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mvasil / fashion-compatibility

Learning Type-Aware Embeddings for Fashion Compatibility
BSD 3-Clause "New" or "Revised" License
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File "main.py", line 222, in train losses.update(loss_triplet.data[0], num_items) IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number #28

Closed monajalal closed 3 years ago

monajalal commented 3 years ago 
(fashcomp) [jalal@goku fashion-compatibility]$ python main.py --name test_baseline --learned --l2_embed --datadir ../../../data/fashion/
/scratch3/venv/fashcomp/lib/python3.8/site-packages/torchvision/transforms/transforms.py:310: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
  warnings.warn("The use of the transforms.Scale transform is deprecated, " +
  + Number of params: 3191808
<class 'torch.utils.data.dataloader.DataLoader'>
/scratch3/venv/fashcomp/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Traceback (most recent call last):
  File "main.py", line 324, in <module>
    main()    
  File "main.py", line 167, in main
    train(train_loader, tnet, criterion, optimizer, epoch)
  File "main.py", line 222, in train
    losses.update(loss_triplet.data[0], num_items)
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
monajalal commented 3 years ago

[Not sure if 100% makes sense or correct] This change (## refers to old code)

    # measure accuracy and record loss
    ##losses.update(loss_triplet.data[0], num_items)
    ##accs.update(acc.data[0], num_items)
    ##emb_norms.update(loss_embed.data[0])
    ##mask_norms.update(loss_mask.data[0])

    losses.update(loss_triplet.data, num_items)
    accs.update(acc.data, num_items)
    emb_norms.update(loss_embed.data)
    mask_norms.update(loss_mask.data)

based on this suggestion https://github.com/NVIDIA/flownet2-pytorch/issues/113#issuecomment-450802359 makes any sense if the error i was getting is non-existent?