CODEWITHSUNDEEP
pythonpytorchhuggingface-transformers
KOB
KOB

Reputation: 4545

Pytorch complaining about input and label batch size mismatch

I am using Huggingface to implement a BERT model using BertForSequenceClassification.from_pretrained().

The model is trying to predict 1 of 24 classes. I am using a batch size of 32 and a sequence length of 66.

When I try to call the model in training, I get the following error:

ValueError: Expected input batch_size (32) to match target batch_size (768).

However, my target shape is 32x24. It seems like somewhere when the model is called, this is being flattened to 768x1. Here is a test I ran to check:

for i in train_dataloader:
    i = tuple(t.to(device) for t in i)
    print(i[0].shape, i[1].shape, i[2].shape) # here i[2].shape is (32, 24)
    output = model(i[0], attention_mask=i[1], labels=i[2]) # here PyTorch complains that i[2]'s shape is now (768, 1)
    print(output.logits.shape)
    break

This outputs:

torch.Size([32, 66]) torch.Size([32, 66]) torch.Size([32, 24])
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-68-c69db6168cc3> in <module>
      2     i = tuple(t.to(device) for t in i)
      3     print(i[0].shape, i[1].shape, i[2].shape)
----> 4     output = model(i[0], attention_mask=i[1], labels=i[2])
      5     print(output.logits.shape)
      6     break

4 frames
/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
   3024     if size_average is not None or reduce is not None:
   3025         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 3026     return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
   3027 
   3028 

ValueError: Expected input batch_size (32) to match target batch_size (768).

Upvotes: 1

Views: 1376

Answers (1)

DerekG
DerekG

Reputation: 3938

Pytorch's implementation of CrossEntropyLoss expects targets to be integer indices, not one-hot class vectors. Thus target should be of size [batch_size], not [batch_size,n_classes].

You can ravel your classes quite simply as follows (provided each class vector is indeed one-hot):

raveler = torch.arange(0,n_classes).unsqueeze(0).expand(batch_size,n_classes)
target = (target * raveler).sum(dim = 1)

Upvotes: 2

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