基于YOLOv5的道路标志识别项目（yolov5界面GUI）
\n\n\n\n这是一个关于yolov5的道路标志识别项目，使用Pyqt5开发界面，Yolov5训练模型，数据库Mysql，包含五个模块：初始化参数、标志识别、数据库、数据分析和图像处理。\n\n## 软件截图\n\n* ### 标志识别模块\n`左下角三个勾选框分别是结果保存、启动数据库录入、以及模型可视化分析`\n\n \n\n \n \n* ### 图像处理与数据增强模块\n`右侧栏是自定义参数的批量图像数据增强（按一定概率对一个文件夹所有图片使用勾选的数据增加方法）`\n\n \n\n* ### 初始化参数模块\n` 模型基本参数勾选配置`\n\n \n\n\n* ### 数据库模块\n \n \n \n* ### 数据分析模块\n \n* ### 登录界面\n \n\n### 演示视频\n\n[基于YOLOV5的道路标志识别系统](https://www.bilibili.com/video/BV1Ck4y1Y7Bk/?spm_id_from=333.999.0.0&vd_source=40d9cda43378fbc89cd5184e09bf1272)\n\n\n### 安装依赖\n\npip install -r requirements.txt\n\n---\n## **快速开始**\n\n### 1. **设置数据库**\n\n为了运行应用程序，您需要设置您的 MySQL 数据库。按照以下步骤准备您的数据库：\n\n- **自动数据库创建（可选）**：\n - 如果您喜欢自动设置，我们提供了一个批处理脚本。运行 **`setup_database.bat`** 脚本来创建数据库。这需要在您的系统上安装并配置 MySQL。\n- **手动数据库创建**：\n - 或者，您可以在 MySQL 中手动创建数据库。在您的 MySQL 环境中导入并执行 **`data/regn_mysql.sql`** 文件，以设置必要的数据库和表。\n\n### 2. **在代码中配置数据库连接**\n\n设置数据库之后，请更新代码中的连接设置，请更改成你本地数据库的身份验证信息（这4个变量在代码的开头 , 大约在59行，具体如下）；附：这些身份验证信息在代码中被两次调用(大约第111行和第1783行)\n\n```python\n# 数据库连接设置作为全局变量\nDB_HOST = 'localhost' # 数据库主机\nDB_USER = 'root' # 数据库用户\nDB_PASSWORD = '1234' # 数据库密码\nDB_NAME = 'traffic_sign_recognition' # 数据库名\n```\n\n### **关于数据库链接的注意事项**\n\n如果遇到 **`RuntimeError: 'cryptography' package is required for sha256_password or caching_sha2_password auth methods`** 错误，这是因为数据库身份验证出错了，需要正确地创建数据库并输入密码。\n并且如果你本地没有启动mysql服务，也会报这个错，所以请确保你的mysql服务已经启动。\n### 3. 运行 `main.py`。\n\n### 4. 输入您的账号和密码以登录。\n\n以下是默认的登录凭据：\n\n| 用户名 | 密码 |\n|----------|----------|\n| admin | 123456 |\n| 1 | 2 |\n\n或者修改`main.py`中的主函数：删除登陆逻辑，以直接进入系统而无需进行身份验证。\n\n---\n\n## 项目模块\n\n- `pt`文件夹：存放模型(best.pt是道路标志识别模型)\n- `main_with`文件夹：`login.py`(登陆ui)、`win.py`(主ui)\n- `dialog`文件夹：rtsp弹出界面\n- `apprcc_rc.py`：资源文件\n- `login_ji.py`：界面登陆逻辑文件\n- `data/run/run-exp52`：300轮训练后的道路标志识别模型\n- `utils/tt100k_to_voc-main`文件夹：json转yolo格式\n- `result`保存一些推理文件，`run`保存训练文件\n- 数据集：[TT100k : Traffic-Sign Detection and Classification in the Wild](https://cg.cs.tsinghua.edu.cn/traffic-sign/)\n- 数据库文件：`data`文件夹下`-regn_mysql.sql`\n\n\n> 由于这个项目是在我学习YOLOv5时完成的（已经过了很长一段时间），因此主要的逻辑代码都集中在main.py文件中。换句话说，我没有将不同功能模块化，没有进行模块结构的清晰划分。虽然现在我想给它划分一下模块结构，但是我还是太懒了，嘻嘻 :smile:。如果您有兴趣，可以将其模块化，这样它就会更加清晰。\n\n\n\n## 致谢\n\n- [将TT100K数据集转成VOC格式，并且用Python脚本选出45类超过100张的图片和XML](https://blog.csdn.net/Hankerchen/article/details/120727299?spm=1001.2014.3001.5502)\n- https://github.com/Javacr/PyQt5-YOLOv5\n\n## 联系我们\n> 微信：AIGC004\n> \n> 邮箱: 2545197649@qq.com\n> \n> 公众号：AI进修生\n\n\n## Star History\n\n[](https://star-history.com/#Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI&Date)\n" }, { "path": "data/regn_mysql.sql", "content": "/*\r\nSQLyog Community v13.2.0 (64 bit)\r\nMySQL - 8.0.32 : Database - traffic_sign_recognition\r\n*********************************************************************\r\n*/\r\n\r\n/*!40101 SET NAMES utf8 */;\r\n\r\n/*!40101 SET SQL_MODE=''*/;\r\n\r\n/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;\r\n/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;\r\n/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;\r\n/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;\r\nCREATE DATABASE /*!32312 IF NOT EXISTS*/`traffic_sign_recognition` /*!40100 DEFAULT CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci */ /*!80016 DEFAULT ENCRYPTION='N' */;\r\n\r\nUSE `traffic_sign_recognition`;\r\n\r\n/*Table structure for table `detection_results` */\r\n\r\nDROP TABLE IF EXISTS `detection_results`;\r\n\r\nCREATE TABLE `detection_results` (\r\n `id` int NOT NULL AUTO_INCREMENT,\r\n `sign_type` varchar(255) NOT NULL,\r\n `sign_count` int NOT NULL,\r\n `detection_time` varchar(255) DEFAULT NULL,\r\n `additional_info` text,\r\n PRIMARY KEY (`id`)\r\n) ENGINE=InnoDB AUTO_INCREMENT=456 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;\r\n\r\n/*Data for the table `detection_results` */\r\n\r\ninsert into `detection_results`(`id`,`sign_type`,`sign_count`,`detection_time`,`additional_info`) values \r\n(1,'stop',5,'2022-10-02 15:30:00','Weather condition: sunny'),\r\n(3,'pn',1,'2023-04-09 20:00:01','Confidence: 0.91'),\r\n(4,'pl40',1,'2023-04-09 20:00:01','Confidence: 0.93'),\r\n(5,'i2',1,'2023-04-09 20:00:01','Confidence: 0.53'),\r\n(6,'i4',1,'2023-04-09 20:00:01','Confidence: 0.88'),\r\n(8,'pl40',1,'2023-04-09 20:00:01','Confidence: 0.93'),\r\n(14,'pn',1,'2023-04-09 20:00:01','Confidence: 0.90'),\r\n(15,'pl40',1,'2023-04-09 20:00:01','Confidence: 0.94'),\r\n(16,'i2',1,'2023-04-09 20:00:01','Confidence: 0.95'),\r\n(17,'i4',1,'2023-04-09 20:00:02','Confidence: 0.89'),\r\n(18,'pn',1,'2023-04-09 20:00:02','Confidence: 0.90'),\r\n(19,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.94'),\r\n(20,'i2',1,'2023-04-09 20:00:02','Confidence: 0.96'),\r\n(21,'pn',1,'2023-04-09 20:00:02','Confidence: 0.89'),\r\n(22,'i4',1,'2023-04-09 20:00:02','Confidence: 0.91'),\r\n(23,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.94'),\r\n(24,'i2',1,'2023-04-09 20:00:02','Confidence: 0.94'),\r\n(25,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.92'),\r\n(26,'i4',1,'2023-04-09 20:00:02','Confidence: 0.92'),\r\n(27,'pn',1,'2023-04-09 20:00:02','Confidence: 0.93'),\r\n(28,'i2',1,'2023-04-09 20:00:02','Confidence: 0.96'),\r\n(29,'pn',1,'2023-04-09 20:00:02','Confidence: 0.91'),\r\n(30,'i4',1,'2023-04-09 20:00:02','Confidence: 0.92'),\r\n(31,'pl40',1,'2023-04-09 20:00:02','Confidence: 0.94'),\r\n(32,'i2',1,'2023-04-09 20:00:02','Confidence: 0.97'),\r\n(33,'pn',1,'2023-04-09 20:00:03','Confidence: 0.92'),\r\n(34,'i4',1,'2023-04-09 20:00:03','Confidence: 0.92'),\r\n(35,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.94'),\r\n(36,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),\r\n(37,'pn',1,'2023-04-09 20:00:03','Confidence: 0.89'),\r\n(38,'i4',1,'2023-04-09 20:00:03','Confidence: 0.93'),\r\n(39,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.94'),\r\n(40,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),\r\n(41,'pn',1,'2023-04-09 20:00:03','Confidence: 0.88'),\r\n(42,'i4',1,'2023-04-09 20:00:03','Confidence: 0.93'),\r\n(43,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.94'),\r\n(44,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),\r\n(45,'pn',1,'2023-04-09 20:00:03','Confidence: 0.90'),\r\n(46,'i4',1,'2023-04-09 20:00:03','Confidence: 0.94'),\r\n(47,'pl40',1,'2023-04-09 20:00:03','Confidence: 0.95'),\r\n(48,'i2',1,'2023-04-09 20:00:03','Confidence: 0.96'),\r\n(49,'pn',1,'2023-04-09 20:00:04','Confidence: 0.91'),\r\n(50,'i4',1,'2023-04-09 20:00:04','Confidence: 0.93'),\r\n(51,'pl40',1,'2023-04-09 20:00:04','Confidence: 0.94'),\r\n(52,'i2',1,'2023-04-09 20:00:04','Confidence: 0.96'),\r\n(53,'pn',1,'2023-04-09 20:00:04','Confidence: 0.92'),\r\n(54,'i4',1,'2023-04-09 20:00:04','Confidence: 0.93'),\r\n(55,'pl40',1,'2023-04-09 20:00:04','Confidence: 0.94'),\r\n(56,'i2',1,'2023-04-09 20:00:04','Confidence: 0.95'),\r\n(57,'pn',1,'2023-04-09 20:00:04','Confidence: 0.91'),\r\n(58,'i4',1,'2023-04-09 20:00:04','Confidence: 0.93'),\r\n(59,'i2',1,'2023-04-09 20:00:04','Confidence: 0.93'),\r\n(60,'pl40',1,'2023-04-09 20:00:04','Confidence: 0.95'),\r\n(61,'pn',1,'2023-04-09 20:00:04','Confidence: 0.93'),\r\n(62,'i4',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(63,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.95'),\r\n(64,'i2',1,'2023-04-09 20:00:05','Confidence: 0.96'),\r\n(65,'i4',1,'2023-04-09 20:00:05','Confidence: 0.92'),\r\n(66,'pn',1,'2023-04-09 20:00:05','Confidence: 0.92'),\r\n(67,'i2',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(68,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(69,'pn',1,'2023-04-09 20:00:05','Confidence: 0.92'),\r\n(70,'i4',1,'2023-04-09 20:00:05','Confidence: 0.93'),\r\n(71,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.95'),\r\n(72,'i2',1,'2023-04-09 20:00:05','Confidence: 0.95'),\r\n(73,'pn',1,'2023-04-09 20:00:05','Confidence: 0.93'),\r\n(74,'i4',1,'2023-04-09 20:00:05','Confidence: 0.93'),\r\n(75,'pl40',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(76,'i2',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(77,'i4',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(78,'pn',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(79,'i2',1,'2023-04-09 20:00:05','Confidence: 0.94'),\r\n(80,'pl40',1,'2023-04-09 20:00:06','Confidence: 0.95'),\r\n(81,'i4',1,'2023-04-09 20:00:06','Confidence: 0.92'),\r\n(82,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),\r\n(83,'pl40',1,'2023-04-09 20:00:06','Confidence: 0.95'),\r\n(84,'i2',1,'2023-04-09 20:00:06','Confidence: 0.95'),\r\n(85,'i2',1,'2023-04-09 20:00:06','Confidence: 0.70'),\r\n(86,'pl40',1,'2023-04-09 20:00:06','Confidence: 0.91'),\r\n(87,'i4',1,'2023-04-09 20:00:06','Confidence: 0.91'),\r\n(88,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),\r\n(89,'i2',2,'2023-04-09 20:00:06','Confidence: 0.97'),\r\n(90,'p26',1,'2023-04-09 20:00:06','Confidence: 0.62'),\r\n(91,'i4',1,'2023-04-09 20:00:06','Confidence: 0.93'),\r\n(92,'i2',1,'2023-04-09 20:00:06','Confidence: 0.94'),\r\n(93,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),\r\n(94,'i4',1,'2023-04-09 20:00:06','Confidence: 0.91'),\r\n(95,'i2',1,'2023-04-09 20:00:06','Confidence: 0.94'),\r\n(96,'pn',1,'2023-04-09 20:00:06','Confidence: 0.94'),\r\n(97,'i2',1,'2023-04-09 20:00:06','Confidence: 0.92'),\r\n(98,'pn',1,'2023-04-09 20:00:06','Confidence: 0.93'),\r\n(99,'i4',1,'2023-04-09 20:00:06','Confidence: 0.93'),\r\n(100,'i2',1,'2023-04-09 20:00:07','Confidence: 0.91'),\r\n(101,'i4',1,'2023-04-09 20:00:07','Confidence: 0.92'),\r\n(102,'pn',1,'2023-04-09 20:00:07','Confidence: 0.93'),\r\n(103,'i5',1,'2023-04-09 20:00:07','Confidence: 0.53'),\r\n(104,'i2',1,'2023-04-09 20:00:07','Confidence: 0.89'),\r\n(105,'i4',1,'2023-04-09 20:00:07','Confidence: 0.91'),\r\n(106,'i4',1,'2023-04-09 20:00:07','Confidence: 0.89'),\r\n(107,'i2',1,'2023-04-09 20:00:07','Confidence: 0.93'),\r\n(108,'i2',1,'2023-04-09 20:00:07','Confidence: 0.77'),\r\n(109,'i4',1,'2023-04-09 20:00:07','Confidence: 0.80'),\r\n(110,'pl40',1,'2023-04-09 20:00:12','Confidence: 0.80'),\r\n(111,'pl40',1,'2023-04-09 20:00:12','Confidence: 0.78'),\r\n(112,'pl40',1,'2023-04-09 20:00:13','Confidence: 0.43'),\r\n(113,'pl40',1,'2023-04-09 20:00:13','Confidence: 0.57'),\r\n(114,'pr40',1,'2023-04-09 20:00:13','Confidence: 0.76'),\r\n(115,'ph4',1,'2023-04-09 20:00:13','Confidence: 0.42'),\r\n(116,'p19',1,'2023-04-09 20:00:13','Confidence: 0.47'),\r\n(117,'pl20',1,'2023-04-09 20:00:13','Confidence: 0.69'),\r\n(118,'ph4',1,'2023-04-09 20:00:13','Confidence: 0.46'),\r\n(119,'pl20',1,'2023-04-09 20:00:13','Confidence: 0.60'),\r\n(120,'pl20',1,'2023-04-09 20:00:14','Confidence: 0.55'),\r\n(121,'p19',1,'2023-04-09 20:00:14','Confidence: 0.43'),\r\n(122,'pl20',1,'2023-04-09 20:00:14','Confidence: 0.43'),\r\n(123,'p10',1,'2023-04-09 20:00:14','Confidence: 0.43'),\r\n(124,'pl40',1,'2023-04-09 20:00:14','Confidence: 0.44'),\r\n(125,'ph4',1,'2023-04-09 20:00:14','Confidence: 0.46'),\r\n(126,'p6',1,'2023-04-09 20:00:14','Confidence: 0.48'),\r\n(127,'p19',1,'2023-04-09 20:00:14','Confidence: 0.58'),\r\n(128,'p10',1,'2023-04-09 20:00:14','Confidence: 0.44'),\r\n(129,'ph4',1,'2023-04-09 20:00:14','Confidence: 0.60'),\r\n(130,'pm20',1,'2023-04-09 20:00:14','Confidence: 0.71'),\r\n(131,'p10',1,'2023-04-09 20:00:15','Confidence: 0.57'),\r\n(132,'pl40',1,'2023-04-09 20:00:15','Confidence: 0.80'),\r\n(133,'p10',1,'2023-04-09 20:00:15','Confidence: 0.68'),\r\n(134,'pl40',1,'2023-04-09 20:00:15','Confidence: 0.81'),\r\n(135,'pn',1,'2023-04-09 20:25:49','Confidence: 0.50'),\r\n(136,'pl40',1,'2023-04-09 20:25:49','Confidence: 0.89'),\r\n(137,'pl40',1,'2023-04-09 20:25:49','Confidence: 0.91'),\r\n(138,'pn',1,'2023-04-09 20:25:49','Confidence: 0.52'),\r\n(139,'pl40',1,'2023-04-09 20:25:49','Confidence: 0.92'),\r\n(140,'pn',1,'2023-04-09 20:25:49','Confidence: 0.82'),\r\n(141,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.92'),\r\n(142,'pn',1,'2023-04-09 20:25:50','Confidence: 0.82'),\r\n(143,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.91'),\r\n(144,'pn',1,'2023-04-09 20:25:50','Confidence: 0.45'),\r\n(145,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.85'),\r\n(146,'pn',1,'2023-04-09 20:25:50','Confidence: 0.82'),\r\n(147,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.91'),\r\n(148,'pn',1,'2023-04-09 20:25:50','Confidence: 0.85'),\r\n(149,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.91'),\r\n(150,'pn',1,'2023-04-09 20:25:50','Confidence: 0.90'),\r\n(151,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.90'),\r\n(152,'pn',1,'2023-04-09 20:25:50','Confidence: 0.89'),\r\n(153,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.91'),\r\n(154,'pl40',1,'2023-04-09 20:25:50','Confidence: 0.89'),\r\n(155,'pn',1,'2023-04-09 20:25:50','Confidence: 0.89'),\r\n(156,'pn',1,'2023-04-09 20:25:51','Confidence: 0.78'),\r\n(157,'pl40',1,'2023-04-09 20:25:51','Confidence: 0.89'),\r\n(158,'pl40',1,'2023-04-09 20:25:51','Confidence: 0.89'),\r\n(159,'pn',1,'2023-04-09 20:25:51','Confidence: 0.90'),\r\n(160,'pn',1,'2023-04-09 20:25:51','Confidence: 0.88'),\r\n(161,'pl40',1,'2023-04-09 20:25:51','Confidence: 