wehub-resource-sync
201 lines
7.9 KiB
Python
201 lines
7.9 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.nn.functional.pairwise_distance and pdist
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# 自动生成的单测，覆盖 paddle.nn.functional.distance 模块中未覆盖的代码
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# Target: cover uncovered lines 95-124 in paddle/python/paddle/nn/functional/distance.py
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"""
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测试模块：paddle.nn.functional.distance
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Test Module: paddle.nn.functional.distance
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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. pairwise_distance - 计算成对向量距离 / Compute pairwise vector distance
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- 动态图模式下的各种参数组合 / Dynamic graph mode with various parameter combinations
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- keepdim 参数测试 / keepdim parameter test
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- 不同 p 范数测试 / Different p-norm tests
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- epsilon 参数测试 / epsilon parameter test
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- 1D输入测试 / 1D input test
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2. pdist - 计算行向量之间的距离 / Compute row-wise pairwise distances
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覆盖的未覆盖行：95-124（静态图路径）
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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 TestPairwiseDistanceDynamic(unittest.TestCase):
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"""测试动态图模式下的pairwise_distance
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Test pairwise_distance in dynamic graph mode"""
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def setUp(self):
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"""设置测试环境 / Set up test environment"""
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paddle.disable_static()
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def test_basic_l2_distance(self):
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"""测试基本的L2距离计算
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Test basic L2 distance computation"""
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]])
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y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]])
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dist = paddle.nn.functional.pairwise_distance(x, y)
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self.assertEqual(list(dist.shape), [2])
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def test_keepdim_true(self):
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"""测试keepdim=True时输出形状
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Test output shape when keepdim=True"""
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]])
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y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]])
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dist = paddle.nn.functional.pairwise_distance(x, y, keepdim=True)
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self.assertEqual(list(dist.shape), [2, 1])
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def test_l1_distance(self):
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"""测试L1距离 (p=1.0)
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Test L1 distance with p=1.0"""
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x = paddle.to_tensor([[1.0, 0.0]], dtype='float64')
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y = paddle.to_tensor([[0.0, 0.0]], dtype='float64')
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dist = paddle.nn.functional.pairwise_distance(x, y, p=1.0, epsilon=0.0)
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np.testing.assert_allclose(dist.numpy(), [1.0], atol=1e-5)
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def test_linf_distance(self):
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"""测试L-inf距离 (p=float('inf'))
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Test L-inf distance with p=inf"""
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x = paddle.to_tensor([[1.0, 3.0, 5.0]])
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y = paddle.to_tensor([[0.0, 0.0, 0.0]])
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dist = paddle.nn.functional.pairwise_distance(
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x, y, p=float('inf'), epsilon=0.0
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)
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np.testing.assert_allclose(dist.numpy(), [5.0], atol=1e-5)
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def test_zero_epsilon(self):
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"""测试epsilon=0.0时的行为
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Test behavior when epsilon=0.0"""
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x = paddle.to_tensor([[1.0, 2.0]])
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y = paddle.to_tensor([[1.0, 2.0]])
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dist = paddle.nn.functional.pairwise_distance(x, y, epsilon=0.0)
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np.testing.assert_allclose(dist.numpy(), [0.0], atol=1e-5)
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def test_1d_input(self):
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"""测试1D输入（无batch维度）
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Test 1D input without batch dimension"""
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x = paddle.to_tensor([1.0, 2.0, 3.0])
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y = paddle.to_tensor([4.0, 5.0, 6.0])
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dist = paddle.nn.functional.pairwise_distance(x, y)
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self.assertEqual(len(dist.shape), 0) # scalar output
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def test_float16_input(self):
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"""测试float32输入
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Test float32 input"""
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x = paddle.to_tensor([[1.0, 2.0]], dtype='float32')
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y = paddle.to_tensor([[3.0, 4.0]], dtype='float32')
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dist = paddle.nn.functional.pairwise_distance(x, y)
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self.assertEqual(dist.dtype, paddle.float32)
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class TestPdist(unittest.TestCase):
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"""测试pdist函数
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Test pdist function"""
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def setUp(self):
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paddle.disable_static()
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def test_pdist_basic(self):
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"""测试基本的pdist计算
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Test basic pdist computation"""
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paddle.seed(2023)
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x = paddle.randn([4, 5])
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result = paddle.pdist(x)
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# 4行向量的成对距离数应为 C(4,2)=6
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# Number of pairwise distances for 4 rows should be C(4,2)=6
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self.assertEqual(list(result.shape), [6])
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def test_pdist_l1(self):
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"""测试pdist使用L1范数
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Test pdist with L1 norm"""
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x = paddle.to_tensor([[1.0, 0.0], [0.0, 1.0], [0.0, 0.0]])
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result = paddle.pdist(x, p=1.0)
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# C(3,2)=3 pairs
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self.assertEqual(list(result.shape), [3])
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def test_pdist_two_rows(self):
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"""测试只有两行的输入
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Test input with only two rows"""
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x = paddle.to_tensor([[1.0, 0.0], [0.0, 1.0]])
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result = paddle.pdist(x)
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self.assertEqual(list(result.shape), [1])
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class TestPairwiseDistanceStatic(unittest.TestCase):
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"""测试静态图模式下的pairwise_distance，覆盖未覆盖的静态图代码路径
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Test pairwise_distance in static graph mode to cover uncovered static graph code paths"""
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def test_static_graph_basic(self):
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"""测试静态图模式下的基本距离计算
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Test basic distance computation in static graph mode"""
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paddle.enable_static()
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try:
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(name='x', shape=[2, 3], dtype='float32')
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y = paddle.static.data(name='y', shape=[2, 3], dtype='float32')
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dist = paddle.nn.functional.pairwise_distance(
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x, y, p=2.0, epsilon=1e-6, keepdim=False
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_prog)
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x_np = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype='float32')
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y_np = np.array([[7.0, 8.0, 9.0], [1.0, 1.0, 1.0]], dtype='float32')
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result = exe.run(
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main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[dist]
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)
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self.assertEqual(len(result[0].shape), 1)
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self.assertEqual(result[0].shape[0], 2)
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finally:
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paddle.disable_static()
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def test_static_graph_keepdim(self):
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"""测试静态图模式下keepdim=True
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Test keepdim=True in static graph mode"""
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paddle.enable_static()
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try:
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(name='x', shape=[2, 3], dtype='float64')
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y = paddle.static.data(name='y', shape=[2, 3], dtype='float64')
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dist = paddle.nn.functional.pairwise_distance(
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x, y, p=2.0, epsilon=0.0, keepdim=True
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_prog)
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x_np = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype='float64')
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y_np = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype='float64')
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result = exe.run(
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main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[dist]
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)
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self.assertEqual(list(result[0].shape), [2, 1])
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finally:
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paddle.disable_static()
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if __name__ == '__main__':
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unittest.main()
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