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#!/usr/bin/env python
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# 变化检测模型FC-Siam-conc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/fc_siam_conc/'
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# 下载和解压AirChange数据集
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airchange_dataset = 'http://mplab.sztaki.hu/~bcsaba/test/SZTAKI_AirChange_Benchmark.zip'
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pdrs.utils.download_and_decompress(airchange_dataset, path=DATA_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/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FC-Siam-conc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/apis/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/change_detector.py
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model = pdrs.tasks.FCSiamConc()
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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=3,
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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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