commit f9dff0891f02dfc93f1bcc7e10cc6bdee1be0568 Author: zhengsl Date: Mon Jul 27 17:51:49 2026 +0800 Initial commit diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..cd9cbdf --- /dev/null +++ b/.env.example @@ -0,0 +1,24 @@ +DJANGO_DEBUG=true +DJANGO_SECRET_KEY=change-me +DJANGO_ALLOWED_HOSTS=127.0.0.1,localhost,192.168.1.174 +DJANGO_CORS_ALLOWED_ORIGINS=http://localhost:3000,ws://localhost:3000 + +DB_ENGINE=django.db.backends.sqlite3 +DB_NAME= +DB_USER= +DB_PASSWORD= +DB_HOST=127.0.0.1 +DB_PORT=5432 + +REDIS_URL=redis://127.0.0.1:6379/1 + +SM4_PRIVATE_KEY= +FILESPACE_ROOT_PATH=D:\AI-TrainPrediction\AI-TrainPrediction\project_space +TRAIN_LOG_DIR=D:\AI-TrainPrediction\AI-TrainPrediction\project_space +MEDIA_URL=/image/ +MEDIA_ROOT=D:\AI-TrainPrediction\AI-TrainPrediction\project_space +IMAGE_BASE_URL=http://localhost:8000/image/ +TASK_CALL_BACK_URL=http://localhost:8000/server/ + +JWT_ACCESS_MINUTES=60 +JWT_REFRESH_DAYS=1 diff --git a/MinIO文件存储接口文档.md b/MinIO文件存储接口文档.md new file mode 100644 index 0000000..1406c87 --- /dev/null +++ b/MinIO文件存储接口文档.md @@ -0,0 +1,709 @@ +# MinIO 文件存储接口文档 + +## 一、概述 + +本项目已集成 MinIO 对象存储，并在 Django 后端提供统一的文件存储 API。 + +### MinIO 服务信息 + +| 项目 | 值 | +|------|-----| +| API 地址 | `http://127.0.0.1:9000` | +| Web 控制台 | `http://127.0.0.1:9001` | +| 用户名 | `admin` | +| 密码 | `zhengsl2026` | +| 默认 Bucket | `ai-trainprediction` | + +### Django 配置 + +```python +MINIO_ENDPOINT = os.environ.get("MINIO_ENDPOINT", "127.0.0.1:9000") +MINIO_ACCESS_KEY = os.environ.get("MINIO_ACCESS_KEY", "admin") +MINIO_SECRET_KEY = os.environ.get("MINIO_SECRET_KEY", "zhengsl2026") +MINIO_SECURE = _env_bool("MINIO_SECURE", default=False) +MINIO_DEFAULT_BUCKET = os.environ.get("MINIO_DEFAULT_BUCKET", "ai-trainprediction") +MINIO_PRESIGN_EXPIRES = int(os.environ.get("MINIO_PRESIGN_EXPIRES", "3600")) +``` + +### 认证要求 + +所有 MinIO 接口均要求 JWT 认证： + +```http +Authorization: Bearer {access_token} +``` + +### 接口前缀 + +```text +/server/minio/ +``` + +--- + +## 二、接口总览 + +| 方法 | 路径 | 功能 | +|------|------|------| +| POST | `/server/minio/bucket/create/` | 创建 Bucket | +| GET | `/server/minio/bucket/list/` | 列出 Bucket | +| DELETE | `/server/minio/bucket/delete/` | 删除 Bucket | +| POST | `/server/minio/file/upload/` | 单文件上传 | +| POST | `/server/minio/files/upload/` | 批量文件上传 | +| POST | `/server/minio/file/upload-zip/` | ZIP 解压后批量入库 | +| POST | `/server/minio/file/upload-video-frames/` | 视频抽帧后图片入库 | +| POST | `/server/minio/file/upload-base64/` | Base64 文件上传 | +| GET | `/server/minio/file/download/` | 流式下载文件 | +| GET | `/server/minio/file/presign/` | 获取预签名下载 URL | +| GET | `/server/minio/file/presign-upload/` | 获取预签名上传 URL | +| GET | `/server/minio/file/preview/` | 流式预览文件 | +| GET | `/server/minio/file/info/` | 获取文件元信息 | +| GET | `/server/minio/files/` | 列出对象 | +| DELETE | `/server/minio/file/delete/` | 删除单个文件 | +| DELETE | `/server/minio/files/delete/` | 批量删除文件 | +| POST | `/server/minio/file/copy/` | 复制文件 | + +--- + +## 三、Bucket 管理 + +### 3.1 创建 Bucket + +```http +POST /server/minio/bucket/create/ +Content-Type: application/json +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `bucket_name` | string | 否 | Bucket 名称，默认 `ai-trainprediction` | + +成功响应： + +```json +{ + "code": 200, + "data": { + "bucket_name": "my-bucket", + "created": true + }, + "msg": "Bucket 创建成功" +} +``` + +说明： +- 若 Bucket 已存在，返回 `created: false` +- MinIO 返回 `BucketAlreadyExists` / `BucketAlreadyOwnedByYou` 时会转换为 `409` + +### 3.2 列出 Bucket + +```http +GET /server/minio/bucket/list/ +``` + +成功响应： + +```json +{ + "code": 200, + "data": ["ai-trainprediction", "my-bucket"], + "msg": "获取成功" +} +``` + +### 3.3 删除 Bucket + +```http +DELETE /server/minio/bucket/delete/ +Content-Type: application/json +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `bucket_name` | string | 是 | Bucket 名称 | + +成功响应： + +```json +{ + "code": 200, + "data": { + "bucket_name": "my-bucket" + }, + "msg": "Bucket 已删除" +} +``` + +错误说明： +- `404`：Bucket 不存在 +- `409`：Bucket 非空，无法删除 + +--- + +## 四、文件上传 + +### 4.1 单文件上传 + +```http +POST /server/minio/file/upload/ +Content-Type: multipart/form-data +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `file` | File | 是 | 上传文件 | +| `bucket_name` | string | 否 | 目标 Bucket，默认 `ai-trainprediction` | +| `prefix` | string | 否 | 对象前缀，如 `images/datasets` | +| `object_name` | string | 否 | 指定对象名；不传则自动生成 | + +自动命名规则： + +```text +{prefix}/YYYY-MM-DD/{uuid12}_{original_filename} +``` + +成功响应： + +```json +{ + "code": 200, + "data": { + "bucket_name": "ai-trainprediction", + "object_name": "images/2026-07-01/a1b2c3d4e5f6_photo.jpg", + "original_name": "photo.jpg", + "size": 102400, + "content_type": "image/jpeg", + "presigned_url": "http://127.0.0.1:9000/..." + }, + "msg": "上传成功" +} +``` + +说明： +- 该接口已改为流式上传，不会把整文件一次性读入内存 +- 若 `object_name` 明确指定且对象已存在，则会被覆盖 + +### 4.2 批量文件上传 + +```http +POST /server/minio/files/upload/ +Content-Type: multipart/form-data +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `files` | File[] | 是 | 多个文件 | +| `bucket_name` | string | 否 | 目标 Bucket | +| `prefix` | string | 否 | 对象前缀 | + +成功响应： + +```json +{ + "code": 200, + "data": [ + { + "object_name": "images/2026-07-01/a1b2_photo1.jpg", + "original_name": "photo1.jpg", + "size": 102400, + "content_type": "image/jpeg", + "presigned_url": "http://127.0.0.1:9000/..." + } + ], + "msg": "全部上传成功，共 1 个文件" +} +``` + +### 4.3 Base64 文件上传 + +```http +POST /server/minio/file/upload-base64/ +Content-Type: application/json +``` + +请求体： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `file_base64` | string | 是 | Base64 编码内容 | +| `filename` | string | 是 | 原始文件名 | +| `bucket_name` | string | 否 | 目标 Bucket | +| `prefix` | string | 否 | 对象前缀 | + +请求示例： + +```json +{ + "file_base64": "/9j/4AAQSkZJRgABAQ...", + "filename": "avatar.png", + "bucket_name": "ai-trainprediction", + "prefix": "avatars" +} +``` + +成功响应： + +```json +{ + "code": 200, + "data": { + "bucket_name": "ai-trainprediction", + "object_name": "avatars/2026-07-01/a1b2c3d4e5f6_avatar.png", + "original_name": "avatar.png", + "size": 51200, + "content_type": "image/png", + "presigned_url": "http://127.0.0.1:9000/..." + }, + "msg": "上传成功" +} +``` + +说明： +- 此接口当前只支持 `application/json` +- Base64 解码失败时返回 `400` + +### 4.4 ZIP 解压批量入库 + +```http +POST /server/minio/file/upload-zip/ +Content-Type: multipart/form-data +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `file` | File | 是 | ZIP 压缩包 | +| `bucket_name` | string | 否 | 目标 Bucket | +| `prefix` | string | 否 | MinIO 对象前缀 | +| `images_only` | bool | 否 | 是否仅入库图片文件，默认 `false` | + +说明： +- 接口会自动解压 ZIP 内文件并逐个上传到 MinIO +- 默认保留 ZIP 内相对目录结构 +- 会自动过滤目录项、隐藏文件和非法路径片段（如 `..`） +- `images_only=true` 时，仅上传图片扩展名文件：`jpg/jpeg/png/bmp/gif/webp/tif/tiff` + +成功响应： + +```json +{ + "code": 200, + "data": { + "bucket_name": "ai-trainprediction", + "zip_name": "dataset.zip", + "uploaded_count": 2, + "uploaded": [ + { + "source_name": "images/cat.jpg", + "object_name": "datasets/images/cat.jpg", + "size": 102400, + "content_type": "image/jpeg" + } + ], + "skipped": ["docs/readme.txt"] + }, + "msg": "ZIP 解压入库成功，共上传 2 个文件" +} +``` + +### 4.5 视频抽帧图片入库 + +```http +POST /server/minio/file/upload-video-frames/ +Content-Type: multipart/form-data +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `file` | File | 是 | 视频文件 | +| `bucket_name` | string | 否 | 目标 Bucket | +| `prefix` | string | 否 | MinIO 对象前缀 | +| `interval_seconds` | int | 否 | 抽帧时间间隔，单位秒，默认 `1` | +| `start_second` | int | 否 | 开始抽帧时间，默认 `0` | +| `end_second` | int | 否 | 结束抽帧时间，不传则到视频末尾 | +| `max_frames` | int | 否 | 最多生成图片数，默认 `100` | +| `image_format` | string | 否 | 输出图片格式，支持 `jpg/jpeg/png`，默认 `jpg` | + +说明： +- 当前支持的视频扩展名：`mp4/avi/mov/mkv/wmv/flv/mpeg/mpg/webm` +- 需要服务端安装 `opencv-python` +- 抽出的图片会存入 `{prefix}/{视频名}_frames/` 目录 +- 文件名规则：`frame_00001_1200ms.jpg` + +成功响应： + +```json +{ + "code": 200, + "data": { + "bucket_name": "ai-trainprediction", + "video_name": "demo.mp4", + "fps": 25.0, + "duration_seconds": 12.4, + "interval_seconds": 1, + "start_second": 0, + "end_second": 10, + "max_frames": 20, + "extracted_count": 10, + "extracted": [ + { + "object_name": "videos/demo_frames/frame_00001_0ms.jpg", + "frame_index": 0, + "time_second": 0.0 + } + ] + }, + "msg": "视频抽帧入库成功，共生成 10 张图片" +} +``` + +### 4.6 获取预签名上传 URL + +```http +GET /server/minio/file/presign-upload/?bucket_name={bucket}&object_name={object}&expires={seconds} +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `bucket_name` | string | 否 | 目标 Bucket | +| `object_name` | string | 是 | 目标对象名 | +| `expires` | int | 否 | 有效期秒数，默认 `3600`，最大 `604800` | + +成功响应： + +```json +{ + "code": 200, + "data": { + "presigned_url": "http://127.0.0.1:9000/ai-trainprediction/path/file.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&...", + "expires_in": 3600 + }, + "msg": "获取成功" +} +``` + +--- + +## 五、文件下载与预览 + +### 5.1 流式下载文件 + +```http +GET /server/minio/file/download/?bucket_name={bucket}&object_name={object} +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `bucket_name` | string | 否 | Bucket 名称 | +| `object_name` | string | 是 | 对象名 | + +说明： +- 返回流式响应，适合大文件下载 +- `Content-Disposition` 为 `attachment` + +### 5.2 获取预签名下载 URL + +```http +GET /server/minio/file/presign/?bucket_name={bucket}&object_name={object}&expires={seconds} +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `bucket_name` | string | 否 | Bucket 名称 | +| `object_name` | string | 是 | 对象名 | +| `expires` | int | 否 | 有效期秒数，默认 `3600`，最大 `604800` | + +成功响应： + +```json +{ + "code": 200, + "data": { + "presigned_url": "http://127.0.0.1:9000/ai-trainprediction/path/file.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&...", + "expires_in": 3600 + }, + "msg": "获取成功" +} +``` + +说明： +- `expires <= 0` 时返回 `400` +- 超过 7 天会被自动截断为 `604800` + +### 5.3 流式预览文件 + +```http +GET /server/minio/file/preview/?bucket_name={bucket}&object_name={object} +``` + +说明： +- 返回流式响应 +- `Content-Disposition` 为 `inline` +- 适合图片、PDF、文本等浏览器可直接预览的文件 + +--- + +## 六、文件管理 + +### 6.1 获取文件元信息 + +```http +GET /server/minio/file/info/?bucket_name={bucket}&object_name={object} +``` + +成功响应： + +```json +{ + "code": 200, + "data": { + "name": "images/2026-07-01/a1b2_photo.jpg", + "size": 102400, + "etag": "d41d8cd98f00b204e9800998ecf8427e", + "content_type": "image/jpeg", + "last_modified": "2026-07-01T12:00:00+00:00", + "presigned_url": "http://127.0.0.1:9000/..." + }, + "msg": "获取成功" +} +``` + +### 6.2 列出对象 + +```http +GET /server/minio/files/?bucket_name={bucket}&prefix={prefix} +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `bucket_name` | string | 否 | Bucket 名称 | +| `prefix` | string | 否 | 仅列出指定前缀下的对象 | + +成功响应： + +```json +{ + "code": 200, + "data": [ + { + "name": "images/2026-07-01/a1b2_photo.jpg", + "size": 102400, + "last_modified": "2026-07-01T12:00:00+00:00", + "etag": "d41d8cd98f00b204e9800998ecf8427e", + "content_type": "image/jpeg", + "is_dir": false + } + ], + "msg": "共 1 个对象" +} +``` + +### 6.3 删除单个文件 + +```http +DELETE /server/minio/file/delete/ +Content-Type: application/json +``` + +请求体： + +```json +{ + "bucket_name": "ai-trainprediction", + "object_name": "images/2026-07-01/a1b2_photo.jpg" +} +``` + +成功响应： + +```json +{ + "code": 200, + "data": { + "object_name": "images/2026-07-01/a1b2_photo.jpg" + }, + "msg": "删除成功" +} +``` + +### 6.4 批量删除文件 + +```http +DELETE /server/minio/files/delete/ +Content-Type: application/json +``` + +请求体： + +```json +{ + "bucket_name": "ai-trainprediction", + "object_names": [ + "images/photo1.jpg", + "images/photo2.png" + ] +} +``` + +成功响应： + +```json +{ + "code": 200, + "data": { + "deleted": 2 + }, + "msg": "全部删除成功" +} +``` + +部分成功响应： + +```json +{ + "code": 207, + "data": { + "deleted": 1, + "errors": ["images/photo2.png"] + }, + "msg": "部分删除，1 个失败" +} +``` + +### 6.5 复制文件 + +```http +POST /server/minio/file/copy/ +Content-Type: application/json +``` + +请求体： + +```json +{ + "src_bucket": "ai-trainprediction", + "src_object": "images/photo.jpg", + "dst_bucket": "ai-trainprediction", + "dst_object": "backup/photo.jpg" +} +``` + +请求参数： + +| 参数 | 类型 | 必填 | 说明 | +|------|------|------|------| +| `src_bucket` | string | 否 | 源 Bucket，默认 `ai-trainprediction` | +| `src_object` | string | 是 | 源对象名 | +| `dst_bucket` | string | 否 | 目标 Bucket，默认 `ai-trainprediction` | +| `dst_object` | string | 是 | 目标对象名 | + +成功响应： + +```json +{ + "code": 200, + "data": { + "object_name": "backup/photo.jpg" + }, + "msg": "复制成功" +} +``` + +说明： +- 若目标 Bucket 不存在，接口会自动创建 + +--- + +## 七、错误码说明 + +| HTTP 状态码 | 含义 | +|-------------|------| +| `200` | 成功 | +| `207` | 批量删除部分成功 | +| `400` | 参数错误、`expires` 不合法、Base64 解码失败等 | +| `401` | 未携带或携带了无效 JWT Token | +| `403` | MinIO 拒绝访问 | +| `404` | Bucket 或对象不存在 | +| `409` | Bucket 已存在、Bucket 非空等冲突 | +| `500` | 其他 MinIO / 服务端错误 | + +错误响应格式： + +```json +{ + "code": 409, + "msg": "Bucket 非空，无法删除", + "detail": "S3 operation failed; code: BucketNotEmpty, ..." +} +``` + +--- + +## 八、代码结构 + +```text +backend/ +├── config/settings/base.py +├── config/api_urls.py +└── apps/common/ + ├── minio_client.py + └── views/minio_storage.py +``` + +### `minio_client.py` + +封装的核心方法包括： + +| 方法 | 功能 | +|------|------| +| `bucket_exists()` | 检查 Bucket 是否存在 | +| `create_bucket()` | 创建 Bucket | +| `list_buckets()` | 列出 Bucket | +| `remove_bucket()` | 删除空 Bucket | +| `upload_file()` | 上传本地文件 | +| `upload_bytes()` | 上传字节数据 | +| `upload_stream()` | 流式上传文件对象 | +| `download_file()` | 下载到本地 | +| `download_bytes()` | 下载为 bytes | +| `get_object()` | 获取对象流响应 | +| `get_object_info()` | 获取对象元信息 | +| `object_exists()` | 判断对象是否存在 | +| `list_objects()` | 列出对象 | +| `delete_object()` | 删除单个对象 | +| `delete_objects()` | 批量删除对象 | +| `presign_get()` | 获取预签名下载 URL | +| `presign_upload()` | 获取预签名上传 URL | +| `generate_object_name()` | 生成唯一对象名 | +| `copy_object()` | 复制对象 | + +--- + +## 九、注意事项 + +1. 所有接口均要求 JWT 认证 +2. 下载和预览接口已经改为流式响应，适合较大文件 +3. `upload-base64` 当前只支持 `application/json` +4. ZIP 入库默认保留压缩包中的相对目录结构，并会过滤非法路径 +5. 视频抽帧依赖 `opencv-python` +6. `expires` 必须大于 0，最大有效期为 7 天 +7. 指定 `object_name` 时可能覆盖已有对象，请谨慎使用 +8. 删除 Bucket 前必须先清空其中所有对象 diff --git a/SD_AIBOX_B_H.sql b/SD_AIBOX_B_H.sql new file mode 100644 index 0000000..2baa86e --- /dev/null +++ b/SD_AIBOX_B_H.sql @@ -0,0 +1,136 @@ +/* + Navicat Premium Data Transfer + + Source Server : 全过程新框架数据库190 + Source Server Type : Oracle + Source Server Version : 110200 + Source Host : 172.16.31.190:1521 + Source Schema : QGC_REFA + + Target Server Type : Oracle + Target Server Version : 110200 + File Encoding : 65001 + + Date: 08/07/2026 18:30:27 +*/ + + +-- ---------------------------- +-- Table structure for SD_AIBOX_B_H +-- ---------------------------- +DROP TABLE "QGC_REFA"."SD_AIBOX_B_H"; +CREATE TABLE "QGC_REFA"."SD_AIBOX_B_H" ( + "STCD" VARCHAR2(36 BYTE) NOT NULL , + "STNM" VARCHAR2(255 BYTE) NOT NULL , + "STTP" VARCHAR2(64 BYTE) NOT NULL , + "TM" DATE , + "LGTD" NUMBER(10,6) , + "LTTD" NUMBER(10,6) , + "ELEV" NUMBER(10,6) , + "STLC" VARCHAR2(200 BYTE) , + "JCDT" DATE , + "WDDT" DATE , + "BLDSTT_CODE" NUMBER(1) , + "USFL" NUMBER(1) , + "DTIN" NUMBER(1) , + "DTIN_TM" DATE , + "MWAY" NUMBER(1) , + "STINDX" VARCHAR2(1000 BYTE) , + "ORDER_INDEX" NUMBER(8) , + "RSTCD" VARCHAR2(64 BYTE) , + "BASE_ID" VARCHAR2(64 BYTE) , + "HBRVCD" VARCHAR2(64 BYTE) , + "RVCD" VARCHAR2(64 BYTE) , + "INTRODUCE" VARCHAR2(3000 BYTE) , + "LOGO" VARCHAR2(255 BYTE) , + "INFFILE" VARCHAR2(1000 BYTE) , + "FWDX" VARCHAR2(1000 BYTE) , + "GDFS" VARCHAR2(512 BYTE) , + "AIPZ" VARCHAR2(1000 BYTE) , + "MXBB" VARCHAR2(512 BYTE) , + "IPADDR" VARCHAR2(512 BYTE) , + "SIMINFO" VARCHAR2(512 BYTE) , + "LXR" VARCHAR2(512 BYTE) , + "PURPOSE" VARCHAR2(512 BYTE) , + "DTFRQCY" NUMBER(7) , + "REMARK" VARCHAR2(512 BYTE) , + "VLSR" VARCHAR2(512 BYTE) , + "VLSR_TM" DATE , + "RECORD_USER" VARCHAR2(36 BYTE) , + "RECORD_TIME" DATE DEFAULT SYSDATE NOT NULL , + "MODIFY_USER" VARCHAR2(36 BYTE) , + "MODIFY_TIME" DATE , + "IS_DELETED" NUMBER(1) DEFAULT 0 NOT NULL , + "DELETE_USER" VARCHAR2(36 BYTE) , + "DELETE_TIME" DATE +) +TABLESPACE "QGC_REFA_DATA" +LOGGING +NOCOMPRESS +PCTFREE 10 +INITRANS 1 +STORAGE ( + BUFFER_POOL DEFAULT +) +PARALLEL 1 +NOCACHE +DISABLE ROW MOVEMENT +; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."STCD" IS '站码'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."STNM" IS '站名'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."STTP" IS '站类'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."TM" IS '数据时间'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."LGTD" IS '经度（单位：°）'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."LTTD" IS '纬度（单位：°）'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."ELEV" IS '高程（单位：m）'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."STLC" IS '站址/位置'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."JCDT" IS '建成日期'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."WDDT" IS '退役/拆除日期'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."BLDSTT_CODE" IS '建设状态分类'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."USFL" IS '是否启用'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."DTIN" IS '数据是否接入'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."DTIN_TM" IS '数据接入时间'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."MWAY" IS '监测方式'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."STINDX" IS '监测指标'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."ORDER_INDEX" IS '排序'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."RSTCD" IS '所属电站编码'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."BASE_ID" IS '所属水电基地编码'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."HBRVCD" IS '所属水电基地流域编码'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."RVCD" IS '所属流域编码'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."INTRODUCE" IS '简介'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."LOGO" IS 'LOGO图片'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."INFFILE" IS '介绍弹窗图片'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."FWDX" IS '服务对象'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."GDFS" IS '供电方式'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."AIPZ" IS 'AI盒子配置'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."MXBB" IS '模型版本'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."IPADDR" IS 'IP地址'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."SIMINFO" IS 'SIM卡信息'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."LXR" IS '联系人及联系电话'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."PURPOSE" IS '用途'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."DTFRQCY" IS '数据监测频次（单位：min）'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."REMARK" IS '备注'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."VLSR" IS '数据来源'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."VLSR_TM" IS '数据来源时间'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."RECORD_USER" IS '创建人：关联SYS_USER.ID'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."RECORD_TIME" IS '创建时间'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."MODIFY_USER" IS '更新人：关联SYS_USER.ID'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."MODIFY_TIME" IS '更新时间'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."IS_DELETED" IS '是否已删除：0=未删除 1=已删除；存储字典值，字典项为comm.is_deleted'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."DELETE_USER" IS '删除人：关联SYS_USER.ID'; +COMMENT ON COLUMN "QGC_REFA"."SD_AIBOX_B_H"."DELETE_TIME" IS '删除时间'; +COMMENT ON TABLE "QGC_REFA"."SD_AIBOX_B_H" IS 'AI边缘计算盒子基本属性表'; + +-- ---------------------------- +-- Primary Key structure for table SD_AIBOX_B_H +-- ---------------------------- +ALTER TABLE "QGC_REFA"."SD_AIBOX_B_H" ADD CONSTRAINT "SD_AIBOX_B_H_PK" PRIMARY KEY ("STCD"); + +-- ---------------------------- +-- Checks structure for table SD_AIBOX_B_H +-- ---------------------------- +ALTER TABLE "QGC_REFA"."SD_AIBOX_B_H" ADD CONSTRAINT "SYS_C0057611" CHECK ("STCD" IS NOT NULL) NOT DEFERRABLE INITIALLY IMMEDIATE NORELY VALIDATE; +ALTER TABLE "QGC_REFA"."SD_AIBOX_B_H" ADD CONSTRAINT "SYS_C0057612" CHECK ("STNM" IS NOT NULL) NOT DEFERRABLE INITIALLY IMMEDIATE NORELY VALIDATE; +ALTER TABLE "QGC_REFA"."SD_AIBOX_B_H" ADD CONSTRAINT "SYS_C0057613" CHECK ("STTP" IS NOT NULL) NOT DEFERRABLE INITIALLY IMMEDIATE NORELY VALIDATE; +ALTER TABLE "QGC_REFA"."SD_AIBOX_B_H" ADD CONSTRAINT "SYS_C0057615" CHECK ("RECORD_TIME" IS NOT NULL) NOT DEFERRABLE INITIALLY IMMEDIATE NORELY VALIDATE; +ALTER TABLE "QGC_REFA"."SD_AIBOX_B_H" ADD CONSTRAINT "SYS_C0057616" CHECK ("IS_DELETED" IS NOT NULL) NOT DEFERRABLE INITIALLY IMMEDIATE NORELY VALIDATE; diff --git a/backend/apps/__init__.py b/backend/apps/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/algorithms/__init__.py b/backend/apps/algorithms/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/algorithms/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/algorithms/algorithm/DFINE/.pre-commit-config.yaml b/backend/apps/algorithms/algorithm/DFINE/.pre-commit-config.yaml new file mode 100644 index 0000000..c7d884a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/.pre-commit-config.yaml @@ -0,0 +1,68 @@ +# Copyright The Lightning team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +default_language_version: + python: python3 + +ci: + autofix_prs: true + autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions" + autoupdate_schedule: quarterly + # submodules: true + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + # - id: check-json # skip for incompatibility with .devcontainer/devcontainer.json + - id: check-yaml + - id: check-toml + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: check-case-conflict + # - id: check-added-large-files + # args: ["--maxkb=100", "--enforce-all"] + - id: detect-private-key + + # - repo: https://github.com/PyCQA/docformatter + # rev: v1.7.5 + # hooks: + # - id: docformatter + # additional_dependencies: [tomli] + # args: ["--in-place"] + + - repo: https://github.com/executablebooks/mdformat + rev: 0.7.17 + hooks: + - id: mdformat + exclude: '^.*\.md$' + args: ["--number"] + additional_dependencies: + - mdformat-gfm + - mdformat-black + - mdformat_frontmatter + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.6.9 + hooks: + # try to fix what is possible + - id: ruff + args: ["--fix", "--ignore", "E501,F401,F403,F841,E741"] + # # perform formatting updates + # - id: ruff-format + # validate if all is fine with preview mode + - id: ruff + args: ["--ignore", "E501,F401,F403,F841,E741"] diff --git a/backend/apps/algorithms/algorithm/DFINE/1.jpg b/backend/apps/algorithms/algorithm/DFINE/1.jpg new file mode 100644 index 0000000..e5fe69a Binary files /dev/null and b/backend/apps/algorithms/algorithm/DFINE/1.jpg differ diff --git a/backend/apps/algorithms/algorithm/DFINE/3.png b/backend/apps/algorithms/algorithm/DFINE/3.png new file mode 100644 index 0000000..27fab8a Binary files /dev/null and b/backend/apps/algorithms/algorithm/DFINE/3.png differ diff --git a/backend/apps/algorithms/algorithm/DFINE/DfineDetector.py b/backend/apps/algorithms/algorithm/DFINE/DfineDetector.py new file mode 100644 index 0000000..a28f832 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/DfineDetector.py @@ -0,0 +1,132 @@ +import base64 +import json +from datetime import datetime +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torchvision.transforms as T +from PIL import Image, ImageDraw + +class DfineDetector: + def __init__(self, config_path: str, model_path: str, label_map: str, device="cpu", score_thresh=0.3): + # 初始化配置和模型 + from src.core import YAMLConfig + cfg = YAMLConfig(config_path, resume=model_path) + if "HGNetv2" in cfg.yaml_cfg: + cfg.yaml_cfg["HGNetv2"]["pretrained"] = False + + checkpoint = torch.load(model_path, map_location="cpu") + if "ema" in checkpoint: + state = checkpoint["ema"]["module"] + else: + state = checkpoint["model"] + + cfg.model.load_state_dict(state) + + class Model(nn.Module): + def __init__(self): + super().__init__() + self.model = cfg.model.deploy() + self.postprocessor = cfg.postprocessor.deploy() + + def forward(self, images, orig_target_sizes): + outputs = self.model(images) + outputs = self.postprocessor(outputs, orig_target_sizes) + return outputs + + self.device = device + self.model = Model().to(device) + self.model.eval() + self.label_map = json.loads(label_map) # list[dict{"label":"类别英文","name":"中文"}] + self.score_thresh = score_thresh + + def detect(self, image_path: str): + try: + im_pil = Image.open(image_path).convert("RGB") + w, h = im_pil.size + orig_size = torch.tensor([[w, h]]).to(self.device) + transforms = T.Compose([ + T.Resize((640, 640)), + T.ToTensor(), + ]) + im_data = transforms(im_pil).unsqueeze(0).to(self.device) + with torch.no_grad(): + output = self.model(im_data, orig_size) + labels, boxes, scores = output + idx = scores[0] > self.score_thresh + + result_labels = labels[0][idx].detach().cpu().numpy() + result_boxes = boxes[0][idx].detach().cpu().numpy() + result_scores = scores[0][idx].detach().cpu().numpy() + + obj_list = [] + for l, b, s in zip(result_labels, result_boxes, result_scores): + code = int(l) + # 默认 label_map 结构: [{"label":"person","name":"人"}] + label_str = str(code) + code_name = None + for item in self.label_map: + # 支持数字 or 英文名的映射 + if str(item.get("label")) == str(code): + label_str = item["label"] + code_name = item.get("name", "") + break + one_obj = { + "code": label_str, # YOLO 是英文类别，这里建议保持和 label_map 结构统一 + "name": code_name, + "score": round(float(s), 2), + "x1": int(b[0]), + "y1": int(b[1]), + "x2": int(b[2]), + "y2": int(b[3]) + } + obj_list.append(one_obj) + + detection_image = self.draw_and_encode(im_pil, obj_list) + + result_json = { + "image": detection_image, + "obj_list": obj_list, + "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S") + } + return json.dumps(result_json, ensure_ascii=False) + + except Exception as e: + print(f"Error during detection: {e}") + return json.dumps({'error': str(e)}, ensure_ascii=False) + + def draw_and_encode(self, im_pil, obj_list): + # 转 opencv 画框 + img = np.array(im_pil) + if img.shape[-1] == 3: + pass + else: + img = img[:, :, :3] + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + preset_colors = [ + (0, 0, 255), (20, 20, 255), (20, 40, 255), (0, 60, 255), (0, 80, 255), + (0, 100, 255), (0, 120, 255), (0, 140, 255), (0, 160, 255), (0, 180, 255), + (0, 200, 255), (0, 220, 255), (0, 255, 255), + ] + category_colors = {} + + for i, obj in enumerate(obj_list): + category = obj['code'] + if category not in category_colors: + category_colors[category] = preset_colors[i % len(preset_colors)] + + x1, y1, x2, y2 = obj['x1'], obj['y1'], obj['x2'], obj['y2'] + color = category_colors[category] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) + + font = cv2.FONT_HERSHEY_SIMPLEX + text = f"{category}-{obj['score']}" + (text_width, _), _ = cv2.getTextSize(text, font, 0.5, 1) + cv2.rectangle(img, (x1, y1 - 20), (x1 + text_width, y1), color, -1) + cv2.putText(img, text, (x1, y1 - 5), font, 0.5, (255, 255, 255), 1) + + _, img_encoded = cv2.imencode('.jpg', img) + img_base64 = base64.b64encode(img_encoded.tobytes()).decode('utf-8') + return img_base64 diff --git a/backend/apps/algorithms/algorithm/DFINE/Dockerfile b/backend/apps/algorithms/algorithm/DFINE/Dockerfile new file mode 100644 index 0000000..9d8af8b --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/Dockerfile @@ -0,0 +1,48 @@ +FROM registry.cn-hangzhou.aliyuncs.com/peterande/dfine:v1 + +# FULL BUILDING INFO: + +# docker login --username=xxx registry.cn-hangzhou.aliyuncs.com +# cd [PATH_2_Dockerfile] +# docker build -t xxx:v1 . +# docker tag xxx:v1 registry.cn-hangzhou.aliyuncs.com/xxx/xxx:v1 +# docker push registry.cn-hangzhou.aliyuncs.com/xxx/xxx:v1 + +# FROM dockerpull.com/nvidia/cuda:12.0.1-cudnn8-devel-ubuntu18.04 +# ARG DEBIAN_FRONTEND=noninteractive +# ENV PATH="/root/miniconda3/bin:${PATH}" +# ARG PATH="/root/miniconda3/bin:${PATH}" + +# RUN sed -i "s/archive.ubuntu./mirrors.aliyun./g" /etc/apt/sources.list +# RUN sed -i "s/deb.debian.org/mirrors.aliyun.com/g" /etc/apt/sources.list +# RUN sed -i "s/security.debian.org/mirrors.aliyun.com\/debian-security/g" /etc/apt/sources.list +# RUN sed -i 's/archive.ubuntu.com/mirrors.ustc.edu.cn/g' /etc/apt/sources.list + +# RUN apt-get update && apt-get install -y --no-install-recommends apt-utils && \ +# apt-get upgrade -y && \ +# apt-get install -y vim git libgl1-mesa-glx libglib2.0-0 libsm6 && \ +# apt-get install -y libxrender1 libxext6 tmux wget htop && \ +# apt-get install -y build-essential gcc g++ gdb binutils pciutils net-tools iputils-ping iproute2 git vim wget curl make openssh-server openssh-client tmux tree man unzip unrar + +# ENV PYTHONIOENCODING=UTF-8 + +# RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \ +# mkdir /root/.conda && \ +# bash Miniconda3-latest-Linux-x86_64.sh -b && \ +# rm -f Miniconda3-latest-Linux-x86_64.sh && \ +# conda init bash + +# RUN conda config --set show_channel_urls yes \ +# && echo "channels:" > ~/.condarc \ +# && echo " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/" >> ~/.condarc \ +# && echo " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/" >> ~/.condarc \ +# && echo "show_channel_urls: true" \ +# && cat ~/.condarc \ +# && pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple \ +# && cat ~/.config/pip/pip.conf + +# RUN python3 -m pip install --upgrade pip && \ +# python3 -m pip install --upgrade setuptools + +# RUN python3 -m pip install jupyterlab pycocotools PyYAML tensorboard scipy +# RUN python3 -m pip --default-timeout=10000 install torch torchvision diff --git a/backend/apps/algorithms/algorithm/DFINE/LICENSE b/backend/apps/algorithms/algorithm/DFINE/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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+ D-FINE: Redefine Regression Task of DETRs as Fine‑grained Distribution Refinement +

+ + + +

+ + hf + + + license + + + prs + + + issues + + + arXiv + + + + pre-commit.ci status + + + stars + +

+ + + +

+ 📄 This is the official implementation of the paper: +
+ D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +

+ + + +

+Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, and Feng Wu +

+ +

+University of Science and Technology of China +

+ +

+ + sota + +

+ + + +

+If you like D-FINE, please give us a ⭐! Your support motivates us to keep improving! +

+ +

+ +

+ +D-FINE is a powerful real-time object detector that redefines the bounding box regression task in DETRs as Fine-grained Distribution Refinement (FDR) and introduces Global Optimal Localization Self-Distillation (GO-LSD), achieving outstanding performance without introducing additional inference and training costs. + +
+ Video + +We conduct object detection using D-FINE and YOLO11 on a complex street scene video from [YouTube](https://www.youtube.com/watch?v=CfhEWj9sd9A). Despite challenging conditions such as backlighting, motion blur, and dense crowds, D-FINE-X successfully detects nearly all targets, including subtle small objects like backpacks, bicycles, and traffic lights. Its confidence scores and the localization precision for blurred edges are significantly higher than those of YOLO11. + + + +https://github.com/user-attachments/assets/e5933d8e-3c8a-400e-870b-4e452f5321d9 + +
+ +## 🚀 Updates +- [x] **\[2024.10.18\]** Release D-FINE series. +- [x] **\[2024.10.25\]** Add custom dataset finetuning configs ([#7](https://github.com/Peterande/D-FINE/issues/7)). +- [x] **\[2024.10.30\]** Update D-FINE-L (E25) pretrained model, with performance improved by 2.0%. +- [x] **\[2024.11.07\]** Release **D-FINE-N**, achiving 42.8% APval on COCO @ 472 FPST4! + +## Model Zoo + +### COCO +| Model | Dataset | APval | #Params | Latency | GFLOPs | config | checkpoint | logs | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑N** | COCO | **42.8** | 4M | 2.12ms | 7 | [yml](./configs/dfine/dfine_hgnetv2_n_coco.yml) | [42.8](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_n_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_n_coco_log.txt) +**D‑FINE‑S** | COCO | **48.5** | 10M | 3.49ms | 25 | [yml](./configs/dfine/dfine_hgnetv2_s_coco.yml) | [48.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_s_coco_log.txt) +**D‑FINE‑M** | COCO | **52.3** | 19M | 5.62ms | 57 | [yml](./configs/dfine/dfine_hgnetv2_m_coco.yml) | [52.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_m_coco_log.txt) +**D‑FINE‑L** | COCO | **54.0** | 31M | 8.07ms | 91 | [yml](./configs/dfine/dfine_hgnetv2_l_coco.yml) | [54.0](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_l_coco_log.txt) +**D‑FINE‑X** | COCO | **55.8** | 62M | 12.89ms | 202 | [yml](./configs/dfine/dfine_hgnetv2_x_coco.yml) | [55.8](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_x_coco_log.txt) + + +### Objects365+COCO +| Model | Dataset | APval | #Params | Latency | GFLOPs | config | checkpoint | logs | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑S** | Objects365+COCO | **50.7** | 10M | 3.49ms | 25 | [yml](./configs/dfine/objects365/dfine_hgnetv2_s_obj2coco.yml) | [50.7](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_s_obj2coco_log.txt) +**D‑FINE‑M** | Objects365+COCO | **55.1** | 19M | 5.62ms | 57 | [yml](./configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml) | [55.1](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_m_obj2coco_log.txt) +**D‑FINE‑L** | Objects365+COCO | **57.3** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj2coco.yml) | [57.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj2coco_e25.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_l_obj2coco_log_e25.txt) +**D‑FINE‑X** | Objects365+COCO | **59.3** | 62M | 12.89ms | 202 | [yml](./configs/dfine/objects365/dfine_hgnetv2_x_obj2coco.yml) | [59.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_x_obj2coco_log.txt) + +**We highly recommend that you use the Objects365 pre-trained model for fine-tuning:** + +⚠️ **Important**: Please note that this is generally beneficial for complex scene understanding. If your categories are very simple, it might lead to overfitting and suboptimal performance. +
+ 🔥 Pretrained Models on Objects365 (Best generalization) + +| Model | Dataset | APval | AP5000 | #Params | Latency | GFLOPs | config | checkpoint | logs | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑S** | Objects365 | **31.0** | **30.5** | 10M | 3.49ms | 25 | [yml](./configs/dfine/objects365/dfine_hgnetv2_s_obj365.yml) | [30.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_s_obj365_log.txt) +**D‑FINE‑M** | Objects365 | **38.6** | **37.4** | 19M | 5.62ms | 57 | [yml](./configs/dfine/objects365/dfine_hgnetv2_m_obj365.yml) | [37.4](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_m_obj365_log.txt) +**D‑FINE‑L** | Objects365 | - | **40.6** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml) | [40.6](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_l_obj365_log.txt) +**D‑FINE‑L (E25)** | Objects365 | **44.7** | **42.6** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml) | [42.6](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj365_e25.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_l_obj365_log_e25.txt) +**D‑FINE‑X** | Objects365 | **49.5** | **46.5** | 62M | 12.89ms | 202 | [yml](./configs/dfine/objects365/dfine_hgnetv2_x_obj365.yml) | [46.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_x_obj365_log.txt) +- **E25**: Re-trained and extended the pretraining to 25 epochs. +- **APval** is evaluated on *Objects365* full validation set. +- **AP5000** is evaluated on the first 5000 samples of the *Objects365* validation set. +
+ +**Notes:** +- **APval** is evaluated on *MSCOCO val2017* dataset. +- **Latency** is evaluated on a single T4 GPU with $batch\\_size = 1$, $fp16$, and $TensorRT==10.4.0$. +- **Objects365+COCO** means finetuned model on *COCO* using pretrained weights trained on *Objects365*. + + + +## Quick start + +### Setup + +```shell +conda create -n dfine python=3.11.9 +conda activate dfine +pip install -r requirements.txt +``` + + +### Data Preparation + +
+ COCO2017 Dataset + +1. Download COCO2017 from [OpenDataLab](https://opendatalab.com/OpenDataLab/COCO_2017) or [COCO](https://cocodataset.org/#download). +1. Modify paths in [coco_detection.yml](./configs/dataset/coco_detection.yml) + + ```yaml + train_dataloader: + img_folder: /data/COCO2017/train2017/ + ann_file: /data/COCO2017/annotations/instances_train2017.json + val_dataloader: + img_folder: /data/COCO2017/val2017/ + ann_file: /data/COCO2017/annotations/instances_val2017.json + ``` + +
+ +
+ Objects365 Dataset + +1. Download Objects365 from [OpenDataLab](https://opendatalab.com/OpenDataLab/Objects365). + +2. Set the Base Directory: +```shell +export BASE_DIR=/data/Objects365/data +``` + +3. Extract and organize the downloaded files, resulting directory structure: + +```shell +${BASE_DIR}/train +├── images +│ ├── v1 +│ │ ├── patch0 +│ │ │ ├── 000000000.jpg +│ │ │ ├── 000000001.jpg +│ │ │ └── ... (more images) +│ ├── v2 +│ │ ├── patchx +│ │ │ ├── 000000000.jpg +│ │ │ ├── 000000001.jpg +│ │ │ └── ... (more images) +├── zhiyuan_objv2_train.json +``` + +```shell +${BASE_DIR}/val +├── images +│ ├── v1 +│ │ ├── patch0 +│ │ │ ├── 000000000.jpg +│ │ │ └── ... (more images) +│ ├── v2 +│ │ ├── patchx +│ │ │ ├── 000000000.jpg +│ │ │ └── ... (more images) +├── zhiyuan_objv2_val.json +``` + +4. Create a New Directory to Store Images from the Validation Set: +```shell +mkdir -p ${BASE_DIR}/train/images_from_val +``` + +5. Copy the v1 and v2 folders from the val directory into the train/images_from_val directory +```shell +cp -r ${BASE_DIR}/val/images/v1 ${BASE_DIR}/train/images_from_val/ +cp -r ${BASE_DIR}/val/images/v2 ${BASE_DIR}/train/images_from_val/ +``` + +6. Run remap_obj365.py to merge a subset of the validation set into the training set. Specifically, this script moves samples with indices between 5000 and 800000 from the validation set to the training set. +```shell +python tools/remap_obj365.py --base_dir ${BASE_DIR} +``` + + +7. Run the resize_obj365.py script to resize any images in the dataset where the maximum edge length exceeds 640 pixels. Use the updated JSON file generated in Step 5 to process the sample data. Ensure that you resize images in both the train and val datasets to maintain consistency. +```shell +python tools/resize_obj365.py --base_dir ${BASE_DIR} +``` + +8. Modify paths in [obj365_detection.yml](./configs/dataset/obj365_detection.yml) + + ```yaml + train_dataloader: + img_folder: /data/Objects365/data/train + ann_file: /data/Objects365/data/train/new_zhiyuan_objv2_train_resized.json + val_dataloader: + img_folder: /data/Objects365/data/val/ + ann_file: /data/Objects365/data/val/new_zhiyuan_objv2_val_resized.json + ``` + + +
+ +
+CrowdHuman + +Download COCO format dataset here: [url](https://aistudio.baidu.com/datasetdetail/231455) + +
+ +
+Custom Dataset + +To train on your custom dataset, you need to organize it in the COCO format. Follow the steps below to prepare your dataset: + +1. **Set `remap_mscoco_category` to `False`:** + + This prevents the automatic remapping of category IDs to match the MSCOCO categories. + + ```yaml + remap_mscoco_category: False + ``` + +2. **Organize Images:** + + Structure your dataset directories as follows: + + ```shell + dataset/ + ├── images/ + │ ├── train/ + │ │ ├── image1.jpg + │ │ ├── image2.jpg + │ │ └── ... + │ ├── val/ + │ │ ├── image1.jpg + │ │ ├── image2.jpg + │ │ └── ... + └── annotations/ + ├── instances_train.json + ├── instances_val.json + └── ... + ``` + + - **`images/train/`**: Contains all training images. + - **`images/val/`**: Contains all validation images. + - **`annotations/`**: Contains COCO-formatted annotation files. + +3. **Convert Annotations to COCO Format:** + + If your annotations are not already in COCO format, you'll need to convert them. You can use the following Python script as a reference or utilize existing tools: + + ```python + import json + + def convert_to_coco(input_annotations, output_annotations): + # Implement conversion logic here + pass + + if __name__ == "__main__": + convert_to_coco('path/to/your_annotations.json', 'dataset/annotations/instances_train.json') + ``` + +4. **Update Configuration Files:** + + Modify your [custom_detection.yml](./configs/dataset/custom_detection.yml). + + ```yaml + task: detection + + evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + + num_classes: 777 # your dataset classes + remap_mscoco_category: False + + train_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/train + ann_file: /data/yourdataset/train/train.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 4 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + val_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/val + ann_file: /data/yourdataset/val/ann.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 4 + drop_last: False + collate_fn: + type: BatchImageCollateFunction + ``` + +
+ + +## Usage +
+ COCO2017 + + +1. Set Model +```shell +export model=l # n s m l x +``` + +2. Training +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --use-amp --seed=0 +``` + + +3. Testing +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --test-only -r model.pth +``` + + +4. Tuning +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --use-amp --seed=0 -t model.pth +``` +
+ + +
+ Objects365 to COCO2017 + +1. Set Model +```shell +export model=l # n s m l x +``` + +2. Training on Objects365 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/objects365/dfine_hgnetv2_${model}_obj365.yml --use-amp --seed=0 +``` + +3. Tuning on COCO2017 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/objects365/dfine_hgnetv2_${model}_obj2coco.yml --use-amp --seed=0 -t model.pth +``` + + +4. Testing +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --test-only -r model.pth +``` +
+ + +
+ Custom Dataset + +1. Set Model +```shell +export model=l # n s m l x +``` + +2. Training on Custom Dataset +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --use-amp --seed=0 +``` + +3. Testing +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --test-only -r model.pth +``` + +4. Tuning on Custom Dataset +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/objects365/dfine_hgnetv2_${model}_obj2custom.yml --use-amp --seed=0 -t model.pth +``` + +5. **[Optional]** Modify Class Mappings: + +When using the Objects365 pre-trained weights to train on your custom dataset, the example assumes that your dataset only contains the classes `'Person'` and `'Car'`. For faster convergence, you can modify `self.obj365_ids` in `src/solver/_solver.py` as follows: + + +```python +self.obj365_ids = [0, 5] # Person, Cars +``` +You can replace these with any corresponding classes from your dataset. The list of Objects365 classes with their corresponding IDs: +https://github.com/Peterande/D-FINE/blob/352a94ece291e26e1957df81277bef00fe88a8e3/src/solver/_solver.py#L330 + +New training command: + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --use-amp --seed=0 -t model.pth +``` + +However, if you don't wish to modify the class mappings, the pre-trained Objects365 weights will still work without any changes. Modifying the class mappings is optional and can potentially accelerate convergence for specific tasks. + + + +
+ +
+ Customizing Batch Size + +For example, if you want to double the total batch size when training D-FINE-L on COCO2017, here are the steps you should follow: + +1. **Modify your [dataloader.yml](./configs/dfine/include/dataloader.yml)** to increase the `total_batch_size`: + + ```yaml + train_dataloader: + total_batch_size: 64 # Previously it was 32, now doubled + ``` + +2. **Modify your [dfine_hgnetv2_l_coco.yml](./configs/dfine/dfine_hgnetv2_l_coco.yml)**. Here’s how the key parameters should be adjusted: + + ```yaml + optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 # doubled, linear scaling law + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.0005 # doubled, linear scaling law + betas: [0.9, 0.999] + weight_decay: 0.0001 # need a grid search + + ema: # added EMA settings + decay: 0.9998 # adjusted by 1 - (1 - decay) * 2 + warmups: 500 # halved + + lr_warmup_scheduler: + warmup_duration: 250 # halved + ``` + +
+ + +
+ Customizing Input Size + +If you'd like to train **D-FINE-L** on COCO2017 with an input size of 320x320, follow these steps: + +1. **Modify your [dataloader.yml](./configs/dfine/include/dataloader.yml)**: + + ```yaml + + train_dataloader: + dataset: + transforms: + ops: + - {type: Resize, size: [320, 320], } + collate_fn: + base_size: 320 + dataset: + transforms: + ops: + - {type: Resize, size: [320, 320], } + ``` + +2. **Modify your [dfine_hgnetv2.yml](./configs/dfine/include/dfine_hgnetv2.yml)**: + + ```yaml + eval_spatial_size: [320, 320] + ``` + +
+ +## Tools +
+ Deployment + + +1. Setup +```shell +pip install onnx onnxsim +export model=l # n s m l x +``` + +2. Export onnx +```shell +python tools/deployment/export_onnx.py --check -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth +``` + +3. Export [tensorrt](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) +```shell +trtexec --onnx="model.onnx" --saveEngine="model.engine" --fp16 +``` + +
+ +
+ Inference (Visualization) + + +1. Setup +```shell +pip install -r tools/inference/requirements.txt +export model=l # n s m l x +``` + + + +2. Inference (onnxruntime / tensorrt / torch) + +Inference on images and videos is now supported. +```shell +python tools/inference/onnx_inf.py --onnx model.onnx --input image.jpg # video.mp4 +python tools/inference/trt_inf.py --trt model.engine --input image.jpg +python tools/inference/torch_inf.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth --input image.jpg --device cuda:0 +``` +
+ +
+ Benchmark + +1. Setup +```shell +pip install -r tools/benchmark/requirements.txt +export model=l # n s m l x +``` + + +2. Model FLOPs, MACs, and Params +```shell +python tools/benchmark/get_info.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml +``` + +2. TensorRT Latency +```shell +python tools/benchmark/trt_benchmark.py --COCO_dir path/to/COCO2017 --engine_dir model.engine +``` +
+ +
+ Fiftyone Visualization + +1. Setup +```shell +pip install fiftyone +export model=l # n s m l x +``` +4. Voxel51 Fiftyone Visualization ([fiftyone](https://github.com/voxel51/fiftyone)) +```shell +python tools/visualization/fiftyone_vis.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth +``` +
+ +
+ Others + +1. Auto Resume Training +```shell +bash reference/safe_training.sh +``` + +2. Converting Model Weights +```shell +python reference/convert_weight.py model.pth +``` +
+ +## Figures and Visualizations + +
+ FDR and GO-LSD + +1. Overview of D-FINE with FDR. The probability distributions that act as a more fine- +grained intermediate representation are iteratively refined by the decoder layers in a residual manner. +Non-uniform weighting functions are applied to allow for finer localization. + +

+ Fine-grained Distribution Refinement Process +

+ +2. Overview of GO-LSD process. Localization knowledge from the final layer’s refined +distributions is distilled into earlier layers through DDF loss with decoupled weighting strategies. + +

+ GO-LSD Process +

+ +
+ +
+ Distributions + +Visualizations of FDR across detection scenarios with initial and refined bounding boxes, along with unweighted and weighted distributions. + +

+ +

+ +
+ +
+ Hard Cases + +The following visualization demonstrates D-FINE's predictions in various complex detection scenarios. These include cases with occlusion, low-light conditions, motion blur, depth of field effects, and densely populated scenes. Despite these challenges, D-FINE consistently produces accurate localization results. + +

+ D-FINE Predictions in Challenging Scenarios +

+ +
+ + + + + + + +## Citation +If you use `D-FINE` or its methods in your work, please cite the following BibTeX entries: +
+ bibtex + +```latex +@misc{peng2024dfine, + title={D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement}, + author={Yansong Peng and Hebei Li and Peixi Wu and Yueyi Zhang and Xiaoyan Sun and Feng Wu}, + year={2024}, + eprint={2410.13842}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` +
+ +## Acknowledgement +Our work is built upon [RT-DETR](https://github.com/lyuwenyu/RT-DETR). +Thanks to the inspirations from [RT-DETR](https://github.com/lyuwenyu/RT-DETR), [GFocal](https://github.com/implus/GFocal), [LD](https://github.com/HikariTJU/LD), and [YOLOv9](https://github.com/WongKinYiu/yolov9). + +✨ Feel free to contribute and reach out if you have any questions! ✨ diff --git a/backend/apps/algorithms/algorithm/DFINE/README_cn.md b/backend/apps/algorithms/algorithm/DFINE/README_cn.md new file mode 100644 index 0000000..c9beb8e --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/README_cn.md @@ -0,0 +1,673 @@ + + +[English](README.md) | 简体中文 | [日本語](README_ja.md) | [English Blog](src/zoo/dfine/blog.md) | [中文博客](src/zoo/dfine/blog_cn.md) + +

+ D-FINE: Redefine Regression Task of DETRs as Fine‑grained Distribution Refinement +

+ +

+ + + license + + + prs + + + issues + + + arXiv + + + + pre-commit.ci status + + + stars + +

+ +

+ 📄 这是该文章的官方实现: +
+ D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +

+ + +

+彭岩松，李和倍，吴沛熹，张越一，孙晓艳，吴枫 +

+ +

+中国科学技术大学 +

+ +

+ + sota + +

+ + +

如果你喜欢 D-FINE，请给我们一个 ⭐！你的支持激励我们不断前进！

+ +

+ +

+ +D-FINE 是一个强大的实时目标检测器，将 DETR 中的边界框回归任务重新定义为了细粒度的分布优化（FDR），并引入全局最优的定位自蒸馏（GO-LSD），在不增加额外推理和训练成本的情况下，实现了卓越的性能。 + +
+ 视频 + +我们分别使用 D-FINE 和 YOLO11 对 [YouTube](https://www.youtube.com/watch?v=CfhEWj9sd9A) 上的一段复杂街景视频进行了目标检测。尽管存在逆光、虚化模糊和密集遮挡等不利因素，D-FINE-X 依然成功检测出几乎所有目标，包括背包、自行车和信号灯等难以察觉的小目标，其置信度、以及模糊边缘的定位准确度明显高于 YOLO11x。 + +https://github.com/user-attachments/assets/e5933d8e-3c8a-400e-870b-4e452f5321d9 + +
+ +## 🚀 Updates +- [x] **\[2024.10.18\]** 发布 D-FINE 系列。 +- [x] **\[2024.10.25\]** 添加了自定义数据集微调配置文件 ([#7](https://github.com/Peterande/D-FINE/issues/7))。 +- [x] **\[2024.10.30\]** 更新 D-FINE-L (E25) 预训练模型，性能提升了 2.0%。 +- [x] **\[2024.11.07\]** 发布 **D-FINE-N**, 在 COCO 上达到 42.8% APval @ 472 FPST4! + +## 模型库 + +### COCO +| 模型 | 数据集 | APval | 参数量 | 时延 (ms) | GFLOPs | 配置 | 权重 | 日志 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑N** | COCO | **42.8** | 4M | 2.12ms | 7 | [yml](./configs/dfine/dfine_hgnetv2_n_coco.yml) | [42.8](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_n_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_n_coco_log.txt) +**D‑FINE‑S** | COCO | **48.5** | 10M | 3.49ms | 25 | [yml](./configs/dfine/dfine_hgnetv2_s_coco.yml) | [48.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_s_coco_log.txt) +**D‑FINE‑M** | COCO | **52.3** | 19M | 5.62ms | 57 | [yml](./configs/dfine/dfine_hgnetv2_m_coco.yml) | [52.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_m_coco_log.txt) +**D‑FINE‑L** | COCO | **54.0** | 31M | 8.07ms | 91 | [yml](./configs/dfine/dfine_hgnetv2_l_coco.yml) | [54.0](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_l_coco_log.txt) +**D‑FINE‑X** | COCO | **55.8** | 62M | 12.89ms | 202 | [yml](./configs/dfine/dfine_hgnetv2_x_coco.yml) | [55.8](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_x_coco_log.txt) + +### Objects365+COCO +| 模型 | 数据集 | APval | 参数量 | 时延 (ms) | GFLOPs | 配置 | 权重 | 日志 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑S** | Objects365+COCO | **50.7** | 10M | 3.49ms | 25 | [yml](./configs/dfine/objects365/dfine_hgnetv2_s_obj2coco.yml) | [50.7](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_s_obj2coco_log.txt) +**D‑FINE‑M** | Objects365+COCO | **55.1** | 19M | 5.62ms | 57 | [yml](./configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml) | [55.1](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_m_obj2coco_log.txt) +**D‑FINE‑L** | Objects365+COCO | **57.3** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj2coco.yml) | [57.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj2coco_e25.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_l_obj2coco_log_e25.txt) +**D‑FINE‑X** | Objects365+COCO | **59.3** | 62M | 12.89ms | 202 | [yml](./configs/dfine/objects365/dfine_hgnetv2_x_obj2coco.yml) | [59.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_x_obj2coco_log.txt) + +**我们强烈推荐您使用 Objects365 预训练模型进行微调：** + +⚠️ 重要提醒：通常这种预训练模型对复杂场景的理解非常有用。如果您的类别非常简单，请注意，这可能会导致过拟合和次优性能。 + +
🔥 Objects365 预训练模型（泛化性最好） + +| 模型 | 数据集 | APval | AP5000 | 参数量 | 时延 (ms) | GFLOPs | 配置 | 权重 | 日志 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑S** | Objects365 | **31.0** | **30.5** | 10M | 3.49ms | 25 | [yml](./configs/dfine/objects365/dfine_hgnetv2_s_obj365.yml) | [30.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_s_obj365_log.txt) +**D‑FINE‑M** | Objects365 | **38.6** | **37.4** | 19M | 5.62ms | 57 | [yml](./configs/dfine/objects365/dfine_hgnetv2_m_obj365.yml) | [37.4](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_m_obj365_log.txt) +**D‑FINE‑L** | Objects365 | - | **40.6** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml) | [40.6](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_l_obj365_log.txt) +**D‑FINE‑L (E25)** | Objects365 | **44.7** | **42.6** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml) | [42.6](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj365_e25.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_l_obj365_log_e25.txt) +**D‑FINE‑X** | Objects365 | **49.5** | **46.5** | 62M | 12.89ms | 202 | [yml](./configs/dfine/objects365/dfine_hgnetv2_x_obj365.yml) | [46.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_x_obj365_log.txt) +- **E25**: 重新训练，并将训练延长至 25 个 epoch。 +- **APval** 是在 *Objects365* 完整的验证集上进行评估的。 +- **AP5000** 是在 *Objects365* 验证集的前5000个样本上评估的。 +
+ +**注意：** +- **APval** 是在 *MSCOCO val2017* 数据集上评估的。 +- **时延** 是在单张 T4 GPU 上以 $batch\\_size = 1$, $fp16$, 和 $TensorRT==10.4.0$ 评估的。 +- **Objects365+COCO** 表示使用在 *Objects365* 上预训练的权重在 *COCO* 上微调的模型。 + + + +## 快速开始 + +### 设置 + +```shell +conda create -n dfine python=3.11.9 +conda activate dfine +pip install -r requirements.txt +``` + + + + + +### 数据集准备 + + +
+ + COCO2017 数据集 + +1. 从 [OpenDataLab](https://opendatalab.com/OpenDataLab/COCO_2017) 或者 [COCO](https://cocodataset.org/#download) 下载 COCO2017。 +1.修改 [coco_detection.yml](./configs/dataset/coco_detection.yml) 中的路径。 + + ```yaml + train_dataloader: + img_folder: /data/COCO2017/train2017/ + ann_file: /data/COCO2017/annotations/instances_train2017.json + val_dataloader: + img_folder: /data/COCO2017/val2017/ + ann_file: /data/COCO2017/annotations/instances_val2017.json + ``` + +
+ +
+ Objects365 数据集 + +1. 从 [OpenDataLab](https://opendatalab.com/OpenDataLab/Objects365) 下载 Objects365。 + +2. 设置数据集的基础目录： +```shell +export BASE_DIR=/data/Objects365/data +``` + +3. 解压并整理目录结构如下： + +```shell +${BASE_DIR}/train +├── images +│ ├── v1 +│ │ ├── patch0 +│ │ │ ├── 000000000.jpg +│ │ │ ├── 000000001.jpg +│ │ │ └── ... (more images) +│ ├── v2 +│ │ ├── patchx +│ │ │ ├── 000000000.jpg +│ │ │ ├── 000000001.jpg +│ │ │ └── ... (more images) +├── zhiyuan_objv2_train.json +``` + +```shell +${BASE_DIR}/val +├── images +│ ├── v1 +│ │ ├── patch0 +│ │ │ ├── 000000000.jpg +│ │ │ └── ... (more images) +│ ├── v2 +│ │ ├── patchx +│ │ │ ├── 000000000.jpg +│ │ │ └── ... (more images) +├── zhiyuan_objv2_val.json +``` + + +4. 创建一个新目录来存储验证集中的图像： +```shell +mkdir -p ${BASE_DIR}/train/images_from_val +``` + +5. 将 val 目录中的 v1 和 v2 文件夹复制到 train/images_from_val 目录中 +```shell +cp -r ${BASE_DIR}/val/images/v1 ${BASE_DIR}/train/images_from_val/ +cp -r ${BASE_DIR}/val/images/v2 ${BASE_DIR}/train/images_from_val/ +``` + + +6. 运行 remap_obj365.py 将验证集中的部分样本合并到训练集中。具体来说，该脚本将索引在 5000 到 800000 之间的样本从验证集移动到训练集。 +```shell +python tools/remap_obj365.py --base_dir ${BASE_DIR} +``` + + +7. 运行 resize_obj365.py 脚本，将数据集中任何最大边长超过 640 像素的图像进行大小调整。使用步骤 5 中生成的更新后的 JSON 文件处理样本数据。 +```shell +python tools/resize_obj365.py --base_dir ${BASE_DIR} +``` + +8. 修改 [obj365_detection.yml](./configs/dataset/obj365_detection.yml) 中的路径。 + + ```yaml + train_dataloader: + img_folder: /data/Objects365/data/train + ann_file: /data/Objects365/data/train/new_zhiyuan_objv2_train_resized.json + val_dataloader: + img_folder: /data/Objects365/data/val/ + ann_file: /data/Objects365/data/val/new_zhiyuan_objv2_val_resized.json + ``` + + +
+ +
+CrowdHuman + +在此下载 COCO 格式的数据集：[链接](https://aistudio.baidu.com/datasetdetail/231455) + +
+ +
+自定义数据集 + +要在你的自定义数据集上训练，你需要将其组织为 COCO 格式。请按照以下步骤准备你的数据集： + +1. **将 `remap_mscoco_category` 设置为 `False`:** + + 这可以防止类别 ID 自动映射以匹配 MSCOCO 类别。 + + ```yaml + remap_mscoco_category: False + ``` + +2. **组织图像：** + + 按以下结构组织你的数据集目录： + + ```shell + dataset/ + ├── images/ + │ ├── train/ + │ │ ├── image1.jpg + │ │ ├── image2.jpg + │ │ └── ... + │ ├── val/ + │ │ ├── image1.jpg + │ │ ├── image2.jpg + │ │ └── ... + └── annotations/ + ├── instances_train.json + ├── instances_val.json + └── ... + ``` + + - **`images/train/`**: 包含所有训练图像。 + - **`images/val/`**: 包含所有验证图像。 + - **`annotations/`**: 包含 COCO 格式的注释文件。 + +3. **将注释转换为 COCO 格式：** + + 如果你的注释尚未为 COCO 格式，你需要进行转换。你可以参考以下 Python 脚本或使用现有工具： + + ```python + import json + + def convert_to_coco(input_annotations, output_annotations): + # Implement conversion logic here + pass + + if __name__ == "__main__": + convert_to_coco('path/to/your_annotations.json', 'dataset/annotations/instances_train.json') + ``` + +4. **更新配置文件：** + + 修改你的 [custom_detection.yml](./configs/dataset/custom_detection.yml)。 + + ```yaml + task: detection + + evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + + num_classes: 777 # your dataset classes + remap_mscoco_category: False + + train_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/train + ann_file: /data/yourdataset/train/train.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 4 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + val_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/val + ann_file: /data/yourdataset/val/ann.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 4 + drop_last: False + collate_fn: + type: BatchImageCollateFunction + ``` +
+ + +## 使用方法 +
+ COCO2017 + + +1. 设置模型 +```shell +export model=l # n s m l x +``` + +2. 训练 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --use-amp --seed=0 +``` + + +3. 测试 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --test-only -r model.pth +``` + + +4. 微调 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --use-amp --seed=0 -t model.pth +``` +
+ + +
+ 在 Objects365 上训练，在COCO2017上微调 + +1. 设置模型 +```shell +export model=l # n s m l x +``` + +2. 在 Objects365 上训练 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/objects365/dfine_hgnetv2_${model}_obj365.yml --use-amp --seed=0 +``` + +3. 在 COCO2017 上微调 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/objects365/dfine_hgnetv2_${model}_obj2coco.yml --use-amp --seed=0 -t model.pth +``` + + +4. 测试 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --test-only -r model.pth +``` +
+ + +
+ 自定义数据集 + +1. 设置模型 +```shell +export model=l # n s m l x +``` + +2. 在自定义数据集上训练 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --use-amp --seed=0 +``` + +3. 测试 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --test-only -r model.pth +``` + +4. 在自定义数据集上微调 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/objects365/dfine_hgnetv2_${model}_obj2custom.yml --use-amp --seed=0 -t model.pth +``` + +5. **[可选项]** 修改类映射: + +在使用 Objects365 预训练权重训练自定义数据集时，示例中假设自定义数据集仅有 `'Person'` 和 `'Car'` 类，您可以将其替换为数据集中对应的任何类别。为了加快收敛，可以在 `src/solver/_solver.py` 中修改 `self.obj365_ids`，如下所示： + +```python +self.obj365_ids = [0, 5] # Person, Cars +``` +Objects365 类及其对应 ID 的完整列表: +https://github.com/Peterande/D-FINE/blob/352a94ece291e26e1957df81277bef00fe88a8e3/src/solver/_solver.py#L330 + +新的训练启动命令： + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --use-amp --seed=0 -t model.pth +``` + +如果您不想修改类映射，预训练的 Objects365 权重依然可以不做任何更改直接使用。修改类映射是可选的，但针对特定任务可能会加快收敛速度。 + +
+ +
+ 自定义批次大小 + +例如，如果你想在训练 D-FINE-L 时将 COCO2017 的总批次大小增加一倍，请按照以下步骤操作： + +1. **修改你的 [dataloader.yml](./configs/dfine/include/dataloader.yml)**，增加 `total_batch_size`： + + ```yaml + train_dataloader: + total_batch_size: 64 # 原来是 32，现在增加了一倍 + ``` + +2. **修改你的 [dfine_hgnetv2_l_coco.yml](./configs/dfine/dfine_hgnetv2_l_coco.yml)**。 + + ```yaml + optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 # 翻倍，线性缩放原则 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.0005 # 翻倍，线性缩放原则 + betas: [0.9, 0.999] + weight_decay: 0.0001 # 需要网格搜索找到最优值 + + ema: # 添加 EMA 设置 + decay: 0.9998 # 根据 1 - (1 - decay) * 2 调整 + warmups: 500 # 减半 + + lr_warmup_scheduler: + warmup_duration: 250 # 减半 + ``` + +
+ +
+ 自定义输入尺寸 + +如果你想在 COCO2017 上使用 **D-FINE-L** 进行 320x320 尺寸的图片训练，按照以下步骤操作： + +1. **修改你的 [dataloader.yml](./configs/dfine/include/dataloader.yml)**： + + ```yaml + + train_dataloader: + dataset: + transforms: + ops: + - {type: Resize, size: [320, 320], } + collate_fn: + base_size: 320 + dataset: + transforms: + ops: + - {type: Resize, size: [320, 320], } + ``` + +2. **修改你的 [dfine_hgnetv2.yml](./configs/dfine/include/dfine_hgnetv2.yml)**： + + ```yaml + eval_spatial_size: [320, 320] + ``` + +
+ + +## 工具 + +
+ 部署 + + +1. 设置 +```shell +pip install onnx onnxsim onnxruntime +export model=l # n s m l x +``` + +2. 导出 onnx +```shell +python tools/export_onnx.py --check -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth +``` + +3. 导出 [tensorrt](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) +```shell +trtexec --onnx="model.onnx" --saveEngine="model.engine" --fp16 +``` + +
+ +
+ 推理（可视化） + + +1. 设置 +```shell +pip install -r tools/inference/requirements.txt +export model=l # n s m l x +``` + + + +2. 推理 (onnxruntime / tensorrt / torch) + +目前支持对图像和视频的推理。 +```shell +python tools/inference/onnx_inf.py --onnx model.onnx --input image.jpg # video.mp4 +python tools/inference/trt_inf.py --trt model.engine --input image.jpg +python tools/inference/torch_inf.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth --input image.jpg --device cuda:0 +``` +
+ +
+ 基准测试 + +1. 设置 +```shell +pip install -r tools/benchmark/requirements.txt +export model=l # n s m l x +``` + + +2. 模型 FLOPs、MACs、参数量 +```shell +python tools/benchmark/get_info.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml +``` + +2. TensorRT 延迟 +```shell +python tools/benchmark/trt_benchmark.py --COCO_dir path/to/COCO2017 --engine_dir model.engine +``` +
+ +
+ Voxel51 Fiftyone 可视化 + +1. 设置 +```shell +pip install fiftyone +export model=l # n s m l x +``` +4. Voxel51 Fiftyone 可视化 ([fiftyone](https://github.com/voxel51/fiftyone)) +```shell +python tools/visualization/fiftyone_vis.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth +``` +
+ +
+ 其他 + +1. 自动恢复（Auto Resume）训练 +```shell +bash reference/safe_training.sh +``` + +2. 模型权重转换 +```shell +python reference/convert_weight.py model.pth +``` +
+ +## 图表与可视化 + +
+ FDR 和 GO-LSD + +D-FINE与FDR概览。概率分布作为更细粒度的中间表征，通过解码器层以残差方式进行迭代优化。应用非均匀加权函数以实现更精细的定位。 +

