doodle.py 文件源码

def style_loss(self):
        """Returns a list of loss components as Theano expressions. Finds the best style patch for each patch in the
        current image using normalized cross-correlation, then computes the mean squared error for all patches.
        """
        style_loss = []
        if args.style_weight == 0.0:
            return style_loss

        # Extract the patches from the current image, as well as their magnitude.
        result = self.do_extract_patches(zip(self.style_layers, self.model.get_outputs('conv', self.style_layers)))

        # Multiple style layers are optimized separately, usually conv3_1 and conv4_1 — semantic data not used here.
        for l, matches, patches in zip(self.style_layers, self.tensor_matches, result[0::3]):
            # Compute the mean squared error between the current patch and the best matching style patch.
            # Ignore the last channels (from semantic map) so errors returned are indicative of image only.
            loss = T.mean((patches - matches[:,:self.model.channels[l]]) ** 2.0)
            style_loss.append(('style', l, args.style_weight * loss))
        return style_loss