wehub-resource-sync
154 lines
4.6 KiB
Python
154 lines
4.6 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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模型训练循环测试 / Model Training Loop Tests
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测试目标 / Test Target:
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完整训练循环组件
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覆盖的模块 / Covered Modules:
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- paddle.Model 高级API
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- model.fit / model.evaluate / model.predict
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- callback机制
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作用 / Purpose:
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补充高级模型训练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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from paddle.io import DataLoader, Dataset
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paddle.disable_static()
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class SimpleDataset(Dataset):
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def __init__(self, size=200):
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self.data = np.random.randn(size, 4).astype('float32')
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self.labels = np.random.randint(0, 2, size).astype('int64')
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def __getitem__(self, idx):
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return self.data[idx], self.labels[idx]
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def __len__(self):
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return len(self.data)
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class SimpleNet(nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc = nn.Linear(4, 2)
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def forward(self, x):
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return self.fc(x)
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class TestPaddleModelAPI(unittest.TestCase):
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"""测试paddle.Model高级API / Test paddle.Model high-level API"""
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def setUp(self):
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"""设置测试环境 / Setup test environment"""
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self.model = paddle.Model(SimpleNet())
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self.model.prepare(
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optimizer=paddle.optimizer.Adam(
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parameters=self.model.parameters(), learning_rate=0.001
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),
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loss=nn.CrossEntropyLoss(),
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metrics=paddle.metric.Accuracy(),
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)
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self.train_dataset = SimpleDataset(100)
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self.val_dataset = SimpleDataset(40)
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def test_model_fit(self):
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"""测试model.fit / Test model.fit"""
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self.model.fit(
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self.train_dataset,
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eval_data=self.val_dataset,
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batch_size=32,
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epochs=2,
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verbose=0,
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)
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def test_model_evaluate(self):
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"""测试model.evaluate / Test model.evaluate"""
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result = self.model.evaluate(self.val_dataset, batch_size=16, verbose=0)
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self.assertIsNotNone(result)
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def test_model_predict(self):
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"""测试model.predict / Test model.predict"""
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result = self.model.predict(self.val_dataset, batch_size=16, verbose=0)
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self.assertIsNotNone(result)
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class TestManualTrainingLoop(unittest.TestCase):
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"""测试手动训练循环 / Test manual training loop"""
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def test_basic_training(self):
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"""测试基本训练循环 / Test basic training loop"""
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model = SimpleNet()
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optimizer = paddle.optimizer.Adam(parameters=model.parameters())
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criterion = nn.CrossEntropyLoss()
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dataset = SimpleDataset(100)
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loader = DataLoader(dataset, batch_size=32)
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model.train()
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for x, y in loader:
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pred = model(x)
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loss = criterion(pred, y)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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def test_training_with_eval(self):
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"""测试含评估的训练循环 / Test training loop with evaluation"""
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model = SimpleNet()
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optimizer = paddle.optimizer.Adam(parameters=model.parameters())
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criterion = nn.CrossEntropyLoss()
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train_set = SimpleDataset(80)
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val_set = SimpleDataset(20)
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train_loader = DataLoader(train_set, batch_size=32)
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val_loader = DataLoader(val_set, batch_size=20)
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# Train one epoch
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model.train()
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for x, y in train_loader:
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pred = model(x)
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loss = criterion(pred, y)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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# Evaluate
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model.eval()
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total_correct = 0
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total_samples = 0
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with paddle.no_grad():
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for x, y in val_loader:
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pred = model(x)
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correct = (pred.argmax(axis=1) == y).sum()
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total_correct += int(correct.numpy())
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total_samples += len(y)
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accuracy = total_correct / total_samples
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self.assertGreaterEqual(accuracy, 0.0)
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self.assertLessEqual(accuracy, 1.0)
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if __name__ == '__main__':
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unittest.main()
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