如何用tf.data.Dataset.map无限期地迭代两个tf.data.Dataset的元素求和？

Question

我想在基于tf.data的管道中编写一个mixup数据增强[1]函数。

我用我的训练示例生成一个tf.data.Dataset，并用我想用来扩充我的训练示例的示例。

我想将数据集_train的元素feat_train，label_train映射为feat_train + feat_aug，label_train，label_aug，feat_aug和label_aug dataset_aug的元素，这样两个数据集都可以无限期地进行迭代，例如对于包含3个元素的dataset_train和具有2个元素的dataset_aug：

feat_train [0]，label_train [0]-> feat_train [0] + feat_aug [0]，label_train [0] + label_aug [0]feat_train [1]，label_train [1]-> feat_train [1] + feat_aug [1]，label_train [1] + label_aug [1]feat_train [2]，label_train [2]-> feat_train [2] + feat_aug [0]，label_train [2] + label_aug [0]feat_train [0]，label_train [0]-> feat_train [0] + feat_aug [1]，label_train [0] + label_aug [1]feat_train [1]，label_train [1]-> feat_train [1] + feat_aug [0]，label_train [1] + label_aug [0]...

如何在我的混搭功能中获得这种行为？是否有其他建议的方法可以对2个[[tf.data.Datasets进行无限迭代？[[1] Zhang，Hongyi，et al。 “混合：超越经验风险最小化。” arXiv预印本arXiv：1710.09412（2017）。

# files_train and files_aug are lists of TFRecord files.

# parse TFRecords to get training example features and
# one-hot encoded labels
dataset_train = tf.data.TFRecordDataset(files_train)
dataset_train = dataset_train.map(
    lambda x: serialized2data(x, feature_shape, class_list))
dataset_train = dataset_train.shuffle(10000)
dataset_train = dataset_train.repeat()  # Repeat indefinitely.

# parse TFRecords to get augmentation example features and
# one-hot encoded labels
dataset_aug = tf.data.TFRecordDataset(files_aug)
dataset_aug = dataset_aug.map(
    lambda x: serialized2data(x, feature_shape, class_list))
dataset_aug = dataset_aug.repeat()  # Repeat indefinitely.

# augment data (mixup)
# Here how can I write a map function so that the features of every item
# of dataset_train is mixed with an item of dataset_aug ?
# something like
# dataset_train = dataset_train.map(
#     lambda feat_train, label_train: mixup(
#         feat_train, label_train, feat_aug, label_aug)
# )
# ?
# but how can I iterate dataset_aug to get feat_aug and label_aug ?

# make batch
dataset_train = dataset_train.batch(batch_size, drop_remainder=True)

return dataset


def mixup(feat_train, label_train, feat_aug, label_aug):
    # Shown as an example. This will be more complicated...
    return (feat_train + feat_aug,
            label_train + label_aug)


def serialized2data(
        serialized_data,
        feature_shape,
        class_list,
        data_format='channels_first',
        training=True):
    """Generate features, labels and, if training is False, filenames and times.
    Labels are indices of original label in class_list.

    Args:
        serialized_data: data serialized using utils.tf_utils.serialize_data
        feature_shape: shape of the features. Can be obtained with
            feature_extractor.feature_shape (see utils.feature_utils)
        class_list: list of class ids (used for one-hot encoding the labels)
        data_format: 'channels_first' (NCHW) or 'channels_last' (NHWC).
            Default is set to 'channels_first' because it is faster on GPU
            (https://www.tensorflow.org/guide/performance/overview#data_formats).
    """

    features = {
        'filename': tf.io.FixedLenFeature([], tf.string),
        'times': tf.io.FixedLenFeature([2], tf.float32),
        'data': tf.io.FixedLenFeature(feature_shape, tf.float32),
        'labels': tf.io.FixedLenFeature([], tf.string),
    }
    example = tf.io.parse_single_example(serialized_data, features)

    # reshape data to channels_first format
    if data_format == 'channels_first':
        data = tf.reshape(example['data'], (1, feature_shape[0], feature_shape[1]))
    else:
        data = tf.reshape(example['data'], (feature_shape[0], feature_shape[1], 1))

    # one-hot encode labels
    labels = tf.strings.to_number(
        tf.string_split([example['labels']], '#').values,
        out_type=tf.int32
    )

    # get intersection of class_list and labels
    labels = tf.squeeze(
        tf.sparse.to_dense(
            tf.sets.intersection(
                tf.expand_dims(labels, axis=0),
                tf.expand_dims(class_list, axis=0)
            )
        ),
        axis=0
    )

    # sort class_list and get indices of labels in class_list
    class_list = tf.sort(class_list)
    labels = tf.where(
        tf.equal(
            tf.expand_dims(labels, axis=1),
            class_list)
    )[:,1]

    tf.cond(
        tf.math.logical_and(training, tf.equal(tf.size(labels), 0)),
        true_fn=lambda:myprint(tf.strings.format('File {} has no label', example['filename'])),
        false_fn=lambda:1
    )

    one_hot = tf.cond(
        tf.equal(tf.size(labels), 0),
        true_fn=lambda: tf.zeros(tf.size(class_list)),
        false_fn=lambda: tf.reduce_max(tf.one_hot(labels, tf.size(class_list)), 0)
    )

    if training:
        return (data, one_hot)
    else:
        return (data, one_hot, example['filename'], example['times'])

我想在基于tf.data的管道中编写一个混合数据增强[1]函数。我用我的训练示例生成一个tf.data.Dataset，并用我想用来扩展的示例生成一个tf.data.Dataset ...

Answer 1

我正在提供一个示例代码，说明如何实现您所要求的目标。我分别创建了长度为3和2的train_dataset和aug_dataset。两者都有图像和标签。图像的形状为（64，64，3）。 train的标签为[10，20，30]，aug的标签为[1，2]。