I am trying to normalize my targets (here landmark values) but to_tensor is not forcing the values to be between 0 and 1. How should that be enforced?
Also, am I looking for only one value for mean and one value for std, right? Each image has 4 landmarks and each landmark has x and y coordinates.
to_tensor = transforms.ToTensor()

landmarks_arr = []

for i in range(len(train_dataset)):
    landmarks = to_tensor(train_dataset[i]['landmarks'])
    landmarks[landmarks != landmarks] = 0  # this will set all nan to zero
    landmarks_arr.append(landmarks)
    
mean = torch.mean(torch.stack(landmarks_arr, dim=0))#, dim=(0, 2, 3))
std = torch.std(torch.stack(landmarks_arr, dim=0)) #, dim=(0, 2, 3))

print(mean.shape)
print("mean is {} and std is {}".format(mean, std))

result is:
torch.Size([])
mean is 367.7143527453419 and std is 160.8974583202625

This is how landmarks are:
landmarks_arr[0]

tensor([[[502.2869, 240.4949],
         [688.0000, 293.0000],
         [346.0000, 317.0000],
         [560.8283, 322.6830]]], dtype=torch.float64)

On a side note, is this line even correct?
mean = torch.mean(torch.stack(landmarks_arr, dim=0))#, dim=(0, 2, 3))