ReverseSequence#

ReverseSequence - 10

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

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

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

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([1, 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')

Resize

RoiAlign

ReverseSequence#

ReverseSequence - 10#

ReverseSequence - 10 #