Hi,
I am doing transfer learning from large network based in Resnet.
It is related to image classification that outputs labels.
When I feed input with one image and corresponding target as below:
torch.Size([1, 1, 2500, 1900]) 
tensor([[0., 1.]], device='cuda:1') torch.Size([1, 2])


I have correct output like:
outputs: tensor([[[-0.4243, -1.0620],
                  [-0.0913, -2.4389]]], device='cuda:1', grad_fn=<SliceBackward>) torch.Size([1, 2, 2])


When I input a 2 image batch (using Dataloader)
The input/target are like that:
torch.Size([2, 1, 2500, 1900]) 
tensor([[1., 0.], [1., 0.]], device='cuda:1') torch.Size([2, 2])


And I get following output:
outputs: tensor([[[-0.2792, -1.4123],
                  [-0.0257, -3.6729]],

                 [[-0.2532, -1.4974],
                  [-0.0518, -2.9860]]], device='cuda:1', grad_fn=<SliceBackward>) torch.Size([2, 2, 2])


–> Correct too.
But after that I got an error when I try input/target:
torch.Size([3, 1, 2500, 1900]) 
tensor([[0., 1.],
        [0., 1.],
        [1., 0.]], device='cuda:1') torch.Size([3, 2])


The output tensor becomes:
outputs: tensor([[[-0.1289, -2.1125],
                  [-0.0123, -4.4008]],

                 [[-0.2694, -1.4434],
                  [-0.0322, -3.4504]]], device='cuda:1', grad_fn=<SliceBackward>) torch.Size([2, 2, 2])


Which is not 3 in first dimension as I expected, and happens an error message below:
/home/dpetrini/.local/lib/python3.6/site-packages/torch/nn/modules/loss.py:498: UserWarning: Using a target size (torch.Size([3, 2])) that is different to the input size (torch.Size([2, 2])) is deprecated. Please ensure they have the same size.
return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
Traceback (most recent call last):
File “src/modeling/train_maca.py”, line 593, in 
main()
File “src/modeling/train_maca.py”, line 553, in main
loss_func, optimizer, device, mini_batch, num_epochs)
File “src/modeling/train_maca.py”, line 97, in train_and_validate
loss = loss_criterion(y_hat, labels)    # compute loss
File “/home/dpetrini/.local/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 547, in call
result = self.forward(*input, **kwargs)
File “/home/dpetrini/.local/lib/python3.6/site-packages/torch/nn/modules/loss.py”, line 498, in forward
return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
File “/home/dpetrini/.local/lib/python3.6/site-packages/torch/nn/functional.py”, line 2044, in binary_cross_entropy
“!= input nelement ({})”.format(target.numel(), input.numel()))
ValueError: Target and input must have the same number of elements. target nelement (6) != input nelement (4)
The model is a Resnet plus the layers below:
  (fc1): Linear(in_features=256, out_features=256, bias=True)
  (output_layer): OutputLayer(
    (fc_layer): Linear(in_features=256, out_features=4, bias=True)
  )

I appreciate any help or suggestion. It prevents me to better use GPU filling one or two more samples for each batch and making it faster.
Thanks