RuntimeError: dimension out of range (expected to be in range of [-1, 0], but got 1)
Im using a Pytorch Unet model to which i am feeding in a image as input and along with that i am feeding the label as the input image mask and traning the dataset on it.
The Unet model i have picked up from somewhere else, and i am using the cross-entropy loss as a loss function but i get this dimension out of range error,
RuntimeError                              
Traceback (most recent call last)
&lt;ipython-input-358-fa0ef49a43ae&gt; in &lt;module&gt;()
     16 for epoch in range(0, num_epochs):
     17     # train for one epoch
---&gt; 18     curr_loss = train(train_loader, model, criterion, epoch, num_epochs)
     19 
     20     # store best loss and save a model checkpoint

&lt;ipython-input-356-1bd6c6c281fb&gt; in train(train_loader, model, criterion, epoch, num_epochs)
     16         # measure loss
     17         print (outputs.size(),labels.size())
---&gt; 18         loss = criterion(outputs, labels)
     19         losses.update(loss.data[0], images.size(0))
     20 

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in     _ _call__(self, *input, **kwargs)
    323         for hook in self._forward_pre_hooks.values():
    324             hook(self, input)
--&gt; 325         result = self.forward(*input, **kwargs)
    326         for hook in self._forward_hooks.values():
    327             hook_result = hook(self, input, result)

&lt;ipython-input-355-db66abcdb074&gt; in forward(self, logits, targets)
      9         probs_flat = probs.view(-1)
     10         targets_flat = targets.view(-1)
---&gt; 11         return self.crossEntropy_loss(probs_flat, targets_flat)

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in     __call__(self, *input, **kwargs)
    323         for hook in self._forward_pre_hooks.values():
    324             hook(self, input)
  --&gt; 325         result = self.forward(*input, **kwargs)
    326         for hook in self._forward_hooks.values():
    327             hook_result = hook(self, input, result)

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/loss.py in f orward(self, input, target)
    599         _assert_no_grad(target)
    600         return F.cross_entropy(input, target, self.weight, self.size_average,
--&gt; 601                                self.ignore_index, self.reduce)
    602 
    603 

/usr/local/lib/python3.5/dist-packages/torch/nn/functional.py in     cross_entropy(input, target, weight, size_average, ignore_index, reduce)
   1138         &gt;&gt;&gt; loss.backward()
   1139     &quot;&quot;&quot;
-&gt; 1140     return nll_loss(log_softmax(input, 1), target, weight, size_average, ignore_index, reduce)
   1141 
   1142 

/usr/local/lib/python3.5/dist-packages/torch/nn/functional.py in     log_softmax(input, dim, _stacklevel)
    784     if dim is None:
    785         dim = _get_softmax_dim('log_softmax', input.dim(),      _stacklevel)
--&gt; 786     return torch._C._nn.log_softmax(input, dim)
    787 
    788 

RuntimeError: dimension out of range (expected to be in range of [-1, 0], but got 1)

Part of my code looks like this
class crossEntropy(nn.Module):
    def __init__(self, weight = None, size_average = True):
        super(crossEntropy, self).__init__()
        self.crossEntropy_loss = nn.CrossEntropyLoss(weight, size_average)
        
    def forward(self, logits, targets):
        probs = F.sigmoid(logits)
        probs_flat = probs.view(-1)
        targets_flat = targets.view(-1)
        return self.crossEntropy_loss(probs_flat, targets_flat)


class UNet(nn.Module):
    def __init__(self, imsize):
        super(UNet, self).__init__()
        self.imsize = imsize

        self.activation = F.relu
        
        self.pool1 = nn.MaxPool2d(2)
        self.pool2 = nn.MaxPool2d(2)
        self.pool3 = nn.MaxPool2d(2)
        self.pool4 = nn.MaxPool2d(2)
        self.conv_block1_64 = UNetConvBlock(4, 64)
        self.conv_block64_128 = UNetConvBlock(64, 128)
        self.conv_block128_256 = UNetConvBlock(128, 256)
        self.conv_block256_512 = UNetConvBlock(256, 512)
        self.conv_block512_1024 = UNetConvBlock(512, 1024)

        self.up_block1024_512 = UNetUpBlock(1024, 512)
        self.up_block512_256 = UNetUpBlock(512, 256)
        self.up_block256_128 = UNetUpBlock(256, 128)
        self.up_block128_64 = UNetUpBlock(128, 64)

        self.last = nn.Conv2d(64, 2, 1)


    def forward(self, x):
        block1 = self.conv_block1_64(x)
        pool1 = self.pool1(block1)

        block2 = self.conv_block64_128(pool1)
        pool2 = self.pool2(block2)

        block3 = self.conv_block128_256(pool2)
        pool3 = self.pool3(block3)

        block4 = self.conv_block256_512(pool3)
        pool4 = self.pool4(block4)

        block5 = self.conv_block512_1024(pool4)

        up1 = self.up_block1024_512(block5, block4)

        up2 = self.up_block512_256(up1, block3)

        up3 = self.up_block256_128(up2, block2)

        up4 = self.up_block128_64(up3, block1)

        return F.log_softmax(self.last(up4))


According to your code:

probs_flat = probs.view(-1)
targets_flat = targets.view(-1)
return self.crossEntropy_loss(probs_flat, targets_flat)


You are giving two 1d tensor to nn.CrossEntropyLoss but according to documentation, it expects:

Input: (N,C) where C = number of classes
Target: (N) where each value is 0 &lt;= targets[i] &lt;= C-1
Output: scalar. If reduce is False, then (N) instead.


I believe that is the cause of the problem you are encountering.
