Bobholamovic
2 years ago
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# PaddleRS科研实战：设计深度学习变化检测模型 |
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## 环境配置 |
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本案例演示如何使用PaddleRS设计变化检测模型，并开展消融实验和对比实验。 |
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## 数据准备 |
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## 1 环境配置 |
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## 模型设计与验证 |
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根据[教程](https://github.com/PaddlePaddle/PaddleRS/tree/develop/tutorials/train#环境准备)安装PaddleRS及相关依赖。在本项目中，GDAL库并不是必需的。 |
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## 获取对比算法指标 |
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配置好环境后，在PaddleRS仓库根目录中执行如下指令切换到本案例所在目录： |
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```shell |
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cd examples/rs_research |
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``` |
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## 2 数据准备 |
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本案例在[LEVIR-CD数据集](https://www.mdpi.com/2072-4292/12/10/1662)[1]和[synthetic images and real season-varying remote sensing images（SVCD）数据集](https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/565/2018/isprs-archives-XLII-2-565-2018.pdf)[2]上开展实验。请在[LEVIR-CD数据集下载链接](https://justchenhao.github.io/LEVIR/)和[SVCD数据集下载链接](https://drive.google.com/file/d/1GX656JqqOyBi_Ef0w65kDGVto-nHrNs9/edit)分别下载这两个数据集，解压至本地目录，并执行如下指令： |
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```shell |
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mkdir data/ |
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python ../../tools/prepare_dataset/prepare_levircd.py \ |
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--in_dataset_dir {LEVIR-CD数据集存放目录路径} \ |
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--out_dataset_dir "data/levircd" \ |
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--crop_size 256 \ |
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--crop_stride 256 |
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python ../../tools/prepare_dataset/prepare_svcd.py \ |
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--in_dataset_dir {SVCD数据集存放目录路径} \ |
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--out_dataset_dir "data/svcd" |
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``` |
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以上指令利用PaddleRS提供的数据集准备工具完成数据集切分、file list创建等操作。具体而言，对于LEVIR-CD数据集，使用官方的训练/验证/测试集划分，并将原始的`1024x1024`大小的影像切分为无重叠的`256x256`的小块（参考[3]中的做法）；对于SVCD数据集，使用官方的训练/验证/测试集划分，不做其它额外处理。 |
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## 3 模型设计与验证 |
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### 3.1 问题分析与思路拟定 |
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科学研究是为了解决实际问题的，本案例也不例外。本案例的研究动机如下：随着深度学习技术应用的不断深入，变化检测领域涌现了许多。与之相对应的是，模型的参数量也越来越大。 |
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[近年来变化检测模型]() |
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诚然，。 |
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1. 存储开销。 |
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2. 过拟合。 |
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为了解决上述问题，本案例拟提出一种基于网络迭代优化思想的深度学习变化检测算法。本案例的基本思路是，构造一个轻量级的变化检测模型，并以其作为基础迭代单元。每次迭代开始时，由上一次迭代输出的概率图以及原始的输入影像对构造新的输入，实现coarse-to-fine优化。考虑到增加迭代单元的数量将使模型参数量成倍增加，在迭代过程中始终复用同一迭代单元的参数，充分挖掘变化检测网络的拟合能力，迫使其学习到更加有效的特征。这一做法类似[循环神经网络](https://baike.baidu.com/item/%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/23199490)。根据此思路可以绘制框图如下： |
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[思路展示]() |
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### 3.2 确定baseline |
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科研工作往往需要“站在巨人的肩膀上”，在前人工作的基础上做“增量创新”。因此，对模型设计类工作而言，选用一个合适的baseline网络至关重要。考虑到本案例的出发点是解决，并且使用了。 |
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### 3.3 定义新模型 |
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[算法整体框图]() |
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### 3.4 进行参数分析与消融实验 |
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#### 3.4.1 实验设置 |
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#### 3.4.2 实验结果 |
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### 3.5 开展特征可视化实验 |
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## 4 对比实验 |
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### 4.1 确定对比算法 |
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### 4.2 准备对比算法配置文件 |
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### 4.3 实验结果 |
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#### 4.3.1 LEVIR-CD数据集上的对比结果 |
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#### 4.3.2 SVCD数据集上的对比结果 |
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精度、FLOPs、运行时间 |
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## 5 总结与展望 |
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## 参考文献 |
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> [1] Chen, Hao, and Zhenwei Shi. "A spatial-temporal attention-based method and a new dataset for remote sensing image change detection." *Remote Sensing* 12.10 (2020): 1662. |
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[2] Lebedev, M. A., et al. "CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS." *International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences* 42.2 (2018). |
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[3] Chen, Hao, Zipeng Qi, and Zhenwei Shi. "Remote sensing image change detection with transformers." *IEEE Transactions on Geoscience and Remote Sensing* 60 (2021): 1-14. |
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[4] Daudt, Rodrigo Caye, Bertr Le Saux, and Alexandre Boulch. "Fully convolutional siamese networks for change detection." *2018 25th IEEE International Conference on Image Processing (ICIP)*. IEEE, 2018. |
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import argparse |
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import os |
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import os.path as osp |
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from glob import glob |
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from itertools import count |
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from functools import partial |
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from concurrent.futures import ThreadPoolExecutor |
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from skimage.io import imread, imsave |
