I know this has been asked but I could not apply the suggestions there to solve my problem. My code is a simple copy of Pytorch’s tutorial to classify CIFAR-10 dataset and looks like this:

from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def init(self):
super(Net, self).init()
self.conv1 = nn.Conv2d(3, 32, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 3)
self.conv3 = nn.Conv2d(64, 128, 3)
self.conv4 = nn.Conv2d(128, 256, 3)
self.conv5 = nn.Conv2d(256, 512, 3)
self.conv6 = nn.Conv2d(512, 1024, 3)
self.fc1 = nn.Linear(36864, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
    #x = self.pool(F.relu(self.conv1(x)))
    x = F.relu(self.conv1(x))
    #x = self.pool(F.relu(self.conv2(x)))
    x = F.relu(self.conv2(x))
    x = self.pool(F.relu(self.conv3(x)))
    x = F.relu(self.conv4(x))
    x = F.relu(self.conv5(x))
    x = self.pool(F.relu(self.conv6(x)))        
    x = x.view(-1, 36864)
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x
    #print (x.size())

net = Net()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2):  # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
    # get the inputs
    inputs, labels = data

    # wrap them in Variable
    inputs, labels = Variable(inputs), Variable(labels)

    # zero the parameter gradients
    optimizer.zero_grad()

    # forward + backward + optimize
    outputs = net(inputs)
    print(outputs.size())
    print(outputs)
    print(labels)
    print(labels.size())
    loss = criterion(outputs, labels)
    print(output.size())
    print(labels.size())
    loss.backward()
    optimizer.step()

    # print statistics
    running_loss += loss.data[0]
    if i % 2000 == 1999:    # print every 2000 mini-batches
        print('[%d, %5d] loss: %.3f' %
              (epoch + 1, i + 1, running_loss / 2000))
        running_loss = 0.0

print(‘Finished Training’)

and the error is this:

torch.Size([1, 10])
Variable containing:
1.00000e-02 *
-4.6679 -1.3744  0.1477  5.1275 -8.8241 -0.7043 -1.2692 -7.0015  7.4909 -9.7750
[torch.FloatTensor of size 1x10]
Variable containing:
4
1
1
9
[torch.LongTensor of size 4]
torch.Size([4])
RuntimeErrorTraceback (most recent call last)
 in ()
18         print(labels)
19         print(labels.size())
—> 20         loss = criterion(outputs, labels)
21         print(output.size())
22         print(labels.size())
/home/jigyasa/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.pyc in call(self, *input, **kwargs)
222         for hook in self._forward_pre_hooks.values():
223             hook(self, input)
–> 224         result = self.forward(*input, **kwargs)
225         for hook in self._forward_hooks.values():
226             hook_result = hook(self, input, result)
/home/jigyasa/anaconda2/lib/python2.7/site-packages/torch/nn/modules/loss.pyc in forward(self, input, target)
480         _assert_no_grad(target)
481         return F.cross_entropy(input, target, self.weight, self.size_average,
–> 482                                self.ignore_index)
483
484
/home/jigyasa/anaconda2/lib/python2.7/site-packages/torch/nn/functional.pyc in cross_entropy(input, target, weight, size_average, ignore_index)
744                 True, the loss is averaged over non-ignored targets.
745     “”"
–> 746     return nll_loss(log_softmax(input), target, weight, size_average, ignore_index)
747
748
/home/jigyasa/anaconda2/lib/python2.7/site-packages/torch/nn/functional.pyc in nll_loss(input, target, weight, size_average, ignore_index)
670     dim = input.dim()
671     if dim == 2:
–> 672         return _functions.thnn.NLLLoss.apply(input, target, weight, size_average, ignore_index)
673     elif dim == 4:
674         return _functions.thnn.NLLLoss2d.apply(input, target, weight, size_average, ignore_index)
/home/jigyasa/anaconda2/lib/python2.7/site-packages/torch/nn/_functions/thnn/auto.pyc in forward(ctx, input, target, *args)
45         output = input.new(1)
46         getattr(ctx._backend, update_output.name)(ctx._backend.library_state, input, target,
—> 47                                                   output, *ctx.additional_args)
48         return output
49
RuntimeError: Assertion `THIndexTensor_(size)(target, 0) == batch_size’ failed.  at /opt/conda/conda-bld/pytorch_1503966894950/work/torch/lib/THNN/generic/ClassNLLCriterion.c:54

The batch size I’ve taken is 4. But I think the output of print(labels.size()) should be 10 ijstead of 4 because that is the total number of classes.
What am I missing?