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https://github.com/luxfi/onnx-source-snapshot.git
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107 lines
3.8 KiB
Python
107 lines
3.8 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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def pow(x, y): # type: ignore # noqa: A001
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return np.power(x, y).astype(x.dtype)
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class Pow(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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"Pow",
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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 = pow(x, y) # expected output [1., 32., 729.]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_example")
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x = np.arange(60).reshape(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 = pow(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_pow")
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@staticmethod
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def export_pow_broadcast() -> None:
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node = onnx.helper.make_node(
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"Pow",
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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(2).astype(np.float32)
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z = pow(x, y) # expected output [1., 4., 9.]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar")
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node = onnx.helper.make_node(
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"Pow",
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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], [4, 5, 6]]).astype(np.float32)
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y = np.array([1, 2, 3]).astype(np.float32)
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# expected output [[1, 4, 27], [4, 25, 216]]
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z = pow(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array")
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@staticmethod
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def export_types() -> None:
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node = onnx.helper.make_node(
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"Pow",
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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.int64)
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z = pow(x, y) # expected output [1., 32., 729.]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64")
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x = np.array([1, 2, 3]).astype(np.int64)
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y = np.array([4, 5, 6]).astype(np.float32)
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z = pow(x, y) # expected output [1, 32, 729]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32")
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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.int32)
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z = pow(x, y) # expected output [1., 32., 729.]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32")
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x = np.array([1, 2, 3]).astype(np.int32)
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y = np.array([4, 5, 6]).astype(np.float32)
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z = pow(x, y) # expected output [1, 32, 729]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32")
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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.uint64)
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z = pow(x, y) # expected output [1., 32., 729.]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64")
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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.uint32)
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z = pow(x, y) # expected output [1., 32., 729.]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32")
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x = np.array([1, 2, 3]).astype(np.int64)
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y = np.array([4, 5, 6]).astype(np.int64)
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z = pow(x, y) # expected output [1, 32, 729]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64")
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x = np.array([1, 2, 3]).astype(np.int32)
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y = np.array([4, 5, 6]).astype(np.int32)
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z = pow(x, y) # expected output [1, 32, 729]
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expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32")
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