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https://github.com/luxfi/onnx-source-snapshot.git
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74 lines
2.5 KiB
Python
74 lines
2.5 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 Mul(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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"Mul",
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inputs=["x", "y"],
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outputs=["z"],
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)
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x = np.array([1, 2, 3]).astype(np.float32)
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y = np.array([4, 5, 6]).astype(np.float32)
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z = x * y # expected output [4., 10., 18.]
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_example")
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.randn(3, 4, 5).astype(np.float32)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul")
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x = np.random.randint(4, size=(3, 4, 5), dtype=np.int8)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_int8")
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x = np.random.randint(4, size=(3, 4, 5), dtype=np.int16)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_int16")
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x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint8")
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x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint16)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint16")
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x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint32)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint32")
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x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint64)
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y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint64")
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@staticmethod
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def export_mul_broadcast() -> None:
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node = onnx.helper.make_node(
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"Mul",
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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.randn(5).astype(np.float32)
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z = x * y
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expect(node, inputs=[x, y], outputs=[z], name="test_mul_bcast")
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