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#!/usr/bin/env python
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# 图像分割模型Fast-SCNN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/rsseg/'
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# 训练集`file_list`文件路径
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TRAIN_FILE_LIST_PATH = './data/rsseg/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/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/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/fast_scnn/'
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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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pdrs.utils.download_and_decompress(
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'https://paddlers.bj.bcebos.com/datasets/rsseg.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/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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# 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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T.ArrangeSegmenter('train')
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])
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eval_transforms = T.Compose([
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T.DecodeImg(),
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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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T.ReloadMask(),
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T.ArrangeSegmenter('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.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Fast-SCNN模型
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# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md
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# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
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model = pdrs.tasks.seg.FastSCNN(
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in_channels=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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