ucf11.py 文件源码

def _read_record(self, filename_queue):

        class FrameSeqRecord(object):
            pass

        record = FrameSeqRecord()
        record.height = self._data_img_size[0]
        record.width = self._data_img_size[1]
        record.depth = self._data_img_size[2]

        input_seq_length = self.input_shape[0]
        target_seq_length = self.target_shape[0]
        total_seq_length = input_seq_length + target_seq_length

        frame_bytes = record.height * record.width * record.depth
        record_bytes = frame_bytes * (total_seq_length)
        total_file_bytes = frame_bytes * self._serialized_sequence_length

        with tf.name_scope('read_record'):
            reader = tf.FixedLengthRecordReader(total_file_bytes)

            record.key, value = reader.read(filename_queue)
            decoded_record_bytes = tf.decode_raw(value, tf.uint8)

            decoded_record_bytes = tf.reshape(decoded_record_bytes,
                                              [self._serialized_sequence_length, record.height, record.width, record.depth])

            # calculcate tensors [start, 0, 0, 0]
            rnd_start_index = tf.to_int32(tf.random_uniform([1], 0, self._serialized_sequence_length - (total_seq_length), 
                                                            tf.int32))
            seq_start_offset = tf.SparseTensor(indices=[[0]], values=rnd_start_index, dense_shape=[4])
            sequence_start = tf.sparse_tensor_to_dense(seq_start_offset)

            # take a random slice of frames as input
            record.data = tf.slice(decoded_record_bytes, sequence_start,
                                   [total_seq_length, record.height, record.width, record.depth])
            return record