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from tqdm import tqdm |
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def get_default_parser(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'--in_dataset_dir', |
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type=str, |
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required=True, |
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help="Input dataset directory.") |
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parser.add_argument( |
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'--out_dataset_dir', type=str, help="Output dataset directory.") |
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return parser |
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def add_crop_options(parser): |
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parser.add_argument( |
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'--crop_size', type=int, help="Size of cropped patches.") |
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parser.add_argument( |
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'--crop_stride', |
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type=int, |
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help="Stride of sliding windows when cropping patches. `crop_size` will be used only if `crop_size` is not None.", |
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) |
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return parser |
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def crop_and_save(path, out_subdir, crop_size, stride): |
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name, ext = osp.splitext(osp.basename(path)) |
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out_subsubdir = osp.join(out_subdir, name) |
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if not osp.exists(out_subsubdir): |
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os.makedirs(out_subsubdir) |
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img = imread(path) |
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w, h = img.shape[:2] |
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counter = count() |
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for i in range(0, h - crop_size + 1, stride): |
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for j in range(0, w - crop_size + 1, stride): |
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imsave( |
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osp.join(out_subsubdir, '{}_{}{}'.format(name, |
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next(counter), ext)), |
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img[i:i + crop_size, j:j + crop_size], |
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check_contrast=False) |
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def crop_patches(crop_size, |
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stride, |
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data_dir, |
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out_dir, |
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subsets=('train', 'val', 'test'), |
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subdirs=('A', 'B', 'label'), |
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glob_pattern='*', |
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max_workers=0): |
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if max_workers < 0: |
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raise ValueError("`max_workers` must be a non-negative integer!") |
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if max_workers == 0: |
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for subset in subsets: |
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for subdir in subdirs: |
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paths = glob( |
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osp.join(data_dir, subset, subdir, glob_pattern), |
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recursive=True) |
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out_subdir = osp.join(out_dir, subset, subdir) |
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for p in tqdm(paths): |
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crop_and_save( |
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p, |
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out_subdir=out_subdir, |
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crop_size=crop_size, |
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stride=stride) |
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else: |
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# Concurrently crop image patches |
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with ThreadPoolExecutor(max_workers=max_workers) as executor: |
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for subset in subsets: |
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for subdir in subdirs: |
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paths = glob( |
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osp.join(data_dir, subset, subdir, glob_pattern), |
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recursive=True) |
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out_subdir = osp.join(out_dir, subset, subdir) |
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for _ in tqdm( |
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executor.map(partial( |
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crop_and_save, |
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out_subdir=out_subdir, |
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crop_size=crop_size, |
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stride=stride), |
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paths), |
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total=len(paths)): |
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pass |
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def get_path_tuples(*dirs, glob_pattern='*', data_dir=None): |
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all_paths = [] |
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for dir_ in dirs: |
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paths = glob(osp.join(dir_, glob_pattern), recursive=True) |
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paths = sorted(paths) |
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if data_dir is not None: |
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paths = [osp.relpath(p, data_dir) for p in paths] |
