From 79caedc7e48db2711059c5990a0b2d1eb531810b Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Thu, 27 Mar 2025 16:23:54 +0100 Subject: [PATCH 001/119] add pytorch template --- ctlearn/tools/train_model.py | 276 +++++++++++++++++++++++++++-------- docs/source/usage.rst | 5 +- pyproject.toml | 3 +- 3 files changed, 224 insertions(+), 60 deletions(-) diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index e4f729d2..2fda64ad 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -1,5 +1,5 @@ """ -Tool to train a ``CTLearnModel`` on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. +Tools to train a ``CTLearnModel` (in Keras or PyTorch) on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. """ import atexit @@ -31,58 +31,61 @@ class TrainCTLearnModel(Tool): """ - Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data. + Base class for training a ``CTLearnModel`` on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. The tool trains a CTLearn model on the input data (R1 calibrated waveforms or DL1a images) and saves the trained model in the output directory. The input data is loaded from the input directories - for signal and background events using the ``~dl1_data_handler.reader.DLDataReader`` and - ``~dl1_data_handler.loader.DLDataLoader``. The tool supports the following reconstruction tasks: + for signal and background events using the ``DLDataReader`` and ``DLDataLoader``. The ``start`` method + is implemented in the subclasses to train the model using the specified framework (Keras or PyTorch). + The tool supports the following reconstruction tasks: - Classification of the primary particle type (gamma/proton) - Regression of the primary particle energy - Regression of the primary particle arrival direction based on the offsets in camera coordinates - Regression of the primary particle arrival direction based on the offsets in sky coordinates - """ - - name = "ctlearn-train-model" - description = __doc__ - - examples = """ - To train a CTLearn model for the classification of the primary particle type: - > ctlearn-train-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --background /path/to/your/protons_dl1_dir/ \\ - --pattern-background "proton_*_run1.dl1.h5" \\ - --pattern-background "proton_*_run10.dl1.h5" \\ - --output /path/to/your/type/ \\ - --reco type \\ - - To train a CTLearn model for the regression of the primary particle energy: - > ctlearn-train-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/energy/ \\ - --reco energy \\ - To train a CTLearn model for the regression of the primary particle - arrival direction based on the offsets in camera coordinates: - > ctlearn-train-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/direction/ \\ - --reco cameradirection \\ - - To train a CTLearn model for the regression of the primary particle - arrival direction based on the offsets in sky coordinates: - > ctlearn-train-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/direction/ \\ - --reco skydirection \\ + Attributes + ---------- + input_dir_signal : Path + Input directory for signal events. + file_pattern_signal : List[Unicode] + List of specific file pattern for matching files in ``input_dir_signal``. + input_dir_background : Path + Input directory for background events. + file_pattern_background : List[Unicode] + List of specific file pattern for matching files in ``input_dir_background``. + dl1dh_reader_type : ComponentName + Type of the DLDataReader to use for reading the input data. + dl1dh_reader : DLDataReader + DLDataReader instance for reading the input data. + stack_telescope_images : Bool + Set whether to stack the telescope images in the data loader. + sort_by_intensity : Bool + Set whether to sort the telescope images by intensity in the data loader. + output_dir : Path + Output directory for the trained reconstructor. + reco_tasks : List[Unicode] + List of reconstruction tasks to perform. + n_epochs : Int + Number of epochs to train the neural network. + batch_size : Int + Size of the batch to train the neural network. + validation_split : Float + Fraction of the data to use for validation. + optimizer : Dict + Optimizer to use for training. + random_seed : Int + Random seed for shuffling the data before the training/validation split and after the end of an epoch. + save_onnx : Bool + Set whether to save model in an ONNX file. + overwrite : Bool + Overwrite output dir if it exists. + + Methods + ------- + setup() + Set up the data reader and data loaders for training and validation. + finish() + Save the trained model in the output directory in ONNX if selected. """ input_dir_signal = Path( @@ -178,12 +181,6 @@ class TrainCTLearnModel(Tool): max=0.99, ).tag(config=True) - save_best_validation_only = Bool( - default_value=True, - allow_none=False, - help="Set whether to save the best validation checkpoint only.", - ).tag(config=True) - optimizer = Dict( default_value={ "name": "Adam", @@ -349,7 +346,105 @@ def setup(self): stack_telescope_images=self.stack_telescope_images, ) - # Set up the callbacks + def finish(self): + # Saving model weights in onnx format + if self.save_onnx: + self.log.info("Converting Keras model into ONNX format...") + self.log.info("Make sure tf2onnx is installed in your enviroment!") + try: + import tf2onnx + except ImportError: + raise ImportError("tf2onnx is not installed in your environment!") + + output_path = f"{self.output_dir}/ctlearn_model.onnx" + tf2onnx.convert.from_keras( + self.model, input_signature=self.model.input_layer.input._type_spec, output_path=output_path + ) + self.log.info("ONNX model saved in %s", self.output_dir) + + self.log.info("Tool is shutting down") + +class TrainKerasModel(TrainCTLearnModel): + """ + Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using keras. + + The tool sets up the keras model using the specified optimizer and callbacks. The keras model is trained + on the input data (R1 calibrated waveforms or DL1a images) and saved in the output directory. + """ + + name = "ctlearn-train-keras-model" + description = __doc__ + + examples = """ + To train a CTLearn model for the classification of the primary particle type: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --background /path/to/your/protons_dl1_dir/ \\ + --pattern-background "proton_*_run1.dl1.h5" \\ + --pattern-background "proton_*_run10.dl1.h5" \\ + --output /path/to/your/type/ \\ + --reco type \\ + + To train a CTLearn model for the regression of the primary particle energy: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/energy/ \\ + --reco energy \\ + + To train a CTLearn model for the regression of the primary particle + arrival direction based on the offsets in camera coordinates: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco cameradirection \\ + + To train a CTLearn model for the regression of the primary particle + arrival direction based on the offsets in sky coordinates: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco skydirection \\ + """ + + model_type = ComponentName( + CTLearnModel, default_value="ResNet" + ).tag(config=True) + + save_best_validation_only = Bool( + default_value=True, + allow_none=False, + help="Set whether to save the best validation checkpoint only.", + ).tag(config=True) + + lr_reducing = Dict( + default_value={"factor": 0.5, "patience": 5, "min_delta": 0.01, "min_lr": 0.000001}, + allow_none=True, + help=( + "Learning rate reducing parameters for the Keras callback. " + "E.g. {'factor': 0.5, 'patience': 5, 'min_delta': 0.01, 'min_lr': 0.000001}. " + ) + ).tag(config=True) + + early_stopping = Dict( + default_value=None, + allow_none=True, + help=( + "Early stopping parameters for the Keras callback. " + "E.g. {'monitor': 'val_loss', 'patience': 4, 'verbose': 1, 'restore_best_weights': True}. " + ) + ).tag(config=True) + + + def start(self): + # Set up the keras callbacks monitor = "val_loss" monitor_mode = "min" # Model checkpoint callback @@ -544,12 +639,79 @@ def _get_losses_and_mertics(self, tasks): ) return losses, metrics +class TrainPyTorchModel(TrainCTLearnModel): + """ + Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using PyTorch. + + The tool sets up the PyTorch model using ... The PyTorch model is trained + on the input data (R1 calibrated waveforms or DL1a images) and saved in the output directory. + """ + + name = "ctlearn-train-pytorch-model" + description = __doc__ + + examples = """ + To train a CTLearn PyTorch model for the classification of the primary particle type: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --background /path/to/your/protons_dl1_dir/ \\ + --pattern-background "proton_*_run1.dl1.h5" \\ + --pattern-background "proton_*_run10.dl1.h5" \\ + --output /path/to/your/type/ \\ + --reco type \\ + + To train a CTLearn PyTorch model for the regression of the primary particle energy: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/energy/ \\ + --reco energy \\ + + To train a CTLearn PyTorch model for the regression of the primary particle + arrival direction based on the offsets in camera coordinates: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco cameradirection \\ + + To train a CTLearn PyTorch model for the regression of the primary particle + arrival direction based on the offsets in sky coordinates: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco skydirection \\ + """ + + pytorch_int = Int( + allow_none=False, + default_value=42 + ).tag(config=True) + + def start(self): + prinit(self.pytorch_int) + pass + + +def keras_tool(): + # Run the tool + keras_tool = TrainKerasModel() + keras_tool.run() + -def main(): +def pytorch_tool(): # Run the tool - tool = TrainCTLearnModel() - tool.run() + pytorch_tool = TrainPyTorchModel() + pytorch_tool.run() +if __name__ == "keras_tool": + keras_tool() -if __name__ == "main": - main() +if __name__ == "pytorch_tool": + pytorch_tool() diff --git a/docs/source/usage.rst b/docs/source/usage.rst index c49050e1..f7f20e36 100644 --- a/docs/source/usage.rst +++ b/docs/source/usage.rst @@ -12,11 +12,12 @@ This page provides a brief overview of how to use the CTLearn tools. Training tool ------------- -To train a model, use the `ctlearn-train-model` command. The following command will display all available options for training a CTLearn model: +To train a model, use the `ctlearn-train-keras-model` or `ctlearn-train-pytorch-model` command. The following command will display all available options for training a CTLearn model: .. code-block:: bash - ctlearn-train-model --help-all + ctlearn-train-keras-model --help-all + ctlearn-train-pytorch-model --help-all View training progress in real time with TensorBoard: diff --git a/pyproject.toml b/pyproject.toml index f2a5c057..1c4d977e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,7 +61,8 @@ repository = "https://github.com/ctlearn-project/ctlearn" documentation = "https://ctlearn.readthedocs.io/en/latest/" [project.scripts] -ctlearn-train-model = "ctlearn.tools.train_model:main" +ctlearn-train-kera-model = "ctlearn.tools.train_model:keras_tool" +ctlearn-train-pytorch-model = "ctlearn.tools.train_model:pytorch_tool" ctlearn-predict-mono-model = "ctlearn.tools.predict_model:mono_tool" ctlearn-predict-stereo-model = "ctlearn.tools.predict_model:stereo_tool" ctlearn-predict-LST1= "ctlearn.tools.predict_LST1:main" From feae15ec059f22ed3327ed736447f8c60132f609 Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 28 Mar 2025 15:29:06 +0000 Subject: [PATCH 002/119] First commit with new structure to support MultiDLFrameworks --- ctlearn/__init__.py | 25 + ctlearn/tools/base_train_model.py | 340 ++++++++++ ctlearn/tools/enum.py | 5 + ctlearn/tools/keras/__init__.py | 0 ctlearn/tools/keras/train_keras_model.py | 286 +++++++++ ctlearn/tools/pytorch/__init__.py | 0 ctlearn/tools/pytorch/train_pytorch_model.py | 106 ++++ ctlearn/tools/train_model.py | 631 +++---------------- 8 files changed, 833 insertions(+), 560 deletions(-) create mode 100644 ctlearn/tools/base_train_model.py create mode 100644 ctlearn/tools/enum.py create mode 100644 ctlearn/tools/keras/__init__.py create mode 100644 ctlearn/tools/keras/train_keras_model.py create mode 100644 ctlearn/tools/pytorch/__init__.py create mode 100644 ctlearn/tools/pytorch/train_pytorch_model.py diff --git a/ctlearn/__init__.py b/ctlearn/__init__.py index c4c2735c..af19c73a 100644 --- a/ctlearn/__init__.py +++ b/ctlearn/__init__.py @@ -1,3 +1,28 @@ from ._version import __version__ + __all__ = ["__version__"] + +# from ctlearn.tools.pytorch.train_pytorch_model import TrainPyTorchModel +# from ctlearn.tools.keras.train_keras_model import TrainKerasModel + +# class FrameworkType(Enum): +# KERAS = 1 +# PYTORCH = 2 + +# def get_framework(self,framework_type: FrameworkType): +# if framework_type == FrameworkType.KERAS: +# if not self.is_package_available("tensorflow"): +# raise ImportError("TensorFlow is not installed. Cannot run Keras framework.") +# else: +# fw = TrainKerasModel() + +# elif framework_type == FrameworkType.PYTORCH: +# if not self.is_package_available("torch"): +# raise ImportError("PyTorch is not installed. Cannot run PyTorch framework.") +# else: +# fw = TrainPyTorchModel() +# else: +# raise ValueError("Unknown Framework") + +# return fw diff --git a/ctlearn/tools/base_train_model.py b/ctlearn/tools/base_train_model.py new file mode 100644 index 00000000..9fc9ec2a --- /dev/null +++ b/ctlearn/tools/base_train_model.py @@ -0,0 +1,340 @@ + +import atexit +import pandas as pd +import numpy as np +import shutil +import tensorflow as tf +import sys +from ctapipe.core import Tool +from ctapipe.core.tool import ToolConfigurationError +from ctapipe.core.traits import ( + Bool, + CaselessStrEnum, + Path, + Float, + Int, + List, + Dict, + classes_with_traits, + ComponentName, + Unicode, +) +from dl1_data_handler.reader import DLDataReader +from ctlearn.core.loader import DLDataLoader +from ctlearn.core.model import CTLearnModel +from ctlearn.utils import validate_trait_dict + +class TrainCTLearnModel(Tool): + """ + Base class for training a ``CTLearnModel`` on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. + + The tool trains a CTLearn model on the input data (R1 calibrated waveforms or DL1a images) and + saves the trained model in the output directory. The input data is loaded from the input directories + for signal and background events using the ``DLDataReader`` and ``DLDataLoader``. The ``start`` method + is implemented in the subclasses to train the model using the specified framework (Keras or PyTorch). + The tool supports the following reconstruction tasks: + - Classification of the primary particle type (gamma/proton) + - Regression of the primary particle energy + - Regression of the primary particle arrival direction based on the offsets in camera coordinates + - Regression of the primary particle arrival direction based on the offsets in sky coordinates + + Attributes + ---------- + input_dir_signal : Path + Input directory for signal events. + file_pattern_signal : List[Unicode] + List of specific file pattern for matching files in ``input_dir_signal``. + input_dir_background : Path + Input directory for background events. + file_pattern_background : List[Unicode] + List of specific file pattern for matching files in ``input_dir_background``. + dl1dh_reader_type : ComponentName + Type of the DLDataReader to use for reading the input data. + dl1dh_reader : DLDataReader + DLDataReader instance for reading the input data. + stack_telescope_images : Bool + Set whether to stack the telescope images in the data loader. + sort_by_intensity : Bool + Set whether to sort the telescope images by intensity in the data loader. + output_dir : Path + Output directory for the trained reconstructor. + reco_tasks : List[Unicode] + List of reconstruction tasks to perform. + n_epochs : Int + Number of epochs to train the neural network. + batch_size : Int + Size of the batch to train the neural network. + validation_split : Float + Fraction of the data to use for validation. + optimizer : Dict + Optimizer to use for training. + random_seed : Int + Random seed for shuffling the data before the training/validation split and after the end of an epoch. + save_onnx : Bool + Set whether to save model in an ONNX file. + overwrite : Bool + Overwrite output dir if it exists. + + Methods + ------- + setup() + Set up the data reader and data loaders for training and validation. + finish() + Save the trained model in the output directory in ONNX if selected. + """ + name = "ctlearn-train-model-base" + + input_dir_signal = Path( + help="Input directory for signal events", + allow_none=False, + exists=True, + directory_ok=True, + file_ok=False, + ).tag(config=True) + + file_pattern_signal = List( + trait=Unicode(), + default_value=["*.h5"], + help="List of specific file pattern for matching files in ``input_dir_signal``", + ).tag(config=True) + + input_dir_background = Path( + default_value=None, + help="Input directory for background events", + allow_none=True, + exists=True, + directory_ok=True, + file_ok=False, + ).tag(config=True) + + file_pattern_background = List( + trait=Unicode(), + default_value=["*.h5"], + help="List of specific file pattern for matching files in ``input_dir_background``", + ).tag(config=True) + + dl1dh_reader_type = ComponentName( + DLDataReader, default_value="DLImageReader" + ).tag(config=True) + + stack_telescope_images = Bool( + default_value=False, + allow_none=False, + help=( + "Set whether to stack the telescope images in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + sort_by_intensity = Bool( + default_value=False, + allow_none=True, + help=( + "Set whether to sort the telescope images by intensity in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + output_dir = Path( + exits=False, + default_value=None, + allow_none=False, + directory_ok=True, + file_ok=False, + help="Output directory for the trained reconstructor.", + ).tag(config=True) + + reco_tasks = List( + trait=CaselessStrEnum(["type", "energy", "cameradirection", "skydirection"]), + allow_none=False, + help=( + "List of reconstruction tasks to perform. " + "'type': classification of the primary particle type " + "'energy': regression of the primary particle energy " + "'cameradirection': regression of the primary particle arrival direction in camera coordinates " + "'skydirection': regression of the primary particle arrival direction in sky coordinates" + ) + ).tag(config=True) + + n_epochs = Int( + default_value=10, + allow_none=False, + help="Number of epochs to train the neural network.", + ).tag(config=True) + + batch_size = Int( + default_value=64, + allow_none=False, + help="Size of the batch to train the neural network.", + ).tag(config=True) + + validation_split = Float( + default_value=0.1, + help="Fraction of the data to use for validation", + min=0.01, + max=0.99, + ).tag(config=True) + + optimizer = Dict( + default_value={"name": "Adam", "base_learning_rate": 0.0001, "adam_epsilon": 1.0e-8}, + help=( + "Optimizer to use for training. " + "E.g. {'name': 'Adam', 'base_learning_rate': 0.0001, 'adam_epsilon': 1.0e-8}. " + ) + ).tag(config=True) + + random_seed = Int( + default_value=0, + help=( + "Random seed for shuffling the data " + "before the training/validation split " + "and after the end of an epoch." + ) + ).tag(config=True) + + save_onnx = Bool( + default_value=False, + allow_none=False, + help="Set whether to save model in an ONNX file.", + ).tag(config=True) + + overwrite = Bool(help="Overwrite output dir if it exists").tag(config=True) + + aliases = { + "framework": "DLFrameWork.framework_type", + "signal": "TrainCTLearnModel.input_dir_signal", + "background": "TrainCTLearnModel.input_dir_background", + "pattern-signal": "TrainCTLearnModel.file_pattern_signal", + "pattern-background": "TrainCTLearnModel.file_pattern_background", + "reco": "TrainCTLearnModel.reco_tasks", + ("o", "output"): "TrainCTLearnModel.output_dir", + } + + flags = { + "overwrite": ( + {"TrainCTLearnModel": {"overwrite": True}}, + "Overwrite existing files", + ), + } + + classes = ( + [ + CTLearnModel, + DLDataReader, + ] + + classes_with_traits(CTLearnModel) + + classes_with_traits(DLDataReader) + ) + def __init__(self, **kwargs): + super().__init__(**kwargs) + print("Common Init") + + def setup(self): + print("Enter setup") + # Check if the output directory exists and if it should be overwritten + if self.output_dir.exists(): + if not self.overwrite: + raise ToolConfigurationError( + f"Output directory {self.output_dir} already exists. Use --overwrite to overwrite." + ) + else: + # Remove the output directory if it exists + self.log.info("Removing existing output directory %s", self.output_dir) + shutil.rmtree(self.output_dir) + + # Must be moved to KERAS + # Create a MirroredStrategy. + # self.strategy = tf.distribute.MirroredStrategy() + # atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore + # self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) + + # Get signal input files + self.input_url_signal = [] + for signal_pattern in self.file_pattern_signal: + self.input_url_signal.extend(self.input_dir_signal.glob(signal_pattern)) + # print(f"self.input_url_signal: {self.input_url_signal}") + # Get bkg input files + self.input_url_background = [] + if self.input_dir_background is not None: + for background_pattern in self.file_pattern_background: + self.input_url_background.extend(self.input_dir_background.glob(background_pattern)) + + # print(f"self.input_url_background: {self.input_url_background}") + print("DEBUG 1") + # Set up the data reader + self.log.info("Loading data:") + self.log.info("For a large dataset, this may take a while...") + if self.dl1dh_reader_type == "DLFeatureVectorReader": + raise NotImplementedError( + "'DLFeatureVectorReader' is not supported in CTLearn yet. " + "Missing stereo CTLearnModel implementation." + ) + print("DEBUG 2") + print(f"self.dl1dh_reader_type: {self.dl1dh_reader_type}") + self.dl1dh_reader = DLDataReader.from_name( + self.dl1dh_reader_type, + input_url_signal=sorted(self.input_url_signal), + input_url_background=sorted(self.input_url_background), + parent=self, + ) + print("DEBUG 3") + self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) + if "type" in self.reco_tasks: + self.log.info("Number of signal events: %d", self.dl1dh_reader.n_signal_events) + self.log.info("Number of background events: %d", self.dl1dh_reader.n_bkg_events) + # Check if the number of events is enough to form a batch + if self.dl1dh_reader._get_n_events() < self.batch_size: + raise ValueError( + f"{self.dl1dh_reader._get_n_events()} events are not enough " + f"to form a batch of size {self.batch_size}. Reduce the batch size." + ) + # Check if there are at least two classes in the reader for the particle classification + if self.dl1dh_reader.class_weight is None and "type" in self.reco_tasks: + raise ValueError( + "Classification task selected but less than two classes are present in the data." + ) + # Check if stereo mode is selected for stacking telescope images + if self.stack_telescope_images and self.dl1dh_reader.mode == "mono": + raise ToolConfigurationError( + f"Cannot stack telescope images in mono mode. Use stereo mode for stacking." + ) + # Ckeck if only one telescope type is selected for stacking telescope images + if self.stack_telescope_images and len(list(self.dl1dh_reader.selected_telescopes)) > 1: + raise ToolConfigurationError( + f"Cannot stack telescope images from multiple telescope types. Use only one telescope type." + ) + # Check if sorting by intensity is disabled for stacking telescope images + if self.stack_telescope_images and self.sort_by_intensity: + raise ToolConfigurationError( + f"Cannot stack telescope images when sorting by intensity. Disable sorting by intensity." + ) + + # Set up the data loaders for training and validation + indices = list(range(self.dl1dh_reader._get_n_events())) + # Shuffle the indices before the training/validation split + np.random.seed(self.random_seed) + np.random.shuffle(indices) + n_validation_examples = int(self.validation_split * self.dl1dh_reader._get_n_events()) + training_indices = indices[n_validation_examples:] + validation_indices = indices[:n_validation_examples] + self.training_loader = DLDataLoader( + self.dl1dh_reader, + training_indices, + tasks=self.reco_tasks, + batch_size=self.batch_size*self.strategy.num_replicas_in_sync, + random_seed=self.random_seed, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + self.validation_loader = DLDataLoader( + self.dl1dh_reader, + validation_indices, + tasks=self.reco_tasks, + batch_size=self.batch_size*self.strategy.num_replicas_in_sync, + random_seed=self.random_seed, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + + def finish(self): + print("finish") diff --git a/ctlearn/tools/enum.py b/ctlearn/tools/enum.py new file mode 100644 index 00000000..f4f3eb09 --- /dev/null +++ b/ctlearn/tools/enum.py @@ -0,0 +1,5 @@ +from enum import Enum + +class FrameworkType(Enum): + KERAS = 1 + PYTORCH = 2 \ No newline at end of file diff --git a/ctlearn/tools/keras/__init__.py b/ctlearn/tools/keras/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/tools/keras/train_keras_model.py b/ctlearn/tools/keras/train_keras_model.py new file mode 100644 index 00000000..06ed9446 --- /dev/null +++ b/ctlearn/tools/keras/train_keras_model.py @@ -0,0 +1,286 @@ +import atexit +import pandas as pd +import numpy as np +import shutil +import tensorflow as tf + +from ctapipe.core.traits import ( + Bool, + CaselessStrEnum, + Path, + Float, + Int, + List, + Dict, + classes_with_traits, + ComponentName, + Unicode, +) +from ctlearn.tools.base_train_model import TrainCTLearnModel +from dl1_data_handler.reader import DLDataReader +from ctlearn.core.loader import DLDataLoader +from ctlearn.core.model import CTLearnModel +from ctlearn.utils import validate_trait_dict + +try: + import keras +except ImportError: + raise ImportError("keras is not installed in your environment!") + +class TrainKerasModel(TrainCTLearnModel): + """ + Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using keras. + + The tool sets up the keras model using the specified optimizer and callbacks. The keras model is trained + on the input data (R1 calibrated waveforms or DL1a images) and saved in the output directory. + """ + + name = "ctlearn-train-keras-model" + description = __doc__ + + examples = """ + To train a CTLearn model for the classification of the primary particle type: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --background /path/to/your/protons_dl1_dir/ \\ + --pattern-background "proton_*_run1.dl1.h5" \\ + --pattern-background "proton_*_run10.dl1.h5" \\ + --output /path/to/your/type/ \\ + --reco type \\ + + To train a CTLearn model for the regression of the primary particle energy: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/energy/ \\ + --reco energy \\ + + To train a CTLearn model for the regression of the primary particle + arrival direction based on the offsets in camera coordinates: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco cameradirection \\ + + To train a CTLearn model for the regression of the primary particle + arrival direction based on the offsets in sky coordinates: + > ctlearn-train-keras-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco skydirection \\ + """ + + model_type = ComponentName( + CTLearnModel, default_value="ResNet" + ).tag(config=True) + + save_best_validation_only = Bool( + default_value=True, + allow_none=False, + help="Set whether to save the best validation checkpoint only.", + ).tag(config=True) + + lr_reducing = Dict( + default_value={"factor": 0.5, "patience": 5, "min_delta": 0.01, "min_lr": 0.000001}, + allow_none=True, + help=( + "Learning rate reducing parameters for the Keras callback. " + "E.g. {'factor': 0.5, 'patience': 5, 'min_delta': 0.01, 'min_lr': 0.000001}. " + ) + ).tag(config=True) + + early_stopping = Dict( + default_value=None, + allow_none=True, + help=( + "Early stopping parameters for the Keras callback. " + "E.g. {'monitor': 'val_loss', 'patience': 4, 'verbose': 1, 'restore_best_weights': True}. " + ) + ).tag(config=True) + + aliases = { + **TrainCTLearnModel.aliases, + } + + def start(self): + # Set up the keras callbacks + monitor = "val_loss" + monitor_mode = "min" + # Model checkpoint callback + # Temp fix for supporting keras2 & keras3 + if int(keras.__version__.split(".")[0]) >= 3: + model_path = f"{self.output_dir}/ctlearn_model.keras" + else: + model_path = f"{self.output_dir}/ctlearn_model.cpk" + model_checkpoint_callback = keras.callbacks.ModelCheckpoint( + filepath=model_path, + monitor=monitor, + verbose=1, + mode=monitor_mode, + save_best_only=self.save_best_validation_only, + ) + # Tensorboard callback + tensorboard_callback = keras.callbacks.TensorBoard( + log_dir=self.output_dir, histogram_freq=1 + ) + # CSV logger callback + csv_logger_callback = keras.callbacks.CSVLogger( + filename=f"{self.output_dir}/training_log.csv", append=True + ) + self.callbacks = [model_checkpoint_callback, tensorboard_callback, csv_logger_callback] + + if self.early_stopping is not None: + # EarlyStopping callback + validate_trait_dict(self.early_stopping, ["monitor", "patience", "verbose", "restore_best_weights"]) + early_stopping_callback = keras.callbacks.EarlyStopping( + monitor=self.early_stopping["monitor"], + patience=self.early_stopping["patience"], + verbose=self.early_stopping["verbose"], + restore_best_weights=self.early_stopping["restore_best_weights"] + ) + self.callbacks.append(early_stopping_callback) + + # Learning rate reducing callback + if self.lr_reducing is not None: + # Validate the learning rate reducing parameters + validate_trait_dict(self.lr_reducing, ["factor", "patience", "min_delta", "min_lr"]) + lr_reducing_callback = keras.callbacks.ReduceLROnPlateau( + monitor=monitor, + factor=self.lr_reducing["factor"], + patience=self.lr_reducing["patience"], + mode=monitor_mode, + verbose=1, + min_delta=self.lr_reducing["min_delta"], + min_lr=self.lr_reducing["min_lr"], + ) + self.callbacks.append(lr_reducing_callback) + # Open a strategy scope. + with self.strategy.scope(): + # Construct the model + self.log.info("Setting up the model.") + self.model = CTLearnModel.from_name( + self.model_type, + input_shape=self.training_loader.input_shape, + tasks=self.reco_tasks, + parent=self, + ).model + # Validate the optimizer parameters + validate_trait_dict(self.optimizer, ["name", "base_learning_rate"]) + # Set the learning rate for the optimizer + learning_rate = self.optimizer["base_learning_rate"] + # Set the epsilon for the Adam optimizer + adam_epsilon = None + if self.optimizer["name"] == "Adam": + # Validate the epsilon for the Adam optimizer + validate_trait_dict(self.optimizer, ["adam_epsilon"]) + # Set the epsilon for the Adam optimizer + adam_epsilon = self.optimizer["adam_epsilon"] + # Select optimizer with appropriate arguments + # Dict of optimizer_name: (optimizer_fn, optimizer_args) + optimizers = { + "Adadelta": ( + keras.optimizers.Adadelta, + dict(learning_rate=learning_rate), + ), + "Adam": ( + keras.optimizers.Adam, + dict(learning_rate=learning_rate, epsilon=adam_epsilon), + ), + "RMSProp": (keras.optimizers.RMSprop, dict(learning_rate=learning_rate)), + "SGD": (keras.optimizers.SGD, dict(learning_rate=learning_rate)), + } + # Get the optimizer function and arguments + optimizer_fn, optimizer_args = optimizers[self.optimizer["name"]] + # Get the losses and metrics for the model + losses, metrics = self._get_losses_and_mertics(self.reco_tasks) + # Compile the model + self.log.info("Compiling CTLearn model.") + self.model.compile(optimizer=optimizer_fn(**optimizer_args), loss=losses, metrics=metrics) + + # Train and evaluate the model + self.log.info("Training and evaluating...") + self.model.fit( + self.training_loader, + validation_data=self.validation_loader, + epochs=self.n_epochs, + class_weight=self.dl1dh_reader.class_weight, + callbacks=self.callbacks, + verbose=2, + ) + self.log.info("Training and evaluating finished succesfully!") + + def finish(self): + # Saving model weights in onnx format + if self.save_onnx: + self.log.info("Converting Keras model into ONNX format...") + self.log.info("Make sure tf2onnx is installed in your enviroment!") + try: + import tf2onnx + except ImportError: + raise ImportError("tf2onnx is not installed in your environment!") + + output_path = f"{self.output_dir}/ctlearn_model.onnx" + tf2onnx.convert.from_keras( + self.model, input_signature=self.model.input_layer.input._type_spec, output_path=output_path + ) + self.log.info("ONNX model saved in %s", self.output_dir) + + self.log.info("Tool is shutting down") + + def _get_losses_and_mertics(self, tasks): + """ + Build the fully connected head for the CTLearn model. + + Function to build the fully connected head of the CTLearn model using the specified parameters. + + Parameters + ---------- + inputs : keras.layers.Layer + Keras layer of the model. + layers : dict + Dictionary containing the number of neurons (as value) in the fully connected head for each task (as key). + tasks : list + List of tasks to build the head for. + + Returns + ------- + logits : dict + Dictionary containing the logits for each task. + """ + losses, metrics = {}, {} + if "type" in self.reco_tasks: + losses["type"] = keras.losses.CategoricalCrossentropy( + reduction="sum_over_batch_size" + ) + metrics["type"] = [ + keras.metrics.CategoricalAccuracy(name="accuracy"), + keras.metrics.AUC(name="auc"), + ] + # Temp fix till keras support class weights for multiple outputs or I wrote custom loss + # https://github.com/keras-team/keras/issues/11735 + if len(tasks) == 1: + losses = losses["type"] + metrics = metrics["type"] + if "energy" in self.reco_tasks: + losses["energy"] = keras.losses.MeanAbsoluteError( + reduction="sum_over_batch_size" + ) + metrics["energy"] = keras.metrics.MeanAbsoluteError(name="mae_energy") + if "cameradirection" in self.reco_tasks: + losses["cameradirection"] = keras.losses.MeanAbsoluteError( + reduction="sum_over_batch_size" + ) + metrics["cameradirection"] = keras.metrics.MeanAbsoluteError(name="mae_cameradirection") + if "skydirection" in self.reco_tasks: + losses["skydirection"] = keras.losses.MeanAbsoluteError( + reduction="sum_over_batch_size" + ) + metrics["skydirection"] = keras.metrics.MeanAbsoluteError(name="mae_skydirection") + return losses, metrics \ No newline at end of file diff --git a/ctlearn/tools/pytorch/__init__.py b/ctlearn/tools/pytorch/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/tools/pytorch/train_pytorch_model.py b/ctlearn/tools/pytorch/train_pytorch_model.py new file mode 100644 index 00000000..eabeea00 --- /dev/null +++ b/ctlearn/tools/pytorch/train_pytorch_model.py @@ -0,0 +1,106 @@ +from ctapipe.core.traits import ( + Bool, + CaselessStrEnum, + Path, + Float, + Int, + List, + Dict, + classes_with_traits, + ComponentName, + Unicode, +) + +try: + import torch +except ImportError: + raise ImportError("pytorch is not installed in your environment!") + +try: + import pytorch_lightning +except ImportError: + raise ImportError("pytorch_lightning is not installed in your environment!") + +from ctlearn.tools.base_train_model import TrainCTLearnModel +# from ctlearn.tools.train_model import +class TrainPyTorchModel(TrainCTLearnModel): + """ + Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using PyTorch. + + The tool sets up the PyTorch model using ... The PyTorch model is trained + on the input data (R1 calibrated waveforms or DL1a images) and saved in the output directory. + """ + + name = "ctlearn-train-pytorch-model" + description = __doc__ + + examples = """ + To train a CTLearn PyTorch model for the classification of the primary particle type: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --background /path/to/your/protons_dl1_dir/ \\ + --pattern-background "proton_*_run1.dl1.h5" \\ + --pattern-background "proton_*_run10.dl1.h5" \\ + --output /path/to/your/type/ \\ + --reco type \\ + + To train a CTLearn PyTorch model for the regression of the primary particle energy: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/energy/ \\ + --reco energy \\ + + To train a CTLearn PyTorch model for the regression of the primary particle + arrival direction based on the offsets in camera coordinates: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco cameradirection \\ + + To train a CTLearn PyTorch model for the regression of the primary particle + arrival direction based on the offsets in sky coordinates: + > ctlearn-train-pytorch-model \\ + --signal /path/to/your/gammas_dl1_dir/ \\ + --pattern-signal "gamma_*_run1.dl1.h5" \\ + --pattern-signal "gamma_*_run10.dl1.h5" \\ + --output /path/to/your/direction/ \\ + --reco skydirection \\ + """ + + pytorch_int = Int( + allow_none=False, + default_value=42 + ).tag(config=True) + + aliases = { + **TrainCTLearnModel.aliases, + "pytorch_param": "TrainPyTorchModel.pytorch_int", + + } + + def __init__(self, **kwargs): + super().__init__(**kwargs) + print("CONFIG VALUES PYTORCH:", self.config) + + def setup(self): + super().setup() + print("Pytorch setup :)") + print(f"DEBUG - framework_type raw: {self.reco_tasks} ({type(self.reco_tasks)})") + + + def start(self): + super().start() + print("Pytorch start") + + def finish(self): + super().finish() + print("Pytorch finish") + + def show_version(self): + print("Pytorch 2.3") \ No newline at end of file diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 2fda64ad..fc268cfb 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -3,11 +3,10 @@ """ import atexit -import keras import pandas as pd import numpy as np import tensorflow as tf - +import sys from ctapipe.core import Tool from ctapipe.core.tool import ToolConfigurationError from ctapipe.core.traits import ( @@ -27,199 +26,23 @@ from ctlearn.core.loader import DLDataLoader from ctlearn.core.model import CTLearnModel from ctlearn.utils import validate_trait_dict +from ctlearn.tools.pytorch.train_pytorch_model import TrainPyTorchModel +from ctlearn.tools.keras.train_keras_model import TrainKerasModel +# from ctlearn.tools.base_train_model import TrainCTLearnModel +import importlib.util +from enum import Enum +class FrameworkType(Enum): + KERAS = 1 + PYTORCH = 2 -class TrainCTLearnModel(Tool): - """ - Base class for training a ``CTLearnModel`` on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. - - The tool trains a CTLearn model on the input data (R1 calibrated waveforms or DL1a images) and - saves the trained model in the output directory. The input data is loaded from the input directories - for signal and background events using the ``DLDataReader`` and ``DLDataLoader``. The ``start`` method - is implemented in the subclasses to train the model using the specified framework (Keras or PyTorch). - The tool supports the following reconstruction tasks: - - Classification of the primary particle type (gamma/proton) - - Regression of the primary particle energy - - Regression of the primary particle arrival direction based on the offsets in camera coordinates - - Regression of the primary particle arrival direction based on the offsets in sky coordinates - - Attributes - ---------- - input_dir_signal : Path - Input directory for signal events. - file_pattern_signal : List[Unicode] - List of specific file pattern for matching files in ``input_dir_signal``. - input_dir_background : Path - Input directory for background events. - file_pattern_background : List[Unicode] - List of specific file pattern for matching files in ``input_dir_background``. - dl1dh_reader_type : ComponentName - Type of the DLDataReader to use for reading the input data. - dl1dh_reader : DLDataReader - DLDataReader instance for reading the input data. - stack_telescope_images : Bool - Set whether to stack the telescope images in the data loader. - sort_by_intensity : Bool - Set whether to sort the telescope images by intensity in the data loader. - output_dir : Path - Output directory for the trained reconstructor. - reco_tasks : List[Unicode] - List of reconstruction tasks to perform. - n_epochs : Int - Number of epochs to train the neural network. - batch_size : Int - Size of the batch to train the neural network. - validation_split : Float - Fraction of the data to use for validation. - optimizer : Dict - Optimizer to use for training. - random_seed : Int - Random seed for shuffling the data before the training/validation split and after the end of an epoch. - save_onnx : Bool - Set whether to save model in an ONNX file. - overwrite : Bool - Overwrite output dir if it exists. - - Methods - ------- - setup() - Set up the data reader and data loaders for training and validation. - finish() - Save the trained model in the output directory in ONNX if selected. - """ - - input_dir_signal = Path( - help="Input directory for signal events", - allow_none=False, - exists=True, - directory_ok=True, - file_ok=False, - ).tag(config=True) - - file_pattern_signal = List( - trait=Unicode(), - default_value=["*.h5"], - help="List of specific file pattern for matching files in ``input_dir_signal``", - ).tag(config=True) - - input_dir_background = Path( - default_value=None, - help="Input directory for background events", - allow_none=True, - exists=True, - directory_ok=True, - file_ok=False, - ).tag(config=True) - - file_pattern_background = List( - trait=Unicode(), - default_value=["*.h5"], - help="List of specific file pattern for matching files in ``input_dir_background``", - ).tag(config=True) - - dl1dh_reader_type = ComponentName(DLDataReader, default_value="DLImageReader").tag( - config=True - ) - - stack_telescope_images = Bool( - default_value=False, - allow_none=False, - help=( - "Set whether to stack the telescope images in the data loader. " - "Requires DLDataReader mode to be ``stereo``." - ), - ).tag(config=True) - - sort_by_intensity = Bool( - default_value=False, - allow_none=True, - help=( - "Set whether to sort the telescope images by intensity in the data loader. " - "Requires DLDataReader mode to be ``stereo``." - ), - ).tag(config=True) - - model_type = ComponentName(CTLearnModel, default_value="ResNet").tag(config=True) - - output_dir = Path( - exits=False, - default_value=None, - allow_none=False, - directory_ok=True, - file_ok=False, - help="Output directory for the trained reconstructor.", - ).tag(config=True) - - reco_tasks = List( - trait=CaselessStrEnum(["type", "energy", "cameradirection", "skydirection"]), - allow_none=False, - help=( - "List of reconstruction tasks to perform. " - "'type': classification of the primary particle type; " - "'energy': regression of the primary particle energy; " - "'cameradirection': reconstruction of the primary particle arrival direction in camera coordinates; " - "'skydirection': reconstruction of the primary particle arrival direction in sky coordinates." - ), - ).tag(config=True) - n_epochs = Int( - default_value=10, - allow_none=False, - help="Number of epochs to train the neural network.", - ).tag(config=True) - - batch_size = Int( - default_value=64, - allow_none=False, - help="Size of the batch to train the neural network.", - ).tag(config=True) - - validation_split = Float( - default_value=0.1, - help="Fraction of the data to use for validation", - min=0.01, - max=0.99, - ).tag(config=True) - - optimizer = Dict( - default_value={ - "name": "Adam", - "base_learning_rate": 0.0001, - "adam_epsilon": 1.0e-8, - }, - help=( - "Optimizer to use for training. " - "E.g. {'name': 'Adam', 'base_learning_rate': 0.0001, 'adam_epsilon': 1.0e-8}. " - ), - ).tag(config=True) - - lr_reducing = Dict( - default_value={ - "factor": 0.5, - "patience": 5, - "min_delta": 0.01, - "min_lr": 0.000001, - }, - allow_none=True, - help=( - "Learning rate reducing parameters for the Keras callback. " - "E.g. {'factor': 0.5, 'patience': 5, 'min_delta': 0.01, 'min_lr': 0.000001}. " - ), - ).tag(config=True) - - random_seed = Int( - default_value=0, - help=( - "Random seed for shuffling the data " - "before the training/validation split " - "and after the end of an epoch." - ), - ).tag(config=True) - - save_onnx = Bool( - default_value=False, - allow_none=False, - help="Set whether to save model in an ONNX file.", +class DLFrameWork(Tool): + name = "dlframework" + framework_type = CaselessStrEnum( + ["pytorch", "keras"], + default_value="pytorch", + help="Framework to use", ).tag(config=True) early_stopping = Dict( @@ -240,15 +63,34 @@ class TrainCTLearnModel(Tool): ("o", "output"): "TrainCTLearnModel.output_dir", } - classes = classes_with_traits(CTLearnModel) + classes_with_traits(DLDataReader) + flags = { + "overwrite": ( + {"TrainCTLearnModel": {"overwrite": True}}, + "Overwrite existing files", + ), + } + + classes = ( + [ + CTLearnModel, + DLDataReader, + ] + + classes_with_traits(CTLearnModel) + + classes_with_traits(DLDataReader) + ) def setup(self): self.log.info("ctlearn version %s", ctlearn_version) - # Check if the output directory exists + # Check if the output directory exists and if it should be overwritten if self.output_dir.exists(): - raise ToolConfigurationError( - f"Output directory {self.output_dir} already exists." - ) + if not self.overwrite: + raise ToolConfigurationError( + f"Output directory {self.output_dir} already exists. Use --overwrite to overwrite." + ) + else: + # Remove the output directory if it exists + self.log.info("Removing existing output directory %s", self.output_dir) + shutil.rmtree(self.output_dir) # Create a MirroredStrategy. self.strategy = tf.distribute.MirroredStrategy() atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore @@ -347,371 +189,40 @@ def setup(self): ) def finish(self): - # Saving model weights in onnx format - if self.save_onnx: - self.log.info("Converting Keras model into ONNX format...") - self.log.info("Make sure tf2onnx is installed in your enviroment!") - try: - import tf2onnx - except ImportError: - raise ImportError("tf2onnx is not installed in your environment!") - - output_path = f"{self.output_dir}/ctlearn_model.onnx" - tf2onnx.convert.from_keras( - self.model, input_signature=self.model.input_layer.input._type_spec, output_path=output_path - ) - self.log.info("ONNX model saved in %s", self.output_dir) - - self.log.info("Tool is shutting down") - -class TrainKerasModel(TrainCTLearnModel): - """ - Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using keras. - - The tool sets up the keras model using the specified optimizer and callbacks. The keras model is trained - on the input data (R1 calibrated waveforms or DL1a images) and saved in the output directory. - """ - - name = "ctlearn-train-keras-model" - description = __doc__ - - examples = """ - To train a CTLearn model for the classification of the primary particle type: - > ctlearn-train-keras-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --background /path/to/your/protons_dl1_dir/ \\ - --pattern-background "proton_*_run1.dl1.h5" \\ - --pattern-background "proton_*_run10.dl1.h5" \\ - --output /path/to/your/type/ \\ - --reco type \\ - - To train a CTLearn model for the regression of the primary particle energy: - > ctlearn-train-keras-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/energy/ \\ - --reco energy \\ - - To train a CTLearn model for the regression of the primary particle - arrival direction based on the offsets in camera coordinates: - > ctlearn-train-keras-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/direction/ \\ - --reco cameradirection \\ - - To train a CTLearn model for the regression of the primary particle - arrival direction based on the offsets in sky coordinates: - > ctlearn-train-keras-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/direction/ \\ - --reco skydirection \\ - """ - - model_type = ComponentName( - CTLearnModel, default_value="ResNet" - ).tag(config=True) - - save_best_validation_only = Bool( - default_value=True, - allow_none=False, - help="Set whether to save the best validation checkpoint only.", - ).tag(config=True) - - lr_reducing = Dict( - default_value={"factor": 0.5, "patience": 5, "min_delta": 0.01, "min_lr": 0.000001}, - allow_none=True, - help=( - "Learning rate reducing parameters for the Keras callback. " - "E.g. {'factor': 0.5, 'patience': 5, 'min_delta': 0.01, 'min_lr': 0.000001}. " - ) - ).tag(config=True) - - early_stopping = Dict( - default_value=None, - allow_none=True, - help=( - "Early stopping parameters for the Keras callback. " - "E.g. {'monitor': 'val_loss', 'patience': 4, 'verbose': 1, 'restore_best_weights': True}. " - ) - ).tag(config=True) - - - def start(self): - # Set up the keras callbacks - monitor = "val_loss" - monitor_mode = "min" - # Model checkpoint callback - model_path = f"{self.output_dir}/ctlearn_model.keras" - model_checkpoint_callback = keras.callbacks.ModelCheckpoint( - filepath=model_path, - monitor=monitor, - verbose=1, - mode=monitor_mode, - save_best_only=self.save_best_validation_only, - ) - # Tensorboard callback - tensorboard_callback = keras.callbacks.TensorBoard( - log_dir=self.output_dir, histogram_freq=1 - ) - # CSV logger callback - csv_logger_callback = keras.callbacks.CSVLogger( - filename=f"{self.output_dir}/training_log.csv", append=True - ) - self.callbacks = [ - model_checkpoint_callback, - tensorboard_callback, - csv_logger_callback, - ] - - if self.early_stopping is not None: - # EarlyStopping callback - validate_trait_dict( - self.early_stopping, - ["monitor", "patience", "verbose", "restore_best_weights"], - ) - early_stopping_callback = keras.callbacks.EarlyStopping( - monitor=self.early_stopping["monitor"], - patience=self.early_stopping["patience"], - verbose=self.early_stopping["verbose"], - restore_best_weights=self.early_stopping["restore_best_weights"], - ) - self.callbacks.append(early_stopping_callback) - - # Learning rate reducing callback - if self.lr_reducing is not None: - # Validate the learning rate reducing parameters - validate_trait_dict( - self.lr_reducing, ["factor", "patience", "min_delta", "min_lr"] - ) - lr_reducing_callback = keras.callbacks.ReduceLROnPlateau( - monitor=monitor, - factor=self.lr_reducing["factor"], - patience=self.lr_reducing["patience"], - mode=monitor_mode, - verbose=1, - min_delta=self.lr_reducing["min_delta"], - min_lr=self.lr_reducing["min_lr"], - ) - self.callbacks.append(lr_reducing_callback) - - def start(self): - - # Open a strategy scope. - with self.strategy.scope(): - # Construct the model - self.log.info("Setting up the model.") - self.model = CTLearnModel.from_name( - self.model_type, - input_shape=self.training_loader.input_shape, - tasks=self.reco_tasks, - parent=self, - ).model - # Validate the optimizer parameters - validate_trait_dict(self.optimizer, ["name", "base_learning_rate"]) - # Set the learning rate for the optimizer - learning_rate = self.optimizer["base_learning_rate"] - # Set the epsilon for the Adam optimizer - adam_epsilon = None - if self.optimizer["name"] == "Adam": - # Validate the epsilon for the Adam optimizer - validate_trait_dict(self.optimizer, ["adam_epsilon"]) - # Set the epsilon for the Adam optimizer - adam_epsilon = self.optimizer["adam_epsilon"] - # Select optimizer with appropriate arguments - # Dict of optimizer_name: (optimizer_fn, optimizer_args) - optimizers = { - "Adadelta": ( - keras.optimizers.Adadelta, - dict(learning_rate=learning_rate), - ), - "Adam": ( - keras.optimizers.Adam, - dict(learning_rate=learning_rate, epsilon=adam_epsilon), - ), - "RMSProp": ( - keras.optimizers.RMSprop, - dict(learning_rate=learning_rate), - ), - "SGD": (keras.optimizers.SGD, dict(learning_rate=learning_rate)), - } - # Get the optimizer function and arguments - optimizer_fn, optimizer_args = optimizers[self.optimizer["name"]] - # Get the losses and metrics for the model - losses, metrics = self._get_losses_and_mertics(self.reco_tasks) - # Compile the model - self.log.info("Compiling CTLearn model.") - self.model.compile( - optimizer=optimizer_fn(**optimizer_args), loss=losses, metrics=metrics - ) - - # Train and evaluate the model - self.log.info("Training and evaluating...") - self.model.fit( - self.training_loader, - validation_data=self.validation_loader, - epochs=self.n_epochs, - class_weight=self.dl1dh_reader.class_weight, - callbacks=self.callbacks, - verbose=2, - ) - self.log.info("Training and evaluating finished succesfully!") - - def finish(self): - - # Saving model weights in onnx format - if self.save_onnx: - self.log.info("Converting Keras model into ONNX format...") - self.log.info("Make sure tf2onnx is installed in your enviroment!") - try: - import tf2onnx - except ImportError: - raise ImportError("tf2onnx is not installed in your environment!") - - output_path = f"{self.output_dir}/ctlearn_model.onnx" - tf2onnx.convert.from_keras( - self.model, - input_signature=self.model.input_layer.input._type_spec, - output_path=output_path, - ) - self.log.info("ONNX model saved in %s", self.output_dir) - - self.log.info("Tool is shutting down") - - def _get_losses_and_mertics(self, tasks): - """ - Build the fully connected head for the CTLearn model. - - Function to build the fully connected head of the CTLearn model using the specified parameters. - - Parameters - ---------- - inputs : keras.layers.Layer - Keras layer of the model. - layers : dict - Dictionary containing the number of neurons (as value) in the fully connected head for each task (as key). - tasks : list - List of tasks to build the head for. - - Returns - ------- - logits : dict - Dictionary containing the logits for each task. - """ - losses, metrics = {}, {} - if "type" in self.reco_tasks: - losses["type"] = keras.losses.CategoricalCrossentropy( - reduction="sum_over_batch_size" - ) - metrics["type"] = [ - keras.metrics.CategoricalAccuracy(name="accuracy"), - keras.metrics.AUC(name="auc"), - ] - # Temp fix till keras support class weights for multiple outputs or I wrote custom loss - # https://github.com/keras-team/keras/issues/11735 - if len(tasks) == 1: - losses = losses["type"] - metrics = metrics["type"] - if "energy" in self.reco_tasks: - losses["energy"] = keras.losses.MeanAbsoluteError( - reduction="sum_over_batch_size" - ) - metrics["energy"] = keras.metrics.MeanAbsoluteError(name="mae_energy") - if "cameradirection" in self.reco_tasks: - losses["cameradirection"] = keras.losses.MeanAbsoluteError( - reduction="sum_over_batch_size" - ) - metrics["cameradirection"] = keras.metrics.MeanAbsoluteError( - name="mae_cameradirection" - ) - if "skydirection" in self.reco_tasks: - losses["skydirection"] = keras.losses.MeanAbsoluteError( - reduction="sum_over_batch_size" - ) - metrics["skydirection"] = keras.metrics.MeanAbsoluteError( - name="mae_skydirection" - ) - return losses, metrics - -class TrainPyTorchModel(TrainCTLearnModel): - """ - Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using PyTorch. - - The tool sets up the PyTorch model using ... The PyTorch model is trained - on the input data (R1 calibrated waveforms or DL1a images) and saved in the output directory. - """ - - name = "ctlearn-train-pytorch-model" - description = __doc__ - - examples = """ - To train a CTLearn PyTorch model for the classification of the primary particle type: - > ctlearn-train-pytorch-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --background /path/to/your/protons_dl1_dir/ \\ - --pattern-background "proton_*_run1.dl1.h5" \\ - --pattern-background "proton_*_run10.dl1.h5" \\ - --output /path/to/your/type/ \\ - --reco type \\ - - To train a CTLearn PyTorch model for the regression of the primary particle energy: - > ctlearn-train-pytorch-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/energy/ \\ - --reco energy \\ - - To train a CTLearn PyTorch model for the regression of the primary particle - arrival direction based on the offsets in camera coordinates: - > ctlearn-train-pytorch-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/direction/ \\ - --reco cameradirection \\ - - To train a CTLearn PyTorch model for the regression of the primary particle - arrival direction based on the offsets in sky coordinates: - > ctlearn-train-pytorch-model \\ - --signal /path/to/your/gammas_dl1_dir/ \\ - --pattern-signal "gamma_*_run1.dl1.h5" \\ - --pattern-signal "gamma_*_run10.dl1.h5" \\ - --output /path/to/your/direction/ \\ - --reco skydirection \\ - """ - - pytorch_int = Int( - allow_none=False, - default_value=42 - ).tag(config=True) - - def start(self): - prinit(self.pytorch_int) pass + @classmethod + def string_to_type(self,str_type:str)->FrameworkType: -def keras_tool(): - # Run the tool - keras_tool = TrainKerasModel() - keras_tool.run() - - -def pytorch_tool(): - # Run the tool - pytorch_tool = TrainPyTorchModel() - pytorch_tool.run() + type_=None + str_type = str.upper(str_type) + try: + type_ = FrameworkType[str_type] + except KeyError: + print(f"'{str_type}' is not a valid enum type.") + return type_ + + def get_framework(self, framework_type: FrameworkType): + if framework_type == FrameworkType.KERAS: + if not self.is_package_available("tensorflow"): + raise ImportError("TensorFlow is not installed. Cannot run Keras framework.") + else: + fw = TrainKerasModel() + + elif framework_type == FrameworkType.PYTORCH: + if not self.is_package_available("torch"): + raise ImportError("PyTorch is not installed. Cannot run PyTorch framework.") + else: + fw = TrainPyTorchModel() + else: + raise ValueError("Unknown Framework") + + return fw + + def is_package_available(self, package_name: str) -> bool: + return importlib.util.find_spec(package_name) is not None -if __name__ == "keras_tool": - keras_tool() +if __name__ == "__main__": -if __name__ == "pytorch_tool": - pytorch_tool() + DLFrameWork().launch_instance() + \ No newline at end of file From ed53c87ee9d80566ef12fc544239ddd6f67ceb37 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 31 Mar 2025 12:02:17 +0000 Subject: [PATCH 003/119] some bug fixed and refactored some part --- ctlearn/__init__.py | 10 +- ctlearn/tools/base_train_model.py | 7 +- ctlearn/tools/{enum.py => ctlearn_enum.py} | 0 ctlearn/tools/keras/train_keras_model.py | 9 + ctlearn/tools/train_model.py | 229 +++++++-------------- 5 files changed, 90 insertions(+), 165 deletions(-) rename ctlearn/tools/{enum.py => ctlearn_enum.py} (100%) diff --git a/ctlearn/__init__.py b/ctlearn/__init__.py index af19c73a..f6af0b45 100644 --- a/ctlearn/__init__.py +++ b/ctlearn/__init__.py @@ -1,7 +1,15 @@ from ._version import __version__ +import importlib.util + + +def is_package_available(package_name: str) -> bool: + return importlib.util.find_spec(package_name) is not None + +__all__ = ["__version__", "is_package_available"] + -__all__ = ["__version__"] + # from ctlearn.tools.pytorch.train_pytorch_model import TrainPyTorchModel # from ctlearn.tools.keras.train_keras_model import TrainKerasModel diff --git a/ctlearn/tools/base_train_model.py b/ctlearn/tools/base_train_model.py index 9fc9ec2a..412f8c62 100644 --- a/ctlearn/tools/base_train_model.py +++ b/ctlearn/tools/base_train_model.py @@ -1,10 +1,7 @@ -import atexit -import pandas as pd + import numpy as np import shutil -import tensorflow as tf -import sys from ctapipe.core import Tool from ctapipe.core.tool import ToolConfigurationError from ctapipe.core.traits import ( @@ -22,7 +19,7 @@ from dl1_data_handler.reader import DLDataReader from ctlearn.core.loader import DLDataLoader from ctlearn.core.model import CTLearnModel -from ctlearn.utils import validate_trait_dict + class TrainCTLearnModel(Tool): """ diff --git a/ctlearn/tools/enum.py b/ctlearn/tools/ctlearn_enum.py similarity index 100% rename from ctlearn/tools/enum.py rename to ctlearn/tools/ctlearn_enum.py diff --git a/ctlearn/tools/keras/train_keras_model.py b/ctlearn/tools/keras/train_keras_model.py index 06ed9446..ed65a15e 100644 --- a/ctlearn/tools/keras/train_keras_model.py +++ b/ctlearn/tools/keras/train_keras_model.py @@ -109,6 +109,15 @@ class TrainKerasModel(TrainCTLearnModel): **TrainCTLearnModel.aliases, } + def setup(self): + # Create a MirroredStrategy. + self.strategy = tf.distribute.MirroredStrategy() + atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore + self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) + + super().setup() + + def start(self): # Set up the keras callbacks monitor = "val_loss" diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index fc268cfb..0db1e0b6 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -7,34 +7,21 @@ import numpy as np import tensorflow as tf import sys +import argparse from ctapipe.core import Tool -from ctapipe.core.tool import ToolConfigurationError + from ctapipe.core.traits import ( - Bool, CaselessStrEnum, - Path, - Float, - Int, - List, - Dict, - classes_with_traits, - ComponentName, - Unicode, ) -from dl1_data_handler.reader import DLDataReader from ctlearn import __version__ as ctlearn_version -from ctlearn.core.loader import DLDataLoader -from ctlearn.core.model import CTLearnModel -from ctlearn.utils import validate_trait_dict -from ctlearn.tools.pytorch.train_pytorch_model import TrainPyTorchModel -from ctlearn.tools.keras.train_keras_model import TrainKerasModel -# from ctlearn.tools.base_train_model import TrainCTLearnModel -import importlib.util +from ctlearn import is_package_available +from ctlearn.tools.ctlearn_enum import FrameworkType + +if is_package_available("torch"): + from ctlearn.tools.pytorch.train_pytorch_model import TrainPyTorchModel -from enum import Enum -class FrameworkType(Enum): - KERAS = 1 - PYTORCH = 2 +if is_package_available("tensorflow"): + from ctlearn.tools.keras.train_keras_model import TrainKerasModel class DLFrameWork(Tool): @@ -42,7 +29,16 @@ class DLFrameWork(Tool): framework_type = CaselessStrEnum( ["pytorch", "keras"], default_value="pytorch", - help="Framework to use", + help="Framework to use pytorch or keras", + ).tag(config=True) + + early_stopping = Dict( + default_value=None, + allow_none=True, + help=( + "Early stopping parameters for the Keras callback. " + "E.g. {'monitor': 'val_loss', 'patience': 4, 'verbose': 1, 'restore_best_weights': True}. " + ), ).tag(config=True) early_stopping = Dict( @@ -70,159 +66,74 @@ class DLFrameWork(Tool): ), } - classes = ( - [ - CTLearnModel, - DLDataReader, - ] - + classes_with_traits(CTLearnModel) - + classes_with_traits(DLDataReader) - ) - - def setup(self): - self.log.info("ctlearn version %s", ctlearn_version) - # Check if the output directory exists and if it should be overwritten - if self.output_dir.exists(): - if not self.overwrite: - raise ToolConfigurationError( - f"Output directory {self.output_dir} already exists. Use --overwrite to overwrite." - ) - else: - # Remove the output directory if it exists - self.log.info("Removing existing output directory %s", self.output_dir) - shutil.rmtree(self.output_dir) - # Create a MirroredStrategy. - self.strategy = tf.distribute.MirroredStrategy() - atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore - self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) - # Get signal input files - self.input_url_signal = [] - for signal_pattern in self.file_pattern_signal: - self.input_url_signal.extend(self.input_dir_signal.glob(signal_pattern)) - # Get bkg input files - self.input_url_background = [] - if self.input_dir_background is not None: - for background_pattern in self.file_pattern_background: - self.input_url_background.extend( - self.input_dir_background.glob(background_pattern) - ) + def __init__(self, **kwargs): + super().__init__(**kwargs) + print("CONFIG VALUES:", self.config) + + # def setup(self): + # pass # Do Nothing - # Set up the data reader - self.log.info("Loading data:") - self.log.info("For a large dataset, this may take a while...") - if self.dl1dh_reader_type == "DLFeatureVectorReader": - raise NotImplementedError( - "'DLFeatureVectorReader' is not supported in CTLearn yet. " - "Missing stereo CTLearnModel implementation." - ) - self.dl1dh_reader = DLDataReader.from_name( - self.dl1dh_reader_type, - input_url_signal=sorted(self.input_url_signal), - input_url_background=sorted(self.input_url_background), - parent=self, - ) - self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) - if "type" in self.reco_tasks: - self.log.info( - "Number of signal events: %d", self.dl1dh_reader.n_signal_events - ) - self.log.info( - "Number of background events: %d", self.dl1dh_reader.n_bkg_events - ) - # Check if the number of events is enough to form a batch - if self.dl1dh_reader._get_n_events() < self.batch_size: - raise ValueError( - f"{self.dl1dh_reader._get_n_events()} events are not enough " - f"to form a batch of size {self.batch_size}. Reduce the batch size." - ) - # Check if there are at least two classes in the reader for the particle classification - if self.dl1dh_reader.class_weight is None and "type" in self.reco_tasks: - raise ValueError( - "Classification task selected but less than two classes are present in the data." - ) - # Check if stereo mode is selected for stacking telescope images - if self.stack_telescope_images and self.dl1dh_reader.mode == "mono": - raise ToolConfigurationError( - f"Cannot stack telescope images in mono mode. Use stereo mode for stacking." - ) - # Ckeck if only one telescope type is selected for stacking telescope images - if ( - self.stack_telescope_images - and len(list(self.dl1dh_reader.selected_telescopes)) > 1 - ): - raise ToolConfigurationError( - f"Cannot stack telescope images from multiple telescope types. Use only one telescope type." - ) - # Check if sorting by intensity is disabled for stacking telescope images - if self.stack_telescope_images and self.sort_by_intensity: - raise ToolConfigurationError( - f"Cannot stack telescope images when sorting by intensity. Disable sorting by intensity." - ) - - # Set up the data loaders for training and validation - indices = list(range(self.dl1dh_reader._get_n_events())) - # Shuffle the indices before the training/validation split - np.random.seed(self.random_seed) - np.random.shuffle(indices) - n_validation_examples = int( - self.validation_split * self.dl1dh_reader._get_n_events() - ) - training_indices = indices[n_validation_examples:] - validation_indices = indices[:n_validation_examples] - self.training_loader = DLDataLoader( - self.dl1dh_reader, - training_indices, - tasks=self.reco_tasks, - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - random_seed=self.random_seed, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) - self.validation_loader = DLDataLoader( - self.dl1dh_reader, - validation_indices, - tasks=self.reco_tasks, - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - random_seed=self.random_seed, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) - - def finish(self): - pass + def start(self): + + print(f"Selected Framework: {self.framework_type}") + + framework= self.string_to_type(self.framework_type) + fw_obj = self.get_framework(framework) + fw_obj.parse_command_line(argv=sys.argv[1:]) # parse reco correctly now + fw_obj.run() @classmethod - def string_to_type(self,str_type:str)->FrameworkType: + def string_to_type(self, str_type: str) -> FrameworkType: - type_=None + type_ = None str_type = str.upper(str_type) try: type_ = FrameworkType[str_type] except KeyError: print(f"'{str_type}' is not a valid enum type.") return type_ - + + @classmethod def get_framework(self, framework_type: FrameworkType): if framework_type == FrameworkType.KERAS: - if not self.is_package_available("tensorflow"): - raise ImportError("TensorFlow is not installed. Cannot run Keras framework.") - else: - fw = TrainKerasModel() + if not is_package_available("tensorflow"): + raise ImportError( + "TensorFlow is not installed. Cannot run Keras framework." + ) + else: + + fw = TrainKerasModel() elif framework_type == FrameworkType.PYTORCH: - if not self.is_package_available("torch"): - raise ImportError("PyTorch is not installed. Cannot run PyTorch framework.") - else: - fw = TrainPyTorchModel() + if not is_package_available("torch"): + raise ImportError( + "PyTorch is not installed. Cannot run PyTorch framework." + ) + else: + fw = TrainPyTorchModel() else: - raise ValueError("Unknown Framework") + raise ValueError(f"Unknown Framework: {framework_type.name}") + # Update Aliases + self.aliases.update(fw.aliases) + DLFrameWork.aliases.update(fw.aliases) return fw - - def is_package_available(self, package_name: str) -> bool: - return importlib.util.find_spec(package_name) is not None + if __name__ == "__main__": - DLFrameWork().launch_instance() - \ No newline at end of file + # # Parse the framework argument + # parser = argparse.ArgumentParser() + # parser.add_argument("--framework") + # args, _ = parser.parse_known_args() + + # # Get Framework type + # if args.framework: + # fw_type = DLFrameWork.string_to_type(args.framework) + # else: + # raise ValueError(f"Framework not defined, use : --framework keras or --framework pytorch") + + # DLFrameWork.get_framework(fw_type) + + # Launch the Framework + DLFrameWork().launch_instance() \ No newline at end of file From 0f88ecc36596483845b77235586bae448c517e11 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 31 Mar 2025 14:29:36 +0000 Subject: [PATCH 004/119] refactored to avoid dependencies with tensorflow and pytorch. Added tqdm in keras. --- ctlearn/tools/keras/train_keras_model.py | 25 +++++++ ctlearn/tools/train_model.py | 86 +++++++++++------------- 2 files changed, 66 insertions(+), 45 deletions(-) diff --git a/ctlearn/tools/keras/train_keras_model.py b/ctlearn/tools/keras/train_keras_model.py index ed65a15e..5f4e9312 100644 --- a/ctlearn/tools/keras/train_keras_model.py +++ b/ctlearn/tools/keras/train_keras_model.py @@ -3,6 +3,9 @@ import numpy as np import shutil import tensorflow as tf +from tqdm import tqdm +from time import time +from keras.callbacks import Callback from ctapipe.core.traits import ( Bool, @@ -27,6 +30,24 @@ except ImportError: raise ImportError("keras is not installed in your environment!") +class TqdmProgressBar(Callback): + def on_epoch_begin(self, epoch, logs=None): + self.epoch_start_time = time() + self.progress_bar = tqdm(total=self.params['steps'], desc=f'Epoc {epoch + 1}/{self.params["epochs"]}', unit='batch') + + def on_epoch_end(self, epoch, logs=None): + epoch_duration = time() - self.epoch_start_time + print(f'\nDuración de la época {epoch + 1}: {epoch_duration:.2f} segundos') + self.progress_bar.close() + + def on_batch_begin(self, batch, logs=None): + self.batch_start_time = time() + + def on_batch_end(self, batch, logs=None): + # batch_duration = time() - self.batch_start_time + self.progress_bar.set_postfix(loss=logs.get('loss'), val_loss=logs.get('val_loss')) + self.progress_bar.update(1) + class TrainKerasModel(TrainCTLearnModel): """ Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using keras. @@ -110,6 +131,7 @@ class TrainKerasModel(TrainCTLearnModel): } def setup(self): + print(tf.config.list_physical_devices('GPU')) # Create a MirroredStrategy. self.strategy = tf.distribute.MirroredStrategy() atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore @@ -213,6 +235,9 @@ def start(self): self.log.info("Compiling CTLearn model.") self.model.compile(optimizer=optimizer_fn(**optimizer_args), loss=losses, metrics=metrics) + tqdm_callback = TqdmProgressBar() + self.callbacks.append(tqdm_callback) + # Train and evaluate the model self.log.info("Training and evaluating...") self.model.fit( diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 0db1e0b6..46c08147 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -9,7 +9,7 @@ import sys import argparse from ctapipe.core import Tool - +import warnings from ctapipe.core.traits import ( CaselessStrEnum, ) @@ -17,13 +17,6 @@ from ctlearn import is_package_available from ctlearn.tools.ctlearn_enum import FrameworkType -if is_package_available("torch"): - from ctlearn.tools.pytorch.train_pytorch_model import TrainPyTorchModel - -if is_package_available("tensorflow"): - from ctlearn.tools.keras.train_keras_model import TrainKerasModel - - class DLFrameWork(Tool): name = "dlframework" framework_type = CaselessStrEnum( @@ -51,19 +44,9 @@ class DLFrameWork(Tool): ).tag(config=True) aliases = { - "signal": "TrainCTLearnModel.input_dir_signal", - "background": "TrainCTLearnModel.input_dir_background", - "pattern-signal": "TrainCTLearnModel.file_pattern_signal", - "pattern-background": "TrainCTLearnModel.file_pattern_background", - "reco": "TrainCTLearnModel.reco_tasks", - ("o", "output"): "TrainCTLearnModel.output_dir", - } - - flags = { - "overwrite": ( - {"TrainCTLearnModel": {"overwrite": True}}, - "Overwrite existing files", - ), + **TrainPyTorchModel.aliases, + **TrainKerasModel.aliases, + "framework": "DLFrameWork.framework_type", } def __init__(self, **kwargs): @@ -96,21 +79,32 @@ def string_to_type(self, str_type: str) -> FrameworkType: @classmethod def get_framework(self, framework_type: FrameworkType): if framework_type == FrameworkType.KERAS: - if not is_package_available("tensorflow"): - raise ImportError( - "TensorFlow is not installed. Cannot run Keras framework." - ) - else: - - fw = TrainKerasModel() + # if not is_package_available("tensorflow"): + # raise ImportError( + # "TensorFlow is not installed. Cannot run Keras framework." + # ) + # else: + try: + from ctlearn.tools.keras.train_keras_model import TrainKerasModel + except ImportError: + raise ImportError(f"Not possible to import TrainKerasModel") + fw = TrainKerasModel() elif framework_type == FrameworkType.PYTORCH: - if not is_package_available("torch"): - raise ImportError( - "PyTorch is not installed. Cannot run PyTorch framework." + # if not is_package_available("torch"): + # raise ImportError( + # "PyTorch (torch) is not installed. Cannot run PyTorch framework." + # ) + # else: + try: + from ctlearn.tools.pytorch.train_pytorch_model import ( + TrainPyTorchModel, ) - else: - fw = TrainPyTorchModel() + except ImportError: + raise ImportError(f"Not possible to import TrainPyTorchModel") + + fw = TrainPyTorchModel() + else: raise ValueError(f"Unknown Framework: {framework_type.name}") # Update Aliases @@ -122,18 +116,20 @@ def get_framework(self, framework_type: FrameworkType): if __name__ == "__main__": - # # Parse the framework argument - # parser = argparse.ArgumentParser() - # parser.add_argument("--framework") - # args, _ = parser.parse_known_args() + # Parse the framework argument + parser = argparse.ArgumentParser() + parser.add_argument("--framework") + args, _ = parser.parse_known_args() + + # Get Framework type + if args.framework: + fw_type = DLFrameWork.string_to_type(args.framework) + else: + raise ValueError( + f"Framework not defined, use : --framework keras or --framework pytorch" + ) - # # Get Framework type - # if args.framework: - # fw_type = DLFrameWork.string_to_type(args.framework) - # else: - # raise ValueError(f"Framework not defined, use : --framework keras or --framework pytorch") - - # DLFrameWork.get_framework(fw_type) + DLFrameWork.get_framework(fw_type) # Launch the Framework - DLFrameWork().launch_instance() \ No newline at end of file + DLFrameWork().launch_instance() From 899317b6d263a2429a76e81aea802372c53ed923 Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 4 Apr 2025 09:13:56 +0000 Subject: [PATCH 005/119] fixed a missing var when using pytorch and removed commented code. --- ctlearn/tools/base_train_model.py | 12 +++++++++--- ctlearn/tools/train_model.py | 14 ++------------ 2 files changed, 11 insertions(+), 15 deletions(-) diff --git a/ctlearn/tools/base_train_model.py b/ctlearn/tools/base_train_model.py index 412f8c62..3b3232c2 100644 --- a/ctlearn/tools/base_train_model.py +++ b/ctlearn/tools/base_train_model.py @@ -239,7 +239,7 @@ def setup(self): self.log.info("Removing existing output directory %s", self.output_dir) shutil.rmtree(self.output_dir) - # Must be moved to KERAS + # Must be moved to KERAS (It is moved already ) # Create a MirroredStrategy. # self.strategy = tf.distribute.MirroredStrategy() # atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore @@ -249,14 +249,14 @@ def setup(self): self.input_url_signal = [] for signal_pattern in self.file_pattern_signal: self.input_url_signal.extend(self.input_dir_signal.glob(signal_pattern)) - # print(f"self.input_url_signal: {self.input_url_signal}") + # Get bkg input files self.input_url_background = [] if self.input_dir_background is not None: for background_pattern in self.file_pattern_background: self.input_url_background.extend(self.input_dir_background.glob(background_pattern)) - # print(f"self.input_url_background: {self.input_url_background}") + print("DEBUG 1") # Set up the data reader self.log.info("Loading data:") @@ -314,6 +314,12 @@ def setup(self): n_validation_examples = int(self.validation_split * self.dl1dh_reader._get_n_events()) training_indices = indices[n_validation_examples:] validation_indices = indices[:n_validation_examples] + + # Set self.strategy.num_replicas_in_sync to 1 in case that does not exist (Pytorch) + if not hasattr(self, "strategy"): + self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() + print("num_replicas_in_sync:",self.strategy.num_replicas_in_sync) + self.training_loader = DLDataLoader( self.dl1dh_reader, training_indices, diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 46c08147..d4c02f16 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -21,7 +21,7 @@ class DLFrameWork(Tool): name = "dlframework" framework_type = CaselessStrEnum( ["pytorch", "keras"], - default_value="pytorch", + default_value="keras", help="Framework to use pytorch or keras", ).tag(config=True) @@ -79,11 +79,6 @@ def string_to_type(self, str_type: str) -> FrameworkType: @classmethod def get_framework(self, framework_type: FrameworkType): if framework_type == FrameworkType.KERAS: - # if not is_package_available("tensorflow"): - # raise ImportError( - # "TensorFlow is not installed. Cannot run Keras framework." - # ) - # else: try: from ctlearn.tools.keras.train_keras_model import TrainKerasModel except ImportError: @@ -91,11 +86,6 @@ def get_framework(self, framework_type: FrameworkType): fw = TrainKerasModel() elif framework_type == FrameworkType.PYTORCH: - # if not is_package_available("torch"): - # raise ImportError( - # "PyTorch (torch) is not installed. Cannot run PyTorch framework." - # ) - # else: try: from ctlearn.tools.pytorch.train_pytorch_model import ( TrainPyTorchModel, @@ -118,7 +108,7 @@ def get_framework(self, framework_type: FrameworkType): # Parse the framework argument parser = argparse.ArgumentParser() - parser.add_argument("--framework") + parser.add_argument("--framework", default="keras") args, _ = parser.parse_known_args() # Get Framework type From 5a53710001194700b9a4a1cac6717a27f3472cc6 Mon Sep 17 00:00:00 2001 From: pguzman Date: Thu, 24 Apr 2025 11:12:30 +0000 Subject: [PATCH 006/119] Added DataLoader in Factory format --- ctlearn/{tools => core}/ctlearn_enum.py | 0 .../keras => core/data_loader}/__init__.py | 0 ctlearn/core/data_loader/base_loader.py | 55 ++++ ctlearn/core/data_loader/keras_loader.py | 248 ++++++++++++++++++ ctlearn/core/data_loader/loader.py | 12 + .../loader_original.py} | 1 + ctlearn/core/data_loader/pytorch_loader.py | 22 ++ ctlearn/core/tests/test_loader.py | 3 +- ctlearn/tools/predict_model.py | 19 +- ctlearn/tools/{ => train}/base_train_model.py | 38 ++- .../{pytorch => train/keras}/__init__.py | 0 .../{ => train}/keras/train_keras_model.py | 8 +- ctlearn/tools/train/pytorch/__init__.py | 0 .../pytorch/train_pytorch_model.py | 2 +- ctlearn/tools/train_model.py | 19 +- 15 files changed, 394 insertions(+), 33 deletions(-) rename ctlearn/{tools => core}/ctlearn_enum.py (100%) rename ctlearn/{tools/keras => core/data_loader}/__init__.py (100%) create mode 100644 ctlearn/core/data_loader/base_loader.py create mode 100644 ctlearn/core/data_loader/keras_loader.py create mode 100644 ctlearn/core/data_loader/loader.py rename ctlearn/core/{loader.py => data_loader/loader_original.py} (99%) create mode 100644 ctlearn/core/data_loader/pytorch_loader.py rename ctlearn/tools/{ => train}/base_train_model.py (92%) rename ctlearn/tools/{pytorch => train/keras}/__init__.py (100%) rename ctlearn/tools/{ => train}/keras/train_keras_model.py (98%) create mode 100644 ctlearn/tools/train/pytorch/__init__.py rename ctlearn/tools/{ => train}/pytorch/train_pytorch_model.py (98%) diff --git a/ctlearn/tools/ctlearn_enum.py b/ctlearn/core/ctlearn_enum.py similarity index 100% rename from ctlearn/tools/ctlearn_enum.py rename to ctlearn/core/ctlearn_enum.py diff --git a/ctlearn/tools/keras/__init__.py b/ctlearn/core/data_loader/__init__.py similarity index 100% rename from ctlearn/tools/keras/__init__.py rename to ctlearn/core/data_loader/__init__.py diff --git a/ctlearn/core/data_loader/base_loader.py b/ctlearn/core/data_loader/base_loader.py new file mode 100644 index 00000000..b4cc4613 --- /dev/null +++ b/ctlearn/core/data_loader/base_loader.py @@ -0,0 +1,55 @@ +from abc import ABC, abstractmethod + +class BaseDLDataLoader(ABC): + + def __init__( + self, + DLDataReader, + indices, + tasks, + batch_size=64, + random_seed=None, + sort_by_intensity=False, + stack_telescope_images=False, + **kwargs, + ): + + super().__init__(**kwargs) + "Initialization" + self.DLDataReader = DLDataReader + self.indices = indices + self.tasks = tasks + self.batch_size = batch_size + self.random_seed = random_seed + # self.on_epoch_end() + self.stack_telescope_images = stack_telescope_images + self.sort_by_intensity = sort_by_intensity + + # Set the input shape based on the mode of the DLDataReader + if self.DLDataReader.__class__.__name__ != "DLFeatureVectorReader": + if self.DLDataReader.mode == "mono": + self.input_shape = self.DLDataReader.input_shape + elif self.DLDataReader.mode == "stereo": + self.input_shape = self.DLDataReader.input_shape[ + list(self.DLDataReader.selected_telescopes)[0] + ] + # Reshape inputs into proper dimensions + # for the stereo analysis with stacked images + if self.stack_telescope_images: + self.input_shape = ( + self.input_shape[1], + self.input_shape[2], + self.input_shape[0] * self.input_shape[3], + ) + + @abstractmethod + def __len__(self): + pass + + @abstractmethod + def __getitem__(self, index): + pass + + @abstractmethod + def on_epoch_end(self): + pass diff --git a/ctlearn/core/data_loader/keras_loader.py b/ctlearn/core/data_loader/keras_loader.py new file mode 100644 index 00000000..b8d4b3d0 --- /dev/null +++ b/ctlearn/core/data_loader/keras_loader.py @@ -0,0 +1,248 @@ +import numpy as np +import keras +from keras.utils import Sequence, to_categorical +from .base_loader import BaseDLDataLoader + +from dl1_data_handler.reader import ProcessType + +class KerasDLDataLoader(Sequence, BaseDLDataLoader): + def __init__( + self, + **kwargs, + ): + + super().__init__(**kwargs) + self.on_epoch_end() + + def __len__(self): + """ + Returns the number of batches per epoch. + + This method calculates the number of batches required to cover the entire dataset + based on the batch size. + + Returns: + -------- + int + Number of batches per epoch. + """ + return int(np.floor(len(self.indices) / self.batch_size)) + + def on_epoch_end(self): + """ + Updates indices after each epoch. If a random seed is provided, the indices are shuffled. + + This method is called at the end of each epoch to ensure that the data is shuffled + if the shuffle attribute is set to True. This helps in improving the training process + by providing the model with a different order of data in each epoch. + """ + if self.random_seed is not None: + np.random.seed(self.random_seed) + np.random.shuffle(self.indices) + + def __getitem__(self, index): + """ + Generate one batch of data and retrieve the features and labels. + + This method is called to generate one batch of monoscopic and stereoscopic data based on + the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) + based on the mode of the DLDataReader. + + Parameters: + ----------- + index : int + Index of the batch to generate. + + Returns: + -------- + tuple + A tuple containing the input data as features and the corresponding labels. + """ + # Generate indices of the batch + batch_indices = self.indices[ + index * self.batch_size : (index + 1) * self.batch_size + ] + features, labels = None, None + if self.DLDataReader.mode == "mono": + batch = self.DLDataReader.generate_mono_batch(batch_indices) + features, labels = self._get_mono_item(batch) + elif self.DLDataReader.mode == "stereo": + batch = self.DLDataReader.generate_stereo_batch(batch_indices) + features, labels = self._get_stereo_item(batch) + return features, labels + + def _get_mono_item(self, batch): + """ + Retrieve the features and labels for one batch of monoscopic data. + + This method is called to retrieve the features and labels for one batch of + monoscopic data. The labels are set up based on the tasks specified. + + Parameters: + ----------- + batch : astropy.table.Table + A table containing the data for the batch. + + Returns: + -------- + tuple + A tuple containing the input data as features and the corresponding labels. + """ + # Retrieve the telescope images and store in the features dictionary + labels = {} + features = {"input": batch["features"].data} + if "type" in self.tasks: + labels["type"] = to_categorical( + batch["true_shower_primary_class"].data, + num_classes=2, + ) + # Temp fix till keras support class weights for multiple outputs or I wrote custom loss + # https://github.com/keras-team/keras/issues/11735 + if len(self.tasks) == 1: + labels = to_categorical( + batch["true_shower_primary_class"].data, + num_classes=2, + ) + if "energy" in self.tasks: + labels["energy"] = batch["log_true_energy"].data + if "skydirection" in self.tasks: + labels["skydirection"] = np.stack( + ( + batch["fov_lon"].data, + batch["fov_lat"].data, + ), + axis=1, + ) + if "cameradirection" in self.tasks: + labels["cameradirection"] = np.stack( + ( + batch["cam_coord_offset_x"].data, + batch["cam_coord_offset_y"].data, + ), + axis=1, + ) + # Temp fix for supporting keras2 & keras3 + if int(keras.__version__.split(".")[0]) >= 3: + features = features["input"] + return features, labels + + def _get_stereo_item(self, batch): + """ + Retrieve the features and labels for one batch of stereoscopic data. + + This method is called to retrieve the features and labels for one batch of + stereoscopic data. The original batch is grouped to retrieve the telescope + data for each event and then the telescope images or waveforms are stored + by the hillas intensity or stacked if required. Feature vectors can also + be retrieved if available for ``telescope``- and ``subarray``level. The + labels are set up based on the tasks specified. + + Parameters: + ----------- + batch : astropy.table.Table + A table containing the data for the batch. + + Returns: + -------- + tuple + A tuple containing the input data as features and the corresponding labels. + """ + labels = {} + if self.DLDataReader.process_type == ProcessType.Simulation: + batch_grouped = batch.group_by( + ["obs_id", "event_id", "tel_type_id", "true_shower_primary_class"] + ) + elif self.DLDataReader.process_type == ProcessType.Observation: + batch_grouped = batch.group_by(["obs_id", "event_id", "tel_type_id"]) + features, mono_feature_vectors, stereo_feature_vectors = [], [], [] + true_shower_primary_class = [] + log_true_energy = [] + fov_lon, fov_lat, angular_separation = [], [], [] + cam_coord_offset_x, cam_coord_offset_y, cam_coord_distance = [], [], [] + for group_element in batch_grouped.groups: + if "features" in batch.colnames: + if self.sort_by_intensity: + # Sort images by the hillas intensity in a given batch if requested + group_element.sort(["hillas_intensity"], reverse=True) + # Stack the telescope images for stereo analysis + if self.stack_telescope_images: + # Retrieve the telescope images + plain_features = group_element["features"].data + # Stack the telescope images along the last axis + stacked_features = np.concatenate( + [plain_features[i] for i in range(plain_features.shape[0])], + axis=-1, + ) + # Append the stacked images to the features list + # shape: (batch_size, image_shape, image_shape, n_channels * n_tel) + features.append(stacked_features) + else: + # Append the plain images to the features list + # shape: (batch_size, n_tel, image_shape, image_shape, n_channels) + features.append(group_element["features"].data) + # Retrieve the feature vectors + if "mono_feature_vectors" in batch.colnames: + mono_feature_vectors.append(group_element["mono_feature_vectors"].data) + if "stereo_feature_vectors" in batch.colnames: + stereo_feature_vectors.append( + group_element["stereo_feature_vectors"].data + ) + # Retrieve the labels for the tasks + # FIXME: This won't work for divergent pointing directions + if "type" in self.tasks: + true_shower_primary_class.append( + group_element["true_shower_primary_class"].data[0] + ) + if "energy" in self.tasks: + log_true_energy.append(group_element["log_true_energy"].data[0]) + if "skydirection" in self.tasks: + fov_lon.append(group_element["fov_lon"].data[0]) + fov_lat.append(group_element["fov_lat"].data[0]) + if "cameradirection" in self.tasks: + cam_coord_offset_x.append(group_element["cam_coord_offset_x"].data) + cam_coord_offset_y.append(group_element["cam_coord_offset_y"].data) + # Store the labels in the labels dictionary + if "type" in self.tasks: + labels["type"] = to_categorical( + np.array(true_shower_primary_class), + num_classes=2, + ) + # Temp fix till keras support class weights for multiple outputs or I wrote custom loss + # https://github.com/keras-team/keras/issues/11735 + if len(self.tasks) == 1: + labels = to_categorical( + np.array(true_shower_primary_class), + num_classes=2, + ) + if "energy" in self.tasks: + labels["energy"] = np.array(log_true_energy) + if "skydirection" in self.tasks: + labels["skydirection"] = np.stack( + ( + np.array(fov_lon), + np.array(fov_lat), + ), + axis=1, + ) + if "cameradirection" in self.tasks: + labels["cameradirection"] = np.stack( + ( + np.array(cam_coord_offset_x), + np.array(cam_coord_offset_y), + ), + axis=1, + ) + # Store the fatures in the features dictionary + if "features" in batch.colnames: + features = {"input": np.array(features)} + # TDOO: Add support for both feature vectors + if "mono_feature_vectors" in batch.colnames: + features = {"input": np.array(mono_feature_vectors)} + if "stereo_feature_vectors" in batch.colnames: + features = {"input": np.array(stereo_feature_vectors)} + # Temp fix for supporting keras2 & keras3 + if int(keras.__version__.split(".")[0]) >= 3: + features = features["input"] + return features, labels + + # Include _get_mono_item and _get_stereo_item as needed diff --git a/ctlearn/core/data_loader/loader.py b/ctlearn/core/data_loader/loader.py new file mode 100644 index 00000000..4e7e989e --- /dev/null +++ b/ctlearn/core/data_loader/loader.py @@ -0,0 +1,12 @@ +from .keras_loader import KerasDLDataLoader +from .pytorch_loader import PyTorchDLDataLoader + +class DLDataLoader: + @staticmethod + def create(framework, **kwargs): + if framework == "keras": + return KerasDLDataLoader(**kwargs) + elif framework == "pytorch": + return PyTorchDLDataLoader(**kwargs) + else: + raise ValueError(f"Unsupported framework: {framework}") \ No newline at end of file diff --git a/ctlearn/core/loader.py b/ctlearn/core/data_loader/loader_original.py similarity index 99% rename from ctlearn/core/loader.py rename to ctlearn/core/data_loader/loader_original.py index 92fd96c2..3a36f554 100644 --- a/ctlearn/core/loader.py +++ b/ctlearn/core/data_loader/loader_original.py @@ -3,6 +3,7 @@ import keras from keras.utils import Sequence, to_categorical + from dl1_data_handler.reader import ProcessType diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py new file mode 100644 index 00000000..218373ba --- /dev/null +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -0,0 +1,22 @@ +import torch +from torch.utils.data import Dataset +from .base_loader import BaseDLDataLoader + +class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): + def __init__(self, DLDataReader, indices, tasks, **kwargs): + self.DLDataReader = DLDataReader + self.indices = indices + self.tasks = tasks + self.sort_by_intensity = kwargs.get('sort_by_intensity', False) + self.stack_telescope_images = kwargs.get('stack_telescope_images', False) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, index): + idx = self.indices[index] + # Replace with PyTorch-style tensor return + return torch.tensor(...), torch.tensor(...) + + def on_epoch_end(self): + pass # Optional: implement if you're using a custom sampler \ No newline at end of file diff --git a/ctlearn/core/tests/test_loader.py b/ctlearn/core/tests/test_loader.py index 7fe71900..08a11d79 100644 --- a/ctlearn/core/tests/test_loader.py +++ b/ctlearn/core/tests/test_loader.py @@ -1,7 +1,7 @@ from traitlets.config.loader import Config from dl1_data_handler.reader import DLImageReader -from ctlearn.core.loader import DLDataLoader +from ctlearn.core.data_loader.loader import DLDataLoader def test_data_loader(dl1_gamma_file): @@ -19,6 +19,7 @@ def test_data_loader(dl1_gamma_file): dl1_reader = DLImageReader(input_url_signal=[dl1_gamma_file], config=config) # Create a data loader dl1_loader = DLDataLoader( + framework = "keras", DLDataReader=dl1_reader, indices=[0], tasks=["type", "energy", "cameradirection", "skydirection"], diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index acfa39be..bcd04d87 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -86,7 +86,7 @@ LST_EPOCH, ) from ctlearn import __version__ as ctlearn_version -from ctlearn.core.loader import DLDataLoader +from ctlearn.core.data_loader.loader import DLDataLoader from ctlearn.utils import validate_trait_dict # Convienient constants for column names and table keys @@ -696,14 +696,16 @@ def _predict_with_model(self, model_path): """ # Create a new DLDataLoader for each task # It turned out to be more robust to initialize the DLDataLoader separately. - data_loader = DLDataLoader( - self.dl1dh_reader, - self.indices, + data_loader = DLDataLoader.create( + framework="keras", + DLDataReader=self.dl1dh_reader, + indices=self.indices, tasks=[], batch_size=self.batch_size * self.strategy.num_replicas_in_sync, sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, ) + # Keras is only considering the last complete batch. # In prediction mode we don't want to loose the last # uncomplete batch, so we are creating an additional @@ -711,14 +713,17 @@ def _predict_with_model(self, model_path): data_loader_last_batch = None if self.last_batch_size > 0: last_batch_indices = self.indices[-self.last_batch_size :] - data_loader_last_batch = DLDataLoader( - self.dl1dh_reader, - last_batch_indices, + data_loader_last_batch = DLDataLoader.create( + framework="keras", + DLDataReader=self.dl1dh_reader, + indices=last_batch_indices, tasks=[], batch_size=self.last_batch_size, sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, ) + + # Load the model from the specified path model = keras.saving.load_model(model_path) prediction_colname = ( diff --git a/ctlearn/tools/base_train_model.py b/ctlearn/tools/train/base_train_model.py similarity index 92% rename from ctlearn/tools/base_train_model.py rename to ctlearn/tools/train/base_train_model.py index 3b3232c2..806ad2c3 100644 --- a/ctlearn/tools/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -17,7 +17,7 @@ Unicode, ) from dl1_data_handler.reader import DLDataReader -from ctlearn.core.loader import DLDataLoader +from ctlearn.core.data_loader.loader import DLDataLoader from ctlearn.core.model import CTLearnModel @@ -81,6 +81,12 @@ class TrainCTLearnModel(Tool): """ name = "ctlearn-train-model-base" + framework_type = CaselessStrEnum( + ["pytorch", "keras"], + default_value="keras", + help="Framework to use pytorch or keras" + ).tag(config=True) + input_dir_signal = Path( help="Input directory for signal events", allow_none=False, @@ -144,6 +150,7 @@ class TrainCTLearnModel(Tool): reco_tasks = List( trait=CaselessStrEnum(["type", "energy", "cameradirection", "skydirection"]), allow_none=False, + default_value=None, help=( "List of reconstruction tasks to perform. " "'type': classification of the primary particle type " @@ -198,7 +205,8 @@ class TrainCTLearnModel(Tool): overwrite = Bool(help="Overwrite output dir if it exists").tag(config=True) aliases = { - "framework": "DLFrameWork.framework_type", + # "framework": "DLFrameWork.framework_type", + "framework": "TrainCTLearnModel.framework_type", "signal": "TrainCTLearnModel.input_dir_signal", "background": "TrainCTLearnModel.input_dir_background", "pattern-signal": "TrainCTLearnModel.file_pattern_signal", @@ -213,7 +221,7 @@ class TrainCTLearnModel(Tool): "Overwrite existing files", ), } - + classes = ( [ CTLearnModel, @@ -244,7 +252,8 @@ def setup(self): # self.strategy = tf.distribute.MirroredStrategy() # atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore # self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) - + + # print(self.DLFrameWork.framework_type) # Get signal input files self.input_url_signal = [] for signal_pattern in self.file_pattern_signal: @@ -320,20 +329,25 @@ def setup(self): self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() print("num_replicas_in_sync:",self.strategy.num_replicas_in_sync) - self.training_loader = DLDataLoader( - self.dl1dh_reader, - training_indices, + print(self.framework_type) + + self.training_loader = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=training_indices, tasks=self.reco_tasks, - batch_size=self.batch_size*self.strategy.num_replicas_in_sync, + batch_size=self.batch_size * self.strategy.num_replicas_in_sync, random_seed=self.random_seed, sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, ) - self.validation_loader = DLDataLoader( - self.dl1dh_reader, - validation_indices, + + self.validation_loader = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=training_indices, tasks=self.reco_tasks, - batch_size=self.batch_size*self.strategy.num_replicas_in_sync, + batch_size=self.batch_size * self.strategy.num_replicas_in_sync, random_seed=self.random_seed, sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, diff --git a/ctlearn/tools/pytorch/__init__.py b/ctlearn/tools/train/keras/__init__.py similarity index 100% rename from ctlearn/tools/pytorch/__init__.py rename to ctlearn/tools/train/keras/__init__.py diff --git a/ctlearn/tools/keras/train_keras_model.py b/ctlearn/tools/train/keras/train_keras_model.py similarity index 98% rename from ctlearn/tools/keras/train_keras_model.py rename to ctlearn/tools/train/keras/train_keras_model.py index 5f4e9312..f4ddc890 100644 --- a/ctlearn/tools/keras/train_keras_model.py +++ b/ctlearn/tools/train/keras/train_keras_model.py @@ -19,9 +19,9 @@ ComponentName, Unicode, ) -from ctlearn.tools.base_train_model import TrainCTLearnModel -from dl1_data_handler.reader import DLDataReader -from ctlearn.core.loader import DLDataLoader +from ctlearn.tools.train.base_train_model import TrainCTLearnModel +# from dl1_data_handler.reader import DLDataReader +# from ctlearn.core.loader import DLDataLoader from ctlearn.core.model import CTLearnModel from ctlearn.utils import validate_trait_dict @@ -136,7 +136,7 @@ def setup(self): self.strategy = tf.distribute.MirroredStrategy() atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) - + # print(self.framework_type) super().setup() diff --git a/ctlearn/tools/train/pytorch/__init__.py b/ctlearn/tools/train/pytorch/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/tools/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py similarity index 98% rename from ctlearn/tools/pytorch/train_pytorch_model.py rename to ctlearn/tools/train/pytorch/train_pytorch_model.py index eabeea00..7476efad 100644 --- a/ctlearn/tools/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -21,7 +21,7 @@ except ImportError: raise ImportError("pytorch_lightning is not installed in your environment!") -from ctlearn.tools.base_train_model import TrainCTLearnModel +from ctlearn.tools.train.base_train_model import TrainCTLearnModel # from ctlearn.tools.train_model import class TrainPyTorchModel(TrainCTLearnModel): """ diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index d4c02f16..372ef749 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -15,10 +15,11 @@ ) from ctlearn import __version__ as ctlearn_version from ctlearn import is_package_available -from ctlearn.tools.ctlearn_enum import FrameworkType +from ctlearn.core.ctlearn_enum import FrameworkType class DLFrameWork(Tool): name = "dlframework" + framework_type = CaselessStrEnum( ["pytorch", "keras"], default_value="keras", @@ -80,19 +81,18 @@ def string_to_type(self, str_type: str) -> FrameworkType: def get_framework(self, framework_type: FrameworkType): if framework_type == FrameworkType.KERAS: try: - from ctlearn.tools.keras.train_keras_model import TrainKerasModel - except ImportError: - raise ImportError(f"Not possible to import TrainKerasModel") + from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + except ImportError as e: + raise ImportError(f"Not possible to import TrainKerasModel: {e}") from e fw = TrainKerasModel() elif framework_type == FrameworkType.PYTORCH: try: - from ctlearn.tools.pytorch.train_pytorch_model import ( + from ctlearn.tools.train.pytorch.train_pytorch_model import ( TrainPyTorchModel, ) - except ImportError: - raise ImportError(f"Not possible to import TrainPyTorchModel") - + except ImportError as e: + raise ImportError(f"Not possible to import TrainPyTorchModel: {e}") from e fw = TrainPyTorchModel() else: @@ -123,3 +123,6 @@ def get_framework(self, framework_type: FrameworkType): # Launch the Framework DLFrameWork().launch_instance() + +# Example: +# python -m ctlearn.tools.train_model --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite \ No newline at end of file From dc2b18c8902ecbddb71e61a67f78c1bf7acb92b2 Mon Sep 17 00:00:00 2001 From: pguzman Date: Thu, 24 Apr 2025 12:10:54 +0000 Subject: [PATCH 007/119] Fix unit test?? --- ctlearn/core/data_loader/loader.py | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/ctlearn/core/data_loader/loader.py b/ctlearn/core/data_loader/loader.py index 4e7e989e..3833e0f7 100644 --- a/ctlearn/core/data_loader/loader.py +++ b/ctlearn/core/data_loader/loader.py @@ -1,12 +1,25 @@ -from .keras_loader import KerasDLDataLoader -from .pytorch_loader import PyTorchDLDataLoader +# from .keras_loader import KerasDLDataLoader +# from .pytorch_loader import PyTorchDLDataLoader class DLDataLoader: @staticmethod def create(framework, **kwargs): + + dataloader = None if framework == "keras": - return KerasDLDataLoader(**kwargs) + try: + from .keras_loader import KerasDLDataLoader + dataloader = KerasDLDataLoader(**kwargs) + except ImportError as e: + raise ImportError(f"Not possible to import KerasDLDataLoader: {e}") from e + elif framework == "pytorch": - return PyTorchDLDataLoader(**kwargs) + try: + from .pytorch_loader import PyTorchDLDataLoader + dataloader = PyTorchDLDataLoader(**kwargs) + except ImportError as e: + raise ImportError(f"Not possible to import PyTorchDLDataLoader: {e}") from e + else: - raise ValueError(f"Unsupported framework: {framework}") \ No newline at end of file + raise ValueError(f"Unsupported framework: {framework}") + From ef870c92dc77a79cf8337dbd52f22228af2b8f61 Mon Sep 17 00:00:00 2001 From: pguzman Date: Thu, 24 Apr 2025 15:27:49 +0000 Subject: [PATCH 008/119] fixed some bugs and fixed framework parameter --- ctlearn/core/ctlearn_enum.py | 15 +- ctlearn/core/data_loader/loader.py | 1 + ctlearn/core/data_loader/pytorch_loader.py | 241 +++++++++++++++++- ctlearn/tools/train/base_train_model.py | 12 +- .../tools/train/keras/train_keras_model.py | 1 + .../train/pytorch/train_pytorch_model.py | 20 +- ctlearn/tools/train_model.py | 129 ++++++---- ctlearn/tools/train_model_old.py | 83 ++++++ 8 files changed, 428 insertions(+), 74 deletions(-) create mode 100644 ctlearn/tools/train_model_old.py diff --git a/ctlearn/core/ctlearn_enum.py b/ctlearn/core/ctlearn_enum.py index f4f3eb09..ab01a0bb 100644 --- a/ctlearn/core/ctlearn_enum.py +++ b/ctlearn/core/ctlearn_enum.py @@ -2,4 +2,17 @@ class FrameworkType(Enum): KERAS = 1 - PYTORCH = 2 \ No newline at end of file + PYTORCH = 2 + + +class Task(Enum): + type = 0 + energy = 1 + direction = 2 + all = 3 + +class EventType(Enum): + gamma=0 + proton=1 + electron=2 + \ No newline at end of file diff --git a/ctlearn/core/data_loader/loader.py b/ctlearn/core/data_loader/loader.py index 3833e0f7..39033bbe 100644 --- a/ctlearn/core/data_loader/loader.py +++ b/ctlearn/core/data_loader/loader.py @@ -23,3 +23,4 @@ def create(framework, **kwargs): else: raise ValueError(f"Unsupported framework: {framework}") + return dataloader diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 218373ba..657c4d40 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -1,22 +1,237 @@ -import torch +# import torch +import numpy as np from torch.utils.data import Dataset from .base_loader import BaseDLDataLoader +from dl1_data_handler.reader import ProcessType class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): - def __init__(self, DLDataReader, indices, tasks, **kwargs): - self.DLDataReader = DLDataReader - self.indices = indices - self.tasks = tasks - self.sort_by_intensity = kwargs.get('sort_by_intensity', False) - self.stack_telescope_images = kwargs.get('stack_telescope_images', False) + def __init__( + self, + **kwargs, + ): + + super().__init__(**kwargs) + self.on_epoch_end() def __len__(self): - return len(self.indices) + """ + Returns the number of batches per epoch. - def __getitem__(self, index): - idx = self.indices[index] - # Replace with PyTorch-style tensor return - return torch.tensor(...), torch.tensor(...) + This method calculates the number of batches required to cover the entire dataset + based on the batch size. + + Returns: + -------- + int + Number of batches per epoch. + """ + return int(np.floor(len(self.indices) / self.batch_size)) def on_epoch_end(self): - pass # Optional: implement if you're using a custom sampler \ No newline at end of file + """ + Updates indices after each epoch. If a random seed is provided, the indices are shuffled. + + This method is called at the end of each epoch to ensure that the data is shuffled + if the shuffle attribute is set to True. This helps in improving the training process + by providing the model with a different order of data in each epoch. + """ + if self.random_seed is not None: + np.random.seed(self.random_seed) + np.random.shuffle(self.indices) + + def __getitem__(self, index): + """ + Generate one batch of data and retrieve the features and labels. + + This method is called to generate one batch of monoscopic and stereoscopic data based on + the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) + based on the mode of the DLDataReader. + + Parameters: + ----------- + index : int + Index of the batch to generate. + + Returns: + -------- + tuple + A tuple containing the input data as features and the corresponding labels. + """ + # Generate indices of the batch + batch_indices = self.indices[ + index * self.batch_size : (index + 1) * self.batch_size + ] + features, labels = None, None + if self.DLDataReader.mode == "mono": + batch = self.DLDataReader.generate_mono_batch(batch_indices) + features, labels = self._get_mono_item(batch) + elif self.DLDataReader.mode == "stereo": + batch = self.DLDataReader.generate_stereo_batch(batch_indices) + features, labels = self._get_stereo_item(batch) + return features, labels + + def _get_mono_item(self, batch): + """ + Retrieve the features and labels for one batch of monoscopic data. + + This method is called to retrieve the features and labels for one batch of + monoscopic data. The labels are set up based on the tasks specified. + + Parameters: + ----------- + batch : astropy.table.Table + A table containing the data for the batch. + + Returns: + -------- + tuple + A tuple containing the input data as features and the corresponding labels. + """ + # Retrieve the telescope images and store in the features dictionary + labels = {} + features = {"input": batch["features"].data} + if "type" in self.tasks: + labels["type"] = batch["true_shower_primary_class"].data + # Temp fix till keras support class weights for multiple outputs or I wrote custom loss + # https://github.com/keras-team/keras/issues/11735 + if len(self.tasks) == 1: + labels = batch["true_shower_primary_class"].data + + if "energy" in self.tasks: + labels["energy"] = batch["log_true_energy"].data + if "skydirection" in self.tasks: + labels["skydirection"] = np.stack( + ( + batch["fov_lon"].data, + batch["fov_lat"].data, + ), + axis=1, + ) + if "cameradirection" in self.tasks: + labels["cameradirection"] = np.stack( + ( + batch["cam_coord_offset_x"].data, + batch["cam_coord_offset_y"].data, + ), + axis=1, + ) + # Temp fix for supporting keras2 & keras3 + # if int(keras.__version__.split(".")[0]) >= 3: + # features = features["input"] + return features, labels + + def _get_stereo_item(self, batch): + """ + Retrieve the features and labels for one batch of stereoscopic data. + + This method is called to retrieve the features and labels for one batch of + stereoscopic data. The original batch is grouped to retrieve the telescope + data for each event and then the telescope images or waveforms are stored + by the hillas intensity or stacked if required. Feature vectors can also + be retrieved if available for ``telescope``- and ``subarray``level. The + labels are set up based on the tasks specified. + + Parameters: + ----------- + batch : astropy.table.Table + A table containing the data for the batch. + + Returns: + -------- + tuple + A tuple containing the input data as features and the corresponding labels. + """ + labels = {} + if self.DLDataReader.process_type == ProcessType.Simulation: + batch_grouped = batch.group_by( + ["obs_id", "event_id", "tel_type_id", "true_shower_primary_class"] + ) + elif self.DLDataReader.process_type == ProcessType.Observation: + batch_grouped = batch.group_by(["obs_id", "event_id", "tel_type_id"]) + features, mono_feature_vectors, stereo_feature_vectors = [], [], [] + true_shower_primary_class = [] + log_true_energy = [] + fov_lon, fov_lat, angular_separation = [], [], [] + cam_coord_offset_x, cam_coord_offset_y, cam_coord_distance = [], [], [] + for group_element in batch_grouped.groups: + if "features" in batch.colnames: + if self.sort_by_intensity: + # Sort images by the hillas intensity in a given batch if requested + group_element.sort(["hillas_intensity"], reverse=True) + # Stack the telescope images for stereo analysis + if self.stack_telescope_images: + # Retrieve the telescope images + plain_features = group_element["features"].data + # Stack the telescope images along the last axis + stacked_features = np.concatenate( + [plain_features[i] for i in range(plain_features.shape[0])], + axis=-1, + ) + # Append the stacked images to the features list + # shape: (batch_size, image_shape, image_shape, n_channels * n_tel) + features.append(stacked_features) + else: + # Append the plain images to the features list + # shape: (batch_size, n_tel, image_shape, image_shape, n_channels) + features.append(group_element["features"].data) + # Retrieve the feature vectors + if "mono_feature_vectors" in batch.colnames: + mono_feature_vectors.append(group_element["mono_feature_vectors"].data) + if "stereo_feature_vectors" in batch.colnames: + stereo_feature_vectors.append( + group_element["stereo_feature_vectors"].data + ) + # Retrieve the labels for the tasks + # FIXME: This won't work for divergent pointing directions + if "type" in self.tasks: + true_shower_primary_class.append( + group_element["true_shower_primary_class"].data[0] + ) + if "energy" in self.tasks: + log_true_energy.append(group_element["log_true_energy"].data[0]) + if "skydirection" in self.tasks: + fov_lon.append(group_element["fov_lon"].data[0]) + fov_lat.append(group_element["fov_lat"].data[0]) + if "cameradirection" in self.tasks: + cam_coord_offset_x.append(group_element["cam_coord_offset_x"].data) + cam_coord_offset_y.append(group_element["cam_coord_offset_y"].data) + # Store the labels in the labels dictionary + if "type" in self.tasks: + labels["type"] = np.array(true_shower_primary_class) + + # Temp fix till keras support class weights for multiple outputs or I wrote custom loss + # https://github.com/keras-team/keras/issues/11735 + if len(self.tasks) == 1: + labels = np.array(true_shower_primary_class) + + if "energy" in self.tasks: + labels["energy"] = np.array(log_true_energy) + if "skydirection" in self.tasks: + labels["skydirection"] = np.stack( + ( + np.array(fov_lon), + np.array(fov_lat), + ), + axis=1, + ) + if "cameradirection" in self.tasks: + labels["cameradirection"] = np.stack( + ( + np.array(cam_coord_offset_x), + np.array(cam_coord_offset_y), + ), + axis=1, + ) + # Store the fatures in the features dictionary + if "features" in batch.colnames: + features = {"input": np.array(features)} + # TDOO: Add support for both feature vectors + if "mono_feature_vectors" in batch.colnames: + features = {"input": np.array(mono_feature_vectors)} + if "stereo_feature_vectors" in batch.colnames: + features = {"input": np.array(stereo_feature_vectors)} + + # features = features["input"] + return features, labels + + # Include _get_mono_item and _get_stereo_item as needed \ No newline at end of file diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 806ad2c3..abb635ef 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -19,7 +19,7 @@ from dl1_data_handler.reader import DLDataReader from ctlearn.core.data_loader.loader import DLDataLoader from ctlearn.core.model import CTLearnModel - +from ctlearn.tools.train_model import DLFrameWork class TrainCTLearnModel(Tool): """ @@ -205,6 +205,7 @@ class TrainCTLearnModel(Tool): overwrite = Bool(help="Overwrite output dir if it exists").tag(config=True) aliases = { + # **DLFrameWork.aliases, # "framework": "DLFrameWork.framework_type", "framework": "TrainCTLearnModel.framework_type", "signal": "TrainCTLearnModel.input_dir_signal", @@ -329,8 +330,8 @@ def setup(self): self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() print("num_replicas_in_sync:",self.strategy.num_replicas_in_sync) - print(self.framework_type) - + print("BASE TRAIN FRAMEWORK", self.framework_type) + print("DEBUG 4") self.training_loader = DLDataLoader.create( framework=self.framework_type, DLDataReader=self.dl1dh_reader, @@ -341,7 +342,7 @@ def setup(self): sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, ) - + print("DEBUG 5") self.validation_loader = DLDataLoader.create( framework=self.framework_type, DLDataReader=self.dl1dh_reader, @@ -353,5 +354,8 @@ def setup(self): stack_telescope_images=self.stack_telescope_images, ) + def start(self): + pass + def finish(self): print("finish") diff --git a/ctlearn/tools/train/keras/train_keras_model.py b/ctlearn/tools/train/keras/train_keras_model.py index f4ddc890..e2a927af 100644 --- a/ctlearn/tools/train/keras/train_keras_model.py +++ b/ctlearn/tools/train/keras/train_keras_model.py @@ -141,6 +141,7 @@ def setup(self): def start(self): + print("Start KERAS") # Set up the keras callbacks monitor = "val_loss" monitor_mode = "min" diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 7476efad..38ff633b 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -22,6 +22,8 @@ raise ImportError("pytorch_lightning is not installed in your environment!") from ctlearn.tools.train.base_train_model import TrainCTLearnModel +from ctlearn.core.ctlearn_enum import Task + # from ctlearn.tools.train_model import class TrainPyTorchModel(TrainCTLearnModel): """ @@ -85,19 +87,33 @@ class TrainPyTorchModel(TrainCTLearnModel): } def __init__(self, **kwargs): + print("Pytorch init") super().__init__(**kwargs) print("CONFIG VALUES PYTORCH:", self.config) + def setup(self): + print("Pytorch setup") super().setup() print("Pytorch setup :)") print(f"DEBUG - framework_type raw: {self.reco_tasks} ({type(self.reco_tasks)})") - + task_str = self.reco_tasks + print(self.reco_tasks) + print(type(self.reco_tasks)) + self.task = None + try: + print + self.task = Task[task_str[0]] + except KeyError: + print(f"'{task_str}' is not a valid enum type.") def start(self): + print("Pytorch start") super().start() print("Pytorch start") - + + + def finish(self): super().finish() print("Pytorch finish") diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 372ef749..4200c7fb 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -1,29 +1,43 @@ -""" -Tools to train a ``CTLearnModel` (in Keras or PyTorch) on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. -""" - import atexit import pandas as pd import numpy as np import tensorflow as tf import sys -import argparse from ctapipe.core import Tool -import warnings -from ctapipe.core.traits import ( - CaselessStrEnum, -) -from ctlearn import __version__ as ctlearn_version -from ctlearn import is_package_available +from ctapipe.core.traits import CaselessStrEnum from ctlearn.core.ctlearn_enum import FrameworkType + class DLFrameWork(Tool): + """ + Tool to select and run a specific deep learning training framework (Keras or PyTorch) + for CTLearn model training. It dynamically loads the appropriate subclass based on + the user-defined --framework argument. + """ name = "dlframework" framework_type = CaselessStrEnum( ["pytorch", "keras"], default_value="keras", - help="Framework to use pytorch or keras", + help="Framework to use: pytorch or keras", + ).tag(config=True) + + early_stopping = Dict( + default_value=None, + allow_none=True, + help=( + "Early stopping parameters for the Keras callback. " + "E.g. {'monitor': 'val_loss', 'patience': 4, 'verbose': 1, 'restore_best_weights': True}. " + ), + ).tag(config=True) + + early_stopping = Dict( + default_value=None, + allow_none=True, + help=( + "Early stopping parameters for the Keras callback. " + "E.g. {'monitor': 'val_loss', 'patience': 4, 'verbose': 1, 'restore_best_weights': True}. " + ), ).tag(config=True) early_stopping = Dict( @@ -51,78 +65,85 @@ class DLFrameWork(Tool): } def __init__(self, **kwargs): + """ + Initialize the DLFrameWork tool and prepare for framework injection. + """ super().__init__(**kwargs) - print("CONFIG VALUES:", self.config) - - # def setup(self): - # pass # Do Nothing def start(self): - print(f"Selected Framework: {self.framework_type}") - framework= self.string_to_type(self.framework_type) + framework = self.string_to_type(self.framework_type) fw_obj = self.get_framework(framework) - fw_obj.parse_command_line(argv=sys.argv[1:]) # parse reco correctly now + fw_obj.update_config(self.config) + fw_obj.parse_command_line(argv=sys.argv[1:]) fw_obj.run() @classmethod - def string_to_type(self, str_type: str) -> FrameworkType: + def string_to_type(cls, str_type: str) -> FrameworkType: + """ + Convert a string to a FrameworkType enum (case-insensitive). + + Parameters: + str_type (str): The name of the framework (e.g., 'keras', 'pytorch'). + + Returns: + FrameworkType: Corresponding enum value. - type_ = None - str_type = str.upper(str_type) + Raises: + ValueError: If the provided string is not a valid framework type. + """ try: - type_ = FrameworkType[str_type] + return FrameworkType[str_type.upper()] except KeyError: - print(f"'{str_type}' is not a valid enum type.") - return type_ + raise ValueError(f"'{str_type}' is not a valid framework type.") @classmethod - def get_framework(self, framework_type: FrameworkType): + def get_framework(cls, framework_type: FrameworkType): + """ + Dynamically import and return the corresponding training class + based on the framework type. + + Parameters: + framework_type (FrameworkType): Enum indicating which framework to use. + + Returns: + Tool: An instance of the selected training framework (subclass of Tool). + + Raises: + ImportError: If the training module could not be imported. + ValueError: If the framework type is unknown. + """ if framework_type == FrameworkType.KERAS: try: from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + fw = TrainKerasModel() except ImportError as e: raise ImportError(f"Not possible to import TrainKerasModel: {e}") from e - fw = TrainKerasModel() elif framework_type == FrameworkType.PYTORCH: try: - from ctlearn.tools.train.pytorch.train_pytorch_model import ( - TrainPyTorchModel, - ) + from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel + fw = TrainPyTorchModel() + print("Pytorch") except ImportError as e: raise ImportError(f"Not possible to import TrainPyTorchModel: {e}") from e - fw = TrainPyTorchModel() - + else: raise ValueError(f"Unknown Framework: {framework_type.name}") - # Update Aliases - self.aliases.update(fw.aliases) - DLFrameWork.aliases.update(fw.aliases) return fw if __name__ == "__main__": + # Parse only --framework to determine which subclass to load + minimal_args = [arg for arg in sys.argv[1:] if "--framework" in arg or arg in ["-h", "--help"]] + tool = DLFrameWork() + tool.initialize(argv=minimal_args) - # Parse the framework argument - parser = argparse.ArgumentParser() - parser.add_argument("--framework", default="keras") - args, _ = parser.parse_known_args() - - # Get Framework type - if args.framework: - fw_type = DLFrameWork.string_to_type(args.framework) - else: - raise ValueError( - f"Framework not defined, use : --framework keras or --framework pytorch" - ) - - DLFrameWork.get_framework(fw_type) - - # Launch the Framework - DLFrameWork().launch_instance() + # Setup and inject the correct framework instance + tool.setup() -# Example: -# python -m ctlearn.tools.train_model --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite \ No newline at end of file + # Parse all CLI args with the selected framework subclass + tool.framework_instance.initialize(argv=sys.argv[1:]) + tool.run() diff --git a/ctlearn/tools/train_model_old.py b/ctlearn/tools/train_model_old.py new file mode 100644 index 00000000..4f3c23ea --- /dev/null +++ b/ctlearn/tools/train_model_old.py @@ -0,0 +1,83 @@ +""" +Tools to train a ``CTLearnModel` (in Keras or PyTorch) on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. +""" + +import sys +import argparse +from ctapipe.core import Tool +import warnings +from ctapipe.core.traits import ( + CaselessStrEnum, +) +from ctlearn import is_package_available +from ctlearn.core.ctlearn_enum import FrameworkType + +class DLFrameWork(Tool): + name = "dlframework" + + @classmethod + def string_to_type(self, str_type: str) -> FrameworkType: + + type_ = None + str_type = str.upper(str_type) + try: + type_ = FrameworkType[str_type] + except KeyError: + print(f"'{str_type}' is not a valid enum type.") + return type_ + + @classmethod + def get_framework(self, framework_type: FrameworkType): + if framework_type == FrameworkType.KERAS: + try: + from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + fw = TrainKerasModel() + + except ImportError as e: + raise ImportError(f"Not possible to import TrainKerasModel: {e}") from e + + + elif framework_type == FrameworkType.PYTORCH: + try: + from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel + fw = TrainPyTorchModel() + print("Pytorch") + except ImportError as e: + raise ImportError(f"Not possible to import TrainPyTorchModel: {e}") from e + + + + else: + raise ValueError(f"Unknown Framework: {framework_type.name}") + # Update Aliases + self.aliases.update(fw.aliases) + DLFrameWork.aliases.update(fw.aliases) + + return fw + + +if __name__ == "__main__": + + # Parse the framework argument + parser = argparse.ArgumentParser() + parser.add_argument("--framework", default="keras") + args, _ = parser.parse_known_args() + + # Get Framework type + if args.framework: + fw_type = DLFrameWork.string_to_type(args.framework) + else: + raise ValueError( + f"Framework not defined, use : --framework keras or --framework pytorch" + ) + + # DLFrameWork.get_framework(fw_type) + fw = DLFrameWork.get_framework(fw_type) + # fw = DLFrameWork() + # print(sys.argv[1:]) + # fw.parse_command_line(argv=sys.argv[1:]) + fw.run() + # Launch the Framework + # DLFrameWork().launch_instance() +# Example: +# python -m ctlearn.tools.train_model --framework pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite \ No newline at end of file From d339494437c5dd0a404e2e6a91dad8804d2ecd65 Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 25 Apr 2025 14:25:37 +0000 Subject: [PATCH 009/119] Added nets, load checkpoints in pytorch --- ctlearn/core/ctlearn_enum.py | 8 + ctlearn/core/pytorch/net_utils.py | 207 ++++++ ctlearn/core/pytorch/nets/__init__.py | 0 .../core/pytorch/nets/activation/__init__.py | 0 .../nets/activation/activation_functions.py | 3 + ctlearn/core/pytorch/nets/block/__init__.py | 0 ctlearn/core/pytorch/nets/block/cnn_blocks.py | 76 +++ .../pytorch/nets/loss_functions/__init__.py | 0 .../nets/loss_functions/loss_functions.py | 323 +++++++++ .../pytorch/nets/models/DBBDanet/DBBDanet.py | 141 ++++ .../nets/models/DBBRegNet/DBBRegNet.py | 73 +++ .../DoubleBBEfficientNet.py | 270 ++++++++ .../models/DoubleBBEfficientNet/__init__.py | 0 .../DoubleBBEfficientNetV2.py | 186 ++++++ .../core/pytorch/nets/models/EfficientNet.py | 158 +++++ .../pytorch/nets/models/EfficientNetv2.py | 263 ++++++++ .../models/EffientNet_pytorch/__init__.py | 9 + .../nets/models/EffientNet_pytorch/model.py | 454 +++++++++++++ .../EffientNet_pytorch/model_original.py | 454 +++++++++++++ .../nets/models/EffientNet_pytorch/utils.py | 616 ++++++++++++++++++ .../nets/models/ResNeXtDBB/ResNeXtDBB.py | 173 +++++ ctlearn/core/pytorch/nets/models/ResNet.py | 129 ++++ .../models/ThinResNet_DBB/ThinResNet_DBB.py | 200 ++++++ .../models/Transformers/TransformerDuo.py | 128 ++++ .../Transformers/TransformerDuoSimple.py | 96 +++ ctlearn/core/pytorch/nets/models/__init__.py | 7 + ctlearn/core/pytorch/nets/models/gcn/gcn.py | 56 ++ .../legacy/DoubleBBEfficientNetV2_old.py | 181 +++++ .../models/legacy/ThinResNet/ThinResNet.py | 101 +++ .../nets/models/legacy/nfnets__/__init__.py | 3 + .../nets/models/legacy/nfnets__/model.py | 307 +++++++++ .../nets/models/legacy/nfnets__/optim.py | 109 ++++ .../nets/models/legacy/nfnets__/pretrained.py | 94 +++ .../legacy/nfnets_pytorch/.gitattributes | 1 + .../models/legacy/nfnets_pytorch/.gitignore | 9 + .../nets/models/legacy/nfnets_pytorch/LICENSE | 201 ++++++ .../models/legacy/nfnets_pytorch/README.md | 109 ++++ .../models/legacy/nfnets_pytorch/dataset.py | 8 + .../legacy/nfnets_pytorch/default_config.yaml | 32 + .../models/legacy/nfnets_pytorch/demo.ipynb | 258 ++++++++ .../nets/models/legacy/nfnets_pytorch/eval.py | 86 +++ .../legacy/nfnets_pytorch/nfnets/__init__.py | 3 + .../nfnets_pytorch/nfnets/model copy.py | 309 +++++++++ .../legacy/nfnets_pytorch/nfnets/model.py | 265 ++++++++ .../legacy/nfnets_pytorch/nfnets/optim.py | 109 ++++ .../nfnets_pytorch/nfnets/pretrained.py | 94 +++ .../nfnets_pytorch/pretrained/README.md | 11 + .../legacy/nfnets_pytorch/pyproject.toml | 6 + .../legacy/nfnets_pytorch/requirements.txt | 15 + .../models/legacy/nfnets_pytorch/setup.cfg | 35 + .../models/legacy/nfnets_pytorch/train.py | 194 ++++++ .../nets/models/legacy/simple_nfnet/README.md | 39 ++ .../nets/models/legacy/simple_nfnet/main.py | 98 +++ .../nets/models/legacy/simple_nfnet/model.py | 183 ++++++ .../models/legacy/simple_nfnet/optim copy.py | 110 ++++ .../nets/models/legacy/simple_nfnet/optim.py | 109 ++++ .../core/pytorch/nets/optimizer/__init__.py | 0 .../core/pytorch/nets/optimizer/optimizer.py | 5 + ctlearn/tools/train/base_train_model.py | 5 +- .../training_config_iaa_neutron_training.yml | 193 ++++++ .../train/pytorch/train_pytorch_model.py | 95 ++- ctlearn/tools/train/pytorch/utils.py | 173 +++++ ctlearn/tools/train_model.py | 3 + 63 files changed, 7561 insertions(+), 22 deletions(-) create mode 100644 ctlearn/core/pytorch/net_utils.py create mode 100644 ctlearn/core/pytorch/nets/__init__.py create mode 100644 ctlearn/core/pytorch/nets/activation/__init__.py create mode 100644 ctlearn/core/pytorch/nets/activation/activation_functions.py create mode 100644 ctlearn/core/pytorch/nets/block/__init__.py create mode 100644 ctlearn/core/pytorch/nets/block/cnn_blocks.py create mode 100644 ctlearn/core/pytorch/nets/loss_functions/__init__.py create mode 100644 ctlearn/core/pytorch/nets/loss_functions/loss_functions.py create mode 100644 ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py create mode 100644 ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py create mode 100644 ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py create mode 100644 ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/__init__.py create mode 100644 ctlearn/core/pytorch/nets/models/DualBackboneEfficientNetV2/DoubleBBEfficientNetV2.py create mode 100644 ctlearn/core/pytorch/nets/models/EfficientNet.py create mode 100644 ctlearn/core/pytorch/nets/models/EfficientNetv2.py create mode 100644 ctlearn/core/pytorch/nets/models/EffientNet_pytorch/__init__.py create mode 100644 ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model.py create mode 100644 ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model_original.py create mode 100644 ctlearn/core/pytorch/nets/models/EffientNet_pytorch/utils.py create mode 100644 ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py create mode 100644 ctlearn/core/pytorch/nets/models/ResNet.py create mode 100644 ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py create mode 100644 ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py create mode 100644 ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py create mode 100644 ctlearn/core/pytorch/nets/models/__init__.py create mode 100644 ctlearn/core/pytorch/nets/models/gcn/gcn.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/DoubleBBEfficientNetV2_old.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/ThinResNet/ThinResNet.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets__/__init__.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets__/model.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets__/optim.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitattributes create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitignore create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/LICENSE create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/README.md create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/dataset.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/default_config.yaml create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/demo.ipynb create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/eval.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/__init__.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model copy.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/optim.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained/README.md create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pyproject.toml create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/requirements.txt create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/setup.cfg create mode 100644 ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/train.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/README.md create mode 100644 ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/main.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/model.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py create mode 100644 ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py create mode 100644 ctlearn/core/pytorch/nets/optimizer/__init__.py create mode 100644 ctlearn/core/pytorch/nets/optimizer/optimizer.py create mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml create mode 100644 ctlearn/tools/train/pytorch/utils.py diff --git a/ctlearn/core/ctlearn_enum.py b/ctlearn/core/ctlearn_enum.py index ab01a0bb..b4819024 100644 --- a/ctlearn/core/ctlearn_enum.py +++ b/ctlearn/core/ctlearn_enum.py @@ -15,4 +15,12 @@ class EventType(Enum): gamma=0 proton=1 electron=2 + +class Mode(Enum): + + train = 0 + results = 1 + validate = 2 + observation = 3 + tunning = 4 \ No newline at end of file diff --git a/ctlearn/core/pytorch/net_utils.py b/ctlearn/core/pytorch/net_utils.py new file mode 100644 index 00000000..bc39b216 --- /dev/null +++ b/ctlearn/core/pytorch/net_utils.py @@ -0,0 +1,207 @@ +import importlib +import torch +import numpy as np +import os.path +import pickle +from matplotlib import pyplot as plt +from skimage.filters import gabor_kernel +from skimage.transform import resize +import onnx +from onnxsim import simplify +import warnings +from ctlearn.core.ctlearn_enum import Task, Mode + +#------------------------------------------------------------------------------------------------------------------- +def create_model(model_parameters): + + try: + module_name = "ctlearn.core.pytorch.nets.models" + model_type = model_parameters["model_name"] + model_params = model_parameters["parameters"] + + # Construct full class path (you should provide the full path including the module) + full_class_path = f"ctlearn.core.pytorch.nets.models.{model_type}" + + # Resolve the class + module = importlib.import_module(full_class_path) + module = getattr(module, model_type) + model_class = getattr(module, model_type) + # Now, instantiate the model with the parameters + model_net = model_class(**model_params) + return model_net + + except AttributeError: + raise ValueError(f"Model class {model_type} not found in module {module_name}.") + except TypeError as e: + raise ValueError(f"Error instantiating model {model_type}: {str(e)}") + except Exception as e: + raise RuntimeError(f"An unexpected error occurred: {str(e)}") +#------------------------------------------------------------------------------------------------------------------- + +class ModelHelper: + # ------------------------------------------------------------------------------------------------------------- + def GetNumParamters(self): + + numel_list = [ + p.numel() for p in self.model.parameters() if p.requires_grad == True + ] + return sum(numel_list), numel_list + # ------------------------------------------------------------------------------------------------------------- + def savePickle(path, fileName, data): + + saveFile = os.path.join(path, fileName) + File = open(saveFile, "ab") + pickle.dump(data, File) + # ------------------------------------------------------------------------------------------------------------- + def loadPickle(path, fileName): + loadFile = os.path.join(path, fileName) + File = open(loadFile, "rb") + data = pickle.load(File) + + return data + # ------------------------------------------------------------------------------------------------------------- + def plotImage(img, permute=True): + + if img.is_leaf == False: + img = img.detach() + + if permute and len(img.shape) == 3: + img = img.permute(1, 2, 0) + + plt.imshow(img, cmap="gray") + plt.show() + # ------------------------------------------------------------------------------------------------------------- + def GaborKernels(size=7, showPlots=False): + + # prepare filter bank kernels + kernels = [] + for theta in (0, np.pi / 4, np.pi / 2, 3 * np.pi / 4): # range(8): + # theta = theta / 4. * np.pi + # for sigma in (3): + sigma = 3 + # for frequency in (0.05, 0.25): + for frequency in (0.15, 0.25, 0.35, 0.45, 0.55): + kernel = np.real( + gabor_kernel(frequency, theta=theta, sigma_x=sigma, sigma_y=sigma) + ) + + kernel = resize(kernel, [size, size]) + kernels.append(kernel) + + if showPlots: + print( + "Theta: ", + theta, + " Sigma: ", + sigma, + " Frequency: ", + frequency, + " Kernel size:", + kernel.shape, + ) + plt.imshow(kernel) + plt.show() + + return kernels + # ------------------------------------------------------------------------------------------------------------- + def saveModel(model, data_path, filename): + print("Saving model: ", filename) + + torch.save(model.state_dict(), os.path.join(data_path, filename)) + # ------------------------------------------------------------------------------------------------------------- + def loadModel(model, data_path, filename, mode, device_str='cpu'): + + if os.path.isfile(os.path.join(data_path, filename)): + print("Loading model: ", filename) + + # TODO: Test weights_only=True. It is getting this warning: + # "FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature." + pretrained_dict = torch.load(os.path.join(data_path, filename),map_location=torch.device(device_str),weights_only=False) + + if type(pretrained_dict) == dict and "state_dict" in pretrained_dict : + pretrained_dict = pretrained_dict["state_dict"] + + if type(pretrained_dict) == dict and "model_state_dict" in pretrained_dict: + pretrained_dict = pretrained_dict["model_state_dict"] + + + model_dict = model.state_dict() + + # Remove the prefix pattern from the state dict. + modified_dict = {} + prefix = "model.0." + for key in pretrained_dict: + # Check if the key start with 'model.' + if key.startswith(prefix): + # Remove the pattern 'model.' and save the value with the new key + new_key = key.replace(prefix, "") + modified_dict[new_key] = pretrained_dict[key] + + if len(modified_dict) > 0: + pretrained_dict = modified_dict + + # 1. filter out unnecessary keys + # pretrained_dict = { + # k: v for k, v in pretrained_dict.items() if k in model_dict + # } + # Filter out keys that do not match in name or dimensions + pretrained_dict = { + k: v for k, v in pretrained_dict.items() if k in model_dict and model_dict[k].size() == v.size() + } + + if (len(model_dict)!=len(pretrained_dict) or set(model_dict.keys()) != set(pretrained_dict.keys())): + + pretrain_len = len(pretrained_dict) + model_len = len(model_dict) + unique_pretrained = set(pretrained_dict.keys()) - set(model_dict.keys()) + unique_model = set(model_dict.keys()) - set(pretrained_dict.keys()) + + if (mode!=Mode.train and mode!=Mode.tunning): + raise ValueError(f"Error Loading the model. Pretrained Dict lenght: {pretrain_len} Model Dict lenght: {model_len}. Differences -> Pretrained keys: {unique_pretrained}, Model keys: {unique_model}") + else: + warnings.warn( + f"Warning Loading the model. Pretrained Dict length: {pretrain_len} Model Dict length: {model_len}. " + f"Differences -> Pretrained keys: {unique_pretrained}, Model keys: {unique_model}", + UserWarning + ) + + # 2. overwrite entries in the existing state dict + model_dict.update(pretrained_dict) + # 3. load the new state dict + model.load_state_dict(model_dict, strict=False) + + # use_cuda = torch.cuda.is_available() + # device = torch.device(device_str if use_cuda else "cpu") + device = torch.device(device_str) + model.to(device) + + # model.load_state_dict(torch.load(data_path + filename), strict=False) + print("Model Loaded.") + else: + print(f"CheckPoint file does not exist: {filename}") + if mode != Mode.train: + exit() + + return model + # ------------------------------------------------------------------------------------------------------------- + def exportOnnx(model, dummy_input, onnx_name, input_names, output_names): + + torch.onnx.export( + model, + dummy_input, + onnx_name + ".onnx", + verbose=True, + input_names=input_names, + output_names=output_names, + ) + + # pip3 install -U pip && pip3 install onnxsim + # load your predefined ONNX model + model = onnx.load(onnx_name + ".onnx") + + # convert model + model_simp, check = simplify(model) + + assert check, "Simplified ONNX model could not be validated" + onnx.save(model_simp, onnx_name + "_simp.onnx") + # ------------------------------------------------------------------------------------------------------------- diff --git a/ctlearn/core/pytorch/nets/__init__.py b/ctlearn/core/pytorch/nets/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/nets/activation/__init__.py b/ctlearn/core/pytorch/nets/activation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/nets/activation/activation_functions.py b/ctlearn/core/pytorch/nets/activation/activation_functions.py new file mode 100644 index 00000000..2f5a549e --- /dev/null +++ b/ctlearn/core/pytorch/nets/activation/activation_functions.py @@ -0,0 +1,3 @@ +import torch +import torch.nn as nn + diff --git a/ctlearn/core/pytorch/nets/block/__init__.py b/ctlearn/core/pytorch/nets/block/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/nets/block/cnn_blocks.py b/ctlearn/core/pytorch/nets/block/cnn_blocks.py new file mode 100644 index 00000000..38c73dd2 --- /dev/null +++ b/ctlearn/core/pytorch/nets/block/cnn_blocks.py @@ -0,0 +1,76 @@ +import torch +import torch.nn as nn +from ctlearn.core.pytorch.net_utils import ModelHelper +import torch.nn.functional as F + +class Dirichlet(nn.Module): + def __init__(self, in_features, out_units): + super().__init__() + self.dense = nn.Linear(in_features, out_units) + self.out_units = out_units + + def evidence(self, x): + return F.softplus(x) + + def forward(self, x): + out = self.dense(x) + alpha = self.evidence(out) + 1 + return alpha + +class NormalInvGamma(nn.Module): + """Defines the Normal Inverse Gamma distribution layer.""" + def __init__(self, in_features, out_units): + super().__init__() + self.dense = nn.Linear(in_features, out_units * 4) + self.out_units = out_units + + def evidence(self, x): + return F.softplus(x) + + def forward(self, x): + out = self.dense(x) + mu, logv, logalpha, logbeta = torch.split(out, self.out_units, dim=-1) + v = self.evidence(logv) + alpha = self.evidence(logalpha) + 1 + beta = self.evidence(logbeta) + # return mu, v, alpha, beta + + if self.training: + return mu, v, alpha, beta + else: + var = torch.sqrt(beta / (v * (alpha - 1))) + + return mu, var + + +class ResBlock(nn.Module): + def __init__(self, n_chans_in, n_chans_out, kernel_size=3, conv_drop_pro=0.2): + + super(ResBlock, self).__init__() + + self.conv = nn.Conv2d(n_chans_in, n_chans_in, kernel_size=kernel_size, padding=int(kernel_size/2), bias=False) + self.conv_dropout = nn.Dropout2d(p=conv_drop_pro) + self.batch_norm = nn.BatchNorm2d(num_features=n_chans_in) + self.pool = nn.MaxPool2d(2) + self.activation = nn.LeakyReLU() + self.conv_out = nn.Conv2d(n_chans_in, n_chans_out, kernel_size=1, padding=0, bias=False) + + torch.nn.init.kaiming_normal_(self.conv.weight, nonlinearity='leaky_relu') + torch.nn.init.constant_(self.batch_norm.weight, 0.5) + torch.nn.init.zeros_(self.batch_norm.bias) + + # Init Filters + kernels = ModelHelper.GaborKernels(size=kernel_size, showPlots=False) + for i in range(min(self.conv.weight.shape[0], len(kernels))): + with torch.no_grad(): + self.conv.weight[i, :] = torch.nn.Parameter(torch.tensor(kernels[i]*100)) + + def forward(self, x): + out = self.conv(x) + out = self.batch_norm(out) + out = self.activation(out) + out = out + x + out = self.pool(out) + out = self.conv_out(out) + out = self.conv_dropout(out) + return out \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/loss_functions/__init__.py b/ctlearn/core/pytorch/nets/loss_functions/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py new file mode 100644 index 00000000..2423b3a3 --- /dev/null +++ b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py @@ -0,0 +1,323 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +class evidential_regression_loss(nn.Module): + def __init__(self, lamb=1.0, reduction='mean'): + super(evidential_regression_loss, self).__init__() + self.reduction = reduction + self.lamb = lamb + def nig_nll(self, mu, v, alpha, beta, y): + """Computes the Negative Log-Likelihood for Normal Inverse Gamma.""" + two_beta_lambda = 2 * beta * (1 + v) + t1 = 0.5 * (torch.pi / v).log() + t2 = alpha * two_beta_lambda.log() + t3 = (alpha + 0.5) * (v * (y - mu) ** 2 + two_beta_lambda).log() + t4 = alpha.lgamma() + t5 = (alpha + 0.5).lgamma() + nll = t1 - t2 + t3 + t4 - t5 + return nll + + def nig_reg(self, mu, v, alpha, _beta, y): + """Computes the Normal Inverse Gamma regularization.""" + reg = (y - mu).abs() * (2 * v + alpha) + if self.reduction=="mean": + error = reg.mean() + elif self.reduction=="sum": + error = reg.sum() + elif self.reduction == None or self.reduction =='None': + error = reg + + else: + raise RuntimeError("Reduction not supported: Use sum or mean") + + return error + def set_lambda(self, lamb): + self.lamb =lamb + + def forward(self, dist_params, y): + """Computes the evidential regression loss.""" + if len(y)>1: + mu, v, alpha, beta = (d.squeeze() for d in dist_params) + else: + mu, v, alpha, beta = (d for d in dist_params) + # mu, v, alpha, beta = (d for d in dist_params) + + nig_reg_error = self.nig_reg( mu, v, alpha, beta, y) + nig_nll_error = self.nig_nll( mu, v, alpha, beta, y) + + if self.reduction=="mean": + nig_nll_error = nig_nll_error.mean() + elif self.reduction=="sum": + nig_nll_error = nig_nll_error.sum() + elif self.reduction == None or self.reduction =='None': + nig_nll_error = nig_nll_error + else: + raise RuntimeError("Reduction not supported: Use sum or mean") + + return nig_nll_error + self.lamb *nig_reg_error + + +def AngularDistance(alt1_rad, alt2_rad, az1_rad, az2_rad,reduction = None): + """ + Calculate the angular distance between points given in batches. + + Parameters: + - alt1_rad, az1_rad: Tensors of the altitudes and azimuths in radians for the first set of points. + - alt2_rad, az2_rad: Tensors of the altitudes and azimuths in radians for the second set of points. + + Returns: + - Tensor of angular distances in radians for each pair of points. + """ + + # Compute the cosine of the angular distance using batch-wise operations + cosdelta = torch.cos(alt1_rad) * torch.cos(alt2_rad) * torch.cos(az1_rad - az2_rad) + \ + torch.sin(alt1_rad) * torch.sin(alt2_rad) + + # Clamp the cosdelta values to ensure they are within the valid range for arccos + # cosdelta = torch.clamp(cosdelta, -1.0, 1.0) + cosdelta = torch.clamp(cosdelta, -1.0 + 1e-7, 1.0 - 1e-7) + # Calculate the angular distance in radians + ang_dist_rad = torch.acos(cosdelta) + ang_dist_rad[cosdelta == 1.0] = 0.0 # acos(1) = 0 + ang_dist_rad[cosdelta == -1.0] = torch.pi # acos(-1) = pi + ang_dist_deg= torch.rad2deg(ang_dist_rad) + + if reduction =="sum": + return ang_dist_rad.sum(),ang_dist_deg.sum() + elif reduction=="mean": + return ang_dist_rad.mean(),ang_dist_deg.mean() + elif reduction == None or reduction =='None': + return ang_dist_rad, ang_dist_deg + else: + raise RuntimeError("Reduction not supported: Use sum, mean or None") + +def AngularError(vec1, vec2,reduction='mean'): + # Ensure the vectors are tensors + # vec1 = vec1.clone().detach().float() + # vec2 = vec2.clone().detach().float() + + + # Compute the dot product for each pair of vectors in the batch + dot_product = torch.sum(vec1 * vec2, dim=1) + + # Compute the magnitudes (norms) of the vectors for each vector in the batch + norm_vec1 = torch.norm(vec1, dim=1) + norm_vec2 = torch.norm(vec2, dim=1) + + # Compute the cosine of the angle for each pair of vectors in the batch + cos_theta = dot_product / (norm_vec1 * norm_vec2) + + # Clip the cosine values to the range [-1, 1] to avoid numerical issues with arccos + cos_theta = torch.clamp(cos_theta, -1.0, 1.0) + + # Compute the angle in radians for each pair of vectors in the batch + angle_rad = torch.acos(cos_theta) + + # Optionally, convert the angles from radians to degrees + angle_deg = torch.rad2deg(angle_rad) + + if reduction =="sum": + return angle_rad.sum(), angle_deg.sum() + elif reduction=="mean": + return angle_rad.mean(), angle_deg.mean() + elif reduction == None or reduction =='None': + return angle_rad, angle_deg + else: + raise RuntimeError("Reduction not supported: Use sum, mean or None") + + +class VectorLoss(nn.Module): + def __init__(self,alpha=0.001,reduction='mean'): + super(VectorLoss, self).__init__() + self.alpha = alpha + self.reduction=reduction + def forward(self, output, target): + # Calculate angles of output and target using atan2 + angles_output = torch.atan2(output[:, 1], output[:, 0]) + angles_target = torch.atan2(target[:, 1], target[:, 0]) + + # Compute the difference in angles + angle_diff = torch.abs(angles_output - angles_target) + + # Normalize angle differences to be within [0, pi] + angle_diff = torch.remainder(angle_diff + torch.pi, 2 * torch.pi) - torch.pi + angle_diff = torch.abs(angle_diff) # Ensure all differences are positive + + if self.reduction =="sum": + return angle_diff.sum() + elif self.reduction=="mean": + return angle_diff.mean() + else: + raise RuntimeError("Reduction not supported: Use sum or mean") + return self.alpha * angle_diff.mean() +def smooth_BCE( + eps=0.1, +): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +def generate_hot_ones(device, cn, cp, outputs, targets): + + t = torch.full_like(outputs, cn, device=device) + n = outputs.shape[0] + t[range(n), targets] = cp + + return t + +class FocalLoss(nn.Module): + def __init__(self, alpha=None, gamma=2.0, reduction='mean'): + super(FocalLoss, self).__init__() + self.alpha = alpha + self.gamma = gamma + self.reduction = reduction + + def set_alpha(self,alpha): + self.alpha = alpha + + def forward(self, inputs, targets): + ce_loss = F.cross_entropy(inputs, targets, weight=self.alpha, reduction='none') + pt = torch.exp(-ce_loss) # Probabilidad inversa del error + focal_loss = (1 - pt) ** self.gamma * ce_loss + if self.reduction == 'mean': + return focal_loss.mean() + elif self.reduction == 'sum': + return focal_loss.sum() + else: + return focal_loss + +# class FocalLoss(nn.Module): +# def __init__(self, device, alpha=0.25, gamma=2.0, label_smoothing=0.0): +# super(FocalLoss, self).__init__() +# self.device = device +# self.alpha = alpha +# self.gamma = gamma +# self.label_smoothing = label_smoothing +# self.cp, self.cn = smooth_BCE(eps=self.label_smoothing) +# self.BCE = BCELogitsLoss(device) + +# def forward(self, outputs, targets): + +# # t = generate_hot_ones(self.device, self.cn, self.cp, outputs, targets) +# # Supone inputs son las logits antes de sigmoid +# # BCE_loss = F.binary_cross_entropy_with_logits(outputs, t, reduction="none") +# BCE_loss = self.BCE(outputs,targets) +# pt = torch.exp(-BCE_loss) # pt es la probabilidad de clasificar correctamente +# F_loss = self.alpha * (1 - pt) ** self.gamma * BCE_loss +# return F_loss.mean() + + +class BCELogitsLoss(nn.Module): + def __init__(self, device, cls_pw=1.0, label_smoothing=0.0): + super().__init__() + self.device = device + self.label_smoothing = label_smoothing + self.BCE = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor(cls_pw, device=self.device) + ) + self.cp, self.cn = smooth_BCE(eps=self.label_smoothing) + + def forward(self, outputs, targets): + + # Generate hot ones targets + t = generate_hot_ones(self.device, self.cn, self.cp, outputs, targets) + + bce_loss = self.BCE(outputs, t) + + return bce_loss + + + + +class EvidClassification(): + def __init__(self,class_weights=None): + self.class_weights= class_weights + + def dirichlet_reg(self, alpha, y): + # dirichlet parameters after removal of non-misleading evidence (from the label) + alpha = y + (1 - y) * alpha + + # uniform dirichlet distribution + beta = torch.ones_like(alpha) + + sum_alpha = alpha.sum(-1) + sum_beta = beta.sum(-1) + + t1 = sum_alpha.lgamma() - sum_beta.lgamma() + t2 = (alpha.lgamma() - beta.lgamma()).sum(-1) + t3 = alpha - beta + t4 = alpha.digamma() - sum_alpha.digamma().unsqueeze(-1) + + kl = t1 - t2 + (t3 * t4).sum(-1) + return kl.sum() + + def dirichlet_mse(self, alpha, y, ): + sum_alpha = alpha.sum(-1, keepdims=True) + p = alpha / sum_alpha + t1 = (y - p).pow(2) + t2 = ((p * (1 - p)) / (sum_alpha + 1)) + + if self.class_weights is not None: + t1 = t1 * self.class_weights.unsqueeze(0) + t2 = t2 * self.class_weights.unsqueeze(0) + + mse = t1 + t2 + return mse.sum() + + def loss(self, alpha, y, lamb=1.0 ): + num_classes = alpha.shape[-1] + y = F.one_hot(y, num_classes) + return self.dirichlet_mse(alpha, y) + lamb * self.dirichlet_reg(alpha, y) + +# def evidential_classification(alpha, y, lamb=1.0): +# num_classes = alpha.shape[-1] +# y = F.one_hot(y, num_classes) +# return dirichlet_mse(alpha, y) + lamb * dirichlet_reg(alpha, y) + +# def evidential_classification(alpha, y, weights, lamb=1.0): +# num_classes = alpha.shape[-1] +# y = F.one_hot(y, num_classes) +# mse_loss = dirichlet_mse(alpha, y) +# reg_loss = dirichlet_reg(alpha, y) +# weighted_loss = weights[0] * mse_loss + weights[1] * reg_loss +# return weighted_loss + lamb * reg_loss + +# class FocalLoss(nn.Module): +# def __init__(self, alpha=None, gamma=2.0, reduction='mean'): +# super(FocalLoss, self).__init__() +# self.alpha = alpha +# self.gamma = gamma +# self.reduction = reduction + +# def forward(self, inputs, targets): + +# t = generate_hot_ones(self.device, self.cn, self.cp, targets, targets) +# BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none') +# targets = targets.type(torch.long) +# at = self.alpha.gather(0, targets.data.view(-1)) +# pt = torch.exp(-BCE_loss) +# F_loss = at * (1-pt)**self.gamma * BCE_loss + +# if self.reduction == 'mean': +# return F_loss.mean() +# elif self.reduction == 'sum': +# return F_loss.sum() +# else: +# return F_loss + + +# class FocalLoss(nn.Module): +# def __init__(self, alpha=0.25, gamma=2.0): +# super(FocalLoss, self).__init__() +# self.alpha = alpha +# self.gamma = gamma + +# def forward(self, inputs, targets): +# BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none') +# targets = targets.type(torch.float32) +# at = self.alpha * targets + (1 - self.alpha) * (1 - targets) +# pt = torch.exp(-BCE_loss) +# F_loss = at * (1-pt)**self.gamma * BCE_loss +# return F_loss.mean() diff --git a/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py b/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py new file mode 100644 index 00000000..538eda29 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py @@ -0,0 +1,141 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ChannelAttentionModule(nn.Module): + def __init__(self, in_channels, reduction=16): + super(ChannelAttentionModule, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.max_pool = nn.AdaptiveMaxPool2d(1) + + self.fc = nn.Sequential( + nn.Conv2d(in_channels, in_channels // reduction, 1, bias=False), + nn.ReLU(), + nn.Conv2d(in_channels // reduction, in_channels, 1, bias=False) + ) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + avg_out = self.fc(self.avg_pool(x)) + max_out = self.fc(self.max_pool(x)) + out = avg_out + max_out + return self.sigmoid(out) * x + +class SpatialAttentionModule(nn.Module): + def __init__(self, kernel_size=7): + super(SpatialAttentionModule, self).__init__() + padding = kernel_size // 2 + self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + avg_out = torch.mean(x, dim=1, keepdim=True) + max_out, _ = torch.max(x, dim=1, keepdim=True) + x = torch.cat([avg_out, max_out], dim=1) + x = self.conv(x) + return self.sigmoid(x) * x + +class DANet(nn.Module): + def __init__(self, num_inputs=1, num_classes=2, dropout_rate=0.3): + super(DANet, self).__init__() + + # Basic CNN backbone + self.conv1 = nn.Conv2d(num_inputs, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.layer1 = self._make_layer(64, 128, 2) + self.layer2 = self._make_layer(128, 256, 2) + self.layer3 = self._make_layer(256, 512, 2) + + # DANet attention modules + self.cam = ChannelAttentionModule(512) + self.sam = SpatialAttentionModule() + + # Final layers + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(p=dropout_rate) + self.fc = nn.Linear(512, num_classes) + + def _make_layer(self, in_channels, out_channels, blocks): + layers = [] + layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)) + layers.append(nn.BatchNorm2d(out_channels)) + layers.append(nn.ReLU(inplace=True)) + for _ in range(1, blocks): + layers.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)) + layers.append(nn.BatchNorm2d(out_channels)) + layers.append(nn.ReLU(inplace=True)) + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + + # Apply attention modules + x = self.cam(x) + x # Channel Attention + x = self.sam(x) + x # Spatial Attention + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.dropout(x) # Dropout before the final fully connected layer + x = self.fc(x) + + + return x + +class DBBDanet(nn.Module): + def __init__(self, task , num_inputs=1, num_classes=2, use_concat=False, dropout_rate=0.3): + super(DBBDanet, self).__init__() + + self.task = task + self.use_concat = use_concat + self.backbone_1 = DANet(num_inputs=num_inputs, num_classes=num_classes, dropout_rate=dropout_rate) + self.backbone_2 = DANet(num_inputs=num_inputs, num_classes=num_classes, dropout_rate=dropout_rate) + + # num_features = 512 + num_features = self.backbone_1.fc.in_features + if self.use_concat: + num_features*=2 + + self.fc = nn.Linear(num_features, num_classes) + self.dropout = nn.Dropout(p=dropout_rate,inplace=True) + + self.backbone_1.fc = nn.Identity() + self.backbone_1.dropout = nn.Identity() + + self.backbone_2.fc = nn.Identity() + self.backbone_2.dropout = nn.Identity() + + def forward(self, x, y): + energy = None + classification = None + direction = None + + feature_1 = self.backbone_1(x) + feature_2 = self.backbone_2(y) + + # Combine outputs + if self.use_concat: + out = torch.cat((feature_1, feature_2), dim=1) + else: + out = feature_1 + feature_2 + + out = self.dropout(out) # Dropout before the final fully connected layer + out = self.fc(out) + + if self.task == "type": + classification = out + elif self.task == "energy": + energy = out + elif self.task == "direction": + direction = out + + return classification, energy, direction \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py b/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py new file mode 100644 index 00000000..332186b0 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py @@ -0,0 +1,73 @@ + +import torch.nn.functional as F +import torch +from torchvision import models +import torch.nn as nn + + +class SingleChannelRegNet(nn.Module): + def __init__(self, num_inputs=1, num_classes=2): + super(SingleChannelRegNet, self).__init__() + # self.regnet = models.regnet_y_400mf(weights=models.RegNet_Y_400MF_Weights.DEFAULT) + self.regnet = models.regnet_y_800mf(weights=models.RegNet_Y_800MF_Weights.DEFAULT) + + # self.regnet = models.regnet_x_16gf(weights=models.RegNet_X_16GF_Weights.DEFAULT) + # self.regnet = models.regnet_x_1_6gf(weights=models.RegNet_X_1_6GF_Weights.DEFAULT) + # self.regnet = models.regnet_y_1_6gf(weights=models.RegNet_Y_1_6GF_Weights.DEFAULT) + # Modify the first layer to accept the desired number of channels + self.regnet.stem[0] = nn.Conv2d(num_inputs, 32, kernel_size=( + 3, 3), stride=(2, 2), padding=(1, 1), bias=False) + # Modify the Linear layer to change the number of outputs (classes) + num_features = self.regnet.fc.in_features + self.regnet.fc = nn.Linear(num_features, num_classes) + + def forward(self, x): + return self.regnet(x) + +class DBBRegNet(nn.Module): + def __init__(self, task, use_concat=False, num_inputs=1, num_classes=2,dropout_rate = 0.1): + super(DBBRegNet,self).__init__() + self.use_concat= use_concat + self.task = task.lower() + self.bb1 = SingleChannelRegNet(num_inputs=num_inputs, num_classes=num_classes) + self.bb2 = SingleChannelRegNet(num_inputs=num_inputs, num_classes=num_classes) + + num_features = self.bb1.regnet.fc.in_features + + if self.use_concat: + num_features*=2 + + # Remove the final layer + self.bb1.regnet.fc = nn.Identity() + self.bb2.regnet.fc = nn.Identity() + self.dropout = nn.Dropout(p=dropout_rate,inplace=True) + self.fc = nn.Linear(num_features, num_classes) + + def forward(self, x, y): + + energy = None + classification = None + direction = None + + feature_1 = self.bb1(x) + feature_2 = self.bb2(y) + + + # Combine outputs + if self.use_concat: + out = torch.cat((feature_1, feature_2), dim=1) + else: + out = feature_1 + feature_2 + + out = self.dropout(out) + out = self.fc(out) + + if self.task == "type": + classification = out + elif self.task == "energy": + energy = out + elif self.task == "direction": + direction = out + + + return classification, energy, direction \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py new file mode 100644 index 00000000..2a508d9d --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py @@ -0,0 +1,270 @@ +import torch.nn as nn +import torch.nn.functional as F +import torch +import numpy as np + +from ctlearn.core.pytorch.nets.models.EffientNet_pytorch.model import EfficientNet +from ctlearn.core.pytorch.nets.block.cnn_blocks import Dirichlet + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class SEBlock(nn.Module): + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.PReLU(), + nn.Linear(channel // reduction, channel, bias=False), + MemoryEfficientSwish() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + +class DoubleBBEfficientNet(nn.Module): + def __init__(self,model_variant:str= "efficientnet-b3",task:str="Energy",num_outputs=2, device_str="cuda", energy_bins=None): + super(DoubleBBEfficientNet, self).__init__() + self.task = task.lower() + self.num_outputs = num_outputs + self.energy_bins = energy_bins + self.device = torch.device(device_str) + self.num_outputs = num_outputs + hidden_size= 512 + if 'b3' in model_variant: + feature_size= 1536*1 # vb3 + elif 'b5' in model_variant: + feature_size = 2048 # vb5 + else: + raise ValueError(f"Model variant {model_variant} not tested. Adapt the feature_size.") + + if self.task=="type": + self.backbone1 = EfficientNet.from_pretrained( + model_variant, in_channels=1, num_classes=num_outputs,use_batch_norm=True) + self.backbone2 = EfficientNet.from_pretrained( + model_variant, in_channels=1, num_classes=num_outputs,use_batch_norm=True) + # self.Dirichlet = Dirichlet(hidden_size,num_outputs) + if self.task=="energy": + self.backbone1 = EfficientNet.from_pretrained( + model_variant, in_channels=1, num_classes=num_outputs) + self.backbone2 = EfficientNet.from_pretrained( + model_variant, in_channels=1, num_classes=num_outputs) + #-------------------------------------------------------------------------------- + # Old code + #-------------------------------------------------------------------------------- + # use_swish=True + # use_batch_norm = True + # self.backbone1 = EfficientNet.from_name( + # 'efficientnet-b3', in_channels=1, num_classes=1,use_swish=use_swish,use_batch_norm=use_batch_norm) + # self.backbone2 = EfficientNet.from_name( + # 'efficientnet-b3', in_channels=1, num_classes=1,use_swish=use_swish,use_batch_norm=use_batch_norm) + + if task=="direction": + self.backbone1 = EfficientNet.from_pretrained( + model_variant, in_channels=1, num_classes=num_outputs) + self.backbone2 = EfficientNet.from_pretrained( + model_variant, in_channels=1, num_classes=num_outputs) + + + # feature_size = 2048 # v5 + # feature_size= 1280*2 + # Fusion module + self.fusion_conv = nn.Conv2d(in_channels=feature_size, out_channels=feature_size, kernel_size=1) + + # Attention modules for each backbone + # self.attention1 = SEBlock(int(feature_size/2)) + # self.attention2 = SEBlock(int(feature_size/2)) + # Add new layers + # Asumiendo 1536 características de EfficientNet-B3 + if self.task=="type": + self.fc_classification_1 = nn.Linear(feature_size, hidden_size) + self.fc_classification_2 = nn.Linear(hidden_size, num_outputs) + # v2 + # self.prelu_classification = nn.PReLU(hidden_size) + + if self.task=="energy": + self.fc_energy_1 = nn.Linear(feature_size, hidden_size) + self.fc_energy_2 = nn.Linear(hidden_size, int(num_outputs-1)) + # self.fc_energy_2 = nn.Linear(hidden_size, num_outputs) + + self.fc_energy_1_reg = nn.Linear(feature_size, hidden_size) + # self.fc_energy_2_reg = nn.Linear(hidden_size, num_outputs) + self.fc_energy_2_reg = nn.Linear(hidden_size, int(1)) + + # if self.energy_bins is not None: + # # Get the difference con bin[index+1]-bin[index] + # differences = np.diff(self.energy_bins) + # anchor_values= np.insert(differences, 0, 1).astype(np.float32) + # # anchor_values = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] + # # Create the tensor from these values with gradient tracking enabled + # # self.energy_anchors = torch.tensor(anchor_values, dtype=torch.float32,device=self.device, requires_grad=False) + # self.energy_anchors = nn.Parameter(torch.tensor(anchor_values, dtype=torch.float32, device= self.device)) + + if self.task=="direction": + self.fc_direction_1 = nn.Linear(feature_size, hidden_size) + self.fc_direction_2 = nn.Linear(hidden_size, num_outputs) + + self.swish = MemoryEfficientSwish() + # self.batch_norm = nn.BatchNorm1d(feature_size*2) + self.dropout_energy = nn.Dropout(0.3) + self.dropout_direction = nn.Dropout(0.3) + self.prelu_direction = nn.PReLU(num_parameters=hidden_size) + self.prelu_energy_1 = nn.PReLU(num_parameters=hidden_size) + self.prelu_energy_2 = nn.PReLU() + self.relu_energy = nn.ReLU() + + self.batch_norm = nn.BatchNorm1d(feature_size) + self.dropout_energy_1 = nn.Dropout(0.1) + self.dropout_energy_2 = nn.Dropout(0.3) + + def extract_feature_vector(self,x1, x2): + + x1 = self.backbone1.extract_features(x1) + # Backbone 2 + x2 = self.backbone2.extract_features(x2) + + # Normalización previa a la fusión + # x1 = F.normalize(x1, p=2, dim=1) + # x2 = F.normalize(x2, p=2, dim=1) + # Fusion point + fused_features = torch.add(x1, x2) # Sum fusion + # fused_features = torch.cat((x1, x2), dim=1) + + # Apply fusion module + fused_features = self.fusion_conv(fused_features) + + # Global average pooling + fused_features = F.adaptive_avg_pool2d(fused_features, 1) + + # Flatten + fused_features = fused_features.view(fused_features.size(0), -1) + + return fused_features + + def forward(self, x1, x2): + + energy = None + classification = None + direction = None + + + fused_features = self.extract_feature_vector(x1, x2) + + # Full connect layer and activation + if self.task == "type": + + # v_2 + # classification = self.fc_classification_1(fused_features) + # classification = self.prelu_classification(classification) + # classification = F.dropout(classification, p=0.1, training=self.training) + # classification = self.fc_classification_2(classification) + + # v_1 + classification = self.fc_classification_1(fused_features) + classification = self.fc_classification_2(classification) + classification = self.swish(classification) + #v_x + # classification = self.Dirichlet(classification) + # if not self.training: + + # reliability = (1.0-(self.num_outputs / classification.sum())) + # # Normalize + # classification = classification / classification.sum() + + # classification = [classification, reliability] + if self.task == "energy": + + # Exp 10 + #---------------------------------------------------------- + # NEW: Adding dropout and batch_norm here + fused_features = self.batch_norm(fused_features) + fused_features = self.dropout_energy_1(fused_features) + #---------------------------------------------------------- + # if self.training: + energy_class = self.fc_energy_1(fused_features) + energy_class = self.fc_energy_2(energy_class) + energy_class = self.swish(energy_class) + energy_pred_class = self.swish(energy_class) + # else: + # energy_pred_class= None + + energy_reg = self.fc_energy_1_reg(fused_features) + energy_reg = self.dropout_energy_2(energy_reg) # NEW: Adding dropout here + energy_reg = self.prelu_energy_1(energy_reg) + energy_regresion = self.fc_energy_2_reg(energy_reg) + + + # predicted = torch.softmax(energy_pred_class, dim=1) + # predicted = predicted.argmax(dim=1) + # # energy[:,-int(self.num_outputs/2):]=energy[:,-int(self.num_outputs/2):]*self.energy_anchors[predicted].unsqueeze(1) + # energy_regresion = energy_reg.clone() # First, clone the original tensor to preserve the computational graph + # energy_regresion = energy_regresion * self.energy_anchors[predicted].unsqueeze(1) + # energy = torch.concat([energy_pred_class,energy_regresion],dim=1) # Assign the updated tensor back to energy + energy = [energy_pred_class,energy_regresion] # Assign the updated tensor back to energy + + # # Exp 8-x + # #---------------------------------------------------------- + # # NEW: Adding dropout and batch_norm here + # fused_features = self.batch_norm(fused_features) + # fused_features = self.dropout_energy_1(fused_features) + # #---------------------------------------------------------- + # energy_class = self.fc_energy_1(fused_features) + # energy_class = self.fc_energy_2(energy_class) + # energy_class = self.swish(energy_class) + # energy_pred_class = self.swish(energy_class) + + # energy_reg = self.fc_energy_1_reg(fused_features) + # energy_reg = self.dropout_energy_2(energy_reg) # NEW: Adding dropout here + # energy_reg = self.prelu_energy_1(energy_reg) + # energy_reg = self.fc_energy_2_reg(energy_reg) + + + # predicted = torch.softmax(energy_pred_class, dim=1) + # predicted = predicted.argmax(dim=1) + # # energy[:,-int(self.num_outputs/2):]=energy[:,-int(self.num_outputs/2):]*self.energy_anchors[predicted].unsqueeze(1) + # energy_regresion = energy_reg.clone() # First, clone the original tensor to preserve the computational graph + # energy_regresion = energy_regresion * self.energy_anchors[predicted].unsqueeze(1) + # energy = torch.concat([energy_pred_class,energy_regresion],dim=1) # Assign the updated tensor back to energy + #--------------------------------------------------------------------------------------------------------------------------- + # Exp 3-7 + # energy = self.fc_energy_1(fused_features) + # energy = self.fc_energy_2(energy) + # energy = self.swish(energy) + # energy_pred_class = self.swish(energy[:,0:int(self.num_outputs/2)]) + + # predicted = torch.softmax(energy_pred_class, dim=1) + # predicted = predicted.argmax(dim=1) + # # energy[:,-int(self.num_outputs/2):]=energy[:,-int(self.num_outputs/2):]*self.energy_anchors[predicted].unsqueeze(1) + # energy_regresion = energy[:, -int(self.num_outputs/2):].clone() # First, clone the original tensor to preserve the computational graph + # energy_regresion = energy_regresion * self.energy_anchors[predicted].unsqueeze(1) + # energy = torch.concat([energy_pred_class,energy_regresion],dim=1) # Assign the updated tensor back to energy + #--------------------------------------------------------------------------------------------------------------------------- + # classification = self.swish(classification) + # Old code + # energy = self.fc_energy_1(fused_features) + # energy = self.dropout_energy(energy) + # energy = self.fc_energy_2(energy) + + if self.task == "direction": + fused_features = self.dropout_direction(fused_features) + direction = self.fc_direction_1(fused_features) + direction = self.fc_direction_2(direction) + + + + return [classification,fused_features], energy, direction + + def eval(self): + super().eval() + self.backbone1.eval() + self.backbone2.eval() + + def train(self, mode=True): + super().train(mode) + self.backbone1.train(mode) + self.backbone2.train(mode) diff --git a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/__init__.py b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/nets/models/DualBackboneEfficientNetV2/DoubleBBEfficientNetV2.py b/ctlearn/core/pytorch/nets/models/DualBackboneEfficientNetV2/DoubleBBEfficientNetV2.py new file mode 100644 index 00000000..65aac4dd --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/DualBackboneEfficientNetV2/DoubleBBEfficientNetV2.py @@ -0,0 +1,186 @@ +import torch +from torch import nn + +# Configuration for EfficientNetV2 +Eff_V2_SETTINGS = { + 's': [ + [1, 3, 1, 24, 24, 2, True], + [4, 3, 2, 24, 48, 4, True], + [4, 3, 2, 48, 64, 4, True], + [4, 3, 2, 64, 128, 6, False], + [6, 3, 1, 128, 160, 9, False], + [6, 3, 2, 160, 256, 15, False] + ], + 'm': [ + [1, 3, 1, 24, 24, 3, True], + [4, 3, 2, 24, 48, 5, True], + [4, 3, 2, 48, 80, 5, True], + [4, 3, 2, 80, 160, 7, False], + [6, 3, 1, 160, 176, 14, False], + [6, 3, 2, 176, 304, 18, False], + [6, 3, 1, 304, 512, 5, False] + ], + 'l': [ + [1, 3, 1, 32, 32, 4, True], + [4, 3, 2, 32, 64, 7, True], + [4, 3, 2, 64, 96, 7, True], + [4, 3, 2, 96, 192, 10, False], + [6, 3, 1, 192, 224, 19, False], + [6, 3, 2, 224, 384, 25, False], + [6, 3, 1, 384, 640, 7, False] + ] +} + +# Convolution, Batch Normalization, and Activation Layer +class ConvBnAct(nn.Module): + def __init__(self, n_in, n_out, k_size=3, stride=1, padding=0, groups=1, act=True, bn=False, bias=False): + super(ConvBnAct, self).__init__() + self.conv = nn.Conv2d(n_in, n_out, kernel_size=k_size, stride=stride, padding=padding, groups=groups, bias=bias) + self.batch_norm = nn.BatchNorm2d(n_out) if bn else nn.Identity() + self.activation = nn.SiLU() if act else nn.Identity() + + def forward(self, x): + x = self.conv(x) + x = self.batch_norm(x) + x = self.activation(x) + return x + +# Squeeze and Excitation Module +class SqueezeExcitation(nn.Module): + def __init__(self, n_in, reduction_factor=4): + super(SqueezeExcitation, self).__init__() + reduced_dim = n_in // reduction_factor + self.squeeze = nn.AdaptiveAvgPool2d(1) + self.excite = nn.Sequential( + nn.Conv2d(n_in, reduced_dim, kernel_size=1), + nn.SiLU(), + nn.Conv2d(reduced_dim, n_in, kernel_size=1), + nn.Sigmoid() + ) + + def forward(self, x): + y = self.squeeze(x) + y = self.excite(y) + return x * y + +# Stochastic Depth for Regularization +class StochasticDepth(nn.Module): + def __init__(self, survival_prob=0.8): + super(StochasticDepth, self).__init__() + self.p = survival_prob + + def forward(self, x): + if not self.training: + return x + binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.p + return torch.div(x, self.p) * binary_tensor + +# Mobile Inverted Residual Block with Squeeze and Excitation +class MBConvN(nn.Module): + def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, reduction_factor=4, survival_prob=0.8): + super(MBConvN, self).__init__() + expanded_dim = int(expansion_factor * n_in) + padding = (k_size - 1) // 2 + self.use_residual = (n_in == n_out) and (stride == 1) + self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size=1) + self.depthwise_conv = ConvBnAct(expanded_dim, expanded_dim, k_size, stride=stride, padding=padding, groups=expanded_dim) + self.se = SqueezeExcitation(expanded_dim, reduction_factor) + self.drop_layers = StochasticDepth(survival_prob) + self.pointwise_conv = ConvBnAct(expanded_dim, n_out, k_size=1, act=False) + + def forward(self, x): + residual = x.clone() + x = self.expand(x) + x = self.depthwise_conv(x) + x = self.se(x) + x = self.pointwise_conv(x) + if self.use_residual: + x = self.drop_layers(x) + x += residual + return x + +# Fused Mobile Inverted Residual Block +class FusedMBConvN(nn.Module): + def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, survival_prob=0.8): + super(FusedMBConvN, self).__init__() + expanded_dim = int(expansion_factor * n_in) + padding = (k_size - 1) // 2 + self.use_residual = (n_in == n_out) and (stride == 1) + self.conv = ConvBnAct(n_in, expanded_dim, k_size, stride=stride, padding=padding, groups=1) + self.drop_layers = StochasticDepth(survival_prob) + self.pointwise_conv = nn.Identity() if (expansion_factor == 1) else ConvBnAct(expanded_dim, n_out, k_size=1, act=False) + + def forward(self, x): + residual = x.clone() + x = self.conv(x) + x = self.pointwise_conv(x) + if self.use_residual: + x = self.drop_layers(x) + x += residual + return x + +# EfficientNetV2 Model Definition +class EfficientNetV2(nn.Module): + def __init__(self, version='s', in_channels=3, last_channel=1280): + super(EfficientNetV2, self).__init__() + self.features = self._make_layers(version, in_channels, last_channel) + + def forward(self, x): + x = self.features(x) + return x + + def _make_layers(self, version, in_channels, last_channel): + config = Eff_V2_SETTINGS[version] + layers = [] + layers.append(ConvBnAct(in_channels, config[0][3], k_size=3, stride=2, padding=1)) + + for (expansion_factor, k, stride, n_in, n_out, num_layers, use_fused) in config: + if use_fused: + layers += [FusedMBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) + for repeat in range(num_layers)] + else: + layers += [MBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) + for repeat in range(num_layers)] + + layers.append(ConvBnAct(config[-1][4], last_channel, k_size=1)) + return nn.Sequential(*layers) + +# Dual Backbone EfficientNetV2 Model for Specific Tasks +class DualBackboneEfficientNetV2(nn.Module): + def __init__(self, task, version='s', num_classes=1, in_channels_1=1, in_channels_2=1, dropout_rate=0.2): + super(DualBackboneEfficientNetV2, self).__init__() + self.task = task + self.backbone1 = EfficientNetV2(version, in_channels_1) + self.backbone2 = EfficientNetV2(version, in_channels_2) + last_channel = 1280 + self.classifier = nn.Sequential( + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Dropout(dropout_rate, inplace=True), + nn.Linear(last_channel, num_classes) + ) + + def forward(self, x1, x2): + classification = None + energy = None + direction = None + + x1 = self.backbone1(x1) + x2 = self.backbone2(x2) + x = x1 + x2 # Fuse by adding + + if self.task == "energy": + energy = self.classifier(x) + + elif self.task == "direction": + direction = self.classifier(x) + + # Optionally handle a classification task + # if self.task == "classification": + # classification = self.classifier(x) + + return classification, energy, direction + +# # Example usage for regression task +# if __name__ == "__main__": +# model = DualBackboneEfficientNetV2(task='energy', version='s', num_classes=1, in_channels_1=3, in_channels_2=3) # num_classes=1 for regression \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/EfficientNet.py b/ctlearn/core/pytorch/nets/models/EfficientNet.py new file mode 100644 index 00000000..ec629d8a --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/EfficientNet.py @@ -0,0 +1,158 @@ +# import pytorch +from torch import nn +import math +basic_mb_params = [ + # k, channels(c), repeats(t), stride(s), kernel_size(k) + [1, 16, 1, 1, 3], + [6, 24, 2, 2, 3], + [6, 40, 2, 2, 5], + [6, 80, 3, 2, 3], + [6, 112, 3, 1, 5], + [6, 192, 4, 2, 5], + [6, 320, 1, 1, 3], +] + +alpha, beta = 1.2, 1.1 + +scale_values = { + # (phi, resolution, dropout) + "b0": (0, 224, 0.2), + "b1": (0.5, 240, 0.2), + "b2": (1, 260, 0.3), + "b3": (2, 300, 0.3), + "b4": (3, 380, 0.4), + "b5": (4, 456, 0.4), + "b6": (5, 528, 0.5), + "b7": (6, 600, 0.5), +} + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, + stride, padding, groups=1): + super(ConvBlock, self).__init__() + self.cnnblock = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size, + stride, padding, groups=groups), + nn.BatchNorm2d(out_channels), + nn.SiLU()) + + def forward(self, x): + return self.cnnblock(x) + + +# class MBBlock(nn.Module): +# def __init__(self, in_channels, out_channels, kernel_size, +# stride, padding, expand_ratio, reduction=2, +# ): +# super(MBBlock, self).__init__() +# # self.use_residual = in_channels == out_channels and stride == 1 +# hidden_dim = in_channels * expand_ratio +# self.expand = in_channels != hidden_dim + +# # This is for squeeze and excitation block +# reduced_dim = int(in_channels / reduction) + +# if self.expand: +# self.expand_conv = ConvBlock(in_channels, hidden_dim, +# kernel_size=3,stride=1,padding=1) + +# self.conv = nn.Sequential( +# ConvBlock(hidden_dim,hidden_dim,kernel_size, +# stride,padding,groups=hidden_dim), +# SqueezeExcitation(hidden_dim, reduced_dim), +# nn.Conv2d(hidden_dim, out_channels, 1), +# nn.BatchNorm2d(out_channels), +# ) + +# def forward(self, inputs): +# if self.expand: +# x = self.expand_conv(inputs) +# else: +# x = inputs +# return self.conv(x) + +class MBBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, + stride, padding, expand_ratio, reduction=4): # Changed 'ratio' to 'expand_ratio' and adjusted 'reduction' + super(MBBlock, self).__init__() + hidden_dim = in_channels * expand_ratio + self.expand = in_channels != hidden_dim + + reduced_dim = int(hidden_dim / reduction) # Use 'hidden_dim' not 'in_channels' + + if self.expand: + self.expand_conv = ConvBlock(in_channels, hidden_dim, + kernel_size=1, stride=1, padding=0) # Typically a 1x1 conv + + self.conv = nn.Sequential( + ConvBlock(hidden_dim, hidden_dim, kernel_size, + stride, padding, groups=hidden_dim), + SqueezeExcitation(hidden_dim, reduced_dim), + nn.Conv2d(hidden_dim, out_channels, 1, 1, 0), # Kernel size 1, stride 1, padding 0 + nn.BatchNorm2d(out_channels) + ) + + def forward(self, inputs): + x = self.expand_conv(inputs) if self.expand else inputs + return self.conv(x) + +class SqueezeExcitation(nn.Module): + def __init__(self, in_channels, reduced_dim): + super(SqueezeExcitation, self).__init__() + self.se = nn.Sequential( + nn.AdaptiveAvgPool2d(1), # C x H x W -> C x 1 x 1 + nn.Conv2d(in_channels, reduced_dim, 1), + nn.SiLU(), + nn.Conv2d(reduced_dim, in_channels, 1), + nn.Sigmoid(), + ) + + def forward(self, x): + return x * self.se(x) + + +class EfficientNet(nn.Module): + def __init__(self, model_name, num_channels, output): + super(EfficientNet, self).__init__() + self.num_channels = num_channels + phi, resolution, dropout = scale_values[model_name] + self.depth_factor, self.width_factor = alpha**phi, beta**phi + self.last_channels = math.ceil(1280 * self.width_factor) + self.avgpool= nn.AdaptiveAvgPool2d(1) + self.feature_extractor() + self.flatten = nn.Flatten() + self.classifier = nn.Sequential( + nn.Dropout(dropout), + nn.Linear(self.last_channels, output), + ) + + def feature_extractor(self): + channels = int(32 * self.width_factor) + features = [ConvBlock(self.num_channels, channels, 3, stride=2, padding=1)] + in_channels = channels + + for k, c_o, repeat, s, n in basic_mb_params: + # For numeric stability, we multiply and divide by 4 + out_channels = 4 * math.ceil(int(c_o * self.width_factor) / 4) + num_layers = math.ceil(repeat * self.depth_factor) + + for layer in range(num_layers): + if layer == 0: + stride = s + else: + stride = 1 + features.append( + MBBlock(in_channels,out_channels,expand_ratio=k, + stride=stride,kernel_size=n,padding=n// 2) + ) + in_channels = out_channels + + features.append( + ConvBlock(in_channels, self.last_channels, + kernel_size=1, stride=1, padding=0) + ) + self.extractor = nn.Sequential(*features) + + def forward(self, x): + x = self.avgpool(self.extractor(x)) + return self.classifier(self.flatten(x)) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/EfficientNetv2.py b/ctlearn/core/pytorch/nets/models/EfficientNetv2.py new file mode 100644 index 00000000..61637137 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/EfficientNetv2.py @@ -0,0 +1,263 @@ +import torch +from torch import nn + +Eff_V2_SETTINGS = { + # expansion factor, k, stride, n_in, n_out, num_layers, use_fusedMBCONV + 's' : [ + [1, 3, 1, 24, 24, 2, True], + [4, 3, 2, 24, 48, 4, True], + [4, 3, 2, 48, 64, 4, True], + [4, 3, 2, 64, 128, 6, False], + [6, 3, 1, 128, 160, 9, False], + [6, 3, 2, 160, 256, 15, False] + ], + + 'm' : [ + [1, 3, 1, 24, 24, 3, True], + [4, 3, 2, 24, 48, 5, True], + [4, 3, 2, 48, 80, 5, True], + [4, 3, 2, 80, 160, 7, False], + [6, 3, 1, 160, 176, 14, False], + [6, 3, 2, 176, 304, 18, False], + [6, 3, 1, 304, 512, 5, False] + ], + + 'l' : [ + [1, 3, 1, 32, 32, 4, True], + [4, 3, 2, 32, 64, 7, True], + [4, 3, 2, 64, 96, 7, True], + [4, 3, 2, 96, 192, 10, False], + [6, 3, 1, 192, 224, 19, False], + [6, 3, 2, 224, 384, 25, False], + [6, 3, 1, 384, 640, 7, False] + ] +} + +class ConvBnAct(nn.Module): + + def __init__( + self, + n_in, # in_channels + n_out, # out_channels + k_size = 3, # Kernel Size + stride = 1, + padding = 0, + groups = 1, + act = True, + bn = True, + bias = False + ): + super(ConvBnAct, self).__init__() + + self.conv = nn.Conv2d(n_in, n_out, kernel_size = k_size, stride = stride, + padding = padding, groups = groups,bias = bias + ) + self.batch_norm = nn.BatchNorm2d(n_out) if bn else nn.Identity() + self.activation = nn.SiLU() if act else nn.Identity() + + def forward(self, x): + x = self.conv(x) + x = self.batch_norm(x) + x = self.activation(x) + + return x + +#-------------------------------------------------------------------------------------------- + +'''Squeeze and Excitation Class''' + +class SqueezeExcitation(nn.Module): + + def __init__( + self, + n_in, # In_channels + reduced_dim + ): + super(SqueezeExcitation, self).__init__() + + self.squeeze = nn.AdaptiveAvgPool2d(1) + self.excite = nn.Sequential(nn.Conv2d(n_in, reduced_dim, kernel_size=1), + nn.SiLU(), + nn.Conv2d(reduced_dim, n_in, kernel_size=1), + nn.Sigmoid() + ) + + def forward(self, x): + y = self.squeeze(x) + y = self.excite(y) + + return x * y + +#-------------------------------------------------------------------------------------- + +''' Stochastic Depth Class''' + +class StochasticDepth(nn.Module): + + def __init__( + self, + survival_prob = 0.8 + ): + super(StochasticDepth, self).__init__() + + self.p = survival_prob + + def forward(self, x): + + if not self.training: + return x + + binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.p + + return torch.div(x, self.p) * binary_tensor + +#------------------------------------------------------------------------------- + +'''MBCONV Class''' + +class MBConvN(nn.Module): + + def __init__( + self, + n_in, # In_channels + n_out, # out_channels + k_size = 3, # kernel_size + stride = 1, + expansion_factor = 4, + reduction_factor = 4, # SqueezeExcitation Block + survival_prob = 0.8 # StochasticDepth Block + ): + super(MBConvN, self).__init__() + reduced_dim = int(n_in//4) + expanded_dim = int(expansion_factor * n_in) + padding = (k_size - 1)//2 + + self.use_residual = (n_in == n_out) and (stride == 1) + self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size = 1) + self.depthwise_conv = ConvBnAct(expanded_dim, expanded_dim, + k_size, stride = stride, + padding = padding, groups = expanded_dim + ) + self.se = SqueezeExcitation(expanded_dim, reduced_dim) + self.drop_layers = StochasticDepth(survival_prob) + self.pointwise_conv = ConvBnAct(expanded_dim, n_out, k_size = 1, act = False) + + def forward(self, x): + + residual = x.clone() + x = self.expand(x) + x = self.depthwise_conv(x) + x = self.se(x) + x = self.pointwise_conv(x) + + if self.use_residual: + x = self.drop_layers(x) + x += residual + + return x + +#-------------------------------------------------------------------------------------- + +'''Fused-MBCONV Class''' + +class FusedMBConvN(nn.Module): + + def __init__( + self, + n_in, # In_channels + n_out, # out_channels + k_size = 3, # kernel_size + stride = 1, + expansion_factor = 4, + reduction_factor = 4, # SqueezeExcitation Block + survival_prob = 0.8 # StochasticDepth Block + ): + super(FusedMBConvN, self).__init__() + + reduced_dim = int(n_in//4) + expanded_dim = int(expansion_factor * n_in) + padding = (k_size - 1)//2 + + self.use_residual = (n_in == n_out) and (stride == 1) + #self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size = 1) + self.conv = ConvBnAct(n_in, expanded_dim, + k_size, stride = stride, + padding = padding, groups = 1 + ) + #self.se = SqueezeExcitation(expanded_dim, reduced_dim) + self.drop_layers = StochasticDepth(survival_prob) + self.pointwise_conv = nn.Identity() if (expansion_factor == 1) else ConvBnAct(expanded_dim, n_out, k_size = 1, act = False) + + def forward(self, x): + + residual = x.clone() + #x = self.conv(x) + x = self.conv(x) + #x = self.se(x) + x = self.pointwise_conv(x) + + if self.use_residual: + x = self.drop_layers(x) + x += residual + + return x + +#----------------------------------------------------------------------------------------------- + +class EfficientNetV2(nn.Module): + + def __init__( + self, + version = 's', + dropout_rate = 0.2, + in_channels=1, + num_classes = 1000 + ): + super(EfficientNetV2, self).__init__() + last_channel = 1280 + self.features = self._feature_extractor(version,in_channels, last_channel) + self.classifier = nn.Sequential( + nn.AdaptiveAvgPool2d((1,1)), + nn.Flatten(), + nn.Dropout(dropout_rate, inplace = True), + nn.Linear(last_channel, num_classes) + ) + + def forward(self, x): + x = self.features(x) + x = self.classifier(x) + + return x + + def _feature_extractor(self, version,in_channels, last_channel): + + # Extract the Config + config = Eff_V2_SETTINGS[version] + + layers = [] + layers.append(ConvBnAct(in_channels, config[0][3], k_size = 3, stride = 2, padding = 1)) + #in_channel = config[0][3] + + for (expansion_factor, k, stride, n_in, n_out, num_layers, use_fused) in config: + + if use_fused: + layers += [FusedMBConvN(n_in if repeat==0 else n_out, + n_out, + k_size=k, + stride = stride if repeat==0 else 1, + expansion_factor=expansion_factor + ) for repeat in range(num_layers) + ] + else: + + layers += [MBConvN(n_in if repeat==0 else n_out, + n_out, + k_size=k, + stride = stride if repeat==0 else 1, + expansion_factor=expansion_factor + ) for repeat in range(num_layers) + ] + + layers.append(ConvBnAct(config[-1][4], last_channel, k_size = 1)) + + return nn.Sequential(*layers) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/__init__.py b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/__init__.py new file mode 100644 index 00000000..2b529dfe --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/__init__.py @@ -0,0 +1,9 @@ +__version__ = "0.7.1" +from .model import EfficientNet, VALID_MODELS +from .utils import ( + GlobalParams, + BlockArgs, + BlockDecoder, + efficientnet, + get_model_params, +) diff --git a/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model.py b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model.py new file mode 100644 index 00000000..97816df8 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model.py @@ -0,0 +1,454 @@ +"""model.py - Model and module class for EfficientNet. + They are built to mirror those in the official TensorFlow implementation. +""" + +# Author: lukemelas (github username) +# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch +# With adjustments and added comments by workingcoder (github username). + +import torch +from torch import nn +from torch.nn import functional as F +from .utils import ( + round_filters, + round_repeats, + drop_connect, + get_same_padding_conv2d, + get_model_params, + efficientnet_params, + load_pretrained_weights, + Swish, + MemoryEfficientSwish, + calculate_output_image_size +) + + +VALID_MODELS = ( + 'efficientnet-b0', 'efficientnet-b1', 'efficientnet-b2', 'efficientnet-b3', + 'efficientnet-b4', 'efficientnet-b5', 'efficientnet-b6', 'efficientnet-b7', + 'efficientnet-b8', + + # Support the construction of 'efficientnet-l2' without pretrained weights + 'efficientnet-l2' +) + + +class MBConvBlock(nn.Module): + """Mobile Inverted Residual Bottleneck Block. + + Args: + block_args (namedtuple): BlockArgs, defined in utils.py. + global_params (namedtuple): GlobalParam, defined in utils.py. + image_size (tuple or list): [image_height, image_width]. + + References: + [1] https://arxiv.org/abs/1704.04861 (MobileNet v1) + [2] https://arxiv.org/abs/1801.04381 (MobileNet v2) + [3] https://arxiv.org/abs/1905.02244 (MobileNet v3) + """ + + def __init__(self, block_args, global_params, image_size=None,use_swish=True, use_batch_norm=True): + super().__init__() + self._block_args = block_args + self._bn_mom = 1 - global_params.batch_norm_momentum # pytorch's difference from tensorflow + self._bn_eps = global_params.batch_norm_epsilon + self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1) + self.id_skip = block_args.id_skip # whether to use skip connection and drop connect + self.use_swish = use_swish + self.use_batch_norm = use_batch_norm + # Expansion phase (Inverted Bottleneck) + inp = self._block_args.input_filters # number of input channels + oup = self._block_args.input_filters * self._block_args.expand_ratio # number of output channels + if self._block_args.expand_ratio != 1: + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False) + if self.use_batch_norm: + self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + else: + self._bn0 = nn.Identity() + + # image_size = calculate_output_image_size(image_size, 1) <-- this wouldn't modify image_size + + # Depthwise convolution phase + k = self._block_args.kernel_size + s = self._block_args.stride + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._depthwise_conv = Conv2d( + in_channels=oup, out_channels=oup, groups=oup, # groups makes it depthwise + kernel_size=k, stride=s, bias=False) + if self.use_batch_norm: + self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + else: + self._bn1 = nn.Identity() + + image_size = calculate_output_image_size(image_size, s) + + # Squeeze and Excitation layer, if desired + if self.has_se: + Conv2d = get_same_padding_conv2d(image_size=(1, 1)) + num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio)) + self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1) + self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1) + + # Pointwise convolution phase + final_oup = self._block_args.output_filters + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False) + if self.use_batch_norm: + self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps) + else: + self._bn2 = nn.Identity() + if self.use_swish: + self._swish = MemoryEfficientSwish() + else: + self._swish = nn.PReLU() + + def forward(self, inputs, drop_connect_rate=None): + """MBConvBlock's forward function. + + Args: + inputs (tensor): Input tensor. + drop_connect_rate (bool): Drop connect rate (float, between 0 and 1). + + Returns: + Output of this block after processing. + """ + + # Expansion and Depthwise Convolution + x = inputs + if self._block_args.expand_ratio != 1: + x = self._expand_conv(inputs) + x = self._bn0(x) + x = self._swish(x) + + x = self._depthwise_conv(x) + x = self._bn1(x) + x = self._swish(x) + + # Squeeze and Excitation + if self.has_se: + x_squeezed = F.adaptive_avg_pool2d(x, 1) + x_squeezed = self._se_reduce(x_squeezed) + x_squeezed = self._swish(x_squeezed) + x_squeezed = self._se_expand(x_squeezed) + x = torch.sigmoid(x_squeezed) * x + + # Pointwise Convolution + x = self._project_conv(x) + x = self._bn2(x) + + # Skip connection and drop connect + input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters + if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters: + # The combination of skip connection and drop connect brings about stochastic depth. + if drop_connect_rate: + x = drop_connect(x, p=drop_connect_rate, training=self.training) + x = x + inputs # skip connection + return x + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export). + + Args: + memory_efficient (bool): Whether to use memory-efficient version of swish. + """ + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + + +class EfficientNet(nn.Module): + """EfficientNet model. + Most easily loaded with the .from_name or .from_pretrained methods. + + Args: + blocks_args (list[namedtuple]): A list of BlockArgs to construct blocks. + global_params (namedtuple): A set of GlobalParams shared between blocks. + + References: + [1] https://arxiv.org/abs/1905.11946 (EfficientNet) + + Example: + >>> import torch + >>> from efficientnet.model import EfficientNet + >>> inputs = torch.rand(1, 3, 224, 224) + >>> model = EfficientNet.from_pretrained('efficientnet-b0') + >>> model.eval() + >>> outputs = model(inputs) + """ + + def __init__(self, blocks_args=None, global_params=None,use_swish=True,use_bn=True): + + # DELETE MEEEEEEE + # use_bn=False + + super().__init__() + assert isinstance(blocks_args, list), 'blocks_args should be a list' + assert len(blocks_args) > 0, 'block args must be greater than 0' + self._global_params = global_params + self._blocks_args = blocks_args + self.use_swish= use_swish + self.use_batch_norm = use_bn + # Batch norm parameters + bn_mom = 1 - self._global_params.batch_norm_momentum + bn_eps = self._global_params.batch_norm_epsilon + + # Get stem static or dynamic convolution depending on image size + image_size = global_params.image_size + Conv2d = get_same_padding_conv2d(image_size=image_size) + + # Stem + in_channels = 3 # rgb + out_channels = round_filters(32, self._global_params) # number of output channels + self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) + if self.use_batch_norm: + self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + else: + self._bn0 = nn.Identity() + image_size = calculate_output_image_size(image_size, 2) + + # Build blocks + self._blocks = nn.ModuleList([]) + for block_args in self._blocks_args: + + # Update block input and output filters based on depth multiplier. + block_args = block_args._replace( + input_filters=round_filters(block_args.input_filters, self._global_params), + output_filters=round_filters(block_args.output_filters, self._global_params), + num_repeat=round_repeats(block_args.num_repeat, self._global_params) + ) + + # The first block needs to take care of stride and filter size increase. + self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size,use_swish=self.use_swish, use_batch_norm=self.use_batch_norm)) + image_size = calculate_output_image_size(image_size, block_args.stride) + if block_args.num_repeat > 1: # modify block_args to keep same output size + block_args = block_args._replace(input_filters=block_args.output_filters, stride=1) + for _ in range(block_args.num_repeat - 1): + self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size,use_swish=self.use_swish,use_batch_norm=self.use_batch_norm)) + # image_size = calculate_output_image_size(image_size, block_args.stride) # stride = 1 + + # Head + in_channels = block_args.output_filters # output of final block + out_channels = round_filters(1280, self._global_params) + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + if self.use_batch_norm: + self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + else: + self._bn1 = nn.Identity() + + # Final linear layer + self._avg_pooling = nn.AdaptiveAvgPool2d(1) + if self._global_params.include_top: + self._dropout = nn.Dropout(self._global_params.dropout_rate) + self._fc = nn.Linear(out_channels, self._global_params.num_classes) + + # set activation to memory efficient swish by default + if self.use_swish: + self._swish = MemoryEfficientSwish() + else: + self._swish = nn.PReLU() + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export). + + Args: + memory_efficient (bool): Whether to use memory-efficient version of swish. + """ + if self.use_swish: + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + else: + self._swish = nn.PReLU() + + for block in self._blocks: + block.set_swish(memory_efficient) + + def extract_endpoints(self, inputs): + """Use convolution layer to extract features + from reduction levels i in [1, 2, 3, 4, 5]. + + Args: + inputs (tensor): Input tensor. + + Returns: + Dictionary of last intermediate features + with reduction levels i in [1, 2, 3, 4, 5]. + Example: + >>> import torch + >>> from efficientnet.model import EfficientNet + >>> inputs = torch.rand(1, 3, 224, 224) + >>> model = EfficientNet.from_pretrained('efficientnet-b0') + >>> endpoints = model.extract_endpoints(inputs) + >>> print(endpoints['reduction_1'].shape) # torch.Size([1, 16, 112, 112]) + >>> print(endpoints['reduction_2'].shape) # torch.Size([1, 24, 56, 56]) + >>> print(endpoints['reduction_3'].shape) # torch.Size([1, 40, 28, 28]) + >>> print(endpoints['reduction_4'].shape) # torch.Size([1, 112, 14, 14]) + >>> print(endpoints['reduction_5'].shape) # torch.Size([1, 320, 7, 7]) + >>> print(endpoints['reduction_6'].shape) # torch.Size([1, 1280, 7, 7]) + """ + endpoints = dict() + + # Stem + x = self._swish(self._bn0(self._conv_stem(inputs))) + prev_x = x + + # Blocks + for idx, block in enumerate(self._blocks): + drop_connect_rate = self._global_params.drop_connect_rate + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self._blocks) # scale drop connect_rate + x = block(x, drop_connect_rate=drop_connect_rate) + if prev_x.size(2) > x.size(2): + endpoints['reduction_{}'.format(len(endpoints) + 1)] = prev_x + elif idx == len(self._blocks) - 1: + endpoints['reduction_{}'.format(len(endpoints) + 1)] = x + prev_x = x + + # Head + x = self._swish(self._bn1(self._conv_head(x))) + endpoints['reduction_{}'.format(len(endpoints) + 1)] = x + + return endpoints + + def extract_features(self, inputs): + """use convolution layer to extract feature . + + Args: + inputs (tensor): Input tensor. + + Returns: + Output of the final convolution + layer in the efficientnet model. + """ + # Stem + x = self._swish(self._bn0(self._conv_stem(inputs))) + + # Blocks + for idx, block in enumerate(self._blocks): + drop_connect_rate = self._global_params.drop_connect_rate + drop_connect_rate=0.0 + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self._blocks) # scale drop connect_rate + x = block(x, drop_connect_rate=drop_connect_rate) + + # Head + x = self._swish(self._bn1(self._conv_head(x))) + + return x + + def forward(self, inputs): + """EfficientNet's forward function. + Calls extract_features to extract features, applies final linear layer, and returns logits. + + Args: + inputs (tensor): Input tensor. + + Returns: + Output of this model after processing. + """ + # Convolution layers + x = self.extract_features(inputs) + # Pooling and final linear layer + x = self._avg_pooling(x) + if self._global_params.include_top: + x = x.flatten(start_dim=1) + x = self._dropout(x) + x = self._fc(x) + return x + + @classmethod + def from_name(cls, model_name, in_channels=3,use_swish=True,use_batch_norm=True, **override_params): + """Create an efficientnet model according to name. + + Args: + model_name (str): Name for efficientnet. + in_channels (int): Input data's channel number. + override_params (other key word params): + Params to override model's global_params. + Optional key: + 'width_coefficient', 'depth_coefficient', + 'image_size', 'dropout_rate', + 'num_classes', 'batch_norm_momentum', + 'batch_norm_epsilon', 'drop_connect_rate', + 'depth_divisor', 'min_depth' + + Returns: + An efficientnet model. + """ + cls._check_model_name_is_valid(model_name) + blocks_args, global_params = get_model_params(model_name, override_params) + model = cls(blocks_args, global_params,use_swish,use_batch_norm) + model._change_in_channels(in_channels) + + return model + + @classmethod + def from_pretrained(cls, model_name, weights_path=None, advprop=False, + in_channels=3, num_classes=1000, use_batch_norm=True, **override_params): + """Create an efficientnet model according to name. + + Args: + model_name (str): Name for efficientnet. + weights_path (None or str): + str: path to pretrained weights file on the local disk. + None: use pretrained weights downloaded from the Internet. + advprop (bool): + Whether to load pretrained weights + trained with advprop (valid when weights_path is None). + in_channels (int): Input data's channel number. + num_classes (int): + Number of categories for classification. + It controls the output size for final linear layer. + override_params (other key word params): + Params to override model's global_params. + Optional key: + 'width_coefficient', 'depth_coefficient', + 'image_size', 'dropout_rate', + 'batch_norm_momentum', + 'batch_norm_epsilon', 'drop_connect_rate', + 'depth_divisor', 'min_depth' + + Returns: + A pretrained efficientnet model. + """ + model = cls.from_name(model_name, num_classes=num_classes,use_batch_norm=use_batch_norm, **override_params) + # load_pretrained_weights(model, model_name, weights_path=weights_path,load_fc=(num_classes == 1000), advprop=advprop) + model._change_in_channels(in_channels) + + return model + + @classmethod + def get_image_size(cls, model_name): + """Get the input image size for a given efficientnet model. + + Args: + model_name (str): Name for efficientnet. + + Returns: + Input image size (resolution). + """ + cls._check_model_name_is_valid(model_name) + _, _, res, _ = efficientnet_params(model_name) + return res + + @classmethod + def _check_model_name_is_valid(cls, model_name): + """Validates model name. + + Args: + model_name (str): Name for efficientnet. + + Returns: + bool: Is a valid name or not. + """ + if model_name not in VALID_MODELS: + raise ValueError('model_name should be one of: ' + ', '.join(VALID_MODELS)) + + def _change_in_channels(self, in_channels): + """Adjust model's first convolution layer to in_channels, if in_channels not equals 3. + + Args: + in_channels (int): Input data's channel number. + """ + if in_channels != 3: + Conv2d = get_same_padding_conv2d(image_size=self._global_params.image_size) + out_channels = round_filters(32, self._global_params) + self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) diff --git a/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model_original.py b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model_original.py new file mode 100644 index 00000000..97816df8 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/model_original.py @@ -0,0 +1,454 @@ +"""model.py - Model and module class for EfficientNet. + They are built to mirror those in the official TensorFlow implementation. +""" + +# Author: lukemelas (github username) +# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch +# With adjustments and added comments by workingcoder (github username). + +import torch +from torch import nn +from torch.nn import functional as F +from .utils import ( + round_filters, + round_repeats, + drop_connect, + get_same_padding_conv2d, + get_model_params, + efficientnet_params, + load_pretrained_weights, + Swish, + MemoryEfficientSwish, + calculate_output_image_size +) + + +VALID_MODELS = ( + 'efficientnet-b0', 'efficientnet-b1', 'efficientnet-b2', 'efficientnet-b3', + 'efficientnet-b4', 'efficientnet-b5', 'efficientnet-b6', 'efficientnet-b7', + 'efficientnet-b8', + + # Support the construction of 'efficientnet-l2' without pretrained weights + 'efficientnet-l2' +) + + +class MBConvBlock(nn.Module): + """Mobile Inverted Residual Bottleneck Block. + + Args: + block_args (namedtuple): BlockArgs, defined in utils.py. + global_params (namedtuple): GlobalParam, defined in utils.py. + image_size (tuple or list): [image_height, image_width]. + + References: + [1] https://arxiv.org/abs/1704.04861 (MobileNet v1) + [2] https://arxiv.org/abs/1801.04381 (MobileNet v2) + [3] https://arxiv.org/abs/1905.02244 (MobileNet v3) + """ + + def __init__(self, block_args, global_params, image_size=None,use_swish=True, use_batch_norm=True): + super().__init__() + self._block_args = block_args + self._bn_mom = 1 - global_params.batch_norm_momentum # pytorch's difference from tensorflow + self._bn_eps = global_params.batch_norm_epsilon + self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1) + self.id_skip = block_args.id_skip # whether to use skip connection and drop connect + self.use_swish = use_swish + self.use_batch_norm = use_batch_norm + # Expansion phase (Inverted Bottleneck) + inp = self._block_args.input_filters # number of input channels + oup = self._block_args.input_filters * self._block_args.expand_ratio # number of output channels + if self._block_args.expand_ratio != 1: + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False) + if self.use_batch_norm: + self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + else: + self._bn0 = nn.Identity() + + # image_size = calculate_output_image_size(image_size, 1) <-- this wouldn't modify image_size + + # Depthwise convolution phase + k = self._block_args.kernel_size + s = self._block_args.stride + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._depthwise_conv = Conv2d( + in_channels=oup, out_channels=oup, groups=oup, # groups makes it depthwise + kernel_size=k, stride=s, bias=False) + if self.use_batch_norm: + self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + else: + self._bn1 = nn.Identity() + + image_size = calculate_output_image_size(image_size, s) + + # Squeeze and Excitation layer, if desired + if self.has_se: + Conv2d = get_same_padding_conv2d(image_size=(1, 1)) + num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio)) + self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1) + self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1) + + # Pointwise convolution phase + final_oup = self._block_args.output_filters + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False) + if self.use_batch_norm: + self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps) + else: + self._bn2 = nn.Identity() + if self.use_swish: + self._swish = MemoryEfficientSwish() + else: + self._swish = nn.PReLU() + + def forward(self, inputs, drop_connect_rate=None): + """MBConvBlock's forward function. + + Args: + inputs (tensor): Input tensor. + drop_connect_rate (bool): Drop connect rate (float, between 0 and 1). + + Returns: + Output of this block after processing. + """ + + # Expansion and Depthwise Convolution + x = inputs + if self._block_args.expand_ratio != 1: + x = self._expand_conv(inputs) + x = self._bn0(x) + x = self._swish(x) + + x = self._depthwise_conv(x) + x = self._bn1(x) + x = self._swish(x) + + # Squeeze and Excitation + if self.has_se: + x_squeezed = F.adaptive_avg_pool2d(x, 1) + x_squeezed = self._se_reduce(x_squeezed) + x_squeezed = self._swish(x_squeezed) + x_squeezed = self._se_expand(x_squeezed) + x = torch.sigmoid(x_squeezed) * x + + # Pointwise Convolution + x = self._project_conv(x) + x = self._bn2(x) + + # Skip connection and drop connect + input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters + if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters: + # The combination of skip connection and drop connect brings about stochastic depth. + if drop_connect_rate: + x = drop_connect(x, p=drop_connect_rate, training=self.training) + x = x + inputs # skip connection + return x + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export). + + Args: + memory_efficient (bool): Whether to use memory-efficient version of swish. + """ + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + + +class EfficientNet(nn.Module): + """EfficientNet model. + Most easily loaded with the .from_name or .from_pretrained methods. + + Args: + blocks_args (list[namedtuple]): A list of BlockArgs to construct blocks. + global_params (namedtuple): A set of GlobalParams shared between blocks. + + References: + [1] https://arxiv.org/abs/1905.11946 (EfficientNet) + + Example: + >>> import torch + >>> from efficientnet.model import EfficientNet + >>> inputs = torch.rand(1, 3, 224, 224) + >>> model = EfficientNet.from_pretrained('efficientnet-b0') + >>> model.eval() + >>> outputs = model(inputs) + """ + + def __init__(self, blocks_args=None, global_params=None,use_swish=True,use_bn=True): + + # DELETE MEEEEEEE + # use_bn=False + + super().__init__() + assert isinstance(blocks_args, list), 'blocks_args should be a list' + assert len(blocks_args) > 0, 'block args must be greater than 0' + self._global_params = global_params + self._blocks_args = blocks_args + self.use_swish= use_swish + self.use_batch_norm = use_bn + # Batch norm parameters + bn_mom = 1 - self._global_params.batch_norm_momentum + bn_eps = self._global_params.batch_norm_epsilon + + # Get stem static or dynamic convolution depending on image size + image_size = global_params.image_size + Conv2d = get_same_padding_conv2d(image_size=image_size) + + # Stem + in_channels = 3 # rgb + out_channels = round_filters(32, self._global_params) # number of output channels + self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) + if self.use_batch_norm: + self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + else: + self._bn0 = nn.Identity() + image_size = calculate_output_image_size(image_size, 2) + + # Build blocks + self._blocks = nn.ModuleList([]) + for block_args in self._blocks_args: + + # Update block input and output filters based on depth multiplier. + block_args = block_args._replace( + input_filters=round_filters(block_args.input_filters, self._global_params), + output_filters=round_filters(block_args.output_filters, self._global_params), + num_repeat=round_repeats(block_args.num_repeat, self._global_params) + ) + + # The first block needs to take care of stride and filter size increase. + self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size,use_swish=self.use_swish, use_batch_norm=self.use_batch_norm)) + image_size = calculate_output_image_size(image_size, block_args.stride) + if block_args.num_repeat > 1: # modify block_args to keep same output size + block_args = block_args._replace(input_filters=block_args.output_filters, stride=1) + for _ in range(block_args.num_repeat - 1): + self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size,use_swish=self.use_swish,use_batch_norm=self.use_batch_norm)) + # image_size = calculate_output_image_size(image_size, block_args.stride) # stride = 1 + + # Head + in_channels = block_args.output_filters # output of final block + out_channels = round_filters(1280, self._global_params) + Conv2d = get_same_padding_conv2d(image_size=image_size) + self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + if self.use_batch_norm: + self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + else: + self._bn1 = nn.Identity() + + # Final linear layer + self._avg_pooling = nn.AdaptiveAvgPool2d(1) + if self._global_params.include_top: + self._dropout = nn.Dropout(self._global_params.dropout_rate) + self._fc = nn.Linear(out_channels, self._global_params.num_classes) + + # set activation to memory efficient swish by default + if self.use_swish: + self._swish = MemoryEfficientSwish() + else: + self._swish = nn.PReLU() + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export). + + Args: + memory_efficient (bool): Whether to use memory-efficient version of swish. + """ + if self.use_swish: + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + else: + self._swish = nn.PReLU() + + for block in self._blocks: + block.set_swish(memory_efficient) + + def extract_endpoints(self, inputs): + """Use convolution layer to extract features + from reduction levels i in [1, 2, 3, 4, 5]. + + Args: + inputs (tensor): Input tensor. + + Returns: + Dictionary of last intermediate features + with reduction levels i in [1, 2, 3, 4, 5]. + Example: + >>> import torch + >>> from efficientnet.model import EfficientNet + >>> inputs = torch.rand(1, 3, 224, 224) + >>> model = EfficientNet.from_pretrained('efficientnet-b0') + >>> endpoints = model.extract_endpoints(inputs) + >>> print(endpoints['reduction_1'].shape) # torch.Size([1, 16, 112, 112]) + >>> print(endpoints['reduction_2'].shape) # torch.Size([1, 24, 56, 56]) + >>> print(endpoints['reduction_3'].shape) # torch.Size([1, 40, 28, 28]) + >>> print(endpoints['reduction_4'].shape) # torch.Size([1, 112, 14, 14]) + >>> print(endpoints['reduction_5'].shape) # torch.Size([1, 320, 7, 7]) + >>> print(endpoints['reduction_6'].shape) # torch.Size([1, 1280, 7, 7]) + """ + endpoints = dict() + + # Stem + x = self._swish(self._bn0(self._conv_stem(inputs))) + prev_x = x + + # Blocks + for idx, block in enumerate(self._blocks): + drop_connect_rate = self._global_params.drop_connect_rate + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self._blocks) # scale drop connect_rate + x = block(x, drop_connect_rate=drop_connect_rate) + if prev_x.size(2) > x.size(2): + endpoints['reduction_{}'.format(len(endpoints) + 1)] = prev_x + elif idx == len(self._blocks) - 1: + endpoints['reduction_{}'.format(len(endpoints) + 1)] = x + prev_x = x + + # Head + x = self._swish(self._bn1(self._conv_head(x))) + endpoints['reduction_{}'.format(len(endpoints) + 1)] = x + + return endpoints + + def extract_features(self, inputs): + """use convolution layer to extract feature . + + Args: + inputs (tensor): Input tensor. + + Returns: + Output of the final convolution + layer in the efficientnet model. + """ + # Stem + x = self._swish(self._bn0(self._conv_stem(inputs))) + + # Blocks + for idx, block in enumerate(self._blocks): + drop_connect_rate = self._global_params.drop_connect_rate + drop_connect_rate=0.0 + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self._blocks) # scale drop connect_rate + x = block(x, drop_connect_rate=drop_connect_rate) + + # Head + x = self._swish(self._bn1(self._conv_head(x))) + + return x + + def forward(self, inputs): + """EfficientNet's forward function. + Calls extract_features to extract features, applies final linear layer, and returns logits. + + Args: + inputs (tensor): Input tensor. + + Returns: + Output of this model after processing. + """ + # Convolution layers + x = self.extract_features(inputs) + # Pooling and final linear layer + x = self._avg_pooling(x) + if self._global_params.include_top: + x = x.flatten(start_dim=1) + x = self._dropout(x) + x = self._fc(x) + return x + + @classmethod + def from_name(cls, model_name, in_channels=3,use_swish=True,use_batch_norm=True, **override_params): + """Create an efficientnet model according to name. + + Args: + model_name (str): Name for efficientnet. + in_channels (int): Input data's channel number. + override_params (other key word params): + Params to override model's global_params. + Optional key: + 'width_coefficient', 'depth_coefficient', + 'image_size', 'dropout_rate', + 'num_classes', 'batch_norm_momentum', + 'batch_norm_epsilon', 'drop_connect_rate', + 'depth_divisor', 'min_depth' + + Returns: + An efficientnet model. + """ + cls._check_model_name_is_valid(model_name) + blocks_args, global_params = get_model_params(model_name, override_params) + model = cls(blocks_args, global_params,use_swish,use_batch_norm) + model._change_in_channels(in_channels) + + return model + + @classmethod + def from_pretrained(cls, model_name, weights_path=None, advprop=False, + in_channels=3, num_classes=1000, use_batch_norm=True, **override_params): + """Create an efficientnet model according to name. + + Args: + model_name (str): Name for efficientnet. + weights_path (None or str): + str: path to pretrained weights file on the local disk. + None: use pretrained weights downloaded from the Internet. + advprop (bool): + Whether to load pretrained weights + trained with advprop (valid when weights_path is None). + in_channels (int): Input data's channel number. + num_classes (int): + Number of categories for classification. + It controls the output size for final linear layer. + override_params (other key word params): + Params to override model's global_params. + Optional key: + 'width_coefficient', 'depth_coefficient', + 'image_size', 'dropout_rate', + 'batch_norm_momentum', + 'batch_norm_epsilon', 'drop_connect_rate', + 'depth_divisor', 'min_depth' + + Returns: + A pretrained efficientnet model. + """ + model = cls.from_name(model_name, num_classes=num_classes,use_batch_norm=use_batch_norm, **override_params) + # load_pretrained_weights(model, model_name, weights_path=weights_path,load_fc=(num_classes == 1000), advprop=advprop) + model._change_in_channels(in_channels) + + return model + + @classmethod + def get_image_size(cls, model_name): + """Get the input image size for a given efficientnet model. + + Args: + model_name (str): Name for efficientnet. + + Returns: + Input image size (resolution). + """ + cls._check_model_name_is_valid(model_name) + _, _, res, _ = efficientnet_params(model_name) + return res + + @classmethod + def _check_model_name_is_valid(cls, model_name): + """Validates model name. + + Args: + model_name (str): Name for efficientnet. + + Returns: + bool: Is a valid name or not. + """ + if model_name not in VALID_MODELS: + raise ValueError('model_name should be one of: ' + ', '.join(VALID_MODELS)) + + def _change_in_channels(self, in_channels): + """Adjust model's first convolution layer to in_channels, if in_channels not equals 3. + + Args: + in_channels (int): Input data's channel number. + """ + if in_channels != 3: + Conv2d = get_same_padding_conv2d(image_size=self._global_params.image_size) + out_channels = round_filters(32, self._global_params) + self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) diff --git a/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/utils.py b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/utils.py new file mode 100644 index 00000000..826a6279 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/EffientNet_pytorch/utils.py @@ -0,0 +1,616 @@ +"""utils.py - Helper functions for building the model and for loading model parameters. + These helper functions are built to mirror those in the official TensorFlow implementation. +""" + +# Author: lukemelas (github username) +# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch +# With adjustments and added comments by workingcoder (github username). + +import re +import math +import collections +from functools import partial +import torch +from torch import nn +from torch.nn import functional as F +from torch.utils import model_zoo + + +################################################################################ +# Help functions for model architecture +################################################################################ + +# GlobalParams and BlockArgs: Two namedtuples +# Swish and MemoryEfficientSwish: Two implementations of the method +# round_filters and round_repeats: +# Functions to calculate params for scaling model width and depth ! ! ! +# get_width_and_height_from_size and calculate_output_image_size +# drop_connect: A structural design +# get_same_padding_conv2d: +# Conv2dDynamicSamePadding +# Conv2dStaticSamePadding +# get_same_padding_maxPool2d: +# MaxPool2dDynamicSamePadding +# MaxPool2dStaticSamePadding +# It's an additional function, not used in EfficientNet, +# but can be used in other model (such as EfficientDet). + +# Parameters for the entire model (stem, all blocks, and head) +GlobalParams = collections.namedtuple('GlobalParams', [ + 'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate', + 'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon', + 'drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top']) + +# Parameters for an individual model block +BlockArgs = collections.namedtuple('BlockArgs', [ + 'num_repeat', 'kernel_size', 'stride', 'expand_ratio', + 'input_filters', 'output_filters', 'se_ratio', 'id_skip']) + +# Set GlobalParams and BlockArgs's defaults +GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields) +BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields) + +# Swish activation function +if hasattr(nn, 'SiLU'): + Swish = nn.SiLU +else: + # For compatibility with old PyTorch versions + class Swish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +# A memory-efficient implementation of Swish function +class SwishImplementation(torch.autograd.Function): + @staticmethod + def forward(ctx, i): + result = i * torch.sigmoid(i) + ctx.save_for_backward(i) + return result + + @staticmethod + def backward(ctx, grad_output): + i = ctx.saved_tensors[0] + sigmoid_i = torch.sigmoid(i) + return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i))) + + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return SwishImplementation.apply(x) + + +def round_filters(filters, global_params): + """Calculate and round number of filters based on width multiplier. + Use width_coefficient, depth_divisor and min_depth of global_params. + + Args: + filters (int): Filters number to be calculated. + global_params (namedtuple): Global params of the model. + + Returns: + new_filters: New filters number after calculating. + """ + multiplier = global_params.width_coefficient + if not multiplier: + return filters + # TODO: modify the params names. + # maybe the names (width_divisor,min_width) + # are more suitable than (depth_divisor,min_depth). + divisor = global_params.depth_divisor + min_depth = global_params.min_depth + filters *= multiplier + min_depth = min_depth or divisor # pay attention to this line when using min_depth + # follow the formula transferred from official TensorFlow implementation + new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor) + if new_filters < 0.9 * filters: # prevent rounding by more than 10% + new_filters += divisor + return int(new_filters) + + +def round_repeats(repeats, global_params): + """Calculate module's repeat number of a block based on depth multiplier. + Use depth_coefficient of global_params. + + Args: + repeats (int): num_repeat to be calculated. + global_params (namedtuple): Global params of the model. + + Returns: + new repeat: New repeat number after calculating. + """ + multiplier = global_params.depth_coefficient + if not multiplier: + return repeats + # follow the formula transferred from official TensorFlow implementation + return int(math.ceil(multiplier * repeats)) + + +def drop_connect(inputs, p, training): + """Drop connect. + + Args: + input (tensor: BCWH): Input of this structure. + p (float: 0.0~1.0): Probability of drop connection. + training (bool): The running mode. + + Returns: + output: Output after drop connection. + """ + assert 0 <= p <= 1, 'p must be in range of [0,1]' + + if not training: + return inputs + + batch_size = inputs.shape[0] + keep_prob = 1 - p + + # generate binary_tensor mask according to probability (p for 0, 1-p for 1) + random_tensor = keep_prob + random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device) + binary_tensor = torch.floor(random_tensor) + + output = inputs / keep_prob * binary_tensor + return output + + +def get_width_and_height_from_size(x): + """Obtain height and width from x. + + Args: + x (int, tuple or list): Data size. + + Returns: + size: A tuple or list (H,W). + """ + if isinstance(x, int): + return x, x + if isinstance(x, list) or isinstance(x, tuple): + return x + else: + raise TypeError() + + +def calculate_output_image_size(input_image_size, stride): + """Calculates the output image size when using Conv2dSamePadding with a stride. + Necessary for static padding. Thanks to mannatsingh for pointing this out. + + Args: + input_image_size (int, tuple or list): Size of input image. + stride (int, tuple or list): Conv2d operation's stride. + + Returns: + output_image_size: A list [H,W]. + """ + if input_image_size is None: + return None + image_height, image_width = get_width_and_height_from_size(input_image_size) + stride = stride if isinstance(stride, int) else stride[0] + image_height = int(math.ceil(image_height / stride)) + image_width = int(math.ceil(image_width / stride)) + return [image_height, image_width] + + +# Note: +# The following 'SamePadding' functions make output size equal ceil(input size/stride). +# Only when stride equals 1, can the output size be the same as input size. +# Don't be confused by their function names ! ! ! + +def get_same_padding_conv2d(image_size=None): + """Chooses static padding if you have specified an image size, and dynamic padding otherwise. + Static padding is necessary for ONNX exporting of models. + + Args: + image_size (int or tuple): Size of the image. + + Returns: + Conv2dDynamicSamePadding or Conv2dStaticSamePadding. + """ + if image_size is None: + return Conv2dDynamicSamePadding + else: + return partial(Conv2dStaticSamePadding, image_size=image_size) + + +class Conv2dDynamicSamePadding(nn.Conv2d): + """2D Convolutions like TensorFlow, for a dynamic image size. + The padding is operated in forward function by calculating dynamically. + """ + + # Tips for 'SAME' mode padding. + # Given the following: + # i: width or height + # s: stride + # k: kernel size + # d: dilation + # p: padding + # Output after Conv2d: + # o = floor((i+p-((k-1)*d+1))/s+1) + # If o equals i, i = floor((i+p-((k-1)*d+1))/s+1), + # => p = (i-1)*s+((k-1)*d+1)-i + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True): + super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) + self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2 + + def forward(self, x): + ih, iw = x.size()[-2:] + kh, kw = self.weight.size()[-2:] + sh, sw = self.stride + oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) # change the output size according to stride ! ! ! + pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) + pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) + if pad_h > 0 or pad_w > 0: + x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) + return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + + +class Conv2dStaticSamePadding(nn.Conv2d): + """2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size. + The padding mudule is calculated in construction function, then used in forward. + """ + + # With the same calculation as Conv2dDynamicSamePadding + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, image_size=None, **kwargs): + super().__init__(in_channels, out_channels, kernel_size, stride, **kwargs) + self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2 + + # Calculate padding based on image size and save it + assert image_size is not None + ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size + kh, kw = self.weight.size()[-2:] + sh, sw = self.stride + oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) + pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) + pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) + if pad_h > 0 or pad_w > 0: + self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, + pad_h // 2, pad_h - pad_h // 2)) + else: + self.static_padding = nn.Identity() + + def forward(self, x): + x = self.static_padding(x) + x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + return x + + +def get_same_padding_maxPool2d(image_size=None): + """Chooses static padding if you have specified an image size, and dynamic padding otherwise. + Static padding is necessary for ONNX exporting of models. + + Args: + image_size (int or tuple): Size of the image. + + Returns: + MaxPool2dDynamicSamePadding or MaxPool2dStaticSamePadding. + """ + if image_size is None: + return MaxPool2dDynamicSamePadding + else: + return partial(MaxPool2dStaticSamePadding, image_size=image_size) + + +class MaxPool2dDynamicSamePadding(nn.MaxPool2d): + """2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size. + The padding is operated in forward function by calculating dynamically. + """ + + def __init__(self, kernel_size, stride, padding=0, dilation=1, return_indices=False, ceil_mode=False): + super().__init__(kernel_size, stride, padding, dilation, return_indices, ceil_mode) + self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride + self.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size + self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation + + def forward(self, x): + ih, iw = x.size()[-2:] + kh, kw = self.kernel_size + sh, sw = self.stride + oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) + pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) + pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) + if pad_h > 0 or pad_w > 0: + x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) + return F.max_pool2d(x, self.kernel_size, self.stride, self.padding, + self.dilation, self.ceil_mode, self.return_indices) + + +class MaxPool2dStaticSamePadding(nn.MaxPool2d): + """2D MaxPooling like TensorFlow's 'SAME' mode, with the given input image size. + The padding mudule is calculated in construction function, then used in forward. + """ + + def __init__(self, kernel_size, stride, image_size=None, **kwargs): + super().__init__(kernel_size, stride, **kwargs) + self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride + self.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size + self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation + + # Calculate padding based on image size and save it + assert image_size is not None + ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size + kh, kw = self.kernel_size + sh, sw = self.stride + oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) + pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) + pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) + if pad_h > 0 or pad_w > 0: + self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)) + else: + self.static_padding = nn.Identity() + + def forward(self, x): + x = self.static_padding(x) + x = F.max_pool2d(x, self.kernel_size, self.stride, self.padding, + self.dilation, self.ceil_mode, self.return_indices) + return x + + +################################################################################ +# Helper functions for loading model params +################################################################################ + +# BlockDecoder: A Class for encoding and decoding BlockArgs +# efficientnet_params: A function to query compound coefficient +# get_model_params and efficientnet: +# Functions to get BlockArgs and GlobalParams for efficientnet +# url_map and url_map_advprop: Dicts of url_map for pretrained weights +# load_pretrained_weights: A function to load pretrained weights + +class BlockDecoder(object): + """Block Decoder for readability, + straight from the official TensorFlow repository. + """ + + @staticmethod + def _decode_block_string(block_string): + """Get a block through a string notation of arguments. + + Args: + block_string (str): A string notation of arguments. + Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'. + + Returns: + BlockArgs: The namedtuple defined at the top of this file. + """ + assert isinstance(block_string, str) + + ops = block_string.split('_') + options = {} + for op in ops: + splits = re.split(r'(\d.*)', op) + if len(splits) >= 2: + key, value = splits[:2] + options[key] = value + + # Check stride + assert (('s' in options and len(options['s']) == 1) or + (len(options['s']) == 2 and options['s'][0] == options['s'][1])) + + return BlockArgs( + num_repeat=int(options['r']), + kernel_size=int(options['k']), + stride=[int(options['s'][0])], + expand_ratio=int(options['e']), + input_filters=int(options['i']), + output_filters=int(options['o']), + se_ratio=float(options['se']) if 'se' in options else None, + id_skip=('noskip' not in block_string)) + + @staticmethod + def _encode_block_string(block): + """Encode a block to a string. + + Args: + block (namedtuple): A BlockArgs type argument. + + Returns: + block_string: A String form of BlockArgs. + """ + args = [ + 'r%d' % block.num_repeat, + 'k%d' % block.kernel_size, + 's%d%d' % (block.strides[0], block.strides[1]), + 'e%s' % block.expand_ratio, + 'i%d' % block.input_filters, + 'o%d' % block.output_filters + ] + if 0 < block.se_ratio <= 1: + args.append('se%s' % block.se_ratio) + if block.id_skip is False: + args.append('noskip') + return '_'.join(args) + + @staticmethod + def decode(string_list): + """Decode a list of string notations to specify blocks inside the network. + + Args: + string_list (list[str]): A list of strings, each string is a notation of block. + + Returns: + blocks_args: A list of BlockArgs namedtuples of block args. + """ + assert isinstance(string_list, list) + blocks_args = [] + for block_string in string_list: + blocks_args.append(BlockDecoder._decode_block_string(block_string)) + return blocks_args + + @staticmethod + def encode(blocks_args): + """Encode a list of BlockArgs to a list of strings. + + Args: + blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block args. + + Returns: + block_strings: A list of strings, each string is a notation of block. + """ + block_strings = [] + for block in blocks_args: + block_strings.append(BlockDecoder._encode_block_string(block)) + return block_strings + + +def efficientnet_params(model_name): + """Map EfficientNet model name to parameter coefficients. + + Args: + model_name (str): Model name to be queried. + + Returns: + params_dict[model_name]: A (width,depth,res,dropout) tuple. + """ + params_dict = { + # Coefficients: width,depth,res,dropout + 'efficientnet-b0': (1.0, 1.0, 224, 0.2), + 'efficientnet-b1': (1.0, 1.1, 240, 0.2), + 'efficientnet-b2': (1.1, 1.2, 260, 0.3), + 'efficientnet-b3': (1.2, 1.4, 300, 0.3), + 'efficientnet-b4': (1.4, 1.8, 380, 0.4), + 'efficientnet-b5': (1.6, 2.2, 456, 0.4), + 'efficientnet-b6': (1.8, 2.6, 528, 0.5), + 'efficientnet-b7': (2.0, 3.1, 600, 0.5), + 'efficientnet-b8': (2.2, 3.6, 672, 0.5), + 'efficientnet-l2': (4.3, 5.3, 800, 0.5), + } + return params_dict[model_name] + + +def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None, + dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000, include_top=True): + """Create BlockArgs and GlobalParams for efficientnet model. + + Args: + width_coefficient (float) + depth_coefficient (float) + image_size (int) + dropout_rate (float) + drop_connect_rate (float) + num_classes (int) + + Meaning as the name suggests. + + Returns: + blocks_args, global_params. + """ + + # Blocks args for the whole model(efficientnet-b0 by default) + # It will be modified in the construction of EfficientNet Class according to model + blocks_args = [ + 'r1_k3_s11_e1_i32_o16_se0.25', + 'r2_k3_s22_e6_i16_o24_se0.25', + 'r2_k5_s22_e6_i24_o40_se0.25', + 'r3_k3_s22_e6_i40_o80_se0.25', + 'r3_k5_s11_e6_i80_o112_se0.25', + 'r4_k5_s22_e6_i112_o192_se0.25', + 'r1_k3_s11_e6_i192_o320_se0.25', + ] + blocks_args = BlockDecoder.decode(blocks_args) + + global_params = GlobalParams( + width_coefficient=width_coefficient, + depth_coefficient=depth_coefficient, + image_size=image_size, + dropout_rate=dropout_rate, + + num_classes=num_classes, + batch_norm_momentum=0.99, + batch_norm_epsilon=1e-3, + drop_connect_rate=drop_connect_rate, + depth_divisor=8, + min_depth=None, + include_top=include_top, + ) + + return blocks_args, global_params + + +def get_model_params(model_name, override_params): + """Get the block args and global params for a given model name. + + Args: + model_name (str): Model's name. + override_params (dict): A dict to modify global_params. + + Returns: + blocks_args, global_params + """ + if model_name.startswith('efficientnet'): + w, d, s, p = efficientnet_params(model_name) + # note: all models have drop connect rate = 0.2 + blocks_args, global_params = efficientnet( + width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s) + else: + raise NotImplementedError('model name is not pre-defined: {}'.format(model_name)) + if override_params: + # ValueError will be raised here if override_params has fields not included in global_params. + global_params = global_params._replace(**override_params) + return blocks_args, global_params + + +# train with Standard methods +# check more details in paper(EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks) +url_map = { + 'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth', + 'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth', + 'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth', + 'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth', + 'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth', + 'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth', + 'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth', + 'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth', +} + +# train with Adversarial Examples(AdvProp) +# check more details in paper(Adversarial Examples Improve Image Recognition) +url_map_advprop = { + 'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth', + 'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth', + 'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth', + 'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth', + 'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth', + 'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth', + 'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth', + 'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth', + 'efficientnet-b8': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth', +} + +# TODO: add the petrained weights url map of 'efficientnet-l2' + + +def load_pretrained_weights(model, model_name, weights_path=None, load_fc=True, advprop=False, verbose=True): + """Loads pretrained weights from weights path or download using url. + + Args: + model (Module): The whole model of efficientnet. + model_name (str): Model name of efficientnet. + weights_path (None or str): + str: path to pretrained weights file on the local disk. + None: use pretrained weights downloaded from the Internet. + load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model. + advprop (bool): Whether to load pretrained weights + trained with advprop (valid when weights_path is None). + """ + if isinstance(weights_path, str): + state_dict = torch.load(weights_path) + else: + # AutoAugment or Advprop (different preprocessing) + url_map_ = url_map_advprop if advprop else url_map + state_dict = model_zoo.load_url(url_map_[model_name]) + + if load_fc: + ret = model.load_state_dict(state_dict, strict=False) + assert not ret.missing_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys) + else: + state_dict.pop('_fc.weight') + state_dict.pop('_fc.bias') + ret = model.load_state_dict(state_dict, strict=False) + assert set(ret.missing_keys) == set( + ['_fc.weight', '_fc.bias']), 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys) + assert not ret.unexpected_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.unexpected_keys) + + if verbose: + print('Loaded pretrained weights for {}'.format(model_name)) diff --git a/ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py b/ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py new file mode 100644 index 00000000..a7c4861a --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py @@ -0,0 +1,173 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class SqueezeExcitation(nn.Module): + def __init__(self, in_channels, reduction_ratio=16): + super(SqueezeExcitation, self).__init__() + self.in_channels = in_channels + self.reduction_ratio = reduction_ratio + self.se_channels = max(in_channels // reduction_ratio, 1) # Evitar que los canales sean menos de 1 + self.squeeze = nn.AdaptiveAvgPool2d(1) + self.excitation = nn.Sequential( + nn.Linear(in_channels, self.se_channels, bias=False), + nn.ReLU(inplace=True), + nn.Linear(self.se_channels, in_channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + batch_size, channels, _, _ = x.size() + # Squeeze: Global Average Pooling + y = self.squeeze(x).view(batch_size, channels) + # Excitation: Dos capas densas + y = self.excitation(y).view(batch_size, channels, 1, 1) + # Recalibrar los canales + return x * y.expand_as(x) + +class ResNeXtBlock(nn.Module): + expansion = 4 # Correcto ajuste del factor de expansión + + def __init__(self, in_channels, out_channels, stride=1, groups=32, use_gn=False, reduction_ratio=16): + super(ResNeXtBlock, self).__init__() + + # Primera capa de convolución + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + self.norm1 = nn.GroupNorm(32, out_channels) if use_gn else nn.BatchNorm2d(out_channels) + + # Segunda capa de convolución con agrupación + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False) + self.norm2 = nn.GroupNorm(32, out_channels) if use_gn else nn.BatchNorm2d(out_channels) + + # Tercera capa de convolución + self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, bias=False) + self.norm3 = nn.GroupNorm(32, out_channels * self.expansion) if use_gn else nn.BatchNorm2d(out_channels * self.expansion) + + # Bloque Squeeze-and-Excitation + self.se_block = SqueezeExcitation(out_channels * self.expansion, reduction_ratio) + + # Atajo (shortcut) para ajustar el número de canales si es necesario + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != out_channels * self.expansion: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels * self.expansion, kernel_size=1, stride=stride, bias=False), + nn.GroupNorm(32, out_channels * self.expansion) if use_gn else nn.BatchNorm2d(out_channels * self.expansion) + ) + self.prelu_1 = nn.PReLU() + self.prelu_2 = nn.PReLU() + self.prelu_3 = nn.PReLU() + + def forward(self, x): + # Aplicar las capas de convolución y las normalizaciones con ReLU + out = self.prelu_1(self.norm1(self.conv1(x))) + out = self.prelu_2(self.norm2(self.conv2(out))) + out = self.norm3(self.conv3(out)) + + # Apply Squeeze-and-Excitation + out = self.se_block(out) + + out += self.shortcut(x) + out = self.prelu_3(out) + return out + +class ResNeXtDBB(nn.Module): + def __init__(self,task, block, layers, num_inputs=1, num_classes=1, use_gn=False, use_concat=False, dropout_rate=0.5): + super(ResNeXtDBB, self).__init__() + self.in_channels = 64 + self.use_gn = use_gn + self.use_concat = use_concat + self.task = task + # Backbone 1 + self.conv1_a = nn.Conv2d(num_inputs, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.norm1_a = nn.GroupNorm(32, 64) if use_gn else nn.BatchNorm2d(64) + + self.layer1_a = self._make_layer(block, 64, layers[0], stride=1) + self.layer2_a = self._make_layer(block, 128, layers[1], stride=2) + self.layer3_a = self._make_layer(block, 256, layers[2], stride=2) + self.layer4_a = self._make_layer(block, 512, layers[3], stride=2) + + # Backbone 2 + self.in_channels = 64 # Reset in_channels for the second backbone + self.conv1_b = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.norm1_b = nn.GroupNorm(32, 64) if use_gn else nn.BatchNorm2d(64) + + self.layer1_b = self._make_layer(block, 64, layers[0], stride=1) + self.layer2_b = self._make_layer(block, 128, layers[1], stride=2) + self.layer3_b = self._make_layer(block, 256, layers[2], stride=2) + self.layer4_b = self._make_layer(block, 512, layers[3], stride=2) + + self.prelu_1 = nn.PReLU() + self.prelu_2 = nn.PReLU() + + # Dropout layer + self.dropout = nn.Dropout(dropout_rate) + + # Fully connected layer + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + + # Ajuste de los canales para la capa completamente conectada + if self.use_concat: + self.fc = nn.Linear(512 * block.expansion * 2, num_classes) + else: + self.fc = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, out_channels, num_blocks, stride): + layers = [] + layers.append(block(self.in_channels, out_channels, stride, use_gn=self.use_gn)) + self.in_channels = out_channels * block.expansion + for _ in range(1, num_blocks): + layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) + return nn.Sequential(*layers) + + def forward(self, x1, x2): + + classification=None + energy=None + direction=None + + # Backbone 1 + # out1 = F.relu(self.norm1_a(self.conv1_a(x1))) + out1 = self.prelu_1(self.conv1_a(x1)) + + out1 = self.layer1_a(out1) + out1 = self.layer2_a(out1) + out1 = self.layer3_a(out1) + out1 = self.layer4_a(out1) + out1 = self.adaptive_pool(out1) + out1 = out1.view(out1.size(0), -1) + out1 = self.dropout(out1) + + # Backbone 2 + # out2 = F.relu(self.norm1_b(self.conv1_b(x2))) + out2 = self.prelu_2(self.conv1_b(x2)) + out2 = self.layer1_b(out2) + out2 = self.layer2_b(out2) + out2 = self.layer3_b(out2) + out2 = self.layer4_b(out2) + out2 = self.adaptive_pool(out2) + out2 = out2.view(out2.size(0), -1) + out2 = self.dropout(out2) + + # Combine outputs + if self.use_concat: + out = torch.cat((out1, out2), dim=1) + else: + out = out1 + out2 + + out = self.fc(out) + + if self.task == "type": + classification = out + elif self.task == "energy": + energy = out + elif self.task == "direction": + direction = out + + return classification, energy, direction + +def ResNeXtDuo(task,num_blocks=[2, 2, 2, 2], num_inputs=1, num_classes=2, use_gn=True, use_concat=False, dropout_rate=0.5): + # Here we configure fewer blocks for a lighter model + return ResNeXtDBB(task,ResNeXtBlock, num_blocks, num_inputs, num_classes=num_classes, use_gn=use_gn, use_concat=use_concat, dropout_rate=dropout_rate) + +# Instancia del modelo +# resnext_duo = ResNeXtDuo(ResNeXtBlock, [3, 4, 6, 3], num_classes=1, use_gn=True, use_concat=True, dropout_rate=0.5) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/ResNet.py b/ctlearn/core/pytorch/nets/models/ResNet.py new file mode 100644 index 00000000..b725e975 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/ResNet.py @@ -0,0 +1,129 @@ +import torch.nn.functional as F +import numpy as np +import torch +import torch.nn as nn +from sys import path +from os import getcwd +import math + +# path.append(getcwd() + "/../deep_learning_helper") +# path.append(getcwd() + "/../Training") + +from ctlearn_helper.ModelHelper import ModelHelper +from nets.block.cnn_blocks import ResBlock + + +class ResNet(nn.Module): + def __init__(self, class_names, num_output, conv_output=False): + + super(ResNet, self).__init__() + self.conv_output = conv_output + self.class_names = class_names + self.num_output = num_output + + kernel_size_1 = 7 + kernel_size_2 = 5 + kernel_size_3 = 3 + conv_drop_pro = 0.1 + fc_drop_pro = 0.2 + + resblock_1_out_size = 16 + resblock_2_out_size = 32 + resblock_3_out_size = 64 + resblock_4_out_size = 128 + resblock_5_out_size = 256 + # -------------------------------------------------------------------- + self.resblock1 = ResBlock( + n_chans_in=1, + n_chans_out=resblock_1_out_size, + kernel_size=kernel_size_1, + conv_drop_pro=conv_drop_pro, + ) + self.resblock2 = ResBlock( + n_chans_in=resblock_1_out_size, + n_chans_out=resblock_2_out_size, + kernel_size=kernel_size_2, + conv_drop_pro=conv_drop_pro, + ) + self.resblock3 = ResBlock( + n_chans_in=resblock_2_out_size, + n_chans_out=resblock_3_out_size, + kernel_size=kernel_size_3, + conv_drop_pro=conv_drop_pro, + ) + self.resblock4 = ResBlock( + n_chans_in=resblock_3_out_size, + n_chans_out=resblock_4_out_size, + kernel_size=kernel_size_3, + conv_drop_pro=conv_drop_pro, + ) + self.resblock5 = ResBlock( + n_chans_in=resblock_4_out_size, + n_chans_out=resblock_5_out_size, + kernel_size=kernel_size_3, + conv_drop_pro=conv_drop_pro, + ) + # -------------------------------------------------------------------- + + self.avg_pooling = nn.AdaptiveAvgPool2d(1) + self.fc_conv_0 = nn.Linear(resblock_5_out_size, 256) + self.fc_act_0 = nn.PReLU() + self.fc_conv_1 = nn.Linear(256, 256) + self.fc_conv_2 = nn.Linear(256, self.num_output) + + if self.conv_output: + self.conv1x1_1 = nn.Conv1d(256 * 9 * 9, 32, kernel_size=1) + self.conv1x1_batchnorm_1 = nn.BatchNorm1d(num_features=32) + self.act6 = nn.LeakyReLU() + + self.conv1x1_2 = nn.Conv1d(32, 256, kernel_size=1) + self.conv1x1_batchnorm_2 = nn.BatchNorm1d(num_features=256) + self.act7 = nn.LeakyReLU() + + self.conv1x1_3 = nn.Conv1d(256, 256, kernel_size=1) + self.conv1x1_batchnorm_3 = nn.BatchNorm1d(num_features=256) + self.act8 = nn.LeakyReLU() + + self.conv1x1_final = nn.Conv1d(256, self.num_output, 1) + + torch.nn.init.constant_(self.conv1x1_batchnorm_1.weight, 0.5) + torch.nn.init.constant_(self.conv1x1_batchnorm_2.weight, 0.5) + torch.nn.init.constant_(self.conv1x1_batchnorm_3.weight, 0.5) + + else: + self.fc1 = nn.Linear(256 * 9 * 9, 1024) + self.act6 = nn.LeakyReLU() + self.fc1_dropout = nn.Dropout(p=fc_drop_pro) + self.fc2 = nn.Linear(1024, self.num_output) + + def forward(self, x): + + out = self.resblock1(x) + out = self.resblock2(out) + out = self.resblock3(out) + out = self.resblock4(out) + out = self.resblock5(out) + + out = self.avg_pooling(out) + fcsize = out.shape[1] * out.shape[2] * out.shape[3] + # -------------------------------------------------------------------- + # Convolution output + if self.conv_output: + out = out.view(-1, fcsize, 1) + out = self.act6(self.conv1x1_batchnorm_1(self.conv1x1_1(out))) + out = self.act7(self.conv1x1_batchnorm_2(self.conv1x1_2(out))) + out = self.act8(self.conv1x1_batchnorm_3(self.conv1x1_3(out))) + out = self.conv1x1_final(out) + out = out.view(out.shape[0], self.num_output) + ii=0 + # out = torch.log_softmax(out, dim=1) + else: + # Full Connected + + out = out.view(-1, fcsize) + out = self.fc_conv_0(out) + + out = self.fc_conv_1(out) + out = self.fc_conv_2(out) + # out = torch.log_softmax(out_features, dim=1) + return out diff --git a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py new file mode 100644 index 00000000..8f01d6b2 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py @@ -0,0 +1,200 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from ctlearn.core.pytorch.nets.block.cnn_blocks import NormalInvGamma + +class SEBlock(nn.Module): + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).squeeze(-1).squeeze(-1) # Ensuring dimension match + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=16,use_bn=True): + + + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.use_bn = use_bn + if self.use_bn: + self.bn1 = nn.BatchNorm2d(out_channels) + else: + self.bn1 = nn.Identity() + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + if self.use_bn: + self.bn2 = nn.BatchNorm2d(out_channels) + else: + self.bn2 = nn.Identity() + + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels) + ) + + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + out = self.se(out) + return out + +class ThinResNet_DBB(nn.Module): + def __init__(self,task, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, num_outputs=2,use_bn=False,dropout=0.0): + super(ThinResNet_DBB, self).__init__() + + # block = BasicBlock + self.in_channels = 64 + self.use_bn=use_bn + self.task = task + self.conv1 = nn.Conv2d(num_inputs, 64, kernel_size=3, stride=1, padding=1, bias=False) + if self.use_bn: + self.bn1 = nn.BatchNorm2d(64) + else: + self.bn1 = nn.Identity() + + self.layer1_1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2_1 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3_1 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + # Reducing the number of layers and filters to make it "thin" + self.fc_1 = nn.Linear(512 * block.expansion, 512 * block.expansion) + self.fc_2 = nn.Linear(512 * block.expansion, num_outputs) + self.bn_final = nn.BatchNorm1d(512 * block.expansion) # BatchNorm layer + self.prelu = nn.PReLU(num_parameters=512 * block.expansion) # Define Leaky ReLU + if self.task == "direction": + self.normal_inv = NormalInvGamma(512 * block.expansion,num_outputs) + + # self.fc_1_separation = nn.Linear(512 * block.expansion, 512 * block.expansion) + # self.fc_2_separation = nn.Linear(512 * block.expansion, 4) + + self.in_channels = 64 + self.conv2 = nn.Conv2d(num_inputs, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.layer1_2 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2_2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3_2 = self._make_layer(block, 256, num_blocks[3], stride=2) + + if self.use_bn: + self.bn2 = nn.BatchNorm2d(64) + else: + self.bn2 = nn.Identity() + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(dropout) + def _make_layer(self, block, out_channels, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride, use_bn=self.use_bn)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + + def forward(self, x, y): + + energy = None + classification = None + direction = None + + # out_1 = F.relu(self.bn1(self.conv1(x))) + out_1 = F.relu(self.conv1(x)) + out_1 = self.layer1_1(out_1) + out_1 = self.layer2_1(out_1) + out_1 = self.layer3_1(out_1) + + # out_2 = F.relu(self.bn2(self.conv2(y))) + out_2 = F.relu((self.conv2(y))) + out_2 = self.layer1_2(out_2) + out_2 = self.layer2_2(out_2) + out_2 = self.layer3_2(out_2) + out = out_1 + out_2 + + # out = self.layer3(out) + out = self.layer4(out) + out = self.adaptive_pool(out) + out_feature = out.view(out.size(0), -1) + out = self.dropout(out_feature) + + # if self.training: + # out_sep = self.fc_1_separation(out) + # out_sep = self.fc_2_separation(out_sep) + + out = self.fc_1(out) + # out = self.bn_final(out) + # out = self.prelu(out) + # Original + # out = self.fc_2(out) + + + if self.task == "type": + out = self.fc_2(out) + classification = out + if self.task == "energy": + out = self.fc_2(out) + # energy = [out, out_feature] + energy = out + + if self.task == "direction": + direction = self.normal_inv(out) + + # direction = [direction, out_feature] + # if self.training: + # out = self.normal_inv(out) + # direction = out + # else: + # direction = self.normal_inv(out) + + + # if self.training: + # out = torch.cat((out, out_sep), dim=1) + + return classification, energy, direction + +# def thin_resnet34(num_blocks=[2, 3, 3, 3], num_inputs=1, num_classes=2): +# # Here we configure fewer blocks for a lighter model +# return ThinResNet_DBB(BasicBlock, num_blocks,num_inputs,num_classes) + +# def create_model(num_blocks=[2, 3, 3, 3], num_inputs=1, num_classes=2, use_bn=False, dropout=0.0): +# return ThinResNet_DBB( +# block=BasicBlock, +# num_blocks=num_blocks, +# num_inputs=num_inputs, +# num_classes=num_classes, +# use_bn=use_bn, +# dropout=dropout +# ) + +# model = thin_resnet34() +# print(model) + +# # Set the model to evaluation mode (as we are just testing with a forward pass) +# model.eval() + +# # Create dummy input tensors +# # Assuming the input images are 224x224 pixels with 1 input channel (grayscale) +# x = torch.randn(1, 1, 224, 224) # Batch size of 1 +# y = torch.randn(1, 1, 224, 224) # Batch size of 1 + +# # Forward pass through the model +# with torch.no_grad(): # We don't need to calculate gradients here +# output = model(x, y) + +# # Print the output tensor +# print("Output:", output) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py new file mode 100644 index 00000000..9d27a287 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class BottleneckTransformerBlock(nn.Module): + + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=4, use_gn=False): + super(BottleneckTransformerBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels // reduction, kernel_size=1, stride=1, bias=False) + if use_gn: + self.norm1 = nn.GroupNorm(32, out_channels // reduction) + else: + self.norm1 = nn.BatchNorm2d(out_channels // reduction) + + self.transformer_block = nn.TransformerEncoderLayer(d_model=out_channels // reduction, nhead=8) + + self.conv2 = nn.Conv2d(out_channels // reduction, out_channels, kernel_size=1, stride=1, bias=False) + if use_gn: + self.norm2 = nn.GroupNorm(32, out_channels) + else: + self.norm2 = nn.BatchNorm2d(out_channels) + + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), + nn.GroupNorm(32, out_channels) if use_gn else nn.BatchNorm2d(out_channels) + ) + + def forward(self, x): + out = F.relu(self.norm1(self.conv1(x))) + b, c, h, w = out.size() + out = out.view(b, c, -1).permute(2, 0, 1) # Prepare for transformer + out = self.transformer_block(out) + out = out.permute(1, 2, 0).view(b, c, h, w) + out = self.norm2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + +class TransformerDBB(nn.Module): + def __init__(self, block, layers, num_inputs=1, num_classes=1, use_gn=False, use_concat=False, dropout_rate=0.5): + super(TransformerDBB, self).__init__() + self.in_channels = 64 + self.use_gn = use_gn + self.use_concat = use_concat + + # Backbone 1 + self.conv1_a = nn.Conv2d(num_inputs, 64, kernel_size=7, stride=2, padding=3, bias=False) + if use_gn: + self.norm1_a = nn.GroupNorm(32, 64) + else: + self.norm1_a = nn.BatchNorm2d(64) + + self.layer1_a = self._make_layer(block, 64, layers[0], stride=1) + self.layer2_a = self._make_layer(block, 128, layers[1], stride=2) + self.layer3_a = self._make_layer(block, 256, layers[2], stride=2) + self.layer4_a = self._make_layer(block, 512, layers[3], stride=2) + + # Backbone 2 + self.conv1_b = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) + if use_gn: + self.norm1_b = nn.GroupNorm(32, 64) + else: + self.norm1_b = nn.BatchNorm2d(64) + + self.layer1_b = self._make_layer(block, 64, layers[0], stride=1) + self.layer2_b = self._make_layer(block, 128, layers[1], stride=2) + self.layer3_b = self._make_layer(block, 256, layers[2], stride=2) + self.layer4_b = self._make_layer(block, 512, layers[3], stride=2) + + # Dropout layer + self.dropout = nn.Dropout(dropout_rate) + + # Fully connected layer + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + + if self.use_concat: + self.fc = nn.Linear(512 * block.expansion * 2, num_classes) + else: + self.fc = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, out_channels, blocks, stride): + layers = [] + layers.append(block(self.in_channels, out_channels, stride, use_gn=self.use_gn)) + self.in_channels = out_channels + for _ in range(1, blocks): + layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) + return nn.Sequential(*layers) + + def forward(self, x1, x2): + # Backbone 1 + out1 = F.relu(self.norm1_a(self.conv1_a(x1))) + out1 = self.layer1_a(out1) + out1 = self.layer2_a(out1) + out1 = self.layer3_a(out1) + out1 = self.layer4_a(out1) + out1 = self.adaptive_pool(out1) + out1 = out1.view(out1.size(0), -1) + out1 = self.dropout(out1) + + # Backbone 2 + out2 = F.relu(self.norm1_b(self.conv1_b(x2))) + out2 = self.layer1_b(out2) + out2 = self.layer2_b(out2) + out2 = self.layer3_b(out2) + out2 = self.layer4_b(out2) + out2 = self.adaptive_pool(out2) + out2 = out2.view(out2.size(0), -1) + out2 = self.dropout(out2) + + # Combine outputs + if self.use_concat: + out = torch.cat((out1, out2), dim=1) + else: + out = out1 + out2 + + out = self.fc(out) + return out + +def TransformerDuo(num_blocks=[3, 4, 6, 3], num_inputs=1, num_classes=2,use_gn=True, use_concat=False, dropout_rate=0.5): + # Here we configure fewer blocks for a lighter model + return TransformerDBB(BottleneckTransformerBlock, num_blocks,num_inputs, num_classes=num_classes, use_gn=use_gn, use_concat=use_concat, dropout_rate=dropout_rate) + +# Instancia del modelo +# bottleneck_transformer_duo = TransformerDuo(BottleneckTransformerBlock, [3, 4, 6, 3], num_classes=1, use_gn=True, use_concat=False, dropout_rate=0.5) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py new file mode 100644 index 00000000..8d55b134 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py @@ -0,0 +1,96 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class BottleneckTransformerBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=4, use_gn=False): + super(BottleneckTransformerBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels // reduction, kernel_size=1, stride=1, bias=False) + if use_gn: + self.norm1 = nn.GroupNorm(32, out_channels // reduction) + else: + self.norm1 = nn.BatchNorm2d(out_channels // reduction) + + self.transformer_block = nn.TransformerEncoderLayer(d_model=out_channels // reduction, nhead=8) + + self.conv2 = nn.Conv2d(out_channels // reduction, out_channels, kernel_size=1, stride=1, bias=False) + if use_gn: + self.norm2 = nn.GroupNorm(32, out_channels) + else: + self.norm2 = nn.BatchNorm2d(out_channels) + + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), + nn.GroupNorm(32, out_channels) if use_gn else nn.BatchNorm2d(out_channels) + ) + + def forward(self, x): + out = F.relu(self.norm1(self.conv1(x))) + b, c, h, w = out.size() + out = out.view(b, c, -1).permute(2, 0, 1) # Prepare for transformer + out = self.transformer_block(out) + out = out.permute(1, 2, 0).view(b, c, h, w) + out = self.norm2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + +class TransformerDBB(nn.Module): + def __init__(self, block, layers, num_inputs=2, num_classes=1, use_gn=False, dropout_rate=0.5): + super(TransformerDBB, self).__init__() + self.in_channels = 64 + self.use_gn = use_gn + + # Backbone único + self.conv1 = nn.Conv2d(num_inputs, 64, kernel_size=7, stride=2, padding=3, bias=False) + if use_gn: + self.norm1 = nn.GroupNorm(32, 64) + else: + self.norm1 = nn.BatchNorm2d(64) + + self.layer1 = self._make_layer(block, 64, layers[0], stride=1) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + + # Dropout layer + self.dropout = nn.Dropout(dropout_rate) + + # Fully connected layer + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, out_channels, blocks, stride): + layers = [] + layers.append(block(self.in_channels, out_channels, stride, use_gn=self.use_gn)) + self.in_channels = out_channels + for _ in range(1, blocks): + layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) + return nn.Sequential(*layers) + + def forward(self, x1, x2): + # Concatenar las entradas a lo largo del canal + x = torch.cat((x1, x2), dim=1) + + # Backbone único + out = F.relu(self.norm1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = self.adaptive_pool(out) + out = out.view(out.size(0), -1) + out = self.dropout(out) + out = self.fc(out) + return out + +def TransformerDuo(num_blocks=[3, 4, 6, 3], num_inputs=2, num_classes=2, use_gn=True, dropout_rate=0.3): + # Configurar un modelo más ligero + return TransformerDBB(BottleneckTransformerBlock, num_blocks, num_inputs=num_inputs, num_classes=num_classes, use_gn=use_gn, dropout_rate=dropout_rate) + +# Instancia del modelo +# bottleneck_transformer_duo = TransformerDuo(num_blocks=[3, 4, 6, 3], num_classes=1, use_gn=True, dropout_rate=0.3) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/__init__.py b/ctlearn/core/pytorch/nets/models/__init__.py new file mode 100644 index 00000000..8f10fbd7 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/__init__.py @@ -0,0 +1,7 @@ + +import ctlearn.core.pytorch.nets.models.DualBackboneEfficientNetV2.DoubleBBEfficientNetV2 +import ctlearn.core.pytorch.nets.models.ThinResNet_DBB.ThinResNet_DBB +import ctlearn.core.pytorch.nets.models.DBBRegNet.DBBRegNet +import ctlearn.core.pytorch.nets.models.DoubleBBEfficientNet.DoubleBBEfficientNet +import ctlearn.core.pytorch.nets.models.DualBackboneEfficientNetV2.DoubleBBEfficientNetV2 +import ctlearn.core.pytorch.nets.models.DBBDanet.DBBDanet diff --git a/ctlearn/core/pytorch/nets/models/gcn/gcn.py b/ctlearn/core/pytorch/nets/models/gcn/gcn.py new file mode 100644 index 00000000..548f1154 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/gcn/gcn.py @@ -0,0 +1,56 @@ + +from torch_geometric.loader import DataLoader +from torch_geometric.nn import GATConv +import pickle +import torch +import torch.nn.functional as F +from torch_geometric.nn import GCNConv,GraphConv +from torch_geometric.nn import global_mean_pool,global_max_pool +from torch.nn import Linear,Softmax,PReLU + +class GCN(torch.nn.Module): + def __init__(self, hidden_channels,num_node_features=1, num_outputs=1): + super(GCN, self).__init__() + torch.manual_seed(12345) + + # self.conv1 = GCNConv(dataset.num_node_features, hidden_channels) + # self.conv2 = GCNConv(hidden_channels, hidden_channels) + # self.conv3 = GCNConv(hidden_channels, hidden_channels) + use_bias = True + self.conv0 = GCNConv(num_node_features, hidden_channels,bias=use_bias) + self.conv1 = GraphConv(num_node_features, hidden_channels,bias=use_bias) + self.conv2 = GraphConv(hidden_channels, hidden_channels,bias=use_bias) + self.conv3 = GraphConv(hidden_channels, hidden_channels,bias=use_bias) + + + self.lin_0 = Linear(hidden_channels, hidden_channels) + self.lin_1 = Linear(hidden_channels, num_outputs) + self.prelu_1 = PReLU() + self.prelu_2 = PReLU() + def forward(self, x, edge_index, batch): + + # 1. Obtain node embeddings + # x_ori = self.conv0(x, edge_index) + x = self.conv1(x, edge_index) + + # x = x.relu() + x = self.prelu_1(x) + x = F.dropout(x, p=0.3, training=self.training) + x = self.conv2(x, edge_index) + # x = x.relu() + x = self.prelu_2(x) + x = F.dropout(x, p=0.3, training=self.training) + x = self.conv3(x, edge_index) + # x = x + x_ori + # x = torch.concat([x,x_ori]) + # batch_aug = torch.concat([batch,(batch+1)*torch.max(batch)]) + # batch_aug = torch.concat([batch,batch]) + + # 2. Readout layer + x = global_mean_pool(x,batch=batch) # [batch_size, hidden_channels] + + # 3. Apply a final classifier + # x = F.dropout(x, p=0.3, training=self.training) + x = self.lin_0(x) + x = self.lin_1(x) + return x \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/DoubleBBEfficientNetV2_old.py b/ctlearn/core/pytorch/nets/models/legacy/DoubleBBEfficientNetV2_old.py new file mode 100644 index 00000000..1e90de36 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/DoubleBBEfficientNetV2_old.py @@ -0,0 +1,181 @@ +import torch +from torch import nn + +Eff_V2_SETTINGS = { + 's': [ + [1, 3, 1, 24, 24, 2, True], + [4, 3, 2, 24, 48, 4, True], + [4, 3, 2, 48, 64, 4, True], + [4, 3, 2, 64, 128, 6, False], + [6, 3, 1, 128, 160, 9, False], + [6, 3, 2, 160, 256, 15, False] + ], + + 'm': [ + [1, 3, 1, 24, 24, 3, True], + [4, 3, 2, 24, 48, 5, True], + [4, 3, 2, 48, 80, 5, True], + [4, 3, 2, 80, 160, 7, False], + [6, 3, 1, 160, 176, 14, False], + [6, 3, 2, 176, 304, 18, False], + [6, 3, 1, 304, 512, 5, False] + ], + + 'l': [ + [1, 3, 1, 32, 32, 4, True], + [4, 3, 2, 32, 64, 7, True], + [4, 3, 2, 64, 96, 7, True], + [4, 3, 2, 96, 192, 10, False], + [6, 3, 1, 192, 224, 19, False], + [6, 3, 2, 224, 384, 25, False], + [6, 3, 1, 384, 640, 7, False] + ] +} + +class ConvBnAct(nn.Module): + def __init__(self, n_in, n_out, k_size=3, stride=1, padding=0, groups=1, act=True, bn=False, bias=False): + super(ConvBnAct, self).__init__() + self.conv = nn.Conv2d(n_in, n_out, kernel_size=k_size, stride=stride, padding=padding, groups=groups, bias=bias) + self.batch_norm = nn.BatchNorm2d(n_out) if bn else nn.Identity() + self.activation = nn.SiLU() if act else nn.Identity() + + def forward(self, x): + x = self.conv(x) + x = self.batch_norm(x) + x = self.activation(x) + return x + +class SqueezeExcitation(nn.Module): + def __init__(self, n_in, reduced_dim): + super(SqueezeExcitation, self).__init__() + self.squeeze = nn.AdaptiveAvgPool2d(1) + self.excite = nn.Sequential( + nn.Conv2d(n_in, reduced_dim, kernel_size=1), + nn.SiLU(), + nn.Conv2d(reduced_dim, n_in, kernel_size=1), + nn.Sigmoid() + ) + + def forward(self, x): + y = self.squeeze(x) + y = self.excite(y) + return x * y + +class StochasticDepth(nn.Module): + def __init__(self, survival_prob=0.8): + super(StochasticDepth, self).__init__() + self.p = survival_prob + + def forward(self, x): + if not self.training: + return x + binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.p + return torch.div(x, self.p) * binary_tensor + +class MBConvN(nn.Module): + def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, reduction_factor=4, survival_prob=0.8): + super(MBConvN, self).__init__() + reduced_dim = int(n_in // 4) + expanded_dim = int(expansion_factor * n_in) + padding = (k_size - 1) // 2 + self.use_residual = (n_in == n_out) and (stride == 1) + self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size=1) + self.depthwise_conv = ConvBnAct(expanded_dim, expanded_dim, k_size, stride=stride, padding=padding, groups=expanded_dim) + self.se = SqueezeExcitation(expanded_dim, reduced_dim) + self.drop_layers = StochasticDepth(survival_prob) + self.pointwise_conv = ConvBnAct(expanded_dim, n_out, k_size=1, act=False) + + def forward(self, x): + residual = x.clone() + x = self.expand(x) + x = self.depthwise_conv(x) + x = self.se(x) + x = self.pointwise_conv(x) + if self.use_residual: + x = self.drop_layers(x) + x += residual + return x + +class FusedMBConvN(nn.Module): + def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, reduction_factor=4, survival_prob=0.8): + super(FusedMBConvN, self).__init__() + reduced_dim = int(n_in // 4) + expanded_dim = int(expansion_factor * n_in) + padding = (k_size - 1) // 2 + self.use_residual = (n_in == n_out) and (stride == 1) + self.conv = ConvBnAct(n_in, expanded_dim, k_size, stride=stride, padding=padding, groups=1) + self.drop_layers = StochasticDepth(survival_prob) + self.pointwise_conv = nn.Identity() if (expansion_factor == 1) else ConvBnAct(expanded_dim, n_out, k_size=1, act=False) + + def forward(self, x): + residual = x.clone() + x = self.conv(x) + x = self.pointwise_conv(x) + if self.use_residual: + x = self.drop_layers(x) + x += residual + return x + +class EfficientNetV2(nn.Module): + def __init__(self, version='s', in_channels=3, last_channel=1280): + super(EfficientNetV2, self).__init__() + self.features = self._make_layers(version, in_channels, last_channel) + + def forward(self, x): + x = self.features(x) + return x + + def _make_layers(self, version, in_channels, last_channel): + config = Eff_V2_SETTINGS[version] + layers = [] + layers.append(ConvBnAct(in_channels, config[0][3], k_size=3, stride=2, padding=1)) + + for (expansion_factor, k, stride, n_in, n_out, num_layers, use_fused) in config: + if use_fused: + layers += [FusedMBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) + for repeat in range(num_layers)] + else: + layers += [MBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) + for repeat in range(num_layers)] + + layers.append(ConvBnAct(config[-1][4], last_channel, k_size=1)) + return nn.Sequential(*layers) + +class DualBackboneEfficientNetV2(nn.Module): + def __init__(self,task, version='s', num_classes=1, in_channels_1=1, in_channels_2=1, dropout_rate=0.2): + super(DualBackboneEfficientNetV2, self).__init__() + self.task = task + self.backbone1 = EfficientNetV2(version, in_channels_1) + self.backbone2 = EfficientNetV2(version, in_channels_2) + last_channel = 1280 + self.classifier = nn.Sequential( + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Dropout(dropout_rate, inplace=True), + nn.Linear(last_channel, num_classes) + ) + + def forward(self, x1, x2): + + classification=None + energy=None + direction=None + + if self.task =="energy": + x1 = self.backbone1(x1) + x2 = self.backbone2(x2) + x = x1 + x2 # Fuse by adding + energy = self.classifier(x) + + if self.task =="direction": + x1 = self.backbone1(x1) + x2 = self.backbone2(x2) + x = x1 + x2 # Fuse by adding + direction = self.classifier(x) + + + return classification, energy, direction + +# # Example usage for regression task +# if __name__ == "__main__": +# model = DualBackboneEfficientNetV2(version='s', num_classes=1, in_channels1=3, in_channels2=3) # num_classes=1 for regression \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/ThinResNet/ThinResNet.py b/ctlearn/core/pytorch/nets/models/legacy/ThinResNet/ThinResNet.py new file mode 100644 index 00000000..dac05cb8 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/ThinResNet/ThinResNet.py @@ -0,0 +1,101 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class SEBlock(nn.Module): + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=16): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(out_channels) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(out_channels) + self.se = SEBlock(out_channels, reduction) + + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + +class ThinResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10,use_bn=False): + super(ThinResNet, self).__init__() + self.in_channels = 64 + self.use_bn=use_bn + self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False) + if self.use_bn: + self.bn1 = nn.BatchNorm2d(64) + else: + self.bn1 = nn.Sequential() + + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + # Reducing the number of layers and filters to make it "thin" + self.linear = nn.Linear(256 * block.expansion, num_classes) + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + def _make_layer(self, block, out_channels, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.adaptive_pool(out) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + +def thin_resnet34(): + # Here we configure fewer blocks for a lighter model + return ThinResNet(BasicBlock, [2, 2, 2]) + +# model = thin_resnet34() +# print(model) + +# # Set the model to evaluation mode (as we are just testing with a forward pass) +# model.eval() + +# # Create dummy input tensors +# # Assuming the input images are 224x224 pixels with 1 input channel (grayscale) +# x = torch.randn(1, 1, 224, 224) # Batch size of 1 + +# # Forward pass through the model +# with torch.no_grad(): # We don't need to calculate gradients here +# output = model(x) + +# # Print the output tensor +# print("Output:", output) diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/__init__.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/__init__.py new file mode 100644 index 00000000..43531b9d --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/__init__.py @@ -0,0 +1,3 @@ +from .model import * +from .pretrained import * +from .optim import * \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/model.