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#!/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=val_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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