+细粒度分布优化过程

+GO-LSD流程概览。通过DDF损失函数和解耦加权策略将最终层分布中的定位知识蒸馏到前面的层中。 +

GO-LSD流程

+ +
+ +
+ 分布可视化 + +FDR在检测场景中的可视化，包括初始和优化后的边界框，以及未加权和加权的分布图。 + +

+ +

+ +
+ +
+ 困难场景 + +以下可视化展示了D-FINE在各种复杂检测场景中的预测结果。这些场景包括遮挡、低光条件、运动模糊、景深效果和密集场景。尽管面临这些挑战，D-FINE依然能够生成准确的定位结果。 + +

+D-FINE在挑战性场景中的预测 +

+ +
+ + + + +## 引用 +如果你在工作中使用了 `D-FINE` 或其方法，请引用以下 BibTeX 条目： +
+ bibtex + +```latex +@misc{peng2024dfine, + title={D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement}, + author={Yansong Peng and Hebei Li and Peixi Wu and Yueyi Zhang and Xiaoyan Sun and Feng Wu}, + year={2024}, + eprint={2410.13842}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` +
+ +## 致谢 +我们的工作基于 [RT-DETR](https://github.com/lyuwenyu/RT-DETR)。 +感谢 [RT-DETR](https://github.com/lyuwenyu/RT-DETR), [GFocal](https://github.com/implus/GFocal), [LD](https://github.com/HikariTJU/LD), 和 [YOLOv9](https://github.com/WongKinYiu/yolov9) 的启发。 + +✨ 欢迎贡献并在有任何问题时联系我！ ✨ diff --git a/backend/apps/algorithms/algorithm/DFINE/README_ja.md b/backend/apps/algorithms/algorithm/DFINE/README_ja.md new file mode 100644 index 0000000..e67c239 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/README_ja.md @@ -0,0 +1,698 @@ + + +[English](README.md) | [简体中文](README_cn.md) | 日本語 | [English Blog](src/zoo/dfine/blog.md) | [中文博客](src/zoo/dfine/blog_cn.md) + +

+ D-FINE: Redefine Regression Task of DETRs as Fine‑grained Distribution Refinement +

+ + + +

+ + license + + + prs + + + issues + + + arXiv + + + + pre-commit.ci status + + + stars + +

+ + + +

+ 📄 これは論文の公式実装です: +
+ D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +

+

+ D-FINE: DETRの回帰タスクを細粒度分布最適化として再定義 +

+ + + +

+Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, and Feng Wu +

+ +

+中国科学技術大学 +

+ +

+ + sota + +

+ + + +

+もしD-FINEが気に入ったら、ぜひ⭐をください！あなたのサポートが私たちのモチベーションになります！ +

+ +

+ +

+ +D-FINEは、DETRの境界ボックス回帰タスクを細粒度分布最適化（FDR）として再定義し、グローバル最適な位置特定自己蒸留（GO-LSD）を導入することで、追加の推論およびトレーニングコストを増やすことなく、優れたパフォーマンスを実現する強力なリアルタイムオブジェクト検出器です。 + +
+ ビデオ + +D-FINEとYOLO11を使用して、[YouTube](https://www.youtube.com/watch?v=CfhEWj9sd9A)の複雑な街並みのビデオでオブジェクト検出を行いました。逆光、モーションブラー、密集した群衆などの厳しい条件にもかかわらず、D-FINE-Xはほぼすべてのターゲットを検出し、バックパック、自転車、信号機などの微妙な小さなオブジェクトも含まれます。その信頼スコアとぼやけたエッジの位置特定精度はYOLO11よりもはるかに高いです。 + + + +https://github.com/user-attachments/assets/e5933d8e-3c8a-400e-870b-4e452f5321d9 + +
+ +## 🚀 更新情報 +- [x] **\[2024.10.18\]** D-FINEシリーズをリリース。 +- [x] **\[2024.10.25\]** カスタムデータセットの微調整設定を追加 ([#7](https://github.com/Peterande/D-FINE/issues/7))。 +- [x] **\[2024.10.30\]** D-FINE-L (E25) 事前トレーニングモデルを更新し、パフォーマンスが2.0%向上。 +- [x] **\[2024.11.07\]** **D-FINE-N** をリリース, COCO で 42.8% の APval を達成 @ 472 FPST4! + +## モデルズー + +### COCO +| モデル | データセット | APval | パラメータ数 | レイテンシ | GFLOPs | config | checkpoint | logs | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑N** | COCO | **42.8** | 4M | 2.12ms | 7 | [yml](./configs/dfine/dfine_hgnetv2_n_coco.yml) | [42.8](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_n_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_n_coco_log.txt) +**D‑FINE‑S** | COCO | **48.5** | 10M | 3.49ms | 25 | [yml](./configs/dfine/dfine_hgnetv2_s_coco.yml) | [48.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_s_coco_log.txt) +**D‑FINE‑M** | COCO | **52.3** | 19M | 5.62ms | 57 | [yml](./configs/dfine/dfine_hgnetv2_m_coco.yml) | [52.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_m_coco_log.txt) +**D‑FINE‑L** | COCO | **54.0** | 31M | 8.07ms | 91 | [yml](./configs/dfine/dfine_hgnetv2_l_coco.yml) | [54.0](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_l_coco_log.txt) +**D‑FINE‑X** | COCO | **55.8** | 62M | 12.89ms | 202 | [yml](./configs/dfine/dfine_hgnetv2_x_coco.yml) | [55.8](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/coco/dfine_x_coco_log.txt) + + +### Objects365+COCO +| モデル | データセット | APval | パラメータ数 | レイテンシ | GFLOPs | config | checkpoint | logs | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑S** | Objects365+COCO | **50.7** | 10M | 3.49ms | 25 | [yml](./configs/dfine/objects365/dfine_hgnetv2_s_obj2coco.yml) | [50.7](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_s_obj2coco_log.txt) +**D‑FINE‑M** | Objects365+COCO | **55.1** | 19M | 5.62ms | 57 | [yml](./configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml) | [55.1](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_m_obj2coco_log.txt) +**D‑FINE‑L** | Objects365+COCO | **57.3** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj2coco.yml) | [57.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj2coco_e25.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_l_obj2coco_log_e25.txt) +**D‑FINE‑X** | Objects365+COCO | **59.3** | 62M | 12.89ms | 202 | [yml](./configs/dfine/objects365/dfine_hgnetv2_x_obj2coco.yml) | [59.3](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_obj2coco.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj2coco/dfine_x_obj2coco_log.txt) + +**微調整のために Objects365 の事前学習モデルを使用することを強くお勧めします：** + +⚠️ 重要なお知らせ：このプリトレインモデルは複雑なシーンの理解に有益ですが、カテゴリが非常に単純な場合、過学習や最適ではない性能につながる可能性がありますので、ご注意ください。 + +
🔥 Objects365で事前トレーニングされたモデル（最良の汎化性能） + + +| モデル | データセット | APval | AP5000 | パラメータ数 | レイテンシ | GFLOPs | config | checkpoint | logs | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +**D‑FINE‑S** | Objects365 | **31.0** | **30.5** | 10M | 3.49ms | 25 | [yml](./configs/dfine/objects365/dfine_hgnetv2_s_obj365.yml) | [30.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_s_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_s_obj365_log.txt) +**D‑FINE‑M** | Objects365 | **38.6** | **37.4** | 19M | 5.62ms | 57 | [yml](./configs/dfine/objects365/dfine_hgnetv2_m_obj365.yml) | [37.4](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_m_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_m_obj365_log.txt) +**D‑FINE‑L** | Objects365 | - | **40.6** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml) | [40.6](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_l_obj365_log.txt) +**D‑FINE‑L (E25)** | Objects365 | **44.7** | **42.6** | 31M | 8.07ms | 91 | [yml](./configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml) | [42.6](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_l_obj365_e25.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_l_obj365_log_e25.txt) +**D‑FINE‑X** | Objects365 | **49.5** | **46.5** | 62M | 12.89ms | 202 | [yml](./configs/dfine/objects365/dfine_hgnetv2_x_obj365.yml) | [46.5](https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_x_obj365.pth) | [url](https://raw.githubusercontent.com/Peterande/storage/refs/heads/master/logs/obj365/dfine_x_obj365_log.txt) +- **E25**: 再トレーニングし、事前トレーニングを25エポックに延長。 +- **APval** は *Objects365* のフルバリデーションセットで評価されます。 +- **AP5000** は *Objects365* 検証セットの最初の5000サンプルで評価されます。 +
+ +**注意事項:** +- **APval** は *MSCOCO val2017* データセットで評価されます。 +- **レイテンシ** は単一のT4 GPUで $batch\\_size = 1$, $fp16$, および $TensorRT==10.4.0$ で評価されます。 +- **Objects365+COCO** は *Objects365* で事前トレーニングされた重みを使用して *COCO* で微調整されたモデルを意味します。 + + + +## クイックスタート + +### セットアップ + +```shell +conda create -n dfine python=3.11.9 +conda activate dfine +pip install -r requirements.txt +``` + + +### データ準備 + +
+ COCO2017 データセット + +1. [OpenDataLab](https://opendatalab.com/OpenDataLab/COCO_2017) または [COCO](https://cocodataset.org/#download) からCOCO2017をダウンロードします。 +1. [coco_detection.yml](./configs/dataset/coco_detection.yml) のパスを修正します。 + + ```yaml + train_dataloader: + img_folder: /data/COCO2017/train2017/ + ann_file: /data/COCO2017/annotations/instances_train2017.json + val_dataloader: + img_folder: /data/COCO2017/val2017/ + ann_file: /data/COCO2017/annotations/instances_val2017.json + ``` + +
+ +
+ Objects365 データセット + +1. [OpenDataLab](https://opendatalab.com/OpenDataLab/Objects365) からObjects365をダウンロードします。 + +2. ベースディレクトリを設定します： +```shell +export BASE_DIR=/data/Objects365/data +``` + +3. ダウンロードしたファイルを解凍し、以下のディレクトリ構造に整理します： + +```shell +${BASE_DIR}/train +├── images +│ ├── v1 +│ │ ├── patch0 +│ │ │ ├── 000000000.jpg +│ │ │ ├── 000000001.jpg +│ │ │ └── ... (more images) +│ ├── v2 +│ │ ├── patchx +│ │ │ ├── 000000000.jpg +│ │ │ ├── 000000001.jpg +│ │ │ └── ... (more images) +├── zhiyuan_objv2_train.json +``` + +```shell +${BASE_DIR}/val +├── images +│ ├── v1 +│ │ ├── patch0 +│ │ │ ├── 000000000.jpg +│ │ │ └── ... (more images) +│ ├── v2 +│ │ ├── patchx +│ │ │ ├── 000000000.jpg +│ │ │ └── ... (more images) +├── zhiyuan_objv2_val.json +``` + +4. 検証セットの画像を保存する新しいディレクトリを作成します： +```shell +mkdir -p ${BASE_DIR}/train/images_from_val +``` + +5. valディレクトリのv1およびv2フォルダをtrain/images_from_valディレクトリにコピーします +```shell +cp -r ${BASE_DIR}/val/images/v1 ${BASE_DIR}/train/images_from_val/ +cp -r ${BASE_DIR}/val/images/v2 ${BASE_DIR}/train/images_from_val/ +``` + +6. remap_obj365.pyを実行して、検証セットの一部をトレーニングセットにマージします。具体的には、このスクリプトはインデックスが5000から800000のサンプルを検証セットからトレーニングセットに移動します。 +```shell +python tools/remap_obj365.py --base_dir ${BASE_DIR} +``` + + +7. resize_obj365.pyスクリプトを実行して、データセット内の最大エッジ長が640ピクセルを超える画像をリサイズします。ステップ5で生成された更新されたJSONファイルを使用してサンプルデータを処理します。トレーニングセットと検証セットの両方の画像をリサイズして、一貫性を保ちます。 +```shell +python tools/resize_obj365.py --base_dir ${BASE_DIR} +``` + +8. [obj365_detection.yml](./configs/dataset/obj365_detection.yml) のパスを修正します。 + + ```yaml + train_dataloader: + img_folder: /data/Objects365/data/train + ann_file: /data/Objects365/data/train/new_zhiyuan_objv2_train_resized.json + val_dataloader: + img_folder: /data/Objects365/data/val/ + ann_file: /data/Objects365/data/val/new_zhiyuan_objv2_val_resized.json + ``` + + +
+ +
+CrowdHuman + +こちらからCOCOフォーマットのデータセットをダウンロードしてください：[リンク](https://aistudio.baidu.com/datasetdetail/231455) + +
+ +
+カスタムデータセット + +カスタムデータセットでトレーニングするには、COCO形式で整理する必要があります。以下の手順に従ってデータセットを準備してください： + +1. **`remap_mscoco_category` を `False` に設定します**： + + これにより、カテゴリIDがMSCOCOカテゴリに自動的にマッピングされるのを防ぎます。 + + ```yaml + remap_mscoco_category: False + ``` + +2. **画像を整理します**： + + データセットディレクトリを以下のように構造化します： + + ```shell + dataset/ + ├── images/ + │ ├── train/ + │ │ ├── image1.jpg + │ │ ├── image2.jpg + │ │ └── ... + │ ├── val/ + │ │ ├── image1.jpg + │ │ ├── image2.jpg + │ │ └── ... + └── annotations/ + ├── instances_train.json + ├── instances_val.json + └── ... + ``` + + - **`images/train/`**: すべてのトレーニング画像を含みます。 + - **`images/val/`**: すべての検証画像を含みます。 + - **`annotations/`**: COCO形式の注釈ファイルを含みます。 + +3. **注釈をCOCO形式に変換します**： + + 注釈がまだCOCO形式でない場合は、変換する必要があります。以下のPythonスクリプトを参考にするか、既存のツールを利用してください： + + ```python + import json + + def convert_to_coco(input_annotations, output_annotations): + # 変換ロジックをここに実装します + pass + + if __name__ == "__main__": + convert_to_coco('path/to/your_annotations.json', 'dataset/annotations/instances_train.json') + ``` + +4. **設定ファイルを更新します**： + + [custom_detection.yml](./configs/dataset/custom_detection.yml) を修正します。 + + ```yaml + task: detection + + evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + + num_classes: 777 # データセットのクラス数 + remap_mscoco_category: False + + train_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/train + ann_file: /data/yourdataset/train/train.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 4 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + val_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/val + ann_file: /data/yourdataset/val/ann.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 4 + drop_last: False + collate_fn: + type: BatchImageCollateFunction + ``` + +
+ + +## 使用方法 +
+ COCO2017 + + +1. モデルを設定します +```shell +export model=l # n s m l x +``` + +2. トレーニング +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --use-amp --seed=0 +``` + + +3. テスト +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --test-only -r model.pth +``` + + +4. 微調整 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --use-amp --seed=0 -t model.pth +``` +
+ + +
+ Objects365からCOCO2017へ + +1. モデルを設定します +```shell +export model=l # n s m l x +``` + +2. Objects365でトレーニング +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/objects365/dfine_hgnetv2_${model}_obj365.yml --use-amp --seed=0 +``` + +3. COCO2017で微調整 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/objects365/dfine_hgnetv2_${model}_obj2coco.yml --use-amp --seed=0 -t model.pth +``` + + +4. テスト +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml --test-only -r model.pth +``` +
+ + +
+ カスタムデータセット + +1. モデルを設定します +```shell +export model=l # n s m l x +``` + +2. カスタムデータセットでトレーニング +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --use-amp --seed=0 +``` + +3. テスト +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --test-only -r model.pth +``` + +4. カスタムデータセットで微調整 +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/objects365/dfine_hgnetv2_${model}_obj2custom.yml --use-amp --seed=0 -t model.pth +``` + +5. **[オプション]** クラスマッピングを変更します： + +Objects365の事前トレーニング済みの重みを使用してカスタムデータセットでトレーニングする場合、例ではデータセットに `'Person'` と `'Car'` クラスのみが含まれていると仮定しています。特定のタスクに対して収束を早めるために、`src/solver/_solver.py` の `self.obj365_ids` を以下のように変更できます： + +```python +self.obj365_ids = [0, 5] # Person, Cars +``` +これらをデータセットの対応するクラスに置き換えることができます。Objects365クラスとその対応IDのリスト： +https://github.com/Peterande/D-FINE/blob/352a94ece291e26e1957df81277bef00fe88a8e3/src/solver/_solver.py#L330 + +新しいトレーニングコマンド： + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/dfine/custom/dfine_hgnetv2_${model}_custom.yml --use-amp --seed=0 -t model.pth +``` + +ただし、クラスマッピングを変更したくない場合、事前トレーニング済みのObjects365の重みは変更なしでそのまま使用できます。クラスマッピングの変更はオプションであり、特定のタスクに対して収束を早める可能性があります。 + + + +
+ +
+ バッチサイズのカスタマイズ + +例えば、COCO2017でD-FINE-Lをトレーニングする際にバッチサイズを2倍にしたい場合、以下の手順に従ってください： + +1. **[dataloader.yml](./configs/dfine/include/dataloader.yml) を修正して `total_batch_size` を増やします**： + + ```yaml + train_dataloader: + total_batch_size: 64 # 以前は32、今は2倍 + ``` + +2. **[dfine_hgnetv2_l_coco.yml](./configs/dfine/dfine_hgnetv2_l_coco.yml) を修正します**。以下のように主要なパラメータを調整します： + + ```yaml + optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 # 2倍、線形スケーリング法則 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.0005 # 2倍、線形スケーリング法則 + betas: [0.9, 0.999] + weight_decay: 0.0001 # グリッドサーチが必要です + + ema: # EMA設定を追加 + decay: 0.9998 # 1 - (1 - decay) * 2 によって調整 + warmups: 500 # 半分 + + lr_warmup_scheduler: + warmup_duration: 250 # 半分 + ``` + +
+ + +
+ 入力サイズのカスタマイズ + +COCO2017で **D-FINE-L** を320x320の入力サイズでトレーニングしたい場合、以下の手順に従ってください： + +1. **[dataloader.yml](./configs/dfine/include/dataloader.yml) を修正します**： + + ```yaml + + train_dataloader: + dataset: + transforms: + ops: + - {type: Resize, size: [320, 320], } + collate_fn: + base_size: 320 + dataset: + transforms: + ops: + - {type: Resize, size: [320, 320], } + ``` + +2. **[dfine_hgnetv2.yml](./configs/dfine/include/dfine_hgnetv2.yml) を修正します**： + + ```yaml + eval_spatial_size: [320, 320] + ``` + +
+ +## ツール +
+ デプロイ + + +1. セットアップ +```shell +pip install onnx onnxsim +export model=l # n s m l x +``` + +2. onnxのエクスポート +```shell +python tools/deployment/export_onnx.py --check -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth +``` + +3. [tensorrt](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) のエクスポート +```shell +trtexec --onnx="model.onnx" --saveEngine="model.engine" --fp16 +``` + +
+ +
+ 推論（可視化） + + +1. セットアップ +```shell +pip install -r tools/inference/requirements.txt +export model=l # n s m l x +``` + + + +2. 推論 (onnxruntime / tensorrt / torch) + +現在、画像とビデオの推論がサポートされています。 +```shell +python tools/inference/onnx_inf.py --onnx model.onnx --input image.jpg # video.mp4 +python tools/inference/trt_inf.py --trt model.engine --input image.jpg +python tools/inference/torch_inf.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth --input image.jpg --device cuda:0 +``` +
+ +
+ ベンチマーク + +1. セットアップ +```shell +pip install -r tools/benchmark/requirements.txt +export model=l # n s m l x +``` + + +2. モデルのFLOPs、MACs、およびパラメータ数 +```shell +python tools/benchmark/get_info.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml +``` + +2. TensorRTのレイテンシ +```shell +python tools/benchmark/trt_benchmark.py --COCO_dir path/to/COCO2017 --engine_dir model.engine +``` +
+ +
+ Fiftyoneの可視化 + +1. セットアップ +```shell +pip install fiftyone +export model=l # n s m l x +``` +4. Voxel51 Fiftyoneの可視化 ([fiftyone](https://github.com/voxel51/fiftyone)) +```shell +python tools/visualization/fiftyone_vis.py -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth +``` +
+ +
+ その他 + +1. 自動再開トレーニング +```shell +bash reference/safe_training.sh +``` + +2. モデルの重みの変換 +```shell +python reference/convert_weight.py model.pth +``` +
+ +## 図と可視化 + +
+ FDRとGO-LSD + +1. FDRを搭載したD-FINEの概要。より細粒度の中間表現として機能する確率分布は、残差的にデコーダ層によって逐次最適化されます。 +不均一な重み付け関数が適用され、より細かい位置特定が可能になります。 + +

+ 細粒度分布最適化プロセス +

+ +2. GO-LSDプロセスの概要。最終層の最適化された分布からの位置特定知識は、デカップリングされた重み付け戦略を使用してDDF損失を通じて前の層に蒸留されます。 + +

+ GO-LSDプロセス +

+ +
+ +
+ 分布 + +初期および最適化された境界ボックスと、未重み付けおよび重み付けされた分布とともに、さまざまな検出シナリオにおけるFDRの可視化。 + +

+ +

+ +
+ +
+ 難しいケース + +以下の可視化は、さまざまな複雑な検出シナリオにおけるD-FINEの予測を示しています。これらのシナリオには、遮蔽、低光条件、モーションブラー、被写界深度効果、および密集したシーンが含まれます。これらの課題にもかかわらず、D-FINEは一貫して正確な位置特定結果を生成します。 + +

