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 :)