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379 lines
12 KiB
Python
379 lines
12 KiB
Python
# Copyright (c) ONNX Project Contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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import onnx
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from onnx.backend.test.case.base import Base
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from onnx.backend.test.case.node import expect
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class Split(Base):
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@staticmethod
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def export_1d_opset13() -> None:
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node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input"],
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outputs=["output_1", "output_2", "output_3"],
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axis=0,
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0]).astype(np.float32),
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np.array([5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_equal_parts_1d_opset13",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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)
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split = np.array([2, 4]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input", "split"],
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outputs=["output_1", "output_2"],
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axis=0,
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_variable_parts_1d_opset13",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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)
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@staticmethod
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def export_2d_opset13() -> None:
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node_input = np.array(
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[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
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).astype(np.float32)
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node = onnx.helper.make_node(
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"Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1
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)
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expected_outputs = [
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np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
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np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_equal_parts_2d_opset13",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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)
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split = np.array([2, 4]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input", "split"],
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outputs=["output_1", "output_2"],
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axis=1,
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)
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expected_outputs = [
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np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
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np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
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np.float32
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),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_variable_parts_2d_opset13",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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)
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@staticmethod
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def export_default_values_opset13() -> None:
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node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
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# If axis is not specified, split is applied on default axis 0
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node = onnx.helper.make_node(
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"Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"]
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0]).astype(np.float32),
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np.array([5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_equal_parts_default_axis_opset13",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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)
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split = np.array([2, 4]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_variable_parts_default_axis_opset13",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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)
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@staticmethod
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def export_zero_size_splits_opset13() -> None:
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# 1-dimensional tensor with dimension_size=0
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node_input = np.array([]).astype(np.float32)
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# Split empty tensor to tensors of size zero
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split = np.array([0, 0, 0]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input", "split"],
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outputs=["output_1", "output_2", "output_3"],
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)
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expected_outputs = [
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np.array([]).astype(np.float32),
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np.array([]).astype(np.float32),
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np.array([]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_zero_size_splits_opset13",
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opset_imports=[onnx.helper.make_opsetid("", 13)],
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)
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@staticmethod
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def export_1d_opset18() -> None:
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node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input"],
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outputs=["output_1", "output_2", "output_3"],
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axis=0,
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num_outputs=3,
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0]).astype(np.float32),
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np.array([5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_equal_parts_1d_opset18",
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)
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split = np.array([2, 4]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input", "split"],
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outputs=["output_1", "output_2"],
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axis=0,
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_variable_parts_1d_opset18",
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)
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@staticmethod
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def export_2d_opset18() -> None:
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node_input = np.array(
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[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
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).astype(np.float32)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input"],
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outputs=["output_1", "output_2"],
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axis=1,
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num_outputs=2,
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)
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expected_outputs = [
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np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
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np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_equal_parts_2d",
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)
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split = np.array([2, 4]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input", "split"],
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outputs=["output_1", "output_2"],
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axis=1,
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)
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expected_outputs = [
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np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
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np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
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np.float32
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),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_variable_parts_2d_opset18",
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)
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@staticmethod
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def export_default_values_opset18() -> None:
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node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
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# If axis is not specified, split is applied on default axis 0
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node = onnx.helper.make_node(
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"Split",
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inputs=["input"],
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outputs=["output_1", "output_2", "output_3"],
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num_outputs=3,
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0]).astype(np.float32),
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np.array([5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_equal_parts_default_axis_opset18",
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)
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split = np.array([2, 4]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_variable_parts_default_axis_opset18",
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)
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@staticmethod
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def export_zero_size_splits_opset18() -> None:
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# 1-dimensional tensor with dimension_size=0
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node_input = np.array([]).astype(np.float32)
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# Split empty tensor to tensors of size zero
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split = np.array([0, 0, 0]).astype(np.int64)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input", "split"],
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outputs=["output_1", "output_2", "output_3"],
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)
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expected_outputs = [
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np.array([]).astype(np.float32),
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np.array([]).astype(np.float32),
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np.array([]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input, split],
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outputs=expected_outputs,
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name="test_split_zero_size_splits_opset18",
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)
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@staticmethod
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def export_1d_uneven_split_opset18() -> None:
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node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32)
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# If axis is not specified, split is applied on default axis 0
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node = onnx.helper.make_node(
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"Split",
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inputs=["input"],
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outputs=["output_1", "output_2", "output_3", "output_4"],
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num_outputs=4,
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)
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expected_outputs = [
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np.array([1.0, 2.0]).astype(np.float32),
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np.array([3.0, 4.0]).astype(np.float32),
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np.array([5.0, 6.0]).astype(np.float32),
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np.array([7.0]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_1d_uneven_split_opset18",
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)
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@staticmethod
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def export_2d_uneven_split_opset18() -> None:
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node_input = np.array(
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[
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[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
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[9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0],
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]
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).astype(np.float32)
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node = onnx.helper.make_node(
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"Split",
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inputs=["input"],
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outputs=["output_1", "output_2", "output_3"],
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axis=1,
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num_outputs=3,
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)
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expected_outputs = [
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np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32),
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np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32),
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np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32),
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]
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expect(
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node,
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inputs=[node_input],
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outputs=expected_outputs,
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name="test_split_2d_uneven_split_opset18",
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)
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