+ 複雑なシナリオにおけるD-FINEの予測 +

+ +
+ + + + + + + +## 引用 +もし`D-FINE`やその方法をあなたの仕事で使用する場合、以下のBibTeXエントリを引用してください： +
+ bibtex + +```latex +@misc{peng2024dfine, + title={D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement}, + author={Yansong Peng and Hebei Li and Peixi Wu and Yueyi Zhang and Xiaoyan Sun and Feng Wu}, + year={2024}, + eprint={2410.13842}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` +
+ +## 謝辞 +私たちの仕事は [RT-DETR](https://github.com/lyuwenyu/RT-DETR) に基づいています。 +[RT-DETR](https://github.com/lyuwenyu/RT-DETR), [GFocal](https://github.com/implus/GFocal), [LD](https://github.com/HikariTJU/LD), および [YOLOv9](https://github.com/WongKinYiu/yolov9) からのインスピレーションに感謝します。 + +✨ 貢献を歓迎し、質問があればお気軽にお問い合わせください！ ✨ diff --git a/backend/apps/algorithms/algorithm/DFINE/collect_train_dataset_voctococo.py b/backend/apps/algorithms/algorithm/DFINE/collect_train_dataset_voctococo.py new file mode 100644 index 0000000..c1576df --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/collect_train_dataset_voctococo.py @@ -0,0 +1,91 @@ +import os +import json +import xml.etree.ElementTree as ET +from tqdm import tqdm +def collect_train_dataset_voctococo(): + XML_DIR = r"D:\project_space\01\006\datasets\002\images" # VOC格式xml文件夹路径 + IMG_DIR = r"D:\project_space\01\006\datasets\002\images" # 图片文件夹路径 + SAVE_PATH = r"D:\project_space\01\006\datasets\002\images\coco.json" # COCO格式json保存路径 + CLASSES = ['0'] # 改成你自己的类别 + + def get_category_id(name): + return CLASSES.index(name) + 1 + + # 3. 开始转换 + image_id = 1 + annotation_id = 1 + images = [] + annotations = [] + categories = [] + + # 4. 构建categories + for idx, name in enumerate(CLASSES): + categories.append({ + "id": idx + 1, + "name": name, + "supercategory": "none" + }) + + xml_files = [f for f in os.listdir(XML_DIR) if f.endswith('.xml')] + for xml_file in tqdm(xml_files): + xml_path = os.path.join(XML_DIR, xml_file) + tree = ET.parse(xml_path) + root = tree.getroot() + filename = root.find('filename').text + img_path = os.path.join(IMG_DIR, filename) + + # 获取图片尺寸 + size = root.find('size') + width = int(size.find('width').text) + height = int(size.find('height').text) + + # images部分 + images.append({ + "file_name": filename, + "height": height, + "width": width, + "id": image_id + }) + + # annotations部分 + for obj in root.findall('object'): + name = obj.find('name').text + if name not in CLASSES: + continue + category_id = get_category_id(name) + bndbox = obj.find('bndbox') + xmin = int(float(bndbox.find('xmin').text)) + ymin = int(float(bndbox.find('ymin').text)) + xmax = int(float(bndbox.find('xmax').text)) + ymax = int(float(bndbox.find('ymax').text)) + w = xmax - xmin + h = ymax - ymin + + # coco标注 + annotations.append({ + "id": annotation_id, + "image_id": image_id, + "category_id": category_id, + "bbox": [xmin, ymin, w, h], + "area": w * h, + "iscrowd": 0, + "segmentation": [] + }) + annotation_id += 1 + + image_id += 1 + + # 5. 整合并保存 + coco_format = { + "images": images, + "type": "instances", + "annotations": annotations, + "categories": categories + } + with open(SAVE_PATH, 'w', encoding='utf-8') as f: + json.dump(coco_format, f, ensure_ascii=False, indent=4) + + print(f"转换完成，共有 {len(images)} 张图片，{len(annotations)} 个标注。输出: {SAVE_PATH}") + +if __name__ == '__main__': + collect_train_dataset_voctococo() \ No newline at end of file diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dataset/coco_detection.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/coco_detection.yml new file mode 100644 index 0000000..df6a7ce --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/coco_detection.yml @@ -0,0 +1,41 @@ +task: detection + +evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + +num_classes: 1 +remap_mscoco_category: True + +train_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: D:\project_space\01\006\datasets\002\images + ann_file: D:\project_space\01\006\datasets\002\images\coco.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 2 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + +val_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: D:\project_space\01\006\datasets\002\images + ann_file: D:\project_space\01\006\datasets\002\images\coco.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 2 + drop_last: False + collate_fn: + type: BatchImageCollateFunction diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dataset/crowdhuman_detection.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/crowdhuman_detection.yml new file mode 100644 index 0000000..0a4bbad --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/crowdhuman_detection.yml @@ -0,0 +1,41 @@ +task: detection + +evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + +num_classes: 1 # your dataset classes +remap_mscoco_category: False + +train_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/CrowdHuman/coco/CrowdHuman_train + ann_file: /data/CrowdHuman/coco/Chuman-train.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 4 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + +val_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/CrowdHuman/coco/CrowdHuman_val + ann_file: /data/CrowdHuman/coco/Chuman-val.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 4 + drop_last: False + collate_fn: + type: BatchImageCollateFunction diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dataset/custom_detection.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/custom_detection.yml new file mode 100644 index 0000000..35435ad --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/custom_detection.yml @@ -0,0 +1,41 @@ +task: detection + +evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + +num_classes: 777 # your dataset classes +remap_mscoco_category: False + +train_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/train + ann_file: /data/yourdataset/train/train.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 4 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + +val_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/yourdataset/val + ann_file: /data/yourdataset/val/val.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 4 + drop_last: False + collate_fn: + type: BatchImageCollateFunction diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dataset/obj365_detection.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/obj365_detection.yml new file mode 100644 index 0000000..e58fdbc --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/obj365_detection.yml @@ -0,0 +1,41 @@ +task: detection + +evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + +num_classes: 366 +remap_mscoco_category: False + +train_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/Objects365/data/train + ann_file: /data/Objects365/data/train/new_zhiyuan_objv2_train_resized.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 4 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + +val_dataloader: + type: DataLoader + dataset: + type: CocoDetection + img_folder: /data/Objects365/data/val/ + ann_file: /data/Objects365/data/val/new_zhiyuan_objv2_val_resized.json + return_masks: False + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 4 + drop_last: False + collate_fn: + type: BatchImageCollateFunction diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dataset/voc_detection.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/voc_detection.yml new file mode 100644 index 0000000..1f9ceeb --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dataset/voc_detection.yml @@ -0,0 +1,40 @@ +task: detection + +evaluator: + type: CocoEvaluator + iou_types: ['bbox', ] + +num_classes: 20 + +train_dataloader: + type: DataLoader + dataset: + type: VOCDetection + root: ./dataset/voc/ + ann_file: trainval.txt + label_file: label_list.txt + transforms: + type: Compose + ops: ~ + shuffle: True + num_workers: 4 + drop_last: True + collate_fn: + type: BatchImageCollateFunction + + +val_dataloader: + type: DataLoader + dataset: + type: VOCDetection + root: ./dataset/voc/ + ann_file: test.txt + label_file: label_list.txt + transforms: + type: Compose + ops: ~ + shuffle: False + num_workers: 4 + drop_last: False + collate_fn: + type: BatchImageCollateFunction diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_l_ch.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_l_ch.yml new file mode 100644 index 0000000..761bcf8 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_l_ch.yml @@ -0,0 +1,44 @@ +__include__: [ + '../../dataset/crowdhuman_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_l_crowdhuman + + +HGNetv2: + name: 'B4' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000125 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 140 +train_dataloader: + dataset: + transforms: + policy: + epoch: 120 + collate_fn: + stop_epoch: 120 + ema_restart_decay: 0.9999 + base_size_repeat: 4 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_m_ch.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_m_ch.yml new file mode 100644 index 0000000..48d37f1 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_m_ch.yml @@ -0,0 +1,60 @@ +__include__: [ + '../../dataset/crowdhuman_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_m_crowdhuman + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B2' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 4 # 5 6 + eval_idx: -1 # -2 -3 + +HybridEncoder: + in_channels: [384, 768, 1536] + hidden_dim: 256 + depth_mult: 0.67 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000025 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 220 +train_dataloader: + dataset: + transforms: + policy: + epoch: 200 + collate_fn: + stop_epoch: 200 + ema_restart_decay: 0.9999 + base_size_repeat: 6 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_n_ch.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_n_ch.yml new file mode 100644 index 0000000..51528bc --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_n_ch.yml @@ -0,0 +1,82 @@ +__include__: [ + '../../dataset/crowdhuman_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_n_crowdhuman + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + + +HybridEncoder: + in_channels: [512, 1024] + feat_strides: [16, 32] + + # intra + hidden_dim: 128 + use_encoder_idx: [1] + dim_feedforward: 512 + + # cross + expansion: 0.34 + depth_mult: 0.5 + + +DFINETransformer: + feat_channels: [128, 128] + feat_strides: [16, 32] + hidden_dim: 128 + dim_feedforward: 512 + num_levels: 2 + + num_layers: 3 + eval_idx: -1 + + num_points: [6, 6] + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0004 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0004 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0008 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + +# Increase to search for the optimal ema +epochs: 220 +train_dataloader: + total_batch_size: 128 + dataset: + transforms: + policy: + epoch: 200 + collate_fn: + stop_epoch: 200 + ema_restart_decay: 0.9999 + base_size_repeat: ~ + +val_dataloader: + total_batch_size: 256 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_s_ch.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_s_ch.yml new file mode 100644 index 0000000..55ab8b8 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_s_ch.yml @@ -0,0 +1,65 @@ +__include__: [ + '../../dataset/crowdhuman_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_s_crowdhuman + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 3 # 4 5 6 + eval_idx: -1 # -2 -3 -4 + +HybridEncoder: + in_channels: [256, 512, 1024] + hidden_dim: 256 + depth_mult: 0.34 + expansion: 0.5 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0002 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0002 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0004 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + +# Increase to search for the optimal ema +epochs: 220 +train_dataloader: + total_batch_size: 64 + dataset: + transforms: + policy: + epoch: 200 + collate_fn: + stop_epoch: 200 + ema_restart_decay: 0.9999 + base_size_repeat: 20 + +val_dataloader: + total_batch_size: 128 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_x_ch.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_x_ch.yml new file mode 100644 index 0000000..2ab49a0 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/crowdhuman/dfine_hgnetv2_x_ch.yml @@ -0,0 +1,55 @@ +__include__: [ + '../../dataset/crowdhuman_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_x_crowdhuman + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B5' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +HybridEncoder: + hidden_dim: 384 + dim_feedforward: 2048 + +DFINETransformer: + feat_channels: [384, 384, 384] + reg_scale: 8 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000025 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 140 +train_dataloader: + dataset: + transforms: + policy: + epoch: 120 + collate_fn: + stop_epoch: 120 + ema_restart_decay: 0.9998 + base_size_repeat: 3 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_l_custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_l_custom.yml new file mode 100644 index 0000000..7526401 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_l_custom.yml @@ -0,0 +1,44 @@ +__include__: [ + '../../dataset/custom_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_l_custom + + +HGNetv2: + name: 'B4' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000125 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 80 # 72 + 2n +train_dataloader: + dataset: + transforms: + policy: + epoch: 72 + collate_fn: + stop_epoch: 72 + ema_restart_decay: 0.9999 + base_size_repeat: 4 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_m_custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_m_custom.yml new file mode 100644 index 0000000..bf31c1c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_m_custom.yml @@ -0,0 +1,60 @@ +__include__: [ + '../../dataset/custom_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_m_custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B2' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 4 # 5 6 + eval_idx: -1 # -2 -3 + +HybridEncoder: + in_channels: [384, 768, 1536] + hidden_dim: 256 + depth_mult: 0.67 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000025 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 132 # 120 + 4n +train_dataloader: + dataset: + transforms: + policy: + epoch: 120 + collate_fn: + stop_epoch: 120 + ema_restart_decay: 0.9999 + base_size_repeat: 6 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_n_custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_n_custom.yml new file mode 100644 index 0000000..6da7702 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_n_custom.yml @@ -0,0 +1,82 @@ +__include__: [ + '../../dataset/custom_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_n_custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + + +HybridEncoder: + in_channels: [512, 1024] + feat_strides: [16, 32] + + # intra + hidden_dim: 128 + use_encoder_idx: [1] + dim_feedforward: 512 + + # cross + expansion: 0.34 + depth_mult: 0.5 + + +DFINETransformer: + feat_channels: [128, 128] + feat_strides: [16, 32] + hidden_dim: 128 + dim_feedforward: 512 + num_levels: 2 + + num_layers: 3 + eval_idx: -1 + + num_points: [6, 6] + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0004 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0004 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0008 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + +# Increase to search for the optimal ema +epochs: 220 +train_dataloader: + total_batch_size: 128 + dataset: + transforms: + policy: + epoch: 200 + collate_fn: + stop_epoch: 200 + ema_restart_decay: 0.9999 + base_size_repeat: ~ + +val_dataloader: + total_batch_size: 256 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_s_custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_s_custom.yml new file mode 100644 index 0000000..285cb77 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_s_custom.yml @@ -0,0 +1,65 @@ +__include__: [ + '../../dataset/custom_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_s_custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 3 # 4 5 6 + eval_idx: -1 # -2 -3 -4 + +HybridEncoder: + in_channels: [256, 512, 1024] + hidden_dim: 256 + depth_mult: 0.34 + expansion: 0.5 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0002 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0002 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0004 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + +# Increase to search for the optimal ema +epochs: 220 +train_dataloader: + total_batch_size: 64 + dataset: + transforms: + policy: + epoch: 200 + collate_fn: + stop_epoch: 200 + ema_restart_decay: 0.9999 + base_size_repeat: 20 + +val_dataloader: + total_batch_size: 128 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_x_custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_x_custom.yml new file mode 100644 index 0000000..2ba5646 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/dfine_hgnetv2_x_custom.yml @@ -0,0 +1,55 @@ +__include__: [ + '../../dataset/custom_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_x_custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B5' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +HybridEncoder: + hidden_dim: 384 + dim_feedforward: 2048 + +DFINETransformer: + feat_channels: [384, 384, 384] + reg_scale: 8 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000025 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 80 # 72 + 2n +train_dataloader: + dataset: + transforms: + policy: + epoch: 72 + collate_fn: + stop_epoch: 72 + ema_restart_decay: 0.9998 + base_size_repeat: 3 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_l_obj2custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_l_obj2custom.yml new file mode 100644 index 0000000..ef6a95d --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_l_obj2custom.yml @@ -0,0 +1,53 @@ +__include__: [ + '../../../dataset/custom_detection.yml', + '../../../runtime.yml', + '../../include/dataloader.yml', + '../../include/optimizer.yml', + '../../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_l_obj2custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B4' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + pretrained: False + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000125 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 36 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 30 + collate_fn: + stop_epoch: 30 + ema_restart_decay: 0.9999 + base_size_repeat: 4 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_m_obj2custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_m_obj2custom.yml new file mode 100644 index 0000000..f21fbbc --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_m_obj2custom.yml @@ -0,0 +1,66 @@ +__include__: [ + '../../../dataset/custom_detection.yml', + '../../../runtime.yml', + '../../include/dataloader.yml', + '../../include/optimizer.yml', + '../../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_m_obj2custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B2' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + pretrained: False + +DFINETransformer: + num_layers: 4 # 5 6 + eval_idx: -1 # -2 -3 + +HybridEncoder: + in_channels: [384, 768, 1536] + hidden_dim: 256 + depth_mult: 0.67 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000025 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 56 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 48 + collate_fn: + stop_epoch: 48 + ema_restart_decay: 0.9999 + base_size_repeat: 6 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_s_obj2custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_s_obj2custom.yml new file mode 100644 index 0000000..9a74e34 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_s_obj2custom.yml @@ -0,0 +1,67 @@ +__include__: [ + '../../../dataset/custom_detection.yml', + '../../../runtime.yml', + '../../include/dataloader.yml', + '../../include/optimizer.yml', + '../../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_s_obj2custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + pretrained: False + +DFINETransformer: + num_layers: 3 # 4 5 6 + eval_idx: -1 # -2 -3 -4 + +HybridEncoder: + in_channels: [256, 512, 1024] + hidden_dim: 256 + depth_mult: 0.34 + expansion: 0.5 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000125 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000125 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 64 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 56 + collate_fn: + stop_epoch: 56 + ema_restart_decay: 0.9999 + base_size_repeat: 10 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_x_obj2custom.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_x_obj2custom.yml new file mode 100644 index 0000000..717812f --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/custom/objects365/dfine_hgnetv2_x_obj2custom.yml @@ -0,0 +1,62 @@ +__include__: [ + '../../../dataset/custom_detection.yml', + '../../../runtime.yml', + '../../include/dataloader.yml', + '../../include/optimizer.yml', + '../../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_x_obj2custom + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B5' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + pretrained: False + +HybridEncoder: + # intra + hidden_dim: 384 + dim_feedforward: 2048 + +DFINETransformer: + feat_channels: [384, 384, 384] + reg_scale: 8 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000025 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 36 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 30 + collate_fn: + stop_epoch: 30 + ema_restart_decay: 0.9999 + base_size_repeat: 3 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_l_coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_l_coco.yml new file mode 100644 index 0000000..c276ad5 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_l_coco.yml @@ -0,0 +1,44 @@ +__include__: [ + '../dataset/coco_detection.yml', + '../runtime.yml', + './include/dataloader.yml', + './include/optimizer.yml', + './include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_l_coco + + +HGNetv2: + name: 'B4' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000125 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 8 # 72 + 2n +train_dataloader: + dataset: + transforms: + policy: + epoch: 7 + collate_fn: + stop_epoch: 7 + ema_restart_decay: 0.9999 + base_size_repeat: 4 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_m_coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_m_coco.yml new file mode 100644 index 0000000..9837eac --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_m_coco.yml @@ -0,0 +1,60 @@ +__include__: [ + '../dataset/coco_detection.yml', + '../runtime.yml', + './include/dataloader.yml', + './include/optimizer.yml', + './include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_m_coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B2' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 4 # 5 6 + eval_idx: -1 # -2 -3 + +HybridEncoder: + in_channels: [384, 768, 1536] + hidden_dim: 256 + depth_mult: 0.67 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.00002 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.00002 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0002 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + +# Increase to search for the optimal ema +epochs: 132 # 120 + 4n +train_dataloader: + dataset: + transforms: + policy: + epoch: 120 + collate_fn: + stop_epoch: 120 + ema_restart_decay: 0.9999 + base_size_repeat: 6 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_n_coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_n_coco.yml new file mode 100644 index 0000000..f0c3a1a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_n_coco.yml @@ -0,0 +1,82 @@ +__include__: [ + '../dataset/coco_detection.yml', + '../runtime.yml', + './include/dataloader.yml', + './include/optimizer.yml', + './include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_n_coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + + +HybridEncoder: + in_channels: [512, 1024] + feat_strides: [16, 32] + + # intra + hidden_dim: 128 + use_encoder_idx: [1] + dim_feedforward: 512 + + # cross + expansion: 0.34 + depth_mult: 0.5 + + +DFINETransformer: + feat_channels: [128, 128] + feat_strides: [16, 32] + hidden_dim: 128 + dim_feedforward: 512 + num_levels: 2 + + num_layers: 3 + eval_idx: -1 + + num_points: [6, 6] + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0004 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0004 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0008 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + +# Increase to search for the optimal ema +epochs: 160 # 148 + 4n +train_dataloader: + total_batch_size: 128 + dataset: + transforms: + policy: + epoch: 148 + collate_fn: + stop_epoch: 148 + ema_restart_decay: 0.9999 + base_size_repeat: ~ + +val_dataloader: + total_batch_size: 256 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_s_coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_s_coco.yml new file mode 100644 index 0000000..e7aaa22 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_s_coco.yml @@ -0,0 +1,61 @@ +__include__: [ + '../dataset/coco_detection.yml', + '../runtime.yml', + './include/dataloader.yml', + './include/optimizer.yml', + './include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_s_coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 3 # 4 5 6 + eval_idx: -1 # -2 -3 -4 + +HybridEncoder: + in_channels: [256, 512, 1024] + hidden_dim: 256 + depth_mult: 0.34 + expansion: 0.5 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0001 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0001 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0002 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + +# Increase to search for the optimal ema +epochs: 132 # 120 + 4n +train_dataloader: + dataset: + transforms: + policy: + epoch: 120 + collate_fn: + stop_epoch: 120 + ema_restart_decay: 0.9999 + base_size_repeat: 20 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_x_coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_x_coco.yml new file mode 100644 index 0000000..136d799 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/dfine_hgnetv2_x_coco.yml @@ -0,0 +1,56 @@ +__include__: [ + '../dataset/coco_detection.yml', + '../runtime.yml', + './include/dataloader.yml', + './include/optimizer.yml', + './include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_x_coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B5' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +HybridEncoder: + # intra + hidden_dim: 384 + dim_feedforward: 2048 + +DFINETransformer: + feat_channels: [384, 384, 384] + reg_scale: 8 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000025 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +# Increase to search for the optimal ema +epochs: 8 # 72 + 2n +train_dataloader: + dataset: + transforms: + policy: + epoch: 7 + collate_fn: + stop_epoch: 7 + ema_restart_decay: 0.9998 + base_size_repeat: 3 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/dataloader.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/dataloader.yml new file mode 100644 index 0000000..3f7c67c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/dataloader.yml @@ -0,0 +1,39 @@ + +train_dataloader: + dataset: + transforms: + ops: + - {type: RandomPhotometricDistort, p: 0.5} + - {type: RandomZoomOut, fill: 0} + - {type: RandomIoUCrop, p: 0.8} + - {type: SanitizeBoundingBoxes, min_size: 1} + - {type: RandomHorizontalFlip} + - {type: Resize, size: [640, 640], } + - {type: SanitizeBoundingBoxes, min_size: 1} + - {type: ConvertPILImage, dtype: 'float32', scale: True} + - {type: ConvertBoxes, fmt: 'cxcywh', normalize: True} + policy: + name: stop_epoch + epoch: 72 # epoch in [71, ~) stop `ops` + ops: ['RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop'] + + collate_fn: + type: BatchImageCollateFunction + base_size: 640 + base_size_repeat: 3 + stop_epoch: 72 # epoch in [72, ~) stop `multiscales` + + shuffle: True + total_batch_size: 32 # total batch size equals to 32 (4 * 8) + num_workers: 4 + + +val_dataloader: + dataset: + transforms: + ops: + - {type: Resize, size: [640, 640], } + - {type: ConvertPILImage, dtype: 'float32', scale: True} + shuffle: False + total_batch_size: 64 + num_workers: 4 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/dfine_hgnetv2.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/dfine_hgnetv2.yml new file mode 100644 index 0000000..91ad4a0 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/dfine_hgnetv2.yml @@ -0,0 +1,82 @@ +task: detection + +model: DFINE +criterion: DFINECriterion +postprocessor: DFINEPostProcessor + +use_focal_loss: True +eval_spatial_size: [640, 640] # h w + +DFINE: + backbone: HGNetv2 + encoder: HybridEncoder + decoder: DFINETransformer + +HGNetv2: + pretrained: True + local_model_dir: weight/hgnetv2/ + +HybridEncoder: + in_channels: [512, 1024, 2048] + feat_strides: [8, 16, 32] + + # intra + hidden_dim: 256 + use_encoder_idx: [2] + num_encoder_layers: 1 + nhead: 8 + dim_feedforward: 1024 + dropout: 0. + enc_act: 'gelu' + + # cross + expansion: 1.0 + depth_mult: 1 + act: 'silu' + + +DFINETransformer: + feat_channels: [256, 256, 256] + feat_strides: [8, 16, 32] + hidden_dim: 256 + num_levels: 3 + + num_layers: 6 + eval_idx: -1 + num_queries: 300 + + num_denoising: 100 + label_noise_ratio: 0.5 + box_noise_scale: 1.0 + + # NEW + reg_max: 32 + reg_scale: 4 + + # Auxiliary decoder layers dimension scaling + # "eg. If num_layers: 6 eval_idx: -4, + # then layer 3, 4, 5 are auxiliary decoder layers." + layer_scale: 1 # 2 + + + num_points: [3, 6, 3] # [4, 4, 4] [3, 6, 3] + cross_attn_method: default # default, discrete + query_select_method: default # default, agnostic + + +DFINEPostProcessor: + num_top_queries: 300 + + +DFINECriterion: + weight_dict: {loss_vfl: 1, loss_bbox: 5, loss_giou: 2, loss_fgl: 0.15, loss_ddf: 1.5} + losses: ['vfl', 'boxes', 'local'] + alpha: 0.75 + gamma: 2.0 + reg_max: 32 + + matcher: + type: HungarianMatcher + weight_dict: {cost_class: 2, cost_bbox: 5, cost_giou: 2} + alpha: 0.25 + gamma: 2.0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/optimizer.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/optimizer.yml new file mode 100644 index 0000000..8ec87c7 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/include/optimizer.yml @@ -0,0 +1,36 @@ +use_amp: True +use_ema: True +ema: + type: ModelEMA + decay: 0.9999 + warmups: 1000 + start: 0 + + +epochs: 72 +clip_max_norm: 0.1 + + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm).*$' + lr: 0.0000125 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +lr_scheduler: + type: MultiStepLR + milestones: [500] + gamma: 0.1 + +lr_warmup_scheduler: + type: LinearWarmup + warmup_duration: 500 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_l_obj2coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_l_obj2coco.yml new file mode 100644 index 0000000..f4fda63 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_l_obj2coco.yml @@ -0,0 +1,52 @@ +__include__: [ + '../../dataset/coco_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_l_obj2coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B4' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000125 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 36 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 30 + collate_fn: + stop_epoch: 30 + ema_restart_decay: 0.9999 + base_size_repeat: 4 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml new file mode 100644 index 0000000..c7a2876 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_l_obj365.yml @@ -0,0 +1,49 @@ +__include__: [ + '../../dataset/obj365_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_l_obj365 + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B4' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000125 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 +# weight_decay: 0.00005 # Faster convergence (optional) + + +epochs: 24 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 500 + collate_fn: + stop_epoch: 500 + base_size_repeat: 4 + +checkpoint_freq: 1 +print_freq: 1000 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml new file mode 100644 index 0000000..fd77cca --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml @@ -0,0 +1,65 @@ +__include__: [ + '../../dataset/coco_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_m_obj2coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B2' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 4 # 5 6 + eval_idx: -1 # -2 -3 + +HybridEncoder: + in_channels: [384, 768, 1536] + hidden_dim: 256 + depth_mult: 0.67 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000025 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 56 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 48 + collate_fn: + stop_epoch: 48 + ema_restart_decay: 0.9999 + base_size_repeat: 6 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_m_obj365.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_m_obj365.yml new file mode 100644 index 0000000..108d4e6 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_m_obj365.yml @@ -0,0 +1,62 @@ +__include__: [ + '../../dataset/obj365_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: .output/dfine_hgnetv2_s_obj365 + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B2' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 4 # 5 6 + eval_idx: -1 # -2 -3 + +HybridEncoder: + in_channels: [384, 768, 1536] + hidden_dim: 256 + depth_mult: 0.67 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000025 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000025 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 +# weight_decay: 0.00005 # Faster convergence (optional) + + +epochs: 36 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 500 + collate_fn: + stop_epoch: 500 + base_size_repeat: 6 + +checkpoint_freq: 1 +print_freq: 1000 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_n_obj2coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_n_obj2coco.yml new file mode 100644 index 0000000..6febf65 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_n_obj2coco.yml @@ -0,0 +1,88 @@ +__include__: [ + '../../dataset/coco_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_n_obj2coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + + +HybridEncoder: + in_channels: [512, 1024] + feat_strides: [16, 32] + + # intra + hidden_dim: 128 + use_encoder_idx: [1] + dim_feedforward: 512 + + # cross + expansion: 0.34 + depth_mult: 0.5 + + +DFINETransformer: + feat_channels: [128, 128] + feat_strides: [16, 32] + hidden_dim: 128 + dim_feedforward: 512 + num_levels: 2 + + num_layers: 3 + eval_idx: -1 + + num_points: [6, 6] + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0004 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0004 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0008 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + + +epochs: 64 # Early stop +train_dataloader: + total_batch_size: 128 + dataset: + transforms: + policy: + epoch: 56 + collate_fn: + stop_epoch: 56 + ema_restart_decay: 0.9999 + base_size_repeat: ~ + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 + +val_dataloader: + total_batch_size: 256 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_n_obj365.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_n_obj365.yml new file mode 100644 index 0000000..b5f148a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_n_obj365.yml @@ -0,0 +1,84 @@ +__include__: [ + '../../dataset/obj365_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_n_obj365 + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + + +HybridEncoder: + in_channels: [512, 1024] + feat_strides: [16, 32] + + # intra + hidden_dim: 128 + use_encoder_idx: [1] + dim_feedforward: 512 + + # cross + expansion: 0.34 + depth_mult: 0.5 + + +DFINETransformer: + feat_channels: [128, 128] + feat_strides: [16, 32] + hidden_dim: 128 + dim_feedforward: 512 + num_levels: 2 + + num_layers: 3 + eval_idx: -1 + + num_points: [6, 6] + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0004 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.0004 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.0008 + betas: [0.9, 0.999] + weight_decay: 0.0001 + + + +epochs: 48 # Early stop +train_dataloader: + total_batch_size: 128 + dataset: + transforms: + policy: + epoch: 500 + collate_fn: + stop_epoch: 500 + base_size_repeat: ~ + +checkpoint_freq: 1 +print_freq: 500 + +val_dataloader: + total_batch_size: 256 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_s_obj2coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_s_obj2coco.yml new file mode 100644 index 0000000..9fa01e2 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_s_obj2coco.yml @@ -0,0 +1,66 @@ +__include__: [ + '../../dataset/coco_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_s_obj2coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 3 # 4 5 6 + eval_idx: -1 # -2 -3 -4 + +HybridEncoder: + in_channels: [256, 512, 1024] + hidden_dim: 256 + depth_mult: 0.34 + expansion: 0.5 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000125 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000125 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 64 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 56 + collate_fn: + stop_epoch: 56 + ema_restart_decay: 0.9999 + base_size_repeat: 10 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_s_obj365.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_s_obj365.yml new file mode 100644 index 0000000..1e98a0d --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_s_obj365.yml @@ -0,0 +1,63 @@ +__include__: [ + '../../dataset/obj365_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_s_obj365 + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B0' + return_idx: [1, 2, 3] + freeze_at: -1 + freeze_norm: False + use_lab: True + +DFINETransformer: + num_layers: 3 # 4 5 6 + eval_idx: -1 # -2 -3 -4 + +HybridEncoder: + in_channels: [256, 512, 1024] + hidden_dim: 256 + depth_mult: 0.34 + expansion: 0.5 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.000125 + - + params: '^(?=.*backbone)(?=.*norm|bn).*$' + lr: 0.000125 + weight_decay: 0. + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 +# weight_decay: 0.00005 # Faster convergence (optional) + + +epochs: 36 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 500 + collate_fn: + stop_epoch: 500 + base_size_repeat: 20 + +checkpoint_freq: 1 +print_freq: 1000 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_x_obj2coco.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_x_obj2coco.yml new file mode 100644 index 0000000..738c75d --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_x_obj2coco.yml @@ -0,0 +1,61 @@ +__include__: [ + '../../dataset/coco_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_x_obj2coco + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B5' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +HybridEncoder: + # intra + hidden_dim: 384 + dim_feedforward: 2048 + +DFINETransformer: + feat_channels: [384, 384, 384] + reg_scale: 8 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000025 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 + + +epochs: 36 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 30 + collate_fn: + stop_epoch: 30 + ema_restart_decay: 0.9999 + base_size_repeat: 3 + +ema: + warmups: 0 + +lr_warmup_scheduler: + warmup_duration: 0 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_x_obj365.yml b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_x_obj365.yml new file mode 100644 index 0000000..0449d72 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/dfine/objects365/dfine_hgnetv2_x_obj365.yml @@ -0,0 +1,58 @@ +__include__: [ + '../../dataset/obj365_detection.yml', + '../../runtime.yml', + '../include/dataloader.yml', + '../include/optimizer.yml', + '../include/dfine_hgnetv2.yml', +] + +output_dir: ./output/dfine_hgnetv2_x_obj365 + + +DFINE: + backbone: HGNetv2 + +HGNetv2: + name: 'B5' + return_idx: [1, 2, 3] + freeze_stem_only: True + freeze_at: 0 + freeze_norm: True + +HybridEncoder: + # intra + hidden_dim: 384 + dim_feedforward: 2048 + +DFINETransformer: + feat_channels: [384, 384, 384] + reg_scale: 8 + +optimizer: + type: AdamW + params: + - + params: '^(?=.*backbone)(?!.*norm|bn).*$' + lr: 0.0000025 + - + params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$' + weight_decay: 0. + + lr: 0.00025 + betas: [0.9, 0.999] + weight_decay: 0.000125 +# weight_decay: 0.00005 # Faster convergence (optional) + + +epochs: 24 # Early stop +train_dataloader: + dataset: + transforms: + policy: + epoch: 500 + collate_fn: + stop_epoch: 500 + base_size_repeat: 3 + +checkpoint_freq: 1 +print_freq: 1000 diff --git a/backend/apps/algorithms/algorithm/DFINE/configs/runtime.yml b/backend/apps/algorithms/algorithm/DFINE/configs/runtime.yml new file mode 100644 index 0000000..b99af4a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/configs/runtime.yml @@ -0,0 +1,24 @@ +print_freq: 100 +output_dir: './logs' +checkpoint_freq: 12 + + +sync_bn: True +find_unused_parameters: False + + +use_amp: False +scaler: + type: GradScaler + enabled: True + + +use_ema: False +ema: + type: ModelEMA + decay: 0.9999 + warmups: 1000 + +use_wandb: False +project_name: D-FINE # for wandb +exp_name: baseline # wandb experiment name diff --git a/backend/apps/algorithms/algorithm/DFINE/reference/convert_weight.py b/backend/apps/algorithms/algorithm/DFINE/reference/convert_weight.py new file mode 100644 index 0000000..6dd3207 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/reference/convert_weight.py @@ -0,0 +1,30 @@ +import argparse +import os + +import torch + + +def save_only_ema_weights(checkpoint_file): + """Extract and save only the EMA weights.""" + checkpoint = torch.load(checkpoint_file, map_location="cpu") + + weights = {} + if "ema" in checkpoint: + weights["model"] = checkpoint["ema"]["module"] + else: + raise ValueError("The checkpoint does not contain 'ema'.") + + dir_name, base_name = os.path.split(checkpoint_file) + name, ext = os.path.splitext(base_name) + output_file = os.path.join(dir_name, f"{name}_converted{ext}") + + torch.save(weights, output_file) + print(f"EMA weights saved to {output_file}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Extract and save only EMA weights.") + parser.add_argument("checkpoint_file", type=str, help="Path to the input checkpoint file.") + + args = parser.parse_args() + save_only_ema_weights(args.checkpoint_file) diff --git a/backend/apps/algorithms/algorithm/DFINE/reference/safe_training.sh b/backend/apps/algorithms/algorithm/DFINE/reference/safe_training.sh new file mode 100644 index 0000000..d3c752a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/reference/safe_training.sh @@ -0,0 +1,97 @@ +#!/bin/bash + +# Function to display the menu for selecting model size +select_model_size() { + echo "Select model size:" + select size in s m l x; do + case $size in + s|m|l|x) + echo "You selected model size: $size" + MODEL_SIZE=$size + break + ;; + *) + echo "Invalid selection. Please try again." + ;; + esac + done +} + +# Function to display the menu for selecting task +select_task() { + echo "Select task:" + select task in obj365 obj2coco coco; do + case $task in + obj365|obj2coco|coco) + echo "You selected task: $task" + TASK=$task + break + ;; + *) + echo "Invalid selection. Please try again." + ;; + esac + done +} + +# Function to ask if the user wants to save logs to a txt file +ask_save_logs() { + while true; do + read -p "Do you want to save logs to a txt file? (y/n): " yn + case $yn in + [Yy]* ) + SAVE_LOGS=true + break + ;; + [Nn]* ) + SAVE_LOGS=false + break + ;; + * ) echo "Please answer yes or no.";; + esac + done +} + +# Call the functions to let the user select +select_model_size +select_task +ask_save_logs + +# Set config file and output directory based on selection +if [ "$TASK" = "coco" ]; then + CONFIG_FILE="configs/dfine/dfine_hgnetv2_${MODEL_SIZE}_${TASK}.yml" +else + CONFIG_FILE="configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_${TASK}.yml" +fi + +OUTPUT_DIR="output/${MODEL_SIZE}_${TASK}" + +# Construct the training command +TRAIN_CMD="CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c $CONFIG_FILE --use-amp --seed=0 --output-dir $OUTPUT_DIR" + +# Append log redirection if SAVE_LOGS is true +if [ "$SAVE_LOGS" = true ]; then + LOG_FILE="${MODEL_SIZE}_${TASK}.txt" + TRAIN_CMD="$TRAIN_CMD &> \"$LOG_FILE\" 2>&1 &" +else + TRAIN_CMD="$TRAIN_CMD &" +fi + +# Run the training command +eval $TRAIN_CMD +if [ $? -ne 0 ]; then + echo "First training failed, restarting with resume option..." + while true; do + RESUME_CMD="CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c $CONFIG_FILE --use-amp --seed=0 --output-dir $OUTPUT_DIR -r ${OUTPUT_DIR}/last.pth" + if [ "$SAVE_LOGS" = true ]; then + LOG_FILE="${MODEL_SIZE}_${TASK}_2.txt" + RESUME_CMD="$RESUME_CMD &> \"$LOG_FILE\" 2>&1 &" + else + RESUME_CMD="$RESUME_CMD &" + fi + eval $RESUME_CMD + if [ $? -eq 0 ]; then + break + fi + done +fi diff --git a/backend/apps/algorithms/algorithm/DFINE/requirements.txt b/backend/apps/algorithms/algorithm/DFINE/requirements.txt new file mode 100644 index 0000000..aadfb81 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/requirements.txt @@ -0,0 +1,9 @@ +torch>=2.0.1 +torchvision>=0.15.2 +faster-coco-eval>=1.6.6 +PyYAML +tensorboard +scipy +calflops +transformers +loguru diff --git a/backend/apps/algorithms/algorithm/DFINE/src/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/__init__.py new file mode 100644 index 0000000..15e3d5a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +# for register purpose +from . import data, nn, optim, zoo diff --git a/backend/apps/algorithms/algorithm/DFINE/src/core/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/core/__init__.py new file mode 100644 index 0000000..a938d56 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/core/__init__.py @@ -0,0 +1,9 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from ._config import BaseConfig +from .workspace import GLOBAL_CONFIG, create, register +from .yaml_config import YAMLConfig +from .yaml_utils import * diff --git a/backend/apps/algorithms/algorithm/DFINE/src/core/_config.py b/backend/apps/algorithms/algorithm/DFINE/src/core/_config.py new file mode 100644 index 0000000..3bef075 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/core/_config.py @@ -0,0 +1,299 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from pathlib import Path +from typing import Callable, Dict, List + +import torch +import torch.nn as nn +from torch.cuda.amp.grad_scaler import GradScaler +from torch.optim import Optimizer +from torch.optim.lr_scheduler import LRScheduler +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter + +__all__ = [ + "BaseConfig", +] + + +class BaseConfig(object): + # TODO property + + def __init__(self) -> None: + super().__init__() + + self.task: str = None + + # instance / function + self._model: nn.Module = None + self._postprocessor: nn.Module = None + self._criterion: nn.Module = None + self._optimizer: Optimizer = None + self._lr_scheduler: LRScheduler = None + self._lr_warmup_scheduler: LRScheduler = None + self._train_dataloader: DataLoader = None + self._val_dataloader: DataLoader = None + self._ema: nn.Module = None + self._scaler: GradScaler = None + self._train_dataset: Dataset = None + self._val_dataset: Dataset = None + self._collate_fn: Callable = None + self._evaluator: Callable[[nn.Module, DataLoader, str],] = None + self._writer: SummaryWriter = None + + # dataset + self.num_workers: int = 0 + self.batch_size: int = None + self._train_batch_size: int = None + self._val_batch_size: int = None + self._train_shuffle: bool = None + self._val_shuffle: bool = None + + # runtime + self.resume: str = None + self.tuning: str = None + + self.epochs: int = None + self.last_epoch: int = -1 + + self.use_amp: bool = False + self.use_ema: bool = False + self.ema_decay: float = 0.9999 + self.ema_warmups: int = 2000 + self.sync_bn: bool = False + self.clip_max_norm: float = 0.0 + self.find_unused_parameters: bool = None + + self.seed: int = None + self.print_freq: int = None + self.checkpoint_freq: int = 1 + self.output_dir: str = None + self.summary_dir: str = None + self.device: str = "" + + @property + def model(self) -> nn.Module: + return self._model + + @model.setter + def model(self, m): + assert isinstance(m, nn.Module), f"{type(m)} != nn.Module, please check your model class" + self._model = m + + @property + def postprocessor(self) -> nn.Module: + return self._postprocessor + + @postprocessor.setter + def postprocessor(self, m): + assert isinstance(m, nn.Module), f"{type(m)} != nn.Module, please check your model class" + self._postprocessor = m + + @property + def criterion(self) -> nn.Module: + return self._criterion + + @criterion.setter + def criterion(self, m): + assert isinstance(m, nn.Module), f"{type(m)} != nn.Module, please check your model class" + self._criterion = m + + @property + def optimizer(self) -> Optimizer: + return self._optimizer + + @optimizer.setter + def optimizer(self, m): + assert isinstance( + m, Optimizer + ), f"{type(m)} != optim.Optimizer, please check your model class" + self._optimizer = m + + @property + def lr_scheduler(self) -> LRScheduler: + return self._lr_scheduler + + @lr_scheduler.setter + def lr_scheduler(self, m): + assert isinstance( + m, LRScheduler + ), f"{type(m)} != LRScheduler, please check your model class" + self._lr_scheduler = m + + @property + def lr_warmup_scheduler(self) -> LRScheduler: + return self._lr_warmup_scheduler + + @lr_warmup_scheduler.setter + def lr_warmup_scheduler(self, m): + self._lr_warmup_scheduler = m + + @property + def train_dataloader(self) -> DataLoader: + if self._train_dataloader is None and self.train_dataset is not None: + loader = DataLoader( + self.train_dataset, + batch_size=self.train_batch_size, + num_workers=self.num_workers, + collate_fn=self.collate_fn, + shuffle=self.train_shuffle, + ) + loader.shuffle = self.train_shuffle + self._train_dataloader = loader + + return self._train_dataloader + + @train_dataloader.setter + def train_dataloader(self, loader): + self._train_dataloader = loader + + @property + def val_dataloader(self) -> DataLoader: + if self._val_dataloader is None and self.val_dataset is not None: + loader = DataLoader( + self.val_dataset, + batch_size=self.val_batch_size, + num_workers=self.num_workers, + drop_last=False, + collate_fn=self.collate_fn, + shuffle=self.val_shuffle, + persistent_workers=True, + ) + loader.shuffle = self.val_shuffle + self._val_dataloader = loader + + return self._val_dataloader + + @val_dataloader.setter + def val_dataloader(self, loader): + self._val_dataloader = loader + + @property + def ema(self) -> nn.Module: + if self._ema is None and self.use_ema and self.model is not None: + from ..optim import ModelEMA + + self._ema = ModelEMA(self.model, self.ema_decay, self.ema_warmups) + return self._ema + + @ema.setter + def ema(self, obj): + self._ema = obj + + @property + def scaler(self) -> GradScaler: + if self._scaler is None and self.use_amp and torch.cuda.is_available(): + self._scaler = GradScaler() + return self._scaler + + @scaler.setter + def scaler(self, obj: GradScaler): + self._scaler = obj + + @property + def val_shuffle(self) -> bool: + if self._val_shuffle is None: + print("warning: set default val_shuffle=False") + return False + return self._val_shuffle + + @val_shuffle.setter + def val_shuffle(self, shuffle): + assert isinstance(shuffle, bool), "shuffle must be bool" + self._val_shuffle = shuffle + + @property + def train_shuffle(self) -> bool: + if self._train_shuffle is None: + print("warning: set default train_shuffle=True") + return True + return self._train_shuffle + + @train_shuffle.setter + def train_shuffle(self, shuffle): + assert isinstance(shuffle, bool), "shuffle must be bool" + self._train_shuffle = shuffle + + @property + def train_batch_size(self) -> int: + if self._train_batch_size is None and isinstance(self.batch_size, int): + print(f"warning: set train_batch_size=batch_size={self.batch_size}") + return self.batch_size + return self._train_batch_size + + @train_batch_size.setter + def train_batch_size(self, batch_size): + assert isinstance(batch_size, int), "batch_size must be int" + self._train_batch_size = batch_size + + @property + def val_batch_size(self) -> int: + if self._val_batch_size is None: + print(f"warning: set val_batch_size=batch_size={self.batch_size}") + return self.batch_size + return self._val_batch_size + + @val_batch_size.setter + def val_batch_size(self, batch_size): + assert isinstance(batch_size, int), "batch_size must be int" + self._val_batch_size = batch_size + + @property + def train_dataset(self) -> Dataset: + return self._train_dataset + + @train_dataset.setter + def train_dataset(self, dataset): + assert isinstance(dataset, Dataset), f"{type(dataset)} must be Dataset" + self._train_dataset = dataset + + @property + def val_dataset(self) -> Dataset: + return self._val_dataset + + @val_dataset.setter + def val_dataset(self, dataset): + assert isinstance(dataset, Dataset), f"{type(dataset)} must be Dataset" + self._val_dataset = dataset + + @property + def collate_fn(self) -> Callable: + return self._collate_fn + + @collate_fn.setter + def collate_fn(self, fn): + assert isinstance(fn, Callable), f"{type(fn)} must be Callable" + self._collate_fn = fn + + @property + def evaluator(self) -> Callable: + return self._evaluator + + @evaluator.setter + def evaluator(self, fn): + assert isinstance(fn, Callable), f"{type(fn)} must be Callable" + self._evaluator = fn + + @property + def writer(self) -> SummaryWriter: + if self._writer is None: + if self.summary_dir: + self._writer = SummaryWriter(self.summary_dir) + elif self.output_dir: + self._writer = SummaryWriter(Path(self.output_dir) / "summary") + return self._writer + + @writer.setter + def writer(self, m): + assert isinstance(m, SummaryWriter), f"{type(m)} must be SummaryWriter" + self._writer = m + + def __repr__(self): + s = "" + for k, v in self.__dict__.items(): + if not k.startswith("_"): + s += f"{k}: {v}\n" + return s diff --git a/backend/apps/algorithms/algorithm/DFINE/src/core/workspace.py b/backend/apps/algorithms/algorithm/DFINE/src/core/workspace.py new file mode 100644 index 0000000..b99e317 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/core/workspace.py @@ -0,0 +1,178 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import functools +import importlib +import inspect +from collections import defaultdict +from typing import Any, Dict, List, Optional + +GLOBAL_CONFIG = defaultdict(dict) + + +def register(dct: Any = GLOBAL_CONFIG, name=None, force=False): + """ + dct: + if dct is Dict, register foo into dct as key-value pair + if dct is Clas, register as modules attibute + force + whether force register. + """ + + def decorator(foo): + register_name = foo.__name__ if name is None else name + if not force: + if inspect.isclass(dct): + assert not hasattr(dct, foo.__name__), f"module {dct.__name__} has {foo.__name__}" + else: + assert foo.__name__ not in dct, f"{foo.__name__} has been already registered" + + if inspect.isfunction(foo): + + @functools.wraps(foo) + def wrap_func(*args, **kwargs): + return foo(*args, **kwargs) + + if isinstance(dct, dict): + dct[foo.__name__] = wrap_func + elif inspect.isclass(dct): + setattr(dct, foo.__name__, wrap_func) + else: + raise AttributeError("") + return wrap_func + + elif inspect.isclass(foo): + dct[register_name] = extract_schema(foo) + + else: + raise ValueError(f"Do not support {type(foo)} register") + + return foo + + return decorator + + +def extract_schema(module: type): + """ + Args: + module (type), + Return: + Dict, + """ + argspec = inspect.getfullargspec(module.__init__) + arg_names = [arg for arg in argspec.args if arg != "self"] + num_defualts = len(argspec.defaults) if argspec.defaults is not None else 0 + num_requires = len(arg_names) - num_defualts + + schame = dict() + schame["_name"] = module.__name__ + schame["_pymodule"] = importlib.import_module(module.__module__) + schame["_inject"] = getattr(module, "__inject__", []) + schame["_share"] = getattr(module, "__share__", []) + schame["_kwargs"] = {} + for i, name in enumerate(arg_names): + if name in schame["_share"]: + assert i >= num_requires, "share config must have default value." + value = argspec.defaults[i - num_requires] + + elif i >= num_requires: + value = argspec.defaults[i - num_requires] + + else: + value = None + + schame[name] = value + schame["_kwargs"][name] = value + + return schame + + +def create(type_or_name, global_cfg=GLOBAL_CONFIG, **kwargs): + """ """ + assert type(type_or_name) in (type, str), "create should be modules or name." + + name = type_or_name if isinstance(type_or_name, str) else type_or_name.__name__ + + if name in global_cfg: + if hasattr(global_cfg[name], "__dict__"): + return global_cfg[name] + else: + raise ValueError("The module {} is not registered".format(name)) + + cfg = global_cfg[name] + + if isinstance(cfg, dict) and "type" in cfg: + _cfg: dict = global_cfg[cfg["type"]] + # clean args + _keys = [k for k in _cfg.keys() if not k.startswith("_")] + for _arg in _keys: + del _cfg[_arg] + _cfg.update(_cfg["_kwargs"]) # restore default args + _cfg.update(cfg) # load config args + _cfg.update(kwargs) # TODO recive extra kwargs + name = _cfg.pop("type") # pop extra key `type` (from cfg) + + return create(name, global_cfg) + + module = getattr(cfg["_pymodule"], name) + module_kwargs = {} + module_kwargs.update(cfg) + + # shared var + for k in cfg["_share"]: + if k in global_cfg: + module_kwargs[k] = global_cfg[k] + else: + module_kwargs[k] = cfg[k] + + # inject + for k in cfg["_inject"]: + _k = cfg[k] + + if _k is None: + continue + + if isinstance(_k, str): + if _k not in global_cfg: + raise ValueError(f"Missing inject config of {_k}.") + + _cfg = global_cfg[_k] + + if isinstance(_cfg, dict): + module_kwargs[k] = create(_cfg["_name"], global_cfg) + else: + module_kwargs[k] = _cfg + + elif isinstance(_k, dict): + if "type" not in _k.keys(): + raise ValueError("Missing inject for `type` style.") + + _type = str(_k["type"]) + if _type not in global_cfg: + raise ValueError(f"Missing {_type} in inspect stage.") + + # TODO + _cfg: dict = global_cfg[_type] + # clean args + _keys = [k for k in _cfg.keys() if not k.startswith("_")] + for _arg in _keys: + del _cfg[_arg] + _cfg.update(_cfg["_kwargs"]) # restore default values + _cfg.update(_k) # load config args + name = _cfg.pop("type") # pop extra key (`type` from _k) + module_kwargs[k] = create(name, global_cfg) + + else: + raise ValueError(f"Inject does not support {_k}") + + # TODO hard code + module_kwargs = {k: v for k, v in module_kwargs.items() if not k.startswith("_")} + + # TODO for **kwargs + # extra_args = set(module_kwargs.keys()) - set(arg_names) + # if len(extra_args) > 0: + # raise RuntimeError(f'Error: unknown args {extra_args} for {module}') + + return module(**module_kwargs) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/core/yaml_config.py b/backend/apps/algorithms/algorithm/DFINE/src/core/yaml_config.py new file mode 100644 index 0000000..0f7faf4 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/core/yaml_config.py @@ -0,0 +1,187 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import copy +import re + +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader + +from ._config import BaseConfig +from .workspace import create +from .yaml_utils import load_config, merge_config, merge_dict + + +class YAMLConfig(BaseConfig): + def __init__(self, cfg_path: str, **kwargs) -> None: + super().__init__() + + cfg = load_config(cfg_path) + cfg = merge_dict(cfg, kwargs) + + self.yaml_cfg = copy.deepcopy(cfg) + + for k in super().__dict__: + if not k.startswith("_") and k in cfg: + self.__dict__[k] = cfg[k] + + @property + def global_cfg(self): + return merge_config(self.yaml_cfg, inplace=False, overwrite=False) + + @property + def model(self) -> torch.nn.Module: + if self._model is None and "model" in self.yaml_cfg: + self._model = create(self.yaml_cfg["model"], self.global_cfg) + return super().model + + @property + def postprocessor(self) -> torch.nn.Module: + if self._postprocessor is None and "postprocessor" in self.yaml_cfg: + self._postprocessor = create(self.yaml_cfg["postprocessor"], self.global_cfg) + return super().postprocessor + + @property + def criterion(self) -> torch.nn.Module: + if self._criterion is None and "criterion" in self.yaml_cfg: + self._criterion = create(self.yaml_cfg["criterion"], self.global_cfg) + return super().criterion + + @property + def optimizer(self) -> optim.Optimizer: + if self._optimizer is None and "optimizer" in self.yaml_cfg: + params = self.get_optim_params(self.yaml_cfg["optimizer"], self.model) + self._optimizer = create("optimizer", self.global_cfg, params=params) + return super().optimizer + + @property + def lr_scheduler(self) -> optim.lr_scheduler.LRScheduler: + if self._lr_scheduler is None and "lr_scheduler" in self.yaml_cfg: + self._lr_scheduler = create("lr_scheduler", self.global_cfg, optimizer=self.optimizer) + print(f"Initial lr: {self._lr_scheduler.get_last_lr()}") + return super().lr_scheduler + + @property + def lr_warmup_scheduler(self) -> optim.lr_scheduler.LRScheduler: + if self._lr_warmup_scheduler is None and "lr_warmup_scheduler" in self.yaml_cfg: + self._lr_warmup_scheduler = create( + "lr_warmup_scheduler", self.global_cfg, lr_scheduler=self.lr_scheduler + ) + return super().lr_warmup_scheduler + + @property + def train_dataloader(self) -> DataLoader: + if self._train_dataloader is None and "train_dataloader" in self.yaml_cfg: + self._train_dataloader = self.build_dataloader("train_dataloader") + return super().train_dataloader + + @property + def val_dataloader(self) -> DataLoader: + if self._val_dataloader is None and "val_dataloader" in self.yaml_cfg: + self._val_dataloader = self.build_dataloader("val_dataloader") + return super().val_dataloader + + @property + def ema(self) -> torch.nn.Module: + if self._ema is None and self.yaml_cfg.get("use_ema", False): + self._ema = create("ema", self.global_cfg, model=self.model) + return super().ema + + @property + def scaler(self): + if self._scaler is None and self.yaml_cfg.get("use_amp", False): + self._scaler = create("scaler", self.global_cfg) + return super().scaler + + @property + def evaluator(self): + if self._evaluator is None and "evaluator" in self.yaml_cfg: + if self.yaml_cfg["evaluator"]["type"] == "CocoEvaluator": + from ..data import get_coco_api_from_dataset + + base_ds = get_coco_api_from_dataset(self.val_dataloader.dataset) + self._evaluator = create("evaluator", self.global_cfg, coco_gt=base_ds) + else: + raise NotImplementedError(f"{self.yaml_cfg['evaluator']['type']}") + return super().evaluator + + @property + def use_wandb(self) -> bool: + return self.yaml_cfg.get("use_wandb", False) + + @staticmethod + def get_optim_params(cfg: dict, model: nn.Module): + """ + E.g.: + ^(?=.*a)(?=.*b).*$ means including a and b + ^(?=.*(?:a|b)).*$ means including a or b + ^(?=.*a)(?!.*b).*$ means including a, but not b + """ + assert "type" in cfg, "" + cfg = copy.deepcopy(cfg) + + if "params" not in cfg: + return model.parameters() + + assert isinstance(cfg["params"], list), "" + + param_groups = [] + visited = [] + for pg in cfg["params"]: + pattern = pg["params"] + params = { + k: v + for k, v in model.named_parameters() + if v.requires_grad and len(re.findall(pattern, k)) > 0 + } + pg["params"] = params.values() + param_groups.append(pg) + visited.extend(list(params.keys())) + # print(params.keys()) + + names = [k for k, v in model.named_parameters() if v.requires_grad] + + if len(visited) < len(names): + unseen = set(names) - set(visited) + params = {k: v for k, v in model.named_parameters() if v.requires_grad and k in unseen} + param_groups.append({"params": params.values()}) + visited.extend(list(params.keys())) + # print(params.keys()) + + assert len(visited) == len(names), "" + + return param_groups + + @staticmethod + def get_rank_batch_size(cfg): + """compute batch size for per rank if total_batch_size is provided.""" + assert ("total_batch_size" in cfg or "batch_size" in cfg) and not ( + "total_batch_size" in cfg and "batch_size" in cfg + ), "`batch_size` or `total_batch_size` should be choosed one" + + total_batch_size = cfg.get("total_batch_size", None) + if total_batch_size is None: + bs = cfg.get("batch_size") + else: + from ..misc import dist_utils + + assert ( + total_batch_size % dist_utils.get_world_size() == 0 + ), "total_batch_size should be divisible by world size" + bs = total_batch_size // dist_utils.get_world_size() + return bs + + def build_dataloader(self, name: str): + bs = self.get_rank_batch_size(self.yaml_cfg[name]) + global_cfg = self.global_cfg + if "total_batch_size" in global_cfg[name]: + # pop unexpected key for dataloader init + _ = global_cfg[name].pop("total_batch_size") + print(f"building {name} with batch_size={bs}...") + loader = create(name, global_cfg, batch_size=bs) + loader.shuffle = self.yaml_cfg[name].get("shuffle", False) + return loader diff --git a/backend/apps/algorithms/algorithm/DFINE/src/core/yaml_utils.py b/backend/apps/algorithms/algorithm/DFINE/src/core/yaml_utils.py new file mode 100644 index 0000000..b77e071 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/core/yaml_utils.py @@ -0,0 +1,126 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import copy +import os +from typing import Any, Dict, List, Optional + +import yaml + +from .workspace import GLOBAL_CONFIG + +__all__ = [ + "load_config", + "merge_config", + "merge_dict", + "parse_cli", +] + + +INCLUDE_KEY = "__include__" + + +def load_config(file_path, cfg=dict()): + """load config""" + _, ext = os.path.splitext(file_path) + assert ext in [".yml", ".yaml"], "only support yaml files" + + with open(file_path) as f: + file_cfg = yaml.load(f, Loader=yaml.Loader) + if file_cfg is None: + return {} + + if INCLUDE_KEY in file_cfg: + base_yamls = list(file_cfg[INCLUDE_KEY]) + for base_yaml in base_yamls: + if base_yaml.startswith("~"): + base_yaml = os.path.expanduser(base_yaml) + + if not base_yaml.startswith("/"): + base_yaml = os.path.join(os.path.dirname(file_path), base_yaml) + + with open(base_yaml) as f: + base_cfg = load_config(base_yaml, cfg) + merge_dict(cfg, base_cfg) + + return merge_dict(cfg, file_cfg) + + +def merge_dict(dct, another_dct, inplace=True) -> Dict: + """merge another_dct into dct""" + + def _merge(dct, another) -> Dict: + for k in another: + if k in dct and isinstance(dct[k], dict) and isinstance(another[k], dict): + _merge(dct[k], another[k]) + else: + dct[k] = another[k] + + return dct + + if not inplace: + dct = copy.deepcopy(dct) + + return _merge(dct, another_dct) + + +def dictify(s: str, v: Any) -> Dict: + if "." not in s: + return {s: v} + key, rest = s.split(".", 1) + return {key: dictify(rest, v)} + + +def parse_cli(nargs: List[str]) -> Dict: + """ + parse command-line arguments + convert `a.c=3 b=10` to `{'a': {'c': 3}, 'b': 10}` + """ + cfg = {} + if nargs is None or len(nargs) == 0: + return cfg + + for s in nargs: + s = s.strip() + k, v = s.split("=", 1) + d = dictify(k, yaml.load(v, Loader=yaml.Loader)) + cfg = merge_dict(cfg, d) + + return cfg + + +def merge_config(cfg, another_cfg=GLOBAL_CONFIG, inplace: bool = False, overwrite: bool = False): + """ + Merge another_cfg into cfg, return the merged config + + Example: + + cfg1 = load_config('./dfine_r18vd_6x_coco.yml') + cfg1 = merge_config(cfg, inplace=True) + + cfg2 = load_config('./dfine_r50vd_6x_coco.yml') + cfg2 = merge_config(cfg2, inplace=True) + + model1 = create(cfg1['model'], cfg1) + model2 = create(cfg2['model'], cfg2) + """ + + def _merge(dct, another): + for k in another: + if k not in dct: + dct[k] = another[k] + + elif isinstance(dct[k], dict) and isinstance(another[k], dict): + _merge(dct[k], another[k]) + + elif overwrite: + dct[k] = another[k] + + return cfg + + if not inplace: + cfg = copy.deepcopy(cfg) + + return _merge(cfg, another_cfg) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/data/__init__.py new file mode 100644 index 0000000..bb6b8d4 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/__init__.py @@ -0,0 +1,20 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from ._misc import convert_to_tv_tensor +from .dataloader import * +from .dataset import * +from .transforms import * + + +# def set_epoch(self, epoch) -> None: +# self.epoch = epoch +# def _set_epoch_func(datasets): +# """Add `set_epoch` for datasets +# """ +# from ..core import register +# for ds in datasets: +# register(ds)(set_epoch) +# _set_epoch_func([CIFAR10, VOCDetection, CocoDetection]) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/_misc.py b/backend/apps/algorithms/algorithm/DFINE/src/data/_misc.py new file mode 100644 index 0000000..74c1e3f --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/_misc.py @@ -0,0 +1,62 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import importlib.metadata + +from torch import Tensor + +if "0.15.2" in importlib.metadata.version("torchvision"): + import torchvision + + torchvision.disable_beta_transforms_warning() + + from torchvision.datapoints import BoundingBox as BoundingBoxes + from torchvision.datapoints import BoundingBoxFormat, Image, Mask, Video + from torchvision.transforms.v2 import SanitizeBoundingBox as SanitizeBoundingBoxes + + _boxes_keys = ["format", "spatial_size"] + +elif "0.17" > importlib.metadata.version("torchvision") >= "0.16": + import torchvision + + torchvision.disable_beta_transforms_warning() + + from torchvision.transforms.v2 import SanitizeBoundingBoxes + from torchvision.tv_tensors import BoundingBoxes, BoundingBoxFormat, Image, Mask, Video + + _boxes_keys = ["format", "canvas_size"] + +elif importlib.metadata.version("torchvision") >= "0.17": + import torchvision + from torchvision.transforms.v2 import SanitizeBoundingBoxes + from torchvision.tv_tensors import BoundingBoxes, BoundingBoxFormat, Image, Mask, Video + + _boxes_keys = ["format", "canvas_size"] + +else: + raise RuntimeError("Please make sure torchvision version >= 0.15.2") + + +def convert_to_tv_tensor(tensor: Tensor, key: str, box_format="xyxy", spatial_size=None) -> Tensor: + """ + Args: + tensor (Tensor): input tensor + key (str): transform to key + + Return: + Dict[str, TV_Tensor] + """ + assert key in ( + "boxes", + "masks", + ), "Only support 'boxes' and 'masks'" + + if key == "boxes": + box_format = getattr(BoundingBoxFormat, box_format.upper()) + _kwargs = dict(zip(_boxes_keys, [box_format, spatial_size])) + return BoundingBoxes(tensor, **_kwargs) + + if key == "masks": + return Mask(tensor) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataloader.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataloader.py new file mode 100644 index 0000000..ce2c0a1 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataloader.py @@ -0,0 +1,122 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import random +from functools import partial + +import torch +import torch.nn.functional as F +import torch.utils.data as data +import torchvision +import torchvision.transforms.v2 as VT +from torch.utils.data import default_collate +from torchvision.transforms.v2 import InterpolationMode +from torchvision.transforms.v2 import functional as VF + +from ..core import register + +torchvision.disable_beta_transforms_warning() + + +__all__ = [ + "DataLoader", + "BaseCollateFunction", + "BatchImageCollateFunction", + "batch_image_collate_fn", +] + + +@register() +class DataLoader(data.DataLoader): + __inject__ = ["dataset", "collate_fn"] + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + for n in ["dataset", "batch_size", "num_workers", "drop_last", "collate_fn"]: + format_string += "\n" + format_string += " {0}: {1}".format(n, getattr(self, n)) + format_string += "\n)" + return format_string + + def set_epoch(self, epoch): + self._epoch = epoch + self.dataset.set_epoch(epoch) + self.collate_fn.set_epoch(epoch) + + @property + def epoch(self): + return self._epoch if hasattr(self, "_epoch") else -1 + + @property + def shuffle(self): + return self._shuffle + + @shuffle.setter + def shuffle(self, shuffle): + assert isinstance(shuffle, bool), "shuffle must be a boolean" + self._shuffle = shuffle + + +@register() +def batch_image_collate_fn(items): + """only batch image""" + return torch.cat([x[0][None] for x in items], dim=0), [x[1] for x in items] + + +class BaseCollateFunction(object): + def set_epoch(self, epoch): + self._epoch = epoch + + @property + def epoch(self): + return self._epoch if hasattr(self, "_epoch") else -1 + + def __call__(self, items): + raise NotImplementedError("") + + +def generate_scales(base_size, base_size_repeat): + scale_repeat = (base_size - int(base_size * 0.75 / 32) * 32) // 32 + scales = [int(base_size * 0.75 / 32) * 32 + i * 32 for i in range(scale_repeat)] + scales += [base_size] * base_size_repeat + scales += [int(base_size * 1.25 / 32) * 32 - i * 32 for i in range(scale_repeat)] + return scales + + +@register() +class BatchImageCollateFunction(BaseCollateFunction): + def __init__( + self, + stop_epoch=None, + ema_restart_decay=0.9999, + base_size=640, + base_size_repeat=None, + ) -> None: + super().__init__() + self.base_size = base_size + self.scales = ( + generate_scales(base_size, base_size_repeat) if base_size_repeat is not None else None + ) + self.stop_epoch = stop_epoch if stop_epoch is not None else 100000000 + self.ema_restart_decay = ema_restart_decay + # self.interpolation = interpolation + + def __call__(self, items): + images = torch.cat([x[0][None] for x in items], dim=0) + targets = [x[1] for x in items] + + if self.scales is not None and self.epoch < self.stop_epoch: + # sz = random.choice(self.scales) + # sz = [sz] if isinstance(sz, int) else list(sz) + # VF.resize(inpt, sz, interpolation=self.interpolation) + + sz = random.choice(self.scales) + images = F.interpolate(images, size=sz) + if "masks" in targets[0]: + for tg in targets: + tg["masks"] = F.interpolate(tg["masks"], size=sz, mode="nearest") + raise NotImplementedError("") + + return images, targets diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/__init__.py new file mode 100644 index 0000000..eb75888 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/__init__.py @@ -0,0 +1,17 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +# from ._dataset import DetDataset +from .cifar_dataset import CIFAR10 +from .coco_dataset import ( + CocoDetection, + mscoco_category2label, + mscoco_category2name, + mscoco_label2category, +) +from .coco_eval import CocoEvaluator +from .coco_utils import get_coco_api_from_dataset +from .voc_detection import VOCDetection +from .voc_eval import VOCEvaluator diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/_dataset.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/_dataset.py new file mode 100644 index 0000000..62b131e --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/_dataset.py @@ -0,0 +1,27 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torch.utils.data as data + + +class DetDataset(data.Dataset): + def __getitem__(self, index): + img, target = self.load_item(index) + if self.transforms is not None: + img, target, _ = self.transforms(img, target, self) + return img, target + + def load_item(self, index): + raise NotImplementedError( + "Please implement this function to return item before `transforms`." + ) + + def set_epoch(self, epoch) -> None: + self._epoch = epoch + + @property + def epoch(self): + return self._epoch if hasattr(self, "_epoch") else -1 diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/cifar_dataset.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/cifar_dataset.py new file mode 100644 index 0000000..e825da7 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/cifar_dataset.py @@ -0,0 +1,25 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from typing import Callable, Optional + +import torchvision + +from ...core import register + + +@register() +class CIFAR10(torchvision.datasets.CIFAR10): + __inject__ = ["transform", "target_transform"] + + def __init__( + self, + root: str, + train: bool = True, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, train, transform, target_transform, download) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_dataset.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_dataset.py new file mode 100644 index 0000000..22b3c37 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_dataset.py @@ -0,0 +1,280 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py + +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import faster_coco_eval.core.mask as coco_mask +from faster_coco_eval.utils.pytorch import FasterCocoDetection +import torch +import torchvision +import os +from PIL import Image + +from ...core import register +from .._misc import convert_to_tv_tensor +from ._dataset import DetDataset + +torchvision.disable_beta_transforms_warning() +Image.MAX_IMAGE_PIXELS = None + +__all__ = ["CocoDetection"] + + +@register() +class CocoDetection(FasterCocoDetection, DetDataset): + __inject__ = [ + "transforms", + ] + __share__ = ["remap_mscoco_category"] + + def __init__( + self, img_folder, ann_file, transforms, return_masks=False, remap_mscoco_category=False + ): + super(FasterCocoDetection, self).__init__(img_folder, ann_file) + self._transforms = transforms + self.prepare = ConvertCocoPolysToMask(return_masks) + self.img_folder = img_folder + self.ann_file = ann_file + self.return_masks = return_masks + self.remap_mscoco_category = remap_mscoco_category + + def __getitem__(self, idx): + img, target = self.load_item(idx) + if self._transforms is not None: + img, target, _ = self._transforms(img, target, self) + return img, target + + def load_item(self, idx): + image, target = super(FasterCocoDetection, self).__getitem__(idx) + image_id = self.ids[idx] + image_path = os.path.join(self.img_folder, self.coco.loadImgs(image_id)[0]["file_name"]) + target = {"image_id": image_id, "image_path": image_path, "annotations": target} + + if self.remap_mscoco_category: + image, target = self.prepare(image, target, category2label=mscoco_category2label) + else: + image, target = self.prepare(image, target) + + target["idx"] = torch.tensor([idx]) + + if "boxes" in target: + target["boxes"] = convert_to_tv_tensor( + target["boxes"], key="boxes", spatial_size=image.size[::-1] + ) + + if "masks" in target: + target["masks"] = convert_to_tv_tensor(target["masks"], key="masks") + + return image, target + + def extra_repr(self) -> str: + s = f" img_folder: {self.img_folder}\n ann_file: {self.ann_file}\n" + s += f" return_masks: {self.return_masks}\n" + if hasattr(self, "_transforms") and self._transforms is not None: + s += f" transforms:\n {repr(self._transforms)}" + if hasattr(self, "_preset") and self._preset is not None: + s += f" preset:\n {repr(self._preset)}" + return s + + @property + def categories( + self, + ): + return self.coco.dataset["categories"] + + @property + def category2name( + self, + ): + return {cat["id"]: cat["name"] for cat in self.categories} + + @property + def category2label( + self, + ): + return {cat["id"]: i for i, cat in enumerate(self.categories)} + + @property + def label2category( + self, + ): + return {i: cat["id"] for i, cat in enumerate(self.categories)} + + +def convert_coco_poly_to_mask(segmentations, height, width): + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = torch.as_tensor(mask, dtype=torch.uint8) + mask = mask.any(dim=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, dim=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8) + return masks + + +class ConvertCocoPolysToMask(object): + def __init__(self, return_masks=False): + self.return_masks = return_masks + + def __call__(self, image: Image.Image, target, **kwargs): + w, h = image.size + + image_id = target["image_id"] + image_id = torch.tensor([image_id]) + + image_path = target["image_path"] + + anno = target["annotations"] + + anno = [obj for obj in anno if "iscrowd" not in obj or obj["iscrowd"] == 0] + + boxes = [obj["bbox"] for obj in anno] + # guard against no boxes via resizing + boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2].clamp_(min=0, max=w) + boxes[:, 1::2].clamp_(min=0, max=h) + + category2label = kwargs.get("category2label", None) + if category2label is not None: + labels = [category2label[obj["category_id"]] for obj in anno] + else: + labels = [obj["category_id"] for obj in anno] + + labels = torch.tensor(labels, dtype=torch.int64) + + if self.return_masks: + segmentations = [obj["segmentation"] for obj in anno] + masks = convert_coco_poly_to_mask(segmentations, h, w) + + keypoints = None + if anno and "keypoints" in anno[0]: + keypoints = [obj["keypoints"] for obj in anno] + keypoints = torch.as_tensor(keypoints, dtype=torch.float32) + num_keypoints = keypoints.shape[0] + if num_keypoints: + keypoints = keypoints.view(num_keypoints, -1, 3) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + boxes = boxes[keep] + labels = labels[keep] + if self.return_masks: + masks = masks[keep] + if keypoints is not None: + keypoints = keypoints[keep] + + target = {} + target["boxes"] = boxes + target["labels"] = labels + if self.return_masks: + target["masks"] = masks + target["image_id"] = image_id + target["image_path"] = image_path + if keypoints is not None: + target["keypoints"] = keypoints + + # for conversion to coco api + area = torch.tensor([obj["area"] for obj in anno]) + iscrowd = torch.tensor([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno]) + target["area"] = area[keep] + target["iscrowd"] = iscrowd[keep] + + target["orig_size"] = torch.as_tensor([int(w), int(h)]) + # target["size"] = torch.as_tensor([int(w), int(h)]) + + return image, target + + +mscoco_category2name = { + 1: "person", + 2: "bicycle", + 3: "car", + 4: "motorcycle", + 5: "airplane", + 6: "bus", + 7: "train", + 8: "truck", + 9: "boat", + 10: "traffic light", + 11: "fire hydrant", + 13: "stop sign", + 14: "parking meter", + 15: "bench", + 16: "bird", + 17: "cat", + 18: "dog", + 19: "horse", + 20: "sheep", + 21: "cow", + 22: "elephant", + 23: "bear", + 24: "zebra", + 25: "giraffe", + 27: "backpack", + 28: "umbrella", + 31: "handbag", + 32: "tie", + 33: "suitcase", + 34: "frisbee", + 35: "skis", + 36: "snowboard", + 37: "sports ball", + 38: "kite", + 39: "baseball bat", + 40: "baseball glove", + 41: "skateboard", + 42: "surfboard", + 43: "tennis racket", + 44: "bottle", + 46: "wine glass", + 47: "cup", + 48: "fork", + 49: "knife", + 50: "spoon", + 51: "bowl", + 52: "banana", + 53: "apple", + 54: "sandwich", + 55: "orange", + 56: "broccoli", + 57: "carrot", + 58: "hot dog", + 59: "pizza", + 60: "donut", + 61: "cake", + 62: "chair", + 63: "couch", + 64: "potted plant", + 65: "bed", + 67: "dining table", + 70: "toilet", + 72: "tv", + 73: "laptop", + 74: "mouse", + 75: "remote", + 76: "keyboard", + 77: "cell phone", + 78: "microwave", + 79: "oven", + 80: "toaster", + 81: "sink", + 82: "refrigerator", + 84: "book", + 85: "clock", + 86: "vase", + 87: "scissors", + 88: "teddy bear", + 89: "hair drier", + 90: "toothbrush", +} + +mscoco_category2label = {k: i for i, k in enumerate(mscoco_category2name.keys())} +mscoco_label2category = {v: k for k, v in mscoco_category2label.items()} diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_eval.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_eval.py new file mode 100644 index 0000000..ac0cb63 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_eval.py @@ -0,0 +1,22 @@ +""" +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +COCO evaluator that works in distributed mode. +Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py +The difference is that there is less copy-pasting from pycocotools +in the end of the file, as python3 can suppress prints with contextlib + +# MiXaiLL76 replacing pycocotools with faster-coco-eval for better performance and support. +""" + +from faster_coco_eval.utils.pytorch import FasterCocoEvaluator + +from ...core import register + +__all__ = [ + "CocoEvaluator", +] + + +@register() +class CocoEvaluator(FasterCocoEvaluator): + pass diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_utils.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_utils.py new file mode 100644 index 0000000..216816c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/coco_utils.py @@ -0,0 +1,191 @@ +""" +copy and modified https://github.com/pytorch/vision/blob/main/references/detection/coco_utils.py + +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import faster_coco_eval.core.mask as coco_mask +import torch +import torch.utils.data +import torchvision +import torchvision.transforms.functional as TVF +from faster_coco_eval import COCO + + +def convert_coco_poly_to_mask(segmentations, height, width): + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = torch.as_tensor(mask, dtype=torch.uint8) + mask = mask.any(dim=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, dim=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8) + return masks + + +class ConvertCocoPolysToMask: + def __call__(self, image, target): + w, h = image.size + + image_id = target["image_id"] + + anno = target["annotations"] + + anno = [obj for obj in anno if obj["iscrowd"] == 0] + + boxes = [obj["bbox"] for obj in anno] + # guard against no boxes via resizing + boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2].clamp_(min=0, max=w) + boxes[:, 1::2].clamp_(min=0, max=h) + + classes = [obj["category_id"] for obj in anno] + classes = torch.tensor(classes, dtype=torch.int64) + + segmentations = [obj["segmentation"] for obj in anno] + masks = convert_coco_poly_to_mask(segmentations, h, w) + + keypoints = None + if anno and "keypoints" in anno[0]: + keypoints = [obj["keypoints"] for obj in anno] + keypoints = torch.as_tensor(keypoints, dtype=torch.float32) + num_keypoints = keypoints.shape[0] + if num_keypoints: + keypoints = keypoints.view(num_keypoints, -1, 3) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + boxes = boxes[keep] + classes = classes[keep] + masks = masks[keep] + if keypoints is not None: + keypoints = keypoints[keep] + + target = {} + target["boxes"] = boxes + target["labels"] = classes + target["masks"] = masks + target["image_id"] = image_id + if keypoints is not None: + target["keypoints"] = keypoints + + # for conversion to coco api + area = torch.tensor([obj["area"] for obj in anno]) + iscrowd = torch.tensor([obj["iscrowd"] for obj in anno]) + target["area"] = area + target["iscrowd"] = iscrowd + + return image, target + + +def _coco_remove_images_without_annotations(dataset, cat_list=None): + def _has_only_empty_bbox(anno): + return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno) + + def _count_visible_keypoints(anno): + return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno) + + min_keypoints_per_image = 10 + + def _has_valid_annotation(anno): + # if it's empty, there is no annotation + if len(anno) == 0: + return False + # if all boxes have close to zero area, there is no annotation + if _has_only_empty_bbox(anno): + return False + # keypoints task have a slight different criteria for considering + # if an annotation is valid + if "keypoints" not in anno[0]: + return True + # for keypoint detection tasks, only consider valid images those + # containing at least min_keypoints_per_image + if _count_visible_keypoints(anno) >= min_keypoints_per_image: + return True + return False + + ids = [] + for ds_idx, img_id in enumerate(dataset.ids): + ann_ids = dataset.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = dataset.coco.loadAnns(ann_ids) + if cat_list: + anno = [obj for obj in anno if obj["category_id"] in cat_list] + if _has_valid_annotation(anno): + ids.append(ds_idx) + + dataset = torch.utils.data.Subset(dataset, ids) + return dataset + + +def convert_to_coco_api(ds): + coco_ds = COCO() + # annotation IDs need to start at 1, not 0, see torchvision issue #1530 + ann_id = 1 + dataset = {"images": [], "categories": [], "annotations": []} + categories = set() + for img_idx in range(len(ds)): + # find better way to get target + # targets = ds.get_annotations(img_idx) + # img, targets = ds[img_idx] + + img, targets = ds.load_item(img_idx) + width, height = img.size + + image_id = targets["image_id"].item() + img_dict = {} + img_dict["id"] = image_id + img_dict["width"] = width + img_dict["height"] = height + dataset["images"].append(img_dict) + bboxes = targets["boxes"].clone() + bboxes[:, 2:] -= bboxes[:, :2] # xyxy -> xywh + bboxes = bboxes.tolist() + labels = targets["labels"].tolist() + areas = targets["area"].tolist() + iscrowd = targets["iscrowd"].tolist() + if "masks" in targets: + masks = targets["masks"] + # make masks Fortran contiguous for coco_mask + masks = masks.permute(0, 2, 1).contiguous().permute(0, 2, 1) + if "keypoints" in targets: + keypoints = targets["keypoints"] + keypoints = keypoints.reshape(keypoints.shape[0], -1).tolist() + num_objs = len(bboxes) + for i in range(num_objs): + ann = {} + ann["image_id"] = image_id + ann["bbox"] = bboxes[i] + ann["category_id"] = labels[i] + categories.add(labels[i]) + ann["area"] = areas[i] + ann["iscrowd"] = iscrowd[i] + ann["id"] = ann_id + if "masks" in targets: + ann["segmentation"] = coco_mask.encode(masks[i].numpy()) + if "keypoints" in targets: + ann["keypoints"] = keypoints[i] + ann["num_keypoints"] = sum(k != 0 for k in keypoints[i][2::3]) + dataset["annotations"].append(ann) + ann_id += 1 + dataset["categories"] = [{"id": i} for i in sorted(categories)] + coco_ds.dataset = dataset + coco_ds.createIndex() + return coco_ds + + +def get_coco_api_from_dataset(dataset): + # FIXME: This is... awful? + for _ in range(10): + if isinstance(dataset, torchvision.datasets.CocoDetection): + break + if isinstance(dataset, torch.utils.data.Subset): + dataset = dataset.dataset + if isinstance(dataset, torchvision.datasets.CocoDetection): + return dataset.coco + return convert_to_coco_api(dataset) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/voc_detection.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/voc_detection.py new file mode 100644 index 0000000..e673560 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/voc_detection.py @@ -0,0 +1,86 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import os +from typing import Callable, Optional + +import torch +import torchvision +import torchvision.transforms.functional as TVF +from PIL import Image +from sympy import im + +try: + from defusedxml.ElementTree import parse as ET_parse +except ImportError: + from xml.etree.ElementTree import parse as ET_parse + +from ...core import register +from .._misc import convert_to_tv_tensor +from ._dataset import DetDataset + + +@register() +class VOCDetection(torchvision.datasets.VOCDetection, DetDataset): + __inject__ = [ + "transforms", + ] + + def __init__( + self, + root: str, + ann_file: str = "trainval.txt", + label_file: str = "label_list.txt", + transforms: Optional[Callable] = None, + ): + with open(os.path.join(root, ann_file), "r") as f: + lines = [x.strip() for x in f.readlines()] + lines = [x.split(" ") for x in lines] + + self.images = [os.path.join(root, lin[0]) for lin in lines] + self.targets = [os.path.join(root, lin[1]) for lin in lines] + assert len(self.images) == len(self.targets) + + with open(os.path.join(root + label_file), "r") as f: + labels = f.readlines() + labels = [lab.strip() for lab in labels] + + self.transforms = transforms + self.labels_map = {lab: i for i, lab in enumerate(labels)} + + def __getitem__(self, index: int): + image, target = self.load_item(index) + if self.transforms is not None: + image, target, _ = self.transforms(image, target, self) + # target["orig_size"] = torch.tensor(TVF.get_image_size(image)) + return image, target + + def load_item(self, index: int): + image = Image.open(self.images[index]).convert("RGB") + target = self.parse_voc_xml(ET_parse(self.annotations[index]).getroot()) + + output = {} + output["image_id"] = torch.tensor([index]) + for k in ["area", "boxes", "labels", "iscrowd"]: + output[k] = [] + + for blob in target["annotation"]["object"]: + box = [float(v) for v in blob["bndbox"].values()] + output["boxes"].append(box) + output["labels"].append(blob["name"]) + output["area"].append((box[2] - box[0]) * (box[3] - box[1])) + output["iscrowd"].append(0) + + w, h = image.size + boxes = torch.tensor(output["boxes"]) if len(output["boxes"]) > 0 else torch.zeros(0, 4) + output["boxes"] = convert_to_tv_tensor( + boxes, "boxes", box_format="xyxy", spatial_size=[h, w] + ) + output["labels"] = torch.tensor([self.labels_map[lab] for lab in output["labels"]]) + output["area"] = torch.tensor(output["area"]) + output["iscrowd"] = torch.tensor(output["iscrowd"]) + output["orig_size"] = torch.tensor([w, h]) + + return image, output diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/voc_eval.py b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/voc_eval.py new file mode 100644 index 0000000..0bee50a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/dataset/voc_eval.py @@ -0,0 +1,12 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torchvision + + +class VOCEvaluator(object): + def __init__(self) -> None: + pass diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/__init__.py new file mode 100644 index 0000000..f555c9f --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/__init__.py @@ -0,0 +1,21 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from ._transforms import ( + ConvertBoxes, + ConvertPILImage, + EmptyTransform, + Normalize, + PadToSize, + RandomCrop, + RandomHorizontalFlip, + RandomIoUCrop, + RandomPhotometricDistort, + RandomZoomOut, + Resize, + SanitizeBoundingBoxes, +) +from .container import Compose +from .mosaic import Mosaic diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/_transforms.py b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/_transforms.py new file mode 100644 index 0000000..a3fe917 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/_transforms.py @@ -0,0 +1,161 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from typing import Any, Dict, List, Optional + +import PIL +import PIL.Image +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms.v2 as T +import torchvision.transforms.v2.functional as F + +from ...core import register +from .._misc import ( + BoundingBoxes, + Image, + Mask, + SanitizeBoundingBoxes, + Video, + _boxes_keys, + convert_to_tv_tensor, +) + +torchvision.disable_beta_transforms_warning() + + +RandomPhotometricDistort = register()(T.RandomPhotometricDistort) +RandomZoomOut = register()(T.RandomZoomOut) +RandomHorizontalFlip = register()(T.RandomHorizontalFlip) +Resize = register()(T.Resize) +# ToImageTensor = register()(T.ToImageTensor) +# ConvertDtype = register()(T.ConvertDtype) +# PILToTensor = register()(T.PILToTensor) +SanitizeBoundingBoxes = register(name="SanitizeBoundingBoxes")(SanitizeBoundingBoxes) +RandomCrop = register()(T.RandomCrop) +Normalize = register()(T.Normalize) + + +@register() +class EmptyTransform(T.Transform): + def __init__( + self, + ) -> None: + super().__init__() + + def forward(self, *inputs): + inputs = inputs if len(inputs) > 1 else inputs[0] + return inputs + + +@register() +class PadToSize(T.Pad): + _transformed_types = ( + PIL.Image.Image, + Image, + Video, + Mask, + BoundingBoxes, + ) + + def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + sp = F.get_spatial_size(flat_inputs[0]) + h, w = self.size[1] - sp[0], self.size[0] - sp[1] + self.padding = [0, 0, w, h] + return dict(padding=self.padding) + + def __init__(self, size, fill=0, padding_mode="constant") -> None: + if isinstance(size, int): + size = (size, size) + self.size = size + super().__init__(0, fill, padding_mode) + + def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + fill = self._fill[type(inpt)] + padding = params["padding"] + return F.pad(inpt, padding=padding, fill=fill, padding_mode=self.padding_mode) # type: ignore[arg-type] + + def __call__(self, *inputs: Any) -> Any: + outputs = super().forward(*inputs) + if len(outputs) > 1 and isinstance(outputs[1], dict): + outputs[1]["padding"] = torch.tensor(self.padding) + return outputs + + +@register() +class RandomIoUCrop(T.RandomIoUCrop): + def __init__( + self, + min_scale: float = 0.3, + max_scale: float = 1, + min_aspect_ratio: float = 0.5, + max_aspect_ratio: float = 2, + sampler_options: Optional[List[float]] = None, + trials: int = 40, + p: float = 1.0, + ): + super().__init__( + min_scale, max_scale, min_aspect_ratio, max_aspect_ratio, sampler_options, trials + ) + self.p = p + + def __call__(self, *inputs: Any) -> Any: + if torch.rand(1) >= self.p: + return inputs if len(inputs) > 1 else inputs[0] + + return super().forward(*inputs) + + +@register() +class ConvertBoxes(T.Transform): + _transformed_types = (BoundingBoxes,) + + def __init__(self, fmt="", normalize=False) -> None: + super().__init__() + self.fmt = fmt + self.normalize = normalize + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._transform(inpt, params) + + def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + spatial_size = getattr(inpt, _boxes_keys[1]) + if self.fmt: + in_fmt = inpt.format.value.lower() + inpt = torchvision.ops.box_convert(inpt, in_fmt=in_fmt, out_fmt=self.fmt.lower()) + inpt = convert_to_tv_tensor( + inpt, key="boxes", box_format=self.fmt.upper(), spatial_size=spatial_size + ) + + if self.normalize: + inpt = inpt / torch.tensor(spatial_size[::-1]).tile(2)[None] + + return inpt + + +@register() +class ConvertPILImage(T.Transform): + _transformed_types = (PIL.Image.Image,) + + def __init__(self, dtype="float32", scale=True) -> None: + super().__init__() + self.dtype = dtype + self.scale = scale + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._transform(inpt, params) + + def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + inpt = F.pil_to_tensor(inpt) + if self.dtype == "float32": + inpt = inpt.float() + + if self.scale: + inpt = inpt / 255.0 + + inpt = Image(inpt) + + return inpt diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/container.py b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/container.py new file mode 100644 index 0000000..b40a0d6 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/container.py @@ -0,0 +1,99 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms.v2 as T + +from ...core import GLOBAL_CONFIG, register +from ._transforms import EmptyTransform + +torchvision.disable_beta_transforms_warning() + + +@register() +class Compose(T.Compose): + def __init__(self, ops, policy=None) -> None: + transforms = [] + if ops is not None: + for op in ops: + if isinstance(op, dict): + name = op.pop("type") + transform = getattr( + GLOBAL_CONFIG[name]["_pymodule"], GLOBAL_CONFIG[name]["_name"] + )(**op) + transforms.append(transform) + op["type"] = name + + elif isinstance(op, nn.Module): + transforms.append(op) + + else: + raise ValueError("") + else: + transforms = [ + EmptyTransform(), + ] + + super().__init__(transforms=transforms) + + if policy is None: + policy = {"name": "default"} + + self.policy = policy + self.global_samples = 0 + + def forward(self, *inputs: Any) -> Any: + return self.get_forward(self.policy["name"])(*inputs) + + def get_forward(self, name): + forwards = { + "default": self.default_forward, + "stop_epoch": self.stop_epoch_forward, + "stop_sample": self.stop_sample_forward, + } + return forwards[name] + + def default_forward(self, *inputs: Any) -> Any: + sample = inputs if len(inputs) > 1 else inputs[0] + for transform in self.transforms: + sample = transform(sample) + return sample + + def stop_epoch_forward(self, *inputs: Any): + sample = inputs if len(inputs) > 1 else inputs[0] + dataset = sample[-1] + cur_epoch = dataset.epoch + policy_ops = self.policy["ops"] + policy_epoch = self.policy["epoch"] + + for transform in self.transforms: + if type(transform).__name__ in policy_ops and cur_epoch >= policy_epoch: + pass + else: + sample = transform(sample) + + return sample + + def stop_sample_forward(self, *inputs: Any): + sample = inputs if len(inputs) > 1 else inputs[0] + dataset = sample[-1] + + cur_epoch = dataset.epoch + policy_ops = self.policy["ops"] + policy_sample = self.policy["sample"] + + for transform in self.transforms: + if type(transform).__name__ in policy_ops and self.global_samples >= policy_sample: + pass + else: + sample = transform(sample) + + self.global_samples += 1 + + return sample diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/functional.py b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/functional.py new file mode 100644 index 0000000..10ef8b5 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/functional.py @@ -0,0 +1,172 @@ +from typing import List, Optional + +import torch + +# needed due to empty tensor bug in pytorch and torchvision 0.5 +import torchvision +import torchvision.transforms.functional as F +from packaging import version +from torch import Tensor + +if version.parse(torchvision.__version__) < version.parse("0.7"): + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if version.parse(torchvision.__version__) < version.parse("0.7"): + if input.numel() > 0: + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) + + +def crop(image, target, region): + cropped_image = F.crop(image, *region) + + target = target.copy() + i, j, h, w = region + + # should we do something wrt the original size? + target["size"] = torch.tensor([h, w]) + + fields = ["labels", "area", "iscrowd"] + + if "boxes" in target: + boxes = target["boxes"] + max_size = torch.as_tensor([w, h], dtype=torch.float32) + cropped_boxes = boxes - torch.as_tensor([j, i, j, i]) + cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size) + cropped_boxes = cropped_boxes.clamp(min=0) + area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1) + target["boxes"] = cropped_boxes.reshape(-1, 4) + target["area"] = area + fields.append("boxes") + + if "masks" in target: + # FIXME should we update the area here if there are no boxes? + target["masks"] = target["masks"][:, i : i + h, j : j + w] + fields.append("masks") + + # remove elements for which the boxes or masks that have zero area + if "boxes" in target or "masks" in target: + # favor boxes selection when defining which elements to keep + # this is compatible with previous implementation + if "boxes" in target: + cropped_boxes = target["boxes"].reshape(-1, 2, 2) + keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1) + else: + keep = target["masks"].flatten(1).any(1) + + for field in fields: + target[field] = target[field][keep] + + return cropped_image, target + + +def hflip(image, target): + flipped_image = F.hflip(image) + + w, h = image.size + + target = target.copy() + if "boxes" in target: + boxes = target["boxes"] + boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor( + [w, 0, w, 0] + ) + target["boxes"] = boxes + + if "masks" in target: + target["masks"] = target["masks"].flip(-1) + + return flipped_image, target + + +def resize(image, target, size, max_size=None): + # size can be min_size (scalar) or (w, h) tuple + + def get_size_with_aspect_ratio(image_size, size, max_size=None): + w, h = image_size + if max_size is not None: + min_original_size = float(min((w, h))) + max_original_size = float(max((w, h))) + if max_original_size / min_original_size * size > max_size: + size = int(round(max_size * min_original_size / max_original_size)) + + if (w <= h and w == size) or (h <= w and h == size): + return (h, w) + + if w < h: + ow = size + oh = int(size * h / w) + else: + oh = size + ow = int(size * w / h) + + # r = min(size / min(h, w), max_size / max(h, w)) + # ow = int(w * r) + # oh = int(h * r) + + return (oh, ow) + + def get_size(image_size, size, max_size=None): + if isinstance(size, (list, tuple)): + return size[::-1] + else: + return get_size_with_aspect_ratio(image_size, size, max_size) + + size = get_size(image.size, size, max_size) + rescaled_image = F.resize(image, size) + + if target is None: + return rescaled_image, None + + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)) + ratio_width, ratio_height = ratios + + target = target.copy() + if "boxes" in target: + boxes = target["boxes"] + scaled_boxes = boxes * torch.as_tensor( + [ratio_width, ratio_height, ratio_width, ratio_height] + ) + target["boxes"] = scaled_boxes + + if "area" in target: + area = target["area"] + scaled_area = area * (ratio_width * ratio_height) + target["area"] = scaled_area + + h, w = size + target["size"] = torch.tensor([h, w]) + + if "masks" in target: + target["masks"] = ( + interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5 + ) + + return rescaled_image, target + + +def pad(image, target, padding): + # assumes that we only pad on the bottom right corners + padded_image = F.pad(image, (0, 0, padding[0], padding[1])) + if target is None: + return padded_image, None + target = target.copy() + # should we do something wrt the original size? + target["size"] = torch.tensor(padded_image.size[::-1]) + if "masks" in target: + target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1])) + return padded_image, target diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/mosaic.py b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/mosaic.py new file mode 100644 index 0000000..413900c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/mosaic.py @@ -0,0 +1,83 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import random + +import torch +import torchvision +import torchvision.transforms.v2 as T +import torchvision.transforms.v2.functional as F +from PIL import Image + +from ...core import register +from .._misc import convert_to_tv_tensor + +torchvision.disable_beta_transforms_warning() + + +@register() +class Mosaic(T.Transform): + def __init__( + self, + size, + max_size=None, + ) -> None: + super().__init__() + self.resize = T.Resize(size=size, max_size=max_size) + self.crop = T.RandomCrop(size=max_size if max_size else size) + + # TODO add arg `output_size` for affine` + # self.random_perspective = T.RandomPerspective(distortion_scale=0.5, p=1., ) + self.random_affine = T.RandomAffine( + degrees=0, translate=(0.1, 0.1), scale=(0.5, 1.5), fill=114 + ) + + def forward(self, *inputs): + inputs = inputs if len(inputs) > 1 else inputs[0] + image, target, dataset = inputs + + images = [] + targets = [] + indices = random.choices(range(len(dataset)), k=3) + for i in indices: + image, target = dataset.load_item(i) + image, target = self.resize(image, target) + images.append(image) + targets.append(target) + + h, w = F.get_spatial_size(images[0]) + offset = [[0, 0], [w, 0], [0, h], [w, h]] + image = Image.new(mode=images[0].mode, size=(w * 2, h * 2), color=0) + for i, im in enumerate(images): + image.paste(im, offset[i]) + + offset = torch.tensor([[0, 0], [w, 0], [0, h], [w, h]]).repeat(1, 2) + target = {} + for k in targets[0]: + if k == "boxes": + v = [t[k] + offset[i] for i, t in enumerate(targets)] + else: + v = [t[k] for t in targets] + + if isinstance(v[0], torch.Tensor): + v = torch.cat(v, dim=0) + + target[k] = v + + if "boxes" in target: + # target['boxes'] = target['boxes'].clamp(0, 640 * 2 - 1) + w, h = image.size + target["boxes"] = convert_to_tv_tensor( + target["boxes"], "boxes", box_format="xyxy", spatial_size=[h, w] + ) + + if "masks" in target: + target["masks"] = convert_to_tv_tensor(target["masks"], "masks") + + image, target = self.random_affine(image, target) + # image, target = self.resize(image, target) + image, target = self.crop(image, target) + + return image, target, dataset diff --git a/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/presets.py b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/presets.py new file mode 100644 index 0000000..6f6d59c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/data/transforms/presets.py @@ -0,0 +1,4 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" diff --git a/backend/apps/algorithms/algorithm/DFINE/src/misc/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/misc/__init__.py new file mode 100644 index 0000000..3d1f955 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/misc/__init__.py @@ -0,0 +1,9 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from .dist_utils import setup_print, setup_seed +from .logger import * +from .profiler_utils import stats +from .visualizer import * diff --git a/backend/apps/algorithms/algorithm/DFINE/src/misc/box_ops.py b/backend/apps/algorithms/algorithm/DFINE/src/misc/box_ops.py new file mode 100644 index 0000000..64dff0d --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/misc/box_ops.py @@ -0,0 +1,106 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from typing import List, Tuple + +import torch +import torchvision +from torch import Tensor + + +def generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor: + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + return torchvision.ops.generalized_box_iou(boxes1, boxes2) + + +# elementwise +def elementwise_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor: + """ + Args: + boxes1, [N, 4] + boxes2, [N, 4] + Returns: + iou, [N, ] + union, [N, ] + """ + area1 = torchvision.ops.box_area(boxes1) # [N, ] + area2 = torchvision.ops.box_area(boxes2) # [N, ] + lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N, 2] + rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N, 2] + wh = (rb - lt).clamp(min=0) # [N, 2] + inter = wh[:, 0] * wh[:, 1] # [N, ] + union = area1 + area2 - inter + iou = inter / union + return iou, union + + +def elementwise_generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor: + """ + Args: + boxes1, [N, 4] with [x1, y1, x2, y2] + boxes2, [N, 4] with [x1, y1, x2, y2] + Returns: + giou, [N, ] + """ + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + iou, union = elementwise_box_iou(boxes1, boxes2) + lt = torch.min(boxes1[:, :2], boxes2[:, :2]) # [N, 2] + rb = torch.max(boxes1[:, 2:], boxes2[:, 2:]) # [N, 2] + wh = (rb - lt).clamp(min=0) # [N, 2] + area = wh[:, 0] * wh[:, 1] + return iou - (area - union) / area + + +def check_point_inside_box(points: Tensor, boxes: Tensor, eps=1e-9) -> Tensor: + """ + Args: + points, [K, 2], (x, y) + boxes, [N, 4], (x1, y1, y2, y2) + Returns: + Tensor (bool), [K, N] + """ + x, y = [p.unsqueeze(-1) for p in points.unbind(-1)] + x1, y1, x2, y2 = [x.unsqueeze(0) for x in boxes.unbind(-1)] + + l = x - x1 + t = y - y1 + r = x2 - x + b = y2 - y + + ltrb = torch.stack([l, t, r, b], dim=-1) + mask = ltrb.min(dim=-1).values > eps + + return mask + + +def point_box_distance(points: Tensor, boxes: Tensor) -> Tensor: + """ + Args: + boxes, [N, 4], (x1, y1, x2, y2) + points, [N, 2], (x, y) + Returns: + Tensor (N, 4), (l, t, r, b) + """ + x1y1, x2y2 = torch.split(boxes, 2, dim=-1) + lt = points - x1y1 + rb = x2y2 - points + return torch.concat([lt, rb], dim=-1) + + +def point_distance_box(points: Tensor, distances: Tensor) -> Tensor: + """ + Args: + points (Tensor), [N, 2], (x, y) + distances (Tensor), [N, 4], (l, t, r, b) + Returns: + boxes (Tensor), (N, 4), (x1, y1, x2, y2) + """ + lt, rb = torch.split(distances, 2, dim=-1) + x1y1 = -lt + points + x2y2 = rb + points + boxes = torch.concat([x1y1, x2y2], dim=-1) + return boxes diff --git a/backend/apps/algorithms/algorithm/DFINE/src/misc/dist_utils.py b/backend/apps/algorithms/algorithm/DFINE/src/misc/dist_utils.py new file mode 100644 index 0000000..959ded6 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/misc/dist_utils.py @@ -0,0 +1,281 @@ +""" +reference +- https://github.com/pytorch/vision/blob/main/references/detection/utils.py +- https://github.com/facebookresearch/detr/blob/master/util/misc.py#L406 + +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import atexit +import os +import random +import time + +import numpy as np +import torch +import torch.backends.cudnn +import torch.distributed +import torch.nn as nn +from torch.distributed.fsdp import FullyShardedDataParallel as FSDP +from torch.nn.parallel import DataParallel as DP +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DistributedSampler + +# from torch.utils.data.dataloader import DataLoader +from ..data import DataLoader + + +def setup_distributed( + print_rank: int = 0, + print_method: str = "builtin", + seed: int = None, +): + """ + env setup + args: + print_rank, + print_method, (builtin, rich) + seed, + """ + try: + # https://pytorch.org/docs/stable/elastic/run.html + RANK = int(os.getenv("RANK", -1)) + LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) + WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) + + # torch.distributed.init_process_group(backend=backend, init_method='env://') + torch.distributed.init_process_group(init_method="env://") + torch.distributed.barrier() + + rank = torch.distributed.get_rank() + torch.cuda.set_device(rank) + torch.cuda.empty_cache() + enabled_dist = True + if get_rank() == print_rank: + print("Initialized distributed mode...") + + except Exception: + enabled_dist = False + print("Not init distributed mode.") + + setup_print(get_rank() == print_rank, method=print_method) + if seed is not None: + setup_seed(seed) + + return enabled_dist + + +def setup_print(is_main, method="builtin"): + """This function disables printing when not in master process""" + import builtins as __builtin__ + + if method == "builtin": + builtin_print = __builtin__.print + + elif method == "rich": + import rich + + builtin_print = rich.print + + else: + raise AttributeError("") + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_main or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_available_and_initialized(): + if not torch.distributed.is_available(): + return False + if not torch.distributed.is_initialized(): + return False + return True + + +@atexit.register +def cleanup(): + """cleanup distributed environment""" + if is_dist_available_and_initialized(): + torch.distributed.barrier() + torch.distributed.destroy_process_group() + + +def get_rank(): + if not is_dist_available_and_initialized(): + return 0 + return torch.distributed.get_rank() + + +def get_world_size(): + if not is_dist_available_and_initialized(): + return 1 + return torch.distributed.get_world_size() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def warp_model( + model: torch.nn.Module, + sync_bn: bool = False, + dist_mode: str = "ddp", + find_unused_parameters: bool = False, + compile: bool = False, + compile_mode: str = "reduce-overhead", + **kwargs, +): + if is_dist_available_and_initialized(): + rank = get_rank() + model = nn.SyncBatchNorm.convert_sync_batchnorm(model) if sync_bn else model + if dist_mode == "dp": + model = DP(model, device_ids=[rank], output_device=rank) + elif dist_mode == "ddp": + model = DDP( + model, + device_ids=[rank], + output_device=rank, + find_unused_parameters=find_unused_parameters, + ) + else: + raise AttributeError("") + + if compile: + model = torch.compile(model, mode=compile_mode) + + return model + + +def de_model(model): + return de_parallel(de_complie(model)) + + +def warp_loader(loader, shuffle=False): + if is_dist_available_and_initialized(): + sampler = DistributedSampler(loader.dataset, shuffle=shuffle) + loader = DataLoader( + loader.dataset, + loader.batch_size, + sampler=sampler, + drop_last=loader.drop_last, + collate_fn=loader.collate_fn, + pin_memory=loader.pin_memory, + num_workers=loader.num_workers, + ) + return loader + + +def is_parallel(model) -> bool: + # Returns True if model is of type DP or DDP + return type(model) in ( + torch.nn.parallel.DataParallel, + torch.nn.parallel.DistributedDataParallel, + ) + + +def de_parallel(model) -> nn.Module: + # De-parallelize a model: returns single-GPU model if model is of type DP or DDP + return model.module if is_parallel(model) else model + + +def reduce_dict(data, avg=True): + """ + Args + data dict: input, {k: v, ...} + avg bool: true + """ + world_size = get_world_size() + if world_size < 2: + return data + + with torch.no_grad(): + keys, values = [], [] + for k in sorted(data.keys()): + keys.append(k) + values.append(data[k]) + + values = torch.stack(values, dim=0) + torch.distributed.all_reduce(values) + + if avg is True: + values /= world_size + + return {k: v for k, v in zip(keys, values)} + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + data_list = [None] * world_size + torch.distributed.all_gather_object(data_list, data) + return data_list + + +def sync_time(): + """sync_time""" + if torch.cuda.is_available(): + torch.cuda.synchronize() + + return time.time() + + +def setup_seed(seed: int, deterministic=False): + """setup_seed for reproducibility + torch.manual_seed(3407) is all you need. https://arxiv.org/abs/2109.08203 + """ + seed = seed + get_rank() + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + # memory will be large when setting deterministic to True + if torch.backends.cudnn.is_available() and deterministic: + torch.backends.cudnn.deterministic = True + + +# for torch.compile +def check_compile(): + import warnings + + import torch + + gpu_ok = False + if torch.cuda.is_available(): + device_cap = torch.cuda.get_device_capability() + if device_cap in ((7, 0), (8, 0), (9, 0)): + gpu_ok = True + if not gpu_ok: + warnings.warn( + "GPU is not NVIDIA V100, A100, or H100. Speedup numbers may be lower " "than expected." + ) + return gpu_ok + + +def is_compile(model): + import torch._dynamo + + return type(model) in (torch._dynamo.OptimizedModule,) + + +def de_complie(model): + return model._orig_mod if is_compile(model) else model diff --git a/backend/apps/algorithms/algorithm/DFINE/src/misc/lazy_loader.py b/backend/apps/algorithms/algorithm/DFINE/src/misc/lazy_loader.py new file mode 100644 index 0000000..208fb12 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/misc/lazy_loader.py @@ -0,0 +1,70 @@ +""" +https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/util/lazy_loader.py +""" + +import importlib +import types + + +class LazyLoader(types.ModuleType): + """Lazily import a module, mainly to avoid pulling in large dependencies. + + `paddle`, and `ffmpeg` are examples of modules that are large and not always + needed, and this allows them to only be loaded when they are used. + """ + + # The lint error here is incorrect. + def __init__(self, local_name, parent_module_globals, name, warning=None): + self._local_name = local_name + self._parent_module_globals = parent_module_globals + self._warning = warning + + # These members allows doctest correctly process this module member without + # triggering self._load(). self._load() mutates parant_module_globals and + # triggers a dict mutated during iteration error from doctest.py. + # - for from_module() + self.__module__ = name.rsplit(".", 1)[0] + # - for is_routine() + self.__wrapped__ = None + + super(LazyLoader, self).__init__(name) + + def _load(self): + """Load the module and insert it into the parent's globals.""" + # Import the target module and insert it into the parent's namespace + module = importlib.import_module(self.__name__) + self._parent_module_globals[self._local_name] = module + + # Emit a warning if one was specified + if self._warning: + # logging.warning(self._warning) + # Make sure to only warn once. + self._warning = None + + # Update this object's dict so that if someone keeps a reference to the + # LazyLoader, lookups are efficient (__getattr__ is only called on lookups + # that fail). + self.__dict__.update(module.__dict__) + + return module + + def __getattr__(self, item): + module = self._load() + return getattr(module, item) + + def __repr__(self): + # Carefully to not trigger _load, since repr may be called in very + # sensitive places. + return f"" + + def __dir__(self): + module = self._load() + return dir(module) + + +# import paddle.nn as nn +# nn = LazyLoader("nn", globals(), "paddle.nn") + +# class M(nn.Layer): +# def __init__(self) -> None: +# super().