0.92'),\r\n(162,'pn',1,'2023-04-09 20:25:51','Confidence: 0.86'),\r\n(163,'pl40',1,'2023-04-09 20:25:51','Confidence: 0.91'),\r\n(164,'pn',1,'2023-04-09 20:25:51','Confidence: 0.78'),\r\n(165,'pl40',1,'2023-04-09 20:25:51','Confidence: 0.91'),\r\n(166,'pn',1,'2023-04-09 20:25:51','Confidence: 0.90'),\r\n(167,'pl40',1,'2023-04-09 20:25:51','Confidence: 0.91'),\r\n(168,'pn',1,'2023-04-09 20:25:51','Confidence: 0.90'),\r\n(169,'pl40',1,'2023-04-09 20:25:51','Confidence: 0.91'),\r\n(170,'pn',1,'2023-04-09 20:25:52','Confidence: 0.91'),\r\n(171,'pl40',1,'2023-04-09 20:25:52','Confidence: 0.93'),\r\n(172,'i2',1,'2023-04-09 20:25:52','Confidence: 0.53'),\r\n(173,'i4',1,'2023-04-09 20:25:52','Confidence: 0.88'),\r\n(174,'pn',1,'2023-04-09 20:25:52','Confidence: 0.91'),\r\n(175,'pl40',1,'2023-04-09 20:25:52','Confidence: 0.93'),\r\n(176,'pn',1,'2023-04-09 20:25:52','Confidence: 0.86'),\r\n(177,'i4',1,'2023-04-09 20:25:52','Confidence: 0.88'),\r\n(178,'pl40',1,'2023-04-09 20:25:52','Confidence: 0.91'),\r\n(179,'i2',1,'2023-04-09 20:25:52','Confidence: 0.94'),\r\n(180,'i4',1,'2023-04-09 20:25:52','Confidence: 0.87'),\r\n(181,'pn',1,'2023-04-09 20:25:52','Confidence: 0.90'),\r\n(182,'pl40',1,'2023-04-09 20:25:52','Confidence: 0.94'),\r\n(183,'i2',1,'2023-04-09 20:25:52','Confidence: 0.95'),\r\n(184,'i4',1,'2023-04-09 20:25:52','Confidence: 0.89'),\r\n(185,'pn',1,'2023-04-09 20:25:52','Confidence: 0.90'),\r\n(186,'pl40',1,'2023-04-09 20:25:52','Confidence: 0.94'),\r\n(187,'i2',1,'2023-04-09 20:25:52','Confidence: 0.96'),\r\n(188,'pn',1,'2023-04-09 20:25:53','Confidence: 0.89'),\r\n(189,'i4',1,'2023-04-09 20:25:53','Confidence: 0.91'),\r\n(190,'pl40',1,'2023-04-09 20:25:53','Confidence: 0.94'),\r\n(191,'i2',1,'2023-04-09 20:25:53','Confidence: 0.94'),\r\n(192,'pl40',1,'2023-04-09 20:25:53','Confidence: 0.92'),\r\n(193,'i4',1,'2023-04-09 20:25:53','Confidence: 0.92'),\r\n(194,'pn',1,'2023-04-09 20:25:53','Confidence: 0.93'),\r\n(195,'i2',1,'2023-04-09 20:25:53','Confidence: 0.96'),\r\n(196,'pn',1,'2023-04-09 20:25:53','Confidence: 0.91'),\r\n(197,'i4',1,'2023-04-09 20:25:53','Confidence: 0.92'),\r\n(198,'pl40',1,'2023-04-09 20:25:53','Confidence: 0.94'),\r\n(199,'i2',1,'2023-04-09 20:25:53','Confidence: 0.97'),\r\n(200,'pn',1,'2023-04-09 20:25:53','Confidence: 0.92'),\r\n(201,'i4',1,'2023-04-09 20:25:53','Confidence: 0.92'),\r\n(202,'pl40',1,'2023-04-09 20:25:53','Confidence: 0.94'),\r\n(203,'i2',1,'2023-04-09 20:25:53','Confidence: 0.96'),\r\n(204,'pn',1,'2023-04-09 20:25:53','Confidence: 0.89'),\r\n(205,'i4',1,'2023-04-09 20:25:53','Confidence: 0.93'),\r\n(206,'pl40',1,'2023-04-09 20:25:53','Confidence: 0.94'),\r\n(207,'i2',1,'2023-04-09 20:25:54','Confidence: 0.96'),\r\n(208,'pn',1,'2023-04-09 20:25:54','Confidence: 0.88'),\r\n(209,'i4',1,'2023-04-09 20:25:54','Confidence: 0.93'),\r\n(210,'pl40',1,'2023-04-09 20:25:54','Confidence: 0.94'),\r\n(211,'i2',1,'2023-04-09 20:25:54','Confidence: 0.96'),\r\n(212,'pn',1,'2023-04-09 20:25:54','Confidence: 0.90'),\r\n(213,'i4',1,'2023-04-09 20:25:54','Confidence: 0.94'),\r\n(214,'pl40',1,'2023-04-09 20:25:54','Confidence: 0.95'),\r\n(215,'i2',1,'2023-04-09 20:25:54','Confidence: 0.96'),\r\n(216,'pn',1,'2023-04-09 20:25:54','Confidence: 0.91'),\r\n(217,'i4',1,'2023-04-09 20:25:54','Confidence: 0.93'),\r\n(218,'pl40',1,'2023-04-09 20:25:54','Confidence: 0.94'),\r\n(219,'i2',1,'2023-04-09 20:25:54','Confidence: 0.96'),\r\n(220,'pn',1,'2023-04-09 20:25:54','Confidence: 0.92'),\r\n(221,'i4',1,'2023-04-09 20:25:55','Confidence: 0.93'),\r\n(222,'pl40',1,'2023-04-09 20:25:55','Confidence: 0.94'),\r\n(223,'i2',1,'2023-04-09 20:25:55','Confidence: 0.95'),\r\n(224,'pn',1,'2023-04-09 20:25:55','Confidence: 0.91'),\r\n(225,'i4',1,'2023-04-09 20:25:55','Confidence: 0.93'),\r\n(226,'i2',1,'2023-04-09 20:25:55','Confidence: 0.93'),\r\n(227,'pl40',1,'2023-04-09 20:25:55','Confidence: 0.95'),\r\n(228,'pn',1,'2023-04-09 20:25:55','Confidence: 0.93'),\r\n(229,'i4',1,'2023-04-09 20:25:55','Confidence: 0.94'),\r\n(230,'pl40',1,'2023-04-09 20:25:55','Confidence: 0.95'),\r\n(231,'i2',1,'2023-04-09 20:25:55','Confidence: 0.96'),\r\n(232,'i4',1,'2023-04-09 20:25:55','Confidence: 0.92'),\r\n(233,'pn',1,'2023-04-09 20:25:55','Confidence: 0.92'),\r\n(234,'i2',1,'2023-04-09 20:25:55','Confidence: 0.94'),\r\n(235,'pl40',1,'2023-04-09 20:25:55','Confidence: 0.94'),\r\n(236,'pn',1,'2023-04-09 20:25:55','Confidence: 0.92'),\r\n(237,'i4',1,'2023-04-09 20:25:55','Confidence: 0.93'),\r\n(238,'pl40',1,'2023-04-09 20:25:56','Confidence: 0.95'),\r\n(239,'i2',1,'2023-04-09 20:25:56','Confidence: 0.95'),\r\n(240,'pn',1,'2023-04-09 20:25:56','Confidence: 0.93'),\r\n(241,'i4',1,'2023-04-09 20:25:56','Confidence: 0.93'),\r\n(242,'pl40',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(243,'i2',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(244,'i4',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(245,'pn',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(246,'i2',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(247,'pl40',1,'2023-04-09 20:25:56','Confidence: 0.95'),\r\n(248,'i4',1,'2023-04-09 20:25:56','Confidence: 0.92'),\r\n(249,'pn',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(250,'pl40',1,'2023-04-09 20:25:56','Confidence: 0.95'),\r\n(251,'i2',1,'2023-04-09 20:25:56','Confidence: 0.95'),\r\n(252,'i2',1,'2023-04-09 20:25:56','Confidence: 0.70'),\r\n(253,'pl40',1,'2023-04-09 20:25:56','Confidence: 0.91'),\r\n(254,'i4',1,'2023-04-09 20:25:56','Confidence: 0.91'),\r\n(255,'pn',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(256,'i2',2,'2023-04-09 20:25:56','Confidence: 0.97'),\r\n(257,'p26',1,'2023-04-09 20:25:56','Confidence: 0.62'),\r\n(258,'i4',1,'2023-04-09 20:25:56','Confidence: 0.93'),\r\n(259,'i2',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(260,'pn',1,'2023-04-09 20:25:56','Confidence: 0.94'),\r\n(261,'i4',1,'2023-04-09 20:25:57','Confidence: 0.91'),\r\n(262,'i2',1,'2023-04-09 20:25:57','Confidence: 0.94'),\r\n(263,'pn',1,'2023-04-09 20:25:57','Confidence: 0.94'),\r\n(264,'i2',1,'2023-04-09 20:25:57','Confidence: 0.92'),\r\n(265,'pn',1,'2023-04-09 20:25:57','Confidence: 0.93'),\r\n(266,'i4',1,'2023-04-09 20:25:57','Confidence: 0.93'),\r\n(267,'i2',1,'2023-04-09 20:25:57','Confidence: 0.91'),\r\n(268,'i4',1,'2023-04-09 20:25:57','Confidence: 0.92'),\r\n(269,'pn',1,'2023-04-09 20:25:57','Confidence: 0.93'),\r\n(270,'i5',1,'2023-04-09 20:25:57','Confidence: 0.53'),\r\n(271,'i2',1,'2023-04-09 20:25:57','Confidence: 0.89'),\r\n(272,'i4',1,'2023-04-09 20:25:57','Confidence: 0.91'),\r\n(273,'i4',1,'2023-04-09 20:25:57','Confidence: 0.89'),\r\n(274,'i2',1,'2023-04-09 20:25:57','Confidence: 0.93'),\r\n(275,'i2',1,'2023-04-09 20:25:57','Confidence: 0.77'),\r\n(276,'i4',1,'2023-04-09 20:25:57','Confidence: 0.80'),\r\n(277,'pl40',1,'2023-04-09 20:26:02','Confidence: 0.35'),\r\n(278,'pl40',1,'2023-04-09 20:26:02','Confidence: 0.80'),\r\n(279,'pl40',1,'2023-04-09 20:26:02','Confidence: 0.78'),\r\n(280,'pl40',1,'2023-04-09 20:26:02','Confidence: 0.43'),\r\n(281,'pl40',1,'2023-04-09 20:26:02','Confidence: 0.57'),\r\n(282,'pr40',1,'2023-04-09 20:26:03','Confidence: 0.76'),\r\n(283,'ph4',1,'2023-04-09 20:26:03','Confidence: 0.42'),\r\n(284,'p19',1,'2023-04-09 20:26:03','Confidence: 0.47'),\r\n(285,'pl20',1,'2023-04-09 20:26:03','Confidence: 0.69'),\r\n(286,'ph4',1,'2023-04-09 20:26:03','Confidence: 0.46'),\r\n(287,'pl20',1,'2023-04-09 20:26:03','Confidence: 0.60'),\r\n(288,'p10',1,'2023-04-09 20:26:03','Confidence: 0.37'),\r\n(289,'pl20',1,'2023-04-09 20:26:04','Confidence: 0.55'),\r\n(290,'p19',1,'2023-04-09 20:26:04','Confidence: 0.43'),\r\n(291,'pl20',1,'2023-04-09 20:26:04','Confidence: 0.43'),\r\n(292,'pl40',1,'2023-04-09 20:26:04','Confidence: 0.38'),\r\n(293,'p10',1,'2023-04-09 20:26:04','Confidence: 0.43'),\r\n(294,'pl40',1,'2023-04-09 20:26:04','Confidence: 0.44'),\r\n(295,'ph4',1,'2023-04-09 20:26:04','Confidence: 0.46'),\r\n(296,'p6',1,'2023-04-09 20:26:04','Confidence: 0.48'),\r\n(297,'p19',1,'2023-04-09 20:26:04','Confidence: 0.58'),\r\n(298,'p10',1,'2023-04-09 20:26:04','Confidence: 0.44'),\r\n(299,'ph4',1,'2023-04-09 20:26:04','Confidence: 0.60'),\r\n(300,'pm20',1,'2023-04-09 20:26:04','Confidence: 0.71'),\r\n(301,'p10',1,'2023-04-09 20:26:04','Confidence: 0.37'),\r\n(302,'p10',1,'2023-04-09 20:26:05','Confidence: 0.57'),\r\n(303,'pl40',1,'2023-04-09 20:26:05','Confidence: 0.80'),\r\n(304,'p10',1,'2023-04-09 20:26:05','Confidence: 0.68'),\r\n(305,'pl40',1,'2023-04-09 20:26:05','Confidence: 0.81'),\r\n(306,'p6',1,'2023-04-09 20:26:05','Confidence: 0.37'),\r\n(307,'w32',1,'2023-04-09 20:26:05','Confidence: 0.44'),\r\n(308,'pl70',1,'2023-04-09 20:26:05','Confidence: 0.49'),\r\n(309,'p10',1,'2023-04-09 20:26:05','Confidence: 0.55'),\r\n(310,'pl40',1,'2023-04-09 20:26:05','Confidence: 0.64'),\r\n(311,'pl40',1,'2023-04-09 20:26:05','Confidence: 0.81'),\r\n(312,'w32',1,'2023-04-09 20:26:05','Confidence: 0.39'),\r\n(313,'p10',1,'2023-04-09 20:26:05','Confidence: 0.84'),\r\n(314,'pl40',1,'2023-04-09 20:26:05','Confidence: 0.88'),\r\n(315,'w32',1,'2023-04-09 20:26:05','Confidence: 0.48'),\r\n(316,'pl20',1,'2023-04-09 20:26:06','Confidence: 0.49'),\r\n(317,'w32',1,'2023-04-09 20:26:06','Confidence: 0.43'),\r\n(318,'p19',1,'2023-04-09 20:26:06','Confidence: 0.82'),\r\n(319,'pl40',1,'2023-04-09 20:26:06','Confidence: 0.92'),\r\n(320,'p23',1,'2023-04-09 20:26:06','Confidence: 0.40'),\r\n(321,'p19',1,'2023-04-09 20:26:06','Confidence: 0.68'),\r\n(322,'pl40',1,'2023-04-09 20:26:06','Confidence: 0.83'),\r\n(323,'pl40',1,'2023-04-09 20:26:06','Confidence: 0.37'),\r\n(324,'w32',1,'2023-04-09 20:26:06','Confidence: 0.40'),\r\n(325,'p10',1,'2023-04-09 20:26:06','Confidence: 0.75'),\r\n(326,'pl60',1,'2023-04-09 20:26:06','Confidence: 0.86'),\r\n(327,'w32',1,'2023-04-09 20:26:06','Confidence: 0.53'),\r\n(328,'p10',1,'2023-04-09 20:26:06','Confidence: 0.53'),\r\n(329,'p6',1,'2023-04-09 20:26:06','Confidence: 0.53'),\r\n(330,'pm30',1,'2023-04-09 20:26:06','Confidence: 0.55'),\r\n(331,'pl60',1,'2023-04-09 20:26:06','Confidence: 0.81'),\r\n(332,'p6',1,'2023-04-09 20:26:06','Confidence: 0.43'),\r\n(333,'w32',1,'2023-04-09 20:26:07','Confidence: 0.58'),\r\n(334,'pl40',1,'2023-04-09 20:26:07','Confidence: 0.64'),\r\n(335,'w32',1,'2023-04-09 20:26:07','Confidence: 0.51'),\r\n(336,'pm20',1,'2023-04-09 20:26:07','Confidence: 0.82'),\r\n(337,'p10',1,'2023-04-09 20:26:07','Confidence: 0.86'),\r\n(338,'w32',1,'2023-04-09 20:26:07','Confidence: 0.41'),\r\n(339,'pl60',1,'2023-04-09 20:26:07','Confidence: 0.66'),\r\n(340,'p19',1,'2023-04-09 20:26:07','Confidence: 0.87'),\r\n(341,'p10',1,'2023-04-09 20:26:07','Confidence: 0.43'),\r\n(342,'w32',1,'2023-04-09 20:26:07','Confidence: 0.47'),\r\n(343,'pl40',1,'2023-04-09 20:26:07','Confidence: 0.53'),\r\n(344,'p6',1,'2023-04-09 20:26:07','Confidence: 0.69'),\r\n(345,'pl40',1,'2023-04-09 20:26:07','Confidence: 0.43'),\r\n(346,'p23',1,'2023-04-09 20:26:07','Confidence: 0.53'),\r\n(347,'pm30',1,'2023-04-09 20:26:07','Confidence: 0.44'),\r\n(348,'pm20',1,'2023-04-09 20:26:07','Confidence: 0.88'),\r\n(349,'p23',1,'2023-04-09 20:26:08','Confidence: 0.48'),\r\n(350,'p19',1,'2023-04-09 20:26:08','Confidence: 0.61'),\r\n(351,'pm20',1,'2023-04-09 20:26:08','Confidence: 0.87'),\r\n(352,'w32',1,'2023-04-09 20:26:08','Confidence: 0.40'),\r\n(353,'p19',1,'2023-04-09 20:26:08','Confidence: 0.89'),\r\n(354,'pm20',1,'2023-04-09 20:26:08','Confidence: 0.90'),\r\n(355,'p19',1,'2023-04-09 20:26:08','Confidence: 0.40'),\r\n(356,'w32',1,'2023-04-09 20:26:08','Confidence: 0.49'),\r\n(357,'p10',1,'2023-04-09 20:26:08','Confidence: 0.52'),\r\n(358,'p23',1,'2023-04-09 20:26:08','Confidence: 0.61'),\r\n(359,'pm20',1,'2023-04-09 20:26:08','Confidence: 0.74'),\r\n(360,'pm30',1,'2023-04-09 20:26:08','Confidence: 0.80'),\r\n(362,'w55',1,'2023-05-12 19:24:42','Confidence: 0.90'),\r\n(363,'w55',1,'2023-05-12 19:24:45','Confidence: 0.92'),\r\n(364,'w55',1,'2023-05-12 19:24:45','Confidence: 0.90'),\r\n(365,'w55',1,'2023-05-12 19:24:45','Confidence: 0.93'),\r\n(366,'w55',1,'2023-05-12 19:24:45','Confidence: 0.90'),\r\n(367,'w55',1,'2023-05-12 19:24:45','Confidence: 0.93'),\r\n(368,'w13',1,'2023-05-12 19:24:46','Confidence: 0.81'),\r\n(369,'w13',1,'2023-05-12 19:24:46','Confidence: 0.69'),\r\n(370,'w13',1,'2023-05-12 19:24:46','Confidence: 0.80'),\r\n(371,'w55',1,'2023-05-12 19:24:46','Confidence: 0.74'),\r\n(372,'pl40',1,'2023-05-25 17:27:24','Confidence: 0.80'),\r\n(373,'pl40',1,'2023-05-25 17:27:25','Confidence: 0.78'),\r\n(374,'pl40',1,'2023-05-25 17:27:25','Confidence: 0.43'),\r\n(375,'pl40',1,'2023-05-25 17:27:25','Confidence: 0.57'),\r\n(376,'pr40',1,'2023-05-25 17:27:25','Confidence: 0.76'),\r\n(377,'ph4',1,'2023-05-25 17:27:25','Confidence: 0.42'),\r\n(378,'p19',1,'2023-05-25 17:27:25','Confidence: 0.47'),\r\n(379,'pl20',1,'2023-05-25 17:27:26','Confidence: 0.69'),\r\n(380,'ph4',1,'2023-05-25 17:27:26','Confidence: 0.46'),\r\n(381,'pl20',1,'2023-05-25 17:27:26','Confidence: 0.60'),\r\n(382,'pl20',1,'2023-05-25 17:27:26','Confidence: 0.55'),\r\n(383,'p19',1,'2023-05-25 17:27:26','Confidence: 