corr.py

import tensorflow as tf
import numpy as np
import math

BATCH_SIZE = 8
HEIGHT = 30
WIDTH = 60
CHANNELS = 3

NEIGHBORHOOD_SIZE = 41
MAX_DISPLACEMENT = int(math.ceil(NEIGHBORHOOD_SIZE / 2.0))
STRIDE_2 = 2

assert(STRIDE_2 <= NEIGHBORHOOD_SIZE)

# Define two feature maps
fmA = tf.ones((BATCH_SIZE, HEIGHT, WIDTH, CHANNELS), dtype=tf.int32)
fmB = tf.convert_to_tensor(np.random.randint(5, size=(BATCH_SIZE, HEIGHT, WIDTH, CHANNELS)), dtype=tf.int32)

depth = int(math.floor((2.0 * MAX_DISPLACEMENT + 1) / STRIDE_2) ** 2)

print 'Output should be size:', (BATCH_SIZE, HEIGHT, WIDTH, depth)
print 'Striding at values: ', [e for e in range(-MAX_DISPLACEMENT + 1, MAX_DISPLACEMENT, STRIDE_2)]

def main():
    out = []
    for i in range(-MAX_DISPLACEMENT + 1, MAX_DISPLACEMENT, STRIDE_2): # height
        for j in range(-MAX_DISPLACEMENT + 1, MAX_DISPLACEMENT, STRIDE_2): # width
            padded_a = tf.pad(fmA, [[0,0], [0, abs(i)], [0, abs(j)], [0, 0]])
            padded_b = tf.pad(fmB, [[0, 0], [abs(i), 0], [abs(j), 0], [0, 0]])
            m = padded_a * padded_b

            height_start_idx = 0 if i <= 0 else i
            height_end_idx = height_start_idx + HEIGHT
            width_start_idx = 0 if j <= 0 else j
            width_end_idx = width_start_idx + WIDTH
            cut = m[:, height_start_idx:height_end_idx, width_start_idx:width_end_idx, :]

            final = tf.reduce_sum(cut, 3)
            out.append(final)
    corr = tf.stack(out, 3)
    print 'Output size: ', corr.shape

main()