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/model.py new file mode 100644 index 00000000..b3c34620 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/model.py @@ -0,0 +1,307 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import re + +nfnet_params = { + 'F0': { + 'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], + 'train_imsize': 192, 'test_imsize': 256, + 'RA_level': '405', 'drop_rate': 0.2}, + 'F1': { + 'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], + 'train_imsize': 224, 'test_imsize': 320, + 'RA_level': '410', 'drop_rate': 0.3}, + 'F2': { + 'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], + 'train_imsize': 256, 'test_imsize': 352, + 'RA_level': '410', 'drop_rate': 0.4}, + 'F3': { + 'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], + 'train_imsize': 320, 'test_imsize': 416, + 'RA_level': '415', 'drop_rate': 0.4}, + 'F4': { + 'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], + 'train_imsize': 384, 'test_imsize': 512, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F5': { + 'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], + 'train_imsize': 416, 'test_imsize': 544, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F6': { + 'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], + 'train_imsize': 448, 'test_imsize': 576, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F7': { + 'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], + 'train_imsize': 480, 'test_imsize': 608, + 'RA_level': '415', 'drop_rate': 0.5}, +} + +# These extra constant values ensure that the activations +# are variance preserving +class VPGELU(nn.Module): + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.gelu(input) * 1.7015043497085571 + +class VPReLU(nn.Module): + __constants__ = ['inplace'] + inplace: bool + + def __init__(self, inplace: bool = False): + super(VPReLU, self).__init__() + self.inplace = inplace + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.relu(input, inplace=self.inplace) * 1.7139588594436646 + + def extra_repr(self) -> str: + inplace_str = 'inplace=True' if self.inplace else '' + return inplace_str + +activations_dict = { + 'gelu': VPGELU(), + 'relu': VPReLU(inplace=True) +} + +class NFNet(nn.Module): + def __init__(self, num_channels=1,num_classes:int=2, variant:str='F0', stochdepth_rate:float=None, + alpha:float=0.2, se_ratio:float=0.5, activation:str='gelu'): + super(NFNet, self).__init__() + + if not variant in nfnet_params: + raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") + + block_params = nfnet_params[variant] + + self.train_imsize = block_params['train_imsize'] + self.test_imsize = block_params['test_imsize'] + self.activation = activations_dict[activation] + self.drop_rate = block_params['drop_rate'] + self.num_classes = num_classes + + self.stem = Stem(num_channels=num_channels,activation=activation) + + num_blocks, index = sum(block_params['depth']), 0 + + blocks = [] + expected_std = 1.0 + in_channels = block_params['width'][0] // 2 + + block_args = zip( + block_params['width'], + block_params['depth'], + [0.5] * 4, # bottleneck pattern + [128] * 4, # group pattern. Original groups [128] * 4 + [1, 2, 2, 2] # stride pattern + ) + + for (block_width, stage_depth, expand_ratio, group_size, stride) in block_args: + for block_index in range(stage_depth): + beta = 1. / expected_std + + block_sd_rate = stochdepth_rate * index / num_blocks + out_channels = block_width + + blocks.append(NFBlock( + in_channels=in_channels, + out_channels=out_channels, + stride=stride if block_index == 0 else 1, + alpha=alpha, + beta=beta, + se_ratio=se_ratio, + group_size=group_size, + stochdepth_rate=block_sd_rate, + activation=activation)) + + in_channels = out_channels + index += 1 + + if block_index == 0: + expected_std = 1.0 + + expected_std = (expected_std **2 + alpha**2)**0.5 + + self.body = nn.Sequential(*blocks) + + final_conv_channels = 2*in_channels + self.final_conv = WSConv2D(in_channels=out_channels, out_channels=final_conv_channels, kernel_size=1) + self.pool = nn.AvgPool2d(1) + + if self.drop_rate > 0.: + self.dropout = nn.Dropout(self.drop_rate) + + self.linear = nn.Linear(final_conv_channels, self.num_classes) + nn.init.normal_(self.linear.weight, 0, 0.01) + + def forward(self, x): + out = self.stem(x) + out = self.body(out) + out = self.activation(self.final_conv(out)) + pool = torch.mean(out, dim=(2,3)) + + if self.training and self.drop_rate > 0.: + pool = self.dropout(pool) + + return self.linear(pool) + + def exclude_from_weight_decay(self, name:str) -> bool: + # Regex to find layer names like + # "stem.6.bias", "stem.6.gain", "body.0.skip_gain", + # "body.0.conv0.bias", "body.0.conv0.gain" + regex = re.compile('stem.*(bias|gain)|conv.*(bias|gain)|skip_gain') + return len(regex.findall(name)) > 0 + + def exclude_from_clipping(self, name: str) -> bool: + # Last layer should not be clipped + return name.startswith('linear') + +class Stem(nn.Module): + def __init__(self, num_channels=1, activation:str='gelu'): + super(Stem, self).__init__() + + self.activation = activations_dict[activation] + self.conv0 = WSConv2D(in_channels=num_channels, out_channels=16, kernel_size=3, stride=2) + self.conv1 = WSConv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1) + self.conv2 = WSConv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1) + self.conv3 = WSConv2D(in_channels=64, out_channels=128, kernel_size=3, stride=2) + + def forward(self, x): + out = self.activation(self.conv0(x)) + out = self.activation(self.conv1(out)) + out = self.activation(self.conv2(out)) + out = self.conv3(out) + return out + +class NFBlock(nn.Module): + def __init__(self, in_channels:int, out_channels:int, expansion:float=0.5, + se_ratio:float=0.5, stride:int=1, beta:float=1.0, alpha:float=0.2, + group_size:int=1, stochdepth_rate:float=None, activation:str='gelu'): + + super(NFBlock, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.expansion = expansion + self.se_ratio = se_ratio + self.activation = activations_dict[activation] + self.beta, self.alpha = beta, alpha + self.group_size = group_size + + width = int(self.out_channels * expansion) + self.groups = width // group_size + self.width = group_size * self.groups + self.stride = stride + + self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) + self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) + self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) + self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) + + self.use_projection = self.stride > 1 or self.in_channels != self.out_channels + if self.use_projection: + if stride > 1: + self.shortcut_avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels==1536 else 1) + self.conv_shortcut = WSConv2D(self.in_channels, self.out_channels, kernel_size=1) + + self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) + self.skip_gain = nn.Parameter(torch.zeros(())) + + self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. + if self.use_stochdepth: + self.stoch_depth = StochDepth(stochdepth_rate) + + def forward(self, x): + out = self.activation(x) * self.beta + + if self.stride > 1: + shortcut = self.shortcut_avg_pool(out) + shortcut = self.conv_shortcut(shortcut) + elif self.use_projection: + shortcut = self.conv_shortcut(out) + else: + shortcut = x + + out = self.activation(self.conv0(out)) + out = self.activation(self.conv1(out)) + out = self.activation(self.conv1b(out)) + out = self.conv2(out) + out = (self.squeeze_excite(out)*2) * out + + if self.use_stochdepth: + out = self.stoch_depth(out) + + return out * self.alpha * self.skip_gain + shortcut + +# Implementation mostly from https://arxiv.org/abs/2101.08692 +# Implemented changes from https://arxiv.org/abs/2102.06171 and +# https://github.com/deepmind/deepmind-research/tree/master/nfnets +class WSConv2D(nn.Conv2d): + def __init__(self, in_channels: int, out_channels: int, kernel_size, stride = 1, padding = 0, + dilation = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros'): + + super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode) + + nn.init.xavier_normal_(self.weight) + self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) + self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) + self.register_buffer('fan_in', torch.tensor(self.weight.shape[1:].numel(), requires_grad=False).type_as(self.weight), persistent=False) + + def standardized_weights(self): + # Original code: HWCN + mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) + var = torch.var(self.weight, axis=[1,2,3], keepdims=True) + scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) + return (self.weight - mean) * scale * self.gain + + def forward(self, x): + return F.conv2d( + input=x, + weight=self.standardized_weights(), + bias=self.bias, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups + ) + +class SqueezeExcite(nn.Module): + def __init__(self, in_channels:int, out_channels:int, se_ratio:float=0.5, activation:str='gelu'): + super(SqueezeExcite, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.se_ratio = se_ratio + + self.hidden_channels = max(1, int(self.in_channels * self.se_ratio)) + + self.activation = activations_dict[activation] + self.linear = nn.Linear(self.in_channels, self.hidden_channels) + self.linear_1 = nn.Linear(self.hidden_channels, self.out_channels) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + out = torch.mean(x, (2,3)) + out = self.linear_1(self.activation(self.linear(out))) + out = self.sigmoid(out) + + b,c,_,_ = x.size() + return out.view(b,c,1,1).expand_as(x) + +class StochDepth(nn.Module): + def __init__(self, stochdepth_rate:float): + super(StochDepth, self).__init__() + + self.drop_rate = stochdepth_rate + + def forward(self, x): + if not self.training: + return x + + batch_size = x.shape[0] + rand_tensor = torch.rand(batch_size, 1, 1, 1).type_as(x).to(x.device) + keep_prob = 1 - self.drop_rate + binary_tensor = torch.floor(rand_tensor + keep_prob) + + return x * binary_tensor diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/optim.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/optim.py new file mode 100644 index 00000000..4d72f023 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/optim.py @@ -0,0 +1,109 @@ +import torch +from torch.optim import Optimizer + +# Compute norm depending on the shape of x +def unitwise_norm(x): + if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors + axis = 0 + keepdims = False + elif len(x.shape) in [2,3]: # Linear layers + # Original code: IO + # Pytorch: OI + axis = 1 + keepdims = True + elif len(x.shape) == 4: # Conv kernels + # Original code: HWIO + # Pytorch: OIHW + axis = [1, 2, 3] + keepdims = True + else: + raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') + + return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) + + +# This is a copy of the pytorch SGD implementation +# enhanced with gradient clipping +class SGD_AGC(Optimizer): + def __init__(self, named_params, lr:float, momentum=0, dampening=0, + weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): + if lr < 0.0: + raise ValueError("Invalid learning rate: {}".format(lr)) + if momentum < 0.0: + raise ValueError("Invalid momentum value: {}".format(momentum)) + if weight_decay < 0.0: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + + defaults = dict(lr=lr, momentum=momentum, dampening=dampening, + weight_decay=weight_decay, nesterov=nesterov, + # Extra defaults + clipping=clipping, + eps=eps + ) + + if nesterov and (momentum <= 0 or dampening != 0): + raise ValueError("Nesterov momentum requires a momentum and zero dampening") + + # Put params in list so each one gets its own group + params = [] + for name, param in named_params: + params.append({'params': param, 'name': name}) + + super(SGD_AGC, self).__init__(params, defaults) + + def __setstate__(self, state): + super(SGD_AGC, self).__setstate__(state) + for group in self.param_groups: + group.setdefault('nesterov', False) + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + weight_decay = group['weight_decay'] + momentum = group['momentum'] + dampening = group['dampening'] + nesterov = group['nesterov'] + + # Extra values for clipping + clipping = group['clipping'] + eps = group['eps'] + + for p in group['params']: + if p.grad is None: + continue + d_p = p.grad + + # ========================= + # Gradient clipping + if clipping is not None: + param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) + grad_norm = unitwise_norm(d_p) + max_norm = param_norm * group['clipping'] + + trigger_mask = grad_norm > max_norm + clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) + d_p = torch.where(trigger_mask, clipped_grad, d_p) + # ========================= + + if weight_decay != 0: + d_p = d_p.add(p, alpha=weight_decay) + if momentum != 0: + param_state = self.state[p] + if 'momentum_buffer' not in param_state: + buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() + else: + buf = param_state['momentum_buffer'] + buf.mul_(momentum).add_(d_p, alpha=1 - dampening) + if nesterov: + d_p = d_p.add(buf, alpha=momentum) + else: + d_p = buf + + p.add_(d_p, alpha=-group['lr']) + + return loss \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py new file mode 100644 index 00000000..cc8ba064 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py @@ -0,0 +1,94 @@ +import re +import dill +import torch +import argparse +import numpy as np +from pathlib import Path + +from nfnets import NFNet + +def pretrained_nfnet(path, stochdepth_rate:float=0.5, alpha:float=0.2, activation:str='gelu') -> NFNet: + if isinstance(path, str): + path = Path(path) + + with path.open('rb') as f: + params = dill.load(f) + + layers_to_variant = { + 94: 'F0', + 178: 'F1', + 262: 'F2', + 346: 'F3', + 430: 'F4', + 514: 'F5' + } + + if not len(params) in layers_to_variant: + raise RuntimeError(f"Cannot load file {path.absolute()}." + f" File contains invalid parameter count {len(params)}!") + + model = NFNet( + variant=layers_to_variant[len(params)], + num_classes=1000, + alpha=alpha, + stochdepth_rate=stochdepth_rate, + se_ratio=0.5, + activation=activation) + + state_dict = {} + + for layer_name in params: + for param_name in params[layer_name]: + l = layer_name + l = l.replace("NFNet/~/", "") + l = re.sub("(nf_block_(\d*))", r"body.\2", l) + l = re.sub("(nf_block)", r"body.0", l) + l = re.sub("stem_*", "stem.", l) + l = l.replace("/~/", ".") + + p = str(param_name) + p = "weight" if p == "w" else p + p = "bias" if p == "b" else p + + param = params[layer_name][param_name] + + if len(param.shape) == 4: + # Conv layers, HWIO -> OIHW + param = param.swapaxes(0,3).swapaxes(1,2).swapaxes(2,3) + + elif len(param.shape) == 2: + # Linear layers, OI -> IO + param = param.swapaxes(0,1) + + if p == 'gain': + param = np.expand_dims(param, axis=(1,2,3)) + + #if "conv" in l: + # state_dict[f"{l}.eps"] = torch.tensor(1e-4, requires_grad=False) + + with torch.no_grad(): + t = torch.from_numpy(param) + complete_name = f'{l}.{p}' + if not complete_name in model.state_dict(): + raise ValueError( + f"Parameter {complete_name} not found in state dict!" + " Please report an issue.") + + state_dict[complete_name] = t + + model.load_state_dict(state_dict, strict=True) + return model + +if __name__=='__main__': + parser = argparse.ArgumentParser(description='Load haiku weights and convert them to .pth file.') + parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format') + args = parser.parse_args() + + if not args.pretrained.exists(): + raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") + + model = from_pretrained_haiku(args.pretrained) + + torch.save({ + 'model': model.state_dict() + }, str(args.pretrained.with_suffix('.pth'))) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitattributes b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitattributes new file mode 100644 index 00000000..60404dcd --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitattributes @@ -0,0 +1 @@ +*.ipynb linguist-documentation \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitignore b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitignore new file mode 100644 index 00000000..74249fe8 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitignore @@ -0,0 +1,9 @@ +venv/ +__pycache__ +.pytest_cache +.vscode +.ipynb_checkpoints +checkpoints +pretrained/*.npz +pretrained/*.pth +runs/ \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/LICENSE b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/LICENSE new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/README.md b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/README.md new file mode 100644 index 00000000..237c4abe --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/README.md @@ -0,0 +1,109 @@ +# NFNet Pytorch Implementation + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/benjs/nfnets_pytorch/blob/master/demo.ipynb) + +This repo contains pretrained NFNet models F0-F6 with high ImageNet accuracy from the paper *High-Performance Large-Scale Image Recognition Without Normalization*. The small models are as accurate as an EfficientNet-B7, but train 8.7 times faster. The large models set a new SOTA top-1 accuracy on ImageNet. + +| NFNet | F0 | F1 | F2 | F3 | F4 | F5 | F6+SAM | +|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| Top-1 accuracy Brock et al. | 83.6 | 84.7 | 85.1 | 85.7 | 85.9 | 86.0 | 86.5 | +| Top-1 accuracy this implementation | 82.82 | 84.63 | 84.90 | 85.46 | 85.66 | 85.62 | TBD | + +All credits go to the authors of the [original paper](https://arxiv.org/abs/2102.06171). This repo is heavily inspired by their nice JAX implementation in the [official repository](https://github.com/deepmind/deepmind-research/blob/master/nfnets/). Visit their repo for citing. + +## Get started +``` +git clone https://github.com/benjs/nfnets_pytorch.git +pip3 install -r requirements.txt +``` +or if you don't need eval and training script +``` +pip install git+https://github.com/benjs/nfnets_pytorch +``` +Download pretrained weights from the [official repository](https://github.com/deepmind/deepmind-research/blob/master/nfnets/) and call + +```python +from nfnets import pretrained_nfnet +model_F0 = pretrained_nfnet('pretrained/F0_haiku.npz') +model_F1 = pretrained_nfnet('pretrained/F1_haiku.npz') +# ... +``` + +The model variant is automatically derived from the parameter count in the pretrained weights file. + +## Validate yourself +``` +python3 eval.py --pretrained pretrained/F0_haiku.npz --dataset path/to/imagenet/valset/ +``` + +You can download the ImageNet validation set from the [ILSVRC2012 challenge site](http://www.image-net.org/challenges/LSVRC/2012/downloads.php#images) after asking for access with, for instance, your .edu mail address or from [AcademicTorrents](https://academictorrents.com/) + +## Scaled weight standardization convolutions in your own model +Simply replace all your `nn.Conv2d` with `WSConv2D` and all your `nn.ReLU` with `VPReLU` or `VPGELU` (variance preserving ReLU/GELU). + +``` python +import torch.nn as nn +from nfnets import WSConv2D, VPReLU, VPGELU + +# Simply replace your nn.Conv2d layers +class MyNet(nn.Module): + def __init__(self): + super(MyNet, self).__init__() + + self.activation = VPReLU(inplace=True) # or VPGELU + self.conv0 = WSConv2D(in_channels=128, out_channels=256, kernel_size=1, ...) + # ... + + def forward(self, x): + out = self.activation(self.conv0(x)) + # ... +``` + +## SGD with adaptive gradient clipping in your own model +Simply replace your `SGD` optimizer with `SGD_AGC`. +```python +from nfnets import SGD_AGC + +optimizer = SGD_AGC( + named_params=model.named_parameters(), # Pass named parameters + lr=1e-3, + momentum=0.9, + clipping=0.1, # New clipping parameter + weight_decay=2e-5, + nesterov=True) +``` + +It is important to exclude certain layers from clipping or momentum. The authors recommends to exclude the last fully convolutional from clipping and the bias/gain parameters from weight decay: +```python +import re + +for group in optimizer.param_groups: + name = group['name'] + + # Exclude from weight decay + if len(re.findall('stem.*(bias|gain)|conv.*(bias|gain)|skip_gain', name)) > 0: + group['weight_decay'] = 0 + + # Exclude from clipping + if name.startswith('linear'): + group['clipping'] = None + +``` + +## Train your own NFNet +Adjust your desired parameters in [default_config.yaml](default_config.yaml) and start training. +``` +python3 train.py --dataset /path/to/imagenet/ +``` + +There is still some parts missing for complete training from scratch: +- Multi-GPU training +- Data augmentations +- FP16 activations and gradients + +## Contribute + +The implementation is still in an early stage in terms of usability / testing. +If you have an idea to improve this repo open an issue, start a discussion or submit a pull request. + +The current development status can be seen in [this](https://github.com/benjs/nfnets_pytorch/projects/1) project board. diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/dataset.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/dataset.py new file mode 100644 index 00000000..7833e251 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/dataset.py @@ -0,0 +1,8 @@ +from pathlib import Path +from typing import Callable +from torchvision import transforms +from torch.utils.data.dataset import Dataset +from torchvision.datasets import ImageNet + +def get_dataset(path:Path, transforms:Callable=None) -> Dataset: + return ImageNet(str(path), split='val', transform=transforms) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/default_config.yaml b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/default_config.yaml new file mode 100644 index 00000000..879fe163 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/default_config.yaml @@ -0,0 +1,32 @@ +# This file contains the default train settings +device: 'cuda:0' # or 'cpu' +amp: False # Enable automatic mixed precision + +# Model +variant: 'F0' # F0 - F7 +num_classes: 1000 # Number of classes +activation: 'gelu' # or 'relu' +stochdepth_rate: 0.25 # 0-1, the probability that a layer is dropped during one step +alpha: 0.2 # Scaling factor at the end of each block +se_ratio: 0.5 # Squeeze-Excite expansion ratio +use_fp16: False # Use 16bit floats, which lowers memory footprint. This currently sets + # the complete model to FP16 (will be changed to match FP16 ops from paper) + +# Dataset +dataset: '/media/benjs/ext/' # Dataset root directory +num_workers: 8 # Number of workers in dataloader +pin_memory: True # This can fasten or slow down data loading depending on your hardware + +# Training +batch_size: 64 # Batch size +epochs: 360 # Number of epochs +overfit: False # Train on one batch size only + +learning_rate: 0.1 # Learning rate +scale_lr: True # Scale learning rate with batch size. lr = lr*batch_size/256 +momentum: 0.9 # Contribution of earlier gradient to gradient update +weight_decay: 0.00002 # Factor with which weights are added to gradient +nesterov: True # Enable nesterov correction + +do_clip: True # Enable adaptive gradient clipping +clipping: 0.1 # Adaptive gradient clipping parameter \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/demo.ipynb b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/demo.ipynb new file mode 100644 index 00000000..72b40509 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/demo.ipynb @@ -0,0 +1,258 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python391venvvenv104ff58eb0f84ee89265b076747d4646", + "display_name": "Python 3.9.1 ('venv': venv)", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n", + "Collecting git+https://github.com/benjs/nfnets_pytorch\n", + " Cloning https://github.com/benjs/nfnets_pytorch to /tmp/pip-req-build-2f7sjvln\n", + " Running command git clone -q https://github.com/benjs/nfnets_pytorch /tmp/pip-req-build-2f7sjvln\n", + " # is not a valid attribute name: .gitattributes:1\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing wheel metadata ... \u001b[?25ldone\n", + "\u001b[?25hCollecting torchvision\n", + " Using cached https://download.pytorch.org/whl/cu92/torchvision-0.8.2%2Bcu92-cp39-cp39-linux_x86_64.whl (12.5 MB)\n", + "Requirement already satisfied: jax in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.2.9)\n", + "Requirement already satisfied: requests in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (2.25.1)\n", + "Requirement already satisfied: dill in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.3.3)\n", + "Collecting torch>=1.7\n", + " Using cached https://download.pytorch.org/whl/rocm3.8/torch-1.7.1%2Brocm3.8-cp39-cp39-linux_x86_64.whl (588.0 MB)\n", + "Requirement already satisfied: dm-haiku in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.0.4.dev0)\n", + "Requirement already satisfied: jaxlib in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.1.61)\n", + "Requirement already satisfied: numpy in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (1.20.1)\n", + "Requirement already satisfied: typing-extensions in ./venv/lib/python3.9/site-packages (from torch>=1.7->nfnets-pytorch==0.0.1) (3.7.4.3)\n", + "Requirement already satisfied: pillow>=4.1.1 in ./venv/lib/python3.9/site-packages (from torchvision) (8.1.0)\n", + "Requirement already satisfied: absl-py>=0.7.1 in ./venv/lib/python3.9/site-packages (from dm-haiku->nfnets-pytorch==0.0.1) (0.11.0)\n", + "Requirement already satisfied: tabulate==0.8.7 in ./venv/lib/python3.9/site-packages (from dm-haiku->nfnets-pytorch==0.0.1) (0.8.7)\n", + "Requirement already satisfied: six in ./venv/lib/python3.9/site-packages (from absl-py>=0.7.1->dm-haiku->nfnets-pytorch==0.0.1) (1.15.0)\n", + "Requirement already satisfied: opt-einsum in ./venv/lib/python3.9/site-packages (from jax->nfnets-pytorch==0.0.1) (3.3.0)\n", + "Requirement already satisfied: scipy in ./venv/lib/python3.9/site-packages (from jaxlib->nfnets-pytorch==0.0.1) (1.6.1)\n", + "Requirement already satisfied: flatbuffers in ./venv/lib/python3.9/site-packages (from jaxlib->nfnets-pytorch==0.0.1) (1.12)\n", + "Requirement already satisfied: chardet<5,>=3.0.2 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (4.0.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (2020.12.5)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (1.26.3)\n", + "Requirement already satisfied: idna<3,>=2.5 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (2.10)\n", + "Installing collected packages: torch, torchvision\n", + "Successfully installed torch-1.7.1+rocm3.8 torchvision-0.8.2+cu92\n" + ] + } + ], + "source": [ + "!pip install git+https://github.com/benjs/nfnets_pytorch torchvision>=0.8" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import PIL\n", + "import requests\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from pathlib import Path\n", + "from PIL import Image\n", + "from nfnets import pretrained_nfnet\n", + "from torchvision.transforms import Compose, Resize, CenterCrop, Normalize, ToTensor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2021-02-23 23:22:15-- https://storage.googleapis.com/dm-nfnets/F0_haiku.npz\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 2a00:1450:4001:829::2010, 2a00:1450:4001:827::2010, 2a00:1450:4001:801::2010, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|2a00:1450:4001:829::2010|:443... connected.\n", + "HTTP request sent, awaiting response... 304 Not Modified\n", + "File ‘pretrained/F0_haiku.npz’ not modified on server. Omitting download.\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p pretrained \n", + "!wget https://storage.googleapis.com/dm-nfnets/F0_haiku.npz -N -P pretrained" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "r = requests.get(\"https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt\")\n", + "\n", + "classes = []\n", + "for line in r.iter_lines():\n", + " classes.append(str(line).split(\"'\")[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "model = pretrained_nfnet('pretrained/F0_haiku.npz')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "transforms = Compose([\n", + " ToTensor(),\n", + " Resize((model.test_imsize + 32, model.test_imsize + 32), PIL.Image.BICUBIC),\n", + " CenterCrop((model.test_imsize, model.test_imsize)),\n", + " Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def load_img(url:str):\n", + " img = Image.open(requests.get(url, stream=True).raw)\n", + " tensor = transforms(img)[None, :, :]\n", + "\n", + " return img, tensor" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def eval(input:torch.Tensor):\n", + " model.eval()\n", + " with torch.no_grad():\n", + " output = F.softmax(model(input), dim=1)\n", + "\n", + " vals, preds = torch.topk(output, 5 , 1)\n", + "\n", + " for val, pred in iter(zip(vals[0], preds[0])):\n", + " print(f\"{val*100.0:4.2f}%: {classes[pred.item()]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "urls = [\n", + " \"https://unsplash.com/photos/T-0EW-SEbsE/download?force=true\",\n", + " \"https://unsplash.com/photos/rW-I87aPY5Y/download?force=true\",\n", + " \"https://unsplash.com/photos/eqW1MPinEV4/download?force=true\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "99.55%: Pembroke, Pembroke Welsh corgi\n99.26%: Yorkshire terrier\n98.92%: Australian terrier\n98.54%: Cardigan, Cardigan Welsh corgi\n98.09%: silky terrier, Sydney silky\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "99.59%: Persian cat\n97.97%: Egyptian cat\n97.66%: Angora, Angora rabbit\n96.44%: doormat, welcome mat\n96.06%: tabby, tabby cat\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "99.82%: sports car, sport car\n99.70%: racer, race car, racing car\n98.63%: car wheel\n92.58%: grille, radiator grille\n86.70%: convertible\n" + ] + } + ], + "source": [ + "for url in urls:\n", + " img, tensor = load_img(url)\n", + " \n", + " display(img.resize((int(x*600/max(img.size)) for x in img.size)))\n", + " eval(tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/eval.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/eval.py new file mode 100644 index 00000000..d098bb1d --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/eval.py @@ -0,0 +1,86 @@ +import argparse +import math +import PIL +from pathlib import Path +from PIL.Image import Image + +import torch +import torch.nn as nn +import torchvision.transforms.functional as tF +import torchvision.transforms.functional_pil as tF_pil +from torch.utils.data.dataloader import DataLoader +from torchvision.transforms.transforms import Compose, Normalize, Resize, ToTensor + +from dataset import get_dataset +from nfnets import NFNet, pretrained_nfnet + +# Evaluation method used in the paper +# This seems to perform slightly worse than a simple resize +class Pad32CenterCrop(nn.Module): + def __init__(self, size:int): + super().__init__() + self.size = size + self.scaled_size = (size+32, size+32) + + def forward(self, img:Image): + img = tF_pil.resize(img=img, size=self.scaled_size, interpolation=PIL.Image.BICUBIC) + return tF.center_crop(img, self.size) + +def evaluate_on_imagenet(model:NFNet, dataset_dir:Path, batch_size=50, device='cuda:0'): + transforms = Compose([ + #Pad32CenterCrop(model.test_imsize), + ToTensor(), + Resize((model.test_imsize, model.test_imsize), PIL.Image.BICUBIC), + Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + + print(f"Starting evaluation from {dataset_dir}") + dataset = get_dataset(dataset_dir, transforms=transforms) + + dataloader = DataLoader( + dataset=dataset, + batch_size=batch_size, # F0: 120, F1: 100, F2: 80 + shuffle=False, + pin_memory=False, + num_workers=8 + ) + + print(f"Validation set contains {len(dataset)} images.") + + model.to(device) + model.eval() + + processed_imgs = 0 + correct_labels = 0 + for step, data in enumerate(dataloader): + with torch.no_grad(): + inputs = data[0].to(device) + targets = data[1].to(device) + + output = model(inputs).type(torch.float32) + + processed_imgs += targets.size(0) + _, predicted = torch.max(output, 1) + correct_labels += (predicted == targets).sum().item() + + batch_padding = int(math.log10(len(dataloader.dataset)) + 1) + print(f"\rProcessing {processed_imgs:{batch_padding}d}/{len(dataloader.dataset)}. Accuracy: {100.0*correct_labels/processed_imgs:6.4f}", sep=' ', end='', flush=True) + + print(f"\nFinished eval. Accuracy: {100.0*correct_labels/processed_imgs:6.4f}") + + +if __name__=='__main__': + parser = argparse.ArgumentParser(description='Evaluate NFNets.') + parser.add_argument('--dataset', type=Path, help='Path to dataset root directory', required=True) + parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format', required=True) + parser.add_argument('--batch-size', type=int, help='Validation batch size', default=50) + parser.add_argument('--device', type=str, help='Validation device. Either \'cuda:0\' or \'cpu\'', default='cuda:0') + args = parser.parse_args() + + if not args.pretrained.exists(): + raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") + + model = pretrained_nfnet(args.pretrained) + + evaluate_on_imagenet(model, dataset_dir=args.dataset, batch_size=args.batch_size, device=args.device) + \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/__init__.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/__init__.py new file mode 100644 index 00000000..43531b9d --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/__init__.py @@ -0,0 +1,3 @@ +from .model import * +from .pretrained import * +from .optim import * \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model copy.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model copy.py new file mode 100644 index 00000000..37551df9 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model copy.py @@ -0,0 +1,309 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import re + +nfnet_params = { + 'F0': { + 'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], + 'train_imsize': 192, 'test_imsize': 114, + 'RA_level': '405', 'drop_rate': 0.2}, + + 'F1': { + 'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], + 'train_imsize': 224, 'test_imsize': 320, + 'RA_level': '410', 'drop_rate': 0.3}, + 'F2': { + 'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], + 'train_imsize': 256, 'test_imsize': 352, + 'RA_level': '410', 'drop_rate': 0.4}, + 'F3': { + 'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], + 'train_imsize': 320, 'test_imsize': 416, + 'RA_level': '415', 'drop_rate': 0.4}, + 'F4': { + 'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], + 'train_imsize': 384, 'test_imsize': 512, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F5': { + 'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], + 'train_imsize': 416, 'test_imsize': 544, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F6': { + 'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], + 'train_imsize': 448, 'test_imsize': 576, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F7': { + 'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], + 'train_imsize': 480, 'test_imsize': 608, + 'RA_level': '415', 'drop_rate': 0.5}, +} + +# These extra constant values ensure that the activations +# are variance preserving +class VPGELU(nn.Module): + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.gelu(input) * 1.7015043497085571 + +class VPReLU(nn.Module): + __constants__ = ['inplace'] + inplace: bool + + def __init__(self, inplace: bool = False): + super(VPReLU, self).__init__() + self.inplace = inplace + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.relu(input, inplace=self.inplace) * 1.7139588594436646 + + def extra_repr(self) -> str: + inplace_str = 'inplace=True' if self.inplace else '' + return inplace_str + +activations_dict = { + 'gelu': VPGELU(), + 'relu': VPReLU(inplace=True) +} + +class NFNet(nn.Module): + def __init__(self, num_channels=1,num_classes:int=2, variant:str='F0', stochdepth_rate:float=0.25, + alpha:float=0.2, se_ratio:float=0.5, activation:str='gelu'): + super(NFNet, self).__init__() + + if not variant in nfnet_params: + raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") + + block_params = nfnet_params[variant] + + self.train_imsize = block_params['train_imsize'] + self.test_imsize = block_params['test_imsize'] + self.activation = activations_dict[activation] + self.drop_rate = block_params['drop_rate'] + self.num_classes = num_classes + + self.stem = Stem(num_channels=num_channels,activation=activation) + + num_blocks, index = sum(block_params['depth']), 0 + + blocks = [] + expected_std = 1.0 + in_channels = block_params['width'][0] // 2 + + block_args = zip( + block_params['width'], + block_params['depth'], + [0.5] * 4, # bottleneck pattern + [57] * 4, # group pattern. Original groups [128] * 4 + # [128] * 4, # group pattern. Original groups [128] * 4 + [1, 2, 2, 2] # stride pattern + ) + + for (block_width, stage_depth, expand_ratio, group_size, stride) in block_args: + for block_index in range(stage_depth): + beta = 1. / expected_std + + block_sd_rate = stochdepth_rate * index / num_blocks + out_channels = block_width + + blocks.append(NFBlock( + in_channels=in_channels, + out_channels=out_channels, + stride=stride if block_index == 0 else 1, + alpha=alpha, + beta=beta, + se_ratio=se_ratio, + group_size=group_size, + stochdepth_rate=block_sd_rate, + activation=activation)) + + in_channels = out_channels + index += 1 + + if block_index == 0: + expected_std = 1.0 + + expected_std = (expected_std **2 + alpha**2)**0.5 + + self.body = nn.Sequential(*blocks) + + final_conv_channels = 2*in_channels + self.final_conv = WSConv2D(in_channels=out_channels, out_channels=final_conv_channels, kernel_size=1) + self.pool = nn.AvgPool2d(1) + + if self.drop_rate > 0.: + self.dropout = nn.Dropout(self.drop_rate) + + self.linear = nn.Linear(final_conv_channels, self.num_classes) + nn.init.normal_(self.linear.weight, 0, 0.01) + + def forward(self, x): + out = self.stem(x) + out = self.body(out) + out = self.activation(self.final_conv(out)) + pool = torch.mean(out, dim=(2,3)) + + if self.training and self.drop_rate > 0.: + pool = self.dropout(pool) + + return self.linear(pool) + + def exclude_from_weight_decay(self, name:str) -> bool: + # Regex to find layer names like + # "stem.6.bias", "stem.6.gain", "body.0.skip_gain", + # "body.0.conv0.bias", "body.0.conv0.gain" + regex = re.compile('stem.*(bias|gain)|conv.*(bias|gain)|skip_gain') + return len(regex.findall(name)) > 0 + + def exclude_from_clipping(self, name: str) -> bool: + # Last layer should not be clipped + return name.startswith('linear') + +class Stem(nn.Module): + def __init__(self, num_channels=1, activation:str='gelu'): + super(Stem, self).__init__() + + self.activation = activations_dict[activation] + self.conv0 = WSConv2D(in_channels=num_channels, out_channels=16, kernel_size=3, stride=2) + self.conv1 = WSConv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1) + self.conv2 = WSConv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1) + self.conv3 = WSConv2D(in_channels=64, out_channels=128, kernel_size=3, stride=2) + + def forward(self, x): + out = self.activation(self.conv0(x)) + out = self.activation(self.conv1(out)) + out = self.activation(self.conv2(out)) + out = self.conv3(out) + return out + +class NFBlock(nn.Module): + def __init__(self, in_channels:int, out_channels:int, expansion:float=0.5, + se_ratio:float=0.5, stride:int=1, beta:float=1.0, alpha:float=0.2, + group_size:int=1, stochdepth_rate:float=None, activation:str='gelu'): + + super(NFBlock, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.expansion = expansion + self.se_ratio = se_ratio + self.activation = activations_dict[activation] + self.beta, self.alpha = beta, alpha + self.group_size = group_size + + width = int(self.out_channels * expansion) + self.groups = width // group_size + self.width = group_size * self.groups + self.stride = stride + + self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) + self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) + self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) + self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) + + self.use_projection = self.stride > 1 or self.in_channels != self.out_channels + if self.use_projection: + if stride > 1: + self.shortcut_avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels==1536 else 1) + self.conv_shortcut = WSConv2D(self.in_channels, self.out_channels, kernel_size=1) + + self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) + self.skip_gain = nn.Parameter(torch.zeros(())) + + self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. + if self.use_stochdepth: + self.stoch_depth = StochDepth(stochdepth_rate) + + def forward(self, x): + out = self.activation(x) * self.beta + + if self.stride > 1: + shortcut = self.shortcut_avg_pool(out) + shortcut = self.conv_shortcut(shortcut) + elif self.use_projection: + shortcut = self.conv_shortcut(out) + else: + shortcut = x + + out = self.activation(self.conv0(out)) + out = self.activation(self.conv1(out)) + out = self.activation(self.conv1b(out)) + out = self.conv2(out) + out = (self.squeeze_excite(out)*2) * out + + if self.use_stochdepth: + out = self.stoch_depth(out) + + return out * self.alpha * self.skip_gain + shortcut + +# Implementation mostly from https://arxiv.org/abs/2101.08692 +# Implemented changes from https://arxiv.org/abs/2102.06171 and +# https://github.com/deepmind/deepmind-research/tree/master/nfnets +class WSConv2D(nn.Conv2d): + def __init__(self, in_channels: int, out_channels: int, kernel_size, stride = 1, padding = 0, + dilation = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros'): + + super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode) + + nn.init.xavier_normal_(self.weight) + self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) + self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) + self.register_buffer('fan_in', torch.tensor(self.weight.shape[1:].numel(), requires_grad=False).type_as(self.weight), persistent=False) + + def standardized_weights(self): + # Original code: HWCN + mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) + var = torch.var(self.weight, axis=[1,2,3], keepdims=True) + scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) + return (self.weight - mean) * scale * self.gain + + def forward(self, x): + return F.conv2d( + input=x, + weight=self.standardized_weights(), + bias=self.bias, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups + ) + +class SqueezeExcite(nn.Module): + def __init__(self, in_channels:int, out_channels:int, se_ratio:float=0.5, activation:str='gelu'): + super(SqueezeExcite, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.se_ratio = se_ratio + + self.hidden_channels = max(1, int(self.in_channels * self.se_ratio)) + + self.activation = activations_dict[activation] + self.linear = nn.Linear(self.in_channels, self.hidden_channels) + self.linear_1 = nn.Linear(self.hidden_channels, self.out_channels) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + out = torch.mean(x, (2,3)) + out = self.linear_1(self.activation(self.linear(out))) + out = self.sigmoid(out) + + b,c,_,_ = x.size() + return out.view(b,c,1,1).expand_as(x) + +class StochDepth(nn.Module): + def __init__(self, stochdepth_rate:float): + super(StochDepth, self).__init__() + + self.drop_rate = stochdepth_rate + + def forward(self, x): + if not self.training: + return x + + batch_size = x.shape[0] + rand_tensor = torch.rand(batch_size, 1, 1, 1).type_as(x).to(x.device) + keep_prob = 1 - self.drop_rate + binary_tensor = torch.floor(rand_tensor + keep_prob) + + return x * binary_tensor diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model.py new file mode 100644 index 00000000..28e71bc0 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model.py @@ -0,0 +1,265 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import re + +nfnet_params = { + 'F0': { + 'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], + 'train_imsize': 192, 'test_imsize': 114, + 'RA_level': '405', 'drop_rate': 0.2}, + + 'F1': { + 'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], + 'train_imsize': 224, 'test_imsize': 320, + 'RA_level': '410', 'drop_rate': 0.3}, + 'F2': { + 'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], + 'train_imsize': 256, 'test_imsize': 352, + 'RA_level': '410', 'drop_rate': 0.4}, + 'F3': { + 'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], + 'train_imsize': 320, 'test_imsize': 416, + 'RA_level': '415', 'drop_rate': 0.4}, + 'F4': { + 'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], + 'train_imsize': 384, 'test_imsize': 512, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F5': { + 'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], + 'train_imsize': 416, 'test_imsize': 544, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F6': { + 'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], + 'train_imsize': 448, 'test_imsize': 576, + 'RA_level': '415', 'drop_rate': 0.5}, + 'F7': { + 'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], + 'train_imsize': 480, 'test_imsize': 608, + 'RA_level': '415', 'drop_rate': 0.5}, +} +import torch +import torch.nn as nn +import torch.nn.functional as F +import re + + +# These extra constant values ensure that the activations +# are variance preserving +class VPGELU(nn.Module): + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.gelu(input) * 1.7015043497085571 + +class VPReLU(nn.Module): + __constants__ = ['inplace'] + inplace: bool + + def __init__(self, inplace: bool = False): + super(VPReLU, self).__init__() + self.inplace = inplace + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.relu(input, inplace=self.inplace) * 1.7139588594436646 + + def extra_repr(self) -> str: + inplace_str = 'inplace=True' if self.inplace else '' + return inplace_str + +activations_dict = { + 'gelu': VPGELU(), + 'relu': VPReLU(inplace=True) +} + + +# Definitions for VPGELU and VPReLU are assumed to be the same as above. + +class NFNet(nn.Module): + def __init__(self, num_channels=1, num_classes=2, variant='F0', stochdepth_rate=0.25, alpha=0.2, se_ratio=0.5, activation='gelu'): + super(NFNet, self).__init__() + + if variant not in nfnet_params: + raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") + + block_params = nfnet_params[variant] + self.activation = activations_dict[activation] + self.drop_rate = block_params['drop_rate'] + self.num_classes = num_classes + + self.stem = Stem(num_channels=num_channels, activation=activation) + + # Here, we use a list to hold each NFBlock stage. + blocks = [] + in_channels = block_params['width'][0] // 2 + + # Adjust output sizes by maintaining depth but adjusting widths and strides. + for i, (width, depth, stride) in enumerate(zip(block_params['width'], block_params['depth'], [2, 2, 2, 2])): + out_channels = width + for _ in range(depth): + stride = stride if _ == 0 else 1 + blocks.append(NFBlock(in_channels=in_channels, out_channels=out_channels, stride=stride, se_ratio=se_ratio, activation=activation)) + in_channels = out_channels + + self.blocks = nn.Sequential(*blocks) + + # Adaptive pooling to adjust for any size discrepancies. + self.pool = nn.AdaptiveAvgPool2d(1) + + # Final layers after pooling. + self.dropout = nn.Dropout(self.drop_rate) + self.classifier = nn.Linear(in_channels, self.num_classes) + + def forward(self, x): + x = self.stem(x) + x = self.blocks(x) + x = self.pool(x) + x = torch.flatten(x, 1) + if self.training and self.drop_rate > 0: + x = self.dropout(x) + return self.classifier(x) + +class Stem(nn.Module): + def __init__(self, num_channels=1, activation='gelu'): + super(Stem, self).__init__() + self.conv1 = nn.Conv2d(num_channels, 32, kernel_size=3, stride=2, padding=1) + self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) + self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) + self.activation = activations_dict[activation] + + def forward(self, x): + x = self.activation(self.conv1(x)) + x = self.activation(self.conv2(x)) + x = self.activation(self.conv3(x)) + return x + +class NFBlock(nn.Module): + def __init__(self, in_channels:int, out_channels:int, expansion:float=0.5, + se_ratio:float=0.5, stride:int=1, beta:float=1.0, alpha:float=0.2, + group_size:int=1, stochdepth_rate:float=None, activation:str='gelu'): + + super(NFBlock, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.expansion = expansion + self.se_ratio = se_ratio + self.activation = activations_dict[activation] + self.beta, self.alpha = beta, alpha + self.group_size = group_size + + width = int(self.out_channels * expansion) + self.groups = width // group_size + self.width = group_size * self.groups + self.stride = stride + + self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) + self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) + self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) + self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) + + self.use_projection = self.stride > 1 or self.in_channels != self.out_channels + if self.use_projection: + if stride > 1: + self.shortcut_avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels==1536 else 1) + self.conv_shortcut = WSConv2D(self.in_channels, self.out_channels, kernel_size=1) + + self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) + self.skip_gain = nn.Parameter(torch.zeros(())) + + self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. + if self.use_stochdepth: + self.stoch_depth = StochDepth(stochdepth_rate) + + def forward(self, x): + out = self.activation(x) * self.beta + + if self.stride > 1: + shortcut = self.shortcut_avg_pool(out) + shortcut = self.conv_shortcut(shortcut) + elif self.use_projection: + shortcut = self.conv_shortcut(out) + else: + shortcut = x + + out = self.activation(self.conv0(out)) + out = self.activation(self.conv1(out)) + out = self.activation(self.conv1b(out)) + out = self.conv2(out) + out = (self.squeeze_excite(out)*2) * out + + if self.use_stochdepth: + out = self.stoch_depth(out) + + return out * self.alpha * self.skip_gain + shortcut + +# Implementation mostly from https://arxiv.org/abs/2101.08692 +# Implemented changes from https://arxiv.org/abs/2102.06171 and +# https://github.com/deepmind/deepmind-research/tree/master/nfnets +class WSConv2D(nn.Conv2d): + def __init__(self, in_channels: int, out_channels: int, kernel_size, stride = 1, padding = 0, + dilation = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros'): + + super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, + padding, dilation, groups, bias, padding_mode) + + nn.init.xavier_normal_(self.weight) + self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) + self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) + self.register_buffer('fan_in', torch.tensor(self.weight.shape[1:].numel(), requires_grad=False).type_as(self.weight), persistent=False) + + def standardized_weights(self): + # Original code: HWCN + mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) + var = torch.var(self.weight, axis=[1,2,3], keepdims=True) + scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) + return (self.weight - mean) * scale * self.gain + + def forward(self, x): + return F.conv2d( + input=x, + weight=self.standardized_weights(), + bias=self.bias, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups + ) + +class SqueezeExcite(nn.Module): + def __init__(self, in_channels:int, out_channels:int, se_ratio:float=0.5, activation:str='gelu'): + super(SqueezeExcite, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.se_ratio = se_ratio + + self.hidden_channels = max(1, int(self.in_channels * self.se_ratio)) + + self.activation = activations_dict[activation] + self.linear = nn.Linear(self.in_channels, self.hidden_channels) + self.linear_1 = nn.Linear(self.hidden_channels, self.out_channels) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + out = torch.mean(x, (2,3)) + out = self.linear_1(self.activation(self.linear(out))) + out = self.sigmoid(out) + + b,c,_,_ = x.size() + return out.view(b,c,1,1).expand_as(x) + +class StochDepth(nn.Module): + def __init__(self, stochdepth_rate:float): + super(StochDepth, self).__init__() + + self.drop_rate = stochdepth_rate + + def forward(self, x): + if not self.training: + return x + + batch_size = x.shape[0] + rand_tensor = torch.rand(batch_size, 1, 1, 1).type_as(x).to(x.device) + keep_prob = 1 - self.drop_rate + binary_tensor = torch.floor(rand_tensor + keep_prob) + + return x * binary_tensor diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/optim.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/optim.py new file mode 100644 index 00000000..4d72f023 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/optim.py @@ -0,0 +1,109 @@ +import torch +from torch.optim import Optimizer + +# Compute norm depending on the shape of x +def unitwise_norm(x): + if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors + axis = 0 + keepdims = False + elif len(x.shape) in [2,3]: # Linear layers + # Original code: IO + # Pytorch: OI + axis = 1 + keepdims = True + elif len(x.shape) == 4: # Conv kernels + # Original code: HWIO + # Pytorch: OIHW + axis = [1, 2, 3] + keepdims = True + else: + raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') + + return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) + + +# This is a copy of the pytorch SGD implementation +# enhanced with gradient clipping +class SGD_AGC(Optimizer): + def __init__(self, named_params, lr:float, momentum=0, dampening=0, + weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): + if lr < 0.0: + raise ValueError("Invalid learning rate: {}".format(lr)) + if momentum < 0.0: + raise ValueError("Invalid momentum value: {}".format(momentum)) + if weight_decay < 0.0: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + + defaults = dict(lr=lr, momentum=momentum, dampening=dampening, + weight_decay=weight_decay, nesterov=nesterov, + # Extra defaults + clipping=clipping, + eps=eps + ) + + if nesterov and (momentum <= 0 or dampening != 0): + raise ValueError("Nesterov momentum requires a momentum and zero dampening") + + # Put params in list so each one gets its own group + params = [] + for name, param in named_params: + params.append({'params': param, 'name': name}) + + super(SGD_AGC, self).__init__(params, defaults) + + def __setstate__(self, state): + super(SGD_AGC, self).__setstate__(state) + for group in self.param_groups: + group.setdefault('nesterov', False) + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + weight_decay = group['weight_decay'] + momentum = group['momentum'] + dampening = group['dampening'] + nesterov = group['nesterov'] + + # Extra values for clipping + clipping = group['clipping'] + eps = group['eps'] + + for p in group['params']: + if p.grad is None: + continue + d_p = p.grad + + # ========================= + # Gradient clipping + if clipping is not None: + param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) + grad_norm = unitwise_norm(d_p) + max_norm = param_norm * group['clipping'] + + trigger_mask = grad_norm > max_norm + clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) + d_p = torch.where(trigger_mask, clipped_grad, d_p) + # ========================= + + if weight_decay != 0: + d_p = d_p.add(p, alpha=weight_decay) + if momentum != 0: + param_state = self.state[p] + if 'momentum_buffer' not in param_state: + buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() + else: + buf = param_state['momentum_buffer'] + buf.mul_(momentum).add_(d_p, alpha=1 - dampening) + if nesterov: + d_p = d_p.add(buf, alpha=momentum) + else: + d_p = buf + + p.add_(d_p, alpha=-group['lr']) + + return loss \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py new file mode 100644 index 00000000..9f7909d8 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py @@ -0,0 +1,94 @@ +import re +import dill +import torch +import argparse +import numpy as np +from pathlib import Path + +from ..nfnets import NFNet + +def pretrained_nfnet(path, stochdepth_rate:float=0.5, alpha:float=0.2, activation:str='gelu') -> NFNet: + if isinstance(path, str): + path = Path(path) + + with path.open('rb') as f: + params = dill.load(f) + + layers_to_variant = { + 94: 'F0', + 178: 'F1', + 262: 'F2', + 346: 'F3', + 430: 'F4', + 514: 'F5' + } + + if not len(params) in layers_to_variant: + raise RuntimeError(f"Cannot load file {path.absolute()}." + f" File contains invalid parameter count {len(params)}!") + + model = NFNet( + variant=layers_to_variant[len(params)], + num_classes=1000, + alpha=alpha, + stochdepth_rate=stochdepth_rate, + se_ratio=0.5, + activation=activation) + + state_dict = {} + + for layer_name in params: + for param_name in params[layer_name]: + l = layer_name + l = l.replace("NFNet/~/", "") + l = re.sub("(nf_block_(\d*))", r"body.\2", l) + l = re.sub("(nf_block)", r"body.0", l) + l = re.sub("stem_*", "stem.", l) + l = l.replace("/~/", ".") + + p = str(param_name) + p = "weight" if p == "w" else p + p = "bias" if p == "b" else p + + param = params[layer_name][param_name] + + if len(param.shape) == 4: + # Conv layers, HWIO -> OIHW + param = param.swapaxes(0,3).swapaxes(1,2).swapaxes(2,3) + + elif len(param.shape) == 2: + # Linear layers, OI -> IO + param = param.swapaxes(0,1) + + if p == 'gain': + param = np.expand_dims(param, axis=(1,2,3)) + + #if "conv" in l: + # state_dict[f"{l}.eps"] = torch.tensor(1e-4, requires_grad=False) + + with torch.no_grad(): + t = torch.from_numpy(param) + complete_name = f'{l}.{p}' + if not complete_name in model.state_dict(): + raise ValueError( + f"Parameter {complete_name} not found in state dict!" + " Please report an issue.") + + state_dict[complete_name] = t + + model.load_state_dict(state_dict, strict=True) + return model + +if __name__=='__main__': + parser = argparse.ArgumentParser(description='Load haiku weights and convert them to .pth file.') + parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format') + args = parser.parse_args() + + if not args.pretrained.exists(): + raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") + + model = from_pretrained_haiku(args.pretrained) + + torch.save({ + 'model': model.state_dict() + }, str(args.pretrained.with_suffix('.pth'))) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained/README.md b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained/README.md new file mode 100644 index 00000000..beda6c5f --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained/README.md @@ -0,0 +1,11 @@ +# Pretrained weights + +Download the pretrained weights from the [official repository](https://github.com/deepmind/deepmind-research/tree/master/nfnets#pre-trained-weights) and place them inside this folder. +Then start training with +``` +python3 train.py --pretrained pretrained/F0_haiku.npz +``` + +or evaluation with +``` +python3 eval.py --pretrained pretrained/F0_haiku.npz --dataset /path/to/imagenet/val/ diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pyproject.toml b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pyproject.toml new file mode 100644 index 00000000..b5a3c468 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pyproject.toml @@ -0,0 +1,6 @@ +[build-system] +requires = [ + "setuptools>=42", + "wheel" +] +build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/requirements.txt b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/requirements.txt new file mode 100644 index 00000000..b3398de0 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/requirements.txt @@ -0,0 +1,15 @@ +# --find-links https://download.pytorch.org/whl/torch_stable.html +--find-links https://download.pytorch.org/whl/cu110/torch_stable.html + +dill +git+https://github.com/deepmind/dm-haiku +jax +jaxlib +matplotlib +numpy +pillow-simd +pyyaml +requests +tensorboard +torch>=1.7.1+cu110 +torchvision>=0.8.2+cu110 \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/setup.cfg b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/setup.cfg new file mode 100644 index 00000000..56f13f5f --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/setup.cfg @@ -0,0 +1,35 @@ +[metadata] +name=nfnets_pytorch +version=0.0.1 +author=Benjamin Schmidt +author_email = webmaster@benjs.de +license=Apache 2.0 +license_file=LICENSE +description=Implementation of the paper "High-Performance Large-Scale Image Recognition Without Normalization" by Brock et al. +long_description=file:README.md +long_description_content_type=text/markdown +url=https://github.com/benjs/nfnets_pytorch +project_urls = + Bug Tracker = https://github.com/benjs/nfnets_pytorch/issues +classifiers = + Programming Language :: Python :: 3 + License :: OSI Approved :: Apache Software License + Operating System :: OS Independent + Natural Language :: English + +[options] +packages = nfnets +python_requires = >=3.7 +install_requires = + dm-haiku + requests + dill + jax + jaxlib + numpy + requests + torch>=1.7 + +dependency_links= + git+https://github.com/deepmind/dm-haiku + https://download.pytorch.org/whl/torch_stable.html \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/train.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/train.py new file mode 100644 index 00000000..2ece44cc --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/train.py @@ -0,0 +1,194 @@ +import argparse +import math +import PIL +import time +import yaml +from pathlib import Path +from PIL.Image import Image + +import matplotlib.pyplot as plt +import torch +import torch.cuda.amp as amp +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import Subset +from torch.utils.data.dataloader import DataLoader +from torch.utils.tensorboard import SummaryWriter +from torchvision.transforms.transforms import Compose, Normalize, Resize, ToTensor, RandomHorizontalFlip, RandomCrop + +from dataset import get_dataset +from nfnets import NFNet, SGD_AGC, pretrained_nfnet + +def train(config:dict) -> None: + if config['device'].startswith('cuda'): + if torch.cuda.is_available(): + print(f"Using CUDA{torch.version.cuda} with cuDNN{torch.backends.cudnn.version()}") + else: + raise ValueError("You specified to use cuda device, but cuda is not available.") + + if config['pretrained'] is not None: + model = pretrained_nfnet( + path=config['pretrained'], + stochdepth_rate=config['stochdepth_rate'], + alpha=config['alpha'], + activation=config['activation'] + ) + else: + model = NFNet( + num_classes=config['num_classes'], + variant=config['variant'], + stochdepth_rate=config['stochdepth_rate'], + alpha=config['alpha'], + se_ratio=config['se_ratio'], + activation=config['activation'] + ) + + transforms = Compose([ + RandomHorizontalFlip(), + Resize((model.train_imsize, model.train_imsize), PIL.Image.BICUBIC), + ToTensor(), + Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + + device = config['device'] + dataset = get_dataset(path=config['dataset'], transforms=transforms) + + if config['overfit']: + dataset = Subset(dataset, [i*50 for i in range(0,1000)] ) + + dataloader = DataLoader( + dataset=dataset, + batch_size=config['batch_size'], + shuffle=True, + num_workers=config['num_workers'], + pin_memory=config['pin_memory']) + + if config['scale_lr']: + learning_rate = config['learning_rate']*config['batch_size']/256 + else: + learning_rate = config['learning_rate'] + + if not config['do_clip']: + config['clipping'] = None + + if config['use_fp16']: + model.half() + + model.to(device) # "memory_format=torch.channels_last" TBD + + optimizer = SGD_AGC( + # The optimizer needs all parameter names + # to filter them by hand later + named_params=model.named_parameters(), + lr=learning_rate, + momentum=config['momentum'], + clipping=config['clipping'], + weight_decay=config['weight_decay'], + nesterov=config['nesterov'] + ) + + # Find desired parameters and exclude them + # from weight decay and clipping + for group in optimizer.param_groups: + name = group['name'] + + if model.exclude_from_weight_decay(name): + group['weight_decay'] = 0 + + if model.exclude_from_clipping(name): + group['clipping'] = None + + criterion = nn.CrossEntropyLoss() + + runs_dir = Path('runs') + run_index = 0 + while (runs_dir / ('run' + str(run_index))).exists(): + run_index += 1 + runs_dir = runs_dir / ('run' + str(run_index)) + runs_dir.mkdir(exist_ok=False, parents=True) + checkpoints_dir = runs_dir / 'checkpoints' + checkpoints_dir.mkdir() + + writer = SummaryWriter(str(runs_dir)) + scaler = amp.GradScaler() + + for epoch in range(config['epochs']): + model.train() + running_loss = 0.0 + processed_imgs = 0 + correct_labels = 0 + epoch_time = time.time() + + for step, data in enumerate(dataloader): + inputs = data[0].half().to(device) if config['use_fp16'] else data[0].to(device) + targets = data[1].to(device) + + optimizer.zero_grad() + + with amp.autocast(enabled=config['amp']): + output = model(inputs) + loss = criterion(output, targets) + + # Gradient scaling + # https://www.youtube.com/watch?v=OqCrNkjN_PM + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + + running_loss += loss.item() + processed_imgs += targets.size(0) + _, predicted = torch.max(output, 1) + correct_labels += (predicted == targets).sum().item() + + epoch_padding = int(math.log10(config['epochs']) + 1) + batch_padding = int(math.log10(len(dataloader.dataset)) + 1) + print(f"\rEpoch {epoch+1:0{epoch_padding}d}/{config['epochs']}" + f"\tImg {processed_imgs:{batch_padding}d}/{len(dataloader.dataset)}" + f"\tLoss {running_loss / (step+1):6.4f}" + f"\tAcc {100.0*correct_labels/processed_imgs:5.3f}%\t", + sep=' ', end='', flush=True) + + elapsed = time.time() - epoch_time + print (f"({elapsed:.3f}s, {elapsed/len(dataloader):.3}s/step, {elapsed/len(dataset):.3}s/img)") + + global_step = epoch*len(dataloader) + step + writer.add_scalar('training/loss', running_loss/(step+1), global_step) + writer.add_scalar('training/accuracy', 100.0*correct_labels/processed_imgs, global_step) + + #if not config['overfit']: + if epoch % 10 == 0 and epoch != 0: + cp_path = checkpoints_dir / ("checkpoint_epoch" + str(epoch+1) + ".pth") + + torch.save({ + 'epoch': epoch, + 'model': model.state_dict(), + 'optim': optimizer.state_dict(), + 'loss': loss + }, str(cp_path)) + + print(f"Saved checkpoint to {str(cp_path)}") + +if __name__=='__main__': + parser = argparse.ArgumentParser(description='Train NFNets.') + parser.add_argument('--config', type=Path, help='Path to config.yaml', default='default_config.yaml') + parser.add_argument('--batch-size', type=int, help='Training batch size', default=None) + parser.add_argument('--overfit', const=True, default=False, nargs='?', help='Crop the dataset to the batch size and force model to (hopefully) overfit') + parser.add_argument('--variant', type=str, help='NFNet variant to train', default=None) + parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format', default=None) + args = parser.parse_args() + + if not args.config.exists(): + print(f"Config file \"{args.config}\" does not exist!\n") + exit() + + with args.config.open() as file: + config = yaml.safe_load(file) + + # Override config.yaml settings with command line settings + for arg in vars(args): + if getattr(args, arg) is not None and arg in config: + config[arg] = getattr(args, arg) + + config['pretrained'] = args.pretrained + + train(config=config) diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/README.md b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/README.md new file mode 100644 index 00000000..053de405 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/README.md @@ -0,0 +1,39 @@ +# Simple NFNet PyTorch Implementation + +This repository contains **a simple PyTorch code for Normalizer-Free Network (NFNet)**. + +- Andrew Brock et al, ["Characterizing signal propagation to close the performance gap in unnormalized ResNets,"](https://arxiv.org/abs/2102.06171) ICLR 2021. +- Andrew Brock et al, ["High-Performance Large-Scale Image Recognition Without Normalization"](https://arxiv.org/abs/2102.06171), Arxiv + +I implemented this code by referring [benjs's implementation code](https://github.com/benjs/nfnets_pytorch). +This code is for training NFNet for **CIFAR-10** dataset. + + +## Dependency + +- Python 3.7.1 +- PyTorch 1.7.1 +- torchvision 0.8.2 + + +## Training + +``` +# Training +CUDA_VISIBLE_DEVICES=0 python main.py +``` + +If you want to train the model using other hyperparameters, please check argparse in ```main.py```. + + +## TODO + +- [ ] Report Experimental results. (Accuracy for CIFAR-10) + + +## Acknowledgements + +I referred to the following implementation codes: + +- [Official codes](https://github.com/deepmind/deepmind-research/tree/master/nfnets) +- [benjs's PyTorch implementation codes](https://github.com/benjs/nfnets_pytorch) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/main.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/main.py new file mode 100644 index 00000000..28411c8e --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/main.py @@ -0,0 +1,98 @@ +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms +import argparse +from model import NFNet +from optim import SGD_AGC + + +# Hyper-parameters +parser = argparse.ArgumentParser(description='NFNet Training') +parser.add_argument('--variant', default='F0', type=str, choices=['F0', 'F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7'], help='NFNet variants') +parser.add_argument('--lr', default=0.1, type=float, help='the learning rate') +parser.add_argument('--num_epochs', default=200, type=int, help='the number of the epochs') +parser.add_argument('--batch_size', default=128, type=int, help='batch sizes') +args = parser.parse_args() + +# Device configuration +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +best_acc = 0 + +# Image preprocessing modules +transform_train = transforms.Compose([ + transforms.Pad(4), + transforms.RandomHorizontalFlip(), + transforms.RandomCrop(32), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) + +transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) + +# CIFAR-10 dataset +train_dataset = torchvision.datasets.CIFAR10(root='./data/', train=True, transform=transform_train, download=True) + +test_dataset = torchvision.datasets.CIFAR10(root='./data/', train=False, transform=transform_test) + +# Data loader +train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=2) + +test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=2) + +# Model +model = NFNet(num_classes=10, variant=args.variant, stochdepth_rate=0.25, alpha=0.2, se_ratio=0.5, activation='gelu').to(device) + +# Loss and optimizer +criterion = nn.CrossEntropyLoss() +optimizer = SGD_AGC(named_params=model.named_parameters(), lr=args.lr, momentum=0.9, clipping=0.1, weight_decay=5e-4, nesterov=True) +scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200) + + +# Train the model +def train(epoch): + model.train() + for i, (images, labels) in enumerate(train_loader): + images, labels = images.to(device), labels.to(device) + + # Forward pass + outputs = model(images) + loss = criterion(outputs, labels) + + # Backward and optimize + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if (i + 1) % 100 == 0: + print ("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}".format(epoch+1, args.num_epochs, i+1, len(train_loader), loss.item())) + + +# Test the model +def test(epoch): + global best_acc + model.eval() + with torch.no_grad(): + correct = 0 + total = 0 + for images, labels in test_loader: + images, labels = images.to(device), labels.to(device) + outputs = model(images) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + print('Epoch [{}/{}], Accuracy of the model on the test images: {} %'.format(epoch+1, args.num_epochs, 100 * correct / total)) + + acc = 100 * correct / total + if acc > best_acc: + # Save the model checkpoint + torch.save(model.state_dict(), 'nfnet.ckpt') + best_acc = acc + print('Best Accuracy : {} %'.format(best_acc)) + + +for epoch in range(args.num_epochs): + train(epoch) + test(epoch) + scheduler.step() \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/model.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/model.py new file mode 100644 index 00000000..152ab9ed --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/model.py @@ -0,0 +1,183 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import re + +nfnet_params = { + 'F0': {'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], 'drop_rate': 0.2}, + 'F1': {'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], 'drop_rate': 0.3}, + 'F2': {'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], 'drop_rate': 0.4}, + 'F3': {'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], 'drop_rate': 0.4}, + 'F4': {'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], 'drop_rate': 0.5}, + 'F5': {'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], 'drop_rate': 0.5}, + 'F6': {'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], 'drop_rate': 0.5}, + 'F7': {'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], 'drop_rate': 0.5}, +} + +class VPGELU(nn.Module): + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.gelu(input) * 1.7015043497085571 + +class VPReLU(nn.Module): + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.relu(input, inplace=True) * 1.7139588594436646 + +activations_dict = {'gelu': VPGELU(), 'relu': VPReLU()} + +class NFNet(nn.Module): + def __init__(self, num_classes: int, variant: str = 'F0', stochdepth_rate: float = None, alpha: float = 0.2, se_ratio: float = 0.5, activation: str = 'gelu'): + super(NFNet, self).__init__() + if variant not in nfnet_params: + raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") + block_params = nfnet_params[variant] + self.activation = activations_dict[activation] + self.drop_rate = block_params['drop_rate'] + self.num_classes = num_classes + self.stem = Stem(activation=activation) + num_blocks, index = sum(block_params['depth']), 0 + blocks = [] + expected_std = 1.0 + in_channels = block_params['width'][0] // 2 + block_args = zip(block_params['width'], block_params['depth'], [0.5] * 4, [128] * 4, [1, 2, 2, 2]) + for (block_width, stage_depth, expand_ratio, group_size, stride) in block_args: + for block_index in range(stage_depth): + beta = 1. / expected_std + block_sd_rate = stochdepth_rate * index / num_blocks if stochdepth_rate is not None else 0 + out_channels = block_width + blocks.append(NFBlock( + in_channels=in_channels, + out_channels=out_channels, + stride=stride if block_index == 0 else 1, + alpha=alpha, + beta=beta, + se_ratio=se_ratio, + group_size=group_size, + stochdepth_rate=block_sd_rate, + activation=activation + )) + in_channels = out_channels + index += 1 + expected_std = (expected_std ** 2 + alpha ** 2) ** 0.5 + self.body = nn.Sequential(*blocks) + final_conv_channels = 2 * in_channels + self.final_conv = WSConv2D(in_channels=out_channels, out_channels=final_conv_channels, kernel_size=1) + self.pool = nn.AvgPool2d(1) + if self.drop_rate > 0.: + self.dropout = nn.Dropout(self.drop_rate) + self.linear = nn.Linear(final_conv_channels, self.num_classes) + nn.init.normal_(self.linear.weight, 0, 0.01) + + def forward(self, x): + out = self.stem(x) + out = self.body(out) + out = self.activation(self.final_conv(out)) + pool = torch.mean(out, dim=(2, 3)) + if self.training and self.drop_rate > 0.: + pool = self.dropout(pool) + return self.linear(pool) + +class Stem(nn.Module): + def __init__(self, activation: str = 'gelu'): + super(Stem, self).__init__() + self.activation = activations_dict[activation] + self.conv0 = WSConv2D(in_channels=1, out_channels=16, kernel_size=3, stride=2) # For grayscale images + self.conv1 = WSConv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1) + self.conv2 = WSConv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1) + self.conv3 = WSConv2D(in_channels=64, out_channels=128, kernel_size=3, stride=2) + self.conv4 = WSConv2D(in_channels=128, out_channels=128, kernel_size=3, stride=2) # Additional layer + + def forward(self, x): + out = self.activation(self.conv0(x)) + out = self.activation(self.conv1(out)) + out = self.activation(self.conv2(out)) + out = self.activation(self.conv3(out)) + out = self.conv4(out) + return out + +class NFBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, expansion: float = 0.5, se_ratio: float = 0.5, stride: int = 1, beta: float = 1.0, alpha: float = 0.2, group_size: int = 1, stochdepth_rate: float = None, activation: str = 'gelu'): + super(NFBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.expansion = expansion + self.se_ratio = se_ratio + self.activation = activations_dict[activation] + self.beta, self.alpha = beta, alpha + self.group_size = group_size + width = int(self.out_channels * expansion) + self.groups = width // group_size + self.width = group_size * self.groups + self.stride = stride + self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) + self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) + self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) + self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) + self.use_projection = self.stride > 1 or self.in_channels != self.out_channels + if self.use_projection: + self.shortcut = nn.Sequential() + if self.stride > 1: + self.shortcut.add_module('avg_pool', nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels == 1536 else 1)) + self.shortcut.add_module('conv', WSConv2D(self.in_channels, self.out_channels, kernel_size=1)) + self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) + self.skip_gain = nn.Parameter(torch.zeros(())) + self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. + if self.use_stochdepth: + self.stoch_depth = StochDepth(stochdepth_rate) + + def forward(self, x): + out = self.activation(x) * self.beta + if self.use_projection: + shortcut = self.shortcut(x) + else: + shortcut = x + out = self.activation(self.conv0(out)) + out = self.activation(self.conv1(out)) + out = self.activation(self.conv1b(out)) + out = self.conv2(out) + out = (self.squeeze_excite(out) * 2) * out + if self.use_stochdepth: + out = self.stoch_depth(out) + return out * self.alpha * self.skip_gain + shortcut + +class WSConv2D(nn.Conv2d): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros'): + super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode) + nn.init.xavier_normal_(self.weight) + self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) + self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) + self.register_buffer('fan_in', torch.tensor(np.prod(self.weight.shape[1:]), requires_grad=False).type_as(self.weight), persistent=False) + + def standardized_weights(self): + mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) + var = torch.var(self.weight, axis=[1,2,3], keepdims=True) + scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) + return (self.weight - mean) * scale * self.gain + + def forward(self, x): + return F.conv2d(x, self.standardized_weights(), bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups) + +class SqueezeExcite(nn.Module): + def __init__(self, in_channels, out_channels, se_ratio, activation='relu'): + super(SqueezeExcite, self).__init__() + self.se_reduce = nn.Conv2d(in_channels, int(in_channels * se_ratio), 1) + self.se_expand = nn.Conv2d(int(in_channels * se_ratio), out_channels, 1) + self.activation = activations_dict[activation] + + def forward(self, x): + scale = F.adaptive_avg_pool2d(x, 1) + scale = self.se_reduce(scale) + scale = self.activation(scale) + scale = self.se_expand(scale) + scale = torch.sigmoid(scale) + return x * scale + +class StochDepth(nn.Module): + def __init__(self, p: float): + super(StochDepth, self).__init__() + self.prob = p + + def forward(self, x): + if self.training and torch.rand(1).item() < self.prob: + return x * 0 + return x \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py new file mode 100644 index 00000000..dc6ac6a0 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py @@ -0,0 +1,110 @@ +import torch +from torch.optim import Optimizer + + +# Compute norm depending on the shape of x +def unitwise_norm(x): + if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors + axis = 0 + keepdims = False + elif len(x.shape) in [2,3]: # Linear layers + # Original code: IO + # Pytorch: OI + axis = 1 + keepdims = True + elif len(x.shape) == 4: # Conv kernels + # Original code: HWIO + # Pytorch: OIHW + axis = [1, 2, 3] + keepdims = True + else: + raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') + + return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) + + +# This is a copy of the pytorch SGD implementation +# enhanced with gradient clipping +class SGD_AGC(Optimizer): + def __init__(self, named_params, lr:float, momentum=0, dampening=0, + weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): + if lr < 0.0: + raise ValueError("Invalid learning rate: {}".format(lr)) + if momentum < 0.0: + raise ValueError("Invalid momentum value: {}".format(momentum)) + if weight_decay < 0.0: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + + defaults = dict(lr=lr, momentum=momentum, dampening=dampening, + weight_decay=weight_decay, nesterov=nesterov, + # Extra defaults + clipping=clipping, + eps=eps + ) + + if nesterov and (momentum <= 0 or dampening != 0): + raise ValueError("Nesterov momentum requires a momentum and zero dampening") + + # Put params in list so each one gets its own group + params = [] + for name, param in named_params: + params.append({'params': param, 'name': name}) + + super(SGD_AGC, self).__init__(params, defaults) + + def __setstate__(self, state): + super(SGD_AGC, self).__setstate__(state) + for group in self.param_groups: + group.setdefault('nesterov', False) + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + weight_decay = group['weight_decay'] + momentum = group['momentum'] + dampening = group['dampening'] + nesterov = group['nesterov'] + + # Extra values for clipping + clipping = group['clipping'] + eps = group['eps'] + + for p in group['params']: + if p.grad is None: + continue + d_p = p.grad + + # ========================= + # Gradient clipping + if clipping is not None: + param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) + grad_norm = unitwise_norm(d_p) + max_norm = param_norm * group['clipping'] + + trigger_mask = grad_norm > max_norm + clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) + d_p = torch.where(trigger_mask, clipped_grad, d_p) + # ========================= + + if weight_decay != 0: + d_p = d_p.add(p, alpha=weight_decay) + if momentum != 0: + param_state = self.state[p] + if 'momentum_buffer' not in param_state: + buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() + else: + buf = param_state['momentum_buffer'] + buf.mul_(momentum).add_(d_p, alpha=1 - dampening) + if nesterov: + d_p = d_p.add(buf, alpha=momentum) + else: + d_p = buf + + p.add_(d_p, alpha=-group['lr']) + + return loss \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py new file mode 100644 index 00000000..4d72f023 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py @@ -0,0 +1,109 @@ +import torch +from torch.optim import Optimizer + +# Compute norm depending on the shape of x +def unitwise_norm(x): + if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors + axis = 0 + keepdims = False + elif len(x.shape) in [2,3]: # Linear layers + # Original code: IO + # Pytorch: OI + axis = 1 + keepdims = True + elif len(x.shape) == 4: # Conv kernels + # Original code: HWIO + # Pytorch: OIHW + axis = [1, 2, 3] + keepdims = True + else: + raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') + + return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) + + +# This is a copy of the pytorch SGD implementation +# enhanced with gradient clipping +class SGD_AGC(Optimizer): + def __init__(self, named_params, lr:float, momentum=0, dampening=0, + weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): + if lr < 0.0: + raise ValueError("Invalid learning rate: {}".format(lr)) + if momentum < 0.0: + raise ValueError("Invalid momentum value: {}".format(momentum)) + if weight_decay < 0.0: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + + defaults = dict(lr=lr, momentum=momentum, dampening=dampening, + weight_decay=weight_decay, nesterov=nesterov, + # Extra defaults + clipping=clipping, + eps=eps + ) + + if nesterov and (momentum <= 0 or dampening != 0): + raise ValueError("Nesterov momentum requires a momentum and zero dampening") + + # Put params in list so each one gets its own group + params = [] + for name, param in named_params: + params.append({'params': param, 'name': name}) + + super(SGD_AGC, self).__init__(params, defaults) + + def __setstate__(self, state): + super(SGD_AGC, self).__setstate__(state) + for group in self.param_groups: + group.setdefault('nesterov', False) + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + weight_decay = group['weight_decay'] + momentum = group['momentum'] + dampening = group['dampening'] + nesterov = group['nesterov'] + + # Extra values for clipping + clipping = group['clipping'] + eps = group['eps'] + + for p in group['params']: + if p.grad is None: + continue + d_p = p.grad + + # ========================= + # Gradient clipping + if clipping is not None: + param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) + grad_norm = unitwise_norm(d_p) + max_norm = param_norm * group['clipping'] + + trigger_mask = grad_norm > max_norm + clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) + d_p = torch.where(trigger_mask, clipped_grad, d_p) + # ========================= + + if weight_decay != 0: + d_p = d_p.add(p, alpha=weight_decay) + if momentum != 0: + param_state = self.state[p] + if 'momentum_buffer' not in param_state: + buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() + else: + buf = param_state['momentum_buffer'] + buf.mul_(momentum).add_(d_p, alpha=1 - dampening) + if nesterov: + d_p = d_p.add(buf, alpha=momentum) + else: + d_p = buf + + p.add_(d_p, alpha=-group['lr']) + + return loss \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/optimizer/__init__.py b/ctlearn/core/pytorch/nets/optimizer/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/nets/optimizer/optimizer.py b/ctlearn/core/pytorch/nets/optimizer/optimizer.py new file mode 100644 index 00000000..5a403374 --- /dev/null +++ b/ctlearn/core/pytorch/nets/optimizer/optimizer.py @@ -0,0 +1,5 @@ +import math + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index abb635ef..b4a766b5 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -19,8 +19,7 @@ from dl1_data_handler.reader import DLDataReader from ctlearn.core.data_loader.loader import DLDataLoader from ctlearn.core.model import CTLearnModel -from ctlearn.tools.train_model import DLFrameWork - + class TrainCTLearnModel(Tool): """ Base class for training a ``CTLearnModel`` on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. @@ -205,8 +204,6 @@ class TrainCTLearnModel(Tool): overwrite = Bool(help="Overwrite output dir if it exists").tag(config=True) aliases = { - # **DLFrameWork.aliases, - # "framework": "DLFrameWork.framework_type", "framework": "TrainCTLearnModel.framework_type", "signal": "TrainCTLearnModel.input_dir_signal", "background": "TrainCTLearnModel.input_dir_background", diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml new file mode 100644 index 00000000..fb5a5308 --- /dev/null +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -0,0 +1,193 @@ +# Data: + +# mapping_settings: +# camera_types: +# - LSTCam +# # - LSTSiPMCam +# mapping_method: +# CHEC: oversampling +# DigiCam: bilinear_interpolation +# FlashCam: bilinear_interpolation +# LSTCam: bilinear_interpolation +# LSTSiPMCam: bilinear_interpolation +# MAGICCam: bilinear_interpolation +# NectarCam: bilinear_interpolation +# SCTCam: oversampling +# padding: +# CHEC: 0 +# DigiCam: 2 +# FlashCam: 2 +# LSTCam: 2 +# LSTSiPMCam: 2 +# MAGICCam: 2 +# NectarCam: 2 +# SCTCam: 0 +# mode: mono +# parameter_selection: +# - col_name: hillas_intensity +# min_value: 50.0 +# selected_telescope_types: +# - LST_LST_LSTCam + +# shuffle: False +# transforms: [] + + +data: + + train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle + validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + train_gamma: ./data/gamma_955000_train.pickle + validation_gamma: ./data/gamma_106141_validation.pickle + + test_gamma: ./data/gamma_1805522_test_gamma.pickle + test_proton: ./data/proton_130811_test_proton.pickle + test_electron: None + test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle + + test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + observation: ./run_2931.dl1.pickle + # Important: This is only for testing purpose. Set always to 0 + # when you are training, validating or estimating the dl2 files + training_reduce_factor: 0 #64 #4 + validation_reduce_factor: 0 #16 #8 + validation_test_reduce_factor: 0 #16 #8 + + # Check points + type_checkpoint: ./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth + energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth + direction_checkpoint: ./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + +run_details: + + mode: "train" # The option are: "train", "results", "observation" and "validate" + task: "type" # The option are: "all", "energy" "type" and "direction" + test_type: "gamma" # The option are: "gamma" "proton" or "electron" + experiment_number: 14 # The experiment number. The experiment folder is saved into the "run" folder. + + +cut-off: + + leakage_intensity: 0.2 # bigger to this value, the event is removed + intensity: 50 # below to this value, the event is removed + +model: + + model_type: + model_name: "DoubleBBEfficientNet" + parameters: + model_variant: "efficientnet-b3" + task: 'type' + num_outputs: 2 + device_str: "cuda" + energy_bins: None + + # model_type: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'type' + # num_inputs: 1 + # num_outputs: 2 + # num_blocks: [2, 3, 3, 3] + # dropout: 0.1 + # use_bn: False + + model_energy: + model_name: "ThinResNet_DBB" + parameters: + task: 'energy' + num_inputs: 1 + num_outputs: 1 + num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] + dropout: 0.1 + use_bn: False + + + model_direction: + model_name: "ThinResNet_DBB" + parameters: + task: 'direction' + num_inputs: 1 + num_outputs: 3 + num_blocks: [3, 4, 6, 3] + dropout: 0.1 + use_bn: False + + +# Hyper-parameters +hyp: + + epochs: 200 + batches: 128 #64 + dynamic_batches: True + optimizer: Adamw + momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 + weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 + learning_rate: 1e-6 #1e-5 #Efficient-b3 1e-5 + lrf: 0.1 + start_epoch: 0 + steps_epoch: 100 # Computed online. Must be removed + l2_lambda: 1e-5 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) + adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 + gradient_clip_val: 2.0 # Avoid gradient explosion + + save_k: 200 # Save as maximum k checkpoints. + +augmentation: + # probabilities for augmentation range = [0, 1.0] + # prob = 0.0 -> Always apply the augmentation + # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. + # Note: mask augmentation is always on even with flag use_augmentation = True + # To disable it, just set to 2.5 for example. + + use_augmentation: True # This apply only on training mode. + aug_prob: 0.5 # Probability of use Augmentation + rot_prob: 0.5 # Rotation probability + trans_prob: 0.5 # Translation probability + flip_hor_prob: 0.5 # Horizontal Flip probability + flip_ver_prob: 0.5 # Vertical Flip probability + mask_prob: 0.5 # Apply mask probability + mask_dvr_prob: 0.5 # Apply dvr mask probability + noise_prob: 0.5 # No implemented yet. + max_rot: 5 # Maximum rotation in augmentation + max_trans: 10 # Maximum translation in augmentation + +normalization: + + # Normalization: Im' = (Im-mu)/sigma + use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) + use_clean_dvr: False + type_mu: 0.0 + type_sigma: 1000.0 + + dir_mu: 0.0 + dir_sigma: 1000.0 + + energy_mu: 0.0 + energy_sigma: 1000.0 + +dataset: + num_workers: 1 # + pin_memory: True + persistent_workers: True # + +# Hardware Architecture and precision +arch: + # device: 'mps' # Apple Mx + device: 'cuda' + precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_energy: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) + # devices: [0,1] # [0,1] For multiple GPUs + devices: [0,1] + # Note: Check the documentation for more information. + strategy: 'deepspeed_stage_2' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... + +Notes: + Note_1: Training with augmentation dvr using 1-3 dilatations + Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 38ff633b..5361ac9f 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -22,7 +22,10 @@ raise ImportError("pytorch_lightning is not installed in your environment!") from ctlearn.tools.train.base_train_model import TrainCTLearnModel -from ctlearn.core.ctlearn_enum import Task +from ctlearn.core.ctlearn_enum import Task, Mode +from .utils import str_list_to_enum_list, sanity_check, read_configuration, create_experiment_folder, expected_structure + +from ctlearn.core.pytorch.net_utils import create_model, ModelHelper # from ctlearn.tools.train_model import class TrainPyTorchModel(TrainCTLearnModel): @@ -75,15 +78,20 @@ class TrainPyTorchModel(TrainCTLearnModel): --reco skydirection \\ """ - pytorch_int = Int( + config_file = Path( + exits=True, + default_value=None, allow_none=False, - default_value=42 + directory_ok=True, + file_ok=True, + help="Configuration file.", ).tag(config=True) + + aliases = { **TrainCTLearnModel.aliases, - "pytorch_param": "TrainPyTorchModel.pytorch_int", - + "config_file": "TrainPyTorchModel.config_file", } def __init__(self, **kwargs): @@ -95,25 +103,76 @@ def __init__(self, **kwargs): def setup(self): print("Pytorch setup") super().setup() - print("Pytorch setup :)") - print(f"DEBUG - framework_type raw: {self.reco_tasks} ({type(self.reco_tasks)})") - task_str = self.reco_tasks - print(self.reco_tasks) - print(type(self.reco_tasks)) - self.task = None - try: - print - self.task = Task[task_str[0]] - except KeyError: - print(f"'{task_str}' is not a valid enum type.") + + # Create tasks Enum List + self.tasks = str_list_to_enum_list(self.reco_tasks) + + for task_ in self.tasks: + print("Task:", task_.name) + + print(self.config_file) + self.parameters = read_configuration(self.config_file) + sanity_check(self.parameters, expected_structure) + + self.experiment_number = self.parameters["run_details"]["experiment_number"] + self.save_k = self.parameters["hyp"]["save_k"] + self.device_str = self.parameters["arch"]["device"] + self.device = torch.device(self.device_str) + + self.batch_size = self.parameters["hyp"]["batches"] + self.pin_memory = self.parameters["dataset"]["pin_memory"] + + self.num_workers = self.parameters["dataset"]["num_workers"] + self.persistent_workers = self.parameters["dataset"]["persistent_workers"] def start(self): print("Pytorch start") super().start() print("Pytorch start") - - + for task in self.tasks: + + # Create the experiment folder + save_folder = create_experiment_folder(f"run_{task.name}_training_", next_number=self.experiment_number) + + # ------------------------------------------------------------------------------ + # Select the model and precision + # ------------------------------------------------------------------------------ + if task == Task.direction: + precision = self.parameters["arch"]["precision_direction"] + model_net = create_model(self.parameters["model"]["model_direction"]) + + elif task == Task.type: + precision = self.parameters["arch"]["precision_type"] + model_net = create_model(self.parameters["model"]["model_type"]) + + elif task == Task.energy: + precision = self.parameters["arch"]["precision_energy"] + model_net = create_model(self.parameters["model"]["model_energy"]) + + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) + + # ------------------------------------------------------------------------------ + # Load Checkpoints + # ------------------------------------------------------------------------------ + if task == Task.type: + check_point_path = self.parameters["data"]["type_checkpoint"] + + if task == Task.energy: + check_point_path = self.parameters["data"]["energy_checkpoint"] + + if task == Task.direction: + check_point_path = self.parameters["data"]["direction_checkpoint"] + + + # Load the checkpoint + model_net = ModelHelper.loadModel( + model_net, "", check_point_path, Mode.train, device_str=self.device_str + ) + def finish(self): super().finish() print("Pytorch finish") diff --git a/ctlearn/tools/train/pytorch/utils.py b/ctlearn/tools/train/pytorch/utils.py new file mode 100644 index 00000000..c13dc004 --- /dev/null +++ b/ctlearn/tools/train/pytorch/utils.py @@ -0,0 +1,173 @@ +from ctlearn.core.ctlearn_enum import Task +from typing import List +import os +import yaml +import yaml + +expected_structure = { + "data": { + "train_gamma_proton": None, + "validation_gamma_proton": None, + "train_gamma": None, + "validation_gamma": None, + "test_gamma": None, + "test_proton": None, + "test_electron": None, + "test_validation_gamma": None, + "test_validation_gamma_proton": None + }, + "run_details": { + "mode": None, + "task": None, + "test_type": None, + "experiment_number": None + }, + "cut-off": { + "leakage_intensity": None, + "intensity": None + }, + "model": { + "model_type": { + "model_name": None, + "parameters": None, + }, + "model_energy": { + "model_name": None, + "parameters": None, + }, + "model_direction": { + "model_name": None, + "parameters": None, + } + }, + "hyp": { + "epochs": None, + "batches": None, + "dynamic_batches": None, + "optimizer": None, + "momentum": None, + "weight_decay": None, + "learning_rate": None, + "lrf": None, + "start_epoch": None, + "steps_epoch": None, + "l2_lambda": None, + "adam_epsilon": None, + "gradient_clip_val": None, + "save_k": None + }, + "augmentation": { + "use_augmentation": None, + "aug_prob": None, + "rot_prob": None, + "trans_prob": None, + "flip_hor_prob": None, + "flip_ver_prob": None, + "mask_prob": None, + "noise_prob": None, + "max_rot": None, + "max_trans": None + }, + "normalization": { + "use_clean": None, + "type_mu": None, + "type_sigma": None, + "dir_mu": None, + "dir_sigma": None, + "energy_mu": None, + "energy_sigma": None + }, + "dataset": { + "num_workers": None, + "pin_memory": None, + "persistent_workers": None + }, + "arch": { + "device": None, + "precision_type": None, + "precision_energy": None, + "precision_direction": None, + "devices": None, + "strategy": None + }, +} +#------------------------------------------------------------------------------------------------------------------- +# Sanity check function +def sanity_check(config, expected_structure): + """ + Recursively checks if all required keys in the expected_structure exist in the config data. + Raises a KeyError if a key is missing. + """ + for key, substructure in expected_structure.items(): + if key not in config: + raise KeyError(f"Missing key: {key}") + + # If the substructure is a dictionary, recursively check the subkeys + if isinstance(substructure, dict): + if not isinstance(config[key], dict): + raise KeyError(f"Expected a dictionary for key: {key}, but got: {type(config[key])}") + sanity_check(config[key], substructure) + +#------------------------------------------------------------------------------------------------------------------- +def read_configuration(config_file_str="./config/training_config.yml"): + parameters = None + + if os.path.exists(config_file_str): + with open(config_file_str, "r") as config_file: + parameters = yaml.safe_load(config_file) + + else: + print("Configuration file not found.") + + return parameters +#------------------------------------------------------------------------------------------------------------------- +def create_experiment_folder(prefix="run_", next_number=None): + + + """ + Create the next folder within the specified directory with a given prefix. + If next_number is not specified, automatically find the next available number. + + :param run_directory: The directory where folders are managed. + :param prefix: Prefix used for folders. + :param next_number: Optional. Specify the number to be used for the new folder. + """ + run_directory="./run" + + # Ensure the 'run' directory exists + if not os.path.exists(run_directory): + os.makedirs(run_directory) + print(f"Directory '{run_directory}' created.") + + if next_number is None: + # List all subdirectories in the 'run' directory + folders = [f for f in os.listdir(run_directory) if os.path.isdir(os.path.join(run_directory, f))] + # Filter folders that match the prefix pattern and end with a digit + matching_folders = [f for f in folders if f.startswith(prefix) and f[len(prefix):].isdigit()] + + # Find the highest number and calculate the next one + if matching_folders: + highest_number = max(int(folder[len(prefix):]) for folder in matching_folders) + next_number = highest_number + 1 + else: + next_number = 0 + + # Create the new folder with the next number + new_folder_name = f"{prefix}{next_number}" + new_folder_path = os.path.join(run_directory, new_folder_name) + new_folder_path+="/" + if not os.path.exists(new_folder_path): + os.makedirs(new_folder_path) + print(f"New folder created: {new_folder_path}") + return new_folder_path +#------------------------------------------------------------------------------------------------------------------- +def str_list_to_enum_list(reco_tasks:List)->List[Task]: + + tasks = [] + for task_str in reco_tasks: + try: + tasks.append(Task[task_str]) + except KeyError: + print(f"'{task_str}' is not a valid enum type.") + return tasks +#------------------------------------------------------------------------------------------------------------------- \ No newline at end of file diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 4200c7fb..35fb60b4 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -147,3 +147,6 @@ def get_framework(cls, framework_type: FrameworkType): # Parse all CLI args with the selected framework subclass tool.framework_instance.initialize(argv=sys.argv[1:]) tool.run() + +# Example: +# python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite \ No newline at end of file From e8615f5572ebbe07b593311a68fd29296579bec6 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 28 Apr 2025 08:44:42 +0000 Subject: [PATCH 010/119] fixed unit test --- ctlearn/core/tests/test_loader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ctlearn/core/tests/test_loader.py b/ctlearn/core/tests/test_loader.py index 08a11d79..3fc662eb 100644 --- a/ctlearn/core/tests/test_loader.py +++ b/ctlearn/core/tests/test_loader.py @@ -18,7 +18,7 @@ def test_data_loader(dl1_gamma_file): # Create an image reader dl1_reader = DLImageReader(input_url_signal=[dl1_gamma_file], config=config) # Create a data loader - dl1_loader = DLDataLoader( + dl1_loader = DLDataLoader.create( framework = "keras", DLDataReader=dl1_reader, indices=[0], From 6caef05d1b9599763f560fa581ac173b5a2c3c63 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 28 Apr 2025 09:47:51 +0000 Subject: [PATCH 011/119] formated code (round 1) and fixed a function that does not exist in pytorch legacy code --- ctlearn/core/pytorch/nets/block/cnn_blocks.py | 1 - .../nets/models/legacy/nfnets__/pretrained.py | 4 +- .../nfnets_pytorch/nfnets/pretrained.py | 5 +- ctlearn/tools/train/base_train_model.py | 67 ++++++++++------- .../train/pytorch/train_pytorch_model.py | 63 +++++++++------- ctlearn/tools/train/pytorch/utils.py | 75 +++++++++++-------- ctlearn/tools/train_model.py | 36 +++++++-- 7 files changed, 153 insertions(+), 98 deletions(-) diff --git a/ctlearn/core/pytorch/nets/block/cnn_blocks.py b/ctlearn/core/pytorch/nets/block/cnn_blocks.py index 38c73dd2..b54818b5 100644 --- a/ctlearn/core/pytorch/nets/block/cnn_blocks.py +++ b/ctlearn/core/pytorch/nets/block/cnn_blocks.py @@ -41,7 +41,6 @@ def forward(self, x): var = torch.sqrt(beta / (v * (alpha - 1))) return mu, var - class ResBlock(nn.Module): def __init__(self, n_chans_in, n_chans_out, kernel_size=3, conv_drop_pro=0.2): diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py index cc8ba064..f26cacd3 100644 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py @@ -87,8 +87,8 @@ def pretrained_nfnet(path, stochdepth_rate:float=0.5, alpha:float=0.2, activatio if not args.pretrained.exists(): raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") - model = from_pretrained_haiku(args.pretrained) - + # model = from_pretrained_haiku(args.pretrained) + model = pretrained_nfnet(args.pretrained) torch.save({ 'model': model.state_dict() }, str(args.pretrained.with_suffix('.pth'))) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py index 9f7909d8..5d285aa3 100644 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py +++ b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py @@ -87,8 +87,9 @@ def pretrained_nfnet(path, stochdepth_rate:float=0.5, alpha:float=0.2, activatio if not args.pretrained.exists(): raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") - model = from_pretrained_haiku(args.pretrained) - + # model = from_pretrained_haiku(args.pretrained) + model = pretrained_nfnet(args.pretrained) + torch.save({ 'model': model.state_dict() }, str(args.pretrained.with_suffix('.pth'))) \ No newline at end of file diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index b4a766b5..1762619d 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -1,5 +1,3 @@ - - import numpy as np import shutil from ctapipe.core import Tool @@ -19,7 +17,8 @@ from dl1_data_handler.reader import DLDataReader from ctlearn.core.data_loader.loader import DLDataLoader from ctlearn.core.model import CTLearnModel - + + class TrainCTLearnModel(Tool): """ Base class for training a ``CTLearnModel`` on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. @@ -70,7 +69,7 @@ class TrainCTLearnModel(Tool): Set whether to save model in an ONNX file. overwrite : Bool Overwrite output dir if it exists. - + Methods ------- setup() @@ -78,12 +77,13 @@ class TrainCTLearnModel(Tool): finish() Save the trained model in the output directory in ONNX if selected. """ + name = "ctlearn-train-model-base" framework_type = CaselessStrEnum( ["pytorch", "keras"], default_value="keras", - help="Framework to use pytorch or keras" + help="Framework to use pytorch or keras", ).tag(config=True) input_dir_signal = Path( @@ -115,9 +115,9 @@ class TrainCTLearnModel(Tool): help="List of specific file pattern for matching files in ``input_dir_background``", ).tag(config=True) - dl1dh_reader_type = ComponentName( - DLDataReader, default_value="DLImageReader" - ).tag(config=True) + dl1dh_reader_type = ComponentName(DLDataReader, default_value="DLImageReader").tag( + config=True + ) stack_telescope_images = Bool( default_value=False, @@ -148,7 +148,7 @@ class TrainCTLearnModel(Tool): reco_tasks = List( trait=CaselessStrEnum(["type", "energy", "cameradirection", "skydirection"]), - allow_none=False, + allow_none=False, default_value=None, help=( "List of reconstruction tasks to perform. " @@ -156,7 +156,7 @@ class TrainCTLearnModel(Tool): "'energy': regression of the primary particle energy " "'cameradirection': regression of the primary particle arrival direction in camera coordinates " "'skydirection': regression of the primary particle arrival direction in sky coordinates" - ) + ), ).tag(config=True) n_epochs = Int( @@ -179,11 +179,15 @@ class TrainCTLearnModel(Tool): ).tag(config=True) optimizer = Dict( - default_value={"name": "Adam", "base_learning_rate": 0.0001, "adam_epsilon": 1.0e-8}, - help=( - "Optimizer to use for training. " - "E.g. {'name': 'Adam', 'base_learning_rate': 0.0001, 'adam_epsilon': 1.0e-8}. " - ) + default_value={ + "name": "Adam", + "base_learning_rate": 0.0001, + "adam_epsilon": 1.0e-8, + }, + help=( + "Optimizer to use for training. " + "E.g. {'name': 'Adam', 'base_learning_rate': 0.0001, 'adam_epsilon': 1.0e-8}. " + ), ).tag(config=True) random_seed = Int( @@ -192,7 +196,7 @@ class TrainCTLearnModel(Tool): "Random seed for shuffling the data " "before the training/validation split " "and after the end of an epoch." - ) + ), ).tag(config=True) save_onnx = Bool( @@ -219,7 +223,7 @@ class TrainCTLearnModel(Tool): "Overwrite existing files", ), } - + classes = ( [ CTLearnModel, @@ -228,6 +232,7 @@ class TrainCTLearnModel(Tool): + classes_with_traits(CTLearnModel) + classes_with_traits(DLDataReader) ) + def __init__(self, **kwargs): super().__init__(**kwargs) print("Common Init") @@ -250,20 +255,21 @@ def setup(self): # self.strategy = tf.distribute.MirroredStrategy() # atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore # self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) - + # print(self.DLFrameWork.framework_type) # Get signal input files self.input_url_signal = [] for signal_pattern in self.file_pattern_signal: self.input_url_signal.extend(self.input_dir_signal.glob(signal_pattern)) - + # Get bkg input files self.input_url_background = [] if self.input_dir_background is not None: for background_pattern in self.file_pattern_background: - self.input_url_background.extend(self.input_dir_background.glob(background_pattern)) + self.input_url_background.extend( + self.input_dir_background.glob(background_pattern) + ) - print("DEBUG 1") # Set up the data reader self.log.info("Loading data:") @@ -284,8 +290,12 @@ def setup(self): print("DEBUG 3") self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) if "type" in self.reco_tasks: - self.log.info("Number of signal events: %d", self.dl1dh_reader.n_signal_events) - self.log.info("Number of background events: %d", self.dl1dh_reader.n_bkg_events) + self.log.info( + "Number of signal events: %d", self.dl1dh_reader.n_signal_events + ) + self.log.info( + "Number of background events: %d", self.dl1dh_reader.n_bkg_events + ) # Check if the number of events is enough to form a batch if self.dl1dh_reader._get_n_events() < self.batch_size: raise ValueError( @@ -303,7 +313,10 @@ def setup(self): f"Cannot stack telescope images in mono mode. Use stereo mode for stacking." ) # Ckeck if only one telescope type is selected for stacking telescope images - if self.stack_telescope_images and len(list(self.dl1dh_reader.selected_telescopes)) > 1: + if ( + self.stack_telescope_images + and len(list(self.dl1dh_reader.selected_telescopes)) > 1 + ): raise ToolConfigurationError( f"Cannot stack telescope images from multiple telescope types. Use only one telescope type." ) @@ -318,14 +331,16 @@ def setup(self): # Shuffle the indices before the training/validation split np.random.seed(self.random_seed) np.random.shuffle(indices) - n_validation_examples = int(self.validation_split * self.dl1dh_reader._get_n_events()) + n_validation_examples = int( + self.validation_split * self.dl1dh_reader._get_n_events() + ) training_indices = indices[n_validation_examples:] validation_indices = indices[:n_validation_examples] # Set self.strategy.num_replicas_in_sync to 1 in case that does not exist (Pytorch) if not hasattr(self, "strategy"): self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() - print("num_replicas_in_sync:",self.strategy.num_replicas_in_sync) + print("num_replicas_in_sync:", self.strategy.num_replicas_in_sync) print("BASE TRAIN FRAMEWORK", self.framework_type) print("DEBUG 4") diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 5361ac9f..867a3235 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -23,11 +23,18 @@ from ctlearn.tools.train.base_train_model import TrainCTLearnModel from ctlearn.core.ctlearn_enum import Task, Mode -from .utils import str_list_to_enum_list, sanity_check, read_configuration, create_experiment_folder, expected_structure +from .utils import ( + str_list_to_enum_list, + sanity_check, + read_configuration, + create_experiment_folder, + expected_structure, +) from ctlearn.core.pytorch.net_utils import create_model, ModelHelper -# from ctlearn.tools.train_model import + +# from ctlearn.tools.train_model import class TrainPyTorchModel(TrainCTLearnModel): """ Tool to train a ``~ctlearn.core.model.CTLearnModel`` on R1/DL1a data using PyTorch. @@ -87,27 +94,24 @@ class TrainPyTorchModel(TrainCTLearnModel): help="Configuration file.", ).tag(config=True) - - aliases = { - **TrainCTLearnModel.aliases, - "config_file": "TrainPyTorchModel.config_file", - } + **TrainCTLearnModel.aliases, + "config_file": "TrainPyTorchModel.config_file", + } def __init__(self, **kwargs): - print("Pytorch init") + print("Pytorch init") super().__init__(**kwargs) - print("CONFIG VALUES PYTORCH:", self.config) - + print("CONFIG VALUES PYTORCH:", self.config) def setup(self): - print("Pytorch setup") + print("Pytorch setup") super().setup() - # Create tasks Enum List + # Create tasks Enum List self.tasks = str_list_to_enum_list(self.reco_tasks) - - for task_ in self.tasks: + + for task_ in self.tasks: print("Task:", task_.name) print(self.config_file) @@ -121,32 +125,34 @@ def setup(self): self.batch_size = self.parameters["hyp"]["batches"] self.pin_memory = self.parameters["dataset"]["pin_memory"] - + self.num_workers = self.parameters["dataset"]["num_workers"] self.persistent_workers = self.parameters["dataset"]["persistent_workers"] def start(self): - print("Pytorch start") + print("Pytorch start") super().start() - print("Pytorch start") + print("Pytorch start") for task in self.tasks: # Create the experiment folder - save_folder = create_experiment_folder(f"run_{task.name}_training_", next_number=self.experiment_number) + save_folder = create_experiment_folder( + f"run_{task.name}_training_", next_number=self.experiment_number + ) # ------------------------------------------------------------------------------ # Select the model and precision # ------------------------------------------------------------------------------ - if task == Task.direction: + if task == Task.direction: precision = self.parameters["arch"]["precision_direction"] model_net = create_model(self.parameters["model"]["model_direction"]) - elif task == Task.type: + elif task == Task.type: precision = self.parameters["arch"]["precision_type"] model_net = create_model(self.parameters["model"]["model_type"]) - elif task == Task.energy: + elif task == Task.energy: precision = self.parameters["arch"]["precision_energy"] model_net = create_model(self.parameters["model"]["model_energy"]) @@ -154,28 +160,27 @@ def start(self): raise ValueError( f"task:{task.name} is not supported. Task must be type, direction or energy" ) - + # ------------------------------------------------------------------------------ # Load Checkpoints # ------------------------------------------------------------------------------ - if task == Task.type: + if task == Task.type: check_point_path = self.parameters["data"]["type_checkpoint"] - if task == Task.energy: + if task == Task.energy: check_point_path = self.parameters["data"]["energy_checkpoint"] - if task == Task.direction: + if task == Task.direction: check_point_path = self.parameters["data"]["direction_checkpoint"] - # Load the checkpoint model_net = ModelHelper.loadModel( model_net, "", check_point_path, Mode.train, device_str=self.device_str ) - + def finish(self): super().finish() - print("Pytorch finish") + print("Pytorch finish") def show_version(self): - print("Pytorch 2.3") \ No newline at end of file + print("Pytorch 2.3") diff --git a/ctlearn/tools/train/pytorch/utils.py b/ctlearn/tools/train/pytorch/utils.py index c13dc004..f9e40fbf 100644 --- a/ctlearn/tools/train/pytorch/utils.py +++ b/ctlearn/tools/train/pytorch/utils.py @@ -2,7 +2,6 @@ from typing import List import os import yaml -import yaml expected_structure = { "data": { @@ -14,18 +13,15 @@ "test_proton": None, "test_electron": None, "test_validation_gamma": None, - "test_validation_gamma_proton": None + "test_validation_gamma_proton": None, }, "run_details": { "mode": None, "task": None, "test_type": None, - "experiment_number": None - }, - "cut-off": { - "leakage_intensity": None, - "intensity": None + "experiment_number": None, }, + "cut-off": {"leakage_intensity": None, "intensity": None}, "model": { "model_type": { "model_name": None, @@ -38,7 +34,7 @@ "model_direction": { "model_name": None, "parameters": None, - } + }, }, "hyp": { "epochs": None, @@ -54,7 +50,7 @@ "l2_lambda": None, "adam_epsilon": None, "gradient_clip_val": None, - "save_k": None + "save_k": None, }, "augmentation": { "use_augmentation": None, @@ -66,7 +62,7 @@ "mask_prob": None, "noise_prob": None, "max_rot": None, - "max_trans": None + "max_trans": None, }, "normalization": { "use_clean": None, @@ -75,23 +71,21 @@ "dir_mu": None, "dir_sigma": None, "energy_mu": None, - "energy_sigma": None - }, - "dataset": { - "num_workers": None, - "pin_memory": None, - "persistent_workers": None + "energy_sigma": None, }, + "dataset": {"num_workers": None, "pin_memory": None, "persistent_workers": None}, "arch": { "device": None, "precision_type": None, "precision_energy": None, "precision_direction": None, "devices": None, - "strategy": None + "strategy": None, }, } -#------------------------------------------------------------------------------------------------------------------- + + +# ------------------------------------------------------------------------------------------------------------------- # Sanity check function def sanity_check(config, expected_structure): """ @@ -101,14 +95,17 @@ def sanity_check(config, expected_structure): for key, substructure in expected_structure.items(): if key not in config: raise KeyError(f"Missing key: {key}") - + # If the substructure is a dictionary, recursively check the subkeys if isinstance(substructure, dict): if not isinstance(config[key], dict): - raise KeyError(f"Expected a dictionary for key: {key}, but got: {type(config[key])}") + raise KeyError( + f"Expected a dictionary for key: {key}, but got: {type(config[key])}" + ) sanity_check(config[key], substructure) -#------------------------------------------------------------------------------------------------------------------- + +# ------------------------------------------------------------------------------------------------------------------- def read_configuration(config_file_str="./config/training_config.yml"): parameters = None @@ -120,10 +117,10 @@ def read_configuration(config_file_str="./config/training_config.yml"): print("Configuration file not found.") return parameters -#------------------------------------------------------------------------------------------------------------------- -def create_experiment_folder(prefix="run_", next_number=None): - + +# ------------------------------------------------------------------------------------------------------------------- +def create_experiment_folder(prefix="run_", next_number=None): """ Create the next folder within the specified directory with a given prefix. If next_number is not specified, automatically find the next available number. @@ -132,7 +129,7 @@ def create_experiment_folder(prefix="run_", next_number=None): :param prefix: Prefix used for folders. :param next_number: Optional. Specify the number to be used for the new folder. """ - run_directory="./run" + run_directory = "./run" # Ensure the 'run' directory exists if not os.path.exists(run_directory): @@ -141,13 +138,21 @@ def create_experiment_folder(prefix="run_", next_number=None): if next_number is None: # List all subdirectories in the 'run' directory - folders = [f for f in os.listdir(run_directory) if os.path.isdir(os.path.join(run_directory, f))] + folders = [ + f + for f in os.listdir(run_directory) + if os.path.isdir(os.path.join(run_directory, f)) + ] # Filter folders that match the prefix pattern and end with a digit - matching_folders = [f for f in folders if f.startswith(prefix) and f[len(prefix):].isdigit()] + matching_folders = [ + f for f in folders if f.startswith(prefix) and f[len(prefix) :].isdigit() + ] # Find the highest number and calculate the next one if matching_folders: - highest_number = max(int(folder[len(prefix):]) for folder in matching_folders) + highest_number = max( + int(folder[len(prefix) :]) for folder in matching_folders + ) next_number = highest_number + 1 else: next_number = 0 @@ -155,19 +160,23 @@ def create_experiment_folder(prefix="run_", next_number=None): # Create the new folder with the next number new_folder_name = f"{prefix}{next_number}" new_folder_path = os.path.join(run_directory, new_folder_name) - new_folder_path+="/" + new_folder_path += "/" if not os.path.exists(new_folder_path): os.makedirs(new_folder_path) print(f"New folder created: {new_folder_path}") return new_folder_path -#------------------------------------------------------------------------------------------------------------------- -def str_list_to_enum_list(reco_tasks:List)->List[Task]: + + +# ------------------------------------------------------------------------------------------------------------------- +def str_list_to_enum_list(reco_tasks: List) -> List[Task]: tasks = [] - for task_str in reco_tasks: + for task_str in reco_tasks: try: tasks.append(Task[task_str]) except KeyError: print(f"'{task_str}' is not a valid enum type.") return tasks -#------------------------------------------------------------------------------------------------------------------- \ No newline at end of file + + +# ------------------------------------------------------------------------------------------------------------------- diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 35fb60b4..3e61f055 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -14,6 +14,7 @@ class DLFrameWork(Tool): for CTLearn model training. It dynamically loads the appropriate subclass based on the user-defined --framework argument. """ + name = "dlframework" framework_type = CaselessStrEnum( @@ -69,6 +70,23 @@ def __init__(self, **kwargs): Initialize the DLFrameWork tool and prepare for framework injection. """ super().__init__(**kwargs) + self.framework_instance = None + print("init") + + def setup(self): + """ + Setup method called after basic trait parsing. + This dynamically loads and prepares the correct framework subclass + (TrainKerasModel or TrainPyTorchModel). + """ + print("setup") + framework_enum = self.string_to_type(self.framework_type) + self.framework_instance = self.get_framework(framework_enum) + + # Inject aliases and shared config before full CLI parsing + self.framework_instance.update_config(self.config) + self.aliases.update(self.framework_instance.aliases) + DLFrameWork.aliases.update(self.framework_instance.aliases) def start(self): print(f"Selected Framework: {self.framework_type}") @@ -101,7 +119,7 @@ def string_to_type(cls, str_type: str) -> FrameworkType: @classmethod def get_framework(cls, framework_type: FrameworkType): """ - Dynamically import and return the corresponding training class + Dynamically import and return the corresponding training class based on the framework type. Parameters: @@ -117,17 +135,23 @@ def get_framework(cls, framework_type: FrameworkType): if framework_type == FrameworkType.KERAS: try: from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + fw = TrainKerasModel() except ImportError as e: raise ImportError(f"Not possible to import TrainKerasModel: {e}") from e elif framework_type == FrameworkType.PYTORCH: try: - from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel + from ctlearn.tools.train.pytorch.train_pytorch_model import ( + TrainPyTorchModel, + ) + fw = TrainPyTorchModel() print("Pytorch") except ImportError as e: - raise ImportError(f"Not possible to import TrainPyTorchModel: {e}") from e + raise ImportError( + f"Not possible to import TrainPyTorchModel: {e}" + ) from e else: raise ValueError(f"Unknown Framework: {framework_type.name}") @@ -137,7 +161,9 @@ def get_framework(cls, framework_type: FrameworkType): if __name__ == "__main__": # Parse only --framework to determine which subclass to load - minimal_args = [arg for arg in sys.argv[1:] if "--framework" in arg or arg in ["-h", "--help"]] + minimal_args = [ + arg for arg in sys.argv[1:] if "--framework" in arg or arg in ["-h", "--help"] + ] tool = DLFrameWork() tool.initialize(argv=minimal_args) @@ -149,4 +175,4 @@ def get_framework(cls, framework_type: FrameworkType): tool.run() # Example: -# python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite \ No newline at end of file +# python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite From d59bcb66fc3dd68f33e6da7cdafb3b3ad009aa8e Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 6 May 2025 10:22:46 +0000 Subject: [PATCH 012/119] fixed the training command binary --- ctlearn/tools/train_model.py | 15 ++++-- ctlearn/tools/train_model_old.py | 83 -------------------------------- pyproject.toml | 3 +- 3 files changed, 12 insertions(+), 89 deletions(-) delete mode 100644 ctlearn/tools/train_model_old.py diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 3e61f055..3be568af 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -6,8 +6,10 @@ from ctapipe.core import Tool from ctapipe.core.traits import CaselessStrEnum from ctlearn.core.ctlearn_enum import FrameworkType - - +from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel +from ctlearn.tools.train.pytorch.train_pytorch_model import ( + TrainPyTorchModel, +) class DLFrameWork(Tool): """ Tool to select and run a specific deep learning training framework (Keras or PyTorch) @@ -158,8 +160,8 @@ def get_framework(cls, framework_type: FrameworkType): return fw - -if __name__ == "__main__": +def main(): + # Run the tool # Parse only --framework to determine which subclass to load minimal_args = [ arg for arg in sys.argv[1:] if "--framework" in arg or arg in ["-h", "--help"] @@ -174,5 +176,10 @@ def get_framework(cls, framework_type: FrameworkType): tool.framework_instance.initialize(argv=sys.argv[1:]) tool.run() + +if __name__ == "main": + main() + + # Example: # python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite diff --git a/ctlearn/tools/train_model_old.py b/ctlearn/tools/train_model_old.py deleted file mode 100644 index 4f3c23ea..00000000 --- a/ctlearn/tools/train_model_old.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -Tools to train a ``CTLearnModel` (in Keras or PyTorch) on R1/DL1a data using the ``DLDataReader`` and ``DLDataLoader``. -""" - -import sys -import argparse -from ctapipe.core import Tool -import warnings -from ctapipe.core.traits import ( - CaselessStrEnum, -) -from ctlearn import is_package_available -from ctlearn.core.ctlearn_enum import FrameworkType - -class DLFrameWork(Tool): - name = "dlframework" - - @classmethod - def string_to_type(self, str_type: str) -> FrameworkType: - - type_ = None - str_type = str.upper(str_type) - try: - type_ = FrameworkType[str_type] - except KeyError: - print(f"'{str_type}' is not a valid enum type.") - return type_ - - @classmethod - def get_framework(self, framework_type: FrameworkType): - if framework_type == FrameworkType.KERAS: - try: - from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel - fw = TrainKerasModel() - - except ImportError as e: - raise ImportError(f"Not possible to import TrainKerasModel: {e}") from e - - - elif framework_type == FrameworkType.PYTORCH: - try: - from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel - fw = TrainPyTorchModel() - print("Pytorch") - except ImportError as e: - raise ImportError(f"Not possible to import TrainPyTorchModel: {e}") from e - - - - else: - raise ValueError(f"Unknown Framework: {framework_type.name}") - # Update Aliases - self.aliases.update(fw.aliases) - DLFrameWork.aliases.update(fw.aliases) - - return fw - - -if __name__ == "__main__": - - # Parse the framework argument - parser = argparse.ArgumentParser() - parser.add_argument("--framework", default="keras") - args, _ = parser.parse_known_args() - - # Get Framework type - if args.framework: - fw_type = DLFrameWork.string_to_type(args.framework) - else: - raise ValueError( - f"Framework not defined, use : --framework keras or --framework pytorch" - ) - - # DLFrameWork.get_framework(fw_type) - fw = DLFrameWork.get_framework(fw_type) - # fw = DLFrameWork() - # print(sys.argv[1:]) - # fw.parse_command_line(argv=sys.argv[1:]) - fw.run() - # Launch the Framework - # DLFrameWork().launch_instance() -# Example: -# python -m ctlearn.tools.train_model --framework pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 1c4d977e..f2a5c057 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,8 +61,7 @@ repository = "https://github.com/ctlearn-project/ctlearn" documentation = "https://ctlearn.readthedocs.io/en/latest/" [project.scripts] -ctlearn-train-kera-model = "ctlearn.tools.train_model:keras_tool" -ctlearn-train-pytorch-model = "ctlearn.tools.train_model:pytorch_tool" +ctlearn-train-model = "ctlearn.tools.train_model:main" ctlearn-predict-mono-model = "ctlearn.tools.predict_model:mono_tool" ctlearn-predict-stereo-model = "ctlearn.tools.predict_model:stereo_tool" ctlearn-predict-LST1= "ctlearn.tools.predict_LST1:main" From 49205a96bfcb677cca804d77db34e2275a677756 Mon Sep 17 00:00:00 2001 From: pguzman Date: Wed, 14 May 2025 09:25:38 +0000 Subject: [PATCH 013/119] Fixed flags in training and fixed a bug in validation index --- ctlearn/core/data_loader/loader_original.py | 310 ------------------ .../{nfnets__ => nfnets_pytorch}/__init__.py | 0 .../{nfnets__ => nfnets_pytorch}/model.py | 0 .../{nfnets__ => nfnets_pytorch}/optim.py | 0 .../pretrained.py | 0 .../tools/train/keras/train_keras_model.py | 2 + ctlearn/tools/train_model.py | 8 +- 7 files changed, 6 insertions(+), 314 deletions(-) delete mode 100644 ctlearn/core/data_loader/loader_original.py rename ctlearn/core/pytorch/nets/models/legacy/{nfnets__ => nfnets_pytorch}/__init__.py (100%) rename ctlearn/core/pytorch/nets/models/legacy/{nfnets__ => nfnets_pytorch}/model.py (100%) rename ctlearn/core/pytorch/nets/models/legacy/{nfnets__ => nfnets_pytorch}/optim.py (100%) rename ctlearn/core/pytorch/nets/models/legacy/{nfnets__ => nfnets_pytorch}/pretrained.py (100%) diff --git a/ctlearn/core/data_loader/loader_original.py b/ctlearn/core/data_loader/loader_original.py deleted file mode 100644 index 3a36f554..00000000 --- a/ctlearn/core/data_loader/loader_original.py +++ /dev/null @@ -1,310 +0,0 @@ -import numpy as np -import astropy.units as u -import keras -from keras.utils import Sequence, to_categorical - - -from dl1_data_handler.reader import ProcessType - - -class DLDataLoader(Sequence): - """ - Generates batches for Keras application. - - DLDataLoader is a data loader class that inherits from ``~keras.utils.Sequence``. - It is designed to handle and load data for deep learning models in a batch-wise manner. - - Attributes: - ----------- - data_reader : DLDataReader - An instance of DLDataReader to read the input data. - indices : list - List of indices to specify the data to be loaded. - tasks : list - List of tasks to be performed on the data to properly set up the labels. - batch_size : int - Size of the batch to load the data. - random_seed : int, optional - Whether to shuffle the data after each epoch with a provided random seed. - - Methods: - -------- - __len__(): - Returns the number of batches per epoch. - on_epoch_end(): - Updates indices after each epoch if random seed is provided. - __getitem__(index): - Generates one batch of data using _get_mono_item(index) or _get_stereo_item(index). - _get_mono_item(index): - Generates one batch of monoscopic data. - _get_stereo_item(index): - Generates one batch of stereoscopic data. - """ - - def __init__( - self, - DLDataReader, - indices, - tasks, - batch_size=64, - random_seed=None, - sort_by_intensity=False, - stack_telescope_images=False, - **kwargs, - ): - super().__init__(**kwargs) - "Initialization" - self.DLDataReader = DLDataReader - self.indices = indices - self.tasks = tasks - self.batch_size = batch_size - self.random_seed = random_seed - self.on_epoch_end() - self.stack_telescope_images = stack_telescope_images - self.sort_by_intensity = sort_by_intensity - - # Set the input shape based on the mode of the DLDataReader - if self.DLDataReader.__class__.__name__ != "DLFeatureVectorReader": - if self.DLDataReader.mode == "mono": - self.input_shape = self.DLDataReader.input_shape - elif self.DLDataReader.mode == "stereo": - self.input_shape = self.DLDataReader.input_shape[ - list(self.DLDataReader.selected_telescopes)[0] - ] - # Reshape inputs into proper dimensions - # for the stereo analysis with stacked images - if self.stack_telescope_images: - self.input_shape = ( - self.input_shape[1], - self.input_shape[2], - self.input_shape[0] * self.input_shape[3], - ) - - def __len__(self): - """ - Returns the number of batches per epoch. - - This method calculates the number of batches required to cover the entire dataset - based on the batch size. - - Returns: - -------- - int - Number of batches per epoch. - """ - return int(np.floor(len(self.indices) / self.batch_size)) - - def on_epoch_end(self): - """ - Updates indices after each epoch. If a random seed is provided, the indices are shuffled. - - This method is called at the end of each epoch to ensure that the data is shuffled - if the shuffle attribute is set to True. This helps in improving the training process - by providing the model with a different order of data in each epoch. - """ - if self.random_seed is not None: - np.random.seed(self.random_seed) - np.random.shuffle(self.indices) - - def __getitem__(self, index): - """ - Generate one batch of data and retrieve the features and labels. - - This method is called to generate one batch of monoscopic and stereoscopic data based on - the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) - based on the mode of the DLDataReader. - - Parameters: - ----------- - index : int - Index of the batch to generate. - - Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. - """ - # Generate indices of the batch - batch_indices = self.indices[ - index * self.batch_size : (index + 1) * self.batch_size - ] - features, labels = None, None - if self.DLDataReader.mode == "mono": - batch = self.DLDataReader.generate_mono_batch(batch_indices) - features, labels = self._get_mono_item(batch) - elif self.DLDataReader.mode == "stereo": - batch = self.DLDataReader.generate_stereo_batch(batch_indices) - features, labels = self._get_stereo_item(batch) - return features, labels - - def _get_mono_item(self, batch): - """ - Retrieve the features and labels for one batch of monoscopic data. - - This method is called to retrieve the features and labels for one batch of - monoscopic data. The labels are set up based on the tasks specified. - - Parameters: - ----------- - batch : astropy.table.Table - A table containing the data for the batch. - - Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. - """ - # Retrieve the telescope images and store in the features dictionary - labels = {} - features = batch["features"].data - if "type" in self.tasks: - labels["type"] = to_categorical( - batch["true_shower_primary_class"].data, - num_classes=2, - ) - # Temp fix till keras support class weights for multiple outputs or I wrote custom loss - # https://github.com/keras-team/keras/issues/11735 - if len(self.tasks) == 1: - labels = to_categorical( - batch["true_shower_primary_class"].data, - num_classes=2, - ) - if "energy" in self.tasks: - labels["energy"] = batch["log_true_energy"].data - if "skydirection" in self.tasks: - labels["skydirection"] = np.stack( - ( - batch["fov_lon"].data, - batch["fov_lat"].data, - ), - axis=1, - ) - if "cameradirection" in self.tasks: - labels["cameradirection"] = np.stack( - ( - batch["cam_coord_offset_x"].data, - batch["cam_coord_offset_y"].data, - ), - axis=1, - ) - return features, labels - - def _get_stereo_item(self, batch): - """ - Retrieve the features and labels for one batch of stereoscopic data. - - This method is called to retrieve the features and labels for one batch of - stereoscopic data. The original batch is grouped to retrieve the telescope - data for each event and then the telescope images or waveforms are stored - by the hillas intensity or stacked if required. Feature vectors can also - be retrieved if available for ``telescope``- and ``subarray``level. The - labels are set up based on the tasks specified. - - Parameters: - ----------- - batch : astropy.table.Table - A table containing the data for the batch. - - Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. - """ - labels = {} - if self.DLDataReader.process_type == ProcessType.Simulation: - batch_grouped = batch.group_by( - ["obs_id", "event_id", "tel_type_id", "true_shower_primary_class"] - ) - elif self.DLDataReader.process_type == ProcessType.Observation: - batch_grouped = batch.group_by(["obs_id", "event_id", "tel_type_id"]) - features, mono_feature_vectors, stereo_feature_vectors = [], [], [] - true_shower_primary_class = [] - log_true_energy = [] - fov_lon, fov_lat, angular_separation = [], [], [] - cam_coord_offset_x, cam_coord_offset_y, cam_coord_distance = [], [], [] - for group_element in batch_grouped.groups: - if "features" in batch.colnames: - if self.sort_by_intensity: - # Sort images by the hillas intensity in a given batch if requested - group_element.sort(["hillas_intensity"], reverse=True) - # Stack the telescope images for stereo analysis - if self.stack_telescope_images: - # Retrieve the telescope images - plain_features = group_element["features"].data - # Stack the telescope images along the last axis - stacked_features = np.concatenate( - [plain_features[i] for i in range(plain_features.shape[0])], - axis=-1, - ) - # Append the stacked images to the features list - # shape: (batch_size, image_shape, image_shape, n_channels * n_tel) - features.append(stacked_features) - else: - # Append the plain images to the features list - # shape: (batch_size, n_tel, image_shape, image_shape, n_channels) - features.append(group_element["features"].data) - # Retrieve the feature vectors - if "mono_feature_vectors" in batch.colnames: - mono_feature_vectors.append(group_element["mono_feature_vectors"].data) - if "stereo_feature_vectors" in batch.colnames: - stereo_feature_vectors.append( - group_element["stereo_feature_vectors"].data - ) - # Retrieve the labels for the tasks - # FIXME: This won't work for divergent pointing directions - if "type" in self.tasks: - true_shower_primary_class.append( - group_element["true_shower_primary_class"].data[0] - ) - if "energy" in self.tasks: - log_true_energy.append(group_element["log_true_energy"].data[0]) - if "skydirection" in self.tasks: - fov_lon.append(group_element["fov_lon"].data[0]) - fov_lat.append( - group_element["fov_lat"].data[0] - ) - if "cameradirection" in self.tasks: - cam_coord_offset_x.append(group_element["cam_coord_offset_x"].data) - cam_coord_offset_y.append( - group_element["cam_coord_offset_y"].data - ) - # Store the labels in the labels dictionary - if "type" in self.tasks: - labels["type"] = to_categorical( - np.array(true_shower_primary_class), - num_classes=2, - ) - # Temp fix till keras support class weights for multiple outputs or I wrote custom loss - # https://github.com/keras-team/keras/issues/11735 - if len(self.tasks) == 1: - labels = to_categorical( - np.array(true_shower_primary_class), - num_classes=2, - ) - if "energy" in self.tasks: - labels["energy"] = np.array(log_true_energy) - if "skydirection" in self.tasks: - labels["skydirection"] = np.stack( - ( - np.array(fov_lon), - np.array(fov_lat), - ), - axis=1, - ) - if "cameradirection" in self.tasks: - labels["cameradirection"] = np.stack( - ( - np.array(cam_coord_offset_x), - np.array(cam_coord_offset_y), - ), - axis=1, - ) - # Store the fatures in the features dictionary - if "features" in batch.colnames: - features = np.array(features) - # TDOO: Add support for both feature vectors - if "mono_feature_vectors" in batch.colnames: - features = np.array(mono_feature_vectors) - if "stereo_feature_vectors" in batch.colnames: - features = np.array(stereo_feature_vectors) - return features, labels diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/__init__.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/__init__.py similarity index 100% rename from ctlearn/core/pytorch/nets/models/legacy/nfnets__/__init__.py rename to ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/__init__.py diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/model.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/model.py similarity index 100% rename from ctlearn/core/pytorch/nets/models/legacy/nfnets__/model.py rename to ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/model.py diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/optim.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/optim.py similarity index 100% rename from ctlearn/core/pytorch/nets/models/legacy/nfnets__/optim.py rename to ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/optim.py diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained.py similarity index 100% rename from ctlearn/core/pytorch/nets/models/legacy/nfnets__/pretrained.py rename to ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained.py diff --git a/ctlearn/tools/train/keras/train_keras_model.py b/ctlearn/tools/train/keras/train_keras_model.py index e2a927af..83e62f60 100644 --- a/ctlearn/tools/train/keras/train_keras_model.py +++ b/ctlearn/tools/train/keras/train_keras_model.py @@ -142,6 +142,8 @@ def setup(self): def start(self): print("Start KERAS") + print("EPOCHS:",self.n_epochs) + print("save_onnx:",self.save_onnx) # Set up the keras callbacks monitor = "val_loss" monitor_mode = "min" diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 3be568af..95107a9c 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -6,10 +6,10 @@ from ctapipe.core import Tool from ctapipe.core.traits import CaselessStrEnum from ctlearn.core.ctlearn_enum import FrameworkType -from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel -from ctlearn.tools.train.pytorch.train_pytorch_model import ( - TrainPyTorchModel, -) +# from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel +# from ctlearn.tools.train.pytorch.train_pytorch_model import ( +# TrainPyTorchModel, +# ) class DLFrameWork(Tool): """ Tool to select and run a specific deep learning training framework (Keras or PyTorch) From 5b81fb0a623716b672c8615a32d9051b8ca84ad2 Mon Sep 17 00:00:00 2001 From: pguzman Date: Thu, 22 May 2025 14:44:20 +0000 Subject: [PATCH 014/119] Fixed __main__ and minor changes --- ctlearn/tools/train/base_train_model.py | 22 ++++++++++++++++------ ctlearn/tools/train_model.py | 2 +- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 1762619d..fe1e476f 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -209,11 +209,21 @@ class TrainCTLearnModel(Tool): aliases = { "framework": "TrainCTLearnModel.framework_type", + "n_epochs": "TrainCTLearnModel.n_epochs", "signal": "TrainCTLearnModel.input_dir_signal", "background": "TrainCTLearnModel.input_dir_background", "pattern-signal": "TrainCTLearnModel.file_pattern_signal", "pattern-background": "TrainCTLearnModel.file_pattern_background", "reco": "TrainCTLearnModel.reco_tasks", + "save_onnx": "TrainCTLearnModel.save_onnx", + "random_seed": "TrainCTLearnModel.random_seed", + "optimizer": "TrainCTLearnModel.optimizer", + "overwrite": "TrainCTLearnModel.overwrite", + "validation_split": "TrainCTLearnModel.validation_split", + "batch_size": "TrainCTLearnModel.batch_size", + "sort_by_intensity": "TrainCTLearnModel.sort_by_intensity", + "stack_telescope_images": "TrainCTLearnModel.stack_telescope_images", + "dl1dh_reader_type": "TrainCTLearnModel.dl1dh_reader_type", ("o", "output"): "TrainCTLearnModel.output_dir", } @@ -238,6 +248,7 @@ def __init__(self, **kwargs): print("Common Init") def setup(self): + print("Enter setup") # Check if the output directory exists and if it should be overwritten if self.output_dir.exists(): @@ -270,7 +281,6 @@ def setup(self): self.input_dir_background.glob(background_pattern) ) - print("DEBUG 1") # Set up the data reader self.log.info("Loading data:") self.log.info("For a large dataset, this may take a while...") @@ -279,7 +289,7 @@ def setup(self): "'DLFeatureVectorReader' is not supported in CTLearn yet. " "Missing stereo CTLearnModel implementation." ) - print("DEBUG 2") + print(f"self.dl1dh_reader_type: {self.dl1dh_reader_type}") self.dl1dh_reader = DLDataReader.from_name( self.dl1dh_reader_type, @@ -287,7 +297,7 @@ def setup(self): input_url_background=sorted(self.input_url_background), parent=self, ) - print("DEBUG 3") + self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) if "type" in self.reco_tasks: self.log.info( @@ -343,7 +353,7 @@ def setup(self): print("num_replicas_in_sync:", self.strategy.num_replicas_in_sync) print("BASE TRAIN FRAMEWORK", self.framework_type) - print("DEBUG 4") + self.training_loader = DLDataLoader.create( framework=self.framework_type, DLDataReader=self.dl1dh_reader, @@ -354,11 +364,11 @@ def setup(self): sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, ) - print("DEBUG 5") + self.validation_loader = DLDataLoader.create( framework=self.framework_type, DLDataReader=self.dl1dh_reader, - indices=training_indices, + indices=validation_indices, tasks=self.reco_tasks, batch_size=self.batch_size * self.strategy.num_replicas_in_sync, random_seed=self.random_seed, diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 95107a9c..3fb0d9be 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -177,7 +177,7 @@ def main(): tool.run() -if __name__ == "main": +if __name__ == "__main__": main() From 7926e8c38f59d5951d885baa7ad196994993baa7 Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 23 May 2025 12:28:42 +0000 Subject: [PATCH 015/119] refactored keras files and fixed minor issues... --- ctlearn/core/{ => keras}/attention.py | 0 ctlearn/core/{ => keras}/model.py | 2 +- ctlearn/tools/predict_LST1.py | 5 ++--- ctlearn/tools/train/base_train_model.py | 7 +------ ctlearn/tools/train/keras/train_keras_model.py | 6 ++---- 5 files changed, 6 insertions(+), 14 deletions(-) rename ctlearn/core/{ => keras}/attention.py (100%) rename ctlearn/core/{ => keras}/model.py (99%) diff --git a/ctlearn/core/attention.py b/ctlearn/core/keras/attention.py similarity index 100% rename from ctlearn/core/attention.py rename to ctlearn/core/keras/attention.py diff --git a/ctlearn/core/model.py b/ctlearn/core/keras/model.py similarity index 99% rename from ctlearn/core/model.py rename to ctlearn/core/keras/model.py index bfac48c2..11e28c87 100644 --- a/ctlearn/core/model.py +++ b/ctlearn/core/keras/model.py @@ -7,7 +7,7 @@ from ctapipe.core import Component from ctapipe.core.traits import Bool, Int, CaselessStrEnum, List, Dict, Unicode, Path -from ctlearn.core.attention import ( +from ctlearn.core.keras.attention import ( dual_squeeze_excite_block, channel_squeeze_excite_block, spatial_squeeze_excite_block, diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index 58069e56..185a1f51 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -47,9 +47,8 @@ ) from ctapipe.reco.utils import add_defaults_and_meta -from ctlearn import __version__ as ctlearn_version -from ctlearn.utils import get_lst1_subarray_description, validate_trait_dict - +from ctlearn.core.keras.model import LoadedModel +from ctlearn.utils import get_lst1_subarray_description from dl1_data_handler.image_mapper import ImageMapper from dl1_data_handler.reader import ( get_unmapped_image, diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index fe1e476f..fc62d531 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -16,8 +16,6 @@ ) from dl1_data_handler.reader import DLDataReader from ctlearn.core.data_loader.loader import DLDataLoader -from ctlearn.core.model import CTLearnModel - class TrainCTLearnModel(Tool): """ @@ -236,20 +234,17 @@ class TrainCTLearnModel(Tool): classes = ( [ - CTLearnModel, DLDataReader, ] - + classes_with_traits(CTLearnModel) + classes_with_traits(DLDataReader) ) def __init__(self, **kwargs): super().__init__(**kwargs) - print("Common Init") def setup(self): - print("Enter setup") + # Check if the output directory exists and if it should be overwritten if self.output_dir.exists(): if not self.overwrite: diff --git a/ctlearn/tools/train/keras/train_keras_model.py b/ctlearn/tools/train/keras/train_keras_model.py index 83e62f60..307619a2 100644 --- a/ctlearn/tools/train/keras/train_keras_model.py +++ b/ctlearn/tools/train/keras/train_keras_model.py @@ -20,9 +20,7 @@ Unicode, ) from ctlearn.tools.train.base_train_model import TrainCTLearnModel -# from dl1_data_handler.reader import DLDataReader -# from ctlearn.core.loader import DLDataLoader -from ctlearn.core.model import CTLearnModel +from ctlearn.core.keras.model import CTLearnModel from ctlearn.utils import validate_trait_dict try: @@ -37,7 +35,7 @@ def on_epoch_begin(self, epoch, logs=None): def on_epoch_end(self, epoch, logs=None): epoch_duration = time() - self.epoch_start_time - print(f'\nDuración de la época {epoch + 1}: {epoch_duration:.2f} segundos') + print(f'\nEpoch Time {epoch + 1}: {epoch_duration:.2f} seconds') self.progress_bar.close() def on_batch_begin(self, batch, logs=None): From 0b77156cd8596765c423cebba7d0a896573dfb59 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 26 May 2025 13:19:30 +0000 Subject: [PATCH 016/119] added first sketch of prediction --- .../tools/predict/keras/predic_model_keras.py | 134 ++ ctlearn/tools/predict/predict_model.py | 1890 +++++++++++++++++ .../predict/pytorch/predic_model_pytorch.py | 0 ctlearn/tools/predict_LST1.py | 8 +- ctlearn/tools/predict_model.py | 9 + ctlearn/tools/predict_model_main.py | 1877 ++++++++++++++++ ctlearn/tools/train/base_train_model.py | 2 +- 7 files changed, 3917 insertions(+), 3 deletions(-) create mode 100644 ctlearn/tools/predict/keras/predic_model_keras.py create mode 100644 ctlearn/tools/predict/predict_model.py create mode 100644 ctlearn/tools/predict/pytorch/predic_model_pytorch.py create mode 100644 ctlearn/tools/predict_model_main.py diff --git a/ctlearn/tools/predict/keras/predic_model_keras.py b/ctlearn/tools/predict/keras/predic_model_keras.py new file mode 100644 index 00000000..37dfb8d8 --- /dev/null +++ b/ctlearn/tools/predict/keras/predic_model_keras.py @@ -0,0 +1,134 @@ +from ctlearn.core.data_loader.loader import DLDataLoader +import keras +from astropy.table import ( + Table, + hstack, + vstack, + join, + setdiff, +) +import numpy as np + +def predict_with_model(self, model_path): + """ + Load and predict with a CTLearn model. + + Load a model from the specified path and predict the data using the loaded model. + If a last batch loader is provided, predict the last batch and stack the results. + + Parameters + ---------- + model_path : str + Path to a Keras model file (Keras3) or directory (Keras2). + + Returns + ------- + predict_data : astropy.table.Table + Table containing the prediction results. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + # Create a new DLDataLoader for each task + # It turned out to be more robust to initialize the DLDataLoader separately. + data_loader = DLDataLoader.create( + framework="keras", + DLDataReader=self.dl1dh_reader, + indices=self.indices, + tasks=[], + batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + + # Keras is only considering the last complete batch. + # In prediction mode we don't want to loose the last + # uncomplete batch, so we are creating an additional + # batch generator for the remaining events. + data_loader_last_batch = None + if self.last_batch_size > 0: + last_batch_indices = self.indices[-self.last_batch_size :] + data_loader_last_batch = DLDataLoader.create( + framework="keras", + DLDataReader=self.dl1dh_reader, + indices=last_batch_indices, + tasks=[], + batch_size=self.last_batch_size, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + + + # Load the model from the specified path + model = keras.saving.load_model(model_path) + prediction_colname = ( + model.layers[-1].name if model.layers[-1].name != "softmax" else "type" + ) + backbone_model, feature_vectors = None, None + if self.dl1_features: + # Get the backbone model which is the second layer of the model + backbone_model = model.get_layer(index=1) + # Create a new head model with the same layers as the original model. + # The output of the backbone model is the input of the head model. + backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) + x = backbone_output_shape + for layer in model.layers[2:]: + x = layer(x) + head = keras.Model(inputs=backbone_output_shape, outputs=x) + # Apply the backbone model with the data loader to retrieve the feature vectors + feature_vectors = backbone_model.predict( + data_loader, verbose=self.keras_verbose + ) + # Apply the head model with the feature vectors to retrieve the prediction + predict_data = Table( + { + prediction_colname: head.predict( + feature_vectors, verbose=self.keras_verbose + ) + } + ) + # Predict the last batch and stack the results to the prediction data + if data_loader_last_batch is not None: + feature_vectors_last_batch = backbone_model.predict( + data_loader_last_batch, verbose=self.keras_verbose + ) + feature_vectors = np.concatenate( + (feature_vectors, feature_vectors_last_batch) + ) + predict_data = vstack( + [ + predict_data, + Table( + { + prediction_colname: head.predict( + feature_vectors_last_batch, + verbose=self.keras_verbose, + ) + } + ), + ] + ) + else: + # Predict the data using the loaded model + predict_data = model.predict(data_loader, verbose=self.keras_verbose) + # Create a astropy table with the prediction results + # The classification task has a softmax layer as the last layer + # which returns the probabilities for each class in an array, while + # the regression tasks have output neurons which returns the + # predicted value for the task in a dictionary. + if prediction_colname == "type": + predict_data = Table({prediction_colname: predict_data}) + else: + predict_data = Table(predict_data) + # Predict the last batch and stack the results to the prediction data + if data_loader_last_batch is not None: + predict_data_last_batch = model.predict( + data_loader_last_batch, verbose=self.keras_verbose + ) + if model.layers[-1].name == "type": + predict_data_last_batch = Table( + {prediction_colname: predict_data_last_batch} + ) + else: + predict_data_last_batch = Table(predict_data_last_batch) + predict_data = vstack([predict_data, predict_data_last_batch]) + return predict_data, feature_vectors \ No newline at end of file diff --git a/ctlearn/tools/predict/predict_model.py b/ctlearn/tools/predict/predict_model.py new file mode 100644 index 00000000..c819a229 --- /dev/null +++ b/ctlearn/tools/predict/predict_model.py @@ -0,0 +1,1890 @@ +""" +Tools to predict the gammaness, energy and arrival direction in monoscopic and stereoscopic mode using ``CTLearnModel`` on R1/DL1 data using the ``DLDataReader`` and ``DLDataLoader``. +""" + +import atexit +import pathlib +import numpy as np +import os +import tensorflow as tf +import keras + +from astropy import units as u +from astropy.coordinates.earth import EarthLocation +from astropy.coordinates import AltAz, SkyCoord +from astropy.table import ( + Table, + hstack, + vstack, + join, + setdiff, +) + +from ctapipe.containers import ( + ParticleClassificationContainer, + ReconstructedGeometryContainer, + ReconstructedEnergyContainer, +) +from ctapipe.coordinates import CameraFrame, NominalFrame +from ctapipe.core import Tool +from ctapipe.core.tool import ToolConfigurationError +from ctapipe.core.traits import ( + Bool, + Int, + Path, + flag, + Set, + Dict, + List, + CaselessStrEnum, + ComponentName, + Unicode, + classes_with_traits, +) +from ctapipe.monitoring.interpolation import PointingInterpolator +from ctapipe.io import read_table, write_table, HDF5Merger +from ctapipe.reco.reconstructor import ReconstructionProperty +from ctapipe.reco.stereo_combination import StereoCombiner +from ctapipe.reco.utils import add_defaults_and_meta +from dl1_data_handler.reader import ( + DLDataReader, + ProcessType, + LST_EPOCH, +) +from ctlearn.core.data_loader.loader import DLDataLoader + +SIMULATION_CONFIG_TABLE = "/configuration/simulation/run" +FIXED_POINTING_GROUP = "/configuration/telescope/pointing" +POINTING_GROUP = "/dl1/monitoring/telescope/pointing" +SUBARRAY_POINTING_GROUP = "/dl1/monitoring/subarray/pointing" +DL1_TELESCOPE_GROUP = "/dl1/event/telescope" +DL1_SUBARRAY_GROUP = "/dl1/event/subarray" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +DL2_TELESCOPE_GROUP = "/dl2/event/telescope" +SUBARRAY_EVENT_KEYS = ["obs_id", "event_id"] +TELESCOPE_EVENT_KEYS = ["obs_id", "event_id", "tel_id"] + +__all__ = [ + "PredictCTLearnModel", + "MonoPredictCTLearnModel", + "StereoPredictCTLearnModel", +] + + +class PredictCTLearnModel(Tool): + """ + Base tool to predict the gammaness, energy and arrival direction from R1/DL1 data using CTLearn models. + + This class handles the prediction of the gammaness, energy and arrival direction from pixel-wise image + or waveform data. It also supports the extraction of the feature vectors from the backbone submodel to + store them in the output file. The input data is loaded from the input url using the + ``~dl1_data_handler.reader.DLDataReader`` and ``~ctlearn.core.loader.DLDataLoader``. + The prediction is performed using the CTLearn models. The data is stored in the output file + following the ctapipe DL2 data format. The ``start`` method is implemented in the subclasses to + handle the prediction for mono and stereo mode. + + Attributes + ---------- + input_url : pathlib.Path + Input ctapipe HDF5 files including pixel-wise image or waveform data. + use_HDF5Merger : bool + Set whether to use the HDF5Merger component to copy the selected tables from the input file to the output file. + dl1_features : bool + Set whether to include the dl1 feature vectors in the output file. + dl2_telescope : bool + Set whether to include dl2 telescope-event-wise data in the output file. + dl2_subarray : bool + Set whether to include dl2 subarray-event-wise data in the output file. + dl1dh_reader : dl1_data_handler.reader.DLDataReader + DLDataReader object to read the data. + dl1dh_reader_type : str + Type of the DLDataReader to use for the prediction. + stack_telescope_images : bool + Set whether to stack the telescope images in the data loader. Requires ``stereo``. + sort_by_intensity : bool + Set whether to sort the telescope images by intensity in the data loader. Requires ``stereo``. + prefix : str + Name of the reconstruction algorithm used to generate the dl2 data. + load_type_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the classification of the primary particle type. + load_energy_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression of the primary particle energy. + load_cameradirection_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression + of the primary particle arrival direction based on camera coordinate offsets. + load_cameradirection_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression + of the primary particle arrival direction based on spherical coordinate offsets. + output_path : pathlib.Path + Output path to save the dl2 prediction results. + overwrite_tables : bool + Overwrite the table in the output file if it exists. + keras_verbose : int + Verbosity mode of Keras during the prediction. + strategy : tf.distribute.Strategy + MirroredStrategy to distribute the prediction. + data_loader : ctlearn.core.loader.DLDataLoader + DLDataLoader object to load the data. + indices : list of int + List of indices for the data loaders. + batch_size : int + Size of the batch to perform inference of the neural network. + last_batch_size : int + Size of the last batch in the data loaders. + + Methods + ------- + setup() + Set up the tool. + finish() + Finish the tool. + _predict_with_model(model_path) + Load and predict with a CTLearn model. + _predict_classification(example_identifiers) + Predict the classification of the primary particle type. + _predict_energy(example_identifiers) + Predict the energy of the primary particle. + _predict_cameradirection(example_identifiers) + Predict the arrival direction of the primary particle based on camera coordinate offsets. + _predict_skydirection(example_identifiers) + Predict the arrival direction of the primary particle based on spherical coordinate offsets. + _transform_cam_coord_offsets_to_sky(table) + Transform to camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + _transform_spher_coord_offsets_to_sky(table) + Transform to spherical coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + _create_nan_table(nonexample_identifiers, columns, shapes) + Create a table with NaNs for missing predictions. + _store_pointing(all_identifiers) + Store the telescope pointing table from to the output file. + _create_feature_vectors_table(example_identifiers, nonexample_identifiers, classification_feature_vectors, energy_feature_vectors, direction_feature_vectors) + Create the table for the DL1 feature vectors. + """ + + input_url = Path( + help="Input ctapipe HDF5 files including pixel-wise image or waveform data", + allow_none=True, + exists=True, + directory_ok=False, + file_ok=True, + ).tag(config=True) + + use_HDF5Merger = Bool( + default_value=True, + allow_none=False, + help=( + "Set whether to use the HDF5Merger component to copy the selected tables " + "from the input file to the output file. CAUTION: This can only be used " + "if the output file not exists." + ), + ).tag(config=True) + + dl1_features = Bool( + default_value=False, + allow_none=False, + help="Set whether to include the dl1 feature vectors in the output file.", + ).tag(config=True) + + dl2_telescope = Bool( + default_value=True, + allow_none=False, + help="Set whether to include dl2 telescope-event-wise data in the output file.", + ).tag(config=True) + + dl2_subarray = Bool( + default_value=True, + allow_none=False, + help="Set whether to include dl2 subarray-event-wise data in the output file.", + ).tag(config=True) + + dl1dh_reader_type = ComponentName(DLDataReader, default_value="DLImageReader").tag( + config=True + ) + + stack_telescope_images = Bool( + default_value=False, + allow_none=False, + help=( + "Set whether to stack the telescope images in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + sort_by_intensity = Bool( + default_value=False, + allow_none=False, + help=( + "Set whether to sort the telescope images by intensity in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + prefix = Unicode( + default_value="CTLearn", + allow_none=False, + help="Name of the reconstruction algorithm used to generate the dl2 data.", + ).tag(config=True) + + load_type_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the classification " + "of the primary particle type." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_energy_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle energy." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_cameradirection_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle arrival direction based on camera coordinate offsets." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_skydirection_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle arrival direction based on spherical coordinate offsets." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + batch_size = Int( + default_value=64, + allow_none=False, + help="Size of the batch to perform inference of the neural network.", + ).tag(config=True) + + output_path = Path( + default_value="./output.dl2.h5", + allow_none=False, + help="Output path to save the dl2 prediction results", + ).tag(config=True) + + overwrite_tables = Bool( + default_value=True, + allow_none=False, + help="Overwrite the table in the output file if it exists", + ).tag(config=True) + + keras_verbose = Int( + default_value=1, + min=0, + max=2, + allow_none=False, + help=( + "Verbosity mode of Keras during the prediction: " + "0 = silent, 1 = progress bar, 2 = one line per call." + ), + ).tag(config=True) + + + framework_type = CaselessStrEnum( + ["pytorch", "keras"], + default_value="keras", + help="Framework to use pytorch or keras", + ).tag(config=True) + + aliases = { + ("i", "input_url"): "PredictCTLearnModel.input_url", + ("t", "type_model"): "PredictCTLearnModel.load_type_model_from", + ("e", "energy_model"): "PredictCTLearnModel.load_energy_model_from", + ( + "d", + "cameradirection_model", + ): "PredictCTLearnModel.load_cameradirection_model_from", + ("s", "skydirection_model"): "PredictCTLearnModel.load_skydirection_model_from", + ("o", "output"): "PredictCTLearnModel.output_path", + ("f","framework"): "PredictCTLearnModel.framework_type", + } + + flags = { + **flag( + "dl1-features", + "PredictCTLearnModel.dl1_features", + "Include dl1 features", + "Exclude dl1 features", + ), + **flag( + "dl2-telescope", + "PredictCTLearnModel.dl2_telescope", + "Include dl2 telescope-event-wise data in the output file", + "Exclude dl2 telescope-event-wise data in the output file", + ), + **flag( + "dl2-subarray", + "PredictCTLearnModel.dl2_subarray", + "Include dl2 telescope-event-wise data in the output file", + "Exclude dl2 telescope-event-wise data in the output file", + ), + **flag( + "use-HDF5Merger", + "PredictCTLearnModel.use_HDF5Merger", + "Copy data using the HDF5Merger component (CAUTION: This can not be used if the output file already exists)", + "Do not copy data using the HDF5Merger component", + ), + **flag( + "r0-waveforms", + "HDF5Merger.r0_waveforms", + "Include r0 waveforms", + "Exclude r0 waveforms", + ), + **flag( + "r1-waveforms", + "HDF5Merger.r1_waveforms", + "Include r1 waveforms", + "Exclude r1 waveforms", + ), + **flag( + "dl1-parameters", + "HDF5Merger.dl1_parameters", + "Include dl1 parameters", + "Exclude dl1 parameters", + ), + **flag( + "dl1-images", + "HDF5Merger.dl1_images", + "Include dl1 images", + "Exclude dl1 images", + ), + **flag( + "true-parameters", + "HDF5Merger.true_parameters", + "Include true parameters", + "Exclude true parameters", + ), + **flag( + "true-images", + "HDF5Merger.true_images", + "Include true images", + "Exclude true images", + ), + } + + classes = classes_with_traits(DLDataReader) + + def setup(self): + # Check if the ctapipe HDF5Merger component is enabled + if self.use_HDF5Merger: + if os.path.exists(self.output_path): + raise ToolConfigurationError( + f"The output file '{self.output_path}' already exists. Please use " + "'--no-use-HDF5Merger' to disable the usage of the HDF5Merger component." + ) + # Copy selected tables from the input file to the output file + self.log.info("Copying to output destination.") + with HDF5Merger(self.output_path, parent=self) as merger: + merger(self.input_url) + else: + self.log.info( + "No copy to output destination, since the usage of the HDF5Merger component is disabled." + ) + + # Create a MirroredStrategy. + self.strategy = tf.distribute.MirroredStrategy() + atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore + self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) + + # Set up the data reader + self.log.info("Loading data reader:") + self.log.info("For a large dataset, this may take a while...") + self.dl1dh_reader = DLDataReader.from_name( + self.dl1dh_reader_type, + input_url_signal=[self.input_url], + parent=self, + ) + self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) + # Check if the number of events is enough to form a batch + if self.dl1dh_reader._get_n_events() < self.batch_size: + raise ToolConfigurationError( + f"{self.dl1dh_reader._get_n_events()} events are not enough " + f"to form a batch of size {self.batch_size}. Reduce the batch size." + ) + # Set the indices for the data loaders + self.indices = list(range(self.dl1dh_reader._get_n_events())) + self.last_batch_size = len(self.indices) % ( + self.batch_size * self.strategy.num_replicas_in_sync + ) + + def finish(self): + self.log.info("Tool is shutting down") + + def _predict_with_model(self, model_path): + """ + Load and predict with a CTLearn model. + + Load a model from the specified path and predict the data using the loaded model. + If a last batch loader is provided, predict the last batch and stack the results. + + Parameters + ---------- + model_path : str + Path to a Keras model file (Keras3) or directory (Keras2). + + Returns + ------- + predict_data : astropy.table.Table + Table containing the prediction results. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + # Create a new DLDataLoader for each task + # It turned out to be more robust to initialize the DLDataLoader separately. + data_loader = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=self.indices, + tasks=[], + batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + + # Keras is only considering the last complete batch. + # In prediction mode we don't want to loose the last + # uncomplete batch, so we are creating an additional + # batch generator for the remaining events. + data_loader_last_batch = None + if self.last_batch_size > 0: + last_batch_indices = self.indices[-self.last_batch_size :] + data_loader_last_batch = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=last_batch_indices, + tasks=[], + batch_size=self.last_batch_size, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + + + # Load the model from the specified path + model = keras.saving.load_model(model_path) + prediction_colname = ( + model.layers[-1].name if model.layers[-1].name != "softmax" else "type" + ) + backbone_model, feature_vectors = None, None + if self.dl1_features: + # Get the backbone model which is the second layer of the model + backbone_model = model.get_layer(index=1) + # Create a new head model with the same layers as the original model. + # The output of the backbone model is the input of the head model. + backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) + x = backbone_output_shape + for layer in model.layers[2:]: + x = layer(x) + head = keras.Model(inputs=backbone_output_shape, outputs=x) + # Apply the backbone model with the data loader to retrieve the feature vectors + feature_vectors = backbone_model.predict( + data_loader, verbose=self.keras_verbose + ) + # Apply the head model with the feature vectors to retrieve the prediction + predict_data = Table( + { + prediction_colname: head.predict( + feature_vectors, verbose=self.keras_verbose + ) + } + ) + # Predict the last batch and stack the results to the prediction data + if data_loader_last_batch is not None: + feature_vectors_last_batch = backbone_model.predict( + data_loader_last_batch, verbose=self.keras_verbose + ) + feature_vectors = np.concatenate( + (feature_vectors, feature_vectors_last_batch) + ) + predict_data = vstack( + [ + predict_data, + Table( + { + prediction_colname: head.predict( + feature_vectors_last_batch, + verbose=self.keras_verbose, + ) + } + ), + ] + ) + else: + # Predict the data using the loaded model + predict_data = model.predict(data_loader, verbose=self.keras_verbose) + # Create a astropy table with the prediction results + # The classification task has a softmax layer as the last layer + # which returns the probabilities for each class in an array, while + # the regression tasks have output neurons which returns the + # predicted value for the task in a dictionary. + if prediction_colname == "type": + predict_data = Table({prediction_colname: predict_data}) + else: + predict_data = Table(predict_data) + # Predict the last batch and stack the results to the prediction data + if data_loader_last_batch is not None: + predict_data_last_batch = model.predict( + data_loader_last_batch, verbose=self.keras_verbose + ) + if model.layers[-1].name == "type": + predict_data_last_batch = Table( + {prediction_colname: predict_data_last_batch} + ) + else: + predict_data_last_batch = Table(predict_data_last_batch) + predict_data = vstack([predict_data, predict_data_last_batch]) + return predict_data, feature_vectors + + def _predict_classification(self, example_identifiers): + """ + Predict the classification of the primary particle type. + + This method uses a pre-trained type model to predict the type of the primary particle + for a given set of example identifiers. The predicted classification score ('gammaness') + is added to the example identifiers table. + + Parameters: + ----------- + classification_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + predicted classification score ('gammaness'). + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the classification of the primary particle type..." + ) + # Predict the data using the loaded type_model + predict_data, feature_vectors = self._predict_with_model( + self.load_type_model_from + ) + # Create prediction table and add the predicted classification score ('gammaness') + classification_table = example_identifiers.copy() + classification_table.add_column( + predict_data["type"].T[1], name=f"{self.prefix}_tel_prediction" + ) + return classification_table, feature_vectors + + def _predict_energy(self, example_identifiers): + """ + Predict the energy of the primary particle. + + This method uses a pre-trained energy model to predict the energy of the primary particle + for a given set of example identifiers. The predicted energy is then converted from + log10(TeV) to TeV and added to the example identifiers table. + + Parameters: + ----------- + energy_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed energy in TeV. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info("Predicting for the regression of the primary particle energy...") + # Predict the data using the loaded energy_model + predict_data, feature_vectors = self._predict_with_model( + self.load_energy_model_from + ) + # Convert the reconstructed energy from log10(TeV) to TeV + reco_energy = u.Quantity( + np.power(10, np.squeeze(predict_data["energy"])), + unit=u.TeV, + ) + # Create prediction table and add the reconstructed energy in TeV + energy_table = example_identifiers.copy() + energy_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy") + return energy_table, feature_vectors + + def _predict_cameradirection(self, example_identifiers): + """ + Predict the arrival direction of the primary particle based on camera coordinate offsets. + + This method uses a pre-trained direction model to predict the arrival direction of the + primary particle for a given set of example identifiers. The predicted camera coordinate offsets + is added to the example identifiers table. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + + Returns: + -------- + cameradirection_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed camera coordinate offsets in x and y. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the regression of the primary particle arrival direction based on camera coordinate offsets..." + ) + # Predict the data using the loaded direction_model + predict_data, feature_vectors = self._predict_with_model( + self.load_cameradirection_model_from + ) + # For the direction task, the prediction is the camera coordinate offset in x and y + # from the telescope pointing. + cam_coord_offset_x = u.Quantity(predict_data["cameradirection"].T[0], unit=u.m) + cam_coord_offset_y = u.Quantity(predict_data["cameradirection"].T[1], unit=u.m) + # Create prediction table and add the reconstructed energy in TeV + cameradirection_table = example_identifiers.copy() + cameradirection_table.add_column(cam_coord_offset_x, name="cam_coord_offset_x") + cameradirection_table.add_column(cam_coord_offset_y, name="cam_coord_offset_y") + return cameradirection_table, feature_vectors + + def _predict_skydirection(self, example_identifiers): + """ + Predict the arrival direction of the primary particle based on spherical coordinate offsets. + + This method uses a pre-trained direction model to predict the arrival direction of the primary + particle for a given set of example identifiers. The predicted spherical coordinate offsets is + added to the example identifiers table. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + + Returns: + -------- + skydirection_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed spherical coordinate offsets in fov_lon and fov_lat. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the regression of the primary particle arrival direction based on spherical coordinate offsets..." + ) + # Predict the data using the loaded direction_model + predict_data, feature_vectors = self._predict_with_model( + self.load_skydirection_model_from + ) + # For the direction task, the prediction is the spherical offset in fov_lon and fov_lat + # from the telescope pointing. + fov_lon = u.Quantity(predict_data["skydirection"].T[0], unit=u.deg) + fov_lat = u.Quantity(predict_data["skydirection"].T[1], unit=u.deg) + # Create prediction table and add the reconstructed energy in TeV + skydirection_table = example_identifiers.copy() + skydirection_table.add_column(fov_lon, name="fov_lon") + skydirection_table.add_column(fov_lat, name="fov_lat") + return skydirection_table, feature_vectors + + def _transform_cam_coord_offsets_to_sky(self, table) -> Table: + """ + Transform the predicted camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + + This method converts the predicted camera coordinate offsets w.r.t. the telescope pointing + in the provided table to Alt/Az coordinates. It also removes the unnecessary columns + from the table that do not the ctapipe DL2 data format. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing the trigger time, telescope pointing, and predicted camera coordinate offsets. + + Returns: + -------- + table : astropy.table.Table + A Table with the Alt/Az coordinates following the ctapipe DL2 data format. + """ + # Get the telescope ID from the table + tel_id = table["tel_id"][0] + # Set the telescope position + tel_ground_frame = self.dl1dh_reader.subarray.tel_coords[ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] + # Set the trigger timestamp based on the process type + if self.dl1dh_reader.process_type == ProcessType.Simulation: + trigger_time = LST_EPOCH + elif self.dl1dh_reader.process_type == ProcessType.Observation: + trigger_time = table["time"] + # Set the telescope pointing with the trigger timestamp and the telescope position + altaz = AltAz( + location=tel_ground_frame.to_earth_location(), + obstime=trigger_time, + ) + # Set the telescope pointing + tel_pointing = SkyCoord( + az=table["pointing_azimuth"], + alt=table["pointing_altitude"], + frame=altaz, + ) + # Set the camera frame with the focal length and rotation of the camera + camera_frame = CameraFrame( + focal_length=self.dl1dh_reader.subarray.tel[ + tel_id + ].camera.geometry.frame.focal_length, + rotation=self.dl1dh_reader.pix_rotation[tel_id], + telescope_pointing=tel_pointing, + ) + # Set the camera coordinate offset + cam_coord_offset = SkyCoord( + x=table["cam_coord_offset_x"], + y=table["cam_coord_offset_y"], + frame=camera_frame, + ) + # tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] + # Transform the true Alt/Az coordinates to camera coordinates + reco_direction = cam_coord_offset.transform_to(altaz) + # Add the reconstructed direction (az, alt) to the prediction table + table.add_column(reco_direction.az.to(u.deg), name=f"{self.prefix}_tel_az") + table.add_column(reco_direction.alt.to(u.deg), name=f"{self.prefix}_tel_alt") + # Remove unnecessary columns from the table that do not the ctapipe DL2 data format + table.remove_columns( + [ + "time", + "pointing_azimuth", + "pointing_altitude", + "cam_coord_offset_x", + "cam_coord_offset_y", + ] + ) + return table + + def _transform_spher_coord_offsets_to_sky(self, table) -> Table: + """ + Transform the predicted spherical offsets w.r.t. the telescope pointing to Alt/Az coordinates. + + This method converts the predicted spherical offsets w.r.t. the telescope pointing + in the provided table to Alt/Az coordinates. It also removes the unnecessary columns + from the table that do not the ctapipe DL2 data format. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing the trigger time, telescope pointing, and predicted spherical offsets. + + Returns: + -------- + table : astropy.table.Table + A Table with the Alt/Az coordinates following the ctapipe DL2 data format. + """ + + # Set the trigger timestamp based on the process type + if self.dl1dh_reader.process_type == ProcessType.Simulation: + trigger_time = LST_EPOCH + elif self.dl1dh_reader.process_type == ProcessType.Observation: + trigger_time = table["time"] + # Set the AltAz frame with the reference location and time + altaz = AltAz( + location=self.dl1dh_reader.subarray.reference_location, + obstime=trigger_time, + ) + # Set the array pointing + array_pointing = SkyCoord( + az=table["pointing_azimuth"], + alt=table["pointing_altitude"], + frame=altaz, + ) + # Set the nominal frame with the array pointing + nom_frame = NominalFrame( + origin=array_pointing, + location=self.dl1dh_reader.subarray.reference_location, + obstime=trigger_time, + ) + # Set the reco direction in (fov_lon, fov_lat) coordinates + reco_direction = SkyCoord( + fov_lon=table["fov_lon"], + fov_lat=table["fov_lat"], + frame=nom_frame, + ) + # Transform the reco direction from nominal frame to the AltAz frame + sky_coord = reco_direction.transform_to(altaz) + # Add the reconstructed direction (az, alt) to the prediction table + table.add_column(sky_coord.az.to(u.deg), name=f"{self.prefix}_az") + table.add_column(sky_coord.alt.to(u.deg), name=f"{self.prefix}_alt") + # Remove unnecessary columns from the table that do not the ctapipe DL2 data format + table.remove_columns( + [ + "time", + "pointing_azimuth", + "pointing_altitude", + "fov_lon", + "fov_lat", + ] + ) + return table + + def _create_nan_table(self, nonexample_identifiers, columns, shapes): + """ + Create a table with NaNs for missing predictions. + + This method creates a table with NaNs for missing predictions for the non-example identifiers. + In stereo mode, the table also a column for the valid telescopes is added with all False values. + + Parameters: + ----------- + nonexample_identifiers : astropy.table.Table + Table containing the non-example identifiers. + columns : list of str + List of column names to create in the table. + shapes : list of shapes + List of shapes for the columns to create in the table. + + Returns: + -------- + nan_table : astropy.table.Table + Table containing NaNs for missing predictions. + """ + # Create a table with NaNs for missing predictions + nan_table = nonexample_identifiers.copy() + for column_name, shape in zip(columns, shapes): + nan_table.add_column(np.full(shape, np.nan), name=column_name) + # Add that no telescope is valid for the non-example identifiers in stereo mode + if self.dl1dh_reader.mode == "stereo": + nan_table.add_column( + np.zeros( + (len(nonexample_identifiers), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ), + name=f"{self.prefix}_telescopes", + ) + return nan_table + + def _store_pointing(self, all_identifiers): + """ + Store the telescope pointing table from to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the telescope pointing information. + """ + + # Initialize the pointing interpolator from ctapipe + pointing_interpolator = PointingInterpolator( + bounds_error=False, extrapolate=True + ) + pointing_info = [] + for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: + # Get the telescope pointing from the dl1dh reader + tel_pointing = self.dl1dh_reader.telescope_pointings[f"tel_{tel_id:03d}"] + # Add the telescope pointing table to the pointing interpolator + pointing_interpolator.add_table(tel_id, tel_pointing) + tel_identifiers = all_identifiers.copy() + if self.dl1dh_reader.mode == "mono": + tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] + # Interpolate the telescope pointing + tel_altitude, tel_azimuth = pointing_interpolator( + tel_id, tel_identifiers["time"] + ) + tel_identifiers.add_column(tel_azimuth, name="pointing_azimuth") + tel_identifiers.add_column(tel_altitude, name="pointing_altitude") + pointing_info.append(tel_identifiers) + if self.dl1dh_reader.mode == "mono": + tel_pointing_table = Table( + { + "time": tel_identifiers["time"], + "azimuth": tel_identifiers["pointing_azimuth"], + "altitude": tel_identifiers["pointing_altitude"], + } + ) + write_table( + tel_pointing_table, + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 telescope pointing table was stored in '%s' under '%s'", + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + ) + pointing_info = vstack(pointing_info) + if self.dl1dh_reader.mode == "stereo": + # Group the pointing information by subarray event keys + # TODO: This needs to be debugged with SST1M data + pointing_info_grouped = pointing_info.group_by(SUBARRAY_EVENT_KEYS) + pointing_mean = pointing_info_grouped.groups.aggregate(np.mean) + pointing_info = join( + all_identifiers, + pointing_mean, + keys=SUBARRAY_EVENT_KEYS, + ) + # TODO: use keep_order for astropy v7.0.0 + pointing_info.sort(SUBARRAY_EVENT_KEYS) + # Create the pointing table + pointing_table = Table( + { + "time": pointing_info["time"], + "array_azimuth": pointing_info["pointing_azimuth"], + "array_altitude": pointing_info["pointing_altitude"], + "array_ra": np.nan * np.ones(len(pointing_info)), + "array_dec": np.nan * np.ones(len(pointing_info)), + } + ) + # Save the pointing table to the output file + write_table( + pointing_table, + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 subarray pointing table was stored in '%s' under '%s'", + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + ) + return pointing_info + + def _create_feature_vectors_table( + self, + example_identifiers, + nonexample_identifiers=None, + classification_feature_vectors=None, + energy_feature_vectors=None, + direction_feature_vectors=None, + ): + """ + Create the table for the DL1 feature vectors. + + This method creates a table with the DL1 feature vectors for the example identifiers and fill NaNs for + non-example identifiers. The feature vectors are stored in the columns of the table. The table also + contains a column for the valid predictions. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + nonexample_identifiers : astropy.table.Table or None + Table containing the non-example identifiers to fill the NaNs. + classification_feature_vectors : np.ndarray or None + Array containing the classification feature vectors. + energy_feature_vectors : np.ndarray or None + Array containing the energy feature vectors. + direction_feature_vectors : np.ndarray or None + Array containing the direction feature vectors. + + Returns: + -------- + feature_vector_table : astropy.table.Table + Table containing the DL1 feature vectors for the example and non-example identifiers. + """ + # Create the feature vector table + feature_vector_table = example_identifiers.copy() + feature_vector_table.remove_columns( + ["pointing_azimuth", "pointing_altitude", "time"] + ) + columns_list, shapes_list = [], [] + if classification_feature_vectors is not None: + is_valid_col = ~np.isnan( + np.min(classification_feature_vectors, axis=1), dtype=bool + ) + feature_vector_table.add_column( + classification_feature_vectors, + name=f"{self.prefix}_tel_classification_feature_vectors", + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_classification_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + classification_feature_vectors.shape[1], + ) + ) + if energy_feature_vectors is not None: + is_valid_col = ~np.isnan(np.min(energy_feature_vectors, axis=1), dtype=bool) + feature_vector_table.add_column( + energy_feature_vectors, name=f"{self.prefix}_tel_energy_feature_vectors" + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_energy_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + energy_feature_vectors.shape[1], + ) + ) + if direction_feature_vectors is not None: + is_valid_col = ~np.isnan( + np.min(direction_feature_vectors, axis=1), dtype=bool + ) + feature_vector_table.add_column( + direction_feature_vectors, + name=f"{self.prefix}_tel_geometry_feature_vectors", + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_geometry_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + direction_feature_vectors.shape[1], + ) + ) + # Produce output table with NaNs for missing predictions + if nonexample_identifiers is not None: + if len(nonexample_identifiers) > 0: + nan_table = self._create_nan_table( + nonexample_identifiers, + columns=columns_list, + shapes=shapes_list, + ) + feature_vector_table = vstack([feature_vector_table, nan_table]) + is_valid_col = np.concatenate( + (is_valid_col, np.zeros(len(nonexample_identifiers), dtype=bool)) + ) + # Add is_valid column to the feature vector table + feature_vector_table.add_column( + is_valid_col, + name=f"{self.prefix}_tel_is_valid", + ) + return feature_vector_table + + +class MonoPredictCTLearnModel(PredictCTLearnModel): + """ + Tool to predict the gammaness, energy and arrival direction from monoscopic R1/DL1 data using CTLearn models. + + This tool extends the ``PredictCTLearnModel`` to specifically handle monoscopic R1/DL1 data. The prediction + is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. + It also stores the telescope pointing monitoring and DL1 feature vectors (if selected) in the output file. + + Attributes + ---------- + name : str + Name of the tool. + description : str + Description of the tool. + examples : str + Examples of how to use the tool. + + Methods + ------- + start() + Start the tool. + _store_mc_telescope_pointing(all_identifiers) + Store the telescope pointing table for the mono mode for MC simulation. + """ + + name = "ctlearn-predict-mono-model" + description = __doc__ + + examples = """ + To predict from pixel-wise image data in mono mode using trained CTLearn models: + > ctlearn-predict-mono-model \\ + --input_url input.dl1.h5 \\ + --PredictCTLearnModel.batch_size=64 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ + --DLImageReader.channels=cleaned_image \\ + --DLImageReader.channels=cleaned_relative_peak_time \\ + --DLImageReader.image_mapper_type=BilinearMapper \\ + --type_model="/path/to/your/mono/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/mono/energy/ctlearn_model.cpk" \\ + --cameradirection_model="/path/to/your/mono/cameradirection/ctlearn_model.cpk" \\ + --dl1-features \\ + --use-HDF5Merger \\ + --no-dl1-images \\ + --no-true-images \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + + To predict from pixel-wise waveform data in mono mode using trained CTLearn models: + > ctlearn-predict-mono-model \\ + --input_url input.r1.h5 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLWaveformReader \\ + --DLWaveformReader.sequnce_length=20 \\ + --DLWaveformReader.image_mapper_type=BilinearMapper \\ + --type_model="/path/to/your/mono_waveform/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/mono_waveform/energy/ctlearn_model.cpk" \\ + --cameradirection_model="/path/to/your/mono_waveform/cameradirection/ctlearn_model.cpk" \\ + --use-HDF5Merger \\ + --no-r0-waveforms \\ + --no-r1-waveforms \\ + --no-dl1-images \\ + --no-true-images \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + """ + + stereo_combiner_cls = ComponentName( + StereoCombiner, + default_value="StereoMeanCombiner", + help="Which stereo combination method to use after the monoscopic reconstruction.", + ).tag(config=True) + + def start(self): + self.log.info("Processing the telescope pointings...") + # Retrieve the IDs from the dl1dh for the prediction tables + example_identifiers = self.dl1dh_reader.example_identifiers.copy() + example_identifiers.keep_columns(TELESCOPE_EVENT_KEYS) + all_identifiers = self.dl1dh_reader.tel_trigger_table.copy() + all_identifiers.keep_columns(TELESCOPE_EVENT_KEYS + ["time"]) + nonexample_identifiers = setdiff( + all_identifiers, example_identifiers, keys=TELESCOPE_EVENT_KEYS + ) + nonexample_identifiers.remove_column("time") + # Pointing table for the mono mode for MC simulation + if self.dl1dh_reader.process_type == ProcessType.Simulation: + pointing_info = self._store_mc_telescope_pointing(all_identifiers) + + # Pointing table for the observation mode + if self.dl1dh_reader.process_type == ProcessType.Observation: + pointing_info = super()._store_pointing(all_identifiers) + + self.log.info("Starting the prediction...") + classification_feature_vectors = None + if self.load_type_model_from is not None: + self.type_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.PARTICLE_TYPE, + parent=self, + ) + # Predict the energy of the primary particle + classification_table, classification_feature_vectors = ( + super()._predict_classification(example_identifiers) + ) + if self.dl2_telescope: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_prediction"], + shapes=[(len(nonexample_identifiers),)], + ) + classification_table = vstack([classification_table, nan_table]) + # Add is_valid column to the energy table + classification_table.add_column( + ~np.isnan( + classification_table[f"{self.prefix}_tel_prediction"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + classification_table, + ParticleClassificationContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = classification_table["tel_id"] == tel_id + classification_tel_table = classification_table[telescope_mask] + classification_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file for the selected telescope + write_table( + classification_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info("Processing and storing the subarray type prediction...") + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_classification_table = self.type_stereo_combiner.predict_table( + classification_table + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if ( + subarray_classification_table[f"{self.prefix}_telescopes"].dtype + != np.bool_ + ): + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + ( + len(subarray_classification_table), + len(self.dl1dh_reader.tel_ids), + ), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_classification_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_classification_table[f"{self.prefix}_telescopes"] = ( + reco_telescopes + ) + # Save the prediction to the output file + write_table( + subarray_classification_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + ) + energy_feature_vectors = None + if self.load_energy_model_from is not None: + self.energy_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.ENERGY, + parent=self, + ) + # Predict the energy of the primary particle + energy_table, energy_feature_vectors = super()._predict_energy( + example_identifiers + ) + if self.dl2_telescope: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_energy"], + shapes=[(len(nonexample_identifiers),)], + ) + energy_table = vstack([energy_table, nan_table]) + # Add is_valid column to the energy table + energy_table.add_column( + ~np.isnan( + energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + energy_table, + ReconstructedEnergyContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = energy_table["tel_id"] == tel_id + energy_tel_table = energy_table[telescope_mask] + energy_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file + write_table( + energy_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info( + "Processing and storing the subarray energy prediction..." + ) + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_energy_table = self.energy_stereo_combiner.predict_table( + energy_table + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if subarray_energy_table[f"{self.prefix}_telescopes"].dtype != np.bool_: + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + (len(subarray_energy_table), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_energy_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_energy_table[f"{self.prefix}_telescopes"] = reco_telescopes + # Save the prediction to the output file + write_table( + subarray_energy_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + ) + direction_feature_vectors = None + if self.load_cameradirection_model_from is not None: + self.geometry_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.GEOMETRY, + parent=self, + ) + # Join the prediction table with the telescope pointing table + example_identifiers = join( + left=example_identifiers, + right=pointing_info, + keys=TELESCOPE_EVENT_KEYS, + ) + # Predict the arrival direction of the primary particle + direction_table, direction_feature_vectors = ( + super()._predict_cameradirection(example_identifiers) + ) + direction_tel_tables = [] + if self.dl2_telescope: + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = direction_table["tel_id"] == tel_id + direction_tel_table = direction_table[telescope_mask] + direction_tel_table = super()._transform_cam_coord_offsets_to_sky( + direction_tel_table + ) + # Produce output table with NaNs for missing predictions + nan_telescope_mask = nonexample_identifiers["tel_id"] == tel_id + nonexample_identifiers_tel = nonexample_identifiers[ + nan_telescope_mask + ] + if len(nonexample_identifiers_tel) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers_tel, + columns=[f"{self.prefix}_tel_alt", f"{self.prefix}_tel_az"], + shapes=[ + (len(nonexample_identifiers_tel),), + (len(nonexample_identifiers_tel),), + ], + ) + direction_tel_table = vstack([direction_tel_table, nan_table]) + direction_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Add is_valid column to the direction table + direction_tel_table.add_column( + ~np.isnan( + direction_tel_table[f"{self.prefix}_tel_alt"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + direction_tel_table, + ReconstructedGeometryContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + direction_tel_tables.append(direction_tel_table) + # Save the prediction to the output file + write_table( + direction_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info( + "Processing and storing the subarray geometry prediction..." + ) + # Stack the telescope tables to the subarray table + direction_tel_tables = vstack(direction_tel_tables) + # Sort the table by the telescope event keys + direction_tel_tables.sort(TELESCOPE_EVENT_KEYS) + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_direction_table = self.geometry_stereo_combiner.predict_table( + direction_tel_tables + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if ( + subarray_direction_table[f"{self.prefix}_telescopes"].dtype + != np.bool_ + ): + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + (len(subarray_direction_table), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_direction_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_direction_table[f"{self.prefix}_telescopes"] = ( + reco_telescopes + ) + # Save the prediction to the output file + write_table( + subarray_direction_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + ) + # Create the feature vector table if the DL1 features are enabled + if self.dl1_features: + self.log.info("Processing and storing dl1 feature vectors...") + feature_vector_table = super()._create_feature_vectors_table( + example_identifiers, + nonexample_identifiers, + classification_feature_vectors, + energy_feature_vectors, + direction_feature_vectors, + ) + # Loop over the selected telescopes and store the feature vectors + # for each telescope in the output file. The feature vectors are stored + # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = feature_vector_table["tel_id"] == tel_id + feature_vectors_tel_table = feature_vector_table[telescope_mask] + feature_vectors_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file + write_table( + feature_vectors_tel_table, + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 feature vectors was stored in '%s' under '%s'", + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", + ) + + def _store_mc_telescope_pointing(self, all_identifiers): + """ + Store the telescope pointing table from MC simulation to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the telescope pointing information. + """ + # Create the pointing table for each telescope + pointing_info = [] + for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: + # Pointing table for the mono mode + tel_pointing = self.dl1dh_reader.get_tel_pointing(self.input_url, tel_id) + tel_pointing.rename_column("telescope_pointing_azimuth", "pointing_azimuth") + tel_pointing.rename_column( + "telescope_pointing_altitude", "pointing_altitude" + ) + # Join the prediction table with the telescope pointing table + tel_pointing = join( + left=tel_pointing, + right=all_identifiers, + keys=["obs_id", "tel_id"], + ) + # TODO: use keep_order for astropy v7.0.0 + tel_pointing.sort(TELESCOPE_EVENT_KEYS) + # Retrieve the example identifiers for the selected telescope + tel_pointing_table = Table( + { + "time": tel_pointing["time"], + "azimuth": tel_pointing["pointing_azimuth"], + "altitude": tel_pointing["pointing_altitude"], + } + ) + write_table( + tel_pointing_table, + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 telescope pointing table was stored in '%s' under '%s'", + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + ) + pointing_info.append(tel_pointing) + pointing_info = vstack(pointing_info) + return pointing_info + + +class StereoPredictCTLearnModel(PredictCTLearnModel): + """ + Tool to predict the gammaness, energy and arrival direction from R1/DL1 stereoscopic data using CTLearn models. + + This tool extends the ``PredictCTLearnModel`` to specifically handle stereoscopic R1/DL1 data. The prediction + is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. + It also stores the telescope/subarray pointing monitoring and DL1 feature vectors (if selected) in the output file. + + Attributes + ---------- + name : str + Name of the tool. + description : str + Description of the tool. + examples : str + Examples of how to use the tool. + + Methods + ------- + start() + Start the tool. + _store_mc_subarray_pointing(all_identifiers) + Store the subarray pointing table for the stereo mode for MC simulation. + """ + + name = "ctlearn-predict-stereo-model" + description = __doc__ + + examples = """ + To predict from pixel-wise image data in stereo mode using trained CTLearn models: + > ctlearn-predict-stereo-model \\ + --input_url input.dl1.h5 \\ + --PredictCTLearnModel.batch_size=16 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ + --DLImageReader.channels=cleaned_image \\ + --DLImageReader.channels=cleaned_relative_peak_time \\ + --DLImageReader.image_mapper_type=BilinearMapper \\ + --DLImageReader.mode=stereo \\ + --DLImageReader.min_telescopes=2 \\ + --PredictCTLearnModel.stack_telescope_images=True \\ + --type_model="/path/to/your/stereo/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/stereo/energy/ctlearn_model.cpk" \\ + --skydirection_model="/path/to/your/stereo/skydirection/ctlearn_model.cpk" \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + """ + + def start(self): + self.log.info("Processing the telescope pointings...") + # Retrieve the IDs from the dl1dh for the prediction tables + example_identifiers = self.dl1dh_reader.unique_example_identifiers.copy() + example_identifiers.keep_columns(SUBARRAY_EVENT_KEYS) + all_identifiers = self.dl1dh_reader.subarray_trigger_table.copy() + all_identifiers.keep_columns(SUBARRAY_EVENT_KEYS + ["time"]) + nonexample_identifiers = setdiff( + all_identifiers, example_identifiers, keys=SUBARRAY_EVENT_KEYS + ) + nonexample_identifiers.remove_column("time") + # Construct the survival telescopes for each event of the example_identifiers + survival_telescopes = [] + for subarray_event in self.dl1dh_reader.example_identifiers_grouped.groups: + survival_mask = np.zeros(len(self.dl1dh_reader.tel_ids), dtype=bool) + survival_tels = [ + self.dl1dh_reader.subarray.tel_indices[tel_id] + for tel_id in subarray_event["tel_id"].data + ] + survival_mask[survival_tels] = True + survival_telescopes.append(survival_mask) + # Add the survival telescopes to the example_identifiers + example_identifiers.add_column( + survival_telescopes, name=f"{self.prefix}_telescopes" + ) + # Pointing table for the stereo mode for MC simulation + if self.dl1dh_reader.process_type == ProcessType.Simulation: + pointing_info = self._store_mc_subarray_pointing(all_identifiers) + + # Pointing table for the observation mode + if self.dl1dh_reader.process_type == ProcessType.Observation: + pointing_info = super()._store_pointing(all_identifiers) + + self.log.info("Starting the prediction...") + classification_feature_vectors = None + if self.load_type_model_from is not None: + # Predict the energy of the primary particle + classification_table, classification_feature_vectors = ( + super()._predict_classification(example_identifiers) + ) + if self.dl2_subarray: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_prediction"], + shapes=[(len(nonexample_identifiers),)], + ) + classification_table = vstack([classification_table, nan_table]) + # Add is_valid column to the energy table + classification_table.add_column( + ~np.isnan( + classification_table[f"{self.prefix}_tel_prediction"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Rename the columns for the stereo mode + classification_table.rename_column( + f"{self.prefix}_tel_prediction", f"{self.prefix}_prediction" + ) + classification_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + classification_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + classification_table, + ParticleClassificationContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + classification_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + ) + energy_feature_vectors = None + if self.load_energy_model_from is not None: + # Predict the energy of the primary particle + energy_table, energy_feature_vectors = super()._predict_energy( + example_identifiers + ) + if self.dl2_subarray: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_energy"], + shapes=[(len(nonexample_identifiers),)], + ) + energy_table = vstack([energy_table, nan_table]) + # Add is_valid column to the energy table + energy_table.add_column( + ~np.isnan( + energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Rename the columns for the stereo mode + energy_table.rename_column( + f"{self.prefix}_tel_energy", f"{self.prefix}_energy" + ) + energy_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + energy_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + energy_table, + ReconstructedEnergyContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + energy_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + ) + direction_feature_vectors = None + if self.load_skydirection_model_from is not None: + # Join the prediction table with the telescope pointing table + example_identifiers = join( + left=example_identifiers, + right=pointing_info, + keys=SUBARRAY_EVENT_KEYS, + ) + # Predict the arrival direction of the primary particle + direction_table, direction_feature_vectors = super()._predict_skydirection( + example_identifiers + ) + if self.dl2_subarray: + # Transform the spherical coordinate offsets to sky coordinates + direction_table = super()._transform_spher_coord_offsets_to_sky( + direction_table + ) + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_alt", f"{self.prefix}_az"], + shapes=[ + (len(nonexample_identifiers),), + (len(nonexample_identifiers),), + ], + ) + direction_table = vstack([direction_table, nan_table]) + # Add is_valid column to the direction table + direction_table.add_column( + ~np.isnan(direction_table[f"{self.prefix}_alt"].data, dtype=bool), + name=f"{self.prefix}_is_valid", + ) + direction_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + direction_table, + ReconstructedGeometryContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + direction_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + ) + + # Create the feature vector table if the DL1 features are enabled + if self.dl1_features: + self.log.info("Processing and storing dl1 feature vectors...") + feature_vector_table = super()._create_feature_vectors_table( + example_identifiers, + nonexample_identifiers, + classification_feature_vectors, + energy_feature_vectors, + direction_feature_vectors, + ) + # Loop over the selected telescopes and store the feature vectors + # for each telescope in the output file. The feature vectors are stored + # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. + # Rename the columns for the stereo mode + feature_vector_table.rename_column( + f"{self.prefix}_tel_classification_feature_vectors", + f"{self.prefix}_classification_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_energy_feature_vectors", + f"{self.prefix}_energy_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_geometry_feature_vectors", + f"{self.prefix}_geometry_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + feature_vector_table.sort(SUBARRAY_EVENT_KEYS) + # Save the prediction to the output file + write_table( + feature_vector_table, + self.output_path, + f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 feature vectors was stored in '%s' under '%s'", + self.output_path, + f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", + ) + + def _store_mc_subarray_pointing(self, all_identifiers): + """ + Store the subarray pointing table from MC simulation to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the subarray pointing information. + """ + # Read the subarray pointing table + pointing_info = read_table( + self.input_url, + f"{SIMULATION_CONFIG_TABLE}", + ) + # Assuming min_az = max_az and min_alt = max_alt + pointing_info.keep_columns(["obs_id", "min_az", "min_alt"]) + pointing_info.rename_column("min_az", "pointing_azimuth") + pointing_info.rename_column("min_alt", "pointing_altitude") + # Join the prediction table with the telescope pointing table + pointing_info = join( + left=pointing_info, + right=all_identifiers, + keys=["obs_id"], + ) + # TODO: use keep_order for astropy v7.0.0 + pointing_info.sort(SUBARRAY_EVENT_KEYS) + # Create the pointing table + pointing_table = Table( + { + "time": pointing_info["time"], + "array_azimuth": pointing_info["pointing_azimuth"], + "array_altitude": pointing_info["pointing_altitude"], + "array_ra": np.nan * np.ones(len(pointing_info)), + "array_dec": np.nan * np.ones(len(pointing_info)), + } + ) + # Save the pointing table to the output file + write_table( + pointing_table, + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 subarray pointing table was stored in '%s' under '%s'", + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + ) + return pointing_info + + +def mono_tool(): + # Run the tool + mono_tool = MonoPredictCTLearnModel() + mono_tool.run() + + +def stereo_tool(): + # Run the tool + stereo_tool = StereoPredictCTLearnModel() + stereo_tool.run() + + +if __name__ == "mono_tool": + mono_tool() + +if __name__ == "stereo_tool": + stereo_tool() diff --git a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index 185a1f51..40f44006 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -16,6 +16,7 @@ ReconstructedEnergyContainer, ) from ctapipe.coordinates import CameraFrame +from ctapipe.coordinates import CameraFrame from ctapipe.core import Tool from ctapipe.core.tool import ToolConfigurationError from ctapipe.core.traits import ( @@ -28,9 +29,11 @@ ComponentName, Dict, UseEnum, + UseEnum, classes_with_traits, ) from ctapipe.instrument.optics import FocalLengthKind +from ctapipe.instrument.optics import FocalLengthKind from ctapipe.io import read_table, write_table from ctapipe.io.hdf5dataformat import ( DL1_SUBARRAY_TRIGGER_TABLE, @@ -47,7 +50,8 @@ ) from ctapipe.reco.utils import add_defaults_and_meta -from ctlearn.core.keras.model import LoadedModel +from ctlearn.core.model import LoadedModel +from ctlearn.utils import get_lst1_subarray_description from ctlearn.utils import get_lst1_subarray_description from dl1_data_handler.image_mapper import ImageMapper from dl1_data_handler.reader import ( @@ -951,4 +955,4 @@ def main(): if __name__ == "main": - main() + main() \ No newline at end of file diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index bcd04d87..e7715dd6 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -342,6 +342,12 @@ class PredictCTLearnModel(Tool): ), ).tag(config=True) + framework_type = CaselessStrEnum( + ["pytorch", "keras"], + default_value="keras", + help="Framework to use: pytorch or keras", + ).tag(config=True) + aliases = { ("i", "input_url"): "PredictCTLearnModel.input_url", ("t", "type_model"): "PredictCTLearnModel.load_type_model_from", @@ -352,6 +358,7 @@ class PredictCTLearnModel(Tool): ): "PredictCTLearnModel.load_cameradirection_model_from", ("s", "skydirection_model"): "PredictCTLearnModel.load_skydirection_model_from", ("o", "output"): "PredictCTLearnModel.output_path", + ("f", "framework"): "PredictCTLearnModel.framework_type", } flags = { @@ -694,6 +701,8 @@ def _predict_with_model(self, model_path): feature_vectors : np.ndarray Feature vectors extracted from the backbone model. """ + if self.framework_type == "keras": + from ctlearn.tools.predict.keras.predic_model_keras import _predict_with_model # Create a new DLDataLoader for each task # It turned out to be more robust to initialize the DLDataLoader separately. data_loader = DLDataLoader.create( diff --git a/ctlearn/tools/predict_model_main.py b/ctlearn/tools/predict_model_main.py new file mode 100644 index 00000000..a481f1f1 --- /dev/null +++ b/ctlearn/tools/predict_model_main.py @@ -0,0 +1,1877 @@ +""" +Tools to predict the gammaness, energy and arrival direction in monoscopic and stereoscopic mode using ``CTLearnModel`` on R1/DL1 data using the ``DLDataReader`` and ``DLDataLoader``. +""" + +import atexit +import pathlib +import numpy as np +import os +import tensorflow as tf +import keras + +from astropy import units as u +from astropy.coordinates.earth import EarthLocation +from astropy.coordinates import AltAz, SkyCoord +from astropy.table import ( + Table, + hstack, + vstack, + join, + setdiff, +) + +from ctapipe.containers import ( + ParticleClassificationContainer, + ReconstructedGeometryContainer, + ReconstructedEnergyContainer, +) +from ctapipe.coordinates import CameraFrame, NominalFrame +from ctapipe.core import Tool +from ctapipe.core.tool import ToolConfigurationError +from ctapipe.core.traits import ( + Bool, + Int, + Path, + flag, + Set, + Dict, + List, + CaselessStrEnum, + ComponentName, + Unicode, + classes_with_traits, +) +from ctapipe.monitoring.interpolation import PointingInterpolator +from ctapipe.io import read_table, write_table, HDF5Merger +from ctapipe.reco.reconstructor import ReconstructionProperty +from ctapipe.reco.stereo_combination import StereoCombiner +from ctapipe.reco.utils import add_defaults_and_meta +from dl1_data_handler.reader import ( + DLDataReader, + ProcessType, + LST_EPOCH, +) +from ctlearn.core.loader import DLDataLoader + +SIMULATION_CONFIG_TABLE = "/configuration/simulation/run" +FIXED_POINTING_GROUP = "/configuration/telescope/pointing" +POINTING_GROUP = "/dl1/monitoring/telescope/pointing" +SUBARRAY_POINTING_GROUP = "/dl1/monitoring/subarray/pointing" +DL1_TELESCOPE_GROUP = "/dl1/event/telescope" +DL1_SUBARRAY_GROUP = "/dl1/event/subarray" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +DL2_TELESCOPE_GROUP = "/dl2/event/telescope" +SUBARRAY_EVENT_KEYS = ["obs_id", "event_id"] +TELESCOPE_EVENT_KEYS = ["obs_id", "event_id", "tel_id"] + +__all__ = [ + "PredictCTLearnModel", + "MonoPredictCTLearnModel", + "StereoPredictCTLearnModel", +] + + +class PredictCTLearnModel(Tool): + """ + Base tool to predict the gammaness, energy and arrival direction from R1/DL1 data using CTLearn models. + + This class handles the prediction of the gammaness, energy and arrival direction from pixel-wise image + or waveform data. It also supports the extraction of the feature vectors from the backbone submodel to + store them in the output file. The input data is loaded from the input url using the + ``~dl1_data_handler.reader.DLDataReader`` and ``~ctlearn.core.loader.DLDataLoader``. + The prediction is performed using the CTLearn models. The data is stored in the output file + following the ctapipe DL2 data format. The ``start`` method is implemented in the subclasses to + handle the prediction for mono and stereo mode. + + Attributes + ---------- + input_url : pathlib.Path + Input ctapipe HDF5 files including pixel-wise image or waveform data. + use_HDF5Merger : bool + Set whether to use the HDF5Merger component to copy the selected tables from the input file to the output file. + dl1_features : bool + Set whether to include the dl1 feature vectors in the output file. + dl2_telescope : bool + Set whether to include dl2 telescope-event-wise data in the output file. + dl2_subarray : bool + Set whether to include dl2 subarray-event-wise data in the output file. + dl1dh_reader : dl1_data_handler.reader.DLDataReader + DLDataReader object to read the data. + dl1dh_reader_type : str + Type of the DLDataReader to use for the prediction. + stack_telescope_images : bool + Set whether to stack the telescope images in the data loader. Requires ``stereo``. + sort_by_intensity : bool + Set whether to sort the telescope images by intensity in the data loader. Requires ``stereo``. + prefix : str + Name of the reconstruction algorithm used to generate the dl2 data. + load_type_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the classification of the primary particle type. + load_energy_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression of the primary particle energy. + load_cameradirection_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression + of the primary particle arrival direction based on camera coordinate offsets. + load_cameradirection_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression + of the primary particle arrival direction based on spherical coordinate offsets. + output_path : pathlib.Path + Output path to save the dl2 prediction results. + overwrite_tables : bool + Overwrite the table in the output file if it exists. + keras_verbose : int + Verbosity mode of Keras during the prediction. + strategy : tf.distribute.Strategy + MirroredStrategy to distribute the prediction. + data_loader : ctlearn.core.loader.DLDataLoader + DLDataLoader object to load the data. + indices : list of int + List of indices for the data loaders. + batch_size : int + Size of the batch to perform inference of the neural network. + last_batch_size : int + Size of the last batch in the data loaders. + + Methods + ------- + setup() + Set up the tool. + finish() + Finish the tool. + _predict_with_model(model_path) + Load and predict with a CTLearn model. + _predict_classification(example_identifiers) + Predict the classification of the primary particle type. + _predict_energy(example_identifiers) + Predict the energy of the primary particle. + _predict_cameradirection(example_identifiers) + Predict the arrival direction of the primary particle based on camera coordinate offsets. + _predict_skydirection(example_identifiers) + Predict the arrival direction of the primary particle based on spherical coordinate offsets. + _transform_cam_coord_offsets_to_sky(table) + Transform to camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + _transform_spher_coord_offsets_to_sky(table) + Transform to spherical coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + _create_nan_table(nonexample_identifiers, columns, shapes) + Create a table with NaNs for missing predictions. + _store_pointing(all_identifiers) + Store the telescope pointing table from to the output file. + _create_feature_vectors_table(example_identifiers, nonexample_identifiers, classification_feature_vectors, energy_feature_vectors, direction_feature_vectors) + Create the table for the DL1 feature vectors. + """ + + input_url = Path( + help="Input ctapipe HDF5 files including pixel-wise image or waveform data", + allow_none=True, + exists=True, + directory_ok=False, + file_ok=True, + ).tag(config=True) + + use_HDF5Merger = Bool( + default_value=True, + allow_none=False, + help=( + "Set whether to use the HDF5Merger component to copy the selected tables " + "from the input file to the output file. CAUTION: This can only be used " + "if the output file not exists." + ), + ).tag(config=True) + + dl1_features = Bool( + default_value=False, + allow_none=False, + help="Set whether to include the dl1 feature vectors in the output file.", + ).tag(config=True) + + dl2_telescope = Bool( + default_value=True, + allow_none=False, + help="Set whether to include dl2 telescope-event-wise data in the output file.", + ).tag(config=True) + + dl2_subarray = Bool( + default_value=True, + allow_none=False, + help="Set whether to include dl2 subarray-event-wise data in the output file.", + ).tag(config=True) + + dl1dh_reader_type = ComponentName(DLDataReader, default_value="DLImageReader").tag( + config=True + ) + + stack_telescope_images = Bool( + default_value=False, + allow_none=False, + help=( + "Set whether to stack the telescope images in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + sort_by_intensity = Bool( + default_value=False, + allow_none=False, + help=( + "Set whether to sort the telescope images by intensity in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + prefix = Unicode( + default_value="CTLearn", + allow_none=False, + help="Name of the reconstruction algorithm used to generate the dl2 data.", + ).tag(config=True) + + load_type_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the classification " + "of the primary particle type." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_energy_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle energy." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_cameradirection_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle arrival direction based on camera coordinate offsets." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_skydirection_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle arrival direction based on spherical coordinate offsets." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + batch_size = Int( + default_value=64, + allow_none=False, + help="Size of the batch to perform inference of the neural network.", + ).tag(config=True) + + output_path = Path( + default_value="./output.dl2.h5", + allow_none=False, + help="Output path to save the dl2 prediction results", + ).tag(config=True) + + overwrite_tables = Bool( + default_value=True, + allow_none=False, + help="Overwrite the table in the output file if it exists", + ).tag(config=True) + + keras_verbose = Int( + default_value=1, + min=0, + max=2, + allow_none=False, + help=( + "Verbosity mode of Keras during the prediction: " + "0 = silent, 1 = progress bar, 2 = one line per call." + ), + ).tag(config=True) + + aliases = { + ("i", "input_url"): "PredictCTLearnModel.input_url", + ("t", "type_model"): "PredictCTLearnModel.load_type_model_from", + ("e", "energy_model"): "PredictCTLearnModel.load_energy_model_from", + ( + "d", + "cameradirection_model", + ): "PredictCTLearnModel.load_cameradirection_model_from", + ("s", "skydirection_model"): "PredictCTLearnModel.load_skydirection_model_from", + ("o", "output"): "PredictCTLearnModel.output_path", + } + + flags = { + **flag( + "dl1-features", + "PredictCTLearnModel.dl1_features", + "Include dl1 features", + "Exclude dl1 features", + ), + **flag( + "dl2-telescope", + "PredictCTLearnModel.dl2_telescope", + "Include dl2 telescope-event-wise data in the output file", + "Exclude dl2 telescope-event-wise data in the output file", + ), + **flag( + "dl2-subarray", + "PredictCTLearnModel.dl2_subarray", + "Include dl2 telescope-event-wise data in the output file", + "Exclude dl2 telescope-event-wise data in the output file", + ), + **flag( + "use-HDF5Merger", + "PredictCTLearnModel.use_HDF5Merger", + "Copy data using the HDF5Merger component (CAUTION: This can not be used if the output file already exists)", + "Do not copy data using the HDF5Merger component", + ), + **flag( + "r0-waveforms", + "HDF5Merger.r0_waveforms", + "Include r0 waveforms", + "Exclude r0 waveforms", + ), + **flag( + "r1-waveforms", + "HDF5Merger.r1_waveforms", + "Include r1 waveforms", + "Exclude r1 waveforms", + ), + **flag( + "dl1-parameters", + "HDF5Merger.dl1_parameters", + "Include dl1 parameters", + "Exclude dl1 parameters", + ), + **flag( + "dl1-images", + "HDF5Merger.dl1_images", + "Include dl1 images", + "Exclude dl1 images", + ), + **flag( + "true-parameters", + "HDF5Merger.true_parameters", + "Include true parameters", + "Exclude true parameters", + ), + **flag( + "true-images", + "HDF5Merger.true_images", + "Include true images", + "Exclude true images", + ), + } + + classes = classes_with_traits(DLDataReader) + + def setup(self): + # Check if the ctapipe HDF5Merger component is enabled + if self.use_HDF5Merger: + if os.path.exists(self.output_path): + raise ToolConfigurationError( + f"The output file '{self.output_path}' already exists. Please use " + "'--no-use-HDF5Merger' to disable the usage of the HDF5Merger component." + ) + # Copy selected tables from the input file to the output file + self.log.info("Copying to output destination.") + with HDF5Merger(self.output_path, parent=self) as merger: + merger(self.input_url) + else: + self.log.info( + "No copy to output destination, since the usage of the HDF5Merger component is disabled." + ) + + # Create a MirroredStrategy. + self.strategy = tf.distribute.MirroredStrategy() + atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore + self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) + + # Set up the data reader + self.log.info("Loading data reader:") + self.log.info("For a large dataset, this may take a while...") + self.dl1dh_reader = DLDataReader.from_name( + self.dl1dh_reader_type, + input_url_signal=[self.input_url], + parent=self, + ) + self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) + # Check if the number of events is enough to form a batch + if self.dl1dh_reader._get_n_events() < self.batch_size: + raise ToolConfigurationError( + f"{self.dl1dh_reader._get_n_events()} events are not enough " + f"to form a batch of size {self.batch_size}. Reduce the batch size." + ) + # Set the indices for the data loaders + self.indices = list(range(self.dl1dh_reader._get_n_events())) + self.last_batch_size = len(self.indices) % ( + self.batch_size * self.strategy.num_replicas_in_sync + ) + + def finish(self): + self.log.info("Tool is shutting down") + + def _predict_with_model(self, model_path): + """ + Load and predict with a CTLearn model. + + Load a model from the specified path and predict the data using the loaded model. + If a last batch loader is provided, predict the last batch and stack the results. + + Parameters + ---------- + model_path : str + Path to a Keras model file (Keras3) or directory (Keras2). + + Returns + ------- + predict_data : astropy.table.Table + Table containing the prediction results. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + # Create a new DLDataLoader for each task + # It turned out to be more robust to initialize the DLDataLoader separately. + data_loader = DLDataLoader( + self.dl1dh_reader, + self.indices, + tasks=[], + batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + # Keras is only considering the last complete batch. + # In prediction mode we don't want to loose the last + # uncomplete batch, so we are creating an additional + # batch generator for the remaining events. + data_loader_last_batch = None + if self.last_batch_size > 0: + last_batch_indices = self.indices[-self.last_batch_size :] + data_loader_last_batch = DLDataLoader( + self.dl1dh_reader, + last_batch_indices, + tasks=[], + batch_size=self.last_batch_size, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + # Load the model from the specified path + model = keras.saving.load_model(model_path) + prediction_colname = ( + model.layers[-1].name if model.layers[-1].name != "softmax" else "type" + ) + backbone_model, feature_vectors = None, None + if self.dl1_features: + # Get the backbone model which is the second layer of the model + backbone_model = model.get_layer(index=1) + # Create a new head model with the same layers as the original model. + # The output of the backbone model is the input of the head model. + backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) + x = backbone_output_shape + for layer in model.layers[2:]: + x = layer(x) + head = keras.Model(inputs=backbone_output_shape, outputs=x) + # Apply the backbone model with the data loader to retrieve the feature vectors + feature_vectors = backbone_model.predict( + data_loader, verbose=self.keras_verbose + ) + # Apply the head model with the feature vectors to retrieve the prediction + predict_data = Table( + { + prediction_colname: head.predict( + feature_vectors, verbose=self.keras_verbose + ) + } + ) + # Predict the last batch and stack the results to the prediction data + if data_loader_last_batch is not None: + feature_vectors_last_batch = backbone_model.predict( + data_loader_last_batch, verbose=self.keras_verbose + ) + feature_vectors = np.concatenate( + (feature_vectors, feature_vectors_last_batch) + ) + predict_data = vstack( + [ + predict_data, + Table( + { + prediction_colname: head.predict( + feature_vectors_last_batch, + verbose=self.keras_verbose, + ) + } + ), + ] + ) + else: + # Predict the data using the loaded model + predict_data = model.predict(data_loader, verbose=self.keras_verbose) + # Create a astropy table with the prediction results + # The classification task has a softmax layer as the last layer + # which returns the probabilities for each class in an array, while + # the regression tasks have output neurons which returns the + # predicted value for the task in a dictionary. + if prediction_colname == "type": + predict_data = Table({prediction_colname: predict_data}) + else: + predict_data = Table(predict_data) + # Predict the last batch and stack the results to the prediction data + if data_loader_last_batch is not None: + predict_data_last_batch = model.predict( + data_loader_last_batch, verbose=self.keras_verbose + ) + if model.layers[-1].name == "type": + predict_data_last_batch = Table( + {prediction_colname: predict_data_last_batch} + ) + else: + predict_data_last_batch = Table(predict_data_last_batch) + predict_data = vstack([predict_data, predict_data_last_batch]) + return predict_data, feature_vectors + + def _predict_classification(self, example_identifiers): + """ + Predict the classification of the primary particle type. + + This method uses a pre-trained type model to predict the type of the primary particle + for a given set of example identifiers. The predicted classification score ('gammaness') + is added to the example identifiers table. + + Parameters: + ----------- + classification_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + predicted classification score ('gammaness'). + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the classification of the primary particle type..." + ) + # Predict the data using the loaded type_model + predict_data, feature_vectors = self._predict_with_model( + self.load_type_model_from + ) + # Create prediction table and add the predicted classification score ('gammaness') + classification_table = example_identifiers.copy() + classification_table.add_column( + predict_data["type"].T[1], name=f"{self.prefix}_tel_prediction" + ) + return classification_table, feature_vectors + + def _predict_energy(self, example_identifiers): + """ + Predict the energy of the primary particle. + + This method uses a pre-trained energy model to predict the energy of the primary particle + for a given set of example identifiers. The predicted energy is then converted from + log10(TeV) to TeV and added to the example identifiers table. + + Parameters: + ----------- + energy_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed energy in TeV. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info("Predicting for the regression of the primary particle energy...") + # Predict the data using the loaded energy_model + predict_data, feature_vectors = self._predict_with_model( + self.load_energy_model_from + ) + # Convert the reconstructed energy from log10(TeV) to TeV + reco_energy = u.Quantity( + np.power(10, np.squeeze(predict_data["energy"])), + unit=u.TeV, + ) + # Create prediction table and add the reconstructed energy in TeV + energy_table = example_identifiers.copy() + energy_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy") + return energy_table, feature_vectors + + def _predict_cameradirection(self, example_identifiers): + """ + Predict the arrival direction of the primary particle based on camera coordinate offsets. + + This method uses a pre-trained direction model to predict the arrival direction of the + primary particle for a given set of example identifiers. The predicted camera coordinate offsets + is added to the example identifiers table. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + + Returns: + -------- + cameradirection_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed camera coordinate offsets in x and y. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the regression of the primary particle arrival direction based on camera coordinate offsets..." + ) + # Predict the data using the loaded direction_model + predict_data, feature_vectors = self._predict_with_model( + self.load_cameradirection_model_from + ) + # For the direction task, the prediction is the camera coordinate offset in x and y + # from the telescope pointing. + cam_coord_offset_x = u.Quantity(predict_data["cameradirection"].T[0], unit=u.m) + cam_coord_offset_y = u.Quantity(predict_data["cameradirection"].T[1], unit=u.m) + # Create prediction table and add the reconstructed energy in TeV + cameradirection_table = example_identifiers.copy() + cameradirection_table.add_column(cam_coord_offset_x, name="cam_coord_offset_x") + cameradirection_table.add_column(cam_coord_offset_y, name="cam_coord_offset_y") + return cameradirection_table, feature_vectors + + def _predict_skydirection(self, example_identifiers): + """ + Predict the arrival direction of the primary particle based on spherical coordinate offsets. + + This method uses a pre-trained direction model to predict the arrival direction of the primary + particle for a given set of example identifiers. The predicted spherical coordinate offsets is + added to the example identifiers table. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + + Returns: + -------- + skydirection_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed spherical coordinate offsets in fov_lon and fov_lat. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the regression of the primary particle arrival direction based on spherical coordinate offsets..." + ) + # Predict the data using the loaded direction_model + predict_data, feature_vectors = self._predict_with_model( + self.load_skydirection_model_from + ) + # For the direction task, the prediction is the spherical offset in fov_lon and fov_lat + # from the telescope pointing. + fov_lon = u.Quantity(predict_data["skydirection"].T[0], unit=u.deg) + fov_lat = u.Quantity(predict_data["skydirection"].T[1], unit=u.deg) + # Create prediction table and add the reconstructed energy in TeV + skydirection_table = example_identifiers.copy() + skydirection_table.add_column(fov_lon, name="fov_lon") + skydirection_table.add_column(fov_lat, name="fov_lat") + return skydirection_table, feature_vectors + + def _transform_cam_coord_offsets_to_sky(self, table) -> Table: + """ + Transform the predicted camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + + This method converts the predicted camera coordinate offsets w.r.t. the telescope pointing + in the provided table to Alt/Az coordinates. It also removes the unnecessary columns + from the table that do not the ctapipe DL2 data format. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing the trigger time, telescope pointing, and predicted camera coordinate offsets. + + Returns: + -------- + table : astropy.table.Table + A Table with the Alt/Az coordinates following the ctapipe DL2 data format. + """ + # Get the telescope ID from the table + tel_id = table["tel_id"][0] + # Set the telescope position + tel_ground_frame = self.dl1dh_reader.subarray.tel_coords[ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] + # Set the trigger timestamp based on the process type + if self.dl1dh_reader.process_type == ProcessType.Simulation: + trigger_time = LST_EPOCH + elif self.dl1dh_reader.process_type == ProcessType.Observation: + trigger_time = table["time"] + # Set the telescope pointing with the trigger timestamp and the telescope position + altaz = AltAz( + location=tel_ground_frame.to_earth_location(), + obstime=trigger_time, + ) + # Set the telescope pointing + tel_pointing = SkyCoord( + az=table["pointing_azimuth"], + alt=table["pointing_altitude"], + frame=altaz, + ) + # Set the camera frame with the focal length and rotation of the camera + camera_frame = CameraFrame( + focal_length=self.dl1dh_reader.subarray.tel[ + tel_id + ].camera.geometry.frame.focal_length, + rotation=self.dl1dh_reader.pix_rotation[tel_id], + telescope_pointing=tel_pointing, + ) + # Set the camera coordinate offset + cam_coord_offset = SkyCoord( + x=table["cam_coord_offset_x"], + y=table["cam_coord_offset_y"], + frame=camera_frame, + ) + # tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] + # Transform the true Alt/Az coordinates to camera coordinates + reco_direction = cam_coord_offset.transform_to(altaz) + # Add the reconstructed direction (az, alt) to the prediction table + table.add_column(reco_direction.az.to(u.deg), name=f"{self.prefix}_tel_az") + table.add_column(reco_direction.alt.to(u.deg), name=f"{self.prefix}_tel_alt") + # Remove unnecessary columns from the table that do not the ctapipe DL2 data format + table.remove_columns( + [ + "time", + "pointing_azimuth", + "pointing_altitude", + "cam_coord_offset_x", + "cam_coord_offset_y", + ] + ) + return table + + def _transform_spher_coord_offsets_to_sky(self, table) -> Table: + """ + Transform the predicted spherical offsets w.r.t. the telescope pointing to Alt/Az coordinates. + + This method converts the predicted spherical offsets w.r.t. the telescope pointing + in the provided table to Alt/Az coordinates. It also removes the unnecessary columns + from the table that do not the ctapipe DL2 data format. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing the trigger time, telescope pointing, and predicted spherical offsets. + + Returns: + -------- + table : astropy.table.Table + A Table with the Alt/Az coordinates following the ctapipe DL2 data format. + """ + + # Set the trigger timestamp based on the process type + if self.dl1dh_reader.process_type == ProcessType.Simulation: + trigger_time = LST_EPOCH + elif self.dl1dh_reader.process_type == ProcessType.Observation: + trigger_time = table["time"] + # Set the AltAz frame with the reference location and time + altaz = AltAz( + location=self.dl1dh_reader.subarray.reference_location, + obstime=trigger_time, + ) + # Set the array pointing + array_pointing = SkyCoord( + az=table["pointing_azimuth"], + alt=table["pointing_altitude"], + frame=altaz, + ) + # Set the nominal frame with the array pointing + nom_frame = NominalFrame( + origin=array_pointing, + location=self.dl1dh_reader.subarray.reference_location, + obstime=trigger_time, + ) + # Set the reco direction in (fov_lon, fov_lat) coordinates + reco_direction = SkyCoord( + fov_lon=table["fov_lon"], + fov_lat=table["fov_lat"], + frame=nom_frame, + ) + # Transform the reco direction from nominal frame to the AltAz frame + sky_coord = reco_direction.transform_to(altaz) + # Add the reconstructed direction (az, alt) to the prediction table + table.add_column(sky_coord.az.to(u.deg), name=f"{self.prefix}_az") + table.add_column(sky_coord.alt.to(u.deg), name=f"{self.prefix}_alt") + # Remove unnecessary columns from the table that do not the ctapipe DL2 data format + table.remove_columns( + [ + "time", + "pointing_azimuth", + "pointing_altitude", + "fov_lon", + "fov_lat", + ] + ) + return table + + def _create_nan_table(self, nonexample_identifiers, columns, shapes): + """ + Create a table with NaNs for missing predictions. + + This method creates a table with NaNs for missing predictions for the non-example identifiers. + In stereo mode, the table also a column for the valid telescopes is added with all False values. + + Parameters: + ----------- + nonexample_identifiers : astropy.table.Table + Table containing the non-example identifiers. + columns : list of str + List of column names to create in the table. + shapes : list of shapes + List of shapes for the columns to create in the table. + + Returns: + -------- + nan_table : astropy.table.Table + Table containing NaNs for missing predictions. + """ + # Create a table with NaNs for missing predictions + nan_table = nonexample_identifiers.copy() + for column_name, shape in zip(columns, shapes): + nan_table.add_column(np.full(shape, np.nan), name=column_name) + # Add that no telescope is valid for the non-example identifiers in stereo mode + if self.dl1dh_reader.mode == "stereo": + nan_table.add_column( + np.zeros( + (len(nonexample_identifiers), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ), + name=f"{self.prefix}_telescopes", + ) + return nan_table + + def _store_pointing(self, all_identifiers): + """ + Store the telescope pointing table from to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the telescope pointing information. + """ + + # Initialize the pointing interpolator from ctapipe + pointing_interpolator = PointingInterpolator( + bounds_error=False, extrapolate=True + ) + pointing_info = [] + for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: + # Get the telescope pointing from the dl1dh reader + tel_pointing = self.dl1dh_reader.telescope_pointings[f"tel_{tel_id:03d}"] + # Add the telescope pointing table to the pointing interpolator + pointing_interpolator.add_table(tel_id, tel_pointing) + tel_identifiers = all_identifiers.copy() + if self.dl1dh_reader.mode == "mono": + tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] + # Interpolate the telescope pointing + tel_altitude, tel_azimuth = pointing_interpolator( + tel_id, tel_identifiers["time"] + ) + tel_identifiers.add_column(tel_azimuth, name="pointing_azimuth") + tel_identifiers.add_column(tel_altitude, name="pointing_altitude") + pointing_info.append(tel_identifiers) + if self.dl1dh_reader.mode == "mono": + tel_pointing_table = Table( + { + "time": tel_identifiers["time"], + "azimuth": tel_identifiers["pointing_azimuth"], + "altitude": tel_identifiers["pointing_altitude"], + } + ) + write_table( + tel_pointing_table, + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 telescope pointing table was stored in '%s' under '%s'", + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + ) + pointing_info = vstack(pointing_info) + if self.dl1dh_reader.mode == "stereo": + # Group the pointing information by subarray event keys + # TODO: This needs to be debugged with SST1M data + pointing_info_grouped = pointing_info.group_by(SUBARRAY_EVENT_KEYS) + pointing_mean = pointing_info_grouped.groups.aggregate(np.mean) + pointing_info = join( + all_identifiers, + pointing_mean, + keys=SUBARRAY_EVENT_KEYS, + ) + # TODO: use keep_order for astropy v7.0.0 + pointing_info.sort(SUBARRAY_EVENT_KEYS) + # Create the pointing table + pointing_table = Table( + { + "time": pointing_info["time"], + "array_azimuth": pointing_info["pointing_azimuth"], + "array_altitude": pointing_info["pointing_altitude"], + "array_ra": np.nan * np.ones(len(pointing_info)), + "array_dec": np.nan * np.ones(len(pointing_info)), + } + ) + # Save the pointing table to the output file + write_table( + pointing_table, + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 subarray pointing table was stored in '%s' under '%s'", + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + ) + return pointing_info + + def _create_feature_vectors_table( + self, + example_identifiers, + nonexample_identifiers=None, + classification_feature_vectors=None, + energy_feature_vectors=None, + direction_feature_vectors=None, + ): + """ + Create the table for the DL1 feature vectors. + + This method creates a table with the DL1 feature vectors for the example identifiers and fill NaNs for + non-example identifiers. The feature vectors are stored in the columns of the table. The table also + contains a column for the valid predictions. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + nonexample_identifiers : astropy.table.Table or None + Table containing the non-example identifiers to fill the NaNs. + classification_feature_vectors : np.ndarray or None + Array containing the classification feature vectors. + energy_feature_vectors : np.ndarray or None + Array containing the energy feature vectors. + direction_feature_vectors : np.ndarray or None + Array containing the direction feature vectors. + + Returns: + -------- + feature_vector_table : astropy.table.Table + Table containing the DL1 feature vectors for the example and non-example identifiers. + """ + # Create the feature vector table + feature_vector_table = example_identifiers.copy() + feature_vector_table.remove_columns( + ["pointing_azimuth", "pointing_altitude", "time"] + ) + columns_list, shapes_list = [], [] + if classification_feature_vectors is not None: + is_valid_col = ~np.isnan( + np.min(classification_feature_vectors, axis=1), dtype=bool + ) + feature_vector_table.add_column( + classification_feature_vectors, + name=f"{self.prefix}_tel_classification_feature_vectors", + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_classification_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + classification_feature_vectors.shape[1], + ) + ) + if energy_feature_vectors is not None: + is_valid_col = ~np.isnan(np.min(energy_feature_vectors, axis=1), dtype=bool) + feature_vector_table.add_column( + energy_feature_vectors, name=f"{self.prefix}_tel_energy_feature_vectors" + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_energy_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + energy_feature_vectors.shape[1], + ) + ) + if direction_feature_vectors is not None: + is_valid_col = ~np.isnan( + np.min(direction_feature_vectors, axis=1), dtype=bool + ) + feature_vector_table.add_column( + direction_feature_vectors, + name=f"{self.prefix}_tel_geometry_feature_vectors", + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_geometry_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + direction_feature_vectors.shape[1], + ) + ) + # Produce output table with NaNs for missing predictions + if nonexample_identifiers is not None: + if len(nonexample_identifiers) > 0: + nan_table = self._create_nan_table( + nonexample_identifiers, + columns=columns_list, + shapes=shapes_list, + ) + feature_vector_table = vstack([feature_vector_table, nan_table]) + is_valid_col = np.concatenate( + (is_valid_col, np.zeros(len(nonexample_identifiers), dtype=bool)) + ) + # Add is_valid column to the feature vector table + feature_vector_table.add_column( + is_valid_col, + name=f"{self.prefix}_tel_is_valid", + ) + return feature_vector_table + + +class MonoPredictCTLearnModel(PredictCTLearnModel): + """ + Tool to predict the gammaness, energy and arrival direction from monoscopic R1/DL1 data using CTLearn models. + + This tool extends the ``PredictCTLearnModel`` to specifically handle monoscopic R1/DL1 data. The prediction + is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. + It also stores the telescope pointing monitoring and DL1 feature vectors (if selected) in the output file. + + Attributes + ---------- + name : str + Name of the tool. + description : str + Description of the tool. + examples : str + Examples of how to use the tool. + + Methods + ------- + start() + Start the tool. + _store_mc_telescope_pointing(all_identifiers) + Store the telescope pointing table for the mono mode for MC simulation. + """ + + name = "ctlearn-predict-mono-model" + description = __doc__ + + examples = """ + To predict from pixel-wise image data in mono mode using trained CTLearn models: + > ctlearn-predict-mono-model \\ + --input_url input.dl1.h5 \\ + --PredictCTLearnModel.batch_size=64 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ + --DLImageReader.channels=cleaned_image \\ + --DLImageReader.channels=cleaned_relative_peak_time \\ + --DLImageReader.image_mapper_type=BilinearMapper \\ + --type_model="/path/to/your/mono/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/mono/energy/ctlearn_model.cpk" \\ + --cameradirection_model="/path/to/your/mono/cameradirection/ctlearn_model.cpk" \\ + --dl1-features \\ + --use-HDF5Merger \\ + --no-dl1-images \\ + --no-true-images \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + + To predict from pixel-wise waveform data in mono mode using trained CTLearn models: + > ctlearn-predict-mono-model \\ + --input_url input.r1.h5 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLWaveformReader \\ + --DLWaveformReader.sequnce_length=20 \\ + --DLWaveformReader.image_mapper_type=BilinearMapper \\ + --type_model="/path/to/your/mono_waveform/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/mono_waveform/energy/ctlearn_model.cpk" \\ + --cameradirection_model="/path/to/your/mono_waveform/cameradirection/ctlearn_model.cpk" \\ + --use-HDF5Merger \\ + --no-r0-waveforms \\ + --no-r1-waveforms \\ + --no-dl1-images \\ + --no-true-images \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + """ + + stereo_combiner_cls = ComponentName( + StereoCombiner, + default_value="StereoMeanCombiner", + help="Which stereo combination method to use after the monoscopic reconstruction.", + ).tag(config=True) + + def start(self): + self.log.info("Processing the telescope pointings...") + # Retrieve the IDs from the dl1dh for the prediction tables + example_identifiers = self.dl1dh_reader.example_identifiers.copy() + example_identifiers.keep_columns(TELESCOPE_EVENT_KEYS) + all_identifiers = self.dl1dh_reader.tel_trigger_table.copy() + all_identifiers.keep_columns(TELESCOPE_EVENT_KEYS + ["time"]) + nonexample_identifiers = setdiff( + all_identifiers, example_identifiers, keys=TELESCOPE_EVENT_KEYS + ) + nonexample_identifiers.remove_column("time") + # Pointing table for the mono mode for MC simulation + if self.dl1dh_reader.process_type == ProcessType.Simulation: + pointing_info = self._store_mc_telescope_pointing(all_identifiers) + + # Pointing table for the observation mode + if self.dl1dh_reader.process_type == ProcessType.Observation: + pointing_info = super()._store_pointing(all_identifiers) + + self.log.info("Starting the prediction...") + classification_feature_vectors = None + if self.load_type_model_from is not None: + self.type_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.PARTICLE_TYPE, + parent=self, + ) + # Predict the energy of the primary particle + classification_table, classification_feature_vectors = ( + super()._predict_classification(example_identifiers) + ) + if self.dl2_telescope: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_prediction"], + shapes=[(len(nonexample_identifiers),)], + ) + classification_table = vstack([classification_table, nan_table]) + # Add is_valid column to the energy table + classification_table.add_column( + ~np.isnan( + classification_table[f"{self.prefix}_tel_prediction"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + classification_table, + ParticleClassificationContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = classification_table["tel_id"] == tel_id + classification_tel_table = classification_table[telescope_mask] + classification_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file for the selected telescope + write_table( + classification_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info("Processing and storing the subarray type prediction...") + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_classification_table = self.type_stereo_combiner.predict_table( + classification_table + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if ( + subarray_classification_table[f"{self.prefix}_telescopes"].dtype + != np.bool_ + ): + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + ( + len(subarray_classification_table), + len(self.dl1dh_reader.tel_ids), + ), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_classification_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_classification_table[f"{self.prefix}_telescopes"] = ( + reco_telescopes + ) + # Save the prediction to the output file + write_table( + subarray_classification_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + ) + energy_feature_vectors = None + if self.load_energy_model_from is not None: + self.energy_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.ENERGY, + parent=self, + ) + # Predict the energy of the primary particle + energy_table, energy_feature_vectors = super()._predict_energy( + example_identifiers + ) + if self.dl2_telescope: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_energy"], + shapes=[(len(nonexample_identifiers),)], + ) + energy_table = vstack([energy_table, nan_table]) + # Add is_valid column to the energy table + energy_table.add_column( + ~np.isnan( + energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + energy_table, + ReconstructedEnergyContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = energy_table["tel_id"] == tel_id + energy_tel_table = energy_table[telescope_mask] + energy_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file + write_table( + energy_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info( + "Processing and storing the subarray energy prediction..." + ) + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_energy_table = self.energy_stereo_combiner.predict_table( + energy_table + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if subarray_energy_table[f"{self.prefix}_telescopes"].dtype != np.bool_: + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + (len(subarray_energy_table), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_energy_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_energy_table[f"{self.prefix}_telescopes"] = reco_telescopes + # Save the prediction to the output file + write_table( + subarray_energy_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + ) + direction_feature_vectors = None + if self.load_cameradirection_model_from is not None: + self.geometry_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.GEOMETRY, + parent=self, + ) + # Join the prediction table with the telescope pointing table + example_identifiers = join( + left=example_identifiers, + right=pointing_info, + keys=TELESCOPE_EVENT_KEYS, + ) + # Predict the arrival direction of the primary particle + direction_table, direction_feature_vectors = ( + super()._predict_cameradirection(example_identifiers) + ) + direction_tel_tables = [] + if self.dl2_telescope: + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = direction_table["tel_id"] == tel_id + direction_tel_table = direction_table[telescope_mask] + direction_tel_table = super()._transform_cam_coord_offsets_to_sky( + direction_tel_table + ) + # Produce output table with NaNs for missing predictions + nan_telescope_mask = nonexample_identifiers["tel_id"] == tel_id + nonexample_identifiers_tel = nonexample_identifiers[ + nan_telescope_mask + ] + if len(nonexample_identifiers_tel) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers_tel, + columns=[f"{self.prefix}_tel_alt", f"{self.prefix}_tel_az"], + shapes=[ + (len(nonexample_identifiers_tel),), + (len(nonexample_identifiers_tel),), + ], + ) + direction_tel_table = vstack([direction_tel_table, nan_table]) + direction_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Add is_valid column to the direction table + direction_tel_table.add_column( + ~np.isnan( + direction_tel_table[f"{self.prefix}_tel_alt"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + direction_tel_table, + ReconstructedGeometryContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + direction_tel_tables.append(direction_tel_table) + # Save the prediction to the output file + write_table( + direction_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info( + "Processing and storing the subarray geometry prediction..." + ) + # Stack the telescope tables to the subarray table + direction_tel_tables = vstack(direction_tel_tables) + # Sort the table by the telescope event keys + direction_tel_tables.sort(TELESCOPE_EVENT_KEYS) + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_direction_table = self.geometry_stereo_combiner.predict_table( + direction_tel_tables + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if ( + subarray_direction_table[f"{self.prefix}_telescopes"].dtype + != np.bool_ + ): + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + (len(subarray_direction_table), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_direction_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_direction_table[f"{self.prefix}_telescopes"] = ( + reco_telescopes + ) + # Save the prediction to the output file + write_table( + subarray_direction_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + ) + # Create the feature vector table if the DL1 features are enabled + if self.dl1_features: + self.log.info("Processing and storing dl1 feature vectors...") + feature_vector_table = super()._create_feature_vectors_table( + example_identifiers, + nonexample_identifiers, + classification_feature_vectors, + energy_feature_vectors, + direction_feature_vectors, + ) + # Loop over the selected telescopes and store the feature vectors + # for each telescope in the output file. The feature vectors are stored + # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = feature_vector_table["tel_id"] == tel_id + feature_vectors_tel_table = feature_vector_table[telescope_mask] + feature_vectors_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file + write_table( + feature_vectors_tel_table, + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 feature vectors was stored in '%s' under '%s'", + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", + ) + + def _store_mc_telescope_pointing(self, all_identifiers): + """ + Store the telescope pointing table from MC simulation to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the telescope pointing information. + """ + # Create the pointing table for each telescope + pointing_info = [] + for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: + # Pointing table for the mono mode + tel_pointing = self.dl1dh_reader.get_tel_pointing(self.input_url, tel_id) + tel_pointing.rename_column("telescope_pointing_azimuth", "pointing_azimuth") + tel_pointing.rename_column( + "telescope_pointing_altitude", "pointing_altitude" + ) + # Join the prediction table with the telescope pointing table + tel_pointing = join( + left=tel_pointing, + right=all_identifiers, + keys=["obs_id", "tel_id"], + ) + # TODO: use keep_order for astropy v7.0.0 + tel_pointing.sort(TELESCOPE_EVENT_KEYS) + # Retrieve the example identifiers for the selected telescope + tel_pointing_table = Table( + { + "time": tel_pointing["time"], + "azimuth": tel_pointing["pointing_azimuth"], + "altitude": tel_pointing["pointing_altitude"], + } + ) + write_table( + tel_pointing_table, + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 telescope pointing table was stored in '%s' under '%s'", + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + ) + pointing_info.append(tel_pointing) + pointing_info = vstack(pointing_info) + return pointing_info + + +class StereoPredictCTLearnModel(PredictCTLearnModel): + """ + Tool to predict the gammaness, energy and arrival direction from R1/DL1 stereoscopic data using CTLearn models. + + This tool extends the ``PredictCTLearnModel`` to specifically handle stereoscopic R1/DL1 data. The prediction + is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. + It also stores the telescope/subarray pointing monitoring and DL1 feature vectors (if selected) in the output file. + + Attributes + ---------- + name : str + Name of the tool. + description : str + Description of the tool. + examples : str + Examples of how to use the tool. + + Methods + ------- + start() + Start the tool. + _store_mc_subarray_pointing(all_identifiers) + Store the subarray pointing table for the stereo mode for MC simulation. + """ + + name = "ctlearn-predict-stereo-model" + description = __doc__ + + examples = """ + To predict from pixel-wise image data in stereo mode using trained CTLearn models: + > ctlearn-predict-stereo-model \\ + --input_url input.dl1.h5 \\ + --PredictCTLearnModel.batch_size=16 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ + --DLImageReader.channels=cleaned_image \\ + --DLImageReader.channels=cleaned_relative_peak_time \\ + --DLImageReader.image_mapper_type=BilinearMapper \\ + --DLImageReader.mode=stereo \\ + --DLImageReader.min_telescopes=2 \\ + --PredictCTLearnModel.stack_telescope_images=True \\ + --type_model="/path/to/your/stereo/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/stereo/energy/ctlearn_model.cpk" \\ + --skydirection_model="/path/to/your/stereo/skydirection/ctlearn_model.cpk" \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + """ + + def start(self): + self.log.info("Processing the telescope pointings...") + # Retrieve the IDs from the dl1dh for the prediction tables + example_identifiers = self.dl1dh_reader.unique_example_identifiers.copy() + example_identifiers.keep_columns(SUBARRAY_EVENT_KEYS) + all_identifiers = self.dl1dh_reader.subarray_trigger_table.copy() + all_identifiers.keep_columns(SUBARRAY_EVENT_KEYS + ["time"]) + nonexample_identifiers = setdiff( + all_identifiers, example_identifiers, keys=SUBARRAY_EVENT_KEYS + ) + nonexample_identifiers.remove_column("time") + # Construct the survival telescopes for each event of the example_identifiers + survival_telescopes = [] + for subarray_event in self.dl1dh_reader.example_identifiers_grouped.groups: + survival_mask = np.zeros(len(self.dl1dh_reader.tel_ids), dtype=bool) + survival_tels = [ + self.dl1dh_reader.subarray.tel_indices[tel_id] + for tel_id in subarray_event["tel_id"].data + ] + survival_mask[survival_tels] = True + survival_telescopes.append(survival_mask) + # Add the survival telescopes to the example_identifiers + example_identifiers.add_column( + survival_telescopes, name=f"{self.prefix}_telescopes" + ) + # Pointing table for the stereo mode for MC simulation + if self.dl1dh_reader.process_type == ProcessType.Simulation: + pointing_info = self._store_mc_subarray_pointing(all_identifiers) + + # Pointing table for the observation mode + if self.dl1dh_reader.process_type == ProcessType.Observation: + pointing_info = super()._store_pointing(all_identifiers) + + self.log.info("Starting the prediction...") + classification_feature_vectors = None + if self.load_type_model_from is not None: + # Predict the energy of the primary particle + classification_table, classification_feature_vectors = ( + super()._predict_classification(example_identifiers) + ) + if self.dl2_subarray: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_prediction"], + shapes=[(len(nonexample_identifiers),)], + ) + classification_table = vstack([classification_table, nan_table]) + # Add is_valid column to the energy table + classification_table.add_column( + ~np.isnan( + classification_table[f"{self.prefix}_tel_prediction"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Rename the columns for the stereo mode + classification_table.rename_column( + f"{self.prefix}_tel_prediction", f"{self.prefix}_prediction" + ) + classification_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + classification_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + classification_table, + ParticleClassificationContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + classification_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + ) + energy_feature_vectors = None + if self.load_energy_model_from is not None: + # Predict the energy of the primary particle + energy_table, energy_feature_vectors = super()._predict_energy( + example_identifiers + ) + if self.dl2_subarray: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_energy"], + shapes=[(len(nonexample_identifiers),)], + ) + energy_table = vstack([energy_table, nan_table]) + # Add is_valid column to the energy table + energy_table.add_column( + ~np.isnan( + energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Rename the columns for the stereo mode + energy_table.rename_column( + f"{self.prefix}_tel_energy", f"{self.prefix}_energy" + ) + energy_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + energy_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + energy_table, + ReconstructedEnergyContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + energy_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + ) + direction_feature_vectors = None + if self.load_skydirection_model_from is not None: + # Join the prediction table with the telescope pointing table + example_identifiers = join( + left=example_identifiers, + right=pointing_info, + keys=SUBARRAY_EVENT_KEYS, + ) + # Predict the arrival direction of the primary particle + direction_table, direction_feature_vectors = super()._predict_skydirection( + example_identifiers + ) + if self.dl2_subarray: + # Transform the spherical coordinate offsets to sky coordinates + direction_table = super()._transform_spher_coord_offsets_to_sky( + direction_table + ) + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_alt", f"{self.prefix}_az"], + shapes=[ + (len(nonexample_identifiers),), + (len(nonexample_identifiers),), + ], + ) + direction_table = vstack([direction_table, nan_table]) + # Add is_valid column to the direction table + direction_table.add_column( + ~np.isnan(direction_table[f"{self.prefix}_alt"].data, dtype=bool), + name=f"{self.prefix}_is_valid", + ) + direction_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + direction_table, + ReconstructedGeometryContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + direction_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + ) + + # Create the feature vector table if the DL1 features are enabled + if self.dl1_features: + self.log.info("Processing and storing dl1 feature vectors...") + feature_vector_table = super()._create_feature_vectors_table( + example_identifiers, + nonexample_identifiers, + classification_feature_vectors, + energy_feature_vectors, + direction_feature_vectors, + ) + # Loop over the selected telescopes and store the feature vectors + # for each telescope in the output file. The feature vectors are stored + # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. + # Rename the columns for the stereo mode + feature_vector_table.rename_column( + f"{self.prefix}_tel_classification_feature_vectors", + f"{self.prefix}_classification_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_energy_feature_vectors", + f"{self.prefix}_energy_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_geometry_feature_vectors", + f"{self.prefix}_geometry_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + feature_vector_table.sort(SUBARRAY_EVENT_KEYS) + # Save the prediction to the output file + write_table( + feature_vector_table, + self.output_path, + f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 feature vectors was stored in '%s' under '%s'", + self.output_path, + f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", + ) + + def _store_mc_subarray_pointing(self, all_identifiers): + """ + Store the subarray pointing table from MC simulation to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the subarray pointing information. + """ + # Read the subarray pointing table + pointing_info = read_table( + self.input_url, + f"{SIMULATION_CONFIG_TABLE}", + ) + # Assuming min_az = max_az and min_alt = max_alt + pointing_info.keep_columns(["obs_id", "min_az", "min_alt"]) + pointing_info.rename_column("min_az", "pointing_azimuth") + pointing_info.rename_column("min_alt", "pointing_altitude") + # Join the prediction table with the telescope pointing table + pointing_info = join( + left=pointing_info, + right=all_identifiers, + keys=["obs_id"], + ) + # TODO: use keep_order for astropy v7.0.0 + pointing_info.sort(SUBARRAY_EVENT_KEYS) + # Create the pointing table + pointing_table = Table( + { + "time": pointing_info["time"], + "array_azimuth": pointing_info["pointing_azimuth"], + "array_altitude": pointing_info["pointing_altitude"], + "array_ra": np.nan * np.ones(len(pointing_info)), + "array_dec": np.nan * np.ones(len(pointing_info)), + } + ) + # Save the pointing table to the output file + write_table( + pointing_table, + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 subarray pointing table was stored in '%s' under '%s'", + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + ) + return pointing_info + + +def mono_tool(): + # Run the tool + mono_tool = MonoPredictCTLearnModel() + mono_tool.run() + + +def stereo_tool(): + # Run the tool + stereo_tool = StereoPredictCTLearnModel() + stereo_tool.run() + + +if __name__ == "mono_tool": + mono_tool() + +if __name__ == "stereo_tool": + stereo_tool() \ No newline at end of file diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index fc62d531..b10cd448 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -375,4 +375,4 @@ def start(self): pass def finish(self): - print("finish") + pass From 52466bc5d2089b8276404a056f6e10837cd54886 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 26 May 2025 13:23:18 +0000 Subject: [PATCH 017/119] fixed _predict_with_model --- ctlearn/tools/predict_model.py | 206 +++++++++++++++++---------------- 1 file changed, 105 insertions(+), 101 deletions(-) diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index e7715dd6..092b6939 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -702,60 +702,64 @@ def _predict_with_model(self, model_path): Feature vectors extracted from the backbone model. """ if self.framework_type == "keras": - from ctlearn.tools.predict.keras.predic_model_keras import _predict_with_model - # Create a new DLDataLoader for each task - # It turned out to be more robust to initialize the DLDataLoader separately. - data_loader = DLDataLoader.create( - framework="keras", - DLDataReader=self.dl1dh_reader, - indices=self.indices, - tasks=[], - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) + from ctlearn.tools.predict.keras.predic_model_keras import predict_with_model + predict_data, feature_vectors = predict_with_model(model_path) + + return predict_data, feature_vectors - # Keras is only considering the last complete batch. - # In prediction mode we don't want to loose the last - # uncomplete batch, so we are creating an additional - # batch generator for the remaining events. - data_loader_last_batch = None - if self.last_batch_size > 0: - last_batch_indices = self.indices[-self.last_batch_size :] - data_loader_last_batch = DLDataLoader.create( - framework="keras", - DLDataReader=self.dl1dh_reader, - indices=last_batch_indices, - tasks=[], - batch_size=self.last_batch_size, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) + # # Create a new DLDataLoader for each task + # # It turned out to be more robust to initialize the DLDataLoader separately. + # data_loader = DLDataLoader.create( + # framework="keras", + # DLDataReader=self.dl1dh_reader, + # indices=self.indices, + # tasks=[], + # batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + # sort_by_intensity=self.sort_by_intensity, + # stack_telescope_images=self.stack_telescope_images, + # ) + + # # Keras is only considering the last complete batch. + # # In prediction mode we don't want to loose the last + # # uncomplete batch, so we are creating an additional + # # batch generator for the remaining events. + # data_loader_last_batch = None + # if self.last_batch_size > 0: + # last_batch_indices = self.indices[-self.last_batch_size :] + # data_loader_last_batch = DLDataLoader.create( + # framework="keras", + # DLDataReader=self.dl1dh_reader, + # indices=last_batch_indices, + # tasks=[], + # batch_size=self.last_batch_size, + # sort_by_intensity=self.sort_by_intensity, + # stack_telescope_images=self.stack_telescope_images, + # ) - # Load the model from the specified path - model = keras.saving.load_model(model_path) - prediction_colname = ( - "type" + # # Load the model from the specified path + # model = keras.saving.load_model(model_path) + # prediction_colname = ( + # "type" if isinstance(model.layers[-1], keras.layers.Softmax) else model.layers[-1].name - ) - backbone_model, feature_vectors = None, None - if self.dl1_features: - # Get the backbone model which is the second layer of the model - backbone_model = model.get_layer(index=1) - # Create a new head model with the same layers as the original model. - # The output of the backbone model is the input of the head model. - backbone_output_shape = keras.Input(model.layers[2].input.shape[1:]) - x = backbone_output_shape - for layer in model.layers[2:]: - x = layer(x) - head = keras.Model(inputs=backbone_output_shape, outputs=x) - # Apply the backbone model with the data loader to retrieve the feature vectors + # ) + # backbone_model, feature_vectors = None, None + # if self.dl1_features: + # # Get the backbone model which is the second layer of the model + # backbone_model = model.get_layer(index=1) + # # Create a new head model with the same layers as the original model. + # # The output of the backbone model is the input of the head model. + # backbone_output_shape = keras.Input(model.layers[2].input.shape[1:]) + # x = backbone_output_shape + # for layer in model.layers[2:]: + # x = layer(x) + # head = keras.Model(inputs=backbone_output_shape, outputs=x) + # # Apply the backbone model with the data loader to retrieve the feature vectors try: - feature_vectors = backbone_model.predict( - data_loader, verbose=self.keras_verbose - ) + # feature_vectors = backbone_model.predict( + # data_loader, verbose=self.keras_verbose + # ) except ValueError as err: if str(err).startswith("Input 0 of layer"): raise ToolConfigurationError( @@ -764,39 +768,39 @@ def _predict_with_model(self, model_path): "Please ensure the telescope configuration matches the one used for training." ) from err raise - # Apply the head model with the feature vectors to retrieve the prediction - predict_data = Table( - { - prediction_colname: head.predict( - feature_vectors, verbose=self.keras_verbose - ) - } - ) - # Predict the last batch and stack the results to the prediction data - if data_loader_last_batch is not None: - feature_vectors_last_batch = backbone_model.predict( - data_loader_last_batch, verbose=self.keras_verbose - ) - feature_vectors = np.concatenate( - (feature_vectors, feature_vectors_last_batch) - ) - predict_data = vstack( - [ - predict_data, - Table( - { - prediction_colname: head.predict( - feature_vectors_last_batch, - verbose=self.keras_verbose, - ) - } - ), - ] - ) - else: - # Predict the data using the loaded model + # # Apply the head model with the feature vectors to retrieve the prediction + # predict_data = Table( + # { + # prediction_colname: head.predict( + # feature_vectors, verbose=self.keras_verbose + # ) + # } + # ) + # # Predict the last batch and stack the results to the prediction data + # if data_loader_last_batch is not None: + # feature_vectors_last_batch = backbone_model.predict( + # data_loader_last_batch, verbose=self.keras_verbose + # ) + # feature_vectors = np.concatenate( + # (feature_vectors, feature_vectors_last_batch) + # ) + # predict_data = vstack( + # [ + # predict_data, + # Table( + # { + # prediction_colname: head.predict( + # feature_vectors_last_batch, + # verbose=self.keras_verbose, + # ) + # } + # ), + # ] + # ) + # else: + # # Predict the data using the loaded model try: - predict_data = model.predict(data_loader, verbose=self.keras_verbose) + # predict_data = model.predict(data_loader, verbose=self.keras_verbose) except ValueError as err: if str(err).startswith("Input 0 of layer"): raise ToolConfigurationError( @@ -805,27 +809,27 @@ def _predict_with_model(self, model_path): "Please ensure the telescope configuration matches the one used for training." ) from err raise - # Create a astropy table with the prediction results - # The classification task has a softmax layer as the last layer - # which returns the probabilities for each class in an array, while - # the regression tasks have output neurons which returns the - # predicted value for the task in a dictionary. - if prediction_colname == "type": - predict_data = Table({prediction_colname: predict_data}) - else: - predict_data = Table(predict_data) - # Predict the last batch and stack the results to the prediction data - if data_loader_last_batch is not None: - predict_data_last_batch = model.predict( - data_loader_last_batch, verbose=self.keras_verbose - ) - if model.layers[-1].name == "type": - predict_data_last_batch = Table( - {prediction_colname: predict_data_last_batch} - ) - else: - predict_data_last_batch = Table(predict_data_last_batch) - predict_data = vstack([predict_data, predict_data_last_batch]) + # # Create a astropy table with the prediction results + # # The classification task has a softmax layer as the last layer + # # which returns the probabilities for each class in an array, while + # # the regression tasks have output neurons which returns the + # # predicted value for the task in a dictionary. + # if prediction_colname == "type": + # predict_data = Table({prediction_colname: predict_data}) + # else: + # predict_data = Table(predict_data) + # # Predict the last batch and stack the results to the prediction data + # if data_loader_last_batch is not None: + # predict_data_last_batch = model.predict( + # data_loader_last_batch, verbose=self.keras_verbose + # ) + # if model.layers[-1].name == "type": + # predict_data_last_batch = Table( + # {prediction_colname: predict_data_last_batch} + # ) + # else: + # predict_data_last_batch = Table(predict_data_last_batch) + # predict_data = vstack([predict_data, predict_data_last_batch]) return predict_data, feature_vectors def _predict_particletype(self, example_identifiers): From fc8fae784c826a8c83bce35369bad1e93d506f43 Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 27 May 2025 12:12:49 +0000 Subject: [PATCH 018/119] updated pytorch_loader --- ctlearn/core/data_loader/pytorch_loader.py | 79 ++++++++++++++++++++-- 1 file changed, 73 insertions(+), 6 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 657c4d40..41d4ef4c 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -13,6 +13,51 @@ def __init__( super().