0.43'),\r\n(384,'pl20',1,'2023-05-25 17:27:26','Confidence: 0.43'),\r\n(385,'p10',1,'2023-05-25 17:27:26','Confidence: 0.43'),\r\n(386,'pl40',1,'2023-05-25 17:27:26','Confidence: 0.44'),\r\n(387,'ph4',1,'2023-05-25 17:27:26','Confidence: 0.46'),\r\n(388,'p6',1,'2023-05-25 17:27:26','Confidence: 0.48'),\r\n(389,'p19',1,'2023-05-25 17:27:26','Confidence: 0.58'),\r\n(390,'p10',1,'2023-05-25 17:27:27','Confidence: 0.44'),\r\n(391,'ph4',1,'2023-05-25 17:27:27','Confidence: 0.60'),\r\n(392,'pm20',1,'2023-05-25 17:27:27','Confidence: 0.71'),\r\n(393,'p10',1,'2023-05-25 17:27:27','Confidence: 0.57'),\r\n(394,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.80'),\r\n(395,'p10',1,'2023-05-25 17:27:27','Confidence: 0.68'),\r\n(396,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.81'),\r\n(397,'w32',1,'2023-05-25 17:27:27','Confidence: 0.44'),\r\n(398,'pl70',1,'2023-05-25 17:27:27','Confidence: 0.49'),\r\n(399,'p10',1,'2023-05-25 17:27:27','Confidence: 0.55'),\r\n(400,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.64'),\r\n(401,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.81'),\r\n(402,'p10',1,'2023-05-25 17:27:27','Confidence: 0.84'),\r\n(403,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.88'),\r\n(404,'w32',1,'2023-05-25 17:27:27','Confidence: 0.48'),\r\n(405,'pl20',1,'2023-05-25 17:27:27','Confidence: 0.49'),\r\n(406,'w32',1,'2023-05-25 17:27:27','Confidence: 0.43'),\r\n(407,'p19',1,'2023-05-25 17:27:27','Confidence: 0.82'),\r\n(408,'pl40',1,'2023-05-25 17:27:27','Confidence: 0.92'),\r\n(409,'p19',1,'2023-05-25 17:27:28','Confidence: 0.68'),\r\n(410,'pl40',1,'2023-05-25 17:27:28','Confidence: 0.83'),\r\n(411,'p10',1,'2023-05-25 17:27:28','Confidence: 0.75'),\r\n(412,'pl60',1,'2023-05-25 17:27:28','Confidence: 0.86'),\r\n(413,'w32',1,'2023-05-25 17:27:28','Confidence: 0.53'),\r\n(414,'p10',1,'2023-05-25 17:27:28','Confidence: 0.53'),\r\n(415,'p6',1,'2023-05-25 17:27:28','Confidence: 0.53'),\r\n(416,'pm30',1,'2023-05-25 17:27:28','Confidence: 0.55'),\r\n(417,'pl60',1,'2023-05-25 17:27:28','Confidence: 0.81'),\r\n(418,'p6',1,'2023-05-25 17:27:35','Confidence: 0.43'),\r\n(419,'w32',1,'2023-05-25 17:27:35','Confidence: 0.58'),\r\n(420,'pl40',1,'2023-05-25 17:27:35','Confidence: 0.64'),\r\n(421,'w32',1,'2023-05-25 17:27:35','Confidence: 0.51'),\r\n(422,'pm20',1,'2023-05-25 17:27:36','Confidence: 0.82'),\r\n(423,'p10',1,'2023-05-25 17:27:36','Confidence: 0.86'),\r\n(424,'w32',1,'2023-05-25 17:27:36','Confidence: 0.41'),\r\n(425,'pl60',1,'2023-05-25 17:27:36','Confidence: 0.66'),\r\n(426,'p19',1,'2023-05-25 17:27:36','Confidence: 0.87'),\r\n(427,'p10',1,'2023-05-25 17:27:36','Confidence: 0.43'),\r\n(428,'w32',1,'2023-05-25 17:27:36','Confidence: 0.47'),\r\n(429,'pl40',1,'2023-05-25 17:27:36','Confidence: 0.53'),\r\n(430,'p6',1,'2023-05-25 17:27:36','Confidence: 0.69'),\r\n(431,'pl40',1,'2023-05-25 17:27:36','Confidence: 0.43'),\r\n(432,'p23',1,'2023-05-25 17:27:36','Confidence: 0.53'),\r\n(433,'pm30',1,'2023-05-25 17:27:37','Confidence: 0.44'),\r\n(434,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.88'),\r\n(435,'p23',1,'2023-05-25 17:27:37','Confidence: 0.48'),\r\n(436,'p19',1,'2023-05-25 17:27:37','Confidence: 0.61'),\r\n(437,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.87'),\r\n(438,'p19',1,'2023-05-25 17:27:37','Confidence: 0.89'),\r\n(439,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.90'),\r\n(440,'w32',1,'2023-05-25 17:27:37','Confidence: 0.49'),\r\n(441,'p10',1,'2023-05-25 17:27:37','Confidence: 0.52'),\r\n(442,'p23',1,'2023-05-25 17:27:37','Confidence: 0.61'),\r\n(443,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.74'),\r\n(444,'pm30',1,'2023-05-25 17:27:37','Confidence: 0.80'),\r\n(445,'p23',1,'2023-05-25 17:27:37','Confidence: 0.55'),\r\n(446,'w32',1,'2023-05-25 17:27:37','Confidence: 0.56'),\r\n(447,'pm20',1,'2023-05-25 17:27:37','Confidence: 0.75'),\r\n(448,'p19',1,'2023-05-25 17:27:37','Confidence: 0.76'),\r\n(449,'p19',1,'2023-05-25 17:27:38','Confidence: 0.59'),\r\n(450,'pm20',1,'2023-05-25 17:27:38','Confidence: 0.78'),\r\n(451,'p23',1,'2023-05-25 17:27:38','Confidence: 0.84'),\r\n(452,'pl40',1,'2023-05-25 17:27:38','Confidence: 0.45'),\r\n(453,'w32',1,'2023-05-25 17:27:38','Confidence: 0.50'),\r\n(454,'pm20',1,'2023-05-25 17:27:38','Confidence: 0.85'),\r\n(455,'p19',1,'2023-05-25 17:27:38','Confidence: 0.91');\r\n\r\n/*!40101 SET SQL_MODE=@OLD_SQL_MODE */;\r\n/*!40014 SET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS */;\r\n/*!40014 SET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS */;\r\n/*!40111 SET SQL_NOTES=@OLD_SQL_NOTES */;\r\n" }, { "path": "data/run/exp52/hyp.yaml", "content": "lr0: 0.001\nlrf: 0.01\nmomentum: 0.937\nweight_decay: 0.0005\nwarmup_epochs: 3.0\nwarmup_momentum: 0.8\nwarmup_bias_lr: 0.1\nbox: 0.05\ncls: 0.5\ncls_pw: 1.0\nobj: 1.0\nobj_pw: 1.0\niou_t: 0.2\nanchor_t: 4.0\nfl_gamma: 0.0\nhsv_h: 0.015\nhsv_s: 0.7\nhsv_v: 0.4\ndegrees: 0.0\ntranslate: 0.1\nscale: 0.5\nshear: 0.0\nperspective: 0.0\nflipud: 0.0\nfliplr: 0.5\nmosaic: 1.0\nmixup: 0.0\ncopy_paste: 0.0\n" }, { "path": "data/run/exp52/opt.yaml", "content": "weights: runs\\train\\exp51\\weights\\best.pt\ncfg: ''\ndata: E:\\Desktop\\yolov5-6.0\\data\\1.yaml\nhyp: data\\hyps\\hyp.scratch-low.yaml\nepochs: 20\nbatch_size: 8\nimgsz: 1280\nrect: false\nresume: false\nnosave: false\nnoval: false\nnoautoanchor: false\nevolve: null\nbucket: ''\ncache: null\nimage_weights: true\ndevice: ''\nmulti_scale: false\nsingle_cls: false\nadam: true\nsync_bn: false\nworkers: 4\nproject: runs\\train\nname: exp\nexist_ok: false\nquad: false\nlinear_lr: false\nlabel_smoothing: 0.0\npatience: 100\nfreeze: 0\nsave_period: -1\nlocal_rank: -1\nentity: null\nupload_dataset: false\nbbox_interval: -1\nartifact_alias: latest\nsave_dir: runs\\train\\exp52\n" }, { "path": "data/scripts/download_weights.sh", "content": "#!/bin/bash\n# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n# Download latest models from https://github.com/ultralytics/yolov5/releases\n# Example usage: bash path/to/download_weights.sh\n# parent\n# └── yolov5\n# ├── yolov5s.pt ← downloads here\n# ├── yolov5m.pt\n# └── ...\n\npython - <图像增强操作详细信息：
\"\r\n \"- \"\r\n \"
- 翻转：水平或垂直翻转图像。 \"\r\n \"
- 随机裁剪：从图像中随机裁剪一部分。 \"\r\n \"
- 随机缩放：对图像进行随机缩放。 \"\r\n \"
- 随机旋转：在一定范围内随机旋转图像。 \"\r\n \"
- 随机亮度/对比度/饱和度/色调：调整图像的亮度、对比度、饱和度和色调。 \"\r\n \"
- 随机擦除：在图像上随机擦除一个矩形区域。 \"\r\n \"
- 随机噪声：在图像上添加随机噪声。 \"\r\n \"
- 剪切：在图像上随机剪切一个矩形区域。 \"\r\n \"
- Mixup：将两个图像按一定比例混合在一起。 \"\r\n \"
- Mosaic：将四个图像组合成一个马赛克图像。 \"\r\n \"
注意：Mixup 和 Mosaic 操作不能与其他操作同时使用。
\"\r\n \"建议的图像增强组合：
\"\r\n \"- \"\r\n \"
- 翻转 + 随机裁剪 + 随机缩放 + 随机旋转 \"\r\n \"
- 随机亮度/对比度/饱和度/色调 + 随机擦除 + 随机噪声 + 剪切 \"\r\n \"
- 使用 Mixup 或 Mosaic 作为单独操作。 \"\r\n \"
scaleSlider
\"))\r\n self.previousImageButton.setText(_translate(\"mainWindow\", \"上一个\"))\r\n self.nextImageButton.setText(_translate(\"mainWindow\", \"下一个\"))\r\n self.enhanceMethod1Checkbox.setText(_translate(\"mainWindow\", \"Image flip\"))\r\n self.enhanceMethod2Checkbox.setText(_translate(\"mainWindow\", \"Random Crop\"))\r\n self.enhanceMethod3Checkbox.setText(_translate(\"mainWindow\", \"Random Resize\"))\r\n self.enhanceMethod4Checkbox.setText(_translate(\"mainWindow\", \"Random Rotation\"))\r\n self.enhanceMethod5Checkbox.setText(_translate(\"mainWindow\", \"Random BCSH\"))\r\n self.enhanceMethod6Checkbox.setText(_translate(\"mainWindow\", \"Random Wipe\"))\r\n self.enhanceMethod7Checkbox.setText(_translate(\"mainWindow\", \"Weighted Random Noise\"))\r\n self.enhanceMethod8Checkbox.setText(_translate(\"mainWindow\", \"CutOut\"))\r\n self.enhanceMethod9Checkbox.setText(_translate(\"mainWindow\", \"Mixup\"))\r\n self.enhanceMethod10Checkbox.setText(_translate(\"mainWindow\", \"Mosaic\"))\r\n self.helpButton.setToolTip(_translate(\"mainWindow\", \"查看参数详情
\"))\r\n self.advancedSettingsButton.setText(_translate(\"mainWindow\", \"高级参数设置\"))\r\n self.enhanceProbabilityInput.setToolTip(_translate(\"mainWindow\", \"请输入增强概率
\"))\r\n self.enhanceProbabilityInput.setText(_translate(\"mainWindow\", \"0.5\"))\r\n self.label_18.setText(_translate(\"mainWindow\", \"图像增强概率\"))\r\n self.inputFolderButton.setText(_translate(\"mainWindow\", \"输入文件夹\"))\r\n self.outputFolderButton.setText(_translate(\"mainWindow\", \"输出文件夹\"))\r\n self.startProcessingButton.setText(_translate(\"mainWindow\", \"开始批处理\"))\r\n self.hide_labelscheckbox_5.setText(_translate(\"mainWindow\", \"Show results\"))\r\n self.save_cropcheckbox_2.setText(_translate(\"mainWindow\", \"Save Cropped Images\"))\r\n self.agnostic_nmscheckbox_2.setText(_translate(\"mainWindow\", \"The file is created automatically\"))\r\n self.halfcheckbox_2.setText(_translate(\"mainWindow\", \"Enable Half Precision Inference\"))\r\n self.agnostic_nmscheckbox.setText(_translate(\"mainWindow\", \"Apply Class-Agnostic NMS\"))\r\n self.augmentedcheckbox_3.setText(_translate(\"mainWindow\", \"Use parameter enhancement\"))\r\n self.hide_labelscheckbox_4.setText(_translate(\"mainWindow\", \"Hidden tag\"))\r\n self.hide_confscheckbox_6.setText(_translate(\"mainWindow\", \"Hidden conf\"))\r\n self.label_16.setText(_translate(\"mainWindow\", \"Device Choose\"))\r\n self.comboBox_2.setItemText(0, _translate(\"mainWindow\", \"0\"))\r\n self.comboBox_2.setItemText(1, _translate(\"mainWindow\", \"1\"))\r\n self.comboBox_2.setItemText(2, _translate(\"mainWindow\", \"2\"))\r\n self.comboBox_2.setItemText(3, _translate(\"mainWindow\", \"3\"))\r\n self.comboBox_2.setItemText(4, _translate(\"mainWindow\", \"cpu\"))\r\n self.label_12.setText(_translate(\"mainWindow\", \"Input image size\"))\r\n self.label_14.setText(_translate(\"mainWindow\", \"Bounding box thickness\"))\r\n self.label_15.setText(_translate(\"mainWindow\", \"Maximum detections per image\"))\r\n self.label_13.setText(_translate(\"mainWindow\", \"Category filtering\"))\r\n self.browse_result_folder_button.setText(_translate(\"mainWindow\", \"Select Result Folder\"))\r\n\r\nfrom MouseLabel import Label_click_Mouse\r\nimport apprcc_rc\r\n" }, { "path": "main_win/win.ui", "content": "\r\n\n ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n![\"Screen](\"https://user-images.githubusercontent.com/26833433/135392431-1ab7920a-c49d-450a-b0b0-0c86ec86100e.png\")
\n\n\n
\n\n## Viewing Runs\nToggle Details
\nWhen you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device.\n\nW&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as:\n\n ```shell\n $ python train.py --project ... --name ...\n ```\n\nYOLOv5 notebook example: \n\n\n\n \n
\n\n ## Disabling wandb\n* training after running `wandb disabled` inside that directory creates no wandb run\n\n\n* To enable wandb again, run `wandb online`\n\n\n## Advanced Usage\nYou can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.\nToggle Details
\nRun information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in realtime . All important information is logged:\n\n * Training & Validation losses\n * Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95\n * Learning Rate over time\n * A bounding box debugging panel, showing the training progress over time\n * GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage**\n * System: Disk I/0, CPU utilization, RAM memory usage\n * Your trained model as W&B Artifact\n * Environment: OS and Python types, Git repository and state, **training command**\n\n![\"Weights](\"https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png\")
\n
\n\n 1: Train and Log Evaluation simultaneousy
\n This is an extension of the previous section, but it'll also training after uploading the dataset. This also evaluation Table\n Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,\n so no images will be uploaded from your system more than once.\n\n
\n\n Usage
\n Code $ python train.py --upload_data val\n\n\n 2. Visualize and Version Datasets
\n Log, visualize, dynamically query, and understand your data with W&B Tables. You can use the following command to log your dataset as a W&B Table. This will generate a{dataset}_wandb.yaml file which can be used to train from dataset artifact.\n \n
\n\n Usage
\n Code $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. \n\n \n 3: Train using dataset artifact
\n When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that\n can be used to train a model directly from the dataset artifact. This also logs evaluation \n\n
\n\n Usage
\n Code $ python train.py --data {data}_wandb.yaml \n\n\n 4: Save model checkpoints as artifacts
\n To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.\n You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged\n\n\n
\n\nUsage
\n Code $ python train.py --save_period 1 \n\n\n 5: Resume runs from checkpoint artifacts.
\nAny run can be resumed using artifacts if the--resume argument starts with wandb-artifact:// prefix followed by the run path, i.e, wandb-artifact://username/project/runid . This doesn't require the model checkpoint to be present on the local system.\n\n \n
\n\n Usage
\n Code $ python train.py --resume wandb-artifact://{run_path} \n\n\n 6: Resume runs from dataset artifact & checkpoint artifacts.
