File "main.py", line 240, in train print('Train Epoch: {} [{}/{}]\t' File "/scratch3/venv/fashcomp/lib/python3.8/site-packages/torch/_tensor.py", line 561, in __format__ return object.__format__(self, format_spec) TypeError: unsupported format string passed to Tensor.__format__

[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 329, in <module>
    main()    
  File "main.py", line 167, in main
    train(train_loader, tnet, criterion, optimizer, epoch)
  File "main.py", line 240, in train
    print('Train Epoch: {} [{}/{}]\t'
  File "/scratch3/venv/fashcomp/lib/python3.8/site-packages/torch/_tensor.py", line 561, in __format__
    return object.__format__(self, format_spec)
TypeError: unsupported format string passed to Tensor.__format__

[monajalal]

This (adding .item() to each tensor) worked for me:

    if batch_idx % args.log_interval == 0:
        print('Train Epoch: {} [{}/{}]\t'
              'Loss: {:.4f} ({:.4f}) \t'
              'Acc: {:.2f}% ({:.2f}%) \t'
              'Emb_Norm: {:.2f} ({:.2f})'.format(
            epoch, batch_idx * num_items, len(train_loader.dataset),
            losses.val.item(), losses.avg.item(), 
            100. * accs.val.item(), 100. * accs.avg.item(), emb_norms.val.item(), emb_norms.avg.item()))