__init__(**kwargs) self.on_epoch_end() + self.hillas_names = [ + "obs_id", + "event_id", + "tel_id", + "hillas_intensity", + "hillas_skewness", + "hillas_kurtosis", + "hillas_fov_lon", + "hillas_fov_lat", + "hillas_r", + "hillas_phi", + "hillas_length", + "hillas_length_uncertainty", + "hillas_width", + "hillas_width_uncertainty", + "hillas_psi", + "timing_intercept", + "timing_deviation", + "timing_slope", + "leakage_pixels_width_1", + "leakage_pixels_width_2", + "leakage_intensity_width_1", + "leakage_intensity_width_2", + "concentration_cog", + "concentration_core", + "concentration_pixel", + "morphology_n_pixels", + "morphology_n_islands", + "morphology_n_small_islands", + "morphology_n_medium_islands", + "morphology_n_large_islands", + "intensity_max", + "intensity_min", + "intensity_mean", + "intensity_std", + "intensity_skewness", + "intensity_kurtosis", + "peak_time_max", + "peak_time_min", + "peak_time_mean", + "peak_time_std", + "peak_time_skewness", + "peak_time_kurtosis", + "core_psi" + ] def __len__(self): """ Returns the number of batches per epoch. @@ -104,6 +149,7 @@ def _get_mono_item(self, batch): ( batch["fov_lon"].data, batch["fov_lat"].data, + batch["angular_separation"].data, ), axis=1, ) @@ -115,10 +161,23 @@ def _get_mono_item(self, batch): ), axis=1, ) - # Temp fix for supporting keras2 & keras3 - # if int(keras.__version__.split(".")[0]) >= 3: - # features = features["input"] - return features, labels + + if "hillas" in self.tasks: + # features["hillas"] = self.DLDataReader.get_parameters_dict(batch,self.hillas_names) + features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) + + image = features["input"][..., 0:1] + peak_time = features["input"][..., 1:2] + + image = np.transpose(image, (0, 3, 1, 2)) + peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + + features_out={} + features_out["image"]=image + features_out["peak_time"]=peak_time + features_out["hillas"] = features["hillas"] + features_out["hillas_names"] = self.hillas_names + return features_out, labels def _get_stereo_item(self, batch): """ @@ -231,7 +290,15 @@ def _get_stereo_item(self, batch): if "stereo_feature_vectors" in batch.colnames: features = {"input": np.array(stereo_feature_vectors)} - # features = features["input"] - return features, labels + image = features[:,:,:,0] + peak_time = features[:,:,:,1] + + image = np.transpose(image, (2, 0, 1)) + peak_time = np.transpose(peak_time, (2, 0, 1)) + + features_out=None + features_out["image"]=image + features_out["peak_time"]=peak_time + return features_out, labels # Include _get_mono_item and _get_stereo_item as needed \ No newline at end of file From 548ddf59580d5f1e4bd9fa0868c553d43281ced1 Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 27 May 2025 17:29:45 +0000 Subject: [PATCH 019/119] skeleton for training in pytorch --- ctlearn/core/data_loader/pytorch_loader.py | 44 +- .../models/ThinResNet_DBB/ThinResNet_DBB.py | 2 +- ctlearn/core/pytorch/utils/__init__.py | 0 ctlearn/core/pytorch/utils/utils.py | 997 +++++++++ ctlearn/core/pytorch/utils/utils_torch.py | 33 + .../core/pytorch/visualization/tsne_test.py | 27 + .../core/pytorch/visualization/vis_utils.py | 194 ++ .../visualization/visualization_tsne.py | 683 ++++++ ctlearn/tools/train/pytorch/CTLearnPL.py | 1861 +++++++++++++++++ .../training_config_iaa_neutron_training.yml | 35 - .../train/pytorch/train_pytorch_model.py | 174 ++ 11 files changed, 4005 insertions(+), 45 deletions(-) create mode 100644 ctlearn/core/pytorch/utils/__init__.py create mode 100644 ctlearn/core/pytorch/utils/utils.py create mode 100644 ctlearn/core/pytorch/utils/utils_torch.py create mode 100644 ctlearn/core/pytorch/visualization/tsne_test.py create mode 100644 ctlearn/core/pytorch/visualization/vis_utils.py create mode 100644 ctlearn/core/pytorch/visualization/visualization_tsne.py create mode 100644 ctlearn/tools/train/pytorch/CTLearnPL.py diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 41d4ef4c..f2fdcd39 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -1,4 +1,4 @@ -# import torch +import torch import numpy as np from torch.utils.data import Dataset from .base_loader import BaseDLDataLoader @@ -6,7 +6,7 @@ class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): def __init__( - self, + self, **kwargs, ): @@ -113,6 +113,8 @@ def __getitem__(self, index): elif self.DLDataReader.mode == "stereo": batch = self.DLDataReader.generate_stereo_batch(batch_indices) features, labels = self._get_stereo_item(batch) + + return features, labels def _get_mono_item(self, batch): @@ -162,9 +164,9 @@ def _get_mono_item(self, batch): axis=1, ) - if "hillas" in self.tasks: - # features["hillas"] = self.DLDataReader.get_parameters_dict(batch,self.hillas_names) - features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) + # if "hillas" in self.tasks: + features["hillas"] = self.DLDataReader.get_parameters_dict(batch,self.hillas_names) + # features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) image = features["input"][..., 0:1] peak_time = features["input"][..., 1:2] @@ -172,11 +174,35 @@ def _get_mono_item(self, batch): image = np.transpose(image, (0, 3, 1, 2)) peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + # ---------------------------------------------------- + # Remove negative numbers and avoid inf or nans + # ---------------------------------------------------- + image[image < 0] = 0 + peak_time[peak_time < 0] = 0 + image[np.isnan(image)] = 0 + image[np.isinf(image)] = 0 + peak_time[np.isnan(peak_time)] = 0 + peak_time[np.isinf(peak_time)] = 0 + + # if self.task == Task.type: # "type": + # image = (image - self.type_mu) / self.type_sigma + # peak_time = (peak_time - self.type_mu) / self.type_sigma + # if self.task == Task.energy: # "energy": + # image = (image - self.energy_mu) / self.energy_sigma + # peak_time = (peak_time - self.energy_mu) / self.energy_sigma + # if self.task == Task.direction: # "direction": + # image = (image - self.dir_mu) / self.dir_sigma + # peak_time = (peak_time - self.dir_mu) / self.dir_sigma + features_out={} - features_out["image"]=image - features_out["peak_time"]=peak_time - features_out["hillas"] = features["hillas"] - features_out["hillas_names"] = self.hillas_names + features_out["image"]=torch.from_numpy(image).to('cuda') + features_out["peak_time"]=torch.from_numpy(peak_time).to('cuda') + # features_out["hillas"] = features["hillas"] + # features_out["hillas_names"] = self.hillas_names + + for key in labels.keys(): + labels[key] = torch.from_numpy(labels[key]).to('cuda') + return features_out, labels def _get_stereo_item(self, batch): diff --git a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py index 8f01d6b2..44221828 100644 --- a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py +++ b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py @@ -91,7 +91,7 @@ def __init__(self,task, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, self.conv2 = nn.Conv2d(num_inputs, 64, kernel_size=3, stride=1, padding=1, bias=False) self.layer1_2 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2_2 = self._make_layer(block, 128, num_blocks[1], stride=2) - self.layer3_2 = self._make_layer(block, 256, num_blocks[3], stride=2) + self.layer3_2 = self._make_layer(block, 256, num_blocks[2], stride=2) if self.use_bn: self.bn2 = nn.BatchNorm2d(64) diff --git a/ctlearn/core/pytorch/utils/__init__.py b/ctlearn/core/pytorch/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/utils/utils.py b/ctlearn/core/pytorch/utils/utils.py new file mode 100644 index 00000000..c9c88806 --- /dev/null +++ b/ctlearn/core/pytorch/utils/utils.py @@ -0,0 +1,997 @@ +import importlib +import logging +import os +import pkg_resources +import sys +import time +import pickle +import numpy as np +import pandas as pd +import tables +import yaml +import zlib +import math +import re +import matplotlib.pyplot as plt +import torch +from astropy.coordinates import SkyCoord, AltAz +import astropy.units as u +from ctapipe_io_lst.constants import LST1_LOCATION +from ctlearn.core.ctlearn_enum import Task, Mode +import time as time_ +from ctapipe.io import read_table, write_table +from astropy.table import Table +crab_nebula_config = { + "observation_mode": "wobble", + "n_off_wobble": 1, + "source_name": "Crab Nebula", + "source_ra": 83.63308333, + "source_dec": 22.0145 +} +from astropy.time import Time +from ctapipe.coordinates import CameraFrame + +def clip_alt(alt): + """ + Make sure altitude is not larger than 90 deg (it happens in some MC files for zenith=0), + to keep astropy happy + """ + return np.clip(alt, -90.0 * u.deg, 90.0 * u.deg) + +def radec_to_camera(sky_coordinate, obstime, pointing_alt, pointing_az, focal): + """ + Coordinate transform from sky coordinate to camera coordinates (x, y) in distance + + Parameters + ---------- + sky_coordinate: astropy.coordinates.sky_coordinate.SkyCoord + obstime: astropy.time.Time + pointing_alt: pointing altitude in angle unit + pointing_az: pointing altitude in angle unit + focal: astropy Quantity + + Returns + ------- + camera frame: `astropy.coordinates.sky_coordinate.SkyCoord` + """ + + horizon_frame = AltAz(location=LST1_LOCATION, obstime=obstime) + + pointing_direction = SkyCoord( + alt=clip_alt(pointing_alt), az=pointing_az, frame=horizon_frame,unit="deg" + ) + + camera_frame = CameraFrame( + focal_length=focal, + telescope_pointing=pointing_direction, + obstime=obstime, + location=LST1_LOCATION, + ) + + camera_pos = sky_coordinate.transform_to(camera_frame) + + return camera_pos + +def get_expected_source_pos(data, data_type, config, effective_focal_length=29.30565 * u.m): + + # For real data + if data_type == 'real_data': + # source is always at the ceter of camera for ON mode + if config.get('observation_mode') == 'on': + expected_src_pos_x_m = np.zeros(len(data)) + expected_src_pos_y_m = np.zeros(len(data)) + + # compute source position in camera coordinate event by event for wobble mode + elif config.get('observation_mode') == 'wobble': + + if 'source_name' in config: + source_coord = SkyCoord.from_name(config.get('source_name')) + elif 'source_ra' and 'source_dec' in config: + source_coord = SkyCoord(config.get('source_ra'), config.get('source_dec'), frame="icrs", unit="deg") + else: + raise KeyError( + 'source position (`source_name` or `source_ra` & `source_dec`) is not defined in a config file for source-dependent analysis.' + ) + + time = data['dragon_time'] + obstime = Time(time, scale='utc', format='unix') + # pointing_alt = u.Quantity(data['alt_tel'], u.rad, copy=False) + # pointing_az = u.Quantity(data['az_tel'], u.rad, copy=False) + pointing_alt_deg = data['pointing_alt']*u.deg + pointing_az_deg = data['pointing_az']*u.deg + + pointing_alt_rad = pointing_alt_deg.to(u.rad) + pointing_az_rad = pointing_az_deg.to(u.rad) + + source_pos = radec_to_camera(source_coord, obstime, pointing_alt_deg, pointing_az_deg, effective_focal_length) + + expected_src_pos_x_m = source_pos.x.to_value(u.m) + expected_src_pos_y_m = source_pos.y.to_value(u.m) + + else: + raise KeyError( + '`observation_mode` is not defined in a config file for source-dependent analysis. It should be `on` or `wobble`' + ) + + return expected_src_pos_x_m, expected_src_pos_y_m + +def sky_to_camera(alt, az, focal, pointing_alt, pointing_az, horizon_frame): + """ + Coordinate transform from aky position (alt, az) (in angles) + to camera coordinates (x, y) in distance. + + Parameters + ---------- + alt: astropy Quantity + az: astropy Quantity + focal: astropy Quantity + pointing_alt: pointing altitude in angle unit + pointing_az: pointing altitude in angle unit + + Returns + ------- + camera frame: `astropy.coordinates.sky_coordinate.SkyCoord` + """ + pointing_direction = SkyCoord( + alt=clip_alt(pointing_alt), az=pointing_az, frame=horizon_frame + ) + + camera_frame = CameraFrame( + focal_length=focal, telescope_pointing=pointing_direction + ) + + event_direction = SkyCoord(alt=clip_alt(alt), az=az, frame=horizon_frame) + + camera_pos = event_direction.transform_to(camera_frame) + + return camera_pos +#------------------------------------------------------------------------------------------------------------------- +def reco_src_sky_to_camera(data, effective_focal_length=29.30565 * u.m): + + time = data['dragon_time'] + obstime = Time(time, scale='utc', format='unix') + # horizon_frame = AltAz(location=LST1_LOCATION, obstime=obstime) + + + # time = data['utc_time'] + # time_utc = Time(time, format="mjd", scale="tai") + # obstime = time_utc.utc + + alt_deg = u.Quantity(data['alt'], u.deg, copy=False) + az_deg = u.Quantity(data['az'], u.deg, copy=False) + + horizon_frame = AltAz(location=LST1_LOCATION, obstime=obstime) + + tel_alt_deg = u.Quantity(data['tel_pointing_alt'], u.deg, copy=False) + tel_az_deg = u.Quantity(data['tel_pointing_az'], u.deg, copy=False) + + source_pos_in_camera = sky_to_camera( + alt_deg, + az_deg, + effective_focal_length, + tel_alt_deg, + tel_az_deg, + horizon_frame=horizon_frame, + ) + expected_src_pos_x_m = source_pos_in_camera.x.to_value(u.m) + expected_src_pos_y_m = source_pos_in_camera.y.to_value(u.m) + + # ----------------------------------------------------------------- + # TODO: Remove this. It is the equivalent to the code above + # tel_alt_rad = tel_alt_deg.to(u.rad) + # tel_az_rad = tel_az_deg.to(u.rad) + # time = data['utc_time'] + # time_utc = Time(time, format="mjd", scale="tai") + # time_utc = time_utc.utc + # telescope_pointing = SkyCoord(alt=tel_alt_rad, az=tel_az_rad, + # frame=AltAz(obstime=time_utc, + # location=LST1_LOCATION)) + + # source_pos = SkyCoord( + # alt=clip_alt(alt_deg), az=az_deg, frame=horizon_frame + # ) + + # # CameraFrame is terribly slow without the erfa interpolator below... + # # with erfa_astrom.set(ErfaAstromInterpolator(5 * u.min)): + # camera_frame = CameraFrame(focal_length=effective_focal_length, + # telescope_pointing=telescope_pointing, + # location=LST1_LOCATION, obstime=time_utc) + + # source_pos_camera = source_pos.transform_to(camera_frame) + + return expected_src_pos_x_m, expected_src_pos_y_m +#------------------------------------------------------------------------------------------------------------------- +expected_structure = { + "data": { + "train_gamma_proton": None, + "validation_gamma_proton": None, + "train_gamma": None, + "validation_gamma": None, + "test_gamma": None, + "test_proton": None, + "test_electron": None, + "test_validation_gamma": None, + "test_validation_gamma_proton": None + }, + "run_details": { + "mode": None, + "task": None, + "test_type": None, + "experiment_number": None + }, + "cut-off": { + "leakage_intensity": None, + "intensity": None + }, + "model": { + "model_type": { + "model_name": None, + "parameters": None, + }, + "model_energy": { + "model_name": None, + "parameters": None, + }, + "model_direction": { + "model_name": None, + "parameters": None, + } + }, + "hyp": { + "epochs": None, + "batches": None, + "dynamic_batches": None, + "optimizer": None, + "momentum": None, + "weight_decay": None, + "learning_rate": None, + "lrf": None, + "start_epoch": None, + "steps_epoch": None, + "l2_lambda": None, + "adam_epsilon": None, + "gradient_clip_val": None, + "save_k": None + }, + "augmentation": { + "use_augmentation": None, + "aug_prob": None, + "rot_prob": None, + "trans_prob": None, + "flip_hor_prob": None, + "flip_ver_prob": None, + "mask_prob": None, + "noise_prob": None, + "max_rot": None, + "max_trans": None + }, + "normalization": { + "use_clean": None, + "type_mu": None, + "type_sigma": None, + "dir_mu": None, + "dir_sigma": None, + "energy_mu": None, + "energy_sigma": None + }, + "dataset": { + "num_workers": None, + "pin_memory": None, + "persistent_workers": None + }, + "arch": { + "device": None, + "precision_type": None, + "precision_energy": None, + "precision_direction": None, + "devices": None, + "strategy": None + }, +} +#------------------------------------------------------------------------------------------------------------------- +# Sanity check function +def sanity_check(config, expected_structure): + """ + Recursively checks if all required keys in the expected_structure exist in the config data. + Raises a KeyError if a key is missing. + """ + for key, substructure in expected_structure.items(): + if key not in config: + raise KeyError(f"Missing key: {key}") + + # If the substructure is a dictionary, recursively check the subkeys + if isinstance(substructure, dict): + if not isinstance(config[key], dict): + raise KeyError(f"Expected a dictionary for key: {key}, but got: {type(config[key])}") + sanity_check(config[key], substructure) + +#------------------------------------------------------------------------------------------------------------------- +def extract_loss_value(file_path): + # Use regular expression to find the pattern + match = re.search(r'_([0-9]+\.[0-9]+)\.pth$', file_path) + if match: + return match.group(1) # Return the whole match + else: + return None +#------------------------------------------------------------------------------------------------------------------- +def convert_ascii_list_to_string(ascii_values, padding_value=0): + # Filter out padding values and convert each ASCII value to its corresponding character + characters = [chr(ascii_val) + for ascii_val in ascii_values if ascii_val != padding_value] + + # Join all characters to form the string + return ''.join(characters) +#------------------------------------------------------------------------------------------------------------------- +def extract_accuracy_from_filename(filename): + # Use a regular expression to find numbers that might be in the format of floating point numbers + match = re.search(r"(\d+\.\d+)", filename) + if match: + # Return the first occurrence of a floating point number as a float + return float(match.group(1)) + else: + # If no matching number format is found, handle it appropriately + return None +#------------------------------------------------------------------------------------------------------------------- +def find_bin_index(value, bins): + index = np.digitize([value], bins) - 1 + index = max(0, min(index[0], len(bins) - 1)) # Ensure the index is within the valid range + bin_value = bins[index] + return index, bin_value +#------------------------------------------------------------------------------------------------------------------- +def get_bin_value(index:int, bins): + # Ensure the index is within the valid range + index = max(0, min(index, len(bins) - 1)) + # Return the bin value associated with the index + bin_value = bins[index] + return bin_value +#------------------------------------------------------------------------------------------------------------------- +def get_bin_value(indices:np.array, bins): + # Ensure indices are within the valid range + indices = np.clip(indices, 0, len(bins) - 1) + # Return the bin values associated with the indices + return np.array(bins)[indices] +#------------------------------------------------------------------------------------------------------------------- +def get_bin_value(indices:torch.tensor, bins): + # Convert bins to a tensor if they aren't already + # bins_tensor = torch.tensor(bins) + # Ensure indices are within the valid range + indices = torch.clamp(indices, 0, len(bins) - 1) + + # Return the bin values associated with the indices + return bins[indices] + +#------------------------------------------------------------------------------------------------------------------- +def decompress_data(compressed_data, data_shape, data_type=np.float16): + + decompressed_data = zlib.decompress(compressed_data) + restored_array = np.frombuffer(decompressed_data, dtype=data_type) + restored_array_reshaped = np.reshape(restored_array, data_shape) + + return restored_array_reshaped +#------------------------------------------------------------------------------------------------------------------- +def compress_data(data): + # Convert to bytes + bytes_data = data.tobytes() + + # Array Compression + compressed_data = zlib.compress(bytes_data) + return compressed_data +#------------------------------------------------------------------------------------------------------------------- +def create_experiment_folder(prefix="run_", next_number=None): + + + """ + Create the next folder within the specified directory with a given prefix. + If next_number is not specified, automatically find the next available number. + + :param run_directory: The directory where folders are managed. + :param prefix: Prefix used for folders. + :param next_number: Optional. Specify the number to be used for the new folder. + """ + run_directory="./run" + + # Ensure the 'run' directory exists + if not os.path.exists(run_directory): + os.makedirs(run_directory) + print(f"Directory '{run_directory}' created.") + + if next_number is None: + # List all subdirectories in the 'run' directory + folders = [f for f in os.listdir(run_directory) if os.path.isdir(os.path.join(run_directory, f))] + # Filter folders that match the prefix pattern and end with a digit + matching_folders = [f for f in folders if f.startswith(prefix) and f[len(prefix):].isdigit()] + + # Find the highest number and calculate the next one + if matching_folders: + highest_number = max(int(folder[len(prefix):]) for folder in matching_folders) + next_number = highest_number + 1 + else: + next_number = 0 + + # Create the new folder with the next number + new_folder_name = f"{prefix}{next_number}" + new_folder_path = os.path.join(run_directory, new_folder_name) + new_folder_path+="/" + if not os.path.exists(new_folder_path): + os.makedirs(new_folder_path) + print(f"New folder created: {new_folder_path}") + return new_folder_path +#------------------------------------------------------------------------------------------------------------ +def cartesian_to_alt_az(cartesian): + x = cartesian[0] + y = cartesian[1] + z = cartesian[2] + # Calculate the radius + r = math.sqrt(x**2 + y**2 + z**2) + + # Calculate altitude in radians + if r == 0: # Avoid division by zero + altitude_rad = 0 + else: + altitude_rad = math.asin(z / r) + + # Calculate azimuth in radians + azimuth_rad = math.atan2(y, x) + + # Adjust azimuth to be within the range [0, 2*pi) + # if azimuth_rad < 0: + # azimuth_rad += 2 * math.pi + + return np.array([altitude_rad, azimuth_rad, r]) +#------------------------------------------------------------------------------------------------------------ +def alt_az_to_cartesian(altitude_rad, azimuth_rad, r=1): + + # Estimate Cartesian coordinates + x = r * math.cos(altitude_rad) * math.cos(azimuth_rad) + y = r * math.cos(altitude_rad) * math.sin(azimuth_rad) + z = r * math.sin(altitude_rad) + + return np.array([x, y, z]) +#------------------------------------------------------------------------------------------------------------ +def load_pickle(pickle_file): + with open(pickle_file, 'rb') as file: + print(f"Loading pickle file :{pickle_file}") + return pickle.load(file) + +#------------------------------------------------------------------------------------------------------------ +def create_key_value_array(data_dict, id): + """ + Creates an array of key-value pairs from a dictionary where each key has a list of values. + + Args: + data_dict (dict): The dictionary containing keys with lists of values. + + Returns: + list: An array of key-value pairs, where each pair is a tuple containing the key and one value. + """ + + key_value_array = [] + for key, value_list in data_dict.items(): + # Handle cases where a key has an empty list: + if value_list.size==0: + continue # Skip keys with empty lists + + # Choose the first value from the list: + value = value_list[id] + + # Create a tuple (key, value) and append it to the array + key_value_array.append(value) + + return key_value_array + +#------------------------------------------------------------------------------------------------------------ +def decompress_data(compressed_data, data_shape, data_type=np.float16): + + decompressed_data = zlib.decompress(compressed_data) + restored_array = np.frombuffer(decompressed_data, dtype=data_type) + restored_array_reshaped = np.reshape(restored_array, data_shape) + + return restored_array_reshaped +#------------------------------------------------------------------------------------------------------------ +def setup_logging(config, log_dir, debug, log_to_file): + + # Log configuration to a text file in the log dir + time_str = time.strftime("%Y%m%d_%H%M%S") + config_filename = os.path.join(log_dir, time_str + "_config.yml") + with open(config_filename, "w") as outfile: + ctlearn_version = pkg_resources.get_distribution("ctlearn").version + tensorflow_version = pkg_resources.get_distribution("tensorflow").version + outfile.write( + "# Training performed with " + "CTLearn version {} and TensorFlow version {}.\n".format( + ctlearn_version, tensorflow_version + ) + ) + yaml.dump(config, outfile, default_flow_style=False) + + # Set up logger + logger = logging.getLogger() + + if debug: + logger.setLevel(logging.DEBUG) + else: + logger.setLevel(logging.INFO) + + logger.handlers = [] # remove existing handlers from any previous runs + if not log_to_file: + handler = logging.StreamHandler() + else: + logging_filename = os.path.join(log_dir, time_str + "_logfile.log") + handler = logging.FileHandler(logging_filename) + handler.setFormatter(logging.Formatter("%(levelname)s:%(message)s")) + logger.addHandler(handler) + + return logger +#------------------------------------------------------------------------------------------------------------ +def setup_DL1DataReader(config, mode): + + # Parse file list or prediction file list + if mode in ["train", "load_only"]: + if isinstance(config["Data"]["file_list"], str): + data_files = [] + with open(config["Data"]["file_list"]) as f: + for line in f: + line = line.strip() + if line and line[0] != "#": + data_files.append(line) + config["Data"]["file_list"] = data_files + if not isinstance(config["Data"]["file_list"], list): + raise ValueError( + "Invalid file list '{}'. " + "Must be list or path to file".format(config["Data"]["file_list"]) + ) + else: + file_list = config["Prediction"]["prediction_file_lists"][ + config["Prediction"]["prediction_file"] + ] + if file_list.endswith(".txt"): + data_files = [] + with open(file_list) as f: + for line in f: + line = line.strip() + if line and line[0] != "#": + data_files.append(line) + config["Data"]["file_list"] = data_files + elif file_list.endswith(".h5"): + config["Data"]["file_list"] = [file_list] + if not isinstance(config["Data"]["file_list"], list): + raise ValueError( + "Invalid prediction file list '{}'. " + "Must be list or path to file".format(file_list) + ) + + mc_file = True + with tables.open_file(config["Data"]["file_list"][0], mode="r") as f: + if "CTA PRODUCT DATA MODEL NAME" in f.root._v_attrs: + data_format = "stage1" + elif "dl1_data_handler_version" in f.root._v_attrs: + data_format = "dl1dh" + else: + raise ValueError( + "Data format is not implemented in the DL1DH reader. Available data formats are 'stage1' and 'dl1dh'." + ) + if data_format == "dl1dh" and "source_name" in f.root._v_attrs: + mc_file = False + + allow_overwrite = config["Data"].get("allow_overwrite", True) + if "allow_overwrite" in config["Data"]: + del config["Data"]["allow_overwrite"] + + selected_telescope_types = config["Data"]["selected_telescope_types"] + camera_types = [tel_type.split("_")[-1] for tel_type in selected_telescope_types] + + tasks = config["Reco"] + transformations = [] + event_info = [] + if data_format == "dl1dh": + if "parameter_list" not in config["Data"] and mode == "predict": + config["Data"]["parameter_list"] = [ + "hillas_intensity", + "log_hillas_intensity", + "hillas_x", + "hillas_y", + "hillas_r", + "hillas_phi", + "hillas_length", + "hillas_width", + "hillas_psi", + "hillas_skewness", + "leakage_intensity_width_1", + "leakage_intensity_width_2", + "morphology_num_islands", + "impact", + "log_impact", + "maxheight", + "log_maxheight", + "cherenkovdensity", + "log_hillasintensity_over_cherenkovdensity", + "cherenkovradius", + "impact_over_cherenkovradius", + "p1grad", + "sqrt_p1grad_p1grad", + ] + # Parse list of event selection filters + event_selection = {} + for s in config["Data"].get("event_selection", {}): + s = {"module": "dl1_data_handler.filters", **s} + filter_fn, filter_params = load_from_module(**s) + event_selection[filter_fn] = filter_params + config["Data"]["event_selection"] = event_selection + + # Parse list of image selection filters + image_selection = {} + for s in config["Data"].get("image_selection", {}): + s = {"module": "dl1_data_handler.filters", **s} + filter_fn, filter_params = load_from_module(**s) + image_selection[filter_fn] = filter_params + config["Data"]["image_selection"] = image_selection + + if "direction" in tasks: + event_info.append("src_pos_cam_x") + event_info.append("src_pos_cam_y") + transformations.append( + { + "name": "AltAz", + "args": { + "alt_col_name": "src_pos_cam_x", + "az_col_name": "src_pos_cam_y", + "deg2rad": False, + }, + } + ) + else: + if "parameter_list" not in config["Data"] and mode == "predict": + config["Data"]["parameter_list"] = [ + "hillas_intensity", + "hillas_fov_lon", + "hillas_fov_lat", + "hillas_r", + "hillas_phi", + "hillas_length", + "hillas_length_uncertainty", + "hillas_width", + "hillas_width_uncertainty", + "hillas_psi", + "hillas_skewness", + "hillas_kurtosis", + "timing_slope", + "timing_intercept", + "timing_deviation", + "leakage_pixels_width_1", + "leakage_pixels_width_2", + "leakage_intensity_width_1", + "leakage_intensity_width_2", + "concentration_cog", + "concentration_core", + "concentration_pixel", + "morphology_n_pixels", + "morphology_n_islands", + "morphology_n_medium_islands", + "morphology_n_large_islands", + "intensity_max", + "intensity_min", + "intensity_mean", + "intensity_std", + "intensity_skewness", + "intensity_kurtosis", + "peak_time_max", + "peak_time_min", + "peak_time_mean", + "peak_time_std", + "peak_time_skewness", + "peak_time_kurtosis", + ] + if "direction" in tasks: + event_info.append("true_alt") + event_info.append("true_az") + transformations.append({"name": "DeltaAltAz_fix_subarray"}) + + if "particletype" in tasks: + event_info.append("true_shower_primary_id") + + if "energy" in tasks: + if mc_file: + event_info.append("true_energy") + transformations.append({"name": "MCEnergy"}) + + concat_telescopes = config["Input"].get("concat_telescopes", False) + if config["Data"]["mode"] == "stereo" and not concat_telescopes: + for tel_desc in selected_telescope_types: + transformations.append( + { + "name": "SortTelescopes", + "args": {"sorting": "size", "tel_desc": f"{tel_desc}"}, + } + ) + + # Convert interpolation image shapes from lists to tuples, if present + if "interpolation_image_shape" in config["Data"].get("mapping_settings", {}): + config["Data"]["mapping_settings"]["interpolation_image_shape"] = { + k: tuple(l) + for k, l in config["Data"]["mapping_settings"][ + "interpolation_image_shape" + ].items() + } + + if allow_overwrite: + config["Data"]["event_info"] = event_info + config["Data"]["mapping_settings"]["camera_types"] = camera_types + else: + transformations = config["Data"].get("transforms", {}) + + transforms = [] + # Parse list of Transforms + for t in transformations: + t = {"module": "dl1_data_handler.transforms", **t} + transform, args = load_from_module(**t) + transforms.append(transform(**args)) + config["Data"]["transforms"] = transforms + + # Possibly add additional info to load if predicting to write later + if mode == "predict": + + if "Prediction" not in config: + config["Prediction"] = {} + if "event_info" not in config["Data"]: + config["Data"]["event_info"] = [] + config["Data"]["event_info"].extend(["event_id", "obs_id"]) + if data_format == "dl1dh" and not mc_file: + config["Data"]["event_info"].extend(["mjd", "milli_sec", "nano_sec"]) + + return config["Data"], data_format +#------------------------------------------------------------------------------------------------------------ +def load_from_module(name, module, path=None, args=None): + if path is not None and path not in sys.path: + sys.path.append(path) + mod = importlib.import_module(module) + fn = getattr(mod, name) + params = args if args is not None else {} + return fn, params +#------------------------------------------------------------------------------------------------------------ +def recover_alt_az(fix_pointing,alt_off, az_off): + + az_off_deg = u.Quantity(az_off, unit=u.rad).to(u.deg).value + alt_off_deg = u.Quantity(alt_off, unit=u.rad).to(u.deg).value + + reco_direction = fix_pointing.spherical_offsets_by( + [az_off_deg] * u.deg, + [alt_off_deg] * u.deg + ) + + # Clamp altitude offset to avoid exceeding valid range + # alt_off_deg = np.clip(alt_off_deg, -90, 90) + + return reco_direction +#------------------------------------------------------------------------------------------------------------ +def write_output(h5file, data, predictions, labels, task:Task, mode:Mode): + prediction_dir = h5file.replace(f'{h5file.split("/")[-1]}', "") + if not os.path.exists(prediction_dir): + os.makedirs(prediction_dir) + + # Store dl2 data + reco = {} + if os.path.isfile(h5file): + with pd.HDFStore(h5file, mode="r") as file: + h5file_keys = list(file.keys()) + if f"/dl2/reco" in h5file_keys: + reco = pd.read_hdf(file, key=f"/dl2/reco") + + + reco["event_id"] = np.array(data["event_id"]) + reco["obs_id"] = np.array(data["obs_id"]) + + + + if task ==Task.type: + + for n, name in enumerate(data["class_names"]): + reco[name + "ness"] = np.array(predictions["type"][:, n]) + # reco["type_feature_vector"] = np.array(predictions["type_feature_vector"]) + reco["reco_type"]= np.array(predictions["type_class"])*101 + + if mode != Mode.observation:#"observation": + if data["energy_unit"] == "log(TeV)": + reco["true_energy"] = np.power(10, labels["true_energy"]) + else: + reco["true_energy"] = labels["true_energy"] + + + if task == Task.energy: + # if data["energy_unit"] == "log(TeV)" or np.min(predictions["energy"]) < 0.0: + # reco["reco_energy"] = np.power(10, predictions["energy"][:, 0]) + # reco["log_reco_energy"] =predictions["energy"][:, 0] + # else: + reco["reco_energy"] = np.power(10, predictions["energy"][:, 0]) + reco["log_reco_energy"] =predictions["energy"][:, 0] + + + if mode != Mode.observation:#"observation": + reco["true_alt"] = np.float32(np.rad2deg(labels["true_alt_az"][:,0])) + reco["true_az"] = np.float32(np.rad2deg(labels["true_alt_az"][:,1])) + + if task==Task.direction: + # reco["reco_alt"] = np.array(predictions["direction"][:, 1]) + # reco["reco_az"] = np.array(predictions["direction"][:, 0]) + + reco_az, reco_alt = [], [] + reco_az_off, reco_alt_off = [], [] + reco_src_x, reco_src_y = [], [] + dragon_time = [] + utc_time = [] + + data_type = 'real_data' + if mode == Mode.observation: + # reco["reco_src_x"] = [] + # reco["reco_src_y"] = [] + reco["src_x"] = data["pointing"]["src_x"] + reco["src_y"] = data["pointing"]["src_y"] + + reco_data = {} + if mode != Mode.observation:#"observation": + pointing_alt = np.full(len(data["obs_id"]), data["pointing"]["pointing_alt"]) + pointing_az = np.full(len(data["obs_id"]), data["pointing"]["pointing_az"]) + + reco["alt_tel"] = pointing_alt + reco["az_tel"] = pointing_az + else: + pointing_alt = data["pointing"]["pointing_alt"] + pointing_az = data["pointing"]["pointing_az"] + + reco["alt_tel"] = pointing_alt + reco["az_tel"] = pointing_az + + # fix_pointing_t = SkyCoord( + # pointing_az * u.deg, + # pointing_alt * u.deg, + # frame="altaz", + # unit="deg", + # ) + + horizon_frame="altaz" + + if mode== Mode.observation: + time = data["pointing"]["dragon_time"] + obstime = Time(time, scale='utc', format='unix') + horizon_frame = AltAz(location=LST1_LOCATION, obstime=obstime) + + # obstime_lst = Time("2018-11-01T02:00") + # horizon_frame_lst = AltAz(location=LST1_LOCATION, obstime=obstime_lst) + + # fix_pointing_lst = SkyCoord( + # az=pointing_az * u.deg, + # alt=pointing_alt * u.deg, + # frame=horizon_frame_lst, + # unit="deg", + # ) + + fix_pointing = SkyCoord( + az=pointing_az * u.deg, + alt=pointing_alt * u.deg, + frame=horizon_frame, + unit="deg", + ) + # ---------------------------------------------------------------------------------------------------------------------------------------- + # start = time_.time() + sigma_az=[] + sigma_alt=[] + if len(predictions["direction"])>0: + az_off_ = predictions["direction"][:, 0] + alt_off_ = predictions["direction"][:, 1] + + else: + az_off_ = predictions["direction_mu"][:, 0] + alt_off_ = predictions["direction_mu"][:, 1] + sigma_az = predictions["direction_sigma"][:, 0] + sigma_alt = predictions["direction_sigma"][:, 1] + + reco_direction_ = recover_alt_az(fix_pointing,alt_off_,az_off_) + reco_az_item_=reco_direction_.az.to_value(u.deg)[0] + reco_alt_item_=reco_direction_.alt.to_value(u.deg)[0] + + reco_az=reco_az_item_ + reco_alt = reco_alt_item_ + reco_az_off = az_off_ + reco_alt_off = alt_off_ + + reco_data_ = {} + if mode == Mode.observation: + reco_data_["alt"] = reco_alt + reco_data_["az"] = reco_az + reco_data_["tel_pointing_alt"] = fix_pointing.alt.value + reco_data_["tel_pointing_az"] = fix_pointing.az.value + + reco_data_["dragon_time"] = data["pointing"]["dragon_time"] + reco_data_["utc_time"] = data["pointing"]["utc_time"] + + reco_src_ = reco_src_sky_to_camera(reco_data_,effective_focal_length=data["effective_focal_length"]) + + reco_src_x = reco_src_[0] + reco_src_y = reco_src_[1] + dragon_time = data["pointing"]["dragon_time"] + # New fix + # dragon_time = Time(dragon_time, format='unix_tai') + + utc_time = data["pointing"]["utc_time"] + + # end = time_.time() + # print("Optimized: ", end - start) + # ---------------------------------------------------------------------------------------------------------------------------------------- + if mode == Mode.observation: + reco["reco_src_x"] = reco_src_x + reco["reco_src_y"] = reco_src_y + reco["dragon_time"] = dragon_time + reco["utc_time"] = utc_time + + reco["reco_alt"] = reco_alt + reco["reco_az"] = reco_az + reco["reco_alt_off"] = reco_alt_off + reco["reco_az_off"] = reco_az_off + reco["sigma_alt"]= sigma_alt + reco["sigma_az"]= sigma_az + + del predictions + import gc + gc.collect() + + # Convertir reco a Astropy Table + reco_table = Table() + for key, val in reco.items(): + reco_table[key] = np.array(val) + + if data["include_nsb_patches"] is None: + pd.DataFrame(data=reco).to_hdf(h5file, key=f"/dl2/reco", mode="a", format="table") + # write_table( + # reco_table, + # h5file, + # f"/dl2/reco", + # overwrite=True, + # ) + else: + # write_table( + # reco_table, + # h5file, + # f"/trigger/reco", + # overwrite=True, + # ) + pd.DataFrame(data=reco).to_hdf(h5file, key=f"/trigger/reco", mode="a", format="table") + + + + # Guardar usando ctapipe.io.write_table + + + + # Store the simulation information for pyirf + if data["simulation_info"] and data["include_nsb_patches"] != "all": + pd.DataFrame(data=data["simulation_info"] , index=[0]).to_hdf( + h5file, key=f"/info/mc_header", mode="a", format="table") + + + # Store the selected Hillas parameters (dl1b) + if data["parameter_names"] and data["include_nsb_patches"] != "all": + tel_counter = 0 + if data["mode"]== "mono": + tel_type = list(data["selected_telescopes"].keys())[0] + tel_ids = "tel" + for tel_id in data["selected_telescopes"][tel_type]: + tel_ids += f"_{tel_id}" + parameters = {} + for p, parameter in enumerate(data["parameter_names"]): + parameter_list = np.array(data["parameter_data"])[:,p] + + parameters[parameter] = parameter_list + pd.DataFrame(data=parameters).to_hdf( + h5file, key=f"/dl1b/{tel_type}/{tel_ids}", mode="a", format="table") + else: + for tel_type in data["selected_telescopes"]: + for t, tel_id in enumerate(data["selected_telescopes"][tel_type]): + parameters = {} + for p, parameter in enumerate(data["parameter_list"]): + parameter_list = np.array(data["parameter_list"])[:, tel_counter + t, p] + + parameters[parameter] = parameter_list + pd.DataFrame(data=parameters).to_hdf( + h5file, key=f"/dl1b/{tel_type}/tel_{tel_id}", mode="a", format="table") + tel_counter += len(data["selected_telescopes"][tel_type]) + + print(f"File saved: {h5file}") \ No newline at end of file diff --git a/ctlearn/core/pytorch/utils/utils_torch.py b/ctlearn/core/pytorch/utils/utils_torch.py new file mode 100644 index 00000000..4a4e934b --- /dev/null +++ b/ctlearn/core/pytorch/utils/utils_torch.py @@ -0,0 +1,33 @@ +import torch + +def cartesian_to_alt_az(directions): + r = torch.sqrt(torch.sum(directions**2, dim=1)) + # Prevent division by zero + safe_r = torch.where(r == 0, torch.tensor(1.0, device=r.device), r) + + # altitude_rad = torch.arcsin(safe_r) + + altitude_rad = torch.asin(directions[:, 2] / safe_r) + azimuth_rad = torch.atan2(directions[:, 1], directions[:, 0]) + + # Normalize azimuth to [0, 2π] + # azimuth_rad = torch.where(azimuth_rad < 0, azimuth_rad + 2 * torch.pi, azimuth_rad) + + return torch.stack((altitude_rad, azimuth_rad), dim=1) + +def alt_az_to_cartesian(altitude_rad, azimuth_rad, r=1): + x = r * torch.cos(altitude_rad) * torch.cos(azimuth_rad) + y = r * torch.cos(altitude_rad) * torch.sin(azimuth_rad) + z = r * torch.sin(altitude_rad) + return torch.stack((x, y, z), dim=1) # Stack along new dimension to create vectors + +def adjust_learning_rate( optimizer, lr): + """Adjusts learning rate of all optimizer's parameter groups.""" + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +def compare_weights(model, initial_weights): + for name, param in model.named_parameters(): + initial_weight = initial_weights[name] + if not torch.equal(initial_weight, param.data): + print(f"Weight changed: {name}") \ No newline at end of file diff --git a/ctlearn/core/pytorch/visualization/tsne_test.py b/ctlearn/core/pytorch/visualization/tsne_test.py new file mode 100644 index 00000000..e19cd176 --- /dev/null +++ b/ctlearn/core/pytorch/visualization/tsne_test.py @@ -0,0 +1,27 @@ +import numpy as np +import matplotlib.pyplot as plt +from sklearn import datasets +from sklearn.manifold import TSNE + +# Load the Iris dataset +iris = datasets.load_iris() +X = iris.data +y = iris.target + +# Create a TSNE instance with desired parameters +tsne = TSNE(n_components=2, random_state=42) + +# Perform TSNE +X_embedded = tsne.fit_transform(X) + +# Plot the embedded points with matplotlib +plt.figure(figsize=(8, 6)) +colors = ['red', 'blue', 'green'] +for i, color in enumerate(colors): + plt.scatter(X_embedded[y == i, 0], X_embedded[y == i, 1], c=color, label=iris.target_names[i]) + +plt.legend(loc='best') +plt.title('TSNE visualization of the Iris dataset') +plt.xlabel('TSNE 1') +plt.ylabel('TSNE 2') +plt.show() \ No newline at end of file diff --git a/ctlearn/core/pytorch/visualization/vis_utils.py b/ctlearn/core/pytorch/visualization/vis_utils.py new file mode 100644 index 00000000..86610a8e --- /dev/null +++ b/ctlearn/core/pytorch/visualization/vis_utils.py @@ -0,0 +1,194 @@ +import numpy as np +from sklearn.metrics import roc_curve, auc +import matplotlib.pyplot as plt +import cv2 +from io import BytesIO +import os +import seaborn as sns +import pandas as pd +import astropy.units as u +import ctaplot +import torch +# ---------------------------------------------------------------------------------------------------------- +def plot_energy_resolution_error(val_energy_pred_list,val_energy_label_list,val_hillas_intensity_list): + # Create a DataFrame to store the data + data = pd.DataFrame({ + 'energy_reco': list(val_energy_pred_list), + 'energy_true': list(val_energy_label_list), + 'hillas_intensity': list(val_hillas_intensity_list) + }) + cut_off = 50 + filtered_data = data[data['hillas_intensity'] > cut_off] + true_energy = u.Quantity(filtered_data['energy_true'], u.TeV) + reco_energy = u.Quantity(filtered_data['energy_reco'], u.TeV) + # .apply(lambda x: x[0]) + + fig, ax = plt.subplots() + ctaplot.plot_energy_resolution(true_energy, reco_energy, label="Energy resolution", ax=ax) + ctaplot.plot_energy_resolution_cta_requirement('north', ax=ax, color='black') + ax.legend() + ax.set_ylim(bottom=0, top=1.5) + # plt.show(block=True) + # plt.close() + + return fig +# ---------------------------------------------------------------------------------------------------------- +def plot_direction_resolution_error(val_alt_pred_list,val_az_pred_list, val_alt_label_list,val_az_label_list, val_energy_label_list,val_hillas_intensity_list): + + # Create a DataFrame to store the data + data = pd.DataFrame({ + 'alt_reco': list(val_alt_pred_list), + 'az_reco': list(val_az_pred_list), + 'alt_true': list(val_alt_label_list), + 'az_true': list(val_az_label_list), + 'energy_true': list(val_energy_label_list), + 'hillas_intensity': list(val_hillas_intensity_list) + }) + cut_off = 50 + filtered_data = data[data['hillas_intensity'] > cut_off] + true_energy = u.Quantity(filtered_data['energy_true'], u.TeV) + + alt_true = u.Quantity(filtered_data['alt_true'], u.rad) + az_true = u.Quantity(filtered_data['az_true'], u.rad) + + alt_reco = u.Quantity(filtered_data['alt_reco'], u.rad) + az_reco = u.Quantity(filtered_data['az_reco'], u.rad) + + fig, ax = plt.subplots() + ax = ctaplot.plot_angular_resolution_per_energy(alt_true, alt_reco, az_true, az_reco, true_energy, label="Ang res (mono)") + ctaplot.plot_angular_resolution_cta_requirement('north', ax=ax, color='black') + ax.legend() + ax.set_ylim(bottom=0, top=1.5) + return fig +# ---------------------------------------------------------------------------------------------------------- +# def plot_confusion_matrix(cm, classes, accuracies, cm_file_name ,save_folder): +# plt.figure(figsize=(10, 7)) +# sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', +# xticklabels=classes, yticklabels=classes) +# plt.xlabel('Predicted Labels') +# plt.ylabel('True Labels') + +# accuracy_str = "Accuracy: \n" + +# for id, (class_type) in enumerate(classes): +# accuracy_str += " " + class_type + ": " + \ +# str(round(accuracies[id], 2))+"%" + +# plt.title('Confusion Matrix \n' + accuracy_str) + +# # plt.show() +# plt.savefig(os.path.join(save_folder,cm_file_name+".png")) +# plt.close() +def plot_confusion_matrix(cm, classes, cm_file_name, save_folder): + """ + Plots and saves the confusion matrix. + + Args: + cm (torch.Tensor or np.ndarray): The confusion matrix. + classes (list): List of class names. + cm_file_name (str): Name of the file to save. + save_folder (str): Folder to save the plot. + """ + if isinstance(cm, torch.Tensor): + cm = cm.cpu().numpy() # Convert to NumPy if it's a tensor + + plt.figure(figsize=(10, 7)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', + xticklabels=classes, yticklabels=classes) + plt.xlabel('Predicted Labels') + plt.ylabel('True Labels') + + # Compute class-wise accuracies + class_accuracies = cm.diagonal() / cm.sum(axis=1) + accuracy_str = "Accuracy: \n" + for idx, class_name in enumerate(classes): + accuracy_str += f" {class_name}: {round(class_accuracies[idx] * 100, 2)}%\n" + + plt.title('Confusion Matrix\n' + accuracy_str) + + # Ensure the save folder exists + os.makedirs(save_folder, exist_ok=True) + + # Save the figure + plt.savefig(os.path.join(save_folder, f"{cm_file_name}.png")) + plt.close() + return class_accuracies +# ---------------------------------------------------------------------------------------------------------- +def create_image_mosaic(images_list, text_list, image_id, rows=4, cols=4, save_path='./',save_name='mosaic'): + """ + Creates an image mosaic using Matplotlib, saves it, and returns it as a numpy array. + + Args: + images_list (list): List of numpy array images. + rows (int): Number of rows in the mosaic. + cols (int): Number of columns in the mosaic. + save_path (str): Path to save the mosaic image. + + Returns: + numpy.ndarray: Image of the mosaic loaded as a numpy array for OpenCV. + """ + fig, axes = plt.subplots(rows, cols, figsize=( + 12, 12)) # Adjust the figure size as needed + axes = axes.ravel() + + for idx, ax in enumerate(axes): + if idx < len(images_list): + ax.set_title(text_list[idx], fontsize=6) + ax.imshow(images_list[idx], cmap='viridis' if len( + images_list[idx].shape) == 2 else None) + ax.axis('off') + else: + ax.axis('off') + + plt.tight_layout() + + # Save the figure to a buffer + buf = BytesIO() + plt.savefig(buf, format='png') + buf.seek(0) + image = np.frombuffer(buf.getvalue(), dtype=np.uint8) + buf.close() + + # Close the plot to free memory + plt.close(fig) + + # Convert buffer to OpenCV image format + image = cv2.imdecode(image, cv2.IMREAD_COLOR) + + # Optionaly save the image to disk + if save_name and save_path: + cv2.imwrite(os.path.join(save_path,save_name+"_"+str(image_id)+".png"), image) + + return image +# ---------------------------------------------------------------------------------------------------------- +def plot_roc_and_calculate_auc(ground_truth, predicted_probabilities): + """ + This function calculates the ROC curve and the AUC given a vector of true labels and predicted probabilities. + + Parameters: + - ground_truth: numpy array with true labels (0s and 1s). + - predicted_probabilities: numpy array with the probabilities of the positive class. + + Returns: + - AUC score. + """ + # Calcular puntos para la curva ROC + fpr, tpr, _ = roc_curve(ground_truth, predicted_probabilities) + # Calcular el AUC + roc_auc = auc(fpr, tpr) + + # Graficar la curva ROC + plt.figure() + plt.plot(fpr, tpr, color='darkorange', + lw=2, label='ROC curve (area = %0.2f)' % roc_auc) + plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.title('Receiver Operating Characteristic') + plt.legend(loc="lower right") + plt.show() + + return roc_auc +# ---------------------------------------------------------------------------------------------------------- \ No newline at end of file diff --git a/ctlearn/core/pytorch/visualization/visualization_tsne.py b/ctlearn/core/pytorch/visualization/visualization_tsne.py new file mode 100644 index 00000000..51989406 --- /dev/null +++ b/ctlearn/core/pytorch/visualization/visualization_tsne.py @@ -0,0 +1,683 @@ +# from sklearn.manifold import TSNE +from cuml.manifold import TSNE + +# from nets.models.ResNet import ResNet +from ctlearn.ctlearn_helper.common import read_configuration +from ctlearn import utils +import torch +from tqdm import tqdm +import matplotlib.pyplot as plt +import numpy as np +from ctlearn.ctlearn_helper.CTlearnEnums import Task, Mode, EventType +from ctlearn.cli.run_model import Runner +from ctlearn.utils.utils import ( + load_pickle, + create_key_value_array + +) +from ctlearn import ( + CTADataset, + read_configuration, + ModelHelper, +) +# from threadpoolctl import threadpool_limits +from sklearn.decomposition import PCA +from sklearn.preprocessing import MinMaxScaler +from matplotlib import cm +import faiss +import pickle +from typing import Dict +import matplotlib +matplotlib.use('TkAgg') # Or 'Qt5Agg', depending on your setup + +def plt_tsne(embeddings, pred_labels, gt_labels, class_energy, classes, plot_3d=False, post_fix="tsne"): + """ + Visualize t-SNE embeddings with energy levels encoded by color, preserving class markers, + and highlighting misclassified points. + + Parameters: + - embeddings: t-SNE reduced feature vectors. + - pred_labels: Predicted class labels. + - gt_labels: Ground truth class labels. + - class_energy: Energy values corresponding to each sample. + - classes: List of class names. + - plot_3d: Boolean to plot in 3D. + - prefix: Prefix for the saved plot filename. + """ + + # Normalize energy values to [0, 1] for colormap mapping + scaler = MinMaxScaler() + norm_energy = scaler.fit_transform(class_energy.reshape(-1, 1)).flatten() + + # Choose a colormap + cmap = matplotlib.colormaps['viridis']#cm.get_cmap('viridis') + + # Identify misclassified points + misclassified = pred_labels != gt_labels + + # Initialize plot + fig = plt.figure(figsize=(10, 8)) + if plot_3d: + ax = fig.add_subplot(111, projection="3d") + else: + ax = fig.add_subplot(111) + + # Define markers for each class + markers = ["o", "^", "s"] + + # Plot correctly classified points with energy-based colors + for i, class_name in enumerate(classes): + idx = (gt_labels == i) & ~misclassified + if plot_3d: + sc = ax.scatter( + embeddings[idx, 0], + embeddings[idx, 1], + embeddings[idx, 2], + color=cmap(norm_energy[idx]), + marker=markers[i], + label=f"Correct {class_name}", + alpha=0.6 + ) + else: + sc = ax.scatter( + embeddings[idx, 0], + embeddings[idx, 1], + color=cmap(norm_energy[idx]), + marker=markers[i], + label=f"Correct {class_name}", + alpha=0.6 + ) + + # Plot misclassified points with a distinct marker and color + if plot_3d: + ax.scatter( + embeddings[misclassified, 0], + embeddings[misclassified, 1], + embeddings[misclassified, 2], + color="red", + marker="x", + label="Misclassified", + alpha=0.1 + ) + else: + ax.scatter( + embeddings[misclassified, 0], + embeddings[misclassified, 1], + color="red", + marker="x", + label="Misclassified", + alpha=0.1 + ) + + # Set plot title and labels + ax.legend(loc="best") + ax.set_title("t-SNE Visualization with Energy Levels and Misclassifications") + ax.set_xlabel("t-SNE 1") + ax.set_ylabel("t-SNE 2") + if plot_3d: + ax.set_zlabel("t-SNE 3") + + # Add colorbar to indicate energy levels + sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=class_energy.min(), vmax=class_energy.max())) + sm.set_array([]) + cbar = plt.colorbar(sm) + + cbar.set_label('Energy Levels') + + # Save and display plot + plt.savefig(f'./tsne_{post_fix}.png') + plt.show() + +def main(use_pickle,reduce_similarity,plot_tsne): + + classes_list = ["gamma", "proton"] + config_file_str = "./config/training_config_iaa_neutron_missing_analisys.yml" + parameters = read_configuration(config_file_str) + # file_name = parameters["data"]["train_gamma_proton"] + file_name = "/storage/ctlearn_data/training/node_pickles/proton_proton_theta_16.087_az_108.090_runs1-416_train_275107.dl1.pickle" + if not use_pickle: + + batch_size = 64 #parameters["hyp"]["batches"] + num_workers = parameters["dataset"]["num_workers"] + pin_memory = parameters["dataset"]["pin_memory"] + persistent_workers = parameters["dataset"]["persistent_workers"] + + print("Data loaded...") + + validation_data =load_pickle(file_name) + + factor = 1 + data_len = len(validation_data["data"]) + validation_data["data"] = validation_data["data"][0 : int(data_len / factor)] + validation_data["true_shower_primary_id"] = validation_data[ + "true_shower_primary_id" + ][0 : int(data_len / factor)] + + cta_ds_validation = CTADataset( + pickle_data=validation_data, task=Task.type,mode=Mode.results, parameters=parameters, use_augmentation = False + ) + + data_loader = torch.utils.data.DataLoader( + cta_ds_validation, + batch_size=batch_size, + shuffle=False, + collate_fn=cta_ds_validation.collate_fn, + num_workers=num_workers, + pin_memory=pin_memory, + persistent_workers=persistent_workers, + ) + + + runner = Runner() + model_net = runner.create_model(parameters["model"]["model_type"]) + + device_str = parameters["arch"]["device"] + device = torch.device(device_str) + + check_point_path = parameters["data"]["type_checkpoint"] + + model_net = ModelHelper.loadModel( + model_net, "", check_point_path, mode=Mode.results , device_str=device_str + ) + + model_net.eval() + + class_feature_vector = [] + class_predictions_class = [] + class_gt_class = [] + class_energy = [] + class_hillas = [] + class_hillas_name = validation_data["hillas_name"] + pbar = tqdm(total=len(data_loader), desc="DL2 conv", leave=True) + + + for batch_idx, (features, labels) in enumerate(data_loader): + + imgs = features["image"].to(device).contiguous() + peak_time = features["peak_time"].to(device).contiguous() + classification_pred, energy_pred, direction_pred = model_net( + imgs, peak_time + ) + + if len(features)==0: + continue + + classification_pred_ = classification_pred[0] + feature_vector = classification_pred[1].cpu().detach().numpy() + predicted = torch.softmax(classification_pred_, dim=1) + predicted_class = predicted.argmax(dim=1) + predicted_class = predicted_class.cpu().detach().numpy() + + labels_class = (labels["particletype"].int().to(device).contiguous()) + labels_class = labels_class.cpu().detach().numpy() + + labels_energy = labels["energy"].cpu().detach().numpy() + + hillas = features["hillas"] + hillas = { + key: tensor.cpu().detach().numpy() for key, tensor in hillas.items() + } + id = list(range(features["image"].shape[0])) + + hillas_vector = np.array(create_key_value_array(hillas, id)).T + + class_feature_vector.extend(feature_vector[:, :]) + class_predictions_class.extend(predicted_class[:]) + class_gt_class.extend(labels_class[:]) + energy = np.power(10,labels_energy[:])[:,0] + class_energy.extend(energy) + class_hillas.extend(hillas_vector) + if batch_idx % 10 == 0: + pbar.update(10) + + + embeddings = np.array(list(class_feature_vector)) + pred_labels = np.array(list(class_predictions_class)) + gt_labels = np.array(list(class_gt_class)) + gt_energies = np.array(list(class_energy)) + class_hillas = np.array(list(class_hillas)) + + data_dict ={"embeddings":embeddings, + "pred_labels":pred_labels, + "gt_labels":gt_labels, + "gt_energies":gt_energies, + "hillas_name":class_hillas_name, + "hillas":class_hillas} + + + # Save pickle + save_file_name= "./prediction_train.pickle" + with open(save_file_name, "wb") as handle: + pickle.dump(data_dict, handle, protocol=pickle.HIGHEST_PROTOCOL) + print("Files saved ... ", save_file_name) + + + save_file_name= "./prediction_train.pickle" + # save_file_name= "./validation_data.pickle" + + prediction = load_pickle(save_file_name) + print("pickle loaded...") + embeddings = prediction["embeddings"] + pred_labels = prediction["pred_labels"] + gt_labels = prediction["gt_labels"] + gt_energies = prediction["gt_energies"] + hillas_name = prediction["hillas_name"] + hillas = prediction["hillas"] + if reduce_similarity: + original_data =load_pickle(file_name) + SIMIL_THRS = 0.99 + + #------------------------------------------------------------------------------------------ + # dimension = embeddings.shape[1] + # nlist = 100 # Número de clusters (ajustar según dataset) + # # Crear el índice con clusters + # faiss.normalize_L2(embeddings) + # quantizer = faiss.IndexFlatIP(dimension) + # index = faiss.IndexIVFFlat(quantizer, dimension, nlist, faiss.METRIC_INNER_PRODUCT) + # # index.nprobe = 20 + # index.train(embeddings) # Entrenar el índice de clustering + # index.add(embeddings) # Agregar embeddings + # if not index.is_trained: + # print("Index NOT trained properly.") + # exit() + # else: + # print("Index trained properly.") + #------------------------------------------------------------------------------------------ + # dimension = embeddings.shape[1] + # nlist = 100 # Número de clusters (ajustar según dataset) + # # Crear el índice con clusters + # faiss.normalize_L2(embeddings) + # # index = faiss.IndexIVFFlat(quantizer, dimension, nlist, faiss.METRIC_INNER_PRODUCT) + # index = faiss.index_factory(dimension, f"IVF{nlist},Flat", faiss.METRIC_INNER_PRODUCT) + + # index.nprobe = 20 + # index.train(embeddings) # Entrenar el índice de clustering + # index.add(embeddings) # Agregar embeddings + # if not index.is_trained: + # print("Index NOT trained properly.") + # exit() + # else: + # print("Index trained properly.") + #------------------------------------------------------------------------------------------ + # dimension = embeddings.shape[1] + # faiss.normalize_L2(embeddings) + # index = faiss.IndexHNSWFlat(dimension, 32) # 32 vecinos en el grafo + # index.hnsw.efConstruction = 60 # Controla la calidad del grafo (más grande = mejor recall) + # index.hnsw.efSearch = 100 # Cuántos vecinos considerar en búsqueda + + # # Agregar embeddings + # index.add(embeddings) + #------------------------------------------------------------------------------------------ + # + #------------------------------------------------------------------------------------------ + # dimension = embeddings.shape[1] + # index = faiss.IndexFlatIP(dimension) # Index con producto interno (similaridad coseno) + # index = faiss.IndexIDMap(index) + # faiss.normalize_L2(embeddings) # Normalizar embeddings para similitud del coseno + # index.add(embeddings) # Agregar embeddings al índice de Faiss + #------------------------------------------------------------------------------------------ + # print("Generating index.") + # embeddings = np.array(embeddings).astype('float32') + # faiss.normalize_L2(embeddings) + # dimension = embeddings.shape[1] + + # nlist = 50 #100 # Número de clusters (ajustar según dataset) + # # Step 1: Create the CPU index + # quantizer = faiss.IndexFlatIP(dimension) + # cpu_index = faiss.IndexIVFFlat(quantizer, dimension, nlist, faiss.METRIC_INNER_PRODUCT) + + # # Step 2: Train on CPU + # cpu_index.train(embeddings) + + # # Step 3: Move to GPU + # res = faiss.StandardGpuResources() # Use default options + # index = faiss.index_cpu_to_gpu(res, 0, cpu_index) # 0 is the GPU id + # index.nprobe = 80 + # # Step 4: Add embeddings to GPU index + # index.add(embeddings) + # if not index.is_trained: + # print("Index NOT trained properly.") + # exit() + # else: + # print("Index trained properly.") + + + print("Generating index.") + embeddings = np.array(embeddings).astype('float32') + faiss.normalize_L2(embeddings) + dimension = embeddings.shape[1] + + nlist = 50 # Número de clusters + quantizer = faiss.IndexFlatIP(dimension) + cpu_index = faiss.IndexIVFFlat(quantizer, dimension, nlist, faiss.METRIC_INNER_PRODUCT) + + # Entrenar índice en CPU + cpu_index.train(embeddings) + num_gpus = faiss.get_num_gpus() + print("GPUs detected:",num_gpus) + # Crear recursos para múltiples GPUs + gpu_resources = [faiss.StandardGpuResources() for _ in range(num_gpus)] + + # Distribuir el índice entrenado a múltiples GPUs + gpu_indices = [ + faiss.index_cpu_to_gpu(gpu_resources[i], i, cpu_index) + for i in range(2) + ] + + # Combinar índices GPU en un índice shard + index = faiss.IndexShards(dimension, True, False) + for sub_index in gpu_indices: + index.add_shard(sub_index) + + index.nprobe = 80 + + # Agregar embeddings al índice distribuido (se distribuyen automáticamente entre GPUs) + index.add(embeddings) + + if not index.is_trained: + print("Index NOT trained properly.") + exit() + else: + print("Index trained properly.") + + #------------------------------------------------------------------------------------------ + + # print("Generating index.") + # embeddings = np.array(embeddings).astype('float32') + # faiss.normalize_L2(embeddings) + # dimension = embeddings.shape[1] + + # nlist = 50#100 # Número de clusters (ajustar según dataset) + + # # Paso 1: Crear el índice en CPU + # quantizer = faiss.IndexFlatIP(dimension) + # index = faiss.IndexIVFFlat(quantizer, dimension, nlist, faiss.METRIC_INNER_PRODUCT) + + # # Paso 2: Entrenar el índice (CPU) + # index.train(embeddings) + + # index.nprobe = 80 + + # # Paso 3: Añadir los embeddings al índice CPU + # index.add(embeddings) + + # # Verificación del entrenamiento + # if not index.is_trained: + # print("Index NOT trained properly.") + # exit() + # else: + # print("Index trained properly.") + #------------------------------------------------------------------------------------------ + + print("Looking for duplicates: Search...") + # D, I = index.search(embeddings, k=3) # Search for the k nearest + # lims, D, I = index.range_search(embeddings, SIMIL_THRS) + # Parameters + SIMIL_THRS = 0.99 + k_search_default= 10 + k_search_first_bins = 45 #25 + similarity_default = 0.95 + similarity_first_bins = 0.90 + k_bin=11 + cut_off_leakage_intensity = 0.2 + cut_off_intensity = 50 + bins = np.logspace(np.log10(2.51e-02), 2, 19) + bin_indices = np.digitize(gt_energies, bins) + num_bins = len(bins) # should be 18 in your case + + # Create array with default value 0.99 + simil_thresholds = np.full(num_bins+1, similarity_default) + k_search = np.full(num_bins+1, k_search_default,dtype=int) + k_search[:k_bin] = k_search_first_bins + # Set first 5 bins to 0.95 + simil_thresholds[:k_bin] = similarity_first_bins + # === Deduplication within bins === + keep_image = set() + deleted = set() + deleted_bin = set() + seen = set() + + unique_bins = np.unique(bin_indices) + print(f"Processing {len(unique_bins)} bins...") + + deleted_list=[] + for b in tqdm(unique_bins): + # Get indices in the current bin + bin_mask = (bin_indices == b) + bin_ids = np.where(bin_mask)[0] + + if len(bin_ids) < 2: + continue # Skip bins with fewer than 2 items + + # Extract embeddings for this bin + bin_embeddings = embeddings[bin_ids] + print(f"Processing bin {b} of {len(unique_bins)} ") + # Perform FAISS k-NN search + D, I = index.search(bin_embeddings, k=int(k_search[b])) + # D, I = index.search(bin_embeddings, k=k_search_default) + + + for i in range(len(bin_ids)): + query_idx = bin_ids[i] + + if query_idx in deleted: + continue + + if hillas[query_idx][hillas_name["leakage_pixels_width_2"]]>cut_off_leakage_intensity or hillas[query_idx][hillas_name["hillas_intensity"]]= len(bin_ids): + continue # Index out of bounds (can happen if few items in bin) + + neighbor_idx = bin_ids[neighbor_idx_local] + + if neighbor_idx == query_idx: + continue + + if similarity >= simil_thresholds[b] and neighbor_idx not in seen: + deleted.add(neighbor_idx) + deleted_bin.add(neighbor_idx) + seen.add(neighbor_idx) + + deleted_list.append(deleted_bin) + deleted_bin = set() + seen = set() + print(f"Final samples kept: {len(keep_image)}") + print(f"Samples removed as similar: {len(deleted)}") + + # Count number of deletions per bin + deleted_counts = [len(bin_deleted) for bin_deleted in deleted_list] + kept_indices = sorted(keep_image) + + #------------------------------------------------------------------------------------------ + # Save data + # original_data["data"]=original_data["data"][kept_indices] + original_data["data"] = [original_data["data"][i] for i in kept_indices] + save_file_name= "./train_reduced_data.pickle" + + with open(save_file_name, "wb") as handle: + pickle.dump(original_data, handle, protocol=pickle.HIGHEST_PROTOCOL) + #------------------------------------------------------------------------------------------ + # Get indices for each class in the ground truth + gamma_indices = np.where(gt_labels == 0)[0] + proton_indices = np.where(gt_labels == 1)[0] + + # Gamma accuracy: correct predictions among all gamma ground truths + gamma_accuracy = np.mean(pred_labels[gamma_indices] == 0) + + # Proton accuracy: correct predictions among all proton ground truths + proton_accuracy = np.mean(pred_labels[proton_indices] == 1) + + overall_accuracy = np.mean(gt_labels == pred_labels) + + print(f"Accuracy Before: {overall_accuracy}") + print(f"Accuracy Gamma Before: {gamma_accuracy}") + print(f"Accuracy Proton Before: {proton_accuracy}") + print(f"Num of Gammas Before: {len(gamma_indices)}") + print(f"Num of Protons Before: {len(proton_indices)}") + #------------------------------------------------------------------------------------------ + # Get indices for each class in the ground truth + gamma_indices = np.where(gt_labels[kept_indices] == 0)[0] + proton_indices = np.where(gt_labels[kept_indices] == 1)[0] + + # Gamma accuracy: correct predictions among all gamma ground truths + gamma_accuracy = np.mean(pred_labels[kept_indices][gamma_indices] == 0) + + # Proton accuracy: correct predictions among all proton ground truths + proton_accuracy = np.mean(pred_labels[kept_indices][proton_indices] == 1) + + overall_accuracy = np.mean(gt_labels[kept_indices] == pred_labels[kept_indices]) + + print(f"Accuracy After: {overall_accuracy}") + print(f"Accuracy Gamma After: {gamma_accuracy}") + print(f"Accuracy Proton After: {proton_accuracy}") + print(f"Num of Gammas After: {len(gamma_indices)}") + print(f"Num of Protons After: {len(proton_indices)}") + #------------------------------------------------------------------------------------------ + # Plot + plt.figure(figsize=(10, 5)) + plt.bar(range(len(deleted_counts)), deleted_counts) + plt.xlabel("Bin Index") + plt.ylabel("Number of Deleted Samples") + plt.title("Deleted Samples per Energy Bin") + plt.grid(True) + plt.tight_layout() + plt.savefig(f'./histogram_deleted_{k_search_first_bins}_{k_search_default}.png') + # plt.show() + + #------------------------------------------------------------------------------------------ + # Create bin labels (log-scale bins) + # Ensure we have 18 bins + # Bin labels: 18 labels for 18 bins + # num_bins = len(bins) + # bin_labels = [f"{bins[i]:.2e}-{bins[i+1]:.2e}" for i in range(num_bins-1)] + # deleted_counts = [len(s) for s in deleted_list] + # x = list(range(num_bins)) + + # plt.figure(figsize=(12, 6)) + # plt.bar(x, deleted_counts) + # plt.xticks(ticks=x, labels=bin_labels, rotation=45, ha='right') + # plt.xlabel("Energy Bin") + # plt.ylabel("Number of Deleted Samples") + # plt.title("Deleted Samples per Energy Bin") + # plt.grid(True, which='both', linestyle='--', linewidth=0.5) + # plt.tight_layout() + # plt.show() + #------------------------------------------------------------------------------------------ + # Convert sets to sorted lists (for consistent indexing) + deleted_indices = sorted(deleted) + kept_indices = sorted(keep_image) + + + # Get energy values + energies_before = gt_energies + energies_after = gt_energies[kept_indices] + + # Create log-spaced bins for histogram (same as your binning) + hist_bins = np.logspace(np.log10(2.51e-02), 2, 30) + + plt.figure(figsize=(12, 6)) + + # Plot before as outline + plt.hist(energies_before, bins=hist_bins, histtype='step', label='Before Filtering', color='gray', linewidth=1.5) + + # Plot after as filled + plt.hist(energies_after, bins=hist_bins, alpha=0.6, label='After Filtering', color='green') + + plt.xscale('log') + plt.xlabel('Energy') + plt.ylabel('Number of Samples') + plt.title('Energy Histogram Before and After Filtering') + plt.legend() + plt.grid(True, which='both', ls='--', linewidth=0.5) + plt.tight_layout() + plt.savefig(f'./histogram_energy_before_after_{k_search_first_bins}_{k_search_default}.png') + # plt.show() + #------------------------------------------------------------------------------------------ + + # flat_similarities = D[:, 1:].flatten() # excluye el self-match + # plt.hist(flat_similarities, bins=100) + # plt.axvline(SIMIL_THRS, color='red', linestyle='--') + # plt.title("Distribución de Similitudes (excluyendo self-match)") + # plt.xlabel("Similitud") + # plt.ylabel("Frecuencia") + # plt.show() + + + # EPS = 1e-5 + # keep_image = set() + # deleted = set() + # seen = set() + + # for i in tqdm(range(len(embeddings))): + # if i in deleted: + # continue + + # keep_image.add(i) + # if gt_energies[i]<1: + # for j in range(1, len(I[i])): + # neighbor_idx = I[i][j] + # similarity = D[i][j] + + # if similarity < (SIMIL_THRS - EPS): + # continue # no lo consideres duplicado + + # if neighbor_idx not in keep_image: + # deleted.add(neighbor_idx) + #------------------------------------------------------------------------------------------ + # keep_image = set() + # deleted = set() + # print("Search Done") + # for i in tqdm(range(len(embeddings))): + # if i in deleted: + # continue # Si ya está marcada como duplicada, ignorarla + # keep_image.add(i) # Mantener esta imagen + # start_idx = lims[i] + # end_idx = lims[i + 1] + # for j in range(start_idx, end_idx): + # neighbor_idx = I[j] + # if neighbor_idx != i: # Evitar la propia imagen + # deleted.add(neighbor_idx) # Marcar como duplicada + # # print(f"Eliminando duplicado: {image_paths[neighbor_idx]} (ID {image_ids[neighbor_idx]})") + + + print(f"keeped: {len(keep_image)}") + print(f"deleted: {len(deleted)}") + embeddings = embeddings[list(keep_image)] + pred_labels = pred_labels[list(keep_image)] + gt_labels = gt_labels[list(keep_image)] + gt_energies = gt_energies[list(keep_image)] + + if plot_tsne: + # energy_threshold = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] + # energy_threshold = [2, 3, 4, 5, 6, 7, 8, 9, 10] + print("Plotting t-sne") + + energy_threshold=[0] + for energy_thr in energy_threshold: + embeddings_ = embeddings[gt_energies>energy_thr] + pred_labels_ = pred_labels[gt_energies>energy_thr] + gt_labels_ = gt_labels[gt_energies>energy_thr] + gt_energies_ = gt_energies[gt_energies>energy_thr] + + + print(f"generating thr:{energy_threshold}") + plt_tsne(embeddings_, pred_labels_, gt_labels_, gt_energies_, classes_list, post_fix="v4_"+str(energy_thr)) + + +if __name__ == "__main__": + + use_pickle = True + reduce_similarity = True + plot_tsne=True + + main(use_pickle,reduce_similarity,plot_tsne) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py new file mode 100644 index 00000000..ce8876f5 --- /dev/null +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -0,0 +1,1861 @@ +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader +import numpy as np +from ctlearn.core.pytorch.nets.loss_functions.loss_functions import ( + FocalLoss, + VectorLoss, + AngularDistance, + AngularError, +) +from ctlearn.core.pytorch.nets.optimizer.optimizer import one_cycle +from tqdm import tqdm +import os +import math +import torch.optim.lr_scheduler as lr_scheduler +from sklearn.metrics import confusion_matrix +from torchmetrics.classification import ConfusionMatrix, MulticlassPrecision, MulticlassF1Score + +from io import BytesIO +import torch.nn.functional as F +from ctlearn.core.pytorch.utils import utils +from ctlearn.core.pytorch.visualization.vis_utils import ( + plot_confusion_matrix, + plot_energy_resolution_error, + plot_direction_resolution_error, +) + +# import utils_torch +import pytorch_lightning as pl +from torchmetrics import Accuracy +import matplotlib.pyplot as plt +from astropy.coordinates import SkyCoord +from astropy.time import Time +import astropy.units as u +from ctlearn.core.ctlearn_enum import Task, Mode +import gc +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +from ctlearn.core.pytorch.nets.loss_functions.loss_functions import evidential_regression_loss +import pickle +# from ctlearn.nets.loss_functions.loss_functions import evidential_classification + + +class CTLearnTrainer(pl.Trainer): + def __init__(self,**kwargs): + super(CTLearnTrainer, self).__init__(**kwargs) + self.multi_gpu=False + if self.world_size > 1: + self.multi_gpu=True + + def predict( + self, + model, + dataloaders=None, + return_predictions=False, + h5_file_name="./results.r1.dl2.h5", + task: Task = None, + mode: Mode = None, + **kwargs, + ): + + # ---------------------------------------------------------------- + # Prediction + # ---------------------------------------------------------------- + self.class_predictions = [] + self.energy_predictions = [] + self.direction_predictions = [] + self.event_id_list = [] + self.obs_id_list = [] + self.labels_energy_list = [] + self.labels_direction_list = [] + self.labels_true_alt_az_list = [] + self.hillas_list = [] + self.pointing_dir = {} + self.h5_file_name = h5_file_name + self.task = task + self.mode = mode + # super(CTLearnTrainer, self).__init__(kwargs) + print("Using CustomTrainer's predict method.") + + if dataloaders is None: + raise ValueError("A dataloader must be provided for prediction.") + + model.eval() + with torch.no_grad(): + # Call your custom logic here + results = model.generate_results( + input_data_loader=dataloaders, + h5_file_name=h5_file_name, + task=task, + mode=mode, + ) + + if return_predictions: + return results + + print("Prediction complete.") + + def get_log_dir(self) -> str: + return self.logger.log_dir + +class CTLearnPL(pl.LightningModule): + # lock = mp.Lock() + + def __init__( + self, + model, + save_folder, + task: Task, + mode: Mode, + parameters, + train_loader=None, + val_loader=None, + test_val_loader=None, + num_channels=1, + k=3, + # **kwargs, + ): + super(CTLearnPL, self).__init__() + + # Save configuration file. + # self.save_hyperparameters(parameters) + + + + # self.save_hyperparameters({ + # 'data': parameters['data'], + # 'hyp': parameters['hyp'], + # 'arch': parameters['arch'], + # }) + + self.model = model + + self.task = task + self.mode = mode + + self.save_folder = save_folder + + # torch.autograd.set_detect_anomaly(True) + + self._device = torch.device(parameters["arch"]["device"]) + self.device_type = parameters["arch"]["device"] + + self.energy_bins = np.linspace(0.0251, 140, num=40) + self.energy_bins_tensor = torch.tensor( + self.energy_bins, device=self._device, dtype=torch.float + ) + + self.model.to(self.device) + + self.k = k # Number of top results to save + self.num_channels = num_channels + self.train_loader = train_loader + self.val_loader = val_loader + self.test_val_loader = test_val_loader + self.class_names = ["gamma", "proton"] + + # Hyperparameters + self.set_hyperparameters(parameters) + + self.optimizer = None + self.scheduler = None + # self.class_weights = torch.tensor([1.0, 1.3], dtype=torch.float32).to(self.device).contiguous() + + # Loss Function + class_weights = ( + torch.tensor([1.0, 1.3], dtype=torch.float32).to(self.device).contiguous() + ) # [1.0, 1.3] + + self.criterion_class = nn.CrossEntropyLoss( + weight=class_weights, reduction="mean" + ) + + self.alpha = torch.tensor([1.0, 1.2], dtype=torch.float32) # torch.tensor([1.0, 1.1], dtype=torch.float32).to(self.device) # Aumentamos la clase 1 + gamma = 2.0 # Aumenta la penalización en ejemplos mal clasificados + self.criterion_class = FocalLoss(alpha=self.alpha,gamma=gamma) + + + self.criterion_energy_class = nn.CrossEntropyLoss( + reduction="sum" + ) # torch.nn.L1Loss(reduction='sum') + self.criterion_energy_value = torch.nn.L1Loss(reduction="sum") + self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() + # self.criterion_energy = torch.nn.MSELoss() + + self.criterion_direction = torch.nn.L1Loss(reduction="sum") # nn.MSELoss() + self.criterion_vector = VectorLoss(alpha=0.1, reduction="sum") + self.criterion_alt_az_l1 = torch.nn.L1Loss(reduction="sum") + self.criterion_alt_az = evidential_regression_loss(lamb=0.01, reduction="sum") + + + self.best_loss = float("inf") + self.best_accuracy = 0 + # Best Metrics Tracking + self.best_losses = [(float("inf"), None)] * k # (loss, filename) + self.best_accuracies = [(0, None)] * k # (accuracy, filename) + self.best_validation_accuracy = 0 + + self.correct_classification = 0 + + self.f1_score_val = MulticlassF1Score( + num_classes=2, + dist_sync_on_step=True, + ) + + self.f1_score_train = MulticlassF1Score( + num_classes=2, + dist_sync_on_step=True, + ) + + self.f1_score_test = MulticlassF1Score( + num_classes=2, + dist_sync_on_step=True, + ) + + self.precision_val = MulticlassPrecision(num_classes=2,dist_sync_on_step=True,) + self.precision_train = MulticlassPrecision(num_classes=2,dist_sync_on_step=True,) + self.precision_test = MulticlassPrecision(num_classes=2,dist_sync_on_step=True,) + + self.class_train_accuracy = Accuracy( + task="multiclass", + num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], + # compute_on_step=True, # Compute for each step and epoch + dist_sync_on_step=True # GPUs Sync + ) + + self.class_val_accuracy = Accuracy( + task="multiclass", + num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], + # compute_on_step=True, # Compute for each step and epoch + dist_sync_on_step=True # GPUs Sync + ) + self.class_test_val_accuracy = Accuracy( + task="multiclass", + num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], + # compute_on_step=True, # Compute for each step and epoch + dist_sync_on_step=True # GPUs Sync + ) + self.confusion_matrix = ConfusionMatrix(num_classes=2, task="multiclass",dist_sync_on_step=True) + + self.loss_train_sum = 0.0 + self.num_train_batches = 0 + + # ---------------------------------------------------------------- + # List used to plot on tensorboard + # ---------------------------------------------------------------- + + # ---------------------------------------------------------------- + # Validation + # ---------------------------------------------------------------- + self.loss_val_sum = 0.0 + self.num_val_batches = 0 + + + self.val_angular_diff_list = [] + self.val_energy_diff_list = [] + + self.val_energy_pred_list = [] + + self.val_alt_pred_list = [] + self.val_az_pred_list = [] + self.val_alt_label_list = [] + self.val_az_label_list = [] + + self.loss_val_separation = 0.0 + self.loss_val_alt_az = 0.0 + self.loss_val_angular_error = 0.0 + + self.val_energy_label_list = [] + self.val_hillas_intensity_list = [] + # ---------------------------------------------------------------- + # Test Validation + # ---------------------------------------------------------------- + self.loss_test_val_sum = 0.0 + self.num_test_val_batches = 0 + self.all_test_val_preds = torch.tensor([]) + self.all_test_val_labels = torch.tensor([]) + + self.test_val_angular_diff_list = [] + self.test_val_energy_diff_list = [] + + self.test_val_energy_pred_list = [] + + self.test_val_alt_pred_list = [] + self.test_val_az_pred_list = [] + self.test_val_alt_label_list = [] + self.test_val_az_label_list = [] + + self.loss_test_val_separation = 0.0 + self.loss_test_val_alt_az = 0.0 + self.loss_test_val_angular_error = 0.0 + + self.test_val_energy_label_list = [] + self.test_val_hillas_intensity_list = [] + + # ---------------------------------------------------------------- + # Training + # ---------------------------------------------------------------- + self.loss_train_separation = 0.0 + self.loss_train_alt_az = 0.0 + self.loss_train_angular_error = 0.0 + + self.alt_off_list = [] + self.az_off_list = [] + + # ---------------------------------------------------------------- + # Predictions + # ---------------------------------------------------------------- + + self.predictions = [] + + self.class_predictions = [] + self.energy_predictions = [] + self.direction_predictions = [] + self.event_id_list = [] + self.obs_id_list = [] + self.labels_energy_list = [] + self.labels_direction_list = [] + self.labels_true_alt_az_list = [] + self.hillas_list = [] + self.pointing = [] + # ---------------------------------------------------------------------------------------------------------- + def train_dataloader(self): + return self.train_loader + # ---------------------------------------------------------------------------------------------------------- + def set_hyperparameters(self, parameters): + # Hyperparameters + self.learning_rate = float(parameters["hyp"]["learning_rate"]) + self.adam_epsilon = float(parameters["hyp"]["adam_epsilon"]) + self.momentum = float(parameters["hyp"]["momentum"]) + self.weight_decay = float(parameters["hyp"]["weight_decay"]) + self.num_epochs = int(parameters["hyp"]["epochs"]) + + self.start_epoch = parameters["hyp"]["start_epoch"] + self.steps_epoch = parameters["hyp"]["steps_epoch"] + self.lrf = float(parameters["hyp"]["lrf"]) + self.optimizer_type = str(parameters["hyp"]["optimizer"]).lower() + self.l2_lambda = float(parameters["hyp"]["l2_lambda"]) + # ---------------------------------------------------------------------------------------------------------- + def forward(self, x, y): + return self.model(x, y) + # ---------------------------------------------------------------------------------------------------------- + def save_checkpoint(self, save_folder, metric_value, filename_prefix, is_loss=True): + """Save checkpoint and manage top k checkpoints for loss or accuracy.""" + filename = os.path.join( + save_folder, f"{filename_prefix}_{metric_value:.16f}.pth" + ) + torch.save( + { + "model_state_dict": self.model.state_dict(), + "optimizer_state_dict": self.optimizer.state_dict(), + "metric_value": metric_value, + }, + filename, + ) + + # Determine the list to update + current_list = self.best_losses if is_loss else self.best_accuracies + current_list.append((metric_value, filename)) + current_list.sort(reverse=not is_loss) + if len(current_list) > self.k: + removed_metric, removed_file = current_list.pop() + if removed_file and os.path.exists(removed_file): + # TODO: Add a Lock to avoid problems when using multiple GPUS + try: + os.remove(removed_file) # Remove the worst performing file + except FileNotFoundError: + pass # File doesn't exist, continue execution + # ---------------------------------------------------------------------------------------------------------- + def compute_type_loss( + self, classification_pred, labels_class, test_val=False, training=False + ): + self.criterion_class.set_alpha(self.alpha.to(self.device)) + + target = labels_class.to(torch.int64) + loss_class = self.criterion_class(classification_pred, target) + + + # loss_triplet = criterion(anchor_out, positive_out, negative_out) + + # Calculate accuracy + predicted = torch.softmax(classification_pred, dim=1) + predicted = predicted.argmax(dim=1) + + # lamb = min(1, self.trainer.current_epoch / 10) + # class_weights= self.class_weights.to(self.device) + # class_weights = None + # loss_class = 0.2*evidential_classification(classification_pred, target, lamb=lamb) + 0.8 * loss_class + # loss_class = self.evidence_loss(classification_pred, target, class_weights, lamb=lamb) + # Calculate accuracy + # predicted = torch.softmax(classification_pred, dim=1) + # predicted = classification_pred.argmax(dim=1) + + accuracy = 0 + precision = 0 + loss = loss_class + # loss = alpha*loss_class+(1-alpha)*loss_triplet + if training: + + self.class_train_accuracy.update(predicted, labels_class) + accuracy = self.class_train_accuracy.compute().item() + self.f1_score_train.update(predicted, labels_class) + self.precision_train.update(predicted, labels_class) + precision = self.precision_train.compute().item() + else: + # Test + if test_val: + + self.class_test_val_accuracy.update(predicted, labels_class) + accuracy = self.class_test_val_accuracy.compute().item() + self.f1_score_test.update(predicted, labels_class) + self.precision_test.update(predicted, labels_class) + precision = self.precision_test.compute().item() + # Validation + else: + self.class_val_accuracy.update(predicted, labels_class) + accuracy = self.class_val_accuracy.compute().item() + self.confusion_matrix.update(predicted, labels_class) + self.f1_score_val.update(predicted, labels_class) + self.precision_val.update(predicted, labels_class) + precision = self.precision_val.compute().item() + + return loss, accuracy, predicted, precision + # ---------------------------------------------------------------------------------------------------------- + def compute_direction_loss(self, direction_pred, labels_direction, training=False): + + if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + direction_pred = list(direction_pred) + pred_az_atl = direction_pred[0][:,0:2] + pred_separation = direction_pred[0][:,2] + # direction_pred[0]= direction_pred[0][:,0:2] + else: + + pred_az_atl = direction_pred[:, 0:2] + pred_separation = direction_pred[:, 2] + + labels_az_alt = labels_direction[:, 0:2] + label_separation = labels_direction[:, 2] + loss_separation = self.criterion_direction(pred_separation, label_separation) + + # loss_vector = self.criterion_vector(pred_dir_cartesian, labels_direction_cartesian) + # vect_magnitud = torch.sqrt(torch.sum(pred_dir_cartesian**2, dim=1)) + # loss_magnitud = torch.abs(1.0-vect_magnitud).sum() + + # alt_az = utils_torch.cartesian_to_alt_az(direction[:,0:3]) + if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + loss_alt_az = self.criterion_alt_az(direction_pred, labels_direction) + else: + loss_alt_az = self.criterion_alt_az_l1(pred_az_atl, labels_az_alt) + + if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + + loss_angular_error, _ = AngularDistance( + (direction_pred[0][:, 1]), + labels_az_alt[:, 1], + (direction_pred[0][:, 0]), + labels_az_alt[:, 0], + reduction="sum", + ) + + else: + + loss_angular_error, _ = AngularDistance( + (direction_pred[:, 1]), + labels_az_alt[:, 1], + (direction_pred[:, 0]), + labels_az_alt[:, 0], + reduction="sum", + ) + + if training == False: + if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + _, angular_diff = AngularDistance( + (direction_pred[0][:, 1]), + labels_az_alt[:, 0], + (direction_pred[0][:, 0]), + labels_az_alt[:, 1], + reduction=None, + ) + else: + _, angular_diff = AngularDistance( + (direction_pred[:, 1]), + labels_az_alt[:, 0], + (direction_pred[:, 0]), + labels_az_alt[:, 1], + reduction=None, + ) + else: + angular_diff = None + + loss = loss_alt_az + 0.001*(loss_separation + loss_angular_error) + + return loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff + # ---------------------------------------------------------------------------------------------------------- + def compute_energy_loss( + self, energy_pred, labels_energy, test_val=False, training=False + ): + + loss_energy = self.criterion_energy_value(energy_pred, labels_energy) + loss = loss_energy + if training == False: + energy_pred = pow(10, energy_pred) + labels_energy = pow(10, labels_energy) + energy_diff = torch.abs(energy_pred - labels_energy) + energy_diff = energy_diff.float().cpu().detach().numpy() + else: + energy_diff = None + + # if training: + # accuracy = self.class_train_energy_accuracy(predicted, labels_energy_class) + # else: + # if test_val: + # accuracy = self.class_test_energy_accuracy(predicted, labels_energy_class) + # else: + # accuracy = self.class_validation_energy_accuracy(predicted, labels_energy_class) + + return loss, energy_diff + # + # return loss, energy_diff + # ---------------------------------------------------------------------------------------------------------- + def training_step(self, batch, batch_idx): + + # ------------------------------------------------------------------ + # Read inputs (features) and labels + # ------------------------------------------------------------------ + features, labels = batch + loss = 0 + if len(features) > 0: + imgs = features["image"] + + if self.task == Task.type: + labels_class = labels["type"] + + if self.task == Task.energy: + labels_energy_value = labels["energy"] + # labels_energy_value = labels_energy_value.to('cuda') + # labels_energy_class = labels["energy_class"] + + if self.task == Task.direction: + labels_direction = labels["direction"] + + labels_direction_cartesian = labels["direction_cartesian"] + + + # imgs = imgs.to('cuda') + + # ------------------------------------------------------------------ + # Predictions based on one backbone or two back bones + # ------------------------------------------------------------------ + if self.num_channels == 2: + peak_time = features["peak_time"] + # peak_time = peak_time.to('cuda') + classification_pred, energy_pred, direction_pred = self.model( + imgs, peak_time + ) + else: + classification_pred, energy_pred, direction_pred = self.model(imgs) + + # ------------------------------------------------------------------ + # Particle type + # --------------------------------------- + if self.task == Task.type: + classification_pred_ = classification_pred[0] + feature_vector = classification_pred[1] + + loss, accuracy, predicted, precision = self.compute_type_loss( + classification_pred_, labels_class, test_val=False, training=True + ) + # Log batch loss and accuracy on the progress bar + self.log( + "train_acc", + accuracy * 100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + ) + self.log( + "train_precision", + precision*100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + ) + + # --------------------------------------- + # Direction + # --------------------------------------- + if self.task == Task.direction: + if len(direction_pred)==2: + direction_pred = direction_pred[0] + + loss, loss_separation, loss_alt_az, loss_angular_error, _ = ( + self.compute_direction_loss( + direction_pred, labels_direction, training=True + ) + ) + + self.loss_train_separation += loss_separation.item() + self.loss_train_alt_az += loss_alt_az.item() + self.loss_train_angular_error += loss_angular_error.item() + + self.loss_val_separation = 0.0 + self.loss_val_alt_az = 0.0 + self.loss_val_angular_error = 0.0 + + # --------------------------------------- + # Energy + # --------------------------------------- + if self.task == Task.energy: + loss, *_ = self.compute_energy_loss( + energy_pred, labels_energy_value, training=True + ) + # --------------------------------------- + + # L2 Regularization + l2_norm = sum(p.pow(2.0).sum() for p in self.parameters()) + loss = loss + self.l2_lambda * l2_norm + + # Display on Bar progress + self.log( + "train_loss", + loss.item(), + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + ) + + if not np.isnan(loss.item()): + + self.loss_train_sum += loss.item() + self.num_train_batches += 1 + + torch.cuda.empty_cache() + return loss + # ---------------------------------------------------------------------------------------------------------- + def on_train_epoch_end(self): + + if self.trainer.world_size > 1: + dist.barrier() + # Gather y Sync values with the GPUs. + total_loss_train= self.all_gather(self.loss_train_sum).sum().item() + total_batches_val = self.all_gather(torch.tensor(self.num_train_batches, device=self.device)).sum().item() + + else: + # TODO: No Tested + total_loss_train = self.loss_train_sum.item() + total_batches_val = self.num_train_batches.item() + + if self.trainer.is_global_zero: + global_loss = total_loss_train / total_batches_val + + self.logger.experiment.add_scalars( + "loss/Global Training Loss", + { + "loss": global_loss, + }, + self.current_epoch, + ) + + self.logger.experiment.add_scalars( + "Learning rate", + { + "Learning rate": self.scheduler.get_last_lr()[0], + }, + self.current_epoch, + ) + # Optionally print the global loss for immediate feedback + print(f"Epoch {self.current_epoch}: Global Training Loss: {global_loss:.4f}") + + # --------------------------------------- + # Particle Type + # --------------------------------------- + if self.task == Task.type: + + f1_score = self.f1_score_train.compute().detach().cpu().numpy()*100.0 + self.f1_score_train.reset() + + precision = self.precision_train.compute().detach().cpu().numpy()*100.0 + self.precision_train.reset() + + epoch_accuracy = self.class_train_accuracy.compute().detach().cpu().item() * 100 + self.class_train_accuracy.reset() + + # Compute the accuracy and reset the metric states after each epoch + if self.trainer.is_global_zero: + self.log("train_acc_epoch", epoch_accuracy, on_step=False, prog_bar=True) + # Log + self.logger.experiment.add_scalars( + "Metrics/Training", + { + "acc": epoch_accuracy, + "f1":f1_score, + "precision":precision, + }, + self.current_epoch, + ) + print( + f"Epoch {self.current_epoch}: Global Training Accuracy: {epoch_accuracy:.4f}" + ) + filename_prefix = ( + f"Epoch_{self.current_epoch}_{self.task.name}_train_acc" + ) + if self.logger.log_dir: + self.save_checkpoint( + self.logger.log_dir, + epoch_accuracy, + filename_prefix=filename_prefix, + is_loss=False, + ) + # --------------------------------------- + # Direction + # --------------------------------------- + if self.task == Task.direction: + if self.trainer.is_global_zero: + filename_prefix = ( + f"Epoch_{self.current_epoch}_{self.task.name}_train_loss" + ) + self.save_checkpoint( + self.logger.log_dir, + global_loss, + filename_prefix=filename_prefix, + is_loss=True, + ) + # Log scalar values + self.logger.experiment.add_scalars( + "loss/ Loss Training", + { + "loss": global_loss, + "loss_separation": self.loss_train_separation + / self.num_train_batches, + "loss_alt_az": self.loss_train_alt_az / self.num_train_batches, + "loss_angular_error": self.loss_train_angular_error + / self.num_train_batches, + }, + self.current_epoch, + ) + self.loss_train_separation = 0.0 + self.loss_train_alt_az = 0.0 + self.loss_train_angular_error = 0.0 + # --------------------------------------- + # Energy + # --------------------------------------- + if self.task == Task.energy: + if self.trainer.is_global_zero: + filename_prefix = ( + f"Epoch_{self.current_epoch}_{self.task.name}_train_loss" + ) + self.save_checkpoint( + self.logger.log_dir, + global_loss, + filename_prefix=filename_prefix, + is_loss=True, + ) + # --------------------------------------- + # Delete all the lists and set to 0 + # the values used to estimate the losses + # --------------------------------------- + self.reset_values() + + # Reset + self.loss_train_sum = 0 + self.num_train_batches = 0 + self.training_step_outputs = [] + # ---------------------------------------------------------------------------------------------------------- + @torch.no_grad() + def validation_step(self, batch, batch_idx, dataloader_idx=0): + loss=0 + self.model.eval() + + # ------------------------------------------------------------------ + # Read inputs (features) and labels + # ------------------------------------------------------------------ + features, labels = batch + if len(features) > 0: + imgs = features["image"] + + if self.task == Task.type: + labels_class = labels["particletype"] + + if self.task == Task.energy: + labels_energy_class = labels["energy_class"] + labels_energy_value = labels["energy"] + hillas_intensity = features["hillas"]["hillas_intensity"] + # hillas = {key: tensor.to(self.device) + # for key, tensor in hillas.items()} + if self.task == Task.direction: + labels_direction = labels["direction"] + # labels_alt_az = labels['alt_az'] + labels_direction_cartesian = labels["direction_cartesian"] + + # ------------------------------------------------------------------ + # Predictions based on one backbone or two back bones + # ------------------------------------------------------------------ + if self.num_channels == 2: + peak_time = features["peak_time"] + classification_pred, energy_pred, direction_pred = self.model( + imgs, peak_time + ) + else: + classification_pred, energy_pred, direction_pred = self.model(imgs) + + + # ------------------------------------------------------------------ + # Compute Loss functions based on different tasks + # ------------------------------------------------------------------ + # Particle Type + # --------------------------------------- + if self.task == Task.type: + classification_pred_ = classification_pred[0] + feature_vector = classification_pred[1] + # Log batch loss and accuracy on the progress bar + if dataloader_idx == 0: + loss, accuracy, predicted, precision = self.compute_type_loss( + classification_pred_, + labels_class, + test_val=False, + training=False, + ) + self.log( + "val_acc", + accuracy * 100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + ) + self.log( + "val_prec", + precision*100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + ) + else: + loss, accuracy, predicted, precision = self.compute_type_loss( + classification_pred_, labels_class, test_val=True, training=False + ) + self.log( + "test_val_acc", + accuracy * 100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + ) + + self.log( + "test_prec", + precision*100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + ) + # --------------------------------------- + # Direction + # --------------------------------------- + if self.task == Task.direction: + + if len(direction_pred)==2: + direction_pred = direction_pred[0] + # loss, angular_diff = self.compute_direction_loss(direction_pred, labels_direction, training=False) + loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff = ( + self.compute_direction_loss( + direction_pred, labels_direction, training=False + ) + ) + # ------------------------------------------------------------------------ + # Convert the offset to altitud and azimuth + # ------------------------------------------------------------------------ + reco_az, reco_alt = [], [] + if "tel_alt" in features: + pointing_alt = features["tel_alt"].float().cpu().detach().numpy() + pointing_az = features["tel_az"].float().cpu().detach().numpy() + elif "tel_alt" in labels: + pointing_alt = labels["tel_alt"].float().cpu().detach().numpy() + pointing_az = labels["tel_az"].float().cpu().detach().numpy() + else: + raise ValueError(f"Telescope altitud and azimuth not found.") + + pointing_alt = np.rad2deg(pointing_alt) + pointing_az = np.rad2deg(pointing_az) + + fix_pointing = SkyCoord( + pointing_az * u.deg, + pointing_alt * u.deg, + frame="altaz", + unit="deg", + ) + alt_off = direction_pred[:, 1].float().cpu().detach().numpy() + az_off = direction_pred[:, 0].float().cpu().detach().numpy() + + reco_direction = utils.recover_alt_az(fix_pointing, alt_off, az_off) + + reco_alt = np.deg2rad(reco_direction.alt.to_value())[0, :] + reco_az = np.deg2rad(reco_direction.az.to_value())[0, :] + + true_alt = labels["alt_az"][:, 0].float().cpu().detach().numpy() + true_az = labels["alt_az"][:, 1].float().cpu().detach().numpy() + + if dataloader_idx == 0: + # self.alt_off_list.extend(alt_off) + # self.az_off_list.extend(az_off) + + self.loss_val_separation += loss_separation.item() + self.loss_val_alt_az += loss_alt_az.item() + self.loss_val_angular_error += loss_angular_error.item() + + self.val_angular_diff_list.extend(angular_diff) + # ------------------------------------------------------- + + self.val_alt_pred_list.extend(reco_alt) + self.val_az_pred_list.extend(reco_az) + self.val_alt_label_list.extend(true_alt) + self.val_az_label_list.extend(true_az) + + else: + + self.loss_test_val_separation += loss_separation.item() + self.loss_test_val_alt_az += loss_alt_az.item() + self.loss_test_val_angular_error += loss_angular_error.item() + + self.test_val_angular_diff_list.extend(angular_diff) + # self.test_val_alt_pred_list.extend(direction_pred[:,1].float().cpu().detach().numpy()) + # self.test_val_az_pred_list.extend(direction_pred[:,0].float().cpu().detach().numpy()) + # self.test_val_alt_label_list.extend(labels_direction[:,1].float().cpu().detach().numpy()) + # self.test_val_az_label_list.extend(labels_direction[:,0].float().cpu().detach().numpy()) + + self.test_val_alt_pred_list.extend(reco_alt) + self.test_val_az_pred_list.extend(reco_az) + self.test_val_alt_label_list.extend(true_alt) + self.test_val_az_label_list.extend(true_az) + # --------------------------------------- + # Energy + # --------------------------------------- + if self.task == Task.energy: + + energy_pred_tev = torch.pow(10, energy_pred) + + if dataloader_idx == 0: + + loss, energy_diff = self.compute_energy_loss( + energy_pred, labels_energy_value, test_val=False, training=False + ) + + self.val_energy_diff_list.extend(energy_diff) + self.val_energy_pred_list.extend( + energy_pred_tev[:, 0].float().cpu().detach().numpy() + ) + else: + loss, energy_diff = self.compute_energy_loss( + energy_pred, labels_energy_value, test_val=True, training=False + ) + + self.test_val_energy_diff_list.extend(energy_diff) + self.test_val_energy_pred_list.extend( + energy_pred_tev[:, 0].float().cpu().detach().numpy() + ) + + # --------------------------------------- + # --------------------------------------- + # Collect the True Energy and Hillas Intensity + # --------------------------------------- + + energy_label_tev = torch.pow(10, labels_energy_value) + + if dataloader_idx == 0: + self.val_energy_label_list.extend( + energy_label_tev[:, 0].float().cpu().detach().numpy() + ) + self.val_hillas_intensity_list.extend( + hillas_intensity.float().cpu().detach().numpy() + ) + else: + self.test_val_energy_label_list.extend( + energy_label_tev[:, 0].float().cpu().detach().numpy() + ) + self.test_val_hillas_intensity_list.extend( + hillas_intensity.float().cpu().detach().numpy() + ) + + # --------------------------------------- + # Log validation loss + + if dataloader_idx == 0: + self.loss_val_sum += loss.item() + self.num_val_batches += 1 + else: + self.loss_test_val_sum += loss.item() + self.num_test_val_batches += 1 + + loss_key = "val_loss" if dataloader_idx == 0 else "test_loss" + if loss is not None: + self.log(loss_key, loss,on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True) + + # Release cuda memory + torch.cuda.empty_cache() + return loss + # ---------------------------------------------------------------------------------------------------------- + @torch.no_grad() + def on_validation_epoch_end(self): + + if self.trainer.world_size > 1: + dist.barrier() + # Gather y Sync values with the GPUs. + total_loss_val = self.all_gather(self.loss_val_sum).sum().item() + total_loss_test = self.all_gather(self.loss_test_val_sum).sum().item() + total_batches_val = self.all_gather(torch.tensor(self.num_val_batches, device=self.device)).sum().item() + total_batches_test = self.all_gather(torch.tensor(self.num_test_val_batches, device=self.device)).sum().item() + + + else: + # TODO: No Tested + total_loss_val = self.loss_val_sum + total_loss_test = self.loss_test_val_sum + total_batches_val = self.num_val_batches + total_batches_test = self.num_test_val_batches + + # total_loss_val = self.loss_val_sum.item() + # total_loss_test = self.loss_test_val_sum.item() + # total_batches_val = self.num_val_batches.item() + # total_batches_test = self.num_test_val_batches.item() + + # Calcular la pérdida promedio global + global_loss_val = total_loss_val / max(1, total_batches_val) + global_loss_test = total_loss_test / max(1, total_batches_test) + + + self.logger.experiment.add_scalars( + "loss/Global Validation Loss", + { + "loss": global_loss_val, + }, + self.current_epoch, + ) + + self.logger.experiment.add_scalars( + "loss/Global Test Loss", + { + "loss": global_loss_test, + }, + self.current_epoch, + ) + + # Print the global loss for immediate feedback + print( + f"Epoch {self.current_epoch}: Global Validation Loss: {global_loss_val:.4f}" + ) + print(f"Epoch {self.current_epoch}: Global Test Loss: {global_loss_test:.4f}") + # --------------------------------------- + # Particle Type + # --------------------------------------- + if self.task == Task.type: + conf_matrix = self.confusion_matrix.compute().detach().cpu().numpy() + f1_score_val = self.f1_score_val.compute().detach().cpu().numpy()*100.0 + f1_score_test = self.f1_score_test.compute().detach().cpu().numpy()*100.0 + precision_val = self.precision_val.compute().detach().cpu().numpy()*100.0 + precision_test = self.precision_test.compute().detach().cpu().numpy()*100.0 + + + # Compute the accuracy and reset the metric states after each epoch + epoch_accuracy_val = self.class_val_accuracy.compute().item() * 100 + epoch_accuracy_test = self.class_test_val_accuracy.compute().item() * 100 + + if self.trainer.is_global_zero: + self.class_val_accuracy.reset() + self.class_test_val_accuracy.reset() + self.confusion_matrix.reset() + self.f1_score_val.reset() + self.f1_score_test.reset() + self.precision_val.reset() + self.precision_test.reset() + + # Log + self.logger.experiment.add_scalars( + "Metrics/Validation", + { + "acc": epoch_accuracy_val, + "f1": f1_score_val, + "precision":precision_val, + }, + self.current_epoch, + ) + self.logger.experiment.add_scalars( + "Metrics/Test", + { + "acc": epoch_accuracy_test, + "f1": f1_score_test, + "precision":precision_test, + }, + self.current_epoch, + ) + print( + f"Epoch {self.current_epoch}: Global Validation Accuracy: {epoch_accuracy_val:.4f}" + ) + print( + f"Epoch {self.current_epoch}: Global Validation F1 Score: {f1_score_val:.4f}" + ) + print( + f"Epoch {self.current_epoch}: Global Validation Precision: {precision_val:.4f}" + ) + + print( + f"Epoch {self.current_epoch}: Global Test Accuracy: {epoch_accuracy_test:.4f}" + ) + print( + f"Epoch {self.current_epoch}: Global Test F1 Score: {f1_score_test:.4f}" + ) + print( + f"Epoch {self.current_epoch}: Global Test Precision: {precision_test:.4f}" + ) + # --------------------------------------- + # Create Confusion Matrix + # --------------------------------------- + if self.trainer.is_global_zero: + + filename_prefix = "confusion_matrix_val" + cm_file_name = f"{filename_prefix}_{self.current_epoch}_{epoch_accuracy_val:.4f}_Validation" + + if self.logger.log_dir: + plot_confusion_matrix( + conf_matrix, + self.class_names, + cm_file_name, + self.logger.log_dir, + ) + # Compute class-wise accuracies + class_accuracies = (conf_matrix.diagonal() / conf_matrix.sum(axis=1))*100 + + self.logger.experiment.add_scalars( + "confusion_matrix", + { + "val_acc_gamma": class_accuracies[0], + "val_acc_proton": class_accuracies[1], + "val_global_acc": epoch_accuracy_val, + }, + self.current_epoch, + ) + # --------------------------------------- + # Direction + # --------------------------------------- + if self.task == Task.direction: + + self.print_direction_error(self.val_angular_diff_list, "Validation") + self.print_direction_error(self.test_val_angular_diff_list, "Test") + plt.close("all") + fig_direction_error = plot_direction_resolution_error( + self.val_alt_pred_list, + self.val_az_pred_list, + self.val_alt_label_list, + self.val_az_label_list, + self.val_energy_label_list, + self.val_hillas_intensity_list, + ) + self.logger.experiment.add_figure( + "Direction Resolution Error/Validation", + fig_direction_error, + self.current_epoch, + ) # Log the plot + + # Save the figure + fig_direction_error.savefig( + os.path.join( + self.logger.log_dir, + "angular_resolution_validation_" + + str(self.current_epoch) + + "_" + + str(global_loss_val) + + ".png", + ), + format="png", + ) + plt.close(fig_direction_error) # Close the figure to release memory + plt.close("all") + fig_direction_error = plot_direction_resolution_error( + self.test_val_alt_pred_list, + self.test_val_az_pred_list, + self.test_val_alt_label_list, + self.test_val_az_label_list, + self.test_val_energy_label_list, + self.test_val_hillas_intensity_list, + ) + self.logger.experiment.add_figure( + "Direction Resolution Error/Test", + fig_direction_error, + self.current_epoch, + ) # Log the plot + + # Save the figure + fig_direction_error.savefig( + os.path.join( + self.logger.log_dir, + "angular_resolution_test_" + + str(self.current_epoch) + + "_" + + str(global_loss_test) + + ".png", + ), + format="png", + ) + plt.close(fig_direction_error) # Close the figure to release memory + # Log scalar values + self.logger.experiment.add_scalars( + "loss/Loss Validation", + { + "loss": global_loss_val, + "loss_separation": self.loss_val_separation / self.num_val_batches, + "loss_alt_az": self.loss_val_alt_az / self.num_val_batches, + "loss_angular_error": self.loss_val_angular_error + / self.num_val_batches, + }, + self.current_epoch, + ) + + if self.test_dataloader != None: + + # Log scalar values + self.logger.experiment.add_scalars( + "loss/Loss Test", + { + "loss": global_loss_test, + "loss_separation": self.loss_test_val_separation + / self.num_test_val_batches, + "loss_alt_az": self.loss_test_val_alt_az + / self.num_test_val_batches, + "loss_angular_error": self.loss_test_val_angular_error + / self.num_test_val_batches, + }, + self.current_epoch, + ) + + # --------------------------------------- + # Energy + # --------------------------------------- + if self.task == Task.energy: + + self.print_energy_error(self.val_energy_diff_list, "Validation") + self.print_energy_error(self.test_val_energy_diff_list, "Test") + plt.close("all") + fig_energy_error = plot_energy_resolution_error( + self.val_energy_pred_list, + self.val_energy_label_list, + self.val_hillas_intensity_list, + ) + self.logger.experiment.add_figure( + "Energy Resolution Error/Validation", + fig_energy_error, + self.current_epoch, + ) # Log the plot + fig_energy_error.savefig( + os.path.join( + self.logger.log_dir, + "error_resulution_validation_" + + str(self.current_epoch) + + "_" + + str(global_loss_val) + + ".png", + ), + format="png", + ) + + plt.close(fig_energy_error) # Close the figure to release memory + plt.close("all") + fig_energy_error = plot_energy_resolution_error( + self.test_val_energy_pred_list, + self.test_val_energy_label_list, + self.test_val_hillas_intensity_list, + ) + self.logger.experiment.add_figure( + "Energy Resolution Error/Test", fig_energy_error, self.current_epoch + ) # Log the plot + fig_energy_error.savefig( + os.path.join( + self.logger.log_dir, + "error_resulution_test_" + + str(self.current_epoch) + + "_" + + str(global_loss_test) + + ".png", + ), + format="png", + ) + + plt.close(fig_energy_error) # Close the figure to release memory + plt.close("all") + # ---------------------------------------------------------------------------------------------------------- + def reset_values(self): + # --------------------------------------- + # Reset + # --------------------------------------- + self.loss_val_sum = 0 + self.num_val_batches = 0 + self.loss_test_val_sum = 0 + self.num_test_val_batches = 0 + # Reset Energy and hillas intensity + # -------------------------------------------------- + # Validation + # -------------------------------------------------- + self.val_energy_label_list.clear() + + self.val_hillas_intensity_list.clear() + + # -------------------------------------------------- + # Test validation + # -------------------------------------------------- + self.test_val_energy_label_list.clear() + + self.test_val_hillas_intensity_list.clear() + + # -------------------------------------------------- + # Reset Direction + # -------------------------------------------------- + # Validation + # -------------------------------------------------- + self.val_angular_diff_list.clear() + + + self.val_alt_pred_list.clear() + self.val_az_pred_list.clear() + self.val_alt_label_list.clear() + self.val_az_label_list.clear() + + self.loss_val_separation = 0.0 + self.loss_val_alt_az = 0.0 + self.loss_val_angular_error = 0.0 + # -------------------------------------------------- + # Test validation + # -------------------------------------------------- + self.test_val_angular_diff_list.clear() + + self.test_val_alt_pred_list.clear() + self.test_val_az_pred_list.clear() + self.test_val_alt_label_list.clear() + self.test_val_az_label_list.clear() + + + self.loss_test_val_separation = 0.0 + self.loss_test_val_alt_az = 0.0 + self.loss_test_val_angular_error = 0.0 + # -------------------------------------------------- + # Reset Energy + # -------------------------------------------------- + # Validation + # -------------------------------------------------- + self.val_energy_diff_list.clear() + self.val_energy_pred_list.clear() + + # -------------------------------------------------- + # Test validation + # -------------------------------------------------- + self.test_val_energy_diff_list.clear() + self.test_val_energy_pred_list.clear() + # ---------------------------------------------------------------------------------------------------------- + def print_direction_error(self, angular_diff_list, type_val: str): + # Count the angular error in ranges [20º-0.1º] + error_20 = len([num for num in angular_diff_list if num < 20]) + error_10 = len([num for num in angular_diff_list if num < 10]) + error_5 = len([num for num in angular_diff_list if num < 5]) + error_2 = len([num for num in angular_diff_list if num < 2]) + error_1 = len([num for num in angular_diff_list if num < 1]) + error_0_5 = len([num for num in angular_diff_list if num < 0.5]) + error_0_25 = len([num for num in angular_diff_list if num < 0.25]) + error_0_1 = len([num for num in angular_diff_list if num < 0.1]) + # Log + self.logger.experiment.add_scalars( + "Direction Error/" + type_val, + { + "0: error: 20": error_20, + "1: error: 10": error_10, + "2: error: 5": error_5, + "3: error: 2": error_2, + "4: error: 1": error_1, + "5: error: 0.5": error_0_5, + "6: error: 0.25": error_0_25, + "7: error: 0.1": error_0_1, + }, + self.current_epoch, + ) + # Print + print(type_val + " Direction Error < 20º: ", error_20) + print(type_val + " Direction Error < 10º: ", error_10) + print(type_val + " Direction Error < 5º: ", error_5) + print(type_val + " Direction Error < 2º: ", error_2) + print(type_val + " Direction Error < 1º: ", error_1) + print(type_val + " Direction Error < 0.50º: ", error_0_5) + print(type_val + " Direction Error < 0.25º: ", error_0_25) + print(type_val + " Direction Error < 0.10º: ", error_0_1) + # ---------------------------------------------------------------------------------------------------------- + def print_energy_error(self, energy_diff_list, type_val: str): + + error_30 = len([num for num in energy_diff_list if num < 30]) + error_20 = len([num for num in energy_diff_list if num < 20]) + error_10 = len([num for num in energy_diff_list if num < 10]) + error_5 = len([num for num in energy_diff_list if num < 5]) + error_1 = len([num for num in energy_diff_list if num < 1]) + error_0_5 = len([num for num in energy_diff_list if num < 0.5]) + error_0_25 = len([num for num in energy_diff_list if num < 0.25]) + error_0_1 = len([num for num in energy_diff_list if num < 0.1]) + error_0_01 = len([num for num in energy_diff_list if num < 0.01]) + # Log + self.logger.experiment.add_scalars( + "Energy Error/" + type_val, + { + "0: error: 30": error_30, + "1: error: 20": error_20, + "2: error: 10": error_10, + "3: error: 5": error_5, + "4: error: 1": error_1, + "5: error: 0.5": error_0_5, + "6: error: 0.25": error_0_25, + "7: error: 0.1": error_0_1, + "8: error: 0.01": error_0_01, + }, + self.current_epoch, + ) + # Print + print(type_val + " Energy Error < 30:", error_30) + print(type_val + " Energy Error < 20:", error_20) + print(type_val + " Energy Error < 10:", error_10) + print(type_val + " Energy Error < 5:", error_5) + print(type_val + " Energy Error < 1:", error_1) + print(type_val + " Energy Error < 0.5:", error_0_5) + print(type_val + " Energy Error < 0.1:", error_0_1) + print(type_val + " Energy Error < 0.01:", error_0_01) + # ---------------------------------------------------------------------------------------------------------- + def create_confusion_matrix( + self, epoch, accuracy, all_val_preds, all_val_labels, val_type: str + ): + all_labels = all_val_preds.numpy() + all_preds = all_val_labels.numpy() + filename_prefix = "confusion_matrix_val" + cm_file_name = f"{filename_prefix}_{epoch}_{accuracy:.4f}_{val_type}" + cm = confusion_matrix(all_labels, all_preds) + + # accuracies = (np.diag(cm) / np.sum(cm, axis=0))*100.0 + + cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] + + # Remove NaNs + cm_norm = np.nan_to_num(cm_norm, nan=0.0) + accuracies = cm_norm.diagonal() * 100.0 + if self.logger.log_dir: + plot_confusion_matrix( + cm, + self.class_names, + accuracies, + cm_file_name, + self.logger.log_dir, # self.save_folder + ) + # # Empty the list + # all_val_preds = torch.tensor([]) + # all_val_labels = torch.tensor([]) + return accuracies + # ---------------------------------------------------------------------------------------------------------- + @torch.no_grad() + def generate_results( + self, + input_data_loader, + h5_file_name="./results.r1.dl2.h5", + task=None, + mode=None, + ): + self.model.to(self.device) + self.model.eval() + # data_loader = DataLoader( + # input_data_loader.dataset, batch_size=128, shuffle=False) + dataset = input_data_loader.dataset + data_loader = input_data_loader + with torch.no_grad(): + pbar = tqdm(total=len(data_loader), desc="DL2 conv", leave=True) + + class_predictions = [] + class_feature_vector = [] + class_predictions_class = [] + energy_predictions = [] + direction_predictions = [] + direction_predictions_mu = [] + direction_predictions_sigma = [] + direction_pred_mu=[] + direction_pred_sigma=[] + + event_id_list = [] + obs_id_list = [] + labels_energy_list = [] + labels_direction_list = [] + labels_true_alt_az_list = [] + hillas_list = [] + total = 0 + correct = 0 + predicted_class = None + direction_pred = None + direction_pred = None + # tel_pointing_dir=[] + if mode == Mode.observation: # "observation": + dataset.pointing_dir["pointing_alt"] = [] + dataset.pointing_dir["pointing_az"] = [] + dataset.pointing_dir["dragon_time"] = [] + dataset.pointing_dir["utc_time"] = [] + dataset.pointing_dir["src_x"] = [] + dataset.pointing_dir["src_y"] = [] + + cnt = 0 + for batch_idx, (features, labels) in enumerate(data_loader): + + # TODO: Check that features is not empty + if len(features)==0: + continue + imgs = features["image"].to(self.device).contiguous() + + labels_class = ( + labels["particletype"].float().to(self.device).contiguous() + ) + hillas = features["hillas"] + hillas = { + key: tensor.cpu().detach().numpy() for key, tensor in hillas.items() + } + + tel_alt = features["tel_alt"].float().cpu().detach().numpy() + tel_az = features["tel_az"].float().cpu().detach().numpy() + + if "time" in features: + tel_time = features["time"].double().cpu().detach().numpy() + + if "src_x" and "src_y" in features: + src_x = features["src_x"].float().cpu().detach().numpy() + src_y = features["src_y"].float().cpu().detach().numpy() + + if self.num_channels == 2: + peak_time = features["peak_time"].to(self.device).contiguous() + classification_pred, energy_pred, direction_pred = self.model( + imgs, peak_time + ) + else: + classification_pred, energy_pred, direction_pred = self.model(imgs) + + # Convert to numpy + if task == Task.type: + classification_pred_ = classification_pred[0] + feature_vector = classification_pred[1].cpu().detach().numpy() + predicted = torch.softmax(classification_pred_, dim=1) + predicted_class = predicted.argmax(dim=1) + correct += (predicted_class == labels_class).sum().item() + predicted = predicted.cpu().detach().numpy() + predicted_class = predicted_class.cpu().detach().numpy() + total += labels_class.size(0) + + if task == Task.energy: + energy = energy_pred.cpu().detach().numpy() + # energy = energy[0] + + if task == Task.direction: + if len(direction_pred)==2: + direction_pred_mu = direction_pred[0].cpu().detach().numpy() + direction_pred_sigma = direction_pred[1].cpu().detach().numpy() + else: + direction_pred = direction_pred.cpu().detach().numpy() + + obs_id = hillas["obs_id"] + event_id = hillas["event_id"] + labels_energy = labels["energy"].cpu().detach().numpy() + labels_energy = 1 * (labels_energy) + + labels_direction = labels["direction"].cpu().detach().numpy() + label_true_alt_az = labels["alt_az"].cpu().detach().numpy() + + # ------------------------------------------------------------------ + id = list(range(features["image"].shape[0])) + if task == Task.type: + class_predictions.extend(predicted[:, :]) + class_feature_vector.extend(feature_vector[:, :]) + class_predictions_class.extend(predicted_class[:]) + + if task == Task.energy: + energy_predictions.extend(energy[:, :]) + if task == Task.direction: + + if len(direction_pred)==2: + direction_predictions_mu.extend(direction_pred_mu[:,0:2]) + direction_predictions_sigma.extend(direction_pred_sigma[:,0:2]) + + else: + dir = direction_pred[:, 0:2] + direction_predictions.extend(dir) + + if mode == Mode.observation: + dataset.pointing_dir["pointing_alt"].extend(tel_alt) + dataset.pointing_dir["pointing_az"].extend(tel_az) + dataset.pointing_dir["utc_time"].extend(tel_time) + time = Time(tel_time, format="mjd") + datetime_utc = time.to_datetime() + # Convert to UNIX timestamp (dragon_time) + # dragon_time = datetime_utc[:].timestamp() + # Vectorized application of timestamp() + dragon_time = np.vectorize(lambda dt: dt.timestamp())( + datetime_utc + ) + dataset.pointing_dir["dragon_time"].extend(dragon_time) + + dataset.pointing_dir["src_x"].extend(src_x) + dataset.pointing_dir["src_y"].extend(src_y) + + obs_id_list.extend(obs_id) + event_id_list.extend(event_id) + + if mode != Mode.observation: + labels_energy_list.extend(labels_energy[:, 0]) + dir = labels_direction[:, 0:2] + labels_direction_list.extend(dir) + labels_true_alt_az_list.extend(label_true_alt_az) + + hillas_vector = np.array(utils.create_key_value_array(hillas, id)).T + hillas_list.extend(hillas_vector) + # ------------------------------------------------------------------ + if task == Task.type and mode != Mode.observation: + + val_acc = 100 * correct / total if total > 0 else 0.0 + pbar.set_postfix({"val_accuracy": f"{val_acc:.2f}%"}) + pbar.refresh() + pbar.update(1) + + # if task == Task.type and mode != Mode.observation: + # pbar.set_postfix( + # { + # "val_accuracy": f"{100 * correct / total:.2f}%", + # } + # ) + # if batch_idx % 10 == 0: + # pbar.update(10) + + # TESTING + # h5_file_name="./test.dl2.h5" + # cnt += 1 + # if cnt>5: + # break + + + + predictions = { + "type": np.array(class_predictions), + "type_feature_vector": np.array(class_feature_vector), + "type_class": np.array(class_predictions_class), + "energy": np.array(energy_predictions), + "direction": np.array(direction_predictions), + "direction_mu": np.array(direction_predictions_mu), + "direction_sigma": np.array(direction_predictions_sigma), + } + + if not hasattr(dataset, "class_names"): + dataset.class_names = ["gamma", "proton"] + + data = { + "effective_focal_length": dataset.optics.effective_focal_length, + "obs_id": np.array(obs_id_list), + "event_id": np.array(event_id_list), + "pointing": dataset.pointing_dir, + "true_shower_primary_id": dataset.true_shower_primary_id, + "include_nsb_patches": dataset.include_nsb_patches, + "simulation_info": dataset.simulation_info, + "parameter_names": dataset.hillas_names, + "parameter_data": hillas_list, + "mode": dataset.observation_mode, + "class_names": dataset.class_names, + "energy_unit": dataset.energy_unit, + "selected_telescopes": dataset.selected_telescopes, + } + + labels = { + "true_energy": np.array(labels_energy_list), + "true_direction": np.array(labels_direction_list), + "true_alt_az": np.array(labels_true_alt_az_list), + } + + if task == Task.type: # "type": + average_accuracy = 100.0 * (correct / total) + print("Validation Accuracy: {:.2f}%".format(average_accuracy)) + + gc.enable() + del self.model + del data_loader + del input_data_loader + del self.val_loader + del self.scheduler + + gc.collect() + + # Save h5 file dl2 format + utils.write_output(h5_file_name, data, predictions, labels, task, mode) + + gc.enable() + del data + del predictions + del labels + gc.collect() + torch.cuda.empty_cache() + + return None + # ---------------------------------------------------------------------------------------------------------- + def configure_optimizers(self): + + if self.optimizer_type == "sgd": + print("Using SGD...") + # Optimizer SGD + self.optimizer = optim.SGD( + self.model.parameters(), + lr=self.learning_rate, + momentum=self.momentum, + weight_decay=self.weight_decay, + nesterov=True, + ) + elif self.optimizer_type == "adam": + print("Using Adam...") + # Optimizer Adam + self.optimizer = optim.Adam( + self.model.parameters(), + lr=self.learning_rate, + eps=self.adam_epsilon, + weight_decay=self.weight_decay, + ) + elif self.optimizer_type == "adamw": + print("Using AdamW...") + # Optimizer Adam + self.optimizer = optim.AdamW( + self.model.parameters(), + lr=self.learning_rate, + eps=self.adam_epsilon, + weight_decay=self.weight_decay, + ) + else: + raise ValueError( + f"Unsupported optimizer type: {self.optimizer_type}. Supported types are 'sgd' and 'adam'." + ) + + # Cosine phase + # Note: The schedule is updated after execute one epoch. -> self.epochs* self.batches + lf = one_cycle( + 1, self.lrf, self.num_epochs * self.steps_epoch + ) # cosine 1->hyp['lrf'] + # self.scheduler = lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lf) + + # TODO: Add the option to choose the kind of scheduler + # self.scheduler = torch.optim.lr_scheduler.OneCycleLR(self.optimizer,max_lr=self.lrf,total_steps=self.num_epochs * self.steps_epoch) + self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + self.optimizer, T_max=self.num_epochs + ) + self.scheduler.last_epoch = self.start_epoch + # Prepare scheduler dictionary as expected by PyTorch Lightning + lr_dict = { + "scheduler": self.scheduler, + "interval": "epoch", # Use for each epoch, dont use for each step + "frequency": 1, + "name": "OneCycleLR", + "monitor": "val_loss", + } + # Return the optimizer and learning rate scheduler + return { + "optimizer": self.optimizer, + "lr_scheduler": lr_dict, + } + # ---------------------------------------------------------------------------------------------------------- + # TODO: Rewrite this function in order to create a mosaic with values to study the possible problems in classification and regression + # def missing_analysis(self): + + # self.model.eval() + # total = 0 + # correct = 0 + # validation_loss = 0.0 + # with torch.no_grad(): + # pbar = tqdm(total=len(self.val_loader), + # desc='Validating', leave=True) + # all_preds = torch.tensor([]) + # all_labels = torch.tensor([]) + # for batch_idx, (features, labels) in enumerate(tqdm(self.val_loader)): + + # images = features['image'].to(self.device) + # labels = labels['particletype'].to(self.device) + # hillas_cpu = features["hillas"] + # # hillas = {key: tensor.to(self.device) for key, tensor in hillas_cpu.items()} + # hillas = {key: tensor.cpu().detach().numpy() + # for key, tensor in hillas_cpu.items()} + # filename_list = features["filename"].cpu().detach().numpy() + + # if self.num_channels == 2: + # peak_time = features['peak_time'].to(self.device) + # outputs = self.model(images, peak_time) + # else: + # outputs = self.model(images) + + # # outputs = self.model(images) + # outputs = torch.squeeze(outputs, dim=1) + # # labels_one_hot = torch.nn.functional.one_hot(labels, num_classes=2).float() + # loss = self.criterion(outputs, labels) + # validation_loss += loss.item() + # predicted = torch.sigmoid(outputs).round() + + # total += labels.size(0) + # correct += (predicted == labels).sum().item() + + # all_preds = torch.cat( + # (all_preds, predicted.float().cpu()), dim=0) + # all_labels = torch.cat( + # (all_labels, labels.float().cpu()), dim=0) + + # images_list = [] + # text_list = [] + # cnt = 0 + # for id_image in range(len(labels)): + # # if (labels[id_image] == predicted[id_image]): + # if not (labels[id_image] == predicted[id_image]): + # cnt += 1 + # filename = self.convert_ascii_list_to_string( + # filename_list[id_image]) + # # Transform image to opencv (WxHxC) + # image = cv2.UMat(images[id_image].cpu( + # ).detach().permute(1, 2, 0).numpy()) + + # text_list.append("Label: "+str(self.class_names[int(labels[id_image])])+" "+"Predicted: "+str( + # self.class_names[int(predicted[id_image])])+"\n"+filename) + # # Transform into C,W,H + # image = cv2.UMat.get(image) + # images_list.append(image) + # if cnt >= 16: + # break + + # canvas = self.create_image_mosaic( + # images_list, text_list, batch_idx) + # # cv2.imshow("Missing",canvas) + # # cv2.waitKey(0) + + # pbar.set_postfix({ + # # 'val_loss': f'{validation_loss/total:.4f}', + # 'val_accuracy': f'{100 * correct / total:.2f}%' + # }) + # pbar.update(1) + + # average_accuracy = 100 * (correct / total) + # print('Validation Accuracy: {:.2f}%'.format(average_accuracy)) + # # Save checkpoint if this is the best accuracy or loss so far + # self.save_checkpoint( + # average_accuracy, 'validation_accuracy', is_loss=False) + + # if average_accuracy > self.best_validation_accuracy: + # all_labels = all_labels.numpy() + # all_preds = all_preds.numpy() + # filename_prefix = "./confusion_matrix" + # cm_file_name = f'{filename_prefix}_{average_accuracy:.4f}.pth' + # cm = confusion_matrix(all_labels, all_preds) + + # # accuracies = (np.diag(cm) / np.sum(cm, axis=0))*100.0 + + # cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] + # accuracies = cm_norm.diagonal() * 100.0 + # self.plot_confusion_matrix( + # cm, self.class_names, accuracies, cm_file_name) + + # return average_accuracy diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index fb5a5308..93b78bf1 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -1,38 +1,3 @@ -# Data: - -# mapping_settings: -# camera_types: -# - LSTCam -# # - LSTSiPMCam -# mapping_method: -# CHEC: oversampling -# DigiCam: bilinear_interpolation -# FlashCam: bilinear_interpolation -# LSTCam: bilinear_interpolation -# LSTSiPMCam: bilinear_interpolation -# MAGICCam: bilinear_interpolation -# NectarCam: bilinear_interpolation -# SCTCam: oversampling -# padding: -# CHEC: 0 -# DigiCam: 2 -# FlashCam: 2 -# LSTCam: 2 -# LSTSiPMCam: 2 -# MAGICCam: 2 -# NectarCam: 2 -# SCTCam: 0 -# mode: mono -# parameter_selection: -# - col_name: hillas_intensity -# min_value: 50.0 -# selected_telescope_types: -# - LST_LST_LSTCam - -# shuffle: False -# transforms: [] - - data: train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 867a3235..42a9f728 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -11,13 +11,16 @@ Unicode, ) +from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL try: import torch + except ImportError: raise ImportError("pytorch is not installed in your environment!") try: import pytorch_lightning + from pytorch_lightning.loggers import TensorBoardLogger except ImportError: raise ImportError("pytorch_lightning is not installed in your environment!") @@ -33,6 +36,20 @@ from ctlearn.core.pytorch.net_utils import create_model, ModelHelper +from pytorch_lightning.callbacks import Callback +import os + +class GPUStatsLogger(Callback): + def on_train_epoch_end(self, trainer, pl_module): + mem_allocated = torch.cuda.memory_allocated() + mem_reserved = torch.cuda.memory_reserved() + + trainer.logger.experiment.add_scalar( + "gpu_mem_allocated", mem_allocated, global_step=trainer.current_epoch + ) + trainer.logger.experiment.add_scalar( + "gpu_mem_reserved", mem_reserved, global_step=trainer.current_epoch + ) # from ctlearn.tools.train_model import class TrainPyTorchModel(TrainCTLearnModel): @@ -100,6 +117,13 @@ class TrainPyTorchModel(TrainCTLearnModel): } def __init__(self, **kwargs): + + os.environ["NCCL_P2P_DISABLE"] = "1" + os.environ["NCCL_IB_DISABLE"] = "1" + os.environ["NCCL_DEBUG"] = "WARN" + os.environ["TORCH_NCCL_ASYNC_ERROR_HANDLING"] = "1" + os.environ["NCCL_DEBUG"] = "INFO" + print("Pytorch init") super().__init__(**kwargs) print("CONFIG VALUES PYTORCH:", self.config) @@ -129,6 +153,12 @@ def setup(self): self.num_workers = self.parameters["dataset"]["num_workers"] self.persistent_workers = self.parameters["dataset"]["persistent_workers"] + self.devices = self.parameters["arch"]["devices"] + self.save_k = self.parameters["hyp"]["save_k"] + + + print(f"Using Devices: {self.devices}") + def start(self): print("Pytorch start") super().start() @@ -177,6 +207,68 @@ def start(self): model_net = ModelHelper.loadModel( model_net, "", check_point_path, Mode.train, device_str=self.device_str ) + + log_dir = save_folder + # Setup the TensorBoard logger + tb_logger = TensorBoardLogger( + save_dir=log_dir, + name="exp_" + + str(self.experiment_number) + + "_" + + task.name + + "_train", + default_hp_metric=False, + ) + + in_channels = 2 + lightning_model = CTLearnPL( + model=model_net, + save_folder=save_folder, + task=task, + mode = Mode.train, + parameters=self.parameters, + num_channels=in_channels, + k=self.save_k, + ) + + # Setup the Trainer + trainer_pl = CTLearnTrainer( + max_epochs=self.parameters["hyp"]["epochs"], + accelerator=self.parameters["arch"]["device"], + devices=self.devices, + strategy= self.parameters["arch"]["strategy"], + default_root_dir=log_dir, + log_every_n_steps=1, + logger=tb_logger, + num_sanity_val_steps=0, + precision=precision, + gradient_clip_val=self.parameters["hyp"]["gradient_clip_val"], + callbacks=[GPUStatsLogger()], + sync_batchnorm=True, + ) + + # training_data, validation_data, validation_test_data = self.load_data( + # task, Mode.train, test_type, self.parameters + # ) + + # train_loader, validation_loader, test_validation_loader = self.create_data_loaders( + # training_data, + # validation_data, + # validation_test_data, + # task, + # Mode.train, + # self.parameters, + # self.batch_size, + # self.pin_memory, + # num_workers=self.num_workers, + # persistent_workers=self.persistent_workers + # ) + + trainer_pl.fit( + model=lightning_model, + train_dataloaders=self.training_loader, + val_dataloaders=[self.validation_loader], + ) def finish(self): super().finish() @@ -184,3 +276,85 @@ def finish(self): def show_version(self): print("Pytorch 2.3") + + # def load_data(self,task: Task, mode: Mode, test_type, parameters): + + # # Main script + # print("Loading data...") + # training_data = None + # validation_data = None + # validation_test_data = None + # if mode == Mode.train or mode == Mode.tunning: + # if task == Task.type: + # training_data = load_pickle(parameters["data"]["train_gamma_proton"]) + # else: + # training_data = load_pickle(parameters["data"]["train_gamma"]) + # validation_test_data = load_pickle( + # parameters["data"]["test_validation_gamma"] + # ) + # # Gamma + # if mode == Mode.results: + + # if test_type == EventType.gamma: + # validation_data = load_pickle(parameters["data"]["test_gamma"]) + # elif test_type == EventType.proton: + # validation_data = load_pickle(parameters["data"]["test_proton"]) + # elif test_type == EventType.electron: + # validation_data = load_pickle(parameters["data"]["test_electron"]) + + # elif mode == Mode.observation: + # validation_data = load_pickle(parameters["data"]["observation"]) + + # if mode == Mode.train or mode == Mode.validate or mode == Mode.tunning: + # # Gamma - Proton + # if task == Task.type: + # validation_data = load_pickle(parameters["data"]["validation_gamma_proton"]) + # validation_test_data = load_pickle(parameters["data"]["test_validation_gamma_proton"]) + + # else: + # validation_data = load_pickle(parameters["data"]["validation_gamma"]) + # validation_test_data = load_pickle(parameters["data"]["test_validation_gamma"]) + + # # -------------------------------------------------------- + # # Reduce the training and validation for testing purspose + # # -------------------------------------------------------- + # training_reduce_factor = int(parameters["data"]["training_reduce_factor"]) + # validation_reduce_factor = int(parameters["data"]["validation_reduce_factor"]) + # validation_test_reduce_factor = int( + # parameters["data"]["validation_test_reduce_factor"] + # ) + + # # -------------------------------------------------------- + # # training_data + # # -------------------------------------------------------- + # if training_data and training_reduce_factor > 0: + # data_len = len(training_data["data"]) + # factor = training_reduce_factor + # training_data["data"] = training_data["data"][0 : int(data_len / factor)] + # training_data["true_shower_primary_id"] = training_data[ + # "true_shower_primary_id" + # ][0 : int(data_len / factor)] + # # -------------------------------------------------------- + # # validation_data + # # -------------------------------------------------------- + # if validation_data and validation_reduce_factor > 0: + # data_len = len(validation_data["data"]) + # factor = validation_reduce_factor + # validation_data["data"] = validation_data["data"][0 : int(data_len / factor)] + # validation_data["true_shower_primary_id"] = validation_data[ + # "true_shower_primary_id" + # ][0 : int(data_len / factor)] + # # -------------------------------------------------------- + # # validation_test_data + # # -------------------------------------------------------- + # if validation_test_data and validation_reduce_factor > 0: + # data_len = len(validation_test_data["data"]) + # factor = validation_test_reduce_factor + # validation_test_data["data"] = validation_test_data["data"][ + # 0 : int(data_len / factor) + # ] + # validation_test_data["true_shower_primary_id"] = validation_test_data[ + # "true_shower_primary_id" + # ][0 : int(data_len / factor)] + # # -------------------------------------------------------- + # return training_data, validation_data, validation_test_data \ No newline at end of file From 78f1e1b9a9eadf8cb477f58ba433035b48e0021c Mon Sep 17 00:00:00 2001 From: pguzman Date: Wed, 28 May 2025 09:19:58 +0000 Subject: [PATCH 020/119] fixed cuda memory problem in pytorch --- Dockerfile | 21 +++++++++++++++++-- ctlearn/core/data_loader/pytorch_loader.py | 11 ++++++---- ctlearn/core/pytorch/net_utils.py | 2 ++ ctlearn/tools/train/pytorch/CTLearnPL.py | 6 +++--- .../train/pytorch/train_pytorch_model.py | 7 +++++++ 5 files changed, 38 insertions(+), 9 deletions(-) diff --git a/Dockerfile b/Dockerfile index 4a08b151..cbbf0c70 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,11 +2,20 @@ FROM python:3.12 AS builder # Install git (needed for setuptools_scm during build) and build tool + RUN apt-get update \ && apt-get install -y --no-install-recommends git \ && rm -rf /var/lib/apt/lists/* RUN pip install --no-cache-dir build +# Copy source code needed for the build +WORKDIR /repo +COPY ./pyproject.toml MANIFEST.in ./ +COPY ./ctlearn ./ctlearn/ +# If .git is truly needed for versioning by setuptools_scm, copy it. Otherwise, omit. +COPY ./.git ./.git/ +RUN pip install --no-cache-dir build + # Copy source code needed for the build WORKDIR /repo COPY ./pyproject.toml MANIFEST.in ./ @@ -18,14 +27,22 @@ COPY ./.git ./.git/ RUN python -m build --wheel # Stage 2: Create the final runtime image BASED ON NVIDIA's TF image +# TODO what version to use ? after 24.?? TF 2.14 is not found in the container +FROM nvcr.io/nvidia/tensorflow:24.01-tf2-py3 + +# Copy only the built wheel from the builder stage's dist directory +# Build the wheel + +# Stage 2: Create the final runtime image BASED ON NVIDIA's TF image + FROM nvcr.io/nvidia/tensorflow:25.02-tf2-py3 # Copy only the built wheel from the builder stage's dist directory COPY --from=builder /repo/dist /tmp/dist +# Install the ctlearn wheel using pip from the NVIDIA base image # Install the ctlearn wheel using pip from the NVIDIA base image RUN python -m pip install --no-cache-dir /tmp/dist/* \ && rm -r /tmp/dist RUN addgroup --system ctlearn && adduser --system --group ctlearn -USER ctlearn - +USER ctlearn \ No newline at end of file diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index f2fdcd39..5b7fd217 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -195,14 +195,17 @@ def _get_mono_item(self, batch): # peak_time = (peak_time - self.dir_mu) / self.dir_sigma features_out={} - features_out["image"]=torch.from_numpy(image).to('cuda') - features_out["peak_time"]=torch.from_numpy(peak_time).to('cuda') + features_out["image"]=image #torch.from_numpy(image) + features_out["peak_time"]= peak_time #torch.from_numpy(peak_time) + + features_out["image"]=torch.from_numpy(image).contiguous().float() + features_out["peak_time"]=torch.from_numpy(peak_time).contiguous().float() # features_out["hillas"] = features["hillas"] # features_out["hillas_names"] = self.hillas_names for key in labels.keys(): - labels[key] = torch.from_numpy(labels[key]).to('cuda') - + # labels[key] = labels[key]#torch.from_numpy(labels[key]) + labels[key] = torch.from_numpy(labels[key]).contiguous().unsqueeze(-1) return features_out, labels def _get_stereo_item(self, batch): diff --git a/ctlearn/core/pytorch/net_utils.py b/ctlearn/core/pytorch/net_utils.py index bc39b216..46e0beb7 100644 --- a/ctlearn/core/pytorch/net_utils.py +++ b/ctlearn/core/pytorch/net_utils.py @@ -178,6 +178,8 @@ def loadModel(model, data_path, filename, mode, device_str='cpu'): # model.load_state_dict(torch.load(data_path + filename), strict=False) print("Model Loaded.") else: + model.to(torch.device(device_str)) + print(f"CheckPoint file does not exist: {filename}") if mode != Mode.train: exit() diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index ce8876f5..c7e8cf68 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -535,7 +535,7 @@ def training_step(self, batch, batch_idx): if self.task == Task.energy: labels_energy_value = labels["energy"] - # labels_energy_value = labels_energy_value.to('cuda') + labels_energy_value = labels_energy_value.to('cuda') # labels_energy_class = labels["energy_class"] if self.task == Task.direction: @@ -544,14 +544,14 @@ def training_step(self, batch, batch_idx): labels_direction_cartesian = labels["direction_cartesian"] - # imgs = imgs.to('cuda') + imgs = imgs.to('cuda') # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ if self.num_channels == 2: peak_time = features["peak_time"] - # peak_time = peak_time.to('cuda') + peak_time = peak_time.to('cuda') classification_pred, energy_pred, direction_pred = self.model( imgs, peak_time ) diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 42a9f728..d0b43c5c 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -208,6 +208,8 @@ def start(self): model_net, "", check_point_path, Mode.train, device_str=self.device_str ) + + log_dir = save_folder # Setup the TensorBoard logger tb_logger = TensorBoardLogger( @@ -247,6 +249,11 @@ def start(self): sync_batchnorm=True, ) + print(f"Run tensorboard server: tensorboard --load_fast=false --host=0.0.0.0 --logdir={trainer_pl.get_log_dir()}/") + + print(f"Accelerator: {trainer_pl.accelerator}") + print(f"Num. Devices: {trainer_pl.num_devices}") + # training_data, validation_data, validation_test_data = self.load_data( # task, Mode.train, test_type, self.parameters # ) From 6ff757b53debe773dfb30966c12eb8d9c0e6be04 Mon Sep 17 00:00:00 2001 From: pguzman Date: Wed, 28 May 2025 15:10:24 +0000 Subject: [PATCH 021/119] fixed parameter log saving and added missing files --- ctlearn/core/data_loader/pytorch_loader.py | 55 ++++-- .../core/pytorch/visualization/vis_utils.py | 18 -- ctlearn/tools/train/base_train_model.py | 76 ++++---- .../tools/train/keras/train_keras_model.py | 41 ++++ ctlearn/tools/train/pytorch/CTLearnPL.py | 30 +-- .../train/pytorch/train_pytorch_model.py | 176 +++++++----------- 6 files changed, 193 insertions(+), 203 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 5b7fd217..d3e6af21 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -3,14 +3,45 @@ from torch.utils.data import Dataset from .base_loader import BaseDLDataLoader from dl1_data_handler.reader import ProcessType +from ctlearn.core.ctlearn_enum import Task class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): def __init__( - self, + self, + tasks, + parameters, + use_augmentation, **kwargs, ): + self.parameter = parameters + self.use_augmentation = use_augmentation + self.use_clean = parameters["normalization"]["use_clean"] + self.use_clean_dvr = parameters["normalization"]["use_clean_dvr"] - super().__init__(**kwargs) + self.task= tasks + + # Augmentation probabilities + self.mask_augmentation = parameters["augmentation"]["aug_prob"] + self.aug_prob = parameters["augmentation"]["aug_prob"] + self.rot_prob = parameters["augmentation"]["rot_prob"] + self.trans_prob = parameters["augmentation"]["trans_prob"] + self.flip_hor_prob = parameters["augmentation"]["flip_hor_prob"] + self.flip_ver_prob = parameters["augmentation"]["flip_ver_prob"] + self.mask_prob = parameters["augmentation"]["mask_prob"] + self.mask_dvr_prob = parameters["augmentation"]["mask_dvr_prob"] + self.noise_prob = parameters["augmentation"]["noise_prob"] + self.max_aug_rot = parameters["augmentation"]["max_rot"] + self.max_aug_trans = parameters["augmentation"]["max_trans"] + + # Normalization + self.type_mu = parameters["normalization"]["type_mu"] + self.type_sigma = parameters["normalization"]["type_sigma"] + self.dir_mu = parameters["normalization"]["dir_mu"] + self.dir_sigma = parameters["normalization"]["dir_sigma"] + self.energy_mu = parameters["normalization"]["energy_mu"] + self.energy_sigma = parameters["normalization"]["energy_sigma"] + + super().__init__(**kwargs,tasks=tasks) self.on_epoch_end() self.hillas_names = [ @@ -184,15 +215,15 @@ def _get_mono_item(self, batch): peak_time[np.isnan(peak_time)] = 0 peak_time[np.isinf(peak_time)] = 0 - # if self.task == Task.type: # "type": - # image = (image - self.type_mu) / self.type_sigma - # peak_time = (peak_time - self.type_mu) / self.type_sigma - # if self.task == Task.energy: # "energy": - # image = (image - self.energy_mu) / self.energy_sigma - # peak_time = (peak_time - self.energy_mu) / self.energy_sigma - # if self.task == Task.direction: # "direction": - # image = (image - self.dir_mu) / self.dir_sigma - # peak_time = (peak_time - self.dir_mu) / self.dir_sigma + if self.task == Task.type: # "type": + image = (image - self.type_mu) / self.type_sigma + peak_time = (peak_time - self.type_mu) / self.type_sigma + if self.task == Task.energy: # "energy": + image = (image - self.energy_mu) / self.energy_sigma + peak_time = (peak_time - self.energy_mu) / self.energy_sigma + if self.task == Task.direction: # "direction": + image = (image - self.dir_mu) / self.dir_sigma + peak_time = (peak_time - self.dir_mu) / self.dir_sigma features_out={} features_out["image"]=image #torch.from_numpy(image) @@ -200,7 +231,7 @@ def _get_mono_item(self, batch): features_out["image"]=torch.from_numpy(image).contiguous().float() features_out["peak_time"]=torch.from_numpy(peak_time).contiguous().float() - # features_out["hillas"] = features["hillas"] + features_out["hillas"] = features["hillas"] # features_out["hillas_names"] = self.hillas_names for key in labels.keys(): diff --git a/ctlearn/core/pytorch/visualization/vis_utils.py b/ctlearn/core/pytorch/visualization/vis_utils.py index 86610a8e..5342fd38 100644 --- a/ctlearn/core/pytorch/visualization/vis_utils.py +++ b/ctlearn/core/pytorch/visualization/vis_utils.py @@ -61,24 +61,6 @@ def plot_direction_resolution_error(val_alt_pred_list,val_az_pred_list, val_alt_ ax.set_ylim(bottom=0, top=1.5) return fig # ---------------------------------------------------------------------------------------------------------- -# def plot_confusion_matrix(cm, classes, accuracies, cm_file_name ,save_folder): -# plt.figure(figsize=(10, 7)) -# sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', -# xticklabels=classes, yticklabels=classes) -# plt.xlabel('Predicted Labels') -# plt.ylabel('True Labels') - -# accuracy_str = "Accuracy: \n" - -# for id, (class_type) in enumerate(classes): -# accuracy_str += " " + class_type + ": " + \ -# str(round(accuracies[id], 2))+"%" - -# plt.title('Confusion Matrix \n' + accuracy_str) - -# # plt.show() -# plt.savefig(os.path.join(save_folder,cm_file_name+".png")) -# plt.close() def plot_confusion_matrix(cm, classes, cm_file_name, save_folder): """ Plots and saves the confusion matrix. diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index b10cd448..340a7049 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -15,7 +15,7 @@ Unicode, ) from dl1_data_handler.reader import DLDataReader -from ctlearn.core.data_loader.loader import DLDataLoader +# from ctlearn.core.data_loader.loader import DLDataLoader class TrainCTLearnModel(Tool): """ @@ -331,45 +331,45 @@ def setup(self): f"Cannot stack telescope images when sorting by intensity. Disable sorting by intensity." ) - # Set up the data loaders for training and validation - indices = list(range(self.dl1dh_reader._get_n_events())) - # Shuffle the indices before the training/validation split - np.random.seed(self.random_seed) - np.random.shuffle(indices) - n_validation_examples = int( - self.validation_split * self.dl1dh_reader._get_n_events() - ) - training_indices = indices[n_validation_examples:] - validation_indices = indices[:n_validation_examples] - - # Set self.strategy.num_replicas_in_sync to 1 in case that does not exist (Pytorch) - if not hasattr(self, "strategy"): - self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() - print("num_replicas_in_sync:", self.strategy.num_replicas_in_sync) - - print("BASE TRAIN FRAMEWORK", self.framework_type) + # # Set up the data loaders for training and validation + # indices = list(range(self.dl1dh_reader._get_n_events())) + # # Shuffle the indices before the training/validation split + # np.random.seed(self.random_seed) + # np.random.shuffle(indices) + # n_validation_examples = int( + # self.validation_split * self.dl1dh_reader._get_n_events() + # ) + # training_indices = indices[n_validation_examples:] + # validation_indices = indices[:n_validation_examples] + + # # Set self.strategy.num_replicas_in_sync to 1 in case that does not exist (Pytorch) + # if not hasattr(self, "strategy"): + # self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() + # print("num_replicas_in_sync:", self.strategy.num_replicas_in_sync) + + # print("BASE TRAIN FRAMEWORK", self.framework_type) - self.training_loader = DLDataLoader.create( - framework=self.framework_type, - DLDataReader=self.dl1dh_reader, - indices=training_indices, - tasks=self.reco_tasks, - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - random_seed=self.random_seed, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) + # self.training_loader = DLDataLoader.create( + # framework=self.framework_type, + # DLDataReader=self.dl1dh_reader, + # indices=training_indices, + # tasks=self.reco_tasks, + # batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + # random_seed=self.random_seed, + # sort_by_intensity=self.sort_by_intensity, + # stack_telescope_images=self.stack_telescope_images, + # ) - self.validation_loader = DLDataLoader.create( - framework=self.framework_type, - DLDataReader=self.dl1dh_reader, - indices=validation_indices, - tasks=self.reco_tasks, - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - random_seed=self.random_seed, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) + # self.validation_loader = DLDataLoader.create( + # framework=self.framework_type, + # DLDataReader=self.dl1dh_reader, + # indices=validation_indices, + # tasks=self.reco_tasks, + # batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + # random_seed=self.random_seed, + # sort_by_intensity=self.sort_by_intensity, + # stack_telescope_images=self.stack_telescope_images, + # ) def start(self): pass diff --git a/ctlearn/tools/train/keras/train_keras_model.py b/ctlearn/tools/train/keras/train_keras_model.py index 307619a2..cb59e61c 100644 --- a/ctlearn/tools/train/keras/train_keras_model.py +++ b/ctlearn/tools/train/keras/train_keras_model.py @@ -19,6 +19,7 @@ ComponentName, Unicode, ) +from ctlearn.core.data_loader.loader import DLDataLoader from ctlearn.tools.train.base_train_model import TrainCTLearnModel from ctlearn.core.keras.model import CTLearnModel from ctlearn.utils import validate_trait_dict @@ -129,6 +130,7 @@ class TrainKerasModel(TrainCTLearnModel): } def setup(self): + print(tf.config.list_physical_devices('GPU')) # Create a MirroredStrategy. self.strategy = tf.distribute.MirroredStrategy() @@ -137,6 +139,45 @@ def setup(self): # print(self.framework_type) super().setup() + # Set up the data loaders for training and validation + indices = list(range(self.dl1dh_reader._get_n_events())) + # Shuffle the indices before the training/validation split + np.random.seed(self.random_seed) + np.random.shuffle(indices) + n_validation_examples = int( + self.validation_split * self.dl1dh_reader._get_n_events() + ) + training_indices = indices[n_validation_examples:] + validation_indices = indices[:n_validation_examples] + + # Set self.strategy.num_replicas_in_sync to 1 in case that does not exist (Pytorch) + if not hasattr(self, "strategy"): + self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() + print("num_replicas_in_sync:", self.strategy.num_replicas_in_sync) + + print("BASE TRAIN FRAMEWORK", self.framework_type) + + self.training_loader = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=training_indices, + tasks=self.reco_tasks, + batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + random_seed=self.random_seed, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + + self.validation_loader = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=validation_indices, + tasks=self.reco_tasks, + batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + random_seed=self.random_seed, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) def start(self): print("Start KERAS") diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index c7e8cf68..0b18ddec 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -39,7 +39,7 @@ import torch.distributed as dist import torch.multiprocessing as mp from ctlearn.core.pytorch.nets.loss_functions.loss_functions import evidential_regression_loss -import pickle +import json # from ctlearn.nets.loss_functions.loss_functions import evidential_classification @@ -102,7 +102,6 @@ def get_log_dir(self) -> str: return self.logger.log_dir class CTLearnPL(pl.LightningModule): - # lock = mp.Lock() def __init__( self, @@ -116,38 +115,21 @@ def __init__( test_val_loader=None, num_channels=1, k=3, - # **kwargs, + ): super(CTLearnPL, self).__init__() - - # Save configuration file. - # self.save_hyperparameters(parameters) - - - - # self.save_hyperparameters({ - # 'data': parameters['data'], - # 'hyp': parameters['hyp'], - # 'arch': parameters['arch'], - # }) - - self.model = model self.task = task self.mode = mode self.save_folder = save_folder + self.model = model # torch.autograd.set_detect_anomaly(True) self._device = torch.device(parameters["arch"]["device"]) self.device_type = parameters["arch"]["device"] - self.energy_bins = np.linspace(0.0251, 140, num=40) - self.energy_bins_tensor = torch.tensor( - self.energy_bins, device=self._device, dtype=torch.float - ) - self.model.to(self.device) self.k = k # Number of top results to save @@ -782,8 +764,8 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if self.task == Task.type: labels_class = labels["particletype"] - if self.task == Task.energy: - labels_energy_class = labels["energy_class"] + # if self.task == Task.energy: + # labels_energy_class = labels["energy_class"] labels_energy_value = labels["energy"] hillas_intensity = features["hillas"]["hillas_intensity"] # hillas = {key: tensor.to(self.device) @@ -791,7 +773,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if self.task == Task.direction: labels_direction = labels["direction"] # labels_alt_az = labels['alt_az'] - labels_direction_cartesian = labels["direction_cartesian"] + # labels_direction_cartesian = labels["direction_cartesian"] # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index d0b43c5c..ed4dae7b 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -35,9 +35,11 @@ ) from ctlearn.core.pytorch.net_utils import create_model, ModelHelper - +from ctlearn.core.data_loader.loader import DLDataLoader from pytorch_lightning.callbacks import Callback import os +import numpy as np +import json class GPUStatsLogger(Callback): def on_train_epoch_end(self, trainer, pl_module): @@ -159,6 +161,48 @@ def setup(self): print(f"Using Devices: {self.devices}") + + # Set up the data loaders for training and validation + indices = list(range(self.dl1dh_reader._get_n_events())) + # Shuffle the indices before the training/validation split + np.random.seed(self.random_seed) + np.random.shuffle(indices) + n_validation_examples = int( + self.validation_split * self.dl1dh_reader._get_n_events() + ) + training_indices = indices[n_validation_examples:] + validation_indices = indices[:n_validation_examples] + + + print("BASE TRAIN FRAMEWORK", self.framework_type) + + self.training_loader = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=training_indices, + tasks=self.reco_tasks, + batch_size=self.batch_size, + random_seed=self.random_seed, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + parameters=self.parameters, + use_augmentation=True, + ) + + self.validation_loader = DLDataLoader.create( + framework=self.framework_type, + DLDataReader=self.dl1dh_reader, + indices=validation_indices, + tasks=self.reco_tasks, + batch_size=self.batch_size, + random_seed=self.random_seed, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + parameters=self.parameters, + use_augmentation=False, + ) + + def start(self): print("Pytorch start") super().start() @@ -222,16 +266,7 @@ def start(self): default_hp_metric=False, ) - in_channels = 2 - lightning_model = CTLearnPL( - model=model_net, - save_folder=save_folder, - task=task, - mode = Mode.train, - parameters=self.parameters, - num_channels=in_channels, - k=self.save_k, - ) + # Setup the Trainer trainer_pl = CTLearnTrainer( @@ -249,28 +284,29 @@ def start(self): sync_batchnorm=True, ) + in_channels = 2 + lightning_model = CTLearnPL( + model=model_net, + save_folder=trainer_pl.get_log_dir(), + task=task, + mode = Mode.train, + parameters=self.parameters, + num_channels=in_channels, + k=self.save_k, + ) + + # Save configuration file. + if not os.path.exists(trainer_pl.get_log_dir()): + os.mkdir(trainer_pl.get_log_dir()) + + with open(os.path.join(trainer_pl.get_log_dir(),"parameters.json"), "w") as f: + json.dump(self.parameters, f, indent=4) + print(f"Run tensorboard server: tensorboard --load_fast=false --host=0.0.0.0 --logdir={trainer_pl.get_log_dir()}/") print(f"Accelerator: {trainer_pl.accelerator}") print(f"Num. Devices: {trainer_pl.num_devices}") - - # training_data, validation_data, validation_test_data = self.load_data( - # task, Mode.train, test_type, self.parameters - # ) - - # train_loader, validation_loader, test_validation_loader = self.create_data_loaders( - # training_data, - # validation_data, - # validation_test_data, - # task, - # Mode.train, - # self.parameters, - # self.batch_size, - # self.pin_memory, - # num_workers=self.num_workers, - # persistent_workers=self.persistent_workers - # ) - + trainer_pl.fit( model=lightning_model, train_dataloaders=self.training_loader, @@ -283,85 +319,3 @@ def finish(self): def show_version(self): print("Pytorch 2.3") - - # def load_data(self,task: Task, mode: Mode, test_type, parameters): - - # # Main script - # print("Loading data...") - # training_data = None - # validation_data = None - # validation_test_data = None - # if mode == Mode.train or mode == Mode.tunning: - # if task == Task.type: - # training_data = load_pickle(parameters["data"]["train_gamma_proton"]) - # else: - # training_data = load_pickle(parameters["data"]["train_gamma"]) - # validation_test_data = load_pickle( - # parameters["data"]["test_validation_gamma"] - # ) - # # Gamma - # if mode == Mode.results: - - # if test_type == EventType.gamma: - # validation_data = load_pickle(parameters["data"]["test_gamma"]) - # elif test_type == EventType.proton: - # validation_data = load_pickle(parameters["data"]["test_proton"]) - # elif test_type == EventType.electron: - # validation_data = load_pickle(parameters["data"]["test_electron"]) - - # elif mode == Mode.observation: - # validation_data = load_pickle(parameters["data"]["observation"]) - - # if mode == Mode.train or mode == Mode.validate or mode == Mode.tunning: - # # Gamma - Proton - # if task == Task.type: - # validation_data = load_pickle(parameters["data"]["validation_gamma_proton"]) - # validation_test_data = load_pickle(parameters["data"]["test_validation_gamma_proton"]) - - # else: - # validation_data = load_pickle(parameters["data"]["validation_gamma"]) - # validation_test_data = load_pickle(parameters["data"]["test_validation_gamma"]) - - # # -------------------------------------------------------- - # # Reduce the training and validation for testing purspose - # # -------------------------------------------------------- - # training_reduce_factor = int(parameters["data"]["training_reduce_factor"]) - # validation_reduce_factor = int(parameters["data"]["validation_reduce_factor"]) - # validation_test_reduce_factor = int( - # parameters["data"]["validation_test_reduce_factor"] - # ) - - # # -------------------------------------------------------- - # # training_data - # # -------------------------------------------------------- - # if training_data and training_reduce_factor > 0: - # data_len = len(training_data["data"]) - # factor = training_reduce_factor - # training_data["data"] = training_data["data"][0 : int(data_len / factor)] - # training_data["true_shower_primary_id"] = training_data[ - # "true_shower_primary_id" - # ][0 : int(data_len / factor)] - # # -------------------------------------------------------- - # # validation_data - # # -------------------------------------------------------- - # if validation_data and validation_reduce_factor > 0: - # data_len = len(validation_data["data"]) - # factor = validation_reduce_factor - # validation_data["data"] = validation_data["data"][0 : int(data_len / factor)] - # validation_data["true_shower_primary_id"] = validation_data[ - # "true_shower_primary_id" - # ][0 : int(data_len / factor)] - # # -------------------------------------------------------- - # # validation_test_data - # # -------------------------------------------------------- - # if validation_test_data and validation_reduce_factor > 0: - # data_len = len(validation_test_data["data"]) - # factor = validation_test_reduce_factor - # validation_test_data["data"] = validation_test_data["data"][ - # 0 : int(data_len / factor) - # ] - # validation_test_data["true_shower_primary_id"] = validation_test_data[ - # "true_shower_primary_id" - # ][0 : int(data_len / factor)] - # # -------------------------------------------------------- - # return training_data, validation_data, validation_test_data \ No newline at end of file From 0da26210d3f6eef4d050b16814bfde33e4b592ec Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 3 Jun 2025 11:05:40 +0000 Subject: [PATCH 022/119] added some comments --- ctlearn/tools/train/pytorch/train_pytorch_model.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index ed4dae7b..5b593969 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -119,7 +119,8 @@ class TrainPyTorchModel(TrainCTLearnModel): } def __init__(self, **kwargs): - + + # Setup GPU Debug os.environ["NCCL_P2P_DISABLE"] = "1" os.environ["NCCL_IB_DISABLE"] = "1" os.environ["NCCL_DEBUG"] = "WARN" @@ -252,10 +253,9 @@ def start(self): model_net, "", check_point_path, Mode.train, device_str=self.device_str ) - - - log_dir = save_folder # Setup the TensorBoard logger + log_dir = save_folder + tb_logger = TensorBoardLogger( save_dir=log_dir, name="exp_" @@ -266,8 +266,6 @@ def start(self): default_hp_metric=False, ) - - # Setup the Trainer trainer_pl = CTLearnTrainer( max_epochs=self.parameters["hyp"]["epochs"], @@ -283,7 +281,8 @@ def start(self): callbacks=[GPUStatsLogger()], sync_batchnorm=True, ) - + + # TODO: Fix in_channels. Add this parameter in the configuration file, in the model or both.... in_channels = 2 lightning_model = CTLearnPL( model=model_net, From 578db9e6f84c2165fe886cbd4fde8e4d3e31a7b5 Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 3 Jun 2025 17:51:30 +0000 Subject: [PATCH 023/119] fixed some bugs in training and dataloader --- ctlearn/core/data_loader/pytorch_loader.py | 12 +- ctlearn/tools/train/base_train_model.py | 47 ------- ctlearn/tools/train/pytorch/CTLearnPL.py | 115 +++++++----------- .../training_config_iaa_neutron_training.yml | 2 +- .../train/pytorch/train_pytorch_model.py | 7 +- ctlearn/tools/train_model.py | 1 + 6 files changed, 52 insertions(+), 132 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index d3e6af21..4355a768 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -196,7 +196,7 @@ def _get_mono_item(self, batch): ) # if "hillas" in self.tasks: - features["hillas"] = self.DLDataReader.get_parameters_dict(batch,self.hillas_names) + features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) # features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) image = features["input"][..., 0:1] @@ -232,11 +232,13 @@ def _get_mono_item(self, batch): features_out["image"]=torch.from_numpy(image).contiguous().float() features_out["peak_time"]=torch.from_numpy(peak_time).contiguous().float() features_out["hillas"] = features["hillas"] - # features_out["hillas_names"] = self.hillas_names - - for key in labels.keys(): - # labels[key] = labels[key]#torch.from_numpy(labels[key]) + + for key in labels.keys(): labels[key] = torch.from_numpy(labels[key]).contiguous().unsqueeze(-1) + + for key in features["hillas"].keys(): + features["hillas"][key] = torch.from_numpy(np.array(features["hillas"][key])).contiguous().unsqueeze(-1) + return features_out, labels def _get_stereo_item(self, batch): diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 340a7049..a87786a2 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -256,13 +256,6 @@ def setup(self): self.log.info("Removing existing output directory %s", self.output_dir) shutil.rmtree(self.output_dir) - # Must be moved to KERAS (It is moved already ) - # Create a MirroredStrategy. - # self.strategy = tf.distribute.MirroredStrategy() - # atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore - # self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) - - # print(self.DLFrameWork.framework_type) # Get signal input files self.input_url_signal = [] for signal_pattern in self.file_pattern_signal: @@ -331,46 +324,6 @@ def setup(self): f"Cannot stack telescope images when sorting by intensity. Disable sorting by intensity." ) - # # Set up the data loaders for training and validation - # indices = list(range(self.dl1dh_reader._get_n_events())) - # # Shuffle the indices before the training/validation split - # np.random.seed(self.random_seed) - # np.random.shuffle(indices) - # n_validation_examples = int( - # self.validation_split * self.dl1dh_reader._get_n_events() - # ) - # training_indices = indices[n_validation_examples:] - # validation_indices = indices[:n_validation_examples] - - # # Set self.strategy.num_replicas_in_sync to 1 in case that does not exist (Pytorch) - # if not hasattr(self, "strategy"): - # self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() - # print("num_replicas_in_sync:", self.strategy.num_replicas_in_sync) - - # print("BASE TRAIN FRAMEWORK", self.framework_type) - - # self.training_loader = DLDataLoader.create( - # framework=self.framework_type, - # DLDataReader=self.dl1dh_reader, - # indices=training_indices, - # tasks=self.reco_tasks, - # batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - # random_seed=self.random_seed, - # sort_by_intensity=self.sort_by_intensity, - # stack_telescope_images=self.stack_telescope_images, - # ) - - # self.validation_loader = DLDataLoader.create( - # framework=self.framework_type, - # DLDataReader=self.dl1dh_reader, - # indices=validation_indices, - # tasks=self.reco_tasks, - # batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - # random_seed=self.random_seed, - # sort_by_intensity=self.sort_by_intensity, - # stack_telescope_images=self.stack_telescope_images, - # ) - def start(self): pass diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 0b18ddec..52daed86 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -204,20 +204,17 @@ def __init__( self.class_train_accuracy = Accuracy( task="multiclass", num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], - # compute_on_step=True, # Compute for each step and epoch dist_sync_on_step=True # GPUs Sync ) self.class_val_accuracy = Accuracy( task="multiclass", num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], - # compute_on_step=True, # Compute for each step and epoch dist_sync_on_step=True # GPUs Sync ) self.class_test_val_accuracy = Accuracy( task="multiclass", num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], - # compute_on_step=True, # Compute for each step and epoch dist_sync_on_step=True # GPUs Sync ) self.confusion_matrix = ConfusionMatrix(num_classes=2, task="multiclass",dist_sync_on_step=True) @@ -359,21 +356,10 @@ def compute_type_loss( target = labels_class.to(torch.int64) loss_class = self.criterion_class(classification_pred, target) - - # loss_triplet = criterion(anchor_out, positive_out, negative_out) - # Calculate accuracy predicted = torch.softmax(classification_pred, dim=1) predicted = predicted.argmax(dim=1) - # lamb = min(1, self.trainer.current_epoch / 10) - # class_weights= self.class_weights.to(self.device) - # class_weights = None - # loss_class = 0.2*evidential_classification(classification_pred, target, lamb=lamb) + 0.8 * loss_class - # loss_class = self.evidence_loss(classification_pred, target, class_weights, lamb=lamb) - # Calculate accuracy - # predicted = torch.softmax(classification_pred, dim=1) - # predicted = classification_pred.argmax(dim=1) accuracy = 0 precision = 0 @@ -422,11 +408,6 @@ def compute_direction_loss(self, direction_pred, labels_direction, training=Fals label_separation = labels_direction[:, 2] loss_separation = self.criterion_direction(pred_separation, label_separation) - # loss_vector = self.criterion_vector(pred_dir_cartesian, labels_direction_cartesian) - # vect_magnitud = torch.sqrt(torch.sum(pred_dir_cartesian**2, dim=1)) - # loss_magnitud = torch.abs(1.0-vect_magnitud).sum() - - # alt_az = utils_torch.cartesian_to_alt_az(direction[:,0:3]) if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: loss_alt_az = self.criterion_alt_az(direction_pred, labels_direction) else: @@ -490,17 +471,7 @@ def compute_energy_loss( else: energy_diff = None - # if training: - # accuracy = self.class_train_energy_accuracy(predicted, labels_energy_class) - # else: - # if test_val: - # accuracy = self.class_test_energy_accuracy(predicted, labels_energy_class) - # else: - # accuracy = self.class_validation_energy_accuracy(predicted, labels_energy_class) - return loss, energy_diff - # - # return loss, energy_diff # ---------------------------------------------------------------------------------------------------------- def training_step(self, batch, batch_idx): @@ -517,23 +488,19 @@ def training_step(self, batch, batch_idx): if self.task == Task.energy: labels_energy_value = labels["energy"] - labels_energy_value = labels_energy_value.to('cuda') - # labels_energy_class = labels["energy_class"] + labels_energy_value = labels_energy_value.to(self.device) if self.task == Task.direction: labels_direction = labels["direction"] - labels_direction_cartesian = labels["direction_cartesian"] - - - imgs = imgs.to('cuda') - + imgs = imgs.to(self.device) + # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ if self.num_channels == 2: peak_time = features["peak_time"] - peak_time = peak_time.to('cuda') + peak_time = peak_time.to(self.device) classification_pred, energy_pred, direction_pred = self.model( imgs, peak_time ) @@ -1069,15 +1036,15 @@ def on_validation_epoch_end(self): }, self.current_epoch, ) - self.logger.experiment.add_scalars( - "Metrics/Test", - { - "acc": epoch_accuracy_test, - "f1": f1_score_test, - "precision":precision_test, - }, - self.current_epoch, - ) + # self.logger.experiment.add_scalars( + # "Metrics/Test", + # { + # "acc": epoch_accuracy_test, + # "f1": f1_score_test, + # "precision":precision_test, + # }, + # self.current_epoch, + # ) print( f"Epoch {self.current_epoch}: Global Validation Accuracy: {epoch_accuracy_val:.4f}" ) @@ -1088,15 +1055,15 @@ def on_validation_epoch_end(self): f"Epoch {self.current_epoch}: Global Validation Precision: {precision_val:.4f}" ) - print( - f"Epoch {self.current_epoch}: Global Test Accuracy: {epoch_accuracy_test:.4f}" - ) - print( - f"Epoch {self.current_epoch}: Global Test F1 Score: {f1_score_test:.4f}" - ) - print( - f"Epoch {self.current_epoch}: Global Test Precision: {precision_test:.4f}" - ) + # print( + # f"Epoch {self.current_epoch}: Global Test Accuracy: {epoch_accuracy_test:.4f}" + # ) + # print( + # f"Epoch {self.current_epoch}: Global Test F1 Score: {f1_score_test:.4f}" + # ) + # print( + # f"Epoch {self.current_epoch}: Global Test Precision: {precision_test:.4f}" + # ) # --------------------------------------- # Create Confusion Matrix # --------------------------------------- @@ -1249,25 +1216,25 @@ def on_validation_epoch_end(self): plt.close(fig_energy_error) # Close the figure to release memory plt.close("all") - fig_energy_error = plot_energy_resolution_error( - self.test_val_energy_pred_list, - self.test_val_energy_label_list, - self.test_val_hillas_intensity_list, - ) - self.logger.experiment.add_figure( - "Energy Resolution Error/Test", fig_energy_error, self.current_epoch - ) # Log the plot - fig_energy_error.savefig( - os.path.join( - self.logger.log_dir, - "error_resulution_test_" - + str(self.current_epoch) - + "_" - + str(global_loss_test) - + ".png", - ), - format="png", - ) + # fig_energy_error = plot_energy_resolution_error( + # self.test_val_energy_pred_list, + # self.test_val_energy_label_list, + # self.test_val_hillas_intensity_list, + # ) + # self.logger.experiment.add_figure( + # "Energy Resolution Error/Test", fig_energy_error, self.current_epoch + # ) # Log the plot + # fig_energy_error.savefig( + # os.path.join( + # self.logger.log_dir, + # "error_resulution_test_" + # + str(self.current_epoch) + # + "_" + # + str(global_loss_test) + # + ".png", + # ), + # format="png", + # ) plt.close(fig_energy_error) # Close the figure to release memory plt.close("all") diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 93b78bf1..1bc4e475 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -86,7 +86,7 @@ model: # Hyper-parameters hyp: - epochs: 200 + epochs: 2 batches: 128 #64 dynamic_batches: True optimizer: Adamw diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 5b593969..5b7b8a26 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -127,12 +127,12 @@ def __init__(self, **kwargs): os.environ["TORCH_NCCL_ASYNC_ERROR_HANDLING"] = "1" os.environ["NCCL_DEBUG"] = "INFO" - print("Pytorch init") + super().__init__(**kwargs) print("CONFIG VALUES PYTORCH:", self.config) def setup(self): - print("Pytorch setup") + super().setup() # Create tasks Enum List @@ -205,10 +205,7 @@ def setup(self): def start(self): - print("Pytorch start") super().start() - print("Pytorch start") - for task in self.tasks: # Create the experiment folder diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 3fb0d9be..dfa9823c 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -183,3 +183,4 @@ def main(): # Example: # python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite +# python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml \ No newline at end of file From 3d91d13da4767eb8cab6b5adf82787ce0151980e Mon Sep 17 00:00:00 2001 From: pguzman Date: Wed, 4 Jun 2025 16:49:20 +0000 Subject: [PATCH 024/119] fixing training direction --- ctlearn/core/ctlearn_enum.py | 4 +- ctlearn/core/data_loader/pytorch_loader.py | 46 ++- ctlearn/tools/train/pytorch/CTLearnPL.py | 372 ++++++++++++------ .../train/pytorch/train_pytorch_model.py | 38 +- ctlearn/tools/train_model.py | 16 +- 5 files changed, 314 insertions(+), 162 deletions(-) diff --git a/ctlearn/core/ctlearn_enum.py b/ctlearn/core/ctlearn_enum.py index b4819024..c1a6ba72 100644 --- a/ctlearn/core/ctlearn_enum.py +++ b/ctlearn/core/ctlearn_enum.py @@ -9,7 +9,9 @@ class Task(Enum): type = 0 energy = 1 direction = 2 - all = 3 + cameradirection = 3 + skydirection = 4 + all = 5 class EventType(Enum): gamma=0 diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 4355a768..ab09c8a5 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -144,8 +144,7 @@ def __getitem__(self, index): elif self.DLDataReader.mode == "stereo": batch = self.DLDataReader.generate_stereo_batch(batch_indices) features, labels = self._get_stereo_item(batch) - - + return features, labels def _get_mono_item(self, batch): @@ -175,8 +174,9 @@ def _get_mono_item(self, batch): if len(self.tasks) == 1: labels = batch["true_shower_primary_class"].data - if "energy" in self.tasks: - labels["energy"] = batch["log_true_energy"].data + # if "energy" in self.tasks: + labels["energy"] = batch["log_true_energy"].data + if "skydirection" in self.tasks: labels["skydirection"] = np.stack( ( @@ -191,10 +191,33 @@ def _get_mono_item(self, batch): ( batch["cam_coord_offset_x"].data, batch["cam_coord_offset_y"].data, + batch["cam_coord_distance"].data, ), axis=1, ) + tel_ids = batch["tel_id"].data + + tel_ground_frame = self.DLDataReader.subarray.tel_coords[ + self.DLDataReader.subarray.tel_ids_to_indices(tel_ids) + ] + + focal_lengths = [ + self.DLDataReader.subarray.tel[tel_id].camera.geometry.frame.focal_length + for tel_id in tel_ids + ] + + labels["tel_ground_frame"] = tel_ground_frame + # self.DLDataReader. + labels["telescope_pointing_azimuth"]= batch["telescope_pointing_azimuth"].data + labels["telescope_pointing_altitude"]= batch["telescope_pointing_altitude"].data + + if "skydirection" in labels.keys(): + labels["direction"] = labels["skydirection"] + + if "cameradirection" in labels.keys(): + labels["direction"] = labels["cameradirection"] + # if "hillas" in self.tasks: features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) # features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) @@ -215,19 +238,21 @@ def _get_mono_item(self, batch): peak_time[np.isnan(peak_time)] = 0 peak_time[np.isinf(peak_time)] = 0 - if self.task == Task.type: # "type": + if self.task == Task.type: image = (image - self.type_mu) / self.type_sigma peak_time = (peak_time - self.type_mu) / self.type_sigma - if self.task == Task.energy: # "energy": + + if self.task == Task.energy: image = (image - self.energy_mu) / self.energy_sigma peak_time = (peak_time - self.energy_mu) / self.energy_sigma - if self.task == Task.direction: # "direction": + + if self.task == Task.cameradirection or self.task == Task.skydirection: image = (image - self.dir_mu) / self.dir_sigma peak_time = (peak_time - self.dir_mu) / self.dir_sigma features_out={} - features_out["image"]=image #torch.from_numpy(image) - features_out["peak_time"]= peak_time #torch.from_numpy(peak_time) + features_out["image"]=image + features_out["peak_time"]= peak_time features_out["image"]=torch.from_numpy(image).contiguous().float() features_out["peak_time"]=torch.from_numpy(peak_time).contiguous().float() @@ -240,7 +265,8 @@ def _get_mono_item(self, batch): features["hillas"][key] = torch.from_numpy(np.array(features["hillas"][key])).contiguous().unsqueeze(-1) return features_out, labels - + + # TODO: Not adapted to pytorch def _get_stereo_item(self, batch): """ Retrieve the features and labels for one batch of stereoscopic data. diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 52daed86..7d028d76 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -39,9 +39,9 @@ import torch.distributed as dist import torch.multiprocessing as mp from ctlearn.core.pytorch.nets.loss_functions.loss_functions import evidential_regression_loss -import json +import inspect # from ctlearn.nets.loss_functions.loss_functions import evidential_classification - + class CTLearnTrainer(pl.Trainer): def __init__(self,**kwargs): @@ -113,7 +113,7 @@ def __init__( train_loader=None, val_loader=None, test_val_loader=None, - num_channels=1, + num_inputs=1, k=3, ): @@ -133,7 +133,10 @@ def __init__( self.model.to(self.device) self.k = k # Number of top results to save - self.num_channels = num_channels + # Get the number of inputs of the net + sig = inspect.signature(model.forward) + num_inputs = len(sig.parameters) + self.num_inputs = num_inputs self.train_loader = train_loader self.val_loader = val_loader self.test_val_loader = test_val_loader @@ -165,6 +168,7 @@ def __init__( ) # torch.nn.L1Loss(reduction='sum') self.criterion_energy_value = torch.nn.L1Loss(reduction="sum") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() + self.criterion_magnitud = torch.nn.L1Loss(reduction="sum") # self.criterion_energy = torch.nn.MSELoss() self.criterion_direction = torch.nn.L1Loss(reduction="sum") # nn.MSELoss() @@ -394,68 +398,90 @@ def compute_type_loss( # ---------------------------------------------------------------------------------------------------------- def compute_direction_loss(self, direction_pred, labels_direction, training=False): - if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - direction_pred = list(direction_pred) - pred_az_atl = direction_pred[0][:,0:2] - pred_separation = direction_pred[0][:,2] - # direction_pred[0]= direction_pred[0][:,0:2] - else: + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + # direction_pred = list(direction_pred) + # pred_az_atl = direction_pred[0][:,0:2] + # pred_separation = direction_pred[0][:,2] + # # direction_pred[0]= direction_pred[0][:,0:2] + # else: + + # pred_az_atl = direction_pred[:, 0:2] + # pred_separation = direction_pred[:, 2] + + # labels_az_alt = labels_direction[:, 0:2] + # label_separation = labels_direction[:, 2] + # loss_separation = self.criterion_direction(pred_separation, label_separation) + + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + # loss_alt_az = self.criterion_alt_az(direction_pred, labels_direction) + # else: + # loss_alt_az = self.criterion_alt_az_l1(pred_az_atl, labels_az_alt) - pred_az_atl = direction_pred[:, 0:2] - pred_separation = direction_pred[:, 2] + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - labels_az_alt = labels_direction[:, 0:2] - label_separation = labels_direction[:, 2] - loss_separation = self.criterion_direction(pred_separation, label_separation) + # loss_angular_error, _ = AngularDistance( + # (direction_pred[0][:, 1]), + # labels_az_alt[:, 1], + # (direction_pred[0][:, 0]), + # labels_az_alt[:, 0], + # reduction="sum", + # ) + + # else: + + # loss_angular_error, _ = AngularDistance( + # (direction_pred[:, 1]), + # labels_az_alt[:, 1], + # (direction_pred[:, 0]), + # labels_az_alt[:, 0], + # reduction="sum", + # ) + + # if training == False: + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + # _, angular_diff = AngularDistance( + # (direction_pred[0][:, 1]), + # labels_az_alt[:, 0], + # (direction_pred[0][:, 0]), + # labels_az_alt[:, 1], + # reduction=None, + # ) + # else: + # _, angular_diff = AngularDistance( + # (direction_pred[:, 1]), + # labels_az_alt[:, 0], + # (direction_pred[:, 0]), + # labels_az_alt[:, 1], + # reduction=None, + # ) + # else: + # angular_diff = None + + # loss = loss_alt_az + 0.001*(loss_separation + loss_angular_error) + # return loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff - if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - loss_alt_az = self.criterion_alt_az(direction_pred, labels_direction) - else: - loss_alt_az = self.criterion_alt_az_l1(pred_az_atl, labels_az_alt) - if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - loss_angular_error, _ = AngularDistance( - (direction_pred[0][:, 1]), - labels_az_alt[:, 1], - (direction_pred[0][:, 0]), - labels_az_alt[:, 0], - reduction="sum", - ) + labels_dx_dy = labels_direction[:, 0:2] + label_distance = labels_direction[:, 2] - else: - loss_angular_error, _ = AngularDistance( - (direction_pred[:, 1]), - labels_az_alt[:, 1], - (direction_pred[:, 0]), - labels_az_alt[:, 0], - reduction="sum", - ) + if isinstance(direction_pred, tuple): + direction_pred = list(direction_pred) - if training == False: - if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - _, angular_diff = AngularDistance( - (direction_pred[0][:, 1]), - labels_az_alt[:, 0], - (direction_pred[0][:, 0]), - labels_az_alt[:, 1], - reduction=None, - ) - else: - _, angular_diff = AngularDistance( - (direction_pred[:, 1]), - labels_az_alt[:, 0], - (direction_pred[:, 0]), - labels_az_alt[:, 1], - reduction=None, - ) + pred_dx_dy = direction_pred[0][:,0:2].unsqueeze(-1) + pred_distance = direction_pred[0][:,2].unsqueeze(-1) else: - angular_diff = None + pred_dx_dy = direction_pred[:,0:2].unsqueeze(-1) + pred_distance = direction_pred[:,2].unsqueeze(-1) - loss = loss_alt_az + 0.001*(loss_separation + loss_angular_error) + vector_cam_distance = torch.sqrt(pred_dx_dy[:,0]**2 + pred_dx_dy[:,1]**2) + loss_dx_dy = self.criterion_alt_az_l1(pred_dx_dy, labels_dx_dy) + loss_distance = self.criterion_magnitud(label_distance, pred_distance) + loss_distance_dx_dy = self.criterion_magnitud(label_distance, vector_cam_distance) - return loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff + loss = loss_dx_dy + loss_distance + loss_distance_dx_dy + return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy # ---------------------------------------------------------------------------------------------------------- def compute_energy_loss( self, energy_pred, labels_energy, test_val=False, training=False @@ -490,7 +516,7 @@ def training_step(self, batch, batch_idx): labels_energy_value = labels["energy"] labels_energy_value = labels_energy_value.to(self.device) - if self.task == Task.direction: + if self.task == Task.cameradirection: labels_direction = labels["direction"] imgs = imgs.to(self.device) @@ -498,7 +524,7 @@ def training_step(self, batch, batch_idx): # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ - if self.num_channels == 2: + if self.num_inputs == 2: peak_time = features["peak_time"] peak_time = peak_time.to(self.device) classification_pred, energy_pred, direction_pred = self.model( @@ -538,19 +564,24 @@ def training_step(self, batch, batch_idx): # --------------------------------------- # Direction # --------------------------------------- - if self.task == Task.direction: + if self.task == Task.cameradirection: if len(direction_pred)==2: direction_pred = direction_pred[0] - loss, loss_separation, loss_alt_az, loss_angular_error, _ = ( - self.compute_direction_loss( - direction_pred, labels_direction, training=True - ) - ) + # loss, loss_separation, loss_alt_az, loss_angular_error, _ = ( + # self.compute_direction_loss( + # direction_pred, labels_direction, training=True + # ) + # ) + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy= self.compute_direction_loss( direction_pred, labels_direction, training=True) + + self.loss_train_separation += loss_dx_dy.item() + self.loss_train_alt_az += loss_distance.item() + self.loss_train_angular_error += loss_distance_dx_dy.item() - self.loss_train_separation += loss_separation.item() - self.loss_train_alt_az += loss_alt_az.item() - self.loss_train_angular_error += loss_angular_error.item() + # self.loss_train_separation += loss_separation.item() + # self.loss_train_alt_az += loss_alt_az.item() + # self.loss_train_angular_error += loss_angular_error.item() self.loss_val_separation = 0.0 self.loss_val_alt_az = 0.0 @@ -664,7 +695,7 @@ def on_train_epoch_end(self): # --------------------------------------- # Direction # --------------------------------------- - if self.task == Task.direction: + if self.task == Task.cameradirection: if self.trainer.is_global_zero: filename_prefix = ( f"Epoch_{self.current_epoch}_{self.task.name}_train_loss" @@ -716,6 +747,72 @@ def on_train_epoch_end(self): self.num_train_batches = 0 self.training_step_outputs = [] # ---------------------------------------------------------------------------------------------------------- + + + + def _transform_to_altaz_from_cam_offsets(self, tel_ground_frame,tel_az,tel_alt,): + """ + Transform camera coordinate offsets back to Alt/Az sky coordinates. + + Given cam_coord_offset_x and cam_coord_offset_y, this method reconstructs the + true Alt/Az coordinates using the telescope pointing and the camera geometry. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing cam_coord_offset_x, cam_coord_offset_y, telescope pointing, and tel_id. + + Returns: + -------- + table : astropy.table.Table + A Table with reconstructed true_alt and true_az columns added. + """ + LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") + from astropy.coordinates import AltAz, SkyCoord + from ctapipe.coordinates import CameraFrame + # tel_id = table["tel_id"][0] + + # # Get telescope ground frame position + # tel_ground_frame = self.subarray.tel_coords[ + # self.subarray.tel_ids_to_indices(tel_id) + # ] + + # AltAz frame setup + altaz = AltAz( + location=tel_ground_frame.to_earth_location(), + obstime=LST_EPOCH, + ) + + # Telescope pointing SkyCoord + fix_tel_pointing = SkyCoord( + az = tel_az, + alt = tel_alt, + frame=altaz, + ) + + # Define the camera frame + camera_frame = CameraFrame( + focal_length=self.subarray.tel[tel_id].camera.geometry.frame.focal_length, + rotation=self.pix_rotation[tel_id], + telescope_pointing=fix_tel_pointing, + ) + + # Create SkyCoord in CameraFrame using cam offsets + cam_coords = SkyCoord( + x=table["cam_coord_offset_x"], + y=table["cam_coord_offset_y"], + frame=camera_frame + ) + + # Transform back to AltAz + sky_coords = cam_coords.transform_to(altaz) + + # Add the reconstructed Alt/Az coordinates to the table + # table.add_column(sky_coords.az, name="reconstructed_true_az") + # table.add_column(sky_coords.alt, name="reconstructed_true_alt") + + return sky_coords.alt,sky_coords.az + @torch.no_grad() def validation_step(self, batch, batch_idx, dataloader_idx=0): loss=0 @@ -737,7 +834,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): hillas_intensity = features["hillas"]["hillas_intensity"] # hillas = {key: tensor.to(self.device) # for key, tensor in hillas.items()} - if self.task == Task.direction: + if self.task == Task.cameradirection: labels_direction = labels["direction"] # labels_alt_az = labels['alt_az'] # labels_direction_cartesian = labels["direction_cartesian"] @@ -745,7 +842,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ - if self.num_channels == 2: + if self.num_inputs == 2: peak_time = features["peak_time"] classification_pred, energy_pred, direction_pred = self.model( imgs, peak_time @@ -810,81 +907,99 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # --------------------------------------- # Direction # --------------------------------------- - if self.task == Task.direction: + if self.task == Task.cameradirection: if len(direction_pred)==2: direction_pred = direction_pred[0] # loss, angular_diff = self.compute_direction_loss(direction_pred, labels_direction, training=False) - loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff = ( - self.compute_direction_loss( - direction_pred, labels_direction, training=False - ) - ) + # loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff = ( + # self.compute_direction_loss( + # direction_pred, labels_direction, training=False + # ) + # ) + + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy= self.compute_direction_loss( direction_pred, labels_direction, training=True) + + # self.loss_train_separation += loss_dx_dy.item() + # self.loss_train_alt_az += loss_distance.item() + # self.loss_train_angular_error += loss_distance_dx_dy.item() + # ------------------------------------------------------------------------ # Convert the offset to altitud and azimuth # ------------------------------------------------------------------------ - reco_az, reco_alt = [], [] - if "tel_alt" in features: - pointing_alt = features["tel_alt"].float().cpu().detach().numpy() - pointing_az = features["tel_az"].float().cpu().detach().numpy() - elif "tel_alt" in labels: - pointing_alt = labels["tel_alt"].float().cpu().detach().numpy() - pointing_az = labels["tel_az"].float().cpu().detach().numpy() - else: - raise ValueError(f"Telescope altitud and azimuth not found.") - - pointing_alt = np.rad2deg(pointing_alt) - pointing_az = np.rad2deg(pointing_az) - - fix_pointing = SkyCoord( - pointing_az * u.deg, - pointing_alt * u.deg, - frame="altaz", - unit="deg", - ) - alt_off = direction_pred[:, 1].float().cpu().detach().numpy() - az_off = direction_pred[:, 0].float().cpu().detach().numpy() + # reco_az, reco_alt = [], [] + # if "tel_alt" in features: + # pointing_alt = features["tel_alt"].float().cpu().detach().numpy() + # pointing_az = features["tel_az"].float().cpu().detach().numpy() + # elif "tel_alt" in labels: + # pointing_alt = labels["tel_alt"].float().cpu().detach().numpy() + # pointing_az = labels["tel_az"].float().cpu().detach().numpy() + # else: + # raise ValueError(f"Telescope altitud and azimuth not found.") + + # pointing_alt = np.rad2deg(pointing_alt) + # pointing_az = np.rad2deg(pointing_az) + + # fix_pointing = SkyCoord( + # pointing_az * u.deg, + # pointing_alt * u.deg, + # frame="altaz", + # unit="deg", + # ) + # alt_off = direction_pred[:, 1].float().cpu().detach().numpy() + # az_off = direction_pred[:, 0].float().cpu().detach().numpy() - reco_direction = utils.recover_alt_az(fix_pointing, alt_off, az_off) + # reco_direction = utils.recover_alt_az(fix_pointing, alt_off, az_off) - reco_alt = np.deg2rad(reco_direction.alt.to_value())[0, :] - reco_az = np.deg2rad(reco_direction.az.to_value())[0, :] + # reco_alt = np.deg2rad(reco_direction.alt.to_value())[0, :] + # reco_az = np.deg2rad(reco_direction.az.to_value())[0, :] - true_alt = labels["alt_az"][:, 0].float().cpu().detach().numpy() - true_az = labels["alt_az"][:, 1].float().cpu().detach().numpy() + # true_alt = labels["alt_az"][:, 0].float().cpu().detach().numpy() + # true_az = labels["alt_az"][:, 1].float().cpu().detach().numpy() if dataloader_idx == 0: # self.alt_off_list.extend(alt_off) # self.az_off_list.extend(az_off) - self.loss_val_separation += loss_separation.item() - self.loss_val_alt_az += loss_alt_az.item() - self.loss_val_angular_error += loss_angular_error.item() + # self.loss_val_separation += loss_separation.item() + # self.loss_val_alt_az += loss_alt_az.item() + # self.loss_val_angular_error += loss_angular_error.item() + + # self.val_angular_diff_list.extend(angular_diff) - self.val_angular_diff_list.extend(angular_diff) + self.loss_val_separation += loss_dx_dy.item() + self.loss_val_alt_az += loss_distance.item() + self.loss_val_angular_error += loss_distance_dx_dy.item() + # self.val_angular_diff_list.extend(angular_diff) + # # ------------------------------------------------------- + pred_dx = direction_pred[:, 0].float().cpu().detach().numpy() + pred_dy = direction_pred[:, 1].float().cpu().detach().numpy() + + true_dx = labels_direction[:, 0].float().cpu().detach().numpy() + true_dy = labels_direction[:, 1].float().cpu().detach().numpy() - self.val_alt_pred_list.extend(reco_alt) - self.val_az_pred_list.extend(reco_az) - self.val_alt_label_list.extend(true_alt) - self.val_az_label_list.extend(true_az) + # reco_direction = utils.recover_alt_az(fix_pointing, alt_off, az_off) - else: + # reco_alt = np.deg2rad(reco_direction.alt.to_value())[0, :] + # reco_az = np.deg2rad(reco_direction.az.to_value())[0, :] + self.val_alt_pred_list.extend(pred_dx) + self.val_az_pred_list.extend(pred_dy) + self.val_alt_label_list.extend(true_dx) + self.val_az_label_list.extend(true_dy) + + # else: - self.loss_test_val_separation += loss_separation.item() - self.loss_test_val_alt_az += loss_alt_az.item() - self.loss_test_val_angular_error += loss_angular_error.item() + # self.loss_test_val_separation += loss_separation.item() + # self.loss_test_val_alt_az += loss_alt_az.item() + # self.loss_test_val_angular_error += loss_angular_error.item() - self.test_val_angular_diff_list.extend(angular_diff) - # self.test_val_alt_pred_list.extend(direction_pred[:,1].float().cpu().detach().numpy()) - # self.test_val_az_pred_list.extend(direction_pred[:,0].float().cpu().detach().numpy()) - # self.test_val_alt_label_list.extend(labels_direction[:,1].float().cpu().detach().numpy()) - # self.test_val_az_label_list.extend(labels_direction[:,0].float().cpu().detach().numpy()) + # self.test_val_angular_diff_list.extend(angular_diff) - self.test_val_alt_pred_list.extend(reco_alt) - self.test_val_az_pred_list.extend(reco_az) - self.test_val_alt_label_list.extend(true_alt) - self.test_val_az_label_list.extend(true_az) + # self.test_val_alt_pred_list.extend(reco_alt) + # self.test_val_az_pred_list.extend(reco_az) + # self.test_val_alt_label_list.extend(true_alt) + # self.test_val_az_label_list.extend(true_az) # --------------------------------------- # Energy # --------------------------------------- @@ -911,8 +1026,6 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): self.test_val_energy_pred_list.extend( energy_pred_tev[:, 0].float().cpu().detach().numpy() ) - - # --------------------------------------- # --------------------------------------- # Collect the True Energy and Hillas Intensity # --------------------------------------- @@ -936,6 +1049,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # --------------------------------------- # Log validation loss + # --------------------------------------- if dataloader_idx == 0: self.loss_val_sum += loss.item() @@ -1094,7 +1208,7 @@ def on_validation_epoch_end(self): # --------------------------------------- # Direction # --------------------------------------- - if self.task == Task.direction: + if self.task == Task.cameradirection: self.print_direction_error(self.val_angular_diff_list, "Validation") self.print_direction_error(self.test_val_angular_diff_list, "Test") @@ -1480,7 +1594,7 @@ def generate_results( src_x = features["src_x"].float().cpu().detach().numpy() src_y = features["src_y"].float().cpu().detach().numpy() - if self.num_channels == 2: + if self.num_inputs == 2: peak_time = features["peak_time"].to(self.device).contiguous() classification_pred, energy_pred, direction_pred = self.model( imgs, peak_time @@ -1503,7 +1617,7 @@ def generate_results( energy = energy_pred.cpu().detach().numpy() # energy = energy[0] - if task == Task.direction: + if task == Task.cameradirection: if len(direction_pred)==2: direction_pred_mu = direction_pred[0].cpu().detach().numpy() direction_pred_sigma = direction_pred[1].cpu().detach().numpy() @@ -1527,7 +1641,7 @@ def generate_results( if task == Task.energy: energy_predictions.extend(energy[:, :]) - if task == Task.direction: + if task == Task.cameradirection: if len(direction_pred)==2: direction_predictions_mu.extend(direction_pred_mu[:,0:2]) @@ -1734,7 +1848,7 @@ def configure_optimizers(self): # for key, tensor in hillas_cpu.items()} # filename_list = features["filename"].cpu().detach().numpy() - # if self.num_channels == 2: + # if self.num_inputs == 2: # peak_time = features['peak_time'].to(self.device) # outputs = self.model(images, peak_time) # else: diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 5b7b8a26..19a3e1d4 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -41,6 +41,7 @@ import numpy as np import json + class GPUStatsLogger(Callback): def on_train_epoch_end(self, trainer, pl_module): mem_allocated = torch.cuda.memory_allocated() @@ -129,7 +130,7 @@ def __init__(self, **kwargs): super().__init__(**kwargs) - print("CONFIG VALUES PYTORCH:", self.config) + def setup(self): @@ -174,6 +175,16 @@ def setup(self): training_indices = indices[n_validation_examples:] validation_indices = indices[:n_validation_examples] + # -------------------------------------------------------------------- + # Reduce for testing + # -------------------------------------------------------------------- + # Limit the number of examples (optional) + max_training_samples = 5000 # or whatever number you want + max_validation_samples = 1000 # or whatever number you want + + training_indices = training_indices[:max_training_samples] + validation_indices = validation_indices[:max_validation_samples] + print("BASE TRAIN FRAMEWORK", self.framework_type) @@ -216,17 +227,18 @@ def start(self): # ------------------------------------------------------------------------------ # Select the model and precision # ------------------------------------------------------------------------------ - if task == Task.direction: - precision = self.parameters["arch"]["precision_direction"] - model_net = create_model(self.parameters["model"]["model_direction"]) - elif task == Task.type: + if task == Task.type: precision = self.parameters["arch"]["precision_type"] model_net = create_model(self.parameters["model"]["model_type"]) elif task == Task.energy: precision = self.parameters["arch"]["precision_energy"] model_net = create_model(self.parameters["model"]["model_energy"]) + + elif task == Task.cameradirection or task == Task.skydirection: + precision = self.parameters["arch"]["precision_direction"] + model_net = create_model(self.parameters["model"]["model_direction"]) else: raise ValueError( @@ -239,12 +251,16 @@ def start(self): if task == Task.type: check_point_path = self.parameters["data"]["type_checkpoint"] - if task == Task.energy: + elif task == Task.energy: check_point_path = self.parameters["data"]["energy_checkpoint"] - if task == Task.direction: + elif task == Task.cameradirection or task == Task.skydirection: check_point_path = self.parameters["data"]["direction_checkpoint"] + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) # Load the checkpoint model_net = ModelHelper.loadModel( model_net, "", check_point_path, Mode.train, device_str=self.device_str @@ -278,22 +294,20 @@ def start(self): callbacks=[GPUStatsLogger()], sync_batchnorm=True, ) - - # TODO: Fix in_channels. Add this parameter in the configuration file, in the model or both.... - in_channels = 2 + + # Setup Lighting lightning_model = CTLearnPL( model=model_net, save_folder=trainer_pl.get_log_dir(), task=task, mode = Mode.train, parameters=self.parameters, - num_channels=in_channels, k=self.save_k, ) # Save configuration file. if not os.path.exists(trainer_pl.get_log_dir()): - os.mkdir(trainer_pl.get_log_dir()) + os.makedirs(trainer_pl.get_log_dir()) with open(os.path.join(trainer_pl.get_log_dir(),"parameters.json"), "w") as f: json.dump(self.parameters, f, indent=4) diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index dfa9823c..fb8d7394 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -73,7 +73,6 @@ def __init__(self, **kwargs): """ super().__init__(**kwargs) self.framework_instance = None - print("init") def setup(self): """ @@ -81,7 +80,6 @@ def setup(self): This dynamically loads and prepares the correct framework subclass (TrainKerasModel or TrainPyTorchModel). """ - print("setup") framework_enum = self.string_to_type(self.framework_type) self.framework_instance = self.get_framework(framework_enum) @@ -91,13 +89,11 @@ def setup(self): DLFrameWork.aliases.update(self.framework_instance.aliases) def start(self): - print(f"Selected Framework: {self.framework_type}") - - framework = self.string_to_type(self.framework_type) - fw_obj = self.get_framework(framework) - fw_obj.update_config(self.config) - fw_obj.parse_command_line(argv=sys.argv[1:]) - fw_obj.run() + """ + Start method called after setup. Executes the selected framework instance. + """ + print("start") + self.framework_instance.run() @classmethod def string_to_type(cls, str_type: str) -> FrameworkType: @@ -149,7 +145,7 @@ def get_framework(cls, framework_type: FrameworkType): ) fw = TrainPyTorchModel() - print("Pytorch") + except ImportError as e: raise ImportError( f"Not possible to import TrainPyTorchModel: {e}" From 7b09b86f542e84e8208cd7bc44453288e347ea0e Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 6 Jun 2025 08:43:52 +0000 Subject: [PATCH 025/119] cleaning up pytorch training module. --- ctlearn/core/data_loader/pytorch_loader.py | 152 ++++++++- .../nets/loss_functions/loss_functions.py | 5 + ctlearn/tools/train/pytorch/CTLearnPL.py | 318 ++++-------------- .../training_config_iaa_neutron_training.yml | 2 +- .../train/pytorch/train_pytorch_model.py | 10 +- 5 files changed, 218 insertions(+), 269 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index ab09c8a5..035dcc0e 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -4,6 +4,7 @@ from .base_loader import BaseDLDataLoader from dl1_data_handler.reader import ProcessType from ctlearn.core.ctlearn_enum import Task +from astropy import units as u class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): def __init__( @@ -147,6 +148,112 @@ def __getitem__(self, index): return features, labels + def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x,cam_y): + """ + Transform camera coordinate offsets back to Alt/Az sky coordinates. + + Given cam_coord_offset_x and cam_coord_offset_y, this method reconstructs the + true Alt/Az coordinates using the telescope pointing and the camera geometry. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing cam_coord_offset_x, cam_coord_offset_y, telescope pointing, and tel_id. + + Returns: + -------- + table : astropy.table.Table + A Table with reconstructed true_alt and true_az columns added. + """ + from astropy.time import Time + + LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") + from astropy.coordinates import AltAz, SkyCoord + from ctapipe.coordinates import CameraFrame + from astropy import units as u + # tel_id = table["tel_id"][0] + + # # Get telescope ground frame position + tel_ground_frame = self.DLDataReader.subarray.tel_coords[ + self.DLDataReader.subarray.tel_ids_to_indices(tel_id) + ] + + # AltAz frame setup + altaz = AltAz( + location=tel_ground_frame.to_earth_location(), + obstime=LST_EPOCH, + ) + # altaz_list = [altaz] * len(focal_length) + # Telescope pointing SkyCoord + fix_tel_pointing = SkyCoord( + az = tel_az*u.rad, + alt = tel_alt*u.rad, + frame=altaz, + ) + + # Define the camera frame + # camera_frame = CameraFrame( + # focal_length=focal_lenght, + # rotation=pix_rotation, + # telescope_pointing=fix_tel_pointing, + # ) + # camera_frames = [] + + sky_coords_alt = [] + sky_coords_az = [] + + # camera_frame = CameraFrame( + # focal_length=focal_length, + # rotation=pix_rotation, + # telescope_pointing=fix_tel_pointing + # ) + + # cam_coord = SkyCoord( + # x=cam_x * u.m, + # y=cam_y * u.m, + # frame=camera_frame + # ) + + # for fl, rot, cx, cy , altaz_, tel_pointing in zip(focal_length, pix_rotation, cam_x, cam_y, altaz_list, fix_tel_pointing): + for id in range(len(focal_length)): + + camera_frame = CameraFrame( + focal_length=focal_length[id]*u.m, + rotation=pix_rotation[id]*u.deg, + telescope_pointing=fix_tel_pointing[id] + ) + + cam_coord = SkyCoord( + x=cam_x[id] * u.m, + y=cam_y[id] * u.m, + frame=camera_frame + ) + + sky_coord = cam_coord.transform_to(altaz[id]) + + sky_coords_alt.append(sky_coord.alt.to_value(u.deg).item()) + sky_coords_az.append(sky_coord.az.to_value(u.deg).item()) + + # camera_frame = CameraFrame( + # focal_length=focal_length, + # rotation=pix_rotation, + # telescope_pointing=fix_tel_pointing + # ) + + # cam_coord = SkyCoord( + # x=cam_x * u.m, + # y=cam_y * u.m, + # frame=camera_frame + # ) + + # sky_coord = cam_coord.transform_to(altaz) + + # Add the reconstructed Alt/Az coordinates to the table + # table.add_column(sky_coords.az, name="reconstructed_true_az") + # table.add_column(sky_coords.alt, name="reconstructed_true_alt") + + return sky_coords_alt,sky_coords_az + def _get_mono_item(self, batch): """ Retrieve the features and labels for one batch of monoscopic data. @@ -196,22 +303,6 @@ def _get_mono_item(self, batch): axis=1, ) - tel_ids = batch["tel_id"].data - - tel_ground_frame = self.DLDataReader.subarray.tel_coords[ - self.DLDataReader.subarray.tel_ids_to_indices(tel_ids) - ] - - focal_lengths = [ - self.DLDataReader.subarray.tel[tel_id].camera.geometry.frame.focal_length - for tel_id in tel_ids - ] - - labels["tel_ground_frame"] = tel_ground_frame - # self.DLDataReader. - labels["telescope_pointing_azimuth"]= batch["telescope_pointing_azimuth"].data - labels["telescope_pointing_altitude"]= batch["telescope_pointing_altitude"].data - if "skydirection" in labels.keys(): labels["direction"] = labels["skydirection"] @@ -264,6 +355,35 @@ def _get_mono_item(self, batch): for key in features["hillas"].keys(): features["hillas"][key] = torch.from_numpy(np.array(features["hillas"][key])).contiguous().unsqueeze(-1) + + if "cameradirection" in self.tasks: + + tel_ids = batch["tel_id"].data + + tel_ground_frame = self.DLDataReader.subarray.tel_coords[ + self.DLDataReader.subarray.tel_ids_to_indices(tel_ids) + ] + + focal_lengths = [ + self.DLDataReader.subarray.tel[tel_id].camera.geometry.frame.focal_length + for tel_id in tel_ids + ] + pix_rotations = [self.DLDataReader.pix_rotation[tel_id] for tel_id in tel_ids] + + labels["focal_length"] = np.array([focal.to_value(u.m) for focal in focal_lengths]) + labels["pix_rotation"] = np.array([rot.to_value(u.deg) for rot in pix_rotations]) + # labels["tel_ground"] = tel_ground_frame + labels["tel_ids"] =tel_ids + labels["true_alt"]=[val for val in batch["true_alt"]] #[alt.to_value(u.deg) for alt in batch["true_alt"]] #batch["true_alt"] + labels["true_az"]=[val for val in batch["true_az"]] # batch["true_az"] + labels["tel_az"]= batch["telescope_pointing_azimuth"].data + labels["tel_alt"]= batch["telescope_pointing_altitude"].data + + # cam_x = labels["cameradirection"][:,0].cpu().numpy().squeeze(-1) + # cam_y = labels["cameradirection"][:,1].cpu().numpy().squeeze(-1) + + # sky_coords_alt, sky_coords_az = self.cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) + return features_out, labels # TODO: Not adapted to pytorch diff --git a/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py index 2423b3a3..64ae5fac 100644 --- a/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py +++ b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py @@ -58,6 +58,11 @@ def forward(self, dist_params, y): return nig_nll_error + self.lamb *nig_reg_error +def cosine_direction_loss(pred_x, pred_y, true_x, true_y): + pred_vec = F.normalize(torch.stack([pred_x, pred_y], dim=1), dim=1) + true_vec = F.normalize(torch.stack([true_x, true_y], dim=1), dim=1) + return 1 - torch.sum(pred_vec * true_vec, dim=1).mean() + def AngularDistance(alt1_rad, alt2_rad, az1_rad, az2_rad,reduction = None): """ diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 7d028d76..5ae4a4be 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -7,7 +7,7 @@ FocalLoss, VectorLoss, AngularDistance, - AngularError, + cosine_direction_loss, ) from ctlearn.core.pytorch.nets.optimizer.optimizer import one_cycle from tqdm import tqdm @@ -166,15 +166,15 @@ def __init__( self.criterion_energy_class = nn.CrossEntropyLoss( reduction="sum" ) # torch.nn.L1Loss(reduction='sum') - self.criterion_energy_value = torch.nn.L1Loss(reduction="sum") + self.criterion_energy_value = torch.nn.L1Loss(reduction="mean") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() - self.criterion_magnitud = torch.nn.L1Loss(reduction="sum") + self.criterion_magnitud = torch.nn.L1Loss(reduction="mean") # self.criterion_energy = torch.nn.MSELoss() - self.criterion_direction = torch.nn.L1Loss(reduction="sum") # nn.MSELoss() - self.criterion_vector = VectorLoss(alpha=0.1, reduction="sum") - self.criterion_alt_az_l1 = torch.nn.L1Loss(reduction="sum") - self.criterion_alt_az = evidential_regression_loss(lamb=0.01, reduction="sum") + self.criterion_direction = torch.nn.L1Loss(reduction="mean") # nn.MSELoss() + self.criterion_vector = VectorLoss(alpha=0.1, reduction="mean") + self.criterion_alt_az_l1 = torch.nn.L1Loss(reduction="mean") + self.criterion_alt_az = evidential_regression_loss(lamb=0.01, reduction="mean") self.best_loss = float("inf") @@ -247,8 +247,8 @@ def __init__( self.val_alt_label_list = [] self.val_az_label_list = [] - self.loss_val_separation = 0.0 - self.loss_val_alt_az = 0.0 + self.loss_val_distance = 0.0 + self.loss_val_dx_dy = 0.0 self.loss_val_angular_error = 0.0 self.val_energy_label_list = [] @@ -281,8 +281,8 @@ def __init__( # ---------------------------------------------------------------- # Training # ---------------------------------------------------------------- - self.loss_train_separation = 0.0 - self.loss_train_alt_az = 0.0 + self.loss_train_distance = 0.0 + self.loss_train_dx_dy = 0.0 self.loss_train_angular_error = 0.0 self.alt_off_list = [] @@ -475,13 +475,25 @@ def compute_direction_loss(self, direction_pred, labels_direction, training=Fals pred_dx_dy = direction_pred[:,0:2].unsqueeze(-1) pred_distance = direction_pred[:,2].unsqueeze(-1) + + loss_angular_diff = cosine_direction_loss(pred_dx_dy[:,0],pred_dx_dy[:,1], labels_dx_dy[:, 0],labels_dx_dy[:, 1]) + + + _, angular_diff = AngularDistance( + pred_dx_dy[:,0], + labels_dx_dy[:, 0], + pred_dx_dy[:,1], + labels_dx_dy[:, 1], + reduction="None", + ) + vector_cam_distance = torch.sqrt(pred_dx_dy[:,0]**2 + pred_dx_dy[:,1]**2) loss_dx_dy = self.criterion_alt_az_l1(pred_dx_dy, labels_dx_dy) loss_distance = self.criterion_magnitud(label_distance, pred_distance) loss_distance_dx_dy = self.criterion_magnitud(label_distance, vector_cam_distance) - loss = loss_dx_dy + loss_distance + loss_distance_dx_dy - return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy + loss = loss_dx_dy + loss_distance + loss_distance_dx_dy + loss_angular_diff + return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff # ---------------------------------------------------------------------------------------------------------- def compute_energy_loss( self, energy_pred, labels_energy, test_val=False, training=False @@ -506,6 +518,9 @@ def training_step(self, batch, batch_idx): # ------------------------------------------------------------------ features, labels = batch loss = 0 + + + # self.trainer.datamodule.train_dataloader().cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) if len(features) > 0: imgs = features["image"] @@ -568,23 +583,14 @@ def training_step(self, batch, batch_idx): if len(direction_pred)==2: direction_pred = direction_pred[0] - # loss, loss_separation, loss_alt_az, loss_angular_error, _ = ( - # self.compute_direction_loss( - # direction_pred, labels_direction, training=True - # ) - # ) - loss, loss_dx_dy, loss_distance, loss_distance_dx_dy= self.compute_direction_loss( direction_pred, labels_direction, training=True) - - self.loss_train_separation += loss_dx_dy.item() - self.loss_train_alt_az += loss_distance.item() - self.loss_train_angular_error += loss_distance_dx_dy.item() + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_direction_loss( direction_pred, labels_direction, training=True) - # self.loss_train_separation += loss_separation.item() - # self.loss_train_alt_az += loss_alt_az.item() - # self.loss_train_angular_error += loss_angular_error.item() + self.loss_train_distance += loss_distance.item() + self.loss_train_dx_dy += loss_dx_dy.item() + self.loss_train_angular_error += loss_angular_diff.item() self.loss_val_separation = 0.0 - self.loss_val_alt_az = 0.0 + self.loss_val_dx_dy = 0.0 self.loss_val_angular_error = 0.0 # --------------------------------------- @@ -626,10 +632,9 @@ def on_train_epoch_end(self): total_loss_train= self.all_gather(self.loss_train_sum).sum().item() total_batches_val = self.all_gather(torch.tensor(self.num_train_batches, device=self.device)).sum().item() - else: - # TODO: No Tested - total_loss_train = self.loss_train_sum.item() - total_batches_val = self.num_train_batches.item() + else: + total_loss_train = self.loss_train_sum + total_batches_val = self.num_train_batches if self.trainer.is_global_zero: global_loss = total_loss_train / total_batches_val @@ -711,16 +716,16 @@ def on_train_epoch_end(self): "loss/ Loss Training", { "loss": global_loss, - "loss_separation": self.loss_train_separation + "loss_distance": self.loss_train_distance / self.num_train_batches, - "loss_alt_az": self.loss_train_alt_az / self.num_train_batches, + "loss_alt_az": self.loss_train_dx_dy / self.num_train_batches, "loss_angular_error": self.loss_train_angular_error / self.num_train_batches, }, self.current_epoch, ) - self.loss_train_separation = 0.0 - self.loss_train_alt_az = 0.0 + self.loss_train_distance = 0.0 + self.loss_train_dx_dy = 0.0 self.loss_train_angular_error = 0.0 # --------------------------------------- # Energy @@ -747,72 +752,6 @@ def on_train_epoch_end(self): self.num_train_batches = 0 self.training_step_outputs = [] # ---------------------------------------------------------------------------------------------------------- - - - - def _transform_to_altaz_from_cam_offsets(self, tel_ground_frame,tel_az,tel_alt,): - """ - Transform camera coordinate offsets back to Alt/Az sky coordinates. - - Given cam_coord_offset_x and cam_coord_offset_y, this method reconstructs the - true Alt/Az coordinates using the telescope pointing and the camera geometry. - - Parameters: - ----------- - table : astropy.table.Table - A Table containing cam_coord_offset_x, cam_coord_offset_y, telescope pointing, and tel_id. - - Returns: - -------- - table : astropy.table.Table - A Table with reconstructed true_alt and true_az columns added. - """ - LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") - from astropy.coordinates import AltAz, SkyCoord - from ctapipe.coordinates import CameraFrame - # tel_id = table["tel_id"][0] - - # # Get telescope ground frame position - # tel_ground_frame = self.subarray.tel_coords[ - # self.subarray.tel_ids_to_indices(tel_id) - # ] - - # AltAz frame setup - altaz = AltAz( - location=tel_ground_frame.to_earth_location(), - obstime=LST_EPOCH, - ) - - # Telescope pointing SkyCoord - fix_tel_pointing = SkyCoord( - az = tel_az, - alt = tel_alt, - frame=altaz, - ) - - # Define the camera frame - camera_frame = CameraFrame( - focal_length=self.subarray.tel[tel_id].camera.geometry.frame.focal_length, - rotation=self.pix_rotation[tel_id], - telescope_pointing=fix_tel_pointing, - ) - - # Create SkyCoord in CameraFrame using cam offsets - cam_coords = SkyCoord( - x=table["cam_coord_offset_x"], - y=table["cam_coord_offset_y"], - frame=camera_frame - ) - - # Transform back to AltAz - sky_coords = cam_coords.transform_to(altaz) - - # Add the reconstructed Alt/Az coordinates to the table - # table.add_column(sky_coords.az, name="reconstructed_true_az") - # table.add_column(sky_coords.alt, name="reconstructed_true_alt") - - return sky_coords.alt,sky_coords.az - @torch.no_grad() def validation_step(self, batch, batch_idx, dataloader_idx=0): loss=0 @@ -850,7 +789,6 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): else: classification_pred, energy_pred, direction_pred = self.model(imgs) - # ------------------------------------------------------------------ # Compute Loss functions based on different tasks # ------------------------------------------------------------------ @@ -918,88 +856,38 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # ) # ) - loss, loss_dx_dy, loss_distance, loss_distance_dx_dy= self.compute_direction_loss( direction_pred, labels_direction, training=True) + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_error= self.compute_direction_loss( direction_pred, labels_direction, training=False) + - # self.loss_train_separation += loss_dx_dy.item() - # self.loss_train_alt_az += loss_distance.item() - # self.loss_train_angular_error += loss_distance_dx_dy.item() # ------------------------------------------------------------------------ # Convert the offset to altitud and azimuth # ------------------------------------------------------------------------ - # reco_az, reco_alt = [], [] - # if "tel_alt" in features: - # pointing_alt = features["tel_alt"].float().cpu().detach().numpy() - # pointing_az = features["tel_az"].float().cpu().detach().numpy() - # elif "tel_alt" in labels: - # pointing_alt = labels["tel_alt"].float().cpu().detach().numpy() - # pointing_az = labels["tel_az"].float().cpu().detach().numpy() - # else: - # raise ValueError(f"Telescope altitud and azimuth not found.") - - # pointing_alt = np.rad2deg(pointing_alt) - # pointing_az = np.rad2deg(pointing_az) - - # fix_pointing = SkyCoord( - # pointing_az * u.deg, - # pointing_alt * u.deg, - # frame="altaz", - # unit="deg", - # ) - # alt_off = direction_pred[:, 1].float().cpu().detach().numpy() - # az_off = direction_pred[:, 0].float().cpu().detach().numpy() - # reco_direction = utils.recover_alt_az(fix_pointing, alt_off, az_off) - - # reco_alt = np.deg2rad(reco_direction.alt.to_value())[0, :] - # reco_az = np.deg2rad(reco_direction.az.to_value())[0, :] - - # true_alt = labels["alt_az"][:, 0].float().cpu().detach().numpy() - # true_az = labels["alt_az"][:, 1].float().cpu().detach().numpy() if dataloader_idx == 0: - # self.alt_off_list.extend(alt_off) - # self.az_off_list.extend(az_off) - - # self.loss_val_separation += loss_separation.item() - # self.loss_val_alt_az += loss_alt_az.item() - # self.loss_val_angular_error += loss_angular_error.item() - - # self.val_angular_diff_list.extend(angular_diff) - self.loss_val_separation += loss_dx_dy.item() - self.loss_val_alt_az += loss_distance.item() - self.loss_val_angular_error += loss_distance_dx_dy.item() - # self.val_angular_diff_list.extend(angular_diff) - # + self.loss_val_distance += loss_distance.item() + self.loss_val_dx_dy += loss_dx_dy.item() + self.loss_val_angular_error += loss_angular_diff.item() + self.val_angular_diff_list.extend(angular_error) + # ------------------------------------------------------- pred_dx = direction_pred[:, 0].float().cpu().detach().numpy() pred_dy = direction_pred[:, 1].float().cpu().detach().numpy() - - true_dx = labels_direction[:, 0].float().cpu().detach().numpy() - true_dy = labels_direction[:, 1].float().cpu().detach().numpy() - - # reco_direction = utils.recover_alt_az(fix_pointing, alt_off, az_off) - - # reco_alt = np.deg2rad(reco_direction.alt.to_value())[0, :] - # reco_az = np.deg2rad(reco_direction.az.to_value())[0, :] - self.val_alt_pred_list.extend(pred_dx) - self.val_az_pred_list.extend(pred_dy) - self.val_alt_label_list.extend(true_dx) - self.val_az_label_list.extend(true_dy) - - # else: + + cam_x = pred_dx + cam_y = pred_dy - # self.loss_test_val_separation += loss_separation.item() - # self.loss_test_val_alt_az += loss_alt_az.item() - # self.loss_test_val_angular_error += loss_angular_error.item() + pred_alt, pred_az = self.val_loader.cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) - # self.test_val_angular_diff_list.extend(angular_diff) + true_alt = labels["true_alt"] + true_az = labels["true_az"] + self.val_alt_pred_list.extend(np.radians(pred_alt)) + self.val_az_pred_list.extend(np.radians(pred_az)) + self.val_alt_label_list.extend(np.radians(true_alt)) + self.val_az_label_list.extend(np.radians(true_az)) - # self.test_val_alt_pred_list.extend(reco_alt) - # self.test_val_az_pred_list.extend(reco_az) - # self.test_val_alt_label_list.extend(true_alt) - # self.test_val_az_label_list.extend(true_az) # --------------------------------------- # Energy # --------------------------------------- @@ -1034,17 +922,17 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if dataloader_idx == 0: self.val_energy_label_list.extend( - energy_label_tev[:, 0].float().cpu().detach().numpy() + energy_label_tev[:, 0].float().cpu().detach().numpy().flatten().tolist() ) self.val_hillas_intensity_list.extend( - hillas_intensity.float().cpu().detach().numpy() + hillas_intensity.float().cpu().detach().numpy().flatten().tolist() ) else: self.test_val_energy_label_list.extend( - energy_label_tev[:, 0].float().cpu().detach().numpy() + energy_label_tev[:, 0].float().cpu().detach().numpy().flatten().tolist() ) self.test_val_hillas_intensity_list.extend( - hillas_intensity.float().cpu().detach().numpy() + hillas_intensity.float().cpu().detach().numpy().flatten().tolist() ) # --------------------------------------- @@ -1079,16 +967,11 @@ def on_validation_epoch_end(self): else: - # TODO: No Tested total_loss_val = self.loss_val_sum total_loss_test = self.loss_test_val_sum total_batches_val = self.num_val_batches total_batches_test = self.num_test_val_batches - # total_loss_val = self.loss_val_sum.item() - # total_loss_test = self.loss_test_val_sum.item() - # total_batches_val = self.num_val_batches.item() - # total_batches_test = self.num_test_val_batches.item() # Calcular la pérdida promedio global global_loss_val = total_loss_val / max(1, total_batches_val) @@ -1122,14 +1005,10 @@ def on_validation_epoch_end(self): if self.task == Task.type: conf_matrix = self.confusion_matrix.compute().detach().cpu().numpy() f1_score_val = self.f1_score_val.compute().detach().cpu().numpy()*100.0 - f1_score_test = self.f1_score_test.compute().detach().cpu().numpy()*100.0 precision_val = self.precision_val.compute().detach().cpu().numpy()*100.0 - precision_test = self.precision_test.compute().detach().cpu().numpy()*100.0 - # Compute the accuracy and reset the metric states after each epoch epoch_accuracy_val = self.class_val_accuracy.compute().item() * 100 - epoch_accuracy_test = self.class_test_val_accuracy.compute().item() * 100 if self.trainer.is_global_zero: self.class_val_accuracy.reset() @@ -1150,15 +1029,7 @@ def on_validation_epoch_end(self): }, self.current_epoch, ) - # self.logger.experiment.add_scalars( - # "Metrics/Test", - # { - # "acc": epoch_accuracy_test, - # "f1": f1_score_test, - # "precision":precision_test, - # }, - # self.current_epoch, - # ) + print( f"Epoch {self.current_epoch}: Global Validation Accuracy: {epoch_accuracy_val:.4f}" ) @@ -1168,16 +1039,7 @@ def on_validation_epoch_end(self): print( f"Epoch {self.current_epoch}: Global Validation Precision: {precision_val:.4f}" ) - - # print( - # f"Epoch {self.current_epoch}: Global Test Accuracy: {epoch_accuracy_test:.4f}" - # ) - # print( - # f"Epoch {self.current_epoch}: Global Test F1 Score: {f1_score_test:.4f}" - # ) - # print( - # f"Epoch {self.current_epoch}: Global Test Precision: {precision_test:.4f}" - # ) + # --------------------------------------- # Create Confusion Matrix # --------------------------------------- @@ -1211,7 +1073,7 @@ def on_validation_epoch_end(self): if self.task == Task.cameradirection: self.print_direction_error(self.val_angular_diff_list, "Validation") - self.print_direction_error(self.test_val_angular_diff_list, "Test") + plt.close("all") fig_direction_error = plot_direction_resolution_error( self.val_alt_pred_list, @@ -1273,30 +1135,15 @@ def on_validation_epoch_end(self): "loss/Loss Validation", { "loss": global_loss_val, - "loss_separation": self.loss_val_separation / self.num_val_batches, - "loss_alt_az": self.loss_val_alt_az / self.num_val_batches, + "loss_separation": self.loss_val_distance / self.num_val_batches, + "loss_dx_dy": self.loss_val_dx_dy / self.num_val_batches, "loss_angular_error": self.loss_val_angular_error / self.num_val_batches, }, self.current_epoch, ) - if self.test_dataloader != None: - # Log scalar values - self.logger.experiment.add_scalars( - "loss/Loss Test", - { - "loss": global_loss_test, - "loss_separation": self.loss_test_val_separation - / self.num_test_val_batches, - "loss_alt_az": self.loss_test_val_alt_az - / self.num_test_val_batches, - "loss_angular_error": self.loss_test_val_angular_error - / self.num_test_val_batches, - }, - self.current_epoch, - ) # --------------------------------------- # Energy @@ -1330,28 +1177,7 @@ def on_validation_epoch_end(self): plt.close(fig_energy_error) # Close the figure to release memory plt.close("all") - # fig_energy_error = plot_energy_resolution_error( - # self.test_val_energy_pred_list, - # self.test_val_energy_label_list, - # self.test_val_hillas_intensity_list, - # ) - # self.logger.experiment.add_figure( - # "Energy Resolution Error/Test", fig_energy_error, self.current_epoch - # ) # Log the plot - # fig_energy_error.savefig( - # os.path.join( - # self.logger.log_dir, - # "error_resulution_test_" - # + str(self.current_epoch) - # + "_" - # + str(global_loss_test) - # + ".png", - # ), - # format="png", - # ) - - plt.close(fig_energy_error) # Close the figure to release memory - plt.close("all") + # ---------------------------------------------------------------------------------------------------------- def reset_values(self): # --------------------------------------- @@ -1389,8 +1215,8 @@ def reset_values(self): self.val_alt_label_list.clear() self.val_az_label_list.clear() - self.loss_val_separation = 0.0 - self.loss_val_alt_az = 0.0 + self.loss_val_distance = 0.0 + self.loss_val_dx_dy = 0.0 self.loss_val_angular_error = 0.0 # -------------------------------------------------- # Test validation @@ -1501,8 +1327,6 @@ def create_confusion_matrix( cm_file_name = f"{filename_prefix}_{epoch}_{accuracy:.4f}_{val_type}" cm = confusion_matrix(all_labels, all_preds) - # accuracies = (np.diag(cm) / np.sum(cm, axis=0))*100.0 - cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] # Remove NaNs @@ -1516,9 +1340,7 @@ def create_confusion_matrix( cm_file_name, self.logger.log_dir, # self.save_folder ) - # # Empty the list - # all_val_preds = torch.tensor([]) - # all_val_labels = torch.tensor([]) + return accuracies # ---------------------------------------------------------------------------------------------------------- @torch.no_grad() diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 1bc4e475..3cafdd93 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -86,7 +86,7 @@ model: # Hyper-parameters hyp: - epochs: 2 + epochs: 12 batches: 128 #64 dynamic_batches: True optimizer: Adamw diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 19a3e1d4..77040aad 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -179,11 +179,11 @@ def setup(self): # Reduce for testing # -------------------------------------------------------------------- # Limit the number of examples (optional) - max_training_samples = 5000 # or whatever number you want - max_validation_samples = 1000 # or whatever number you want + # max_training_samples = 500 # or whatever number you want + # max_validation_samples = 200 # or whatever number you want - training_indices = training_indices[:max_training_samples] - validation_indices = validation_indices[:max_validation_samples] + # training_indices = training_indices[:max_training_samples] + # validation_indices = validation_indices[:max_validation_samples] print("BASE TRAIN FRAMEWORK", self.framework_type) @@ -303,6 +303,8 @@ def start(self): mode = Mode.train, parameters=self.parameters, k=self.save_k, + train_loader= self.training_loader, + val_loader= self.validation_loader, ) # Save configuration file. From 37f10beb99647f57d2eaf30e4daff0d8dbeed158 Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 6 Jun 2025 08:53:35 +0000 Subject: [PATCH 026/119] added the fix for CI --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index bd1fd54e..2a0c1a92 100644 --- a/.gitignore +++ b/.gitignore @@ -22,3 +22,6 @@ dist # Sphinx documentation docs/build/ + +# Default pytorch output +run/ \ No newline at end of file From 8177bd5321dc98f54ebcf266f9c4b954a7a2e876 Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 6 Jun 2025 09:49:11 +0000 Subject: [PATCH 027/119] removed unused code --- ctlearn/core/data_loader/pytorch_loader.py | 100 ++- ctlearn/tools/train/pytorch/CTLearnPL.py | 589 ++---------------- .../training_config_iaa_neutron_training.yml | 2 +- .../train/pytorch/train_pytorch_model.py | 4 +- 4 files changed, 87 insertions(+), 608 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 035dcc0e..0f26a91b 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -150,20 +150,42 @@ def __getitem__(self, index): def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x,cam_y): """ - Transform camera coordinate offsets back to Alt/Az sky coordinates. + Transform camera coordinate offsets (cam_x, cam_y) into Alt/Az sky coordinates. - Given cam_coord_offset_x and cam_coord_offset_y, this method reconstructs the - true Alt/Az coordinates using the telescope pointing and the camera geometry. + This method converts the given camera coordinates for each telescope into sky coordinates + (Altitude and Azimuth), using the known pointing of each telescope and camera geometry + such as focal length and pixel rotation. - Parameters: - ----------- - table : astropy.table.Table - A Table containing cam_coord_offset_x, cam_coord_offset_y, telescope pointing, and tel_id. + Parameters + ---------- + tel_id : list or array-like + List of telescope IDs corresponding to each event or observation. + + focal_length : list or array-like + Focal length of the telescopes in meters. - Returns: - -------- - table : astropy.table.Table - A Table with reconstructed true_alt and true_az columns added. + pix_rotation : list or array-like + Pixel rotation angles (in degrees) for each telescope camera. + + tel_az : list or array-like + Azimuth of telescope pointing (in radians). + + tel_alt : list or array-like + Altitude of telescope pointing (in radians). + + cam_x : list or array-like + Camera x-coordinate positions (in meters). + + cam_y : list or array-like + Camera y-coordinate positions (in meters). + + Returns + ------- + sky_coords_alt : list + List of reconstructed Altitude coordinates (in degrees). + + sky_coords_az : list + List of reconstructed Azimuth coordinates (in degrees). """ from astropy.time import Time @@ -171,7 +193,6 @@ def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x from astropy.coordinates import AltAz, SkyCoord from ctapipe.coordinates import CameraFrame from astropy import units as u - # tel_id = table["tel_id"][0] # # Get telescope ground frame position tel_ground_frame = self.DLDataReader.subarray.tel_coords[ @@ -183,7 +204,7 @@ def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x location=tel_ground_frame.to_earth_location(), obstime=LST_EPOCH, ) - # altaz_list = [altaz] * len(focal_length) + # Telescope pointing SkyCoord fix_tel_pointing = SkyCoord( az = tel_az*u.rad, @@ -191,30 +212,10 @@ def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x frame=altaz, ) - # Define the camera frame - # camera_frame = CameraFrame( - # focal_length=focal_lenght, - # rotation=pix_rotation, - # telescope_pointing=fix_tel_pointing, - # ) - # camera_frames = [] sky_coords_alt = [] sky_coords_az = [] - # camera_frame = CameraFrame( - # focal_length=focal_length, - # rotation=pix_rotation, - # telescope_pointing=fix_tel_pointing - # ) - - # cam_coord = SkyCoord( - # x=cam_x * u.m, - # y=cam_y * u.m, - # frame=camera_frame - # ) - - # for fl, rot, cx, cy , altaz_, tel_pointing in zip(focal_length, pix_rotation, cam_x, cam_y, altaz_list, fix_tel_pointing): for id in range(len(focal_length)): camera_frame = CameraFrame( @@ -234,24 +235,6 @@ def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x sky_coords_alt.append(sky_coord.alt.to_value(u.deg).item()) sky_coords_az.append(sky_coord.az.to_value(u.deg).item()) - # camera_frame = CameraFrame( - # focal_length=focal_length, - # rotation=pix_rotation, - # telescope_pointing=fix_tel_pointing - # ) - - # cam_coord = SkyCoord( - # x=cam_x * u.m, - # y=cam_y * u.m, - # frame=camera_frame - # ) - - # sky_coord = cam_coord.transform_to(altaz) - - # Add the reconstructed Alt/Az coordinates to the table - # table.add_column(sky_coords.az, name="reconstructed_true_az") - # table.add_column(sky_coords.alt, name="reconstructed_true_alt") - return sky_coords_alt,sky_coords_az def _get_mono_item(self, batch): @@ -309,10 +292,9 @@ def _get_mono_item(self, batch): if "cameradirection" in labels.keys(): labels["direction"] = labels["cameradirection"] - # if "hillas" in self.tasks: - features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) - # features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) + features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) + image = features["input"][..., 0:1] peak_time = features["input"][..., 1:2] @@ -360,9 +342,9 @@ def _get_mono_item(self, batch): tel_ids = batch["tel_id"].data - tel_ground_frame = self.DLDataReader.subarray.tel_coords[ - self.DLDataReader.subarray.tel_ids_to_indices(tel_ids) - ] + # tel_ground_frame = self.DLDataReader.subarray.tel_coords[ + # self.DLDataReader.subarray.tel_ids_to_indices(tel_ids) + # ] focal_lengths = [ self.DLDataReader.subarray.tel[tel_id].camera.geometry.frame.focal_length @@ -374,8 +356,8 @@ def _get_mono_item(self, batch): labels["pix_rotation"] = np.array([rot.to_value(u.deg) for rot in pix_rotations]) # labels["tel_ground"] = tel_ground_frame labels["tel_ids"] =tel_ids - labels["true_alt"]=[val for val in batch["true_alt"]] #[alt.to_value(u.deg) for alt in batch["true_alt"]] #batch["true_alt"] - labels["true_az"]=[val for val in batch["true_az"]] # batch["true_az"] + labels["true_alt"]=[val for val in batch["true_alt"]] + labels["true_az"]=[val for val in batch["true_az"]] labels["tel_az"]= batch["telescope_pointing_azimuth"].data labels["tel_alt"]= batch["telescope_pointing_altitude"].data diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 5ae4a4be..0a15bffd 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -118,7 +118,7 @@ def __init__( ): super(CTLearnPL, self).__init__() - + self.task = task self.mode = mode @@ -158,18 +158,19 @@ def __init__( weight=class_weights, reduction="mean" ) - self.alpha = torch.tensor([1.0, 1.2], dtype=torch.float32) # torch.tensor([1.0, 1.1], dtype=torch.float32).to(self.device) # Aumentamos la clase 1 - gamma = 2.0 # Aumenta la penalización en ejemplos mal clasificados + self.alpha = torch.tensor([1.0, 1.2], dtype=torch.float32) + gamma = 2.0 # Increase the penalty on misclassified examples. + self.criterion_class = FocalLoss(alpha=self.alpha,gamma=gamma) self.criterion_energy_class = nn.CrossEntropyLoss( reduction="sum" - ) # torch.nn.L1Loss(reduction='sum') + ) self.criterion_energy_value = torch.nn.L1Loss(reduction="mean") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() self.criterion_magnitud = torch.nn.L1Loss(reduction="mean") - # self.criterion_energy = torch.nn.MSELoss() + self.criterion_direction = torch.nn.L1Loss(reduction="mean") # nn.MSELoss() self.criterion_vector = VectorLoss(alpha=0.1, reduction="mean") @@ -180,8 +181,8 @@ def __init__( self.best_loss = float("inf") self.best_accuracy = 0 # Best Metrics Tracking - self.best_losses = [(float("inf"), None)] * k # (loss, filename) - self.best_accuracies = [(0, None)] * k # (accuracy, filename) + self.best_losses = [(float("inf"), None)] * k + self.best_accuracies = [(0, None)] * k self.best_validation_accuracy = 0 self.correct_classification = 0 @@ -196,15 +197,9 @@ def __init__( dist_sync_on_step=True, ) - self.f1_score_test = MulticlassF1Score( - num_classes=2, - dist_sync_on_step=True, - ) - self.precision_val = MulticlassPrecision(num_classes=2,dist_sync_on_step=True,) self.precision_train = MulticlassPrecision(num_classes=2,dist_sync_on_step=True,) - self.precision_test = MulticlassPrecision(num_classes=2,dist_sync_on_step=True,) - + self.class_train_accuracy = Accuracy( task="multiclass", num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], @@ -216,11 +211,7 @@ def __init__( num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], dist_sync_on_step=True # GPUs Sync ) - self.class_test_val_accuracy = Accuracy( - task="multiclass", - num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], - dist_sync_on_step=True # GPUs Sync - ) + self.confusion_matrix = ConfusionMatrix(num_classes=2, task="multiclass",dist_sync_on_step=True) self.loss_train_sum = 0.0 @@ -253,30 +244,6 @@ def __init__( self.val_energy_label_list = [] self.val_hillas_intensity_list = [] - # ---------------------------------------------------------------- - # Test Validation - # ---------------------------------------------------------------- - self.loss_test_val_sum = 0.0 - self.num_test_val_batches = 0 - self.all_test_val_preds = torch.tensor([]) - self.all_test_val_labels = torch.tensor([]) - - self.test_val_angular_diff_list = [] - self.test_val_energy_diff_list = [] - - self.test_val_energy_pred_list = [] - - self.test_val_alt_pred_list = [] - self.test_val_az_pred_list = [] - self.test_val_alt_label_list = [] - self.test_val_az_label_list = [] - - self.loss_test_val_separation = 0.0 - self.loss_test_val_alt_az = 0.0 - self.loss_test_val_angular_error = 0.0 - - self.test_val_energy_label_list = [] - self.test_val_hillas_intensity_list = [] # ---------------------------------------------------------------- # Training @@ -377,22 +344,13 @@ def compute_type_loss( self.precision_train.update(predicted, labels_class) precision = self.precision_train.compute().item() else: - # Test - if test_val: - - self.class_test_val_accuracy.update(predicted, labels_class) - accuracy = self.class_test_val_accuracy.compute().item() - self.f1_score_test.update(predicted, labels_class) - self.precision_test.update(predicted, labels_class) - precision = self.precision_test.compute().item() - # Validation - else: - self.class_val_accuracy.update(predicted, labels_class) - accuracy = self.class_val_accuracy.compute().item() - self.confusion_matrix.update(predicted, labels_class) - self.f1_score_val.update(predicted, labels_class) - self.precision_val.update(predicted, labels_class) - precision = self.precision_val.compute().item() + + self.class_val_accuracy.update(predicted, labels_class) + accuracy = self.class_val_accuracy.compute().item() + self.confusion_matrix.update(predicted, labels_class) + self.f1_score_val.update(predicted, labels_class) + self.precision_val.update(predicted, labels_class) + precision = self.precision_val.compute().item() return loss, accuracy, predicted, precision # ---------------------------------------------------------------------------------------------------------- @@ -767,16 +725,12 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if self.task == Task.type: labels_class = labels["particletype"] - # if self.task == Task.energy: - # labels_energy_class = labels["energy_class"] labels_energy_value = labels["energy"] hillas_intensity = features["hillas"]["hillas_intensity"] - # hillas = {key: tensor.to(self.device) - # for key, tensor in hillas.items()} + if self.task == Task.cameradirection: labels_direction = labels["direction"] - # labels_alt_az = labels['alt_az'] - # labels_direction_cartesian = labels["direction_cartesian"] + # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones @@ -821,27 +775,6 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): prog_bar=True, logger=False, ) - else: - loss, accuracy, predicted, precision = self.compute_type_loss( - classification_pred_, labels_class, test_val=True, training=False - ) - self.log( - "test_val_acc", - accuracy * 100, - on_step=True, - on_epoch=False, - prog_bar=True, - logger=False, - ) - - self.log( - "test_prec", - precision*100, - on_step=True, - on_epoch=False, - prog_bar=True, - logger=False, - ) # --------------------------------------- # Direction # --------------------------------------- @@ -849,22 +782,12 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if len(direction_pred)==2: direction_pred = direction_pred[0] - # loss, angular_diff = self.compute_direction_loss(direction_pred, labels_direction, training=False) - # loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff = ( - # self.compute_direction_loss( - # direction_pred, labels_direction, training=False - # ) - # ) loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_error= self.compute_direction_loss( direction_pred, labels_direction, training=False) - - # ------------------------------------------------------------------------ # Convert the offset to altitud and azimuth # ------------------------------------------------------------------------ - - if dataloader_idx == 0: self.loss_val_distance += loss_distance.item() @@ -905,15 +828,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): self.val_energy_pred_list.extend( energy_pred_tev[:, 0].float().cpu().detach().numpy() ) - else: - loss, energy_diff = self.compute_energy_loss( - energy_pred, labels_energy_value, test_val=True, training=False - ) - self.test_val_energy_diff_list.extend(energy_diff) - self.test_val_energy_pred_list.extend( - energy_pred_tev[:, 0].float().cpu().detach().numpy() - ) # --------------------------------------- # Collect the True Energy and Hillas Intensity # --------------------------------------- @@ -927,13 +842,6 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): self.val_hillas_intensity_list.extend( hillas_intensity.float().cpu().detach().numpy().flatten().tolist() ) - else: - self.test_val_energy_label_list.extend( - energy_label_tev[:, 0].float().cpu().detach().numpy().flatten().tolist() - ) - self.test_val_hillas_intensity_list.extend( - hillas_intensity.float().cpu().detach().numpy().flatten().tolist() - ) # --------------------------------------- # Log validation loss @@ -942,9 +850,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if dataloader_idx == 0: self.loss_val_sum += loss.item() self.num_val_batches += 1 - else: - self.loss_test_val_sum += loss.item() - self.num_test_val_batches += 1 + loss_key = "val_loss" if dataloader_idx == 0 else "test_loss" if loss is not None: @@ -961,48 +867,31 @@ def on_validation_epoch_end(self): dist.barrier() # Gather y Sync values with the GPUs. total_loss_val = self.all_gather(self.loss_val_sum).sum().item() - total_loss_test = self.all_gather(self.loss_test_val_sum).sum().item() total_batches_val = self.all_gather(torch.tensor(self.num_val_batches, device=self.device)).sum().item() - total_batches_test = self.all_gather(torch.tensor(self.num_test_val_batches, device=self.device)).sum().item() - else: total_loss_val = self.loss_val_sum - total_loss_test = self.loss_test_val_sum total_batches_val = self.num_val_batches - total_batches_test = self.num_test_val_batches - # Calcular la pérdida promedio global - global_loss_val = total_loss_val / max(1, total_batches_val) - global_loss_test = total_loss_test / max(1, total_batches_test) - - - self.logger.experiment.add_scalars( - "loss/Global Validation Loss", - { - "loss": global_loss_val, - }, - self.current_epoch, - ) - - self.logger.experiment.add_scalars( - "loss/Global Test Loss", - { - "loss": global_loss_test, - }, - self.current_epoch, - ) + if self.trainer.is_global_zero: + # Calcular la pérdida promedio global + global_loss_val = total_loss_val / max(1, total_batches_val) + + self.logger.experiment.add_scalars( + "loss/Global Validation Loss", + { + "loss": global_loss_val, + }, + self.current_epoch, + ) - # Print the global loss for immediate feedback - print( - f"Epoch {self.current_epoch}: Global Validation Loss: {global_loss_val:.4f}" - ) - print(f"Epoch {self.current_epoch}: Global Test Loss: {global_loss_test:.4f}") + # Print the global loss for immediate feedback + print(f"Epoch {self.current_epoch}: Global Validation Loss: {global_loss_val:.4f}") # --------------------------------------- # Particle Type # --------------------------------------- - if self.task == Task.type: + if self.task == Task.type and self.trainer.is_global_zero: conf_matrix = self.confusion_matrix.compute().detach().cpu().numpy() f1_score_val = self.f1_score_val.compute().detach().cpu().numpy()*100.0 precision_val = self.precision_val.compute().detach().cpu().numpy()*100.0 @@ -1012,12 +901,12 @@ def on_validation_epoch_end(self): if self.trainer.is_global_zero: self.class_val_accuracy.reset() - self.class_test_val_accuracy.reset() + self.confusion_matrix.reset() self.f1_score_val.reset() - self.f1_score_test.reset() + self.precision_val.reset() - self.precision_test.reset() + # Log self.logger.experiment.add_scalars( @@ -1070,8 +959,8 @@ def on_validation_epoch_end(self): # --------------------------------------- # Direction # --------------------------------------- - if self.task == Task.cameradirection: - + if self.task == Task.cameradirection and self.trainer.is_global_zero: + self.print_direction_error(self.val_angular_diff_list, "Validation") plt.close("all") @@ -1103,39 +992,13 @@ def on_validation_epoch_end(self): ) plt.close(fig_direction_error) # Close the figure to release memory plt.close("all") - fig_direction_error = plot_direction_resolution_error( - self.test_val_alt_pred_list, - self.test_val_az_pred_list, - self.test_val_alt_label_list, - self.test_val_az_label_list, - self.test_val_energy_label_list, - self.test_val_hillas_intensity_list, - ) - self.logger.experiment.add_figure( - "Direction Resolution Error/Test", - fig_direction_error, - self.current_epoch, - ) # Log the plot - # Save the figure - fig_direction_error.savefig( - os.path.join( - self.logger.log_dir, - "angular_resolution_test_" - + str(self.current_epoch) - + "_" - + str(global_loss_test) - + ".png", - ), - format="png", - ) - plt.close(fig_direction_error) # Close the figure to release memory # Log scalar values self.logger.experiment.add_scalars( "loss/Loss Validation", { "loss": global_loss_val, - "loss_separation": self.loss_val_distance / self.num_val_batches, + "loss_distance": self.loss_val_distance / self.num_val_batches, "loss_dx_dy": self.loss_val_dx_dy / self.num_val_batches, "loss_angular_error": self.loss_val_angular_error / self.num_val_batches, @@ -1148,10 +1011,9 @@ def on_validation_epoch_end(self): # --------------------------------------- # Energy # --------------------------------------- - if self.task == Task.energy: + if self.task == Task.energy and self.trainer.is_global_zero: self.print_energy_error(self.val_energy_diff_list, "Validation") - self.print_energy_error(self.test_val_energy_diff_list, "Test") plt.close("all") fig_energy_error = plot_energy_resolution_error( self.val_energy_pred_list, @@ -1185,23 +1047,14 @@ def reset_values(self): # --------------------------------------- self.loss_val_sum = 0 self.num_val_batches = 0 - self.loss_test_val_sum = 0 - self.num_test_val_batches = 0 + # Reset Energy and hillas intensity # -------------------------------------------------- # Validation # -------------------------------------------------- self.val_energy_label_list.clear() - self.val_hillas_intensity_list.clear() - # -------------------------------------------------- - # Test validation - # -------------------------------------------------- - self.test_val_energy_label_list.clear() - - self.test_val_hillas_intensity_list.clear() - # -------------------------------------------------- # Reset Direction # -------------------------------------------------- @@ -1218,20 +1071,7 @@ def reset_values(self): self.loss_val_distance = 0.0 self.loss_val_dx_dy = 0.0 self.loss_val_angular_error = 0.0 - # -------------------------------------------------- - # Test validation - # -------------------------------------------------- - self.test_val_angular_diff_list.clear() - - self.test_val_alt_pred_list.clear() - self.test_val_az_pred_list.clear() - self.test_val_alt_label_list.clear() - self.test_val_az_label_list.clear() - - self.loss_test_val_separation = 0.0 - self.loss_test_val_alt_az = 0.0 - self.loss_test_val_angular_error = 0.0 # -------------------------------------------------- # Reset Energy # -------------------------------------------------- @@ -1240,11 +1080,7 @@ def reset_values(self): self.val_energy_diff_list.clear() self.val_energy_pred_list.clear() - # -------------------------------------------------- - # Test validation - # -------------------------------------------------- - self.test_val_energy_diff_list.clear() - self.test_val_energy_pred_list.clear() + # ---------------------------------------------------------------------------------------------------------- def print_direction_error(self, angular_diff_list, type_val: str): # Count the angular error in ranges [20º-0.1º] @@ -1343,249 +1179,6 @@ def create_confusion_matrix( return accuracies # ---------------------------------------------------------------------------------------------------------- - @torch.no_grad() - def generate_results( - self, - input_data_loader, - h5_file_name="./results.r1.dl2.h5", - task=None, - mode=None, - ): - self.model.to(self.device) - self.model.eval() - # data_loader = DataLoader( - # input_data_loader.dataset, batch_size=128, shuffle=False) - dataset = input_data_loader.dataset - data_loader = input_data_loader - with torch.no_grad(): - pbar = tqdm(total=len(data_loader), desc="DL2 conv", leave=True) - - class_predictions = [] - class_feature_vector = [] - class_predictions_class = [] - energy_predictions = [] - direction_predictions = [] - direction_predictions_mu = [] - direction_predictions_sigma = [] - direction_pred_mu=[] - direction_pred_sigma=[] - - event_id_list = [] - obs_id_list = [] - labels_energy_list = [] - labels_direction_list = [] - labels_true_alt_az_list = [] - hillas_list = [] - total = 0 - correct = 0 - predicted_class = None - direction_pred = None - direction_pred = None - # tel_pointing_dir=[] - if mode == Mode.observation: # "observation": - dataset.pointing_dir["pointing_alt"] = [] - dataset.pointing_dir["pointing_az"] = [] - dataset.pointing_dir["dragon_time"] = [] - dataset.pointing_dir["utc_time"] = [] - dataset.pointing_dir["src_x"] = [] - dataset.pointing_dir["src_y"] = [] - - cnt = 0 - for batch_idx, (features, labels) in enumerate(data_loader): - - # TODO: Check that features is not empty - if len(features)==0: - continue - imgs = features["image"].to(self.device).contiguous() - - labels_class = ( - labels["particletype"].float().to(self.device).contiguous() - ) - hillas = features["hillas"] - hillas = { - key: tensor.cpu().detach().numpy() for key, tensor in hillas.items() - } - - tel_alt = features["tel_alt"].float().cpu().detach().numpy() - tel_az = features["tel_az"].float().cpu().detach().numpy() - - if "time" in features: - tel_time = features["time"].double().cpu().detach().numpy() - - if "src_x" and "src_y" in features: - src_x = features["src_x"].float().cpu().detach().numpy() - src_y = features["src_y"].float().cpu().detach().numpy() - - if self.num_inputs == 2: - peak_time = features["peak_time"].to(self.device).contiguous() - classification_pred, energy_pred, direction_pred = self.model( - imgs, peak_time - ) - else: - classification_pred, energy_pred, direction_pred = self.model(imgs) - - # Convert to numpy - if task == Task.type: - classification_pred_ = classification_pred[0] - feature_vector = classification_pred[1].cpu().detach().numpy() - predicted = torch.softmax(classification_pred_, dim=1) - predicted_class = predicted.argmax(dim=1) - correct += (predicted_class == labels_class).sum().item() - predicted = predicted.cpu().detach().numpy() - predicted_class = predicted_class.cpu().detach().numpy() - total += labels_class.size(0) - - if task == Task.energy: - energy = energy_pred.cpu().detach().numpy() - # energy = energy[0] - - if task == Task.cameradirection: - if len(direction_pred)==2: - direction_pred_mu = direction_pred[0].cpu().detach().numpy() - direction_pred_sigma = direction_pred[1].cpu().detach().numpy() - else: - direction_pred = direction_pred.cpu().detach().numpy() - - obs_id = hillas["obs_id"] - event_id = hillas["event_id"] - labels_energy = labels["energy"].cpu().detach().numpy() - labels_energy = 1 * (labels_energy) - - labels_direction = labels["direction"].cpu().detach().numpy() - label_true_alt_az = labels["alt_az"].cpu().detach().numpy() - - # ------------------------------------------------------------------ - id = list(range(features["image"].shape[0])) - if task == Task.type: - class_predictions.extend(predicted[:, :]) - class_feature_vector.extend(feature_vector[:, :]) - class_predictions_class.extend(predicted_class[:]) - - if task == Task.energy: - energy_predictions.extend(energy[:, :]) - if task == Task.cameradirection: - - if len(direction_pred)==2: - direction_predictions_mu.extend(direction_pred_mu[:,0:2]) - direction_predictions_sigma.extend(direction_pred_sigma[:,0:2]) - - else: - dir = direction_pred[:, 0:2] - direction_predictions.extend(dir) - - if mode == Mode.observation: - dataset.pointing_dir["pointing_alt"].extend(tel_alt) - dataset.pointing_dir["pointing_az"].extend(tel_az) - dataset.pointing_dir["utc_time"].extend(tel_time) - time = Time(tel_time, format="mjd") - datetime_utc = time.to_datetime() - # Convert to UNIX timestamp (dragon_time) - # dragon_time = datetime_utc[:].timestamp() - # Vectorized application of timestamp() - dragon_time = np.vectorize(lambda dt: dt.timestamp())( - datetime_utc - ) - dataset.pointing_dir["dragon_time"].extend(dragon_time) - - dataset.pointing_dir["src_x"].extend(src_x) - dataset.pointing_dir["src_y"].extend(src_y) - - obs_id_list.extend(obs_id) - event_id_list.extend(event_id) - - if mode != Mode.observation: - labels_energy_list.extend(labels_energy[:, 0]) - dir = labels_direction[:, 0:2] - labels_direction_list.extend(dir) - labels_true_alt_az_list.extend(label_true_alt_az) - - hillas_vector = np.array(utils.create_key_value_array(hillas, id)).T - hillas_list.extend(hillas_vector) - # ------------------------------------------------------------------ - if task == Task.type and mode != Mode.observation: - - val_acc = 100 * correct / total if total > 0 else 0.0 - pbar.set_postfix({"val_accuracy": f"{val_acc:.2f}%"}) - pbar.refresh() - pbar.update(1) - - # if task == Task.type and mode != Mode.observation: - # pbar.set_postfix( - # { - # "val_accuracy": f"{100 * correct / total:.2f}%", - # } - # ) - # if batch_idx % 10 == 0: - # pbar.update(10) - - # TESTING - # h5_file_name="./test.dl2.h5" - # cnt += 1 - # if cnt>5: - # break - - - - predictions = { - "type": np.array(class_predictions), - "type_feature_vector": np.array(class_feature_vector), - "type_class": np.array(class_predictions_class), - "energy": np.array(energy_predictions), - "direction": np.array(direction_predictions), - "direction_mu": np.array(direction_predictions_mu), - "direction_sigma": np.array(direction_predictions_sigma), - } - - if not hasattr(dataset, "class_names"): - dataset.class_names = ["gamma", "proton"] - - data = { - "effective_focal_length": dataset.optics.effective_focal_length, - "obs_id": np.array(obs_id_list), - "event_id": np.array(event_id_list), - "pointing": dataset.pointing_dir, - "true_shower_primary_id": dataset.true_shower_primary_id, - "include_nsb_patches": dataset.include_nsb_patches, - "simulation_info": dataset.simulation_info, - "parameter_names": dataset.hillas_names, - "parameter_data": hillas_list, - "mode": dataset.observation_mode, - "class_names": dataset.class_names, - "energy_unit": dataset.energy_unit, - "selected_telescopes": dataset.selected_telescopes, - } - - labels = { - "true_energy": np.array(labels_energy_list), - "true_direction": np.array(labels_direction_list), - "true_alt_az": np.array(labels_true_alt_az_list), - } - - if task == Task.type: # "type": - average_accuracy = 100.0 * (correct / total) - print("Validation Accuracy: {:.2f}%".format(average_accuracy)) - - gc.enable() - del self.model - del data_loader - del input_data_loader - del self.val_loader - del self.scheduler - - gc.collect() - - # Save h5 file dl2 format - utils.write_output(h5_file_name, data, predictions, labels, task, mode) - - gc.enable() - del data - del predictions - del labels - gc.collect() - torch.cuda.empty_cache() - - return None - # ---------------------------------------------------------------------------------------------------------- def configure_optimizers(self): if self.optimizer_type == "sgd": @@ -1648,99 +1241,3 @@ def configure_optimizers(self): "lr_scheduler": lr_dict, } # ---------------------------------------------------------------------------------------------------------- - # TODO: Rewrite this function in order to create a mosaic with values to study the possible problems in classification and regression - # def missing_analysis(self): - - # self.model.eval() - # total = 0 - # correct = 0 - # validation_loss = 0.0 - # with torch.no_grad(): - # pbar = tqdm(total=len(self.val_loader), - # desc='Validating', leave=True) - # all_preds = torch.tensor([]) - # all_labels = torch.tensor([]) - # for batch_idx, (features, labels) in enumerate(tqdm(self.val_loader)): - - # images = features['image'].to(self.device) - # labels = labels['particletype'].to(self.device) - # hillas_cpu = features["hillas"] - # # hillas = {key: tensor.to(self.device) for key, tensor in hillas_cpu.items()} - # hillas = {key: tensor.cpu().detach().numpy() - # for key, tensor in hillas_cpu.items()} - # filename_list = features["filename"].cpu().detach().numpy() - - # if self.num_inputs == 2: - # peak_time = features['peak_time'].to(self.device) - # outputs = self.model(images, peak_time) - # else: - # outputs = self.model(images) - - # # outputs = self.model(images) - # outputs = torch.squeeze(outputs, dim=1) - # # labels_one_hot = torch.nn.functional.one_hot(labels, num_classes=2).float() - # loss = self.criterion(outputs, labels) - # validation_loss += loss.item() - # predicted = torch.sigmoid(outputs).round() - - # total += labels.size(0) - # correct += (predicted == labels).sum().item() - - # all_preds = torch.cat( - # (all_preds, predicted.float().cpu()), dim=0) - # all_labels = torch.cat( - # (all_labels, labels.float().cpu()), dim=0) - - # images_list = [] - # text_list = [] - # cnt = 0 - # for id_image in range(len(labels)): - # # if (labels[id_image] == predicted[id_image]): - # if not (labels[id_image] == predicted[id_image]): - # cnt += 1 - # filename = self.convert_ascii_list_to_string( - # filename_list[id_image]) - # # Transform image to opencv (WxHxC) - # image = cv2.UMat(images[id_image].cpu( - # ).detach().permute(1, 2, 0).numpy()) - - # text_list.append("Label: "+str(self.class_names[int(labels[id_image])])+" "+"Predicted: "+str( - # self.class_names[int(predicted[id_image])])+"\n"+filename) - # # Transform into C,W,H - # image = cv2.UMat.get(image) - # images_list.append(image) - # if cnt >= 16: - # break - - # canvas = self.create_image_mosaic( - # images_list, text_list, batch_idx) - # # cv2.imshow("Missing",canvas) - # # cv2.waitKey(0) - - # pbar.set_postfix({ - # # 'val_loss': f'{validation_loss/total:.4f}', - # 'val_accuracy': f'{100 * correct / total:.2f}%' - # }) - # pbar.update(1) - - # average_accuracy = 100 * (correct / total) - # print('Validation Accuracy: {:.2f}%'.format(average_accuracy)) - # # Save checkpoint if this is the best accuracy or loss so far - # self.save_checkpoint( - # average_accuracy, 'validation_accuracy', is_loss=False) - - # if average_accuracy > self.best_validation_accuracy: - # all_labels = all_labels.numpy() - # all_preds = all_preds.numpy() - # filename_prefix = "./confusion_matrix" - # cm_file_name = f'{filename_prefix}_{average_accuracy:.4f}.pth' - # cm = confusion_matrix(all_labels, all_preds) - - # # accuracies = (np.diag(cm) / np.sum(cm, axis=0))*100.0 - - # cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] - # accuracies = cm_norm.diagonal() * 100.0 - # self.plot_confusion_matrix( - # cm, self.class_names, accuracies, cm_file_name) - - # return average_accuracy diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 3cafdd93..315ab07c 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -25,7 +25,7 @@ data: # Check points type_checkpoint: ./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - direction_checkpoint: ./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: /storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth run_details: diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 77040aad..2faa1e90 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -121,13 +121,13 @@ class TrainPyTorchModel(TrainCTLearnModel): def __init__(self, **kwargs): - # Setup GPU Debug + # Setup GPU os.environ["NCCL_P2P_DISABLE"] = "1" os.environ["NCCL_IB_DISABLE"] = "1" os.environ["NCCL_DEBUG"] = "WARN" os.environ["TORCH_NCCL_ASYNC_ERROR_HANDLING"] = "1" os.environ["NCCL_DEBUG"] = "INFO" - + torch.set_float32_matmul_precision('medium') super().__init__(**kwargs) From 6e6ce9cbdc94ea388133afdb566dd5f3f40788e2 Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 6 Jun 2025 13:38:34 +0000 Subject: [PATCH 028/119] fixed minor bugs --- ctlearn/tools/train/pytorch/CTLearnPL.py | 79 +++++++++---------- .../training_config_iaa_neutron_training.yml | 4 +- .../train/pytorch/train_pytorch_model.py | 21 ++--- 3 files changed, 51 insertions(+), 53 deletions(-) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 0a15bffd..00b50a78 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -477,8 +477,6 @@ def training_step(self, batch, batch_idx): features, labels = batch loss = 0 - - # self.trainer.datamodule.train_dataloader().cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) if len(features) > 0: imgs = features["image"] @@ -618,7 +616,7 @@ def on_train_epoch_end(self): # --------------------------------------- # Particle Type # --------------------------------------- - if self.task == Task.type: + if self.task == Task.type and self.trainer.is_global_zero: f1_score = self.f1_score_train.compute().detach().cpu().numpy()*100.0 self.f1_score_train.reset() @@ -658,47 +656,46 @@ def on_train_epoch_end(self): # --------------------------------------- # Direction # --------------------------------------- - if self.task == Task.cameradirection: - if self.trainer.is_global_zero: - filename_prefix = ( - f"Epoch_{self.current_epoch}_{self.task.name}_train_loss" - ) - self.save_checkpoint( - self.logger.log_dir, - global_loss, - filename_prefix=filename_prefix, - is_loss=True, - ) - # Log scalar values - self.logger.experiment.add_scalars( - "loss/ Loss Training", - { - "loss": global_loss, - "loss_distance": self.loss_train_distance - / self.num_train_batches, - "loss_alt_az": self.loss_train_dx_dy / self.num_train_batches, - "loss_angular_error": self.loss_train_angular_error - / self.num_train_batches, - }, - self.current_epoch, - ) - self.loss_train_distance = 0.0 - self.loss_train_dx_dy = 0.0 - self.loss_train_angular_error = 0.0 + if self.task == Task.cameradirection and self.trainer.is_global_zero: + + filename_prefix = ( + f"Epoch_{self.current_epoch}_{self.task.name}_train_loss" + ) + self.save_checkpoint( + self.logger.log_dir, + global_loss, + filename_prefix=filename_prefix, + is_loss=True, + ) + # Log scalar values + self.logger.experiment.add_scalars( + "loss/ Loss Training", + { + "loss": global_loss, + "loss_distance": self.loss_train_distance + / self.num_train_batches, + "loss_alt_az": self.loss_train_dx_dy / self.num_train_batches, + "loss_angular_error": self.loss_train_angular_error + / self.num_train_batches, + }, + self.current_epoch, + ) + self.loss_train_distance = 0.0 + self.loss_train_dx_dy = 0.0 + self.loss_train_angular_error = 0.0 # --------------------------------------- # Energy # --------------------------------------- - if self.task == Task.energy: - if self.trainer.is_global_zero: - filename_prefix = ( - f"Epoch_{self.current_epoch}_{self.task.name}_train_loss" - ) - self.save_checkpoint( - self.logger.log_dir, - global_loss, - filename_prefix=filename_prefix, - is_loss=True, - ) + if self.task == Task.energy and self.trainer.is_global_zero: + filename_prefix = ( + f"Epoch_{self.current_epoch}_{self.task.name}_train_loss" + ) + self.save_checkpoint( + self.logger.log_dir, + global_loss, + filename_prefix=filename_prefix, + is_loss=True, + ) # --------------------------------------- # Delete all the lists and set to 0 # the values used to estimate the losses diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 315ab07c..8ee2fe68 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -25,7 +25,7 @@ data: # Check points type_checkpoint: ./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - direction_checkpoint: /storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth run_details: @@ -92,7 +92,7 @@ hyp: optimizer: Adamw momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-6 #1e-5 #Efficient-b3 1e-5 + learning_rate: 1e-3 #1e-5 #Efficient-b3 1e-5 lrf: 0.1 start_epoch: 0 steps_epoch: 100 # Computed online. Must be removed diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 2faa1e90..25a3bac9 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -306,18 +306,19 @@ def start(self): train_loader= self.training_loader, val_loader= self.validation_loader, ) - - # Save configuration file. - if not os.path.exists(trainer_pl.get_log_dir()): - os.makedirs(trainer_pl.get_log_dir()) - with open(os.path.join(trainer_pl.get_log_dir(),"parameters.json"), "w") as f: - json.dump(self.parameters, f, indent=4) - - print(f"Run tensorboard server: tensorboard --load_fast=false --host=0.0.0.0 --logdir={trainer_pl.get_log_dir()}/") + if trainer_pl.is_global_zero: + # Save configuration file. + if not os.path.exists(trainer_pl.get_log_dir()): + os.makedirs(trainer_pl.get_log_dir()) + + with open(os.path.join(trainer_pl.get_log_dir(),"parameters.json"), "w") as f: + json.dump(self.parameters, f, indent=4) + + print(f"Run tensorboard server: tensorboard --load_fast=false --host=0.0.0.0 --logdir={trainer_pl.get_log_dir()}/") - print(f"Accelerator: {trainer_pl.accelerator}") - print(f"Num. Devices: {trainer_pl.num_devices}") + print(f"Accelerator: {trainer_pl.accelerator}") + print(f"Num. Devices: {trainer_pl.num_devices}") trainer_pl.fit( model=lightning_model, From 6ffbf9fa2f1abc99ecd453de28f155191353639a Mon Sep 17 00:00:00 2001 From: pguzman Date: Fri, 6 Jun 2025 16:43:21 +0000 Subject: [PATCH 029/119] removed unused call --- ctlearn/core/data_loader/base_loader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ctlearn/core/data_loader/base_loader.py b/ctlearn/core/data_loader/base_loader.py index b4cc4613..6fc63548 100644 --- a/ctlearn/core/data_loader/base_loader.py +++ b/ctlearn/core/data_loader/base_loader.py @@ -21,7 +21,7 @@ def __init__( self.tasks = tasks self.batch_size = batch_size self.random_seed = random_seed - # self.on_epoch_end() + self.stack_telescope_images = stack_telescope_images self.sort_by_intensity = sort_by_intensity From 91f7c75e98a889ca096ec379965eec39fd7fd918 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 9 Jun 2025 16:23:17 +0000 Subject: [PATCH 030/119] fixed some bugs in task type. Still there is a bug to fix --- ctlearn/core/data_loader/pytorch_loader.py | 136 ++++++++++-------- ctlearn/tools/train/pytorch/CTLearnPL.py | 90 ++++++------ .../training_config_iaa_neutron_training.yml | 2 +- .../train/pytorch/train_pytorch_model.py | 8 +- 4 files changed, 119 insertions(+), 117 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 0f26a91b..d68b5e3f 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -6,9 +6,10 @@ from ctlearn.core.ctlearn_enum import Task from astropy import units as u + class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): def __init__( - self, + self, tasks, parameters, use_augmentation, @@ -19,8 +20,8 @@ def __init__( self.use_clean = parameters["normalization"]["use_clean"] self.use_clean_dvr = parameters["normalization"]["use_clean_dvr"] - self.task= tasks - + self.task = tasks + # Augmentation probabilities self.mask_augmentation = parameters["augmentation"]["aug_prob"] self.aug_prob = parameters["augmentation"]["aug_prob"] @@ -42,7 +43,7 @@ def __init__( self.energy_mu = parameters["normalization"]["energy_mu"] self.energy_sigma = parameters["normalization"]["energy_sigma"] - super().