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79 lines
2.8 KiB
Python
79 lines
2.8 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 Div(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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"Div",
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inputs=["x", "y"],
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outputs=["z"],
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)
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x = np.array([3, 4]).astype(np.float32)
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y = np.array([1, 2]).astype(np.float32)
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z = x / y # expected output [3., 2.]
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expect(node, inputs=[x, y], outputs=[z], name="test_div_example")
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
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z = x / y
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expect(node, inputs=[x, y], outputs=[z], name="test_div")
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x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8) + 1
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z = x // y
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expect(node, inputs=[x, y], outputs=[z], name="test_div_int8")
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x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16) + 1
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z = x // y
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expect(node, inputs=[x, y], outputs=[z], name="test_div_int16")
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x = np.array([-3, 3, -3, 3], dtype=np.int32)
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y = np.array([2, 2, -2, -2], dtype=np.int32)
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z = np.array([-1, 1, 1, -1], dtype=np.int32)
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expect(node, inputs=[x, y], outputs=[z], name="test_div_int32_trunc")
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x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1
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z = x // y
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expect(node, inputs=[x, y], outputs=[z], name="test_div_uint8")
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x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16) + 1
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z = x // y
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expect(node, inputs=[x, y], outputs=[z], name="test_div_uint16")
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x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32) + 1
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z = x // y
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expect(node, inputs=[x, y], outputs=[z], name="test_div_uint32")
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x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64) + 1
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z = x // y
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expect(node, inputs=[x, y], outputs=[z], name="test_div_uint64")
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@staticmethod
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def export_div_broadcast() -> None:
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node = onnx.helper.make_node(
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"Div",
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inputs=["x", "y"],
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outputs=["z"],
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)
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.rand(5).astype(np.float32) + 1.0
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z = x / y
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expect(node, inputs=[x, y], outputs=[z], name="test_div_bcast")
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