resnetbm.py 文件源码

def distorted_inputs():
    data = load_data(FLAGS.data_dir)

    filenames = [ d['filename'] for d in data ]
    label_indexes = [ d['label_index'] for d in data ]

    filename, label_index = tf.train.slice_input_producer([filenames, label_indexes], shuffle=True)

    num_preprocess_threads = 4
    images_and_labels = []
    for thread_id in range(num_preprocess_threads):
        image_buffer = tf.read_file(filename)

        bbox = []
        train = True
        image = image_preprocessing(image_buffer, bbox, train, thread_id)
        images_and_labels.append([image, label_index])

    images, label_index_batch = tf.train.batch_join(
        images_and_labels,
        batch_size=FLAGS.batch_size,
        capacity=2 * num_preprocess_threads * FLAGS.batch_size)

    height = FLAGS.input_size
    width = FLAGS.input_size
    depth = 3

    images = tf.cast(images, tf.float32)
    images = tf.reshape(images, shape=[FLAGS.batch_size, height, width, depth])

    return images, tf.reshape(label_index_batch, [FLAGS.batch_size])