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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()
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