wehub-resource-sync
211 lines
7.3 KiB
Python
211 lines
7.3 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [AUTO-GENERATED] Unit test for paddle.tensor.math
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# 自动生成的单测，覆盖 paddle.tensor.math 模块中未覆盖的代码
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"""
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测试模块：paddle.tensor.math (inner, outer, dist, lerp, frac, diff, logit, i0)
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Test Module: paddle.tensor.math
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本测试覆盖以下功能：
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This test covers the following functions:
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1. inner - 内积 / Inner product of tensors
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2. outer - 外积 / Outer product of vectors
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3. dist - 距离 / Distance with various p values
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4. lerp - 线性插值 / Linear interpolation
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5. frac - 小数部分 / Fractional part
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6. diff - 差分 / Difference
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7. logit - logit函数 / Logit function
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覆盖的未覆盖行：inner/outer各分支, dist p值分支, lerp/frac动态图路径
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"""
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import unittest
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import numpy as np
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import paddle
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class TestInner(unittest.TestCase):
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"""测试inner内积运算
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Test inner product"""
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def setUp(self):
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paddle.disable_static()
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def test_inner_1d(self):
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"""1D向量内积 / 1D vector inner product"""
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x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
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y = paddle.to_tensor([4.0, 5.0, 6.0], dtype='float32')
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out = paddle.inner(x, y)
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np.testing.assert_allclose(float(out.numpy()), 32.0, rtol=1e-5)
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def test_inner_2d(self):
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"""2D矩阵内积 / 2D matrix inner product"""
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
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y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype='float32')
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out = paddle.inner(x, y)
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self.assertEqual(list(out.shape), [2, 2])
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class TestOuter(unittest.TestCase):
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"""测试outer外积运算
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Test outer product"""
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def setUp(self):
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paddle.disable_static()
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def test_outer_basic(self):
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"""基本外积 / Basic outer product"""
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x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
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y = paddle.to_tensor([4.0, 5.0], dtype='float32')
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out = paddle.outer(x, y)
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expected = np.array([[4.0, 5.0], [8.0, 10.0], [12.0, 15.0]])
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np.testing.assert_allclose(out.numpy(), expected, rtol=1e-5)
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def test_outer_shape(self):
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"""外积形状验证 / Outer product shape"""
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x = paddle.ones([5], dtype='float32')
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y = paddle.ones([3], dtype='float32')
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out = paddle.outer(x, y)
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self.assertEqual(list(out.shape), [5, 3])
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class TestDist(unittest.TestCase):
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"""测试dist距离计算
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Test dist function"""
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def setUp(self):
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paddle.disable_static()
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def test_dist_p2(self):
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"""L2距离 / L2 distance"""
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x = paddle.to_tensor([1.0, 0.0, 0.0], dtype='float32')
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y = paddle.to_tensor([0.0, 1.0, 0.0], dtype='float32')
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out = paddle.dist(x, y, p=2)
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np.testing.assert_allclose(float(out.numpy()), np.sqrt(2.0), rtol=1e-5)
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def test_dist_p1(self):
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"""L1距离 / L1 distance"""
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x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
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y = paddle.to_tensor([4.0, 5.0, 6.0], dtype='float32')
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out = paddle.dist(x, y, p=1)
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np.testing.assert_allclose(float(out.numpy()), 9.0, rtol=1e-5)
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def test_dist_inf(self):
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"""无穷距离 / Infinity distance"""
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x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
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y = paddle.to_tensor([4.0, 5.0, 10.0], dtype='float32')
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out = paddle.dist(x, y, p=float('inf'))
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np.testing.assert_allclose(float(out.numpy()), 7.0, rtol=1e-5)
