Performance issue in the definition of read_and_decode, examples/data_process/tutorial_tfrecord.py(P1) about tensorlayer HOT 2 CLOSED

def read_and_decode(filename):
    # generate a queue with a given file name
    raw_dataset = tf.data.TFRecordDataset([filename]).shuffle(1000).batch(4)
    features = {}
    for serialized_example in raw_dataset:
        features['label'] = tf.io.FixedLenFeature([], tf.int64)
        features['img_raw'] = tf.io.FixedLenFeature([], tf.string)
        features = tf.io.parse_example(serialized_example, features)
        # You can do more image distortion here for training data
        img_batch = tf.io.decode_raw(features['img_raw'], tf.uint8)
        img_batch = tf.reshape(img_batch, [4, 224, 224, 3])
        # img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
        label_batch = tf.cast(features['label'], tf.int32)
        yield img_batch, label_batch