Prepare the v0.2.0 release: version, changelog, citation

CHANGELOG.md

@@ -1,5 +1,51 @@
 # Changelog
 
+## 0.2.0 (2026-11-12)
+
+Second experimental release. It adds a foundation-model workflow, ViT and DPT conversion,
+more raster blocks for standard CNN encoders, and a plain-language way in.
+
+- **Prithvi-EO-1.0 Sen1Floods11.** `tensor_ee.models.prithvi_sen1floods11` composes the
+  100M-parameter flood model from window blocks and runs it in staged exports, as one
+  224 × 224 crop or a region of whole crops, from its released trained checkpoint. It was
+  checked live against PyTorch on synthetic crops and on one real Sentinel-2 crop from the
+  held-out Bolivia event: logits within 1.41e-5, with 0 argmax disagreements. Water IoU
+  against that crop's hand label was 0.911, an example and not an accuracy score. A 2 × 2
+  region of synthetic crops matched within 6.11e-6. Merged stages run a crop in 16 exports
+  instead of 33.
+- **ViT-B/16 and DPT conversion.** The converter recognizes timm ViT-B/16 encoders and SMP
+  0.5.0 DPT models at 224 × 224, and converts them to graphs byte-identical to hand-composed
+  targets. The numerical evidence is narrower than for the other models: random weights
+  only; offline, the full-width head and logits are within 2.06e-5 of float32 PyTorch; live
+  agreement covers reduced twins and individual blocks, not the full-width model.
+- **Ten new blocks, 88 in all** (23 raster, 65 window). Raster: `relu6`, `hardsigmoid`,
+  `hardswish`, `batch_norm`, `bilinear_upsample`, `grouped_conv2d` (grouped and depthwise)
+  and `standardize`. Window: `class_token_attention`, `concat_rows` and
+  `window_depth_to_space`. Each was validated live at the settings its catalogue row lists.
+- **New settings on existing blocks,** each keyword-only, validated live, with today's
+  default graph unchanged: stride 2 and per-crop padding for `conv3x3_window`, epsilon 1e-6
+  for `layer_norm`, ×2 with aligned corners for `window_resize`, and an opt-in slice gather
+  for `relative_position_bias`.
+- **SMP adapter.** Unet on `resnext50_32x4d` and `mobilenet_v2` encoders now builds its graph
+  offline through the grouped and depthwise convolution. Neither whole model has been
+  checked in Earth Engine.
+- **A way in.** A plain-language start guide and three Colab notebooks: a quickstart with the
+  trained U-Net, an offline check of whether your own model is supported, and your own CNN
+  from checkpoint to an exported region.
+- **Numerical comparisons.** The 1e-4 absolute target against float32 PyTorch applies to
+  block fixtures and to a model's final outputs. A model's intermediate outputs are checked
+  against float64 PyTorch instead.
+- **For contributors.** Gate fixtures are generated from integer arithmetic, so the
+  graph-identity checks can be judged on Linux as well as on macOS arm64.
+
+`pip install tensor-ee` provides the library, the converter and the models. The notebooks,
+the tools that drive the Prithvi staged runs, the demos and the tests need a checkout.
+
+Support and numerical evidence apply only to the documented settings, and numerical
+agreement does not establish segmentation accuracy. The Prithvi checkpoint is a separate
+third-party release under its own terms. The SegWater and Swin-T checkpoint files and the
+full training dataset description are not yet public.
+
 ## 0.1.0 (2026-10-21)
 
 First experimental release of TensorEE, for expressing supported PyTorch models as

CITATION.cff

@@ -8,14 +8,15 @@ authors:
     given-names: Noel Ivan
 # Concept DOI: resolves to the latest archived release on Zenodo.
 doi: 10.5281/zenodo.23524569
-version: 0.1.0
-date-released: 2026-10-21
+version: 0.2.0
+date-released: 2026-11-12
 license: MIT
 abstract: >-
   Experimental building blocks that run pretrained PyTorch models natively in
   Google Earth Engine, as Earth Engine raster and array operations with no
   model-serving endpoint. A restricted CNN compiler translates the SegWater
   ResNet18 U-Net and standard segmentation-models-pytorch Unet and UnetPlusPlus
   models on ResNet and VGG encoders, a converter maps supported attention models (SMP SegFormer-B0,
-  and SMP UPerNet with timm's Swin-T) onto the library's blocks, and a planner
+  SMP UPerNet with timm's Swin-T, and SMP DPT with timm's ViT-B/16) onto the library's blocks,
+  a hand-composed model runs the Prithvi-EO-1.0 Sen1Floods11 flood checkpoint, and a planner
   and runner split their graphs into staged exports.

docs/getting-started.md

@@ -22,7 +22,7 @@ python -m pip install tensor-ee
 ```
 
 pip ends with `Successfully installed`, followed by what it installed; the distribution
-is `tensor-ee` 0.1.0. Allow several minutes: most of the download is PyTorch, about 600 MB
+is `tensor-ee` 0.2.0. Allow several minutes: most of the download is PyTorch, about 600 MB
 once installed on macOS. On Linux, pip's default PyTorch build also brings the CUDA
 libraries, a few gigabytes; to skip them, install the CPU-only build before installing
 TensorEE, as the continuous integration does:

pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tensor-ee"
-version = "0.1.0"
-description = "Experimental building blocks for running pretrained PyTorch models natively in Google Earth Engine: a restricted CNN compiler (the SegWater U-Net; standard SMP Unet and UnetPlusPlus on ResNet and VGG encoders), a converter for supported attention models (SMP SegFormer-B0; SMP UPerNet with timm's Swin-T), and a planner and runner for staged exports."
+version = "0.2.0"
+description = "Experimental building blocks for running pretrained PyTorch models natively in Google Earth Engine: a restricted CNN compiler (the SegWater U-Net; standard SMP Unet and UnetPlusPlus on ResNet and VGG encoders), a converter for supported attention models (SMP SegFormer-B0; SMP UPerNet with timm's Swin-T; SMP DPT with timm's ViT-B/16), a hand-composed Prithvi-EO-1.0 Sen1Floods11 model, and a planner and runner for staged exports."
 readme = "README.md"
 license = "MIT"
 license-files = ["LICENSE"]

tensor_ee/__init__.py

@@ -5,7 +5,7 @@
 try:
     __version__ = _distribution_version("tensor-ee")
 except _PackageNotFoundError:
-    __version__ = "0.1.0"  # Source checkouts need no installed distribution metadata.
+    __version__ = "0.2.0"  # Source checkouts need no installed distribution metadata.
 
 from .comparison import PixelTensor, compare_segmentation
 from .compatibility import CompatibilityReport, check_compatibility