__init__() diff --git a/backend/apps/algorithms/algorithm/DFINE/src/misc/logger.py b/backend/apps/algorithms/algorithm/DFINE/src/misc/logger.py new file mode 100644 index 0000000..0c1ca18 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/misc/logger.py @@ -0,0 +1,255 @@ +""" +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +https://github.com/facebookresearch/detr/blob/main/util/misc.py +Mostly copy-paste from torchvision references. +""" + +import datetime +import pickle +import time +from collections import defaultdict, deque +from typing import Dict + +import torch +import torch.distributed as tdist + +from .dist_utils import get_world_size, is_dist_available_and_initialized + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_available_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") + tdist.barrier() + tdist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value, + ) + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + tdist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + tdist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True) -> Dict[str, torch.Tensor]: + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + tdist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {}".format(name, str(meter))) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = "" + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt="{avg:.4f}") + data_time = SmoothedValue(fmt="{avg:.4f}") + space_fmt = ":" + str(len(str(len(iterable)))) + "d" + if torch.cuda.is_available(): + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + "max mem: {memory:.0f}", + ] + ) + else: + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + ] + ) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB, + ) + ) + else: + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + ) + ) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print( + "{} Total time: {} ({:.4f} s / it)".format( + header, total_time_str, total_time / len(iterable) + ) + ) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/misc/profiler_utils.py b/backend/apps/algorithms/algorithm/DFINE/src/misc/profiler_utils.py new file mode 100644 index 0000000..84db32a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/misc/profiler_utils.py @@ -0,0 +1,30 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import copy +from typing import Tuple + +from calflops import calculate_flops + + +def stats( + cfg, + input_shape: Tuple = (1, 3, 640, 640), +) -> Tuple[int, dict]: + base_size = cfg.train_dataloader.collate_fn.base_size + input_shape = (1, 3, base_size, base_size) + + model_for_info = copy.deepcopy(cfg.model).deploy() + + flops, macs, _ = calculate_flops( + model=model_for_info, + input_shape=input_shape, + output_as_string=True, + output_precision=4, + print_detailed=False, + ) + params = sum(p.numel() for p in model_for_info.parameters()) + del model_for_info + + return params, {"Model FLOPs:%s MACs:%s Params:%s" % (flops, macs, params)} diff --git a/backend/apps/algorithms/algorithm/DFINE/src/misc/visualizer.py b/backend/apps/algorithms/algorithm/DFINE/src/misc/visualizer.py new file mode 100644 index 0000000..fa82f7c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/misc/visualizer.py @@ -0,0 +1,121 @@ +""" " +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import PIL +import numpy as np +import torch +import torch.utils.data +import torchvision +from typing import List, Dict + +torchvision.disable_beta_transforms_warning() + +__all__ = ["show_sample", "save_samples"] + +def save_samples(samples: torch.Tensor, targets: List[Dict], output_dir: str, split: str, normalized: bool, box_fmt: str): + ''' + normalized: whether the boxes are normalized to [0, 1] + box_fmt: 'xyxy', 'xywh', 'cxcywh', D-FINE uses 'cxcywh' for training, 'xyxy' for validation + ''' + from torchvision.transforms.functional import to_pil_image + from torchvision.ops import box_convert + from pathlib import Path + from PIL import ImageDraw, ImageFont + import os + + os.makedirs(Path(output_dir) / Path(f"{split}_samples"), exist_ok=True) + # Predefined colors (standard color names recognized by PIL) + BOX_COLORS = [ + "red", "blue", "green", "orange", "purple", + "cyan", "magenta", "yellow", "lime", "pink", + "teal", "lavender", "brown", "beige", "maroon", + "navy", "olive", "coral", "turquoise", "gold" + ] + + LABEL_TEXT_COLOR = "white" + + font = ImageFont.load_default() + font.size = 32 + + for i, (sample, target) in enumerate(zip(samples, targets)): + sample_visualization = sample.clone().cpu() + target_boxes = target["boxes"].clone().cpu() + target_labels = target["labels"].clone().cpu() + target_image_id = target["image_id"].item() + target_image_path = target["image_path"] + target_image_path_stem = Path(target_image_path).stem + + sample_visualization = to_pil_image(sample_visualization) + sample_visualization_w, sample_visualization_h = sample_visualization.size + + # normalized to pixel space + if normalized: + target_boxes[:, 0] = target_boxes[:, 0] * sample_visualization_w + target_boxes[:, 2] = target_boxes[:, 2] * sample_visualization_w + target_boxes[:, 1] = target_boxes[:, 1] * sample_visualization_h + target_boxes[:, 3] = target_boxes[:, 3] * sample_visualization_h + + # any box format -> xyxy + target_boxes = box_convert(target_boxes, in_fmt=box_fmt, out_fmt="xyxy") + + # clip to image size + target_boxes[:, 0] = torch.clamp(target_boxes[:, 0], 0, sample_visualization_w) + target_boxes[:, 1] = torch.clamp(target_boxes[:, 1], 0, sample_visualization_h) + target_boxes[:, 2] = torch.clamp(target_boxes[:, 2], 0, sample_visualization_w) + target_boxes[:, 3] = torch.clamp(target_boxes[:, 3], 0, sample_visualization_h) + + target_boxes = target_boxes.numpy().astype(np.int32) + target_labels = target_labels.numpy().astype(np.int32) + + draw = ImageDraw.Draw(sample_visualization) + + # draw target boxes + for box, label in zip(target_boxes, target_labels): + x1, y1, x2, y2 = box + + # Select color based on class ID + box_color = BOX_COLORS[int(label) % len(BOX_COLORS)] + + # Draw box (thick) + draw.rectangle([x1, y1, x2, y2], outline=box_color, width=3) + + label_text = f"{label}" + + # Measure text size + text_width, text_height = draw.textbbox((0, 0), label_text, font=font)[2:4] + + # Draw text background + padding = 2 + draw.rectangle( + [x1, y1 - text_height - padding * 2, x1 + text_width + padding * 2, y1], + fill=box_color + ) + + # Draw text (LABEL_TEXT_COLOR) + draw.text((x1 + padding, y1 - text_height - padding), label_text, + fill=LABEL_TEXT_COLOR, font=font) + + save_path = Path(output_dir) / f"{split}_samples" / f"{target_image_id}_{target_image_path_stem}.webp" + sample_visualization.save(save_path) + +def show_sample(sample): + """for coco dataset/dataloader""" + import matplotlib.pyplot as plt + from torchvision.transforms.v2 import functional as F + from torchvision.utils import draw_bounding_boxes + + image, target = sample + if isinstance(image, PIL.Image.Image): + image = F.to_image_tensor(image) + + image = F.convert_dtype(image, torch.uint8) + annotated_image = draw_bounding_boxes(image, target["boxes"], colors="yellow", width=3) + + fig, ax = plt.subplots() + ax.imshow(annotated_image.permute(1, 2, 0).numpy()) + ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) + fig.tight_layout() + fig.show() + plt.show() diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/__init__.py new file mode 100644 index 0000000..4544b3c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/__init__.py @@ -0,0 +1,16 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from .arch import * + +# +from .backbone import * +from .backbone import ( + FrozenBatchNorm2d, + freeze_batch_norm2d, + get_activation, +) +from .criterion import * +from .postprocessor import * diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/__init__.py new file mode 100644 index 0000000..29f908c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/__init__.py @@ -0,0 +1,7 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from .classification import ClassHead, Classification +from .yolo import YOLO diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/classification.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/classification.py new file mode 100644 index 0000000..d287543 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/classification.py @@ -0,0 +1,45 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torch.nn as nn + +from ...core import register + +__all__ = ["Classification", "ClassHead"] + + +@register() +class Classification(torch.nn.Module): + __inject__ = ["backbone", "head"] + + def __init__(self, backbone: nn.Module, head: nn.Module = None): + super().__init__() + + self.backbone = backbone + self.head = head + + def forward(self, x): + x = self.backbone(x) + + if self.head is not None: + x = self.head(x) + + return x + + +@register() +class ClassHead(nn.Module): + def __init__(self, hidden_dim, num_classes): + super().__init__() + self.pool = nn.AdaptiveAvgPool2d(1) + self.proj = nn.Linear(hidden_dim, num_classes) + + def forward(self, x): + x = x[0] if isinstance(x, (list, tuple)) else x + x = self.pool(x) + x = x.reshape(x.shape[0], -1) + x = self.proj(x) + return x diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/yolo.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/yolo.py new file mode 100644 index 0000000..c40a306 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/arch/yolo.py @@ -0,0 +1,42 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch + +from ...core import register + +__all__ = [ + "YOLO", +] + + +@register() +class YOLO(torch.nn.Module): + __inject__ = [ + "backbone", + "neck", + "head", + ] + + def __init__(self, backbone: torch.nn.Module, neck, head): + super().__init__() + self.backbone = backbone + self.neck = neck + self.head = head + + def forward(self, x, **kwargs): + x = self.backbone(x) + x = self.neck(x) + x = self.head(x) + return x + + def deploy( + self, + ): + self.eval() + for m in self.modules(): + if m is not self and hasattr(m, "deploy"): + m.deploy() + return self diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/__init__.py new file mode 100644 index 0000000..497d8df --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/__init__.py @@ -0,0 +1,17 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from .common import ( + FrozenBatchNorm2d, + freeze_batch_norm2d, + get_activation, +) +from .csp_darknet import CSPPAN, CSPDarkNet +from .csp_resnet import CSPResNet +from .hgnetv2 import HGNetv2 +from .presnet import PResNet +from .test_resnet import MResNet +from .timm_model import TimmModel +from .torchvision_model import TorchVisionModel diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/common.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/common.py new file mode 100644 index 0000000..fcdb906 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/common.py @@ -0,0 +1,117 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torch.nn as nn + + +class ConvNormLayer(nn.Module): + def __init__(self, ch_in, ch_out, kernel_size, stride, padding=None, bias=False, act=None): + super().__init__() + self.conv = nn.Conv2d( + ch_in, + ch_out, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=bias, + ) + self.norm = nn.BatchNorm2d(ch_out) + self.act = nn.Identity() if act is None else get_activation(act) + + def forward(self, x): + return self.act(self.norm(self.conv(x))) + + +class FrozenBatchNorm2d(nn.Module): + """copy and modified from https://github.com/facebookresearch/detr/blob/master/models/backbone.py + BatchNorm2d where the batch statistics and the affine parameters are fixed. + Copy-paste from torchvision.misc.ops with added eps before rqsrt, + without which any other models than torchvision.models.resnet[18,34,50,101] + produce nans. + """ + + def __init__(self, num_features, eps=1e-5): + super(FrozenBatchNorm2d, self).__init__() + n = num_features + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + self.eps = eps + self.num_features = n + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super(FrozenBatchNorm2d, self)._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + scale = w * (rv + self.eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + def extra_repr(self): + return "{num_features}, eps={eps}".format(**self.__dict__) + + +def freeze_batch_norm2d(module: nn.Module) -> nn.Module: + if isinstance(module, nn.BatchNorm2d): + module = FrozenBatchNorm2d(module.num_features) + else: + for name, child in module.named_children(): + _child = freeze_batch_norm2d(child) + if _child is not child: + setattr(module, name, _child) + return module + + +def get_activation(act: str, inplace: bool = True): + """get activation""" + if act is None: + return nn.Identity() + + elif isinstance(act, nn.Module): + return act + + act = act.lower() + + if act == "silu" or act == "swish": + m = nn.SiLU() + + elif act == "relu": + m = nn.ReLU() + + elif act == "leaky_relu": + m = nn.LeakyReLU() + + elif act == "silu": + m = nn.SiLU() + + elif act == "gelu": + m = nn.GELU() + + elif act == "hardsigmoid": + m = nn.Hardsigmoid() + + else: + raise RuntimeError("") + + if hasattr(m, "inplace"): + m.inplace = inplace + + return m diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/csp_darknet.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/csp_darknet.py new file mode 100644 index 0000000..f6efa01 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/csp_darknet.py @@ -0,0 +1,203 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import math +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...core import register +from .common import get_activation + + +def autopad(k, p=None): + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] + return p + + +def make_divisible(c, d): + return math.ceil(c / d) * d + + +class Conv(nn.Module): + def __init__(self, cin, cout, k=1, s=1, p=None, g=1, act="silu") -> None: + super().__init__() + self.conv = nn.Conv2d(cin, cout, k, s, autopad(k, p), groups=g, bias=False) + self.bn = nn.BatchNorm2d(cout) + self.act = get_activation(act, inplace=True) + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + +class Bottleneck(nn.Module): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, act="silu"): + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1, act=act) + self.cv2 = Conv(c_, c2, 3, 1, g=g, act=act) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class C3(nn.Module): + # CSP Bottleneck with 3 convolutions + def __init__( + self, c1, c2, n=1, shortcut=True, g=1, e=0.5, act="silu" + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1, act=act) + self.cv2 = Conv(c1, c_, 1, 1, act=act) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0, act=act) for _ in range(n))) + self.cv3 = Conv(2 * c_, c2, 1, act=act) + + def forward(self, x): + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) + + +class SPPF(nn.Module): + # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher + def __init__(self, c1, c2, k=5, act="silu"): # equivalent to SPP(k=(5, 9, 13)) + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1, act=act) + self.cv2 = Conv(c_ * 4, c2, 1, 1, act=act) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) + + +@register() +class CSPDarkNet(nn.Module): + __share__ = ["depth_multi", "width_multi"] + + def __init__( + self, + in_channels=3, + width_multi=1.0, + depth_multi=1.0, + return_idx=[2, 3, -1], + act="silu", + ) -> None: + super().__init__() + + channels = [64, 128, 256, 512, 1024] + channels = [make_divisible(c * width_multi, 8) for c in channels] + + depths = [3, 6, 9, 3] + depths = [max(round(d * depth_multi), 1) for d in depths] + + self.layers = nn.ModuleList([Conv(in_channels, channels[0], 6, 2, 2, act=act)]) + for i, (c, d) in enumerate(zip(channels, depths), 1): + layer = nn.Sequential( + *[Conv(c, channels[i], 3, 2, act=act), C3(channels[i], channels[i], n=d, act=act)] + ) + self.layers.append(layer) + + self.layers.append(SPPF(channels[-1], channels[-1], k=5, act=act)) + + self.return_idx = return_idx + self.out_channels = [channels[i] for i in self.return_idx] + self.strides = [[2, 4, 8, 16, 32][i] for i in self.return_idx] + self.depths = depths + self.act = act + + def forward(self, x): + outputs = [] + for _, m in enumerate(self.layers): + x = m(x) + outputs.append(x) + + return [outputs[i] for i in self.return_idx] + + +@register() +class CSPPAN(nn.Module): + """ + P5 ---> 1x1 ---------------------------------> concat --> c3 --> det + | up | conv /2 + P4 ---> concat ---> c3 ---> 1x1 --> concat ---> c3 -----------> det + | up | conv /2 + P3 -----------------------> concat ---> c3 ---------------------> det + """ + + __share__ = [ + "depth_multi", + ] + + def __init__(self, in_channels=[256, 512, 1024], depth_multi=1.0, act="silu") -> None: + super().__init__() + depth = max(round(3 * depth_multi), 1) + + self.out_channels = in_channels + self.fpn_stems = nn.ModuleList( + [ + Conv(cin, cout, 1, 1, act=act) + for cin, cout in zip(in_channels[::-1], in_channels[::-1][1:]) + ] + ) + self.fpn_csps = nn.ModuleList( + [ + C3(cin, cout, depth, False, act=act) + for cin, cout in zip(in_channels[::-1], in_channels[::-1][1:]) + ] + ) + + self.pan_stems = nn.ModuleList([Conv(c, c, 3, 2, act=act) for c in in_channels[:-1]]) + self.pan_csps = nn.ModuleList([C3(c, c, depth, False, act=act) for c in in_channels[1:]]) + + def forward(self, feats): + fpn_feats = [] + for i, feat in enumerate(feats[::-1]): + if i == 0: + feat = self.fpn_stems[i](feat) + fpn_feats.append(feat) + else: + _feat = F.interpolate(fpn_feats[-1], scale_factor=2, mode="nearest") + feat = torch.concat([_feat, feat], dim=1) + feat = self.fpn_csps[i - 1](feat) + if i < len(self.fpn_stems): + feat = self.fpn_stems[i](feat) + fpn_feats.append(feat) + + pan_feats = [] + for i, feat in enumerate(fpn_feats[::-1]): + if i == 0: + pan_feats.append(feat) + else: + _feat = self.pan_stems[i - 1](pan_feats[-1]) + feat = torch.concat([_feat, feat], dim=1) + feat = self.pan_csps[i - 1](feat) + pan_feats.append(feat) + + return pan_feats + + +if __name__ == "__main__": + data = torch.rand(1, 3, 320, 640) + + width_multi = 0.75 + depth_multi = 0.33 + + m = CSPDarkNet(3, width_multi=width_multi, depth_multi=depth_multi, act="silu") + outputs = m(data) + print([o.shape for o in outputs]) + + m = CSPPAN(in_channels=m.out_channels, depth_multi=depth_multi, act="silu") + outputs = m(outputs) + print([o.shape for o in outputs]) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/csp_resnet.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/csp_resnet.py new file mode 100644 index 0000000..17887c7 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/csp_resnet.py @@ -0,0 +1,302 @@ +""" +https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.6/ppdet/modeling/backbones/cspresnet.py + +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...core import register +from .common import get_activation + +__all__ = ["CSPResNet"] + + +donwload_url = { + "s": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_s_pretrained_from_paddle.pth", + "m": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_m_pretrained_from_paddle.pth", + "l": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_l_pretrained_from_paddle.pth", + "x": "https://github.com/lyuwenyu/storage/releases/download/v0.1/CSPResNetb_x_pretrained_from_paddle.pth", +} + + +class ConvBNLayer(nn.Module): + def __init__(self, ch_in, ch_out, filter_size=3, stride=1, groups=1, padding=0, act=None): + super().__init__() + self.conv = nn.Conv2d( + ch_in, ch_out, filter_size, stride, padding, groups=groups, bias=False + ) + self.bn = nn.BatchNorm2d(ch_out) + self.act = get_activation(act) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.conv(x) + x = self.bn(x) + x = self.act(x) + return x + + +class RepVggBlock(nn.Module): + def __init__(self, ch_in, ch_out, act="relu", alpha: bool = False): + super().__init__() + self.ch_in = ch_in + self.ch_out = ch_out + self.conv1 = ConvBNLayer(ch_in, ch_out, 3, stride=1, padding=1, act=None) + self.conv2 = ConvBNLayer(ch_in, ch_out, 1, stride=1, padding=0, act=None) + self.act = get_activation(act) + + if alpha: + self.alpha = nn.Parameter( + torch.ones( + 1, + ) + ) + else: + self.alpha = None + + def forward(self, x): + if hasattr(self, "conv"): + y = self.conv(x) + else: + if self.alpha: + y = self.conv1(x) + self.alpha * self.conv2(x) + else: + y = self.conv1(x) + self.conv2(x) + y = self.act(y) + return y + + def convert_to_deploy(self): + if not hasattr(self, "conv"): + self.conv = nn.Conv2d(self.ch_in, self.ch_out, 3, 1, padding=1) + + kernel, bias = self.get_equivalent_kernel_bias() + self.conv.weight.data = kernel + self.conv.bias.data = bias + + def get_equivalent_kernel_bias(self): + kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1) + kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2) + + if self.alpha: + return kernel3x3 + self.alpha * self._pad_1x1_to_3x3_tensor( + kernel1x1 + ), bias3x3 + self.alpha * bias1x1 + else: + return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1), bias3x3 + bias1x1 + + def _pad_1x1_to_3x3_tensor(self, kernel1x1): + if kernel1x1 is None: + return 0 + else: + return F.pad(kernel1x1, [1, 1, 1, 1]) + + def _fuse_bn_tensor(self, branch: ConvBNLayer): + if branch is None: + return 0, 0 + kernel = branch.conv.weight + running_mean = branch.norm.running_mean + running_var = branch.norm.running_var + gamma = branch.norm.weight + beta = branch.norm.bias + eps = branch.norm.eps + std = (running_var + eps).sqrt() + t = (gamma / std).reshape(-1, 1, 1, 1) + return kernel * t, beta - running_mean * gamma / std + + +class BasicBlock(nn.Module): + def __init__(self, ch_in, ch_out, act="relu", shortcut=True, use_alpha=False): + super().__init__() + assert ch_in == ch_out + self.conv1 = ConvBNLayer(ch_in, ch_out, 3, stride=1, padding=1, act=act) + self.conv2 = RepVggBlock(ch_out, ch_out, act=act, alpha=use_alpha) + self.shortcut = shortcut + + def forward(self, x): + y = self.conv1(x) + y = self.conv2(y) + if self.shortcut: + return x + y + else: + return y + + +class EffectiveSELayer(nn.Module): + """Effective Squeeze-Excitation + From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667 + """ + + def __init__(self, channels, act="hardsigmoid"): + super(EffectiveSELayer, self).__init__() + self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0) + self.act = get_activation(act) + + def forward(self, x: torch.Tensor): + x_se = x.mean((2, 3), keepdim=True) + x_se = self.fc(x_se) + x_se = self.act(x_se) + return x * x_se + + +class CSPResStage(nn.Module): + def __init__(self, block_fn, ch_in, ch_out, n, stride, act="relu", attn="eca", use_alpha=False): + super().__init__() + ch_mid = (ch_in + ch_out) // 2 + if stride == 2: + self.conv_down = ConvBNLayer(ch_in, ch_mid, 3, stride=2, padding=1, act=act) + else: + self.conv_down = None + self.conv1 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act) + self.conv2 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act) + self.blocks = nn.Sequential( + *[ + block_fn(ch_mid // 2, ch_mid // 2, act=act, shortcut=True, use_alpha=use_alpha) + for i in range(n) + ] + ) + if attn: + self.attn = EffectiveSELayer(ch_mid, act="hardsigmoid") + else: + self.attn = None + + self.conv3 = ConvBNLayer(ch_mid, ch_out, 1, act=act) + + def forward(self, x): + if self.conv_down is not None: + x = self.conv_down(x) + y1 = self.conv1(x) + y2 = self.blocks(self.conv2(x)) + y = torch.concat([y1, y2], dim=1) + if self.attn is not None: + y = self.attn(y) + y = self.conv3(y) + return y + + +@register() +class CSPResNet(nn.Module): + layers = [3, 6, 6, 3] + channels = [64, 128, 256, 512, 1024] + model_cfg = { + "s": { + "depth_mult": 0.33, + "width_mult": 0.50, + }, + "m": { + "depth_mult": 0.67, + "width_mult": 0.75, + }, + "l": { + "depth_mult": 1.00, + "width_mult": 1.00, + }, + "x": { + "depth_mult": 1.33, + "width_mult": 1.25, + }, + } + + def __init__( + self, + name: str, + act="silu", + return_idx=[1, 2, 3], + use_large_stem=True, + use_alpha=False, + pretrained=False, + ): + super().__init__() + depth_mult = self.model_cfg[name]["depth_mult"] + width_mult = self.model_cfg[name]["width_mult"] + + channels = [max(round(c * width_mult), 1) for c in self.channels] + layers = [max(round(l * depth_mult), 1) for l in self.layers] + act = get_activation(act) + + if use_large_stem: + self.stem = nn.Sequential( + OrderedDict( + [ + ( + "conv1", + ConvBNLayer(3, channels[0] // 2, 3, stride=2, padding=1, act=act), + ), + ( + "conv2", + ConvBNLayer( + channels[0] // 2, channels[0] // 2, 3, stride=1, padding=1, act=act + ), + ), + ( + "conv3", + ConvBNLayer( + channels[0] // 2, channels[0], 3, stride=1, padding=1, act=act + ), + ), + ] + ) + ) + else: + self.stem = nn.Sequential( + OrderedDict( + [ + ( + "conv1", + ConvBNLayer(3, channels[0] // 2, 3, stride=2, padding=1, act=act), + ), + ( + "conv2", + ConvBNLayer( + channels[0] // 2, channels[0], 3, stride=1, padding=1, act=act + ), + ), + ] + ) + ) + + n = len(channels) - 1 + self.stages = nn.Sequential( + OrderedDict( + [ + ( + str(i), + CSPResStage( + BasicBlock, + channels[i], + channels[i + 1], + layers[i], + 2, + act=act, + use_alpha=use_alpha, + ), + ) + for i in range(n) + ] + ) + ) + + self._out_channels = channels[1:] + self._out_strides = [4 * 2**i for i in range(n)] + self.return_idx = return_idx + + if pretrained: + if isinstance(pretrained, bool) or "http" in pretrained: + state = torch.hub.load_state_dict_from_url(donwload_url[name], map_location="cpu") + else: + state = torch.load(pretrained, map_location="cpu") + self.load_state_dict(state) + print(f"Load CSPResNet_{name} state_dict") + + def forward(self, x): + x = self.stem(x) + outs = [] + for idx, stage in enumerate(self.stages): + x = stage(x) + if idx in self.return_idx: + outs.append(x) + + return outs diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/hgnetv2.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/hgnetv2.py new file mode 100644 index 0000000..85d99d6 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/hgnetv2.py @@ -0,0 +1,579 @@ +""" +reference +- https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py + +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import logging +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...core import register +from .common import FrozenBatchNorm2d + +# Constants for initialization +kaiming_normal_ = nn.init.kaiming_normal_ +zeros_ = nn.init.zeros_ +ones_ = nn.init.ones_ + +__all__ = ["HGNetv2"] + +def safe_barrier(): + if torch.distributed.is_available() and torch.distributed.is_initialized(): + torch.distributed.barrier() + else: + pass + +def safe_get_rank(): + if torch.distributed.is_available() and torch.distributed.is_initialized(): + return torch.distributed.get_rank() + else: + return 0 + +class LearnableAffineBlock(nn.Module): + def __init__(self, scale_value=1.0, bias_value=0.0): + super().__init__() + self.scale = nn.Parameter(torch.tensor([scale_value]), requires_grad=True) + self.bias = nn.Parameter(torch.tensor([bias_value]), requires_grad=True) + + def forward(self, x): + return self.scale * x + self.bias + + +class ConvBNAct(nn.Module): + def __init__( + self, + in_chs, + out_chs, + kernel_size, + stride=1, + groups=1, + padding="", + use_act=True, + use_lab=False, + ): + super().__init__() + self.use_act = use_act + self.use_lab = use_lab + if padding == "same": + self.conv = nn.Sequential( + nn.ZeroPad2d([0, 1, 0, 1]), + nn.Conv2d(in_chs, out_chs, kernel_size, stride, groups=groups, bias=False), + ) + else: + self.conv = nn.Conv2d( + in_chs, + out_chs, + kernel_size, + stride, + padding=(kernel_size - 1) // 2, + groups=groups, + bias=False, + ) + self.bn = nn.BatchNorm2d(out_chs) + if self.use_act: + self.act = nn.ReLU() + else: + self.act = nn.Identity() + if self.use_act and self.use_lab: + self.lab = LearnableAffineBlock() + else: + self.lab = nn.Identity() + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + x = self.act(x) + x = self.lab(x) + return x + + +class LightConvBNAct(nn.Module): + def __init__( + self, + in_chs, + out_chs, + kernel_size, + groups=1, + use_lab=False, + ): + super().__init__() + self.conv1 = ConvBNAct( + in_chs, + out_chs, + kernel_size=1, + use_act=False, + use_lab=use_lab, + ) + self.conv2 = ConvBNAct( + out_chs, + out_chs, + kernel_size=kernel_size, + groups=out_chs, + use_act=True, + use_lab=use_lab, + ) + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + return x + + +class StemBlock(nn.Module): + # for HGNetv2 + def __init__(self, in_chs, mid_chs, out_chs, use_lab=False): + super().__init__() + self.stem1 = ConvBNAct( + in_chs, + mid_chs, + kernel_size=3, + stride=2, + use_lab=use_lab, + ) + self.stem2a = ConvBNAct( + mid_chs, + mid_chs // 2, + kernel_size=2, + stride=1, + use_lab=use_lab, + ) + self.stem2b = ConvBNAct( + mid_chs // 2, + mid_chs, + kernel_size=2, + stride=1, + use_lab=use_lab, + ) + self.stem3 = ConvBNAct( + mid_chs * 2, + mid_chs, + kernel_size=3, + stride=2, + use_lab=use_lab, + ) + self.stem4 = ConvBNAct( + mid_chs, + out_chs, + kernel_size=1, + stride=1, + use_lab=use_lab, + ) + self.pool = nn.MaxPool2d(kernel_size=2, stride=1, ceil_mode=True) + + def forward(self, x): + x = self.stem1(x) + x = F.pad(x, (0, 1, 0, 1)) + x2 = self.stem2a(x) + x2 = F.pad(x2, (0, 1, 0, 1)) + x2 = self.stem2b(x2) + x1 = self.pool(x) + x = torch.cat([x1, x2], dim=1) + x = self.stem3(x) + x = self.stem4(x) + return x + + +class EseModule(nn.Module): + def __init__(self, chs): + super().__init__() + self.conv = nn.Conv2d( + chs, + chs, + kernel_size=1, + stride=1, + padding=0, + ) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + identity = x + x = x.mean((2, 3), keepdim=True) + x = self.conv(x) + x = self.sigmoid(x) + return torch.mul(identity, x) + + +class HG_Block(nn.Module): + def __init__( + self, + in_chs, + mid_chs, + out_chs, + layer_num, + kernel_size=3, + residual=False, + light_block=False, + use_lab=False, + agg="ese", + drop_path=0.0, + ): + super().__init__() + self.residual = residual + + self.layers = nn.ModuleList() + for i in range(layer_num): + if light_block: + self.layers.append( + LightConvBNAct( + in_chs if i == 0 else mid_chs, + mid_chs, + kernel_size=kernel_size, + use_lab=use_lab, + ) + ) + else: + self.layers.append( + ConvBNAct( + in_chs if i == 0 else mid_chs, + mid_chs, + kernel_size=kernel_size, + stride=1, + use_lab=use_lab, + ) + ) + + # feature aggregation + total_chs = in_chs + layer_num * mid_chs + if agg == "se": + aggregation_squeeze_conv = ConvBNAct( + total_chs, + out_chs // 2, + kernel_size=1, + stride=1, + use_lab=use_lab, + ) + aggregation_excitation_conv = ConvBNAct( + out_chs // 2, + out_chs, + kernel_size=1, + stride=1, + use_lab=use_lab, + ) + self.aggregation = nn.Sequential( + aggregation_squeeze_conv, + aggregation_excitation_conv, + ) + else: + aggregation_conv = ConvBNAct( + total_chs, + out_chs, + kernel_size=1, + stride=1, + use_lab=use_lab, + ) + att = EseModule(out_chs) + self.aggregation = nn.Sequential( + aggregation_conv, + att, + ) + + self.drop_path = nn.Dropout(drop_path) if drop_path else nn.Identity() + + def forward(self, x): + identity = x + output = [x] + for layer in self.layers: + x = layer(x) + output.append(x) + x = torch.cat(output, dim=1) + x = self.aggregation(x) + if self.residual: + x = self.drop_path(x) + identity + return x + + +class HG_Stage(nn.Module): + def __init__( + self, + in_chs, + mid_chs, + out_chs, + block_num, + layer_num, + downsample=True, + light_block=False, + kernel_size=3, + use_lab=False, + agg="se", + drop_path=0.0, + ): + super().__init__() + self.downsample = downsample + if downsample: + self.downsample = ConvBNAct( + in_chs, + in_chs, + kernel_size=3, + stride=2, + groups=in_chs, + use_act=False, + use_lab=use_lab, + ) + else: + self.downsample = nn.Identity() + + blocks_list = [] + for i in range(block_num): + blocks_list.append( + HG_Block( + in_chs if i == 0 else out_chs, + mid_chs, + out_chs, + layer_num, + residual=False if i == 0 else True, + kernel_size=kernel_size, + light_block=light_block, + use_lab=use_lab, + agg=agg, + drop_path=drop_path[i] if isinstance(drop_path, (list, tuple)) else drop_path, + ) + ) + self.blocks = nn.Sequential(*blocks_list) + + def forward(self, x): + x = self.downsample(x) + x = self.blocks(x) + return x + + +@register() +class HGNetv2(nn.Module): + """ + HGNetV2 + Args: + stem_channels: list. Number of channels for the stem block. + stage_type: str. The stage configuration of HGNet. such as the number of channels, stride, etc. + use_lab: boolean. Whether to use LearnableAffineBlock in network. + lr_mult_list: list. Control the learning rate of different stages. + Returns: + model: nn.Layer. Specific HGNetV2 model depends on args. + """ + + arch_configs = { + "B0": { + "stem_channels": [3, 16, 16], + "stage_config": { + # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num + "stage1": [16, 16, 64, 1, False, False, 3, 3], + "stage2": [64, 32, 256, 1, True, False, 3, 3], + "stage3": [256, 64, 512, 2, True, True, 5, 3], + "stage4": [512, 128, 1024, 1, True, True, 5, 3], + }, + "url": "https://github.com/Peterande/storage/releases/download/dfinev1.0/PPHGNetV2_B0_stage1.pth", + }, + "B1": { + "stem_channels": [3, 24, 32], + "stage_config": { + # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num + "stage1": [32, 32, 64, 1, False, False, 3, 3], + "stage2": [64, 48, 256, 1, True, False, 3, 3], + "stage3": [256, 96, 512, 2, True, True, 5, 3], + "stage4": [512, 192, 1024, 1, True, True, 5, 3], + }, + "url": "https://github.com/Peterande/storage/releases/download/dfinev1.0/PPHGNetV2_B1_stage1.pth", + }, + "B2": { + "stem_channels": [3, 24, 32], + "stage_config": { + # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num + "stage1": [32, 32, 96, 1, False, False, 3, 4], + "stage2": [96, 64, 384, 1, True, False, 3, 4], + "stage3": [384, 128, 768, 3, True, True, 5, 4], + "stage4": [768, 256, 1536, 1, True, True, 5, 4], + }, + "url": "https://github.com/Peterande/storage/releases/download/dfinev1.0/PPHGNetV2_B2_stage1.pth", + }, + "B3": { + "stem_channels": [3, 24, 32], + "stage_config": { + # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num + "stage1": [32, 32, 128, 1, False, False, 3, 5], + "stage2": [128, 64, 512, 1, True, False, 3, 5], + "stage3": [512, 128, 1024, 3, True, True, 5, 5], + "stage4": [1024, 256, 2048, 1, True, True, 5, 5], + }, + "url": "https://github.com/Peterande/storage/releases/download/dfinev1.0/PPHGNetV2_B3_stage1.pth", + }, + "B4": { + "stem_channels": [3, 32, 48], + "stage_config": { + # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num + "stage1": [48, 48, 128, 1, False, False, 3, 6], + "stage2": [128, 96, 512, 1, True, False, 3, 6], + "stage3": [512, 192, 1024, 3, True, True, 5, 6], + "stage4": [1024, 384, 2048, 1, True, True, 5, 6], + }, + "url": "https://github.com/Peterande/storage/releases/download/dfinev1.0/PPHGNetV2_B4_stage1.pth", + }, + "B5": { + "stem_channels": [3, 32, 64], + "stage_config": { + # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num + "stage1": [64, 64, 128, 1, False, False, 3, 6], + "stage2": [128, 128, 512, 2, True, False, 3, 6], + "stage3": [512, 256, 1024, 5, True, True, 5, 6], + "stage4": [1024, 512, 2048, 2, True, True, 5, 6], + }, + "url": "https://github.com/Peterande/storage/releases/download/dfinev1.0/PPHGNetV2_B5_stage1.pth", + }, + "B6": { + "stem_channels": [3, 48, 96], + "stage_config": { + # in_channels, mid_channels, out_channels, num_blocks, downsample, light_block, kernel_size, layer_num + "stage1": [96, 96, 192, 2, False, False, 3, 6], + "stage2": [192, 192, 512, 3, True, False, 3, 6], + "stage3": [512, 384, 1024, 6, True, True, 5, 6], + "stage4": [1024, 768, 2048, 3, True, True, 5, 6], + }, + "url": "https://github.com/Peterande/storage/releases/download/dfinev1.0/PPHGNetV2_B6_stage1.pth", + }, + } + + def __init__( + self, + name, + use_lab=False, + return_idx=[1, 2, 3], + freeze_stem_only=True, + freeze_at=0, + freeze_norm=True, + pretrained=True, + local_model_dir="weight/hgnetv2/", + ): + super().__init__() + self.use_lab = use_lab + self.return_idx = return_idx + + stem_channels = self.arch_configs[name]["stem_channels"] + stage_config = self.arch_configs[name]["stage_config"] + download_url = self.arch_configs[name]["url"] + + self._out_strides = [4, 8, 16, 32] + self._out_channels = [stage_config[k][2] for k in stage_config] + + # stem + self.stem = StemBlock( + in_chs=stem_channels[0], + mid_chs=stem_channels[1], + out_chs=stem_channels[2], + use_lab=use_lab, + ) + + # stages + self.stages = nn.ModuleList() + for i, k in enumerate(stage_config): + ( + in_channels, + mid_channels, + out_channels, + block_num, + downsample, + light_block, + kernel_size, + layer_num, + ) = stage_config[k] + self.stages.append( + HG_Stage( + in_channels, + mid_channels, + out_channels, + block_num, + layer_num, + downsample, + light_block, + kernel_size, + use_lab, + ) + ) + + if freeze_at >= 0: + self._freeze_parameters(self.stem) + if not freeze_stem_only: + for i in range(min(freeze_at + 1, len(self.stages))): + self._freeze_parameters(self.stages[i]) + + if freeze_norm: + self._freeze_norm(self) + + if pretrained: + RED, GREEN, RESET = "\033[91m", "\033[92m", "\033[0m" + try: + # If the file doesn't exist locally, download from the URL + if safe_get_rank() == 0: + print( + GREEN + + "If the pretrained HGNetV2 can't be downloaded automatically. Please check your network connection." + + RESET + ) + print( + GREEN + + "Please check your network connection. Or download the model manually from " + + RESET + + f"{download_url}" + + GREEN + + " to " + + RESET + + f"{local_model_dir}." + + RESET + ) + state = torch.hub.load_state_dict_from_url( + download_url, map_location="cpu", model_dir=local_model_dir + ) + print(f"Loaded stage1 {name} HGNetV2 from URL.") + + # Wait for rank 0 to download the model + safe_barrier() + + # All processes load the downloaded model + model_path = local_model_dir + "PPHGNetV2_" + name + "_stage1.pth" + state = torch.load(model_path, map_location="cpu") + + self.load_state_dict(state) + print(f"Loaded stage1 {name} HGNetV2 from URL.") + + except (Exception, KeyboardInterrupt) as e: + if safe_get_rank() == 0: + print(f"{str(e)}") + logging.error( + RED + "CRITICAL WARNING: Failed to load pretrained HGNetV2 model" + RESET + ) + logging.error( + GREEN + + "Please check your network connection. Or download the model manually from " + + RESET + + f"{download_url}" + + GREEN + + " to " + + RESET + + f"{local_model_dir}." + + RESET + ) + exit() + + def _freeze_norm(self, m: nn.Module): + if isinstance(m, nn.BatchNorm2d): + m = FrozenBatchNorm2d(m.num_features) + else: + for name, child in m.named_children(): + _child = self._freeze_norm(child) + if _child is not child: + setattr(m, name, _child) + return m + + def _freeze_parameters(self, m: nn.Module): + for p in m.parameters(): + p.requires_grad = False + + def forward(self, x): + x = self.stem(x) + outs = [] + for idx, stage in enumerate(self.stages): + x = stage(x) + if idx in self.return_idx: + outs.append(x) + return outs diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/presnet.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/presnet.py new file mode 100644 index 0000000..5543fe8 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/presnet.py @@ -0,0 +1,263 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...core import register +from .common import FrozenBatchNorm2d, get_activation + +__all__ = ["PResNet"] + + +ResNet_cfg = { + 18: [2, 2, 2, 2], + 34: [3, 4, 6, 3], + 50: [3, 4, 6, 3], + 101: [3, 4, 23, 3], + # 152: [3, 8, 36, 3], +} + + +donwload_url = { + 18: "https://github.com/lyuwenyu/storage/releases/download/v0.1/ResNet18_vd_pretrained_from_paddle.pth", + 34: "https://github.com/lyuwenyu/storage/releases/download/v0.1/ResNet34_vd_pretrained_from_paddle.pth", + 50: "https://github.com/lyuwenyu/storage/releases/download/v0.1/ResNet50_vd_ssld_v2_pretrained_from_paddle.pth", + 101: "https://github.com/lyuwenyu/storage/releases/download/v0.1/ResNet101_vd_ssld_pretrained_from_paddle.pth", +} + + +class ConvNormLayer(nn.Module): + def __init__(self, ch_in, ch_out, kernel_size, stride, padding=None, bias=False, act=None): + super().__init__() + self.conv = nn.Conv2d( + ch_in, + ch_out, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=bias, + ) + self.norm = nn.BatchNorm2d(ch_out) + self.act = get_activation(act) + + def forward(self, x): + return self.act(self.norm(self.conv(x))) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, ch_in, ch_out, stride, shortcut, act="relu", variant="b"): + super().__init__() + + self.shortcut = shortcut + + if not shortcut: + if variant == "d" and stride == 2: + self.short = nn.Sequential( + OrderedDict( + [ + ("pool", nn.AvgPool2d(2, 2, 0, ceil_mode=True)), + ("conv", ConvNormLayer(ch_in, ch_out, 1, 1)), + ] + ) + ) + else: + self.short = ConvNormLayer(ch_in, ch_out, 1, stride) + + self.branch2a = ConvNormLayer(ch_in, ch_out, 3, stride, act=act) + self.branch2b = ConvNormLayer(ch_out, ch_out, 3, 1, act=None) + self.act = nn.Identity() if act is None else get_activation(act) + + def forward(self, x): + out = self.branch2a(x) + out = self.branch2b(out) + if self.shortcut: + short = x + else: + short = self.short(x) + + out = out + short + out = self.act(out) + + return out + + +class BottleNeck(nn.Module): + expansion = 4 + + def __init__(self, ch_in, ch_out, stride, shortcut, act="relu", variant="b"): + super().__init__() + + if variant == "a": + stride1, stride2 = stride, 1 + else: + stride1, stride2 = 1, stride + + width = ch_out + + self.branch2a = ConvNormLayer(ch_in, width, 1, stride1, act=act) + self.branch2b = ConvNormLayer(width, width, 3, stride2, act=act) + self.branch2c = ConvNormLayer(width, ch_out * self.expansion, 1, 1) + + self.shortcut = shortcut + if not shortcut: + if variant == "d" and stride == 2: + self.short = nn.Sequential( + OrderedDict( + [ + ("pool", nn.AvgPool2d(2, 2, 0, ceil_mode=True)), + ("conv", ConvNormLayer(ch_in, ch_out * self.expansion, 1, 1)), + ] + ) + ) + else: + self.short = ConvNormLayer(ch_in, ch_out * self.expansion, 1, stride) + + self.act = nn.Identity() if act is None else get_activation(act) + + def forward(self, x): + out = self.branch2a(x) + out = self.branch2b(out) + out = self.branch2c(out) + + if self.shortcut: + short = x + else: + short = self.short(x) + + out = out + short + out = self.act(out) + + return out + + +class Blocks(nn.Module): + def __init__(self, block, ch_in, ch_out, count, stage_num, act="relu", variant="b"): + super().__init__() + + self.blocks = nn.ModuleList() + for i in range(count): + self.blocks.append( + block( + ch_in, + ch_out, + stride=2 if i == 0 and stage_num != 2 else 1, + shortcut=False if i == 0 else True, + variant=variant, + act=act, + ) + ) + + if i == 0: + ch_in = ch_out * block.expansion + + def forward(self, x): + out = x + for block in self.blocks: + out = block(out) + return out + + +@register() +class PResNet(nn.Module): + def __init__( + self, + depth, + variant="d", + num_stages=4, + return_idx=[0, 1, 2, 3], + act="relu", + freeze_at=-1, + freeze_norm=True, + pretrained=False, + ): + super().__init__() + + block_nums = ResNet_cfg[depth] + ch_in = 64 + if variant in ["c", "d"]: + conv_def = [ + [3, ch_in // 2, 3, 2, "conv1_1"], + [ch_in // 2, ch_in // 2, 3, 1, "conv1_2"], + [ch_in // 2, ch_in, 3, 1, "conv1_3"], + ] + else: + conv_def = [[3, ch_in, 7, 2, "conv1_1"]] + + self.conv1 = nn.Sequential( + OrderedDict( + [ + (name, ConvNormLayer(cin, cout, k, s, act=act)) + for cin, cout, k, s, name in conv_def + ] + ) + ) + + ch_out_list = [64, 128, 256, 512] + block = BottleNeck if depth >= 50 else BasicBlock + + _out_channels = [block.expansion * v for v in ch_out_list] + _out_strides = [4, 8, 16, 32] + + self.res_layers = nn.ModuleList() + for i in range(num_stages): + stage_num = i + 2 + self.res_layers.append( + Blocks( + block, ch_in, ch_out_list[i], block_nums[i], stage_num, act=act, variant=variant + ) + ) + ch_in = _out_channels[i] + + self.return_idx = return_idx + self.out_channels = [_out_channels[_i] for _i in return_idx] + self.out_strides = [_out_strides[_i] for _i in return_idx] + + if freeze_at >= 0: + self._freeze_parameters(self.conv1) + for i in range(min(freeze_at, num_stages)): + self._freeze_parameters(self.res_layers[i]) + + if freeze_norm: + self._freeze_norm(self) + + if pretrained: + if isinstance(pretrained, bool) or "http" in pretrained: + state = torch.hub.load_state_dict_from_url( + donwload_url[depth], map_location="cpu", model_dir="weight" + ) + else: + state = torch.load(pretrained, map_location="cpu") + self.load_state_dict(state) + print(f"Load PResNet{depth} state_dict") + + def _freeze_parameters(self, m: nn.Module): + for p in m.parameters(): + p.requires_grad = False + + def _freeze_norm(self, m: nn.Module): + if isinstance(m, nn.BatchNorm2d): + m = FrozenBatchNorm2d(m.num_features) + else: + for name, child in m.named_children(): + _child = self._freeze_norm(child) + if _child is not child: + setattr(m, name, _child) + return m + + def forward(self, x): + conv1 = self.conv1(x) + x = F.max_pool2d(conv1, kernel_size=3, stride=2, padding=1) + outs = [] + for idx, stage in enumerate(self.res_layers): + x = stage(x) + if idx in self.return_idx: + outs.append(x) + return outs diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/test_resnet.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/test_resnet.py new file mode 100644 index 0000000..ab877ff --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/test_resnet.py @@ -0,0 +1,83 @@ +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...core import register + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + + self.conv1 = nn.Conv2d( + in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(planes) + + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False + ), + nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class _ResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super().__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +@register() +class MResNet(nn.Module): + def __init__(self, num_classes=10, num_blocks=[2, 2, 2, 2]) -> None: + super().__init__() + self.model = _ResNet(BasicBlock, num_blocks, num_classes) + + def forward(self, x): + return self.model(x) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/timm_model.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/timm_model.py new file mode 100644 index 0000000..d2258a5 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/timm_model.py @@ -0,0 +1,66 @@ +"""Copyright(c) 2023 lyuwenyu. All Rights Reserved. + +https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055#0583 +""" + +import torch +from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names + +from ...core import register +from .utils import IntermediateLayerGetter + + +@register() +class TimmModel(torch.nn.Module): + def __init__( + self, name, return_layers, pretrained=False, exportable=True, features_only=True, **kwargs + ) -> None: + super().__init__() + + import timm + + model = timm.create_model( + name, + pretrained=pretrained, + exportable=exportable, + features_only=features_only, + **kwargs, + ) + # nodes, _ = get_graph_node_names(model) + # print(nodes) + # features = {'': ''} + # model = create_feature_extractor(model, return_nodes=features) + + assert set(return_layers).issubset( + model.feature_info.module_name() + ), f"return_layers should be a subset of {model.feature_info.module_name()}" + + # self.model = model + self.model = IntermediateLayerGetter(model, return_layers) + + return_idx = [model.feature_info.module_name().index(name) for name in return_layers] + self.strides = [model.feature_info.reduction()[i] for i in return_idx] + self.channels = [model.feature_info.channels()[i] for i in return_idx] + self.return_idx = return_idx + self.return_layers = return_layers + + def forward(self, x: torch.Tensor): + outputs = self.model(x) + # outputs = [outputs[i] for i in self.return_idx] + return outputs + + +if __name__ == "__main__": + model = TimmModel(name="resnet34", return_layers=["layer2", "layer3"]) + data = torch.rand(1, 3, 640, 640) + outputs = model(data) + + for output in outputs: + print(output.shape) + + """ + model: + type: TimmModel + name: resnet34 + return_layers: ['layer2', 'layer4'] + """ diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/torchvision_model.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/torchvision_model.py new file mode 100644 index 0000000..dd207d9 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/torchvision_model.py @@ -0,0 +1,50 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torchvision + +from ...core import register +from .utils import IntermediateLayerGetter + +__all__ = ["TorchVisionModel"] + + +@register() +class TorchVisionModel(torch.nn.Module): + def __init__(self, name, return_layers, weights=None, **kwargs) -> None: + super().__init__() + + if weights is not None: + weights = getattr(torchvision.models.get_model_weights(name), weights) + + model = torchvision.models.get_model(name, weights=weights, **kwargs) + + # TODO hard code. + if hasattr(model, "features"): + model = IntermediateLayerGetter(model.features, return_layers) + else: + model = IntermediateLayerGetter(model, return_layers) + + self.model = model + + def forward(self, x): + return self.model(x) + + +# TorchVisionModel('swin_t', return_layers=['5', '7']) +# TorchVisionModel('resnet34', return_layers=['layer2','layer3', 'layer4']) + +# TorchVisionModel: +# name: swin_t +# return_layers: ['5', '7'] +# weights: DEFAULT + + +# model: +# type: TorchVisionModel +# name: resnet34 +# return_layers: ['layer2','layer3', 'layer4'] +# weights: DEFAULT diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/utils.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/utils.py new file mode 100644 index 0000000..58efeb3 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/backbone/utils.py @@ -0,0 +1,56 @@ +""" +https://github.com/pytorch/vision/blob/main/torchvision/models/_utils.py + +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from collections import OrderedDict +from typing import Dict, List + +import torch.nn as nn + + +class IntermediateLayerGetter(nn.ModuleDict): + """ + Module wrapper that returns intermediate layers from a model + + It has a strong assumption that the modules have been registered + into the model in the same order as they are used. + This means that one should **not** reuse the same nn.Module + twice in the forward if you want this to work. + + Additionally, it is only able to query submodules that are directly + assigned to the model. So if `model` is passed, `model.feature1` can + be returned, but not `model.feature1.layer2`. + """ + + _version = 3 + + def __init__(self, model: nn.Module, return_layers: List[str]) -> None: + if not set(return_layers).issubset([name for name, _ in model.named_children()]): + raise ValueError( + "return_layers are not present in model. {}".format( + [name for name, _ in model.named_children()] + ) + ) + orig_return_layers = return_layers + return_layers = {str(k): str(k) for k in return_layers} + layers = OrderedDict() + for name, module in model.named_children(): + layers[name] = module + if name in return_layers: + del return_layers[name] + if not return_layers: + break + + super().__init__(layers) + self.return_layers = orig_return_layers + + def forward(self, x): + outputs = [] + for name, module in self.items(): + x = module(x) + if name in self.return_layers: + outputs.append(x) + + return outputs diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/criterion/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/criterion/__init__.py new file mode 100644 index 0000000..da74acf --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/criterion/__init__.py @@ -0,0 +1,11 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch.nn as nn + +from ...core import register +from .det_criterion import DetCriterion + +CrossEntropyLoss = register()(nn.CrossEntropyLoss) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/criterion/det_criterion.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/criterion/det_criterion.py new file mode 100644 index 0000000..d5dbde5 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/criterion/det_criterion.py @@ -0,0 +1,188 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torch.distributed +import torch.nn.functional as F +import torchvision + +from ...core import register +from ...misc import box_ops, dist_utils + + +@register() +class DetCriterion(torch.nn.Module): + """Default Detection Criterion""" + + __share__ = ["num_classes"] + __inject__ = ["matcher"] + + def __init__( + self, + losses, + weight_dict, + num_classes=80, + alpha=0.75, + gamma=2.0, + box_fmt="cxcywh", + matcher=None, + ): + """ + Args: + losses (list[str]): requested losses, support ['boxes', 'vfl', 'focal'] + weight_dict (dict[str, float)]: corresponding losses weight, including + ['loss_bbox', 'loss_giou', 'loss_vfl', 'loss_focal'] + box_fmt (str): in box format, 'cxcywh' or 'xyxy' + matcher (Matcher): matcher used to match source to target + """ + super().__init__() + self.losses = losses + self.weight_dict = weight_dict + self.alpha = alpha + self.gamma = gamma + self.num_classes = num_classes + self.box_fmt = box_fmt + assert matcher is not None, "" + self.matcher = matcher + + def forward(self, outputs, targets, **kwargs): + """ + Args: + outputs: Dict[Tensor], 'pred_boxes', 'pred_logits', 'meta'. + targets, List[Dict[str, Tensor]], len(targets) == batch_size. + kwargs, store other information such as current epoch id. + Return: + losses, Dict[str, Tensor] + """ + matched = self.matcher(outputs, targets) + values = matched["values"] + indices = matched["indices"] + num_boxes = self._get_positive_nums(indices) + + # Compute all the requested losses + losses = {} + for loss in self.losses: + l_dict = self.get_loss(loss, outputs, targets, indices, num_boxes) + l_dict = {k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict} + losses.update(l_dict) + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def _get_positive_nums(self, indices): + # number of positive samples + num_pos = sum(len(i) for (i, _) in indices) + num_pos = torch.as_tensor([num_pos], dtype=torch.float32, device=indices[0][0].device) + if dist_utils.is_dist_available_and_initialized(): + torch.distributed.all_reduce(num_pos) + num_pos = torch.clamp(num_pos / dist_utils.get_world_size(), min=1).item() + return num_pos + + def loss_labels_focal(self, outputs, targets, indices, num_boxes): + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"] + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][j] for t, (_, j) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + + target = F.one_hot(target_classes, num_classes=self.num_classes + 1)[..., :-1].to( + src_logits.dtype + ) + loss = torchvision.ops.sigmoid_focal_loss( + src_logits, target, self.alpha, self.gamma, reduction="none" + ) + loss = loss.sum() / num_boxes + return {"loss_focal": loss} + + def loss_labels_vfl(self, outputs, targets, indices, num_boxes): + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][j] for t, (_, j) in zip(targets, indices)], dim=0) + + src_boxes = torchvision.ops.box_convert(src_boxes, in_fmt=self.box_fmt, out_fmt="xyxy") + target_boxes = torchvision.ops.box_convert( + target_boxes, in_fmt=self.box_fmt, out_fmt="xyxy" + ) + iou, _ = box_ops.elementwise_box_iou(src_boxes.detach(), target_boxes) + + src_logits: torch.Tensor = outputs["pred_logits"] + target_classes_o = torch.cat([t["labels"][j] for t, (_, j) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + target = F.one_hot(target_classes, num_classes=self.num_classes + 1)[..., :-1] + + target_score_o = torch.zeros_like(target_classes, dtype=src_logits.dtype) + target_score_o[idx] = iou.to(src_logits.dtype) + target_score = target_score_o.unsqueeze(-1) * target + + src_score = F.sigmoid(src_logits.detach()) + weight = self.alpha * src_score.pow(self.gamma) * (1 - target) + target_score + + loss = F.binary_cross_entropy_with_logits( + src_logits, target_score, weight=weight, reduction="none" + ) + loss = loss.sum() / num_boxes + return {"loss_vfl": loss} + + def loss_boxes(self, outputs, targets, indices, num_boxes): + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + losses = {} + loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + src_boxes = torchvision.ops.box_convert(src_boxes, in_fmt=self.box_fmt, out_fmt="xyxy") + target_boxes = torchvision.ops.box_convert( + target_boxes, in_fmt=self.box_fmt, out_fmt="xyxy" + ) + loss_giou = 1 - box_ops.elementwise_generalized_box_iou(src_boxes, target_boxes) + losses["loss_giou"] = loss_giou.sum() / num_boxes + return losses + + def loss_boxes_giou(self, outputs, targets, indices, num_boxes): + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + losses = {} + src_boxes = torchvision.ops.box_convert(src_boxes, in_fmt=self.box_fmt, out_fmt="xyxy") + target_boxes = torchvision.ops.box_convert( + target_boxes, in_fmt=self.box_fmt, out_fmt="xyxy" + ) + loss_giou = 1 - box_ops.elementwise_generalized_box_iou(src_boxes, target_boxes) + losses["loss_giou"] = loss_giou.sum() / num_boxes + return losses + + def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs): + loss_map = { + "boxes": self.loss_boxes, + "giou": self.loss_boxes_giou, + "vfl": self.loss_labels_vfl, + "focal": self.loss_labels_focal, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/__init__.py new file mode 100644 index 0000000..0949998 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/__init__.py @@ -0,0 +1,6 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from .nms_postprocessor import DetNMSPostProcessor diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/box_revert.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/box_revert.py new file mode 100644 index 0000000..c40f0c1 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/box_revert.py @@ -0,0 +1,66 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from enum import Enum + +import torch +import torchvision +from torch import Tensor + + +class BoxProcessFormat(Enum): + """Box process format + + Available formats are + * ``RESIZE`` + * ``RESIZE_KEEP_RATIO`` + * ``RESIZE_KEEP_RATIO_PADDING`` + """ + + RESIZE = 1 + RESIZE_KEEP_RATIO = 2 + RESIZE_KEEP_RATIO_PADDING = 3 + + +def box_revert( + boxes: Tensor, + orig_sizes: Tensor = None, + eval_sizes: Tensor = None, + inpt_sizes: Tensor = None, + inpt_padding: Tensor = None, + normalized: bool = True, + in_fmt: str = "cxcywh", + out_fmt: str = "xyxy", + process_fmt=BoxProcessFormat.RESIZE, +) -> Tensor: + """ + Args: + boxes(Tensor), [N, :, 4], (x1, y1, x2, y2), pred boxes. + inpt_sizes(Tensor), [N, 2], (w, h). input sizes. + orig_sizes(Tensor), [N, 2], (w, h). origin sizes. + inpt_padding (Tensor), [N, 2], (w_pad, h_pad, ...). + (inpt_sizes + inpt_padding) == eval_sizes + """ + assert in_fmt in ("cxcywh", "xyxy"), "" + + if normalized and eval_sizes is not None: + boxes = boxes * eval_sizes.repeat(1, 2).unsqueeze(1) + + if inpt_padding is not None: + if in_fmt == "xyxy": + boxes -= inpt_padding[:, :2].repeat(1, 2).unsqueeze(1) + elif in_fmt == "cxcywh": + boxes[..., :2] -= inpt_padding[:, :2].repeat(1, 2).unsqueeze(1) + + if orig_sizes is not None: + orig_sizes = orig_sizes.repeat(1, 2).unsqueeze(1) + if inpt_sizes is not None: + inpt_sizes = inpt_sizes.repeat(1, 2).unsqueeze(1) + boxes = boxes * (orig_sizes / inpt_sizes) + else: + boxes = boxes * orig_sizes + + boxes = torchvision.ops.box_convert(boxes, in_fmt=in_fmt, out_fmt=out_fmt) + return boxes diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/detr_postprocessor.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/detr_postprocessor.py new file mode 100644 index 0000000..6cccf72 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/detr_postprocessor.py @@ -0,0 +1,86 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +__all__ = ["DetDETRPostProcessor"] + +from .box_revert import BoxProcessFormat, box_revert + + +def mod(a, b): + out = a - a // b * b + return out + + +class DetDETRPostProcessor(nn.Module): + def __init__( + self, + num_classes=80, + use_focal_loss=True, + num_top_queries=300, + box_process_format=BoxProcessFormat.RESIZE, + ) -> None: + super().__init__() + self.use_focal_loss = use_focal_loss + self.num_top_queries = num_top_queries + self.num_classes = int(num_classes) + self.box_process_format = box_process_format + self.deploy_mode = False + + def extra_repr(self) -> str: + return f"use_focal_loss={self.use_focal_loss}, num_classes={self.num_classes}, num_top_queries={self.num_top_queries}" + + def forward(self, outputs, **kwargs): + logits, boxes = outputs["pred_logits"], outputs["pred_boxes"] + + if self.use_focal_loss: + scores = F.sigmoid(logits) + scores, index = torch.topk(scores.flatten(1), self.num_top_queries, dim=-1) + labels = index % self.num_classes + # labels = mod(index, self.num_classes) # for tensorrt + index = index // self.num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + + else: + scores = F.softmax(logits)[:, :, :-1] + scores, labels = scores.max(dim=-1) + if scores.shape[1] > self.num_top_queries: + scores, index = torch.topk(scores, self.num_top_queries, dim=-1) + labels = torch.gather(labels, dim=1, index=index) + boxes = torch.gather( + boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1]) + ) + + if kwargs is not None: + boxes = box_revert( + boxes, + in_fmt="cxcywh", + out_fmt="xyxy", + process_fmt=self.box_process_format, + normalized=True, + **kwargs, + ) + + # TODO for onnx export + if self.deploy_mode: + return labels, boxes, scores + + results = [] + for lab, box, sco in zip(labels, boxes, scores): + result = dict(labels=lab, boxes=box, scores=sco) + results.append(result) + + return results + + def deploy( + self, + ): + self.eval() + self.deploy_mode = True + return self diff --git a/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/nms_postprocessor.py b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/nms_postprocessor.py new file mode 100644 index 0000000..260e08c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/nn/postprocessor/nms_postprocessor.py @@ -0,0 +1,86 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from typing import Dict + +import torch +import torch.distributed +import torch.nn.functional as F +import torchvision +from torch import Tensor + +from ...core import register + +__all__ = [ + "DetNMSPostProcessor", +] + + +@register() +class DetNMSPostProcessor(torch.nn.Module): + def __init__( + self, + iou_threshold=0.7, + score_threshold=0.01, + keep_topk=300, + box_fmt="cxcywh", + logit_fmt="sigmoid", + ) -> None: + super().__init__() + self.iou_threshold = iou_threshold + self.score_threshold = score_threshold + self.keep_topk = keep_topk + self.box_fmt = box_fmt.lower() + self.logit_fmt = logit_fmt.lower() + self.logit_func = getattr(F, self.logit_fmt, None) + self.deploy_mode = False + + def forward(self, outputs: Dict[str, Tensor], orig_target_sizes: Tensor): + logits, boxes = outputs["pred_logits"], outputs["pred_boxes"] + pred_boxes = torchvision.ops.box_convert(boxes, in_fmt=self.box_fmt, out_fmt="xyxy") + pred_boxes *= orig_target_sizes.repeat(1, 2).unsqueeze(1) + + values, pred_labels = torch.max(logits, dim=-1) + + if self.logit_func: + pred_scores = self.logit_func(values) + else: + pred_scores = values + + # TODO for onnx export + if self.deploy_mode: + blobs = { + "pred_labels": pred_labels, + "pred_boxes": pred_boxes, + "pred_scores": pred_scores, + } + return blobs + + results = [] + for i in range(logits.shape[0]): + score_keep = pred_scores[i] > self.score_threshold + pred_box = pred_boxes[i][score_keep] + pred_label = pred_labels[i][score_keep] + pred_score = pred_scores[i][score_keep] + + keep = torchvision.ops.batched_nms(pred_box, pred_score, pred_label, self.iou_threshold) + keep = keep[: self.keep_topk] + + blob = { + "labels": pred_label[keep], + "boxes": pred_box[keep], + "scores": pred_score[keep], + } + + results.append(blob) + + return results + + def deploy( + self, + ): + self.eval() + self.deploy_mode = True + return self diff --git a/backend/apps/algorithms/algorithm/DFINE/src/optim/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/optim/__init__.py new file mode 100644 index 0000000..fbc06c0 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/optim/__init__.py @@ -0,0 +1,9 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from .amp import * +from .ema import * +from .optim import * +from .warmup import * diff --git a/backend/apps/algorithms/algorithm/DFINE/src/optim/amp.py b/backend/apps/algorithms/algorithm/DFINE/src/optim/amp.py new file mode 100644 index 0000000..5392899 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/optim/amp.py @@ -0,0 +1,12 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch.cuda.amp as amp + +from ..core import register + +__all__ = ["GradScaler"] + +GradScaler = register()(amp.grad_scaler.GradScaler) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/optim/ema.py b/backend/apps/algorithms/algorithm/DFINE/src/optim/ema.py new file mode 100644 index 0000000..9e508b6 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/optim/ema.py @@ -0,0 +1,108 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import math +from copy import deepcopy + +import torch +import torch.nn as nn + +from ..core import register +from ..misc import dist_utils + +__all__ = ["ModelEMA"] + + +@register() +class ModelEMA(object): + """ + Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models + Keep a moving average of everything in the model state_dict (parameters and buffers). + This is intended to allow functionality like + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + A smoothed version of the weights is necessary for some training schemes to perform well. + This class is sensitive where it is initialized in the sequence of model init, + GPU assignment and distributed training wrappers. + """ + + def __init__( + self, model: nn.Module, decay: float = 0.9999, warmups: int = 1000, start: int = 0 + ): + super().__init__() + + self.module = deepcopy(dist_utils.de_parallel(model)).eval() + # if next(model.parameters()).device.type != 'cpu': + # self.module.half() # FP16 EMA + + self.decay = decay + self.warmups = warmups + self.before_start = 0 + self.start = start + self.updates = 0 # number of EMA updates + if warmups == 0: + self.decay_fn = lambda x: decay + else: + self.decay_fn = lambda x: decay * ( + 1 - math.exp(-x / warmups) + ) # decay exponential ramp (to help early epochs) + + for p in self.module.parameters(): + p.requires_grad_(False) + + def update(self, model: nn.Module): + if self.before_start < self.start: + self.before_start += 1 + return + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay_fn(self.updates) + msd = dist_utils.de_parallel(model).state_dict() + for k, v in self.module.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1 - d) * msd[k].detach() + + def to(self, *args, **kwargs): + self.module = self.module.to(*args, **kwargs) + return self + + def state_dict( + self, + ): + return dict(module=self.module.state_dict(), updates=self.updates) + + def load_state_dict(self, state, strict=True): + self.module.load_state_dict(state["module"], strict=strict) + if "updates" in state: + self.updates = state["updates"] + + def forwad( + self, + ): + raise RuntimeError("ema...") + + def extra_repr(self) -> str: + return f"decay={self.decay}, warmups={self.warmups}" + + +class ExponentialMovingAverage(torch.optim.swa_utils.AveragedModel): + """Maintains moving averages of model parameters using an exponential decay. + ``ema_avg = decay * avg_model_param + (1 - decay) * model_param`` + `torch.optim.swa_utils.AveragedModel `_ + is used to compute the EMA. + """ + + def __init__(self, model, decay, device="cpu", use_buffers=True): + self.decay_fn = lambda x: decay * (1 - math.exp(-x / 2000)) + + def ema_avg(avg_model_param, model_param, num_averaged): + decay = self.decay_fn(num_averaged) + return decay * avg_model_param + (1 - decay) * model_param + + super().__init__(model, device, ema_avg, use_buffers=use_buffers) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/optim/optim.py b/backend/apps/algorithms/algorithm/DFINE/src/optim/optim.py new file mode 100644 index 0000000..cec3030 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/optim/optim.py @@ -0,0 +1,22 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch.optim as optim +import torch.optim.lr_scheduler as lr_scheduler + +from ..core import register + +__all__ = ["AdamW", "SGD", "Adam", "MultiStepLR", "CosineAnnealingLR", "OneCycleLR", "LambdaLR"] + + +SGD = register()(optim.SGD) +Adam = register()(optim.Adam) +AdamW = register()(optim.AdamW) + + +MultiStepLR = register()(lr_scheduler.MultiStepLR) +CosineAnnealingLR = register()(lr_scheduler.CosineAnnealingLR) +OneCycleLR = register()(lr_scheduler.OneCycleLR) +LambdaLR = register()(lr_scheduler.LambdaLR) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/optim/warmup.py b/backend/apps/algorithms/algorithm/DFINE/src/optim/warmup.py new file mode 100644 index 0000000..cd475ef --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/optim/warmup.py @@ -0,0 +1,56 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from torch.optim.lr_scheduler import LRScheduler + +from ..core import register + + +class Warmup(object): + def __init__( + self, lr_scheduler: LRScheduler, warmup_duration: int, last_step: int = -1 + ) -> None: + self.lr_scheduler = lr_scheduler + self.warmup_end_values = [pg["lr"] for pg in lr_scheduler.optimizer.param_groups] + self.last_step = last_step + self.warmup_duration = warmup_duration + self.step() + + def state_dict(self): + return {k: v for k, v in self.__dict__.items() if k != "lr_scheduler"} + + def load_state_dict(self, state_dict): + self.__dict__.update(state_dict) + + def get_warmup_factor(self, step, **kwargs): + raise NotImplementedError + + def step( + self, + ): + self.last_step += 1 + if self.last_step >= self.warmup_duration: + return + factor = self.get_warmup_factor(self.last_step) + for i, pg in enumerate(self.lr_scheduler.optimizer.param_groups): + pg["lr"] = factor * self.warmup_end_values[i] + + def finished( + self, + ): + if self.last_step >= self.warmup_duration: + return True + return False + + +@register() +class LinearWarmup(Warmup): + def __init__( + self, lr_scheduler: LRScheduler, warmup_duration: int, last_step: int = -1 + ) -> None: + super().__init__(lr_scheduler, warmup_duration, last_step) + + def get_warmup_factor(self, step): + return min(1.0, (step + 1) / self.warmup_duration) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/solver/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/solver/__init__.py new file mode 100644 index 0000000..440182a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/solver/__init__.py @@ -0,0 +1,15 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from typing import Dict + +from ._solver import BaseSolver +from .clas_solver import ClasSolver +from .det_solver import DetSolver + +TASKS: Dict[str, BaseSolver] = { + "classification": ClasSolver, + "detection": DetSolver, +} diff --git a/backend/apps/algorithms/algorithm/DFINE/src/solver/_solver.py b/backend/apps/algorithms/algorithm/DFINE/src/solver/_solver.py new file mode 100644 index 0000000..3120437 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/solver/_solver.py @@ -0,0 +1,783 @@ +import atexit +from datetime import datetime +from pathlib import Path +from typing import Dict + +import torch +import torch.nn as nn + +from ..core import BaseConfig +from ..misc import dist_utils + + +def to(m: nn.Module, device: str): + if m is None: + return None + return m.to(device) + + +def remove_module_prefix(state_dict): + new_state_dict = {} + for k, v in state_dict.items(): + if k.startswith("module."): + new_state_dict[k[7:]] = v + else: + new_state_dict[k] = v + return new_state_dict + + +class BaseSolver(object): + def __init__(self, cfg: BaseConfig) -> None: + self.cfg = cfg + self.obj365_ids = [ + 0, + 46, + 5, + 58, + 114, + 55, + 116, + 65, + 21, + 40, + 176, + 127, + 249, + 24, + 56, + 139, + 92, + 78, + 99, + 96, + 144, + 295, + 178, + 180, + 38, + 39, + 13, + 43, + 120, + 219, + 148, + 173, + 165, + 154, + 137, + 113, + 145, + 146, + 204, + 8, + 35, + 10, + 88, + 84, + 93, + 26, + 112, + 82, + 265, + 104, + 141, + 152, + 234, + 143, + 150, + 97, + 2, + 50, + 25, + 75, + 98, + 153, + 37, + 73, + 115, + 132, + 106, + 61, + 163, + 134, + 277, + 81, + 133, + 18, + 94, + 30, + 169, + 70, + 328, + 226, + ] + + def _setup(self): + """Avoid instantiating unnecessary classes""" + cfg = self.cfg + if cfg.device: + device = torch.device(cfg.device) + else: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + self.model = cfg.model + + # NOTE: Must load_tuning_state before EMA instance building + if self.cfg.tuning: + print(f"Tuning checkpoint from {self.cfg.tuning}") + self.load_tuning_state(self.cfg.tuning) + + self.model = dist_utils.warp_model( + self.model.to(device), + sync_bn=cfg.sync_bn, + find_unused_parameters=cfg.find_unused_parameters, + ) + + self.criterion = self.to(cfg.criterion, device) + self.postprocessor = self.to(cfg.postprocessor, device) + + self.ema = self.to(cfg.ema, device) + self.scaler = cfg.scaler + + self.device = device + self.last_epoch = self.cfg.last_epoch + + self.output_dir = Path(cfg.output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.writer = cfg.writer + + if self.writer: + atexit.register(self.writer.close) + if dist_utils.is_main_process(): + self.writer.add_text("config", "{:s}".format(cfg.__repr__()), 0) + self.use_wandb = self.cfg.use_wandb + if self.use_wandb: + try: + import wandb + self.use_wandb = True + except ImportError: + self.use_wandb = False + + def cleanup(self): + if self.writer: + atexit.register(self.writer.close) + + def train(self): + self._setup() + self.optimizer = self.cfg.optimizer + self.lr_scheduler = self.cfg.lr_scheduler + self.lr_warmup_scheduler = self.cfg.lr_warmup_scheduler + + self.train_dataloader = dist_utils.warp_loader( + self.cfg.train_dataloader, shuffle=self.cfg.train_dataloader.shuffle + ) + self.val_dataloader = dist_utils.warp_loader( + self.cfg.val_dataloader, shuffle=self.cfg.val_dataloader.shuffle + ) + + self.evaluator = self.cfg.evaluator + + # NOTE: Instantiating order + if self.cfg.resume: + print(f"Resume checkpoint from {self.cfg.resume}") + self.load_resume_state(self.cfg.resume) + + def eval(self): + self._setup() + + self.val_dataloader = dist_utils.warp_loader( + self.cfg.val_dataloader, shuffle=self.cfg.val_dataloader.shuffle + ) + + self.evaluator = self.cfg.evaluator + + if self.cfg.resume: + print(f"Resume checkpoint from {self.cfg.resume}") + self.load_resume_state(self.cfg.resume) + + def to(self, module, device): + return module.to(device) if hasattr(module, "to") else module + + def state_dict(self): + """State dict, train/eval""" + state = {} + state["date"] = datetime.now().isoformat() + + # For resume + state["last_epoch"] = self.last_epoch + + for k, v in self.__dict__.items(): + if hasattr(v, "state_dict"): + v = dist_utils.de_parallel(v) + state[k] = v.state_dict() + + return state + + def load_state_dict(self, state): + """Load state dict, train/eval""" + if "last_epoch" in state: + self.last_epoch = state["last_epoch"] + print("Load last_epoch") + + for k, v in self.__dict__.items(): + if hasattr(v, "load_state_dict") and k in state: + v = dist_utils.de_parallel(v) + v.load_state_dict(state[k]) + print(f"Load {k}.state_dict") + + if hasattr(v, "load_state_dict") and k not in state: + if k == "ema": + model = getattr(self, "model", None) + if model is not None: + ema = dist_utils.de_parallel(v) + model_state_dict = remove_module_prefix(model.state_dict()) + ema.load_state_dict({"module": model_state_dict}) + print(f"Load {k}.state_dict from model.state_dict") + else: + print(f"Not load {k}.state_dict") + + def load_resume_state(self, path: str): + """Load resume""" + if path.startswith("http"): + state = torch.hub.load_state_dict_from_url(path, map_location="cpu") + else: + state = torch.load(path, map_location="cpu") + + # state['model'] = remove_module_prefix(state['model']) + self.load_state_dict(state) + + def load_tuning_state(self, path: str): + """Load model for tuning and adjust mismatched head parameters""" + if path.startswith("http"): + state = torch.hub.load_state_dict_from_url(path, map_location="cpu") + else: + state = torch.load(path, map_location="cpu") + + module = dist_utils.de_parallel(self.model) + + # Load the appropriate state dict + if "ema" in state: + pretrain_state_dict = state["ema"]["module"] + else: + pretrain_state_dict = state["model"] + + # Adjust head parameters between datasets + try: + adjusted_state_dict = self._adjust_head_parameters( + module.state_dict(), pretrain_state_dict + ) + stat, infos = self._matched_state(module.state_dict(), adjusted_state_dict) + except Exception: + stat, infos = self._matched_state(module.state_dict(), pretrain_state_dict) + + module.load_state_dict(stat, strict=False) + print(f"Load model.state_dict, {infos}") + + @staticmethod + def _matched_state(state: Dict[str, torch.Tensor], params: Dict[str, torch.Tensor]): + missed_list = [] + unmatched_list = [] + matched_state = {} + for k, v in state.items(): + if k in params: + if v.shape == params[k].shape: + matched_state[k] = params[k] + else: + unmatched_list.append(k) + else: + missed_list.append(k) + + return matched_state, {"missed": missed_list, "unmatched": unmatched_list} + + def _adjust_head_parameters(self, cur_state_dict, pretrain_state_dict): + """Adjust head parameters between datasets.""" + # List of parameters to adjust + if ( + pretrain_state_dict["decoder.denoising_class_embed.weight"].size() + != cur_state_dict["decoder.denoising_class_embed.weight"].size() + ): + del pretrain_state_dict["decoder.denoising_class_embed.weight"] + + head_param_names = ["decoder.enc_score_head.weight", "decoder.enc_score_head.bias"] + for i in range(8): + head_param_names.append(f"decoder.dec_score_head.{i}.weight") + head_param_names.append(f"decoder.dec_score_head.{i}.bias") + + adjusted_params = [] + + for param_name in head_param_names: + if param_name in cur_state_dict and param_name in pretrain_state_dict: + cur_tensor = cur_state_dict[param_name] + pretrain_tensor = pretrain_state_dict[param_name] + adjusted_tensor = self.map_class_weights(cur_tensor, pretrain_tensor) + if adjusted_tensor is not None: + pretrain_state_dict[param_name] = adjusted_tensor + adjusted_params.append(param_name) + else: + print(f"Cannot adjust parameter '{param_name}' due to size mismatch.") + + return pretrain_state_dict + + def map_class_weights(self, cur_tensor, pretrain_tensor): + """Map class weights from pretrain model to current model based on class IDs.""" + if pretrain_tensor.size() == cur_tensor.size(): + return pretrain_tensor + + adjusted_tensor = cur_tensor.clone() + adjusted_tensor.requires_grad = False + + if pretrain_tensor.size() > cur_tensor.size(): + for coco_id, obj_id in enumerate(self.obj365_ids): + adjusted_tensor[coco_id] = pretrain_tensor[obj_id + 1] + else: + for coco_id, obj_id in enumerate(self.obj365_ids): + adjusted_tensor[obj_id + 1] = pretrain_tensor[coco_id] + + return adjusted_tensor + + def fit(self): + raise NotImplementedError("") + + def val(self): + raise NotImplementedError("") + + +# obj365_classes = [ +# 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', +# 'Bottle', 'Desk', 'Cup', 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', +# 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book', 'Gloves', 'Storage box', +# 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', +# 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', +# 'Belt', 'Moniter/TV', 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', +# 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', 'Stool', 'Barrel/bucket', 'Van', +# 'Couch', 'Sandals', 'Bakset', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird', 'High Heels', +# 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck', +# 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', +# 'Laptop', 'Awning', 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', +# 'Sink', 'Apple', 'Air Conditioner', 'Knife', 'Hockey Stick', 'Paddle', 'Pickup Truck', +# 'Fork', 'Traffic Sign', 'Ballon', 'Tripod', 'Dog', 'Spoon', 'Clock', 'Pot', 'Cow', +# 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', +# 'Other Fish', 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', +# 'Machinery Vehicle', 'Fan', 'Green Vegetables', 'Banana', 'Baseball Glove', +# 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', 'Luggage', 'Nightstand', +# 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign', 'Dessert', +# 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', +# 'Baseball Bat', 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', +# 'Elephant', 'Skateboard', 'Surfboard', 'Gun', 'Skating and Skiing shoes', 'Gas stove', +# 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry', 'Other Balls', 'Shovel', +# 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks', 'Microwave', +# 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors', +# 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', +# 'Zebra', 'Grape', 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', +# 'Fire Extinguisher', 'Candy', 'Fire Truck', 'Billards', 'Converter', 'Bathtub', +# 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette ', 'Paint Brush', +# 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extention Cord', 'Tong', +# 'Tennis Racket', 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', +# 'Tennis', 'Ship', 'Swing', 'Coffee Machine', 'Slide', 'Carriage', 'Onion', +# 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine', 'Chicken', +# 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', +# 'Hotair ballon', 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', +# 'Blender', 'Peach', 'Rice', 'Wallet/Purse', 'Volleyball', 'Deer', 'Goose', 'Tape', +# 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball', 'Ambulance', 'Parking meter', +# 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', +# 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', +# 'Sandwich', 'Nuts', 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', +# 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit', 'Router/modem', 'Poker Card', 'Toaster', +# 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD', 'Pasta', 'Hammer', +# 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroon', 'Screwdriver', 'Soap', 'Recorder', +# 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measur/ Ruler', 'Pig', +# 'Showerhead', 'Globe', 'Chips', 'Steak', 'Crosswalk Sign', 'Stapler', 'Campel', +# 'Formula 1 ', 'Pomegranate', 'Dishwasher', 'Crab', 'Hoverboard', 'Meat ball', +# 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', +# 'Buttefly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', +# 'Hair Dryer', 'Egg tart', 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', +# 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French', 'Spring Rolls', 'Monkey', +# 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', +# 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Teniis paddle', +# 'Cosmetics Brush/Eyeliner Pencil', 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', +# 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis ' +# ] + +# coco_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', +# 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', +# 'stop sign', 'parking meter', 'bench', 'wild bird', 'cat', 'dog', +# 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', +# 'backpack', 'umbrella', 'handbag/satchel', 'tie', 'luggage', 'frisbee', +# 'skating and skiing shoes', 'snowboard', 'baseball', 'kite', 'baseball bat', +# 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', +# 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl/basin', +# 'banana', 'apple', 'sandwich', 'orange/tangerine', 'broccoli', 'carrot', +# 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', +# 'potted plant', 'bed', 'dinning table', 'toilet', 'moniter/tv', 'laptop', +# 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', +# 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', +# 'vase', 'scissors', 'stuffed toy', 'hair dryer', 'toothbrush'] + + +# obj365_classes = [ +# (0, 'Person'), +# (1, 'Sneakers'), +# (2, 'Chair'), +# (3, 'Other Shoes'), +# (4, 'Hat'), +# (5, 'Car'), +# (6, 'Lamp'), +# (7, 'Glasses'), +# (8, 'Bottle'), +# (9, 'Desk'), +# (10, 'Cup'), +# (11, 'Street Lights'), +# (12, 'Cabinet/shelf'), +# (13, 'Handbag/Satchel'), +# (14, 'Bracelet'), +# (15, 'Plate'), +# (16, 'Picture/Frame'), +# (17, 'Helmet'), +# (18, 'Book'), +# (19, 'Gloves'), +# (20, 'Storage box'), +# (21, 'Boat'), +# (22, 'Leather Shoes'), +# (23, 'Flower'), +# (24, 'Bench'), +# (25, 'Potted Plant'), +# (26, 'Bowl/Basin'), +# (27, 'Flag'), +# (28, 'Pillow'), +# (29, 'Boots'), +# (30, 'Vase'), +# (31, 'Microphone'), +# (32, 'Necklace'), +# (33, 'Ring'), +# (34, 'SUV'), +# (35, 'Wine Glass'), +# (36, 'Belt'), +# (37, 'Monitor/TV'), +# (38, 'Backpack'), +# (39, 'Umbrella'), +# (40, 'Traffic Light'), +# (41, 'Speaker'), +# (42, 'Watch'), +# (43, 'Tie'), +# (44, 'Trash bin Can'), +# (45, 'Slippers'), +# (46, 'Bicycle'), +# (47, 'Stool'), +# (48, 'Barrel/bucket'), +# (49, 'Van'), +# (50, 'Couch'), +# (51, 'Sandals'), +# (52, 'Basket'), +# (53, 'Drum'), +# (54, 'Pen/Pencil'), +# (55, 'Bus'), +# (56, 'Wild Bird'), +# (57, 'High Heels'), +# (58, 'Motorcycle'), +# (59, 'Guitar'), +# (60, 'Carpet'), +# (61, 'Cell Phone'), +# (62, 'Bread'), +# (63, 'Camera'), +# (64, 'Canned'), +# (65, 'Truck'), +# (66, 'Traffic cone'), +# (67, 'Cymbal'), +# (68, 'Lifesaver'), +# (69, 'Towel'), +# (70, 'Stuffed Toy'), +# (71, 'Candle'), +# (72, 'Sailboat'), +# (73, 'Laptop'), +# (74, 'Awning'), +# (75, 'Bed'), +# (76, 'Faucet'), +# (77, 'Tent'), +# (78, 'Horse'), +# (79, 'Mirror'), +# (80, 'Power outlet'), +# (81, 'Sink'), +# (82, 'Apple'), +# (83, 'Air Conditioner'), +# (84, 'Knife'), +# (85, 'Hockey Stick'), +# (86, 'Paddle'), +# (87, 'Pickup Truck'), +# (88, 'Fork'), +# (89, 'Traffic Sign'), +# (90, 'Balloon'), +# (91, 'Tripod'), +# (92, 'Dog'), +# (93, 'Spoon'), +# (94, 'Clock'), +# (95, 'Pot'), +# (96, 'Cow'), +# (97, 'Cake'), +# (98, 'Dining Table'), +# (99, 'Sheep'), +# (100, 'Hanger'), +# (101, 'Blackboard/Whiteboard'), +# (102, 'Napkin'), +# (103, 'Other Fish'), +# (104, 'Orange/Tangerine'), +# (105, 'Toiletry'), +# (106, 'Keyboard'), +# (107, 'Tomato'), +# (108, 'Lantern'), +# (109, 'Machinery Vehicle'), +# (110, 'Fan'), +# (111, 'Green Vegetables'), +# (112, 'Banana'), +# (113, 'Baseball Glove'), +# (114, 'Airplane'), +# (115, 'Mouse'), +# (116, 'Train'), +# (117, 'Pumpkin'), +# (118, 'Soccer'), +# (119, 'Skiboard'), +# (120, 'Luggage'), +# (121, 'Nightstand'), +# (122, 'Tea pot'), +# (123, 'Telephone'), +# (124, 'Trolley'), +# (125, 'Head Phone'), +# (126, 'Sports Car'), +# (127, 'Stop Sign'), +# (128, 'Dessert'), +# (129, 'Scooter'), +# (130, 'Stroller'), +# (131, 'Crane'), +# (132, 'Remote'), +# (133, 'Refrigerator'), +# (134, 'Oven'), +# (135, 'Lemon'), +# (136, 'Duck'), +# (137, 'Baseball Bat'), +# (138, 'Surveillance Camera'), +# (139, 'Cat'), +# (140, 'Jug'), +# (141, 'Broccoli'), +# (142, 'Piano'), +# (143, 'Pizza'), +# (144, 'Elephant'), +# (145, 'Skateboard'), +# (146, 'Surfboard'), +# (147, 'Gun'), +# (148, 'Skating and Skiing Shoes'), +# (149, 'Gas Stove'), +# (150, 'Donut'), +# (151, 'Bow Tie'), +# (152, 'Carrot'), +# (153, 'Toilet'), +# (154, 'Kite'), +# (155, 'Strawberry'), +# (156, 'Other Balls'), +# (157, 'Shovel'), +# (158, 'Pepper'), +# (159, 'Computer Box'), +# (160, 'Toilet Paper'), +# (161, 'Cleaning Products'), +# (162, 'Chopsticks'), +# (163, 'Microwave'), +# (164, 'Pigeon'), +# (165, 'Baseball'), +# (166, 'Cutting/chopping Board'), +# (167, 'Coffee Table'), +# (168, 'Side Table'), +# (169, 'Scissors'), +# (170, 'Marker'), +# (171, 'Pie'), +# (172, 'Ladder'), +# (173, 'Snowboard'), +# (174, 'Cookies'), +# (175, 'Radiator'), +# (176, 'Fire Hydrant'), +# (177, 'Basketball'), +# (178, 'Zebra'), +# (179, 'Grape'), +# (180, 'Giraffe'), +# (181, 'Potato'), +# (182, 'Sausage'), +# (183, 'Tricycle'), +# (184, 'Violin'), +# (185, 'Egg'), +# (186, 'Fire Extinguisher'), +# (187, 'Candy'), +# (188, 'Fire Truck'), +# (189, 'Billiards'), +# (190, 'Converter'), +# (191, 'Bathtub'), +# (192, 'Wheelchair'), +# (193, 'Golf Club'), +# (194, 'Briefcase'), +# (195, 'Cucumber'), +# (196, 'Cigar/Cigarette'), +# (197, 'Paint Brush'), +# (198, 'Pear'), +# (199, 'Heavy Truck'), +# (200, 'Hamburger'), +# (201, 'Extractor'), +# (202, 'Extension Cord'), +# (203, 'Tong'), +# (204, 'Tennis Racket'), +# (205, 'Folder'), +# (206, 'American Football'), +# (207, 'Earphone'), +# (208, 'Mask'), +# (209, 'Kettle'), +# (210, 'Tennis'), +# (211, 'Ship'), +# (212, 'Swing'), +# (213, 'Coffee Machine'), +# (214, 'Slide'), +# (215, 'Carriage'), +# (216, 'Onion'), +# (217, 'Green Beans'), +# (218, 'Projector'), +# (219, 'Frisbee'), +# (220, 'Washing Machine/Drying Machine'), +# (221, 'Chicken'), +# (222, 'Printer'), +# (223, 'Watermelon'), +# (224, 'Saxophone'), +# (225, 'Tissue'), +# (226, 'Toothbrush'), +# (227, 'Ice Cream'), +# (228, 'Hot Air Balloon'), +# (229, 'Cello'), +# (230, 'French Fries'), +# (231, 'Scale'), +# (232, 'Trophy'), +# (233, 'Cabbage'), +# (234, 'Hot Dog'), +# (235, 'Blender'), +# (236, 'Peach'), +# (237, 'Rice'), +# (238, 'Wallet/Purse'), +# (239, 'Volleyball'), +# (240, 'Deer'), +# (241, 'Goose'), +# (242, 'Tape'), +# (243, 'Tablet'), +# (244, 'Cosmetics'), +# (245, 'Trumpet'), +# (246, 'Pineapple'), +# (247, 'Golf Ball'), +# (248, 'Ambulance'), +# (249, 'Parking Meter'), +# (250, 'Mango'), +# (251, 'Key'), +# (252, 'Hurdle'), +# (253, 'Fishing Rod'), +# (254, 'Medal'), +# (255, 'Flute'), +# (256, 'Brush'), +# (257, 'Penguin'), +# (258, 'Megaphone'), +# (259, 'Corn'), +# (260, 'Lettuce'), +# (261, 'Garlic'), +# (262, 'Swan'), +# (263, 'Helicopter'), +# (264, 'Green Onion'), +# (265, 'Sandwich'), +# (266, 'Nuts'), +# (267, 'Speed Limit Sign'), +# (268, 'Induction Cooker'), +# (269, 'Broom'), +# (270, 'Trombone'), +# (271, 'Plum'), +# (272, 'Rickshaw'), +# (273, 'Goldfish'), +# (274, 'Kiwi Fruit'), +# (275, 'Router/Modem'), +# (276, 'Poker Card'), +# (277, 'Toaster'), +# (278, 'Shrimp'), +# (279, 'Sushi'), +# (280, 'Cheese'), +# (281, 'Notepaper'), +# (282, 'Cherry'), +# (283, 'Pliers'), +# (284, 'CD'), +# (285, 'Pasta'), +# (286, 'Hammer'), +# (287, 'Cue'), +# (288, 'Avocado'), +# (289, 'Hami Melon'), +# (290, 'Flask'), +# (291, 'Mushroom'), +# (292, 'Screwdriver'), +# (293, 'Soap'), +# (294, 'Recorder'), +# (295, 'Bear'), +# (296, 'Eggplant'), +# (297, 'Board Eraser'), +# (298, 'Coconut'), +# (299, 'Tape Measure/Ruler'), +# (300, 'Pig'), +# (301, 'Showerhead'), +# (302, 'Globe'), +# (303, 'Chips'), +# (304, 'Steak'), +# (305, 'Crosswalk Sign'), +# (306, 'Stapler'), +# (307, 'Camel'), +# (308, 'Formula 1'), +# (309, 'Pomegranate'), +# (310, 'Dishwasher'), +# (311, 'Crab'), +# (312, 'Hoverboard'), +# (313, 'Meatball'), +# (314, 'Rice Cooker'), +# (315, 'Tuba'), +# (316, 'Calculator'), +# (317, 'Papaya'), +# (318, 'Antelope'), +# (319, 'Parrot'), +# (320, 'Seal'), +# (321, 'Butterfly'), +# (322, 'Dumbbell'), +# (323, 'Donkey'), +# (324, 'Lion'), +# (325, 'Urinal'), +# (326, 'Dolphin'), +# (327, 'Electric Drill'), +# (328, 'Hair Dryer'), +# (329, 'Egg Tart'), +# (330, 'Jellyfish'), +# (331, 'Treadmill'), +# (332, 'Lighter'), +# (333, 'Grapefruit'), +# (334, 'Game Board'), +# (335, 'Mop'), +# (336, 'Radish'), +# (337, 'Baozi'), +# (338, 'Target'), +# (339, 'French'), +# (340, 'Spring Rolls'), +# (341, 'Monkey'), +# (342, 'Rabbit'), +# (343, 'Pencil Case'), +# (344, 'Yak'), +# (345, 'Red Cabbage'), +# (346, 'Binoculars'), +# (347, 'Asparagus'), +# (348, 'Barbell'), +# (349, 'Scallop'), +# (350, 'Noodles'), +# (351, 'Comb'), +# (352, 'Dumpling'), +# (353, 'Oyster'), +# (354, 'Table Tennis Paddle'), +# (355, 'Cosmetics Brush/Eyeliner Pencil'), +# (356, 'Chainsaw'), +# (357, 'Eraser'), +# (358, 'Lobster'), +# (359, 'Durian'), +# (360, 'Okra'), +# (361, 'Lipstick'), +# (362, 'Cosmetics Mirror'), +# (363, 'Curling'), +# (364, 'Table Tennis') +# ] diff --git a/backend/apps/algorithms/algorithm/DFINE/src/solver/clas_engine.py b/backend/apps/algorithms/algorithm/DFINE/src/solver/clas_engine.py new file mode 100644 index 0000000..fc5a680 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/solver/clas_engine.py @@ -0,0 +1,74 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torch.nn as nn + +from ..misc import MetricLogger, SmoothedValue, reduce_dict + + +def train_one_epoch( + model: nn.Module, criterion: nn.Module, dataloader, optimizer, ema, epoch, device +): + """ """ + model.train() + + metric_logger = MetricLogger(delimiter=" ") + metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{value:.6f}")) + print_freq = 100 + header = "Epoch: [{}]".format(epoch) + + for imgs, labels in metric_logger.log_every(dataloader, print_freq, header): + imgs = imgs.to(device) + labels = labels.to(device) + + preds = model(imgs) + loss: torch.Tensor = criterion(preds, labels, epoch) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if ema is not None: + ema.update(model) + + loss_reduced_values = {k: v.item() for k, v in reduce_dict({"loss": loss}).items()} + metric_logger.update(**loss_reduced_values) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + + stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} + return stats + + +@torch.no_grad() +def evaluate(model, criterion, dataloader, device): + model.eval() + + metric_logger = MetricLogger(delimiter=" ") + # metric_logger.add_meter('acc', SmoothedValue(window_size=1, fmt='{global_avg:.4f}')) + # metric_logger.add_meter('loss', SmoothedValue(window_size=1, fmt='{value:.2f}')) + metric_logger.add_meter("acc", SmoothedValue(window_size=1)) + metric_logger.add_meter("loss", SmoothedValue(window_size=1)) + + header = "Test:" + for imgs, labels in metric_logger.log_every(dataloader, 10, header): + imgs, labels = imgs.to(device), labels.to(device) + preds = model(imgs) + + acc = (preds.argmax(dim=-1) == labels).sum() / preds.shape[0] + loss = criterion(preds, labels) + + dict_reduced = reduce_dict({"acc": acc, "loss": loss}) + reduced_values = {k: v.item() for k, v in dict_reduced.items()} + metric_logger.update(**reduced_values) + + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + + stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} + return stats diff --git a/backend/apps/algorithms/algorithm/DFINE/src/solver/clas_solver.py b/backend/apps/algorithms/algorithm/DFINE/src/solver/clas_solver.py new file mode 100644 index 0000000..aefb3d0 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/solver/clas_solver.py @@ -0,0 +1,75 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import datetime +import json +import time +from pathlib import Path + +import torch +import torch.nn as nn + +from ..misc import dist_utils +from ._solver import BaseSolver +from .clas_engine import evaluate, train_one_epoch + + +class ClasSolver(BaseSolver): + def fit( + self, + ): + print("Start training") + self.train() + args = self.cfg + + n_parameters = sum(p.numel() for p in self.model.parameters() if p.requires_grad) + print("Number of params:", n_parameters) + + output_dir = Path(args.output_dir) + output_dir.mkdir(exist_ok=True) + + start_time = time.time() + start_epoch = self.last_epoch + 1 + for epoch in range(start_epoch, args.epochs): + if dist_utils.is_dist_available_and_initialized(): + self.train_dataloader.sampler.set_epoch(epoch) + + train_stats = train_one_epoch( + self.model, + self.criterion, + self.train_dataloader, + self.optimizer, + self.ema, + epoch=epoch, + device=self.device, + ) + self.lr_scheduler.step() + self.last_epoch += 1 + + if output_dir: + checkpoint_paths = [output_dir / "checkpoint.pth"] + # extra checkpoint before LR drop and every 100 epochs + if (epoch + 1) % args.checkpoint_freq == 0: + checkpoint_paths.append(output_dir / f"checkpoint{epoch:04}.pth") + for checkpoint_path in checkpoint_paths: + dist_utils.save_on_master(self.state_dict(epoch), checkpoint_path) + + module = self.ema.module if self.ema else self.model + test_stats = evaluate(module, self.criterion, self.val_dataloader, self.device) + + log_stats = { + **{f"train_{k}": v for k, v in train_stats.items()}, + **{f"test_{k}": v for k, v in test_stats.items()}, + "epoch": epoch, + "n_parameters": n_parameters, + } + + if output_dir and dist_utils.is_main_process(): + with (output_dir / "log.txt").open("a") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print("Training time {}".format(total_time_str)) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/solver/det_engine.py b/backend/apps/algorithms/algorithm/DFINE/src/solver/det_engine.py new file mode 100644 index 0000000..b877b09 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/solver/det_engine.py @@ -0,0 +1,259 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from DETR (https://github.com/facebookresearch/detr/blob/main/engine.py) +Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" + +import math +import sys +from typing import Dict, Iterable, List + +import numpy as np +import torch +import torch.amp +from torch.cuda.amp.grad_scaler import GradScaler +from torch.utils.tensorboard import SummaryWriter + +from ..data import CocoEvaluator +from ..data.dataset import mscoco_category2label +from ..misc import MetricLogger, SmoothedValue, dist_utils, save_samples +from ..optim import ModelEMA, Warmup +from .validator import Validator, scale_boxes + + +def train_one_epoch( + model: torch.nn.Module, + criterion: torch.nn.Module, + data_loader: Iterable, + optimizer: torch.optim.Optimizer, + device: torch.device, + epoch: int, + use_wandb: bool, + max_norm: float = 0, + **kwargs, +): + if use_wandb: + import wandb + + model.train() + criterion.train() + metric_logger = MetricLogger(delimiter=" ") + metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{value:.6f}")) + + epochs = kwargs.get("epochs", None) + header = "Epoch: [{}]".format(epoch) if epochs is None else "Epoch: [{}/{}]".format(epoch, epochs) + + print_freq = kwargs.get("print_freq", 10) + writer: SummaryWriter = kwargs.get("writer", None) + + ema: ModelEMA = kwargs.get("ema", None) + scaler: GradScaler = kwargs.get("scaler", None) + lr_warmup_scheduler: Warmup = kwargs.get("lr_warmup_scheduler", None) + losses = [] + + output_dir = kwargs.get("output_dir", None) + num_visualization_sample_batch = kwargs.get("num_visualization_sample_batch", 1) + + for i, (samples, targets) in enumerate( + metric_logger.log_every(data_loader, print_freq, header) + ): + global_step = epoch * len(data_loader) + i + metas = dict(epoch=epoch, step=i, global_step=global_step, epoch_step=len(data_loader)) + + if global_step < num_visualization_sample_batch and output_dir is not None and dist_utils.is_main_process(): + save_samples(samples, targets, output_dir, "train", normalized=True, box_fmt="cxcywh") + + samples = samples.to(device) + targets = [{k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in t.items()} for t in targets] + + if scaler is not None: + with torch.autocast(device_type=str(device), cache_enabled=True): + outputs = model(samples, targets=targets) + + if torch.isnan(outputs["pred_boxes"]).any() or torch.isinf(outputs["pred_boxes"]).any(): + print(outputs["pred_boxes"]) + state = model.state_dict() + new_state = {} + for key, value in model.state_dict().items(): + # Replace 'module' with 'model' in each key + new_key = key.replace("module.", "") + # Add the updated key-value pair to the state dictionary + state[new_key] = value + new_state["model"] = state + dist_utils.save_on_master(new_state, "./NaN.pth") + + with torch.autocast(device_type=str(device), enabled=False): + loss_dict = criterion(outputs, targets, **metas) + + loss = sum(loss_dict.values()) + scaler.scale(loss).backward() + + if max_norm > 0: + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + else: + outputs = model(samples, targets=targets) + loss_dict = criterion(outputs, targets, **metas) + + loss: torch.Tensor = sum(loss_dict.values()) + optimizer.zero_grad() + loss.backward() + + if max_norm > 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) + + optimizer.step() + + # ema + if ema is not None: + ema.update(model) + + if lr_warmup_scheduler is not None: + lr_warmup_scheduler.step() + + loss_dict_reduced = dist_utils.reduce_dict(loss_dict) + loss_value = sum(loss_dict_reduced.values()) + losses.append(loss_value.detach().cpu().numpy()) + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + print(loss_dict_reduced) + sys.exit(1) + + metric_logger.update(loss=loss_value, **loss_dict_reduced) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + + if writer and dist_utils.is_main_process() and global_step % 10 == 0: + writer.add_scalar("Loss/total", loss_value.item(), global_step) + for j, pg in enumerate(optimizer.param_groups): + writer.add_scalar(f"Lr/pg_{j}", pg["lr"], global_step) + for k, v in loss_dict_reduced.items(): + writer.add_scalar(f"Loss/{k}", v.item(), global_step) + + if use_wandb: + wandb.log( + {"lr": optimizer.param_groups[0]["lr"], "epoch": epoch, "train/loss": np.mean(losses)} + ) + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +@torch.no_grad() +def evaluate( + model: torch.nn.Module, + criterion: torch.nn.Module, + postprocessor, + data_loader, + coco_evaluator: CocoEvaluator, + device, + epoch: int, + use_wandb: bool, + **kwargs, +): + if use_wandb: + import wandb + + model.eval() + criterion.eval() + coco_evaluator.cleanup() + + metric_logger = MetricLogger(delimiter=" ") + # metric_logger.add_meter('class_error', SmoothedValue(window_size=1, fmt='{value:.2f}')) + header = "Test:" + + # iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessor.keys()) + iou_types = coco_evaluator.iou_types + # coco_evaluator = CocoEvaluator(base_ds, iou_types) + # coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75] + + gt: List[Dict[str, torch.Tensor]] = [] + preds: List[Dict[str, torch.Tensor]] = [] + + output_dir = kwargs.get("output_dir", None) + num_visualization_sample_batch = kwargs.get("num_visualization_sample_batch", 1) + + for i, (samples, targets) in enumerate(metric_logger.log_every(data_loader, 10, header)): + global_step = epoch * len(data_loader) + i + + if global_step < num_visualization_sample_batch and output_dir is not None and dist_utils.is_main_process(): + save_samples(samples, targets, output_dir, "val", normalized=False, box_fmt="xyxy") + + samples = samples.to(device) + targets = [{k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in t.items()} for t in targets] + + outputs = model(samples) + # with torch.autocast(device_type=str(device)): + # outputs = model(samples) + + # TODO (lyuwenyu), fix dataset converted using `convert_to_coco_api`? + orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0) + # orig_target_sizes = torch.tensor([[samples.shape[-1], samples.shape[-2]]], device=samples.device) + + results = postprocessor(outputs, orig_target_sizes) + + # if 'segm' in postprocessor.keys(): + # target_sizes = torch.stack([t["size"] for t in targets], dim=0) + # results = postprocessor['segm'](results, outputs, orig_target_sizes, target_sizes) + + res = {target["image_id"].item(): output for target, output in zip(targets, results)} + if coco_evaluator is not None: + coco_evaluator.update(res) + + # validator format for metrics + for idx, (target, result) in enumerate(zip(targets, results)): + gt.append( + { + "boxes": scale_boxes( # from model input size to original img size + target["boxes"], + (target["orig_size"][1], target["orig_size"][0]), + (samples[idx].shape[-1], samples[idx].shape[-2]), + ), + "labels": target["labels"], + } + ) + labels = ( + torch.tensor([mscoco_category2label[int(x.item())] for x in result["labels"].flatten()]) + .to(result["labels"].device) + .reshape(result["labels"].shape) + ) if postprocessor.remap_mscoco_category else result["labels"] + preds.append( + {"boxes": result["boxes"], "labels": labels, "scores": result["scores"]} + ) + + # Conf matrix, F1, Precision, Recall, box IoU + metrics = Validator(gt, preds).compute_metrics() + print("Metrics:", metrics) + if use_wandb: + metrics = {f"metrics/{k}": v for k, v in metrics.items()} + metrics["epoch"] = epoch + wandb.log(metrics) + + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + if coco_evaluator is not None: + coco_evaluator.synchronize_between_processes() + + # accumulate predictions from all images + if coco_evaluator is not None: + coco_evaluator.accumulate() + coco_evaluator.summarize() + + stats = {} + # stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} + if coco_evaluator is not None: + if "bbox" in iou_types: + stats["coco_eval_bbox"] = coco_evaluator.coco_eval["bbox"].stats.tolist() + if "segm" in iou_types: + stats["coco_eval_masks"] = coco_evaluator.coco_eval["segm"].stats.tolist() + + return stats, coco_evaluator diff --git a/backend/apps/algorithms/algorithm/DFINE/src/solver/det_solver.py b/backend/apps/algorithms/algorithm/DFINE/src/solver/det_solver.py new file mode 100644 index 0000000..9e57702 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/solver/det_solver.py @@ -0,0 +1,235 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import datetime +import json +import sys +import time + +import torch + +from ..misc import dist_utils, stats +from ._solver import BaseSolver +from .det_engine import evaluate, train_one_epoch + + +class DetSolver(BaseSolver): + def fit(self): + self.train() + args = self.cfg + metric_names = ["AP50:95", "AP50", "AP75", "APsmall", "APmedium", "APlarge"] + + if self.use_wandb: + import wandb + + wandb.init( + project=args.yaml_cfg["project_name"], + name=args.yaml_cfg["exp_name"], + config=args.yaml_cfg, + ) + wandb.watch(self.model) + + n_parameters, model_stats = stats(self.cfg) + print(model_stats) + sys.stdout.flush() + print("-" * 42 + "Start training" + "-" * 43) + sys.stdout.flush() + top1 = 0 + best_stat = { + "epoch": -1, + } + if self.last_epoch > 0: + module = self.ema.module if self.ema else self.model + test_stats, coco_evaluator = evaluate( + module, + self.criterion, + self.postprocessor, + self.val_dataloader, + self.evaluator, + self.device, + self.last_epoch, + self.use_wandb + ) + for k in test_stats: + best_stat["epoch"] = self.last_epoch + best_stat[k] = test_stats[k][0] + top1 = test_stats[k][0] + print(f"best_stat: {best_stat}") + sys.stdout.flush() + + best_stat_print = best_stat.copy() + start_time = time.time() + start_epoch = self.last_epoch + 1 + for epoch in range(start_epoch, args.epochs): + self.train_dataloader.set_epoch(epoch) + # self.train_dataloader.dataset.set_epoch(epoch) + if dist_utils.is_dist_available_and_initialized(): + self.train_dataloader.sampler.set_epoch(epoch) + + if epoch == self.train_dataloader.collate_fn.stop_epoch: + self.load_resume_state(str(self.output_dir / "best_stg1.pth")) + if self.ema: + self.ema.decay = self.train_dataloader.collate_fn.ema_restart_decay + print(f"Refresh EMA at epoch {epoch} with decay {self.ema.decay}") + sys.stdout.flush() + + train_stats = train_one_epoch( + self.model, + self.criterion, + self.train_dataloader, + self.optimizer, + self.device, + epoch, + epochs=args.epochs, + max_norm=args.clip_max_norm, + print_freq=args.print_freq, + ema=self.ema, + scaler=self.scaler, + lr_warmup_scheduler=self.lr_warmup_scheduler, + writer=self.writer, + use_wandb=self.use_wandb, + output_dir=self.output_dir, + ) + + if self.lr_warmup_scheduler is None or self.lr_warmup_scheduler.finished(): + self.lr_scheduler.step() + + self.last_epoch += 1 + + if self.output_dir and epoch < self.train_dataloader.collate_fn.stop_epoch: + checkpoint_paths = [self.output_dir / "last.pth"] + # extra checkpoint before LR drop and every 100 epochs + if (epoch + 1) % args.checkpoint_freq == 0: + checkpoint_paths.append(self.output_dir / f"checkpoint{epoch:04}.pth") + for checkpoint_path in checkpoint_paths: + dist_utils.save_on_master(self.state_dict(), checkpoint_path) + + module = self.ema.module if self.ema else self.model + test_stats, coco_evaluator = evaluate( + module, + self.criterion, + self.postprocessor, + self.val_dataloader, + self.evaluator, + self.device, + epoch, + self.use_wandb, + output_dir=self.output_dir, + ) + + # TODO + for k in test_stats: + if self.writer and dist_utils.is_main_process(): + for i, v in enumerate(test_stats[k]): + self.writer.add_scalar(f"Test/{k}_{i}".format(k), v, epoch) + + if k in best_stat: + best_stat["epoch"] = ( + epoch if test_stats[k][0] > best_stat[k] else best_stat["epoch"] + ) + best_stat[k] = max(best_stat[k], test_stats[k][0]) + else: + best_stat["epoch"] = epoch + best_stat[k] = test_stats[k][0] + + if best_stat[k] > top1: + best_stat_print["epoch"] = epoch + top1 = best_stat[k] + if self.output_dir: + if epoch >= self.train_dataloader.collate_fn.stop_epoch: + dist_utils.save_on_master( + self.state_dict(), self.output_dir / "best_stg2.pth" + ) + else: + dist_utils.save_on_master( + self.state_dict(), self.output_dir / "best_stg1.pth" + ) + + best_stat_print[k] = max(best_stat[k], top1) + print(f"best_stat: {best_stat_print}") # global best + sys.stdout.flush() + + if best_stat["epoch"] == epoch and self.output_dir: + if epoch >= self.train_dataloader.collate_fn.stop_epoch: + if test_stats[k][0] > top1: + top1 = test_stats[k][0] + dist_utils.save_on_master( + self.state_dict(), self.output_dir / "best_stg2.pth" + ) + else: + top1 = max(test_stats[k][0], top1) + dist_utils.save_on_master( + self.state_dict(), self.output_dir / "best_stg1.pth" + ) + + elif epoch >= self.train_dataloader.collate_fn.stop_epoch: + best_stat = { + "epoch": -1, + } + if self.ema: + self.ema.decay -= 0.0001 + self.load_resume_state(str(self.output_dir / "best_stg1.pth")) + print(f"Refresh EMA at epoch {epoch} with decay {self.ema.decay}") + sys.stdout.flush() + + log_stats = { + **{f"train_{k}": v for k, v in train_stats.items()}, + **{f"test_{k}": v for k, v in test_stats.items()}, + "epoch": epoch, + "n_parameters": n_parameters, + } + if self.use_wandb: + wandb_logs = {} + for idx, metric_name in enumerate(metric_names): + wandb_logs[f"metrics/{metric_name}"] = test_stats["coco_eval_bbox"][idx] + wandb_logs["epoch"] = epoch + wandb.log(wandb_logs) + + if self.output_dir and dist_utils.is_main_process(): + with (self.output_dir / "log.txt").open("a") as f: + f.write(json.dumps(log_stats) + "\n") + + # for evaluation logs + if coco_evaluator is not None: + (self.output_dir / "eval").mkdir(exist_ok=True) + if "bbox" in coco_evaluator.coco_eval: + filenames = ["latest.pth"] + if epoch % 50 == 0: + filenames.append(f"{epoch:03}.pth") + for name in filenames: + torch.save( + coco_evaluator.coco_eval["bbox"].eval, + self.output_dir / "eval" / name, + ) + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print("Training time {}".format(total_time_str)) + sys.stdout.flush() + + def val(self): + self.eval() + + module = self.ema.module if self.ema else self.model + test_stats, coco_evaluator = evaluate( + module, + self.criterion, + self.postprocessor, + self.val_dataloader, + self.evaluator, + self.device, + epoch=-1, + use_wandb=False, + ) + + if self.output_dir: + dist_utils.save_on_master( + coco_evaluator.coco_eval["bbox"].eval, self.output_dir / "eval.pth" + ) + + return diff --git a/backend/apps/algorithms/algorithm/DFINE/src/solver/validator.py b/backend/apps/algorithms/algorithm/DFINE/src/solver/validator.py new file mode 100644 index 0000000..e38308c --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/solver/validator.py @@ -0,0 +1,347 @@ +import copy +from collections import defaultdict +from pathlib import Path +from typing import Dict, List + +import matplotlib.pyplot as plt +import numpy as np +import torch +from loguru import logger +from torchvision.ops import box_iou + + +class Validator: + def __init__( + self, + gt: List[Dict[str, torch.Tensor]], + preds: List[Dict[str, torch.Tensor]], + conf_thresh=0.5, + iou_thresh=0.5, + ) -> None: + """ + Format example: + gt = [{'labels': tensor([0]), 'boxes': tensor([[561.0, 297.0, 661.0, 359.0]])}, ...] + len(gt) is the number of images + bboxes are in format [x1, y1, x2, y2], absolute values + """ + self.gt = gt + self.preds = preds + self.conf_thresh = conf_thresh + self.iou_thresh = iou_thresh + self.thresholds = np.arange(0.2, 1.0, 0.05) + self.conf_matrix = None + + def compute_metrics(self, extended=False) -> Dict[str, float]: + filtered_preds = filter_preds(copy.deepcopy(self.preds), self.conf_thresh) + metrics = self._compute_main_metrics(filtered_preds) + if not extended: + metrics.pop("extended_metrics", None) + return metrics + + def _compute_main_metrics(self, preds): + ( + self.metrics_per_class, + self.conf_matrix, + self.class_to_idx, + ) = self._compute_metrics_and_confusion_matrix(preds) + tps, fps, fns = 0, 0, 0 + ious = [] + extended_metrics = {} + for key, value in self.metrics_per_class.items(): + tps += value["TPs"] + fps += value["FPs"] + fns += value["FNs"] + ious.extend(value["IoUs"]) + + extended_metrics[f"precision_{key}"] = ( + value["TPs"] / (value["TPs"] + value["FPs"]) + if value["TPs"] + value["FPs"] > 0 + else 0 + ) + extended_metrics[f"recall_{key}"] = ( + value["TPs"] / (value["TPs"] + value["FNs"]) + if value["TPs"] + value["FNs"] > 0 + else 0 + ) + + extended_metrics[f"iou_{key}"] = np.mean(value["IoUs"]) + + precision = tps / (tps + fps) if (tps + fps) > 0 else 0 + recall = tps / (tps + fns) if (tps + fns) > 0 else 0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 + iou = np.mean(ious).item() if ious else 0 + return { + "f1": f1, + "precision": precision, + "recall": recall, + "iou": iou, + "TPs": tps, + "FPs": fps, + "FNs": fns, + "extended_metrics": extended_metrics, + } + + def _compute_matrix_multi_class(self, preds): + metrics_per_class = defaultdict(lambda: {"TPs": 0, "FPs": 0, "FNs": 0, "IoUs": []}) + for pred, gt in zip(preds, self.gt): + pred_boxes = pred["boxes"] + pred_labels = pred["labels"] + gt_boxes = gt["boxes"] + gt_labels = gt["labels"] + + # isolate each class + labels = torch.unique(torch.cat([pred_labels, gt_labels])) + for label in labels: + pred_cl_boxes = pred_boxes[pred_labels == label] # filter by bool mask + gt_cl_boxes = gt_boxes[gt_labels == label] + + n_preds = len(pred_cl_boxes) + n_gts = len(gt_cl_boxes) + if not (n_preds or n_gts): + continue + if not n_preds: + metrics_per_class[label.item()]["FNs"] += n_gts + metrics_per_class[label.item()]["IoUs"].extend([0] * n_gts) + continue + if not n_gts: + metrics_per_class[label.item()]["FPs"] += n_preds + metrics_per_class[label.item()]["IoUs"].extend([0] * n_preds) + continue + + ious = box_iou(pred_cl_boxes, gt_cl_boxes) # matrix of all IoUs + ious_mask = ious >= self.iou_thresh + + # indeces of boxes that have IoU >= threshold + pred_indices, gt_indices = torch.nonzero(ious_mask, as_tuple=True) + + if not pred_indices.numel(): # no predicts matched gts + metrics_per_class[label.item()]["FNs"] += n_gts + metrics_per_class[label.item()]["IoUs"].extend([0] * n_gts) + metrics_per_class[label.item()]["FPs"] += n_preds + metrics_per_class[label.item()]["IoUs"].extend([0] * n_preds) + continue + + iou_values = ious[pred_indices, gt_indices] + + # sorting by IoU to match hgihest scores first + sorted_indices = torch.argsort(-iou_values) + pred_indices = pred_indices[sorted_indices] + gt_indices = gt_indices[sorted_indices] + iou_values = iou_values[sorted_indices] + + matched_preds = set() + matched_gts = set() + for pred_idx, gt_idx, iou in zip(pred_indices, gt_indices, iou_values): + if gt_idx.item() not in matched_gts and pred_idx.item() not in matched_preds: + matched_preds.add(pred_idx.item()) + matched_gts.add(gt_idx.item()) + metrics_per_class[label.item()]["TPs"] += 1 + metrics_per_class[label.item()]["IoUs"].append(iou.item()) + + unmatched_preds = set(range(n_preds)) - matched_preds + unmatched_gts = set(range(n_gts)) - matched_gts + metrics_per_class[label.item()]["FPs"] += len(unmatched_preds) + metrics_per_class[label.item()]["IoUs"].extend([0] * len(unmatched_preds)) + metrics_per_class[label.item()]["FNs"] += len(unmatched_gts) + metrics_per_class[label.item()]["IoUs"].extend([0] * len(unmatched_gts)) + return metrics_per_class + + def _compute_metrics_and_confusion_matrix(self, preds): + # Initialize per-class metrics + metrics_per_class = defaultdict(lambda: {"TPs": 0, "FPs": 0, "FNs": 0, "IoUs": []}) + + # Collect all class IDs + all_classes = set() + for pred in preds: + all_classes.update(pred["labels"].tolist()) + for gt in self.gt: + all_classes.update(gt["labels"].tolist()) + all_classes = sorted(list(all_classes)) + class_to_idx = {cls_id: idx for idx, cls_id in enumerate(all_classes)} + n_classes = len(all_classes) + conf_matrix = np.zeros((n_classes + 1, n_classes + 1), dtype=int) # +1 for background class + + for pred, gt in zip(preds, self.gt): + pred_boxes = pred["boxes"] + pred_labels = pred["labels"] + gt_boxes = gt["boxes"] + gt_labels = gt["labels"] + + n_preds = len(pred_boxes) + n_gts = len(gt_boxes) + + if n_preds == 0 and n_gts == 0: + continue + + ious = box_iou(pred_boxes, gt_boxes) if n_preds > 0 and n_gts > 0 else torch.tensor([]) + # Assign matches between preds and gts + matched_pred_indices = set() + matched_gt_indices = set() + + if ious.numel() > 0: + # For each pred box, find the gt box with highest IoU + ious_mask = ious >= self.iou_thresh + pred_indices, gt_indices = torch.nonzero(ious_mask, as_tuple=True) + iou_values = ious[pred_indices, gt_indices] + + # Sorting by IoU to match highest scores first + sorted_indices = torch.argsort(-iou_values) + pred_indices = pred_indices[sorted_indices] + gt_indices = gt_indices[sorted_indices] + iou_values = iou_values[sorted_indices] + + for pred_idx, gt_idx, iou in zip(pred_indices, gt_indices, iou_values): + if ( + pred_idx.item() in matched_pred_indices + or gt_idx.item() in matched_gt_indices + ): + continue + matched_pred_indices.add(pred_idx.item()) + matched_gt_indices.add(gt_idx.item()) + + pred_label = pred_labels[pred_idx].item() + gt_label = gt_labels[gt_idx].item() + + pred_cls_idx = class_to_idx[pred_label] + gt_cls_idx = class_to_idx[gt_label] + + # Update confusion matrix + conf_matrix[gt_cls_idx, pred_cls_idx] += 1 + + # Update per-class metrics + if pred_label == gt_label: + metrics_per_class[gt_label]["TPs"] += 1 + metrics_per_class[gt_label]["IoUs"].append(iou.item()) + else: + # Misclassification + metrics_per_class[gt_label]["FNs"] += 1 + metrics_per_class[pred_label]["FPs"] += 1 + metrics_per_class[gt_label]["IoUs"].append(0) + metrics_per_class[pred_label]["IoUs"].append(0) + + # Unmatched predictions (False Positives) + unmatched_pred_indices = set(range(n_preds)) - matched_pred_indices + for pred_idx in unmatched_pred_indices: + pred_label = pred_labels[pred_idx].item() + pred_cls_idx = class_to_idx[pred_label] + # Update confusion matrix: background row + conf_matrix[n_classes, pred_cls_idx] += 1 + # Update per-class metrics + metrics_per_class[pred_label]["FPs"] += 1 + metrics_per_class[pred_label]["IoUs"].append(0) + + # Unmatched ground truths (False Negatives) + unmatched_gt_indices = set(range(n_gts)) - matched_gt_indices + for gt_idx in unmatched_gt_indices: + gt_label = gt_labels[gt_idx].item() + gt_cls_idx = class_to_idx[gt_label] + # Update confusion matrix: background column + conf_matrix[gt_cls_idx, n_classes] += 1 + # Update per-class metrics + metrics_per_class[gt_label]["FNs"] += 1 + metrics_per_class[gt_label]["IoUs"].append(0) + + return metrics_per_class, conf_matrix, class_to_idx + + def save_plots(self, path_to_save) -> None: + path_to_save = Path(path_to_save) + path_to_save.mkdir(parents=True, exist_ok=True) + + if self.conf_matrix is not None: + class_labels = [str(cls_id) for cls_id in self.class_to_idx.keys()] + ["background"] + + plt.figure(figsize=(10, 8)) + plt.imshow(self.conf_matrix, interpolation="nearest", cmap=plt.cm.Blues) + plt.title("Confusion Matrix") + plt.colorbar() + tick_marks = np.arange(len(class_labels)) + plt.xticks(tick_marks, class_labels, rotation=45) + plt.yticks(tick_marks, class_labels) + + # Add labels to each cell + thresh = self.conf_matrix.max() / 2.0 + for i in range(self.conf_matrix.shape[0]): + for j in range(self.conf_matrix.shape[1]): + plt.text( + j, + i, + format(self.conf_matrix[i, j], "d"), + horizontalalignment="center", + color="white" if self.conf_matrix[i, j] > thresh else "black", + ) + + plt.ylabel("True label") + plt.xlabel("Predicted label") + plt.tight_layout() + plt.savefig(path_to_save / "confusion_matrix.png") + plt.close() + + thresholds = self.thresholds + precisions, recalls, f1_scores = [], [], [] + + # Store the original predictions to reset after each threshold + original_preds = copy.deepcopy(self.preds) + + for threshold in thresholds: + # Filter predictions based on the current threshold + filtered_preds = filter_preds(copy.deepcopy(original_preds), threshold) + # Compute metrics with the filtered predictions + metrics = self._compute_main_metrics(filtered_preds) + precisions.append(metrics["precision"]) + recalls.append(metrics["recall"]) + f1_scores.append(metrics["f1"]) + + # Plot Precision and Recall vs Threshold + plt.figure() + plt.plot(thresholds, precisions, label="Precision", marker="o") + plt.plot(thresholds, recalls, label="Recall", marker="o") + plt.xlabel("Threshold") + plt.ylabel("Value") + plt.title("Precision and Recall vs Threshold") + plt.legend() + plt.grid(True) + plt.savefig(path_to_save / "precision_recall_vs_threshold.png") + plt.close() + + # Plot F1 Score vs Threshold + plt.figure() + plt.plot(thresholds, f1_scores, label="F1 Score", marker="o") + plt.xlabel("Threshold") + plt.ylabel("F1 Score") + plt.title("F1 Score vs Threshold") + plt.grid(True) + plt.savefig(path_to_save / "f1_score_vs_threshold.png") + plt.close() + + # Find the best threshold based on F1 Score (last occurence) + best_idx = len(f1_scores) - np.argmax(f1_scores[::-1]) - 1 + best_threshold = thresholds[best_idx] + best_f1 = f1_scores[best_idx] + + logger.info( + f"Best Threshold: {round(best_threshold, 2)} with F1 Score: {round(best_f1, 3)}" + ) + + +def filter_preds(preds, conf_thresh): + for pred in preds: + keep_idxs = pred["scores"] >= conf_thresh + pred["scores"] = pred["scores"][keep_idxs] + pred["boxes"] = pred["boxes"][keep_idxs] + pred["labels"] = pred["labels"][keep_idxs] + return preds + + +def scale_boxes(boxes, orig_shape, resized_shape): + """ + boxes in format: [x1, y1, x2, y2], absolute values + orig_shape: [height, width] + resized_shape: [height, width] + """ + scale_x = orig_shape[1] / resized_shape[1] + scale_y = orig_shape[0] / resized_shape[0] + boxes[:, 0] *= scale_x + boxes[:, 2] *= scale_x + boxes[:, 1] *= scale_y + boxes[:, 3] *= scale_y + return boxes diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/__init__.py new file mode 100644 index 0000000..de38c3a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/__init__.py @@ -0,0 +1,6 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from . import dfine diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/__init__.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/__init__.py new file mode 100644 index 0000000..f61af2a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/__init__.py @@ -0,0 +1,11 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +from .dfine import DFINE +from .dfine_criterion import DFINECriterion +from .dfine_decoder import DFINETransformer +from .hybrid_encoder import HybridEncoder +from .matcher import HungarianMatcher +from .postprocessor import DFINEPostProcessor diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/blog.md b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/blog.md new file mode 100644 index 0000000..24abf57 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/blog.md @@ -0,0 +1,90 @@ +English Blog | [中文博客](blog_cn.md) + +## 🔥 Revolutionizing Real-Time Object Detection: D-FINE vs. YOLO and Other DETR Models + +In the rapidly evolving field of real-time object detection, **D-FINE** emerges as a revolutionary approach that significantly surpasses existing models like **YOLOv10**, **YOLO11**, and **RT-DETR v1/v2/v3**, raising the performance ceiling for real-time object detection. After pretraining on the large-scale dataset Objects365, **D-FINE** far exceeds its competitor **LW-DETR**, achieving up to **59.3%** AP on the COCO dataset while maintaining excellent frame rates, parameter counts, and computational complexity. This positions **D-FINE** as a leader in the realm of real-time object detection, laying the groundwork for future research advancements. + +Currently, all code, weights, logs, compilation tools, and the FiftyOne visualization tool for **D-FINE** have been fully open-sourced, thanks to the codebase provided by **RT-DETR**. This includes pretraining tutorials, custom dataset tutorials, and more. We will continue to update with improvement insights and tuning strategies. We welcome everyone to raise issues and collectively promote the **D-FINE** series. We also hope you can leave a ⭐; it's the best encouragement for us. + +**GitHub Repo**: https://github.com/Peterande/D-FINE + +**ArXiv Paper**: https://arxiv.org/abs/2410.13842 + +--- + +### 🔍 Exploring the Key Innovations Behind D-FINE + +**D-FINE** redefines the regression task in DETR-based object detectors as **FDR**, and based on this, develops a performance-enhancing self-distillation mechanism **GO-LSD**. Below is a brief introduction to **FDR** and **GO-LSD**: + +#### **FDR (Fine-grained Distribution Refinement)** Decouples the Bounding Box Generation Process: + +1. **Initial Box Prediction**: Similar to traditional DETR methods, the decoder of **D-FINE** transforms object queries into several initial bounding boxes in the first layer. These boxes do not need to be highly accurate and serve only as an initialization. +2. **Fine-Grained Distribution Optimization**: Unlike traditional methods that directly decode new bounding boxes, **D-FINE** generates four sets of probability distributions based on these initial bounding boxes in the decoder layers and iteratively optimizes these distributions layer by layer. These distributions essentially act as a "fine-grained intermediate representation" of the detection boxes. Coupled with a carefully designed weighting function **W(n)**, **D-FINE** can adjust the initial bounding boxes by fine-tuning these representations, allowing for subtle modifications or significant shifts of the edges (top, bottom, left, right). The specific process is illustrated in the figure: + +

