readers.py 文件源码

def prepare_reader(self, filename_queue):

    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)

    contexts, features = tf.parse_single_sequence_example(
        serialized_example,
        context_features={
            "video_id": tf.FixedLenFeature([], tf.string),
            "labels": tf.VarLenFeature(tf.int64)},
        sequence_features={
            "rgb": tf.FixedLenSequenceFeature([], dtype=tf.string),
            "audio": tf.FixedLenSequenceFeature([], dtype=tf.string),
        })

    # read ground truth labels
    labels = (tf.cast(
        tf.sparse_to_dense(contexts["labels"].values, (self.num_classes,), 1,
            validate_indices=False),
        tf.bool))

    rgbs, num_frames = self.get_video_matrix(features["rgb"], 1024, self.max_frames)
    audios, num_frames = self.get_video_matrix(features["audio"], 1024, self.max_frames)

    batch_video_ids = tf.expand_dims(contexts["video_id"], 0)
    batch_rgbs = tf.expand_dims(rgbs, 0)
    batch_audios = tf.expand_dims(audios, 0)
    batch_labels = tf.expand_dims(labels, 0)
    batch_frames = tf.expand_dims(num_frames, 0)

    return batch_video_ids, batch_rgbs, batch_audios, batch_labels, batch_frames