enhance_me/zero_dce/losses/spatial_constancy.py

import tensorflow as tf
from tensorflow.keras import losses


class SpatialConsistencyLoss(losses.Loss):
    def __init__(self, **kwargs):
        super(SpatialConsistencyLoss, self).__init__(reduction="none")

        self.left_kernel = tf.constant(
            [[[[0, 0, 0]], [[-1, 1, 0]], [[0, 0, 0]]]], dtype=tf.float32
        )
        self.right_kernel = tf.constant(
            [[[[0, 0, 0]], [[0, 1, -1]], [[0, 0, 0]]]], dtype=tf.float32
        )
        self.up_kernel = tf.constant(
            [[[[0, -1, 0]], [[0, 1, 0]], [[0, 0, 0]]]], dtype=tf.float32
        )
        self.down_kernel = tf.constant(
            [[[[0, 0, 0]], [[0, 1, 0]], [[0, -1, 0]]]], dtype=tf.float32
        )

    def call(self, y_true, y_pred):

        original_mean = tf.reduce_mean(y_true, 3, keepdims=True)
        enhanced_mean = tf.reduce_mean(y_pred, 3, keepdims=True)
        original_pool = tf.nn.avg_pool2d(
            original_mean, ksize=4, strides=4, padding="VALID"
        )
        enhanced_pool = tf.nn.avg_pool2d(
            enhanced_mean, ksize=4, strides=4, padding="VALID"
        )

        d_original_left = tf.nn.conv2d(
            original_pool, self.left_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_original_right = tf.nn.conv2d(
            original_pool, self.right_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_original_up = tf.nn.conv2d(
            original_pool, self.up_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_original_down = tf.nn.conv2d(
            original_pool, self.down_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )

        d_enhanced_left = tf.nn.conv2d(
            enhanced_pool, self.left_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_enhanced_right = tf.nn.conv2d(
            enhanced_pool, self.right_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_enhanced_up = tf.nn.conv2d(
            enhanced_pool, self.up_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_enhanced_down = tf.nn.conv2d(
            enhanced_pool, self.down_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )

        d_left = tf.square(d_original_left - d_enhanced_left)
        d_right = tf.square(d_original_right - d_enhanced_right)
        d_up = tf.square(d_original_up - d_enhanced_up)
        d_down = tf.square(d_original_down - d_enhanced_down)
        return d_left + d_right + d_up + d_down