SequenceInsert

SequenceInsert - 11

Version

• name: SequenceInsert (GitHub)

• domain: main

• since_version: 11

• function:

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 11.

Summary

Inputs

Between 2 and 3 inputs.

• input_sequence (heterogeneous) - S:

• tensor (heterogeneous) - T:

• position (optional, heterogeneous) - I:

Outputs

• output_sequence (heterogeneous) - S:

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) ): Constrain to any tensor type.

• S in ( seq(tensor(bool)), seq(tensor(complex128)), seq(tensor(complex64)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)) ): Constrain to any tensor type.

• I in ( tensor(int32), tensor(int64) ): Constrain position to integral tensor. It must be a scalar(tensor of empty shape).

Examples

default

import numpy as np
import onnx

test_cases = {
    "at_back": [np.array([10, 11, 12]).astype(np.int64)],
    "at_front": [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)],
}
sequence = [
    np.array([1, 2, 3, 4]).astype(np.int64),
    np.array([5, 6, 7]).astype(np.int64),
    np.array([8, 9]).astype(np.int64),
]

for test_name, test_inputs in test_cases.items():
    tensor = test_inputs[0].astype(np.int64)

    if len(test_inputs) > 1:
        node = onnx.helper.make_node(
            "SequenceInsert",
            inputs=["sequence", "tensor", "position"],
            outputs=["output_sequence"],
        )
        position = test_inputs[1]
        inserted = sequence_insert_reference_implementation(
            sequence, tensor, position
        )
        expect(
            node,
            inputs=[sequence, tensor, position],
            outputs=[inserted],
            name="test_sequence_insert_" + test_name,
        )
    else:
        node = onnx.helper.make_node(
            "SequenceInsert",
            inputs=["sequence", "tensor"],
            outputs=["output_sequence"],
        )
        inserted = sequence_insert_reference_implementation(sequence, tensor)
        expect(
            node,
            inputs=[sequence, tensor],
            outputs=[inserted],
            name="test_sequence_insert_" + test_name,
        )