code:
for i, (input, target) in enumerate(test_loader):
	target = target.cuda(async=True) # in test loader, pin_memory = True
	input_var = torch.autograd.Variable(input, volatile=False)

	# (Batch_Size, 10L, 3L, 32L, 224L, 224L)
	b, s, c, t, h, w = input_var.size()
	# view in (Batch_Size * 10L, 3L, 32L, 224L, 224L)
	input_var = input_var.view(-1, c, t, h, w)
	# forward
	output = model(input_var)
	# split in (Batch_Size, 10L, 400L)
	output = output.view(b, s, args.num_classes)
	# softmax
	scores = torch.sum(F.softmax(output, dim=2), dim=1, keepdim=False)
	# in-place average scores
	scores, indices = scores.div_(10).sort(dim=1, descending=True)

error:
Traceback (most recent call last):
  File "inference.py", line 301, in <module>
    main()
  File "inference.py", line 164, in main
    inference(test_loader, model)
  File "inference.py", line 195, in inference
    output = model(input_var)
  File "/home/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.py", line 357, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/anaconda2/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 73, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/anaconda2/lib/python2.7/site-packages/torch/nn/parallel/data_parallel.py", line 83, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/home/anaconda2/lib/python2.7/site-packages/torch/nn/parallel/parallel_apply.py", line 67, in parallel_apply
    raise output
RuntimeError: all tensors must be on devices[0]

Thanks.