How to use Tencrop

transform = Compose([
TenCrop(size), # this is a list of PIL Images
Lambda(lambda crops: torch.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor
])
#In your test loop you can do the following:
input, target = batch # input is a 5d tensor, target is 2d
bs, ncrops, c, h, w = input.size()
result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops
result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops

If I want to use result_max instead of `result_avg, then

result_max = torch.max(result.view(`bs, ncrops, -1), 1) is ok?

torch.max will return the values and indices for the specified dimension, so you might want to access the desired attribute.
Besides that you would get the result containing the max. pixel values from all crops.