objectives -> losses

keras_contrib/losses.py

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+from __future__ import absolute_import
+from keras.objectives import *
+import keras_contrib.backend as KC
+
+
+
+class DSSIMObjective():
+    def __init__(self, k1=0.01, k2=0.03, kernel_size=3, max_value=1.0):
+        """
+        Difference of Structural Similarity (DSSIM loss function). Clipped between 0 and 0.5
+        Note : You should add a regularization term like a l2 loss in addition to this one.
+        :param k1: Parameter of the SSIM (default 0.01)
+        :param k2: Parameter of the SSIM (default 0.03)
+        :param kernel_size: Size of the sliding window (default 3)
+        :param max_value: Max value of the output (default 1.0)
+        """
+        self.__name__ = "DSSIMObjective"
+        self.kernel_size = kernel_size
+        self.k1 = k1
+        self.k2 = k2
+        self.max_value = max_value
+        self.c1 = (self.k1 * self.max_value) ** 2
+        self.c2 = (self.k2 * self.max_value) ** 2
+        self.dim_ordering = K.image_dim_ordering()
+        self.backend = KC.backend()
+
+    def __int_shape(self, x):
+        return KC.int_shape(x) if self.backend == "tensorflow" else KC.shape(x)
+
+    def __call__(self, y_true, y_pred):
+        # There are additional parameters for this function
+        # Note: some of the 'modes' for edge behavior do not yet have a gradient definition in the Theano tree
+        #   and cannot be used for learning
+
+        kernel = [self.kernel_size, self.kernel_size]
+        y_true = KC.reshape(y_true, [-1] + list(self.__int_shape(y_pred)[1:]))
+        y_pred = KC.reshape(y_pred, [-1] + list(self.__int_shape(y_pred)[1:]))
+        patches_pred = KC.extract_image_patches(y_pred, kernel, kernel, "valid", self.dim_ordering)
+        patches_true = KC.extract_image_patches(y_true, kernel, kernel, "valid", self.dim_ordering)
+
+        # Reshape to get the var in the cells
+        bs, w, h, c1, c2, c3 = self.__int_shape(patches_pred)
+        patches_pred = KC.reshape(patches_pred, [-1, w, h, c1 * c2 * c3])
+        patches_true = KC.reshape(patches_true, [-1, w, h, c1 * c2 * c3])
+        # Get mean
+        u_true = KC.mean(patches_true, axis=-1)
+        u_pred = KC.mean(patches_pred, axis=-1)
+        # Get variance
+        var_true = K.var(patches_true, axis=-1)
+        var_pred = K.var(patches_pred, axis=-1)
+        # Get std dev
+        std_true = K.sqrt(var_true + KC.epsilon())
+        std_pred = K.sqrt(var_pred + KC.epsilon())
+
+        ssim = (2 * u_true * u_pred + self.c1) * (2 * std_pred * std_true + self.c2)
+        denom = (K.square(u_true) + K.square(u_pred) + self.c1) * (var_pred + var_true + self.c2)
+        ssim /= denom  # no need for clipping, c1 and c2 make the denom non-zero
+        return K.mean((1.0 - ssim) / 2.0)