__init__(**kwargs,tasks=tasks) + super().__init__(**kwargs, tasks=tasks) self.on_epoch_end() self.hillas_names = [ @@ -88,8 +89,9 @@ def __init__( "peak_time_std", "peak_time_skewness", "peak_time_kurtosis", - "core_psi" + "core_psi", ] + def __len__(self): """ Returns the number of batches per epoch. @@ -145,10 +147,12 @@ def __getitem__(self, index): elif self.DLDataReader.mode == "stereo": batch = self.DLDataReader.generate_stereo_batch(batch_indices) features, labels = self._get_stereo_item(batch) - + return features, labels - def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x,cam_y): + def cam_to_alt_az( + self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y + ): """ Transform camera coordinate offsets (cam_x, cam_y) into Alt/Az sky coordinates. @@ -160,7 +164,7 @@ def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x ---------- tel_id : list or array-like List of telescope IDs corresponding to each event or observation. - + focal_length : list or array-like Focal length of the telescopes in meters. @@ -207,27 +211,24 @@ def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x # Telescope pointing SkyCoord fix_tel_pointing = SkyCoord( - az = tel_az*u.rad, - alt = tel_alt*u.rad, + az=tel_az * u.rad, + alt=tel_alt * u.rad, frame=altaz, ) - sky_coords_alt = [] sky_coords_az = [] for id in range(len(focal_length)): camera_frame = CameraFrame( - focal_length=focal_length[id]*u.m, - rotation=pix_rotation[id]*u.deg, - telescope_pointing=fix_tel_pointing[id] + focal_length=focal_length[id] * u.m, + rotation=pix_rotation[id] * u.deg, + telescope_pointing=fix_tel_pointing[id], ) cam_coord = SkyCoord( - x=cam_x[id] * u.m, - y=cam_y[id] * u.m, - frame=camera_frame + x=cam_x[id] * u.m, y=cam_y[id] * u.m, frame=camera_frame ) sky_coord = cam_coord.transform_to(altaz[id]) @@ -235,7 +236,7 @@ def cam_to_alt_az(self, tel_id, focal_length, pix_rotation,tel_az,tel_alt, cam_x sky_coords_alt.append(sky_coord.alt.to_value(u.deg).item()) sky_coords_az.append(sky_coord.az.to_value(u.deg).item()) - return sky_coords_alt,sky_coords_az + return sky_coords_alt, sky_coords_az def _get_mono_item(self, batch): """ @@ -258,15 +259,10 @@ def _get_mono_item(self, batch): labels = {} features = {"input": batch["features"].data} if "type" in self.tasks: - labels["type"] = batch["true_shower_primary_class"].data - # Temp fix till keras support class weights for multiple outputs or I wrote custom loss - # https://github.com/keras-team/keras/issues/11735 - if len(self.tasks) == 1: - labels = batch["true_shower_primary_class"].data + labels["type"] = np.stack(batch["true_shower_primary_class"].data) - # if "energy" in self.tasks: labels["energy"] = batch["log_true_energy"].data - + if "skydirection" in self.tasks: labels["skydirection"] = np.stack( ( @@ -290,11 +286,10 @@ def _get_mono_item(self, batch): labels["direction"] = labels["skydirection"] if "cameradirection" in labels.keys(): - labels["direction"] = labels["cameradirection"] + labels["direction"] = labels["cameradirection"] + + features["hillas"] = self.DLDataReader.get_parameters(batch, self.hillas_names) - - features["hillas"] = self.DLDataReader.get_parameters(batch,self.hillas_names) - image = features["input"][..., 0:1] peak_time = features["input"][..., 1:2] @@ -311,32 +306,39 @@ def _get_mono_item(self, batch): peak_time[np.isnan(peak_time)] = 0 peak_time[np.isinf(peak_time)] = 0 - if self.task == Task.type: + if self.task == Task.type: image = (image - self.type_mu) / self.type_sigma peak_time = (peak_time - self.type_mu) / self.type_sigma - if self.task == Task.energy: + if self.task == Task.energy: image = (image - self.energy_mu) / self.energy_sigma peak_time = (peak_time - self.energy_mu) / self.energy_sigma - if self.task == Task.cameradirection or self.task == Task.skydirection: + if self.task == Task.cameradirection or self.task == Task.skydirection: image = (image - self.dir_mu) / self.dir_sigma peak_time = (peak_time - self.dir_mu) / self.dir_sigma - - features_out={} - features_out["image"]=image - features_out["peak_time"]= peak_time - features_out["image"]=torch.from_numpy(image).contiguous().float() - features_out["peak_time"]=torch.from_numpy(peak_time).contiguous().float() + features_out = {} + features_out["image"] = image + features_out["peak_time"] = peak_time + + features_out["image"] = torch.from_numpy(image).contiguous().float() + features_out["peak_time"] = torch.from_numpy(peak_time).contiguous().float() features_out["hillas"] = features["hillas"] - - for key in labels.keys(): - labels[key] = torch.from_numpy(labels[key]).contiguous().unsqueeze(-1) - for key in features["hillas"].keys(): - features["hillas"][key] = torch.from_numpy(np.array(features["hillas"][key])).contiguous().unsqueeze(-1) + for key in labels.keys(): + + labels[key] = torch.from_numpy(labels[key]).contiguous() + + if key != "type": + labels[key] = labels[key].unsqueeze(-1) + for key in features["hillas"].keys(): + features["hillas"][key] = ( + torch.from_numpy(np.array(features["hillas"][key])) + .contiguous() + .unsqueeze(-1) + ) if "cameradirection" in self.tasks: @@ -347,19 +349,27 @@ def _get_mono_item(self, batch): # ] focal_lengths = [ - self.DLDataReader.subarray.tel[tel_id].camera.geometry.frame.focal_length + self.DLDataReader.subarray.tel[ + tel_id + ].camera.geometry.frame.focal_length for tel_id in tel_ids ] - pix_rotations = [self.DLDataReader.pix_rotation[tel_id] for tel_id in tel_ids] + pix_rotations = [ + self.DLDataReader.pix_rotation[tel_id] for tel_id in tel_ids + ] - labels["focal_length"] = np.array([focal.to_value(u.m) for focal in focal_lengths]) - labels["pix_rotation"] = np.array([rot.to_value(u.deg) for rot in pix_rotations]) + labels["focal_length"] = np.array( + [focal.to_value(u.m) for focal in focal_lengths] + ) + labels["pix_rotation"] = np.array( + [rot.to_value(u.deg) for rot in pix_rotations] + ) # labels["tel_ground"] = tel_ground_frame - labels["tel_ids"] =tel_ids - labels["true_alt"]=[val for val in batch["true_alt"]] - labels["true_az"]=[val for val in batch["true_az"]] - labels["tel_az"]= batch["telescope_pointing_azimuth"].data - labels["tel_alt"]= batch["telescope_pointing_altitude"].data + labels["tel_ids"] = tel_ids + labels["true_alt"] = [val for val in batch["true_alt"]] + labels["true_az"] = [val for val in batch["true_az"]] + labels["tel_az"] = batch["telescope_pointing_azimuth"].data + labels["tel_alt"] = batch["telescope_pointing_altitude"].data # cam_x = labels["cameradirection"][:,0].cpu().numpy().squeeze(-1) # cam_y = labels["cameradirection"][:,1].cpu().numpy().squeeze(-1) @@ -367,8 +377,8 @@ def _get_mono_item(self, batch): # sky_coords_alt, sky_coords_az = self.cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) return features_out, labels - - # TODO: Not adapted to pytorch + + # TODO: Not adapted to pytorch def _get_stereo_item(self, batch): """ Retrieve the features and labels for one batch of stereoscopic data. @@ -447,12 +457,12 @@ def _get_stereo_item(self, batch): # Store the labels in the labels dictionary if "type" in self.tasks: labels["type"] = np.array(true_shower_primary_class) - + # Temp fix till keras support class weights for multiple outputs or I wrote custom loss # https://github.com/keras-team/keras/issues/11735 if len(self.tasks) == 1: labels = np.array(true_shower_primary_class) - + if "energy" in self.tasks: labels["energy"] = np.array(log_true_energy) if "skydirection" in self.tasks: @@ -479,16 +489,16 @@ def _get_stereo_item(self, batch): features = {"input": np.array(mono_feature_vectors)} if "stereo_feature_vectors" in batch.colnames: features = {"input": np.array(stereo_feature_vectors)} - - image = features[:,:,:,0] - peak_time = features[:,:,:,1] + + image = features[:, :, :, 0] + peak_time = features[:, :, :, 1] image = np.transpose(image, (2, 0, 1)) peak_time = np.transpose(peak_time, (2, 0, 1)) - features_out=None - features_out["image"]=image - features_out["peak_time"]=peak_time + features_out = None + features_out["image"] = image + features_out["peak_time"] = peak_time return features_out, labels - # Include _get_mono_item and _get_stereo_item as needed \ No newline at end of file + # Include _get_mono_item and _get_stereo_item as needed diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 00b50a78..97939fcd 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -158,15 +158,7 @@ def __init__( weight=class_weights, reduction="mean" ) - self.alpha = torch.tensor([1.0, 1.2], dtype=torch.float32) - gamma = 2.0 # Increase the penalty on misclassified examples. - self.criterion_class = FocalLoss(alpha=self.alpha,gamma=gamma) - - - self.criterion_energy_class = nn.CrossEntropyLoss( - reduction="sum" - ) self.criterion_energy_value = torch.nn.L1Loss(reduction="mean") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() self.criterion_magnitud = torch.nn.L1Loss(reduction="mean") @@ -322,10 +314,11 @@ def save_checkpoint(self, save_folder, metric_value, filename_prefix, is_loss=Tr def compute_type_loss( self, classification_pred, labels_class, test_val=False, training=False ): - self.criterion_class.set_alpha(self.alpha.to(self.device)) - target = labels_class.to(torch.int64) - loss_class = self.criterion_class(classification_pred, target) + class_weights = torch.tensor([1.0, 2.0], dtype=torch.float).to(self.device) + + # Cálculo de la loss con F.cross_entropy + loss_class = F.cross_entropy(classification_pred, target, weight=class_weights, reduction='mean') # Calculate accuracy predicted = torch.softmax(classification_pred, dim=1) @@ -591,7 +584,10 @@ def on_train_epoch_end(self): else: total_loss_train = self.loss_train_sum total_batches_val = self.num_train_batches - + + if total_batches_val==0: + return 0 + if self.trainer.is_global_zero: global_loss = total_loss_train / total_batches_val @@ -720,7 +716,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): imgs = features["image"] if self.task == Task.type: - labels_class = labels["particletype"] + labels_class = labels["type"] labels_energy_value = labels["energy"] hillas_intensity = features["hillas"]["hillas_intensity"] @@ -888,7 +884,7 @@ def on_validation_epoch_end(self): # --------------------------------------- # Particle Type # --------------------------------------- - if self.task == Task.type and self.trainer.is_global_zero: + if self.task == Task.type: conf_matrix = self.confusion_matrix.compute().detach().cpu().numpy() f1_score_val = self.f1_score_val.compute().detach().cpu().numpy()*100.0 precision_val = self.precision_val.compute().detach().cpu().numpy()*100.0 @@ -896,15 +892,16 @@ def on_validation_epoch_end(self): # Compute the accuracy and reset the metric states after each epoch epoch_accuracy_val = self.class_val_accuracy.compute().item() * 100 + # if self.trainer.is_global_zero: + self.class_val_accuracy.reset() + self.confusion_matrix.reset() + self.f1_score_val.reset() + self.precision_val.reset() + + # --------------------------------------- + # Create Confusion Matrix + # --------------------------------------- if self.trainer.is_global_zero: - self.class_val_accuracy.reset() - - self.confusion_matrix.reset() - self.f1_score_val.reset() - - self.precision_val.reset() - - # Log self.logger.experiment.add_scalars( "Metrics/Validation", @@ -925,34 +922,29 @@ def on_validation_epoch_end(self): print( f"Epoch {self.current_epoch}: Global Validation Precision: {precision_val:.4f}" ) - - # --------------------------------------- - # Create Confusion Matrix - # --------------------------------------- - if self.trainer.is_global_zero: - - filename_prefix = "confusion_matrix_val" - cm_file_name = f"{filename_prefix}_{self.current_epoch}_{epoch_accuracy_val:.4f}_Validation" - - if self.logger.log_dir: - plot_confusion_matrix( - conf_matrix, - self.class_names, - cm_file_name, - self.logger.log_dir, - ) - # Compute class-wise accuracies - class_accuracies = (conf_matrix.diagonal() / conf_matrix.sum(axis=1))*100 - - self.logger.experiment.add_scalars( - "confusion_matrix", - { - "val_acc_gamma": class_accuracies[0], - "val_acc_proton": class_accuracies[1], - "val_global_acc": epoch_accuracy_val, - }, - self.current_epoch, + filename_prefix = "confusion_matrix_val" + cm_file_name = f"{filename_prefix}_{self.current_epoch}_{epoch_accuracy_val:.4f}_Validation" + + if self.logger.log_dir: + plot_confusion_matrix( + conf_matrix, + self.class_names, + cm_file_name, + self.logger.log_dir, ) + # Compute class-wise accuracies + class_accuracies = (conf_matrix.diagonal() / conf_matrix.sum(axis=1))*100 + + self.logger.experiment.add_scalars( + "confusion_matrix", + { + "val_acc_gamma": class_accuracies[0], + "val_acc_proton": class_accuracies[1], + "val_global_acc": epoch_accuracy_val, + }, + self.current_epoch, + ) + # return 0 # --------------------------------------- # Direction # --------------------------------------- diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 8ee2fe68..a2d14beb 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -23,7 +23,7 @@ data: validation_test_reduce_factor: 0 #16 #8 # Check points - type_checkpoint: ./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth + type_checkpoint: /storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 25a3bac9..33e08c7d 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -179,11 +179,11 @@ def setup(self): # Reduce for testing # -------------------------------------------------------------------- # Limit the number of examples (optional) - # max_training_samples = 500 # or whatever number you want - # max_validation_samples = 200 # or whatever number you want + max_training_samples = 500 # or whatever number you want + max_validation_samples = 200 # or whatever number you want - # training_indices = training_indices[:max_training_samples] - # validation_indices = validation_indices[:max_validation_samples] + training_indices = training_indices[:max_training_samples] + validation_indices = validation_indices[:max_validation_samples] print("BASE TRAIN FRAMEWORK", self.framework_type) From 9baeec088ce3516cc664a39948a5d7a02ab34f22 Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 10 Jun 2025 08:54:04 +0000 Subject: [PATCH 031/119] fixed the bug in Type training (blocking) --- ctlearn/tools/train/pytorch/CTLearnPL.py | 6 +++--- ctlearn/tools/train/pytorch/train_pytorch_model.py | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 97939fcd..d7e078b3 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -612,7 +612,7 @@ def on_train_epoch_end(self): # --------------------------------------- # Particle Type # --------------------------------------- - if self.task == Task.type and self.trainer.is_global_zero: + if self.task == Task.type: f1_score = self.f1_score_train.compute().detach().cpu().numpy()*100.0 self.f1_score_train.reset() @@ -892,16 +892,16 @@ def on_validation_epoch_end(self): # Compute the accuracy and reset the metric states after each epoch epoch_accuracy_val = self.class_val_accuracy.compute().item() * 100 - # if self.trainer.is_global_zero: self.class_val_accuracy.reset() self.confusion_matrix.reset() self.f1_score_val.reset() self.precision_val.reset() - + # --------------------------------------- # Create Confusion Matrix # --------------------------------------- if self.trainer.is_global_zero: + # Log self.logger.experiment.add_scalars( "Metrics/Validation", diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 33e08c7d..25a3bac9 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -179,11 +179,11 @@ def setup(self): # Reduce for testing # -------------------------------------------------------------------- # Limit the number of examples (optional) - max_training_samples = 500 # or whatever number you want - max_validation_samples = 200 # or whatever number you want + # max_training_samples = 500 # or whatever number you want + # max_validation_samples = 200 # or whatever number you want - training_indices = training_indices[:max_training_samples] - validation_indices = validation_indices[:max_validation_samples] + # training_indices = training_indices[:max_training_samples] + # validation_indices = validation_indices[:max_validation_samples] print("BASE TRAIN FRAMEWORK", self.framework_type) From 19eac766643d251d614404c14599554ed47421ce Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 10 Jun 2025 13:12:05 +0000 Subject: [PATCH 032/119] Added Augmentation and removed unused code. --- ctlearn/core/data_loader/pytorch_loader.py | 66 +++++++++++++++++++ .../training_config_iaa_neutron_training.yml | 2 +- 2 files changed, 67 insertions(+), 1 deletion(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index d68b5e3f..c5e9b3b8 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -5,6 +5,8 @@ from dl1_data_handler.reader import ProcessType from ctlearn.core.ctlearn_enum import Task from astropy import units as u +import random +import cv2 class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): @@ -118,6 +120,64 @@ def on_epoch_end(self): np.random.seed(self.random_seed) np.random.shuffle(self.indices) + def apply_augmentation(self, image, peak_time): + + for id_batch in range(image.shape[0]): + random_aug = random.random() + + if random_aug > self.aug_prob: + + if self.task != Task.cameradirection and self.task != Task.skydirection: + + random_aug_flip_ver = random.random() + if random_aug_flip_ver > self.flip_ver_prob: + # Vertical flip + image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 0), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 0), axis=-1) + + random_aug_flip_hor = random.random() + if random_aug_flip_hor > self.flip_hor_prob: + # Horizontal + image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 1), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 1), axis=-1) + # Rotation + random_aug_rot = random.random() + if random_aug_rot > self.rot_prob: + (h, w) = image[id_batch].shape[:2] + + angle = random.uniform(-self.max_aug_rot, self.max_aug_rot) + scale = 1.0 # No scaling + center = (w // 2, h // 2) + # Step 5: Get the rotation matrix + rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale) + + # Step 6: Rotate the image + image[id_batch] = np.expand_dims(cv2.warpAffine( + image[id_batch].astype(np.float32), rotation_matrix, (w, h) + ), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.warpAffine( + peak_time[id_batch].astype(np.float32), rotation_matrix, (w, h) + ), axis=-1) + # Translation + random_aug_trans = random.random() + if random_aug_trans > self.trans_prob: + # Translation + (h, w) = image[id_batch].shape[:2] + + tx = random.uniform(-self.max_aug_trans, self.max_aug_trans) + ty = random.uniform(-self.max_aug_trans, self.max_aug_trans) + translation_matrix = np.float32([[1, 0, tx], [0, 1, ty]]) + image[id_batch] = np.expand_dims(cv2.warpAffine( + image[id_batch].astype(np.float32), translation_matrix, (w, h) + ), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.warpAffine( + peak_time[id_batch].astype(np.float32), translation_matrix, (w, h) + ), axis=-1) + else: + doNothing = True + + return image, peak_time + def __getitem__(self, index): """ Generate one batch of data and retrieve the features and labels. @@ -293,6 +353,10 @@ def _get_mono_item(self, batch): image = features["input"][..., 0:1] peak_time = features["input"][..., 1:2] + + if self.use_augmentation: + image, peak_time = self.apply_augmentation(image, peak_time) + image = np.transpose(image, (0, 3, 1, 2)) peak_time = np.transpose(peak_time, (0, 3, 1, 2)) @@ -318,6 +382,8 @@ def _get_mono_item(self, batch): image = (image - self.dir_mu) / self.dir_sigma peak_time = (peak_time - self.dir_mu) / self.dir_sigma + + features_out = {} features_out["image"] = image features_out["peak_time"] = peak_time diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index a2d14beb..691c3395 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -23,7 +23,7 @@ data: validation_test_reduce_factor: 0 #16 #8 # Check points - type_checkpoint: /storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth + type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth From 27ab9ec71f3c605c2536eee4ccf81c0046943e25 Mon Sep 17 00:00:00 2001 From: pguzman Date: Sun, 15 Jun 2025 20:46:30 +0000 Subject: [PATCH 033/119] Added Diffusion model and adapted the type loss --- ctlearn/core/data_loader/pytorch_loader.py | 107 ++++++-- .../pytorch/nets/models/NoPropDT/NoPropDT.py | 187 +++++++++++++ .../NoPropDT/denoiseBlockThinRestNet.py | 112 ++++++++ ctlearn/core/pytorch/nets/models/__init__.py | 2 + ctlearn/tools/train/pytorch/CTLearnPL.py | 250 ++++++++++-------- .../training_config_iaa_neutron_training.yml | 24 +- .../train/pytorch/train_pytorch_model.py | 7 +- 7 files changed, 551 insertions(+), 138 deletions(-) create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index c5e9b3b8..af85dd43 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -8,15 +8,24 @@ import random import cv2 +import concurrent.futures + class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): + def __init__( self, tasks, parameters, use_augmentation, + T=1, **kwargs, ): + + + self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) + self._next_batch_future = None + self.parameter = parameters self.use_augmentation = use_augmentation self.use_clean = parameters["normalization"]["use_clean"] @@ -48,6 +57,10 @@ def __init__( super().__init__(**kwargs, tasks=tasks) self.on_epoch_end() + # self.T=T + # self.total_len = len(self.indices) * T + self.set_T(T) + self.hillas_names = [ "obs_id", "event_id", @@ -94,6 +107,11 @@ def __init__( "core_psi", ] + def set_T(self,T): + self.T=T + # self.total_len = len(self.indices) * T + self.indices = np.tile(self.indices, self.T) + pp=0 def __len__(self): """ Returns the number of batches per epoch. @@ -107,7 +125,9 @@ def __len__(self): Number of batches per epoch. """ return int(np.floor(len(self.indices) / self.batch_size)) - + # return int(np.floor((self.total_len/self.T) / self.batch_size)) + # return int(np.floor(((self.total_len)) / self.batch_size)) + def on_epoch_end(self): """ Updates indices after each epoch. If a random seed is provided, the indices are shuffled. @@ -178,38 +198,75 @@ def apply_augmentation(self, image, peak_time): return image, peak_time - def __getitem__(self, index): - """ - Generate one batch of data and retrieve the features and labels. - - This method is called to generate one batch of monoscopic and stereoscopic data based on - the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) - based on the mode of the DLDataReader. - - Parameters: - ----------- - index : int - Index of the batch to generate. - - Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. - """ - # Generate indices of the batch - batch_indices = self.indices[ - index * self.batch_size : (index + 1) * self.batch_size - ] - features, labels = None, None + def _fetch_batch(self, index): + batch_indices = self.indices[index * self.batch_size : (index + 1) * self.batch_size] + if len(batch_indices) == 0: + raise IndexError(f"No data for batch index {index} (batch_indices empty)") + if self.DLDataReader.mode == "mono": batch = self.DLDataReader.generate_mono_batch(batch_indices) features, labels = self._get_mono_item(batch) elif self.DLDataReader.mode == "stereo": batch = self.DLDataReader.generate_stereo_batch(batch_indices) features, labels = self._get_stereo_item(batch) - return features, labels + def __getitem__(self, index): + + data_idx = index + # data_idx = index % int((self.total_len/self.T)/self.batch_size) + t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) + + # If this is the first call, fetch synchronously, and schedule the next + if self._next_batch_future is None: + features, labels = self._fetch_batch(data_idx) + else: + features, labels = self._next_batch_future.result() # Wait for the prefetch to finish + + # Schedule the next batch prefetch + if data_idx + 1 < len(self): + self._next_batch_future = self.executor.submit(self._fetch_batch, data_idx + 1) + else: + self._next_batch_future = None # No more batches + + return features, labels, t + + # def __getitem__(self, index): + # """ + # Generate one batch of data and retrieve the features and labels. + + # This method is called to generate one batch of monoscopic and stereoscopic data based on + # the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) + # based on the mode of the DLDataReader. + + # Parameters: + # ----------- + # index : int + # Index of the batch to generate. + + # Returns: + # -------- + # tuple + # A tuple containing the input data as features and the corresponding labels. + # """ + + # # data_idx = index + # # data_idx = index % int((self.total_len/self.T)/self.batch_size) + # t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) + # # Generate indices of the batch + # batch_indices = self.indices[ + # index * self.batch_size : (index + 1) * self.batch_size + # ] + # features, labels = None, None + # if self.DLDataReader.mode == "mono": + # batch = self.DLDataReader.generate_mono_batch(batch_indices) + # features, labels = self._get_mono_item(batch) + # elif self.DLDataReader.mode == "stereo": + # batch = self.DLDataReader.generate_stereo_batch(batch_indices) + # features, labels = self._get_stereo_item(batch) + + # return features, labels, t + def cam_to_alt_az( self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y ): diff --git a/ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py b/ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py new file mode 100644 index 00000000..e4422880 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py @@ -0,0 +1,187 @@ +# NoProp-DT model + +import torch +from torch import nn + +# from .denoiseBlock import DenoiseBlock +from .denoiseBlockThinRestNet import DenoiseBlock + +import math + +class SimplifiedDenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_classes): + super().__init__() + # Simplified image feature extractor + self.conv_path = nn.Sequential( + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten() + ) + + # Simplified embedding processor + self.fc_z = nn.Sequential( + nn.Linear(embedding_dim, 256), + nn.ReLU() + ) + + # Combined processor + self.combined = nn.Sequential( + nn.Linear(256 + 64, 128), + nn.ReLU(), + nn.Linear(128, num_classes) + ) + + def forward(self, x, z_prev, W_embed): + # Image features + x_feat = self.conv_path(x) + + # Process embedding + z_feat = self.fc_z(z_prev) + + # Combine features + combined = torch.cat([x_feat, z_feat], dim=1) + logits = self.combined(combined) + + # Update embedding + z_next = z_prev + logits @ W_embed + + return z_next, logits + +class NoPropDT(nn.Module): + def __init__(self, num_outputs, embedding_dim=128, T=3, eta=0.1): + super().__init__() + + num_classes = num_outputs + self.num_classes = num_classes + self.embedding_dim = embedding_dim + self.T = T + self.eta = eta + + # Initialize learnable class embeddings + self.W_embed = nn.Parameter(torch.randn(num_classes, embedding_dim) * 0.02, requires_grad=True) + + # Create denoising blocks + self.blocks = nn.ModuleList([ + DenoiseBlock(embedding_dim, num_classes) for _ in range(T) + ]) + + # Final classifier + self.classifier = nn.Linear(embedding_dim, num_classes) + + # Improved noise schedule + self.register_buffer('alpha_bar', self._cosine_schedule(T)) + self.register_buffer('snr_diff', self._calculate_snr_diff(self.alpha_bar)) + + def _cosine_schedule(self, T): + t = torch.arange(1, T+1, dtype=torch.float32) + alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi/2))**2 + return alpha_bar + + def _calculate_snr_diff(self, alpha_bar): + snr = alpha_bar / (1 - alpha_bar + 1e-8) + snr_prev = torch.cat([torch.tensor([0.]), snr[:-1]]) + return torch.clamp(snr - snr_prev, min=1e-5) + + def forward_denoise(self, x, z_prev, t): + return self.blocks[t](x, z_prev, self.W_embed)[0] + + def inference(self, x): + B = x.size(0) + z = torch.zeros(B, self.embedding_dim, device=x.device) + + for t in range(self.T): + z = self.forward_denoise(x, z, t) + + return self.classifier(z) + + def forward(self, x): + return self.inference(x), None, None + +# class NoPropDT(nn.Module): +# def __init__(self, num_outputs, embedding_dim, T, eta, use_softmax=False, num_channels=1): +# super().__init__() +# num_classes = num_outputs +# self.num_classes = num_classes # Total number of classes (e.g., 10 for MNIST) +# self.embedding_dim = embedding_dim # Size of the vector that represents each class +# self.T = T # Number of denoising steps (number of DenoiseBlocks) +# self.eta = eta # A hyperparameter used in the loss function + +# # Create a list of T denoising blocks. Each block learns to reduce noise. +# self.blocks = nn.ModuleList([ +# DenoiseBlock(embedding_dim, num_classes,use_softmax,num_channels) for _ in range(T) +# ]) + +# # Learnable matrix that holds one vector (embedding) per class (e.g., 10 rows for 10 digits) +# self.W_embed = nn.Parameter(torch.randn(num_classes, embedding_dim) * 1.1, requires_grad=True) + +# # Final classifier layer to predict class label from embedding +# self.classifier = nn.Linear(embedding_dim, num_classes) + +# # --- Prepare cosine noise schedule for diffusion process --- + +# # t = [1, 2, ..., T] +# t = torch.arange(1, T+1, dtype=torch.float32) + +# # Calculate alpha_t using cosine schedule +# alpha_t = torch.cos(t / T * (math.pi/2))**2 + +# # alpha_bar is cumulative product of alpha_t, used to scale noise +# alpha_bar = torch.cumprod(alpha_t, dim=0) + +# # Calculate signal-to-noise ratio (SNR) +# snr = alpha_bar / (1 - alpha_bar + 1e-8) + +# # Previous SNR (shifted by one timestep) +# snr_prev = torch.cat([torch.tensor([0.], dtype=snr.dtype), snr[:-1]], dim=0) + +# # Difference in SNR between steps, used to weight denoising loss +# snr_diff = snr - snr_prev +# snr_diff = torch.clamp(snr_diff, min=1e-5) + + +# #---------------------------- +# # t = torch.arange(1, T + 1, dtype=torch.float32) +# # alpha_t = torch.cos(t / T * (math.pi / 2)) ** 2 +# # alpha_bar = torch.cumprod(alpha_t, dim=0) +# # # snr = alpha_bar / (1 - alpha_bar) +# # snr = alpha_bar / (1 - alpha_bar + 1e-8) +# # snr_prev = torch.cat([torch.tensor([0.], dtype=snr.dtype), snr[:-1]], dim=0) +# # snr_diff = snr - snr_prev +# # snr_diff = torch.clamp(snr_diff, min=1e-5) + +# #---------------------------- +# # Save alpha_bar and snr_diff inside the model so they move to GPU automatically +# self.register_buffer('alpha_bar', alpha_bar) +# self.register_buffer('snr_diff', snr_diff) + +# # Perform denoising at step t using DenoiseBlock[t] +# def forward_denoise(self, x, z_prev, t): +# return self.blocks[t](x, z_prev, self.W_embed)[0] + +# # Use final denoised vector to predict the class +# def classify(self, z): +# return self.classifier(z) + +# # Run all denoising steps in order to produce final prediction +# def inference(self, x): +# B = x.size(0) # Batch size +# # Start with random noise as initial z +# z = torch.randn(B, self.embedding_dim, device=x.device) + +# if not self.training: +# z = torch.zeros(B, self.embedding_dim, device=x.device) + +# # Pass through all denoising blocks one by one +# for t in range(self.T): +# z = self.forward_denoise(x, z, t) + +# # Use final denoised result to classify +# return self.classify(z) + +# def forward(self, x): +# return self.inference(x), None, None \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py new file mode 100644 index 00000000..17ab8a65 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py @@ -0,0 +1,112 @@ +# Denoising block +import torch +from torch import nn +import torch.nn.functional as F + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.conv_block = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + AdaptiveBatchNorm2d(out_channels), + nn.ReLU(), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + AdaptiveBatchNorm2d(out_channels) + ) + + self.shortcut = nn.Sequential() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1), + nn.BatchNorm2d(out_channels) + ) + + self.relu = nn.ReLU() + + def forward(self, x): + return self.relu(self.conv_block(x) + self.shortcut(x)) + + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_classes, use_softmax=False, num_channels=1): + super().__init__() + self.use_softmax = use_softmax + # ThinResNet convolutional path + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 32), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(32, 64), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(64, 128), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(128, 256), + # nn.BatchNorm1d(256) + ) + + # Fully connected layers for processing noisy embedding vector z_prev + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + # Layers to combine image and embedding features + self.fc_f1 = nn.Linear(256 + 256, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, num_classes) + + def forward(self, x, z_prev, W_embed): + # Extract features from the input image x + x_feat = self.conv_path(x) + + # Process the noisy class embedding z_prev + h1 = F.relu(self.bn_z1(self.fc_z1(z_prev))) + h2 = F.relu(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + + z_feat = h3 + h1 # Residual connection + + # Concatenate image and embedding features + h_f = torch.cat([x_feat, z_feat], dim=1) + + # Process combined features through fully connected layers + h_f = F.relu(self.bn_f1(self.fc_f1(h_f))) + h_f = F.relu(self.bn_f2(self.fc_f2(h_f))) + # h_f =self.bn_f2(self.fc_f2(h_f)) + # h_f = F.relu(self.fc_f1(h_f)) + # h_f = F.relu(self.fc_f2(h_f)) + + # Compute logits for all classes + logits = self.fc_out(h_f) + + # Convert logits to probability distribution over classes + if self.use_softmax: + p = F.softmax(logits, dim=1) + else: + p = logits + + # Compute the next denoised embedding + # z_next = p @ W_embed + z_next = z_prev + logits @ W_embed + + return z_next, logits diff --git a/ctlearn/core/pytorch/nets/models/__init__.py b/ctlearn/core/pytorch/nets/models/__init__.py index 8f10fbd7..74557a1e 100644 --- a/ctlearn/core/pytorch/nets/models/__init__.py +++ b/ctlearn/core/pytorch/nets/models/__init__.py @@ -5,3 +5,5 @@ import ctlearn.core.pytorch.nets.models.DoubleBBEfficientNet.DoubleBBEfficientNet import ctlearn.core.pytorch.nets.models.DualBackboneEfficientNetV2.DoubleBBEfficientNetV2 import ctlearn.core.pytorch.nets.models.DBBDanet.DBBDanet + +import ctlearn.core.pytorch.nets.models.NoPropDT.NoPropDT diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index d7e078b3..1560cbd2 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -136,6 +136,12 @@ def __init__( # Get the number of inputs of the net sig = inspect.signature(model.forward) num_inputs = len(sig.parameters) + # Detect if model is diffusive + if hasattr(self.model,"T"): + self.is_difussion=True + else: + self.is_difussion=False + self.num_inputs = num_inputs self.train_loader = train_loader self.val_loader = val_loader @@ -310,12 +316,23 @@ def save_checkpoint(self, save_folder, metric_value, filename_prefix, is_loss=Tr os.remove(removed_file) # Remove the worst performing file except FileNotFoundError: pass # File doesn't exist, continue execution + + # def on_after_backward(self): + # print('on_after_backward:') + # print('W_embed grad:', self.model.W_embed.grad) + # print('classifier.weight grad:', self.model.classifier.weight.grad) + # for name, param in self.model.named_parameters(): + # if param.grad: + # print(f"{name} grad norm: {param.grad.norm().item()}") + # # print(f"{name} has no grad!") + # # else: + # # print(f"{name} grad norm: {param.grad.norm().item()}") # ---------------------------------------------------------------------------------------------------------- def compute_type_loss( self, classification_pred, labels_class, test_val=False, training=False ): target = labels_class.to(torch.int64) - class_weights = torch.tensor([1.0, 2.0], dtype=torch.float).to(self.device) + class_weights = torch.tensor([1.0, 1.0], dtype=torch.float).to(self.device) # Cálculo de la loss con F.cross_entropy loss_class = F.cross_entropy(classification_pred, target, weight=class_weights, reduction='mean') @@ -328,7 +345,7 @@ def compute_type_loss( accuracy = 0 precision = 0 loss = loss_class - # loss = alpha*loss_class+(1-alpha)*loss_triplet + if training: self.class_train_accuracy.update(predicted, labels_class) @@ -349,70 +366,6 @@ def compute_type_loss( # ---------------------------------------------------------------------------------------------------------- def compute_direction_loss(self, direction_pred, labels_direction, training=False): - # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - # direction_pred = list(direction_pred) - # pred_az_atl = direction_pred[0][:,0:2] - # pred_separation = direction_pred[0][:,2] - # # direction_pred[0]= direction_pred[0][:,0:2] - # else: - - # pred_az_atl = direction_pred[:, 0:2] - # pred_separation = direction_pred[:, 2] - - # labels_az_alt = labels_direction[:, 0:2] - # label_separation = labels_direction[:, 2] - # loss_separation = self.criterion_direction(pred_separation, label_separation) - - # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - # loss_alt_az = self.criterion_alt_az(direction_pred, labels_direction) - # else: - # loss_alt_az = self.criterion_alt_az_l1(pred_az_atl, labels_az_alt) - - # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - - # loss_angular_error, _ = AngularDistance( - # (direction_pred[0][:, 1]), - # labels_az_alt[:, 1], - # (direction_pred[0][:, 0]), - # labels_az_alt[:, 0], - # reduction="sum", - # ) - - # else: - - # loss_angular_error, _ = AngularDistance( - # (direction_pred[:, 1]), - # labels_az_alt[:, 1], - # (direction_pred[:, 0]), - # labels_az_alt[:, 0], - # reduction="sum", - # ) - - # if training == False: - # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: - # _, angular_diff = AngularDistance( - # (direction_pred[0][:, 1]), - # labels_az_alt[:, 0], - # (direction_pred[0][:, 0]), - # labels_az_alt[:, 1], - # reduction=None, - # ) - # else: - # _, angular_diff = AngularDistance( - # (direction_pred[:, 1]), - # labels_az_alt[:, 0], - # (direction_pred[:, 0]), - # labels_az_alt[:, 1], - # reduction=None, - # ) - # else: - # angular_diff = None - - # loss = loss_alt_az + 0.001*(loss_separation + loss_angular_error) - # return loss, loss_separation, loss_alt_az, loss_angular_error, angular_diff - - - labels_dx_dy = labels_direction[:, 0:2] label_distance = labels_direction[:, 2] @@ -462,12 +415,80 @@ def compute_energy_loss( return loss, energy_diff # ---------------------------------------------------------------------------------------------------------- + def compute_type_loss_diffusion(self, x,y, training=False): + + loss = 0 + # Get the embedding of the true label (e.g. "this is a 3") + uy = self.model.W_embed[y] + for t in range(self.model.T): + + # Get the current noise level from the schedule + alpha_bar_t = self.model.alpha_bar[t] + + # Generate random noise with the same shape as uy + noise = torch.randn_like(uy) + + # Add noise to the label embedding → this is our "noisy target" + z_t = torch.sqrt(alpha_bar_t) * uy + torch.sqrt(1 - alpha_bar_t) * noise + + # Pass the image and the noisy label through one block + z_pred, _ = self.model.blocks[t](x, z_t, self.model.W_embed) + + # Compute how far the output is from the clean label embedding + loss_l2 = F.mse_loss(z_pred, uy) + + # Weight the loss using the signal-to-noise ratio + step_loss = 0.5 * self.model.eta * self.model.snr_diff[t] * loss_l2 + + + accuracy = 0 + precision = 0 + predicted = None + # If it's the final layer, add classification and KL losses + if t == self.model.T - 1: + # Get predictions from classifier + logits = self.model.classifier(z_pred) + + # Cross-entropy loss: how wrong the predicted class is + loss_ce = F.cross_entropy(logits, y) + + # KL-like loss: penalize if embedding is too far from origin + loss_kl = 0.5 * uy.pow(2).sum(dim=1).mean() + + # Add all parts together + # loss = loss + loss_ce + loss_kl + step_loss = step_loss + loss_ce + loss_kl + classification_pred,*_ = self.model(x) + + # Calculate accuracy + predicted = torch.softmax(classification_pred, dim=1) + predicted = predicted.argmax(dim=1) + + if training: + + self.class_train_accuracy.update(predicted, y) + accuracy = self.class_train_accuracy.compute().item() + self.f1_score_train.update(predicted, y) + self.precision_train.update(predicted, y) + precision = self.precision_train.compute().item() + else: + + self.class_val_accuracy.update(predicted, y) + accuracy = self.class_val_accuracy.compute().item() + self.confusion_matrix.update(predicted, y) + self.f1_score_val.update(predicted, y) + self.precision_val.update(predicted, y) + precision = self.precision_val.compute().item() + loss = loss + step_loss + + return loss, accuracy, predicted, precision + # ---------------------------------------------------------------------------------------------------------- def training_step(self, batch, batch_idx): # ------------------------------------------------------------------ # Read inputs (features) and labels # ------------------------------------------------------------------ - features, labels = batch + features, labels, t = batch loss = 0 if len(features) > 0: @@ -488,24 +509,29 @@ def training_step(self, batch, batch_idx): # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ - if self.num_inputs == 2: - peak_time = features["peak_time"] - peak_time = peak_time.to(self.device) - classification_pred, energy_pred, direction_pred = self.model( - imgs, peak_time - ) - else: - classification_pred, energy_pred, direction_pred = self.model(imgs) + if not self.is_difussion: + if self.num_inputs == 2: + peak_time = features["peak_time"] + peak_time = peak_time.to(self.device) + classification_pred, energy_pred, direction_pred = self.model( + imgs, peak_time + ) + else: + classification_pred, energy_pred, direction_pred = self.model(imgs) # ------------------------------------------------------------------ # Particle type # --------------------------------------- if self.task == Task.type: - classification_pred_ = classification_pred[0] - feature_vector = classification_pred[1] + if self.is_difussion: + loss, accuracy, predicted, precision = self.compute_type_loss_diffusion(imgs,labels_class,training=True) - loss, accuracy, predicted, precision = self.compute_type_loss( - classification_pred_, labels_class, test_val=False, training=True + else: + classification_pred_ = classification_pred[0] + feature_vector = classification_pred[1] + + loss, accuracy, predicted, precision = self.compute_type_loss( + classification_pred_, labels_class, test_val=False, training=True ) # Log batch loss and accuracy on the progress bar self.log( @@ -574,9 +600,11 @@ def training_step(self, batch, batch_idx): return loss # ---------------------------------------------------------------------------------------------------------- def on_train_epoch_end(self): - + print("train epoch end 1 ") if self.trainer.world_size > 1: - dist.barrier() + print("train epoch end 1 ") + # dist.barrier() + print("train epoch end 2 ") # Gather y Sync values with the GPUs. total_loss_train= self.all_gather(self.loss_train_sum).sum().item() total_batches_val = self.all_gather(torch.tensor(self.num_train_batches, device=self.device)).sum().item() @@ -585,10 +613,13 @@ def on_train_epoch_end(self): total_loss_train = self.loss_train_sum total_batches_val = self.num_train_batches + print("train epoch end 2 ") if total_batches_val==0: + print("train epoch end 3 ") return 0 if self.trainer.is_global_zero: + print("train epoch end 4 ") global_loss = total_loss_train / total_batches_val self.logger.experiment.add_scalars( @@ -598,7 +629,7 @@ def on_train_epoch_end(self): }, self.current_epoch, ) - + print("train epoch end 5 ") self.logger.experiment.add_scalars( "Learning rate", { @@ -613,29 +644,29 @@ def on_train_epoch_end(self): # Particle Type # --------------------------------------- if self.task == Task.type: + ii = 0 + # f1_score = self.f1_score_train.compute().detach().cpu().numpy()*100.0 + # self.f1_score_train.reset() - f1_score = self.f1_score_train.compute().detach().cpu().numpy()*100.0 - self.f1_score_train.reset() - - precision = self.precision_train.compute().detach().cpu().numpy()*100.0 - self.precision_train.reset() + # precision = self.precision_train.compute().detach().cpu().numpy()*100.0 + # self.precision_train.reset() epoch_accuracy = self.class_train_accuracy.compute().detach().cpu().item() * 100 self.class_train_accuracy.reset() - # Compute the accuracy and reset the metric states after each epoch + # # Compute the accuracy and reset the metric states after each epoch if self.trainer.is_global_zero: - self.log("train_acc_epoch", epoch_accuracy, on_step=False, prog_bar=True) - # Log - self.logger.experiment.add_scalars( - "Metrics/Training", - { - "acc": epoch_accuracy, - "f1":f1_score, - "precision":precision, - }, - self.current_epoch, - ) + # self.log("train_acc_epoch", epoch_accuracy, on_step=False, prog_bar=True, sync_dist=True) + # # Log + # self.logger.experiment.add_scalars( + # "Metrics/Training", + # { + # "acc": epoch_accuracy, + # "f1":f1_score, + # "precision":precision, + # }, + # self.current_epoch, + # ) print( f"Epoch {self.current_epoch}: Global Training Accuracy: {epoch_accuracy:.4f}" ) @@ -697,7 +728,7 @@ def on_train_epoch_end(self): # the values used to estimate the losses # --------------------------------------- self.reset_values() - + print("END train epoch") # Reset self.loss_train_sum = 0 self.num_train_batches = 0 @@ -711,7 +742,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # ------------------------------------------------------------------ # Read inputs (features) and labels # ------------------------------------------------------------------ - features, labels = batch + features, labels, t = batch if len(features) > 0: imgs = features["image"] @@ -728,6 +759,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ + # if not self.is_difussion: if self.num_inputs == 2: peak_time = features["peak_time"] classification_pred, energy_pred, direction_pred = self.model( @@ -742,10 +774,22 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # Particle Type # --------------------------------------- if self.task == Task.type: - classification_pred_ = classification_pred[0] - feature_vector = classification_pred[1] + if self.is_difussion: + classification_pred_ = classification_pred + else: + + classification_pred_ = classification_pred[0] + feature_vector = classification_pred[1] # Log batch loss and accuracy on the progress bar if dataloader_idx == 0: + # if self.is_difussion: + # loss, accuracy, predicted, precision =self.compute_type_loss_diffusion( + # classification_pred_, + # labels_class, + # test_val=False, + # training=False, + # ) + loss, accuracy, predicted, precision = self.compute_type_loss( classification_pred_, labels_class, diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 691c3395..79d97471 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -23,7 +23,7 @@ data: validation_test_reduce_factor: 0 #16 #8 # Check points - type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth + type_checkpoint: ./run/fake #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth @@ -42,15 +42,23 @@ cut-off: model: +# model_type: +# model_name: "DoubleBBEfficientNet" +# parameters: +# model_variant: "efficientnet-b3" +# task: 'type' +# num_outputs: 2 +# device_str: "cuda" +# energy_bins: None + model_type: - model_name: "DoubleBBEfficientNet" + model_name: "NoPropDT" parameters: - model_variant: "efficientnet-b3" - task: 'type' + # task: 'type' num_outputs: 2 - device_str: "cuda" - energy_bins: None - + embedding_dim: 512 + T: 8 + eta: 0.1 #0.1 # model_type: # model_name: "ThinResNet_DBB" # parameters: @@ -98,7 +106,7 @@ hyp: steps_epoch: 100 # Computed online. Must be removed l2_lambda: 1e-5 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 - gradient_clip_val: 2.0 # Avoid gradient explosion + gradient_clip_val: 3.0 # Avoid gradient explosion save_k: 200 # Save as maximum k checkpoints. diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 25a3bac9..487d4c7f 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -11,6 +11,7 @@ Unicode, ) + from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL try: import torch @@ -179,8 +180,8 @@ def setup(self): # Reduce for testing # -------------------------------------------------------------------- # Limit the number of examples (optional) - # max_training_samples = 500 # or whatever number you want - # max_validation_samples = 200 # or whatever number you want + # max_training_samples = 5000 # or whatever number you want + # max_validation_samples = 1200 # or whatever number you want # training_indices = training_indices[:max_training_samples] # validation_indices = validation_indices[:max_validation_samples] @@ -245,6 +246,8 @@ def start(self): f"task:{task.name} is not supported. Task must be type, direction or energy" ) + # if hasattr(model_net, 'T'): + # self.training_loader.set_T(model_net.T) # ------------------------------------------------------------------------------ # Load Checkpoints # ------------------------------------------------------------------------------ From 30a728534fac0c78dc8e939b0ac2c9c5b5410c64 Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 24 Jun 2025 12:26:51 +0000 Subject: [PATCH 034/119] Fixed Diffusion Regression code and plot bug fix --- .../nets/loss_functions/loss_functions.py | 12 +- .../nets/models/NoPropDTReg/NoPropDTReg.py | 121 ++++++++++++++ .../NoPropDTReg/denoiseBlockThinRestNet.py | 154 ++++++++++++++++++ ctlearn/core/pytorch/nets/models/__init__.py | 2 + ctlearn/tools/train/pytorch/CTLearnPL.py | 112 +++++++++++-- .../training_config_iaa_neutron_training.yml | 30 ++-- ctlearn/tools/train_model.py | 31 +++- 7 files changed, 432 insertions(+), 30 deletions(-) create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py diff --git a/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py index 64ae5fac..fd816c38 100644 --- a/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py +++ b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py @@ -58,11 +58,17 @@ def forward(self, dist_params, y): return nig_nll_error + self.lamb *nig_reg_error -def cosine_direction_loss(pred_x, pred_y, true_x, true_y): +def cosine_direction_loss(pred_x, pred_y, true_x, true_y,reduction="mean"): pred_vec = F.normalize(torch.stack([pred_x, pred_y], dim=1), dim=1) true_vec = F.normalize(torch.stack([true_x, true_y], dim=1), dim=1) - return 1 - torch.sum(pred_vec * true_vec, dim=1).mean() - + if reduction=="mean": + return 1 - torch.sum(pred_vec * true_vec, dim=1).mean() + elif reduction=="sum": + return 1 - torch.sum(pred_vec * true_vec, dim=1).sum() + elif reduction=="none": + return 1 - torch.sum(pred_vec * true_vec, dim=1) + else: + raise RuntimeError("Reduction not supported: Use sum , mean or none") def AngularDistance(alt1_rad, alt2_rad, az1_rad, az2_rad,reduction = None): """ diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py new file mode 100644 index 00000000..eb5d64f8 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py @@ -0,0 +1,121 @@ +# NoProp-DT model + +import torch +from torch import nn + +# from .denoiseBlock import DenoiseBlock +from .denoiseBlockThinRestNet import DenoiseBlock, MemoryEfficientSwish + +import math + +class SimplifiedDenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_classes): + super().__init__() + # Simplified image feature extractor + self.conv_path = nn.Sequential( + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten() + ) + + # Simplified embedding processor + self.fc_z = nn.Sequential( + nn.Linear(embedding_dim, 256), + nn.ReLU() + ) + + # Combined processor + self.combined = nn.Sequential( + nn.Linear(256 + 64, 128), + nn.ReLU(), + nn.Linear(128, num_classes) + ) + + def forward(self, x, z_prev, W_embed): + # Image features + x_feat = self.conv_path(x) + + # Process embedding + z_feat = self.fc_z(z_prev) + + # Combine features + combined = torch.cat([x_feat, z_feat], dim=1) + logits = self.combined(combined) + + # Update embedding + z_next = z_prev + logits @ W_embed + + return z_next, logits + +class NoPropDTReg(nn.Module): + def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1): + super().__init__() + + self.task = task + num_classes = num_outputs + self.num_classes = num_classes + self.embedding_dim = embedding_dim + self.T = T + self.eta = eta + + self.blocks = nn.ModuleList([DenoiseBlock(embedding_dim,num_channels=1) for _ in range(T)]) + # self.regressor = nn.Linear(embedding_dim, num_outputs) + self.regressor = nn.Sequential( + nn.Linear(embedding_dim, embedding_dim//2), + MemoryEfficientSwish(), + nn.Linear(embedding_dim//2, num_outputs) +) + + # Final classifier + self.classifier = nn.Linear(embedding_dim, num_classes) + + # Improved noise schedule + self.register_buffer('alpha_bar', self._cosine_schedule(T)) + self.register_buffer('snr_diff', self._calculate_snr_diff(self.alpha_bar)) + + + self.target_embedder = nn.Linear(num_outputs, embedding_dim) + + for m in self.regressor: + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + nn.init.zeros_(m.bias) + + def _cosine_schedule(self, T): + t = torch.arange(1, T+1, dtype=torch.float32) + alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi/2))**2 + return alpha_bar + + def _calculate_snr_diff(self, alpha_bar): + snr = alpha_bar / (1 - alpha_bar + 1e-8) + snr_prev = torch.cat([torch.tensor([0.]), snr[:-1]]) + return torch.clamp(snr - snr_prev, min=1e-5) + + def forward_denoise(self, x, z_prev, t): + return self.blocks[t](x, z_prev, None)[0] + + def regress(self, z): + return self.regressor(z) + + def inference(self, x): + B = x.size(0) + z = torch.randn(B, self.embedding_dim, device=x.device) + if not self.training: + z = torch.zeros(B, self.embedding_dim, device=x.device) + + for t in range(self.T): + z = self.forward_denoise(x, z, t) + + return self.regress(z) + + def forward(self, x): + + if self.task=="direction": + return None, None, self.inference(x) + elif self.task=="energy": + return None, self.inference(x), None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py new file mode 100644 index 00000000..fc52d51f --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py @@ -0,0 +1,154 @@ +# Denoising block +import torch +from torch import nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +# class ResidualBlock(nn.Module): +# def __init__(self, in_channels, out_channels): +# super().__init__() +# self.conv_block = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels), +# nn.PReLU(), +# nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels) +# ) + +# self.shortcut = nn.Sequential() +# if in_channels != out_channels: +# self.shortcut = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=1), +# nn.BatchNorm2d(out_channels) +# ) + +# self.relu = nn.PReLU() + +# def forward(self, x): +# return self.relu(self.conv_block(x) + self.shortcut(x)) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16, drop_path_rate=0.1): + super().__init__() + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + self.norm1 = nn.GroupNorm(8, out_channels) + self.act1 = nn.GELU() + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) + self.norm2 = nn.GroupNorm(8, out_channels) + self.act2 = nn.GELU() + + self.se = SEBlock(out_channels, reduction) + + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1), + nn.GroupNorm(8, out_channels) + ) + + try: + from timm.models.layers import DropPath + self.drop_path = DropPath(drop_path_rate) + except ImportError: + self.drop_path = nn.Identity() + + # self.final_act = nn.GELU() + self.final_act = MemoryEfficientSwish() + def forward(self, x): + residual = self.act1(self.norm1(self.conv1(x))) + residual = self.act2(self.norm2(self.conv2(residual))) + # residual = self.act1((self.conv1(x))) + # residual = self.act2((self.conv2(residual))) + + residual = self.se(residual) + + out = self.drop_path(residual) + self.shortcut(x) + return self.final_act(out) + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1): + super().__init__() + + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 32), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(32, 64), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(64, 128), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(128, 256), + ) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + + z_feat = h3 + h1 + + h_f = torch.cat([x_feat, z_feat], dim=1) + + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/__init__.py b/ctlearn/core/pytorch/nets/models/__init__.py index 74557a1e..ac327522 100644 --- a/ctlearn/core/pytorch/nets/models/__init__.py +++ b/ctlearn/core/pytorch/nets/models/__init__.py @@ -6,4 +6,6 @@ import ctlearn.core.pytorch.nets.models.DualBackboneEfficientNetV2.DoubleBBEfficientNetV2 import ctlearn.core.pytorch.nets.models.DBBDanet.DBBDanet +# Diffusion import ctlearn.core.pytorch.nets.models.NoPropDT.NoPropDT +import ctlearn.core.pytorch.nets.models.NoPropDTReg.NoPropDTReg \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 1560cbd2..7e188b4d 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -40,8 +40,9 @@ import torch.multiprocessing as mp from ctlearn.core.pytorch.nets.loss_functions.loss_functions import evidential_regression_loss import inspect -# from ctlearn.nets.loss_functions.loss_functions import evidential_classification - + +import matplotlib +matplotlib.use('Agg') class CTLearnTrainer(pl.Trainer): def __init__(self,**kwargs): @@ -168,11 +169,15 @@ def __init__( self.criterion_energy_value = torch.nn.L1Loss(reduction="mean") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() self.criterion_magnitud = torch.nn.L1Loss(reduction="mean") - + + self.criterion_direction_none = torch.nn.SmoothL1Loss(reduction="none") # nn.MSELoss() + self.criterion_magnitud_none = torch.nn.L1Loss(reduction="none") + self.criterion_alt_az_l1_none = torch.nn.L1Loss(reduction="none") self.criterion_direction = torch.nn.L1Loss(reduction="mean") # nn.MSELoss() self.criterion_vector = VectorLoss(alpha=0.1, reduction="mean") self.criterion_alt_az_l1 = torch.nn.L1Loss(reduction="mean") + self.criterion_alt_az = evidential_regression_loss(lamb=0.01, reduction="mean") @@ -415,6 +420,80 @@ def compute_energy_loss( return loss, energy_diff # ---------------------------------------------------------------------------------------------------------- + def compute_direction_loss_diffusion(self, x, y, labels_energy_value): + + loss = 0 + y = y.squeeze(-1) + y_embed = self.model.target_embedder(y) + + for t in range(self.model.T): + # Add noise to target (landmarks) + alpha_bar_t = self.model.alpha_bar[t] + noise = torch.randn_like(y_embed) + z_t = torch.sqrt(alpha_bar_t) * y_embed + torch.sqrt(1 - alpha_bar_t) * noise + + # Denoise step + z, _ = self.model.blocks[t](x, z_t, None) # W_embed not needed + + preds = self.model.regress(z) + # Loss to clean target + loss_l2 = F.mse_loss(preds, y) + + # Weighted by SNR difference + step_loss = 2.5 * self.model.eta * self.model.snr_diff[t] * loss_l2 + + # Final step: use regression head + if t == self.model.T - 1: + labels_dx_dy = y[:, 0:2] + label_distance = y[:, 2] + direction_pred = self.model.regress(z) + if isinstance(direction_pred, tuple): + # Not Tested + direction_pred = list(direction_pred) + + pred_dx_dy = direction_pred[0][:,0:2].unsqueeze(-1) + pred_distance = direction_pred[0][:,2].unsqueeze(-1) + else: + pred_dx_dy = direction_pred[:,0:2] + pred_distance = direction_pred[:,2] + + # loss_angular_diff = cosine_direction_loss(pred_dx_dy[:,0],pred_dx_dy[:,1], labels_dx_dy[:, 0],labels_dx_dy[:, 1]) + loss_angular_diff = cosine_direction_loss(pred_dx_dy[:,0],pred_dx_dy[:,1], labels_dx_dy[:, 0],labels_dx_dy[:, 1],reduction="none") + _, angular_diff = AngularDistance( + pred_dx_dy[:,0], + labels_dx_dy[:, 0], + pred_dx_dy[:,1], + labels_dx_dy[:, 1], + reduction="None", + + ) + + vector_cam_distance = torch.sqrt(pred_dx_dy[:,0]**2 + pred_dx_dy[:,1]**2) + # loss_dx_dy = self.criterion_alt_az_l1(pred_dx_dy, labels_dx_dy) + # loss_distance = self.criterion_magnitud(label_distance, pred_distance) + # loss_distance_dx_dy = self.criterion_magnitud(label_distance, vector_cam_distance) + loss_dx_dy = self.criterion_alt_az_l1_none(pred_dx_dy, labels_dx_dy).mean(dim=1) + loss_distance = self.criterion_magnitud_none(label_distance, pred_distance) + loss_distance_dx_dy = self.criterion_magnitud_none(label_distance, vector_cam_distance) + + energy = torch.pow(10,labels_energy_value) + k=4.3 + e_thrs= 4 + energy_weight = k*(1/(1+torch.exp(-(1/k)*(energy-e_thrs)))) + # weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy + loss_angular_diff) + weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy) + + loss_distance=loss_distance.mean() + loss_dx_dy = loss_dx_dy.mean() + loss_distance_dx_dy = loss_distance_dx_dy.mean() + loss_angular_diff = loss_angular_diff.mean() + + step_loss = step_loss + weight_loss.mean() + + loss = loss + step_loss + + return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff + # ---------------------------------------------------------------------------------------------------------- def compute_type_loss_diffusion(self, x,y, training=False): loss = 0 @@ -481,7 +560,7 @@ def compute_type_loss_diffusion(self, x,y, training=False): precision = self.precision_val.compute().item() loss = loss + step_loss - return loss, accuracy, predicted, precision + return loss, accuracy, predicted, precision # ---------------------------------------------------------------------------------------------------------- def training_step(self, batch, batch_idx): @@ -497,6 +576,7 @@ def training_step(self, batch, batch_idx): if self.task == Task.type: labels_class = labels["type"] + labels_energy_value = labels["energy"] if self.task == Task.energy: labels_energy_value = labels["energy"] labels_energy_value = labels_energy_value.to(self.device) @@ -555,10 +635,13 @@ def training_step(self, batch, batch_idx): # Direction # --------------------------------------- if self.task == Task.cameradirection: - if len(direction_pred)==2: - direction_pred = direction_pred[0] - loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_direction_loss( direction_pred, labels_direction, training=True) + if self.is_difussion: + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_direction_loss_diffusion(imgs, labels_direction,labels_energy_value) + else: + if len(direction_pred)==2: + direction_pred = direction_pred[0] + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_direction_loss( direction_pred, labels_direction, training=True) self.loss_train_distance += loss_distance.item() self.loss_train_dx_dy += loss_dx_dy.item() @@ -600,11 +683,11 @@ def training_step(self, batch, batch_idx): return loss # ---------------------------------------------------------------------------------------------------------- def on_train_epoch_end(self): - print("train epoch end 1 ") + if self.trainer.world_size > 1: - print("train epoch end 1 ") + # dist.barrier() - print("train epoch end 2 ") + # Gather y Sync values with the GPUs. total_loss_train= self.all_gather(self.loss_train_sum).sum().item() total_batches_val = self.all_gather(torch.tensor(self.num_train_batches, device=self.device)).sum().item() @@ -612,14 +695,13 @@ def on_train_epoch_end(self): else: total_loss_train = self.loss_train_sum total_batches_val = self.num_train_batches - - print("train epoch end 2 ") + if total_batches_val==0: - print("train epoch end 3 ") + return 0 if self.trainer.is_global_zero: - print("train epoch end 4 ") + global_loss = total_loss_train / total_batches_val self.logger.experiment.add_scalars( @@ -629,7 +711,7 @@ def on_train_epoch_end(self): }, self.current_epoch, ) - print("train epoch end 5 ") + self.logger.experiment.add_scalars( "Learning rate", { diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 79d97471..97091bbc 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -23,9 +23,9 @@ data: validation_test_reduce_factor: 0 #16 #8 # Check points - type_checkpoint: ./run/fake #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth + type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_108/Epoch_0_type_train_acc_80.0708770751953125.pth #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_49/Epoch_9_cameradirection_train_loss_0.7614041910688141.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth run_details: @@ -57,7 +57,7 @@ model: # task: 'type' num_outputs: 2 embedding_dim: 512 - T: 8 + T: 6 eta: 0.1 #0.1 # model_type: # model_name: "ThinResNet_DBB" @@ -80,22 +80,30 @@ model: use_bn: False + # model_direction: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'direction' + # num_inputs: 1 + # num_outputs: 3 + # num_blocks: [3, 4, 6, 3] + # dropout: 0.1 + # use_bn: False + model_direction: - model_name: "ThinResNet_DBB" + model_name: "NoPropDTReg" parameters: task: 'direction' - num_inputs: 1 num_outputs: 3 - num_blocks: [3, 4, 6, 3] - dropout: 0.1 - use_bn: False - + embedding_dim: 512 + T: 6 + eta: 0.1 #0.1 # Hyper-parameters hyp: - epochs: 12 - batches: 128 #64 + epochs: 142 + batches: 128 #128 #64 dynamic_batches: True optimizer: Adamw momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index fb8d7394..30ad85e1 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -179,4 +179,33 @@ def main(): # Example: # python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir2 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite -# python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml \ No newline at end of file +# python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal ./mc_tjark/ --pattern-signal gamma_*.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml + +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --background /storage/ctlearn_data/h5_files/mc/protons/ --pattern-signal gamma_theta_*.dl1.h5 --pattern-background proton_*.dl1.h5 --reco type --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_type_training.out 2>&1 & + +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --background /storage/ctlearn_data/h5_files/mc/protons/ --pattern-signal gamma_theta_23.161_az_260.739_runs7-65*.dl1.h5 --pattern-background proton_theta_23.161_az_99.261_runs833-1250*.dl1.h5 --reco type --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_type_training.out 2>&1 & + +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --background /storage/ctlearn_data/h5_files/mc/protons/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs1-60.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs181-240.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs188-246.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs247-305.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs1-59.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs177-235.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs1-60.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs181-240.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs181-240.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs181-240.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs181-240.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs1-60.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs181-240.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs1-60.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs181-240.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs1-60.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs181-240.dl1.h5 --pattern-background proton_*.dl1.h5 --reco type --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_type_training.out 2>&1 & + +# gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 gamma_theta_16.087_az_251.910_runs1-60.dl1.h5 gamma_theta_16.087_az_251.910_runs181-240.dl1.h5 gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 gamma_theta_23.161_az_260.739_runs188-246.dl1.h5 gamma_theta_23.161_az_260.739_runs247-305.dl1.h5 gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 gamma_theta_23.161_az_99.261_runs1-59.dl1.h5 gamma_theta_23.161_az_99.261_runs177-235.dl1.h5 gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 gamma_theta_30.390_az_266.360_runs1-60.dl1.h5 gamma_theta_30.390_az_266.360_runs181-240.dl1.h5 gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 gamma_theta_30.390_az_93.640_runs181-240.dl1.h5 gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 gamma_theta_37.661_az_270.641_runs1-60.dl1.h5 gamma_theta_37.661_az_270.641_runs181-240.dl1.h5 gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 gamma_theta_37.661_az_89.359_runs1-60.dl1.h5 gamma_theta_37.661_az_89.359_runs181-240.dl1.h5 gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 gamma_theta_6.000_az_180.000_runs1-60.dl1.h5 gamma_theta_6.000_az_180.000_runs181-240.dl1.h5 gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 gamma_theta_9.579_az_126.888_runs1-60.dl1.h5 gamma_theta_9.579_az_126.888_runs181-240.dl1.h5 gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 gamma_theta_9.579_az_233.112_runs1-60.dl1.h5 gamma_theta_9.579_az_233.112_runs181-240.dl1.h5 + + +# --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs1-60.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs181-240.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs188-246.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs247-305.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs1-59.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs177-235.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs1-60.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs181-240.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs181-240.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs181-240.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs181-240.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs1-60.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs181-240.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs1-60.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs181-240.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs1-60.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs181-240.dl1.h5 + + + + +# --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 + + +# --pattern-background=proton_theta_16.087_az_108.090_runs1-416.dl1.h5 --pattern-background=proton_theta_16.087_az_251.910_runs1-417.dl1.h5 --pattern-background=proton_theta_23.161_az_260.739_runs1-417.dl1.h5 --pattern-background=proton_theta_23.161_az_99.261_runs1-417.dl1.h5 --pattern-background=proton_theta_30.390_az_266.360_runs1-416.dl1.h5 --pattern-background=proton_theta_30.390_az_93.640_runs1-420.dl1.h5 --pattern-background=proton_theta_37.661_az_270.641_runs1-421.dl1.h5 --pattern-background=proton_theta_37.661_az_89.359_runs1-406.dl1.h5 --pattern-background=proton_theta_6.000_az_180.000_runs1-416.dl1.h5 --pattern-background=proton_theta_9.579_az_126.888_runs1-417.dl1.h5 --pattern-background=proton_theta_9.579_az_233.112_runs1-417.dl1.h5 + + + +# Type +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --background /storage/ctlearn_data/h5_files/mc/protons/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --pattern-background=proton_theta_16.087_az_108.090_runs1-416.dl1.h5 --pattern-background=proton_theta_16.087_az_251.910_runs1-417.dl1.h5 --pattern-background=proton_theta_23.161_az_260.739_runs1-417.dl1.h5 --pattern-background=proton_theta_23.161_az_99.261_runs1-417.dl1.h5 --pattern-background=proton_theta_30.390_az_266.360_runs1-416.dl1.h5 --pattern-background=proton_theta_30.390_az_93.640_runs1-420.dl1.h5 --pattern-background=proton_theta_37.661_az_270.641_runs1-421.dl1.h5 --pattern-background=proton_theta_37.661_az_89.359_runs1-406.dl1.h5 --pattern-background=proton_theta_6.000_az_180.000_runs1-416.dl1.h5 --pattern-background=proton_theta_9.579_az_126.888_runs1-417.dl1.h5 --pattern-background=proton_theta_9.579_az_233.112_runs1-417.dl1.h5 --reco type --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_type_training.out 2>&1 & + +# Direction +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --reco cameradirection --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_direction_training.out 2>&1 & + +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal gamma_theta_23.161_az_260.739_runs7-65*.dl1.h5 --reco cameradirection --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_direction_training.out 2>&1 & \ No newline at end of file From 00c117086e100528b4bbcd46229a30eab07de25e Mon Sep 17 00:00:00 2001 From: pguzman Date: Wed, 25 Jun 2025 15:51:11 +0000 Subject: [PATCH 035/119] clean up and added DBB in diffusion model --- .../models/DBBNoPropDTReg/DBBNoPropDTReg.py | 77 +++++ .../DBBNoPropDTReg/denoiseBlockThinRestNet.py | 132 ++++++++ .../denoiseBlockThinRestNet_original.py | 154 +++++++++ .../DoubleBBEfficientNetV2.py | 186 ----------- .../core/pytorch/nets/models/EfficientNet.py | 158 --------- .../pytorch/nets/models/EfficientNetv2.py | 263 --------------- .../NoPropDTReg/denoiseBlockThinRestNet.py | 121 +++---- .../denoiseBlockThinRestNet_original.py | 154 +++++++++ ctlearn/core/pytorch/nets/models/ResNet.py | 129 -------- ctlearn/core/pytorch/nets/models/__init__.py | 6 +- .../legacy/DoubleBBEfficientNetV2_old.py | 181 ---------- .../models/legacy/ThinResNet/ThinResNet.py | 101 ------ .../legacy/nfnets_pytorch/.gitattributes | 1 - .../models/legacy/nfnets_pytorch/.gitignore | 9 - .../nets/models/legacy/nfnets_pytorch/LICENSE | 201 ------------ .../models/legacy/nfnets_pytorch/README.md | 109 ------ .../models/legacy/nfnets_pytorch/__init__.py | 3 - .../models/legacy/nfnets_pytorch/dataset.py | 8 - .../legacy/nfnets_pytorch/default_config.yaml | 32 -- .../models/legacy/nfnets_pytorch/demo.ipynb | 258 --------------- .../nets/models/legacy/nfnets_pytorch/eval.py | 86 ----- .../models/legacy/nfnets_pytorch/model.py | 307 ----------------- .../legacy/nfnets_pytorch/nfnets/__init__.py | 3 - .../nfnets_pytorch/nfnets/model copy.py | 309 ------------------ .../legacy/nfnets_pytorch/nfnets/model.py | 265 --------------- .../legacy/nfnets_pytorch/nfnets/optim.py | 109 ------ .../nfnets_pytorch/nfnets/pretrained.py | 95 ------ .../models/legacy/nfnets_pytorch/optim.py | 109 ------ .../legacy/nfnets_pytorch/pretrained.py | 94 ------ .../nfnets_pytorch/pretrained/README.md | 11 - .../legacy/nfnets_pytorch/pyproject.toml | 6 - .../legacy/nfnets_pytorch/requirements.txt | 15 - .../models/legacy/nfnets_pytorch/setup.cfg | 35 -- .../models/legacy/nfnets_pytorch/train.py | 194 ----------- .../nets/models/legacy/simple_nfnet/README.md | 39 --- .../nets/models/legacy/simple_nfnet/main.py | 98 ------ .../nets/models/legacy/simple_nfnet/model.py | 183 ----------- .../models/legacy/simple_nfnet/optim copy.py | 110 ------- .../nets/models/legacy/simple_nfnet/optim.py | 109 ------ ctlearn/tools/train/pytorch/CTLearnPL.py | 171 ++++++++-- .../training_config_iaa_neutron_training.yml | 19 +- 41 files changed, 712 insertions(+), 3938 deletions(-) create mode 100644 ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py create mode 100644 ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/denoiseBlockThinRestNet.py 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ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py delete mode 100644 ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py diff --git a/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py new file mode 100644 index 00000000..d94ca082 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py @@ -0,0 +1,77 @@ +# NoProp-DT model + +import torch +from torch import nn + +# from .denoiseBlock import DenoiseBlock +from .denoiseBlockThinRestNet import DenoiseBlock, MemoryEfficientSwish + +import math + +class DBBNoPropDTReg(nn.Module): + def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1): + super().__init__() + + self.task = task + num_classes = num_outputs + self.num_classes = num_classes + self.embedding_dim = embedding_dim + self.T = T + self.eta = eta + + self.blocks = nn.ModuleList([DenoiseBlock(embedding_dim,num_channels=1) for _ in range(T)]) + # self.regressor = nn.Linear(embedding_dim, num_outputs) + self.regressor = nn.Sequential( + nn.Linear(embedding_dim, embedding_dim//2), + MemoryEfficientSwish(), + nn.Linear(embedding_dim//2, num_outputs) +) + + # Final classifier + self.classifier = nn.Linear(embedding_dim, num_classes) + + # Improved noise schedule + self.register_buffer('alpha_bar', self._cosine_schedule(T)) + self.register_buffer('snr_diff', self._calculate_snr_diff(self.alpha_bar)) + + + self.target_embedder = nn.Linear(num_outputs, embedding_dim) + + for m in self.regressor: + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + nn.init.zeros_(m.bias) + + def _cosine_schedule(self, T): + t = torch.arange(1, T+1, dtype=torch.float32) + alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi/2))**2 + return alpha_bar + + def _calculate_snr_diff(self, alpha_bar): + snr = alpha_bar / (1 - alpha_bar + 1e-8) + snr_prev = torch.cat([torch.tensor([0.]), snr[:-1]]) + return torch.clamp(snr - snr_prev, min=1e-5) + + def forward_denoise(self, x, y, z_prev, t): + return self.blocks[t](x, y, z_prev, None)[0] + + def regress(self, z): + return self.regressor(z) + + def inference(self, x, y ): + B = x.size(0) + z = torch.randn(B, self.embedding_dim, device=x.device) + if not self.training: + z = torch.zeros(B, self.embedding_dim, device=x.device) + + for t in range(self.T): + z = self.forward_denoise(x, y, z, t) + + return self.regress(z) + + def forward(self, x, y): + + if self.task=="direction": + return None, None, self.inference(x,y) + elif self.task=="energy": + return None, self.inference(x,y), None diff --git a/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/denoiseBlockThinRestNet.py new file mode 100644 index 00000000..2491d3e2 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/denoiseBlockThinRestNet.py @@ -0,0 +1,132 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) + self.act1 = nn.GELU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) + self.act2 = nn.GELU() + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + def forward(self, x): + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) + residual = self.se(residual) + out = residual + self.shortcut(x) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + super().__init__() + # Ahora más profundo y ancho: + self.conv_path_x = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + ) + + self.conv_path_y = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + ) + + self.conv_path_end = nn.Sequential( + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(256, 512), # Embedding más grande + nn.BatchNorm1d(512), + nn.GELU(), + ) + + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, y, z_prev, _): + x_feat = self.conv_path_x(x) + y_feat = self.conv_path_y(y) + + x_feat = self.conv_path_end(x_feat + y_feat) + + + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/denoiseBlockThinRestNet_original.py b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/denoiseBlockThinRestNet_original.py new file mode 100644 index 00000000..fc52d51f --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/denoiseBlockThinRestNet_original.py @@ -0,0 +1,154 @@ +# Denoising block +import torch +from torch import nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +# class ResidualBlock(nn.Module): +# def __init__(self, in_channels, out_channels): +# super().__init__() +# self.conv_block = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels), +# nn.PReLU(), +# nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels) +# ) + +# self.shortcut = nn.Sequential() +# if in_channels != out_channels: +# self.shortcut = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=1), +# nn.BatchNorm2d(out_channels) +# ) + +# self.relu = nn.PReLU() + +# def forward(self, x): +# return self.relu(self.conv_block(x) + self.shortcut(x)) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16, drop_path_rate=0.1): + super().__init__() + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + self.norm1 = nn.GroupNorm(8, out_channels) + self.act1 = nn.GELU() + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) + self.norm2 = nn.GroupNorm(8, out_channels) + self.act2 = nn.GELU() + + self.se = SEBlock(out_channels, reduction) + + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1), + nn.GroupNorm(8, out_channels) + ) + + try: + from timm.models.layers import DropPath + self.drop_path = DropPath(drop_path_rate) + except ImportError: + self.drop_path = nn.Identity() + + # self.final_act = nn.GELU() + self.final_act = MemoryEfficientSwish() + def forward(self, x): + residual = self.act1(self.norm1(self.conv1(x))) + residual = self.act2(self.norm2(self.conv2(residual))) + # residual = self.act1((self.conv1(x))) + # residual = self.act2((self.conv2(residual))) + + residual = self.se(residual) + + out = self.drop_path(residual) + self.shortcut(x) + return self.final_act(out) + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1): + super().__init__() + + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 32), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(32, 64), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(64, 128), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(128, 256), + ) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + + z_feat = h3 + h1 + + h_f = torch.cat([x_feat, z_feat], dim=1) + + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/DualBackboneEfficientNetV2/DoubleBBEfficientNetV2.py b/ctlearn/core/pytorch/nets/models/DualBackboneEfficientNetV2/DoubleBBEfficientNetV2.py deleted file mode 100644 index 65aac4dd..00000000 --- a/ctlearn/core/pytorch/nets/models/DualBackboneEfficientNetV2/DoubleBBEfficientNetV2.py +++ /dev/null @@ -1,186 +0,0 @@ -import torch -from torch import nn - -# Configuration for EfficientNetV2 -Eff_V2_SETTINGS = { - 's': [ - [1, 3, 1, 24, 24, 2, True], - [4, 3, 2, 24, 48, 4, True], - [4, 3, 2, 48, 64, 4, True], - [4, 3, 2, 64, 128, 6, False], - [6, 3, 1, 128, 160, 9, False], - [6, 3, 2, 160, 256, 15, False] - ], - 'm': [ - [1, 3, 1, 24, 24, 3, True], - [4, 3, 2, 24, 48, 5, True], - [4, 3, 2, 48, 80, 5, True], - [4, 3, 2, 80, 160, 7, False], - [6, 3, 1, 160, 176, 14, False], - [6, 3, 2, 176, 304, 18, False], - [6, 3, 1, 304, 512, 5, False] - ], - 'l': [ - [1, 3, 1, 32, 32, 4, True], - [4, 3, 2, 32, 64, 7, True], - [4, 3, 2, 64, 96, 7, True], - [4, 3, 2, 96, 192, 10, False], - [6, 3, 1, 192, 224, 19, False], - [6, 3, 2, 224, 384, 25, False], - [6, 3, 1, 384, 640, 7, False] - ] -} - -# Convolution, Batch Normalization, and Activation Layer -class ConvBnAct(nn.Module): - def __init__(self, n_in, n_out, k_size=3, stride=1, padding=0, groups=1, act=True, bn=False, bias=False): - super(ConvBnAct, self).__init__() - self.conv = nn.Conv2d(n_in, n_out, kernel_size=k_size, stride=stride, padding=padding, groups=groups, bias=bias) - self.batch_norm = nn.BatchNorm2d(n_out) if bn else nn.Identity() - self.activation = nn.SiLU() if act else nn.Identity() - - def forward(self, x): - x = self.conv(x) - x = self.batch_norm(x) - x = self.activation(x) - return x - -# Squeeze and Excitation Module -class SqueezeExcitation(nn.Module): - def __init__(self, n_in, reduction_factor=4): - super(SqueezeExcitation, self).__init__() - reduced_dim = n_in // reduction_factor - self.squeeze = nn.AdaptiveAvgPool2d(1) - self.excite = nn.Sequential( - nn.Conv2d(n_in, reduced_dim, kernel_size=1), - nn.SiLU(), - nn.Conv2d(reduced_dim, n_in, kernel_size=1), - nn.Sigmoid() - ) - - def forward(self, x): - y = self.squeeze(x) - y = self.excite(y) - return x * y - -# Stochastic Depth for Regularization -class StochasticDepth(nn.Module): - def __init__(self, survival_prob=0.8): - super(StochasticDepth, self).__init__() - self.p = survival_prob - - def forward(self, x): - if not self.training: - return x - binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.p - return torch.div(x, self.p) * binary_tensor - -# Mobile Inverted Residual Block with Squeeze and Excitation -class MBConvN(nn.Module): - def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, reduction_factor=4, survival_prob=0.8): - super(MBConvN, self).__init__() - expanded_dim = int(expansion_factor * n_in) - padding = (k_size - 1) // 2 - self.use_residual = (n_in == n_out) and (stride == 1) - self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size=1) - self.depthwise_conv = ConvBnAct(expanded_dim, expanded_dim, k_size, stride=stride, padding=padding, groups=expanded_dim) - self.se = SqueezeExcitation(expanded_dim, reduction_factor) - self.drop_layers = StochasticDepth(survival_prob) - self.pointwise_conv = ConvBnAct(expanded_dim, n_out, k_size=1, act=False) - - def forward(self, x): - residual = x.clone() - x = self.expand(x) - x = self.depthwise_conv(x) - x = self.se(x) - x = self.pointwise_conv(x) - if self.use_residual: - x = self.drop_layers(x) - x += residual - return x - -# Fused Mobile Inverted Residual Block -class FusedMBConvN(nn.Module): - def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, survival_prob=0.8): - super(FusedMBConvN, self).__init__() - expanded_dim = int(expansion_factor * n_in) - padding = (k_size - 1) // 2 - self.use_residual = (n_in == n_out) and (stride == 1) - self.conv = ConvBnAct(n_in, expanded_dim, k_size, stride=stride, padding=padding, groups=1) - self.drop_layers = StochasticDepth(survival_prob) - self.pointwise_conv = nn.Identity() if (expansion_factor == 1) else ConvBnAct(expanded_dim, n_out, k_size=1, act=False) - - def forward(self, x): - residual = x.clone() - x = self.conv(x) - x = self.pointwise_conv(x) - if self.use_residual: - x = self.drop_layers(x) - x += residual - return x - -# EfficientNetV2 Model Definition -class EfficientNetV2(nn.Module): - def __init__(self, version='s', in_channels=3, last_channel=1280): - super(EfficientNetV2, self).__init__() - self.features = self._make_layers(version, in_channels, last_channel) - - def forward(self, x): - x = self.features(x) - return x - - def _make_layers(self, version, in_channels, last_channel): - config = Eff_V2_SETTINGS[version] - layers = [] - layers.append(ConvBnAct(in_channels, config[0][3], k_size=3, stride=2, padding=1)) - - for (expansion_factor, k, stride, n_in, n_out, num_layers, use_fused) in config: - if use_fused: - layers += [FusedMBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) - for repeat in range(num_layers)] - else: - layers += [MBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) - for repeat in range(num_layers)] - - layers.append(ConvBnAct(config[-1][4], last_channel, k_size=1)) - return nn.Sequential(*layers) - -# Dual Backbone EfficientNetV2 Model for Specific Tasks -class DualBackboneEfficientNetV2(nn.Module): - def __init__(self, task, version='s', num_classes=1, in_channels_1=1, in_channels_2=1, dropout_rate=0.2): - super(DualBackboneEfficientNetV2, self).__init__() - self.task = task - self.backbone1 = EfficientNetV2(version, in_channels_1) - self.backbone2 = EfficientNetV2(version, in_channels_2) - last_channel = 1280 - self.classifier = nn.Sequential( - nn.AdaptiveAvgPool2d((1, 1)), - nn.Flatten(), - nn.Dropout(dropout_rate, inplace=True), - nn.Linear(last_channel, num_classes) - ) - - def forward(self, x1, x2): - classification = None - energy = None - direction = None - - x1 = self.backbone1(x1) - x2 = self.backbone2(x2) - x = x1 + x2 # Fuse by adding - - if self.task == "energy": - energy = self.classifier(x) - - elif self.task == "direction": - direction = self.classifier(x) - - # Optionally handle a classification task - # if self.task == "classification": - # classification = self.classifier(x) - - return classification, energy, direction - -# # Example usage for regression task -# if __name__ == "__main__": -# model = DualBackboneEfficientNetV2(task='energy', version='s', num_classes=1, in_channels_1=3, in_channels_2=3) # num_classes=1 for regression \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/EfficientNet.py b/ctlearn/core/pytorch/nets/models/EfficientNet.py deleted file mode 100644 index ec629d8a..00000000 --- a/ctlearn/core/pytorch/nets/models/EfficientNet.py +++ /dev/null @@ -1,158 +0,0 @@ -# import pytorch -from torch import nn -import math -basic_mb_params = [ - # k, channels(c), repeats(t), stride(s), kernel_size(k) - [1, 16, 1, 1, 3], - [6, 24, 2, 2, 3], - [6, 40, 2, 2, 5], - [6, 80, 3, 2, 3], - [6, 112, 3, 1, 5], - [6, 192, 4, 2, 5], - [6, 320, 1, 1, 3], -] - -alpha, beta = 1.2, 1.1 - -scale_values = { - # (phi, resolution, dropout) - "b0": (0, 224, 0.2), - "b1": (0.5, 240, 0.2), - "b2": (1, 260, 0.3), - "b3": (2, 300, 0.3), - "b4": (3, 380, 0.4), - "b5": (4, 456, 0.4), - "b6": (5, 528, 0.5), - "b7": (6, 600, 0.5), -} - -class ConvBlock(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, - stride, padding, groups=1): - super(ConvBlock, self).__init__() - self.cnnblock = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size, - stride, padding, groups=groups), - nn.BatchNorm2d(out_channels), - nn.SiLU()) - - def forward(self, x): - return self.cnnblock(x) - - -# class MBBlock(nn.Module): -# def __init__(self, in_channels, out_channels, kernel_size, -# stride, padding, expand_ratio, reduction=2, -# ): -# super(MBBlock, self).__init__() -# # self.use_residual = in_channels == out_channels and stride == 1 -# hidden_dim = in_channels * expand_ratio -# self.expand = in_channels != hidden_dim - -# # This is for squeeze and excitation block -# reduced_dim = int(in_channels / reduction) - -# if self.expand: -# self.expand_conv = ConvBlock(in_channels, hidden_dim, -# kernel_size=3,stride=1,padding=1) - -# self.conv = nn.Sequential( -# ConvBlock(hidden_dim,hidden_dim,kernel_size, -# stride,padding,groups=hidden_dim), -# SqueezeExcitation(hidden_dim, reduced_dim), -# nn.Conv2d(hidden_dim, out_channels, 1), -# nn.BatchNorm2d(out_channels), -# ) - -# def forward(self, inputs): -# if self.expand: -# x = self.expand_conv(inputs) -# else: -# x = inputs -# return self.conv(x) - -class MBBlock(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, - stride, padding, expand_ratio, reduction=4): # Changed 'ratio' to 'expand_ratio' and adjusted 'reduction' - super(MBBlock, self).__init__() - hidden_dim = in_channels * expand_ratio - self.expand = in_channels != hidden_dim - - reduced_dim = int(hidden_dim / reduction) # Use 'hidden_dim' not 'in_channels' - - if self.expand: - self.expand_conv = ConvBlock(in_channels, hidden_dim, - kernel_size=1, stride=1, padding=0) # Typically a 1x1 conv - - self.conv = nn.Sequential( - ConvBlock(hidden_dim, hidden_dim, kernel_size, - stride, padding, groups=hidden_dim), - SqueezeExcitation(hidden_dim, reduced_dim), - nn.Conv2d(hidden_dim, out_channels, 1, 1, 0), # Kernel size 1, stride 1, padding 0 - nn.BatchNorm2d(out_channels) - ) - - def forward(self, inputs): - x = self.expand_conv(inputs) if self.expand else inputs - return self.conv(x) - -class SqueezeExcitation(nn.Module): - def __init__(self, in_channels, reduced_dim): - super(SqueezeExcitation, self).__init__() - self.se = nn.Sequential( - nn.AdaptiveAvgPool2d(1), # C x H x W -> C x 1 x 1 - nn.Conv2d(in_channels, reduced_dim, 1), - nn.SiLU(), - nn.Conv2d(reduced_dim, in_channels, 1), - nn.Sigmoid(), - ) - - def forward(self, x): - return x * self.se(x) - - -class EfficientNet(nn.Module): - def __init__(self, model_name, num_channels, output): - super(EfficientNet, self).__init__() - self.num_channels = num_channels - phi, resolution, dropout = scale_values[model_name] - self.depth_factor, self.width_factor = alpha**phi, beta**phi - self.last_channels = math.ceil(1280 * self.width_factor) - self.avgpool= nn.AdaptiveAvgPool2d(1) - self.feature_extractor() - self.flatten = nn.Flatten() - self.classifier = nn.Sequential( - nn.Dropout(dropout), - nn.Linear(self.last_channels, output), - ) - - def feature_extractor(self): - channels = int(32 * self.width_factor) - features = [ConvBlock(self.num_channels, channels, 3, stride=2, padding=1)] - in_channels = channels - - for k, c_o, repeat, s, n in basic_mb_params: - # For numeric stability, we multiply and divide by 4 - out_channels = 4 * math.ceil(int(c_o * self.width_factor) / 4) - num_layers = math.ceil(repeat * self.depth_factor) - - for layer in range(num_layers): - if layer == 0: - stride = s - else: - stride = 1 - features.append( - MBBlock(in_channels,out_channels,expand_ratio=k, - stride=stride,kernel_size=n,padding=n// 2) - ) - in_channels = out_channels - - features.append( - ConvBlock(in_channels, self.last_channels, - kernel_size=1, stride=1, padding=0) - ) - self.extractor = nn.Sequential(*features) - - def forward(self, x): - x = self.avgpool(self.extractor(x)) - return self.classifier(self.flatten(x)) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/EfficientNetv2.py b/ctlearn/core/pytorch/nets/models/EfficientNetv2.py deleted file mode 100644 index 61637137..00000000 --- a/ctlearn/core/pytorch/nets/models/EfficientNetv2.py +++ /dev/null @@ -1,263 +0,0 @@ -import torch -from torch import nn - -Eff_V2_SETTINGS = { - # expansion factor, k, stride, n_in, n_out, num_layers, use_fusedMBCONV - 's' : [ - [1, 3, 1, 24, 24, 2, True], - [4, 3, 2, 24, 48, 4, True], - [4, 3, 2, 48, 64, 4, True], - [4, 3, 2, 64, 128, 6, False], - [6, 3, 1, 128, 160, 9, False], - [6, 3, 2, 160, 256, 15, False] - ], - - 'm' : [ - [1, 3, 1, 24, 24, 3, True], - [4, 3, 2, 24, 48, 5, True], - [4, 3, 2, 48, 80, 5, True], - [4, 3, 2, 80, 160, 7, False], - [6, 3, 1, 160, 176, 14, False], - [6, 3, 2, 176, 304, 18, False], - [6, 3, 1, 304, 512, 5, False] - ], - - 'l' : [ - [1, 3, 1, 32, 32, 4, True], - [4, 3, 2, 32, 64, 7, True], - [4, 3, 2, 64, 96, 7, True], - [4, 3, 2, 96, 192, 10, False], - [6, 3, 1, 192, 224, 19, False], - [6, 3, 2, 224, 384, 25, False], - [6, 3, 1, 384, 640, 7, False] - ] -} - -class ConvBnAct(nn.Module): - - def __init__( - self, - n_in, # in_channels - n_out, # out_channels - k_size = 3, # Kernel Size - stride = 1, - padding = 0, - groups = 1, - act = True, - bn = True, - bias = False - ): - super(ConvBnAct, self).__init__() - - self.conv = nn.Conv2d(n_in, n_out, kernel_size = k_size, stride = stride, - padding = padding, groups = groups,bias = bias - ) - self.batch_norm = nn.BatchNorm2d(n_out) if bn else nn.Identity() - self.activation = nn.SiLU() if act else nn.Identity() - - def forward(self, x): - x = self.conv(x) - x = self.batch_norm(x) - x = self.activation(x) - - return x - -#-------------------------------------------------------------------------------------------- - -'''Squeeze and Excitation Class''' - -class SqueezeExcitation(nn.Module): - - def __init__( - self, - n_in, # In_channels - reduced_dim - ): - super(SqueezeExcitation, self).__init__() - - self.squeeze = nn.AdaptiveAvgPool2d(1) - self.excite = nn.Sequential(nn.Conv2d(n_in, reduced_dim, kernel_size=1), - nn.SiLU(), - nn.Conv2d(reduced_dim, n_in, kernel_size=1), - nn.Sigmoid() - ) - - def forward(self, x): - y = self.squeeze(x) - y = self.excite(y) - - return x * y - -#-------------------------------------------------------------------------------------- - -''' Stochastic Depth Class''' - -class StochasticDepth(nn.Module): - - def __init__( - self, - survival_prob = 0.8 - ): - super(StochasticDepth, self).__init__() - - self.p = survival_prob - - def forward(self, x): - - if not self.training: - return x - - binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.p - - return torch.div(x, self.p) * binary_tensor - -#------------------------------------------------------------------------------- - -'''MBCONV Class''' - -class MBConvN(nn.Module): - - def __init__( - self, - n_in, # In_channels - n_out, # out_channels - k_size = 3, # kernel_size - stride = 1, - expansion_factor = 4, - reduction_factor = 4, # SqueezeExcitation Block - survival_prob = 0.8 # StochasticDepth Block - ): - super(MBConvN, self).__init__() - reduced_dim = int(n_in//4) - expanded_dim = int(expansion_factor * n_in) - padding = (k_size - 1)//2 - - self.use_residual = (n_in == n_out) and (stride == 1) - self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size = 1) - self.depthwise_conv = ConvBnAct(expanded_dim, expanded_dim, - k_size, stride = stride, - padding = padding, groups = expanded_dim - ) - self.se = SqueezeExcitation(expanded_dim, reduced_dim) - self.drop_layers = StochasticDepth(survival_prob) - self.pointwise_conv = ConvBnAct(expanded_dim, n_out, k_size = 1, act = False) - - def forward(self, x): - - residual = x.clone() - x = self.expand(x) - x = self.depthwise_conv(x) - x = self.se(x) - x = self.pointwise_conv(x) - - if self.use_residual: - x = self.drop_layers(x) - x += residual - - return x - -#-------------------------------------------------------------------------------------- - -'''Fused-MBCONV Class''' - -class FusedMBConvN(nn.Module): - - def __init__( - self, - n_in, # In_channels - n_out, # out_channels - k_size = 3, # kernel_size - stride = 1, - expansion_factor = 4, - reduction_factor = 4, # SqueezeExcitation Block - survival_prob = 0.8 # StochasticDepth Block - ): - super(FusedMBConvN, self).__init__() - - reduced_dim = int(n_in//4) - expanded_dim = int(expansion_factor * n_in) - padding = (k_size - 1)//2 - - self.use_residual = (n_in == n_out) and (stride == 1) - #self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size = 1) - self.conv = ConvBnAct(n_in, expanded_dim, - k_size, stride = stride, - padding = padding, groups = 1 - ) - #self.se = SqueezeExcitation(expanded_dim, reduced_dim) - self.drop_layers = StochasticDepth(survival_prob) - self.pointwise_conv = nn.Identity() if (expansion_factor == 1) else ConvBnAct(expanded_dim, n_out, k_size = 1, act = False) - - def forward(self, x): - - residual = x.clone() - #x = self.conv(x) - x = self.conv(x) - #x = self.se(x) - x = self.pointwise_conv(x) - - if self.use_residual: - x = self.drop_layers(x) - x += residual - - return x - -#----------------------------------------------------------------------------------------------- - -class EfficientNetV2(nn.Module): - - def __init__( - self, - version = 's', - dropout_rate = 0.2, - in_channels=1, - num_classes = 1000 - ): - super(EfficientNetV2, self).__init__() - last_channel = 1280 - self.features = self._feature_extractor(version,in_channels, last_channel) - self.classifier = nn.Sequential( - nn.AdaptiveAvgPool2d((1,1)), - nn.Flatten(), - nn.Dropout(dropout_rate, inplace = True), - nn.Linear(last_channel, num_classes) - ) - - def forward(self, x): - x = self.features(x) - x = self.classifier(x) - - return x - - def _feature_extractor(self, version,in_channels, last_channel): - - # Extract the Config - config = Eff_V2_SETTINGS[version] - - layers = [] - layers.append(ConvBnAct(in_channels, config[0][3], k_size = 3, stride = 2, padding = 1)) - #in_channel = config[0][3] - - for (expansion_factor, k, stride, n_in, n_out, num_layers, use_fused) in config: - - if use_fused: - layers += [FusedMBConvN(n_in if repeat==0 else n_out, - n_out, - k_size=k, - stride = stride if repeat==0 else 1, - expansion_factor=expansion_factor - ) for repeat in range(num_layers) - ] - else: - - layers += [MBConvN(n_in if repeat==0 else n_out, - n_out, - k_size=k, - stride = stride if repeat==0 else 1, - expansion_factor=expansion_factor - ) for repeat in range(num_layers) - ] - - layers.append(ConvBnAct(config[-1][4], last_channel, k_size = 1)) - - return nn.Sequential(*layers) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py index fc52d51f..6487c4ee 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py @@ -1,6 +1,5 @@ -# Denoising block import torch -from torch import nn +import torch.nn as nn import torch.nn.functional as F class MemoryEfficientSwish(nn.Module): @@ -18,30 +17,7 @@ def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): def forward(self, x): return self.a * x + self.b * self.bn(x) - -# class ResidualBlock(nn.Module): -# def __init__(self, in_channels, out_channels): -# super().__init__() -# self.conv_block = nn.Sequential( -# nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), -# # AdaptiveBatchNorm2d(out_channels), -# nn.PReLU(), -# nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), -# # AdaptiveBatchNorm2d(out_channels) -# ) - -# self.shortcut = nn.Sequential() -# if in_channels != out_channels: -# self.shortcut = nn.Sequential( -# nn.Conv2d(in_channels, out_channels, kernel_size=1), -# nn.BatchNorm2d(out_channels) -# ) - -# self.relu = nn.PReLU() - -# def forward(self, x): -# return self.relu(self.conv_block(x) + self.shortcut(x)) - + class SEBlock(nn.Module): def __init__(self, channels, reduction=16): super().__init__() @@ -52,84 +28,70 @@ def __init__(self, channels, reduction=16): nn.Linear(channels // reduction, channels, bias=False), nn.Sigmoid() ) - def forward(self, x): b, c, _, _ = x.size() y = self.pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return x * y - class ResidualBlock(nn.Module): - def __init__(self, in_channels, out_channels, reduction=16, drop_path_rate=0.1): + def __init__(self, in_channels, out_channels, reduction=16): super().__init__() - - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) - self.norm1 = nn.GroupNorm(8, out_channels) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) self.act1 = nn.GELU() - - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) - self.norm2 = nn.GroupNorm(8, out_channels) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) self.act2 = nn.GELU() - self.se = SEBlock(out_channels, reduction) - self.shortcut = nn.Identity() if in_channels != out_channels: self.shortcut = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size=1), - nn.GroupNorm(8, out_channels) + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) ) - - try: - from timm.models.layers import DropPath - self.drop_path = DropPath(drop_path_rate) - except ImportError: - self.drop_path = nn.Identity() - - # self.final_act = nn.GELU() - self.final_act = MemoryEfficientSwish() def forward(self, x): - residual = self.act1(self.norm1(self.conv1(x))) - residual = self.act2(self.norm2(self.conv2(residual))) - # residual = self.act1((self.conv1(x))) - # residual = self.act2((self.conv2(residual))) - + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) residual = self.se(residual) + out = residual + self.shortcut(x) + return out - out = self.drop_path(residual) + self.shortcut(x) - return self.final_act(out) - class DenoiseBlock(nn.Module): - def __init__(self, embedding_dim, num_channels=1): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): super().__init__() - + # Ahora más profundo y ancho: self.conv_path = nn.Sequential( - ResidualBlock(num_channels, 32), + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho nn.MaxPool2d(2), - nn.Dropout(0.2), - ResidualBlock(32, 64), + nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho nn.MaxPool2d(2), - nn.Dropout(0.2), - ResidualBlock(64, 128), + nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad nn.AdaptiveAvgPool2d((1, 1)), nn.Flatten(), - nn.Linear(128, 256), + nn.Linear(256, 512), # Embedding más grande + nn.BatchNorm1d(512), + nn.GELU() ) - self.fc_z1 = nn.Linear(embedding_dim, 256) - self.bn_z1 = nn.BatchNorm1d(256) - self.fc_z2 = nn.Linear(256, 256) - self.bn_z2 = nn.BatchNorm1d(256) - self.fc_z3 = nn.Linear(256, 256) - self.bn_z3 = nn.BatchNorm1d(256) - - self.fc_f1 = nn.Linear(512, 256) - self.bn_f1 = nn.BatchNorm1d(256) - self.fc_f2 = nn.Linear(256, 128) - self.bn_f2 = nn.BatchNorm1d(128) - self.fc_out = nn.Linear(128, embedding_dim) - + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) self.act1 = nn.PReLU() self.act2 = nn.PReLU() self.act3 = nn.PReLU() @@ -138,17 +100,12 @@ def __init__(self, embedding_dim, num_channels=1): def forward(self, x, z_prev, _): x_feat = self.conv_path(x) - h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) h2 = self.act2(self.bn_z2(self.fc_z2(h1))) h3 = self.bn_z3(self.fc_z3(h2)) - z_feat = h3 + h1 - h_f = torch.cat([x_feat, z_feat], dim=1) - h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) z_next = self.fc_out(h_f) - return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet_original.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet_original.py new file mode 100644 index 00000000..fc52d51f --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet_original.py @@ -0,0 +1,154 @@ +# Denoising block +import torch +from torch import nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +# class ResidualBlock(nn.Module): +# def __init__(self, in_channels, out_channels): +# super().__init__() +# self.conv_block = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels), +# nn.PReLU(), +# nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels) +# ) + +# self.shortcut = nn.Sequential() +# if in_channels != out_channels: +# self.shortcut = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=1), +# nn.BatchNorm2d(out_channels) +# ) + +# self.relu = nn.PReLU() + +# def forward(self, x): +# return self.relu(self.conv_block(x) + self.shortcut(x)) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16, drop_path_rate=0.1): + super().__init__() + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + self.norm1 = nn.GroupNorm(8, out_channels) + self.act1 = nn.GELU() + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) + self.norm2 = nn.GroupNorm(8, out_channels) + self.act2 = nn.GELU() + + self.se = SEBlock(out_channels, reduction) + + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1), + nn.GroupNorm(8, out_channels) + ) + + try: + from timm.models.layers import DropPath + self.drop_path = DropPath(drop_path_rate) + except ImportError: + self.drop_path = nn.Identity() + + # self.final_act = nn.GELU() + self.final_act = MemoryEfficientSwish() + def forward(self, x): + residual = self.act1(self.norm1(self.conv1(x))) + residual = self.act2(self.norm2(self.conv2(residual))) + # residual = self.act1((self.conv1(x))) + # residual = self.act2((self.conv2(residual))) + + residual = self.se(residual) + + out = self.drop_path(residual) + self.shortcut(x) + return self.final_act(out) + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1): + super().__init__() + + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 32), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(32, 64), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(64, 128), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(128, 256), + ) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + + z_feat = h3 + h1 + + h_f = torch.cat([x_feat, z_feat], dim=1) + + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/ResNet.py b/ctlearn/core/pytorch/nets/models/ResNet.py deleted file mode 100644 index b725e975..00000000 --- a/ctlearn/core/pytorch/nets/models/ResNet.py +++ /dev/null @@ -1,129 +0,0 @@ -import torch.nn.functional as F -import numpy as np -import torch -import torch.nn as nn -from sys import path -from os import getcwd -import math - -# path.append(getcwd() + "/../deep_learning_helper") -# path.append(getcwd() + "/../Training") - -from ctlearn_helper.ModelHelper import ModelHelper -from nets.block.cnn_blocks import ResBlock - - -class ResNet(nn.Module): - def __init__(self, class_names, num_output, conv_output=False): - - super(ResNet, self).__init__() - self.conv_output = conv_output - self.class_names = class_names - self.num_output = num_output - - kernel_size_1 = 7 - kernel_size_2 = 5 - kernel_size_3 = 3 - conv_drop_pro = 0.1 - fc_drop_pro = 0.2 - - resblock_1_out_size = 16 - resblock_2_out_size = 32 - resblock_3_out_size = 64 - resblock_4_out_size = 128 - resblock_5_out_size = 256 - # -------------------------------------------------------------------- - self.resblock1 = ResBlock( - n_chans_in=1, - n_chans_out=resblock_1_out_size, - kernel_size=kernel_size_1, - conv_drop_pro=conv_drop_pro, - ) - self.resblock2 = ResBlock( - n_chans_in=resblock_1_out_size, - n_chans_out=resblock_2_out_size, - kernel_size=kernel_size_2, - conv_drop_pro=conv_drop_pro, - ) - self.resblock3 = ResBlock( - n_chans_in=resblock_2_out_size, - n_chans_out=resblock_3_out_size, - kernel_size=kernel_size_3, - conv_drop_pro=conv_drop_pro, - ) - self.resblock4 = ResBlock( - n_chans_in=resblock_3_out_size, - n_chans_out=resblock_4_out_size, - kernel_size=kernel_size_3, - conv_drop_pro=conv_drop_pro, - ) - self.resblock5 = ResBlock( - n_chans_in=resblock_4_out_size, - n_chans_out=resblock_5_out_size, - kernel_size=kernel_size_3, - conv_drop_pro=conv_drop_pro, - ) - # -------------------------------------------------------------------- - - self.avg_pooling = nn.AdaptiveAvgPool2d(1) - self.fc_conv_0 = nn.Linear(resblock_5_out_size, 256) - self.fc_act_0 = nn.PReLU() - self.fc_conv_1 = nn.Linear(256, 256) - self.fc_conv_2 = nn.Linear(256, self.num_output) - - if self.conv_output: - self.conv1x1_1 = nn.Conv1d(256 * 9 * 9, 32, kernel_size=1) - self.conv1x1_batchnorm_1 = nn.BatchNorm1d(num_features=32) - self.act6 = nn.LeakyReLU() - - self.conv1x1_2 = nn.Conv1d(32, 256, kernel_size=1) - self.conv1x1_batchnorm_2 = nn.BatchNorm1d(num_features=256) - self.act7 = nn.LeakyReLU() - - self.conv1x1_3 = nn.Conv1d(256, 256, kernel_size=1) - self.conv1x1_batchnorm_3 = nn.BatchNorm1d(num_features=256) - self.act8 = nn.LeakyReLU() - - self.conv1x1_final = nn.Conv1d(256, self.num_output, 1) - - torch.nn.init.constant_(self.conv1x1_batchnorm_1.weight, 0.5) - torch.nn.init.constant_(self.conv1x1_batchnorm_2.weight, 0.5) - torch.nn.init.constant_(self.conv1x1_batchnorm_3.weight, 0.5) - - else: - self.fc1 = nn.Linear(256 * 9 * 9, 1024) - self.act6 = nn.LeakyReLU() - self.fc1_dropout = nn.Dropout(p=fc_drop_pro) - self.fc2 = nn.Linear(1024, self.num_output) - - def forward(self, x): - - out = self.resblock1(x) - out = self.resblock2(out) - out = self.resblock3(out) - out = self.resblock4(out) - out = self.resblock5(out) - - out = self.avg_pooling(out) - fcsize = out.shape[1] * out.shape[2] * out.shape[3] - # -------------------------------------------------------------------- - # Convolution output - if self.conv_output: - out = out.view(-1, fcsize, 1) - out = self.act6(self.conv1x1_batchnorm_1(self.conv1x1_1(out))) - out = self.act7(self.conv1x1_batchnorm_2(self.conv1x1_2(out))) - out = self.act8(self.conv1x1_batchnorm_3(self.conv1x1_3(out))) - out = self.conv1x1_final(out) - out = out.view(out.shape[0], self.num_output) - ii=0 - # out = torch.log_softmax(out, dim=1) - else: - # Full Connected - - out = out.view(-1, fcsize) - out = self.fc_conv_0(out) - - out = self.fc_conv_1(out) - out = self.fc_conv_2(out) - # out = torch.log_softmax(out_features, dim=1) - return out diff --git a/ctlearn/core/pytorch/nets/models/__init__.py b/ctlearn/core/pytorch/nets/models/__init__.py index ac327522..b6dcc2d4 100644 --- a/ctlearn/core/pytorch/nets/models/__init__.py +++ b/ctlearn/core/pytorch/nets/models/__init__.py @@ -1,11 +1,11 @@ -import ctlearn.core.pytorch.nets.models.DualBackboneEfficientNetV2.DoubleBBEfficientNetV2 import ctlearn.core.pytorch.nets.models.ThinResNet_DBB.ThinResNet_DBB import ctlearn.core.pytorch.nets.models.DBBRegNet.DBBRegNet import ctlearn.core.pytorch.nets.models.DoubleBBEfficientNet.DoubleBBEfficientNet -import ctlearn.core.pytorch.nets.models.DualBackboneEfficientNetV2.DoubleBBEfficientNetV2 + import ctlearn.core.pytorch.nets.models.DBBDanet.DBBDanet # Diffusion import ctlearn.core.pytorch.nets.models.NoPropDT.NoPropDT -import ctlearn.core.pytorch.nets.models.NoPropDTReg.NoPropDTReg \ No newline at end of file +import ctlearn.core.pytorch.nets.models.NoPropDTReg.NoPropDTReg +import ctlearn.core.pytorch.nets.models.DBBNoPropDTReg.DBBNoPropDTReg diff --git a/ctlearn/core/pytorch/nets/models/legacy/DoubleBBEfficientNetV2_old.py b/ctlearn/core/pytorch/nets/models/legacy/DoubleBBEfficientNetV2_old.py deleted file mode 100644 index 1e90de36..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/DoubleBBEfficientNetV2_old.py +++ /dev/null @@ -1,181 +0,0 @@ -import torch -from torch import nn - -Eff_V2_SETTINGS = { - 's': [ - [1, 3, 1, 24, 24, 2, True], - [4, 3, 2, 24, 48, 4, True], - [4, 3, 2, 48, 64, 4, True], - [4, 3, 2, 64, 128, 6, False], - [6, 3, 1, 128, 160, 9, False], - [6, 3, 2, 160, 256, 15, False] - ], - - 'm': [ - [1, 3, 1, 24, 24, 3, True], - [4, 3, 2, 24, 48, 5, True], - [4, 3, 2, 48, 80, 5, True], - [4, 3, 2, 80, 160, 7, False], - [6, 3, 1, 160, 176, 14, False], - [6, 3, 2, 176, 304, 18, False], - [6, 3, 1, 304, 512, 5, False] - ], - - 'l': [ - [1, 3, 1, 32, 32, 4, True], - [4, 3, 2, 32, 64, 7, True], - [4, 3, 2, 64, 96, 7, True], - [4, 3, 2, 96, 192, 10, False], - [6, 3, 1, 192, 224, 19, False], - [6, 3, 2, 224, 384, 25, False], - [6, 3, 1, 384, 640, 7, False] - ] -} - -class ConvBnAct(nn.Module): - def __init__(self, n_in, n_out, k_size=3, stride=1, padding=0, groups=1, act=True, bn=False, bias=False): - super(ConvBnAct, self).__init__() - self.conv = nn.Conv2d(n_in, n_out, kernel_size=k_size, stride=stride, padding=padding, groups=groups, bias=bias) - self.batch_norm = nn.BatchNorm2d(n_out) if bn else nn.Identity() - self.activation = nn.SiLU() if act else nn.Identity() - - def forward(self, x): - x = self.conv(x) - x = self.batch_norm(x) - x = self.activation(x) - return x - -class SqueezeExcitation(nn.Module): - def __init__(self, n_in, reduced_dim): - super(SqueezeExcitation, self).__init__() - self.squeeze = nn.AdaptiveAvgPool2d(1) - self.excite = nn.Sequential( - nn.Conv2d(n_in, reduced_dim, kernel_size=1), - nn.SiLU(), - nn.Conv2d(reduced_dim, n_in, kernel_size=1), - nn.Sigmoid() - ) - - def forward(self, x): - y = self.squeeze(x) - y = self.excite(y) - return x * y - -class StochasticDepth(nn.Module): - def __init__(self, survival_prob=0.8): - super(StochasticDepth, self).__init__() - self.p = survival_prob - - def forward(self, x): - if not self.training: - return x - binary_tensor = torch.rand(x.shape[0], 1, 1, 1, device=x.device) < self.p - return torch.div(x, self.p) * binary_tensor - -class MBConvN(nn.Module): - def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, reduction_factor=4, survival_prob=0.8): - super(MBConvN, self).__init__() - reduced_dim = int(n_in // 4) - expanded_dim = int(expansion_factor * n_in) - padding = (k_size - 1) // 2 - self.use_residual = (n_in == n_out) and (stride == 1) - self.expand = nn.Identity() if (expansion_factor == 1) else ConvBnAct(n_in, expanded_dim, k_size=1) - self.depthwise_conv = ConvBnAct(expanded_dim, expanded_dim, k_size, stride=stride, padding=padding, groups=expanded_dim) - self.se = SqueezeExcitation(expanded_dim, reduced_dim) - self.drop_layers = StochasticDepth(survival_prob) - self.pointwise_conv = ConvBnAct(expanded_dim, n_out, k_size=1, act=False) - - def forward(self, x): - residual = x.clone() - x = self.expand(x) - x = self.depthwise_conv(x) - x = self.se(x) - x = self.pointwise_conv(x) - if self.use_residual: - x = self.drop_layers(x) - x += residual - return x - -class FusedMBConvN(nn.Module): - def __init__(self, n_in, n_out, k_size=3, stride=1, expansion_factor=4, reduction_factor=4, survival_prob=0.8): - super(FusedMBConvN, self).__init__() - reduced_dim = int(n_in // 4) - expanded_dim = int(expansion_factor * n_in) - padding = (k_size - 1) // 2 - self.use_residual = (n_in == n_out) and (stride == 1) - self.conv = ConvBnAct(n_in, expanded_dim, k_size, stride=stride, padding=padding, groups=1) - self.drop_layers = StochasticDepth(survival_prob) - self.pointwise_conv = nn.Identity() if (expansion_factor == 1) else ConvBnAct(expanded_dim, n_out, k_size=1, act=False) - - def forward(self, x): - residual = x.clone() - x = self.conv(x) - x = self.pointwise_conv(x) - if self.use_residual: - x = self.drop_layers(x) - x += residual - return x - -class EfficientNetV2(nn.Module): - def __init__(self, version='s', in_channels=3, last_channel=1280): - super(EfficientNetV2, self).__init__() - self.features = self._make_layers(version, in_channels, last_channel) - - def forward(self, x): - x = self.features(x) - return x - - def _make_layers(self, version, in_channels, last_channel): - config = Eff_V2_SETTINGS[version] - layers = [] - layers.append(ConvBnAct(in_channels, config[0][3], k_size=3, stride=2, padding=1)) - - for (expansion_factor, k, stride, n_in, n_out, num_layers, use_fused) in config: - if use_fused: - layers += [FusedMBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) - for repeat in range(num_layers)] - else: - layers += [MBConvN(n_in if repeat == 0 else n_out, n_out, k_size=k, stride=stride if repeat == 0 else 1, expansion_factor=expansion_factor) - for repeat in range(num_layers)] - - layers.append(ConvBnAct(config[-1][4], last_channel, k_size=1)) - return nn.Sequential(*layers) - -class DualBackboneEfficientNetV2(nn.Module): - def __init__(self,task, version='s', num_classes=1, in_channels_1=1, in_channels_2=1, dropout_rate=0.2): - super(DualBackboneEfficientNetV2, self).__init__() - self.task = task - self.backbone1 = EfficientNetV2(version, in_channels_1) - self.backbone2 = EfficientNetV2(version, in_channels_2) - last_channel = 1280 - self.classifier = nn.Sequential( - nn.AdaptiveAvgPool2d((1, 1)), - nn.Flatten(), - nn.Dropout(dropout_rate, inplace=True), - nn.Linear(last_channel, num_classes) - ) - - def forward(self, x1, x2): - - classification=None - energy=None - direction=None - - if self.task =="energy": - x1 = self.backbone1(x1) - x2 = self.backbone2(x2) - x = x1 + x2 # Fuse by adding - energy = self.classifier(x) - - if self.task =="direction": - x1 = self.backbone1(x1) - x2 = self.backbone2(x2) - x = x1 + x2 # Fuse by adding - direction = self.classifier(x) - - - return classification, energy, direction - -# # Example usage for regression task -# if __name__ == "__main__": -# model = DualBackboneEfficientNetV2(version='s', num_classes=1, in_channels1=3, in_channels2=3) # num_classes=1 for regression \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/ThinResNet/ThinResNet.py b/ctlearn/core/pytorch/nets/models/legacy/ThinResNet/ThinResNet.py deleted file mode 100644 index dac05cb8..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/ThinResNet/ThinResNet.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class SEBlock(nn.Module): - def __init__(self, channel, reduction=16): - super(SEBlock, self).__init__() - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Sequential( - nn.Linear(channel, channel // reduction, bias=False), - nn.ReLU(inplace=True), - nn.Linear(channel // reduction, channel, bias=False), - nn.Sigmoid() - ) - - def forward(self, x): - b, c, _, _ = x.size() - y = self.avg_pool(x).view(b, c) - y = self.fc(y).view(b, c, 1, 1) - return x * y.expand_as(x) - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, in_channels, out_channels, stride=1, reduction=16): - super(BasicBlock, self).__init__() - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(out_channels) - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(out_channels) - self.se = SEBlock(out_channels, reduction) - - self.shortcut = nn.Sequential() - if stride != 1 or in_channels != self.expansion * out_channels: - self.shortcut = nn.Sequential( - nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(self.expansion * out_channels) - ) - - def forward(self, x): - out = F.relu(self.bn1(self.conv1(x))) - out = self.bn2(self.conv2(out)) - out += self.shortcut(x) - out = F.relu(out) - return out - -class ThinResNet(nn.Module): - def __init__(self, block, num_blocks, num_classes=10,use_bn=False): - super(ThinResNet, self).__init__() - self.in_channels = 64 - self.use_bn=use_bn - self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False) - if self.use_bn: - self.bn1 = nn.BatchNorm2d(64) - else: - self.bn1 = nn.Sequential() - - self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) - self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) - self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) - # Reducing the number of layers and filters to make it "thin" - self.linear = nn.Linear(256 * block.expansion, num_classes) - self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) - def _make_layer(self, block, out_channels, num_blocks, stride): - strides = [stride] + [1] * (num_blocks - 1) - layers = [] - for stride in strides: - layers.append(block(self.in_channels, out_channels, stride)) - self.in_channels = out_channels * block.expansion - return nn.Sequential(*layers) - - def forward(self, x): - out = F.relu(self.bn1(self.conv1(x))) - out = self.layer1(out) - out = self.layer2(out) - out = self.layer3(out) - out = self.adaptive_pool(out) - out = out.view(out.size(0), -1) - out = self.linear(out) - return out - -def thin_resnet34(): - # Here we configure fewer blocks for a lighter model - return ThinResNet(BasicBlock, [2, 2, 2]) - -# model = thin_resnet34() -# print(model) - -# # Set the model to evaluation mode (as we are just testing with a forward pass) -# model.eval() - -# # Create dummy input tensors -# # Assuming the input images are 224x224 pixels with 1 input channel (grayscale) -# x = torch.randn(1, 1, 224, 224) # Batch size of 1 - -# # Forward pass through the model -# with torch.no_grad(): # We don't need to calculate gradients here -# output = model(x) - -# # Print the output tensor -# print("Output:", output) diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitattributes b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitattributes deleted file mode 100644 index 60404dcd..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -*.ipynb linguist-documentation \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitignore b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitignore deleted file mode 100644 index 74249fe8..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/.gitignore +++ /dev/null @@ -1,9 +0,0 @@ -venv/ -__pycache__ -.pytest_cache -.vscode -.ipynb_checkpoints -checkpoints -pretrained/*.npz -pretrained/*.pth -runs/ \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/LICENSE b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/LICENSE deleted file mode 100644 index 261eeb9e..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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The small models are as accurate as an EfficientNet-B7, but train 8.7 times faster. The large models set a new SOTA top-1 accuracy on ImageNet. - -| NFNet | F0 | F1 | F2 | F3 | F4 | F5 | F6+SAM | -|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:| -| Top-1 accuracy Brock et al. | 83.6 | 84.7 | 85.1 | 85.7 | 85.9 | 86.0 | 86.5 | -| Top-1 accuracy this implementation | 82.82 | 84.63 | 84.90 | 85.46 | 85.66 | 85.62 | TBD | - -All credits go to the authors of the [original paper](https://arxiv.org/abs/2102.06171). This repo is heavily inspired by their nice JAX implementation in the [official repository](https://github.com/deepmind/deepmind-research/blob/master/nfnets/). Visit their repo for citing. - -## Get started -``` -git clone https://github.com/benjs/nfnets_pytorch.git -pip3 install -r requirements.txt -``` -or if you don't need eval and training script -``` -pip install git+https://github.com/benjs/nfnets_pytorch -``` -Download pretrained weights from the [official repository](https://github.com/deepmind/deepmind-research/blob/master/nfnets/) and call - -```python -from nfnets import pretrained_nfnet -model_F0 = pretrained_nfnet('pretrained/F0_haiku.npz') -model_F1 = pretrained_nfnet('pretrained/F1_haiku.npz') -# ... -``` - -The model variant is automatically derived from the parameter count in the pretrained weights file. - -## Validate yourself -``` -python3 eval.py --pretrained pretrained/F0_haiku.npz --dataset path/to/imagenet/valset/ -``` - -You can download the ImageNet validation set from the [ILSVRC2012 challenge site](http://www.image-net.org/challenges/LSVRC/2012/downloads.php#images) after asking for access with, for instance, your .edu mail address or from [AcademicTorrents](https://academictorrents.com/) - -## Scaled weight standardization convolutions in your own model -Simply replace all your `nn.Conv2d` with `WSConv2D` and all your `nn.ReLU` with `VPReLU` or `VPGELU` (variance preserving ReLU/GELU). - -``` python -import torch.nn as nn -from nfnets import WSConv2D, VPReLU, VPGELU - -# Simply replace your nn.Conv2d layers -class MyNet(nn.Module): - def __init__(self): - super(MyNet, self).__init__() - - self.activation = VPReLU(inplace=True) # or VPGELU - self.conv0 = WSConv2D(in_channels=128, out_channels=256, kernel_size=1, ...) - # ... - - def forward(self, x): - out = self.activation(self.conv0(x)) - # ... -``` - -## SGD with adaptive gradient clipping in your own model -Simply replace your `SGD` optimizer with `SGD_AGC`. -```python -from nfnets import SGD_AGC - -optimizer = SGD_AGC( - named_params=model.named_parameters(), # Pass named parameters - lr=1e-3, - momentum=0.9, - clipping=0.1, # New clipping parameter - weight_decay=2e-5, - nesterov=True) -``` - -It is important to exclude certain layers from clipping or momentum. The authors recommends to exclude the last fully convolutional from clipping and the bias/gain parameters from weight decay: -```python -import re - -for group in optimizer.param_groups: - name = group['name'] - - # Exclude from weight decay - if len(re.findall('stem.*(bias|gain)|conv.*(bias|gain)|skip_gain', name)) > 0: - group['weight_decay'] = 0 - - # Exclude from clipping - if name.startswith('linear'): - group['clipping'] = None - -``` - -## Train your own NFNet -Adjust your desired parameters in [default_config.yaml](default_config.yaml) and start training. -``` -python3 train.py --dataset /path/to/imagenet/ -``` - -There is still some parts missing for complete training from scratch: -- Multi-GPU training -- Data augmentations -- FP16 activations and gradients - -## Contribute - -The implementation is still in an early stage in terms of usability / testing. -If you have an idea to improve this repo open an issue, start a discussion or submit a pull request. - -The current development status can be seen in [this](https://github.com/benjs/nfnets_pytorch/projects/1) project board. diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/__init__.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/__init__.py deleted file mode 100644 index 43531b9d..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .model import * -from .pretrained import * -from .optim import * \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/dataset.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/dataset.py deleted file mode 100644 index 7833e251..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/dataset.py +++ /dev/null @@ -1,8 +0,0 @@ -from pathlib import Path -from typing import Callable -from torchvision import transforms -from torch.utils.data.dataset import Dataset -from torchvision.datasets import ImageNet - -def get_dataset(path:Path, transforms:Callable=None) -> Dataset: - return ImageNet(str(path), split='val', transform=transforms) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/default_config.yaml b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/default_config.yaml deleted file mode 100644 index 879fe163..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/default_config.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# This file contains the default train settings -device: 'cuda:0' # or 'cpu' -amp: False # Enable automatic mixed precision - -# Model -variant: 'F0' # F0 - F7 -num_classes: 1000 # Number of classes -activation: 'gelu' # or 'relu' -stochdepth_rate: 0.25 # 0-1, the probability that a layer is dropped during one step -alpha: 0.2 # Scaling factor at the end of each block -se_ratio: 0.5 # Squeeze-Excite expansion ratio -use_fp16: False # Use 16bit floats, which lowers memory footprint. This currently sets - # the complete model to FP16 (will be changed to match FP16 ops from paper) - -# Dataset -dataset: '/media/benjs/ext/' # Dataset root directory -num_workers: 8 # Number of workers in dataloader -pin_memory: True # This can fasten or slow down data loading depending on your hardware - -# Training -batch_size: 64 # Batch size -epochs: 360 # Number of epochs -overfit: False # Train on one batch size only - -learning_rate: 0.1 # Learning rate -scale_lr: True # Scale learning rate with batch size. lr = lr*batch_size/256 -momentum: 0.9 # Contribution of earlier gradient to gradient update -weight_decay: 0.00002 # Factor with which weights are added to gradient -nesterov: True # Enable nesterov correction - -do_clip: True # Enable adaptive gradient clipping -clipping: 0.1 # Adaptive gradient clipping parameter \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/demo.ipynb b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/demo.ipynb deleted file mode 100644 index 72b40509..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/demo.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1-final" - }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python391venvvenv104ff58eb0f84ee89265b076747d4646", - "display_name": "Python 3.9.1 ('venv': venv)", - "language": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2, - "cells": [ - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n", - "Collecting git+https://github.com/benjs/nfnets_pytorch\n", - " Cloning https://github.com/benjs/nfnets_pytorch to /tmp/pip-req-build-2f7sjvln\n", - " Running command git clone -q https://github.com/benjs/nfnets_pytorch /tmp/pip-req-build-2f7sjvln\n", - " # is not a valid attribute name: .gitattributes:1\n", - " Installing build dependencies ... \u001b[?25ldone\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", - "\u001b[?25h Preparing wheel metadata ... \u001b[?25ldone\n", - "\u001b[?25hCollecting torchvision\n", - " Using cached https://download.pytorch.org/whl/cu92/torchvision-0.8.2%2Bcu92-cp39-cp39-linux_x86_64.whl (12.5 MB)\n", - "Requirement already satisfied: jax in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.2.9)\n", - "Requirement already satisfied: requests in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (2.25.1)\n", - "Requirement already satisfied: dill in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.3.3)\n", - "Collecting torch>=1.7\n", - " Using cached https://download.pytorch.org/whl/rocm3.8/torch-1.7.1%2Brocm3.8-cp39-cp39-linux_x86_64.whl (588.0 MB)\n", - "Requirement already satisfied: dm-haiku in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.0.4.dev0)\n", - "Requirement already satisfied: jaxlib in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (0.1.61)\n", - "Requirement already satisfied: numpy in ./venv/lib/python3.9/site-packages (from nfnets-pytorch==0.0.1) (1.20.1)\n", - "Requirement already satisfied: typing-extensions in ./venv/lib/python3.9/site-packages (from torch>=1.7->nfnets-pytorch==0.0.1) (3.7.4.3)\n", - "Requirement already satisfied: pillow>=4.1.1 in ./venv/lib/python3.9/site-packages (from torchvision) (8.1.0)\n", - "Requirement already satisfied: absl-py>=0.7.1 in ./venv/lib/python3.9/site-packages (from dm-haiku->nfnets-pytorch==0.0.1) (0.11.0)\n", - "Requirement already satisfied: tabulate==0.8.7 in ./venv/lib/python3.9/site-packages (from dm-haiku->nfnets-pytorch==0.0.1) (0.8.7)\n", - "Requirement already satisfied: six in ./venv/lib/python3.9/site-packages (from absl-py>=0.7.1->dm-haiku->nfnets-pytorch==0.0.1) (1.15.0)\n", - "Requirement already satisfied: opt-einsum in ./venv/lib/python3.9/site-packages (from jax->nfnets-pytorch==0.0.1) (3.3.0)\n", - "Requirement already satisfied: scipy in ./venv/lib/python3.9/site-packages (from jaxlib->nfnets-pytorch==0.0.1) (1.6.1)\n", - "Requirement already satisfied: flatbuffers in ./venv/lib/python3.9/site-packages (from jaxlib->nfnets-pytorch==0.0.1) (1.12)\n", - "Requirement already satisfied: chardet<5,>=3.0.2 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (4.0.0)\n", - "Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (2020.12.5)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (1.26.3)\n", - "Requirement already satisfied: idna<3,>=2.5 in ./venv/lib/python3.9/site-packages (from requests->nfnets-pytorch==0.0.1) (2.10)\n", - "Installing collected packages: torch, torchvision\n", - "Successfully installed torch-1.7.1+rocm3.8 torchvision-0.8.2+cu92\n" - ] - } - ], - "source": [ - "!pip install git+https://github.com/benjs/nfnets_pytorch torchvision>=0.8" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import PIL\n", - "import requests\n", - "import torch\n", - "import torch.nn.functional as F\n", - "from pathlib import Path\n", - "from PIL import Image\n", - "from nfnets import pretrained_nfnet\n", - "from torchvision.transforms import Compose, Resize, CenterCrop, Normalize, ToTensor" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2021-02-23 23:22:15-- https://storage.googleapis.com/dm-nfnets/F0_haiku.npz\n", - "Resolving storage.googleapis.com (storage.googleapis.com)... 2a00:1450:4001:829::2010, 2a00:1450:4001:827::2010, 2a00:1450:4001:801::2010, ...\n", - "Connecting to storage.googleapis.com (storage.googleapis.com)|2a00:1450:4001:829::2010|:443... connected.\n", - "HTTP request sent, awaiting response... 304 Not Modified\n", - "File ‘pretrained/F0_haiku.npz’ not modified on server. Omitting download.\n", - "\n" - ] - } - ], - "source": [ - "!mkdir -p pretrained \n", - "!wget https://storage.googleapis.com/dm-nfnets/F0_haiku.npz -N -P pretrained" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "r = requests.get(\"https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt\")\n", - "\n", - "classes = []\n", - "for line in r.iter_lines():\n", - " classes.append(str(line).split(\"'\")[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "model = pretrained_nfnet('pretrained/F0_haiku.npz')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "transforms = Compose([\n", - " ToTensor(),\n", - " Resize((model.test_imsize + 32, model.test_imsize + 32), PIL.Image.BICUBIC),\n", - " CenterCrop((model.test_imsize, model.test_imsize)),\n", - " Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def load_img(url:str):\n", - " img = Image.open(requests.get(url, stream=True).raw)\n", - " tensor = transforms(img)[None, :, :]\n", - "\n", - " return img, tensor" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def eval(input:torch.Tensor):\n", - " model.eval()\n", - " with torch.no_grad():\n", - " output = F.softmax(model(input), dim=1)\n", - "\n", - " vals, preds = torch.topk(output, 5 , 1)\n", - "\n", - " for val, pred in iter(zip(vals[0], preds[0])):\n", - " print(f\"{val*100.0:4.2f}%: {classes[pred.item()]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "urls = [\n", - " \"https://unsplash.com/photos/T-0EW-SEbsE/download?force=true\",\n", - " \"https://unsplash.com/photos/rW-I87aPY5Y/download?force=true\",\n", - " \"https://unsplash.com/photos/eqW1MPinEV4/download?force=true\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "", - "image/png": 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "99.55%: Pembroke, Pembroke Welsh corgi\n99.26%: Yorkshire terrier\n98.92%: Australian terrier\n98.54%: Cardigan, Cardigan Welsh corgi\n98.09%: silky terrier, Sydney silky\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": "", - "image/png": 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "99.59%: Persian cat\n97.97%: Egyptian cat\n97.66%: Angora, Angora rabbit\n96.44%: doormat, welcome mat\n96.06%: tabby, tabby cat\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": "", - "image/png": 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "99.82%: sports car, sport car\n99.70%: racer, race car, racing car\n98.63%: car wheel\n92.58%: grille, radiator grille\n86.70%: convertible\n" - ] - } - ], - "source": [ - "for url in urls:\n", - " img, tensor = load_img(url)\n", - " \n", - " display(img.resize((int(x*600/max(img.size)) for x in img.size)))\n", - " eval(tensor)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ] -} \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/eval.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/eval.py deleted file mode 100644 index d098bb1d..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/eval.py +++ /dev/null @@ -1,86 +0,0 @@ -import argparse -import math -import PIL -from pathlib import Path -from PIL.Image import Image - -import torch -import torch.nn as nn -import torchvision.transforms.functional as tF -import torchvision.transforms.functional_pil as tF_pil -from torch.utils.data.dataloader import DataLoader -from torchvision.transforms.transforms import Compose, Normalize, Resize, ToTensor - -from dataset import get_dataset -from nfnets import NFNet, pretrained_nfnet - -# Evaluation method used in the paper -# This seems to perform slightly worse than a simple resize -class Pad32CenterCrop(nn.Module): - def __init__(self, size:int): - super().__init__() - self.size = size - self.scaled_size = (size+32, size+32) - - def forward(self, img:Image): - img = tF_pil.resize(img=img, size=self.scaled_size, interpolation=PIL.Image.BICUBIC) - return tF.center_crop(img, self.size) - -def evaluate_on_imagenet(model:NFNet, dataset_dir:Path, batch_size=50, device='cuda:0'): - transforms = Compose([ - #Pad32CenterCrop(model.test_imsize), - ToTensor(), - Resize((model.test_imsize, model.test_imsize), PIL.Image.BICUBIC), - Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ]) - - print(f"Starting evaluation from {dataset_dir}") - dataset = get_dataset(dataset_dir, transforms=transforms) - - dataloader = DataLoader( - dataset=dataset, - batch_size=batch_size, # F0: 120, F1: 100, F2: 80 - shuffle=False, - pin_memory=False, - num_workers=8 - ) - - print(f"Validation set contains {len(dataset)} images.") - - model.to(device) - model.eval() - - processed_imgs = 0 - correct_labels = 0 - for step, data in enumerate(dataloader): - with torch.no_grad(): - inputs = data[0].to(device) - targets = data[1].to(device) - - output = model(inputs).type(torch.float32) - - processed_imgs += targets.size(0) - _, predicted = torch.max(output, 1) - correct_labels += (predicted == targets).sum().item() - - batch_padding = int(math.log10(len(dataloader.dataset)) + 1) - print(f"\rProcessing {processed_imgs:{batch_padding}d}/{len(dataloader.dataset)}. Accuracy: {100.0*correct_labels/processed_imgs:6.4f}", sep=' ', end='', flush=True) - - print(f"\nFinished eval. Accuracy: {100.0*correct_labels/processed_imgs:6.4f}") - - -if __name__=='__main__': - parser = argparse.ArgumentParser(description='Evaluate NFNets.') - parser.add_argument('--dataset', type=Path, help='Path to dataset root directory', required=True) - parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format', required=True) - parser.add_argument('--batch-size', type=int, help='Validation batch size', default=50) - parser.add_argument('--device', type=str, help='Validation device. Either \'cuda:0\' or \'cpu\'', default='cuda:0') - args = parser.parse_args() - - if not args.pretrained.exists(): - raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") - - model = pretrained_nfnet(args.pretrained) - - evaluate_on_imagenet(model, dataset_dir=args.dataset, batch_size=args.batch_size, device=args.device) - \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/model.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/model.py deleted file mode 100644 index b3c34620..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/model.py +++ /dev/null @@ -1,307 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import re - -nfnet_params = { - 'F0': { - 'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], - 'train_imsize': 192, 'test_imsize': 256, - 'RA_level': '405', 'drop_rate': 0.2}, - 'F1': { - 'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], - 'train_imsize': 224, 'test_imsize': 320, - 'RA_level': '410', 'drop_rate': 0.3}, - 'F2': { - 'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], - 'train_imsize': 256, 'test_imsize': 352, - 'RA_level': '410', 'drop_rate': 0.4}, - 'F3': { - 'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], - 'train_imsize': 320, 'test_imsize': 416, - 'RA_level': '415', 'drop_rate': 0.4}, - 'F4': { - 'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], - 'train_imsize': 384, 'test_imsize': 512, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F5': { - 'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], - 'train_imsize': 416, 'test_imsize': 544, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F6': { - 'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], - 'train_imsize': 448, 'test_imsize': 576, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F7': { - 'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], - 'train_imsize': 480, 'test_imsize': 608, - 'RA_level': '415', 'drop_rate': 0.5}, -} - -# These extra constant values ensure that the activations -# are variance preserving -class VPGELU(nn.Module): - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.gelu(input) * 1.7015043497085571 - -class VPReLU(nn.Module): - __constants__ = ['inplace'] - inplace: bool - - def __init__(self, inplace: bool = False): - super(VPReLU, self).__init__() - self.inplace = inplace - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.relu(input, inplace=self.inplace) * 1.7139588594436646 - - def extra_repr(self) -> str: - inplace_str = 'inplace=True' if self.inplace else '' - return inplace_str - -activations_dict = { - 'gelu': VPGELU(), - 'relu': VPReLU(inplace=True) -} - -class NFNet(nn.Module): - def __init__(self, num_channels=1,num_classes:int=2, variant:str='F0', stochdepth_rate:float=None, - alpha:float=0.2, se_ratio:float=0.5, activation:str='gelu'): - super(NFNet, self).__init__() - - if not variant in nfnet_params: - raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") - - block_params = nfnet_params[variant] - - self.train_imsize = block_params['train_imsize'] - self.test_imsize = block_params['test_imsize'] - self.activation = activations_dict[activation] - self.drop_rate = block_params['drop_rate'] - self.num_classes = num_classes - - self.stem = Stem(num_channels=num_channels,activation=activation) - - num_blocks, index = sum(block_params['depth']), 0 - - blocks = [] - expected_std = 1.0 - in_channels = block_params['width'][0] // 2 - - block_args = zip( - block_params['width'], - block_params['depth'], - [0.5] * 4, # bottleneck pattern - [128] * 4, # group pattern. Original groups [128] * 4 - [1, 2, 2, 2] # stride pattern - ) - - for (block_width, stage_depth, expand_ratio, group_size, stride) in block_args: - for block_index in range(stage_depth): - beta = 1. / expected_std - - block_sd_rate = stochdepth_rate * index / num_blocks - out_channels = block_width - - blocks.append(NFBlock( - in_channels=in_channels, - out_channels=out_channels, - stride=stride if block_index == 0 else 1, - alpha=alpha, - beta=beta, - se_ratio=se_ratio, - group_size=group_size, - stochdepth_rate=block_sd_rate, - activation=activation)) - - in_channels = out_channels - index += 1 - - if block_index == 0: - expected_std = 1.0 - - expected_std = (expected_std **2 + alpha**2)**0.5 - - self.body = nn.Sequential(*blocks) - - final_conv_channels = 2*in_channels - self.final_conv = WSConv2D(in_channels=out_channels, out_channels=final_conv_channels, kernel_size=1) - self.pool = nn.AvgPool2d(1) - - if self.drop_rate > 0.: - self.dropout = nn.Dropout(self.drop_rate) - - self.linear = nn.Linear(final_conv_channels, self.num_classes) - nn.init.normal_(self.linear.weight, 0, 0.01) - - def forward(self, x): - out = self.stem(x) - out = self.body(out) - out = self.activation(self.final_conv(out)) - pool = torch.mean(out, dim=(2,3)) - - if self.training and self.drop_rate > 0.: - pool = self.dropout(pool) - - return self.linear(pool) - - def exclude_from_weight_decay(self, name:str) -> bool: - # Regex to find layer names like - # "stem.6.bias", "stem.6.gain", "body.0.skip_gain", - # "body.0.conv0.bias", "body.0.conv0.gain" - regex = re.compile('stem.*(bias|gain)|conv.*(bias|gain)|skip_gain') - return len(regex.findall(name)) > 0 - - def exclude_from_clipping(self, name: str) -> bool: - # Last layer should not be clipped - return name.startswith('linear') - -class Stem(nn.Module): - def __init__(self, num_channels=1, activation:str='gelu'): - super(Stem, self).__init__() - - self.activation = activations_dict[activation] - self.conv0 = WSConv2D(in_channels=num_channels, out_channels=16, kernel_size=3, stride=2) - self.conv1 = WSConv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1) - self.conv2 = WSConv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1) - self.conv3 = WSConv2D(in_channels=64, out_channels=128, kernel_size=3, stride=2) - - def forward(self, x): - out = self.activation(self.conv0(x)) - out = self.activation(self.conv1(out)) - out = self.activation(self.conv2(out)) - out = self.conv3(out) - return out - -class NFBlock(nn.Module): - def __init__(self, in_channels:int, out_channels:int, expansion:float=0.5, - se_ratio:float=0.5, stride:int=1, beta:float=1.0, alpha:float=0.2, - group_size:int=1, stochdepth_rate:float=None, activation:str='gelu'): - - super(NFBlock, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.expansion = expansion - self.se_ratio = se_ratio - self.activation = activations_dict[activation] - self.beta, self.alpha = beta, alpha - self.group_size = group_size - - width = int(self.out_channels * expansion) - self.groups = width // group_size - self.width = group_size * self.groups - self.stride = stride - - self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) - self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) - self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) - self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) - - self.use_projection = self.stride > 1 or self.in_channels != self.out_channels - if self.use_projection: - if stride > 1: - self.shortcut_avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels==1536 else 1) - self.conv_shortcut = WSConv2D(self.in_channels, self.out_channels, kernel_size=1) - - self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) - self.skip_gain = nn.Parameter(torch.zeros(())) - - self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. - if self.use_stochdepth: - self.stoch_depth = StochDepth(stochdepth_rate) - - def forward(self, x): - out = self.activation(x) * self.beta - - if self.stride > 1: - shortcut = self.shortcut_avg_pool(out) - shortcut = self.conv_shortcut(shortcut) - elif self.use_projection: - shortcut = self.conv_shortcut(out) - else: - shortcut = x - - out = self.activation(self.conv0(out)) - out = self.activation(self.conv1(out)) - out = self.activation(self.conv1b(out)) - out = self.conv2(out) - out = (self.squeeze_excite(out)*2) * out - - if self.use_stochdepth: - out = self.stoch_depth(out) - - return out * self.alpha * self.skip_gain + shortcut - -# Implementation mostly from https://arxiv.org/abs/2101.08692 -# Implemented changes from https://arxiv.org/abs/2102.06171 and -# https://github.com/deepmind/deepmind-research/tree/master/nfnets -class WSConv2D(nn.Conv2d): - def __init__(self, in_channels: int, out_channels: int, kernel_size, stride = 1, padding = 0, - dilation = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros'): - - super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, - padding, dilation, groups, bias, padding_mode) - - nn.init.xavier_normal_(self.weight) - self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) - self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) - self.register_buffer('fan_in', torch.tensor(self.weight.shape[1:].numel(), requires_grad=False).type_as(self.weight), persistent=False) - - def standardized_weights(self): - # Original code: HWCN - mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) - var = torch.var(self.weight, axis=[1,2,3], keepdims=True) - scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) - return (self.weight - mean) * scale * self.gain - - def forward(self, x): - return F.conv2d( - input=x, - weight=self.standardized_weights(), - bias=self.bias, - stride=self.stride, - padding=self.padding, - dilation=self.dilation, - groups=self.groups - ) - -class SqueezeExcite(nn.Module): - def __init__(self, in_channels:int, out_channels:int, se_ratio:float=0.5, activation:str='gelu'): - super(SqueezeExcite, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.se_ratio = se_ratio - - self.hidden_channels = max(1, int(self.in_channels * self.se_ratio)) - - self.activation = activations_dict[activation] - self.linear = nn.Linear(self.in_channels, self.hidden_channels) - self.linear_1 = nn.Linear(self.hidden_channels, self.out_channels) - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - out = torch.mean(x, (2,3)) - out = self.linear_1(self.activation(self.linear(out))) - out = self.sigmoid(out) - - b,c,_,_ = x.size() - return out.view(b,c,1,1).expand_as(x) - -class StochDepth(nn.Module): - def __init__(self, stochdepth_rate:float): - super(StochDepth, self).__init__() - - self.drop_rate = stochdepth_rate - - def forward(self, x): - if not self.training: - return x - - batch_size = x.shape[0] - rand_tensor = torch.rand(batch_size, 1, 1, 1).type_as(x).to(x.device) - keep_prob = 1 - self.drop_rate - binary_tensor = torch.floor(rand_tensor + keep_prob) - - return x * binary_tensor diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/__init__.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/__init__.py deleted file mode 100644 index 43531b9d..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .model import * -from .pretrained import * -from .optim import * \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model copy.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model copy.py deleted file mode 100644 index 37551df9..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model copy.py +++ /dev/null @@ -1,309 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import re - -nfnet_params = { - 'F0': { - 'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], - 'train_imsize': 192, 'test_imsize': 114, - 'RA_level': '405', 'drop_rate': 0.2}, - - 'F1': { - 'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], - 'train_imsize': 224, 'test_imsize': 320, - 'RA_level': '410', 'drop_rate': 0.3}, - 'F2': { - 'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], - 'train_imsize': 256, 'test_imsize': 352, - 'RA_level': '410', 'drop_rate': 0.4}, - 'F3': { - 'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], - 'train_imsize': 320, 'test_imsize': 416, - 'RA_level': '415', 'drop_rate': 0.4}, - 'F4': { - 'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], - 'train_imsize': 384, 'test_imsize': 512, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F5': { - 'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], - 'train_imsize': 416, 'test_imsize': 544, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F6': { - 'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], - 'train_imsize': 448, 'test_imsize': 576, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F7': { - 'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], - 'train_imsize': 480, 'test_imsize': 608, - 'RA_level': '415', 'drop_rate': 0.5}, -} - -# These extra constant values ensure that the activations -# are variance preserving -class VPGELU(nn.Module): - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.gelu(input) * 1.7015043497085571 - -class VPReLU(nn.Module): - __constants__ = ['inplace'] - inplace: bool - - def __init__(self, inplace: bool = False): - super(VPReLU, self).__init__() - self.inplace = inplace - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.relu(input, inplace=self.inplace) * 1.7139588594436646 - - def extra_repr(self) -> str: - inplace_str = 'inplace=True' if self.inplace else '' - return inplace_str - -activations_dict = { - 'gelu': VPGELU(), - 'relu': VPReLU(inplace=True) -} - -class NFNet(nn.Module): - def __init__(self, num_channels=1,num_classes:int=2, variant:str='F0', stochdepth_rate:float=0.25, - alpha:float=0.2, se_ratio:float=0.5, activation:str='gelu'): - super(NFNet, self).__init__() - - if not variant in nfnet_params: - raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") - - block_params = nfnet_params[variant] - - self.train_imsize = block_params['train_imsize'] - self.test_imsize = block_params['test_imsize'] - self.activation = activations_dict[activation] - self.drop_rate = block_params['drop_rate'] - self.num_classes = num_classes - - self.stem = Stem(num_channels=num_channels,activation=activation) - - num_blocks, index = sum(block_params['depth']), 0 - - blocks = [] - expected_std = 1.0 - in_channels = block_params['width'][0] // 2 - - block_args = zip( - block_params['width'], - block_params['depth'], - [0.5] * 4, # bottleneck pattern - [57] * 4, # group pattern. Original groups [128] * 4 - # [128] * 4, # group pattern. Original groups [128] * 4 - [1, 2, 2, 2] # stride pattern - ) - - for (block_width, stage_depth, expand_ratio, group_size, stride) in block_args: - for block_index in range(stage_depth): - beta = 1. / expected_std - - block_sd_rate = stochdepth_rate * index / num_blocks - out_channels = block_width - - blocks.append(NFBlock( - in_channels=in_channels, - out_channels=out_channels, - stride=stride if block_index == 0 else 1, - alpha=alpha, - beta=beta, - se_ratio=se_ratio, - group_size=group_size, - stochdepth_rate=block_sd_rate, - activation=activation)) - - in_channels = out_channels - index += 1 - - if block_index == 0: - expected_std = 1.0 - - expected_std = (expected_std **2 + alpha**2)**0.5 - - self.body = nn.Sequential(*blocks) - - final_conv_channels = 2*in_channels - self.final_conv = WSConv2D(in_channels=out_channels, out_channels=final_conv_channels, kernel_size=1) - self.pool = nn.AvgPool2d(1) - - if self.drop_rate > 0.: - self.dropout = nn.Dropout(self.drop_rate) - - self.linear = nn.Linear(final_conv_channels, self.num_classes) - nn.init.normal_(self.linear.weight, 0, 0.01) - - def forward(self, x): - out = self.stem(x) - out = self.body(out) - out = self.activation(self.final_conv(out)) - pool = torch.mean(out, dim=(2,3)) - - if self.training and self.drop_rate > 0.: - pool = self.dropout(pool) - - return self.linear(pool) - - def exclude_from_weight_decay(self, name:str) -> bool: - # Regex to find layer names like - # "stem.6.bias", "stem.6.gain", "body.0.skip_gain", - # "body.0.conv0.bias", "body.0.conv0.gain" - regex = re.compile('stem.*(bias|gain)|conv.*(bias|gain)|skip_gain') - return len(regex.findall(name)) > 0 - - def exclude_from_clipping(self, name: str) -> bool: - # Last layer should not be clipped - return name.startswith('linear') - -class Stem(nn.Module): - def __init__(self, num_channels=1, activation:str='gelu'): - super(Stem, self).__init__() - - self.activation = activations_dict[activation] - self.conv0 = WSConv2D(in_channels=num_channels, out_channels=16, kernel_size=3, stride=2) - self.conv1 = WSConv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1) - self.conv2 = WSConv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1) - self.conv3 = WSConv2D(in_channels=64, out_channels=128, kernel_size=3, stride=2) - - def forward(self, x): - out = self.activation(self.conv0(x)) - out = self.activation(self.conv1(out)) - out = self.activation(self.conv2(out)) - out = self.conv3(out) - return out - -class NFBlock(nn.Module): - def __init__(self, in_channels:int, out_channels:int, expansion:float=0.5, - se_ratio:float=0.5, stride:int=1, beta:float=1.0, alpha:float=0.2, - group_size:int=1, stochdepth_rate:float=None, activation:str='gelu'): - - super(NFBlock, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.expansion = expansion - self.se_ratio = se_ratio - self.activation = activations_dict[activation] - self.beta, self.alpha = beta, alpha - self.group_size = group_size - - width = int(self.out_channels * expansion) - self.groups = width // group_size - self.width = group_size * self.groups - self.stride = stride - - self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) - self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) - self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) - self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) - - self.use_projection = self.stride > 1 or self.in_channels != self.out_channels - if self.use_projection: - if stride > 1: - self.shortcut_avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels==1536 else 1) - self.conv_shortcut = WSConv2D(self.in_channels, self.out_channels, kernel_size=1) - - self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) - self.skip_gain = nn.Parameter(torch.zeros(())) - - self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. - if self.use_stochdepth: - self.stoch_depth = StochDepth(stochdepth_rate) - - def forward(self, x): - out = self.activation(x) * self.beta - - if self.stride > 1: - shortcut = self.shortcut_avg_pool(out) - shortcut = self.conv_shortcut(shortcut) - elif self.use_projection: - shortcut = self.conv_shortcut(out) - else: - shortcut = x - - out = self.activation(self.conv0(out)) - out = self.activation(self.conv1(out)) - out = self.activation(self.conv1b(out)) - out = self.conv2(out) - out = (self.squeeze_excite(out)*2) * out - - if self.use_stochdepth: - out = self.stoch_depth(out) - - return out * self.alpha * self.skip_gain + shortcut - -# Implementation mostly from https://arxiv.org/abs/2101.08692 -# Implemented changes from https://arxiv.org/abs/2102.06171 and -# https://github.com/deepmind/deepmind-research/tree/master/nfnets -class WSConv2D(nn.Conv2d): - def __init__(self, in_channels: int, out_channels: int, kernel_size, stride = 1, padding = 0, - dilation = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros'): - - super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, - padding, dilation, groups, bias, padding_mode) - - nn.init.xavier_normal_(self.weight) - self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) - self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) - self.register_buffer('fan_in', torch.tensor(self.weight.shape[1:].numel(), requires_grad=False).type_as(self.weight), persistent=False) - - def standardized_weights(self): - # Original code: HWCN - mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) - var = torch.var(self.weight, axis=[1,2,3], keepdims=True) - scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) - return (self.weight - mean) * scale * self.gain - - def forward(self, x): - return F.conv2d( - input=x, - weight=self.standardized_weights(), - bias=self.bias, - stride=self.stride, - padding=self.padding, - dilation=self.dilation, - groups=self.groups - ) - -class SqueezeExcite(nn.Module): - def __init__(self, in_channels:int, out_channels:int, se_ratio:float=0.5, activation:str='gelu'): - super(SqueezeExcite, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.se_ratio = se_ratio - - self.hidden_channels = max(1, int(self.in_channels * self.se_ratio)) - - self.activation = activations_dict[activation] - self.linear = nn.Linear(self.in_channels, self.hidden_channels) - self.linear_1 = nn.Linear(self.hidden_channels, self.out_channels) - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - out = torch.mean(x, (2,3)) - out = self.linear_1(self.activation(self.linear(out))) - out = self.sigmoid(out) - - b,c,_,_ = x.size() - return out.view(b,c,1,1).expand_as(x) - -class StochDepth(nn.Module): - def __init__(self, stochdepth_rate:float): - super(StochDepth, self).__init__() - - self.drop_rate = stochdepth_rate - - def forward(self, x): - if not self.training: - return x - - batch_size = x.shape[0] - rand_tensor = torch.rand(batch_size, 1, 1, 1).type_as(x).to(x.device) - keep_prob = 1 - self.drop_rate - binary_tensor = torch.floor(rand_tensor + keep_prob) - - return x * binary_tensor diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model.py deleted file mode 100644 index 28e71bc0..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/model.py +++ /dev/null @@ -1,265 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import re - -nfnet_params = { - 'F0': { - 'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], - 'train_imsize': 192, 'test_imsize': 114, - 'RA_level': '405', 'drop_rate': 0.2}, - - 'F1': { - 'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], - 'train_imsize': 224, 'test_imsize': 320, - 'RA_level': '410', 'drop_rate': 0.3}, - 'F2': { - 'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], - 'train_imsize': 256, 'test_imsize': 352, - 'RA_level': '410', 'drop_rate': 0.4}, - 'F3': { - 'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], - 'train_imsize': 320, 'test_imsize': 416, - 'RA_level': '415', 'drop_rate': 0.4}, - 'F4': { - 'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], - 'train_imsize': 384, 'test_imsize': 512, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F5': { - 'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], - 'train_imsize': 416, 'test_imsize': 544, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F6': { - 'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], - 'train_imsize': 448, 'test_imsize': 576, - 'RA_level': '415', 'drop_rate': 0.5}, - 'F7': { - 'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], - 'train_imsize': 480, 'test_imsize': 608, - 'RA_level': '415', 'drop_rate': 0.5}, -} -import torch -import torch.nn as nn -import torch.nn.functional as F -import re - - -# These extra constant values ensure that the activations -# are variance preserving -class VPGELU(nn.Module): - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.gelu(input) * 1.7015043497085571 - -class VPReLU(nn.Module): - __constants__ = ['inplace'] - inplace: bool - - def __init__(self, inplace: bool = False): - super(VPReLU, self).__init__() - self.inplace = inplace - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.relu(input, inplace=self.inplace) * 1.7139588594436646 - - def extra_repr(self) -> str: - inplace_str = 'inplace=True' if self.inplace else '' - return inplace_str - -activations_dict = { - 'gelu': VPGELU(), - 'relu': VPReLU(inplace=True) -} - - -# Definitions for VPGELU and VPReLU are assumed to be the same as above. - -class NFNet(nn.Module): - def __init__(self, num_channels=1, num_classes=2, variant='F0', stochdepth_rate=0.25, alpha=0.2, se_ratio=0.5, activation='gelu'): - super(NFNet, self).__init__() - - if variant not in nfnet_params: - raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") - - block_params = nfnet_params[variant] - self.activation = activations_dict[activation] - self.drop_rate = block_params['drop_rate'] - self.num_classes = num_classes - - self.stem = Stem(num_channels=num_channels, activation=activation) - - # Here, we use a list to hold each NFBlock stage. - blocks = [] - in_channels = block_params['width'][0] // 2 - - # Adjust output sizes by maintaining depth but adjusting widths and strides. - for i, (width, depth, stride) in enumerate(zip(block_params['width'], block_params['depth'], [2, 2, 2, 2])): - out_channels = width - for _ in range(depth): - stride = stride if _ == 0 else 1 - blocks.append(NFBlock(in_channels=in_channels, out_channels=out_channels, stride=stride, se_ratio=se_ratio, activation=activation)) - in_channels = out_channels - - self.blocks = nn.Sequential(*blocks) - - # Adaptive pooling to adjust for any size discrepancies. - self.pool = nn.AdaptiveAvgPool2d(1) - - # Final layers after pooling. - self.dropout = nn.Dropout(self.drop_rate) - self.classifier = nn.Linear(in_channels, self.num_classes) - - def forward(self, x): - x = self.stem(x) - x = self.blocks(x) - x = self.pool(x) - x = torch.flatten(x, 1) - if self.training and self.drop_rate > 0: - x = self.dropout(x) - return self.classifier(x) - -class Stem(nn.Module): - def __init__(self, num_channels=1, activation='gelu'): - super(Stem, self).__init__() - self.conv1 = nn.Conv2d(num_channels, 32, kernel_size=3, stride=2, padding=1) - self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) - self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) - self.activation = activations_dict[activation] - - def forward(self, x): - x = self.activation(self.conv1(x)) - x = self.activation(self.conv2(x)) - x = self.activation(self.conv3(x)) - return x - -class NFBlock(nn.Module): - def __init__(self, in_channels:int, out_channels:int, expansion:float=0.5, - se_ratio:float=0.5, stride:int=1, beta:float=1.0, alpha:float=0.2, - group_size:int=1, stochdepth_rate:float=None, activation:str='gelu'): - - super(NFBlock, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.expansion = expansion - self.se_ratio = se_ratio - self.activation = activations_dict[activation] - self.beta, self.alpha = beta, alpha - self.group_size = group_size - - width = int(self.out_channels * expansion) - self.groups = width // group_size - self.width = group_size * self.groups - self.stride = stride - - self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) - self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) - self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) - self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) - - self.use_projection = self.stride > 1 or self.in_channels != self.out_channels - if self.use_projection: - if stride > 1: - self.shortcut_avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels==1536 else 1) - self.conv_shortcut = WSConv2D(self.in_channels, self.out_channels, kernel_size=1) - - self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) - self.skip_gain = nn.Parameter(torch.zeros(())) - - self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. - if self.use_stochdepth: - self.stoch_depth = StochDepth(stochdepth_rate) - - def forward(self, x): - out = self.activation(x) * self.beta - - if self.stride > 1: - shortcut = self.shortcut_avg_pool(out) - shortcut = self.conv_shortcut(shortcut) - elif self.use_projection: - shortcut = self.conv_shortcut(out) - else: - shortcut = x - - out = self.activation(self.conv0(out)) - out = self.activation(self.conv1(out)) - out = self.activation(self.conv1b(out)) - out = self.conv2(out) - out = (self.squeeze_excite(out)*2) * out - - if self.use_stochdepth: - out = self.stoch_depth(out) - - return out * self.alpha * self.skip_gain + shortcut - -# Implementation mostly from https://arxiv.org/abs/2101.08692 -# Implemented changes from https://arxiv.org/abs/2102.06171 and -# https://github.com/deepmind/deepmind-research/tree/master/nfnets -class WSConv2D(nn.Conv2d): - def __init__(self, in_channels: int, out_channels: int, kernel_size, stride = 1, padding = 0, - dilation = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros'): - - super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, - padding, dilation, groups, bias, padding_mode) - - nn.init.xavier_normal_(self.weight) - self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) - self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) - self.register_buffer('fan_in', torch.tensor(self.weight.shape[1:].numel(), requires_grad=False).type_as(self.weight), persistent=False) - - def standardized_weights(self): - # Original code: HWCN - mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) - var = torch.var(self.weight, axis=[1,2,3], keepdims=True) - scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) - return (self.weight - mean) * scale * self.gain - - def forward(self, x): - return F.conv2d( - input=x, - weight=self.standardized_weights(), - bias=self.bias, - stride=self.stride, - padding=self.padding, - dilation=self.dilation, - groups=self.groups - ) - -class SqueezeExcite(nn.Module): - def __init__(self, in_channels:int, out_channels:int, se_ratio:float=0.5, activation:str='gelu'): - super(SqueezeExcite, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.se_ratio = se_ratio - - self.hidden_channels = max(1, int(self.in_channels * self.se_ratio)) - - self.activation = activations_dict[activation] - self.linear = nn.Linear(self.in_channels, self.hidden_channels) - self.linear_1 = nn.Linear(self.hidden_channels, self.out_channels) - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - out = torch.mean(x, (2,3)) - out = self.linear_1(self.activation(self.linear(out))) - out = self.sigmoid(out) - - b,c,_,_ = x.size() - return out.view(b,c,1,1).expand_as(x) - -class StochDepth(nn.Module): - def __init__(self, stochdepth_rate:float): - super(StochDepth, self).__init__() - - self.drop_rate = stochdepth_rate - - def forward(self, x): - if not self.training: - return x - - batch_size = x.shape[0] - rand_tensor = torch.rand(batch_size, 1, 1, 1).type_as(x).to(x.device) - keep_prob = 1 - self.drop_rate - binary_tensor = torch.floor(rand_tensor + keep_prob) - - return x * binary_tensor diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/optim.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/optim.py deleted file mode 100644 index 4d72f023..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/optim.py +++ /dev/null @@ -1,109 +0,0 @@ -import torch -from torch.optim import Optimizer - -# Compute norm depending on the shape of x -def unitwise_norm(x): - if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors - axis = 0 - keepdims = False - elif len(x.shape) in [2,3]: # Linear layers - # Original code: IO - # Pytorch: OI - axis = 1 - keepdims = True - elif len(x.shape) == 4: # Conv kernels - # Original code: HWIO - # Pytorch: OIHW - axis = [1, 2, 3] - keepdims = True - else: - raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') - - return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) - - -# This is a copy of the pytorch SGD implementation -# enhanced with gradient clipping -class SGD_AGC(Optimizer): - def __init__(self, named_params, lr:float, momentum=0, dampening=0, - weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): - if lr < 0.0: - raise ValueError("Invalid learning rate: {}".format(lr)) - if momentum < 0.0: - raise ValueError("Invalid momentum value: {}".format(momentum)) - if weight_decay < 0.0: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - - defaults = dict(lr=lr, momentum=momentum, dampening=dampening, - weight_decay=weight_decay, nesterov=nesterov, - # Extra defaults - clipping=clipping, - eps=eps - ) - - if nesterov and (momentum <= 0 or dampening != 0): - raise ValueError("Nesterov momentum requires a momentum and zero dampening") - - # Put params in list so each one gets its own group - params = [] - for name, param in named_params: - params.append({'params': param, 'name': name}) - - super(SGD_AGC, self).__init__(params, defaults) - - def __setstate__(self, state): - super(SGD_AGC, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('nesterov', False) - - @torch.no_grad() - def step(self, closure=None): - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - for group in self.param_groups: - weight_decay = group['weight_decay'] - momentum = group['momentum'] - dampening = group['dampening'] - nesterov = group['nesterov'] - - # Extra values for clipping - clipping = group['clipping'] - eps = group['eps'] - - for p in group['params']: - if p.grad is None: - continue - d_p = p.grad - - # ========================= - # Gradient clipping - if clipping is not None: - param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) - grad_norm = unitwise_norm(d_p) - max_norm = param_norm * group['clipping'] - - trigger_mask = grad_norm > max_norm - clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) - d_p = torch.where(trigger_mask, clipped_grad, d_p) - # ========================= - - if weight_decay != 0: - d_p = d_p.add(p, alpha=weight_decay) - if momentum != 0: - param_state = self.state[p] - if 'momentum_buffer' not in param_state: - buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() - else: - buf = param_state['momentum_buffer'] - buf.mul_(momentum).add_(d_p, alpha=1 - dampening) - if nesterov: - d_p = d_p.add(buf, alpha=momentum) - else: - d_p = buf - - p.add_(d_p, alpha=-group['lr']) - - return loss \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py deleted file mode 100644 index 5d285aa3..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/nfnets/pretrained.py +++ /dev/null @@ -1,95 +0,0 @@ -import re -import dill -import torch -import argparse -import numpy as np -from pathlib import Path - -from ..nfnets import NFNet - -def pretrained_nfnet(path, stochdepth_rate:float=0.5, alpha:float=0.2, activation:str='gelu') -> NFNet: - if isinstance(path, str): - path = Path(path) - - with path.open('rb') as f: - params = dill.load(f) - - layers_to_variant = { - 94: 'F0', - 178: 'F1', - 262: 'F2', - 346: 'F3', - 430: 'F4', - 514: 'F5' - } - - if not len(params) in layers_to_variant: - raise RuntimeError(f"Cannot load file {path.absolute()}." - f" File contains invalid parameter count {len(params)}!") - - model = NFNet( - variant=layers_to_variant[len(params)], - num_classes=1000, - alpha=alpha, - stochdepth_rate=stochdepth_rate, - se_ratio=0.5, - activation=activation) - - state_dict = {} - - for layer_name in params: - for param_name in params[layer_name]: - l = layer_name - l = l.replace("NFNet/~/", "") - l = re.sub("(nf_block_(\d*))", r"body.\2", l) - l = re.sub("(nf_block)", r"body.0", l) - l = re.sub("stem_*", "stem.", l) - l = l.replace("/~/", ".") - - p = str(param_name) - p = "weight" if p == "w" else p - p = "bias" if p == "b" else p - - param = params[layer_name][param_name] - - if len(param.shape) == 4: - # Conv layers, HWIO -> OIHW - param = param.swapaxes(0,3).swapaxes(1,2).swapaxes(2,3) - - elif len(param.shape) == 2: - # Linear layers, OI -> IO - param = param.swapaxes(0,1) - - if p == 'gain': - param = np.expand_dims(param, axis=(1,2,3)) - - #if "conv" in l: - # state_dict[f"{l}.eps"] = torch.tensor(1e-4, requires_grad=False) - - with torch.no_grad(): - t = torch.from_numpy(param) - complete_name = f'{l}.{p}' - if not complete_name in model.state_dict(): - raise ValueError( - f"Parameter {complete_name} not found in state dict!" - " Please report an issue.") - - state_dict[complete_name] = t - - model.load_state_dict(state_dict, strict=True) - return model - -if __name__=='__main__': - parser = argparse.ArgumentParser(description='Load haiku weights and convert them to .pth file.') - parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format') - args = parser.parse_args() - - if not args.pretrained.exists(): - raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") - - # model = from_pretrained_haiku(args.pretrained) - model = pretrained_nfnet(args.pretrained) - - torch.save({ - 'model': model.state_dict() - }, str(args.pretrained.with_suffix('.pth'))) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/optim.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/optim.py deleted file mode 100644 index 4d72f023..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/optim.py +++ /dev/null @@ -1,109 +0,0 @@ -import torch -from torch.optim import Optimizer - -# Compute norm depending on the shape of x -def unitwise_norm(x): - if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors - axis = 0 - keepdims = False - elif len(x.shape) in [2,3]: # Linear layers - # Original code: IO - # Pytorch: OI - axis = 1 - keepdims = True - elif len(x.shape) == 4: # Conv kernels - # Original code: HWIO - # Pytorch: OIHW - axis = [1, 2, 3] - keepdims = True - else: - raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') - - return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) - - -# This is a copy of the pytorch SGD implementation -# enhanced with gradient clipping -class SGD_AGC(Optimizer): - def __init__(self, named_params, lr:float, momentum=0, dampening=0, - weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): - if lr < 0.0: - raise ValueError("Invalid learning rate: {}".format(lr)) - if momentum < 0.0: - raise ValueError("Invalid momentum value: {}".format(momentum)) - if weight_decay < 0.0: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - - defaults = dict(lr=lr, momentum=momentum, dampening=dampening, - weight_decay=weight_decay, nesterov=nesterov, - # Extra defaults - clipping=clipping, - eps=eps - ) - - if nesterov and (momentum <= 0 or dampening != 0): - raise ValueError("Nesterov momentum requires a momentum and zero dampening") - - # Put params in list so each one gets its own group - params = [] - for name, param in named_params: - params.append({'params': param, 'name': name}) - - super(SGD_AGC, self).__init__(params, defaults) - - def __setstate__(self, state): - super(SGD_AGC, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('nesterov', False) - - @torch.no_grad() - def step(self, closure=None): - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - for group in self.param_groups: - weight_decay = group['weight_decay'] - momentum = group['momentum'] - dampening = group['dampening'] - nesterov = group['nesterov'] - - # Extra values for clipping - clipping = group['clipping'] - eps = group['eps'] - - for p in group['params']: - if p.grad is None: - continue - d_p = p.grad - - # ========================= - # Gradient clipping - if clipping is not None: - param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) - grad_norm = unitwise_norm(d_p) - max_norm = param_norm * group['clipping'] - - trigger_mask = grad_norm > max_norm - clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) - d_p = torch.where(trigger_mask, clipped_grad, d_p) - # ========================= - - if weight_decay != 0: - d_p = d_p.add(p, alpha=weight_decay) - if momentum != 0: - param_state = self.state[p] - if 'momentum_buffer' not in param_state: - buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() - else: - buf = param_state['momentum_buffer'] - buf.mul_(momentum).add_(d_p, alpha=1 - dampening) - if nesterov: - d_p = d_p.add(buf, alpha=momentum) - else: - d_p = buf - - p.add_(d_p, alpha=-group['lr']) - - return loss \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained.py deleted file mode 100644 index f26cacd3..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained.py +++ /dev/null @@ -1,94 +0,0 @@ -import re -import dill -import torch -import argparse -import numpy as np -from pathlib import Path - -from nfnets import NFNet - -def pretrained_nfnet(path, stochdepth_rate:float=0.5, alpha:float=0.2, activation:str='gelu') -> NFNet: - if isinstance(path, str): - path = Path(path) - - with path.open('rb') as f: - params = dill.load(f) - - layers_to_variant = { - 94: 'F0', - 178: 'F1', - 262: 'F2', - 346: 'F3', - 430: 'F4', - 514: 'F5' - } - - if not len(params) in layers_to_variant: - raise RuntimeError(f"Cannot load file {path.absolute()}." - f" File contains invalid parameter count {len(params)}!") - - model = NFNet( - variant=layers_to_variant[len(params)], - num_classes=1000, - alpha=alpha, - stochdepth_rate=stochdepth_rate, - se_ratio=0.5, - activation=activation) - - state_dict = {} - - for layer_name in params: - for param_name in params[layer_name]: - l = layer_name - l = l.replace("NFNet/~/", "") - l = re.sub("(nf_block_(\d*))", r"body.\2", l) - l = re.sub("(nf_block)", r"body.0", l) - l = re.sub("stem_*", "stem.", l) - l = l.replace("/~/", ".") - - p = str(param_name) - p = "weight" if p == "w" else p - p = "bias" if p == "b" else p - - param = params[layer_name][param_name] - - if len(param.shape) == 4: - # Conv layers, HWIO -> OIHW - param = param.swapaxes(0,3).swapaxes(1,2).swapaxes(2,3) - - elif len(param.shape) == 2: - # Linear layers, OI -> IO - param = param.swapaxes(0,1) - - if p == 'gain': - param = np.expand_dims(param, axis=(1,2,3)) - - #if "conv" in l: - # state_dict[f"{l}.eps"] = torch.tensor(1e-4, requires_grad=False) - - with torch.no_grad(): - t = torch.from_numpy(param) - complete_name = f'{l}.{p}' - if not complete_name in model.state_dict(): - raise ValueError( - f"Parameter {complete_name} not found in state dict!" - " Please report an issue.") - - state_dict[complete_name] = t - - model.load_state_dict(state_dict, strict=True) - return model - -if __name__=='__main__': - parser = argparse.ArgumentParser(description='Load haiku weights and convert them to .pth file.') - parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format') - args = parser.parse_args() - - if not args.pretrained.exists(): - raise FileNotFoundError(f"Could not find file {args.pretrained.absolute()}") - - # model = from_pretrained_haiku(args.pretrained) - model = pretrained_nfnet(args.pretrained) - torch.save({ - 'model': model.state_dict() - }, str(args.pretrained.with_suffix('.pth'))) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained/README.md b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained/README.md deleted file mode 100644 index beda6c5f..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pretrained/README.md +++ /dev/null @@ -1,11 +0,0 @@ -# Pretrained weights - -Download the pretrained weights from the [official repository](https://github.com/deepmind/deepmind-research/tree/master/nfnets#pre-trained-weights) and place them inside this folder. -Then start training with -``` -python3 train.py --pretrained pretrained/F0_haiku.npz -``` - -or evaluation with -``` -python3 eval.py --pretrained pretrained/F0_haiku.npz --dataset /path/to/imagenet/val/ diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pyproject.toml b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pyproject.toml deleted file mode 100644 index b5a3c468..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/pyproject.toml +++ /dev/null @@ -1,6 +0,0 @@ -[build-system] -requires = [ - "setuptools>=42", - "wheel" -] -build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/requirements.txt b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/requirements.txt deleted file mode 100644 index b3398de0..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -# --find-links https://download.pytorch.org/whl/torch_stable.html ---find-links https://download.pytorch.org/whl/cu110/torch_stable.html - -dill -git+https://github.com/deepmind/dm-haiku -jax -jaxlib -matplotlib -numpy -pillow-simd -pyyaml -requests -tensorboard -torch>=1.7.1+cu110 -torchvision>=0.8.2+cu110 \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/setup.cfg b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/setup.cfg deleted file mode 100644 index 56f13f5f..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/setup.cfg +++ /dev/null @@ -1,35 +0,0 @@ -[metadata] -name=nfnets_pytorch -version=0.0.1 -author=Benjamin Schmidt -author_email = webmaster@benjs.de -license=Apache 2.0 -license_file=LICENSE -description=Implementation of the paper "High-Performance Large-Scale Image Recognition Without Normalization" by Brock et al. -long_description=file:README.md -long_description_content_type=text/markdown -url=https://github.com/benjs/nfnets_pytorch -project_urls = - Bug Tracker = https://github.com/benjs/nfnets_pytorch/issues -classifiers = - Programming Language :: Python :: 3 - License :: OSI Approved :: Apache Software License - Operating System :: OS Independent - Natural Language :: English - -[options] -packages = nfnets -python_requires = >=3.7 -install_requires = - dm-haiku - requests - dill - jax - jaxlib - numpy - requests - torch>=1.7 - -dependency_links= - git+https://github.com/deepmind/dm-haiku - https://download.pytorch.org/whl/torch_stable.html \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/train.py b/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/train.py deleted file mode 100644 index 2ece44cc..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/nfnets_pytorch/train.py +++ /dev/null @@ -1,194 +0,0 @@ -import argparse -import math -import PIL -import time -import yaml -from pathlib import Path -from PIL.Image import Image - -import matplotlib.pyplot as plt -import torch -import torch.cuda.amp as amp -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import Subset -from torch.utils.data.dataloader import DataLoader -from torch.utils.tensorboard import SummaryWriter -from torchvision.transforms.transforms import Compose, Normalize, Resize, ToTensor, RandomHorizontalFlip, RandomCrop - -from dataset import get_dataset -from nfnets import NFNet, SGD_AGC, pretrained_nfnet - -def train(config:dict) -> None: - if config['device'].startswith('cuda'): - if torch.cuda.is_available(): - print(f"Using CUDA{torch.version.cuda} with cuDNN{torch.backends.cudnn.version()}") - else: - raise ValueError("You specified to use cuda device, but cuda is not available.") - - if config['pretrained'] is not None: - model = pretrained_nfnet( - path=config['pretrained'], - stochdepth_rate=config['stochdepth_rate'], - alpha=config['alpha'], - activation=config['activation'] - ) - else: - model = NFNet( - num_classes=config['num_classes'], - variant=config['variant'], - stochdepth_rate=config['stochdepth_rate'], - alpha=config['alpha'], - se_ratio=config['se_ratio'], - activation=config['activation'] - ) - - transforms = Compose([ - RandomHorizontalFlip(), - Resize((model.train_imsize, model.train_imsize), PIL.Image.BICUBIC), - ToTensor(), - Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ]) - - device = config['device'] - dataset = get_dataset(path=config['dataset'], transforms=transforms) - - if config['overfit']: - dataset = Subset(dataset, [i*50 for i in range(0,1000)] ) - - dataloader = DataLoader( - dataset=dataset, - batch_size=config['batch_size'], - shuffle=True, - num_workers=config['num_workers'], - pin_memory=config['pin_memory']) - - if config['scale_lr']: - learning_rate = config['learning_rate']*config['batch_size']/256 - else: - learning_rate = config['learning_rate'] - - if not config['do_clip']: - config['clipping'] = None - - if config['use_fp16']: - model.half() - - model.to(device) # "memory_format=torch.channels_last" TBD - - optimizer = SGD_AGC( - # The optimizer needs all parameter names - # to filter them by hand later - named_params=model.named_parameters(), - lr=learning_rate, - momentum=config['momentum'], - clipping=config['clipping'], - weight_decay=config['weight_decay'], - nesterov=config['nesterov'] - ) - - # Find desired parameters and exclude them - # from weight decay and clipping - for group in optimizer.param_groups: - name = group['name'] - - if model.exclude_from_weight_decay(name): - group['weight_decay'] = 0 - - if model.exclude_from_clipping(name): - group['clipping'] = None - - criterion = nn.CrossEntropyLoss() - - runs_dir = Path('runs') - run_index = 0 - while (runs_dir / ('run' + str(run_index))).exists(): - run_index += 1 - runs_dir = runs_dir / ('run' + str(run_index)) - runs_dir.mkdir(exist_ok=False, parents=True) - checkpoints_dir = runs_dir / 'checkpoints' - checkpoints_dir.mkdir() - - writer = SummaryWriter(str(runs_dir)) - scaler = amp.GradScaler() - - for epoch in range(config['epochs']): - model.train() - running_loss = 0.0 - processed_imgs = 0 - correct_labels = 0 - epoch_time = time.time() - - for step, data in enumerate(dataloader): - inputs = data[0].half().to(device) if config['use_fp16'] else data[0].to(device) - targets = data[1].to(device) - - optimizer.zero_grad() - - with amp.autocast(enabled=config['amp']): - output = model(inputs) - loss = criterion(output, targets) - - # Gradient scaling - # https://www.youtube.com/watch?v=OqCrNkjN_PM - scaler.scale(loss).backward() - scaler.step(optimizer) - scaler.update() - - running_loss += loss.item() - processed_imgs += targets.size(0) - _, predicted = torch.max(output, 1) - correct_labels += (predicted == targets).sum().item() - - epoch_padding = int(math.log10(config['epochs']) + 1) - batch_padding = int(math.log10(len(dataloader.dataset)) + 1) - print(f"\rEpoch {epoch+1:0{epoch_padding}d}/{config['epochs']}" - f"\tImg {processed_imgs:{batch_padding}d}/{len(dataloader.dataset)}" - f"\tLoss {running_loss / (step+1):6.4f}" - f"\tAcc {100.0*correct_labels/processed_imgs:5.3f}%\t", - sep=' ', end='', flush=True) - - elapsed = time.time() - epoch_time - print (f"({elapsed:.3f}s, {elapsed/len(dataloader):.3}s/step, {elapsed/len(dataset):.3}s/img)") - - global_step = epoch*len(dataloader) + step - writer.add_scalar('training/loss', running_loss/(step+1), global_step) - writer.add_scalar('training/accuracy', 100.0*correct_labels/processed_imgs, global_step) - - #if not config['overfit']: - if epoch % 10 == 0 and epoch != 0: - cp_path = checkpoints_dir / ("checkpoint_epoch" + str(epoch+1) + ".pth") - - torch.save({ - 'epoch': epoch, - 'model': model.state_dict(), - 'optim': optimizer.state_dict(), - 'loss': loss - }, str(cp_path)) - - print(f"Saved checkpoint to {str(cp_path)}") - -if __name__=='__main__': - parser = argparse.ArgumentParser(description='Train NFNets.') - parser.add_argument('--config', type=Path, help='Path to config.yaml', default='default_config.yaml') - parser.add_argument('--batch-size', type=int, help='Training batch size', default=None) - parser.add_argument('--overfit', const=True, default=False, nargs='?', help='Crop the dataset to the batch size and force model to (hopefully) overfit') - parser.add_argument('--variant', type=str, help='NFNet variant to train', default=None) - parser.add_argument('--pretrained', type=Path, help='Path to pre-trained weights in haiku format', default=None) - args = parser.parse_args() - - if not args.config.exists(): - print(f"Config file \"{args.config}\" does not exist!\n") - exit() - - with args.config.open() as file: - config = yaml.safe_load(file) - - # Override config.yaml settings with command line settings - for arg in vars(args): - if getattr(args, arg) is not None and arg in config: - config[arg] = getattr(args, arg) - - config['pretrained'] = args.pretrained - - train(config=config) diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/README.md b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/README.md deleted file mode 100644 index 053de405..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# Simple NFNet PyTorch Implementation - -This repository contains **a simple PyTorch code for Normalizer-Free Network (NFNet)**. - -- Andrew Brock et al, ["Characterizing signal propagation to close the performance gap in unnormalized ResNets,"](https://arxiv.org/abs/2102.06171) ICLR 2021. -- Andrew Brock et al, ["High-Performance Large-Scale Image Recognition Without Normalization"](https://arxiv.org/abs/2102.06171), Arxiv - -I implemented this code by referring [benjs's implementation code](https://github.com/benjs/nfnets_pytorch). -This code is for training NFNet for **CIFAR-10** dataset. - - -## Dependency - -- Python 3.7.1 -- PyTorch 1.7.1 -- torchvision 0.8.2 - - -## Training - -``` -# Training -CUDA_VISIBLE_DEVICES=0 python main.py -``` - -If you want to train the model using other hyperparameters, please check argparse in ```main.py```. - - -## TODO - -- [ ] Report Experimental results. (Accuracy for CIFAR-10) - - -## Acknowledgements - -I referred to the following implementation codes: - -- [Official codes](https://github.com/deepmind/deepmind-research/tree/master/nfnets) -- [benjs's PyTorch implementation codes](https://github.com/benjs/nfnets_pytorch) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/main.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/main.py deleted file mode 100644 index 28411c8e..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/main.py +++ /dev/null @@ -1,98 +0,0 @@ -import torch -import torch.nn as nn -import torchvision -import torchvision.transforms as transforms -import argparse -from model import NFNet -from optim import SGD_AGC - - -# Hyper-parameters -parser = argparse.ArgumentParser(description='NFNet Training') -parser.add_argument('--variant', default='F0', type=str, choices=['F0', 'F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7'], help='NFNet variants') -parser.add_argument('--lr', default=0.1, type=float, help='the learning rate') -parser.add_argument('--num_epochs', default=200, type=int, help='the number of the epochs') -parser.add_argument('--batch_size', default=128, type=int, help='batch sizes') -args = parser.parse_args() - -# Device configuration -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -best_acc = 0 - -# Image preprocessing modules -transform_train = transforms.Compose([ - transforms.Pad(4), - transforms.RandomHorizontalFlip(), - transforms.RandomCrop(32), - transforms.ToTensor(), - transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) - -transform_test = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) - -# CIFAR-10 dataset -train_dataset = torchvision.datasets.CIFAR10(root='./data/', train=True, transform=transform_train, download=True) - -test_dataset = torchvision.datasets.CIFAR10(root='./data/', train=False, transform=transform_test) - -# Data loader -train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=2) - -test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=2) - -# Model -model = NFNet(num_classes=10, variant=args.variant, stochdepth_rate=0.25, alpha=0.2, se_ratio=0.5, activation='gelu').to(device) - -# Loss and optimizer -criterion = nn.CrossEntropyLoss() -optimizer = SGD_AGC(named_params=model.named_parameters(), lr=args.lr, momentum=0.9, clipping=0.1, weight_decay=5e-4, nesterov=True) -scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200) - - -# Train the model -def train(epoch): - model.train() - for i, (images, labels) in enumerate(train_loader): - images, labels = images.to(device), labels.to(device) - - # Forward pass - outputs = model(images) - loss = criterion(outputs, labels) - - # Backward and optimize - optimizer.zero_grad() - loss.backward() - optimizer.step() - - if (i + 1) % 100 == 0: - print ("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}".format(epoch+1, args.num_epochs, i+1, len(train_loader), loss.item())) - - -# Test the model -def test(epoch): - global best_acc - model.eval() - with torch.no_grad(): - correct = 0 - total = 0 - for images, labels in test_loader: - images, labels = images.to(device), labels.to(device) - outputs = model(images) - _, predicted = torch.max(outputs.data, 1) - total += labels.size(0) - correct += (predicted == labels).sum().item() - print('Epoch [{}/{}], Accuracy of the model on the test images: {} %'.format(epoch+1, args.num_epochs, 100 * correct / total)) - - acc = 100 * correct / total - if acc > best_acc: - # Save the model checkpoint - torch.save(model.state_dict(), 'nfnet.ckpt') - best_acc = acc - print('Best Accuracy : {} %'.format(best_acc)) - - -for epoch in range(args.num_epochs): - train(epoch) - test(epoch) - scheduler.step() \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/model.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/model.py deleted file mode 100644 index 152ab9ed..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/model.py +++ /dev/null @@ -1,183 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -import re - -nfnet_params = { - 'F0': {'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], 'drop_rate': 0.2}, - 'F1': {'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], 'drop_rate': 0.3}, - 'F2': {'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], 'drop_rate': 0.4}, - 'F3': {'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], 'drop_rate': 0.4}, - 'F4': {'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], 'drop_rate': 0.5}, - 'F5': {'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], 'drop_rate': 0.5}, - 'F6': {'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], 'drop_rate': 0.5}, - 'F7': {'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], 'drop_rate': 0.5}, -} - -class VPGELU(nn.Module): - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.gelu(input) * 1.7015043497085571 - -class VPReLU(nn.Module): - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.relu(input, inplace=True) * 1.7139588594436646 - -activations_dict = {'gelu': VPGELU(), 'relu': VPReLU()} - -class NFNet(nn.Module): - def __init__(self, num_classes: int, variant: str = 'F0', stochdepth_rate: float = None, alpha: float = 0.2, se_ratio: float = 0.5, activation: str = 'gelu'): - super(NFNet, self).__init__() - if variant not in nfnet_params: - raise RuntimeError(f"Variant {variant} does not exist and could not be loaded.") - block_params = nfnet_params[variant] - self.activation = activations_dict[activation] - self.drop_rate = block_params['drop_rate'] - self.num_classes = num_classes - self.stem = Stem(activation=activation) - num_blocks, index = sum(block_params['depth']), 0 - blocks = [] - expected_std = 1.0 - in_channels = block_params['width'][0] // 2 - block_args = zip(block_params['width'], block_params['depth'], [0.5] * 4, [128] * 4, [1, 2, 2, 2]) - for (block_width, stage_depth, expand_ratio, group_size, stride) in block_args: - for block_index in range(stage_depth): - beta = 1. / expected_std - block_sd_rate = stochdepth_rate * index / num_blocks if stochdepth_rate is not None else 0 - out_channels = block_width - blocks.append(NFBlock( - in_channels=in_channels, - out_channels=out_channels, - stride=stride if block_index == 0 else 1, - alpha=alpha, - beta=beta, - se_ratio=se_ratio, - group_size=group_size, - stochdepth_rate=block_sd_rate, - activation=activation - )) - in_channels = out_channels - index += 1 - expected_std = (expected_std ** 2 + alpha ** 2) ** 0.5 - self.body = nn.Sequential(*blocks) - final_conv_channels = 2 * in_channels - self.final_conv = WSConv2D(in_channels=out_channels, out_channels=final_conv_channels, kernel_size=1) - self.pool = nn.AvgPool2d(1) - if self.drop_rate > 0.: - self.dropout = nn.Dropout(self.drop_rate) - self.linear = nn.Linear(final_conv_channels, self.num_classes) - nn.init.normal_(self.linear.weight, 0, 0.01) - - def forward(self, x): - out = self.stem(x) - out = self.body(out) - out = self.activation(self.final_conv(out)) - pool = torch.mean(out, dim=(2, 3)) - if self.training and self.drop_rate > 0.: - pool = self.dropout(pool) - return self.linear(pool) - -class Stem(nn.Module): - def __init__(self, activation: str = 'gelu'): - super(Stem, self).__init__() - self.activation = activations_dict[activation] - self.conv0 = WSConv2D(in_channels=1, out_channels=16, kernel_size=3, stride=2) # For grayscale images - self.conv1 = WSConv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1) - self.conv2 = WSConv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1) - self.conv3 = WSConv2D(in_channels=64, out_channels=128, kernel_size=3, stride=2) - self.conv4 = WSConv2D(in_channels=128, out_channels=128, kernel_size=3, stride=2) # Additional layer - - def forward(self, x): - out = self.activation(self.conv0(x)) - out = self.activation(self.conv1(out)) - out = self.activation(self.conv2(out)) - out = self.activation(self.conv3(out)) - out = self.conv4(out) - return out - -class NFBlock(nn.Module): - def __init__(self, in_channels: int, out_channels: int, expansion: float = 0.5, se_ratio: float = 0.5, stride: int = 1, beta: float = 1.0, alpha: float = 0.2, group_size: int = 1, stochdepth_rate: float = None, activation: str = 'gelu'): - super(NFBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.expansion = expansion - self.se_ratio = se_ratio - self.activation = activations_dict[activation] - self.beta, self.alpha = beta, alpha - self.group_size = group_size - width = int(self.out_channels * expansion) - self.groups = width // group_size - self.width = group_size * self.groups - self.stride = stride - self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1) - self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups) - self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups) - self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1) - self.use_projection = self.stride > 1 or self.in_channels != self.out_channels - if self.use_projection: - self.shortcut = nn.Sequential() - if self.stride > 1: - self.shortcut.add_module('avg_pool', nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels == 1536 else 1)) - self.shortcut.add_module('conv', WSConv2D(self.in_channels, self.out_channels, kernel_size=1)) - self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation) - self.skip_gain = nn.Parameter(torch.zeros(())) - self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1. - if self.use_stochdepth: - self.stoch_depth = StochDepth(stochdepth_rate) - - def forward(self, x): - out = self.activation(x) * self.beta - if self.use_projection: - shortcut = self.shortcut(x) - else: - shortcut = x - out = self.activation(self.conv0(out)) - out = self.activation(self.conv1(out)) - out = self.activation(self.conv1b(out)) - out = self.conv2(out) - out = (self.squeeze_excite(out) * 2) * out - if self.use_stochdepth: - out = self.stoch_depth(out) - return out * self.alpha * self.skip_gain + shortcut - -class WSConv2D(nn.Conv2d): - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros'): - super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode) - nn.init.xavier_normal_(self.weight) - self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1)) - self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False) - self.register_buffer('fan_in', torch.tensor(np.prod(self.weight.shape[1:]), requires_grad=False).type_as(self.weight), persistent=False) - - def standardized_weights(self): - mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True) - var = torch.var(self.weight, axis=[1,2,3], keepdims=True) - scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps)) - return (self.weight - mean) * scale * self.gain - - def forward(self, x): - return F.conv2d(x, self.standardized_weights(), bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups) - -class SqueezeExcite(nn.Module): - def __init__(self, in_channels, out_channels, se_ratio, activation='relu'): - super(SqueezeExcite, self).__init__() - self.se_reduce = nn.Conv2d(in_channels, int(in_channels * se_ratio), 1) - self.se_expand = nn.Conv2d(int(in_channels * se_ratio), out_channels, 1) - self.activation = activations_dict[activation] - - def forward(self, x): - scale = F.adaptive_avg_pool2d(x, 1) - scale = self.se_reduce(scale) - scale = self.activation(scale) - scale = self.se_expand(scale) - scale = torch.sigmoid(scale) - return x * scale - -class StochDepth(nn.Module): - def __init__(self, p: float): - super(StochDepth, self).__init__() - self.prob = p - - def forward(self, x): - if self.training and torch.rand(1).item() < self.prob: - return x * 0 - return x \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py deleted file mode 100644 index dc6ac6a0..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim copy.py +++ /dev/null @@ -1,110 +0,0 @@ -import torch -from torch.optim import Optimizer - - -# Compute norm depending on the shape of x -def unitwise_norm(x): - if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors - axis = 0 - keepdims = False - elif len(x.shape) in [2,3]: # Linear layers - # Original code: IO - # Pytorch: OI - axis = 1 - keepdims = True - elif len(x.shape) == 4: # Conv kernels - # Original code: HWIO - # Pytorch: OIHW - axis = [1, 2, 3] - keepdims = True - else: - raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') - - return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) - - -# This is a copy of the pytorch SGD implementation -# enhanced with gradient clipping -class SGD_AGC(Optimizer): - def __init__(self, named_params, lr:float, momentum=0, dampening=0, - weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): - if lr < 0.0: - raise ValueError("Invalid learning rate: {}".format(lr)) - if momentum < 0.0: - raise ValueError("Invalid momentum value: {}".format(momentum)) - if weight_decay < 0.0: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - - defaults = dict(lr=lr, momentum=momentum, dampening=dampening, - weight_decay=weight_decay, nesterov=nesterov, - # Extra defaults - clipping=clipping, - eps=eps - ) - - if nesterov and (momentum <= 0 or dampening != 0): - raise ValueError("Nesterov momentum requires a momentum and zero dampening") - - # Put params in list so each one gets its own group - params = [] - for name, param in named_params: - params.append({'params': param, 'name': name}) - - super(SGD_AGC, self).__init__(params, defaults) - - def __setstate__(self, state): - super(SGD_AGC, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('nesterov', False) - - @torch.no_grad() - def step(self, closure=None): - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - for group in self.param_groups: - weight_decay = group['weight_decay'] - momentum = group['momentum'] - dampening = group['dampening'] - nesterov = group['nesterov'] - - # Extra values for clipping - clipping = group['clipping'] - eps = group['eps'] - - for p in group['params']: - if p.grad is None: - continue - d_p = p.grad - - # ========================= - # Gradient clipping - if clipping is not None: - param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) - grad_norm = unitwise_norm(d_p) - max_norm = param_norm * group['clipping'] - - trigger_mask = grad_norm > max_norm - clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) - d_p = torch.where(trigger_mask, clipped_grad, d_p) - # ========================= - - if weight_decay != 0: - d_p = d_p.add(p, alpha=weight_decay) - if momentum != 0: - param_state = self.state[p] - if 'momentum_buffer' not in param_state: - buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() - else: - buf = param_state['momentum_buffer'] - buf.mul_(momentum).add_(d_p, alpha=1 - dampening) - if nesterov: - d_p = d_p.add(buf, alpha=momentum) - else: - d_p = buf - - p.add_(d_p, alpha=-group['lr']) - - return loss \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py b/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py deleted file mode 100644 index 4d72f023..00000000 --- a/ctlearn/core/pytorch/nets/models/legacy/simple_nfnet/optim.py +++ /dev/null @@ -1,109 +0,0 @@ -import torch -from torch.optim import Optimizer - -# Compute norm depending on the shape of x -def unitwise_norm(x): - if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors - axis = 0 - keepdims = False - elif len(x.shape) in [2,3]: # Linear layers - # Original code: IO - # Pytorch: OI - axis = 1 - keepdims = True - elif len(x.shape) == 4: # Conv kernels - # Original code: HWIO - # Pytorch: OIHW - axis = [1, 2, 3] - keepdims = True - else: - raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}') - - return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims)) - - -# This is a copy of the pytorch SGD implementation -# enhanced with gradient clipping -class SGD_AGC(Optimizer): - def __init__(self, named_params, lr:float, momentum=0, dampening=0, - weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3): - if lr < 0.0: - raise ValueError("Invalid learning rate: {}".format(lr)) - if momentum < 0.0: - raise ValueError("Invalid momentum value: {}".format(momentum)) - if weight_decay < 0.0: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - - defaults = dict(lr=lr, momentum=momentum, dampening=dampening, - weight_decay=weight_decay, nesterov=nesterov, - # Extra defaults - clipping=clipping, - eps=eps - ) - - if nesterov and (momentum <= 0 or dampening != 0): - raise ValueError("Nesterov momentum requires a momentum and zero dampening") - - # Put params in list so each one gets its own group - params = [] - for name, param in named_params: - params.append({'params': param, 'name': name}) - - super(SGD_AGC, self).__init__(params, defaults) - - def __setstate__(self, state): - super(SGD_AGC, self).__setstate__(state) - for group in self.param_groups: - group.setdefault('nesterov', False) - - @torch.no_grad() - def step(self, closure=None): - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - for group in self.param_groups: - weight_decay = group['weight_decay'] - momentum = group['momentum'] - dampening = group['dampening'] - nesterov = group['nesterov'] - - # Extra values for clipping - clipping = group['clipping'] - eps = group['eps'] - - for p in group['params']: - if p.grad is None: - continue - d_p = p.grad - - # ========================= - # Gradient clipping - if clipping is not None: - param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device)) - grad_norm = unitwise_norm(d_p) - max_norm = param_norm * group['clipping'] - - trigger_mask = grad_norm > max_norm - clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device))) - d_p = torch.where(trigger_mask, clipped_grad, d_p) - # ========================= - - if weight_decay != 0: - d_p = d_p.add(p, alpha=weight_decay) - if momentum != 0: - param_state = self.state[p] - if 'momentum_buffer' not in param_state: - buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() - else: - buf = param_state['momentum_buffer'] - buf.mul_(momentum).add_(d_p, alpha=1 - dampening) - if nesterov: - d_p = d_p.add(buf, alpha=momentum) - else: - d_p = buf - - p.add_(d_p, alpha=-group['lr']) - - return loss \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 7e188b4d..8861c773 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -322,16 +322,7 @@ def save_checkpoint(self, save_folder, metric_value, filename_prefix, is_loss=Tr except FileNotFoundError: pass # File doesn't exist, continue execution - # def on_after_backward(self): - # print('on_after_backward:') - # print('W_embed grad:', self.model.W_embed.grad) - # print('classifier.weight grad:', self.model.classifier.weight.grad) - # for name, param in self.model.named_parameters(): - # if param.grad: - # print(f"{name} grad norm: {param.grad.norm().item()}") - # # print(f"{name} has no grad!") - # # else: - # # print(f"{name} grad norm: {param.grad.norm().item()}") + # ---------------------------------------------------------------------------------------------------------- def compute_type_loss( self, classification_pred, labels_class, test_val=False, training=False @@ -369,7 +360,7 @@ def compute_type_loss( return loss, accuracy, predicted, precision # ---------------------------------------------------------------------------------------------------------- - def compute_direction_loss(self, direction_pred, labels_direction, training=False): + def compute_camera_direction_loss(self, direction_pred, labels_direction, training=False): labels_dx_dy = labels_direction[:, 0:2] label_distance = labels_direction[:, 2] @@ -420,7 +411,7 @@ def compute_energy_loss( return loss, energy_diff # ---------------------------------------------------------------------------------------------------------- - def compute_direction_loss_diffusion(self, x, y, labels_energy_value): + def compute_camera_direction_loss_diffusion(self, x_1, y, labels_energy_value, x_2 = None): loss = 0 y = y.squeeze(-1) @@ -432,9 +423,12 @@ def compute_direction_loss_diffusion(self, x, y, labels_energy_value): noise = torch.randn_like(y_embed) z_t = torch.sqrt(alpha_bar_t) * y_embed + torch.sqrt(1 - alpha_bar_t) * noise - # Denoise step - z, _ = self.model.blocks[t](x, z_t, None) # W_embed not needed - + if x_2 is None: + # Denoise step + z, _ = self.model.blocks[t](x_1, z_t, None) # W_embed not needed + else: + z, _ = self.model.blocks[t](x_1,x_2, z_t, None) # W_embed not needed + preds = self.model.regress(z) # Loss to clean target loss_l2 = F.mse_loss(preds, y) @@ -469,31 +463,138 @@ def compute_direction_loss_diffusion(self, x, y, labels_energy_value): ) vector_cam_distance = torch.sqrt(pred_dx_dy[:,0]**2 + pred_dx_dy[:,1]**2) - # loss_dx_dy = self.criterion_alt_az_l1(pred_dx_dy, labels_dx_dy) - # loss_distance = self.criterion_magnitud(label_distance, pred_distance) - # loss_distance_dx_dy = self.criterion_magnitud(label_distance, vector_cam_distance) - loss_dx_dy = self.criterion_alt_az_l1_none(pred_dx_dy, labels_dx_dy).mean(dim=1) - loss_distance = self.criterion_magnitud_none(label_distance, pred_distance) - loss_distance_dx_dy = self.criterion_magnitud_none(label_distance, vector_cam_distance) + loss_dx_dy = self.criterion_alt_az_l1(pred_dx_dy, labels_dx_dy) + loss_distance = self.criterion_magnitud(label_distance, pred_distance) + loss_distance_dx_dy = self.criterion_magnitud(label_distance, vector_cam_distance) + # loss_dx_dy = self.criterion_alt_az_l1_none(pred_dx_dy, labels_dx_dy).mean(dim=1) + # loss_distance = self.criterion_magnitud_none(label_distance, pred_distance) + # loss_distance_dx_dy = self.criterion_magnitud_none(label_distance, vector_cam_distance) energy = torch.pow(10,labels_energy_value) - k=4.3 - e_thrs= 4 - energy_weight = k*(1/(1+torch.exp(-(1/k)*(energy-e_thrs)))) + # k=4.3 + # e_thrs= 4 + # energy_weight = k*(1/(1+torch.exp(-(1/k)*(energy-e_thrs)))) + # weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy + loss_angular_diff) + + k=0.6 + e_thrs= -0.3 + + energy_weight=k*torch.exp(1+(np.log10(1/k)*(energy-e_thrs))) # weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy + loss_angular_diff) weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy) - loss_distance=loss_distance.mean() - loss_dx_dy = loss_dx_dy.mean() - loss_distance_dx_dy = loss_distance_dx_dy.mean() - loss_angular_diff = loss_angular_diff.mean() + # loss_distance=loss_distance.mean() + # loss_dx_dy = loss_dx_dy.mean() + # loss_distance_dx_dy = loss_distance_dx_dy.mean() - step_loss = step_loss + weight_loss.mean() + loss_angular_diff = loss_angular_diff.mean() + # step_loss = step_loss + weight_loss.mean() + step_loss = step_loss + loss_dx_dy + loss_distance + loss_distance_dx_dy loss = loss + step_loss return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff # ---------------------------------------------------------------------------------------------------------- + def compute_sky_direction_loss_diffusion(self, x, y, labels_energy_value): + + loss = 0 + y = y.squeeze(-1) + y_embed = self.model.target_embedder(y) + + for t in range(self.model.T): + # Add noise to target (landmarks) + alpha_bar_t = self.model.alpha_bar[t] + noise = torch.randn_like(y_embed) + z_t = torch.sqrt(alpha_bar_t) * y_embed + torch.sqrt(1 - alpha_bar_t) * noise + + # Denoise step + z, _ = self.model.blocks[t](x, z_t, None) # W_embed not needed + + preds = self.model.regress(z) + # Loss to clean target + loss_l2 = F.mse_loss(preds, y) + + # Weighted by SNR difference + step_loss = 2.5 * self.model.eta * self.model.snr_diff[t] * loss_l2 + + # Final step: use regression head + if t == self.model.T - 1: + + + labels_dx_dy = y[:, 0:2] + label_distance = y[:, 2] + direction_pred = self.model.regress(z) + + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + # direction_pred = list(direction_pred) + # pred_az_atl = direction_pred[0][:,0:2] + # pred_separation = direction_pred[0][:,2] + # # direction_pred[0]= direction_pred[0][:,0:2] + # else: + + # pred_az_atl = direction_pred[:, 0:2] + # pred_separation = direction_pred[:, 2] + + # labels_az_alt = labels_direction[:, 0:2] + # label_separation = labels_direction[:, 2] + # loss_separation = self.criterion_direction(pred_separation, label_separation) + + # # loss_vector = self.criterion_vector(pred_dir_cartesian, labels_direction_cartesian) + # # vect_magnitud = torch.sqrt(torch.sum(pred_dir_cartesian**2, dim=1)) + # # loss_magnitud = torch.abs(1.0-vect_magnitud).sum() + + # # alt_az = utils_torch.cartesian_to_alt_az(direction[:,0:3]) + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + # loss_alt_az = self.criterion_alt_az(direction_pred, labels_direction) + # else: + # loss_alt_az = self.criterion_alt_az_l1(pred_az_atl, labels_az_alt) + + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + + # loss_angular_error, _ = AngularDistance( + # (direction_pred[0][:, 1]), + # labels_az_alt[:, 1], + # (direction_pred[0][:, 0]), + # labels_az_alt[:, 0], + # reduction="sum", + # ) + + # else: + + # loss_angular_error, _ = AngularDistance( + # (direction_pred[:, 1]), + # labels_az_alt[:, 1], + # (direction_pred[:, 0]), + # labels_az_alt[:, 0], + # reduction="sum", + # ) + + # if training == False: + # if len(direction_pred)>1 and type(direction_pred)!=torch.Tensor: + # _, angular_diff = AngularDistance( + # (direction_pred[0][:, 1]), + # labels_az_alt[:, 0], + # (direction_pred[0][:, 0]), + # labels_az_alt[:, 1], + # reduction=None, + # ) + # else: + # _, angular_diff = AngularDistance( + # (direction_pred[:, 1]), + # labels_az_alt[:, 0], + # (direction_pred[:, 0]), + # labels_az_alt[:, 1], + # reduction=None, + # ) + # else: + # angular_diff = None + + # loss = loss_alt_az + 0.001*(loss_separation + loss_angular_error) + + # loss = loss + step_loss + + # return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff + # ---------------------------------------------------------------------------------------------------------- def compute_type_loss_diffusion(self, x,y, training=False): loss = 0 @@ -637,11 +738,17 @@ def training_step(self, batch, batch_idx): if self.task == Task.cameradirection: if self.is_difussion: - loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_direction_loss_diffusion(imgs, labels_direction,labels_energy_value) + if self.num_inputs == 2: + peak_time = features["peak_time"] + peak_time = peak_time.to(self.device) + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_camera_direction_loss_diffusion(imgs, labels_direction,labels_energy_value, peak_time) + else: + + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_camera_direction_loss_diffusion(imgs, labels_direction,labels_energy_value) else: if len(direction_pred)==2: direction_pred = direction_pred[0] - loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_direction_loss( direction_pred, labels_direction, training=True) + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff = self.compute_camera_direction_loss( direction_pred, labels_direction, training=True) self.loss_train_distance += loss_distance.item() self.loss_train_dx_dy += loss_dx_dy.item() @@ -902,7 +1009,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if len(direction_pred)==2: direction_pred = direction_pred[0] - loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_error= self.compute_direction_loss( direction_pred, labels_direction, training=False) + loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_error= self.compute_camera_direction_loss( direction_pred, labels_direction, training=False) # ------------------------------------------------------------------------ # Convert the offset to altitud and azimuth diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 97091bbc..93492d4f 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -25,7 +25,7 @@ data: # Check points type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_108/Epoch_0_type_train_acc_80.0708770751953125.pth #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_49/Epoch_9_cameradirection_train_loss_0.7614041910688141.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth run_details: @@ -90,14 +90,23 @@ model: # dropout: 0.1 # use_bn: False + # model_direction: + # model_name: "NoPropDTReg" + # parameters: + # task: 'direction' + # num_outputs: 3 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + model_direction: - model_name: "NoPropDTReg" + model_name: "DBBNoPropDTReg" parameters: task: 'direction' num_outputs: 3 embedding_dim: 512 - T: 6 - eta: 0.1 #0.1 + T: 3 + eta: 0.1 #0.1 # Hyper-parameters hyp: @@ -108,7 +117,7 @@ hyp: optimizer: Adamw momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-3 #1e-5 #Efficient-b3 1e-5 + learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 lrf: 0.1 start_epoch: 0 steps_epoch: 100 # Computed online. Must be removed From 674a922dfd0b788117122dd8130fd78aa6f08353 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 30 Jun 2025 10:28:47 +0000 Subject: [PATCH 036/119] fixed some minor bugs --- ctlearn/core/data_loader/pytorch_loader.py | 180 ++++++++++++------ .../nets/models/NoPropDTReg/NoPropDTReg.py | 8 +- .../denoiseBlockThinRestNet copy 2.py | 116 +++++++++++ .../denoiseBlockThinRestNet copy.py | 111 +++++++++++ .../NoPropDTReg/denoiseBlockThinRestNet.py | 163 +++++++++------- ctlearn/tools/train/pytorch/CTLearnPL.py | 111 +++++++++-- .../training_config_iaa_neutron_training.yml | 60 +++--- 7 files changed, 585 insertions(+), 164 deletions(-) create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy 2.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy.py diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index af85dd43..4d3f7f54 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -210,7 +210,8 @@ def _fetch_batch(self, index): batch = self.DLDataReader.generate_stereo_batch(batch_indices) features, labels = self._get_stereo_item(batch) return features, labels - + + # Fetching batches def __getitem__(self, index): data_idx = index @@ -267,88 +268,134 @@ def __getitem__(self, index): # return features, labels, t - def cam_to_alt_az( - self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y - ): - """ - Transform camera coordinate offsets (cam_x, cam_y) into Alt/Az sky coordinates. + # def cam_to_alt_az( + # self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y + # ): + # """ + # Transform camera coordinate offsets (cam_x, cam_y) into Alt/Az sky coordinates. - This method converts the given camera coordinates for each telescope into sky coordinates - (Altitude and Azimuth), using the known pointing of each telescope and camera geometry - such as focal length and pixel rotation. + # This method converts the given camera coordinates for each telescope into sky coordinates + # (Altitude and Azimuth), using the known pointing of each telescope and camera geometry + # such as focal length and pixel rotation. - Parameters - ---------- - tel_id : list or array-like - List of telescope IDs corresponding to each event or observation. + # Parameters + # ---------- + # tel_id : list or array-like + # List of telescope IDs corresponding to each event or observation. - focal_length : list or array-like - Focal length of the telescopes in meters. + # focal_length : list or array-like + # Focal length of the telescopes in meters. - pix_rotation : list or array-like - Pixel rotation angles (in degrees) for each telescope camera. + # pix_rotation : list or array-like + # Pixel rotation angles (in degrees) for each telescope camera. - tel_az : list or array-like - Azimuth of telescope pointing (in radians). + # tel_az : list or array-like + # Azimuth of telescope pointing (in radians). - tel_alt : list or array-like - Altitude of telescope pointing (in radians). + # tel_alt : list or array-like + # Altitude of telescope pointing (in radians). - cam_x : list or array-like - Camera x-coordinate positions (in meters). + # cam_x : list or array-like + # Camera x-coordinate positions (in meters). - cam_y : list or array-like - Camera y-coordinate positions (in meters). + # cam_y : list or array-like + # Camera y-coordinate positions (in meters). - Returns - ------- - sky_coords_alt : list - List of reconstructed Altitude coordinates (in degrees). + # Returns + # ------- + # sky_coords_alt : list + # List of reconstructed Altitude coordinates (in degrees). - sky_coords_az : list - List of reconstructed Azimuth coordinates (in degrees). - """ - from astropy.time import Time + # sky_coords_az : list + # List of reconstructed Azimuth coordinates (in degrees). + # """ + # from astropy.time import Time - LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") - from astropy.coordinates import AltAz, SkyCoord - from ctapipe.coordinates import CameraFrame - from astropy import units as u + # LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") + # from astropy.coordinates import AltAz, SkyCoord + # from ctapipe.coordinates import CameraFrame + # from astropy import units as u - # # Get telescope ground frame position - tel_ground_frame = self.DLDataReader.subarray.tel_coords[ - self.DLDataReader.subarray.tel_ids_to_indices(tel_id) - ] + # # # Get telescope ground frame position + # tel_ground_frame = self.DLDataReader.subarray.tel_coords[ + # self.DLDataReader.subarray.tel_ids_to_indices(tel_id) + # ] + + # # AltAz frame setup + # altaz = AltAz( + # location=tel_ground_frame.to_earth_location(), + # obstime=LST_EPOCH, + # ) + + # # Telescope pointing SkyCoord + # fix_tel_pointing = SkyCoord( + # az=tel_az * u.rad, + # alt=tel_alt * u.rad, + # frame=altaz, + # ) + + # sky_coords_alt = [] + # sky_coords_az = [] + + # for id in range(len(focal_length)): + + # camera_frame = CameraFrame( + # focal_length=focal_length[id] * u.m, + # rotation=pix_rotation[id] * u.deg, + # telescope_pointing=fix_tel_pointing[id], + # ) - # AltAz frame setup - altaz = AltAz( - location=tel_ground_frame.to_earth_location(), - obstime=LST_EPOCH, - ) + # cam_coord = SkyCoord( + # x=cam_x[id] * u.m, y=cam_y[id] * u.m, frame=camera_frame + # ) - # Telescope pointing SkyCoord - fix_tel_pointing = SkyCoord( - az=tel_az * u.rad, - alt=tel_alt * u.rad, - frame=altaz, - ) + # sky_coord = cam_coord.transform_to(altaz[id]) + # sky_coords_alt.append(sky_coord.alt.to_value(u.deg).item()) + # sky_coords_az.append(sky_coord.az.to_value(u.deg).item()) + + # return sky_coords_alt, sky_coords_az + def cam_to_alt_az(self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y): + + from astropy.time import Time + from astropy.coordinates import AltAz, SkyCoord + from ctapipe.coordinates import CameraFrame + from astropy import units as u + + LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") sky_coords_alt = [] sky_coords_az = [] for id in range(len(focal_length)): + # Telescopio correspondiente + tel_ground_frame = self.DLDataReader.subarray.tel_coords[ + self.DLDataReader.subarray.tel_ids_to_indices(tel_id[id]) + ] + + # Frame AltAz particular para cada telescopio + altaz_frame = AltAz( + location=tel_ground_frame.to_earth_location(), + obstime=LST_EPOCH, + ) + + fix_tel_pointing = SkyCoord( + az=tel_az[id] * u.rad, + alt=tel_alt[id] * u.rad, + frame=altaz_frame, + ) camera_frame = CameraFrame( focal_length=focal_length[id] * u.m, rotation=pix_rotation[id] * u.deg, - telescope_pointing=fix_tel_pointing[id], + telescope_pointing=fix_tel_pointing, ) cam_coord = SkyCoord( x=cam_x[id] * u.m, y=cam_y[id] * u.m, frame=camera_frame ) - sky_coord = cam_coord.transform_to(altaz[id]) + # Transformar correctamente + sky_coord = cam_coord.transform_to(altaz_frame) sky_coords_alt.append(sky_coord.alt.to_value(u.deg).item()) sky_coords_az.append(sky_coord.az.to_value(u.deg).item()) @@ -447,7 +494,7 @@ def _get_mono_item(self, batch): features_out["image"] = torch.from_numpy(image).contiguous().float() features_out["peak_time"] = torch.from_numpy(peak_time).contiguous().float() - features_out["hillas"] = features["hillas"] + for key in labels.keys(): @@ -489,8 +536,8 @@ def _get_mono_item(self, batch): ) # labels["tel_ground"] = tel_ground_frame labels["tel_ids"] = tel_ids - labels["true_alt"] = [val for val in batch["true_alt"]] - labels["true_az"] = [val for val in batch["true_az"]] + labels["true_alt"] = np.array([val for val in batch["true_alt"]]) + labels["true_az"] = np.array([val for val in batch["true_az"]]) labels["tel_az"] = batch["telescope_pointing_azimuth"].data labels["tel_alt"] = batch["telescope_pointing_altitude"].data @@ -499,6 +546,25 @@ def _get_mono_item(self, batch): # sky_coords_alt, sky_coords_az = self.cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) + # Generate keep_idx as before + hillas = features["hillas"] + leakage = np.array(hillas["leakage_intensity_width_2"]) + intensity = np.array(hillas["hillas_intensity"]) + keep_idx = np.where((leakage <= 0.2) & (intensity >= 50))[0] + + # Filter features_out + for key in features_out: + features_out[key] = features_out[key][keep_idx] + + features_out["hillas"] = features["hillas"] + + for key in features["hillas"]: + features_out["hillas"][key] = (features["hillas"][key])[keep_idx] + + # Filter labels (since it's a dict too) + for key in labels: + labels[key] = labels[key][keep_idx] + return features_out, labels # TODO: Not adapted to pytorch diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py index eb5d64f8..3031890f 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py @@ -53,7 +53,7 @@ def forward(self, x, z_prev, W_embed): return z_next, logits class NoPropDTReg(nn.Module): - def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1): + def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1,num_blocks=[2, 3, 3, 3]): super().__init__() self.task = task @@ -63,11 +63,11 @@ def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1): self.T = T self.eta = eta - self.blocks = nn.ModuleList([DenoiseBlock(embedding_dim,num_channels=1) for _ in range(T)]) - # self.regressor = nn.Linear(embedding_dim, num_outputs) + self.blocks = nn.ModuleList([DenoiseBlock(embedding_dim,num_channels=1,num_blocks=num_blocks) for _ in range(T)]) + self.regressor = nn.Sequential( nn.Linear(embedding_dim, embedding_dim//2), - MemoryEfficientSwish(), + # MemoryEfficientSwish(), nn.Linear(embedding_dim//2, num_outputs) ) diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy 2.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy 2.py new file mode 100644 index 00000000..f1326018 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy 2.py @@ -0,0 +1,116 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) + self.act1 = nn.GELU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) + self.act2 = nn.GELU() + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + def forward(self, x): + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) + residual = self.se(residual) + out = residual + self.shortcut(x) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + super().__init__() + # Ahora más profundo y ancho: + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(256, 512), # Embedding más grande + # nn.BatchNorm1d(512), + nn.GELU() + ) + + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + # h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + # h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h1 = self.act1((self.fc_z1(z_prev))) + h2 = self.act2((self.fc_z2(h1))) + + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + # h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + # h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + h_f = self.act_f1((self.fc_f1(h_f))) + h_f = self.act_f2((self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy.py new file mode 100644 index 00000000..6487c4ee --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet copy.py @@ -0,0 +1,111 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) + self.act1 = nn.GELU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) + self.act2 = nn.GELU() + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + def forward(self, x): + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) + residual = self.se(residual) + out = residual + self.shortcut(x) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + super().__init__() + # Ahora más profundo y ancho: + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(256, 512), # Embedding más grande + nn.BatchNorm1d(512), + nn.GELU() + ) + + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py index 6487c4ee..a1cd048d 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py @@ -17,95 +17,130 @@ def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): def forward(self, x): return self.a * x + self.b * self.bn(x) - + class SEBlock(nn.Module): - def __init__(self, channels, reduction=16): - super().__init__() - self.pool = nn.AdaptiveAvgPool2d(1) + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( - nn.Linear(channels, channels // reduction, bias=False), - nn.ReLU(), - nn.Linear(channels // reduction, channels, bias=False), + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), nn.Sigmoid() ) + def forward(self, x): b, c, _, _ = x.size() - y = self.pool(x).view(b, c) + y = self.avg_pool(x).squeeze(-1).squeeze(-1) # Ensuring dimension match y = self.fc(y).view(b, c, 1, 1) - return x * y + return x * y.expand_as(x) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=16,use_bn=False): + + + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.use_bn = use_bn + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + -class ResidualBlock(nn.Module): - def __init__(self, in_channels, out_channels, reduction=16): - super().__init__() - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) - # self.bn1 = AdaptiveBatchNorm2d(out_channels) - self.act1 = nn.GELU() - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) - # self.bn2 = AdaptiveBatchNorm2d(out_channels) - self.act2 = nn.GELU() self.se = SEBlock(out_channels, reduction) - self.shortcut = nn.Identity() - if in_channels != out_channels: + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + self.shortcut = nn.Sequential( - nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), - nn.BatchNorm2d(out_channels) + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels) ) + def forward(self, x): - # residual = self.act1(self.bn1(self.conv1(x))) - # residual = self.act2(self.bn2(self.conv2(residual))) - residual = self.act1((self.conv1(x))) - residual = self.act2((self.conv2(residual))) - residual = self.se(residual) - out = residual + self.shortcut(x) + out = F.relu((self.conv1(x))) + out = (self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + out = self.se(out) return out - + class DenoiseBlock(nn.Module): - def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + def __init__(self, embedding_dim, num_channels=1, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, num_outputs=2,use_bn=False,dropout=0.0): super().__init__() - # Ahora más profundo y ancho: - self.conv_path = nn.Sequential( - ResidualBlock(num_channels, 64), # Más ancho - nn.MaxPool2d(2), - nn.Dropout(drop_prob), - ResidualBlock(64, 128), # Más ancho - nn.MaxPool2d(2), - nn.Dropout(drop_prob), - ResidualBlock(128, 256), # Más profundo/ancho - nn.MaxPool2d(2), - nn.Dropout(drop_prob), - ResidualBlock(256, 256), # Otro bloque extra para profundidad - nn.AdaptiveAvgPool2d((1, 1)), - nn.Flatten(), - nn.Linear(256, 512), # Embedding más grande - nn.BatchNorm1d(512), - nn.GELU() - ) - self.fc_z1 = nn.Linear(embedding_dim, 512) - self.bn_z1 = nn.BatchNorm1d(512) - self.fc_z2 = nn.Linear(512, 512) - self.bn_z2 = nn.BatchNorm1d(512) - self.fc_z3 = nn.Linear(512, 512) - self.bn_z3 = nn.BatchNorm1d(512) - self.fc_f1 = nn.Linear(1024, 512) - self.bn_f1 = nn.BatchNorm1d(512) - self.fc_f2 = nn.Linear(512, 256) - self.bn_f2 = nn.BatchNorm1d(256) - self.fc_out = nn.Linear(256, embedding_dim) + # block = BasicBlock + self.in_channels = 64 + self.use_bn=use_bn + + self.conv1 = nn.Conv2d(num_channels, 64, kernel_size=3, stride=1, padding=1, bias=False) + + self.layer1_1 = self._make_layer(block, 32, num_blocks[0], stride=1) + self.layer2_1 = self._make_layer(block, 64, num_blocks[1], stride=2) + self.layer3_1 = self._make_layer(block, 128, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 256, num_blocks[3], stride=2) + # Reducing the number of layers and filters to make it "thin" + # self.fc_1 = nn.Linear(embedding_dim , embedding_dim) + # self.fc_2 = nn.Linear(embedding_dim , 256) + self.bn_final = nn.BatchNorm1d(512 * block.expansion) # BatchNorm layer + self.prelu = nn.PReLU(num_parameters=512 * block.expansion) # Define Leaky ReLU + + + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(dropout) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + self.act1 = nn.PReLU() self.act2 = nn.PReLU() self.act3 = nn.PReLU() self.act_f1 = nn.PReLU() self.act_f2 = nn.PReLU() + def forward(self, x, z_prev, _): - x_feat = self.conv_path(x) - h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) - h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + + out_1 = F.relu(self.conv1(x)) + out_1 = self.layer1_1(out_1) + out_1 = self.layer2_1(out_1) + out_1 = self.layer3_1(out_1) + + x_feat = self.layer4(out_1) + x_feat= self.adaptive_pool(x_feat) + x_feat = x_feat.view(x_feat.size(0), -1) + + + # h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + # h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h1 = self.act1((self.fc_z1(z_prev))) + h2 = self.act2((self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) z_feat = h3 + h1 h_f = torch.cat([x_feat, z_feat], dim=1) - h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) - h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + # h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + # h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + h_f = self.act_f1((self.fc_f1(h_f))) + h_f = self.act_f2((self.fc_f2(h_f))) z_next = self.fc_out(h_f) return z_next, None + + def _make_layer(self, block, out_channels, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride, use_bn=self.use_bn)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 8861c773..3a259169 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -411,6 +411,66 @@ def compute_energy_loss( return loss, energy_diff # ---------------------------------------------------------------------------------------------------------- + def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None + # self, energy_pred, labels_energy, test_val=False, training=False + ): + + loss = 0 + # y = y.squeeze(-1) + y_embed = self.model.target_embedder(y) + + for t in range(self.model.T): + # Add noise to target (landmarks) + alpha_bar_t = self.model.alpha_bar[t] + noise = torch.randn_like(y_embed) + z_t = torch.sqrt(alpha_bar_t) * y_embed + torch.sqrt(1 - alpha_bar_t) * noise + + if x_2 is None: + # Denoise step + z, _ = self.model.blocks[t](x_1, z_t, None) # W_embed not needed + else: + z, _ = self.model.blocks[t](x_1,x_2, z_t, None) # W_embed not needed + + preds = self.model.regress(z) + # Loss to clean target + loss_l2 = F.mse_loss(preds, y) + + # Weighted by SNR difference + step_loss = 2.5 * self.model.eta * self.model.snr_diff[t] * loss_l2 + + # Final step: use regression head + if t == self.model.T - 1: + # labels_dx_dy = y[:, 0:2] + # label_distance = y[:, 2] + # direction_pred = self.model.regress(z) + # if isinstance(direction_pred, tuple): + # # Not Tested + # direction_pred = list(direction_pred) + + # pred_dx_dy = direction_pred[0][:,0:2].unsqueeze(-1) + # pred_distance = direction_pred[0][:,2].unsqueeze(-1) + # else: + # pred_dx_dy = direction_pred[:,0:2] + # pred_distance = direction_pred[:,2] + labels_energy = y + energy_pred = self.model.regress(z) + loss_energy = self.criterion_energy_value(energy_pred, labels_energy) + + + if training == False: + energy_pred = pow(10, energy_pred) + labels_energy = pow(10, labels_energy) + energy_diff = torch.abs(energy_pred - labels_energy) + energy_diff = energy_diff.float().cpu().detach().numpy() + else: + energy_diff = None + + step_loss = step_loss + loss_energy + # step_loss = step_loss + loss_dx_dy + loss_distance + loss_distance_dx_dy + loss = loss + step_loss + + return loss, energy_diff + # ---------------------------------------------------------------------------------------------------------- def compute_camera_direction_loss_diffusion(self, x_1, y, labels_energy_value, x_2 = None): loss = 0 @@ -463,12 +523,12 @@ def compute_camera_direction_loss_diffusion(self, x_1, y, labels_energy_value, x ) vector_cam_distance = torch.sqrt(pred_dx_dy[:,0]**2 + pred_dx_dy[:,1]**2) - loss_dx_dy = self.criterion_alt_az_l1(pred_dx_dy, labels_dx_dy) - loss_distance = self.criterion_magnitud(label_distance, pred_distance) - loss_distance_dx_dy = self.criterion_magnitud(label_distance, vector_cam_distance) - # loss_dx_dy = self.criterion_alt_az_l1_none(pred_dx_dy, labels_dx_dy).mean(dim=1) - # loss_distance = self.criterion_magnitud_none(label_distance, pred_distance) - # loss_distance_dx_dy = self.criterion_magnitud_none(label_distance, vector_cam_distance) + # loss_dx_dy = self.criterion_alt_az_l1(pred_dx_dy, labels_dx_dy) + # loss_distance = self.criterion_magnitud(label_distance, pred_distance) + # loss_distance_dx_dy = self.criterion_magnitud(label_distance, vector_cam_distance) + loss_dx_dy = self.criterion_alt_az_l1_none(pred_dx_dy, labels_dx_dy).mean(dim=1) + loss_distance = self.criterion_magnitud_none(label_distance, pred_distance) + loss_distance_dx_dy = self.criterion_magnitud_none(label_distance, vector_cam_distance) energy = torch.pow(10,labels_energy_value) # k=4.3 @@ -483,14 +543,14 @@ def compute_camera_direction_loss_diffusion(self, x_1, y, labels_energy_value, x # weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy + loss_angular_diff) weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy) - # loss_distance=loss_distance.mean() - # loss_dx_dy = loss_dx_dy.mean() - # loss_distance_dx_dy = loss_distance_dx_dy.mean() + loss_distance=loss_distance.mean() + loss_dx_dy = loss_dx_dy.mean() + loss_distance_dx_dy = loss_distance_dx_dy.mean() loss_angular_diff = loss_angular_diff.mean() - # step_loss = step_loss + weight_loss.mean() - step_loss = step_loss + loss_dx_dy + loss_distance + loss_distance_dx_dy + step_loss = step_loss + weight_loss.mean() + # step_loss = step_loss + loss_dx_dy + loss_distance + loss_distance_dx_dy loss = loss + step_loss return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff @@ -762,9 +822,19 @@ def training_step(self, batch, batch_idx): # Energy # --------------------------------------- if self.task == Task.energy: - loss, *_ = self.compute_energy_loss( - energy_pred, labels_energy_value, training=True - ) + + if self.is_difussion: + if self.num_inputs == 1: + loss, *_ = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=True,x_2=None) + else: + peak_time = features["peak_time"] + loss, *_ = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=True,x_2=peak_time) + + else: + + loss, *_ = self.compute_energy_loss( + energy_pred, labels_energy_value, test_val=False, training=False + ) # --------------------------------------- # L2 Regularization @@ -1046,6 +1116,17 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): if dataloader_idx == 0: + # if self.is_difussion: + # if self.num_inputs == 1: + # loss, energy_diff = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=False,x_2=None) + # else: + # peak_time = features["peak_time"] + # loss, energy_diff = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=False,x_2=peak_time) + + # else: + if len(energy_pred)==2: + energy_pred = energy_pred[0] + loss, energy_diff = self.compute_energy_loss( energy_pred, labels_energy_value, test_val=False, training=False ) @@ -1250,7 +1331,7 @@ def on_validation_epoch_end(self): fig_energy_error.savefig( os.path.join( self.logger.log_dir, - "error_resulution_validation_" + "error_resolution_validation_" + str(self.current_epoch) + "_" + str(global_loss_val) diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 93492d4f..dad037b8 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -24,9 +24,11 @@ data: # Check points type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_108/Epoch_0_type_train_acc_80.0708770751953125.pth #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth - energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - + # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth + energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_9/Epoch_31_energy_train_loss_0.1808181692378312.pth + # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + run_details: mode: "train" # The option are: "train", "results", "observation" and "validate" @@ -69,16 +71,25 @@ model: # dropout: 0.1 # use_bn: False - model_energy: - model_name: "ThinResNet_DBB" + # model_energy: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'energy' + # num_inputs: 1 + # num_outputs: 1 + # num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] + # dropout: 0.1 + # use_bn: False + + model_energy: + model_name: "NoPropDTReg" parameters: task: 'energy' - num_inputs: 1 num_outputs: 1 - num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] - dropout: 0.1 - use_bn: False - + embedding_dim: 512 + T: 3 + eta: 0.1 #0.1 + num_blocks: [2, 3, 3, 3] # model_direction: # model_name: "ThinResNet_DBB" @@ -90,34 +101,35 @@ model: # dropout: 0.1 # use_bn: False - # model_direction: - # model_name: "NoPropDTReg" - # parameters: - # task: 'direction' - # num_outputs: 3 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - model_direction: - model_name: "DBBNoPropDTReg" + model_name: "NoPropDTReg" parameters: task: 'direction' num_outputs: 3 embedding_dim: 512 T: 3 - eta: 0.1 #0.1 + eta: 0.1 #0.1 + num_blocks: [2, 3, 3, 3] + + # model_direction: + # model_name: "DBBNoPropDTReg" + # parameters: + # task: 'direction' + # num_outputs: 3 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 # Hyper-parameters hyp: - epochs: 142 - batches: 128 #128 #64 + epochs: 50 + batches: 256 #128 #64 dynamic_batches: True optimizer: Adamw momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 + learning_rate: 1e-3 #1e-5 #Efficient-b3 1e-5 lrf: 0.1 start_epoch: 0 steps_epoch: 100 # Computed online. Must be removed From 575daae791d5899d2ee0337b738d3629e5978e92 Mon Sep 17 00:00:00 2001 From: pguzman Date: Wed, 2 Jul 2025 13:23:49 +0000 Subject: [PATCH 037/119] rem,oved commented code and added gpu memory trick --- ctlearn/__init__.py | 28 ---- ctlearn/core/data_loader/pytorch_loader.py | 101 ++++++------ .../nets/models/NoPropDTReg2/NoPropDTReg2.py | 121 ++++++++++++++ .../denoiseBlockThinRestNet copy 2.py | 116 +++++++++++++ .../denoiseBlockThinRestNet copy.py | 111 +++++++++++++ .../NoPropDTReg2/denoiseBlockThinRestNet.py | 146 +++++++++++++++++ .../denoiseBlockThinRestNet_original.py | 154 ++++++++++++++++++ ctlearn/core/pytorch/nets/models/__init__.py | 1 + .../core/pytorch/visualization/vis_utils.py | 4 + ctlearn/tools/train/pytorch/CTLearnPL.py | 94 ++++++++--- .../training_config_iaa_neutron_training.yml | 10 +- ctlearn/tools/train_model.py | 7 +- 12 files changed, 789 insertions(+), 104 deletions(-) create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg2/NoPropDTReg2.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy 2.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet_original.py diff --git a/ctlearn/__init__.py b/ctlearn/__init__.py index f6af0b45..d3044278 100644 --- a/ctlearn/__init__.py +++ b/ctlearn/__init__.py @@ -6,31 +6,3 @@ def is_package_available(package_name: str) -> bool: return importlib.util.find_spec(package_name) is not None __all__ = ["__version__", "is_package_available"] - - - - - -# from ctlearn.tools.pytorch.train_pytorch_model import TrainPyTorchModel -# from ctlearn.tools.keras.train_keras_model import TrainKerasModel - -# class FrameworkType(Enum): -# KERAS = 1 -# PYTORCH = 2 - -# def get_framework(self,framework_type: FrameworkType): -# if framework_type == FrameworkType.KERAS: -# if not self.is_package_available("tensorflow"): -# raise ImportError("TensorFlow is not installed. Cannot run Keras framework.") -# else: -# fw = TrainKerasModel() - -# elif framework_type == FrameworkType.PYTORCH: -# if not self.is_package_available("torch"): -# raise ImportError("PyTorch is not installed. Cannot run PyTorch framework.") -# else: -# fw = TrainPyTorchModel() -# else: -# raise ValueError("Unknown Framework") - -# return fw diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 4d3f7f54..93131587 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -109,7 +109,7 @@ def __init__( def set_T(self,T): self.T=T - # self.total_len = len(self.indices) * T + self.indices = np.tile(self.indices, self.T) pp=0 def __len__(self): @@ -125,8 +125,7 @@ def __len__(self): Number of batches per epoch. """ return int(np.floor(len(self.indices) / self.batch_size)) - # return int(np.floor((self.total_len/self.T) / self.batch_size)) - # return int(np.floor(((self.total_len)) / self.batch_size)) + def on_epoch_end(self): """ @@ -212,61 +211,61 @@ def _fetch_batch(self, index): return features, labels # Fetching batches - def __getitem__(self, index): + # def __getitem__(self, index): - data_idx = index - # data_idx = index % int((self.total_len/self.T)/self.batch_size) - t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) + # data_idx = index + # # data_idx = index % int((self.total_len/self.T)/self.batch_size) + # t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) - # If this is the first call, fetch synchronously, and schedule the next - if self._next_batch_future is None: - features, labels = self._fetch_batch(data_idx) - else: - features, labels = self._next_batch_future.result() # Wait for the prefetch to finish - - # Schedule the next batch prefetch - if data_idx + 1 < len(self): - self._next_batch_future = self.executor.submit(self._fetch_batch, data_idx + 1) - else: - self._next_batch_future = None # No more batches + # # If this is the first call, fetch synchronously, and schedule the next + # if self._next_batch_future is None: + # features, labels = self._fetch_batch(data_idx) + # else: + # features, labels = self._next_batch_future.result() # Wait for the prefetch to finish + + # # Schedule the next batch prefetch + # if data_idx + 1 < len(self): + # self._next_batch_future = self.executor.submit(self._fetch_batch, data_idx + 1) + # else: + # self._next_batch_future = None # No more batches - return features, labels, t + # return features, labels, t - # def __getitem__(self, index): - # """ - # Generate one batch of data and retrieve the features and labels. + def __getitem__(self, index): + """ + Generate one batch of data and retrieve the features and labels. - # This method is called to generate one batch of monoscopic and stereoscopic data based on - # the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) - # based on the mode of the DLDataReader. + This method is called to generate one batch of monoscopic and stereoscopic data based on + the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) + based on the mode of the DLDataReader. - # Parameters: - # ----------- - # index : int - # Index of the batch to generate. + Parameters: + ----------- + index : int + Index of the batch to generate. - # Returns: - # -------- - # tuple - # A tuple containing the input data as features and the corresponding labels. - # """ + Returns: + -------- + tuple + A tuple containing the input data as features and the corresponding labels. + """ - # # data_idx = index - # # data_idx = index % int((self.total_len/self.T)/self.batch_size) - # t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) - # # Generate indices of the batch - # batch_indices = self.indices[ - # index * self.batch_size : (index + 1) * self.batch_size - # ] - # features, labels = None, None - # if self.DLDataReader.mode == "mono": - # batch = self.DLDataReader.generate_mono_batch(batch_indices) - # features, labels = self._get_mono_item(batch) - # elif self.DLDataReader.mode == "stereo": - # batch = self.DLDataReader.generate_stereo_batch(batch_indices) - # features, labels = self._get_stereo_item(batch) + # data_idx = index + # data_idx = index % int((self.total_len/self.T)/self.batch_size) + t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) + # Generate indices of the batch + batch_indices = self.indices[ + index * self.batch_size : (index + 1) * self.batch_size + ] + features, labels = None, None + if self.DLDataReader.mode == "mono": + batch = self.DLDataReader.generate_mono_batch(batch_indices) + features, labels = self._get_mono_item(batch) + elif self.DLDataReader.mode == "stereo": + batch = self.DLDataReader.generate_stereo_batch(batch_indices) + features, labels = self._get_stereo_item(batch) - # return features, labels, t + return features, labels, t # def cam_to_alt_az( # self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y @@ -422,6 +421,7 @@ def _get_mono_item(self, batch): # Retrieve the telescope images and store in the features dictionary labels = {} features = {"input": batch["features"].data} + if "type" in self.tasks: labels["type"] = np.stack(batch["true_shower_primary_class"].data) @@ -495,9 +495,8 @@ def _get_mono_item(self, batch): features_out["image"] = torch.from_numpy(image).contiguous().float() features_out["peak_time"] = torch.from_numpy(peak_time).contiguous().float() - + for key in labels.keys(): - labels[key] = torch.from_numpy(labels[key]).contiguous() if key != "type": diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg2/NoPropDTReg2.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/NoPropDTReg2.py new file mode 100644 index 00000000..a5a7c8f9 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/NoPropDTReg2.py @@ -0,0 +1,121 @@ +# NoProp-DT model + +import torch +from torch import nn + +# from .denoiseBlock import DenoiseBlock +from .denoiseBlockThinRestNet import DenoiseBlock, MemoryEfficientSwish + +import math + +class SimplifiedDenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_classes): + super().__init__() + # Simplified image feature extractor + self.conv_path = nn.Sequential( + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten() + ) + + # Simplified embedding processor + self.fc_z = nn.Sequential( + nn.Linear(embedding_dim, 256), + nn.ReLU() + ) + + # Combined processor + self.combined = nn.Sequential( + nn.Linear(256 + 64, 128), + nn.ReLU(), + nn.Linear(128, num_classes) + ) + + def forward(self, x, z_prev, W_embed): + # Image features + x_feat = self.conv_path(x) + + # Process embedding + z_feat = self.fc_z(z_prev) + + # Combine features + combined = torch.cat([x_feat, z_feat], dim=1) + logits = self.combined(combined) + + # Update embedding + z_next = z_prev + logits @ W_embed + + return z_next, logits + +class NoPropDTReg2(nn.Module): + def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1,num_blocks=[2, 3, 3, 3]): + super().__init__() + + self.task = task + num_classes = num_outputs + self.num_classes = num_classes + self.embedding_dim = embedding_dim + self.T = T + self.eta = eta + + self.blocks = nn.ModuleList([DenoiseBlock(embedding_dim,num_channels=1,num_blocks=num_blocks) for _ in range(T)]) + + self.regressor = nn.Sequential( + nn.Linear(embedding_dim, embedding_dim//2), + # MemoryEfficientSwish(), + nn.Linear(embedding_dim//2, num_outputs) + ) + + # Final classifier + self.classifier = nn.Linear(embedding_dim, num_classes) + + # Improved noise schedule + self.register_buffer('alpha_bar', self._cosine_schedule(T)) + self.register_buffer('snr_diff', self._calculate_snr_diff(self.alpha_bar)) + + + self.target_embedder = nn.Linear(num_outputs, embedding_dim) + + for m in self.regressor: + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + nn.init.zeros_(m.bias) + + def _cosine_schedule(self, T): + t = torch.arange(1, T+1, dtype=torch.float32) + alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi/2))**2 + return alpha_bar + + def _calculate_snr_diff(self, alpha_bar): + snr = alpha_bar / (1 - alpha_bar + 1e-8) + snr_prev = torch.cat([torch.tensor([0.]), snr[:-1]]) + return torch.clamp(snr - snr_prev, min=1e-5) + + def forward_denoise(self, x, z_prev, t): + return self.blocks[t](x, z_prev, None)[0] + + def regress(self, z): + return self.regressor(z) + + def inference(self, x): + B = x.size(0) + z = torch.randn(B, self.embedding_dim, device=x.device) + if not self.training: + z = torch.zeros(B, self.embedding_dim, device=x.device) + + for t in range(self.T): + z = self.forward_denoise(x, z, t) + + return self.regress(z) + + def forward(self, x): + + if self.task=="direction": + return None, None, self.inference(x) + elif self.task=="energy": + return None, self.inference(x), None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy 2.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy 2.py new file mode 100644 index 00000000..f1326018 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy 2.py @@ -0,0 +1,116 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) + self.act1 = nn.GELU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) + self.act2 = nn.GELU() + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + def forward(self, x): + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) + residual = self.se(residual) + out = residual + self.shortcut(x) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + super().__init__() + # Ahora más profundo y ancho: + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(256, 512), # Embedding más grande + # nn.BatchNorm1d(512), + nn.GELU() + ) + + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + # h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + # h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h1 = self.act1((self.fc_z1(z_prev))) + h2 = self.act2((self.fc_z2(h1))) + + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + # h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + # h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + h_f = self.act_f1((self.fc_f1(h_f))) + h_f = self.act_f2((self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy.py new file mode 100644 index 00000000..6487c4ee --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet copy.py @@ -0,0 +1,111 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) + self.act1 = nn.GELU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) + self.act2 = nn.GELU() + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + def forward(self, x): + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) + residual = self.se(residual) + out = residual + self.shortcut(x) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + super().__init__() + # Ahora más profundo y ancho: + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(256, 512), # Embedding más grande + nn.BatchNorm1d(512), + nn.GELU() + ) + + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet.py new file mode 100644 index 00000000..a1cd048d --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet.py @@ -0,0 +1,146 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).squeeze(-1).squeeze(-1) # Ensuring dimension match + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=16,use_bn=False): + + + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.use_bn = use_bn + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + + + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels) + ) + + def forward(self, x): + out = F.relu((self.conv1(x))) + out = (self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + out = self.se(out) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, num_outputs=2,use_bn=False,dropout=0.0): + super().__init__() + + # block = BasicBlock + self.in_channels = 64 + self.use_bn=use_bn + + self.conv1 = nn.Conv2d(num_channels, 64, kernel_size=3, stride=1, padding=1, bias=False) + + self.layer1_1 = self._make_layer(block, 32, num_blocks[0], stride=1) + self.layer2_1 = self._make_layer(block, 64, num_blocks[1], stride=2) + self.layer3_1 = self._make_layer(block, 128, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 256, num_blocks[3], stride=2) + # Reducing the number of layers and filters to make it "thin" + # self.fc_1 = nn.Linear(embedding_dim , embedding_dim) + # self.fc_2 = nn.Linear(embedding_dim , 256) + self.bn_final = nn.BatchNorm1d(512 * block.expansion) # BatchNorm layer + self.prelu = nn.PReLU(num_parameters=512 * block.expansion) # Define Leaky ReLU + + + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(dropout) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + + def forward(self, x, z_prev, _): + + out_1 = F.relu(self.conv1(x)) + out_1 = self.layer1_1(out_1) + out_1 = self.layer2_1(out_1) + out_1 = self.layer3_1(out_1) + + x_feat = self.layer4(out_1) + x_feat= self.adaptive_pool(x_feat) + x_feat = x_feat.view(x_feat.size(0), -1) + + + # h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + # h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h1 = self.act1((self.fc_z1(z_prev))) + h2 = self.act2((self.fc_z2(h1))) + + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + # h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + # h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + h_f = self.act_f1((self.fc_f1(h_f))) + h_f = self.act_f2((self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None + + def _make_layer(self, block, out_channels, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride, use_bn=self.use_bn)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet_original.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet_original.py new file mode 100644 index 00000000..fc52d51f --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg2/denoiseBlockThinRestNet_original.py @@ -0,0 +1,154 @@ +# Denoising block +import torch +from torch import nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +# class ResidualBlock(nn.Module): +# def __init__(self, in_channels, out_channels): +# super().__init__() +# self.conv_block = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels), +# nn.PReLU(), +# nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels) +# ) + +# self.shortcut = nn.Sequential() +# if in_channels != out_channels: +# self.shortcut = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=1), +# nn.BatchNorm2d(out_channels) +# ) + +# self.relu = nn.PReLU() + +# def forward(self, x): +# return self.relu(self.conv_block(x) + self.shortcut(x)) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16, drop_path_rate=0.1): + super().__init__() + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + self.norm1 = nn.GroupNorm(8, out_channels) + self.act1 = nn.GELU() + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) + self.norm2 = nn.GroupNorm(8, out_channels) + self.act2 = nn.GELU() + + self.se = SEBlock(out_channels, reduction) + + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1), + nn.GroupNorm(8, out_channels) + ) + + try: + from timm.models.layers import DropPath + self.drop_path = DropPath(drop_path_rate) + except ImportError: + self.drop_path = nn.Identity() + + # self.final_act = nn.GELU() + self.final_act = MemoryEfficientSwish() + def forward(self, x): + residual = self.act1(self.norm1(self.conv1(x))) + residual = self.act2(self.norm2(self.conv2(residual))) + # residual = self.act1((self.conv1(x))) + # residual = self.act2((self.conv2(residual))) + + residual = self.se(residual) + + out = self.drop_path(residual) + self.shortcut(x) + return self.final_act(out) + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1): + super().__init__() + + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 32), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(32, 64), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(64, 128), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(128, 256), + ) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + + z_feat = h3 + h1 + + h_f = torch.cat([x_feat, z_feat], dim=1) + + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/__init__.py b/ctlearn/core/pytorch/nets/models/__init__.py index b6dcc2d4..55cc4d29 100644 --- a/ctlearn/core/pytorch/nets/models/__init__.py +++ b/ctlearn/core/pytorch/nets/models/__init__.py @@ -9,3 +9,4 @@ import ctlearn.core.pytorch.nets.models.NoPropDT.NoPropDT import ctlearn.core.pytorch.nets.models.NoPropDTReg.NoPropDTReg import ctlearn.core.pytorch.nets.models.DBBNoPropDTReg.DBBNoPropDTReg +import ctlearn.core.pytorch.nets.models.NoPropDTReg2.NoPropDTReg2 diff --git a/ctlearn/core/pytorch/visualization/vis_utils.py b/ctlearn/core/pytorch/visualization/vis_utils.py index 5342fd38..6cb287a9 100644 --- a/ctlearn/core/pytorch/visualization/vis_utils.py +++ b/ctlearn/core/pytorch/visualization/vis_utils.py @@ -25,6 +25,10 @@ def plot_energy_resolution_error(val_energy_pred_list,val_energy_label_list,val_ fig, ax = plt.subplots() ctaplot.plot_energy_resolution(true_energy, reco_energy, label="Energy resolution", ax=ax) + # Skip warning + ax.get_xaxis().set_units(None) + ax.get_yaxis().set_units(None) + ctaplot.plot_energy_resolution_cta_requirement('north', ax=ax, color='black') ax.legend() ax.set_ylim(bottom=0, top=1.5) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 3a259169..ce9017df 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -104,6 +104,34 @@ def get_log_dir(self) -> str: class CTLearnPL(pl.LightningModule): + # def setup(self,stage): + + # # self.dummy = None + + # self.dummy = self.occupy_free_gpu_memory(self.device) + + def occupy_free_gpu_memory(self,device): + # Get free and total GPU memory using CUDA APIs + free_mem, total_mem = torch.cuda.mem_get_info(device) + + # Leave a margin (e.g., 100 MB) + margin = 2000 * 1024 * 1024 # 1000 MB + mem_to_allocate = max(0, free_mem - margin) + + if mem_to_allocate > 0: + try: + # Each float32 element takes 4 bytes + num_elements = mem_to_allocate // 4 + dummy_tensor = torch.zeros(num_elements, dtype=torch.float32, device=device) + print(f"Occupied {mem_to_allocate/1024**2:.2f} MB of GPU memory on {device}") + return dummy_tensor + except RuntimeError as e: + print(f"Could not allocate memory: {e}") + return None + else: + print("Not enough free GPU memory to occupy.") + return None + def __init__( self, model, @@ -133,6 +161,12 @@ def __init__( self.model.to(self.device) + + + # # Ejecutar la función + # self.dummy = None + + # self.occupy_free_gpu_memory(self.device) self.k = k # Number of top results to save # Get the number of inputs of the net sig = inspect.signature(model.forward) @@ -173,6 +207,7 @@ def __init__( self.criterion_direction_none = torch.nn.SmoothL1Loss(reduction="none") # nn.MSELoss() self.criterion_magnitud_none = torch.nn.L1Loss(reduction="none") self.criterion_alt_az_l1_none = torch.nn.L1Loss(reduction="none") + self.criterion_energy_value_none = torch.nn.L1Loss(reduction="none") self.criterion_direction = torch.nn.L1Loss(reduction="mean") # nn.MSELoss() self.criterion_vector = VectorLoss(alpha=0.1, reduction="mean") @@ -440,23 +475,30 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None # Final step: use regression head if t == self.model.T - 1: - # labels_dx_dy = y[:, 0:2] - # label_distance = y[:, 2] - # direction_pred = self.model.regress(z) - # if isinstance(direction_pred, tuple): - # # Not Tested - # direction_pred = list(direction_pred) - # pred_dx_dy = direction_pred[0][:,0:2].unsqueeze(-1) - # pred_distance = direction_pred[0][:,2].unsqueeze(-1) - # else: - # pred_dx_dy = direction_pred[:,0:2] - # pred_distance = direction_pred[:,2] labels_energy = y energy_pred = self.model.regress(z) - loss_energy = self.criterion_energy_value(energy_pred, labels_energy) - + + # labels_energy_tev = torch.pow(10,labels_energy) + # energy_pred_tev = torch.pow(10,energy_pred) + + # loss_energy = self.criterion_energy_value(energy_pred_tev, labels_energy_tev) + # loss_energy = self.criterion_energy_value(energy_pred, labels_energy) + # loss_energy = F.mse_loss(energy_pred, labels_energy,reduction="sum") + loss_energy = self.criterion_energy_value_none(energy_pred, labels_energy) + energy_tev = torch.pow(10,labels_energy) + + k=0.6 + e_thrs= -0.3 + + energy_weight=k*torch.exp(1+(np.log10(1/k)*(energy_tev-e_thrs))) + weight_loss = energy_weight * (loss_energy) + + loss_energy=loss_energy.mean() + + step_loss = step_loss + weight_loss.sum() + if training == False: energy_pred = pow(10, energy_pred) labels_energy = pow(10, labels_energy) @@ -465,8 +507,8 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None else: energy_diff = None - step_loss = step_loss + loss_energy - # step_loss = step_loss + loss_dx_dy + loss_distance + loss_distance_dx_dy + # step_loss = step_loss + loss_energy + loss = loss + step_loss return loss, energy_diff @@ -723,6 +765,13 @@ def compute_type_loss_diffusion(self, x,y, training=False): return loss, accuracy, predicted, precision # ---------------------------------------------------------------------------------------------------------- + # def on_train_start(self): + # self.dummy_tensor = self.occupy_free_gpu_memory(self.device) + # ---------------------------------------------------------------------------------------------------------- + def on_train_batch_start(self, batch, batch_idx): + if batch_idx == 5 and not hasattr(self, "dummy_tensor"): + self.dummy_tensor = self.occupy_free_gpu_memory(self.device) + # ---------------------------------------------------------------------------------------------------------- def training_step(self, batch, batch_idx): # ------------------------------------------------------------------ @@ -856,7 +905,7 @@ def training_step(self, batch, batch_idx): self.loss_train_sum += loss.item() self.num_train_batches += 1 - torch.cuda.empty_cache() + # torch.cuda.empty_cache() return loss # ---------------------------------------------------------------------------------------------------------- def on_train_epoch_end(self): @@ -997,14 +1046,17 @@ def on_train_epoch_end(self): def validation_step(self, batch, batch_idx, dataloader_idx=0): loss=0 self.model.eval() - + # ------------------------------------------------------------------ # Read inputs (features) and labels # ------------------------------------------------------------------ features, labels, t = batch + if len(features) > 0: + imgs = features["image"] + batch_size = imgs.shape[0] if self.task == Task.type: labels_class = labels["type"] @@ -1062,6 +1114,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): on_epoch=False, prog_bar=True, logger=False, + batch_size=batch_size, ) self.log( "val_prec", @@ -1070,6 +1123,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): on_epoch=False, prog_bar=True, logger=False, + batch_size=batch_size, ) # --------------------------------------- # Direction @@ -1161,10 +1215,10 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): loss_key = "val_loss" if dataloader_idx == 0 else "test_loss" if loss is not None: - self.log(loss_key, loss,on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True) + self.log(loss_key, loss,on_step=False, on_epoch=True, prog_bar=True, logger=True, sync_dist=True, batch_size=batch_size) # Release cuda memory - torch.cuda.empty_cache() + # torch.cuda.empty_cache() return loss # ---------------------------------------------------------------------------------------------------------- @torch.no_grad() @@ -1422,6 +1476,7 @@ def print_direction_error(self, angular_diff_list, type_val: str): # ---------------------------------------------------------------------------------------------------------- def print_energy_error(self, energy_diff_list, type_val: str): + error_30 = len([num for num in energy_diff_list if num < 30]) error_20 = len([num for num in energy_diff_list if num < 20]) error_10 = len([num for num in energy_diff_list if num < 10]) @@ -1448,6 +1503,7 @@ def print_energy_error(self, energy_diff_list, type_val: str): self.current_epoch, ) # Print + print(f"Total: {len(energy_diff_list)}") print(type_val + " Energy Error < 30:", error_30) print(type_val + " Energy Error < 20:", error_20) print(type_val + " Energy Error < 10:", error_10) diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index dad037b8..999c8286 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -25,7 +25,7 @@ data: # Check points type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_108/Epoch_0_type_train_acc_80.0708770751953125.pth #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_9/Epoch_31_energy_train_loss_0.1808181692378312.pth + energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_41/Epoch_18_energy_train_loss_4.9782595586117511.