21 lines
637 B
Python
21 lines
637 B
Python
import tensorflow as tf
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import tensorflow.keras.backend as K
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def dense_depth_loss_function(y, y_pred):
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"""
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Implementation of the loss from the dense depth paper https://arxiv.org/pdf/1812.11941.pdf
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"""
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# Point-wise L1 loss
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l_depth = K.mean(K.abs(y_pred - y), axis=-1)
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# L1 loss over image gradients
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dy, dx = tf.image.image_gradients(y)
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dy_pred, dx_pred = tf.image.image_gradients(y_pred)
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l_grad = K.mean(K.abs(dy_pred - dy) + K.abs(dx_pred - dx), axis=-1)
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# Structural Similarity (SSIM)
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l_ssim = (1 - tf.image.ssim(y, y_pred, 500)) / 2
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return 0.1 * K.mean(l_depth) + l_grad + l_ssim
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