+ Fine-grained Distribution Refinement Process +

+ +For readability, we will not elaborate on the mathematical formulas and the Fine-Grained Localization (FGL) Loss that aids optimization here. Interested readers can refer to the original paper for derivations. + +The main advantages of redefining the bounding box regression task as **FDR** are: + +1. **Simplified Supervision**: While optimizing detection boxes using traditional L1 loss and IoU loss, the "residual" between labels and predictions can be additionally used to constrain these intermediate probability distributions. This allows each decoder layer to more effectively focus on and address the localization errors it currently faces. As the number of layers increases, their optimization objectives become progressively simpler, thereby simplifying the overall optimization process. + +2. **Robustness in Complex Scenarios**: The values of these probability distributions inherently represent the confidence level of fine-tuning for each edge. This enables **D-FINE** to independently model the uncertainty of each edge at different network depths, thereby exhibiting stronger robustness in complex real-world scenarios such as occlusion, motion blur, and low-light conditions, compared to directly regressing four fixed values. + +3. **Flexible Optimization Mechanism**: The probability distributions are transformed into final bounding box offsets through a weighted sum. The carefully designed weighting function ensures fine-grained adjustments when the initial box is accurate and provides significant corrections when necessary. + +4. **Research Potential and Scalability**: By transforming the regression task into a probability distribution prediction problem consistent with classification tasks, **FDR** not only enhances compatibility with other tasks but also enables object detection models to benefit from innovations in areas such as knowledge distillation, multi-task learning, and distribution optimization, opening new avenues for future research. + +--- + +#### **GO-LSD (Global Optimal Localization Self-Distillation)** Integrates Knowledge Distillation into FDR-Based Detectors Seamlessly + +Based on the above, object detectors equipped with the **FDR** framework satisfy the following two points: + +1. **Ability to Achieve Knowledge Transfer**: As Hinton mentioned in the paper *"Distilling the Knowledge in a Neural Network"*, probabilities are "knowledge." The network's output becomes probability distributions, and these distributions carry localization knowledge. By calculating the KLD loss, this "knowledge" can be transferred from deeper layers to shallower layers. This is something that traditional fixed box representations (Dirac δ functions) cannot achieve. + +2. **Consistent Optimization Objectives**: Since each decoder layer in the **FDR** framework shares a common goal: reducing the residual between the initial bounding box and the ground truth bounding box, the precise probability distributions generated by the final layer can serve as the ultimate target for each preceding layer and guide them through distillation. + +Thus, based on **FDR**, we propose **GO-LSD (Global Optimal Localization Self-Distillation)**. By implementing localization knowledge distillation between network layers, we further extend the capabilities of **D-FINE**. The specific process is illustrated in the figure: + +

