wehub-resource-sync
155 lines
5.2 KiB
Python
155 lines
5.2 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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模型剪枝和压缩测试 / Model Pruning and Compression Tests
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测试目标 / Test Target:
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paddle.nn 模型结构操作
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覆盖的模块 / Covered Modules:
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- nn.Layer.parameters(): 参数访问
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- nn.Layer.sublayers(): 子层访问
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- nn.Layer.named_parameters(): 命名参数
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- 模型迭代和修改
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作用 / Purpose:
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补充模型结构操作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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from paddle import nn
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paddle.disable_static()
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class TestModelStructure(unittest.TestCase):
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"""测试模型结构 / Test model structure"""
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def setUp(self):
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"""设置测试模型 / Setup test model"""
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self.model = nn.Sequential(
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nn.Linear(4, 8),
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nn.ReLU(),
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nn.Linear(8, 4),
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nn.ReLU(),
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nn.Linear(4, 2),
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)
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def test_parameters_count(self):
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"""测试参数数量 / Test parameter count"""
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params = list(self.model.parameters())
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# 3 Linear layers, each with weight and bias = 6 params
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self.assertEqual(len(params), 6)
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def test_named_parameters(self):
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"""测试命名参数 / Test named parameters"""
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named_params = dict(self.model.named_parameters())
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self.assertIn('0.weight', named_params)
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self.assertIn('0.bias', named_params)
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def test_sublayers(self):
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"""测试子层 / Test sublayers"""
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sublayers = self.model.sublayers()
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self.assertEqual(len(sublayers), 5) # 3 Linear + 2 ReLU
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def test_named_sublayers(self):
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"""测试命名子层 / Test named sublayers"""
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named_sublayers = dict(self.model.named_sublayers())
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self.assertIn('0', named_sublayers)
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self.assertIn('2', named_sublayers)
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def test_total_params_count(self):
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"""测试总参数量 / Test total parameter count"""
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total = sum(p.numel() for p in self.model.parameters())
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# Linear(4,8): 4*8+8=40, Linear(8,4): 8*4+4=36, Linear(4,2): 4*2+2=10 = 86
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self.assertEqual(total, 86)
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class TestModelModification(unittest.TestCase):
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"""测试模型修改 / Test model modification"""
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def test_freeze_parameters(self):
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"""测试冻结参数 / Test freezing parameters"""
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model = nn.Linear(4, 2)
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# Freeze all parameters
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for param in model.parameters():
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param.stop_gradient = True
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# Verify all frozen
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for param in model.parameters():
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self.assertTrue(param.stop_gradient)
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def test_selective_freeze(self):
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"""测试选择性冻结 / Test selective freeze"""
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model = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 2))
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# Freeze first layer only
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for param in model[0].parameters():
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param.stop_gradient = True
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# First layer frozen
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for param in model[0].parameters():
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self.assertTrue(param.stop_gradient)
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# Second layer not frozen
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for param in model[1].parameters():
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self.assertFalse(param.stop_gradient)
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def test_parameter_count_with_frozen(self):
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"""测试带冻结参数的训练 / Test training with frozen parameters"""
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model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
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# Freeze first linear
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for param in model[0].parameters():
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param.stop_gradient = True
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# Only second linear should have trainable params
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trainable = [p for p in model.parameters() if not p.stop_gradient]
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# Linear(8,2): 8*2+2=18 trainable params
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self.assertEqual(sum(p.numel() for p in trainable), 18)
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class TestModelClone(unittest.TestCase):
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"""测试模型克隆 / Test model cloning"""
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def test_model_copy(self):
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"""测试模型复制 / Test model copy"""
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import copy
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model1 = nn.Linear(4, 2)
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model2 = copy.deepcopy(model1)
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# Verify weights are the same
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np.testing.assert_allclose(model1.weight.numpy(), model2.weight.numpy())
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# Modify model2 and verify model1 is unchanged
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with paddle.no_grad():
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model2.weight[:] = paddle.zeros_like(model2.weight)
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self.assertFalse(
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np.allclose(model1.weight.numpy(), model2.weight.numpy())
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)
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def test_sequential_access(self):
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"""测试Sequential层访问 / Test Sequential layer access"""
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model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
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# Access layers by index
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first_layer = model[0]
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self.assertIsInstance(first_layer, nn.Linear)
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last_layer = model[-1]
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self.assertIsInstance(last_layer, nn.Linear)
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if __name__ == '__main__':
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unittest.main()
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