commit 28c518b355a658eb457417112a6db6a22b1d63eb Author: suyiiyii Date: Wed Nov 27 23:22:08 2024 +0800 init diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ebfcc9e --- /dev/null +++ b/.gitignore @@ -0,0 +1,206 @@ +.DS_Store +node_modules +*.log +explorations +TODOs.md +RELEASE_NOTE*.md +packages/server-renderer/basic.js +packages/server-renderer/build.dev.js +packages/server-renderer/build.prod.js +packages/server-renderer/server-plugin.js +packages/server-renderer/client-plugin.js +packages/template-compiler/build.js +packages/template-compiler/browser.js +.vscode +dist +temp +types/v3-generated.d.ts + + +# Compiled class file +*.class + +# Log file +*.log + +# BlueJ files +*.ctxt + +# Mobile Tools for Java (J2ME) +.mtj.tmp/ + +# Package Files # +*.jar +*.war +*.nar +*.ear +*.zip +*.tar.gz +*.rar + +# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml +hs_err_pid* +replay_pid* + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ \ No newline at end of file diff --git a/backend2/.gitignore b/backend2/.gitignore new file mode 100644 index 0000000..549e00a --- /dev/null +++ b/backend2/.gitignore @@ -0,0 +1,33 @@ +HELP.md +target/ +!.mvn/wrapper/maven-wrapper.jar +!**/src/main/**/target/ +!**/src/test/**/target/ + +### STS ### +.apt_generated +.classpath +.factorypath +.project +.settings +.springBeans +.sts4-cache + +### IntelliJ IDEA ### +.idea +*.iws +*.iml +*.ipr + +### NetBeans ### +/nbproject/private/ +/nbbuild/ +/dist/ +/nbdist/ +/.nb-gradle/ +build/ +!**/src/main/**/build/ +!**/src/test/**/build/ + +### VS Code ### +.vscode/ diff --git a/backend2/pom.xml b/backend2/pom.xml new file mode 100644 index 0000000..7e03b5c --- /dev/null +++ b/backend2/pom.xml @@ -0,0 +1,86 @@ + + + 4.0.0 + + org.springframework.boot + spring-boot-starter-parent + 2.6.11 + + + com.common + backend2 + 0.0.1-SNAPSHOT + backend2 + backend2 + + + 1.8 + + + + + org.springframework.boot + spring-boot-starter-web + + + + org.springframework.boot + spring-boot-starter-jdbc + + + mysql + mysql-connector-java + runtime + + + com.baomidou + mybatis-plus-boot-starter + 2.2.0 + + + + org.apache.commons + commons-lang3 + 3.9 + + + + org.projectlombok + lombok + true + + + org.springframework.boot + spring-boot-starter-test + test + + + + com.fasterxml.jackson.core + jackson-databind + + + + + + + + + org.apache.maven.plugins + maven-compiler-plugin + 3.8.1 + + 1.8 + 1.8 + UTF-8 + + + + org.springframework.boot + spring-boot-maven-plugin + + + + + diff --git a/backend2/src/main/java/com/common/backend2/Backend2Application.java b/backend2/src/main/java/com/common/backend2/Backend2Application.java new file mode 100644 index 0000000..d0c5c09 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/Backend2Application.java @@ -0,0 +1,13 @@ +package com.common.backend2; + +import org.springframework.boot.SpringApplication; +import org.springframework.boot.autoconfigure.SpringBootApplication; + +@SpringBootApplication +public class Backend2Application { + + public static void main(String[] args) { + SpringApplication.run(Backend2Application.class, args); + } + +} diff --git a/backend2/src/main/java/com/common/backend2/config/CommonInterceptor.java b/backend2/src/main/java/com/common/backend2/config/CommonInterceptor.java new file mode 100644 index 0000000..9c10b9f --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/config/CommonInterceptor.java @@ -0,0 +1,63 @@ +package com.common.backend2.config; + +import org.springframework.web.servlet.HandlerInterceptor; +import org.springframework.web.servlet.ModelAndView; + +import javax.servlet.http.HttpServletRequest; +import javax.servlet.http.HttpServletResponse; +import java.util.List; + +public class CommonInterceptor implements HandlerInterceptor { + private List excludedUrls; + + public List getExcludedUrls() { + return excludedUrls; + } + + public void setExcludedUrls(List excludedUrls) { + this.excludedUrls = excludedUrls; + } + + /** + * + * 在业务处理器处理请求之前被调用 如果返回false + * 从当前的拦截器往回执行所有拦截器的afterCompletion(), + * 再退出拦截器链, 如果返回true 执行下一个拦截器, + * 直到所有的拦截器都执行完毕 再执行被拦截的Controller + * 然后进入拦截器链, + * 从最后一个拦截器往回执行所有的postHandle() + * 接着再从最后一个拦截器往回执行所有的afterCompletion() + * @param request + * @param response + */ + public boolean preHandle(HttpServletRequest request, HttpServletResponse response, + Object handler) throws Exception { + response.setHeader("Access-Control-Allow-Origin", "*"); + response.setHeader("Access-Control-Allow-Methods", "*"); + response.setHeader("Access-Control-Max-Age", "3600"); + response.setHeader("Access-Control-Allow-Headers", + "Origin, X-Requested-With, Content-Type, Accept"); + return true; + } + + // 在业务处理器处理请求执行完成后,生成视图之前执行的动作 + public void postHandle(HttpServletRequest request, HttpServletResponse response, Object handler, + ModelAndView modelAndView) throws Exception { + + } + + /** + * 在DispatcherServlet完全处理完请求后被调用 + * 当有拦截器抛出异常时, + * 会从当前拦截器往回执行所有的拦截器的afterCompletion() + * @param request + * @param response + * @param handler + * + */ + public void afterCompletion(HttpServletRequest request, HttpServletResponse response, + Object handler, Exception ex) throws Exception { + + } + +} diff --git a/backend2/src/main/java/com/common/backend2/config/ResTemplateConfig.java b/backend2/src/main/java/com/common/backend2/config/ResTemplateConfig.java new file mode 100644 index 0000000..72ff975 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/config/ResTemplateConfig.java @@ -0,0 +1,25 @@ +package com.common.backend2.config; + +import org.springframework.context.annotation.Bean; +import org.springframework.context.annotation.Configuration; +import org.springframework.http.client.ClientHttpRequestFactory; +import org.springframework.http.client.SimpleClientHttpRequestFactory; +import org.springframework.web.client.RestTemplate; + +@Configuration +public class ResTemplateConfig { + @Bean + public RestTemplate restTemplate(ClientHttpRequestFactory factory) { + return new RestTemplate(factory); + } + + @Bean + public ClientHttpRequestFactory simpleClientHttpRequestFactory() { + SimpleClientHttpRequestFactory factory = new SimpleClientHttpRequestFactory(); + //超时设置 + factory.setReadTimeout(10000);//ms + factory.setConnectTimeout(15000);//ms + return factory; + } +} + diff --git a/backend2/src/main/java/com/common/backend2/config/WebMvcConfig.java b/backend2/src/main/java/com/common/backend2/config/WebMvcConfig.java new file mode 100644 index 0000000..7878b76 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/config/WebMvcConfig.java @@ -0,0 +1,55 @@ +package com.common.backend2.config; + +import org.springframework.context.annotation.Bean; +import org.springframework.context.annotation.Configuration; +import org.springframework.web.servlet.config.annotation.CorsRegistry; +import org.springframework.web.servlet.config.annotation.InterceptorRegistry; +import org.springframework.web.servlet.config.annotation.ResourceHandlerRegistry; +import org.springframework.web.servlet.config.annotation.WebMvcConfigurer; + +import java.io.File; + +@Configuration +public class WebMvcConfig implements WebMvcConfigurer { + @Override + public void addCorsMappings(CorsRegistry registry) { + registry.addMapping("/**") + .allowedHeaders("*") + .allowedOriginPatterns("*") + .allowedMethods("POST", "GET", "PUT", "OPTIONS", "DELETE") + .maxAge(3600) + .allowCredentials(true); + } + + @Override + public void addInterceptors(InterceptorRegistry registry) { + WebMvcConfigurer.super.addInterceptors(registry); + registry.addInterceptor(CommonInterceptor()).addPathPatterns("/**"); + } + + @Override + public void addResourceHandlers(ResourceHandlerRegistry registry) { + /** + * 资源映射路径 + * addResourceHandler：访问映射路径 + * addResourceLocations：资源绝对路径 + */ + //String projectRootDirectoryPath = System.getProperty("user.dir"); + // 通过 File 对象的 getParent() 方法获取到根目录的上级目录 + //String parentPath = new File(projectRootDirectoryPath).getParent(); + +// registry.addResourceHandler("/result/**") +// .addResourceLocations("file:///"+parentPath+"/RED-CNN-master(Lite)\\save/fig/"); + registry.addResourceHandler("/result/**") + .addResourceLocations("file:///E:/Document/project/temp/save/"); + registry.addResourceHandler("/assets/**") + .addResourceLocations("file:///E:/Document/project/temp/assets/"); + } + + + + @Bean + public CommonInterceptor CommonInterceptor() { + return new CommonInterceptor(); + } +} diff --git a/backend2/src/main/java/com/common/backend2/controller/FileController.java b/backend2/src/main/java/com/common/backend2/controller/FileController.java new file mode 100644 index 0000000..680a440 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/controller/FileController.java @@ -0,0 +1,109 @@ +package com.common.backend2.controller; + + +import com.common.backend2.entity.File; +import com.common.backend2.response.Result; +import com.common.backend2.response.ResponseCode; +import com.common.backend2.service.FileService; +import org.springframework.beans.factory.annotation.Autowired; +import org.springframework.web.bind.annotation.*; +import org.springframework.web.multipart.MultipartFile; + +import javax.servlet.http.HttpServletRequest; +import javax.servlet.http.HttpServletResponse; +import java.io.BufferedInputStream; +import java.io.IOException; +import java.io.InputStream; +import java.io.OutputStream; +import java.nio.charset.StandardCharsets; +import java.util.LinkedList; +import java.util.Queue; + +@CrossOrigin +@RestController +@RequestMapping("/api") +public class FileController { + + + @Autowired + private FileService fileService; + + + Queue queue =new LinkedList(); //test_patient + + @RequestMapping(value="/multi/uploadMultiImage",method=RequestMethod.POST) + public Result uploadMultiImage(@RequestParam("files") MultipartFile[] files,HttpServletRequest request){ + //files 就是前端传来的多文件数组 + if (files.length<=0) { + return new Result(ResponseCode.FILE_EMPTY.getCode(), ResponseCode.FILE_EMPTY.getMsg(), null); + } + for(MultipartFile file :files){ + fileService.upLoadFiles(file,request); + } + return new Result(ResponseCode.SUCCESS.getCode(), ResponseCode.SUCCESS.getMsg(), "数据上传成功"); + } + + @RequestMapping(value = "/upload",method = RequestMethod.POST) + public Result upLoadFile(@RequestParam("file") MultipartFile multipartFile,HttpServletRequest request) { + if (multipartFile.isEmpty()) { + return new Result(ResponseCode.FILE_EMPTY.getCode(), ResponseCode.FILE_EMPTY.getMsg(), null); + } + return fileService.upLoadFiles(multipartFile,request); + + } + @RequestMapping(value="/predict/{fileId}",method=RequestMethod.GET) + public Result predictAndreturnResult (@PathVariable("fileId")Integer id){ + + return fileService.processFile(id); + } + @RequestMapping(value = "/download/{id}",method = RequestMethod.GET) + public void downloadFiles(@PathVariable("id") String id, HttpServletRequest request, HttpServletResponse response){ + OutputStream outputStream=null; + InputStream inputStream=null; + BufferedInputStream bufferedInputStream=null; + byte[] bytes=new byte[1024]; + File file = fileService.getFileById(id); + String fileName = file.getFileName(); + // 获取输出流 + try { + response.setHeader("Content-Disposition", "attachment;filename=" + new String(fileName.getBytes(StandardCharsets.UTF_8), StandardCharsets.ISO_8859_1)); + response.setContentType("application/force-download"); + inputStream=fileService.getFileInputStream(file); + bufferedInputStream=new BufferedInputStream(inputStream); + outputStream = response.getOutputStream(); + int i=bufferedInputStream.read(bytes); + while (i!=-1){ + outputStream.write(bytes,0,i); + i=bufferedInputStream.read(bytes); + } + } catch (IOException e) { + e.printStackTrace(); + }finally { + try { + if (inputStream!=null){ + inputStream.close(); + } + if (outputStream!=null){ + outputStream.close(); + } + if (bufferedInputStream!=null){ + bufferedInputStream.close(); + } + } catch (IOException e) { + e.printStackTrace(); + } + + } + } + + @RequestMapping(value="/getResult",method=RequestMethod.GET) + public Result getUrlsByIp(HttpServletRequest request,HttpServletResponse response){ + return fileService.returnUrls(request,response); + } + @RequestMapping(value="/delete/{id}",method=RequestMethod.DELETE) + public Result deleteOriginAndProcess(@PathVariable("id")Integer id ){ + return fileService.deleteOne(id); + } + + +} diff --git a/backend2/src/main/java/com/common/backend2/entity/File.java b/backend2/src/main/java/com/common/backend2/entity/File.java new file mode 100644 index 0000000..3a64020 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/entity/File.java @@ -0,0 +1,56 @@ +package com.common.backend2.entity; + +import com.baomidou.mybatisplus.annotations.TableId; +import com.baomidou.mybatisplus.annotations.TableName; +import com.baomidou.mybatisplus.enums.IdType; +import lombok.*; + +import java.io.Serializable; + + + +@Data +@AllArgsConstructor +@NoArgsConstructor +@Getter +@Setter +@EqualsAndHashCode +@TableName(value = "file") +public class File implements Serializable { + + private static final long serialVersionUID=1L; + + /** + * 文件id + */ + @TableId(value="id",type = IdType.AUTO) + private Integer fileId; + /** + * 文件存储路径 + */ + private String filePath; + /** + * 文件存储路径 + */ + private String savePath; + /** + * 文件名称 + */ + private String fileName; + /** + * + * 是否处理过 + * + */ + private Integer processed; + + private String result; + + + public File(Object o, String saveChildPath, String resultChildPath, String fileName) { + this.fileId=null; + this.filePath= saveChildPath; + this.savePath = resultChildPath; + this.fileName = fileName; + } +} diff --git a/backend2/src/main/java/com/common/backend2/mapper/FileMapper.java b/backend2/src/main/java/com/common/backend2/mapper/FileMapper.java new file mode 100644 index 0000000..cbadebc --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/mapper/FileMapper.java @@ -0,0 +1,25 @@ +package com.common.backend2.mapper; + + +import com.baomidou.mybatisplus.mapper.BaseMapper; + +import com.common.backend2.entity.File; +import org.apache.ibatis.annotations.Insert; +import org.apache.ibatis.annotations.Mapper; +import org.apache.ibatis.annotations.Select; +import org.springframework.stereotype.Repository; + +import java.util.List; + + +@Mapper +@Repository +public interface FileMapper extends BaseMapper { + @Select("select * from file") + ListfindAll(); + @Select("SELECT LAST_INSERT_ID();") + Integer getId(); + + + +} diff --git a/backend2/src/main/java/com/common/backend2/response/ResponseCode.java b/backend2/src/main/java/com/common/backend2/response/ResponseCode.java new file mode 100644 index 0000000..c385fc7 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/response/ResponseCode.java @@ -0,0 +1,53 @@ +package com.common.backend2.response; + + +public enum ResponseCode { + // 系统模块 + SUCCESS(0, "操作成功"), + ERROR(1, "操作失败"), + SERVER_ERROR(500, "服务器异常"), + + // 通用模块 1xxxx + ILLEGAL_ARGUMENT(10000, "参数不合法"), + REPETITIVE_OPERATION(10001, "请勿重复操作"), + ACCESS_LIMIT(10002, "请求太频繁, 请稍后再试"), + MAIL_SEND_SUCCESS(10003, "邮件发送成功"), + + // 用户模块 2xxxx + NEED_LOGIN(20001, "登录失效"), + USERNAME_OR_PASSWORD_EMPTY(20002, "用户名或密码不能为空"), + USERNAME_OR_PASSWORD_WRONG(20003, "用户名或密码错误"), + USER_NOT_EXISTS(20004, "用户不存在"), + WRONG_PASSWORD(20005, "密码错误"), + + // 文件模块 3xxxx + FILE_EMPTY(30001,"文件不能空"), + FILE_NAME_EMPTY(30002,"文件名称不能为空"), + FILE_MAX_SIZE(30003,"文件大小超出"), + ; + + ResponseCode(Integer code, String msg) { + this.code = code; + this.msg = msg; + } + + private Integer code; + + private String msg; + + public Integer getCode() { + return code; + } + + public void setCode(Integer code) { + this.code = code; + } + + public String getMsg() { + return msg; + } + + public void setMsg(String msg) { + this.msg = msg; + } +} diff --git a/backend2/src/main/java/com/common/backend2/response/Result.java b/backend2/src/main/java/com/common/backend2/response/Result.java new file mode 100644 index 0000000..8cf0a88 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/response/Result.java @@ -0,0 +1,16 @@ +package com.common.backend2.response; + +import lombok.AllArgsConstructor; +import lombok.Data; +import lombok.NoArgsConstructor; + + +@Data +@AllArgsConstructor +@NoArgsConstructor +public class Result { + private int code; + private String message; + private Object data; +} + diff --git a/backend2/src/main/java/com/common/backend2/service/FileService.java b/backend2/src/main/java/com/common/backend2/service/FileService.java new file mode 100644 index 0000000..cb19ac5 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/service/FileService.java @@ -0,0 +1,52 @@ +package com.common.backend2.service; + + + +import com.baomidou.mybatisplus.service.IService; +import com.common.backend2.entity.File; +import com.common.backend2.response.Result; +import org.springframework.web.multipart.MultipartFile; + +import javax.servlet.http.HttpServletRequest; +import javax.servlet.http.HttpServletResponse; +import java.io.InputStream; + + +public interface FileService extends IService { + + /** + * 文件上传接口 + * @param file + * @return + */ + Result upLoadFiles(MultipartFile file, HttpServletRequest request); + + /** + * 根据id获取文件 + * @param id + * @return + */ + File getFileById(String id); + + /** + * 根据id获取数据流 + * @param file + * @return + */ + InputStream getFileInputStream(File file); + + + + /** + * 对文件进行处理 + * @param + * @return: 返回处理完的文件的id + */ + Result processFile(Integer id); + + + Result returnUrls(HttpServletRequest request, HttpServletResponse response); + + Result deleteOne(Integer id); + +} diff --git a/backend2/src/main/java/com/common/backend2/service/Impl/FileServiceImpl.java b/backend2/src/main/java/com/common/backend2/service/Impl/FileServiceImpl.java new file mode 100644 index 0000000..d512293 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/service/Impl/FileServiceImpl.java @@ -0,0 +1,209 @@ +package com.common.backend2.service.Impl; + + +import com.baomidou.mybatisplus.service.impl.ServiceImpl; + +import com.common.backend2.entity.File; +import com.common.backend2.mapper.FileMapper; +import com.common.backend2.response.ResponseCode; +import com.common.backend2.response.Result; + +import com.common.backend2.service.FileService; +import com.common.backend2.utils.IpUtil; +import com.fasterxml.jackson.databind.JsonNode; +import com.fasterxml.jackson.databind.ObjectMapper; +import org.apache.commons.lang3.StringUtils; +import org.springframework.beans.factory.annotation.Autowired; +import org.springframework.beans.factory.annotation.Value; +import org.springframework.stereotype.Service; +import org.springframework.transaction.annotation.Transactional; +import org.springframework.web.multipart.MultipartFile; + +import javax.servlet.http.HttpServletRequest; +import javax.servlet.http.HttpServletResponse; +import java.io.*; +import java.text.SimpleDateFormat; +import java.util.ArrayList; +import java.util.Date; +import java.util.List; + + +@Service +public class FileServiceImpl extends ServiceImpl implements FileService { + + + @Value("${file.data-path}") + private String dataPath; + // @Value("${file.parent-path}") +// private String parentPath; + @Value("${file.result-path}") + private String resultPath; + @Value("${url.get-result-url}") + private String getResultUrl; + @Autowired + private FileMapper fileMapper; + + @Autowired + private RestTemplateMethods restTemplateMethods; + + @Transactional + public Result upLoadFiles(MultipartFile file, HttpServletRequest request) { + long MAX_SIZE = 2097152L; + + String fileName = file.getOriginalFilename(); //获取原名 + if (StringUtils.isEmpty(fileName)) { + return new Result(ResponseCode.FILE_NAME_EMPTY.getCode(), ResponseCode.FILE_NAME_EMPTY.getMsg(), null); + } // 判断不为空 + if (file.getSize() > MAX_SIZE) { + return new Result(ResponseCode.FILE_MAX_SIZE.getCode(), ResponseCode.FILE_MAX_SIZE.getMsg(), null); + } // 判断大小是否超出限制 + // String suffixName = fileName.contains(".") ? fileName.substring(fileName.lastIndexOf(".")) : null; //获取没有后缀的文件名 + // 获取当前时间戳作为文件名 + Date date = new Date(); + SimpleDateFormat formatter = new SimpleDateFormat("yyyy-MM-dd-HH:mm:ss"); + String dateString = formatter.format(date); + + String saveChildPath = dataPath ;//+ "/" + dateString; // 使用时间戳字符串存储 + + String resultChildPath = resultPath ;//+ "/" + dateString; + java.io.File newFile = new java.io.File(saveChildPath, fileName); //新建一个文件进行存储 + if (!newFile.getParentFile().exists()) { + newFile.getParentFile().mkdirs(); + } //文件创建 + try { + //文件写入--文件的转存 + file.transferTo(newFile); + } catch (IOException e) { + e.printStackTrace(); + } // 转存到文件中 + File files = new File(null, saveChildPath, resultChildPath, fileName); // 信息包装成对象准备存入数据库 + fileMapper.insert(files); //插入到数据库 + int fildId = fileMapper.getId(); + + return new Result(ResponseCode.SUCCESS.getCode(), ResponseCode.SUCCESS.getMsg(), fildId); + } + + public File getFileById(String id) { + return fileMapper.selectById(id); + + } + + public InputStream getFileInputStream(File files) { + java.io.File file = new java.io.File(files.getFilePath()); + try { + return new FileInputStream(file); + } catch (FileNotFoundException e) { + e.printStackTrace(); + } + return null; + } + + + public Result processFile(Integer id) { +// +// File file = fileMapper.selectById(id); +// String fileName = file.getFileName(); +// String classAndScore=restTemplateMethods.RestTemplatePost(fileName); +// String resultUrl=getResultUrl+"/result/"+fileName; +// ArrayList result =new ArrayList<>(); +// result.add(resultUrl); +// result.add(classAndScore); +// //从结果文件夹发回--这个可以用一个get来请求或者使用轮询 +// return new Result(ResponseCode.SUCCESS.getCode(), ResponseCode.SUCCESS.getMsg(), result); + + File file = fileMapper.selectById(id); + Integer processed = file.getProcessed(); + String fileName = file.getFileName(); + ArrayList result =new ArrayList<>(); + String resultUrl=getResultUrl+"/result/"+fileName; + result.add(resultUrl); + if(processed!=0){ + result.add(file.getResult()); + } + else{ + String classAndScore=restTemplateMethods.RestTemplatePost(fileName); + //将结果字符串保存至数据库 + file.setResult(classAndScore); + file.setProcessed(1); + fileMapper.updateById(file); + + result.add(classAndScore); + + //从结果文件夹发回--这个可以用一个get来请求或者使用轮询 + + } + return new Result(ResponseCode.SUCCESS.getCode(), ResponseCode.SUCCESS.getMsg(), result); + } + + @Override + public Result returnUrls(HttpServletRequest request, HttpServletResponse response) { + + String ip = IpUtil.getClientIpAddr(request); + String path = resultPath + "/" + ip; //python 图片目录 + String RetPath = getResultUrl + "/result/" + ip + "/"; + java.io.File file = new java.io.File(path); + java.io.File[] files = file.listFiles(); //获取所有的文件名 + List ResultUrls = new ArrayList<>(); + for (java.io.File file1 : files) { + ResultUrls.add(RetPath + file1.getName()); + } + return new Result(ResponseCode.SUCCESS.getCode(), ResponseCode.SUCCESS.getMsg(), ResultUrls); + } + + @Override + public Result deleteOne(Integer id) { + File file =fileMapper.selectById(id); + // 获取路径 + String originPath = file.getFilePath(); + String resultPath = file.getSavePath(); + String fileName = file.getFileName(); + String originFullPath = originPath+"/"+fileName; + String resultFullPath = resultPath+"/"+fileName; + + //删除文件 + java.io.File originFile = new java.io.File(originFullPath); + java.io.File resultFile = new java.io.File(resultFullPath); + + // 检查文件是否存在 + if (originFile.exists()) { + // 尝试删除文件 + boolean deleted = originFile.delete(); + + if (deleted) { + System.out.println("Origin文件已成功删除"); + } else { + System.out.println("无法删除Origin文件"); + return new Result(ResponseCode.ERROR.getCode(), ResponseCode.ERROR.getMsg(), "Origin删除失败"); + } + } else { + System.out.println("Origin文件不存在"); + return new Result(ResponseCode.ERROR.getCode(), ResponseCode.ERROR.getMsg(), "Origin文件不存在"); + } + + if (resultFile.exists()) { + // 尝试删除文件 + boolean deleted = resultFile.delete(); + + if (deleted) { + System.out.println("Result文件已成功删除"); + } else { + System.out.println("无法删除Result文件"); + return new Result(ResponseCode.ERROR.getCode(), ResponseCode.ERROR.getMsg(), "Result删除失败"); + } + } else { + System.out.println("Result文件不存在"); + } + //删除数据库数据 + int result = fileMapper.deleteById(file); + + if (result > 0) { + // 删除成功 + return new Result(ResponseCode.SUCCESS.getCode(), ResponseCode.SUCCESS.getMsg(), "删除成功"); + } else { + // 删除失败 + return new Result(ResponseCode.ERROR.getCode(), ResponseCode.ERROR.getMsg(), "数据库删除失败"); + } + + } + +} diff --git a/backend2/src/main/java/com/common/backend2/service/Impl/RestTemplateMethods.java b/backend2/src/main/java/com/common/backend2/service/Impl/RestTemplateMethods.java new file mode 100644 index 0000000..7b483ad --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/service/Impl/RestTemplateMethods.java @@ -0,0 +1,51 @@ +package com.common.backend2.service.Impl; + + +import jdk.nashorn.internal.parser.JSONParser; +import org.springframework.beans.factory.annotation.Autowired; +import org.springframework.beans.factory.annotation.Value; +import org.springframework.stereotype.Service; +import org.springframework.util.LinkedMultiValueMap; +import org.springframework.web.client.RestTemplate; + +import java.io.IOException; +import java.net.HttpURLConnection; +import java.net.MalformedURLException; +import java.net.URL; + +@Service +public class RestTemplateMethods { + @Autowired + private RestTemplate restTemplate; + @Value("${url.predict-url}") + String predictUrl ; + + public String RestTemplatePost(String fileName) { + + + + LinkedMultiValueMap request = new LinkedMultiValueMap<>(); + //request.set("className",dateString); + request.set("fileName", fileName); + try { + URL url1 = new URL(predictUrl); + HttpURLConnection con =(HttpURLConnection)url1.openConnection(); + con.setRequestMethod("POST"); + con.setDoOutput(true); + con.setDoInput(true); + con.setUseCaches(false); + con.setRequestProperty("Content-Type","application/from-data"); + } catch (MalformedURLException e) { + e.printStackTrace(); + } catch (IOException e) { + e.printStackTrace(); + } + //请求 + String result = restTemplate.postForObject(predictUrl, request, String.class); + + System.out.println(result); + return result; + + } + +} diff --git a/backend2/src/main/java/com/common/backend2/utils/IpUtil.java b/backend2/src/main/java/com/common/backend2/utils/IpUtil.java new file mode 100644 index 0000000..dcdd626 --- /dev/null +++ b/backend2/src/main/java/com/common/backend2/utils/IpUtil.java @@ -0,0 +1,71 @@ +package com.common.backend2.utils; +import javax.servlet.http.HttpServletRequest; +import java.net.InetAddress; +import java.net.UnknownHostException; + + +public class IpUtil { + /** + * @description: 获取请求的ip + * @param request + * @return: java.lang.String + */ + public static String getClientIpAddr(HttpServletRequest request) { + String ip = request.getHeader("X-Forwarded-For"); + if (ip != null && ip.length() != 0 && !"unknown".equalsIgnoreCase(ip)) { + // 多次反向代理后会有多个ip值，第一个ip才是真实ip + int index = ip.indexOf(','); + if (index != -1) { + ip = ip.substring(0, index); + } + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("Proxy-Client-IP"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("WL-Proxy-Client-IP"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("HTTP_X_FORWARDED_FOR"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("HTTP_X_FORWARDED"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("HTTP_X_CLUSTER_CLIENT_IP"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("HTTP_CLIENT_IP"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("HTTP_FORWARDED_FOR"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("HTTP_FORWARDED"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("HTTP_VIA"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("REMOTE_ADDR"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getHeader("X-Real-IP"); + } + if (ip == null || ip.length() == 0 || "unknown".equalsIgnoreCase(ip)) { + ip = request.getRemoteAddr(); + if (ip.equals("127.0.0.1") || ip.equals("0:0:0:0:0:0:0:1")) { + //根据网卡取本机配置的IP + InetAddress inet = null; + try { + inet = InetAddress.getLocalHost(); + } catch (UnknownHostException e) { + e.printStackTrace(); + } + ip = inet.getHostAddress(); + } + } + return ip; + } +} + diff --git a/backend2/src/main/resources/application.yml b/backend2/src/main/resources/application.yml new file mode 100644 index 0000000..f37f502 --- /dev/null +++ b/backend2/src/main/resources/application.yml @@ -0,0 +1,41 @@ +server: + port: 5000 + #port: 80 + servlet: + encoding: + charset: utf-8 + enabled: true + force: true +spring: + datasource: + driver-class-name: com.mysql.cj.jdbc.Driver + name: defaultDataSource + # url: jdbc:mysql://175.178.186.100:31441/DiatomRecognition?useUnicode=true&characterEncoding=UTF-8&serverTimezone=UTC + url: jdbc:mysql://192.168.161.130:3306/DiatomRecognition?useUnicode=true&characterEncoding=UTF-8&serverTimezone=UTC + username: root + # password: xiyi409@ + password: 123456 + +mybatis-plus: + typeAliasesPackage: com.common.backend2.entity + mapperLocations: classpath:mapper/*.xml +file: # 服务器 + data-path: /home/hejinwen/detect/data/upload + result-path: /home/hejinwen/detect/data/save +url: # 服务器 + predict-url: http://127.0.0.1:2000/predict + get-result-url: http://192.168.161.130:80 # 走nginx的代理 +#url: # 服务器测试 +# predict-url: http://175.178.186.100:5000/predict +# get-result-url: wadouri:http://175.178.186.100:6000 +#file: # 本地测试 +# parent-path: E:\Document\project\temp +# data-path: E:/Document/project/temp/data +# result-path: E:\Document\project\temp/save +#url: # 本地测试 +# predict-url: http://127.0.0.1:2000/predict +# get-result-url: http://127.0.0.1:5000 +logging: + level: + root: INFO + web: DEBUG diff --git a/backend2/src/test/java/com/common/backend2/Backend2ApplicationTests.java b/backend2/src/test/java/com/common/backend2/Backend2ApplicationTests.java new file mode 100644 index 0000000..fde79eb --- /dev/null +++ b/backend2/src/test/java/com/common/backend2/Backend2ApplicationTests.java @@ -0,0 +1,13 @@ +package com.common.backend2; + +import org.junit.jupiter.api.Test; +import org.springframework.boot.test.context.SpringBootTest; + +@SpringBootTest +class Backend2ApplicationTests { + + @Test + void contextLoads() { + } + +} diff --git a/bigview/babel.config.js b/bigview/babel.config.js new file mode 100644 index 0000000..e955840 --- /dev/null +++ b/bigview/babel.config.js @@ -0,0 +1,5 @@ +module.exports = { + presets: [ + '@vue/cli-plugin-babel/preset' + ] +} diff --git a/bigview/jsconfig.json b/bigview/jsconfig.json new file mode 100644 index 0000000..4aafc5f --- /dev/null +++ b/bigview/jsconfig.json @@ -0,0 +1,19 @@ +{ + "compilerOptions": { + "target": "es5", + "module": "esnext", + "baseUrl": "./", + "moduleResolution": "node", + "paths": { + "@/*": [ + "src/*" + ] + }, + "lib": [ + "esnext", + "dom", + "dom.iterable", + "scripthost" + ] + } +} diff --git a/bigview/mock/bigScreen-mock.js b/bigview/mock/bigScreen-mock.js new file mode 100644 index 0000000..a49cba4 --- /dev/null +++ b/bigview/mock/bigScreen-mock.js @@ -0,0 +1,15 @@ +export default [ + // 获取天气情况 + { + method: 'get', + url: '/getWeather', + data: { + code: 200, + message: 'success', + data: { + weather: '小雨转多云', + temperature: '13℃~18℃' + } + } + } +] \ No newline at end of file diff --git a/bigview/mock/index.js b/bigview/mock/index.js new file mode 100644 index 0000000..e2726c9 --- /dev/null +++ b/bigview/mock/index.js @@ -0,0 +1,25 @@ +const Mock = require('mockjs') + +// 遍历所有mock文件 +const files = require.context('.', true, /\.js$/) +let mockList = files.keys() + .filter(key => + key !== './index.js' && files(key).default + ) + .map(key => + files(key).default + ); + +// 开始注册所有mock服务 +for (let list of mockList) { //遍历所有模块 + // 遍历模块中的所有api + for (let item of list) { + 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"scripts": { + "serve": "vue-cli-service serve", + "build": "vue-cli-service build", + "lint": "vue-cli-service lint" + }, + "dependencies": { + "axios": "^1.4.0", + "core-js": "^3.8.3", + "dayjs": "^1.11.8", + "echarts": "^5.4.2", + "element-ui": "^2.15.13", + "mockjs": "^1.1.0", + "vue": "^2.6.14", + "vue-baidu-map": "^0.21.22" + }, + "devDependencies": { + "@babel/core": "^7.12.16", + "@babel/eslint-parser": "^7.12.16", + "@vue/cli-plugin-babel": "~5.0.0", + "@vue/cli-plugin-eslint": "~5.0.0", + "@vue/cli-service": "~5.0.0", + "eslint": "^7.32.0", + "eslint-plugin-vue": "^8.0.3", + "less": "^4.1.3", + "less-loader": "^11.1.3", + "vue-template-compiler": "^2.6.14" + }, + "eslintConfig": { + "root": true, + "env": { + "node": true + }, + "extends": [ + "plugin:vue/essential", + "eslint:recommended" + ], + "parserOptions": { + "parser": "@babel/eslint-parser" + }, + "rules": {} + }, + "browserslist": [ + "> 1%", + "last 2 versions", + "not dead" + ] +} diff --git a/bigview/public/favicon.ico b/bigview/public/favicon.ico new file mode 100644 index 0000000..df36fcf Binary files /dev/null and b/bigview/public/favicon.ico differ diff --git a/bigview/public/index.html b/bigview/public/index.html new file mode 100644 index 0000000..48fd4b1 --- /dev/null +++ b/bigview/public/index.html @@ -0,0 +1,38 @@ + + + + + + + + + + <%= htmlWebpackPlugin.options.title %> + + + + + + + + +