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def test_dist_p0(self):
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"""L0距离 / L0 distance"""
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x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
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y = paddle.to_tensor([1.0, 5.0, 6.0], dtype='float32')
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out = paddle.dist(x, y, p=0)
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np.testing.assert_allclose(float(out.numpy()), 2.0, rtol=1e-5)
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class TestLerp(unittest.TestCase):
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"""测试lerp线性插值
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Test lerp function"""
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def setUp(self):
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paddle.disable_static()
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def test_lerp_basic(self):
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"""基本线性插值 / Basic lerp"""
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x = paddle.to_tensor([0.0, 0.0], dtype='float32')
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y = paddle.to_tensor([10.0, 10.0], dtype='float32')
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out = paddle.lerp(x, y, 0.5)
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np.testing.assert_allclose(out.numpy(), [5.0, 5.0], rtol=1e-5)
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def test_lerp_weight_zero(self):
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"""weight=0返回x / Lerp with weight=0 returns x"""
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x = paddle.to_tensor([1.0, 2.0], dtype='float32')
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y = paddle.to_tensor([10.0, 20.0], dtype='float32')
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out = paddle.lerp(x, y, 0.0)
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np.testing.assert_allclose(out.numpy(), [1.0, 2.0], rtol=1e-5)
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def test_lerp_weight_one(self):
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"""weight=1返回y / Lerp with weight=1 returns y"""
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x = paddle.to_tensor([1.0, 2.0], dtype='float32')
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y = paddle.to_tensor([10.0, 20.0], dtype='float32')
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out = paddle.lerp(x, y, 1.0)
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np.testing.assert_allclose(out.numpy(), [10.0, 20.0], rtol=1e-5)
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def test_lerp_tensor_weight(self):
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"""Tensor权重 / Lerp with tensor weight"""
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x = paddle.zeros([3], dtype='float32')
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y = paddle.ones([3], dtype='float32')
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weight = paddle.to_tensor([0.0, 0.5, 1.0], dtype='float32')
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out = paddle.lerp(x, y, weight)
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np.testing.assert_allclose(out.numpy(), [0.0, 0.5, 1.0], rtol=1e-5)
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class TestFrac(unittest.TestCase):
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"""测试frac小数部分
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Test frac function"""
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def setUp(self):
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paddle.disable_static()
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def test_frac_positive(self):
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"""正数小数部分 / Frac of positive numbers"""
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x = paddle.to_tensor([1.5, 2.7, 3.0], dtype='float32')
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out = paddle.frac(x)
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np.testing.assert_allclose(out.numpy(), [0.5, 0.7, 0.0], rtol=1e-4)
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def test_frac_negative(self):
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"""负数小数部分 / Frac of negative numbers"""
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x = paddle.to_tensor([-1.5, -2.3], dtype='float32')
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out = paddle.frac(x)
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np.testing.assert_allclose(out.numpy(), [-0.5, -0.3], rtol=1e-4)
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class TestDiff(unittest.TestCase):
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"""测试diff差分
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Test diff function"""
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def setUp(self):
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paddle.disable_static()
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def test_diff_basic(self):
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"""基本差分 / Basic diff"""
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x = paddle.to_tensor([1.0, 4.0, 9.0, 16.0], dtype='float32')
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out = paddle.diff(x)
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np.testing.assert_allclose(out.numpy(), [3.0, 5.0, 7.0], rtol=1e-5)
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def test_diff_n2(self):
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"""二阶差分 / Second order diff"""
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x = paddle.to_tensor([1.0, 4.0, 9.0, 16.0], dtype='float32')
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out = paddle.diff(x, n=2)
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np.testing.assert_allclose(out.numpy(), [2.0, 2.0], rtol=1e-5)
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def test_diff_2d(self):
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"""2D差分 / 2D diff"""
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x = paddle.to_tensor(
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[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype='float32'
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)
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out = paddle.diff(x, axis=1)
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expected = np.array([[1.0, 1.0], [1.0, 1.0]])
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np.testing.assert_allclose(out.numpy(), expected, rtol=1e-5)
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if __name__ == '__main__':
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unittest.main()
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