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89 lines
3.0 KiB
89 lines
3.0 KiB
#!/usr/bin/env python |
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# 图像分割模型UNet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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# 下载文件存放目录 |
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DOWNLOAD_DIR = './data/rsseg/' |
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# 数据集存放目录 |
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DATA_DIR = './data/rsseg/remote_sensing_seg/' |
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# 训练集`file_list`文件路径 |
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TRAIN_FILE_LIST_PATH = './data/rsseg/remote_sensing_seg/train.txt' |
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# 验证集`file_list`文件路径 |
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EVAL_FILE_LIST_PATH = './data/rsseg/remote_sensing_seg/val.txt' |
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# 数据集类别信息文件路径 |
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LABEL_LIST_PATH = './data/rsseg/remote_sensing_seg/labels.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/unet/' |
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# 影像波段数量 |
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NUM_BANDS = 10 |
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# 下载和解压多光谱地块分类数据集 |
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seg_dataset = 'https://paddleseg.bj.bcebos.com/dataset/remote_sensing_seg.zip' |
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pdrs.utils.download_and_decompress(seg_dataset, path=DOWNLOAD_DIR) |
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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/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/transforms.md |
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train_transforms = T.Compose([ |
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# 将影像缩放到512x512大小 |
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T.Resize(target_size=512), |
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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] * NUM_BANDS, std=[0.5] * NUM_BANDS), |
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]) |
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eval_transforms = T.Compose([ |
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T.Resize(target_size=512), |
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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] * NUM_BANDS, std=[0.5] * NUM_BANDS), |
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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.SegDataset( |
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data_dir=DATA_DIR, |
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file_list=TRAIN_FILE_LIST_PATH, |
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label_list=LABEL_LIST_PATH, |
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transforms=train_transforms, |
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num_workers=0, |
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shuffle=True) |
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eval_dataset = pdrs.datasets.SegDataset( |
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data_dir=DATA_DIR, |
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file_list=EVAL_FILE_LIST_PATH, |
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label_list=LABEL_LIST_PATH, |
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transforms=eval_transforms, |
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num_workers=0, |
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shuffle=False) |
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# 构建UNet模型 |
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# 目前已支持的模型请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/docs/apis/model_zoo.md |
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# 模型输入参数请参考：https://github.com/PaddleCV-SIG/PaddleRS/blob/develop/paddlers/tasks/segmenter.py |
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model = pdrs.tasks.UNet( |
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input_channel=NUM_BANDS, num_classes=len(train_dataset.labels)) |
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# 执行模型训练 |
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model.train( |
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num_epochs=10, |
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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=5, |
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# 每多少次迭代记录一次日志 |
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log_interval_steps=4, |
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save_dir=EXP_DIR, |
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# 初始学习率大小 |
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learning_rate=0.001, |
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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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