Hi.
Whenever I Normalize my input images with its mean and std, and when i plot the images to visualize i get this message along with plots of images which are distorted in a way.
Clipping input data to the valid range for imshow with RGB data ([0…1] for floats or [0…255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0…1] for floats or [0…255] for integers).
If anyone could help me understand why this is happening it will be great.
torchvision.transforms.Normalize will use the mean and std to standardize the inputs, so that they would have a zero mean and unit variance.
Your current library to show these images (probably matplotlib) will clip the values of these float image arrays to [0, 1], which will distort them.
Thank you very much @ptrblck . Yes i am using matplotlib to show the images. Getting distorted images , does it mean that I am doing something wrong ? , If not is there a way i could plot the images without them being distorted ?
The easiest way would be to plot them before normalizing.
However, if that’s not possible, you could also undo the normalization:
x = torch.empty(3, 224, 224).uniform_(0, 1)
mean = (0.5, 0.5, 0.5)
std = (0.5, 0.5, 0.5)
norm = transforms.Normalize(mean, std)
x_norm = norm(x)
x_restore = x_norm * torch.tensor(std).view(3, 1, 1) + torch.tensor(mean).view(3, 1, 1)
print((x_restore - x).abs().max())
> tensor(0.)
