wehub-resource-sync
166 lines
6.0 KiB
Python
166 lines
6.0 KiB
Python
# Copyright (c) 2026 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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"""
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随机操作高级测试 / Advanced Random Operations Tests
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测试目标 / Test Target:
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paddle.tensor.random 随机张量操作
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覆盖的模块 / Covered Modules:
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- paddle.seed/get_cuda_rng_state/set_cuda_rng_state: 随机状态
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- paddle.randperm: 随机排列
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- paddle.bernoulli: 伯努利采样
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- paddle.multinomial: 多项式采样
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作用 / Purpose:
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补充随机操作API的测试，提升覆盖率。
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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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paddle.disable_static()
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class TestRandomSeed(unittest.TestCase):
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"""测试随机种子 / Test random seed"""
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def test_seed_reproducibility(self):
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"""测试随机种子可重复性 / Test seed reproducibility"""
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paddle.seed(42)
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x1 = paddle.randn([5])
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paddle.seed(42)
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x2 = paddle.randn([5])
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np.testing.assert_allclose(x1.numpy(), x2.numpy())
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def test_different_seeds(self):
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"""测试不同种子 / Test different seeds"""
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paddle.seed(42)
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x1 = paddle.randn([10])
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paddle.seed(99)
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x2 = paddle.randn([10])
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# Different seeds should produce different results (very likely)
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self.assertFalse(np.allclose(x1.numpy(), x2.numpy()))
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class TestRandperm(unittest.TestCase):
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"""测试随机排列 / Test random permutation"""
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def test_randperm_basic(self):
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"""测试基本随机排列 / Test basic randperm"""
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result = paddle.randperm(10)
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self.assertEqual(result.shape[0], 10)
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# All values 0-9 should be present
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sorted_result = np.sort(result.numpy())
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np.testing.assert_array_equal(sorted_result, np.arange(10))
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def test_randperm_dtype(self):
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"""测试随机排列数据类型 / Test randperm dtype"""
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result = paddle.randperm(5, dtype='int64')
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self.assertEqual(result.dtype, paddle.int64)
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def test_randperm_shuffle(self):
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"""测试随机排列用于数据打乱 / Test randperm for shuffling"""
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data = paddle.to_tensor([10.0, 20.0, 30.0, 40.0, 50.0])
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perm = paddle.randperm(5)
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shuffled = data[perm]
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self.assertEqual(shuffled.shape, [5])
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# All values should still be present
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np.testing.assert_array_equal(
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np.sort(shuffled.numpy()), [10.0, 20.0, 30.0, 40.0, 50.0]
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)
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class TestBernoulli(unittest.TestCase):
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"""测试伯努利采样 / Test Bernoulli sampling"""
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def test_bernoulli_basic(self):
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"""测试基本伯努利采样 / Test basic Bernoulli sampling"""
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probs = paddle.to_tensor([0.5, 0.5, 0.5, 0.5, 0.5])
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result = paddle.bernoulli(probs)
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# All values should be 0 or 1
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self.assertTrue(bool(((result == 0) | (result == 1)).all().numpy()))
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def test_bernoulli_all_ones(self):
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"""测试全1伯努利采样 / Test Bernoulli with all ones"""
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probs = paddle.ones([5])
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result = paddle.bernoulli(probs)
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np.testing.assert_array_equal(result.numpy(), np.ones(5))
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def test_bernoulli_all_zeros(self):
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"""测试全0伯努利采样 / Test Bernoulli with all zeros"""
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probs = paddle.zeros([5])
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result = paddle.bernoulli(probs)
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np.testing.assert_array_equal(result.numpy(), np.zeros(5))
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class TestMultinomial(unittest.TestCase):
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"""测试多项式采样 / Test multinomial sampling"""
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def test_multinomial_basic(self):
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"""测试基本多项式采样 / Test basic multinomial sampling"""
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weights = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
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result = paddle.multinomial(weights, num_samples=100, replacement=True)
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self.assertEqual(result.shape, [100])
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# All indices should be valid
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self.assertTrue(bool((result >= 0).all().numpy()))
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self.assertTrue(bool((result < 4).all().numpy()))
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def test_multinomial_without_replacement(self):
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"""测试无重复多项式采样 / Test multinomial without replacement"""
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weights = paddle.to_tensor([1.0, 1.0, 1.0, 1.0, 1.0])
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result = paddle.multinomial(weights, num_samples=3, replacement=False)
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self.assertEqual(result.shape, [3])
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# All values should be unique
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self.assertEqual(len(np.unique(result.numpy())), 3)
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def test_multinomial_2d(self):
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"""测试2D多项式采样 / Test 2D multinomial sampling"""
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weights = paddle.to_tensor([[1.0, 2.0, 3.0], [3.0, 2.0, 1.0]])
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result = paddle.multinomial(weights, num_samples=5, replacement=True)
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self.assertEqual(result.shape, [2, 5])
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class TestRandomDropout(unittest.TestCase):
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"""测试随机丢弃 / Test random dropout"""
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def test_dropout_training(self):
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"""测试训练模式dropout / Test dropout in training mode"""
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dropout = paddle.nn.Dropout(p=0.5)
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dropout.train()
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x = paddle.ones([100, 100])
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result = dropout(x)
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# In training mode, some values should be 0
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zero_fraction = float((result == 0).sum().numpy()) / result.numel()
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# Should be approximately 0.5 (with tolerance)
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self.assertGreater(zero_fraction, 0.3)
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self.assertLess(zero_fraction, 0.7)
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def test_dropout_eval(self):
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"""测试评估模式dropout / Test dropout in eval mode"""
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dropout = paddle.nn.Dropout(p=0.5)
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dropout.eval()
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x = paddle.ones([10, 10])
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result = dropout(x)
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# In eval mode, no dropout
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np.testing.assert_allclose(result.numpy(), x.numpy())
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if __name__ == '__main__':
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unittest.main()
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