\n Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device \n The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot--upload_dataset or\n train from _wandb.yaml file and set --save_period\n\n \n
\n\n\n\n Usage
\n Code $ python train.py --resume wandb-artifact://{run_path} \n\n\n Reports
\nW&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)).\n\n![\"Weights](\"https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png\")
\n\n\n## Environments\n\nYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n\n- **Google Colab and Kaggle** notebooks with free GPU: \n- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![\"Docker](\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\")
\n\n\n## Status\n\n\n\nIf this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
},
{
"path": "utils/loggers/wandb/__init__.py",
"content": ""
},
{
"path": "utils/loggers/wandb/log_dataset.py",
"content": "import argparse\n\nfrom wandb_utils import WandbLogger\n\nfrom utils.general import LOGGER\n\nWANDB_ARTIFACT_PREFIX = 'wandb-artifact://'\n\n\ndef create_dataset_artifact(opt):\n logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused\n if not logger.wandb:\n LOGGER.info(\"install wandb using `pip install wandb` to log the dataset\")\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')\n parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')\n parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')\n parser.add_argument('--entity', default=None, help='W&B entity')\n parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')\n\n opt = parser.parse_args()\n opt.resume = False # Explicitly disallow resume check for dataset upload job\n\n create_dataset_artifact(opt)\n"
},
{
"path": "utils/loggers/wandb/sweep.py",
"content": "import sys\nfrom pathlib import Path\n\nimport wandb\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[3] # YOLOv5 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\n\nfrom train import parse_opt, train\nfrom utils.callbacks import Callbacks\nfrom utils.general import increment_path\nfrom utils.torch_utils import select_device\n\n\ndef sweep():\n wandb.init()\n # Get hyp dict from sweep agent\n hyp_dict = vars(wandb.config).get(\"_items\")\n\n # Workaround: get necessary opt args\n opt = parse_opt(known=True)\n opt.batch_size = hyp_dict.get(\"batch_size\")\n opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))\n opt.epochs = hyp_dict.get(\"epochs\")\n opt.nosave = True\n opt.data = hyp_dict.get(\"data\")\n opt.weights = str(opt.weights)\n opt.cfg = str(opt.cfg)\n opt.data = str(opt.data)\n opt.hyp = str(opt.hyp)\n opt.project = str(opt.project)\n device = select_device(opt.device, batch_size=opt.batch_size)\n\n # train\n train(hyp_dict, opt, device, callbacks=Callbacks())\n\n\nif __name__ == \"__main__\":\n sweep()\n"
},
{
"path": "utils/loggers/wandb/sweep.yaml",
"content": "# Hyperparameters for training\n# To set range-\n# Provide min and max values as:\n# parameter:\n#\n# min: scalar\n# max: scalar\n# OR\n#\n# Set a specific list of search space-\n# parameter:\n# values: [scalar1, scalar2, scalar3...]\n#\n# You can use grid, bayesian and hyperopt search strategy\n# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration\n\nprogram: utils/loggers/wandb/sweep.py\nmethod: random\nmetric:\n name: metrics/mAP_0.5\n goal: maximize\n\nparameters:\n # hyperparameters: set either min, max range or values list\n data:\n value: \"data/coco128.yaml\"\n batch_size:\n values: [64]\n epochs:\n values: [10]\n\n lr0:\n distribution: uniform\n min: 1e-5\n max: 1e-1\n lrf:\n distribution: uniform\n min: 0.01\n max: 1.0\n momentum:\n distribution: uniform\n min: 0.6\n max: 0.98\n weight_decay:\n distribution: uniform\n min: 0.0\n max: 0.001\n warmup_epochs:\n distribution: uniform\n min: 0.0\n max: 5.0\n warmup_momentum:\n distribution: uniform\n min: 0.0\n max: 0.95\n warmup_bias_lr:\n distribution: uniform\n min: 0.0\n max: 0.2\n box:\n distribution: uniform\n min: 0.02\n max: 0.2\n cls:\n distribution: uniform\n min: 0.2\n max: 4.0\n cls_pw:\n distribution: uniform\n min: 0.5\n max: 2.0\n obj:\n distribution: uniform\n min: 0.2\n max: 4.0\n obj_pw:\n distribution: uniform\n min: 0.5\n max: 2.0\n iou_t:\n distribution: uniform\n min: 0.1\n max: 0.7\n anchor_t:\n distribution: uniform\n min: 2.0\n max: 8.0\n fl_gamma:\n distribution: uniform\n min: 0.0\n max: 0.1\n hsv_h:\n distribution: uniform\n min: 0.0\n max: 0.1\n hsv_s:\n distribution: uniform\n min: 0.0\n max: 0.9\n hsv_v:\n distribution: uniform\n min: 0.0\n max: 0.9\n degrees:\n distribution: uniform\n min: 0.0\n max: 45.0\n translate:\n distribution: uniform\n min: 0.0\n max: 0.9\n scale:\n distribution: uniform\n min: 0.0\n max: 0.9\n shear:\n distribution: uniform\n min: 0.0\n max: 10.0\n perspective:\n distribution: uniform\n min: 0.0\n max: 0.001\n flipud:\n distribution: uniform\n min: 0.0\n max: 1.0\n fliplr:\n distribution: uniform\n min: 0.0\n max: 1.0\n mosaic:\n distribution: uniform\n min: 0.0\n max: 1.0\n mixup:\n distribution: uniform\n min: 0.0\n max: 1.0\n copy_paste:\n distribution: uniform\n min: 0.0\n max: 1.0\n"
},
{
"path": "utils/loggers/wandb/wandb_utils.py",
"content": "\"\"\"Utilities and tools for tracking runs with Weights & Biases.\"\"\"\n\nimport logging\nimport os\nimport sys\nfrom contextlib import contextmanager\nfrom pathlib import Path\nfrom typing import Dict\n\nimport yaml\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[3] # YOLOv5 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\n\nfrom utils.datasets import LoadImagesAndLabels, img2label_paths\nfrom utils.general import LOGGER, check_dataset, check_file\n\ntry:\n import wandb\n\n assert hasattr(wandb, '__version__') # verify package import not local dir\nexcept (ImportError, AssertionError):\n wandb = None\n\nRANK = int(os.getenv('RANK', -1))\nWANDB_ARTIFACT_PREFIX = 'wandb-artifact://'\n\n\ndef remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):\n return from_string[len(prefix):]\n\n\ndef check_wandb_config_file(data_config_file):\n wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path\n if Path(wandb_config).is_file():\n return wandb_config\n return data_config_file\n\n\ndef check_wandb_dataset(data_file):\n is_trainset_wandb_artifact = False\n is_valset_wandb_artifact = False\n if check_file(data_file) and data_file.endswith('.yaml'):\n with open(data_file, errors='ignore') as f:\n data_dict = yaml.safe_load(f)\n is_trainset_wandb_artifact = (isinstance(data_dict['train'], str) and\n data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX))\n is_valset_wandb_artifact = (isinstance(data_dict['val'], str) and\n data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX))\n if is_trainset_wandb_artifact or is_valset_wandb_artifact:\n return data_dict\n else:\n return check_dataset(data_file)\n\n\ndef get_run_info(run_path):\n run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))\n run_id = run_path.stem\n project = run_path.parent.stem\n entity = run_path.parent.parent.stem\n model_artifact_name = 'run_' + run_id + '_model'\n return entity, project, run_id, model_artifact_name\n\n\ndef check_wandb_resume(opt):\n process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None\n if isinstance(opt.resume, str):\n if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):\n if RANK not in [-1, 0]: # For resuming DDP runs\n entity, project, run_id, model_artifact_name = get_run_info(opt.resume)\n api = wandb.Api()\n artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')\n modeldir = artifact.download()\n opt.weights = str(Path(modeldir) / \"last.pt\")\n return True\n return None\n\n\ndef process_wandb_config_ddp_mode(opt):\n with open(check_file(opt.data), errors='ignore') as f:\n data_dict = yaml.safe_load(f) # data dict\n train_dir, val_dir = None, None\n if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):\n api = wandb.Api()\n train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)\n train_dir = train_artifact.download()\n train_path = Path(train_dir) / 'data/images/'\n data_dict['train'] = str(train_path)\n\n if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):\n api = wandb.Api()\n val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)\n val_dir = val_artifact.download()\n val_path = Path(val_dir) / 'data/images/'\n data_dict['val'] = str(val_path)\n if train_dir or val_dir:\n ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')\n with open(ddp_data_path, 'w') as f:\n yaml.safe_dump(data_dict, f)\n opt.data = ddp_data_path\n\n\nclass WandbLogger():\n \"\"\"Log training runs, datasets, models, and predictions to Weights & Biases.\n\n This logger sends information to W&B at wandb.ai. By default, this information\n includes hyperparameters, system configuration and metrics, model metrics,\n and basic data metrics and analyses.\n\n By providing additional command line arguments to train.py, datasets,\n models and predictions can also be logged.\n\n For more on how this logger is used, see the Weights & Biases documentation:\n https://docs.wandb.com/guides/integrations/yolov5\n \"\"\"\n\n def __init__(self, opt, run_id=None, job_type='Training'):\n \"\"\"\n - Initialize WandbLogger instance\n - Upload dataset if opt.upload_dataset is True\n - Setup trainig processes if job_type is 'Training'\n\n arguments:\n opt (namespace) -- Commandline arguments for this run\n run_id (str) -- Run ID of W&B run to be resumed\n job_type (str) -- To set the job_type for this run\n\n \"\"\"\n # Pre-training routine --\n self.job_type = job_type\n self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run\n self.val_artifact, self.train_artifact = None, None\n self.train_artifact_path, self.val_artifact_path = None, None\n self.result_artifact = None\n self.val_table, self.result_table = None, None\n self.bbox_media_panel_images = []\n self.val_table_path_map = None\n self.max_imgs_to_log = 16\n self.wandb_artifact_data_dict = None\n self.data_dict = None\n # It's more elegant to stick to 1 wandb.init call,\n # but useful config data is overwritten in the WandbLogger's wandb.init call\n if isinstance(opt.resume, str): # checks resume from artifact\n if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):\n entity, project, run_id, model_artifact_name = get_run_info(opt.resume)\n model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name\n assert wandb, 'install wandb to resume wandb runs'\n # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config\n self.wandb_run = wandb.init(id=run_id,\n project=project,\n entity=entity,\n resume='allow',\n allow_val_change=True)\n opt.resume = model_artifact_name\n elif self.wandb:\n self.wandb_run = wandb.init(config=opt,\n resume=\"allow\",\n project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,\n entity=opt.entity,\n name=opt.name if opt.name != 'exp' else None,\n job_type=job_type,\n id=run_id,\n allow_val_change=True) if not wandb.run else wandb.run\n if self.wandb_run:\n if self.job_type == 'Training':\n if opt.upload_dataset:\n if not opt.resume:\n self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)\n\n if opt.resume:\n # resume from artifact\n if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):\n self.data_dict = dict(self.wandb_run.config.data_dict)\n else: # local resume\n self.data_dict = check_wandb_dataset(opt.data)\n else:\n self.data_dict = check_wandb_dataset(opt.data)\n self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict\n\n # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.\n self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict},\n allow_val_change=True)\n self.setup_training(opt)\n\n if self.job_type == 'Dataset Creation':\n self.wandb_run.config.update({\"upload_dataset\": True})\n self.data_dict = self.check_and_upload_dataset(opt)\n\n def check_and_upload_dataset(self, opt):\n \"\"\"\n Check if the dataset format is compatible and upload it as W&B artifact\n\n arguments:\n opt (namespace)-- Commandline arguments for current run\n\n returns:\n Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.\n \"\"\"\n assert wandb, 'Install wandb to upload dataset'\n config_path = self.log_dataset_artifact(opt.data,\n opt.single_cls,\n 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)\n with open(config_path, errors='ignore') as f:\n wandb_data_dict = yaml.safe_load(f)\n return wandb_data_dict\n\n def setup_training(self, opt):\n \"\"\"\n Setup the necessary processes for training YOLO models:\n - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX\n - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded\n - Setup log_dict, initialize bbox_interval\n\n arguments:\n opt (namespace) -- commandline arguments for this run\n\n \"\"\"\n self.log_dict, self.current_epoch = {}, 0\n self.bbox_interval = opt.bbox_interval\n if isinstance(opt.resume, str):\n modeldir, _ = self.download_model_artifact(opt)\n if modeldir:\n self.weights = Path(modeldir) / \"last.pt\"\n config = self.wandb_run.config\n opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(\n self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs,\\\n config.hyp, config.imgsz\n data_dict = self.data_dict\n if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download\n self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),\n opt.artifact_alias)\n self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),\n opt.artifact_alias)\n\n if self.train_artifact_path is not None:\n train_path = Path(self.train_artifact_path) / 'data/images/'\n data_dict['train'] = str(train_path)\n if self.val_artifact_path is not None:\n val_path = Path(self.val_artifact_path) / 'data/images/'\n data_dict['val'] = str(val_path)\n\n if self.val_artifact is not None:\n self.result_artifact = wandb.Artifact(\"run_\" + wandb.run.id + \"_progress\", \"evaluation\")\n columns = [\"epoch\", \"id\", \"ground truth\", \"prediction\"]\n columns.extend(self.data_dict['names'])\n self.result_table = wandb.Table(columns)\n self.val_table = self.val_artifact.get(\"val\")\n if self.val_table_path_map is None:\n self.map_val_table_path()\n if opt.bbox_interval == -1:\n self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1\n if opt.evolve:\n self.bbox_interval = opt.bbox_interval = opt.epochs + 1\n train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None\n # Update the the data_dict to point to local artifacts dir\n if train_from_artifact:\n self.data_dict = data_dict\n\n def download_dataset_artifact(self, path, alias):\n \"\"\"\n download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX\n\n arguments:\n path -- path of the dataset to be used for training\n alias (str)-- alias of the artifact to be download/used for training\n\n returns:\n (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset\n is found otherwise returns (None, None)\n \"\"\"\n if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):\n artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + \":\" + alias)\n dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace(\"\\\\\", \"/\"))\n assert dataset_artifact is not None, \"'Error: W&B dataset artifact doesn\\'t exist'\"\n datadir = dataset_artifact.download()\n return datadir, dataset_artifact\n return None, None\n\n def download_model_artifact(self, opt):\n \"\"\"\n download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX\n\n arguments:\n opt (namespace) -- Commandline arguments for this run\n \"\"\"\n if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):\n model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + \":latest\")\n assert model_artifact is not None, 'Error: W&B model artifact doesn\\'t exist'\n modeldir = model_artifact.download()\n # epochs_trained = model_artifact.metadata.get('epochs_trained')\n total_epochs = model_artifact.metadata.get('total_epochs')\n is_finished = total_epochs is None\n assert not is_finished, 'training is finished, can only resume incomplete runs.'\n return modeldir, model_artifact\n return None, None\n\n def log_model(self, path, opt, epoch, fitness_score, best_model=False):\n \"\"\"\n Log the model checkpoint as W&B artifact\n\n arguments:\n path (Path) -- Path of directory containing the checkpoints\n opt (namespace) -- Command line arguments for this run\n epoch (int) -- Current epoch number\n fitness_score (float) -- fitness score for current epoch\n best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.\n \"\"\"\n model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={\n 'original_url': str(path),\n 'epochs_trained': epoch + 1,\n 'save period': opt.save_period,\n 'project': opt.project,\n 'total_epochs': opt.epochs,\n 'fitness_score': fitness_score\n })\n model_artifact.add_file(str(path / 'last.pt'), name='last.pt')\n wandb.log_artifact(model_artifact,\n aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])\n LOGGER.info(f\"Saving model artifact on epoch {epoch + 1}\")\n\n def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):\n \"\"\"\n Log the dataset as W&B artifact and return the new data file with W&B links\n\n arguments:\n data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.\n single_class (boolean) -- train multi-class data as single-class\n project (str) -- project name. Used to construct the artifact path\n overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new\n file with _wandb postfix. Eg -> data_wandb.yaml\n\n returns:\n the new .yaml file with artifact links. it can be used to start training directly from artifacts\n \"\"\"\n upload_dataset = self.wandb_run.config.upload_dataset\n log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val'\n self.data_dict = check_dataset(data_file) # parse and check\n data = dict(self.data_dict)\n nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])\n names = {k: v for k, v in enumerate(names)} # to index dictionary\n\n # log train set\n if not log_val_only:\n self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(\n data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None\n if data.get('train'):\n data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')\n\n self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(\n data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None\n if data.get('val'):\n data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')\n\n path = Path(data_file)\n # create a _wandb.yaml file with artifacts links if both train and test set are logged\n if not log_val_only:\n path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml' # updated data.yaml path\n path = ROOT / 'data' / path\n data.pop('download', None)\n data.pop('path', None)\n with open(path, 'w') as f:\n yaml.safe_dump(data, f)\n LOGGER.info(f\"Created dataset config file {path}\")\n\n if self.job_type == 'Training': # builds correct artifact pipeline graph\n if not log_val_only:\n self.wandb_run.log_artifact(\n self.train_artifact) # calling use_artifact downloads the dataset. NOT NEEDED!\n self.wandb_run.use_artifact(self.val_artifact)\n self.val_artifact.wait()\n self.val_table = self.val_artifact.get('val')\n self.map_val_table_path()\n else:\n self.wandb_run.log_artifact(self.train_artifact)\n self.wandb_run.log_artifact(self.val_artifact)\n return path\n\n def map_val_table_path(self):\n \"\"\"\n Map the validation dataset Table like name of file -> it's id in the W&B Table.\n Useful for - referencing artifacts for evaluation.\n \"\"\"\n self.val_table_path_map = {}\n LOGGER.info(\"Mapping dataset\")\n for i, data in enumerate(tqdm(self.val_table.data)):\n self.val_table_path_map[data[3]] = data[0]\n\n def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'):\n \"\"\"\n Create and return W&B artifact containing W&B Table of the dataset.\n\n arguments:\n dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table\n class_to_id -- hash map that maps class ids to labels\n name -- name of the artifact\n\n returns:\n dataset artifact to be logged or used\n \"\"\"\n # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging\n artifact = wandb.Artifact(name=name, type=\"dataset\")\n img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None\n img_files = tqdm(dataset.img_files) if not img_files else img_files\n for img_file in img_files:\n if Path(img_file).is_dir():\n artifact.add_dir(img_file, name='data/images')\n labels_path = 'labels'.join(dataset.path.rsplit('images', 1))\n artifact.add_dir(labels_path, name='data/labels')\n else:\n artifact.add_file(img_file, name='data/images/' + Path(img_file).name)\n label_file = Path(img2label_paths([img_file])[0])\n artifact.add_file(str(label_file),\n name='data/labels/' + label_file.name) if label_file.exists() else None\n table = wandb.Table(columns=[\"id\", \"train_image\", \"Classes\", \"name\"])\n class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])\n for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):\n box_data, img_classes = [], {}\n for cls, *xywh in labels[:, 1:].tolist():\n cls = int(cls)\n box_data.append({\"position\": {\"middle\": [xywh[0], xywh[1]], \"width\": xywh[2], \"height\": xywh[3]},\n \"class_id\": cls,\n \"box_caption\": \"%s\" % (class_to_id[cls])})\n img_classes[cls] = class_to_id[cls]\n boxes = {\"ground_truth\": {\"box_data\": box_data, \"class_labels\": class_to_id}} # inference-space\n table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),\n Path(paths).name)\n artifact.add(table, name)\n return artifact\n\n def log_training_progress(self, predn, path, names):\n \"\"\"\n Build evaluation Table. Uses reference from validation dataset table.