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]

(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]

[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 NVIDIA/flownet2-pytorch#113 (comment) makes any sense if the error i was getting is non-existent?