Understanding transform.Normalize( )

vision
18

The messy output is quite normal, as matplotlib either slips the input or tries to scale it, which creates these kind of artifacts (also because you are normalizing channel-wise with different values).

If you would like to visualize the images, you should use the raw images (in [0, 255]) or the normalized ones (in [0, 1]).
Alternatively, you could also unnormalize them, but I think the first approach would be simpler.

If you are using a custom Dataset, just add another load_data function and use it for visualization:

class MyDataset(Dataset):
    def __init__(self, image_paths, targets, transform=None):
        self.image_paths = image_paths
        self.targets = targets
        self.transform = transform

    def load_image(self, index):
        image_path = self.image_paths[index]
        img = Image.open(image_path)
        return img

    def __getitem__(self, index):
        x = self.load_image(index)
        y = self.targets[index]
        
        if self.transform:
            x = self.transform(x)
            
        return x, y
    
    def __len__(self):
        return len(self.image_paths)

image_paths = [...]
targets = ...
dataset = MyDataset(image_paths, targets, transform=transforms.ToTensor())
img_to_vis = dataset.load_image(index=0)

PS: Unrelated to your question, but your PyTorch version is quite old. I would recommend to update it to the latest stable version. You’ll find the install instructions here.

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