[FIXED] Why couldn't I feed a 4-tuple to nn.ReplicationPad2d()?

Issue

I'm applying yolov5 on kitti raw image [C, H, W] = [3, 375, 1242]. Therefore I need to pad the image so that the H and W being dividable by 32. I'm using nn.ReplicationPad2d to do the padding: [3, 375, 1242] -> [3, 384, 1248].
In the official tutorial of nn.ReplicationPad2d it was said that we give a 4-tuple to indicate padding sizes for left, right, top and bottom.
The Problem is:
When I give a 4-tuple (0, pad1, 0, pad2), it claims that: 3D tensors expect 2 values for padding
When I give a 2-tuple (pad1, pad2), the pad can be implemented but it seems that only W was padded by pad1+pad2, while H stays unchanged. Because I 'll get a tensor of size [3, 375, 1257].
1257-1242 = 15 = 9+6, where 9 was supposed to pad H and 6 pad W.
I could not figure out what is the problem here...
thanks in advance

Here is my code:

def paddingImage(img, divider=32):
    if img.shape[1]%divider != 0 or img.shape[2]%divider != 0:
        padding1_mult = int(img.shape[1] / divider) + 1
        padding2_mult = int(img.shape[2] / divider) + 1
        pad1 = (divider * padding1_mult) - img.shape[1]
        pad2 = (divider * padding2_mult) - img.shape[2]

        # pad1 = 32 - (img.shape[1]%32)
        # pad2 = 32 - (img.shape[2]%32)
        # pad1 = 384 - 375    # 9
        # pad2 = 1248 - 1242  # 6

        #################### PROBLEM ####################
        padding = nn.ReplicationPad2d((pad1, pad2))
        #################### PROBLEM ####################

        return padding(img)
    else:
        return img

Where img was given as a torch.Tensor in the main function:

# ...
image_tensor = torch.from_numpy(image_np).type(torch.float32)
image_tensor = paddingImage(image_tensor)
image_np = image_tensor.numpy()
# ...

Solution

PyTorch expects the input to ReplicationPad2d to be batched image tensors. Therefore, we can unsqueeze to add a 'batch dimension'.

def paddingImage(img, divider=32):
    if img.shape[1]%divider != 0 or img.shape[2]%divider != 0:
        padding1_mult = int(img.shape[1] / divider) + 1
        padding2_mult = int(img.shape[2] / divider) + 1
        pad1 = (divider * padding1_mult) - img.shape[1]
        pad2 = (divider * padding2_mult) - img.shape[2]

        # pad1 = 32 - (img.shape[1]%32)
        # pad2 = 32 - (img.shape[2]%32)
        # pad1 = 384 - 375    # 9
        # pad2 = 1248 - 1242  # 6

        padding = nn.ReplicationPad2d((0, pad2, 0, pad1))
        # Add a extra batch-dimension, pad, and then remove batch-dimension
        return torch.squeeze(padding(torch.unsqueeze(img,0)),0)
    else:
        return img

Hope this helps!

EDIT As GoodDeeds mentions, this is resolved with later versions of PyTorch. Either upgrade PyTorch, or, if that's not an option, use the code above.

Answered By - Ludvig J.