So Im confuse here.
I am using the tutorial on the pytorch website.
I am reading the images from the cifar10 and for the initial stage im doing some preprocessing on them.
here i just normalize each channel for batch size of 1 between 0-1, but it does not sound like that it is working…
I am not sure if it is a bug or if im doing a mistake here…
So here is my code:

BatchSize=1;

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))
    ])

TrainSet = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(TrainSet, batch_size=BatchSize,
dataiter = iter(trainloader)
images, labels = dataiter.next()

and here i just wanted to check the values:
print('Images size is: {} and it has min and max of {} and {}, \n\n \
      1st channel is {}\n \
      min and max of the 1st channel is {} and {} \n \n \
      2st channel is {}\n \
      min and max of the 2st channel is {} and {} \n \n \
      3rd channel is {} \n \
      min and max of the 3st channel is {} and {} \n' \
      .format(images.size(), torch.min(images[0,:,:,:]), torch.max(images[0,:,:,:]),\
              images[0,0,:,:], torch.min(images[0,0,:,:]), torch.max(images[0,0,:,:]),\
              images[0,1,:,:], torch.min(images[0,1,:,:]), torch.max(images[0,1,:,:]),\
              images[0,2,:,:], torch.min(images[0,2,:,:]), torch.max(images[0,2,:,:]),\
             ) )

And the results were:

Images size is: torch.Size([1, 3, 32, 32]) and it has min and max of 0.0 and 1.0,
   1st channel is 

0.1647  0.2078  0.2588  …   0.4353  0.4431  0.4353
0.1569  0.2118  0.2078  …   0.4431  0.4392  0.4353
0.1686  0.1922  0.1961  …   0.4275  0.4392  0.4353
…             ⋱             …
0.2824  0.2706  0.2745  …   0.3490  0.3647  0.3765
0.2667  0.2667  0.2706  …   0.3961  0.4000  0.4078
0.2588  0.2588  0.2627  …   0.4118  0.4078  0.4000
[torch.FloatTensor of size 32x32]
   min and max of the 1st channel is 0.0549019612372 and 1.0 

   2st channel is 

0.1843  0.2314  0.2863  …   0.5216  0.5333  0.5255
0.1804  0.2392  0.2353  …   0.5333  0.5294  0.5216
0.1922  0.2157  0.2235  …   0.5255  0.5294  0.5216
…             ⋱             …
0.2902  0.2784  0.2824  …   0.3725  0.3882  0.4000
0.2784  0.2784  0.2824  …   0.4196  0.4235  0.4314
0.2706  0.2706  0.2745  …   0.4353  0.4314  0.4235
[torch.FloatTensor of size 32x32]
   min and max of the 2st channel is 0.0470588244498 and 1.0 

   3rd channel is 

0.1451  0.1765  0.2235  …   0.7922  0.8039  0.7922
0.1412  0.1843  0.1686  …   0.7765  0.7961  0.8000
0.1529  0.1608  0.1569  …   0.7333  0.7961  0.8078
…             ⋱             …
0.3765  0.3686  0.3686  …   0.4745  0.4902  0.5020
0.3529  0.3529  0.3569  …   0.5216  0.5255  0.5333
0.3373  0.3333  0.3412  …   0.5373  0.5333  0.5255
[torch.FloatTensor of size 32x32]
   min and max of the 3st channel is 0.0 and 0.815686285496 


as you can see all three channels are normalized together and have value between 0-1, but the channels separately does not sound like that are normalized.
I thought when I defined it like above it should normalized them between 0-1 separately. Can you please tell me how should i do that if I want each channel separately become normalized.
Thanks