88b164b98c209ea8e2afbfae93a01b9aacd8b0ab,tools/train_shadownet.py,,train_shadownet,#Any#Any#Any#Any#,65

Before Change

    images, labels, imagenames = decoder.read_features(ops.join(cfg.PATH.TFRECORDS_DIR, "train_feature.tfrecords"),
                                                       num_epochs=None, input_size=cfg.ARCH.INPUT_SIZE,
                                                       input_channels=cfg.ARCH.INPUT_CHANNELS)
    inputdata, input_labels, input_imagenames = tf.train.shuffle_batch(
        tensors=[images, labels, imagenames], batch_size=cfg.TRAIN.BATCH_SIZE,
        capacity=1000 + 2*cfg.TRAIN.BATCH_SIZE, min_after_dequeue=100, num_threads=num_threads)

    inputdata = tf.cast(x=inputdata, dtype=tf.float32)

    // initialise the net model

After Change

    dataset = tf.data.TFRecordDataset(cfg.PATH.TFRECORDS_DIR)
    dataset = dataset.batch(cfg.TRAIN.BATCH_SIZE, drop_remainder=True)
    dataset = dataset.map(decoder.extract_features_batch, num_parallel_calls=num_threads)
    // dataset = dataset.apply(tf.contrib.data.map_and_batch(map_func=decoder.extract_features,
    //                                                       batch_size=config.cfg.TRAIN.BATCH_SIZE,
    //                                                       num_parallel_batches=num_threads,
    //                                                       drop_remainder=True))
    dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(cfg.TRAIN.BATCH_SIZE*num_threads*16))
    dataset = dataset.prefetch(buffer_size=cfg.TRAIN.BATCH_SIZE*num_threads)
    iterator = dataset.make_one_shot_iterator()
    input_images, input_labels, input_image_names = iterator.get_next()

    // initialise the net model
    shadownet = crnn_model.ShadowNet(phase="Train",
                                     hidden_nums=cfg.ARCH.HIDDEN_UNITS,

Instances