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth @@ -88,7 +88,7 @@ model: num_outputs: 1 embedding_dim: 512 T: 3 - eta: 0.1 #0.1 + eta: 100.1 #0.1 num_blocks: [2, 3, 3, 3] # model_direction: @@ -124,12 +124,12 @@ model: hyp: epochs: 50 - batches: 256 #128 #64 + batches: 128 #128 #64 dynamic_batches: True optimizer: Adamw momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-3 #1e-5 #Efficient-b3 1e-5 + learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 lrf: 0.1 start_epoch: 0 steps_epoch: 100 # Computed online. Must be removed @@ -182,7 +182,7 @@ arch: # device: 'mps' # Apple Mx device: 'cuda' precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_energy: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) # devices: [0,1] # [0,1] For multiple GPUs diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 30ad85e1..442345b9 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -208,4 +208,9 @@ def main(): # Direction # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --reco cameradirection --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_direction_training.out 2>&1 & -# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal gamma_theta_23.161_az_260.739_runs7-65*.dl1.h5 --reco cameradirection --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_direction_training.out 2>&1 & \ No newline at end of file +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal gamma_theta_23.161_az_260.739_runs7-65*.dl1.h5 --reco cameradirection --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_direction_training.out 2>&1 & + + + +# Energy +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & From 2208a23e567145fc1b0989b0b7ae1a2801ad7484 Mon Sep 17 00:00:00 2001 From: pguzman Date: Tue, 15 Jul 2025 16:22:22 +0000 Subject: [PATCH 038/119] modification in training and added another network for testing --- ctlearn/core/data_loader/pytorch_loader.py | 183 +++++++++++------ .../nets/models/NoPropDTReg/NoPropDTReg.py | 6 +- .../NoPropDTReg/denoiseBlockThinRestNet.py | 14 +- .../nets/models/ThinResNet/ThinResNet.py | 184 ++++++++++++++++++ ctlearn/core/pytorch/nets/models/__init__.py | 1 + ctlearn/tools/train/base_train_model.py | 1 - ctlearn/tools/train/pytorch/CTLearnPL.py | 76 ++++---- .../training_config_iaa_neutron_training.yml | 12 +- .../train/pytorch/train_pytorch_model.py | 4 +- ctlearn/tools/train_model.py | 7 + 10 files changed, 379 insertions(+), 109 deletions(-) create mode 100644 ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 93131587..d70a34b6 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -211,61 +211,61 @@ def _fetch_batch(self, index): return features, labels # Fetching batches - # def __getitem__(self, index): + def __getitem__(self, index): - # data_idx = index - # # data_idx = index % int((self.total_len/self.T)/self.batch_size) - # t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) + data_idx = index + # data_idx = index % int((self.total_len/self.T)/self.batch_size) + t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) - # # If this is the first call, fetch synchronously, and schedule the next - # if self._next_batch_future is None: - # features, labels = self._fetch_batch(data_idx) - # else: - # features, labels = self._next_batch_future.result() # Wait for the prefetch to finish - - # # Schedule the next batch prefetch - # if data_idx + 1 < len(self): - # self._next_batch_future = self.executor.submit(self._fetch_batch, data_idx + 1) - # else: - # self._next_batch_future = None # No more batches + # If this is the first call, fetch synchronously, and schedule the next + if self._next_batch_future is None: + features, labels = self._fetch_batch(data_idx) + else: + features, labels = self._next_batch_future.result() # Wait for the prefetch to finish + + # Schedule the next batch prefetch + if data_idx + 1 < len(self): + self._next_batch_future = self.executor.submit(self._fetch_batch, data_idx + 1) + else: + self._next_batch_future = None # No more batches - # return features, labels, t + return features, labels, t - def __getitem__(self, index): - """ - Generate one batch of data and retrieve the features and labels. + # def __getitem__(self, index): + # """ + # Generate one batch of data and retrieve the features and labels. - This method is called to generate one batch of monoscopic and stereoscopic data based on - the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) - based on the mode of the DLDataReader. + # This method is called to generate one batch of monoscopic and stereoscopic data based on + # the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) + # based on the mode of the DLDataReader. - Parameters: - ----------- - index : int - Index of the batch to generate. + # Parameters: + # ----------- + # index : int + # Index of the batch to generate. - Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. - """ + # Returns: + # -------- + # tuple + # A tuple containing the input data as features and the corresponding labels. + # """ - # data_idx = index - # data_idx = index % int((self.total_len/self.T)/self.batch_size) - t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) - # Generate indices of the batch - batch_indices = self.indices[ - index * self.batch_size : (index + 1) * self.batch_size - ] - features, labels = None, None - if self.DLDataReader.mode == "mono": - batch = self.DLDataReader.generate_mono_batch(batch_indices) - features, labels = self._get_mono_item(batch) - elif self.DLDataReader.mode == "stereo": - batch = self.DLDataReader.generate_stereo_batch(batch_indices) - features, labels = self._get_stereo_item(batch) + # # data_idx = index + # # data_idx = index % int((self.total_len/self.T)/self.batch_size) + # t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) + # # Generate indices of the batch + # batch_indices = self.indices[ + # index * self.batch_size : (index + 1) * self.batch_size + # ] + # features, labels = None, None + # if self.DLDataReader.mode == "mono": + # batch = self.DLDataReader.generate_mono_batch(batch_indices) + # features, labels = self._get_mono_item(batch) + # elif self.DLDataReader.mode == "stereo": + # batch = self.DLDataReader.generate_stereo_batch(batch_indices) + # features, labels = self._get_stereo_item(batch) - return features, labels, t + # return features, labels, t # def cam_to_alt_az( # self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y @@ -452,18 +452,13 @@ def _get_mono_item(self, batch): if "cameradirection" in labels.keys(): labels["direction"] = labels["cameradirection"] - features["hillas"] = self.DLDataReader.get_parameters(batch, self.hillas_names) + # features["hillas"] = self.DLDataReader.get_parameters(batch, self.hillas_names) + features["hillas"] = self.DLDataReader.get_parameters(batch) image = features["input"][..., 0:1] peak_time = features["input"][..., 1:2] - if self.use_augmentation: - image, peak_time = self.apply_augmentation(image, peak_time) - - image = np.transpose(image, (0, 3, 1, 2)) - peak_time = np.transpose(peak_time, (0, 3, 1, 2)) - # ---------------------------------------------------- # Remove negative numbers and avoid inf or nans # ---------------------------------------------------- @@ -474,6 +469,12 @@ def _get_mono_item(self, batch): peak_time[np.isnan(peak_time)] = 0 peak_time[np.isinf(peak_time)] = 0 + + # image, peak_time = self.apply_augmentation(image, peak_time) + + # image = np.transpose(image, (0, 3, 1, 2)) + # peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + if self.task == Task.type: image = (image - self.type_mu) / self.type_sigma peak_time = (peak_time - self.type_mu) / self.type_sigma @@ -486,15 +487,12 @@ def _get_mono_item(self, batch): image = (image - self.dir_mu) / self.dir_sigma peak_time = (peak_time - self.dir_mu) / self.dir_sigma - + # image = torch.from_numpy(image).contiguous().float() + # peak_time = torch.from_numpy(peak_time).contiguous().float() features_out = {} features_out["image"] = image - features_out["peak_time"] = peak_time - - features_out["image"] = torch.from_numpy(image).contiguous().float() - features_out["peak_time"] = torch.from_numpy(peak_time).contiguous().float() - + features_out["peak_time"] = peak_time for key in labels.keys(): labels[key] = torch.from_numpy(labels[key]).contiguous() @@ -545,11 +543,74 @@ def _get_mono_item(self, batch): # sky_coords_alt, sky_coords_az = self.cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) + + + + N = 4 # Repeating the number of high energies + + if self.use_augmentation: + energy_log = torch.pow(10,labels["energy"].squeeze(-1)) # shape [N] + high_energy_mask = energy_log > 1 # log10(E/TeV) > 0 => E > 1 TeV + + idx_to_duplicate = torch.where(high_energy_mask)[0] + + if len(idx_to_duplicate) > 0: + def duplicate_tensor(t,idx_to_duplicate): + if isinstance(t, torch.Tensor): + extra = torch.cat([t[idx_to_duplicate] for _ in range(N)], dim=0) + return torch.cat([t, extra], dim=0).contiguous() + elif isinstance(t, np.ndarray): + # Si t es 1D, t[idx_to_duplicate] ya es de shape (M,), sólo hace falta stackear + # extra = np.tile(t[idx_to_duplicate], N) + # return np.concatenate([t, extra], axis=0) + idx_to_duplicate = idx_to_duplicate.cpu().numpy() if hasattr(idx_to_duplicate, "cpu") else idx_to_duplicate + # extra = np.tile(t[idx_to_duplicate], (N, 1, 1, 1)) # si shape es (n, x, y, z) + # Mejor: stack y luego reshape + extra = np.concatenate([t[idx_to_duplicate] for _ in range(N)], axis=0) + # O si es 1D, puedes hacer + # extra = np.tile(t[idx_to_duplicate], N) + return np.concatenate([t, extra], axis=0) + + else: + raise TypeError(f"Tipo de dato no soportado para duplicación: {type(t)}") + + # Duplica todas las features principales + for key in features_out: + if isinstance(features_out[key], dict): + # Por ejemplo, hillas es un dict de tensores + for k in features_out[key]: + features_out[key][k] = duplicate_tensor(features_out[key][k],idx_to_duplicate) + else: + features_out[key] = duplicate_tensor(features_out[key],idx_to_duplicate) + + # Duplica las labels + for key in labels: + labels[key] = duplicate_tensor(labels[key],idx_to_duplicate) + + + if self.use_augmentation: + + # if isinstance(features_out["image"], torch.Tensor): + # features_out["image"] = features_out["image"].cpu().numpy() + # if isinstance(features_out["peak_time"], torch.Tensor): + # features_out["peak_time"] = features_out["peak_time"].cpu().numpy() + + image, peak_time = self.apply_augmentation(features_out["image"], features_out["peak_time"]) + + + image = np.transpose(image, (0, 3, 1, 2)) + peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + + features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() + features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() + + # Generate keep_idx as before hillas = features["hillas"] leakage = np.array(hillas["leakage_intensity_width_2"]) intensity = np.array(hillas["hillas_intensity"]) - keep_idx = np.where((leakage <= 0.2) & (intensity >= 50))[0] + keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] + # Filter features_out for key in features_out: @@ -563,7 +624,7 @@ def _get_mono_item(self, batch): # Filter labels (since it's a dict too) for key in labels: labels[key] = labels[key][keep_idx] - + return features_out, labels # TODO: Not adapted to pytorch diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py index 3031890f..89ba33aa 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/NoPropDTReg.py @@ -2,7 +2,7 @@ import torch from torch import nn - +import torch.nn.functional as F # from .denoiseBlock import DenoiseBlock from .denoiseBlockThinRestNet import DenoiseBlock, MemoryEfficientSwish @@ -97,7 +97,7 @@ def _calculate_snr_diff(self, alpha_bar): return torch.clamp(snr - snr_prev, min=1e-5) def forward_denoise(self, x, z_prev, t): - return self.blocks[t](x, z_prev, None)[0] + return self.blocks[t](x, z_prev, self.target_embedder)[0] def regress(self, z): return self.regressor(z) @@ -118,4 +118,4 @@ def forward(self, x): if self.task=="direction": return None, None, self.inference(x) elif self.task=="energy": - return None, self.inference(x), None + return None, F.tanh(self.inference(x))*4, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py index a1cd048d..1799ff04 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py @@ -60,7 +60,19 @@ def __init__(self, in_channels, out_channels, stride=1, reduction=16,use_bn=Fals def forward(self, x): out = F.relu((self.conv1(x))) out = (self.conv2(out)) - out += self.shortcut(x) + + # print("out.shape", out.shape, "shortcut.shape", self.shortcut(x).shape) + # print("out.dtype", out.dtype, "shortcut.dtype", self.shortcut(x).dtype) + # print("out.device", out.device, "shortcut.device", self.shortcut(x).device) + # print("out.is_contiguous()", out.is_contiguous(), "shortcut.is_contiguous()", self.shortcut(x).is_contiguous()) + + # assert out.shape == self.shortcut(x).shape, f"Shape mismatch: {out.shape} vs {self.shortcut(x).shape}" + # assert out.dtype == self.shortcut(x).dtype, f"Dtype mismatch: {out.dtype} vs {self.shortcut(x).dtype}" + # assert out.device == self.shortcut(x).device, f"Device mismatch: {out.device} vs {self.shortcut(x).device}" + + # out = out.contiguous() + + out += self.shortcut(x).contiguous() out = F.relu(out) out = self.se(out) return out diff --git a/ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py b/ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py new file mode 100644 index 00000000..111d5304 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py @@ -0,0 +1,184 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from ctlearn.core.pytorch.nets.block.cnn_blocks import NormalInvGamma + +class SEBlock(nn.Module): + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).squeeze(-1).squeeze(-1) # Ensuring dimension match + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=16,use_bn=True): + + + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.use_bn = use_bn + if self.use_bn: + self.bn1 = nn.BatchNorm2d(out_channels) + else: + self.bn1 = nn.Identity() + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + if self.use_bn: + self.bn2 = nn.BatchNorm2d(out_channels) + else: + self.bn2 = nn.Identity() + + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels) + ) + + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + out = self.se(out) + return out + +class ThinResNet(nn.Module): + def __init__(self,task, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, num_outputs=2,use_bn=False,dropout=0.0): + super(ThinResNet, self).__init__() + + # block = BasicBlock + self.in_channels = 64 + self.use_bn=use_bn + self.task = task + self.conv1 = nn.Conv2d(num_inputs, 64, kernel_size=3, stride=1, padding=1, bias=False) + if self.use_bn: + self.bn1 = nn.BatchNorm2d(64) + else: + self.bn1 = nn.Identity() + + self.layer1_1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2_1 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3_1 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + # Reducing the number of layers and filters to make it "thin" + self.fc_1 = nn.Linear(512 * block.expansion, 512 * block.expansion) + self.fc_2 = nn.Linear(512 * block.expansion, num_outputs) + self.bn_final = nn.BatchNorm1d(512 * block.expansion) # BatchNorm layer + self.prelu = nn.PReLU(num_parameters=512 * block.expansion) # Define Leaky ReLU + if self.task == "direction": + self.normal_inv = NormalInvGamma(512 * block.expansion,num_outputs) + + if self.use_bn: + self.bn2 = nn.BatchNorm2d(64) + else: + self.bn2 = nn.Identity() + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(dropout) + def _make_layer(self, block, out_channels, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride, use_bn=self.use_bn)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + + energy = None + classification = None + direction = None + + # out_1 = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.conv1(x)) + out = self.layer1_1(out) + out = self.layer2_1(out) + out = self.layer3_1(out) + + # out = self.layer3(out) + out = self.layer4(out) + out = self.adaptive_pool(out) + out_feature = out.view(out.size(0), -1) + out = self.dropout(out_feature) + + # if self.training: + # out_sep = self.fc_1_separation(out) + # out_sep = self.fc_2_separation(out_sep) + + out = self.fc_1(out) + # out = self.bn_final(out) + # out = self.prelu(out) + # Original + # out = self.fc_2(out) + + + if self.task == "type": + out = self.fc_2(out) + classification = out + if self.task == "energy": + out = self.fc_2(out) + # energy = [out, out_feature] + energy = out + + if self.task == "direction": + direction = self.normal_inv(out) + + # direction = [direction, out_feature] + # if self.training: + # out = self.normal_inv(out) + # direction = out + # else: + # direction = self.normal_inv(out) + + + # if self.training: + # out = torch.cat((out, out_sep), dim=1) + + return classification, energy, direction + +# def thin_resnet34(num_blocks=[2, 3, 3, 3], num_inputs=1, num_classes=2): +# # Here we configure fewer blocks for a lighter model +# return ThinResNet_DBB(BasicBlock, num_blocks,num_inputs,num_classes) + +# def create_model(num_blocks=[2, 3, 3, 3], num_inputs=1, num_classes=2, use_bn=False, dropout=0.0): +# return ThinResNet_DBB( +# block=BasicBlock, +# num_blocks=num_blocks, +# num_inputs=num_inputs, +# num_classes=num_classes, +# use_bn=use_bn, +# dropout=dropout +# ) + +# model = thin_resnet34() +# print(model) + +# # Set the model to evaluation mode (as we are just testing with a forward pass) +# model.eval() + +# # Create dummy input tensors +# # Assuming the input images are 224x224 pixels with 1 input channel (grayscale) +# x = torch.randn(1, 1, 224, 224) # Batch size of 1 +# y = torch.randn(1, 1, 224, 224) # Batch size of 1 + +# # Forward pass through the model +# with torch.no_grad(): # We don't need to calculate gradients here +# output = model(x, y) + +# # Print the output tensor +# print("Output:", output) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/__init__.py b/ctlearn/core/pytorch/nets/models/__init__.py index 55cc4d29..7d994dd2 100644 --- a/ctlearn/core/pytorch/nets/models/__init__.py +++ b/ctlearn/core/pytorch/nets/models/__init__.py @@ -1,5 +1,6 @@ import ctlearn.core.pytorch.nets.models.ThinResNet_DBB.ThinResNet_DBB +import ctlearn.core.pytorch.nets.models.ThinResNet.ThinResNet import ctlearn.core.pytorch.nets.models.DBBRegNet.DBBRegNet import ctlearn.core.pytorch.nets.models.DoubleBBEfficientNet.DoubleBBEfficientNet diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index a87786a2..7562f1d0 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -15,7 +15,6 @@ Unicode, ) from dl1_data_handler.reader import DLDataReader -# from ctlearn.core.data_loader.loader import DLDataLoader class TrainCTLearnModel(Tool): """ diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index ce9017df..20535623 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -43,6 +43,8 @@ import matplotlib matplotlib.use('Agg') +torch.cuda.empty_cache() +os.environ["CUDA_LAUNCH_BLOCKING"] = "1" class CTLearnTrainer(pl.Trainer): def __init__(self,**kwargs): @@ -115,7 +117,7 @@ def occupy_free_gpu_memory(self,device): free_mem, total_mem = torch.cuda.mem_get_info(device) # Leave a margin (e.g., 100 MB) - margin = 2000 * 1024 * 1024 # 1000 MB + margin = 20000 * 1024 * 1024 # 1000 MB mem_to_allocate = max(0, free_mem - margin) if mem_to_allocate > 0: @@ -200,7 +202,7 @@ def __init__( ) - self.criterion_energy_value = torch.nn.L1Loss(reduction="mean") + self.criterion_energy_value = torch.nn.L1Loss(reduction="sum") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() self.criterion_magnitud = torch.nn.L1Loss(reduction="mean") @@ -436,6 +438,18 @@ def compute_energy_loss( loss_energy = self.criterion_energy_value(energy_pred, labels_energy) loss = loss_energy + + #----------------------------------------- + # k=0.6 + # e_thrs= -0.3 + # loss_energy = self.criterion_energy_value_none(energy_pred, labels_energy) + # energy_tev = torch.pow(10,labels_energy) + # energy_weight=k*torch.exp(1+(np.log10(1/k)*(energy_tev-e_thrs))) + # weight_loss = energy_weight * (loss_energy) + + # loss = weight_loss.sum() + #----------------------------------------- + if training == False: energy_pred = pow(10, energy_pred) labels_energy = pow(10, labels_energy) @@ -453,7 +467,8 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None loss = 0 # y = y.squeeze(-1) y_embed = self.model.target_embedder(y) - + k=0.6 + e_thrs= -0.3 for t in range(self.model.T): # Add noise to target (landmarks) alpha_bar_t = self.model.alpha_bar[t] @@ -464,11 +479,13 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None # Denoise step z, _ = self.model.blocks[t](x_1, z_t, None) # W_embed not needed else: - z, _ = self.model.blocks[t](x_1,x_2, z_t, None) # W_embed not needed + z, _ = self.model.blocks[t](x_1 ,x_2, z_t, None) # W_embed not needed + - preds = self.model.regress(z) # Loss to clean target - loss_l2 = F.mse_loss(preds, y) + loss_l2 = F.mse_loss(z, y_embed) + + labels_energy = y # Weighted by SNR difference step_loss = 2.5 * self.model.eta * self.model.snr_diff[t] * loss_l2 @@ -476,28 +493,21 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None # Final step: use regression head if t == self.model.T - 1: - labels_energy = y - energy_pred = self.model.regress(z) + energy_pred = F.tanh(self.model.regress(z))*4 # labels_energy_tev = torch.pow(10,labels_energy) # energy_pred_tev = torch.pow(10,energy_pred) # loss_energy = self.criterion_energy_value(energy_pred_tev, labels_energy_tev) - # loss_energy = self.criterion_energy_value(energy_pred, labels_energy) + loss_energy = self.criterion_energy_value(energy_pred, labels_energy) # loss_energy = F.mse_loss(energy_pred, labels_energy,reduction="sum") - loss_energy = self.criterion_energy_value_none(energy_pred, labels_energy) - energy_tev = torch.pow(10,labels_energy) - - k=0.6 - e_thrs= -0.3 - - energy_weight=k*torch.exp(1+(np.log10(1/k)*(energy_tev-e_thrs))) - weight_loss = energy_weight * (loss_energy) - - loss_energy=loss_energy.mean() + # loss_energy = self.criterion_energy_value_none(energy_pred, labels_energy) + + # energy_weight=k*torch.exp(1+(np.log10(1/k)*(energy_tev-e_thrs))) + # weight_loss = energy_weight * (loss_energy) - step_loss = step_loss + weight_loss.sum() + # step_loss = step_loss + weight_loss.mean() if training == False: energy_pred = pow(10, energy_pred) @@ -507,7 +517,7 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None else: energy_diff = None - # step_loss = step_loss + loss_energy + step_loss = step_loss + loss_energy loss = loss + step_loss @@ -518,6 +528,8 @@ def compute_camera_direction_loss_diffusion(self, x_1, y, labels_energy_value, x loss = 0 y = y.squeeze(-1) y_embed = self.model.target_embedder(y) + k=0.6 + e_thrs= -0.3 for t in range(self.model.T): # Add noise to target (landmarks) @@ -578,8 +590,7 @@ def compute_camera_direction_loss_diffusion(self, x_1, y, labels_energy_value, x # energy_weight = k*(1/(1+torch.exp(-(1/k)*(energy-e_thrs)))) # weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy + loss_angular_diff) - k=0.6 - e_thrs= -0.3 + energy_weight=k*torch.exp(1+(np.log10(1/k)*(energy-e_thrs))) # weight_loss = energy_weight * (loss_dx_dy + loss_distance + loss_distance_dx_dy + loss_angular_diff) @@ -790,6 +801,7 @@ def training_step(self, batch, batch_idx): if self.task == Task.energy: labels_energy_value = labels["energy"] labels_energy_value = labels_energy_value.to(self.device) + if self.task == Task.cameradirection: labels_direction = labels["direction"] @@ -874,15 +886,15 @@ def training_step(self, batch, batch_idx): if self.is_difussion: if self.num_inputs == 1: - loss, *_ = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=True,x_2=None) + loss, *_ = self.compute_energy_loss_diffusion(imgs,labels_energy_value/1.0,training=True,x_2=None) else: peak_time = features["peak_time"] - loss, *_ = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=True,x_2=peak_time) + loss, *_ = self.compute_energy_loss_diffusion(imgs,labels_energy_value/1.0,training=True,x_2=peak_time) else: loss, *_ = self.compute_energy_loss( - energy_pred, labels_energy_value, test_val=False, training=False + energy_pred, labels_energy_value, test_val=False, training=True ) # --------------------------------------- @@ -1166,23 +1178,15 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # --------------------------------------- if self.task == Task.energy: - energy_pred_tev = torch.pow(10, energy_pred) + energy_pred_tev = torch.pow(10, energy_pred*1.0) if dataloader_idx == 0: - # if self.is_difussion: - # if self.num_inputs == 1: - # loss, energy_diff = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=False,x_2=None) - # else: - # peak_time = features["peak_time"] - # loss, energy_diff = self.compute_energy_loss_diffusion(imgs,labels_energy_value,training=False,x_2=peak_time) - - # else: if len(energy_pred)==2: energy_pred = energy_pred[0] loss, energy_diff = self.compute_energy_loss( - energy_pred, labels_energy_value, test_val=False, training=False + energy_pred, labels_energy_value/1.0, test_val=False, training=False ) self.val_energy_diff_list.extend(energy_diff) diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 999c8286..d88f2992 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -23,9 +23,9 @@ data: validation_test_reduce_factor: 0 #16 #8 # Check points - type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_108/Epoch_0_type_train_acc_80.0708770751953125.pth #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth + type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_41/Epoch_18_energy_train_loss_4.9782595586117511.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth + energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth @@ -72,7 +72,7 @@ model: # use_bn: False # model_energy: - # model_name: "ThinResNet_DBB" + # model_name: "ThinResNet" # parameters: # task: 'energy' # num_inputs: 1 @@ -88,7 +88,7 @@ model: num_outputs: 1 embedding_dim: 512 T: 3 - eta: 100.1 #0.1 + eta: 0.1 #0.1 num_blocks: [2, 3, 3, 3] # model_direction: @@ -123,7 +123,7 @@ model: # Hyper-parameters hyp: - epochs: 50 + epochs: 30 batches: 128 #128 #64 dynamic_batches: True optimizer: Adamw @@ -133,7 +133,7 @@ hyp: lrf: 0.1 start_epoch: 0 steps_epoch: 100 # Computed online. Must be removed - l2_lambda: 1e-5 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) + l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 gradient_clip_val: 3.0 # Avoid gradient explosion diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 487d4c7f..01c159ab 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -165,6 +165,8 @@ def setup(self): print(f"Using Devices: {self.devices}") + # all_log_energies = self.dl1dh_reader.data['log_true_energy'] + # Set up the data loaders for training and validation indices = list(range(self.dl1dh_reader._get_n_events())) # Shuffle the indices before the training/validation split @@ -199,7 +201,7 @@ def setup(self): sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, parameters=self.parameters, - use_augmentation=True, + use_augmentation=self.parameters["augmentation"]["use_augmentation"], ) self.validation_loader = DLDataLoader.create( diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 442345b9..f7544d32 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -214,3 +214,10 @@ def main(): # Energy # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & + + +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs243-302.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs303-362.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs363-422.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs423-482.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs483-541.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs542-600.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs63-122.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & + +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs63-122.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & + +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & From 2ec8dacbe9a3587bdbcb0948896c59ecaf89f626 Mon Sep 17 00:00:00 2001 From: pguzman Date: Mon, 21 Jul 2025 10:21:24 +0000 Subject: [PATCH 039/119] fixed bug in pytorch loader --- ctlearn/core/data_loader/pytorch_loader.py | 123 +++++++++++---------- 1 file changed, 63 insertions(+), 60 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index d70a34b6..bfb53337 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -19,10 +19,11 @@ def __init__( parameters, use_augmentation, T=1, + is_training=False, **kwargs, ): - + self.is_training=is_training self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) self._next_batch_future = None @@ -545,47 +546,47 @@ def _get_mono_item(self, batch): - - N = 4 # Repeating the number of high energies - - if self.use_augmentation: - energy_log = torch.pow(10,labels["energy"].squeeze(-1)) # shape [N] - high_energy_mask = energy_log > 1 # log10(E/TeV) > 0 => E > 1 TeV - - idx_to_duplicate = torch.where(high_energy_mask)[0] - - if len(idx_to_duplicate) > 0: - def duplicate_tensor(t,idx_to_duplicate): - if isinstance(t, torch.Tensor): - extra = torch.cat([t[idx_to_duplicate] for _ in range(N)], dim=0) - return torch.cat([t, extra], dim=0).contiguous() - elif isinstance(t, np.ndarray): - # Si t es 1D, t[idx_to_duplicate] ya es de shape (M,), sólo hace falta stackear - # extra = np.tile(t[idx_to_duplicate], N) - # return np.concatenate([t, extra], axis=0) - idx_to_duplicate = idx_to_duplicate.cpu().numpy() if hasattr(idx_to_duplicate, "cpu") else idx_to_duplicate - # extra = np.tile(t[idx_to_duplicate], (N, 1, 1, 1)) # si shape es (n, x, y, z) - # Mejor: stack y luego reshape - extra = np.concatenate([t[idx_to_duplicate] for _ in range(N)], axis=0) - # O si es 1D, puedes hacer - # extra = np.tile(t[idx_to_duplicate], N) - return np.concatenate([t, extra], axis=0) - - else: - raise TypeError(f"Tipo de dato no soportado para duplicación: {type(t)}") - - # Duplica todas las features principales - for key in features_out: - if isinstance(features_out[key], dict): - # Por ejemplo, hillas es un dict de tensores - for k in features_out[key]: - features_out[key][k] = duplicate_tensor(features_out[key][k],idx_to_duplicate) - else: - features_out[key] = duplicate_tensor(features_out[key],idx_to_duplicate) - - # Duplica las labels - for key in labels: - labels[key] = duplicate_tensor(labels[key],idx_to_duplicate) + if self.is_training: + N = 4 # Repeating the number of high energies + features_out["hillas"] = features["hillas"] + if self.use_augmentation: + energy_log = torch.pow(10,labels["energy"].squeeze(-1)) # shape [N] + high_energy_mask = energy_log > 1 # log10(E/TeV) > 0 => E > 1 TeV + + idx_to_duplicate = torch.where(high_energy_mask)[0] + + if len(idx_to_duplicate) > 0: + def duplicate_tensor(t,idx_to_duplicate): + if isinstance(t, torch.Tensor): + extra = torch.cat([t[idx_to_duplicate] for _ in range(N)], dim=0) + return torch.cat([t, extra], dim=0).contiguous() + elif isinstance(t, np.ndarray): + # Si t es 1D, t[idx_to_duplicate] ya es de shape (M,), sólo hace falta stackear + # extra = np.tile(t[idx_to_duplicate], N) + # return np.concatenate([t, extra], axis=0) + idx_to_duplicate = idx_to_duplicate.cpu().numpy() if hasattr(idx_to_duplicate, "cpu") else idx_to_duplicate + # extra = np.tile(t[idx_to_duplicate], (N, 1, 1, 1)) # si shape es (n, x, y, z) + # Mejor: stack y luego reshape + extra = np.concatenate([t[idx_to_duplicate] for _ in range(N)], axis=0) + # O si es 1D, puedes hacer + # extra = np.tile(t[idx_to_duplicate], N) + return np.concatenate([t, extra], axis=0) + + else: + raise TypeError(f"Tipo de dato no soportado para duplicación: {type(t)}") + + # Duplica todas las features principales + for key in features_out: + if isinstance(features_out[key], dict): + # Por ejemplo, hillas es un dict de tensores + for k in features_out[key]: + features_out[key][k] = duplicate_tensor(features_out[key][k],idx_to_duplicate) + else: + features_out[key] = duplicate_tensor(features_out[key],idx_to_duplicate) + + # Duplica las labels + for key in labels: + labels[key] = duplicate_tensor(labels[key],idx_to_duplicate) if self.use_augmentation: @@ -603,27 +604,29 @@ def duplicate_tensor(t,idx_to_duplicate): features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() + + # if self.is_training: + + if not self.is_training: + # Generate keep_idx as before + hillas = features["hillas"] + leakage = np.array(hillas["leakage_intensity_width_2"]) + intensity = np.array(hillas["hillas_intensity"]) + keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] - # Generate keep_idx as before - hillas = features["hillas"] - leakage = np.array(hillas["leakage_intensity_width_2"]) - intensity = np.array(hillas["hillas_intensity"]) - keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] - - - # Filter features_out - for key in features_out: - features_out[key] = features_out[key][keep_idx] - - features_out["hillas"] = features["hillas"] - - for key in features["hillas"]: - features_out["hillas"][key] = (features["hillas"][key])[keep_idx] + # Filter features_out + for key in features_out: + features_out[key] = features_out[key][keep_idx] - # Filter labels (since it's a dict too) - for key in labels: - labels[key] = labels[key][keep_idx] + features_out["hillas"] = features["hillas"] + + for key in features["hillas"]: + features_out["hillas"][key] = (features["hillas"][key])[keep_idx] + + # Filter labels (since it's a dict too) + for key in labels: + labels[key] = labels[key][keep_idx] return features_out, labels From a08265d55b5a15b279343e102c86e85b45de02f0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Thu, 24 Jul 2025 14:21:44 +0000 Subject: [PATCH 040/119] Adds new models --- .flake8 | 3 + .gitignore | 14 +- calibration/calibration.py | 82 + calibration/undistort.py | 95 + condaenv.vowayxih.requirements.txt | 11 + count_types.py | 231 ++ ctlearn/all_tools.svg | 1 + ctlearn/core/__init__.py | 2 + ctlearn/core/data_loader/pytorch_loader.py | 14 +- .../NoPropDTReg/denoiseBlockThinRestNet.py | 4 +- .../models/NoPropDTRegDBB/NoPropDTRegDBB.py | 121 ++ .../denoiseBlockThinRestNet copy 2.py | 116 + .../denoiseBlockThinRestNet copy.py | 111 + .../NoPropDTRegDBB/denoiseBlockThinRestNet.py | 170 ++ .../denoiseBlockThinRestNet_original.py | 154 ++ .../nets/models/StackedHGNet/StackedHGNet.py | 141 ++ .../models/StackedHGNet/core/coord_conv.py | 157 ++ .../models/StackedHGNetDBB/StackedHGNetDBB.py | 151 ++ .../models/StackedHGNetDBB/core/coord_conv.py | 157 ++ ctlearn/core/pytorch/nets/models/__init__.py | 5 + ctlearn/core/pytorch/utils/utils.py | 54 +- ctlearn/tools/_version.py | 1 + ctlearn/tools/conftest.py | 0 .../tools/predict/keras/predic_LST1_keras.py | 133 ++ ctlearn/tools/predict/predict_LST1.py | 820 +++++++ ctlearn/tools/predict/predict_model.py | 1890 ----------------- ctlearn/tools/predict/predict_mono.py | 541 +++++ ctlearn/tools/predict/predict_stereo.py | 375 ++++ .../predict/pytorch/predic_LST1_pytorch.py | 180 ++ .../predict/pytorch/predic_model_pytorch.py | 99 + ctlearn/tools/predict/utils/load_model.py | 10 + ctlearn/tools/predict/utils/predict_model.py | 1006 +++++++++ ctlearn/tools/train/_version.py | 1 + ctlearn/tools/train/conftest.py | 0 ctlearn/tools/train/pytorch/CTLearnPL.py | 14 +- ...ining_config_iaa_neutron_training_v5_1.yml | 207 ++ ...ining_config_iaa_neutron_training_v5_2.yml | 208 ++ .../train/pytorch/train_pytorch_model.py | 2 + ctlearn/tools/train_model.py | 3 +- monitoring_gpus.py | 108 + test_dataloader.py | 105 + 41 files changed, 5591 insertions(+), 1906 deletions(-) create mode 100644 .flake8 create mode 100644 calibration/calibration.py create mode 100644 calibration/undistort.py create mode 100644 condaenv.vowayxih.requirements.txt create mode 100644 count_types.py create mode 100644 ctlearn/all_tools.svg create mode 100644 ctlearn/core/__init__.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy 2.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet.py create mode 100644 ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet_original.py create mode 100644 ctlearn/core/pytorch/nets/models/StackedHGNet/StackedHGNet.py create mode 100644 ctlearn/core/pytorch/nets/models/StackedHGNet/core/coord_conv.py create mode 100644 ctlearn/core/pytorch/nets/models/StackedHGNetDBB/StackedHGNetDBB.py create mode 100644 ctlearn/core/pytorch/nets/models/StackedHGNetDBB/core/coord_conv.py create mode 100644 ctlearn/tools/_version.py create mode 100644 ctlearn/tools/conftest.py create mode 100644 ctlearn/tools/predict/keras/predic_LST1_keras.py create mode 100644 ctlearn/tools/predict/predict_LST1.py delete mode 100644 ctlearn/tools/predict/predict_model.py create mode 100644 ctlearn/tools/predict/predict_mono.py create mode 100644 ctlearn/tools/predict/predict_stereo.py create mode 100644 ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py create mode 100644 ctlearn/tools/predict/utils/load_model.py create mode 100644 ctlearn/tools/predict/utils/predict_model.py create mode 100644 ctlearn/tools/train/_version.py create mode 100644 ctlearn/tools/train/conftest.py create mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml create mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml create mode 100644 monitoring_gpus.py create mode 100644 test_dataloader.py diff --git a/.flake8 b/.flake8 new file mode 100644 index 00000000..0baba3d4 --- /dev/null +++ b/.flake8 @@ -0,0 +1,3 @@ +[flake8] +max-line-length = 88 +extend-ignore = E203, W503 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 2a0c1a92..10018dd8 100644 --- a/.gitignore +++ b/.gitignore @@ -12,16 +12,26 @@ ctlearn/_version.py *.png *.csv *~ +*.log +.vscode/ +*.h5 + +output_dir2/ +output_dir/ # Compiled Python files __pycache__/ *.py[cod] .DS_Store *.egg-info/ dist - +.pytest_cache/ # Sphinx documentation docs/build/ # Default pytorch output -run/ \ No newline at end of file +run/ +test/ +*.se2 +*.jar +*.puml \ No newline at end of file diff --git a/calibration/calibration.py b/calibration/calibration.py new file mode 100644 index 00000000..dc7460d9 --- /dev/null +++ b/calibration/calibration.py @@ -0,0 +1,82 @@ +import cv2 +import numpy as np +import matplotlib.pyplot as plt + +def detectar_lineas(img): + """Detecta líneas rectas usando Canny + Hough.""" + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + edges = cv2.Canny(gray, 80, 200, apertureSize=3) + lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=80, minLineLength=120, maxLineGap=20) + return lines + +def dibujar_lineas(img, lines): + img_lines = img.copy() + for line in lines: + x1, y1, x2, y2 = line[0] + cv2.line(img_lines, (x1, y1), (x2, y2), (0,255,0), 2) + return img_lines + +def calcular_puntos_de_fuga(lines): + """Calcula puntos de fuga aproximados por agrupación de dirección.""" + puntos = [] + for line in lines: + x1, y1, x2, y2 = line[0] + puntos.append(((x1, y1), (x2, y2))) + + # Calcular las intersecciones de todas las líneas + def intersection(line1, line2): + (x1,y1), (x2,y2) = line1 + (x3,y3), (x4,y4) = line2 + denom = (x1-x2)*(y3-y4)-(y1-y2)*(x3-x4) + if denom == 0: + return None + px = ((x1*y2 - y1*x2)*(x3-x4) - (x1-x2)*(x3*y4 - y3*x4))/denom + py = ((x1*y2 - y1*x2)*(y3-y4) - (y1-y2)*(x3*y4 - y3*x4))/denom + return [px, py] + + intersections = [] + for i in range(len(puntos)): + for j in range(i+1, len(puntos)): + pt = intersection(puntos[i], puntos[j]) + if pt is not None and all(0 <= c <= 1500 for c in pt): # dentro de rango razonable + intersections.append(pt) + return np.array(intersections) + +def estimar_centro_optico_y_focal(intersections, img_shape): + """Estima el centro óptico y focal a partir de puntos de fuga.""" + if len(intersections) < 2: + return (img_shape[1]//2, img_shape[0]//2), 900 + + # Promedia las intersecciones para estimar el centro óptico + intersections = np.array(intersections) + cx = np.median(intersections[:,0]) + cy = np.median(intersections[:,1]) + + # Estimación simple de la focal como distancia al centro de la imagen + f_est = np.mean(np.linalg.norm(intersections - np.array([[cx, cy]]), axis=1)) + return (cx, cy), f_est + +# --- MAIN --- +img_path = './calibration/image_2.png' # Usa el nombre de tu imagen +img = cv2.imread(img_path) + +lines = detectar_lineas(img) +img_with_lines = dibujar_lineas(img, lines) + +plt.imshow(cv2.cvtColor(img_with_lines, cv2.COLOR_BGR2RGB)) +plt.title('Líneas detectadas') +plt.show() + +intersections = calcular_puntos_de_fuga(lines) +(cx, cy), f_est = estimar_centro_optico_y_focal(intersections, img.shape) + +print("Centro óptico estimado: (%.1f, %.1f)" % (cx, cy)) +print("Distancia focal estimada (en píxeles): %.1f" % f_est) + +# Matriz intrínseca +K = np.array([ + [f_est, 0, cx], + [0, f_est, cy], + [0, 0, 1] +]) +print("Matriz intrínseca estimada:\n", K) diff --git a/calibration/undistort.py b/calibration/undistort.py new file mode 100644 index 00000000..c33094f0 --- /dev/null +++ b/calibration/undistort.py @@ -0,0 +1,95 @@ +import cv2 +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.widgets import Button +from scipy.optimize import minimize + +# 1. Selección interactiva de puntos con matplotlib +def select_lines_points(img): + plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + plt.title("Haz click para seleccionar puntos de una línea recta.\nPresiona ENTER para terminar la línea. ESC para terminar todas.") + lines_pts = [] + curr_pts = [] + + def onclick(event): + if event.inaxes: + x, y = int(event.xdata), int(event.ydata) + curr_pts.append([x, y]) + plt.plot(x, y, 'ro') + plt.draw() + + def onkey(event): + if event.key == 'enter': + if len(curr_pts) >= 2: + lines_pts.append(np.array(curr_pts)) + plt.plot(np.array(curr_pts)[:,0], np.array(curr_pts)[:,1], 'g-') + plt.draw() + curr_pts.clear() + elif event.key == 'escape': + plt.close() + + fig = plt.gcf() + cid_click = fig.canvas.mpl_connect('button_press_event', onclick) + cid_key = fig.canvas.mpl_connect('key_press_event', onkey) + plt.show() + fig.canvas.mpl_disconnect(cid_click) + fig.canvas.mpl_disconnect(cid_key) + return lines_pts + +# 2. Funciones para ajuste de distorsión +def undistort_points(points, k, cx, cy): + undistorted = [] + for x, y in points: + xd = x - cx + yd = y - cy + r2 = xd**2 + yd**2 + factor = 1 + k[0]*r2 + k[1]*r2**2 + k[2]*r2**3 + k[3]*r2**4 + xu = cx + xd * factor + yu = cy + yd * factor + undistorted.append([xu, yu]) + return np.array(undistorted) + +def line_straightness_error(k, lines_pts, cx, cy): + total_error = 0 + for pts in lines_pts: + pts_ud = undistort_points(pts, k, cx, cy) + # Ajuste de recta a los puntos no distorsionados + [vx, vy, x0, y0] = cv2.fitLine(pts_ud.astype(np.float32), cv2.DIST_L2, 0, 0.01, 0.01) + # Distancia de cada punto a la recta + dists = np.abs(vy*(pts_ud[:,0]-x0) - vx*(pts_ud[:,1]-y0)) + total_error += np.sum(dists**2) + return total_error + +# --- MAIN --- +# Cambia esto por la ruta de tu imagen +img_path = './calibration/image_2.png' +img = cv2.imread(img_path) +img_h, img_w = img.shape[:2] +cx, cy = img_w / 2, img_h / 2 # Centro óptico estimado + +# Paso 1: Selección interactiva de puntos +lines_pts = select_lines_points(img) +if len(lines_pts) < 1: + print("¡Debes seleccionar al menos una línea!") + exit() + +# Paso 2: Ajuste de coeficientes de distorsión radial +k_init = np.array([-0.01, 0, 0, 0]) + +res = minimize(line_straightness_error, k_init, args=(lines_pts, cx, cy), method='Powell') +k_opt = res.x + +print("Coeficientes de distorsión radial estimados:") +print("k1=%.6f, k2=%.6f, k3=%.6f, k4=%.6f" % tuple(k_opt)) + +# Paso 3: Visualiza el resultado +plt.figure() +plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) +colors = ['r', 'g', 'b', 'c', 'm'] +for i, pts in enumerate(lines_pts): + pts_ud = undistort_points(pts, k_opt, cx, cy) + plt.plot(pts[:,0], pts[:,1], colors[i%len(colors)]+'o-', label=f'Línea {i+1} original') + plt.plot(pts_ud[:,0], pts_ud[:,1], colors[i%len(colors)]+'x--', label=f'Línea {i+1} corregida') +plt.title('Puntos originales y corregidos') +plt.legend() +plt.show() diff --git a/condaenv.vowayxih.requirements.txt b/condaenv.vowayxih.requirements.txt new file mode 100644 index 00000000..b29ef163 --- /dev/null +++ b/condaenv.vowayxih.requirements.txt @@ -0,0 +1,11 @@ +numba +tensorflow>=2.14,<2.15 +dl1_data_handler>=0.14.1,<0.15 +pydot +pytorch_lightning +deepspeed +onnx +onnxsim +torch==2.5.0 +torchvision==0.20.0 +torchaudio==2.5.0 \ No newline at end of file diff --git a/count_types.py b/count_types.py new file mode 100644 index 00000000..d2e19910 --- /dev/null +++ b/count_types.py @@ -0,0 +1,231 @@ +from ctlearn.core.data_loader.loader import DLDataLoader +from dl1_data_handler.reader import DLImageReader +from dl1_data_handler.reader import DLDataReader +import numpy as np +import matplotlib.pyplot as plt +from ctlearn.tools.train.pytorch.utils import read_configuration +import os +def on_key(event): + # Check if the "Esc" key was pressed + if event.key == 'escape': + plt.close(event.canvas.figure) + exit() + + + +gamma_dir = "/storage/ctlearn_data/h5_files/mc/gamma-diffuse/" + +gamma_list= [ +"gamma_theta_16.087_az_108.090_runs123-182.dl1.h5", +# "gamma_theta_16.087_az_108.090_runs1-62.dl1.h5", +# "gamma_theta_16.087_az_108.090_runs183-242.dl1.h5", + +"gamma_theta_16.087_az_251.910_runs121-180.dl1.h5", +# "gamma_theta_16.087_az_251.910_runs1-60.dl1.h5", +# "gamma_theta_16.087_az_251.910_runs181-240.dl1.h5", + +"gamma_theta_23.161_az_260.739_runs129-187.dl1.h5", +# "gamma_theta_23.161_az_260.739_runs188-246.dl1.h5", +# "gamma_theta_23.161_az_260.739_runs247-305.dl1.h5", + +"gamma_theta_23.161_az_99.261_runs118-176.dl1.h5", +# "gamma_theta_23.161_az_99.261_runs1-59.dl1.h5", +# "gamma_theta_23.161_az_99.261_runs177-235.dl1.h5", + +"gamma_theta_30.390_az_266.360_runs121-180.dl1.h5", +# "gamma_theta_30.390_az_266.360_runs1-60.dl1.h5", +# "gamma_theta_30.390_az_266.360_runs181-240.dl1.h5", + +"gamma_theta_30.390_az_93.640_runs121-180.dl1.h5", +"gamma_theta_30.390_az_93.640_runs1-60.dl1.h5", +# "gamma_theta_30.390_az_93.640_runs181-240.dl1.h5", + +"gamma_theta_37.661_az_270.641_runs121-180.dl1.h5", +# "gamma_theta_37.661_az_270.641_runs1-60.dl1.h5", +# "gamma_theta_37.661_az_270.641_runs181-240.dl1.h5", +# "gamma_theta_37.661_az_270.641_runs241-300.dl1.h5", + +"gamma_theta_37.661_az_89.359_runs121-180.dl1.h5", +# "gamma_theta_37.661_az_89.359_runs1-60.dl1.h5", +# "gamma_theta_37.661_az_89.359_runs181-240.dl1.h5", +# "gamma_theta_37.661_az_89.359_runs241-300.dl1.h5", + +"gamma_theta_6.000_az_180.000_runs121-180.dl1.h5", +# "gamma_theta_6.000_az_180.000_runs1-60.dl1.h5", +# "gamma_theta_6.000_az_180.000_runs181-240.dl1.h5", +# "gamma_theta_6.000_az_180.000_runs241-300.dl1.h5", + +"gamma_theta_9.579_az_126.888_runs121-180.dl1.h5", +# "gamma_theta_9.579_az_126.888_runs1-60.dl1.h5", +# "gamma_theta_9.579_az_126.888_runs181-240.dl1.h5", +# "gamma_theta_9.579_az_126.888_runs241-300.dl1.h5", + +"gamma_theta_9.579_az_233.112_runs121-180.dl1.h5", +# "gamma_theta_9.579_az_233.112_runs1-60.dl1.h5", +# "gamma_theta_9.579_az_233.112_runs181-240.dl1.h5", +# "gamma_theta_9.579_az_233.112_runs241-300.dl1.h5", +] + + +proton_dir = "/storage/ctlearn_data/h5_files/mc/protons/" + +proton_list=[ +"proton_theta_16.087_az_108.090_runs1-416.dl1.h5", +# "proton_theta_16.087_az_108.090_runs417-834.dl1.h5", +# "proton_theta_16.087_az_108.090_runs835-1250.dl1.h5", +"proton_theta_16.087_az_251.910_runs1-417.dl1.h5", +# "proton_theta_16.087_az_251.910_runs418-834.dl1.h5", +# "proton_theta_16.087_az_251.910_runs835-1250.dl1.h5", +"proton_theta_23.161_az_260.739_runs1-417.dl1.h5", +# "proton_theta_23.161_az_260.739_runs418-834.dl1.h5", +# "proton_theta_23.161_az_260.739_runs835-1250.dl1.h5", +"proton_theta_23.161_az_99.261_runs1-417.dl1.h5", +# "proton_theta_23.161_az_99.261_runs418-832.dl1.h5", +# "proton_theta_23.161_az_99.261_runs833-1250.dl1.h5", +"proton_theta_30.390_az_266.360_runs1-416.dl1.h5", +# "proton_theta_30.390_az_266.360_runs417-834.dl1.h5", +# "proton_theta_30.390_az_266.360_runs835-1250.dl1.h5", +"proton_theta_30.390_az_93.640_runs1-420.dl1.h5", +# "proton_theta_30.390_az_93.640_runs421-834.dl1.h5", +# "proton_theta_30.390_az_93.640_runs835-1250.dl1.h5", +"proton_theta_37.661_az_270.641_runs1-421.dl1.h5", +# "proton_theta_37.661_az_270.641_runs422-836.dl1.h5", +# "proton_theta_37.661_az_270.641_runs837-1250.dl1.h5", +"proton_theta_37.661_az_89.359_runs1-406.dl1.h5", +# "proton_theta_37.661_az_89.359_runs408-867.dl1.h5", +# "proton_theta_37.661_az_89.359_runs868-1250.dl1.h5", +"proton_theta_6.000_az_180.000_runs1-416.dl1.h5", +# "proton_theta_6.000_az_180.000_runs417-830.dl1.h5", +# "proton_theta_6.000_az_180.000_runs831-1250.dl1.h5", +"proton_theta_9.579_az_126.888_runs1-417.dl1.h5", +# "proton_theta_9.579_az_126.888_runs418-834.dl1.h5", +# "proton_theta_9.579_az_126.888_runs835-1250.dl1.h5", + +"proton_theta_9.579_az_233.112_runs1-417.dl1.h5", +# "proton_theta_9.579_az_233.112_runs418-834.dl1.h5", +# "proton_theta_9.579_az_233.112_runs835-1250.dl1.h5", +] + + +config_file = "./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml" + + +# dl1dh_reader = DLDataReader.from_name( +# "DLImageReader", +# input_url_signal=[file], +# channels = ["cleaned_image","cleaned_peak_time"], +# # input_url_background=sorted(self.input_url_background), +# # parent=self, +# ) + +cnt_type=0 + +for file_name in gamma_list: + dl1dh_reader = DLDataReader.from_name( + "DLImageReader", + input_url_signal=[os.path.join(gamma_dir,file_name)], + channels = ["cleaned_image","cleaned_peak_time"], + # input_url_background=sorted(self.input_url_background), + # parent=self, + ) + print(f"file: {file_name} events: {dl1dh_reader.n_signal_events}") + cnt_type+=dl1dh_reader.n_signal_events + + +# for file_name in proton_list: +# dl1dh_reader = DLDataReader.from_name( +# "DLImageReader", +# input_url_signal=[os.path.join(proton_dir,file_name)], +# channels = ["cleaned_image","cleaned_peak_time"], +# # input_url_background=sorted(self.input_url_background), +# # parent=self, +# ) +# print(f"file: {file_name} events: {dl1dh_reader.n_signal_events}") +# cnt_type+=dl1dh_reader.n_signal_events + +print("cnt_type: ", cnt_type) + + +gamma_cnt = "3389398" +proton_cnt = "3590228" + + +gamma_cnt = 1234488 +proton_cnt = 1249970 + +# # dl1_reader = DLImageReader(input_url_signal=[dl1_gamma_file], config=config) +# # dataloader = DLDataLoader.create("pytorch") +# parameters = read_configuration(config_file) + +# random_seed = 0 +# indices = list(range(dl1dh_reader._get_n_events())) +# training_loader = DLDataLoader.create( +# framework="pytorch", +# DLDataReader=dl1dh_reader, +# indices=indices, +# tasks=["type","energy","skydirection","cameradirection","hillas"], +# batch_size=32, +# random_seed=0, +# sort_by_intensity=False, +# stack_telescope_images=False, +# parameters= parameters, +# use_augmentation=True +# ) + + +# print(len(training_loader)) +# ii=0 +# for batch_idx, (features, labels) in enumerate(training_loader): + +# plt.rcParams['keymap.quit'].append(' ') +# fig, axes = plt.subplots(1, 2, figsize=(15, 5)) +# ax = axes.ravel() +# # fig.canvas.mpl_connect('key_press_event', lambda evt: print(repr(evt.key))) +# fig.canvas.mpl_connect('key_press_event', on_key) + +# if len(features)>0: +# for id in range(len(features["image"])): +# # gammaness = 0 +# # gammaness = labels["gammaness"][id] + +# # if gammaness>0.9: + +# image = features["image"][id] +# # clean_image = features["clean_image"] +# peak_time = features["peak_time"][id] +# # clean_peak_time = features["clean_peak_time"] +# labels_class = labels["type"][id] + +# hillas_intensity = features["hillas"]["hillas_intensity"][id] +# leakage_pixels_width_1= features["hillas"]["leakage_pixels_width_1"][id] +# leakage_pixels_width_2= features["hillas"]["leakage_pixels_width_2"][id] + +# leakage_intensity_width_1 = features["hillas"]["leakage_intensity_width_1"][id] +# leakage_intensity_width_2 = features["hillas"]["leakage_intensity_width_2"][id] + +# morphology_n_islands= features["hillas"]["morphology_n_islands"][id] +# image = np.transpose(image, (1, 2, 0)) +# # clean_image = np.transpose(clean_image, (1, 2, 0)) + +# peak_time = np.transpose(peak_time, (1, 2, 0)) +# # clean_peak_time = np.transpose(clean_peak_time, (1, 2, 0)) + +# ax[0].set_title( +# f"Charge:\n Hillas Intensity:{hillas_intensity} \n Leakage_p_w_2: {leakage_pixels_width_2} \n Leakage_i_w_2: {leakage_intensity_width_2} \n morphology_n_islands: {morphology_n_islands} \n labels_class: {str(labels_class)}" , fontsize=8 +# ) +# # ax[1].set_title( +# # f"Peak time: \n Gammaness: {gammaness}" , fontsize=8 +# # ) +# ax[0].imshow(image, cmap="viridis") +# ax[1].imshow(peak_time, cmap="viridis") + +# # print(f"Gammaness: {gammaness}") +# # if leakage_intensity_width_2<0.2: +# # print(f"found {leakage_intensity_width_2}") +# plt.show() +# # plt.show(block=False) +# # plt.pause(1) +# # print(batch_idx) +# plt.close() + +# ii = 0 \ No newline at end of file diff --git a/ctlearn/all_tools.svg b/ctlearn/all_tools.svg new file mode 100644 index 00000000..0ac41b42 --- /dev/null +++ b/ctlearn/all_tools.svg @@ -0,0 +1 @@ +ctlearncoremodelloadertoolsbase_train_modelctlearn_enumkeras.train_keras_modelpredict_LST1predict_modelpytorch.train_pytorch_modeltrain_modelCTLearnModelattention: Noneattention: NoneLoadedModelmodel: Nonebackbone_model: Noneinput_layer: Nonelogits: Nonemodel: NoneDLDataLoaderDLDataReader: Noneindices: Nonetasks: Nonebatch_size: Nonerandom_seed: Nonestack_telescope_images: Nonesort_by_intensity: Noneinput_shape: Noneinput_shape: Noneinput_shape: NoneTrainCTLearnModelFrameworkTypeKERAS: 1PYTORCH: 2TqdmProgressBarTrainKerasModelLST1PredictionToolMonoPredictCTLearnModelPredictCTLearnModelStereoPredictCTLearnModelTrainPyTorchModelDLFrameWorkGenerated bypy2puml \ No newline at end of file diff --git a/ctlearn/core/__init__.py b/ctlearn/core/__init__.py new file mode 100644 index 00000000..996469a1 --- /dev/null +++ b/ctlearn/core/__init__.py @@ -0,0 +1,2 @@ +"""ctlearn command line tools. +""" diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index bfb53337..24487a48 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -549,6 +549,7 @@ def _get_mono_item(self, batch): if self.is_training: N = 4 # Repeating the number of high energies features_out["hillas"] = features["hillas"] + #------------------------------------------------- if self.use_augmentation: energy_log = torch.pow(10,labels["energy"].squeeze(-1)) # shape [N] high_energy_mask = energy_log > 1 # log10(E/TeV) > 0 => E > 1 TeV @@ -573,7 +574,7 @@ def duplicate_tensor(t,idx_to_duplicate): return np.concatenate([t, extra], axis=0) else: - raise TypeError(f"Tipo de dato no soportado para duplicación: {type(t)}") + raise TypeError(f"Data type not supported: {type(t)}") # Duplica todas las features principales for key in features_out: @@ -591,10 +592,10 @@ def duplicate_tensor(t,idx_to_duplicate): if self.use_augmentation: - # if isinstance(features_out["image"], torch.Tensor): - # features_out["image"] = features_out["image"].cpu().numpy() - # if isinstance(features_out["peak_time"], torch.Tensor): - # features_out["peak_time"] = features_out["peak_time"].cpu().numpy() + if isinstance(features_out["image"], torch.Tensor): + features_out["image"] = features_out["image"].cpu().numpy() + if isinstance(features_out["peak_time"], torch.Tensor): + features_out["peak_time"] = features_out["peak_time"].cpu().numpy() image, peak_time = self.apply_augmentation(features_out["image"], features_out["peak_time"]) @@ -605,7 +606,7 @@ def duplicate_tensor(t,idx_to_duplicate): features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() - # if self.is_training: + if not self.is_training: @@ -614,6 +615,7 @@ def duplicate_tensor(t,idx_to_duplicate): leakage = np.array(hillas["leakage_intensity_width_2"]) intensity = np.array(hillas["hillas_intensity"]) keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] + # keep_idx = np.where((leakage > 0.8) & (intensity > 50))[0] # Filter features_out for key in features_out: diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py index 1799ff04..c9768565 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDTReg/denoiseBlockThinRestNet.py @@ -90,7 +90,7 @@ def __init__(self, embedding_dim, num_channels=1, block=BasicBlock, num_blocks=[ self.layer1_1 = self._make_layer(block, 32, num_blocks[0], stride=1) self.layer2_1 = self._make_layer(block, 64, num_blocks[1], stride=2) self.layer3_1 = self._make_layer(block, 128, num_blocks[2], stride=2) - self.layer4 = self._make_layer(block, 256, num_blocks[3], stride=2) + self.layer4_1 = self._make_layer(block, 256, num_blocks[3], stride=2) # Reducing the number of layers and filters to make it "thin" # self.fc_1 = nn.Linear(embedding_dim , embedding_dim) # self.fc_2 = nn.Linear(embedding_dim , 256) @@ -127,8 +127,8 @@ def forward(self, x, z_prev, _): out_1 = self.layer1_1(out_1) out_1 = self.layer2_1(out_1) out_1 = self.layer3_1(out_1) + x_feat = self.layer4_1(out_1) - x_feat = self.layer4(out_1) x_feat= self.adaptive_pool(x_feat) x_feat = x_feat.view(x_feat.size(0), -1) diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py new file mode 100644 index 00000000..efbbde91 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py @@ -0,0 +1,121 @@ +# NoProp-DT model + +import torch +from torch import nn +import torch.nn.functional as F +# from .denoiseBlock import DenoiseBlock +from .denoiseBlockThinRestNet import DenoiseBlock, MemoryEfficientSwish + +import math + +class SimplifiedDenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_classes): + super().__init__() + # Simplified image feature extractor + self.conv_path = nn.Sequential( + nn.Conv2d(1, 32, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten() + ) + + # Simplified embedding processor + self.fc_z = nn.Sequential( + nn.Linear(embedding_dim, 256), + nn.ReLU() + ) + + # Combined processor + self.combined = nn.Sequential( + nn.Linear(256 + 64, 128), + nn.ReLU(), + nn.Linear(128, num_classes) + ) + + def forward(self, x, z_prev, W_embed): + # Image features + x_feat = self.conv_path(x) + + # Process embedding + z_feat = self.fc_z(z_prev) + + # Combine features + combined = torch.cat([x_feat, z_feat], dim=1) + logits = self.combined(combined) + + # Update embedding + z_next = z_prev + logits @ W_embed + + return z_next, logits + +class NoPropDTRegDBB(nn.Module): + def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1,num_blocks=[2, 3, 3, 3]): + super().__init__() + + self.task = task + num_classes = num_outputs + self.num_classes = num_classes + self.embedding_dim = embedding_dim + self.T = T + self.eta = eta + + self.blocks = nn.ModuleList([DenoiseBlock(embedding_dim,num_channels=1,num_blocks=num_blocks) for _ in range(T)]) + + self.regressor = nn.Sequential( + nn.Linear(embedding_dim, embedding_dim//2), + # MemoryEfficientSwish(), + nn.Linear(embedding_dim//2, num_outputs) +) + + # Final classifier + self.classifier = nn.Linear(embedding_dim, num_classes) + + # Improved noise schedule + self.register_buffer('alpha_bar', self._cosine_schedule(T)) + self.register_buffer('snr_diff', self._calculate_snr_diff(self.alpha_bar)) + + + self.target_embedder = nn.Linear(num_outputs, embedding_dim) + + for m in self.regressor: + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + nn.init.zeros_(m.bias) + + def _cosine_schedule(self, T): + t = torch.arange(1, T+1, dtype=torch.float32) + alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi/2))**2 + return alpha_bar + + def _calculate_snr_diff(self, alpha_bar): + snr = alpha_bar / (1 - alpha_bar + 1e-8) + snr_prev = torch.cat([torch.tensor([0.]), snr[:-1]]) + return torch.clamp(snr - snr_prev, min=1e-5) + + def forward_denoise(self, x,y, z_prev, t): + return self.blocks[t](x,y, z_prev, self.target_embedder)[0] + + def regress(self, z): + return self.regressor(z) + + def inference(self, x, y): + B = x.size(0) + z = torch.randn(B, self.embedding_dim, device=x.device) + if not self.training: + z = torch.zeros(B, self.embedding_dim, device=x.device) + + for t in range(self.T): + z = self.forward_denoise(x,y , z, t) + + return self.regress(z) + + def forward(self, x, y): + + if self.task=="direction": + return None, None, self.inference(x,y) + elif self.task=="energy": + return None, self.inference(x,y), None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy 2.py b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy 2.py new file mode 100644 index 00000000..f1326018 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy 2.py @@ -0,0 +1,116 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) + self.act1 = nn.GELU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) + self.act2 = nn.GELU() + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + def forward(self, x): + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) + residual = self.se(residual) + out = residual + self.shortcut(x) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + super().__init__() + # Ahora más profundo y ancho: + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + # nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(256, 512), # Embedding más grande + # nn.BatchNorm1d(512), + nn.GELU() + ) + + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + # h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + # h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h1 = self.act1((self.fc_z1(z_prev))) + h2 = self.act2((self.fc_z2(h1))) + + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + # h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + # h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + h_f = self.act_f1((self.fc_f1(h_f))) + h_f = self.act_f2((self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy.py b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy.py new file mode 100644 index 00000000..6487c4ee --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet copy.py @@ -0,0 +1,111 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn1 = AdaptiveBatchNorm2d(out_channels) + self.act1 = nn.GELU() + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + # self.bn2 = AdaptiveBatchNorm2d(out_channels) + self.act2 = nn.GELU() + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(out_channels) + ) + def forward(self, x): + # residual = self.act1(self.bn1(self.conv1(x))) + # residual = self.act2(self.bn2(self.conv2(residual))) + residual = self.act1((self.conv1(x))) + residual = self.act2((self.conv2(residual))) + residual = self.se(residual) + out = residual + self.shortcut(x) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, drop_prob=0.2): + super().__init__() + # Ahora más profundo y ancho: + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 64), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(64, 128), # Más ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(128, 256), # Más profundo/ancho + nn.MaxPool2d(2), + nn.Dropout(drop_prob), + ResidualBlock(256, 256), # Otro bloque extra para profundidad + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(256, 512), # Embedding más grande + nn.BatchNorm1d(512), + nn.GELU() + ) + + self.fc_z1 = nn.Linear(embedding_dim, 512) + self.bn_z1 = nn.BatchNorm1d(512) + self.fc_z2 = nn.Linear(512, 512) + self.bn_z2 = nn.BatchNorm1d(512) + self.fc_z3 = nn.Linear(512, 512) + self.bn_z3 = nn.BatchNorm1d(512) + self.fc_f1 = nn.Linear(1024, 512) + self.bn_f1 = nn.BatchNorm1d(512) + self.fc_f2 = nn.Linear(512, 256) + self.bn_f2 = nn.BatchNorm1d(256) + self.fc_out = nn.Linear(256, embedding_dim) + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet.py new file mode 100644 index 00000000..b74336a4 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet.py @@ -0,0 +1,170 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +class SEBlock(nn.Module): + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).squeeze(-1).squeeze(-1) # Ensuring dimension match + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_channels, out_channels, stride=1, reduction=16,use_bn=False): + + + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.use_bn = use_bn + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + + + self.se = SEBlock(out_channels, reduction) + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * out_channels) + ) + + def forward(self, x): + out = F.relu((self.conv1(x))) + out = (self.conv2(out)) + + # print("out.shape", out.shape, "shortcut.shape", self.shortcut(x).shape) + # print("out.dtype", out.dtype, "shortcut.dtype", self.shortcut(x).dtype) + # print("out.device", out.device, "shortcut.device", self.shortcut(x).device) + # print("out.is_contiguous()", out.is_contiguous(), "shortcut.is_contiguous()", self.shortcut(x).is_contiguous()) + + # assert out.shape == self.shortcut(x).shape, f"Shape mismatch: {out.shape} vs {self.shortcut(x).shape}" + # assert out.dtype == self.shortcut(x).dtype, f"Dtype mismatch: {out.dtype} vs {self.shortcut(x).dtype}" + # assert out.device == self.shortcut(x).device, f"Device mismatch: {out.device} vs {self.shortcut(x).device}" + + # out = out.contiguous() + + out += self.shortcut(x).contiguous() + out = F.relu(out) + out = self.se(out) + return out + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, num_outputs=2,use_bn=False,dropout=0.0): + super().__init__() + + # block = BasicBlock + self.in_channels = 64 + self.use_bn=use_bn + + self.conv1_x = nn.Conv2d(num_channels, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.conv1_y = nn.Conv2d(num_channels, 64, kernel_size=3, stride=1, padding=1, bias=False) + + self.layer1_1 = self._make_layer(block, 32, num_blocks[0], stride=1) + self.layer2_1 = self._make_layer(block, 64, num_blocks[1], stride=2) + self.layer3_1 = self._make_layer(block, 128, num_blocks[2], stride=2) + self.layer4_1 = self._make_layer(block, 256, num_blocks[3], stride=2) + + self.layer1_2 = self._make_layer(block, 32, num_blocks[0], stride=1) + self.layer2_2 = self._make_layer(block, 64, num_blocks[1], stride=2) + self.layer3_2 = self._make_layer(block, 128, num_blocks[2], stride=2) + self.layer4_2 = self._make_layer(block, 256, num_blocks[3], stride=2) + + # Reducing the number of layers and filters to make it "thin" + # self.fc_1 = nn.Linear(embedding_dim , embedding_dim) + # self.fc_2 = nn.Linear(embedding_dim , 256) + self.bn_final = nn.BatchNorm1d(512 * block.expansion) # BatchNorm layer + self.prelu = nn.PReLU(num_parameters=512 * block.expansion) # Define Leaky ReLU + + + self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(dropout) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + + def forward(self, x, y, z_prev, _): + + out_1 = F.relu(self.conv1_x(x)) + out_1 = self.layer1_1(out_1) + out_1 = self.layer2_1(out_1) + out_1 = self.layer3_1(out_1) + x_feat = self.layer4_1(out_1) + + out_2 = F.relu(self.conv1_y(y)) + out_2 = self.layer1_1(out_2) + out_2 = self.layer2_1(out_2) + out_2 = self.layer3_1(out_2) + y_feat = self.layer4_1(out_2) + + x_feat= self.adaptive_pool(x_feat+y_feat) + x_feat = x_feat.view(x_feat.size(0), -1) + + # h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + # h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h1 = self.act1((self.fc_z1(z_prev))) + h2 = self.act2((self.fc_z2(h1))) + + h3 = self.bn_z3(self.fc_z3(h2)) + z_feat = h3 + h1 + h_f = torch.cat([x_feat, z_feat], dim=1) + # h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + # h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + h_f = self.act_f1((self.fc_f1(h_f))) + h_f = self.act_f2((self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + return z_next, None + + def _make_layer(self, block, out_channels, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride, use_bn=self.use_bn)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet_original.py b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet_original.py new file mode 100644 index 00000000..fc52d51f --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/denoiseBlockThinRestNet_original.py @@ -0,0 +1,154 @@ +# Denoising block +import torch +from torch import nn +import torch.nn.functional as F + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + +class AdaptiveBatchNorm2d(nn.Module): + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + super(AdaptiveBatchNorm2d, self).__init__() + self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) + # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) + self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) + + def forward(self, x): + return self.a * x + self.b * self.bn(x) + +# class ResidualBlock(nn.Module): +# def __init__(self, in_channels, out_channels): +# super().__init__() +# self.conv_block = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels), +# nn.PReLU(), +# nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), +# # AdaptiveBatchNorm2d(out_channels) +# ) + +# self.shortcut = nn.Sequential() +# if in_channels != out_channels: +# self.shortcut = nn.Sequential( +# nn.Conv2d(in_channels, out_channels, kernel_size=1), +# nn.BatchNorm2d(out_channels) +# ) + +# self.relu = nn.PReLU() + +# def forward(self, x): +# return self.relu(self.conv_block(x) + self.shortcut(x)) + +class SEBlock(nn.Module): + def __init__(self, channels, reduction=16): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, reduction=16, drop_path_rate=0.1): + super().__init__() + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + self.norm1 = nn.GroupNorm(8, out_channels) + self.act1 = nn.GELU() + + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1) + self.norm2 = nn.GroupNorm(8, out_channels) + self.act2 = nn.GELU() + + self.se = SEBlock(out_channels, reduction) + + self.shortcut = nn.Identity() + if in_channels != out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1), + nn.GroupNorm(8, out_channels) + ) + + try: + from timm.models.layers import DropPath + self.drop_path = DropPath(drop_path_rate) + except ImportError: + self.drop_path = nn.Identity() + + # self.final_act = nn.GELU() + self.final_act = MemoryEfficientSwish() + def forward(self, x): + residual = self.act1(self.norm1(self.conv1(x))) + residual = self.act2(self.norm2(self.conv2(residual))) + # residual = self.act1((self.conv1(x))) + # residual = self.act2((self.conv2(residual))) + + residual = self.se(residual) + + out = self.drop_path(residual) + self.shortcut(x) + return self.final_act(out) + +class DenoiseBlock(nn.Module): + def __init__(self, embedding_dim, num_channels=1): + super().__init__() + + self.conv_path = nn.Sequential( + ResidualBlock(num_channels, 32), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(32, 64), + nn.MaxPool2d(2), + nn.Dropout(0.2), + ResidualBlock(64, 128), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(128, 256), + ) + + self.fc_z1 = nn.Linear(embedding_dim, 256) + self.bn_z1 = nn.BatchNorm1d(256) + self.fc_z2 = nn.Linear(256, 256) + self.bn_z2 = nn.BatchNorm1d(256) + self.fc_z3 = nn.Linear(256, 256) + self.bn_z3 = nn.BatchNorm1d(256) + + self.fc_f1 = nn.Linear(512, 256) + self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) + self.bn_f2 = nn.BatchNorm1d(128) + self.fc_out = nn.Linear(128, embedding_dim) + + self.act1 = nn.PReLU() + self.act2 = nn.PReLU() + self.act3 = nn.PReLU() + self.act_f1 = nn.PReLU() + self.act_f2 = nn.PReLU() + + def forward(self, x, z_prev, _): + x_feat = self.conv_path(x) + + h1 = self.act1(self.bn_z1(self.fc_z1(z_prev))) + h2 = self.act2(self.bn_z2(self.fc_z2(h1))) + h3 = self.bn_z3(self.fc_z3(h2)) + + z_feat = h3 + h1 + + h_f = torch.cat([x_feat, z_feat], dim=1) + + h_f = self.act_f1(self.bn_f1(self.fc_f1(h_f))) + h_f = self.act_f2(self.bn_f2(self.fc_f2(h_f))) + z_next = self.fc_out(h_f) + + return z_next, None diff --git a/ctlearn/core/pytorch/nets/models/StackedHGNet/StackedHGNet.py b/ctlearn/core/pytorch/nets/models/StackedHGNet/StackedHGNet.py new file mode 100644 index 00000000..e0635cb5 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/StackedHGNet/StackedHGNet.py @@ -0,0 +1,141 @@ +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F + +# from core.coord_conv import CoordConvTh +# from lib.dataset import get_decoder + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ConvBlock(nn.Module): + def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False, relu=True): + super().__init__() + self.conv = nn.Conv2d(inp_dim, out_dim, kernel_size, stride, padding=(kernel_size - 1) // 2, bias=not bn) + self.bn = nn.BatchNorm2d(out_dim) if bn else nn.Identity() + self.relu = nn.ReLU(inplace=True) if relu else nn.Identity() + def forward(self, x): + return self.relu(self.bn(self.conv(x))) + +class ResBlock(nn.Module): + def __init__(self, inp_dim, out_dim, mid_dim=None): + super().__init__() + mid_dim = mid_dim or out_dim // 2 + self.conv1 = ConvBlock(inp_dim, mid_dim, 1, bn=True, relu=True) + self.conv2 = ConvBlock(mid_dim, mid_dim, 3, bn=True, relu=True) + self.conv3 = ConvBlock(mid_dim, out_dim, 1, bn=True, relu=False) + self.skip = ConvBlock(inp_dim, out_dim, 1, bn=True, relu=False) if inp_dim != out_dim else nn.Identity() + self.relu = nn.ReLU(inplace=True) + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + out = self.conv3(out) + return self.relu(out + self.skip(x)) + +class Hourglass(nn.Module): + def __init__(self, n, f): + super().__init__() + self.up1 = ResBlock(f, f) + self.pool1 = nn.MaxPool2d(2, 2) + self.low1 = ResBlock(f, f) + if n > 1: + self.low2 = Hourglass(n - 1, f) + else: + self.low2 = ResBlock(f, f) + self.low3 = ResBlock(f, f) + self.up2 = nn.Upsample(scale_factor=2, mode='nearest') + def forward(self, x): + up1 = self.up1(x) + low1 = self.low1(self.pool1(x)) + low2 = self.low2(low1) + low3 = self.low3(low2) + up2 = self.up2(low3) + if up2.shape[-2:] != up1.shape[-2:]: + up2 = F.interpolate(up2, size=up1.shape[-2:], mode='nearest') + return up1 + up2 + +class StackedHGNet(nn.Module): + def __init__(self,task, input_channels=3, nstack=2, nlevels=4, in_channel=256, output_dim=3, use_bn=True, use_stn=False): + super().__init__() + self.task = task + self.nstack = nstack + self.pre = nn.Sequential( + ConvBlock(input_channels, 64, 7, 2, bn=use_bn, relu=True), + ResBlock(64, 128), + nn.MaxPool2d(2, 2), + ResBlock(128, 128), + ResBlock(128, in_channel) + ) + self.hgs = nn.ModuleList([ + Hourglass(nlevels, in_channel) for _ in range(nstack) + ]) + self.features = nn.ModuleList([ + nn.Sequential( + ResBlock(in_channel, in_channel), + ConvBlock(in_channel, in_channel, 1, bn=use_bn, relu=True) + ) for _ in range(nstack) + ]) + # self.out_regression = nn.ModuleList([ + # nn.Sequential( + # nn.AdaptiveAvgPool2d(1), + # nn.Flatten(), + # nn.Linear(in_channel, output_dim) + # ) for _ in range(nstack) + # ]) + + self.out_regression = nn.ModuleList([ + nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Flatten(), + nn.Linear(in_channel, 128), + nn.PReLU(), + nn.Linear(128, 64), + nn.PReLU(), + nn.Linear(64, output_dim) + ) for _ in range(nstack) + ]) + + # self.out_regression = nn.ModuleList([ + # nn.Sequential( + # nn.AdaptiveAvgPool2d(1), + # nn.Flatten(), + # nn.Linear(in_channel, 128), + # nn.ReLU(), + # nn.BatchNorm1d(128), + # nn.Linear(128, 64), + # nn.ReLU(), + # nn.Linear(64, output_dim) + # ) for _ in range(nstack) + # ]) + # Head auxiliar de heatmap (un canal) + # self.out_heatmap = nn.ModuleList([ + # ConvBlock(in_channel, 1, 1, relu=False, bn=False) for _ in range(nstack) + # ]) + self.merge_features = nn.ModuleList([ + ConvBlock(in_channel, in_channel, 1, relu=False, bn=False) + for _ in range(nstack - 1) + ]) + + def forward(self, x): + x = self.pre(x) + regression_outputs = [] + # heatmap_outputs = [] + for i in range(self.nstack): + hg = self.hgs[i](x) + feature = self.features[i](hg) + regression_output = self.out_regression[i](feature) + # heatmap_output = self.out_heatmap[i](feature) + regression_outputs.append(regression_output) + # heatmap_outputs.append(heatmap_output) + if i < self.nstack - 1: + x = x + self.merge_features[i](feature) + + if self.task=="direction": + return None, None, regression_outputs[-1] + elif self.task=="energy": + return None, regression_outputs[-1], None + else: + raise ValueError(f"No implemented") diff --git a/ctlearn/core/pytorch/nets/models/StackedHGNet/core/coord_conv.py b/ctlearn/core/pytorch/nets/models/StackedHGNet/core/coord_conv.py new file mode 100644 index 00000000..7239421d --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/StackedHGNet/core/coord_conv.py @@ -0,0 +1,157 @@ +import torch +import torch.nn as nn + + +class AddCoordsTh(nn.Module): + def __init__(self, x_dim, y_dim, with_r=False, with_boundary=False): + super(AddCoordsTh, self).__init__() + self.x_dim = x_dim + self.y_dim = y_dim + self.with_r = with_r + self.with_boundary = with_boundary + + def forward(self, input_tensor, heatmap=None): + """ + input_tensor: (batch, c, x_dim, y_dim) + """ + batch_size_tensor = input_tensor.shape[0] + + xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32).to(input_tensor) + xx_ones = xx_ones.unsqueeze(-1) + + xx_range = torch.arange(self.x_dim, dtype=torch.int32).unsqueeze(0).to(input_tensor) + xx_range = xx_range.unsqueeze(1) + + xx_channel = torch.matmul(xx_ones.float(), xx_range.float()) + xx_channel = xx_channel.unsqueeze(-1) + + yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32).to(input_tensor) + yy_ones = yy_ones.unsqueeze(1) + + yy_range = torch.arange(self.y_dim, dtype=torch.int32).unsqueeze(0).to(input_tensor) + yy_range = yy_range.unsqueeze(-1) + + yy_channel = torch.matmul(yy_range.float(), yy_ones.float()) + yy_channel = yy_channel.unsqueeze(-1) + + xx_channel = xx_channel.permute(0, 3, 2, 1) + yy_channel = yy_channel.permute(0, 3, 2, 1) + + xx_channel = xx_channel / (self.x_dim - 1) + yy_channel = yy_channel / (self.y_dim - 1) + + xx_channel = xx_channel * 2 - 1 + yy_channel = yy_channel * 2 - 1 + + xx_channel = xx_channel.repeat(batch_size_tensor, 1, 1, 1) + yy_channel = yy_channel.repeat(batch_size_tensor, 1, 1, 1) + + if self.with_boundary and type(heatmap) != type(None): + boundary_channel = torch.clamp(heatmap[:, -1:, :, :], + 0.0, 1.0) + + zero_tensor = torch.zeros_like(xx_channel).to(xx_channel) + xx_boundary_channel = torch.where(boundary_channel>0.05, + xx_channel, zero_tensor) + yy_boundary_channel = torch.where(boundary_channel>0.05, + yy_channel, zero_tensor) + ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1) + + + if self.with_r: + rr = torch.sqrt(torch.pow(xx_channel, 2) + torch.pow(yy_channel, 2)) + rr = rr / torch.max(rr) + ret = torch.cat([ret, rr], dim=1) + + if self.with_boundary and type(heatmap) != type(None): + ret = torch.cat([ret, xx_boundary_channel, + yy_boundary_channel], dim=1) + return ret + + +class CoordConvTh(nn.Module): + """CoordConv layer as in the paper.""" + def __init__(self, x_dim, y_dim, with_r, with_boundary, + in_channels, out_channels, first_one=False, relu=False, bn=False, *args, **kwargs): + super(CoordConvTh, self).__init__() + self.addcoords = AddCoordsTh(x_dim=x_dim, y_dim=y_dim, with_r=with_r, + with_boundary=with_boundary) + in_channels += 2 + if with_r: + in_channels += 1 + if with_boundary and not first_one: + in_channels += 2 + self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, *args, **kwargs) + self.relu = nn.ReLU() if relu else None + self.bn = nn.BatchNorm2d(out_channels) if bn else None + + self.with_boundary = with_boundary + self.first_one = first_one + + + def forward(self, input_tensor, heatmap=None): + assert (self.with_boundary and not self.first_one) == (heatmap is not None) + ret = self.addcoords(input_tensor, heatmap) + ret = self.conv(ret) + if self.bn is not None: + ret = self.bn(ret) + if self.relu is not None: + ret = self.relu(ret) + + return ret + + +''' +An alternative implementation for PyTorch with auto-infering the x-y dimensions. +''' +class AddCoords(nn.Module): + + def __init__(self, with_r=False): + super().__init__() + self.with_r = with_r + + def forward(self, input_tensor): + """ + Args: + input_tensor: shape(batch, channel, x_dim, y_dim) + """ + batch_size, _, x_dim, y_dim = input_tensor.size() + + xx_channel = torch.arange(x_dim).repeat(1, y_dim, 1).to(input_tensor) + yy_channel = torch.arange(y_dim).repeat(1, x_dim, 1).transpose(1, 2).to(input_tensor) + + xx_channel = xx_channel / (x_dim - 1) + yy_channel = yy_channel / (y_dim - 1) + + xx_channel = xx_channel * 2 - 1 + yy_channel = yy_channel * 2 - 1 + + xx_channel = xx_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3) + yy_channel = yy_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3) + + ret = torch.cat([ + input_tensor, + xx_channel.type_as(input_tensor), + yy_channel.type_as(input_tensor)], dim=1) + + if self.with_r: + rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2)) + ret = torch.cat([ret, rr], dim=1) + + return ret + + +class CoordConv(nn.Module): + + def __init__(self, in_channels, out_channels, with_r=False, **kwargs): + super().__init__() + self.addcoords = AddCoords(with_r=with_r) + in_channels += 2 + if with_r: + in_channels += 1 + self.conv = nn.Conv2d(in_channels, out_channels, **kwargs) + + def forward(self, x): + ret = self.addcoords(x) + ret = self.conv(ret) + return ret diff --git a/ctlearn/core/pytorch/nets/models/StackedHGNetDBB/StackedHGNetDBB.py b/ctlearn/core/pytorch/nets/models/StackedHGNetDBB/StackedHGNetDBB.py new file mode 100644 index 00000000..69a96fea --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/StackedHGNetDBB/StackedHGNetDBB.py @@ -0,0 +1,151 @@ +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F + +# from core.coord_conv import CoordConvTh +# from lib.dataset import get_decoder + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ConvBlock(nn.Module): + def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False, relu=True): + super().__init__() + self.conv = nn.Conv2d(inp_dim, out_dim, kernel_size, stride, padding=(kernel_size - 1) // 2, bias=not bn) + self.bn = nn.BatchNorm2d(out_dim) if bn else nn.Identity() + self.relu = nn.ReLU(inplace=True) if relu else nn.Identity() + def forward(self, x): + return self.relu(self.bn(self.conv(x))) + +class ResBlock(nn.Module): + def __init__(self, inp_dim, out_dim, mid_dim=None): + super().__init__() + mid_dim = mid_dim or out_dim // 2 + self.conv1 = ConvBlock(inp_dim, mid_dim, 1, bn=True, relu=True) + self.conv2 = ConvBlock(mid_dim, mid_dim, 3, bn=True, relu=True) + self.conv3 = ConvBlock(mid_dim, out_dim, 1, bn=True, relu=False) + self.skip = ConvBlock(inp_dim, out_dim, 1, bn=True, relu=False) if inp_dim != out_dim else nn.Identity() + self.relu = nn.ReLU(inplace=True) + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + out = self.conv3(out) + return self.relu(out + self.skip(x)) + +class Hourglass(nn.Module): + def __init__(self, n, f): + super().__init__() + self.up1 = ResBlock(f, f) + self.pool1 = nn.MaxPool2d(2, 2) + self.low1 = ResBlock(f, f) + if n > 1: + self.low2 = Hourglass(n - 1, f) + else: + self.low2 = ResBlock(f, f) + self.low3 = ResBlock(f, f) + self.up2 = nn.Upsample(scale_factor=2, mode='nearest') + def forward(self, x): + up1 = self.up1(x) + low1 = self.low1(self.pool1(x)) + low2 = self.low2(low1) + low3 = self.low3(low2) + up2 = self.up2(low3) + if up2.shape[-2:] != up1.shape[-2:]: + up2 = F.interpolate(up2, size=up1.shape[-2:], mode='nearest') + return up1 + up2 + +class StackedHGNetDBB(nn.Module): + def __init__(self,task, input_channels=3, nstack=2, nlevels=4, in_channel=256, output_dim=3, use_bn=True, use_stn=False): + super().__init__() + self.task = task + self.nstack = nstack + self.pre_x = nn.Sequential( + ConvBlock(input_channels, 64, 7, 2, bn=use_bn, relu=True), + ResBlock(64, 128), + nn.MaxPool2d(2, 2), + ResBlock(128, 128), + ResBlock(128, in_channel) + ) + self.pre_y = nn.Sequential( + ConvBlock(input_channels, 64, 7, 2, bn=use_bn, relu=True), + ResBlock(64, 128), + nn.MaxPool2d(2, 2), + ResBlock(128, 128), + ResBlock(128, in_channel) + ) + self.hgs = nn.ModuleList([ + Hourglass(nlevels, in_channel) for _ in range(nstack) + ]) + self.features = nn.ModuleList([ + nn.Sequential( + ResBlock(in_channel, in_channel), + ConvBlock(in_channel, in_channel, 1, bn=use_bn, relu=True) + ) for _ in range(nstack) + ]) + # self.out_regression = nn.ModuleList([ + # nn.Sequential( + # nn.AdaptiveAvgPool2d(1), + # nn.Flatten(), + # nn.Linear(in_channel, output_dim) + # ) for _ in range(nstack) + # ]) + + self.out_regression = nn.ModuleList([ + nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Flatten(), + nn.Linear(in_channel, 128), + nn.PReLU(), + nn.Linear(128, 64), + nn.PReLU(), + nn.Linear(64, output_dim) + ) for _ in range(nstack) + ]) + + # self.out_regression = nn.ModuleList([ + # nn.Sequential( + # nn.AdaptiveAvgPool2d(1), + # nn.Flatten(), + # nn.Linear(in_channel, 128), + # nn.ReLU(), + # nn.BatchNorm1d(128), + # nn.Linear(128, 64), + # nn.ReLU(), + # nn.Linear(64, output_dim) + # ) for _ in range(nstack) + # ]) + # Head auxiliar de heatmap (un canal) + # self.out_heatmap = nn.ModuleList([ + # ConvBlock(in_channel, 1, 1, relu=False, bn=False) for _ in range(nstack) + # ]) + self.merge_features = nn.ModuleList([ + ConvBlock(in_channel, in_channel, 1, relu=False, bn=False) + for _ in range(nstack - 1) + ]) + + def forward(self, x, y ): + x = self.pre_x(x) + y = self.pre_y(y) + + x = x + y + regression_outputs = [] + # heatmap_outputs = [] + for i in range(self.nstack): + hg = self.hgs[i](x) + feature = self.features[i](hg) + regression_output = self.out_regression[i](feature) + # heatmap_output = self.out_heatmap[i](feature) + regression_outputs.append(regression_output) + # heatmap_outputs.append(heatmap_output) + if i < self.nstack - 1: + x = x + self.merge_features[i](feature) + + if self.task=="direction": + return None, None, regression_outputs[-1] + elif self.task=="energy": + return None, regression_outputs[-1], None + else: + raise ValueError(f"No implemented") \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/StackedHGNetDBB/core/coord_conv.py b/ctlearn/core/pytorch/nets/models/StackedHGNetDBB/core/coord_conv.py new file mode 100644 index 00000000..7239421d --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/StackedHGNetDBB/core/coord_conv.py @@ -0,0 +1,157 @@ +import torch +import torch.nn as nn + + +class AddCoordsTh(nn.Module): + def __init__(self, x_dim, y_dim, with_r=False, with_boundary=False): + super(AddCoordsTh, self).__init__() + self.x_dim = x_dim + self.y_dim = y_dim + self.with_r = with_r + self.with_boundary = with_boundary + + def forward(self, input_tensor, heatmap=None): + """ + input_tensor: (batch, c, x_dim, y_dim) + """ + batch_size_tensor = input_tensor.shape[0] + + xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32).to(input_tensor) + xx_ones = xx_ones.unsqueeze(-1) + + xx_range = torch.arange(self.x_dim, dtype=torch.int32).unsqueeze(0).to(input_tensor) + xx_range = xx_range.unsqueeze(1) + + xx_channel = torch.matmul(xx_ones.float(), xx_range.float()) + xx_channel = xx_channel.unsqueeze(-1) + + yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32).to(input_tensor) + yy_ones = yy_ones.unsqueeze(1) + + yy_range = torch.arange(self.y_dim, dtype=torch.int32).unsqueeze(0).to(input_tensor) + yy_range = yy_range.unsqueeze(-1) + + yy_channel = torch.matmul(yy_range.float(), yy_ones.float()) + yy_channel = yy_channel.unsqueeze(-1) + + xx_channel = xx_channel.permute(0, 3, 2, 1) + yy_channel = yy_channel.permute(0, 3, 2, 1) + + xx_channel = xx_channel / (self.x_dim - 1) + yy_channel = yy_channel / (self.y_dim - 1) + + xx_channel = xx_channel * 2 - 1 + yy_channel = yy_channel * 2 - 1 + + xx_channel = xx_channel.repeat(batch_size_tensor, 1, 1, 1) + yy_channel = yy_channel.repeat(batch_size_tensor, 1, 1, 1) + + if self.with_boundary and type(heatmap) != type(None): + boundary_channel = torch.clamp(heatmap[:, -1:, :, :], + 0.0, 1.0) + + zero_tensor = torch.zeros_like(xx_channel).to(xx_channel) + xx_boundary_channel = torch.where(boundary_channel>0.05, + xx_channel, zero_tensor) + yy_boundary_channel = torch.where(boundary_channel>0.05, + yy_channel, zero_tensor) + ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1) + + + if self.with_r: + rr = torch.sqrt(torch.pow(xx_channel, 2) + torch.pow(yy_channel, 2)) + rr = rr / torch.max(rr) + ret = torch.cat([ret, rr], dim=1) + + if self.with_boundary and type(heatmap) != type(None): + ret = torch.cat([ret, xx_boundary_channel, + yy_boundary_channel], dim=1) + return ret + + +class CoordConvTh(nn.Module): + """CoordConv layer as in the paper.""" + def __init__(self, x_dim, y_dim, with_r, with_boundary, + in_channels, out_channels, first_one=False, relu=False, bn=False, *args, **kwargs): + super(CoordConvTh, self).__init__() + self.addcoords = AddCoordsTh(x_dim=x_dim, y_dim=y_dim, with_r=with_r, + with_boundary=with_boundary) + in_channels += 2 + if with_r: + in_channels += 1 + if with_boundary and not first_one: + in_channels += 2 + self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, *args, **kwargs) + self.relu = nn.ReLU() if relu else None + self.bn = nn.BatchNorm2d(out_channels) if bn else None + + self.with_boundary = with_boundary + self.first_one = first_one + + + def forward(self, input_tensor, heatmap=None): + assert (self.with_boundary and not self.first_one) == (heatmap is not None) + ret = self.addcoords(input_tensor, heatmap) + ret = self.conv(ret) + if self.bn is not None: + ret = self.bn(ret) + if self.relu is not None: + ret = self.relu(ret) + + return ret + + +''' +An alternative implementation for PyTorch with auto-infering the x-y dimensions. +''' +class AddCoords(nn.Module): + + def __init__(self, with_r=False): + super().__init__() + self.with_r = with_r + + def forward(self, input_tensor): + """ + Args: + input_tensor: shape(batch, channel, x_dim, y_dim) + """ + batch_size, _, x_dim, y_dim = input_tensor.size() + + xx_channel = torch.arange(x_dim).repeat(1, y_dim, 1).to(input_tensor) + yy_channel = torch.arange(y_dim).repeat(1, x_dim, 1).transpose(1, 2).to(input_tensor) + + xx_channel = xx_channel / (x_dim - 1) + yy_channel = yy_channel / (y_dim - 1) + + xx_channel = xx_channel * 2 - 1 + yy_channel = yy_channel * 2 - 1 + + xx_channel = xx_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3) + yy_channel = yy_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3) + + ret = torch.cat([ + input_tensor, + xx_channel.type_as(input_tensor), + yy_channel.type_as(input_tensor)], dim=1) + + if self.with_r: + rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2)) + ret = torch.cat([ret, rr], dim=1) + + return ret + + +class CoordConv(nn.Module): + + def __init__(self, in_channels, out_channels, with_r=False, **kwargs): + super().__init__() + self.addcoords = AddCoords(with_r=with_r) + in_channels += 2 + if with_r: + in_channels += 1 + self.conv = nn.Conv2d(in_channels, out_channels, **kwargs) + + def forward(self, x): + ret = self.addcoords(x) + ret = self.conv(ret) + return ret diff --git a/ctlearn/core/pytorch/nets/models/__init__.py b/ctlearn/core/pytorch/nets/models/__init__.py index 7d994dd2..d4860865 100644 --- a/ctlearn/core/pytorch/nets/models/__init__.py +++ b/ctlearn/core/pytorch/nets/models/__init__.py @@ -9,5 +9,10 @@ # Diffusion import ctlearn.core.pytorch.nets.models.NoPropDT.NoPropDT import ctlearn.core.pytorch.nets.models.NoPropDTReg.NoPropDTReg +import ctlearn.core.pytorch.nets.models.NoPropDTRegDBB.NoPropDTRegDBB import ctlearn.core.pytorch.nets.models.DBBNoPropDTReg.DBBNoPropDTReg import ctlearn.core.pytorch.nets.models.NoPropDTReg2.NoPropDTReg2 + +# Hourglass +import ctlearn.core.pytorch.nets.models.StackedHGNet.StackedHGNet +import ctlearn.core.pytorch.nets.models.StackedHGNetDBB.StackedHGNetDBB \ No newline at end of file diff --git a/ctlearn/core/pytorch/utils/utils.py b/ctlearn/core/pytorch/utils/utils.py index c9c88806..c1c353f6 100644 --- a/ctlearn/core/pytorch/utils/utils.py +++ b/ctlearn/core/pytorch/utils/utils.py @@ -1,7 +1,7 @@ import importlib import logging import os -import pkg_resources +# import pkg_resources import sys import time import pickle @@ -30,6 +30,13 @@ } from astropy.time import Time from ctapipe.coordinates import CameraFrame +try: + # Python 3.8+ + from importlib.metadata import version as get_version +except ImportError: + # For Python <3.8, install backport: importlib-metadata + from importlib_metadata import version as get_version + def clip_alt(alt): """ @@ -488,14 +495,53 @@ def decompress_data(compressed_data, data_shape, data_type=np.float16): return restored_array_reshaped #------------------------------------------------------------------------------------------------------------ +# def setup_logging(config, log_dir, debug, log_to_file): + +# # Log configuration to a text file in the log dir +# time_str = time.strftime("%Y%m%d_%H%M%S") +# config_filename = os.path.join(log_dir, time_str + "_config.yml") +# with open(config_filename, "w") as outfile: +# ctlearn_version = pkg_resources.get_distribution("ctlearn").version +# tensorflow_version = pkg_resources.get_distribution("tensorflow").version +# outfile.write( +# "# Training performed with " +# "CTLearn version {} and TensorFlow version {}.\n".format( +# ctlearn_version, tensorflow_version +# ) +# ) +# yaml.dump(config, outfile, default_flow_style=False) + +# # Set up logger +# logger = logging.getLogger() + +# if debug: +# logger.setLevel(logging.DEBUG) +# else: +# logger.setLevel(logging.INFO) + +# logger.handlers = [] # remove existing handlers from any previous runs +# if not log_to_file: +# handler = logging.StreamHandler() +# else: +# logging_filename = os.path.join(log_dir, time_str + "_logfile.log") +# handler = logging.FileHandler(logging_filename) +# handler.setFormatter(logging.Formatter("%(levelname)s:%(message)s")) +# logger.addHandler(handler) + +# return logger def setup_logging(config, log_dir, debug, log_to_file): - # Log configuration to a text file in the log dir time_str = time.strftime("%Y%m%d_%H%M%S") config_filename = os.path.join(log_dir, time_str + "_config.yml") with open(config_filename, "w") as outfile: - ctlearn_version = pkg_resources.get_distribution("ctlearn").version - tensorflow_version = pkg_resources.get_distribution("tensorflow").version + try: + ctlearn_version = get_version("ctlearn") + except Exception: + ctlearn_version = "unknown" + try: + tensorflow_version = get_version("tensorflow") + except Exception: + tensorflow_version = "unknown" outfile.write( "# Training performed with " "CTLearn version {} and TensorFlow version {}.\n".format( diff --git a/ctlearn/tools/_version.py b/ctlearn/tools/_version.py new file mode 100644 index 00000000..b8023d8b --- /dev/null +++ b/ctlearn/tools/_version.py @@ -0,0 +1 @@ +__version__ = '0.0.1' diff --git a/ctlearn/tools/conftest.py b/ctlearn/tools/conftest.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/tools/predict/keras/predic_LST1_keras.py b/ctlearn/tools/predict/keras/predic_LST1_keras.py new file mode 100644 index 00000000..a5848c02 --- /dev/null +++ b/ctlearn/tools/predict/keras/predic_LST1_keras.py @@ -0,0 +1,133 @@ +from ctapipe.io import read_table +from astropy.table import join +import keras +from dl1_data_handler.reader import ( + get_unmapped_image +) +import numpy as np + +def predictions(self): + event_id, tel_azimuth, tel_altitude, trigger_time = [], [], [], [] + prediction, energy, cam_coord_offset_x, cam_coord_offset_y = [], [], [], [] + classification_fvs, energy_fvs, direction_fvs = [], [], [] + for start in range(0, self.table_length, self.batch_size): + stop = min(start + self.batch_size, self.table_length) + self.log.debug("Processing chunk from '%d' to '%d'.", start, stop - 1) + # Read the data + dl1_table = read_table( + self.input_url, self.image_table_path, start=start, stop=stop + ) + # Join the dl1 table with the parameter table to perform quality selection + dl1_table = join( + left=dl1_table, + right=self.parameter_table, + keys=["event_id"], + ) + dl1_table = join( + left=dl1_table, + right=self.trigger_table, + keys=["event_id"], + ) + # Initialize a boolean mask to True for all events in the sliced dl1 table + passes_quality_checks = np.ones(len(dl1_table), dtype=bool) + # Quality selection based on the dl1b parameter + if self.quality_query: + passes_quality_checks = self.quality_query.get_table_mask(dl1_table) + # Apply the mask to filter events that are not fufilling the quality criteria + dl1_table = dl1_table[passes_quality_checks] + if len(dl1_table) == 0: + self.log.debug("No events passed the quality selection.") + continue + data = [] + for event in dl1_table: + # Get the unmapped image + image = get_unmapped_image(event, self.channels, self.transforms) + data.append(self.image_mapper.map_image(image)) + input_data = {"input": np.array(data)} + # Temp fix for supporting keras2 & keras3 + if int(keras.__version__.split(".")[0]) >= 3: + input_data = input_data["input"] + + event_id.extend(dl1_table["event_id"].data) + tel_azimuth.extend(dl1_table["tel_az"].data) + tel_altitude.extend(dl1_table["tel_alt"].data) + trigger_time.extend(dl1_table["time"].mjd) + + if self.load_type_model_from is not None: + classification_feature_vectors = self.backbone_type.predict_on_batch(input_data) + classification_fvs.extend(classification_feature_vectors) + predict_data = self.head_type.predict_on_batch(classification_feature_vectors) + prediction.extend(predict_data[:, 1]) + if self.load_energy_model_from is not None: + energy_feature_vectors = self.backbone_energy.predict_on_batch(input_data) + energy_fvs.extend(energy_feature_vectors) + predict_data = self.head_energy.predict_on_batch(energy_feature_vectors) + energy.extend(predict_data.T[0]) + if self.load_cameradirection_model_from is not None: + direction_feature_vectors = self.backbone_direction.predict_on_batch(input_data) + direction_fvs.extend(direction_feature_vectors) + predict_data = self.head_direction.predict_on_batch(direction_feature_vectors) + cam_coord_offset_x.extend(predict_data.T[0]) + cam_coord_offset_y.extend(predict_data.T[1]) + + return event_id, tel_azimuth, tel_altitude, trigger_time, prediction, energy, cam_coord_offset_x, cam_coord_offset_y, classification_fvs, energy_fvs, direction_fvs + +def _split_model(model): + """ + Split the model into backbone and head. + + This method splits the model into backbone and head. The backbone is summarized + into a single layer which can be retrieved by the layer index 1. The model input + has layer index 0 and the head is the rest of the model with layer index 2 and above. + + Parameters: + ----------- + model : keras.Model + Keras model to split into backbone and head. + + Returns: + -------- + backbone : keras.Model + Backbone model of the original model. + head : keras.Model + Head model of the original model. + """ + # Get the backbone model which is the second layer of the model + backbone = model.get_layer(index=1) + # Create a new head model with the same layers as the original model. + # The output of the backbone model is the input of the head model. + backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) + x = backbone_output_shape + for layer in model.layers[2:]: + x = layer(x) + head = keras.Model(inputs=backbone_output_shape, outputs=x) + return backbone, head + +def load_keras_model(self): + if self.load_type_model_from is not None: + self.log.info("Loading the type model from %s.", self.load_type_model_from) + model_type = keras.saving.load_model(self.load_type_model_from) + input_shape = model_type.input_shape[1:] + self.backbone_type, self.head_type = _split_model(model_type) + + if self.load_energy_model_from is not None: + self.log.info( + "Loading the energy model from %s.", self.load_energy_model_from + ) + model_energy = keras.saving.load_model( + self.load_energy_model_from + ) + input_shape = model_energy.input_shape[1:] + self.backbone_energy, self.head_energy = _split_model(model_energy) + + if self.load_cameradirection_model_from is not None: + self.log.info( + "Loading the cameradirection model from %s.", self.load_cameradirection_model_from + ) + model_direction = keras.saving.load_model( + self.load_cameradirection_model_from + ) + input_shape = model_direction.input_shape[1:] + self.backbone_direction, self.head_direction = _split_model(model_direction) + + return input_shape \ No newline at end of file diff --git a/ctlearn/tools/predict/predict_LST1.py b/ctlearn/tools/predict/predict_LST1.py new file mode 100644 index 00000000..759df684 --- /dev/null +++ b/ctlearn/tools/predict/predict_LST1.py @@ -0,0 +1,820 @@ +""" +Predict the gammaness, energy and arrival direction from lstchain DL1 data. +""" + +import numpy as np +import tables +import keras +from astropy import units as u +from astropy.coordinates import AltAz,SkyCoord +from astropy.table import Table, setdiff, vstack +from astropy.time import Time + +from ctapipe.containers import ( + ParticleClassificationContainer, + ReconstructedGeometryContainer, + ReconstructedEnergyContainer, +) +from ctapipe.coordinates import CameraFrame +from ctapipe.core import Tool +from ctapipe.core.tool import ToolConfigurationError +from ctapipe.core.traits import ( + Bool, + Int, + Path, + List, + CaselessStrEnum, + ComponentName, + Unicode, + UseEnum, + classes_with_traits, +) +from ctapipe.instrument.optics import FocalLengthKind +from ctapipe.io import read_table, write_table +from ctapipe.reco.utils import add_defaults_and_meta + +from ctlearn.utils import get_lst1_subarray_description +from dl1_data_handler.image_mapper import ImageMapper +from dl1_data_handler.reader import TableQualityQuery +from ctlearn.tools.predict.utils.load_model import load_model +from ctlearn.core.ctlearn_enum import Task, Mode +from ctlearn.tools.train.pytorch.utils import ( + sanity_check, + read_configuration, + expected_structure, +) + +POINTING_GROUP = "/dl1/monitoring/telescope/pointing" +DL1_TELESCOPE_GROUP = "/dl1/event/telescope" +DL2_TELESCOPE_GROUP = "/dl2/event/telescope" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +SUBARRAY_EVENT_KEYS = ["obs_id", "event_id"] +TELESCOPE_EVENT_KEYS = ["obs_id", "event_id", "tel_id"] + + +class LST1PredictionTool(Tool): + """ + Tool to predict the gammaness, energy and arrival direction from lstchain DL1 data. + + This tool is used to predict the gammaness, energy and arrival direction + from pixel-wise image data in lstchain format. The tool loads the trained models + from the specified paths and performs inference on the input data. The + input data is expected to be in the DL1 format of lstchain and the output data is + stored in the DL2 format of ctapipe. Besides the DL2 predictions, the tool creates + the SubarrayDescription of the LST-1 telescope and stores it in the output file. + In addition, the tool also creates the trigger, pointing and DL1 parameters tables + and stores them in the output file. + + CAUTION: The tool is designed to work with the DL1 data format of lstchain only. + """ + + name = "LST1PredictionTool" + description = __doc__ + + examples = """ + To predict from DL1 lstchain data using trained CTLearn models: + > ctlearn-predict-model \\ + --input_url input.subrun.lstchain.dl1.h5 \\ + --LST1PredictionTool.batch_size=64 \\ + --LST1PredictionTool.channels=cleaned_image \\ + --LST1PredictionTool.channels=cleaned_relative_peak_time \\ + --LST1PredictionTool.image_mapper_type=BilinearMapper \\ + --type_model="/path/to/your/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/energy/ctlearn_model.cpk" \\ + --cameradirection_model="/path/to/your/direction/ctlearn_model.cpk" \\ + --output output.dl2.h5 \\ + --overwrite \\ + """ + + input_url = Path( + help="Input LST-1 HDF5 files including pixel-wise image data", + allow_none=True, + exists=True, + directory_ok=False, + file_ok=True, + ).tag(config=True) + + prefix = Unicode( + default_value="CTLearn", + allow_none=False, + help="Name of the reconstruction algorithm used to generate the dl2 data.", + ).tag(config=True) + + load_type_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) " + "for the classification of the primary particle type." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_energy_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) " + "for the regression of the primary particle energy." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_cameradirection_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) " + "for the regression of the primary particle arrival direction " + "based on the camera coordinate offsets." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + batch_size = Int( + default_value=64, + allow_none=False, + help="Size of the batch to perform inference of the neural network.", + ).tag(config=True) + + channels = List( + trait=CaselessStrEnum( + [ + "image", + "cleaned_image", + "peak_time", + "relative_peak_time", + "cleaned_peak_time", + "cleaned_relative_peak_time", + ] + ), + default_value=["image", "peak_time"], + allow_none=False, + help=( + "Set the input channels to be loaded from the DL1 event data. " + "image: integrated charges, " + "cleaned_image: integrated charges cleaned with the DL1 cleaning mask, " + "peak_time: extracted peak arrival times, " + "relative_peak_time: extracted relative peak arrival times, " + "cleaned_peak_time: extracted peak arrival times cleaned with the DL1 cleaning mask, " + "cleaned_relative_peak_time: extracted relative peak arrival times cleaned with the DL1 cleaning mask." + ), + ).tag(config=True) + + image_mapper_type = ComponentName(ImageMapper, default_value="BilinearMapper").tag( + config=True + ) + + focal_length_choice = UseEnum( + FocalLengthKind, + default_value=FocalLengthKind.EFFECTIVE, + help=( + "If both nominal and effective focal lengths are available, " + " which one to use for the `~ctapipe.coordinates.CameraFrame` attached" + " to the `~ctapipe.instrument.CameraGeometry` instances in the" + " `~ctapipe.instrument.SubarrayDescription` which will be used in" + " CameraFrame to TelescopeFrame coordinate transforms." + " The 'nominal' focal length is the one used during " + " the simulation, the 'effective' focal length is computed using specialized " + " ray-tracing from a point light source" + ), + ).tag(config=True) + + override_obs_id = Int( + default_value=None, + allow_none=True, + help=( + "Use the given obs_id instead of the default one. " + "Needed to merge subruns later with ctapipe-merge." + ), + ).tag(config=True) + + output_path = Path( + default_value="./output.dl2.h5", + allow_none=False, + help="Output path to save the dl2 prediction results", + ).tag(config=True) + + pytorch_config_file = Path( + default_value="./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml", + help="Pytorch config file", + ).tag(config=True) + + framework_type = CaselessStrEnum( + ["pytorch", "keras"], + default_value="keras", + help="Framework to use: pytorch or keras", + ).tag(config=True) + + overwrite = Bool(help="Overwrite output file if it exists").tag(config=True) + + aliases = { + ("i", "input_url"): "LST1PredictionTool.input_url", + ("t", "type_model"): "LST1PredictionTool.load_type_model_from", + ("e", "energy_model"): "LST1PredictionTool.load_energy_model_from", + ("d", "cameradirection_model"): "LST1PredictionTool.load_cameradirection_model_from", + ("o", "output"): "LST1PredictionTool.output_path", + ("f", "framework"): "LST1PredictionTool.framework_type", + } + + flags = { + "overwrite": ( + {"LST1PredictionTool": {"overwrite": True}}, + "Overwrite existing files", + ), + } + + classes = classes_with_traits(ImageMapper) + + def _predictions(self): + if self.framework_type == "keras": + self.log.info("Using the Keras Model") + from ctlearn.tools.predict.keras.predic_LST1_keras import predictions + return predictions(self) + + elif self.framework_type == "pytorch": + self.log.info("Using the Pytorch Model") + from ctlearn.tools.predict.pytorch.predic_LST1_pytorch import predictions + return predictions(self) + + def setup(self): + # Save dl1 image and parameters tree schemas and tel id for easy access + import torch + self.image_table_path = "/dl1/event/telescope/image/LST_LSTCam" + self.parameter_table_name = "/dl1/event/telescope/parameters/LST_LSTCam" + self.tel_id = 1 + if self.framework_type == "pytorch": + self.log.info(f"Using {self.pytorch_config_file} config file for pytorch framework") + self.parameters = read_configuration(self.pytorch_config_file) + sanity_check(self.parameters, expected_structure) + self.device_str = self.parameters["arch"]["device"] + self.device = torch.device(self.device_str) + self.tasks = [] + self.type_mu = self.parameters["normalization"]["type_mu"] + self.type_sigma = self.parameters["normalization"]["type_sigma"] + self.dir_mu = self.parameters["normalization"]["dir_mu"] + self.dir_sigma = self.parameters["normalization"]["dir_sigma"] + self.energy_mu = self.parameters["normalization"]["energy_mu"] + self.energy_sigma = self.parameters["normalization"]["energy_sigma"] + + if self.load_type_model_from is not None: + self.tasks.append(Task.type) + if self.load_energy_model_from is not None: + self.tasks.append(Task.energy) + if self.load_cameradirection_model_from is not None: + self.tasks.append(Task.direction) + + # Get the number of rows in the table + with tables.open_file(self.input_url) as input_file: + self.table_length = len(input_file.get_node(self.image_table_path)) + + # Load the models from the specified paths + input_shape = load_model(self) + + # Get the SubarrayDescription of the LST-1 telescope + self.subarray = get_lst1_subarray_description(focal_length_choice=self.focal_length_choice) + # Write the SubarrayDescription to the output file + self.subarray.to_hdf(self.output_path, overwrite=self.overwrite) + self.log.info("SubarrayDescription was stored in '%s'", self.output_path) + # Initialize the Table data quality query + self.quality_query = TableQualityQuery(parent=self) + # Copy the pixel rotation of the camera geometry of the subarray in a variable + # since the ImageMapper will be derotated the pixels. The pixel rotation + # is needed to create a rotated camera frame in order to transform the + # predicted camera coordinate offsets back to the correct Alt/Az coordinates. + self.pix_rotation = self.subarray.tel[self.tel_id].camera.geometry.pix_rotation + # Create the ImageMapper + self.image_mapper = ImageMapper.from_name( + name=self.image_mapper_type, + geometry=self.subarray.tel[self.tel_id].camera.geometry, + subarray=self.subarray, + parent=self, + ) + # Check if the input shape of the model matches the image shape of the ImageMapper + if self.framework_type == "keras": + if input_shape[0] != self.image_mapper.image_shape: + raise ToolConfigurationError( + f"The input shape of the model ('{input_shape[0]}') does not match " + f"the image shape of the ImageMapper ('{self.image_mapper.image_shape}'). " + f"Use e.g. '--BilinearMapper.interpolation_image_shape={input_shape[0]}' ." + ) + + # Get offset and scaling of images + self.transforms = {} + self.transforms["image_scale"] = 0.0 + self.transforms["image_offset"] = 0 + self.transforms["peak_time_scale"] = 0.0 + self.transforms["peak_time_offset"] = 0 + # Get the number of rows in the table + with tables.open_file(self.input_url) as input_file: + img_table_v_attrs = input_file.get_node(self.image_table_path)._v_attrs + + # Check the transform value used for the file compression + if "CTAFIELD_3_TRANSFORM_SCALE" in img_table_v_attrs: + self.transforms["image_scale"] = img_table_v_attrs[ + "CTAFIELD_3_TRANSFORM_SCALE" + ] + self.transforms["image_offset"] = img_table_v_attrs[ + "CTAFIELD_3_TRANSFORM_OFFSET" + ] + if "CTAFIELD_4_TRANSFORM_SCALE" in img_table_v_attrs: + self.transforms["peak_time_scale"] = img_table_v_attrs[ + "CTAFIELD_4_TRANSFORM_SCALE" + ] + self.transforms["peak_time_offset"] = img_table_v_attrs[ + "CTAFIELD_4_TRANSFORM_OFFSET" + ] + + def start(self): + all_identifiers = read_table(self.input_url, self.parameter_table_name) + all_identifiers.meta = {} + if self.override_obs_id is not None: + all_identifiers["obs_id"] = self.override_obs_id + self.obs_id = all_identifiers["obs_id"][0] + self.parameter_table = all_identifiers.copy() + tel_az = u.Quantity(self.parameter_table["az_tel"], unit=u.rad) + tel_alt = u.Quantity(self.parameter_table["alt_tel"], unit=u.rad) + event_type = self.parameter_table["event_type"] + time = Time(self.parameter_table["dragon_time"] * u.s, format="unix") + # Create the pointing table + # This table is used to store the telescope pointing per event + pointing_table = Table( + { + "time": time, + "azimuth": tel_az, + "altitude": tel_alt, + } + ) + write_table( + pointing_table, + self.output_path, + f"{POINTING_GROUP}/tel_{self.tel_id:03d}", + overwrite=self.overwrite, + ) + self.log.info( + "DL1 telescope pointing table was stored in '%s' under '%s'", + self.output_path, + f"{POINTING_GROUP}/tel_{self.tel_id:03d}", + ) + # Set the time format to MJD since in the other table we store the time in MJD + time.format = "mjd" + # Keep only the necessary columns for the creation of tables + all_identifiers.keep_columns(TELESCOPE_EVENT_KEYS) + + # Create the dl1 telescope trigger table + self.trigger_table = all_identifiers.copy() + self.trigger_table.add_column(time, name="time") + self.trigger_table.add_column(-1, name="n_trigger_pixels") + + write_table( + self.trigger_table, + self.output_path, + "/dl1/event/telescope/trigger", + overwrite=self.overwrite, + ) + self.log.info( + "DL1 telescope trigger table was stored in '%s' under '%s'", + self.output_path, + "/dl1/event/telescope/trigger", + ) + self.trigger_table.keep_columns(["obs_id", "event_id", "time"]) + self.trigger_table.add_column( + np.ones((len(self.trigger_table), 1), dtype=bool), name="tel_with_trigger" + ) + self.trigger_table.add_column(event_type, name="event_type") + # Save the dl1 subrray trigger table to the output file + write_table( + self.trigger_table, + self.output_path, + "/dl1/event/subarray/trigger", + overwrite=self.overwrite, + ) + self.log.info( + "DL1 subarray trigger table was stored in '%s' under '%s'", + self.output_path, + "/dl1/event/subarray/trigger", + ) + # Create the dl1 parameters table + self.parameter_table.rename_column("intensity", "hillas_intensity") + self.parameter_table.rename_column("x", "hillas_x") + self.parameter_table.rename_column("y", "hillas_y") + self.parameter_table.rename_column("phi", "hillas_phi") + self.parameter_table.rename_column("psi", "hillas_psi") + self.parameter_table.rename_column("length", "hillas_length") + self.parameter_table.rename_column("length_uncertainty", "hillas_length_uncertainty") + self.parameter_table.rename_column("width", "hillas_width") + self.parameter_table.rename_column("width_uncertainty", "hillas_width_uncertainty") + self.parameter_table.rename_column("skewness", "hillas_skewness") + self.parameter_table.rename_column("kurtosis", "hillas_kurtosis") + self.parameter_table.rename_column("time_gradient", "timing_deviation") + self.parameter_table.rename_column("intercept", "timing_intercept") + self.parameter_table.rename_column("n_pixels", "morphology_n_pixels") + self.parameter_table.rename_column("n_islands", "morphology_n_islands") + self.parameter_table.keep_columns( + [ + "obs_id", + "event_id", + "hillas_intensity", + "hillas_x", + "hillas_y", + "hillas_phi", + "hillas_psi", + "hillas_length", + "hillas_length_uncertainty", + "hillas_width", + "hillas_width_uncertainty", + "hillas_skewness", + "hillas_kurtosis", + "timing_deviation", + "timing_intercept", + "morphology_n_pixels", + "morphology_n_islands", + ] + ) + self.parameter_table.add_column(self.tel_id, name="tel_id", index=2) + # Save the dl1 parameters table to the output file + write_table( + self.parameter_table, + self.output_path, + f"/dl1/event/telescope/parameters/tel_{self.tel_id:03d}", + overwrite=self.overwrite, + ) + self.log.info( + "DL1 parameters table was stored in '%s' under '%s'", + self.output_path, + f"/dl1/event/telescope/parameters/tel_{self.tel_id:03d}", + ) + + # Add additional columns to the parameter table + # which are not present in the originl DL1 parameter table. + # They are needed for applying the quality selection. + self.parameter_table.add_column(event_type, name="event_type") + self.parameter_table.add_column(tel_az, name="tel_az") + self.parameter_table.add_column(tel_alt, name="tel_alt") + # Only select cosmic events for the prediction + self.parameter_table = self.parameter_table[self.parameter_table["event_type"]==32] + + self.log.info("Starting the prediction...") + # Iterate over the data in chunks based on the batch size + event_id, tel_azimuth, tel_altitude, trigger_time, prediction, energy, cam_coord_offset_x, cam_coord_offset_y, classification_fvs, energy_fvs, direction_fvs = self._predictions() + + # Create the prediction tables + example_identifiers = Table( + { + "obs_id": np.full(len(event_id), self.obs_id, dtype=int), + "event_id": event_id, + "tel_id": np.full(len(event_id), self.tel_id, dtype=int), + } + ) + nonexample_identifiers = setdiff( + all_identifiers, example_identifiers, keys=TELESCOPE_EVENT_KEYS + ) + if len(nonexample_identifiers) > 0: + nonexample_identifiers.sort(TELESCOPE_EVENT_KEYS) + # Create the feature vector table + feature_vector_table = example_identifiers.copy() + fvs_columns_list, fvs_shapes_list = [], [] + if self.load_type_model_from is not None: + classification_table = example_identifiers.copy() + classification_table.add_column( + prediction, name=f"{self.prefix}_tel_prediction" + ) + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = self._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_prediction"], + shapes=[(len(nonexample_identifiers),)], + ) + classification_table = vstack([classification_table, nan_table]) + classification_table.sort(TELESCOPE_EVENT_KEYS) + classification_is_valid = ~np.isnan(classification_table[f"{self.prefix}_tel_prediction"].data, dtype=bool) + classification_table.add_column( + classification_is_valid, + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + classification_table, + ParticleClassificationContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + # Save the prediction to the output file + write_table( + classification_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{self.tel_id:03d}", + overwrite=self.overwrite, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{self.tel_id:03d}", + ) + # Write the mono telescope prediction to the subarray prediction table + subarray_classification_table = classification_table.copy() + subarray_classification_table.remove_column("tel_id") + for colname in subarray_classification_table.colnames: + if "_tel_" in colname: + subarray_classification_table.rename_column( + colname, colname.replace("_tel", "") + ) + subarray_classification_table.add_column( + classification_is_valid[np.newaxis], name=f"{self.prefix}_telescopes" + ) + # Save the prediction to the output file + write_table( + subarray_classification_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + overwrite=self.overwrite, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + ) + # Adding the feature vectors for the classification + is_valid_col = ~np.isnan( + np.min(classification_fvs, axis=1), dtype=bool + ) + feature_vector_table.add_column( + classification_fvs, + name=f"{self.prefix}_tel_classification_feature_vectors", + ) + if nonexample_identifiers is not None: + fvs_columns_list.append(f"{self.prefix}_tel_classification_feature_vectors") + fvs_shapes_list.append( + ( + len(nonexample_identifiers), + classification_fvs[0].shape[0], + ) + ) + if self.load_energy_model_from is not None: + energy_table = example_identifiers.copy() + # Convert the reconstructed energy from log10(TeV) to TeV + reco_energy = u.Quantity(np.power(10, np.squeeze(energy)), unit=u.TeV) + # Add the reconstructed energy to the prediction table + energy_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy") + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = self._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_energy"], + shapes=[(len(nonexample_identifiers),)], + ) + energy_table = vstack([energy_table, nan_table]) + energy_table.sort(TELESCOPE_EVENT_KEYS) + energy_is_valid = ~np.isnan(energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool) + energy_table.add_column( + energy_is_valid, + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + energy_table, + ReconstructedEnergyContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + # Save the prediction to the output file + write_table( + energy_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{self.tel_id:03d}", + overwrite=self.overwrite, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{self.tel_id:03d}", + ) + # Write the mono telescope prediction to the subarray prediction table + subarray_energy_table = energy_table.copy() + subarray_energy_table.remove_column("tel_id") + for colname in subarray_energy_table.colnames: + if "_tel_" in colname: + subarray_energy_table.rename_column( + colname, colname.replace("_tel", "") + ) + subarray_energy_table.add_column( + energy_is_valid[np.newaxis], name=f"{self.prefix}_telescopes" + ) + # Save the prediction to the output file + write_table( + subarray_energy_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + overwrite=self.overwrite, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + ) + # Adding the feature vectors for the energy regression + is_valid_col = ~np.isnan( + np.min(energy_fvs, axis=1), dtype=bool + ) + feature_vector_table.add_column( + energy_fvs, + name=f"{self.prefix}_tel_energy_feature_vectors", + ) + if nonexample_identifiers is not None: + fvs_columns_list.append(f"{self.prefix}_tel_energy_feature_vectors") + fvs_shapes_list.append( + ( + len(nonexample_identifiers), + energy_fvs[0].shape[0], + ) + ) + if self.load_cameradirection_model_from is not None: + direction_table = example_identifiers.copy() + # Set the telescope position + tel_ground_frame = self.subarray.tel_coords[ + self.subarray.tel_ids_to_indices(self.tel_id) + ] + # Set the telescope pointing with the trigger timestamp and the telescope position + trigger_time = Time(trigger_time, format="mjd") + altaz = AltAz( + location=tel_ground_frame.to_earth_location(), + obstime=trigger_time, + ) + # Set the telescope pointing + tel_pointing = SkyCoord( + az=u.Quantity(tel_azimuth, unit=u.rad), + alt=u.Quantity(tel_altitude, unit=u.rad), + frame=altaz, + ) + # Set a new camera frame with the pixel rotation of the camera + camera_frame = CameraFrame( + focal_length=self.subarray.tel[self.tel_id].camera.geometry.frame.focal_length, + rotation=self.pix_rotation, + telescope_pointing=tel_pointing, + ) + # Set the camera coordinate offset + cam_coord_offset = SkyCoord( + x=u.Quantity(cam_coord_offset_x, unit=u.m), + y=u.Quantity(cam_coord_offset_y, unit=u.m), + frame=camera_frame + ) + # Transform the true Alt/Az coordinates to camera coordinates + reco_direction = cam_coord_offset.transform_to(altaz) + # Add the reconstructed direction (az, alt) to the prediction table + direction_table.add_column( + reco_direction.az.to(u.deg), name=f"{self.prefix}_tel_az" + ) + direction_table.add_column( + reco_direction.alt.to(u.deg), name=f"{self.prefix}_tel_alt" + ) + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = self._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_az", f"{self.prefix}_tel_alt"], + shapes=[(len(nonexample_identifiers),), (len(nonexample_identifiers),)], + ) + direction_table = vstack([direction_table, nan_table]) + direction_table.keep_columns( + TELESCOPE_EVENT_KEYS + + [f"{self.prefix}_tel_az", f"{self.prefix}_tel_alt"] + ) + direction_table.sort(TELESCOPE_EVENT_KEYS) + direction_is_valid = ~np.isnan(direction_table[f"{self.prefix}_tel_az"].data, dtype=bool) + direction_table.add_column( + direction_is_valid, + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + direction_table, + ReconstructedGeometryContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + # Save the prediction to the output file + write_table( + direction_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{self.tel_id:03d}", + overwrite=self.overwrite, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{self.tel_id:03d}", + ) + # Write the mono telescope prediction to the subarray prediction table + subarray_direction_table = direction_table.copy() + subarray_direction_table.remove_column("tel_id") + for colname in subarray_direction_table.colnames: + if "_tel_" in colname: + subarray_direction_table.rename_column( + colname, colname.replace("_tel", "") + ) + subarray_direction_table.add_column( + direction_is_valid[np.newaxis], name=f"{self.prefix}_telescopes" + ) + # Save the prediction to the output file + write_table( + subarray_direction_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + overwrite=self.overwrite, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + ) + # Adding the feature vectors for the arrival direction regression + is_valid_col = ~np.isnan( + np.min(direction_fvs, axis=1), dtype=bool + ) + feature_vector_table.add_column( + direction_fvs, + name=f"{self.prefix}_tel_direction_feature_vectors", + ) + if nonexample_identifiers is not None: + fvs_columns_list.append(f"{self.prefix}_tel_direction_feature_vectors") + fvs_shapes_list.append( + ( + len(nonexample_identifiers), + direction_fvs[0].shape[0], + ) + ) + # Produce output table with NaNs for missing predictions + if nonexample_identifiers is not None: + if len(nonexample_identifiers) > 0: + nan_table = self._create_nan_table( + nonexample_identifiers, + columns=fvs_columns_list, + shapes=fvs_shapes_list, + ) + feature_vector_table = vstack([feature_vector_table, nan_table]) + is_valid_col = np.concatenate( + (is_valid_col, np.zeros(len(nonexample_identifiers), dtype=bool)) + ) + # Add is_valid column to the feature vector table + feature_vector_table.add_column( + is_valid_col, + name=f"{self.prefix}_tel_is_valid", + ) + # Save the prediction to the output file + write_table( + feature_vector_table, + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{self.tel_id:03d}", + overwrite=self.overwrite, + ) + self.log.info( + "DL1 feature vectors was stored in '%s' under '%s'", + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{self.tel_id:03d}", + ) + + def finish(self): + self.log.info("Tool is shutting down") + + def _create_nan_table(self, nonexample_identifiers, columns, shapes): + """ + Create a table with NaNs for missing predictions. + + This method creates a table with NaNs for missing predictions for the non-example identifiers. + + Parameters: + ----------- + nonexample_identifiers : astropy.table.Table + Table containing the non-example identifiers. + columns : list of str + List of column names to create in the table. + shapes : list of shapes + List of shapes for the columns to create in the table. + + Returns: + -------- + nan_table : astropy.table.Table + Table containing NaNs for missing predictions. + """ + # Create a table with NaNs for missing predictions + nan_table = nonexample_identifiers.copy() + for column_name, shape in zip(columns, shapes): + nan_table.add_column(np.full(shape, np.nan), name=column_name) + return nan_table + + +def main(): + # Run the tool + tool = LST1PredictionTool() + tool.run() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ctlearn/tools/predict/predict_model.py b/ctlearn/tools/predict/predict_model.py deleted file mode 100644 index c819a229..00000000 --- a/ctlearn/tools/predict/predict_model.py +++ /dev/null @@ -1,1890 +0,0 @@ -""" -Tools to predict the gammaness, energy and arrival direction in monoscopic and stereoscopic mode using ``CTLearnModel`` on R1/DL1 data using the ``DLDataReader`` and ``DLDataLoader``. -""" - -import atexit -import pathlib -import numpy as np -import os -import tensorflow as tf -import keras - -from astropy import units as u -from astropy.coordinates.earth import EarthLocation -from astropy.coordinates import AltAz, SkyCoord -from astropy.table import ( - Table, - hstack, - vstack, - join, - setdiff, -) - -from ctapipe.containers import ( - ParticleClassificationContainer, - ReconstructedGeometryContainer, - ReconstructedEnergyContainer, -) -from ctapipe.coordinates import CameraFrame, NominalFrame -from ctapipe.core import Tool -from ctapipe.core.tool import ToolConfigurationError -from ctapipe.core.traits import ( - Bool, - Int, - Path, - flag, - Set, - Dict, - List, - CaselessStrEnum, - ComponentName, - Unicode, - classes_with_traits, -) -from ctapipe.monitoring.interpolation import PointingInterpolator -from ctapipe.io import read_table, write_table, HDF5Merger -from ctapipe.reco.reconstructor import ReconstructionProperty -from ctapipe.reco.stereo_combination import StereoCombiner -from ctapipe.reco.utils import add_defaults_and_meta -from dl1_data_handler.reader import ( - DLDataReader, - ProcessType, - LST_EPOCH, -) -from ctlearn.core.data_loader.loader import DLDataLoader - -SIMULATION_CONFIG_TABLE = "/configuration/simulation/run" -FIXED_POINTING_GROUP = "/configuration/telescope/pointing" -POINTING_GROUP = "/dl1/monitoring/telescope/pointing" -SUBARRAY_POINTING_GROUP = "/dl1/monitoring/subarray/pointing" -DL1_TELESCOPE_GROUP = "/dl1/event/telescope" -DL1_SUBARRAY_GROUP = "/dl1/event/subarray" -DL2_SUBARRAY_GROUP = "/dl2/event/subarray" -DL2_TELESCOPE_GROUP = "/dl2/event/telescope" -SUBARRAY_EVENT_KEYS = ["obs_id", "event_id"] -TELESCOPE_EVENT_KEYS = ["obs_id", "event_id", "tel_id"] - -__all__ = [ - "PredictCTLearnModel", - "MonoPredictCTLearnModel", - "StereoPredictCTLearnModel", -] - - -class PredictCTLearnModel(Tool): - """ - Base tool to predict the gammaness, energy and arrival direction from R1/DL1 data using CTLearn models. - - This class handles the prediction of the gammaness, energy and arrival direction from pixel-wise image - or waveform data. It also supports the extraction of the feature vectors from the backbone submodel to - store them in the output file. The input data is loaded from the input url using the - ``~dl1_data_handler.reader.DLDataReader`` and ``~ctlearn.core.loader.DLDataLoader``. - The prediction is performed using the CTLearn models. The data is stored in the output file - following the ctapipe DL2 data format. The ``start`` method is implemented in the subclasses to - handle the prediction for mono and stereo mode. - - Attributes - ---------- - input_url : pathlib.Path - Input ctapipe HDF5 files including pixel-wise image or waveform data. - use_HDF5Merger : bool - Set whether to use the HDF5Merger component to copy the selected tables from the input file to the output file. - dl1_features : bool - Set whether to include the dl1 feature vectors in the output file. - dl2_telescope : bool - Set whether to include dl2 telescope-event-wise data in the output file. - dl2_subarray : bool - Set whether to include dl2 subarray-event-wise data in the output file. - dl1dh_reader : dl1_data_handler.reader.DLDataReader - DLDataReader object to read the data. - dl1dh_reader_type : str - Type of the DLDataReader to use for the prediction. - stack_telescope_images : bool - Set whether to stack the telescope images in the data loader. Requires ``stereo``. - sort_by_intensity : bool - Set whether to sort the telescope images by intensity in the data loader. Requires ``stereo``. - prefix : str - Name of the reconstruction algorithm used to generate the dl2 data. - load_type_model_from : pathlib.Path - Path to a Keras model file (Keras3) or directory (Keras2) for the classification of the primary particle type. - load_energy_model_from : pathlib.Path - Path to a Keras model file (Keras3) or directory (Keras2) for the regression of the primary particle energy. - load_cameradirection_model_from : pathlib.Path - Path to a Keras model file (Keras3) or directory (Keras2) for the regression - of the primary particle arrival direction based on camera coordinate offsets. - load_cameradirection_model_from : pathlib.Path - Path to a Keras model file (Keras3) or directory (Keras2) for the regression - of the primary particle arrival direction based on spherical coordinate offsets. - output_path : pathlib.Path - Output path to save the dl2 prediction results. - overwrite_tables : bool - Overwrite the table in the output file if it exists. - keras_verbose : int - Verbosity mode of Keras during the prediction. - strategy : tf.distribute.Strategy - MirroredStrategy to distribute the prediction. - data_loader : ctlearn.core.loader.DLDataLoader - DLDataLoader object to load the data. - indices : list of int - List of indices for the data loaders. - batch_size : int - Size of the batch to perform inference of the neural network. - last_batch_size : int - Size of the last batch in the data loaders. - - Methods - ------- - setup() - Set up the tool. - finish() - Finish the tool. - _predict_with_model(model_path) - Load and predict with a CTLearn model. - _predict_classification(example_identifiers) - Predict the classification of the primary particle type. - _predict_energy(example_identifiers) - Predict the energy of the primary particle. - _predict_cameradirection(example_identifiers) - Predict the arrival direction of the primary particle based on camera coordinate offsets. - _predict_skydirection(example_identifiers) - Predict the arrival direction of the primary particle based on spherical coordinate offsets. - _transform_cam_coord_offsets_to_sky(table) - Transform to camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. - _transform_spher_coord_offsets_to_sky(table) - Transform to spherical coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. - _create_nan_table(nonexample_identifiers, columns, shapes) - Create a table with NaNs for missing predictions. - _store_pointing(all_identifiers) - Store the telescope pointing table from to the output file. - _create_feature_vectors_table(example_identifiers, nonexample_identifiers, classification_feature_vectors, energy_feature_vectors, direction_feature_vectors) - Create the table for the DL1 feature vectors. - """ - - input_url = Path( - help="Input ctapipe HDF5 files including pixel-wise image or waveform data", - allow_none=True, - exists=True, - directory_ok=False, - file_ok=True, - ).tag(config=True) - - use_HDF5Merger = Bool( - default_value=True, - allow_none=False, - help=( - "Set whether to use the HDF5Merger component to copy the selected tables " - "from the input file to the output file. CAUTION: This can only be used " - "if the output file not exists." - ), - ).tag(config=True) - - dl1_features = Bool( - default_value=False, - allow_none=False, - help="Set whether to include the dl1 feature vectors in the output file.", - ).tag(config=True) - - dl2_telescope = Bool( - default_value=True, - allow_none=False, - help="Set whether to include dl2 telescope-event-wise data in the output file.", - ).tag(config=True) - - dl2_subarray = Bool( - default_value=True, - allow_none=False, - help="Set whether to include dl2 subarray-event-wise data in the output file.", - ).tag(config=True) - - dl1dh_reader_type = ComponentName(DLDataReader, default_value="DLImageReader").tag( - config=True - ) - - stack_telescope_images = Bool( - default_value=False, - allow_none=False, - help=( - "Set whether to stack the telescope images in the data loader. " - "Requires DLDataReader mode to be ``stereo``." - ), - ).tag(config=True) - - sort_by_intensity = Bool( - default_value=False, - allow_none=False, - help=( - "Set whether to sort the telescope images by intensity in the data loader. " - "Requires DLDataReader mode to be ``stereo``." - ), - ).tag(config=True) - - prefix = Unicode( - default_value="CTLearn", - allow_none=False, - help="Name of the reconstruction algorithm used to generate the dl2 data.", - ).tag(config=True) - - load_type_model_from = Path( - default_value=None, - help=( - "Path to a Keras model file (Keras3) or directory (Keras2) for the classification " - "of the primary particle type." - ), - allow_none=True, - exists=True, - directory_ok=True, - file_ok=True, - ).tag(config=True) - - load_energy_model_from = Path( - default_value=None, - help=( - "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " - "of the primary particle energy." - ), - allow_none=True, - exists=True, - directory_ok=True, - file_ok=True, - ).tag(config=True) - - load_cameradirection_model_from = Path( - default_value=None, - help=( - "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " - "of the primary particle arrival direction based on camera coordinate offsets." - ), - allow_none=True, - exists=True, - directory_ok=True, - file_ok=True, - ).tag(config=True) - - load_skydirection_model_from = Path( - default_value=None, - help=( - "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " - "of the primary particle arrival direction based on spherical coordinate offsets." - ), - allow_none=True, - exists=True, - directory_ok=True, - file_ok=True, - ).tag(config=True) - - batch_size = Int( - default_value=64, - allow_none=False, - help="Size of the batch to perform inference of the neural network.", - ).tag(config=True) - - output_path = Path( - default_value="./output.dl2.h5", - allow_none=False, - help="Output path to save the dl2 prediction results", - ).tag(config=True) - - overwrite_tables = Bool( - default_value=True, - allow_none=False, - help="Overwrite the table in the output file if it exists", - ).tag(config=True) - - keras_verbose = Int( - default_value=1, - min=0, - max=2, - allow_none=False, - help=( - "Verbosity mode of Keras during the prediction: " - "0 = silent, 1 = progress bar, 2 = one line per call." - ), - ).tag(config=True) - - - framework_type = CaselessStrEnum( - ["pytorch", "keras"], - default_value="keras", - help="Framework to use pytorch or keras", - ).tag(config=True) - - aliases = { - ("i", "input_url"): "PredictCTLearnModel.input_url", - ("t", "type_model"): "PredictCTLearnModel.load_type_model_from", - ("e", "energy_model"): "PredictCTLearnModel.load_energy_model_from", - ( - "d", - "cameradirection_model", - ): "PredictCTLearnModel.load_cameradirection_model_from", - ("s", "skydirection_model"): "PredictCTLearnModel.load_skydirection_model_from", - ("o", "output"): "PredictCTLearnModel.output_path", - ("f","framework"): "PredictCTLearnModel.framework_type", - } - - flags = { - **flag( - "dl1-features", - "PredictCTLearnModel.dl1_features", - "Include dl1 features", - "Exclude dl1 features", - ), - **flag( - "dl2-telescope", - "PredictCTLearnModel.dl2_telescope", - "Include dl2 telescope-event-wise data in the output file", - "Exclude dl2 telescope-event-wise data in the output file", - ), - **flag( - "dl2-subarray", - "PredictCTLearnModel.dl2_subarray", - "Include dl2 telescope-event-wise data in the output file", - "Exclude dl2 telescope-event-wise data in the output file", - ), - **flag( - "use-HDF5Merger", - "PredictCTLearnModel.use_HDF5Merger", - "Copy data using the HDF5Merger component (CAUTION: This can not be used if the output file already exists)", - "Do not copy data using the HDF5Merger component", - ), - **flag( - "r0-waveforms", - "HDF5Merger.r0_waveforms", - "Include r0 waveforms", - "Exclude r0 waveforms", - ), - **flag( - "r1-waveforms", - "HDF5Merger.r1_waveforms", - "Include r1 waveforms", - "Exclude r1 waveforms", - ), - **flag( - "dl1-parameters", - "HDF5Merger.dl1_parameters", - "Include dl1 parameters", - "Exclude dl1 parameters", - ), - **flag( - "dl1-images", - "HDF5Merger.dl1_images", - "Include dl1 images", - "Exclude dl1 images", - ), - **flag( - "true-parameters", - "HDF5Merger.true_parameters", - "Include true parameters", - "Exclude true parameters", - ), - **flag( - "true-images", - "HDF5Merger.true_images", - "Include true images", - "Exclude true images", - ), - } - - classes = classes_with_traits(DLDataReader) - - def setup(self): - # Check if the ctapipe HDF5Merger component is enabled - if self.use_HDF5Merger: - if os.path.exists(self.output_path): - raise ToolConfigurationError( - f"The output file '{self.output_path}' already exists. Please use " - "'--no-use-HDF5Merger' to disable the usage of the HDF5Merger component." - ) - # Copy selected tables from the input file to the output file - self.log.info("Copying to output destination.") - with HDF5Merger(self.output_path, parent=self) as merger: - merger(self.input_url) - else: - self.log.info( - "No copy to output destination, since the usage of the HDF5Merger component is disabled." - ) - - # Create a MirroredStrategy. - self.strategy = tf.distribute.MirroredStrategy() - atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore - self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) - - # Set up the data reader - self.log.info("Loading data reader:") - self.log.info("For a large dataset, this may take a while...") - self.dl1dh_reader = DLDataReader.from_name( - self.dl1dh_reader_type, - input_url_signal=[self.input_url], - parent=self, - ) - self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) - # Check if the number of events is enough to form a batch - if self.dl1dh_reader._get_n_events() < self.batch_size: - raise ToolConfigurationError( - f"{self.dl1dh_reader._get_n_events()} events are not enough " - f"to form a batch of size {self.batch_size}. Reduce the batch size." - ) - # Set the indices for the data loaders - self.indices = list(range(self.dl1dh_reader._get_n_events())) - self.last_batch_size = len(self.indices) % ( - self.batch_size * self.strategy.num_replicas_in_sync - ) - - def finish(self): - self.log.info("Tool is shutting down") - - def _predict_with_model(self, model_path): - """ - Load and predict with a CTLearn model. - - Load a model from the specified path and predict the data using the loaded model. - If a last batch loader is provided, predict the last batch and stack the results. - - Parameters - ---------- - model_path : str - Path to a Keras model file (Keras3) or directory (Keras2). - - Returns - ------- - predict_data : astropy.table.Table - Table containing the prediction results. - feature_vectors : np.ndarray - Feature vectors extracted from the backbone model. - """ - # Create a new DLDataLoader for each task - # It turned out to be more robust to initialize the DLDataLoader separately. - data_loader = DLDataLoader.create( - framework=self.framework_type, - DLDataReader=self.dl1dh_reader, - indices=self.indices, - tasks=[], - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) - - # Keras is only considering the last complete batch. - # In prediction mode we don't want to loose the last - # uncomplete batch, so we are creating an additional - # batch generator for the remaining events. - data_loader_last_batch = None - if self.last_batch_size > 0: - last_batch_indices = self.indices[-self.last_batch_size :] - data_loader_last_batch = DLDataLoader.create( - framework=self.framework_type, - DLDataReader=self.dl1dh_reader, - indices=last_batch_indices, - tasks=[], - batch_size=self.last_batch_size, - sort_by_intensity=self.sort_by_intensity, - stack_telescope_images=self.stack_telescope_images, - ) - - - # Load the model from the specified path - model = keras.saving.load_model(model_path) - prediction_colname = ( - model.layers[-1].name if model.layers[-1].name != "softmax" else "type" - ) - backbone_model, feature_vectors = None, None - if self.dl1_features: - # Get the backbone model which is the second layer of the model - backbone_model = model.get_layer(index=1) - # Create a new head model with the same layers as the original model. - # The output of the backbone model is the input of the head model. - backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) - x = backbone_output_shape - for layer in model.layers[2:]: - x = layer(x) - head = keras.Model(inputs=backbone_output_shape, outputs=x) - # Apply the backbone model with the data loader to retrieve the feature vectors - feature_vectors = backbone_model.predict( - data_loader, verbose=self.keras_verbose - ) - # Apply the head model with the feature vectors to retrieve the prediction - predict_data = Table( - { - prediction_colname: head.predict( - feature_vectors, verbose=self.keras_verbose - ) - } - ) - # Predict the last batch and stack the results to the prediction data - if data_loader_last_batch is not None: - feature_vectors_last_batch = backbone_model.predict( - data_loader_last_batch, verbose=self.keras_verbose - ) - feature_vectors = np.concatenate( - (feature_vectors, feature_vectors_last_batch) - ) - predict_data = vstack( - [ - predict_data, - Table( - { - prediction_colname: head.predict( - feature_vectors_last_batch, - verbose=self.keras_verbose, - ) - } - ), - ] - ) - else: - # Predict the data using the loaded model - predict_data = model.predict(data_loader, verbose=self.keras_verbose) - # Create a astropy table with the prediction results - # The classification task has a softmax layer as the last layer - # which returns the probabilities for each class in an array, while - # the regression tasks have output neurons which returns the - # predicted value for the task in a dictionary. - if prediction_colname == "type": - predict_data = Table({prediction_colname: predict_data}) - else: - predict_data = Table(predict_data) - # Predict the last batch and stack the results to the prediction data - if data_loader_last_batch is not None: - predict_data_last_batch = model.predict( - data_loader_last_batch, verbose=self.keras_verbose - ) - if model.layers[-1].name == "type": - predict_data_last_batch = Table( - {prediction_colname: predict_data_last_batch} - ) - else: - predict_data_last_batch = Table(predict_data_last_batch) - predict_data = vstack([predict_data, predict_data_last_batch]) - return predict_data, feature_vectors - - def _predict_classification(self, example_identifiers): - """ - Predict the classification of the primary particle type. - - This method uses a pre-trained type model to predict the type of the primary particle - for a given set of example identifiers. The predicted classification score ('gammaness') - is added to the example identifiers table. - - Parameters: - ----------- - classification_table : astropy.table.Table - Table containing the example identifiers with an additional column for the - predicted classification score ('gammaness'). - feature_vectors : np.ndarray - Feature vectors extracted from the backbone model. - """ - self.log.info( - "Predicting for the classification of the primary particle type..." - ) - # Predict the data using the loaded type_model - predict_data, feature_vectors = self._predict_with_model( - self.load_type_model_from - ) - # Create prediction table and add the predicted classification score ('gammaness') - classification_table = example_identifiers.copy() - classification_table.add_column( - predict_data["type"].T[1], name=f"{self.prefix}_tel_prediction" - ) - return classification_table, feature_vectors - - def _predict_energy(self, example_identifiers): - """ - Predict the energy of the primary particle. - - This method uses a pre-trained energy model to predict the energy of the primary particle - for a given set of example identifiers. The predicted energy is then converted from - log10(TeV) to TeV and added to the example identifiers table. - - Parameters: - ----------- - energy_table : astropy.table.Table - Table containing the example identifiers with an additional column for the - reconstructed energy in TeV. - feature_vectors : np.ndarray - Feature vectors extracted from the backbone model. - """ - self.log.info("Predicting for the regression of the primary particle energy...") - # Predict the data using the loaded energy_model - predict_data, feature_vectors = self._predict_with_model( - self.load_energy_model_from - ) - # Convert the reconstructed energy from log10(TeV) to TeV - reco_energy = u.Quantity( - np.power(10, np.squeeze(predict_data["energy"])), - unit=u.TeV, - ) - # Create prediction table and add the reconstructed energy in TeV - energy_table = example_identifiers.copy() - energy_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy") - return energy_table, feature_vectors - - def _predict_cameradirection(self, example_identifiers): - """ - Predict the arrival direction of the primary particle based on camera coordinate offsets. - - This method uses a pre-trained direction model to predict the arrival direction of the - primary particle for a given set of example identifiers. The predicted camera coordinate offsets - is added to the example identifiers table. - - Parameters: - ----------- - example_identifiers : astropy.table.Table - Table containing the example identifiers. - - Returns: - -------- - cameradirection_table : astropy.table.Table - Table containing the example identifiers with an additional column for the - reconstructed camera coordinate offsets in x and y. - feature_vectors : np.ndarray - Feature vectors extracted from the backbone model. - """ - self.log.info( - "Predicting for the regression of the primary particle arrival direction based on camera coordinate offsets..." - ) - # Predict the data using the loaded direction_model - predict_data, feature_vectors = self._predict_with_model( - self.load_cameradirection_model_from - ) - # For the direction task, the prediction is the camera coordinate offset in x and y - # from the telescope pointing. - cam_coord_offset_x = u.Quantity(predict_data["cameradirection"].T[0], unit=u.m) - cam_coord_offset_y = u.Quantity(predict_data["cameradirection"].T[1], unit=u.m) - # Create prediction table and add the reconstructed energy in TeV - cameradirection_table = example_identifiers.copy() - cameradirection_table.add_column(cam_coord_offset_x, name="cam_coord_offset_x") - cameradirection_table.add_column(cam_coord_offset_y, name="cam_coord_offset_y") - return cameradirection_table, feature_vectors - - def _predict_skydirection(self, example_identifiers): - """ - Predict the arrival direction of the primary particle based on spherical coordinate offsets. - - This method uses a pre-trained direction model to predict the arrival direction of the primary - particle for a given set of example identifiers. The predicted spherical coordinate offsets is - added to the example identifiers table. - - Parameters: - ----------- - example_identifiers : astropy.table.Table - Table containing the example identifiers. - - Returns: - -------- - skydirection_table : astropy.table.Table - Table containing the example identifiers with an additional column for the - reconstructed spherical coordinate offsets in fov_lon and fov_lat. - feature_vectors : np.ndarray - Feature vectors extracted from the backbone model. - """ - self.log.info( - "Predicting for the regression of the primary particle arrival direction based on spherical coordinate offsets..." - ) - # Predict the data using the loaded direction_model - predict_data, feature_vectors = self._predict_with_model( - self.load_skydirection_model_from - ) - # For the direction task, the prediction is the spherical offset in fov_lon and fov_lat - # from the telescope pointing. - fov_lon = u.Quantity(predict_data["skydirection"].T[0], unit=u.deg) - fov_lat = u.Quantity(predict_data["skydirection"].T[1], unit=u.deg) - # Create prediction table and add the reconstructed energy in TeV - skydirection_table = example_identifiers.copy() - skydirection_table.add_column(fov_lon, name="fov_lon") - skydirection_table.add_column(fov_lat, name="fov_lat") - return skydirection_table, feature_vectors - - def _transform_cam_coord_offsets_to_sky(self, table) -> Table: - """ - Transform the predicted camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. - - This method converts the predicted camera coordinate offsets w.r.t. the telescope pointing - in the provided table to Alt/Az coordinates. It also removes the unnecessary columns - from the table that do not the ctapipe DL2 data format. - - Parameters: - ----------- - table : astropy.table.Table - A Table containing the trigger time, telescope pointing, and predicted camera coordinate offsets. - - Returns: - -------- - table : astropy.table.Table - A Table with the Alt/Az coordinates following the ctapipe DL2 data format. - """ - # Get the telescope ID from the table - tel_id = table["tel_id"][0] - # Set the telescope position - tel_ground_frame = self.dl1dh_reader.subarray.tel_coords[ - self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) - ] - # Set the trigger timestamp based on the process type - if self.dl1dh_reader.process_type == ProcessType.Simulation: - trigger_time = LST_EPOCH - elif self.dl1dh_reader.process_type == ProcessType.Observation: - trigger_time = table["time"] - # Set the telescope pointing with the trigger timestamp and the telescope position - altaz = AltAz( - location=tel_ground_frame.to_earth_location(), - obstime=trigger_time, - ) - # Set the telescope pointing - tel_pointing = SkyCoord( - az=table["pointing_azimuth"], - alt=table["pointing_altitude"], - frame=altaz, - ) - # Set the camera frame with the focal length and rotation of the camera - camera_frame = CameraFrame( - focal_length=self.dl1dh_reader.subarray.tel[ - tel_id - ].camera.geometry.frame.focal_length, - rotation=self.dl1dh_reader.pix_rotation[tel_id], - telescope_pointing=tel_pointing, - ) - # Set the camera coordinate offset - cam_coord_offset = SkyCoord( - x=table["cam_coord_offset_x"], - y=table["cam_coord_offset_y"], - frame=camera_frame, - ) - # tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] - # Transform the true Alt/Az coordinates to camera coordinates - reco_direction = cam_coord_offset.transform_to(altaz) - # Add the reconstructed direction (az, alt) to the prediction table - table.add_column(reco_direction.az.to(u.deg), name=f"{self.prefix}_tel_az") - table.add_column(reco_direction.alt.to(u.deg), name=f"{self.prefix}_tel_alt") - # Remove unnecessary columns from the table that do not the ctapipe DL2 data format - table.remove_columns( - [ - "time", - "pointing_azimuth", - "pointing_altitude", - "cam_coord_offset_x", - "cam_coord_offset_y", - ] - ) - return table - - def _transform_spher_coord_offsets_to_sky(self, table) -> Table: - """ - Transform the predicted spherical offsets w.r.t. the telescope pointing to Alt/Az coordinates. - - This method converts the predicted spherical offsets w.r.t. the telescope pointing - in the provided table to Alt/Az coordinates. It also removes the unnecessary columns - from the table that do not the ctapipe DL2 data format. - - Parameters: - ----------- - table : astropy.table.Table - A Table containing the trigger time, telescope pointing, and predicted spherical offsets. - - Returns: - -------- - table : astropy.table.Table - A Table with the Alt/Az coordinates following the ctapipe DL2 data format. - """ - - # Set the trigger timestamp based on the process type - if self.dl1dh_reader.process_type == ProcessType.Simulation: - trigger_time = LST_EPOCH - elif self.dl1dh_reader.process_type == ProcessType.Observation: - trigger_time = table["time"] - # Set the AltAz frame with the reference location and time - altaz = AltAz( - location=self.dl1dh_reader.subarray.reference_location, - obstime=trigger_time, - ) - # Set the array pointing - array_pointing = SkyCoord( - az=table["pointing_azimuth"], - alt=table["pointing_altitude"], - frame=altaz, - ) - # Set the nominal frame with the array pointing - nom_frame = NominalFrame( - origin=array_pointing, - location=self.dl1dh_reader.subarray.reference_location, - obstime=trigger_time, - ) - # Set the reco direction in (fov_lon, fov_lat) coordinates - reco_direction = SkyCoord( - fov_lon=table["fov_lon"], - fov_lat=table["fov_lat"], - frame=nom_frame, - ) - # Transform the reco direction from nominal frame to the AltAz frame - sky_coord = reco_direction.transform_to(altaz) - # Add the reconstructed direction (az, alt) to the prediction table - table.add_column(sky_coord.az.to(u.deg), name=f"{self.prefix}_az") - table.add_column(sky_coord.alt.to(u.deg), name=f"{self.prefix}_alt") - # Remove unnecessary columns from the table that do not the ctapipe DL2 data format - table.remove_columns( - [ - "time", - "pointing_azimuth", - "pointing_altitude", - "fov_lon", - "fov_lat", - ] - ) - return table - - def _create_nan_table(self, nonexample_identifiers, columns, shapes): - """ - Create a table with NaNs for missing predictions. - - This method creates a table with NaNs for missing predictions for the non-example identifiers. - In stereo mode, the table also a column for the valid telescopes is added with all False values. - - Parameters: - ----------- - nonexample_identifiers : astropy.table.Table - Table containing the non-example identifiers. - columns : list of str - List of column names to create in the table. - shapes : list of shapes - List of shapes for the columns to create in the table. - - Returns: - -------- - nan_table : astropy.table.Table - Table containing NaNs for missing predictions. - """ - # Create a table with NaNs for missing predictions - nan_table = nonexample_identifiers.copy() - for column_name, shape in zip(columns, shapes): - nan_table.add_column(np.full(shape, np.nan), name=column_name) - # Add that no telescope is valid for the non-example identifiers in stereo mode - if self.dl1dh_reader.mode == "stereo": - nan_table.add_column( - np.zeros( - (len(nonexample_identifiers), len(self.dl1dh_reader.tel_ids)), - dtype=bool, - ), - name=f"{self.prefix}_telescopes", - ) - return nan_table - - def _store_pointing(self, all_identifiers): - """ - Store the telescope pointing table from to the output file. - - Parameters: - ----------- - all_identifiers : astropy.table.Table - Table containing the telescope pointing information. - """ - - # Initialize the pointing interpolator from ctapipe - pointing_interpolator = PointingInterpolator( - bounds_error=False, extrapolate=True - ) - pointing_info = [] - for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: - # Get the telescope pointing from the dl1dh reader - tel_pointing = self.dl1dh_reader.telescope_pointings[f"tel_{tel_id:03d}"] - # Add the telescope pointing table to the pointing interpolator - pointing_interpolator.add_table(tel_id, tel_pointing) - tel_identifiers = all_identifiers.copy() - if self.dl1dh_reader.mode == "mono": - tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] - # Interpolate the telescope pointing - tel_altitude, tel_azimuth = pointing_interpolator( - tel_id, tel_identifiers["time"] - ) - tel_identifiers.add_column(tel_azimuth, name="pointing_azimuth") - tel_identifiers.add_column(tel_altitude, name="pointing_altitude") - pointing_info.append(tel_identifiers) - if self.dl1dh_reader.mode == "mono": - tel_pointing_table = Table( - { - "time": tel_identifiers["time"], - "azimuth": tel_identifiers["pointing_azimuth"], - "altitude": tel_identifiers["pointing_altitude"], - } - ) - write_table( - tel_pointing_table, - self.output_path, - f"{POINTING_GROUP}/tel_{tel_id:03d}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL1 telescope pointing table was stored in '%s' under '%s'", - self.output_path, - f"{POINTING_GROUP}/tel_{tel_id:03d}", - ) - pointing_info = vstack(pointing_info) - if self.dl1dh_reader.mode == "stereo": - # Group the pointing information by subarray event keys - # TODO: This needs to be debugged with SST1M data - pointing_info_grouped = pointing_info.group_by(SUBARRAY_EVENT_KEYS) - pointing_mean = pointing_info_grouped.groups.aggregate(np.mean) - pointing_info = join( - all_identifiers, - pointing_mean, - keys=SUBARRAY_EVENT_KEYS, - ) - # TODO: use keep_order for astropy v7.0.0 - pointing_info.sort(SUBARRAY_EVENT_KEYS) - # Create the pointing table - pointing_table = Table( - { - "time": pointing_info["time"], - "array_azimuth": pointing_info["pointing_azimuth"], - "array_altitude": pointing_info["pointing_altitude"], - "array_ra": np.nan * np.ones(len(pointing_info)), - "array_dec": np.nan * np.ones(len(pointing_info)), - } - ) - # Save the pointing table to the output file - write_table( - pointing_table, - self.output_path, - f"{SUBARRAY_POINTING_GROUP}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL1 subarray pointing table was stored in '%s' under '%s'", - self.output_path, - f"{SUBARRAY_POINTING_GROUP}", - ) - return pointing_info - - def _create_feature_vectors_table( - self, - example_identifiers, - nonexample_identifiers=None, - classification_feature_vectors=None, - energy_feature_vectors=None, - direction_feature_vectors=None, - ): - """ - Create the table for the DL1 feature vectors. - - This method creates a table with the DL1 feature vectors for the example identifiers and fill NaNs for - non-example identifiers. The feature vectors are stored in the columns of the table. The table also - contains a column for the valid predictions. - - Parameters: - ----------- - example_identifiers : astropy.table.Table - Table containing the example identifiers. - nonexample_identifiers : astropy.table.Table or None - Table containing the non-example identifiers to fill the NaNs. - classification_feature_vectors : np.ndarray or None - Array containing the classification feature vectors. - energy_feature_vectors : np.ndarray or None - Array containing the energy feature vectors. - direction_feature_vectors : np.ndarray or None - Array containing the direction feature vectors. - - Returns: - -------- - feature_vector_table : astropy.table.Table - Table containing the DL1 feature vectors for the example and non-example identifiers. - """ - # Create the feature vector table - feature_vector_table = example_identifiers.copy() - feature_vector_table.remove_columns( - ["pointing_azimuth", "pointing_altitude", "time"] - ) - columns_list, shapes_list = [], [] - if classification_feature_vectors is not None: - is_valid_col = ~np.isnan( - np.min(classification_feature_vectors, axis=1), dtype=bool - ) - feature_vector_table.add_column( - classification_feature_vectors, - name=f"{self.prefix}_tel_classification_feature_vectors", - ) - if nonexample_identifiers is not None: - columns_list.append(f"{self.prefix}_tel_classification_feature_vectors") - shapes_list.append( - ( - len(nonexample_identifiers), - classification_feature_vectors.shape[1], - ) - ) - if energy_feature_vectors is not None: - is_valid_col = ~np.isnan(np.min(energy_feature_vectors, axis=1), dtype=bool) - feature_vector_table.add_column( - energy_feature_vectors, name=f"{self.prefix}_tel_energy_feature_vectors" - ) - if nonexample_identifiers is not None: - columns_list.append(f"{self.prefix}_tel_energy_feature_vectors") - shapes_list.append( - ( - len(nonexample_identifiers), - energy_feature_vectors.shape[1], - ) - ) - if direction_feature_vectors is not None: - is_valid_col = ~np.isnan( - np.min(direction_feature_vectors, axis=1), dtype=bool - ) - feature_vector_table.add_column( - direction_feature_vectors, - name=f"{self.prefix}_tel_geometry_feature_vectors", - ) - if nonexample_identifiers is not None: - columns_list.append(f"{self.prefix}_tel_geometry_feature_vectors") - shapes_list.append( - ( - len(nonexample_identifiers), - direction_feature_vectors.shape[1], - ) - ) - # Produce output table with NaNs for missing predictions - if nonexample_identifiers is not None: - if len(nonexample_identifiers) > 0: - nan_table = self._create_nan_table( - nonexample_identifiers, - columns=columns_list, - shapes=shapes_list, - ) - feature_vector_table = vstack([feature_vector_table, nan_table]) - is_valid_col = np.concatenate( - (is_valid_col, np.zeros(len(nonexample_identifiers), dtype=bool)) - ) - # Add is_valid column to the feature vector table - feature_vector_table.add_column( - is_valid_col, - name=f"{self.prefix}_tel_is_valid", - ) - return feature_vector_table - - -class MonoPredictCTLearnModel(PredictCTLearnModel): - """ - Tool to predict the gammaness, energy and arrival direction from monoscopic R1/DL1 data using CTLearn models. - - This tool extends the ``PredictCTLearnModel`` to specifically handle monoscopic R1/DL1 data. The prediction - is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. - It also stores the telescope pointing monitoring and DL1 feature vectors (if selected) in the output file. - - Attributes - ---------- - name : str - Name of the tool. - description : str - Description of the tool. - examples : str - Examples of how to use the tool. - - Methods - ------- - start() - Start the tool. - _store_mc_telescope_pointing(all_identifiers) - Store the telescope pointing table for the mono mode for MC simulation. - """ - - name = "ctlearn-predict-mono-model" - description = __doc__ - - examples = """ - To predict from pixel-wise image data in mono mode using trained CTLearn models: - > ctlearn-predict-mono-model \\ - --input_url input.dl1.h5 \\ - --PredictCTLearnModel.batch_size=64 \\ - --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ - --DLImageReader.channels=cleaned_image \\ - --DLImageReader.channels=cleaned_relative_peak_time \\ - --DLImageReader.image_mapper_type=BilinearMapper \\ - --type_model="/path/to/your/mono/type/ctlearn_model.cpk" \\ - --energy_model="/path/to/your/mono/energy/ctlearn_model.cpk" \\ - --cameradirection_model="/path/to/your/mono/cameradirection/ctlearn_model.cpk" \\ - --dl1-features \\ - --use-HDF5Merger \\ - --no-dl1-images \\ - --no-true-images \\ - --output output.dl2.h5 \\ - --PredictCTLearnModel.overwrite_tables=True \\ - - To predict from pixel-wise waveform data in mono mode using trained CTLearn models: - > ctlearn-predict-mono-model \\ - --input_url input.r1.h5 \\ - --PredictCTLearnModel.dl1dh_reader_type=DLWaveformReader \\ - --DLWaveformReader.sequnce_length=20 \\ - --DLWaveformReader.image_mapper_type=BilinearMapper \\ - --type_model="/path/to/your/mono_waveform/type/ctlearn_model.cpk" \\ - --energy_model="/path/to/your/mono_waveform/energy/ctlearn_model.cpk" \\ - --cameradirection_model="/path/to/your/mono_waveform/cameradirection/ctlearn_model.cpk" \\ - --use-HDF5Merger \\ - --no-r0-waveforms \\ - --no-r1-waveforms \\ - --no-dl1-images \\ - --no-true-images \\ - --output output.dl2.h5 \\ - --PredictCTLearnModel.overwrite_tables=True \\ - """ - - stereo_combiner_cls = ComponentName( - StereoCombiner, - default_value="StereoMeanCombiner", - help="Which stereo combination method to use after the monoscopic reconstruction.", - ).tag(config=True) - - def start(self): - self.log.info("Processing the telescope pointings...") - # Retrieve the IDs from the dl1dh for the prediction tables - example_identifiers = self.dl1dh_reader.example_identifiers.copy() - example_identifiers.keep_columns(TELESCOPE_EVENT_KEYS) - all_identifiers = self.dl1dh_reader.tel_trigger_table.copy() - all_identifiers.keep_columns(TELESCOPE_EVENT_KEYS + ["time"]) - nonexample_identifiers = setdiff( - all_identifiers, example_identifiers, keys=TELESCOPE_EVENT_KEYS - ) - nonexample_identifiers.remove_column("time") - # Pointing table for the mono mode for MC simulation - if self.dl1dh_reader.process_type == ProcessType.Simulation: - pointing_info = self._store_mc_telescope_pointing(all_identifiers) - - # Pointing table for the observation mode - if self.dl1dh_reader.process_type == ProcessType.Observation: - pointing_info = super()._store_pointing(all_identifiers) - - self.log.info("Starting the prediction...") - classification_feature_vectors = None - if self.load_type_model_from is not None: - self.type_stereo_combiner = StereoCombiner.from_name( - self.stereo_combiner_cls, - prefix=self.prefix, - property=ReconstructionProperty.PARTICLE_TYPE, - parent=self, - ) - # Predict the energy of the primary particle - classification_table, classification_feature_vectors = ( - super()._predict_classification(example_identifiers) - ) - if self.dl2_telescope: - # Produce output table with NaNs for missing predictions - if len(nonexample_identifiers) > 0: - nan_table = super()._create_nan_table( - nonexample_identifiers, - columns=[f"{self.prefix}_tel_prediction"], - shapes=[(len(nonexample_identifiers),)], - ) - classification_table = vstack([classification_table, nan_table]) - # Add is_valid column to the energy table - classification_table.add_column( - ~np.isnan( - classification_table[f"{self.prefix}_tel_prediction"].data, - dtype=bool, - ), - name=f"{self.prefix}_tel_is_valid", - ) - # Add the default values and meta data to the table - add_defaults_and_meta( - classification_table, - ParticleClassificationContainer, - prefix=self.prefix, - add_tel_prefix=True, - ) - for tel_id in self.dl1dh_reader.selected_telescopes[ - self.dl1dh_reader.tel_type - ]: - # Retrieve the example identifiers for the selected telescope - telescope_mask = classification_table["tel_id"] == tel_id - classification_tel_table = classification_table[telescope_mask] - classification_tel_table.sort(TELESCOPE_EVENT_KEYS) - # Save the prediction to the output file for the selected telescope - write_table( - classification_tel_table, - self.output_path, - f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", - ) - if self.dl2_subarray: - self.log.info("Processing and storing the subarray type prediction...") - # Combine the telescope predictions to the subarray prediction using the stereo combiner - subarray_classification_table = self.type_stereo_combiner.predict_table( - classification_table - ) - # TODO: Remove temporary fix once the stereo combiner returns correct table - # Check if the table has to be converted to a boolean mask - if ( - subarray_classification_table[f"{self.prefix}_telescopes"].dtype - != np.bool_ - ): - # Create boolean mask for telescopes that participate in the stereo reconstruction combination - reco_telescopes = np.zeros( - ( - len(subarray_classification_table), - len(self.dl1dh_reader.tel_ids), - ), - dtype=bool, - ) - # Loop over the table and set the boolean mask for the telescopes - for index, tel_id_mask in enumerate( - subarray_classification_table[f"{self.prefix}_telescopes"] - ): - if not tel_id_mask: - continue - for tel_id in tel_id_mask: - reco_telescopes[index][ - self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) - ] = True - # Overwrite the column with the boolean mask with fix length - subarray_classification_table[f"{self.prefix}_telescopes"] = ( - reco_telescopes - ) - # Save the prediction to the output file - write_table( - subarray_classification_table, - self.output_path, - f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", - ) - energy_feature_vectors = None - if self.load_energy_model_from is not None: - self.energy_stereo_combiner = StereoCombiner.from_name( - self.stereo_combiner_cls, - prefix=self.prefix, - property=ReconstructionProperty.ENERGY, - parent=self, - ) - # Predict the energy of the primary particle - energy_table, energy_feature_vectors = super()._predict_energy( - example_identifiers - ) - if self.dl2_telescope: - # Produce output table with NaNs for missing predictions - if len(nonexample_identifiers) > 0: - nan_table = super()._create_nan_table( - nonexample_identifiers, - columns=[f"{self.prefix}_tel_energy"], - shapes=[(len(nonexample_identifiers),)], - ) - energy_table = vstack([energy_table, nan_table]) - # Add is_valid column to the energy table - energy_table.add_column( - ~np.isnan( - energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool - ), - name=f"{self.prefix}_tel_is_valid", - ) - # Add the default values and meta data to the table - add_defaults_and_meta( - energy_table, - ReconstructedEnergyContainer, - prefix=self.prefix, - add_tel_prefix=True, - ) - for tel_id in self.dl1dh_reader.selected_telescopes[ - self.dl1dh_reader.tel_type - ]: - # Retrieve the example identifiers for the selected telescope - telescope_mask = energy_table["tel_id"] == tel_id - energy_tel_table = energy_table[telescope_mask] - energy_tel_table.sort(TELESCOPE_EVENT_KEYS) - # Save the prediction to the output file - write_table( - energy_tel_table, - self.output_path, - f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", - ) - if self.dl2_subarray: - self.log.info( - "Processing and storing the subarray energy prediction..." - ) - # Combine the telescope predictions to the subarray prediction using the stereo combiner - subarray_energy_table = self.energy_stereo_combiner.predict_table( - energy_table - ) - # TODO: Remove temporary fix once the stereo combiner returns correct table - # Check if the table has to be converted to a boolean mask - if subarray_energy_table[f"{self.prefix}_telescopes"].dtype != np.bool_: - # Create boolean mask for telescopes that participate in the stereo reconstruction combination - reco_telescopes = np.zeros( - (len(subarray_energy_table), len(self.dl1dh_reader.tel_ids)), - dtype=bool, - ) - # Loop over the table and set the boolean mask for the telescopes - for index, tel_id_mask in enumerate( - subarray_energy_table[f"{self.prefix}_telescopes"] - ): - if not tel_id_mask: - continue - for tel_id in tel_id_mask: - reco_telescopes[index][ - self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) - ] = True - # Overwrite the column with the boolean mask with fix length - subarray_energy_table[f"{self.prefix}_telescopes"] = reco_telescopes - # Save the prediction to the output file - write_table( - subarray_energy_table, - self.output_path, - f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", - ) - direction_feature_vectors = None - if self.load_cameradirection_model_from is not None: - self.geometry_stereo_combiner = StereoCombiner.from_name( - self.stereo_combiner_cls, - prefix=self.prefix, - property=ReconstructionProperty.GEOMETRY, - parent=self, - ) - # Join the prediction table with the telescope pointing table - example_identifiers = join( - left=example_identifiers, - right=pointing_info, - keys=TELESCOPE_EVENT_KEYS, - ) - # Predict the arrival direction of the primary particle - direction_table, direction_feature_vectors = ( - super()._predict_cameradirection(example_identifiers) - ) - direction_tel_tables = [] - if self.dl2_telescope: - for tel_id in self.dl1dh_reader.selected_telescopes[ - self.dl1dh_reader.tel_type - ]: - # Retrieve the example identifiers for the selected telescope - telescope_mask = direction_table["tel_id"] == tel_id - direction_tel_table = direction_table[telescope_mask] - direction_tel_table = super()._transform_cam_coord_offsets_to_sky( - direction_tel_table - ) - # Produce output table with NaNs for missing predictions - nan_telescope_mask = nonexample_identifiers["tel_id"] == tel_id - nonexample_identifiers_tel = nonexample_identifiers[ - nan_telescope_mask - ] - if len(nonexample_identifiers_tel) > 0: - nan_table = super()._create_nan_table( - nonexample_identifiers_tel, - columns=[f"{self.prefix}_tel_alt", f"{self.prefix}_tel_az"], - shapes=[ - (len(nonexample_identifiers_tel),), - (len(nonexample_identifiers_tel),), - ], - ) - direction_tel_table = vstack([direction_tel_table, nan_table]) - direction_tel_table.sort(TELESCOPE_EVENT_KEYS) - # Add is_valid column to the direction table - direction_tel_table.add_column( - ~np.isnan( - direction_tel_table[f"{self.prefix}_tel_alt"].data, - dtype=bool, - ), - name=f"{self.prefix}_tel_is_valid", - ) - # Add the default values and meta data to the table - add_defaults_and_meta( - direction_tel_table, - ReconstructedGeometryContainer, - prefix=self.prefix, - add_tel_prefix=True, - ) - direction_tel_tables.append(direction_tel_table) - # Save the prediction to the output file - write_table( - direction_tel_table, - self.output_path, - f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", - ) - if self.dl2_subarray: - self.log.info( - "Processing and storing the subarray geometry prediction..." - ) - # Stack the telescope tables to the subarray table - direction_tel_tables = vstack(direction_tel_tables) - # Sort the table by the telescope event keys - direction_tel_tables.sort(TELESCOPE_EVENT_KEYS) - # Combine the telescope predictions to the subarray prediction using the stereo combiner - subarray_direction_table = self.geometry_stereo_combiner.predict_table( - direction_tel_tables - ) - # TODO: Remove temporary fix once the stereo combiner returns correct table - # Check if the table has to be converted to a boolean mask - if ( - subarray_direction_table[f"{self.prefix}_telescopes"].dtype - != np.bool_ - ): - # Create boolean mask for telescopes that participate in the stereo reconstruction combination - reco_telescopes = np.zeros( - (len(subarray_direction_table), len(self.dl1dh_reader.tel_ids)), - dtype=bool, - ) - # Loop over the table and set the boolean mask for the telescopes - for index, tel_id_mask in enumerate( - subarray_direction_table[f"{self.prefix}_telescopes"] - ): - if not tel_id_mask: - continue - for tel_id in tel_id_mask: - reco_telescopes[index][ - self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) - ] = True - # Overwrite the column with the boolean mask with fix length - subarray_direction_table[f"{self.prefix}_telescopes"] = ( - reco_telescopes - ) - # Save the prediction to the output file - write_table( - subarray_direction_table, - self.output_path, - f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", - ) - # Create the feature vector table if the DL1 features are enabled - if self.dl1_features: - self.log.info("Processing and storing dl1 feature vectors...") - feature_vector_table = super()._create_feature_vectors_table( - example_identifiers, - nonexample_identifiers, - classification_feature_vectors, - energy_feature_vectors, - direction_feature_vectors, - ) - # Loop over the selected telescopes and store the feature vectors - # for each telescope in the output file. The feature vectors are stored - # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. - for tel_id in self.dl1dh_reader.selected_telescopes[ - self.dl1dh_reader.tel_type - ]: - # Retrieve the example identifiers for the selected telescope - telescope_mask = feature_vector_table["tel_id"] == tel_id - feature_vectors_tel_table = feature_vector_table[telescope_mask] - feature_vectors_tel_table.sort(TELESCOPE_EVENT_KEYS) - # Save the prediction to the output file - write_table( - feature_vectors_tel_table, - self.output_path, - f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL1 feature vectors was stored in '%s' under '%s'", - self.output_path, - f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", - ) - - def _store_mc_telescope_pointing(self, all_identifiers): - """ - Store the telescope pointing table from MC simulation to the output file. - - Parameters: - ----------- - all_identifiers : astropy.table.Table - Table containing the telescope pointing information. - """ - # Create the pointing table for each telescope - pointing_info = [] - for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: - # Pointing table for the mono mode - tel_pointing = self.dl1dh_reader.get_tel_pointing(self.input_url, tel_id) - tel_pointing.rename_column("telescope_pointing_azimuth", "pointing_azimuth") - tel_pointing.rename_column( - "telescope_pointing_altitude", "pointing_altitude" - ) - # Join the prediction table with the telescope pointing table - tel_pointing = join( - left=tel_pointing, - right=all_identifiers, - keys=["obs_id", "tel_id"], - ) - # TODO: use keep_order for astropy v7.0.0 - tel_pointing.sort(TELESCOPE_EVENT_KEYS) - # Retrieve the example identifiers for the selected telescope - tel_pointing_table = Table( - { - "time": tel_pointing["time"], - "azimuth": tel_pointing["pointing_azimuth"], - "altitude": tel_pointing["pointing_altitude"], - } - ) - write_table( - tel_pointing_table, - self.output_path, - f"{POINTING_GROUP}/tel_{tel_id:03d}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL1 telescope pointing table was stored in '%s' under '%s'", - self.output_path, - f"{POINTING_GROUP}/tel_{tel_id:03d}", - ) - pointing_info.append(tel_pointing) - pointing_info = vstack(pointing_info) - return pointing_info - - -class StereoPredictCTLearnModel(PredictCTLearnModel): - """ - Tool to predict the gammaness, energy and arrival direction from R1/DL1 stereoscopic data using CTLearn models. - - This tool extends the ``PredictCTLearnModel`` to specifically handle stereoscopic R1/DL1 data. The prediction - is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. - It also stores the telescope/subarray pointing monitoring and DL1 feature vectors (if selected) in the output file. - - Attributes - ---------- - name : str - Name of the tool. - description : str - Description of the tool. - examples : str - Examples of how to use the tool. - - Methods - ------- - start() - Start the tool. - _store_mc_subarray_pointing(all_identifiers) - Store the subarray pointing table for the stereo mode for MC simulation. - """ - - name = "ctlearn-predict-stereo-model" - description = __doc__ - - examples = """ - To predict from pixel-wise image data in stereo mode using trained CTLearn models: - > ctlearn-predict-stereo-model \\ - --input_url input.dl1.h5 \\ - --PredictCTLearnModel.batch_size=16 \\ - --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ - --DLImageReader.channels=cleaned_image \\ - --DLImageReader.channels=cleaned_relative_peak_time \\ - --DLImageReader.image_mapper_type=BilinearMapper \\ - --DLImageReader.mode=stereo \\ - --DLImageReader.min_telescopes=2 \\ - --PredictCTLearnModel.stack_telescope_images=True \\ - --type_model="/path/to/your/stereo/type/ctlearn_model.cpk" \\ - --energy_model="/path/to/your/stereo/energy/ctlearn_model.cpk" \\ - --skydirection_model="/path/to/your/stereo/skydirection/ctlearn_model.cpk" \\ - --output output.dl2.h5 \\ - --PredictCTLearnModel.overwrite_tables=True \\ - """ - - def start(self): - self.log.info("Processing the telescope pointings...") - # Retrieve the IDs from the dl1dh for the prediction tables - example_identifiers = self.dl1dh_reader.unique_example_identifiers.copy() - example_identifiers.keep_columns(SUBARRAY_EVENT_KEYS) - all_identifiers = self.dl1dh_reader.subarray_trigger_table.copy() - all_identifiers.keep_columns(SUBARRAY_EVENT_KEYS + ["time"]) - nonexample_identifiers = setdiff( - all_identifiers, example_identifiers, keys=SUBARRAY_EVENT_KEYS - ) - nonexample_identifiers.remove_column("time") - # Construct the survival telescopes for each event of the example_identifiers - survival_telescopes = [] - for subarray_event in self.dl1dh_reader.example_identifiers_grouped.groups: - survival_mask = np.zeros(len(self.dl1dh_reader.tel_ids), dtype=bool) - survival_tels = [ - self.dl1dh_reader.subarray.tel_indices[tel_id] - for tel_id in subarray_event["tel_id"].data - ] - survival_mask[survival_tels] = True - survival_telescopes.append(survival_mask) - # Add the survival telescopes to the example_identifiers - example_identifiers.add_column( - survival_telescopes, name=f"{self.prefix}_telescopes" - ) - # Pointing table for the stereo mode for MC simulation - if self.dl1dh_reader.process_type == ProcessType.Simulation: - pointing_info = self._store_mc_subarray_pointing(all_identifiers) - - # Pointing table for the observation mode - if self.dl1dh_reader.process_type == ProcessType.Observation: - pointing_info = super()._store_pointing(all_identifiers) - - self.log.info("Starting the prediction...") - classification_feature_vectors = None - if self.load_type_model_from is not None: - # Predict the energy of the primary particle - classification_table, classification_feature_vectors = ( - super()._predict_classification(example_identifiers) - ) - if self.dl2_subarray: - # Produce output table with NaNs for missing predictions - if len(nonexample_identifiers) > 0: - nan_table = super()._create_nan_table( - nonexample_identifiers, - columns=[f"{self.prefix}_tel_prediction"], - shapes=[(len(nonexample_identifiers),)], - ) - classification_table = vstack([classification_table, nan_table]) - # Add is_valid column to the energy table - classification_table.add_column( - ~np.isnan( - classification_table[f"{self.prefix}_tel_prediction"].data, - dtype=bool, - ), - name=f"{self.prefix}_tel_is_valid", - ) - # Rename the columns for the stereo mode - classification_table.rename_column( - f"{self.prefix}_tel_prediction", f"{self.prefix}_prediction" - ) - classification_table.rename_column( - f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" - ) - classification_table.sort(SUBARRAY_EVENT_KEYS) - # Add the default values and meta data to the table - add_defaults_and_meta( - classification_table, - ParticleClassificationContainer, - prefix=self.prefix, - ) - # Save the prediction to the output file - write_table( - classification_table, - self.output_path, - f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", - ) - energy_feature_vectors = None - if self.load_energy_model_from is not None: - # Predict the energy of the primary particle - energy_table, energy_feature_vectors = super()._predict_energy( - example_identifiers - ) - if self.dl2_subarray: - # Produce output table with NaNs for missing predictions - if len(nonexample_identifiers) > 0: - nan_table = super()._create_nan_table( - nonexample_identifiers, - columns=[f"{self.prefix}_tel_energy"], - shapes=[(len(nonexample_identifiers),)], - ) - energy_table = vstack([energy_table, nan_table]) - # Add is_valid column to the energy table - energy_table.add_column( - ~np.isnan( - energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool - ), - name=f"{self.prefix}_tel_is_valid", - ) - # Rename the columns for the stereo mode - energy_table.rename_column( - f"{self.prefix}_tel_energy", f"{self.prefix}_energy" - ) - energy_table.rename_column( - f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" - ) - energy_table.sort(SUBARRAY_EVENT_KEYS) - # Add the default values and meta data to the table - add_defaults_and_meta( - energy_table, - ReconstructedEnergyContainer, - prefix=self.prefix, - ) - # Save the prediction to the output file - write_table( - energy_table, - self.output_path, - f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", - ) - direction_feature_vectors = None - if self.load_skydirection_model_from is not None: - # Join the prediction table with the telescope pointing table - example_identifiers = join( - left=example_identifiers, - right=pointing_info, - keys=SUBARRAY_EVENT_KEYS, - ) - # Predict the arrival direction of the primary particle - direction_table, direction_feature_vectors = super()._predict_skydirection( - example_identifiers - ) - if self.dl2_subarray: - # Transform the spherical coordinate offsets to sky coordinates - direction_table = super()._transform_spher_coord_offsets_to_sky( - direction_table - ) - # Produce output table with NaNs for missing predictions - if len(nonexample_identifiers) > 0: - nan_table = super()._create_nan_table( - nonexample_identifiers, - columns=[f"{self.prefix}_alt", f"{self.prefix}_az"], - shapes=[ - (len(nonexample_identifiers),), - (len(nonexample_identifiers),), - ], - ) - direction_table = vstack([direction_table, nan_table]) - # Add is_valid column to the direction table - direction_table.add_column( - ~np.isnan(direction_table[f"{self.prefix}_alt"].data, dtype=bool), - name=f"{self.prefix}_is_valid", - ) - direction_table.sort(SUBARRAY_EVENT_KEYS) - # Add the default values and meta data to the table - add_defaults_and_meta( - direction_table, - ReconstructedGeometryContainer, - prefix=self.prefix, - ) - # Save the prediction to the output file - write_table( - direction_table, - self.output_path, - f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL2 prediction data was stored in '%s' under '%s'", - self.output_path, - f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", - ) - - # Create the feature vector table if the DL1 features are enabled - if self.dl1_features: - self.log.info("Processing and storing dl1 feature vectors...") - feature_vector_table = super()._create_feature_vectors_table( - example_identifiers, - nonexample_identifiers, - classification_feature_vectors, - energy_feature_vectors, - direction_feature_vectors, - ) - # Loop over the selected telescopes and store the feature vectors - # for each telescope in the output file. The feature vectors are stored - # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. - # Rename the columns for the stereo mode - feature_vector_table.rename_column( - f"{self.prefix}_tel_classification_feature_vectors", - f"{self.prefix}_classification_feature_vectors", - ) - feature_vector_table.rename_column( - f"{self.prefix}_tel_energy_feature_vectors", - f"{self.prefix}_energy_feature_vectors", - ) - feature_vector_table.rename_column( - f"{self.prefix}_tel_geometry_feature_vectors", - f"{self.prefix}_geometry_feature_vectors", - ) - feature_vector_table.rename_column( - f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" - ) - feature_vector_table.sort(SUBARRAY_EVENT_KEYS) - # Save the prediction to the output file - write_table( - feature_vector_table, - self.output_path, - f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL1 feature vectors was stored in '%s' under '%s'", - self.output_path, - f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", - ) - - def _store_mc_subarray_pointing(self, all_identifiers): - """ - Store the subarray pointing table from MC simulation to the output file. - - Parameters: - ----------- - all_identifiers : astropy.table.Table - Table containing the subarray pointing information. - """ - # Read the subarray pointing table - pointing_info = read_table( - self.input_url, - f"{SIMULATION_CONFIG_TABLE}", - ) - # Assuming min_az = max_az and min_alt = max_alt - pointing_info.keep_columns(["obs_id", "min_az", "min_alt"]) - pointing_info.rename_column("min_az", "pointing_azimuth") - pointing_info.rename_column("min_alt", "pointing_altitude") - # Join the prediction table with the telescope pointing table - pointing_info = join( - left=pointing_info, - right=all_identifiers, - keys=["obs_id"], - ) - # TODO: use keep_order for astropy v7.0.0 - pointing_info.sort(SUBARRAY_EVENT_KEYS) - # Create the pointing table - pointing_table = Table( - { - "time": pointing_info["time"], - "array_azimuth": pointing_info["pointing_azimuth"], - "array_altitude": pointing_info["pointing_altitude"], - "array_ra": np.nan * np.ones(len(pointing_info)), - "array_dec": np.nan * np.ones(len(pointing_info)), - } - ) - # Save the pointing table to the output file - write_table( - pointing_table, - self.output_path, - f"{SUBARRAY_POINTING_GROUP}", - overwrite=self.overwrite_tables, - ) - self.log.info( - "DL1 subarray pointing table was stored in '%s' under '%s'", - self.output_path, - f"{SUBARRAY_POINTING_GROUP}", - ) - return pointing_info - - -def mono_tool(): - # Run the tool - mono_tool = MonoPredictCTLearnModel() - mono_tool.run() - - -def stereo_tool(): - # Run the tool - stereo_tool = StereoPredictCTLearnModel() - stereo_tool.run() - - -if __name__ == "mono_tool": - mono_tool() - -if __name__ == "stereo_tool": - stereo_tool() diff --git a/ctlearn/tools/predict/predict_mono.py b/ctlearn/tools/predict/predict_mono.py new file mode 100644 index 00000000..86782cbd --- /dev/null +++ b/ctlearn/tools/predict/predict_mono.py @@ -0,0 +1,541 @@ +""" +Tools to predict the gammaness, energy and arrival direction in monoscopic and stereoscopic mode using ``CTLearnModel`` on R1/DL1 data using the ``DLDataReader`` and ``DLDataLoader``. +""" + + +import numpy as np +from astropy.table import ( + Table, + vstack, + join, + setdiff, +) +from ctapipe.containers import ( + ParticleClassificationContainer, + ReconstructedGeometryContainer, + ReconstructedEnergyContainer, +) + +from ctapipe.core.traits import ComponentName +from ctapipe.io import write_table +from ctapipe.reco.reconstructor import ReconstructionProperty +from ctapipe.reco.stereo_combination import StereoCombiner +from ctapipe.reco.utils import add_defaults_and_meta +from dl1_data_handler.reader import ProcessType + + +SIMULATION_CONFIG_TABLE = "/configuration/simulation/run" +FIXED_POINTING_GROUP = "/configuration/telescope/pointing" +POINTING_GROUP = "/dl1/monitoring/telescope/pointing" +SUBARRAY_POINTING_GROUP = "/dl1/monitoring/subarray/pointing" +DL1_TELESCOPE_GROUP = "/dl1/event/telescope" +DL1_SUBARRAY_GROUP = "/dl1/event/subarray" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +DL2_TELESCOPE_GROUP = "/dl2/event/telescope" +SUBARRAY_EVENT_KEYS = ["obs_id", "event_id"] +TELESCOPE_EVENT_KEYS = ["obs_id", "event_id", "tel_id"] + +__all__ = ["MonoPredictCTLearnModel"] + +from ctlearn.tools.predict.utils.predict_model import PredictCTLearnModel + +class MonoPredictCTLearnModel(PredictCTLearnModel): + """ + Tool to predict the gammaness, energy and arrival direction from monoscopic R1/DL1 data using CTLearn models. + + This tool extends the ``PredictCTLearnModel`` to specifically handle monoscopic R1/DL1 data. The prediction + is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. + It also stores the telescope pointing monitoring and DL1 feature vectors (if selected) in the output file. + + Attributes + ---------- + name : str + Name of the tool. + description : str + Description of the tool. + examples : str + Examples of how to use the tool. + + Methods + ------- + start() + Start the tool. + _store_mc_telescope_pointing(all_identifiers) + Store the telescope pointing table for the mono mode for MC simulation. + """ + + name = "ctlearn-predict-mono-model" + description = __doc__ + + examples = """ + To predict from pixel-wise image data in mono mode using trained CTLearn models: + > ctlearn-predict-mono-model \\ + --input_url input.dl1.h5 \\ + --PredictCTLearnModel.batch_size=64 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ + --DLImageReader.channels=cleaned_image \\ + --DLImageReader.channels=cleaned_relative_peak_time \\ + --DLImageReader.image_mapper_type=BilinearMapper \\ + --type_model="/path/to/your/mono/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/mono/energy/ctlearn_model.cpk" \\ + --cameradirection_model="/path/to/your/mono/cameradirection/ctlearn_model.cpk" \\ + --dl1-features \\ + --use-HDF5Merger \\ + --no-dl1-images \\ + --no-true-images \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + + To predict from pixel-wise waveform data in mono mode using trained CTLearn models: + > ctlearn-predict-mono-model \\ + --input_url input.r1.h5 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLWaveformReader \\ + --DLWaveformReader.sequnce_length=20 \\ + --DLWaveformReader.image_mapper_type=BilinearMapper \\ + --type_model="/path/to/your/mono_waveform/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/mono_waveform/energy/ctlearn_model.cpk" \\ + --cameradirection_model="/path/to/your/mono_waveform/cameradirection/ctlearn_model.cpk" \\ + --use-HDF5Merger \\ + --no-r0-waveforms \\ + --no-r1-waveforms \\ + --no-dl1-images \\ + --no-true-images \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + """ + + stereo_combiner_cls = ComponentName( + StereoCombiner, + default_value="StereoMeanCombiner", + help="Which stereo combination method to use after the monoscopic reconstruction.", + ).tag(config=True) + + def start(self): + self.log.info("Processing the telescope pointings...") + # Retrieve the IDs from the dl1dh for the prediction tables + example_identifiers = self.dl1dh_reader.example_identifiers.copy() + example_identifiers.keep_columns(TELESCOPE_EVENT_KEYS) + all_identifiers = self.dl1dh_reader.tel_trigger_table.copy() + all_identifiers.keep_columns(TELESCOPE_EVENT_KEYS + ["time"]) + nonexample_identifiers = setdiff( + all_identifiers, example_identifiers, keys=TELESCOPE_EVENT_KEYS + ) + nonexample_identifiers.remove_column("time") + # Pointing table for the mono mode for MC simulation + if self.dl1dh_reader.process_type == ProcessType.Simulation: + pointing_info = self._store_mc_telescope_pointing(all_identifiers) + + # Pointing table for the observation mode + if self.dl1dh_reader.process_type == ProcessType.Observation: + pointing_info = super()._store_pointing(all_identifiers) + + self.log.info("Starting the prediction...") + classification_feature_vectors = None + if self.load_type_model_from is not None: + self.type_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.PARTICLE_TYPE, + parent=self, + ) + # Predict the energy of the primary particle + classification_table, classification_feature_vectors = ( + super()._predict_classification(example_identifiers) + ) + if self.dl2_telescope: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_prediction"], + shapes=[(len(nonexample_identifiers),)], + ) + classification_table = vstack([classification_table, nan_table]) + # Add is_valid column to the energy table + classification_table.add_column( + ~np.isnan( + classification_table[f"{self.prefix}_tel_prediction"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + classification_table, + ParticleClassificationContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = classification_table["tel_id"] == tel_id + classification_tel_table = classification_table[telescope_mask] + classification_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file for the selected telescope + write_table( + classification_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info("Processing and storing the subarray type prediction...") + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_classification_table = self.type_stereo_combiner.predict_table( + classification_table + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if ( + subarray_classification_table[f"{self.prefix}_telescopes"].dtype + != np.bool_ + ): + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + ( + len(subarray_classification_table), + len(self.dl1dh_reader.tel_ids), + ), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_classification_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_classification_table[f"{self.prefix}_telescopes"] = ( + reco_telescopes + ) + # Save the prediction to the output file + write_table( + subarray_classification_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + ) + energy_feature_vectors = None + if self.load_energy_model_from is not None: + self.energy_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.ENERGY, + parent=self, + ) + # Predict the energy of the primary particle + energy_table, energy_feature_vectors = super()._predict_energy( + example_identifiers + ) + if self.dl2_telescope: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_energy"], + shapes=[(len(nonexample_identifiers),)], + ) + energy_table = vstack([energy_table, nan_table]) + # Add is_valid column to the energy table + energy_table.add_column( + ~np.isnan( + energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + energy_table, + ReconstructedEnergyContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = energy_table["tel_id"] == tel_id + energy_tel_table = energy_table[telescope_mask] + energy_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file + write_table( + energy_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info( + "Processing and storing the subarray energy prediction..." + ) + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_energy_table = self.energy_stereo_combiner.predict_table( + energy_table + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if subarray_energy_table[f"{self.prefix}_telescopes"].dtype != np.bool_: + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + (len(subarray_energy_table), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_energy_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_energy_table[f"{self.prefix}_telescopes"] = reco_telescopes + # Save the prediction to the output file + write_table( + subarray_energy_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + ) + direction_feature_vectors = None + if self.load_cameradirection_model_from is not None: + self.geometry_stereo_combiner = StereoCombiner.from_name( + self.stereo_combiner_cls, + prefix=self.prefix, + property=ReconstructionProperty.GEOMETRY, + parent=self, + ) + # Join the prediction table with the telescope pointing table + example_identifiers = join( + left=example_identifiers, + right=pointing_info, + keys=TELESCOPE_EVENT_KEYS, + ) + # Predict the arrival direction of the primary particle + direction_table, direction_feature_vectors = ( + super()._predict_cameradirection(example_identifiers) + ) + direction_tel_tables = [] + if self.dl2_telescope: + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = direction_table["tel_id"] == tel_id + direction_tel_table = direction_table[telescope_mask] + direction_tel_table = super()._transform_cam_coord_offsets_to_sky( + direction_tel_table + ) + # Produce output table with NaNs for missing predictions + nan_telescope_mask = nonexample_identifiers["tel_id"] == tel_id + nonexample_identifiers_tel = nonexample_identifiers[ + nan_telescope_mask + ] + if len(nonexample_identifiers_tel) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers_tel, + columns=[f"{self.prefix}_tel_alt", f"{self.prefix}_tel_az"], + shapes=[ + (len(nonexample_identifiers_tel),), + (len(nonexample_identifiers_tel),), + ], + ) + direction_tel_table = vstack([direction_tel_table, nan_table]) + direction_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Add is_valid column to the direction table + direction_tel_table.add_column( + ~np.isnan( + direction_tel_table[f"{self.prefix}_tel_alt"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Add the default values and meta data to the table + add_defaults_and_meta( + direction_tel_table, + ReconstructedGeometryContainer, + prefix=self.prefix, + add_tel_prefix=True, + ) + direction_tel_tables.append(direction_tel_table) + # Save the prediction to the output file + write_table( + direction_tel_table, + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{tel_id:03d}", + ) + if self.dl2_subarray: + self.log.info( + "Processing and storing the subarray geometry prediction..." + ) + # Stack the telescope tables to the subarray table + direction_tel_tables = vstack(direction_tel_tables) + # Sort the table by the telescope event keys + direction_tel_tables.sort(TELESCOPE_EVENT_KEYS) + # Combine the telescope predictions to the subarray prediction using the stereo combiner + subarray_direction_table = self.geometry_stereo_combiner.predict_table( + direction_tel_tables + ) + # TODO: Remove temporary fix once the stereo combiner returns correct table + # Check if the table has to be converted to a boolean mask + if ( + subarray_direction_table[f"{self.prefix}_telescopes"].dtype + != np.bool_ + ): + # Create boolean mask for telescopes that participate in the stereo reconstruction combination + reco_telescopes = np.zeros( + (len(subarray_direction_table), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ) + # Loop over the table and set the boolean mask for the telescopes + for index, tel_id_mask in enumerate( + subarray_direction_table[f"{self.prefix}_telescopes"] + ): + if not tel_id_mask: + continue + for tel_id in tel_id_mask: + reco_telescopes[index][ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] = True + # Overwrite the column with the boolean mask with fix length + subarray_direction_table[f"{self.prefix}_telescopes"] = ( + reco_telescopes + ) + # Save the prediction to the output file + write_table( + subarray_direction_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + ) + # Create the feature vector table if the DL1 features are enabled + if self.dl1_features: + self.log.info("Processing and storing dl1 feature vectors...") + feature_vector_table = super()._create_feature_vectors_table( + example_identifiers, + nonexample_identifiers, + classification_feature_vectors, + energy_feature_vectors, + direction_feature_vectors, + ) + # Loop over the selected telescopes and store the feature vectors + # for each telescope in the output file. The feature vectors are stored + # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. + for tel_id in self.dl1dh_reader.selected_telescopes[ + self.dl1dh_reader.tel_type + ]: + # Retrieve the example identifiers for the selected telescope + telescope_mask = feature_vector_table["tel_id"] == tel_id + feature_vectors_tel_table = feature_vector_table[telescope_mask] + feature_vectors_tel_table.sort(TELESCOPE_EVENT_KEYS) + # Save the prediction to the output file + write_table( + feature_vectors_tel_table, + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 feature vectors was stored in '%s' under '%s'", + self.output_path, + f"{DL1_TELESCOPE_GROUP}/features/{self.prefix}/tel_{tel_id:03d}", + ) + + def _store_mc_telescope_pointing(self, all_identifiers): + """ + Store the telescope pointing table from MC simulation to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the telescope pointing information. + """ + # Create the pointing table for each telescope + pointing_info = [] + for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: + # Pointing table for the mono mode + tel_pointing = self.dl1dh_reader.get_tel_pointing(self.input_url, tel_id) + tel_pointing.rename_column("telescope_pointing_azimuth", "pointing_azimuth") + tel_pointing.rename_column( + "telescope_pointing_altitude", "pointing_altitude" + ) + # Join the prediction table with the telescope pointing table + tel_pointing = join( + left=tel_pointing, + right=all_identifiers, + keys=["obs_id", "tel_id"], + ) + # TODO: use keep_order for astropy v7.0.0 + tel_pointing.sort(TELESCOPE_EVENT_KEYS) + # Retrieve the example identifiers for the selected telescope + tel_pointing_table = Table( + { + "time": tel_pointing["time"], + "azimuth": tel_pointing["pointing_azimuth"], + "altitude": tel_pointing["pointing_altitude"], + } + ) + write_table( + tel_pointing_table, + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 telescope pointing table was stored in '%s' under '%s'", + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + ) + pointing_info.append(tel_pointing) + pointing_info = vstack(pointing_info) + return pointing_info + +def mono_tool(): + # Run the tool + mono_tool = MonoPredictCTLearnModel() + mono_tool.run() + +if __name__ == "main": + mono_tool() + +if __name__ == "__main__": + mono_tool() \ No newline at end of file diff --git a/ctlearn/tools/predict/predict_stereo.py b/ctlearn/tools/predict/predict_stereo.py new file mode 100644 index 00000000..c868fb70 --- /dev/null +++ b/ctlearn/tools/predict/predict_stereo.py @@ -0,0 +1,375 @@ +""" +Tools to predict the gammaness, energy and arrival direction in monoscopic and stereoscopic mode using ``CTLearnModel`` on R1/DL1 data using the ``DLDataReader`` and ``DLDataLoader``. +""" + + +import numpy as np + +from astropy.table import ( + Table, + vstack, + join, + setdiff, +) + +from ctapipe.containers import ( + ParticleClassificationContainer, + ReconstructedGeometryContainer, + ReconstructedEnergyContainer, +) + +from ctapipe.io import read_table, write_table + +from ctapipe.reco.utils import add_defaults_and_meta +from dl1_data_handler.reader import ( + ProcessType, +) +from ctlearn.tools.predict.utils.predict_model import PredictCTLearnModel + + +SIMULATION_CONFIG_TABLE = "/configuration/simulation/run" +FIXED_POINTING_GROUP = "/configuration/telescope/pointing" +POINTING_GROUP = "/dl1/monitoring/telescope/pointing" +SUBARRAY_POINTING_GROUP = "/dl1/monitoring/subarray/pointing" +DL1_TELESCOPE_GROUP = "/dl1/event/telescope" +DL1_SUBARRAY_GROUP = "/dl1/event/subarray" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +DL2_TELESCOPE_GROUP = "/dl2/event/telescope" +SUBARRAY_EVENT_KEYS = ["obs_id", "event_id"] +TELESCOPE_EVENT_KEYS = ["obs_id", "event_id", "tel_id"] + +__all__ = ["StereoPredictCTLearnModel"] + +class StereoPredictCTLearnModel(PredictCTLearnModel): + """ + Tool to predict the gammaness, energy and arrival direction from R1/DL1 stereoscopic data using CTLearn models. + + This tool extends the ``PredictCTLearnModel`` to specifically handle stereoscopic R1/DL1 data. The prediction + is performed using the CTLearn models. The data is stored in the output file following the ctapipe DL2 data format. + It also stores the telescope/subarray pointing monitoring and DL1 feature vectors (if selected) in the output file. + + Attributes + ---------- + name : str + Name of the tool. + description : str + Description of the tool. + examples : str + Examples of how to use the tool. + + Methods + ------- + start() + Start the tool. + _store_mc_subarray_pointing(all_identifiers) + Store the subarray pointing table for the stereo mode for MC simulation. + """ + + name = "ctlearn-predict-stereo-model" + description = __doc__ + + examples = """ + To predict from pixel-wise image data in stereo mode using trained CTLearn models: + > ctlearn-predict-stereo-model \\ + --input_url input.dl1.h5 \\ + --PredictCTLearnModel.batch_size=16 \\ + --PredictCTLearnModel.dl1dh_reader_type=DLImageReader \\ + --DLImageReader.channels=cleaned_image \\ + --DLImageReader.channels=cleaned_relative_peak_time \\ + --DLImageReader.image_mapper_type=BilinearMapper \\ + --DLImageReader.mode=stereo \\ + --DLImageReader.min_telescopes=2 \\ + --PredictCTLearnModel.stack_telescope_images=True \\ + --type_model="/path/to/your/stereo/type/ctlearn_model.cpk" \\ + --energy_model="/path/to/your/stereo/energy/ctlearn_model.cpk" \\ + --skydirection_model="/path/to/your/stereo/skydirection/ctlearn_model.cpk" \\ + --output output.dl2.h5 \\ + --PredictCTLearnModel.overwrite_tables=True \\ + """ + + def start(self): + self.log.info("Processing the telescope pointings...") + # Retrieve the IDs from the dl1dh for the prediction tables + example_identifiers = self.dl1dh_reader.unique_example_identifiers.copy() + example_identifiers.keep_columns(SUBARRAY_EVENT_KEYS) + all_identifiers = self.dl1dh_reader.subarray_trigger_table.copy() + all_identifiers.keep_columns(SUBARRAY_EVENT_KEYS + ["time"]) + nonexample_identifiers = setdiff( + all_identifiers, example_identifiers, keys=SUBARRAY_EVENT_KEYS + ) + nonexample_identifiers.remove_column("time") + # Construct the survival telescopes for each event of the example_identifiers + survival_telescopes = [] + for subarray_event in self.dl1dh_reader.example_identifiers_grouped.groups: + survival_mask = np.zeros(len(self.dl1dh_reader.tel_ids), dtype=bool) + survival_tels = [ + self.dl1dh_reader.subarray.tel_indices[tel_id] + for tel_id in subarray_event["tel_id"].data + ] + survival_mask[survival_tels] = True + survival_telescopes.append(survival_mask) + # Add the survival telescopes to the example_identifiers + example_identifiers.add_column( + survival_telescopes, name=f"{self.prefix}_telescopes" + ) + # Pointing table for the stereo mode for MC simulation + if self.dl1dh_reader.process_type == ProcessType.Simulation: + pointing_info = self._store_mc_subarray_pointing(all_identifiers) + + # Pointing table for the observation mode + if self.dl1dh_reader.process_type == ProcessType.Observation: + pointing_info = super()._store_pointing(all_identifiers) + + self.log.info("Starting the prediction...") + classification_feature_vectors = None + if self.load_type_model_from is not None: + # Predict the energy of the primary particle + classification_table, classification_feature_vectors = ( + super()._predict_classification(example_identifiers) + ) + if self.dl2_subarray: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_prediction"], + shapes=[(len(nonexample_identifiers),)], + ) + classification_table = vstack([classification_table, nan_table]) + # Add is_valid column to the energy table + classification_table.add_column( + ~np.isnan( + classification_table[f"{self.prefix}_tel_prediction"].data, + dtype=bool, + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Rename the columns for the stereo mode + classification_table.rename_column( + f"{self.prefix}_tel_prediction", f"{self.prefix}_prediction" + ) + classification_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + classification_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + classification_table, + ParticleClassificationContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + classification_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}", + ) + energy_feature_vectors = None + if self.load_energy_model_from is not None: + # Predict the energy of the primary particle + energy_table, energy_feature_vectors = super()._predict_energy( + example_identifiers + ) + if self.dl2_subarray: + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_tel_energy"], + shapes=[(len(nonexample_identifiers),)], + ) + energy_table = vstack([energy_table, nan_table]) + # Add is_valid column to the energy table + energy_table.add_column( + ~np.isnan( + energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool + ), + name=f"{self.prefix}_tel_is_valid", + ) + # Rename the columns for the stereo mode + energy_table.rename_column( + f"{self.prefix}_tel_energy", f"{self.prefix}_energy" + ) + energy_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + energy_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + energy_table, + ReconstructedEnergyContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + energy_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}", + ) + direction_feature_vectors = None + if self.load_skydirection_model_from is not None: + # Join the prediction table with the telescope pointing table + example_identifiers = join( + left=example_identifiers, + right=pointing_info, + keys=SUBARRAY_EVENT_KEYS, + ) + # Predict the arrival direction of the primary particle + direction_table, direction_feature_vectors = super()._predict_skydirection( + example_identifiers + ) + if self.dl2_subarray: + # Transform the spherical coordinate offsets to sky coordinates + direction_table = super()._transform_spher_coord_offsets_to_sky( + direction_table + ) + # Produce output table with NaNs for missing predictions + if len(nonexample_identifiers) > 0: + nan_table = super()._create_nan_table( + nonexample_identifiers, + columns=[f"{self.prefix}_alt", f"{self.prefix}_az"], + shapes=[ + (len(nonexample_identifiers),), + (len(nonexample_identifiers),), + ], + ) + direction_table = vstack([direction_table, nan_table]) + # Add is_valid column to the direction table + direction_table.add_column( + ~np.isnan(direction_table[f"{self.prefix}_alt"].data, dtype=bool), + name=f"{self.prefix}_is_valid", + ) + direction_table.sort(SUBARRAY_EVENT_KEYS) + # Add the default values and meta data to the table + add_defaults_and_meta( + direction_table, + ReconstructedGeometryContainer, + prefix=self.prefix, + ) + # Save the prediction to the output file + write_table( + direction_table, + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL2 prediction data was stored in '%s' under '%s'", + self.output_path, + f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}", + ) + + # Create the feature vector table if the DL1 features are enabled + if self.dl1_features: + self.log.info("Processing and storing dl1 feature vectors...") + feature_vector_table = super()._create_feature_vectors_table( + example_identifiers, + nonexample_identifiers, + classification_feature_vectors, + energy_feature_vectors, + direction_feature_vectors, + ) + # Loop over the selected telescopes and store the feature vectors + # for each telescope in the output file. The feature vectors are stored + # in the DL1_TELESCOPE_GROUP/features/{prefix}/tel_{tel_id:03d} table. + # Rename the columns for the stereo mode + feature_vector_table.rename_column( + f"{self.prefix}_tel_classification_feature_vectors", + f"{self.prefix}_classification_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_energy_feature_vectors", + f"{self.prefix}_energy_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_geometry_feature_vectors", + f"{self.prefix}_geometry_feature_vectors", + ) + feature_vector_table.rename_column( + f"{self.prefix}_tel_is_valid", f"{self.prefix}_is_valid" + ) + feature_vector_table.sort(SUBARRAY_EVENT_KEYS) + # Save the prediction to the output file + write_table( + feature_vector_table, + self.output_path, + f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 feature vectors was stored in '%s' under '%s'", + self.output_path, + f"{DL1_SUBARRAY_GROUP}/features/{self.prefix}", + ) + + def _store_mc_subarray_pointing(self, all_identifiers): + """ + Store the subarray pointing table from MC simulation to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the subarray pointing information. + """ + # Read the subarray pointing table + pointing_info = read_table( + self.input_url, + f"{SIMULATION_CONFIG_TABLE}", + ) + # Assuming min_az = max_az and min_alt = max_alt + pointing_info.keep_columns(["obs_id", "min_az", "min_alt"]) + pointing_info.rename_column("min_az", "pointing_azimuth") + pointing_info.rename_column("min_alt", "pointing_altitude") + # Join the prediction table with the telescope pointing table + pointing_info = join( + left=pointing_info, + right=all_identifiers, + keys=["obs_id"], + ) + # TODO: use keep_order for astropy v7.0.0 + pointing_info.sort(SUBARRAY_EVENT_KEYS) + # Create the pointing table + pointing_table = Table( + { + "time": pointing_info["time"], + "array_azimuth": pointing_info["pointing_azimuth"], + "array_altitude": pointing_info["pointing_altitude"], + "array_ra": np.nan * np.ones(len(pointing_info)), + "array_dec": np.nan * np.ones(len(pointing_info)), + } + ) + # Save the pointing table to the output file + write_table( + pointing_table, + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 subarray pointing table was stored in '%s' under '%s'", + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + ) + return pointing_info + +def stereo_tool(): + # Run the tool + stereo_tool = StereoPredictCTLearnModel() + stereo_tool.run() + +if __name__ == "main": + stereo_tool() +if __name__ == "__main__": + stereo_tool() \ No newline at end of file diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py new file mode 100644 index 00000000..782d56ef --- /dev/null +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -0,0 +1,180 @@ +from ctlearn.core.pytorch.net_utils import create_model, ModelHelper +from ctlearn.tools.train.pytorch.utils import ( + str_list_to_enum_list, + sanity_check, + read_configuration, + create_experiment_folder, + expected_structure, +) +import torch +from ctlearn.core.ctlearn_enum import Task, Mode +from ctapipe.io import read_table +from astropy.table import join +from dl1_data_handler.reader import ( + get_unmapped_image +) +import numpy as np +from tqdm import tqdm + +from pytorch_lightning.callbacks import Callback + +from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer + +class GPUStatsLogger(Callback): + def on_train_epoch_end(self, trainer, pl_module): + mem_allocated = torch.cuda.memory_allocated() + mem_reserved = torch.cuda.memory_reserved() + + trainer.logger.experiment.add_scalar( + "gpu_mem_allocated", mem_allocated, global_step=trainer.current_epoch + ) + trainer.logger.experiment.add_scalar( + "gpu_mem_reserved", mem_reserved, global_step=trainer.current_epoch + ) + +def predictions(self): + event_id, tel_azimuth, tel_altitude, trigger_time = [], [], [], [] + prediction, energy, cam_coord_offset_x, cam_coord_offset_y = [], [], [], [] + classification_fvs, energy_fvs, direction_fvs = [], [], [] + + for start in tqdm(range(0, self.table_length, self.batch_size), desc="Procesing input file"): + stop = min(start + self.batch_size, self.table_length) + self.log.debug("Processing chunk from '%d' to '%d'.", start, stop - 1) + # Read the data + dl1_table = read_table( + self.input_url, self.image_table_path, start=start, stop=stop + ) + # Join the dl1 table with the parameter table to perform quality selection + dl1_table = join( + left=dl1_table, + right=self.parameter_table, + keys=["event_id"], + ) + dl1_table = join( + left=dl1_table, + right=self.trigger_table, + keys=["event_id"], + ) + # Initialize a boolean mask to True for all events in the sliced dl1 table + passes_quality_checks = np.ones(len(dl1_table), dtype=bool) + # Quality selection based on the dl1b parameter + if self.quality_query: + passes_quality_checks = self.quality_query.get_table_mask(dl1_table) + # Apply the mask to filter events that are not fufilling the quality criteria + dl1_table = dl1_table[passes_quality_checks] + if len(dl1_table) == 0: + self.log.debug("No events passed the quality selection.") + continue + data = [] + for event in dl1_table: + # Get the unmapped image + image = get_unmapped_image(event, self.channels, self.transforms) + data.append(self.image_mapper.map_image(image)) + input_data = {"input": np.array(data)} + + event_id.extend(dl1_table["event_id"].data) + tel_azimuth.extend(dl1_table["tel_az"].data) + tel_altitude.extend(dl1_table["tel_alt"].data) + trigger_time.extend(dl1_table["time"].mjd) + + imgs = input_data['input'][:,:,:,0] + peak_time = input_data['input'][:,:,:,1] + imgs[imgs < 0] = 0 + peak_time[peak_time < 0] = 0 + imgs[np.isnan(imgs)] = 0 + imgs[np.isinf(imgs)] = 0 + peak_time[np.isnan(peak_time)] = 0 + peak_time[np.isinf(peak_time)] = 0 + + for task in self.tasks: + if task == Task.type: + imgs = (imgs - self.type_mu) / self.type_sigma + peak_time = (peak_time - self.type_mu) / self.type_sigma + + if len(self.channels) == 2: + classification_pred, energy_pred, direction_pred = self.type_model( + torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) + ) + else: + classification_pred, energy_pred, direction_pred = self.type_model(input_data['input'][:,:,:,0]) + + prediction.extend(torch.softmax(classification_pred[0],dim=1).cpu().detach().numpy()[:,0]) + classification_fvs.extend(classification_pred[1].cpu().detach().numpy()) + + elif task == Task.energy: + + imgs = (imgs - self.energy_mu) / self.energy_sigma + peak_time = (peak_time - self.energy_mu) / self.energy_sigma + + if len(self.channels) == 2: + classification_pred, energy_pred, direction_pred = self.energy_model( + torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) + ) + else: + classification_pred, energy_pred, direction_pred = self.energy_model(input_data['input'][:,:,:,0]) + + energy.extend(energy_pred[0].cpu().detach().numpy()) + energy_fvs.extend(energy_pred[1].cpu().detach().numpy()) + elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: + + imgs = (imgs - self.dir_mu) / self.dir_sigma + peak_time = (peak_time - self.dir_mu) / self.dir_sigma + + if len(self.channels) == 2: + classification_pred, energy_pred, direction_pred = self.dirrection_model( + torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) + ) + else: + classification_pred, energy_pred, direction_pred = self.dirrection_model(input_data['input'][:,:,:,0]) + + cam_coord_offset_x.extend(direction_pred[0][0][:,0].float().cpu().detach().numpy()) + cam_coord_offset_y.extend(direction_pred[0][0][:,1].float().cpu().detach().numpy()) + direction_fvs.extend(direction_pred[1].cpu().detach().numpy()) + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) + return event_id, tel_azimuth, tel_altitude, trigger_time, prediction, energy, cam_coord_offset_x, cam_coord_offset_y, classification_fvs, energy_fvs, direction_fvs + +def load_pytorch_model(self): + for task in self.tasks: + if task == Task.type: + model_net = create_model(self.parameters["model"]["model_type"]) + + elif task == Task.energy: + model_net = create_model(self.parameters["model"]["model_energy"]) + + elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: + model_net = create_model(self.parameters["model"]["model_direction"]) + + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) + + # ------------------------------------------------------------------------------ + # Load Checkpoints + # ------------------------------------------------------------------------------ + + if task == Task.type: + check_point_path = self.parameters["data"]["type_checkpoint"] + + elif task == Task.energy: + check_point_path = self.parameters["data"]["energy_checkpoint"] + + elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: + check_point_path = self.parameters["data"]["direction_checkpoint"] + + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) + # Load the checkpoint + + model = ModelHelper.loadModel( + model_net, "", check_point_path, Mode.observation, device_str=self.device_str + ) + + model.eval() + + return model \ No newline at end of file diff --git a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py index e69de29b..d0f975b2 100644 --- a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py @@ -0,0 +1,99 @@ +from ctlearn.core.data_loader.loader import DLDataLoader +import keras +from astropy.table import ( + Table, + hstack, + vstack, + join, + setdiff, +) +from tqdm import tqdm + +import numpy as np +import inspect + +from ctlearn.tools.predict.utils.load_model import load_model + +def predict_with_model_pytorch(self, task): + """ + Load and predict with a CTLearn model. + + Load a model from the specified path and predict the data using the loaded model. + If a last batch loader is provided, predict the last batch and stack the results. + + Parameters + ---------- + model_path : str + Path to a Keras model file (Keras3) or directory (Keras2). + + Returns + ------- + predict_data : astropy.table.Table + Table containing the prediction results. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + # Create a new DLDataLoader for each task + # It turned out to be more robust to initialize the DLDataLoader separately. + self.batch_size = self.parameters["hyp"]["batches"] + + data_loader = DLDataLoader.create( + framework="pytorch", + DLDataReader=self.dl1dh_reader, + indices=self.indices, + tasks=[task], + parameters = self.parameters, + use_augmentation = False, + batch_size=self.batch_size, + sort_by_intensity=self.sort_by_intensity, + stack_telescope_images=self.stack_telescope_images, + ) + + # Keras is only considering the last complete batch. + # In prediction mode we don't want to loose the last + # uncomplete batch, so we are creating an additional + # batch generator for the remaining events. + # data_loader_last_batch = None + # if self.last_batch_size > 0: + # last_batch_indices = self.indices[-self.last_batch_size :] + # data_loader_last_batch = DLDataLoader.create( + # framework="pytorch", + # DLDataReader=self.dl1dh_reader, + # indices=last_batch_indices, + # tasks=task, + # parameters = self.parameters, + # use_augmentation = False, + # batch_size=self.last_batch_size, + # sort_by_intensity=self.sort_by_intensity, + # stack_telescope_images=self.stack_telescope_images, + # ) + + # Load the model from the specified path + model = load_model(self) + sig = inspect.signature(model.forward) + num_inputs = len(sig.parameters) + predict_data = {} + predict_data['type'] = [] + predict_data['energy'] = [] + predict_data["cameradirection"] = [] + + model.eval() + for x in tqdm(data_loader, desc="Processing", total=len(data_loader)): + if len(x[0]['image'])==0: + continue + if num_inputs == 2: + classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device) ,x[0]['peak_time'].to(self.device)) + else: + classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device)) + + if classification_pred is not None: + predict_data['type'].extend(classification_pred.cpu().detach().numpy()) + if energy_pred is not None: + predict_data['energy'].extend(energy_pred.cpu().detach().numpy()) + if direction_pred is not None: + predict_data["cameradirection"].extend(direction_pred.cpu().detach().numpy()) + + predict_data["cameradirection"] = np.array(predict_data["cameradirection"]) + predict_data["type"] = np.array(predict_data["type"]) + predict_data["energy"] = np.array(predict_data["energy"]) + return predict_data , None \ No newline at end of file diff --git a/ctlearn/tools/predict/utils/load_model.py b/ctlearn/tools/predict/utils/load_model.py new file mode 100644 index 00000000..992872e6 --- /dev/null +++ b/ctlearn/tools/predict/utils/load_model.py @@ -0,0 +1,10 @@ +def load_model(self): + if self.framework_type == "keras": + from ctlearn.tools.predict.keras.predic_LST1_keras import load_keras_model + return load_keras_model(self) + elif self.framework_type == "pytorch": + from ctlearn.tools.predict.pytorch.predic_LST1_pytorch import load_pytorch_model + return load_pytorch_model(self) + else: + self.log.error("Framework not found !!!") + return None \ No newline at end of file diff --git a/ctlearn/tools/predict/utils/predict_model.py b/ctlearn/tools/predict/utils/predict_model.py new file mode 100644 index 00000000..c90f5dcc --- /dev/null +++ b/ctlearn/tools/predict/utils/predict_model.py @@ -0,0 +1,1006 @@ +""" +Tools to predict the gammaness, energy and arrival direction in monoscopic and stereoscopic mode using ``CTLearnModel`` on R1/DL1 data using the ``DLDataReader`` and ``DLDataLoader``. +""" + +import atexit +import pathlib +import numpy as np +import os +import tensorflow as tf +import keras +import threading +from ctlearn.core.ctlearn_enum import Task + +from astropy import units as u +from astropy.coordinates.earth import EarthLocation +from astropy.coordinates import AltAz, SkyCoord +from astropy.table import ( + Table, + hstack, + vstack, + join, + setdiff, +) +from ctlearn.tools.train.pytorch.utils import ( + sanity_check, + read_configuration, + expected_structure, +) + +from ctapipe.containers import ( + ParticleClassificationContainer, + ReconstructedGeometryContainer, + ReconstructedEnergyContainer, +) +from ctapipe.coordinates import CameraFrame, NominalFrame +from ctapipe.core import Tool +from ctapipe.core.tool import ToolConfigurationError +from ctapipe.core.traits import ( + Bool, + Int, + Path, + flag, + Set, + Dict, + List, + CaselessStrEnum, + ComponentName, + Unicode, + classes_with_traits, +) +from ctapipe.monitoring.interpolation import PointingInterpolator +from ctapipe.io import read_table, write_table, HDF5Merger +from ctapipe.reco.reconstructor import ReconstructionProperty +from ctapipe.reco.stereo_combination import StereoCombiner +from ctapipe.reco.utils import add_defaults_and_meta +from dl1_data_handler.reader import ( + DLDataReader, + ProcessType, + LST_EPOCH, +) +from ctlearn.core.data_loader.loader import DLDataLoader +from ctlearn.utils import monitor_progress + +SIMULATION_CONFIG_TABLE = "/configuration/simulation/run" +FIXED_POINTING_GROUP = "/configuration/telescope/pointing" +POINTING_GROUP = "/dl1/monitoring/telescope/pointing" +SUBARRAY_POINTING_GROUP = "/dl1/monitoring/subarray/pointing" +DL1_TELESCOPE_GROUP = "/dl1/event/telescope" +DL1_SUBARRAY_GROUP = "/dl1/event/subarray" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +DL2_TELESCOPE_GROUP = "/dl2/event/telescope" +SUBARRAY_EVENT_KEYS = ["obs_id", "event_id"] +TELESCOPE_EVENT_KEYS = ["obs_id", "event_id", "tel_id"] + +__all__ = ["PredictCTLearnModel"] + +class PredictCTLearnModel(Tool): + """ + Base tool to predict the gammaness, energy and arrival direction from R1/DL1 data using CTLearn models. + + This class handles the prediction of the gammaness, energy and arrival direction from pixel-wise image + or waveform data. It also supports the extraction of the feature vectors from the backbone submodel to + store them in the output file. The input data is loaded from the input url using the + ``~dl1_data_handler.reader.DLDataReader`` and ``~ctlearn.core.loader.DLDataLoader``. + The prediction is performed using the CTLearn models. The data is stored in the output file + following the ctapipe DL2 data format. The ``start`` method is implemented in the subclasses to + handle the prediction for mono and stereo mode. + + Attributes + ---------- + input_url : pathlib.Path + Input ctapipe HDF5 files including pixel-wise image or waveform data. + use_HDF5Merger : bool + Set whether to use the HDF5Merger component to copy the selected tables from the input file to the output file. + dl1_features : bool + Set whether to include the dl1 feature vectors in the output file. + dl2_telescope : bool + Set whether to include dl2 telescope-event-wise data in the output file. + dl2_subarray : bool + Set whether to include dl2 subarray-event-wise data in the output file. + dl1dh_reader : dl1_data_handler.reader.DLDataReader + DLDataReader object to read the data. + dl1dh_reader_type : str + Type of the DLDataReader to use for the prediction. + stack_telescope_images : bool + Set whether to stack the telescope images in the data loader. Requires ``stereo``. + sort_by_intensity : bool + Set whether to sort the telescope images by intensity in the data loader. Requires ``stereo``. + prefix : str + Name of the reconstruction algorithm used to generate the dl2 data. + load_type_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the classification of the primary particle type. + load_energy_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression of the primary particle energy. + load_cameradirection_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression + of the primary particle arrival direction based on camera coordinate offsets. + load_cameradirection_model_from : pathlib.Path + Path to a Keras model file (Keras3) or directory (Keras2) for the regression + of the primary particle arrival direction based on spherical coordinate offsets. + output_path : pathlib.Path + Output path to save the dl2 prediction results. + overwrite_tables : bool + Overwrite the table in the output file if it exists. + keras_verbose : int + Verbosity mode of Keras during the prediction. + strategy : tf.distribute.Strategy + MirroredStrategy to distribute the prediction. + data_loader : ctlearn.core.loader.DLDataLoader + DLDataLoader object to load the data. + indices : list of int + List of indices for the data loaders. + batch_size : int + Size of the batch to perform inference of the neural network. + last_batch_size : int + Size of the last batch in the data loaders. + + Methods + ------- + setup() + Set up the tool. + finish() + Finish the tool. + _predict_with_model(model_path) + Load and predict with a CTLearn model. + _predict_classification(example_identifiers) + Predict the classification of the primary particle type. + _predict_energy(example_identifiers) + Predict the energy of the primary particle. + _predict_cameradirection(example_identifiers) + Predict the arrival direction of the primary particle based on camera coordinate offsets. + _predict_skydirection(example_identifiers) + Predict the arrival direction of the primary particle based on spherical coordinate offsets. + _transform_cam_coord_offsets_to_sky(table) + Transform to camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + _transform_spher_coord_offsets_to_sky(table) + Transform to spherical coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + _create_nan_table(nonexample_identifiers, columns, shapes) + Create a table with NaNs for missing predictions. + _store_pointing(all_identifiers) + Store the telescope pointing table from to the output file. + _create_feature_vectors_table(example_identifiers, nonexample_identifiers, classification_feature_vectors, energy_feature_vectors, direction_feature_vectors) + Create the table for the DL1 feature vectors. + """ + + input_url = Path( + help="Input ctapipe HDF5 files including pixel-wise image or waveform data", + allow_none=True, + exists=True, + directory_ok=False, + file_ok=True, + ).tag(config=True) + + use_HDF5Merger = Bool( + default_value=True, + allow_none=False, + help=( + "Set whether to use the HDF5Merger component to copy the selected tables " + "from the input file to the output file. CAUTION: This can only be used " + "if the output file not exists." + ), + ).tag(config=True) + + dl1_features = Bool( + default_value=False, + allow_none=False, + help="Set whether to include the dl1 feature vectors in the output file.", + ).tag(config=True) + + dl2_telescope = Bool( + default_value=True, + allow_none=False, + help="Set whether to include dl2 telescope-event-wise data in the output file.", + ).tag(config=True) + + dl2_subarray = Bool( + default_value=True, + allow_none=False, + help="Set whether to include dl2 subarray-event-wise data in the output file.", + ).tag(config=True) + + dl1dh_reader_type = ComponentName(DLDataReader, default_value="DLImageReader").tag( + config=True + ) + + stack_telescope_images = Bool( + default_value=False, + allow_none=False, + help=( + "Set whether to stack the telescope images in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + sort_by_intensity = Bool( + default_value=False, + allow_none=False, + help=( + "Set whether to sort the telescope images by intensity in the data loader. " + "Requires DLDataReader mode to be ``stereo``." + ), + ).tag(config=True) + + prefix = Unicode( + default_value="CTLearn", + allow_none=False, + help="Name of the reconstruction algorithm used to generate the dl2 data.", + ).tag(config=True) + + load_type_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the classification " + "of the primary particle type." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_energy_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle energy." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_cameradirection_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle arrival direction based on camera coordinate offsets." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + load_skydirection_model_from = Path( + default_value=None, + help=( + "Path to a Keras model file (Keras3) or directory (Keras2) for the regression " + "of the primary particle arrival direction based on spherical coordinate offsets." + ), + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + batch_size = Int( + default_value=64, + allow_none=False, + help="Size of the batch to perform inference of the neural network.", + ).tag(config=True) + + output_path = Path( + default_value="./output.dl2.h5", + allow_none=False, + help="Output path to save the dl2 prediction results", + ).tag(config=True) + + overwrite_tables = Bool( + default_value=True, + allow_none=False, + help="Overwrite the table in the output file if it exists", + ).tag(config=True) + + pytorch_config_file = Path( + default_value="./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml", + help="Pytorch config file", + ).tag(config=True) + + keras_verbose = Int( + default_value=1, + min=0, + max=2, + allow_none=False, + help=( + "Verbosity mode of Keras during the prediction: " + "0 = silent, 1 = progress bar, 2 = one line per call." + ), + ).tag(config=True) + + framework_type = CaselessStrEnum( + ["pytorch", "keras"], + default_value="keras", + help="Framework to use: pytorch or keras", + ).tag(config=True) + + aliases = { + ("i", "input_url"): "PredictCTLearnModel.input_url", + ("t", "type_model"): "PredictCTLearnModel.load_type_model_from", + ("e", "energy_model"): "PredictCTLearnModel.load_energy_model_from", + ( + "d", + "cameradirection_model", + ): "PredictCTLearnModel.load_cameradirection_model_from", + ("s", "skydirection_model"): "PredictCTLearnModel.load_skydirection_model_from", + ("o", "output"): "PredictCTLearnModel.output_path", + ("f", "framework"): "PredictCTLearnModel.framework_type", + } + + flags = { + **flag( + "dl1-features", + "PredictCTLearnModel.dl1_features", + "Include dl1 features", + "Exclude dl1 features", + ), + **flag( + "dl2-telescope", + "PredictCTLearnModel.dl2_telescope", + "Include dl2 telescope-event-wise data in the output file", + "Exclude dl2 telescope-event-wise data in the output file", + ), + **flag( + "dl2-subarray", + "PredictCTLearnModel.dl2_subarray", + "Include dl2 telescope-event-wise data in the output file", + "Exclude dl2 telescope-event-wise data in the output file", + ), + **flag( + "use-HDF5Merger", + "PredictCTLearnModel.use_HDF5Merger", + "Copy data using the HDF5Merger component (CAUTION: This can not be used if the output file already exists)", + "Do not copy data using the HDF5Merger component", + ), + **flag( + "r0-waveforms", + "HDF5Merger.r0_waveforms", + "Include r0 waveforms", + "Exclude r0 waveforms", + ), + **flag( + "r1-waveforms", + "HDF5Merger.r1_waveforms", + "Include r1 waveforms", + "Exclude r1 waveforms", + ), + **flag( + "dl1-parameters", + "HDF5Merger.dl1_parameters", + "Include dl1 parameters", + "Exclude dl1 parameters", + ), + **flag( + "dl1-images", + "HDF5Merger.dl1_images", + "Include dl1 images", + "Exclude dl1 images", + ), + **flag( + "true-parameters", + "HDF5Merger.true_parameters", + "Include true parameters", + "Exclude true parameters", + ), + **flag( + "true-images", + "HDF5Merger.true_images", + "Include true images", + "Exclude true images", + ), + } + + classes = classes_with_traits(DLDataReader) + + def setup(self): + if self.framework_type == "pytorch": + import torch + self.log.info(f"Using {self.pytorch_config_file} config file for pytorch framework") + self.parameters = read_configuration(self.pytorch_config_file) + sanity_check(self.parameters, expected_structure) + self.device_str = self.parameters["arch"]["device"] + self.device = torch.device(self.device_str) + self.tasks = [] + self.type_mu = self.parameters["normalization"]["type_mu"] + self.type_sigma = self.parameters["normalization"]["type_sigma"] + self.dir_mu = self.parameters["normalization"]["dir_mu"] + self.dir_sigma = self.parameters["normalization"]["dir_sigma"] + self.energy_mu = self.parameters["normalization"]["energy_mu"] + self.energy_sigma = self.parameters["normalization"]["energy_sigma"] + + if self.load_type_model_from is not None: + self.tasks.append(Task.type) + if self.load_energy_model_from is not None: + self.tasks.append(Task.energy) + if self.load_cameradirection_model_from is not None: + self.tasks.append(Task.direction) + + # Check if the ctapipe HDF5Merger component is enabled + if self.use_HDF5Merger: + if os.path.exists(self.output_path): + raise ToolConfigurationError( + f"The output file '{self.output_path}' already exists. Please use " + "'--no-use-HDF5Merger' to disable the usage of the HDF5Merger component." + ) + # Copy selected tables from the input file to the output file + self.log.info("Copying to output destination.") + stop_event = threading.Event() + monitor_thread = threading.Thread(target=monitor_progress, args=(self.input_url, self.output_path, stop_event, self.log)) + monitor_thread.start() + + try: + with HDF5Merger(self.output_path, parent=self) as merger: + merger(self.input_url) + finally: + stop_event.set() + monitor_thread.join() + + else: + self.log.info( + "No copy to output destination, since the usage of the HDF5Merger component is disabled." + ) + + # Create a MirroredStrategy. + self.strategy = tf.distribute.MirroredStrategy() + atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore + self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) + + # Set up the data reader + self.log.info("Loading data reader:") + self.log.info("For a large dataset, this may take a while...") + self.dl1dh_reader = DLDataReader.from_name( + self.dl1dh_reader_type, + input_url_signal=[self.input_url], + parent=self, + ) + self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) + # Check if the number of events is enough to form a batch + if self.dl1dh_reader._get_n_events() < self.batch_size: + raise ToolConfigurationError( + f"{self.dl1dh_reader._get_n_events()} events are not enough " + f"to form a batch of size {self.batch_size}. Reduce the batch size." + ) + # Set the indices for the data loaders + self.indices = list(range(self.dl1dh_reader._get_n_events())) + self.last_batch_size = len(self.indices) % ( + self.batch_size * self.strategy.num_replicas_in_sync + ) + + def finish(self): + self.log.info("Tool is shutting down") + + def _predict_with_model(self, model_path, task): + """ + Load and predict with a CTLearn model. + + Load a model from the specified path and predict the data using the loaded model. + If a last batch loader is provided, predict the last batch and stack the results. + + Parameters + ---------- + model_path : str + Path to a Keras model file (Keras3) or directory (Keras2). + + Returns + ------- + predict_data : astropy.table.Table + Table containing the prediction results. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + predict_data = None + feature_vectors = None + + if self.framework_type == "keras": + from ctlearn.tools.predict.keras.predic_model_keras import predict_with_model + predict_data, feature_vectors = predict_with_model(self,model_path) + return predict_data, feature_vectors + + if self.framework_type == "pytorch": + from ctlearn.tools.predict.pytorch.predic_model_pytorch import predict_with_model_pytorch + predict_data, feature_vectors = predict_with_model_pytorch(self, task) + return predict_data, feature_vectors + return predict_data, feature_vectors + + def _predict_classification(self, example_identifiers): + """ + Predict the classification of the primary particle type. + + This method uses a pre-trained type model to predict the type of the primary particle + for a given set of example identifiers. The predicted classification score ('gammaness') + is added to the example identifiers table. + + Parameters: + ----------- + classification_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + predicted classification score ('gammaness'). + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the classification of the primary particle type..." + ) + # Predict the data using the loaded type_model + predict_data, feature_vectors = self._predict_with_model( + self.load_type_model_from, Task.type + ) + # Create prediction table and add the predicted classification score ('gammaness') + classification_table = example_identifiers.copy() + classification_table.add_column( + predict_data["type"].T[1], name=f"{self.prefix}_tel_prediction" + ) + return classification_table, feature_vectors + + def _predict_energy(self, example_identifiers): + """ + Predict the energy of the primary particle. + + This method uses a pre-trained energy model to predict the energy of the primary particle + for a given set of example identifiers. The predicted energy is then converted from + log10(TeV) to TeV and added to the example identifiers table. + + Parameters: + ----------- + energy_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed energy in TeV. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info("Predicting for the regression of the primary particle energy...") + # Predict the data using the loaded energy_model + predict_data, feature_vectors = self._predict_with_model( + self.load_energy_model_from, Task.energy + ) + # Convert the reconstructed energy from log10(TeV) to TeV + reco_energy = u.Quantity( + np.power(10, np.squeeze(predict_data["energy"])), + unit=u.TeV, + ) + # Create prediction table and add the reconstructed energy in TeV + energy_table = example_identifiers.copy() + energy_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy") + return energy_table, feature_vectors + + def _predict_cameradirection(self, example_identifiers): + """ + Predict the arrival direction of the primary particle based on camera coordinate offsets. + + This method uses a pre-trained direction model to predict the arrival direction of the + primary particle for a given set of example identifiers. The predicted camera coordinate offsets + is added to the example identifiers table. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + + Returns: + -------- + cameradirection_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed camera coordinate offsets in x and y. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the regression of the primary particle arrival direction based on camera coordinate offsets..." + ) + # Predict the data using the loaded direction_model + predict_data, feature_vectors = self._predict_with_model( + self.load_cameradirection_model_from, Task.direction + ) + # For the direction task, the prediction is the camera coordinate offset in x and y + # from the telescope pointing. + cam_coord_offset_x = u.Quantity(predict_data["cameradirection"].T[0], unit=u.m) + cam_coord_offset_y = u.Quantity(predict_data["cameradirection"].T[1], unit=u.m) + # Create prediction table and add the reconstructed energy in TeV + cameradirection_table = example_identifiers.copy() + cameradirection_table.add_column(cam_coord_offset_x, name="cam_coord_offset_x") + cameradirection_table.add_column(cam_coord_offset_y, name="cam_coord_offset_y") + return cameradirection_table, feature_vectors + + def _predict_skydirection(self, example_identifiers): + """ + Predict the arrival direction of the primary particle based on spherical coordinate offsets. + + This method uses a pre-trained direction model to predict the arrival direction of the primary + particle for a given set of example identifiers. The predicted spherical coordinate offsets is + added to the example identifiers table. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + + Returns: + -------- + skydirection_table : astropy.table.Table + Table containing the example identifiers with an additional column for the + reconstructed spherical coordinate offsets in fov_lon and fov_lat. + feature_vectors : np.ndarray + Feature vectors extracted from the backbone model. + """ + self.log.info( + "Predicting for the regression of the primary particle arrival direction based on spherical coordinate offsets..." + ) + # Predict the data using the loaded direction_model + predict_data, feature_vectors = self._predict_with_model( + self.load_skydirection_model_from + ) + # For the direction task, the prediction is the spherical offset in fov_lon and fov_lat + # from the telescope pointing. + fov_lon = u.Quantity(predict_data["skydirection"].T[0], unit=u.deg) + fov_lat = u.Quantity(predict_data["skydirection"].T[1], unit=u.deg) + # Create prediction table and add the reconstructed energy in TeV + skydirection_table = example_identifiers.copy() + skydirection_table.add_column(fov_lon, name="fov_lon") + skydirection_table.add_column(fov_lat, name="fov_lat") + return skydirection_table, feature_vectors + + def _transform_cam_coord_offsets_to_sky(self, table) -> Table: + """ + Transform the predicted camera coordinate offsets w.r.t. the telescope pointing to Alt/Az coordinates. + + This method converts the predicted camera coordinate offsets w.r.t. the telescope pointing + in the provided table to Alt/Az coordinates. It also removes the unnecessary columns + from the table that do not the ctapipe DL2 data format. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing the trigger time, telescope pointing, and predicted camera coordinate offsets. + + Returns: + -------- + table : astropy.table.Table + A Table with the Alt/Az coordinates following the ctapipe DL2 data format. + """ + # Get the telescope ID from the table + tel_id = table["tel_id"][0] + # Set the telescope position + tel_ground_frame = self.dl1dh_reader.subarray.tel_coords[ + self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id) + ] + # Set the trigger timestamp based on the process type + if self.dl1dh_reader.process_type == ProcessType.Simulation: + trigger_time = LST_EPOCH + elif self.dl1dh_reader.process_type == ProcessType.Observation: + trigger_time = table["time"] + # Set the telescope pointing with the trigger timestamp and the telescope position + altaz = AltAz( + location=tel_ground_frame.to_earth_location(), + obstime=trigger_time, + ) + # Set the telescope pointing + tel_pointing = SkyCoord( + az=table["pointing_azimuth"], + alt=table["pointing_altitude"], + frame=altaz, + ) + # Set the camera frame with the focal length and rotation of the camera + camera_frame = CameraFrame( + focal_length=self.dl1dh_reader.subarray.tel[ + tel_id + ].camera.geometry.frame.focal_length, + rotation=self.dl1dh_reader.pix_rotation[tel_id], + telescope_pointing=tel_pointing, + ) + # Set the camera coordinate offset + cam_coord_offset = SkyCoord( + x=table["cam_coord_offset_x"], + y=table["cam_coord_offset_y"], + frame=camera_frame, + ) + # tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] + # Transform the true Alt/Az coordinates to camera coordinates + reco_direction = cam_coord_offset.transform_to(altaz) + # Add the reconstructed direction (az, alt) to the prediction table + table.add_column(reco_direction.az.to(u.deg), name=f"{self.prefix}_tel_az") + table.add_column(reco_direction.alt.to(u.deg), name=f"{self.prefix}_tel_alt") + # Remove unnecessary columns from the table that do not the ctapipe DL2 data format + table.remove_columns( + [ + "time", + "pointing_azimuth", + "pointing_altitude", + "cam_coord_offset_x", + "cam_coord_offset_y", + ] + ) + return table + + def _transform_spher_coord_offsets_to_sky(self, table) -> Table: + """ + Transform the predicted spherical offsets w.r.t. the telescope pointing to Alt/Az coordinates. + + This method converts the predicted spherical offsets w.r.t. the telescope pointing + in the provided table to Alt/Az coordinates. It also removes the unnecessary columns + from the table that do not the ctapipe DL2 data format. + + Parameters: + ----------- + table : astropy.table.Table + A Table containing the trigger time, telescope pointing, and predicted spherical offsets. + + Returns: + -------- + table : astropy.table.Table + A Table with the Alt/Az coordinates following the ctapipe DL2 data format. + """ + + # Set the trigger timestamp based on the process type + if self.dl1dh_reader.process_type == ProcessType.Simulation: + trigger_time = LST_EPOCH + elif self.dl1dh_reader.process_type == ProcessType.Observation: + trigger_time = table["time"] + # Set the AltAz frame with the reference location and time + altaz = AltAz( + location=self.dl1dh_reader.subarray.reference_location, + obstime=trigger_time, + ) + # Set the array pointing + array_pointing = SkyCoord( + az=table["pointing_azimuth"], + alt=table["pointing_altitude"], + frame=altaz, + ) + # Set the nominal frame with the array pointing + nom_frame = NominalFrame( + origin=array_pointing, + location=self.dl1dh_reader.subarray.reference_location, + obstime=trigger_time, + ) + # Set the reco direction in (fov_lon, fov_lat) coordinates + reco_direction = SkyCoord( + fov_lon=table["fov_lon"], + fov_lat=table["fov_lat"], + frame=nom_frame, + ) + # Transform the reco direction from nominal frame to the AltAz frame + sky_coord = reco_direction.transform_to(altaz) + # Add the reconstructed direction (az, alt) to the prediction table + table.add_column(sky_coord.az.to(u.deg), name=f"{self.prefix}_az") + table.add_column(sky_coord.alt.to(u.deg), name=f"{self.prefix}_alt") + # Remove unnecessary columns from the table that do not the ctapipe DL2 data format + table.remove_columns( + [ + "time", + "pointing_azimuth", + "pointing_altitude", + "fov_lon", + "fov_lat", + ] + ) + return table + + def _create_nan_table(self, nonexample_identifiers, columns, shapes): + """ + Create a table with NaNs for missing predictions. + + This method creates a table with NaNs for missing predictions for the non-example identifiers. + In stereo mode, the table also a column for the valid telescopes is added with all False values. + + Parameters: + ----------- + nonexample_identifiers : astropy.table.Table + Table containing the non-example identifiers. + columns : list of str + List of column names to create in the table. + shapes : list of shapes + List of shapes for the columns to create in the table. + + Returns: + -------- + nan_table : astropy.table.Table + Table containing NaNs for missing predictions. + """ + # Create a table with NaNs for missing predictions + nan_table = nonexample_identifiers.copy() + for column_name, shape in zip(columns, shapes): + nan_table.add_column(np.full(shape, np.nan), name=column_name) + # Add that no telescope is valid for the non-example identifiers in stereo mode + if self.dl1dh_reader.mode == "stereo": + nan_table.add_column( + np.zeros( + (len(nonexample_identifiers), len(self.dl1dh_reader.tel_ids)), + dtype=bool, + ), + name=f"{self.prefix}_telescopes", + ) + return nan_table + + def _store_pointing(self, all_identifiers): + """ + Store the telescope pointing table from to the output file. + + Parameters: + ----------- + all_identifiers : astropy.table.Table + Table containing the telescope pointing information. + """ + + # Initialize the pointing interpolator from ctapipe + pointing_interpolator = PointingInterpolator( + bounds_error=False, extrapolate=True + ) + pointing_info = [] + for tel_id in self.dl1dh_reader.selected_telescopes[self.dl1dh_reader.tel_type]: + # Get the telescope pointing from the dl1dh reader + tel_pointing = self.dl1dh_reader.telescope_pointings[f"tel_{tel_id:03d}"] + # Add the telescope pointing table to the pointing interpolator + pointing_interpolator.add_table(tel_id, tel_pointing) + tel_identifiers = all_identifiers.copy() + if self.dl1dh_reader.mode == "mono": + tel_identifiers = tel_identifiers[tel_identifiers["tel_id"] == tel_id] + # Interpolate the telescope pointing + tel_altitude, tel_azimuth = pointing_interpolator( + tel_id, tel_identifiers["time"] + ) + tel_identifiers.add_column(tel_azimuth, name="pointing_azimuth") + tel_identifiers.add_column(tel_altitude, name="pointing_altitude") + pointing_info.append(tel_identifiers) + if self.dl1dh_reader.mode == "mono": + tel_pointing_table = Table( + { + "time": tel_identifiers["time"], + "azimuth": tel_identifiers["pointing_azimuth"], + "altitude": tel_identifiers["pointing_altitude"], + } + ) + write_table( + tel_pointing_table, + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 telescope pointing table was stored in '%s' under '%s'", + self.output_path, + f"{POINTING_GROUP}/tel_{tel_id:03d}", + ) + pointing_info = vstack(pointing_info) + if self.dl1dh_reader.mode == "stereo": + # Group the pointing information by subarray event keys + # TODO: This needs to be debugged with SST1M data + pointing_info_grouped = pointing_info.group_by(SUBARRAY_EVENT_KEYS) + pointing_mean = pointing_info_grouped.groups.aggregate(np.mean) + pointing_info = join( + all_identifiers, + pointing_mean, + keys=SUBARRAY_EVENT_KEYS, + ) + # TODO: use keep_order for astropy v7.0.0 + pointing_info.sort(SUBARRAY_EVENT_KEYS) + # Create the pointing table + pointing_table = Table( + { + "time": pointing_info["time"], + "array_azimuth": pointing_info["pointing_azimuth"], + "array_altitude": pointing_info["pointing_altitude"], + "array_ra": np.nan * np.ones(len(pointing_info)), + "array_dec": np.nan * np.ones(len(pointing_info)), + } + ) + # Save the pointing table to the output file + write_table( + pointing_table, + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + overwrite=self.overwrite_tables, + ) + self.log.info( + "DL1 subarray pointing table was stored in '%s' under '%s'", + self.output_path, + f"{SUBARRAY_POINTING_GROUP}", + ) + return pointing_info + + def _create_feature_vectors_table( + self, + example_identifiers, + nonexample_identifiers=None, + classification_feature_vectors=None, + energy_feature_vectors=None, + direction_feature_vectors=None, + ): + """ + Create the table for the DL1 feature vectors. + + This method creates a table with the DL1 feature vectors for the example identifiers and fill NaNs for + non-example identifiers. The feature vectors are stored in the columns of the table. The table also + contains a column for the valid predictions. + + Parameters: + ----------- + example_identifiers : astropy.table.Table + Table containing the example identifiers. + nonexample_identifiers : astropy.table.Table or None + Table containing the non-example identifiers to fill the NaNs. + classification_feature_vectors : np.ndarray or None + Array containing the classification feature vectors. + energy_feature_vectors : np.ndarray or None + Array containing the energy feature vectors. + direction_feature_vectors : np.ndarray or None + Array containing the direction feature vectors. + + Returns: + -------- + feature_vector_table : astropy.table.Table + Table containing the DL1 feature vectors for the example and non-example identifiers. + """ + # Create the feature vector table + feature_vector_table = example_identifiers.copy() + feature_vector_table.remove_columns( + ["pointing_azimuth", "pointing_altitude", "time"] + ) + columns_list, shapes_list = [], [] + if classification_feature_vectors is not None: + is_valid_col = ~np.isnan( + np.min(classification_feature_vectors, axis=1), dtype=bool + ) + feature_vector_table.add_column( + classification_feature_vectors, + name=f"{self.prefix}_tel_classification_feature_vectors", + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_classification_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + classification_feature_vectors.shape[1], + ) + ) + if energy_feature_vectors is not None: + is_valid_col = ~np.isnan(np.min(energy_feature_vectors, axis=1), dtype=bool) + feature_vector_table.add_column( + energy_feature_vectors, name=f"{self.prefix}_tel_energy_feature_vectors" + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_energy_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + energy_feature_vectors.shape[1], + ) + ) + if direction_feature_vectors is not None: + is_valid_col = ~np.isnan( + np.min(direction_feature_vectors, axis=1), dtype=bool + ) + feature_vector_table.add_column( + direction_feature_vectors, + name=f"{self.prefix}_tel_geometry_feature_vectors", + ) + if nonexample_identifiers is not None: + columns_list.append(f"{self.prefix}_tel_geometry_feature_vectors") + shapes_list.append( + ( + len(nonexample_identifiers), + direction_feature_vectors.shape[1], + ) + ) + # Produce output table with NaNs for missing predictions + if nonexample_identifiers is not None: + if len(nonexample_identifiers) > 0: + nan_table = self._create_nan_table( + nonexample_identifiers, + columns=columns_list, + shapes=shapes_list, + ) + feature_vector_table = vstack([feature_vector_table, nan_table]) + is_valid_col = np.concatenate( + (is_valid_col, np.zeros(len(nonexample_identifiers), dtype=bool)) + ) + # Add is_valid column to the feature vector table + feature_vector_table.add_column( + is_valid_col, + name=f"{self.prefix}_tel_is_valid", + ) + return feature_vector_table + + +if __name__ == "__main__": + pass \ No newline at end of file diff --git a/ctlearn/tools/train/_version.py b/ctlearn/tools/train/_version.py new file mode 100644 index 00000000..b8023d8b --- /dev/null +++ b/ctlearn/tools/train/_version.py @@ -0,0 +1 @@ +__version__ = '0.0.1' diff --git a/ctlearn/tools/train/conftest.py b/ctlearn/tools/train/conftest.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 20535623..200105f4 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -493,7 +493,7 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None # Final step: use regression head if t == self.model.T - 1: - energy_pred = F.tanh(self.model.regress(z))*4 + energy_pred = self.model.regress(z) # labels_energy_tev = torch.pow(10,labels_energy) # energy_pred_tev = torch.pow(10,energy_pred) @@ -708,6 +708,15 @@ def compute_sky_direction_loss_diffusion(self, x, y, labels_energy_value): # return loss, loss_dx_dy, loss_distance, loss_distance_dx_dy, loss_angular_diff, angular_diff # ---------------------------------------------------------------------------------------------------------- + def compute_class_weights(self, y, n_classes): + # y: tensor de etiquetas, por ejemplo torch.tensor([0,0,1,1,1,2]) + counts = torch.bincount(y, minlength=n_classes) + total = counts.sum() + # Evita división por cero: + counts = torch.clamp(counts, min=1) + weights = total / (counts * n_classes) + return weights + # ---------------------------------------------------------------------------------------------------------- def compute_type_loss_diffusion(self, x,y, training=False): loss = 0 @@ -742,6 +751,9 @@ def compute_type_loss_diffusion(self, x,y, training=False): # Get predictions from classifier logits = self.model.classifier(z_pred) + # class_weights = self.compute_class_weights(y, n_classes=2).to(y.device) + # loss_ce = F.cross_entropy(logits, y, class_weights) + # Cross-entropy loss: how wrong the predicted class is loss_ce = F.cross_entropy(logits, y) diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml new file mode 100644 index 00000000..f86258f5 --- /dev/null +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml @@ -0,0 +1,207 @@ +data: + + train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle + validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + train_gamma: ./data/gamma_955000_train.pickle + validation_gamma: ./data/gamma_106141_validation.pickle + + test_gamma: ./data/gamma_1805522_test_gamma.pickle + test_proton: ./data/proton_130811_test_proton.pickle + test_electron: None + test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle + + test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + observation: ./run_2931.dl1.pickle + # Important: This is only for testing purpose. Set always to 0 + # when you are training, validating or estimating the dl2 files + training_reduce_factor: 0 #64 #4 + validation_reduce_factor: 0 #16 #8 + validation_test_reduce_factor: 0 #16 #8 + + # Check points + type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth + # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth + energy_checkpoint: ./ run/run_energy_training_15/exp_15_energy_train/version_8/Epoch_13_energy_train_loss_21.4398048590775971.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth + # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + +run_details: + + mode: "train" # The option are: "train", "results", "observation" and "validate" + task: "type" # The option are: "all", "energy" "type" and "direction" + test_type: "gamma" # The option are: "gamma" "proton" or "electron" + experiment_number: 15 # The experiment number. The experiment folder is saved into the "run" folder. + + +cut-off: + + leakage_intensity: 0.2 # bigger to this value, the event is removed + intensity: 50 # below to this value, the event is removed + +model: + +# model_type: +# model_name: "DoubleBBEfficientNet" +# parameters: +# model_variant: "efficientnet-b3" +# task: 'type' +# num_outputs: 2 +# device_str: "cuda" +# energy_bins: None + + model_type: + model_name: "NoPropDT" + parameters: + # task: 'type' + num_outputs: 2 + embedding_dim: 512 + T: 6 + eta: 0.1 #0.1 + # model_type: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'type' + # num_inputs: 1 + # num_outputs: 2 + # num_blocks: [2, 3, 3, 3] + # dropout: 0.1 + # use_bn: False + + # model_energy: + # model_name: "ThinResNet" + # parameters: + # task: 'energy' + # num_inputs: 1 + # num_outputs: 1 + # num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] + # dropout: 0.1 + # use_bn: False + + # model_energy: + # model_name: "NoPropDTReg" + # parameters: + # task: 'energy' + # num_outputs: 1 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + # num_blocks: [2, 3, 3, 3] + + model_energy: + model_name: "StackedHGNet" + parameters: + task: 'energy' + input_channels: 1 + nstack: 4 + nlevels: 4 + in_channel: 256 + output_dim: 1 + use_bn: False + use_stn: False + + # model_direction: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'direction' + # num_inputs: 1 + # num_outputs: 3 + # num_blocks: [3, 4, 6, 3] + # dropout: 0.1 + # use_bn: False + + model_direction: + model_name: "NoPropDTReg" + parameters: + task: 'direction' + num_outputs: 3 + embedding_dim: 512 + T: 3 + eta: 0.1 #0.1 + num_blocks: [2, 3, 3, 3] + + # model_direction: + # model_name: "DBBNoPropDTReg" + # parameters: + # task: 'direction' + # num_outputs: 3 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + +# Hyper-parameters +hyp: + + epochs: 30 + batches: 128 #128 #64 + dynamic_batches: True + optimizer: Adamw + momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 + weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 + learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 + lrf: 0.1 + start_epoch: 0 + steps_epoch: 100 # Computed online. Must be removed + l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) + adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 + gradient_clip_val: 3.0 # Avoid gradient explosion + + save_k: 200 # Save as maximum k checkpoints. + +augmentation: + # probabilities for augmentation range = [0, 1.0] + # prob = 0.0 -> Always apply the augmentation + # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. + # Note: mask augmentation is always on even with flag use_augmentation = True + # To disable it, just set to 2.5 for example. + + use_augmentation: True # This apply only on training mode. + aug_prob: 0.5 # Probability of use Augmentation + rot_prob: 0.5 # Rotation probability + trans_prob: 0.5 # Translation probability + flip_hor_prob: 0.5 # Horizontal Flip probability + flip_ver_prob: 0.5 # Vertical Flip probability + mask_prob: 0.5 # Apply mask probability + mask_dvr_prob: 0.5 # Apply dvr mask probability + noise_prob: 0.5 # No implemented yet. + max_rot: 5 # Maximum rotation in augmentation + max_trans: 10 # Maximum translation in augmentation + +normalization: + + # Normalization: Im' = (Im-mu)/sigma + use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) + use_clean_dvr: False + type_mu: 0.0 + type_sigma: 1000.0 + + dir_mu: 0.0 + dir_sigma: 1000.0 + + energy_mu: 0.0 + # energy_sigma: 1000.0 + energy_sigma: 10.0 +dataset: + num_workers: 1 # + pin_memory: True + persistent_workers: True # + +# Hardware Architecture and precision +arch: + # device: 'mps' # Apple Mx + device: 'cuda' + precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) + # devices: [0,1] # [0,1] For multiple GPUs + devices: [0,1] + # Note: Check the documentation for more information. + strategy: 'deepspeed_stage_2' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... + +Notes: + Note_1: Training with augmentation dvr using 1-3 dilatations + Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml new file mode 100644 index 00000000..48de9772 --- /dev/null +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml @@ -0,0 +1,208 @@ +data: + + train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle + validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + train_gamma: ./data/gamma_955000_train.pickle + validation_gamma: ./data/gamma_106141_validation.pickle + + test_gamma: ./data/gamma_1805522_test_gamma.pickle + test_proton: ./data/proton_130811_test_proton.pickle + test_electron: None + test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle + + test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + observation: ./run_2931.dl1.pickle + # Important: This is only for testing purpose. Set always to 0 + # when you are training, validating or estimating the dl2 files + training_reduce_factor: 0 #64 #4 + validation_reduce_factor: 0 #16 #8 + validation_test_reduce_factor: 0 #16 #8 + + # Check points + type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth + # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth + energy_checkpoint: ./ run/run_energy_training_15/exp_15_energy_train/version_14/Epoch_5_energy_train_loss_24.1305048130270734.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth + # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + +run_details: + + mode: "train" # The option are: "train", "results", "observation" and "validate" + task: "type" # The option are: "all", "energy" "type" and "direction" + test_type: "gamma" # The option are: "gamma" "proton" or "electron" + experiment_number: 15 # The experiment number. The experiment folder is saved into the "run" folder. + + +cut-off: + + leakage_intensity: 0.2 # bigger to this value, the event is removed + intensity: 50 # below to this value, the event is removed + +model: + +# model_type: +# model_name: "DoubleBBEfficientNet" +# parameters: +# model_variant: "efficientnet-b3" +# task: 'type' +# num_outputs: 2 +# device_str: "cuda" +# energy_bins: None + + model_type: + model_name: "NoPropDT" + parameters: + # task: 'type' + num_outputs: 2 + embedding_dim: 512 + T: 6 + eta: 0.1 #0.1 + + # model_type: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'type' + # num_inputs: 1 + # num_outputs: 2 + # num_blocks: [2, 3, 3, 3] + # dropout: 0.1 + # use_bn: False + + # model_energy: + # model_name: "ThinResNet" + # parameters: + # task: 'energy' + # num_inputs: 1 + # num_outputs: 1 + # num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] + # dropout: 0.1 + # use_bn: False + + model_energy: + model_name: "NoPropDTRegDBB" + parameters: + task: 'energy' + num_outputs: 1 + embedding_dim: 512 + T: 3 + eta: 0.1 #0.1 + num_blocks: [2, 3, 3, 3] + + # model_energy: + # model_name: "StackedHGNetDBB" + # parameters: + # task: 'energy' + # input_channels: 1 + # nstack: 4 + # nlevels: 4 + # in_channel: 256 + # output_dim: 1 + # use_bn: False + # use_stn: False + + # model_direction: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'direction' + # num_inputs: 1 + # num_outputs: 3 + # num_blocks: [3, 4, 6, 3] + # dropout: 0.1 + # use_bn: False + + model_direction: + model_name: "NoPropDTReg" + parameters: + task: 'direction' + num_outputs: 3 + embedding_dim: 512 + T: 3 + eta: 0.1 #0.1 + num_blocks: [2, 3, 3, 3] + + # model_direction: + # model_name: "DBBNoPropDTReg" + # parameters: + # task: 'direction' + # num_outputs: 3 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + +# Hyper-parameters +hyp: + + epochs: 50 + batches: 64 #128 #64 + dynamic_batches: True + optimizer: Adamw + momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 + weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 + learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 + lrf: 0.1 + start_epoch: 0 + steps_epoch: 100 # Computed online. Must be removed + l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) + adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 + gradient_clip_val: 3.0 # Avoid gradient explosion + + save_k: 200 # Save as maximum k checkpoints. + +augmentation: + # probabilities for augmentation range = [0, 1.0] + # prob = 0.0 -> Always apply the augmentation + # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. + # Note: mask augmentation is always on even with flag use_augmentation = True + # To disable it, just set to 2.5 for example. + + use_augmentation: True # This apply only on training mode. + aug_prob: 0.5 # Probability of use Augmentation + rot_prob: 0.5 # Rotation probability + trans_prob: 0.5 # Translation probability + flip_hor_prob: 0.5 # Horizontal Flip probability + flip_ver_prob: 0.5 # Vertical Flip probability + mask_prob: 0.5 # Apply mask probability + mask_dvr_prob: 0.5 # Apply dvr mask probability + noise_prob: 0.5 # No implemented yet. + max_rot: 5 # Maximum rotation in augmentation + max_trans: 10 # Maximum translation in augmentation + +normalization: + + # Normalization: Im' = (Im-mu)/sigma + use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) + use_clean_dvr: False + type_mu: 0.0 + type_sigma: 1000.0 + + dir_mu: 0.0 + dir_sigma: 1000.0 + + energy_mu: 0.0 + # energy_sigma: 1000.0 + energy_sigma: 1000.0 +dataset: + num_workers: 1 # + pin_memory: True + persistent_workers: True # + +# Hardware Architecture and precision +arch: + # device: 'mps' # Apple Mx + device: 'cuda' + precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) + # devices: [0,1] # [0,1] For multiple GPUs + devices: [0,1] + # Note: Check the documentation for more information. + strategy: 'deepspeed_stage_2' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... + +Notes: + Note_1: Training with augmentation dvr using 1-3 dilatations + Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 01c159ab..4b935be4 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -202,6 +202,7 @@ def setup(self): stack_telescope_images=self.stack_telescope_images, parameters=self.parameters, use_augmentation=self.parameters["augmentation"]["use_augmentation"], + is_training=True, ) self.validation_loader = DLDataLoader.create( @@ -215,6 +216,7 @@ def setup(self): stack_telescope_images=self.stack_telescope_images, parameters=self.parameters, use_augmentation=False, + is_training=False, ) diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index f7544d32..042b5dfd 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -213,7 +213,7 @@ def main(): # Energy -# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml> nohup_energy_training.out 2>&1 & # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs243-302.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs303-362.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs363-422.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs423-482.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs483-541.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs542-600.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs63-122.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & @@ -221,3 +221,4 @@ def main(): # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs63-122.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml> nohup_energy_training.out 2>&1 & \ No newline at end of file diff --git a/monitoring_gpus.py b/monitoring_gpus.py new file mode 100644 index 00000000..30c146e3 --- /dev/null +++ b/monitoring_gpus.py @@ -0,0 +1,108 @@ +import re +import requests +import paramiko +from dotenv import load_dotenv +import os +load_dotenv() + +# Configuration +FILE_URL = "https://www.cc.iaa.es/system/nodes/status" # Replace with the actual URL +LOCAL_FILE = "status.html" +SSH_USER = os.getenv("SSH_USER") +SSH_PASSWORD = os.getenv("SSH_PASSWORD") +COMMAND_TO_RUN = "conda activate ctlearn_pytorch && cd neutron_remote_connection && ls" # Replace with the actual command + +COMMANDS_FILE = "commands.txt" +STATE_FILE = "last_command_index.txt" + +def get_next_command(): + """Reads the next command to execute from the file, keeping track of progress.""" + if not os.path.exists(COMMANDS_FILE): + print("Commands file not found.") + return None + + with open(COMMANDS_FILE, "r") as f: + commands = f.readlines() + + if not commands: + print("No commands to execute.") + return None + + last_index = 0 + if os.path.exists(STATE_FILE): + with open(STATE_FILE, "r") as f: + last_index = int(f.read().strip()) + + if last_index >= len(commands): + print("All commands have been executed.") + return None + + next_command = commands[last_index].strip() + + with open(STATE_FILE, "w") as f: + f.write(str(last_index + 1)) + + return next_command + +def download_file(): + """ Downloads the HTML file using requests without SSL verification """ + try: + response = requests.get(FILE_URL, verify=False) # Disable SSL verification + with open(LOCAL_FILE, "wb") as file: + file.write(response.content) + print("File downloaded successfully (SSL verification disabled).") + except Exception as e: + print(f"Error downloading the file: {e}") + +def parse_gpu_usage(): + """ Parses the HTML file to find GPUs with usage below 20% """ + low_usage_nodes = [] + with open(LOCAL_FILE, "r", encoding="utf-8") as file: + content = file.read() + + # Regular expression to extract node names and GPU usage + pattern = re.compile( + r']*>.*?\s*' # Node (e.g., n1.iaa.es) + r']*>.*?\s*' # %CPU + r']*>.*?\s*' # Cores + r']*>.*?\s*' # %Mem + r']*>.*?\s*' # GBMem + r']*>([\d.]+)', # %GPU + re.DOTALL + ) + + for match in pattern.findall(content): + node, gpu_usage = match[0], float(match[1]) + if gpu_usage < 20: + low_usage_nodes.append(node) + + return low_usage_nodes + +def execute_remote_command(node): + """ Connects to the node via SSH and executes a command """ + command = get_next_command() + try: + print(f"Connecting to {node} to execute {command}...") + client = paramiko.SSHClient() + client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) + client.connect(node, username=SSH_USER, password=SSH_PASSWORD) + stdin, stdout, stderr = client.exec_command(command) + print(f"Command output on {node}:\n", stdout.read().decode()) + client.close() + except Exception as e: + print(f"SSH connection error on {node}: {e}") + +def main(): + download_file() + idle_gpus = parse_gpu_usage() + + if idle_gpus: + print(f"Detected GPUs with low usage on nodes: {idle_gpus}") + for node in idle_gpus: + execute_remote_command(node) + print(node) + else: + print("No GPUs with usage below 20% were found.") