torchvision.transforms.Normalize() slows down learning when adding to torchvision,transforms.Compose()
when i use
train_transforms = torchvision.transforms.Compose([
  torchvision.transforms.ToTensor(), 
  torchvision.transforms.Normalize((0.1307,), (0.3081,))
])

for loading MNIST dataset, it slows down learning even with mean = 0 and std = 1.

The transformations are performed on CPU, and it doesn't matter if the mean/std are all zeros (BTW, don't set std to 0). To speed up the transform you have two options:

If you don't have any data augmentations in your flow, just transform the data and save it as normalized tensors (pickled or something).
You can also use torch.utils.data.DataLoader with some arguments: for example num_workers specifies how many CPU processes to use to transform the data. THere is also pin_memory which will speed up the whole thing if you are using CUDA.