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all_paths.append(paths) |
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all_paths = list(zip(*all_paths)) |
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return all_paths |
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def create_file_list(file_list, path_tuples, sep=' '): |
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with open(file_list, 'w') as f: |
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for tup in path_tuples: |
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line = sep.join(tup) |
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f.write(line + '\n') |
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def link_dataset(src, dst): |
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if osp.exists(dst) and not osp.isdir(dst): |
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raise ValueError(f"{dst} exists and is not a directory.") |
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elif not osp.exists(dst): |
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os.makedirs(dst) |
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name = osp.basename(osp.normpath(src)) |
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os.symlink(src, osp.join(dst, name), target_is_directory=True) |
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#!/usr/bin/env python |
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import os.path as osp |
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from common import (get_default_parser, add_crop_options, crop_patches, |
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get_path_tuples, create_file_list, link_dataset) |
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SUBSETS = ('train', 'val', 'test') |
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SUBDIRS = ('A', 'B', 'label') |
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FILE_LIST_PATTERN = "{subset}.txt" |
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URL = "" |
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if __name__ == '__main__': |
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parser = get_default_parser() |
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parser = add_crop_options(parser) |
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args = parser.parse_args() |
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out_dir = osp.join(args.out_dataset_dir, |
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osp.basename(osp.normpath(args.in_dataset_dir))) |
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if args.crop_size is not None: |
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crop_patches( |
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args.crop_size, |
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args.crop_stride, |
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data_dir=args.in_dataset_dir, |
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out_dir=out_dir, |
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subsets=SUBSETS, |
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subdirs=SUBDIRS, |
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glob_pattern='*.png', |
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max_workers=0) |
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else: |
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link_dataset(args.in_dataset_dir, args.out_dataset_dir) |
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for subset in SUBSETS: |
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path_tuples = get_path_tuples( |
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*(osp.join(out_dir, subset, subdir) for subdir in SUBDIRS), |
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glob_pattern='**/*.png', |
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data_dir=args.out_dataset_dir) |
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file_list = osp.join( |
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args.out_dataset_dir, FILE_LIST_PATTERN.format(subset=subset)) |
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create_file_list(file_list, path_tuples) |
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print(f"Write file list to {file_list}.") |
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#!/usr/bin/env python |
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import os.path as osp |
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from common import (get_default_parser, get_path_tuples, create_file_list, |
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link_dataset) |
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SUBSETS = ('train', 'val', 'test') |
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SUBDIRS = ('A', 'B', 'OUT') |
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FILE_LIST_PATTERN = "{subset}.txt" |
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URL = "" |
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if __name__ == '__main__': |
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parser = get_default_parser() |
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args = parser.parse_args() |
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out_dir = osp.join(args.out_dataset_dir, |
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osp.basename(osp.normpath(args.in_dataset_dir))) |
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link_dataset(args.in_dataset_dir, args.out_dataset_dir) |
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for subset in SUBSETS: |
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# NOTE: Only use cropped real samples. |
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path_tuples = get_path_tuples( |
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*(osp.join(out_dir, 'Real', 'subset', subset, subdir) |
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for subdir in SUBDIRS), |
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data_dir=args.out_dataset_dir) |
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file_list = osp.join( |
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args.out_dataset_dir, FILE_LIST_PATTERN.format(subset=subset)) |
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create_file_list(file_list, path_tuples) |
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print(f"Write file list to {file_list}.") |
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