image_converter.py 文件源码

def convert_debug(self, content_img, init_img, output_directory, max_iteration=1000, debug_span=100,
                      random_init=False):
        init_array = self.xp.array(neural_art.utility.img2array(init_img))
        if random_init:
            init_array = self.xp.array(self.xp.random.uniform(-20, 20, init_array.shape), dtype=init_array.dtype)
        content_array = self.xp.array(neural_art.utility.img2array(content_img))
        content_layers = self.model.forward_layers(chainer.Variable(content_array),
                                                   average_pooling=self.average_pooling)

        parameter_now = chainer.links.Parameter(init_array)
        self.optimizer.setup(parameter_now)
        for i in range(max_iteration + 1):
            neural_art.utility.print_ltsv({"iteration": i})
            if i % debug_span == 0 and i > 0:
                print("dump to {}".format(os.path.join(output_directory, "{}.png".format(i))))
                neural_art.utility.array2img(chainer.cuda.to_cpu(parameter_now.W.data)).save(
                    os.path.join(output_directory, "{}.png".format(i)))
            parameter_now.zerograds()
            x = parameter_now.W
            layers = self.model.forward_layers(x, average_pooling=self.average_pooling)

            loss_texture = self._texture_loss(layers)
            loss_content = self._contents_loss(layers, content_layers)
            loss = self.texture_weight * loss_texture + self.content_weight * loss_content
            loss.backward()
            parameter_now.W.grad = x.grad
            self.optimizer.update()
        return neural_art.utility.array2img(chainer.cuda.to_cpu(parameter_now.W.data))