\n\n arguments:\n predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]\n path (str): local path of the current evaluation image\n names (dict(int, str)): hash map that maps class ids to labels\n \"\"\"\n class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])\n box_data = []\n avg_conf_per_class = [0] * len(self.data_dict['names'])\n pred_class_count = {}\n for *xyxy, conf, cls in predn.tolist():\n if conf >= 0.25:\n cls = int(cls)\n box_data.append(\n {\"position\": {\"minX\": xyxy[0], \"minY\": xyxy[1], \"maxX\": xyxy[2], \"maxY\": xyxy[3]},\n \"class_id\": cls,\n \"box_caption\": f\"{names[cls]} {conf:.3f}\",\n \"scores\": {\"class_score\": conf},\n \"domain\": \"pixel\"})\n avg_conf_per_class[cls] += conf\n\n if cls in pred_class_count:\n pred_class_count[cls] += 1\n else:\n pred_class_count[cls] = 1\n\n for pred_class in pred_class_count.keys():\n avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class]\n\n boxes = {\"predictions\": {\"box_data\": box_data, \"class_labels\": names}} # inference-space\n id = self.val_table_path_map[Path(path).name]\n self.result_table.add_data(self.current_epoch,\n id,\n self.val_table.data[id][1],\n wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),\n *avg_conf_per_class\n )\n\n def val_one_image(self, pred, predn, path, names, im):\n \"\"\"\n Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel\n\n arguments:\n pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]\n predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]\n path (str): local path of the current evaluation image\n \"\"\"\n if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact\n self.log_training_progress(predn, path, names)\n\n if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:\n if self.current_epoch % self.bbox_interval == 0:\n box_data = [{\"position\": {\"minX\": xyxy[0], \"minY\": xyxy[1], \"maxX\": xyxy[2], \"maxY\": xyxy[3]},\n \"class_id\": int(cls),\n \"box_caption\": f\"{names[int(cls)]} {conf:.3f}\",\n \"scores\": {\"class_score\": conf},\n \"domain\": \"pixel\"} for *xyxy, conf, cls in pred.tolist()]\n boxes = {\"predictions\": {\"box_data\": box_data, \"class_labels\": names}} # inference-space\n self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))\n\n def log(self, log_dict):\n \"\"\"\n save the metrics to the logging dictionary\n\n arguments:\n log_dict (Dict) -- metrics/media to be logged in current step\n \"\"\"\n if self.wandb_run:\n for key, value in log_dict.items():\n self.log_dict[key] = value\n\n def end_epoch(self, best_result=False):\n \"\"\"\n commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.\n\n arguments:\n best_result (boolean): Boolean representing if the result of this evaluation is best or not\n \"\"\"\n if self.wandb_run:\n with all_logging_disabled():\n if self.bbox_media_panel_images:\n self.log_dict[\"BoundingBoxDebugger\"] = self.bbox_media_panel_images\n try:\n wandb.log(self.log_dict)\n except BaseException as e:\n LOGGER.info(\n f\"An error occurred in wandb logger. The training will proceed without interruption. More info\\n{e}\")\n self.wandb_run.finish()\n self.wandb_run = None\n\n self.log_dict = {}\n self.bbox_media_panel_images = []\n if self.result_artifact:\n self.result_artifact.add(self.result_table, 'result')\n wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),\n ('best' if best_result else '')])\n\n wandb.log({\"evaluation\": self.result_table})\n columns = [\"epoch\", \"id\", \"ground truth\", \"prediction\"]\n columns.extend(self.data_dict['names'])\n self.result_table = wandb.Table(columns)\n self.result_artifact = wandb.Artifact(\"run_\" + wandb.run.id + \"_progress\", \"evaluation\")\n\n def finish_run(self):\n \"\"\"\n Log metrics if any and finish the current W&B run\n \"\"\"\n if self.wandb_run:\n if self.log_dict:\n with all_logging_disabled():\n wandb.log(self.log_dict)\n wandb.run.finish()\n\n\n@contextmanager\ndef all_logging_disabled(highest_level=logging.CRITICAL):\n \"\"\" source - https://gist.github.com/simon-weber/7853144\n A context manager that will prevent any logging messages triggered during the body from being processed.\n :param highest_level: the maximum logging level in use.\n This would only need to be changed if a custom level greater than CRITICAL is defined.\n \"\"\"\n previous_level = logging.root.manager.disable\n logging.disable(highest_level)\n try:\n yield\n finally:\n logging.disable(previous_level)\n"
},
{
"path": "utils/loss.py",
"content": "# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nLoss functions\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom utils.metrics import bbox_iou\nfrom utils.torch_utils import de_parallel\n\n\ndef smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441\n # return positive, negative label smoothing BCE targets\n return 1.0 - 0.5 * eps, 0.5 * eps\n\n\nclass BCEBlurWithLogitsLoss(nn.Module):\n # BCEwithLogitLoss() with reduced missing label effects.\n def __init__(self, alpha=0.05):\n super().__init__()\n self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()\n self.alpha = alpha\n\n def forward(self, pred, true):\n loss = self.loss_fcn(pred, true)\n pred = torch.sigmoid(pred) # prob from logits\n dx = pred - true # reduce only missing label effects\n # dx = (pred - true).abs() # reduce missing label and false label effects\n alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))\n loss *= alpha_factor\n return loss.mean()\n\n\nclass FocalLoss(nn.Module):\n # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)\n def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):\n super().__init__()\n self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()\n self.gamma = gamma\n self.alpha = alpha\n self.reduction = loss_fcn.reduction\n self.loss_fcn.reduction = 'none' # required to apply FL to each element\n\n def forward(self, pred, true):\n loss = self.loss_fcn(pred, true)\n # p_t = torch.exp(-loss)\n # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability\n\n # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py\n pred_prob = torch.sigmoid(pred) # prob from logits\n p_t = true * pred_prob + (1 - true) * (1 - pred_prob)\n alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)\n modulating_factor = (1.0 - p_t) ** self.gamma\n loss *= alpha_factor * modulating_factor\n\n if self.reduction == 'mean':\n return loss.mean()\n elif self.reduction == 'sum':\n return loss.sum()\n else: # 'none'\n return loss\n\n\nclass QFocalLoss(nn.Module):\n # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)\n def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):\n super().__init__()\n self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()\n self.gamma = gamma\n self.alpha = alpha\n self.reduction = loss_fcn.reduction\n self.loss_fcn.reduction = 'none' # required to apply FL to each element\n\n def forward(self, pred, true):\n loss = self.loss_fcn(pred, true)\n\n pred_prob = torch.sigmoid(pred) # prob from logits\n alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)\n modulating_factor = torch.abs(true - pred_prob) ** self.gamma\n loss *= alpha_factor * modulating_factor\n\n if self.reduction == 'mean':\n return loss.mean()\n elif self.reduction == 'sum':\n return loss.sum()\n else: # 'none'\n return loss\n\n\nclass ComputeLoss:\n # Compute losses\n def __init__(self, model, autobalance=False):\n self.sort_obj_iou = False\n device = next(model.parameters()).device # get model device\n h = model.hyp # hyperparameters\n\n # Define criteria\n BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))\n BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))\n\n # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3\n self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets\n\n # Focal loss\n g = h['fl_gamma'] # focal loss gamma\n if g > 0:\n BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)\n\n det = de_parallel(model).model[-1] # Detect() module\n self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7\n self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index\n self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance\n for k in 'na', 'nc', 'nl', 'anchors':\n setattr(self, k, getattr(det, k))\n\n def __call__(self, p, targets): # predictions, targets, model\n device = targets.device\n lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)\n tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets\n\n # Losses\n for i, pi in enumerate(p): # layer index, layer predictions\n b, a, gj, gi = indices[i] # image, anchor, gridy, gridx\n tobj = torch.zeros_like(pi[..., 0], device=device) # target obj\n\n n = b.shape[0] # number of targets\n if n:\n ps = pi[b, a, gj, gi] # prediction subset corresponding to targets\n\n # Regression\n pxy = ps[:, :2].sigmoid() * 2 - 0.5\n pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]\n pbox = torch.cat((pxy, pwh), 1) # predicted box\n iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)\n lbox += (1.0 - iou).mean() # iou loss\n\n # Objectness\n score_iou = iou.detach().clamp(0).type(tobj.dtype)\n if self.sort_obj_iou:\n sort_id = torch.argsort(score_iou)\n b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id]\n tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou # iou ratio\n\n # Classification\n if self.nc > 1: # cls loss (only if multiple classes)\n t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets\n t[range(n), tcls[i]] = self.cp\n lcls += self.BCEcls(ps[:, 5:], t) # BCE\n\n # Append targets to text file\n # with open('targets.txt', 'a') as file:\n # [file.write('%11.5g ' * 4 % tuple(x) + '\\n') for x in torch.cat((txy[i], twh[i]), 1)]\n\n obji = self.BCEobj(pi[..., 4], tobj)\n lobj += obji * self.balance[i] # obj loss\n if self.autobalance:\n self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()\n\n if self.autobalance:\n self.balance = [x / self.balance[self.ssi] for x in self.balance]\n lbox *= self.hyp['box']\n lobj *= self.hyp['obj']\n lcls *= self.hyp['cls']\n bs = tobj.shape[0] # batch size\n\n return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()\n\n def build_targets(self, p, targets):\n # Build targets for compute_loss(), input targets(image,class,x,y,w,h)\n na, nt = self.na, targets.shape[0] # number of anchors, targets\n tcls, tbox, indices, anch = [], [], [], []\n gain = torch.ones(7, device=targets.device) # normalized to gridspace gain\n ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)\n targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices\n\n g = 0.5 # bias\n off = torch.tensor([[0, 0],\n [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m\n # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm\n ], device=targets.device).float() * g # offsets\n\n for i in range(self.nl):\n anchors = self.anchors[i]\n gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain\n\n # Match targets to anchors\n t = targets * gain\n if nt:\n # Matches\n r = t[:, :, 4:6] / anchors[:, None] # wh ratio\n j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare\n # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))\n t = t[j] # filter\n\n # Offsets\n gxy = t[:, 2:4] # grid xy\n gxi = gain[[2, 3]] - gxy # inverse\n j, k = ((gxy % 1 < g) & (gxy > 1)).T\n l, m = ((gxi % 1 < g) & (gxi > 1)).T\n j = torch.stack((torch.ones_like(j), j, k, l, m))\n t = t.repeat((5, 1, 1))[j]\n offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]\n else:\n t = targets[0]\n offsets = 0\n\n # Define\n b, c = t[:, :2].long().T # image, class\n gxy = t[:, 2:4] # grid xy\n gwh = t[:, 4:6] # grid wh\n gij = (gxy - offsets).long()\n gi, gj = gij.T # grid xy indices\n\n # Append\n a = t[:, 6].long() # anchor indices\n indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices\n tbox.append(torch.cat((gxy - gij, gwh), 1)) # box\n anch.append(anchors[a]) # anchors\n tcls.append(c) # class\n\n return tcls, tbox, indices, anch\n"
},
{
"path": "utils/metrics.py",
"content": "# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nModel validation metrics\n\"\"\"\n\nimport math\nimport warnings\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\n\ndef fitness(x):\n # Model fitness as a weighted combination of metrics\n w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]\n return (x[:, :4] * w).sum(1)\n\n\ndef ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):\n \"\"\" Compute the average precision, given the recall and precision curves.\n Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.\n # Arguments\n tp: True positives (nparray, nx1 or nx10).\n conf: Objectness value from 0-1 (nparray).\n pred_cls: Predicted object classes (nparray).\n target_cls: True object classes (nparray).\n plot: Plot precision-recall curve at mAP@0.5\n save_dir: Plot save directory\n # Returns\n The average precision as computed in py-faster-rcnn.\n \"\"\"\n\n # Sort by objectness\n i = np.argsort(-conf)\n tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]\n\n # Find unique classes\n unique_classes, nt = np.unique(target_cls, return_counts=True)\n nc = unique_classes.shape[0] # number of classes, number of detections\n\n # Create Precision-Recall curve and compute AP for each class\n px, py = np.linspace(0, 1, 1000), [] # for plotting\n ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))\n for ci, c in enumerate(unique_classes):\n i = pred_cls == c\n n_l = nt[ci] # number of labels\n n_p = i.sum() # number of predictions\n\n if n_p == 0 or n_l == 0:\n continue\n else:\n # Accumulate FPs and TPs\n fpc = (1 - tp[i]).cumsum(0)\n tpc = tp[i].cumsum(0)\n\n # Recall\n recall = tpc / (n_l + eps) # recall curve\n r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases\n\n # Precision\n precision = tpc / (tpc + fpc) # precision curve\n p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score\n\n # AP from recall-precision curve\n for j in range(tp.shape[1]):\n ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])\n if plot and j == 0:\n py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5\n\n # Compute F1 (harmonic mean of precision and recall)\n f1 = 2 * p * r / (p + r + eps)\n names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data\n names = {i: v for i, v in enumerate(names)} # to dict\n if plot:\n plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)\n plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')\n plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')\n plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')\n\n i = f1.mean(0).argmax() # max F1 index\n p, r, f1 = p[:, i], r[:, i], f1[:, i]\n tp = (r * nt).round() # true positives\n fp = (tp / (p + eps) - tp).round() # false positives\n return tp, fp, p, r, f1, ap, unique_classes.astype('int32')\n\n\ndef compute_ap(recall, precision):\n \"\"\" Compute the average precision, given the recall and precision curves\n # Arguments\n recall: The recall curve (list)\n precision: The precision curve (list)\n # Returns\n Average precision, precision curve, recall curve\n \"\"\"\n\n # Append sentinel values to beginning and end\n mrec = np.concatenate(([0.0], recall, [1.0]))\n mpre = np.concatenate(([1.0], precision, [0.0]))\n\n # Compute the precision envelope\n mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))\n\n # Integrate area under curve\n method = 'interp' # methods: 'continuous', 'interp'\n if method == 'interp':\n x = np.linspace(0, 1, 101) # 101-point interp (COCO)\n ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate\n else: # 'continuous'\n i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes\n ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve\n\n return ap, mpre, mrec\n\n\nclass ConfusionMatrix:\n # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix\n def __init__(self, nc, conf=0.25, iou_thres=0.45):\n self.matrix = np.zeros((nc + 1, nc + 1))\n self.nc = nc # number of classes\n self.conf = conf\n self.iou_thres = iou_thres\n\n def process_batch(self, detections, labels):\n \"\"\"\n Return intersection-over-union (Jaccard index) of boxes.\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n Arguments:\n detections (Array[N, 6]), x1, y1, x2, y2, conf, class\n labels (Array[M, 5]), class, x1, y1, x2, y2\n Returns:\n None, updates confusion matrix accordingly\n \"\"\"\n detections = detections[detections[:, 4] > self.conf]\n gt_classes = labels[:, 0].int()\n detection_classes = detections[:, 5].int()\n iou = box_iou(labels[:, 1:], detections[:, :4])\n\n x = torch.where(iou > self.iou_thres)\n if x[0].shape[0]:\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n else:\n matches = np.zeros((0, 3))\n\n n = matches.shape[0] > 0\n m0, m1, _ = matches.transpose().astype(np.int16)\n for i, gc in enumerate(gt_classes):\n j = m0 == i\n if n and sum(j) == 1:\n self.matrix[detection_classes[m1[j]], gc] += 1 # correct\n else:\n self.matrix[self.nc, gc] += 1 # background FP\n\n if n:\n for i, dc in enumerate(detection_classes):\n if not any(m1 == i):\n self.matrix[dc, self.nc] += 1 # background FN\n\n def matrix(self):\n return self.matrix\n\n def tp_fp(self):\n tp = self.matrix.diagonal() # true positives\n fp = self.matrix.sum(1) - tp # false positives\n # fn = self.matrix.sum(0) - tp # false negatives (missed detections)\n return tp[:-1], fp[:-1] # remove background class\n\n def plot(self, normalize=True, save_dir='', names=()):\n try:\n import seaborn as sn\n\n array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns\n array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)\n\n fig = plt.figure(figsize=(12, 9), tight_layout=True)\n nc, nn = self.nc, len(names) # number of classes, names\n sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size\n labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels\n with warnings.catch_warnings():\n warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered\n sn.heatmap(array, annot=nc < 30, annot_kws={\"size\": 8}, cmap='Blues', fmt='.2f', square=True, vmin=0.0,\n xticklabels=names + ['background FP'] if labels else \"auto\",\n yticklabels=names + ['background FN'] if labels else \"auto\").set_facecolor((1, 1, 1))\n fig.axes[0].set_xlabel('True')\n fig.axes[0].set_ylabel('Predicted')\n fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)\n plt.close()\n except Exception as e:\n print(f'WARNING: ConfusionMatrix plot failure: {e}')\n\n def print(self):\n for i in range(self.nc + 1):\n print(' '.join(map(str, self.matrix[i])))\n\n\ndef bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):\n # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4\n box2 = box2.T\n\n # Get the coordinates of bounding boxes\n if x1y1x2y2: # x1, y1, x2, y2 = box1\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]\n else: # transform from xywh to xyxy\n b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2\n b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2\n b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2\n b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2\n\n # Intersection area\n inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \\\n (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)\n\n # Union Area\n w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps\n w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps\n union = w1 * h1 + w2 * h2 - inter + eps\n\n iou = inter / union\n if CIoU or DIoU or GIoU:\n cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width\n ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height\n if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1\n c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared\n rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +\n (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared\n if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47\n v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)\n with torch.no_grad():\n alpha = v / (v - iou + (1 + eps))\n return iou - (rho2 / c2 + v * alpha) # CIoU\n return iou - rho2 / c2 # DIoU\n c_area = cw * ch + eps # convex area\n return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf\n return iou # IoU\n\n\ndef box_iou(box1, box2):\n # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\n \"\"\"\n Return intersection-over-union (Jaccard index) of boxes.\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n Arguments:\n box1 (Tensor[N, 4])\n box2 (Tensor[M, 4])\n Returns:\n iou (Tensor[N, M]): the NxM matrix containing the pairwise\n IoU values for every element in boxes1 and boxes2\n \"\"\"\n\n def box_area(box):\n # box = 4xn\n return (box[2] - box[0]) * (box[3] - box[1])\n\n area1 = box_area(box1.T)\n area2 = box_area(box2.T)\n\n # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\n inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)\n return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)\n\n\ndef bbox_ioa(box1, box2, eps=1E-7):\n \"\"\" Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2\n box1: np.array of shape(4)\n box2: np.array of shape(nx4)\n returns: np.array of shape(n)\n \"\"\"\n\n box2 = box2.transpose()\n\n # Get the coordinates of bounding boxes\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]\n\n # Intersection area\n inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \\\n (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)\n\n # box2 area\n box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps\n\n # Intersection over box2 area\n return inter_area / box2_area\n\n\ndef wh_iou(wh1, wh2):\n # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2\n wh1 = wh1[:, None] # [N,1,2]\n wh2 = wh2[None] # [1,M,2]\n inter = torch.min(wh1, wh2).prod(2) # [N,M]\n return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter)\n\n\n# Plots ----------------------------------------------------------------------------------------------------------------\n\ndef plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):\n # Precision-recall curve\n fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)\n py = np.stack(py, axis=1)\n\n if 0 < len(names) < 21: # display per-class legend if < 21 classes\n for i, y in enumerate(py.T):\n ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision)\n else:\n ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)\n\n ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())\n ax.set_xlabel('Recall')\n ax.set_ylabel('Precision')\n ax.set_xlim(0, 1)\n ax.set_ylim(0, 1)\n plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n fig.savefig(Path(save_dir), dpi=250)\n plt.close()\n\n\ndef plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):\n # Metric-confidence curve\n fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)\n\n if 0 < len(names) < 21: # display per-class legend if < 21 classes\n for i, y in enumerate(py):\n ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric)\n else:\n ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)\n\n y = py.mean(0)\n ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n ax.set_xlim(0, 1)\n ax.set_ylim(0, 1)\n plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n fig.savefig(Path(save_dir), dpi=250)\n plt.close()\n"
