Generator.py 文件源码

def sample(self, nb_samples):
        sampled_character_folders = random.sample(self.character_folders, nb_samples)
        random.shuffle(sampled_character_folders)

        example_inputs = np.zeros((self.batch_size, nb_samples * self.nb_samples_per_class, np.prod(self.img_size)), dtype=np.float32)
        example_outputs = np.zeros((self.batch_size, nb_samples * self.nb_samples_per_class), dtype=np.float32)     #notice hardcoded np.float32 here and above, change it to something else in tf

        for i in range(self.batch_size):
            labels_and_images = get_shuffled_images(sampled_character_folders, range(nb_samples), nb_samples=self.nb_samples_per_class)
            sequence_length = len(labels_and_images)
            labels, image_files = zip(*labels_and_images)

            angles = np.random.uniform(-self.max_rotation, self.max_rotation, size=sequence_length)
            shifts = np.random.uniform(-self.max_shift, self.max_shift, size=sequence_length)

            example_inputs[i] = np.asarray([load_transform(filename, angle=angle, s=shift, size=self.img_size).flatten() \
                                            for (filename, angle, shift) in zip(image_files, angles, shifts)], dtype=np.float32)
            example_outputs[i] = np.asarray(labels, dtype=np.int32)

        return example_inputs, example_outputs