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https://github.com/luxfi/onnx-source-snapshot.git
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43 lines
1.3 KiB
Python
43 lines
1.3 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 Where(Base):
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@staticmethod
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def export() -> None:
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node = onnx.helper.make_node(
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"Where",
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inputs=["condition", "x", "y"],
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outputs=["z"],
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)
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condition = np.array([[1, 0], [1, 1]], dtype=bool)
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x = np.array([[1, 2], [3, 4]], dtype=np.float32)
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y = np.array([[9, 8], [7, 6]], dtype=np.float32)
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z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]]
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expect(node, inputs=[condition, x, y], outputs=[z], name="test_where_example")
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@staticmethod
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def export_long() -> None:
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node = onnx.helper.make_node(
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"Where",
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inputs=["condition", "x", "y"],
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outputs=["z"],
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)
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condition = np.array([[1, 0], [1, 1]], dtype=bool)
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x = np.array([[1, 2], [3, 4]], dtype=np.int64)
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y = np.array([[9, 8], [7, 6]], dtype=np.int64)
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z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]]
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expect(
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node, inputs=[condition, x, y], outputs=[z], name="test_where_long_example"
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)
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