},
{
"path": "utils/plots.py",
"content": "# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nPlotting utils\n\"\"\"\n\nimport math\nimport os\nfrom copy import copy\nfrom pathlib import Path\n\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport torch\nfrom PIL import Image, ImageDraw, ImageFont\n\nfrom utils.general import (CONFIG_DIR, FONT, LOGGER, Timeout, check_font, check_requirements, clip_coords,\n increment_path, is_ascii, is_chinese, try_except, xywh2xyxy, xyxy2xywh)\nfrom utils.metrics import fitness\n\n# Settings\nRANK = int(os.getenv('RANK', -1))\nmatplotlib.rc('font', **{'size': 11})\nmatplotlib.use('Agg') # for writing to files only\n\n\nclass Colors:\n # Ultralytics color palette https://ultralytics.com/\n def __init__(self):\n # hex = matplotlib.colors.TABLEAU_COLORS.values()\n hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',\n '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')\n self.palette = [self.hex2rgb('#' + c) for c in hex]\n self.n = len(self.palette)\n\n def __call__(self, i, bgr=False):\n c = self.palette[int(i) % self.n]\n return (c[2], c[1], c[0]) if bgr else c\n\n @staticmethod\n def hex2rgb(h): # rgb order (PIL)\n return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))\n\n\ncolors = Colors() # create instance for 'from utils.plots import colors'\n\n\ndef check_pil_font(font=FONT, size=10):\n # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary\n font = Path(font)\n font = font if font.exists() else (CONFIG_DIR / font.name)\n try:\n return ImageFont.truetype(str(font) if font.exists() else font.name, size)\n except Exception: # download if missing\n check_font(font)\n try:\n return ImageFont.truetype(str(font), size)\n except TypeError:\n check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374\n\n\nclass Annotator:\n if RANK in (-1, 0):\n check_pil_font() # download TTF if necessary\n\n # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations\n def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):\n assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'\n self.pil = pil or not is_ascii(example) or is_chinese(example)\n if self.pil: # use PIL\n self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)\n self.draw = ImageDraw.Draw(self.im)\n self.font = check_pil_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,\n size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))\n else: # use cv2\n self.im = im\n self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width\n\n def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):\n # Add one xyxy box to image with label\n if self.pil or not is_ascii(label):\n self.draw.rectangle(box, width=self.lw, outline=color) # box\n if label:\n w, h = self.font.getsize(label) # text width, height\n outside = box[1] - h >= 0 # label fits outside box\n self.draw.rectangle((box[0],\n box[1] - h if outside else box[1],\n box[0] + w + 1,\n box[1] + 1 if outside else box[1] + h + 1), fill=color)\n # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0\n self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)\n else: # cv2\n p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))\n cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)\n if label:\n tf = max(self.lw - 1, 1) # font thickness\n w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height\n outside = p1[1] - h - 3 >= 0 # label fits outside box\n p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3\n cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled\n cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,\n thickness=tf, lineType=cv2.LINE_AA)\n\n def rectangle(self, xy, fill=None, outline=None, width=1):\n # Add rectangle to image (PIL-only)\n self.draw.rectangle(xy, fill, outline, width)\n\n def text(self, xy, text, txt_color=(255, 255, 255)):\n # Add text to image (PIL-only)\n w, h = self.font.getsize(text) # text width, height\n self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)\n\n def result(self):\n # Return annotated image as array\n return np.asarray(self.im)\n\n\ndef feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):\n \"\"\"\n x: Features to be visualized\n module_type: Module type\n stage: Module stage within model\n n: Maximum number of feature maps to plot\n save_dir: Directory to save results\n \"\"\"\n if 'Detect' not in module_type:\n batch, channels, height, width = x.shape # batch, channels, height, width\n if height > 1 and width > 1:\n f = save_dir / f\"stage{stage}_{module_type.split('.')[-1]}_features.png\" # filename\n\n blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels\n n = min(n, channels) # number of plots\n fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols\n ax = ax.ravel()\n plt.subplots_adjust(wspace=0.05, hspace=0.05)\n for i in range(n):\n ax[i].imshow(blocks[i].squeeze()) # cmap='gray'\n ax[i].axis('off')\n\n LOGGER.info(f'Saving {f}... ({n}/{channels})')\n plt.savefig(f, dpi=300, bbox_inches='tight')\n plt.close()\n np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save\n\n\ndef hist2d(x, y, n=100):\n # 2d histogram used in labels.png and evolve.png\n xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)\n hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))\n xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)\n yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)\n return np.log(hist[xidx, yidx])\n\n\ndef butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):\n from scipy.signal import butter, filtfilt\n\n # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy\n def butter_lowpass(cutoff, fs, order):\n nyq = 0.5 * fs\n normal_cutoff = cutoff / nyq\n return butter(order, normal_cutoff, btype='low', analog=False)\n\n b, a = butter_lowpass(cutoff, fs, order=order)\n return filtfilt(b, a, data) # forward-backward filter\n\n\ndef output_to_target(output):\n # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]\n targets = []\n for i, o in enumerate(output):\n for *box, conf, cls in o.cpu().numpy():\n targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])\n return np.array(targets)\n\n\ndef plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=1920, max_subplots=16):\n # Plot image grid with labels\n if isinstance(images, torch.Tensor):\n images = images.cpu().float().numpy()\n if isinstance(targets, torch.Tensor):\n targets = targets.cpu().numpy()\n if np.max(images[0]) <= 1:\n images *= 255 # de-normalise (optional)\n bs, _, h, w = images.shape # batch size, _, height, width\n bs = min(bs, max_subplots) # limit plot images\n ns = np.ceil(bs ** 0.5) # number of subplots (square)\n\n # Build Image\n mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init\n for i, im in enumerate(images):\n if i == max_subplots: # if last batch has fewer images than we expect\n break\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n im = im.transpose(1, 2, 0)\n mosaic[y:y + h, x:x + w, :] = im\n\n # Resize (optional)\n scale = max_size / ns / max(h, w)\n if scale < 1:\n h = math.ceil(scale * h)\n w = math.ceil(scale * w)\n mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))\n\n # Annotate\n fs = int((h + w) * ns * 0.01) # font size\n annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)\n for i in range(i + 1):\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders\n if paths:\n annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames\n if len(targets) > 0:\n ti = targets[targets[:, 0] == i] # image targets\n boxes = xywh2xyxy(ti[:, 2:6]).T\n classes = ti[:, 1].astype('int')\n labels = ti.shape[1] == 6 # labels if no conf column\n conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)\n\n if boxes.shape[1]:\n if boxes.max() <= 1.01: # if normalized with tolerance 0.01\n boxes[[0, 2]] *= w # scale to pixels\n boxes[[1, 3]] *= h\n elif scale < 1: # absolute coords need scale if image scales\n boxes *= scale\n boxes[[0, 2]] += x\n boxes[[1, 3]] += y\n for j, box in enumerate(boxes.T.tolist()):\n cls = classes[j]\n color = colors(cls)\n cls = names[cls] if names else cls\n if labels or conf[j] > 0.25: # 0.25 conf thresh\n label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'\n annotator.box_label(box, label, color=color)\n annotator.im.save(fname) # save\n\n\ndef plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):\n # Plot LR simulating training for full epochs\n optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals\n y = []\n for _ in range(epochs):\n scheduler.step()\n y.append(optimizer.param_groups[0]['lr'])\n plt.plot(y, '.-', label='LR')\n plt.xlabel('epoch')\n plt.ylabel('LR')\n plt.grid()\n plt.xlim(0, epochs)\n plt.ylim(0)\n plt.savefig(Path(save_dir) / 'LR.png', dpi=200)\n plt.close()\n\n\ndef plot_val_txt(): # from utils.plots import *; plot_val()\n # Plot val.txt histograms\n x = np.loadtxt('val.txt', dtype=np.float32)\n box = xyxy2xywh(x[:, :4])\n cx, cy = box[:, 0], box[:, 1]\n\n fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)\n ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)\n ax.set_aspect('equal')\n plt.savefig('hist2d.png', dpi=300)\n\n fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)\n ax[0].hist(cx, bins=600)\n ax[1].hist(cy, bins=600)\n plt.savefig('hist1d.png', dpi=200)\n\n\ndef plot_targets_txt(): # from utils.plots import *; plot_targets_txt()\n # Plot targets.txt histograms\n x = np.loadtxt('targets.txt', dtype=np.float32).T\n s = ['x targets', 'y targets', 'width targets', 'height targets']\n fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)\n ax = ax.ravel()\n for i in range(4):\n ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')\n ax[i].legend()\n ax[i].set_title(s[i])\n plt.savefig('targets.jpg', dpi=200)\n\n\ndef plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()\n # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)\n save_dir = Path(file).parent if file else Path(dir)\n plot2 = False # plot additional results\n if plot2:\n ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()\n\n fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)\n # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:\n for f in sorted(save_dir.glob('study*.txt')):\n y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T\n x = np.arange(y.shape[1]) if x is None else np.array(x)\n if plot2:\n s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']\n for i in range(7):\n ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)\n ax[i].set_title(s[i])\n\n j = y[3].argmax() + 1\n ax2.plot(y[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,\n label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))\n\n ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],\n 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')\n\n ax2.grid(alpha=0.2)\n ax2.set_yticks(np.arange(20, 60, 5))\n ax2.set_xlim(0, 57)\n ax2.set_ylim(25, 55)\n ax2.set_xlabel('GPU Speed (ms/img)')\n ax2.set_ylabel('COCO AP val')\n ax2.legend(loc='lower right')\n f = save_dir / 'study.png'\n print(f'Saving {f}...')\n plt.savefig(f, dpi=300)\n\n\n@try_except # known issue https://github.com/ultralytics/yolov5/issues/5395\n@Timeout(30) # known issue https://github.com/ultralytics/yolov5/issues/5611\ndef plot_labels(labels, names=(), save_dir=Path('')):\n # plot dataset labels\n LOGGER.info(f\"Plotting labels to {save_dir / 'labels.jpg'}... \")\n c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes\n nc = int(c.max() + 1) # number of classes\n x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])\n\n # seaborn correlogram\n sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))\n plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)\n plt.close()\n\n # matplotlib labels\n matplotlib.use('svg') # faster\n ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()\n y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)\n try: # color histogram bars by class\n [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195\n except Exception:\n pass\n ax[0].set_ylabel('instances')\n if 0 < len(names) < 30:\n ax[0].set_xticks(range(len(names)))\n ax[0].set_xticklabels(names, rotation=90, fontsize=10)\n else:\n ax[0].set_xlabel('classes')\n sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)\n sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)\n\n # rectangles\n labels[:, 1:3] = 0.5 # center\n labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000\n img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)\n for cls, *box in labels[:1000]:\n ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot\n ax[1].imshow(img)\n ax[1].axis('off')\n\n for a in [0, 1, 2, 3]:\n for s in ['top', 'right', 'left', 'bottom']:\n ax[a].spines[s].set_visible(False)\n\n plt.savefig(save_dir / 'labels.jpg', dpi=200)\n matplotlib.use('Agg')\n plt.close()\n\n\ndef plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()\n # Plot evolve.csv hyp evolution results\n evolve_csv = Path(evolve_csv)\n data = pd.read_csv(evolve_csv)\n keys = [x.strip() for x in data.columns]\n x = data.values\n f = fitness(x)\n j = np.argmax(f) # max fitness index\n plt.figure(figsize=(10, 12), tight_layout=True)\n matplotlib.rc('font', **{'size': 8})\n print(f'Best results from row {j} of {evolve_csv}:')\n for i, k in enumerate(keys[7:]):\n v = x[:, 7 + i]\n mu = v[j] # best single result\n plt.subplot(6, 5, i + 1)\n plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')\n plt.plot(mu, f.max(), 'k+', markersize=15)\n plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters\n if i % 5 != 0:\n plt.yticks([])\n print(f'{k:>15}: {mu:.3g}')\n f = evolve_csv.with_suffix('.png') # filename\n plt.savefig(f, dpi=200)\n plt.close()\n print(f'Saved {f}')\n\n\ndef plot_results(file='path/to/results.csv', dir=''):\n # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')\n save_dir = Path(file).parent if file else Path(dir)\n fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)\n ax = ax.ravel()\n files = list(save_dir.glob('results*.csv'))\n assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'\n for fi, f in enumerate(files):\n try:\n data = pd.read_csv(f)\n s = [x.strip() for x in data.columns]\n x = data.values[:, 0]\n for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):\n y = data.values[:, j]\n # y[y == 0] = np.nan # don't show zero values\n ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)\n ax[i].set_title(s[j], fontsize=12)\n # if j in [8, 9, 10]: # share train and val loss y axes\n # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])\n except Exception as e:\n LOGGER.info(f'Warning: Plotting error for {f}: {e}')\n ax[1].legend()\n fig.savefig(save_dir / 'results.png', dpi=200)\n plt.close()\n\n\ndef profile_idetection(start=0, stop=0, labels=(), save_dir=''):\n # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()\n ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()\n s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']\n files = list(Path(save_dir).glob('frames*.txt'))\n for fi, f in enumerate(files):\n try:\n results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows\n n = results.shape[1] # number of rows\n x = np.arange(start, min(stop, n) if stop else n)\n results = results[:, x]\n t = (results[0] - results[0].min()) # set t0=0s\n results[0] = x\n for i, a in enumerate(ax):\n if i < len(results):\n label = labels[fi] if len(labels) else f.stem.replace('frames_', '')\n a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)\n a.set_title(s[i])\n a.set_xlabel('time (s)')\n # if fi == len(files) - 1:\n # a.set_ylim(bottom=0)\n for side in ['top', 'right']:\n a.spines[side].set_visible(False)\n else:\n a.remove()\n except Exception as e:\n print(f'Warning: Plotting error for {f}; {e}')\n ax[1].legend()\n plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)\n\n\ndef save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):\n # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop\n xyxy = torch.tensor(xyxy).view(-1, 4)\n b = xyxy2xywh(xyxy) # boxes\n if square:\n b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square\n b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad\n xyxy = xywh2xyxy(b).long()\n clip_coords(xyxy, im.shape)\n crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]\n if save:\n file.parent.mkdir(parents=True, exist_ok=True) # make directory\n cv2.imwrite(str(increment_path(file).with_suffix('.jpg')), crop)\n return crop\n"
},
{
"path": "utils/torch_utils.py",
