When I run the pytorch_examples/imagenet example with inception_v3 model I get:
Traceback (most recent call last):
  File "/home/nadavb/pytorch_examples/examples/imagenet/main.py", line 287, in <module>
    main()
  File "/home/nadavb/pytorch_examples/examples/imagenet/main.py", line 130, in main
    train(train_loader, model, criterion, optimizer, epoch)
  File "/home/nadavb/pytorch_examples/examples/imagenet/main.py", line 166, in train
    output = model(input_var)
  File "/home/nadavb/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.py", line 206, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/nadavb/anaconda2/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 61, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/nadavb/anaconda2/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 71, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs)
  File "/home/nadavb/anaconda2/lib/python2.7/site-packages/torch/nn/parallel/parallel_apply.py", line 45, in parallel_apply
    raise output
RuntimeError: CHECK_ARG(input->nDimension == output->nDimension) failed at torch/csrc/cudnn/Conv.cpp:275

It looks like I’m missing something obvious which is related to the input size. I’m not sure what exactly…
Is there a different pre-processing done for inception models?