Tensorflow - Same Padding Calculation - tensorflow

Question

I have the following parameters:
in_height = 28
in_width = 28
stride (s) = 2
padding (p) = 'SAME'
The idea of 'SAME' padding is when s = 1 then input map and output map dimensions (height, width) should remain same
So if I should be able to get the padding size using the following:
(28 + 2*p - 5) + 1 = 28
Solving gives p = 2
which means there should be a padding on each side of 2
Using p=2 the output map size would be:
(28 + 4 -5)/2 + 1 = 14
From Tensorflow documentation, Same Padding:
out_height = ceil(float(in_height) / float(strides[1]))
out_width = ceil(float(in_width) / float(strides[2]))
pad_along_height = max((out_height - 1) * strides[1] +
filter_height - in_height, 0)
pad_along_width = max((out_width - 1) * strides[2] +
filter_width - in_width, 0)
pad_top = pad_along_height // 2
pad_bottom = pad_along_height - pad_top
pad_left = pad_along_width // 2
pad_right = pad_along_width - pad_left
To follow the above:
out_height = ceil(28.0/2.0) = 14.0
out_width = ceil(28.0/2.0) = 14.0
Hence
pad_along_height = max((14.0 -1)*2 + 5 - 28,0) = 3
pad_along_width = max((14.0 -1)*2 + 5 - 28,0) = 3
pad_top = 3 // 2 = 1
pad_bottom = 3//2 - pad_top = 2
pad_left = pad_along_width // 2 = 1
pad_right = pad_along_width - pad_left = 2
So does it mean that the image should be padded 1 on top and 2 on bottom similarly on the left and right?

Answer 1

I was looking at the Tensorflow documentation they actually validate the thought:
Note that the division by 2 means that there might be cases when the
padding on both sides (top vs bottom, right vs left) are off by one.
In this case, the bottom and right sides always get the one additional
padded pixel. For example, when pad_along_height is 5, we pad 2 pixels
at the top and 3 pixels at the bottom. Note that this is different
from existing libraries such as cuDNN and Caffe, which explicitly
specify the number of padded pixels and always pad the same number of
pixels on both sides.