What an honor to be replied by you, smth.
But the pytorch imagenet example is also very different from 0.5, 0.5, 0.5.
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])

train_loader = torch.utils.data.DataLoader(
    datasets.ImageFolder(traindir, transforms.Compose([
        transforms.RandomSizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize,
    ])),
    batch_size=args.batch_size, shuffle=True,
    num_workers=args.workers, pin_memory=True)

I guess in the pytorch tutorial we are getting a normalization from a range 0 to 1 to -1 to 1 for each image, not considering the mean-std of the whole dataset.
David