Function to compute the amount of stride to add before and after the data arrays to obtain a 'same' convolution using a 'valid' convolution.

conv2D_padding.py

import math

import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.backend as K

def compute_amount_padding(L, F, S):
  '''Compute the amount of padding to add prior to a 'valid' convolution to obtain
  the same output size as for a 'same' convolution.

  L: length of data
  F: filter size
  S: stride

  Condition: S <= F

  Return: tuple of two elements with the amount of padding to add before and after the data, respectively'''
  L_target = math.ceil(L / S)
  P = ((L_target - 1)*S - L + F)
  # If stride is larger then filter size: makes no sense
  if S > F:
    print("WARNING in compute_amount_padding: stride is larger then filter size, which makes no sense")
    return 0

  return [math.floor(P/2), math.ceil(P/2)]

# Verify validity for a bunch of combinations:
data_lengths = range(1,11)
filter_sizes = range(1,12)
for length in data_lengths:
  for filter in filter_sizes:
    strides = range(1,filter)
    for stride in strides:
      # input tensor
      input = keras.Input(shape=(length, length, 3,), name="img_content")

      # "same" convolution
      output_same = keras.layers.Conv2D(1,kernel_size=filter,strides=stride,padding="same")(input)

      # "padding + valid convolution"
      P = compute_amount_padding(length,filter,stride)
      input_padded = keras.layers.Lambda( lambda xi: tf.pad(xi, [[0,0], P, P ,[0,0]], "SYMMETRIC"))(input)
      output_pad = keras.layers.Conv2D(1,kernel_size=filter,strides=stride,padding="valid")(input_padded)

      # check if same output size
      condition = output_same.shape[1:3] == output_pad.shape[1:3]
      print(output_same.shape[1:3],output_pad.shape[1:3],condition)
      assert(condition)