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Difference of stuctural similarity using Tensorflow and keras. Works ONLY on tf >= 0.11
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import keras.backend as K | |
import tensorflow as tf | |
class Model: | |
def __init__(self,batch_size): | |
self.batch_size = batch_size | |
def loss_DSSIS_tf11(self, y_true, y_pred): | |
"""Need tf0.11rc to work""" | |
y_true = tf.reshape(y_true, [self.batch_size] + get_shape(y_pred)[1:]) | |
y_pred = tf.reshape(y_pred, [self.batch_size] + get_shape(y_pred)[1:]) | |
y_true = tf.transpose(y_true, [0, 2, 3, 1]) | |
y_pred = tf.transpose(y_pred, [0, 2, 3, 1]) | |
patches_true = tf.extract_image_patches(y_true, [1, 5, 5, 1], [1, 2, 2, 1], [1, 1, 1, 1], "SAME") | |
patches_pred = tf.extract_image_patches(y_pred, [1, 5, 5, 1], [1, 2, 2, 1], [1, 1, 1, 1], "SAME") | |
u_true = K.mean(patches_true, axis=3) | |
u_pred = K.mean(patches_pred, axis=3) | |
var_true = K.var(patches_true, axis=3) | |
var_pred = K.var(patches_pred, axis=3) | |
std_true = K.sqrt(var_true) | |
std_pred = K.sqrt(var_pred) | |
c1 = 0.01 ** 2 | |
c2 = 0.03 ** 2 | |
ssim = (2 * u_true * u_pred + c1) * (2 * std_pred * std_true + c2) | |
denom = (u_true ** 2 + u_pred ** 2 + c1) * (var_pred + var_true + c2) | |
ssim /= denom | |
ssim = tf.select(tf.is_nan(ssim), K.zeros_like(ssim), ssim) | |
return K.mean(((1.0 - ssim) / 2)) |
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