+ + + + \ No newline at end of file diff --git a/bigview/src/App.vue b/bigview/src/App.vue new file mode 100644 index 0000000..fc4f229 --- /dev/null +++ b/bigview/src/App.vue @@ -0,0 +1,589 @@ + + + + + diff --git a/bigview/src/api/bigScreen-api.js b/bigview/src/api/bigScreen-api.js new file mode 100644 index 0000000..9c6f889 --- /dev/null +++ b/bigview/src/api/bigScreen-api.js @@ -0,0 +1,10 @@ +import request from '@/utils/request' + +// 获取天气情况 +export function getWeather (param) { + return request({ + url: '/getWeather', + method: 'get', + params: param + }) +} \ No newline at end of file diff --git a/bigview/src/assets/image/a.png b/bigview/src/assets/image/a.png new file mode 100644 index 0000000..e15ab18 Binary files /dev/null and b/bigview/src/assets/image/a.png differ diff --git a/bigview/src/assets/image/b.png b/bigview/src/assets/image/b.png new file mode 100644 index 0000000..49e92e4 Binary files /dev/null and b/bigview/src/assets/image/b.png differ diff --git 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a/bigview/src/assets/image/logo.png b/bigview/src/assets/image/logo.png new file mode 100644 index 0000000..5ec310b Binary files /dev/null and b/bigview/src/assets/image/logo.png differ diff --git a/bigview/src/assets/image/logo2.jpg b/bigview/src/assets/image/logo2.jpg new file mode 100644 index 0000000..ae03065 Binary files /dev/null and b/bigview/src/assets/image/logo2.jpg differ diff --git a/bigview/src/assets/image/no-data.png b/bigview/src/assets/image/no-data.png new file mode 100644 index 0000000..ceb94ba Binary files /dev/null and b/bigview/src/assets/image/no-data.png differ diff --git a/bigview/src/main.js b/bigview/src/main.js new file mode 100644 index 0000000..7057902 --- /dev/null +++ b/bigview/src/main.js @@ -0,0 +1,14 @@ +import Vue from 'vue' +import App from './App.vue' + +Vue.config.productionTip = false + +import ElementUI from 'element-ui' +import 'element-ui/lib/theme-chalk/index.css' +Vue.use(ElementUI) + +import '../mock/index' + +new Vue({ + render: h => h(App), +}).$mount('#app') diff --git a/bigview/src/utils/request.js b/bigview/src/utils/request.js new file mode 100644 index 0000000..f55e956 --- /dev/null +++ b/bigview/src/utils/request.js @@ -0,0 +1,89 @@ +import axios from 'axios' +import { Notification } from 'element-ui' +// 创建axios实例 +const service = axios.create({ + baseURL: '/api', + timeout: 80000, // 请求超时时间 + withCredentials: true, + // crossDomain: true +}) + +// request拦截器 +service.interceptors.request.use( + config => { + // if (getToken()) { + // config.headers['Authorization'] = getToken() // 让每个请求携带自定义token 请根据实际情况自行修改 + // } + var lang = localStorage.getItem('lang')//因为项目中使用到了i18n国际化语言配置，请根据实际情况自行修改 + if (!lang) { + lang = 'zh_CN' + } + config.headers['Accept-Language'] = lang.replace(/_/g, '-') + config.headers['Content-Type'] = 'application/json' + return config + }, + error => { + Promise.reject(error) + } +) + +// response 拦截器 +service.interceptors.response.use( + response => { + return response.data + }, + error => { + // 兼容blob下载出错json提示 + if (error.response.data instanceof Blob && error.response.data.type.toLowerCase().indexOf('json') !== -1) { + const reader = new FileReader() + reader.readAsText(error.response.data, 'utf-8') + reader.onload = function () { + const errorMsg = JSON.parse(reader.result).message + Notification.error({ + title: errorMsg, + duration: 5000 + }) + } + } else { + let code = 0 + try { + code = error.response.data.status + } catch (e) { + if (error.toString().indexOf('Error: timeout') !== -1) { + Notification.error({ + title: '网络请求超时', + duration: 5000 + }) + return Promise.reject(error) + } + } + + if (code) { + // if (code === 401) { + // store.dispatch('LogOut').then(() => { + // // 用户登录界面提示 + // Cookies.set('point', 401) + // location.reload() + // }) + // } else if (code === 403) { + // router.push({ path: '/401' }) + // } else { + // const errorMsg = error.response.data.message + // if (errorMsg !== undefined) { + // Notification.error({ + // title: errorMsg, + // duration: 0 + // }) + // } + // } + } else { + Notification.error({ + title: '接口请求失败', + duration: 5000 + }) + } + } + return Promise.reject(error) + } +) +export default service \ No newline at end of file diff --git a/bigview/vue.config.js b/bigview/vue.config.js new file mode 100644 index 0000000..a592b46 --- /dev/null +++ b/bigview/vue.config.js @@ -0,0 +1,16 @@ +const { defineConfig } = require('@vue/cli-service') +module.exports = defineConfig({ + transpileDependencies: true, + devServer: { + open: true, // 运行后自动打开浏览器 + port: 8080, + // proxy: { + // '/api': { + // target: 'http://192.168.1.102:8088', + // ws: false, + // changeOrigin: true, + // logLevel: 'debug' + // } + // } + }, +}) diff --git a/yolox-pytorch/.gitignore b/yolox-pytorch/.gitignore new file mode 100644 index 0000000..dc0f659 --- /dev/null +++ b/yolox-pytorch/.gitignore @@ -0,0 +1,140 @@ +# ignore map, miou, datasets +map_out/ +miou_out/ +VOCdevkit/ +datasets/ +Medical_Datasets/ +lfw/ +logs/ +model_data/ +.temp_map_out/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/yolox-pytorch/2007_train.txt 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[仓库更新 Top News](#仓库更新) +2. [相关仓库 Related code](#相关仓库) +3. [性能情况 Performance](#性能情况) +4. [实现的内容 Achievement](#实现的内容) +5. [所需环境 Environment](#所需环境) +6. [文件下载 Download](#文件下载) +7. [训练步骤 How2train](#训练步骤) +8. [预测步骤 How2predict](#预测步骤) +9. [评估步骤 How2eval](#评估步骤) +10. [参考资料 Reference](#Reference) + +## Top News +**`2022-04`**:**支持多GPU训练，新增各个种类目标数量计算，新增heatmap。** + +**`2022-03`**:**进行了大幅度的更新，支持step、cos学习率下降法、支持adam、sgd优化器选择、支持学习率根据batch_size自适应调整、新增图片裁剪。** +BiliBili视频中的原仓库地址为：https://github.com/bubbliiiing/yolox-pytorch/tree/bilibili + +**`2021-10`**:**创建仓库，支持不同尺寸模型训练、支持大量可调整参数，支持fps、视频预测、批量预测等功能。**   + +## 相关仓库 +| 模型 | 路径 | +| :----- | :----- | +YoloV3 | https://github.com/bubbliiiing/yolo3-pytorch +Efficientnet-Yolo3 | https://github.com/bubbliiiing/efficientnet-yolo3-pytorch +YoloV4 | https://github.com/bubbliiiing/yolov4-pytorch +YoloV4-tiny | https://github.com/bubbliiiing/yolov4-tiny-pytorch +Mobilenet-Yolov4 | https://github.com/bubbliiiing/mobilenet-yolov4-pytorch +YoloV5-V5.0 | https://github.com/bubbliiiing/yolov5-pytorch +YoloV5-V6.1 | https://github.com/bubbliiiing/yolov5-v6.1-pytorch +YoloX | https://github.com/bubbliiiing/yolox-pytorch +YoloV7 | https://github.com/bubbliiiing/yolov7-pytorch +YoloV7-tiny | https://github.com/bubbliiiing/yolov7-tiny-pytorch + +## 性能情况 +| 训练数据集 | 权值文件名称 | 测试数据集 | 输入图片大小 | mAP 0.5:0.95 | mAP 0.5 | +| :-----: | :-----: | :------: | :------: | :------: | :-----: | +| COCO-Train2017 | [yolox_nano.pth](https://github.com/bubbliiiing/yolox-pytorch/releases/download/v1.0/yolox_nano.pth) | COCO-Val2017 | 640x640 | 27.4 | 44.5 +| COCO-Train2017 | [yolox_tiny.pth](https://github.com/bubbliiiing/yolox-pytorch/releases/download/v1.0/yolox_tiny.pth) | COCO-Val2017 | 640x640 | 34.7 | 53.6 +| COCO-Train2017 | [yolox_s.pth](https://github.com/bubbliiiing/yolox-pytorch/releases/download/v1.0/yolox_s.pth) | COCO-Val2017 | 640x640 | 38.2 | 57.7 +| COCO-Train2017 | [yolox_m.pth](https://github.com/bubbliiiing/yolox-pytorch/releases/download/v1.0/yolox_m.pth) | COCO-Val2017 | 640x640 | 44.8 | 63.9 +| COCO-Train2017 | [yolox_l.pth](https://github.com/bubbliiiing/yolox-pytorch/releases/download/v1.0/yolox_l.pth) | COCO-Val2017 | 640x640 | 47.9 | 66.6 +| COCO-Train2017 | [yolox_x.pth](https://github.com/bubbliiiing/yolox-pytorch/releases/download/v1.0/yolox_x.pth) | COCO-Val2017 | 640x640 | 49.0 | 67.7 + +## 实现的内容 +- [x] 主干特征提取网络：使用了Focus网络结构。 +- [x] 分类回归层：Decoupled Head，在YoloX中，Yolo Head被分为了分类回归两部分，最后预测的时候才整合在一起。 +- [x] 训练用到的小技巧：Mosaic数据增强、IOU和GIOU、学习率余弦退火衰减。 +- [x] Anchor Free：不使用先验框 +- [x] SimOTA：为不同大小的目标动态匹配正样本。 + +## 所需环境 +pytorch==1.2.0 + +## 文件下载 +训练所需的权值可在百度网盘中下载。 +链接: https://pan.baidu.com/s/1bi2UBwwIHES0OpLeyYuBFg +提取码: f4ni + +VOC数据集下载地址如下，里面已经包括了训练集、测试集、验证集（与测试集一样），无需再次划分： +链接: https://pan.baidu.com/s/19Mw2u_df_nBzsC2lg20fQA +提取码: j5ge + +## 训练步骤 +### a、训练VOC07+12数据集 +1. 数据集的准备 +**本文使用VOC格式进行训练，训练前需要下载好VOC07+12的数据集，解压后放在根目录** + +2. 数据集的处理 +修改voc_annotation.py里面的annotation_mode=2，运行voc_annotation.py生成根目录下的2007_train.txt和2007_val.txt。 + +3. 开始网络训练 +train.py的默认参数用于训练VOC数据集，直接运行train.py即可开始训练。 + +4. 训练结果预测 +训练结果预测需要用到两个文件，分别是yolo.py和predict.py。我们首先需要去yolo.py里面修改model_path以及classes_path，这两个参数必须要修改。 +**model_path指向训练好的权值文件，在logs文件夹里。 +classes_path指向检测类别所对应的txt。** +完成修改后就可以运行predict.py进行检测了。运行后输入图片路径即可检测。 + +### b、训练自己的数据集 +1. 数据集的准备 +**本文使用VOC格式进行训练，训练前需要自己制作好数据集，** +训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的Annotation中。 +训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。 + +2. 数据集的处理 +在完成数据集的摆放之后，我们需要利用voc_annotation.py获得训练用的2007_train.txt和2007_val.txt。 +修改voc_annotation.py里面的参数。第一次训练可以仅修改classes_path，classes_path用于指向检测类别所对应的txt。 +训练自己的数据集时，可以自己建立一个cls_classes.txt，里面写自己所需要区分的类别。 +model_data/cls_classes.txt文件内容为： +```python +cat +dog +... +``` +修改voc_annotation.py中的classes_path，使其对应cls_classes.txt，并运行voc_annotation.py。 + +3. 开始网络训练 +**训练的参数较多，均在train.py中，大家可以在下载库后仔细看注释，其中最重要的部分依然是train.py里的classes_path。** +**classes_path用于指向检测类别所对应的txt，这个txt和voc_annotation.py里面的txt一样！训练自己的数据集必须要修改！** +修改完classes_path后就可以运行train.py开始训练了，在训练多个epoch后，权值会生成在logs文件夹中。 + +4. 训练结果预测 +训练结果预测需要用到两个文件，分别是yolo.py和predict.py。在yolo.py里面修改model_path以及classes_path。 +**model_path指向训练好的权值文件，在logs文件夹里。 +classes_path指向检测类别所对应的txt。** +完成修改后就可以运行predict.py进行检测了。运行后输入图片路径即可检测。 + +## 预测步骤 +### a、使用预训练权重 +1. 下载完库后解压，在百度网盘下载yolo_weights.pth，放入model_data，运行predict.py，输入 +```python +img/street.jpg +``` +2. 在predict.py里面进行设置可以进行fps测试和video视频检测。 +### b、使用自己训练的权重 +1. 按照训练步骤训练。 +2. 在yolo.py文件里面，在如下部分修改model_path和classes_path使其对应训练好的文件；**model_path对应logs文件夹下面的权值文件，classes_path是model_path对应分的类**。 +```python +_defaults = { + #--------------------------------------------------------------------------# + # 使用自己训练好的模型进行预测一定要修改model_path和classes_path！ + # model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt + # 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改 + #--------------------------------------------------------------------------# + "model_path" : 'model_data/yolox_s.pth', + "classes_path" : 'model_data/coco_classes.txt', + #---------------------------------------------------------------------# + # 输入图片的大小，必须为32的倍数。 + #---------------------------------------------------------------------# + "input_shape" : [640, 640], + #---------------------------------------------------------------------# + # 所使用的YoloX的版本。nano、tiny、s、m、l、x + #---------------------------------------------------------------------# + "phi" : 's', + #---------------------------------------------------------------------# + # 只有得分大于置信度的预测框会被保留下来 + #---------------------------------------------------------------------# + "confidence" : 0.5, + #---------------------------------------------------------------------# + # 非极大抑制所用到的nms_iou大小 + #---------------------------------------------------------------------# + "nms_iou" : 0.3, + #---------------------------------------------------------------------# + # 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize， + # 在多次测试后，发现关闭letterbox_image直接resize的效果更好 + #---------------------------------------------------------------------# + "letterbox_image" : True, + #-------------------------------# + # 是否使用Cuda + # 没有GPU可以设置成False + #-------------------------------# + "cuda" : True, +} +``` +3. 运行predict.py，输入 +```python +img/street.jpg +``` +4. 在predict.py里面进行设置可以进行fps测试和video视频检测。 + +## 评估步骤 +### a、评估VOC07+12的测试集 +1. 本文使用VOC格式进行评估。VOC07+12已经划分好了测试集，无需利用voc_annotation.py生成ImageSets文件夹下的txt。 +2. 在yolo.py里面修改model_path以及classes_path。**model_path指向训练好的权值文件，在logs文件夹里。classes_path指向检测类别所对应的txt。** +3. 运行get_map.py即可获得评估结果，评估结果会保存在map_out文件夹中。 + +### b、评估自己的数据集 +1. 本文使用VOC格式进行评估。 +2. 如果在训练前已经运行过voc_annotation.py文件，代码会自动将数据集划分成训练集、验证集和测试集。如果想要修改测试集的比例，可以修改voc_annotation.py文件下的trainval_percent。trainval_percent用于指定(训练集+验证集)与测试集的比例，默认情况下 (训练集+验证集):测试集 = 9:1。train_percent用于指定(训练集+验证集)中训练集与验证集的比例，默认情况下 训练集:验证集 = 9:1。 +3. 利用voc_annotation.py划分测试集后，前往get_map.py文件修改classes_path，classes_path用于指向检测类别所对应的txt，这个txt和训练时的txt一样。评估自己的数据集必须要修改。 +4. 在yolo.py里面修改model_path以及classes_path。**model_path指向训练好的权值文件，在logs文件夹里。classes_path指向检测类别所对应的txt。** +5. 运行get_map.py即可获得评估结果，评估结果会保存在map_out文件夹中。 + +## Reference +https://github.com/Megvii-BaseDetection/YOLOX diff --git a/yolox-pytorch/display_image.html b/yolox-pytorch/display_image.html new file mode 100644 index 0000000..a90a2e4 --- /dev/null +++ b/yolox-pytorch/display_image.html @@ -0,0 +1,17 @@ + + + + + Display Image + + + Uploaded Image + + diff --git a/yolox-pytorch/flask_11 b/yolox-pytorch/flask_11 new file mode 100644 index 0000000..aed78d1 --- /dev/null +++ b/yolox-pytorch/flask_11 @@ -0,0 +1,95 @@ +''' +Author: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git +Date: 2023-03-29 20:22:34 +LastEditors: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git +LastEditTime: 2023-04-05 15:46:07 +FilePath: /luyuetong-data/yolox-pytorch/flask_on.py +Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://githaub.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE +''' +from flask import Flask, make_response, request, jsonify,send_file +from flask_cors import CORS +from werkzeug.utils import secure_filename +from PIL import Image +from yolo import YOLO +from predict import PREDICT +import os +import quanju +import requests +import urllib.request +from io import BytesIO +import base64 +import io + + +app = Flask(__name__) +CORS(app) + +@app.route('/uploads', methods=['GET','POST']) +def uploads(): + global filename + # 检查是否收到了POST请求 + if request.method == 'POST': + + + # 检查是否收到了文件 + if 'file' not in request.files: + return jsonify({'error': 'No file uploaded.'}), 400 + + file = request.files['file'] + filename = file.read() #此时转换出来的是二进制内容 + file_path = filename.decode("utf-8") + + print(file_path) + print(f"file:{file}") + + # 将blob转换为base64编码的字符串 + image_data = base64.b64encode(file.read()).decode('utf-8') + + # 将base64编码的字符串解码为图片数据 + img = Image.open(io.BytesIO(base64.b64decode(image_data))) + Image.open(img) + # 将bytes类型传递给预测函数进行处理 + result = PREDICT(img) + + + # + # print(file) + # filename = secure_filename(file.filename) + # file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) + # result = PREDICT() + + #检查是否选择了文件 + if file.filename == '': + return jsonify({'error': 'No file selected.'}), 400 + + + # # 检查文件类型是否合法 + # # if file and allowed_file(file.filename): + # filename = secure_filename(file.filename) + #保存文件到本地 + # file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) + + #自定义一个全局变量承接名字(自定义库.变量) + # quanju.mingzi = filename + return "ok" + + # 将文件路径传递给模型进行预测 + # result = PREDICT(os.path.join(app.config['UPLOAD_FOLDER'], filename)) + + # return send_file(f'./results/result_{quanju.mingzi}', mimetype='image/jpeg') + # return send_file('results/result_{}'.format(quanju.mingzi), mimetype='image/jpeg') + + # return jsonify({'error': 'Invalid file type.'}), 400 + + + +def allowed_file(filename): + # 检查文件类型是否合法，这里仅接受jpeg、jpg、png类型的图片文件 + return '.' in filename and filename.rsplit('.', 1)[1].lower() in {'jpeg', 'jpg', 'png'} + + +if __name__ == '__main__': + # app.config['UPLOAD_FOLDER'] = './uploads' + app.config['UPLOAD_FOLDER'] = 'yolox-pytorch/uploads' + # app.debug = False + app.run(host='10.21.19.104', port=8000) diff --git a/yolox-pytorch/flask_on.py b/yolox-pytorch/flask_on.py new file mode 100644 index 0000000..2a239b7 --- /dev/null +++ b/yolox-pytorch/flask_on.py @@ -0,0 +1,42 @@ +''' +Author: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git +Date: 2023-03-29 20:22:34 +LastEditors: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git +LastEditTime: 2023-04-05 15:27:13 +FilePath: /luyuetong-data/yolox-pytorch/flask_on.py +Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://githaub.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE +''' +import os + +from flask import Flask, request, jsonify +from gevent import pywsgi + +from predict import PREDICT + +app = Flask(__name__) +# 设置未处理文件路径以及处理保存路径 +# dataPath = '/home/DiatomRecognition/upload' +# resultPath = '/home/DiatomRecognition/result' +# localhost 测试 +dataPath = 'E:/Document/project/temp/data' +resultPath = r'E:\Document\project\temp/save' + + +@app.route('/predict', methods=['POST']) +def predict_one(): + if not os.path.exists(resultPath): + # 如果不存在，创建文件夹及其所有父文件夹 + os.makedirs(resultPath) + # 从请求参数中提取文件名 + fileName = request.form['fileName'] + # 将文件路径传递给模型进行预测 + result = PREDICT(dataPath, resultPath, fileName) # predicted_class，score如果需要再处理 + + return result + + +if __name__ == '__main__': + # app.debug = False + server = pywsgi.WSGIServer(('0.0.0.0', 2000), app) + app.config["JSON_AS_ASCII"] = False + server.serve_forever() diff --git a/yolox-pytorch/get_map.py b/yolox-pytorch/get_map.py new file mode 100644 index 0000000..4f14f96 --- /dev/null +++ b/yolox-pytorch/get_map.py @@ -0,0 +1,138 @@ +import os +import xml.etree.ElementTree as ET + +from PIL import Image +from tqdm import tqdm + +from utils.utils import get_classes +from utils.utils_map import get_coco_map, get_map +from yolo import YOLO + +if __name__ == "__main__": + ''' + Recall和Precision不像AP是一个面积的概念，因此在门限值（Confidence）不同时，网络的Recall和Precision值是不同的。 + 默认情况下，本代码计算的Recall和Precision代表的是当门限值（Confidence）为0.5时，所对应的Recall和Precision值。 + + 受到mAP计算原理的限制，网络在计算mAP时需要获得近乎所有的预测框，这样才可以计算不同门限条件下的Recall和Precision值 + 因此，本代码获得的map_out/detection-results/里面的txt的框的数量一般会比直接predict多一些，目的是列出所有可能的预测框， + ''' + #------------------------------------------------------------------------------------------------------------------# + # map_mode用于指定该文件运行时计算的内容 + # map_mode为0代表整个map计算流程，包括获得预测结果、获得真实框、计算VOC_map。 + # map_mode为1代表仅仅获得预测结果。 + # map_mode为2代表仅仅获得真实框。 + # map_mode为3代表仅仅计算VOC_map。 + # map_mode为4代表利用COCO工具箱计算当前数据集的0.50:0.95map。需要获得预测结果、获得真实框后并安装pycocotools才行 + #-------------------------------------------------------------------------------------------------------------------# + map_mode = 0 + #--------------------------------------------------------------------------------------# + # 此处的classes_path用于指定需要测量VOC_map的类别 + # 一般情况下与训练和预测所用的classes_path一致即可 + #--------------------------------------------------------------------------------------# + classes_path = 'model_data/voc_classes.txt' #.. + #--------------------------------------------------------------------------------------# + # MINOVERLAP用于指定想要获得的mAP0.x，mAP0.x的意义是什么请同学们百度一下。 + # 比如计算mAP0.75，可以设定MINOVERLAP = 0.75。 + # + # 当某一预测框与真实框重合度大于MINOVERLAP时，该预测框被认为是正样本，否则为负样本。 + # 因此MINOVERLAP的值越大，预测框要预测的越准确才能被认为是正样本，此时算出来的mAP值越低， + #--------------------------------------------------------------------------------------# + MINOVERLAP = 0.5 + #--------------------------------------------------------------------------------------# + # 受到mAP计算原理的限制，网络在计算mAP时需要获得近乎所有的预测框，这样才可以计算mAP + # 因此，confidence的值应当设置的尽量小进而获得全部可能的预测框。 + # + # 该值一般不调整。因为计算mAP需要获得近乎所有的预测框，此处的confidence不能随便更改。 + # 想要获得不同门限值下的Recall和Precision值，请修改下方的score_threhold。 + #--------------------------------------------------------------------------------------# + confidence = 0.001 + #--------------------------------------------------------------------------------------# + # 预测时使用到的非极大抑制值的大小，越大表示非极大抑制越不严格。 + # + # 该值一般不调整。 + #--------------------------------------------------------------------------------------# + nms_iou = 0.5 + #---------------------------------------------------------------------------------------------------------------# + # Recall和Precision不像AP是一个面积的概念，因此在门限值不同时，网络的Recall和Precision值是不同的。 + # + # 默认情况下，本代码计算的Recall和Precision代表的是当门限值为0.5（此处定义为score_threhold）时所对应的Recall和Precision值。 + # 因为计算mAP需要获得近乎所有的预测框，上面定义的confidence不能随便更改。 + # 这里专门定义一个score_threhold用于代表门限值，进而在计算mAP时找到门限值对应的Recall和Precision值。 + #---------------------------------------------------------------------------------------------------------------# + score_threhold = 0.5 + #-------------------------------------------------------# + # map_vis用于指定是否开启VOC_map计算的可视化 + #-------------------------------------------------------# + map_vis = False + #-------------------------------------------------------# + # 指向VOC数据集所在的文件夹 + # 默认指向根目录下的VOC数据集 + #-------------------------------------------------------# + VOCdevkit_path = 'VOCdevkit' + #-------------------------------------------------------# + # 结果输出的文件夹，默认为map_out + #-------------------------------------------------------# + map_out_path = 'map_out' + + image_ids = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Main/test.txt")).read().strip().split() + + if not os.path.exists(map_out_path): + os.makedirs(map_out_path) + if not os.path.exists(os.path.join(map_out_path, 'ground-truth')): + os.makedirs(os.path.join(map_out_path, 'ground-truth')) + if not os.path.exists(os.path.join(map_out_path, 'detection-results')): + os.makedirs(os.path.join(map_out_path, 'detection-results')) + if not os.path.exists(os.path.join(map_out_path, 'images-optional')): + os.makedirs(os.path.join(map_out_path, 'images-optional')) + + class_names, _ = get_classes(classes_path) + + if map_mode == 0 or map_mode == 1: + print("Load model.") + yolo = YOLO(confidence = confidence, nms_iou = nms_iou) + print("Load model done.") + + print("Get predict result.") + for image_id in tqdm(image_ids): + image_path = os.path.join(VOCdevkit_path, "VOC2007/JPEGImages/"+image_id+".jpg") + image = Image.open(image_path) + if map_vis: + image.save(os.path.join(map_out_path, "images-optional/" + image_id + ".jpg")) + yolo.get_map_txt(image_id, image, class_names, map_out_path) + print("Get predict result done.") + + if map_mode == 0 or map_mode == 2: + print("Get ground truth result.") + for image_id in tqdm(image_ids): + with open(os.path.join(map_out_path, "ground-truth/"+image_id+".txt"), "w") as new_f: + root = ET.parse(os.path.join(VOCdevkit_path, "VOC2007/Annotations/"+image_id+".xml")).getroot() + for obj in root.findall('object'): + difficult_flag = False + if obj.find('difficult')!=None: + difficult = obj.find('difficult').text + if int(difficult)==1: + difficult_flag = True + obj_name = obj.find('name').text + if obj_name not in class_names: + continue + bndbox = obj.find('bndbox') + left = bndbox.find('xmin').text + top = bndbox.find('ymin').text + right = bndbox.find('xmax').text + bottom = bndbox.find('ymax').text + + if difficult_flag: + new_f.write("%s %s %s %s %s difficult\n" % (obj_name, left, top, right, bottom)) + else: + new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom)) + print("Get ground truth result done.") + + if map_mode == 0 or map_mode == 3: + print("Get map.") + get_map(MINOVERLAP, True, score_threhold = score_threhold, path = map_out_path) + print("Get map done.") + + if map_mode == 4: + print("Get map.") + get_coco_map(class_names = class_names, path = map_out_path) + print("Get map done.") diff --git a/yolox-pytorch/img/000009.jpg b/yolox-pytorch/img/000009.jpg new file mode 100644 index 0000000..bc42d1e Binary files /dev/null and b/yolox-pytorch/img/000009.jpg differ diff --git a/yolox-pytorch/img/000924.jpg b/yolox-pytorch/img/000924.jpg new file mode 100644 index 0000000..bbb85cc Binary files /dev/null and b/yolox-pytorch/img/000924.jpg differ diff --git a/yolox-pytorch/img/001258.jpg b/yolox-pytorch/img/001258.jpg new file mode 100644 index 0000000..078dd8f Binary files /dev/null and b/yolox-pytorch/img/001258.jpg differ diff --git a/yolox-pytorch/img/001630.jpg b/yolox-pytorch/img/001630.jpg new file mode 100644 index 0000000..c99c65b Binary files /dev/null and b/yolox-pytorch/img/001630.jpg differ diff --git a/yolox-pytorch/img/001889.jpg b/yolox-pytorch/img/001889.jpg new file mode 100644 index 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+# -*- coding:utf-8 -*- +# Copyright (c) Megvii, Inc. and its affiliates. + +import torch +from torch import nn + +class SiLU(nn.Module): + @staticmethod + def forward(x): + return x * torch.sigmoid(x) + +def get_activation(name="silu", inplace=True): + if name == "silu": + module = SiLU() + elif name == "relu": + module = nn.ReLU(inplace=inplace) + elif name == "lrelu": + module = nn.LeakyReLU(0.1, inplace=inplace) + else: + raise AttributeError("Unsupported act type: {}".format(name)) + return module + +class Focus(nn.Module): + def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu"): + super().__init__() + self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act) + + def forward(self, x): + patch_top_left = x[..., ::2, ::2] + patch_bot_left = x[..., 1::2, ::2] + patch_top_right = x[..., ::2, 1::2] + patch_bot_right = x[..., 1::2, 1::2] + x = torch.cat((patch_top_left, patch_bot_left, patch_top_right, patch_bot_right,), dim=1,) + return self.conv(x) + +class BaseConv(nn.Module): + def __init__(self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"): + super().__init__() + pad = (ksize - 1) // 2 + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=ksize, stride=stride, padding=pad, groups=groups, bias=bias) + self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.03) + self.act = get_activation(act, inplace=True) + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def fuseforward(self, x): + return self.act(self.conv(x)) + +class DWConv(nn.Module): + def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"): + super().__init__() + self.dconv = BaseConv(in_channels, in_channels, ksize=ksize, stride=stride, groups=in_channels, act=act,) + self.pconv = BaseConv(in_channels, out_channels, ksize=1, stride=1, groups=1, act=act) + + def forward(self, x): + x = self.dconv(x) + return self.pconv(x) + +class SPPBottleneck(nn.Module): + def __init__(self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu"): + super().__init__() + hidden_channels = in_channels // 2 + self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2) for ks in kernel_sizes]) + conv2_channels = hidden_channels * (len(kernel_sizes) + 1) + self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation) + + def forward(self, x): + x = self.conv1(x) + x = torch.cat([x] + [m(x) for m in self.m], dim=1) + x = self.conv2(x) + return x + +#--------------------------------------------------# +# 残差结构的构建，小的残差结构 +#--------------------------------------------------# +class Bottleneck(nn.Module): + # Standard bottleneck + def __init__(self, in_channels, out_channels, shortcut=True, expansion=0.5, depthwise=False, act="silu",): + super().__init__() + hidden_channels = int(out_channels * expansion) + Conv = DWConv if depthwise else BaseConv + #--------------------------------------------------# + # 利用1x1卷积进行通道数的缩减。缩减率一般是50% + #--------------------------------------------------# + self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act) + #--------------------------------------------------# + # 利用3x3卷积进行通道数的拓张。