+ GO-LSD Process +

+ +Similarly, for readability, we will not elaborate on the mathematical formulas and the Decoupled Distillation Focal (DDF) Loss that aids optimization here. Interested readers can refer to the original paper for derivations. + +This results in a synergistic win-win effect: as training progresses, the predictions of the final layer become increasingly accurate, and its generated soft labels can better help the preceding layers improve prediction accuracy. Conversely, the earlier layers learn to localize accurately more quickly, simplifying the optimization tasks of the deeper layers and further enhancing overall accuracy. + +--- + +### Visualization of D-FINE Predictions + +The following visualization showcases **D-FINE**'s predictions in various complex detection scenarios. These scenarios include occlusion, low-light conditions, motion blur, depth-of-field effects, and densely populated scenes. Despite these challenges, **D-FINE** still produces accurate localization results. + +

+ D-FINE Predictions in Complex Scenarios +

+ +Additionally, the visualization below shows the prediction results of the first layer and the last layer, the corresponding distributions of the four edges, and the weighted distributions. It can be seen that the localization of the predicted boxes becomes more precise as the distributions are optimized. + +

+ +

+ +--- + +### Frequently Asked Questions + +#### Question 1: Will FDR and GO-LSD increase the inference cost? + +**Answer**: No, FDR and the original prediction have almost no difference in speed, parameter count, and computational complexity, making it a seamless replacement. + +#### Question 2: Will FDR and GO-LSD increase the training cost? + +**Answer**: The increase in training cost mainly comes from how to generate the labels of the distributions. We have optimized this process, keeping the increase in training time and memory consumption to 6% and 2%, respectively, making it almost negligible. + +#### Question 3: Why is D-FINE faster and more lightweight than the RT-DETR series? + +**Answer**: Directly applying FDR and GO-LSD will significantly improve performance but will not make the network faster or lighter. Therefore, we performed a series of lightweight optimizations on RT-DETR. These adjustments led to a performance drop, but our methods compensated for these losses, achieving a perfect balance of speed, parameters, computational complexity, and performance. diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/blog_cn.md b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/blog_cn.md new file mode 100644 index 0000000..04fd5df --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/blog_cn.md @@ -0,0 +1,90 @@ +[English Blog](blog.md) | 中文博客 + +## 🔥 革新实时目标检测：D-FINE 与 YOLO 和其他 DETR 模型的对比 + +在快速发展的实时目标检测领域，**D-FINE** 作为一项革命性的方法，显著超越了现有模型（如 **YOLOv10**、**YOLO11** 及 **RT-DETR v1/v2/v3**），提升了实时目标检测的性能上限。经过大规模数据集 Objects365 的预训练，**D-FINE** 远超其竞争对手 **LW-DETR**，在 COCO 数据集上实现了高达 **59.3%** 的 AP，同时保持了卓越的帧率、参数量和计算复杂度。这使得 **D-FINE** 成为实时目标检测领域的佼佼者，为未来的研究奠定了基础。 + +目前，D-FINE 的所有代码、权重、日志、编译工具，以及 FiftyOne 可视化工具已经全部开源，感谢 RT-DETR 提供的 codebase。其中还包括了预训练教程、自定义数据集教程等。之后还会陆续更新一些改进心得和调参攻略，欢迎大家多提 issue，共同将 D-FINE 系列发扬光大。同时希望您能随手留下一颗 ⭐，这是对我们最好的鼓励。 + +**Github Repo**: https://github.com/Peterande/D-FINE + +**Arxiv Paper**: https://arxiv.org/abs/2410.13842 + +--- + +### 🔍 探索 D-FINE 背后的关键创新 + +**D-FINE** 将基于 DETR 的目标检测器中的回归任务重新定义为 FDR，并在此基础上开发出了无感提升性能的自蒸馏机制 GO-LSD。下面对 FDR 和 GO-LSD 进行简要介绍： + +#### FDR (Fine-grained Distribution Refinement) 将检测框的生成过程拆解为： + +1. **初始框预测**：与传统 DETR 方法类似，**D-FINE** 的解码器 (decoder) 会在第一层将 object queries 转变为若干个初始的边界框，这些框不需要特别精准，仅作为一种初始化。 +2. **细粒度的分布优化**：**D-FINE** 解码层不会像传统方法那样直接解码出新的边界框，而是基于这些初始化的边界框，生成四组概率分布；并迭代地对这四组概率分布进行逐层优化。这些分布本质上是作为检测框的一种“细粒度中间表征”；配合精心设计的加权函数 W(n)，**D-FINE** 能够通过微调这些表征来实现对初始边界框的调整，包含对其上下左右边缘进行细微的小幅度修正亦或是大幅度的搬移，具体的流程如图： + +

+ 精细分布优化过程 +

+ +为了方便阅读，我们不在此赘述数学公式及帮助优化的损失函数 Fine-Grained Localization (FGL) Loss，有兴趣的可以根据原文推导。 + +将边界框回归任务重新定义为 FDR 的主要优势在于： + +1. **简化的监督**：在使用传统的 L1 损失、IoU 损失优化检测框的同时，可以额外用标签和预测结果之间的“残差”来约束这些中间态的概率分布。这使每个解码层 (decoder layer) 能够更有效地关注并解决其当前面临的定位误差，随着层数加深，其优化目标变得越来越简单，从而简化了整体优化过程。 + +2. **复杂场景下的鲁棒性**：这些概率分布的值本质上代表了对每个边界“微调”的自信程度。这使 **D-FINE** 能够在不同网络深度独立建模每个边界的不确定性，从而在遮挡、运动模糊和低光照等复杂的实际场景下表现出更强的鲁棒性，相比直接回归四个固定值要更为稳健。 + +3. **灵活的优化机制**：概率分布通过加权求和转化为最终的边界框偏移值。精心设计的加权函数确保在初始框准确时进行细微调整，而在必要时则提供较大的修正。 + +4. **研究潜力与可扩展性**：FDR 通过将回归任务转变为同分类任务一致的概率分布预测问题，不仅提高了与其他任务的兼容性，还使得目标检测模型可以受益于知识蒸馏、多任务学习和分布优化等更多领域的创新，为未来的研究打开了新的大门。 + +--- + +#### GO-LSD (Global Optimal Localization Self-Distillation) 将知识蒸馏无痛应用到 FDR 框架检测器 + +根据上文，搭载 FDR 框架的目标检测器满足了以下两点： + +1. **能够实现知识传递**：Hinton 早在 *"Distilling the Knowledge in a Neural Network"* 一文中就说过：概率即“知识”；网络输出变成了概率分布，而概率分布携带定位知识 (Localization Knowledge)，而通过计算 KLD 损失，可以将这些“知识”从深层传递到浅层。这是传统固定框表示（狄拉克 δ 函数）无法实现的。 + +2. **一致的优化目标**：由于 FDR 架构中每一个解码层都共享一个共同目标：减少初始边界框与真实边界框之间的残差；因此最后一层生成的精确概率分布可以作为前面每一层的最终目标，并通过蒸馏引导前几层。 + +于是，基于 FDR，我们提出了 GO-LSD（全局最优定位自蒸馏）。通过在网络层间实现定位知识蒸馏，进一步扩展了 **D-FINE** 的能力，具体流程如图： + +

+ GO-LSD过程 +

+ +同样的，为了方便阅读，我们不在此赘述数学公式及帮助优化的损失函数 Decoupled Distillation Focal (DDF) Loss，有兴趣的可以根据原文推导。 + +这产生了一种双赢的协同效应：随着训练的进行，最后一层的预测变得越来越准确，其生成的软标签能够更好地帮助前几层提高预测准确性。反过来，前几层学会更快地定位到准确位置，简化了深层的优化任务，进一步提高了整体准确性。 + +--- + +### D-FINE 预测的可视化 + +以下可视化展示了 **D-FINE** 在各种复杂检测场景中的预测结果。这些场景包括遮挡、低光照、运动模糊、景深效果和密集场景。尽管面对这些挑战，**D-FINE** 依然能够产生准确的定位结果。 + +

+ D-FINE在复杂场景中的预测 +

+ +同时下面给出的可视化结果展示了第一层和最后一层的预测结果、对应四条边的分布、以及加权后的分布。可以看到，预测框的定位会随着分布的优化而变得更加精准。 + +

