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Remove usage of keras

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Piv
2021-04-14 12:45:01 +09:30
parent cf7d2561ec
commit acdb58396c
1 changed files with 3 additions and 4 deletions
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7
losses.py
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@@ -1,5 +1,4 @@
import tensorflow as tf import tensorflow as tf
import tensorflow.keras.backend as K
def dense_depth_loss_function(y, y_pred): def dense_depth_loss_function(y, y_pred):
@@ -7,14 +6,14 @@ def dense_depth_loss_function(y, y_pred):
Implementation of the loss from the dense depth paper https://arxiv.org/pdf/1812.11941.pdf Implementation of the loss from the dense depth paper https://arxiv.org/pdf/1812.11941.pdf
""" """
# Point-wise L1 loss # Point-wise L1 loss
l_depth = K.mean(K.abs(y_pred - y), axis=-1) l_depth = tf.reduce_mean(tf.math.abs(y_pred - y), axis=-1)
# L1 loss over image gradients # L1 loss over image gradients
dy, dx = tf.image.image_gradients(y) dy, dx = tf.image.image_gradients(y)
dy_pred, dx_pred = tf.image.image_gradients(y_pred) dy_pred, dx_pred = tf.image.image_gradients(y_pred)
l_grad = K.mean(K.abs(dy_pred - dy) + K.abs(dx_pred - dx), axis=-1) l_grad = tf.reduce_mean(tf.math.abs(dy_pred - dy) + tf.math.abs(dx_pred - dx), axis=-1)
# Structural Similarity (SSIM) # Structural Similarity (SSIM)
l_ssim = (1 - tf.image.ssim(y, y_pred, 500)) / 2 l_ssim = (1 - tf.image.ssim(y, y_pred, 500)) / 2
return 0.1 * K.mean(l_depth) + l_grad + l_ssim return 0.1 * tf.reduce_mean(l_depth) + l_grad + l_ssim