并且完成特征提取 + #--------------------------------------------------# + self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act) + self.use_add = shortcut and in_channels == out_channels + + def forward(self, x): + y = self.conv2(self.conv1(x)) + if self.use_add: + y = y + x + return y + +class CSPLayer(nn.Module): + def __init__(self, in_channels, out_channels, n=1, shortcut=True, expansion=0.5, depthwise=False, act="silu",): + # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + hidden_channels = int(out_channels * expansion) + #--------------------------------------------------# + # 主干部分的初次卷积 + #--------------------------------------------------# + self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act) + #--------------------------------------------------# + # 大的残差边部分的初次卷积 + #--------------------------------------------------# + self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act) + #-----------------------------------------------# + # 对堆叠的结果进行卷积的处理 + #-----------------------------------------------# + self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act) + + #--------------------------------------------------# + # 根据循环的次数构建上述Bottleneck残差结构 + #--------------------------------------------------# + module_list = [Bottleneck(hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act) for _ in range(n)] + self.m = nn.Sequential(*module_list) + + def forward(self, x): + #-------------------------------# + # x_1是主干部分 + #-------------------------------# + x_1 = self.conv1(x) + #-------------------------------# + # x_2是大的残差边部分 + #-------------------------------# + x_2 = self.conv2(x) + + #-----------------------------------------------# + # 主干部分利用残差结构堆叠继续进行特征提取 + #-----------------------------------------------# + x_1 = self.m(x_1) + #-----------------------------------------------# + # 主干部分和大的残差边部分进行堆叠 + #-----------------------------------------------# + x = torch.cat((x_1, x_2), dim=1) + #-----------------------------------------------# + # 对堆叠的结果进行卷积的处理 + #-----------------------------------------------# + return self.conv3(x) + +class CSPDarknet(nn.Module): + def __init__(self, dep_mul, wid_mul, out_features=("dark3", "dark4", "dark5"), depthwise=False, act="silu",): + super().__init__() + assert out_features, "please provide output features of Darknet" + self.out_features = out_features + Conv = DWConv if depthwise else BaseConv + + #-----------------------------------------------# + # 输入图片是640, 640, 3 + # 初始的基本通道是64 + #-----------------------------------------------# + base_channels = int(wid_mul * 64) # 64 + base_depth = max(round(dep_mul * 3), 1) # 3 + + #-----------------------------------------------# + # 利用focus网络结构进行特征提取 + # 640, 640, 3 -> 320, 320, 12 -> 320, 320, 64 + #-----------------------------------------------# + self.stem = Focus(3, base_channels, ksize=3, act=act) + + #-----------------------------------------------# + # 完成卷积之后，320, 320, 64 -> 160, 160, 128 + # 完成CSPlayer之后，160, 160, 128 -> 160, 160, 128 + #-----------------------------------------------# + self.dark2 = nn.Sequential( + Conv(base_channels, base_channels * 2, 3, 2, act=act), + CSPLayer(base_channels * 2, base_channels * 2, n=base_depth, depthwise=depthwise, act=act), + ) + + #-----------------------------------------------# + # 完成卷积之后，160, 160, 128 -> 80, 80, 256 + # 完成CSPlayer之后，80, 80, 256 -> 80, 80, 256 + #-----------------------------------------------# + self.dark3 = nn.Sequential( + Conv(base_channels * 2, base_channels * 4, 3, 2, act=act), + CSPLayer(base_channels * 4, base_channels * 4, n=base_depth * 3, depthwise=depthwise, act=act), + ) + + #-----------------------------------------------# + # 完成卷积之后，80, 80, 256 -> 40, 40, 512 + # 完成CSPlayer之后，40, 40, 512 -> 40, 40, 512 + #-----------------------------------------------# + self.dark4 = nn.Sequential( + Conv(base_channels * 4, base_channels * 8, 3, 2, act=act), + CSPLayer(base_channels * 8, base_channels * 8, n=base_depth * 3, depthwise=depthwise, act=act), + ) + + #-----------------------------------------------# + # 完成卷积之后，40, 40, 512 -> 20, 20, 1024 + # 完成SPP之后，20, 20, 1024 -> 20, 20, 1024 + # 完成CSPlayer之后，20, 20, 1024 -> 20, 20, 1024 + #-----------------------------------------------# + self.dark5 = nn.Sequential( + Conv(base_channels * 8, base_channels * 16, 3, 2, act=act), + SPPBottleneck(base_channels * 16, base_channels * 16, activation=act), + CSPLayer(base_channels * 16, base_channels * 16, n=base_depth, shortcut=False, depthwise=depthwise, act=act), + ) + + def forward(self, x): + outputs = {} + x = self.stem(x) + outputs["stem"] = x + x = self.dark2(x) + outputs["dark2"] = x + #-----------------------------------------------# + # dark3的输出为80, 80, 256，是一个有效特征层 + #-----------------------------------------------# + x = self.dark3(x) + outputs["dark3"] = x + #-----------------------------------------------# + # dark4的输出为40, 40, 512，是一个有效特征层 + #-----------------------------------------------# + x = self.dark4(x) + outputs["dark4"] = x + #-----------------------------------------------# + # dark5的输出为20, 20, 1024，是一个有效特征层 + #-----------------------------------------------# + x = self.dark5(x) + outputs["dark5"] = x + return {k: v for k, v in outputs.items() if k in self.out_features} + + +if __name__ == '__main__': + print(CSPDarknet(1, 1)) \ No newline at end of file diff --git a/yolox-pytorch/nets/yolo.py b/yolox-pytorch/nets/yolo.py new file mode 100644 index 0000000..2009069 --- /dev/null +++ b/yolox-pytorch/nets/yolo.py @@ -0,0 +1,247 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +# Copyright (c) Megvii, Inc. and its affiliates. + +import torch +import torch.nn as nn + +from .darknet import BaseConv, CSPDarknet, CSPLayer, DWConv + + +class YOLOXHead(nn.Module): + def __init__(self, num_classes, width = 1.0, in_channels = [256, 512, 1024], act = "silu", depthwise = False,): + super().__init__() + Conv = DWConv if depthwise else BaseConv + + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + self.cls_preds = nn.ModuleList() + self.reg_preds = nn.ModuleList() + self.obj_preds = nn.ModuleList() + self.stems = nn.ModuleList() + + for i in range(len(in_channels)): + self.stems.append(BaseConv(in_channels = int(in_channels[i] * width), out_channels = int(256 * width), ksize = 1, stride = 1, act = act)) + self.cls_convs.append(nn.Sequential(*[ + Conv(in_channels = int(256 * width), out_channels = int(256 * width), ksize = 3, stride = 1, act = act), + Conv(in_channels = int(256 * width), out_channels = int(256 * width), ksize = 3, stride = 1, act = act), + ])) + self.cls_preds.append( + nn.Conv2d(in_channels = int(256 * width), out_channels = num_classes, kernel_size = 1, stride = 1, padding = 0) + ) + + + self.reg_convs.append(nn.Sequential(*[ + Conv(in_channels = int(256 * width), out_channels = int(256 * width), ksize = 3, stride = 1, act = act), + Conv(in_channels = int(256 * width), out_channels = int(256 * width), ksize = 3, stride = 1, act = act) + ])) + self.reg_preds.append( + nn.Conv2d(in_channels = int(256 * width), out_channels = 4, kernel_size = 1, stride = 1, padding = 0) + ) + self.obj_preds.append( + nn.Conv2d(in_channels = int(256 * width), out_channels = 1, kernel_size = 1, stride = 1, padding = 0) + ) + + def forward(self, inputs): + #---------------------------------------------------# + # inputs输入 + # P3_out 80, 80, 256 + # P4_out 40, 40, 512 + # P5_out 20, 20, 1024 + #---------------------------------------------------# + outputs = [] + for k, x in enumerate(inputs): + #---------------------------------------------------# + # 利用1x1卷积进行通道整合 + #---------------------------------------------------# + x = self.stems[k](x) + #---------------------------------------------------# + # 利用两个卷积标准化激活函数来进行特征提取 + #---------------------------------------------------# + cls_feat = self.cls_convs[k](x) + #---------------------------------------------------# + # 判断特征点所属的种类 + # 80, 80, num_classes + # 40, 40, num_classes + # 20, 20, num_classes + #---------------------------------------------------# + cls_output = self.cls_preds[k](cls_feat) + + #---------------------------------------------------# + # 利用两个卷积标准化激活函数来进行特征提取 + #---------------------------------------------------# + reg_feat = self.reg_convs[k](x) + #---------------------------------------------------# + # 特征点的回归系数 + # reg_pred 80, 80, 4 + # reg_pred 40, 40, 4 + # reg_pred 20, 20, 4 + #---------------------------------------------------# + reg_output = self.reg_preds[k](reg_feat) + #---------------------------------------------------# + # 判断特征点是否有对应的物体 + # obj_pred 80, 80, 1 + # obj_pred 40, 40, 1 + # obj_pred 20, 20, 1 + #---------------------------------------------------# + obj_output = self.obj_preds[k](reg_feat) + + output = torch.cat([reg_output, obj_output, cls_output], 1) + outputs.append(output) + return outputs + +class YOLOPAFPN(nn.Module): + def __init__(self, depth = 1.0, width = 1.0, in_features = ("dark3", "dark4", "dark5"), in_channels = [256, 512, 1024], depthwise = False, act = "silu"): + super().__init__() + Conv = DWConv if depthwise else BaseConv + self.backbone = CSPDarknet(depth, width, depthwise = depthwise, act = act) + self.in_features = in_features + + self.upsample = nn.Upsample(scale_factor=2, mode="nearest") + + #-------------------------------------------# + # 20, 20, 1024 -> 20, 20, 512 + #-------------------------------------------# + self.lateral_conv0 = BaseConv(int(in_channels[2] * width), int(in_channels[1] * width), 1, 1, act=act) + + #-------------------------------------------# + # 40, 40, 1024 -> 40, 40, 512 + #-------------------------------------------# + self.C3_p4 = CSPLayer( + int(2 * in_channels[1] * width), + int(in_channels[1] * width), + round(3 * depth), + False, + depthwise = depthwise, + act = act, + ) + + #-------------------------------------------# + # 40, 40, 512 -> 40, 40, 256 + #-------------------------------------------# + self.reduce_conv1 = BaseConv(int(in_channels[1] * width), int(in_channels[0] * width), 1, 1, act=act) + #-------------------------------------------# + # 80, 80, 512 -> 80, 80, 256 + #-------------------------------------------# + self.C3_p3 = CSPLayer( + int(2 * in_channels[0] * width), + int(in_channels[0] * width), + round(3 * depth), + False, + depthwise = depthwise, + act = act, + ) + + #-------------------------------------------# + # 80, 80, 256 -> 40, 40, 256 + #-------------------------------------------# + self.bu_conv2 = Conv(int(in_channels[0] * width), int(in_channels[0] * width), 3, 2, act=act) + #-------------------------------------------# + # 40, 40, 256 -> 40, 40, 512 + #-------------------------------------------# + self.C3_n3 = CSPLayer( + int(2 * in_channels[0] * width), + int(in_channels[1] * width), + round(3 * depth), + False, + depthwise = depthwise, + act = act, + ) + + #-------------------------------------------# + # 40, 40, 512 -> 20, 20, 512 + #-------------------------------------------# + self.bu_conv1 = Conv(int(in_channels[1] * width), int(in_channels[1] * width), 3, 2, act=act) + #-------------------------------------------# + # 20, 20, 1024 -> 20, 20, 1024 + #-------------------------------------------# + self.C3_n4 = CSPLayer( + int(2 * in_channels[1] * width), + int(in_channels[2] * width), + round(3 * depth), + False, + depthwise = depthwise, + act = act, + ) + + def forward(self, input): + out_features = self.backbone.forward(input) + [feat1, feat2, feat3] = [out_features[f] for f in self.in_features] + + #-------------------------------------------# + # 20, 20, 1024 -> 20, 20, 512 + #-------------------------------------------# + P5 = self.lateral_conv0(feat3) + #-------------------------------------------# + # 20, 20, 512 -> 40, 40, 512 + #-------------------------------------------# + P5_upsample = self.upsample(P5) + #-------------------------------------------# + # 40, 40, 512 + 40, 40, 512 -> 40, 40, 1024 + #-------------------------------------------# + P5_upsample = torch.cat([P5_upsample, feat2], 1) + #-------------------------------------------# + # 40, 40, 1024 -> 40, 40, 512 + #-------------------------------------------# + P5_upsample = self.C3_p4(P5_upsample) + + #-------------------------------------------# + # 40, 40, 512 -> 40, 40, 256 + #-------------------------------------------# + P4 = self.reduce_conv1(P5_upsample) + #-------------------------------------------# + # 40, 40, 256 -> 80, 80, 256 + #-------------------------------------------# + P4_upsample = self.upsample(P4) + #-------------------------------------------# + # 80, 80, 256 + 80, 80, 256 -> 80, 80, 512 + #-------------------------------------------# + P4_upsample = torch.cat([P4_upsample, feat1], 1) + #-------------------------------------------# + # 80, 80, 512 -> 80, 80, 256 + #-------------------------------------------# + P3_out = self.C3_p3(P4_upsample) + + #-------------------------------------------# + # 80, 80, 256 -> 40, 40, 256 + #-------------------------------------------# + P3_downsample = self.bu_conv2(P3_out) + #-------------------------------------------# + # 40, 40, 256 + 40, 40, 256 -> 40, 40, 512 + #-------------------------------------------# + P3_downsample = torch.cat([P3_downsample, P4], 1) + #-------------------------------------------# + # 40, 40, 256 -> 40, 40, 512 + #-------------------------------------------# + P4_out = self.C3_n3(P3_downsample) + + #-------------------------------------------# + # 40, 40, 512 -> 20, 20, 512 + #-------------------------------------------# + P4_downsample = self.bu_conv1(P4_out) + #-------------------------------------------# + # 20, 20, 512 + 20, 20, 512 -> 20, 20, 1024 + #-------------------------------------------# + P4_downsample = torch.cat([P4_downsample, P5], 1) + #-------------------------------------------# + # 20, 20, 1024 -> 20, 20, 1024 + #-------------------------------------------# + P5_out = self.C3_n4(P4_downsample) + + return (P3_out, P4_out, P5_out) + +class YoloBody(nn.Module): + def __init__(self, num_classes, phi): + super().__init__() + depth_dict = {'nano': 0.33, 'tiny': 0.33, 's' : 0.33, 'm' : 0.67, 'l' : 1.00, 'x' : 1.33,} + width_dict = {'nano': 0.25, 'tiny': 0.375, 's' : 0.50, 'm' : 0.75, 'l' : 1.00, 'x' : 1.25,} + depth, width = depth_dict[phi], width_dict[phi] + depthwise = True if phi == 'nano' else False + + self.backbone = YOLOPAFPN(depth, width, depthwise=depthwise) + self.head = YOLOXHead(num_classes, width, depthwise=depthwise) + + def forward(self, x): + fpn_outs = self.backbone.forward(x) + outputs = self.head.forward(fpn_outs) + return outputs diff --git a/yolox-pytorch/nets/yolo_training.py b/yolox-pytorch/nets/yolo_training.py new file mode 100644 index 0000000..5ca3890 --- /dev/null +++ b/yolox-pytorch/nets/yolo_training.py @@ -0,0 +1,488 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +# Copyright (c) Megvii, Inc. and its affiliates. +import math +from copy import deepcopy +from functools import partial + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class IOUloss(nn.Module): + def __init__(self, reduction="none", loss_type="iou"): + super(IOUloss, self).__init__() + self.reduction = reduction + self.loss_type = loss_type + + def forward(self, pred, target): + assert pred.shape[0] == target.shape[0] + + pred = pred.view(-1, 4) + target = target.view(-1, 4) + tl = torch.max( + (pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2) + ) + br = torch.min( + (pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2) + ) + + area_p = torch.prod(pred[:, 2:], 1) + area_g = torch.prod(target[:, 2:], 1) + + en = (tl < br).type(tl.type()).prod(dim=1) + area_i = torch.prod(br - tl, 1) * en + area_u = area_p + area_g - area_i + iou = (area_i) / (area_u + 1e-16) + + if self.loss_type == "iou": + loss = 1 - iou ** 2 + elif self.loss_type == "giou": + c_tl = torch.min( + (pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2) + ) + c_br = torch.max( + (pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2) + ) + area_c = torch.prod(c_br - c_tl, 1) + giou = iou - (area_c - area_u) / area_c.clamp(1e-16) + loss = 1 - giou.clamp(min=-1.0, max=1.0) + + if self.reduction == "mean": + loss = loss.mean() + elif self.reduction == "sum": + loss = loss.sum() + + return loss + +class YOLOLoss(nn.Module): + def __init__(self, num_classes, fp16, strides=[8, 16, 32]): + super().__init__() + self.num_classes = num_classes + self.strides = strides + + self.bcewithlog_loss = nn.BCEWithLogitsLoss(reduction="none") + self.iou_loss = IOUloss(reduction="none") + self.grids = [torch.zeros(1)] * len(strides) + self.fp16 = fp16 + + def forward(self, inputs, labels=None): + outputs = [] + x_shifts = [] + y_shifts = [] + expanded_strides = [] + + #-----------------------------------------------# + # inputs [[batch_size, num_classes + 5, 20, 20] + # [batch_size, num_classes + 5, 40, 40] + # [batch_size, num_classes + 5, 80, 80]] + # outputs [[batch_size, 400, num_classes + 5] + # [batch_size, 1600, num_classes + 5] + # [batch_size, 6400, num_classes + 5]] + # x_shifts [[batch_size, 400] + # [batch_size, 1600] + # [batch_size, 6400]] + #-----------------------------------------------# + for k, (stride, output) in enumerate(zip(self.strides, inputs)): + output, grid = self.get_output_and_grid(output, k, stride) + x_shifts.append(grid[:, :, 0]) + y_shifts.append(grid[:, :, 1]) + expanded_strides.append(torch.ones_like(grid[:, :, 0]) * stride) + outputs.append(output) + + return self.get_losses(x_shifts, y_shifts, expanded_strides, labels, torch.cat(outputs, 1)) + + def get_output_and_grid(self, output, k, stride): + grid = self.grids[k] + hsize, wsize = output.shape[-2:] + if grid.shape[2:4] != output.shape[2:4]: + yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)]) + grid = torch.stack((xv, yv), 2).view(1, hsize, wsize, 2).type(output.type()) + self.grids[k] = grid + grid = grid.view(1, -1, 2) + + output = output.flatten(start_dim=2).permute(0, 2, 1) + output[..., :2] = (output[..., :2] + grid.type_as(output)) * stride + output[..., 2:4] = torch.exp(output[..., 2:4]) * stride + return output, grid + + def get_losses(self, x_shifts, y_shifts, expanded_strides, labels, outputs): + #-----------------------------------------------# + # [batch, n_anchors_all, 4] + #-----------------------------------------------# + bbox_preds = outputs[:, :, :4] + #-----------------------------------------------# + # [batch, n_anchors_all, 1] + #-----------------------------------------------# + obj_preds = outputs[:, :, 4:5] + #-----------------------------------------------# + # [batch, n_anchors_all, n_cls] + #-----------------------------------------------# + cls_preds = outputs[:, :, 5:] + + total_num_anchors = outputs.shape[1] + #-----------------------------------------------# + # x_shifts [1, n_anchors_all] + # y_shifts [1, n_anchors_all] + # expanded_strides [1, n_anchors_all] + #-----------------------------------------------# + x_shifts = torch.cat(x_shifts, 1).type_as(outputs) + y_shifts = torch.cat(y_shifts, 1).type_as(outputs) + expanded_strides = torch.cat(expanded_strides, 1).type_as(outputs) + + cls_targets = [] + reg_targets = [] + obj_targets = [] + fg_masks = [] + + num_fg = 0.0 + for batch_idx in range(outputs.shape[0]): + num_gt = len(labels[batch_idx]) + if num_gt == 0: + cls_target = outputs.new_zeros((0, self.num_classes)) + reg_target = outputs.new_zeros((0, 4)) + obj_target = outputs.new_zeros((total_num_anchors, 1)) + fg_mask = outputs.new_zeros(total_num_anchors).bool() + else: + #-----------------------------------------------# + # gt_bboxes_per_image [num_gt, num_classes] + # gt_classes [num_gt] + # bboxes_preds_per_image [n_anchors_all, 4] + # cls_preds_per_image [n_anchors_all, num_classes] + # obj_preds_per_image [n_anchors_all, 1] + #-----------------------------------------------# + gt_bboxes_per_image = labels[batch_idx][..., :4].type_as(outputs) + gt_classes = labels[batch_idx][..., 4].type_as(outputs) + bboxes_preds_per_image = bbox_preds[batch_idx] + cls_preds_per_image = cls_preds[batch_idx] + obj_preds_per_image = obj_preds[batch_idx] + + gt_matched_classes, fg_mask, pred_ious_this_matching, matched_gt_inds, num_fg_img = self.get_assignments( + num_gt, total_num_anchors, gt_bboxes_per_image, gt_classes, bboxes_preds_per_image, cls_preds_per_image, obj_preds_per_image, + expanded_strides, x_shifts, y_shifts, + ) + torch.cuda.empty_cache() + num_fg += num_fg_img + cls_target = F.one_hot(gt_matched_classes.to(torch.int64), self.num_classes).float() * pred_ious_this_matching.unsqueeze(-1) + obj_target = fg_mask.unsqueeze(-1) + reg_target = gt_bboxes_per_image[matched_gt_inds] + cls_targets.append(cls_target) + reg_targets.append(reg_target) + obj_targets.append(obj_target.type(cls_target.type())) + fg_masks.append(fg_mask) + + cls_targets = torch.cat(cls_targets, 0) + reg_targets = torch.cat(reg_targets, 0) + obj_targets = torch.cat(obj_targets, 0) + fg_masks = torch.cat(fg_masks, 0) + + num_fg = max(num_fg, 1) + loss_iou = (self.iou_loss(bbox_preds.view(-1, 4)[fg_masks], reg_targets)).sum() + loss_obj = (self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets)).sum() + loss_cls = (self.bcewithlog_loss(cls_preds.view(-1, self.num_classes)[fg_masks], cls_targets)).sum() + reg_weight = 5.0 + loss = reg_weight * loss_iou + loss_obj + loss_cls + + return loss / num_fg + + @torch.no_grad() + def get_assignments(self, num_gt, total_num_anchors, gt_bboxes_per_image, gt_classes, bboxes_preds_per_image, cls_preds_per_image, obj_preds_per_image, expanded_strides, x_shifts, y_shifts): + #-------------------------------------------------------# + # fg_mask [n_anchors_all] + # is_in_boxes_and_center [num_gt, len(fg_mask)] + #-------------------------------------------------------# + fg_mask, is_in_boxes_and_center = self.get_in_boxes_info(gt_bboxes_per_image, expanded_strides, x_shifts, y_shifts, total_num_anchors, num_gt) + + #-------------------------------------------------------# + # fg_mask [n_anchors_all] + # bboxes_preds_per_image [fg_mask, 4] + # cls_preds_ [fg_mask, num_classes] + # obj_preds_ [fg_mask, 1] + #-------------------------------------------------------# + bboxes_preds_per_image = bboxes_preds_per_image[fg_mask] + cls_preds_ = cls_preds_per_image[fg_mask] + obj_preds_ = obj_preds_per_image[fg_mask] + num_in_boxes_anchor = bboxes_preds_per_image.shape[0] + + #-------------------------------------------------------# + # pair_wise_ious [num_gt, fg_mask] + #-------------------------------------------------------# + pair_wise_ious = self.bboxes_iou(gt_bboxes_per_image, bboxes_preds_per_image, False) + pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8) + + #-------------------------------------------------------# + # cls_preds_ [num_gt, fg_mask, num_classes] + # gt_cls_per_image [num_gt, fg_mask, num_classes] + #-------------------------------------------------------# + if self.fp16: + with torch.cuda.amp.autocast(enabled=False): + cls_preds_ = cls_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() * obj_preds_.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + gt_cls_per_image = F.one_hot(gt_classes.to(torch.int64), self.num_classes).float().unsqueeze(1).repeat(1, num_in_boxes_anchor, 1) + pair_wise_cls_loss = F.binary_cross_entropy(cls_preds_.sqrt_(), gt_cls_per_image, reduction="none").sum(-1) + else: + cls_preds_ = cls_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() * obj_preds_.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + gt_cls_per_image = F.one_hot(gt_classes.to(torch.int64), self.num_classes).float().unsqueeze(1).repeat(1, num_in_boxes_anchor, 1) + pair_wise_cls_loss = F.binary_cross_entropy(cls_preds_.sqrt_(), gt_cls_per_image, reduction="none").sum(-1) + del cls_preds_ + + cost = pair_wise_cls_loss + 3.0 * pair_wise_ious_loss + 100000.0 * (~is_in_boxes_and_center).float() + + num_fg, gt_matched_classes, pred_ious_this_matching, matched_gt_inds = self.dynamic_k_matching(cost, pair_wise_ious, gt_classes, num_gt, fg_mask) + del pair_wise_cls_loss, cost, pair_wise_ious, pair_wise_ious_loss + return gt_matched_classes, fg_mask, pred_ious_this_matching, matched_gt_inds, num_fg + + def bboxes_iou(self, bboxes_a, bboxes_b, xyxy=True): + if bboxes_a.shape[1] != 4 or bboxes_b.shape[1] != 4: + raise IndexError + + if xyxy: + tl = torch.max(bboxes_a[:, None, :2], bboxes_b[:, :2]) + br = torch.min(bboxes_a[:, None, 2:], bboxes_b[:, 2:]) + area_a = torch.prod(bboxes_a[:, 2:] - bboxes_a[:, :2], 1) + area_b = torch.prod(bboxes_b[:, 2:] - bboxes_b[:, :2], 1) + else: + tl = torch.max( + (bboxes_a[:, None, :2] - bboxes_a[:, None, 2:] / 2), + (bboxes_b[:, :2] - bboxes_b[:, 2:] / 2), + ) + br = torch.min( + (bboxes_a[:, None, :2] + bboxes_a[:, None, 2:] / 2), + (bboxes_b[:, :2] + bboxes_b[:, 2:] / 2), + ) + + area_a = torch.prod(bboxes_a[:, 2:], 1) + area_b = torch.prod(bboxes_b[:, 2:], 1) + en = (tl < br).type(tl.type()).prod(dim=2) + area_i = torch.prod(br - tl, 2) * en + return area_i / (area_a[:, None] + area_b - area_i) + + def get_in_boxes_info(self, gt_bboxes_per_image, expanded_strides, x_shifts, y_shifts, total_num_anchors, num_gt, center_radius = 2.5): + #-------------------------------------------------------# + # expanded_strides_per_image [n_anchors_all] + # x_centers_per_image [num_gt, n_anchors_all] + # x_centers_per_image [num_gt, n_anchors_all] + #-------------------------------------------------------# + expanded_strides_per_image = expanded_strides[0] + x_centers_per_image = ((x_shifts[0] + 0.5) * expanded_strides_per_image).unsqueeze(0).repeat(num_gt, 1) + y_centers_per_image = ((y_shifts[0] + 0.5) * expanded_strides_per_image).unsqueeze(0).repeat(num_gt, 1) + + #-------------------------------------------------------# + # gt_bboxes_per_image_x [num_gt, n_anchors_all] + #-------------------------------------------------------# + gt_bboxes_per_image_l = (gt_bboxes_per_image[:, 0] - 0.5 * gt_bboxes_per_image[:, 2]).unsqueeze(1).repeat(1, total_num_anchors) + gt_bboxes_per_image_r = (gt_bboxes_per_image[:, 0] + 0.5 * gt_bboxes_per_image[:, 2]).unsqueeze(1).repeat(1, total_num_anchors) + gt_bboxes_per_image_t = (gt_bboxes_per_image[:, 1] - 0.5 * gt_bboxes_per_image[:, 3]).unsqueeze(1).repeat(1, total_num_anchors) + gt_bboxes_per_image_b = (gt_bboxes_per_image[:, 1] + 0.5 * gt_bboxes_per_image[:, 3]).unsqueeze(1).repeat(1, total_num_anchors) + + #-------------------------------------------------------# + # bbox_deltas [num_gt, n_anchors_all, 4] + #-------------------------------------------------------# + b_l = x_centers_per_image - gt_bboxes_per_image_l + b_r = gt_bboxes_per_image_r - x_centers_per_image + b_t = y_centers_per_image - gt_bboxes_per_image_t + b_b = gt_bboxes_per_image_b - y_centers_per_image + bbox_deltas = torch.stack([b_l, b_t, b_r, b_b], 2) + + #-------------------------------------------------------# + # is_in_boxes [num_gt, n_anchors_all] + # is_in_boxes_all [n_anchors_all] + #-------------------------------------------------------# + is_in_boxes = bbox_deltas.min(dim=-1).values > 0.0 + is_in_boxes_all = is_in_boxes.sum(dim=0) > 0 + + gt_bboxes_per_image_l = (gt_bboxes_per_image[:, 0]).unsqueeze(1).repeat(1, total_num_anchors) - center_radius * expanded_strides_per_image.unsqueeze(0) + gt_bboxes_per_image_r = (gt_bboxes_per_image[:, 0]).unsqueeze(1).repeat(1, total_num_anchors) + center_radius * expanded_strides_per_image.unsqueeze(0) + gt_bboxes_per_image_t = (gt_bboxes_per_image[:, 1]).unsqueeze(1).repeat(1, total_num_anchors) - center_radius * expanded_strides_per_image.unsqueeze(0) + gt_bboxes_per_image_b = (gt_bboxes_per_image[:, 1]).unsqueeze(1).repeat(1, total_num_anchors) + center_radius * expanded_strides_per_image.unsqueeze(0) + + #-------------------------------------------------------# + # center_deltas [num_gt, n_anchors_all, 4] + #-------------------------------------------------------# + c_l = x_centers_per_image - gt_bboxes_per_image_l + c_r = gt_bboxes_per_image_r - x_centers_per_image + c_t = y_centers_per_image - gt_bboxes_per_image_t + c_b = gt_bboxes_per_image_b - y_centers_per_image + center_deltas = torch.stack([c_l, c_t, c_r, c_b], 2) + + #-------------------------------------------------------# + # is_in_centers [num_gt, n_anchors_all] + # is_in_centers_all [n_anchors_all] + #-------------------------------------------------------# + is_in_centers = center_deltas.min(dim=-1).values > 0.0 + is_in_centers_all = is_in_centers.sum(dim=0) > 0 + + #-------------------------------------------------------# + # is_in_boxes_anchor [n_anchors_all] + # is_in_boxes_and_center [num_gt, is_in_boxes_anchor] + #-------------------------------------------------------# + is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all + is_in_boxes_and_center = is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor] + return is_in_boxes_anchor, is_in_boxes_and_center + + def dynamic_k_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask): + #-------------------------------------------------------# + # cost [num_gt, fg_mask] + # pair_wise_ious [num_gt, fg_mask] + # gt_classes [num_gt] + # fg_mask [n_anchors_all] + # matching_matrix [num_gt, fg_mask] + #-------------------------------------------------------# + matching_matrix = torch.zeros_like(cost) + + #------------------------------------------------------------# + # 选取iou最大的n_candidate_k个点 + # 然后求和，判断应该有多少点用于该框预测 + # topk_ious [num_gt, n_candidate_k] + # dynamic_ks [num_gt] + # matching_matrix [num_gt, fg_mask] + #------------------------------------------------------------# + n_candidate_k = min(10, pair_wise_ious.size(1)) + topk_ious, _ = torch.topk(pair_wise_ious, n_candidate_k, dim=1) + dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1) + + for gt_idx in range(num_gt): + #------------------------------------------------------------# + # 给每个真实框选取最小的动态k个点 + #------------------------------------------------------------# + _, pos_idx = torch.topk(cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False) + matching_matrix[gt_idx][pos_idx] = 1.0 + del topk_ious, dynamic_ks, pos_idx + + #------------------------------------------------------------# + # anchor_matching_gt [fg_mask] + #------------------------------------------------------------# + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + #------------------------------------------------------------# + # 当某一个特征点指向多个真实框的时候 + # 选取cost最小的真实框。 + #------------------------------------------------------------# + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + #------------------------------------------------------------# + # fg_mask_inboxes [fg_mask] + # num_fg为正样本的特征点个数 + #------------------------------------------------------------# + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + num_fg = fg_mask_inboxes.sum().item() + + #------------------------------------------------------------# + # 对fg_mask进行更新 + #------------------------------------------------------------# + fg_mask[fg_mask.clone()] = fg_mask_inboxes + + #------------------------------------------------------------# + # 获得特征点对应的物品种类 + #------------------------------------------------------------# + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + gt_matched_classes = gt_classes[matched_gt_inds] + + pred_ious_this_matching = (matching_matrix * pair_wise_ious).sum(0)[fg_mask_inboxes] + return num_fg, gt_matched_classes, pred_ious_this_matching, matched_gt_inds + +def is_parallel(model): + # Returns True if model is of type DP or DDP + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + +def de_parallel(model): + # De-parallelize a model: returns single-GPU model if model is of type DP or DDP + return model.module if is_parallel(model) else model + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + +class ModelEMA: + """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models + Keeps a moving average of everything in the model state_dict (parameters and buffers) + For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + """ + + def __init__(self, model, decay=0.9999, tau=2000, updates=0): + # Create EMA + self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA + # if next(model.parameters()).device.type != 'cpu': + # self.ema.half() # FP16 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + msd = de_parallel(model).state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1 - d) * msd[k].detach() + + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) + +def weights_init(net, init_type='normal', init_gain = 0.02): + def init_func(m): + classname = m.__class__.__name__ + if hasattr(m, 'weight') and classname.find('Conv') != -1: + if init_type == 'normal': + torch.nn.init.normal_(m.weight.data, 0.0, init_gain) + elif init_type == 'xavier': + torch.nn.init.xavier_normal_(m.weight.data, gain=init_gain) + elif init_type == 'kaiming': + torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + torch.nn.init.orthogonal_(m.weight.data, gain=init_gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + elif classname.find('BatchNorm2d') != -1: + torch.nn.init.normal_(m.weight.data, 1.0, 0.02) + torch.nn.init.constant_(m.bias.data, 0.0) + print('initialize network with %s type' % init_type) + net.apply(init_func) + +def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10): + def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters): + if iters <= warmup_total_iters: + # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start + lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2) + warmup_lr_start + elif iters >= total_iters - no_aug_iter: + lr = min_lr + else: + lr = min_lr + 0.5 * (lr - min_lr) * ( + 1.0 + math.cos(math.pi* (iters - warmup_total_iters) / (total_iters - warmup_total_iters - no_aug_iter)) + ) + return lr + + def step_lr(lr, decay_rate, step_size, iters): + if step_size < 1: + raise ValueError("step_size must above 1.") + n = iters // step_size + out_lr = lr * decay_rate ** n + return out_lr + + if lr_decay_type == "cos": + warmup_total_iters = min(max(warmup_iters_ratio * total_iters, 1), 3) + warmup_lr_start = max(warmup_lr_ratio * lr, 1e-6) + no_aug_iter = min(max(no_aug_iter_ratio * total_iters, 1), 15) + func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter) + else: + decay_rate = (min_lr / lr) ** (1 / (step_num - 1)) + step_size = total_iters / step_num + func = partial(step_lr, lr, decay_rate, step_size) + + return func + +def set_optimizer_lr(optimizer, lr_scheduler_func, epoch): + lr = lr_scheduler_func(epoch) + for param_group in optimizer.param_groups: + param_group['lr'] = lr diff --git a/yolox-pytorch/predict.py b/yolox-pytorch/predict.py new file mode 100644 index 0000000..d6aec76 --- /dev/null +++ b/yolox-pytorch/predict.py @@ -0,0 +1,180 @@ +# -----------------------------------------------------------------------# +# predict.py将单张图片预测、摄像头检测、FPS测试和目录遍历检测等功能 +# 整合到了一个py文件中，通过指定mode进行模式的修改。 +# -----------------------------------------------------------------------# +import os +import time + +import cv2 +import numpy as np +from PIL import Image +from tqdm import tqdm + +from yolo import YOLO + + +def PREDICT(dataPath, resultPath, fileName): + yolo = YOLO() + # ----------------------------------------------------------------------------------------------------------# + # mode用于指定测试的模式： + # 'predict' 表示单张图片预测，如果想对预测过程进行修改，如保存图片，截取对象等，可以先看下方详细的注释 + # 'video' 表示视频检测，可调用摄像头或者视频进行检测，详情查看下方注释。 + # 'fps' 表示测试fps，使用的图片是img里面的street.jpg，详情查看下方注释。 + # 'dir_predict' 表示遍历文件夹进行检测并保存。默认遍历img文件夹，保存img_out文件夹，详情查看下方注释。 + # 'heatmap' 表示进行预测结果的热力图可视化，详情查看下方注释。 + # 'export_onnx' 表示将模型导出为onnx，需要pytorch1.7.1以上。 + # ----------------------------------------------------------------------------------------------------------# + mode = "predict" + # -------------------------------------------------------------------------# + # crop 指定了是否在单张图片预测后对目标进行截取 + # count 指定了是否进行目标的计数 + # crop、count仅在mode='predict'时有效 + # -------------------------------------------------------------------------# + crop = False + count = False + # ----------------------------------------------------------------------------------------------------------# + # video_path 用于指定视频的路径，当video_path=0时表示检测摄像头 + # 想要检测视频，则设置如video_path = "xxx.mp4"即可，代表读取出根目录下的xxx.mp4文件。 + # video_save_path 表示视频保存的路径，当video_save_path=""时表示不保存 + # 想要保存视频，则设置如video_save_path = "yyy.mp4"即可，代表保存为根目录下的yyy.mp4文件。 + # video_fps 用于保存的视频的fps + # + # video_path、video_save_path和video_fps仅在mode='video'时有效 + # 保存视频时需要ctrl+c退出或者运行到最后一帧才会完成完整的保存步骤。 + # ----------------------------------------------------------------------------------------------------------# + video_path = 0 + video_save_path = "" + video_fps = 25.0 + # ----------------------------------------------------------------------------------------------------------# + # test_interval 用于指定测量fps的时候，图片检测的次数。理论上test_interval越大，fps越准确。 + # fps_image_path 用于指定测试的fps图片 + # + # test_interval和fps_image_path仅在mode='fps'有效 + # ----------------------------------------------------------------------------------------------------------# + test_interval = 100 + fps_image_path = "img/street.jpg" + # -------------------------------------------------------------------------# + # dir_origin_path 指定了用于检测的图片的文件夹路径 + # dir_save_path 指定了检测完图片的保存路径 + # + # dir_origin_path和dir_save_path仅在mode='dir_predict'时有效 + # ----------------- --------------------------------------------------------# + dir_origin_path = "yolox-pytorch/img/" + dir_save_path = "img_out/" + # -------------------------------------------------------------------------# + # heatmap_save_path 热力图的保存路径，默认保存在model_data下 + # + # heatmap_save_path仅在mode='heatmap'有效 + # -------------------------------------------------------------------------# + heatmap_save_path = "model_data/heatmap_vision.png" + # -------------------------------------------------------------------------# + # simplify 使用Simplify onnx + # onnx_save_path 指定了onnx的保存路径 + # -------------------------------------------------------------------------# + simplify = True + onnx_save_path = "model_data/models.onnx" + + if mode == "predict": + ''' + 1、如果想要进行检测完的图片的保存，利用r_image.save("img.jpg")即可保存，直接在predict.py里进行修改即可。 + 2、如果想要获得预测框的坐标，可以进入yolo.detect_image函数，在绘图部分读取top，left，bottom，right这四个值。 + 3、如果想要利用预测框截取下目标，可以进入yolo.detect_image函数，在绘图部分利用获取到的top，left，bottom，right这四个值 + 在原图上利用矩阵的方式进行截取。 + 4、如果想要在预测图上写额外的字，比如检测到的特定目标的数量，可以进入yolo.detect_image函数，在绘图部分对predicted_class进行判断， + 比如判断if predicted_class == 'car': 即可判断当前目标是否为车，然后记录数量即可。利用draw.text即可写字。 + ''' + + imgPath = os.path.join(dataPath, fileName) + resultFilePath = os.path.join(resultPath, fileName) + image = Image.open(imgPath) + + r_image, result = yolo.detect_image(image, crop=crop, count=count) + r_image.show() + + # 保存图片 + r_image.save(resultFilePath) + + return result # 返回预测得到的分类名以及得分 + + elif mode == "video": + capture = cv2.VideoCapture(video_path) + if video_save_path != "": + fourcc = cv2.VideoWriter_fourcc(*'XVID') + size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))) + out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size) + + ref, frame = capture.read() + if not ref: + raise ValueError("未能正确读取摄像头（视频），请注意是否正确安装摄像头（是否正确填写视频路径）。") + + fps = 0.0 + while (True): + t1 = time.time() + # 读取某一帧 + ref, frame = capture.read() + if not ref: + break + # 格式转变，BGRtoRGB + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # 转变成Image + frame = Image.fromarray(np.uint8(frame)) + # 进行检测 + frame = np.array(yolo.detect_image(frame)) + # RGBtoBGR满足opencv显示格式 + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + + fps = (fps + (1. / (time.time() - t1))) / 2 + print("fps= %.2f" % (fps)) + frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + + cv2.imshow("video", frame) + c = cv2.waitKey(1) & 0xff + if video_save_path != "": + out.write(frame) + + if c == 27: + capture.release() + break + + print("Video Detection Done!") + capture.release() + if video_save_path != "": + print("Save processed video to the path :" + video_save_path) + out.release() + cv2.destroyAllWindows() + + elif mode == "fps": + img = Image.open(fps_image_path) + tact_time = yolo.get_FPS(img, test_interval) + print(str(tact_time) + ' seconds, ' + str(1 / tact_time) + 'FPS, @batch_size 1') + + elif mode == "dir_predict": + + img_names = os.listdir(dir_origin_path) + for img_name in tqdm(img_names): + if img_name.lower().endswith( + ('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')): + image_path = os.path.join(dir_origin_path, img_name) + image = Image.open(image_path) + r_image = yolo.detect_image(image) + if not os.path.exists(dir_save_path): + os.makedirs(dir_save_path) + r_image.save(os.path.join(dir_save_path, img_name.replace(".jpg", ".png")), quality=95, subsampling=0) + + elif mode == "heatmap": + while True: + img = input('Input image filename:') + try: + image = Image.open(img) + except: + print('Open Error! Try again!') + continue + else: + yolo.detect_heatmap(image, heatmap_save_path) + + elif mode == "export_onnx": + yolo.convert_to_onnx(simplify, onnx_save_path) + + else: + raise AssertionError( + "Please specify the correct mode: 'predict', 'video', 'fps', 'heatmap', 'export_onnx', 'dir_predict'.") diff --git a/yolox-pytorch/quanju.py b/yolox-pytorch/quanju.py new file mode 100644 index 0000000..4fb30e9 --- /dev/null +++ b/yolox-pytorch/quanju.py @@ -0,0 +1,9 @@ +''' +Author: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git +Date: 2023-04-01 21:34:17 +LastEditors: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git +LastEditTime: 2023-04-01 21:41:27 +FilePath: /luyuetong-data/yolox-pytorch/quanju.py +Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE +''' +mingzi = '' \ No newline at end of file diff --git a/yolox-pytorch/requirements.txt b/yolox-pytorch/requirements.txt new file mode 100644 index 0000000..0ce4ca7 --- /dev/null +++ b/yolox-pytorch/requirements.txt @@ -0,0 +1,9 @@ +scipy==1.2.1 +numpy==1.17.0 +matplotlib==3.1.2 +opencv_python==4.1.2.30 +torch==1.2.0 +torchvision==0.4.0 +tqdm==4.60.0 +Pillow==8.2.0 +h5py==2.10.0 \ No newline at end of file diff --git a/yolox-pytorch/results/result_000009.jpg b/yolox-pytorch/results/result_000009.jpg new file mode 100644 index 0000000..7fe919e Binary files /dev/null and b/yolox-pytorch/results/result_000009.jpg differ diff --git a/yolox-pytorch/results/result_001630.jpg b/yolox-pytorch/results/result_001630.jpg new file mode 100644 index 0000000..9eea533 Binary files /dev/null and b/yolox-pytorch/results/result_001630.jpg differ diff --git a/yolox-pytorch/results/result_002227.jpg b/yolox-pytorch/results/result_002227.jpg new file mode 100644 index 0000000..71b3a18 Binary files /dev/null and b/yolox-pytorch/results/result_002227.jpg differ diff --git a/yolox-pytorch/results/result_c.png b/yolox-pytorch/results/result_c.png new file mode 100644 index 0000000..e1c91d8 Binary files /dev/null and b/yolox-pytorch/results/result_c.png differ diff --git a/yolox-pytorch/results/result_f.png b/yolox-pytorch/results/result_f.png new file mode 100644 index 0000000..f62c191 Binary files /dev/null and b/yolox-pytorch/results/result_f.png differ diff --git a/yolox-pytorch/results/result_h.png b/yolox-pytorch/results/result_h.png new file mode 100644 index 0000000..ef4bbf5 Binary files /dev/null and b/yolox-pytorch/results/result_h.png differ diff --git a/yolox-pytorch/summary.py b/yolox-pytorch/summary.py new file mode 100644 index 0000000..f48b75b --- /dev/null +++ b/yolox-pytorch/summary.py @@ -0,0 +1,31 @@ +#--------------------------------------------# +# 该部分代码用于看网络结构 +#--------------------------------------------# +import torch +from thop import clever_format, profile +from torchsummary import summary + +from nets.yolo import YoloBody + +if __name__ == "__main__": + input_shape = [640, 640] + num_classes = 80 + phi = 'l' + + # 需要使用device来指定网络在GPU还是CPU运行 + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + m = YoloBody(num_classes, phi).to(device) + summary(m, (3, input_shape[0], input_shape[1])) + + dummy_input = torch.randn(1, 3, input_shape[0], input_shape[1]).to(device) + flops, params = profile(m.to(device), (dummy_input, ), verbose=False) + #--------------------------------------------------------# + # flops * 2是因为profile没有将卷积作为两个operations + # 有些论文将卷积算乘法、加法两个operations。此时乘2 + # 有些论文只考虑乘法的运算次数，忽略加法。此时不乘2 + # 本代码选择乘2，参考YOLOX。 + #--------------------------------------------------------# + flops = flops * 2 + flops, params = clever_format([flops, params], "%.3f") + print('Total GFLOPS: %s' % (flops)) + print('Total params: %s' % (params)) diff --git a/yolox-pytorch/test.py b/yolox-pytorch/test.py new file mode 100644 index 0000000..e742cca --- /dev/null +++ b/yolox-pytorch/test.py @@ -0,0 +1,15 @@ +from flask import Flask, jsonify, request, make_response, render_template +from PIL import Image +from yolo import YOLO +from predict import PREDICT +import calendar,time,os + + + +import numpy as np + +# 用np.load加载.npy文件 +data = np.load('E:\Temp/train_64/train_64.npy') + +# 查看数组的形状 +print(data.shape) diff --git a/yolox-pytorch/train.py b/yolox-pytorch/train.py new file mode 100644 index 0000000..5b4d1c0 --- /dev/null +++ b/yolox-pytorch/train.py @@ -0,0 +1,543 @@ +# -------------------------------------# +# 对数据集进行训练 +# -------------------------------------# +import datetime +import os + +import numpy as np +import torch +import torch.backends.cudnn as cudnn +import torch.distributed as dist +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader + +from nets.yolo import YoloBody +from nets.yolo_training import (ModelEMA, YOLOLoss, get_lr_scheduler, + set_optimizer_lr, weights_init) +from utils.callbacks import LossHistory, EvalCallback +from utils.dataloader import YoloDataset, yolo_dataset_collate +from utils.utils import get_classes, show_config +from utils.utils_fit import fit_one_epoch + +''' +训练自己的目标检测模型一定需要注意以下几点： +1、训练前仔细检查自己的格式是否满足要求，该库要求数据集格式为VOC格式，需要准备好的内容有输入图片和标签 + 输入图片为.jpg图片，无需固定大小，传入训练前会自动进行resize。 + 灰度图会自动转成RGB图片进行训练，无需自己修改。 + 输入图片如果后缀非jpg，需要自己批量转成jpg后再开始训练。 + + 标签为.xml格式，文件中会有需要检测的目标信息，标签文件和输入图片文件相对应。 + +2、损失值的大小用于判断是否收敛，比较重要的是有收敛的趋势，即验证集损失不断下降，如果验证集损失基本上不改变的话，模型基本上就收敛了。 + 损失值的具体大小并没有什么意义，大和小只在于损失的计算方式，并不是接近于0才好。如果想要让损失好看点，可以直接到对应的损失函数里面除上10000。 + 训练过程中的损失值会保存在logs文件夹下的loss_%Y_%m_%d_%H_%M_%S文件夹中 + +3、训练好的权值文件保存在logs文件夹中，每个训练世代（Epoch）包含若干训练步长（Step），每个训练步长（Step）进行一次梯度下降。 + 如果只是训练了几个Step是不会保存的，Epoch和Step的概念要捋清楚一下。 +''' +if __name__ == "__main__": + # ---------------------------------# + # Cuda 是否使用Cuda + # 没有GPU可以设置成False + # ---------------------------------# + Cuda = True + # ---------------------------------------------------------------------# + # distributed 用于指定是否使用单机多卡分布式运行 + # 终端指令仅支持Ubuntu。CUDA_VISIBLE_DEVICES用于在Ubuntu下指定显卡。 + # Windows系统下默认使用DP模式调用所有显卡，不支持DDP。 + # DP模式： + # 设置 distributed = False + # 在终端中输入 CUDA_VISIBLE_DEVICES=0,1 python train.py + # DDP模式： + # 设置 distributed = True + # 在终端中输入 CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py + # ---------------------------------------------------------------------# + distributed = False + # ---------------------------------------------------------------------# + # sync_bn 是否使用sync_bn，DDP模式多卡可用 + # ---------------------------------------------------------------------# + sync_bn = False + # ---------------------------------------------------------------------# + # fp16 是否使用混合精度训练 + # 可减少约一半的显存、需要pytorch1.7.1以上 + # ---------------------------------------------------------------------# + fp16 = False + # ---------------------------------------------------------------------# + # classes_path 指向model_data下的txt，与自己训练的数据集相关 + # 训练前一定要修改classes_path，使其对应自己的数据集 + # ---------------------------------------------------------------------# + classes_path = 'model_data/voc_classes.txt' + # ----------------------------------------------------------------------------------------------------------------------------# + # 权值文件的下载请看README，可以通过网盘下载。模型的 预训练权重 对不同数据集是通用的，因为特征是通用的。 + # 模型的 预训练权重 比较重要的部分是 主干特征提取网络的权值部分，用于进行特征提取。 + # 预训练权重对于99%的情况都必须要用，不用的话主干部分的权值太过随机，特征提取效果不明显，网络训练的结果也不会好 + # + # 如果训练过程中存在中断训练的操作，可以将model_path设置成logs文件夹下的权值文件，将已经训练了一部分的权值再次载入。 + # 同时修改下方的 冻结阶段 或者 解冻阶段 的参数，来保证模型epoch的连续性。 + # + # 当model_path = ''的时候不加载整个模型的权值。 + # + # 此处使用的是整个模型的权重，因此是在train.py进行加载的。 + # 如果想要让模型从0开始训练，则设置model_path = ''，下面的Freeze_Train = Fasle，此时从0开始训练，且没有冻结主干的过程。 + # + # 一般来讲，网络从0开始的训练效果会很差，因为权值太过随机，特征提取效果不明显，因此非常、非常、非常不建议大家从0开始训练！ + # 从0开始训练有两个方案： + # 1、得益于Mosaic数据增强方法强大的数据增强能力，将UnFreeze_Epoch设置的较大（300及以上）、batch较大（16及以上）、数据较多（万以上）的情况下， + # 可以设置mosaic=True，直接随机初始化参数开始训练，但得到的效果仍然不如有预训练的情况。（像COCO这样的大数据集可以这样做） + # 2、了解imagenet数据集，首先训练分类模型，获得网络的主干部分权值，分类模型的 主干部分 和该模型通用，基于此进行训练。 + # ----------------------------------------------------------------------------------------------------------------------------# + model_path = 'model_data/yolox_s.pth' # .. + # ------------------------------------------------------# + # input_shape 输入的shape大小，一定要是32的倍数 + # ------------------------------------------------------# + input_shape = [640, 640] + # ------------------------------------------------------# + # 所使用的YoloX的版本。nano、tiny、s、m、l、x + # ------------------------------------------------------# + phi = 's' # .. + # ------------------------------------------------------------------# + # mosaic 马赛克数据增强。 + # mosaic_prob 每个step有多少概率使用mosaic数据增强，默认50%。 + # + # mixup 是否使用mixup数据增强，仅在mosaic=True时有效。 + # 只会对mosaic增强后的图片进行mixup的处理。 + # mixup_prob 有多少概率在mosaic后使用mixup数据增强，默认50%。 + # 总的mixup概率为mosaic_prob * mixup_prob。 + # + # special_aug_ratio 参考YoloX，由于Mosaic生成的训练图片，远远脱离自然图片的真实分布。 + # 当mosaic=True时，本代码会在special_aug_ratio范围内开启mosaic。 + # 默认为前70%个epoch，100个世代会开启70个世代。 + # + # 余弦退火算法的参数放到下面的lr_decay_type中设置 + # ------------------------------------------------------------------# + mosaic = True + mosaic_prob = 0.5 + mixup = True + mixup_prob = 0.5 + special_aug_ratio = 0.7 + + # ----------------------------------------------------------------------------------------------------------------------------# + # 训练分为两个阶段，分别是冻结阶段和解冻阶段。设置冻结阶段是为了满足机器性能不足的同学的训练需求。 + # 冻结训练需要的显存较小，显卡非常差的情况下，可设置Freeze_Epoch等于UnFreeze_Epoch，Freeze_Train = True，此时仅仅进行冻结训练。 + # + # 在此提供若干参数设置建议，各位训练者根据自己的需求进行灵活调整： + # （一）从整个模型的预训练权重开始训练： + # Adam： + # Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 100，Freeze_Train = True，optimizer_type = 'adam'，Init_lr = 1e-3，weight_decay = 0。（冻结） + # Init_Epoch = 0，UnFreeze_Epoch = 100，Freeze_Train = False，optimizer_type = 'adam'，Init_lr = 1e-3，weight_decay = 0。（不冻结） + # SGD： + # Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 300，Freeze_Train = True，optimizer_type = 'sgd'，Init_lr = 1e-2，weight_decay = 5e-4。（冻结） + # Init_Epoch = 0，UnFreeze_Epoch = 300，Freeze_Train = False，optimizer_type = 'sgd'，Init_lr = 1e-2，weight_decay = 5e-4。（不冻结） + # 其中：UnFreeze_Epoch可以在100-300之间调整。 + # （二）从0开始训练： + # Init_Epoch = 0，UnFreeze_Epoch >= 300，Unfreeze_batch_size >= 16，Freeze_Train = False（不冻结训练） + # 其中：UnFreeze_Epoch尽量不小于300。optimizer_type = 'sgd'，Init_lr = 1e-2，mosaic = True。 + # （三）batch_size的设置： + # 在显卡能够接受的范围内，以大为好。显存不足与数据集大小无关，提示显存不足（OOM或者CUDA out of memory）请调小batch_size。 + # 受到BatchNorm层影响，batch_size最小为2，不能为1。 + # 正常情况下Freeze_batch_size建议为Unfreeze_batch_size的1-2倍。不建议设置的差距过大，因为关系到学习率的自动调整。 + # ----------------------------------------------------------------------------------------------------------------------------# + # ------------------------------------------------------------------# + # 冻结阶段训练参数 + # 此时模型的主干被冻结了，特征提取网络不发生改变 + # 占用的显存较小，仅对网络进行微调 + # Init_Epoch 模型当前开始的训练世代，其值可以大于Freeze_Epoch，如设置： + # Init_Epoch = 60、Freeze_Epoch = 50、UnFreeze_Epoch = 100 + # 会跳过冻结阶段，直接从60代开始，并调整对应的学习率。 + # （断点续练时使用） + # Freeze_Epoch 模型冻结训练的Freeze_Epoch + # (当Freeze_Train=False时失效) + # Freeze_batch_size 模型冻结训练的batch_size + # (当Freeze_Train=False时失效) + # ------------------------------------------------------------------# + Init_Epoch = 0 # .. + Freeze_Epoch = 50 # .. + Freeze_batch_size = 16 # .. + # ------------------------------------------------------------------# + # 解冻阶段训练参数 + # 此时模型的主干不被冻结了，特征提取网络会发生改变 + # 占用的显存较大，网络所有的参数都会发生改变 + # UnFreeze_Epoch 模型总共训练的epoch + # SGD需要更长的时间收敛，因此设置较大的UnFreeze_Epoch + # Adam可以使用相对较小的UnFreeze_Epoch + # Unfreeze_batch_size 模型在解冻后的batch_size + # ------------------------------------------------------------------# + UnFreeze_Epoch = 300 # .. + Unfreeze_batch_size = 8 # .. + # ------------------------------------------------------------------# + # Freeze_Train 是否进行冻结训练 + # 默认先冻结主干训练后解冻训练。 + # ------------------------------------------------------------------# + Freeze_Train = True # .. + + # ------------------------------------------------------------------# + # 其它训练参数：学习率、优化器、学习率下降有关 + # ------------------------------------------------------------------# + # ------------------------------------------------------------------# + # Init_lr 模型的最大学习率 + # Min_lr 模型的最小学习率，默认为最大学习率的0.01 + # ------------------------------------------------------------------# + Init_lr = 1e-2 + Min_lr = Init_lr * 0.01 + # ------------------------------------------------------------------# + # optimizer_type 使用到的优化器种类，可选的有adam、sgd + # 当使用Adam优化器时建议设置 Init_lr=1e-3 + # 当使用SGD优化器时建议设置 Init_lr=1e-2 + # momentum 优化器内部使用到的momentum参数 + # weight_decay 权值衰减，可防止过拟合 + # adam会导致weight_decay错误，使用adam时建议设置为0。 + # ------------------------------------------------------------------# + optimizer_type = "sgd" # + momentum = 0.937 + weight_decay = 5e-4 + # ------------------------------------------------------------------# + # lr_decay_type 使用到的学习率下降方式，可选的有step、cos + # ------------------------------------------------------------------# + lr_decay_type = "cos" + # ------------------------------------------------------------------# + # save_period 多少个epoch保存一次权值 + # ------------------------------------------------------------------# + save_period = 10 + # ------------------------------------------------------------------# + # save_dir 权值与日志文件保存的文件夹 + # ------------------------------------------------------------------# + save_dir = 'logs' + # ------------------------------------------------------------------# + # eval_flag 是否在训练时进行评估，评估对象为验证集 + # 安装pycocotools库后，评估体验更佳。 + # eval_period 代表多少个epoch评估一次，不建议频繁的评估 + # 评估需要消耗较多的时间，频繁评估会导致训练非常慢 + # 此处获得的mAP会与get_map.py获得的会有所不同，原因有二： + # （一）此处获得的mAP为验证集的mAP。 + # （二）此处设置评估参数较为保守，目的是加快评估速度。 + # ------------------------------------------------------------------# + eval_flag = True + eval_period = 10 + # ------------------------------------------------------------------# + # num_workers 用于设置是否使用多线程读取数据 + # 开启后会加快数据读取速度，但是会占用更多内存 + # 内存较小的电脑可以设置为2或者0 + # ------------------------------------------------------------------# + num_workers = 4 + + # ----------------------------------------------------# + # 获得图片路径和标签 + # ----------------------------------------------------# + train_annotation_path = '2007_train.txt' + val_annotation_path = '2007_val.txt' + + # ------------------------------------------------------# + # 设置用到的显卡 + # ------------------------------------------------------# + ngpus_per_node = torch.cuda.device_count() + if distributed: + dist.init_process_group(backend="nccl") + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + device = torch.device("cuda", local_rank) + if local_rank == 0: + print(f"[{os.getpid()}] (rank = {rank}, local_rank = {local_rank}) training...") + print("Gpu Device Count : ", ngpus_per_node) + else: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + local_rank = 0 + rank = 0 + + # ----------------------------------------------------# + # 获取classes和anchor + # ----------------------------------------------------# + class_names, num_classes = get_classes(classes_path) + + # ------------------------------------------------------# + # 创建yolo模型 + # ------------------------------------------------------# + model = YoloBody(num_classes, phi) + weights_init(model) + if model_path != '': + # ------------------------------------------------------# + # 权值文件请看README，百度网盘下载 + # ------------------------------------------------------# + if local_rank == 0: + print('Load weights {}.'.format(model_path)) + + # ------------------------------------------------------# + # 根据预训练权重的Key和模型的Key进行加载 + # ------------------------------------------------------# + model_dict = model.state_dict() + pretrained_dict = torch.load(model_path, map_location=device) + load_key, no_load_key, temp_dict = [], [], {} + for k, v in pretrained_dict.items(): + if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v): + temp_dict[k] = v + load_key.append(k) + else: + no_load_key.append(k) + model_dict.update(temp_dict) + model.load_state_dict(model_dict) + # ------------------------------------------------------# + # 显示没有匹配上的Key + # ------------------------------------------------------# + if local_rank == 0: + print("\nSuccessful Load Key:", str(load_key)[:500], "……\nSuccessful Load Key Num:", len(load_key)) + print("\nFail To Load Key:", str(no_load_key)[:500], "……\nFail To Load Key num:", len(no_load_key)) + print("\n\033[1;33;44m温馨提示，head部分没有载入是正常现象，Backbone部分没有载入是错误的。\033[0m") + + # ----------------------# + # 获得损失函数 + # ----------------------# + yolo_loss = YOLOLoss(num_classes, fp16) + # ----------------------# + # 记录Loss + # ----------------------# + if local_rank == 0: + time_str = datetime.datetime.strftime(datetime.datetime.now(), '%Y_%m_%d_%H_%M_%S') + log_dir = os.path.join(save_dir, "loss_" + str(time_str)) + loss_history = LossHistory(log_dir, model, input_shape=input_shape) + else: + loss_history = None + + # ------------------------------------------------------------------# + # torch 1.2不支持amp，建议使用torch 1.7.1及以上正确使用fp16 + # 因此torch1.2这里显示"could not be resolve" + # ------------------------------------------------------------------# + if fp16: + from torch.cuda.amp import GradScaler as GradScaler + + scaler = GradScaler() + else: + scaler = None + + model_train = model.train() + # ----------------------------# + # 多卡同步Bn + # ----------------------------# + if sync_bn and ngpus_per_node > 1 and distributed: + model_train = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model_train) + elif sync_bn: + print("Sync_bn is not support in one gpu or not distributed.") + + if Cuda: + if distributed: + # ----------------------------# + # 多卡平行运行 + # ----------------------------# + model_train = model_train.cuda(local_rank) + model_train = torch.nn.parallel.DistributedDataParallel(model_train, device_ids=[local_rank], + find_unused_parameters=True) + else: + model_train = torch.nn.DataParallel(model) + cudnn.benchmark = True + model_train = model_train.cuda() + + # ----------------------------# + # 权值平滑 + # ----------------------------# + ema = ModelEMA(model_train) + + # ---------------------------# + # 读取数据集对应的txt + # ---------------------------# + with open(train_annotation_path, encoding='utf-8') as f: + train_lines = f.readlines() + with open(val_annotation_path, encoding='utf-8') as f: + val_lines = f.readlines() + num_train = len(train_lines) + num_val = len(val_lines) + + if local_rank == 0: + show_config( + classes_path=classes_path, model_path=model_path, input_shape=input_shape, \ + Init_Epoch=Init_Epoch, Freeze_Epoch=Freeze_Epoch, UnFreeze_Epoch=UnFreeze_Epoch, + Freeze_batch_size=Freeze_batch_size, Unfreeze_batch_size=Unfreeze_batch_size, Freeze_Train=Freeze_Train, \ + Init_lr=Init_lr, Min_lr=Min_lr, optimizer_type=optimizer_type, momentum=momentum, + lr_decay_type=lr_decay_type, \ + save_period=save_period, save_dir=save_dir, num_workers=num_workers, num_train=num_train, num_val=num_val + ) + # ---------------------------------------------------------# + # 总训练世代指的是遍历全部数据的总次数 + # 总训练步长指的是梯度下降的总次数 + # 每个训练世代包含若干训练步长，每个训练步长进行一次梯度下降。 + # 此处仅建议最低训练世代，上不封顶，计算时只考虑了解冻部分 + # ----------------------------------------------------------# + wanted_step = 5e4 if optimizer_type == "sgd" else 1.5e4 + total_step = num_train // Unfreeze_batch_size * UnFreeze_Epoch + if total_step <= wanted_step: + if num_train // Unfreeze_batch_size == 0: + raise ValueError('数据集过小，无法进行训练，请扩充数据集。') + wanted_epoch = wanted_step // (num_train // Unfreeze_batch_size) + 1 + print("\n\033[1;33;44m[Warning] 使用%s优化器时，建议将训练总步长设置到%d以上。