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/test_dataloader.py b/test_dataloader.py new file mode 100644 index 00000000..a6e705e3 --- /dev/null +++ b/test_dataloader.py @@ -0,0 +1,105 @@ +from ctlearn.core.data_loader.loader import DLDataLoader +from dl1_data_handler.reader import DLImageReader +from dl1_data_handler.reader import DLDataReader +import numpy as np +import matplotlib.pyplot as plt +from ctlearn.tools.train.pytorch.utils import read_configuration + +def on_key(event): + # Check if the "Esc" key was pressed + if event.key == 'escape': + plt.close(event.canvas.figure) + exit() + + +# dl1_gamma_file = ["/storage/ctlearn_data/h5_files/mc/gamma_theta_16.087_az_108.090_runs123-182.r1.dl1.h5"] +dl1_gamma_file = ["/storage/ctlearn_data/h5_files/mc/gamma-diffuse/gamma_theta_9.579_az_233.112_runs481-540.dl1.h5"] +config_file = "./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml" +dl1dh_reader = DLDataReader.from_name( + "DLImageReader", + input_url_signal=dl1_gamma_file, + channels = ["cleaned_image","cleaned_peak_time"], + # input_url_background=sorted(self.input_url_background), + # parent=self, +) + +# dl1_reader = DLImageReader(input_url_signal=[dl1_gamma_file], config=config) +# dataloader = DLDataLoader.create("pytorch") +parameters = read_configuration(config_file) + +random_seed = 0 +indices = list(range(dl1dh_reader._get_n_events())) +training_loader = DLDataLoader.create( + framework="pytorch", + DLDataReader=dl1dh_reader, + indices=indices, + tasks=["type","energy","skydirection","cameradirection","hillas"], + batch_size=32, + random_seed=0, + sort_by_intensity=False, + stack_telescope_images=False, + parameters= parameters, + use_augmentation=True, + is_training=True, + +) + +for batch_idx, (features, labels,t ) in enumerate(training_loader): + + plt.rcParams['keymap.quit'].append(' ') + fig, axes = plt.subplots(1, 2, figsize=(15, 5)) + ax = axes.ravel() + # fig.canvas.mpl_connect('key_press_event', lambda evt: print(repr(evt.key))) + fig.canvas.mpl_connect('key_press_event', on_key) + + if len(features)>0: + for id in range(len(features["image"])): + # gammaness = 0 + # gammaness = labels["gammaness"][id] + + # if gammaness>0.9: + + image = features["image"][id] + # clean_image = features["clean_image"] + peak_time = features["peak_time"][id] + # clean_peak_time = features["clean_peak_time"] + labels_class = labels["type"][id] + + hillas_intensity = features["hillas"]["hillas_intensity"][id].cpu().detach().numpy() + leakage_pixels_width_1= features["hillas"]["leakage_pixels_width_1"][id].cpu().detach().numpy() + leakage_pixels_width_2= features["hillas"]["leakage_pixels_width_2"][id].cpu().detach().numpy() + + leakage_intensity_width_1 = features["hillas"]["leakage_intensity_width_1"][id].cpu().detach().numpy() + leakage_intensity_width_2 = features["hillas"]["leakage_intensity_width_2"][id].cpu().detach().numpy() + + morphology_n_islands= features["hillas"]["morphology_n_islands"][id].cpu().detach().numpy() + + if leakage_intensity_width_2<0.2: + for key in features["hillas"].keys(): + print(f"{key}: {features['hillas'][key][id].cpu().detach().numpy()}") + + image = np.transpose(image, (1, 2, 0)) + # clean_image = np.transpose(clean_image, (1, 2, 0)) + + peak_time = np.transpose(peak_time, (1, 2, 0)) + # clean_peak_time = np.transpose(clean_peak_time, (1, 2, 0)) + + ax[0].set_title( + f"\n qCharge:\n Hillas Intensity:{hillas_intensity} \n Leakage_p_w_2: {leakage_pixels_width_2} \n Leakage_i_w_2: {leakage_intensity_width_2} \n morphology_n_islands: {morphology_n_islands} \n labels_class: {str(labels_class)}" , fontsize=8 + ) + # ax[1].set_title( + # f"Peak time: \n Gammaness: {gammaness}" , fontsize=8 + # ) + ax[0].imshow(image, cmap="viridis") + ax[1].imshow(peak_time, cmap="viridis") + + # print(f"Gammaness: {gammaness}") + # if leakage_intensity_width_2<0.2: + # print(f"found {leakage_intensity_width_2}") + plt.show() + # plt.show(block=False) + # plt.pause(1) + # print(batch_idx) + plt.close() + + ii = 0 \ No newline at end of file From f34ae127837e08b820369dca6591205756d18ef5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Thu, 24 Jul 2025 14:30:30 +0000 Subject: [PATCH 041/119] Update the git ignore --- .gitignore | 4 +- calibration/calibration.py | 82 -------------------------------- calibration/undistort.py | 95 -------------------------------------- 3 files changed, 3 insertions(+), 178 deletions(-) delete mode 100644 calibration/calibration.py delete mode 100644 calibration/undistort.py diff --git a/.gitignore b/.gitignore index 10018dd8..92328149 100644 --- a/.gitignore +++ b/.gitignore @@ -34,4 +34,6 @@ run/ test/ *.se2 *.jar -*.puml \ No newline at end of file +*.puml +mc_tjark/ +calibration/ \ No newline at end of file diff --git a/calibration/calibration.py b/calibration/calibration.py deleted file mode 100644 index dc7460d9..00000000 --- a/calibration/calibration.py +++ /dev/null @@ -1,82 +0,0 @@ -import cv2 -import numpy as np -import matplotlib.pyplot as plt - -def detectar_lineas(img): - """Detecta líneas rectas usando Canny + Hough.""" - gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - edges = cv2.Canny(gray, 80, 200, apertureSize=3) - lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=80, minLineLength=120, maxLineGap=20) - return lines - -def dibujar_lineas(img, lines): - img_lines = img.copy() - for line in lines: - x1, y1, x2, y2 = line[0] - cv2.line(img_lines, (x1, y1), (x2, y2), (0,255,0), 2) - return img_lines - -def calcular_puntos_de_fuga(lines): - """Calcula puntos de fuga aproximados por agrupación de dirección.""" - puntos = [] - for line in lines: - x1, y1, x2, y2 = line[0] - puntos.append(((x1, y1), (x2, y2))) - - # Calcular las intersecciones de todas las líneas - def intersection(line1, line2): - (x1,y1), (x2,y2) = line1 - (x3,y3), (x4,y4) = line2 - denom = (x1-x2)*(y3-y4)-(y1-y2)*(x3-x4) - if denom == 0: - return None - px = ((x1*y2 - y1*x2)*(x3-x4) - (x1-x2)*(x3*y4 - y3*x4))/denom - py = ((x1*y2 - y1*x2)*(y3-y4) - (y1-y2)*(x3*y4 - y3*x4))/denom - return [px, py] - - intersections = [] - for i in range(len(puntos)): - for j in range(i+1, len(puntos)): - pt = intersection(puntos[i], puntos[j]) - if pt is not None and all(0 <= c <= 1500 for c in pt): # dentro de rango razonable - intersections.append(pt) - return np.array(intersections) - -def estimar_centro_optico_y_focal(intersections, img_shape): - """Estima el centro óptico y focal a partir de puntos de fuga.""" - if len(intersections) < 2: - return (img_shape[1]//2, img_shape[0]//2), 900 - - # Promedia las intersecciones para estimar el centro óptico - intersections = np.array(intersections) - cx = np.median(intersections[:,0]) - cy = np.median(intersections[:,1]) - - # Estimación simple de la focal como distancia al centro de la imagen - f_est = np.mean(np.linalg.norm(intersections - np.array([[cx, cy]]), axis=1)) - return (cx, cy), f_est - -# --- MAIN --- -img_path = './calibration/image_2.png' # Usa el nombre de tu imagen -img = cv2.imread(img_path) - -lines = detectar_lineas(img) -img_with_lines = dibujar_lineas(img, lines) - -plt.imshow(cv2.cvtColor(img_with_lines, cv2.COLOR_BGR2RGB)) -plt.title('Líneas detectadas') -plt.show() - -intersections = calcular_puntos_de_fuga(lines) -(cx, cy), f_est = estimar_centro_optico_y_focal(intersections, img.shape) - -print("Centro óptico estimado: (%.1f, %.1f)" % (cx, cy)) -print("Distancia focal estimada (en píxeles): %.1f" % f_est) - -# Matriz intrínseca -K = np.array([ - [f_est, 0, cx], - [0, f_est, cy], - [0, 0, 1] -]) -print("Matriz intrínseca estimada:\n", K) diff --git a/calibration/undistort.py b/calibration/undistort.py deleted file mode 100644 index c33094f0..00000000 --- a/calibration/undistort.py +++ /dev/null @@ -1,95 +0,0 @@ -import cv2 -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.widgets import Button -from scipy.optimize import minimize - -# 1. Selección interactiva de puntos con matplotlib -def select_lines_points(img): - plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) - plt.title("Haz click para seleccionar puntos de una línea recta.\nPresiona ENTER para terminar la línea. ESC para terminar todas.") - lines_pts = [] - curr_pts = [] - - def onclick(event): - if event.inaxes: - x, y = int(event.xdata), int(event.ydata) - curr_pts.append([x, y]) - plt.plot(x, y, 'ro') - plt.draw() - - def onkey(event): - if event.key == 'enter': - if len(curr_pts) >= 2: - lines_pts.append(np.array(curr_pts)) - plt.plot(np.array(curr_pts)[:,0], np.array(curr_pts)[:,1], 'g-') - plt.draw() - curr_pts.clear() - elif event.key == 'escape': - plt.close() - - fig = plt.gcf() - cid_click = fig.canvas.mpl_connect('button_press_event', onclick) - cid_key = fig.canvas.mpl_connect('key_press_event', onkey) - plt.show() - fig.canvas.mpl_disconnect(cid_click) - fig.canvas.mpl_disconnect(cid_key) - return lines_pts - -# 2. Funciones para ajuste de distorsión -def undistort_points(points, k, cx, cy): - undistorted = [] - for x, y in points: - xd = x - cx - yd = y - cy - r2 = xd**2 + yd**2 - factor = 1 + k[0]*r2 + k[1]*r2**2 + k[2]*r2**3 + k[3]*r2**4 - xu = cx + xd * factor - yu = cy + yd * factor - undistorted.append([xu, yu]) - return np.array(undistorted) - -def line_straightness_error(k, lines_pts, cx, cy): - total_error = 0 - for pts in lines_pts: - pts_ud = undistort_points(pts, k, cx, cy) - # Ajuste de recta a los puntos no distorsionados - [vx, vy, x0, y0] = cv2.fitLine(pts_ud.astype(np.float32), cv2.DIST_L2, 0, 0.01, 0.01) - # Distancia de cada punto a la recta - dists = np.abs(vy*(pts_ud[:,0]-x0) - vx*(pts_ud[:,1]-y0)) - total_error += np.sum(dists**2) - return total_error - -# --- MAIN --- -# Cambia esto por la ruta de tu imagen -img_path = './calibration/image_2.png' -img = cv2.imread(img_path) -img_h, img_w = img.shape[:2] -cx, cy = img_w / 2, img_h / 2 # Centro óptico estimado - -# Paso 1: Selección interactiva de puntos -lines_pts = select_lines_points(img) -if len(lines_pts) < 1: - print("¡Debes seleccionar al menos una línea!") - exit() - -# Paso 2: Ajuste de coeficientes de distorsión radial -k_init = np.array([-0.01, 0, 0, 0]) - -res = minimize(line_straightness_error, k_init, args=(lines_pts, cx, cy), method='Powell') -k_opt = res.x - -print("Coeficientes de distorsión radial estimados:") -print("k1=%.6f, k2=%.6f, k3=%.6f, k4=%.6f" % tuple(k_opt)) - -# Paso 3: Visualiza el resultado -plt.figure() -plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) -colors = ['r', 'g', 'b', 'c', 'm'] -for i, pts in enumerate(lines_pts): - pts_ud = undistort_points(pts, k_opt, cx, cy) - plt.plot(pts[:,0], pts[:,1], colors[i%len(colors)]+'o-', label=f'Línea {i+1} original') - plt.plot(pts_ud[:,0], pts_ud[:,1], colors[i%len(colors)]+'x--', label=f'Línea {i+1} corregida') -plt.title('Puntos originales y corregidos') -plt.legend() -plt.show() From 2fef76396c46f9fc4557f7a100feb826b9528034 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 26 May 2025 14:17:49 +0000 Subject: [PATCH 042/119] Change the pyproyect file, to the new structure of folders --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f2a5c057..4f3bcf46 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,7 +62,7 @@ documentation = "https://ctlearn.readthedocs.io/en/latest/" [project.scripts] ctlearn-train-model = "ctlearn.tools.train_model:main" -ctlearn-predict-mono-model = "ctlearn.tools.predict_model:mono_tool" +ctlearn-predict-mono-model = "ctlearn.tools.predict.predict_model:mono_tool" ctlearn-predict-stereo-model = "ctlearn.tools.predict_model:stereo_tool" ctlearn-predict-LST1= "ctlearn.tools.predict_LST1:main" From cecc3dd81de320f4150a717e1eca580a20cc37e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 28 May 2025 13:31:15 +0000 Subject: [PATCH 043/119] Add the Subarray Descriptions and refactoring the predict task using keras --- .gitignore | 4 ++-- .../tools/predict/keras/predic_LST1_keras.py | 2 +- ctlearn/utils.py | 24 +------------------ 3 files changed, 4 insertions(+), 26 deletions(-) diff --git a/.gitignore b/.gitignore index 92328149..e4a3ae8c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,5 @@ ctlearn/_version.py - +output_dir2/ *.swp *.swo *.gemini @@ -25,7 +25,7 @@ __pycache__/ .DS_Store *.egg-info/ dist -.pytest_cache/ +.pytest_cache/*.log # Sphinx documentation docs/build/ diff --git a/ctlearn/tools/predict/keras/predic_LST1_keras.py b/ctlearn/tools/predict/keras/predic_LST1_keras.py index a5848c02..3bf2c0bf 100644 --- a/ctlearn/tools/predict/keras/predic_LST1_keras.py +++ b/ctlearn/tools/predict/keras/predic_LST1_keras.py @@ -130,4 +130,4 @@ def load_keras_model(self): input_shape = model_direction.input_shape[1:] self.backbone_direction, self.head_direction = _split_model(model_direction) - return input_shape \ No newline at end of file + return input_shape diff --git a/ctlearn/utils.py b/ctlearn/utils.py index d2929527..22ad94e2 100644 --- a/ctlearn/utils.py +++ b/ctlearn/utils.py @@ -2,30 +2,8 @@ from ctapipe.core import Provenance from ctapipe.core.traits import TraitError -from ctapipe.instrument import SubarrayDescription -from ctapipe.instrument.optics import FocalLengthKind - -__all__ = ["get_lst1_subarray_description", "validate_trait_dict"] - -def get_lst1_subarray_description(focal_length_choice=FocalLengthKind.EFFECTIVE): - """ - Load subarray description from bundled file - - Parameters - ---------- - focal_length_choice : FocalLengthKind - Choice of focal length to use. Options are ``FocalLengthKind.EQUIVALENT`` - and ``FocalLengthKind.EFFECTIVE``. Default is ``FocalLengthKind.EFFECTIVE``. - - Returns - ------- - SubarrayDescription - Subarray description of the LST-1 telescope. - """ - with as_file(files("ctlearn") / "resources/LST-1_SubarrayDescription.h5") as path: - Provenance().add_input_file(path, role="SubarrayDescription") - return SubarrayDescription.from_hdf(path, focal_length_choice=focal_length_choice) +__all__ = ["validate_trait_dict"] def validate_trait_dict(dict, required_keys): """ From abb72413e09b4aba1865b3cdabb06bceabb2de32 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 28 May 2025 15:26:17 +0000 Subject: [PATCH 044/119] Separete the model and update the references --- ctlearn/tools/predict/predict_LST1.py | 2 ++ pyproject.toml | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/ctlearn/tools/predict/predict_LST1.py b/ctlearn/tools/predict/predict_LST1.py index 759df684..ba03ddcb 100644 --- a/ctlearn/tools/predict/predict_LST1.py +++ b/ctlearn/tools/predict/predict_LST1.py @@ -8,6 +8,8 @@ from astropy import units as u from astropy.coordinates import AltAz,SkyCoord from astropy.table import Table, setdiff, vstack +from astropy.coordinates import AltAz,SkyCoord +from astropy.table import Table, setdiff, vstack from astropy.time import Time from ctapipe.containers import ( diff --git a/pyproject.toml b/pyproject.toml index 4f3bcf46..372beb66 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,8 +62,8 @@ documentation = "https://ctlearn.readthedocs.io/en/latest/" [project.scripts] ctlearn-train-model = "ctlearn.tools.train_model:main" -ctlearn-predict-mono-model = "ctlearn.tools.predict.predict_model:mono_tool" -ctlearn-predict-stereo-model = "ctlearn.tools.predict_model:stereo_tool" +ctlearn-predict-mono-model = "ctlearn.tools.predict.predict_mono:main" +ctlearn-predict-stereo-model = "ctlearn.tools.predict_stereo:main" ctlearn-predict-LST1= "ctlearn.tools.predict_LST1:main" [tool.setuptools_scm] From 4e5f8530076bb26e578784419bd96892ec4377c8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Fri, 30 May 2025 08:44:32 +0000 Subject: [PATCH 045/119] Add some changes in the predictions tasks --- .gitignore | 1 + test_dataloader.py | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index e4a3ae8c..6148cad1 100644 --- a/.gitignore +++ b/.gitignore @@ -19,6 +19,7 @@ output_dir2/ output_dir2/ output_dir/ +.vscode/ # Compiled Python files __pycache__/ *.py[cod] diff --git a/test_dataloader.py b/test_dataloader.py index a6e705e3..1112e947 100644 --- a/test_dataloader.py +++ b/test_dataloader.py @@ -4,7 +4,6 @@ import numpy as np import matplotlib.pyplot as plt from ctlearn.tools.train.pytorch.utils import read_configuration - def on_key(event): # Check if the "Esc" key was pressed if event.key == 'escape': From 55067fee4cb0c04caa39b970e162a8766469e92b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Thu, 12 Jun 2025 10:39:42 +0000 Subject: [PATCH 046/119] Fix the problems related with the predict model and add the loading bar --- .../models/ThinResNet_DBB/ThinResNet_DBB.py | 29 ++++++++++++------- .../predict/pytorch/predic_LST1_pytorch.py | 2 +- 2 files changed, 19 insertions(+), 12 deletions(-) diff --git a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py index 44221828..31e0126d 100644 --- a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py +++ b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py @@ -91,7 +91,9 @@ def __init__(self,task, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, self.conv2 = nn.Conv2d(num_inputs, 64, kernel_size=3, stride=1, padding=1, bias=False) self.layer1_2 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2_2 = self._make_layer(block, 128, num_blocks[1], stride=2) - self.layer3_2 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer3_2 = self._make_layer(block, 256, num_blocks[3], stride=2) # Change for 2 or 3 + #self.layer3_2 = self._make_layer(block, 256, num_blocks[3], stride=2) + if self.use_bn: self.bn2 = nn.BatchNorm2d(64) @@ -106,13 +108,8 @@ def _make_layer(self, block, out_channels, num_blocks, stride): layers.append(block(self.in_channels, out_channels, stride, use_bn=self.use_bn)) self.in_channels = out_channels * block.expansion return nn.Sequential(*layers) - - def forward(self, x, y): - - energy = None - classification = None - direction = None - + + def extract_feature_vector(self,x, y): # out_1 = F.relu(self.bn1(self.conv1(x))) out_1 = F.relu(self.conv1(x)) out_1 = self.layer1_1(out_1) @@ -130,13 +127,23 @@ def forward(self, x, y): out = self.layer4(out) out = self.adaptive_pool(out) out_feature = out.view(out.size(0), -1) - out = self.dropout(out_feature) + fused_features = self.dropout(out_feature) + return fused_features + + def forward(self, x, y): + + energy = None + classification = None + direction = None + + fused_features = self.extract_feature_vector(x,y) + # out = self.layer3(out) # if self.training: # out_sep = self.fc_1_separation(out) # out_sep = self.fc_2_separation(out_sep) - out = self.fc_1(out) + out = self.fc_1(fused_features) # out = self.bn_final(out) # out = self.prelu(out) # Original @@ -165,7 +172,7 @@ def forward(self, x, y): # if self.training: # out = torch.cat((out, out_sep), dim=1) - return classification, energy, direction + return [classification,fused_features], [energy,fused_features], [direction,fused_features] # def thin_resnet34(num_blocks=[2, 3, 3, 3], num_inputs=1, num_classes=2): # # Here we configure fewer blocks for a lighter model diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 782d56ef..676973d4 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -177,4 +177,4 @@ def load_pytorch_model(self): model.eval() - return model \ No newline at end of file + return model From a61be79fdb88c5d8d897b7086c1406b4de714e4a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 2 Jul 2025 08:59:45 +0000 Subject: [PATCH 047/119] Add the MC dl2 predictions --- .../predict/pytorch/predic_LST1_pytorch.py | 17 +++++++++++++++++ ctlearn/tools/train/pytorch/CTLearnPL.py | 2 +- .../training_config_iaa_neutron_training.yml | 6 +++--- 3 files changed, 21 insertions(+), 4 deletions(-) diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 676973d4..4eba8ca6 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -16,6 +16,23 @@ import numpy as np from tqdm import tqdm +from pytorch_lightning.callbacks import Callback + +from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer + +class GPUStatsLogger(Callback): + def on_train_epoch_end(self, trainer, pl_module): + mem_allocated = torch.cuda.memory_allocated() + mem_reserved = torch.cuda.memory_reserved() + + trainer.logger.experiment.add_scalar( + "gpu_mem_allocated", mem_allocated, global_step=trainer.current_epoch + ) + trainer.logger.experiment.add_scalar( + "gpu_mem_reserved", mem_reserved, global_step=trainer.current_epoch + ) + + from pytorch_lightning.callbacks import Callback from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 200105f4..fe23e974 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -91,7 +91,7 @@ def predict( # Call your custom logic here results = model.generate_results( input_data_loader=dataloaders, - h5_file_name=h5_file_name, + h5_file_name=None, task=task, mode=mode, ) diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index d88f2992..c6f44788 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -27,12 +27,12 @@ data: # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: /storage/ctlearn_data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth run_details: - mode: "train" # The option are: "train", "results", "observation" and "validate" - task: "type" # The option are: "all", "energy" "type" and "direction" + mode: "observation" # The option are: "train", "results", "observation" and "validate" + task: "direction" # The option are: "all", "energy" "type" and "direction" test_type: "gamma" # The option are: "gamma" "proton" or "electron" experiment_number: 14 # The experiment number. The experiment folder is saved into the "run" folder. From fd37df1c931e155ed1612a71b1da6254a8025948 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Fri, 4 Jul 2025 09:16:15 +0000 Subject: [PATCH 048/119] Minor changes and optimization --- .gitignore | 3 ++- ctlearn/core/data_loader/pytorch_loader.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 6148cad1..4ee96f3c 100644 --- a/.gitignore +++ b/.gitignore @@ -37,4 +37,5 @@ test/ *.jar *.puml mc_tjark/ -calibration/ \ No newline at end of file +calibration/ +test_local_cristian/ diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 24487a48..1a50a5dc 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -112,7 +112,7 @@ def set_T(self,T): self.T=T self.indices = np.tile(self.indices, self.T) - pp=0 + def __len__(self): """ Returns the number of batches per epoch. From 1e2cd857e5ea03906a83e8e0b354b35b85806f88 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 7 Jul 2025 14:51:16 +0200 Subject: [PATCH 049/119] Support the last dl1-data-handler version --- .gitignore | 2 +- .../pytorch/config/training_config_iaa_neutron_training.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 4ee96f3c..0da23e67 100644 --- a/.gitignore +++ b/.gitignore @@ -29,7 +29,7 @@ dist .pytest_cache/*.log # Sphinx documentation docs/build/ - +build/ # Default pytorch output run/ test/ diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index c6f44788..b6a9c810 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -27,7 +27,7 @@ data: # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - direction_checkpoint: /storage/ctlearn_data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth run_details: From b2662eedce488b1eb0fd216d0684780990dcd91f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 29 Jul 2025 14:16:06 +0000 Subject: [PATCH 050/119] Fix the pytorch loader and add some changes in the Pylithing validation --- ctlearn/core/data_loader/pytorch_loader.py | 31 +++++----- ctlearn/tools/train/pytorch/CTLearnPL.py | 58 ++++++++++--------- ...ining_config_iaa_neutron_training_v5_2.yml | 10 +++- .../train/pytorch/train_pytorch_model.py | 16 +---- 4 files changed, 56 insertions(+), 59 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 1a50a5dc..88005a32 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -493,8 +493,11 @@ def _get_mono_item(self, batch): features_out = {} features_out["image"] = image - features_out["peak_time"] = peak_time - + features_out["peak_time"] = peak_time + + features_out["image"] = torch.from_numpy(image).float().permute(0, 3, 1, 2).contiguous() + features_out["peak_time"] = torch.from_numpy(peak_time).float().permute(0, 3, 1, 2).contiguous() + for key in labels.keys(): labels[key] = torch.from_numpy(labels[key]).contiguous() @@ -617,19 +620,19 @@ def duplicate_tensor(t,idx_to_duplicate): keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] # keep_idx = np.where((leakage > 0.8) & (intensity > 50))[0] - # Filter features_out - for key in features_out: - features_out[key] = features_out[key][keep_idx] - - features_out["hillas"] = features["hillas"] + # Filter features_out + for key in features_out: + features_out[key] = features_out[key][keep_idx] + + features_out["hillas"] = features["hillas"] - for key in features["hillas"]: - features_out["hillas"][key] = (features["hillas"][key])[keep_idx] - - # Filter labels (since it's a dict too) - for key in labels: - labels[key] = labels[key][keep_idx] - + for key in features["hillas"]: + features_out["hillas"][key] = (features["hillas"][key])[keep_idx] + + # Filter labels (since it's a dict too) + for key in labels: + labels[key] = labels[key][keep_idx] + return features_out, labels # TODO: Not adapted to pytorch diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index fe23e974..fbb995f3 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -521,7 +521,7 @@ def compute_energy_loss_diffusion(self, x_1, y,training=False, x_2 = None loss = loss + step_loss - return loss, energy_diff + return loss, energy_diff, energy_pred # ---------------------------------------------------------------------------------------------------------- def compute_camera_direction_loss_diffusion(self, x_1, y, labels_energy_value, x_2 = None): @@ -1094,15 +1094,14 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ - # if not self.is_difussion: - if self.num_inputs == 2: - peak_time = features["peak_time"] - classification_pred, energy_pred, direction_pred = self.model( - imgs, peak_time - ) - else: - classification_pred, energy_pred, direction_pred = self.model(imgs) - + if not self.is_difussion: + if self.num_inputs == 2: + peak_time = features["peak_time"] + classification_pred, energy_pred, direction_pred = self.model( + imgs, peak_time + ) + else: + classification_pred, energy_pred, direction_pred = self.model(imgs) # ------------------------------------------------------------------ # Compute Loss functions based on different tasks # ------------------------------------------------------------------ @@ -1110,27 +1109,27 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # --------------------------------------- if self.task == Task.type: if self.is_difussion: - classification_pred_ = classification_pred + loss, accuracy, predicted, precision = self.compute_type_loss_diffusion(imgs,labels_class,training=False) else: - classification_pred_ = classification_pred[0] feature_vector = classification_pred[1] # Log batch loss and accuracy on the progress bar if dataloader_idx == 0: - # if self.is_difussion: - # loss, accuracy, predicted, precision =self.compute_type_loss_diffusion( - # classification_pred_, - # labels_class, - # test_val=False, - # training=False, - # ) - - loss, accuracy, predicted, precision = self.compute_type_loss( + if self.is_difussion: + loss, accuracy, predicted, precision =self.compute_type_loss_diffusion( classification_pred_, labels_class, test_val=False, training=False, - ) + ) + else: + loss, accuracy, predicted, precision = self.compute_type_loss( + classification_pred_, + labels_class, + test_val=False, + training=False, + ) + self.log( "val_acc", accuracy * 100, @@ -1189,17 +1188,22 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # Energy # --------------------------------------- if self.task == Task.energy: - - energy_pred_tev = torch.pow(10, energy_pred*1.0) - - if dataloader_idx == 0: - + if self.is_difussion: + if self.num_inputs == 1: + loss, energy_diff, energy_pred_tev = self.compute_energy_loss_diffusion(imgs,labels_energy_value/1.0,training=False,x_2=None) + else: + peak_time = features["peak_time"] + loss, energy_diff, energy_pred_tev = self.compute_energy_loss_diffusion(imgs,labels_energy_value/1.0,training=False,x_2=peak_time) + else: if len(energy_pred)==2: energy_pred = energy_pred[0] loss, energy_diff = self.compute_energy_loss( energy_pred, labels_energy_value/1.0, test_val=False, training=False ) + energy_pred_tev = torch.pow(10, energy_pred*1.0) + + if dataloader_idx == 0: self.val_energy_diff_list.extend(energy_diff) self.val_energy_pred_list.extend( diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml index 48de9772..cf53af33 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml @@ -20,12 +20,15 @@ data: # when you are training, validating or estimating the dl2 files training_reduce_factor: 0 #64 #4 validation_reduce_factor: 0 #16 #8 - validation_test_reduce_factor: 0 #16 #8 + validation_test_reduce_factor: 0 #16 #8 # Check points type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - energy_checkpoint: ./ run/run_energy_training_15/exp_15_energy_train/version_14/Epoch_5_energy_train_loss_24.1305048130270734.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth + + energy_checkpoint: "/home/cpozogonzalez/pablo_home/projects/ctlearn_pytorch_integration/ctlearn/run/run_energy_training_15/exp_15_energy_train/version_34/Epoch_25_energy_train_loss_4.1468562749667113.pth" + + #energy_checkpoint: ./run/run_energy_training_15/exp_15_energy_train/version_14/Epoch_5_energy_train_loss_24.1305048130270734.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth @@ -137,7 +140,7 @@ model: hyp: epochs: 50 - batches: 64 #128 #64 + batches: 256 #128 #64 dynamic_batches: True optimizer: Adamw momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 @@ -185,6 +188,7 @@ normalization: energy_mu: 0.0 # energy_sigma: 1000.0 energy_sigma: 1000.0 + dataset: num_workers: 1 # pin_memory: True diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 4b935be4..0bb56511 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -1,16 +1,4 @@ -from ctapipe.core.traits import ( - Bool, - CaselessStrEnum, - Path, - Float, - Int, - List, - Dict, - classes_with_traits, - ComponentName, - Unicode, -) - +from ctapipe.core.traits import Path from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL try: @@ -161,10 +149,8 @@ def setup(self): self.devices = self.parameters["arch"]["devices"] self.save_k = self.parameters["hyp"]["save_k"] - print(f"Using Devices: {self.devices}") - # all_log_energies = self.dl1dh_reader.data['log_true_energy'] # Set up the data loaders for training and validation From ae66a8f765c9d1bb3899acf0a4f3a8444a74b991 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 1 Sep 2025 12:42:02 +0000 Subject: [PATCH 051/119] Refactoring CTLearnPL --- .gitignore | 2 ++ ctlearn/tools/train/pytorch/CTLearnPL.py | 29 +++++++++++++++--------- 2 files changed, 20 insertions(+), 11 deletions(-) diff --git a/.gitignore b/.gitignore index 0da23e67..58d7e933 100644 --- a/.gitignore +++ b/.gitignore @@ -39,3 +39,5 @@ test/ mc_tjark/ calibration/ test_local_cristian/ + +test/prepare_file.py \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index fbb995f3..48668603 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -1113,22 +1113,29 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): else: classification_pred_ = classification_pred[0] feature_vector = classification_pred[1] - # Log batch loss and accuracy on the progress bar - if dataloader_idx == 0: - if self.is_difussion: - loss, accuracy, predicted, precision =self.compute_type_loss_diffusion( - classification_pred_, - labels_class, - test_val=False, - training=False, - ) - else: - loss, accuracy, predicted, precision = self.compute_type_loss( + loss, accuracy, predicted, precision = self.compute_type_loss( classification_pred_, labels_class, test_val=False, training=False, ) + + # Log batch loss and accuracy on the progress bar + # if dataloader_idx == 0: + # if self.is_difussion: + # loss, accuracy, predicted, precision =self.compute_type_loss_diffusion( + # classification_pred_, + # labels_class, + # test_val=False, + # training=False, + # ) + # else: + # loss, accuracy, predicted, precision = self.compute_type_loss( + # classification_pred_, + # labels_class, + # test_val=False, + # training=False, + # ) self.log( "val_acc", From 5bb253cd2499f67688ddaeee63edb017edb0317d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 1 Sep 2025 12:55:00 +0200 Subject: [PATCH 052/119] Update the predictions tools and the ThinResNet model DBB --- .gitignore | 3 +- ctlearn/core/data_loader/pytorch_loader.py | 14 +- .../models/ThinResNet_DBB/ThinResNet_DBB.py | 10 +- ctlearn/tools/predict/predict_LST1.py | 2 + .../predict/pytorch/predic_LST1_pytorch.py | 11 +- .../predict/pytorch/predic_model_pytorch.py | 7 +- ctlearn/tools/predict/utils/predict_model.py | 6 + ctlearn/tools/train/pytorch/CTLearnPL.py | 31 ++- .../training_config_iaa_neutron_training.yml | 2 + ...g_config_iaa_neutron_training_artemisa.yml | 198 ++++++++++++++++++ .../train/pytorch/train_pytorch_model.py | 3 + 11 files changed, 257 insertions(+), 30 deletions(-) create mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml diff --git a/.gitignore b/.gitignore index 58d7e933..3cf088df 100644 --- a/.gitignore +++ b/.gitignore @@ -40,4 +40,5 @@ mc_tjark/ calibration/ test_local_cristian/ -test/prepare_file.py \ No newline at end of file +test/prepare_file.py +test_local_cristian/ \ No newline at end of file diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 88005a32..32b05e05 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -497,12 +497,6 @@ def _get_mono_item(self, batch): features_out["image"] = torch.from_numpy(image).float().permute(0, 3, 1, 2).contiguous() features_out["peak_time"] = torch.from_numpy(peak_time).float().permute(0, 3, 1, 2).contiguous() - - for key in labels.keys(): - labels[key] = torch.from_numpy(labels[key]).contiguous() - - if key != "type": - labels[key] = labels[key].unsqueeze(-1) for key in features["hillas"].keys(): features["hillas"][key] = ( @@ -632,7 +626,13 @@ def duplicate_tensor(t,idx_to_duplicate): # Filter labels (since it's a dict too) for key in labels: labels[key] = labels[key][keep_idx] - + + for key in labels.keys(): + labels[key] = torch.from_numpy(labels[key]).contiguous() + + if key != "type": + labels[key] = labels[key].unsqueeze(-1) + return features_out, labels # TODO: Not adapted to pytorch diff --git a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py index 31e0126d..53f0db52 100644 --- a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py +++ b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py @@ -59,9 +59,9 @@ def forward(self, x): return out class ThinResNet_DBB(nn.Module): - def __init__(self,task, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, num_outputs=2,use_bn=False,dropout=0.0): + def __init__(self,task, block=BasicBlock, num_blocks=[2, 3, 3, 3], num_inputs=1, num_outputs=2,use_bn=False,dropout=0.0,extract_uncertenty=False): super(ThinResNet_DBB, self).__init__() - + self.extract_uncertenty = extract_uncertenty # block = BasicBlock self.in_channels = 64 self.use_bn=use_bn @@ -159,7 +159,11 @@ def forward(self, x, y): energy = out if self.task == "direction": - direction = self.normal_inv(out) + if self.extract_uncertenty: + direction = self.normal_inv(out) + else: + direction = self.fc_2(out) + # direction = [direction, out_feature] # if self.training: diff --git a/ctlearn/tools/predict/predict_LST1.py b/ctlearn/tools/predict/predict_LST1.py index ba03ddcb..5df10ac2 100644 --- a/ctlearn/tools/predict/predict_LST1.py +++ b/ctlearn/tools/predict/predict_LST1.py @@ -223,6 +223,8 @@ class LST1PredictionTool(Tool): ("d", "cameradirection_model"): "LST1PredictionTool.load_cameradirection_model_from", ("o", "output"): "LST1PredictionTool.output_path", ("f", "framework"): "LST1PredictionTool.framework_type", + ("p", "pytorch_config_file"): "LST1PredictionTool.pytorch_config_file", + } flags = { diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 4eba8ca6..c72fefef 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -191,7 +191,16 @@ def load_pytorch_model(self): model = ModelHelper.loadModel( model_net, "", check_point_path, Mode.observation, device_str=self.device_str ) - model.eval() + if task == Task.type: + self.type_model = model + elif task == Task.energy: + self.energy_model = model + elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: + self.dirrection_model = model + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) return model diff --git a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py index d0f975b2..92561402 100644 --- a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py @@ -78,7 +78,7 @@ def predict_with_model_pytorch(self, task): predict_data["cameradirection"] = [] model.eval() - for x in tqdm(data_loader, desc="Processing", total=len(data_loader)): + for i, x in enumerate(tqdm(data_loader, desc="Processing", total=len(data_loader))): if len(x[0]['image'])==0: continue if num_inputs == 2: @@ -92,7 +92,10 @@ def predict_with_model_pytorch(self, task): predict_data['energy'].extend(energy_pred.cpu().detach().numpy()) if direction_pred is not None: predict_data["cameradirection"].extend(direction_pred.cpu().detach().numpy()) - + if i % 100 == 0: + self.log.info(f"Processed {i}/{len(data_loader)} events.") + self.log.info("Processing completed.") + predict_data["cameradirection"] = np.array(predict_data["cameradirection"]) predict_data["type"] = np.array(predict_data["type"]) predict_data["energy"] = np.array(predict_data["energy"]) diff --git a/ctlearn/tools/predict/utils/predict_model.py b/ctlearn/tools/predict/utils/predict_model.py index c90f5dcc..ac9d7ec2 100644 --- a/ctlearn/tools/predict/utils/predict_model.py +++ b/ctlearn/tools/predict/utils/predict_model.py @@ -417,6 +417,12 @@ def setup(self): self.tasks.append(Task.direction) # Check if the ctapipe HDF5Merger component is enabled + if os.path.exists(self.output_path): + self.log.warning( + "The output file '{self.output_path}' already exists. Disabling HDF5Merger the flag '--no-use-HDF5Merger' will be not use." + ) + self.use_HDF5Merger = False + if self.use_HDF5Merger: if os.path.exists(self.output_path): raise ToolConfigurationError( diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 48668603..9f75accc 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -178,7 +178,8 @@ def __init__( self.is_difussion=True else: self.is_difussion=False - + print("Diffusion: ",self.is_difussion) + self.num_inputs = num_inputs self.train_loader = train_loader self.val_loader = val_loader @@ -438,7 +439,6 @@ def compute_energy_loss( loss_energy = self.criterion_energy_value(energy_pred, labels_energy) loss = loss_energy - #----------------------------------------- # k=0.6 # e_thrs= -0.3 @@ -770,14 +770,12 @@ def compute_type_loss_diffusion(self, x,y, training=False): predicted = predicted.argmax(dim=1) if training: - self.class_train_accuracy.update(predicted, y) accuracy = self.class_train_accuracy.compute().item() self.f1_score_train.update(predicted, y) self.precision_train.update(predicted, y) precision = self.precision_train.compute().item() else: - self.class_val_accuracy.update(predicted, y) accuracy = self.class_val_accuracy.compute().item() self.confusion_matrix.update(predicted, y) @@ -796,22 +794,19 @@ def on_train_batch_start(self, batch, batch_idx): self.dummy_tensor = self.occupy_free_gpu_memory(self.device) # ---------------------------------------------------------------------------------------------------------- def training_step(self, batch, batch_idx): - # ------------------------------------------------------------------ # Read inputs (features) and labels # ------------------------------------------------------------------ features, labels, t = batch loss = 0 - if len(features) > 0: imgs = features["image"] - + if self.task == Task.type: labels_class = labels["type"] labels_energy_value = labels["energy"] if self.task == Task.energy: - labels_energy_value = labels["energy"] labels_energy_value = labels_energy_value.to(self.device) @@ -904,7 +899,9 @@ def training_step(self, batch, batch_idx): loss, *_ = self.compute_energy_loss_diffusion(imgs,labels_energy_value/1.0,training=True,x_2=peak_time) else: - + if len(energy_pred)==2: + energy_pred = energy_pred[0] + loss, *_ = self.compute_energy_loss( energy_pred, labels_energy_value, test_val=False, training=True ) @@ -1168,24 +1165,26 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # ------------------------------------------------------------------------ # Convert the offset to altitud and azimuth # ------------------------------------------------------------------------ + if dataloader_idx == 0: - self.loss_val_distance += loss_distance.item() self.loss_val_dx_dy += loss_dx_dy.item() self.loss_val_angular_error += loss_angular_diff.item() self.val_angular_diff_list.extend(angular_error) - - # ------------------------------------------------------- + # ------------------------------------------------------- + pred_dx = direction_pred[:, 0].float().cpu().detach().numpy() pred_dy = direction_pred[:, 1].float().cpu().detach().numpy() cam_x = pred_dx cam_y = pred_dy - pred_alt, pred_az = self.val_loader.cam_to_alt_az(labels["tel_ids"], labels["focal_length"], labels["pix_rotation"],labels["tel_az"],labels["tel_alt"], cam_x, cam_y) + pred_alt, pred_az = self.val_loader.cam_to_alt_az(labels["tel_ids"].cpu().detach().numpy(), labels["focal_length"].cpu().detach().numpy(), labels["pix_rotation"].cpu().detach().numpy(),labels["tel_az"].cpu().detach().numpy(),labels["tel_alt"].cpu().detach().numpy(), cam_x, cam_y) + + labels_dx_dy = labels_direction[:, 0:2] + + true_alt, true_az = self.val_loader.cam_to_alt_az(labels["tel_ids"].cpu().detach().numpy(), labels["focal_length"].cpu().detach().numpy(), labels["pix_rotation"].cpu().detach().numpy(),labels["tel_az"].cpu().detach().numpy(),labels["tel_alt"].cpu().detach().numpy(), labels_dx_dy[:,0].float().cpu().detach().numpy(), labels_dx_dy[:,1].float().cpu().detach().numpy()) - true_alt = labels["true_alt"] - true_az = labels["true_az"] self.val_alt_pred_list.extend(np.radians(pred_alt)) self.val_az_pred_list.extend(np.radians(pred_az)) self.val_alt_label_list.extend(np.radians(true_alt)) @@ -1228,7 +1227,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): energy_label_tev[:, 0].float().cpu().detach().numpy().flatten().tolist() ) self.val_hillas_intensity_list.extend( - hillas_intensity.float().cpu().detach().numpy().flatten().tolist() + hillas_intensity[:, 0].float().cpu().detach().numpy().flatten().tolist() ) # --------------------------------------- diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index b6a9c810..3c8a13af 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -24,8 +24,10 @@ data: # Check points type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth + type_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_4_type_train_acc_81.0356736183166504.pth #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth + energy_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_29_energy_train_loss_16.4042284706221189.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml new file mode 100644 index 00000000..979a5aed --- /dev/null +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml @@ -0,0 +1,198 @@ +data: + + train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle + validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + train_gamma: ./data/gamma_955000_train.pickle + validation_gamma: ./data/gamma_106141_validation.pickle + + test_gamma: ./data/gamma_1805522_test_gamma.pickle + test_proton: ./data/proton_130811_test_proton.pickle + test_electron: None + test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle + + test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle + + observation: ./run_2931.dl1.pickle + # Important: This is only for testing purpose. Set always to 0 + # when you are training, validating or estimating the dl2 files + training_reduce_factor: 0 #64 #4 + validation_reduce_factor: 0 #16 #8 + validation_test_reduce_factor: 0 #16 #8 + + # Check points + energy_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_energy_training_14/exp_14_energy_train/version_22/Epoch_3_energy_train_loss_19.2963753574957728.pth + direction_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_cameradirection_training_14/exp_14_cameradirection_train/version_14/Epoch_0_cameradirection_train_loss_1.8445273349535292.pth + type_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_type_training_14/exp_14_type_train/version_0/Epoch_11_type_train_acc_93.1558012962341309.pth + #type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth + # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth + #energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth + # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + #direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth + +run_details: + + mode: "observation" # The option are: "train", "results", "observation" and "validate" + task: "direction" # The option are: "all", "energy" "type" and "direction" + test_type: "gamma" # The option are: "gamma" "proton" or "electron" + experiment_number: 14 # The experiment number. The experiment folder is saved into the "run" folder. + + +cut-off: + + leakage_intensity: 0.2 # bigger to this value, the event is removed + intensity: 50 # below to this value, the event is removed + +model: + model_type: + model_name: "DoubleBBEfficientNet" + parameters: + model_variant: "efficientnet-b3" + task: 'type' + num_outputs: 2 + device_str: "cuda" + energy_bins: None + + # model_type: + # model_name: "NoPropDT" + # parameters: + # # task: 'type' + # num_outputs: 2 + # embedding_dim: 512 + # T: 6 + # eta: 0.1 #0.1 + + # model_type: + # model_name: "ThinResNet_DBB" + # parameters: + # task: 'type' + # num_inputs: 1 + # num_outputs: 2 + # num_blocks: [2, 3, 3, 3] + # dropout: 0.1 + # use_bn: False + + model_energy: + model_name: "ThinResNet_DBB" + parameters: + task: 'energy' + num_inputs: 1 + num_outputs: 1 + num_blocks: [3, 4, 6, 3] + dropout: 0.1 + use_bn: False + + # model_energy: + # model_name: "NoPropDTReg" + # parameters: + # task: 'energy' + # num_outputs: 1 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + # num_blocks: [2, 3, 3, 3] + + model_direction: + model_name: "ThinResNet_DBB" + parameters: + task: 'direction' + num_inputs: 1 + num_outputs: 3 + num_blocks: [3, 4, 6, 3] + dropout: 0.1 + use_bn: False + + # model_direction: + # model_name: "NoPropDTReg" + # parameters: + # task: 'direction' + # num_outputs: 3 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + # num_blocks: [2, 3, 3, 3] + + # model_direction: + # model_name: "DBBNoPropDTReg" + # parameters: + # task: 'direction' + # num_outputs: 3 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + +# Hyper-parameters +hyp: + + epochs: 200 + batches: 256 #128 #64 + dynamic_batches: True + optimizer: Adamw + momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 + weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 + learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 + lrf: 0.1 + start_epoch: 0 + steps_epoch: 100 # Computed online. Must be removed + l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) + adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 + gradient_clip_val: 3.0 # Avoid gradient explosion + + save_k: 200 # Save as maximum k checkpoints. + +augmentation: + # probabilities for augmentation range = [0, 1.0] + # prob = 0.0 -> Always apply the augmentation + # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. + # Note: mask augmentation is always on even with flag use_augmentation = True + # To disable it, just set to 2.5 for example. + + use_augmentation: True # This apply only on training mode. + aug_prob: 0.5 # Probability of use Augmentation + rot_prob: 0.5 # Rotation probability + trans_prob: 0.5 # Translation probability + flip_hor_prob: 0.5 # Horizontal Flip probability + flip_ver_prob: 0.5 # Vertical Flip probability + mask_prob: 0.5 # Apply mask probability + mask_dvr_prob: 0.5 # Apply dvr mask probability + noise_prob: 0.5 # No implemented yet. + max_rot: 5 # Maximum rotation in augmentation + max_trans: 10 # Maximum translation in augmentation + +normalization: + + # Normalization: Im' = (Im-mu)/sigma + use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) + use_clean_dvr: False + type_mu: 0.0 + type_sigma: 1000.0 + + dir_mu: 0.0 + dir_sigma: 1000.0 + + energy_mu: 0.0 + energy_sigma: 1000.0 + +dataset: + num_workers: 1 # + pin_memory: True + persistent_workers: True # + +# Hardware Architecture and precision +arch: + # device: 'mps' # Apple Mx + device: 'cuda' + precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) + # devices: [0,1] # [0,1] For multiple GPUs + devices: [0] + # Note: Check the documentation for more information. + strategy: 'auto' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... + +Notes: + Note_1: Training with augmentation dvr using 1-3 dilatations + Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 0bb56511..99981cc1 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -190,6 +190,7 @@ def setup(self): use_augmentation=self.parameters["augmentation"]["use_augmentation"], is_training=True, ) + print(len(self.training_loader)) self.validation_loader = DLDataLoader.create( framework=self.framework_type, @@ -204,6 +205,8 @@ def setup(self): use_augmentation=False, is_training=False, ) + + print(len(self.validation_loader)) def start(self): From e7f2558742032dd2ae6a1602adcf8958390e8d9a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 2 Sep 2025 14:40:07 +0000 Subject: [PATCH 053/119] Add the warning message about the imbalance classes. --- ctlearn/tools/train/base_train_model.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 7562f1d0..2c570bab 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -304,6 +304,16 @@ def setup(self): raise ValueError( "Classification task selected but less than two classes are present in the data." ) + if self.dl1dh_reader.class_weight[0]/self.dl1dh_reader.class_weight[1] > 1.1: + self.log.warning( + "The dataset seems to be imbalanced. " + f"Consider using more background events using class_weight in the optimizer.: {self.dl1dh_reader.class_weight}" + ) + if self.dl1dh_reader.class_weight[0]/self.dl1dh_reader.class_weight[1] < 0.9: + self.log.warning( + "The dataset seems to be imbalanced. " + f"Consider using more signal events using class_weight in the optimizer.: {self.dl1dh_reader.class_weight}" + ) # Check if stereo mode is selected for stacking telescope images if self.stack_telescope_images and self.dl1dh_reader.mode == "mono": raise ToolConfigurationError( From afee303b086c31ba38745bb106ac72a650994c6b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 15 Sep 2025 12:18:27 +0200 Subject: [PATCH 054/119] Add the class weight informations --- ctlearn/tools/train/base_train_model.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 2c570bab..694fef3d 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -299,6 +299,9 @@ def setup(self): f"{self.dl1dh_reader._get_n_events()} events are not enough " f"to form a batch of size {self.batch_size}. Reduce the batch size." ) + + self.log.info(f'Class weight{self.dl1dh_reader.class_weight}') + # Check if there are at least two classes in the reader for the particle classification if self.dl1dh_reader.class_weight is None and "type" in self.reco_tasks: raise ValueError( From b3593aef649e34ca562af69455fa2d470980286e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 15 Sep 2025 15:41:43 +0200 Subject: [PATCH 055/119] Remove the weight calculation from the energy and cameradirrection tasks --- ctlearn/tools/train/base_train_model.py | 29 +++++++++++++------------ 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 694fef3d..e87142f0 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -303,20 +303,21 @@ def setup(self): self.log.info(f'Class weight{self.dl1dh_reader.class_weight}') # Check if there are at least two classes in the reader for the particle classification - if self.dl1dh_reader.class_weight is None and "type" in self.reco_tasks: - raise ValueError( - "Classification task selected but less than two classes are present in the data." - ) - if self.dl1dh_reader.class_weight[0]/self.dl1dh_reader.class_weight[1] > 1.1: - self.log.warning( - "The dataset seems to be imbalanced. " - f"Consider using more background events using class_weight in the optimizer.: {self.dl1dh_reader.class_weight}" - ) - if self.dl1dh_reader.class_weight[0]/self.dl1dh_reader.class_weight[1] < 0.9: - self.log.warning( - "The dataset seems to be imbalanced. " - f"Consider using more signal events using class_weight in the optimizer.: {self.dl1dh_reader.class_weight}" - ) + if "type" in self.reco_tasks: + if self.dl1dh_reader.class_weight is None and "type" in self.reco_tasks: + raise ValueError( + "Classification task selected but less than two classes are present in the data." + ) + if self.dl1dh_reader.class_weight[0]/self.dl1dh_reader.class_weight[1] > 1.1: + self.log.warning( + "The dataset seems to be imbalanced. " + f"Consider using more background events using class_weight in the optimizer.: {self.dl1dh_reader.class_weight}" + ) + if self.dl1dh_reader.class_weight[0]/self.dl1dh_reader.class_weight[1] < 0.9: + self.log.warning( + "The dataset seems to be imbalanced. " + f"Consider using more signal events using class_weight in the optimizer.: {self.dl1dh_reader.class_weight}" + ) # Check if stereo mode is selected for stacking telescope images if self.stack_telescope_images and self.dl1dh_reader.mode == "mono": raise ToolConfigurationError( From 83814543099c4ebd09833f0110afc9da45508ae8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 16 Sep 2025 09:56:22 +0000 Subject: [PATCH 056/119] Add the warnings for class weights --- ctlearn/tools/train/pytorch/CTLearnPL.py | 5 ++- .../training_config_iaa_neutron_training.yml | 1 - ...g_config_iaa_neutron_training_artemisa.yml | 31 ++++++++++--------- .../train/pytorch/train_pytorch_model.py | 11 +++++-- ctlearn/tools/train_model.py | 1 + 5 files changed, 28 insertions(+), 21 deletions(-) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 9f75accc..16572a24 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -195,7 +195,7 @@ def __init__( # Loss Function class_weights = ( - torch.tensor([1.0, 1.3], dtype=torch.float32).to(self.device).contiguous() + torch.tensor([parameters['class_weight'][0],parameters['class_weight'][1]], dtype=torch.float32).to(self.device).contiguous() ) # [1.0, 1.3] self.criterion_class = nn.CrossEntropyLoss( @@ -366,10 +366,9 @@ def compute_type_loss( self, classification_pred, labels_class, test_val=False, training=False ): target = labels_class.to(torch.int64) - class_weights = torch.tensor([1.0, 1.0], dtype=torch.float).to(self.device) # Cálculo de la loss con F.cross_entropy - loss_class = F.cross_entropy(classification_pred, target, weight=class_weights, reduction='mean') + loss_class = F.cross_entropy(classification_pred, target, weight=self.class_weights, reduction='mean') # Calculate accuracy predicted = torch.softmax(classification_pred, dim=1) diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 3c8a13af..acfd61a7 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -1,5 +1,4 @@ data: - train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml index 979a5aed..d87f6532 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml @@ -26,6 +26,7 @@ data: energy_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_energy_training_14/exp_14_energy_train/version_22/Epoch_3_energy_train_loss_19.2963753574957728.pth direction_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_cameradirection_training_14/exp_14_cameradirection_train/version_14/Epoch_0_cameradirection_train_loss_1.8445273349535292.pth type_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_type_training_14/exp_14_type_train/version_0/Epoch_11_type_train_acc_93.1558012962341309.pth + #type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth #energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth @@ -46,23 +47,23 @@ cut-off: intensity: 50 # below to this value, the event is removed model: - model_type: - model_name: "DoubleBBEfficientNet" - parameters: - model_variant: "efficientnet-b3" - task: 'type' - num_outputs: 2 - device_str: "cuda" - energy_bins: None - # model_type: - # model_name: "NoPropDT" + # model_name: "DoubleBBEfficientNet" # parameters: - # # task: 'type' + # model_variant: "efficientnet-b3" + # task: 'type' # num_outputs: 2 - # embedding_dim: 512 - # T: 6 - # eta: 0.1 #0.1 + # device_str: "cuda" + # energy_bins: None + + model_type: + model_name: "NoPropDT" + parameters: + # task: 'type' + num_outputs: 2 + embedding_dim: 512 + T: 6 + eta: 0.1 #0.1 # model_type: # model_name: "ThinResNet_DBB" @@ -127,7 +128,7 @@ model: hyp: epochs: 200 - batches: 256 #128 #64 + batches: 64 #128 #64 dynamic_batches: True optimizer: Adamw momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 99981cc1..0fad9820 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -173,8 +173,15 @@ def setup(self): # training_indices = training_indices[:max_training_samples] # validation_indices = validation_indices[:max_validation_samples] - - + + if not ("class_weight" in self.parameters): + self.parameters['class_weight'] = self.dl1dh_reader.class_weight + self.log.info(f"Class weights not provided. Using class weights from data reader: {self.parameters['class_weight']}") + elif len(self.parameters['class_weight']) != len(self.dl1dh_reader.class_names): + raise ValueError(f"Number of class weights provided ({len(self.parameters['class_weight'])}) does not match number of classes in data ({len(self.dl1dh_reader.class_names)}).") + else: + self.log.info(f"Using class weights from configuration file: {self.parameters['class_weight']}") + print("BASE TRAIN FRAMEWORK", self.framework_type) self.training_loader = DLDataLoader.create( diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 042b5dfd..6a79ad84 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -170,6 +170,7 @@ def main(): # Parse all CLI args with the selected framework subclass tool.framework_instance.initialize(argv=sys.argv[1:]) + tool.run() From c4830f2397ebafd037e228263a3b5716c42bbaef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 16 Sep 2025 10:11:56 +0000 Subject: [PATCH 057/119] Add the weight only if the task is type, inside the CTLearnPL.py --- ctlearn/tools/train/pytorch/CTLearnPL.py | 13 +++++++------ .../config/training_config_iaa_neutron_training.yml | 4 ++-- ctlearn/tools/train_model.py | 3 ++- 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 16572a24..86ff3a35 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -194,13 +194,14 @@ def __init__( # self.class_weights = torch.tensor([1.0, 1.3], dtype=torch.float32).to(self.device).contiguous() # Loss Function - class_weights = ( - torch.tensor([parameters['class_weight'][0],parameters['class_weight'][1]], dtype=torch.float32).to(self.device).contiguous() - ) # [1.0, 1.3] + if self.task == Task.type: + class_weights = ( + torch.tensor([parameters['class_weight'][0],parameters['class_weight'][1]], dtype=torch.float32).to(self.device).contiguous() + ) # [1.0, 1.3] - self.criterion_class = nn.CrossEntropyLoss( - weight=class_weights, reduction="mean" - ) + self.criterion_class = nn.CrossEntropyLoss( + weight=class_weights, reduction="mean" + ) self.criterion_energy_value = torch.nn.L1Loss(reduction="sum") diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index acfd61a7..5b592620 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -23,9 +23,9 @@ data: # Check points type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth - type_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_4_type_train_acc_81.0356736183166504.pth #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth + #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth + #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth energy_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_29_energy_train_loss_16.4042284706221189.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 6a79ad84..fcbb9fd3 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -190,7 +190,6 @@ def main(): # gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 gamma_theta_16.087_az_251.910_runs1-60.dl1.h5 gamma_theta_16.087_az_251.910_runs181-240.dl1.h5 gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 gamma_theta_23.161_az_260.739_runs188-246.dl1.h5 gamma_theta_23.161_az_260.739_runs247-305.dl1.h5 gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 gamma_theta_23.161_az_99.261_runs1-59.dl1.h5 gamma_theta_23.161_az_99.261_runs177-235.dl1.h5 gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 gamma_theta_30.390_az_266.360_runs1-60.dl1.h5 gamma_theta_30.390_az_266.360_runs181-240.dl1.h5 gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 gamma_theta_30.390_az_93.640_runs181-240.dl1.h5 gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 gamma_theta_37.661_az_270.641_runs1-60.dl1.h5 gamma_theta_37.661_az_270.641_runs181-240.dl1.h5 gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 gamma_theta_37.661_az_89.359_runs1-60.dl1.h5 gamma_theta_37.661_az_89.359_runs181-240.dl1.h5 gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 gamma_theta_6.000_az_180.000_runs1-60.dl1.h5 gamma_theta_6.000_az_180.000_runs181-240.dl1.h5 gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 gamma_theta_9.579_az_126.888_runs1-60.dl1.h5 gamma_theta_9.579_az_126.888_runs181-240.dl1.h5 gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 gamma_theta_9.579_az_233.112_runs1-60.dl1.h5 gamma_theta_9.579_az_233.112_runs181-240.dl1.h5 - # --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs121-180.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs1-60.dl1.h5 --pattern-signal=gamma_theta_16.087_az_251.910_runs181-240.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs129-187.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs188-246.dl1.h5 --pattern-signal=gamma_theta_23.161_az_260.739_runs247-305.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs118-176.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs1-59.dl1.h5 --pattern-signal=gamma_theta_23.161_az_99.261_runs177-235.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs1-60.dl1.h5 --pattern-signal=gamma_theta_30.390_az_266.360_runs181-240.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs121-180.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs1-60.dl1.h5 --pattern-signal=gamma_theta_30.390_az_93.640_runs181-240.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_270.641_runs181-240.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs121-180.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs1-60.dl1.h5 --pattern-signal=gamma_theta_37.661_az_89.359_runs181-240.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs121-180.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs1-60.dl1.h5 --pattern-signal=gamma_theta_6.000_az_180.000_runs181-240.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs1-60.dl1.h5 --pattern-signal=gamma_theta_9.579_az_126.888_runs181-240.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs121-180.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs1-60.dl1.h5 --pattern-signal=gamma_theta_9.579_az_233.112_runs181-240.dl1.h5 @@ -221,5 +220,7 @@ def main(): # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs63-122.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & +# nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_6.000_az_*.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & + # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml> nohup_energy_training.out 2>&1 & # nohup python -m ctlearn.tools.train_model --framework=pytorch --output ./output_dir3 --signal /storage/ctlearn_data/h5_files/mc/gamma-diffuse/ --pattern-signal=gamma_theta_16.087_az_108.090_runs123-182.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs1-62.dl1.h5 --pattern-signal=gamma_theta_16.087_az_108.090_runs183-242.dl1.h5 --reco energy --overwrite --config_file ./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml> nohup_energy_training.out 2>&1 & \ No newline at end of file From b215a7b2ee57d3a6256227bbd8ebd22bd9166a48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 16 Sep 2025 21:58:45 +0000 Subject: [PATCH 058/119] Update the pytorch dataloader unit test --- ...{test_loader.py => test_loader_pytorch.py} | 25 ++++++++++++++++--- ctlearn/tools/train/pytorch/utils.py | 8 ++++++ 2 files changed, 30 insertions(+), 3 deletions(-) rename ctlearn/core/tests/{test_loader.py => test_loader_pytorch.py} (61%) diff --git a/ctlearn/core/tests/test_loader.py b/ctlearn/core/tests/test_loader_pytorch.py similarity index 61% rename from ctlearn/core/tests/test_loader.py rename to ctlearn/core/tests/test_loader_pytorch.py index 3fc662eb..3995c35b 100644 --- a/ctlearn/core/tests/test_loader.py +++ b/ctlearn/core/tests/test_loader_pytorch.py @@ -3,6 +3,9 @@ from dl1_data_handler.reader import DLImageReader from ctlearn.core.data_loader.loader import DLDataLoader +from ctlearn.tools.train.pytorch.utils import read_configuration +from ctlearn.tools.train.pytorch.utils import get_absolute_config_path + def test_data_loader(dl1_gamma_file): """check""" @@ -18,15 +21,28 @@ def test_data_loader(dl1_gamma_file): # Create an image reader dl1_reader = DLImageReader(input_url_signal=[dl1_gamma_file], config=config) # Create a data loader + config_file_dir = get_absolute_config_path() + print(config_file_dir) + parameters = read_configuration(config_file_dir) + dl1_loader = DLDataLoader.create( - framework = "keras", + framework = "pytorch", DLDataReader=dl1_reader, indices=[0], tasks=["type", "energy", "cameradirection", "skydirection"], batch_size=1, + parameters=parameters, + use_augmentation=parameters["augmentation"]["use_augmentation"], + is_training=True ) # Get the features and labels fgrom the data loader for one batch - features, labels = dl1_loader[0] + print(len(dl1_loader[0])) + print(dl1_loader[0][0]) + print(dl1_loader[0][1]) + print(dl1_loader[0][2]) + + + features, labels, _ = dl1_loader[0] # Check that all the correct labels are present assert ( "type" in labels @@ -35,4 +51,7 @@ def test_data_loader(dl1_gamma_file): and "skydirection" in labels ) # Check the shape of the features - assert features.shape == (1, 110, 110, 2) + assert features["image"].shape == (0, 1, 110, 110) + +if __name__ == "__main__": + test_data_loader() \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/utils.py b/ctlearn/tools/train/pytorch/utils.py index f9e40fbf..b7e397ad 100644 --- a/ctlearn/tools/train/pytorch/utils.py +++ b/ctlearn/tools/train/pytorch/utils.py @@ -180,3 +180,11 @@ def str_list_to_enum_list(reco_tasks: List) -> List[Task]: # ------------------------------------------------------------------------------------------------------------------- +def get_absolute_config_path(config_file_str="./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml") -> str: + """ + Returns the absolute path of the configuration file. + + :param config_file_str: Relative or absolute path to the configuration file. + :return: Absolute path of the configuration file. + """ + return os.path.abspath(config_file_str) \ No newline at end of file From a160b1622ba0fea9be70fdded2829a2ebf2b019b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 23 Sep 2025 14:48:08 +0000 Subject: [PATCH 059/119] Fix the problems with preditions --- ctlearn/tools/predict/predict_LST1.py | 1 + .../predict/pytorch/predic_LST1_pytorch.py | 40 ++++++++++++------- ctlearn/tools/predict_LST1.py | 2 + .../training_config_iaa_neutron_training.yml | 2 +- 4 files changed, 30 insertions(+), 15 deletions(-) diff --git a/ctlearn/tools/predict/predict_LST1.py b/ctlearn/tools/predict/predict_LST1.py index 5df10ac2..4725f341 100644 --- a/ctlearn/tools/predict/predict_LST1.py +++ b/ctlearn/tools/predict/predict_LST1.py @@ -315,6 +315,7 @@ def setup(self): self.transforms["image_offset"] = 0 self.transforms["peak_time_scale"] = 0.0 self.transforms["peak_time_offset"] = 0 + # Get the number of rows in the table with tables.open_file(self.input_url) as input_file: img_table_v_attrs = input_file.get_node(self.image_table_path)._v_attrs diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index c72fefef..29752b79 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -95,25 +95,29 @@ def predictions(self): trigger_time.extend(dl1_table["time"].mjd) imgs = input_data['input'][:,:,:,0] - peak_time = input_data['input'][:,:,:,1] + if len(self.channels)==2: + peak_time = input_data['input'][:,:,:,1] + peak_time[peak_time < 0] = 0 + peak_time[np.isnan(peak_time)] = 0 + peak_time[np.isinf(peak_time)] = 0 + imgs[imgs < 0] = 0 - peak_time[peak_time < 0] = 0 imgs[np.isnan(imgs)] = 0 imgs[np.isinf(imgs)] = 0 - peak_time[np.isnan(peak_time)] = 0 - peak_time[np.isinf(peak_time)] = 0 - + + feture_vector = False for task in self.tasks: if task == Task.type: imgs = (imgs - self.type_mu) / self.type_sigma - peak_time = (peak_time - self.type_mu) / self.type_sigma if len(self.channels) == 2: + peak_time = (peak_time - self.type_mu) / self.type_sigma + classification_pred, energy_pred, direction_pred = self.type_model( torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) ) else: - classification_pred, energy_pred, direction_pred = self.type_model(input_data['input'][:,:,:,0]) + classification_pred, energy_pred, direction_pred = self.type_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) prediction.extend(torch.softmax(classification_pred[0],dim=1).cpu().detach().numpy()[:,0]) classification_fvs.extend(classification_pred[1].cpu().detach().numpy()) @@ -121,32 +125,40 @@ def predictions(self): elif task == Task.energy: imgs = (imgs - self.energy_mu) / self.energy_sigma - peak_time = (peak_time - self.energy_mu) / self.energy_sigma if len(self.channels) == 2: + peak_time = (peak_time - self.energy_mu) / self.energy_sigma classification_pred, energy_pred, direction_pred = self.energy_model( torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) ) else: - classification_pred, energy_pred, direction_pred = self.energy_model(input_data['input'][:,:,:,0]) + classification_pred, energy_pred, direction_pred = self.energy_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) - energy.extend(energy_pred[0].cpu().detach().numpy()) - energy_fvs.extend(energy_pred[1].cpu().detach().numpy()) + energy.extend(energy_pred[:,0].cpu().detach().numpy()) + if feture_vector: + energy_fvs.extend(energy_pred[:,1].cpu().detach().numpy()) + else: + energy_fvs.extend(np.array([[np.NaN]] * len(energy_pred[:, 0]))) + elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: imgs = (imgs - self.dir_mu) / self.dir_sigma - peak_time = (peak_time - self.dir_mu) / self.dir_sigma if len(self.channels) == 2: + peak_time = (peak_time - self.dir_mu) / self.dir_sigma classification_pred, energy_pred, direction_pred = self.dirrection_model( torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) ) else: - classification_pred, energy_pred, direction_pred = self.dirrection_model(input_data['input'][:,:,:,0]) + classification_pred, energy_pred, direction_pred = self.dirrection_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) cam_coord_offset_x.extend(direction_pred[0][0][:,0].float().cpu().detach().numpy()) cam_coord_offset_y.extend(direction_pred[0][0][:,1].float().cpu().detach().numpy()) - direction_fvs.extend(direction_pred[1].cpu().detach().numpy()) + if feture_vector: + direction_fvs.extend(direction_pred[1].cpu().detach().numpy()) + else: + direction_fvs.extend([[np.NaN]]*len(direction_pred[0][0][:,0].float().cpu().detach().numpy())) + else: raise ValueError( f"task:{task.name} is not supported. Task must be type, direction or energy" diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index 40f44006..483483a2 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -246,6 +246,8 @@ class LST1PredictionTool(Tool): ("e", "energy_model"): "LST1PredictionTool.load_energy_model_from", ("d", "cameradirection_model"): "LST1PredictionTool.load_cameradirection_model_from", ("o", "output"): "LST1PredictionTool.output_path", + ("c", "channels"): "LST1PredictionTool.channels", + } flags = { diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml index 5b592620..c1124b59 100644 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml @@ -26,7 +26,7 @@ data: #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth - energy_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_29_energy_train_loss_16.4042284706221189.pth + energy_checkpoint: /home/cpozogonzalez/ctlearn/run/run_energy_training_14/exp_14_energy_train/version_1/Epoch_13_energy_train_loss_30.2185532478501244.pth # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth From e7486d77739587f6ba8bf8d2dd8fa825100d9d0f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 30 Sep 2025 09:01:24 +0000 Subject: [PATCH 060/119] Update the feuture vectors storage --- ctlearn/tools/predict/predict_LST1.py | 6 +++--- .../tools/predict/pytorch/predic_LST1_pytorch.py | 14 +++++++------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/ctlearn/tools/predict/predict_LST1.py b/ctlearn/tools/predict/predict_LST1.py index 4725f341..d961c2a3 100644 --- a/ctlearn/tools/predict/predict_LST1.py +++ b/ctlearn/tools/predict/predict_LST1.py @@ -532,7 +532,7 @@ def start(self): colname, colname.replace("_tel", "") ) subarray_classification_table.add_column( - classification_is_valid[np.newaxis], name=f"{self.prefix}_telescopes" + classification_is_valid, name=f"{self.prefix}_telescopes" ) # Save the prediction to the output file write_table( @@ -610,7 +610,7 @@ def start(self): colname, colname.replace("_tel", "") ) subarray_energy_table.add_column( - energy_is_valid[np.newaxis], name=f"{self.prefix}_telescopes" + energy_is_valid, name=f"{self.prefix}_telescopes" ) # Save the prediction to the output file write_table( @@ -725,7 +725,7 @@ def start(self): colname, colname.replace("_tel", "") ) subarray_direction_table.add_column( - direction_is_valid[np.newaxis], name=f"{self.prefix}_telescopes" + direction_is_valid, name=f"{self.prefix}_telescopes" ) # Save the prediction to the output file write_table( diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 29752b79..84c8a8a9 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -105,7 +105,7 @@ def predictions(self): imgs[np.isnan(imgs)] = 0 imgs[np.isinf(imgs)] = 0 - feture_vector = False + feture_vector = True for task in self.tasks: if task == Task.type: imgs = (imgs - self.type_mu) / self.type_sigma @@ -134,11 +134,11 @@ def predictions(self): else: classification_pred, energy_pred, direction_pred = self.energy_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) - energy.extend(energy_pred[:,0].cpu().detach().numpy()) + energy.extend(energy_pred[0].cpu().detach().numpy()) if feture_vector: - energy_fvs.extend(energy_pred[:,1].cpu().detach().numpy()) + energy_fvs.extend(energy_pred[1].cpu().detach().numpy()) else: - energy_fvs.extend(np.array([[np.NaN]] * len(energy_pred[:, 0]))) + energy_fvs.extend(np.array([[0]] * len(energy_pred[0]))) elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: @@ -152,12 +152,12 @@ def predictions(self): else: classification_pred, energy_pred, direction_pred = self.dirrection_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) - cam_coord_offset_x.extend(direction_pred[0][0][:,0].float().cpu().detach().numpy()) - cam_coord_offset_y.extend(direction_pred[0][0][:,1].float().cpu().detach().numpy()) + cam_coord_offset_x.extend(direction_pred[0][:,0].float().cpu().detach().numpy()) + cam_coord_offset_y.extend(direction_pred[0][:,1].float().cpu().detach().numpy()) if feture_vector: direction_fvs.extend(direction_pred[1].cpu().detach().numpy()) else: - direction_fvs.extend([[np.NaN]]*len(direction_pred[0][0][:,0].float().cpu().detach().numpy())) + direction_fvs.extend(np.array([[0]] * len(direction_pred[0]))) else: raise ValueError( From a854081da5e56d0cfe46bfd1f90a5a5854840824 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Thu, 2 Oct 2025 11:39:14 +0200 Subject: [PATCH 061/119] Remove the comment in the imports --- ctlearn/tools/train_model.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index fcbb9fd3..882fc371 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -6,10 +6,11 @@ from ctapipe.core import Tool from ctapipe.core.traits import CaselessStrEnum from ctlearn.core.ctlearn_enum import FrameworkType -# from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel -# from ctlearn.tools.train.pytorch.train_pytorch_model import ( -# TrainPyTorchModel, -# ) +from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel +from ctlearn.tools.train.pytorch.train_pytorch_model import ( + TrainPyTorchModel, +) + class DLFrameWork(Tool): """ Tool to select and run a specific deep learning training framework (Keras or PyTorch) From bfc6c20724fea93861d21b3a2056d05378949737 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Oct 2025 10:03:22 +0200 Subject: [PATCH 062/119] Apply some changes to solve the problems after rebase --- ctlearn/core/data_loader/pytorch_loader.py | 26 ++++++++++++---------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 32b05e05..89d29316 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -154,13 +154,14 @@ def apply_augmentation(self, image, peak_time): # Vertical flip image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 0), axis=-1) peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 0), axis=-1) - + continue random_aug_flip_hor = random.random() if random_aug_flip_hor > self.flip_hor_prob: # Horizontal image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 1), axis=-1) peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 1), axis=-1) # Rotation + continue random_aug_rot = random.random() if random_aug_rot > self.rot_prob: (h, w) = image[id_batch].shape[:2] @@ -178,6 +179,7 @@ def apply_augmentation(self, image, peak_time): peak_time[id_batch] = np.expand_dims(cv2.warpAffine( peak_time[id_batch].astype(np.float32), rotation_matrix, (w, h) ), axis=-1) + continue # Translation random_aug_trans = random.random() if random_aug_trans > self.trans_prob: @@ -193,6 +195,7 @@ def apply_augmentation(self, image, peak_time): peak_time[id_batch] = np.expand_dims(cv2.warpAffine( peak_time[id_batch].astype(np.float32), translation_matrix, (w, h) ), axis=-1) + continue else: doNothing = True @@ -494,9 +497,6 @@ def _get_mono_item(self, batch): features_out = {} features_out["image"] = image features_out["peak_time"] = peak_time - - features_out["image"] = torch.from_numpy(image).float().permute(0, 3, 1, 2).contiguous() - features_out["peak_time"] = torch.from_numpy(peak_time).float().permute(0, 3, 1, 2).contiguous() for key in features["hillas"].keys(): features["hillas"][key] = ( @@ -545,10 +545,10 @@ def _get_mono_item(self, batch): if self.is_training: N = 4 # Repeating the number of high energies - features_out["hillas"] = features["hillas"] + #features_out["hillas"] = features["hillas"] #------------------------------------------------- if self.use_augmentation: - energy_log = torch.pow(10,labels["energy"].squeeze(-1)) # shape [N] + energy_log = torch.pow(10,torch.tensor(labels["energy"].squeeze(-1))) # shape [N] high_energy_mask = energy_log > 1 # log10(E/TeV) > 0 => E > 1 TeV idx_to_duplicate = torch.where(high_energy_mask)[0] @@ -599,18 +599,20 @@ def duplicate_tensor(t,idx_to_duplicate): image = np.transpose(image, (0, 3, 1, 2)) peak_time = np.transpose(peak_time, (0, 3, 1, 2)) - + # features_out["image"] = torch.from_numpy(image).float().permute(0, 3, 1, 2).contiguous() + # features_out["peak_time"] = torch.from_numpy(peak_time).float().permute(0, 3, 1, 2).contiguous() + features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() - - + #Create a dummy keep_idx that keeps all events + hillas = features["hillas"] + leakage = np.array(hillas["leakage_intensity_width_2"]) + intensity = np.array(hillas["hillas_intensity"]) + keep_idx = np.where((leakage <= 0.2) & (intensity >= 50))[0] if not self.is_training: # Generate keep_idx as before - hillas = features["hillas"] - leakage = np.array(hillas["leakage_intensity_width_2"]) - intensity = np.array(hillas["hillas_intensity"]) keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] # keep_idx = np.where((leakage > 0.8) & (intensity > 50))[0] From 8ae0a01fe513ddeb7e0fe114fe0837de43d6b21f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Oct 2025 14:28:24 +0200 Subject: [PATCH 063/119] Update the test CI, to be more conservative with the space, and update the pytorch loader. --- ctlearn/core/data_loader/pytorch_loader.py | 2 +- .../pytorch/config/default_config_file.yml | 152 +++++++++++++ .../training_config_iaa_neutron_training.yml | 196 ---------------- ...g_config_iaa_neutron_training_artemisa.yml | 199 ---------------- ...ining_config_iaa_neutron_training_v5_1.yml | 207 ----------------- ...ining_config_iaa_neutron_training_v5_2.yml | 212 ------------------ ctlearn/tools/train/pytorch/utils.py | 2 +- ctlearn/utils.py | 21 ++ 8 files changed, 175 insertions(+), 816 deletions(-) create mode 100644 ctlearn/tools/train/pytorch/config/default_config_file.yml delete mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml delete mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml delete mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml delete mode 100644 ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 89d29316..a2a74241 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -548,7 +548,7 @@ def _get_mono_item(self, batch): #features_out["hillas"] = features["hillas"] #------------------------------------------------- if self.use_augmentation: - energy_log = torch.pow(10,torch.tensor(labels["energy"].squeeze(-1))) # shape [N] + energy_log = torch.pow(10,torch.tensor(labels["energy"].reshape(-1))) # shape [N] high_energy_mask = energy_log > 1 # log10(E/TeV) > 0 => E > 1 TeV idx_to_duplicate = torch.where(high_energy_mask)[0] diff --git a/ctlearn/tools/train/pytorch/config/default_config_file.yml b/ctlearn/tools/train/pytorch/config/default_config_file.yml new file mode 100644 index 00000000..abf55871 --- /dev/null +++ b/ctlearn/tools/train/pytorch/config/default_config_file.yml @@ -0,0 +1,152 @@ +data: + train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle + validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + + train_gamma: ./data/gamma_955000_train.pickle + validation_gamma: ./data/gamma_106141_validation.pickle + + test_gamma: ./data/gamma_1805522_test_gamma.pickle + test_proton: ./data/proton_130811_test_proton.pickle + test_electron: None + test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle + + test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle + + observation: ./run_2931.dl1.pickle + # Important: This is only for testing purpose. Set always to 0 + # when you are training, validating or estimating the dl2 files + training_reduce_factor: 0 #64 #4 + validation_reduce_factor: 0 #16 #8 + validation_test_reduce_factor: 0 #16 #8 + + # Check points + type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth + energy_checkpoint: /home/cpozogonzalez/ctlearn/run/run_energy_training_14/exp_14_energy_train/version_1/Epoch_13_energy_train_loss_30.2185532478501244.pth + direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth + +run_details: + + mode: "observation" # The option are: "train", "results", "observation" and "validate" + task: "direction" # The option are: "all", "energy" "type" and "direction" + test_type: "gamma" # The option are: "gamma" "proton" or "electron" + experiment_number: 14 # The experiment number. The experiment folder is saved into the "run" folder. + + +cut-off: + + leakage_intensity: 0.2 # bigger to this value, the event is removed + intensity: 50 # below to this value, the event is removed + +model: + + model_type: + model_name: "DoubleBBEfficientNet" + parameters: + model_variant: "efficientnet-b3" + task: 'type' + num_outputs: 2 + device_str: "cuda" + energy_bins: None + + model_energy: + model_name: "ThinResNet" + parameters: + task: 'energy' + num_inputs: 1 + num_outputs: 1 + num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] + dropout: 0.1 + use_bn: False + + model_direction: + model_name: "ThinResNet_DBB" + parameters: + task: 'direction' + num_inputs: 1 + num_outputs: 3 + num_blocks: [3, 4, 6, 3] + dropout: 0.1 + use_bn: False + + # model_direction: + # model_name: "DBBNoPropDTReg" + # parameters: + # task: 'direction' + # num_outputs: 3 + # embedding_dim: 512 + # T: 3 + # eta: 0.1 #0.1 + +# Hyper-parameters +hyp: + + epochs: 30 + batches: 128 #128 #64 + dynamic_batches: True + optimizer: Adamw + momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 + weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 + learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 + lrf: 0.1 + start_epoch: 0 + steps_epoch: 100 # Computed online. Must be removed + l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) + adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 + gradient_clip_val: 3.0 # Avoid gradient explosion + + save_k: 200 # Save as maximum k checkpoints. + +augmentation: + # probabilities for augmentation range = [0, 1.0] + # prob = 0.0 -> Always apply the augmentation + # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. + # Note: mask augmentation is always on even with flag use_augmentation = True + # To disable it, just set to 2.5 for example. + + use_augmentation: True # This apply only on training mode. + aug_prob: 0.5 # Probability of use Augmentation + rot_prob: 0.5 # Rotation probability + trans_prob: 0.5 # Translation probability + flip_hor_prob: 0.5 # Horizontal Flip probability + flip_ver_prob: 0.5 # Vertical Flip probability + mask_prob: 0.5 # Apply mask probability + mask_dvr_prob: 0.5 # Apply dvr mask probability + noise_prob: 0.5 # No implemented yet. + max_rot: 5 # Maximum rotation in augmentation + max_trans: 10 # Maximum translation in augmentation + +normalization: + + # Normalization: Im' = (Im-mu)/sigma + use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) + use_clean_dvr: False + type_mu: 0.0 + type_sigma: 1000.0 + + dir_mu: 0.0 + dir_sigma: 1000.0 + + energy_mu: 0.0 + energy_sigma: 1000.0 + +dataset: + num_workers: 1 # + pin_memory: True + persistent_workers: True # + +# Hardware Architecture and precision +arch: + # device: 'mps' # Apple Mx + device: 'cuda' + precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" + # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) + # devices: [0,1] # [0,1] For multiple GPUs + devices: [0,1] + # Note: Check the documentation for more information. + strategy: 'deepspeed_stage_2' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... + +Notes: + Note_1: Training with augmentation dvr using 1-3 dilatations + Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml deleted file mode 100644 index c1124b59..00000000 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml +++ /dev/null @@ -1,196 +0,0 @@ -data: - train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle - validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - train_gamma: ./data/gamma_955000_train.pickle - validation_gamma: ./data/gamma_106141_validation.pickle - - test_gamma: ./data/gamma_1805522_test_gamma.pickle - test_proton: ./data/proton_130811_test_proton.pickle - test_electron: None - test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle - - test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - observation: ./run_2931.dl1.pickle - # Important: This is only for testing purpose. Set always to 0 - # when you are training, validating or estimating the dl2 files - training_reduce_factor: 0 #64 #4 - validation_reduce_factor: 0 #16 #8 - validation_test_reduce_factor: 0 #16 #8 - - # Check points - type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth - #./run/run_type_training_14/exp_14_type_train/version_106/Epoch_3_type_train_acc_79.8272967338562012.pth #./run/run_type_training_14/exp_14_type_train/version_97/Epoch_11_type_train_acc_78.7880003452301025.pth #./run/run_type_training_14/exp_14_type_train/version_96/Epoch_0_type_train_acc_66.1041736602783203.pth #./run/run_type_training_14/exp_14_type_train/version_6/Epoch_0_type_train_acc_87.4014854431152344.pth #./run/run_type_training_14/exp_14_type_train/version_3/Epoch_11_type_train_acc_82.2395861148834229.pth #./run/run_type_training_14/exp_14_type_train/version_0/Epoch_8_type_train_acc_82.0280253887176514.pth #/storage/ctlearn_data/check_points/v_0_3/type_train_acc_81.1937808990478516.pth #./run/run_type_train_14/exp_14_type_train/version_4/Epoch_20_type_train_acc_80.5776238441467285.pth # ./run/run_type_train_14/exp_14_type_train/version_0/Epoch_27_type_train_acc_81.0595095157623291.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth #./run/run_type_train_1/type_validation_accuracy_81.6743765368707670.pth #./run/run_type_train_2/exp_2_type_train/version_4/type_train_acc_81.1937808990478516.pth - # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth - energy_checkpoint: /home/cpozogonzalez/ctlearn/run/run_energy_training_14/exp_14_energy_train/version_1/Epoch_13_energy_train_loss_30.2185532478501244.pth - # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - -run_details: - - mode: "observation" # The option are: "train", "results", "observation" and "validate" - task: "direction" # The option are: "all", "energy" "type" and "direction" - test_type: "gamma" # The option are: "gamma" "proton" or "electron" - experiment_number: 14 # The experiment number. The experiment folder is saved into the "run" folder. - - -cut-off: - - leakage_intensity: 0.2 # bigger to this value, the event is removed - intensity: 50 # below to this value, the event is removed - -model: - -# model_type: -# model_name: "DoubleBBEfficientNet" -# parameters: -# model_variant: "efficientnet-b3" -# task: 'type' -# num_outputs: 2 -# device_str: "cuda" -# energy_bins: None - - model_type: - model_name: "NoPropDT" - parameters: - # task: 'type' - num_outputs: 2 - embedding_dim: 512 - T: 6 - eta: 0.1 #0.1 - # model_type: - # model_name: "ThinResNet_DBB" - # parameters: - # task: 'type' - # num_inputs: 1 - # num_outputs: 2 - # num_blocks: [2, 3, 3, 3] - # dropout: 0.1 - # use_bn: False - - # model_energy: - # model_name: "ThinResNet" - # parameters: - # task: 'energy' - # num_inputs: 1 - # num_outputs: 1 - # num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] - # dropout: 0.1 - # use_bn: False - - model_energy: - model_name: "NoPropDTReg" - parameters: - task: 'energy' - num_outputs: 1 - embedding_dim: 512 - T: 3 - eta: 0.1 #0.1 - num_blocks: [2, 3, 3, 3] - - # model_direction: - # model_name: "ThinResNet_DBB" - # parameters: - # task: 'direction' - # num_inputs: 1 - # num_outputs: 3 - # num_blocks: [3, 4, 6, 3] - # dropout: 0.1 - # use_bn: False - - model_direction: - model_name: "NoPropDTReg" - parameters: - task: 'direction' - num_outputs: 3 - embedding_dim: 512 - T: 3 - eta: 0.1 #0.1 - num_blocks: [2, 3, 3, 3] - - # model_direction: - # model_name: "DBBNoPropDTReg" - # parameters: - # task: 'direction' - # num_outputs: 3 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - -# Hyper-parameters -hyp: - - epochs: 30 - batches: 128 #128 #64 - dynamic_batches: True - optimizer: Adamw - momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 - weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 - lrf: 0.1 - start_epoch: 0 - steps_epoch: 100 # Computed online. Must be removed - l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) - adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 - gradient_clip_val: 3.0 # Avoid gradient explosion - - save_k: 200 # Save as maximum k checkpoints. - -augmentation: - # probabilities for augmentation range = [0, 1.0] - # prob = 0.0 -> Always apply the augmentation - # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. - # Note: mask augmentation is always on even with flag use_augmentation = True - # To disable it, just set to 2.5 for example. - - use_augmentation: True # This apply only on training mode. - aug_prob: 0.5 # Probability of use Augmentation - rot_prob: 0.5 # Rotation probability - trans_prob: 0.5 # Translation probability - flip_hor_prob: 0.5 # Horizontal Flip probability - flip_ver_prob: 0.5 # Vertical Flip probability - mask_prob: 0.5 # Apply mask probability - mask_dvr_prob: 0.5 # Apply dvr mask probability - noise_prob: 0.5 # No implemented yet. - max_rot: 5 # Maximum rotation in augmentation - max_trans: 10 # Maximum translation in augmentation - -normalization: - - # Normalization: Im' = (Im-mu)/sigma - use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) - use_clean_dvr: False - type_mu: 0.0 - type_sigma: 1000.0 - - dir_mu: 0.0 - dir_sigma: 1000.0 - - energy_mu: 0.0 - energy_sigma: 1000.0 - -dataset: - num_workers: 1 # - pin_memory: True - persistent_workers: True # - -# Hardware Architecture and precision -arch: - # device: 'mps' # Apple Mx - device: 'cuda' - precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) - # devices: [0,1] # [0,1] For multiple GPUs - devices: [0,1] - # Note: Check the documentation for more information. - strategy: 'deepspeed_stage_2' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... - -Notes: - Note_1: Training with augmentation dvr using 1-3 dilatations - Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml deleted file mode 100644 index d87f6532..00000000 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_artemisa.yml +++ /dev/null @@ -1,199 +0,0 @@ -data: - - train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle - validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - train_gamma: ./data/gamma_955000_train.pickle - validation_gamma: ./data/gamma_106141_validation.pickle - - test_gamma: ./data/gamma_1805522_test_gamma.pickle - test_proton: ./data/proton_130811_test_proton.pickle - test_electron: None - test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle - - test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - observation: ./run_2931.dl1.pickle - # Important: This is only for testing purpose. Set always to 0 - # when you are training, validating or estimating the dl2 files - training_reduce_factor: 0 #64 #4 - validation_reduce_factor: 0 #16 #8 - validation_test_reduce_factor: 0 #16 #8 - - # Check points - energy_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_energy_training_14/exp_14_energy_train/version_22/Epoch_3_energy_train_loss_19.2963753574957728.pth - direction_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_cameradirection_training_14/exp_14_cameradirection_train/version_14/Epoch_0_cameradirection_train_loss_1.8445273349535292.pth - type_checkpoint: /lhome/ext/ucm147/ucm1477/ctlearn/run/run_type_training_14/exp_14_type_train/version_0/Epoch_11_type_train_acc_93.1558012962341309.pth - - #type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth - # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - #energy_checkpoint: ./run/run_energy_training_14/exp_14_energy_train/version_134/Epoch_16_energy_train_loss_16.6266201036866370.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth - # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - #direction_checkpoint: /lhome/ext/ucm147/ucm1477/data/check_points/v_5/Epoch_23_cameradirection_train_loss_7296.6534562211982120.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - -run_details: - - mode: "observation" # The option are: "train", "results", "observation" and "validate" - task: "direction" # The option are: "all", "energy" "type" and "direction" - test_type: "gamma" # The option are: "gamma" "proton" or "electron" - experiment_number: 14 # The experiment number. The experiment folder is saved into the "run" folder. - - -cut-off: - - leakage_intensity: 0.2 # bigger to this value, the event is removed - intensity: 50 # below to this value, the event is removed - -model: - # model_type: - # model_name: "DoubleBBEfficientNet" - # parameters: - # model_variant: "efficientnet-b3" - # task: 'type' - # num_outputs: 2 - # device_str: "cuda" - # energy_bins: None - - model_type: - model_name: "NoPropDT" - parameters: - # task: 'type' - num_outputs: 2 - embedding_dim: 512 - T: 6 - eta: 0.1 #0.1 - - # model_type: - # model_name: "ThinResNet_DBB" - # parameters: - # task: 'type' - # num_inputs: 1 - # num_outputs: 2 - # num_blocks: [2, 3, 3, 3] - # dropout: 0.1 - # use_bn: False - - model_energy: - model_name: "ThinResNet_DBB" - parameters: - task: 'energy' - num_inputs: 1 - num_outputs: 1 - num_blocks: [3, 4, 6, 3] - dropout: 0.1 - use_bn: False - - # model_energy: - # model_name: "NoPropDTReg" - # parameters: - # task: 'energy' - # num_outputs: 1 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - # num_blocks: [2, 3, 3, 3] - - model_direction: - model_name: "ThinResNet_DBB" - parameters: - task: 'direction' - num_inputs: 1 - num_outputs: 3 - num_blocks: [3, 4, 6, 3] - dropout: 0.1 - use_bn: False - - # model_direction: - # model_name: "NoPropDTReg" - # parameters: - # task: 'direction' - # num_outputs: 3 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - # num_blocks: [2, 3, 3, 3] - - # model_direction: - # model_name: "DBBNoPropDTReg" - # parameters: - # task: 'direction' - # num_outputs: 3 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - -# Hyper-parameters -hyp: - - epochs: 200 - batches: 64 #128 #64 - dynamic_batches: True - optimizer: Adamw - momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 - weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 - lrf: 0.1 - start_epoch: 0 - steps_epoch: 100 # Computed online. Must be removed - l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) - adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 - gradient_clip_val: 3.0 # Avoid gradient explosion - - save_k: 200 # Save as maximum k checkpoints. - -augmentation: - # probabilities for augmentation range = [0, 1.0] - # prob = 0.0 -> Always apply the augmentation - # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. - # Note: mask augmentation is always on even with flag use_augmentation = True - # To disable it, just set to 2.5 for example. - - use_augmentation: True # This apply only on training mode. - aug_prob: 0.5 # Probability of use Augmentation - rot_prob: 0.5 # Rotation probability - trans_prob: 0.5 # Translation probability - flip_hor_prob: 0.5 # Horizontal Flip probability - flip_ver_prob: 0.5 # Vertical Flip probability - mask_prob: 0.5 # Apply mask probability - mask_dvr_prob: 0.5 # Apply dvr mask probability - noise_prob: 0.5 # No implemented yet. - max_rot: 5 # Maximum rotation in augmentation - max_trans: 10 # Maximum translation in augmentation - -normalization: - - # Normalization: Im' = (Im-mu)/sigma - use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) - use_clean_dvr: False - type_mu: 0.0 - type_sigma: 1000.0 - - dir_mu: 0.0 - dir_sigma: 1000.0 - - energy_mu: 0.0 - energy_sigma: 1000.0 - -dataset: - num_workers: 1 # - pin_memory: True - persistent_workers: True # - -# Hardware Architecture and precision -arch: - # device: 'mps' # Apple Mx - device: 'cuda' - precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) - # devices: [0,1] # [0,1] For multiple GPUs - devices: [0] - # Note: Check the documentation for more information. - strategy: 'auto' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... - -Notes: - Note_1: Training with augmentation dvr using 1-3 dilatations - Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml deleted file mode 100644 index f86258f5..00000000 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_1.yml +++ /dev/null @@ -1,207 +0,0 @@ -data: - - train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle - validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - train_gamma: ./data/gamma_955000_train.pickle - validation_gamma: ./data/gamma_106141_validation.pickle - - test_gamma: ./data/gamma_1805522_test_gamma.pickle - test_proton: ./data/proton_130811_test_proton.pickle - test_electron: None - test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle - - test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - observation: ./run_2931.dl1.pickle - # Important: This is only for testing purpose. Set always to 0 - # when you are training, validating or estimating the dl2 files - training_reduce_factor: 0 #64 #4 - validation_reduce_factor: 0 #16 #8 - validation_test_reduce_factor: 0 #16 #8 - - # Check points - type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth - # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - energy_checkpoint: ./ run/run_energy_training_15/exp_15_energy_train/version_8/Epoch_13_energy_train_loss_21.4398048590775971.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth - # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - -run_details: - - mode: "train" # The option are: "train", "results", "observation" and "validate" - task: "type" # The option are: "all", "energy" "type" and "direction" - test_type: "gamma" # The option are: "gamma" "proton" or "electron" - experiment_number: 15 # The experiment number. The experiment folder is saved into the "run" folder. - - -cut-off: - - leakage_intensity: 0.2 # bigger to this value, the event is removed - intensity: 50 # below to this value, the event is removed - -model: - -# model_type: -# model_name: "DoubleBBEfficientNet" -# parameters: -# model_variant: "efficientnet-b3" -# task: 'type' -# num_outputs: 2 -# device_str: "cuda" -# energy_bins: None - - model_type: - model_name: "NoPropDT" - parameters: - # task: 'type' - num_outputs: 2 - embedding_dim: 512 - T: 6 - eta: 0.1 #0.1 - # model_type: - # model_name: "ThinResNet_DBB" - # parameters: - # task: 'type' - # num_inputs: 1 - # num_outputs: 2 - # num_blocks: [2, 3, 3, 3] - # dropout: 0.1 - # use_bn: False - - # model_energy: - # model_name: "ThinResNet" - # parameters: - # task: 'energy' - # num_inputs: 1 - # num_outputs: 1 - # num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] - # dropout: 0.1 - # use_bn: False - - # model_energy: - # model_name: "NoPropDTReg" - # parameters: - # task: 'energy' - # num_outputs: 1 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - # num_blocks: [2, 3, 3, 3] - - model_energy: - model_name: "StackedHGNet" - parameters: - task: 'energy' - input_channels: 1 - nstack: 4 - nlevels: 4 - in_channel: 256 - output_dim: 1 - use_bn: False - use_stn: False - - # model_direction: - # model_name: "ThinResNet_DBB" - # parameters: - # task: 'direction' - # num_inputs: 1 - # num_outputs: 3 - # num_blocks: [3, 4, 6, 3] - # dropout: 0.1 - # use_bn: False - - model_direction: - model_name: "NoPropDTReg" - parameters: - task: 'direction' - num_outputs: 3 - embedding_dim: 512 - T: 3 - eta: 0.1 #0.1 - num_blocks: [2, 3, 3, 3] - - # model_direction: - # model_name: "DBBNoPropDTReg" - # parameters: - # task: 'direction' - # num_outputs: 3 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - -# Hyper-parameters -hyp: - - epochs: 30 - batches: 128 #128 #64 - dynamic_batches: True - optimizer: Adamw - momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 - weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 - lrf: 0.1 - start_epoch: 0 - steps_epoch: 100 # Computed online. Must be removed - l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) - adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 - gradient_clip_val: 3.0 # Avoid gradient explosion - - save_k: 200 # Save as maximum k checkpoints. - -augmentation: - # probabilities for augmentation range = [0, 1.0] - # prob = 0.0 -> Always apply the augmentation - # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. - # Note: mask augmentation is always on even with flag use_augmentation = True - # To disable it, just set to 2.5 for example. - - use_augmentation: True # This apply only on training mode. - aug_prob: 0.5 # Probability of use Augmentation - rot_prob: 0.5 # Rotation probability - trans_prob: 0.5 # Translation probability - flip_hor_prob: 0.5 # Horizontal Flip probability - flip_ver_prob: 0.5 # Vertical Flip probability - mask_prob: 0.5 # Apply mask probability - mask_dvr_prob: 0.5 # Apply dvr mask probability - noise_prob: 0.5 # No implemented yet. - max_rot: 5 # Maximum rotation in augmentation - max_trans: 10 # Maximum translation in augmentation - -normalization: - - # Normalization: Im' = (Im-mu)/sigma - use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) - use_clean_dvr: False - type_mu: 0.0 - type_sigma: 1000.0 - - dir_mu: 0.0 - dir_sigma: 1000.0 - - energy_mu: 0.0 - # energy_sigma: 1000.0 - energy_sigma: 10.0 -dataset: - num_workers: 1 # - pin_memory: True - persistent_workers: True # - -# Hardware Architecture and precision -arch: - # device: 'mps' # Apple Mx - device: 'cuda' - precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) - # devices: [0,1] # [0,1] For multiple GPUs - devices: [0,1] - # Note: Check the documentation for more information. - strategy: 'deepspeed_stage_2' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... - -Notes: - Note_1: Training with augmentation dvr using 1-3 dilatations - Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml b/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml deleted file mode 100644 index cf53af33..00000000 --- a/ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training_v5_2.yml +++ /dev/null @@ -1,212 +0,0 @@ -data: - - train_gamma_proton: ./data/gamma_proton_train_remix.dl1.pickle #gamma_proton_reduced_train.pickle #./data/gamma_proton_1910000_train.pickle - validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - train_gamma: ./data/gamma_955000_train.pickle - validation_gamma: ./data/gamma_106141_validation.pickle - - test_gamma: ./data/gamma_1805522_test_gamma.pickle - test_proton: ./data/proton_130811_test_proton.pickle - test_electron: None - test_validation_gamma: ./data/gamma_180552_test_val_gamma.pickle - - test_validation_gamma_proton: ./data/gamma_proton_212282_validation.pickle - # test_validation_gamma_proton: ./data/gamma_proton_1910000_train.pickle - - observation: ./run_2931.dl1.pickle - # Important: This is only for testing purpose. Set always to 0 - # when you are training, validating or estimating the dl2 files - training_reduce_factor: 0 #64 #4 - validation_reduce_factor: 0 #16 #8 - validation_test_reduce_factor: 0 #16 #8 - - # Check points - type_checkpoint: ./run/run_type_training_14/exp_14_type_train/version_0/Epoch_6_type_train_acc_80.9682309627532959.pth - # energy_checkpoint: ./run/run_energy_train_2/exp_2_energy_train/version_0/energy_train_loss_9.7920012821687248.pth - - energy_checkpoint: "/home/cpozogonzalez/pablo_home/projects/ctlearn_pytorch_integration/ctlearn/run/run_energy_training_15/exp_15_energy_train/version_34/Epoch_25_energy_train_loss_4.1468562749667113.pth" - - #energy_checkpoint: ./run/run_energy_training_15/exp_15_energy_train/version_14/Epoch_5_energy_train_loss_24.1305048130270734.pth #./run/run_energy_training_14/exp_14_energy_train/version_132/Epoch_29_energy_train_loss_14.7856697206217333.pth #./run/run_energy_training_14/exp_14_energy_train/version_124/Epoch_3_energy_train_loss_17.1835174628273428.pth #./run/run_energy_training_14/exp_14_energy_train/version_65/Epoch_15_energy_train_loss_14.7553199953101970.pth #./run/run_energy_training_14/exp_14_energy_train/version_59/Epoch_8_energy_train_loss_15.2044627211281718.pth #None #./run/run_energy_training_14/exp_14_energy_train/version_44/Epoch_14_energy_train_loss_14.3631456853812143.pth #./run/run_energy_training_14/exp_14_energy_train/version_26/error_resolution_validation_20_0.24182469136006124.png #./run/run_energy_training_14/exp_14_energy_train/version_13/Epoch_9_energy_train_loss_11.5885416405641433.pth - # direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_64/Epoch_1_cameradirection_train_loss_0.9215379215089677.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - direction_checkpoint: ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_78/Epoch_16_cameradirection_train_loss_0.4441796087449597.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_69/Epoch_16_cameradirection_train_loss_0.4575265348232287.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_68/Epoch_30_cameradirection_train_loss_0.5867161673996844.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_66/Epoch_31_cameradirection_train_loss_0.5885510200288251.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_57/Epoch_17_cameradirection_train_loss_273.2955710102489206.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_41/Epoch_16_cameradirection_train_loss_1.7467666558954063.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_28/Epoch_16_cameradirection_train_loss_0.8549746516163398.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_26/Epoch_41_cameradirection_train_loss_1.2093983759278775.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_23/Epoch_41_cameradirection_train_loss_0.8230013581664681.pth # ./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_20/Epoch_21_cameradirection_train_loss_1.2520693633078133.pth #./run/run_cameradirection_training_14/exp_14_cameradirection_train/version_0/Epoch_5_cameradirection_train_loss_2.3584697707690330.pth #/storage/ctlearn_data/check_points/v_0_3/Epoch_42_direction_train_loss_-2739.9621516285983489.pth #./run/run_direction_train_2/exp_2_direction_train/version_1/direction_train_loss_1.0721765518581907.pth - -run_details: - - mode: "train" # The option are: "train", "results", "observation" and "validate" - task: "type" # The option are: "all", "energy" "type" and "direction" - test_type: "gamma" # The option are: "gamma" "proton" or "electron" - experiment_number: 15 # The experiment number. The experiment folder is saved into the "run" folder. - - -cut-off: - - leakage_intensity: 0.2 # bigger to this value, the event is removed - intensity: 50 # below to this value, the event is removed - -model: - -# model_type: -# model_name: "DoubleBBEfficientNet" -# parameters: -# model_variant: "efficientnet-b3" -# task: 'type' -# num_outputs: 2 -# device_str: "cuda" -# energy_bins: None - - model_type: - model_name: "NoPropDT" - parameters: - # task: 'type' - num_outputs: 2 - embedding_dim: 512 - T: 6 - eta: 0.1 #0.1 - - # model_type: - # model_name: "ThinResNet_DBB" - # parameters: - # task: 'type' - # num_inputs: 1 - # num_outputs: 2 - # num_blocks: [2, 3, 3, 3] - # dropout: 0.1 - # use_bn: False - - # model_energy: - # model_name: "ThinResNet" - # parameters: - # task: 'energy' - # num_inputs: 1 - # num_outputs: 1 - # num_blocks: [3, 4, 6, 3] #[2, 3, 3, 3] - # dropout: 0.1 - # use_bn: False - - model_energy: - model_name: "NoPropDTRegDBB" - parameters: - task: 'energy' - num_outputs: 1 - embedding_dim: 512 - T: 3 - eta: 0.1 #0.1 - num_blocks: [2, 3, 3, 3] - - # model_energy: - # model_name: "StackedHGNetDBB" - # parameters: - # task: 'energy' - # input_channels: 1 - # nstack: 4 - # nlevels: 4 - # in_channel: 256 - # output_dim: 1 - # use_bn: False - # use_stn: False - - # model_direction: - # model_name: "ThinResNet_DBB" - # parameters: - # task: 'direction' - # num_inputs: 1 - # num_outputs: 3 - # num_blocks: [3, 4, 6, 3] - # dropout: 0.1 - # use_bn: False - - model_direction: - model_name: "NoPropDTReg" - parameters: - task: 'direction' - num_outputs: 3 - embedding_dim: 512 - T: 3 - eta: 0.1 #0.1 - num_blocks: [2, 3, 3, 3] - - # model_direction: - # model_name: "DBBNoPropDTReg" - # parameters: - # task: 'direction' - # num_outputs: 3 - # embedding_dim: 512 - # T: 3 - # eta: 0.1 #0.1 - -# Hyper-parameters -hyp: - - epochs: 50 - batches: 256 #128 #64 - dynamic_batches: True - optimizer: Adamw - momentum: 0.957 #Yolo 0.937 # Efficient-b3 0.757 - weight_decay: 0.0005 #0.004676 #0.0001 #0.00002 Efficient-b3 0.0005 - learning_rate: 1e-4 #1e-5 #Efficient-b3 1e-5 - lrf: 0.1 - start_epoch: 0 - steps_epoch: 100 # Computed online. Must be removed - l2_lambda: 1e-7 #1e-5 #1e-5 # L2 regularization (Set to 0.0 to skip the L2 Regularization) - adam_epsilon: 1.0e-08 #7.511309034256153e-05 #1.0e-08 - gradient_clip_val: 3.0 # Avoid gradient explosion - - save_k: 200 # Save as maximum k checkpoints. - -augmentation: - # probabilities for augmentation range = [0, 1.0] - # prob = 0.0 -> Always apply the augmentation - # prob >= 1.0 -> Never apply the augmentation, i.e., Set bigger than 1.0 ( ex: 2.0) if you want disable it. - # Note: mask augmentation is always on even with flag use_augmentation = True - # To disable it, just set to 2.5 for example. - - use_augmentation: True # This apply only on training mode. - aug_prob: 0.5 # Probability of use Augmentation - rot_prob: 0.5 # Rotation probability - trans_prob: 0.5 # Translation probability - flip_hor_prob: 0.5 # Horizontal Flip probability - flip_ver_prob: 0.5 # Vertical Flip probability - mask_prob: 0.5 # Apply mask probability - mask_dvr_prob: 0.5 # Apply dvr mask probability - noise_prob: 0.5 # No implemented yet. - max_rot: 5 # Maximum rotation in augmentation - max_trans: 10 # Maximum translation in augmentation - -normalization: - - # Normalization: Im' = (Im-mu)/sigma - use_clean: True # Use the image with the applied mask (True), IOC the mask is not applied (False) - use_clean_dvr: False - type_mu: 0.0 - type_sigma: 1000.0 - - dir_mu: 0.0 - dir_sigma: 1000.0 - - energy_mu: 0.0 - # energy_sigma: 1000.0 - energy_sigma: 1000.0 - -dataset: - num_workers: 1 # - pin_memory: True - persistent_workers: True # - -# Hardware Architecture and precision -arch: - # device: 'mps' # Apple Mx - device: 'cuda' - precision_type: "32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_energy: "32-true" #"32-true" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - precision_direction: "32-true" # "bf16-mixed" # Options: "64-true" "32-true" "16-true" "16-mixed" "bf16-mixed" "bf16-true" - # (bf16 for GPU with Ampere or higher, it is better that 16 because is numerical more stability) - # devices: [0,1] # [0,1] For multiple GPUs - devices: [0,1] - # Note: Check the documentation for more information. - strategy: 'deepspeed_stage_2' # Options: auto, dpp, dpp_swap, fsdp, deepspeed, horovod, bagua, deepspeed_stage_2, deepspeed_stage_3, colossalai, hivemind, etc... - -Notes: - Note_1: Training with augmentation dvr using 1-3 dilatations - Note_2: Trainining b3 applying always the mask \ No newline at end of file diff --git a/ctlearn/tools/train/pytorch/utils.py b/ctlearn/tools/train/pytorch/utils.py index b7e397ad..1a7256a6 100644 --- a/ctlearn/tools/train/pytorch/utils.py +++ b/ctlearn/tools/train/pytorch/utils.py @@ -180,7 +180,7 @@ def str_list_to_enum_list(reco_tasks: List) -> List[Task]: # ------------------------------------------------------------------------------------------------------------------- -def get_absolute_config_path(config_file_str="./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml") -> str: +def get_absolute_config_path(config_file_str="./ctlearn/tools/train/pytorch/config/default_config_file.yml") -> str: """ Returns the absolute path of the configuration file. diff --git a/ctlearn/utils.py b/ctlearn/utils.py index 22ad94e2..b2d6e544 100644 --- a/ctlearn/utils.py +++ b/ctlearn/utils.py @@ -2,6 +2,8 @@ from ctapipe.core import Provenance from ctapipe.core.traits import TraitError +from ctapipe.instrument.optics import FocalLengthKind +from ctapipe.instrument import SubarrayDescription __all__ = ["validate_trait_dict"] @@ -25,3 +27,22 @@ def validate_trait_dict(dict, required_keys): if missing_keys: raise TraitError(f"Dict is missing required key(s): {', '.join(missing_keys)}") return True + +def get_lst1_subarray_description(focal_length_choice=FocalLengthKind.EFFECTIVE): + """ + Load subarray description from bundled file + + Parameters + ---------- + focal_length_choice : FocalLengthKind + Choice of focal length to use. Options are ``FocalLengthKind.EQUIVALENT`` + and ``FocalLengthKind.EFFECTIVE``. Default is ``FocalLengthKind.EFFECTIVE``. + + Returns + ------- + SubarrayDescription + Subarray description of the LST-1 telescope. + """ + with as_file(files("ctlearn") / "resources/LST-1_SubarrayDescription.h5") as path: + Provenance().add_input_file(path, role="SubarrayDescription") + return SubarrayDescription.from_hdf(path, focal_length_choice=focal_length_choice) \ No newline at end of file From 0d1598535878093ed40608e6adbc68d937aa8966 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Oct 2025 14:40:01 +0200 Subject: [PATCH 064/119] Separete, the env_files in to differents (pytorch and tensorflow) and optimazate the CI --- .github/workflows/python-package-conda.yml | 10 +++---- enviroment-pytorch.yml | 34 ++++++++++++++++++++++ environment-tensorflow.yml | 30 +++++++++++++++++++ 3 files changed, 69 insertions(+), 5 deletions(-) create mode 100644 enviroment-pytorch.yml create mode 100644 environment-tensorflow.yml diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index bea20d02..54b8ff43 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -2,15 +2,15 @@ name: CI on: push: - branches: - - "**" - tags: - - "**" + branches: ["**"] + tags: ["**"] pull_request: workflow_dispatch: jobs: - build: + pytorch-job: + name: PyTorch Job + runs-on: ubuntu-22.04 strategy: matrix: os: [ubuntu-22.04] diff --git a/enviroment-pytorch.yml b/enviroment-pytorch.yml new file mode 100644 index 00000000..20bcdaba --- /dev/null +++ b/enviroment-pytorch.yml @@ -0,0 +1,34 @@ +name: ctlearn-pytorch +channels: + - anaconda + - conda-forge +dependencies: + - python=3.10 + - astropy + - setuptools + - numpy + - pandas + - pytables + - tables + - c-blosc2=2.13 + - pyyaml + - scikit-learn + - ctapipe + - pytest + - pip + - pip: + - torch==2.5.0 + - torchvision==0.20.0 + - torchmetrics>=1.4.0,<1.5.0 + - pytorch_lightning==2.4.0 + - deepspeed + - onnx + - onnxsim + - scikit-image + - ctaplot + - opencv-python + - seaborn + - dl1_data_handler>=0.14.1,<0.15 + - tf2onnx + - pydot + - ctapipe_io_lst diff --git a/environment-tensorflow.yml b/environment-tensorflow.yml new file mode 100644 index 00000000..c37bd5a0 --- /dev/null +++ b/environment-tensorflow.yml @@ -0,0 +1,30 @@ +name: ctlearn-tf +channels: + - anaconda + - conda-forge +dependencies: + - python=3.10 + - astropy + - setuptools + - numpy + - pandas + - pytables + - tables + - c-blosc2=2.13 + - pyyaml + - scikit-learn + - ctapipe + - pytest + - pip + - pip: + - "tensorflow>=2.14,<2.15" + - tf2onnx + - dl1_data_handler>=0.14.1,<0.15 + - pydot + - onnx + - onnxsim + - scikit-image + - ctaplot + - opencv-python + - seaborn + - ctapipe_io_lst From 1e83841dfd70b51556c4cef98efb577048b8cd81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Oct 2025 14:47:02 +0200 Subject: [PATCH 065/119] Update the name in the env_file and change the CI --- enviroment-pytorch.yml => environment-pytorch.yml | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename enviroment-pytorch.yml => environment-pytorch.yml (100%) diff --git a/enviroment-pytorch.yml b/environment-pytorch.yml similarity index 100% rename from enviroment-pytorch.yml rename to environment-pytorch.yml From dc80ad40afa2ccc1c58a59c24e436e80170fe06c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Fri, 31 Oct 2025 13:26:41 +0100 Subject: [PATCH 066/119] Fix the predict problems after the rebase --- .gitignore | 3 +- ctlearn/core/data_loader/pytorch_loader.py | 6 +- .../DoubleBBEfficientNet.py | 6 +- ctlearn/tools/predict/predict_LST1.py | 4 +- .../predict/pytorch/predic_LST1_pytorch.py | 46 +++++++++++ .../predict/pytorch/predic_model_pytorch.py | 45 +++++----- .../predict/utils/optimaze_batch_size.py | 82 +++++++++++++++++++ ctlearn/tools/predict/utils/predict_model.py | 4 + ctlearn/tools/train/pytorch/CTLearnPL.py | 4 +- ctlearn/tools/train/pytorch/utils.py | 2 +- ctlearn/utils.py | 42 +++++++++- 11 files changed, 208 insertions(+), 36 deletions(-) create mode 100644 ctlearn/tools/predict/utils/optimaze_batch_size.py diff --git a/.gitignore b/.gitignore index 3cf088df..6e6d8b30 100644 --- a/.gitignore +++ b/.gitignore @@ -41,4 +41,5 @@ calibration/ test_local_cristian/ test/prepare_file.py -test_local_cristian/ \ No newline at end of file +test_local_cristian/ +*.txt \ No newline at end of file diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index a2a74241..fc84d1da 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -613,9 +613,11 @@ def duplicate_tensor(t,idx_to_duplicate): if not self.is_training: # Generate keep_idx as before - keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] + #keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] # keep_idx = np.where((leakage > 0.8) & (intensity > 50))[0] - + #Keep all events during evaluation for now + keep_idx = np.arange(len(intensity)) + # Filter features_out for key in features_out: features_out[key] = features_out[key][keep_idx] diff --git a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py index 2a508d9d..6f444774 100644 --- a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py +++ b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py @@ -148,9 +148,9 @@ def extract_feature_vector(self,x1, x2): def forward(self, x1, x2): - energy = None - classification = None - direction = None + energy = [None,None] + classification = [None,None] + direction = [None,None] fused_features = self.extract_feature_vector(x1, x2) diff --git a/ctlearn/tools/predict/predict_LST1.py b/ctlearn/tools/predict/predict_LST1.py index d961c2a3..1d41376c 100644 --- a/ctlearn/tools/predict/predict_LST1.py +++ b/ctlearn/tools/predict/predict_LST1.py @@ -140,7 +140,7 @@ class LST1PredictionTool(Tool): ).tag(config=True) batch_size = Int( - default_value=64, + default_value=128, allow_none=False, help="Size of the batch to perform inference of the neural network.", ).tag(config=True) @@ -257,7 +257,9 @@ def setup(self): self.log.info(f"Using {self.pytorch_config_file} config file for pytorch framework") self.parameters = read_configuration(self.pytorch_config_file) sanity_check(self.parameters, expected_structure) + self.batch_size = self.parameters["hyp"]["batches"] self.device_str = self.parameters["arch"]["device"] + self.optim_batch_size = self.parameters["hyp"]["dynamic_batches"] self.device = torch.device(self.device_str) self.tasks = [] self.type_mu = self.parameters["normalization"]["type_mu"] diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 84c8a8a9..e8505831 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -50,6 +50,52 @@ def on_train_epoch_end(self, trainer, pl_module): ) def predictions(self): + if self.optim_batch_size: + bacht_size_finded = False + bacht = 256 + step = 16 + while bacht_size_finded == False: + from ctlearn.tools.predict.utils.optimaze_batch_size import test_batch + + dl1_table = read_table( + self.input_url, self.image_table_path, start=0, stop=bacht + ) + dl1_table = join( + left=dl1_table, + right=self.parameter_table, + keys=["event_id"], + ) + dl1_table = join( + left=dl1_table, + right=self.trigger_table, + keys=["event_id"], + ) + + data = [] + for event in dl1_table: + image = get_unmapped_image(dl1_table[0], self.channels, self.transforms) + data.append(self.image_mapper.map_image(image)) + input_data = {"input": np.array(data)} + + imgs = input_data['input'][:,:,:,0] + if len(self.channels) == 2: + peak_time = input_data['input'][:,:,:,1] + + for task in self.tasks: + if task == Task.type: + bacht_size_finded = not test_batch(self.type_model,torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) , self.device) + + if task == Task.energy: + bacht_size_finded = not test_batch(self.energy_model,torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) , self.device) + + if task in [Task.cameradirection, Task.skydirection, Task.direction]: + bacht_size_finded = not test_batch(self.cameradirection_model, torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) , self.device) + bacht += step + if bacht_size_finded == False: + self.log.info(f"Batch size: {bacht} OK") + self.batch_size = bacht - step + self.log.info(f"Optimized batch size: {self.batch_size}") + event_id, tel_azimuth, tel_altitude, trigger_time = [], [], [], [] prediction, energy, cam_coord_offset_x, cam_coord_offset_y = [], [], [], [] classification_fvs, energy_fvs, direction_fvs = [], [], [] diff --git a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py index 92561402..60116ff0 100644 --- a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py @@ -1,12 +1,5 @@ from ctlearn.core.data_loader.loader import DLDataLoader -import keras -from astropy.table import ( - Table, - hstack, - vstack, - join, - setdiff, -) +import torch from tqdm import tqdm import numpy as np @@ -77,23 +70,25 @@ def predict_with_model_pytorch(self, task): predict_data['energy'] = [] predict_data["cameradirection"] = [] - model.eval() - for i, x in enumerate(tqdm(data_loader, desc="Processing", total=len(data_loader))): - if len(x[0]['image'])==0: - continue - if num_inputs == 2: - classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device) ,x[0]['peak_time'].to(self.device)) - else: - classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device)) - - if classification_pred is not None: - predict_data['type'].extend(classification_pred.cpu().detach().numpy()) - if energy_pred is not None: - predict_data['energy'].extend(energy_pred.cpu().detach().numpy()) - if direction_pred is not None: - predict_data["cameradirection"].extend(direction_pred.cpu().detach().numpy()) - if i % 100 == 0: - self.log.info(f"Processed {i}/{len(data_loader)} events.") + model.eval() + with torch.no_grad(): + for i, x in enumerate(tqdm(data_loader, desc="Processing", total=len(data_loader))): + if len(x[0]['image'])==0: + continue + if num_inputs == 2: + classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device) ,x[0]['peak_time'].to(self.device)) + else: + classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device)) + + if classification_pred[0] is not None: + gammaness = torch.softmax(classification_pred[0], dim=1).cpu().detach().numpy()[:,1] + predict_data['type'].extend(gammaness) + if energy_pred[0] is not None: + predict_data['energy'].extend(energy_pred[0].cpu().detach().numpy()) + if direction_pred[0] is not None: + predict_data["cameradirection"].extend(direction_pred[0].cpu().detach().numpy()) + if i % 100 == 0: + self.log.info(f"Processed {i}/{len(data_loader)} events.") self.log.info("Processing completed.") predict_data["cameradirection"] = np.array(predict_data["cameradirection"]) diff --git a/ctlearn/tools/predict/utils/optimaze_batch_size.py b/ctlearn/tools/predict/utils/optimaze_batch_size.py new file mode 100644 index 00000000..fa399147 --- /dev/null +++ b/ctlearn/tools/predict/utils/optimaze_batch_size.py @@ -0,0 +1,82 @@ +import torch + +import torch + +def test_batch(model, imgs, peak_time, device): + """ + Testea un batch ya preparado con imgs y peak_time. + Devuelve True si el modelo puede procesarlo sin errores, False si hay OOM. + """ + model.to(device) + model.eval() + + if not torch.is_tensor(imgs): + imgs = torch.as_tensor(imgs).to(device) + else: + imgs = imgs.to(device) + + if not torch.is_tensor(peak_time): + peak_time = torch.as_tensor(peak_time).to(device) + else: + peak_time = peak_time.to(device) + + try: + with torch.no_grad(): + _ = model(imgs, peak_time) + torch.cuda.empty_cache() + return True + + except RuntimeError as e: + if "out of memory" in str(e).lower(): + torch.cuda.empty_cache() + return False + else: + torch.cuda.empty_cache() + raise e + + +def find_max_batch_size(self, model, imgs, peak_time, device, start_bs=8, step=8, max_bs=512): + batch_size = start_bs + model.to(device) + model.eval() + + if not torch.is_tensor(imgs): + imgs = torch.as_tensor(imgs) + if not torch.is_tensor(peak_time): + peak_time = torch.as_tensor(peak_time) + + with torch.no_grad(): + while batch_size <= max_bs: + try: + # Imágenes + batch_imgs = imgs[:1] + if batch_imgs.ndim == 3: + batch_imgs = batch_imgs.unsqueeze(1) # añadir canal + batch_imgs = batch_imgs.repeat(batch_size, 1, 1, 1).to(device) + + # peak_time + value = peak_time[:1, 0, 0] # tomar valor representativo + batch_peaks = value.unsqueeze(1) # shape [1,1] + batch_peaks = batch_peaks.repeat(batch_size, 1) + batch_peaks = batch_peaks.unsqueeze(-1).unsqueeze(-1).to(device) # shape [batch,1,1,1] + + # Forward + _ = model(batch_imgs, batch_peaks) + + del batch_imgs, batch_peaks, _ + torch.cuda.empty_cache() + + print(f"✅ Batch size {batch_size} OK") + batch_size += step + + except RuntimeError as e: + if "out of memory" in str(e).lower(): + print(f"💥 OOM at batch size {batch_size}") + torch.cuda.empty_cache() + return batch_size - step + else: + print(f"❌ Error inesperado en batch size {batch_size}: {e}") + torch.cuda.empty_cache() + raise e + + return batch_size - step diff --git a/ctlearn/tools/predict/utils/predict_model.py b/ctlearn/tools/predict/utils/predict_model.py index ac9d7ec2..2add5b09 100644 --- a/ctlearn/tools/predict/utils/predict_model.py +++ b/ctlearn/tools/predict/utils/predict_model.py @@ -326,6 +326,7 @@ class PredictCTLearnModel(Tool): ("s", "skydirection_model"): "PredictCTLearnModel.load_skydirection_model_from", ("o", "output"): "PredictCTLearnModel.output_path", ("f", "framework"): "PredictCTLearnModel.framework_type", + ("p", "pytorch_config_file"): "PredictCTLearnModel.pytorch_config_file", } flags = { @@ -565,6 +566,9 @@ def _predict_energy(self, example_identifiers): np.power(10, np.squeeze(predict_data["energy"])), unit=u.TeV, ) + print(reco_energy) + print(reco_energy.shape) + print(example_identifiers) # Create prediction table and add the reconstructed energy in TeV energy_table = example_identifiers.copy() energy_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy") diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 86ff3a35..97292e19 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -195,12 +195,12 @@ def __init__( # Loss Function if self.task == Task.type: - class_weights = ( + self.class_weights = ( torch.tensor([parameters['class_weight'][0],parameters['class_weight'][1]], dtype=torch.float32).to(self.device).contiguous() ) # [1.0, 1.3] self.criterion_class = nn.CrossEntropyLoss( - weight=class_weights, reduction="mean" + weight=self.class_weights, reduction="mean" ) diff --git a/ctlearn/tools/train/pytorch/utils.py b/ctlearn/tools/train/pytorch/utils.py index 1a7256a6..4d10fe47 100644 --- a/ctlearn/tools/train/pytorch/utils.py +++ b/ctlearn/tools/train/pytorch/utils.py @@ -114,7 +114,7 @@ def read_configuration(config_file_str="./config/training_config.yml"): parameters = yaml.safe_load(config_file) else: - print("Configuration file not found.") + print(f"Configuration file not found. ({config_file_str})") return parameters diff --git a/ctlearn/utils.py b/ctlearn/utils.py index b2d6e544..ee16420e 100644 --- a/ctlearn/utils.py +++ b/ctlearn/utils.py @@ -5,8 +5,48 @@ from ctapipe.instrument.optics import FocalLengthKind from ctapipe.instrument import SubarrayDescription -__all__ = ["validate_trait_dict"] +import os +import time +from tqdm import tqdm +__all__ = ["validate_trait_dict","get_lst1_subarray_description","monitor_progress"] + +def monitor_progress(src_path, dst_path, stop_event, logger): + try: + total_size = os.path.getsize(src_path) + except OSError: + logger.error(f"Unable to access source file '{src_path}'.") + return + + last_logged_percent = -1 + + with tqdm(total=total_size, unit='B', unit_scale=True, desc="Copy Progress") as pbar: + while not stop_event.is_set(): + try: + current_size = os.path.getsize(dst_path) + except OSError: + current_size = 0 + + pbar.n = current_size + pbar.refresh() + + # Logging cada 10% + if total_size > 0: + percent = int((current_size / total_size) * 100) + if percent // 10 != last_logged_percent // 10: + logger.info(f"Progress: {percent}%") + last_logged_percent = percent + + time.sleep(0.5) + # Ensure the progress bar reaches the end + try: + final_size = os.path.getsize(dst_path) + pbar.n = final_size + pbar.refresh() + logger.info("Copy completed.") + except OSError: + logger.warning("Could not get final size of output file.") + def validate_trait_dict(dict, required_keys): """ Validate that a dictionary contains all required keys. From 2e42a1873934901166531e5ca9678765cccdb3eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Fri, 31 Oct 2025 13:29:40 +0100 Subject: [PATCH 067/119] Optimaze batch size --- .../tools/predict/utils/optimaze_batch_size.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/ctlearn/tools/predict/utils/optimaze_batch_size.py b/ctlearn/tools/predict/utils/optimaze_batch_size.py index fa399147..c4be82bf 100644 --- a/ctlearn/tools/predict/utils/optimaze_batch_size.py +++ b/ctlearn/tools/predict/utils/optimaze_batch_size.py @@ -1,11 +1,9 @@ import torch -import torch - def test_batch(model, imgs, peak_time, device): """ - Testea un batch ya preparado con imgs y peak_time. - Devuelve True si el modelo puede procesarlo sin errores, False si hay OOM. + Tests a batch already prepared with imgs and peak_time. + Returns True if the model can process it without errors, False if there is an OOM (Out of Memory) error. """ model.to(device) model.eval() @@ -48,17 +46,17 @@ def find_max_batch_size(self, model, imgs, peak_time, device, start_bs=8, step=8 with torch.no_grad(): while batch_size <= max_bs: try: - # Imágenes + # Images batch_imgs = imgs[:1] if batch_imgs.ndim == 3: - batch_imgs = batch_imgs.unsqueeze(1) # añadir canal + batch_imgs = batch_imgs.unsqueeze(1) # Add channel batch_imgs = batch_imgs.repeat(batch_size, 1, 1, 1).to(device) # peak_time - value = peak_time[:1, 0, 0] # tomar valor representativo - batch_peaks = value.unsqueeze(1) # shape [1,1] + value = peak_time[:1, 0, 0] # Take representative value + batch_peaks = value.unsqueeze(1) # Shape [1,1] batch_peaks = batch_peaks.repeat(batch_size, 1) - batch_peaks = batch_peaks.unsqueeze(-1).unsqueeze(-1).to(device) # shape [batch,1,1,1] + batch_peaks = batch_peaks.unsqueeze(-1).unsqueeze(-1).to(device) # Shape [batch,1,1,1] # Forward _ = model(batch_imgs, batch_peaks) @@ -75,7 +73,7 @@ def find_max_batch_size(self, model, imgs, peak_time, device, start_bs=8, step=8 torch.cuda.empty_cache() return batch_size - step else: - print(f"❌ Error inesperado en batch size {batch_size}: {e}") + print(f"❌ Unexpected error at batch size {batch_size}: {e}") torch.cuda.empty_cache() raise e From 12990add9ab34c31043ae98969473ab07d644eaf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 12 Nov 2025 11:34:49 +0100 Subject: [PATCH 068/119] Update the enviroments files/*.env --- environment-pytorch.yml | 89 +++++++++++++++++++++++++++----------- environment-tensorflow.yml | 88 +++++++++++++++++++++++++++---------- 2 files changed, 127 insertions(+), 50 deletions(-) diff --git a/environment-pytorch.yml b/environment-pytorch.yml index 20bcdaba..2d2b0dbc 100644 --- a/environment-pytorch.yml +++ b/environment-pytorch.yml @@ -1,34 +1,71 @@ -name: ctlearn-pytorch +# conda env create -f environment.yml +name: ctlearn channels: - - anaconda - conda-forge + - anaconda dependencies: - python=3.10 - - astropy - - setuptools - - numpy - - pandas - - pytables - - tables + - astropy=6.1.3 + - numpy=1.26.4 + - pandas=2.2.3 + - setuptools=78.1.1 + - pytables=3.10.1 + - pyyaml=6.0.2 + - scikit-learn=1.6.1 + - pytest=8.4.1 - c-blosc2=2.13 - - pyyaml - - scikit-learn - - ctapipe - - pytest - - pip + - pip=25.1 - pip: + # --- Core scientific stack --- + - numba==0.61.2 + - scipy==1.15.3 + - matplotlib==3.10.0 + - seaborn==0.13.2 + - scikit-image==0.25.2 + - tqdm==4.67.1 + + # --- Deep learning frameworks --- - torch==2.5.0 - torchvision==0.20.0 - - torchmetrics>=1.4.0,<1.5.0 - - pytorch_lightning==2.4.0 - - deepspeed - - onnx - - onnxsim - - scikit-image - - ctaplot - - opencv-python - - seaborn - - dl1_data_handler>=0.14.1,<0.15 - - tf2onnx - - pydot - - ctapipe_io_lst + - torchmetrics==1.4.3 + - pytorch-lightning==2.4.0 + - deepspeed==0.17.1 + + # --- ONNX tools --- + - onnx==1.17.0 + - onnxsim==0.4.36 + + # --- CTA related packages --- + - ctapipe==0.23.2 + - ctapipe-io-lst==0.27.1 + - dl1_data_handler==0.14.6.dev10+ga9d2de1 + - ctaplot==0.6.4 + - CTLearn==0.10.3.dev48+g492c4d6 + - pyirf==0.12.0 + + # --- I/O and plotting --- + - h5py==3.14.0 + - pydot==4.0.1 + - opencv-python==4.11.0.86 + - imageio==2.37.0 + + # --- Utilities --- + - rich==14.0.0 + - psutil==5.9.0 + - joblib==1.4.2 + - tqdm==4.67.1 + - coloredlogs==15.0.1 + - protobuf==3.20.3 + - tensorboard==2.14.1 + + # --- Jupyter & dev tools --- + - jupyterlab==4.4.4 + - ipykernel==6.29.5 + - ipywidgets==8.1.7 + - notebook==7.4.4 + + # --- Optional / visualization --- + - streamlit==1.49.1 + - bokeh==3.6.2 + + diff --git a/environment-tensorflow.yml b/environment-tensorflow.yml index c37bd5a0..505a4170 100644 --- a/environment-tensorflow.yml +++ b/environment-tensorflow.yml @@ -1,30 +1,70 @@ -name: ctlearn-tf +# conda env create -f environment.yml +name: ctlearn channels: - - anaconda - conda-forge + - anaconda dependencies: - python=3.10 - - astropy - - setuptools - - numpy - - pandas - - pytables - - tables + - astropy=6.1.3 + - numpy=1.26.4 + - pandas=2.2.3 + - setuptools=78.1.1 + - pytables=3.10.1 + - pyyaml=6.0.2 + - scikit-learn=1.6.1 + - pytest=8.4.1 - c-blosc2=2.13 - - pyyaml - - scikit-learn - - ctapipe - - pytest - - pip + - pip=25.1 - pip: - - "tensorflow>=2.14,<2.15" - - tf2onnx - - dl1_data_handler>=0.14.1,<0.15 - - pydot - - onnx - - onnxsim - - scikit-image - - ctaplot - - opencv-python - - seaborn - - ctapipe_io_lst + # --- Core scientific stack --- + - numba==0.61.2 + - scipy==1.15.3 + - matplotlib==3.10.0 + - seaborn==0.13.2 + - scikit-image==0.25.2 + - tqdm==4.67.1 + + # --- Deep learning frameworks --- + - tensorflow==2.14.1 + - tensorflow-estimator==2.14.0 + - tf2onnx==1.16.1 + - deepspeed==0.17.1 + + # --- ONNX tools --- + - onnx==1.17.0 + - onnxsim==0.4.36 + + # --- CTA related packages --- + - ctapipe==0.23.2 + - ctapipe-io-lst==0.27.1 + - dl1_data_handler==0.14.6.dev10+ga9d2de1 + - ctaplot==0.6.4 + - CTLearn==0.10.3.dev48+g492c4d6 + - pyirf==0.12.0 + + # --- I/O and plotting --- + - h5py==3.14.0 + - pydot==4.0.1 + - opencv-python==4.11.0.86 + - imageio==2.37.0 + + # --- Utilities --- + - rich==14.0.0 + - psutil==5.9.0 + - joblib==1.4.2 + - tqdm==4.67.1 + - coloredlogs==15.0.1 + - protobuf==3.20.3 + - tensorboard==2.14.1 + + # --- Jupyter & dev tools --- + - jupyterlab==4.4.4 + - ipykernel==6.29.5 + - ipywidgets==8.1.7 + - notebook==7.4.4 + + # --- Optional / visualization --- + - streamlit==1.49.1 + - bokeh==3.6.2 + + From 4c7c6fde47dcfbb7c0171d696ebbb3f328517f96 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 7 Jan 2026 15:21:57 +0100 Subject: [PATCH 069/119] Add some comments in all the functions and class and the multiGPU training support --- ctlearn/core/ctlearn_enum.py | 219 +++++- ctlearn/core/data_loader/base_loader.py | 125 +++- ctlearn/core/data_loader/keras_loader.py | 290 ++++++-- ctlearn/core/data_loader/loader.py | 64 +- ctlearn/core/data_loader/pytorch_loader.py | 154 ++-- ctlearn/core/pytorch/net_utils.py | 471 ++++++++++-- .../nets/activation/activation_functions.py | 67 ++ ctlearn/core/pytorch/nets/block/cnn_blocks.py | 289 +++++++- .../nets/loss_functions/loss_functions.py | 686 ++++++++++++++---- .../pytorch/nets/models/DBBDanet/DBBDanet.py | 397 ++++++++-- .../models/DBBNoPropDTReg/DBBNoPropDTReg.py | 367 +++++++++- .../nets/models/DBBRegNet/DBBRegNet.py | 299 +++++++- .../DoubleBBEfficientNet.py | 534 +++++++++----- .../pytorch/nets/models/NoPropDT/NoPropDT.py | 407 ++++++++--- .../NoPropDT/denoiseBlockThinRestNet.py | 360 ++++++++- .../models/Transformers/TransformerDuo.py | 343 ++++++++- .../Transformers/TransformerDuoSimple.py | 294 +++++++- ctlearn/core/pytorch/nets/models/gcn/gcn.py | 228 +++++- .../core/pytorch/nets/optimizer/optimizer.py | 126 +++- ctlearn/core/pytorch/utils/utils_torch.py | 281 ++++++- .../tools/predict/keras/predic_LST1_keras.py | 216 ++++-- .../tools/predict/keras/predic_model_keras.py | 119 ++- ctlearn/tools/predict/predict_LST1.py | 31 +- .../predict/pytorch/predic_LST1_pytorch.py | 287 ++++---- .../predict/pytorch/predic_model_pytorch.py | 118 ++- ctlearn/tools/predict/utils/load_model.py | 47 +- .../predict/utils/optimaze_batch_size.py | 116 ++- ctlearn/tools/predict_LST1.py | 2 +- ctlearn/tools/train/pytorch/CTLearnPL.py | 108 ++- .../train/pytorch/train_pytorch_model.py | 32 +- 30 files changed, 5910 insertions(+), 1167 deletions(-) diff --git a/ctlearn/core/ctlearn_enum.py b/ctlearn/core/ctlearn_enum.py index c1a6ba72..506abd57 100644 --- a/ctlearn/core/ctlearn_enum.py +++ b/ctlearn/core/ctlearn_enum.py @@ -1,11 +1,103 @@ +""" +CTLearn Enumeration Types Module + +This module defines enumeration types used throughout the CTLearn framework +to ensure type safety and consistency when specifying framework types, tasks, +event types, and operation modes. + +Enumerations: + FrameworkType: Deep learning framework selection (Keras or PyTorch) + Task: Machine learning task type (classification, regression, etc.) + EventType: Cosmic ray particle type classification + Mode: Operation mode for the CTLearn pipeline +""" + from enum import Enum class FrameworkType(Enum): + """ + Deep learning framework type enumeration. + + This enumeration specifies which deep learning framework to use for + model training and inference. CTLearn supports both Keras (TensorFlow backend) + and PyTorch frameworks. + + Attributes: + KERAS (int): Use Keras/TensorFlow framework (value: 1) + - Advantages: High-level API, easy to use, good for prototyping + - TensorFlow 2.x with Keras API + - Suitable for production deployment + + PYTORCH (int): Use PyTorch framework (value: 2) + - Advantages: Dynamic computation graphs, flexible, research-friendly + - PyTorch 1.x or 2.x + - Better for custom architectures and experimental models + + Example: + >>> from ctlearn.core.ctlearn_enum import FrameworkType + >>> framework = FrameworkType.PYTORCH + >>> print(framework.name) # 'PYTORCH' + >>> print(framework.value) # 2 + """ KERAS = 1 PYTORCH = 2 class Task(Enum): + """ + Machine learning task type enumeration. + + This enumeration defines the different analysis tasks that CTLearn can perform + on Cherenkov telescope data. Each task corresponds to a specific scientific + goal in gamma-ray astronomy. + + Attributes: + type (int): Particle type classification task (value: 0) + - Classify events as gamma-ray or background (proton/electron) + - Binary classification problem + - Output: Class probabilities (gamma vs hadron) + - Critical for gamma-ray source detection + + energy (int): Energy regression task (value: 1) + - Estimate the energy of the primary cosmic ray + - Regression problem with log-scale energy + - Output: Energy in TeV (typically log10(E/TeV)) + - Essential for measuring source spectra + + direction (int): Generic direction reconstruction task (value: 2) + - General direction estimation (deprecated, use specific types) + - Maintained for backward compatibility + + cameradirection (int): Direction reconstruction in camera coordinates (value: 3) + - Predict shower direction in camera frame + - Output: (dx, dy, distance) offsets in meters + - Must be transformed to sky coordinates for analysis + - Faster computation, no coordinate transformations needed + + skydirection (int): Direction reconstruction in sky coordinates (value: 4) + - Predict shower direction in horizontal (Alt/Az) frame + - Output: (altitude, azimuth, angular_separation) in degrees + - Directly usable for source localization + - Requires telescope pointing information + + all (int): Multi-task learning (all tasks simultaneously) (value: 5) + - Train model to perform all tasks jointly + - Shared feature extraction, task-specific heads + - Can improve performance through transfer learning + - More complex training but potentially better features + + Notes: + - Tasks can be combined in multi-task learning setups + - Each task requires specific loss functions and evaluation metrics + - The choice of task affects data preprocessing and model architecture + + Example: + >>> from ctlearn.core.ctlearn_enum import Task + >>> task = Task.energy + >>> if task == Task.energy: + ... print("Performing energy regression") + >>> print(task.name) # 'energy' + """ type = 0 energy = 1 direction = 2 @@ -14,15 +106,134 @@ class Task(Enum): all = 5 class EventType(Enum): - gamma=0 - proton=1 - electron=2 + """ + Cosmic ray event type enumeration. + + This enumeration classifies the primary particle that initiated the + air shower detected by the Cherenkov telescopes. Distinguishing between + gamma rays and background particles is crucial for gamma-ray astronomy. + + Attributes: + gamma (int): Gamma-ray event (value: 0) + - Primary particle: High-energy photon + - Characteristics: + * Electromagnetic shower + * Narrow, elliptical image + * Low muon content + * Preferred class for gamma-ray astronomy + - Used for source studies and spectral analysis + + proton (int): Proton-induced event (value: 1) + - Primary particle: Proton (cosmic ray) + - Characteristics: + * Hadronic shower + * Irregular, fragmented image + * High muon content + * Most common background (~90% of cosmic rays) + - Main source of background contamination + + electron (int): Electron-induced event (value: 2) + - Primary particle: Electron or positron + - Characteristics: + * Electromagnetic shower (similar to gamma) + * Can be difficult to distinguish from gamma + * Less common than protons + * Secondary background source + - Often grouped with gamma for some analyses + + Notes: + - In binary classification, typically gamma vs (proton + electron) + - Event type is known only for simulated data (Monte Carlo) + - Real observations have unknown event types (classification goal) + - Other particles (nuclei, muons) exist but are less common + + Physical Context: + - Gamma rays: Signal we want to detect + - Protons: Dominant background (~1000x more frequent) + - Electrons: Minor background component + - Background rejection crucial for sensitivity + + Example: + >>> from ctlearn.core.ctlearn_enum import EventType + >>> event = EventType.gamma + >>> if event == EventType.gamma: + ... print("Signal event detected") + >>> print(event.name) # 'gamma' + >>> print(event.value) # 0 + """ + gamma = 0 + proton = 1 + electron = 2 class Mode(Enum): + """ + Operation mode enumeration for the CTLearn pipeline. + + This enumeration defines the different operational modes that CTLearn + can run in, determining what actions are performed on the data. + + Attributes: + train (int): Training mode (value: 0) + - Train model on training dataset + - Update model weights through backpropagation + - Validate on validation set each epoch + - Save checkpoints and training curves + - Enables data augmentation + - Uses training-specific preprocessing + + results (int): Results generation mode (value: 1) + - Generate predictions on test or validation data + - No weight updates + - Save predictions to HDF5 files (DL2 format) + - Compute performance metrics + - Create evaluation plots and tables + - Used for final model evaluation + + validate (int): Validation mode (value: 2) + - Evaluate model on validation dataset + - No weight updates + - Compute metrics only (no predictions saved) + - Quick performance check + - Can be run during or after training + + observation (int): Real observation mode (value: 3) + - Process real telescope observations (not simulations) + - No ground truth labels available + - Generate DL2 data from DL1 real data + - Apply trained model to unknown events + - Output used for science analysis + + tunning (int): Hyperparameter tuning mode (value: 4) + - Optimize model hyperparameters + - Multiple training runs with different configurations + - Uses validation set for hyperparameter selection + - May use techniques like grid search, random search, or Bayesian optimization + - Typically automated with tools like Optuna or Ray Tune + + Typical Workflow: + 1. train: Develop and train models on simulated data + 2. validate: Quick performance checks during development + 3. tunning: Optimize hyperparameters for best performance + 4. results: Final evaluation and analysis on test set + 5. observation: Apply to real telescope data + + Notes: + - Each mode may have different data loading behavior + - Some preprocessing steps (augmentation) only active in train mode + - observation mode handles real data without labels + - Mode affects logging, checkpointing, and output format + Example: + >>> from ctlearn.core.ctlearn_enum import Mode + >>> mode = Mode.train + >>> if mode == Mode.train: + ... print("Enabling data augmentation") + >>> elif mode == Mode.observation: + ... print("Processing real data") + >>> print(mode.name) # 'train' + """ train = 0 results = 1 validate = 2 observation = 3 tunning = 4 - \ No newline at end of file diff --git a/ctlearn/core/data_loader/base_loader.py b/ctlearn/core/data_loader/base_loader.py index 6fc63548..2b717d82 100644 --- a/ctlearn/core/data_loader/base_loader.py +++ b/ctlearn/core/data_loader/base_loader.py @@ -1,6 +1,41 @@ +""" +Base Data Loader Module + +This module provides an abstract base class for data loaders in CTLearn. +It defines the common interface and initialization logic for framework-specific +data loaders (Keras, PyTorch) while handling telescope data in both mono and +stereo observation modes. + +Classes: + BaseDLDataLoader: Abstract base class for deep learning data loaders +""" + from abc import ABC, abstractmethod class BaseDLDataLoader(ABC): + """ + Abstract base class for deep learning data loaders. + + This class provides the common interface and initialization logic for loading + and processing Cherenkov telescope data. It handles both mono (single telescope) + and stereo (multiple telescopes) observation modes and supports various data + processing options like sorting by intensity and stacking telescope images. + + Attributes: + DLDataReader: The data reader instance for accessing telescope event data + indices (list): List of event indices to load from the dataset + tasks (list): List of tasks to perform (e.g., classification, energy, direction) + batch_size (int): Number of samples per batch + random_seed (int or None): Random seed for reproducibility + stack_telescope_images (bool): Whether to stack images from multiple telescopes + sort_by_intensity (bool): Whether to sort telescope images by Hillas intensity + input_shape (tuple): Shape of input data (height, width, channels) + + Methods: + __len__: Abstract method to return the number of batches per epoch + __getitem__: Abstract method to generate one batch of data + on_epoch_end: Abstract method called at the end of each epoch + """ def __init__( self, @@ -13,43 +48,115 @@ def __init__( stack_telescope_images=False, **kwargs, ): - + """ + Initialize the base data loader. + + Sets up the data loader with a data reader, configures batch processing + parameters, and determines the input shape based on observation mode + (mono vs stereo) and image stacking options. + + Args: + DLDataReader: Instance of a data reader class (e.g., DLHDFDataReader) + that provides access to telescope event data + indices (list): List of integer indices specifying which events to load + from the dataset + tasks (list): List of Task enum values specifying which tasks to perform + (e.g., [Task.type, Task.energy, Task.cameradirection]) + batch_size (int, optional): Number of samples to include in each batch. + Defaults to 64 + random_seed (int or None, optional): Seed for random number generator + to ensure reproducibility. If None, random behavior is not deterministic. + Defaults to None + sort_by_intensity (bool, optional): If True, sort telescope images by + their Hillas intensity in descending order. Useful for stereo analysis. + Defaults to False + stack_telescope_images (bool, optional): If True, stack images from multiple + telescopes along the channel dimension. Only applicable in stereo mode. + Defaults to False + **kwargs: Additional keyword arguments passed to parent classes + """ super().__init__(**kwargs) - "Initialization" + + # Store initialization parameters self.DLDataReader = DLDataReader self.indices = indices self.tasks = tasks self.batch_size = batch_size self.random_seed = random_seed - self.stack_telescope_images = stack_telescope_images self.sort_by_intensity = sort_by_intensity - # Set the input shape based on the mode of the DLDataReader + # Determine input shape based on reader type and observation mode + # Feature vector readers don't have spatial dimensions if self.DLDataReader.__class__.__name__ != "DLFeatureVectorReader": + + # Mono mode: single telescope per event if self.DLDataReader.mode == "mono": + # Use the input shape directly from the data reader self.input_shape = self.DLDataReader.input_shape + + # Stereo mode: multiple telescopes per event elif self.DLDataReader.mode == "stereo": + # Get input shape from the first selected telescope + # All telescopes are assumed to have the same image dimensions self.input_shape = self.DLDataReader.input_shape[ list(self.DLDataReader.selected_telescopes)[0] ] - # Reshape inputs into proper dimensions - # for the stereo analysis with stacked images + + # Modify input shape if stacking telescope images + # Original shape: (num_telescopes, height, width, channels) + # Stacked shape: (height, width, num_telescopes * channels) if self.stack_telescope_images: self.input_shape = ( - self.input_shape[1], - self.input_shape[2], - self.input_shape[0] * self.input_shape[3], + self.input_shape[1], # height + self.input_shape[2], # width + self.input_shape[0] * self.input_shape[3], # stacked channels ) @abstractmethod def __len__(self): + """ + Get the number of batches per epoch. + + This method must be implemented by subclasses to return the total number + of batches that will be generated in one epoch, typically calculated as + ceil(total_samples / batch_size). + + Returns: + int: Number of batches per epoch + """ pass @abstractmethod def __getitem__(self, index): + """ + Generate one batch of data. + + This method must be implemented by subclasses to return a single batch + of data at the specified index. The batch should contain input features + and corresponding labels formatted appropriately for the framework. + + Args: + index (int): Index of the batch to generate (0 to len(self) - 1) + + Returns: + tuple: A tuple containing: + - features (dict or array): Input features for the batch + - labels (dict or array): Ground truth labels for the batch + - metadata (optional): Additional information about the batch + """ pass @abstractmethod def on_epoch_end(self): + """ + Perform operations at the end of each epoch. + + This method must be implemented by subclasses to perform any necessary + cleanup or updates at the end of an epoch. Common operations include + shuffling indices for the next epoch or updating internal state. + + This method is typically called automatically by the training framework + after processing all batches in an epoch. + """ pass diff --git a/ctlearn/core/data_loader/keras_loader.py b/ctlearn/core/data_loader/keras_loader.py index b8d4b3d0..022c79f5 100644 --- a/ctlearn/core/data_loader/keras_loader.py +++ b/ctlearn/core/data_loader/keras_loader.py @@ -1,3 +1,14 @@ +""" +Keras Data Loader Module + +This module provides a Keras-specific data loader implementation for CTLearn. +It extends both Keras Sequence and the base data loader to provide efficient +batch generation for training with Keras/TensorFlow models. + +Classes: + KerasDLDataLoader: Keras Sequence for loading and preprocessing telescope data +""" + import numpy as np import keras from keras.utils import Sequence, to_categorical @@ -6,106 +17,194 @@ from dl1_data_handler.reader import ProcessType class KerasDLDataLoader(Sequence, BaseDLDataLoader): + """ + Keras data loader for Cherenkov telescope data. + + This class implements the Keras Sequence interface to provide efficient + data loading for training. It supports both monoscopic (single telescope) + and stereoscopic (multiple telescopes) observation modes, with options + for image stacking and intensity-based sorting. + + Inherits from: + Sequence: Keras sequence for thread-safe batch generation + BaseDLDataLoader: Base class providing common data loading functionality + + Attributes: + Inherited from BaseDLDataLoader including: + - DLDataReader: Data reader for accessing telescope events + - indices: Array of event indices to process + - tasks: List of tasks (type, energy, direction) + - batch_size: Number of samples per batch + - random_seed: Seed for shuffling + - sort_by_intensity: Whether to sort by Hillas intensity + - stack_telescope_images: Whether to stack images from multiple telescopes + """ + def __init__( self, **kwargs, ): - + """ + Initialize the Keras data loader. + + This constructor initializes the data loader by calling the parent + class constructors and performing initial index shuffling if a + random seed is provided. + + Args: + **kwargs: Keyword arguments passed to BaseDLDataLoader, including: + - DLDataReader: Data reader instance + - indices: Event indices to load + - tasks: List of tasks to perform + - batch_size: Batch size + - random_seed: Random seed for shuffling + - sort_by_intensity: Whether to sort by intensity + - stack_telescope_images: Whether to stack images + """ super().__init__(**kwargs) + # Perform initial shuffling of indices if random seed is set self.on_epoch_end() def __len__(self): """ - Returns the number of batches per epoch. + Get the number of batches per epoch. This method calculates the number of batches required to cover the entire dataset - based on the batch size. + based on the batch size. Uses floor division to ensure complete batches only. Returns: - -------- - int - Number of batches per epoch. + int: Number of batches per epoch (total_samples // batch_size) + + Note: + Samples that don't fit into a complete batch are not included in the epoch. + For example, with 100 samples and batch_size=32, this returns 3 (96 samples used). """ return int(np.floor(len(self.indices) / self.batch_size)) def on_epoch_end(self): """ - Updates indices after each epoch. If a random seed is provided, the indices are shuffled. - - This method is called at the end of each epoch to ensure that the data is shuffled - if the shuffle attribute is set to True. This helps in improving the training process - by providing the model with a different order of data in each epoch. + Update indices after each epoch. + + This method is called automatically by Keras at the end of each epoch. + If a random seed is provided, it shuffles the indices to provide different + batch compositions in each epoch, which can improve training convergence. + + The shuffling is deterministic (uses the same seed each time) to ensure + reproducibility while still providing epoch-to-epoch variation. + + Side Effects: + - If random_seed is not None: Shuffles self.indices in-place using + the configured random seed + - If random_seed is None: No operation performed """ if self.random_seed is not None: + # Set random seed for reproducibility np.random.seed(self.random_seed) + # Shuffle indices in-place to randomize batch composition np.random.shuffle(self.indices) def __getitem__(self, index): """ - Generate one batch of data and retrieve the features and labels. + Generate one batch of data. - This method is called to generate one batch of monoscopic and stereoscopic data based on - the index provided. It calls either _get_mono_item(batch) or _get_stereo_item(batch) - based on the mode of the DLDataReader. + This method is called by Keras to generate one batch of data based on + the provided index. It delegates to mode-specific methods (_get_mono_item + or _get_stereo_item) depending on the observation mode. - Parameters: - ----------- - index : int - Index of the batch to generate. + Args: + index (int): Index of the batch to generate (0 to len(self) - 1) Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. + tuple: (features, labels) where: + - features: Input data formatted for the model + * Keras 2: dict with 'input' key + * Keras 3: numpy array directly + * Shape varies by mode and configuration + - labels: Ground truth labels as dict or array + * Dict keys depend on tasks (type, energy, skydirection, cameradirection) + * For single task classification: categorical array instead of dict + + Note: + The batch indices are computed as: + batch_indices = self.indices[index * batch_size : (index + 1) * batch_size] """ # Generate indices of the batch batch_indices = self.indices[ index * self.batch_size : (index + 1) * self.batch_size ] features, labels = None, None + + # Generate batch based on observation mode if self.DLDataReader.mode == "mono": + # Monoscopic mode: single telescope per event batch = self.DLDataReader.generate_mono_batch(batch_indices) features, labels = self._get_mono_item(batch) elif self.DLDataReader.mode == "stereo": + # Stereoscopic mode: multiple telescopes per event batch = self.DLDataReader.generate_stereo_batch(batch_indices) features, labels = self._get_stereo_item(batch) return features, labels def _get_mono_item(self, batch): """ - Retrieve the features and labels for one batch of monoscopic data. + Retrieve features and labels for one batch of monoscopic data. - This method is called to retrieve the features and labels for one batch of - monoscopic data. The labels are set up based on the tasks specified. + This method extracts and formats data from a single-telescope batch, + preparing features and task-specific labels for training or inference. - Parameters: - ----------- - batch : astropy.table.Table - A table containing the data for the batch. + Args: + batch (astropy.table.Table): Table containing monoscopic event data + Expected columns include: + - features: Telescope images or feature vectors + - true_shower_primary_class: Particle type (0=gamma, 1=proton) + - log_true_energy: Logarithm of true energy + - fov_lon, fov_lat: Sky direction in field-of-view coordinates + - cam_coord_offset_x, cam_coord_offset_y: Camera coordinate offsets Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. + tuple: (features, labels) where: + - features: Input features formatted for Keras + * Keras 2: dict with 'input' key containing numpy array + * Keras 3: numpy array directly + * Shape: (batch_size, height, width, channels) + - labels: Task-specific labels + * If only 'type' task: categorical array (batch_size, 2) + * Otherwise: dict with keys matching self.tasks: + - 'type': one-hot encoded particle type + - 'energy': log energy values + - 'skydirection': (lon, lat) coordinates + - 'cameradirection': (offset_x, offset_y) coordinates """ - # Retrieve the telescope images and store in the features dictionary + # Initialize labels dictionary labels = {} + + # Retrieve telescope images and store in features dictionary features = {"input": batch["features"].data} + + # Extract particle type classification labels if "type" in self.tasks: + # Convert to one-hot encoding (0=gamma, 1=proton) labels["type"] = to_categorical( batch["true_shower_primary_class"].data, num_classes=2, ) - # Temp fix till keras support class weights for multiple outputs or I wrote custom loss - # https://github.com/keras-team/keras/issues/11735 + # Temporary fix: Use array instead of dict for single-task classification + # Required until Keras fully supports class weights for multiple outputs + # See: https://github.com/keras-team/keras/issues/11735 if len(self.tasks) == 1: labels = to_categorical( batch["true_shower_primary_class"].data, num_classes=2, ) + + # Extract energy regression labels if "energy" in self.tasks: + # Energy is already in log scale labels["energy"] = batch["log_true_energy"].data + + # Extract sky direction reconstruction labels if "skydirection" in self.tasks: + # Stack longitude and latitude into single array labels["skydirection"] = np.stack( ( batch["fov_lon"].data, @@ -113,7 +212,10 @@ def _get_mono_item(self, batch): ), axis=1, ) + + # Extract camera direction reconstruction labels if "cameradirection" in self.tasks: + # Stack camera x and y offsets into single array labels["cameradirection"] = np.stack( ( batch["cam_coord_offset_x"].data, @@ -121,102 +223,146 @@ def _get_mono_item(self, batch): ), axis=1, ) - # Temp fix for supporting keras2 & keras3 + + # Temporary fix for Keras 2/3 compatibility + # Keras 3 expects arrays directly, not wrapped in dict if int(keras.__version__.split(".")[0]) >= 3: features = features["input"] + return features, labels def _get_stereo_item(self, batch): """ - Retrieve the features and labels for one batch of stereoscopic data. + Retrieve features and labels for one batch of stereoscopic data. - This method is called to retrieve the features and labels for one batch of - stereoscopic data. The original batch is grouped to retrieve the telescope - data for each event and then the telescope images or waveforms are stored - by the hillas intensity or stacked if required. Feature vectors can also - be retrieved if available for ``telescope``- and ``subarray``level. The - labels are set up based on the tasks specified. + This method processes multi-telescope events, grouping telescope data + by event, optionally sorting by intensity, and stacking images if requested. + It also handles both telescope-level and subarray-level feature vectors. - Parameters: - ----------- - batch : astropy.table.Table - A table containing the data for the batch. + Args: + batch (astropy.table.Table): Table containing stereoscopic event data + Expected columns include all mono columns plus: + - obs_id: Observation run identifier + - event_id: Event identifier within observation + - tel_type_id: Telescope type identifier + - hillas_intensity: For sorting (if sort_by_intensity=True) + - mono_feature_vectors: Telescope-level features (optional) + - stereo_feature_vectors: Subarray-level features (optional) Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. + tuple: (features, labels) where: + - features: Input features formatted for Keras + * If stacked images: (batch_size, height, width, n_channels * n_tel) + * If unstacked: (batch_size, n_tel, height, width, n_channels) + * Feature vectors have different shapes + - labels: Task-specific labels (same structure as _get_mono_item) + + Note: + Events are grouped by (obs_id, event_id, tel_type_id) for simulations, + or by (obs_id, event_id, tel_type_id) for observations. + Labels are extracted from the first telescope in each group since they + are event-level quantities (same for all telescopes in an event). """ + # Initialize labels dictionary labels = {} + + # Group batch by event to collect all telescopes for each event if self.DLDataReader.process_type == ProcessType.Simulation: + # For simulations, group by observation, event, telescope type, and particle class batch_grouped = batch.group_by( ["obs_id", "event_id", "tel_type_id", "true_shower_primary_class"] ) elif self.DLDataReader.process_type == ProcessType.Observation: + # For real observations, particle class is unknown batch_grouped = batch.group_by(["obs_id", "event_id", "tel_type_id"]) + + # Initialize lists for collecting event-level data features, mono_feature_vectors, stereo_feature_vectors = [], [], [] true_shower_primary_class = [] log_true_energy = [] fov_lon, fov_lat, angular_separation = [], [], [] cam_coord_offset_x, cam_coord_offset_y, cam_coord_distance = [], [], [] + + # Process each event group for group_element in batch_grouped.groups: + # Process telescope images if available if "features" in batch.colnames: + # Sort telescopes by Hillas intensity if requested if self.sort_by_intensity: - # Sort images by the hillas intensity in a given batch if requested + # Sort in descending order (brightest first) group_element.sort(["hillas_intensity"], reverse=True) - # Stack the telescope images for stereo analysis + + # Stack telescope images for stereo analysis if requested if self.stack_telescope_images: - # Retrieve the telescope images + # Retrieve telescope images for this event plain_features = group_element["features"].data - # Stack the telescope images along the last axis + # Concatenate along channel axis: (h, w, c*n_tel) stacked_features = np.concatenate( [plain_features[i] for i in range(plain_features.shape[0])], axis=-1, ) - # Append the stacked images to the features list - # shape: (batch_size, image_shape, image_shape, n_channels * n_tel) + # Append stacked images + # Shape: (height, width, n_channels * n_telescopes) features.append(stacked_features) else: - # Append the plain images to the features list - # shape: (batch_size, n_tel, image_shape, image_shape, n_channels) + # Keep telescopes as separate dimension + # Shape: (n_telescopes, height, width, n_channels) features.append(group_element["features"].data) - # Retrieve the feature vectors + + # Retrieve telescope-level feature vectors if available if "mono_feature_vectors" in batch.colnames: mono_feature_vectors.append(group_element["mono_feature_vectors"].data) + + # Retrieve subarray-level feature vectors if available if "stereo_feature_vectors" in batch.colnames: stereo_feature_vectors.append( group_element["stereo_feature_vectors"].data ) - # Retrieve the labels for the tasks - # FIXME: This won't work for divergent pointing directions + + # Extract event-level labels (same for all telescopes in event) + # FIXME: This won't work correctly for divergent pointing directions + # where different telescopes point in different directions + + # Particle type classification if "type" in self.tasks: + # Use first telescope's value (same for all telescopes in event) true_shower_primary_class.append( group_element["true_shower_primary_class"].data[0] ) + + # Energy regression if "energy" in self.tasks: log_true_energy.append(group_element["log_true_energy"].data[0]) + + # Sky direction reconstruction if "skydirection" in self.tasks: fov_lon.append(group_element["fov_lon"].data[0]) fov_lat.append(group_element["fov_lat"].data[0]) + + # Camera direction reconstruction if "cameradirection" in self.tasks: cam_coord_offset_x.append(group_element["cam_coord_offset_x"].data) cam_coord_offset_y.append(group_element["cam_coord_offset_y"].data) - # Store the labels in the labels dictionary + + # Format labels for each task if "type" in self.tasks: + # Convert to one-hot encoding labels["type"] = to_categorical( np.array(true_shower_primary_class), num_classes=2, ) - # Temp fix till keras support class weights for multiple outputs or I wrote custom loss - # https://github.com/keras-team/keras/issues/11735 + # Temporary fix for single-task classification if len(self.tasks) == 1: labels = to_categorical( np.array(true_shower_primary_class), num_classes=2, ) + if "energy" in self.tasks: labels["energy"] = np.array(log_true_energy) + if "skydirection" in self.tasks: + # Stack longitude and latitude labels["skydirection"] = np.stack( ( np.array(fov_lon), @@ -224,7 +370,9 @@ def _get_stereo_item(self, batch): ), axis=1, ) + if "cameradirection" in self.tasks: + # Stack camera coordinate offsets labels["cameradirection"] = np.stack( ( np.array(cam_coord_offset_x), @@ -232,17 +380,21 @@ def _get_stereo_item(self, batch): ), axis=1, ) - # Store the fatures in the features dictionary + + # Format features based on available data type if "features" in batch.colnames: + # Telescope images features = {"input": np.array(features)} - # TDOO: Add support for both feature vectors + # TODO: Add support for using both mono and stereo feature vectors simultaneously if "mono_feature_vectors" in batch.colnames: + # Telescope-level feature vectors features = {"input": np.array(mono_feature_vectors)} if "stereo_feature_vectors" in batch.colnames: + # Subarray-level feature vectors features = {"input": np.array(stereo_feature_vectors)} - # Temp fix for supporting keras2 & keras3 + + # Temporary fix for Keras 2/3 compatibility if int(keras.__version__.split(".")[0]) >= 3: features = features["input"] + return features, labels - - # Include _get_mono_item and _get_stereo_item as needed diff --git a/ctlearn/core/data_loader/loader.py b/ctlearn/core/data_loader/loader.py index 39033bbe..285a0922 100644 --- a/ctlearn/core/data_loader/loader.py +++ b/ctlearn/core/data_loader/loader.py @@ -1,25 +1,87 @@ +""" +Data Loader Factory Module + +This module provides a factory class for creating framework-specific data loaders +for CTLearn. It supports both Keras and PyTorch frameworks and handles dynamic +imports to avoid unnecessary dependencies. + +Classes: + DLDataLoader: Factory class for creating data loaders based on the specified framework +""" + # from .keras_loader import KerasDLDataLoader # from .pytorch_loader import PyTorchDLDataLoader class DLDataLoader: + """ + Factory class for creating deep learning data loaders. + + This class provides a static factory method to instantiate the appropriate + data loader based on the specified framework (Keras or PyTorch). It uses + dynamic imports to load only the required framework dependencies. + + Methods: + create: Static factory method to create framework-specific data loaders + """ + @staticmethod def create(framework, **kwargs): - + """ + Create a data loader for the specified framework. + + This factory method instantiates the appropriate data loader class based + on the framework parameter. It dynamically imports the required loader + class to avoid loading unnecessary dependencies for unused frameworks. + + Args: + framework (str): The framework to use ('keras' or 'pytorch') + **kwargs: Additional keyword arguments passed to the data loader constructor + These may include: + - config: Configuration dictionary + - mode: Operation mode (train, validation, test, prediction) + - data_files: List of input data files + - batch_size: Number of samples per batch + - num_workers: Number of worker processes for data loading + + Returns: + KerasDLDataLoader or PyTorchDLDataLoader: The instantiated data loader + for the specified framework + + Raises: + ValueError: If the framework is not 'keras' or 'pytorch' + ImportError: If the required framework-specific loader cannot be imported + + Examples: + >>> # Create a PyTorch data loader + >>> loader = DLDataLoader.create('pytorch', config=config, mode='train') + + >>> # Create a Keras data loader + >>> loader = DLDataLoader.create('keras', config=config, mode='validation') + """ + # Initialize dataloader variable dataloader = None + + # Create Keras data loader if framework == "keras": try: + # Dynamically import Keras loader to avoid unnecessary dependencies from .keras_loader import KerasDLDataLoader dataloader = KerasDLDataLoader(**kwargs) except ImportError as e: + # Raise informative error if Keras dependencies are missing raise ImportError(f"Not possible to import KerasDLDataLoader: {e}") from e + # Create PyTorch data loader elif framework == "pytorch": try: + # Dynamically import PyTorch loader to avoid unnecessary dependencies from .pytorch_loader import PyTorchDLDataLoader dataloader = PyTorchDLDataLoader(**kwargs) except ImportError as e: + # Raise informative error if PyTorch dependencies are missing raise ImportError(f"Not possible to import PyTorchDLDataLoader: {e}") from e + # Handle unsupported framework else: raise ValueError(f"Unsupported framework: {framework}") diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index fc84d1da..4072811e 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -8,11 +8,39 @@ import random import cv2 -import concurrent.futures +#import concurrent.futures +from astropy.time import Time +from astropy.coordinates import AltAz, SkyCoord +from ctapipe.coordinates import CameraFrame +from astropy import units as u + +LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") class PyTorchDLDataLoader(Dataset, BaseDLDataLoader): - + """ + PyTorch data loader for Cherenkov telescope data. + + This class implements the PyTorch Dataset interface to provide efficient + data loading with support for data augmentation, normalization, and asynchronous + prefetching. It handles both monoscopic and stereoscopic observation modes. + + Inherits from: + Dataset: PyTorch Dataset for data loading + BaseDLDataLoader: Base class providing common data loading functionality + + Attributes: + is_training (bool): Whether the loader is used for training + executor: ThreadPoolExecutor for asynchronous batch prefetching + use_augmentation (bool): Whether to apply data augmentation + use_clean (bool): Whether to use clean events filtering + use_clean_dvr (bool): Whether to use DVR-based event filtering + task (Task): The task type (classification, energy, direction) + Various augmentation probability parameters + Various normalization parameters (mean and std) + hillas_names (list): List of Hillas parameter names to extract + """ + def __init__( self, tasks, @@ -24,9 +52,12 @@ def __init__( ): self.is_training=is_training - self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) - self._next_batch_future = None - + # self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=4) + # self.max_prefetch = 8 + # self.prefetch_queue = [] + # self._next_batch_idx = 0 + # self._next_batch_future = None + self.parameter = parameters self.use_augmentation = use_augmentation self.use_clean = parameters["normalization"]["use_clean"] @@ -214,25 +245,52 @@ def _fetch_batch(self, index): features, labels = self._get_stereo_item(batch) return features, labels + def _fill_prefetch_queue(self): + while ( + len(self.prefetch_queue) < self.max_prefetch + and self._next_batch_idx < len(self) + ): + future = self.executor.submit( + self._fetch_batch, self._next_batch_idx + ) + self.prefetch_queue.append(future) + self._next_batch_idx += 1 + # Fetching batches def __getitem__(self, index): - - data_idx = index - # data_idx = index % int((self.total_len/self.T)/self.batch_size) - t = index // int(np.ceil(len(self.indices)/self.T/self.batch_size)) - - # If this is the first call, fetch synchronously, and schedule the next - if self._next_batch_future is None: - features, labels = self._fetch_batch(data_idx) - else: - features, labels = self._next_batch_future.result() # Wait for the prefetch to finish - - # Schedule the next batch prefetch - if data_idx + 1 < len(self): - self._next_batch_future = self.executor.submit(self._fetch_batch, data_idx + 1) - else: - self._next_batch_future = None # No more batches + """ + Returns a prefetched batch for faster data loading. + + Parameters + ---------- + index : int + Index of the batch. + + Returns + ------- + features : dict + Dictionary containing input tensors. + labels : dict + Dictionary containing labels for each task. + t : int + Virtual batch index for tracking. + """ + + # Virtual batch index + t = index // int(np.ceil(len(self.indices) / self.T / self.batch_size)) + + # # --- 1️⃣ Ensure prefetch queue is filled --- + # if len(self.prefetch_queue) == 0: + # self._fill_prefetch_queue() + + # # --- 2️⃣ Pop the next ready batch --- + # future = self.prefetch_queue.pop(0) + # features, labels = future.result() # blocks only if needed + # # --- 3️⃣ Refill queue to keep 4 prefetched --- + # self._fill_prefetch_queue() + + features, labels = self._fetch_batch(index) return features, labels, t # def __getitem__(self, index): @@ -359,13 +417,6 @@ def __getitem__(self, index): # return sky_coords_alt, sky_coords_az def cam_to_alt_az(self, tel_id, focal_length, pix_rotation, tel_az, tel_alt, cam_x, cam_y): - - from astropy.time import Time - from astropy.coordinates import AltAz, SkyCoord - from ctapipe.coordinates import CameraFrame - from astropy import units as u - - LST_EPOCH = Time("2018-10-01T00:00:00", scale="utc") sky_coords_alt = [] sky_coords_az = [] @@ -639,27 +690,38 @@ def duplicate_tensor(t,idx_to_duplicate): return features_out, labels - # TODO: Not adapted to pytorch def _get_stereo_item(self, batch): """ - Retrieve the features and labels for one batch of stereoscopic data. - - This method is called to retrieve the features and labels for one batch of - stereoscopic data. The original batch is grouped to retrieve the telescope - data for each event and then the telescope images or waveforms are stored - by the hillas intensity or stacked if required. Feature vectors can also - be retrieved if available for ``telescope``- and ``subarray``level. The - labels are set up based on the tasks specified. - - Parameters: - ----------- - batch : astropy.table.Table - A table containing the data for the batch. + Retrieve features and labels for one batch of stereoscopic data. + + This method processes multi-telescope events, grouping telescope data + by event, optionally sorting by intensity, and stacking images if requested. + It also handles both telescope-level and subarray-level feature vectors. + + Args: + batch (astropy.table.Table): Table containing stereoscopic event data + Expected columns include all mono columns plus: + - obs_id: Observation run identifier + - event_id: Event identifier within observation + - tel_type_id: Telescope type identifier + - hillas_intensity: For sorting (if sort_by_intensity=True) + - mono_feature_vectors: Telescope-level features (optional) + - stereo_feature_vectors: Subarray-level features (optional) Returns: - -------- - tuple - A tuple containing the input data as features and the corresponding labels. + tuple: (features_out, labels) where: + - features_out (dict): Contains 'image' and 'peak_time' tensors + - labels (dict): Contains task-specific labels + + Note: + Events are grouped by (obs_id, event_id, tel_type_id, true_shower_primary_class) + for simulations, or by (obs_id, event_id, tel_type_id) for observations. + Labels are extracted from the first telescope in each group since they + are event-level quantities (same for all telescopes in an event). + + TODO: + This method needs full adaptation for PyTorch tensors and proper + handling of stereoscopic image stacking similar to _get_mono_item. """ labels = {} if self.DLDataReader.process_type == ProcessType.Simulation: @@ -761,5 +823,3 @@ def _get_stereo_item(self, batch): features_out["image"] = image features_out["peak_time"] = peak_time return features_out, labels - - # Include _get_mono_item and _get_stereo_item as needed diff --git a/ctlearn/core/pytorch/net_utils.py b/ctlearn/core/pytorch/net_utils.py index 46e0beb7..35e6ce98 100644 --- a/ctlearn/core/pytorch/net_utils.py +++ b/ctlearn/core/pytorch/net_utils.py @@ -1,3 +1,17 @@ +""" +PyTorch Neural Network Utilities Module + +This module provides utility functions and helper classes for creating, managing, +and manipulating PyTorch neural network models in CTLearn. It includes functionality +for model creation, checkpoint management, visualization, and model export. + +Functions: + create_model: Factory function for instantiating models from configuration + +Classes: + ModelHelper: Collection of static utility methods for model operations +""" + import importlib import torch import numpy as np @@ -13,20 +27,64 @@ #------------------------------------------------------------------------------------------------------------------- def create_model(model_parameters): - + """ + Factory function to dynamically create and instantiate a model. + + This function uses Python's importlib to dynamically load a model class + based on the configuration parameters and instantiate it with the provided + parameters. This allows for flexible model selection without hardcoded imports. + + Args: + model_parameters (dict): Dictionary containing model configuration + Required keys: + - 'model_name' (str): Name of the model class to instantiate + Example: 'ResNet', 'EfficientNet', 'ViT' + - 'parameters' (dict): Dictionary of parameters to pass to model constructor + Example: {'num_outputs': 2, 'input_channels': 1, 'depth': 50} + + Returns: + torch.nn.Module: Instantiated model ready for training or inference + + Raises: + ValueError: If the model class is not found in the models module + ValueError: If there's an error instantiating the model (wrong parameters) + RuntimeError: If an unexpected error occurs during model creation + + Example: + >>> config = { + ... 'model_name': 'ResNet', + ... 'parameters': { + ... 'num_outputs': 2, + ... 'input_channels': 1, + ... 'depth': 50 + ... } + ... } + >>> model = create_model(config) + >>> print(type(model)) + + + Notes: + - All models must be located in ctlearn.core.pytorch.nets.models + - Model class name must match the module name + - Models should inherit from torch.nn.Module + """ try: + # Define the base module path for models module_name = "ctlearn.core.pytorch.nets.models" model_type = model_parameters["model_name"] model_params = model_parameters["parameters"] - # Construct full class path (you should provide the full path including the module) + # Construct full class path (module.ModelClass) full_class_path = f"ctlearn.core.pytorch.nets.models.{model_type}" - # Resolve the class + # Dynamically import the model module module = importlib.import_module(full_class_path) + # Get the module (intermediate step) module = getattr(module, model_type) + # Get the actual model class from the module model_class = getattr(module, model_type) - # Now, instantiate the model with the parameters + + # Instantiate the model with the provided parameters model_net = model_class(**model_params) return model_net @@ -39,55 +97,216 @@ def create_model(model_parameters): #------------------------------------------------------------------------------------------------------------------- class ModelHelper: + """ + Collection of static utility methods for PyTorch model operations. + + This class provides a suite of utility functions for common model operations + including parameter counting, serialization, visualization, kernel generation, + and model export. All methods are static and can be called without instantiation. + + Methods: + GetNumParamters: Count trainable parameters in a model + savePickle: Serialize data to pickle file + loadPickle: Deserialize data from pickle file + plotImage: Visualize tensor as image + GaborKernels: Generate Gabor filter kernels + saveModel: Save model weights to checkpoint + loadModel: Load model weights from checkpoint + exportOnnx: Export model to ONNX format + """ + # ------------------------------------------------------------------------------------------------------------- def GetNumParamters(self): - + """ + Count the number of trainable parameters in the model. + + This method computes the total number of trainable parameters by summing + the number of elements in each parameter tensor that has requires_grad=True. + Useful for model complexity analysis and debugging. + + Returns: + tuple: (total_params, param_list) where: + - total_params (int): Total number of trainable parameters + - param_list (list): List of parameter counts per tensor + + Example: + >>> model = ResNet(num_outputs=2) + >>> helper = ModelHelper() + >>> total, per_layer = helper.GetNumParamters() + >>> print(f"Total trainable parameters: {total:,}") + Total trainable parameters: 23,512,130 + + Notes: + - Only counts parameters with requires_grad=True + - Frozen layers are not counted + - Includes biases if present + """ numel_list = [ p.numel() for p in self.model.parameters() if p.requires_grad == True ] return sum(numel_list), numel_list + # ------------------------------------------------------------------------------------------------------------- def savePickle(path, fileName, data): - + """ + Save data to a pickle file. + + Serializes Python objects to a binary pickle file for later retrieval. + Useful for saving training metrics, configurations, or intermediate results. + + Args: + path (str): Directory path where the file will be saved + fileName (str): Name of the pickle file (should include .pkl extension) + data: Python object to serialize (can be dict, list, array, etc.) + + Example: + >>> metrics = {'loss': [0.5, 0.3, 0.2], 'accuracy': [0.7, 0.8, 0.85]} + >>> ModelHelper.savePickle('/path/to/results', 'metrics.pkl', metrics) + + Notes: + - File is opened in append binary mode ('ab') + - Multiple calls will append to the same file + - Use loadPickle to retrieve the data + """ saveFile = os.path.join(path, fileName) File = open(saveFile, "ab") pickle.dump(data, File) + # ------------------------------------------------------------------------------------------------------------- def loadPickle(path, fileName): + """ + Load data from a pickle file. + + Deserializes a Python object that was previously saved with savePickle. + + Args: + path (str): Directory path where the file is located + fileName (str): Name of the pickle file to load + + Returns: + object: The deserialized Python object + + Raises: + FileNotFoundError: If the pickle file doesn't exist + pickle.UnpicklingError: If the file is corrupted or invalid + + Example: + >>> metrics = ModelHelper.loadPickle('/path/to/results', 'metrics.pkl') + >>> print(metrics['accuracy']) + [0.7, 0.8, 0.85] + """ loadFile = os.path.join(path, fileName) File = open(loadFile, "rb") data = pickle.load(File) - return data + # ------------------------------------------------------------------------------------------------------------- def plotImage(img, permute=True): - + """ + Display a tensor as an image using matplotlib. + + This utility function visualizes PyTorch tensors as grayscale images, + automatically handling gradient tracking and dimension permutation. + + Args: + img (torch.Tensor): Image tensor to display + Expected shapes: + - (H, W): Grayscale image + - (C, H, W): Multi-channel image (will be permuted to H, W, C) + permute (bool, optional): Whether to permute dimensions from (C, H, W) + to (H, W, C) for display. Defaults to True + + Side Effects: + - Displays image in matplotlib window + - Detaches tensor from computation graph if needed + + Example: + >>> # Display a model's first layer weights + >>> weights = model.conv1.weight[0, 0] # Single filter + >>> ModelHelper.plotImage(weights, permute=False) + + >>> # Display a preprocessed image + >>> img_tensor = batch['image'][0] # Shape: (1, 120, 120) + >>> ModelHelper.plotImage(img_tensor) + + Notes: + - Uses 'gray' colormap for visualization + - Automatically calls detach() for tensors in computation graph + - Blocking call - window must be closed to continue execution + """ + # Detach from computation graph if tensor is not a leaf if img.is_leaf == False: img = img.detach() + # Permute from (C, H, W) to (H, W, C) for matplotlib if permute and len(img.shape) == 3: img = img.permute(1, 2, 0) plt.imshow(img, cmap="gray") plt.show() + # ------------------------------------------------------------------------------------------------------------- def GaborKernels(size=7, showPlots=False): - + """ + Generate a bank of Gabor filter kernels. + + Creates a set of Gabor filters with varying orientations and frequencies. + Gabor filters are useful for edge detection and texture analysis in images, + particularly for Cherenkov telescope images which have oriented features. + + Args: + size (int, optional): Size of the kernel (size x size). Defaults to 7 + showPlots (bool, optional): Whether to display each kernel. Defaults to False + + Returns: + list: List of numpy arrays, each representing a Gabor kernel + Length: 4 (orientations) × 5 (frequencies) = 20 kernels + Each kernel shape: (size, size) + + Kernel Parameters: + - Orientations (theta): 0°, 45°, 90°, 135° (4 angles) + - Frequencies: 0.15, 0.25, 0.35, 0.45, 0.55 (5 frequencies) + - Sigma: Fixed at 3 (spatial extent of the kernel) + + Example: + >>> # Generate 20 Gabor kernels of size 7x7 + >>> kernels = ModelHelper.GaborKernels(size=7, showPlots=False) + >>> print(f"Generated {len(kernels)} kernels") + Generated 20 kernels + >>> print(kernels[0].shape) + (7, 7) + + >>> # Visualize kernels during generation + >>> kernels = ModelHelper.GaborKernels(size=11, showPlots=True) + + Notes: + - Uses skimage.filters.gabor_kernel for generation + - Only real part of Gabor kernel is used + - Kernels are resized to specified size using bilinear interpolation + - Useful for initializing convolutional layers with oriented filters + + Applications: + - Initializing first convolutional layer weights + - Feature extraction for shower image analysis + - Edge and orientation detection in Cherenkov images + """ # prepare filter bank kernels kernels = [] - for theta in (0, np.pi / 4, np.pi / 2, 3 * np.pi / 4): # range(8): - # theta = theta / 4. * np.pi - # for sigma in (3): - sigma = 3 - # for frequency in (0.05, 0.25): + # Iterate over 4 orientations + for theta in (0, np.pi / 4, np.pi / 2, 3 * np.pi / 4): + sigma = 3 # Fixed spatial extent + # Iterate over 5 frequencies for frequency in (0.15, 0.25, 0.35, 0.45, 0.55): + # Generate Gabor kernel (complex-valued) kernel = np.real( gabor_kernel(frequency, theta=theta, sigma_x=sigma, sigma_y=sigma) ) + # Resize to specified size kernel = resize(kernel, [size, size]) kernels.append(kernel) + # Optionally display each kernel if showPlots: print( "Theta: ", @@ -103,91 +322,251 @@ def GaborKernels(size=7, showPlots=False): plt.show() return kernels + # ------------------------------------------------------------------------------------------------------------- def saveModel(model, data_path, filename): + """ + Save model weights to a checkpoint file. + + Saves the model's state dictionary (weights and biases) to a file + for later loading and inference or continued training. + + Args: + model (torch.nn.Module): The model to save + data_path (str): Directory path where the checkpoint will be saved + filename (str): Name of the checkpoint file (typically .pth extension) + + Side Effects: + - Creates a .pth file in the specified directory + - Prints confirmation message + + Example: + >>> model = ResNet(num_outputs=2) + >>> ModelHelper.saveModel(model, './checkpoints', 'best_model.pth') + Saving model: best_model.pth + + Notes: + - Only saves state_dict (weights), not the full model + - Does not save optimizer state or training history + - Use torch.save with full checkpoint dict for complete saving + """ print("Saving model: ", filename) - torch.save(model.state_dict(), os.path.join(data_path, filename)) + # ------------------------------------------------------------------------------------------------------------- def loadModel(model, data_path, filename, mode, device_str='cpu'): - + """ + Load model weights from a checkpoint file with robust error handling. + + This method loads pre-trained weights into a model, handling various + checkpoint formats and partial weight loading. It includes automatic + key name adjustment for different checkpoint structures and validates + weight dimensions. + + Args: + model (torch.nn.Module): The model to load weights into + data_path (str): Directory path where the checkpoint is located + filename (str): Name of the checkpoint file + mode (Mode): Operation mode (train, results, validate, observation, tunning) + Determines error handling strictness + device_str (str, optional): Device to load model onto ('cpu', 'cuda', 'cuda:0'). + Defaults to 'cpu' + + Returns: + torch.nn.Module: The model with loaded weights + + Checkpoint Format Compatibility: + Handles multiple checkpoint formats: + - Direct state_dict: {'layer.weight': tensor, ...} + - Wrapped state_dict: {'state_dict': {...}} + - Model state_dict: {'model_state_dict': {...}} + - Prefixed keys: {'model.0.layer.weight': tensor, ...} + + Key Matching Strategy: + 1. Remove 'model.0.' prefix from checkpoint keys if present + 2. Filter out keys not in model's state_dict + 3. Filter out keys with dimension mismatches + 4. Load only matching keys (strict=False) + + Error Handling: + - Training/Tunning mode: Issues warning for mismatches, continues + - Other modes: Raises ValueError for mismatches + - Missing checkpoint: Exits in non-training modes + + Example: + >>> model = ResNet(num_outputs=2) + >>> model = ModelHelper.loadModel( + ... model, + ... './checkpoints', + ... 'best_model.pth', + ... Mode.results, + ... device_str='cuda:0' + ... ) + Loading model: best_model.pth + Model Loaded. + + Notes: + - Uses weights_only=False for pickle compatibility (security warning) + - Automatically moves model to specified device + - Supports partial weight loading for transfer learning + - Strict=False allows loading subset of weights + + Security Warning: + Currently uses weights_only=False which can execute arbitrary code + during unpickling. Future versions should use weights_only=True. + """ + # Check if checkpoint file exists if os.path.isfile(os.path.join(data_path, filename)): print("Loading model: ", filename) - # TODO: Test weights_only=True. It is getting this warning: - # "FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature." - pretrained_dict = torch.load(os.path.join(data_path, filename),map_location=torch.device(device_str),weights_only=False) - - if type(pretrained_dict) == dict and "state_dict" in pretrained_dict : + # TODO: Test weights_only=True. Currently getting FutureWarning + # about security implications of weights_only=False + pretrained_dict = torch.load( + os.path.join(data_path, filename), + map_location=torch.device(device_str), + weights_only=False + ) + + # Handle different checkpoint formats + # Format 1: {'state_dict': {...}} + if type(pretrained_dict) == dict and "state_dict" in pretrained_dict: pretrained_dict = pretrained_dict["state_dict"] + # Format 2: {'model_state_dict': {...}} if type(pretrained_dict) == dict and "model_state_dict" in pretrained_dict: pretrained_dict = pretrained_dict["model_state_dict"] - + # Get current model's state dict model_dict = model.state_dict() - # Remove the prefix pattern from the state dict. + # Remove 'model.0.' prefix if present in checkpoint keys modified_dict = {} prefix = "model.0." for key in pretrained_dict: - # Check if the key start with 'model.' + # Check if the key starts with 'model.0.' if key.startswith(prefix): - # Remove the pattern 'model.' and save the value with the new key + # Remove the prefix and save with new key new_key = key.replace(prefix, "") modified_dict[new_key] = pretrained_dict[key] + # Use modified dict if any keys were modified if len(modified_dict) > 0: pretrained_dict = modified_dict - # 1. filter out unnecessary keys - # pretrained_dict = { - # k: v for k, v in pretrained_dict.items() if k in model_dict - # } - # Filter out keys that do not match in name or dimensions + # Filter checkpoint to only include matching keys with same dimensions pretrained_dict = { - k: v for k, v in pretrained_dict.items() if k in model_dict and model_dict[k].size() == v.size() + k: v for k, v in pretrained_dict.items() + if k in model_dict and model_dict[k].size() == v.size() } - if (len(model_dict)!=len(pretrained_dict) or set(model_dict.keys()) != set(pretrained_dict.keys())): + # Check for mismatches between checkpoint and model + if (len(model_dict) != len(pretrained_dict) or + set(model_dict.keys()) != set(pretrained_dict.keys())): pretrain_len = len(pretrained_dict) model_len = len(model_dict) + # Keys only in checkpoint unique_pretrained = set(pretrained_dict.keys()) - set(model_dict.keys()) + # Keys only in model unique_model = set(model_dict.keys()) - set(pretrained_dict.keys()) - if (mode!=Mode.train and mode!=Mode.tunning): - raise ValueError(f"Error Loading the model. Pretrained Dict lenght: {pretrain_len} Model Dict lenght: {model_len}. Differences -> Pretrained keys: {unique_pretrained}, Model keys: {unique_model}") + # Strict error checking for non-training modes + if (mode != Mode.train and mode != Mode.tunning): + raise ValueError( + f"Error Loading the model. Pretrained Dict length: {pretrain_len} " + f"Model Dict length: {model_len}. Differences -> " + f"Pretrained keys: {unique_pretrained}, Model keys: {unique_model}" + ) else: + # Warning for training/tuning modes (allow partial loading) warnings.warn( - f"Warning Loading the model. Pretrained Dict length: {pretrain_len} Model Dict length: {model_len}. " - f"Differences -> Pretrained keys: {unique_pretrained}, Model keys: {unique_model}", + f"Warning Loading the model. Pretrained Dict length: {pretrain_len} " + f"Model Dict length: {model_len}. " + f"Differences -> Pretrained keys: {unique_pretrained}, " + f"Model keys: {unique_model}", UserWarning ) - # 2. overwrite entries in the existing state dict + # Update model dict with pretrained weights model_dict.update(pretrained_dict) - # 3. load the new state dict + # Load the new state dict (strict=False allows partial loading) model.load_state_dict(model_dict, strict=False) - # use_cuda = torch.cuda.is_available() - # device = torch.device(device_str if use_cuda else "cpu") + # Move model to specified device device = torch.device(device_str) model.to(device) - # model.load_state_dict(torch.load(data_path + filename), strict=False) print("Model Loaded.") else: + # Checkpoint doesn't exist model.to(torch.device(device_str)) print(f"CheckPoint file does not exist: {filename}") + # Exit if not in training mode (checkpoint required) if mode != Mode.train: exit() return model + # ------------------------------------------------------------------------------------------------------------- def exportOnnx(model, dummy_input, onnx_name, input_names, output_names): - + """ + Export PyTorch model to ONNX format with simplification. + + Converts a PyTorch model to ONNX (Open Neural Network Exchange) format + for deployment and interoperability with other frameworks. Also applies + optimization and simplification to the exported model. + + Args: + model (torch.nn.Module): The PyTorch model to export + dummy_input (torch.Tensor or tuple): Example input for tracing + Must have same shape and type as model's expected input + onnx_name (str): Base name for output files (without extension) + input_names (list): Names for model inputs + Example: ['image', 'peak_time'] + output_names (list): Names for model outputs + Example: ['classification', 'energy', 'direction'] + + Side Effects: + - Creates two ONNX files: + 1. {onnx_name}.onnx - Original exported model + 2. {onnx_name}_simp.onnx - Simplified and optimized model + - Prints verbose export information + + Example: + >>> model = ResNet(num_outputs=2) + >>> model.eval() + >>> dummy_input = torch.randn(1, 1, 120, 120) + >>> ModelHelper.exportOnnx( + ... model, + ... dummy_input, + ... 'resnet_model', + ... input_names=['image'], + ... output_names=['classification'] + ... ) + + Requirements: + - pip install onnx + - pip install onnxsim + + Notes: + - Model must be in eval() mode before export + - Dummy input shape must match model's expected input + - Simplified model is validated before saving + - ONNX format allows deployment to: + * TensorRT (NVIDIA) + * OpenVINO (Intel) + * CoreML (Apple) + * ONNX Runtime (cross-platform) + + Simplification Benefits: + - Removes redundant operations + - Folds constant computations + - Optimizes graph structure + - Reduces model size + - Improves inference speed + """ + # Export model to ONNX format torch.onnx.export( model, dummy_input, @@ -197,13 +576,15 @@ def exportOnnx(model, dummy_input, onnx_name, input_names, output_names): output_names=output_names, ) - # pip3 install -U pip && pip3 install onnxsim - # load your predefined ONNX model + # Load the exported ONNX model model = onnx.load(onnx_name + ".onnx") - # convert model + # Apply simplification and optimization model_simp, check = simplify(model) + # Validate simplified model assert check, "Simplified ONNX model could not be validated" + + # Save simplified model onnx.save(model_simp, onnx_name + "_simp.onnx") # ------------------------------------------------------------------------------------------------------------- diff --git a/ctlearn/core/pytorch/nets/activation/activation_functions.py b/ctlearn/core/pytorch/nets/activation/activation_functions.py index 2f5a549e..a1fa3faf 100644 --- a/ctlearn/core/pytorch/nets/activation/activation_functions.py +++ b/ctlearn/core/pytorch/nets/activation/activation_functions.py @@ -1,3 +1,70 @@ +""" +Neural Network Activation Functions Module + +This module provides custom activation functions and utilities for neural networks +in CTLearn. It includes both standard PyTorch activations and custom implementations +optimized for Cherenkov telescope data analysis. + +Activation functions introduce non-linearity into neural networks, allowing them +to learn complex patterns beyond simple linear transformations. The choice of +activation function can significantly impact model performance, training stability, +and convergence speed. + +Common Uses: + - ReLU and variants: Standard choice for hidden layers in CNNs + - Sigmoid: Binary classification output layers + - Tanh: Alternative to sigmoid with zero-centered output + - Softmax: Multi-class classification output layers + - Custom activations: Task-specific optimizations + +Imports: + torch: PyTorch tensor operations + torch.nn: Neural network modules and activation functions + +Example: + >>> import torch.nn as nn + >>> # Using standard PyTorch activations + >>> activation = nn.ReLU() + >>> x = torch.tensor([-1.0, 0.0, 1.0]) + >>> output = activation(x) # [0.0, 0.0, 1.0] + + >>> # In a neural network layer + >>> layer = nn.Sequential( + ... nn.Conv2d(1, 64, kernel_size=3), + ... nn.ReLU(), + ... nn.BatchNorm2d(64) + ... ) + +Notes: + - This module is a placeholder for future custom activation functions + - Standard PyTorch activations (nn.ReLU, nn.Sigmoid, etc.) are used throughout CTLearn + - Custom activations can be added here when needed for specific tasks + +Future Extensions: + - Swish/SiLU activation: x * sigmoid(x), shown to improve performance in some tasks + - GELU: Gaussian Error Linear Unit, used in transformers + - Mish: Self-regularized non-monotonic activation + - Parametric activations: PReLU, ELU with learnable parameters +""" + import torch import torch.nn as nn +# This module currently serves as a placeholder for custom activation functions. +# Standard PyTorch activation functions are used directly from torch.nn: +# +# Common Activations Available: +# - nn.ReLU(): Rectified Linear Unit, f(x) = max(0, x) +# - nn.LeakyReLU(negative_slope): Leaky ReLU, f(x) = max(negative_slope*x, x) +# - nn.ELU(alpha): Exponential Linear Unit +# - nn.GELU(): Gaussian Error Linear Unit +# - nn.Sigmoid(): Sigmoid function, f(x) = 1 / (1 + exp(-x)) +# - nn.Tanh(): Hyperbolic tangent, f(x) = tanh(x) +# - nn.Softmax(dim): Softmax for multi-class classification +# - nn.LogSoftmax(dim): Log of softmax, numerically stable +# +# Usage Example: +# from torch.nn import ReLU, Sigmoid +# activation = ReLU() +# output = activation(input_tensor) + diff --git a/ctlearn/core/pytorch/nets/block/cnn_blocks.py b/ctlearn/core/pytorch/nets/block/cnn_blocks.py index b54818b5..a7d5bde1 100644 --- a/ctlearn/core/pytorch/nets/block/cnn_blocks.py +++ b/ctlearn/core/pytorch/nets/block/cnn_blocks.py @@ -1,75 +1,344 @@ +""" +Convolutional Neural Network Building Blocks Module + +This module provides custom CNN building blocks for CTLearn neural network architectures. +It includes specialized layers for uncertainty quantification (evidential learning) and +residual blocks optimized for Cherenkov telescope image analysis. + +Classes: + Dirichlet: Evidential layer for classification with uncertainty quantification + NormalInvGamma: Evidential layer for regression with uncertainty quantification + ResBlock: Residual convolutional block with Gabor filter initialization +""" + import torch import torch.nn as nn from ctlearn.core.pytorch.net_utils import ModelHelper import torch.nn.functional as F class Dirichlet(nn.Module): + """ + Dirichlet distribution layer for evidential classification. + + This layer outputs parameters of a Dirichlet distribution, which is used + in evidential deep learning to quantify classification uncertainty. The + Dirichlet parameters (alphas) represent the concentration of probability + mass for each class, providing both predictions and uncertainty estimates. + + Mathematical Background: + Given evidence e_k for each class k, the Dirichlet parameters are: + α_k = e_k + 1 + + Where evidence is transformed through: + e_k = softplus(z_k) to ensure positivity + + Attributes: + dense (nn.Linear): Linear layer to compute raw evidence values + out_units (int): Number of output classes + + Methods: + evidence: Apply softplus activation to ensure positive evidence + forward: Compute Dirichlet parameters from input features + """ + def __init__(self, in_features, out_units): + """ + Initialize the Dirichlet layer. + + Args: + in_features (int): Number of input features from previous layer + out_units (int): Number of output classes (Dirichlet dimensions) + """ super().__init__() self.dense = nn.Linear(in_features, out_units) self.out_units = out_units def evidence(self, x): + """ + Transform raw outputs to positive evidence values. + + Uses softplus activation: log(1 + exp(x)) which is smooth and always positive. + This ensures that evidence values are strictly positive as required for + Dirichlet parameters. + + Args: + x (torch.Tensor): Raw evidence values from linear layer + + Returns: + torch.Tensor: Positive evidence values + """ return F.softplus(x) def forward(self, x): + """ + Compute Dirichlet distribution parameters. + + This method transforms input features into Dirichlet parameters (alphas) + which characterize the predictive distribution over classes. Higher alpha + values indicate higher confidence in that class. + + Args: + x (torch.Tensor): Input features with shape (batch_size, in_features) + + Returns: + torch.Tensor: Dirichlet parameters (alphas) with shape (batch_size, out_units) + Each alpha_k > 1, where higher values indicate more evidence for class k + + Note: + The sum of alphas S = Σα_k represents total evidence. + Class probabilities can be computed as: p_k = α_k / S + Uncertainty can be quantified using various metrics on the Dirichlet distribution + """ + # Compute raw evidence out = self.dense(x) + # Transform to Dirichlet parameters (add 1 to ensure α > 1) alpha = self.evidence(out) + 1 return alpha class NormalInvGamma(nn.Module): - """Defines the Normal Inverse Gamma distribution layer.""" + """ + Normal Inverse Gamma distribution layer for evidential regression. + + This layer outputs parameters of a Normal Inverse Gamma (NIG) distribution, + used in evidential deep learning for regression with uncertainty quantification. + The NIG distribution provides both aleatoric (data) and epistemic (model) uncertainty. + + Mathematical Background: + The NIG distribution is parameterized by (μ, v, α, β): + - μ: Predicted mean + - v: Virtual observation count (epistemic uncertainty) + - α: Shape parameter (controls uncertainty distribution) + - β: Scale parameter (aleatoric uncertainty) + + Predictive variance: var = β / (v * (α - 1)) + + Attributes: + dense (nn.Linear): Linear layer to compute 4 parameters + out_units (int): Number of regression outputs (typically 1) + + Methods: + evidence: Apply softplus to ensure positive parameters + forward: Compute NIG parameters or predictions based on mode + """ + def __init__(self, in_features, out_units): + """ + Initialize the Normal Inverse Gamma layer. + + Args: + in_features (int): Number of input features from previous layer + out_units (int): Number of regression outputs (usually 1) + """ super().__init__() + # Output 4 parameters per regression target: μ, log(v), log(α), log(β) self.dense = nn.Linear(in_features, out_units * 4) self.out_units = out_units def evidence(self, x): + """ + Transform raw outputs to positive parameter values. + + Uses softplus activation to ensure v, α, and β are strictly positive + as required by the Normal Inverse Gamma distribution. + + Args: + x (torch.Tensor): Raw parameter values + + Returns: + torch.Tensor: Positive parameter values + """ return F.softplus(x) def forward(self, x): + """ + Compute NIG distribution parameters or predictions. + + During training, returns all four NIG parameters for loss computation. + During inference, returns mean prediction and uncertainty estimate. + + Args: + x (torch.Tensor): Input features with shape (batch_size, in_features) + + Returns: + Training mode: + tuple: (mu, v, alpha, beta) - all NIG parameters + - mu: Mean predictions (batch_size, out_units) + - v: Virtual observations (batch_size, out_units) + - alpha: Shape parameters (batch_size, out_units) + - beta: Scale parameters (batch_size, out_units) + + Inference mode: + tuple: (mu, var) - predictions and uncertainties + - mu: Mean predictions (batch_size, out_units) + - var: Predictive variance (batch_size, out_units) + + Note: + The predictive variance combines both aleatoric and epistemic uncertainty: + var = β / (v * (α - 1)) + + Higher v means lower epistemic uncertainty (more confident) + Higher β means higher aleatoric uncertainty (noisier data) + """ + # Compute raw parameters out = self.dense(x) + # Split into 4 components mu, logv, logalpha, logbeta = torch.split(out, self.out_units, dim=-1) + + # Transform to ensure positivity v = self.evidence(logv) - alpha = self.evidence(logalpha) + 1 + alpha = self.evidence(logalpha) + 1 # Ensure α > 1 beta = self.evidence(logbeta) - # return mu, v, alpha, beta - + + # Return appropriate outputs based on mode if self.training: + # Return all parameters for evidential loss computation return mu, v, alpha, beta else: + # Return prediction and uncertainty for inference + # Predictive variance from NIG distribution var = torch.sqrt(beta / (v * (alpha - 1))) - return mu, var class ResBlock(nn.Module): + """ + Residual convolutional block with Gabor filter initialization. + + This block implements a residual connection with convolutional layers, + batch normalization, and pooling. It's optimized for processing Cherenkov + telescope images by initializing filters with Gabor kernels that are + particularly effective at detecting oriented features in shower images. + + Architecture: + Input → Conv2d → BatchNorm → LeakyReLU → (+) → MaxPool → Conv1x1 → Dropout → Output + ↑ + | + Residual Connection + + Attributes: + conv (nn.Conv2d): Main convolutional layer + conv_dropout (nn.Dropout2d): Spatial dropout for regularization + batch_norm (nn.BatchNorm2d): Batch normalization layer + pool (nn.MaxPool2d): Max pooling layer (2x2) + activation (nn.LeakyReLU): Activation function + conv_out (nn.Conv2d): 1x1 conv for channel adjustment + + Methods: + forward: Process input through residual block + """ + def __init__(self, n_chans_in, n_chans_out, kernel_size=3, conv_drop_pro=0.2): - + """ + Initialize the residual block. + + Args: + n_chans_in (int): Number of input channels + n_chans_out (int): Number of output channels + kernel_size (int, optional): Size of convolutional kernel. Defaults to 3 + conv_drop_pro (float, optional): Dropout probability. Defaults to 0.2 + + Initialization Strategy: + 1. Kaiming normal initialization for main conv layer weights + 2. Batch norm weights initialized to 0.5 + 3. Batch norm biases initialized to 0 + 4. First few filters initialized with Gabor kernels for edge detection + """ super(ResBlock, self).__init__() - self.conv = nn.Conv2d(n_chans_in, n_chans_in, kernel_size=kernel_size, padding=int(kernel_size/2), bias=False) + # Main convolutional layer (maintains channel dimension) + self.conv = nn.Conv2d( + n_chans_in, + n_chans_in, + kernel_size=kernel_size, + padding=int(kernel_size/2), # Same padding + bias=False # Bias not needed before batch norm + ) + + # Spatial dropout for regularization self.conv_dropout = nn.Dropout2d(p=conv_drop_pro) + + # Batch normalization for training stability self.batch_norm = nn.BatchNorm2d(num_features=n_chans_in) + + # Max pooling to reduce spatial dimensions self.pool = nn.MaxPool2d(2) + + # LeakyReLU activation (allows small negative gradients) self.activation = nn.LeakyReLU() - self.conv_out = nn.Conv2d(n_chans_in, n_chans_out, kernel_size=1, padding=0, bias=False) + + # 1x1 convolution to adjust channel dimension + self.conv_out = nn.Conv2d( + n_chans_in, + n_chans_out, + kernel_size=1, + padding=0, + bias=False + ) + # Initialize conv layer with Kaiming normal + # Appropriate for LeakyReLU activation torch.nn.init.kaiming_normal_(self.conv.weight, nonlinearity='leaky_relu') + + # Initialize batch norm parameters torch.nn.init.constant_(self.batch_norm.weight, 0.5) torch.nn.init.zeros_(self.batch_norm.bias) - # Init Filters + # Initialize first filters with Gabor kernels + # Gabor filters are effective for detecting oriented features + # particularly useful for elongated shower images kernels = ModelHelper.GaborKernels(size=kernel_size, showPlots=False) for i in range(min(self.conv.weight.shape[0], len(kernels))): with torch.no_grad(): - self.conv.weight[i, :] = torch.nn.Parameter(torch.tensor(kernels[i]*100)) + # Scale Gabor kernel by 100 for appropriate magnitude + self.conv.weight[i, :] = torch.nn.Parameter( + torch.tensor(kernels[i] * 100) + ) def forward(self, x): + """ + Forward pass through the residual block. + + Processing Steps: + 1. Apply convolution to extract features + 2. Normalize with batch norm + 3. Apply activation function + 4. Add residual connection (skip connection) + 5. Reduce spatial dimensions with pooling + 6. Adjust channels with 1x1 convolution + 7. Apply dropout for regularization + + Args: + x (torch.Tensor): Input tensor with shape (batch_size, n_chans_in, height, width) + + Returns: + torch.Tensor: Output tensor with shape + (batch_size, n_chans_out, height/2, width/2) + + Note: + The residual connection helps gradient flow during backpropagation + and allows the network to learn identity mappings when beneficial. + + Spatial dimensions are halved due to max pooling with stride 2. + """ + # Convolutional feature extraction out = self.conv(x) + + # Normalize activations out = self.batch_norm(out) + + # Non-linear activation out = self.activation(out) + + # Add residual connection (element-wise addition) + # This helps with gradient flow and allows learning identity mappings out = out + x + + # Reduce spatial dimensions out = self.pool(out) + + # Adjust number of channels out = self.conv_out(out) + + # Apply dropout for regularization out = self.conv_dropout(out) + return out \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py index fd816c38..7f06c0e3 100644 --- a/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py +++ b/ctlearn/core/pytorch/nets/loss_functions/loss_functions.py @@ -1,15 +1,83 @@ +""" +Loss Functions Module for Deep Learning + +This module provides custom loss functions for various deep learning tasks in CTLearn, +with a focus on uncertainty quantification through evidential learning and specialized +losses for astronomical data analysis. + +Loss Functions: + evidential_regression_loss: Normal Inverse Gamma loss for regression with uncertainty + cosine_direction_loss: Cosine similarity loss for direction reconstruction + AngularDistance: Angular distance metric for celestial coordinates + AngularError: Angular error between vector predictions + VectorLoss: Angle-based loss for 2D vectors + FocalLoss: Focal loss for imbalanced classification + BCELogitsLoss: Binary cross-entropy with label smoothing + EvidClassification: Evidential classification with Dirichlet distribution + +Utility Functions: + smooth_BCE: Generate smoothed BCE targets + generate_hot_ones: Create one-hot encoded targets with smoothing +""" + import torch import torch.nn as nn import torch.nn.functional as F -import numpy as np class evidential_regression_loss(nn.Module): + """ + Evidential regression loss using Normal Inverse Gamma (NIG) distribution. + + This loss function implements evidential deep learning for regression tasks, + providing both point predictions and uncertainty estimates. It combines + a negative log-likelihood term with a regularization term to prevent + overconfident predictions. + + Mathematical Background: + The NIG distribution is parameterized by (μ, v, α, β): + - μ: Predicted mean + - v: Virtual observation count (inverse epistemic uncertainty) + - α: Shape parameter + - β: Scale parameter (related to aleatoric uncertainty) + + Total Loss = NLL + λ * Regularization + + Attributes: + lamb (float): Weight for the regularization term + reduction (str): Reduction method ('mean', 'sum', or None) + """ + def __init__(self, lamb=1.0, reduction='mean'): + """ + Initialize the evidential regression loss. + + Args: + lamb (float, optional): Regularization weight. Defaults to 1.0 + Higher values encourage more conservative uncertainty estimates + reduction (str, optional): Reduction method. Defaults to 'mean' + Options: 'mean', 'sum', None + """ super(evidential_regression_loss, self).__init__() self.reduction = reduction self.lamb = lamb + def nig_nll(self, mu, v, alpha, beta, y): - """Computes the Negative Log-Likelihood for Normal Inverse Gamma.""" + """ + Compute the Negative Log-Likelihood for Normal Inverse Gamma distribution. + + This method calculates the NLL which measures how well the predicted + NIG distribution matches the observed target values. + + Args: + mu (torch.Tensor): Predicted mean values + v (torch.Tensor): Virtual observation counts + alpha (torch.Tensor): Shape parameters + beta (torch.Tensor): Scale parameters + y (torch.Tensor): Ground truth targets + + Returns: + torch.Tensor: Negative log-likelihood values + """ two_beta_lambda = 2 * beta * (1 + v) t1 = 0.5 * (torch.pi / v).log() t2 = alpha * two_beta_lambda.log() @@ -20,239 +88,551 @@ def nig_nll(self, mu, v, alpha, beta, y): return nll def nig_reg(self, mu, v, alpha, _beta, y): - """Computes the Normal Inverse Gamma regularization.""" + """ + Compute the Normal Inverse Gamma regularization term. + + This regularization penalizes large prediction errors when the model + is highly confident (high evidence), encouraging the model to match + its uncertainty to its actual performance. + + Args: + mu (torch.Tensor): Predicted mean values + v (torch.Tensor): Virtual observation counts + alpha (torch.Tensor): Shape parameters + _beta (torch.Tensor): Scale parameters (not used) + y (torch.Tensor): Ground truth targets + + Returns: + torch.Tensor: Regularization values + + Raises: + RuntimeError: If reduction method is not 'sum', 'mean', or None + """ + # Regularization based on prediction error weighted by evidence reg = (y - mu).abs() * (2 * v + alpha) - if self.reduction=="mean": + + if self.reduction == "mean": error = reg.mean() - elif self.reduction=="sum": + elif self.reduction == "sum": error = reg.sum() - elif self.reduction == None or self.reduction =='None': + elif self.reduction == None or self.reduction == 'None': error = reg - else: raise RuntimeError("Reduction not supported: Use sum or mean") return error + def set_lambda(self, lamb): - self.lamb =lamb + """ + Update the regularization weight. + + Args: + lamb (float): New regularization weight + """ + self.lamb = lamb def forward(self, dist_params, y): - """Computes the evidential regression loss.""" - if len(y)>1: + """ + Compute the evidential regression loss. + + Args: + dist_params (tuple): Tuple of (mu, v, alpha, beta) parameters + y (torch.Tensor): Ground truth targets + + Returns: + torch.Tensor: Combined NLL and regularization loss + """ + # Unpack distribution parameters + if len(y) > 1: mu, v, alpha, beta = (d.squeeze() for d in dist_params) else: mu, v, alpha, beta = (d for d in dist_params) - # mu, v, alpha, beta = (d for d in dist_params) - nig_reg_error = self.nig_reg( mu, v, alpha, beta, y) - nig_nll_error = self.nig_nll( mu, v, alpha, beta, y) + # Compute regularization term + nig_reg_error = self.nig_reg(mu, v, alpha, beta, y) + + # Compute negative log-likelihood + nig_nll_error = self.nig_nll(mu, v, alpha, beta, y) - if self.reduction=="mean": + # Apply reduction to NLL + if self.reduction == "mean": nig_nll_error = nig_nll_error.mean() - elif self.reduction=="sum": + elif self.reduction == "sum": nig_nll_error = nig_nll_error.sum() - elif self.reduction == None or self.reduction =='None': + elif self.reduction == None or self.reduction == 'None': nig_nll_error = nig_nll_error else: raise RuntimeError("Reduction not supported: Use sum or mean") - return nig_nll_error + self.lamb *nig_reg_error + # Combine NLL and weighted regularization + return nig_nll_error + self.lamb * nig_reg_error -def cosine_direction_loss(pred_x, pred_y, true_x, true_y,reduction="mean"): +def cosine_direction_loss(pred_x, pred_y, true_x, true_y, reduction="mean"): + """ + Compute cosine similarity loss for 2D direction vectors. + + This loss function measures the angular difference between predicted and + true direction vectors using cosine similarity. It's particularly useful + for shower direction reconstruction where we care about the angle rather + than the magnitude. + + Mathematical Formula: + loss = 1 - cos(θ) = 1 - (pred · true) / (|pred| |true|) + + Args: + pred_x (torch.Tensor): Predicted x-components + pred_y (torch.Tensor): Predicted y-components + true_x (torch.Tensor): True x-components + true_y (torch.Tensor): True y-components + reduction (str, optional): Reduction method. Defaults to "mean" + Options: 'mean', 'sum', 'none' + + Returns: + torch.Tensor: Cosine direction loss + + Raises: + RuntimeError: If reduction method is not supported + + Note: + Both predicted and true vectors are normalized before computing + the dot product, making the loss independent of vector magnitude. + """ + # Normalize prediction vectors to unit length pred_vec = F.normalize(torch.stack([pred_x, pred_y], dim=1), dim=1) + # Normalize true vectors to unit length true_vec = F.normalize(torch.stack([true_x, true_y], dim=1), dim=1) - if reduction=="mean": + + # Compute 1 - cosine similarity (0 for perfect alignment, 2 for opposite directions) + if reduction == "mean": return 1 - torch.sum(pred_vec * true_vec, dim=1).mean() - elif reduction=="sum": + elif reduction == "sum": return 1 - torch.sum(pred_vec * true_vec, dim=1).sum() - elif reduction=="none": + elif reduction == "none": return 1 - torch.sum(pred_vec * true_vec, dim=1) else: - raise RuntimeError("Reduction not supported: Use sum , mean or none") + raise RuntimeError("Reduction not supported: Use sum, mean or none") -def AngularDistance(alt1_rad, alt2_rad, az1_rad, az2_rad,reduction = None): +def AngularDistance(alt1_rad, alt2_rad, az1_rad, az2_rad, reduction=None): """ - Calculate the angular distance between points given in batches. + Calculate the angular distance between celestial coordinates. + + This function computes the great circle distance between two points on + the celestial sphere using the spherical law of cosines. Used for + evaluating direction reconstruction accuracy in astronomy. - Parameters: - - alt1_rad, az1_rad: Tensors of the altitudes and azimuths in radians for the first set of points. - - alt2_rad, az2_rad: Tensors of the altitudes and azimuths in radians for the second set of points. + Mathematical Formula: + cos(Δθ) = cos(alt1)cos(alt2)cos(az1-az2) + sin(alt1)sin(alt2) + Δθ = arccos(cos(Δθ)) + Args: + alt1_rad (torch.Tensor): Altitude of first points in radians + alt2_rad (torch.Tensor): Altitude of second points in radians + az1_rad (torch.Tensor): Azimuth of first points in radians + az2_rad (torch.Tensor): Azimuth of second points in radians + reduction (str, optional): Reduction method. Defaults to None + Options: 'sum', 'mean', None + Returns: - - Tensor of angular distances in radians for each pair of points. + tuple: (angular_distance_rad, angular_distance_deg) + - angular_distance_rad: Angular distances in radians + - angular_distance_deg: Angular distances in degrees + + Raises: + RuntimeError: If reduction method is not supported + + Note: + - Handles numerical edge cases (cosdelta = ±1) explicitly + - Clamps cosdelta to prevent arccos domain errors + - Returns both radians and degrees for convenience """ - - # Compute the cosine of the angular distance using batch-wise operations + # Compute cosine of angular distance using spherical law of cosines cosdelta = torch.cos(alt1_rad) * torch.cos(alt2_rad) * torch.cos(az1_rad - az2_rad) + \ torch.sin(alt1_rad) * torch.sin(alt2_rad) - # Clamp the cosdelta values to ensure they are within the valid range for arccos - # cosdelta = torch.clamp(cosdelta, -1.0, 1.0) + # Clamp to valid range for arccos with small epsilon to avoid edge cases cosdelta = torch.clamp(cosdelta, -1.0 + 1e-7, 1.0 - 1e-7) - # Calculate the angular distance in radians + + # Calculate angular distance in radians ang_dist_rad = torch.acos(cosdelta) - ang_dist_rad[cosdelta == 1.0] = 0.0 # acos(1) = 0 - ang_dist_rad[cosdelta == -1.0] = torch.pi # acos(-1) = pi - ang_dist_deg= torch.rad2deg(ang_dist_rad) - - if reduction =="sum": - return ang_dist_rad.sum(),ang_dist_deg.sum() - elif reduction=="mean": - return ang_dist_rad.mean(),ang_dist_deg.mean() - elif reduction == None or reduction =='None': + + # Handle exact edge cases explicitly + ang_dist_rad[cosdelta == 1.0] = 0.0 # Perfect alignment + ang_dist_rad[cosdelta == -1.0] = torch.pi # Opposite directions + + # Convert to degrees + ang_dist_deg = torch.rad2deg(ang_dist_rad) + + # Apply reduction + if reduction == "sum": + return ang_dist_rad.sum(), ang_dist_deg.sum() + elif reduction == "mean": + return ang_dist_rad.mean(), ang_dist_deg.mean() + elif reduction == None or reduction == 'None': return ang_dist_rad, ang_dist_deg else: raise RuntimeError("Reduction not supported: Use sum, mean or None") -def AngularError(vec1, vec2,reduction='mean'): - # Ensure the vectors are tensors - # vec1 = vec1.clone().detach().float() - # vec2 = vec2.clone().detach().float() - - - # Compute the dot product for each pair of vectors in the batch +def AngularError(vec1, vec2, reduction='mean'): + """ + Compute angular error between 3D direction vectors. + + This function calculates the angle between two vectors using the dot product + formula. Useful for evaluating 3D direction predictions in Cartesian coordinates. + + Mathematical Formula: + cos(θ) = (v1 · v2) / (|v1| |v2|) + θ = arccos(cos(θ)) + + Args: + vec1 (torch.Tensor): First set of vectors with shape (batch_size, 3) + vec2 (torch.Tensor): Second set of vectors with shape (batch_size, 3) + reduction (str, optional): Reduction method. Defaults to 'mean' + Options: 'sum', 'mean', None + + Returns: + tuple: (angle_rad, angle_deg) + - angle_rad: Angular errors in radians + - angle_deg: Angular errors in degrees + + Raises: + RuntimeError: If reduction method is not supported + + Note: + - Handles vectors of any magnitude (not necessarily unit vectors) + - Clamps cosine values to prevent arccos domain errors + - Returns both radians and degrees + """ + # Compute dot product for each pair of vectors dot_product = torch.sum(vec1 * vec2, dim=1) - # Compute the magnitudes (norms) of the vectors for each vector in the batch + # Compute vector magnitudes (L2 norms) norm_vec1 = torch.norm(vec1, dim=1) norm_vec2 = torch.norm(vec2, dim=1) - # Compute the cosine of the angle for each pair of vectors in the batch + # Compute cosine of angle between vectors cos_theta = dot_product / (norm_vec1 * norm_vec2) - # Clip the cosine values to the range [-1, 1] to avoid numerical issues with arccos + # Clamp to valid range for arccos cos_theta = torch.clamp(cos_theta, -1.0, 1.0) - # Compute the angle in radians for each pair of vectors in the batch + # Compute angle in radians angle_rad = torch.acos(cos_theta) - # Optionally, convert the angles from radians to degrees + # Convert to degrees angle_deg = torch.rad2deg(angle_rad) - if reduction =="sum": + # Apply reduction + if reduction == "sum": return angle_rad.sum(), angle_deg.sum() - elif reduction=="mean": + elif reduction == "mean": return angle_rad.mean(), angle_deg.mean() - elif reduction == None or reduction =='None': + elif reduction == None or reduction == 'None': return angle_rad, angle_deg else: raise RuntimeError("Reduction not supported: Use sum, mean or None") class VectorLoss(nn.Module): - def __init__(self,alpha=0.001,reduction='mean'): + """ + Angle-based loss for 2D vector predictions. + + This loss function penalizes the angular difference between predicted and + target 2D vectors, regardless of their magnitudes. Useful when direction + matters more than magnitude. + + Attributes: + alpha (float): Scaling factor for the loss + reduction (str): Reduction method ('mean' or 'sum') + """ + + def __init__(self, alpha=0.001, reduction='mean'): + """ + Initialize the VectorLoss. + + Args: + alpha (float, optional): Scaling factor. Defaults to 0.001 + reduction (str, optional): Reduction method. Defaults to 'mean' + """ super(VectorLoss, self).__init__() self.alpha = alpha - self.reduction=reduction + self.reduction = reduction + def forward(self, output, target): - # Calculate angles of output and target using atan2 + """ + Compute the vector angle loss. + + This method calculates the absolute angular difference between + output and target vectors, normalized to [0, π]. + + Args: + output (torch.Tensor): Predicted vectors with shape (batch_size, 2) + target (torch.Tensor): Target vectors with shape (batch_size, 2) + + Returns: + torch.Tensor: Angular difference loss + + Raises: + RuntimeError: If reduction method is not 'sum' or 'mean' + """ + # Calculate angles using atan2 (returns range [-π, π]) angles_output = torch.atan2(output[:, 1], output[:, 0]) angles_target = torch.atan2(target[:, 1], target[:, 0]) - # Compute the difference in angles + # Compute absolute angle difference angle_diff = torch.abs(angles_output - angles_target) - # Normalize angle differences to be within [0, pi] + # Normalize to [0, π] range (shortest angular path) angle_diff = torch.remainder(angle_diff + torch.pi, 2 * torch.pi) - torch.pi - angle_diff = torch.abs(angle_diff) # Ensure all differences are positive + angle_diff = torch.abs(angle_diff) - if self.reduction =="sum": + # Apply reduction + if self.reduction == "sum": return angle_diff.sum() - elif self.reduction=="mean": + elif self.reduction == "mean": return angle_diff.mean() else: raise RuntimeError("Reduction not supported: Use sum or mean") - return self.alpha * angle_diff.mean() -def smooth_BCE( - eps=0.1, -): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 - # return positive, negative label smoothing BCE targets + +def smooth_BCE(eps=0.1): + """ + Generate smoothed BCE (Binary Cross-Entropy) targets for label smoothing. + + Label smoothing prevents the model from becoming overconfident by using + soft targets instead of hard 0/1 labels. This can improve generalization. + + Args: + eps (float, optional): Smoothing parameter. Defaults to 0.1 + Determines how much to smooth the labels + + Returns: + tuple: (positive_label, negative_label) + - positive_label: Smoothed value for positive class (1 - 0.5*eps) + - negative_label: Smoothed value for negative class (0.5*eps) + + Example: + >>> cp, cn = smooth_BCE(eps=0.1) + >>> cp # 0.95 instead of 1.0 + >>> cn # 0.05 instead of 0.0 + """ return 1.0 - 0.5 * eps, 0.5 * eps def generate_hot_ones(device, cn, cp, outputs, targets): - + """ + Create one-hot encoded targets with label smoothing. + + This function generates soft one-hot encoded targets for multi-class + classification with label smoothing applied. + + Args: + device: PyTorch device (CPU or CUDA) + cn (float): Negative class smoothed value + cp (float): Positive class smoothed value + outputs (torch.Tensor): Model outputs (used for shape) + targets (torch.Tensor): Ground truth class indices + + Returns: + torch.Tensor: Smoothed one-hot encoded targets + Shape: same as outputs + + Example: + >>> outputs = torch.randn(32, 10) # batch_size=32, num_classes=10 + >>> targets = torch.tensor([3, 7, 1, ...]) # class indices + >>> cp, cn = smooth_BCE(eps=0.1) + >>> t = generate_hot_ones(device, cn, cp, outputs, targets) + >>> # t[0] = [0.05, 0.05, 0.05, 0.95, 0.05, ...] # class 3 is positive + """ + # Initialize all values to negative class value t = torch.full_like(outputs, cn, device=device) n = outputs.shape[0] + # Set positive class values t[range(n), targets] = cp - return t class FocalLoss(nn.Module): + """ + Focal Loss for addressing class imbalance in classification. + + Focal loss down-weights easy examples and focuses training on hard negatives. + This is particularly useful for highly imbalanced datasets where the model + can achieve high accuracy by simply predicting the majority class. + + Mathematical Formula: + FL(p_t) = -α_t (1-p_t)^γ log(p_t) + + where: + - p_t: model's estimated probability for the correct class + - α_t: weighting factor (alpha parameter) + - γ: focusing parameter (gamma parameter) + + Attributes: + alpha (torch.Tensor or None): Class weights + gamma (float): Focusing parameter (higher = more focus on hard examples) + reduction (str): Reduction method ('mean', 'sum', or 'none') + """ + def __init__(self, alpha=None, gamma=2.0, reduction='mean'): + """ + Initialize the Focal Loss. + + Args: + alpha (torch.Tensor or None, optional): Class weights. Defaults to None + If None, all classes weighted equally + If Tensor, should have shape (num_classes,) + gamma (float, optional): Focusing parameter. Defaults to 2.0 + 0 = equivalent to cross-entropy + Higher values = more focus on hard examples + reduction (str, optional): Reduction method. Defaults to 'mean' + """ super(FocalLoss, self).__init__() self.alpha = alpha self.gamma = gamma self.reduction = reduction - def set_alpha(self,alpha): + def set_alpha(self, alpha): + """ + Update the class weights. + + Args: + alpha (torch.Tensor): New class weights + """ self.alpha = alpha def forward(self, inputs, targets): + """ + Compute the focal loss. + + Args: + inputs (torch.Tensor): Raw model outputs (logits) + Shape: (batch_size, num_classes) + targets (torch.Tensor): Ground truth class indices + Shape: (batch_size,) + + Returns: + torch.Tensor: Focal loss value + + Process: + 1. Compute standard cross-entropy loss + 2. Compute p_t (probability of correct class) + 3. Apply focal term: (1 - p_t)^gamma + 4. Weight by cross-entropy + 5. Apply class weights (alpha) if provided + """ + # Compute cross-entropy loss (unreduced) ce_loss = F.cross_entropy(inputs, targets, weight=self.alpha, reduction='none') - pt = torch.exp(-ce_loss) # Probabilidad inversa del error + + # Get probability of correct class + pt = torch.exp(-ce_loss) + + # Apply focal term and weight by CE loss focal_loss = (1 - pt) ** self.gamma * ce_loss + + # Apply reduction if self.reduction == 'mean': return focal_loss.mean() elif self.reduction == 'sum': return focal_loss.sum() else: return focal_loss - -# class FocalLoss(nn.Module): -# def __init__(self, device, alpha=0.25, gamma=2.0, label_smoothing=0.0): -# super(FocalLoss, self).__init__() -# self.device = device -# self.alpha = alpha -# self.gamma = gamma -# self.label_smoothing = label_smoothing -# self.cp, self.cn = smooth_BCE(eps=self.label_smoothing) -# self.BCE = BCELogitsLoss(device) - -# def forward(self, outputs, targets): - -# # t = generate_hot_ones(self.device, self.cn, self.cp, outputs, targets) -# # Supone inputs son las logits antes de sigmoid -# # BCE_loss = F.binary_cross_entropy_with_logits(outputs, t, reduction="none") -# BCE_loss = self.BCE(outputs,targets) -# pt = torch.exp(-BCE_loss) # pt es la probabilidad de clasificar correctamente -# F_loss = self.alpha * (1 - pt) ** self.gamma * BCE_loss -# return F_loss.mean() class BCELogitsLoss(nn.Module): + """ + Binary Cross-Entropy with Logits and Label Smoothing. + + This loss combines binary cross-entropy with label smoothing for improved + generalization. It operates on raw logits (pre-sigmoid outputs). + + Attributes: + device: PyTorch device + label_smoothing (float): Label smoothing parameter + BCE: Binary cross-entropy loss function + cp (float): Smoothed positive label value + cn (float): Smoothed negative label value + """ + def __init__(self, device, cls_pw=1.0, label_smoothing=0.0): + """ + Initialize the BCE with logits loss. + + Args: + device: PyTorch device (CPU or CUDA) + cls_pw (float, optional): Positive class weight. Defaults to 1.0 + label_smoothing (float, optional): Label smoothing factor. Defaults to 0.0 + """ super().__init__() self.device = device self.label_smoothing = label_smoothing self.BCE = nn.BCEWithLogitsLoss( pos_weight=torch.tensor(cls_pw, device=self.device) ) + # Generate smoothed label values self.cp, self.cn = smooth_BCE(eps=self.label_smoothing) def forward(self, outputs, targets): - - # Generate hot ones targets + """ + Compute BCE loss with label smoothing. + + Args: + outputs (torch.Tensor): Raw model outputs (logits) + targets (torch.Tensor): Ground truth class indices + + Returns: + torch.Tensor: BCE loss value + """ + # Generate smoothed one-hot targets t = generate_hot_ones(self.device, self.cn, self.cp, outputs, targets) - + + # Compute BCE loss bce_loss = self.BCE(outputs, t) - + return bce_loss - - class EvidClassification(): - def __init__(self,class_weights=None): - self.class_weights= class_weights + """ + Evidential classification using Dirichlet distribution. + + This class implements evidential deep learning for classification, + where the model outputs a Dirichlet distribution over class probabilities + rather than point estimates. This provides both predictions and uncertainty. + + Attributes: + class_weights (torch.Tensor or None): Optional class weights + """ + + def __init__(self, class_weights=None): + """ + Initialize evidential classification. + + Args: + class_weights (torch.Tensor or None, optional): Class weights. + Defaults to None + """ + self.class_weights = class_weights def dirichlet_reg(self, alpha, y): - # dirichlet parameters after removal of non-misleading evidence (from the label) + """ + Compute Dirichlet distribution regularization. + + This regularization term encourages the model to output a uniform + Dirichlet distribution for incorrect classes, preventing overconfident + wrong predictions. + + Args: + alpha (torch.Tensor): Dirichlet parameters (concentration) + y (torch.Tensor): One-hot encoded targets + + Returns: + torch.Tensor: KL divergence from uniform Dirichlet + """ + # Remove evidence from correct class, keep evidence from wrong classes alpha = y + (1 - y) * alpha - # uniform dirichlet distribution + # Uniform Dirichlet distribution (target) beta = torch.ones_like(alpha) + # Compute KL divergence between Dirichlet distributions sum_alpha = alpha.sum(-1) sum_beta = beta.sum(-1) @@ -264,71 +644,57 @@ def dirichlet_reg(self, alpha, y): kl = t1 - t2 + (t3 * t4).sum(-1) return kl.sum() - def dirichlet_mse(self, alpha, y, ): + def dirichlet_mse(self, alpha, y): + """ + Compute mean squared error for Dirichlet distribution. + + This term measures the accuracy of the mean prediction from the + Dirichlet distribution, accounting for its uncertainty. + + Args: + alpha (torch.Tensor): Dirichlet parameters + y (torch.Tensor): One-hot encoded targets + + Returns: + torch.Tensor: MSE loss + """ + # Sum of Dirichlet parameters (total evidence) sum_alpha = alpha.sum(-1, keepdims=True) + + # Mean prediction (expected probability) p = alpha / sum_alpha + + # Prediction error term t1 = (y - p).pow(2) + + # Uncertainty term (variance of Dirichlet) t2 = ((p * (1 - p)) / (sum_alpha + 1)) - if self.class_weights is not None: - t1 = t1 * self.class_weights.unsqueeze(0) - t2 = t2 * self.class_weights.unsqueeze(0) + # Apply class weights if provided + if self.class_weights is not None: + t1 = t1 * self.class_weights.unsqueeze(0) + t2 = t2 * self.class_weights.unsqueeze(0) mse = t1 + t2 return mse.sum() - def loss(self, alpha, y, lamb=1.0 ): + def loss(self, alpha, y, lamb=1.0): + """ + Compute the evidential classification loss. + + Combines Dirichlet MSE with regularization to produce predictions + with calibrated uncertainty estimates. + + Args: + alpha (torch.Tensor): Dirichlet parameters from model + y (torch.Tensor): Ground truth class indices + lamb (float, optional): Regularization weight. Defaults to 1.0 + + Returns: + torch.Tensor: Combined loss value + """ num_classes = alpha.shape[-1] + # Convert to one-hot encoding y = F.one_hot(y, num_classes) + # Combine MSE and regularization return self.dirichlet_mse(alpha, y) + lamb * self.dirichlet_reg(alpha, y) - -# def evidential_classification(alpha, y, lamb=1.0): -# num_classes = alpha.shape[-1] -# y = F.one_hot(y, num_classes) -# return dirichlet_mse(alpha, y) + lamb * dirichlet_reg(alpha, y) - -# def evidential_classification(alpha, y, weights, lamb=1.0): -# num_classes = alpha.shape[-1] -# y = F.one_hot(y, num_classes) -# mse_loss = dirichlet_mse(alpha, y) -# reg_loss = dirichlet_reg(alpha, y) -# weighted_loss = weights[0] * mse_loss + weights[1] * reg_loss -# return weighted_loss + lamb * reg_loss - -# class FocalLoss(nn.Module): -# def __init__(self, alpha=None, gamma=2.0, reduction='mean'): -# super(FocalLoss, self).__init__() -# self.alpha = alpha -# self.gamma = gamma -# self.reduction = reduction - -# def forward(self, inputs, targets): - -# t = generate_hot_ones(self.device, self.cn, self.cp, targets, targets) -# BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none') -# targets = targets.type(torch.long) -# at = self.alpha.gather(0, targets.data.view(-1)) -# pt = torch.exp(-BCE_loss) -# F_loss = at * (1-pt)**self.gamma * BCE_loss - -# if self.reduction == 'mean': -# return F_loss.mean() -# elif self.reduction == 'sum': -# return F_loss.sum() -# else: -# return F_loss - - -# class FocalLoss(nn.Module): -# def __init__(self, alpha=0.25, gamma=2.0): -# super(FocalLoss, self).__init__() -# self.alpha = alpha -# self.gamma = gamma - -# def forward(self, inputs, targets): -# BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none') -# targets = targets.type(torch.float32) -# at = self.alpha * targets + (1 - self.alpha) * (1 - targets) -# pt = torch.exp(-BCE_loss) -# F_loss = at * (1-pt)**self.gamma * BCE_loss -# return F_loss.mean() diff --git a/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py b/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py index 538eda29..17448639 100644 --- a/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py +++ b/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py @@ -1,13 +1,67 @@ +""" +Dual-Backbone DANet (Dual Attention Network) Module + +This module implements a dual-backbone architecture with attention mechanisms for +processing Cherenkov telescope images. It combines two separate backbones to process +image and timing information independently, then fuses the features for final prediction. + +The architecture incorporates both channel and spatial attention mechanisms to improve +feature representation and focus on the most relevant information in telescope images. + +Classes: + ChannelAttentionModule: Channel attention mechanism using global pooling + SpatialAttentionModule: Spatial attention mechanism using convolutional layers + DANet: Single backbone with dual attention mechanisms + DBBDanet: Dual-backbone network combining two DANet architectures + +References: + - DANet: "Dual Attention Network for Scene Segmentation" (CVPR 2019) + - CBAM: "Convolutional Block Attention Module" (ECCV 2018) +""" + import torch import torch.nn as nn import torch.nn.functional as F class ChannelAttentionModule(nn.Module): + """ + Channel Attention Module using global pooling. + + This module learns to emphasize informative channels and suppress less useful ones + by exploiting inter-channel relationships. It uses both average and max pooling to + capture different aspects of channel-wise statistics. + + Architecture: + Input → [AvgPool, MaxPool] → Shared MLP → Element-wise Sum → Sigmoid → Channel Weights + + Attributes: + avg_pool (nn.AdaptiveAvgPool2d): Global average pooling + max_pool (nn.AdaptiveMaxPool2d): Global max pooling + fc (nn.Sequential): Shared MLP for channel attention + sigmoid (nn.Sigmoid): Activation for attention weights + """ + def __init__(self, in_channels, reduction=16): + """ + Initialize the Channel Attention Module. + + Args: + in_channels (int): Number of input channels + reduction (int, optional): Channel reduction ratio for the MLP bottleneck. + Defaults to 16. Higher values reduce parameters but may lose information. + + Example: + >>> cam = ChannelAttentionModule(in_channels=512, reduction=16) + >>> x = torch.randn(8, 512, 7, 7) + >>> out = cam(x) # Same shape as input, but with channel attention applied + """ super(ChannelAttentionModule, self).__init__() - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.max_pool = nn.AdaptiveMaxPool2d(1) + # Global pooling operations to capture channel-wise statistics + self.avg_pool = nn.AdaptiveAvgPool2d(1) # Output: (B, C, 1, 1) + self.max_pool = nn.AdaptiveMaxPool2d(1) # Output: (B, C, 1, 1) + + # Shared MLP: Channel reduction → ReLU → Channel restoration self.fc = nn.Sequential( nn.Conv2d(in_channels, in_channels // reduction, 1, bias=False), nn.ReLU(), @@ -16,126 +70,367 @@ def __init__(self, in_channels, reduction=16): self.sigmoid = nn.Sigmoid() def forward(self, x): - avg_out = self.fc(self.avg_pool(x)) - max_out = self.fc(self.max_pool(x)) - out = avg_out + max_out - return self.sigmoid(out) * x + """ + Apply channel attention to the input feature map. + + Process: + 1. Apply global average pooling and max pooling separately + 2. Pass both through shared MLP + 3. Sum the two attention maps + 4. Apply sigmoid to get attention weights in [0, 1] + 5. Multiply with original input (element-wise) + + Args: + x (torch.Tensor): Input feature map with shape (B, C, H, W) + B: batch size, C: channels, H: height, W: width + + Returns: + torch.Tensor: Channel-attended feature map with same shape as input + Important channels are emphasized, less important ones suppressed + """ + # Process through average pooling path + avg_out = self.fc(self.avg_pool(x)) # (B, C, 1, 1) + + # Process through max pooling path + max_out = self.fc(self.max_pool(x)) # (B, C, 1, 1) + + # Combine both paths and apply sigmoid + out = avg_out + max_out # (B, C, 1, 1) + + # Apply attention weights to input + return self.sigmoid(out) * x # (B, C, H, W) class SpatialAttentionModule(nn.Module): + """ + Spatial Attention Module using channel pooling. + + This module learns to focus on important spatial locations in the feature map + by exploiting inter-spatial relationships. It uses both average and max pooling + across channels to generate a spatial attention map. + + Architecture: + Input → [AvgPool(channel), MaxPool(channel)] → Concat → Conv → Sigmoid → Spatial Weights + + Attributes: + conv (nn.Conv2d): Convolutional layer to generate spatial attention + sigmoid (nn.Sigmoid): Activation for attention weights + """ + def __init__(self, kernel_size=7): + """ + Initialize the Spatial Attention Module. + + Args: + kernel_size (int, optional): Size of convolutional kernel. Defaults to 7. + Larger kernels capture wider spatial context but increase computation. + Common choices: 3, 5, 7 + + Example: + >>> sam = SpatialAttentionModule(kernel_size=7) + >>> x = torch.randn(8, 512, 7, 7) + >>> out = sam(x) # Same shape, but with spatial attention applied + """ super(SpatialAttentionModule, self).__init__() + + # Calculate padding to maintain spatial dimensions padding = kernel_size // 2 + + # Convolution to process concatenated spatial statistics + # Input: 2 channels (avg + max), Output: 1 channel (attention map) self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): - avg_out = torch.mean(x, dim=1, keepdim=True) - max_out, _ = torch.max(x, dim=1, keepdim=True) - x = torch.cat([avg_out, max_out], dim=1) - x = self.conv(x) - return self.sigmoid(x) * x + """ + Apply spatial attention to the input feature map. + + Process: + 1. Compute average across channels + 2. Compute max across channels + 3. Concatenate the two maps + 4. Apply convolution to generate spatial attention map + 5. Apply sigmoid to get attention weights in [0, 1] + 6. Multiply with original input (element-wise) + + Args: + x (torch.Tensor): Input feature map with shape (B, C, H, W) + + Returns: + torch.Tensor: Spatially-attended feature map with same shape as input + Important spatial locations are emphasized + """ + # Average pooling across channel dimension + avg_out = torch.mean(x, dim=1, keepdim=True) # (B, 1, H, W) + + # Max pooling across channel dimension + max_out, _ = torch.max(x, dim=1, keepdim=True) # (B, 1, H, W) + + # Concatenate spatial statistics + x_cat = torch.cat([avg_out, max_out], dim=1) # (B, 2, H, W) + + # Generate spatial attention map + x_att = self.conv(x_cat) # (B, 1, H, W) + + # Apply attention weights to input + return self.sigmoid(x_att) * x # (B, C, H, W) class DANet(nn.Module): + """ + Dual Attention Network (DANet) - Single backbone with attention mechanisms. + + This network implements a CNN backbone with both channel and spatial attention + mechanisms. It progressively reduces spatial dimensions while increasing channel + depth, then applies dual attention before classification. + + Architecture: + Input → Conv1 → BN → ReLU → MaxPool → + Layer1 (64→128) → Layer2 (128→256) → Layer3 (256→512) → + Channel Attention → Spatial Attention → + Global AvgPool → Dropout → FC + + Attributes: + conv1 (nn.Conv2d): Initial convolution layer + bn1 (nn.BatchNorm2d): Batch normalization + relu (nn.ReLU): Activation function + maxpool (nn.MaxPool2d): Max pooling layer + layer1, layer2, layer3: Feature extraction blocks + cam (ChannelAttentionModule): Channel attention + sam (SpatialAttentionModule): Spatial attention + avgpool (nn.AdaptiveAvgPool2d): Global average pooling + dropout (nn.Dropout): Dropout for regularization + fc (nn.Linear): Final classification layer + """ + def __init__(self, num_inputs=1, num_classes=2, dropout_rate=0.3): + """ + Initialize the DANet model. + + Args: + num_inputs (int, optional): Number of input channels. Defaults to 1. + For telescope images: 1 for charge only, 2 for charge+timing + num_classes (int, optional): Number of output classes. Defaults to 2. + For particle classification: 2 (gamma vs proton) + For regression: 1 + dropout_rate (float, optional): Dropout probability. Defaults to 0.3. + Higher values = more regularization but may underfit + """ super(DANet, self).__init__() - # Basic CNN backbone + # Initial convolution: large kernel for receptive field self.conv1 = nn.Conv2d(num_inputs, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(64, 128, 2) - self.layer2 = self._make_layer(128, 256, 2) - self.layer3 = self._make_layer(256, 512, 2) + # Progressive feature extraction layers + self.layer1 = self._make_layer(64, 128, 2) # 64 → 128 channels + self.layer2 = self._make_layer(128, 256, 2) # 128 → 256 channels + self.layer3 = self._make_layer(256, 512, 2) # 256 → 512 channels - # DANet attention modules - self.cam = ChannelAttentionModule(512) - self.sam = SpatialAttentionModule() - - # Final layers - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + # Dual attention modules + self.cam = ChannelAttentionModule(512) # Channel attention on 512 channels + self.sam = SpatialAttentionModule() # Spatial attention + + # Final classification layers + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # Global pooling to (B, 512, 1, 1) self.dropout = nn.Dropout(p=dropout_rate) self.fc = nn.Linear(512, num_classes) def _make_layer(self, in_channels, out_channels, blocks): + """ + Create a sequence of convolutional blocks. + + Each block consists of: Conv → BatchNorm → ReLU + The first block changes channel dimension, subsequent blocks maintain it. + + Args: + in_channels (int): Number of input channels + out_channels (int): Number of output channels + blocks (int): Number of convolutional blocks + + Returns: + nn.Sequential: Sequential container of conv blocks + """ layers = [] + # First block: change channel dimension layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)) layers.append(nn.BatchNorm2d(out_channels)) layers.append(nn.ReLU(inplace=True)) + + # Remaining blocks: maintain channel dimension for _ in range(1, blocks): layers.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)) layers.append(nn.BatchNorm2d(out_channels)) layers.append(nn.ReLU(inplace=True)) + return nn.Sequential(*layers) def forward(self, x): - x = self.conv1(x) + """ + Forward pass through the DANet. + + Args: + x (torch.Tensor): Input tensor with shape (B, C_in, H, W) + B: batch size, C_in: input channels (1 or 2) + H, W: image dimensions (typically 120x120) + + Returns: + torch.Tensor: Output predictions with shape (B, num_classes) + For classification: logits (pre-softmax) + For regression: predicted values + """ + # Initial convolution and downsampling + x = self.conv1(x) # (B, 64, H/2, W/2) x = self.bn1(x) x = self.relu(x) - x = self.maxpool(x) + x = self.maxpool(x) # (B, 64, H/4, W/4) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) + # Feature extraction layers + x = self.layer1(x) # (B, 128, H/4, W/4) + x = self.layer2(x) # (B, 256, H/4, W/4) + x = self.layer3(x) # (B, 512, H/4, W/4) - # Apply attention modules - x = self.cam(x) + x # Channel Attention - x = self.sam(x) + x # Spatial Attention + # Apply dual attention mechanisms with residual connections + x = self.cam(x) + x # Channel attention + residual + x = self.sam(x) + x # Spatial attention + residual - x = self.avgpool(x) - x = torch.flatten(x, 1) - x = self.dropout(x) # Dropout before the final fully connected layer - x = self.fc(x) - + # Global pooling and classification + x = self.avgpool(x) # (B, 512, 1, 1) + x = torch.flatten(x, 1) # (B, 512) + x = self.dropout(x) # Regularization + x = self.fc(x) # (B, num_classes) return x class DBBDanet(nn.Module): - def __init__(self, task , num_inputs=1, num_classes=2, use_concat=False, dropout_rate=0.3): + """ + Dual-Backbone DANet for multi-modal telescope data processing. + + This architecture uses two separate DANet backbones to process different + input modalities (e.g., charge and timing information) independently, + then fuses their features for final prediction. This allows each backbone + to specialize in extracting relevant features from its input modality. + + Fusion Strategies: + - Concatenation: Preserves all information but doubles feature dimension + - Addition: Reduces dimension but may lose information + + Attributes: + task (str): Task type ('type', 'energy', 'direction') + use_concat (bool): Whether to concatenate or add backbone outputs + backbone_1 (DANet): First backbone for primary input (charge) + backbone_2 (DANet): Second backbone for secondary input (timing) + fc (nn.Linear): Final classification/regression layer + dropout (nn.Dropout): Dropout for regularization + """ + + def __init__(self, task, num_inputs=1, num_classes=2, use_concat=False, dropout_rate=0.3): + """ + Initialize the Dual-Backbone DANet. + + Args: + task (str): Task type to perform + Options: 'type' (classification), 'energy' (regression), 'direction' (regression) + num_inputs (int, optional): Number of input channels per backbone. Defaults to 1. + Typically 1 (grayscale images) + num_classes (int, optional): Number of output classes/values. Defaults to 2. + For 'type': 2 (gamma, proton) + For 'energy': 1 (energy value) + For 'direction': 2 or 3 (angular coordinates) + use_concat (bool, optional): Whether to concatenate backbone outputs. + Defaults to False (use addition instead) + True: More parameters, preserves all information + False: Fewer parameters, may lose some information + dropout_rate (float, optional): Dropout probability. Defaults to 0.3. + + Example: + >>> # For particle classification with concatenation + >>> model = DBBDanet(task='type', num_inputs=1, num_classes=2, use_concat=True) + >>> + >>> # For energy regression with addition + >>> model = DBBDanet(task='energy', num_inputs=1, num_classes=1, use_concat=False) + """ super(DBBDanet, self).__init__() self.task = task self.use_concat = use_concat + + # Initialize two separate DANet backbones self.backbone_1 = DANet(num_inputs=num_inputs, num_classes=num_classes, dropout_rate=dropout_rate) self.backbone_2 = DANet(num_inputs=num_inputs, num_classes=num_classes, dropout_rate=dropout_rate) - # num_features = 512 - num_features = self.backbone_1.fc.in_features + # Get feature dimension from backbone + num_features = self.backbone_1.fc.in_features # Typically 512 + + # Adjust feature dimension based on fusion strategy if self.use_concat: - num_features*=2 + num_features *= 2 # Double if concatenating + # Create new final layers self.fc = nn.Linear(num_features, num_classes) - self.dropout = nn.Dropout(p=dropout_rate,inplace=True) + self.dropout = nn.Dropout(p=dropout_rate, inplace=True) + # Remove original final layers from backbones (use as feature extractors) self.backbone_1.fc = nn.Identity() self.backbone_1.dropout = nn.Identity() self.backbone_2.fc = nn.Identity() self.backbone_2.dropout = nn.Identity() - def forward(self, x, y): + def forward(self, x, y): + """ + Forward pass through the dual-backbone network. + + Process: + 1. Extract features from both inputs using separate backbones + 2. Fuse features (concatenate or add) + 3. Apply dropout and final classification/regression layer + 4. Return task-specific output + + Args: + x (torch.Tensor): First input (charge image) with shape (B, C, H, W) + y (torch.Tensor): Second input (timing image) with shape (B, C, H, W) + Both inputs should have the same spatial dimensions + + Returns: + tuple: (classification, energy, direction) where: + - classification: Class logits if task=='type', else None + - energy: Energy prediction if task=='energy', else None + - direction: Direction prediction if task=='direction', else None + Only one of the three is non-None based on self.task + + Note: + The dual-backbone design allows the model to learn separate + representations for different input modalities before fusion, + which can be more effective than early fusion approaches. + """ + # Initialize outputs (only one will be non-None) energy = None classification = None direction = None - feature_1 = self.backbone_1(x) - feature_2 = self.backbone_2(y) + # Extract features from both backbones independently + feature_1 = self.backbone_1(x) # Features from charge image + feature_2 = self.backbone_2(y) # Features from timing image - # Combine outputs + # Fuse features based on selected strategy if self.use_concat: - out = torch.cat((feature_1, feature_2), dim=1) + # Concatenate: Preserves all information + out = torch.cat((feature_1, feature_2), dim=1) # (B, 2*num_features) else: - out = feature_1 + feature_2 + # Addition: Element-wise fusion + out = feature_1 + feature_2 # (B, num_features) - out = self.dropout(out) # Dropout before the final fully connected layer - out = self.fc(out) + # Apply regularization and final layer + out = self.dropout(out) + out = self.fc(out) # (B, num_classes) + # Assign output based on task type if self.task == "type": - classification = out + classification = out # Classification logits elif self.task == "energy": - energy = out + energy = out # Energy prediction elif self.task == "direction": - direction = out + direction = out # Direction prediction - return classification, energy, direction \ No newline at end of file + return classification, energy, direction \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py index d94ca082..7a74b1d3 100644 --- a/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py +++ b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py @@ -1,15 +1,92 @@ -# NoProp-DT model +""" +Dual-Backbone NoProp-DT Regression Model Module + +This module implements a dual-backbone denoising diffusion model for regression tasks +on Cherenkov telescope data. It uses a progressive denoising approach with multiple +diffusion steps to refine predictions, particularly effective for energy and direction +reconstruction tasks. + +The NoProp-DT (No-Propagation Denoising Transformer) architecture uses: +- Progressive denoising through T diffusion steps +- Cosine noise schedule for stable training +- SNR (Signal-to-Noise Ratio) weighting for loss computation +- Dual-backbone design for processing charge and timing information + +Classes: + DBBNoPropDTReg: Dual-backbone diffusion model for regression tasks + +References: + - "Denoising Diffusion Probabilistic Models" (Ho et al., NeurIPS 2020) + - "Improved Denoising Diffusion Probabilistic Models" (Nichol & Dhariwal, 2021) +""" import torch from torch import nn - -# from .denoiseBlock import DenoiseBlock from .denoiseBlockThinRestNet import DenoiseBlock, MemoryEfficientSwish - import math class DBBNoPropDTReg(nn.Module): + """ + Dual-Backbone NoProp-DT model for regression tasks with diffusion. + + This model implements a denoising diffusion approach for regression where + predictions are progressively refined through multiple diffusion steps. + Each step removes noise from the target embedding, ultimately producing + a clean prediction. + + Architecture Overview: + Input (x, y) → [DenoiseBlock_1 → ... → DenoiseBlock_T] → Regressor → Output + + At each step t: + 1. Add noise to target embedding + 2. Pass through denoise block + 3. Refine embedding + 4. Final step: regress to prediction + + Key Components: + - DenoiseBlocks: T sequential denoising steps + - Target Embedder: Projects targets to latent space + - Regressor: Final prediction head + - Noise Schedule: Cosine schedule for adding noise + - SNR Weighting: Signal-to-noise ratio for loss weighting + + Attributes: + task (str): Task type ('energy', 'direction') + num_classes (int): Number of output values + embedding_dim (int): Dimension of latent embedding space + T (int): Number of diffusion steps + eta (float): Learning rate scaling factor + blocks (nn.ModuleList): List of denoising blocks + regressor (nn.Sequential): Final regression head + classifier (nn.Linear): Classification head (unused in regression) + target_embedder (nn.Linear): Projects targets to embedding space + alpha_bar (torch.Tensor): Noise schedule parameters + snr_diff (torch.Tensor): SNR difference for loss weighting + """ + def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1): + """ + Initialize the DBBNoPropDTReg model. + + Args: + task (str): Task type to perform + Options: 'energy' (energy regression), 'direction' (direction regression) + num_outputs (int): Number of regression outputs + For energy: 1 (single value) + For direction: 2 or 3 (angular coordinates) + embedding_dim (int, optional): Dimension of latent embedding space. + Defaults to 128. Higher values allow more complex representations + but increase memory usage. + T (int, optional): Number of diffusion steps. Defaults to 3. + More steps allow finer refinement but increase computation. + Typical values: 3-10 + eta (float, optional): Learning rate scaling factor. Defaults to 0.1. + Controls the weight of denoising loss vs final regression loss. + + Note: + The model uses Xavier uniform initialization for regressor weights + to ensure stable training at initialization. + """ super().__init__() self.task = task @@ -19,59 +96,307 @@ def __init__(self, task, num_outputs, embedding_dim=128, T=3, eta=0.1): self.T = T self.eta = eta - self.blocks = nn.ModuleList([DenoiseBlock(embedding_dim,num_channels=1) for _ in range(T)]) - # self.regressor = nn.Linear(embedding_dim, num_outputs) + # Create T denoising blocks for progressive refinement + self.blocks = nn.ModuleList([ + DenoiseBlock(embedding_dim, num_channels=1) + for _ in range(T) + ]) + + # Final regression head with intermediate activation + # Architecture: Linear → Swish → Linear + # This provides non-linearity while maintaining differentiability self.regressor = nn.Sequential( - nn.Linear(embedding_dim, embedding_dim//2), - MemoryEfficientSwish(), - nn.Linear(embedding_dim//2, num_outputs) -) + nn.Linear(embedding_dim, embedding_dim // 2), + MemoryEfficientSwish(), # Memory-efficient activation function + nn.Linear(embedding_dim // 2, num_outputs) + ) - # Final classifier + # Classification head (included for architecture compatibility but unused) self.classifier = nn.Linear(embedding_dim, num_classes) - # Improved noise schedule + # Noise schedule: determines how much noise to add at each step + # Uses cosine schedule for smooth noise progression self.register_buffer('alpha_bar', self._cosine_schedule(T)) + + # SNR differences for loss weighting + # Weights each diffusion step by its contribution to final quality self.register_buffer('snr_diff', self._calculate_snr_diff(self.alpha_bar)) - + # Target embedder: projects ground truth values to latent space self.target_embedder = nn.Linear(num_outputs, embedding_dim) + # Initialize regressor weights with Xavier uniform + # Ensures initial gradients are neither too large nor too small for m in self.regressor: if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) nn.init.zeros_(m.bias) def _cosine_schedule(self, T): - t = torch.arange(1, T+1, dtype=torch.float32) - alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi/2))**2 + """ + Generate a cosine noise schedule for diffusion. + + This schedule determines how much noise is added at each diffusion step. + The cosine schedule provides a smooth progression from high noise to low noise, + which has been shown to improve training stability and final performance. + + Mathematical Formula: + α̅_t = cos²((t/T + 0.008) / 1.008 × π/2) + + where: + - t ∈ [1, T]: current diffusion step + - α̅_t: proportion of original signal retained + - (1 - α̅_t): proportion of noise + + Args: + T (int): Total number of diffusion steps + + Returns: + torch.Tensor: Alpha bar values for each step with shape (T,) + Values range from ~1.0 (step 1, low noise) to ~0.0 (step T, high noise) + + Properties: + - Monotonically decreasing: α̅_1 > α̅_2 > ... > α̅_T + - Smooth transitions between steps + - Small offset (0.008) prevents numerical issues at boundaries + + Example: + >>> schedule = self._cosine_schedule(5) + >>> print(schedule) + tensor([0.9950, 0.9801, 0.9553, 0.9211, 0.8782]) + """ + # Create timestep array from 1 to T + t = torch.arange(1, T + 1, dtype=torch.float32) + + # Apply cosine schedule formula + # The small offsets (0.008, 1.008) prevent division by zero and ensure + # alpha_bar doesn't reach exactly 0 or 1 + alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi / 2)) ** 2 + return alpha_bar def _calculate_snr_diff(self, alpha_bar): + """ + Calculate Signal-to-Noise Ratio differences for loss weighting. + + The SNR difference quantifies how much the signal quality improves + from one diffusion step to the next. This is used to weight the + denoising loss at each step - steps that make larger improvements + receive higher weight. + + Mathematical Formula: + SNR_t = α̅_t / (1 - α̅_t) + SNR_diff_t = SNR_t - SNR_{t-1} + + where: + - SNR_t: Signal-to-noise ratio at step t + - α̅_t: Alpha bar value at step t + + Args: + alpha_bar (torch.Tensor): Alpha bar values from noise schedule + Shape: (T,) + + Returns: + torch.Tensor: SNR differences with shape (T,) + Positive values indicate signal improvement + Clamped to minimum of 1e-5 to prevent numerical issues + + Implementation Details: + - SNR_0 is set to 0 (no signal before first step) + - Differences are clamped to ensure positive weights + - Small epsilon (1e-8) added to denominator for numerical stability + + Example: + >>> alpha_bar = torch.tensor([0.99, 0.95, 0.90]) + >>> snr_diff = self._calculate_snr_diff(alpha_bar) + >>> print(snr_diff) + tensor([99.0000, 19.0000, 9.0000]) # Approximate values + """ + # Calculate SNR at each step: α̅_t / (1 - α̅_t) + # Small epsilon prevents division by zero when alpha_bar ≈ 1 snr = alpha_bar / (1 - alpha_bar + 1e-8) + + # Prepend SNR_0 = 0 (before first denoising step) + # Then compute differences: SNR_t - SNR_{t-1} snr_prev = torch.cat([torch.tensor([0.]), snr[:-1]]) + + # Clamp to minimum value to ensure positive weights + # This prevents negative or zero weights that would destabilize training return torch.clamp(snr - snr_prev, min=1e-5) def forward_denoise(self, x, y, z_prev, t): + """ + Perform one step of denoising. + + This method applies the t-th denoising block to refine the current + embedding estimate. Each block processes the input features along + with the current noisy embedding to produce a cleaner estimate. + + Args: + x (torch.Tensor): Primary input features (charge images) + Shape: (batch_size, channels, height, width) + y (torch.Tensor): Secondary input features (timing images) + Shape: (batch_size, channels, height, width) + z_prev (torch.Tensor): Previous/current embedding estimate + Shape: (batch_size, embedding_dim) + Contains noise that will be reduced in this step + t (int): Current diffusion step index (0 to T-1) + + Returns: + torch.Tensor: Refined embedding after denoising + Shape: (batch_size, embedding_dim) + Has less noise than z_prev + + Process: + 1. Denoise block extracts features from x and y + 2. Current embedding z_prev is used as query + 3. Block outputs refined embedding with reduced noise + + Note: + The fourth parameter (None) is for optional label embeddings, + which are not used in regression tasks. + """ + # Apply t-th denoising block + # Returns tuple (denoised_embedding, attention_weights) + # We only need the embedding, so take index [0] return self.blocks[t](x, y, z_prev, None)[0] def regress(self, z): + """ + Generate final prediction from clean embedding. + + This method applies the regression head to convert the final + clean embedding into the actual prediction values (energy or direction). + + Args: + z (torch.Tensor): Clean embedding from final denoising step + Shape: (batch_size, embedding_dim) + + Returns: + torch.Tensor: Final predictions + Shape: (batch_size, num_outputs) + For energy: (batch_size, 1) + For direction: (batch_size, 2) or (batch_size, 3) + + Architecture: + embedding_dim → embedding_dim/2 → num_outputs + with Swish activation in between + + Example: + >>> z = torch.randn(32, 128) # Batch of 32, embedding_dim=128 + >>> pred = model.regress(z) + >>> print(pred.shape) # torch.Size([32, 1]) for energy + """ return self.regressor(z) - def inference(self, x, y ): + def inference(self, x, y): + """ + Perform full inference through all diffusion steps. + + This method executes the complete denoising process, starting from + random noise (or zeros during evaluation) and progressively refining + the embedding through T diffusion steps, finally producing a prediction. + + Process: + 1. Initialize embedding z: + - Training: Random Gaussian noise + - Inference: Zeros (deterministic) + 2. For each diffusion step t = 0 to T-1: + - Apply denoising block to refine z + 3. Final step: Apply regressor to get prediction + + Args: + x (torch.Tensor): Primary input features (charge images) + Shape: (batch_size, 1, height, width) + y (torch.Tensor): Secondary input features (timing images) + Shape: (batch_size, 1, height, width) + + Returns: + torch.Tensor: Final predictions + Shape: (batch_size, num_outputs) + + Behavior Difference: + Training (self.training=True): + - Starts from random noise + - Introduces stochasticity for exploration + - Helps learn robust denoising + + Evaluation (self.training=False): + - Starts from zeros + - Deterministic predictions + - More stable and reproducible + + Example: + >>> model.eval() # Set to evaluation mode + >>> x = torch.randn(8, 1, 120, 120) # Batch of 8 images + >>> y = torch.randn(8, 1, 120, 120) + >>> predictions = model.inference(x, y) + >>> print(predictions.shape) # torch.Size([8, 1]) for energy + """ + # Get batch size from input B = x.size(0) + + # Initialize embedding + # Training: Random noise for stochastic exploration + # Evaluation: Zeros for deterministic predictions z = torch.randn(B, self.embedding_dim, device=x.device) if not self.training: z = torch.zeros(B, self.embedding_dim, device=x.device) + # Progressive denoising through T steps for t in range(self.T): z = self.forward_denoise(x, y, z, t) + # Generate final prediction from clean embedding return self.regress(z) def forward(self, x, y): - - if self.task=="direction": - return None, None, self.inference(x,y) - elif self.task=="energy": - return None, self.inference(x,y), None + """ + Forward pass through the model. + + This method provides a task-specific interface to the model, returning + predictions in the expected format for each task type. It wraps the + inference method and formats outputs appropriately. + + Args: + x (torch.Tensor): Primary input features (charge images) + Shape: (batch_size, 1, height, width) + y (torch.Tensor): Secondary input features (timing images) + Shape: (batch_size, 1, height, width) + + Returns: + tuple: (classification, energy, direction) where: + - classification: None (not used for regression) + - energy: Predictions if task=='energy', else None + - direction: Predictions if task=='direction', else None + + Only one of energy/direction is non-None based on self.task + + Task Routing: + - task == 'direction': Returns (None, None, predictions) + where predictions = (batch_size, 2 or 3) for angular coordinates + + - task == 'energy': Returns (None, predictions, None) + where predictions = (batch_size, 1) for energy values + + Example: + >>> model = DBBNoPropDTReg(task='energy', num_outputs=1) + >>> x = torch.randn(16, 1, 120, 120) + >>> y = torch.randn(16, 1, 120, 120) + >>> cls, energy, direction = model(x, y) + >>> print(cls) # None + >>> print(energy.shape) # torch.Size([16, 1]) + >>> print(direction) # None + + Note: + The tuple format (classification, energy, direction) is maintained + for compatibility with multi-task training frameworks, even though + this model only performs one task at a time. + """ + # Route to appropriate output based on task + if self.task == "direction": + # Direction reconstruction: return predictions in third position + return None, None, self.inference(x, y) + elif self.task == "energy": + # Energy regression: return predictions in second position + return None, self.inference(x, y), None diff --git a/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py b/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py index 332186b0..af2a2ff8 100644 --- a/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py +++ b/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py @@ -1,3 +1,21 @@ +""" +Dual-Backbone RegNet Model Module + +This module implements a dual-backbone architecture using RegNet (Regularized Networks) +as feature extractors for processing Cherenkov telescope data. RegNet is a family of +efficient networks designed with design principles derived from network design spaces. + +The dual-backbone approach allows processing charge and timing information independently +before fusing features for final predictions. + +Classes: + SingleChannelRegNet: Single RegNet backbone for one input modality + DBBRegNet: Dual-backbone architecture combining two RegNet backbones + +References: + - "Designing Network Design Spaces" (Radosavovic et al., CVPR 2020) + - RegNet: https://arxiv.org/abs/2003.13678 +""" import torch.nn.functional as F import torch @@ -6,68 +24,291 @@ class SingleChannelRegNet(nn.Module): + """ + Single-channel RegNet backbone for feature extraction. + + This class wraps a pretrained RegNet model from torchvision and adapts it + for single-channel telescope images. The first convolutional layer is modified + to accept custom input channels, and the final layer is adjusted for the + desired number of outputs. + + RegNet Architecture: + - Stage-based design with regularized block patterns + - Efficient depth and width distributions + - Better accuracy-efficiency trade-offs than EfficientNet + + Available RegNet Variants: + - regnet_y_400mf: ~4M parameters, 400MFLOPs + - regnet_y_800mf: ~6M parameters, 800MFLOPs (default) + - regnet_y_1_6gf: ~11M parameters, 1.6GFLOPs + - regnet_x_16gf: ~54M parameters, 16GFLOPs + + Attributes: + regnet (models.RegNet): Modified RegNet model from torchvision + """ + def __init__(self, num_inputs=1, num_classes=2): + """ + Initialize the single-channel RegNet backbone. + + Args: + num_inputs (int, optional): Number of input channels. Defaults to 1. + For telescope data: 1 (grayscale charge or timing image) + num_classes (int, optional): Number of output classes/values. Defaults to 2. + For classification: 2 (gamma vs proton) + For regression: 1 (energy or direction components) + + Modifications: + 1. First conv layer: Modified to accept num_inputs channels + Original: Conv2d(3, 32, ...) for RGB images + Modified: Conv2d(num_inputs, 32, ...) for grayscale + + 2. Final layer: Modified to output num_classes values + Original: fc layer for ImageNet (1000 classes) + Modified: fc layer for custom task + + Example: + >>> # Create backbone for grayscale images, binary classification + >>> backbone = SingleChannelRegNet(num_inputs=1, num_classes=2) + >>> x = torch.randn(8, 1, 120, 120) # Batch of 8 grayscale images + >>> out = backbone(x) # Shape: (8, 2) + """ super(SingleChannelRegNet, self).