"content": "# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nPyTorch utils\n\"\"\"\n\nimport datetime\nimport math\nimport os\nimport platform\nimport subprocess\nimport time\nimport warnings\nfrom contextlib import contextmanager\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom utils.general import LOGGER\n\ntry:\n import thop # for FLOPs computation\nexcept ImportError:\n thop = None\n\n# Suppress PyTorch warnings\nwarnings.filterwarnings('ignore', message='User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n\n\n@contextmanager\ndef torch_distributed_zero_first(local_rank: int):\n \"\"\"\n Decorator to make all processes in distributed training wait for each local_master to do something.\n \"\"\"\n if local_rank not in [-1, 0]:\n dist.barrier(device_ids=[local_rank])\n yield\n if local_rank == 0:\n dist.barrier(device_ids=[0])\n\n\ndef date_modified(path=__file__):\n # return human-readable file modification date, i.e. '2021-3-26'\n t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)\n return f'{t.year}-{t.month}-{t.day}'\n\n\ndef git_describe(path=Path(__file__).parent): # path must be a directory\n # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe\n s = f'git -C {path} describe --tags --long --always'\n try:\n return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]\n except subprocess.CalledProcessError:\n return '' # not a git repository\n\n\ndef device_count():\n # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Only works on Linux.\n assert platform.system() == 'Linux', 'device_count() function only works on Linux'\n try:\n cmd = 'nvidia-smi -L | wc -l'\n return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])\n except Exception:\n return 0\n\n\ndef select_device(device='', batch_size=0, newline=True):\n # device = 'cpu' or '0' or '0,1,2,3'\n s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string\n device = str(device).strip().lower().replace('cuda:', '') # to string, 'cuda:0' to '0'\n cpu = device == 'cpu'\n if cpu:\n os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False\n elif device: # non-cpu device requested\n os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()\n assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \\\n f\"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)\"\n\n cuda = not cpu and torch.cuda.is_available()\n if cuda:\n devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7\n n = len(devices) # device count\n if n > 1 and batch_size > 0: # check batch_size is divisible by device_count\n assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'\n space = ' ' * (len(s) + 1)\n for i, d in enumerate(devices):\n p = torch.cuda.get_device_properties(i)\n s += f\"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2:.0f}MiB)\\n\" # bytes to MB\n else:\n s += 'CPU\\n'\n\n if not newline:\n s = s.rstrip()\n LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe\n return torch.device('cuda:0' if cuda else 'cpu')\n\n\ndef time_sync():\n # pytorch-accurate time\n if torch.cuda.is_available():\n torch.cuda.synchronize()\n return time.time()\n\n\ndef profile(input, ops, n=10, device=None):\n # YOLOv5 speed/memory/FLOPs profiler\n #\n # Usage:\n # input = torch.randn(16, 3, 640, 640)\n # m1 = lambda x: x * torch.sigmoid(x)\n # m2 = nn.SiLU()\n # profile(input, [m1, m2], n=100) # profile over 100 iterations\n\n results = []\n device = device or select_device()\n print(f\"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}\"\n f\"{'input':>24s}{'output':>24s}\")\n\n for x in input if isinstance(input, list) else [input]:\n x = x.to(device)\n x.requires_grad = True\n for m in ops if isinstance(ops, list) else [ops]:\n m = m.to(device) if hasattr(m, 'to') else m # device\n m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m\n tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward\n try:\n flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs\n except Exception:\n flops = 0\n\n try:\n for _ in range(n):\n t[0] = time_sync()\n y = m(x)\n t[1] = time_sync()\n try:\n _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()\n t[2] = time_sync()\n except Exception: # no backward method\n # print(e) # for debug\n t[2] = float('nan')\n tf += (t[1] - t[0]) * 1000 / n # ms per op forward\n tb += (t[2] - t[1]) * 1000 / n # ms per op backward\n mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)\n s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'\n s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'\n p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters\n print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')\n results.append([p, flops, mem, tf, tb, s_in, s_out])\n except Exception as e:\n print(e)\n results.append(None)\n torch.cuda.empty_cache()\n return results\n\n\ndef is_parallel(model):\n # Returns True if model is of type DP or DDP\n return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)\n\n\ndef de_parallel(model):\n # De-parallelize a model: returns single-GPU model if model is of type DP or DDP\n return model.module if is_parallel(model) else model\n\n\ndef initialize_weights(model):\n for m in model.modules():\n t = type(m)\n if t is nn.Conv2d:\n pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif t is nn.BatchNorm2d:\n m.eps = 1e-3\n m.momentum = 0.03\n elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:\n m.inplace = True\n\n\ndef find_modules(model, mclass=nn.Conv2d):\n # Finds layer indices matching module class 'mclass'\n return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]\n\n\ndef sparsity(model):\n # Return global model sparsity\n a, b = 0, 0\n for p in model.parameters():\n a += p.numel()\n b += (p == 0).sum()\n return b / a\n\n\ndef prune(model, amount=0.3):\n # Prune model to requested global sparsity\n import torch.nn.utils.prune as prune\n print('Pruning model... ', end='')\n for name, m in model.named_modules():\n if isinstance(m, nn.Conv2d):\n prune.l1_unstructured(m, name='weight', amount=amount) # prune\n prune.remove(m, 'weight') # make permanent\n print(' %.3g global sparsity' % sparsity(model))\n\n\ndef fuse_conv_and_bn(conv, bn):\n # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/\n fusedconv = nn.Conv2d(conv.in_channels,\n conv.out_channels,\n kernel_size=conv.kernel_size,\n stride=conv.stride,\n padding=conv.padding,\n groups=conv.groups,\n bias=True).requires_grad_(False).to(conv.weight.device)\n\n # prepare filters\n w_conv = conv.weight.clone().view(conv.out_channels, -1)\n w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))\n fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))\n\n # prepare spatial bias\n b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias\n b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))\n fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)\n\n return fusedconv\n\n\ndef model_info(model, verbose=False, img_size=640):\n # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]\n n_p = sum(x.numel() for x in model.parameters()) # number parameters\n n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients\n if verbose:\n print(f\"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}\")\n for i, (name, p) in enumerate(model.named_parameters()):\n name = name.replace('module_list.', '')\n print('%5g %40s %9s %12g %20s %10.3g %10.3g' %\n (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))\n\n try: # FLOPs\n from thop import profile\n stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32\n img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input\n flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs\n img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float\n fs = ', %.1f GFLOPs' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPs\n except (ImportError, Exception):\n fs = ''\n\n LOGGER.info(f\"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}\")\n\n\ndef scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)\n # scales img(bs,3,y,x) by ratio constrained to gs-multiple\n if ratio == 1.0:\n return img\n else:\n h, w = img.shape[2:]\n s = (int(h * ratio), int(w * ratio)) # new size\n img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize\n if not same_shape: # pad/crop img\n h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))\n return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean\n\n\ndef copy_attr(a, b, include=(), exclude=()):\n # Copy attributes from b to a, options to only include [...] and to exclude [...]\n for k, v in b.__dict__.items():\n if (len(include) and k not in include) or k.startswith('_') or k in exclude:\n continue\n else:\n setattr(a, k, v)\n\n\nclass EarlyStopping:\n # YOLOv5 simple early stopper\n def __init__(self, patience=30):\n self.best_fitness = 0.0 # i.e. mAP\n self.best_epoch = 0\n self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop\n self.possible_stop = False # possible stop may occur next epoch\n\n def __call__(self, epoch, fitness):\n if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training\n self.best_epoch = epoch\n self.best_fitness = fitness\n delta = epoch - self.best_epoch # epochs without improvement\n self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch\n stop = delta >= self.patience # stop training if patience exceeded\n if stop:\n LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '\n f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\\n'\n f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '\n f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.')\n return stop\n\n\nclass ModelEMA:\n \"\"\" Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models\n Keeps a moving average of everything in the model state_dict (parameters and buffers)\n For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage\n \"\"\"\n\n def __init__(self, model, decay=0.9999, updates=0):\n # Create EMA\n self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA\n # if next(model.parameters()).device.type != 'cpu':\n # self.ema.half() # FP16 EMA\n self.updates = updates # number of EMA updates\n self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs)\n for p in self.ema.parameters():\n p.requires_grad_(False)\n\n def update(self, model):\n # Update EMA parameters\n with torch.no_grad():\n self.updates += 1\n d = self.decay(self.updates)\n\n msd = de_parallel(model).state_dict() # model state_dict\n for k, v in self.ema.state_dict().items():\n if v.dtype.is_floating_point:\n v *= d\n v += (1 - d) * msd[k].detach()\n\n def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):\n # Update EMA attributes\n copy_attr(self.ema, model, include, exclude)\n"
},
{
"path": "utils/tt100k_to_voc-main/1.py",
"content": "# @Time : 2023-02-18 21:15\n# @Author : AItrainee\n# @File : 1.py\n#计算E:\\Desktop\\tt100k_to_voc-main\\xmlLabel1\\train下的xml文件的数量\nimport os\n#获取当前文件夹下的文件名称列表\npath = r'E:\\Desktop\\tt100k_to_voc-main\\xmlLabel1\\test'\nfilelist = os.listdir(path)\n#统计文件夹下文件个数\ntotal_num = len(filelist)\nprint(total_num)\n\n"
},
{
"path": "utils/tt100k_to_voc-main/1_build_voc_dir.py",
"content": "import os\n\n# 建立相关文件夹\n# build voc2007 folder structure\ndef make_voc_dir():\n root_dir = os.getcwd()\n os.makedirs(root_dir+'/VOC2007')\n os.makedirs(\"VOC2007\"+'/Annotations')\n os.makedirs(\"VOC2007\" + '/JPEGImages/ ')\n os.makedirs(\"VOC2007\"+'/ImageSets')\n os.makedirs(\"VOC2007\"+'/ImageSets/Main')\n\nif __name__ == '__main__':\n make_voc_dir()"
},
{
"path": "utils/tt100k_to_voc-main/2_json2xml.py",
"content": "import os\nimport json\nfrom lxml import etree as ET\nfrom xml.dom import minidom\n#找出训练集和测试集中的不在45类的标注图片的id\ndef edit_xml(objects, id, dir):\n save_xml_path = os.path.join(dir, \"%s.xml\" % id) # xml\n\n root = ET.Element(\"annotation\")\n # root.set(\"version\", \"1.0\") \n folder = ET.SubElement(root, \"folder\")\n folder.text = \"none\"\n filename = ET.SubElement(root, \"filename\")\n filename.text = id + \".jpg\"\n source = ET.SubElement(root, \"source\")\n source.text = \"none\"\n owner = ET.SubElement(root, \"owner\")\n owner.text = \"halftop\"\n size = ET.SubElement(root, \"size\")\n width = ET.SubElement(size, \"width\")\n width.text = str(2048)\n height = ET.SubElement(size, \"height\")\n height.text = str(2048)\n depth = ET.SubElement(size, \"depth\")\n depth.text = \"3\"\n segmented = ET.SubElement(root, \"segmented\")\n segmented.text = \"0\"\n for obj in objects: # \n object = ET.SubElement(root, \"object\")\n name = ET.SubElement(object, \"name\") # number\n name.text = obj[\"category\"]\n\n # meaning = ET.SubElement(object, \"meaning\") # name\n # meaning.text = inf_value[0]\n pose = ET.SubElement(object, \"pose\")\n pose.text = \"Unspecified\"\n truncated = ET.SubElement(object, \"truncated\")\n truncated.text = \"0\"\n difficult = ET.SubElement(object, \"difficult\")\n difficult.text = \"0\"\n bndbox = ET.SubElement(object, \"bndbox\")\n xmin = ET.SubElement(bndbox, \"xmin\")\n xmin.text = str(int(obj[\"bbox\"][\"xmin\"]))\n ymin = ET.SubElement(bndbox, \"ymin\")\n ymin.text = str(int(obj[\"bbox\"][\"ymin\"]))\n xmax = ET.SubElement(bndbox, \"xmax\")\n xmax.text = str(int(obj[\"bbox\"][\"xmax\"]))\n ymax = ET.SubElement(bndbox, \"ymax\")\n ymax.text = str(int(obj[\"bbox\"][\"ymax\"]))\n tree = ET.ElementTree(root)\n tree.write(save_xml_path, encoding=\"UTF-8\", xml_declaration=True)\n root = ET.parse(save_xml_path) \n file_lines = minidom.parseString(ET.tostring(root, encoding=\"Utf-8\")).toprettyxml(\n indent=\"\\t\") \n file_line = open(save_xml_path, \"w\", encoding=\"utf-8\") \n file_line.write(file_lines)\n file_line.close()\n \ndef getDirId(dir): # get the id list of id.png\n names = os.listdir(dir)\n ids = []\n for name in names:\n # path = os.path.join(dir, name)\n # img = cv2.imread(path)\n # w, h, c = img.shape\n # if name.endswith(\".jpg\") or name.endswith(\".png\"):\n # ids[\"%s\" % name.split(\".\")[0]] = [w, h, c]\n ids.append(name.split(\".\")[0])\n return ids\n\ndef is_tt45(objects):\n flag = True\n json_file = open('TT100K_VOC_classes.json', 'r')\n results = json.load(json_file)\n for obj in objects:\n text = obj[\"category\"]\n for key in results.keys():\n flag1 = False\n if key == text:\n flag1 =True\n break\n if flag1 == False:\n flag = False\n break\n return flag\nfiledir = \"annotations_all.json\"\nannos = json.loads(open(filedir).read())\n\ntrainIds = getDirId(\"train/\")\ntestIds = getDirId(\"test/\")\n\nids = annos[\"imgs\"].keys() # all img ids in .json \n\ndir_train = \"xmlLabel1/train\"\ndir_test = \"xmlLabel1/test\"\nif not os.path.exists(dir_train):\n os.makedirs(dir_train)\nif not os.path.exists(dir_test):\n os.makedirs(dir_test)\n\nNot_TT45_list_train = []\nNot_TT45_list_val = []\nfor id in ids:\n# json 中的ID图片有待检测目标，且该id图片在 train文件夹中\n if len(annos[\"imgs\"][id][\"objects\"]) > 0 and (id in trainIds):\n objects = annos[\"imgs\"][id][\"objects\"]\n flag = is_tt45(objects)\n if flag is False:\n Not_TT45_list_train.append(id+'\\n')\n edit_xml(objects, id, dir=dir_train)\n \n elif len(annos[\"imgs\"][id][\"objects\"]) > 0 and (id in testIds):\n objects = annos[\"imgs\"][id][\"objects\"]\n flag = is_tt45(objects)\n if flag is False:\n Not_TT45_list_val.append(id+'\\n')\n edit_xml(objects, id, dir=dir_test)\nwith open(\"Not_TT45_list_train.txt\", \"a\") as f:\n f.writelines(Not_TT45_list_train)\nwith open(\"Not_TT45_list_val.txt\", \"a\") as f:\n f.writelines(Not_TT45_list_val)"
},
{
"path": "utils/tt100k_to_voc-main/3_delete_jpg_and_xml.py",
"content": "import os\nimport glob\n\n# 删除txt中id对应XML和图片\ndef delete_train_jpg(train_txt):\n root_dir = os.getcwd()\n for line in open(train_txt ,\"r\"):\n file_id = line.strip()\n # print(file_id)\n file_path = os.path.join(root_dir,\"train\",file_id+'.jpg')\n # print(file_path)\n os.remove(file_path)\n\n\ndef delete_test_jpg(test_txt):\n root_dir = os.getcwd()\n for line in open(test_txt ,\"r\"):\n file_id = line.strip()\n # print(file_id)\n file_path = os.path.join(root_dir,\"test\",file_id+'.jpg')\n # print(file_path)\n os.remove(file_path)\n\ndef delete_train_xml(train_txt):\n root_dir = os.getcwd()\n root_path = os.path.join(root_dir,\"xmlLabel1\")\n for line in open(train_txt,\"r\"):\n file_id = line.strip()\n # print(file_id)\n file_path = os.path.join(root_path,\"train\",file_id+'.xml')\n # print(file_path)\n os.remove(file_path)\n\ndef delete_test_xml(test_txt):\n root_dir = os.getcwd()\n root_path = os.path.join(root_dir,\"xmlLabel1\")\n for line in open(test_txt,\"r\"):\n file_id = line.strip()\n # print(file_id)\n file_path = os.path.join(root_path,\"test\",file_id+'.xml')\n # print(file_path)\n os.remove(file_path)\nif __name__ == '__main__':\n train_txt = \"Not_TT45_list_train.txt\"\n test_txt = \"Not_TT45_list_val.txt\"\n delete_train_jpg(train_txt)\n delete_train_xml(train_txt)\n delete_test_jpg(test_txt)\n delete_test_xml(test_txt)"
},
{
"path": "utils/tt100k_to_voc-main/4_spilt_data.py",
"content": "import os\nimport random\n\n\nfiles_path = r\"E:\\Desktop\\tt100k_to_voc-main\\xmlLabel1\\test\"\nif not os.path.exists(files_path):\n print(\"文件夹不存在\")\n exit(1)\n\nval_rate = 1\n\nfiles_name = sorted([file.split(\".\")[0] for file in os.listdir(files_path)])\nfiles_num = len(files_name)\nval_index = random.sample(range(0, files_num), k=int(files_num*val_rate))\ntrain_files = []\nval_files = []\nfor index, file_name in enumerate(files_name):\n if index in val_index:\n val_files.append(file_name)\n else:\n train_files.append(file_name)\n\ntry:\n # train_f = open(\"VOC2007/ImageSets/Main/train.txt\", \"x\")\n eval_f = open(\"VOC2007/ImageSets/Main/val.txt\", \"x\")\n # train_f.write(\"\\n\".join(train_files))\n eval_f.write(\"\\n\".join(val_files))\nexcept FileExistsError as e:\n print(e)\n exit(1)\n\n\n\n"
},
{
"path": "utils/tt100k_to_voc-main/5_label.py",
"content": "import xml.etree.ElementTree as ET\n\nimport pickle\nimport os\nfrom os import listdir, getcwd\nfrom os.path import join\nimport glob\n\nls={\n \"i2\":1,\n \"i4\":2,\n \"i5\":3,\n \"il100\":4,\n \"il60\":5,\n \"il80\":6,\n \"io\":7,\n \"ip\":8,\n \"p10\":9,\n \"p11\":10,\n \"p12\":11,\n \"p19\":12,\n \"p23\":13,\n \"p26\":14,\n \"p27\":15,\n \"p3\":16,\n \"p5\":17,\n \"p6\":18,\n \"pg\":19,\n \"ph4\":20,\n \"ph4.5\":21,\n \"ph5\":22,\n \"pl100\":23,\n \"pl120\":24,\n \"pl20\":25,\n \"pl30\":26,\n \"pl40\":27,\n \"pl5\":28,\n \"pl50\":29,\n \"pl60\":30,\n \"pl70\":31,\n \"pl80\":32,\n \"pm20\":33,\n \"pm30\":34,\n \"pm55\":35,\n \"pn\":36,\n \"pne\":37,\n \"po\":38,\n \"pr40\":39,\n \"w13\":40,\n \"w32\":41,\n \"w55\":42,\n \"w57\":43,\n \"w59\":44,\n \"wo\":45\n}\n#\nclasses = []# 输入缺陷名称，必须与xml标注名称一致\n#classes =ls.keys()\nfor i in ls.keys():\n classes.append(i)\nprint(classes)\n\n\n\ndef convert(size, box):\n dw = 1.0 / size[0]\n dh = 1.0 / size[1]\n x = (box[0] + box[1]) / 2.0\n y = (box[2] + box[3]) / 2.0\n w = box[1] - box[0]\n h = box[3] - box[2]\n x = x * dw\n w = w * dw\n y = y * dh\n h = h * dh\n return (x, y, w, h)\n\n\n\n# 注意如果是在windows系统下那么路径里的反斜杠\\应该多写一个，否则会和python的‘\\’产生语义冲突\ndef convert_annotation(image_name):\n in_file = open(r'E:\\Desktop\\新建文件夹 (3)\\Annotations\\%s' % (image_name)) # 读取xml文件路径\n\n out_file = open(r'E:\\Desktop\\新建文件夹 (3)\\%s.txt' % (image_name), 'w') # 需要保存的txt格式文件路径\n f = in_file\n xml_text = f.read()\n root = ET.fromstring(xml_text)\n f.close()\n\n size = root.find('size')\n w = int(size.find('width').text)\n h = int(size.find('height').text)\n\n for obj in root.iter('object'):\n cls = obj.find('name').text\n if cls not in classes:\n print(cls)\n continue\n cls_id = classes.index(cls)\n xmlbox = obj.find('bndbox')\n b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),\n float(xmlbox.find('ymax').text))\n bb = convert((w, h), b)\n out_file.write(str(cls_id) + \" \" + \" \".join([str(a) for a in bb]) + '\\n')\n\n\nwd = getcwd()\n\nif __name__ == '__main__':\n\n filenames = os.listdir( r\"E:\\Desktop\\新建文件夹 (3)\\Annotations\") # xml文件路径，这样输出的txt文件和xml一一对应\n\n for label_path in filenames:\n print(label_path)\n convert_annotation(label_path)\n\n"
},
{
"path": "utils/tt100k_to_voc-main/Not_TT45_list_train.txt",
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},
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"path": "utils/tt100k_to_voc-main/Not_TT45_list_val.txt",
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\"objects\": [{\"bbox\": {\"xmin\": 1916.0, \"ymin\": 801.0, \"xmax\": 1988.0, \"ymax\": 888.0}, \"category\": \"pl40\"}, {\"bbox\": {\"xmin\": 1919.0, \"ymin\": 889.0, \"xmax\": 1993.0, \"ymax\": 981.0}, \"category\": \"pn\"}]}, \"66363\": {\"path\": \"other/66363.jpg\", \"id\": 66363, \"objects\": [{\"bbox\": {\"xmin\": 1568.9840087890625, \"ymin\": 974.7886962890625, \"xmax\": 1586.2589111328125, \"ymax\": 992.0008544921875}, \"category\": \"w63\"}]}, \"92425\": {\"path\": \"train/92425.jpg\", \"id\": 92425, \"objects\": [{\"bbox\": {\"xmin\": 1266.67, \"ymin\": 946.667, \"xmax\": 1322.6667, \"ymax\": 1000.0}, \"category\": \"pl120\"}]}, \"25806\": {\"path\": \"train/25806.jpg\", \"id\": 25806, \"objects\": [{\"bbox\": {\"xmin\": 807.895, \"ymin\": 718.421, \"xmax\": 871.0526, \"ymax\": 786.8421000000001}, \"category\": \"i5\"}, {\"bbox\": {\"xmin\": 546.053, \"ymin\": 750.0, \"xmax\": 597.3684, \"ymax\": 811.8421}, \"category\": \"pne\"}]}, \"5992\": {\"path\": \"test/5992.jpg\", \"id\": 5992, \"objects\": [{\"bbox\": {\"xmin\": 778.0, \"ymin\": 1004.0, \"xmax\": 811.0, \"ymax\": 1035.0}, \"category\": \"ip\"}, {\"bbox\": {\"xmin\": 1145.0, \"ymin\": 780.0, \"xmax\": 1195.0, \"ymax\": 832.0}, \"category\": \"pl60\"}, {\"bbox\": {\"xmin\": 1281.0, \"ymin\": 983.0, \"xmax\": 1312.0, \"ymax\": 1014.0}, \"category\": \"pl60\"}, {\"bbox\": {\"xmin\": 1404.0, \"ymin\": 866.0, \"xmax\": 1455.0, \"ymax\": 919.0}, \"category\": \"pmr\"}]}, \"23120\": {\"path\": \"train/23120.jpg\", \"id\": 23120, \"objects\": [{\"bbox\": {\"xmin\": 1454.0, \"ymin\": 902.0, \"xmax\": 1518.0, \"ymax\": 972.0}, \"category\": \"pl120\"}]}, \"96224\": {\"path\": \"test/96224.jpg\", \"id\": 96224, \"objects\": [{\"bbox\": {\"xmin\": 1805.0, \"ymin\": 910.0, \"xmax\": 1853.0, \"ymax\": 976.0}, \"category\": \"ip\"}]}, \"83925\": {\"path\": \"train/83925.jpg\", \"id\": 83925, \"objects\": [{\"bbox\": {\"xmin\": 1793.33, \"ymin\": 860.0, \"xmax\": 1860.0, \"ymax\": 934.6667}, \"category\": \"pm55\"}]}, \"68952\": {\"path\": \"train/68952.jpg\", \"id\": 68952, \"objects\": [{\"bbox\": {\"xmin\": 1199.43, \"ymin\": 934.659, \"xmax\": 1218.7464, \"ymax\": 956.8181999999999}, \"category\": \"i5\"}, {\"bbox\": {\"xmin\": 1792.61, \"ymin\": 1009.66, \"xmax\": 1809.0891, \"ymax\": 1028.9736}, \"category\": \"pl30\"}, {\"bbox\": {\"xmin\": 1147.16, \"ymin\": 934.091, \"xmax\": 1168.7482, \"ymax\": 956.25}, \"category\": \"pne\"}]}, \"73507\": {\"path\": \"train/73507.jpg\", \"id\": 73507, \"objects\": [{\"bbox\": {\"xmin\": 1009.33, \"ymin\": 901.333, \"xmax\": 1034.6667, \"ymax\": 924.0}, \"category\": \"p5\"}, {\"bbox\": {\"xmin\": 584.0, \"ymin\": 292.0, \"xmax\": 758.667, \"ymax\": 445.33299999999997}, \"category\": \"pl30\"}, {\"bbox\": {\"xmin\": 758.667, \"ymin\": 242.667, \"xmax\": 946.667, \"ymax\": 376.0}, \"category\": \"w57\"}, {\"bbox\": {\"xmin\": 1036.0, \"ymin\": 897.333, \"xmax\": 1060.0, \"ymax\": 925.3333}, \"category\": \"pcl\"}]}, \"8292\": {\"path\": \"train/8292.jpg\", \"id\": 8292, \"objects\": [{\"bbox\": {\"xmin\": 635.0, \"ymin\": 1004.0, \"xmax\": 666.0, \"ymax\": 1038.0}, \"category\": \"i5\"}, {\"bbox\": {\"xmin\": 1375.0, \"ymin\": 472.0, \"xmax\": 1505.0, \"ymax\": 592.0}, \"category\": \"p26\"}]}, \"47301\": {\"path\": \"train/47301.jpg\", \"id\": 47301, \"objects\": [{\"bbox\": {\"xmin\": 794.667, \"ymin\": 772.0, \"xmax\": 866.6667, \"ymax\": 837.3333}, \"category\": \"pr40\"}]}, \"39900\": {\"path\": \"train/39900.jpg\", \"id\": 39900, \"objects\": [{\"bbox\": {\"xmin\": 1762.75, \"ymin\": 867.638, \"xmax\": 1825.4933, \"ymax\": 1021.782}, \"category\": \"pne\"}]}, \"7955\": {\"path\": \"train/7955.jpg\", \"id\": 7955, \"objects\": [{\"bbox\": {\"xmin\": 1297.73, \"ymin\": 759.091, \"xmax\": 1348.8682000000001, \"ymax\": 814.7727}, \"category\": \"p1\"}, {\"bbox\": {\"xmin\": 1353.98, \"ymin\": 764.205, \"xmax\": 1403.9773, \"ymax\": 816.4776}, \"category\": \"ph5\"}, {\"bbox\": {\"xmin\": 1241.48, \"ymin\": 752.841, \"xmax\": 1295.4545, \"ymax\": 809.0909}, \"category\": \"pl60\"}, {\"bbox\": {\"xmin\": 1408.52, \"ymin\": 767.045, \"xmax\": 1457.9545, \"ymax\": 820.4544999999999}, \"category\": \"pn\"}]}, \"38560\": {\"path\": \"train/38560.jpg\", \"id\": 38560, \"objects\": [{\"bbox\": {\"xmin\": 1019.74, \"ymin\": 327.632, \"xmax\": 1211.835, \"ymax\": 484.211}, \"category\": \"pl60\"}, {\"bbox\": {\"xmin\": 1244.74, \"ymin\": 335.526, \"xmax\": 1432.891, \"ymax\": 492.105}, \"category\": \"pn\"}]}, \"68645\": {\"path\": \"train/68645.jpg\", \"id\": 68645, \"objects\": [{\"bbox\": {\"xmin\": 458.416, \"ymin\": 983.168, \"xmax\": 485.1485, \"ymax\": 1016.8317}, \"category\": \"p11\"}, {\"bbox\": {\"xmin\": 461.386, \"ymin\": 948.515, \"xmax\": 484.15840000000003, \"ymax\": 984.1584}, \"category\": \"pl5\"}]}, \"43541\": {\"path\": \"test/43541.jpg\", \"id\": 43541, \"objects\": [{\"bbox\": {\"xmin\": 1927.49, \"ymin\": 1183.67, \"xmax\": 1958.1625, \"ymax\": 1221.5139000000001}, \"category\": \"pne\"}]}, \"61598\": {\"path\": \"train/61598.jpg\", \"id\": 61598, \"objects\": [{\"bbox\": {\"xmin\": 1301.13, \"ymin\": 989.773, \"xmax\": 1326.1327, \"ymax\": 1014.7727}, \"category\": \"pg\"}, {\"bbox\": {\"xmin\": 1352.84, \"ymin\": 799.432, \"xmax\": 1401.7044999999998, \"ymax\": 851.7044}, \"category\": \"pl20\"}, {\"bbox\": {\"xmin\": 1309.66, \"ymin\": 802.273, \"xmax\": 1355.1136000000001, \"ymax\": 847.1596000000001}, \"category\": \"w13\"}]}, \"81220\": {\"path\": \"train/81220.jpg\", \"id\": 81220, \"objects\": [{\"bbox\": {\"xmin\": 106.667, \"ymin\": 841.333, \"xmax\": 132.0, \"ymax\": 880.0}, \"category\": \"p25\"}, {\"bbox\": {\"xmin\": 101.333, \"ymin\": 964.0, \"xmax\": 129.3333, \"ymax\": 998.6667}, \"category\": \"p26\"}]}, \"58681\": {\"path\": \"train/58681.jpg\", \"id\": 58681, \"objects\": [{\"bbox\": {\"xmin\": 1708.0, \"ymin\": 952.0, \"xmax\": 1753.3333, \"ymax\": 1002.6667}, \"category\": \"pl30\"}]}, \"49063\": {\"path\": \"test/49063.jpg\", \"id\": 49063, \"objects\": [{\"bbox\": {\"xmin\": 1498.295166015625, \"ymin\": 913.6915893554688, \"xmax\": 1518.445556640625, \"ymax\": 934.3252563476562}, \"category\": \"pn\"}, {\"bbox\": {\"xmin\": 1473.81, \"ymin\": 911.111, \"xmax\": 1494.4443999999999, \"ymax\": 936.5079}, \"category\": \"pl50\"}]}}}"
