ReverseSequence¶

ReverseSequence - 10¶

Version¶

name: ReverseSequence (GitHub)
domain: main
since_version: 10
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 10.

Summary¶

Reverse batch of sequences having different lengths specified by sequence_lens.

For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis, and copies elements whose index’s beyond sequence_lens[i] to the output. So the output slice i contains reversed sequences on the first sequence_lens[i] elements, then have original values copied for the other elements.

Example 1: input = [[0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0]] sequence_lens = [4, 3, 2, 1] time_axis = 0 batch_axis = 1

output = [[3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0]]

Example 2: input = [[0.0, 1.0, 2.0, 3.0 ], [4.0, 5.0, 6.0, 7.0 ], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0]] sequence_lens = [1, 2, 3, 4] time_axis = 1 batch_axis = 0

output = [[0.0, 1.0, 2.0, 3.0 ], [5.0, 4.0, 6.0, 7.0 ], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0]]

Attributes¶

batch_axis - INT (default is 1):

(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.
time_axis - INT (default is 0):

(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.

Inputs¶

input (heterogeneous) - T:

Tensor of rank r >= 2.
sequence_lens (heterogeneous) - tensor(int64):

Tensor specifying lengths of the sequences in a batch. It has shape [batch_size].

Outputs¶

Y (heterogeneous) - T:

Tensor with same shape of input.

Type Constraints¶

T in ( tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

Input and output types can be of any tensor type.

Examples¶

_reversesequence_time¶

import numpy as np
import onnx

node = onnx.helper.make_node(
    "ReverseSequence",
    inputs=["x", "sequence_lens"],
    outputs=["y"],
    time_axis=0,
    batch_axis=1,
)
x = np.array(
    [
        [0.0, 4.0, 8.0, 12.0],
        [1.0, 5.0, 9.0, 13.0],
        [2.0, 6.0, 10.0, 14.0],
        [3.0, 7.0, 11.0, 15.0],
    ],
    dtype=np.float32,
)
sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64)

y = np.array(
    [
        [3.0, 6.0, 9.0, 12.0],
        [2.0, 5.0, 8.0, 13.0],
        [1.0, 4.0, 10.0, 14.0],
        [0.0, 7.0, 11.0, 15.0],
    ],
    dtype=np.float32,
)

expect(
    node,
    inputs=[x, sequence_lens],
    outputs=[y],
    name="test_reversesequence_time",
)

_reversesequence_batch¶

import numpy as np
import onnx

node = onnx.helper.make_node(
    "ReverseSequence",
    inputs=["x", "sequence_lens"],
    outputs=["y"],
    time_axis=1,
    batch_axis=0,
)
x = np.array(
    [
        [0.0, 1.0, 2.0, 3.0],
        [4.0, 5.0, 6.0, 7.0],
        [8.0, 9.0, 10.0, 11.0],
        [12.0, 13.0, 14.0, 15.0],
    ],
    dtype=np.float32,
)
sequence_lens = np.array([0, 2, 3, 4], dtype=np.int64)

y = np.array(
    [
        [0.0, 1.0, 2.0, 3.0],
        [5.0, 4.0, 6.0, 7.0],
        [10.0, 9.0, 8.0, 11.0],
        [15.0, 14.0, 13.0, 12.0],
    ],
    dtype=np.float32,
)

expect(
    node,
    inputs=[x, sequence_lens],
    outputs=[y],
    name="test_reversesequence_batch",
)