trainer.py 文件源码

def eval_one_dataset(self, sess, dataset, save_dir, subset='train'):
        count = 0
        print('num_examples:', dataset._num_examples)
        while count < dataset._num_examples:
            start = count % dataset._num_examples
            images, embeddings_batchs, filenames, _ =\
                dataset.next_batch_test(self.batch_size, start, 1)
            print('count = ', count, 'start = ', start)
            for i in range(len(embeddings_batchs)):
                samples_batchs = []
                # Generate up to 16 images for each sentence,
                # with randomness from noise z and conditioning augmentation.
                for j in range(np.minimum(16, cfg.TRAIN.NUM_COPY)):
                    samples = sess.run(self.fake_images,
                                       {self.embeddings: embeddings_batchs[i]})
                    samples_batchs.append(samples)
                self.save_super_images(images, samples_batchs,
                                       filenames, i, save_dir,
                                       subset)

            count += self.batch_size