Some code and model path errors.

[duaghk]

Dear author.

Hello.

Thanks for your nice tools.

When I using ERGO-II, I found some errors in code.

1. TCR ae model location error.

_python3 ./Predict.py vdjdb ./example.csv 
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/decorators.py:13: UserWarning: data_loader decorator deprecated in 0.7.0. Will remove 0.9.0
  warnings.warn(w)
Traceback (most recent call last):
  File "./Predict.py", line 104, in <module>
    df = predict(sys.argv[1], sys.argv[2])
  File "./Predict.py", line 86, in predict
    model, train_file = get_model(dataset)
  File "./Predict.py", line 72, in get_model
    model = load_model(hparams, checkpoint)
  File "./Predict.py", line 43, in load_model
    model = ERGOLightning(hparams)
  File "/opt/ERGO-II/Trainer.py", line 49, in __init__
    self.tcra_encoder = AE_Encoder(encoding_dim=self.encoding_dim, tcr_type='alpha', max_len=34)
  File "/opt/ERGO-II/Models.py", line 102, in __init__
    self.init_ae_params(train_ae)
  File "/opt/ERGO-II/Models.py", line 110, in init_ae_params
    checkpoint = torch.load(ae_file)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 525, in load
    with _open_file_like(f, 'rb') as opened_file:
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 212, in _open_file_like
    return _open_file(name_or_buffer, mode)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 193, in __init__
    super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'TCR_Autoencoder/tcra_ae_dim_100.pt'

so, I copy the directory.
( in ERGO2 directory:) cp -r ./Models/AE ./TCR_Autoencoder
and it works well.

2. Torch CPU-only error.

python3 ./Predict.py vdjdb ./example.csv 
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/decorators.py:13: UserWarning: data_loader decorator deprecated in 0.7.0. Will remove 0.9.0
  warnings.warn(w)
Traceback (most recent call last):
  File "./Predict.py", line 105, in <module>
    df = predict(sys.argv[1], sys.argv[2])
  File "./Predict.py", line 87, in predict
    model, train_file = get_model(dataset)
  File "./Predict.py", line 73, in get_model
    model = load_model(hparams, checkpoint)
  File "./Predict.py", line 43, in load_model
    model = ERGOLightning(hparams)
  File "/opt/ERGO-II/Trainer.py", line 49, in __init__
    self.tcra_encoder = AE_Encoder(encoding_dim=self.encoding_dim, tcr_type='alpha', max_len=34)
  File "/opt/ERGO-II/Models.py", line 102, in __init__
    self.init_ae_params(train_ae)
  File "/opt/ERGO-II/Models.py", line 110, in init_ae_params
    checkpoint = torch.load(ae_file)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 529, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 702, in _legacy_load
    result = unpickler.load()
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 665, in persistent_load
    deserialized_objects[root_key] = restore_location(obj, location)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 156, in default_restore_location
    result = fn(storage, location)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 132, in _cuda_deserialize
    device = validate_cuda_device(location)
  File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 116, in validate_cuda_device
    raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.

I used CPU-only machine, and torch need to set map_locations.
in ERGO-II/Models.py : checkpoint = torch.load(ae_file) -> checkpoint = torch.load(ae_file, map_location=torch.device('cpu' ))
it works well.

I wish it will be helpful.

Best regards.

Jeongjun Chae

[BrunoFant]

This was incredibly useful, thank you ! Ran into the exact same issues and your fixes worked perfectly. Appreciate it :)