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94 lines
3.1 KiB
94 lines
3.1 KiB
#!/usr/bin/env python |
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# 变化检测模型FCCDN训练示例脚本 |
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# 执行此脚本前，请确认已正确安装PaddleRS库 |
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import paddlers as pdrs |
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from paddlers import transforms as T |
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# 数据集存放目录 |
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DATA_DIR = './data/airchange/' |
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# 训练集`file_list`文件路径 |
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TRAIN_FILE_LIST_PATH = './data/airchange/train.txt' |
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# 验证集`file_list`文件路径 |
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EVAL_FILE_LIST_PATH = './data/airchange/eval.txt' |
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# 实验目录，保存输出的模型权重和结果 |
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EXP_DIR = './output/fccdn/' |
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# 下载和解压AirChange数据集 |
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pdrs.utils.download_and_decompress( |
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'https://paddlers.bj.bcebos.com/datasets/airchange.zip', path='./data/') |
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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/transforms.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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# 随机裁剪 |
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T.RandomCrop( |
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# 裁剪区域将被缩放到256x256 |
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crop_size=256, |
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# 裁剪区域的横纵比在0.5-2之间变动 |
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aspect_ratio=[0.5, 2.0], |
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# 裁剪区域相对原始影像长宽比例在一定范围内变动，最小不低于原始长宽的1/5 |
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scaling=[0.2, 1.0]), |
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# 以50%的概率实施随机水平翻转 |
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T.RandomHorizontalFlip(prob=0.5), |
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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.ReloadMask(), |
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T.ArrangeChangeDetector('eval') |
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]) |
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# 分别构建训练和验证所用的数据集 |
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train_dataset = pdrs.datasets.CDDataset( |
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data_dir=DATA_DIR, |
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file_list=TRAIN_FILE_LIST_PATH, |
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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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eval_dataset = pdrs.datasets.CDDataset( |
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data_dir=DATA_DIR, |
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file_list=EVAL_FILE_LIST_PATH, |
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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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# 使用默认参数构建FCCDN模型 |
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# 目前已支持的模型及模型输入参数请参考： |
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# https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/change_detector.py |
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model = pdrs.tasks.cd.FCCDN() |
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# 执行模型训练 |
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model.train( |
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num_epochs=5, |
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train_dataset=train_dataset, |
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train_batch_size=4, |
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eval_dataset=eval_dataset, |
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save_interval_epochs=2, |
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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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