trainer.py 文件源码

def epoch_sum_images(self, sess, n):
        images_train, _, embeddings_train, captions_train, _ =\
            self.dataset.train.next_batch(n * n, cfg.TRAIN.NUM_EMBEDDING)
        images_train = self.preprocess(images_train, n)
        embeddings_train = self.preprocess(embeddings_train, n)

        images_test, _, embeddings_test, captions_test, _ = \
            self.dataset.test.next_batch(n * n, 1)
        images_test = self.preprocess(images_test, n)
        embeddings_test = self.preprocess(embeddings_test, n)

        images = np.concatenate([images_train, images_test], axis=0)
        embeddings =\
            np.concatenate([embeddings_train, embeddings_test], axis=0)

        if self.batch_size > 2 * n * n:
            images_pad, _, embeddings_pad, _, _ =\
                self.dataset.test.next_batch(self.batch_size - 2 * n * n, 1)
            images = np.concatenate([images, images_pad], axis=0)
            embeddings = np.concatenate([embeddings, embeddings_pad], axis=0)
        feed_dict = {self.images: images,
                     self.embeddings: embeddings}
        gen_samples, img_summary =\
            sess.run([self.superimages, self.image_summary], feed_dict)

        # save images generated for train and test captions
        scipy.misc.imsave('%s/train.jpg' % (self.log_dir), gen_samples[0])
        scipy.misc.imsave('%s/test.jpg' % (self.log_dir), gen_samples[1])

        # pfi_train = open(self.log_dir + "/train.txt", "w")
        pfi_test = open(self.log_dir + "/test.txt", "w")
        for row in range(n):
            # pfi_train.write('\n***row %d***\n' % row)
            # pfi_train.write(captions_train[row * n])

            pfi_test.write('\n***row %d***\n' % row)
            pfi_test.write(captions_test[row * n])
        # pfi_train.close()
        pfi_test.close()

        return img_summary