predict.py 文件源码

def _reverse_seq(input_seq, lengths):
    """Reverse a list of Tensors up to specified lengths.
    Args:
        input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)
                   or nested tuples of tensors.
        lengths:   A `Tensor` of dimension batch_size, containing lengths for each
                   sequence in the batch. If "None" is specified, simply reverses
                   the list.
    Returns:
        time-reversed sequence
    """
    if lengths is None:
        return list(reversed(input_seq))

    flat_input_seq = tuple(nest.flatten(input_) for input_ in input_seq)

    flat_results = [[] for _ in range(len(input_seq))]
    for sequence in zip(*flat_input_seq):
        input_shape = tensor_shape.unknown_shape(
                ndims=sequence[0].get_shape().ndims)
        for input_ in sequence:
            input_shape.merge_with(input_.get_shape())
            input_.set_shape(input_shape)

        # Join into (time, batch_size, depth)
        s_joined = array_ops.pack(sequence)

        # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
        if lengths is not None:
            lengths = math_ops.to_int64(lengths)

        # Reverse along dimension 0
        s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
        # Split again into list
        result = array_ops.unpack(s_reversed)
        for r, flat_result in zip(result, flat_results):
            r.set_shape(input_shape)
            flat_result.append(r)

    results = [nest.pack_sequence_as(structure=input_, flat_sequence=flat_result)
               for input_, flat_result in zip(input_seq, flat_results)]
    return results