},
{
"path": "utils/tt100k_to_voc-main/新建 Internet 快捷方式.url",
"content": "[{000214A0-0000-0000-C000-000000000046}]\nProp3=19,11\n[InternetShortcut]\nIDList=\nURL=https://blog.csdn.net/Hankerchen/article/details/120727299\n"
},
{
"path": "utils/wandb_logging/__init__.py",
"content": ""
},
{
"path": "utils/wandb_logging/log_dataset.py",
"content": "import argparse\n\nimport yaml\n\nfrom wandb_utils import WandbLogger\n\nWANDB_ARTIFACT_PREFIX = 'wandb-artifact://'\n\n\ndef create_dataset_artifact(opt):\n with open(opt.data) as f:\n data = yaml.safe_load(f) # data dict\n logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')\n parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')\n parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')\n parser.add_argument('--entity', default=None, help='W&B entity')\n\n opt = parser.parse_args()\n opt.resume = False # Explicitly disallow resume check for dataset upload job\n\n create_dataset_artifact(opt)\n"
},
{
"path": "utils/wandb_logging/sweep.py",
"content": "import sys\nfrom pathlib import Path\nimport wandb\n\nFILE = Path(__file__).absolute()\nsys.path.append(FILE.parents[2].as_posix()) # add utils/ to path\n\nfrom train import train, parse_opt\nimport test\nfrom utils.general import increment_path\nfrom utils.torch_utils import select_device\n\n\ndef sweep():\n wandb.init()\n # Get hyp dict from sweep agent\n hyp_dict = vars(wandb.config).get(\"_items\")\n\n # Workaround: get necessary opt args\n opt = parse_opt(known=True)\n opt.batch_size = hyp_dict.get(\"batch_size\")\n opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))\n opt.epochs = hyp_dict.get(\"epochs\")\n opt.nosave = True\n opt.data = hyp_dict.get(\"data\")\n device = select_device(opt.device, batch_size=opt.batch_size)\n\n # train\n train(hyp_dict, opt, device)\n\n\nif __name__ == \"__main__\":\n sweep()\n"
},
{
"path": "utils/wandb_logging/sweep.yaml",
"content": "# Hyperparameters for training\n# To set range- \n# Provide min and max values as:\n# parameter:\n# \n# min: scalar\n# max: scalar\n# OR\n#\n# Set a specific list of search space-\n# parameter: \n# values: [scalar1, scalar2, scalar3...]\n# \n# You can use grid, bayesian and hyperopt search strategy \n# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration\n\nprogram: utils/wandb_logging/sweep.py\nmethod: random\nmetric:\n name: metrics/mAP_0.5\n goal: maximize\n\nparameters:\n # hyperparameters: set either min, max range or values list\n data:\n value: \"data/coco128.yaml\"\n batch_size:\n values: [ 64 ]\n epochs:\n values: [ 10 ]\n\n lr0:\n distribution: uniform\n min: 1e-5\n max: 1e-1\n lrf:\n distribution: uniform\n min: 0.01\n max: 1.0\n momentum:\n distribution: uniform\n min: 0.6\n max: 0.98\n weight_decay:\n distribution: uniform\n min: 0.0\n max: 0.001\n warmup_epochs:\n distribution: uniform\n min: 0.0\n max: 5.0\n warmup_momentum:\n distribution: uniform\n min: 0.0\n max: 0.95\n warmup_bias_lr:\n distribution: uniform\n min: 0.0\n max: 0.2\n box:\n distribution: uniform\n min: 0.02\n max: 0.2\n cls:\n distribution: uniform\n min: 0.2\n max: 4.0\n cls_pw:\n distribution: uniform\n min: 0.5\n max: 2.0\n obj:\n distribution: uniform\n min: 0.2\n max: 4.0\n obj_pw:\n distribution: uniform\n min: 0.5\n max: 2.0\n iou_t:\n distribution: uniform\n min: 0.1\n max: 0.7\n anchor_t:\n distribution: uniform\n min: 2.0\n max: 8.0\n fl_gamma:\n distribution: uniform\n min: 0.0\n max: 0.1\n hsv_h:\n distribution: uniform\n min: 0.0\n max: 0.1\n hsv_s:\n distribution: uniform\n min: 0.0\n max: 0.9\n hsv_v:\n distribution: uniform\n min: 0.0\n max: 0.9\n degrees:\n distribution: uniform\n min: 0.0\n max: 45.0\n translate:\n distribution: uniform\n min: 0.0\n max: 0.9\n scale:\n distribution: uniform\n min: 0.0\n max: 0.9\n shear:\n distribution: uniform\n min: 0.0\n max: 10.0\n perspective:\n distribution: uniform\n min: 0.0\n max: 0.001\n flipud:\n distribution: uniform\n min: 0.0\n max: 1.0\n fliplr:\n distribution: uniform\n min: 0.0\n max: 1.0\n mosaic:\n distribution: uniform\n min: 0.0\n max: 1.0\n mixup:\n distribution: uniform\n min: 0.0\n max: 1.0\n copy_paste:\n distribution: uniform\n min: 0.0\n max: 1.0\n"
},
{
"path": "utils/wandb_logging/wandb_utils.py",
"content": "\"\"\"Utilities and tools for tracking runs with Weights & Biases.\"\"\"\nimport logging\nimport os\nimport sys\nfrom contextlib import contextmanager\nfrom pathlib import Path\n\nimport yaml\nfrom tqdm import tqdm\n\nsys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path\nfrom utils.datasets import LoadImagesAndLabels\nfrom utils.datasets import img2label_paths\nfrom utils.general import colorstr, check_dataset, check_file\n\ntry:\n import wandb\n from wandb import init, finish\nexcept ImportError:\n wandb = None\n\nRANK = int(os.getenv('RANK', -1))\nWANDB_ARTIFACT_PREFIX = 'wandb-artifact://'\n\n\ndef remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):\n return from_string[len(prefix):]\n\n\ndef check_wandb_config_file(data_config_file):\n wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path\n if Path(wandb_config).is_file():\n return wandb_config\n return data_config_file\n\n\ndef get_run_info(run_path):\n run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))\n run_id = run_path.stem\n project = run_path.parent.stem\n entity = run_path.parent.parent.stem\n model_artifact_name = 'run_' + run_id + '_model'\n return entity, project, run_id, model_artifact_name\n\n\ndef check_wandb_resume(opt):\n process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None\n if isinstance(opt.resume, str):\n if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):\n if RANK not in [-1, 0]: # For resuming DDP runs\n entity, project, run_id, model_artifact_name = get_run_info(opt.resume)\n api = wandb.Api()\n artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')\n modeldir = artifact.download()\n opt.weights = str(Path(modeldir) / \"last.pt\")\n return True\n return None\n\n\ndef process_wandb_config_ddp_mode(opt):\n with open(check_file(opt.data)) as f:\n data_dict = yaml.safe_load(f) # data dict\n train_dir, val_dir = None, None\n if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):\n api = wandb.Api()\n train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)\n train_dir = train_artifact.download()\n train_path = Path(train_dir) / 'data/images/'\n data_dict['train'] = str(train_path)\n\n if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):\n api = wandb.Api()\n val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)\n val_dir = val_artifact.download()\n val_path = Path(val_dir) / 'data/images/'\n data_dict['val'] = str(val_path)\n if train_dir or val_dir:\n ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')\n with open(ddp_data_path, 'w') as f:\n yaml.safe_dump(data_dict, f)\n opt.data = ddp_data_path\n\n\nclass WandbLogger():\n \"\"\"Log training runs, datasets, models, and predictions to Weights & Biases.\n\n This logger sends information to W&B at wandb.ai. By default, this information\n includes hyperparameters, system configuration and metrics, model metrics,\n and basic data metrics and analyses.\n\n By providing additional command line arguments to train.py, datasets,\n models and predictions can also be logged.\n\n For more on how this logger is used, see the Weights & Biases documentation:\n https://docs.wandb.com/guides/integrations/yolov5\n \"\"\"\n\n def __init__(self, opt, name, run_id, data_dict, job_type='Training'):\n # Pre-training routine --\n self.job_type = job_type\n self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run\n self.val_artifact, self.train_artifact = None, None\n self.train_artifact_path, self.val_artifact_path = None, None\n self.result_artifact = None\n self.val_table, self.result_table = None, None\n self.data_dict = data_dict\n self.bbox_media_panel_images = []\n self.val_table_path_map = None\n self.max_imgs_to_log = 16 \n # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call\n if isinstance(opt.resume, str): # checks resume from artifact\n if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):\n entity, project, run_id, model_artifact_name = get_run_info(opt.resume)\n model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name\n assert wandb, 'install wandb to resume wandb runs'\n # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config\n self.wandb_run = wandb.init(id=run_id,\n project=project,\n entity=entity,\n resume='allow',\n allow_val_change=True)\n opt.resume = model_artifact_name\n elif self.wandb:\n self.wandb_run = wandb.init(config=opt,\n resume=\"allow\",\n project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,\n entity=opt.entity,\n name=name,\n job_type=job_type,\n id=run_id,\n allow_val_change=True) if not wandb.run else wandb.run\n if self.wandb_run:\n if self.job_type == 'Training':\n if not opt.resume:\n wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict\n # Info useful for resuming from artifacts\n self.wandb_run.config.update({'opt': vars(opt), 'data_dict': wandb_data_dict}, allow_val_change=True)\n self.data_dict = self.setup_training(opt, data_dict)\n if self.job_type == 'Dataset Creation':\n self.data_dict = self.check_and_upload_dataset(opt)\n else:\n prefix = colorstr('wandb: ')\n print(f\"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\")\n\n def check_and_upload_dataset(self, opt):\n assert wandb, 'Install wandb to upload dataset'\n config_path = self.log_dataset_artifact(check_file(opt.data),\n opt.single_cls,\n 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)\n print(\"Created dataset config file \", config_path)\n with open(config_path) as f:\n wandb_data_dict = yaml.safe_load(f)\n return wandb_data_dict\n\n def setup_training(self, opt, data_dict):\n self.log_dict, self.current_epoch = {}, 0\n self.bbox_interval = opt.bbox_interval\n if isinstance(opt.resume, str):\n modeldir, _ = self.download_model_artifact(opt)\n if modeldir:\n self.weights = Path(modeldir) / \"last.pt\"\n config = self.wandb_run.config\n opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(\n self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \\\n config.opt['hyp']\n data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume\n if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download\n self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),\n opt.artifact_alias)\n self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),\n opt.artifact_alias)\n \n if self.train_artifact_path is not None:\n train_path = Path(self.train_artifact_path) / 'data/images/'\n data_dict['train'] = str(train_path)\n if self.val_artifact_path is not None:\n val_path = Path(self.val_artifact_path) / 'data/images/'\n data_dict['val'] = str(val_path)\n\n\n if self.val_artifact is not None:\n self.result_artifact = wandb.Artifact(\"run_\" + wandb.run.id + \"_progress\", \"evaluation\")\n self.result_table = wandb.Table([\"epoch\", \"id\", \"ground truth\", \"prediction\", \"avg_confidence\"])\n self.val_table = self.val_artifact.get(\"val\")\n if self.val_table_path_map is None:\n self.map_val_table_path()\n wandb.log({\"validation dataset\": self.val_table})\n if opt.bbox_interval == -1:\n self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1\n return data_dict\n\n def download_dataset_artifact(self, path, alias):\n if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):\n artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + \":\" + alias)\n dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace(\"\\\\\", \"/\"))\n assert dataset_artifact is not None, \"'Error: W&B dataset artifact doesn\\'t exist'\"\n datadir = dataset_artifact.download()\n return datadir, dataset_artifact\n return None, None\n\n def download_model_artifact(self, opt):\n if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):\n model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + \":latest\")\n assert model_artifact is not None, 'Error: W&B model artifact doesn\\'t exist'\n modeldir = model_artifact.download()\n epochs_trained = model_artifact.metadata.get('epochs_trained')\n total_epochs = model_artifact.metadata.get('total_epochs')\n is_finished = total_epochs is None\n assert not is_finished, 'training is finished, can only resume incomplete runs.'\n return modeldir, model_artifact\n return None, None\n\n def log_model(self, path, opt, epoch, fitness_score, best_model=False):\n model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={\n 'original_url': str(path),\n 'epochs_trained': epoch + 1,\n 'save period': opt.save_period,\n 'project': opt.project,\n 'total_epochs': opt.epochs,\n 'fitness_score': fitness_score\n })\n model_artifact.add_file(str(path / 'last.pt'), name='last.pt')\n wandb.log_artifact(model_artifact,\n aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])\n print(\"Saving model artifact on epoch \", epoch + 1)\n\n def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):\n with open(data_file) as f:\n data = yaml.safe_load(f) # data dict\n check_dataset(data)\n nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])\n names = {k: v for k, v in enumerate(names)} # to index dictionary\n self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(\n data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None\n self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(\n data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None\n if data.get('train'):\n data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')\n if data.get('val'):\n data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')\n path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path\n data.pop('download', None)\n data.pop('path', None)\n with open(path, 'w') as f:\n yaml.safe_dump(data, f)\n\n if self.job_type == 'Training': # builds correct artifact pipeline graph\n self.wandb_run.use_artifact(self.val_artifact)\n self.wandb_run.use_artifact(self.train_artifact)\n self.val_artifact.wait()\n self.val_table = self.val_artifact.get('val')\n self.map_val_table_path()\n else:\n self.wandb_run.log_artifact(self.train_artifact)\n self.wandb_run.log_artifact(self.val_artifact)\n return path\n\n def map_val_table_path(self):\n self.val_table_path_map = {}\n print(\"Mapping dataset\")\n for i, data in enumerate(tqdm(self.val_table.data)):\n self.val_table_path_map[data[3]] = data[0]\n\n def create_dataset_table(self, dataset, class_to_id, name='dataset'):\n # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging\n artifact = wandb.Artifact(name=name, type=\"dataset\")\n img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None\n img_files = tqdm(dataset.img_files) if not img_files else img_files\n for img_file in img_files:\n if Path(img_file).is_dir():\n artifact.add_dir(img_file, name='data/images')\n labels_path = 'labels'.join(dataset.path.rsplit('images', 1))\n artifact.add_dir(labels_path, name='data/labels')\n else:\n artifact.add_file(img_file, name='data/images/' + Path(img_file).name)\n label_file = Path(img2label_paths([img_file])[0])\n artifact.add_file(str(label_file),\n name='data/labels/' + label_file.name) if label_file.exists() else None\n table = wandb.Table(columns=[\"id\", \"train_image\", \"Classes\", \"name\"])\n class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])\n for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):\n box_data, img_classes = [], {}\n for cls, *xywh in labels[:, 1:].tolist():\n cls = int(cls)\n box_data.append({\"position\": {\"middle\": [xywh[0], xywh[1]], \"width\": xywh[2], \"height\": xywh[3]},\n \"class_id\": cls,\n \"box_caption\": \"%s\" % (class_to_id[cls])})\n img_classes[cls] = class_to_id[cls]\n boxes = {\"ground_truth\": {\"box_data\": box_data, \"class_labels\": class_to_id}} # inference-space\n table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),\n Path(paths).name)\n artifact.add(table, name)\n return artifact\n\n def log_training_progress(self, predn, path, names):\n class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])\n box_data = []\n total_conf = 0\n for *xyxy, conf, cls in predn.tolist():\n if conf >= 0.25:\n box_data.append(\n {\"position\": {\"minX\": xyxy[0], \"minY\": xyxy[1], \"maxX\": xyxy[2], \"maxY\": xyxy[3]},\n \"class_id\": int(cls),\n \"box_caption\": \"%s %.3f\" % (names[cls], conf),\n \"scores\": {\"class_score\": conf},\n \"domain\": \"pixel\"})\n total_conf = total_conf + conf\n boxes = {\"predictions\": {\"box_data\": box_data, \"class_labels\": names}} # inference-space\n id = self.val_table_path_map[Path(path).name]\n self.result_table.add_data(self.current_epoch,\n id,\n self.val_table.data[id][1],\n wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),\n total_conf / max(1, len(box_data))\n )\n\n def val_one_image(self, pred, predn, path, names, im):\n if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact\n self.log_training_progress(predn, path, names)\n else: # Default to bbox media panelif Val artifact not found\n if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:\n if self.current_epoch % self.bbox_interval == 0:\n box_data = [{\"position\": {\"minX\": xyxy[0], \"minY\": xyxy[1], \"maxX\": xyxy[2], \"maxY\": xyxy[3]},\n \"class_id\": int(cls),\n \"box_caption\": \"%s %.3f\" % (names[cls], conf),\n \"scores\": {\"class_score\": conf},\n \"domain\": \"pixel\"} for *xyxy, conf, cls in pred.tolist()]\n boxes = {\"predictions\": {\"box_data\": box_data, \"class_labels\": names}} # inference-space\n self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))\n\n \n def log(self, log_dict):\n if self.wandb_run:\n for key, value in log_dict.items():\n self.log_dict[key] = value\n\n def end_epoch(self, best_result=False):\n if self.wandb_run:\n with all_logging_disabled():\n if self.bbox_media_panel_images:\n self.log_dict[\"Bounding Box Debugger/Images\"] = self.bbox_media_panel_images\n wandb.log(self.log_dict)\n self.log_dict = {}\n self.bbox_media_panel_images = []\n if self.result_artifact:\n self.result_artifact.add(self.result_table, 'result')\n wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),\n ('best' if best_result else '')])\n\n wandb.log({\"evaluation\": self.result_table})\n self.result_table = wandb.Table([\"epoch\", \"id\", \"ground truth\", \"prediction\", \"avg_confidence\"])\n self.result_artifact = wandb.Artifact(\"run_\" + wandb.run.id + \"_progress\", \"evaluation\")\n\n def finish_run(self):\n if self.wandb_run:\n if self.log_dict:\n with all_logging_disabled():\n wandb.log(self.log_dict)\n wandb.run.finish()\n\n\n@contextmanager\ndef all_logging_disabled(highest_level=logging.CRITICAL):\n \"\"\" source - https://gist.github.com/simon-weber/7853144\n A context manager that will prevent any logging messages triggered during the body from being processed.\n :param highest_level: the maximum logging level in use.\n This would only need to be changed if a custom level greater than CRITICAL is defined.\n \"\"\"\n previous_level = logging.root.manager.disable\n logging.disable(highest_level)\n try:\n yield\n finally:\n logging.disable(previous_level)\n"
}
]