+ +

+ +--- + +### 常见问题解答 + +#### 问题1：FDR 和 GO-LSD 会带来更多的推理成本吗？ + +**回答**：并不会，FDR 和原始的预测几乎没有在速度、参数量和计算复杂度上的任何区别，完全是无感替换。 + +#### 问题2：FDR 和 GO-LSD 会带来更多的训练成本吗？ + +**回答**：训练成本的增加主要来源于如何生成分布的标签。我们已经对该过程进行了优化，将额外训练时长和显存占用控制在了 6% 和 2%，几乎无感。 + +#### 问题3：D-FINE 为什么会比 RT-DETR 系列更快、更轻量？ + +**回答**：直接应用 FDR 和 GO-LSD 只会显著提高性能，并不会让网络更快、更轻。所以我们对 RT-DETR 进行了一系列的轻量化处理，这些处理带来了性能的下降，但我们的方法弥补了这些损失，实现了速度-参数-计算量-性能的完美平衡。 diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/box_ops.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/box_ops.py new file mode 100644 index 0000000..1e2acba --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/box_ops.py @@ -0,0 +1,93 @@ +""" +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +https://github.com/facebookresearch/detr/blob/main/util/box_ops.py +""" + +import torch +from torch import Tensor +from torchvision.ops.boxes import box_area + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [ + (x_c - 0.5 * w.clamp(min=0.0)), + (y_c - 0.5 * h.clamp(min=0.0)), + (x_c + 0.5 * w.clamp(min=0.0)), + (y_c + 0.5 * h.clamp(min=0.0)), + ] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x: Tensor) -> Tensor: + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +# modified from torchvision to also return the union +def box_iou(boxes1: Tensor, boxes2: Tensor): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / area + + +def masks_to_boxes(masks): + """Compute the bounding boxes around the provided masks + + The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. + + Returns a [N, 4] tensors, with the boxes in xyxy format + """ + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + + y = torch.arange(0, h, dtype=torch.float) + x = torch.arange(0, w, dtype=torch.float) + y, x = torch.meshgrid(y, x) + + x_mask = masks * x.unsqueeze(0) + x_max = x_mask.flatten(1).max(-1)[0] + x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + y_mask = masks * y.unsqueeze(0) + y_max = y_mask.flatten(1).max(-1)[0] + y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/denoising.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/denoising.py new file mode 100644 index 0000000..4d2409a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/denoising.py @@ -0,0 +1,121 @@ +"""Copyright(c) 2023 lyuwenyu. All Rights Reserved. +Modifications Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import torch + +from .box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh +from .utils import inverse_sigmoid + + +def get_contrastive_denoising_training_group( + targets, + num_classes, + num_queries, + class_embed, + num_denoising=100, + label_noise_ratio=0.5, + box_noise_scale=1.0, +): + """cnd""" + if num_denoising <= 0: + return None, None, None, None + + num_gts = [len(t["labels"]) for t in targets] + device = targets[0]["labels"].device + + max_gt_num = max(num_gts) + if max_gt_num == 0: + dn_meta = {"dn_positive_idx": None, "dn_num_group": 0, "dn_num_split": [0, num_queries]} + return None, None, None, dn_meta + + num_group = num_denoising // max_gt_num + num_group = 1 if num_group == 0 else num_group + # pad gt to max_num of a batch + bs = len(num_gts) + + input_query_class = torch.full([bs, max_gt_num], num_classes, dtype=torch.int32, device=device) + input_query_bbox = torch.zeros([bs, max_gt_num, 4], device=device) + pad_gt_mask = torch.zeros([bs, max_gt_num], dtype=torch.bool, device=device) + + for i in range(bs): + num_gt = num_gts[i] + if num_gt > 0: + input_query_class[i, :num_gt] = targets[i]["labels"] + input_query_bbox[i, :num_gt] = targets[i]["boxes"] + pad_gt_mask[i, :num_gt] = 1 + # each group has positive and negative queries. + input_query_class = input_query_class.tile([1, 2 * num_group]) + input_query_bbox = input_query_bbox.tile([1, 2 * num_group, 1]) + pad_gt_mask = pad_gt_mask.tile([1, 2 * num_group]) + # positive and negative mask + negative_gt_mask = torch.zeros([bs, max_gt_num * 2, 1], device=device) + negative_gt_mask[:, max_gt_num:] = 1 + negative_gt_mask = negative_gt_mask.tile([1, num_group, 1]) + positive_gt_mask = 1 - negative_gt_mask + # contrastive denoising training positive index + positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask + dn_positive_idx = torch.nonzero(positive_gt_mask)[:, 1] + dn_positive_idx = torch.split(dn_positive_idx, [n * num_group for n in num_gts]) + # total denoising queries + num_denoising = int(max_gt_num * 2 * num_group) + + if label_noise_ratio > 0: + mask = torch.rand_like(input_query_class, dtype=torch.float) < (label_noise_ratio * 0.5) + # randomly put a new one here + new_label = torch.randint_like(mask, 0, num_classes, dtype=input_query_class.dtype) + input_query_class = torch.where(mask & pad_gt_mask, new_label, input_query_class) + + if box_noise_scale > 0: + known_bbox = box_cxcywh_to_xyxy(input_query_bbox) + diff = torch.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale + rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 + rand_part = torch.rand_like(input_query_bbox) + rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask) + # shrink_mask = torch.zeros_like(rand_sign) + # shrink_mask[:, :, :2] = (rand_sign[:, :, :2] == 1) # rand_sign == 1 → (x1, y1) ↘ → smaller bbox + # shrink_mask[:, :, 2:] = (rand_sign[:, :, 2:] == -1) # rand_sign == -1 → (x2, y2) ↖ → smaller bbox + # mask = rand_part > (upper_bound / (upper_bound+1)) + # # this is to make sure the dn bbox can be reversed to the original bbox by dfine head. + # rand_sign = torch.where((shrink_mask * (1 - negative_gt_mask) * mask).bool(), \ + # rand_sign * upper_bound / (upper_bound+1) / rand_part, rand_sign) + known_bbox += rand_sign * rand_part * diff + known_bbox = torch.clip(known_bbox, min=0.0, max=1.0) + input_query_bbox = box_xyxy_to_cxcywh(known_bbox) + input_query_bbox[input_query_bbox < 0] *= -1 + input_query_bbox_unact = inverse_sigmoid(input_query_bbox) + + input_query_logits = class_embed(input_query_class) + + tgt_size = num_denoising + num_queries + attn_mask = torch.full([tgt_size, tgt_size], False, dtype=torch.bool, device=device) + # match query cannot see the reconstruction + attn_mask[num_denoising:, :num_denoising] = True + + # reconstruct cannot see each other + for i in range(num_group): + if i == 0: + attn_mask[ + max_gt_num * 2 * i : max_gt_num * 2 * (i + 1), + max_gt_num * 2 * (i + 1) : num_denoising, + ] = True + if i == num_group - 1: + attn_mask[max_gt_num * 2 * i : max_gt_num * 2 * (i + 1), : max_gt_num * i * 2] = True + else: + attn_mask[ + max_gt_num * 2 * i : max_gt_num * 2 * (i + 1), + max_gt_num * 2 * (i + 1) : num_denoising, + ] = True + attn_mask[max_gt_num * 2 * i : max_gt_num * 2 * (i + 1), : max_gt_num * 2 * i] = True + + dn_meta = { + "dn_positive_idx": dn_positive_idx, + "dn_num_group": num_group, + "dn_num_split": [num_denoising, num_queries], + } + + # print(input_query_class.shape) # torch.Size([4, 196, 256]) + # print(input_query_bbox.shape) # torch.Size([4, 196, 4]) + # print(attn_mask.shape) # torch.Size([496, 496]) + + return input_query_logits, input_query_bbox_unact, attn_mask, dn_meta diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine.py new file mode 100644 index 0000000..b83bd99 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine.py @@ -0,0 +1,47 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import torch.nn as nn + +from ...core import register + +__all__ = [ + "DFINE", +] + + +@register() +class DFINE(nn.Module): + __inject__ = [ + "backbone", + "encoder", + "decoder", + ] + + def __init__( + self, + backbone: nn.Module, + encoder: nn.Module, + decoder: nn.Module, + ): + super().__init__() + self.backbone = backbone + self.decoder = decoder + self.encoder = encoder + + def forward(self, x, targets=None): + x = self.backbone(x) + x = self.encoder(x) + x = self.decoder(x, targets) + + return x + + def deploy( + self, + ): + self.eval() + for m in self.modules(): + if hasattr(m, "convert_to_deploy"): + m.convert_to_deploy() + return self diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_criterion.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_criterion.py new file mode 100644 index 0000000..954d42b --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_criterion.py @@ -0,0 +1,525 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import copy + +import torch +import torch.distributed +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from ...core import register +from ...misc.dist_utils import get_world_size, is_dist_available_and_initialized +from .box_ops import box_cxcywh_to_xyxy, box_iou, generalized_box_iou +from .dfine_utils import bbox2distance + + +@register() +class DFINECriterion(nn.Module): + """This class computes the loss for D-FINE.""" + + __share__ = [ + "num_classes", + ] + __inject__ = [ + "matcher", + ] + + def __init__( + self, + matcher, + weight_dict, + losses, + alpha=0.2, + gamma=2.0, + num_classes=80, + reg_max=32, + boxes_weight_format=None, + share_matched_indices=False, + ): + """Create the criterion. + Parameters: + matcher: module able to compute a matching between targets and proposals. + weight_dict: dict containing as key the names of the losses and as values their relative weight. + losses: list of all the losses to be applied. See get_loss for list of available losses. + num_classes: number of object categories, omitting the special no-object category. + reg_max (int): Max number of the discrete bins in D-FINE. + boxes_weight_format: format for boxes weight (iou, ). + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.losses = losses + self.boxes_weight_format = boxes_weight_format + self.share_matched_indices = share_matched_indices + self.alpha = alpha + self.gamma = gamma + self.fgl_targets, self.fgl_targets_dn = None, None + self.own_targets, self.own_targets_dn = None, None + self.reg_max = reg_max + self.num_pos, self.num_neg = None, None + + def loss_labels_focal(self, outputs, targets, indices, num_boxes): + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"] + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + target = F.one_hot(target_classes, num_classes=self.num_classes + 1)[..., :-1] + loss = torchvision.ops.sigmoid_focal_loss( + src_logits, target, self.alpha, self.gamma, reduction="none" + ) + loss = loss.mean(1).sum() * src_logits.shape[1] / num_boxes + + return {"loss_focal": loss} + + def loss_labels_vfl(self, outputs, targets, indices, num_boxes, values=None): + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + if values is None: + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + ious, _ = box_iou(box_cxcywh_to_xyxy(src_boxes), box_cxcywh_to_xyxy(target_boxes)) + ious = torch.diag(ious).detach() + else: + ious = values + + src_logits = outputs["pred_logits"] + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + target = F.one_hot(target_classes, num_classes=self.num_classes + 1)[..., :-1] + + target_score_o = torch.zeros_like(target_classes, dtype=src_logits.dtype) + target_score_o[idx] = ious.to(target_score_o.dtype) + target_score = target_score_o.unsqueeze(-1) * target + + pred_score = F.sigmoid(src_logits).detach() + weight = self.alpha * pred_score.pow(self.gamma) * (1 - target) + target_score + + loss = F.binary_cross_entropy_with_logits( + src_logits, target_score, weight=weight, reduction="none" + ) + loss = loss.mean(1).sum() * src_logits.shape[1] / num_boxes + return {"loss_vfl": loss} + + def loss_boxes(self, outputs, targets, indices, num_boxes, boxes_weight=None): + """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss + targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] + The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + losses = {} + loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + loss_giou = 1 - torch.diag( + generalized_box_iou(box_cxcywh_to_xyxy(src_boxes), box_cxcywh_to_xyxy(target_boxes)) + ) + loss_giou = loss_giou if boxes_weight is None else loss_giou * boxes_weight + losses["loss_giou"] = loss_giou.sum() / num_boxes + + return losses + + def loss_local(self, outputs, targets, indices, num_boxes, T=5): + """Compute Fine-Grained Localization (FGL) Loss + and Decoupled Distillation Focal (DDF) Loss.""" + + losses = {} + if "pred_corners" in outputs: + idx = self._get_src_permutation_idx(indices) + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + pred_corners = outputs["pred_corners"][idx].reshape(-1, (self.reg_max + 1)) + ref_points = outputs["ref_points"][idx].detach() + with torch.no_grad(): + if self.fgl_targets_dn is None and "is_dn" in outputs: + self.fgl_targets_dn = bbox2distance( + ref_points, + box_cxcywh_to_xyxy(target_boxes), + self.reg_max, + outputs["reg_scale"], + outputs["up"], + ) + if self.fgl_targets is None and "is_dn" not in outputs: + self.fgl_targets = bbox2distance( + ref_points, + box_cxcywh_to_xyxy(target_boxes), + self.reg_max, + outputs["reg_scale"], + outputs["up"], + ) + + target_corners, weight_right, weight_left = ( + self.fgl_targets_dn if "is_dn" in outputs else self.fgl_targets + ) + + ious = torch.diag( + box_iou( + box_cxcywh_to_xyxy(outputs["pred_boxes"][idx]), box_cxcywh_to_xyxy(target_boxes) + )[0] + ) + weight_targets = ious.unsqueeze(-1).repeat(1, 1, 4).reshape(-1).detach() + + losses["loss_fgl"] = self.unimodal_distribution_focal_loss( + pred_corners, + target_corners, + weight_right, + weight_left, + weight_targets, + avg_factor=num_boxes, + ) + + if "teacher_corners" in outputs: + pred_corners = outputs["pred_corners"].reshape(-1, (self.reg_max + 1)) + target_corners = outputs["teacher_corners"].reshape(-1, (self.reg_max + 1)) + if torch.equal(pred_corners, target_corners): + losses["loss_ddf"] = pred_corners.sum() * 0 + else: + weight_targets_local = outputs["teacher_logits"].sigmoid().max(dim=-1)[0] + + mask = torch.zeros_like(weight_targets_local, dtype=torch.bool) + mask[idx] = True + mask = mask.unsqueeze(-1).repeat(1, 1, 4).reshape(-1) + + weight_targets_local[idx] = ious.reshape_as(weight_targets_local[idx]).to( + weight_targets_local.dtype + ) + weight_targets_local = ( + weight_targets_local.unsqueeze(-1).repeat(1, 1, 4).reshape(-1).detach() + ) + + loss_match_local = ( + weight_targets_local + * (T**2) + * ( + nn.KLDivLoss(reduction="none")( + F.log_softmax(pred_corners / T, dim=1), + F.softmax(target_corners.detach() / T, dim=1), + ) + ).sum(-1) + ) + if "is_dn" not in outputs: + batch_scale = ( + 8 / outputs["pred_boxes"].shape[0] + ) # Avoid the influence of batch size per GPU + self.num_pos, self.num_neg = ( + (mask.sum() * batch_scale) ** 0.5, + ((~mask).sum() * batch_scale) ** 0.5, + ) + loss_match_local1 = loss_match_local[mask].mean() if mask.any() else 0 + loss_match_local2 = loss_match_local[~mask].mean() if (~mask).any() else 0 + losses["loss_ddf"] = ( + loss_match_local1 * self.num_pos + loss_match_local2 * self.num_neg + ) / (self.num_pos + self.num_neg) + + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def _get_go_indices(self, indices, indices_aux_list): + """Get a matching union set across all decoder layers.""" + results = [] + for indices_aux in indices_aux_list: + indices = [ + (torch.cat([idx1[0], idx2[0]]), torch.cat([idx1[1], idx2[1]])) + for idx1, idx2 in zip(indices.copy(), indices_aux.copy()) + ] + + for ind in [torch.cat([idx[0][:, None], idx[1][:, None]], 1) for idx in indices]: + unique, counts = torch.unique(ind, return_counts=True, dim=0) + count_sort_indices = torch.argsort(counts, descending=True) + unique_sorted = unique[count_sort_indices] + column_to_row = {} + for idx in unique_sorted: + row_idx, col_idx = idx[0].item(), idx[1].item() + if row_idx not in column_to_row: + column_to_row[row_idx] = col_idx + final_rows = torch.tensor(list(column_to_row.keys()), device=ind.device) + final_cols = torch.tensor(list(column_to_row.values()), device=ind.device) + results.append((final_rows.long(), final_cols.long())) + return results + + def _clear_cache(self): + self.fgl_targets, self.fgl_targets_dn = None, None + self.own_targets, self.own_targets_dn = None, None + self.num_pos, self.num_neg = None, None + + def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs): + loss_map = { + "boxes": self.loss_boxes, + "focal": self.loss_labels_focal, + "vfl": self.loss_labels_vfl, + "local": self.loss_local, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) + + def forward(self, outputs, targets, **kwargs): + """This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if "aux" not in k} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets)["indices"] + self._clear_cache() + + # Get the matching union set across all decoder layers. + if "aux_outputs" in outputs: + indices_aux_list, cached_indices, cached_indices_enc = [], [], [] + for i, aux_outputs in enumerate(outputs["aux_outputs"] + [outputs["pre_outputs"]]): + indices_aux = self.matcher(aux_outputs, targets)["indices"] + cached_indices.append(indices_aux) + indices_aux_list.append(indices_aux) + for i, aux_outputs in enumerate(outputs["enc_aux_outputs"]): + indices_enc = self.matcher(aux_outputs, targets)["indices"] + cached_indices_enc.append(indices_enc) + indices_aux_list.append(indices_enc) + indices_go = self._get_go_indices(indices, indices_aux_list) + + num_boxes_go = sum(len(x[0]) for x in indices_go) + num_boxes_go = torch.as_tensor( + [num_boxes_go], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_available_and_initialized(): + torch.distributed.all_reduce(num_boxes_go) + num_boxes_go = torch.clamp(num_boxes_go / get_world_size(), min=1).item() + else: + assert "aux_outputs" in outputs, "" + + # Compute the average number of target boxes accross all nodes, for normalization purposes + num_boxes = sum(len(t["labels"]) for t in targets) + num_boxes = torch.as_tensor( + [num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_available_and_initialized(): + torch.distributed.all_reduce(num_boxes) + num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + indices_in = indices_go if loss in ["boxes", "local"] else indices + num_boxes_in = num_boxes_go if loss in ["boxes", "local"] else num_boxes + meta = self.get_loss_meta_info(loss, outputs, targets, indices_in) + l_dict = self.get_loss(loss, outputs, targets, indices_in, num_boxes_in, **meta) + l_dict = {k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict} + losses.update(l_dict) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + aux_outputs["up"], aux_outputs["reg_scale"] = outputs["up"], outputs["reg_scale"] + for loss in self.losses: + indices_in = indices_go if loss in ["boxes", "local"] else cached_indices[i] + num_boxes_in = num_boxes_go if loss in ["boxes", "local"] else num_boxes + meta = self.get_loss_meta_info(loss, aux_outputs, targets, indices_in) + l_dict = self.get_loss( + loss, aux_outputs, targets, indices_in, num_boxes_in, **meta + ) + + l_dict = { + k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict + } + l_dict = {k + f"_aux_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + # In case of auxiliary traditional head output at first decoder layer. + if "pre_outputs" in outputs: + aux_outputs = outputs["pre_outputs"] + for loss in self.losses: + indices_in = indices_go if loss in ["boxes", "local"] else cached_indices[-1] + num_boxes_in = num_boxes_go if loss in ["boxes", "local"] else num_boxes + meta = self.get_loss_meta_info(loss, aux_outputs, targets, indices_in) + l_dict = self.get_loss(loss, aux_outputs, targets, indices_in, num_boxes_in, **meta) + + l_dict = { + k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict + } + l_dict = {k + "_pre": v for k, v in l_dict.items()} + losses.update(l_dict) + + # In case of encoder auxiliary losses. + if "enc_aux_outputs" in outputs: + assert "enc_meta" in outputs, "" + class_agnostic = outputs["enc_meta"]["class_agnostic"] + if class_agnostic: + orig_num_classes = self.num_classes + self.num_classes = 1 + enc_targets = copy.deepcopy(targets) + for t in enc_targets: + t["labels"] = torch.zeros_like(t["labels"]) + else: + enc_targets = targets + + for i, aux_outputs in enumerate(outputs["enc_aux_outputs"]): + for loss in self.losses: + indices_in = indices_go if loss == "boxes" else cached_indices_enc[i] + num_boxes_in = num_boxes_go if loss == "boxes" else num_boxes + meta = self.get_loss_meta_info(loss, aux_outputs, enc_targets, indices_in) + l_dict = self.get_loss( + loss, aux_outputs, enc_targets, indices_in, num_boxes_in, **meta + ) + l_dict = { + k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict + } + l_dict = {k + f"_enc_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + if class_agnostic: + self.num_classes = orig_num_classes + + # In case of cdn auxiliary losses. For dfine + if "dn_outputs" in outputs: + assert "dn_meta" in outputs, "" + indices_dn = self.get_cdn_matched_indices(outputs["dn_meta"], targets) + dn_num_boxes = num_boxes * outputs["dn_meta"]["dn_num_group"] + dn_num_boxes = dn_num_boxes if dn_num_boxes > 0 else 1 + + for i, aux_outputs in enumerate(outputs["dn_outputs"]): + aux_outputs["is_dn"] = True + aux_outputs["up"], aux_outputs["reg_scale"] = outputs["up"], outputs["reg_scale"] + for loss in self.losses: + meta = self.get_loss_meta_info(loss, aux_outputs, targets, indices_dn) + l_dict = self.get_loss( + loss, aux_outputs, targets, indices_dn, dn_num_boxes, **meta + ) + l_dict = { + k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict + } + l_dict = {k + f"_dn_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + # In case of auxiliary traditional head output at first decoder layer. + if "dn_pre_outputs" in outputs: + aux_outputs = outputs["dn_pre_outputs"] + for loss in self.losses: + meta = self.get_loss_meta_info(loss, aux_outputs, targets, indices_dn) + l_dict = self.get_loss( + loss, aux_outputs, targets, indices_dn, dn_num_boxes, **meta + ) + l_dict = { + k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict + } + l_dict = {k + "_dn_pre": v for k, v in l_dict.items()} + losses.update(l_dict) + + # For debugging Objects365 pre-train. + losses = {k: torch.nan_to_num(v, nan=0.0) for k, v in losses.items()} + return losses + + def get_loss_meta_info(self, loss, outputs, targets, indices): + if self.boxes_weight_format is None: + return {} + + src_boxes = outputs["pred_boxes"][self._get_src_permutation_idx(indices)] + target_boxes = torch.cat([t["boxes"][j] for t, (_, j) in zip(targets, indices)], dim=0) + + if self.boxes_weight_format == "iou": + iou, _ = box_iou( + box_cxcywh_to_xyxy(src_boxes.detach()), box_cxcywh_to_xyxy(target_boxes) + ) + iou = torch.diag(iou) + elif self.boxes_weight_format == "giou": + iou = torch.diag( + generalized_box_iou( + box_cxcywh_to_xyxy(src_boxes.detach()), box_cxcywh_to_xyxy(target_boxes) + ) + ) + else: + raise AttributeError() + + if loss in ("boxes",): + meta = {"boxes_weight": iou} + elif loss in ("vfl",): + meta = {"values": iou} + else: + meta = {} + + return meta + + @staticmethod + def get_cdn_matched_indices(dn_meta, targets): + """get_cdn_matched_indices""" + dn_positive_idx, dn_num_group = dn_meta["dn_positive_idx"], dn_meta["dn_num_group"] + num_gts = [len(t["labels"]) for t in targets] + device = targets[0]["labels"].device + + dn_match_indices = [] + for i, num_gt in enumerate(num_gts): + if num_gt > 0: + gt_idx = torch.arange(num_gt, dtype=torch.int64, device=device) + gt_idx = gt_idx.tile(dn_num_group) + assert len(dn_positive_idx[i]) == len(gt_idx) + dn_match_indices.append((dn_positive_idx[i], gt_idx)) + else: + dn_match_indices.append( + ( + torch.zeros(0, dtype=torch.int64, device=device), + torch.zeros(0, dtype=torch.int64, device=device), + ) + ) + + return dn_match_indices + + def feature_loss_function(self, fea, target_fea): + loss = (fea - target_fea) ** 2 * ((fea > 0) | (target_fea > 0)).float() + return torch.abs(loss) + + def unimodal_distribution_focal_loss( + self, pred, label, weight_right, weight_left, weight=None, reduction="sum", avg_factor=None + ): + dis_left = label.long() + dis_right = dis_left + 1 + + loss = F.cross_entropy(pred, dis_left, reduction="none") * weight_left.reshape( + -1 + ) + F.cross_entropy(pred, dis_right, reduction="none") * weight_right.reshape(-1) + + if weight is not None: + weight = weight.float() + loss = loss * weight + + if avg_factor is not None: + loss = loss.sum() / avg_factor + elif reduction == "mean": + loss = loss.mean() + elif reduction == "sum": + loss = loss.sum() + + return loss + + def get_gradual_steps(self, outputs): + num_layers = len(outputs["aux_outputs"]) + 1 if "aux_outputs" in outputs else 1 + step = 0.5 / (num_layers - 1) + opt_list = [0.5 + step * i for i in range(num_layers)] if num_layers > 1 else [1] + return opt_list diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_decoder.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_decoder.py new file mode 100644 index 0000000..d7c77f7 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_decoder.py @@ -0,0 +1,959 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import copy +import functools +import math +from collections import OrderedDict +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init + +from ...core import register +from .denoising import get_contrastive_denoising_training_group +from .dfine_utils import distance2bbox, weighting_function +from .utils import ( + bias_init_with_prob, + deformable_attention_core_func_v2, + get_activation, + inverse_sigmoid, +) + +__all__ = ["DFINETransformer"] + + +class MLP(nn.Module): + def __init__(self, input_dim, hidden_dim, output_dim, num_layers, act="relu"): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + self.act = get_activation(act) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +class MSDeformableAttention(nn.Module): + def __init__( + self, + embed_dim=256, + num_heads=8, + num_levels=4, + num_points=4, + method="default", + offset_scale=0.5, + ): + """Multi-Scale Deformable Attention""" + super(MSDeformableAttention, self).__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.num_levels = num_levels + self.offset_scale = offset_scale + + if isinstance(num_points, list): + assert len(num_points) == num_levels, "" + num_points_list = num_points + else: + num_points_list = [num_points for _ in range(num_levels)] + + self.num_points_list = num_points_list + + num_points_scale = [1 / n for n in num_points_list for _ in range(n)] + self.register_buffer( + "num_points_scale", torch.tensor(num_points_scale, dtype=torch.float32) + ) + + self.total_points = num_heads * sum(num_points_list) + self.method = method + + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + + self.sampling_offsets = nn.Linear(embed_dim, self.total_points * 2) + self.attention_weights = nn.Linear(embed_dim, self.total_points) + + self.ms_deformable_attn_core = functools.partial( + deformable_attention_core_func_v2, method=self.method + ) + + self._reset_parameters() + + if method == "discrete": + for p in self.sampling_offsets.parameters(): + p.requires_grad = False + + def _reset_parameters(self): + # sampling_offsets + init.constant_(self.sampling_offsets.weight, 0) + thetas = torch.arange(self.num_heads, dtype=torch.float32) * ( + 2.0 * math.pi / self.num_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = grid_init / grid_init.abs().max(-1, keepdim=True).values + grid_init = grid_init.reshape(self.num_heads, 1, 2).tile([1, sum(self.num_points_list), 1]) + scaling = torch.concat([torch.arange(1, n + 1) for n in self.num_points_list]).reshape( + 1, -1, 1 + ) + grid_init *= scaling + self.sampling_offsets.bias.data[...] = grid_init.flatten() + + # attention_weights + init.constant_(self.attention_weights.weight, 0) + init.constant_(self.attention_weights.bias, 0) + + def forward( + self, + query: torch.Tensor, + reference_points: torch.Tensor, + value: torch.Tensor, + value_spatial_shapes: List[int], + ): + """ + Args: + query (Tensor): [bs, query_length, C] + reference_points (Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0), + bottom-right (1, 1), including padding area + value (Tensor): [bs, value_length, C] + value_spatial_shapes (List): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + + Returns: + output (Tensor): [bs, Length_{query}, C] + """ + bs, Len_q = query.shape[:2] + + sampling_offsets: torch.Tensor = self.sampling_offsets(query) + sampling_offsets = sampling_offsets.reshape( + bs, Len_q, self.num_heads, sum(self.num_points_list), 2 + ) + + attention_weights = self.attention_weights(query).reshape( + bs, Len_q, self.num_heads, sum(self.num_points_list) + ) + attention_weights = F.softmax(attention_weights, dim=-1) + + if reference_points.shape[-1] == 2: + offset_normalizer = torch.tensor(value_spatial_shapes) + offset_normalizer = offset_normalizer.flip([1]).reshape(1, 1, 1, self.num_levels, 1, 2) + sampling_locations = ( + reference_points.reshape(bs, Len_q, 1, self.num_levels, 1, 2) + + sampling_offsets / offset_normalizer + ) + elif reference_points.shape[-1] == 4: + # reference_points [8, 480, None, 1, 4] + # sampling_offsets [8, 480, 8, 12, 2] + num_points_scale = self.num_points_scale.to(dtype=query.dtype).unsqueeze(-1) + offset = ( + sampling_offsets + * num_points_scale + * reference_points[:, :, None, :, 2:] + * self.offset_scale + ) + sampling_locations = reference_points[:, :, None, :, :2] + offset + else: + raise ValueError( + "Last dim of reference_points must be 2 or 4, but get {} instead.".format( + reference_points.shape[-1] + ) + ) + + output = self.ms_deformable_attn_core( + value, value_spatial_shapes, sampling_locations, attention_weights, self.num_points_list + ) + + return output + + +class TransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model=256, + n_head=8, + dim_feedforward=1024, + dropout=0.0, + activation="relu", + n_levels=4, + n_points=4, + cross_attn_method="default", + layer_scale=None, + ): + super(TransformerDecoderLayer, self).__init__() + if layer_scale is not None: + dim_feedforward = round(layer_scale * dim_feedforward) + d_model = round(layer_scale * d_model) + + # self attention + self.self_attn = nn.MultiheadAttention(d_model, n_head, dropout=dropout, batch_first=True) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # cross attention + self.cross_attn = MSDeformableAttention( + d_model, n_head, n_levels, n_points, method=cross_attn_method + ) + self.dropout2 = nn.Dropout(dropout) + + # gate + self.gateway = Gate(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.activation = get_activation(activation) + self.dropout3 = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + self.dropout4 = nn.Dropout(dropout) + self.norm3 = nn.LayerNorm(d_model) + + self._reset_parameters() + + def _reset_parameters(self): + init.xavier_uniform_(self.linear1.weight) + init.xavier_uniform_(self.linear2.weight) + + def with_pos_embed(self, tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, tgt): + return self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) + + def forward( + self, target, reference_points, value, spatial_shapes, attn_mask=None, query_pos_embed=None + ): + # self attention + q = k = self.with_pos_embed(target, query_pos_embed) + + target2, _ = self.self_attn(q, k, value=target, attn_mask=attn_mask) + target = target + self.dropout1(target2) + target = self.norm1(target) + + # cross attention + target2 = self.cross_attn( + self.with_pos_embed(target, query_pos_embed), reference_points, value, spatial_shapes + ) + + target = self.gateway(target, self.dropout2(target2)) + + # ffn + target2 = self.forward_ffn(target) + target = target + self.dropout4(target2) + target = self.norm3(target.clamp(min=-65504, max=65504)) + + return target + + +class Gate(nn.Module): + def __init__(self, d_model): + super(Gate, self).__init__() + self.gate = nn.Linear(2 * d_model, 2 * d_model) + bias = bias_init_with_prob(0.5) + init.constant_(self.gate.bias, bias) + init.constant_(self.gate.weight, 0) + self.norm = nn.LayerNorm(d_model) + + def forward(self, x1, x2): + gate_input = torch.cat([x1, x2], dim=-1) + gates = torch.sigmoid(self.gate(gate_input)) + gate1, gate2 = gates.chunk(2, dim=-1) + return self.norm(gate1 * x1 + gate2 * x2) + + +class Integral(nn.Module): + """ + A static layer that calculates integral results from a distribution. + + This layer computes the target location using the formula: `sum{Pr(n) * W(n)}`, + where Pr(n) is the softmax probability vector representing the discrete + distribution, and W(n) is the non-uniform Weighting Function. + + Args: + reg_max (int): Max number of the discrete bins. Default is 32. + It can be adjusted based on the dataset or task requirements. + """ + + def __init__(self, reg_max=32): + super(Integral, self).__init__() + self.reg_max = reg_max + + def forward(self, x, project): + shape = x.shape + x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1) + x = F.linear(x, project.to(x.device)).reshape(-1, 4) + return x.reshape(list(shape[:-1]) + [-1]) + + +class LQE(nn.Module): + def __init__(self, k, hidden_dim, num_layers, reg_max): + super(LQE, self).__init__() + self.k = k + self.reg_max = reg_max + self.reg_conf = MLP(4 * (k + 1), hidden_dim, 1, num_layers) + init.constant_(self.reg_conf.layers[-1].bias, 0) + init.constant_(self.reg_conf.layers[-1].weight, 0) + + def forward(self, scores, pred_corners): + B, L, _ = pred_corners.size() + prob = F.softmax(pred_corners.reshape(B, L, 4, self.reg_max + 1), dim=-1) + prob_topk, _ = prob.topk(self.k, dim=-1) + stat = torch.cat([prob_topk, prob_topk.mean(dim=-1, keepdim=True)], dim=-1) + quality_score = self.reg_conf(stat.reshape(B, L, -1)) + return scores + quality_score + + +class TransformerDecoder(nn.Module): + """ + Transformer Decoder implementing Fine-grained Distribution Refinement (FDR). + + This decoder refines object detection predictions through iterative updates across multiple layers, + utilizing attention mechanisms, location quality estimators, and distribution refinement techniques + to improve bounding box accuracy and robustness. + """ + + def __init__( + self, + hidden_dim, + decoder_layer, + decoder_layer_wide, + num_layers, + num_head, + reg_max, + reg_scale, + up, + eval_idx=-1, + layer_scale=2, + ): + super(TransformerDecoder, self).__init__() + self.hidden_dim = hidden_dim + self.num_layers = num_layers + self.layer_scale = layer_scale + self.num_head = num_head + self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx + self.up, self.reg_scale, self.reg_max = up, reg_scale, reg_max + self.layers = nn.ModuleList( + [copy.deepcopy(decoder_layer) for _ in range(self.eval_idx + 1)] + + [copy.deepcopy(decoder_layer_wide) for _ in range(num_layers - self.eval_idx - 1)] + ) + self.lqe_layers = nn.ModuleList( + [copy.deepcopy(LQE(4, 64, 2, reg_max)) for _ in range(num_layers)] + ) + + def value_op(self, memory, value_proj, value_scale, memory_mask, memory_spatial_shapes): + """ + Preprocess values for MSDeformableAttention. + """ + value = value_proj(memory) if value_proj is not None else memory + value = F.interpolate(memory, size=value_scale) if value_scale is not None else value + if memory_mask is not None: + value = value * memory_mask.to(value.dtype).unsqueeze(-1) + value = value.reshape(value.shape[0], value.shape[1], self.num_head, -1) + split_shape = [h * w for h, w in memory_spatial_shapes] + return value.permute(0, 2, 3, 1).split(split_shape, dim=-1) + + def convert_to_deploy(self): + self.project = weighting_function(self.reg_max, self.up, self.reg_scale, deploy=True) + self.layers = self.layers[: self.eval_idx + 1] + self.lqe_layers = nn.ModuleList( + [nn.Identity()] * (self.eval_idx) + [self.lqe_layers[self.eval_idx]] + ) + + def forward( + self, + target, + ref_points_unact, + memory, + spatial_shapes, + bbox_head, + score_head, + query_pos_head, + pre_bbox_head, + integral, + up, + reg_scale, + attn_mask=None, + memory_mask=None, + dn_meta=None, + ): + output = target + output_detach = pred_corners_undetach = 0 + value = self.value_op(memory, None, None, memory_mask, spatial_shapes) + + dec_out_bboxes = [] + dec_out_logits = [] + dec_out_pred_corners = [] + dec_out_refs = [] + if not hasattr(self, "project"): + project = weighting_function(self.reg_max, up, reg_scale) + else: + project = self.project + + ref_points_detach = F.sigmoid(ref_points_unact) + + for i, layer in enumerate(self.layers): + ref_points_input = ref_points_detach.unsqueeze(2) + query_pos_embed = query_pos_head(ref_points_detach).clamp(min=-10, max=10) + + # TODO Adjust scale if needed for detachable wider layers + if i >= self.eval_idx + 1 and self.layer_scale > 1: + query_pos_embed = F.interpolate(query_pos_embed, scale_factor=self.layer_scale) + value = self.value_op( + memory, None, query_pos_embed.shape[-1], memory_mask, spatial_shapes + ) + output = F.interpolate(output, size=query_pos_embed.shape[-1]) + output_detach = output.detach() + + output = layer( + output, ref_points_input, value, spatial_shapes, attn_mask, query_pos_embed + ) + + if i == 0: + # Initial bounding box predictions with inverse sigmoid refinement + pre_bboxes = F.sigmoid(pre_bbox_head(output) + inverse_sigmoid(ref_points_detach)) + pre_scores = score_head[0](output) + ref_points_initial = pre_bboxes.detach() + + # Refine bounding box corners using FDR, integrating previous layer's corrections + pred_corners = bbox_head[i](output + output_detach) + pred_corners_undetach + inter_ref_bbox = distance2bbox( + ref_points_initial, integral(pred_corners, project), reg_scale + ) + + if self.training or i == self.eval_idx: + scores = score_head[i](output) + # Lqe does not affect the performance here. + scores = self.lqe_layers[i](scores, pred_corners) + dec_out_logits.append(scores) + dec_out_bboxes.append(inter_ref_bbox) + dec_out_pred_corners.append(pred_corners) + dec_out_refs.append(ref_points_initial) + + if not self.training: + break + + pred_corners_undetach = pred_corners + ref_points_detach = inter_ref_bbox.detach() + output_detach = output.detach() + + return ( + torch.stack(dec_out_bboxes), + torch.stack(dec_out_logits), + torch.stack(dec_out_pred_corners), + torch.stack(dec_out_refs), + pre_bboxes, + pre_scores, + ) + + +@register() +class DFINETransformer(nn.Module): + __share__ = ["num_classes", "eval_spatial_size"] + + def __init__( + self, + num_classes=80, + hidden_dim=256, + num_queries=300, + feat_channels=[512, 1024, 2048], + feat_strides=[8, 16, 32], + num_levels=3, + num_points=4, + nhead=8, + num_layers=6, + dim_feedforward=1024, + dropout=0.0, + activation="relu", + num_denoising=100, + label_noise_ratio=0.5, + box_noise_scale=1.0, + learn_query_content=False, + eval_spatial_size=None, + eval_idx=-1, + eps=1e-2, + aux_loss=True, + cross_attn_method="default", + query_select_method="default", + reg_max=32, + reg_scale=4.0, + layer_scale=1, + ): + super().__init__() + assert len(feat_channels) <= num_levels + assert len(feat_strides) == len(feat_channels) + + for _ in range(num_levels - len(feat_strides)): + feat_strides.append(feat_strides[-1] * 2) + + self.hidden_dim = hidden_dim + scaled_dim = round(layer_scale * hidden_dim) + self.nhead = nhead + self.feat_strides = feat_strides + self.num_levels = num_levels + self.num_classes = num_classes + self.num_queries = num_queries + self.eps = eps + self.num_layers = num_layers + self.eval_spatial_size = eval_spatial_size + self.aux_loss = aux_loss + self.reg_max = reg_max + + assert query_select_method in ("default", "one2many", "agnostic"), "" + assert cross_attn_method in ("default", "discrete"), "" + self.cross_attn_method = cross_attn_method + self.query_select_method = query_select_method + + # backbone feature projection + self._build_input_proj_layer(feat_channels) + + # Transformer module + self.up = nn.Parameter(torch.tensor([0.5]), requires_grad=False) + self.reg_scale = nn.Parameter(torch.tensor([reg_scale]), requires_grad=False) + decoder_layer = TransformerDecoderLayer( + hidden_dim, + nhead, + dim_feedforward, + dropout, + activation, + num_levels, + num_points, + cross_attn_method=cross_attn_method, + ) + decoder_layer_wide = TransformerDecoderLayer( + hidden_dim, + nhead, + dim_feedforward, + dropout, + activation, + num_levels, + num_points, + cross_attn_method=cross_attn_method, + layer_scale=layer_scale, + ) + self.decoder = TransformerDecoder( + hidden_dim, + decoder_layer, + decoder_layer_wide, + num_layers, + nhead, + reg_max, + self.reg_scale, + self.up, + eval_idx, + layer_scale, + ) + # denoising + self.num_denoising = num_denoising + self.label_noise_ratio = label_noise_ratio + self.box_noise_scale = box_noise_scale + if num_denoising > 0: + self.denoising_class_embed = nn.Embedding( + num_classes + 1, hidden_dim, padding_idx=num_classes + ) + init.normal_(self.denoising_class_embed.weight[:-1]) + + # decoder embedding + self.learn_query_content = learn_query_content + if learn_query_content: + self.tgt_embed = nn.Embedding(num_queries, hidden_dim) + self.query_pos_head = MLP(4, 2 * hidden_dim, hidden_dim, 2) + + # if num_select_queries != self.num_queries: + # layer = TransformerEncoderLayer(hidden_dim, nhead, dim_feedforward, activation='gelu') + # self.encoder = TransformerEncoder(layer, 1) + + self.enc_output = nn.Sequential( + OrderedDict( + [ + ("proj", nn.Linear(hidden_dim, hidden_dim)), + ( + "norm", + nn.LayerNorm( + hidden_dim, + ), + ), + ] + ) + ) + + if query_select_method == "agnostic": + self.enc_score_head = nn.Linear(hidden_dim, 1) + else: + self.enc_score_head = nn.Linear(hidden_dim, num_classes) + + self.enc_bbox_head = MLP(hidden_dim, hidden_dim, 4, 3) + + # decoder head + self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx + self.dec_score_head = nn.ModuleList( + [nn.Linear(hidden_dim, num_classes) for _ in range(self.eval_idx + 1)] + + [nn.Linear(scaled_dim, num_classes) for _ in range(num_layers - self.eval_idx - 1)] + ) + self.pre_bbox_head = MLP(hidden_dim, hidden_dim, 4, 3) + self.dec_bbox_head = nn.ModuleList( + [ + MLP(hidden_dim, hidden_dim, 4 * (self.reg_max + 1), 3) + for _ in range(self.eval_idx + 1) + ] + + [ + MLP(scaled_dim, scaled_dim, 4 * (self.reg_max + 1), 3) + for _ in range(num_layers - self.eval_idx - 1) + ] + ) + self.integral = Integral(self.reg_max) + + # init encoder output anchors and valid_mask + if self.eval_spatial_size: + anchors, valid_mask = self._generate_anchors() + self.register_buffer("anchors", anchors) + self.register_buffer("valid_mask", valid_mask) + # init encoder output anchors and valid_mask + if self.eval_spatial_size: + self.anchors, self.valid_mask = self._generate_anchors() + + self._reset_parameters(feat_channels) + + def convert_to_deploy(self): + self.dec_score_head = nn.ModuleList( + [nn.Identity()] * (self.eval_idx) + [self.dec_score_head[self.eval_idx]] + ) + self.dec_bbox_head = nn.ModuleList( + [ + self.dec_bbox_head[i] if i <= self.eval_idx else nn.Identity() + for i in range(len(self.dec_bbox_head)) + ] + ) + + def _reset_parameters(self, feat_channels): + bias = bias_init_with_prob(0.01) + init.constant_(self.enc_score_head.bias, bias) + init.constant_(self.enc_bbox_head.layers[-1].weight, 0) + init.constant_(self.enc_bbox_head.layers[-1].bias, 0) + + init.constant_(self.pre_bbox_head.layers[-1].weight, 0) + init.constant_(self.pre_bbox_head.layers[-1].bias, 0) + + for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head): + init.constant_(cls_.bias, bias) + if hasattr(reg_, "layers"): + init.constant_(reg_.layers[-1].weight, 0) + init.constant_(reg_.layers[-1].bias, 0) + + init.xavier_uniform_(self.enc_output[0].weight) + if self.learn_query_content: + init.xavier_uniform_(self.tgt_embed.weight) + init.xavier_uniform_(self.query_pos_head.layers[0].weight) + init.xavier_uniform_(self.query_pos_head.layers[1].weight) + for m, in_channels in zip(self.input_proj, feat_channels): + if in_channels != self.hidden_dim: + init.xavier_uniform_(m[0].weight) + + def _build_input_proj_layer(self, feat_channels): + self.input_proj = nn.ModuleList() + for in_channels in feat_channels: + if in_channels == self.hidden_dim: + self.input_proj.append(nn.Identity()) + else: + self.input_proj.append( + nn.Sequential( + OrderedDict( + [ + ("conv", nn.Conv2d(in_channels, self.hidden_dim, 1, bias=False)), + ( + "norm", + nn.BatchNorm2d( + self.hidden_dim, + ), + ), + ] + ) + ) + ) + + in_channels = feat_channels[-1] + + for _ in range(self.num_levels - len(feat_channels)): + if in_channels == self.hidden_dim: + self.input_proj.append(nn.Identity()) + else: + self.input_proj.append( + nn.Sequential( + OrderedDict( + [ + ( + "conv", + nn.Conv2d( + in_channels, self.hidden_dim, 3, 2, padding=1, bias=False + ), + ), + ("norm", nn.BatchNorm2d(self.hidden_dim)), + ] + ) + ) + ) + in_channels = self.hidden_dim + + def _get_encoder_input(self, feats: List[torch.Tensor]): + # get projection features + proj_feats = [self.input_proj[i](feat) for i, feat in enumerate(feats)] + if self.num_levels > len(proj_feats): + len_srcs = len(proj_feats) + for i in range(len_srcs, self.num_levels): + if i == len_srcs: + proj_feats.append(self.input_proj[i](feats[-1])) + else: + proj_feats.append(self.input_proj[i](proj_feats[-1])) + + # get encoder inputs + feat_flatten = [] + spatial_shapes = [] + for i, feat in enumerate(proj_feats): + _, _, h, w = feat.shape + # [b, c, h, w] -> [b, h*w, c] + feat_flatten.append(feat.flatten(2).permute(0, 2, 1)) + # [num_levels, 2] + spatial_shapes.append([h, w]) + + # [b, l, c] + feat_flatten = torch.concat(feat_flatten, 1) + return feat_flatten, spatial_shapes + + def _generate_anchors( + self, spatial_shapes=None, grid_size=0.05, dtype=torch.float32, device="cpu" + ): + if spatial_shapes is None: + spatial_shapes = [] + eval_h, eval_w = self.eval_spatial_size + for s in self.feat_strides: + spatial_shapes.append([int(eval_h / s), int(eval_w / s)]) + + anchors = [] + for lvl, (h, w) in enumerate(spatial_shapes): + grid_y, grid_x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij") + grid_xy = torch.stack([grid_x, grid_y], dim=-1) + grid_xy = (grid_xy.unsqueeze(0) + 0.5) / torch.tensor([w, h], dtype=dtype) + wh = torch.ones_like(grid_xy) * grid_size * (2.0**lvl) + lvl_anchors = torch.concat([grid_xy, wh], dim=-1).reshape(-1, h * w, 4) + anchors.append(lvl_anchors) + + anchors = torch.concat(anchors, dim=1).to(device) + valid_mask = ((anchors > self.eps) * (anchors < 1 - self.eps)).all(-1, keepdim=True) + anchors = torch.log(anchors / (1 - anchors)) + anchors = torch.where(valid_mask, anchors, torch.inf) + + return anchors, valid_mask + + def _get_decoder_input( + self, memory: torch.Tensor, spatial_shapes, denoising_logits=None, denoising_bbox_unact=None + ): + # prepare input for decoder + if self.training or self.eval_spatial_size is None: + anchors, valid_mask = self._generate_anchors(spatial_shapes, device=memory.device) + else: + anchors = self.anchors + valid_mask = self.valid_mask + if memory.shape[0] > 1: + anchors = anchors.repeat(memory.shape[0], 1, 1) + + # memory = torch.where(valid_mask, memory, 0) + # TODO fix type error for onnx export + memory = valid_mask.to(memory.dtype) * memory + + output_memory: torch.Tensor = self.enc_output(memory) + enc_outputs_logits: torch.Tensor = self.enc_score_head(output_memory) + + enc_topk_bboxes_list, enc_topk_logits_list = [], [] + enc_topk_memory, enc_topk_logits, enc_topk_anchors = self._select_topk( + output_memory, enc_outputs_logits, anchors, self.num_queries + ) + + enc_topk_bbox_unact: torch.Tensor = self.enc_bbox_head(enc_topk_memory) + enc_topk_anchors + + if self.training: + enc_topk_bboxes = F.sigmoid(enc_topk_bbox_unact) + enc_topk_bboxes_list.append(enc_topk_bboxes) + enc_topk_logits_list.append(enc_topk_logits) + + # if self.num_select_queries != self.num_queries: + # raise NotImplementedError('') + + if self.learn_query_content: + content = self.tgt_embed.weight.unsqueeze(0).tile([memory.shape[0], 1, 1]) + else: + content = enc_topk_memory.detach() + + enc_topk_bbox_unact = enc_topk_bbox_unact.detach() + + if denoising_bbox_unact is not None: + enc_topk_bbox_unact = torch.concat([denoising_bbox_unact, enc_topk_bbox_unact], dim=1) + content = torch.concat([denoising_logits, content], dim=1) + + return content, enc_topk_bbox_unact, enc_topk_bboxes_list, enc_topk_logits_list + + def _select_topk( + self, + memory: torch.Tensor, + outputs_logits: torch.Tensor, + outputs_anchors_unact: torch.Tensor, + topk: int, + ): + if self.query_select_method == "default": + _, topk_ind = torch.topk(outputs_logits.max(-1).values, topk, dim=-1) + + elif self.query_select_method == "one2many": + _, topk_ind = torch.topk(outputs_logits.flatten(1), topk, dim=-1) + topk_ind = topk_ind // self.num_classes + + elif self.query_select_method == "agnostic": + _, topk_ind = torch.topk(outputs_logits.squeeze(-1), topk, dim=-1) + + topk_ind: torch.Tensor + + topk_anchors = outputs_anchors_unact.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_anchors_unact.shape[-1]) + ) + + topk_logits = ( + outputs_logits.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, outputs_logits.shape[-1]) + ) + if self.training + else None + ) + + topk_memory = memory.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, memory.shape[-1]) + ) + + return topk_memory, topk_logits, topk_anchors + + def forward(self, feats, targets=None): + # input projection and embedding + memory, spatial_shapes = self._get_encoder_input(feats) + + # prepare denoising training + if self.training and self.num_denoising > 0: + denoising_logits, denoising_bbox_unact, attn_mask, dn_meta = ( + get_contrastive_denoising_training_group( + targets, + self.num_classes, + self.num_queries, + self.denoising_class_embed, + num_denoising=self.num_denoising, + label_noise_ratio=self.label_noise_ratio, + box_noise_scale=1.0, + ) + ) + else: + denoising_logits, denoising_bbox_unact, attn_mask, dn_meta = None, None, None, None + + init_ref_contents, init_ref_points_unact, enc_topk_bboxes_list, enc_topk_logits_list = ( + self._get_decoder_input(memory, spatial_shapes, denoising_logits, denoising_bbox_unact) + ) + + # decoder + out_bboxes, out_logits, out_corners, out_refs, pre_bboxes, pre_logits = self.decoder( + init_ref_contents, + init_ref_points_unact, + memory, + spatial_shapes, + self.dec_bbox_head, + self.dec_score_head, + self.query_pos_head, + self.pre_bbox_head, + self.integral, + self.up, + self.reg_scale, + attn_mask=attn_mask, + dn_meta=dn_meta, + ) + + if self.training and dn_meta is not None: + dn_pre_logits, pre_logits = torch.split(pre_logits, dn_meta["dn_num_split"], dim=1) + dn_pre_bboxes, pre_bboxes = torch.split(pre_bboxes, dn_meta["dn_num_split"], dim=1) + dn_out_bboxes, out_bboxes = torch.split(out_bboxes, dn_meta["dn_num_split"], dim=2) + dn_out_logits, out_logits = torch.split(out_logits, dn_meta["dn_num_split"], dim=2) + + dn_out_corners, out_corners = torch.split(out_corners, dn_meta["dn_num_split"], dim=2) + dn_out_refs, out_refs = torch.split(out_refs, dn_meta["dn_num_split"], dim=2) + + if self.training: + out = { + "pred_logits": out_logits[-1], + "pred_boxes": out_bboxes[-1], + "pred_corners": out_corners[-1], + "ref_points": out_refs[-1], + "up": self.up, + "reg_scale": self.reg_scale, + } + else: + out = {"pred_logits": out_logits[-1], "pred_boxes": out_bboxes[-1]} + + if self.training and self.aux_loss: + out["aux_outputs"] = self._set_aux_loss2( + out_logits[:-1], + out_bboxes[:-1], + out_corners[:-1], + out_refs[:-1], + out_corners[-1], + out_logits[-1], + ) + out["enc_aux_outputs"] = self._set_aux_loss(enc_topk_logits_list, enc_topk_bboxes_list) + out["pre_outputs"] = {"pred_logits": pre_logits, "pred_boxes": pre_bboxes} + out["enc_meta"] = {"class_agnostic": self.query_select_method == "agnostic"} + + if dn_meta is not None: + out["dn_outputs"] = self._set_aux_loss2( + dn_out_logits, + dn_out_bboxes, + dn_out_corners, + dn_out_refs, + dn_out_corners[-1], + dn_out_logits[-1], + ) + out["dn_pre_outputs"] = {"pred_logits": dn_pre_logits, "pred_boxes": dn_pre_bboxes} + out["dn_meta"] = dn_meta + + return out + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + return [{"pred_logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)] + + @torch.jit.unused + def _set_aux_loss2( + self, + outputs_class, + outputs_coord, + outputs_corners, + outputs_ref, + teacher_corners=None, + teacher_logits=None, + ): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + return [ + { + "pred_logits": a, + "pred_boxes": b, + "pred_corners": c, + "ref_points": d, + "teacher_corners": teacher_corners, + "teacher_logits": teacher_logits, + } + for a, b, c, d in zip(outputs_class, outputs_coord, outputs_corners, outputs_ref) + ] diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_utils.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_utils.py new file mode 100644 index 0000000..512d161 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/dfine_utils.py @@ -0,0 +1,169 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import torch + +from .box_ops import box_xyxy_to_cxcywh + + +def weighting_function(reg_max, up, reg_scale, deploy=False): + """ + Generates the non-uniform Weighting Function W(n) for bounding box regression. + + Args: + reg_max (int): Max number of the discrete bins. + up (Tensor): Controls upper bounds of the sequence, + where maximum offset is ±up * H / W. + reg_scale (float): Controls the curvature of the Weighting Function. + Larger values result in flatter weights near the central axis W(reg_max/2)=0 + and steeper weights at both ends. + deploy (bool): If True, uses deployment mode settings. + + Returns: + Tensor: Sequence of Weighting Function. + """ + if deploy: + upper_bound1 = (abs(up[0]) * abs(reg_scale)).item() + upper_bound2 = (abs(up[0]) * abs(reg_scale) * 2).item() + step = (upper_bound1 + 1) ** (2 / (reg_max - 2)) + left_values = [-((step) ** i) + 1 for i in range(reg_max // 2 - 1, 0, -1)] + right_values = [(step) ** i - 1 for i in range(1, reg_max // 2)] + values = ( + [-upper_bound2] + + left_values + + [torch.zeros_like(up[0][None])] + + right_values + + [upper_bound2] + ) + return torch.tensor(values, dtype=up.dtype, device=up.device) + else: + upper_bound1 = abs(up[0]) * abs(reg_scale) + upper_bound2 = abs(up[0]) * abs(reg_scale) * 2 + step = (upper_bound1 + 1) ** (2 / (reg_max - 2)) + left_values = [-((step) ** i) + 1 for i in range(reg_max // 2 - 1, 0, -1)] + right_values = [(step) ** i - 1 for i in range(1, reg_max // 2)] + values = ( + [-upper_bound2] + + left_values + + [torch.zeros_like(up[0][None])] + + right_values + + [upper_bound2] + ) + return torch.cat(values, 0) + + +def translate_gt(gt, reg_max, reg_scale, up): + """ + Decodes bounding box ground truth (GT) values into distribution-based GT representations. + + This function maps continuous GT values into discrete distribution bins, which can be used + for regression tasks in object detection models. It calculates the indices of the closest + bins to each GT value and assigns interpolation weights to these bins based on their proximity + to the GT value. + + Args: + gt (Tensor): Ground truth bounding box values, shape (N, ). + reg_max (int): Maximum number of discrete bins for the distribution. + reg_scale (float): Controls the curvature of the Weighting Function. + up (Tensor): Controls the upper bounds of the Weighting Function. + + Returns: + Tuple[Tensor, Tensor, Tensor]: + - indices (Tensor): Index of the left bin closest to each GT value, shape (N, ). + - weight_right (Tensor): Weight assigned to the right bin, shape (N, ). + - weight_left (Tensor): Weight assigned to the left bin, shape (N, ). + """ + gt = gt.reshape(-1) + function_values = weighting_function(reg_max, up, reg_scale) + + # Find the closest left-side indices for each value + diffs = function_values.unsqueeze(0) - gt.unsqueeze(1) + mask = diffs <= 0 + closest_left_indices = torch.sum(mask, dim=1) - 1 + + # Calculate the weights for the interpolation + indices = closest_left_indices.float() + + weight_right = torch.zeros_like(indices) + weight_left = torch.zeros_like(indices) + + valid_idx_mask = (indices >= 0) & (indices < reg_max) + valid_indices = indices[valid_idx_mask].long() + + # Obtain distances + left_values = function_values[valid_indices] + right_values = function_values[valid_indices + 1] + + left_diffs = torch.abs(gt[valid_idx_mask] - left_values) + right_diffs = torch.abs(right_values - gt[valid_idx_mask]) + + # Valid weights + weight_right[valid_idx_mask] = left_diffs / (left_diffs + right_diffs) + weight_left[valid_idx_mask] = 1.0 - weight_right[valid_idx_mask] + + # Invalid weights (out of range) + invalid_idx_mask_neg = indices < 0 + weight_right[invalid_idx_mask_neg] = 0.0 + weight_left[invalid_idx_mask_neg] = 1.0 + indices[invalid_idx_mask_neg] = 0.0 + + invalid_idx_mask_pos = indices >= reg_max + weight_right[invalid_idx_mask_pos] = 1.0 + weight_left[invalid_idx_mask_pos] = 0.0 + indices[invalid_idx_mask_pos] = reg_max - 0.1 + + return indices, weight_right, weight_left + + +def distance2bbox(points, distance, reg_scale): + """ + Decodes edge-distances into bounding box coordinates. + + Args: + points (Tensor): (B, N, 4) or (N, 4) format, representing [x, y, w, h], + where (x, y) is the center and (w, h) are width and height. + distance (Tensor): (B, N, 4) or (N, 4), representing distances from the + point to the left, top, right, and bottom boundaries. + + reg_scale (float): Controls the curvature of the Weighting Function. + + Returns: + Tensor: Bounding boxes in (N, 4) or (B, N, 4) format [cx, cy, w, h]. + """ + reg_scale = abs(reg_scale) + x1 = points[..., 0] - (0.5 * reg_scale + distance[..., 0]) * (points[..., 2] / reg_scale) + y1 = points[..., 1] - (0.5 * reg_scale + distance[..., 1]) * (points[..., 3] / reg_scale) + x2 = points[..., 0] + (0.5 * reg_scale + distance[..., 2]) * (points[..., 2] / reg_scale) + y2 = points[..., 1] + (0.5 * reg_scale + distance[..., 3]) * (points[..., 3] / reg_scale) + + bboxes = torch.stack([x1, y1, x2, y2], -1) + + return box_xyxy_to_cxcywh(bboxes) + + +def bbox2distance(points, bbox, reg_max, reg_scale, up, eps=0.1): + """ + Converts bounding box coordinates to distances from a reference point. + + Args: + points (Tensor): (n, 4) [x, y, w, h], where (x, y) is the center. + bbox (Tensor): (n, 4) bounding boxes in "xyxy" format. + reg_max (float): Maximum bin value. + reg_scale (float): Controling curvarture of W(n). + up (Tensor): Controling upper bounds of W(n). + eps (float): Small value to ensure target < reg_max. + + Returns: + Tensor: Decoded distances. + """ + reg_scale = abs(reg_scale) + left = (points[:, 0] - bbox[:, 0]) / (points[..., 2] / reg_scale + 1e-16) - 0.5 * reg_scale + top = (points[:, 1] - bbox[:, 1]) / (points[..., 3] / reg_scale + 1e-16) - 0.5 * reg_scale + right = (bbox[:, 2] - points[:, 0]) / (points[..., 2] / reg_scale + 1e-16) - 0.5 * reg_scale + bottom = (bbox[:, 3] - points[:, 1]) / (points[..., 3] / reg_scale + 1e-16) - 0.5 * reg_scale + four_lens = torch.stack([left, top, right, bottom], -1) + four_lens, weight_right, weight_left = translate_gt(four_lens, reg_max, reg_scale, up) + if reg_max is not None: + four_lens = four_lens.clamp(min=0, max=reg_max - eps) + return four_lens.reshape(-1).detach(), weight_right.detach(), weight_left.detach() diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/hybrid_encoder.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/hybrid_encoder.py new file mode 100644 index 0000000..77af3fb --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/hybrid_encoder.py @@ -0,0 +1,488 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import copy +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...core import register +from .utils import get_activation + +__all__ = ["HybridEncoder"] + + +class ConvNormLayer_fuse(nn.Module): + def __init__(self, ch_in, ch_out, kernel_size, stride, g=1, padding=None, bias=False, act=None): + super().__init__() + padding = (kernel_size - 1) // 2 if padding is None else padding + self.conv = nn.Conv2d( + ch_in, ch_out, kernel_size, stride, groups=g, padding=padding, bias=bias + ) + self.norm = nn.BatchNorm2d(ch_out) + self.act = nn.Identity() if act is None else get_activation(act) + self.ch_in, self.ch_out, self.kernel_size, self.stride, self.g, self.padding, self.bias = ( + ch_in, + ch_out, + kernel_size, + stride, + g, + padding, + bias, + ) + + def forward(self, x): + if hasattr(self, "conv_bn_fused"): + y = self.conv_bn_fused(x) + else: + y = self.norm(self.conv(x)) + return self.act(y) + + def convert_to_deploy(self): + if not hasattr(self, "conv_bn_fused"): + self.conv_bn_fused = nn.Conv2d( + self.ch_in, + self.ch_out, + self.kernel_size, + self.stride, + groups=self.g, + padding=self.padding, + bias=True, + ) + + kernel, bias = self.get_equivalent_kernel_bias() + self.conv_bn_fused.weight.data = kernel + self.conv_bn_fused.bias.data = bias + self.__delattr__("conv") + self.__delattr__("norm") + + def get_equivalent_kernel_bias(self): + kernel3x3, bias3x3 = self._fuse_bn_tensor() + + return kernel3x3, bias3x3 + + def _fuse_bn_tensor(self): + kernel = self.conv.weight + running_mean = self.norm.running_mean + running_var = self.norm.running_var + gamma = self.norm.weight + beta = self.norm.bias + eps = self.norm.eps + std = (running_var + eps).sqrt() + t = (gamma / std).reshape(-1, 1, 1, 1) + return kernel * t, beta - running_mean * gamma / std + + +class ConvNormLayer(nn.Module): + def __init__(self, ch_in, ch_out, kernel_size, stride, g=1, padding=None, bias=False, act=None): + super().__init__() + padding = (kernel_size - 1) // 2 if padding is None else padding + self.conv = nn.Conv2d( + ch_in, ch_out, kernel_size, stride, groups=g, padding=padding, bias=bias + ) + self.norm = nn.BatchNorm2d(ch_out) + self.act = nn.Identity() if act is None else get_activation(act) + + def forward(self, x): + return self.act(self.norm(self.conv(x))) + + +class SCDown(nn.Module): + def __init__(self, c1, c2, k, s): + super().__init__() + self.cv1 = ConvNormLayer_fuse(c1, c2, 1, 1) + self.cv2 = ConvNormLayer_fuse(c2, c2, k, s, c2) + + def forward(self, x): + return self.cv2(self.cv1(x)) + + +class VGGBlock(nn.Module): + def __init__(self, ch_in, ch_out, act="relu"): + super().__init__() + self.ch_in = ch_in + self.ch_out = ch_out + self.conv1 = ConvNormLayer(ch_in, ch_out, 3, 1, padding=1, act=None) + self.conv2 = ConvNormLayer(ch_in, ch_out, 1, 1, padding=0, act=None) + self.act = nn.Identity() if act is None else act + + def forward(self, x): + if hasattr(self, "conv"): + y = self.conv(x) + else: + y = self.conv1(x) + self.conv2(x) + + return self.act(y) + + def convert_to_deploy(self): + if not hasattr(self, "conv"): + self.conv = nn.Conv2d(self.ch_in, self.ch_out, 3, 1, padding=1) + + kernel, bias = self.get_equivalent_kernel_bias() + self.conv.weight.data = kernel + self.conv.bias.data = bias + self.__delattr__("conv1") + self.__delattr__("conv2") + + def get_equivalent_kernel_bias(self): + kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1) + kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2) + + return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1), bias3x3 + bias1x1 + + def _pad_1x1_to_3x3_tensor(self, kernel1x1): + if kernel1x1 is None: + return 0 + else: + return F.pad(kernel1x1, [1, 1, 1, 1]) + + def _fuse_bn_tensor(self, branch: ConvNormLayer): + if branch is None: + return 0, 0 + kernel = branch.conv.weight + running_mean = branch.norm.running_mean + running_var = branch.norm.running_var + gamma = branch.norm.weight + beta = branch.norm.bias + eps = branch.norm.eps + std = (running_var + eps).sqrt() + t = (gamma / std).reshape(-1, 1, 1, 1) + return kernel * t, beta - running_mean * gamma / std + + +class ELAN(nn.Module): + # csp-elan + def __init__(self, c1, c2, c3, c4, n=2, bias=False, act="silu", bottletype=VGGBlock): + super().__init__() + self.c = c3 + self.cv1 = ConvNormLayer_fuse(c1, c3, 1, 1, bias=bias, act=act) + self.cv2 = nn.Sequential( + bottletype(c3 // 2, c4, act=get_activation(act)), + ConvNormLayer_fuse(c4, c4, 3, 1, bias=bias, act=act), + ) + self.cv3 = nn.Sequential( + bottletype(c4, c4, act=get_activation(act)), + ConvNormLayer_fuse(c4, c4, 3, 1, bias=bias, act=act), + ) + self.cv4 = ConvNormLayer_fuse(c3 + (2 * c4), c2, 1, 1, bias=bias, act=act) + + def forward(self, x): + # y = [self.cv1(x)] + y = list(self.cv1(x).chunk(2, 1)) + y.extend((m(y[-1])) for m in [self.cv2, self.cv3]) + return self.cv4(torch.cat(y, 1)) + + +class RepNCSPELAN4(nn.Module): + # csp-elan + def __init__(self, c1, c2, c3, c4, n=3, bias=False, act="silu"): + super().__init__() + self.c = c3 // 2 + self.cv1 = ConvNormLayer_fuse(c1, c3, 1, 1, bias=bias, act=act) + self.cv2 = nn.Sequential( + CSPLayer(c3 // 2, c4, n, 1, bias=bias, act=act, bottletype=VGGBlock), + ConvNormLayer_fuse(c4, c4, 3, 1, bias=bias, act=act), + ) + self.cv3 = nn.Sequential( + CSPLayer(c4, c4, n, 1, bias=bias, act=act, bottletype=VGGBlock), + ConvNormLayer_fuse(c4, c4, 3, 1, bias=bias, act=act), + ) + self.cv4 = ConvNormLayer_fuse(c3 + (2 * c4), c2, 1, 1, bias=bias, act=act) + + def forward_chunk(self, x): + y = list(self.cv1(x).chunk(2, 1)) + y.extend((m(y[-1])) for m in [self.cv2, self.cv3]) + return self.cv4(torch.cat(y, 1)) + + def forward(self, x): + y = list(self.cv1(x).split((self.c, self.c), 1)) + y.extend(m(y[-1]) for m in [self.cv2, self.cv3]) + return self.cv4(torch.cat(y, 1)) + + +class CSPLayer(nn.Module): + def __init__( + self, + in_channels, + out_channels, + num_blocks=3, + expansion=1.0, + bias=False, + act="silu", + bottletype=VGGBlock, + ): + super(CSPLayer, self).__init__() + hidden_channels = int(out_channels * expansion) + self.conv1 = ConvNormLayer_fuse(in_channels, hidden_channels, 1, 1, bias=bias, act=act) + self.conv2 = ConvNormLayer_fuse(in_channels, hidden_channels, 1, 1, bias=bias, act=act) + self.bottlenecks = nn.Sequential( + *[ + bottletype(hidden_channels, hidden_channels, act=get_activation(act)) + for _ in range(num_blocks) + ] + ) + if hidden_channels != out_channels: + self.conv3 = ConvNormLayer_fuse(hidden_channels, out_channels, 1, 1, bias=bias, act=act) + else: + self.conv3 = nn.Identity() + + def forward(self, x): + x_1 = self.conv1(x) + x_1 = self.bottlenecks(x_1) + x_2 = self.conv2(x) + return self.conv3(x_1 + x_2) + + +# transformer +class TransformerEncoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.normalize_before = normalize_before + + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout, batch_first=True) + + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.activation = get_activation(activation) + + @staticmethod + def with_pos_embed(tensor, pos_embed): + return tensor if pos_embed is None else tensor + pos_embed + + def forward(self, src, src_mask=None, pos_embed=None) -> torch.Tensor: + residual = src + if self.normalize_before: + src = self.norm1(src) + q = k = self.with_pos_embed(src, pos_embed) + src, _ = self.self_attn(q, k, value=src, attn_mask=src_mask) + + src = residual + self.dropout1(src) + if not self.normalize_before: + src = self.norm1(src) + + residual = src + if self.normalize_before: + src = self.norm2(src) + src = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = residual + self.dropout2(src) + if not self.normalize_before: + src = self.norm2(src) + return src + + +class TransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers, norm=None): + super(TransformerEncoder, self).__init__() + self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(num_layers)]) + self.num_layers = num_layers + self.norm = norm + + def forward(self, src, src_mask=None, pos_embed=None) -> torch.Tensor: + output = src + for layer in self.layers: + output = layer(output, src_mask=src_mask, pos_embed=pos_embed) + + if self.norm is not None: + output = self.norm(output) + + return output + + +@register() +class HybridEncoder(nn.Module): + __share__ = [ + "eval_spatial_size", + ] + + def __init__( + self, + in_channels=[512, 1024, 2048], + feat_strides=[8, 16, 32], + hidden_dim=256, + nhead=8, + dim_feedforward=1024, + dropout=0.0, + enc_act="gelu", + use_encoder_idx=[2], + num_encoder_layers=1, + pe_temperature=10000, + expansion=1.0, + depth_mult=1.0, + act="silu", + eval_spatial_size=None, + ): + super().__init__() + self.in_channels = in_channels + self.feat_strides = feat_strides + self.hidden_dim = hidden_dim + self.use_encoder_idx = use_encoder_idx + self.num_encoder_layers = num_encoder_layers + self.pe_temperature = pe_temperature + self.eval_spatial_size = eval_spatial_size + self.out_channels = [hidden_dim for _ in range(len(in_channels))] + self.out_strides = feat_strides + + # channel projection + self.input_proj = nn.ModuleList() + for in_channel in in_channels: + proj = nn.Sequential( + OrderedDict( + [ + ("conv", nn.Conv2d(in_channel, hidden_dim, kernel_size=1, bias=False)), + ("norm", nn.BatchNorm2d(hidden_dim)), + ] + ) + ) + + self.input_proj.append(proj) + + # encoder transformer + encoder_layer = TransformerEncoderLayer( + hidden_dim, + nhead=nhead, + dim_feedforward=dim_feedforward, + dropout=dropout, + activation=enc_act, + ) + + self.encoder = nn.ModuleList( + [ + TransformerEncoder(copy.deepcopy(encoder_layer), num_encoder_layers) + for _ in range(len(use_encoder_idx)) + ] + ) + + # top-down fpn + self.lateral_convs = nn.ModuleList() + self.fpn_blocks = nn.ModuleList() + for _ in range(len(in_channels) - 1, 0, -1): + self.lateral_convs.append(ConvNormLayer_fuse(hidden_dim, hidden_dim, 1, 1)) + self.fpn_blocks.append( + RepNCSPELAN4( + hidden_dim * 2, + hidden_dim, + hidden_dim * 2, + round(expansion * hidden_dim // 2), + round(3 * depth_mult), + ) + # CSPLayer(hidden_dim * 2, hidden_dim, round(3 * depth_mult), act=act, expansion=expansion, bottletype=VGGBlock) + ) + + # bottom-up pan + self.downsample_convs = nn.ModuleList() + self.pan_blocks = nn.ModuleList() + for _ in range(len(in_channels) - 1): + self.downsample_convs.append( + nn.Sequential( + SCDown(hidden_dim, hidden_dim, 3, 2), + ) + ) + self.pan_blocks.append( + RepNCSPELAN4( + hidden_dim * 2, + hidden_dim, + hidden_dim * 2, + round(expansion * hidden_dim // 2), + round(3 * depth_mult), + ) + # CSPLayer(hidden_dim * 2, hidden_dim, round(3 * depth_mult), act=act, expansion=expansion, bottletype=VGGBlock) + ) + + self._reset_parameters() + + def _reset_parameters(self): + if self.eval_spatial_size: + for idx in self.use_encoder_idx: + stride = self.feat_strides[idx] + pos_embed = self.build_2d_sincos_position_embedding( + self.eval_spatial_size[1] // stride, + self.eval_spatial_size[0] // stride, + self.hidden_dim, + self.pe_temperature, + ) + setattr(self, f"pos_embed{idx}", pos_embed) + # self.register_buffer(f'pos_embed{idx}', pos_embed) + + @staticmethod + def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.0): + """ """ + grid_w = torch.arange(int(w), dtype=torch.float32) + grid_h = torch.arange(int(h), dtype=torch.float32) + grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij") + assert ( + embed_dim % 4 == 0 + ), "Embed dimension must be divisible by 4 for 2D sin-cos position embedding" + pos_dim = embed_dim // 4 + omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim + omega = 1.0 / (temperature**omega) + + out_w = grid_w.flatten()[..., None] @ omega[None] + out_h = grid_h.flatten()[..., None] @ omega[None] + + return torch.concat([out_w.sin(), out_w.cos(), out_h.sin(), out_h.cos()], dim=1)[None, :, :] + + def forward(self, feats): + assert len(feats) == len(self.in_channels) + proj_feats = [self.input_proj[i](feat) for i, feat in enumerate(feats)] + + # encoder + if self.num_encoder_layers > 0: + for i, enc_ind in enumerate(self.use_encoder_idx): + h, w = proj_feats[enc_ind].shape[2:] + # flatten [B, C, H, W] to [B, HxW, C] + src_flatten = proj_feats[enc_ind].flatten(2).permute(0, 2, 1) + if self.training or self.eval_spatial_size is None: + pos_embed = self.build_2d_sincos_position_embedding( + w, h, self.hidden_dim, self.pe_temperature + ).to(src_flatten.device) + else: + pos_embed = getattr(self, f"pos_embed{enc_ind}", None).to(src_flatten.device) + + memory: torch.Tensor = self.encoder[i](src_flatten, pos_embed=pos_embed) + proj_feats[enc_ind] = ( + memory.permute(0, 2, 1).reshape(-1, self.hidden_dim, h, w).contiguous() + ) + + # broadcasting and fusion + inner_outs = [proj_feats[-1]] + for idx in range(len(self.in_channels) - 1, 0, -1): + feat_heigh = inner_outs[0] + feat_low = proj_feats[idx - 1] + feat_heigh = self.lateral_convs[len(self.in_channels) - 1 - idx](feat_heigh) + inner_outs[0] = feat_heigh + upsample_feat = F.interpolate(feat_heigh, scale_factor=2.0, mode="nearest") + inner_out = self.fpn_blocks[len(self.in_channels) - 1 - idx]( + torch.concat([upsample_feat, feat_low], dim=1) + ) + inner_outs.insert(0, inner_out) + + outs = [inner_outs[0]] + for idx in range(len(self.in_channels) - 1): + feat_low = outs[-1] + feat_height = inner_outs[idx + 1] + downsample_feat = self.downsample_convs[idx](feat_low) + out = self.pan_blocks[idx](torch.concat([downsample_feat, feat_height], dim=1)) + outs.append(out) + + return outs diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/matcher.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/matcher.py new file mode 100644 index 0000000..debe797 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/matcher.py @@ -0,0 +1,160 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +Modules to compute the matching cost and solve the corresponding LSAP. + +Copyright (c) 2024 The D-FINE Authors All Rights Reserved. +""" + +from typing import Dict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from scipy.optimize import linear_sum_assignment + +from ...core import register +from .box_ops import box_cxcywh_to_xyxy, generalized_box_iou + + +@register() +class HungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + __share__ = [ + "use_focal_loss", + ] + + def __init__(self, weight_dict, use_focal_loss=False, alpha=0.25, gamma=2.0): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost + cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost + """ + super().__init__() + self.cost_class = weight_dict["cost_class"] + self.cost_bbox = weight_dict["cost_bbox"] + self.cost_giou = weight_dict["cost_giou"] + + self.use_focal_loss = use_focal_loss + self.alpha = alpha + self.gamma = gamma + + assert ( + self.cost_class != 0 or self.cost_bbox != 0 or self.cost_giou != 0 + ), "all costs cant be 0" + + @torch.no_grad() + def forward(self, outputs: Dict[str, torch.Tensor], targets, return_topk=False): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + bs, num_queries = outputs["pred_logits"].shape[:2] + + # We flatten to compute the cost matrices in a batch + if self.use_focal_loss: + out_prob = F.sigmoid(outputs["pred_logits"].flatten(0, 1)) + else: + out_prob = ( + outputs["pred_logits"].flatten(0, 1).softmax(-1) + ) # [batch_size * num_queries, num_classes] + + out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] + + # Also concat the target labels and boxes + tgt_ids = torch.cat([v["labels"] for v in targets]) + tgt_bbox = torch.cat([v["boxes"] for v in targets]) + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + if self.use_focal_loss: + out_prob = out_prob[:, tgt_ids] + neg_cost_class = ( + (1 - self.alpha) * (out_prob**self.gamma) * (-(1 - out_prob + 1e-8).log()) + ) + pos_cost_class = ( + self.alpha * ((1 - out_prob) ** self.gamma) * (-(out_prob + 1e-8).log()) + ) + cost_class = pos_cost_class - neg_cost_class + else: + cost_class = -out_prob[:, tgt_ids] + + # Compute the L1 cost between boxes + cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1) + + # Compute the giou cost betwen boxes + cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)) + + # Final cost matrix 3 * self.cost_bbox + 2 * self.cost_class + self.cost_giou + C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou + C = C.view(bs, num_queries, -1).cpu() + + sizes = [len(v["boxes"]) for v in targets] + C = torch.nan_to_num(C, nan=1.0) + indices_pre = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] + indices = [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) + for i, j in indices_pre + ] + + # Compute topk indices + if return_topk: + return { + "indices_o2m": self.get_top_k_matches( + C, sizes=sizes, k=return_topk, initial_indices=indices_pre + ) + } + + return {"indices": indices} # , 'indices_o2m': C.min(-1)[1]} + + def get_top_k_matches(self, C, sizes, k=1, initial_indices=None): + indices_list = [] + # C_original = C.clone() + for i in range(k): + indices_k = ( + [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] + if i > 0 + else initial_indices + ) + indices_list.append( + [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) + for i, j in indices_k + ] + ) + for c, idx_k in zip(C.split(sizes, -1), indices_k): + idx_k = np.stack(idx_k) + c[:, idx_k] = 1e6 + indices_list = [ + ( + torch.cat([indices_list[i][j][0] for i in range(k)], dim=0), + torch.cat([indices_list[i][j][1] for i in range(k)], dim=0), + ) + for j in range(len(sizes)) + ] + # C.copy_(C_original) + return indices_list diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/postprocessor.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/postprocessor.py new file mode 100644 index 0000000..b1516b6 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/postprocessor.py @@ -0,0 +1,93 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from ...core import register + +__all__ = ["DFINEPostProcessor"] + + +def mod(a, b): + out = a - a // b * b + return out + + +@register() +class DFINEPostProcessor(nn.Module): + __share__ = ["num_classes", "use_focal_loss", "num_top_queries", "remap_mscoco_category"] + + def __init__( + self, num_classes=80, use_focal_loss=True, num_top_queries=300, remap_mscoco_category=False + ) -> None: + super().__init__() + self.use_focal_loss = use_focal_loss + self.num_top_queries = num_top_queries + self.num_classes = int(num_classes) + self.remap_mscoco_category = remap_mscoco_category + self.deploy_mode = False + + def extra_repr(self) -> str: + return f"use_focal_loss={self.use_focal_loss}, num_classes={self.num_classes}, num_top_queries={self.num_top_queries}" + + # def forward(self, outputs, orig_target_sizes): + def forward(self, outputs, orig_target_sizes: torch.Tensor): + logits, boxes = outputs["pred_logits"], outputs["pred_boxes"] + # orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0) + + bbox_pred = torchvision.ops.box_convert(boxes, in_fmt="cxcywh", out_fmt="xyxy") + bbox_pred *= orig_target_sizes.repeat(1, 2).unsqueeze(1) + + if self.use_focal_loss: + scores = F.sigmoid(logits) + scores, index = torch.topk(scores.flatten(1), self.num_top_queries, dim=-1) + # TODO for older tensorrt + # labels = index % self.num_classes + labels = mod(index, self.num_classes) + index = index // self.num_classes + boxes = bbox_pred.gather( + dim=1, index=index.unsqueeze(-1).repeat(1, 1, bbox_pred.shape[-1]) + ) + + else: + scores = F.softmax(logits)[:, :, :-1] + scores, labels = scores.max(dim=-1) + if scores.shape[1] > self.num_top_queries: + scores, index = torch.topk(scores, self.num_top_queries, dim=-1) + labels = torch.gather(labels, dim=1, index=index) + boxes = torch.gather( + boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1]) + ) + + # TODO for onnx export + if self.deploy_mode: + return labels, boxes, scores + + # TODO + if self.remap_mscoco_category: + from ...data.dataset import mscoco_label2category + + labels = ( + torch.tensor([mscoco_label2category[int(x.item())] for x in labels.flatten()]) + .to(boxes.device) + .reshape(labels.shape) + ) + + results = [] + for lab, box, sco in zip(labels, boxes, scores): + result = dict(labels=lab, boxes=box, scores=sco) + results.append(result) + + return results + + def deploy( + self, + ): + self.eval() + self.deploy_mode = True + return self diff --git a/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/utils.py b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/utils.py new file mode 100644 index 0000000..dffdcb5 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/src/zoo/dfine/utils.py @@ -0,0 +1,182 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import math +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def inverse_sigmoid(x: torch.Tensor, eps: float = 1e-5) -> torch.Tensor: + x = x.clip(min=0.0, max=1.0) + return torch.log(x.clip(min=eps) / (1 - x).clip(min=eps)) + + +def bias_init_with_prob(prior_prob=0.01): + """initialize conv/fc bias value according to a given probability value.""" + bias_init = float(-math.log((1 - prior_prob) / prior_prob)) + return bias_init + + +def deformable_attention_core_func( + value, value_spatial_shapes, sampling_locations, attention_weights +): + """ + Args: + value (Tensor): [bs, value_length, n_head, c] + value_spatial_shapes (Tensor|List): [n_levels, 2] + value_level_start_index (Tensor|List): [n_levels] + sampling_locations (Tensor): [bs, query_length, n_head, n_levels, n_points, 2] + attention_weights (Tensor): [bs, query_length, n_head, n_levels, n_points] + + Returns: + output (Tensor): [bs, Length_{query}, C] + """ + bs, _, n_head, c = value.shape + _, Len_q, _, n_levels, n_points, _ = sampling_locations.shape + + split_shape = [h * w for h, w in value_spatial_shapes] + value_list = value.split(split_shape, dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level, (h, w) in enumerate(value_spatial_shapes): + # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ + value_l_ = value_list[level].flatten(2).permute(0, 2, 1).reshape(bs * n_head, c, h, w) + # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level].permute(0, 2, 1, 3, 4).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample( + value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False + ) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_*M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.permute(0, 2, 1, 3, 4).reshape( + bs * n_head, 1, Len_q, n_levels * n_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .reshape(bs, n_head * c, Len_q) + ) + + return output.permute(0, 2, 1) + + +def deformable_attention_core_func_v2( + value: torch.Tensor, + value_spatial_shapes, + sampling_locations: torch.Tensor, + attention_weights: torch.Tensor, + num_points_list: List[int], + method="default", +): + """ + Args: + value (Tensor): [bs, value_length, n_head, c] + value_spatial_shapes (Tensor|List): [n_levels, 2] + value_level_start_index (Tensor|List): [n_levels] + sampling_locations (Tensor): [bs, query_length, n_head, n_levels * n_points, 2] + attention_weights (Tensor): [bs, query_length, n_head, n_levels * n_points] + + Returns: + output (Tensor): [bs, Length_{query}, C] + """ + bs, n_head, c, _ = value[0].shape + _, Len_q, _, _, _ = sampling_locations.shape + + # sampling_offsets [8, 480, 8, 12, 2] + if method == "default": + sampling_grids = 2 * sampling_locations - 1 + + elif method == "discrete": + sampling_grids = sampling_locations + + sampling_grids = sampling_grids.permute(0, 2, 1, 3, 4).flatten(0, 1) + sampling_locations_list = sampling_grids.split(num_points_list, dim=-2) + + sampling_value_list = [] + for level, (h, w) in enumerate(value_spatial_shapes): + value_l = value[level].reshape(bs * n_head, c, h, w) + sampling_grid_l: torch.Tensor = sampling_locations_list[level] + + if method == "default": + sampling_value_l = F.grid_sample( + value_l, sampling_grid_l, mode="bilinear", padding_mode="zeros", align_corners=False + ) + + elif method == "discrete": + # n * m, seq, n, 2 + sampling_coord = ( + sampling_grid_l * torch.tensor([[w, h]], device=value_l.device) + 0.5 + ).to(torch.int64) + + # FIX ME? for rectangle input + sampling_coord = sampling_coord.clamp(0, h - 1) + sampling_coord = sampling_coord.reshape(bs * n_head, Len_q * num_points_list[level], 2) + + s_idx = ( + torch.arange(sampling_coord.shape[0], device=value_l.device) + .unsqueeze(-1) + .repeat(1, sampling_coord.shape[1]) + ) + sampling_value_l: torch.Tensor = value_l[ + s_idx, :, sampling_coord[..., 1], sampling_coord[..., 0] + ] # n l c + + sampling_value_l = sampling_value_l.permute(0, 2, 1).reshape( + bs * n_head, c, Len_q, num_points_list[level] + ) + + sampling_value_list.append(sampling_value_l) + + attn_weights = attention_weights.permute(0, 2, 1, 3).reshape( + bs * n_head, 1, Len_q, sum(num_points_list) + ) + weighted_sample_locs = torch.concat(sampling_value_list, dim=-1) * attn_weights + output = weighted_sample_locs.sum(-1).reshape(bs, n_head * c, Len_q) + + return output.permute(0, 2, 1) + + +def get_activation(act: str, inpace: bool = True): + """get activation""" + if act is None: + return nn.Identity() + + elif isinstance(act, nn.Module): + return act + + act = act.lower() + + if act == "silu" or act == "swish": + m = nn.SiLU() + + elif act == "relu": + m = nn.ReLU() + + elif act == "leaky_relu": + m = nn.LeakyReLU() + + elif act == "silu": + m = nn.SiLU() + + elif act == "gelu": + m = nn.GELU() + + elif act == "hardsigmoid": + m = nn.Hardsigmoid() + + else: + raise RuntimeError("") + + if hasattr(m, "inplace"): + m.inplace = inpace + + return m diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/dataset.py b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/dataset.py new file mode 100644 index 0000000..0eaeeef --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/dataset.py @@ -0,0 +1,113 @@ +""" +Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright(c) 2023 lyuwenyu. All Rights Reserved. +""" + +import glob +import os + +import torch +import torch.utils.data as data +import torchvision +import torchvision.transforms as T +import torchvision.transforms.functional as F +from PIL import Image + +Image.MAX_IMAGE_PIXELS = None + + +class ToTensor(T.ToTensor): + def __init__(self) -> None: + super().__init__() + + def __call__(self, pic): + if isinstance(pic, torch.Tensor): + return pic + return super().__call__(pic) + + +class PadToSize(T.Pad): + def __init__(self, size, fill=0, padding_mode="constant"): + super().__init__(0, fill, padding_mode) + self.size = size + self.fill = fill + + def __call__(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be padded. + + Returns: + PIL Image or Tensor: Padded image. + """ + w, h = F.get_image_size(img) + padding = (0, 0, self.size[0] - w, self.size[1] - h) + return F.pad(img, padding, self.fill, self.padding_mode) + + +class Dataset(data.Dataset): + def __init__(self, img_dir: str = "", preprocess: T.Compose = None, device="cuda:0") -> None: + super().__init__() + + self.device = device + self.size = 640 + + self.im_path_list = list(glob.glob(os.path.join(img_dir, "*.jpg"))) + + if preprocess is None: + self.preprocess = T.Compose( + [ + T.Resize(size=639, max_size=640), + PadToSize(size=(640, 640), fill=114), + ToTensor(), + T.ConvertImageDtype(torch.float), + ] + ) + else: + self.preprocess = preprocess + + def __len__( + self, + ): + return len(self.im_path_list) + + def __getitem__(self, index): + # im = Image.open(self.img_path_list[index]).convert('RGB') + im = torchvision.io.read_file(self.im_path_list[index]) + im = torchvision.io.decode_jpeg( + im, mode=torchvision.io.ImageReadMode.RGB, device=self.device + ) + _, h, w = im.shape # c,h,w + + im = self.preprocess(im) + + blob = { + "images": im, + "im_shape": torch.tensor([self.size, self.size]).to(im.device), + "scale_factor": torch.tensor([self.size / h, self.size / w]).to(im.device), + "orig_target_sizes": torch.tensor([w, h]).to(im.device), + } + + return blob + + @staticmethod + def post_process(): + pass + + @staticmethod + def collate_fn(): + pass + + +def draw_nms_result(blob, outputs, draw_score_threshold=0.25, name=""): + """show result + Keys: + 'num_dets', 'det_boxes', 'det_scores', 'det_classes' + """ + for i in range(blob["image"].shape[0]): + det_scores = outputs["det_scores"][i] + det_boxes = outputs["det_boxes"][i][det_scores > draw_score_threshold] + + im = (blob["image"][i] * 255).to(torch.uint8) + im = torchvision.utils.draw_bounding_boxes(im, boxes=det_boxes, width=2) + Image.fromarray(im.permute(1, 2, 0).cpu().numpy()).save(f"test_{name}_{i}.jpg") diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/get_info.py b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/get_info.py new file mode 100644 index 0000000..bcef786 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/get_info.py @@ -0,0 +1,60 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) + +import argparse + +import torch +import torch.nn as nn +from calflops import calculate_flops + +from src.core import YAMLConfig + + +def custom_repr(self): + return f"{{Tensor:{tuple(self.shape)}}} {original_repr(self)}" + + +original_repr = torch.Tensor.