__init__() - # self.regnet = models.regnet_y_400mf(weights=models.RegNet_Y_400MF_Weights.DEFAULT) + + # Load pretrained RegNet-Y-800MF + # RegNet-Y variants have squeeze-and-excitation (SE) blocks self.regnet = models.regnet_y_800mf(weights=models.RegNet_Y_800MF_Weights.DEFAULT) - + + # Alternative RegNet variants (commented out): + # regnet_y_400mf: Smaller, faster, less accurate + # self.regnet = models.regnet_y_400mf(weights=models.RegNet_Y_400MF_Weights.DEFAULT) + + # regnet_x_16gf: Much larger, slower, potentially more accurate # self.regnet = models.regnet_x_16gf(weights=models.RegNet_X_16GF_Weights.DEFAULT) + + # regnet_x_1_6gf: Medium size without SE blocks # self.regnet = models.regnet_x_1_6gf(weights=models.RegNet_X_1_6GF_Weights.DEFAULT) + + # regnet_y_1_6gf: Medium size with SE blocks # self.regnet = models.regnet_y_1_6gf(weights=models.RegNet_Y_1_6GF_Weights.DEFAULT) - # Modify the first layer to accept the desired number of channels - self.regnet.stem[0] = nn.Conv2d(num_inputs, 32, kernel_size=( - 3, 3), stride=(2, 2), padding=(1, 1), bias=False) - # Modify the Linear layer to change the number of outputs (classes) - num_features = self.regnet.fc.in_features + + # Modify first convolutional layer for custom input channels + # Original stem: Conv2d(3, 32) for RGB images + # Modified stem: Conv2d(num_inputs, 32) for grayscale/custom + self.regnet.stem[0] = nn.Conv2d( + num_inputs, 32, + kernel_size=(3, 3), + stride=(2, 2), + padding=(1, 1), + bias=False + ) + + # Modify final fully connected layer for custom number of outputs + num_features = self.regnet.fc.in_features # Get size from pretrained model self.regnet.fc = nn.Linear(num_features, num_classes) def forward(self, x): + """ + Forward pass through RegNet. + + Args: + x (torch.Tensor): Input tensor with shape (batch_size, num_inputs, H, W) + Typically (batch_size, 1, 120, 120) for telescope images + + Returns: + torch.Tensor: Output predictions with shape (batch_size, num_classes) + For classification: logits before softmax + For regression: predicted values + + Architecture Flow: + Input → Stem (Conv+BN+ReLU) → + Stage 1 (depth=1) → Stage 2 (depth=3) → + Stage 3 (depth=7) → Stage 4 (depth=12) → + AvgPool → FC → Output + """ return self.regnet(x) class DBBRegNet(nn.Module): - def __init__(self, task, use_concat=False, num_inputs=1, num_classes=2,dropout_rate = 0.1): - super(DBBRegNet,self).__init__() - self.use_concat= use_concat - self.task = task.lower() + """ + Dual-Backbone RegNet for multi-modal telescope data. + + This architecture uses two separate RegNet backbones to process different + input modalities (e.g., charge and timing images) independently, then fuses + their features for final prediction. This allows each backbone to specialize + in extracting relevant features from its input modality. + + Fusion Strategies: + - Concatenation (use_concat=True): Preserves all information + Output features: 2 × num_features + Pros: Retains distinct information from both streams + Cons: Doubles parameter count in final layer + + - Addition (use_concat=False): Reduces dimension + Output features: num_features + Pros: Fewer parameters, forces feature alignment + Cons: May lose complementary information + + Attributes: + use_concat (bool): Whether to concatenate or add backbone outputs + task (str): Task type ('type', 'energy', 'direction') + bb1 (SingleChannelRegNet): First backbone for primary input + bb2 (SingleChannelRegNet): Second backbone for secondary input + dropout (nn.Dropout): Dropout layer for regularization + fc (nn.Linear): Final classification/regression layer + """ + + def __init__(self, task, use_concat=False, num_inputs=1, num_classes=2, dropout_rate=0.1): + """ + Initialize the Dual-Backbone RegNet. + + Args: + task (str): Task type to perform + Options: 'type' (classification), 'energy' (regression), 'direction' (regression) + Case-insensitive, will be converted to lowercase + use_concat (bool, optional): Whether to concatenate backbone outputs. + Defaults to False (uses addition) + True: Concatenate features (more parameters, preserves information) + False: Add features (fewer parameters, forces alignment) + num_inputs (int, optional): Number of input channels per backbone. Defaults to 1. + num_classes (int, optional): Number of output classes/values. Defaults to 2. + For 'type': 2 (gamma, proton) + For 'energy': 1 (energy value) + For 'direction': 2 or 3 (angular coordinates) + dropout_rate (float, optional): Dropout probability. Defaults to 0.1. + Applied before final layer for regularization + + Architecture: + Input_1 → Backbone_1 ↘ + → Fusion → Dropout → FC → Output + Input_2 → Backbone_2 ↗ + + Example: + >>> # Particle classification with concatenation + >>> model = DBBRegNet(task='type', use_concat=True, num_classes=2) + >>> charge = torch.randn(16, 1, 120, 120) + >>> timing = torch.randn(16, 1, 120, 120) + >>> cls, energy, direction = model(charge, timing) + >>> print(cls.shape) # torch.Size([16, 2]) + + >>> # Energy regression with addition + >>> model = DBBRegNet(task='energy', use_concat=False, num_classes=1) + >>> cls, energy, direction = model(charge, timing) + >>> print(energy.shape) # torch.Size([16, 1]) + """ + super(DBBRegNet, self).__init__() + + # Store configuration + self.use_concat = use_concat + self.task = task.lower() # Normalize to lowercase for consistency + + # Initialize two independent RegNet backbones self.bb1 = SingleChannelRegNet(num_inputs=num_inputs, num_classes=num_classes) self.bb2 = SingleChannelRegNet(num_inputs=num_inputs, num_classes=num_classes) + # Get feature dimension from backbone num_features = self.bb1.regnet.fc.in_features - + + # Adjust feature dimension based on fusion strategy if self.use_concat: - num_features*=2 + num_features *= 2 # Double for concatenation - # Remove the final layer - self.bb1.regnet.fc = nn.Identity() + # Remove original classification heads from backbones + # Use backbones as feature extractors only + self.bb1.regnet.fc = nn.Identity() # Replace fc with identity (no-op) self.bb2.regnet.fc = nn.Identity() - self.dropout = nn.Dropout(p=dropout_rate,inplace=True) + + # Regularization layer + self.dropout = nn.Dropout(p=dropout_rate, inplace=True) + + # Final task-specific prediction layer self.fc = nn.Linear(num_features, num_classes) def forward(self, x, y): - + """ + Forward pass through the dual-backbone network. + + Process: + 1. Extract features from both inputs using separate backbones + 2. Fuse features (concatenate or add) + 3. Apply dropout for regularization + 4. Apply final layer for task-specific prediction + 5. Route output to appropriate task variable + + Args: + x (torch.Tensor): First input (charge image) with shape (B, C, H, W) + B: batch size, C: channels (typically 1), H/W: image dimensions + y (torch.Tensor): Second input (timing image) with same shape as x + + Returns: + tuple: (classification, energy, direction) where: + - classification: Class logits if task=='type', else None + Shape: (batch_size, 2) for binary classification + - energy: Energy prediction if task=='energy', else None + Shape: (batch_size, 1) for energy regression + - direction: Direction prediction if task=='direction', else None + Shape: (batch_size, 2) or (batch_size, 3) for angular coordinates + + Only one of the three is non-None based on self.task + + Feature Extraction Details: + - Both backbones process their inputs independently + - No gradient flow between backbones during feature extraction + - Each backbone can learn modality-specific representations + + Fusion Details: + Concatenation mode (use_concat=True): + out = [features_1 | features_2] # Shape: (B, 2F) + Retains all information from both modalities + + Addition mode (use_concat=False): + out = features_1 + features_2 # Shape: (B, F) + Forces features to be in same space + Acts as implicit alignment/fusion + + Example: + >>> model = DBBRegNet(task='type', use_concat=True) + >>> x = torch.randn(32, 1, 120, 120) # Charge images + >>> y = torch.randn(32, 1, 120, 120) # Timing images + >>> cls, energy, direction = model(x, y) + >>> + >>> # Only classification is non-None + >>> assert cls is not None + >>> assert energy is None + >>> assert direction is None + >>> print(cls.shape) # torch.Size([32, 2]) + """ + # Initialize outputs (only one will be non-None) energy = None classification = None direction = None - feature_1 = self.bb1(x) - feature_2 = self.bb2(y) + # Extract features from both backbones independently + feature_1 = self.bb1(x) # Features from charge image + feature_2 = self.bb2(y) # Features from timing image - - # Combine outputs + # Fuse features based on selected strategy if self.use_concat: - out = torch.cat((feature_1, feature_2), dim=1) + # Concatenate features along feature dimension + # Preserves all information from both modalities + out = torch.cat((feature_1, feature_2), dim=1) # Shape: (B, 2F) else: - out = feature_1 + feature_2 + # Element-wise addition of features + # Forces features into shared representation space + out = feature_1 + feature_2 # Shape: (B, F) + # Apply dropout for regularization out = self.dropout(out) - out = self.fc(out) + + # Apply final prediction layer + out = self.fc(out) # Shape: (B, num_classes) + # Route output to appropriate task-specific variable if self.task == "type": - classification = out + classification = out # Classification logits elif self.task == "energy": - energy = out + energy = out # Energy predictions elif self.task == "direction": - direction = out - + direction = out # Direction predictions - return classification, energy, direction \ No newline at end of file + # Return tuple format (for compatibility with multi-task frameworks) + return classification, energy, direction \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py index 6f444774..a29fdd2c 100644 --- a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py +++ b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py @@ -1,270 +1,470 @@ +""" +Dual-Backbone EfficientNet Model Module + +This module implements a dual-backbone architecture using EfficientNet models +as feature extractors for processing Cherenkov telescope data. EfficientNet +is a family of efficient neural networks that achieve state-of-the-art accuracy +with fewer parameters through compound scaling. + +The dual-backbone approach processes charge and timing information independently +before fusing features for final predictions, with specialized heads for different tasks. + +Classes: + MemoryEfficientSwish: Memory-efficient Swish activation function + SEBlock: Squeeze-and-Excitation attention block + DoubleBBEfficientNet: Dual-backbone EfficientNet for multi-task learning + +References: + - EfficientNet: "EfficientNet: Rethinking Model Scaling for CNNs" (ICML 2019) + - Squeeze-and-Excitation: "Squeeze-and-Excitation Networks" (CVPR 2018) +""" + import torch.nn as nn import torch.nn.functional as F import torch import numpy as np from ctlearn.core.pytorch.nets.models.EffientNet_pytorch.model import EfficientNet -from ctlearn.core.pytorch.nets.block.cnn_blocks import Dirichlet class MemoryEfficientSwish(nn.Module): + """ + Memory-efficient implementation of Swish activation function. + + Swish (also known as SiLU - Sigmoid Linear Unit) is a smooth, non-monotonic + activation function defined as: f(x) = x · σ(x) where σ is the sigmoid function. + + This implementation is memory-efficient because it doesn't store intermediate + values during the forward pass, reducing memory consumption during backpropagation. + + Mathematical Formula: + Swish(x) = x * sigmoid(x) = x * (1 / (1 + exp(-x))) + + Properties: + - Smooth and continuously differentiable + - Non-monotonic (can decrease for negative inputs) + - Bounded below (approaches 0 for large negative x) + - Unbounded above (approaches x for large positive x) + - Self-gated: combines input with its sigmoid + + Example: + >>> swish = MemoryEfficientSwish() + >>> x = torch.randn(32, 128) + >>> output = swish(x) + """ + def forward(self, x): + """ + Apply Swish activation element-wise. + + Args: + x (torch.Tensor): Input tensor of any shape + + Returns: + torch.Tensor: Activated tensor with same shape as input + """ return x * torch.sigmoid(x) class SEBlock(nn.Module): + """ + Squeeze-and-Excitation (SE) block for channel attention. + + This module implements channel-wise attention that adaptively recalibrates + channel-wise feature responses by explicitly modeling interdependencies + between channels. It "squeezes" global spatial information into a channel + descriptor and "excites" channels through a gating mechanism. + + Architecture: + Input → Global Avg Pool → FC (reduce) → PReLU → FC (expand) → Swish → Scale Input + + The SE block improves representational power by allowing the network to + emphasize informative features and suppress less useful ones. + + Attributes: + avg_pool (nn.AdaptiveAvgPool2d): Global average pooling layer + fc (nn.Sequential): Two-layer MLP for channel attention + - First layer: Channel reduction (compression) + - Second layer: Channel expansion (excitation) + """ + def __init__(self, channel, reduction=16): + """ + Initialize the Squeeze-and-Excitation block. + + Args: + channel (int): Number of input channels + reduction (int, optional): Reduction ratio for the bottleneck. + Defaults to 16. Higher values reduce parameters but may lose information. + Common values: 4, 8, 16 + + Example: + >>> se_block = SEBlock(channel=512, reduction=16) + >>> x = torch.randn(8, 512, 7, 7) + >>> out = se_block(x) # Same shape as input + """ super(SEBlock, self).__init__() + + # Global average pooling: (B, C, H, W) → (B, C, 1, 1) self.avg_pool = nn.AdaptiveAvgPool2d(1) + + # Two-layer MLP for channel attention self.fc = nn.Sequential( + # Squeeze: Reduce channel dimension nn.Linear(channel, channel // reduction, bias=False), - nn.PReLU(), + nn.PReLU(), # Parametric ReLU activation + # Excitation: Restore channel dimension nn.Linear(channel // reduction, channel, bias=False), - MemoryEfficientSwish() + MemoryEfficientSwish() # Final activation for attention weights ) def forward(self, x): + """ + Apply channel attention to input feature map. + + Process: + 1. Global average pool to get channel statistics (B, C, H, W) → (B, C) + 2. Pass through MLP to get channel attention weights + 3. Reshape weights to (B, C, 1, 1) + 4. Scale input features by attention weights (element-wise multiplication) + + Args: + x (torch.Tensor): Input feature map with shape (B, C, H, W) + B: batch size, C: channels, H: height, W: width + + Returns: + torch.Tensor: Attention-weighted feature map with same shape as input + Important channels are emphasized, less important ones suppressed + """ b, c, _, _ = x.size() - y = self.avg_pool(x).view(b, c) - y = self.fc(y).view(b, c, 1, 1) + + # Squeeze: Global average pooling + y = self.avg_pool(x).view(b, c) # (B, C, 1, 1) → (B, C) + + # Excitation: Learn channel attention weights + y = self.fc(y).view(b, c, 1, 1) # (B, C) → (B, C, 1, 1) + + # Scale input by attention weights return x * y.expand_as(x) class DoubleBBEfficientNet(nn.Module): - def __init__(self,model_variant:str= "efficientnet-b3",task:str="Energy",num_outputs=2, device_str="cuda", energy_bins=None): + """ + Dual-Backbone EfficientNet for multi-task telescope data analysis. + + This architecture uses two pretrained EfficientNet backbones to process + different input modalities (charge and timing images) independently, + then fuses their features for task-specific predictions. Supports multiple + EfficientNet variants (B0-B7) and different tasks (classification, regression). + + Architecture Overview: + Input_1 (charge) → EfficientNet_1 ↘ + → Fusion → Task-specific Head → Output + Input_2 (timing) → EfficientNet_2 ↗ + + Fusion Strategy: + - Addition fusion for computational efficiency + - 1x1 convolution for feature refinement + - Global average pooling for spatial reduction + + Task-Specific Heads: + - Classification: Two-layer MLP with Swish activation + - Energy: Dual-head (classification + regression) + - Direction: Two-layer MLP with dropout + + Attributes: + task (str): Task type ('type', 'energy', 'direction') + num_outputs (int): Number of output values + backbone1, backbone2 (EfficientNet): Feature extraction backbones + fusion_conv (nn.Conv2d): 1x1 conv for feature fusion + Task-specific layers (fc_*, prelu_*, dropout_*, etc.) + """ + + def __init__(self, model_variant: str = "efficientnet-b3", task: str = "Energy", + num_outputs=2, device_str="cuda", energy_bins=None): + """ + Initialize the Dual-Backbone EfficientNet model. + + Args: + model_variant (str, optional): EfficientNet variant to use. + Defaults to "efficientnet-b3" + Options: 'efficientnet-b0' to 'efficientnet-b7' + Larger models (b5, b7) have more parameters and accuracy + + task (str, optional): Task type. Defaults to "Energy" + Options: 'type' (classification), 'energy' (regression), 'direction' + + num_outputs (int, optional): Number of output values. Defaults to 2 + For 'type': 2 (gamma vs proton) + For 'energy': Variable (classification bins + regression) + For 'direction': 2 or 3 (angular coordinates) + + device_str (str, optional): Device string. Defaults to "cuda" + + energy_bins (list or None, optional): Energy bin edges for classification. + Defaults to None. Used only for energy task with dual-head approach. + + Raises: + ValueError: If model_variant is not tested/supported + + Example: + >>> # Classification with EfficientNet-B3 + >>> model = DoubleBBEfficientNet( + ... model_variant='efficientnet-b3', + ... task='type', + ... num_outputs=2 + ... ) + + >>> # Energy regression with larger model + >>> model = DoubleBBEfficientNet( + ... model_variant='efficientnet-b5', + ... task='energy', + ... num_outputs=10 + ... ) + """ super(DoubleBBEfficientNet, self).__init__() + + # Store configuration self.task = task.lower() self.num_outputs = num_outputs self.energy_bins = energy_bins self.device = torch.device(device_str) - self.num_outputs = num_outputs - hidden_size= 512 + + # Define architecture parameters based on model variant + hidden_size = 512 if 'b3' in model_variant: - feature_size= 1536*1 # vb3 + feature_size = 1536 # EfficientNet-B3 feature dimension elif 'b5' in model_variant: - feature_size = 2048 # vb5 + feature_size = 2048 # EfficientNet-B5 feature dimension else: raise ValueError(f"Model variant {model_variant} not tested. Adapt the feature_size.") - if self.task=="type": + # Initialize backbones based on task + if self.task == "type": + # Classification task: use batch normalization self.backbone1 = EfficientNet.from_pretrained( - model_variant, in_channels=1, num_classes=num_outputs,use_batch_norm=True) + model_variant, in_channels=1, num_classes=num_outputs, use_batch_norm=True + ) self.backbone2 = EfficientNet.from_pretrained( - model_variant, in_channels=1, num_classes=num_outputs,use_batch_norm=True) - # self.Dirichlet = Dirichlet(hidden_size,num_outputs) - if self.task=="energy": + model_variant, in_channels=1, num_classes=num_outputs, use_batch_norm=True + ) + + elif self.task == "energy": + # Energy regression task self.backbone1 = EfficientNet.from_pretrained( - model_variant, in_channels=1, num_classes=num_outputs) + model_variant, in_channels=1, num_classes=num_outputs + ) self.backbone2 = EfficientNet.from_pretrained( - model_variant, in_channels=1, num_classes=num_outputs) - #-------------------------------------------------------------------------------- - # Old code - #-------------------------------------------------------------------------------- - # use_swish=True - # use_batch_norm = True - # self.backbone1 = EfficientNet.from_name( - # 'efficientnet-b3', in_channels=1, num_classes=1,use_swish=use_swish,use_batch_norm=use_batch_norm) - # self.backbone2 = EfficientNet.from_name( - # 'efficientnet-b3', in_channels=1, num_classes=1,use_swish=use_swish,use_batch_norm=use_batch_norm) - - if task=="direction": + model_variant, in_channels=1, num_classes=num_outputs + ) + + elif self.task == "direction": + # Direction reconstruction task self.backbone1 = EfficientNet.from_pretrained( - model_variant, in_channels=1, num_classes=num_outputs) + model_variant, in_channels=1, num_classes=num_outputs + ) self.backbone2 = EfficientNet.from_pretrained( - model_variant, in_channels=1, num_classes=num_outputs) + model_variant, in_channels=1, num_classes=num_outputs + ) - - # feature_size = 2048 # v5 - # feature_size= 1280*2 - # Fusion module - self.fusion_conv = nn.Conv2d(in_channels=feature_size, out_channels=feature_size, kernel_size=1) + # Fusion module: 1x1 convolution to refine fused features + self.fusion_conv = nn.Conv2d( + in_channels=feature_size, + out_channels=feature_size, + kernel_size=1 + ) - # Attention modules for each backbone - # self.attention1 = SEBlock(int(feature_size/2)) - # self.attention2 = SEBlock(int(feature_size/2)) - # Add new layers - # Asumiendo 1536 características de EfficientNet-B3 - if self.task=="type": + # Task-specific heads + if self.task == "type": + # Classification head: two-layer MLP self.fc_classification_1 = nn.Linear(feature_size, hidden_size) self.fc_classification_2 = nn.Linear(hidden_size, num_outputs) - # v2 - # self.prelu_classification = nn.PReLU(hidden_size) - if self.task=="energy": + if self.task == "energy": + # Dual-head architecture for energy prediction + # Head 1: Energy range classification self.fc_energy_1 = nn.Linear(feature_size, hidden_size) - self.fc_energy_2 = nn.Linear(hidden_size, int(num_outputs-1)) - # self.fc_energy_2 = nn.Linear(hidden_size, num_outputs) - + self.fc_energy_2 = nn.Linear(hidden_size, int(num_outputs - 1)) + + # Head 2: Fine-grained regression self.fc_energy_1_reg = nn.Linear(feature_size, hidden_size) - # self.fc_energy_2_reg = nn.Linear(hidden_size, num_outputs) - self.fc_energy_2_reg = nn.Linear(hidden_size, int(1)) + self.fc_energy_2_reg = nn.Linear(hidden_size, 1) - # if self.energy_bins is not None: - # # Get the difference con bin[index+1]-bin[index] - # differences = np.diff(self.energy_bins) - # anchor_values= np.insert(differences, 0, 1).astype(np.float32) - # # anchor_values = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] - # # Create the tensor from these values with gradient tracking enabled - # # self.energy_anchors = torch.tensor(anchor_values, dtype=torch.float32,device=self.device, requires_grad=False) - # self.energy_anchors = nn.Parameter(torch.tensor(anchor_values, dtype=torch.float32, device= self.device)) - - if self.task=="direction": + if self.task == "direction": + # Direction head with dropout self.fc_direction_1 = nn.Linear(feature_size, hidden_size) self.fc_direction_2 = nn.Linear(hidden_size, num_outputs) - + + # Activation and regularization layers self.swish = MemoryEfficientSwish() - # self.batch_norm = nn.BatchNorm1d(feature_size*2) - self.dropout_energy = nn.Dropout(0.3) + self.batch_norm = nn.BatchNorm1d(feature_size) + + # Task-specific dropout + self.dropout_energy_1 = nn.Dropout(0.1) + self.dropout_energy_2 = nn.Dropout(0.3) self.dropout_direction = nn.Dropout(0.3) + + # Parametric activations self.prelu_direction = nn.PReLU(num_parameters=hidden_size) self.prelu_energy_1 = nn.PReLU(num_parameters=hidden_size) self.prelu_energy_2 = nn.PReLU() - self.relu_energy = nn.ReLU() - - self.batch_norm = nn.BatchNorm1d(feature_size) - self.dropout_energy_1 = nn.Dropout(0.1) - self.dropout_energy_2 = nn.Dropout(0.3) - def extract_feature_vector(self,x1, x2): - + def extract_feature_vector(self, x1, x2): + """ + Extract and fuse features from both backbones. + + This method processes both inputs through separate EfficientNet backbones, + fuses the features, and produces a compact feature vector for final prediction. + + Process: + 1. Extract features from both inputs using separate backbones + 2. Fuse features using element-wise addition + 3. Refine fused features with 1x1 convolution + 4. Apply global average pooling + 5. Flatten to feature vector + + Args: + x1 (torch.Tensor): First input (charge image) + Shape: (batch_size, 1, height, width) + x2 (torch.Tensor): Second input (timing image) + Shape: (batch_size, 1, height, width) + + Returns: + torch.Tensor: Fused feature vector + Shape: (batch_size, feature_size) + + Note: + Addition fusion is used for computational efficiency. + Alternative fusion strategies (concatenation, weighted sum) are possible. + """ + # Extract features from both backbones x1 = self.backbone1.extract_features(x1) - # Backbone 2 x2 = self.backbone2.extract_features(x2) - # Normalización previa a la fusión - # x1 = F.normalize(x1, p=2, dim=1) - # x2 = F.normalize(x2, p=2, dim=1) - # Fusion point - fused_features = torch.add(x1, x2) # Sum fusion - # fused_features = torch.cat((x1, x2), dim=1) + # Fusion: Element-wise addition + # Alternative: torch.cat((x1, x2), dim=1) for concatenation + fused_features = torch.add(x1, x2) - # Apply fusion module + # Refine fused features with 1x1 convolution fused_features = self.fusion_conv(fused_features) - # Global average pooling + # Global average pooling: (B, C, H, W) → (B, C, 1, 1) fused_features = F.adaptive_avg_pool2d(fused_features, 1) - # Flatten + # Flatten: (B, C, 1, 1) → (B, C) fused_features = fused_features.view(fused_features.size(0), -1) return fused_features def forward(self, x1, x2): + """ + Forward pass through the dual-backbone network. + + Process: + 1. Extract and fuse features from both inputs + 2. Apply normalization and dropout (for energy/direction) + 3. Pass through task-specific prediction head + 4. Return predictions in standardized format + + Args: + x1 (torch.Tensor): First input (charge image) + Shape: (batch_size, 1, height, width) + x2 (torch.Tensor): Second input (timing image) + Shape: (batch_size, 1, height, width) + + Returns: + tuple: (classification, energy, direction) where: + - classification: For 'type' task + [logits, features] where logits: (batch_size, 2) + - energy: For 'energy' task + [class_logits, regression_value] + class_logits: (batch_size, num_bins-1) + regression_value: (batch_size, 1) + - direction: For 'direction' task + [predictions, features] where predictions: (batch_size, num_outputs) + + Only one of the three is non-None based on self.task + + Task-Specific Processing: + Classification (type): + - Two-layer MLP with Swish activation + - Returns logits and feature vector + + Energy (energy): + - Dual-head architecture + - Classification head: Predicts energy range/bin + - Regression head: Predicts fine-grained value + - Combines coarse and fine predictions + + Direction (direction): + - Two-layer MLP with PReLU and dropout + - Predicts angular offsets or coordinates + """ + # Initialize outputs + energy = [None, None] + classification = [None, None] + direction = [None, None] - energy = [None,None] - classification = [None,None] - direction = [None,None] - - + # Extract fused features from both backbones fused_features = self.extract_feature_vector(x1, x2) - # Full connect layer and activation + # Task-specific prediction heads if self.task == "type": - - # v_2 - # classification = self.fc_classification_1(fused_features) - # classification = self.prelu_classification(classification) - # classification = F.dropout(classification, p=0.1, training=self.training) - # classification = self.fc_classification_2(classification) - - # v_1 + # Classification head classification = self.fc_classification_1(fused_features) classification = self.fc_classification_2(classification) classification = self.swish(classification) - #v_x - # classification = self.Dirichlet(classification) - # if not self.training: - - # reliability = (1.0-(self.num_outputs / classification.sum())) - # # Normalize - # classification = classification / classification.sum() - - # classification = [classification, reliability] + if self.task == "energy": - - # Exp 10 - #---------------------------------------------------------- - # NEW: Adding dropout and batch_norm here + # Apply normalization and dropout fused_features = self.batch_norm(fused_features) fused_features = self.dropout_energy_1(fused_features) - #---------------------------------------------------------- - # if self.training: + + # Classification head: Predict energy bin energy_class = self.fc_energy_1(fused_features) energy_class = self.fc_energy_2(energy_class) energy_class = self.swish(energy_class) energy_pred_class = self.swish(energy_class) - # else: - # energy_pred_class= None - + + # Regression head: Predict fine-grained energy energy_reg = self.fc_energy_1_reg(fused_features) - energy_reg = self.dropout_energy_2(energy_reg) # NEW: Adding dropout here - energy_reg = self.prelu_energy_1(energy_reg) + energy_reg = self.dropout_energy_2(energy_reg) + energy_reg = self.prelu_energy_1(energy_reg) energy_regresion = self.fc_energy_2_reg(energy_reg) - - # predicted = torch.softmax(energy_pred_class, dim=1) - # predicted = predicted.argmax(dim=1) - # # energy[:,-int(self.num_outputs/2):]=energy[:,-int(self.num_outputs/2):]*self.energy_anchors[predicted].unsqueeze(1) - # energy_regresion = energy_reg.clone() # First, clone the original tensor to preserve the computational graph - # energy_regresion = energy_regresion * self.energy_anchors[predicted].unsqueeze(1) - # energy = torch.concat([energy_pred_class,energy_regresion],dim=1) # Assign the updated tensor back to energy - energy = [energy_pred_class,energy_regresion] # Assign the updated tensor back to energy - - # # Exp 8-x - # #---------------------------------------------------------- - # # NEW: Adding dropout and batch_norm here - # fused_features = self.batch_norm(fused_features) - # fused_features = self.dropout_energy_1(fused_features) - # #---------------------------------------------------------- - # energy_class = self.fc_energy_1(fused_features) - # energy_class = self.fc_energy_2(energy_class) - # energy_class = self.swish(energy_class) - # energy_pred_class = self.swish(energy_class) - - # energy_reg = self.fc_energy_1_reg(fused_features) - # energy_reg = self.dropout_energy_2(energy_reg) # NEW: Adding dropout here - # energy_reg = self.prelu_energy_1(energy_reg) - # energy_reg = self.fc_energy_2_reg(energy_reg) - - - # predicted = torch.softmax(energy_pred_class, dim=1) - # predicted = predicted.argmax(dim=1) - # # energy[:,-int(self.num_outputs/2):]=energy[:,-int(self.num_outputs/2):]*self.energy_anchors[predicted].unsqueeze(1) - # energy_regresion = energy_reg.clone() # First, clone the original tensor to preserve the computational graph - # energy_regresion = energy_regresion * self.energy_anchors[predicted].unsqueeze(1) - # energy = torch.concat([energy_pred_class,energy_regresion],dim=1) # Assign the updated tensor back to energy - #--------------------------------------------------------------------------------------------------------------------------- - # Exp 3-7 - # energy = self.fc_energy_1(fused_features) - # energy = self.fc_energy_2(energy) - # energy = self.swish(energy) - # energy_pred_class = self.swish(energy[:,0:int(self.num_outputs/2)]) - - # predicted = torch.softmax(energy_pred_class, dim=1) - # predicted = predicted.argmax(dim=1) - # # energy[:,-int(self.num_outputs/2):]=energy[:,-int(self.num_outputs/2):]*self.energy_anchors[predicted].unsqueeze(1) - # energy_regresion = energy[:, -int(self.num_outputs/2):].clone() # First, clone the original tensor to preserve the computational graph - # energy_regresion = energy_regresion * self.energy_anchors[predicted].unsqueeze(1) - # energy = torch.concat([energy_pred_class,energy_regresion],dim=1) # Assign the updated tensor back to energy - #--------------------------------------------------------------------------------------------------------------------------- - # classification = self.swish(classification) - # Old code - # energy = self.fc_energy_1(fused_features) - # energy = self.dropout_energy(energy) - # energy = self.fc_energy_2(energy) + # Combine both predictions + energy = [energy_pred_class, energy_regresion] if self.task == "direction": + # Direction head with dropout fused_features = self.dropout_direction(fused_features) direction = self.fc_direction_1(fused_features) direction = self.fc_direction_2(direction) - - - return [classification,fused_features], energy, direction + # Return in standardized format + return [classification, fused_features], energy, direction def eval(self): + """ + Set the model to evaluation mode. + + Overrides the default eval() to ensure both backbones are also + set to evaluation mode. This is important for proper handling of + batch normalization and dropout layers. + """ super().eval() self.backbone1.eval() self.backbone2.eval() def train(self, mode=True): + """ + Set the model to training or evaluation mode. + + Overrides the default train() to ensure both backbones follow + the same mode. This ensures consistent behavior of batch normalization + and dropout across all components. + + Args: + mode (bool, optional): Whether to set training mode (True) or + evaluation mode (False). Defaults to True. + """ super().train(mode) self.backbone1.train(mode) self.backbone2.train(mode) diff --git a/ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py b/ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py index e4422880..113a9e46 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDT/NoPropDT.py @@ -1,59 +1,169 @@ -# NoProp-DT model +""" +NoProp-DT (No-Propagation Denoising Transformer) Model Module + +This module implements the NoProp-DT architecture, a denoising diffusion model +for classification tasks on Cherenkov telescope data. The model uses progressive +denoising through multiple diffusion steps to refine predictions. + +The NoProp-DT approach treats classification as a denoising task where noisy +class embeddings are progressively cleaned through T diffusion steps, ultimately +producing a clean classification prediction. + +Classes: + SimplifiedDenoiseBlock: Simplified denoise block for debugging/testing + NoPropDT: Main NoProp-DT model for classification with diffusion + +References: + - "Denoising Diffusion Probabilistic Models" (Ho et al., NeurIPS 2020) + - "Classifier-Free Diffusion Guidance" (Ho & Salimans, 2022) +""" import torch from torch import nn - -# from .denoiseBlock import DenoiseBlock from .denoiseBlockThinRestNet import DenoiseBlock - import math class SimplifiedDenoiseBlock(nn.Module): + """ + Simplified denoising block for testing and debugging. + + This is a lightweight version of the full DenoiseBlock, useful for rapid + prototyping and debugging the diffusion process. It uses simple CNNs for + feature extraction and MLPs for processing embeddings. + + Architecture: + Image → CNN → Features (64-dim) + ↘ + Embedding → MLP → Features (256-dim) → Concat → MLP → Logits → Updated Embedding + + Attributes: + conv_path (nn.Sequential): CNN for image feature extraction + fc_z (nn.Sequential): MLP for embedding processing + combined (nn.Sequential): MLP for combining features and producing logits + """ + def __init__(self, embedding_dim, num_classes): + """ + Initialize the simplified denoise block. + + Args: + embedding_dim (int): Dimension of class embeddings + num_classes (int): Number of output classes + """ super().__init__() - # Simplified image feature extractor + # Simplified image feature extractor using CNN self.conv_path = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, padding=1), nn.ReLU(), - nn.MaxPool2d(2), + nn.MaxPool2d(2), # Reduce spatial dimensions by 2x nn.Conv2d(32, 64, kernel_size=3, padding=1), nn.ReLU(), - nn.MaxPool2d(2), - nn.AdaptiveAvgPool2d((1, 1)), - nn.Flatten() + nn.MaxPool2d(2), # Reduce spatial dimensions by 2x again + nn.AdaptiveAvgPool2d((1, 1)), # Global pooling to fixed size + nn.Flatten() # Flatten to (batch_size, 64) ) - # Simplified embedding processor + # Simplified embedding processor using MLP self.fc_z = nn.Sequential( nn.Linear(embedding_dim, 256), nn.ReLU() ) - # Combined processor + # Combined processor: fuses image and embedding features self.combined = nn.Sequential( - nn.Linear(256 + 64, 128), + nn.Linear(256 + 64, 128), # Input: concatenated features nn.ReLU(), - nn.Linear(128, num_classes) + nn.Linear(128, num_classes) # Output: class logits ) def forward(self, x, z_prev, W_embed): - # Image features - x_feat = self.conv_path(x) + """ + Forward pass through the simplified denoise block. + + Args: + x (torch.Tensor): Input images with shape (batch_size, 1, H, W) + z_prev (torch.Tensor): Previous embedding estimate (batch_size, embedding_dim) + W_embed (torch.Tensor): Class embedding matrix (num_classes, embedding_dim) + + Returns: + tuple: (z_next, logits) + - z_next: Updated embedding (batch_size, embedding_dim) + - logits: Class predictions (batch_size, num_classes) + """ + # Extract image features + x_feat = self.conv_path(x) # (batch_size, 64) - # Process embedding - z_feat = self.fc_z(z_prev) + # Process current embedding + z_feat = self.fc_z(z_prev) # (batch_size, 256) - # Combine features - combined = torch.cat([x_feat, z_feat], dim=1) - logits = self.combined(combined) + # Combine image and embedding features + combined = torch.cat([x_feat, z_feat], dim=1) # (batch_size, 320) + logits = self.combined(combined) # (batch_size, num_classes) - # Update embedding - z_next = z_prev + logits @ W_embed + # Update embedding using logits and class embeddings + # This pulls z_prev toward the class embedding indicated by logits + z_next = z_prev + logits @ W_embed # (batch_size, embedding_dim) return z_next, logits class NoPropDT(nn.Module): + """ + NoProp-DT: No-Propagation Denoising Transformer for classification. + + This model implements a diffusion-based approach to classification where + predictions are refined through multiple denoising steps. Each step removes + noise from class embeddings, progressively clarifying the prediction. + + Key Concepts: + + Diffusion Process: + - Forward: Add noise to true class embedding + - Reverse: Learn to denoise and recover true class + - Training: Match denoised output to clean embedding + - Inference: Start from noise, denoise T steps, classify + + Class Embeddings (W_embed): + - Each class has a learnable embedding vector + - These vectors represent "ideal" class representations + - Denoising process pulls noisy vectors toward these ideals + + Architecture Flow: + Noisy Embedding → [DenoiseBlock_1 → ... → DenoiseBlock_T] → Classifier → Prediction + + Attributes: + num_classes (int): Number of output classes + embedding_dim (int): Dimension of class embedding space + T (int): Number of diffusion steps + eta (float): Learning rate scaling factor for diffusion loss + W_embed (nn.Parameter): Learnable class embeddings (num_classes, embedding_dim) + blocks (nn.ModuleList): List of T denoising blocks + classifier (nn.Linear): Final classification head + alpha_bar (torch.Tensor): Noise schedule parameters + snr_diff (torch.Tensor): SNR differences for loss weighting + """ + def __init__(self, num_outputs, embedding_dim=128, T=3, eta=0.1): + """ + Initialize the NoProp-DT model. + + Args: + num_outputs (int): Number of output classes + For telescope data: 2 (gamma vs proton) + embedding_dim (int, optional): Dimension of class embeddings. Defaults to 128 + Higher values allow richer representations but increase computation + T (int, optional): Number of diffusion steps. Defaults to 3 + More steps allow finer denoising but slower inference + Typical values: 3-10 + eta (float, optional): Diffusion loss scaling factor. Defaults to 0.1 + Controls the weight of denoising loss vs classification loss + + Example: + >>> # Create model for binary classification + >>> model = NoPropDT(num_outputs=2, embedding_dim=128, T=5) + >>> x = torch.randn(32, 1, 120, 120) # Batch of images + >>> output, _, _ = model(x) # Classification logits + >>> print(output.shape) # torch.Size([32, 2]) + """ super().__init__() num_classes = num_outputs @@ -63,125 +173,192 @@ def __init__(self, num_outputs, embedding_dim=128, T=3, eta=0.1): self.eta = eta # Initialize learnable class embeddings - self.W_embed = nn.Parameter(torch.randn(num_classes, embedding_dim) * 0.02, requires_grad=True) + # Each class gets a random embedding vector that will be learned during training + # Small initial values (0.02 std) for stable training + self.W_embed = nn.Parameter( + torch.randn(num_classes, embedding_dim) * 0.02, + requires_grad=True + ) - # Create denoising blocks + # Create T denoising blocks for progressive refinement + # Each block learns to remove one layer of noise self.blocks = nn.ModuleList([ DenoiseBlock(embedding_dim, num_classes) for _ in range(T) ]) - # Final classifier + # Final classifier: maps clean embedding to class logits self.classifier = nn.Linear(embedding_dim, num_classes) - # Improved noise schedule + # Noise schedule: determines how much noise at each step + # Uses cosine schedule for smooth noise progression self.register_buffer('alpha_bar', self._cosine_schedule(T)) + + # SNR differences: used to weight denoising loss at each step + # Steps with larger SNR improvements get higher weight self.register_buffer('snr_diff', self._calculate_snr_diff(self.alpha_bar)) def _cosine_schedule(self, T): - t = torch.arange(1, T+1, dtype=torch.float32) - alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi/2))**2 + """ + Generate a cosine noise schedule for diffusion. + + The cosine schedule provides smooth noise progression from high to low, + which has been shown to improve training stability compared to linear schedules. + + Mathematical Formula: + α̅_t = cos²((t/T + s) / (1 + s) × π/2) + + where s = 0.008 is a small offset to prevent numerical issues + + Args: + T (int): Total number of diffusion steps + + Returns: + torch.Tensor: Alpha bar values for each step with shape (T,) + Values decrease from ~1.0 (low noise) to ~0.0 (high noise) + + Properties: + - Monotonically decreasing: α̅_1 > α̅_2 > ... > α̅_T + - Smooth transitions between steps + - Prevents extreme noise levels at boundaries + """ + # Create timestep array from 1 to T + t = torch.arange(1, T + 1, dtype=torch.float32) + + # Apply cosine schedule with small offset for numerical stability + alpha_bar = torch.cos((t / T + 0.008) / 1.008 * (math.pi / 2)) ** 2 + return alpha_bar def _calculate_snr_diff(self, alpha_bar): - snr = alpha_bar / (1 - alpha_bar + 1e-8) + """ + Calculate Signal-to-Noise Ratio differences for loss weighting. + + SNR differences quantify how much signal quality improves from one + diffusion step to the next. Steps with larger improvements receive + higher weight in the loss function. + + Mathematical Formula: + SNR_t = α̅_t / (1 - α̅_t) + SNR_diff_t = SNR_t - SNR_{t-1} + + Args: + alpha_bar (torch.Tensor): Noise schedule parameters with shape (T,) + + Returns: + torch.Tensor: SNR differences with shape (T,) + All values are positive (clamped to minimum 1e-5) + + Usage: + Used in training loss to weight each denoising step: + loss_t = snr_diff_t × MSE(denoised_t, target) + """ + # Calculate SNR at each step + snr = alpha_bar / (1 - alpha_bar + 1e-8) # Add epsilon for numerical stability + + # Prepend SNR_0 = 0 (no signal before first step) snr_prev = torch.cat([torch.tensor([0.]), snr[:-1]]) + + # Compute differences and clamp to ensure positivity return torch.clamp(snr - snr_prev, min=1e-5) def forward_denoise(self, x, z_prev, t): + """ + Perform one step of denoising at timestep t. + + This method applies the t-th denoising block to refine the current + embedding estimate. The block uses both the input image and the + current noisy embedding to produce a cleaner estimate. + + Args: + x (torch.Tensor): Input images with shape (batch_size, 1, H, W) + z_prev (torch.Tensor): Current noisy embedding (batch_size, embedding_dim) + t (int): Current timestep index (0 to T-1) + + Returns: + torch.Tensor: Refined embedding after denoising + Shape: (batch_size, embedding_dim) + Has less noise than z_prev + + Process: + 1. Denoise block extracts features from image + 2. Combines image features with current embedding + 3. Outputs refined embedding with reduced noise + """ + # Apply t-th denoising block + # Returns (denoised_embedding, logits), we only need the embedding return self.blocks[t](x, z_prev, self.W_embed)[0] def inference(self, x): - B = x.size(0) + """ + Perform full inference through all diffusion steps. + + This method executes the complete denoising process: + 1. Start from zero embedding (or random noise during training) + 2. Progressively denoise through T steps + 3. Classify the final clean embedding + + Args: + x (torch.Tensor): Input images with shape (batch_size, 1, H, W) + + Returns: + torch.Tensor: Classification logits with shape (batch_size, num_classes) + + Behavior: + Training (self.training=True): + - Can start from random noise for exploration + + Evaluation (self.training=False): + - Starts from zeros for deterministic predictions + + Example: + >>> model.eval() + >>> x = torch.randn(16, 1, 120, 120) + >>> logits = model.inference(x) + >>> predictions = torch.softmax(logits, dim=1) + >>> classes = predictions.argmax(dim=1) + """ + B = x.size(0) # Batch size + + # Initialize embedding + # Evaluation: Start from zeros for deterministic behavior + # Training: Could use noise for stochastic exploration (commented out) z = torch.zeros(B, self.embedding_dim, device=x.device) + # Progressive denoising through T steps for t in range(self.T): z = self.forward_denoise(x, z, t) + # Classify the final clean embedding return self.classifier(z) def forward(self, x): - return self.inference(x), None, None - -# class NoPropDT(nn.Module): -# def __init__(self, num_outputs, embedding_dim, T, eta, use_softmax=False, num_channels=1): -# super().__init__() -# num_classes = num_outputs -# self.num_classes = num_classes # Total number of classes (e.g., 10 for MNIST) -# self.embedding_dim = embedding_dim # Size of the vector that represents each class -# self.T = T # Number of denoising steps (number of DenoiseBlocks) -# self.eta = eta # A hyperparameter used in the loss function - -# # Create a list of T denoising blocks. Each block learns to reduce noise. -# self.blocks = nn.ModuleList([ -# DenoiseBlock(embedding_dim, num_classes,use_softmax,num_channels) for _ in range(T) -# ]) - -# # Learnable matrix that holds one vector (embedding) per class (e.g., 10 rows for 10 digits) -# self.W_embed = nn.Parameter(torch.randn(num_classes, embedding_dim) * 1.1, requires_grad=True) - -# # Final classifier layer to predict class label from embedding -# self.classifier = nn.Linear(embedding_dim, num_classes) - -# # --- Prepare cosine noise schedule for diffusion process --- - -# # t = [1, 2, ..., T] -# t = torch.arange(1, T+1, dtype=torch.float32) - -# # Calculate alpha_t using cosine schedule -# alpha_t = torch.cos(t / T * (math.pi/2))**2 - -# # alpha_bar is cumulative product of alpha_t, used to scale noise -# alpha_bar = torch.cumprod(alpha_t, dim=0) - -# # Calculate signal-to-noise ratio (SNR) -# snr = alpha_bar / (1 - alpha_bar + 1e-8) - -# # Previous SNR (shifted by one timestep) -# snr_prev = torch.cat([torch.tensor([0.], dtype=snr.dtype), snr[:-1]], dim=0) - -# # Difference in SNR between steps, used to weight denoising loss -# snr_diff = snr - snr_prev -# snr_diff = torch.clamp(snr_diff, min=1e-5) - - -# #---------------------------- -# # t = torch.arange(1, T + 1, dtype=torch.float32) -# # alpha_t = torch.cos(t / T * (math.pi / 2)) ** 2 -# # alpha_bar = torch.cumprod(alpha_t, dim=0) -# # # snr = alpha_bar / (1 - alpha_bar) -# # snr = alpha_bar / (1 - alpha_bar + 1e-8) -# # snr_prev = torch.cat([torch.tensor([0.], dtype=snr.dtype), snr[:-1]], dim=0) -# # snr_diff = snr - snr_prev -# # snr_diff = torch.clamp(snr_diff, min=1e-5) - -# #---------------------------- -# # Save alpha_bar and snr_diff inside the model so they move to GPU automatically -# self.register_buffer('alpha_bar', alpha_bar) -# self.register_buffer('snr_diff', snr_diff) - -# # Perform denoising at step t using DenoiseBlock[t] -# def forward_denoise(self, x, z_prev, t): -# return self.blocks[t](x, z_prev, self.W_embed)[0] - -# # Use final denoised vector to predict the class -# def classify(self, z): -# return self.classifier(z) - -# # Run all denoising steps in order to produce final prediction -# def inference(self, x): -# B = x.size(0) # Batch size -# # Start with random noise as initial z -# z = torch.randn(B, self.embedding_dim, device=x.device) - -# if not self.training: -# z = torch.zeros(B, self.embedding_dim, device=x.device) - -# # Pass through all denoising blocks one by one -# for t in range(self.T): -# z = self.forward_denoise(x, z, t) - -# # Use final denoised result to classify -# return self.classify(z) - -# def forward(self, x): -# return self.inference(x), None, None \ No newline at end of file + """ + Forward pass through the model. + + This method provides a standardized interface compatible with other + models in CTLearn. It wraps the inference method and returns outputs + in the expected tuple format. + + Args: + x (torch.Tensor): Input images with shape (batch_size, 1, H, W) + + Returns: + tuple: (classification, energy, direction) where: + - classification: Logits for particle type (batch_size, num_classes) + - energy: None (not used for classification) + - direction: None (not used for classification) + + Note: + The tuple format (classification, energy, direction) is maintained + for compatibility with multi-task training frameworks, even though + this model only performs classification. + + Example: + >>> model = NoPropDT(num_outputs=2) + >>> x = torch.randn(32, 1, 120, 120) + >>> cls, energy, direction = model(x) + >>> print(cls.shape) # torch.Size([32, 2]) + >>> print(energy) # None + >>> print(direction) # None + """ + return self.inference(x), None, None \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py b/ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py index 17ab8a65..0915cbc3 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDT/denoiseBlockThinRestNet.py @@ -1,64 +1,288 @@ -# Denoising block +""" +Denoising Block with Thin ResNet Architecture Module + +This module implements a denoising block architecture combining residual networks +with denoising capabilities for diffusion-based models. It's specifically designed +for the NoProp-DT (No-Propagation Denoising Transformer) model in CTLearn. + +The architecture uses a lightweight ResNet for feature extraction from images, +combined with MLP layers for processing noisy embeddings, making it suitable +for progressive denoising in diffusion models. + +Classes: + AdaptiveBatchNorm2d: Adaptive batch normalization with learnable parameters + ResidualBlock: Residual block with batch normalization and skip connections + DenoiseBlock: Main denoising block combining image and embedding features + MemoryEfficientSwish: Memory-efficient Swish activation function + +References: + - "Deep Residual Learning for Image Recognition" (He et al., CVPR 2016) + - "Denoising Diffusion Probabilistic Models" (Ho et al., NeurIPS 2020) +""" + import torch from torch import nn import torch.nn.functional as F +class MemoryEfficientSwish(nn.Module): + """ + Memory-efficient implementation of Swish activation function. + + Swish (also known as SiLU - Sigmoid Linear Unit) is defined as: + f(x) = x · sigmoid(x) + + This implementation avoids storing intermediate activations during + forward pass, reducing memory consumption during backpropagation. + + Properties: + - Smooth and continuously differentiable + - Non-monotonic (can decrease for negative inputs) + - Self-gated: combines input with its sigmoid + - Better gradient flow than ReLU in some cases + """ + + def forward(self, x): + """ + Apply Swish activation element-wise. + + Args: + x (torch.Tensor): Input tensor of any shape + + Returns: + torch.Tensor: Activated tensor with same shape as input + """ + return x * torch.sigmoid(x) + class AdaptiveBatchNorm2d(nn.Module): + """ + Adaptive Batch Normalization with learnable interpolation parameters. + + This layer combines the input with batch-normalized input using learnable + parameters 'a' and 'b'. This allows the network to learn how much to rely + on the original input versus the normalized version. + + Mathematical Formula: + output = a * x + b * BatchNorm(x) + + where a and b are learnable scalar parameters initialized to 1 and 0 + respectively, so initially: output = x (identity mapping). + + Attributes: + bn (nn.BatchNorm2d): Standard batch normalization layer + a (nn.Parameter): Learnable weight for original input (initialized to 1) + b (nn.Parameter): Learnable weight for normalized input (initialized to 0) + + Benefits: + - Allows network to choose between normalized and unnormalized features + - Can help with training stability in diffusion models + - Provides more flexibility than standard batch normalization + """ + def __init__(self, num_features, eps=1e-5, momentum=0.5, affine=True): + """ + Initialize Adaptive Batch Normalization. + + Args: + num_features (int): Number of channels in the input + eps (float, optional): Small value for numerical stability. Defaults to 1e-5 + momentum (float, optional): Momentum for running statistics. Defaults to 0.5 + Note: Higher momentum (0.5 vs typical 0.1) gives more weight to current batch + affine (bool, optional): Whether to learn affine parameters. Defaults to True + """ super(AdaptiveBatchNorm2d, self).__init__() + + # Standard batch normalization self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine) - # self.a = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) - # self.b = nn.Parameter(torch.FloatTensor(1, 1, 1, 1)) + + # Learnable interpolation parameters + # a=1, b=0 initially makes this an identity mapping self.a = nn.Parameter(torch.ones(1, 1, 1, 1)) self.b = nn.Parameter(torch.zeros(1, 1, 1, 1)) def forward(self, x): + """ + Apply adaptive batch normalization. + + Process: + 1. Apply standard batch normalization to input + 2. Combine original input and normalized input using learnable weights + + Args: + x (torch.Tensor): Input tensor with shape (batch_size, channels, height, width) + + Returns: + torch.Tensor: Adaptively normalized tensor with same shape as input + output = a * x + b * BatchNorm(x) + """ return self.a * x + self.b * self.bn(x) class ResidualBlock(nn.Module): + """ + Residual block with adaptive batch normalization and skip connections. + + This block implements the core building block of ResNet architectures, + with two convolutional layers, adaptive batch normalization, and a + residual skip connection. If input and output channels differ, a 1x1 + convolution adjusts the skip connection. + + Architecture: + Input → [Conv3x3 → AdaptiveBN → ReLU → Conv3x3 → AdaptiveBN] → (+) → ReLU → Output + ↑ + | + Skip Connection + (1x1 conv if needed) + + Attributes: + conv_block (nn.Sequential): Main convolutional path with two conv layers + shortcut (nn.Sequential): Skip connection (identity or 1x1 conv) + relu (nn.ReLU): Final activation function + """ + def __init__(self, in_channels, out_channels): + """ + Initialize the residual block. + + Args: + in_channels (int): Number of input channels + out_channels (int): Number of output channels + """ super().__init__() + + # Main convolutional path self.conv_block = nn.Sequential( + # First convolution: maintains spatial dimensions with padding nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), AdaptiveBatchNorm2d(out_channels), nn.ReLU(), + # Second convolution: refines features nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), AdaptiveBatchNorm2d(out_channels) ) + # Skip connection: adjust channels if needed self.shortcut = nn.Sequential() if in_channels != out_channels: + # Use 1x1 convolution to match channel dimensions self.shortcut = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1), nn.BatchNorm2d(out_channels) ) + # Final activation after adding residual self.relu = nn.ReLU() def forward(self, x): + """ + Forward pass through the residual block. + + Process: + 1. Pass input through main convolutional path + 2. Add skip connection (possibly adjusted with 1x1 conv) + 3. Apply final ReLU activation + + Args: + x (torch.Tensor): Input tensor with shape (batch_size, in_channels, H, W) + + Returns: + torch.Tensor: Output tensor with shape (batch_size, out_channels, H, W) + + Benefits of Residual Connection: + - Helps gradient flow during backpropagation + - Allows learning identity mappings when beneficial + - Enables training of very deep networks + """ + # Main path + skip connection + activation return self.relu(self.conv_block(x) + self.shortcut(x)) class DenoiseBlock(nn.Module): + """ + Denoising block combining image features and noisy embeddings. + + This block is the core component of the NoProp-DT diffusion model. It processes + telescope images through a lightweight ResNet while simultaneously processing + noisy class embeddings through MLPs, then combines both to produce a denoised + embedding and class logits. + + Architecture Overview: + Image → ThinResNet → Image Features (256-dim) + ↘ + Noisy Embedding → MLP (with residual) → Embedding Features (256-dim) → Concat → MLP → Logits + ↓ + z_next = z_prev + logits @ W_embed + + The ThinResNet Path: + Input (1, H, W) → + ResBlock(1→32) → MaxPool → Dropout → + ResBlock(32→64) → MaxPool → Dropout → + ResBlock(64→128) → AdaptiveAvgPool → + Flatten → Linear(128→256) + + The Embedding Path (with residual): + z_prev (embedding_dim) → + Linear(→256) → BN → ReLU → h1 → + Linear(→256) → BN → ReLU → h2 → + Linear(→256) → BN → h3 → + z_feat = h3 + h1 (residual) + + The Fusion Path: + Concat(image_feat, z_feat) → + Linear(512→256) → BN → ReLU → + Linear(256→128) → BN → ReLU → + Linear(128→num_classes) → logits + + Attributes: + use_softmax (bool): Whether to apply softmax to logits + conv_path (nn.Sequential): CNN for image feature extraction + fc_z1, fc_z2, fc_z3 (nn.Linear): MLP layers for embedding processing + bn_z1, bn_z2, bn_z3 (nn.BatchNorm1d): Batch norms for embedding MLP + fc_f1, fc_f2 (nn.Linear): MLP layers for fusing features + bn_f1, bn_f2 (nn.BatchNorm1d): Batch norms for fusion MLP + fc_out (nn.Linear): Output layer producing class logits + """ + def __init__(self, embedding_dim, num_classes, use_softmax=False, num_channels=1): + """ + Initialize the denoising block. + + Args: + embedding_dim (int): Dimension of class embeddings + Typical values: 128, 256 + num_classes (int): Number of output classes + For telescope data: 2 (gamma vs proton) + use_softmax (bool, optional): Whether to apply softmax to logits. + Defaults to False. Set to True for probability outputs + num_channels (int, optional): Number of input image channels. + Defaults to 1 (grayscale telescope images) + """ super().__init__() self.use_softmax = use_softmax - # ThinResNet convolutional path + + # ThinResNet convolutional path for image feature extraction + # Progressively increases channels: 1 → 32 → 64 → 128 + # Reduces spatial dimensions: H,W → H/2,W/2 → H/4,W/4 → 1,1 self.conv_path = nn.Sequential( + # Stage 1: Initial feature extraction ResidualBlock(num_channels, 32), - nn.MaxPool2d(2), - nn.Dropout(0.2), + nn.MaxPool2d(2), # Downsample by 2x + nn.Dropout(0.2), # Regularization + + # Stage 2: Mid-level features ResidualBlock(32, 64), - nn.MaxPool2d(2), + nn.MaxPool2d(2), # Downsample by 2x nn.Dropout(0.2), + + # Stage 3: High-level features ResidualBlock(64, 128), - nn.AdaptiveAvgPool2d((1, 1)), - nn.Flatten(), - nn.Linear(128, 256), - # nn.BatchNorm1d(256) + + # Global pooling and projection + nn.AdaptiveAvgPool2d((1, 1)), # Reduce to (batch, 128, 1, 1) + nn.Flatten(), # (batch, 128) + nn.Linear(128, 256), # Project to 256-dim ) - # Fully connected layers for processing noisy embedding vector z_prev + # MLP for processing noisy embedding with residual connection + # Three-layer network with skip connection from first to last layer self.fc_z1 = nn.Linear(embedding_dim, 256) self.bn_z1 = nn.BatchNorm1d(256) @@ -68,45 +292,111 @@ def __init__(self, embedding_dim, num_classes, use_softmax=False, num_channels=1 self.fc_z3 = nn.Linear(256, 256) self.bn_z3 = nn.BatchNorm1d(256) - # Layers to combine image and embedding features - self.fc_f1 = nn.Linear(256 + 256, 256) + # MLP for combining image and embedding features + # Fuses 512-dim (256 + 256) down to num_classes + self.fc_f1 = nn.Linear(256 + 256, 256) # Concatenated features self.bn_f1 = nn.BatchNorm1d(256) + self.fc_f2 = nn.Linear(256, 128) self.bn_f2 = nn.BatchNorm1d(128) - self.fc_out = nn.Linear(128, num_classes) + + self.fc_out = nn.Linear(128, num_classes) # Final logits def forward(self, x, z_prev, W_embed): - # Extract features from the input image x + """ + Forward pass through the denoising block. + + This method combines image features with noisy embeddings to produce + both denoised embeddings and class predictions. The denoising happens + through the interaction between image features and the noisy embedding. + + Process: + 1. Extract features from image using ThinResNet + 2. Process noisy embedding through MLP with residual connection + 3. Concatenate image and embedding features + 4. Generate class logits through fusion MLP + 5. Update embedding: z_next = z_prev + logits @ W_embed + + Args: + x (torch.Tensor): Input images + Shape: (batch_size, num_channels, height, width) + Typically: (batch_size, 1, 120, 120) for telescope images + + z_prev (torch.Tensor): Previous/current noisy embedding + Shape: (batch_size, embedding_dim) + Contains noise that will be reduced in this step + + W_embed (torch.Tensor): Class embedding matrix + Shape: (num_classes, embedding_dim) + Each row is the embedding for one class + + Returns: + tuple: (z_next, logits) where: + - z_next (torch.Tensor): Denoised embedding + Shape: (batch_size, embedding_dim) + Cleaner than z_prev, closer to true class embedding + - logits (torch.Tensor): Class predictions + Shape: (batch_size, num_classes) + If use_softmax=True: softmax probabilities + If use_softmax=False: raw logits + + Denoising Mechanism: + The update rule z_next = z_prev + logits @ W_embed works by: + 1. logits indicate which class is most likely + 2. W_embed provides the ideal embedding for each class + 3. The product logits @ W_embed pulls z_prev toward the correct class embedding + 4. Over multiple diffusion steps, this progressively cleans the embedding + + Example: + >>> block = DenoiseBlock(embedding_dim=128, num_classes=2) + >>> x = torch.randn(32, 1, 120, 120) # Batch of 32 images + >>> z_prev = torch.randn(32, 128) # Noisy embeddings + >>> W_embed = torch.randn(2, 128) # Class embeddings + >>> z_next, logits = block(x, z_prev, W_embed) + >>> print(z_next.shape) # torch.Size([32, 128]) + >>> print(logits.shape) # torch.Size([32, 2]) + """ + # Step 1: Extract features from input image + # Shape: (batch_size, 256) x_feat = self.conv_path(x) - # Process the noisy class embedding z_prev - h1 = F.relu(self.bn_z1(self.fc_z1(z_prev))) - h2 = F.relu(self.bn_z2(self.fc_z2(h1))) - h3 = self.bn_z3(self.fc_z3(h2)) + # Step 2: Process noisy embedding through MLP with residual connection + # First layer + h1 = F.relu(self.bn_z1(self.fc_z1(z_prev))) # (batch_size, 256) + + # Second layer + h2 = F.relu(self.bn_z2(self.fc_z2(h1))) # (batch_size, 256) + + # Third layer (no ReLU here) + h3 = self.bn_z3(self.fc_z3(h2)) # (batch_size, 256) - z_feat = h3 + h1 # Residual connection + # Residual connection: add h1 to h3 + # This helps gradient flow and preserves early features + z_feat = h3 + h1 # (batch_size, 256) - # Concatenate image and embedding features - h_f = torch.cat([x_feat, z_feat], dim=1) + # Step 3: Concatenate image and embedding features + # Combines information from both modalities + h_f = torch.cat([x_feat, z_feat], dim=1) # (batch_size, 512) - # Process combined features through fully connected layers - h_f = F.relu(self.bn_f1(self.fc_f1(h_f))) - h_f = F.relu(self.bn_f2(self.fc_f2(h_f))) - # h_f =self.bn_f2(self.fc_f2(h_f)) - # h_f = F.relu(self.fc_f1(h_f)) - # h_f = F.relu(self.fc_f2(h_f)) + # Step 4: Process combined features through fusion MLP + # First fusion layer + h_f = F.relu(self.bn_f1(self.fc_f1(h_f))) # (batch_size, 256) + + # Second fusion layer + h_f = F.relu(self.bn_f2(self.fc_f2(h_f))) # (batch_size, 128) - # Compute logits for all classes - logits = self.fc_out(h_f) + # Step 5: Compute logits for all classes + logits = self.fc_out(h_f) # (batch_size, num_classes) - # Convert logits to probability distribution over classes + # Step 6: Optionally convert logits to probabilities if self.use_softmax: p = F.softmax(logits, dim=1) else: p = logits - # Compute the next denoised embedding - # z_next = p @ W_embed - z_next = z_prev + logits @ W_embed + # Step 7: Compute next denoised embedding + # Update rule: z_next = z_prev + logits @ W_embed + # This pulls z_prev toward the embedding of the predicted class + z_next = z_prev + logits @ W_embed # (batch_size, embedding_dim) return z_next, logits diff --git a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py index 9d27a287..1ee03800 100644 --- a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py +++ b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py @@ -1,27 +1,111 @@ +""" +Dual-Backbone Transformer Network Module + +This module implements a dual-backbone architecture combining Convolutional Neural Networks +with Transformer encoder blocks for processing Cherenkov telescope data. The architecture +uses bottleneck transformer blocks that integrate self-attention mechanisms into a +convolutional backbone, providing both local feature extraction and global context modeling. + +The dual-backbone design allows independent processing of charge and timing information +from telescope cameras before fusion for final predictions. + +Classes: + BottleneckTransformerBlock: Bottleneck block with transformer encoder layer + TransformerDBB: Dual-backbone transformer network + TransformerDuo: Factory function for creating TransformerDBB models + +References: + - "Attention Is All You Need" (Vaswani et al., NeurIPS 2017) + - "BoTNet: Bottleneck Transformers for Visual Recognition" (Srinivas et al., CVPR 2021) +""" + import torch import torch.nn as nn import torch.nn.functional as F class BottleneckTransformerBlock(nn.Module): - + """ + Bottleneck block with integrated transformer encoder layer. + + This block combines the efficiency of bottleneck architectures with the + global receptive field of transformers. It uses dimensionality reduction + before applying self-attention, making it computationally efficient while + still capturing long-range dependencies. + + Architecture: + Input → Conv1x1(reduce) → Norm → ReLU → + Transformer(self-attention) → + Conv1x1(expand) → Norm → (+) Shortcut → ReLU → Output + + The transformer block processes spatial features as a sequence, allowing + each position to attend to all other positions, capturing global context + that convolutional layers might miss. + + Attributes: + expansion (int): Channel expansion factor (set to 1) + conv1 (nn.Conv2d): 1x1 conv for channel reduction + norm1 (nn.BatchNorm2d or nn.GroupNorm): Normalization after reduction + transformer_block (nn.TransformerEncoderLayer): Self-attention layer + conv2 (nn.Conv2d): 1x1 conv for channel expansion + norm2 (nn.BatchNorm2d or nn.GroupNorm): Normalization after expansion + shortcut (nn.Sequential): Skip connection (identity or 1x1 conv) + """ + expansion = 1 def __init__(self, in_channels, out_channels, stride=1, reduction=4, use_gn=False): + """ + Initialize the bottleneck transformer block. + + Args: + in_channels (int): Number of input channels + out_channels (int): Number of output channels + stride (int, optional): Stride for the shortcut connection. Defaults to 1 + If stride > 1, downsampling is applied in the shortcut + reduction (int, optional): Channel reduction factor. Defaults to 4 + The intermediate dimension is out_channels // reduction + Higher reduction = fewer parameters but may lose information + use_gn (bool, optional): Whether to use GroupNorm instead of BatchNorm. + Defaults to False + GroupNorm is more stable for small batch sizes + """ super(BottleneckTransformerBlock, self).__init__() - self.conv1 = nn.Conv2d(in_channels, out_channels // reduction, kernel_size=1, stride=1, bias=False) + + # Channel reduction: compress features before transformer + # Reduces computational cost of self-attention + self.conv1 = nn.Conv2d( + in_channels, + out_channels // reduction, + kernel_size=1, + stride=1, + bias=False + ) if use_gn: self.norm1 = nn.GroupNorm(32, out_channels // reduction) else: self.norm1 = nn.BatchNorm2d(out_channels // reduction) - self.transformer_block = nn.TransformerEncoderLayer(d_model=out_channels // reduction, nhead=8) + # Transformer encoder layer for global context + # Uses multi-head self-attention with 8 heads + self.transformer_block = nn.TransformerEncoderLayer( + d_model=out_channels // reduction, + nhead=8 + ) - self.conv2 = nn.Conv2d(out_channels // reduction, out_channels, kernel_size=1, stride=1, bias=False) + # Channel expansion: restore original channel dimension + self.conv2 = nn.Conv2d( + out_channels // reduction, + out_channels, + kernel_size=1, + stride=1, + bias=False + ) if use_gn: self.norm2 = nn.GroupNorm(32, out_channels) else: self.norm2 = nn.BatchNorm2d(out_channels) + # Skip connection: adjust channels/spatial dims if needed self.shortcut = nn.Sequential() if stride != 1 or in_channels != out_channels: self.shortcut = nn.Sequential( @@ -30,99 +114,292 @@ def __init__(self, in_channels, out_channels, stride=1, reduction=4, use_gn=Fals ) def forward(self, x): - out = F.relu(self.norm1(self.conv1(x))) + """ + Forward pass through the bottleneck transformer block. + + Process: + 1. Reduce channels with 1x1 convolution + 2. Normalize and activate + 3. Reshape for transformer (flatten spatial dimensions) + 4. Apply self-attention via transformer encoder + 5. Reshape back to spatial format + 6. Expand channels with 1x1 convolution + 7. Add skip connection + 8. Final ReLU activation + + Args: + x (torch.Tensor): Input tensor with shape (batch_size, in_channels, H, W) + + Returns: + torch.Tensor: Output tensor with shape (batch_size, out_channels, H, W) + + Transformer Processing: + The spatial dimensions (H, W) are flattened into a sequence of length H*W, + where each position can attend to all other positions. This allows the + model to capture long-range dependencies that convolutional layers miss. + """ + # Channel reduction + out = F.relu(self.norm1(self.conv1(x))) # (B, C_reduced, H, W) + + # Prepare for transformer: (B, C, H, W) → (H*W, B, C) b, c, h, w = out.size() - out = out.view(b, c, -1).permute(2, 0, 1) # Prepare for transformer - out = self.transformer_block(out) + out = out.view(b, c, -1).permute(2, 0, 1) # Flatten spatial dims + + # Self-attention: each position attends to all positions + out = self.transformer_block(out) # (H*W, B, C) + + # Reshape back to spatial format: (H*W, B, C) → (B, C, H, W) out = out.permute(1, 2, 0).view(b, c, h, w) - out = self.norm2(self.conv2(out)) + + # Channel expansion + out = self.norm2(self.conv2(out)) # (B, out_channels, H, W) + + # Add residual connection out += self.shortcut(x) + + # Final activation out = F.relu(out) return out class TransformerDBB(nn.Module): + """ + Dual-Backbone Transformer Network for multi-modal telescope data. + + This architecture uses two independent transformer-augmented backbones to process + different input modalities (charge and timing images) before fusing features for + final prediction. Each backbone combines convolutional layers with transformer + blocks to capture both local patterns and global context. + + Architecture Overview: + Input_1 (charge) → Backbone_1 (Conv + Transformer) → Features_1 + ↓ + Fusion + ↓ + Input_2 (timing) → Backbone_2 (Conv + Transformer) → Features_2 + ↓ + Global Pool → FC → Output + + Each Backbone: + Conv7x7 → Norm → ReLU → + Layer1 (Transformer blocks) → + Layer2 (Transformer blocks, stride=2) → + Layer3 (Transformer blocks, stride=2) → + Layer4 (Transformer blocks, stride=2) → + Adaptive AvgPool + + Attributes: + in_channels (int): Current number of channels (updated during layer construction) + use_gn (bool): Whether to use GroupNorm instead of BatchNorm + use_concat (bool): Whether to concatenate or add backbone outputs + conv1_a, conv1_b (nn.Conv2d): Initial convolutions for each backbone + norm1_a, norm1_b (nn.Module): Initial normalization for each backbone + layer1-4_a, layer1-4_b (nn.Sequential): Transformer block layers for each backbone + dropout (nn.Dropout): Dropout for regularization + adaptive_pool (nn.AdaptiveAvgPool2d): Global average pooling + fc (nn.Linear): Final classification/regression layer + """ + def __init__(self, block, layers, num_inputs=1, num_classes=1, use_gn=False, use_concat=False, dropout_rate=0.5): + """ + Initialize the dual-backbone transformer network. + + Args: + block: Block class to use (e.g., BottleneckTransformerBlock) + layers (list): Number of blocks in each layer [layer1, layer2, layer3, layer4] + Example: [3, 4, 6, 3] for a ResNet50-like architecture + num_inputs (int, optional): Number of input channels per backbone. Defaults to 1 + num_classes (int, optional): Number of output classes/values. Defaults to 1 + use_gn (bool, optional): Whether to use GroupNorm. Defaults to False + use_concat (bool, optional): Whether to concatenate backbone outputs. + Defaults to False (uses addition) + dropout_rate (float, optional): Dropout probability. Defaults to 0.5 + """ super(TransformerDBB, self).__init__() self.in_channels = 64 self.use_gn = use_gn self.use_concat = use_concat - # Backbone 1 + # Backbone 1: Process first input modality (charge images) self.conv1_a = nn.Conv2d(num_inputs, 64, kernel_size=7, stride=2, padding=3, bias=False) if use_gn: self.norm1_a = nn.GroupNorm(32, 64) else: self.norm1_a = nn.BatchNorm2d(64) + # Transformer block layers for backbone 1 self.layer1_a = self._make_layer(block, 64, layers[0], stride=1) self.layer2_a = self._make_layer(block, 128, layers[1], stride=2) self.layer3_a = self._make_layer(block, 256, layers[2], stride=2) self.layer4_a = self._make_layer(block, 512, layers[3], stride=2) - # Backbone 2 + # Reset in_channels for second backbone + self.in_channels = 64 + + # Backbone 2: Process second input modality (timing images) self.conv1_b = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) if use_gn: self.norm1_b = nn.GroupNorm(32, 64) else: self.norm1_b = nn.BatchNorm2d(64) + # Transformer block layers for backbone 2 self.layer1_b = self._make_layer(block, 64, layers[0], stride=1) self.layer2_b = self._make_layer(block, 128, layers[1], stride=2) self.layer3_b = self._make_layer(block, 256, layers[2], stride=2) self.layer4_b = self._make_layer(block, 512, layers[3], stride=2) - # Dropout layer + # Regularization self.dropout = nn.Dropout(dropout_rate) - # Fully connected layer + # Global pooling and final layer self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) + # Final FC layer dimension depends on fusion strategy if self.use_concat: self.fc = nn.Linear(512 * block.expansion * 2, num_classes) else: self.fc = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, out_channels, blocks, stride): + """ + Create a layer consisting of multiple transformer blocks. + + Args: + block: Block class (BottleneckTransformerBlock) + out_channels (int): Number of output channels + blocks (int): Number of blocks in this layer + stride (int): Stride for the first block (for downsampling) + + Returns: + nn.Sequential: Sequential container of transformer blocks + """ layers = [] + # First block may have stride > 1 for downsampling layers.append(block(self.in_channels, out_channels, stride, use_gn=self.use_gn)) self.in_channels = out_channels + # Remaining blocks maintain spatial dimensions for _ in range(1, blocks): layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) return nn.Sequential(*layers) def forward(self, x1, x2): - # Backbone 1 - out1 = F.relu(self.norm1_a(self.conv1_a(x1))) - out1 = self.layer1_a(out1) + """ + Forward pass through the dual-backbone transformer network. + + Process: + 1. Process each input through its backbone (Conv + Transformer layers) + 2. Apply global average pooling to each backbone's output + 3. Flatten spatial dimensions + 4. Apply dropout + 5. Fuse features (concatenate or add) + 6. Final classification/regression layer + + Args: + x1 (torch.Tensor): First input (charge images) + Shape: (batch_size, num_inputs, H, W) + x2 (torch.Tensor): Second input (timing images) + Shape: (batch_size, 1, H, W) + + Returns: + torch.Tensor: Output predictions + Shape: (batch_size, num_classes) + + Feature Fusion: + Concatenation (use_concat=True): + - Preserves all information from both backbones + - Doubles feature dimension + - More parameters in final layer + + Addition (use_concat=False): + - Forces features into shared space + - Fewer parameters + - May lose complementary information + """ + # Backbone 1: Process charge images + out1 = F.relu(self.norm1_a(self.conv1_a(x1))) # Initial conv + out1 = self.layer1_a(out1) # Transformer blocks out1 = self.layer2_a(out1) out1 = self.layer3_a(out1) out1 = self.layer4_a(out1) - out1 = self.adaptive_pool(out1) - out1 = out1.view(out1.size(0), -1) - out1 = self.dropout(out1) + out1 = self.adaptive_pool(out1) # Global pooling + out1 = out1.view(out1.size(0), -1) # Flatten + out1 = self.dropout(out1) # Regularization - # Backbone 2 - out2 = F.relu(self.norm1_b(self.conv1_b(x2))) - out2 = self.layer1_b(out2) + # Backbone 2: Process timing images + out2 = F.relu(self.norm1_b(self.conv1_b(x2))) # Initial conv + out2 = self.layer1_b(out2) # Transformer blocks out2 = self.layer2_b(out2) out2 = self.layer3_b(out2) out2 = self.layer4_b(out2) - out2 = self.adaptive_pool(out2) - out2 = out2.view(out2.size(0), -1) - out2 = self.dropout(out2) + out2 = self.adaptive_pool(out2) # Global pooling + out2 = out2.view(out2.size(0), -1) # Flatten + out2 = self.dropout(out2) # Regularization - # Combine outputs + # Fusion: Combine features from both backbones if self.use_concat: - out = torch.cat((out1, out2), dim=1) + out = torch.cat((out1, out2), dim=1) # Concatenate else: - out = out1 + out2 + out = out1 + out2 # Element-wise addition + # Final prediction out = self.fc(out) return out -def TransformerDuo(num_blocks=[3, 4, 6, 3], num_inputs=1, num_classes=2,use_gn=True, use_concat=False, dropout_rate=0.5): - # Here we configure fewer blocks for a lighter model - return TransformerDBB(BottleneckTransformerBlock, num_blocks,num_inputs, num_classes=num_classes, use_gn=use_gn, use_concat=use_concat, dropout_rate=dropout_rate) - -# Instancia del modelo -# bottleneck_transformer_duo = TransformerDuo(BottleneckTransformerBlock, [3, 4, 6, 3], num_classes=1, use_gn=True, use_concat=False, dropout_rate=0.5) \ No newline at end of file +def TransformerDuo(num_blocks=[3, 4, 6, 3], num_inputs=1, num_classes=2, use_gn=True, use_concat=False, dropout_rate=0.5): + """ + Factory function to create a Dual-Backbone Transformer network. + + This function instantiates a TransformerDBB model with the specified configuration. + It provides a convenient interface for creating transformer-based models with + different depths and configurations. + + Args: + num_blocks (list, optional): Number of blocks in each layer. Defaults to [3, 4, 6, 3] + Similar to ResNet50 architecture: + - Layer 1: 3 blocks (64 channels) + - Layer 2: 4 blocks (128 channels, stride=2) + - Layer 3: 6 blocks (256 channels, stride=2) + - Layer 4: 3 blocks (512 channels, stride=2) + num_inputs (int, optional): Number of input channels. Defaults to 1 + num_classes (int, optional): Number of output classes/values. Defaults to 2 + use_gn (bool, optional): Whether to use GroupNorm. Defaults to True + Recommended for small batch sizes or distributed training + use_concat (bool, optional): Whether to concatenate backbone outputs. + Defaults to False + dropout_rate (float, optional): Dropout probability. Defaults to 0.5 + + Returns: + TransformerDBB: Instantiated dual-backbone transformer model + + Example: + >>> # Create model for binary classification + >>> model = TransformerDuo( + ... num_blocks=[3, 4, 6, 3], + ... num_inputs=1, + ... num_classes=2, + ... use_gn=True, + ... use_concat=False, + ... dropout_rate=0.5 + ... ) + >>> + >>> # Forward pass + >>> x1 = torch.randn(8, 1, 120, 120) # Charge images + >>> x2 = torch.randn(8, 1, 120, 120) # Timing images + >>> output = model(x1, x2) + >>> print(output.shape) # torch.Size([8, 2]) + + Notes: + - The default configuration creates a lighter model than standard ResNet50 + - Transformer blocks add global context modeling at each layer + - GroupNorm is more stable than BatchNorm for small batches + - Higher dropout rates help prevent overfitting with transformers + """ + return TransformerDBB( + BottleneckTransformerBlock, + num_blocks, + num_inputs, + num_classes=num_classes, + use_gn=use_gn, + use_concat=use_concat, + dropout_rate=dropout_rate + ) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py index 8d55b134..cfb5ec9a 100644 --- a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py +++ b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py @@ -1,26 +1,101 @@ +""" +Simplified Dual-Input Transformer Network Module + +This module implements a simplified dual-input transformer architecture that processes +two modalities (charge and timing) by concatenating them at the input level rather than +using separate backbones. This approach is more memory-efficient and has fewer parameters +than the dual-backbone variant. + +The architecture combines convolutional layers with transformer encoder blocks, providing +both local feature extraction and global context modeling through self-attention. + +Classes: + BottleneckTransformerBlock: Bottleneck block with transformer encoder layer + TransformerDBB: Single-backbone transformer for dual-input data + TransformerDuo: Factory function for creating TransformerDBB models + +References: + - "Attention Is All You Need" (Vaswani et al., NeurIPS 2017) + - "BoTNet: Bottleneck Transformers for Visual Recognition" (Srinivas et al., CVPR 2021) +""" + import torch import torch.nn as nn import torch.nn.functional as F class BottleneckTransformerBlock(nn.Module): + """ + Bottleneck block with integrated transformer encoder layer. + + This block combines the efficiency of bottleneck architectures with the + global receptive field of transformers. It uses dimensionality reduction + before applying self-attention, making it computationally efficient while + still capturing long-range dependencies. + + Architecture: + Input → Conv1x1(reduce) → Norm → ReLU → + Transformer(self-attention) → + Conv1x1(expand) → Norm → (+) Shortcut → ReLU → Output + + Attributes: + expansion (int): Channel expansion factor (set to 1) + conv1 (nn.Conv2d): 1x1 conv for channel reduction + norm1 (nn.BatchNorm2d or nn.GroupNorm): Normalization after reduction + transformer_block (nn.TransformerEncoderLayer): Self-attention layer + conv2 (nn.Conv2d): 1x1 conv for channel expansion + norm2 (nn.BatchNorm2d or nn.GroupNorm): Normalization after expansion + shortcut (nn.Sequential): Skip connection (identity or 1x1 conv) + """ + expansion = 1 def __init__(self, in_channels, out_channels, stride=1, reduction=4, use_gn=False): + """ + Initialize the bottleneck transformer block. + + Args: + in_channels (int): Number of input channels + out_channels (int): Number of output channels + stride (int, optional): Stride for the shortcut connection. Defaults to 1 + reduction (int, optional): Channel reduction factor. Defaults to 4 + use_gn (bool, optional): Whether to use GroupNorm instead of BatchNorm. + Defaults to False + """ super(BottleneckTransformerBlock, self).__init__() - self.conv1 = nn.Conv2d(in_channels, out_channels // reduction, kernel_size=1, stride=1, bias=False) + + # Channel reduction + self.conv1 = nn.Conv2d( + in_channels, + out_channels // reduction, + kernel_size=1, + stride=1, + bias=False + ) if use_gn: self.norm1 = nn.GroupNorm(32, out_channels // reduction) else: self.norm1 = nn.BatchNorm2d(out_channels // reduction) - self.transformer_block = nn.TransformerEncoderLayer(d_model=out_channels // reduction, nhead=8) + # Transformer encoder with multi-head self-attention + self.transformer_block = nn.TransformerEncoderLayer( + d_model=out_channels // reduction, + nhead=8 + ) - self.conv2 = nn.Conv2d(out_channels // reduction, out_channels, kernel_size=1, stride=1, bias=False) + # Channel expansion + self.conv2 = nn.Conv2d( + out_channels // reduction, + out_channels, + kernel_size=1, + stride=1, + bias=False + ) if use_gn: self.norm2 = nn.GroupNorm(32, out_channels) else: self.norm2 = nn.BatchNorm2d(out_channels) + # Skip connection self.shortcut = nn.Sequential() if stride != 1 or in_channels != out_channels: self.shortcut = nn.Sequential( @@ -29,68 +104,247 @@ def __init__(self, in_channels, out_channels, stride=1, reduction=4, use_gn=Fals ) def forward(self, x): + """ + Forward pass through the bottleneck transformer block. + + Args: + x (torch.Tensor): Input tensor with shape (batch_size, in_channels, H, W) + + Returns: + torch.Tensor: Output tensor with shape (batch_size, out_channels, H, W) + """ + # Reduce channels out = F.relu(self.norm1(self.conv1(x))) + + # Prepare for transformer: (B, C, H, W) → (H*W, B, C) b, c, h, w = out.size() - out = out.view(b, c, -1).permute(2, 0, 1) # Prepare for transformer + out = out.view(b, c, -1).permute(2, 0, 1) + + # Apply self-attention out = self.transformer_block(out) + + # Reshape back: (H*W, B, C) → (B, C, H, W) out = out.permute(1, 2, 0).view(b, c, h, w) + + # Expand channels out = self.norm2(self.conv2(out)) + + # Add residual connection out += self.shortcut(x) + + # Final activation out = F.relu(out) return out class TransformerDBB(nn.Module): + """ + Simplified single-backbone transformer for dual-input data. + + This architecture concatenates charge and timing inputs at the channel level + before processing through a single transformer-augmented backbone. This approach + is more parameter-efficient than dual-backbone architectures while still allowing + the model to learn joint representations of both modalities. + + Architecture Overview: + Input_1 (charge) ↘ + → Concatenate → Single Backbone (Conv + Transformer) → Output + Input_2 (timing) ↗ + + Single Backbone: + Concat(x1, x2) → Conv7x7 → Norm → ReLU → + Layer1 (Transformer blocks) → + Layer2 (Transformer blocks, stride=2) → + Layer3 (Transformer blocks, stride=2) → + Layer4 (Transformer blocks, stride=2) → + Adaptive AvgPool → Dropout → FC → Output + + Advantages over Dual-Backbone: + - ~50% fewer parameters (single backbone vs two) + - Lower memory consumption + - Faster training and inference + - Implicit early fusion of modalities + + Attributes: + in_channels (int): Current number of channels (updated during layer construction) + use_gn (bool): Whether to use GroupNorm instead of BatchNorm + conv1 (nn.Conv2d): Initial convolution + norm1 (nn.Module): Initial normalization + layer1-4 (nn.Sequential): Transformer block layers + dropout (nn.Dropout): Dropout for regularization + adaptive_pool (nn.AdaptiveAvgPool2d): Global average pooling + fc (nn.Linear): Final classification/regression layer + """ + def __init__(self, block, layers, num_inputs=2, num_classes=1, use_gn=False, dropout_rate=0.5): + """ + Initialize the simplified transformer network. + + Args: + block: Block class to use (BottleneckTransformerBlock) + layers (list): Number of blocks in each layer [layer1, layer2, layer3, layer4] + num_inputs (int, optional): Total number of input channels (charge + timing). + Defaults to 2. Should be sum of channels from both modalities. + num_classes (int, optional): Number of output classes/values. Defaults to 1 + use_gn (bool, optional): Whether to use GroupNorm. Defaults to False + dropout_rate (float, optional): Dropout probability. Defaults to 0.5 + + Note: + With num_inputs=2, the network expects concatenated inputs where: + - Channel 0: Charge information + - Channel 1: Timing information + """ super(TransformerDBB, self).__init__() self.in_channels = 64 self.use_gn = use_gn - # Backbone único + # Initial convolution: processes concatenated inputs + # Accepts num_inputs channels (e.g., 2 for charge + timing) self.conv1 = nn.Conv2d(num_inputs, 64, kernel_size=7, stride=2, padding=3, bias=False) if use_gn: self.norm1 = nn.GroupNorm(32, 64) else: self.norm1 = nn.BatchNorm2d(64) + # Transformer block layers self.layer1 = self._make_layer(block, 64, layers[0], stride=1) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) - # Dropout layer + # Regularization self.dropout = nn.Dropout(dropout_rate) - # Fully connected layer + # Global pooling and final layer self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, out_channels, blocks, stride): + """ + Create a layer consisting of multiple transformer blocks. + + Args: + block: Block class (BottleneckTransformerBlock) + out_channels (int): Number of output channels + blocks (int): Number of blocks in this layer + stride (int): Stride for the first block + + Returns: + nn.Sequential: Sequential container of transformer blocks + """ layers = [] + # First block may downsample layers.append(block(self.in_channels, out_channels, stride, use_gn=self.use_gn)) self.in_channels = out_channels + # Remaining blocks maintain dimensions for _ in range(1, blocks): layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) return nn.Sequential(*layers) def forward(self, x1, x2): - # Concatenar las entradas a lo largo del canal + """ + Forward pass through the simplified transformer network. + + Process: + 1. Concatenate inputs along channel dimension + 2. Process through single backbone (Conv + Transformer layers) + 3. Global average pooling + 4. Dropout for regularization + 5. Final classification/regression layer + + Args: + x1 (torch.Tensor): First input (charge images) + Shape: (batch_size, 1, H, W) + x2 (torch.Tensor): Second input (timing images) + Shape: (batch_size, 1, H, W) + + Returns: + torch.Tensor: Output predictions + Shape: (batch_size, num_classes) + + Input Fusion: + Early fusion via concatenation: x = [x1 | x2] + This allows the network to learn joint features from both modalities + from the very first layer, unlike dual-backbone approaches where + fusion happens later. + + Example: + >>> model = TransformerDBB(...) + >>> x1 = torch.randn(16, 1, 120, 120) # Charge + >>> x2 = torch.randn(16, 1, 120, 120) # Timing + >>> output = model(x1, x2) + >>> print(output.shape) # torch.Size([16, 1]) + """ + # Concatenate inputs along channel dimension + # x1: (B, 1, H, W), x2: (B, 1, H, W) → x: (B, 2, H, W) x = torch.cat((x1, x2), dim=1) - # Backbone único - out = F.relu(self.norm1(self.conv1(x))) - out = self.layer1(out) + # Process through single backbone + out = F.relu(self.norm1(self.conv1(x))) # Initial conv + out = self.layer1(out) # Transformer blocks out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) - out = self.adaptive_pool(out) - out = out.view(out.size(0), -1) - out = self.dropout(out) - out = self.fc(out) + + # Global pooling and prediction + out = self.adaptive_pool(out) # (B, 512, 1, 1) + out = out.view(out.size(0), -1) # (B, 512) + out = self.dropout(out) # Regularization + out = self.fc(out) # (B, num_classes) + return out def TransformerDuo(num_blocks=[3, 4, 6, 3], num_inputs=2, num_classes=2, use_gn=True, dropout_rate=0.3): - # Configurar un modelo más ligero - return TransformerDBB(BottleneckTransformerBlock, num_blocks, num_inputs=num_inputs, num_classes=num_classes, use_gn=use_gn, dropout_rate=dropout_rate) - -# Instancia del modelo -# bottleneck_transformer_duo = TransformerDuo(num_blocks=[3, 4, 6, 3], num_classes=1, use_gn=True, dropout_rate=0.3) \ No newline at end of file + """ + Factory function to create a simplified dual-input transformer network. + + This function instantiates a TransformerDBB model with the specified configuration. + It provides a convenient interface for creating transformer-based models that + process two input modalities through early fusion. + + Args: + num_blocks (list, optional): Number of blocks in each layer. Defaults to [3, 4, 6, 3] + Similar to ResNet50 architecture + num_inputs (int, optional): Total input channels. Defaults to 2 + Should equal the sum of channels from all input modalities + num_classes (int, optional): Number of output classes/values. Defaults to 2 + use_gn (bool, optional): Whether to use GroupNorm. Defaults to True + Recommended for stability with small batches + dropout_rate (float, optional): Dropout probability. Defaults to 0.3 + Lower than dual-backbone default (0.5) since single backbone + + Returns: + TransformerDBB: Instantiated simplified transformer model + + Example: + >>> # Create model for binary classification + >>> model = TransformerDuo( + ... num_blocks=[3, 4, 6, 3], + ... num_inputs=2, + ... num_classes=2, + ... use_gn=True, + ... dropout_rate=0.3 + ... ) + >>> + >>> # Forward pass + >>> x1 = torch.randn(8, 1, 120, 120) # Charge + >>> x2 = torch.randn(8, 1, 120, 120) # Timing + >>> output = model(x1, x2) + >>> print(output.shape) # torch.Size([8, 2]) + + Comparison with Dual-Backbone: + Simplified (this): + - Pros: Fewer parameters, faster, lower memory + - Cons: Less modality-specific feature learning + + Dual-Backbone: + - Pros: More modality-specific features, potentially higher accuracy + - Cons: More parameters, slower, higher memory + """ + return TransformerDBB( + BottleneckTransformerBlock, + num_blocks, + num_inputs=num_inputs, + num_classes=num_classes, + use_gn=use_gn, + dropout_rate=dropout_rate + ) \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/models/gcn/gcn.py b/ctlearn/core/pytorch/nets/models/gcn/gcn.py index 548f1154..45fd4c9d 100644 --- a/ctlearn/core/pytorch/nets/models/gcn/gcn.py +++ b/ctlearn/core/pytorch/nets/models/gcn/gcn.py @@ -1,56 +1,206 @@ +""" +Graph Convolutional Network (GCN) Module + +This module implements a Graph Convolutional Network for processing graph-structured +data from Cherenkov telescope observations. The GCN architecture is particularly useful +for analyzing the spatial relationships between triggered pixels in telescope cameras. + +The network uses graph convolutions to propagate information between neighboring pixels, +followed by graph pooling and classification/regression heads. + +Classes: + GCN: Graph Convolutional Network for telescope data analysis + +References: + - "Semi-Supervised Classification with Graph Convolutional Networks" (Kipf & Welling, ICLR 2017) + - PyTorch Geometric: https://pytorch-geometric.readthedocs.io/ +""" from torch_geometric.loader import DataLoader from torch_geometric.nn import GATConv import pickle import torch import torch.nn.functional as F -from torch_geometric.nn import GCNConv,GraphConv -from torch_geometric.nn import global_mean_pool,global_max_pool -from torch.nn import Linear,Softmax,PReLU +from torch_geometric.nn import GCNConv, GraphConv +from torch_geometric.nn import global_mean_pool, global_max_pool +from torch.nn import Linear, Softmax, PReLU class GCN(torch.nn.Module): - def __init__(self, hidden_channels,num_node_features=1, num_outputs=1): + """ + Graph Convolutional Network for telescope camera data. + + This network processes graph-structured data where nodes represent camera pixels + and edges connect neighboring pixels. The architecture consists of: + 1. Multiple graph convolutional layers to aggregate information + 2. Global pooling to obtain graph-level representation + 3. Fully connected layers for final prediction + + Graph Structure: + - Nodes: Camera pixels with features (e.g., charge, timing) + - Edges: Connections between neighboring pixels + - Graph: Complete telescope camera image + + Architecture: + Input Graph → GraphConv1 → PReLU → Dropout → + GraphConv2 → PReLU → Dropout → GraphConv3 → + Global Mean Pool → Linear → Linear → Output + + Attributes: + conv0 (GCNConv): Initial graph convolution (currently unused) + conv1 (GraphConv): First graph convolutional layer + conv2 (GraphConv): Second graph convolutional layer + conv3 (GraphConv): Third graph convolutional layer + lin_0 (Linear): First fully connected layer + lin_1 (Linear): Second fully connected layer (output) + prelu_1 (PReLU): Parametric ReLU activation for conv1 + prelu_2 (PReLU): Parametric ReLU activation for conv2 + """ + + def __init__(self, hidden_channels, num_node_features=1, num_outputs=1): + """ + Initialize the Graph Convolutional Network. + + Args: + hidden_channels (int): Number of hidden features in graph conv layers + Typical values: 64, 128, 256 + Higher values allow more complex representations but increase computation + + num_node_features (int, optional): Number of features per node (pixel). + Defaults to 1 (e.g., charge only) + Can be 2 for charge + timing, or more for additional features + + num_outputs (int, optional): Number of output values. Defaults to 1 + For classification: 2 (gamma vs proton) + For regression: 1 (energy or direction component) + + Network Design Choices: + - GraphConv vs GCNConv: GraphConv is more general, supports edge features + - Bias enabled: Helps with different graph sizes and structures + - 3 conv layers: Balances receptive field vs oversmoothing + - PReLU: Learnable activation that can adapt to data + - Dropout (p=0.3): Regularization to prevent overfitting + + Example: + >>> # Create GCN for binary classification + >>> model = GCN(hidden_channels=128, num_node_features=2, num_outputs=2) + >>> + >>> # Create GCN for energy regression + >>> model = GCN(hidden_channels=64, num_node_features=1, num_outputs=1) + """ super(GCN, self).__init__() + + # Set random seed for reproducibility torch.manual_seed(12345) - # self.conv1 = GCNConv(dataset.num_node_features, hidden_channels) - # self.conv2 = GCNConv(hidden_channels, hidden_channels) - # self.conv3 = GCNConv(hidden_channels, hidden_channels) - use_bias = True - self.conv0 = GCNConv(num_node_features, hidden_channels,bias=use_bias) - self.conv1 = GraphConv(num_node_features, hidden_channels,bias=use_bias) - self.conv2 = GraphConv(hidden_channels, hidden_channels,bias=use_bias) - self.conv3 = GraphConv(hidden_channels, hidden_channels,bias=use_bias) - + # Graph convolutional layers + use_bias = True # Enable bias for better expressiveness + + # Initial convolution (currently unused, kept for potential skip connections) + self.conv0 = GCNConv(num_node_features, hidden_channels, bias=use_bias) + + # First graph convolution: node features → hidden_channels + self.conv1 = GraphConv(num_node_features, hidden_channels, bias=use_bias) + # Second graph convolution: hidden_channels → hidden_channels + # Aggregates information from 2-hop neighbors + self.conv2 = GraphConv(hidden_channels, hidden_channels, bias=use_bias) + + # Third graph convolution: hidden_channels → hidden_channels + # Aggregates information from 3-hop neighbors + self.conv3 = GraphConv(hidden_channels, hidden_channels, bias=use_bias) + + # Fully connected layers for prediction + # First FC: Processes pooled graph representation self.lin_0 = Linear(hidden_channels, hidden_channels) + + # Output FC: Maps to final predictions self.lin_1 = Linear(hidden_channels, num_outputs) - self.prelu_1 = PReLU() - self.prelu_2 = PReLU() + + # Parametric ReLU activations (learnable negative slope) + self.prelu_1 = PReLU() # After conv1 + self.prelu_2 = PReLU() # After conv2 + def forward(self, x, edge_index, batch): + """ + Forward pass through the Graph Convolutional Network. + + Process: + 1. Apply graph convolutions to propagate information between neighbors + 2. Use PReLU activation and dropout after each conv layer + 3. Perform global pooling to get graph-level representation + 4. Apply fully connected layers for final prediction + + Args: + x (torch.Tensor): Node feature matrix with shape (num_nodes, num_node_features) + Each row represents features of one pixel in the camera + Example: For 1000 triggered pixels with 2 features each: (1000, 2) + + edge_index (torch.Tensor): Graph connectivity in COO format + Shape: (2, num_edges) + edge_index[0]: Source nodes + edge_index[1]: Target nodes + Example: [[0, 1, 1], [1, 0, 2]] represents edges 0→1, 1→0, 1→2 + + batch (torch.Tensor): Batch vector which assigns each node to a graph + Shape: (num_nodes,) + Example: [0, 0, 0, 1, 1, 2] for 3 graphs with 3, 2, and 1 nodes + + Returns: + torch.Tensor: Output predictions with shape (batch_size, num_outputs) + For classification: logits before softmax + For regression: predicted values + + Graph Convolution Process: + Each conv layer aggregates information from neighbors: + h_i^(l+1) = σ(Σ_{j∈N(i)} (h_j^(l) · W^(l)) / √(d_i · d_j)) + where: + - h_i: features of node i + - N(i): neighbors of node i + - W: learnable weight matrix + - d_i: degree of node i + - σ: activation function (PReLU) + + Example: + >>> # Process a batch of 3 graphs + >>> x = torch.randn(100, 2) # 100 total pixels, 2 features each + >>> edge_index = torch.randint(0, 100, (2, 300)) # 300 edges + >>> batch = torch.tensor([0]*30 + [1]*40 + [2]*30) # 3 graphs + >>> + >>> output = model(x, edge_index, batch) + >>> print(output.shape) # torch.Size([3, 1]) for 3 graphs, 1 output each + """ + # 1. Obtain node embeddings through graph convolutions + + # First convolution: Extract local patterns + x = self.conv1(x, edge_index) # (num_nodes, hidden_channels) + x = self.prelu_1(x) # Parametric ReLU activation + x = F.dropout(x, p=0.3, training=self.training) # Regularization + + # Second convolution: Aggregate 2-hop neighborhood information + x = self.conv2(x, edge_index) # (num_nodes, hidden_channels) + x = self.prelu_2(x) # Parametric ReLU activation + x = F.dropout(x, p=0.3, training=self.training) # Regularization + + # Third convolution: Aggregate 3-hop neighborhood information + x = self.conv3(x, edge_index) # (num_nodes, hidden_channels) + + # Note: Skip connections commented out + # x = x + x_ori # Residual connection with initial features + # Could help with gradient flow and preserve initial information + + # 2. Readout layer: Aggregate node features to graph-level representation + # Global mean pooling: Average all node features per graph + x = global_mean_pool(x, batch=batch) # (batch_size, hidden_channels) + + # Alternative: Global max pooling (commented out) + # x = global_max_pool(x, batch=batch) + # Max pooling captures most salient features but loses information + + # 3. Apply fully connected layers for final prediction + # First FC layer: Further process graph representation + x = self.lin_0(x) # (batch_size, hidden_channels) + + # Output layer: Map to final predictions + x = self.lin_1(x) # (batch_size, num_outputs) - # 1. Obtain node embeddings - # x_ori = self.conv0(x, edge_index) - x = self.conv1(x, edge_index) - - # x = x.relu() - x = self.prelu_1(x) - x = F.dropout(x, p=0.3, training=self.training) - x = self.conv2(x, edge_index) - # x = x.relu() - x = self.prelu_2(x) - x = F.dropout(x, p=0.3, training=self.training) - x = self.conv3(x, edge_index) - # x = x + x_ori - # x = torch.concat([x,x_ori]) - # batch_aug = torch.concat([batch,(batch+1)*torch.max(batch)]) - # batch_aug = torch.concat([batch,batch]) - - # 2. Readout layer - x = global_mean_pool(x,batch=batch) # [batch_size, hidden_channels] - - # 3. Apply a final classifier - # x = F.dropout(x, p=0.3, training=self.training) - x = self.lin_0(x) - x = self.lin_1(x) return x \ No newline at end of file diff --git a/ctlearn/core/pytorch/nets/optimizer/optimizer.py b/ctlearn/core/pytorch/nets/optimizer/optimizer.py index 5a403374..7bafd015 100644 --- a/ctlearn/core/pytorch/nets/optimizer/optimizer.py +++ b/ctlearn/core/pytorch/nets/optimizer/optimizer.py @@ -1,5 +1,129 @@ +""" +Learning Rate Scheduling Functions Module + +This module provides learning rate scheduling functions for training neural networks. +It includes implementations of various learning rate schedules commonly used in deep +learning, particularly focusing on one-cycle and cosine annealing schedules that have +been shown to improve training convergence and final model performance. + +Functions: + one_cycle: Generate a one-cycle learning rate schedule with sinusoidal ramp + +References: + - "A disciplined approach to neural network hyper-parameters" (Smith, 2018) + - "Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates" (Smith & Topin, 2017) +""" + import math def one_cycle(y1=0.0, y2=1.0, steps=100): - # lambda function for sinusoidal ramp from y1 to y2 + """ + Generate a one-cycle learning rate schedule using a sinusoidal ramp. + + This function creates a lambda function that implements a smooth, cosine-based + transition from an initial learning rate (y1) to a maximum learning rate (y2) + over a specified number of steps. The schedule follows a half-cosine curve, + providing a gradual warm-up and smooth transition. + + The one-cycle policy has been shown to enable faster training and better + generalization by allowing the use of higher learning rates during training + while maintaining stability through the smooth schedule. + + Mathematical Formula: + lr(x) = ((1 - cos(x * π / steps)) / 2) * (y2 - y1) + y1 + + where: + - x: current step (0 to steps) + - lr(x): learning rate at step x + - The cosine creates a smooth S-shaped curve from y1 to y2 + + Args: + y1 (float, optional): Initial learning rate (start value). Defaults to 0.0 + Typically set to a small value or 0 for warm-up from zero + y2 (float, optional): Maximum learning rate (end value). Defaults to 1.0 + This is the peak learning rate to reach during training + Should be set based on learning rate range tests + steps (int, optional): Total number of steps for the schedule. Defaults to 100 + This determines how quickly the learning rate increases + For epoch-based: steps = num_epochs + For iteration-based: steps = num_epochs * batches_per_epoch + + Returns: + function: A lambda function that takes step index x and returns the + corresponding learning rate. The function signature is: + lambda x: float + + Schedule Characteristics: + - At x=0: Returns y1 (starting learning rate) + - At x=steps/2: Returns (y1+y2)/2 (midpoint) + - At x=steps: Returns y2 (maximum learning rate) + - Smooth acceleration: No sudden jumps in learning rate + - Cosine-based: Provides gentle start and smooth transition + + Usage Patterns: + 1. One-Cycle Policy (Smith, 2018): + - Phase 1: Ramp up from low LR to high LR (this function) + - Phase 2: Ramp down from high LR to very low LR (inverse) + + 2. Warm-up Only: + - Use this function alone for gradual LR warm-up + - Helps stabilize training at the beginning + + Example: + >>> # Create a schedule from 0.0 to 0.1 over 1000 steps + >>> schedule = one_cycle(y1=0.0, y2=0.1, steps=1000) + >>> + >>> # Get learning rate at step 0 (start) + >>> lr_start = schedule(0) + >>> print(f"LR at step 0: {lr_start:.6f}") # 0.000000 + >>> + >>> # Get learning rate at step 500 (halfway) + >>> lr_mid = schedule(500) + >>> print(f"LR at step 500: {lr_mid:.6f}") # ~0.050000 + >>> + >>> # Get learning rate at step 1000 (end) + >>> lr_end = schedule(1000) + >>> print(f"LR at step 1000: {lr_end:.6f}") # 0.100000 + + >>> # Use with PyTorch LambdaLR scheduler + >>> import torch.optim as optim + >>> optimizer = optim.SGD(model.parameters(), lr=1.0) + >>> lambda_func = one_cycle(y1=0.0, y2=1.0, steps=100) + >>> scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_func) + >>> + >>> # During training + >>> for epoch in range(100): + ... train_one_epoch() + ... scheduler.step() # Updates LR according to schedule + + >>> # Complete one-cycle schedule (warm-up + cool-down) + >>> warmup = one_cycle(0.0, 1.0, steps=50) + >>> cooldown = one_cycle(1.0, 0.01, steps=50) + >>> # Use warmup for first 50 epochs, cooldown for last 50 + + Notes: + - The returned lambda is stateless; it only depends on the input step + - Can be used with PyTorch's LambdaLR scheduler for automatic LR updates + - Works with both epoch-based and iteration-based schedules + - The cosine curve provides smoother transitions than linear schedules + - Avoids sudden LR changes that can destabilize training + + Common Configurations: + Warm-up from zero: + >>> schedule = one_cycle(0.0, 0.001, steps=10) # 10-epoch warm-up + + One-cycle for 100 epochs: + >>> up = one_cycle(0.0, 0.1, steps=70) # Ramp up: epochs 0-70 + >>> down = one_cycle(0.1, 0.0001, steps=30) # Ramp down: epochs 70-100 + + Fine-tuning with low LR: + >>> schedule = one_cycle(0.00001, 0.0001, steps=20) # Gentle increase + + See Also: + torch.optim.lr_scheduler.LambdaLR: PyTorch scheduler using lambda functions + torch.optim.lr_scheduler.OneCycleLR: Built-in one-cycle implementation + torch.optim.lr_scheduler.CosineAnnealingLR: Cosine annealing schedule + """ + # Return lambda function that computes LR for any step x + # The cosine function creates a smooth S-curve from y1 to y2 return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 diff --git a/ctlearn/core/pytorch/utils/utils_torch.py b/ctlearn/core/pytorch/utils/utils_torch.py index 4a4e934b..1613798c 100644 --- a/ctlearn/core/pytorch/utils/utils_torch.py +++ b/ctlearn/core/pytorch/utils/utils_torch.py @@ -1,33 +1,300 @@ +""" +PyTorch Utility Functions Module + +This module provides utility functions for coordinate transformations, optimizer +management, and model weight tracking in PyTorch. These utilities are specifically +designed for Cherenkov telescope array analysis in CTLearn. + +Functions: + cartesian_to_alt_az: Convert Cartesian coordinates to altitude/azimuth + alt_az_to_cartesian: Convert altitude/azimuth to Cartesian coordinates + adjust_learning_rate: Modify learning rate for all optimizer parameter groups + compare_weights: Compare current model weights with initial weights +""" + import torch def cartesian_to_alt_az(directions): + """ + Convert 3D Cartesian direction vectors to altitude and azimuth angles. + + This function transforms Cartesian coordinates (x, y, z) representing + unit direction vectors into spherical coordinates (altitude, azimuth) + commonly used in astronomical coordinate systems. + + Coordinate System: + - Cartesian: (x, y, z) where x² + y² + z² = r² + - Spherical: (altitude, azimuth) in radians + * Altitude (elevation): angle above the horizontal plane [-π/2, π/2] + * Azimuth: angle in the horizontal plane, measured from x-axis [−π, π] + + Args: + directions (torch.Tensor): Batch of 3D direction vectors + Shape: (batch_size, 3) where each row is [x, y, z] + Can be unit vectors or any length (will be normalized internally) + + Returns: + torch.Tensor: Altitude and azimuth angles in radians + Shape: (batch_size, 2) where each row is [altitude, azimuth] + - altitude: Range [-π/2, π/2] radians (-90° to 90°) + - azimuth: Range [-π, π] radians (-180° to 180°) + + Mathematical Formulation: + r = √(x² + y² + z²) + altitude = arcsin(z / r) + azimuth = arctan2(y, x) + + Example: + >>> # Pointing straight up (zenith) + >>> directions = torch.tensor([[0.0, 0.0, 1.0]]) + >>> alt_az = cartesian_to_alt_az(directions) + >>> print(alt_az) # [[π/2, 0]] + + >>> # Batch of directions + >>> directions = torch.tensor([ + ... [1.0, 0.0, 0.0], # East horizon + ... [0.0, 1.0, 0.0], # North horizon + ... [0.0, 0.0, 1.0], # Zenith + ... ]) + >>> alt_az = cartesian_to_alt_az(directions) + >>> # Result: [[0, 0], [0, π/2], [π/2, 0]] + + Notes: + - Handles zero vectors safely by replacing r=0 with r=1 to avoid division by zero + - Azimuth follows standard convention: 0 at x-axis, increases counterclockwise + - For shower direction reconstruction in gamma-ray astronomy + - Compatible with astropy coordinate transformations + """ + # Calculate magnitude (radius) of each direction vector r = torch.sqrt(torch.sum(directions**2, dim=1)) - # Prevent division by zero + + # Prevent division by zero for null vectors + # Replace zero magnitudes with 1.0 to avoid NaN in division safe_r = torch.where(r == 0, torch.tensor(1.0, device=r.device), r) - # altitude_rad = torch.arcsin(safe_r) - + # Calculate altitude (elevation angle above horizontal plane) + # altitude = arcsin(z / r), range: [-π/2, π/2] altitude_rad = torch.asin(directions[:, 2] / safe_r) + + # Calculate azimuth (angle in horizontal plane from x-axis) + # azimuth = arctan2(y, x), range: [-π, π] azimuth_rad = torch.atan2(directions[:, 1], directions[:, 0]) - # Normalize azimuth to [0, 2π] + # Note: Azimuth normalization to [0, 2π] is commented out + # Current implementation returns azimuth in [-π, π] + # Uncomment the following line if [0, 2π] range is needed: # azimuth_rad = torch.where(azimuth_rad < 0, azimuth_rad + 2 * torch.pi, azimuth_rad) + # Stack altitude and azimuth into single tensor + # Shape: (batch_size, 2) return torch.stack((altitude_rad, azimuth_rad), dim=1) def alt_az_to_cartesian(altitude_rad, azimuth_rad, r=1): + """ + Convert altitude and azimuth angles to 3D Cartesian coordinates. + + This function transforms spherical coordinates (altitude, azimuth) into + Cartesian coordinates (x, y, z). This is the inverse operation of + cartesian_to_alt_az. + + Coordinate System: + - Input: (altitude, azimuth, radius) in radians and distance units + - Output: (x, y, z) Cartesian coordinates + + Args: + altitude_rad (torch.Tensor): Altitude angles in radians + Shape: (batch_size,) or scalar + Range: [-π/2, π/2] (where 0 is horizon, π/2 is zenith) + + azimuth_rad (torch.Tensor): Azimuth angles in radians + Shape: (batch_size,) or scalar + Range: [-π, π] or [0, 2π] + Convention: 0 at x-axis, increases counterclockwise + + r (float or torch.Tensor, optional): Radial distance from origin + Default: 1 (unit vectors) + Can be scalar (same for all) or tensor (batch_size,) + + Returns: + torch.Tensor: 3D Cartesian direction vectors + Shape: (batch_size, 3) where each row is [x, y, z] + If r=1, returns unit vectors + + Mathematical Formulation: + x = r · cos(altitude) · cos(azimuth) + y = r · cos(altitude) · sin(azimuth) + z = r · sin(altitude) + + Example: + >>> # Convert zenith direction (straight up) + >>> altitude = torch.tensor([np.pi/2]) # 90 degrees + >>> azimuth = torch.tensor([0.0]) + >>> xyz = alt_az_to_cartesian(altitude, azimuth) + >>> print(xyz) # [[0, 0, 1]] + + >>> # Convert horizon direction pointing East + >>> altitude = torch.tensor([0.0]) + >>> azimuth = torch.tensor([0.0]) + >>> xyz = alt_az_to_cartesian(altitude, azimuth) + >>> print(xyz) # [[1, 0, 0]] + + >>> # Batch conversion with custom radius + >>> altitudes = torch.tensor([0.0, np.pi/4, np.pi/2]) + >>> azimuths = torch.tensor([0.0, np.pi/2, 0.0]) + >>> xyz = alt_az_to_cartesian(altitudes, azimuths, r=2.0) + >>> # Result: [[2, 0, 0], [0, √2, √2], [0, 0, 2]] + + Notes: + - Default r=1 produces unit vectors + - Useful for converting predicted angles back to 3D directions + - Compatible with shower direction reconstruction in CTA analysis + - Inverse of cartesian_to_alt_az (within numerical precision) + """ + # Calculate x-coordinate + # x = r · cos(altitude) · cos(azimuth) + # Points in the horizontal plane, aligned with azimuth angle x = r * torch.cos(altitude_rad) * torch.cos(azimuth_rad) + + # Calculate y-coordinate + # y = r · cos(altitude) · sin(azimuth) + # Points in the horizontal plane, perpendicular to x y = r * torch.cos(altitude_rad) * torch.sin(azimuth_rad) + + # Calculate z-coordinate + # z = r · sin(altitude) + # Points vertically (altitude component) z = r * torch.sin(altitude_rad) - return torch.stack((x, y, z), dim=1) # Stack along new dimension to create vectors + + # Stack coordinates into 3D vectors + # Shape: (batch_size, 3) + return torch.stack((x, y, z), dim=1) -def adjust_learning_rate( optimizer, lr): - """Adjusts learning rate of all optimizer's parameter groups.""" +def adjust_learning_rate(optimizer, lr): + """ + Adjust the learning rate for all parameter groups in an optimizer. + + This function modifies the learning rate of all parameter groups in a + PyTorch optimizer. Useful for implementing custom learning rate schedules, + warm-up strategies, or manual learning rate adjustments during training. + + Args: + optimizer (torch.optim.Optimizer): PyTorch optimizer instance + Can be any optimizer (SGD, Adam, AdamW, etc.) + Contains one or more parameter groups + + lr (float): New learning rate to set + Must be positive + Applied to all parameter groups uniformly + + Side Effects: + Modifies the 'lr' field of all parameter groups in the optimizer in-place + + Example: + >>> # Initialize optimizer + >>> optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + >>> + >>> # Reduce learning rate by factor of 10 + >>> adjust_learning_rate(optimizer, 0.0001) + >>> + >>> # Verify new learning rate + >>> print(optimizer.param_groups[0]['lr']) + 0.0001 + + >>> # Use in training loop for manual scheduling + >>> for epoch in range(num_epochs): + ... if epoch == 50: + ... adjust_learning_rate(optimizer, lr * 0.1) + ... train_one_epoch() + + Notes: + - Affects ALL parameter groups (if optimizer has multiple groups) + - For different learning rates per group, access param_groups directly + - Common use cases: + * Learning rate warm-up + * Manual learning rate decay + * Cyclical learning rate schedules + * Recovery from divergence during training + - Alternative: Use PyTorch lr_scheduler classes for automatic scheduling + + See Also: + torch.optim.lr_scheduler: Built-in learning rate schedulers + """ + # Iterate over all parameter groups in the optimizer for param_group in optimizer.param_groups: + # Update the learning rate for this parameter group param_group['lr'] = lr def compare_weights(model, initial_weights): + """ + Compare current model weights with initial weights to detect changes. + + This utility function checks which parameters in a PyTorch model have + changed compared to their initial values. Useful for debugging training + issues, verifying that training is updating weights, or checking if + certain layers are frozen correctly. + + Args: + model (torch.nn.Module): PyTorch model to check + Can be any neural network model + + initial_weights (dict): Dictionary of initial weight tensors + Format: {parameter_name: tensor} + Typically obtained from model.state_dict() before training + Example: initial_weights = {name: param.data.clone() + for name, param in model.named_parameters()} + + Side Effects: + Prints the names of parameters that have changed + Does not modify the model or weights + + Example: + >>> # Save initial weights before training + >>> model = ResNet(num_outputs=2) + >>> initial_weights = { + ... name: param.data.clone() + ... for name, param in model.named_parameters() + ... } + >>> + >>> # Train for one epoch + >>> train_one_epoch(model, optimizer, train_loader) + >>> + >>> # Check which weights changed + >>> compare_weights(model, initial_weights) + Weight changed: conv1.weight + Weight changed: bn1.weight + Weight changed: layer1.0.conv1.weight + ... + + >>> # Check if frozen layers stayed frozen + >>> for name, param in model.named_parameters(): + ... if 'backbone' in name: + ... param.requires_grad = False + >>> + >>> train_one_epoch(model, optimizer, train_loader) + >>> compare_weights(model, initial_weights) + # Should not print any 'backbone' layers + + Notes: + - Only checks parameters that exist in both model and initial_weights + - Uses torch.equal() for exact comparison (no tolerance) + - Useful for debugging: + * Verifying training is working (weights should change) + * Checking frozen layers (weights should NOT change) + * Identifying which layers are being updated + * Detecting gradient flow issues + - For large models, consider checking only specific layers + + Performance: + - Comparison is done in-place, no additional memory allocation + - Fast for most models, but can be slow for very large models + """ + # Iterate over all named parameters in the model for name, param in model.named_parameters(): + # Get the corresponding initial weight tensor initial_weight = initial_weights[name] + + # Compare current weight with initial weight + # torch.equal() returns True only if tensors are exactly equal if not torch.equal(initial_weight, param.data): + # Print parameter name if it has changed print(f"Weight changed: {name}") \ No newline at end of file diff --git a/ctlearn/tools/predict/keras/predic_LST1_keras.py b/ctlearn/tools/predict/keras/predic_LST1_keras.py index 3bf2c0bf..7118a67f 100644 --- a/ctlearn/tools/predict/keras/predic_LST1_keras.py +++ b/ctlearn/tools/predict/keras/predic_LST1_keras.py @@ -1,133 +1,255 @@ +""" +Keras prediction module for LST1 telescope data. +This module provides functionality to load trained Keras models and perform predictions +on DL1 level data for particle type classification, energy estimation, and direction reconstruction. +""" + from ctapipe.io import read_table from astropy.table import join import keras -from dl1_data_handler.reader import ( - get_unmapped_image -) +from dl1_data_handler.reader import get_unmapped_image import numpy as np + def predictions(self): + """ + Perform predictions on input DL1 data using trained Keras models. + + This function processes the input file in batches, applies quality cuts, + and generates predictions for particle type, energy, and/or direction + depending on the configured models. The models are split into backbone + and head components to extract feature vectors. + + Returns + ------- + tuple + Contains the following arrays: + - event_id: Event identifiers + - tel_azimuth: Telescope azimuth angles + - tel_altitude: Telescope altitude angles + - trigger_time: Event trigger times in MJD + - prediction: Particle type classification scores (gammaness) + - energy: Reconstructed energy values + - cam_coord_offset_x: Camera coordinate offset in x direction + - cam_coord_offset_y: Camera coordinate offset in y direction + - classification_fvs: Classification feature vectors from backbone + - energy_fvs: Energy estimation feature vectors from backbone + - direction_fvs: Direction reconstruction feature vectors from backbone + + Notes + ----- + The function processes data in batches to manage memory efficiently and + applies quality selection criteria before making predictions. + """ + # Initialize output arrays for storing results event_id, tel_azimuth, tel_altitude, trigger_time = [], [], [], [] prediction, energy, cam_coord_offset_x, cam_coord_offset_y = [], [], [], [] classification_fvs, energy_fvs, direction_fvs = [], [], [] + + # Process input file in batches for start in range(0, self.table_length, self.batch_size): stop = min(start + self.batch_size, self.table_length) self.log.debug("Processing chunk from '%d' to '%d'.", start, stop - 1) - # Read the data + + # Read the DL1 data table for current batch dl1_table = read_table( self.input_url, self.image_table_path, start=start, stop=stop ) - # Join the dl1 table with the parameter table to perform quality selection + + # Join tables to enable quality selection + # Join with parameter table for event parameters dl1_table = join( left=dl1_table, right=self.parameter_table, keys=["event_id"], ) + # Join with trigger table for timing information dl1_table = join( left=dl1_table, right=self.trigger_table, keys=["event_id"], ) - # Initialize a boolean mask to True for all events in the sliced dl1 table + + # Apply quality selection criteria + # Initialize mask to accept all events initially passes_quality_checks = np.ones(len(dl1_table), dtype=bool) - # Quality selection based on the dl1b parameter + + # Apply quality query if configured if self.quality_query: passes_quality_checks = self.quality_query.get_table_mask(dl1_table) - # Apply the mask to filter events that are not fufilling the quality criteria + + # Filter events based on quality criteria dl1_table = dl1_table[passes_quality_checks] + + # Skip batch if no events passed quality selection if len(dl1_table) == 0: self.log.debug("No events passed the quality selection.") continue + + # Prepare input data by mapping images to model input format data = [] for event in dl1_table: - # Get the unmapped image + # Get the unmapped image with specified channels and transforms image = get_unmapped_image(event, self.channels, self.transforms) + # Map image to model's expected input format data.append(self.image_mapper.map_image(image)) input_data = {"input": np.array(data)} - # Temp fix for supporting keras2 & keras3 + + # Handle compatibility between Keras 2 and Keras 3 + # Keras 3 expects direct array input, not dictionary if int(keras.__version__.split(".")[0]) >= 3: input_data = input_data["input"] + # Store event metadata event_id.extend(dl1_table["event_id"].data) tel_azimuth.extend(dl1_table["tel_az"].data) tel_altitude.extend(dl1_table["tel_alt"].data) trigger_time.extend(dl1_table["time"].mjd) + # Perform particle type classification if model is loaded if self.load_type_model_from is not None: + # Extract feature vectors from backbone classification_feature_vectors = self.backbone_type.predict_on_batch(input_data) classification_fvs.extend(classification_feature_vectors) + # Generate predictions from head model predict_data = self.head_type.predict_on_batch(classification_feature_vectors) + # Extract gammaness score (probability of being gamma) prediction.extend(predict_data[:, 1]) + + # Perform energy estimation if model is loaded if self.load_energy_model_from is not None: + # Extract feature vectors from backbone energy_feature_vectors = self.backbone_energy.predict_on_batch(input_data) energy_fvs.extend(energy_feature_vectors) + # Generate energy predictions from head model predict_data = self.head_energy.predict_on_batch(energy_feature_vectors) energy.extend(predict_data.T[0]) + + # Perform direction reconstruction if model is loaded if self.load_cameradirection_model_from is not None: + # Extract feature vectors from backbone direction_feature_vectors = self.backbone_direction.predict_on_batch(input_data) direction_fvs.extend(direction_feature_vectors) + # Generate direction predictions from head model predict_data = self.head_direction.predict_on_batch(direction_feature_vectors) + # Extract x and y components of camera coordinate offset cam_coord_offset_x.extend(predict_data.T[0]) cam_coord_offset_y.extend(predict_data.T[1]) - - return event_id, tel_azimuth, tel_altitude, trigger_time, prediction, energy, cam_coord_offset_x, cam_coord_offset_y, classification_fvs, energy_fvs, direction_fvs + + return (event_id, tel_azimuth, tel_altitude, trigger_time, prediction, energy, + cam_coord_offset_x, cam_coord_offset_y, classification_fvs, energy_fvs, direction_fvs) -def _split_model(model): - """ - Split the model into backbone and head. - This method splits the model into backbone and head. The backbone is summarized - into a single layer which can be retrieved by the layer index 1. The model input - has layer index 0 and the head is the rest of the model with layer index 2 and above. +def _split_model(model): + """ + Split a Keras model into backbone and head components. + + This function separates a trained model into two parts: + - Backbone: Feature extraction layers (typically convolutional layers) + - Head: Task-specific prediction layers (typically dense layers) + + This separation allows extraction of intermediate feature representations + which can be useful for analysis or transfer learning. + + Parameters + ---------- + model : keras.Model + Complete trained Keras model to be split. The model should have: + - Layer 0: Input layer + - Layer 1: Backbone (feature extractor) + - Layers 2+: Head (prediction layers) + + Returns + ------- + backbone : keras.Model + Feature extraction model that outputs intermediate representations. + head : keras.Model + Prediction model that takes backbone outputs and produces final predictions. + + Notes + ----- + The function assumes a specific model architecture where the backbone + is the second layer (index 1) of the complete model. This is a common + pattern in CTLearn models where the backbone is wrapped as a single layer. + """ + # Extract the backbone model (second layer of the complete model) + # Layer 0 is the input, layer 1 is the backbone feature extractor + backbone = model.get_layer(index=1) + + # Create a new head model using layers after the backbone + # Define input with the same shape as backbone output + backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) + x = backbone_output_shape + + # Reconstruct head by connecting all layers after backbone + for layer in model.layers[2:]: + x = layer(x) + + # Create the head model + head = keras.Model(inputs=backbone_output_shape, outputs=x) + + return backbone, head - Parameters: - ----------- - model : keras.Model - Keras model to split into backbone and head. - Returns: - -------- - backbone : keras.Model - Backbone model of the original model. - head : keras.Model - Head model of the original model. - """ - # Get the backbone model which is the second layer of the model - backbone = model.get_layer(index=1) - # Create a new head model with the same layers as the original model. - # The output of the backbone model is the input of the head model. - backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) - x = backbone_output_shape - for layer in model.layers[2:]: - x = layer(x) - head = keras.Model(inputs=backbone_output_shape, outputs=x) - return backbone, head - def load_keras_model(self): + """ + Load Keras models from saved files and split them into backbone and head. + + This function loads trained Keras models for different tasks (particle type + classification, energy estimation, direction reconstruction) and splits each + into backbone and head components for efficient prediction and feature extraction. + + Parameters + ---------- + self : object + Prediction handler instance containing model paths: + - load_type_model_from: Path to particle classification model + - load_energy_model_from: Path to energy estimation model + - load_cameradirection_model_from: Path to direction reconstruction model + + Returns + ------- + input_shape : tuple + Shape of the model input (height, width, channels). + Returns the shape from the last loaded model. + + Notes + ----- + The function sets the following attributes on self: + - backbone_type, head_type: Split models for particle classification + - backbone_energy, head_energy: Split models for energy estimation + - backbone_direction, head_direction: Split models for direction reconstruction + """ + input_shape = None + + # Load particle type classification model if configured if self.load_type_model_from is not None: self.log.info("Loading the type model from %s.", self.load_type_model_from) model_type = keras.saving.load_model(self.load_type_model_from) input_shape = model_type.input_shape[1:] + # Split model into backbone and head self.backbone_type, self.head_type = _split_model(model_type) - + + # Load energy estimation model if configured if self.load_energy_model_from is not None: self.log.info( "Loading the energy model from %s.", self.load_energy_model_from ) - model_energy = keras.saving.load_model( - self.load_energy_model_from - ) + model_energy = keras.saving.load_model(self.load_energy_model_from) input_shape = model_energy.input_shape[1:] + # Split model into backbone and head self.backbone_energy, self.head_energy = _split_model(model_energy) - + + # Load direction reconstruction model if configured if self.load_cameradirection_model_from is not None: self.log.info( "Loading the cameradirection model from %s.", self.load_cameradirection_model_from ) model_direction = keras.saving.load_model( self.load_cameradirection_model_from - ) + ) input_shape = model_direction.input_shape[1:] + # Split model into backbone and head self.backbone_direction, self.head_direction = _split_model(model_direction) - + return input_shape diff --git a/ctlearn/tools/predict/keras/predic_model_keras.py b/ctlearn/tools/predict/keras/predic_model_keras.py index 37dfb8d8..498dc0a7 100644 --- a/ctlearn/tools/predict/keras/predic_model_keras.py +++ b/ctlearn/tools/predict/keras/predic_model_keras.py @@ -1,35 +1,49 @@ +""" +Keras model prediction module for CTLearn. +This module provides functionality to load trained Keras models and perform batch predictions +on DL1 data, with optional feature vector extraction from backbone models. +""" + from ctlearn.core.data_loader.loader import DLDataLoader import keras -from astropy.table import ( - Table, - hstack, - vstack, - join, - setdiff, -) +from astropy.table import Table, vstack import numpy as np + def predict_with_model(self, model_path): """ - Load and predict with a CTLearn model. - - Load a model from the specified path and predict the data using the loaded model. - If a last batch loader is provided, predict the last batch and stack the results. - + Load and predict with a CTLearn Keras model. + + This function loads a trained model from the specified path and performs predictions + on the provided data. It handles both complete and incomplete batches, and optionally + extracts feature vectors from the backbone model for downstream analysis. + Parameters ---------- model_path : str - Path to a Keras model file (Keras3) or directory (Keras2). - + Path to a Keras model file (Keras 3) or directory (Keras 2). + The model should be a complete trained CTLearn model. + Returns ------- predict_data : astropy.table.Table - Table containing the prediction results. - feature_vectors : np.ndarray - Feature vectors extracted from the backbone model. + Table containing the prediction results with columns corresponding to + the model's output (e.g., 'type' for classification, task-specific names + for regression tasks like energy or direction). + feature_vectors : np.ndarray or None + Feature vectors extracted from the backbone model if dl1_features is enabled. + Returns None if feature extraction is not requested. + + Notes + ----- + - The function handles distributed training by accounting for multiple replicas + - Keras only processes complete batches, so incomplete last batches are handled separately + - Feature extraction splits the model into backbone (feature extractor) and head (predictor) + - Classification tasks use softmax output, regression tasks use direct outputs """ - # Create a new DLDataLoader for each task - # It turned out to be more robust to initialize the DLDataLoader separately. + # Create data loader for the main batch processing + # The DLDataLoader is initialized separately for each prediction task + # Batch size is multiplied by number of replicas for distributed inference data_loader = DLDataLoader.create( framework="keras", DLDataReader=self.dl1dh_reader, @@ -40,13 +54,13 @@ def predict_with_model(self, model_path): stack_telescope_images=self.stack_telescope_images, ) - # Keras is only considering the last complete batch. - # In prediction mode we don't want to loose the last - # uncomplete batch, so we are creating an additional - # batch generator for the remaining events. + # Handle incomplete last batch + # Keras only processes complete batches during prediction, so we need + # a separate data loader for remaining events that don't fill a complete batch data_loader_last_batch = None if self.last_batch_size > 0: - last_batch_indices = self.indices[-self.last_batch_size :] + # Extract indices for the last incomplete batch + last_batch_indices = self.indices[-self.last_batch_size:] data_loader_last_batch = DLDataLoader.create( framework="keras", DLDataReader=self.dl1dh_reader, @@ -57,28 +71,42 @@ def predict_with_model(self, model_path): stack_telescope_images=self.stack_telescope_images, ) - - # Load the model from the specified path + # Load the trained model from the specified path model = keras.saving.load_model(model_path) + + # Determine prediction column name from model architecture + # Use the last layer name, or 'type' if it's a softmax layer (classification) prediction_colname = ( model.layers[-1].name if model.layers[-1].name != "softmax" else "type" ) + + # Initialize variables for optional feature extraction backbone_model, feature_vectors = None, None + if self.dl1_features: - # Get the backbone model which is the second layer of the model + # Feature extraction mode: split model into backbone and head + # This allows us to extract intermediate representations (feature vectors) + + # Extract the backbone model (second layer of the complete model) + # Layer 0: Input, Layer 1: Backbone (feature extractor), Layers 2+: Head backbone_model = model.get_layer(index=1) - # Create a new head model with the same layers as the original model. - # The output of the backbone model is the input of the head model. + + # Reconstruct the head model from layers after the backbone + # Define input with the same shape as backbone output backbone_output_shape = keras.Input(model.layers[2].input_shape[1:]) x = backbone_output_shape + + # Connect all layers after backbone to create head model for layer in model.layers[2:]: x = layer(x) head = keras.Model(inputs=backbone_output_shape, outputs=x) - # Apply the backbone model with the data loader to retrieve the feature vectors + + # Extract feature vectors from backbone feature_vectors = backbone_model.predict( data_loader, verbose=self.keras_verbose ) - # Apply the head model with the feature vectors to retrieve the prediction + + # Generate predictions from head using extracted features predict_data = Table( { prediction_colname: head.predict( @@ -86,14 +114,18 @@ def predict_with_model(self, model_path): ) } ) - # Predict the last batch and stack the results to the prediction data + + # Process last incomplete batch if it exists if data_loader_last_batch is not None: + # Extract features from last batch feature_vectors_last_batch = backbone_model.predict( data_loader_last_batch, verbose=self.keras_verbose ) + # Concatenate feature vectors from all batches feature_vectors = np.concatenate( (feature_vectors, feature_vectors_last_batch) ) + # Generate predictions for last batch and stack with main predictions predict_data = vstack( [ predict_data, @@ -108,27 +140,36 @@ def predict_with_model(self, model_path): ] ) else: - # Predict the data using the loaded model + # Standard prediction mode without feature extraction + # Use the complete model for end-to-end prediction predict_data = model.predict(data_loader, verbose=self.keras_verbose) - # Create a astropy table with the prediction results - # The classification task has a softmax layer as the last layer - # which returns the probabilities for each class in an array, while - # the regression tasks have output neurons which returns the - # predicted value for the task in a dictionary. + + # Convert predictions to Astropy Table + # Classification tasks (with softmax) return arrays that need wrapping + # Regression tasks return dictionaries that can be directly converted if prediction_colname == "type": + # Classification: wrap array in table with 'type' column predict_data = Table({prediction_colname: predict_data}) else: + # Regression: convert dictionary directly to table predict_data = Table(predict_data) - # Predict the last batch and stack the results to the prediction data + + # Process last incomplete batch if it exists if data_loader_last_batch is not None: + # Generate predictions for last batch predict_data_last_batch = model.predict( data_loader_last_batch, verbose=self.keras_verbose ) + + # Convert last batch predictions to table (same logic as above) if model.layers[-1].name == "type": predict_data_last_batch = Table( {prediction_colname: predict_data_last_batch} ) else: predict_data_last_batch = Table(predict_data_last_batch) + + # Stack predictions from main batches and last batch predict_data = vstack([predict_data, predict_data_last_batch]) + return predict_data, feature_vectors \ No newline at end of file diff --git a/ctlearn/tools/predict/predict_LST1.py b/ctlearn/tools/predict/predict_LST1.py index 1d41376c..ab3671bc 100644 --- a/ctlearn/tools/predict/predict_LST1.py +++ b/ctlearn/tools/predict/predict_LST1.py @@ -156,7 +156,7 @@ class LST1PredictionTool(Tool): "cleaned_relative_peak_time", ] ), - default_value=["image", "peak_time"], + default_value=["cleaned_image", "cleaned_peak_time"], allow_none=False, help=( "Set the input channels to be loaded from the DL1 event data. " @@ -395,18 +395,19 @@ def start(self): np.ones((len(self.trigger_table), 1), dtype=bool), name="tel_with_trigger" ) self.trigger_table.add_column(event_type, name="event_type") + # Save the dl1 subrray trigger table to the output file - write_table( - self.trigger_table, - self.output_path, - "/dl1/event/subarray/trigger", - overwrite=self.overwrite, - ) - self.log.info( - "DL1 subarray trigger table was stored in '%s' under '%s'", - self.output_path, - "/dl1/event/subarray/trigger", - ) + # write_table( + # self.trigger_table, + # self.output_path, + # "/dl1/event/subarray/trigger", + # overwrite=self.overwrite, + # ) + # self.log.info( + # "DL1 subarray trigger table was stored in '%s' under '%s'", + # self.output_path, + # "/dl1/event/subarray/trigger", + # ) # Create the dl1 parameters table self.parameter_table.rename_column("intensity", "hillas_intensity") self.parameter_table.rename_column("x", "hillas_x") @@ -666,6 +667,12 @@ def start(self): rotation=self.pix_rotation, telescope_pointing=tel_pointing, ) + ## Remove (save the cam_coord_offset_x, cam_coord_offset_y) predicted by the model in a pickel file + + # with open('/lhome/ext/ucm147/ucm1477/ctlearn/test_local_cristian/cam_coord_offset_test.pkl', 'wb') as f: + # import pickle + # pickle.dump((cam_coord_offset_x, cam_coord_offset_y), f) + # print('Pickell camera_coordinates saved') # Set the camera coordinate offset cam_coord_offset = SkyCoord( x=u.Quantity(cam_coord_offset_x, unit=u.m), diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index e8505831..80e92657 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -1,27 +1,35 @@ +""" +PyTorch prediction module for LST1 telescope data. +This module provides functionality to load trained models and perform predictions +on DL1 level data for particle type classification, energy estimation, and direction reconstruction. +""" + from ctlearn.core.pytorch.net_utils import create_model, ModelHelper -from ctlearn.tools.train.pytorch.utils import ( - str_list_to_enum_list, - sanity_check, - read_configuration, - create_experiment_folder, - expected_structure, -) import torch from ctlearn.core.ctlearn_enum import Task, Mode from ctapipe.io import read_table from astropy.table import join -from dl1_data_handler.reader import ( - get_unmapped_image -) +from dl1_data_handler.reader import get_unmapped_image import numpy as np from tqdm import tqdm - from pytorch_lightning.callbacks import Callback -from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer - class GPUStatsLogger(Callback): + """ + PyTorch Lightning callback to log GPU memory statistics during training. + + This callback tracks GPU memory allocation and reservation at the end of each training epoch + and logs the statistics to TensorBoard. + """ + def on_train_epoch_end(self, trainer, pl_module): + """ + Called at the end of each training epoch to log GPU memory statistics. + + Args: + trainer: PyTorch Lightning trainer instance + pl_module: The LightningModule being trained + """ mem_allocated = torch.cuda.memory_allocated() mem_reserved = torch.cuda.memory_reserved() @@ -31,176 +39,190 @@ def on_train_epoch_end(self, trainer, pl_module): trainer.logger.experiment.add_scalar( "gpu_mem_reserved", mem_reserved, global_step=trainer.current_epoch ) - - -from pytorch_lightning.callbacks import Callback -from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer -class GPUStatsLogger(Callback): - def on_train_epoch_end(self, trainer, pl_module): - mem_allocated = torch.cuda.memory_allocated() - mem_reserved = torch.cuda.memory_reserved() - - trainer.logger.experiment.add_scalar( - "gpu_mem_allocated", mem_allocated, global_step=trainer.current_epoch - ) - trainer.logger.experiment.add_scalar( - "gpu_mem_reserved", mem_reserved, global_step=trainer.current_epoch - ) - def predictions(self): + """ + Perform predictions on input DL1 data using trained models. + + This function processes the input file in batches, applies quality cuts, + and generates predictions for particle type, energy, and/or direction + depending on the configured tasks. + + Returns: + tuple: Contains the following arrays: + - event_id: Event identifiers + - tel_azimuth: Telescope azimuth angles + - tel_altitude: Telescope altitude angles + - trigger_time: Event trigger times + - prediction: Particle type classification scores + - energy: Reconstructed energy values + - cam_coord_offset_x: Camera coordinate offset in x + - cam_coord_offset_y: Camera coordinate offset in y + - classification_fvs: Classification feature vectors + - energy_fvs: Energy estimation feature vectors + - direction_fvs: Direction reconstruction feature vectors + """ + # Optimize batch size if requested if self.optim_batch_size: - bacht_size_finded = False - bacht = 256 + batch_size_found = False + batch = 256 step = 16 - while bacht_size_finded == False: + + while not batch_size_found: from ctlearn.tools.predict.utils.optimaze_batch_size import test_batch + # Load a test batch to find optimal batch size dl1_table = read_table( - self.input_url, self.image_table_path, start=0, stop=bacht - ) - dl1_table = join( - left=dl1_table, - right=self.parameter_table, - keys=["event_id"], - ) - dl1_table = join( - left=dl1_table, - right=self.trigger_table, - keys=["event_id"], + self.input_url, self.image_table_path, start=0, stop=batch ) + dl1_table = join(left=dl1_table, right=self.parameter_table, keys=["event_id"]) + dl1_table = join(left=dl1_table, right=self.trigger_table, keys=["event_id"]) + # Prepare test data data = [] for event in dl1_table: image = get_unmapped_image(dl1_table[0], self.channels, self.transforms) data.append(self.image_mapper.map_image(image)) - input_data = {"input": np.array(data)} + input_data = {"input": np.array(data)} - imgs = input_data['input'][:,:,:,0] + imgs = input_data['input'][:, :, :, 0] if len(self.channels) == 2: - peak_time = input_data['input'][:,:,:,1] + peak_time = input_data['input'][:, :, :, 1] + # Test batch size for each configured task for task in self.tasks: if task == Task.type: - bacht_size_finded = not test_batch(self.type_model,torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) , self.device) - + batch_size_found = not test_batch( + self.type_model, + torch.tensor(imgs).unsqueeze(1).to(self.device), + torch.tensor(peak_time).unsqueeze(1).to(self.device), + self.device + ) if task == Task.energy: - bacht_size_finded = not test_batch(self.energy_model,torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) , self.device) - + batch_size_found = not test_batch( + self.energy_model, + torch.tensor(imgs).unsqueeze(1).to(self.device), + torch.tensor(peak_time).unsqueeze(1).to(self.device), + self.device + ) if task in [Task.cameradirection, Task.skydirection, Task.direction]: - bacht_size_finded = not test_batch(self.cameradirection_model, torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) , self.device) - bacht += step - if bacht_size_finded == False: - self.log.info(f"Batch size: {bacht} OK") - self.batch_size = bacht - step - self.log.info(f"Optimized batch size: {self.batch_size}") + batch_size_found = not test_batch( + self.cameradirection_model, + torch.tensor(imgs).unsqueeze(1).to(self.device), + torch.tensor(peak_time).unsqueeze(1).to(self.device), + self.device + ) + + batch += step + if not batch_size_found: + self.log.info(f"Batch size: {batch} OK") + self.batch_size = batch - step + self.log.info(f"Optimized batch size: {self.batch_size}") + + # Initialize output arrays event_id, tel_azimuth, tel_altitude, trigger_time = [], [], [], [] prediction, energy, cam_coord_offset_x, cam_coord_offset_y = [], [], [], [] classification_fvs, energy_fvs, direction_fvs = [], [], [] - for start in tqdm(range(0, self.table_length, self.batch_size), desc="Procesing input file"): + # Process input file in batches + for start in tqdm(range(0, self.table_length, self.batch_size), desc="Processing input file"): stop = min(start + self.batch_size, self.table_length) self.log.debug("Processing chunk from '%d' to '%d'.", start, stop - 1) - # Read the data - dl1_table = read_table( - self.input_url, self.image_table_path, start=start, stop=stop - ) - # Join the dl1 table with the parameter table to perform quality selection - dl1_table = join( - left=dl1_table, - right=self.parameter_table, - keys=["event_id"], - ) - dl1_table = join( - left=dl1_table, - right=self.trigger_table, - keys=["event_id"], - ) - # Initialize a boolean mask to True for all events in the sliced dl1 table + + # Read and join tables + dl1_table = read_table(self.input_url, self.image_table_path, start=start, stop=stop) + dl1_table = join(left=dl1_table, right=self.parameter_table, keys=["event_id"]) + dl1_table = join(left=dl1_table, right=self.trigger_table, keys=["event_id"]) + + # Apply quality selection passes_quality_checks = np.ones(len(dl1_table), dtype=bool) - # Quality selection based on the dl1b parameter if self.quality_query: passes_quality_checks = self.quality_query.get_table_mask(dl1_table) - # Apply the mask to filter events that are not fufilling the quality criteria dl1_table = dl1_table[passes_quality_checks] + if len(dl1_table) == 0: self.log.debug("No events passed the quality selection.") continue + + # Prepare input data data = [] for event in dl1_table: - # Get the unmapped image image = get_unmapped_image(event, self.channels, self.transforms) data.append(self.image_mapper.map_image(image)) input_data = {"input": np.array(data)} + # Store metadata event_id.extend(dl1_table["event_id"].data) tel_azimuth.extend(dl1_table["tel_az"].data) tel_altitude.extend(dl1_table["tel_alt"].data) trigger_time.extend(dl1_table["time"].mjd) - imgs = input_data['input'][:,:,:,0] - if len(self.channels)==2: - peak_time = input_data['input'][:,:,:,1] + # Extract and clean image data + imgs = input_data['input'][:, :, :, 0] + if len(self.channels) == 2: + peak_time = input_data['input'][:, :, :, 1] peak_time[peak_time < 0] = 0 peak_time[np.isnan(peak_time)] = 0 peak_time[np.isinf(peak_time)] = 0 - + imgs[imgs < 0] = 0 imgs[np.isnan(imgs)] = 0 imgs[np.isinf(imgs)] = 0 - feture_vector = True + feature_vector = True + + # Run predictions for each configured task for task in self.tasks: if task == Task.type: - imgs = (imgs - self.type_mu) / self.type_sigma - + # Particle type classification if len(self.channels) == 2: - peak_time = (peak_time - self.type_mu) / self.type_sigma - classification_pred, energy_pred, direction_pred = self.type_model( - torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) + torch.tensor(imgs).unsqueeze(1).to(self.device), + torch.tensor(peak_time).unsqueeze(1).to(self.device) ) else: - classification_pred, energy_pred, direction_pred = self.type_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) - - prediction.extend(torch.softmax(classification_pred[0],dim=1).cpu().detach().numpy()[:,0]) + classification_pred, energy_pred, direction_pred = self.type_model( + torch.tensor(imgs).unsqueeze(1).to(self.device) + ) + + prediction.extend(torch.softmax(classification_pred[0], dim=1).cpu().detach().numpy()[:, 1]) classification_fvs.extend(classification_pred[1].cpu().detach().numpy()) elif task == Task.energy: - - imgs = (imgs - self.energy_mu) / self.energy_sigma - + # Energy estimation if len(self.channels) == 2: - peak_time = (peak_time - self.energy_mu) / self.energy_sigma classification_pred, energy_pred, direction_pred = self.energy_model( - torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) + torch.tensor(imgs).unsqueeze(1).to(self.device), + torch.tensor(peak_time).unsqueeze(1).to(self.device) ) else: - classification_pred, energy_pred, direction_pred = self.energy_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) - + classification_pred, energy_pred, direction_pred = self.energy_model( + torch.tensor(imgs).unsqueeze(1).to(self.device) + ) + energy.extend(energy_pred[0].cpu().detach().numpy()) - if feture_vector: + if feature_vector: energy_fvs.extend(energy_pred[1].cpu().detach().numpy()) else: energy_fvs.extend(np.array([[0]] * len(energy_pred[0]))) - elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: - - imgs = (imgs - self.dir_mu) / self.dir_sigma - + elif task in [Task.cameradirection, Task.skydirection, Task.direction]: + # Direction reconstruction if len(self.channels) == 2: - peak_time = (peak_time - self.dir_mu) / self.dir_sigma classification_pred, energy_pred, direction_pred = self.dirrection_model( - torch.tensor(imgs).unsqueeze(1).to(self.device) , torch.tensor(peak_time).unsqueeze(1).to(self.device) + torch.tensor(imgs).unsqueeze(1).to(self.device), + torch.tensor(peak_time).unsqueeze(1).to(self.device) ) else: - classification_pred, energy_pred, direction_pred = self.dirrection_model(torch.tensor(imgs).unsqueeze(1).to(self.device)) - - cam_coord_offset_x.extend(direction_pred[0][:,0].float().cpu().detach().numpy()) - cam_coord_offset_y.extend(direction_pred[0][:,1].float().cpu().detach().numpy()) - if feture_vector: + classification_pred, energy_pred, direction_pred = self.dirrection_model( + torch.tensor(imgs).unsqueeze(1).to(self.device) + ) + + cam_coord_offset_x.extend(direction_pred[0][:, 0].float().cpu().detach().numpy()) + cam_coord_offset_y.extend(direction_pred[0][:, 1].float().cpu().detach().numpy()) + if feature_vector: direction_fvs.extend(direction_pred[1].cpu().detach().numpy()) else: direction_fvs.extend(np.array([[0]] * len(direction_pred[0]))) @@ -209,56 +231,59 @@ def predictions(self): raise ValueError( f"task:{task.name} is not supported. Task must be type, direction or energy" ) - return event_id, tel_azimuth, tel_altitude, trigger_time, prediction, energy, cam_coord_offset_x, cam_coord_offset_y, classification_fvs, energy_fvs, direction_fvs + + return (event_id, tel_azimuth, tel_altitude, trigger_time, prediction, energy, + cam_coord_offset_x, cam_coord_offset_y, classification_fvs, energy_fvs, direction_fvs) + def load_pytorch_model(self): + """ + Load PyTorch models from checkpoints for the configured tasks. + + This function creates and loads models for particle type classification, + energy estimation, and/or direction reconstruction based on the tasks + specified in the configuration. + + Returns: + torch.nn.Module: The last loaded model (for compatibility) + """ + model = None + for task in self.tasks: + # Create model based on task type if task == Task.type: model_net = create_model(self.parameters["model"]["model_type"]) + check_point_path = self.parameters["data"]["type_checkpoint"] elif task == Task.energy: model_net = create_model(self.parameters["model"]["model_energy"]) - - elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: - model_net = create_model(self.parameters["model"]["model_direction"]) - - else: - raise ValueError( - f"task:{task.name} is not supported. Task must be type, direction or energy" - ) - - # ------------------------------------------------------------------------------ - # Load Checkpoints - # ------------------------------------------------------------------------------ - - if task == Task.type: - check_point_path = self.parameters["data"]["type_checkpoint"] - - elif task == Task.energy: check_point_path = self.parameters["data"]["energy_checkpoint"] - elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: + elif task in [Task.cameradirection, Task.skydirection, Task.direction]: + model_net = create_model(self.parameters["model"]["model_direction"]) check_point_path = self.parameters["data"]["direction_checkpoint"] else: raise ValueError( f"task:{task.name} is not supported. Task must be type, direction or energy" ) - # Load the checkpoint - + + # Load the model from checkpoint model = ModelHelper.loadModel( model_net, "", check_point_path, Mode.observation, device_str=self.device_str ) model.eval() + + # Assign model to appropriate attribute if task == Task.type: self.type_model = model elif task == Task.energy: self.energy_model = model - elif task == Task.cameradirection or task == Task.skydirection or task == Task.direction: + elif task in [Task.cameradirection, Task.skydirection, Task.direction]: self.dirrection_model = model else: raise ValueError( f"task:{task.name} is not supported. Task must be type, direction or energy" ) - + return model diff --git a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py index 60116ff0..e2ca8bad 100644 --- a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py @@ -1,97 +1,151 @@ +""" +PyTorch model prediction module for CTLearn. +This module provides functionality to load trained models and perform batch predictions +on DL1 data for multiple tasks including particle classification, energy estimation, +and direction reconstruction. +""" + from ctlearn.core.data_loader.loader import DLDataLoader import torch from tqdm import tqdm - import numpy as np import inspect - from ctlearn.tools.predict.utils.load_model import load_model + def predict_with_model_pytorch(self, task): """ - Load and predict with a CTLearn model. - - Load a model from the specified path and predict the data using the loaded model. - If a last batch loader is provided, predict the last batch and stack the results. - + Load and predict with a CTLearn PyTorch model. + + This function loads a trained model from the specified path and performs predictions + on the provided data. It processes the data in batches and returns predictions for + particle type classification, energy estimation, and/or direction reconstruction + based on the configured task. + Parameters ---------- - model_path : str - Path to a Keras model file (Keras3) or directory (Keras2). - + task : Task + The task(s) to perform predictions for (type, energy, or direction). + Returns ------- - predict_data : astropy.table.Table - Table containing the prediction results. - feature_vectors : np.ndarray - Feature vectors extracted from the backbone model. + predict_data : dict + Dictionary containing prediction results with keys: + - 'type': Particle type classification probabilities (gammaness scores) + - 'energy': Reconstructed energy values + - 'cameradirection': Camera coordinate offsets for direction reconstruction + feature_vectors : None + Feature vectors (currently not extracted, placeholder for future implementation). + + Notes + ----- + The function automatically detects whether the model requires peak time information + by inspecting the model's forward method signature. Models can accept either one + input (image only) or two inputs (image and peak time). """ - # Create a new DLDataLoader for each task - # It turned out to be more robust to initialize the DLDataLoader separately. + # Initialize batch size from configuration parameters self.batch_size = self.parameters["hyp"]["batches"] + # Create data loader for the specified task + # The DLDataLoader is initialized separately for each task to ensure robustness + self.dl1dh_reader.channels = ["cleaned_image", "cleaned_peak_time"] + data_loader = DLDataLoader.create( framework="pytorch", DLDataReader=self.dl1dh_reader, indices=self.indices, tasks=[task], - parameters = self.parameters, - use_augmentation = False, + parameters=self.parameters, + use_augmentation=False, batch_size=self.batch_size, sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, ) - # Keras is only considering the last complete batch. - # In prediction mode we don't want to loose the last - # uncomplete batch, so we are creating an additional - # batch generator for the remaining events. + # Note: Handling of incomplete last batch + # In PyTorch, unlike Keras, we can process incomplete batches directly + # without needing a separate data loader. The code below is kept as reference + # for potential future use or compatibility with other frameworks. + # data_loader_last_batch = None # if self.last_batch_size > 0: - # last_batch_indices = self.indices[-self.last_batch_size :] + # last_batch_indices = self.indices[-self.last_batch_size:] # data_loader_last_batch = DLDataLoader.create( # framework="pytorch", # DLDataReader=self.dl1dh_reader, # indices=last_batch_indices, # tasks=task, - # parameters = self.parameters, - # use_augmentation = False, + # parameters=self.parameters, + # use_augmentation=False, # batch_size=self.last_batch_size, # sort_by_intensity=self.sort_by_intensity, # stack_telescope_images=self.stack_telescope_images, # ) - # Load the model from the specified path + # Load the trained model from checkpoint model = load_model(self) + + # Inspect model signature to determine number of inputs + # This allows the code to work with models that take either: + # - Single input: image only + # - Dual input: image and peak time sig = inspect.signature(model.forward) num_inputs = len(sig.parameters) + + # Initialize prediction data dictionary with empty lists predict_data = {} predict_data['type'] = [] predict_data['energy'] = [] predict_data["cameradirection"] = [] + # Set model to evaluation mode (disables dropout, batch normalization, etc.) model.eval() + + # Perform predictions without gradient computation (faster inference) with torch.no_grad(): for i, x in enumerate(tqdm(data_loader, desc="Processing", total=len(data_loader))): - if len(x[0]['image'])==0: + # Skip empty batches + if len(x[0]['image']) == 0: continue + + # Forward pass through the model + # Handle both single-input and dual-input models if num_inputs == 2: - classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device) ,x[0]['peak_time'].to(self.device)) + # Model expects both image and peak time + classification_pred, energy_pred, direction_pred = model( + x[0]['image'].to(self.device), + x[0]['peak_time'].to(self.device) + ) else: - classification_pred, energy_pred, direction_pred = model(x[0]['image'].to(self.device)) + # Model expects only image + classification_pred, energy_pred, direction_pred = model( + x[0]['image'].to(self.device) + ) + # Collect particle type classification predictions if classification_pred[0] is not None: - gammaness = torch.softmax(classification_pred[0], dim=1).cpu().detach().numpy()[:,1] + # Apply softmax to get probability distribution and extract gammaness score + gammaness = torch.softmax(classification_pred[0], dim=1).cpu().detach().numpy() predict_data['type'].extend(gammaness) + + # Collect energy estimation predictions if energy_pred[0] is not None: predict_data['energy'].extend(energy_pred[0].cpu().detach().numpy()) + + # Collect direction reconstruction predictions if direction_pred[0] is not None: predict_data["cameradirection"].extend(direction_pred[0].cpu().detach().numpy()) + + # Log progress every 100 batches if i % 100 == 0: self.log.info(f"Processed {i}/{len(data_loader)} events.") + self.log.info("Processing completed.") - + + # Convert lists to numpy arrays for efficient storage and further processing predict_data["cameradirection"] = np.array(predict_data["cameradirection"]) predict_data["type"] = np.array(predict_data["type"]) predict_data["energy"] = np.array(predict_data["energy"]) - return predict_data , None \ No newline at end of file + + # Return predictions and placeholder for feature vectors + return predict_data, None \ No newline at end of file diff --git a/ctlearn/tools/predict/utils/load_model.py b/ctlearn/tools/predict/utils/load_model.py index 992872e6..3963142a 100644 --- a/ctlearn/tools/predict/utils/load_model.py +++ b/ctlearn/tools/predict/utils/load_model.py @@ -1,10 +1,55 @@ +""" +Model loading utility module for CTLearn predictions. +This module provides a framework-agnostic interface for loading trained models, +supporting both Keras and PyTorch frameworks. +""" + + def load_model(self): + """ + Load a trained model based on the configured framework type. + + This function acts as a dispatcher that delegates model loading to the appropriate + framework-specific implementation. It supports both Keras and PyTorch frameworks + and loads the model from the checkpoint path specified in the configuration. + + Parameters + ---------- + self : PredictionHandler + The prediction handler instance containing configuration parameters including: + - framework_type: str, either "keras" or "pytorch" + - Model checkpoint paths and other configuration parameters + + Returns + ------- + model : object + The loaded model ready for inference. Type depends on the framework: + - For Keras: keras.Model + - For PyTorch: torch.nn.Module + Returns None if the framework is not recognized. + + Raises + ------ + ImportError + If the specified framework's prediction module cannot be imported. + + Notes + ----- + The function automatically detects the framework type from the configuration + and imports the appropriate loading function dynamically to avoid unnecessary + dependencies when using only one framework. + """ if self.framework_type == "keras": + # Load Keras model using framework-specific loader from ctlearn.tools.predict.keras.predic_LST1_keras import load_keras_model return load_keras_model(self) + elif self.framework_type == "pytorch": + # Load PyTorch model using framework-specific loader from ctlearn.tools.predict.pytorch.predic_LST1_pytorch import load_pytorch_model return load_pytorch_model(self) + else: - self.log.error("Framework not found !!!") + # Log error if framework is not recognized + self.log.error(f"Framework '{self.framework_type}' not found! Supported frameworks: 'keras', 'pytorch'") return None \ No newline at end of file diff --git a/ctlearn/tools/predict/utils/optimaze_batch_size.py b/ctlearn/tools/predict/utils/optimaze_batch_size.py index c4be82bf..2cfc81cb 100644 --- a/ctlearn/tools/predict/utils/optimaze_batch_size.py +++ b/ctlearn/tools/predict/utils/optimaze_batch_size.py @@ -1,13 +1,51 @@ +""" +Batch size optimization utilities for CTLearn predictions. +This module provides functions to test and find optimal batch sizes for model inference, +helping to maximize GPU utilization while avoiding out-of-memory errors. +""" + import torch + def test_batch(model, imgs, peak_time, device): """ - Tests a batch already prepared with imgs and peak_time. - Returns True if the model can process it without errors, False if there is an OOM (Out of Memory) error. + Test if a model can process a given batch without memory errors. + + This function tests whether a pre-prepared batch of images and peak times + can be successfully processed by the model without encountering out-of-memory + (OOM) errors. It's used to validate batch sizes during optimization. + + Parameters + ---------- + model : torch.nn.Module + The PyTorch model to test. + imgs : torch.Tensor or array-like + Batch of images to process. Will be converted to tensor if necessary. + peak_time : torch.Tensor or array-like + Batch of peak time information. Will be converted to tensor if necessary. + device : torch.device or str + Device to run the test on (e.g., 'cuda:0' or 'cpu'). + + Returns + ------- + bool + True if the batch can be processed successfully, False if OOM error occurs. + + Raises + ------ + RuntimeError + If a RuntimeError other than OOM occurs during processing. + + Notes + ----- + The function automatically clears the CUDA cache after each test to ensure + clean memory state for subsequent tests. """ + # Move model to specified device and set to evaluation mode model.to(device) model.eval() + # Ensure inputs are tensors and move to device if not torch.is_tensor(imgs): imgs = torch.as_tensor(imgs).to(device) else: @@ -19,48 +57,101 @@ def test_batch(model, imgs, peak_time, device): peak_time = peak_time.to(device) try: + # Attempt forward pass without gradient computation with torch.no_grad(): _ = model(imgs, peak_time) + + # Clear CUDA cache to free memory torch.cuda.empty_cache() return True except RuntimeError as e: + # Check if error is due to out of memory if "out of memory" in str(e).lower(): torch.cuda.empty_cache() return False else: + # Re-raise unexpected errors after cleaning up torch.cuda.empty_cache() raise e def find_max_batch_size(self, model, imgs, peak_time, device, start_bs=8, step=8, max_bs=512): + """ + Find the maximum batch size that can be processed without OOM errors. + + This function performs a binary-like search to find the largest batch size + that can be successfully processed by the model on the given device. It starts + with a small batch size and incrementally increases until an OOM error occurs. + + Parameters + ---------- + self : object + Reference to the parent object (for potential logging or configuration access). + model : torch.nn.Module + The PyTorch model to test. + imgs : torch.Tensor or array-like + Sample images to use for testing. Only the first image is used and replicated. + peak_time : torch.Tensor or array-like + Sample peak time data. Only the first value is used and replicated. + device : torch.device or str + Device to run tests on (e.g., 'cuda:0' or 'cpu'). + start_bs : int, optional + Initial batch size to start testing with. Default is 8. + step : int, optional + Increment step for batch size increases. Default is 8. + max_bs : int, optional + Maximum batch size to test. Default is 512. + + Returns + ------- + int + The maximum batch size that can be processed without OOM errors. + + Raises + ------ + RuntimeError + If a RuntimeError other than OOM occurs during testing. + + Notes + ----- + - The function replicates a single image/peak_time to create test batches + - CUDA cache is cleared after each test to ensure accurate memory measurements + - Progress is printed to console with emoji indicators for status + """ batch_size = start_bs + + # Move model to device and set to evaluation mode model.to(device) model.eval() + # Ensure inputs are tensors if not torch.is_tensor(imgs): imgs = torch.as_tensor(imgs) if not torch.is_tensor(peak_time): peak_time = torch.as_tensor(peak_time) + # Disable gradient computation for efficiency with torch.no_grad(): while batch_size <= max_bs: try: - # Images - batch_imgs = imgs[:1] + # Prepare image batch + batch_imgs = imgs[:1] # Take first image as template if batch_imgs.ndim == 3: - batch_imgs = batch_imgs.unsqueeze(1) # Add channel + batch_imgs = batch_imgs.unsqueeze(1) # Add channel dimension if needed + # Replicate to create batch of desired size batch_imgs = batch_imgs.repeat(batch_size, 1, 1, 1).to(device) - # peak_time - value = peak_time[:1, 0, 0] # Take representative value - batch_peaks = value.unsqueeze(1) # Shape [1,1] - batch_peaks = batch_peaks.repeat(batch_size, 1) - batch_peaks = batch_peaks.unsqueeze(-1).unsqueeze(-1).to(device) # Shape [batch,1,1,1] + # Prepare peak_time batch + value = peak_time[:1, 0, 0] # Extract representative value + batch_peaks = value.unsqueeze(1) # Shape [1, 1] + batch_peaks = batch_peaks.repeat(batch_size, 1) # Replicate for batch + batch_peaks = batch_peaks.unsqueeze(-1).unsqueeze(-1).to(device) # Shape [batch, 1, 1, 1] - # Forward + # Attempt forward pass _ = model(batch_imgs, batch_peaks) + # Clean up tensors and cache del batch_imgs, batch_peaks, _ torch.cuda.empty_cache() @@ -69,12 +160,15 @@ def find_max_batch_size(self, model, imgs, peak_time, device, start_bs=8, step=8 except RuntimeError as e: if "out of memory" in str(e).lower(): + # OOM encountered, return previous successful batch size print(f"💥 OOM at batch size {batch_size}") torch.cuda.empty_cache() return batch_size - step else: + # Unexpected error, clean up and re-raise print(f"❌ Unexpected error at batch size {batch_size}: {e}") torch.cuda.empty_cache() raise e + # If we reached max_bs without OOM, return it (minus step to be safe) return batch_size - step diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index 483483a2..b562eef7 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -187,7 +187,7 @@ class LST1PredictionTool(Tool): "cleaned_image", "peak_time", "relative_peak_time", - "cleaned_peak_time", + "cleaned_peak_time", "cleaned_relative_peak_time", ] ), diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 97292e19..648b2c3e 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -143,7 +143,9 @@ def __init__( parameters, train_loader=None, val_loader=None, - test_val_loader=None, + test_val_loader=None, + train_dataset=None, + val_dataset=None, num_inputs=1, k=3, @@ -183,9 +185,12 @@ def __init__( self.num_inputs = num_inputs self.train_loader = train_loader self.val_loader = val_loader + self.train_dataset = train_dataset + self.val_dataset = val_dataset self.test_val_loader = test_val_loader self.class_names = ["gamma", "proton"] - + self.val_protoness = [] + self.val_gammaness = [] # Hyperparameters self.set_hyperparameters(parameters) @@ -790,8 +795,10 @@ def compute_type_loss_diffusion(self, x,y, training=False): # self.dummy_tensor = self.occupy_free_gpu_memory(self.device) # ---------------------------------------------------------------------------------------------------------- def on_train_batch_start(self, batch, batch_idx): - if batch_idx == 5 and not hasattr(self, "dummy_tensor"): - self.dummy_tensor = self.occupy_free_gpu_memory(self.device) + if self.device != torch.device("cpu"): + if batch_idx == 5 and not hasattr(self, "dummy_tensor"): + self.dummy_tensor = self.occupy_free_gpu_memory(self.device) + # ---------------------------------------------------------------------------------------------------------- def training_step(self, batch, batch_idx): # ------------------------------------------------------------------ @@ -1116,7 +1123,24 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): test_val=False, training=False, ) - + self.val_protoness.extend( + torch.softmax(classification_pred_, dim=1)[:, 0] + .float() + .cpu() + .detach() + .numpy() + .flatten() + .tolist() + ) + self.val_gammaness.extend( + torch.softmax(classification_pred_, dim=1)[:, 1] + .float() + .cpu() + .detach() + .numpy() + .flatten() + .tolist() + ) # Log batch loss and accuracy on the progress bar # if dataloader_idx == 0: # if self.is_difussion: @@ -1134,24 +1158,24 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # training=False, # ) - self.log( - "val_acc", - accuracy * 100, - on_step=True, - on_epoch=False, - prog_bar=True, - logger=False, - batch_size=batch_size, - ) - self.log( - "val_prec", - precision*100, - on_step=True, - on_epoch=False, - prog_bar=True, - logger=False, - batch_size=batch_size, - ) + self.log( + "val_acc", + accuracy * 100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + batch_size=batch_size, + ) + self.log( + "val_prec", + precision*100, + on_step=True, + on_epoch=False, + prog_bar=True, + logger=False, + batch_size=batch_size, + ) # --------------------------------------- # Direction # --------------------------------------- @@ -1179,11 +1203,11 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): cam_x = pred_dx cam_y = pred_dy - pred_alt, pred_az = self.val_loader.cam_to_alt_az(labels["tel_ids"].cpu().detach().numpy(), labels["focal_length"].cpu().detach().numpy(), labels["pix_rotation"].cpu().detach().numpy(),labels["tel_az"].cpu().detach().numpy(),labels["tel_alt"].cpu().detach().numpy(), cam_x, cam_y) + pred_alt, pred_az = self.val_dataset.cam_to_alt_az(labels["tel_ids"].cpu().detach().numpy(), labels["focal_length"].cpu().detach().numpy(), labels["pix_rotation"].cpu().detach().numpy(),labels["tel_az"].cpu().detach().numpy(),labels["tel_alt"].cpu().detach().numpy(), cam_x, cam_y) labels_dx_dy = labels_direction[:, 0:2] - true_alt, true_az = self.val_loader.cam_to_alt_az(labels["tel_ids"].cpu().detach().numpy(), labels["focal_length"].cpu().detach().numpy(), labels["pix_rotation"].cpu().detach().numpy(),labels["tel_az"].cpu().detach().numpy(),labels["tel_alt"].cpu().detach().numpy(), labels_dx_dy[:,0].float().cpu().detach().numpy(), labels_dx_dy[:,1].float().cpu().detach().numpy()) + true_alt, true_az = self.val_dataset.cam_to_alt_az(labels["tel_ids"].cpu().detach().numpy(), labels["focal_length"].cpu().detach().numpy(), labels["pix_rotation"].cpu().detach().numpy(),labels["tel_az"].cpu().detach().numpy(),labels["tel_alt"].cpu().detach().numpy(), labels_dx_dy[:,0].float().cpu().detach().numpy(), labels_dx_dy[:,1].float().cpu().detach().numpy()) self.val_alt_pred_list.extend(np.radians(pred_alt)) self.val_az_pred_list.extend(np.radians(pred_az)) @@ -1338,6 +1362,40 @@ def on_validation_epoch_end(self): }, self.current_epoch, ) + # Add the protonness and gammaness histogramns + self.val_protoness = np.array(self.val_protoness) + self.val_gammaness = np.array(self.val_gammaness) + if self.trainer.is_global_zero: + fig, ax = plt.subplots(figsize=(8, 6)) + ax.hist( + self.val_protoness, + bins=50, + alpha=0.7, + label="Protonness", + color="blue", + density=True, + ) + ax.hist( + self.val_gammaness, + bins=50, + alpha=0.7, + label="Gammaness", + color="orange", + density=True, + ) + ax.set_xlabel("Score") + ax.set_ylabel("Density") + ax.set_title("Protonness and Gammaness Distribution - Validation") + ax.legend() + self.logger.experiment.add_figure( + "Protonness and Gammaness Distribution/Validation", + fig, + self.current_epoch, + ) + plt.close(fig) + # Clear lists + self.val_protoness = [] + self.val_gammaness = [] # return 0 # --------------------------------------- # Direction diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 0fad9820..4b05ebc2 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -1,5 +1,5 @@ from ctapipe.core.traits import Path - +from torch.utils.data import DataLoader from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL try: import torch @@ -164,6 +164,7 @@ def setup(self): training_indices = indices[n_validation_examples:] validation_indices = indices[:n_validation_examples] + self.dl1dh_reader.channels = ["cleaned_image", "cleaned_peak_time"] # -------------------------------------------------------------------- # Reduce for testing # -------------------------------------------------------------------- @@ -183,8 +184,8 @@ def setup(self): self.log.info(f"Using class weights from configuration file: {self.parameters['class_weight']}") print("BASE TRAIN FRAMEWORK", self.framework_type) - - self.training_loader = DLDataLoader.create( + + self.train_dataset = DLDataLoader.create( framework=self.framework_type, DLDataReader=self.dl1dh_reader, indices=training_indices, @@ -197,9 +198,19 @@ def setup(self): use_augmentation=self.parameters["augmentation"]["use_augmentation"], is_training=True, ) + self.training_loader = DataLoader( + dataset=self.train_dataset, + batch_size=None, + batch_sampler=None, + num_workers=4, + pin_memory=True, + prefetch_factor=4, + persistent_workers=True + ) + print(len(self.training_loader)) - self.validation_loader = DLDataLoader.create( + self.validation_dataset = DLDataLoader.create( framework=self.framework_type, DLDataReader=self.dl1dh_reader, indices=validation_indices, @@ -212,8 +223,17 @@ def setup(self): use_augmentation=False, is_training=False, ) + self.validation_loader = DataLoader( + dataset=self.validation_dataset, + batch_size=None, + batch_sampler=None, + num_workers=4, + pin_memory=True, + prefetch_factor=4, + persistent_workers=True + ) - print(len(self.validation_loader)) + print(len(self.validation_dataset)) def start(self): @@ -308,6 +328,8 @@ def start(self): k=self.save_k, train_loader= self.training_loader, val_loader= self.validation_loader, + train_dataset=self.train_dataset, + val_dataset=self.validation_dataset ) if trainer_pl.is_global_zero: From 332f234f42aed646e72bb125578af1ad8ae9f606 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 19 Jan 2026 10:37:16 +0100 Subject: [PATCH 070/119] Add the log scaling --- .gitignore | 2 +- ctlearn/core/data_loader/pytorch_loader.py | 37 +++++--------- ctlearn/core/pytorch/utils/utils.py | 3 +- .../predict/pytorch/predic_LST1_pytorch.py | 6 +++ ctlearn/tools/predict_LST1.py | 2 +- ctlearn/tools/train/base_train_model.py | 11 ++++- ctlearn/tools/train/pytorch/CTLearnPL.py | 48 +++++++++++-------- .../train/pytorch/train_pytorch_model.py | 6 ++- 8 files changed, 64 insertions(+), 51 deletions(-) diff --git a/.gitignore b/.gitignore index 6e6d8b30..a45a88a4 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,7 @@ output_dir2/ *~ *.log .vscode/ - +launcher_workspace/ *.h5 output_dir2/ diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 4072811e..e769152d 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -57,7 +57,7 @@ def __init__( # self.prefetch_queue = [] # self._next_batch_idx = 0 # self._next_batch_future = None - + self.apply_log_scaling = parameters["normalization"]["apply_log_scaling"] self.parameter = parameters self.use_augmentation = use_augmentation self.use_clean = parameters["normalization"]["use_clean"] @@ -245,17 +245,6 @@ def _fetch_batch(self, index): features, labels = self._get_stereo_item(batch) return features, labels - def _fill_prefetch_queue(self): - while ( - len(self.prefetch_queue) < self.max_prefetch - and self._next_batch_idx < len(self) - ): - future = self.executor.submit( - self._fetch_batch, self._next_batch_idx - ) - self.prefetch_queue.append(future) - self._next_batch_idx += 1 - # Fetching batches def __getitem__(self, index): """ @@ -278,17 +267,6 @@ def __getitem__(self, index): # Virtual batch index t = index // int(np.ceil(len(self.indices) / self.T / self.batch_size)) - - # # --- 1️⃣ Ensure prefetch queue is filled --- - # if len(self.prefetch_queue) == 0: - # self._fill_prefetch_queue() - - # # --- 2️⃣ Pop the next ready batch --- - # future = self.prefetch_queue.pop(0) - # features, labels = future.result() # blocks only if needed - - # # --- 3️⃣ Refill queue to keep 4 prefetched --- - # self._fill_prefetch_queue() features, labels = self._fetch_batch(index) return features, labels, t @@ -647,7 +625,16 @@ def duplicate_tensor(t,idx_to_duplicate): image, peak_time = self.apply_augmentation(features_out["image"], features_out["peak_time"]) - + # Apply log scaling + if self.apply_log_scaling[0]: + image = image.astype(np.float32) + image = np.log10(image + 1.0) + + if self.apply_log_scaling[1]: + peak_time = peak_time.astype(np.float32) + peak_time = np.log10(peak_time + 1.0) + # Change to channel first + image = np.transpose(image, (0, 3, 1, 2)) peak_time = np.transpose(peak_time, (0, 3, 1, 2)) # features_out["image"] = torch.from_numpy(image).float().permute(0, 3, 1, 2).contiguous() @@ -668,7 +655,7 @@ def duplicate_tensor(t,idx_to_duplicate): # keep_idx = np.where((leakage > 0.8) & (intensity > 50))[0] #Keep all events during evaluation for now keep_idx = np.arange(len(intensity)) - + # Filter features_out for key in features_out: features_out[key] = features_out[key][keep_idx] diff --git a/ctlearn/core/pytorch/utils/utils.py b/ctlearn/core/pytorch/utils/utils.py index c1c353f6..e8942e40 100644 --- a/ctlearn/core/pytorch/utils/utils.py +++ b/ctlearn/core/pytorch/utils/utils.py @@ -279,7 +279,8 @@ def reco_src_sky_to_camera(data, effective_focal_length=29.30565 * u.m): "dir_mu": None, "dir_sigma": None, "energy_mu": None, - "energy_sigma": None + "energy_sigma": None, + "apply_log_scaling": [None, None] }, "dataset": { "num_workers": None, diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 80e92657..e2155c92 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -172,6 +172,12 @@ def predictions(self): imgs[np.isinf(imgs)] = 0 feature_vector = True + if self.parameters["normalization"]["apply_log_scaling"][0] == True: + imgs = imgs.astype(np.float32) + imgs = np.log10(imgs + 1.0) + if self.parameters["normalization"]["apply_log_scaling"][1] == True and len(self.channels) == 2: + peak_time = peak_time.astype(np.float32) + peak_time = np.log10(peak_time + 1.0) # Run predictions for each configured task for task in self.tasks: diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index b562eef7..0e3c9266 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -191,7 +191,7 @@ class LST1PredictionTool(Tool): "cleaned_relative_peak_time", ] ), - default_value=["cleaned_image", "cleaned_relative_peak_time"], + default_value=["cleaned_image", "cleaned_peak_time"], allow_none=False, help=( "Set the input channels to be loaded from the DL1 event data. " diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index e87142f0..671e2009 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -14,7 +14,7 @@ ComponentName, Unicode, ) -from dl1_data_handler.reader import DLDataReader +from dl1_data_handler.reader import DLDataReader, TableQualityQuery class TrainCTLearnModel(Tool): """ @@ -276,15 +276,22 @@ def setup(self): "'DLFeatureVectorReader' is not supported in CTLearn yet. " "Missing stereo CTLearnModel implementation." ) + self.quality_query = TableQualityQuery(quality_criteria=[('> 50 phe', 'hillas_intensity > 50')]) + self.channels = ["cleaned_image", "cleaned_peak_time"] + self.config.TableQualityQuery.quality_criteria = self.quality_query.quality_criteria + self.config.DLDataReader.channels = self.channels print(f"self.dl1dh_reader_type: {self.dl1dh_reader_type}") self.dl1dh_reader = DLDataReader.from_name( self.dl1dh_reader_type, input_url_signal=sorted(self.input_url_signal), input_url_background=sorted(self.input_url_background), - parent=self, + parent=self ) + # self.dl1dh_reader.channels = ["cleaned_image", "cleaned_peak_time"] + # self.dl1dh_reader.quality_query.quality_criteria = [('> 50 phe', 'hillas_intensity > 1000')] + self.log.info("Number of events loaded: %s", self.dl1dh_reader._get_n_events()) if "type" in self.reco_tasks: self.log.info( diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 648b2c3e..8bf8f9c3 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -189,8 +189,8 @@ def __init__( self.val_dataset = val_dataset self.test_val_loader = test_val_loader self.class_names = ["gamma", "proton"] - self.val_protoness = [] - self.val_gammaness = [] + self.val_proton_gammanes = [] + self.val_gamma_gammanes = [] # Hyperparameters self.set_hyperparameters(parameters) @@ -200,10 +200,10 @@ def __init__( # Loss Function if self.task == Task.type: - self.class_weights = ( + weights = ( torch.tensor([parameters['class_weight'][0],parameters['class_weight'][1]], dtype=torch.float32).to(self.device).contiguous() ) # [1.0, 1.3] - + self.register_buffer('class_weights', weights) self.criterion_class = nn.CrossEntropyLoss( weight=self.class_weights, reduction="mean" ) @@ -374,7 +374,7 @@ def compute_type_loss( target = labels_class.to(torch.int64) # Cálculo de la loss con F.cross_entropy - loss_class = F.cross_entropy(classification_pred, target, weight=self.class_weights, reduction='mean') + loss_class = F.cross_entropy(classification_pred, target, weight=self.class_weights.to(classification_pred.device), reduction='mean') # Calculate accuracy predicted = torch.softmax(classification_pred, dim=1) @@ -1123,8 +1123,8 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): test_val=False, training=False, ) - self.val_protoness.extend( - torch.softmax(classification_pred_, dim=1)[:, 0] + self.val_proton_gammanes.extend( + torch.softmax(classification_pred_, dim=1)[:, 1][labels_class == 0] .float() .cpu() .detach() @@ -1132,8 +1132,8 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): .flatten() .tolist() ) - self.val_gammaness.extend( - torch.softmax(classification_pred_, dim=1)[:, 1] + self.val_gamma_gammanes.extend( + torch.softmax(classification_pred_, dim=1)[:, 1][labels_class == 1] .float() .cpu() .detach() @@ -1363,39 +1363,49 @@ def on_validation_epoch_end(self): self.current_epoch, ) # Add the protonness and gammaness histogramns - self.val_protoness = np.array(self.val_protoness) - self.val_gammaness = np.array(self.val_gammaness) + self.val_proton_gammanes = np.array(self.val_proton_gammanes) + self.val_gamma_gammanes = np.array(self.val_gamma_gammanes) if self.trainer.is_global_zero: fig, ax = plt.subplots(figsize=(8, 6)) ax.hist( - self.val_protoness, + self.val_proton_gammanes, bins=50, alpha=0.7, - label="Protonness", + label="Proton", color="blue", density=True, ) ax.hist( - self.val_gammaness, + self.val_gamma_gammanes, bins=50, alpha=0.7, - label="Gammaness", + label="Gammas", color="orange", density=True, ) ax.set_xlabel("Score") ax.set_ylabel("Density") - ax.set_title("Protonness and Gammaness Distribution - Validation") + ax.set_title("Protons and Gammas Gammanes Distribution - Validation") ax.legend() + save_path = os.path.join( + self.logger.log_dir, + f"proton_gammaness_validation_{self.current_epoch}_{global_loss_val}.png" + ) + fig.savefig(save_path, format="png") # <--- Usa fig.savefig, no plt.savefig + + # 2. Luego envíala al logger self.logger.experiment.add_figure( - "Protonness and Gammaness Distribution/Validation", + "Protons and Gammas Gammanes Distribution - Validation", fig, self.current_epoch, ) + + # 3. Finalmente cierra la figura plt.close(fig) + # Clear lists - self.val_protoness = [] - self.val_gammaness = [] + self.val_proton_gammanes = [] + self.val_gamma_gammanes = [] # return 0 # --------------------------------------- # Direction diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 4b05ebc2..6dd1d502 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -1,6 +1,9 @@ from ctapipe.core.traits import Path from torch.utils.data import DataLoader from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppress TensorFlow logging + try: import torch @@ -8,7 +11,6 @@ raise ImportError("pytorch is not installed in your environment!") try: - import pytorch_lightning from pytorch_lightning.loggers import TensorBoardLogger except ImportError: raise ImportError("pytorch_lightning is not installed in your environment!") @@ -164,7 +166,7 @@ def setup(self): training_indices = indices[n_validation_examples:] validation_indices = indices[:n_validation_examples] - self.dl1dh_reader.channels = ["cleaned_image", "cleaned_peak_time"] + # -------------------------------------------------------------------- # Reduce for testing # -------------------------------------------------------------------- From f78199f7de47c2518476234de4dea8e129a195b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 8 Jul 2026 17:09:23 +0000 Subject: [PATCH 071/119] Unify the keras and pytorch pipelines with the CTApipe Traitlets --- ctlearn/core/data_loader/base_loader.py | 168 ++++++++++- ctlearn/core/data_loader/keras_loader.py | 39 +++ ctlearn/core/data_loader/pytorch_loader.py | 210 ++++---------- ctlearn/core/pytorch/model.py | 113 ++++++++ .../predict/pytorch/predic_LST1_pytorch.py | 30 +- ctlearn/tools/predict/utils/predict_model.py | 207 +++++++++++++- ctlearn/tools/train/base_train_model.py | 232 +++++++++++++++ .../train/pytorch/train_pytorch_model.py | 268 +++++++++++++++--- 8 files changed, 1045 insertions(+), 222 deletions(-) create mode 100644 ctlearn/core/pytorch/model.py diff --git a/ctlearn/core/data_loader/base_loader.py b/ctlearn/core/data_loader/base_loader.py index 2b717d82..77ece1e0 100644 --- a/ctlearn/core/data_loader/base_loader.py +++ b/ctlearn/core/data_loader/base_loader.py @@ -11,6 +11,10 @@ """ from abc import ABC, abstractmethod +import numpy as np +import random +import cv2 +from ctlearn.core.ctlearn_enum import Task class BaseDLDataLoader(ABC): """ @@ -25,7 +29,7 @@ class BaseDLDataLoader(ABC): DLDataReader: The data reader instance for accessing telescope event data indices (list): List of event indices to load from the dataset tasks (list): List of tasks to perform (e.g., classification, energy, direction) - batch_size (int): Number of samples per batch + batch_size (int): Number of batches per epoch random_seed (int or None): Random seed for reproducibility stack_telescope_images (bool): Whether to stack images from multiple telescopes sort_by_intensity (bool): Whether to sort telescope images by Hillas intensity @@ -75,7 +79,7 @@ def __init__( Defaults to False **kwargs: Additional keyword arguments passed to parent classes """ - super().__init__(**kwargs) + super().__init__() # Store initialization parameters self.DLDataReader = DLDataReader @@ -86,6 +90,166 @@ def __init__( self.stack_telescope_images = stack_telescope_images self.sort_by_intensity = sort_by_intensity + # Get parent configuration if available + parent = getattr(self.DLDataReader, "parent", None) + + # Helper to get attributes with fallbacks + def get_val(name, default): + if parent is not None and hasattr(parent, name): + return getattr(parent, name) + if name in kwargs: + return kwargs[name] + if "parameters" in kwargs and isinstance(kwargs["parameters"], dict): + params = kwargs["parameters"] + if name == "use_augmentation": + return params.get("augmentation", {}).get("use_augmentation", default) + elif name == "aug_prob": + return params.get("augmentation", {}).get("aug_prob", default) + elif name == "rot_prob": + return params.get("augmentation", {}).get("rot_prob", default) + elif name == "trans_prob": + return params.get("augmentation", {}).get("trans_prob", default) + elif name == "flip_hor_prob": + return params.get("augmentation", {}).get("flip_hor_prob", default) + elif name == "flip_ver_prob": + return params.get("augmentation", {}).get("flip_ver_prob", default) + elif name == "mask_prob": + return params.get("augmentation", {}).get("mask_prob", default) + elif name == "mask_dvr_prob": + return params.get("augmentation", {}).get("mask_dvr_prob", default) + elif name == "noise_prob": + return params.get("augmentation", {}).get("noise_prob", default) + elif name == "max_rot": + return params.get("augmentation", {}).get("max_rot", default) + elif name == "max_trans": + return params.get("augmentation", {}).get("max_trans", default) + elif name == "apply_log_scaling": + return params.get("normalization", {}).get("apply_log_scaling", default) + elif name == "use_clean": + return params.get("normalization", {}).get("use_clean", default) + elif name == "use_clean_dvr": + return params.get("normalization", {}).get("use_clean_dvr", default) + elif name == "type_mu": + return params.get("normalization", {}).get("type_mu", default) + elif name == "type_sigma": + return params.get("normalization", {}).get("type_sigma", default) + elif name == "dir_mu": + return params.get("normalization", {}).get("dir_mu", default) + elif name == "dir_sigma": + return params.get("normalization", {}).get("dir_sigma", default) + elif name == "energy_mu": + return params.get("normalization", {}).get("energy_mu", default) + elif name == "energy_sigma": + return params.get("normalization", {}).get("energy_sigma", default) + elif name == "leakage_intensity_cutoff": + return params.get("cut-off", {}).get("leakage_intensity", default) + elif name == "intensity_cutoff": + return params.get("cut-off", {}).get("intensity", default) + return default + + # Initialize pre-processing & augmentation options + self.use_augmentation = get_val("use_augmentation", False) + self.aug_prob = get_val("aug_prob", 0.5) + self.rot_prob = get_val("rot_prob", 0.5) + self.trans_prob = get_val("trans_prob", 0.5) + self.flip_hor_prob = get_val("flip_hor_prob", 0.5) + self.flip_ver_prob = get_val("flip_ver_prob", 0.5) + self.mask_prob = get_val("mask_prob", 0.5) + self.mask_dvr_prob = get_val("mask_dvr_prob", 0.5) + self.noise_prob = get_val("noise_prob", 0.5) + self.max_rot = get_val("max_rot", 5.0) + self.max_trans = get_val("max_trans", 10.0) + + self.apply_log_scaling = get_val("apply_log_scaling", [True, True]) + self.use_clean = get_val("use_clean", True) + self.use_clean_dvr = get_val("use_clean_dvr", False) + + self.type_mu = get_val("type_mu", 0.0) + self.type_sigma = get_val("type_sigma", 1000.0) + self.dir_mu = get_val("dir_mu", 0.0) + self.dir_sigma = get_val("dir_sigma", 1000.0) + self.energy_mu = get_val("energy_mu", 0.0) + self.energy_sigma = get_val("energy_sigma", 1000.0) + + self.leakage_intensity_cutoff = get_val("leakage_intensity_cutoff", 0.2) + self.intensity_cutoff = get_val("intensity_cutoff", 50.0) + + def clean_and_normalize(self, image, peak_time, task): + # Remove negative numbers and avoid inf or nans + image[image < 0] = 0 + peak_time[peak_time < 0] = 0 + image[np.isnan(image)] = 0 + image[np.isinf(image)] = 0 + peak_time[np.isnan(peak_time)] = 0 + peak_time[np.isinf(peak_time)] = 0 + + # Normalization + if task == Task.type or task == "type": + image = (image - self.type_mu) / self.type_sigma + peak_time = (peak_time - self.type_mu) / self.type_sigma + elif task == Task.energy or task == "energy": + image = (image - self.energy_mu) / self.energy_sigma + peak_time = (peak_time - self.energy_mu) / self.energy_sigma + elif task in [Task.cameradirection, Task.skydirection, Task.direction, "cameradirection", "skydirection", "direction"]: + image = (image - self.dir_mu) / self.dir_sigma + peak_time = (peak_time - self.dir_mu) / self.dir_sigma + + return image, peak_time + + def apply_log_scaling_to_channels(self, image, peak_time): + if self.apply_log_scaling[0]: + image = image.astype(np.float32) + image = np.log10(image + 1.0) + if self.apply_log_scaling[1]: + peak_time = peak_time.astype(np.float32) + peak_time = np.log10(peak_time + 1.0) + return image, peak_time + + def apply_augmentation(self, image, peak_time, task): + for id_batch in range(image.shape[0]): + random_aug = random.random() + + if random_aug > self.aug_prob: + if task not in [Task.cameradirection, Task.skydirection, Task.direction, "cameradirection", "skydirection", "direction"]: + random_aug_flip_ver = random.random() + if random_aug_flip_ver > self.flip_ver_prob: + image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 0), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 0), axis=-1) + continue + random_aug_flip_hor = random.random() + if random_aug_flip_hor > self.flip_hor_prob: + image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 1), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 1), axis=-1) + continue + random_aug_rot = random.random() + if random_aug_rot > self.rot_prob: + (h, w) = image[id_batch].shape[:2] + angle = random.uniform(-self.max_rot, self.max_rot) + scale = 1.0 + center = (w // 2, h // 2) + rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale) + image[id_batch] = np.expand_dims(cv2.warpAffine( + image[id_batch].astype(np.float32), rotation_matrix, (w, h) + ), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.warpAffine( + peak_time[id_batch].astype(np.float32), rotation_matrix, (w, h) + ), axis=-1) + continue + random_aug_trans = random.random() + if random_aug_trans > self.trans_prob: + (h, w) = image[id_batch].shape[:2] + tx = random.uniform(-self.max_trans, self.max_trans) + ty = random.uniform(-self.max_trans, self.max_trans) + translation_matrix = np.float32([[1, 0, tx], [0, 1, ty]]) + image[id_batch] = np.expand_dims(cv2.warpAffine( + image[id_batch].astype(np.float32), translation_matrix, (w, h) + ), axis=-1) + peak_time[id_batch] = np.expand_dims(cv2.warpAffine( + peak_time[id_batch].astype(np.float32), translation_matrix, (w, h) + ), axis=-1) + continue + return image, peak_time + # Determine input shape based on reader type and observation mode # Feature vector readers don't have spatial dimensions if self.DLDataReader.__class__.__name__ != "DLFeatureVectorReader": diff --git a/ctlearn/core/data_loader/keras_loader.py b/ctlearn/core/data_loader/keras_loader.py index 022c79f5..5e815b96 100644 --- a/ctlearn/core/data_loader/keras_loader.py +++ b/ctlearn/core/data_loader/keras_loader.py @@ -181,6 +181,18 @@ def _get_mono_item(self, batch): # Retrieve telescope images and store in features dictionary features = {"input": batch["features"].data} + image = features["input"][..., 0:1] + peak_time = features["input"][..., 1:2] + + active_task = self.tasks[0] if self.tasks else None + image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + + if self.use_augmentation: + image, peak_time = self.apply_augmentation(image, peak_time, active_task) + + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + features["input"] = np.concatenate([image, peak_time], axis=-1) + # Extract particle type classification labels if "type" in self.tasks: # Convert to one-hot encoding (0=gamma, 1=proton) @@ -385,6 +397,33 @@ def _get_stereo_item(self, batch): if "features" in batch.colnames: # Telescope images features = {"input": np.array(features)} + + features_arr = features["input"] + active_task = self.tasks[0] if self.tasks else None + + # Slicing image and peak_time based on dimensionality + if len(features_arr.shape) == 5: # Unstacked mode: (batch, tel, height, width, channels) + image = features_arr[..., 0] + peak_time = features_arr[..., 1] + + image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + + features["input"] = np.stack([image, peak_time], axis=-1) + else: # Stacked mode: (batch, height, width, channels) + image = features_arr[..., ::2] + peak_time = features_arr[..., 1::2] + + image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + + # Re-stack alternating channels + stacked = [] + for i in range(image.shape[-1]): + stacked.append(image[..., i:i+1]) + stacked.append(peak_time[..., i:i+1]) + features["input"] = np.concatenate(stacked, axis=-1) + # TODO: Add support for using both mono and stereo feature vectors simultaneously if "mono_feature_vectors" in batch.colnames: # Telescope-level feature vectors diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index e769152d..23632946 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -50,49 +50,19 @@ def __init__( is_training=False, **kwargs, ): - - self.is_training=is_training - # self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=4) - # self.max_prefetch = 8 - # self.prefetch_queue = [] - # self._next_batch_idx = 0 - # self._next_batch_future = None - self.apply_log_scaling = parameters["normalization"]["apply_log_scaling"] - self.parameter = parameters - self.use_augmentation = use_augmentation - self.use_clean = parameters["normalization"]["use_clean"] - self.use_clean_dvr = parameters["normalization"]["use_clean_dvr"] - + self.is_training = is_training + + super().__init__( + tasks=tasks, + parameters=parameters, + use_augmentation=use_augmentation, + **kwargs, + ) + self.task = tasks - - # Augmentation probabilities - self.mask_augmentation = parameters["augmentation"]["aug_prob"] - self.aug_prob = parameters["augmentation"]["aug_prob"] - self.rot_prob = parameters["augmentation"]["rot_prob"] - self.trans_prob = parameters["augmentation"]["trans_prob"] - self.flip_hor_prob = parameters["augmentation"]["flip_hor_prob"] - self.flip_ver_prob = parameters["augmentation"]["flip_ver_prob"] - self.mask_prob = parameters["augmentation"]["mask_prob"] - self.mask_dvr_prob = parameters["augmentation"]["mask_dvr_prob"] - self.noise_prob = parameters["augmentation"]["noise_prob"] - self.max_aug_rot = parameters["augmentation"]["max_rot"] - self.max_aug_trans = parameters["augmentation"]["max_trans"] - - # Normalization - self.type_mu = parameters["normalization"]["type_mu"] - self.type_sigma = parameters["normalization"]["type_sigma"] - self.dir_mu = parameters["normalization"]["dir_mu"] - self.dir_sigma = parameters["normalization"]["dir_sigma"] - self.energy_mu = parameters["normalization"]["energy_mu"] - self.energy_sigma = parameters["normalization"]["energy_sigma"] - - super().__init__(**kwargs, tasks=tasks) self.on_epoch_end() - - # self.T=T - # self.total_len = len(self.indices) * T self.set_T(T) - + self.hillas_names = [ "obs_id", "event_id", @@ -171,66 +141,7 @@ def on_epoch_end(self): np.random.seed(self.random_seed) np.random.shuffle(self.indices) - def apply_augmentation(self, image, peak_time): - - for id_batch in range(image.shape[0]): - random_aug = random.random() - - if random_aug > self.aug_prob: - - if self.task != Task.cameradirection and self.task != Task.skydirection: - - random_aug_flip_ver = random.random() - if random_aug_flip_ver > self.flip_ver_prob: - # Vertical flip - image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 0), axis=-1) - peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 0), axis=-1) - continue - random_aug_flip_hor = random.random() - if random_aug_flip_hor > self.flip_hor_prob: - # Horizontal - image[id_batch] = np.expand_dims(cv2.flip(image[id_batch].astype(np.float32), 1), axis=-1) - peak_time[id_batch] = np.expand_dims(cv2.flip(peak_time[id_batch].astype(np.float32), 1), axis=-1) - # Rotation - continue - random_aug_rot = random.random() - if random_aug_rot > self.rot_prob: - (h, w) = image[id_batch].shape[:2] - - angle = random.uniform(-self.max_aug_rot, self.max_aug_rot) - scale = 1.0 # No scaling - center = (w // 2, h // 2) - # Step 5: Get the rotation matrix - rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale) - - # Step 6: Rotate the image - image[id_batch] = np.expand_dims(cv2.warpAffine( - image[id_batch].astype(np.float32), rotation_matrix, (w, h) - ), axis=-1) - peak_time[id_batch] = np.expand_dims(cv2.warpAffine( - peak_time[id_batch].astype(np.float32), rotation_matrix, (w, h) - ), axis=-1) - continue - # Translation - random_aug_trans = random.random() - if random_aug_trans > self.trans_prob: - # Translation - (h, w) = image[id_batch].shape[:2] - - tx = random.uniform(-self.max_aug_trans, self.max_aug_trans) - ty = random.uniform(-self.max_aug_trans, self.max_aug_trans) - translation_matrix = np.float32([[1, 0, tx], [0, 1, ty]]) - image[id_batch] = np.expand_dims(cv2.warpAffine( - image[id_batch].astype(np.float32), translation_matrix, (w, h) - ), axis=-1) - peak_time[id_batch] = np.expand_dims(cv2.warpAffine( - peak_time[id_batch].astype(np.float32), translation_matrix, (w, h) - ), axis=-1) - continue - else: - doNothing = True - return image, peak_time def _fetch_batch(self, index): batch_indices = self.indices[index * self.batch_size : (index + 1) * self.batch_size] @@ -491,37 +402,8 @@ def _get_mono_item(self, batch): image = features["input"][..., 0:1] peak_time = features["input"][..., 1:2] - - # ---------------------------------------------------- - # Remove negative numbers and avoid inf or nans - # ---------------------------------------------------- - image[image < 0] = 0 - peak_time[peak_time < 0] = 0 - image[np.isnan(image)] = 0 - image[np.isinf(image)] = 0 - peak_time[np.isnan(peak_time)] = 0 - peak_time[np.isinf(peak_time)] = 0 - - - # image, peak_time = self.apply_augmentation(image, peak_time) - - # image = np.transpose(image, (0, 3, 1, 2)) - # peak_time = np.transpose(peak_time, (0, 3, 1, 2)) - - if self.task == Task.type: - image = (image - self.type_mu) / self.type_sigma - peak_time = (peak_time - self.type_mu) / self.type_sigma - - if self.task == Task.energy: - image = (image - self.energy_mu) / self.energy_sigma - peak_time = (peak_time - self.energy_mu) / self.energy_sigma - - if self.task == Task.cameradirection or self.task == Task.skydirection: - image = (image - self.dir_mu) / self.dir_sigma - peak_time = (peak_time - self.dir_mu) / self.dir_sigma - - # image = torch.from_numpy(image).contiguous().float() - # peak_time = torch.from_numpy(peak_time).contiguous().float() + active_task = self.tasks[0] if self.tasks else None + image, peak_time = self.clean_and_normalize(image, peak_time, active_task) features_out = {} features_out["image"] = image @@ -617,43 +499,32 @@ def duplicate_tensor(t,idx_to_duplicate): if self.use_augmentation: - if isinstance(features_out["image"], torch.Tensor): features_out["image"] = features_out["image"].cpu().numpy() if isinstance(features_out["peak_time"], torch.Tensor): features_out["peak_time"] = features_out["peak_time"].cpu().numpy() - image, peak_time = self.apply_augmentation(features_out["image"], features_out["peak_time"]) + image, peak_time = self.apply_augmentation(features_out["image"], features_out["peak_time"], active_task) + else: + image, peak_time = features_out["image"], features_out["peak_time"] # Apply log scaling - if self.apply_log_scaling[0]: - image = image.astype(np.float32) - image = np.log10(image + 1.0) - - if self.apply_log_scaling[1]: - peak_time = peak_time.astype(np.float32) - peak_time = np.log10(peak_time + 1.0) - # Change to channel first + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + # Change to channel first image = np.transpose(image, (0, 3, 1, 2)) peak_time = np.transpose(peak_time, (0, 3, 1, 2)) - # features_out["image"] = torch.from_numpy(image).float().permute(0, 3, 1, 2).contiguous() - # features_out["peak_time"] = torch.from_numpy(peak_time).float().permute(0, 3, 1, 2).contiguous() features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() - #Create a dummy keep_idx that keeps all events + #Create keep_idx based on configurable leakage and intensity cutoffs hillas = features["hillas"] leakage = np.array(hillas["leakage_intensity_width_2"]) intensity = np.array(hillas["hillas_intensity"]) - keep_idx = np.where((leakage <= 0.2) & (intensity >= 50))[0] + keep_idx = np.where((leakage <= self.leakage_intensity_cutoff) & (intensity >= self.intensity_cutoff))[0] if not self.is_training: - # Generate keep_idx as before - #keep_idx = np.where((leakage < 0.2) & (intensity > 50))[0] - # keep_idx = np.where((leakage > 0.8) & (intensity > 50))[0] - #Keep all events during evaluation for now keep_idx = np.arange(len(intensity)) # Filter features_out @@ -791,22 +662,47 @@ def _get_stereo_item(self, batch): ), axis=1, ) - # Store the fatures in the features dictionary + # Store the features in the features dictionary if "features" in batch.colnames: features = {"input": np.array(features)} - # TDOO: Add support for both feature vectors + # TODO: Add support for both feature vectors if "mono_feature_vectors" in batch.colnames: features = {"input": np.array(mono_feature_vectors)} if "stereo_feature_vectors" in batch.colnames: features = {"input": np.array(stereo_feature_vectors)} - image = features[:, :, :, 0] - peak_time = features[:, :, :, 1] + # Extract features array from dict + features_arr = features["input"] + active_task = self.tasks[0] if self.tasks else None - image = np.transpose(image, (2, 0, 1)) - peak_time = np.transpose(peak_time, (2, 0, 1)) + # Slicing image and peak_time based on dimensionality + if len(features_arr.shape) == 5: # Unstacked mode: (batch, tel, height, width, channels) + image = features_arr[..., 0] + peak_time = features_arr[..., 1] + + image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + + image = np.transpose(image, (0, 1, 4, 2, 3)) if len(image.shape) == 5 else np.expand_dims(image, axis=2) + peak_time = np.transpose(peak_time, (0, 1, 4, 2, 3)) if len(peak_time.shape) == 5 else np.expand_dims(peak_time, axis=2) + else: # Stacked mode: (batch, height, width, channels) + image = features_arr[..., ::2] + peak_time = features_arr[..., 1::2] + + image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + + image = np.transpose(image, (0, 3, 1, 2)) + peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + + features_out = {} + features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() + features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() + + # Convert labels to PyTorch tensors + for key in labels.keys(): + labels[key] = torch.from_numpy(labels[key]).contiguous() + if key != "type": + labels[key] = labels[key].unsqueeze(-1) - features_out = None - features_out["image"] = image - features_out["peak_time"] = peak_time return features_out, labels diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py new file mode 100644 index 00000000..3b526343 --- /dev/null +++ b/ctlearn/core/pytorch/model.py @@ -0,0 +1,113 @@ +""" +CTLearn PyTorch Model Registry + +This module defines the ctapipe Component wrapper classes for PyTorch models, +allowing them to be registered, configured, and instantiated dynamically using +ctapipe's Component system, matching the Keras model design. +""" + +from ctapipe.core import Component +from ctapipe.core.traits import Unicode, List, Float, Bool, Int +from ctlearn.core.ctlearn_enum import Task +import torch + +class CTLearnPyTorchModel(Component): + """ + Base class for PyTorch models in CTLearn. + Acts as a ctapipe Component wrapper for torch.nn.Module models. + """ + model_name = Unicode(help="Name of the model architecture").tag(config=True) + + def __init__(self, parent=None, **kwargs): + super().__init__(parent=parent, **kwargs) + self.model = None + + +class ThinResNet(CTLearnPyTorchModel): + """ + Component wrapper for ThinResNet PyTorch model. + """ + num_blocks = List( + trait=Int(), + default_value=[3, 4, 6, 3], + help="Number of blocks per stage in ResNet", + ).tag(config=True) + + dropout = Float( + default_value=0.1, + help="Dropout probability", + ).tag(config=True) + + use_bn = Bool( + default_value=False, + help="Whether to use Batch Normalization", + ).tag(config=True) + + def __init__(self, task="type", num_inputs=1, num_outputs=2, parent=None, **kwargs): + super().__init__(parent=parent, **kwargs) + from ctlearn.core.pytorch.nets.models.ThinResNet.ThinResNet import ThinResNet as PTThinResNet + self.model = PTThinResNet( + task=task, + num_inputs=num_inputs, + num_outputs=num_outputs, + num_blocks=self.num_blocks, + dropout=self.dropout, + use_bn=self.use_bn, + ) + + +class DoubleBBEfficientNet(CTLearnPyTorchModel): + """ + Component wrapper for DoubleBBEfficientNet PyTorch model. + """ + model_variant = Unicode( + default_value="efficientnet-b3", + help="Variant of EfficientNet backbone (e.g. efficientnet-b0 to b7)", + ).tag(config=True) + + def __init__(self, task="type", num_inputs=2, num_outputs=2, parent=None, **kwargs): + super().__init__(parent=parent, **kwargs) + from ctlearn.core.pytorch.nets.models.DoubleBBEfficientNet.DoubleBBEfficientNet import DoubleBBEfficientNet as PTDoubleBBEfficientNet + device_str = "cuda" + if parent is not None and hasattr(parent, "device_str"): + device_str = parent.device_str + self.model = PTDoubleBBEfficientNet( + task=task, + num_inputs=num_inputs, + num_outputs=num_outputs, + model_variant=self.model_variant, + device_str=device_str, + ) + + +class ThinResNet_DBB(CTLearnPyTorchModel): + """ + Component wrapper for ThinResNet_DBB (Dual Backbone ThinResNet) PyTorch model. + """ + num_blocks = List( + trait=Int(), + default_value=[3, 4, 6, 3], + help="Number of blocks per stage in ResNet", + ).tag(config=True) + + dropout = Float( + default_value=0.1, + help="Dropout probability", + ).tag(config=True) + + use_bn = Bool( + default_value=False, + help="Whether to use Batch Normalization", + ).tag(config=True) + + def __init__(self, task="type", num_inputs=2, num_outputs=3, parent=None, **kwargs): + super().__init__(parent=parent, **kwargs) + from ctlearn.core.pytorch.nets.models.ThinResNet_DBB.ThinResNet_DBB import ThinResNet_DBB as PTThinResNet_DBB + self.model = PTThinResNet_DBB( + task=task, + num_inputs=num_inputs, + num_outputs=num_outputs, + num_blocks=self.num_blocks, + dropout=self.dropout, + use_bn=self.use_bn, + ) diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index e2155c92..34fcf542 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -254,19 +254,43 @@ def load_pytorch_model(self): torch.nn.Module: The last loaded model (for compatibility) """ model = None + from ctlearn.core.pytorch.model import CTLearnPyTorchModel + + def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): + model_name = model_info.get("model_name", "") + try: + component_cls = CTLearnPyTorchModel.non_abstract_subclasses().get(model_name) + if component_cls is not None: + params = model_info.get("parameters", {}).copy() + params.pop("task", None) + params.pop("num_inputs", None) + params.pop("num_outputs", None) + params["parent"] = self + component = component_cls( + task=task_name, + num_inputs=num_inputs, + num_outputs=num_outputs, + **params + ) + return component.model + except Exception as e: + self.log.warning(f"Failed to load model {model_name} as Component: {e}. Falling back to create_model.") + return create_model(model_info) + + num_inputs = 1 for task in self.tasks: # Create model based on task type if task == Task.type: - model_net = create_model(self.parameters["model"]["model_type"]) + model_net = load_pytorch_model_net(self.parameters["model"]["model_type"], "type", num_inputs, 2) check_point_path = self.parameters["data"]["type_checkpoint"] elif task == Task.energy: - model_net = create_model(self.parameters["model"]["model_energy"]) + model_net = load_pytorch_model_net(self.parameters["model"]["model_energy"], "energy", num_inputs, 1) check_point_path = self.parameters["data"]["energy_checkpoint"] elif task in [Task.cameradirection, Task.skydirection, Task.direction]: - model_net = create_model(self.parameters["model"]["model_direction"]) + model_net = load_pytorch_model_net(self.parameters["model"]["model_direction"], "direction", num_inputs, 3) check_point_path = self.parameters["data"]["direction_checkpoint"] else: diff --git a/ctlearn/tools/predict/utils/predict_model.py b/ctlearn/tools/predict/utils/predict_model.py index 2add5b09..ea8981de 100644 --- a/ctlearn/tools/predict/utils/predict_model.py +++ b/ctlearn/tools/predict/utils/predict_model.py @@ -294,10 +294,87 @@ class PredictCTLearnModel(Tool): ).tag(config=True) pytorch_config_file = Path( - default_value="./ctlearn/tools/train/pytorch/config/training_config_iaa_neutron_training.yml", + default_value=None, + allow_none=True, help="Pytorch config file", ).tag(config=True) - + + # Unified Hardware Architecture & Execution Strategy + device = CaselessStrEnum( + ["cuda", "cpu", "mps"], + default_value="cuda", + help="Device to use: cuda, cpu, or mps", + ).tag(config=True) + + devices = List( + trait=Int(), + default_value=[0], + help="List of GPU device IDs to use", + ).tag(config=True) + + strategy = Unicode( + default_value="auto", + help="Multi-GPU strategy", + ).tag(config=True) + + # Event Cut-offs + leakage_intensity_cutoff = Float( + default_value=0.2, + help="Events with leakage intensity greater than this value are removed", + ).tag(config=True) + + intensity_cutoff = Float( + default_value=50.0, + help="Events with Hillas intensity below this value are removed", + ).tag(config=True) + + # Normalizations + apply_log_scaling = List( + trait=Bool(), + default_value=[True, True], + help="List specifying whether to apply log10(X+1.0) scaling for [charge, peak_time]", + ).tag(config=True) + + use_clean = Bool( + default_value=True, + help="Use the image with the applied mask", + ).tag(config=True) + + use_clean_dvr = Bool( + default_value=False, + help="Use clean DVR mask", + ).tag(config=True) + + type_mu = Float( + default_value=0.0, + help="Mean for type channel normalization", + ).tag(config=True) + + type_sigma = Float( + default_value=1000.0, + help="Std dev for type channel normalization", + ).tag(config=True) + + dir_mu = Float( + default_value=0.0, + help="Mean for direction channel normalization", + ).tag(config=True) + + dir_sigma = Float( + default_value=1000.0, + help="Std dev for direction channel normalization", + ).tag(config=True) + + energy_mu = Float( + default_value=0.0, + help="Mean for energy channel normalization", + ).tag(config=True) + + energy_sigma = Float( + default_value=1000.0, + help="Std dev for energy channel normalization", + ).tag(config=True) + keras_verbose = Int( default_value=1, min=0, @@ -327,6 +404,15 @@ class PredictCTLearnModel(Tool): ("o", "output"): "PredictCTLearnModel.output_path", ("f", "framework"): "PredictCTLearnModel.framework_type", ("p", "pytorch_config_file"): "PredictCTLearnModel.pytorch_config_file", + "device": "PredictCTLearnModel.device", + "devices": "PredictCTLearnModel.devices", + "strategy": "PredictCTLearnModel.strategy", + "type_mu": "PredictCTLearnModel.type_mu", + "type_sigma": "PredictCTLearnModel.type_sigma", + "dir_mu": "PredictCTLearnModel.dir_mu", + "dir_sigma": "PredictCTLearnModel.dir_sigma", + "energy_mu": "PredictCTLearnModel.energy_mu", + "energy_sigma": "PredictCTLearnModel.energy_sigma", } flags = { @@ -397,24 +483,106 @@ class PredictCTLearnModel(Tool): def setup(self): if self.framework_type == "pytorch": import torch - self.log.info(f"Using {self.pytorch_config_file} config file for pytorch framework") - self.parameters = read_configuration(self.pytorch_config_file) - sanity_check(self.parameters, expected_structure) - self.device_str = self.parameters["arch"]["device"] + if self.pytorch_config_file is not None: + self.log.info(f"Using {self.pytorch_config_file} config file for pytorch framework") + legacy_params = read_configuration(self.pytorch_config_file) + sanity_check(legacy_params, expected_structure) + + def get_conf_val(key1, key2, trait_name, default_val): + in_config = False + if "PredictCTLearnModel" in self.config and trait_name in self.config["PredictCTLearnModel"]: + in_config = True + if not in_config: + return legacy_params.get(key1, {}).get(key2, default_val) + return getattr(self, trait_name) + + self.device_str = get_conf_val("arch", "device", "device", self.device.name if hasattr(self.device, "name") else str(self.device)) + self.type_mu = get_conf_val("normalization", "type_mu", "type_mu", self.type_mu) + self.type_sigma = get_conf_val("normalization", "type_sigma", "type_sigma", self.type_sigma) + self.dir_mu = get_conf_val("normalization", "dir_mu", "dir_mu", self.dir_mu) + self.dir_sigma = get_conf_val("normalization", "dir_sigma", "dir_sigma", self.dir_sigma) + self.energy_mu = get_conf_val("normalization", "energy_mu", "energy_mu", self.energy_mu) + self.energy_sigma = get_conf_val("normalization", "energy_sigma", "energy_sigma", self.energy_sigma) + self.leakage_intensity_cutoff = get_conf_val("cut-off", "leakage_intensity", "leakage_intensity_cutoff", self.leakage_intensity_cutoff) + self.intensity_cutoff = get_conf_val("cut-off", "intensity", "intensity_cutoff", self.intensity_cutoff) + + self.parameters = legacy_params + else: + self.log.info("No legacy config file provided. Using standard Traitlets configuration for PyTorch.") + self.device_str = self.device.name if hasattr(self.device, "name") else str(self.device) + + self.parameters = { + "data": { + "type_checkpoint": self.load_type_model_from, + "energy_checkpoint": self.load_energy_model_from, + "direction_checkpoint": self.load_cameradirection_model_from or self.load_skydirection_model_from, + }, + "hyp": { + "batches": self.batch_size, + "dynamic_batches": True, + }, + "cut-off": { + "leakage_intensity": self.leakage_intensity_cutoff, + "intensity": self.intensity_cutoff, + }, + "normalization": { + "apply_log_scaling": self.apply_log_scaling, + "use_clean": self.use_clean, + "use_clean_dvr": self.use_clean_dvr, + "type_mu": self.type_mu, + "type_sigma": self.type_sigma, + "dir_mu": self.dir_mu, + "dir_sigma": self.dir_sigma, + "energy_mu": self.energy_mu, + "energy_sigma": self.energy_sigma, + }, + "arch": { + "device": self.device_str, + "devices": self.devices, + "strategy": self.strategy, + }, + "model": { + "model_type": { + "model_name": "DoubleBBEfficientNet", + "parameters": { + "model_variant": "efficientnet-b3", + "task": "type", + "num_outputs": 2, + "device_str": self.device_str, + "energy_bins": None, + } + }, + "model_energy": { + "model_name": "ThinResNet", + "parameters": { + "task": "energy", + "num_inputs": 1, + "num_outputs": 1, + "num_blocks": [3, 4, 6, 3], + "dropout": 0.1, + "use_bn": False, + } + }, + "model_direction": { + "model_name": "ThinResNet_DBB", + "parameters": { + "task": "direction", + "num_inputs": 1, + "num_outputs": 3, + "num_blocks": [3, 4, 6, 3], + "dropout": 0.1, + "use_bn": False, + } + } + } + } self.device = torch.device(self.device_str) self.tasks = [] - self.type_mu = self.parameters["normalization"]["type_mu"] - self.type_sigma = self.parameters["normalization"]["type_sigma"] - self.dir_mu = self.parameters["normalization"]["dir_mu"] - self.dir_sigma = self.parameters["normalization"]["dir_sigma"] - self.energy_mu = self.parameters["normalization"]["energy_mu"] - self.energy_sigma = self.parameters["normalization"]["energy_sigma"] - if self.load_type_model_from is not None: self.tasks.append(Task.type) if self.load_energy_model_from is not None: self.tasks.append(Task.energy) - if self.load_cameradirection_model_from is not None: + if self.load_cameradirection_model_from is not None or self.load_skydirection_model_from is not None: self.tasks.append(Task.direction) # Check if the ctapipe HDF5Merger component is enabled @@ -506,6 +674,17 @@ def _predict_with_model(self, model_path, task): if self.framework_type == "pytorch": from ctlearn.tools.predict.pytorch.predic_model_pytorch import predict_with_model_pytorch + + task = None + if model_path == self.load_type_model_from: + task = Task.type + elif model_path == self.load_energy_model_from: + task = Task.energy + elif model_path in [self.load_cameradirection_model_from, self.load_skydirection_model_from]: + task = Task.direction + else: + task = Task.type + predict_data, feature_vectors = predict_with_model_pytorch(self, task) return predict_data, feature_vectors return predict_data, feature_vectors diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 671e2009..a816f9cf 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -204,6 +204,228 @@ class TrainCTLearnModel(Tool): overwrite = Bool(help="Overwrite output dir if it exists").tag(config=True) + # Unified Hardware Architecture & Execution Strategy + device = CaselessStrEnum( + ["cuda", "cpu", "mps"], + default_value="cuda", + help="Device to use for training: cuda, cpu, or mps", + ).tag(config=True) + + precision_type = CaselessStrEnum( + ["64-true", "32-true", "16-true", "16-mixed", "bf16-mixed", "bf16-true"], + default_value="32-true", + help="Precision for type classification model", + ).tag(config=True) + + precision_energy = CaselessStrEnum( + ["64-true", "32-true", "16-true", "16-mixed", "bf16-mixed", "bf16-true"], + default_value="32-true", + help="Precision for energy regression model", + ).tag(config=True) + + precision_direction = CaselessStrEnum( + ["64-true", "32-true", "16-true", "16-mixed", "bf16-mixed", "bf16-true"], + default_value="32-true", + help="Precision for direction regression model", + ).tag(config=True) + + devices = List( + trait=Int(), + default_value=[0], + help="List of GPU device IDs to use for training", + ).tag(config=True) + + strategy = Unicode( + default_value="auto", + help="Multi-GPU strategy (e.g. auto, ddp, deepspeed_stage_2, fsdp)", + ).tag(config=True) + + # Optimizer & Learning Rate Hyperparameters + optimizer_momentum = Float( + default_value=0.9, + help="Momentum for optimizer (e.g. SGD, RMSProp, Adamw)", + ).tag(config=True) + + optimizer_weight_decay = Float( + default_value=0.0005, + help="Weight decay for optimizer", + ).tag(config=True) + + lrf = Float( + default_value=0.1, + help="Learning rate factor for learning rate schedulers", + ).tag(config=True) + + l2_lambda = Float( + default_value=1e-7, + help="L2 regularization lambda factor", + ).tag(config=True) + + gradient_clip_val = Float( + default_value=3.0, + allow_none=True, + help="Gradient clipping value to avoid gradient explosion", + ).tag(config=True) + + save_k_checkpoints = Int( + default_value=3, + help="Number of top checkpoints to save", + ).tag(config=True) + + experiment_number = Int( + default_value=1, + help="Experiment run number", + ).tag(config=True) + + # Event Cut-offs + leakage_intensity_cutoff = Float( + default_value=0.2, + help="Events with leakage intensity greater than this value are removed", + ).tag(config=True) + + intensity_cutoff = Float( + default_value=50.0, + help="Events with Hillas intensity below this value are removed", + ).tag(config=True) + + # Normalizations + apply_log_scaling = List( + trait=Bool(), + default_value=[True, True], + help="List specifying whether to apply log10(X+1.0) scaling for [charge, peak_time]", + ).tag(config=True) + + use_clean = Bool( + default_value=True, + help="Use the image with the applied mask (True), otherwise the mask is not applied", + ).tag(config=True) + + use_clean_dvr = Bool( + default_value=False, + help="Use clean DVR mask", + ).tag(config=True) + + type_mu = Float( + default_value=0.0, + help="Mean for type channel normalization", + ).tag(config=True) + + type_sigma = Float( + default_value=1000.0, + help="Std dev for type channel normalization", + ).tag(config=True) + + dir_mu = Float( + default_value=0.0, + help="Mean for direction channel normalization", + ).tag(config=True) + + dir_sigma = Float( + default_value=1000.0, + help="Std dev for direction channel normalization", + ).tag(config=True) + + energy_mu = Float( + default_value=0.0, + help="Mean for energy channel normalization", + ).tag(config=True) + + energy_sigma = Float( + default_value=1000.0, + help="Std dev for energy channel normalization", + ).tag(config=True) + + # Augmentations + use_augmentation = Bool( + default_value=False, + help="Apply data augmentation during training", + ).tag(config=True) + + aug_prob = Float( + default_value=0.5, + help="Probability of applying data augmentation", + ).tag(config=True) + + rot_prob = Float( + default_value=0.5, + help="Probability of applying rotation", + ).tag(config=True) + + trans_prob = Float( + default_value=0.5, + help="Probability of applying translation", + ).tag(config=True) + + flip_hor_prob = Float( + default_value=0.5, + help="Probability of applying horizontal flip", + ).tag(config=True) + + flip_ver_prob = Float( + default_value=0.5, + help="Probability of applying vertical flip", + ).tag(config=True) + + mask_prob = Float( + default_value=0.5, + help="Probability of applying masking", + ).tag(config=True) + + mask_dvr_prob = Float( + default_value=0.5, + help="Probability of applying DVR masking", + ).tag(config=True) + + noise_prob = Float( + default_value=0.5, + help="Probability of applying noise", + ).tag(config=True) + + max_rot = Float( + default_value=5.0, + help="Maximum rotation in augmentation (degrees)", + ).tag(config=True) + + max_trans = Float( + default_value=10.0, + help="Maximum translation in augmentation (pixels)", + ).tag(config=True) + + # Dataset & Loading Settings + num_workers = Int( + default_value=1, + help="Number of workers for data loading", + ).tag(config=True) + + pin_memory = Bool( + default_value=True, + help="Pin memory in PyTorch dataloader for faster GPU transfer", + ).tag(config=True) + + persistent_workers = Bool( + default_value=True, + help="Keep workers alive between epochs", + ).tag(config=True) + + # Checkpoint paths + type_checkpoint = Path( + default_value=None, + allow_none=True, + help="Path to checkpoint for type classification", + ).tag(config=True) + + energy_checkpoint = Path( + default_value=None, + allow_none=True, + help="Path to checkpoint for energy regression", + ).tag(config=True) + + direction_checkpoint = Path( + default_value=None, + allow_none=True, + help="Path to checkpoint for direction regression", + ).tag(config=True) + aliases = { "framework": "TrainCTLearnModel.framework_type", "n_epochs": "TrainCTLearnModel.n_epochs", @@ -222,6 +444,16 @@ class TrainCTLearnModel(Tool): "stack_telescope_images": "TrainCTLearnModel.stack_telescope_images", "dl1dh_reader_type": "TrainCTLearnModel.dl1dh_reader_type", ("o", "output"): "TrainCTLearnModel.output_dir", + "device": "TrainCTLearnModel.device", + "devices": "TrainCTLearnModel.devices", + "strategy": "TrainCTLearnModel.strategy", + "use_augmentation": "TrainCTLearnModel.use_augmentation", + "precision_type": "TrainCTLearnModel.precision_type", + "precision_energy": "TrainCTLearnModel.precision_energy", + "precision_direction": "TrainCTLearnModel.precision_direction", + "type_checkpoint": "TrainCTLearnModel.type_checkpoint", + "energy_checkpoint": "TrainCTLearnModel.energy_checkpoint", + "direction_checkpoint": "TrainCTLearnModel.direction_checkpoint", } flags = { diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 6dd1d502..8add186c 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -99,7 +99,7 @@ class TrainPyTorchModel(TrainCTLearnModel): config_file = Path( exits=True, default_value=None, - allow_none=False, + allow_none=True, directory_ok=True, file_ok=True, help="Configuration file.", @@ -133,28 +133,188 @@ def setup(self): for task_ in self.tasks: print("Task:", task_.name) - print(self.config_file) - self.parameters = read_configuration(self.config_file) - sanity_check(self.parameters, expected_structure) - - self.experiment_number = self.parameters["run_details"]["experiment_number"] - self.save_k = self.parameters["hyp"]["save_k"] - self.device_str = self.parameters["arch"]["device"] - self.device = torch.device(self.device_str) - - self.batch_size = self.parameters["hyp"]["batches"] - self.pin_memory = self.parameters["dataset"]["pin_memory"] - - self.num_workers = self.parameters["dataset"]["num_workers"] - self.persistent_workers = self.parameters["dataset"]["persistent_workers"] + if self.config_file is not None: + self.log.info("Loading configuration from legacy PyTorch config file: %s", self.config_file) + legacy_params = read_configuration(self.config_file) + sanity_check(legacy_params, expected_structure) + + def get_conf_val(key1, key2, trait_name, default_val): + in_config = False + for cls_name in ["TrainPyTorchModel", "TrainCTLearnModel"]: + if cls_name in self.config and trait_name in self.config[cls_name]: + in_config = True + break + if not in_config: + return legacy_params.get(key1, {}).get(key2, default_val) + return getattr(self, trait_name) + + self.type_checkpoint = get_conf_val("data", "type_checkpoint", "type_checkpoint", self.type_checkpoint) + self.energy_checkpoint = get_conf_val("data", "energy_checkpoint", "energy_checkpoint", self.energy_checkpoint) + self.direction_checkpoint = get_conf_val("data", "direction_checkpoint", "direction_checkpoint", self.direction_checkpoint) + + self.experiment_number = get_conf_val("run_details", "experiment_number", "experiment_number", self.experiment_number) + self.save_k_checkpoints = get_conf_val("hyp", "save_k", "save_k_checkpoints", self.save_k_checkpoints) + self.device_str = get_conf_val("arch", "device", "device", self.device) + self.batch_size = get_conf_val("hyp", "batches", "batch_size", self.batch_size) + self.pin_memory = get_conf_val("dataset", "pin_memory", "pin_memory", self.pin_memory) + self.num_workers = get_conf_val("dataset", "num_workers", "num_workers", self.num_workers) + self.persistent_workers = get_conf_val("dataset", "persistent_workers", "persistent_workers", self.persistent_workers) + self.devices = get_conf_val("arch", "devices", "devices", self.devices) + self.strategy = get_conf_val("arch", "strategy", "strategy", self.strategy) + + # Augmentations + self.use_augmentation = get_conf_val("augmentation", "use_augmentation", "use_augmentation", self.use_augmentation) + self.aug_prob = get_conf_val("augmentation", "aug_prob", "aug_prob", self.aug_prob) + self.rot_prob = get_conf_val("augmentation", "rot_prob", "rot_prob", self.rot_prob) + self.trans_prob = get_conf_val("augmentation", "trans_prob", "trans_prob", self.trans_prob) + self.flip_hor_prob = get_conf_val("augmentation", "flip_hor_prob", "flip_hor_prob", self.flip_hor_prob) + self.flip_ver_prob = get_conf_val("augmentation", "flip_ver_prob", "flip_ver_prob", self.flip_ver_prob) + self.mask_prob = get_conf_val("augmentation", "mask_prob", "mask_prob", self.mask_prob) + self.mask_dvr_prob = get_conf_val("augmentation", "mask_dvr_prob", "mask_dvr_prob", self.mask_dvr_prob) + self.noise_prob = get_conf_val("augmentation", "noise_prob", "noise_prob", self.noise_prob) + self.max_rot = get_conf_val("augmentation", "max_rot", "max_rot", self.max_rot) + self.max_trans = get_conf_val("augmentation", "max_trans", "max_trans", self.max_trans) + + # Normalizations + self.use_clean = get_conf_val("normalization", "use_clean", "use_clean", self.use_clean) + self.use_clean_dvr = get_conf_val("normalization", "use_clean_dvr", "use_clean_dvr", self.use_clean_dvr) + self.type_mu = get_conf_val("normalization", "type_mu", "type_mu", self.type_mu) + self.type_sigma = get_conf_val("normalization", "type_sigma", "type_sigma", self.type_sigma) + self.dir_mu = get_conf_val("normalization", "dir_mu", "dir_mu", self.dir_mu) + self.dir_sigma = get_conf_val("normalization", "dir_sigma", "dir_sigma", self.dir_sigma) + self.energy_mu = get_conf_val("normalization", "energy_mu", "energy_mu", self.energy_mu) + self.energy_sigma = get_conf_val("normalization", "energy_sigma", "energy_sigma", self.energy_sigma) + + # Cut-offs + self.leakage_intensity_cutoff = get_conf_val("cut-off", "leakage_intensity", "leakage_intensity_cutoff", self.leakage_intensity_cutoff) + self.intensity_cutoff = get_conf_val("cut-off", "intensity", "intensity_cutoff", self.intensity_cutoff) - self.devices = self.parameters["arch"]["devices"] - self.save_k = self.parameters["hyp"]["save_k"] + self.pytorch_model_configs = legacy_params.get("model", {}) + self.hyp_configs = legacy_params.get("hyp", {}) + else: + self.log.info("No legacy config file provided. Using standard Traitlets configuration.") + self.device_str = self.device + self.pytorch_model_configs = { + "model_type": { + "model_name": "DoubleBBEfficientNet", + "parameters": { + "model_variant": "efficientnet-b3", + "task": "type", + "num_outputs": 2, + "device_str": self.device_str, + "energy_bins": None, + } + }, + "model_energy": { + "model_name": "ThinResNet", + "parameters": { + "task": "energy", + "num_inputs": 1, + "num_outputs": 1, + "num_blocks": [3, 4, 6, 3], + "dropout": 0.1, + "use_bn": False, + } + }, + "model_direction": { + "model_name": "ThinResNet_DBB", + "parameters": { + "task": "direction", + "num_inputs": 1, + "num_outputs": 3, + "num_blocks": [3, 4, 6, 3], + "dropout": 0.1, + "use_bn": False, + } + } + } + self.hyp_configs = { + "epochs": self.n_epochs, + "batches": self.batch_size, + "dynamic_batches": True, + "optimizer": self.optimizer.get("name", "Adam"), + "momentum": self.optimizer_momentum, + "weight_decay": self.optimizer_weight_decay, + "learning_rate": self.optimizer.get("base_learning_rate", 0.0001), + "lrf": self.lrf, + "start_epoch": 0, + "steps_epoch": 100, + "l2_lambda": self.l2_lambda, + "adam_epsilon": self.optimizer.get("adam_epsilon", 1.0e-8), + "gradient_clip_val": self.gradient_clip_val, + "save_k": self.save_k_checkpoints, + } + + self.save_k = self.save_k_checkpoints + + self.parameters = { + "data": { + "train_gamma_proton": None, + "validation_gamma_proton": None, + "train_gamma": None, + "validation_gamma": None, + "test_gamma": None, + "test_proton": None, + "test_electron": None, + "test_validation_gamma": None, + "test_validation_gamma_proton": None, + "type_checkpoint": self.type_checkpoint, + "energy_checkpoint": self.energy_checkpoint, + "direction_checkpoint": self.direction_checkpoint, + }, + "run_details": { + "mode": "train", + "task": self.reco_tasks[0] if self.reco_tasks else "all", + "test_type": "gamma", + "experiment_number": self.experiment_number, + }, + "cut-off": { + "leakage_intensity": self.leakage_intensity_cutoff, + "intensity": self.intensity_cutoff, + }, + "model": self.pytorch_model_configs, + "hyp": self.hyp_configs, + "augmentation": { + "use_augmentation": self.use_augmentation, + "aug_prob": self.aug_prob, + "rot_prob": self.rot_prob, + "trans_prob": self.trans_prob, + "flip_hor_prob": self.flip_hor_prob, + "flip_ver_prob": self.flip_ver_prob, + "mask_prob": self.mask_prob, + "mask_dvr_prob": self.mask_dvr_prob, + "noise_prob": self.noise_prob, + "max_rot": self.max_rot, + "max_trans": self.max_trans, + }, + "normalization": { + "apply_log_scaling": self.apply_log_scaling, + "use_clean": self.use_clean, + "use_clean_dvr": self.use_clean_dvr, + "type_mu": self.type_mu, + "type_sigma": self.type_sigma, + "dir_mu": self.dir_mu, + "dir_sigma": self.dir_sigma, + "energy_mu": self.energy_mu, + "energy_sigma": self.energy_sigma, + }, + "dataset": { + "num_workers": self.num_workers, + "pin_memory": self.pin_memory, + "persistent_workers": self.persistent_workers, + }, + "arch": { + "device": self.device_str, + "precision_type": self.precision_type, + "precision_energy": self.precision_energy, + "precision_direction": self.precision_direction, + "devices": self.devices, + "strategy": self.strategy, + } + } print(f"Using Devices: {self.devices}") - # all_log_energies = self.dl1dh_reader.data['log_true_energy'] - # Set up the data loaders for training and validation indices = list(range(self.dl1dh_reader._get_n_events())) # Shuffle the indices before the training/validation split @@ -166,17 +326,6 @@ def setup(self): training_indices = indices[n_validation_examples:] validation_indices = indices[:n_validation_examples] - - # -------------------------------------------------------------------- - # Reduce for testing - # -------------------------------------------------------------------- - # Limit the number of examples (optional) - # max_training_samples = 5000 # or whatever number you want - # max_validation_samples = 1200 # or whatever number you want - - # training_indices = training_indices[:max_training_samples] - # validation_indices = validation_indices[:max_validation_samples] - if not ("class_weight" in self.parameters): self.parameters['class_weight'] = self.dl1dh_reader.class_weight self.log.info(f"Class weights not provided. Using class weights from data reader: {self.parameters['class_weight']}") @@ -197,17 +346,17 @@ def setup(self): sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, parameters=self.parameters, - use_augmentation=self.parameters["augmentation"]["use_augmentation"], + use_augmentation=self.use_augmentation, is_training=True, ) self.training_loader = DataLoader( dataset=self.train_dataset, batch_size=None, batch_sampler=None, - num_workers=4, - pin_memory=True, - prefetch_factor=4, - persistent_workers=True + num_workers=self.num_workers, + pin_memory=self.pin_memory, + prefetch_factor=4 if self.num_workers > 0 else None, + persistent_workers=self.persistent_workers if self.num_workers > 0 else False ) print(len(self.training_loader)) @@ -229,10 +378,10 @@ def setup(self): dataset=self.validation_dataset, batch_size=None, batch_sampler=None, - num_workers=4, - pin_memory=True, - prefetch_factor=4, - persistent_workers=True + num_workers=self.num_workers, + pin_memory=self.pin_memory, + prefetch_factor=4 if self.num_workers > 0 else None, + persistent_workers=self.persistent_workers if self.num_workers > 0 else False ) print(len(self.validation_dataset)) @@ -251,17 +400,43 @@ def start(self): # Select the model and precision # ------------------------------------------------------------------------------ + from ctlearn.core.pytorch.model import CTLearnPyTorchModel + + def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): + model_name = model_info.get("model_name", "") + try: + component_cls = CTLearnPyTorchModel.non_abstract_subclasses().get(model_name) + if component_cls is not None: + params = model_info.get("parameters", {}).copy() + params.pop("task", None) + params.pop("num_inputs", None) + params.pop("num_outputs", None) + # Ensure device_str is set + params["parent"] = self + component = component_cls( + task=task_name, + num_inputs=num_inputs, + num_outputs=num_outputs, + **params + ) + return component.model + except Exception as e: + self.log.warning(f"Failed to load model {model_name} as Component: {e}. Falling back to create_model.") + return create_model(model_info) + + num_inputs = 1 + if task == Task.type: precision = self.parameters["arch"]["precision_type"] - model_net = create_model(self.parameters["model"]["model_type"]) + model_net = load_pytorch_model_net(self.parameters["model"]["model_type"], "type", num_inputs, 2) elif task == Task.energy: precision = self.parameters["arch"]["precision_energy"] - model_net = create_model(self.parameters["model"]["model_energy"]) + model_net = load_pytorch_model_net(self.parameters["model"]["model_energy"], "energy", num_inputs, 1) elif task == Task.cameradirection or task == Task.skydirection: precision = self.parameters["arch"]["precision_direction"] - model_net = create_model(self.parameters["model"]["model_direction"]) + model_net = load_pytorch_model_net(self.parameters["model"]["model_direction"], "direction", num_inputs, 3) else: raise ValueError( @@ -286,10 +461,11 @@ def start(self): raise ValueError( f"task:{task.name} is not supported. Task must be type, direction or energy" ) - # Load the checkpoint - model_net = ModelHelper.loadModel( - model_net, "", check_point_path, Mode.train, device_str=self.device_str - ) + # Load the checkpoint if provided + if check_point_path: + model_net = ModelHelper.loadModel( + model_net, "", check_point_path, Mode.train, device_str=self.device_str + ) # Setup the TensorBoard logger log_dir = save_folder From 6f73f3fcbc8e1492c38ddc8d1cd24d3d938efb41 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 8 Jul 2026 22:01:40 +0000 Subject: [PATCH 072/119] Update the unit test and fix the onnx train problems --- README.rst | 43 +++++ ctlearn/core/pytorch/net_utils.py | 10 ++ ctlearn/core/tests/test_loader_pytorch.py | 2 + ctlearn/tools/train/base_train_model.py | 11 ++ .../train/pytorch/train_pytorch_model.py | 154 ++++++++++++++++-- environment-pytorch.yml | 1 + 6 files changed, 207 insertions(+), 14 deletions(-) diff --git a/README.rst b/README.rst index c5c4853f..6e808a07 100644 --- a/README.rst +++ b/README.rst @@ -47,6 +47,49 @@ The lastest version fo this package can be installed as a pip package: See the documentation for further information like `installation instructions for the IT-cluster `_, `installation instructions for developers `_, `package usage `_, and `dependencies `_ among other topics. +Running CTLearn Training and Prediction +--------------------------------------- + +CTLearn provides a unified command line interface (CLI) using `ctapipe`'s ``Tool`` and ``Component`` systems, supporting both the **Keras** and **PyTorch** deep learning frameworks. + +Launching training +~~~~~~~~~~~~~~~~~~ + +You can launch a training run using the unified tool ``ctlearn-train``. To run with a specific framework, set the ``--framework`` option (choices: ``keras``, ``pytorch``): + +.. code-block:: bash + + # Launch training with PyTorch + ctlearn-train --framework=pytorch --output ./my_output_dir --signal /path/to/signal/h5/ --pattern-signal "*.dl1.h5" --reco energy --n_epochs=10 --batch_size=32 + + # Launch training with Keras + ctlearn-train --framework=keras --output ./my_output_dir --signal /path/to/signal/h5/ --pattern-signal "*.dl1.h5" --reco energy --n_epochs=10 --batch_size=32 + +Common Training Command Options: +* ``--framework``: Deep learning framework to use (``keras`` or ``pytorch``). +* ``-o``, ``--output``: Directory to save experiment checkpoints, parameters, and logs. +* ``--signal``: Directory containing signal HDF5 data files. +* ``--pattern-signal``: File name pattern for signal files (e.g. ``*.dl1.h5``). +* ``--reco``: Tasks to train (e.g. ``type``, ``energy``, ``cameradirection``, ``skydirection``). Multiple tasks can be provided. +* ``--n_epochs``: Number of epochs to train. +* ``--batch_size``: Batch size for training. +* ``--save_onnx=True``: Export the trained model to ONNX format. +* ``--load_onnx_model=PATH``: Load an existing ONNX model to train/fine-tune. +* ``--overwrite``: Overwrite the output directory if it already exists. + +Launching prediction +~~~~~~~~~~~~~~~~~~~~ + +Similarly, predictions on test/observation data can be executed using the unified prediction tools (for monoscopic or stereoscopic mode): + +.. code-block:: bash + + # Monoscopic prediction with PyTorch + ctlearn-predict-mono-model --framework=pytorch --output ./pred_results --signal /path/to/test/h5/ --pattern-signal "*.dl1.h5" --energy_checkpoint /path/to/checkpoint.pth + + # Monoscopic prediction with Keras + ctlearn-predict-mono-model --framework=keras --output ./pred_results --signal /path/to/test/h5/ --pattern-signal "*.dl1.h5" --energy_checkpoint /path/to/keras_model/ + Citing this software -------------------- diff --git a/ctlearn/core/pytorch/net_utils.py b/ctlearn/core/pytorch/net_utils.py index 35e6ce98..3ea2fd00 100644 --- a/ctlearn/core/pytorch/net_utils.py +++ b/ctlearn/core/pytorch/net_utils.py @@ -566,6 +566,15 @@ def exportOnnx(model, dummy_input, onnx_name, input_names, output_names): - Reduces model size - Improves inference speed """ + # Define dynamic axes to support variable batch sizes + dynamic_axes = {} + if input_names: + for name in input_names: + dynamic_axes[name] = {0: "batch_size"} + if output_names: + for name in output_names: + dynamic_axes[name] = {0: "batch_size"} + # Export model to ONNX format torch.onnx.export( model, @@ -574,6 +583,7 @@ def exportOnnx(model, dummy_input, onnx_name, input_names, output_names): verbose=True, input_names=input_names, output_names=output_names, + dynamic_axes=dynamic_axes, ) # Load the exported ONNX model diff --git a/ctlearn/core/tests/test_loader_pytorch.py b/ctlearn/core/tests/test_loader_pytorch.py index 3995c35b..b89d0df6 100644 --- a/ctlearn/core/tests/test_loader_pytorch.py +++ b/ctlearn/core/tests/test_loader_pytorch.py @@ -1,3 +1,5 @@ +import pytest +torch = pytest.importorskip("torch") from traitlets.config.loader import Config from dl1_data_handler.reader import DLImageReader diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index a816f9cf..59e38b16 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -426,6 +426,13 @@ class TrainCTLearnModel(Tool): help="Path to checkpoint for direction regression", ).tag(config=True) + load_onnx_model = Path( + default_value=None, + allow_none=True, + exists=True, + help="Path to an ONNX model to load and train/fine-tune.", + ).tag(config=True) + aliases = { "framework": "TrainCTLearnModel.framework_type", "n_epochs": "TrainCTLearnModel.n_epochs", @@ -435,6 +442,10 @@ class TrainCTLearnModel(Tool): "pattern-background": "TrainCTLearnModel.file_pattern_background", "reco": "TrainCTLearnModel.reco_tasks", "save_onnx": "TrainCTLearnModel.save_onnx", + "load_onnx_model": "TrainCTLearnModel.load_onnx_model", + "experiment_number": "TrainCTLearnModel.experiment_number", + "apply_log_scaling": "TrainCTLearnModel.apply_log_scaling", + "use_clean": "TrainCTLearnModel.use_clean", "random_seed": "TrainCTLearnModel.random_seed", "optimizer": "TrainCTLearnModel.optimizer", "overwrite": "TrainCTLearnModel.overwrite", diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 8add186c..a75d9b1e 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -151,8 +151,10 @@ def get_conf_val(key1, key2, trait_name, default_val): self.type_checkpoint = get_conf_val("data", "type_checkpoint", "type_checkpoint", self.type_checkpoint) self.energy_checkpoint = get_conf_val("data", "energy_checkpoint", "energy_checkpoint", self.energy_checkpoint) self.direction_checkpoint = get_conf_val("data", "direction_checkpoint", "direction_checkpoint", self.direction_checkpoint) + self.load_onnx_model = get_conf_val("data", "load_onnx_model", "load_onnx_model", self.load_onnx_model) self.experiment_number = get_conf_val("run_details", "experiment_number", "experiment_number", self.experiment_number) + self.save_onnx = get_conf_val("run_details", "save_onnx", "save_onnx", self.save_onnx) self.save_k_checkpoints = get_conf_val("hyp", "save_k", "save_k_checkpoints", self.save_k_checkpoints) self.device_str = get_conf_val("arch", "device", "device", self.device) self.batch_size = get_conf_val("hyp", "batches", "batch_size", self.batch_size) @@ -261,6 +263,7 @@ def get_conf_val(key1, key2, trait_name, default_val): "type_checkpoint": self.type_checkpoint, "energy_checkpoint": self.energy_checkpoint, "direction_checkpoint": self.direction_checkpoint, + "load_onnx_model": self.load_onnx_model, }, "run_details": { "mode": "train", @@ -396,6 +399,12 @@ def start(self): f"run_{task.name}_training_", next_number=self.experiment_number ) + # Save the resolved configuration parameters to a config file in the experiment directory + config_filename = os.path.join(save_folder, "resolved_config.yml") + with open(config_filename, "w") as outfile: + import yaml + yaml.dump(self.parameters, outfile, default_flow_style=False) + # ------------------------------------------------------------------------------ # Select the model and precision # ------------------------------------------------------------------------------ @@ -424,24 +433,88 @@ def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): self.log.warning(f"Failed to load model {model_name} as Component: {e}. Falling back to create_model.") return create_model(model_info) + import torch.nn as nn + import torch + class ONNXModelWrapper(nn.Module): + def __init__(self, onnx_model_net, active_task, onnx_input_shape): + super().__init__() + self.onnx_model_net = onnx_model_net + self.active_task = active_task + self.onnx_input_shape = onnx_input_shape + + self.onnx_channel_last = False + self.expected_channels = 1 + if len(onnx_input_shape) == 4: + if onnx_input_shape[3] in [1, 2, 3] and onnx_input_shape[1] > onnx_input_shape[3]: + self.onnx_channel_last = True + self.expected_channels = onnx_input_shape[3] + else: + self.expected_channels = onnx_input_shape[1] + + def forward(self, x, y=None): + if self.expected_channels == 2 and y is not None: + x = torch.cat([x, y], dim=1) + + import inspect + sig = inspect.signature(self.onnx_model_net.forward) + num_onnx_inputs = len(sig.parameters) + + if num_onnx_inputs == 2 and y is not None: + if self.onnx_channel_last: + x = x.permute(0, 2, 3, 1) + y = y.permute(0, 2, 3, 1) + out = self.onnx_model_net(x, y) + else: + if self.onnx_channel_last: + x = x.permute(0, 2, 3, 1) + out = self.onnx_model_net(x) + + if isinstance(out, (tuple, list)): + if len(out) == 3: + return out + val = out[0] + else: + val = out + + if self.active_task == Task.type: + return val, None, None + elif self.active_task == Task.energy: + return None, val, None + else: + return None, None, val + num_inputs = 1 - if task == Task.type: - precision = self.parameters["arch"]["precision_type"] - model_net = load_pytorch_model_net(self.parameters["model"]["model_type"], "type", num_inputs, 2) + if self.load_onnx_model: + self.log.info(f"Loading ONNX model from {self.load_onnx_model} for training...") + import onnx + from onnx2pytorch import ConvertModel + try: + onnx_proto = onnx.load(self.load_onnx_model) + onnx_model = ConvertModel(onnx_proto) + onnx_input_shape = [dim.dim_value for dim in onnx_proto.graph.input[0].type.tensor_type.shape.dim] + model_net = ONNXModelWrapper(onnx_model, task, onnx_input_shape) + precision = self.parameters["arch"].get(f"precision_{task.name.lower()}", "32-true") + except Exception as e: + self.log.error(f"Failed to load ONNX model: {e}") + raise e + else: + if task == Task.type: + precision = self.parameters["arch"]["precision_type"] + model_net = load_pytorch_model_net(self.parameters["model"]["model_type"], "type", num_inputs, 2) - elif task == Task.energy: - precision = self.parameters["arch"]["precision_energy"] - model_net = load_pytorch_model_net(self.parameters["model"]["model_energy"], "energy", num_inputs, 1) - - elif task == Task.cameradirection or task == Task.skydirection: - precision = self.parameters["arch"]["precision_direction"] - model_net = load_pytorch_model_net(self.parameters["model"]["model_direction"], "direction", num_inputs, 3) + elif task == Task.energy: + precision = self.parameters["arch"]["precision_energy"] + model_net = load_pytorch_model_net(self.parameters["model"]["model_energy"], "energy", num_inputs, 1) + + elif task == Task.cameradirection or task == Task.skydirection: + precision = self.parameters["arch"]["precision_direction"] + model_net = load_pytorch_model_net(self.parameters["model"]["model_direction"], "direction", num_inputs, 3) - else: - raise ValueError( - f"task:{task.name} is not supported. Task must be type, direction or energy" - ) + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) # if hasattr(model_net, 'T'): # self.training_loader.set_T(model_net.T) @@ -480,6 +553,11 @@ def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): default_hp_metric=False, ) + extra_trainer_args = {} + if os.environ.get("CTLEARN_TEST_LIMIT"): + extra_trainer_args["limit_train_batches"] = 5 + extra_trainer_args["limit_val_batches"] = 2 + # Setup the Trainer trainer_pl = CTLearnTrainer( max_epochs=self.parameters["hyp"]["epochs"], @@ -494,6 +572,7 @@ def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): gradient_clip_val=self.parameters["hyp"]["gradient_clip_val"], callbacks=[GPUStatsLogger()], sync_batchnorm=True, + **extra_trainer_args ) # Setup Lighting @@ -529,6 +608,53 @@ def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): val_dataloaders=[self.validation_loader], ) + # Export to ONNX if requested + if self.save_onnx: + self.log.info("Converting PyTorch model into ONNX format...") + try: + # Load the best model weights if available + if trainer_pl.checkpoint_callback and trainer_pl.checkpoint_callback.best_model_path: + best_model_path = trainer_pl.checkpoint_callback.best_model_path + self.log.info(f"Loading best model from {best_model_path} for ONNX export...") + model_net = ModelHelper.loadModel( + model_net, "", best_model_path, Mode.train, device_str=self.device_str + ) + + # Create dummy input dynamically from a batch + batch = next(iter(self.training_loader)) + features, labels, t = batch + + import inspect + sig = inspect.signature(model_net.forward) + num_inputs_sig = len(sig.parameters) + + # Transfer dummy inputs to same device as model + if num_inputs_sig == 2: + dummy_image = torch.randn_like(features["image"][:1]).to(self.device_str) + dummy_peak = torch.randn_like(features["peak_time"][:1]).to(self.device_str) + dummy_input = (dummy_image, dummy_peak) + input_names = ["image", "peak_time"] + else: + dummy_input = torch.randn_like(features["image"][:1]).to(self.device_str) + input_names = ["image"] + + output_names = [task.name] + + # Put model in evaluation mode + model_net.eval() + + onnx_path = os.path.join(save_folder, f"ctlearn_model_{task.name}") + ModelHelper.exportOnnx( + model_net, + dummy_input, + onnx_path, + input_names=input_names, + output_names=output_names + ) + self.log.info(f"ONNX model saved successfully to {onnx_path}.onnx and {onnx_path}_simp.onnx") + except Exception as e: + self.log.error(f"Failed to export model to ONNX: {e}") + def finish(self): super().finish() print("Pytorch finish") diff --git a/environment-pytorch.yml b/environment-pytorch.yml index 2d2b0dbc..7b417e9e 100644 --- a/environment-pytorch.yml +++ b/environment-pytorch.yml @@ -34,6 +34,7 @@ dependencies: # --- ONNX tools --- - onnx==1.17.0 - onnxsim==0.4.36 + - onnx2pytorch==0.5.3 # --- CTA related packages --- - ctapipe==0.23.2 From 9abade2b8808c51cf69cf82ff0039d8e4e348681 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 8 Jul 2026 22:09:28 +0000 Subject: [PATCH 073/119] Update the env files --- environment-pytorch.yml | 3 +-- environment-tensorflow.yml | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/environment-pytorch.yml b/environment-pytorch.yml index 7b417e9e..1d1e32d4 100644 --- a/environment-pytorch.yml +++ b/environment-pytorch.yml @@ -39,9 +39,8 @@ dependencies: # --- CTA related packages --- - ctapipe==0.23.2 - ctapipe-io-lst==0.27.1 - - dl1_data_handler==0.14.6.dev10+ga9d2de1 + - dl1_data_handler==0.14.6 - ctaplot==0.6.4 - - CTLearn==0.10.3.dev48+g492c4d6 - pyirf==0.12.0 # --- I/O and plotting --- diff --git a/environment-tensorflow.yml b/environment-tensorflow.yml index 505a4170..6be35296 100644 --- a/environment-tensorflow.yml +++ b/environment-tensorflow.yml @@ -37,9 +37,8 @@ dependencies: # --- CTA related packages --- - ctapipe==0.23.2 - ctapipe-io-lst==0.27.1 - - dl1_data_handler==0.14.6.dev10+ga9d2de1 + - dl1_data_handler==0.14.6 - ctaplot==0.6.4 - - CTLearn==0.10.3.dev48+g492c4d6 - pyirf==0.12.0 # --- I/O and plotting --- From 25935fe7a1004d11943d17f9579e60ba34189fe1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 8 Jul 2026 22:18:02 +0000 Subject: [PATCH 074/119] Change the name of the envioroments --- environment-pytorch.yml | 2 +- environment-tensorflow.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/environment-pytorch.yml b/environment-pytorch.yml index 1d1e32d4..da703d65 100644 --- a/environment-pytorch.yml +++ b/environment-pytorch.yml @@ -1,5 +1,5 @@ # conda env create -f environment.yml -name: ctlearn +name: ctlearn-pytorch channels: - conda-forge - anaconda diff --git a/environment-tensorflow.yml b/environment-tensorflow.yml index 6be35296..1cb118ab 100644 --- a/environment-tensorflow.yml +++ b/environment-tensorflow.yml @@ -1,5 +1,5 @@ # conda env create -f environment.yml -name: ctlearn +name: ctlearn-tf channels: - conda-forge - anaconda From 6009bd3b329c823aac417c1bb4272f03ec04f2eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Thu, 9 Jul 2026 07:04:44 +0000 Subject: [PATCH 075/119] Solve the problem in the FLoat loading --- ctlearn/tools/predict/utils/predict_model.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ctlearn/tools/predict/utils/predict_model.py b/ctlearn/tools/predict/utils/predict_model.py index ea8981de..2ca79a25 100644 --- a/ctlearn/tools/predict/utils/predict_model.py +++ b/ctlearn/tools/predict/utils/predict_model.py @@ -37,6 +37,7 @@ from ctapipe.core.tool import ToolConfigurationError from ctapipe.core.traits import ( Bool, + Float, Int, Path, flag, From 997e256e378a87b952413ab9a47ac669965580b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 20 Jul 2026 13:35:40 +0000 Subject: [PATCH 076/119] Add the numbers of workers etc options for the dataloader. --- ctlearn/tools/train/base_train_model.py | 3 + ctlearn/tools/train/pytorch/CTLearnPL.py | 80 +++++++++++++++++++ .../train/pytorch/train_pytorch_model.py | 29 +++++-- 3 files changed, 106 insertions(+), 6 deletions(-) diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 59e38b16..048e3e09 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -465,6 +465,9 @@ class TrainCTLearnModel(Tool): "type_checkpoint": "TrainCTLearnModel.type_checkpoint", "energy_checkpoint": "TrainCTLearnModel.energy_checkpoint", "direction_checkpoint": "TrainCTLearnModel.direction_checkpoint", + "num_workers": "TrainCTLearnModel.num_workers", + "pin_memory": "TrainCTLearnModel.pin_memory", + "persistent_workers": "TrainCTLearnModel.persistent_workers", } flags = { diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 8bf8f9c3..68a0c52d 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -821,6 +821,26 @@ def training_step(self, batch, batch_idx): labels_direction = labels["direction"] imgs = imgs.to(self.device) + + # Log sample images to TensorBoard every 5 epochs + if batch_idx == 0 and self.current_epoch % 5 == 0 and self.trainer.is_global_zero: + import torchvision + try: + num_samples = min(4, imgs.shape[0]) + sample_imgs = imgs[:num_samples] + + label_str = "" + if self.task == Task.type: + label_str = str(labels_class[:num_samples].tolist()) + elif self.task == Task.energy: + label_str = str(labels_energy_value[:num_samples].tolist()) + elif self.task == Task.cameradirection: + label_str = str(labels_direction[:num_samples].tolist()) + + grid = torchvision.utils.make_grid(sample_imgs, normalize=True) + self.logger.experiment.add_image(f"Train/Images_labels_{label_str}", grid, self.current_epoch) + except Exception as e: + print(f"Error logging train images: {e}") # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones @@ -1095,6 +1115,25 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): labels_direction = labels["direction"] + # Log sample images to TensorBoard every 5 epochs + if batch_idx == 0 and self.current_epoch % 5 == 0 and self.trainer.is_global_zero: + import torchvision + try: + num_samples = min(4, imgs.shape[0]) + sample_imgs = imgs[:num_samples].to(self.device) + + label_str = "" + if self.task == Task.type: + label_str = str(labels_class[:num_samples].tolist()) + elif self.task == Task.energy: + label_str = str(labels_energy_value[:num_samples].tolist()) + elif self.task == Task.cameradirection: + label_str = str(labels_direction[:num_samples].tolist()) + + grid = torchvision.utils.make_grid(sample_imgs, normalize=True) + self.logger.experiment.add_image(f"Validation/Images_labels_{label_str}", grid, self.current_epoch) + except Exception as e: + print(f"Error logging validation images: {e}") # ------------------------------------------------------------------ # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ @@ -1491,6 +1530,47 @@ def on_validation_epoch_end(self): plt.close(fig_energy_error) # Close the figure to release memory plt.close("all") + # Add histograms for energy labels and predictions + fig_hist, ax_hist = plt.subplots(figsize=(8, 6)) + + import numpy as np + min_val = min(min(self.val_energy_label_list), min(self.val_energy_pred_list)) + max_val = max(max(self.val_energy_label_list), max(self.val_energy_pred_list)) + shared_bins = np.linspace(min_val, max_val, 50) + + ax_hist.hist( + self.val_energy_label_list, + bins=shared_bins, + alpha=0.5, + label="Labels", + color="blue", + ) + ax_hist.hist( + self.val_energy_pred_list, + bins=shared_bins, + alpha=0.5, + label="Predictions", + color="orange", + ) + ax_hist.set_xlabel("Energy (TeV)") + ax_hist.set_ylabel("Counts") + ax_hist.set_title("Energy Distribution - Labels vs Predictions (Validation)") + ax_hist.legend() + + self.logger.experiment.add_figure( + "Energy Distribution - Labels vs Predictions/Validation", + fig_hist, + self.current_epoch, + ) + + save_path = os.path.join( + self.logger.log_dir, + f"energy_distribution_validation_{self.current_epoch}_{global_loss_val}.png" + ) + fig_hist.savefig(save_path, format="png") + plt.close(fig_hist) + plt.close("all") + # ---------------------------------------------------------------------------------------------------------- def reset_values(self): # --------------------------------------- diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index a75d9b1e..13ff34e3 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -1,4 +1,4 @@ -from ctapipe.core.traits import Path +from ctapipe.core.traits import Path, Bool from torch.utils.data import DataLoader from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL import os @@ -105,9 +105,15 @@ class TrainPyTorchModel(TrainCTLearnModel): help="Configuration file.", ).tag(config=True) + disable_progress_bar = Bool( + default_value=False, + help="Disable PyTorch Lightning progress bar.", + ).tag(config=True) + aliases = { **TrainCTLearnModel.aliases, "config_file": "TrainPyTorchModel.config_file", + "disable_progress_bar": "TrainPyTorchModel.disable_progress_bar", } def __init__(self, **kwargs): @@ -483,7 +489,7 @@ def forward(self, x, y=None): else: return None, None, val - num_inputs = 1 + num_inputs = 1 ## Change thiss!!!! if self.load_onnx_model: self.log.info(f"Loading ONNX model from {self.load_onnx_model} for training...") @@ -554,9 +560,14 @@ def forward(self, x, y=None): ) extra_trainer_args = {} - if os.environ.get("CTLEARN_TEST_LIMIT"): - extra_trainer_args["limit_train_batches"] = 5 - extra_trainer_args["limit_val_batches"] = 2 + test_limit = os.environ.get("CTLEARN_TEST_LIMIT") + if test_limit: + try: + limit = int(test_limit) + except ValueError: + limit = 5 + extra_trainer_args["limit_train_batches"] = limit + extra_trainer_args["limit_val_batches"] = limit # Setup the Trainer trainer_pl = CTLearnTrainer( @@ -572,6 +583,7 @@ def forward(self, x, y=None): gradient_clip_val=self.parameters["hyp"]["gradient_clip_val"], callbacks=[GPUStatsLogger()], sync_batchnorm=True, + enable_progress_bar=not self.disable_progress_bar, **extra_trainer_args ) @@ -595,7 +607,12 @@ def forward(self, x, y=None): os.makedirs(trainer_pl.get_log_dir()) with open(os.path.join(trainer_pl.get_log_dir(),"parameters.json"), "w") as f: - json.dump(self.parameters, f, indent=4) + def path_serializer(obj): + import pathlib + if isinstance(obj, pathlib.Path): + return str(obj) + raise TypeError(f"Type {type(obj)} not serializable") + json.dump(self.parameters, f, indent=4, default=path_serializer) print(f"Run tensorboard server: tensorboard --load_fast=false --host=0.0.0.0 --logdir={trainer_pl.get_log_dir()}/") From 45ea6bcc2d305e3b610cf624759ffae094b13b4a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 20 Jul 2026 13:37:00 +0000 Subject: [PATCH 077/119] Add a warning in the pytroch onxx train --- ctlearn/tools/train/pytorch/train_pytorch_model.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 13ff34e3..34757fc4 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -493,6 +493,13 @@ def forward(self, x, y=None): if self.load_onnx_model: self.log.info(f"Loading ONNX model from {self.load_onnx_model} for training...") + self.log.warning( + "WARNING: Training an ONNX model from scratch (untrained) in PyTorch using onnx2pytorch " + "often leads to frozen gradients and the loss not improving. This is because onnx2pytorch " + "is designed primarily for inference, and many operations break the computational graph. " + "It is highly recommended to train the native PyTorch model instead by removing the " + "--load_onnx_model flag." + ) import onnx from onnx2pytorch import ConvertModel try: From 982fc712c9d05e672a9f2c7ef08148a16e604f6a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Jul 2026 10:47:58 +0000 Subject: [PATCH 078/119] Add the Pytorch Keras Resnet Model --- ctlearn/core/pytorch/model.py | 95 ++++- .../models/PyTorchResNet/PyTorchResNet.py | 330 ++++++++++++++++++ .../train/pytorch/train_pytorch_model.py | 27 +- ctlearn/tools/train_model.py | 34 +- 4 files changed, 475 insertions(+), 11 deletions(-) create mode 100644 ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index 3b526343..1cb1a41f 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -7,7 +7,7 @@ """ from ctapipe.core import Component -from ctapipe.core.traits import Unicode, List, Float, Bool, Int +from ctapipe.core.traits import Unicode, List, Float, Bool, Int, Dict, CaselessStrEnum from ctlearn.core.ctlearn_enum import Task import torch @@ -111,3 +111,96 @@ def __init__(self, task="type", num_inputs=2, num_outputs=3, parent=None, **kwar dropout=self.dropout, use_bn=self.use_bn, ) + + +class PyTorchResNet(CTLearnPyTorchModel): + """ + Component wrapper for PyTorchResNet model (from custom_resnet_pytorch). + """ + init_layer = Dict( + default_value=None, + allow_none=True, + help="Parameters for the first convolutional layer.", + ).tag(config=True) + + init_max_pool = Dict( + default_value=None, + allow_none=True, + help="Parameters for the first max pooling layer.", + ).tag(config=True) + + residual_block_type = CaselessStrEnum( + ["basic", "bottleneck"], default_value="bottleneck", allow_none=False + ).tag(config=True) + + architecture = List( + trait=Dict(), + default_value=[ + {"filters": 48, "blocks": 2}, + {"filters": 96, "blocks": 3}, + {"filters": 128, "blocks": 3}, + {"filters": 256, "blocks": 3}, + ], + allow_none=False, + ).tag(config=True) + + init_padding = Int( + default_value=0, + allow_none=False, + min=0, + help="Initial padding to apply to the input data.", + ).tag(config=True) + + head_layers = Dict( + default_value={ + "type": [512, 256, 2], + "energy": [512, 256, 1], + "direction": [512, 256, 2], + }, + allow_none=False, + ).tag(config=True) + + head_activation_function = Dict( + default_value={ + "type": "relu", + "energy": "relu", + "direction": "tanh", + }, + allow_none=False, + ).tag(config=True) + + attention_mechanism = CaselessStrEnum( + ["Dual-SE", "Channel-SE", "Spatial-SE"], + default_value="Dual-SE", + allow_none=True, + ).tag(config=True) + + attention_reduction_ratio = Int( + default_value=16, + allow_none=True, + min=1, + ).tag(config=True) + + def __init__(self, task="type", num_inputs=1, num_outputs=2, parent=None, **kwargs): + super().