\033[0m" % ( + optimizer_type, wanted_step)) + print( + "\033[1;33;44m[Warning] 本次运行的总训练数据量为%d，Unfreeze_batch_size为%d，共训练%d个Epoch，计算出总训练步长为%d。\033[0m" % ( + num_train, Unfreeze_batch_size, UnFreeze_Epoch, total_step)) + print("\033[1;33;44m[Warning] 由于总训练步长为%d，小于建议总步长%d，建议设置总世代为%d。\033[0m" % ( + total_step, wanted_step, wanted_epoch)) + + # ------------------------------------------------------# + # 主干特征提取网络特征通用，冻结训练可以加快训练速度 + # 也可以在训练初期防止权值被破坏。 + # Init_Epoch为起始世代 + # Freeze_Epoch为冻结训练的世代 + # UnFreeze_Epoch总训练世代 + # 提示OOM或者显存不足请调小Batch_size + # ------------------------------------------------------# + if True: + UnFreeze_flag = False + # ------------------------------------# + # 冻结一定部分训练 + # ------------------------------------# + if Freeze_Train: + for param in model.backbone.parameters(): + param.requires_grad = False + + # -------------------------------------------------------------------# + # 如果不冻结训练的话，直接设置batch_size为Unfreeze_batch_size + # -------------------------------------------------------------------# + batch_size = Freeze_batch_size if Freeze_Train else Unfreeze_batch_size + + # -------------------------------------------------------------------# + # 判断当前batch_size，自适应调整学习率 + # -------------------------------------------------------------------# + nbs = 64 + lr_limit_max = 1e-3 if optimizer_type == 'adam' else 5e-2 + lr_limit_min = 3e-4 if optimizer_type == 'adam' else 5e-4 + Init_lr_fit = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max) + Min_lr_fit = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2) + + # ---------------------------------------# + # 根据optimizer_type选择优化器 + # ---------------------------------------# + pg0, pg1, pg2 = [], [], [] + for k, v in model.named_modules(): + if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter): + pg2.append(v.bias) + if isinstance(v, nn.BatchNorm2d) or "bn" in k: + pg0.append(v.weight) + elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter): + pg1.append(v.weight) + optimizer = { + 'adam': optim.Adam(pg0, Init_lr_fit, betas=(momentum, 0.999)), + 'sgd': optim.SGD(pg0, Init_lr_fit, momentum=momentum, nesterov=True) + }[optimizer_type] + optimizer.add_param_group({"params": pg1, "weight_decay": weight_decay}) + optimizer.add_param_group({"params": pg2}) + + # ---------------------------------------# + # 获得学习率下降的公式 + # ---------------------------------------# + lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) + + # ---------------------------------------# + # 判断每一个世代的长度 + # ---------------------------------------# + epoch_step = num_train // batch_size + epoch_step_val = num_val // batch_size + + if epoch_step == 0 or epoch_step_val == 0: + raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") + + if ema: + ema.updates = epoch_step * Init_Epoch + + # ---------------------------------------# + # 构建数据集加载器。 + # ---------------------------------------# + train_dataset = YoloDataset(train_lines, input_shape, num_classes, epoch_length=UnFreeze_Epoch, \ + mosaic=mosaic, mixup=mixup, mosaic_prob=mosaic_prob, mixup_prob=mixup_prob, + train=True, special_aug_ratio=special_aug_ratio) + val_dataset = YoloDataset(val_lines, input_shape, num_classes, epoch_length=UnFreeze_Epoch, \ + mosaic=False, mixup=False, mosaic_prob=0, mixup_prob=0, train=False, + special_aug_ratio=0) + + if distributed: + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True, ) + val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, ) + batch_size = batch_size // ngpus_per_node + shuffle = False + else: + train_sampler = None + val_sampler = None + shuffle = True + + gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, + pin_memory=True, + drop_last=True, collate_fn=yolo_dataset_collate, sampler=train_sampler) + gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, + pin_memory=True, + drop_last=True, collate_fn=yolo_dataset_collate, sampler=val_sampler) + + # ----------------------# + # 记录eval的map曲线 + # ----------------------# + if local_rank == 0: + eval_callback = EvalCallback(model, input_shape, class_names, num_classes, val_lines, log_dir, Cuda, \ + eval_flag=eval_flag, period=eval_period) + else: + eval_callback = None + + # ---------------------------------------# + # 开始模型训练 + # ---------------------------------------# + for epoch in range(Init_Epoch, UnFreeze_Epoch): + # ---------------------------------------# + # 如果模型有冻结学习部分 + # 则解冻，并设置参数 + # ---------------------------------------# + if epoch >= Freeze_Epoch and not UnFreeze_flag and Freeze_Train: + batch_size = Unfreeze_batch_size + + # -------------------------------------------------------------------# + # 判断当前batch_size，自适应调整学习率 + # -------------------------------------------------------------------# + nbs = 64 + lr_limit_max = 1e-3 if optimizer_type == 'adam' else 5e-2 + lr_limit_min = 3e-4 if optimizer_type == 'adam' else 5e-4 + Init_lr_fit = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max) + Min_lr_fit = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2) + # ---------------------------------------# + # 获得学习率下降的公式 + # ---------------------------------------# + lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) + + for param in model.backbone.parameters(): + param.requires_grad = True + + epoch_step = num_train // batch_size + epoch_step_val = num_val // batch_size + + if epoch_step == 0 or epoch_step_val == 0: + raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") + + if distributed: + batch_size = batch_size // ngpus_per_node + + if ema: + ema.updates = epoch_step * epoch + + gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, + pin_memory=True, + drop_last=True, collate_fn=yolo_dataset_collate, sampler=train_sampler) + gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, + pin_memory=True, + drop_last=True, collate_fn=yolo_dataset_collate, sampler=val_sampler) + + UnFreeze_flag = True + + gen.dataset.epoch_now = epoch + gen_val.dataset.epoch_now = epoch + + if distributed: + train_sampler.set_epoch(epoch) + + set_optimizer_lr(optimizer, lr_scheduler_func, epoch) + + fit_one_epoch(model_train, model, ema, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, + epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, + local_rank) + + if distributed: + dist.barrier() + + if local_rank == 0: + loss_history.writer.close() diff --git a/yolox-pytorch/uploads/001630.jpg b/yolox-pytorch/uploads/001630.jpg new file mode 100644 index 0000000..c99c65b Binary files /dev/null and b/yolox-pytorch/uploads/001630.jpg differ diff --git a/yolox-pytorch/uploads/002227.jpg b/yolox-pytorch/uploads/002227.jpg new file mode 100644 index 0000000..e7bffe2 Binary files /dev/null and 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--git a/yolox-pytorch/utils/__init__.py b/yolox-pytorch/utils/__init__.py new file mode 100644 index 0000000..4287ca8 --- /dev/null +++ b/yolox-pytorch/utils/__init__.py @@ -0,0 +1 @@ +# \ No newline at end of file diff --git a/yolox-pytorch/utils/callbacks.py b/yolox-pytorch/utils/callbacks.py new file mode 100644 index 0000000..07ec1c6 --- /dev/null +++ b/yolox-pytorch/utils/callbacks.py @@ -0,0 +1,227 @@ +import os + +import torch +import matplotlib +matplotlib.use('Agg') +import scipy.signal +from matplotlib import pyplot as plt +from torch.utils.tensorboard import SummaryWriter + +import shutil +import numpy as np + +from PIL import Image +from tqdm import tqdm +from .utils import cvtColor, preprocess_input, resize_image +from .utils_bbox import decode_outputs, non_max_suppression +from .utils_map import get_coco_map, get_map + + +class LossHistory(): + def __init__(self, log_dir, model, input_shape): + self.log_dir = log_dir + self.losses = [] + self.val_loss = [] + + os.makedirs(self.log_dir) + self.writer = SummaryWriter(self.log_dir) + try: + dummy_input = torch.randn(2, 3, input_shape[0], input_shape[1]) + self.writer.add_graph(model, dummy_input) + except: + pass + + def append_loss(self, epoch, loss, val_loss): + if not os.path.exists(self.log_dir): + os.makedirs(self.log_dir) + + self.losses.append(loss) + self.val_loss.append(val_loss) + + with open(os.path.join(self.log_dir, "epoch_loss.txt"), 'a') as f: + f.write(str(loss)) + f.write("\n") + with open(os.path.join(self.log_dir, "epoch_val_loss.txt"), 'a') as f: + f.write(str(val_loss)) + f.write("\n") + + self.writer.add_scalar('loss', loss, epoch) + self.writer.add_scalar('val_loss', val_loss, epoch) + self.loss_plot() + + def loss_plot(self): + iters = range(len(self.losses)) + + plt.figure() + plt.plot(iters, self.losses, 'red', linewidth = 2, label='train loss') + plt.plot(iters, self.val_loss, 'coral', linewidth = 2, label='val loss') + try: + if len(self.losses) < 25: + num = 5 + else: + num = 15 + + plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle = '--', linewidth = 2, label='smooth train loss') + plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), '#8B4513', linestyle = '--', linewidth = 2, label='smooth val loss') + except: + pass + + plt.grid(True) + plt.xlabel('Epoch') + plt.ylabel('Loss') + plt.legend(loc="upper right") + + plt.savefig(os.path.join(self.log_dir, "epoch_loss.png")) + + plt.cla() + plt.close("all") + +class EvalCallback(): + def __init__(self, net, input_shape, class_names, num_classes, val_lines, log_dir, cuda, \ + map_out_path=".temp_map_out", max_boxes=100, confidence=0.05, nms_iou=0.5, letterbox_image=True, MINOVERLAP=0.5, eval_flag=True, period=1): + super(EvalCallback, self).__init__() + + self.net = net + self.input_shape = input_shape + self.class_names = class_names + self.num_classes = num_classes + self.val_lines = val_lines + self.log_dir = log_dir + self.cuda = cuda + self.map_out_path = map_out_path + self.max_boxes = max_boxes + self.confidence = confidence + self.nms_iou = nms_iou + self.letterbox_image = letterbox_image + self.MINOVERLAP = MINOVERLAP + self.eval_flag = eval_flag + self.period = period + + self.maps = [0] + self.epoches = [0] + if self.eval_flag: + with open(os.path.join(self.log_dir, "epoch_map.txt"), 'a') as f: + f.write(str(0)) + f.write("\n") + + def get_map_txt(self, image_id, image, class_names, map_out_path): + f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w") + image_shape = np.array(np.shape(image)[0:2]) + #---------------------------------------------------------# + # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 + # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB + #---------------------------------------------------------# + image = cvtColor(image) + #---------------------------------------------------------# + # 给图像增加灰条，实现不失真的resize + # 也可以直接resize进行识别 + #---------------------------------------------------------# + image_data = resize_image(image, (self.input_shape[1],self.input_shape[0]), self.letterbox_image) + #---------------------------------------------------------# + # 添加上batch_size维度 + #---------------------------------------------------------# + image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) + + with torch.no_grad(): + images = torch.from_numpy(image_data) + if self.cuda: + images = images.cuda() + #---------------------------------------------------------# + # 将图像输入网络当中进行预测！ + #---------------------------------------------------------# + outputs = self.net(images) + outputs = decode_outputs(outputs, self.input_shape) + #---------------------------------------------------------# + # 将预测框进行堆叠，然后进行非极大抑制 + #---------------------------------------------------------# + results = non_max_suppression(outputs, self.num_classes, self.input_shape, + image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou) + + if results[0] is None: + return + + top_label = np.array(results[0][:, 6], dtype = 'int32') + top_conf = results[0][:, 4] * results[0][:, 5] + top_boxes = results[0][:, :4] + + top_100 = np.argsort(top_conf)[::-1][:self.max_boxes] + top_boxes = top_boxes[top_100] + top_conf = top_conf[top_100] + top_label = top_label[top_100] + + for i, c in list(enumerate(top_label)): + predicted_class = self.class_names[int(c)] + box = top_boxes[i] + score = str(top_conf[i]) + + top, left, bottom, right = box + if predicted_class not in class_names: + continue + + f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom)))) + + f.close() + return + + def on_epoch_end(self, epoch, model_eval): + if epoch % self.period == 0 and self.eval_flag: + self.net = model_eval + if not os.path.exists(self.map_out_path): + os.makedirs(self.map_out_path) + if not os.path.exists(os.path.join(self.map_out_path, "ground-truth")): + os.makedirs(os.path.join(self.map_out_path, "ground-truth")) + if not os.path.exists(os.path.join(self.map_out_path, "detection-results")): + os.makedirs(os.path.join(self.map_out_path, "detection-results")) + print("Get map.") + for annotation_line in tqdm(self.val_lines): + line = annotation_line.split() + image_id = os.path.basename(line[0]).split('.')[0] + #------------------------------# + # 读取图像并转换成RGB图像 + #------------------------------# + image = Image.open(line[0]) + #------------------------------# + # 获得预测框 + #------------------------------# + gt_boxes = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]]) + #------------------------------# + # 获得预测txt + #------------------------------# + self.get_map_txt(image_id, image, self.class_names, self.map_out_path) + + #------------------------------# + # 获得真实框txt + #------------------------------# + with open(os.path.join(self.map_out_path, "ground-truth/"+image_id+".txt"), "w") as new_f: + for box in gt_boxes: + left, top, right, bottom, obj = box + obj_name = self.class_names[obj] + new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom)) + + print("Calculate Map.") + try: + temp_map = get_coco_map(class_names = self.class_names, path = self.map_out_path)[1] + except: + temp_map = get_map(self.MINOVERLAP, False, path = self.map_out_path) + self.maps.append(temp_map) + self.epoches.append(epoch) + + with open(os.path.join(self.log_dir, "epoch_map.txt"), 'a') as f: + f.write(str(temp_map)) + f.write("\n") + + plt.figure() + plt.plot(self.epoches, self.maps, 'red', linewidth = 2, label='train map') + + plt.grid(True) + plt.xlabel('Epoch') + plt.ylabel('Map %s'%str(self.MINOVERLAP)) + plt.title('A Map Curve') + plt.legend(loc="upper right") + + plt.savefig(os.path.join(self.log_dir, "epoch_map.png")) + plt.cla() + plt.close("all") + + print("Get map done.") + shutil.rmtree(self.map_out_path) diff --git a/yolox-pytorch/utils/dataloader.py b/yolox-pytorch/utils/dataloader.py new file mode 100644 index 0000000..5cac4db --- /dev/null +++ b/yolox-pytorch/utils/dataloader.py @@ -0,0 +1,374 @@ +from random import sample, shuffle + +import cv2 +import numpy as np +import torch +from PIL import Image +from torch.utils.data.dataset import Dataset + +from utils.utils import cvtColor, preprocess_input + + +class YoloDataset(Dataset): + def __init__(self, annotation_lines, input_shape, num_classes, epoch_length, \ + mosaic, mixup, mosaic_prob, mixup_prob, train, special_aug_ratio = 0.7): + super(YoloDataset, self).__init__() + self.annotation_lines = annotation_lines + self.input_shape = input_shape + self.num_classes = num_classes + self.epoch_length = epoch_length + self.mosaic = mosaic + self.mosaic_prob = mosaic_prob + self.mixup = mixup + self.mixup_prob = mixup_prob + self.train = train + self.special_aug_ratio = special_aug_ratio + + self.epoch_now = -1 + self.length = len(self.annotation_lines) + + def __len__(self): + return self.length + + def __getitem__(self, index): + index = index % self.length + + #---------------------------------------------------# + # 训练时进行数据的随机增强 + # 验证时不进行数据的随机增强 + #---------------------------------------------------# + if self.mosaic and self.rand() < self.mosaic_prob and self.epoch_now < self.epoch_length * self.special_aug_ratio: + lines = sample(self.annotation_lines, 3) + lines.append(self.annotation_lines[index]) + shuffle(lines) + image, box = self.get_random_data_with_Mosaic(lines, self.input_shape) + + if self.mixup and self.rand() < self.mixup_prob: + lines = sample(self.annotation_lines, 1) + image_2, box_2 = self.get_random_data(lines[0], self.input_shape, random = self.train) + image, box = self.get_random_data_with_MixUp(image, box, image_2, box_2) + else: + image, box = self.get_random_data(self.annotation_lines[index], self.input_shape, random = self.train) + + image = np.transpose(preprocess_input(np.array(image, dtype=np.float32)), (2, 0, 1)) + box = np.array(box, dtype=np.float32) + if len(box) != 0: + box[:, 2:4] = box[:, 2:4] - box[:, 0:2] + box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2 + return image, box + + def rand(self, a=0, b=1): + return np.random.rand()*(b-a) + a + + def get_random_data(self, annotation_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True): + line = annotation_line.split() + #------------------------------# + # 读取图像并转换成RGB图像 + #------------------------------# + image = Image.open(line[0]) + image = cvtColor(image) + #------------------------------# + # 获得图像的高宽与目标高宽 + #------------------------------# + iw, ih = image.size + h, w = input_shape + #------------------------------# + # 获得预测框 + #------------------------------# + box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]]) + + if not random: + scale = min(w/iw, h/ih) + nw = int(iw*scale) + nh = int(ih*scale) + dx = (w-nw)//2 + dy = (h-nh)//2 + + #---------------------------------# + # 将图像多余的部分加上灰条 + #---------------------------------# + image = image.resize((nw,nh), Image.BICUBIC) + new_image = Image.new('RGB', (w,h), (128,128,128)) + new_image.paste(image, (dx, dy)) + image_data = np.array(new_image, np.float32) + + #---------------------------------# + # 对真实框进行调整 + #---------------------------------# + if len(box)>0: + np.random.shuffle(box) + box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx + box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy + box[:, 0:2][box[:, 0:2]<0] = 0 + box[:, 2][box[:, 2]>w] = w + box[:, 3][box[:, 3]>h] = h + box_w = box[:, 2] - box[:, 0] + box_h = box[:, 3] - box[:, 1] + box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box + + return image_data, box + + #------------------------------------------# + # 对图像进行缩放并且进行长和宽的扭曲 + #------------------------------------------# + new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter) + scale = self.rand(.25, 2) + if new_ar < 1: + nh = int(scale*h) + nw = int(nh*new_ar) + else: + nw = int(scale*w) + nh = int(nw/new_ar) + image = image.resize((nw,nh), Image.BICUBIC) + + #------------------------------------------# + # 将图像多余的部分加上灰条 + #------------------------------------------# + dx = int(self.rand(0, w-nw)) + dy = int(self.rand(0, h-nh)) + new_image = Image.new('RGB', (w,h), (128,128,128)) + new_image.paste(image, (dx, dy)) + image = new_image + + #------------------------------------------# + # 翻转图像 + #------------------------------------------# + flip = self.rand()<.5 + if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) + + image_data = np.array(image, np.uint8) + #---------------------------------# + # 对图像进行色域变换 + # 计算色域变换的参数 + #---------------------------------# + r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1 + #---------------------------------# + # 将图像转到HSV上 + #---------------------------------# + hue, sat, val = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV)) + dtype = image_data.dtype + #---------------------------------# + # 应用变换 + #---------------------------------# + x = np.arange(0, 256, dtype=r.dtype) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) + image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB) + + #---------------------------------# + # 对真实框进行调整 + #---------------------------------# + if len(box)>0: + np.random.shuffle(box) + box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx + box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy + if flip: box[:, [0,2]] = w - box[:, [2,0]] + box[:, 0:2][box[:, 0:2]<0] = 0 + box[:, 2][box[:, 2]>w] = w + box[:, 3][box[:, 3]>h] = h + box_w = box[:, 2] - box[:, 0] + box_h = box[:, 3] - box[:, 1] + box = box[np.logical_and(box_w>1, box_h>1)] + + return image_data, box + + def merge_bboxes(self, bboxes, cutx, cuty): + merge_bbox = [] + for i in range(len(bboxes)): + for box in bboxes[i]: + tmp_box = [] + x1, y1, x2, y2 = box[0], box[1], box[2], box[3] + + if i == 0: + if y1 > cuty or x1 > cutx: + continue + if y2 >= cuty and y1 <= cuty: + y2 = cuty + if x2 >= cutx and x1 <= cutx: + x2 = cutx + + if i == 1: + if y2 < cuty or x1 > cutx: + continue + if y2 >= cuty and y1 <= cuty: + y1 = cuty + if x2 >= cutx and x1 <= cutx: + x2 = cutx + + if i == 2: + if y2 < cuty or x2 < cutx: + continue + if y2 >= cuty and y1 <= cuty: + y1 = cuty + if x2 >= cutx and x1 <= cutx: + x1 = cutx + + if i == 3: + if y1 > cuty or x2 < cutx: + continue + if y2 >= cuty and y1 <= cuty: + y2 = cuty + if x2 >= cutx and x1 <= cutx: + x1 = cutx + tmp_box.append(x1) + tmp_box.append(y1) + tmp_box.append(x2) + tmp_box.append(y2) + tmp_box.append(box[-1]) + merge_bbox.append(tmp_box) + return merge_bbox + + def get_random_data_with_Mosaic(self, annotation_line, input_shape, jitter=0.3, hue=.1, sat=0.7, val=0.4): + h, w = input_shape + min_offset_x = self.rand(0.3, 0.7) + min_offset_y = self.rand(0.3, 0.7) + + image_datas = [] + box_datas = [] + index = 0 + for line in annotation_line: + #---------------------------------# + # 每一行进行分割 + #---------------------------------# + line_content = line.split() + #---------------------------------# + # 打开图片 + #---------------------------------# + image = Image.open(line_content[0]) + image = cvtColor(image) + + #---------------------------------# + # 图片的大小 + #---------------------------------# + iw, ih = image.size + #---------------------------------# + # 保存框的位置 + #---------------------------------# + box = np.array([np.array(list(map(int,box.split(',')))) for box in line_content[1:]]) + + #---------------------------------# + # 是否翻转图片 + #---------------------------------# + flip = self.rand()<.5 + if flip and len(box)>0: + image = image.transpose(Image.FLIP_LEFT_RIGHT) + box[:, [0,2]] = iw - box[:, [2,0]] + + #------------------------------------------# + # 对图像进行缩放并且进行长和宽的扭曲 + #------------------------------------------# + new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter) + scale = self.rand(.4, 1) + if new_ar < 1: + nh = int(scale*h) + nw = int(nh*new_ar) + else: + nw = int(scale*w) + nh = int(nw/new_ar) + image = image.resize((nw, nh), Image.BICUBIC) + + #-----------------------------------------------# + # 将图片进行放置，分别对应四张分割图片的位置 + #-----------------------------------------------# + if index == 0: + dx = int(w*min_offset_x) - nw + dy = int(h*min_offset_y) - nh + elif index == 1: + dx = int(w*min_offset_x) - nw + dy = int(h*min_offset_y) + elif index == 2: + dx = int(w*min_offset_x) + dy = int(h*min_offset_y) + elif index == 3: + dx = int(w*min_offset_x) + dy = int(h*min_offset_y) - nh + + new_image = Image.new('RGB', (w,h), (128,128,128)) + new_image.paste(image, (dx, dy)) + image_data = np.array(new_image) + + index = index + 1 + box_data = [] + #---------------------------------# + # 对box进行重新处理 + #---------------------------------# + if len(box)>0: + np.random.shuffle(box) + box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx + box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy + box[:, 0:2][box[:, 0:2]<0] = 0 + box[:, 2][box[:, 2]>w] = w + box[:, 3][box[:, 3]>h] = h + box_w = box[:, 2] - box[:, 0] + box_h = box[:, 3] - box[:, 1] + box = box[np.logical_and(box_w>1, box_h>1)] + box_data = np.zeros((len(box),5)) + box_data[:len(box)] = box + + image_datas.append(image_data) + box_datas.append(box_data) + + #---------------------------------# + # 将图片分割，放在一起 + #---------------------------------# + cutx = int(w * min_offset_x) + cuty = int(h * min_offset_y) + + new_image = np.zeros([h, w, 3]) + new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :] + new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :] + new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :] + new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :] + + new_image = np.array(new_image, np.uint8) + #---------------------------------# + # 对图像进行色域变换 + # 计算色域变换的参数 + #---------------------------------# + r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1 + #---------------------------------# + # 将图像转到HSV上 + #---------------------------------# + hue, sat, val = cv2.split(cv2.cvtColor(new_image, cv2.COLOR_RGB2HSV)) + dtype = new_image.dtype + #---------------------------------# + # 应用变换 + #---------------------------------# + x = np.arange(0, 256, dtype=r.dtype) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + new_image = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) + new_image = cv2.cvtColor(new_image, cv2.COLOR_HSV2RGB) + + #---------------------------------# + # 对框进行进一步的处理 + #---------------------------------# + new_boxes = self.merge_bboxes(box_datas, cutx, cuty) + + return new_image, new_boxes + + def get_random_data_with_MixUp(self, image_1, box_1, image_2, box_2): + new_image = np.array(image_1, np.float32) * 0.5 + np.array(image_2, np.float32) * 0.5 + if len(box_1) == 0: + new_boxes = box_2 + elif len(box_2) == 0: + new_boxes = box_1 + else: + new_boxes = np.concatenate([box_1, box_2], axis=0) + return new_image, new_boxes + +# DataLoader中collate_fn使用 +def yolo_dataset_collate(batch): + images = [] + bboxes = [] + for img, box in batch: + images.append(img) + bboxes.append(box) + images = torch.from_numpy(np.array(images)).type(torch.FloatTensor) + bboxes = [torch.from_numpy(ann).type(torch.FloatTensor) for ann in bboxes] + return images, bboxes diff --git a/yolox-pytorch/utils/utils.py b/yolox-pytorch/utils/utils.py new file mode 100644 index 0000000..39c70bd --- /dev/null +++ b/yolox-pytorch/utils/utils.py @@ -0,0 +1,64 @@ +import numpy as np +from PIL import Image + + +#---------------------------------------------------------# +# 将图像转换成RGB图像，防止灰度图在预测时报错。 +# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB +#---------------------------------------------------------# +def cvtColor(image): + if len(np.shape(image)) == 3 and np.shape(image)[2] == 3: + return image + else: + image = image.convert('RGB') + return image + +#---------------------------------------------------# +# 对输入图像进行resize +#---------------------------------------------------# +def resize_image(image, size, letterbox_image): + iw, ih = image.size + w, h = size + if letterbox_image: + scale = min(w/iw, h/ih) + nw = int(iw*scale) + nh = int(ih*scale) + + image = image.resize((nw,nh), Image.BICUBIC) + new_image = Image.new('RGB', size, (128,128,128)) + new_image.paste(image, ((w-nw)//2, (h-nh)//2)) + else: + new_image = image.resize((w, h), Image.BICUBIC) + return new_image + +#---------------------------------------------------# +# 获得类 +#---------------------------------------------------# +def get_classes(classes_path): + with open(classes_path, encoding='utf-8') as f: + class_names = f.readlines() + class_names = [c.strip() for c in class_names] + return class_names, len(class_names) + +def preprocess_input(image): + image /= 255.0 + image -= np.array([0.485, 0.456, 0.406]) + image /= np.array([0.229, 0.224, 0.225]) + return image + +#---------------------------------------------------# +# 获得学习率 +#---------------------------------------------------# +def get_lr(optimizer): + for param_group in optimizer.param_groups: + return param_group['lr'] + +def show_config(**kwargs): + print('Configurations:') + print('-' * 70) + print('|%25s | %40s|' % ('keys', 'values')) + print('-' * 70) + for key, value in kwargs.items(): + print('|%25s | %40s|' % (str(key), str(value))) + print('-' * 70) + \ No newline at end of file diff --git a/yolox-pytorch/utils/utils_bbox.py b/yolox-pytorch/utils/utils_bbox.py new file mode 100644 index 0000000..d2cc662 --- /dev/null +++ b/yolox-pytorch/utils/utils_bbox.py @@ -0,0 +1,180 @@ +import numpy as np +import torch +from torchvision.ops import nms, boxes + +def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image): + #-----------------------------------------------------------------# + # 把y轴放前面是因为方便预测框和图像的宽高进行相乘 + #-----------------------------------------------------------------# + box_yx = box_xy[..., ::-1] + box_hw = box_wh[..., ::-1] + input_shape = np.array(input_shape) + image_shape = np.array(image_shape) + + if letterbox_image: + #-----------------------------------------------------------------# + # 这里求出来的offset是图像有效区域相对于图像左上角的偏移情况 + # new_shape指的是宽高缩放情况 + #-----------------------------------------------------------------# + new_shape = np.round(image_shape * np.min(input_shape/image_shape)) + offset = (input_shape - new_shape)/2./input_shape + scale = input_shape/new_shape + + box_yx = (box_yx - offset) * scale + box_hw *= scale + + box_mins = box_yx - (box_hw / 2.) + box_maxes = box_yx + (box_hw / 2.) + boxes = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1) + boxes *= np.concatenate([image_shape, image_shape], axis=-1) + return boxes + +def decode_outputs(outputs, input_shape): + grids = [] + strides = [] + hw = [x.shape[-2:] for x in outputs] + #---------------------------------------------------# + # outputs输入前代表每个特征层的预测结果 + # batch_size, 4 + 1 + num_classes, 80, 80 => batch_size, 4 + 1 + num_classes, 6400 + # batch_size, 5 + num_classes, 40, 40 + # batch_size, 5 + num_classes, 20, 20 + # batch_size, 4 + 1 + num_classes, 6400 + 1600 + 400 -> batch_size, 4 + 1 + num_classes, 8400 + # 堆叠后为batch_size, 8400, 5 + num_classes + #---------------------------------------------------# + outputs = torch.cat([x.flatten(start_dim=2) for x in outputs], dim=2).permute(0, 2, 1) + #---------------------------------------------------# + # 获得每一个特征点属于每一个种类的概率 + #---------------------------------------------------# + outputs[:, :, 4:] = torch.sigmoid(outputs[:, :, 4:]) + for h, w in hw: + #---------------------------# + # 根据特征层的高宽生成网格点 + #---------------------------# + grid_y, grid_x = torch.meshgrid([torch.arange(h), torch.arange(w)]) + #---------------------------# + # 1, 6400, 2 + # 1, 1600, 2 + # 1, 400, 2 + #---------------------------# + grid = torch.stack((grid_x, grid_y), 2).view(1, -1, 2) + shape = grid.shape[:2] + + grids.append(grid) + strides.append(torch.full((shape[0], shape[1], 1), input_shape[0] / h)) + #---------------------------# + # 将网格点堆叠到一起 + # 1, 6400, 2 + # 1, 1600, 2 + # 1, 400, 2 + # + # 1, 8400, 2 + #---------------------------# + grids = torch.cat(grids, dim=1).type(outputs.type()) + strides = torch.cat(strides, dim=1).type(outputs.type()) + #------------------------# + # 根据网格点进行解码 + #------------------------# + outputs[..., :2] = (outputs[..., :2] + grids) * strides + outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides + #-----------------# + # 归一化 + #-----------------# + outputs[..., [0,2]] = outputs[..., [0,2]] / input_shape[1] + outputs[..., [1,3]] = outputs[..., [1,3]] / input_shape[0] + return outputs + +def non_max_suppression(prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4): + #----------------------------------------------------------# + # 将预测结果的格式转换成左上角右下角的格式。 + # prediction [batch_size, num_anchors, 85] + #----------------------------------------------------------# + box_corner = prediction.new(prediction.shape) + box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2 + box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2 + box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2 + box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2 + prediction[:, :, :4] = box_corner[:, :, :4] + + output = [None for _ in range(len(prediction))] + #----------------------------------------------------------# + # 对输入图片进行循环，一般只会进行一次 + #----------------------------------------------------------# + for i, image_pred in enumerate(prediction): + #----------------------------------------------------------# + # 对种类预测部分取max。 + # class_conf [num_anchors, 1] 种类置信度 + # class_pred [num_anchors, 1] 种类 + #----------------------------------------------------------# + class_conf, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True) + + #----------------------------------------------------------# + # 利用置信度进行第一轮筛选 + #----------------------------------------------------------# + conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze() + + if not image_pred.size(0): + continue + #-------------------------------------------------------------------------# + # detections [num_anchors, 7] + # 7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred + #-------------------------------------------------------------------------# + detections = torch.cat((image_pred[:, :5], class_conf, class_pred.float()), 1) + detections = detections[conf_mask] + + nms_out_index = boxes.batched_nms( + detections[:, :4], + detections[:, 4] * detections[:, 5], + detections[:, 6], + nms_thres, + ) + + output[i] = detections[nms_out_index] + + # #------------------------------------------# + # # 获得预测结果中包含的所有种类 + # #------------------------------------------# + # unique_labels = detections[:, -1].cpu().unique() + + # if prediction.is_cuda: + # unique_labels = unique_labels.cuda() + # detections = detections.cuda() + + # for c in unique_labels: + # #------------------------------------------# + # # 获得某一类得分筛选后全部的预测结果 + # #------------------------------------------# + # detections_class = detections[detections[:, -1] == c] + + # #------------------------------------------# + # # 使用官方自带的非极大抑制会速度更快一些！ + # #------------------------------------------# + # keep = nms( + # detections_class[:, :4], + # detections_class[:, 4] * detections_class[:, 5], + # nms_thres + # ) + # max_detections = detections_class[keep] + + # # # 按照存在物体的置信度排序 + # # _, conf_sort_index = torch.sort(detections_class[:, 4]*detections_class[:, 5], descending=True) + # # detections_class = detections_class[conf_sort_index] + # # # 进行非极大抑制 + # # max_detections = [] + # # while detections_class.size(0): + # # # 取出这一类置信度最高的，一步一步往下判断，判断重合程度是否大于nms_thres，如果是则去除掉 + # # max_detections.append(detections_class[0].unsqueeze(0)) + # # if len(detections_class) == 1: + # # break + # # ious = bbox_iou(max_detections[-1], detections_class[1:]) + # # detections_class = detections_class[1:][ious < nms_thres] + # # # 堆叠 + # # max_detections = torch.cat(max_detections).data + + # # Add max detections to outputs + # output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections)) + + if output[i] is not None: + output[i] = output[i].cpu().numpy() + box_xy, box_wh = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2] + output[i][:, :4] = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image) + return output diff --git a/yolox-pytorch/utils/utils_fit.py b/yolox-pytorch/utils/utils_fit.py new file mode 100644 index 0000000..64d8df2 --- /dev/null +++ b/yolox-pytorch/utils/utils_fit.py @@ -0,0 +1,132 @@ +import os + +import torch +from tqdm import tqdm + +from utils.utils import get_lr + + +def fit_one_epoch(model_train, model, ema, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, Epoch, cuda, fp16, scaler, save_period, save_dir, local_rank=0): + loss = 0 + val_loss = 0 + + if local_rank == 0: + print('Start Train') + pbar = tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) + model_train.train() + for iteration, batch in enumerate(gen): + if iteration >= epoch_step: + break + + images, targets = batch[0], batch[1] + with torch.no_grad(): + if cuda: + images = images.cuda(local_rank) + targets = [ann.cuda(local_rank) for ann in targets] + #----------------------# + # 清零梯度 + #----------------------# + optimizer.zero_grad() + if not fp16: + #----------------------# + # 前向传播 + #----------------------# + outputs = model_train(images) + + #----------------------# + # 计算损失 + #----------------------# + loss_value = yolo_loss(outputs, targets) + + #----------------------# + # 反向传播 + #----------------------# + loss_value.backward() + optimizer.step() + else: + from torch.cuda.amp import autocast + with autocast(): + outputs = model_train(images) + #----------------------# + # 计算损失 + #----------------------# + loss_value = yolo_loss(outputs, targets) + + #----------------------# + # 反向传播 + #----------------------# + scaler.scale(loss_value).backward() + scaler.step(optimizer) + scaler.update() + if ema: + ema.update(model_train) + + loss += loss_value.item() + + if local_rank == 0: + pbar.set_postfix(**{'loss' : loss / (iteration + 1), + 'lr' : get_lr(optimizer)}) + pbar.update(1) + + if local_rank == 0: + pbar.close() + print('Finish Train') + print('Start Validation') + pbar = tqdm(total=epoch_step_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) + + if ema: + model_train_eval = ema.ema + else: + model_train_eval = model_train.eval() + + for iteration, batch in enumerate(gen_val): + if iteration >= epoch_step_val: + break + images, targets = batch[0], batch[1] + with torch.no_grad(): + if cuda: + images = images.cuda(local_rank) + targets = [ann.cuda(local_rank) for ann in targets] + #----------------------# + # 清零梯度 + #----------------------# + optimizer.zero_grad() + #----------------------# + # 前向传播 + #----------------------# + outputs = model_train_eval(images) + + #----------------------# + # 计算损失 + #----------------------# + loss_value = yolo_loss(outputs, targets) + + val_loss += loss_value.item() + if local_rank == 0: + pbar.set_postfix(**{'val_loss': val_loss / (iteration + 1)}) + pbar.update(1) + + if local_rank == 0: + pbar.close() + print('Finish Validation') + loss_history.append_loss(epoch + 1, loss / epoch_step, val_loss / epoch_step_val) + eval_callback.on_epoch_end(epoch + 1, model_train_eval) + print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch)) + print('Total Loss: %.3f || Val Loss: %.3f ' % (loss / epoch_step, val_loss / epoch_step_val)) + + #-----------------------------------------------# + # 保存权值 + #-----------------------------------------------# + if ema: + save_state_dict = ema.ema.state_dict() + else: + save_state_dict = model.state_dict() + + if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch: + torch.save(save_state_dict, os.path.join(save_dir, "ep%03d-loss%.3f-val_loss%.3f.pth" % (epoch + 1, loss / epoch_step, val_loss / epoch_step_val))) + + if len(loss_history.val_loss) <= 1 or (val_loss / epoch_step_val) <= min(loss_history.val_loss): + print('Save best model to best_epoch_weights.pth') + torch.save(save_state_dict, os.path.join(save_dir, "best_epoch_weights.pth")) + + torch.save(save_state_dict, os.path.join(save_dir, "last_epoch_weights.pth")) \ No newline at end of file diff --git a/yolox-pytorch/utils/utils_map.py b/yolox-pytorch/utils/utils_map.py new file mode 100644 index 0000000..b49ddba --- /dev/null +++ b/yolox-pytorch/utils/utils_map.py @@ -0,0 +1,923 @@ +import glob +import json +import math +import operator +import os +import shutil +import sys +try: + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval +except: + pass +import cv2 +import matplotlib +matplotlib.use('Agg') +from matplotlib import pyplot as plt +import numpy as np + +''' + 0,0 ------> x (width) + | + | (Left,Top) + | *_________ + | | | + | | + y |_________| + (height) * + (Right,Bottom) +''' + +def log_average_miss_rate(precision, fp_cumsum, num_images): + """ + log-average miss rate: + Calculated by averaging miss rates at 9 evenly spaced FPPI points + between 10e-2 and 10e0, in log-space. + + output: + lamr | log-average miss rate + mr | miss rate + fppi | false positives per image + + references: + [1] Dollar, Piotr, et al. "Pedestrian Detection: An Evaluation of the + State of the Art." Pattern Analysis and Machine Intelligence, IEEE + Transactions on 34.4 (2012): 743 - 761. + """ + + if precision.size == 0: + lamr = 0 + mr = 1 + fppi = 0 + return lamr, mr, fppi + + fppi = fp_cumsum / float(num_images) + mr = (1 - precision) + + fppi_tmp = np.insert(fppi, 0, -1.0) + mr_tmp = np.insert(mr, 0, 1.0) + + ref = np.logspace(-2.0, 0.0, num = 9) + for i, ref_i in enumerate(ref): + j = np.where(fppi_tmp <= ref_i)[-1][-1] + ref[i] = mr_tmp[j] + + lamr = math.exp(np.mean(np.log(np.maximum(1e-10, ref)))) + + return lamr, mr, fppi + +""" + throw error and exit +""" +def error(msg): + print(msg) + sys.exit(0) + +""" + check if the number is a float between 0.0 and 1.0 +""" +def is_float_between_0_and_1(value): + try: + val = float(value) + if val > 0.0 and val < 1.0: + return True + else: + return False + except ValueError: + return False + +""" + Calculate the AP given the recall and precision array + 1st) We compute a version of the measured precision/recall curve with + precision monotonically decreasing + 2nd) We compute the AP as the area under this curve by numerical integration. +""" +def voc_ap(rec, prec): + """ + --- Official matlab code VOC2012--- + mrec=[0 ; rec ; 1]; + mpre=[0 ; prec ; 0]; + for i=numel(mpre)-1:-1:1 + mpre(i)=max(mpre(i),mpre(i+1)); + end + i=find(mrec(2:end)~=mrec(1:end-1))+1; + ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); + """ + rec.insert(0, 0.0) # insert 0.0 at begining of list + rec.append(1.0) # insert 1.0 at end of list + mrec = rec[:] + prec.insert(0, 0.0) # insert 0.0 at begining of list + prec.append(0.0) # insert 0.0 at end of list + mpre = prec[:] + """ + This part makes the precision monotonically decreasing + (goes from the end to the beginning) + matlab: for i=numel(mpre)-1:-1:1 + mpre(i)=max(mpre(i),mpre(i+1)); + """ + for i in range(len(mpre)-2, -1, -1): + mpre[i] = max(mpre[i], mpre[i+1]) + """ + This part creates a list of indexes where the recall changes + matlab: i=find(mrec(2:end)~=mrec(1:end-1))+1; + """ + i_list = [] + for i in range(1, len(mrec)): + if mrec[i] != mrec[i-1]: + i_list.append(i) # if it was matlab would be i + 1 + """ + The Average Precision (AP) is the area under the curve + (numerical integration) + matlab: ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); + """ + ap = 0.0 + for i in i_list: + ap += ((mrec[i]-mrec[i-1])*mpre[i]) + return ap, mrec, mpre + + +""" + Convert the lines of a file to a list +""" +def file_lines_to_list(path): + # open txt file lines to a list + with open(path) as f: + content = f.readlines() + # remove whitespace characters like `\n` at the end of each line + content = [x.strip() for x in content] + return content + +""" + Draws text in image +""" +def draw_text_in_image(img, text, pos, color, line_width): + font = cv2.FONT_HERSHEY_PLAIN + fontScale = 1 + lineType = 1 + bottomLeftCornerOfText = pos + cv2.putText(img, text, + bottomLeftCornerOfText, + font, + fontScale, + color, + lineType) + text_width, _ = cv2.getTextSize(text, font, fontScale, lineType)[0] + return img, (line_width + text_width) + +""" + Plot - adjust axes +""" +def adjust_axes(r, t, fig, axes): + # get text width for re-scaling + bb = t.get_window_extent(renderer=r) + text_width_inches = bb.width / fig.dpi + # get axis width in inches + current_fig_width = fig.get_figwidth() + new_fig_width = current_fig_width + text_width_inches + propotion = new_fig_width / current_fig_width + # get axis limit + x_lim = axes.get_xlim() + axes.set_xlim([x_lim[0], x_lim[1]*propotion]) + +""" + Draw plot using Matplotlib +""" +def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar): + # sort the dictionary by decreasing value, into a list of tuples + sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1)) + # unpacking the list of tuples into two lists + sorted_keys, sorted_values = zip(*sorted_dic_by_value) + # + if true_p_bar != "": + """ + Special case to draw in: + - green -> TP: True Positives (object detected and matches ground-truth) + - red -> FP: False Positives (object detected but does not match ground-truth) + - orange -> FN: False Negatives (object not detected but present in the ground-truth) + """ + fp_sorted = [] + tp_sorted = [] + for key in sorted_keys: + fp_sorted.append(dictionary[key] - true_p_bar[key]) + tp_sorted.append(true_p_bar[key]) + plt.barh(range(n_classes), fp_sorted, align='center', color='crimson', label='False Positive') + plt.barh(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Positive', left=fp_sorted) + # add legend + plt.legend(loc='lower right') + """ + Write number on side of bar + """ + fig = plt.gcf() # gcf - get current figure + axes = plt.gca() + r = fig.canvas.get_renderer() + for i, val in enumerate(sorted_values): + fp_val = fp_sorted[i] + tp_val = tp_sorted[i] + fp_str_val = " " + str(fp_val) + tp_str_val = fp_str_val + " " + str(tp_val) + # trick to paint multicolor with offset: + # first paint everything and then repaint the first number + t = plt.text(val, i, tp_str_val, color='forestgreen', va='center', fontweight='bold') + plt.text(val, i, fp_str_val, color='crimson', va='center', fontweight='bold') + if i == (len(sorted_values)-1): # largest bar + adjust_axes(r, t, fig, axes) + else: + plt.barh(range(n_classes), sorted_values, color=plot_color) + """ + Write number on side of bar + """ + fig = plt.gcf() # gcf - get current figure + axes = plt.gca() + r = fig.canvas.get_renderer() + for i, val in enumerate(sorted_values): + str_val = " " + str(val) # add a space before + if val < 1.0: + str_val = " {0:.2f}".format(val) + t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold') + # re-set axes to show number inside the figure + if i == (len(sorted_values)-1): # largest bar + adjust_axes(r, t, fig, axes) + # set window title + fig.canvas.set_window_title(window_title) + # write classes in y axis + tick_font_size = 12 + plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size) + """ + Re-scale height accordingly + """ + init_height = fig.get_figheight() + # comput the matrix height in points and inches + dpi = fig.dpi + height_pt = n_classes * (tick_font_size * 1.4) # 1.4 (some spacing) + height_in = height_pt / dpi + # compute the required figure height + top_margin = 0.15 # in percentage of the figure height + bottom_margin = 0.05 # in percentage of the figure height + figure_height = height_in / (1 - top_margin - bottom_margin) + # set new height + if figure_height > init_height: + fig.set_figheight(figure_height) + + # set plot title + plt.title(plot_title, fontsize=14) + # set axis titles + # plt.xlabel('classes') + plt.xlabel(x_label, fontsize='large') + # adjust size of window + fig.tight_layout() + # save the plot + fig.savefig(output_path) + # show image + if to_show: + plt.show() + # close the plot + plt.close() + +def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'): + GT_PATH = os.path.join(path, 'ground-truth') + DR_PATH = os.path.join(path, 'detection-results') + IMG_PATH = os.path.join(path, 'images-optional') + TEMP_FILES_PATH = os.path.join(path, '.temp_files') + RESULTS_FILES_PATH = os.path.join(path, 'results') + + show_animation = True + if os.path.exists(IMG_PATH): + for dirpath, dirnames, files in os.walk(IMG_PATH): + if not files: + show_animation = False + else: + show_animation = False + + if not os.path.exists(TEMP_FILES_PATH): + os.makedirs(TEMP_FILES_PATH) + + if os.path.exists(RESULTS_FILES_PATH): + shutil.rmtree(RESULTS_FILES_PATH) + else: + os.makedirs(RESULTS_FILES_PATH) + if draw_plot: + try: + matplotlib.use('TkAgg') + except: + pass + os.makedirs(os.path.join(RESULTS_FILES_PATH, "AP")) + os.makedirs(os.path.join(RESULTS_FILES_PATH, "F1")) + os.makedirs(os.path.join(RESULTS_FILES_PATH, "Recall")) + os.makedirs(os.path.join(RESULTS_FILES_PATH, "Precision")) + if show_animation: + os.makedirs(os.path.join(RESULTS_FILES_PATH, "images", "detections_one_by_one")) + + ground_truth_files_list = glob.glob(GT_PATH + '/*.txt') + if len(ground_truth_files_list) == 0: + error("Error: No ground-truth files found!") + ground_truth_files_list.sort() + gt_counter_per_class = {} + counter_images_per_class = {} + + for txt_file in ground_truth_files_list: + file_id = txt_file.split(".txt", 1)[0] + file_id = os.path.basename(os.path.normpath(file_id)) + temp_path = os.path.join(DR_PATH, (file_id + ".txt")) + if not os.path.exists(temp_path): + error_msg = "Error. File not found: {}\n".format(temp_path) + error(error_msg) + lines_list = file_lines_to_list(txt_file) + bounding_boxes = [] + is_difficult = False + already_seen_classes = [] + for line in lines_list: + try: + if "difficult" in line: + class_name, left, top, right, bottom, _difficult = line.split() + is_difficult = True + else: + class_name, left, top, right, bottom = line.split() + except: + if "difficult" in line: + line_split = line.split() + _difficult = line_split[-1] + bottom = line_split[-2] + right = line_split[-3] + top = line_split[-4] + left = line_split[-5] + class_name = "" + for name in line_split[:-5]: + class_name += name + " " + class_name = class_name[:-1] + is_difficult = True + else: + line_split = line.split() + bottom = line_split[-1] + right = line_split[-2] + top = line_split[-3] + left = line_split[-4] + class_name = "" + for name in line_split[:-4]: + class_name += name + " " + class_name = class_name[:-1] + + bbox = left + " " + top + " " + right + " " + bottom + if is_difficult: + bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True}) + is_difficult = False + else: + bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False}) + if class_name in gt_counter_per_class: + gt_counter_per_class[class_name] += 1 + else: + gt_counter_per_class[class_name] = 1 + + if class_name not in already_seen_classes: + if class_name in counter_images_per_class: + counter_images_per_class[class_name] += 1 + else: + counter_images_per_class[class_name] = 1 + already_seen_classes.append(class_name) + + with open(TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json", 'w') as outfile: + json.dump(bounding_boxes, outfile) + + gt_classes = list(gt_counter_per_class.keys()) + gt_classes = sorted(gt_classes) + n_classes = len(gt_classes) + + dr_files_list = glob.glob(DR_PATH + '/*.txt') + dr_files_list.sort() + for class_index, class_name in enumerate(gt_classes): + bounding_boxes = [] + for txt_file in dr_files_list: + file_id = txt_file.split(".txt",1)[0] + file_id = os.path.basename(os.path.normpath(file_id)) + temp_path = os.path.join(GT_PATH, (file_id + ".txt")) + if class_index == 0: + if not os.path.exists(temp_path): + error_msg = "Error. File not found: {}\n".format(temp_path) + error(error_msg) + lines = file_lines_to_list(txt_file) + for line in lines: + try: + tmp_class_name, confidence, left, top, right, bottom = line.split() + except: + line_split = line.split() + bottom = line_split[-1] + right = line_split[-2] + top = line_split[-3] + left = line_split[-4] + confidence = line_split[-5] + tmp_class_name = "" + for name in line_split[:-5]: + tmp_class_name += name + " " + tmp_class_name = tmp_class_name[:-1] + + if tmp_class_name == class_name: + bbox = left + " " + top + " " + right + " " +bottom + bounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox}) + + bounding_boxes.sort(key=lambda x:float(x['confidence']), reverse=True) + with open(TEMP_FILES_PATH + "/" + class_name + "_dr.json", 'w') as outfile: + json.dump(bounding_boxes, outfile) + + sum_AP = 0.0 + ap_dictionary = {} + lamr_dictionary = {} + with open(RESULTS_FILES_PATH + "/results.txt", 'w') as results_file: + results_file.write("# AP and precision/recall per class\n") + count_true_positives = {} + + for class_index, class_name in enumerate(gt_classes): + count_true_positives[class_name] = 0 + dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json" + dr_data = json.load(open(dr_file)) + + nd = len(dr_data) + tp = [0] * nd + fp = [0] * nd + score = [0] * nd + score_threhold_idx = 0 + for idx, detection in enumerate(dr_data): + file_id = detection["file_id"] + score[idx] = float(detection["confidence"]) + if score[idx] >= score_threhold: + score_threhold_idx = idx + + if show_animation: + ground_truth_img = glob.glob1(IMG_PATH, file_id + ".*") + if len(ground_truth_img) == 0: + error("Error. Image not found with id: " + file_id) + elif len(ground_truth_img) > 1: + error("Error. Multiple image with id: " + file_id) + else: + img = cv2.imread(IMG_PATH + "/" + ground_truth_img[0]) + img_cumulative_path = RESULTS_FILES_PATH + "/images/" + ground_truth_img[0] + if os.path.isfile(img_cumulative_path): + img_cumulative = cv2.imread(img_cumulative_path) + else: + img_cumulative = img.copy() + bottom_border = 60 + BLACK = [0, 0, 0] + img = cv2.copyMakeBorder(img, 0, bottom_border, 0, 0, cv2.BORDER_CONSTANT, value=BLACK) + + gt_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json" + ground_truth_data = json.load(open(gt_file)) + ovmax = -1 + gt_match = -1 + bb = [float(x) for x in detection["bbox"].split()] + for obj in ground_truth_data: + if obj["class_name"] == class_name: + bbgt = [ float(x) for x in obj["bbox"].split() ] + bi = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])] + iw = bi[2] - bi[0] + 1 + ih = bi[3] - bi[1] + 1 + if iw > 0 and ih > 0: + ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0] + + 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih + ov = iw * ih / ua + if ov > ovmax: + ovmax = ov + gt_match = obj + + if show_animation: + status = "NO MATCH FOUND!" + + min_overlap = MINOVERLAP + if ovmax >= min_overlap: + if "difficult" not in gt_match: + if not bool(gt_match["used"]): + tp[idx] = 1 + gt_match["used"] = True + count_true_positives[class_name] += 1 + with open(gt_file, 'w') as f: + f.write(json.dumps(ground_truth_data)) + if show_animation: + status = "MATCH!" + else: + fp[idx] = 1 + if show_animation: + status = "REPEATED MATCH!" + else: + fp[idx] = 1 + if ovmax > 0: + status = "INSUFFICIENT OVERLAP" + + """ + Draw image to show animation + """ + if show_animation: + height, widht = img.shape[:2] + white = (255,255,255) + light_blue = (255,200,100) + green = (0,255,0) + light_red = (30,30,255) + margin = 10 + # 1nd line + v_pos = int(height - margin - (bottom_border / 2.0)) + text = "Image: " + ground_truth_img[0] + " " + img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0) + text = "Class [" + str(class_index) + "/" + str(n_classes) + "]: " + class_name + " " + img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), light_blue, line_width) + if ovmax != -1: + color = light_red + if status == "INSUFFICIENT OVERLAP": + text = "IoU: {0:.2f}% ".format(ovmax*100) + "< {0:.2f}% ".format(min_overlap*100) + else: + text = "IoU: {0:.2f}% ".format(ovmax*100) + ">= {0:.2f}% ".format(min_overlap*100) + color = green + img, _ = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width) + # 2nd line + v_pos += int(bottom_border / 2.0) + rank_pos = str(idx+1) + text = "Detection #rank: " + rank_pos + " confidence: {0:.2f}% ".format(float(detection["confidence"])*100) + img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0) + color = light_red + if status == "MATCH!": + color = green + text = "Result: " + status + " " + img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width) + + font = cv2.FONT_HERSHEY_SIMPLEX + if ovmax > 0: + bbgt = [ int(round(float(x))) for x in gt_match["bbox"].split() ] + cv2.rectangle(img,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2) + cv2.rectangle(img_cumulative,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2) + cv2.putText(img_cumulative, class_name, (bbgt[0],bbgt[1] - 5), font, 0.6, light_blue, 1, cv2.LINE_AA) + bb = [int(i) for i in bb] + cv2.rectangle(img,(bb[0],bb[1]),(bb[2],bb[3]),color,2) + cv2.rectangle(img_cumulative,(bb[0],bb[1]),(bb[2],bb[3]),color,2) + cv2.putText(img_cumulative, class_name, (bb[0],bb[1] - 5), font, 0.6, color, 1, cv2.LINE_AA) + + cv2.imshow("Animation", img) + cv2.waitKey(20) + output_img_path = RESULTS_FILES_PATH + "/images/detections_one_by_one/" + class_name + "_detection" + str(idx) + ".jpg" + cv2.imwrite(output_img_path, img) + cv2.imwrite(img_cumulative_path, img_cumulative) + + cumsum = 0 + for idx, val in enumerate(fp): + fp[idx] += cumsum + cumsum += val + + cumsum = 0 + for idx, val in enumerate(tp): + tp[idx] += cumsum + cumsum += val + + rec = tp[:] + for idx, val in enumerate(tp): + rec[idx] = float(tp[idx]) / np.maximum(gt_counter_per_class[class_name], 1) + + prec = tp[:] + for idx, val in enumerate(tp): + prec[idx] = float(tp[idx]) / np.maximum((fp[idx] + tp[idx]), 1) + + ap, mrec, mprec = voc_ap(rec[:], prec[:]) + F1 = np.array(rec)*np.array(prec)*2 / np.where((np.array(prec)+np.array(rec))==0, 1, (np.array(prec)+np.array(rec))) + + sum_AP += ap + text = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100) + + if len(prec)>0: + F1_text = "{0:.2f}".format(F1[score_threhold_idx]) + " = " + class_name + " F1 " + Recall_text = "{0:.2f}%".format(rec[score_threhold_idx]*100) + " = " + class_name + " Recall " + Precision_text = "{0:.2f}%".format(prec[score_threhold_idx]*100) + " = " + class_name + " Precision " + else: + F1_text = "0.00" + " = " + class_name + " F1 " + Recall_text = "0.00%" + " = " + class_name + " Recall " + Precision_text = "0.00%" + " = " + class_name + " Precision " + + rounded_prec = [ '%.2f' % elem for elem in prec ] + rounded_rec = [ '%.2f' % elem for elem in rec ] + results_file.write(text + "\n Precision: " + str(rounded_prec) + "\n Recall :" + str(rounded_rec) + "\n\n") + + if len(prec)>0: + print(text + "\t||\tscore_threhold=" + str(score_threhold) + " : " + "F1=" + "{0:.2f}".format(F1[score_threhold_idx])\ + + " ; Recall=" + "{0:.2f}%".format(rec[score_threhold_idx]*100) + " ; Precision=" + "{0:.2f}%".format(prec[score_threhold_idx]*100)) + else: + print(text + "\t||\tscore_threhold=" + str(score_threhold) + " : " + "F1=0.00% ; Recall=0.00% ; Precision=0.00%") + ap_dictionary[class_name] = ap + + n_images = counter_images_per_class[class_name] + lamr, mr, fppi = log_average_miss_rate(np.array(rec), np.array(fp), n_images) + lamr_dictionary[class_name] = lamr + + if draw_plot: + plt.plot(rec, prec, '-o') + area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]] + area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]] + plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r') + + fig = plt.gcf() + fig.canvas.set_window_title('AP ' + class_name) + + plt.title('class: ' + text) + plt.xlabel('Recall') + plt.ylabel('Precision') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/AP/" + class_name + ".png") + plt.cla() + + plt.plot(score, F1, "-", color='orangered') + plt.title('class: ' + F1_text + "\nscore_threhold=" + str(score_threhold)) + plt.xlabel('Score_Threhold') + plt.ylabel('F1') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/F1/" + class_name + ".png") + plt.cla() + + plt.plot(score, rec, "-H", color='gold') + plt.title('class: ' + Recall_text + "\nscore_threhold=" + str(score_threhold)) + plt.xlabel('Score_Threhold') + plt.ylabel('Recall') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/Recall/" + class_name + ".png") + plt.cla() + + plt.plot(score, prec, "-s", color='palevioletred') + plt.title('class: ' + Precision_text + "\nscore_threhold=" + str(score_threhold)) + plt.xlabel('Score_Threhold') + plt.ylabel('Precision') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/Precision/" + class_name + ".png") + plt.cla() + + if show_animation: + cv2.destroyAllWindows() + if n_classes == 0: + print("未检测到任何种类，请检查标签信息与get_map.py中的classes_path是否修改。") + return 0 + results_file.write("\n# mAP of all classes\n") + mAP = sum_AP / n_classes + text = "mAP = {0:.2f}%".format(mAP*100) + results_file.write(text + "\n") + print(text) + + shutil.rmtree(TEMP_FILES_PATH) + + """ + Count total of detection-results + """ + det_counter_per_class = {} + for txt_file in dr_files_list: + lines_list = file_lines_to_list(txt_file) + for line in lines_list: + class_name = line.split()[0] + if class_name in det_counter_per_class: + det_counter_per_class[class_name] += 1 + else: + det_counter_per_class[class_name] = 1 + dr_classes = list(det_counter_per_class.keys()) + + """ + Write number of ground-truth