ValueError                                Traceback (most recent call last)
<ipython-input-80-fbef08da00d7> in <module>
     31         #loss = output['loss']
     32         #loss = net(img, target['bbox'], target['labels']).to(device)
---> 33         loss = criterion(output, clas).to(device)
     34         loss_bb = criterion_bb(outputs, box).to(device)
     35         loss.backward()

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
    528 
    529     def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 530         return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
    531 
    532 

/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in binary_cross_entropy(input, target, weight, size_average, reduce, reduction)
   2517     if target.size() != input.size():
   2518         raise ValueError("Using a target size ({}) that is different to the input size ({}) is deprecated. "
-> 2519                          "Please ensure they have the same size.".format(target.size(), input.size()))
   2520 
   2521     if weight is not None:

ValueError: Using a target size (torch.Size([1, 23])) that is different to the input size (torch.Size([1, 19])) is deprecated. Please ensure they have the same size.


for i, (img, boxes, classes) in enumerate(train_loader):
        net.to(device)
        img = img.to(device)  
        box = boxes.to(device) 
        clas = classes.to(device)
        optimizer.zero_grad()
        output = net(img)
        loss = criterion(output, clas).to(device)
        loss_bb = criterion_bb(outputs, box).to(device)
        loss.backward()
        optimizer.step()

I get an error in loss when I pass tensor with lable and bbox into it. model vgg16_bn / I have 19 classes. I assume that the model or loss takes trying to push all the bboxes into the classifier layer, and each image has a different number of objects. how do i feed to bbox model,
label? or maybe this model is not suitable for multi-class + multi-label classification?