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111 lines
3.4 KiB
111 lines
3.4 KiB
#!/usr/bin/env bash |
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import os.path as osp |
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import paddle |
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import paddlers as pdrs |
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from paddlers import transforms as T |
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from custom_model import CustomModel |
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from custom_trainer import make_trainer |
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# 数据集路径 |
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DATA_DIR = 'data/levircd/' |
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# 保存实验结果的路径 |
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EXP_DIR = 'exp/levircd/custom_model/' |
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# 定义训练和验证时使用的数据变换（数据增强、预处理等） |
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# 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行 |
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# API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/data.md |
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train_transforms = T.Compose([ |
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# 读取影像 |
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T.DecodeImg(), |
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# 随机翻转和旋转 |
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T.RandomFlipOrRotate( |
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# 以0.35的概率执行随机翻转，0.35的概率执行随机旋转 |
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probs=[0.35, 0.35], |
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# 以0.5的概率执行随机水平翻转，0.5的概率执行随机垂直翻转 |
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probsf=[0.5, 0.5, 0, 0, 0], |
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# 分别以0.33、0.34和0.33的概率执行90°、180°和270°旋转 |
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probsr=[0.33, 0.34, 0.33]), |
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# 将数据归一化到[-1,1] |
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T.Normalize( |
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mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), |
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T.ArrangeChangeDetector('train') |
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]) |
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eval_transforms = T.Compose([ |
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T.DecodeImg(), |
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# 验证阶段与训练阶段的数据归一化方式必须相同 |
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T.Normalize( |
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mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), |
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T.ArrangeChangeDetector('eval') |
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]) |
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# 分别构建训练、验证和测试所用的数据集 |
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train_dataset = pdrs.datasets.CDDataset( |
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data_dir=DATA_DIR, |
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file_list=osp.join(DATA_DIR, 'train.txt'), |
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label_list=None, |
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transforms=train_transforms, |
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num_workers=0, |
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shuffle=True, |
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with_seg_labels=False, |
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binarize_labels=True) |
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val_dataset = pdrs.datasets.CDDataset( |
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data_dir=DATA_DIR, |
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file_list=osp.join(DATA_DIR, 'val.txt'), |
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label_list=None, |
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transforms=eval_transforms, |
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num_workers=0, |
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shuffle=False, |
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with_seg_labels=False, |
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binarize_labels=True) |
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test_dataset = pdrs.datasets.CDDataset( |
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data_dir=DATA_DIR, |
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file_list=osp.join(DATA_DIR, 'test.txt'), |
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label_list=None, |
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# 与验证阶段使用相同的数据变换算子 |
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transforms=eval_transforms, |
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num_workers=0, |
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shuffle=False, |
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with_seg_labels=False, |
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binarize_labels=True) |
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# 构建自定义模型CustomModel并为其自动生成训练器 |
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# make_trainer()的首个参数为模型类型，剩余参数为模型构造所需参数 |
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model = make_trainer(CustomModel, in_channels=3) |
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# 构建学习率调度器 |
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# 使用定步长学习率衰减策略 |
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lr_scheduler = paddle.optimizer.lr.StepDecay( |
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learning_rate=0.002, step_size=35000, gamma=0.2) |
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# 构建优化器 |
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optimizer = paddle.optimizer.Adam( |
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parameters=model.net.parameters(), learning_rate=lr_scheduler) |
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# 执行模型训练 |
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model.train( |
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num_epochs=50, |
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train_dataset=train_dataset, |
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train_batch_size=8, |
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eval_dataset=eval_dataset, |
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# 每多少个epoch验证并保存一次模型 |
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save_interval_epochs=5, |
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# 每多少次迭代记录一次日志 |
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log_interval_steps=50, |
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save_dir=EXP_DIR, |
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# 是否使用early stopping策略，当精度不再改善时提前终止训练 |
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early_stop=False, |
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# 是否启用VisualDL日志功能 |
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use_vdl=True, |
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# 指定从某个检查点继续训练 |
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resume_checkpoint=None) |
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# 加载验证集上效果最好的模型 |
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model = pdrs.tasks.load_model(osp.join(EXP_DIR, 'best_model')) |
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# 在测试集上计算精度指标 |
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model.evaluate(test_dataset)
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