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Ok. So I reproduced your problem and after some search and reading the API of CrossEntropyLoss(), I have found it's because you have a wrong label dimension.

Offical docs of CrossEntropyLoss here. And you can see

Input: (N,C) where C = number of classes
Target: (N) where each value is 0≤targets[i]≤C−1

While here, in your criterion() function, you have a batchSize x 7 input and batchSize x 1 label. The confusing point is, say your batchSize is 10, a 10x1 tensor can not be regarded as a size-10 tensor, which is what the loss function expectes. You must explictly do the size conversion.

Solution:
Add labels = labels.squeeze_() before you call loss = criterion(y_pred, labels) and do the same thing in your test code. The squeeze_() funciton removes size-1 dimensions inplace. So you have a batchSize-size label now.