__init__(parent=parent, **kwargs) + from ctlearn.core.pytorch.nets.models.PyTorchResNet.PyTorchResNet import PyTorchResNetModel + + # Override output layers dynamically if necessary based on num_outputs argument + head_layers = self.head_layers.copy() + if task in head_layers: + head_layers[task][-1] = num_outputs + + self.model = PyTorchResNetModel( + task=task, + num_inputs=num_inputs, + num_outputs=num_outputs, + init_padding=self.init_padding, + init_layer=self.init_layer, + init_max_pool=self.init_max_pool, + residual_block_type=self.residual_block_type, + architecture=self.architecture, + head_layers=head_layers, + head_activation_function=self.head_activation_function, + attention_mechanism=self.attention_mechanism, + attention_reduction_ratio=self.attention_reduction_ratio, + ) diff --git a/ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py b/ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py new file mode 100644 index 00000000..b88eb9f4 --- /dev/null +++ b/ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py @@ -0,0 +1,330 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class DualSqueezeExciteBlock(nn.Module): + def __init__(self, in_channels, ratio=16): + super().__init__() + self.cse = ChannelSqueezeExciteBlock(in_channels=in_channels, ratio=ratio) + self.sse = SpatialSqueezeExciteBlock(in_channels=in_channels) + + def forward(self, x): + return self.cse(x) + self.sse(x) + +class ChannelSqueezeExciteBlock(nn.Module): + def __init__(self, in_channels, ratio=4): + super().__init__() + self.gate = nn.Sequential( + nn.Conv2d(in_channels, in_channels // ratio, kernel_size=1, bias=True), + nn.ReLU(), + nn.Conv2d(in_channels // ratio, in_channels, kernel_size=1, bias=True), + nn.Sigmoid() + ) + + def forward(self, x): + squeeze = F.adaptive_avg_pool2d(x, (1, 1)) + excitation = self.gate(squeeze) + return x * excitation + +class SpatialSqueezeExciteBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.spatial_conv = nn.Conv2d(in_channels, 1, kernel_size=1, bias=True) + + def forward(self, x): + spatial_mask = torch.sigmoid(self.spatial_conv(x)) + return x * spatial_mask + +class MultiHeadClassifier(nn.Module): + def __init__(self, heads_dict, task): + super().__init__() + self.heads = nn.ModuleDict(heads_dict) + self.task = task + + def forward(self, x): + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + + classification = None + energy = None + direction = None + + if self.task == "type" and "type" in self.heads: + classification = self.heads["type"](x) + if self.task == "energy" and "energy" in self.heads: + energy = self.heads["energy"](x) + if self.task == "direction" and "direction" in self.heads: + direction = self.heads["direction"](x) + + return classification, energy, direction + +def pytorch_build_fully_connect_head(in_features, layers, activation_function, tasks): + heads = {} + act_map = { + "relu": nn.ReLU, + "tanh": nn.Tanh, + "sigmoid": nn.Sigmoid + } + + for task in tasks: + if task not in layers: + continue + task_layers = [] + current_features = in_features + + for i, units in enumerate(layers[task]): + task_layers.append(nn.Linear(current_features, units)) + if i != len(layers[task]) - 1: + act_cls = act_map.get(activation_function[task].lower(), nn.ReLU) + task_layers.append(act_cls()) + current_features = units + + heads[task] = nn.Sequential(*task_layers) + + task_str = tasks[0] if len(tasks) > 0 else "type" + return MultiHeadClassifier(heads, task=task_str) + +class BasicBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride=1, conv_shortcut=True, attention=None): + super().__init__() + self.conv_shortcut = conv_shortcut + self.attention_config = attention + + if conv_shortcut: + self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + else: + self.shortcut = nn.Identity() + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + + self.setup_attention(out_channels) + + def setup_attention(self, channels): + self.attn_layer = None + if self.attention_config: + mech = self.attention_config["mechanism"] + ratio = self.attention_config.get("reduction_ratio", 16) + if mech == "Dual-SE": + self.attn_layer = DualSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Channel-SE": + self.attn_layer = ChannelSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Spatial-SE": + self.attn_layer = SpatialSqueezeExciteBlock(in_channels=channels) + + def forward(self, x): + identity = self.shortcut(x) + + out = F.relu(self.conv1(x)) + out = self.conv2(out) + + if self.attn_layer: + out = self.attn_layer(out) + + out += identity + return F.relu(out) + +class BottleneckBlock(nn.Module): + def __init__(self, in_channels, base_filters, stride=1, conv_shortcut=True, attention=None): + super().__init__() + self.conv_shortcut = conv_shortcut + self.attention_config = attention + out_channels = 4 * base_filters + + if conv_shortcut: + self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + else: + self.shortcut = nn.Identity() + + self.conv1 = nn.Conv2d(in_channels, base_filters, kernel_size=1, stride=stride, bias=False) + self.conv2 = nn.Conv2d(base_filters, base_filters, kernel_size=3, padding=1, bias=False) + self.conv3 = nn.Conv2d(base_filters, out_channels, kernel_size=1, bias=False) + + self.setup_attention(out_channels) + + def setup_attention(self, channels): + self.attn_layer = None + if self.attention_config: + mech = self.attention_config["mechanism"] + ratio = self.attention_config.get("reduction_ratio", 16) + if mech == "Dual-SE": + self.attn_layer = DualSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Channel-SE": + self.attn_layer = ChannelSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Spatial-SE": + self.attn_layer = SpatialSqueezeExciteBlock(in_channels=channels) + + def forward(self, x): + identity = self.shortcut(x) + + out = F.relu(self.conv1(x)) + out = F.relu(self.conv2(out)) + out = self.conv3(out) + + if self.attn_layer: + out = self.attn_layer(out) + + out += identity + return F.relu(out) + +class PyTorchResNetModel(nn.Module): + def __init__( + self, + task="type", + num_inputs=1, + num_outputs=2, + init_padding=0, + init_layer=None, + init_max_pool=None, + residual_block_type="bottleneck", + architecture=None, + head_layers=None, + head_activation_function=None, + attention_mechanism="Dual-SE", + attention_reduction_ratio=16, + ): + super().__init__() + self.task = task + self.init_padding = init_padding + self.init_layer = init_layer + self.init_max_pool = init_max_pool + self.residual_block_type = residual_block_type + + if architecture is None: + architecture = [ + {"filters": 48, "blocks": 2}, + {"filters": 96, "blocks": 3}, + {"filters": 128, "blocks": 3}, + {"filters": 256, "blocks": 3}, + ] + self.architecture = architecture + + if head_layers is None: + head_layers = { + "type": [512, 256, num_outputs], + "energy": [512, 256, 1], + "direction": [512, 256, 2], + } + + if head_activation_function is None: + head_activation_function = { + "type": "relu", + "energy": "relu", + "direction": "tanh", + } + + self.attention = None + if attention_mechanism is not None: + self.attention = { + "mechanism": attention_mechanism, + "reduction_ratio": attention_reduction_ratio, + } + + input_shape = (num_inputs, 224, 224) # Spatial dims don't affect init + self.backbone_model, out_features = self._build_backbone(input_shape) + + self.logits_head = pytorch_build_fully_connect_head( + out_features, head_layers, head_activation_function, [task] + ) + + def _build_backbone(self, input_shape): + in_channels = input_shape[0] + modules = [] + + if self.init_padding > 0: + modules.append(nn.ZeroPad2d(self.init_padding)) + + if self.init_layer is not None: + out_ch = self.init_layer["filters"] + k_size = self.init_layer["kernel_size"] + stride = self.init_layer["strides"] + padding = k_size // 2 + + modules.append(nn.Conv2d(in_channels, out_ch, kernel_size=k_size, stride=stride, padding=padding, bias=False)) + modules.append(nn.ReLU()) + in_channels = out_ch + + if self.init_max_pool is not None: + p_size = self.init_max_pool["size"] + p_stride = self.init_max_pool["strides"] + modules.append(nn.MaxPool2d(kernel_size=p_size, stride=p_stride, padding=p_size // 2)) + + res_blocks, final_channels = self._stacked_res_blocks( + in_channels, + architecture=self.architecture, + residual_block_type=self.residual_block_type, + attention=self.attention + ) + modules.extend(res_blocks) + + class GlobalAvgPool(nn.Module): + def forward(self, x): + return F.adaptive_avg_pool2d(x, (1, 1)) + + modules.append(GlobalAvgPool()) + + return nn.Sequential(*modules), final_channels + + def _stacked_res_blocks(self, in_channels, architecture, residual_block_type, attention): + blocks_list = [] + current_channels = in_channels + + filters_list = [layer["filters"] for layer in architecture] + blocks_count = [layer["blocks"] for layer in architecture] + + blocks_list.extend(self._stack_fn( + current_channels, filters_list[0], blocks_count[0], residual_block_type, stride=1, attention=attention + )) + + multiplier = 4 if residual_block_type == "bottleneck" else 1 + current_channels = filters_list[0] * multiplier + + for filters, blocks in zip(filters_list[1:], blocks_count[1:]): + blocks_list.extend(self._stack_fn( + current_channels, filters, blocks, residual_block_type, stride=2, attention=attention + )) + current_channels = filters * multiplier + + return blocks_list, current_channels + + def _stack_fn(self, in_channels, filters, blocks, residual_block_type, stride=2, attention=None): + block_layer = BasicBlock if residual_block_type == "basic" else BottleneckBlock + stack = [] + + base_kwargs = { + "in_channels": in_channels, + "stride": stride, + "attention": attention + } + + if residual_block_type == "basic": + base_kwargs["out_channels"] = filters + base_kwargs["base_filters"] = filters + else: + base_kwargs["base_filters"] = filters + + stack.append(block_layer(conv_shortcut=True, **base_kwargs)) + + multiplier = 4 if residual_block_type == "bottleneck" else 1 + current_in = filters * multiplier + + for _ in range(1, blocks): + next_kwargs = { + "in_channels": current_in, + "stride": 1, + "attention": attention, + "conv_shortcut": False + } + if residual_block_type == "basic": + next_kwargs["out_channels"] = filters + next_kwargs["base_filters"] = filters + else: + next_kwargs["base_filters"] = filters + + stack.append(block_layer(**next_kwargs)) + + return stack + + def forward(self, x): + features = self.backbone_model(x) + return self.logits_head(features) diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 34757fc4..412042c3 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -1,4 +1,4 @@ -from ctapipe.core.traits import Path, Bool +from ctapipe.core.traits import Path, Bool, Unicode from torch.utils.data import DataLoader from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL import os @@ -110,10 +110,17 @@ class TrainPyTorchModel(TrainCTLearnModel): help="Disable PyTorch Lightning progress bar.", ).tag(config=True) + model_name = Unicode( + default_value=None, + allow_none=True, + help="Model name to override the default model for the reco task.", + ).tag(config=True) + aliases = { **TrainCTLearnModel.aliases, "config_file": "TrainPyTorchModel.config_file", "disable_progress_bar": "TrainPyTorchModel.disable_progress_bar", + "model-name": "TrainPyTorchModel.model_name", } def __init__(self, **kwargs): @@ -253,6 +260,24 @@ def get_conf_val(key1, key2, trait_name, default_val): "save_k": self.save_k_checkpoints, } + # Override model name if passed through command line + if self.model_name is not None: + for task in self.tasks: + task_key = f"model_{task.name}" + if task_key not in self.pytorch_model_configs: + self.pytorch_model_configs[task_key] = {"parameters": {}} + self.pytorch_model_configs[task_key]["model_name"] = self.model_name + + # Clear out parameters to avoid passing the old model's params to the new one + if "parameters" in self.pytorch_model_configs[task_key]: + old_params = self.pytorch_model_configs[task_key]["parameters"] + # Keep basic essential params if they exist + new_params = {} + for key in ["task", "num_inputs", "num_outputs", "device_str"]: + if key in old_params: + new_params[key] = old_params[key] + self.pytorch_model_configs[task_key]["parameters"] = new_params + self.save_k = self.save_k_checkpoints self.parameters = { diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 882fc371..880cde83 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -6,11 +6,6 @@ from ctapipe.core import Tool from ctapipe.core.traits import CaselessStrEnum from ctlearn.core.ctlearn_enum import FrameworkType -from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel -from ctlearn.tools.train.pytorch.train_pytorch_model import ( - TrainPyTorchModel, -) - class DLFrameWork(Tool): """ Tool to select and run a specific deep learning training framework (Keras or PyTorch) @@ -63,10 +58,20 @@ class DLFrameWork(Tool): ).tag(config=True) aliases = { - **TrainPyTorchModel.aliases, - **TrainKerasModel.aliases, "framework": "DLFrameWork.framework_type", } + + try: + from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel + aliases.update(TrainPyTorchModel.aliases) + except ImportError: + pass + + try: + from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + aliases.update(TrainKerasModel.aliases) + except ImportError: + pass def __init__(self, **kwargs): """ @@ -159,11 +164,22 @@ def get_framework(cls, framework_type: FrameworkType): def main(): # Run the tool - # Parse only --framework to determine which subclass to load + tool = DLFrameWork() + + # Manually parse --framework to determine which subclass to load, as traitlets alias + # update can sometimes fail to parse it correctly before setup + framework = "keras" + for i, arg in enumerate(sys.argv[1:]): + if arg.startswith("--framework="): + framework = arg.split("=")[1].strip() + elif arg == "--framework" and i + 2 < len(sys.argv): + framework = sys.argv[i + 2].strip() + + tool.framework_type = framework + minimal_args = [ arg for arg in sys.argv[1:] if "--framework" in arg or arg in ["-h", "--help"] ] - tool = DLFrameWork() tool.initialize(argv=minimal_args) # Setup and inject the correct framework instance From d5cd9a39b261aed4b54755c01257abca0a19a4cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Jul 2026 11:03:31 +0000 Subject: [PATCH 079/119] Update the enviroment files --- environment-pytorch.yml | 1 + environment-tensorflow.yml | 1 + 2 files changed, 2 insertions(+) diff --git a/environment-pytorch.yml b/environment-pytorch.yml index da703d65..87bef7c6 100644 --- a/environment-pytorch.yml +++ b/environment-pytorch.yml @@ -42,6 +42,7 @@ dependencies: - dl1_data_handler==0.14.6 - ctaplot==0.6.4 - pyirf==0.12.0 + - eventio>=1.9.1 # --- I/O and plotting --- - h5py==3.14.0 diff --git a/environment-tensorflow.yml b/environment-tensorflow.yml index 1cb118ab..302662d5 100644 --- a/environment-tensorflow.yml +++ b/environment-tensorflow.yml @@ -40,6 +40,7 @@ dependencies: - dl1_data_handler==0.14.6 - ctaplot==0.6.4 - pyirf==0.12.0 + - eventio>=1.9.1 # --- I/O and plotting --- - h5py==3.14.0 From 790143e8848f4d1e0d72db1fe8a1772a424197f0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Jul 2026 12:28:43 +0000 Subject: [PATCH 080/119] Change the energy criterion by default to mean --- ctlearn/tools/train/pytorch/CTLearnPL.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 68a0c52d..873baf5e 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -209,7 +209,7 @@ def __init__( ) - self.criterion_energy_value = torch.nn.L1Loss(reduction="sum") + self.criterion_energy_value = torch.nn.L1Loss(reduction="mean") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() self.criterion_magnitud = torch.nn.L1Loss(reduction="mean") From e951976c70af66d55199ce70f3ba9932134aed6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 21 Jul 2026 15:54:22 +0000 Subject: [PATCH 081/119] Solve some bug after the rebase --- ctlearn/conftest.py | 11 +- ctlearn/core/data_loader/base_loader.py | 58 +++--- ctlearn/core/data_loader/keras_loader.py | 15 +- ctlearn/core/data_loader/pytorch_loader.py | 14 +- ctlearn/core/tests/test_loader_pytorch.py | 2 +- ctlearn/tools/__init__.py | 4 +- ctlearn/tools/predict_LST1.py | 54 +++-- ctlearn/tools/predict_model.py | 197 ++++-------------- ctlearn/tools/tests/test_train_model.py | 10 +- .../tools/train/keras/train_keras_model.py | 18 +- ctlearn/tools/train_model.py | 17 +- 11 files changed, 166 insertions(+), 234 deletions(-) diff --git a/ctlearn/conftest.py b/ctlearn/conftest.py index cdfcdb18..188de99d 100644 --- a/ctlearn/conftest.py +++ b/ctlearn/conftest.py @@ -14,7 +14,7 @@ from ctapipe.core import run_tool from ctapipe.io import write_table from ctapipe.utils import get_dataset_path -from ctlearn.tools import TrainCTLearnModel +from ctlearn.tools import DLFrameWork from ctlearn.utils import get_lst1_subarray_description @@ -263,12 +263,11 @@ def ctlearn_trained_r1_mono_models(r1_gamma_file, r1_proton_file, tmp_path_facto [ f"--background={background_dir}", "--pattern-background=*.r1.h5", - "--DLWaveformReader.enforce_subarray_equality=False", ] ) # Run training - assert run_tool(TrainCTLearnModel(config=config), argv=argv, cwd=tmp_path) == 0 + assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 ctlearn_trained_r1_mono_models[f"{telescope_type}_{reco_task}"] = ( output_dir / "ctlearn_model.keras" @@ -345,14 +344,13 @@ def ctlearn_trained_dl1_mono_models(dl1_gamma_file, dl1_proton_file, tmp_path_fa [ f"--background={background_dir}", "--pattern-background=*.dl1.h5", - "--DLImageReader.enforce_subarray_equality=False", f"--DLImageReader.image_mapper_type={image_mapper_types[telescope_type]}", ] ) # Run training assert ( - run_tool(TrainCTLearnModel(config=config), argv=argv, cwd=tmp_path) == 0 + run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 ) ctlearn_trained_dl1_mono_models[f"{telescope_type}_{reco_task}"] = ( @@ -427,12 +425,11 @@ def ctlearn_trained_dl1_stereo_models( [ f"--background={background_dir}", "--pattern-background=*.dl1.h5", - "--DLImageReader.enforce_subarray_equality=False", ] ) # Run training - assert run_tool(TrainCTLearnModel(config=config), argv=argv, cwd=tmp_path) == 0 + assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 ctlearn_trained_dl1_stereo_models[f"{telescope_type}_{reco_task}"] = ( output_dir / "ctlearn_model.keras" diff --git a/ctlearn/core/data_loader/base_loader.py b/ctlearn/core/data_loader/base_loader.py index 77ece1e0..5e9e90a5 100644 --- a/ctlearn/core/data_loader/base_loader.py +++ b/ctlearn/core/data_loader/base_loader.py @@ -174,7 +174,34 @@ def get_val(name, default): self.leakage_intensity_cutoff = get_val("leakage_intensity_cutoff", 0.2) self.intensity_cutoff = get_val("intensity_cutoff", 50.0) - def clean_and_normalize(self, image, peak_time, task): + # Determine input shape based on reader type and observation mode + # Feature vector readers don't have spatial dimensions + if self.DLDataReader.__class__.__name__ != "DLFeatureVectorReader": + + # Mono mode: single telescope per event + if self.DLDataReader.mode == "mono": + # Use the input shape directly from the data reader + self.input_shape = self.DLDataReader.input_shape + + # Stereo mode: multiple telescopes per event + elif self.DLDataReader.mode == "stereo": + # Get input shape from the first selected telescope + # All telescopes are assumed to have the same image dimensions + self.input_shape = self.DLDataReader.input_shape[ + list(self.DLDataReader.selected_telescopes)[0] + ] + + # Modify input shape if stacking telescope images + # Original shape: (num_telescopes, height, width, channels) + # Stacked shape: (height, width, num_telescopes * channels) + if self.stack_telescope_images: + self.input_shape = ( + self.input_shape[1], # height + self.input_shape[2], # width + self.input_shape[0] * self.input_shape[3], # stacked channels + ) + + def clean_data(self, image, peak_time): # Remove negative numbers and avoid inf or nans image[image < 0] = 0 peak_time[peak_time < 0] = 0 @@ -182,7 +209,9 @@ def clean_and_normalize(self, image, peak_time, task): image[np.isinf(image)] = 0 peak_time[np.isnan(peak_time)] = 0 peak_time[np.isinf(peak_time)] = 0 + return image, peak_time + def normalize_data(self, image, peak_time, task): # Normalization if task == Task.type or task == "type": image = (image - self.type_mu) / self.type_sigma @@ -250,33 +279,6 @@ def apply_augmentation(self, image, peak_time, task): continue return image, peak_time - # Determine input shape based on reader type and observation mode - # Feature vector readers don't have spatial dimensions - if self.DLDataReader.__class__.__name__ != "DLFeatureVectorReader": - - # Mono mode: single telescope per event - if self.DLDataReader.mode == "mono": - # Use the input shape directly from the data reader - self.input_shape = self.DLDataReader.input_shape - - # Stereo mode: multiple telescopes per event - elif self.DLDataReader.mode == "stereo": - # Get input shape from the first selected telescope - # All telescopes are assumed to have the same image dimensions - self.input_shape = self.DLDataReader.input_shape[ - list(self.DLDataReader.selected_telescopes)[0] - ] - - # Modify input shape if stacking telescope images - # Original shape: (num_telescopes, height, width, channels) - # Stacked shape: (height, width, num_telescopes * channels) - if self.stack_telescope_images: - self.input_shape = ( - self.input_shape[1], # height - self.input_shape[2], # width - self.input_shape[0] * self.input_shape[3], # stacked channels - ) - @abstractmethod def __len__(self): """ diff --git a/ctlearn/core/data_loader/keras_loader.py b/ctlearn/core/data_loader/keras_loader.py index 5e815b96..65b8def3 100644 --- a/ctlearn/core/data_loader/keras_loader.py +++ b/ctlearn/core/data_loader/keras_loader.py @@ -61,7 +61,8 @@ class constructors and performing initial index shuffling if a - sort_by_intensity: Whether to sort by intensity - stack_telescope_images: Whether to stack images """ - super().__init__(**kwargs) + BaseDLDataLoader.__init__(self, **kwargs) + Sequence.__init__(self) # Perform initial shuffling of indices if random seed is set self.on_epoch_end() @@ -185,12 +186,14 @@ def _get_mono_item(self, batch): peak_time = features["input"][..., 1:2] active_task = self.tasks[0] if self.tasks else None - image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + + image, peak_time = self.clean_data(image, peak_time) + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) if self.use_augmentation: image, peak_time = self.apply_augmentation(image, peak_time, active_task) - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + image, peak_time = self.normalize_data(image, peak_time, active_task) features["input"] = np.concatenate([image, peak_time], axis=-1) # Extract particle type classification labels @@ -406,16 +409,18 @@ def _get_stereo_item(self, batch): image = features_arr[..., 0] peak_time = features_arr[..., 1] - image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.clean_data(image, peak_time) image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + image, peak_time = self.normalize_data(image, peak_time, active_task) features["input"] = np.stack([image, peak_time], axis=-1) else: # Stacked mode: (batch, height, width, channels) image = features_arr[..., ::2] peak_time = features_arr[..., 1::2] - image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.clean_data(image, peak_time) image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + image, peak_time = self.normalize_data(image, peak_time, active_task) # Re-stack alternating channels stacked = [] diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 23632946..38550b94 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -403,7 +403,9 @@ def _get_mono_item(self, batch): peak_time = features["input"][..., 1:2] active_task = self.tasks[0] if self.tasks else None - image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + + image, peak_time = self.clean_data(image, peak_time) + image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) features_out = {} features_out["image"] = image @@ -508,8 +510,8 @@ def duplicate_tensor(t,idx_to_duplicate): else: image, peak_time = features_out["image"], features_out["peak_time"] - # Apply log scaling - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + # Normalize data + image, peak_time = self.normalize_data(image, peak_time, active_task) # Change to channel first image = np.transpose(image, (0, 3, 1, 2)) @@ -680,8 +682,9 @@ def _get_stereo_item(self, batch): image = features_arr[..., 0] peak_time = features_arr[..., 1] - image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.clean_data(image, peak_time) image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + image, peak_time = self.normalize_data(image, peak_time, active_task) image = np.transpose(image, (0, 1, 4, 2, 3)) if len(image.shape) == 5 else np.expand_dims(image, axis=2) peak_time = np.transpose(peak_time, (0, 1, 4, 2, 3)) if len(peak_time.shape) == 5 else np.expand_dims(peak_time, axis=2) @@ -689,8 +692,9 @@ def _get_stereo_item(self, batch): image = features_arr[..., ::2] peak_time = features_arr[..., 1::2] - image, peak_time = self.clean_and_normalize(image, peak_time, active_task) + image, peak_time = self.clean_data(image, peak_time) image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) + image, peak_time = self.normalize_data(image, peak_time, active_task) image = np.transpose(image, (0, 3, 1, 2)) peak_time = np.transpose(peak_time, (0, 3, 1, 2)) diff --git a/ctlearn/core/tests/test_loader_pytorch.py b/ctlearn/core/tests/test_loader_pytorch.py index b89d0df6..e96bef40 100644 --- a/ctlearn/core/tests/test_loader_pytorch.py +++ b/ctlearn/core/tests/test_loader_pytorch.py @@ -53,7 +53,7 @@ def test_data_loader(dl1_gamma_file): and "skydirection" in labels ) # Check the shape of the features - assert features["image"].shape == (0, 1, 110, 110) + assert features["image"].shape == (1, 1, 110, 110) if __name__ == "__main__": test_data_loader() \ No newline at end of file diff --git a/ctlearn/tools/__init__.py b/ctlearn/tools/__init__.py index d54df32d..072afa53 100644 --- a/ctlearn/tools/__init__.py +++ b/ctlearn/tools/__init__.py @@ -1,12 +1,12 @@ """ctlearn command line tools. """ -from .train_model import TrainCTLearnModel +from .train_model import DLFrameWork from .predict_LST1 import LST1PredictionTool from .predict_model import MonoPredictCTLearnModel, StereoPredictCTLearnModel __all__ = [ - "TrainCTLearnModel", + "DLFrameWork", "LST1PredictionTool", "MonoPredictCTLearnModel", "StereoPredictCTLearnModel" diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index 0e3c9266..cd616174 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -35,22 +35,41 @@ from ctapipe.instrument.optics import FocalLengthKind from ctapipe.instrument.optics import FocalLengthKind from ctapipe.io import read_table, write_table -from ctapipe.io.hdf5dataformat import ( - DL1_SUBARRAY_TRIGGER_TABLE, - DL1_TEL_GROUP, - DL1_TEL_PARAMETERS_GROUP, - DL1_TEL_POINTING_GROUP, - DL1_TEL_TRIGGER_TABLE, - DL2_TEL_PARTICLETYPE_GROUP, - DL2_TEL_ENERGY_GROUP, - DL2_TEL_GEOMETRY_GROUP, - DL2_SUBARRAY_PARTICLETYPE_GROUP, - DL2_SUBARRAY_ENERGY_GROUP, - DL2_SUBARRAY_GEOMETRY_GROUP, -) + +DL0_TEL_POINTING_GROUP = "/dl0/event/telescope/pointing" +DL1_SUBARRAY_GROUP = "/dl1/event/subarray" +DL1_SUBARRAY_POINTING_GROUP = "/dl1/event/subarray/pointing" +DL1_SUBARRAY_TRIGGER_TABLE = "/dl1/event/subarray/trigger" +DL1_TEL_GROUP = "/dl1/event/telescope" +DL1_TEL_CALIBRATION_GROUP = "/dl1/event/telescope/calibration" +DL1_TEL_ILLUMINATOR_THROUGHPUT_GROUP = "/dl1/event/telescope/illuminator_throughput" +DL1_TEL_IMAGES_GROUP = "/dl1/event/telescope/image" +DL1_TEL_MUON_GROUP = "/dl1/event/telescope/muon" +DL1_TEL_MUON_THROUGHPUT_GROUP = "/dl1/event/telescope/muon_throughput" +DL1_TEL_OPTICAL_PSF_GROUP = "/dl1/event/telescope/optical_psf" +DL1_TEL_PARAMETERS_GROUP = "/dl1/event/telescope/parameters" +DL1_TEL_POINTING_GROUP = "/dl1/event/telescope/pointing" +DL1_TEL_TRIGGER_TABLE = "/dl1/event/telescope/trigger" +DL2_EVENT_STATISTICS_GROUP = "/dl2/event/subarray/statistics" +FIXED_POINTING_GROUP = "/configuration/telescope/pointing" +R0_TEL_GROUP = "/r0/event/telescope" +R1_TEL_GROUP = "/r1/event/telescope" +SIMULATION_IMAGES_GROUP = "/simulation/event/telescope/images" +SIMULATION_IMPACT_GROUP = "/simulation/event/telescope/impact" +SIMULATION_PARAMETERS_GROUP = "/simulation/event/telescope/parameters" +SIMULATION_RUN_TABLE = "/simulation/run_config" +SIMULATION_SHOWER_TABLE = "/simulation/event/subarray/shower" +DL2_TEL_PARTICLETYPE_GROUP = "/dl2/event/telescope/classification" +DL2_TEL_ENERGY_GROUP = "/dl2/event/telescope/energy" +DL2_TEL_GEOMETRY_GROUP = "/dl2/event/telescope/geometry" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +DL2_SUBARRAY_PARTICLETYPE_GROUP = "/dl2/event/subarray/classification" +DL2_SUBARRAY_ENERGY_GROUP = "/dl2/event/subarray/energy" +DL2_SUBARRAY_GEOMETRY_GROUP = "/dl2/event/subarray/geometry" + from ctapipe.reco.utils import add_defaults_and_meta -from ctlearn.core.model import LoadedModel +from ctlearn.core.keras.model import LoadedModel from ctlearn.utils import get_lst1_subarray_description from ctlearn.utils import get_lst1_subarray_description from dl1_data_handler.image_mapper import ImageMapper @@ -279,7 +298,12 @@ class LST1PredictionTool(Tool): } - classes = classes_with_traits(ImageMapper) + @property + def classes(self): + return [ + type(self), + LST1PredictionTool, + ] + classes_with_traits(ImageMapper) def setup(self): self.log.info("ctlearn version %s", ctlearn_version) diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index 092b6939..70d8774e 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -39,44 +39,45 @@ flag, Dict, ComponentName, + CaselessStrEnum, classes_with_traits, ) from ctapipe.monitoring.interpolation import PointingInterpolator from ctapipe.instrument import SubarrayDescription from ctapipe.io import read_table, write_table, HDF5Merger from ctapipe.io.datalevels import DataLevel -from ctapipe.io.hdf5dataformat import ( - DL0_TEL_POINTING_GROUP, - DL1_SUBARRAY_GROUP, - DL1_SUBARRAY_POINTING_GROUP, - DL1_SUBARRAY_TRIGGER_TABLE, - DL1_TEL_GROUP, - DL1_TEL_CALIBRATION_GROUP, - DL1_TEL_ILLUMINATOR_THROUGHPUT_GROUP, - DL1_TEL_IMAGES_GROUP, - DL1_TEL_MUON_GROUP, - DL1_TEL_MUON_THROUGHPUT_GROUP, - DL1_TEL_OPTICAL_PSF_GROUP, - DL1_TEL_PARAMETERS_GROUP, - DL1_TEL_POINTING_GROUP, - DL1_TEL_TRIGGER_TABLE, - DL2_EVENT_STATISTICS_GROUP, - FIXED_POINTING_GROUP, - R0_TEL_GROUP, - R1_TEL_GROUP, - SIMULATION_IMAGES_GROUP, - SIMULATION_IMPACT_GROUP, - SIMULATION_PARAMETERS_GROUP, - SIMULATION_RUN_TABLE, - SIMULATION_SHOWER_TABLE, - DL2_TEL_PARTICLETYPE_GROUP, - DL2_TEL_ENERGY_GROUP, - DL2_TEL_GEOMETRY_GROUP, - DL2_SUBARRAY_GROUP, - DL2_SUBARRAY_PARTICLETYPE_GROUP, - DL2_SUBARRAY_ENERGY_GROUP, - DL2_SUBARRAY_GEOMETRY_GROUP, -) + +DL0_TEL_POINTING_GROUP = "/dl0/event/telescope/pointing" +DL1_SUBARRAY_GROUP = "/dl1/event/subarray" +DL1_SUBARRAY_POINTING_GROUP = "/dl1/event/subarray/pointing" +DL1_SUBARRAY_TRIGGER_TABLE = "/dl1/event/subarray/trigger" +DL1_TEL_GROUP = "/dl1/event/telescope" +DL1_TEL_CALIBRATION_GROUP = "/dl1/event/telescope/calibration" +DL1_TEL_ILLUMINATOR_THROUGHPUT_GROUP = "/dl1/event/telescope/illuminator_throughput" +DL1_TEL_IMAGES_GROUP = "/dl1/event/telescope/image" +DL1_TEL_MUON_GROUP = "/dl1/event/telescope/muon" +DL1_TEL_MUON_THROUGHPUT_GROUP = "/dl1/event/telescope/muon_throughput" +DL1_TEL_OPTICAL_PSF_GROUP = "/dl1/event/telescope/optical_psf" +DL1_TEL_PARAMETERS_GROUP = "/dl1/event/telescope/parameters" +DL1_TEL_POINTING_GROUP = "/dl1/event/telescope/pointing" +DL1_TEL_TRIGGER_TABLE = "/dl1/event/telescope/trigger" +DL2_EVENT_STATISTICS_GROUP = "/dl2/event/subarray/statistics" +FIXED_POINTING_GROUP = "/configuration/telescope/pointing" +R0_TEL_GROUP = "/r0/event/telescope" +R1_TEL_GROUP = "/r1/event/telescope" +SIMULATION_IMAGES_GROUP = "/simulation/event/telescope/images" +SIMULATION_IMPACT_GROUP = "/simulation/event/telescope/impact" +SIMULATION_PARAMETERS_GROUP = "/simulation/event/telescope/parameters" +SIMULATION_RUN_TABLE = "/simulation/run_config" +SIMULATION_SHOWER_TABLE = "/simulation/event/subarray/shower" +DL2_TEL_PARTICLETYPE_GROUP = "/dl2/event/telescope/classification" +DL2_TEL_ENERGY_GROUP = "/dl2/event/telescope/energy" +DL2_TEL_GEOMETRY_GROUP = "/dl2/event/telescope/geometry" +DL2_SUBARRAY_GROUP = "/dl2/event/subarray" +DL2_SUBARRAY_PARTICLETYPE_GROUP = "/dl2/event/subarray/classification" +DL2_SUBARRAY_ENERGY_GROUP = "/dl2/event/subarray/energy" +DL2_SUBARRAY_GEOMETRY_GROUP = "/dl2/event/subarray/geometry" + from ctapipe.reco.reconstructor import ReconstructionProperty from ctapipe.reco.stereo_combination import StereoCombiner from ctapipe.reco.utils import add_defaults_and_meta @@ -424,7 +425,13 @@ class PredictCTLearnModel(Tool): ), } - classes = classes_with_traits(DLDataReader) + @property + def classes(self): + return [ + type(self), + PredictCTLearnModel, + HDF5Merger, + ] + classes_with_traits(DLDataReader) def setup(self): self.activity_start_time = Time.now() @@ -707,129 +714,7 @@ def _predict_with_model(self, model_path): return predict_data, feature_vectors - # # Create a new DLDataLoader for each task - # # It turned out to be more robust to initialize the DLDataLoader separately. - # data_loader = DLDataLoader.create( - # framework="keras", - # DLDataReader=self.dl1dh_reader, - # indices=self.indices, - # tasks=[], - # batch_size=self.batch_size * self.strategy.num_replicas_in_sync, - # sort_by_intensity=self.sort_by_intensity, - # stack_telescope_images=self.stack_telescope_images, - # ) - - # # Keras is only considering the last complete batch. - # # In prediction mode we don't want to loose the last - # # uncomplete batch, so we are creating an additional - # # batch generator for the remaining events. - # data_loader_last_batch = None - # if self.last_batch_size > 0: - # last_batch_indices = self.indices[-self.last_batch_size :] - # data_loader_last_batch = DLDataLoader.create( - # framework="keras", - # DLDataReader=self.dl1dh_reader, - # indices=last_batch_indices, - # tasks=[], - # batch_size=self.last_batch_size, - # sort_by_intensity=self.sort_by_intensity, - # stack_telescope_images=self.stack_telescope_images, - # ) - - - # # Load the model from the specified path - # model = keras.saving.load_model(model_path) - # prediction_colname = ( - # "type" - if isinstance(model.layers[-1], keras.layers.Softmax) - else model.layers[-1].name - # ) - # backbone_model, feature_vectors = None, None - # if self.dl1_features: - # # Get the backbone model which is the second layer of the model - # backbone_model = model.get_layer(index=1) - # # Create a new head model with the same layers as the original model. - # # The output of the backbone model is the input of the head model. - # backbone_output_shape = keras.Input(model.layers[2].input.shape[1:]) - # x = backbone_output_shape - # for layer in model.layers[2:]: - # x = layer(x) - # head = keras.Model(inputs=backbone_output_shape, outputs=x) - # # Apply the backbone model with the data loader to retrieve the feature vectors - try: - # feature_vectors = backbone_model.predict( - # data_loader, verbose=self.keras_verbose - # ) - except ValueError as err: - if str(err).startswith("Input 0 of layer"): - raise ToolConfigurationError( - "Model input shape does not match the prediction data. " - "This is usually caused by selecting the wrong telescope_id. " - "Please ensure the telescope configuration matches the one used for training." - ) from err - raise - # # Apply the head model with the feature vectors to retrieve the prediction - # predict_data = Table( - # { - # prediction_colname: head.predict( - # feature_vectors, verbose=self.keras_verbose - # ) - # } - # ) - # # Predict the last batch and stack the results to the prediction data - # if data_loader_last_batch is not None: - # feature_vectors_last_batch = backbone_model.predict( - # data_loader_last_batch, verbose=self.keras_verbose - # ) - # feature_vectors = np.concatenate( - # (feature_vectors, feature_vectors_last_batch) - # ) - # predict_data = vstack( - # [ - # predict_data, - # Table( - # { - # prediction_colname: head.predict( - # feature_vectors_last_batch, - # verbose=self.keras_verbose, - # ) - # } - # ), - # ] - # ) - # else: - # # Predict the data using the loaded model - try: - # predict_data = model.predict(data_loader, verbose=self.keras_verbose) - except ValueError as err: - if str(err).startswith("Input 0 of layer"): - raise ToolConfigurationError( - "Model input shape does not match the prediction data. " - "This is usually caused by selecting the wrong telescope_id. " - "Please ensure the telescope configuration matches the one used for training." - ) from err - raise - # # Create a astropy table with the prediction results - # # The classification task has a softmax layer as the last layer - # # which returns the probabilities for each class in an array, while - # # the regression tasks have output neurons which returns the - # # predicted value for the task in a dictionary. - # if prediction_colname == "type": - # predict_data = Table({prediction_colname: predict_data}) - # else: - # predict_data = Table(predict_data) - # # Predict the last batch and stack the results to the prediction data - # if data_loader_last_batch is not None: - # predict_data_last_batch = model.predict( - # data_loader_last_batch, verbose=self.keras_verbose - # ) - # if model.layers[-1].name == "type": - # predict_data_last_batch = Table( - # {prediction_colname: predict_data_last_batch} - # ) - # else: - # predict_data_last_batch = Table(predict_data_last_batch) - # predict_data = vstack([predict_data, predict_data_last_batch]) + return predict_data, feature_vectors def _predict_particletype(self, example_identifiers): diff --git a/ctlearn/tools/tests/test_train_model.py b/ctlearn/tools/tests/test_train_model.py index d2291652..e2ef5936 100644 --- a/ctlearn/tools/tests/test_train_model.py +++ b/ctlearn/tools/tests/test_train_model.py @@ -1,13 +1,15 @@ import pandas as pd import pytest import shutil +from unittest import mock from ctapipe.core import run_tool -from ctlearn.tools import TrainCTLearnModel +from ctlearn.tools import DLFrameWork @pytest.mark.parametrize("reco_task", ["type", "energy", "cameradirection"]) -def test_train_ctlearn_model(reco_task, dl1_gamma_file, dl1_proton_file, tmp_path): +@mock.patch("ctapipe.instrument.SubarrayDescription.__eq__", return_value=True) +def test_train_ctlearn_model(mock_eq, reco_task, dl1_gamma_file, dl1_proton_file, tmp_path): """ Test training CTLearn model using the DL1 gamma and proton files for all reconstruction tasks. Each test run gets its own isolated temp directories. @@ -40,18 +42,16 @@ def test_train_ctlearn_model(reco_task, dl1_gamma_file, dl1_proton_file, tmp_pat f"--DLImageReader.allowed_tels={allowed_tels}", ] - # Include background only for classification task if reco_task == "type": argv.extend( [ f"--background={background_dir}", "--pattern-background=*.dl1.h5", - "--DLImageReader.enforce_subarray_equality=False", ] ) # Run training - assert run_tool(TrainCTLearnModel(), argv=argv, cwd=tmp_path) == 0 + assert run_tool(DLFrameWork(), argv=argv, cwd=tmp_path) == 0 # --- Additional checks --- # Check that the trained model exists diff --git a/ctlearn/tools/train/keras/train_keras_model.py b/ctlearn/tools/train/keras/train_keras_model.py index cb59e61c..4828c6a4 100644 --- a/ctlearn/tools/train/keras/train_keras_model.py +++ b/ctlearn/tools/train/keras/train_keras_model.py @@ -133,9 +133,9 @@ def setup(self): print(tf.config.list_physical_devices('GPU')) # Create a MirroredStrategy. - self.strategy = tf.distribute.MirroredStrategy() - atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore - self.log.info("Number of devices: %s", self.strategy.num_replicas_in_sync) + self._keras_strategy = tf.distribute.MirroredStrategy() + atexit.register(self._keras_strategy._extended._collective_ops._lock.locked) # type: ignore + self.log.info("Number of devices: %s", self._keras_strategy.num_replicas_in_sync) # print(self.framework_type) super().setup() @@ -151,9 +151,9 @@ def setup(self): validation_indices = indices[:n_validation_examples] # Set self.strategy.num_replicas_in_sync to 1 in case that does not exist (Pytorch) - if not hasattr(self, "strategy"): - self.strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() - print("num_replicas_in_sync:", self.strategy.num_replicas_in_sync) + if not hasattr(self, "_keras_strategy"): + self._keras_strategy = type("FakeStrategy", (), {"num_replicas_in_sync": 1})() + print("num_replicas_in_sync:", self._keras_strategy.num_replicas_in_sync) print("BASE TRAIN FRAMEWORK", self.framework_type) @@ -162,7 +162,7 @@ def setup(self): DLDataReader=self.dl1dh_reader, indices=training_indices, tasks=self.reco_tasks, - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + batch_size=self.batch_size * self._keras_strategy.num_replicas_in_sync, random_seed=self.random_seed, sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, @@ -173,7 +173,7 @@ def setup(self): DLDataReader=self.dl1dh_reader, indices=validation_indices, tasks=self.reco_tasks, - batch_size=self.batch_size * self.strategy.num_replicas_in_sync, + batch_size=self.batch_size * self._keras_strategy.num_replicas_in_sync, random_seed=self.random_seed, sort_by_intensity=self.sort_by_intensity, stack_telescope_images=self.stack_telescope_images, @@ -235,7 +235,7 @@ def start(self): ) self.callbacks.append(lr_reducing_callback) # Open a strategy scope. - with self.strategy.scope(): + with self._keras_strategy.scope(): # Construct the model self.log.info("Setting up the model.") self.model = CTLearnModel.from_name( diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 880cde83..420b5ec5 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -4,7 +4,7 @@ import tensorflow as tf import sys from ctapipe.core import Tool -from ctapipe.core.traits import CaselessStrEnum +from ctapipe.core.traits import CaselessStrEnum, Dict from ctlearn.core.ctlearn_enum import FrameworkType class DLFrameWork(Tool): """ @@ -162,6 +162,21 @@ def get_framework(cls, framework_type: FrameworkType): return fw + @property + def classes(self): + from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel + from ctlearn.tools.train.base_train_model import TrainCTLearnModel + from ctapipe.core.traits import classes_with_traits + from dl1_data_handler.reader import DLDataReader + + return [ + type(self), + TrainCTLearnModel, + TrainKerasModel, + TrainPyTorchModel, + ] + classes_with_traits(DLDataReader) + def main(): # Run the tool tool = DLFrameWork() From a61a306e4dbbfd2c1e2cc6be27cdc54a24ae5d63 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 22 Jul 2026 15:12:19 +0000 Subject: [PATCH 082/119] Update the CI to include test --- .github/workflows/python-package-conda.yml | 28 ++++++++++------------ ctlearn/tools/predict_LST1.py | 8 ++++++- ctlearn/tools/predict_model.py | 12 ++++++++-- ctlearn/tools/predict_model_main.py | 11 +++++++-- ctlearn/tools/tests/test_predict_LST1.py | 2 ++ ctlearn/tools/tests/test_predict_model.py | 2 ++ ctlearn/tools/tests/test_train_model.py | 2 ++ ctlearn/tools/train_model.py | 1 - pyproject.toml | 17 ++++++++++--- 9 files changed, 58 insertions(+), 25 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 54b8ff43..87a6931e 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -8,23 +8,18 @@ on: workflow_dispatch: jobs: - pytorch-job: - name: PyTorch Job + test-job: + name: Test Job runs-on: ubuntu-22.04 strategy: matrix: os: [ubuntu-22.04] - python-version: ['3.12', '3.13', '3.14'] + python-version: ['3.12', '3.13'] dl1dh-version: ['latest', 'nightly'] - tensorflow-version: ['latest', '2.16.*'] - exclude: - - python-version: '3.13' - tensorflow-version: '2.16.*' - - python-version: '3.14' - tensorflow-version: '2.16.*' + framework: ['tensorflow', 'pytorch', 'all'] max-parallel: 6 runs-on: ${{ matrix.os }} - continue-on-error: ${{ matrix.dl1dh-version == 'nightly' || matrix.python-version == '3.14' }} + continue-on-error: ${{ matrix.dl1dh-version == 'nightly' }} steps: - uses: actions/checkout@v4 @@ -56,11 +51,6 @@ jobs: else pip install dl1-data-handler fi - if [ "${{ matrix.tensorflow-version }}" = "latest" ]; then - pip install --upgrade tensorflow - else - pip install "tensorflow==${{ matrix.tensorflow-version }}" - fi - name: Add MKL_THREADING_LAYER variable run: echo "MKL_THREADING_LAYER=GNU" >> $GITHUB_ENV @@ -76,7 +66,13 @@ jobs: run: | source $HOME/miniconda/etc/profile.d/conda.sh conda activate ctlearn - pip install -e . + if [ "${{ matrix.framework }}" = "tensorflow" ]; then + pip install -e .[tf] + elif [ "${{ matrix.framework }}" = "pytorch" ]; then + pip install -e .[pytorch] + else + pip install -e .[all] + fi - name: Run pytest run: | diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index cd616174..fa5e5054 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -4,7 +4,10 @@ import numpy as np import tables -import keras +try: + import keras +except ImportError: + keras = None from astropy import units as u from astropy.coordinates import AltAz, SkyCoord from astropy.table import Table, join, setdiff, vstack @@ -319,6 +322,9 @@ def setup(self): # Get the number of rows in the table with tables.open_file(self.input_url) as input_file: self.table_length = len(input_file.get_node(self.image_table_path)) + + if keras is None: + raise ImportError("TensorFlow/Keras is required for prediction. Install it with 'pip install ctlearn[tf]' or 'pip install ctlearn[all]'.") # Load the models from the specified paths if self.load_type_model_from is not None: diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index 70d8774e..6e11943d 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -8,8 +8,12 @@ import numpy as np import tables -import tensorflow as tf -import keras +try: + import tensorflow as tf + import keras +except ImportError: + tf = None + keras = None from astropy import units as u from astropy.coordinates.earth import EarthLocation @@ -446,6 +450,10 @@ def setup(self): self.output_path, dl2_subarray=False, dl2_telescope=False, parent=self ) as merger: merger(self.input_url) + + if tf is None: + raise ImportError("TensorFlow is required for prediction. Install it with 'pip install ctlearn[tf]' or 'pip install ctlearn[all]'.") + # Create a MirroredStrategy. self.strategy = tf.distribute.MirroredStrategy() atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore diff --git a/ctlearn/tools/predict_model_main.py b/ctlearn/tools/predict_model_main.py index a481f1f1..4b64001b 100644 --- a/ctlearn/tools/predict_model_main.py +++ b/ctlearn/tools/predict_model_main.py @@ -6,8 +6,12 @@ import pathlib import numpy as np import os -import tensorflow as tf -import keras +try: + import tensorflow as tf + import keras +except ImportError: + tf = None + keras = None from astropy import units as u from astropy.coordinates.earth import EarthLocation @@ -395,6 +399,9 @@ def setup(self): "No copy to output destination, since the usage of the HDF5Merger component is disabled." ) + if tf is None: + raise ImportError("TensorFlow is required for prediction. Install it with 'pip install ctlearn[tf]' or 'pip install ctlearn[all]'.") + # Create a MirroredStrategy. self.strategy = tf.distribute.MirroredStrategy() atexit.register(self.strategy._extended._collective_ops._lock.locked) # type: ignore diff --git a/ctlearn/tools/tests/test_predict_LST1.py b/ctlearn/tools/tests/test_predict_LST1.py index 62969979..da72dded 100644 --- a/ctlearn/tools/tests/test_predict_LST1.py +++ b/ctlearn/tools/tests/test_predict_LST1.py @@ -2,6 +2,8 @@ import numpy as np import pytest +pytest.importorskip("tensorflow") + from ctapipe.core import run_tool from ctapipe.io import TableLoader from ctlearn.tools import LST1PredictionTool diff --git a/ctlearn/tools/tests/test_predict_model.py b/ctlearn/tools/tests/test_predict_model.py index 5f17fc34..81093c6a 100644 --- a/ctlearn/tools/tests/test_predict_model.py +++ b/ctlearn/tools/tests/test_predict_model.py @@ -2,6 +2,8 @@ import numpy as np import pytest +pytest.importorskip("tensorflow") + from ctapipe.core import run_tool from ctapipe.io import TableLoader from ctlearn.tools import MonoPredictCTLearnModel, StereoPredictCTLearnModel diff --git a/ctlearn/tools/tests/test_train_model.py b/ctlearn/tools/tests/test_train_model.py index e2ef5936..71f3f6fb 100644 --- a/ctlearn/tools/tests/test_train_model.py +++ b/ctlearn/tools/tests/test_train_model.py @@ -1,6 +1,8 @@ import pandas as pd import pytest import shutil + +pytest.importorskip("tensorflow") from unittest import mock from ctapipe.core import run_tool diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 420b5ec5..89348958 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -1,7 +1,6 @@ import atexit import pandas as pd import numpy as np -import tensorflow as tf import sys from ctapipe.core import Tool from ctapipe.core.traits import CaselessStrEnum, Dict diff --git a/pyproject.toml b/pyproject.toml index 372beb66..3a7ccbfe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,9 +36,7 @@ dependencies = [ "pyyaml", "scikit-learn", "numba", - "tensorflow>=2.16", "tensorboard", - "pydot", "setuptools", "ctapipe[all]>=0.29", ] @@ -49,12 +47,25 @@ dynamic = ["version"] packages = ["ctlearn"] [project.optional-dependencies] +tf = [ + "tensorflow>=2.16", + "tensorflow-estimator>=2.14.0", + "tf2onnx", + "pydot", +] +pytorch = [ + "torch>=2.0.0", + "torchvision", + "pytorch-lightning", + "torchmetrics", + "onnx2pytorch", +] doc = [ "sphinx", "sphinx-rtd-theme", ] # self reference allows all to be defined in terms of other extras -all = ["ctlearn[doc]"] +all = ["ctlearn[tf,pytorch,doc]"] [project.urls] repository = "https://github.com/ctlearn-project/ctlearn" From 5a238b84c5621a5739896ceac01766782abffbd3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 22 Jul 2026 15:19:05 +0000 Subject: [PATCH 083/119] Fix the CI --- .github/workflows/python-package-conda.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 87a6931e..2d80e498 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -10,7 +10,6 @@ on: jobs: test-job: name: Test Job - runs-on: ubuntu-22.04 strategy: matrix: os: [ubuntu-22.04] From 92dae045cea63f8fc62d941ad3e579de0a0fa345 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 22 Jul 2026 15:30:59 +0000 Subject: [PATCH 084/119] Solving flake 8 problems --- ctlearn/tools/predict_LST1.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index fa5e5054..c7b8c436 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -73,8 +73,9 @@ from ctapipe.reco.utils import add_defaults_and_meta from ctlearn.core.keras.model import LoadedModel +from ctlearn import __version__ as ctlearn_version from ctlearn.utils import get_lst1_subarray_description -from ctlearn.utils import get_lst1_subarray_description +from ctlearn.utils import validate_trait_dict from dl1_data_handler.image_mapper import ImageMapper from dl1_data_handler.reader import ( get_unmapped_image, From 03efde1a86b12b6381dd02d1ead583e82d67f907 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 22 Jul 2026 15:50:48 +0000 Subject: [PATCH 085/119] Fix test --- ctlearn/conftest.py | 178 +++++++++++++++++---------------- ctlearn/tools/predict_model.py | 12 ++- ctlearn/tools/train_model.py | 22 ++-- 3 files changed, 115 insertions(+), 97 deletions(-) diff --git a/ctlearn/conftest.py b/ctlearn/conftest.py index 188de99d..822f7c81 100644 --- a/ctlearn/conftest.py +++ b/ctlearn/conftest.py @@ -7,6 +7,7 @@ import numpy as np import pytest import shutil +from unittest import mock from astropy import units as u from astropy.table import Column, Table from traitlets.config.loader import Config @@ -240,34 +241,35 @@ def ctlearn_trained_r1_mono_models(r1_gamma_file, r1_proton_file, tmp_path_facto telescope_type = "LST" # Loop over reconstruction tasks and train models for each combination ctlearn_trained_r1_mono_models = {} - for reco_task in ["type", "energy", "cameradirection"]: - # Output directory for trained model - output_dir = tmp_path / f"ctlearn_{telescope_type}_{reco_task}" - - # Build command-line arguments - argv = [ - f"--signal={signal_dir}", - "--pattern-signal=*.r1.h5", - f"--output={output_dir}", - f"--reco={reco_task}", - "--TrainCTLearnModel.n_epochs=1", - "--TrainCTLearnModel.batch_size=2", - "--TrainCTLearnModel.dl1dh_reader_type=DLWaveformReader", - "--DLWaveformReader.sequence_length=5", - "--DLWaveformReader.focal_length_choice=EQUIVALENT", - ] - - # Include background only for classification task - if reco_task == "type": - argv.extend( - [ - f"--background={background_dir}", - "--pattern-background=*.r1.h5", - ] - ) - - # Run training - assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 + with mock.patch("ctapipe.instrument.SubarrayDescription.__eq__", return_value=True): + for reco_task in ["type", "energy", "cameradirection"]: + # Output directory for trained model + output_dir = tmp_path / f"ctlearn_{telescope_type}_{reco_task}" + + # Build command-line arguments + argv = [ + f"--signal={signal_dir}", + "--pattern-signal=*.r1.h5", + f"--output={output_dir}", + f"--reco={reco_task}", + "--TrainCTLearnModel.n_epochs=1", + "--TrainCTLearnModel.batch_size=2", + "--TrainCTLearnModel.dl1dh_reader_type=DLWaveformReader", + "--DLWaveformReader.sequence_length=5", + "--DLWaveformReader.focal_length_choice=EQUIVALENT", + ] + + # Include background only for classification task + if reco_task == "type": + argv.extend( + [ + f"--background={background_dir}", + "--pattern-background=*.r1.h5", + ] + ) + + # Run training + assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 ctlearn_trained_r1_mono_models[f"{telescope_type}_{reco_task}"] = ( output_dir / "ctlearn_model.keras" @@ -321,38 +323,39 @@ def ctlearn_trained_dl1_mono_models(dl1_gamma_file, dl1_proton_file, tmp_path_fa # Loop over telescope types and reconstruction tasks # and train models for each combination ctlearn_trained_dl1_mono_models = {} - for telescope_type, allowed_tels in telescope_types.items(): - for reco_task in ["type", "energy", "cameradirection"]: - # Output directory for trained model - output_dir = tmp_path / f"ctlearn_{telescope_type}_{reco_task}" - - # Build command-line arguments - argv = [ - f"--signal={signal_dir}", - "--pattern-signal=*.dl1.h5", - f"--output={output_dir}", - f"--reco={reco_task}", - "--TrainCTLearnModel.n_epochs=1", - "--TrainCTLearnModel.batch_size=2", - "--DLImageReader.focal_length_choice=EQUIVALENT", - f"--DLImageReader.allowed_tels={allowed_tels}", - ] - - # Include background only for classification task - if reco_task == "type": - argv.extend( - [ - f"--background={background_dir}", - "--pattern-background=*.dl1.h5", - f"--DLImageReader.image_mapper_type={image_mapper_types[telescope_type]}", - ] + with mock.patch("ctapipe.instrument.SubarrayDescription.__eq__", return_value=True): + for telescope_type, allowed_tels in telescope_types.items(): + for reco_task in ["type", "energy", "cameradirection"]: + # Output directory for trained model + output_dir = tmp_path / f"ctlearn_{telescope_type}_{reco_task}" + + # Build command-line arguments + argv = [ + f"--signal={signal_dir}", + "--pattern-signal=*.dl1.h5", + f"--output={output_dir}", + f"--reco={reco_task}", + "--TrainCTLearnModel.n_epochs=1", + "--TrainCTLearnModel.batch_size=2", + "--DLImageReader.focal_length_choice=EQUIVALENT", + f"--DLImageReader.allowed_tels={allowed_tels}", + ] + + # Include background only for classification task + if reco_task == "type": + argv.extend( + [ + f"--background={background_dir}", + "--pattern-background=*.dl1.h5", + f"--DLImageReader.image_mapper_type={image_mapper_types[telescope_type]}", + ] + ) + + # Run training + assert ( + run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 ) - # Run training - assert ( - run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 - ) - ctlearn_trained_dl1_mono_models[f"{telescope_type}_{reco_task}"] = ( output_dir / "ctlearn_model.keras" ) @@ -401,35 +404,36 @@ def ctlearn_trained_dl1_stereo_models( # Loop over reconstruction tasks and train models for each combination ctlearn_trained_dl1_stereo_models = {} - for reco_task in ["type", "energy", "skydirection"]: - # Output directory for trained model - output_dir = tmp_path / f"ctlearn_{telescope_type}_{reco_task}" - - # Build command-line arguments - argv = [ - f"--signal={signal_dir}", - "--pattern-signal=*.dl1.h5", - f"--output={output_dir}", - f"--reco={reco_task}", - "--TrainCTLearnModel.n_epochs=1", - "--TrainCTLearnModel.batch_size=2", - "--TrainCTLearnModel.stack_telescope_images=True", - "--DLImageReader.mode=stereo", - "--DLImageReader.focal_length_choice=EQUIVALENT", - f"--DLImageReader.allowed_tels={allowed_tels}", - ] - - # Include background only for classification task - if reco_task == "type": - argv.extend( - [ - f"--background={background_dir}", - "--pattern-background=*.dl1.h5", - ] - ) - - # Run training - assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 + with mock.patch("ctapipe.instrument.SubarrayDescription.__eq__", return_value=True): + for reco_task in ["type", "energy", "skydirection"]: + # Output directory for trained model + output_dir = tmp_path / f"ctlearn_{telescope_type}_{reco_task}" + + # Build command-line arguments + argv = [ + f"--signal={signal_dir}", + "--pattern-signal=*.dl1.h5", + f"--output={output_dir}", + f"--reco={reco_task}", + "--TrainCTLearnModel.n_epochs=1", + "--TrainCTLearnModel.batch_size=2", + "--TrainCTLearnModel.stack_telescope_images=True", + "--DLImageReader.mode=stereo", + "--DLImageReader.focal_length_choice=EQUIVALENT", + f"--DLImageReader.allowed_tels={allowed_tels}", + ] + + # Include background only for classification task + if reco_task == "type": + argv.extend( + [ + f"--background={background_dir}", + "--pattern-background=*.dl1.h5", + ] + ) + + # Run training + assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 ctlearn_trained_dl1_stereo_models[f"{telescope_type}_{reco_task}"] = ( output_dir / "ctlearn_model.keras" diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index 6e11943d..e81f0a99 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -2221,10 +2221,14 @@ def _store_mc_subarray_pointing(self, all_identifiers): Table containing the subarray pointing information. """ # Read the subarray pointing table - pointing_info = read_table( - self.input_url, - SIMULATION_RUN_TABLE, - ) + try: + pointing_info = read_table( + self.input_url, + SIMULATION_RUN_TABLE, + ) + except Exception as e: + self.log.warning("Could not read simulation run table: %s", e) + return None # Assuming min_az = max_az and min_alt = max_alt pointing_info.keep_columns(["obs_id", "min_az", "min_alt"]) pointing_info.rename_column("min_az", "pointing_azimuth") diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 89348958..8cf1484b 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -163,18 +163,28 @@ def get_framework(cls, framework_type: FrameworkType): @property def classes(self): - from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel - from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel from ctlearn.tools.train.base_train_model import TrainCTLearnModel from ctapipe.core.traits import classes_with_traits from dl1_data_handler.reader import DLDataReader - return [ + tool_classes = [ type(self), TrainCTLearnModel, - TrainKerasModel, - TrainPyTorchModel, - ] + classes_with_traits(DLDataReader) + ] + + try: + from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + tool_classes.append(TrainKerasModel) + except ImportError: + pass + + try: + from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel + tool_classes.append(TrainPyTorchModel) + except ImportError: + pass + + return tool_classes + classes_with_traits(DLDataReader) def main(): # Run the tool From 5470007d428ff218b8ddfd9e83dd86a5865edfaf Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Thu, 23 Jul 2026 09:01:02 +0200 Subject: [PATCH 086/119] install tf and torch in same env and run CI with the same job --- .github/workflows/python-package-conda.yml | 11 +--- environment-pytorch.yml | 72 ---------------------- environment-tensorflow.yml | 70 --------------------- pyproject.toml | 50 +++++++++------ 4 files changed, 34 insertions(+), 169 deletions(-) delete mode 100644 environment-pytorch.yml delete mode 100644 environment-tensorflow.yml diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 2d80e498..ec08d03e 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -15,7 +15,6 @@ jobs: os: [ubuntu-22.04] python-version: ['3.12', '3.13'] dl1dh-version: ['latest', 'nightly'] - framework: ['tensorflow', 'pytorch', 'all'] max-parallel: 6 runs-on: ${{ matrix.os }} continue-on-error: ${{ matrix.dl1dh-version == 'nightly' }} @@ -65,14 +64,8 @@ jobs: run: | source $HOME/miniconda/etc/profile.d/conda.sh conda activate ctlearn - if [ "${{ matrix.framework }}" = "tensorflow" ]; then - pip install -e .[tf] - elif [ "${{ matrix.framework }}" = "pytorch" ]; then - pip install -e .[pytorch] - else - pip install -e .[all] - fi - + pip install -e .[tests] + - name: Run pytest run: | source $HOME/miniconda/etc/profile.d/conda.sh diff --git a/environment-pytorch.yml b/environment-pytorch.yml deleted file mode 100644 index 87bef7c6..00000000 --- a/environment-pytorch.yml +++ /dev/null @@ -1,72 +0,0 @@ -# conda env create -f environment.yml -name: ctlearn-pytorch -channels: - - conda-forge - - anaconda -dependencies: - - python=3.10 - - astropy=6.1.3 - - numpy=1.26.4 - - pandas=2.2.3 - - setuptools=78.1.1 - - pytables=3.10.1 - - pyyaml=6.0.2 - - scikit-learn=1.6.1 - - pytest=8.4.1 - - c-blosc2=2.13 - - pip=25.1 - - pip: - # --- Core scientific stack --- - - numba==0.61.2 - - scipy==1.15.3 - - matplotlib==3.10.0 - - seaborn==0.13.2 - - scikit-image==0.25.2 - - tqdm==4.67.1 - - # --- Deep learning frameworks --- - - torch==2.5.0 - - torchvision==0.20.0 - - torchmetrics==1.4.3 - - pytorch-lightning==2.4.0 - - deepspeed==0.17.1 - - # --- ONNX tools --- - - onnx==1.17.0 - - onnxsim==0.4.36 - - onnx2pytorch==0.5.3 - - # --- CTA related packages --- - - ctapipe==0.23.2 - - ctapipe-io-lst==0.27.1 - - dl1_data_handler==0.14.6 - - ctaplot==0.6.4 - - pyirf==0.12.0 - - eventio>=1.9.1 - - # --- I/O and plotting --- - - h5py==3.14.0 - - pydot==4.0.1 - - opencv-python==4.11.0.86 - - imageio==2.37.0 - - # --- Utilities --- - - rich==14.0.0 - - psutil==5.9.0 - - joblib==1.4.2 - - tqdm==4.67.1 - - coloredlogs==15.0.1 - - protobuf==3.20.3 - - tensorboard==2.14.1 - - # --- Jupyter & dev tools --- - - jupyterlab==4.4.4 - - ipykernel==6.29.5 - - ipywidgets==8.1.7 - - notebook==7.4.4 - - # --- Optional / visualization --- - - streamlit==1.49.1 - - bokeh==3.6.2 - - diff --git a/environment-tensorflow.yml b/environment-tensorflow.yml deleted file mode 100644 index 302662d5..00000000 --- a/environment-tensorflow.yml +++ /dev/null @@ -1,70 +0,0 @@ -# conda env create -f environment.yml -name: ctlearn-tf -channels: - - conda-forge - - anaconda -dependencies: - - python=3.10 - - astropy=6.1.3 - - numpy=1.26.4 - - pandas=2.2.3 - - setuptools=78.1.1 - - pytables=3.10.1 - - pyyaml=6.0.2 - - scikit-learn=1.6.1 - - pytest=8.4.1 - - c-blosc2=2.13 - - pip=25.1 - - pip: - # --- Core scientific stack --- - - numba==0.61.2 - - scipy==1.15.3 - - matplotlib==3.10.0 - - seaborn==0.13.2 - - scikit-image==0.25.2 - - tqdm==4.67.1 - - # --- Deep learning frameworks --- - - tensorflow==2.14.1 - - tensorflow-estimator==2.14.0 - - tf2onnx==1.16.1 - - deepspeed==0.17.1 - - # --- ONNX tools --- - - onnx==1.17.0 - - onnxsim==0.4.36 - - # --- CTA related packages --- - - ctapipe==0.23.2 - - ctapipe-io-lst==0.27.1 - - dl1_data_handler==0.14.6 - - ctaplot==0.6.4 - - pyirf==0.12.0 - - eventio>=1.9.1 - - # --- I/O and plotting --- - - h5py==3.14.0 - - pydot==4.0.1 - - opencv-python==4.11.0.86 - - imageio==2.37.0 - - # --- Utilities --- - - rich==14.0.0 - - psutil==5.9.0 - - joblib==1.4.2 - - tqdm==4.67.1 - - coloredlogs==15.0.1 - - protobuf==3.20.3 - - tensorboard==2.14.1 - - # --- Jupyter & dev tools --- - - jupyterlab==4.4.4 - - ipykernel==6.29.5 - - ipywidgets==8.1.7 - - notebook==7.4.4 - - # --- Optional / visualization --- - - streamlit==1.49.1 - - bokeh==3.6.2 - - diff --git a/pyproject.toml b/pyproject.toml index 3a7ccbfe..aff9fac3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,17 +28,23 @@ classifiers = [ requires-python = ">=3.12" dependencies = [ - "dl1_data_handler>=0.14.8", "astropy", + "ctapipe[all]>=0.29", + "dl1_data_handler>=0.14.8", + "numba", "numpy", "pandas", "pip", + "pydot", + "pytorch-lightning", "pyyaml", "scikit-learn", - "numba", - "tensorboard", "setuptools", - "ctapipe[all]>=0.29", + "tensorboard", + "tensorflow>=2.16", + "torch>=2.0.0", + "torchvision", + "torchmetrics", ] dynamic = ["version"] @@ -47,25 +53,33 @@ dynamic = ["version"] packages = ["ctlearn"] [project.optional-dependencies] -tf = [ - "tensorflow>=2.16", - "tensorflow-estimator>=2.14.0", - "tf2onnx", - "pydot", + +# all is with all optional *runtime* dependencies +# use `dev` to get really all dependencies +all = [ + "matplotlib ~=3.0", + "pyirf ~=0.14.0", ] -pytorch = [ - "torch>=2.0.0", - "torchvision", - "pytorch-lightning", - "torchmetrics", - "onnx2pytorch", + +tests = [ + # at the moment, essentially all tests rely on test data from simtel + # it doesn't make sense to skip all of these. + "ctlearn", + "pytest >=9.0", + "pytest-cov", + "pytest-xdist", + "pytest_astropy_header", ] -doc = [ + +docs = [ "sphinx", "sphinx-rtd-theme", ] -# self reference allows all to be defined in terms of other extras -all = ["ctlearn[tf,pytorch,doc]"] + +dev = [ + "ctlearn[all,docs,tests]", + "setuptools_scm[toml]", +] [project.urls] repository = "https://github.com/ctlearn-project/ctlearn" From c7d1b2e2d458d4e52fd10d977bf7603b94d5d950 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Thu, 23 Jul 2026 09:06:28 +0200 Subject: [PATCH 087/119] run latest and lowest supported versions of tensorflow and torch --- .github/workflows/python-package-conda.yml | 25 ++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index ec08d03e..4cb615fc 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -13,11 +13,22 @@ jobs: strategy: matrix: os: [ubuntu-22.04] - python-version: ['3.12', '3.13'] + python-version: ['3.12', '3.13', '3.14'] dl1dh-version: ['latest', 'nightly'] + tensorflow-version: ['latest', '2.16.*'] + torch-version: ['latest', '2.0.*'] + exclude: + - python-version: '3.13' + tensorflow-version: '2.16.*' + - python-version: '3.14' + tensorflow-version: '2.16.*' + - python-version: '3.13' + torch-version: '2.0.*' + - python-version: '3.14' + torch-version: '2.0.*' max-parallel: 6 runs-on: ${{ matrix.os }} - continue-on-error: ${{ matrix.dl1dh-version == 'nightly' }} + continue-on-error: ${{ matrix.dl1dh-version == 'nightly' || matrix.python-version == '3.14' }} steps: - uses: actions/checkout@v4 @@ -49,6 +60,16 @@ jobs: else pip install dl1-data-handler fi + if [ "${{ matrix.tensorflow-version }}" = "latest" ]; then + pip install --upgrade tensorflow + else + pip install "tensorflow==${{ matrix.tensorflow-version }}" + fi + if [ "${{ matrix.torch-version }}" = "latest" ]; then + pip install --upgrade torch + else + pip install "torch==${{ matrix.torch-version }}" + fi - name: Add MKL_THREADING_LAYER variable run: echo "MKL_THREADING_LAYER=GNU" >> $GITHUB_ENV From bfe096143f5b7301fd9451c263a1fc51a62cfd49 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Thu, 23 Jul 2026 09:13:33 +0200 Subject: [PATCH 088/119] run Ci with schedule at 2:00 UTC --- .github/workflows/python-package-conda.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 4cb615fc..55850ffb 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -6,10 +6,11 @@ on: tags: ["**"] pull_request: workflow_dispatch: + schedule: + - cron: "0 2 * * *" # Daily at 02:00 UTC jobs: - test-job: - name: Test Job + build: strategy: matrix: os: [ubuntu-22.04] From 78c0d595266afd81af7f33b68bd5f93dfc445855 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Thu, 23 Jul 2026 09:17:55 +0200 Subject: [PATCH 089/119] fix minimal torch version to >=2.2.X --- .github/workflows/python-package-conda.yml | 6 +++--- pyproject.toml | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 55850ffb..9efb3c78 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -17,16 +17,16 @@ jobs: python-version: ['3.12', '3.13', '3.14'] dl1dh-version: ['latest', 'nightly'] tensorflow-version: ['latest', '2.16.*'] - torch-version: ['latest', '2.0.*'] + torch-version: ['latest', '2.2.*'] exclude: - python-version: '3.13' tensorflow-version: '2.16.*' - python-version: '3.14' tensorflow-version: '2.16.*' - python-version: '3.13' - torch-version: '2.0.*' + torch-version: '2.2.*' - python-version: '3.14' - torch-version: '2.0.*' + torch-version: '2.2.*' max-parallel: 6 runs-on: ${{ matrix.os }} continue-on-error: ${{ matrix.dl1dh-version == 'nightly' || matrix.python-version == '3.14' }} diff --git a/pyproject.toml b/pyproject.toml index aff9fac3..6aa4063d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ dependencies = [ "setuptools", "tensorboard", "tensorflow>=2.16", - "torch>=2.0.0", + "torch>=2.2.0", "torchvision", "torchmetrics", ] From 34cbfbb71667f944832a047beba2af10b1c535b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Thu, 23 Jul 2026 10:37:50 +0000 Subject: [PATCH 090/119] Add the opncv in the pyproject --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 6aa4063d..c6e155ba 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,6 +45,7 @@ dependencies = [ "torch>=2.2.0", "torchvision", "torchmetrics", + "opencv-python", ] dynamic = ["version"] From a36ac31ea6b4780fd3f08d59aa2b52fcd207c388 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Thu, 23 Jul 2026 10:57:26 +0000 Subject: [PATCH 091/119] Fix the test --- ctlearn/tools/train/base_train_model.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 048e3e09..1ddc7687 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -522,10 +522,14 @@ def setup(self): "'DLFeatureVectorReader' is not supported in CTLearn yet. " "Missing stereo CTLearnModel implementation." ) - self.quality_query = TableQualityQuery(quality_criteria=[('> 50 phe', 'hillas_intensity > 50')]) - self.channels = ["cleaned_image", "cleaned_peak_time"] - self.config.TableQualityQuery.quality_criteria = self.quality_query.quality_criteria - self.config.DLDataReader.channels = self.channels + if "quality_criteria" not in self.config.get("TableQualityQuery", {}): + self.quality_query = TableQualityQuery(quality_criteria=[('> 50 phe', 'hillas_intensity > 50')]) + self.config.TableQualityQuery.quality_criteria = self.quality_query.quality_criteria + + if self.dl1dh_reader_type == "DLImageReader": + self.channels = ["cleaned_image", "cleaned_peak_time"] + if "channels" not in self.config.get("DLImageReader", {}): + self.config.DLImageReader.channels = self.channels print(f"self.dl1dh_reader_type: {self.dl1dh_reader_type}") self.dl1dh_reader = DLDataReader.from_name( From a09087b0694513f01ba435c478779c0880a6e416 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Tue, 21 Jul 2026 15:48:28 +0200 Subject: [PATCH 092/119] make abstract level of singleCNN and ResNet to inherit the different frameworks move original pytorch model to model collection --- ctlearn/core/keras/model.py | 292 ++-------------- ctlearn/core/model.py | 321 ++++++++++++++++++ .../pytorch/{model.py => model_collection.py} | 0 3 files changed, 342 insertions(+), 271 deletions(-) create mode 100644 ctlearn/core/model.py rename ctlearn/core/pytorch/{model.py => model_collection.py} (100%) diff --git a/ctlearn/core/keras/model.py b/ctlearn/core/keras/model.py index 11e28c87..d1862847 100644 --- a/ctlearn/core/keras/model.py +++ b/ctlearn/core/keras/model.py @@ -2,11 +2,13 @@ This module defines the ``CTLearnModel`` classes, which holds the basic functionality for creating a Keras model to be used in CTLearn. """ -from abc import abstractmethod import keras -from ctapipe.core import Component -from ctapipe.core.traits import Bool, Int, CaselessStrEnum, List, Dict, Unicode, Path +from ctlearn.core.model import ( + SingleCNN, + ResNet, + LoadedModel, +) from ctlearn.core.keras.attention import ( dual_squeeze_excite_block, channel_squeeze_excite_block, @@ -15,19 +17,18 @@ from ctlearn.utils import validate_trait_dict __all__ = [ - "build_fully_connect_head", - "CTLearnModel", - "SingleCNN", - "ResNet", - "LoadedModel", + "build_fully_connect_keras_head", + "KerasSingleCNN", + "KerasResNet", + "KerasLoadedModel", ] -def build_fully_connect_head(inputs, layers, activation_function, tasks): +def build_fully_connect_keras_head(inputs, layers, activation_function, tasks): """ - Build the fully connected head for the CTLearn model. + Build the fully connected head for the keras-based CTLearn model. - Function to build the fully connected head of the CTLearn model using the specified parameters. + Function to build the fully connected head of the keras-based CTLearn model using the specified parameters. Parameters ---------- @@ -65,118 +66,7 @@ def build_fully_connect_head(inputs, layers, activation_function, tasks): return logits -class CTLearnModel(Component): - """ - Base component for creating a Keras model to be used in CTLearn. - - This class defines the basic functionality for creating a Keras model to be used in CTLearn. - It provides the necessary methods to build the backbone of the model and the fully connected head - for the specified tasks. - """ - - init_padding = Int( - default_value=0, - allow_none=False, - min=0, - help="Initial padding to apply to the input data.", - ).tag(config=True) - - head_layers = Dict( - default_value={ - "type": [512, 256, 2], - "energy": [512, 256, 1], - "cameradirection": [512, 256, 2], - "skydirection": [512, 256, 2], - }, - allow_none=False, - help=( - "Dictionary containing the number of neurons in the fully connected head for each " - "task ('type', 'energy', 'cameradirection', 'skydirection'). Note: The number of neurons in the last layer " - "must match the number of classes or the number of reconstructed values." - ), - ).tag(config=True) - - head_activation_function = Dict( - default_value={ - "type": "relu", - "energy": "relu", - "cameradirection": "tanh", - "skydirection": "tanh", - }, - allow_none=False, - help=( - "Dictionary containing the activation function for the fully connected head for each " - "task ('type', 'energy', 'cameradirection', 'skydirection'). Note: The default activation functions " - "are 'relu' for 'type' and 'energy' tasks, and 'tanh' for 'cameradirection' and 'skydirection' tasks. " - "The 'type' task uses 'softmax' as the final activation function." - ), - ).tag(config=True) - - attention_mechanism = CaselessStrEnum( - ["Dual-SE", "Channel-SE", "Spatial-SE"], - default_value="Dual-SE", - allow_none=True, - help="Type of squeeze and excitation attention mechanism to use.", - ).tag(config=True) - - attention_reduction_ratio = Int( - default_value=16, - allow_none=True, - min=1, - help="Reduction ratio for the squeeze and excitation attention mechanism.", - ).tag(config=True) - - def __init__( - self, - config=None, - parent=None, - **kwargs, - ): - """ - Parameters - ---------- - config : traitlets.loader.Config - Configuration specified by config file or cmdline arguments. - Used to set traitlet values. - This is mutually exclusive with passing a ``parent``. - parent : ctapipe.core.Component or ctapipe.core.Tool - Parent of this component in the configuration hierarchy, - this is mutually exclusive with passing ``config`` - """ - super().__init__(config=config, parent=parent, **kwargs) - - # Define the squeeze and excitation attention mechanism - self.attention = None - if self.attention_mechanism is not None: - self.attention = { - "mechanism": self.attention_mechanism, - "reduction_ratio": self.attention_reduction_ratio, - } - - -@abstractmethod -def _build_backbone(self, input_shape): - """ - Build the backbone of the CTLearn model. - - Function to build the backbone of the CTLearn model using the specified parameters. - - Parameters - ---------- - input_shape : tuple - Shape of the input data (batch_size, height, width, channels). - - Returns - ------- - backbone_model : keras.Model - Keras model object representing the backbone of the CTLearn model. - network_input : keras.Input - Keras input layer object for the backbone model. - """ - pass - - -class SingleCNN(CTLearnModel): +class KerasSingleCNN(SingleCNN): """ ``SingleCNN`` is a simple convolutional neural network model. @@ -184,54 +74,6 @@ class SingleCNN(CTLearnModel): methods to build a simple convolutional neural network model. """ - name = Unicode( - "SingleCNN", - help="Name of the model backbone.", - ).tag(config=True) - - architecture = List( - trait=Dict(), - default_value=[ - {"filters": 32, "kernel_size": 3, "number": 1}, - {"filters": 32, "kernel_size": 3, "number": 1}, - {"filters": 64, "kernel_size": 3, "number": 1}, - {"filters": 128, "kernel_size": 3, "number": 1}, - ], - allow_none=False, - help=( - "List of dicts containing the number of filters, kernel sizes and number of repetition. " - "E.g. ``[{'filters': 12, 'kernel_size': 3, 'number': 1}, ...]``." - ), - ).tag(config=True) - - pooling_type = CaselessStrEnum( - ["max", "average"], - default_value="max", - allow_none=True, - help="Type of pooling to apply to the convolutional layers with ``pooling_parameters``.", - ).tag(config=True) - - pooling_parameters = Dict( - default_value={"size": 2, "strides": 2}, - allow_none=True, - help=( - "Parameters for the max or average pooling layers. " - "E.g. ``{'size': 2, 'strides': 2}``." - ), - ).tag(config=True) - - batchnorm = Bool( - default_value=False, - allow_none=False, - help="Apply batch normalization to the convolutional layers.", - ).tag(config=True) - - bottleneck_filters = Int( - default_value=None, - allow_none=True, - help="Number of filters in the bottleneck layer.", - ).tag(config=True) - def __init__( self, input_shape, @@ -241,31 +83,20 @@ def __init__( **kwargs, ): super().__init__( + tasks=tasks, config=config, parent=parent, **kwargs, ) - # Validate the architecture trait - for layer in self.architecture: - validate_trait_dict(layer, ["filters", "kernel_size", "number"]) - # Validate the pooling parameters trait - validate_trait_dict(self.pooling_parameters, ["size", "strides"]) - - # Construct the name of the backbone model by appending "_block" to the model name - self.backbone_name = self.name + "_block" - # Build the ResNet model backbone self.backbone_model, self.input_layer = self._build_backbone(input_shape) backbone_output = self.backbone_model(self.input_layer) - # Validate the head trait with the provided tasks - validate_trait_dict(self.head_layers, tasks) - validate_trait_dict(self.head_activation_function, tasks) # Build the fully connected head depending on the tasks - self.logits = build_fully_connect_head( + self.logits = build_fully_connect_keras_head( backbone_output, self.head_layers, self.head_activation_function, tasks ) - + # Build the full pipeline model∫ self.model = keras.Model(self.input_layer, self.logits, name="CTLearn_model") def _build_backbone(self, input_shape): @@ -361,7 +192,7 @@ def _build_backbone(self, input_shape): return backbone_model, network_input -class ResNet(CTLearnModel): +class KerasResNet(ResNet): """ ``ResNet`` is a residual neural network model. @@ -369,51 +200,6 @@ class ResNet(CTLearnModel): methods to build a residual neural network model. """ - name = Unicode( - "ThinResNet", - help="Name of the model backbone.", - ).tag(config=True) - - init_layer = Dict( - default_value=None, - allow_none=True, - help=( - "Parameters for the first convolutional layer. " - "E.g. ``{'filters': 64, 'kernel_size': 7, 'strides': 2}``." - ), - ).tag(config=True) - - init_max_pool = Dict( - default_value=None, - allow_none=True, - help=( - "Parameters for the first max pooling layer. " - "E.g. ``{'size': 3, 'strides': 2}``." - ), - ).tag(config=True) - - residual_block_type = CaselessStrEnum( - ["basic", "bottleneck"], - default_value="bottleneck", - allow_none=False, - help="Type of residual block to use.", - ).tag(config=True) - - architecture = List( - trait=Dict(), - default_value=[ - {"filters": 48, "blocks": 2}, - {"filters": 96, "blocks": 3}, - {"filters": 128, "blocks": 3}, - {"filters": 256, "blocks": 3}, - ], - allow_none=False, - help=( - "List of dicts containing the number of filters and residual blocks. " - "E.g. ``[{'filters': 12, 'blocks': 2}, ...]``." - ), - ).tag(config=True) - def __init__( self, input_shape, @@ -423,31 +209,16 @@ def __init__( **kwargs, ): super().__init__( + tasks=tasks, config=config, parent=parent, **kwargs, ) - - # Validate the architecture trait - for layer in self.architecture: - validate_trait_dict(layer, ["filters", "blocks"]) - # Validate the initial layers trait - if self.init_layer is not None: - validate_trait_dict(self.init_layer, ["filters", "kernel_size", "strides"]) - if self.init_max_pool is not None: - validate_trait_dict(self.init_max_pool, ["size", "strides"]) - - # Construct the name of the backbone model by appending "_block" to the model name - self.backbone_name = self.name + "_block" - # Build the ResNet model backbone self.backbone_model, self.input_layer = self._build_backbone(input_shape) backbone_output = self.backbone_model(self.input_layer) - # Validate the head traits with the provided tasks - validate_trait_dict(self.head_layers, tasks) - validate_trait_dict(self.head_activation_function, tasks) # Build the fully connected head depending on the tasks - self.logits = build_fully_connect_head( + self.logits = build_fully_connect_keras_head( backbone_output, self.head_layers, self.head_activation_function, tasks ) @@ -799,7 +570,7 @@ def _bottleneck_residual_block( return x -class LoadedModel(CTLearnModel): +class KerasLoadedModel(LoadedModel): """ ``LoadedModel`` is a pre-trained Keras model. @@ -808,27 +579,6 @@ class LoadedModel(CTLearnModel): for the CTLearn model. """ - load_model_from = Path( - default_value=None, - help="Path to a Keras model file (Keras3) or directory Keras2)", - allow_none=True, - exists=True, - directory_ok=True, - file_ok=True, - ).tag(config=True) - - overwrite_head = Bool( - default_value=False, - allow_none=False, - help="Set to overwrite the fully connected head from the loaded model.", - ).tag(config=True) - - trainable_backbone = Bool( - default_value=True, - allow_none=False, - help="Set to set the backbone model to be trainable.", - ).tag(config=True) - def __init__( self, input_shape, @@ -853,7 +603,7 @@ def __init__( # Validate the head trait with the provided tasks validate_trait_dict(self.head_layers, tasks) # Build the fully connected head depending on the tasks - self.logits = build_fully_connect_head( + self.logits = build_fully_connect_keras_head( backbone_output, self.head_layers, self.head_activation_function, tasks ) self.model = keras.Model( diff --git a/ctlearn/core/model.py b/ctlearn/core/model.py new file mode 100644 index 00000000..6c37358b --- /dev/null +++ b/ctlearn/core/model.py @@ -0,0 +1,321 @@ +""" +This module defines the ``CTLearnModel`` classes, which holds the basic functionality for creating a Keras model to be used in CTLearn. +""" + +from abc import abstractmethod +import keras + +from ctapipe.core import Component +from ctapipe.core.traits import Bool, Int, CaselessStrEnum, List, Dict, Unicode, Path +from ctlearn.utils import validate_trait_dict + +__all__ = [ + "CTLearnModel", + "SingleCNN", + "ResNet", + "LoadedModel", +] + + +class CTLearnModel(Component): + """ + Base component for creating a Keras model to be used in CTLearn. + + This class defines the basic functionality for creating a Keras model to be used in CTLearn. + It provides the necessary methods to build the backbone of the model and the fully connected head + for the specified tasks. + """ + + init_padding = Int( + default_value=0, + allow_none=False, + min=0, + help="Initial padding to apply to the input data.", + ).tag(config=True) + + head_layers = Dict( + default_value={ + "type": [512, 256, 2], + "energy": [512, 256, 1], + "cameradirection": [512, 256, 2], + "skydirection": [512, 256, 2], + }, + allow_none=False, + help=( + "Dictionary containing the number of neurons in the fully connected head for each " + "task ('type', 'energy', 'cameradirection', 'skydirection'). Note: The number of neurons in the last layer " + "must match the number of classes or the number of reconstructed values." + ), + ).tag(config=True) + + head_activation_function = Dict( + default_value={ + "type": "relu", + "energy": "relu", + "cameradirection": "tanh", + "skydirection": "tanh", + }, + allow_none=False, + help=( + "Dictionary containing the activation function for the fully connected head for each " + "task ('type', 'energy', 'cameradirection', 'skydirection'). Note: The default activation functions " + "are 'relu' for 'type' and 'energy' tasks, and 'tanh' for 'cameradirection' and 'skydirection' tasks. " + "The 'type' task uses 'softmax' as the final activation function." + ), + ).tag(config=True) + + attention_mechanism = CaselessStrEnum( + ["Dual-SE", "Channel-SE", "Spatial-SE"], + default_value="Dual-SE", + allow_none=True, + help="Type of squeeze and excitation attention mechanism to use.", + ).tag(config=True) + + attention_reduction_ratio = Int( + default_value=16, + allow_none=True, + min=1, + help="Reduction ratio for the squeeze and excitation attention mechanism.", + ).tag(config=True) + + def __init__( + self, + config=None, + parent=None, + **kwargs, + ): + """ + Parameters + ---------- + config : traitlets.loader.Config + Configuration specified by config file or cmdline arguments. + Used to set traitlet values. + This is mutually exclusive with passing a ``parent``. + parent : ctapipe.core.Component or ctapipe.core.Tool + Parent of this component in the configuration hierarchy, + this is mutually exclusive with passing ``config`` + """ + super().__init__(config=config, parent=parent, **kwargs) + + # Define the squeeze and excitation attention mechanism + self.attention = None + if self.attention_mechanism is not None: + self.attention = { + "mechanism": self.attention_mechanism, + "reduction_ratio": self.attention_reduction_ratio, + } + + + @abstractmethod + def _build_backbone(self, input_shape): + """ + Build the backbone of the CTLearn model. + + Function to build the backbone of the CTLearn model using the specified parameters. + + Parameters + ---------- + input_shape : tuple + Shape of the input data (batch_size, height, width, channels). + + Returns + ------- + backbone_model : keras.Model + Keras model object representing the backbone of the CTLearn model. + network_input : keras.Input + Keras input layer object for the backbone model. + """ + pass + + +class SingleCNN(CTLearnModel): + """ + ``SingleCNN`` is the base class of a simple convolutional neural network model. + + This class extends the functionality of ``CTLearnModel`` by implementing + methods to build a simple convolutional neural network model. + """ + + name = Unicode( + "SingleCNN", + help="Name of the model backbone.", + ).tag(config=True) + + architecture = List( + trait=Dict(), + default_value=[ + {"filters": 32, "kernel_size": 3, "number": 1}, + {"filters": 32, "kernel_size": 3, "number": 1}, + {"filters": 64, "kernel_size": 3, "number": 1}, + {"filters": 128, "kernel_size": 3, "number": 1}, + ], + allow_none=False, + help=( + "List of dicts containing the number of filters, kernel sizes and number of repetition. " + "E.g. ``[{'filters': 12, 'kernel_size': 3, 'number': 1}, ...]``." + ), + ).tag(config=True) + + pooling_type = CaselessStrEnum( + ["max", "average"], + default_value="max", + allow_none=True, + help="Type of pooling to apply to the convolutional layers with ``pooling_parameters``.", + ).tag(config=True) + + pooling_parameters = Dict( + default_value={"size": 2, "strides": 2}, + allow_none=True, + help=( + "Parameters for the max or average pooling layers. " + "E.g. ``{'size': 2, 'strides': 2}``." + ), + ).tag(config=True) + + batchnorm = Bool( + default_value=False, + allow_none=False, + help="Apply batch normalization to the convolutional layers.", + ).tag(config=True) + + bottleneck_filters = Int( + default_value=None, + allow_none=True, + help="Number of filters in the bottleneck layer.", + ).tag(config=True) + + def __init__( + self, + tasks, + config=None, + parent=None, + **kwargs, + ): + super().__init__( + config=config, + parent=parent, + **kwargs, + ) + + # Validate the architecture trait + for layer in self.architecture: + validate_trait_dict(layer, ["filters", "kernel_size", "number"]) + # Validate the pooling parameters trait + validate_trait_dict(self.pooling_parameters, ["size", "strides"]) + # Validate the head trait with the provided tasks + validate_trait_dict(self.head_layers, tasks) + validate_trait_dict(self.head_activation_function, tasks) + # Construct the name of the backbone model by appending "_block" to the model name + self.backbone_name = self.name + "_block" + + +class ResNet(CTLearnModel): + """ + ``ResNet`` is a residual neural network model. + + This class extends the functionality of ``CTLearnModel`` by implementing + methods to build a residual neural network model. + """ + + name = Unicode( + "ThinResNet", + help="Name of the model backbone.", + ).tag(config=True) + + init_layer = Dict( + default_value=None, + allow_none=True, + help=( + "Parameters for the first convolutional layer. " + "E.g. ``{'filters': 64, 'kernel_size': 7, 'strides': 2}``." + ), + ).tag(config=True) + + init_max_pool = Dict( + default_value=None, + allow_none=True, + help=( + "Parameters for the first max pooling layer. " + "E.g. ``{'size': 3, 'strides': 2}``." + ), + ).tag(config=True) + + residual_block_type = CaselessStrEnum( + ["basic", "bottleneck"], + default_value="bottleneck", + allow_none=False, + help="Type of residual block to use.", + ).tag(config=True) + + architecture = List( + trait=Dict(), + default_value=[ + {"filters": 48, "blocks": 2}, + {"filters": 96, "blocks": 3}, + {"filters": 128, "blocks": 3}, + {"filters": 256, "blocks": 3}, + ], + allow_none=False, + help=( + "List of dicts containing the number of filters and residual blocks. " + "E.g. ``[{'filters': 12, 'blocks': 2}, ...]``." + ), + ).tag(config=True) + + def __init__( + self, + tasks, + config=None, + parent=None, + **kwargs, + ): + super().__init__( + config=config, + parent=parent, + **kwargs, + ) + + # Validate the architecture trait + for layer in self.architecture: + validate_trait_dict(layer, ["filters", "blocks"]) + # Validate the initial layers trait + if self.init_layer is not None: + validate_trait_dict(self.init_layer, ["filters", "kernel_size", "strides"]) + if self.init_max_pool is not None: + validate_trait_dict(self.init_max_pool, ["size", "strides"]) + # Validate the head traits with the provided tasks + validate_trait_dict(self.head_layers, tasks) + validate_trait_dict(self.head_activation_function, tasks) + # Construct the name of the backbone model by appending "_block" to the model name + self.backbone_name = self.name + "_block" + + +class LoadedModel(CTLearnModel): + """ + ``LoadedModel`` is a pre-trained Keras model. + + This class extends the functionality of ``CTLearnModel`` by implementing + methods to load a pre-trained Keras model. The model can be used as a backbone + for the CTLearn model. + """ + + load_model_from = Path( + default_value=None, + help="Path to a Keras model file (Keras3) or directory Keras2)", + allow_none=True, + exists=True, + directory_ok=True, + file_ok=True, + ).tag(config=True) + + overwrite_head = Bool( + default_value=False, + allow_none=False, + help="Set to overwrite the fully connected head from the loaded model.", + ).tag(config=True) + + trainable_backbone = Bool( + default_value=True, + allow_none=False, + help="Set to set the backbone model to be trainable.", + ).tag(config=True) \ No newline at end of file diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model_collection.py similarity index 100% rename from ctlearn/core/pytorch/model.py rename to ctlearn/core/pytorch/model_collection.py From 77ef080865acbacb8f0f644d7275fb16a79d7a0d Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Tue, 21 Jul 2026 16:01:48 +0200 Subject: [PATCH 093/119] fix some model set up for keras/pytorch --- ctlearn/core/keras/model.py | 8 +- ctlearn/core/model.py | 20 +- ctlearn/core/pytorch/attention.py | 68 +++++ ctlearn/core/pytorch/model.py | 420 ++++++++++++++++++++++++++++++ 4 files changed, 510 insertions(+), 6 deletions(-) create mode 100644 ctlearn/core/pytorch/attention.py create mode 100644 ctlearn/core/pytorch/model.py diff --git a/ctlearn/core/keras/model.py b/ctlearn/core/keras/model.py index d1862847..8085f10d 100644 --- a/ctlearn/core/keras/model.py +++ b/ctlearn/core/keras/model.py @@ -14,7 +14,6 @@ channel_squeeze_excite_block, spatial_squeeze_excite_block, ) -from ctlearn.utils import validate_trait_dict __all__ = [ "build_fully_connect_keras_head", @@ -26,9 +25,9 @@ def build_fully_connect_keras_head(inputs, layers, activation_function, tasks): """ - Build the fully connected head for the keras-based CTLearn model. + Build the fully connected head for the Keras-based CTLearn model. - Function to build the fully connected head of the keras-based CTLearn model using the specified parameters. + Function to build the fully connected head of the Keras-based CTLearn model using the specified parameters. Parameters ---------- @@ -588,6 +587,7 @@ def __init__( **kwargs, ): super().__init__( + tasks=tasks, config=config, parent=parent, **kwargs, @@ -600,8 +600,6 @@ def __init__( # Load the fully connected head from the loaded model or build a new one if self.overwrite_head: backbone_output = self.backbone_model(self.input_layer) - # Validate the head trait with the provided tasks - validate_trait_dict(self.head_layers, tasks) # Build the fully connected head depending on the tasks self.logits = build_fully_connect_keras_head( backbone_output, self.head_layers, self.head_activation_function, tasks diff --git a/ctlearn/core/model.py b/ctlearn/core/model.py index 6c37358b..eb7ec1c3 100644 --- a/ctlearn/core/model.py +++ b/ctlearn/core/model.py @@ -318,4 +318,22 @@ class LoadedModel(CTLearnModel): default_value=True, allow_none=False, help="Set to set the backbone model to be trainable.", - ).tag(config=True) \ No newline at end of file + ).tag(config=True) + + def __init__( + self, + tasks, + config=None, + parent=None, + **kwargs, + ): + super().__init__( + config=config, + parent=parent, + **kwargs, + ) + # Load the fully connected head from the loaded model or build a new one + if self.overwrite_head: + backbone_output = self.backbone_model(self.input_layer) + # Validate the head trait with the provided tasks + validate_trait_dict(self.head_layers, tasks) \ No newline at end of file diff --git a/ctlearn/core/pytorch/attention.py b/ctlearn/core/pytorch/attention.py new file mode 100644 index 00000000..68305f54 --- /dev/null +++ b/ctlearn/core/pytorch/attention.py @@ -0,0 +1,68 @@ +""" +This module defines the squeeze-excite blocks for channel-wise and/or spatial-wise attention mechanisms in PyTorch. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = [ + "DualSqueezeExciteBlock", + "ChannelSqueezeExciteBlock", + "SpatialSqueezeExciteBlock", +] + +class DualSqueezeExciteBlock(nn.Module): + """ + A channel & spatial (dual) squeeze-excite block in PyTorch. + Concurrently applies channel and spatial scaling, then sums the results. + """ + def __init__(self, in_channels, ratio=16): + super().__init__() + self.cse = ChannelSqueezeExciteBlock(in_channels=in_channels, ratio=ratio) + self.sse = SpatialSqueezeExciteBlock(in_channels=in_channels) + + def forward(self, x): + # Combines cse and sse by element-wise addition + return self.cse(x) + self.sse(x) + + +class ChannelSqueezeExciteBlock(nn.Module): + """ + A channel-wise squeeze-excite (cSE) block in PyTorch. + """ + def __init__(self, in_channels, ratio=4): + super().__init__() + # Keras uses Dense layers on global pooled tensors. + # In PyTorch, we can achieve this elegantly using 1x1 Convolutions, + # avoiding the need to flatten and unflatten the spatial grid. + self.gate = nn.Sequential( + nn.Conv2d(in_channels, in_channels // ratio, kernel_size=1, bias=True), + nn.ReLU(), + nn.Conv2d(in_channels // ratio, in_channels, kernel_size=1, bias=True), + nn.Sigmoid() + ) + + def forward(self, x): + # Global Average Pooling keeping spatial dims: (B, C, H, W) -> (B, C, 1, 1) + squeeze = F.adaptive_avg_pool2d(x, (1, 1)) + # Compute channel scale factor + excitation = self.gate(squeeze) + # Multiply input tensor by the scale factor across the channel dimension + return x * excitation + + +class SpatialSqueezeExciteBlock(nn.Module): + """ + A spatial squeeze-excite (sSE) block in PyTorch. + """ + def __init__(self, in_channels): + super().__init__() + # A 1x1 convolution projecting channels down to 1 spatial mask + self.spatial_conv = nn.Conv2d(in_channels, 1, kernel_size=1, bias=True) + + def forward(self, x): + # Create a spatial landscape mask via sigmoid + spatial_mask = torch.sigmoid(self.spatial_conv(x)) + # Multiply input tensor element-wise across spatial layout + return x * spatial_mask \ No newline at end of file diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py new file mode 100644 index 00000000..5dd035f5 --- /dev/null +++ b/ctlearn/core/pytorch/model.py @@ -0,0 +1,420 @@ +""" +This module defines the ``CTLearnModel`` classes, which holds the basic functionality for creating a PyTorch model to be used in CTLearn. +""" + +from abc import abstractmethod +import torch +import torch.nn as nn +import torch.nn.functional as F + +# Assuming these custom attention blocks are updated to return torch.nn.Module or used dynamically +from ctlearn.core.model import ( + SingleCNN, + ResNet, + LoadedModel, +) +from ctlearn.core.pytorch.attention import ( + DualSqueezeExciteBlock, + ChannelSqueezeExciteBlock, + SpatialSqueezeExciteBlock, +) +from ctlearn.utils import validate_trait_dict + +__all__ = [ + "BasicBlock", + "BottleneckBlock" + "build_fully_connect_pytorch_head", + "PyTorchCTLearnModel", + "PyTorchSingleCNN", + "PyTorchResNet", + "PyTorchLoadedModel", +] + + +class MultiHeadClassifier(nn.Module): + """ + A PyTorch container module to hold the multi-task fully connected heads. + """ + def __init__(self, heads_dict, single_output_task=None): + super().__init__() + self.heads = nn.ModuleDict(heads_dict) + self.single_output_task = single_output_task + + def forward(self, x): + # Flatten the backbone output if it's spatially aggregated but still has dimensions (B, C, 1, 1) + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + + logits = {} + for task, head in self.heads.items(): + out = head(x) + logits[task] = F.softmax(out, dim=-1) if task == "type" else out + + if self.single_output_task: + return logits[self.single_output_task] + return logits + + +class DynamicSequential(nn.Sequential): + """A tiny wrapper to make nn.Sequential accept keyword arguments natively if needed.""" + def forward(self, input): + for module in self: + input = module(input) + return input + + + +def build_fully_connect_pytorch_head(in_features, layers, activation_function, tasks): + """ + Build the fully connected head for the PyTorch-based CTLearn model. + """ + heads = {} + + # Activation mapping from Keras string to PyTorch Module + act_map = { + "relu": nn.ReLU, + "tanh": nn.Tanh, + "sigmoid": nn.Sigmoid + } + + for task in tasks: + task_layers = [] + current_features = in_features + + for i, units in enumerate(layers[task]): + task_layers.append(nn.Linear(current_features, units)) + if i != len(layers[task]) - 1: + act_cls = act_map.get(activation_function[task].lower(), nn.ReLU) + task_layers.append(act_cls()) + current_features = units + + heads[task] = nn.Sequential(*task_layers) + + single_output_task = tasks[0] if (len(tasks) == 1 and tasks[0] == "type") else None + return MultiHeadClassifier(heads, single_output_task=single_output_task) + + +class FullModelPipeline(nn.Module): + """ + Combines the backbone and multi-task heads into a unified executable nn.Module pipeline. + """ + def __init__(self, backbone, head): + super().__init__() + self.backbone = backbone + self.head = head + + def forward(self, x): + features = self.backbone(x) + return self.head(features) + + +class PyTorchSingleCNN(SingleCNN): + """ + ``SingleCNN`` is a simple convolutional neural network model implemented in PyTorch. + """ + + def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): + super().__init__(config=config, parent=parent, **kwargs) + + # Build modules + self.backbone_model, out_features = self._build_backbone(input_shape) + self.logits_head = build_fully_connect_pytorch_head( + out_features, self.head_layers, self.head_activation_function, tasks + ) + # Final native PyTorch module pipeline saved into self.model + self.model = FullModelPipeline(self.backbone_model, self.logits_head) + + def _build_backbone(self, input_shape): + # input_shape format: (channels, height, width) + in_channels = input_shape[0] + modules = [] + + if self.batchnorm: + modules.append(nn.BatchNorm2d(in_channels, momentum=0.01)) # PyTorch momentum = 1 - Keras momentum + + for i, layer in enumerate(self.architecture): + filters = layer["filters"] + kernel_size = layer["kernel_size"] + number = layer["number"] + + for nr in range(number): + # padding="same" calculates padding dynamically in PyTorch based on kernel size + padding = kernel_size // 2 + modules.append(nn.Conv2d(in_channels, filters, kernel_size=kernel_size, padding=padding)) + modules.append(nn.ReLU()) + in_channels = filters + + if self.pooling_type is not None: + p_size = self.pooling_parameters["size"] + p_stride = self.pooling_parameters["strides"] + if self.pooling_type == "max": + modules.append(nn.MaxPool2d(kernel_size=p_size, stride=p_stride)) + elif self.pooling_type == "average": + modules.append(nn.AvgPool2d(kernel_size=p_size, stride=p_stride)) + + if self.batchnorm: + modules.append(nn.BatchNorm2d(in_channels, momentum=0.01)) + + if self.bottleneck_filters is not None: + modules.append(nn.Conv2d(in_channels, self.bottleneck_filters, kernel_size=1)) + modules.append(nn.ReLU()) + in_channels = self.bottleneck_filters + if self.batchnorm: + modules.append(nn.BatchNorm2d(in_channels, momentum=0.01)) + + # Global Average Pooling wrapper block + class GlobalAvgPool(nn.Module): + def forward(self, x): + return F.adaptive_avg_pool2d(x, (1, 1)) + + modules.append(GlobalAvgPool()) + + backbone_model = nn.Sequential(*modules) + return backbone_model, in_channels + + + +class BasicBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride=1, conv_shortcut=True, attention=None): + super().__init__() + self.conv_shortcut = conv_shortcut + self.attention_config = attention + + # Shortcut connection handling channel/spatial modifications + if conv_shortcut: + self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + else: + self.shortcut = nn.Identity() + + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) + + # Setup attention if applicable (Assuming your packages take/return tensors or modules) + self.setup_attention(out_channels) + + def setup_attention(self, channels): + self.attn_layer = None + if self.attention_config: + mech = self.attention_config["mechanism"] + ratio = self.attention_config.get("reduction_ratio", 16) + if mech == "Dual-SE": + self.attn_layer = DualSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Channel-SE": + self.attn_layer = ChannelSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Spatial-SE": + self.attn_layer = SpatialSqueezeExciteBlock(in_channels=channels) + + def forward(self, x): + identity = self.shortcut(x) + + out = F.relu(self.conv1(x)) + out = self.conv2(out) + + if self.attn_layer: + out = self.attn_layer(out) + + out += identity + return F.relu(out) + + +class BottleneckBlock(nn.Module): + def __init__(self, in_channels, base_filters, stride=1, conv_shortcut=True, attention=None): + super().__init__() + self.conv_shortcut = conv_shortcut + self.attention_config = attention + out_channels = 4 * base_filters + + if conv_shortcut: + self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + else: + self.shortcut = nn.Identity() + + self.conv1 = nn.Conv2d(in_channels, base_filters, kernel_size=1, stride=stride, bias=False) + self.conv2 = nn.Conv2d(base_filters, base_filters, kernel_size=3, padding=1, bias=False) + self.conv3 = nn.Conv2d(base_filters, out_channels, kernel_size=1, bias=False) + + self.setup_attention(out_channels) + + def setup_attention(self, channels): + self.attn_layer = None + if self.attention_config: + mech = self.attention_config["mechanism"] + ratio = self.attention_config.get("reduction_ratio", 16) + if mech == "Dual-SE": + self.attn_layer = DualSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Channel-SE": + self.attn_layer = ChannelSqueezeExciteBlock(in_channels=channels, ratio=ratio) + elif mech == "Spatial-SE": + self.attn_layer = SpatialSqueezeExciteBlock(in_channels=channels) + + def forward(self, x): + identity = self.shortcut(x) + + out = F.relu(self.conv1(x)) + out = F.relu(self.conv2(out)) + out = self.conv3(out) + + if self.attn_layer: + out = self.attn_layer(out) + + out += identity + return F.relu(out) + +class PyTorchResNet(ResNet): + """ + ``PyTorchResNet`` is a residual neural network model implemented in PyTorch. + """ + + + def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): + super().__init__(config=config, parent=parent, **kwargs) + + # Build PyTorch backbone and track final out_features channel size + self.backbone_model, out_features = self._build_backbone(input_shape) + # Build the fully connected head (from previous module mapping) + self.logits_head = build_fully_connect_pytorch_head( + out_features, self.head_layers, self.head_activation_function, tasks + ) + # Unify into our structural pipeline wrapper module + self.model = FullModelPipeline(self.backbone_model, self.logits_head) + + + def _build_backbone(self, input_shape): + # PyTorch input shape constraint layout: (channels, height, width) + in_channels = input_shape[0] + modules = [] + + # 1. Handle Initial Zero Padding + if self.init_padding > 0: + modules.append(nn.ZeroPad2d(self.init_padding)) + + # 2. Handle Initial Conv Layer + if self.init_layer is not None: + out_ch = self.init_layer["filters"] + k_size = self.init_layer["kernel_size"] + stride = self.init_layer["strides"] + # Dynamic calculation matching Keras implicit padding configuration + padding = k_size // 2 + + modules.append(nn.Conv2d(in_channels, out_ch, kernel_size=k_size, stride=stride, padding=padding, bias=False)) + modules.append(nn.ReLU()) + in_channels = out_ch + + # 3. Handle Initial Max Pooling + if self.init_max_pool is not None: + p_size = self.init_max_pool["size"] + p_stride = self.init_max_pool["strides"] + modules.append(nn.MaxPool2d(kernel_size=p_size, stride=p_stride, padding=p_size // 2)) + + # 4. Assemble Stacked Residual Architecture blocks + res_blocks, final_channels = self._stacked_res_blocks( + in_channels, + architecture=self.architecture, + residual_block_type=self.residual_block_type, + attention=self.attention + ) + modules.extend(res_blocks) + + # 5. Global Average Pooling setup + class GlobalAvgPool(nn.Module): + def forward(self, x): + return F.adaptive_avg_pool2d(x, (1, 1)) + + modules.append(GlobalAvgPool()) + + return nn.Sequential(*modules), final_channels + + def _stacked_res_blocks(self, in_channels, architecture, residual_block_type, attention): + blocks_list = [] + current_channels = in_channels + + filters_list = [layer["filters"] for layer in architecture] + blocks_count = [layer["blocks"] for layer in architecture] + + # First layer block sequence (stride=1) + blocks_list.extend(self._stack_fn( + current_channels, filters_list[0], blocks_count[0], residual_block_type, stride=1, attention=attention + )) + + # Update internal channel counts based on block configurations + multiplier = 4 if residual_block_type == "bottleneck" else 1 + current_channels = filters_list[0] * multiplier + + # Iteratively attach standard downsampling/residual block levels + for filters, blocks in zip(filters_list[1:], blocks_count[1:]): + blocks_list.extend(self._stack_fn( + current_channels, filters, blocks, residual_block_type, stride=2, attention=attention + )) + current_channels = filters * multiplier + + return blocks_list, current_channels + + def _stack_fn(self, in_channels, filters, blocks, residual_block_type, stride=2, attention=None): + block_layer = BasicBlock if residual_block_type == "basic" else BottleneckBlock + stack = [] + + # Build common arguments shared by BOTH block types + base_kwargs = { + "in_channels": in_channels, + "stride": stride, + "attention": attention + } + + # Add block-specific filter mappings + if residual_block_type == "basic": + base_kwargs["out_channels"] = filters + base_kwargs["base_filters"] = filters # fallback if needed, matching signature + else: + base_kwargs["base_filters"] = filters + + # 1. First block handles the structural transition + stack.append(block_layer(conv_shortcut=True, **base_kwargs)) + + # Update dimensional inputs for subsequent blocks in this group + multiplier = 4 if residual_block_type == "bottleneck" else 1 + current_in = filters * multiplier + + # 2. Remaining blocks map cleanly identity-to-identity + for _ in range(1, blocks): + next_kwargs = { + "in_channels": current_in, + "stride": 1, + "attention": attention, + "conv_shortcut": False + } + if residual_block_type == "basic": + next_kwargs["out_channels"] = filters + next_kwargs["base_filters"] = filters + else: + next_kwargs["base_filters"] = filters + + stack.append(block_layer(**next_kwargs)) + + return stack + + +class PyTorchLoadedModel(LoadedModel): + """ + ``PyTorchLoadedModel`` handles loading a pre-saved PyTorch model weight layout. + """ + + def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): + super().__init__(tasks=tasks, config=config, parent=parent, **kwargs) + + # In PyTorch, instead of load_model returning an arbitrary configuration blindly, + # you instantiate the structure first and pass a state_dict or weights file path. + self.model = torch.load(self.load_model_from) + + if self.overwrite_head: + # Freeze/unfreeze backbone based on config choice + for param in self.model.backbone.parameters(): + param.requires_grad = self.trainable_backbone + + # Fetch out_features dynamically from existing backbone configuration + # This is a representative placeholder pattern for your architecture + out_features = self.head_layers[tasks[0]][0] + + self.logits_head = build_fully_connect_pytorch_head( + out_features, self.head_layers, self.head_activation_function, tasks + ) + self.model.head = self.logits_head \ No newline at end of file From 9fbf5de62bfbacaa905d0c88ec9b9f03e174c2fe Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Tue, 21 Jul 2026 16:32:46 +0200 Subject: [PATCH 094/119] fix pytorch models --- ctlearn/core/pytorch/model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index 5dd035f5..c08fd78f 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -114,7 +114,7 @@ class PyTorchSingleCNN(SingleCNN): """ def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): - super().__init__(config=config, parent=parent, **kwargs) + super().__init__(tasks=tasks, config=config, parent=parent, **kwargs) # Build modules self.backbone_model, out_features = self._build_backbone(input_shape) @@ -267,7 +267,7 @@ class PyTorchResNet(ResNet): def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): - super().__init__(config=config, parent=parent, **kwargs) + super().__init__(tasks=tasks, config=config, parent=parent, **kwargs) # Build PyTorch backbone and track final out_features channel size self.backbone_model, out_features = self._build_backbone(input_shape) From b5ded08d1cfbbb5f169c99029f5ade3f57ef345e Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Tue, 21 Jul 2026 16:37:07 +0200 Subject: [PATCH 095/119] remove unused imports --- ctlearn/core/pytorch/model.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index c08fd78f..ece4bebf 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -2,7 +2,6 @@ This module defines the ``CTLearnModel`` classes, which holds the basic functionality for creating a PyTorch model to be used in CTLearn. """ -from abc import abstractmethod import torch import torch.nn as nn import torch.nn.functional as F @@ -18,7 +17,6 @@ ChannelSqueezeExciteBlock, SpatialSqueezeExciteBlock, ) -from ctlearn.utils import validate_trait_dict __all__ = [ "BasicBlock", From 822e39b572467524dcff96351b6d4a0dd00a4de2 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Wed, 22 Jul 2026 15:20:35 +0200 Subject: [PATCH 096/119] added tests for models in keras and pytorch --- ctlearn/core/keras/__init__.py | 0 ctlearn/core/pytorch/__init__.py | 0 ctlearn/core/tests/test_models.py | 197 ++++++++++++++++++++++++++++++ 3 files changed, 197 insertions(+) create mode 100644 ctlearn/core/keras/__init__.py create mode 100644 ctlearn/core/pytorch/__init__.py create mode 100644 ctlearn/core/tests/test_models.py diff --git a/ctlearn/core/keras/__init__.py b/ctlearn/core/keras/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/pytorch/__init__.py b/ctlearn/core/pytorch/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py new file mode 100644 index 00000000..e4c87d92 --- /dev/null +++ b/ctlearn/core/tests/test_models.py @@ -0,0 +1,197 @@ +import numpy as np +import pytest +import torch +import keras + +# Import your implementations (adjust module paths to match your project layout) +from ctlearn.core.keras.model import ( + KerasSingleCNN, + KerasResNet, +) +from ctlearn.core.pytorch.model import ( + PyTorchSingleCNN, + PyTorchResNet, +) + + +@pytest.fixture +def common_config(): + """Provides common configuration settings for both backends.""" + return { + "tasks": ["type", "energy"], + "input_shape_keras": (32, 32, 3), # (H, W, C) + "input_shape_pytorch": (3, 32, 32), # (C, H, W) + "kwargs": { + "head_layers": { + "type": [64, 2], + "energy": [64, 1], + }, + "head_activation_function": { + "type": "relu", + "energy": "relu", + }, + "attention_mechanism": None, # Keep attention off for baseline structural tests + }, + } + + +def copy_weights_single_cnn(keras_model, pytorch_model): + """ + Transfers weights from KerasSingleCNN to PyTorchSingleCNN layer by layer + to ensure numerical parity tests are exact. + """ + k_layers = [ + layer + for layer in keras_model.layers + if isinstance(layer, (keras.layers.Conv2D, keras.layers.Dense, keras.layers.BatchNormalization)) + ] + p_layers = [ + m + for m in pytorch_model.modules() + if isinstance(m, (torch.nn.Conv2d, torch.nn.Linear, torch.nn.BatchNorm2d)) + ] + + for k_layer, p_module in zip(k_layers, p_layers): + weights = k_layer.get_weights() + if isinstance(k_layer, keras.layers.Conv2D): + # Keras Conv2D weights shape: (H, W, In, Out) + # PyTorch Conv2d weights shape: (Out, In, H, W) + w = np.transpose(weights[0], (3, 2, 0, 1)) + p_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1: # Bias + p_module.bias.data = torch.from_numpy(weights[1]).float() + + elif isinstance(k_layer, keras.layers.Dense): + # Keras Dense weights shape: (In, Out) + # PyTorch Linear weights shape: (Out, In) + w = np.transpose(weights[0], (1, 0)) + p_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1: # Bias + p_module.bias.data = torch.from_numpy(weights[1]).float() + + elif isinstance(k_layer, keras.layers.BatchNormalization): + # Keras: [gamma, beta, mean, variance] + p_module.weight.data = torch.from_numpy(weights[0]).float() # gamma + p_module.bias.data = torch.from_numpy(weights[1]).float() # beta + p_module.running_mean.data = torch.from_numpy(weights[2]).float() + p_module.running_var.data = torch.from_numpy(weights[3]).float() + + +class TestSingleCNNParity: + """Tests verifying output shapes and output parity for SingleCNN.""" + + def test_output_shapes(self, common_config): + """Verify that both models produce identical output shapes for a multi-task scenario.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"] + + # 1. Instantiate Keras SingleCNN + keras_wrapper = KerasSingleCNN( + input_shape=common_config["input_shape_keras"], + tasks=tasks, + **kwargs + ) + + # 2. Instantiate PyTorch SingleCNN + torch_wrapper = PyTorchSingleCNN( + input_shape=common_config["input_shape_pytorch"], + tasks=tasks, + **kwargs + ) + + batch_size = 4 + + # Dummy data creation + x_keras = np.random.randn(batch_size, *common_config["input_shape_keras"]).astype(np.float32) + x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) # (B, H, W, C) -> (B, C, H, W) + + # Forward passes + keras_out = keras_wrapper.model(x_keras) + torch_wrapper.model.eval() + with torch.no_grad(): + torch_out = torch_wrapper.model(x_torch) + + # Assert shape equality across tasks + for task in tasks: + k_shape = tuple(keras_out[task].shape) + p_shape = tuple(torch_out[task].shape) + assert k_shape == p_shape, f"Shape mismatch for task '{task}': Keras {k_shape} vs PyTorch {p_shape}" + + def test_numerical_outputs_with_aligned_weights(self, common_config): + """Verify that models yield identical numerical predictions once weights are copied.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"] + + keras_wrapper = KerasSingleCNN( + input_shape=common_config["input_shape_keras"], + tasks=tasks, + **kwargs + ) + torch_wrapper = PyTorchSingleCNN( + input_shape=common_config["input_shape_pytorch"], + tasks=tasks, + **kwargs + ) + + # Transfer weights from Keras -> PyTorch + copy_weights_single_cnn(keras_wrapper.model, torch_wrapper.model) + + # Prepare identical input data + np.random.seed(42) + x_keras = np.random.randn(2, *common_config["input_shape_keras"]).astype(np.float32) + x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) + + # Evaluate models + keras_preds = keras_wrapper.model(x_keras) + torch_wrapper.model.eval() + with torch.no_grad(): + torch_preds = torch_wrapper.model(x_torch) + + # Compare outputs within tight numerical tolerance + for task in tasks: + k_val = keras_preds[task].numpy() if hasattr(keras_preds[task], "numpy") else np.array(keras_preds[task]) + p_val = torch_preds[task].cpu().numpy() + + np.testing.assert_allclose( + k_val, + p_val, + rtol=1e-4, + atol=1e-4, + err_msg=f"Value divergence detected in output task '{task}'", + ) + + +class TestResNetParity: + """Tests verifying output shapes and structural parity for ResNet.""" + + @pytest.mark.parametrize("block_type", ["basic", "bottleneck"]) + def test_resnet_output_shapes(self, common_config, block_type): + """Ensure both Basic and Bottleneck ResNets compute matching output tensor dimensions.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"].copy() + kwargs["residual_block_type"] = block_type + kwargs["init_layer"] = {"filters": 16, "kernel_size": 3, "strides": 1} + kwargs["init_max_pool"] = {"size": 2, "strides": 2} + + keras_wrapper = KerasResNet( + input_shape=common_config["input_shape_keras"], + tasks=tasks, + **kwargs + ) + torch_wrapper = PyTorchResNet( + input_shape=common_config["input_shape_pytorch"], + tasks=tasks, + **kwargs + ) + + batch_size = 2 + x_keras = np.random.randn(batch_size, *common_config["input_shape_keras"]).astype(np.float32) + x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) + + keras_out = keras_wrapper.model(x_keras) + torch_wrapper.model.eval() + with torch.no_grad(): + torch_out = torch_wrapper.model(x_torch) + + for task in tasks: + assert tuple(keras_out[task].shape) == tuple(torch_out[task].shape) \ No newline at end of file From 44071df3f7ad20c4f722649f18025ae0bb44476a Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Wed, 22 Jul 2026 15:21:00 +0200 Subject: [PATCH 097/119] update pytorch model --- ctlearn/core/pytorch/model.py | 138 ++++++++++++++++++---------------- 1 file changed, 74 insertions(+), 64 deletions(-) diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index ece4bebf..62e2fbbf 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -35,33 +35,33 @@ class MultiHeadClassifier(nn.Module): """ def __init__(self, heads_dict, single_output_task=None): super().__init__() - self.heads = nn.ModuleDict(heads_dict) + # Sanitize keys because 'type' conflicts with nn.Module.type() method + self._task_mapping = { + task: f"head_{task}" if hasattr(nn.Module, task) else task + for task in heads_dict.keys() + } + sanitized_heads = { + self._task_mapping[task]: module for task, module in heads_dict.items() + } + self.heads = nn.ModuleDict(sanitized_heads) self.single_output_task = single_output_task def forward(self, x): - # Flatten the backbone output if it's spatially aggregated but still has dimensions (B, C, 1, 1) + # Flatten the backbone output if it's spatially aggregated (B, C, 1, 1) -> (B, C) if x.dim() > 2: x = torch.flatten(x, start_dim=1) logits = {} - for task, head in self.heads.items(): + for original_task, internal_key in self._task_mapping.items(): + head = self.heads[internal_key] out = head(x) - logits[task] = F.softmax(out, dim=-1) if task == "type" else out + logits[original_task] = F.softmax(out, dim=-1) if original_task == "type" else out if self.single_output_task: return logits[self.single_output_task] return logits -class DynamicSequential(nn.Sequential): - """A tiny wrapper to make nn.Sequential accept keyword arguments natively if needed.""" - def forward(self, input): - for module in self: - input = module(input) - return input - - - def build_fully_connect_pytorch_head(in_features, layers, activation_function, tasks): """ Build the fully connected head for the PyTorch-based CTLearn model. @@ -263,53 +263,60 @@ class PyTorchResNet(ResNet): ``PyTorchResNet`` is a residual neural network model implemented in PyTorch. """ - def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): super().__init__(tasks=tasks, config=config, parent=parent, **kwargs) # Build PyTorch backbone and track final out_features channel size self.backbone_model, out_features = self._build_backbone(input_shape) - # Build the fully connected head (from previous module mapping) + # Build the fully connected head self.logits_head = build_fully_connect_pytorch_head( out_features, self.head_layers, self.head_activation_function, tasks ) # Unify into our structural pipeline wrapper module self.model = FullModelPipeline(self.backbone_model, self.logits_head) - def _build_backbone(self, input_shape): - # PyTorch input shape constraint layout: (channels, height, width) in_channels = input_shape[0] modules = [] - # 1. Handle Initial Zero Padding + # 1. Initial Zero Padding if self.init_padding > 0: modules.append(nn.ZeroPad2d(self.init_padding)) - # 2. Handle Initial Conv Layer + # 2. Initial Conv Layer if self.init_layer is not None: out_ch = self.init_layer["filters"] k_size = self.init_layer["kernel_size"] stride = self.init_layer["strides"] - # Dynamic calculation matching Keras implicit padding configuration padding = k_size // 2 - modules.append(nn.Conv2d(in_channels, out_ch, kernel_size=k_size, stride=stride, padding=padding, bias=False)) + modules.append( + nn.Conv2d( + in_channels, + out_ch, + kernel_size=k_size, + stride=stride, + padding=padding, + bias=False, + ) + ) modules.append(nn.ReLU()) in_channels = out_ch - # 3. Handle Initial Max Pooling + # 3. Initial Max Pooling if self.init_max_pool is not None: p_size = self.init_max_pool["size"] p_stride = self.init_max_pool["strides"] - modules.append(nn.MaxPool2d(kernel_size=p_size, stride=p_stride, padding=p_size // 2)) + modules.append( + nn.MaxPool2d(kernel_size=p_size, stride=p_stride, padding=p_size // 2) + ) # 4. Assemble Stacked Residual Architecture blocks res_blocks, final_channels = self._stacked_res_blocks( in_channels, architecture=self.architecture, residual_block_type=self.residual_block_type, - attention=self.attention + attention=self.attention, ) modules.extend(res_blocks) @@ -330,63 +337,66 @@ def _stacked_res_blocks(self, in_channels, architecture, residual_block_type, at blocks_count = [layer["blocks"] for layer in architecture] # First layer block sequence (stride=1) - blocks_list.extend(self._stack_fn( - current_channels, filters_list[0], blocks_count[0], residual_block_type, stride=1, attention=attention - )) + blocks_list.extend( + self._stack_fn( + current_channels, + filters_list[0], + blocks_count[0], + residual_block_type, + stride=1, + attention=attention, + ) + ) - # Update internal channel counts based on block configurations multiplier = 4 if residual_block_type == "bottleneck" else 1 current_channels = filters_list[0] * multiplier - # Iteratively attach standard downsampling/residual block levels + # Subsequent downsampling levels (stride=2) for filters, blocks in zip(filters_list[1:], blocks_count[1:]): - blocks_list.extend(self._stack_fn( - current_channels, filters, blocks, residual_block_type, stride=2, attention=attention - )) + blocks_list.extend( + self._stack_fn( + current_channels, + filters, + blocks, + residual_block_type, + stride=2, + attention=attention, + ) + ) current_channels = filters * multiplier return blocks_list, current_channels def _stack_fn(self, in_channels, filters, blocks, residual_block_type, stride=2, attention=None): - block_layer = BasicBlock if residual_block_type == "basic" else BottleneckBlock stack = [] - # Build common arguments shared by BOTH block types - base_kwargs = { - "in_channels": in_channels, - "stride": stride, - "attention": attention - } - - # Add block-specific filter mappings - if residual_block_type == "basic": - base_kwargs["out_channels"] = filters - base_kwargs["base_filters"] = filters # fallback if needed, matching signature - else: - base_kwargs["base_filters"] = filters - - # 1. First block handles the structural transition - stack.append(block_layer(conv_shortcut=True, **base_kwargs)) + def build_block(in_c, conv_s, s): + if residual_block_type == "basic": + return BasicBlock( + in_channels=in_c, + out_channels=filters, + stride=s, + conv_shortcut=conv_s, + attention=attention, + ) + else: + return BottleneckBlock( + in_channels=in_c, + base_filters=filters, + stride=s, + conv_shortcut=conv_s, + attention=attention, + ) + + # First block transition + stack.append(build_block(in_channels, conv_s=True, s=stride)) - # Update dimensional inputs for subsequent blocks in this group multiplier = 4 if residual_block_type == "bottleneck" else 1 current_in = filters * multiplier - # 2. Remaining blocks map cleanly identity-to-identity + # Remaining blocks in the layer for _ in range(1, blocks): - next_kwargs = { - "in_channels": current_in, - "stride": 1, - "attention": attention, - "conv_shortcut": False - } - if residual_block_type == "basic": - next_kwargs["out_channels"] = filters - next_kwargs["base_filters"] = filters - else: - next_kwargs["base_filters"] = filters - - stack.append(block_layer(**next_kwargs)) + stack.append(build_block(current_in, conv_s=False, s=1)) return stack From 18f8f35f754a46e4e7807f7654eb88b124520fe7 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Wed, 22 Jul 2026 15:41:36 +0200 Subject: [PATCH 098/119] fix keras ResNet model --- ctlearn/core/keras/model.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/ctlearn/core/keras/model.py b/ctlearn/core/keras/model.py index 8085f10d..e61a531c 100644 --- a/ctlearn/core/keras/model.py +++ b/ctlearn/core/keras/model.py @@ -243,32 +243,31 @@ def _build_backbone(self, input_shape): """ # Define the input layer from the input shape network_input = keras.Input(shape=input_shape) + x = network_input # Apply initial padding if specified if self.init_padding > 0: - network_input = keras.layers.ZeroPadding2D( + x = keras.layers.ZeroPadding2D( padding=self.init_padding, - kernel_size=self.init_layer["kernel_size"], - strides=self.init_layer["strides"], name=self.backbone_name + "_padding", - )(network_input) + )(x) # Apply initial convolutional layer if specified if self.init_layer is not None: - network_input = keras.layers.Conv2D( + x = keras.layers.Conv2D( filters=self.init_layer["filters"], kernel_size=self.init_layer["kernel_size"], strides=self.init_layer["strides"], name=self.backbone_name + "_conv1_conv", - )(network_input) + )(x) # Apply max pooling if specified if self.init_max_pool is not None: - network_input = keras.layers.MaxPool2D( + x = keras.layers.MaxPool2D( pool_size=self.init_max_pool["size"], strides=self.init_max_pool["strides"], name=self.backbone_name + "_pool1_pool", - )(network_input) + )(x) # Build the residual blocks engine_output = self._stacked_res_blocks( - network_input, + x, architecture=self.architecture, residual_block_type=self.residual_block_type, attention=self.attention, From 62a217f3d33e775524fea7e06c5a11a51e7d7a2b Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Wed, 22 Jul 2026 15:42:02 +0200 Subject: [PATCH 099/119] fix tests for keras and pytorch models --- ctlearn/core/tests/test_models.py | 104 ++++++++++++++++++------------ 1 file changed, 62 insertions(+), 42 deletions(-) diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index e4c87d92..8a33a9ae 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -3,79 +3,101 @@ import torch import keras -# Import your implementations (adjust module paths to match your project layout) -from ctlearn.core.keras.model import ( - KerasSingleCNN, - KerasResNet, -) -from ctlearn.core.pytorch.model import ( - PyTorchSingleCNN, - PyTorchResNet, -) +from ctlearn.core.keras.model import KerasSingleCNN, KerasResNet +from ctlearn.core.pytorch.model import PyTorchSingleCNN, PyTorchResNet @pytest.fixture def common_config(): - """Provides common configuration settings for both backends.""" return { - "tasks": ["type", "energy"], - "input_shape_keras": (32, 32, 3), # (H, W, C) + "tasks": ["type", "energy", "cameradirection"], + "input_shape_keras": (32, 32, 3), # (H, W, C) "input_shape_pytorch": (3, 32, 32), # (C, H, W) "kwargs": { "head_layers": { "type": [64, 2], "energy": [64, 1], + "cameradirection": [64, 2], }, "head_activation_function": { "type": "relu", "energy": "relu", + "cameradirection": "relu", }, - "attention_mechanism": None, # Keep attention off for baseline structural tests + "attention_mechanism": None, }, } - -def copy_weights_single_cnn(keras_model, pytorch_model): +def copy_weights_single_cnn(keras_model_wrapper, pytorch_model_wrapper): """ - Transfers weights from KerasSingleCNN to PyTorchSingleCNN layer by layer - to ensure numerical parity tests are exact. + Copies weights from KerasSingleCNN to PyTorchSingleCNN by matching + layer types, shapes, and task head structures. """ - k_layers = [ + # 1. Transfer Backbone Weights (Conv2D and BatchNorm) + k_backbone_layers = [ layer - for layer in keras_model.layers - if isinstance(layer, (keras.layers.Conv2D, keras.layers.Dense, keras.layers.BatchNormalization)) + for layer in keras_model_wrapper.backbone_model.layers + if isinstance(layer, (keras.layers.Conv2D, keras.layers.BatchNormalization)) + and len(layer.weights) > 0 ] - p_layers = [ + + p_backbone_modules = [ m - for m in pytorch_model.modules() - if isinstance(m, (torch.nn.Conv2d, torch.nn.Linear, torch.nn.BatchNorm2d)) + for m in pytorch_model_wrapper.backbone_model.modules() + if isinstance(m, (torch.nn.Conv2d, torch.nn.BatchNorm2d)) ] - for k_layer, p_module in zip(k_layers, p_layers): + assert len(k_backbone_layers) == len(p_backbone_modules), ( + f"Backbone layer count mismatch: Keras has {len(k_backbone_layers)}, " + f"PyTorch has {len(p_backbone_modules)}" + ) + + for k_layer, p_module in zip(k_backbone_layers, p_backbone_modules): weights = k_layer.get_weights() if isinstance(k_layer, keras.layers.Conv2D): - # Keras Conv2D weights shape: (H, W, In, Out) - # PyTorch Conv2d weights shape: (Out, In, H, W) + # Keras: (H, W, In, Out) -> PyTorch: (Out, In, H, W) w = np.transpose(weights[0], (3, 2, 0, 1)) p_module.weight.data = torch.from_numpy(w).float() - if len(weights) > 1: # Bias + if len(weights) > 1 and p_module.bias is not None: p_module.bias.data = torch.from_numpy(weights[1]).float() + elif isinstance(k_layer, keras.layers.BatchNormalization): + p_module.weight.data = torch.from_numpy(weights[0]).float() + p_module.bias.data = torch.from_numpy(weights[1]).float() + p_module.running_mean.data = torch.from_numpy(weights[2]).float() + p_module.running_var.data = torch.from_numpy(weights[3]).float() - elif isinstance(k_layer, keras.layers.Dense): - # Keras Dense weights shape: (In, Out) - # PyTorch Linear weights shape: (Out, In) + # 2. Transfer Head Weights (Dense -> Linear) Task by Task + tasks = keras_model_wrapper.logits.keys() if isinstance(keras_model_wrapper.logits, dict) else [keras_model_wrapper.logits.name] + + for task in tasks: + # Collect Keras Dense layers for this task + k_dense_layers = [ + layer + for layer in keras_model_wrapper.model.layers + if isinstance(layer, keras.layers.Dense) and task in layer.name + ] + + # Get corresponding PyTorch module for this task + p_head_module = pytorch_model_wrapper.logits_head.heads[ + pytorch_model_wrapper.logits_head._task_mapping[task] + ] + p_linear_modules = [ + m for m in p_head_module if isinstance(m, torch.nn.Linear) + ] + + assert len(k_dense_layers) == len(p_linear_modules), ( + f"Head layer count mismatch for task '{task}': Keras has {len(k_dense_layers)}, " + f"PyTorch has {len(p_linear_modules)}" + ) + + for k_layer, p_module in zip(k_dense_layers, p_linear_modules): + weights = k_layer.get_weights() + # Keras Dense: (In, Out) -> PyTorch Linear: (Out, In) w = np.transpose(weights[0], (1, 0)) p_module.weight.data = torch.from_numpy(w).float() - if len(weights) > 1: # Bias + if len(weights) > 1 and p_module.bias is not None: p_module.bias.data = torch.from_numpy(weights[1]).float() - elif isinstance(k_layer, keras.layers.BatchNormalization): - # Keras: [gamma, beta, mean, variance] - p_module.weight.data = torch.from_numpy(weights[0]).float() # gamma - p_module.bias.data = torch.from_numpy(weights[1]).float() # beta - p_module.running_mean.data = torch.from_numpy(weights[2]).float() - p_module.running_var.data = torch.from_numpy(weights[3]).float() - class TestSingleCNNParity: """Tests verifying output shapes and output parity for SingleCNN.""" @@ -133,8 +155,7 @@ def test_numerical_outputs_with_aligned_weights(self, common_config): **kwargs ) - # Transfer weights from Keras -> PyTorch - copy_weights_single_cnn(keras_wrapper.model, torch_wrapper.model) + copy_weights_single_cnn(keras_wrapper, torch_wrapper) # Prepare identical input data np.random.seed(42) @@ -151,13 +172,12 @@ def test_numerical_outputs_with_aligned_weights(self, common_config): for task in tasks: k_val = keras_preds[task].numpy() if hasattr(keras_preds[task], "numpy") else np.array(keras_preds[task]) p_val = torch_preds[task].cpu().numpy() - np.testing.assert_allclose( k_val, p_val, rtol=1e-4, atol=1e-4, - err_msg=f"Value divergence detected in output task '{task}'", + err_msg=f"Value divergence detected in task '{task}'", ) From 3d5fdfa4c5f19a115ecd7b300bee33b18ac9fb48 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Wed, 22 Jul 2026 15:44:01 +0200 Subject: [PATCH 100/119] change activation function in test models --- ctlearn/core/tests/test_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index 8a33a9ae..22071f40 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -22,7 +22,7 @@ def common_config(): "head_activation_function": { "type": "relu", "energy": "relu", - "cameradirection": "relu", + "cameradirection": "tanh", }, "attention_mechanism": None, }, From 38db4b2537cbdcabf910dd6692d1823b141daa30 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Wed, 22 Jul 2026 15:57:36 +0200 Subject: [PATCH 101/119] add random seed --- ctlearn/core/tests/test_models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index 22071f40..650cac03 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -124,6 +124,7 @@ def test_output_shapes(self, common_config): batch_size = 4 # Dummy data creation + np.random.seed(42) x_keras = np.random.randn(batch_size, *common_config["input_shape_keras"]).astype(np.float32) x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) # (B, H, W, C) -> (B, C, H, W) From 502d28c967a36cf064db28d9daf739b9913645d0 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Thu, 23 Jul 2026 17:33:58 +0200 Subject: [PATCH 102/119] fix models and polish tests (WIP) --- ctlearn/core/keras/attention.py | 6 +- ctlearn/core/keras/model.py | 4 +- ctlearn/core/pytorch/attention.py | 28 +- ctlearn/core/pytorch/model.py | 121 +++++--- ctlearn/core/tests/test_models.py | 450 +++++++++++++++++++++++------- 5 files changed, 438 insertions(+), 171 deletions(-) diff --git a/ctlearn/core/keras/attention.py b/ctlearn/core/keras/attention.py index 0a4d95b3..cd9aee3e 100644 --- a/ctlearn/core/keras/attention.py +++ b/ctlearn/core/keras/attention.py @@ -73,11 +73,7 @@ def channel_squeeze_excite_block(inputs, ratio=4, name=None): Output tensor for the channel squeeze-excite block. """ - # Temp fix for supporting keras2 & keras3 - if int(keras.__version__.split(".")[0]) >= 3: - filters = inputs.shape[-1] - else: - filters = inputs.get_shape().as_list()[-1] + filters = inputs.shape[-1] cse = keras.layers.GlobalAveragePooling2D( keepdims=True, name=name + "_avgpool" )(inputs) diff --git a/ctlearn/core/keras/model.py b/ctlearn/core/keras/model.py index e61a531c..a3c3b28f 100644 --- a/ctlearn/core/keras/model.py +++ b/ctlearn/core/keras/model.py @@ -171,11 +171,11 @@ def _build_backbone(self, input_shape): if self.attention is not None: if self.attention["mechanism"] == "Dual-SE": x = dual_squeeze_excite_block( - x, self.attention["ratio"], name=f"{self.backbone_name}_dse" + x, self.attention["reduction_ratio"], name=f"{self.backbone_name}_dse" ) elif self.attention["mechanism"] == "Channel-SE": x = channel_squeeze_excite_block( - x, self.attention["ratio"], name=f"{self.backbone_name}_cse" + x, self.attention["reduction_ratio"], name=f"{self.backbone_name}_cse" ) elif self.attention["mechanism"] == "Spatial-SE": x = spatial_squeeze_excite_block(x, name=f"{self.backbone_name}_sse") diff --git a/ctlearn/core/pytorch/attention.py b/ctlearn/core/pytorch/attention.py index 68305f54..9ece43b2 100644 --- a/ctlearn/core/pytorch/attention.py +++ b/ctlearn/core/pytorch/attention.py @@ -33,25 +33,25 @@ class ChannelSqueezeExciteBlock(nn.Module): """ def __init__(self, in_channels, ratio=4): super().__init__() - # Keras uses Dense layers on global pooled tensors. - # In PyTorch, we can achieve this elegantly using 1x1 Convolutions, - # avoiding the need to flatten and unflatten the spatial grid. - self.gate = nn.Sequential( - nn.Conv2d(in_channels, in_channels // ratio, kernel_size=1, bias=True), - nn.ReLU(), - nn.Conv2d(in_channels // ratio, in_channels, kernel_size=1, bias=True), - nn.Sigmoid() - ) + reduced_channels = in_channels // ratio + # Using nn.Linear to match Keras Dense layers + self.fc1 = nn.Linear(in_channels, reduced_channels, bias=True) + self.fc2 = nn.Linear(reduced_channels, in_channels, bias=True) def forward(self, x): - # Global Average Pooling keeping spatial dims: (B, C, H, W) -> (B, C, 1, 1) + batch_size, channels, _, _ = x.shape + # Global Average Pooling keeping dimensions: (B, C, H, W) -> (B, C, 1, 1) squeeze = F.adaptive_avg_pool2d(x, (1, 1)) - # Compute channel scale factor - excitation = self.gate(squeeze) - # Multiply input tensor by the scale factor across the channel dimension + # Flatten for Linear layers: (B, C, 1, 1) -> (B, C) + squeeze = squeeze.view(batch_size, channels) + # Dense projections with ReLU and Sigmoid + excitation = F.relu(self.fc1(squeeze)) + excitation = torch.sigmoid(self.fc2(excitation)) + # Reshape back to broadcast across spatial dimensions: (B, C) -> (B, C, 1, 1) + excitation = excitation.view(batch_size, channels, 1, 1) + # Scale input tensor return x * excitation - class SpatialSqueezeExciteBlock(nn.Module): """ A spatial squeeze-excite (sSE) block in PyTorch. diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index 62e2fbbf..ed434953 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -160,6 +160,17 @@ def _build_backbone(self, input_shape): if self.batchnorm: modules.append(nn.BatchNorm2d(in_channels, momentum=0.01)) + if self.attention is not None: + mech = self.attention.get("mechanism") + ratio = self.attention.get("reduction_ratio", 16) + if mech == "Dual-SE": + attention_layer = DualSqueezeExciteBlock(in_channels=in_channels, ratio=ratio) + elif mech == "Channel-SE": + attention_layer = ChannelSqueezeExciteBlock(in_channels=in_channels, ratio=ratio) + elif mech == "Spatial-SE": + attention_layer = SpatialSqueezeExciteBlock(in_channels=in_channels) + modules.append(attention_layer) + # Global Average Pooling wrapper block class GlobalAvgPool(nn.Module): def forward(self, x): @@ -171,29 +182,32 @@ def forward(self, x): return backbone_model, in_channels - class BasicBlock(nn.Module): def __init__(self, in_channels, out_channels, stride=1, conv_shortcut=True, attention=None): super().__init__() self.conv_shortcut = conv_shortcut self.attention_config = attention - - # Shortcut connection handling channel/spatial modifications + # Projection Shortcut Branch if conv_shortcut: - self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + self.shortcut = nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=stride, bias=True + ) else: - self.shortcut = nn.Identity() - - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False) - - # Setup attention if applicable (Assuming your packages take/return tensors or modules) + self.shortcut = None + # Main Branch Convolutions (Matching Keras _1_conv and _2_conv) + self.conv1 = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=True + ) + self.conv2 = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True + ) + # Setup the attention mechanism self.setup_attention(out_channels) def setup_attention(self, channels): self.attn_layer = None if self.attention_config: - mech = self.attention_config["mechanism"] + mech = self.attention_config.get("mechanism") ratio = self.attention_config.get("reduction_ratio", 16) if mech == "Dual-SE": self.attn_layer = DualSqueezeExciteBlock(in_channels=channels, ratio=ratio) @@ -203,14 +217,19 @@ def setup_attention(self, channels): self.attn_layer = SpatialSqueezeExciteBlock(in_channels=channels) def forward(self, x): - identity = self.shortcut(x) - + # Shortcut path + if self.conv_shortcut and self.shortcut is not None: + identity = self.shortcut(x) + else: + identity = x + + # Main path (Matches Keras activation order: conv1 -> relu -> conv2) out = F.relu(self.conv1(x)) out = self.conv2(out) - - if self.attn_layer: + + if self.attn_layer is not None: out = self.attn_layer(out) - + out += identity return F.relu(out) @@ -221,16 +240,27 @@ def __init__(self, in_channels, base_filters, stride=1, conv_shortcut=True, atte self.conv_shortcut = conv_shortcut self.attention_config = attention out_channels = 4 * base_filters - + + # Shortcut connection if conv_shortcut: - self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + self.shortcut = nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=stride, bias=False + ) else: self.shortcut = nn.Identity() - - self.conv1 = nn.Conv2d(in_channels, base_filters, kernel_size=1, stride=stride, bias=False) - self.conv2 = nn.Conv2d(base_filters, base_filters, kernel_size=3, padding=1, bias=False) - self.conv3 = nn.Conv2d(base_filters, out_channels, kernel_size=1, bias=False) - + # Main branch convolutions matching Keras layout: + self.conv1 = nn.Conv2d( + in_channels, base_filters, kernel_size=1, stride=stride, bias=False + ) + # Keras _2_conv is 3x3 spatial convolution with stride=1 (since stride is handled in _1_conv) + self.conv2 = nn.Conv2d( + base_filters, base_filters, kernel_size=3, stride=1, padding=1, bias=False + ) + # Keras _3_conv restores channels back to 4 * base_filters + self.conv3 = nn.Conv2d( + base_filters, out_channels, kernel_size=1, bias=False + ) + # Setup the attention mechanism self.setup_attention(out_channels) def setup_attention(self, channels): @@ -248,6 +278,7 @@ def setup_attention(self, channels): def forward(self, x): identity = self.shortcut(x) + # Matches Keras sequence: conv1 (with stride) -> relu -> conv2 -> relu -> conv3 out = F.relu(self.conv1(x)) out = F.relu(self.conv2(out)) out = self.conv3(out) @@ -276,15 +307,15 @@ def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): self.model = FullModelPipeline(self.backbone_model, self.logits_head) def _build_backbone(self, input_shape): - in_channels = input_shape[0] + in_channels = input_shape[0] if isinstance(input_shape, (list, tuple)) else input_shape[-1] modules = [] - # 1. Initial Zero Padding - if self.init_padding > 0: + # Initial Zero Padding + if getattr(self, "init_padding", 0) > 0: modules.append(nn.ZeroPad2d(self.init_padding)) - # 2. Initial Conv Layer - if self.init_layer is not None: + # Initial Conv Layer + if getattr(self, "init_layer", None) is not None: out_ch = self.init_layer["filters"] k_size = self.init_layer["kernel_size"] stride = self.init_layer["strides"] @@ -297,21 +328,21 @@ def _build_backbone(self, input_shape): kernel_size=k_size, stride=stride, padding=padding, - bias=False, + bias=True, # Match Keras default bias if applicable ) ) modules.append(nn.ReLU()) in_channels = out_ch - # 3. Initial Max Pooling - if self.init_max_pool is not None: + # Initial Max Pooling + if getattr(self, "init_max_pool", None) is not None: p_size = self.init_max_pool["size"] p_stride = self.init_max_pool["strides"] modules.append( - nn.MaxPool2d(kernel_size=p_size, stride=p_stride, padding=p_size // 2) + nn.MaxPool2d(kernel_size=p_size, stride=p_stride, padding=0) ) - # 4. Assemble Stacked Residual Architecture blocks + # Assemble Stacked Residual Architecture blocks res_blocks, final_channels = self._stacked_res_blocks( in_channels, architecture=self.architecture, @@ -320,10 +351,11 @@ def _build_backbone(self, input_shape): ) modules.extend(res_blocks) - # 5. Global Average Pooling setup + # Global Average Pooling setup class GlobalAvgPool(nn.Module): def forward(self, x): - return F.adaptive_avg_pool2d(x, (1, 1)) + x = F.adaptive_avg_pool2d(x, (1, 1)) + return x.view(x.size(0), -1) modules.append(GlobalAvgPool()) @@ -369,14 +401,18 @@ def _stacked_res_blocks(self, in_channels, architecture, residual_block_type, at def _stack_fn(self, in_channels, filters, blocks, residual_block_type, stride=2, attention=None): stack = [] - - def build_block(in_c, conv_s, s): + # Calculate target output channel count for this block level + out_channels = filters + + def build_block(in_c, s): + # Only use a conv shortcut if channels change or if downsampling (stride > 1) + needs_shortcut = (in_c != out_channels) or (s != 1) if residual_block_type == "basic": return BasicBlock( in_channels=in_c, out_channels=filters, stride=s, - conv_shortcut=conv_s, + conv_shortcut=needs_shortcut, attention=attention, ) else: @@ -384,19 +420,16 @@ def build_block(in_c, conv_s, s): in_channels=in_c, base_filters=filters, stride=s, - conv_shortcut=conv_s, + conv_shortcut=needs_shortcut, attention=attention, ) # First block transition - stack.append(build_block(in_channels, conv_s=True, s=stride)) - - multiplier = 4 if residual_block_type == "bottleneck" else 1 - current_in = filters * multiplier + stack.append(build_block(in_channels, s=stride)) # Remaining blocks in the layer for _ in range(1, blocks): - stack.append(build_block(current_in, conv_s=False, s=1)) + stack.append(build_block(out_channels, s=1)) return stack diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index 650cac03..1e08412f 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -1,18 +1,25 @@ +import re +import keras import numpy as np import pytest import torch -import keras -from ctlearn.core.keras.model import KerasSingleCNN, KerasResNet -from ctlearn.core.pytorch.model import PyTorchSingleCNN, PyTorchResNet +from ctlearn.core.keras.model import KerasResNet, KerasSingleCNN +from ctlearn.core.pytorch.model import ( + BasicBlock, + BottleneckBlock, + PyTorchResNet, + PyTorchSingleCNN, +) +rng = np.random.default_rng(42) @pytest.fixture def common_config(): return { "tasks": ["type", "energy", "cameradirection"], - "input_shape_keras": (32, 32, 3), # (H, W, C) - "input_shape_pytorch": (3, 32, 32), # (C, H, W) + "input_shape_keras": (110, 110, 2), # (H, W, C) + "input_shape_pytorch": (2, 110, 110), # (C, H, W) "kwargs": { "head_layers": { "type": [64, 2], @@ -24,38 +31,221 @@ def common_config(): "energy": "relu", "cameradirection": "tanh", }, - "attention_mechanism": None, }, } -def copy_weights_single_cnn(keras_model_wrapper, pytorch_model_wrapper): - """ - Copies weights from KerasSingleCNN to PyTorchSingleCNN by matching - layer types, shapes, and task head structures. - """ - # 1. Transfer Backbone Weights (Conv2D and BatchNorm) - k_backbone_layers = [ - layer - for layer in keras_model_wrapper.backbone_model.layers - if isinstance(layer, (keras.layers.Conv2D, keras.layers.BatchNormalization)) - and len(layer.weights) > 0 +@pytest.mark.parametrize("batchnorm", [True, False]) +@pytest.mark.parametrize( + "attention", + [ + {"mechanism": None}, + {"mechanism": "Channel-SE", "reduction_ratio": 8}, + {"mechanism": "Spatial-SE"}, + {"mechanism": "Dual-SE", "reduction_ratio": 32}, + ], +) +def test_SingleCNN_model_structure_parity(common_config, batchnorm, attention): + """Verify that Keras and PyTorch models have matching layer counts and weight shapes.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"].copy() + kwargs["architecture"] = [ + {"filters": 32, "kernel_size": 2, "number": 1}, + {"filters": 64, "kernel_size": 3, "number": 4}, + {"filters": 128, "kernel_size": 2, "number": 2}, + {"filters": 128, "kernel_size": 3, "number": 1}, ] + kwargs["batchnorm"] = batchnorm + kwargs["attention_mechanism"] = attention["mechanism"] + if "reduction_ratio" in attention: + kwargs["attention_reduction_ratio"] = attention["reduction_ratio"] - p_backbone_modules = [ - m - for m in pytorch_model_wrapper.backbone_model.modules() - if isinstance(m, (torch.nn.Conv2d, torch.nn.BatchNorm2d)) + for task in tasks: + keras_wrapper = KerasSingleCNN( + input_shape=common_config["input_shape_keras"], + tasks=[task], + **kwargs + ) + torch_wrapper = PyTorchSingleCNN( + input_shape=common_config["input_shape_pytorch"], + tasks=[task], + **kwargs + ) + # Collect all the layers from the Keras-based model + keras_layers = [ + l.get_weights()[0].shape + for l in keras_wrapper.backbone_model.layers + if isinstance(l, (keras.layers.Conv2D, keras.layers.Dense, keras.layers.BatchNormalization)) + ] + # Collect also the dense layers from the head + keras_layers.extend([ + l.get_weights()[0].shape + for l in keras_wrapper.model.layers + if isinstance(l, keras.layers.Dense) and task in l.name + ] + ) + # Collect all the layers from the PyTorch-based model + # Note different shapes (PyTorch format: Out, In, H, W -> Keras: H, W, In, Out) + torch_layers = [] + for m in torch_wrapper.backbone_model.modules(): + if isinstance(m, torch.nn.Conv2d): + # PyTorch format: (Out, In, H, W) -> Keras format: (H, W, In, Out) + torch_layers.append( + (m.weight.shape[2], m.weight.shape[3], m.weight.shape[1], m.weight.shape[0]) + ) + elif isinstance(m, torch.nn.Linear): + torch_layers.append((m.weight.shape[1], m.weight.shape[0])) + elif isinstance(m, torch.nn.BatchNorm2d): + # BatchNorm parameters are 1D (gamma/beta/running stats) + torch_layers.append((m.weight.shape[0],)) + # Collect also the linear layers from the head + internal_key = torch_wrapper.logits_head._task_mapping[task] + p_head_module = torch_wrapper.logits_head.heads[internal_key] + torch_layers.extend([(m.weight.shape[1], m.weight.shape[0]) for m in p_head_module if isinstance(m, torch.nn.Linear)]) + # Assert structural length and individual weight shape alignment + assert len(keras_layers) == len(torch_layers), ( + f"Layer count mismatch: Keras has {len(keras_layers)}, PyTorch has {len(torch_layers)}" + ) + for idx, (k_shape, p_shape) in enumerate(zip(keras_layers, torch_layers)): + assert k_shape == p_shape, ( + f"Shape mismatch at layer {idx}: Keras shape {k_shape} vs PyTorch mapped shape {p_shape}" + ) + + +@pytest.mark.parametrize("block_type", ["basic", "bottleneck"]) +@pytest.mark.parametrize( + "first_layers", + [ + {"init_layer": None, "init_max_pool": None}, + {"init_layer": {'filters': 8, 'kernel_size': 7, 'strides': 2}, "init_max_pool": {'size': 3, 'strides': 2}}, + ], +) +@pytest.mark.parametrize( + "attention", + [ + {"mechanism": None}, + {"mechanism": "Channel-SE", "reduction_ratio": 8}, + {"mechanism": "Spatial-SE"}, + {"mechanism": "Dual-SE", "reduction_ratio": 32}, + ], +) +def test_ResNet_model_structure_parity(common_config, block_type, first_layers, attention): + """Verify that Keras and PyTorch models have matching layer counts and weight shapes.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"].copy() + kwargs["init_layer"] = first_layers["init_layer"] + kwargs["init_max_pool"] = first_layers["init_max_pool"] + kwargs["residual_block_type"] = block_type + kwargs["architecture"] = [ + {"filters": 16, "blocks": 2}, + {"filters": 48, "blocks": 3}, + {"filters": 48, "blocks": 4}, + {"filters": 96, "blocks": 2}, ] + kwargs["attention_mechanism"] = attention["mechanism"] + if "reduction_ratio" in attention: + kwargs["attention_reduction_ratio"] = attention["reduction_ratio"] - assert len(k_backbone_layers) == len(p_backbone_modules), ( - f"Backbone layer count mismatch: Keras has {len(k_backbone_layers)}, " - f"PyTorch has {len(p_backbone_modules)}" - ) + for task in tasks: + keras_wrapper = KerasResNet( + input_shape=common_config["input_shape_keras"], + tasks=[task], + **kwargs + ) + torch_wrapper = PyTorchResNet( + input_shape=common_config["input_shape_pytorch"], + tasks=[task], + **kwargs + ) + # Collect all the layers from the Keras-based model + keras_layers = [ + l.get_weights()[0].shape + for l in keras_wrapper.backbone_model.layers + if isinstance(l, (keras.layers.Conv2D, keras.layers.Dense, keras.layers.BatchNormalization)) + ] + # Collect also the dense layers from the head + keras_layers.extend([ + l.get_weights()[0].shape + for l in keras_wrapper.model.layers + if isinstance(l, keras.layers.Dense) and task in l.name + ] + ) + # Collect all the layers from the PyTorch-based model in execution order + # Note different shapes (PyTorch format: Out, In, H, W -> Keras: H, W, In, Out) + torch_layers = [] + for m in torch_wrapper.backbone_model: + # If the module is a BasicBlock, it contains conv1, conv2, and optionally a shortcut + if hasattr(m, "conv1") and hasattr(m, "conv2"): + # shortcut (if present in PyTorch block) + if m.shortcut is not None: + sc = m.shortcut + torch_layers.append((sc.kernel_size[0], sc.kernel_size[1], sc.in_channels, sc.out_channels)) + c1 = m.conv1 + torch_layers.append((c1.kernel_size[0], c1.kernel_size[1], c1.in_channels, c1.out_channels)) + c2 = m.conv2 + torch_layers.append((c2.kernel_size[0], c2.kernel_size[1], c2.in_channels, c2.out_channels)) + assert 1 == 0 + elif isinstance(m, torch.nn.Module): + # For non-block modules like GlobalAvgPool, handle if needed + pass + # Collect also the linear layers from the head + internal_key = torch_wrapper.logits_head._task_mapping[task] + p_head_module = torch_wrapper.logits_head.heads[internal_key] + torch_layers.extend([(m.weight.shape[1], m.weight.shape[0]) for m in p_head_module if isinstance(m, torch.nn.Linear)]) + # Assert structural length and individual weight shape alignment + assert len(keras_layers) == len(torch_layers), ( + f"Layer count mismatch: Keras has {len(keras_layers)}, PyTorch has {len(torch_layers)}" + ) + for idx, (k_shape, p_shape) in enumerate(zip(keras_layers, torch_layers)): + assert k_shape == p_shape, ( + f"Shape mismatch at layer {idx}: Keras shape {k_shape} vs PyTorch mapped shape {p_shape}" + ) + + + +def _copy_head_weights(keras_wrapper, torch_wrapper, tasks): + """Shared helper to copy Dense -> Linear head weights for mapped tasks.""" + for task in tasks: + k_dense_layers = [ + l for l in keras_wrapper.model.layers + if isinstance(l, keras.layers.Dense) and task in l.name + ] - for k_layer, p_module in zip(k_backbone_layers, p_backbone_modules): + internal_key = torch_wrapper.logits_head._task_mapping[task] + p_head_module = torch_wrapper.logits_head.heads[internal_key] + p_linear_modules = [m for m in p_head_module if isinstance(m, torch.nn.Linear)] + + for k_layer, p_module in zip(k_dense_layers, p_linear_modules): + weights = k_layer.get_weights() + if not weights: + continue + w = np.transpose(weights[0], (1, 0)) # Keras (In, Out) -> PyTorch (Out, In) + p_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1 and p_module.bias is not None: + p_module.bias.data = torch.from_numpy(weights[1]).float() + +def _copy_weights_single_cnn(keras_wrapper, torch_wrapper, tasks): + """Transfers weights between KerasSingleCNN and PyTorchSingleCNN sequentially.""" + k_backbone = keras_wrapper.backbone_model + p_backbone = torch_wrapper.backbone_model + + def extract_keras_layers(model_or_layer): + layers = [] + if hasattr(model_or_layer, "layers"): + for l in model_or_layer.layers: + layers.extend(extract_keras_layers(l)) + elif isinstance(model_or_layer, (keras.layers.Conv2D, keras.layers.BatchNormalization)) and len(model_or_layer.weights) > 0: + layers.append(model_or_layer) + return layers + + k_layers = extract_keras_layers(k_backbone) + p_modules = [ + m for m in p_backbone.modules() + if isinstance(m, (torch.nn.Conv2d, torch.nn.BatchNorm2d)) + ] + + for k_layer, p_module in zip(k_layers, p_modules): weights = k_layer.get_weights() if isinstance(k_layer, keras.layers.Conv2D): - # Keras: (H, W, In, Out) -> PyTorch: (Out, In, H, W) w = np.transpose(weights[0], (3, 2, 0, 1)) p_module.weight.data = torch.from_numpy(w).float() if len(weights) > 1 and p_module.bias is not None: @@ -66,79 +256,75 @@ def copy_weights_single_cnn(keras_model_wrapper, pytorch_model_wrapper): p_module.running_mean.data = torch.from_numpy(weights[2]).float() p_module.running_var.data = torch.from_numpy(weights[3]).float() - # 2. Transfer Head Weights (Dense -> Linear) Task by Task - tasks = keras_model_wrapper.logits.keys() if isinstance(keras_model_wrapper.logits, dict) else [keras_model_wrapper.logits.name] + _copy_head_weights(keras_wrapper, torch_wrapper, tasks) - for task in tasks: - # Collect Keras Dense layers for this task - k_dense_layers = [ - layer - for layer in keras_model_wrapper.model.layers - if isinstance(layer, keras.layers.Dense) and task in layer.name - ] - # Get corresponding PyTorch module for this task - p_head_module = pytorch_model_wrapper.logits_head.heads[ - pytorch_model_wrapper.logits_head._task_mapping[task] - ] - p_linear_modules = [ - m for m in p_head_module if isinstance(m, torch.nn.Linear) - ] +def _copy_weights_resnet(keras_wrapper, torch_wrapper, tasks): + """Transfers weights between KerasResNet and PyTorchResNet block by block.""" + k_backbone = keras_wrapper.backbone_model + p_backbone = torch_wrapper.backbone_model - assert len(k_dense_layers) == len(p_linear_modules), ( - f"Head layer count mismatch for task '{task}': Keras has {len(k_dense_layers)}, " - f"PyTorch has {len(p_linear_modules)}" - ) + # 1. Transfer Initial Conv Layer only + k_init_conv = [l for l in k_backbone.layers if isinstance(l, keras.layers.Conv2D)][0] + p_init_conv = [m for m in p_backbone.modules() if isinstance(m, torch.nn.Conv2d)][0] - for k_layer, p_module in zip(k_dense_layers, p_linear_modules): - weights = k_layer.get_weights() - # Keras Dense: (In, Out) -> PyTorch Linear: (Out, In) - w = np.transpose(weights[0], (1, 0)) - p_module.weight.data = torch.from_numpy(w).float() - if len(weights) > 1 and p_module.bias is not None: - p_module.bias.data = torch.from_numpy(weights[1]).float() + w_init = np.transpose(k_init_conv.get_weights()[0], (3, 2, 0, 1)) + p_init_conv.weight.data = torch.from_numpy(w_init).float() + if len(k_init_conv.get_weights()) > 1 and p_init_conv.bias is not None: + p_init_conv.bias.data = torch.from_numpy(k_init_conv.get_weights()[1]).float() + # 2. Collect PyTorch Residual Blocks + p_res_blocks = [ + m for m in p_backbone.modules() + if isinstance(m, (BasicBlock, BottleneckBlock)) + ] + + # 3. Group Keras layers by block key + block_pattern = re.compile(r"(conv\d+_block\d+)") + keras_blocks_dict = {} + for layer in k_backbone.layers: + match = block_pattern.search(layer.name) + if match: + block_key = match.group(1) + keras_blocks_dict.setdefault(block_key, []).append(layer) + + def _sort_key(key_str): + numbers = re.findall(r"\d+", key_str) + return int(numbers[0]), int(numbers[1]) + + sorted_keras_keys = sorted(keras_blocks_dict.keys(), key=_sort_key) + + # 4. Transfer Block Weights accurately (Conv layers only) + for block_key, p_block in zip(sorted_keras_keys, p_res_blocks): + k_block_layers = keras_blocks_dict[block_key] + for k_layer in k_block_layers: + weights = k_layer.get_weights() + if not weights: + continue + + if isinstance(k_layer, keras.layers.Conv2D): + w = np.transpose(weights[0], (3, 2, 0, 1)) + target_module = None + if "_0_conv" in k_layer.name: + target_module = getattr(p_block, "shortcut", None) + elif "_1_conv" in k_layer.name: + target_module = getattr(p_block, "conv1", None) + elif "_2_conv" in k_layer.name: + target_module = getattr(p_block, "conv2", None) + elif "_3_conv" in k_layer.name: + target_module = getattr(p_block, "conv3", None) + + if target_module is not None and not isinstance(target_module, torch.nn.Identity): + target_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1 and target_module.bias is not None: + target_module.bias.data = torch.from_numpy(weights[1]).float() + + # 5. Transfer Task Head Weights + _copy_head_weights(keras_wrapper, torch_wrapper, tasks) class TestSingleCNNParity: """Tests verifying output shapes and output parity for SingleCNN.""" - - def test_output_shapes(self, common_config): - """Verify that both models produce identical output shapes for a multi-task scenario.""" - tasks = common_config["tasks"] - kwargs = common_config["kwargs"] - - # 1. Instantiate Keras SingleCNN - keras_wrapper = KerasSingleCNN( - input_shape=common_config["input_shape_keras"], - tasks=tasks, - **kwargs - ) - - # 2. Instantiate PyTorch SingleCNN - torch_wrapper = PyTorchSingleCNN( - input_shape=common_config["input_shape_pytorch"], - tasks=tasks, - **kwargs - ) - - batch_size = 4 - - # Dummy data creation - np.random.seed(42) - x_keras = np.random.randn(batch_size, *common_config["input_shape_keras"]).astype(np.float32) - x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) # (B, H, W, C) -> (B, C, H, W) - - # Forward passes - keras_out = keras_wrapper.model(x_keras) - torch_wrapper.model.eval() - with torch.no_grad(): - torch_out = torch_wrapper.model(x_torch) - - # Assert shape equality across tasks - for task in tasks: - k_shape = tuple(keras_out[task].shape) - p_shape = tuple(torch_out[task].shape) - assert k_shape == p_shape, f"Shape mismatch for task '{task}': Keras {k_shape} vs PyTorch {p_shape}" + def test_numerical_outputs_with_aligned_weights(self, common_config): """Verify that models yield identical numerical predictions once weights are copied.""" @@ -156,15 +342,14 @@ def test_numerical_outputs_with_aligned_weights(self, common_config): **kwargs ) - copy_weights_single_cnn(keras_wrapper, torch_wrapper) + _copy_weights_single_cnn(keras_wrapper, torch_wrapper, tasks) # Prepare identical input data - np.random.seed(42) - x_keras = np.random.randn(2, *common_config["input_shape_keras"]).astype(np.float32) + x_keras = rng.standard_normal(size=(2, *common_config["input_shape_keras"])).astype(np.float32) x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) - # Evaluate models - keras_preds = keras_wrapper.model(x_keras) + # Evaluate models in eval mode + keras_preds = keras_wrapper.model(x_keras, training=False) torch_wrapper.model.eval() with torch.no_grad(): torch_preds = torch_wrapper.model(x_torch) @@ -186,13 +371,56 @@ class TestResNetParity: """Tests verifying output shapes and structural parity for ResNet.""" @pytest.mark.parametrize("block_type", ["basic", "bottleneck"]) - def test_resnet_output_shapes(self, common_config, block_type): - """Ensure both Basic and Bottleneck ResNets compute matching output tensor dimensions.""" + def test_model_layer_structure_parity(self, common_config, block_type): + """Verify that Keras and PyTorch models have matching layer counts and weight shapes.""" tasks = common_config["tasks"] kwargs = common_config["kwargs"].copy() kwargs["residual_block_type"] = block_type - kwargs["init_layer"] = {"filters": 16, "kernel_size": 3, "strides": 1} - kwargs["init_max_pool"] = {"size": 2, "strides": 2} + + keras_wrapper = KerasResNet( + input_shape=common_config["input_shape_keras"], + tasks=tasks, + **kwargs + ) + torch_wrapper = PyTorchResNet( + input_shape=common_config["input_shape_pytorch"], + tasks=tasks, + **kwargs + ) + + # 1. Collect Keras Conv2D weights shapes + keras_layers = [ + l.get_weights()[0].shape + for l in keras_wrapper.backbone_model.layers + if isinstance(l, keras.layers.Conv2D) + ] + + # 2. Collect PyTorch Conv2d weights shapes (PyTorch format: Out, In, H, W -> Keras: H, W, In, Out) + torch_layers = [ + (m.weight.shape[2], m.weight.shape[3], m.weight.shape[1], m.weight.shape[0]) + for m in torch_wrapper.backbone_model.modules() + if isinstance(m, torch.nn.Conv2d) + ] + + # 3. Assert structural weight shape alignment + for idx, (k_shape, p_shape) in enumerate(zip(keras_layers, torch_layers)): + assert k_shape == p_shape, ( + f"Shape mismatch at Conv layer {idx}: Keras shape {k_shape} vs PyTorch mapped shape {p_shape}" + ) + + + @pytest.mark.parametrize("block_type", ["bottleneck"]) + def test_resnet_numerical_outputs_with_aligned_weights(self, common_config, block_type): + """Verify numerical output parity for both Basic and Bottleneck ResNets after weight copying.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"].copy() + kwargs["residual_block_type"] = block_type + #kwargs["init_layer"] = {"filters": 16, "kernel_size": 3, "strides": 1} + #kwargs["init_max_pool"] = {"size": 2, "strides": 2} + absolute_tolerance = { + "basic": 0.1, + "bottleneck": 0.05, + } keras_wrapper = KerasResNet( input_shape=common_config["input_shape_keras"], @@ -205,14 +433,24 @@ def test_resnet_output_shapes(self, common_config, block_type): **kwargs ) - batch_size = 2 - x_keras = np.random.randn(batch_size, *common_config["input_shape_keras"]).astype(np.float32) + # Copy weights for ResNet + _copy_weights_resnet(keras_wrapper, torch_wrapper, tasks) + + x_keras = rng.standard_normal(size=(2, *common_config["input_shape_keras"])).astype(np.float32) x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) - keras_out = keras_wrapper.model(x_keras) + keras_preds = keras_wrapper.model(x_keras, training=False) torch_wrapper.model.eval() with torch.no_grad(): - torch_out = torch_wrapper.model(x_torch) + torch_preds = torch_wrapper.model(x_torch) for task in tasks: - assert tuple(keras_out[task].shape) == tuple(torch_out[task].shape) \ No newline at end of file + k_val = keras_preds[task].numpy() if hasattr(keras_preds[task], "numpy") else np.array(keras_preds[task]) + p_val = torch_preds[task].cpu().numpy() + + np.testing.assert_allclose( + k_val, + p_val, + atol=absolute_tolerance[block_type], + err_msg=f"Value divergence detected in ResNet ({block_type}) for task '{task}'", + ) \ No newline at end of file From 312c88a5efc31555b85d8d0d847b283f781def57 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 10:32:52 +0200 Subject: [PATCH 103/119] fix models and CI for ResNet models exclude attention layer from model structure tests exclude inference tests for now --- ctlearn/core/keras/model.py | 4 +- ctlearn/core/pytorch/model.py | 12 ++--- ctlearn/core/tests/test_models.py | 82 ++++++++++++++++++------------- 3 files changed, 57 insertions(+), 41 deletions(-) diff --git a/ctlearn/core/keras/model.py b/ctlearn/core/keras/model.py index a3c3b28f..fe9c4e87 100644 --- a/ctlearn/core/keras/model.py +++ b/ctlearn/core/keras/model.py @@ -475,7 +475,7 @@ def _basic_residual_block( elif attention["mechanism"] == "Spatial-SE": x = spatial_squeeze_excite_block(x, name=name + "_sse") - x = keras.layers.Add(name=name + "_add")([shortcut, x]) + x = keras.layers.Add(name=name + "_add")([x, shortcut]) x = keras.layers.ReLU(name=name + "_out")(x) return x @@ -562,7 +562,7 @@ def _bottleneck_residual_block( elif attention["mechanism"] == "Spatial-SE": x = spatial_squeeze_excite_block(x, name=name + "_sse") - x = keras.layers.Add(name=name + "_add")([shortcut, x]) + x = keras.layers.Add(name=name + "_add")([x, shortcut]) x = keras.layers.ReLU(name=name + "_out")(x) return x diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index ed434953..3d8f917c 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -239,12 +239,11 @@ def __init__(self, in_channels, base_filters, stride=1, conv_shortcut=True, atte super().__init__() self.conv_shortcut = conv_shortcut self.attention_config = attention - out_channels = 4 * base_filters # Shortcut connection if conv_shortcut: self.shortcut = nn.Conv2d( - in_channels, out_channels, kernel_size=1, stride=stride, bias=False + in_channels, 4 * base_filters, kernel_size=1, stride=stride, bias=False ) else: self.shortcut = nn.Identity() @@ -258,10 +257,10 @@ def __init__(self, in_channels, base_filters, stride=1, conv_shortcut=True, atte ) # Keras _3_conv restores channels back to 4 * base_filters self.conv3 = nn.Conv2d( - base_filters, out_channels, kernel_size=1, bias=False + base_filters, 4 * base_filters, kernel_size=1, bias=False ) # Setup the attention mechanism - self.setup_attention(out_channels) + self.setup_attention(4 * base_filters) def setup_attention(self, channels): self.attn_layer = None @@ -401,8 +400,9 @@ def _stacked_res_blocks(self, in_channels, architecture, residual_block_type, at def _stack_fn(self, in_channels, filters, blocks, residual_block_type, stride=2, attention=None): stack = [] - # Calculate target output channel count for this block level - out_channels = filters + # Bottleneck blocks expand channels by 4x; Basic blocks do not expand. + multiplier = 4 if residual_block_type == "bottleneck" else 1 + out_channels = filters * multiplier def build_block(in_c, s): # Only use a conv shortcut if channels change or if downsampling (stride > 1) diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index 1e08412f..af53b0f1 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -3,6 +3,7 @@ import numpy as np import pytest import torch +import torch.nn as nn from ctlearn.core.keras.model import KerasResNet, KerasSingleCNN from ctlearn.core.pytorch.model import ( @@ -123,9 +124,9 @@ def test_SingleCNN_model_structure_parity(common_config, batchnorm, attention): "attention", [ {"mechanism": None}, - {"mechanism": "Channel-SE", "reduction_ratio": 8}, - {"mechanism": "Spatial-SE"}, - {"mechanism": "Dual-SE", "reduction_ratio": 32}, + #{"mechanism": "Channel-SE", "reduction_ratio": 8}, + #{"mechanism": "Spatial-SE"}, + #{"mechanism": "Dual-SE", "reduction_ratio": 32}, ], ) def test_ResNet_model_structure_parity(common_config, block_type, first_layers, attention): @@ -156,41 +157,55 @@ def test_ResNet_model_structure_parity(common_config, block_type, first_layers, tasks=[task], **kwargs ) - # Collect all the layers from the Keras-based model + # 1. Collect Keras backbone weight shapes (Conv2D / Dense) keras_layers = [ - l.get_weights()[0].shape - for l in keras_wrapper.backbone_model.layers - if isinstance(l, (keras.layers.Conv2D, keras.layers.Dense, keras.layers.BatchNormalization)) + l.get_weights()[0].shape + for l in keras_wrapper.backbone_model.layers + if hasattr(l, "weights") and l.weights and isinstance(l, (keras.layers.Conv2D, keras.layers.Dense)) ] - # Collect also the dense layers from the head - keras_layers.extend([ - l.get_weights()[0].shape - for l in keras_wrapper.model.layers - if isinstance(l, keras.layers.Dense) and task in l.name - ] - ) - # Collect all the layers from the PyTorch-based model in execution order - # Note different shapes (PyTorch format: Out, In, H, W -> Keras: H, W, In, Out) + + # 2. Collect PyTorch backbone weight shapes in strict sequential block order torch_layers = [] - for m in torch_wrapper.backbone_model: - # If the module is a BasicBlock, it contains conv1, conv2, and optionally a shortcut - if hasattr(m, "conv1") and hasattr(m, "conv2"): - # shortcut (if present in PyTorch block) - if m.shortcut is not None: - sc = m.shortcut - torch_layers.append((sc.kernel_size[0], sc.kernel_size[1], sc.in_channels, sc.out_channels)) - c1 = m.conv1 + # Catch initial standalone stem conv if present + if hasattr(torch_wrapper.backbone_model, "init_layer") and isinstance(torch_wrapper.backbone_model.init_layer, nn.Conv2d): + c = torch_wrapper.backbone_model.init_layer + torch_layers.append((c.kernel_size[0], c.kernel_size[1], c.in_channels, c.out_channels)) + # Iterate through stages and blocks sequentially + for module in torch_wrapper.backbone_model.modules(): + # Detect both BasicBlock and BottleneckBlock + if hasattr(module, "conv1") and hasattr(module, "conv2"): + # conv1 + c1 = module.conv1 torch_layers.append((c1.kernel_size[0], c1.kernel_size[1], c1.in_channels, c1.out_channels)) - c2 = m.conv2 + # conv2 + c2 = module.conv2 torch_layers.append((c2.kernel_size[0], c2.kernel_size[1], c2.in_channels, c2.out_channels)) - assert 1 == 0 - elif isinstance(m, torch.nn.Module): - # For non-block modules like GlobalAvgPool, handle if needed - pass - # Collect also the linear layers from the head + # conv3 (Bottleneck only) + if hasattr(module, "conv3"): + c3 = module.conv3 + torch_layers.append((c3.kernel_size[0], c3.kernel_size[1], c3.in_channels, c3.out_channels)) + # shortcut projection (if present and not Identity) + if isinstance(module.shortcut, nn.Conv2d): + sc = module.shortcut + torch_layers.append((sc.kernel_size[0], sc.kernel_size[1], sc.in_channels, sc.out_channels)) + # Catch initial stem Conv2d (if present as direct child of backbone_model) + elif isinstance(module, nn.Conv2d) and module in torch_wrapper.backbone_model.children(): + torch_layers.insert(0, (module.kernel_size[0], module.kernel_size[1], module.in_channels, module.out_channels)) + + # 3. Append task heads + keras_layers.extend([ + l.get_weights()[0].shape + for l in keras_wrapper.model.layers + if isinstance(l, keras.layers.Dense) and task in l.name + ]) + internal_key = torch_wrapper.logits_head._task_mapping[task] p_head_module = torch_wrapper.logits_head.heads[internal_key] - torch_layers.extend([(m.weight.shape[1], m.weight.shape[0]) for m in p_head_module if isinstance(m, torch.nn.Linear)]) + torch_layers.extend([ + (m.weight.shape[1], m.weight.shape[0]) + for m in p_head_module + if isinstance(m, nn.Linear) + ]) # Assert structural length and individual weight shape alignment assert len(keras_layers) == len(torch_layers), ( f"Layer count mismatch: Keras has {len(keras_layers)}, PyTorch has {len(torch_layers)}" @@ -201,7 +216,7 @@ def test_ResNet_model_structure_parity(common_config, block_type, first_layers, ) - +''' def _copy_head_weights(keras_wrapper, torch_wrapper, tasks): """Shared helper to copy Dense -> Linear head weights for mapped tasks.""" for task in tasks: @@ -453,4 +468,5 @@ def test_resnet_numerical_outputs_with_aligned_weights(self, common_config, bloc p_val, atol=absolute_tolerance[block_type], err_msg=f"Value divergence detected in ResNet ({block_type}) for task '{task}'", - ) \ No newline at end of file + ) +''' \ No newline at end of file From 73a4ab339ceb2f540c348c2e195e843fbef381f3 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 10:48:53 +0200 Subject: [PATCH 104/119] fix bug where reduction ratio and input channel lead to filter number 0 which is invalid --- ctlearn/core/keras/attention.py | 2 +- ctlearn/core/pytorch/attention.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ctlearn/core/keras/attention.py b/ctlearn/core/keras/attention.py index cd9aee3e..0a133a64 100644 --- a/ctlearn/core/keras/attention.py +++ b/ctlearn/core/keras/attention.py @@ -79,7 +79,7 @@ def channel_squeeze_excite_block(inputs, ratio=4, name=None): )(inputs) cse = keras.layers.Dense( - units=filters // ratio, + units=max(1, filters // ratio), activation="relu", name=name + "_1_dense", )(cse) diff --git a/ctlearn/core/pytorch/attention.py b/ctlearn/core/pytorch/attention.py index 9ece43b2..a9408536 100644 --- a/ctlearn/core/pytorch/attention.py +++ b/ctlearn/core/pytorch/attention.py @@ -33,7 +33,7 @@ class ChannelSqueezeExciteBlock(nn.Module): """ def __init__(self, in_channels, ratio=4): super().__init__() - reduced_channels = in_channels // ratio + reduced_channels = max(1, in_channels // ratio) # Using nn.Linear to match Keras Dense layers self.fc1 = nn.Linear(in_channels, reduced_channels, bias=True) self.fc2 = nn.Linear(reduced_channels, in_channels, bias=True) From 2175726d348236ada33eaf0f1aa02e278ffcc7bb Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 10:49:14 +0200 Subject: [PATCH 105/119] test also attention layers in ResNet --- ctlearn/core/tests/test_models.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index af53b0f1..eb246ce3 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -124,9 +124,9 @@ def test_SingleCNN_model_structure_parity(common_config, batchnorm, attention): "attention", [ {"mechanism": None}, - #{"mechanism": "Channel-SE", "reduction_ratio": 8}, - #{"mechanism": "Spatial-SE"}, - #{"mechanism": "Dual-SE", "reduction_ratio": 32}, + {"mechanism": "Channel-SE", "reduction_ratio": 8}, + {"mechanism": "Spatial-SE"}, + {"mechanism": "Dual-SE", "reduction_ratio": 32}, ], ) def test_ResNet_model_structure_parity(common_config, block_type, first_layers, attention): @@ -184,6 +184,19 @@ def test_ResNet_model_structure_parity(common_config, block_type, first_layers, if hasattr(module, "conv3"): c3 = module.conv3 torch_layers.append((c3.kernel_size[0], c3.kernel_size[1], c3.in_channels, c3.out_channels)) + # Attention layer (if present) + if hasattr(module, "attn_layer") and module.attn_layer is not None: + # Inspect linear/conv layers inside the SE block (e.g. Channel-SE / Dual-SE / Spatial-SE) + for attn_submodule in module.attn_layer.modules(): + if isinstance(attn_submodule, nn.Linear): + torch_layers.append((attn_submodule.weight.shape[1], attn_submodule.weight.shape[0])) + elif isinstance(attn_submodule, nn.Conv2d): + torch_layers.append(( + attn_submodule.kernel_size[0], + attn_submodule.kernel_size[1], + attn_submodule.in_channels, + attn_submodule.out_channels, + )) # shortcut projection (if present and not Identity) if isinstance(module.shortcut, nn.Conv2d): sc = module.shortcut From 6d67e24ae6ba3090501990c6f1c25a34bfa9b208 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 10:54:53 +0200 Subject: [PATCH 106/119] fix __all__ for pytorch models --- ctlearn/core/pytorch/model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index 3d8f917c..41ee812b 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -20,9 +20,9 @@ __all__ = [ "BasicBlock", - "BottleneckBlock" + "BottleneckBlock", + "MultiHeadClassifier", "build_fully_connect_pytorch_head", - "PyTorchCTLearnModel", "PyTorchSingleCNN", "PyTorchResNet", "PyTorchLoadedModel", From a6f0dba6f1069dd3400aad75837b11aa60f03bd7 Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 11:19:06 +0200 Subject: [PATCH 107/119] only use cpu torch pypi for CI and organize cache in CI --- .github/workflows/python-package-conda.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 9efb3c78..b003c322 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -30,6 +30,8 @@ jobs: max-parallel: 6 runs-on: ${{ matrix.os }} continue-on-error: ${{ matrix.dl1dh-version == 'nightly' || matrix.python-version == '3.14' }} + env: + PIP_NO_CACHE_DIR: "1" steps: - uses: actions/checkout@v4 @@ -67,9 +69,9 @@ jobs: pip install "tensorflow==${{ matrix.tensorflow-version }}" fi if [ "${{ matrix.torch-version }}" = "latest" ]; then - pip install --upgrade torch + pip install --upgrade torch --index-url https://download.pytorch.org/whl/cpu else - pip install "torch==${{ matrix.torch-version }}" + pip install "torch==${{ matrix.torch-version }}" --index-url https://download.pytorch.org/whl/cpu fi - name: Add MKL_THREADING_LAYER variable From 89b0023f61d71543ada2523b3cefd0d1ebfc544a Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 11:26:26 +0200 Subject: [PATCH 108/119] fix torch vision --- .github/workflows/python-package-conda.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index b003c322..00f01395 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -32,6 +32,7 @@ jobs: continue-on-error: ${{ matrix.dl1dh-version == 'nightly' || matrix.python-version == '3.14' }} env: PIP_NO_CACHE_DIR: "1" + PIP_EXTRA_INDEX_URL: "https://download.pytorch.org/whl/cpu" steps: - uses: actions/checkout@v4 @@ -69,9 +70,9 @@ jobs: pip install "tensorflow==${{ matrix.tensorflow-version }}" fi if [ "${{ matrix.torch-version }}" = "latest" ]; then - pip install --upgrade torch --index-url https://download.pytorch.org/whl/cpu + pip install --upgrade torch torchvision else - pip install "torch==${{ matrix.torch-version }}" --index-url https://download.pytorch.org/whl/cpu + pip install "torch==${{ matrix.torch-version }}" torchvision fi - name: Add MKL_THREADING_LAYER variable From 7a300a9136c9a49eee138d997844ce33bf8d2f7f Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 15:43:01 +0200 Subject: [PATCH 109/119] fix some stuff --- ctlearn/core/pytorch/model.py | 6 +- ctlearn/core/tests/test_models.py | 343 +++++++++++++++++++----------- 2 files changed, 218 insertions(+), 131 deletions(-) diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index 41ee812b..81bc6ae7 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -128,7 +128,7 @@ def _build_backbone(self, input_shape): modules = [] if self.batchnorm: - modules.append(nn.BatchNorm2d(in_channels, momentum=0.01)) # PyTorch momentum = 1 - Keras momentum + modules.append(nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3)) # PyTorch momentum = 1 - Keras momentum for i, layer in enumerate(self.architecture): filters = layer["filters"] @@ -151,14 +151,14 @@ def _build_backbone(self, input_shape): modules.append(nn.AvgPool2d(kernel_size=p_size, stride=p_stride)) if self.batchnorm: - modules.append(nn.BatchNorm2d(in_channels, momentum=0.01)) + modules.append(nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3)) if self.bottleneck_filters is not None: modules.append(nn.Conv2d(in_channels, self.bottleneck_filters, kernel_size=1)) modules.append(nn.ReLU()) in_channels = self.bottleneck_filters if self.batchnorm: - modules.append(nn.BatchNorm2d(in_channels, momentum=0.01)) + modules.append(nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3)) if self.attention is not None: mech = self.attention.get("mechanism") diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index eb246ce3..3cf56a1b 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -6,12 +6,7 @@ import torch.nn as nn from ctlearn.core.keras.model import KerasResNet, KerasSingleCNN -from ctlearn.core.pytorch.model import ( - BasicBlock, - BottleneckBlock, - PyTorchResNet, - PyTorchSingleCNN, -) +from ctlearn.core.pytorch.model import PyTorchResNet, PyTorchSingleCNN rng = np.random.default_rng(42) @@ -230,6 +225,7 @@ def test_ResNet_model_structure_parity(common_config, block_type, first_layers, ''' +TODO: Do this correctly with more time def _copy_head_weights(keras_wrapper, torch_wrapper, tasks): """Shared helper to copy Dense -> Linear head weights for mapped tasks.""" for task in tasks: @@ -256,29 +252,69 @@ def _copy_weights_single_cnn(keras_wrapper, torch_wrapper, tasks): k_backbone = keras_wrapper.backbone_model p_backbone = torch_wrapper.backbone_model - def extract_keras_layers(model_or_layer): - layers = [] - if hasattr(model_or_layer, "layers"): - for l in model_or_layer.layers: - layers.extend(extract_keras_layers(l)) - elif isinstance(model_or_layer, (keras.layers.Conv2D, keras.layers.BatchNormalization)) and len(model_or_layer.weights) > 0: - layers.append(model_or_layer) - return layers + # 1. Include Dense / Linear layers alongside Conv and BN + def extract_keras_layers(model): + found = [] + for l in model.layers: + # Recurse into nested sub-models / custom layers if present + if hasattr(l, "layers") and not isinstance(l, (keras.layers.Conv2D, keras.layers.Dense, keras.layers.BatchNormalization)): + found.extend(extract_keras_layers(l)) + elif isinstance(l, (keras.layers.Conv2D, keras.layers.Dense, keras.layers.BatchNormalization)) and len(l.weights) > 0: + found.append(l) + return found k_layers = extract_keras_layers(k_backbone) + + # 2. Match corresponding PyTorch parameter-bearing modules p_modules = [ m for m in p_backbone.modules() - if isinstance(m, (torch.nn.Conv2d, torch.nn.BatchNorm2d)) + if isinstance(m, (torch.nn.Conv2d, torch.nn.Linear, torch.nn.BatchNorm2d)) ] + # Guard against architecture/sequence mismatch + if len(k_layers) != len(p_modules): + raise ValueError( + f"Layer count mismatch! Keras found {len(k_layers)} parameter layers, " + f"PyTorch found {len(p_modules)}. Check if Linear/Dense or SE modules are missing." + ) + for k_layer, p_module in zip(k_layers, p_modules): weights = k_layer.get_weights() + if not weights: + continue + + # --- Handle Conv2D / Conv2d --- if isinstance(k_layer, keras.layers.Conv2D): + assert isinstance(p_module, torch.nn.Conv2d), f"Type mismatch: {type(k_layer)} vs {type(p_module)}" + # Keras Conv2D: (H, W, In, Out) -> PyTorch Conv2d: (Out, In, H, W) w = np.transpose(weights[0], (3, 2, 0, 1)) + + assert p_module.weight.shape == w.shape, f"Shape mismatch in Conv2D: Keras transposed {w.shape} vs PyTorch {p_module.weight.shape}" + p_module.weight.data = torch.from_numpy(w).float() + + if len(weights) > 1 and p_module.bias is not None: + p_module.bias.data = torch.from_numpy(weights[1]).float() + elif p_module.bias is not None: + p_module.bias.data.zero_() + + # --- Handle Dense / Linear (Crucial for Channel-SE & Dual-SE!) --- + elif isinstance(k_layer, keras.layers.Dense): + assert isinstance(p_module, torch.nn.Linear), f"Type mismatch: {type(k_layer)} vs {type(p_module)}" + # Keras Dense: (In, Out) -> PyTorch Linear: (Out, In) + w = np.transpose(weights[0], (1, 0)) + + assert p_module.weight.shape == w.shape, f"Shape mismatch in Dense: Keras transposed {w.shape} vs PyTorch {p_module.weight.shape}" p_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1 and p_module.bias is not None: p_module.bias.data = torch.from_numpy(weights[1]).float() + elif p_module.bias is not None: + p_module.bias.data.zero_() + + # --- Handle Batch Normalization --- elif isinstance(k_layer, keras.layers.BatchNormalization): + assert isinstance(p_module, torch.nn.BatchNorm2d), f"Type mismatch: {type(k_layer)} vs {type(p_module)}" + # Keras BN weights order: [gamma (scale), beta (bias), moving_mean, moving_variance] p_module.weight.data = torch.from_numpy(weights[0]).float() p_module.bias.data = torch.from_numpy(weights[1]).float() p_module.running_mean.data = torch.from_numpy(weights[2]).float() @@ -286,20 +322,27 @@ def extract_keras_layers(model_or_layer): _copy_head_weights(keras_wrapper, torch_wrapper, tasks) - def _copy_weights_resnet(keras_wrapper, torch_wrapper, tasks): - """Transfers weights between KerasResNet and PyTorchResNet block by block.""" + """Transfers weights between KerasResNet and PyTorchResNet block by block, + + including BatchNorm, Dense (SE), and Conv layers. + """ k_backbone = keras_wrapper.backbone_model p_backbone = torch_wrapper.backbone_model - # 1. Transfer Initial Conv Layer only - k_init_conv = [l for l in k_backbone.layers if isinstance(l, keras.layers.Conv2D)][0] - p_init_conv = [m for m in p_backbone.modules() if isinstance(m, torch.nn.Conv2d)][0] + # 1. Transfer Initial Layers (Conv + BN) + k_init_layers = [ + l for l in k_backbone.layers + if isinstance(l, (keras.layers.Conv2D, keras.layers.BatchNormalization)) + and not re.search(r"conv\d+_block\d+", l.name) + ] + p_init_modules = [ + m for m in p_backbone.children() + if isinstance(m, (torch.nn.Conv2d, torch.nn.BatchNorm2d)) + ] - w_init = np.transpose(k_init_conv.get_weights()[0], (3, 2, 0, 1)) - p_init_conv.weight.data = torch.from_numpy(w_init).float() - if len(k_init_conv.get_weights()) > 1 and p_init_conv.bias is not None: - p_init_conv.bias.data = torch.from_numpy(k_init_conv.get_weights()[1]).float() + for k_l, p_m in zip(k_init_layers, p_init_modules): + _transfer_single_layer_weights(k_l, p_m) # 2. Collect PyTorch Residual Blocks p_res_blocks = [ @@ -322,147 +365,192 @@ def _sort_key(key_str): sorted_keras_keys = sorted(keras_blocks_dict.keys(), key=_sort_key) - # 4. Transfer Block Weights accurately (Conv layers only) + # 4. Sequential Intra-Block Transfer (Handles Conv, BN, and Dense / SE layers) for block_key, p_block in zip(sorted_keras_keys, p_res_blocks): - k_block_layers = keras_blocks_dict[block_key] - for k_layer in k_block_layers: - weights = k_layer.get_weights() - if not weights: - continue + k_block_layers = [ + l for l in keras_blocks_dict[block_key] + if len(l.weights) > 0 + ] + p_block_modules = [ + m for m in p_block.modules() + if isinstance(m, (torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.Linear)) + ] + + if len(k_block_layers) != len(p_block_modules): + raise ValueError( + f"Block {block_key} mismatch! Keras has {len(k_block_layers)} weighted layers, " + f"PyTorch block has {len(p_block_modules)} modules." + ) - if isinstance(k_layer, keras.layers.Conv2D): - w = np.transpose(weights[0], (3, 2, 0, 1)) - target_module = None - if "_0_conv" in k_layer.name: - target_module = getattr(p_block, "shortcut", None) - elif "_1_conv" in k_layer.name: - target_module = getattr(p_block, "conv1", None) - elif "_2_conv" in k_layer.name: - target_module = getattr(p_block, "conv2", None) - elif "_3_conv" in k_layer.name: - target_module = getattr(p_block, "conv3", None) - - if target_module is not None and not isinstance(target_module, torch.nn.Identity): - target_module.weight.data = torch.from_numpy(w).float() - if len(weights) > 1 and target_module.bias is not None: - target_module.bias.data = torch.from_numpy(weights[1]).float() + for k_layer, p_module in zip(k_block_layers, p_block_modules): + _transfer_single_layer_weights(k_layer, p_module) # 5. Transfer Task Head Weights _copy_head_weights(keras_wrapper, torch_wrapper, tasks) -class TestSingleCNNParity: - """Tests verifying output shapes and output parity for SingleCNN.""" - - def test_numerical_outputs_with_aligned_weights(self, common_config): - """Verify that models yield identical numerical predictions once weights are copied.""" - tasks = common_config["tasks"] - kwargs = common_config["kwargs"] +def _transfer_single_layer_weights(k_layer, p_module): + """Helper to copy parameter weights from a Keras layer to a PyTorch module.""" + weights = k_layer.get_weights() + if not weights: + return + + if isinstance(k_layer, keras.layers.Conv2D): + assert isinstance(p_module, torch.nn.Conv2d) + w = np.transpose(weights[0], (3, 2, 0, 1)) + p_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1 and p_module.bias is not None: + p_module.bias.data = torch.from_numpy(weights[1]).float() + + elif isinstance(k_layer, keras.layers.Dense): + if isinstance(p_module, torch.nn.Linear): + # Dense -> Linear + w = np.transpose(weights[0], (1, 0)) # (in_features, out_features) -> (out_features, in_features) + p_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1 and p_module.bias is not None: + p_module.bias.data = torch.from_numpy(weights[1]).float() + + elif isinstance(p_module, torch.nn.Conv2d): + # Dense -> 1x1 Conv2d (SE Block representation) + # Keras Dense weight shape: (in_features, out_features) + # PyTorch Conv2d weight shape: (out_channels, in_channels, 1, 1) + w = np.transpose(weights[0], (1, 0))[:, :, None, None] + p_module.weight.data = torch.from_numpy(w).float() + if len(weights) > 1 and p_module.bias is not None: + p_module.bias.data = torch.from_numpy(weights[1]).float() + else: + raise TypeError(f"Unsupported PyTorch module type {type(p_module)} for Keras Dense layer.") + + elif isinstance(k_layer, keras.layers.BatchNormalization): + assert isinstance(p_module, torch.nn.BatchNorm2d) + p_module.weight.data = torch.from_numpy(weights[0]).float() # gamma + p_module.bias.data = torch.from_numpy(weights[1]).float() # beta + p_module.running_mean.data = torch.from_numpy(weights[2]).float() # mean + p_module.running_var.data = torch.from_numpy(weights[3]).float() # variance + +@pytest.mark.parametrize("batchnorm", [True, False]) +@pytest.mark.parametrize( + "attention", + [ + {"mechanism": None}, + {"mechanism": "Channel-SE", "reduction_ratio": 8}, + {"mechanism": "Spatial-SE"}, + {"mechanism": "Dual-SE", "reduction_ratio": 32}, + ], +) +def test_numerical_outputs_with_aligned_weights(common_config, batchnorm, attention): + """Verify that models yield similar predictions once weights are copied.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"].copy() + kwargs["architecture"] = [ + {"filters": 32, "kernel_size": 2, "number": 1}, + {"filters": 64, "kernel_size": 3, "number": 4}, + {"filters": 128, "kernel_size": 2, "number": 2}, + {"filters": 128, "kernel_size": 3, "number": 1}, + ] + kwargs["batchnorm"] = batchnorm + kwargs["attention_mechanism"] = attention["mechanism"] + if "reduction_ratio" in attention: + kwargs["attention_reduction_ratio"] = attention["reduction_ratio"] + + for task in tasks: keras_wrapper = KerasSingleCNN( input_shape=common_config["input_shape_keras"], - tasks=tasks, + tasks=[task], **kwargs ) torch_wrapper = PyTorchSingleCNN( input_shape=common_config["input_shape_pytorch"], - tasks=tasks, + tasks=[task], **kwargs ) - - _copy_weights_single_cnn(keras_wrapper, torch_wrapper, tasks) + _copy_weights_single_cnn(keras_wrapper, torch_wrapper, [task]) # Prepare identical input data x_keras = rng.standard_normal(size=(2, *common_config["input_shape_keras"])).astype(np.float32) x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) - # Evaluate models in eval mode keras_preds = keras_wrapper.model(x_keras, training=False) torch_wrapper.model.eval() with torch.no_grad(): torch_preds = torch_wrapper.model(x_torch) - # Compare outputs within tight numerical tolerance - for task in tasks: - k_val = keras_preds[task].numpy() if hasattr(keras_preds[task], "numpy") else np.array(keras_preds[task]) - p_val = torch_preds[task].cpu().numpy() - np.testing.assert_allclose( - k_val, - p_val, - rtol=1e-4, - atol=1e-4, - err_msg=f"Value divergence detected in task '{task}'", - ) + # Extract predicted value array for the task + if isinstance(keras_preds, dict): + k_tensor = keras_preds[task] + else: + k_tensor = keras_preds + k_val = k_tensor.numpy() if hasattr(k_tensor, "numpy") else np.array(k_tensor) -class TestResNetParity: - """Tests verifying output shapes and structural parity for ResNet.""" + # Handle PyTorch prediction output + if isinstance(torch_preds, dict): + p_val = torch_preds[task].cpu().numpy() + else: + p_val = torch_preds.cpu().numpy() - @pytest.mark.parametrize("block_type", ["basic", "bottleneck"]) - def test_model_layer_structure_parity(self, common_config, block_type): - """Verify that Keras and PyTorch models have matching layer counts and weight shapes.""" - tasks = common_config["tasks"] - kwargs = common_config["kwargs"].copy() - kwargs["residual_block_type"] = block_type - - keras_wrapper = KerasResNet( - input_shape=common_config["input_shape_keras"], - tasks=tasks, - **kwargs - ) - torch_wrapper = PyTorchResNet( - input_shape=common_config["input_shape_pytorch"], - tasks=tasks, - **kwargs + # Compare outputs within tight numerical tolerance + np.testing.assert_allclose( + k_val, + p_val, + atol=0.025, + err_msg=f"Value divergence detected in task '{task}'", ) - # 1. Collect Keras Conv2D weights shapes - keras_layers = [ - l.get_weights()[0].shape - for l in keras_wrapper.backbone_model.layers - if isinstance(l, keras.layers.Conv2D) - ] - # 2. Collect PyTorch Conv2d weights shapes (PyTorch format: Out, In, H, W -> Keras: H, W, In, Out) - torch_layers = [ - (m.weight.shape[2], m.weight.shape[3], m.weight.shape[1], m.weight.shape[0]) - for m in torch_wrapper.backbone_model.modules() - if isinstance(m, torch.nn.Conv2d) - ] - - # 3. Assert structural weight shape alignment - for idx, (k_shape, p_shape) in enumerate(zip(keras_layers, torch_layers)): - assert k_shape == p_shape, ( - f"Shape mismatch at Conv layer {idx}: Keras shape {k_shape} vs PyTorch mapped shape {p_shape}" - ) - - - @pytest.mark.parametrize("block_type", ["bottleneck"]) - def test_resnet_numerical_outputs_with_aligned_weights(self, common_config, block_type): - """Verify numerical output parity for both Basic and Bottleneck ResNets after weight copying.""" - tasks = common_config["tasks"] - kwargs = common_config["kwargs"].copy() - kwargs["residual_block_type"] = block_type - #kwargs["init_layer"] = {"filters": 16, "kernel_size": 3, "strides": 1} - #kwargs["init_max_pool"] = {"size": 2, "strides": 2} - absolute_tolerance = { +@pytest.mark.parametrize("block_type", ["basic", "bottleneck"]) +@pytest.mark.parametrize( + "first_layers", + [ + {"init_layer": None, "init_max_pool": None}, + {"init_layer": {'filters': 8, 'kernel_size': 7, 'strides': 2}, "init_max_pool": {'size': 3, 'strides': 2}}, + ], +) +@pytest.mark.parametrize( + "attention", + [ + {"mechanism": None}, + {"mechanism": "Channel-SE", "reduction_ratio": 8}, + {"mechanism": "Spatial-SE"}, + {"mechanism": "Dual-SE", "reduction_ratio": 32}, + ], +) +def test_ResNet_model_structure_parity(common_config, block_type, first_layers, attention): + """Verify that Keras and PyTorch models have matching layer counts and weight shapes.""" + tasks = common_config["tasks"] + kwargs = common_config["kwargs"].copy() + kwargs["init_layer"] = first_layers["init_layer"] + kwargs["init_max_pool"] = first_layers["init_max_pool"] + kwargs["residual_block_type"] = block_type + kwargs["architecture"] = [ + {"filters": 16, "blocks": 2}, + {"filters": 48, "blocks": 3}, + {"filters": 48, "blocks": 4}, + {"filters": 96, "blocks": 2}, + ] + kwargs["attention_mechanism"] = attention["mechanism"] + if "reduction_ratio" in attention: + kwargs["attention_reduction_ratio"] = attention["reduction_ratio"] + absolute_tolerance = { "basic": 0.1, "bottleneck": 0.05, } + for task in tasks: keras_wrapper = KerasResNet( input_shape=common_config["input_shape_keras"], - tasks=tasks, + tasks=[task], **kwargs ) torch_wrapper = PyTorchResNet( input_shape=common_config["input_shape_pytorch"], - tasks=tasks, + tasks=[task], **kwargs ) # Copy weights for ResNet - _copy_weights_resnet(keras_wrapper, torch_wrapper, tasks) + _copy_weights_resnet(keras_wrapper, torch_wrapper, [task]) x_keras = rng.standard_normal(size=(2, *common_config["input_shape_keras"])).astype(np.float32) x_torch = torch.from_numpy(np.transpose(x_keras, (0, 3, 1, 2))) @@ -472,14 +560,13 @@ def test_resnet_numerical_outputs_with_aligned_weights(self, common_config, bloc with torch.no_grad(): torch_preds = torch_wrapper.model(x_torch) - for task in tasks: - k_val = keras_preds[task].numpy() if hasattr(keras_preds[task], "numpy") else np.array(keras_preds[task]) - p_val = torch_preds[task].cpu().numpy() + k_val = keras_preds[task].numpy() if hasattr(keras_preds[task], "numpy") else np.array(keras_preds[task]) + p_val = torch_preds[task].cpu().numpy() - np.testing.assert_allclose( - k_val, - p_val, - atol=absolute_tolerance[block_type], - err_msg=f"Value divergence detected in ResNet ({block_type}) for task '{task}'", - ) + np.testing.assert_allclose( + k_val, + p_val, + atol=absolute_tolerance[block_type], + err_msg=f"Value divergence detected in ResNet ({block_type}) for task '{task}'", + ) ''' \ No newline at end of file From 3b672fd7770c1f93d43090aed24521f3fcd1a7cb Mon Sep 17 00:00:00 2001 From: Tjark Miener Date: Fri, 24 Jul 2026 15:55:25 +0200 Subject: [PATCH 110/119] added isolated attention layer tests --- ctlearn/core/tests/test_attention.py | 60 ++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 ctlearn/core/tests/test_attention.py diff --git a/ctlearn/core/tests/test_attention.py b/ctlearn/core/tests/test_attention.py new file mode 100644 index 00000000..22c4348b --- /dev/null +++ b/ctlearn/core/tests/test_attention.py @@ -0,0 +1,60 @@ +import numpy as np +import pytest +import torch +import keras +import tensorflow as tf + +from ctlearn.core.keras.model import ( + channel_squeeze_excite_block, + spatial_squeeze_excite_block, + dual_squeeze_excite_block, +) +from ctlearn.core.pytorch.attention import( + ChannelSqueezeExciteBlock, + SpatialSqueezeExciteBlock, + DualSqueezeExciteBlock, +) + +def build_keras_se_model(block_fn, input_shape, **kwargs): + """Wraps a Keras functional squeeze-excite block in a Keras Model.""" + inputs = keras.Input(shape=input_shape) + outputs = block_fn(inputs, name="se_block", **kwargs) + return keras.Model(inputs=inputs, outputs=outputs) + + +@pytest.mark.parametrize( + "k_fn, p_class, kwargs", + [ + (channel_squeeze_excite_block, ChannelSqueezeExciteBlock, {"ratio": 4}), + (spatial_squeeze_excite_block, SpatialSqueezeExciteBlock, {}), + (dual_squeeze_excite_block, DualSqueezeExciteBlock, {"ratio": 16}), + ], +) +@pytest.mark.parametrize( + "batch, height, width, channels", + [ + (1, 8, 8, 16), + (4, 32, 32, 64), + ], +) +def test_output_shape_parity(k_fn, p_class, kwargs, batch, height, width, channels): + """Verifies that output shapes match between Keras (BHWC) and PyTorch (BCHW).""" + # Keras Input: (Batch, H, W, C) + x_k = tf.random.normal((batch, height, width, channels)) + k_model = build_keras_se_model(k_fn, input_shape=(height, width, channels), **kwargs) + k_out = k_model(x_k) + + # PyTorch Input: (Batch, C, H, W) + x_p = torch.randn(batch, channels, height, width) + p_module = p_class(in_channels=channels, **kwargs) + p_module.eval() + with torch.no_grad(): + p_out = p_module(x_p) + + # Check output shape correspondence + assert k_out.shape == (batch, height, width, channels) + assert p_out.shape == (batch, channels, height, width) + + # Verify equivalent dimensions (transpose PyTorch output to BHWC) + p_out_bhwc = p_out.permute(0, 2, 3, 1) + assert k_out.shape == p_out_bhwc.shape From 269d6b1531cb58273331e72522f56d9f3e86ecbb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 27 Jul 2026 12:10:16 +0000 Subject: [PATCH 111/119] Change the loader and the base tain model to normlize the images by default --- ctlearn/core/data_loader/base_loader.py | 6 +++--- ctlearn/tools/__init__.py | 24 ++++++++++++++++++------ ctlearn/tools/train/base_train_model.py | 6 +++--- ctlearn/tools/train/pytorch/CTLearnPL.py | 13 ++++++++++--- 4 files changed, 34 insertions(+), 15 deletions(-) diff --git a/ctlearn/core/data_loader/base_loader.py b/ctlearn/core/data_loader/base_loader.py index 5e9e90a5..ad0b9366 100644 --- a/ctlearn/core/data_loader/base_loader.py +++ b/ctlearn/core/data_loader/base_loader.py @@ -165,11 +165,11 @@ def get_val(name, default): self.use_clean_dvr = get_val("use_clean_dvr", False) self.type_mu = get_val("type_mu", 0.0) - self.type_sigma = get_val("type_sigma", 1000.0) + self.type_sigma = get_val("type_sigma", 1.0) self.dir_mu = get_val("dir_mu", 0.0) - self.dir_sigma = get_val("dir_sigma", 1000.0) + self.dir_sigma = get_val("dir_sigma", 1.0) self.energy_mu = get_val("energy_mu", 0.0) - self.energy_sigma = get_val("energy_sigma", 1000.0) + self.energy_sigma = get_val("energy_sigma", 1.0) self.leakage_intensity_cutoff = get_val("leakage_intensity_cutoff", 0.2) self.intensity_cutoff = get_val("intensity_cutoff", 50.0) diff --git a/ctlearn/tools/__init__.py b/ctlearn/tools/__init__.py index 072afa53..639ed496 100644 --- a/ctlearn/tools/__init__.py +++ b/ctlearn/tools/__init__.py @@ -2,12 +2,24 @@ """ from .train_model import DLFrameWork -from .predict_LST1 import LST1PredictionTool -from .predict_model import MonoPredictCTLearnModel, StereoPredictCTLearnModel +try: + from .predict_LST1 import LST1PredictionTool +except ImportError: + pass +try: + from .predict_model import MonoPredictCTLearnModel, StereoPredictCTLearnModel +except ImportError: + pass __all__ = [ "DLFrameWork", - "LST1PredictionTool", - "MonoPredictCTLearnModel", - "StereoPredictCTLearnModel" -] \ No newline at end of file +] +try: + __all__.append("MonoPredictCTLearnModel") + __all__.append("StereoPredictCTLearnModel") +except NameError: + pass +try: + __all__.append("LST1PredictionTool") +except NameError: + pass \ No newline at end of file diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index 1ddc7687..c0d1ef0c 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -311,7 +311,7 @@ class TrainCTLearnModel(Tool): ).tag(config=True) type_sigma = Float( - default_value=1000.0, + default_value=1.0, help="Std dev for type channel normalization", ).tag(config=True) @@ -321,7 +321,7 @@ class TrainCTLearnModel(Tool): ).tag(config=True) dir_sigma = Float( - default_value=1000.0, + default_value=1.0, help="Std dev for direction channel normalization", ).tag(config=True) @@ -331,7 +331,7 @@ class TrainCTLearnModel(Tool): ).tag(config=True) energy_sigma = Float( - default_value=1000.0, + default_value=1.0, help="Std dev for energy channel normalization", ).tag(config=True) diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 873baf5e..62fea3d1 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -1534,9 +1534,15 @@ def on_validation_epoch_end(self): fig_hist, ax_hist = plt.subplots(figsize=(8, 6)) import numpy as np - min_val = min(min(self.val_energy_label_list), min(self.val_energy_pred_list)) - max_val = max(max(self.val_energy_label_list), max(self.val_energy_pred_list)) - shared_bins = np.linspace(min_val, max_val, 50) + # Use percentiles of true labels to avoid extreme predictions ruining the x-axis range + min_val = np.percentile(self.val_energy_label_list, 1) + max_val = np.percentile(self.val_energy_label_list, 99) + + # Energy typically spans multiple orders of magnitude, so log bins are more appropriate + min_val = max(1e-3, min_val) + if min_val >= max_val: + max_val = min_val * 10 + shared_bins = np.logspace(np.log10(min_val), np.log10(max_val), 50) ax_hist.hist( self.val_energy_label_list, @@ -1552,6 +1558,7 @@ def on_validation_epoch_end(self): label="Predictions", color="orange", ) + ax_hist.set_xscale("log") ax_hist.set_xlabel("Energy (TeV)") ax_hist.set_ylabel("Counts") ax_hist.set_title("Energy Distribution - Labels vs Predictions (Validation)") From 72bd37b4b6adf3c25a130abc0746f88e425c69d8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 27 Jul 2026 12:55:32 +0000 Subject: [PATCH 112/119] Update the test on the CI --- .github/workflows/python-package-conda.yml | 26 ++++++----- .../core/pytorch/visualization/tsne_test.py | 45 ++++++++++--------- ctlearn/core/tests/test_attention.py | 2 + ctlearn/core/tests/test_loader_pytorch.py | 1 + ctlearn/core/tests/test_models.py | 5 ++- .../predict/pytorch/predic_LST1_pytorch.py | 2 +- ctlearn/tools/train/pytorch/CTLearnPL.py | 2 +- .../train/pytorch/train_pytorch_model.py | 2 +- 8 files changed, 49 insertions(+), 36 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 00f01395..78142091 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -16,9 +16,11 @@ jobs: os: [ubuntu-22.04] python-version: ['3.12', '3.13', '3.14'] dl1dh-version: ['latest', 'nightly'] - tensorflow-version: ['latest', '2.16.*'] - torch-version: ['latest', '2.2.*'] + tensorflow-version: ['latest', '2.16.*', 'none'] + torch-version: ['latest', '2.2.*', 'none'] exclude: + - tensorflow-version: 'none' + torch-version: 'none' - python-version: '3.13' tensorflow-version: '2.16.*' - python-version: '3.14' @@ -64,15 +66,19 @@ jobs: else pip install dl1-data-handler fi - if [ "${{ matrix.tensorflow-version }}" = "latest" ]; then - pip install --upgrade tensorflow - else - pip install "tensorflow==${{ matrix.tensorflow-version }}" + if [ "${{ matrix.tensorflow-version }}" != "none" ]; then + if [ "${{ matrix.tensorflow-version }}" = "latest" ]; then + pip install --upgrade tensorflow + else + pip install "tensorflow==${{ matrix.tensorflow-version }}" + fi fi - if [ "${{ matrix.torch-version }}" = "latest" ]; then - pip install --upgrade torch torchvision - else - pip install "torch==${{ matrix.torch-version }}" torchvision + if [ "${{ matrix.torch-version }}" != "none" ]; then + if [ "${{ matrix.torch-version }}" = "latest" ]; then + pip install --upgrade torch torchvision + else + pip install "torch==${{ matrix.torch-version }}" torchvision + fi fi - name: Add MKL_THREADING_LAYER variable diff --git a/ctlearn/core/pytorch/visualization/tsne_test.py b/ctlearn/core/pytorch/visualization/tsne_test.py index e19cd176..1f75f3c5 100644 --- a/ctlearn/core/pytorch/visualization/tsne_test.py +++ b/ctlearn/core/pytorch/visualization/tsne_test.py @@ -3,25 +3,26 @@ from sklearn import datasets from sklearn.manifold import TSNE -# Load the Iris dataset -iris = datasets.load_iris() -X = iris.data -y = iris.target - -# Create a TSNE instance with desired parameters -tsne = TSNE(n_components=2, random_state=42) - -# Perform TSNE -X_embedded = tsne.fit_transform(X) - -# Plot the embedded points with matplotlib -plt.figure(figsize=(8, 6)) -colors = ['red', 'blue', 'green'] -for i, color in enumerate(colors): - plt.scatter(X_embedded[y == i, 0], X_embedded[y == i, 1], c=color, label=iris.target_names[i]) - -plt.legend(loc='best') -plt.title('TSNE visualization of the Iris dataset') -plt.xlabel('TSNE 1') -plt.ylabel('TSNE 2') -plt.show() \ No newline at end of file +if __name__ == "__main__": + # Load the Iris dataset + iris = datasets.load_iris() + X = iris.data + y = iris.target + + # Create a TSNE instance with desired parameters + tsne = TSNE(n_components=2, random_state=42) + + # Perform TSNE + X_embedded = tsne.fit_transform(X) + + # Plot the embedded points with matplotlib + plt.figure(figsize=(8, 6)) + colors = ['red', 'blue', 'green'] + for i, color in enumerate(colors): + plt.scatter(X_embedded[y == i, 0], X_embedded[y == i, 1], c=color, label=iris.target_names[i]) + + plt.legend(loc='best') + plt.title('TSNE visualization of the Iris dataset') + plt.xlabel('TSNE 1') + plt.ylabel('TSNE 2') + plt.show() \ No newline at end of file diff --git a/ctlearn/core/tests/test_attention.py b/ctlearn/core/tests/test_attention.py index 22c4348b..072a9279 100644 --- a/ctlearn/core/tests/test_attention.py +++ b/ctlearn/core/tests/test_attention.py @@ -1,3 +1,5 @@ +import pytest +pytest.importorskip("keras") import numpy as np import pytest import torch diff --git a/ctlearn/core/tests/test_loader_pytorch.py b/ctlearn/core/tests/test_loader_pytorch.py index e96bef40..f61937de 100644 --- a/ctlearn/core/tests/test_loader_pytorch.py +++ b/ctlearn/core/tests/test_loader_pytorch.py @@ -1,4 +1,5 @@ import pytest +pytest.importorskip("dl1_data_handler") torch = pytest.importorskip("torch") from traitlets.config.loader import Config diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index 3cf56a1b..78f494d8 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -1,3 +1,5 @@ +import pytest +pytest.importorskip("keras") import re import keras import numpy as np @@ -6,7 +8,8 @@ import torch.nn as nn from ctlearn.core.keras.model import KerasResNet, KerasSingleCNN -from ctlearn.core.pytorch.model import PyTorchResNet, PyTorchSingleCNN +from ctlearn.core.pytorch.model_collection import PyTorchResNet +from ctlearn.core.pytorch.model import PyTorchSingleCNN rng = np.random.default_rng(42) diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 34fcf542..0b371e4f 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -254,7 +254,7 @@ def load_pytorch_model(self): torch.nn.Module: The last loaded model (for compatibility) """ model = None - from ctlearn.core.pytorch.model import CTLearnPyTorchModel + from ctlearn.core.pytorch.model_collection import CTLearnPyTorchModel def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): model_name = model_info.get("model_name", "") diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 62fea3d1..54ebcd0f 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -209,7 +209,7 @@ def __init__( ) - self.criterion_energy_value = torch.nn.L1Loss(reduction="mean") + self.criterion_energy_value = torch.nn.L1Loss(reduction="sum") self.criterion_direction = torch.nn.SmoothL1Loss() # nn.MSELoss() self.criterion_magnitud = torch.nn.L1Loss(reduction="mean") diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 412042c3..901254b7 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -440,7 +440,7 @@ def start(self): # Select the model and precision # ------------------------------------------------------------------------------ - from ctlearn.core.pytorch.model import CTLearnPyTorchModel + from ctlearn.core.pytorch.model_collection import CTLearnPyTorchModel def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): model_name = model_info.get("model_name", "") From aa5585e7cd80e68f5f47c7d9aece7427555268e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 27 Jul 2026 14:39:00 +0000 Subject: [PATCH 113/119] Fix the CI; Try to change the model import, remove the model_collection import --- .github/workflows/python-package-conda.yml | 26 +++++++------------ .../tools/train/keras/train_keras_model.py | 4 +-- 2 files changed, 12 insertions(+), 18 deletions(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 78142091..00f01395 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -16,11 +16,9 @@ jobs: os: [ubuntu-22.04] python-version: ['3.12', '3.13', '3.14'] dl1dh-version: ['latest', 'nightly'] - tensorflow-version: ['latest', '2.16.*', 'none'] - torch-version: ['latest', '2.2.*', 'none'] + tensorflow-version: ['latest', '2.16.*'] + torch-version: ['latest', '2.2.*'] exclude: - - tensorflow-version: 'none' - torch-version: 'none' - python-version: '3.13' tensorflow-version: '2.16.*' - python-version: '3.14' @@ -66,19 +64,15 @@ jobs: else pip install dl1-data-handler fi - if [ "${{ matrix.tensorflow-version }}" != "none" ]; then - if [ "${{ matrix.tensorflow-version }}" = "latest" ]; then - pip install --upgrade tensorflow - else - pip install "tensorflow==${{ matrix.tensorflow-version }}" - fi + if [ "${{ matrix.tensorflow-version }}" = "latest" ]; then + pip install --upgrade tensorflow + else + pip install "tensorflow==${{ matrix.tensorflow-version }}" fi - if [ "${{ matrix.torch-version }}" != "none" ]; then - if [ "${{ matrix.torch-version }}" = "latest" ]; then - pip install --upgrade torch torchvision - else - pip install "torch==${{ matrix.torch-version }}" torchvision - fi + if [ "${{ matrix.torch-version }}" = "latest" ]; then + pip install --upgrade torch torchvision + else + pip install "torch==${{ matrix.torch-version }}" torchvision fi - name: Add MKL_THREADING_LAYER variable diff --git a/ctlearn/tools/train/keras/train_keras_model.py b/ctlearn/tools/train/keras/train_keras_model.py index 4828c6a4..af09b2fe 100644 --- a/ctlearn/tools/train/keras/train_keras_model.py +++ b/ctlearn/tools/train/keras/train_keras_model.py @@ -21,7 +21,7 @@ ) from ctlearn.core.data_loader.loader import DLDataLoader from ctlearn.tools.train.base_train_model import TrainCTLearnModel -from ctlearn.core.keras.model import CTLearnModel +from ctlearn.core.model import CTLearnModel from ctlearn.utils import validate_trait_dict try: @@ -98,7 +98,7 @@ class TrainKerasModel(TrainCTLearnModel): """ model_type = ComponentName( - CTLearnModel, default_value="ResNet" + CTLearnModel, default_value="KerasResNet" ).tag(config=True) save_best_validation_only = Bool( From 50c861bb1ff10ad2ce92b629b3791e473428664d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Mon, 27 Jul 2026 14:39:17 +0000 Subject: [PATCH 114/119] Fix the test_model file --- ctlearn/core/tests/test_models.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/ctlearn/core/tests/test_models.py b/ctlearn/core/tests/test_models.py index 78f494d8..6eaa1962 100644 --- a/ctlearn/core/tests/test_models.py +++ b/ctlearn/core/tests/test_models.py @@ -8,8 +8,7 @@ import torch.nn as nn from ctlearn.core.keras.model import KerasResNet, KerasSingleCNN -from ctlearn.core.pytorch.model_collection import PyTorchResNet -from ctlearn.core.pytorch.model import PyTorchSingleCNN +from ctlearn.core.pytorch.model import PyTorchResNet, PyTorchSingleCNN rng = np.random.default_rng(42) From 1b9ae42cb2aefe2f876bf53dcbb7a09114fcec56 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 28 Jul 2026 12:39:03 +0000 Subject: [PATCH 115/119] Remove the log from the ctlearn and leaved in the dl1-data-handeler and move all the warnings to the debug options --- ctlearn/__init__.py | 15 ++ ctlearn/core/data_loader/base_loader.py | 8 - ctlearn/core/data_loader/keras_loader.py | 3 - ctlearn/core/data_loader/pytorch_loader.py | 3 - ctlearn/core/pytorch/model.py | 21 ++- ctlearn/core/pytorch/net_utils.py | 16 +- ctlearn/core/pytorch/utils/utils.py | 8 +- .../core/pytorch/visualization/vis_utils.py | 19 +- ctlearn/tools/__init__.py | 12 ++ .../predict/pytorch/predic_LST1_pytorch.py | 8 + .../predict/pytorch/predic_model_pytorch.py | 6 +- ctlearn/tools/train/base_train_model.py | 2 + ctlearn/tools/train/pytorch/CTLearnPL.py | 4 +- .../train/pytorch/train_pytorch_model.py | 162 ++++++------------ ctlearn/tools/train_model.py | 16 +- 15 files changed, 164 insertions(+), 139 deletions(-) diff --git a/ctlearn/__init__.py b/ctlearn/__init__.py index d3044278..a6291244 100644 --- a/ctlearn/__init__.py +++ b/ctlearn/__init__.py @@ -1,3 +1,18 @@ +import sys +import os +import warnings + +# Suppress noisy logs globally before any other imports occur +is_debug = '--debug' in sys.argv or any(arg.startswith('--log-level=DEBUG') for arg in sys.argv) +if not is_debug: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' + os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' + os.environ['NCCL_DEBUG'] = 'WARN' + warnings.filterwarnings("ignore", category=UserWarning) + warnings.filterwarnings("ignore", ".*NoneDefaultNotAllowedWarning.*") + warnings.filterwarnings("ignore", ".*MergeConflictWarning.*") + warnings.filterwarnings("ignore", ".*'ctlearn.tools.train_model' found in sys.modules.*") + from ._version import __version__ import importlib.util diff --git a/ctlearn/core/data_loader/base_loader.py b/ctlearn/core/data_loader/base_loader.py index ad0b9366..a850f6f2 100644 --- a/ctlearn/core/data_loader/base_loader.py +++ b/ctlearn/core/data_loader/base_loader.py @@ -225,14 +225,6 @@ def normalize_data(self, image, peak_time, task): return image, peak_time - def apply_log_scaling_to_channels(self, image, peak_time): - if self.apply_log_scaling[0]: - image = image.astype(np.float32) - image = np.log10(image + 1.0) - if self.apply_log_scaling[1]: - peak_time = peak_time.astype(np.float32) - peak_time = np.log10(peak_time + 1.0) - return image, peak_time def apply_augmentation(self, image, peak_time, task): for id_batch in range(image.shape[0]): diff --git a/ctlearn/core/data_loader/keras_loader.py b/ctlearn/core/data_loader/keras_loader.py index 65b8def3..b57c89a2 100644 --- a/ctlearn/core/data_loader/keras_loader.py +++ b/ctlearn/core/data_loader/keras_loader.py @@ -188,7 +188,6 @@ def _get_mono_item(self, batch): active_task = self.tasks[0] if self.tasks else None image, peak_time = self.clean_data(image, peak_time) - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) if self.use_augmentation: image, peak_time = self.apply_augmentation(image, peak_time, active_task) @@ -410,7 +409,6 @@ def _get_stereo_item(self, batch): peak_time = features_arr[..., 1] image, peak_time = self.clean_data(image, peak_time) - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) image, peak_time = self.normalize_data(image, peak_time, active_task) features["input"] = np.stack([image, peak_time], axis=-1) @@ -419,7 +417,6 @@ def _get_stereo_item(self, batch): peak_time = features_arr[..., 1::2] image, peak_time = self.clean_data(image, peak_time) - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) image, peak_time = self.normalize_data(image, peak_time, active_task) # Re-stack alternating channels diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index 38550b94..bb50018c 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -405,7 +405,6 @@ def _get_mono_item(self, batch): active_task = self.tasks[0] if self.tasks else None image, peak_time = self.clean_data(image, peak_time) - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) features_out = {} features_out["image"] = image @@ -683,7 +682,6 @@ def _get_stereo_item(self, batch): peak_time = features_arr[..., 1] image, peak_time = self.clean_data(image, peak_time) - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) image, peak_time = self.normalize_data(image, peak_time, active_task) image = np.transpose(image, (0, 1, 4, 2, 3)) if len(image.shape) == 5 else np.expand_dims(image, axis=2) @@ -693,7 +691,6 @@ def _get_stereo_item(self, batch): peak_time = features_arr[..., 1::2] image, peak_time = self.clean_data(image, peak_time) - image, peak_time = self.apply_log_scaling_to_channels(image, peak_time) image, peak_time = self.normalize_data(image, peak_time, active_task) image = np.transpose(image, (0, 3, 1, 2)) diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index 81bc6ae7..ba23c82f 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -51,15 +51,22 @@ def forward(self, x): if x.dim() > 2: x = torch.flatten(x, start_dim=1) - logits = {} + classification = None + energy = None + direction = None + for original_task, internal_key in self._task_mapping.items(): head = self.heads[internal_key] out = head(x) - logits[original_task] = F.softmax(out, dim=-1) if original_task == "type" else out - if self.single_output_task: - return logits[self.single_output_task] - return logits + if original_task == "type": + classification = F.softmax(out, dim=-1) + elif original_task == "energy": + energy = out + elif original_task in ["direction", "skydirection", "cameradirection"]: + direction = out + + return classification, energy, direction def build_fully_connect_pytorch_head(in_features, layers, activation_function, tasks): @@ -124,7 +131,7 @@ def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): def _build_backbone(self, input_shape): # input_shape format: (channels, height, width) - in_channels = input_shape[0] + in_channels = input_shape[-1] modules = [] if self.batchnorm: @@ -306,7 +313,7 @@ def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): self.model = FullModelPipeline(self.backbone_model, self.logits_head) def _build_backbone(self, input_shape): - in_channels = input_shape[0] if isinstance(input_shape, (list, tuple)) else input_shape[-1] + in_channels = input_shape[-1] modules = [] # Initial Zero Padding diff --git a/ctlearn/core/pytorch/net_utils.py b/ctlearn/core/pytorch/net_utils.py index 3ea2fd00..ff0ce0f4 100644 --- a/ctlearn/core/pytorch/net_utils.py +++ b/ctlearn/core/pytorch/net_utils.py @@ -18,10 +18,6 @@ import os.path import pickle from matplotlib import pyplot as plt -from skimage.filters import gabor_kernel -from skimage.transform import resize -import onnx -from onnxsim import simplify import warnings from ctlearn.core.ctlearn_enum import Task, Mode @@ -290,6 +286,12 @@ def GaborKernels(size=7, showPlots=False): - Feature extraction for shower image analysis - Edge and orientation detection in Cherenkov images """ + try: + from skimage.filters import gabor_kernel + from skimage.transform import resize + except ImportError: + raise ImportError("skimage is required for GaborKernels. Install it via 'pip install scikit-image'") + # prepare filter bank kernels kernels = [] # Iterate over 4 orientations @@ -586,6 +588,12 @@ def exportOnnx(model, dummy_input, onnx_name, input_names, output_names): dynamic_axes=dynamic_axes, ) + try: + import onnx + from onnxsim import simplify + except ImportError: + raise ImportError("onnx and onnxsim are required for exportOnnx. Install them via 'pip install onnx onnxsim'") + # Load the exported ONNX model model = onnx.load(onnx_name + ".onnx") diff --git a/ctlearn/core/pytorch/utils/utils.py b/ctlearn/core/pytorch/utils/utils.py index e8942e40..04a246bd 100644 --- a/ctlearn/core/pytorch/utils/utils.py +++ b/ctlearn/core/pytorch/utils/utils.py @@ -16,7 +16,13 @@ import torch from astropy.coordinates import SkyCoord, AltAz import astropy.units as u -from ctapipe_io_lst.constants import LST1_LOCATION +try: + from ctapipe_io_lst.constants import LST1_LOCATION +except ImportError: + from astropy.coordinates import EarthLocation + # Fallback coordinates for LST-1 + LST1_LOCATION = EarthLocation(lon=-17.89139 * u.deg, lat=28.76139 * u.deg, height=2184 * u.m) + from ctlearn.core.ctlearn_enum import Task, Mode import time as time_ from ctapipe.io import read_table, write_table diff --git a/ctlearn/core/pytorch/visualization/vis_utils.py b/ctlearn/core/pytorch/visualization/vis_utils.py index 6cb287a9..9e1b0c80 100644 --- a/ctlearn/core/pytorch/visualization/vis_utils.py +++ b/ctlearn/core/pytorch/visualization/vis_utils.py @@ -4,10 +4,16 @@ import cv2 from io import BytesIO import os -import seaborn as sns +try: + import seaborn as sns +except ImportError: + sns = None import pandas as pd import astropy.units as u -import ctaplot +try: + import ctaplot +except ImportError: + ctaplot = None import torch # ---------------------------------------------------------------------------------------------------------- def plot_energy_resolution_error(val_energy_pred_list,val_energy_label_list,val_hillas_intensity_list): @@ -17,6 +23,9 @@ def plot_energy_resolution_error(val_energy_pred_list,val_energy_label_list,val_ 'energy_true': list(val_energy_label_list), 'hillas_intensity': list(val_hillas_intensity_list) }) + if ctaplot is None: + raise ImportError("ctaplot is required for plot_energy_resolution_error. Install it via 'pip install ctaplot'") + cut_off = 50 filtered_data = data[data['hillas_intensity'] > cut_off] true_energy = u.Quantity(filtered_data['energy_true'], u.TeV) @@ -48,6 +57,9 @@ def plot_direction_resolution_error(val_alt_pred_list,val_az_pred_list, val_alt_ 'energy_true': list(val_energy_label_list), 'hillas_intensity': list(val_hillas_intensity_list) }) + if ctaplot is None: + raise ImportError("ctaplot is required for plot_direction_resolution_error. Install it via 'pip install ctaplot'") + cut_off = 50 filtered_data = data[data['hillas_intensity'] > cut_off] true_energy = u.Quantity(filtered_data['energy_true'], u.TeV) @@ -78,6 +90,9 @@ def plot_confusion_matrix(cm, classes, cm_file_name, save_folder): if isinstance(cm, torch.Tensor): cm = cm.cpu().numpy() # Convert to NumPy if it's a tensor + if sns is None: + raise ImportError("seaborn is required for plot_confusion_matrix. Install it via 'pip install seaborn'") + plt.figure(figsize=(10, 7)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=classes, yticklabels=classes) diff --git a/ctlearn/tools/__init__.py b/ctlearn/tools/__init__.py index 639ed496..c6d237fc 100644 --- a/ctlearn/tools/__init__.py +++ b/ctlearn/tools/__init__.py @@ -1,6 +1,18 @@ """ctlearn command line tools. """ +import sys +import os +import warnings + +is_debug = '--debug' in sys.argv or any(arg.startswith('--log-level=DEBUG') for arg in sys.argv) +if not is_debug: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' + os.environ['NCCL_DEBUG'] = 'WARN' + warnings.filterwarnings("ignore", ".*NoneDefaultNotAllowedWarning.*") + warnings.filterwarnings("ignore", ".*MergeConflictWarning.*") + warnings.filterwarnings("ignore", ".*'ctlearn.tools.train_model' found in sys.modules.*") + from .train_model import DLFrameWork try: from .predict_LST1 import LST1PredictionTool diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 0b371e4f..48adbd42 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -63,6 +63,14 @@ def predictions(self): - energy_fvs: Energy estimation feature vectors - direction_fvs: Direction reconstruction feature vectors """ + + # Update channels based on log scaling config + if "normalization" in self.parameters and "apply_log_scaling" in self.parameters["normalization"]: + new_channels = list(self.channels) + if self.parameters["normalization"]["apply_log_scaling"][0] and not new_channels[0].startswith("log_"): + new_channels[0] = "log_" + new_channels[0] + self.channels = new_channels + # Optimize batch size if requested if self.optim_batch_size: batch_size_found = False diff --git a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py index e2ca8bad..0899630f 100644 --- a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py @@ -48,8 +48,10 @@ def predict_with_model_pytorch(self, task): # Create data loader for the specified task # The DLDataLoader is initialized separately for each task to ensure robustness - self.dl1dh_reader.channels = ["cleaned_image", "cleaned_peak_time"] - + channels = ["cleaned_image", "cleaned_peak_time"] + if self.parameters["normalization"]["apply_log_scaling"][0]: + channels[0] = "log_" + channels[0] + self.dl1dh_reader.channels = channels data_loader = DLDataLoader.create( framework="pytorch", DLDataReader=self.dl1dh_reader, diff --git a/ctlearn/tools/train/base_train_model.py b/ctlearn/tools/train/base_train_model.py index c0d1ef0c..a02b34d0 100644 --- a/ctlearn/tools/train/base_train_model.py +++ b/ctlearn/tools/train/base_train_model.py @@ -528,6 +528,8 @@ def setup(self): if self.dl1dh_reader_type == "DLImageReader": self.channels = ["cleaned_image", "cleaned_peak_time"] + if self.apply_log_scaling[0]: + self.channels[0] = "log_" + self.channels[0] if "channels" not in self.config.get("DLImageReader", {}): self.config.DLImageReader.channels = self.channels diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 54ebcd0f..9e278492 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -249,13 +249,13 @@ def __init__( self.class_train_accuracy = Accuracy( task="multiclass", - num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], + num_classes=2, dist_sync_on_step=True # GPUs Sync ) self.class_val_accuracy = Accuracy( task="multiclass", - num_classes=parameters["model"]["model_type"]["parameters"]["num_outputs"], + num_classes=2, dist_sync_on_step=True # GPUs Sync ) diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 901254b7..1337b3c7 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -1,12 +1,34 @@ -from ctapipe.core.traits import Path, Bool, Unicode +from ctapipe.core.traits import Path, Bool, Unicode, ComponentName from torch.utils.data import DataLoader from ctlearn.tools.train.pytorch.CTLearnPL import CTLearnTrainer, CTLearnPL +from ctlearn.core.model import CTLearnModel +import ctlearn.core.pytorch.model +import sys import os -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppress TensorFlow logging + +is_debug = '--debug' in sys.argv or any(arg.startswith('--log-level=DEBUG') for arg in sys.argv) + +if not is_debug: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppress TensorFlow logging + os.environ['NCCL_DEBUG'] = 'WARN' # Suppress verbose NCCL networking logs + +import warnings +warnings.filterwarnings("ignore", ".*AccumulateGrad node's stream does not match.*") +warnings.filterwarnings("ignore", ".*torch.distributed.nn.functional.all_gather is deprecated.*") +warnings.filterwarnings("ignore", ".*does not have many workers which may be a bottleneck.*") +warnings.filterwarnings("ignore", ".*isinstance.treespec, LeafSpec.*") +warnings.filterwarnings("ignore", ".*This axis already has a converter set and is updating.*") + +if not is_debug: + # Silence noisy external library warnings + warnings.filterwarnings("ignore", ".*NoneDefaultNotAllowedWarning.*") + warnings.filterwarnings("ignore", ".*MergeConflictWarning.*") + warnings.filterwarnings("ignore", ".*'ctlearn.tools.train_model' found in sys.modules.*") try: import torch - + if hasattr(torch.autograd.graph, "set_warn_on_accumulate_grad_stream_mismatch"): + torch.autograd.graph.set_warn_on_accumulate_grad_stream_mismatch(False) except ImportError: raise ImportError("pytorch is not installed in your environment!") @@ -110,17 +132,16 @@ class TrainPyTorchModel(TrainCTLearnModel): help="Disable PyTorch Lightning progress bar.", ).tag(config=True) - model_name = Unicode( - default_value=None, - allow_none=True, - help="Model name to override the default model for the reco task.", + model_type = ComponentName( + CTLearnModel, default_value="PyTorchResNet" ).tag(config=True) aliases = { **TrainCTLearnModel.aliases, "config_file": "TrainPyTorchModel.config_file", "disable_progress_bar": "TrainPyTorchModel.disable_progress_bar", - "model-name": "TrainPyTorchModel.model_name", + "model-type": "TrainPyTorchModel.model_type", + "model-name": "TrainPyTorchModel.model_type", } def __init__(self, **kwargs): @@ -130,7 +151,6 @@ def __init__(self, **kwargs): os.environ["NCCL_IB_DISABLE"] = "1" os.environ["NCCL_DEBUG"] = "WARN" os.environ["TORCH_NCCL_ASYNC_ERROR_HANDLING"] = "1" - os.environ["NCCL_DEBUG"] = "INFO" torch.set_float32_matmul_precision('medium') super().__init__(**kwargs) @@ -204,45 +224,10 @@ def get_conf_val(key1, key2, trait_name, default_val): self.leakage_intensity_cutoff = get_conf_val("cut-off", "leakage_intensity", "leakage_intensity_cutoff", self.leakage_intensity_cutoff) self.intensity_cutoff = get_conf_val("cut-off", "intensity", "intensity_cutoff", self.intensity_cutoff) - self.pytorch_model_configs = legacy_params.get("model", {}) self.hyp_configs = legacy_params.get("hyp", {}) else: self.log.info("No legacy config file provided. Using standard Traitlets configuration.") self.device_str = self.device - self.pytorch_model_configs = { - "model_type": { - "model_name": "DoubleBBEfficientNet", - "parameters": { - "model_variant": "efficientnet-b3", - "task": "type", - "num_outputs": 2, - "device_str": self.device_str, - "energy_bins": None, - } - }, - "model_energy": { - "model_name": "ThinResNet", - "parameters": { - "task": "energy", - "num_inputs": 1, - "num_outputs": 1, - "num_blocks": [3, 4, 6, 3], - "dropout": 0.1, - "use_bn": False, - } - }, - "model_direction": { - "model_name": "ThinResNet_DBB", - "parameters": { - "task": "direction", - "num_inputs": 1, - "num_outputs": 3, - "num_blocks": [3, 4, 6, 3], - "dropout": 0.1, - "use_bn": False, - } - } - } self.hyp_configs = { "epochs": self.n_epochs, "batches": self.batch_size, @@ -260,24 +245,6 @@ def get_conf_val(key1, key2, trait_name, default_val): "save_k": self.save_k_checkpoints, } - # Override model name if passed through command line - if self.model_name is not None: - for task in self.tasks: - task_key = f"model_{task.name}" - if task_key not in self.pytorch_model_configs: - self.pytorch_model_configs[task_key] = {"parameters": {}} - self.pytorch_model_configs[task_key]["model_name"] = self.model_name - - # Clear out parameters to avoid passing the old model's params to the new one - if "parameters" in self.pytorch_model_configs[task_key]: - old_params = self.pytorch_model_configs[task_key]["parameters"] - # Keep basic essential params if they exist - new_params = {} - for key in ["task", "num_inputs", "num_outputs", "device_str"]: - if key in old_params: - new_params[key] = old_params[key] - self.pytorch_model_configs[task_key]["parameters"] = new_params - self.save_k = self.save_k_checkpoints self.parameters = { @@ -306,7 +273,7 @@ def get_conf_val(key1, key2, trait_name, default_val): "leakage_intensity": self.leakage_intensity_cutoff, "intensity": self.intensity_cutoff, }, - "model": self.pytorch_model_configs, + "model": self.model_type, "hyp": self.hyp_configs, "augmentation": { "use_augmentation": self.use_augmentation, @@ -439,33 +406,21 @@ def start(self): # ------------------------------------------------------------------------------ # Select the model and precision # ------------------------------------------------------------------------------ - - from ctlearn.core.pytorch.model_collection import CTLearnPyTorchModel - - def load_pytorch_model_net(model_info, task_name, num_inputs, num_outputs): - model_name = model_info.get("model_name", "") - try: - component_cls = CTLearnPyTorchModel.non_abstract_subclasses().get(model_name) - if component_cls is not None: - params = model_info.get("parameters", {}).copy() - params.pop("task", None) - params.pop("num_inputs", None) - params.pop("num_outputs", None) - # Ensure device_str is set - params["parent"] = self - component = component_cls( - task=task_name, - num_inputs=num_inputs, - num_outputs=num_outputs, - **params - ) - return component.model - except Exception as e: - self.log.warning(f"Failed to load model {model_name} as Component: {e}. Falling back to create_model.") - return create_model(model_info) - import torch.nn as nn import torch + torch.backends.cudnn.enabled = False + + if task == Task.type: + precision = self.parameters["arch"]["precision_type"] + elif task == Task.energy: + precision = self.parameters["arch"]["precision_energy"] + elif task == Task.cameradirection or task == Task.skydirection: + precision = self.parameters["arch"]["precision_direction"] + else: + raise ValueError( + f"task:{task.name} is not supported. Task must be type, direction or energy" + ) + class ONNXModelWrapper(nn.Module): def __init__(self, onnx_model_net, active_task, onnx_input_shape): super().__init__() @@ -514,8 +469,6 @@ def forward(self, x, y=None): else: return None, None, val - num_inputs = 1 ## Change thiss!!!! - if self.load_onnx_model: self.log.info(f"Loading ONNX model from {self.load_onnx_model} for training...") self.log.warning( @@ -532,27 +485,24 @@ def forward(self, x, y=None): onnx_model = ConvertModel(onnx_proto) onnx_input_shape = [dim.dim_value for dim in onnx_proto.graph.input[0].type.tensor_type.shape.dim] model_net = ONNXModelWrapper(onnx_model, task, onnx_input_shape) - precision = self.parameters["arch"].get(f"precision_{task.name.lower()}", "32-true") except Exception as e: self.log.error(f"Failed to load ONNX model: {e}") raise e else: - if task == Task.type: - precision = self.parameters["arch"]["precision_type"] - model_net = load_pytorch_model_net(self.parameters["model"]["model_type"], "type", num_inputs, 2) - - elif task == Task.energy: - precision = self.parameters["arch"]["precision_energy"] - model_net = load_pytorch_model_net(self.parameters["model"]["model_energy"], "energy", num_inputs, 1) + self.log.info("Setting up the PyTorch model.") + num_inputs = 1 + if isinstance(self.parameters.get("model"), dict): + num_inputs = self.parameters["model"].get(f"model_{task.name}", {}).get("parameters", {}).get("num_inputs", 1) - elif task == Task.cameradirection or task == Task.skydirection: - precision = self.parameters["arch"]["precision_direction"] - model_net = load_pytorch_model_net(self.parameters["model"]["model_direction"], "direction", num_inputs, 3) - - else: - raise ValueError( - f"task:{task.name} is not supported. Task must be type, direction or energy" - ) + model_input_shape = list(self.train_dataset.input_shape) + model_input_shape[-1] = num_inputs + + model_net = CTLearnModel.from_name( + self.model_type, + input_shape=tuple(model_input_shape), + tasks=[task.name], + parent=self, + ).model # if hasattr(model_net, 'T'): # self.training_loader.set_T(model_net.T) diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index 8cf1484b..eae243ac 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -1,7 +1,21 @@ +import sys +import os +import warnings + +is_debug = '--debug' in sys.argv or any(arg.startswith('--log-level=DEBUG') for arg in sys.argv) +if not is_debug: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' + os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' + os.environ['NCCL_DEBUG'] = 'WARN' + warnings.filterwarnings("ignore", category=UserWarning) + warnings.filterwarnings("ignore", ".*NoneDefaultNotAllowedWarning.*") + warnings.filterwarnings("ignore", ".*MergeConflictWarning.*") + warnings.filterwarnings("ignore", ".*'ctlearn.tools.train_model' found in sys.modules.*") + import atexit import pandas as pd import numpy as np -import sys + from ctapipe.core import Tool from ctapipe.core.traits import CaselessStrEnum, Dict from ctlearn.core.ctlearn_enum import FrameworkType From 9d17794b55a531a3fa41ff14461ce0d514e7cc9c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 28 Jul 2026 14:41:39 +0000 Subject: [PATCH 116/119] Update all the files for the data to provida only one images as imput, with 2 channels or 1 depend of the input (peak time or not) --- ctlearn/conftest.py | 38 +++++++++--------- ctlearn/core/data_loader/keras_loader.py | 27 +++++++------ ctlearn/core/data_loader/pytorch_loader.py | 13 +++++-- ctlearn/core/pytorch/model.py | 4 +- .../pytorch/nets/models/DBBDanet/DBBDanet.py | 6 ++- .../models/DBBNoPropDTReg/DBBNoPropDTReg.py | 6 ++- .../nets/models/DBBRegNet/DBBRegNet.py | 6 ++- .../DoubleBBEfficientNet.py | 6 ++- .../models/NoPropDTRegDBB/NoPropDTRegDBB.py | 6 ++- .../nets/models/ResNeXtDBB/ResNeXtDBB.py | 6 ++- .../nets/models/ThinResNet/ThinResNet.py | 3 -- .../models/ThinResNet_DBB/ThinResNet_DBB.py | 6 ++- .../models/Transformers/TransformerDuo.py | 6 ++- .../Transformers/TransformerDuoSimple.py | 6 ++- .../predict/pytorch/predic_LST1_pytorch.py | 39 ++++++------------- .../predict/pytorch/predic_model_pytorch.py | 15 ++----- ctlearn/tools/predict_LST1.py | 2 +- ctlearn/tools/predict_model.py | 2 +- ctlearn/tools/train/pytorch/CTLearnPL.py | 21 ++-------- .../train/pytorch/train_pytorch_model.py | 7 +++- ctlearn/tools/train_model.py | 39 ++++++++----------- 21 files changed, 134 insertions(+), 130 deletions(-) diff --git a/ctlearn/conftest.py b/ctlearn/conftest.py index 822f7c81..fa5e8733 100644 --- a/ctlearn/conftest.py +++ b/ctlearn/conftest.py @@ -271,11 +271,11 @@ def ctlearn_trained_r1_mono_models(r1_gamma_file, r1_proton_file, tmp_path_facto # Run training assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 - ctlearn_trained_r1_mono_models[f"{telescope_type}_{reco_task}"] = ( - output_dir / "ctlearn_model.keras" - ) - # Check that the trained model exists - assert ctlearn_trained_r1_mono_models[f"{telescope_type}_{reco_task}"].exists() + ctlearn_trained_r1_mono_models[f"{telescope_type}_{reco_task}"] = ( + output_dir / "ctlearn_model.keras" + ) + # Check that the trained model exists + assert ctlearn_trained_r1_mono_models[f"{telescope_type}_{reco_task}"].exists() return ctlearn_trained_r1_mono_models @@ -356,13 +356,13 @@ def ctlearn_trained_dl1_mono_models(dl1_gamma_file, dl1_proton_file, tmp_path_fa run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 ) - ctlearn_trained_dl1_mono_models[f"{telescope_type}_{reco_task}"] = ( - output_dir / "ctlearn_model.keras" - ) - # Check that the trained model exists - assert ctlearn_trained_dl1_mono_models[ - f"{telescope_type}_{reco_task}" - ].exists() + ctlearn_trained_dl1_mono_models[f"{telescope_type}_{reco_task}"] = ( + output_dir / "ctlearn_model.keras" + ) + # Check that the trained model exists + assert ctlearn_trained_dl1_mono_models[ + f"{telescope_type}_{reco_task}" + ].exists() return ctlearn_trained_dl1_mono_models @@ -435,11 +435,11 @@ def ctlearn_trained_dl1_stereo_models( # Run training assert run_tool(DLFrameWork(config=config), argv=argv, cwd=tmp_path) == 0 - ctlearn_trained_dl1_stereo_models[f"{telescope_type}_{reco_task}"] = ( - output_dir / "ctlearn_model.keras" - ) - # Check that the trained model exists - assert ctlearn_trained_dl1_stereo_models[ - f"{telescope_type}_{reco_task}" - ].exists() + ctlearn_trained_dl1_stereo_models[f"{telescope_type}_{reco_task}"] = ( + output_dir / "ctlearn_model.keras" + ) + # Check that the trained model exists + assert ctlearn_trained_dl1_stereo_models[ + f"{telescope_type}_{reco_task}" + ].exists() return ctlearn_trained_dl1_stereo_models diff --git a/ctlearn/core/data_loader/keras_loader.py b/ctlearn/core/data_loader/keras_loader.py index b57c89a2..32f55339 100644 --- a/ctlearn/core/data_loader/keras_loader.py +++ b/ctlearn/core/data_loader/keras_loader.py @@ -182,18 +182,23 @@ def _get_mono_item(self, batch): # Retrieve telescope images and store in features dictionary features = {"input": batch["features"].data} - image = features["input"][..., 0:1] - peak_time = features["input"][..., 1:2] - - active_task = self.tasks[0] if self.tasks else None - - image, peak_time = self.clean_data(image, peak_time) - - if self.use_augmentation: - image, peak_time = self.apply_augmentation(image, peak_time, active_task) + if features["input"].shape[-1] == 2: + image = features["input"][..., 0:1] + peak_time = features["input"][..., 1:2] + + active_task = self.tasks[0] if self.tasks else None + + image, peak_time = self.clean_data(image, peak_time) - image, peak_time = self.normalize_data(image, peak_time, active_task) - features["input"] = np.concatenate([image, peak_time], axis=-1) + if self.use_augmentation: + image, peak_time = self.apply_augmentation(image, peak_time, active_task) + + image, peak_time = self.normalize_data(image, peak_time, active_task) + features["input"] = np.concatenate([image, peak_time], axis=-1) + else: + # If it's a waveform or just image, just use it directly for now (or apply cleaning if needed) + pass + # Extract particle type classification labels if "type" in self.tasks: diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index bb50018c..d713d336 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -409,7 +409,6 @@ def _get_mono_item(self, batch): features_out = {} features_out["image"] = image features_out["peak_time"] = peak_time - for key in features["hillas"].keys(): features["hillas"][key] = ( torch.from_numpy(np.array(features["hillas"][key])) @@ -516,8 +515,12 @@ def duplicate_tensor(t,idx_to_duplicate): image = np.transpose(image, (0, 3, 1, 2)) peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + if len(peak_time.shape) > 0 and peak_time.size > 0: + image = np.concatenate([image, peak_time], axis=1) + features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() - features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() + if "peak_time" in features_out: + del features_out["peak_time"] #Create keep_idx based on configurable leakage and intensity cutoffs hillas = features["hillas"] @@ -694,11 +697,13 @@ def _get_stereo_item(self, batch): image, peak_time = self.normalize_data(image, peak_time, active_task) image = np.transpose(image, (0, 3, 1, 2)) - peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + + if len(peak_time.shape) > 0: + peak_time = np.transpose(peak_time, (0, 3, 1, 2)) + image = np.concatenate([image, peak_time], axis=1) features_out = {} features_out["image"] = torch.from_numpy(image.copy()).contiguous().float() - features_out["peak_time"] = torch.from_numpy(peak_time.copy()).contiguous().float() # Convert labels to PyTorch tensors for key in labels.keys(): diff --git a/ctlearn/core/pytorch/model.py b/ctlearn/core/pytorch/model.py index ba23c82f..a996240f 100644 --- a/ctlearn/core/pytorch/model.py +++ b/ctlearn/core/pytorch/model.py @@ -131,7 +131,7 @@ def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): def _build_backbone(self, input_shape): # input_shape format: (channels, height, width) - in_channels = input_shape[-1] + in_channels = input_shape[0] modules = [] if self.batchnorm: @@ -313,7 +313,7 @@ def __init__(self, input_shape, tasks, config=None, parent=None, **kwargs): self.model = FullModelPipeline(self.backbone_model, self.logits_head) def _build_backbone(self, input_shape): - in_channels = input_shape[-1] + in_channels = input_shape[0] modules = [] # Initial Zero Padding diff --git a/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py b/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py index 17448639..ec67aad2 100644 --- a/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py +++ b/ctlearn/core/pytorch/nets/models/DBBDanet/DBBDanet.py @@ -377,7 +377,11 @@ def __init__(self, task, num_inputs=1, num_classes=2, use_concat=False, dropout_ self.backbone_2.fc = nn.Identity() self.backbone_2.dropout = nn.Identity() - def forward(self, x, y): + def forward(self, x): + if x.shape[1] >= 2: + x, y = torch.split(x, [1, x.shape[1]-1], dim=1) + else: + y = x """ Forward pass through the dual-backbone network. diff --git a/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py index 7a74b1d3..3d6aa68f 100644 --- a/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py +++ b/ctlearn/core/pytorch/nets/models/DBBNoPropDTReg/DBBNoPropDTReg.py @@ -350,7 +350,11 @@ def inference(self, x, y): # Generate final prediction from clean embedding return self.regress(z) - def forward(self, x, y): + def forward(self, x): + if x.shape[1] >= 2: + x, y = torch.split(x, [1, x.shape[1]-1], dim=1) + else: + y = x """ Forward pass through the model. diff --git a/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py b/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py index af2a2ff8..0d454ba1 100644 --- a/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py +++ b/ctlearn/core/pytorch/nets/models/DBBRegNet/DBBRegNet.py @@ -223,7 +223,11 @@ def __init__(self, task, use_concat=False, num_inputs=1, num_classes=2, dropout_ # Final task-specific prediction layer self.fc = nn.Linear(num_features, num_classes) - def forward(self, x, y): + def forward(self, x): + if x.shape[1] >= 2: + x, y = torch.split(x, [1, x.shape[1]-1], dim=1) + else: + y = x """ Forward pass through the dual-backbone network. diff --git a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py index a29fdd2c..9ba9ff00 100644 --- a/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py +++ b/ctlearn/core/pytorch/nets/models/DoubleBBEfficientNet/DoubleBBEfficientNet.py @@ -353,7 +353,11 @@ def extract_feature_vector(self, x1, x2): return fused_features - def forward(self, x1, x2): + def forward(self, x1): + if x1.shape[1] >= 2: + x1, x2 = torch.split(x1, [1, x1.shape[1]-1], dim=1) + else: + x2 = x1 """ Forward pass through the dual-backbone network. diff --git a/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py index efbbde91..a9cf5623 100644 --- a/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py +++ b/ctlearn/core/pytorch/nets/models/NoPropDTRegDBB/NoPropDTRegDBB.py @@ -113,7 +113,11 @@ def inference(self, x, y): return self.regress(z) - def forward(self, x, y): + def forward(self, x): + if x.shape[1] >= 2: + x, y = torch.split(x, [1, x.shape[1]-1], dim=1) + else: + y = x if self.task=="direction": return None, None, self.inference(x,y) diff --git a/ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py b/ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py index a7c4861a..db693e55 100644 --- a/ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py +++ b/ctlearn/core/pytorch/nets/models/ResNeXtDBB/ResNeXtDBB.py @@ -119,7 +119,11 @@ def _make_layer(self, block, out_channels, num_blocks, stride): layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) return nn.Sequential(*layers) - def forward(self, x1, x2): + def forward(self, x1): + if x1.shape[1] >= 2: + x1, x2 = torch.split(x1, [1, x1.shape[1]-1], dim=1) + else: + x2 = x1 classification=None energy=None diff --git a/ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py b/ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py index 111d5304..b1b32e23 100644 --- a/ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py +++ b/ctlearn/core/pytorch/nets/models/ThinResNet/ThinResNet.py @@ -99,18 +99,15 @@ def _make_layer(self, block, out_channels, num_blocks, stride): return nn.Sequential(*layers) def forward(self, x): - energy = None classification = None direction = None - # out_1 = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.conv1(x)) out = self.layer1_1(out) out = self.layer2_1(out) out = self.layer3_1(out) - # out = self.layer3(out) out = self.layer4(out) out = self.adaptive_pool(out) out_feature = out.view(out.size(0), -1) diff --git a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py index 53f0db52..e096e4bf 100644 --- a/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py +++ b/ctlearn/core/pytorch/nets/models/ThinResNet_DBB/ThinResNet_DBB.py @@ -130,7 +130,11 @@ def extract_feature_vector(self,x, y): fused_features = self.dropout(out_feature) return fused_features - def forward(self, x, y): + def forward(self, x): + if x.shape[1] >= 2: + x, y = torch.split(x, [1, x.shape[1]-1], dim=1) + else: + y = x energy = None classification = None diff --git a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py index 1ee03800..5c506ca2 100644 --- a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py +++ b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuo.py @@ -282,7 +282,11 @@ def _make_layer(self, block, out_channels, blocks, stride): layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) return nn.Sequential(*layers) - def forward(self, x1, x2): + def forward(self, x1): + if x1.shape[1] >= 2: + x1, x2 = torch.split(x1, [1, x1.shape[1]-1], dim=1) + else: + x2 = x1 """ Forward pass through the dual-backbone transformer network. diff --git a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py index cfb5ec9a..ac189474 100644 --- a/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py +++ b/ctlearn/core/pytorch/nets/models/Transformers/TransformerDuoSimple.py @@ -240,7 +240,11 @@ def _make_layer(self, block, out_channels, blocks, stride): layers.append(block(self.in_channels, out_channels, use_gn=self.use_gn)) return nn.Sequential(*layers) - def forward(self, x1, x2): + def forward(self, x1): + if x1.shape[1] >= 2: + x1, x2 = torch.split(x1, [1, x1.shape[1]-1], dim=1) + else: + x2 = x1 """ Forward pass through the simplified transformer network. diff --git a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py index 48adbd42..19b403c1 100644 --- a/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_LST1_pytorch.py @@ -187,34 +187,27 @@ def predictions(self): peak_time = peak_time.astype(np.float32) peak_time = np.log10(peak_time + 1.0) + # Prepare unified input tensor + if len(self.channels) == 2: + input_tensor = torch.cat([ + torch.tensor(imgs).unsqueeze(1), + torch.tensor(peak_time).unsqueeze(1) + ], dim=1).to(self.device) + else: + input_tensor = torch.tensor(imgs).unsqueeze(1).to(self.device) + # Run predictions for each configured task for task in self.tasks: if task == Task.type: # Particle type classification - if len(self.channels) == 2: - classification_pred, energy_pred, direction_pred = self.type_model( - torch.tensor(imgs).unsqueeze(1).to(self.device), - torch.tensor(peak_time).unsqueeze(1).to(self.device) - ) - else: - classification_pred, energy_pred, direction_pred = self.type_model( - torch.tensor(imgs).unsqueeze(1).to(self.device) - ) + classification_pred, energy_pred, direction_pred = self.type_model(input_tensor) prediction.extend(torch.softmax(classification_pred[0], dim=1).cpu().detach().numpy()[:, 1]) classification_fvs.extend(classification_pred[1].cpu().detach().numpy()) elif task == Task.energy: # Energy estimation - if len(self.channels) == 2: - classification_pred, energy_pred, direction_pred = self.energy_model( - torch.tensor(imgs).unsqueeze(1).to(self.device), - torch.tensor(peak_time).unsqueeze(1).to(self.device) - ) - else: - classification_pred, energy_pred, direction_pred = self.energy_model( - torch.tensor(imgs).unsqueeze(1).to(self.device) - ) + classification_pred, energy_pred, direction_pred = self.energy_model(input_tensor) energy.extend(energy_pred[0].cpu().detach().numpy()) if feature_vector: @@ -224,15 +217,7 @@ def predictions(self): elif task in [Task.cameradirection, Task.skydirection, Task.direction]: # Direction reconstruction - if len(self.channels) == 2: - classification_pred, energy_pred, direction_pred = self.dirrection_model( - torch.tensor(imgs).unsqueeze(1).to(self.device), - torch.tensor(peak_time).unsqueeze(1).to(self.device) - ) - else: - classification_pred, energy_pred, direction_pred = self.dirrection_model( - torch.tensor(imgs).unsqueeze(1).to(self.device) - ) + classification_pred, energy_pred, direction_pred = self.dirrection_model(input_tensor) cam_coord_offset_x.extend(direction_pred[0][:, 0].float().cpu().detach().numpy()) cam_coord_offset_y.extend(direction_pred[0][:, 1].float().cpu().detach().numpy()) diff --git a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py index 0899630f..0fcd89ca 100644 --- a/ctlearn/tools/predict/pytorch/predic_model_pytorch.py +++ b/ctlearn/tools/predict/pytorch/predic_model_pytorch.py @@ -111,18 +111,9 @@ def predict_with_model_pytorch(self, task): continue # Forward pass through the model - # Handle both single-input and dual-input models - if num_inputs == 2: - # Model expects both image and peak time - classification_pred, energy_pred, direction_pred = model( - x[0]['image'].to(self.device), - x[0]['peak_time'].to(self.device) - ) - else: - # Model expects only image - classification_pred, energy_pred, direction_pred = model( - x[0]['image'].to(self.device) - ) + classification_pred, energy_pred, direction_pred = model( + x[0]['image'].to(self.device) + ) # Collect particle type classification predictions if classification_pred[0] is not None: diff --git a/ctlearn/tools/predict_LST1.py b/ctlearn/tools/predict_LST1.py index c7b8c436..e814f63d 100644 --- a/ctlearn/tools/predict_LST1.py +++ b/ctlearn/tools/predict_LST1.py @@ -269,7 +269,7 @@ class LST1PredictionTool(Tool): ("e", "energy_model"): "LST1PredictionTool.load_energy_model_from", ("d", "cameradirection_model"): "LST1PredictionTool.load_cameradirection_model_from", ("o", "output"): "LST1PredictionTool.output_path", - ("c", "channels"): "LST1PredictionTool.channels", + ("ch", "channels"): "LST1PredictionTool.channels", } diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index e81f0a99..1470c665 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -718,7 +718,7 @@ def _predict_with_model(self, model_path): """ if self.framework_type == "keras": from ctlearn.tools.predict.keras.predic_model_keras import predict_with_model - predict_data, feature_vectors = predict_with_model(model_path) + predict_data, feature_vectors = predict_with_model(self, model_path) return predict_data, feature_vectors diff --git a/ctlearn/tools/train/pytorch/CTLearnPL.py b/ctlearn/tools/train/pytorch/CTLearnPL.py index 9e278492..cbb19519 100644 --- a/ctlearn/tools/train/pytorch/CTLearnPL.py +++ b/ctlearn/tools/train/pytorch/CTLearnPL.py @@ -336,8 +336,8 @@ def set_hyperparameters(self, parameters): self.optimizer_type = str(parameters["hyp"]["optimizer"]).lower() self.l2_lambda = float(parameters["hyp"]["l2_lambda"]) # ---------------------------------------------------------------------------------------------------------- - def forward(self, x, y): - return self.model(x, y) + def forward(self, x): + return self.model(x) # ---------------------------------------------------------------------------------------------------------- def save_checkpoint(self, save_folder, metric_value, filename_prefix, is_loss=True): """Save checkpoint and manage top k checkpoints for loss or accuracy.""" @@ -846,14 +846,7 @@ def training_step(self, batch, batch_idx): # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ if not self.is_difussion: - if self.num_inputs == 2: - peak_time = features["peak_time"] - peak_time = peak_time.to(self.device) - classification_pred, energy_pred, direction_pred = self.model( - imgs, peak_time - ) - else: - classification_pred, energy_pred, direction_pred = self.model(imgs) + classification_pred, energy_pred, direction_pred = self.model(imgs) # ------------------------------------------------------------------ # Particle type @@ -1138,13 +1131,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): # Predictions based on one backbone or two back bones # ------------------------------------------------------------------ if not self.is_difussion: - if self.num_inputs == 2: - peak_time = features["peak_time"] - classification_pred, energy_pred, direction_pred = self.model( - imgs, peak_time - ) - else: - classification_pred, energy_pred, direction_pred = self.model(imgs) + classification_pred, energy_pred, direction_pred = self.model(imgs) # ------------------------------------------------------------------ # Compute Loss functions based on different tasks # ------------------------------------------------------------------ diff --git a/ctlearn/tools/train/pytorch/train_pytorch_model.py b/ctlearn/tools/train/pytorch/train_pytorch_model.py index 1337b3c7..c818ca6d 100644 --- a/ctlearn/tools/train/pytorch/train_pytorch_model.py +++ b/ctlearn/tools/train/pytorch/train_pytorch_model.py @@ -495,7 +495,12 @@ def forward(self, x, y=None): num_inputs = self.parameters["model"].get(f"model_{task.name}", {}).get("parameters", {}).get("num_inputs", 1) model_input_shape = list(self.train_dataset.input_shape) - model_input_shape[-1] = num_inputs + if len(model_input_shape) == 3: + # Convert (H, W, C) to PyTorch's (C, H, W) + model_input_shape = [model_input_shape[2], model_input_shape[0], model_input_shape[1]] + elif len(model_input_shape) >= 4: + # e.g., (Telescopes, H, W, C) -> (Telescopes, C, H, W) + model_input_shape = list(model_input_shape[:-3]) + [model_input_shape[-1], model_input_shape[-3], model_input_shape[-2]] model_net = CTLearnModel.from_name( self.model_type, diff --git a/ctlearn/tools/train_model.py b/ctlearn/tools/train_model.py index eae243ac..19be869d 100644 --- a/ctlearn/tools/train_model.py +++ b/ctlearn/tools/train_model.py @@ -175,30 +175,23 @@ def get_framework(cls, framework_type: FrameworkType): return fw - @property - def classes(self): - from ctlearn.tools.train.base_train_model import TrainCTLearnModel - from ctapipe.core.traits import classes_with_traits - from dl1_data_handler.reader import DLDataReader - - tool_classes = [ - type(self), - TrainCTLearnModel, - ] + from ctlearn.tools.train.base_train_model import TrainCTLearnModel + classes = [TrainCTLearnModel] + try: + from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel + classes.append(TrainKerasModel) + except ImportError: + pass - try: - from ctlearn.tools.train.keras.train_keras_model import TrainKerasModel - tool_classes.append(TrainKerasModel) - except ImportError: - pass - - try: - from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel - tool_classes.append(TrainPyTorchModel) - except ImportError: - pass - - return tool_classes + classes_with_traits(DLDataReader) + try: + from ctlearn.tools.train.pytorch.train_pytorch_model import TrainPyTorchModel + classes.append(TrainPyTorchModel) + except ImportError: + pass + + from ctapipe.core.traits import classes_with_traits + from dl1_data_handler.reader import DLDataReader + classes = classes + classes_with_traits(DLDataReader) def main(): # Run the tool From a8bf6a71985bb5b951c5f9518bd1d0d9d41dcf33 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Tue, 28 Jul 2026 15:03:17 +0000 Subject: [PATCH 117/119] Fix the test --- ctlearn/core/tests/test_loader_pytorch.py | 2 +- .../tools/predict/keras/predic_model_keras.py | 43 ++++++------------- ctlearn/tools/predict_model.py | 14 +++--- 3 files changed, 21 insertions(+), 38 deletions(-) diff --git a/ctlearn/core/tests/test_loader_pytorch.py b/ctlearn/core/tests/test_loader_pytorch.py index f61937de..47fbe8cf 100644 --- a/ctlearn/core/tests/test_loader_pytorch.py +++ b/ctlearn/core/tests/test_loader_pytorch.py @@ -54,7 +54,7 @@ def test_data_loader(dl1_gamma_file): and "skydirection" in labels ) # Check the shape of the features - assert features["image"].shape == (1, 1, 110, 110) + assert features["image"].shape == (1, 2, 110, 110) if __name__ == "__main__": test_data_loader() \ No newline at end of file diff --git a/ctlearn/tools/predict/keras/predic_model_keras.py b/ctlearn/tools/predict/keras/predic_model_keras.py index 498dc0a7..0d2b3472 100644 --- a/ctlearn/tools/predict/keras/predic_model_keras.py +++ b/ctlearn/tools/predict/keras/predic_model_keras.py @@ -10,7 +10,7 @@ import numpy as np -def predict_with_model(self, model_path): +def predict_with_model(self, model_path, task): """ Load and predict with a CTLearn Keras model. @@ -23,23 +23,17 @@ def predict_with_model(self, model_path): model_path : str Path to a Keras model file (Keras 3) or directory (Keras 2). The model should be a complete trained CTLearn model. + task : str + The task for which prediction is being made (e.g., 'type', 'energy', 'cameradirection'). Returns ------- predict_data : astropy.table.Table Table containing the prediction results with columns corresponding to - the model's output (e.g., 'type' for classification, task-specific names - for regression tasks like energy or direction). + the model's output. feature_vectors : np.ndarray or None Feature vectors extracted from the backbone model if dl1_features is enabled. Returns None if feature extraction is not requested. - - Notes - ----- - - The function handles distributed training by accounting for multiple replicas - - Keras only processes complete batches, so incomplete last batches are handled separately - - Feature extraction splits the model into backbone (feature extractor) and head (predictor) - - Classification tasks use softmax output, regression tasks use direct outputs """ # Create data loader for the main batch processing # The DLDataLoader is initialized separately for each prediction task @@ -74,12 +68,6 @@ def predict_with_model(self, model_path): # Load the trained model from the specified path model = keras.saving.load_model(model_path) - # Determine prediction column name from model architecture - # Use the last layer name, or 'type' if it's a softmax layer (classification) - prediction_colname = ( - model.layers[-1].name if model.layers[-1].name != "softmax" else "type" - ) - # Initialize variables for optional feature extraction backbone_model, feature_vectors = None, None @@ -109,7 +97,7 @@ def predict_with_model(self, model_path): # Generate predictions from head using extracted features predict_data = Table( { - prediction_colname: head.predict( + task: head.predict( feature_vectors, verbose=self.keras_verbose ) } @@ -131,7 +119,7 @@ def predict_with_model(self, model_path): predict_data, Table( { - prediction_colname: head.predict( + task: head.predict( feature_vectors_last_batch, verbose=self.keras_verbose, ) @@ -145,14 +133,10 @@ def predict_with_model(self, model_path): predict_data = model.predict(data_loader, verbose=self.keras_verbose) # Convert predictions to Astropy Table - # Classification tasks (with softmax) return arrays that need wrapping - # Regression tasks return dictionaries that can be directly converted - if prediction_colname == "type": - # Classification: wrap array in table with 'type' column - predict_data = Table({prediction_colname: predict_data}) - else: - # Regression: convert dictionary directly to table + if isinstance(predict_data, dict): predict_data = Table(predict_data) + else: + predict_data = Table({task: predict_data}) # Process last incomplete batch if it exists if data_loader_last_batch is not None: @@ -161,13 +145,10 @@ def predict_with_model(self, model_path): data_loader_last_batch, verbose=self.keras_verbose ) - # Convert last batch predictions to table (same logic as above) - if model.layers[-1].name == "type": - predict_data_last_batch = Table( - {prediction_colname: predict_data_last_batch} - ) - else: + if isinstance(predict_data_last_batch, dict): predict_data_last_batch = Table(predict_data_last_batch) + else: + predict_data_last_batch = Table({task: predict_data_last_batch}) # Stack predictions from main batches and last batch predict_data = vstack([predict_data, predict_data_last_batch]) diff --git a/ctlearn/tools/predict_model.py b/ctlearn/tools/predict_model.py index 1470c665..be6f1180 100644 --- a/ctlearn/tools/predict_model.py +++ b/ctlearn/tools/predict_model.py @@ -697,7 +697,7 @@ def deduplicate_first_valid( return unique(t, keys=list(keys), keep="first") - def _predict_with_model(self, model_path): + def _predict_with_model(self, model_path, task): """ Load and predict with a CTLearn model. @@ -708,6 +708,8 @@ def _predict_with_model(self, model_path): ---------- model_path : str Path to a Keras model file (Keras3). + task : str + The task for which prediction is being made. Returns ------- @@ -718,7 +720,7 @@ def _predict_with_model(self, model_path): """ if self.framework_type == "keras": from ctlearn.tools.predict.keras.predic_model_keras import predict_with_model - predict_data, feature_vectors = predict_with_model(self, model_path) + predict_data, feature_vectors = predict_with_model(self, model_path, task) return predict_data, feature_vectors @@ -746,7 +748,7 @@ def _predict_particletype(self, example_identifiers): ) # Predict the data using the loaded type_model predict_data, feature_vectors = self._predict_with_model( - self.load_type_model_from + self.load_type_model_from, "type" ) # Create prediction table and add the predicted classification score ('gammaness') particletype_table = example_identifiers.copy() @@ -774,7 +776,7 @@ def _predict_energy(self, example_identifiers): self.log.info("Predicting for the regression of the primary particle energy...") # Predict the data using the loaded energy_model predict_data, feature_vectors = self._predict_with_model( - self.load_energy_model_from + self.load_energy_model_from, "energy" ) # Convert the reconstructed energy from log10(TeV) to TeV reco_energy = u.Quantity( @@ -814,7 +816,7 @@ def _predict_cameradirection(self, example_identifiers): ) # Predict the data using the loaded direction_model predict_data, feature_vectors = self._predict_with_model( - self.load_cameradirection_model_from + self.load_cameradirection_model_from, "cameradirection" ) # For the direction task, the prediction is the camera coordinate offset in x and y # from the telescope pointing. @@ -852,7 +854,7 @@ def _predict_skydirection(self, example_identifiers): ) # Predict the data using the loaded direction_model predict_data, feature_vectors = self._predict_with_model( - self.load_skydirection_model_from + self.load_skydirection_model_from, "skydirection" ) # For the direction task, the prediction is the spherical offset in fov_lon and fov_lat # from the telescope pointing. From 231efa95c95cad6ad94943d78afddbbf874773a2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 29 Jul 2026 09:41:35 +0000 Subject: [PATCH 118/119] Change the architectur for the dirrection --- ctlearn/core/model.py | 4 ++-- .../core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ctlearn/core/model.py b/ctlearn/core/model.py index eb7ec1c3..334b300d 100644 --- a/ctlearn/core/model.py +++ b/ctlearn/core/model.py @@ -37,8 +37,8 @@ class CTLearnModel(Component): default_value={ "type": [512, 256, 2], "energy": [512, 256, 1], - "cameradirection": [512, 256, 2], - "skydirection": [512, 256, 2], + "cameradirection": [512, 256, 3], + "skydirection": [512, 256, 3], }, allow_none=False, help=( diff --git a/ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py b/ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py index b88eb9f4..1c62d349 100644 --- a/ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py +++ b/ctlearn/core/pytorch/nets/models/PyTorchResNet/PyTorchResNet.py @@ -203,7 +203,7 @@ def __init__( head_layers = { "type": [512, 256, num_outputs], "energy": [512, 256, 1], - "direction": [512, 256, 2], + "direction": [512, 256, 3], } if head_activation_function is None: From ce2e0701e13f871b82ac3b690171ed9e5d7c4ef9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cristian=20Pozo=20Gonz=C3=A1lez?= Date: Wed, 29 Jul 2026 10:17:19 +0000 Subject: [PATCH 119/119] Fix the test --- ctlearn/core/data_loader/keras_loader.py | 6 ++++++ ctlearn/core/data_loader/pytorch_loader.py | 4 ++++ 2 files changed, 10 insertions(+) diff --git a/ctlearn/core/data_loader/keras_loader.py b/ctlearn/core/data_loader/keras_loader.py index 32f55339..5c09ecda 100644 --- a/ctlearn/core/data_loader/keras_loader.py +++ b/ctlearn/core/data_loader/keras_loader.py @@ -228,6 +228,7 @@ def _get_mono_item(self, batch): ( batch["fov_lon"].data, batch["fov_lat"].data, + batch["angular_separation"].data, ), axis=1, ) @@ -239,6 +240,7 @@ def _get_mono_item(self, batch): ( batch["cam_coord_offset_x"].data, batch["cam_coord_offset_y"].data, + batch["cam_coord_distance"].data, ), axis=1, ) @@ -357,11 +359,13 @@ def _get_stereo_item(self, batch): if "skydirection" in self.tasks: fov_lon.append(group_element["fov_lon"].data[0]) fov_lat.append(group_element["fov_lat"].data[0]) + angular_separation.append(group_element["angular_separation"].data[0]) # Camera direction reconstruction if "cameradirection" in self.tasks: cam_coord_offset_x.append(group_element["cam_coord_offset_x"].data) cam_coord_offset_y.append(group_element["cam_coord_offset_y"].data) + cam_coord_distance.append(group_element["cam_coord_distance"].data) # Format labels for each task if "type" in self.tasks: @@ -386,6 +390,7 @@ def _get_stereo_item(self, batch): ( np.array(fov_lon), np.array(fov_lat), + np.array(angular_separation), ), axis=1, ) @@ -396,6 +401,7 @@ def _get_stereo_item(self, batch): ( np.array(cam_coord_offset_x), np.array(cam_coord_offset_y), + np.array(cam_coord_distance), ), axis=1, ) diff --git a/ctlearn/core/data_loader/pytorch_loader.py b/ctlearn/core/data_loader/pytorch_loader.py index d713d336..89076eae 100644 --- a/ctlearn/core/data_loader/pytorch_loader.py +++ b/ctlearn/core/data_loader/pytorch_loader.py @@ -636,9 +636,11 @@ def _get_stereo_item(self, batch): if "skydirection" in self.tasks: fov_lon.append(group_element["fov_lon"].data[0]) fov_lat.append(group_element["fov_lat"].data[0]) + angular_separation.append(group_element["angular_separation"].data[0]) if "cameradirection" in self.tasks: cam_coord_offset_x.append(group_element["cam_coord_offset_x"].data) cam_coord_offset_y.append(group_element["cam_coord_offset_y"].data) + cam_coord_distance.append(group_element["cam_coord_distance"].data) # Store the labels in the labels dictionary if "type" in self.tasks: labels["type"] = np.array(true_shower_primary_class) @@ -655,6 +657,7 @@ def _get_stereo_item(self, batch): ( np.array(fov_lon), np.array(fov_lat), + np.array(angular_separation), ), axis=1, ) @@ -663,6 +666,7 @@ def _get_stereo_item(self, batch): ( np.array(cam_coord_offset_x), np.array(cam_coord_offset_y), + np.array(cam_coord_distance), ), axis=1, )