objects per class to results.txt + """ + with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file: + results_file.write("\n# Number of ground-truth objects per class\n") + for class_name in sorted(gt_counter_per_class): + results_file.write(class_name + ": " + str(gt_counter_per_class[class_name]) + "\n") + + """ + Finish counting true positives + """ + for class_name in dr_classes: + if class_name not in gt_classes: + count_true_positives[class_name] = 0 + + """ + Write number of detected objects per class to results.txt + """ + with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file: + results_file.write("\n# Number of detected objects per class\n") + for class_name in sorted(dr_classes): + n_det = det_counter_per_class[class_name] + text = class_name + ": " + str(n_det) + text += " (tp:" + str(count_true_positives[class_name]) + "" + text += ", fp:" + str(n_det - count_true_positives[class_name]) + ")\n" + results_file.write(text) + + """ + Plot the total number of occurences of each class in the ground-truth + """ + if draw_plot: + window_title = "ground-truth-info" + plot_title = "ground-truth\n" + plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)" + x_label = "Number of objects per class" + output_path = RESULTS_FILES_PATH + "/ground-truth-info.png" + to_show = False + plot_color = 'forestgreen' + draw_plot_func( + gt_counter_per_class, + n_classes, + window_title, + plot_title, + x_label, + output_path, + to_show, + plot_color, + '', + ) + + # """ + # Plot the total number of occurences of each class in the "detection-results" folder + # """ + # if draw_plot: + # window_title = "detection-results-info" + # # Plot title + # plot_title = "detection-results\n" + # plot_title += "(" + str(len(dr_files_list)) + " files and " + # count_non_zero_values_in_dictionary = sum(int(x) > 0 for x in list(det_counter_per_class.values())) + # plot_title += str(count_non_zero_values_in_dictionary) + " detected classes)" + # # end Plot title + # x_label = "Number of objects per class" + # output_path = RESULTS_FILES_PATH + "/detection-results-info.png" + # to_show = False + # plot_color = 'forestgreen' + # true_p_bar = count_true_positives + # draw_plot_func( + # det_counter_per_class, + # len(det_counter_per_class), + # window_title, + # plot_title, + # x_label, + # output_path, + # to_show, + # plot_color, + # true_p_bar + # ) + + """ + Draw log-average miss rate plot (Show lamr of all classes in decreasing order) + """ + if draw_plot: + window_title = "lamr" + plot_title = "log-average miss rate" + x_label = "log-average miss rate" + output_path = RESULTS_FILES_PATH + "/lamr.png" + to_show = False + plot_color = 'royalblue' + draw_plot_func( + lamr_dictionary, + n_classes, + window_title, + plot_title, + x_label, + output_path, + to_show, + plot_color, + "" + ) + + """ + Draw mAP plot (Show AP's of all classes in decreasing order) + """ + if draw_plot: + window_title = "mAP" + plot_title = "mAP = {0:.2f}%".format(mAP*100) + x_label = "Average Precision" + output_path = RESULTS_FILES_PATH + "/mAP.png" + to_show = True + plot_color = 'royalblue' + draw_plot_func( + ap_dictionary, + n_classes, + window_title, + plot_title, + x_label, + output_path, + to_show, + plot_color, + "" + ) + return mAP + +def preprocess_gt(gt_path, class_names): + image_ids = os.listdir(gt_path) + results = {} + + images = [] + bboxes = [] + for i, image_id in enumerate(image_ids): + lines_list = file_lines_to_list(os.path.join(gt_path, image_id)) + boxes_per_image = [] + image = {} + image_id = os.path.splitext(image_id)[0] + image['file_name'] = image_id + '.jpg' + image['width'] = 1 + image['height'] = 1 + #-----------------------------------------------------------------# + # 感谢 多学学英语吧 的提醒 + # 解决了'Results do not correspond to current coco set'问题 + #-----------------------------------------------------------------# + image['id'] = str(image_id) + + for line in lines_list: + difficult = 0 + if "difficult" in line: + line_split = line.split() + left, top, right, bottom, _difficult = line_split[-5:] + class_name = "" + for name in line_split[:-5]: + class_name += name + " " + class_name = class_name[:-1] + difficult = 1 + else: + line_split = line.split() + left, top, right, bottom = line_split[-4:] + class_name = "" + for name in line_split[:-4]: + class_name += name + " " + class_name = class_name[:-1] + + left, top, right, bottom = float(left), float(top), float(right), float(bottom) + if class_name not in class_names: + continue + cls_id = class_names.index(class_name) + 1 + bbox = [left, top, right - left, bottom - top, difficult, str(image_id), cls_id, (right - left) * (bottom - top) - 10.0] + boxes_per_image.append(bbox) + images.append(image) + bboxes.extend(boxes_per_image) + results['images'] = images + + categories = [] + for i, cls in enumerate(class_names): + category = {} + category['supercategory'] = cls + category['name'] = cls + category['id'] = i + 1 + categories.append(category) + results['categories'] = categories + + annotations = [] + for i, box in enumerate(bboxes): + annotation = {} + annotation['area'] = box[-1] + annotation['category_id'] = box[-2] + annotation['image_id'] = box[-3] + annotation['iscrowd'] = box[-4] + annotation['bbox'] = box[:4] + annotation['id'] = i + annotations.append(annotation) + results['annotations'] = annotations + return results + +def preprocess_dr(dr_path, class_names): + image_ids = os.listdir(dr_path) + results = [] + for image_id in image_ids: + lines_list = file_lines_to_list(os.path.join(dr_path, image_id)) + image_id = os.path.splitext(image_id)[0] + for line in lines_list: + line_split = line.split() + confidence, left, top, right, bottom = line_split[-5:] + class_name = "" + for name in line_split[:-5]: + class_name += name + " " + class_name = class_name[:-1] + left, top, right, bottom = float(left), float(top), float(right), float(bottom) + result = {} + result["image_id"] = str(image_id) + if class_name not in class_names: + continue + result["category_id"] = class_names.index(class_name) + 1 + result["bbox"] = [left, top, right - left, bottom - top] + result["score"] = float(confidence) + results.append(result) + return results + +def get_coco_map(class_names, path): + GT_PATH = os.path.join(path, 'ground-truth') + DR_PATH = os.path.join(path, 'detection-results') + COCO_PATH = os.path.join(path, 'coco_eval') + + if not os.path.exists(COCO_PATH): + os.makedirs(COCO_PATH) + + GT_JSON_PATH = os.path.join(COCO_PATH, 'instances_gt.json') + DR_JSON_PATH = os.path.join(COCO_PATH, 'instances_dr.json') + + with open(GT_JSON_PATH, "w") as f: + results_gt = preprocess_gt(GT_PATH, class_names) + json.dump(results_gt, f, indent=4) + + with open(DR_JSON_PATH, "w") as f: + results_dr = preprocess_dr(DR_PATH, class_names) + json.dump(results_dr, f, indent=4) + if len(results_dr) == 0: + print("未检测到任何目标。") + return [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + + cocoGt = COCO(GT_JSON_PATH) + cocoDt = cocoGt.loadRes(DR_JSON_PATH) + cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + return cocoEval.stats \ No newline at end of file diff --git a/yolox-pytorch/utils_coco/coco_annotation.py b/yolox-pytorch/utils_coco/coco_annotation.py new file mode 100644 index 0000000..50f022f --- /dev/null +++ b/yolox-pytorch/utils_coco/coco_annotation.py @@ -0,0 +1,117 @@ +#-------------------------------------------------------# +# 用于处理COCO数据集，根据json文件生成txt文件用于训练 +#-------------------------------------------------------# +import json +import os +from collections import defaultdict + +#-------------------------------------------------------# +# 指向了COCO训练集与验证集图片的路径 +#-------------------------------------------------------# +train_datasets_path = "coco_dataset/train2017" +val_datasets_path = "coco_dataset/val2017" + +#-------------------------------------------------------# +# 指向了COCO训练集与验证集标签的路径 +#-------------------------------------------------------# +train_annotation_path = "coco_dataset/annotations/instances_train2017.json" +val_annotation_path = "coco_dataset/annotations/instances_val2017.json" + +#-------------------------------------------------------# +# 生成的txt文件路径 +#-------------------------------------------------------# +train_output_path = "coco_train.txt" +val_output_path = "coco_val.txt" + +if __name__ == "__main__": + name_box_id = defaultdict(list) + id_name = dict() + f = open(train_annotation_path, encoding='utf-8') + data = json.load(f) + + annotations = data['annotations'] + for ant in annotations: + id = ant['image_id'] + name = os.path.join(train_datasets_path, '%012d.jpg' % id) + cat = ant['category_id'] + if cat >= 1 and cat <= 11: + cat = cat - 1 + elif cat >= 13 and cat <= 25: + cat = cat - 2 + elif cat >= 27 and cat <= 28: + cat = cat - 3 + elif cat >= 31 and cat <= 44: + cat = cat - 5 + elif cat >= 46 and cat <= 65: + cat = cat - 6 + elif cat == 67: + cat = cat - 7 + elif cat == 70: + cat = cat - 9 + elif cat >= 72 and cat <= 82: + cat = cat - 10 + elif cat >= 84 and cat <= 90: + cat = cat - 11 + name_box_id[name].append([ant['bbox'], cat]) + + f = open(train_output_path, 'w') + for key in name_box_id.keys(): + f.write(key) + box_infos = name_box_id[key] + for info in box_infos: + x_min = int(info[0][0]) + y_min = int(info[0][1]) + x_max = x_min + int(info[0][2]) + y_max = y_min + int(info[0][3]) + + box_info = " %d,%d,%d,%d,%d" % ( + x_min, y_min, x_max, y_max, int(info[1])) + f.write(box_info) + f.write('\n') + f.close() + + name_box_id = defaultdict(list) + id_name = dict() + f = open(val_annotation_path, encoding='utf-8') + data = json.load(f) + + annotations = data['annotations'] + for ant in annotations: + id = ant['image_id'] + name = os.path.join(val_datasets_path, '%012d.jpg' % id) + cat = ant['category_id'] + if cat >= 1 and cat <= 11: + cat = cat - 1 + elif cat >= 13 and cat <= 25: + cat = cat - 2 + elif cat >= 27 and cat <= 28: + cat = cat - 3 + elif cat >= 31 and cat <= 44: + cat = cat - 5 + elif cat >= 46 and cat <= 65: + cat = cat - 6 + elif cat == 67: + cat = cat - 7 + elif cat == 70: + cat = cat - 9 + elif cat >= 72 and cat <= 82: + cat = cat - 10 + elif cat >= 84 and cat <= 90: + cat = cat - 11 + name_box_id[name].append([ant['bbox'], cat]) + + f = open(val_output_path, 'w') + for key in name_box_id.keys(): + f.write(key) + box_infos = name_box_id[key] + for info in box_infos: + x_min = int(info[0][0]) + y_min = int(info[0][1]) + x_max = x_min + int(info[0][2]) + y_max = y_min + int(info[0][3]) + + box_info = " %d,%d,%d,%d,%d" % ( + x_min, y_min, x_max, y_max, int(info[1])) + f.write(box_info) + f.write('\n') + f.close() diff --git a/yolox-pytorch/utils_coco/get_map_coco.py b/yolox-pytorch/utils_coco/get_map_coco.py new file mode 100644 index 0000000..98b4c71 --- /dev/null +++ b/yolox-pytorch/utils_coco/get_map_coco.py @@ -0,0 +1,113 @@ +import json +import os + +import numpy as np +import torch +from PIL import Image +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tqdm import tqdm +from utils.utils import cvtColor, get_classes, preprocess_input, resize_image +from utils.utils_bbox import decode_outputs, non_max_suppression +from yolo import YOLO + +#---------------------------------------------------------------------------# +# map_mode用于指定该文件运行时计算的内容 +# map_mode为0代表整个map计算流程，包括获得预测结果、计算map。 +# map_mode为1代表仅仅获得预测结果。 +# map_mode为2代表仅仅获得计算map。 +#---------------------------------------------------------------------------# +map_mode = 0 +#-------------------------------------------------------# +# 指向了验证集标签与图片路径 +#-------------------------------------------------------# +cocoGt_path = 'coco_dataset/annotations/instances_val2017.json' +dataset_img_path = 'coco_dataset/val2017' +#-------------------------------------------------------# +# 结果输出的文件夹，默认为map_out +#-------------------------------------------------------# +temp_save_path = 'map_out/coco_eval' + +class mAP_YOLO(YOLO): + #---------------------------------------------------# + # 检测图片 + #---------------------------------------------------# + def detect_image(self, image_id, image, results): + #---------------------------------------------------# + # 计算输入图片的高和宽 + #---------------------------------------------------# + image_shape = np.array(np.shape(image)[0:2]) + #---------------------------------------------------------# + # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 + # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB + #---------------------------------------------------------# + image = cvtColor(image) + #---------------------------------------------------------# + # 给图像增加灰条，实现不失真的resize + # 也可以直接resize进行识别 + #---------------------------------------------------------# + image_data = resize_image(image, (self.input_shape[1],self.input_shape[0]), self.letterbox_image) + #---------------------------------------------------------# + # 添加上batch_size维度 + #---------------------------------------------------------# + image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) + + with torch.no_grad(): + images = torch.from_numpy(image_data) + if self.cuda: + images = images.cuda() + #---------------------------------------------------------# + # 将图像输入网络当中进行预测！ + #---------------------------------------------------------# + outputs = self.net(images) + outputs = decode_outputs(outputs, self.input_shape) + #---------------------------------------------------------# + # 将预测框进行堆叠，然后进行非极大抑制 + #---------------------------------------------------------# + outputs = non_max_suppression(outputs, self.num_classes, self.input_shape, + image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou) + + if outputs[0] is None: + return results + + top_label = np.array(outputs[0][:, 6], dtype = 'int32') + top_conf = outputs[0][:, 4] * outputs[0][:, 5] + top_boxes = outputs[0][:, :4] + + for i, c in enumerate(top_label): + result = {} + top, left, bottom, right = top_boxes[i] + + result["image_id"] = int(image_id) + result["category_id"] = clsid2catid[c] + result["bbox"] = [float(left),float(top),float(right-left),float(bottom-top)] + result["score"] = float(top_conf[i]) + results.append(result) + return results + +if __name__ == "__main__": + if not os.path.exists(temp_save_path): + os.makedirs(temp_save_path) + + cocoGt = COCO(cocoGt_path) + ids = list(cocoGt.imgToAnns.keys()) + clsid2catid = cocoGt.getCatIds() + + if map_mode == 0 or map_mode == 1: + yolo = mAP_YOLO(confidence = 0.001, nms_iou = 0.65) + + with open(os.path.join(temp_save_path, 'eval_results.json'),"w") as f: + results = [] + for image_id in tqdm(ids): + image_path = os.path.join(dataset_img_path, cocoGt.loadImgs(image_id)[0]['file_name']) + image = Image.open(image_path) + results = yolo.detect_image(image_id, image, results) + json.dump(results, f) + + if map_mode == 0 or map_mode == 2: + cocoDt = cocoGt.loadRes(os.path.join(temp_save_path, 'eval_results.json')) + cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + print("Get map done.") diff --git a/yolox-pytorch/voc_annotation.py b/yolox-pytorch/voc_annotation.py new file mode 100644 index 0000000..4cc0cf2 --- /dev/null +++ b/yolox-pytorch/voc_annotation.py @@ -0,0 +1,153 @@ +import os +import random +import xml.etree.ElementTree as ET + +import numpy as np + +from utils.utils import get_classes + +#--------------------------------------------------------------------------------------------------------------------------------# +# annotation_mode用于指定该文件运行时计算的内容 +# annotation_mode为0代表整个标签处理过程，包括获得VOCdevkit/VOC2007/ImageSets里面的txt以及训练用的2007_train.txt、2007_val.txt +# annotation_mode为1代表获得VOCdevkit/VOC2007/ImageSets里面的txt +# annotation_mode为2代表获得训练用的2007_train.txt、2007_val.txt +#--------------------------------------------------------------------------------------------------------------------------------# +annotation_mode = 0 +#-------------------------------------------------------------------# +# 必须要修改，用于生成2007_train.txt、2007_val.txt的目标信息 +# 与训练和预测所用的classes_path一致即可 +# 如果生成的2007_train.txt里面没有目标信息 +# 那么就是因为classes没有设定正确 +# 仅在annotation_mode为0和2的时候有效 +#-------------------------------------------------------------------# +classes_path = 'model_data/voc_classes.txt' +#--------------------------------------------------------------------------------------------------------------------------------# +# trainval_percent用于指定(训练集+验证集)与测试集的比例，默认情况下 (训练集+验证集):测试集 = 9:1 +# train_percent用于指定(训练集+验证集)中训练集与验证集的比例，默认情况下 训练集:验证集 = 9:1 +# 仅在annotation_mode为0和1的时候有效 +#--------------------------------------------------------------------------------------------------------------------------------# +trainval_percent = 0.8 +train_percent = 0.9 +#-------------------------------------------------------# +# 指向VOC数据集所在的文件夹 +# 默认指向根目录下的VOC数据集 +#-------------------------------------------------------# +VOCdevkit_path = 'VOCdevkit' + +VOCdevkit_sets = [('2007', 'train'), ('2007', 'val')] +classes, _ = get_classes(classes_path) + +#-------------------------------------------------------# +# 统计目标数量 +#-------------------------------------------------------# +photo_nums = np.zeros(len(VOCdevkit_sets)) +nums = np.zeros(len(classes)) +def convert_annotation(year, image_id, list_file): + in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml'%(year, image_id)), encoding='utf-8') + tree=ET.parse(in_file) + root = tree.getroot() + + for obj in root.iter('object'): + difficult = 0 + if obj.find('difficult')!=None: + difficult = obj.find('difficult').text + cls = obj.find('name').text + if cls not in classes or int(difficult)==1: + continue + cls_id = classes.index(cls) + xmlbox = obj.find('bndbox') + b = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text))) + list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) + + nums[classes.index(cls)] = nums[classes.index(cls)] + 1 + +if __name__ == "__main__": + random.seed(0) + if " " in os.path.abspath(VOCdevkit_path): + raise ValueError("数据集存放的文件夹路径与图片名称中不可以存在空格，否则会影响正常的模型训练，请注意修改。") + + if annotation_mode == 0 or annotation_mode == 1: + print("Generate txt in ImageSets.") + xmlfilepath = os.path.join(VOCdevkit_path, 'VOC2007/Annotations') + saveBasePath = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Main') + temp_xml = os.listdir(xmlfilepath) + total_xml = [] + for xml in temp_xml: + if xml.endswith(".xml"): + total_xml.append(xml) + + num = len(total_xml) + list = range(num) + tv = int(num*trainval_percent) + tr = int(tv*train_percent) + trainval= random.sample(list,tv) + train = random.sample(trainval,tr) + + print("train and val size",tv) + print("train size",tr) + ftrainval = open(os.path.join(saveBasePath,'trainval.txt'), 'w') + ftest = open(os.path.join(saveBasePath,'test.txt'), 'w') + ftrain = open(os.path.join(saveBasePath,'train.txt'), 'w') + fval = open(os.path.join(saveBasePath,'val.txt'), 'w') + + for i in list: + name=total_xml[i][:-4]+'\n' + if i in trainval: + ftrainval.write(name) + if i in train: + ftrain.write(name) + else: + fval.write(name) + else: + ftest.write(name) + + ftrainval.close() + ftrain.close() + fval.close() + ftest.close() + print("Generate txt in ImageSets done.") + + if annotation_mode == 0 or annotation_mode == 2: + print("Generate 2007_train.txt and 2007_val.txt for train.") + type_index = 0 + for year, image_set in VOCdevkit_sets: + image_ids = open(os.path.join(VOCdevkit_path, 'VOC%s/ImageSets/Main/%s.txt'%(year, image_set)), encoding='utf-8').read().strip().split() + list_file = open('%s_%s.txt'%(year, image_set), 'w', encoding='utf-8') + for image_id in image_ids: + list_file.write('%s/VOC%s/JPEGImages/%s.jpg'%(os.path.abspath(VOCdevkit_path), year, image_id)) + + convert_annotation(year, image_id, list_file) + list_file.write('\n') + photo_nums[type_index] = len(image_ids) + type_index += 1 + list_file.close() + print("Generate 2007_train.txt and 2007_val.txt for train done.") + + def printTable(List1, List2): + for i in range(len(List1[0])): + print("|", end=' ') + for j in range(len(List1)): + print(List1[j][i].rjust(int(List2[j])), end=' ') + print("|", end=' ') + print() + + str_nums = [str(int(x)) for x in nums] + tableData = [ + classes, str_nums + ] + colWidths = [0]*len(tableData) + len1 = 0 + for i in range(len(tableData)): + for j in range(len(tableData[i])): + if len(tableData[i][j]) > colWidths[i]: + colWidths[i] = len(tableData[i][j]) + printTable(tableData, colWidths) + + if photo_nums[0] <= 500: + print("训练集数量小于500，属于较小的数据量，请注意设置较大的训练世代（Epoch）以满足足够的梯度下降次数（Step）。") + + if np.sum(nums) == 0: + print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！") + print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！") + print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！") + print("（重要的事情说三遍）。") diff --git a/yolox-pytorch/yolo.py b/yolox-pytorch/yolo.py new file mode 100644 index 0000000..80e7098 --- /dev/null +++ b/yolox-pytorch/yolo.py @@ -0,0 +1,432 @@ +import colorsys +import os +import time + +import numpy as np +import torch +import torch.nn as nn +from PIL import ImageDraw, ImageFont + +from nets.yolo import YoloBody +from utils.utils import (cvtColor, get_classes, preprocess_input, resize_image, + show_config) +from utils.utils_bbox import decode_outputs, non_max_suppression + +''' +训练自己的数据集必看注释！ +''' + + +class YOLO(object): + _defaults = { + # --------------------------------------------------------------------------# + # 使用自己训练好的模型进行预测一定要修改model_path和classes_path！ + # model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt + # + # 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。 + # 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。 + # 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改 + # --------------------------------------------------------------------------# + # "model_path": 'yolox-pytorch/logs/ep300-loss1.440-val_loss1.643.pth', # .. + # "classes_path": 'yolox-pytorch/model_data/voc_classes.txt', # .. + "model_path": 'logs/ep300-loss1.440-val_loss1.643.pth', # .. + "classes_path": 'model_data/voc_classes.txt', # .. + # ---------------------------------------------------------------------# + # 输入图片的大小，必须为32的倍数。 + # ---------------------------------------------------------------------# + "input_shape": [640, 640], + # ---------------------------------------------------------------------# + # 所使用的YoloX的版本。nano、tiny、s、m、l、x + # ---------------------------------------------------------------------# + "phi": 's', # .. + # ---------------------------------------------------------------------# + # 只有得分大于置信度的预测框会被保留下来 + # ---------------------------------------------------------------------# + "confidence": 0.5, + # ---------------------------------------------------------------------# + # 非极大抑制所用到的nms_iou大小 + # ---------------------------------------------------------------------# + "nms_iou": 0.3, + # ---------------------------------------------------------------------# + # 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize， + # 在多次测试后，发现关闭letterbox_image直接resize的效果更好 + # ---------------------------------------------------------------------# + "letterbox_image": True, + # -------------------------------# + # 是否使用Cuda + # 没有GPU可以设置成False + # -------------------------------# + "cuda": True, + } + + @classmethod + def get_defaults(cls, n): + if n in cls._defaults: + return cls._defaults[n] + else: + return "Unrecognized attribute name '" + n + "'" + + # ---------------------------------------------------# + # 初始化YOLO + # ---------------------------------------------------# + def __init__(self, **kwargs): + self.__dict__.update(self._defaults) + for name, value in kwargs.items(): + setattr(self, name, value) + self._defaults[name] = value + + # ---------------------------------------------------# + # 获得种类和先验框的数量 + # ---------------------------------------------------# + self.class_names, self.num_classes = get_classes(self.classes_path) + + # ---------------------------------------------------# + # 画框设置不同的颜色 + # ---------------------------------------------------# + hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)] + self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) + self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) + self.generate() + + show_config(**self._defaults) + + # ---------------------------------------------------# + # 生成模型 + # ---------------------------------------------------# + def generate(self, onnx=False): + self.net = YoloBody(self.num_classes, self.phi) + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.net.load_state_dict(torch.load(self.model_path, map_location=device)) + self.net = self.net.eval() + print('{} model, and classes loaded.'.format(self.model_path)) + if not onnx: + if self.cuda: + self.net = nn.DataParallel(self.net) + self.net = self.net.cuda() + + # ---------------------------------------------------# + # 检测图片 + # ---------------------------------------------------# + def detect_image(self, image, crop=False, count=False): + # ---------------------------------------------------# + # 获得输入图片的高和宽 + # ---------------------------------------------------# + image_shape = np.array(np.shape(image)[0:2]) + # ---------------------------------------------------------# + # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 + # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB + # ---------------------------------------------------------# + image = cvtColor(image) + # ---------------------------------------------------------# + # 给图像增加灰条，实现不失真的resize + # 也可以直接resize进行识别 + # ---------------------------------------------------------# + image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) + # ---------------------------------------------------------# + # 添加上batch_size维度 + # ---------------------------------------------------------# + image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) + + predicted_class, score = None, None + + with torch.no_grad(): + images = torch.from_numpy(image_data) + if self.cuda: + images = images.cuda() + # ---------------------------------------------------------# + # 将图像输入网络当中进行预测！ + # ---------------------------------------------------------# + outputs = self.net(images) + outputs = decode_outputs(outputs, self.input_shape) + # ---------------------------------------------------------# + # 将预测框进行堆叠，然后进行非极大抑制 + # ---------------------------------------------------------# + results = non_max_suppression(outputs, self.num_classes, self.input_shape, + image_shape, self.letterbox_image, conf_thres=self.confidence, + nms_thres=self.nms_iou) + + if results[0] is None: + return image + + top_label = np.array(results[0][:, 6], dtype='int32') + top_conf = results[0][:, 4] * results[0][:, 5] + top_boxes = results[0][:, :4] + # ---------------------------------------------------------# + # 设置字体与边框厚度 + # ---------------------------------------------------------# + font = ImageFont.truetype(font='yolox-pytorch/model_data/simhei.ttf', + size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32')) + thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1)) + # ---------------------------------------------------------# + # 计数 + # ---------------------------------------------------------# + if count: + print("top_label:", top_label) + classes_nums = np.zeros([self.num_classes]) + for i in range(self.num_classes): + num = np.sum(top_label == i) + if num > 0: + print(self.class_names[i], " : ", num) + classes_nums[i] = num + print("classes_nums:", classes_nums) + # ---------------------------------------------------------# + # 是否进行目标的裁剪 + # ---------------------------------------------------------# + if crop: + for i, c in list(enumerate(top_label)): + top, left, bottom, right = top_boxes[i] + top = max(0, np.floor(top).astype('int32')) + left = max(0, np.floor(left).astype('int32')) + bottom = min(image.size[1], np.floor(bottom).astype('int32')) + right = min(image.size[0], np.floor(right).astype('int32')) + + dir_save_path = "img_crop" + if not os.path.exists(dir_save_path): + os.makedirs(dir_save_path) + crop_image = image.crop([left, top, right, bottom]) + crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0) + print("save crop_" + str(i) + ".png to " + dir_save_path) + + # ---------------------------------------------------------# + # 图像绘制 + # ---------------------------------------------------------# + # ---------------------------------------------------------# + # 用于返回预测类别以及score的变量设置 + result = [] + # ---------------------------------------------------------# + for i, c in list(enumerate(top_label)): + predicted_class = self.class_names[int(c)] + box = top_boxes[i] + score = top_conf[i] + # 插入结果 + result_one = {"class": predicted_class, "score": str(score)} + result.append(result_one) + top, left, bottom, right = box + + top = max(0, np.floor(top).astype('int32')) + left = max(0, np.floor(left).astype('int32')) + bottom = min(image.size[1], np.floor(bottom).astype('int32')) + right = min(image.size[0], np.floor(right).astype('int32')) + + label = '{} {:.2f}'.format(predicted_class, score) + draw = ImageDraw.Draw(image) + label_size = draw.textsize(label, font) + label = label.encode('utf-8') + # print(label, top, left, bottom, right) + + if top - label_size[1] >= 0: + text_origin = np.array([left, top - label_size[1]]) + else: + text_origin = np.array([left, top + 1]) + + for i in range(thickness): + draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c]) + draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c]) + draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font) # 用于在图像上绘制文本 + del draw + + return image, result # 如果想要返回名称和匹配度的话，就返回多一个predicted_class，然后和flask_on里面做出相应更改 + + def get_FPS(self, image, test_interval): + image_shape = np.array(np.shape(image)[0:2]) + # ---------------------------------------------------------# + # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 + # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB + # ---------------------------------------------------------# + image = cvtColor(image) + # ---------------------------------------------------------# + # 给图像增加灰条，实现不失真的resize + # 也可以直接resize进行识别 + # ---------------------------------------------------------# + image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) + # ---------------------------------------------------------# + # 添加上batch_size维度 + # ---------------------------------------------------------# + image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) + + with torch.no_grad(): + images = torch.from_numpy(image_data) + if self.cuda: + images = images.cuda() + # ---------------------------------------------------------# + # 将图像输入网络当中进行预测！ + # ---------------------------------------------------------# + outputs = self.net(images) + outputs = decode_outputs(outputs, self.input_shape) + # ---------------------------------------------------------# + # 将预测框进行堆叠，然后进行非极大抑制 + # ---------------------------------------------------------# + results = non_max_suppression(outputs, self.num_classes, self.input_shape, + image_shape, self.letterbox_image, conf_thres=self.confidence, + nms_thres=self.nms_iou) + + t1 = time.time() + for _ in range(test_interval): + with torch.no_grad(): + # ---------------------------------------------------------# + # 将图像输入网络当中进行预测！ + # ---------------------------------------------------------# + outputs = self.net(images) + outputs = decode_outputs(outputs, self.input_shape) + # ---------------------------------------------------------# + # 将预测框进行堆叠，然后进行非极大抑制 + # ---------------------------------------------------------# + results = non_max_suppression(outputs, self.num_classes, self.input_shape, + image_shape, self.letterbox_image, conf_thres=self.confidence, + nms_thres=self.nms_iou) + + t2 = time.time() + tact_time = (t2 - t1) / test_interval + return tact_time + + def detect_heatmap(self, image, heatmap_save_path): + import cv2 + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + def sigmoid(x): + y = 1.0 / (1.0 + np.exp(-x)) + return y + + # ---------------------------------------------------# + # 获得输入图片的高和宽 + # ---------------------------------------------------# + image_shape = np.array(np.shape(image)[0:2]) + # ---------------------------------------------------------# + # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 + # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB + # ---------------------------------------------------------# + image = cvtColor(image) + # ---------------------------------------------------------# + # 给图像增加灰条，实现不失真的resize + # 也可以直接resize进行识别 + # ---------------------------------------------------------# + image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) + # ---------------------------------------------------------# + # 添加上batch_size维度 + # ---------------------------------------------------------# + image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) + + with torch.no_grad(): + images = torch.from_numpy(image_data) + if self.cuda: + images = images.cuda() + # ---------------------------------------------------------# + # 将图像输入网络当中进行预测！ + # ---------------------------------------------------------# + outputs = self.net(images) + + outputs = [output.cpu().numpy() for output in outputs] + plt.imshow(image, alpha=1) + plt.axis('off') + mask = np.zeros((image.size[1], image.size[0])) + for sub_output in outputs: + b, c, h, w = np.shape(sub_output) + sub_output = np.transpose(sub_output, [0, 2, 3, 1])[0] + score = np.max(sigmoid(sub_output[..., 5:]), -1) * sigmoid(sub_output[..., 4]) + score = cv2.resize(score, (image.size[0], image.size[1])) + normed_score = (score * 255).astype('uint8') + mask = np.maximum(mask, normed_score) + + plt.imshow(mask, alpha=0.5, interpolation='nearest', cmap="jet") + + plt.axis('off') + plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) + plt.margins(0, 0) + plt.savefig(heatmap_save_path, dpi=200) + print("Save to the " + heatmap_save_path) + plt.cla() + + def convert_to_onnx(self, simplify, model_path): + import onnx + self.generate(onnx=True) + + im = torch.zeros(1, 3, *self.input_shape).to('cpu') # image size(1, 3, 512, 512) BCHW + input_layer_names = ["images"] + output_layer_names = ["output"] + + # Export the model + print(f'Starting export with onnx {onnx.__version__}.') + torch.onnx.export(self.net, + im, + f=model_path, + verbose=False, + opset_version=12, + training=torch.onnx.TrainingMode.EVAL, + do_constant_folding=True, + input_names=input_layer_names, + output_names=output_layer_names, + dynamic_axes=None) + + # Checks + model_onnx = onnx.load(model_path) # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + + # Simplify onnx + if simplify: + import onnxsim + print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.') + model_onnx, check = onnxsim.simplify( + model_onnx, + dynamic_input_shape=False, + input_shapes=None) + assert check, 'assert check failed' + onnx.save(model_onnx, model_path) + + print('Onnx model save as {}'.format(model_path)) + + def get_map_txt(self, image_id, image, class_names, map_out_path): + f = open(os.path.join(map_out_path, "detection-results/" + image_id + ".txt"), "w") + image_shape = np.array(np.shape(image)[0:2]) + # ---------------------------------------------------------# + # 在这里将图像转换成RGB图像，防止灰度图在预测时报错。 + # 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB + # ---------------------------------------------------------# + image = cvtColor(image) + # ---------------------------------------------------------# + # 给图像增加灰条，实现不失真的resize + # 也可以直接resize进行识别 + # ---------------------------------------------------------# + image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) + # ---------------------------------------------------------# + # 添加上batch_size维度 + # ---------------------------------------------------------# + image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0) + + with torch.no_grad(): + images = torch.from_numpy(image_data) + if self.cuda: + images = images.cuda() + # ---------------------------------------------------------# + # 将图像输入网络当中进行预测！ + # ---------------------------------------------------------# + outputs = self.net(images) + outputs = decode_outputs(outputs, self.input_shape) + # ---------------------------------------------------------# + # 将预测框进行堆叠，然后进行非极大抑制 + # ---------------------------------------------------------# + results = non_max_suppression(outputs, self.num_classes, self.input_shape, + image_shape, self.letterbox_image, conf_thres=self.confidence, + nms_thres=self.nms_iou) + + if results[0] is None: + return + + top_label = np.array(results[0][:, 6], dtype='int32') + top_conf = results[0][:, 4] * results[0][:, 5] + top_boxes = results[0][:, :4] + + for i, c in list(enumerate(top_label)): + predicted_class = self.class_names[int(c)] + box = top_boxes[i] + score = str(top_conf[i]) + + top, left, bottom, right = box + if predicted_class not in class_names: + continue + + f.write("%s %s %s %s %s %s\n" % ( + predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom)))) + + f.close() + return diff --git a/yolox-pytorch/常见问题汇总.md b/yolox-pytorch/常见问题汇总.md new file mode 100644 index 0000000..26f3f35 --- /dev/null +++ b/yolox-pytorch/常见问题汇总.md @@ -0,0 +1,626 @@ +问题汇总的博客地址为[https://blog.csdn.net/weixin_44791964/article/details/107517428](https://blog.csdn.net/weixin_44791964/article/details/107517428)。 + +# 问题汇总 +## 1、下载问题 +### a、代码下载 +**问：up主，可以给我发一份代码吗，代码在哪里下载啊？ +答：Github上的地址就在视频简介里。复制一下就能进去下载了。** + +**问：up主，为什么我下载的代码提示压缩包损坏？ +答：重新去Github下载。** + +**问：up主，为什么我下载的代码和你在视频以及博客上的代码不一样？ +答：我常常会对代码进行更新，最终以实际的代码为准。** + +### b、 权值下载 +**问：up主，为什么我下载的代码里面，model_data下面没有.pth或者.h5文件？ +答：我一般会把权值上传到Github和百度网盘，在GITHUB的README里面就能找到。** + +### c、 数据集下载 +**问：up主，XXXX数据集在哪里下载啊？ +答：一般数据集的下载地址我会放在README里面，基本上都有，没有的话请及时联系我添加，直接发github的issue即可**。 + +## 2、环境配置问题 +### a、20系列及以下显卡环境配置 +**pytorch代码对应的pytorch版本为1.2，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/106037141](https://blog.csdn.net/weixin_44791964/article/details/106037141)。 + +**keras代码对应的tensorflow版本为1.13.2，keras版本是2.1.5，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/104702142](https://blog.csdn.net/weixin_44791964/article/details/104702142)。 + +**tf2代码对应的tensorflow版本为2.2.0，无需安装keras，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/109161493](https://blog.csdn.net/weixin_44791964/article/details/109161493)。 + +**问：你的代码某某某版本的tensorflow和pytorch能用嘛？ +答：最好按照我推荐的配置，配置教程也有！其它版本的我没有试过！可能出现问题但是一般问题不大。仅需要改少量代码即可。** + +### b、30系列显卡环境配置 +30系显卡由于框架更新不可使用上述环境配置教程。 +当前我已经测试的可以用的30显卡配置如下： +**pytorch代码对应的pytorch版本为1.7.0，cuda为11.0，cudnn为8.0.5，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/120668551](https://blog.csdn.net/weixin_44791964/article/details/120668551)。 + +**keras代码无法在win10下配置cuda11，在ubuntu下可以百度查询一下，配置tensorflow版本为1.15.4，keras版本是2.1.5或者2.3.1（少量函数接口不同，代码可能还需要少量调整。）** + +**tf2代码对应的tensorflow版本为2.4.0，cuda为11.0，cudnn为8.0.5，博客地址对应为**[https://blog.csdn.net/weixin_44791964/article/details/120657664](https://blog.csdn.net/weixin_44791964/article/details/120657664)。 + +### c、CPU环境配置 +**pytorch代码对应的pytorch-cpu版本为1.2，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/120655098](https://blog.csdn.net/weixin_44791964/article/details/120655098) + +**keras代码对应的tensorflow-cpu版本为1.13.2，keras版本是2.1.5，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/120653717](https://blog.csdn.net/weixin_44791964/article/details/120653717)。 + +**tf2代码对应的tensorflow-cpu版本为2.2.0，无需安装keras，博客地址对应**[https://blog.csdn.net/weixin_44791964/article/details/120656291](https://blog.csdn.net/weixin_44791964/article/details/120656291)。 + + +### d、GPU利用问题与环境使用问题 +**问：为什么我安装了tensorflow-gpu但是却没用利用GPU进行训练呢？ +答：确认tensorflow-gpu已经装好，利用pip list查看tensorflow版本，然后查看任务管理器或者利用nvidia命令看看是否使用了gpu进行训练，任务管理器的话要看显存使用情况。** + +**问：up主，我好像没有在用gpu进行训练啊，怎么看是不是用了GPU进行训练？ +答：查看是否使用GPU进行训练一般使用NVIDIA在命令行的查看命令。在windows电脑中打开cmd然后利用nvidia-smi指令查看GPU利用情况** +![在这里插入图片描述](https://img-blog.csdnimg.cn/f88ef794c9a341918f000eb2b1c67af6.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAQnViYmxpaWlpbmc=,size_20,color_FFFFFF,t_70,g_se,x_16) +**如果要一定看任务管理器的话，请看性能部分GPU的显存是否利用，或者查看任务管理器的Cuda，而非Copy。** +![在这里插入图片描述](https://img-blog.csdnimg.cn/20201013234241524.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center) + +### e、DLL load failed: 找不到指定的模块 +**问：出现如下错误** +```python +Traceback (most recent call last): + File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in + from tensorflow.python.pywrap_tensorflow_internal import * +File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in +pywrap_tensorflow_internal = swig_import_helper() + File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper + _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) +File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\imp.py", line 243, in load_modulereturn load_dynamic(name, filename, file) +File "C:\Users\focus\Anaconda3\ana\envs\tensorflow-gpu\lib\imp.py", line 343, in load_dynamic + return _load(spec) +ImportError: DLL load failed: 找不到指定的模块。 +``` +**答：如果没重启过就重启一下，否则重新按照步骤安装，还无法解决则把你的GPU、CUDA、CUDNN、TF版本以及PYTORCH版本私聊告诉我。** + +### f、no module问题（no module name utils.utils、no module named 'matplotlib' ） +**问：为什么提示说no module name utils.utils（no module name nets.yolo、no module name nets.ssd等一系列问题）啊？ +答：utils并不需要用pip装，它就在我上传的仓库的根目录，出现这个问题的原因是根目录不对，查查相对目录和根目录的概念。查了基本上就明白了。** + +**问：为什么提示说no module name matplotlib（no module name PIL，no module name cv2等等）？ +答：这个库没安装打开命令行安装就好。pip install matplotlib** + +**问：为什么我已经用pip装了opencv（pillow、matplotlib等），还是提示no module name cv2？ +答：没有激活环境装，要激活对应的conda环境进行安装才可以正常使用** + +**问：为什么提示说No module named 'torch' ？ +答：其实我也真的很想知道为什么会有这个问题……这个pytorch没装是什么情况？一般就俩情况，一个是真的没装，还有一个是装到其它环境了，当前激活的环境不是自己装的环境。** + +**问：为什么提示说No module named 'tensorflow' ？ +答：同上。** + +### g、cuda安装失败问题 +一般cuda安装前需要安装Visual Studio，装个2017版本即可。 + +### h、Ubuntu系统问题 +**所有代码在Ubuntu下可以使用，我两个系统都试过。** + +### i、VSCODE提示错误的问题 +**问：为什么在VSCODE里面提示一大堆的错误啊？ +答：我也提示一大堆的错误，但是不影响，是VSCODE的问题，如果不想看错误的话就装Pycharm。 +最好将设置里面的Python:Language Server，调整为Pylance。** + +### j、使用cpu进行训练与预测的问题 +**对于keras和tf2的代码而言，如果想用cpu进行训练和预测，直接装cpu版本的tensorflow就可以了。** + +**对于pytorch的代码而言，如果想用cpu进行训练和预测，需要将cuda=True修改成cuda=False。** + +### k、tqdm没有pos参数问题 +**问：运行代码提示'tqdm' object has no attribute 'pos'。 +答：重装tqdm，换个版本就可以了。** + +### l、提示decode(“utf-8”)的问题 +**由于h5py库的更新，安装过程中会自动安装h5py=3.0.0以上的版本，会导致decode("utf-8")的错误！ +各位一定要在安装完tensorflow后利用命令装h5py=2.10.0！** +``` +pip install h5py==2.10.0 +``` + +### m、提示TypeError: __array__() takes 1 positional argument but 2 were given错误 +可以修改pillow版本解决。 +``` +pip install pillow==8.2.0 +``` +### n、如何查看当前cuda和cudnn +**window下cuda版本查看方式如下： +1、打开cmd窗口。 +2、输入nvcc -V。 +3、Cuda compilation tools, release XXXXXXXX中的XXXXXXXX即cuda版本。** +![在这里插入图片描述](https://img-blog.csdnimg.cn/0389ea35107a408a80ab5cb6590d5a74.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAQnViYmxpaWlpbmc=,size_20,color_FFFFFF,t_70,g_se,x_16) +window下cudnn版本查看方式如下： +1、进入cuda安装目录，进入incude文件夹。 +2、找到cudnn.h文件。 +3、右键文本打开，下拉，看到#define处可获得cudnn版本。 +```python +#define CUDNN_MAJOR 7 +#define CUDNN_MINOR 4 +#define CUDNN_PATCHLEVEL 1 +``` +代表cudnn为7.4.1。 +![在这里插入图片描述](https://img-blog.csdnimg.cn/7a86b68b17c84feaa6fa95780d4ae4b4.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAQnViYmxpaWlpbmc=,size_20,color_FFFFFF,t_70,g_se,x_16) +![在这里插入图片描述](https://img-blog.csdnimg.cn/81bb7c3e13cc492292530e4b69df86a9.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAQnViYmxpaWlpbmc=,size_20,color_FFFFFF,t_70,g_se,x_16) + +### o、为什么按照你的环境配置后还是不能使用 +**问：up主，为什么我按照你的环境配置后还是不能使用？ +答：请把你的GPU、CUDA、CUDNN、TF版本以及PYTORCH版本B站私聊告诉我。** + +### p、其它问题 +**问：为什么提示TypeError: cat() got an unexpected keyword argument 'axis'，Traceback (most recent call last)，AttributeError: 'Tensor' object has no attribute 'bool'？ +答：这是版本问题，建议使用torch1.2以上版本** + +**其它有很多稀奇古怪的问题，很多是版本问题，建议按照我的视频教程安装Keras和tensorflow。比如装的是tensorflow2，就不用问我说为什么我没法运行Keras-yolo啥的。那是必然不行的。** + +## 3、目标检测库问题汇总（人脸检测和分类库也可参考） +### a、shape不匹配问题。 +#### 1）、训练时shape不匹配问题。 +**问：up主，为什么运行train.py会提示shape不匹配啊？ +答：在keras环境中，因为你训练的种类和原始的种类不同，网络结构会变化，所以最尾部的shape会有少量不匹配。** + +#### 2）、预测时shape不匹配问题。 +**问：为什么我运行predict.py会提示我说shape不匹配呀。** +##### i、copying a param with shape torch.Size([75, 704, 1, 1]) from checkpoint +在Pytorch里面是这样的： +![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171631901.png) +##### ii、Shapes are [1,1,1024,75] and [255,1024,1,1]. for 'Assign_360' (op: 'Assign') with input shapes: [1,1,1024,75], [255,1024,1,1]. +在Keras里面是这样的： +![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171523380.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70) +**答：原因主要有仨： +1、训练的classes_path没改，就开始训练了。 +2、训练的model_path没改。 +3、训练的classes_path没改。 +请检查清楚了！确定自己所用的model_path和classes_path是对应的！训练的时候用到的num_classes或者classes_path也需要检查！** + +### b、显存不足问题（OOM、RuntimeError: CUDA out of memory）。 +**问：为什么我运行train.py下面的命令行闪的贼快，还提示OOM啥的？ +答：这是在keras中出现的，爆显存了，可以改小batch_size，SSD的显存占用率是最小的，建议用SSD； +2G显存：SSD、YOLOV4-TINY +4G显存：YOLOV3 +6G显存：YOLOV4、Retinanet、M2det、Efficientdet、Faster RCNN等 +8G+显存：随便选吧。** +**需要注意的是，受到BatchNorm2d影响，batch_size不可为1，至少为2。** + +**问：为什么提示 RuntimeError: CUDA out of memory. Tried to allocate 52.00 MiB (GPU 0; 15.90 GiB total capacity; 14.85 GiB already allocated; 51.88 MiB free; 15.07 GiB reserved in total by PyTorch)？ +答：这是pytorch中出现的，爆显存了，同上。** + +**问：为什么我显存都没利用，就直接爆显存了？ +答：都爆显存了，自然就不利用了，模型没有开始训练。** +### c、为什么要进行冻结训练与解冻训练，不进行行吗？ +**问：为什么要冻结训练和解冻训练呀？ +答：可以不进行，本质上是为了保证性能不足的同学的训练，如果电脑性能完全不够，可以将Freeze_Epoch和UnFreeze_Epoch设置成一样，只进行冻结训练。** + +**同时这也是迁移学习的思想，因为神经网络主干特征提取部分所提取到的特征是通用的，我们冻结起来训练可以加快训练效率，也可以防止权值被破坏。** +在冻结阶段，模型的主干被冻结了，特征提取网络不发生改变。占用的显存较小，仅对网络进行微调。 +在解冻阶段，模型的主干不被冻结了，特征提取网络会发生改变。占用的显存较大，网络所有的参数都会发生改变。 + +### d、我的LOSS好大啊，有问题吗？（我的LOSS好小啊，有问题吗？） +**问：为什么我的网络不收敛啊，LOSS是XXXX。 +答：不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，我的yolo代码都没有归一化，所以LOSS值看起来比较高，LOSS的值不重要，重要的是是否在变小，预测是否有效果。** + +### e、为什么我训练出来的模型没有预测结果？ +**问：为什么我的训练效果不好？预测了没有框（框不准）。 +答：** +考虑几个问题： +1、目标信息问题，查看2007_train.txt文件是否有目标信息，没有的话请修改voc_annotation.py。 +2、数据集问题，小于500的自行考虑增加数据集，同时测试不同的模型，确认数据集是好的。 +3、是否解冻训练，如果数据集分布与常规画面差距过大需要进一步解冻训练，调整主干，加强特征提取能力。 +4、网络问题，比如SSD不适合小目标，因为先验框固定了。 +5、训练时长问题，有些同学只训练了几代表示没有效果，按默认参数训练完。 +6、确认自己是否按照步骤去做了，如果比如voc_annotation.py里面的classes是否修改了等。 +7、不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，LOSS的值不重要，重要的是是否收敛。 +8、是否修改了网络的主干，如果修改了没有预训练权重，网络不容易收敛，自然效果不好。 + +### f、为什么我计算出来的map是0？ +**问：为什么我的训练效果不好？没有map？ +答：** +首先尝试利用predict.py预测一下，如果有效果的话应该是get_map.py里面的classes_path设置错误。如果没有预测结果的话，解决方法同e问题，对下面几点进行检查： +1、目标信息问题，查看2007_train.txt文件是否有目标信息，没有的话请修改voc_annotation.py。 +2、数据集问题，小于500的自行考虑增加数据集，同时测试不同的模型，确认数据集是好的。 +3、是否解冻训练，如果数据集分布与常规画面差距过大需要进一步解冻训练，调整主干，加强特征提取能力。 +4、网络问题，比如SSD不适合小目标，因为先验框固定了。 +5、训练时长问题，有些同学只训练了几代表示没有效果，按默认参数训练完。 +6、确认自己是否按照步骤去做了，如果比如voc_annotation.py里面的classes是否修改了等。 +7、不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，LOSS的值不重要，重要的是是否收敛。 +8、是否修改了网络的主干，如果修改了没有预训练权重，网络不容易收敛，自然效果不好。 + +### g、gbk编码错误（'gbk' codec can't decode byte）。 +**问：我怎么出现了gbk什么的编码错误啊：** +```python +UnicodeDecodeError: 'gbk' codec can't decode byte 0xa6 in position 446: illegal multibyte sequence +``` +**答：标签和路径不要使用中文，如果一定要使用中文，请注意处理的时候编码的问题，改成打开文件的encoding方式改为utf-8。** + +### h、我的图片是xxx*xxx的分辨率的，可以用吗？ +**问：我的图片是xxx*xxx的分辨率的，可以用吗！** +**答：可以用，代码里面会自动进行resize与数据增强。** + +### i、我想进行数据增强！怎么增强？ +**问：我想要进行数据增强！怎么做呢？** +**答：可以用，代码里面会自动进行resize与数据增强。** + +### j、多GPU训练。 +**问：怎么进行多GPU训练？ +答：pytorch的大多数代码可以直接使用gpu训练，keras的话直接百度就好了，实现并不复杂，我没有多卡没法详细测试，还需要各位同学自己努力了。** + +### k、能不能训练灰度图？ +**问：能不能训练灰度图（预测灰度图）啊？ +答：我的大多数库会将灰度图转化成RGB进行训练和预测，如果遇到代码不能训练或者预测灰度图的情况，可以尝试一下在get_random_data里面将Image.open后的结果转换成RGB，预测的时候也这样试试。（仅供参考）** + +### l、断点续练问题。 +**问：我已经训练过几个世代了，能不能从这个基础上继续开始训练 +答：可以，你在训练前，和载入预训练权重一样载入训练过的权重就行了。一般训练好的权重会保存在logs文件夹里面，将model_path修改成你要开始的权值的路径即可。** + +### m、我要训练其它的数据集，预训练权重能不能用？ +**问：如果我要训练其它的数据集，预训练权重要怎么办啊？** +**答：数据的预训练权重对不同数据集是通用的，因为特征是通用的，预训练权重对于99%的情况都必须要用，不用的话权值太过随机，特征提取效果不明显，网络训练的结果也不会好。** + +### n、网络如何从0开始训练？ +**问：我要怎么不使用预训练权重啊？ +答：看一看注释、大多数代码是model_path = ''，Freeze_Train = Fasle**，如果设置model_path无用，**那么把载入预训练权重的代码注释了就行。** + +### o、为什么从0开始训练效果这么差（修改了网络主干，效果不好怎么办）？ +**问：为什么我不使用预训练权重效果这么差啊？ +答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，voc07+12、coco+voc07+12效果都不一样，预训练权重还是非常重要的。** + +**问：up，我修改了网络，预训练权重还能用吗？ +答：修改了主干的话，如果不是用的现有的网络，基本上预训练权重是不能用的，要么就自己判断权值里卷积核的shape然后自己匹配，要么只能自己预训练去了；修改了后半部分的话，前半部分的主干部分的预训练权重还是可以用的，如果是pytorch代码的话，需要自己修改一下载入权值的方式，判断shape后载入，如果是keras代码，直接by_name=True,skip_mismatch=True即可。** +权值匹配的方式可以参考如下： +```python +# 加快模型训练的效率 +print('Loading weights into state dict...') +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +model_dict = model.state_dict() +pretrained_dict = torch.load(model_path, map_location=device) +a = {} +for k, v in pretrained_dict.items(): + try: + if np.shape(model_dict[k]) == np.shape(v): + a[k]=v + except: + pass +model_dict.update(a) +model.load_state_dict(model_dict) +print('Finished!') +``` + +**问：为什么从0开始训练效果这么差（我修改了网络主干，效果不好怎么办）？ +答：一般来讲，网络从0开始的训练效果会很差，因为权值太过随机，特征提取效果不明显，因此非常、非常、非常不建议大家从0开始训练！如果一定要从0开始，可以了解imagenet数据集，首先训练分类模型，获得网络的主干部分权值，分类模型的 主干部分 和该模型通用，基于此进行训练。 +网络修改了主干之后也是同样的问题，随机的权值效果很差。** + +**问：怎么在模型上从0开始训练？ +答：在算力不足与调参能力不足的情况下从0开始训练毫无意义。模型特征提取能力在随机初始化参数的情况下非常差。没有好的参数调节能力和算力，无法使得网络正常收敛。** +如果一定要从0开始，那么训练的时候请注意几点： + - 不载入预训练权重。 + - 不要进行冻结训练，注释冻结模型的代码。 + +**问：为什么我不使用预训练权重效果这么差啊？ +答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，voc07+12、coco+voc07+12效果都不一样，预训练权重还是非常重要的。** + +### p、你的权值都是哪里来的？ +**问：如果网络不能从0开始训练的话你的权值哪里来的？ +答：有些权值是官方转换过来的，有些权值是自己训练出来的，我用到的主干的imagenet的权值都是官方的。** + +### q、视频检测与摄像头检测 +**问：怎么用摄像头检测呀？ +答：predict.py修改参数可以进行摄像头检测，也有视频详细解释了摄像头检测的思路。** + +**问：怎么用视频检测呀？ +答：同上** + +### r、如何保存检测出的图片 +**问：检测完的图片怎么保存？ +答：一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。详细看看predict.py文件的注释。** + +**问：怎么用视频保存呀？ +答：详细看看predict.py文件的注释。** + +### s、遍历问题 +**问：如何对一个文件夹的图片进行遍历？ +答：一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了，详细看看predict.py文件的注释。** + +**问：如何对一个文件夹的图片进行遍历？并且保存。 +答：遍历的话一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了。保存的话一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。如果有些库用的是cv2，那就是查一下cv2怎么保存图片。详细看看predict.py文件的注释。** + +### t、路径问题（No such file or directory、StopIteration: [Errno 13] Permission denied: 'XXXXXX'） +**问：我怎么出现了这样的错误呀：** +```python +FileNotFoundError: 【Errno 2】 No such file or directory +StopIteration: [Errno 13] Permission denied: 'D:\\Study\\Collection\\Dataset\\VOC07+12+test\\VOCdevkit/VOC2007' +…………………………………… +…………………………………… +``` +**答：去检查一下文件夹路径，查看是否有对应文件；并且检查一下2007_train.txt，其中文件路径是否有错。** +关于路径有几个重要的点： +**文件夹名称中一定不要有空格。 +注意相对路径和绝对路径。 +多百度路径相关的知识。** + +**所有的路径问题基本上都是根目录问题，好好查一下相对目录的概念！** +### u、和原版比较问题，你怎么和原版不一样啊？ +**问：原版的代码是XXX，为什么你的代码是XXX？ +答：是啊……这要不怎么说我不是原版呢……** + +**问：你这个代码和原版比怎么样，可以达到原版的效果么？ +答：基本上可以达到，我都用voc数据测过，我没有好显卡，没有能力在coco上测试与训练。** + +**问：你有没有实现yolov4所有的tricks，和原版差距多少？ +答：并没有实现全部的改进部分，由于YOLOV4使用的改进实在太多了，很难完全实现与列出来，这里只列出来了一些我比较感兴趣，而且非常有效的改进。论文中提到的SAM（注意力机制模块），作者自己的源码也没有使用。还有其它很多的tricks，不是所有的tricks都有提升，我也没法实现全部的tricks。至于和原版的比较，我没有能力训练coco数据集，根据使用过的同学反应差距不大。** + +### v、我的检测速度是xxx正常吗？我的检测速度还能增快吗？ +**问：你这个FPS可以到达多少，可以到 XX FPS么？ +答：FPS和机子的配置有关，配置高就快，配置低就慢。** + +**问：我的检测速度是xxx正常吗？我的检测速度还能增快吗？ +答：看配置，配置好速度就快，如果想要配置不变的情况下加快速度，就要修改网络了。** + +**问：为什么我用服务器去测试yolov4（or others）的FPS只有十几？ +答：检查是否正确安装了tensorflow-gpu或者pytorch的gpu版本，如果已经正确安装，可以去利用time.time()的方法查看detect_image里面，哪一段代码耗时更长（不仅只有网络耗时长，其它处理部分也会耗时，如绘图等）。** + +**问：为什么论文中说速度可以达到XX，但是这里却没有？ +答：检查是否正确安装了tensorflow-gpu或者pytorch的gpu版本，如果已经正确安装，可以去利用time.time()的方法查看detect_image里面，哪一段代码耗时更长（不仅只有网络耗时长，其它处理部分也会耗时，如绘图等）。有些论文还会使用多batch进行预测，我并没有去实现这个部分。** + +### w、预测图片不显示问题 +**问：为什么你的代码在预测完成后不显示图片？只是在命令行告诉我有什么目标。 +答：给系统安装一个图片查看器就行了。** + +### x、算法评价问题（目标检测的map、PR曲线、Recall、Precision等） +**问：怎么计算map？ +答：看map视频，都一个流程。** + +**问：计算map的时候，get_map.py里面有一个MINOVERLAP是什么用的，是iou吗？ +答：是iou，它的作用是判断预测框和真实框的重合成度，如果重合程度大于MINOVERLAP，则预测正确。** + +**问：为什么get_map.py里面的self.confidence（self.score）要设置的那么小？ +答：看一下map的视频的原理部分，要知道所有的结果然后再进行pr曲线的绘制。** + +**问：能不能说说怎么绘制PR曲线啥的呀。 +答：可以看mAP视频，结果里面有PR曲线。** + +**问：怎么计算Recall、Precision指标。 +答：这俩指标应该是相对于特定的置信度的，计算map的时候也会获得。** + +### y、coco数据集训练问题 +**问：目标检测怎么训练COCO数据集啊？。 +答：coco数据训练所需要的txt文件可以参考qqwweee的yolo3的库，格式都是一样的。** + +### z、UP，怎么优化模型啊？我想提升效果 +**问：up，怎么修改模型啊，我想发个小论文！ +答：建议看看yolov3和yolov4的区别，然后看看yolov4的论文，作为一个大型调参现场非常有参考意义，使用了很多tricks。我能给的建议就是多看一些经典模型，然后拆解里面的亮点结构并使用。** + +### aa、UP，有Focal LOSS的代码吗？怎么改啊？ +**问：up，YOLO系列使用Focal LOSS的代码你有吗，有提升吗？ +答：很多人试过，提升效果也不大（甚至变的更Low），它自己有自己的正负样本的平衡方式**。改代码的事情，还是自己好好看看代码吧。 + +### ab、部署问题（ONNX、TensorRT等） +我没有具体部署到手机等设备上过，所以很多部署问题我并不了解…… + +## 4、语义分割库问题汇总 +### a、shape不匹配问题 +#### 1）、训练时shape不匹配问题 +**问：up主，为什么运行train.py会提示shape不匹配啊？ +答：在keras环境中，因为你训练的种类和原始的种类不同，网络结构会变化，所以最尾部的shape会有少量不匹配。** + +#### 2）、预测时shape不匹配问题 +**问：为什么我运行predict.py会提示我说shape不匹配呀。** +##### i、copying a param with shape torch.Size([75, 704, 1, 1]) from checkpoint +在Pytorch里面是这样的： +![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171631901.png) +##### ii、Shapes are [1,1,1024,75] and [255,1024,1,1]. for 'Assign_360' (op: 'Assign') with input shapes: [1,1,1024,75], [255,1024,1,1]. +在Keras里面是这样的： +![在这里插入图片描述](https://img-blog.csdnimg.cn/20200722171523380.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70) +**答：原因主要有二： +1、train.py里面的num_classes没改。 +2、预测时num_classes没改。 +3、预测时model_path没改。 +请检查清楚！训练和预测的时候用到的num_classes都需要检查！** + +### b、显存不足问题（OOM、RuntimeError: CUDA out of memory）。 +**问：为什么我运行train.py下面的命令行闪的贼快，还提示OOM啥的？ +答：这是在keras中出现的，爆显存了，可以改小batch_size。** + +**需要注意的是，受到BatchNorm2d影响，batch_size不可为1，至少为2。** + +**问：为什么提示 RuntimeError: CUDA out of memory. Tried to allocate 52.00 MiB (GPU 0; 15.90 GiB total capacity; 14.85 GiB already allocated; 51.88 MiB free; 15.07 GiB reserved in total by PyTorch)？ +答：这是pytorch中出现的，爆显存了，同上。** + +**问：为什么我显存都没利用，就直接爆显存了？ +答：都爆显存了，自然就不利用了，模型没有开始训练。** + +### c、为什么要进行冻结训练与解冻训练，不进行行吗？ +**问：为什么要冻结训练和解冻训练呀？ +答：可以不进行，本质上是为了保证性能不足的同学的训练，如果电脑性能完全不够，可以将Freeze_Epoch和UnFreeze_Epoch设置成一样，只进行冻结训练。** + +**同时这也是迁移学习的思想，因为神经网络主干特征提取部分所提取到的特征是通用的，我们冻结起来训练可以加快训练效率，也可以防止权值被破坏。** +在冻结阶段，模型的主干被冻结了，特征提取网络不发生改变。占用的显存较小，仅对网络进行微调。 +在解冻阶段，模型的主干不被冻结了，特征提取网络会发生改变。占用的显存较大，网络所有的参数都会发生改变。 + +### d、我的LOSS好大啊，有问题吗？（我的LOSS好小啊，有问题吗？） +**问：为什么我的网络不收敛啊，LOSS是XXXX。 +答：不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，我的yolo代码都没有归一化，所以LOSS值看起来比较高，LOSS的值不重要，重要的是是否在变小，预测是否有效果。** + +### e、为什么我训练出来的模型没有预测结果？ +**问：为什么我的训练效果不好？预测了没有框（框不准）。 +答：** +**考虑几个问题： +1、数据集问题，这是最重要的问题。小于500的自行考虑增加数据集；一定要检查数据集的标签，视频中详细解析了VOC数据集的格式，但并不是有输入图片有输出标签即可，还需要确认标签的每一个像素值是否为它对应的种类。很多同学的标签格式不对，最常见的错误格式就是标签的背景为黑，目标为白，此时目标的像素点值为255，无法正常训练，目标需要为1才行。 +2、是否解冻训练，如果数据集分布与常规画面差距过大需要进一步解冻训练，调整主干，加强特征提取能力。 +3、网络问题，可以尝试不同的网络。 +4、训练时长问题，有些同学只训练了几代表示没有效果，按默认参数训练完。 +5、确认自己是否按照步骤去做了。 +6、不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，LOSS的值不重要，重要的是是否收敛。** + +**问：为什么我的训练效果不好？对小目标预测不准确。 +答：对于deeplab和pspnet而言，可以修改一下downsample_factor，当downsample_factor为16的时候下采样倍数过多，效果不太好，可以修改为8。** + +### f、为什么我计算出来的miou是0？ +**问：为什么我的训练效果不好？计算出来的miou是0？。** +答： +与e类似，**考虑几个问题： +1、数据集问题，这是最重要的问题。小于500的自行考虑增加数据集；一定要检查数据集的标签，视频中详细解析了VOC数据集的格式，但并不是有输入图片有输出标签即可，还需要确认标签的每一个像素值是否为它对应的种类。很多同学的标签格式不对，最常见的错误格式就是标签的背景为黑，目标为白，此时目标的像素点值为255，无法正常训练，目标需要为1才行。 +2、是否解冻训练，如果数据集分布与常规画面差距过大需要进一步解冻训练，调整主干，加强特征提取能力。 +3、网络问题，可以尝试不同的网络。 +4、训练时长问题，有些同学只训练了几代表示没有效果，按默认参数训练完。 +5、确认自己是否按照步骤去做了。 +6、不同网络的LOSS不同，LOSS只是一个参考指标，用于查看网络是否收敛，而非评价网络好坏，LOSS的值不重要，重要的是是否收敛。** + +### g、gbk编码错误（'gbk' codec can't decode byte）。 +**问：我怎么出现了gbk什么的编码错误啊：** +```python +UnicodeDecodeError: 'gbk' codec can't decode byte 0xa6 in position 446: illegal multibyte sequence +``` +**答：标签和路径不要使用中文，如果一定要使用中文，请注意处理的时候编码的问题，改成打开文件的encoding方式改为utf-8。** + +### h、我的图片是xxx*xxx的分辨率的，可以用吗？ +**问：我的图片是xxx*xxx的分辨率的，可以用吗！** +**答：可以用，代码里面会自动进行resize与数据增强。** + +### i、我想进行数据增强！怎么增强？ +**问：我想要进行数据增强！怎么做呢？** +**答：可以用，代码里面会自动进行resize与数据增强。** + +### j、多GPU训练。 +**问：怎么进行多GPU训练？ +答：pytorch的大多数代码可以直接使用gpu训练，keras的话直接百度就好了，实现并不复杂，我没有多卡没法详细测试，还需要各位同学自己努力了。** + +### k、能不能训练灰度图？ +**问：能不能训练灰度图（预测灰度图）啊？ +答：我的大多数库会将灰度图转化成RGB进行训练和预测，如果遇到代码不能训练或者预测灰度图的情况，可以尝试一下在get_random_data里面将Image.open后的结果转换成RGB，预测的时候也这样试试。（仅供参考）** + +### l、断点续练问题。 +**问：我已经训练过几个世代了，能不能从这个基础上继续开始训练 +答：可以，你在训练前，和载入预训练权重一样载入训练过的权重就行了。一般训练好的权重会保存在logs文件夹里面，将model_path修改成你要开始的权值的路径即可。** + +### m、我要训练其它的数据集，预训练权重能不能用？ +**问：如果我要训练其它的数据集，预训练权重要怎么办啊？** +**答：数据的预训练权重对不同数据集是通用的，因为特征是通用的，预训练权重对于99%的情况都必须要用，不用的话权值太过随机，特征提取效果不明显，网络训练的结果也不会好。** + +### n、网络如何从0开始训练？ +**问：我要怎么不使用预训练权重啊？ +答：看一看注释、大多数代码是model_path = ''，Freeze_Train = Fasle**，如果设置model_path无用，**那么把载入预训练权重的代码注释了就行。** + +### o、为什么从0开始训练效果这么差（修改了网络主干，效果不好怎么办）？ +**问：为什么我不使用预训练权重效果这么差啊？ +答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，预训练权重还是非常重要的。** + +**问：up，我修改了网络，预训练权重还能用吗？ +答：修改了主干的话，如果不是用的现有的网络，基本上预训练权重是不能用的，要么就自己判断权值里卷积核的shape然后自己匹配，要么只能自己预训练去了；修改了后半部分的话，前半部分的主干部分的预训练权重还是可以用的，如果是pytorch代码的话，需要自己修改一下载入权值的方式，判断shape后载入，如果是keras代码，直接by_name=True,skip_mismatch=True即可。** +权值匹配的方式可以参考如下： +```python +# 加快模型训练的效率 +print('Loading weights into state dict...') +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +model_dict = model.state_dict() +pretrained_dict = torch.load(model_path, map_location=device) +a = {} +for k, v in pretrained_dict.items(): + try: + if np.shape(model_dict[k]) == np.shape(v): + a[k]=v + except: + pass +model_dict.update(a) +model.load_state_dict(model_dict) +print('Finished!') +``` + +**问：为什么从0开始训练效果这么差（我修改了网络主干，效果不好怎么办）？ +答：一般来讲，网络从0开始的训练效果会很差，因为权值太过随机，特征提取效果不明显，因此非常、非常、非常不建议大家从0开始训练！如果一定要从0开始，可以了解imagenet数据集，首先训练分类模型，获得网络的主干部分权值，分类模型的 主干部分 和该模型通用，基于此进行训练。 +网络修改了主干之后也是同样的问题，随机的权值效果很差。** + +**问：怎么在模型上从0开始训练？ +答：在算力不足与调参能力不足的情况下从0开始训练毫无意义。模型特征提取能力在随机初始化参数的情况下非常差。没有好的参数调节能力和算力，无法使得网络正常收敛。** +如果一定要从0开始，那么训练的时候请注意几点： + - 不载入预训练权重。 + - 不要进行冻结训练，注释冻结模型的代码。 + +**问：为什么我不使用预训练权重效果这么差啊？ +答：因为随机初始化的权值不好，提取的特征不好，也就导致了模型训练的效果不好，voc07+12、coco+voc07+12效果都不一样，预训练权重还是非常重要的。** + +### p、你的权值都是哪里来的？ +**问：如果网络不能从0开始训练的话你的权值哪里来的？ +答：有些权值是官方转换过来的，有些权值是自己训练出来的，我用到的主干的imagenet的权值都是官方的。** + + +### q、视频检测与摄像头检测 +**问：怎么用摄像头检测呀？ +答：predict.py修改参数可以进行摄像头检测，也有视频详细解释了摄像头检测的思路。** + +**问：怎么用视频检测呀？ +答：同上** + +### r、如何保存检测出的图片 +**问：检测完的图片怎么保存？ +答：一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。详细看看predict.py文件的注释。** + +**问：怎么用视频保存呀？ +答：详细看看predict.py文件的注释。** + +### s、遍历问题 +**问：如何对一个文件夹的图片进行遍历？ +答：一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了，详细看看predict.py文件的注释。** + +**问：如何对一个文件夹的图片进行遍历？并且保存。 +答：遍历的话一般使用os.listdir先找出文件夹里面的所有图片，然后根据predict.py文件里面的执行思路检测图片就行了。保存的话一般目标检测用的是Image，所以查询一下PIL库的Image如何进行保存。如果有些库用的是cv2，那就是查一下cv2怎么保存图片。详细看看predict.py文件的注释。** + +### t、路径问题（No such file or directory、StopIteration: [Errno 13] Permission denied: 'XXXXXX'） +**问：我怎么出现了这样的错误呀：** +```python +FileNotFoundError: 【Errno 2】 No such file or directory +StopIteration: [Errno 13] Permission denied: 'D:\\Study\\Collection\\Dataset\\VOC07+12+test\\VOCdevkit/VOC2007' +…………………………………… +…………………………………… +``` +**答：去检查一下文件夹路径，查看是否有对应文件；并且检查一下2007_train.txt，其中文件路径是否有错。** +关于路径有几个重要的点： +**文件夹名称中一定不要有空格。 +注意相对路径和绝对路径。 +多百度路径相关的知识。** + +**所有的路径问题基本上都是根目录问题，好好查一下相对目录的概念！** +### u、和原版比较问题，你怎么和原版不一样啊？ +**问：原版的代码是XXX，为什么你的代码是XXX？ +答：是啊……这要不怎么说我不是原版呢……** + +**问：你这个代码和原版比怎么样，可以达到原版的效果么？ +答：基本上可以达到，我都用voc数据测过，我没有好显卡，没有能力在coco上测试与训练。** + +### v、我的检测速度是xxx正常吗？我的检测速度还能增快吗？ +**问：你这个FPS可以到达多少，可以到 XX FPS么？ +答：FPS和机子的配置有关，配置高就快，配置低就慢。** + +**问：我的检测速度是xxx正常吗？我的检测速度还能增快吗？ +答：看配置，配置好速度就快，如果想要配置不变的情况下加快速度，就要修改网络了。** + +**问：为什么论文中说速度可以达到XX，但是这里却没有？ +答：检查是否正确安装了tensorflow-gpu或者pytorch的gpu版本，如果已经正确安装，可以去利用time.time()的方法查看detect_image里面，哪一段代码耗时更长（不仅只有网络耗时长，其它处理部分也会耗时，如绘图等）。有些论文还会使用多batch进行预测，我并没有去实现这个部分。** + +### w、预测图片不显示问题 +**问：为什么你的代码在预测完成后不显示图片？只是在命令行告诉我有什么目标。 +答：给系统安装一个图片查看器就行了。** + +### x、算法评价问题（miou） +**问：怎么计算miou？ +答：参考视频里的miou测量部分。** + +**问：怎么计算Recall、Precision指标。 +答：现有的代码还无法获得，需要各位同学理解一下混淆矩阵的概念，然后自行计算一下。** + +### y、UP，怎么优化模型啊？我想提升效果 +**问：up，怎么修改模型啊，我想发个小论文！ +答：建议目标检测中的yolov4论文，作为一个大型调参现场非常有参考意义，使用了很多tricks。我能给的建议就是多看一些经典模型，然后拆解里面的亮点结构并使用。** + +### z、部署问题（ONNX、TensorRT等） +我没有具体部署到手机等设备上过，所以很多部署问题我并不了解…… + +## 5、交流群问题 +**问：up，有没有QQ群啥的呢？ +答：没有没有，我没有时间管理QQ群……** + +## 6、怎么学习的问题 +**问：up，你的学习路线怎么样的？我是个小白我要怎么学？ +答：这里有几点需要注意哈 +1、我不是高手，很多东西我也不会，我的学习路线也不一定适用所有人。 +2、我实验室不做深度学习，所以我很多东西都是自学，自己摸索，正确与否我也不知道。 +3、我个人觉得学习更靠自学** +学习路线的话，我是先学习了莫烦的python教程，从tensorflow、keras、pytorch入门，入门完之后学的SSD，YOLO，然后了解了很多经典的卷积网，后面就开始学很多不同的代码了，我的学习方法就是一行一行的看，了解整个代码的执行流程，特征层的shape变化等，花了很多时间也没有什么捷径，就是要花时间吧。