This code is working
for k, image_path in enumerate(image_list):
    image = imgproc.loadImage(image_path)
    print(image.shape)
    with torch.no_grad():
        y, feature = net(x)        
    result = image.cuda()

However, when I try to use data loader for the same having batch size more than 1, the code is failing as the size of images are different. I know I can resize the image but not doing it as it might change the aspect ratio of the image and might impact my results.
What alternative solution am i having?
Can we use any bucketing technique here?