ptrblck:

torch.stack([transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(t) for t in tensors]))


Thank you @ptrblck, it worked.
I have another question: Applying TenCrop transforms and using batch (say 16) loading creates a batch with the dimension torch.Size([16, 10, 3, 224, 224]). This means I have 160 images when I reshape it to BCWH, ten of each. I think will be to large a batch size to use and it will also make the batch distribution unrepresentative of the entire dataset.
Please help.
`