tfcycle.py 文件源码

def input_pipeline(filenames, batch_size, num_epochs=None, image_size=142, crop_size=256):
    with tf.device('/cpu:0'):
        filenames = tf.train.match_filenames_once(filenames)
        filename_queue = tf.train.string_input_producer(filenames, num_epochs=num_epochs, shuffle=True)
        reader = tf.WholeFileReader()
        filename, value = reader.read(filename_queue)

        image = tf.image.decode_jpeg(value, channels=3)

        processed = tf.image.resize_images(
            image,
            [image_size, image_size],
            tf.image.ResizeMethod.BILINEAR )

        processed = tf.image.random_flip_left_right(processed)
        processed = tf.random_crop(processed, [crop_size, crop_size, 3] )
        # CHANGE TO 'CHW' DATA_FORMAT FOR FASTER GPU PROCESSING
        processed = tf.transpose(processed, [2, 0, 1])
        processed = (tf.cast(processed, tf.float32) - 128.0) / 128.0

        images = tf.train.batch(
            [processed],
            batch_size = batch_size,
            num_threads = NUM_THREADS,
            capacity=batch_size * 5)

    return images