wehub-resource-sync
241 lines
8.1 KiB
Python
241 lines
8.1 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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Dropout层单元测试 / Dropout Layer Unit Tests
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测试目标 / Test Target:
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paddle.nn.Dropout系列层 (覆盖率较高但部分边界情况未测)
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覆盖的模块 / Covered Modules:
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- paddle.nn.Dropout: 1D Dropout
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- paddle.nn.Dropout2D: 2D通道Dropout
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- paddle.nn.Dropout3D: 3D通道Dropout
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- paddle.nn.AlphaDropout: Alpha Dropout
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作用 / Purpose:
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覆盖Dropout层在训练和评估模式下的代码路径，测试各种参数设置和边界情况。
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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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from paddle import nn
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paddle.disable_static()
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class TestDropout(unittest.TestCase):
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"""测试Dropout / Test Dropout"""
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def test_dropout_eval_mode(self):
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"""测试评估模式下Dropout（无丢弃）/ Test Dropout in eval mode (no dropout)"""
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dropout = nn.Dropout(p=0.5)
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dropout.eval()
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x = paddle.randn([100, 100])
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y = dropout(x)
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np.testing.assert_allclose(x.numpy(), y.numpy())
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def test_dropout_train_mode(self):
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"""测试训练模式下Dropout / Test Dropout in train mode"""
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dropout = nn.Dropout(p=0.5)
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dropout.train()
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x = paddle.ones([1000])
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y = dropout(x)
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# Some values should be zero
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n_zeros = int(paddle.sum(y == 0).numpy())
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self.assertTrue(n_zeros > 0)
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def test_dropout_p0(self):
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"""测试p=0的Dropout（无丢弃）/ Test Dropout with p=0 (no dropout)"""
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dropout = nn.Dropout(p=0.0)
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x = paddle.randn([4, 10])
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y = dropout(x)
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np.testing.assert_allclose(x.numpy(), y.numpy())
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def test_dropout_p1(self):
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"""测试p=1的Dropout（全丢弃）/ Test Dropout with p=1 (all zero)"""
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dropout = nn.Dropout(p=1.0)
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x = paddle.ones([100])
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y = dropout(x)
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# All values should be zero in train mode (default)
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self.assertAlmostEqual(float(y.sum().numpy()), 0.0, places=5)
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def test_dropout_mode_upscale(self):
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"""测试upscale模式 / Test upscale mode"""
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dropout = nn.Dropout(p=0.5, mode='upscale_in_train')
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x = paddle.ones([1000])
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y = dropout(x)
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# Non-zero values should be scaled by 1/(1-p)
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non_zero = y[y != 0]
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if len(non_zero) > 0:
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self.assertAlmostEqual(float(non_zero[0].numpy()), 2.0, places=5)
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def test_dropout_shape_preserved(self):
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"""测试Dropout保留形状 / Test Dropout preserves shape"""
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dropout = nn.Dropout(p=0.3)
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x = paddle.randn([4, 6, 8])
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y = dropout(x)
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self.assertEqual(y.shape, [4, 6, 8])
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def test_dropout_gradient(self):
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"""测试Dropout梯度 / Test Dropout gradient"""
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dropout = nn.Dropout(p=0.0) # p=0: identity
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x = paddle.randn([4, 10])
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x.stop_gradient = False
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y = dropout(x)
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y.sum().backward()
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self.assertIsNotNone(x.grad)
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class TestDropout2D(unittest.TestCase):
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"""测试Dropout2D / Test Dropout2D"""
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def test_dropout2d_basic(self):
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"""测试基本Dropout2D / Test basic Dropout2D"""
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dropout2d = nn.Dropout2D(p=0.5)
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x = paddle.randn([4, 8, 16, 16])
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y = dropout2d(x)
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self.assertEqual(y.shape, [4, 8, 16, 16])
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def test_dropout2d_channel_drop(self):
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"""测试Dropout2D通道丢弃 / Test Dropout2D channel dropping"""
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dropout2d = nn.Dropout2D(p=0.5)
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dropout2d.train()
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x = paddle.ones([4, 100, 8, 8])
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y = dropout2d(x)
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# Some entire channels should be zero
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# Check that some channels are zero
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channel_sums = y.mean(axis=[0, 2, 3])
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n_zero_channels = int(paddle.sum(channel_sums == 0).numpy())
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self.assertTrue(n_zero_channels > 0)
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def test_dropout2d_eval_mode(self):
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"""测试评估模式Dropout2D / Test Dropout2D eval mode"""
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dropout2d = nn.Dropout2D(p=0.5)
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dropout2d.eval()
