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import math
import numpy as np
import tensorflow as tf
# tf.compat.v1.disable_eager_execution()
# tf.compat.v1.enable_eager_execution()
from PIL import Image
from tensorflow.keras import backend as K
from scipy.spatial import distance
from cnn_model import CNNModel
from configuration import DatasetName, D300wConf, LearningConfig
from image_utility import ImageUtility
from pca_utility import PCAUtility
print(tf.__version__)
class Custom_losses:
def MSE(self, x_pr, x_gt):
return tf.losses.mean_squared_error(x_pr, x_gt)
def kd_loss(self, x_pr, x_gt, x_tough, x_tol, alpha_tough, alpha_mi_tough, alpha_tol, alpha_mi_tol,
main_loss_weight, tough_loss_weight, tol_loss_weight):
"""km"""
'''los KD'''
# we revise teachers for reflection:
x_tough = x_gt + tf.sign(x_pr - x_gt) * tf.abs(x_tough - x_gt)
b_tough = x_gt + tf.sign(x_pr - x_gt) * tf.abs(x_tough - x_gt) * 0.15
x_tol = x_gt + tf.sign(x_pr - x_gt) * tf.abs(x_tol - x_gt)
b_tol = x_gt + tf.sign(x_pr - x_gt) * tf.abs(x_tol - x_gt) * 0.15
# Region A: from T -> +inf
tou_pos_map = tf.where(tf.sign(x_pr - x_tough) * tf.sign(x_tough - x_gt) > 0, alpha_tough, 0.0)
tou_neg_map = tf.where(tf.sign(x_tough - x_pr) * tf.sign(x_pr - b_tough) >= 0, alpha_mi_tough, 0.0)
# tou_red_map = tf.where(tf.sign(tf.abs(b_tough) - tf.abs(x_pr))*tf.sign(tf.abs(x_pr) - tf.abs(x_gt)) > 0, 0.1, 0.0)
tou_map = tou_pos_map + tou_neg_map # + tou_red_map
tol_pos_map = tf.where(tf.sign(x_pr - x_tol) * tf.sign(x_tol - x_gt) > 0, alpha_tol, 0.0)
tol_neg_map = tf.where(tf.sign(x_tol - x_pr) * tf.sign(x_pr - b_tol) >= 0, alpha_mi_tol, 0.0)
# tol_red_map = tf.where(tf.sign(tf.abs(b_tol) - tf.abs(x_pr))*tf.sign(tf.abs(x_pr) - tf.abs(x_gt)) > 0, 0.1, 0.0)
tol_map = tol_pos_map + tol_neg_map # + tou_red_map
'''calculate dif map for linear and non-linear part'''
low_diff_main_map = tf.where(tf.abs(x_gt - x_pr) <= tf.abs(x_gt - x_tol), 1.0, 0.0)
high_diff_main_map = tf.where(tf.abs(x_gt - x_pr) > tf.abs(x_gt - x_tol), 1.0, 0.0)
'''calculate loss'''
loss_main_high_dif = tf.reduce_mean(
high_diff_main_map * (tf.square(x_gt - x_pr) + (3 * tf.abs(x_gt - x_tol)) - tf.square(x_gt - x_tol)))
loss_main_low_dif = tf.reduce_mean(low_diff_main_map * (3 * tf.abs(x_gt - x_pr)))
loss_main = main_loss_weight * (loss_main_high_dif + loss_main_low_dif)
loss_tough_assist = tough_loss_weight * tf.reduce_mean(tou_map * tf.abs(x_tough - x_pr))
loss_tol_assist = tol_loss_weight * tf.reduce_mean(tol_map * tf.abs(x_tol - x_pr))
'''dif loss:'''
loss_total = loss_main + loss_tough_assist + loss_tol_assist
return loss_total, loss_main, loss_tough_assist, loss_tol_assist |