| from config import DatasetName | |
| import tensorflow as tf | |
| class ACRLoss: | |
| def acr_loss(self, x_pr, x_gt, phi, lambda_weight, ds_name): | |
| low_map = tf.cast(tf.abs(x_pr - x_gt) <= 1.0, dtype=tf.float32) | |
| high_map = tf.cast(tf.abs(x_pr - x_gt) > 1.0, dtype=tf.float32) | |
| '''Big errors''' | |
| ln_2 = tf.ones_like(x_pr, dtype=tf.float32) * tf.math.log(2.0) | |
| C = tf.cast(tf.cast(phi, dtype=tf.double) * tf.cast(ln_2, dtype=tf.double) - 1.0, dtype=tf.float32) | |
| loss_high = 100 * tf.reduce_mean(tf.math.multiply(high_map, (tf.square(x_pr - x_gt) + C))) | |
| '''Small errors''' | |
| power = tf.cast(2.0 - phi, tf.dtypes.float32) | |
| ll = tf.pow(tf.abs(x_pr - x_gt), power) | |
| loss_low = 100 * tf.reduce_mean(tf.math.multiply(low_map, (lambda_weight * tf.math.log(1.0 + ll)))) | |
| loss_total = loss_low + loss_high | |
| return loss_total, loss_low, loss_high | |