RuntimeError: Expected object of scalar type Long but got scalar type Byte for argument #2 'target'

With optimizer = torch.optim.Adam( model.parameters(), lr = learning_rate, weight_decay = weight_decay)

criterion = nn.CrossEntropyLoss( weight = None, ignore_index = ignore_index, reduce = False)

and then executing

lr_finder = LRFinder(model, optimizer, criterion, device="cuda") lr_finder.range_test( dataLoader[ 'train'], end_lr=100, num_iter=100) lr_finder.plot() # to inspect the loss-learning rate graph lr_finder.reset() # to reset the model and optimizer to their initial state

I am getting the error,

RuntimeError: Expected object of scalar type Long but got scalar type Byte for argument #2 ‘target’

Please find below the whole trace. So far training my models with the above optimizer and criterion I do not have any problem.

[vpapaioannou]

Thanks for your prompt response.

I have been running this code for months now on Google Colab with no problem and with batch size = 256. I observe that after installing the lr-finder the code breaks. Errors are reported where there was no problem.

In other words, I would suggest that it is a compatibility issue. I would be happy, if I could help somehow on that.

Thanks,

Vassilis

[davidtvs]

A new release (v0.2.0) is available that addresses issues like this one by allowing the user to wrap the training and validation DataLoader in a TrainDataLoaderIter and `TrainDataLoaderIter’ and do further data processing/handling/conversions there. For a usage example see: examples/lrfinder_cifar10_dataloader_iter.

However, I still think that the best way to deal with this issue is by placing all needed data processing/handling/conversions in the DataLoader as described in my previous comment.

Closing this in the meantime feel free to reopen if not resolved.