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x = paddle.randn([4, 8, 16, 16])
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y = dropout2d(x)
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np.testing.assert_allclose(x.numpy(), y.numpy())
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def test_dropout2d_nchw(self):
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"""测试NCHW格式的Dropout2D / Test NCHW format Dropout2D"""
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dropout2d = nn.Dropout2D(p=0.3, data_format='NCHW')
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x = paddle.randn([4, 8, 16, 16])
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y = dropout2d(x)
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self.assertEqual(y.shape, [4, 8, 16, 16])
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class TestDropout3D(unittest.TestCase):
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"""测试Dropout3D / Test Dropout3D"""
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def test_dropout3d_basic(self):
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"""测试基本Dropout3D / Test basic Dropout3D"""
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dropout3d = nn.Dropout3D(p=0.5)
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x = paddle.randn([2, 8, 4, 8, 8])
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y = dropout3d(x)
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self.assertEqual(y.shape, [2, 8, 4, 8, 8])
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def test_dropout3d_eval_mode(self):
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"""测试评估模式Dropout3D / Test Dropout3D eval mode"""
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dropout3d = nn.Dropout3D(p=0.5)
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dropout3d.eval()
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x = paddle.randn([2, 8, 4, 8, 8])
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y = dropout3d(x)
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np.testing.assert_allclose(x.numpy(), y.numpy())
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def test_dropout3d_channel_wise(self):
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"""测试Dropout3D通道维度 / Test Dropout3D channel-wise"""
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dropout3d = nn.Dropout3D(p=0.5)
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dropout3d.train()
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x = paddle.ones([2, 100, 4, 8, 8])
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y = dropout3d(x)
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self.assertEqual(y.shape, [2, 100, 4, 8, 8])
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class TestAlphaDropout(unittest.TestCase):
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"""测试AlphaDropout / Test AlphaDropout"""
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def test_alpha_dropout_basic(self):
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"""测试基本AlphaDropout / Test basic AlphaDropout"""
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alpha_dropout = nn.AlphaDropout(p=0.5)
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x = paddle.randn([100, 100])
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y = alpha_dropout(x)
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self.assertEqual(y.shape, [100, 100])
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def test_alpha_dropout_eval_mode(self):
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"""测试评估模式AlphaDropout / Test AlphaDropout eval mode"""
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alpha_dropout = nn.AlphaDropout(p=0.5)
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alpha_dropout.eval()
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x = paddle.randn([100, 100])
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y = alpha_dropout(x)
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np.testing.assert_allclose(x.numpy(), y.numpy())
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def test_alpha_dropout_selu_combination(self):
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"""测试AlphaDropout与SELU组合 / Test AlphaDropout with SELU combination"""
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# AlphaDropout is designed to work with SELU
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model = nn.Sequential(
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nn.Linear(10, 10),
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nn.SELU(),
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nn.AlphaDropout(p=0.1),
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nn.Linear(10, 5),
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)
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model.train()
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x = paddle.randn([4, 10])
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y = model(x)
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self.assertEqual(y.shape, [4, 5])
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class TestDropoutInModel(unittest.TestCase):
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"""测试Dropout在模型中的使用 / Test Dropout in model"""
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def test_dropout_in_sequential(self):
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"""测试Dropout在Sequential中 / Test Dropout in Sequential"""
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model = nn.Sequential(
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nn.Linear(10, 20), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(20, 5)
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)
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x = paddle.randn([4, 10])
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model.train()
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y_train = model(x)
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model.eval()
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y_eval = model(x)
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# Both should have same shape
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self.assertEqual(y_train.shape, [4, 5])
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self.assertEqual(y_eval.shape, [4, 5])
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def test_dropout_train_eval_difference(self):
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"""测试训练和评估模式的不同 / Test difference between train and eval"""
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dropout = nn.Dropout(p=0.5)
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x = paddle.ones([1000])
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# Multiple evaluations with eval mode should give same result
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dropout.eval()
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y1 = dropout(x)
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y2 = dropout(x)
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np.testing.assert_allclose(y1.numpy(), y2.numpy())
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# In train mode, multiple runs may differ
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dropout.train()
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y3 = dropout(x)
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# y3 should have some zeros due to dropout
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self.assertTrue(
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float(y3.sum().numpy()) <= 2000
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) # max is 2000 due to upscaling
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if __name__ == '__main__':
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unittest.main()
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