__repr__ +torch.Tensor.__repr__ = custom_repr + + +def main( + args, +): + """main""" + cfg = YAMLConfig(args.config, resume=None) + + class Model_for_flops(nn.Module): + def __init__( + self, + ) -> None: + super().__init__() + self.model = cfg.model.deploy() + + def forward(self, images): + outputs = self.model(images) + return outputs + + model = Model_for_flops().eval() + + flops, macs, _ = calculate_flops( + model=model, input_shape=(1, 3, 640, 640), output_as_string=True, output_precision=4 + ) + params = sum(p.numel() for p in model.parameters()) + print("Model FLOPs:%s MACs:%s Params:%s \n" % (flops, macs, params)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", "-c", default="configs/dfine/dfine_hgnetv2_l_coco.yml", type=str + ) + args = parser.parse_args() + + main(args) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/requirements.txt b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/requirements.txt new file mode 100644 index 0000000..55a3c0f --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/requirements.txt @@ -0,0 +1,6 @@ +onnxruntime +tensorrt +pycuda +calflops +tqdm +# onnx_graphsurgeon # for YOLOs diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/trt_benchmark.py b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/trt_benchmark.py new file mode 100644 index 0000000..5b8b3b5 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/trt_benchmark.py @@ -0,0 +1,214 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import argparse +import glob +import os +import time +from collections import OrderedDict, namedtuple + +import numpy as np +import pycuda.driver as cuda +import tensorrt as trt +import torch +from dataset import Dataset +from tqdm import tqdm +from utils import TimeProfiler + + +def parse_args(): + parser = argparse.ArgumentParser(description="Argument Parser Example") + parser.add_argument( + "--infer_dir", + type=str, + default="/data/COCO2017/val2017", + help="Directory for images to perform inference on.", + ) + parser.add_argument("--engine_dir", type=str, help="Directory containing model engine files.") + parser.add_argument( + "--busy", action="store_true", help="Flag to indicate that other processes may be running." + ) + args = parser.parse_args() + return args + + +class TRTInference(object): + def __init__( + self, engine_path, device="cuda", backend="torch", max_batch_size=32, verbose=False + ): + self.engine_path = engine_path + self.device = device + self.backend = backend + self.max_batch_size = max_batch_size + + self.logger = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger(trt.Logger.INFO) + self.engine = self.load_engine(engine_path) + self.context = self.engine.create_execution_context() + self.bindings = self.get_bindings( + self.engine, self.context, self.max_batch_size, self.device + ) + self.bindings_addr = OrderedDict((n, v.ptr) for n, v in self.bindings.items()) + self.input_names = self.get_input_names() + self.output_names = self.get_output_names() + + if self.backend == "cuda": + self.stream = cuda.Stream() + self.time_profile = TimeProfiler() + self.time_profile_dataset = TimeProfiler() + + def init(self): + self.dynamic = False + + def load_engine(self, path): + trt.init_libnvinfer_plugins(self.logger, "") + with open(path, "rb") as f, trt.Runtime(self.logger) as runtime: + return runtime.deserialize_cuda_engine(f.read()) + + def get_input_names(self): + names = [] + for _, name in enumerate(self.engine): + if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT: + names.append(name) + return names + + def get_output_names(self): + names = [] + for _, name in enumerate(self.engine): + if self.engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT: + names.append(name) + return names + + def get_bindings(self, engine, context, max_batch_size=32, device=None): + Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) + bindings = OrderedDict() + for i, name in enumerate(engine): + shape = engine.get_tensor_shape(name) + dtype = trt.nptype(engine.get_tensor_dtype(name)) + + if shape[0] == -1: + dynamic = True + shape[0] = max_batch_size + if engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT: + context.set_input_shape(name, shape) + + if self.backend == "cuda": + if engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT: + data = np.random.randn(*shape).astype(dtype) + ptr = cuda.mem_alloc(data.nbytes) + bindings[name] = Binding(name, dtype, shape, data, ptr) + else: + data = cuda.pagelocked_empty(trt.volume(shape), dtype) + ptr = cuda.mem_alloc(data.nbytes) + bindings[name] = Binding(name, dtype, shape, data, ptr) + else: + data = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) + bindings[name] = Binding(name, dtype, shape, data, data.data_ptr()) + return bindings + + def run_torch(self, blob): + for n in self.input_names: + if self.bindings[n].shape != blob[n].shape: + self.context.set_input_shape(n, blob[n].shape) + self.bindings[n] = self.bindings[n]._replace(shape=blob[n].shape) + + self.bindings_addr.update({n: blob[n].data_ptr() for n in self.input_names}) + self.context.execute_v2(list(self.bindings_addr.values())) + outputs = {n: self.bindings[n].data for n in self.output_names} + return outputs + + def async_run_cuda(self, blob): + for n in self.input_names: + cuda.memcpy_htod_async(self.bindings_addr[n], blob[n], self.stream) + + bindings_addr = [int(v) for _, v in self.bindings_addr.items()] + self.context.execute_async_v2(bindings=bindings_addr, stream_handle=self.stream.handle) + + outputs = {} + for n in self.output_names: + cuda.memcpy_dtoh_async(self.bindings[n].data, self.bindings[n].ptr, self.stream) + outputs[n] = self.bindings[n].data + + self.stream.synchronize() + + return outputs + + def __call__(self, blob): + if self.backend == "torch": + return self.run_torch(blob) + elif self.backend == "cuda": + return self.async_run_cuda(blob) + + def synchronize(self): + if self.backend == "torch" and torch.cuda.is_available(): + torch.cuda.synchronize() + elif self.backend == "cuda": + self.stream.synchronize() + + def warmup(self, blob, n): + for _ in range(n): + _ = self(blob) + + def speed(self, blob, n, nonempty_process=False): + times = [] + self.time_profile_dataset.reset() + for i in tqdm(range(n), desc="Running Inference", unit="iteration"): + self.time_profile.reset() + with self.time_profile_dataset: + img = blob[i] + if img["images"] is not None: + img["image"] = img["input"] = img["images"].unsqueeze(0) + else: + img["images"] = img["input"] = img["image"].unsqueeze(0) + with self.time_profile: + _ = self(img) + times.append(self.time_profile.total) + + # end-to-end model only + times = sorted(times) + if len(times) > 100 and nonempty_process: + times = times[:100] + + avg_time = sum(times) / len(times) # Calculate the average of the remaining times + return avg_time + + +def main(): + FLAGS = parse_args() + dataset = Dataset(FLAGS.infer_dir) + im = torch.ones(1, 3, 640, 640).cuda() + blob = { + "image": im, + "images": im, + "input": im, + "im_shape": torch.tensor([640, 640]).to(im.device), + "scale_factor": torch.tensor([1, 1]).to(im.device), + "orig_target_sizes": torch.tensor([640, 640]).to(im.device), + } + + engine_files = glob.glob(os.path.join(FLAGS.engine_dir, "*.engine")) + results = [] + + for engine_file in engine_files: + print(f"Testing engine: {engine_file}") + model = TRTInference(engine_file, max_batch_size=1, verbose=False) + model.init() + model.warmup(blob, 1000) + t = [] + for _ in range(1): + t.append(model.speed(dataset, 1000, FLAGS.busy)) + avg_latency = 1000 * torch.tensor(t).mean() + results.append((engine_file, avg_latency)) + print(f"Engine: {engine_file}, Latency: {avg_latency:.2f} ms") + + del model + torch.cuda.empty_cache() + time.sleep(1) + + sorted_results = sorted(results, key=lambda x: x[1]) + for engine_file, latency in sorted_results: + print(f"Engine: {engine_file}, Latency: {latency:.2f} ms") + + +if __name__ == "__main__": + main() diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/utils.py b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/utils.py new file mode 100644 index 0000000..aaa6c1e --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/benchmark/utils.py @@ -0,0 +1,95 @@ +import contextlib +import time +from collections import OrderedDict + +import numpy as np +import onnx +import onnx_graphsurgeon +import torch +from PIL import Image + + +def to_binary_data(path, size=(640, 640), output_name="input_tensor.bin"): + """--loadInputs='image:input_tensor.bin'""" + im = Image.open(path).resize(size) + data = np.asarray(im, dtype=np.float32).transpose(2, 0, 1)[None] / 255.0 + data.tofile(output_name) + + +def yolo_insert_nms( + path, score_threshold=0.01, iou_threshold=0.7, max_output_boxes=300, simplify=False +): + """ + http://www.xavierdupre.fr/app/onnxcustom/helpsphinx/api/onnxops/onnx__EfficientNMS_TRT.html + https://huggingface.co/spaces/muttalib1326/Punjabi_Character_Detection/blob/3dd1e17054c64e5f6b2254278f96cfa2bf418cd4/utils/add_nms.py + """ + onnx_model = onnx.load(path) + + if simplify: + from onnxsim import simplify + + onnx_model, _ = simplify(onnx_model, overwrite_input_shapes={"image": [1, 3, 640, 640]}) + + graph = onnx_graphsurgeon.import_onnx(onnx_model) + graph.toposort() + graph.fold_constants() + graph.cleanup() + + topk = max_output_boxes + attrs = OrderedDict( + plugin_version="1", + background_class=-1, + max_output_boxes=topk, + score_threshold=score_threshold, + iou_threshold=iou_threshold, + score_activation=False, + box_coding=0, + ) + + outputs = [ + onnx_graphsurgeon.Variable("num_dets", np.int32, [-1, 1]), + onnx_graphsurgeon.Variable("det_boxes", np.float32, [-1, topk, 4]), + onnx_graphsurgeon.Variable("det_scores", np.float32, [-1, topk]), + onnx_graphsurgeon.Variable("det_classes", np.int32, [-1, topk]), + ] + + graph.layer( + op="EfficientNMS_TRT", + name="batched_nms", + inputs=[graph.outputs[0], graph.outputs[1]], + outputs=outputs, + attrs=attrs, + ) + + graph.outputs = outputs + graph.cleanup().toposort() + + onnx.save(onnx_graphsurgeon.export_onnx(graph), "yolo_w_nms.onnx") + + +class TimeProfiler(contextlib.ContextDecorator): + def __init__( + self, + ): + self.total = 0 + + def __enter__( + self, + ): + self.start = self.time() + return self + + def __exit__(self, type, value, traceback): + self.total += self.time() - self.start + + def reset( + self, + ): + self.total = 0 + + def time( + self, + ): + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/dataset/remap_obj365.py b/backend/apps/algorithms/algorithm/DFINE/tools/dataset/remap_obj365.py new file mode 100644 index 0000000..9102eea --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/dataset/remap_obj365.py @@ -0,0 +1,153 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import argparse +import json +import os + + +def update_image_paths(images, new_prefix): + print("Updating image paths with new prefix...") + for img in images: + split = img["file_name"].split("/")[1:] + img["file_name"] = os.path.join(new_prefix, *split) + print("Image paths updated.") + return images + + +def create_split_annotations(original_annotations, split_image_ids, new_prefix, output_file): + print(f"Creating split annotations for {output_file}...") + new_images = [img for img in original_annotations["images"] if img["id"] in split_image_ids] + print(f"Number of images selected: {len(new_images)}") + if new_prefix is not None: + new_images = update_image_paths(new_images, new_prefix) + + new_annotations = { + "images": new_images, + "annotations": [ + ann for ann in original_annotations["annotations"] if ann["image_id"] in split_image_ids + ], + "categories": original_annotations["categories"], + } + print(f'Number of annotations selected: {len(new_annotations["annotations"])}') + with open(output_file, "w") as f: + json.dump(new_annotations, f) + print(f"Annotations saved to {output_file}") + + +def parse_arguments(): + parser = argparse.ArgumentParser(description="Split and update dataset annotations.") + parser.add_argument( + "--base_dir", + type=str, + required=True, + help="Base directory of the dataset, e.g., /data/Objects365/data", + ) + parser.add_argument( + "--new_val_size", + type=int, + default=5000, + help="Number of images to include in the new validation set (default: 5000)", + ) + parser.add_argument( + "--output_suffix", + type=str, + default="new", + help="Suffix to add to new annotation files (default: new)", + ) + return parser.parse_args() + + +def main(): + args = parse_arguments() + base_dir = args.base_dir + new_val_size = args.new_val_size + output_suffix = args.output_suffix + + # Define paths based on the base directory + original_train_ann_file = os.path.join(base_dir, "train", "zhiyuan_objv2_train.json") + original_val_ann_file = os.path.join(base_dir, "val", "zhiyuan_objv2_val.json") + + new_val_ann_file = os.path.join(base_dir, "val", f"{output_suffix}_zhiyuan_objv2_val.json") + new_train_ann_file = os.path.join( + base_dir, "train", f"{output_suffix}_zhiyuan_objv2_train.json" + ) + + # Check if original annotation files exist + if not os.path.isfile(original_train_ann_file): + print(f"Error: Training annotation file not found at {original_train_ann_file}") + return + if not os.path.isfile(original_val_ann_file): + print(f"Error: Validation annotation file not found at {original_val_ann_file}") + return + + # Load the original training and validation annotations + print("Loading original training annotations...") + with open(original_train_ann_file, "r") as f: + train_annotations = json.load(f) + print("Training annotations loaded.") + + print("Loading original validation annotations...") + with open(original_val_ann_file, "r") as f: + val_annotations = json.load(f) + print("Validation annotations loaded.") + + # Extract image IDs from the original validation set + print("Extracting image IDs from the validation set...") + val_image_ids = [img["id"] for img in val_annotations["images"]] + print(f"Total validation images: {len(val_image_ids)}") + + # Split image IDs for the new training and validation sets + print( + f"Splitting validation images into new validation set of size {new_val_size} and training set..." + ) + new_val_image_ids = val_image_ids[:new_val_size] + new_train_image_ids = val_image_ids[new_val_size:] + print(f"New validation set size: {len(new_val_image_ids)}") + print(f"New training set size from validation images: {len(new_train_image_ids)}") + + # Create new validation annotation file + print("Creating new validation annotations...") + create_split_annotations(val_annotations, new_val_image_ids, None, new_val_ann_file) + print("New validation annotations created.") + + # Combine the remaining validation images and annotations with the original training data + print("Preparing new training images and annotations...") + new_train_images = [ + img for img in val_annotations["images"] if img["id"] in new_train_image_ids + ] + print(f"Number of images from validation to add to training: {len(new_train_images)}") + new_train_images = update_image_paths(new_train_images, "images_from_val") + new_train_annotations = [ + ann for ann in val_annotations["annotations"] if ann["image_id"] in new_train_image_ids + ] + print(f"Number of annotations from validation to add to training: {len(new_train_annotations)}") + + # Add the original training images and annotations + print("Adding original training images and annotations...") + new_train_images.extend(train_annotations["images"]) + new_train_annotations.extend(train_annotations["annotations"]) + print(f"Total training images: {len(new_train_images)}") + print(f"Total training annotations: {len(new_train_annotations)}") + + # Create a new training annotation dictionary + print("Creating new training annotations dictionary...") + new_train_annotations_dict = { + "images": new_train_images, + "annotations": new_train_annotations, + "categories": train_annotations["categories"], + } + print("New training annotations dictionary created.") + + # Save the new training annotations + print("Saving new training annotations...") + with open(new_train_ann_file, "w") as f: + json.dump(new_train_annotations_dict, f) + print(f"New training annotations saved to {new_train_ann_file}") + + print("Processing completed successfully.") + + +if __name__ == "__main__": + main() diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/dataset/resize_obj365.py b/backend/apps/algorithms/algorithm/DFINE/tools/dataset/resize_obj365.py new file mode 100644 index 0000000..6ccaeac --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/dataset/resize_obj365.py @@ -0,0 +1,153 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import argparse +import json +import os +from concurrent.futures import ThreadPoolExecutor + +from PIL import Image + + +def resize_image_and_update_annotations(image_path, annotations, max_size=640): + print(f"Processing image: {image_path}") + try: + with Image.open(image_path) as img: + w, h = img.size + if max(w, h) <= max_size: + return annotations, w, h, False # No need to resize + + scale = max_size / max(w, h) + new_w = int(w * scale) + new_h = int(h * scale) + print(f"Resizing image to width={new_w}, height={new_h}") + + img = img.resize((new_w, new_h), Image.Resampling.LANCZOS) + new_image_path = image_path.replace(".jpg", "_resized.jpg") + img.save(new_image_path) + print(f"Resized image saved: {new_image_path}") + print(f"Original size: ({w}, {h}), New size: ({new_w}, {new_h})") + + # Update annotations + for ann in annotations: + ann["area"] = ann["area"] * (scale**2) + ann["bbox"] = [coord * scale for coord in ann["bbox"]] + if "orig_size" in ann: + ann["orig_size"] = (new_w, new_h) + if "size" in ann: + ann["size"] = (new_w, new_h) + + except Exception as e: + print(f"Error processing {image_path}: {e}") + return None + + return annotations, new_w, new_h, True + + +def resize_images_and_update_annotations(base_dir, subset, max_size=640, num_workers=4): + print(f"Starting to resize images and update annotations for subset: {subset}") + json_file = os.path.join(base_dir, subset, "new_zhiyuan_objv2_{}.json".format(subset)) + if not os.path.isfile(json_file): + print(f"Error: JSON file not found at {json_file}") + return + + print(f"Loading JSON file: {json_file}") + with open(json_file, "r") as f: + data = json.load(f) + print("JSON file loaded.") + + print("Preparing image annotations mapping...") + image_annotations = {img["id"]: [] for img in data["images"]} + for ann in data["annotations"]: + image_annotations[ann["image_id"]].append(ann) + print("Image annotations mapping prepared.") + + def process_image(image_info): + image_path = os.path.join(base_dir, subset, image_info["file_name"]) + results = resize_image_and_update_annotations( + image_path, image_annotations[image_info["id"]], max_size + ) + if results is None: + updated_annotations, new_w, new_h, resized = None, None, None, None + else: + updated_annotations, new_w, new_h, resized = results + return image_info, updated_annotations, new_w, new_h, resized + + print(f"Processing images with {num_workers} worker threads...") + with ThreadPoolExecutor(max_workers=num_workers) as executor: + results = list(executor.map(process_image, data["images"])) + print("Image processing completed.") + + new_images = [] + new_annotations = [] + + print("Updating image and annotation data...") + for image_info, updated_annotations, new_w, new_h, resized in results: + if updated_annotations is not None: + image_info["width"] = new_w + image_info["height"] = new_h + image_annotations[image_info["id"]] = updated_annotations + if resized: + image_info["file_name"] = image_info["file_name"].replace(".jpg", "_resized.jpg") + new_images.append(image_info) + new_annotations.extend(updated_annotations) + print(f"Total images processed: {len(new_images)}") + print(f"Total annotations updated: {len(new_annotations)}") + + new_data = { + "images": new_images, + "annotations": new_annotations, + "categories": data["categories"], + } + + new_json_file = json_file.replace(".json", "_resized.json") + print("Saving new training annotations...") + with open(new_json_file, "w") as f: + json.dump(new_data, f) + print(f"New JSON file saved to {new_json_file}") + + +def parse_arguments(): + parser = argparse.ArgumentParser( + description="Resize images and update dataset annotations for both train and val sets." + ) + parser.add_argument( + "--base_dir", + type=str, + required=True, + help="Base directory of the dataset, e.g., /data/Objects365/data", + ) + parser.add_argument( + "--max_size", + type=int, + default=640, + help="Maximum size for the longer side of the image (default: 640)", + ) + parser.add_argument( + "--num_workers", + type=int, + default=4, + help="Number of worker threads for parallel processing (default: 4)", + ) + args = parser.parse_args() + return args + + +def main(): + args = parse_arguments() + base_dir = args.base_dir + max_size = args.max_size + num_workers = args.num_workers + + subsets = ["train", "val"] + for subset in subsets: + print(f"Processing subset: {subset}") + resize_images_and_update_annotations( + base_dir=base_dir, subset=subset, max_size=max_size, num_workers=num_workers + ) + print("All subsets processed.") + + +if __name__ == "__main__": + main() diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/deployment/export_onnx.py b/backend/apps/algorithms/algorithm/DFINE/tools/deployment/export_onnx.py new file mode 100644 index 0000000..58a12cd --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/deployment/export_onnx.py @@ -0,0 +1,128 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import os +import sys + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) + +import torch +import torch.nn as nn + +from src.core import YAMLConfig + + +def main( + args, +): + """main""" + cfg = YAMLConfig(args.config, resume=args.resume) + + if "HGNetv2" in cfg.yaml_cfg: + cfg.yaml_cfg["HGNetv2"]["pretrained"] = False + + if args.resume: + checkpoint = torch.load(args.resume, map_location="cpu") + if "ema" in checkpoint: + state = checkpoint["ema"]["module"] + else: + state = checkpoint["model"] + + # NOTE load train mode state -> convert to deploy mode + cfg.model.load_state_dict(state) + + else: + # raise AttributeError('Only support resume to load model.state_dict by now.') + print("not load model.state_dict, use default init state dict...") + + class Model(nn.Module): + def __init__( + self, + ) -> None: + super().__init__() + self.model = cfg.model.deploy() + self.postprocessor = cfg.postprocessor.deploy() + + def forward(self, images, orig_target_sizes): + outputs = self.model(images) + outputs = self.postprocessor(outputs, orig_target_sizes) + return outputs + + model = Model() + + data = torch.rand(32, 3, 640, 640) + size = torch.tensor([[640, 640]]) + _ = model(data, size) + + dynamic_axes = { + "images": { + 0: "N", + }, + "orig_target_sizes": {0: "N"}, + } + + output_file = args.resume.replace(".pth", ".onnx") if args.resume else "model.onnx" + + torch.onnx.export( + model, + (data, size), + output_file, + input_names=["images", "orig_target_sizes"], + output_names=["labels", "boxes", "scores"], + dynamic_axes=dynamic_axes, + opset_version=16, + verbose=False, + do_constant_folding=True, + ) + + if args.check: + import onnx + + onnx_model = onnx.load(output_file) + onnx.checker.check_model(onnx_model) + print("Check export onnx model done...") + + if args.simplify: + import onnx + import onnxsim + + dynamic = True + # input_shapes = {'images': [1, 3, 640, 640], 'orig_target_sizes': [1, 2]} if dynamic else None + input_shapes = {"images": data.shape, "orig_target_sizes": size.shape} if dynamic else None + onnx_model_simplify, check = onnxsim.simplify(output_file, test_input_shapes=input_shapes) + onnx.save(onnx_model_simplify, output_file) + print(f"Simplify onnx model {check}...") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + "-c", + default="configs/dfine/dfine_hgnetv2_l_coco.yml", + type=str, + ) + parser.add_argument( + "--resume", + "-r", + type=str, + ) + parser.add_argument( + "--check", + action="store_true", + default=True, + ) + parser.add_argument( + "--simplify", + action="store_true", + default=True, + ) + args = parser.parse_args() + main(args) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/deployment/export_yolo_w_nms.py b/backend/apps/algorithms/algorithm/DFINE/tools/deployment/export_yolo_w_nms.py new file mode 100644 index 0000000..206ae26 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/deployment/export_yolo_w_nms.py @@ -0,0 +1,75 @@ +import numpy as np +import onnxruntime as ort +import torch +import torchvision +from utils import yolo_insert_nms + + +class YOLO11(torch.nn.Module): + def __init__(self, name) -> None: + super().__init__() + from ultralytics import YOLO + # Load a model + # build a new model from scratch + # model = YOLO(f'{name}.yaml') + + # load a pretrained model (recommended for training) + model = YOLO("yolo11n.pt") + self.model = model.model + + def forward(self, x): + """https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/tasks.py#L216""" + pred: torch.Tensor = self.model(x)[0] # n 84 8400, + pred = pred.permute(0, 2, 1) + boxes, scores = pred.split([4, 80], dim=-1) + boxes = torchvision.ops.box_convert(boxes, in_fmt="cxcywh", out_fmt="xyxy") + + return boxes, scores + + +def export_onnx(name="yolov8n"): + """export onnx""" + m = YOLO11(name) + + x = torch.rand(1, 3, 640, 640) + dynamic_axes = {"image": {0: "-1"}} + torch.onnx.export( + m, + x, + f"{name}.onnx", + input_names=["image"], + output_names=["boxes", "scores"], + opset_version=13, + dynamic_axes=dynamic_axes, + ) + + data = np.random.rand(1, 3, 640, 640).astype(np.float32) + sess = ort.InferenceSession(f"{name}.onnx") + _ = sess.run(output_names=None, input_feed={"image": data}) + + import onnx + import onnxslim + + model_onnx = onnx.load(f"{name}.onnx") + model_onnx = onnxslim.slim(model_onnx) + onnx.save(model_onnx, f"{name}.onnx") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--name", type=str, default="yolo11n_tuned") + parser.add_argument("--score_threshold", type=float, default=0.01) + parser.add_argument("--iou_threshold", type=float, default=0.6) + parser.add_argument("--max_output_boxes", type=int, default=300) + args = parser.parse_args() + + export_onnx(name=args.name) + + yolo_insert_nms( + path=f"{args.name}.onnx", + score_threshold=args.score_threshold, + iou_threshold=args.iou_threshold, + max_output_boxes=args.max_output_boxes, + ) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/onnx/CMakeLists.txt b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/onnx/CMakeLists.txt new file mode 100644 index 0000000..85f2ade --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/onnx/CMakeLists.txt @@ -0,0 +1,38 @@ +cmake_minimum_required(VERSION 3.5.1) + +project( + onnxExample + LANGUAGES CXX + VERSION 1.0.0 +) + +set(CMAKE_CXX_STANDARD 17) + +# Compiler options +if (MSVC) + add_compile_options(/W4 /permissive-) + add_definitions(-DNOMINMAX) +else() + add_compile_options(-Wall -Wextra -Wpedantic) +endif() + +# Find ONNX Runtime package +find_package(onnxruntime REQUIRED) + +# Find OpenCV +find_package(OpenCV REQUIRED core imgcodecs imgproc) + +# Add executable +add_executable( + onnxExample + onnxExample.cpp +) + +# Link libraries +target_link_libraries( + onnxExample + onnxruntime::onnxruntime + opencv_core + opencv_imgcodecs + opencv_imgproc +) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/onnx/onnxExample.cpp b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/onnx/onnxExample.cpp new file mode 100644 index 0000000..62c98db --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/onnx/onnxExample.cpp @@ -0,0 +1,331 @@ + +#include +#include +#include +#include +#include +#include + + +std::vector input_names = { "images", "orig_target_sizes" }; +std::vector output_names = { "labels", "boxes", "scores" }; + +/** + * @brief Draws bounding boxes, labels, and confidence scores on an image. + * + * This function takes an image, a list of labels, bounding boxes, and their corresponding confidence scores, + * and overlays the bounding boxes and labels on the image. The bounding boxes are adjusted to compensate + * for resizing and padding applied during preprocessing. + * + * @param image The input image (cv::Mat) on which to draw the bounding boxes and labels. + * @param labels A vector of integer labels corresponding to detected objects. + * @param boxes A vector of bounding boxes, where each box is represented as {x1, y1, x2, y2}. + * @param scores A vector of confidence scores corresponding to the bounding boxes. + * @param ratio The scaling factor used to resize the image during preprocessing. + * @param pad_w The horizontal padding applied to the image during preprocessing. + * @param pad_h The vertical padding applied to the image during preprocessing. + * @param thrh The confidence threshold; only boxes with scores above this value will be drawn (default is 0.4). + * @return A cv::Mat object containing the original image with bounding boxes, labels, and scores drawn on it. + */ +cv::Mat draw( + const cv::Mat& image, + const std::vector& labels, + const std::vector>& boxes, + const std::vector& scores, + float ratio, + int pad_w, + int pad_h, + float thrh = 0.4) +{ + // Clone the input image to preserve the original image + cv::Mat img = image.clone(); + + // Iterate over all detected objects + for (size_t i = 0; i < scores.size(); ++i) { + // Only process objects with confidence scores above the threshold + if (scores[i] > thrh) { + // Adjust bounding box coordinates to account for resizing and padding + float x1 = (boxes[i][0] - pad_w) / ratio; // Top-left x-coordinate + float y1 = (boxes[i][1] - pad_h) / ratio; // Top-left y-coordinate + float x2 = (boxes[i][2] - pad_w) / ratio; // Bottom-right x-coordinate + float y2 = (boxes[i][3] - pad_h) / ratio; // Bottom-right y-coordinate + + // Draw the bounding box on the image + cv::rectangle(img, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(0, 0, 255), 1); + + // Prepare the label text with class label and confidence score + std::string label_text = "Label: " + std::to_string(labels[i]) + + " Conf: " + std::to_string(scores[i]); + + // Draw the label text above the bounding box + cv::putText(img, label_text, cv::Point(x1, y1 - 10), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(255, 0, 0), 1); + } + } + + // Return the annotated image + return img; +} + + + +/** + * @brief Resizes an image while maintaining its aspect ratio and pads the resized image to a square of a specified size. + * + * This function scales the input image proportionally to fit within a square of the specified size while preserving + * the aspect ratio. It then pads the resized image with black pixels (value 0) to fill the remaining space, creating + * a square output image. + * + * @param image Input image (cv::Mat) to be resized and padded. + * @param size Target size of the square output image (both width and height will be equal to size). + * @param ratio Output parameter that will contain the scaling factor applied to the image. + * @param pad_w Output parameter that will contain the width of padding applied on the left and right sides. + * @param pad_h Output parameter that will contain the height of padding applied on the top and bottom sides. + * @return A cv::Mat object containing the resized and padded square image. + */ +cv::Mat resizeWithAspectRatio(const cv::Mat& image, int size, float& ratio, int& pad_w, int& pad_h) { + // Get the original width and height of the input image + int original_width = image.cols; + int original_height = image.rows; + + // Compute the scaling ratio to fit the image within the target size while maintaining aspect ratio + ratio = std::min(static_cast(size) / original_width, static_cast(size) / original_height); + int new_width = static_cast(original_width * ratio); // New width after scaling + int new_height = static_cast(original_height * ratio); // New height after scaling + + // Resize the image using the computed dimensions + cv::Mat resized_image; + cv::resize(image, resized_image, cv::Size(new_width, new_height)); + + // Calculate the padding required to center the resized image in the square output + pad_w = (size - new_width) / 2; // Horizontal padding (left and right) + pad_h = (size - new_height) / 2; // Vertical padding (top and bottom) + + // Create a square output image filled with black pixels (value 0) + cv::Mat padded_image(size, size, resized_image.type(), cv::Scalar(0, 0, 0)); + + // Copy the resized image into the center of the square output image + resized_image.copyTo(padded_image(cv::Rect(pad_w, pad_h, new_width, new_height))); + + // Return the resized and padded image + return padded_image; +} + +/** + * @brief Preprocess an input image, run inference using an ONNX model, and process the results. + * + * This function resizes the input image while maintaining its aspect ratio, prepares it for inference, + * runs the inference using the specified ONNX Runtime session, and processes the output to draw + * bounding boxes and labels on the original image. + * + * @param session The ONNX Runtime session used to perform inference. + * @param image The input image (OpenCV Mat) to process. + * @return cv::Mat The result image with bounding boxes and labels drawn. + */ +cv::Mat processImage(Ort::Session& session, const cv::Mat& image) { + float ratio; // Aspect ratio for resizing the image. + int pad_w, pad_h; // Padding added to maintain aspect ratio. + int target_size = 640; // Target size for resizing (typically square). + + // Step 1: Resize and pad the image to the target size while preserving the aspect ratio. + cv::Mat resized_image = resizeWithAspectRatio(image, target_size, ratio, pad_w, pad_h); + + // Step 2: Convert the resized image to RGB format as required by the model. + cv::cvtColor(resized_image, resized_image, cv::COLOR_BGR2RGB); + + // Step 3: Prepare the input tensor in NCHW format (channels-first). + std::vector input_dims = { 1, 3, target_size, target_size }; // Batch size = 1, Channels = 3, HxW = target_size. + std::vector input_tensor_values(input_dims[1] * input_dims[2] * input_dims[3]); + + // Populate the input tensor with normalized pixel values (range 0 to 1). + int index = 0; + for (int c = 0; c < 3; ++c) { // Loop through channels. + for (int i = 0; i < resized_image.rows; ++i) { // Loop through rows. + for (int j = 0; j < resized_image.cols; ++j) { // Loop through columns. + input_tensor_values[index++] = resized_image.at(i, j)[c] / 255.0f; // Normalize pixel value. + } + } + } + + // Step 4: Create ONNX Runtime input tensors. + Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault); + + // Tensor for the preprocessed image. + Ort::Value input_tensor_images = Ort::Value::CreateTensor( + memory_info, input_tensor_values.data(), input_tensor_values.size(), + input_dims.data(), input_dims.size() + ); + + // Tensor for the original target sizes (optional, used for postprocessing). + std::vector orig_size_dims = { 1, 2 }; + std::vector orig_size_values = { + static_cast(resized_image.rows), + static_cast(resized_image.cols) + }; + Ort::Value input_tensor_orig_target_sizes = Ort::Value::CreateTensor( + memory_info, orig_size_values.data(), orig_size_values.size(), + orig_size_dims.data(), orig_size_dims.size() + ); + + // Step 5: Run inference on the session. + auto outputs = session.Run( + Ort::RunOptions{ nullptr }, // Default run options. + input_names.data(), // Names of input nodes. + std::array{std::move(input_tensor_images), std::move(input_tensor_orig_target_sizes)}.data(), + input_names.size(), // Number of inputs. + output_names.data(), // Names of output nodes. + output_names.size() // Number of outputs. + ); + + // Step 6: Extract and process model outputs. + auto labels_ptr = outputs[0].GetTensorMutableData(); // Labels for detected objects. + auto boxes_ptr = outputs[1].GetTensorMutableData(); // Bounding boxes. + auto scores_ptr = outputs[2].GetTensorMutableData(); // Confidence scores. + + size_t num_boxes = outputs[2].GetTensorTypeAndShapeInfo().GetShape()[1]; // Number of detected boxes. + + // Convert raw output to structured data. + std::vector labels(labels_ptr, labels_ptr + num_boxes); + std::vector> boxes; + std::vector scores(scores_ptr, scores_ptr + num_boxes); + + auto boxes_shape = outputs[1].GetTensorTypeAndShapeInfo().GetShape(); + size_t num_coordinates = boxes_shape[2]; // Usually 4 coordinates: (x1, y1, x2, y2). + + // Populate the `boxes` vector. + for (size_t i = 0; i < num_boxes; ++i) { + boxes.push_back({ + boxes_ptr[i * num_coordinates + 0], // x1 + boxes_ptr[i * num_coordinates + 1], // y1 + boxes_ptr[i * num_coordinates + 2], // x2 + boxes_ptr[i * num_coordinates + 3] // y2 + }); + } + + // Step 7: Draw the results on the original image. + cv::Mat result_image = draw(image, labels, boxes, scores, ratio, pad_w, pad_h); + + // Return the annotated image. + return result_image; +} + +/** + * @brief Entry point of the application to perform object detection on an input source using a specified model. + * + * The program loads a pre-trained model, processes an input source (image, video, or webcam), and performs object + * detection using either a CPU or GPU for computation. The results are displayed or saved as appropriate. + * + * @param argc The number of command-line arguments passed to the program. + * @param argv The array of command-line arguments: + * - argv[0]: The name of the executable. + * - argv[1]: The path to the pre-trained model file. + * - argv[2]: The source of the input (image file, video file, or webcam index). + * - argv[3]: Flag to indicate whether to use GPU (1 for GPU, 0 for CPU). + * @return Exit status: + * - Returns 0 on success. + * - Returns -1 if incorrect arguments are provided. + */ +int main(int argc, char** argv) { + // Check if the required number of arguments is provided + if (argc < 4) { + // Display usage instructions if arguments are insufficient + std::cerr << "Usage: " << argv[0] + << " \n"; + return -1; + } + + // Parse arguments + std::string modelPath = argv[1]; + std::string source = argv[2]; + bool useGPU = std::stoi(argv[3]) != 0; + + // Initialize ONNX Runtime environment + Ort::Env env(ORT_LOGGING_LEVEL_ERROR, "ONNXExample"); + Ort::SessionOptions session_options; + session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED); + + if (useGPU) { + OrtCUDAProviderOptions cudaOptions; + cudaOptions.device_id = 0; // Default to GPU 0 + session_options.AppendExecutionProvider_CUDA(cudaOptions); + std::cout << "Using GPU for inference.\n"; + } + else { + std::cout << "Using CPU for inference.\n"; + } + + // Load ONNX model + std::wstring widestr = std::wstring(modelPath.begin(), modelPath.end()); + const wchar_t* model_path = widestr.c_str(); + Ort::Session session(env, model_path, session_options); + + // Open source + cv::VideoCapture cap; + bool isVideo = false; + bool isWebcam = false; + bool isImage = false; + cv::Mat frame; + + if (source == "webcam") { + isWebcam = true; + cap.open(0); // Open webcam + } + else if (source.find(".mp4") != std::string::npos || + source.find(".avi") != std::string::npos || + source.find(".mkv") != std::string::npos) { + isVideo = true; + cap.open(source); // Open video file + } + else { + isImage = true; + frame = cv::imread(source); + if (frame.empty()) { + std::cerr << "Error: Could not read image file.\n"; + return -1; + } + } + + if ((isVideo || isWebcam) && !cap.isOpened()) { + std::cerr << "Error: Could not open video source.\n"; + return -1; + } + + // Process source + do { + if (isWebcam || isVideo) { + cap >> frame; + if (frame.empty()) { + if (isVideo) { + std::cout << "End of video reached.\n"; + } + break; + } + } + + // Process the frame/image with ONNX model + auto result_image = processImage(session, frame); + + cv::imshow("ONNX Result", result_image); + if (isImage) { + cv::waitKey(0); // Wait indefinitely for image + break; + } + else if (cv::waitKey(1) == 27) { // Exit on 'Esc' key for video/webcam + break; + } + + // FPS calculation for video/webcam + static int frame_count = 0; + static auto last_time = std::chrono::high_resolution_clock::now(); + frame_count++; + auto current_time = std::chrono::high_resolution_clock::now(); + std::chrono::duration elapsed = current_time - last_time; + if (elapsed.count() >= 1.0) { + std::cout << "FPS: " << frame_count / elapsed.count() << "\n"; + frame_count = 0; + last_time = current_time; + } + + } while (isWebcam || isVideo); + + return 0; +} diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/openvino/CMakeLists.txt b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/openvino/CMakeLists.txt new file mode 100644 index 0000000..cd2d168 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/openvino/CMakeLists.txt @@ -0,0 +1,31 @@ +cmake_minimum_required(VERSION 3.5.1) + +project( + openvinoExample + LANGUAGES CXX + VERSION 1.0.0 +) + +set(CMAKE_CXX_STANDARD 17) + +if (MSVC) + add_compile_options(-nologo) + add_definitions(-DNOMINMAX) +else() + add_compile_options(-Wall) +endif() + +find_package(OpenVINO CONFIG REQUIRED) +find_package(OpenCV CONFIG REQUIRED core dnn imgcodecs imgproc) + +add_executable( + openvinoExample + openvinoExample.cpp +) +target_link_libraries( + openvinoExample + openvino::runtime + opencv_core + opencv_dnn + opencv_imgcodecs +) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/openvino/openvinoExample.cpp b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/openvino/openvinoExample.cpp new file mode 100644 index 0000000..a799898 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/openvino/openvinoExample.cpp @@ -0,0 +1,42 @@ +#include +#include +#include + +int main(){ + try{ + ov::Core core; + ov::CompiledModel mCompiledModel = core.compile_model("DFINE.onnx","AUTO"); + cv::Mat imageMat = cv::imread("test.png"); + cv::cvtColor(imageMat, imageMat, cv::COLOR_BGR2RGB); + cv::Mat inferMat; + cv::dnn::blobFromImage(imageMat,inferMat,1.0 / 255.0); + std::vector im_shape = { (float)1/imageMat.rows, (float)1/imageMat.cols }; + auto ireq = mCompiledModel.create_infer_request(); + auto inputports = mCompiledModel.inputs(); + ov::Tensor input_tensor1(inputports[0].get_element_type(), { 1,3,640,640 }, inferMat.ptr()); + ireq.set_input_tensor(0,input_tensor1); + ov::Tensor input_tensor2(inputports[1].get_element_type(), { 1,2 }); + int64* input_tensor_data = input_tensor2.data(); + for (int i = 0; i < 2; i++) { + input_tensor_data[i] = 640; + } + ireq.set_input_tensor(1,input_tensor2); + ireq.infer(); + ov::Tensor labels_tensor = ireq.get_output_tensor(0); + ov::Tensor bboxs_tensor = ireq.get_output_tensor(1); + ov::Tensor scores_tensor = ireq.get_output_tensor(2); + float *bo = bboxs_tensor.data(); + + //example + float cx = bo[4] ; + float cy = bo[5] ; + float bx = bo[6] ; + float by = bo[7] ; + cv::rectangle(imageMat, cv::Rect(bo[0],bo[1], bo[2]-bo[0], bo[3]-bo[1]), cv::Scalar(0, 255, 0), 2); + cv::rectangle(imageMat, cv::Rect(cx,cy, bx-cx, by-cy), cv::Scalar(0, 255, 0), 2); + cv::imwrite("aimage.png",imageMat); + } + catch(const ov::Exception& e){ + std::cerr << e.what() << '\n'; + } +} diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/trt/CMakeLists.txt b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/trt/CMakeLists.txt new file mode 100644 index 0000000..452ef21 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/trt/CMakeLists.txt @@ -0,0 +1,35 @@ +cmake_minimum_required(VERSION 3.20) +cmake_policy(SET CMP0146 OLD) # for disabling FindCUDA warning +project(DFineTRT) + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_FLAGS "-O3") + +set(LIB_ROOT "$ENV{HOME}/Documents/libs") + +# opencv +find_package(OpenCV REQUIRED) +find_package(Iconv QUIET) +# cuda +find_package(CUDA REQUIRED) +# tensorrt +set(CUDA_RUNTIME_API_DIRS "/usr/local/cuda-12.9/targets/x86_64-linux/include") + +# dfine +add_executable(trtExample trtExample.cpp) +target_compile_options(trtExample PRIVATE + -Wall + -Wextra + -Wno-unused-function +) +target_link_libraries(trtExample + ${OpenCV_LIBS} + ${CUDA_LIBRARIES} + nvonnxparser + nvinfer +) +target_include_directories(trtExample PRIVATE + ${OpenCV_INCLUDE_DIRS} + ${CUDA_RUNTIME_API_DIRS} +) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/trt/trtExample.cpp b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/trt/trtExample.cpp new file mode 100644 index 0000000..906c02e --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/cppExample/trt/trtExample.cpp @@ -0,0 +1,346 @@ +/** + * NOTE: Convert model with --fp16 may lead to incorrect results + */ +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +#define CUDA_CHECK(call) \ + do \ + { \ + const cudaError_t error_code = call; \ + if (error_code != cudaSuccess) \ + { \ + printf("CUDA_CHECK Error:\n"); \ + printf(" File: %s\n", __FILE__); \ + printf(" Line: %d\n", __LINE__); \ + printf(" Error code: %d\n", error_code); \ + printf(" Error text: %s\n", cudaGetErrorString(error_code)); \ + exit(1); \ + } \ + } while (0) + +struct Object +{ + cv::Rect_ rect; + int label; + float prob; +}; + +const std::vector labels = { + "Person", "Bicycle", "Car", "Motorcycle", "Airplane", "Bus", "Train", + "Truck", "Boat", "Traffic light", "Fire hydrant", "Stop sign", "Parking meter", + "Bench", "Bird", "Cat", "Dog", "Horse", "Sheep", "Cow", "Elephant", "Bear", + "Zebra", "Giraffe", "Backpack", "Umbrella", "Handbag", "Tie", "Suitcase", + "Frisbee", "Skis", "Snowboard", "Sports ball", "Kite", "Baseball bat", + "Baseball glove", "Skateboard", "Surfboard", "Tennis racket", "Bottle", + "Wine glass", "Cup", "Fork", "Knife", "Spoon", "Bowl", "Banana", "Apple", + "Sandwich", "Orange", "Broccoli", "Carrot", "Hot dog", "Pizza", "Donut", + "Cake", "Chair", "Couch", "Potted plant", "Bed", "Dining table", "Toilet", + "Tv", "Laptop", "Mouse", "Remote", "Keyboard", "Cell phone", "Microwave", + "Oven", "Toaster", "Sink", "Refrigerator", "Book", "Clock", "Vase", "Scissors", + "Teddy bear", "Hair drier", "Toothbrush" +}; + +class Logger : public nvinfer1::ILogger +{ +public: + void log(Severity severity, const char *msg) noexcept override + { + if (severity <= nvinfer1::ILogger::Severity::kWARNING) + { + std::cerr << "[TensorRT] "; + switch (severity) + { + case nvinfer1::ILogger::Severity::kINTERNAL_ERROR: std::cerr << "INTERNAL_ERROR: "; break; + case nvinfer1::ILogger::Severity::kERROR: std::cerr << "ERROR: "; break; + case nvinfer1::ILogger::Severity::kWARNING: std::cerr << "WARNING: "; break; + case nvinfer1::ILogger::Severity::kINFO: std::cerr << "INFO: "; break; + case nvinfer1::ILogger::Severity::kVERBOSE: std::cerr << "VERBOSE: "; break; + } + std::cerr << msg << "\n"; + } + } +}; + +static Logger logger; + +bool DrawObjects(cv::Mat &image, const std::vector &objects, + const std::vector &labels, bool isSilent) +{ + for (auto obj : objects) + { + if (obj.label >= static_cast(labels.size())) + return false; + + if (isSilent != true) + std::printf("%s = %.2f%% at (%.1f, %.1f) %.1f x %.1f\n", labels[obj.label].c_str(), obj.prob * 100.0f, + obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height); + + char text[256]; + snprintf(text, sizeof(text), "%s %.1f%%", labels[obj.label].c_str(), obj.prob * 100.0f); + + auto scalar = cv::Scalar(255, 255, 255); + cv::rectangle(image, obj.rect, scalar, 2); + + int baseLine = 5; + cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.75, 1, &baseLine); + + int x = obj.rect.x - 1; + int y = obj.rect.y - label_size.height - baseLine; + y = std::max(0, y); + if (x + label_size.width > image.cols) + x = image.cols - label_size.width; + + cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)), + scalar, -1); + + cv::putText(image, text, cv::Point(x, y + label_size.height + baseLine / 2), + cv::FONT_HERSHEY_SIMPLEX, 0.75, cv::Scalar(0, 0, 0), 2); + } + + return true; +} + +size_t CountElement(const nvinfer1::Dims &dims) +{ + int64_t total = 1; + for (int32_t i = 0; i < dims.nbDims; ++i) + total *= dims.d[i]; + return static_cast(total); +} + +template +T Clamp(T val, T min, T max) +{ + return val > min ? (val < max ? val : max) : min; +} + +void GetLetterboxDimensions( + const int img_rows, const int img_cols, + const int target_size, + int &resize_rows, int &resize_cols, int &pad_rows, int &pad_cols, float &scale +) +{ + scale = static_cast(target_size) / std::max(img_rows, img_cols); + resize_rows = static_cast(std::round(img_rows * scale)); + resize_cols = static_cast(std::round(img_cols * scale)); + pad_rows = target_size - resize_rows; + pad_cols = target_size - resize_cols; +} + +int main(int argc, char *argv[]) +{ + // --- Settings --- + if (argc < 3) + { + std::printf("Usage: %s model image [conf] [target size]\n", argv[0]); + return 0; + } + const std::string model_path = std::string(argv[1]); + float conf_thres = 0.25f; + int target_size = 640; + if (argc >= 4 && std::stof(argv[3]) > 0.0f) + conf_thres = std::stof(argv[3]); + if (argc >= 5 && std::stoi(argv[4]) > 0 && + std::stoi(argv[4]) % 32 == 0 && std::stoi(argv[4]) > 32) + target_size = std::stoi(argv[4]); + + std::cout << "Model: " << model_path << "\n"; + std::cout << "Input: " << argv[2] << "\n"; + std::cout << "Conf: " << conf_thres << "\n"; + std::cout << "Target size: " << target_size << "\n"; + + // --- Init TRT --- + // load model data + std::ifstream engine_file(model_path, std::ios::binary); + if (!engine_file) + { + std::cerr << "Failed to open engine file\n"; + return -1; + } + engine_file.seekg(0, engine_file.end); + std::streamsize engine_size = engine_file.tellg(); + engine_file.seekg(0, engine_file.beg); + std::unique_ptr engine_data{std::make_unique(engine_size)}; + if (!engine_file.read(engine_data.get(), engine_size)) + { + std::cerr << "Failed to read engine file\n"; + return -1; + } + engine_file.close(); + + // create runtime, engine, context, and stream + auto runtime{nvinfer1::createInferRuntime(logger)}; + if (!runtime) + { + std::cerr << "Failed to create runtime\n"; + return -1; + } + auto engine{runtime->deserializeCudaEngine(engine_data.get(), engine_size)}; + if (!engine) + { + std::cerr << "Failed to deserialize engine\n"; + return -1; + } + auto context{engine->createExecutionContext()}; + if (!context) + { + std::cerr << "Failed to create contexts\n"; + return -1; + } + std::unique_ptr stream = std::make_unique(); + CUDA_CHECK(cudaStreamCreate(stream.get())); + + // get model info + std::vector> in_tensor_info, out_tensor_info; + for (int i = 0; i < engine->getNbIOTensors(); ++i) + { + const char *tensor_name = engine->getIOTensorName(i); + nvinfer1::TensorIOMode io_mode = engine->getTensorIOMode(tensor_name); + if (io_mode == nvinfer1::TensorIOMode::kINPUT) + in_tensor_info.push_back({i, std::string(tensor_name)}); + else if (io_mode == nvinfer1::TensorIOMode::kOUTPUT) + out_tensor_info.push_back({i, std::string(tensor_name)}); + } + + // create host memory + size_t max_in0_size_byte = CountElement(context->getTensorShape(in_tensor_info[0].second.c_str())) * sizeof(float); + size_t max_in1_size_byte = CountElement(context->getTensorShape(in_tensor_info[1].second.c_str())) * sizeof(int64_t); + size_t max_out0_size_byte = CountElement(context->getTensorShape(out_tensor_info[0].second.c_str())) * sizeof(int64_t); + size_t max_out1_size_byte = CountElement(context->getTensorShape(out_tensor_info[1].second.c_str())) * sizeof(float); + size_t max_out2_size_byte = CountElement(context->getTensorShape(out_tensor_info[2].second.c_str())) * sizeof(float); + std::vector> host_outs; + host_outs.resize(out_tensor_info.size()); + host_outs[0] = std::make_unique(max_out0_size_byte); + host_outs[1] = std::make_unique(max_out1_size_byte); + host_outs[2] = std::make_unique(max_out2_size_byte); + // create cuda memory + std::vector buffers{}; + buffers.resize(engine->getNbIOTensors()); + CUDA_CHECK(cudaMalloc(&buffers[in_tensor_info[0].first], max_in0_size_byte)); + CUDA_CHECK(cudaMalloc(&buffers[in_tensor_info[1].first], max_in1_size_byte)); + CUDA_CHECK(cudaMalloc(&buffers[out_tensor_info[0].first], max_out0_size_byte)); + CUDA_CHECK(cudaMalloc(&buffers[out_tensor_info[1].first], max_out1_size_byte)); + CUDA_CHECK(cudaMalloc(&buffers[out_tensor_info[2].first], max_out2_size_byte)); + + // set in/out tensor address + context->setInputTensorAddress(in_tensor_info[0].second.c_str(), buffers[in_tensor_info[0].first]); + context->setInputTensorAddress(in_tensor_info[1].second.c_str(), buffers[in_tensor_info[1].first]); + context->setOutputTensorAddress(out_tensor_info[0].second.c_str(), buffers[out_tensor_info[0].first]); + context->setOutputTensorAddress(out_tensor_info[1].second.c_str(), buffers[out_tensor_info[1].first]); + context->setOutputTensorAddress(out_tensor_info[2].second.c_str(), buffers[out_tensor_info[2].first]); + + // --- Detect --- + cv::Mat image = cv::imread(argv[2]); + if (image.empty()) + { + std::cout << "Failed to read image\n"; + return -1; + } + + // preprocessing + int img_rows = image.rows; + int img_cols = image.cols; + float scale; + int resize_rows, resize_cols, pad_rows, pad_cols; + GetLetterboxDimensions( + img_rows, img_cols, target_size, + resize_rows, resize_cols, pad_rows, pad_cols, scale + ); + cv::Mat letterbox, blob; + cv::resize(image, letterbox, cv::Size(resize_cols, resize_rows), 0, 0, cv::INTER_AREA); + cv::copyMakeBorder( + letterbox, letterbox, + pad_rows / 2, pad_rows - pad_rows / 2, + pad_cols / 2, pad_cols - pad_cols / 2, + cv::BORDER_CONSTANT, cv::Scalar(114.0, 114.0, 114.0) + ); + // no normalization + cv::dnn::blobFromImage(letterbox, blob, 1.0f / 255.0f, cv::Size(letterbox.cols, letterbox.rows), cv::Scalar(0, 0, 0), true, false, CV_32F); + + nvinfer1::Dims trt_in0_dims{}, trt_in1_dims{}; + trt_in0_dims.nbDims = 4; + trt_in0_dims.d[0] = 1; + trt_in0_dims.d[1] = 3; + trt_in0_dims.d[2] = letterbox.rows; + trt_in0_dims.d[3] = letterbox.cols; + context->setInputShape(in_tensor_info[0].second.c_str(), trt_in0_dims); + + std::vector orig_size{static_cast(letterbox.rows), static_cast(letterbox.cols)}; + trt_in1_dims.nbDims = 2; + trt_in1_dims.d[0] = 1; + trt_in1_dims.d[1] = 2; + context->setInputShape(in_tensor_info[1].second.c_str(), trt_in1_dims); + + // execute + CUDA_CHECK(cudaMemcpyAsync(buffers[0], blob.data, max_in0_size_byte, cudaMemcpyHostToDevice, *stream)); + CUDA_CHECK(cudaMemcpyAsync(buffers[1], orig_size.data(), max_in1_size_byte, cudaMemcpyHostToDevice, *stream)); + + context->enqueueV3(*stream); + + CUDA_CHECK(cudaMemcpyAsync(host_outs[0].get(), buffers[2], max_out0_size_byte, cudaMemcpyDeviceToHost, *stream)); + CUDA_CHECK(cudaMemcpyAsync(host_outs[1].get(), buffers[3], max_out1_size_byte, cudaMemcpyDeviceToHost, *stream)); + CUDA_CHECK(cudaMemcpyAsync(host_outs[2].get(), buffers[4], max_out2_size_byte, cudaMemcpyDeviceToHost, *stream)); + CUDA_CHECK(cudaStreamSynchronize(*stream)); + + const int64_t *labels_ptr = reinterpret_cast(host_outs[0].get()); + const float *boxes_ptr = reinterpret_cast(host_outs[1].get()); + const float *scores_ptr = reinterpret_cast(host_outs[2].get()); + + size_t num_box = 300; + size_t walk = 4; + float dw = pad_cols / 2, dh = pad_rows / 2; + std::vector objects; + for (size_t i = 0; i < num_box; ++i) + { + if (scores_ptr[i] < conf_thres) + continue; + + float x0 = boxes_ptr[i * walk]; + float y0 = boxes_ptr[i * walk + 1]; + float x1 = boxes_ptr[i * walk + 2]; + float y1 = boxes_ptr[i * walk + 3]; + + x0 = (x0 - dw) / scale; + y0 = (y0 - dh) / scale; + x1 = (x1 - dw) / scale; + y1 = (y1 - dh) / scale; + + x0 = Clamp(x0, 0.0f, static_cast(img_cols)); + y0 = Clamp(y0, 0.0f, static_cast(img_rows)); + x1 = Clamp(x1, x0, static_cast(img_cols)); + y1 = Clamp(y1, y0, static_cast(img_rows)); + + Object object; + object.rect.x = x0; + object.rect.y = y0; + object.rect.width = x1 - x0; + object.rect.height = y1 - y0; + object.prob = scores_ptr[i]; + object.label = static_cast(labels_ptr[i]); + objects.emplace_back(object); + } + + // save results + if (DrawObjects(image, objects, labels, false)) + cv::imwrite("./result.jpg", image); + else + std::cout << "Failed to draw objects\n"; + + // --- Release resources --- + for (const auto &buffer : buffers) + if (buffer) + CUDA_CHECK(cudaFree(buffer)); + if (stream && *stream) + CUDA_CHECK(cudaStreamDestroy(*stream)); + + return 0; +} diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/onnx_inf.py b/backend/apps/algorithms/algorithm/DFINE/tools/inference/onnx_inf.py new file mode 100644 index 0000000..3fb26c7 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/onnx_inf.py @@ -0,0 +1,163 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import cv2 +import numpy as np +import onnxruntime as ort +import torch +import torchvision.transforms as T +from PIL import Image, ImageDraw + + +def resize_with_aspect_ratio(image, size, interpolation=Image.BILINEAR): + """Resizes an image while maintaining aspect ratio and pads it.""" + original_width, original_height = image.size + ratio = min(size / original_width, size / original_height) + new_width = int(original_width * ratio) + new_height = int(original_height * ratio) + image = image.resize((new_width, new_height), interpolation) + + # Create a new image with the desired size and paste the resized image onto it + new_image = Image.new("RGB", (size, size)) + new_image.paste(image, ((size - new_width) // 2, (size - new_height) // 2)) + return new_image, ratio, (size - new_width) // 2, (size - new_height) // 2 + + +def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.4): + result_images = [] + for i, im in enumerate(images): + draw = ImageDraw.Draw(im) + scr = scores[i] + lab = labels[i][scr > thrh] + box = boxes[i][scr > thrh] + scr = scr[scr > thrh] + + ratio = ratios[i] + pad_w, pad_h = paddings[i] + + for lbl, bb in zip(lab, box): + # Adjust bounding boxes according to the resizing and padding + bb = [ + (bb[0] - pad_w) / ratio, + (bb[1] - pad_h) / ratio, + (bb[2] - pad_w) / ratio, + (bb[3] - pad_h) / ratio, + ] + draw.rectangle(bb, outline="red") + draw.text((bb[0], bb[1]), text=str(lbl), fill="blue") + + result_images.append(im) + return result_images + + +def process_image(sess, im_pil): + # Resize image while preserving aspect ratio + resized_im_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(im_pil, 640) + orig_size = torch.tensor([[resized_im_pil.size[1], resized_im_pil.size[0]]]) + + transforms = T.Compose( + [ + T.ToTensor(), + ] + ) + im_data = transforms(resized_im_pil).unsqueeze(0) + + output = sess.run( + output_names=None, + input_feed={"images": im_data.numpy(), "orig_target_sizes": orig_size.numpy()}, + ) + + labels, boxes, scores = output + + result_images = draw([im_pil], labels, boxes, scores, [ratio], [(pad_w, pad_h)]) + result_images[0].save("onnx_result.jpg") + print("Image processing complete. Result saved as 'result.jpg'.") + + +def process_video(sess, video_path): + cap = cv2.VideoCapture(video_path) + + # Get video properties + fps = cap.get(cv2.CAP_PROP_FPS) + orig_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + orig_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + # Define the codec and create VideoWriter object + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + out = cv2.VideoWriter("onnx_result.mp4", fourcc, fps, (orig_w, orig_h)) + + frame_count = 0 + print("Processing video frames...") + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + # Convert frame to PIL image + frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + + # Resize frame while preserving aspect ratio + resized_frame_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(frame_pil, 640) + orig_size = torch.tensor([[resized_frame_pil.size[1], resized_frame_pil.size[0]]]) + + transforms = T.Compose( + [ + T.ToTensor(), + ] + ) + im_data = transforms(resized_frame_pil).unsqueeze(0) + + output = sess.run( + output_names=None, + input_feed={"images": im_data.numpy(), "orig_target_sizes": orig_size.numpy()}, + ) + + labels, boxes, scores = output + + # Draw detections on the original frame + result_images = draw([frame_pil], labels, boxes, scores, [ratio], [(pad_w, pad_h)]) + frame_with_detections = result_images[0] + + # Convert back to OpenCV image + frame = cv2.cvtColor(np.array(frame_with_detections), cv2.COLOR_RGB2BGR) + + # Write the frame + out.write(frame) + frame_count += 1 + + if frame_count % 10 == 0: + print(f"Processed {frame_count} frames...") + + cap.release() + out.release() + print("Video processing complete. Result saved as 'result.mp4'.") + + +def main(args): + """Main function.""" + # Load the ONNX model + sess = ort.InferenceSession(args.onnx) + print(f"Using device: {ort.get_device()}") + + input_path = args.input + + try: + # Try to open the input as an image + im_pil = Image.open(input_path).convert("RGB") + process_image(sess, im_pil) + except IOError: + # Not an image, process as video + process_video(sess, input_path) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--onnx", type=str, required=True, help="Path to the ONNX model file.") + parser.add_argument( + "--input", type=str, required=True, help="Path to the input image or video file." + ) + args = parser.parse_args() + main(args) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/openvino_inf.py b/backend/apps/algorithms/algorithm/DFINE/tools/inference/openvino_inf.py new file mode 100644 index 0000000..6d37e4b --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/openvino_inf.py @@ -0,0 +1,101 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import cv2 +import numpy as np +import openvino +from openvino.runtime import Core + + +class OvInfer: + def __init__(self, model_path, device_name="AUTO"): + self.resized_image = None + self.ratio = None + self.resize_image = None + self.ori_image = None + self.device = device_name + self.model_path = model_path + self.core = Core() + self.available_device = self.core.available_devices + self.compile_model = self.core.compile_model(self.model_path, device_name) + self.target_size = [ + self.compile_model.inputs[0].get_partial_shape()[2].get_length(), + self.compile_model.inputs[0].get_partial_shape()[3].get_length(), + ] + self.query_num = self.compile_model.outputs[0].get_partial_shape()[1].get_length() + + def infer(self, inputs: dict): + infer_request = self.compile_model.create_infer_request() + for input_name, input_data in inputs.items(): + input_tensor = openvino.Tensor(input_data) + infer_request.set_tensor(input_name, input_tensor) + infer_request.infer() + outputs = { + "labels": infer_request.get_tensor("labels").data, + "boxes": infer_request.get_tensor("boxes").data, + "scores": infer_request.get_tensor("scores").data, + } + return outputs + + def process_image(self, ori_image, keep_ratio: bool): + self.ori_image = ori_image + h, w = ori_image.shape[:2] + if keep_ratio: + r = min(self.target_size[0] / h, self.target_size[1] / w) + self.ratio = r + new_w = int(w * r) + new_h = int(h * r) + temp_image = cv2.resize(ori_image, (new_w, new_h), interpolation=cv2.INTER_LINEAR) + resized_image = np.full( + (self.target_size[0], self.target_size[1], 3), 114, dtype=temp_image.dtype + ) + resized_image[:new_h, :new_w, :] = temp_image + self.resized_image = resized_image + else: + self.resized_image = cv2.resize( + ori_image, self.target_size, interpolation=cv2.INTER_LINEAR + ) + blob_image = cv2.dnn.blobFromImage(self.resized_image, 1.0 / 255.0) + orig_size = np.array([self.resized_image.shape[0], self.resized_image.shape[1]], dtype=np.int64).reshape( + 1, 2 + ) + + inputs = { + "images": blob_image, + "orig_target_sizes": orig_size, + } + return inputs + + def get_available_device(self): + return self.available_device + + def draw_and_save_image(self, infer_result, image_path, score_threshold=0.6): + draw_image = self.ori_image + scores = infer_result["scores"] + labels = infer_result["labels"] + boxes = infer_result["boxes"] + for i in range(self.query_num): + if scores[0, i] > score_threshold: + cx = boxes[0, i, 0] / self.ratio + cy = boxes[0, i, 1] / self.ratio + bx = boxes[0, i, 2] / self.ratio + by = boxes[0, i, 3] / self.ratio + cv2.rectangle( + draw_image, (int(cx), int(cy), int(bx - cx), int(by - cy)), (255, 0, 0), 1 + ) + cv2.imwrite(image_path, draw_image) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("-image", "--image", type=str, required=True) + parser.add_argument("-ov_model", "--ov_model", type=str, required=True) + args = parser.parse_args() + img = cv2.imread(args.image) + mOvInfer = OvInfer(args.ov_model) + inputs = mOvInfer.process_image(img, True) + outputs = mOvInfer.infer(inputs) + mOvInfer.draw_and_save_image(outputs, "openvino_result.jpg") diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/requirements.txt b/backend/apps/algorithms/algorithm/DFINE/tools/inference/requirements.txt new file mode 100644 index 0000000..7dfc48a --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/requirements.txt @@ -0,0 +1,3 @@ +onnxruntime +tensorrt +openvino diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/torch_inf.py b/backend/apps/algorithms/algorithm/DFINE/tools/inference/torch_inf.py new file mode 100644 index 0000000..2e0c549 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/torch_inf.py @@ -0,0 +1,178 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import os +import sys + +import cv2 # Added for video processing +import numpy as np +import torch +import torch.nn as nn +import torchvision.transforms as T +from PIL import Image, ImageDraw + +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))) +from src.core import YAMLConfig + + +def draw(images, labels, boxes, scores, thrh=0.4): + for i, im in enumerate(images): + draw = ImageDraw.Draw(im) + + scr = scores[i] + lab = labels[i][scr > thrh] + box = boxes[i][scr > thrh] + scrs = scr[scr > thrh] + + for j, b in enumerate(box): + draw.rectangle(list(b), outline="red") + draw.text( + (b[0], b[1]), + text=f"{lab[j].item()} {round(scrs[j].item(), 2)}", + fill="blue", + ) + + im.save("torch_results.jpg") + + +def process_image(model, device, file_path, score_thresh=0.3): + im_pil = Image.open(file_path).convert("RGB") + w, h = im_pil.size + orig_size = torch.tensor([[w, h]]).to(device) + + transforms = T.Compose( + [ + T.Resize((640, 640)), + T.ToTensor(), + ] + ) + im_data = transforms(im_pil).unsqueeze(0).to(device) + + output = model(im_data, orig_size) + labels, boxes, scores = output + + idx = scores[0] > score_thresh + result_labels = labels[0][idx].detach().cpu().numpy() + result_boxes = boxes[0][idx].detach().cpu().numpy() + result_scores = scores[0][idx].detach().cpu().numpy() + + for l, b, s in zip(result_labels, result_boxes, result_scores): + print(f"Label: {l}, Box: {b}, Score: {s:.2f}") + + draw([im_pil], labels, boxes, scores, thrh=score_thresh) + + return result_labels, result_boxes, result_scores + + +def process_video(model, device, file_path): + cap = cv2.VideoCapture(file_path) + + # Get video properties + fps = cap.get(cv2.CAP_PROP_FPS) + orig_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + orig_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + # Define the codec and create VideoWriter object + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + out = cv2.VideoWriter("torch_results.mp4", fourcc, fps, (orig_w, orig_h)) + + transforms = T.Compose( + [ + T.Resize((640, 640)), + T.ToTensor(), + ] + ) + + frame_count = 0 + print("Processing video frames...") + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + # Convert frame to PIL image + frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + + w, h = frame_pil.size + orig_size = torch.tensor([[w, h]]).to(device) + + im_data = transforms(frame_pil).unsqueeze(0).to(device) + + output = model(im_data, orig_size) + labels, boxes, scores = output + + # Draw detections on the frame + draw([frame_pil], labels, boxes, scores) + + # Convert back to OpenCV image + frame = cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR) + + # Write the frame + out.write(frame) + frame_count += 1 + + if frame_count % 10 == 0: + print(f"Processed {frame_count} frames...") + + cap.release() + out.release() + print("Video processing complete. Result saved as 'results_video.mp4'.") + + +def main(args): + """Main function""" + cfg = YAMLConfig(args.config, resume=args.resume) + + if "HGNetv2" in cfg.yaml_cfg: + cfg.yaml_cfg["HGNetv2"]["pretrained"] = False + + if args.resume: + checkpoint = torch.load(args.resume, map_location="cpu") + if "ema" in checkpoint: + state = checkpoint["ema"]["module"] + else: + state = checkpoint["model"] + else: + raise AttributeError("Only support resume to load model.state_dict by now.") + + # Load train mode state and convert to deploy mode + cfg.model.load_state_dict(state) + + class Model(nn.Module): + def __init__(self): + super().__init__() + self.model = cfg.model.deploy() + self.postprocessor = cfg.postprocessor.deploy() + + def forward(self, images, orig_target_sizes): + outputs = self.model(images) + outputs = self.postprocessor(outputs, orig_target_sizes) + return outputs + + device = args.device + model = Model().to(device) + + # Check if the input file is an image or a video + file_path = args.input + if os.path.splitext(file_path)[-1].lower() in [".jpg", ".jpeg", ".png", ".bmp"]: + result_labels, result_boxes, result_scores = process_image(model, device, file_path, args.score_thresh) + print("识别结果：") + for l, b, s in zip(result_labels, result_boxes, result_scores): + print(f"Label: {l}, Box: {b}, Score: {s:.2f}") + else: + # Process as video + process_video(model, device, file_path) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("-c", "--config", type=str, required=True) + parser.add_argument("-r", "--resume", type=str, required=True) + parser.add_argument("-i", "--input", type=str, required=True) + parser.add_argument("-d", "--device", type=str, default="cpu") + parser.add_argument("--score-thresh", type=float, default=0.3, help="score threshold for filter") + args = parser.parse_args() + main(args) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/inference/trt_inf.py b/backend/apps/algorithms/algorithm/DFINE/tools/inference/trt_inf.py new file mode 100644 index 0000000..731e0b5 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/inference/trt_inf.py @@ -0,0 +1,244 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import collections +import contextlib +import os +import time +from collections import OrderedDict + +import cv2 # Added for video processing +import numpy as np +import tensorrt as trt +import torch +import torchvision.transforms as T +from PIL import Image, ImageDraw + + +class TimeProfiler(contextlib.ContextDecorator): + def __init__(self): + self.total = 0 + + def __enter__(self): + self.start = self.time() + return self + + def __exit__(self, type, value, traceback): + self.total += self.time() - self.start + + def reset(self): + self.total = 0 + + def time(self): + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +class TRTInference(object): + def __init__( + self, engine_path, device="cuda:0", backend="torch", max_batch_size=32, verbose=False + ): + self.engine_path = engine_path + self.device = device + self.backend = backend + self.max_batch_size = max_batch_size + + self.logger = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger(trt.Logger.INFO) + + self.engine = self.load_engine(engine_path) + self.context = self.engine.create_execution_context() + self.bindings = self.get_bindings( + self.engine, self.context, self.max_batch_size, self.device + ) + self.bindings_addr = OrderedDict((n, v.ptr) for n, v in self.bindings.items()) + self.input_names = self.get_input_names() + self.output_names = self.get_output_names() + self.time_profile = TimeProfiler() + + def load_engine(self, path): + trt.init_libnvinfer_plugins(self.logger, "") + with open(path, "rb") as f, trt.Runtime(self.logger) as runtime: + return runtime.deserialize_cuda_engine(f.read()) + + def get_input_names(self): + names = [] + for _, name in enumerate(self.engine): + if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT: + names.append(name) + return names + + def get_output_names(self): + names = [] + for _, name in enumerate(self.engine): + if self.engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT: + names.append(name) + return names + + def get_bindings(self, engine, context, max_batch_size=32, device=None) -> OrderedDict: + Binding = collections.namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) + bindings = OrderedDict() + + for i, name in enumerate(engine): + shape = engine.get_tensor_shape(name) + dtype = trt.nptype(engine.get_tensor_dtype(name)) + + if shape[0] == -1: + shape[0] = max_batch_size + if engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT: + context.set_input_shape(name, shape) + + data = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) + bindings[name] = Binding(name, dtype, shape, data, data.data_ptr()) + + return bindings + + def run_torch(self, blob): + for n in self.input_names: + if blob[n].dtype is not self.bindings[n].data.dtype: + blob[n] = blob[n].to(dtype=self.bindings[n].data.dtype) + if self.bindings[n].shape != blob[n].shape: + self.context.set_input_shape(n, blob[n].shape) + self.bindings[n] = self.bindings[n]._replace(shape=blob[n].shape) + + assert self.bindings[n].data.dtype == blob[n].dtype, "{} dtype mismatch".format(n) + + self.bindings_addr.update({n: blob[n].data_ptr() for n in self.input_names}) + self.context.execute_v2(list(self.bindings_addr.values())) + outputs = {n: self.bindings[n].data for n in self.output_names} + + return outputs + + def __call__(self, blob): + if self.backend == "torch": + return self.run_torch(blob) + else: + raise NotImplementedError("Only 'torch' backend is implemented.") + + def synchronize(self): + if self.backend == "torch" and torch.cuda.is_available(): + torch.cuda.synchronize() + + +def draw(images, labels, boxes, scores, thrh=0.4): + for i, im in enumerate(images): + draw = ImageDraw.Draw(im) + scr = scores[i] + lab = labels[i][scr > thrh] + box = boxes[i][scr > thrh] + scrs = scr[scr > thrh] + + for j, b in enumerate(box): + draw.rectangle(list(b), outline="red") + draw.text( + (b[0], b[1]), + text=f"{lab[j].item()} {round(scrs[j].item(), 2)}", + fill="blue", + ) + + return images + + +def process_image(m, file_path, device): + im_pil = Image.open(file_path).convert("RGB") + w, h = im_pil.size + orig_size = torch.tensor([w, h])[None].to(device) + + transforms = T.Compose( + [ + T.Resize((640, 640)), + T.ToTensor(), + ] + ) + im_data = transforms(im_pil)[None] + + blob = { + "images": im_data.to(device), + "orig_target_sizes": orig_size.to(device), + } + + output = m(blob) + result_images = draw([im_pil], output["labels"], output["boxes"], output["scores"]) + result_images[0].save("trt_result.jpg") + print("Image processing complete. Result saved as 'result.jpg'.") + + +def process_video(m, file_path, device): + cap = cv2.VideoCapture(file_path) + + # Get video properties + fps = cap.get(cv2.CAP_PROP_FPS) + orig_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + orig_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + # Define the codec and create VideoWriter object + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + out = cv2.VideoWriter("trt_result.mp4", fourcc, fps, (orig_w, orig_h)) + + transforms = T.Compose( + [ + T.Resize((640, 640)), + T.ToTensor(), + ] + ) + + frame_count = 0 + print("Processing video frames...") + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + # Convert frame to PIL image + frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + + w, h = frame_pil.size + orig_size = torch.tensor([w, h])[None].to(device) + + im_data = transforms(frame_pil)[None] + + blob = { + "images": im_data.to(device), + "orig_target_sizes": orig_size.to(device), + } + + output = m(blob) + + # Draw detections on the frame + result_images = draw([frame_pil], output["labels"], output["boxes"], output["scores"]) + + # Convert back to OpenCV image + frame = cv2.cvtColor(np.array(result_images[0]), cv2.COLOR_RGB2BGR) + + # Write the frame + out.write(frame) + frame_count += 1 + + if frame_count % 10 == 0: + print(f"Processed {frame_count} frames...") + + cap.release() + out.release() + print("Video processing complete. Result saved as 'result_video.mp4'.") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("-trt", "--trt", type=str, required=True) + parser.add_argument("-i", "--input", type=str, required=True) + parser.add_argument("-d", "--device", type=str, default="cuda:0") + + args = parser.parse_args() + + m = TRTInference(args.trt, device=args.device) + + file_path = args.input + if os.path.splitext(file_path)[-1].lower() in [".jpg", ".jpeg", ".png", ".bmp"]: + # Process as image + process_image(m, file_path, args.device) + else: + # Process as video + process_video(m, file_path, args.device) diff --git a/backend/apps/algorithms/algorithm/DFINE/tools/visualization/fiftyone_vis.py b/backend/apps/algorithms/algorithm/DFINE/tools/visualization/fiftyone_vis.py new file mode 100644 index 0000000..cded6d9 --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/tools/visualization/fiftyone_vis.py @@ -0,0 +1,406 @@ +""" +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +""" + +import argparse +import os +import subprocess +import sys +import time + +import fiftyone as fo +import fiftyone.core.fields as fof +import fiftyone.core.labels as fol +import fiftyone.core.models as fom +import fiftyone.zoo as foz +import torch +import torchvision.transforms as transforms +import tqdm +from fiftyone import ViewField as F +from PIL import Image + +from src.core import YAMLConfig + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "../..")) + + +def kill_existing_mongod(): + try: + result = subprocess.run(["ps", "aux"], stdout=subprocess.PIPE) + processes = result.stdout.decode("utf-8").splitlines() + + for process in processes: + if "mongod" in process and "--dbpath" in process: + # find mongod PID + pid = int(process.split()[1]) + print(f"Killing existing mongod process with PID: {pid}") + # kill mongod session + os.kill(pid, 9) + except Exception as e: + print(f"Error occurred while killing mongod: {e}") + + +kill_existing_mongod() + + +label_map = { + 1: "person", + 2: "bicycle", + 3: "car", + 4: "motorbike", + 5: "aeroplane", + 6: "bus", + 7: "train", + 8: "truck", + 9: "boat", + 10: "trafficlight", + 11: "firehydrant", + 12: "streetsign", + 13: "stopsign", + 14: "parkingmeter", + 15: "bench", + 16: "bird", + 17: "cat", + 18: "dog", + 19: "horse", + 20: "sheep", + 21: "cow", + 22: "elephant", + 23: "bear", + 24: "zebra", + 25: "giraffe", + 26: "hat", + 27: "backpack", + 28: "umbrella", + 29: "shoe", + 30: "eyeglasses", + 31: "handbag", + 32: "tie", + 33: "suitcase", + 34: "frisbee", + 35: "skis", + 36: "snowboard", + 37: "sportsball", + 38: "kite", + 39: "baseballbat", + 40: "baseballglove", + 41: "skateboard", + 42: "surfboard", + 43: "tennisracket", + 44: "bottle", + 45: "plate", + 46: "wineglass", + 47: "cup", + 48: "fork", + 49: "knife", + 50: "spoon", + 51: "bowl", + 52: "banana", + 53: "apple", + 54: "sandwich", + 55: "orange", + 56: "broccoli", + 57: "carrot", + 58: "hotdog", + 59: "pizza", + 60: "donut", + 61: "cake", + 62: "chair", + 63: "sofa", + 64: "pottedplant", + 65: "bed", + 66: "mirror", + 67: "diningtable", + 68: "window", + 69: "desk", + 70: "toilet", + 71: "door", + 72: "tv", + 73: "laptop", + 74: "mouse", + 75: "remote", + 76: "keyboard", + 77: "cellphone", + 78: "microwave", + 79: "oven", + 80: "toaster", + 81: "sink", + 82: "refrigerator", + 83: "blender", + 84: "book", + 85: "clock", + 86: "vase", + 87: "scissors", + 88: "teddybear", + 89: "hairdrier", + 90: "toothbrush", + 91: "hairbrush", +} + + +class CustomModel(fom.Model): + def __init__(self, cfg): + super().__init__() + self.model = cfg.model.eval().cuda() + self.postprocessor = cfg.postprocessor.eval().cuda() + self.transform = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Resize((640, 640)), # Resize to the size expected by your model + # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + ] + ) + + @property + def media_type(self): + return "image" + + @property + def has_logits(self): + return False + + @property + def has_embeddings(self): + return False + + @property + def ragged_batches(self): + return False + + @property + def transforms(self): + return None + + @property + def preprocess(self): + return True + + @preprocess.setter + def preprocess(self, value): + pass + + def _convert_predictions(self, predictions): + class_labels, bboxes, scores = ( + predictions[0]["labels"], + predictions[0]["boxes"], + predictions[0]["scores"], + ) + + detections = [] + for label, bbox, score in zip(class_labels, bboxes, scores): + detection = fol.Detection( + label=label_map[label.item()], + bounding_box=[ + bbox[0] / 640, # Normalized coordinates + bbox[1] / 640, + (bbox[2] - bbox[0]) / 640, + (bbox[3] - bbox[1]) / 640, + ], + confidence=score, + ) + detections.append(detection) + + return fol.Detections(detections=detections) + + def predict(self, image): + image = Image.fromarray(image).convert("RGB") + image_tensor = self.transform(image).unsqueeze(0).cuda() + outputs = self.model(image_tensor) + orig_target_sizes = torch.tensor([[640, 640]]).cuda() + predictions = self.postprocessor(outputs, orig_target_sizes) + return self._convert_predictions(predictions) + + def predict_all(self, images): + image_tensors = [] + for image in images: + image = Image.fromarray(image) + image_tensor = self.transform(image) + image_tensors.append(image_tensor) + image_tensors = torch.stack(image_tensors).cuda() + outputs = self.model(image_tensors) + orig_target_sizes = torch.tensor([[640, 640] for image in images]).cuda() + predictions = self.postprocessor(outputs, orig_target_sizes) + converted_predictions = [self._convert_predictions(pred) for pred in predictions] + + # Ensure the output is a list of lists of Detections + return converted_predictions + + +def filter_by_predictions5_confidence(predictions_view, confidence_threshold=0.3): + for j, sample in tqdm.tqdm(enumerate(predictions_view), total=len(predictions_view)): + has_modified = False + for i, detection in enumerate(sample["predictions0"].detections): + if "original_confidence" not in detection: + detection["original_confidence"] = detection["confidence"] + + if ( + detection["confidence"] <= confidence_threshold + and sample["predictions5"].detections[i]["confidence"] >= confidence_threshold + ) or ( + detection["confidence"] >= confidence_threshold + and sample["predictions5"].detections[i]["confidence"] <= confidence_threshold + ): + sample["predictions0"].detections[i]["confidence"] = sample[ + "predictions5" + ].detections[i]["confidence"] + has_modified = True + if has_modified: + sample.save() + + +def restore_confidence(predictions_view): + for j, sample in tqdm.tqdm(enumerate(predictions_view), total=len(predictions_view)): + for i, detection in enumerate(sample["predictions0"].detections): + if "original_confidence" in detection: + detection["confidence"] = detection["original_confidence"] + sample.save() + + +def fast_iou(bbox1, bbox2): + x1, y1, w1, h1 = bbox1 + x2, y2, w2, h2 = bbox2 + xA = max(x1, x2) + yA = max(y1, y2) + xB = min(x1 + w1, x2 + w2) + yB = min(y1 + h1, y2 + h2) + interArea = max(0, xB - xA) * max(0, yB - yA) + boxAArea = w1 * h1 + boxBArea = w2 * h2 + iou = interArea / float(boxAArea + boxBArea - interArea) + return iou + + +def assign_iou_diff(predictions_view): + for sample in predictions_view: + ious_0 = [ + detection.eval0_iou if "eval0_iou" in detection else None + for detection in sample["predictions0"].detections + ] + ious_5 = [ + detection.eval5_iou if "eval5_iou" in detection else None + for detection in sample["predictions5"].detections + ] + bbox_0 = [detection.bounding_box for detection in sample["predictions0"].detections] + bbox_5 = [detection.bounding_box for detection in sample["predictions5"].detections] + # iou_diffs = [abs(iou_5 - iou_0) if iou_0 is not None and iou_5 is not None else -1 for iou_0, iou_5 in zip(ious_0, ious_5)] + iou_inter = [fast_iou(b0, b5) for b0, b5 in zip(bbox_0, bbox_5)] + iou_diffs = [ + abs(iou_5 - iou_0) + if iou_0 is not None and iou_5 is not None and iou_inter > 0.5 + else -1 + for iou_0, iou_5, iou_inter in zip(ious_0, ious_5, iou_inter) + ] + + for detection, iou_diff in zip(sample["predictions0"].detections, iou_diffs): + detection["iou_diff"] = iou_diff + for detection, iou_diff in zip(sample["predictions5"].detections, iou_diffs): + detection["iou_diff"] = iou_diff + # for detection, iou_diff in zip(sample["predictions100"].detections, iou_diffs): + # detection["iou_diff"] = iou_diff + sample.save() + + +def main(args): + try: + if os.path.exists("saved_predictions_view") and os.path.exists("saved_filtered_view"): + print("Loading saved predictions and filtered views...") + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name="evaluate-detections-tutorial", + dataset_dir="data/fiftyone", + ) + + dataset.persistent = True + session = fo.launch_app(dataset) + + predictions_view = fo.Dataset.from_dir( + dataset_dir="saved_predictions_view", dataset_type=fo.types.FiftyOneDataset + ).view() + filtered_view = fo.Dataset.from_dir( + dataset_dir="saved_filtered_view", dataset_type=fo.types.FiftyOneDataset + ).view() + else: + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name="evaluate-detections-tutorial", + dataset_dir="data/fiftyone", + ) + + dataset.persistent = True + + session = fo.launch_app(dataset) + cfg = YAMLConfig(args.config, resume=args.resume) + if "HGNetv2" in cfg.yaml_cfg: + cfg.yaml_cfg["HGNetv2"]["pretrained"] = False + if args.resume: + checkpoint = torch.load(args.resume, map_location="cpu") + if "ema" in checkpoint: + state = checkpoint["ema"]["module"] + else: + state = checkpoint["model"] + else: + raise AttributeError("only support resume to load model.state_dict by now.") + + # NOTE load train mode state -> convert to deploy mode + cfg.model.load_state_dict(state) + predictions_view = dataset.take(100, seed=51) + + model = CustomModel(cfg) + L = model.model.decoder.decoder.eval_idx + # Apply models and save predictions in different label fields + for i in [L]: + model.model.decoder.decoder.eval_idx = i + label_field = "predictions{:d}".format(i) + predictions_view.apply_model(model, label_field=label_field) + + # filter_by_predictions5_confidence(predictions_view, confidence_threshold=0.3) + for i in [L]: + label_field = "predictions{:d}".format(i) + predictions_view = predictions_view.filter_labels( + label_field, F("confidence") > 0.5, only_matches=False + ) + eval_key = "eval{:d}".format(i) + _ = predictions_view.evaluate_detections( + label_field, + gt_field="ground_truth", + eval_key=eval_key, + compute_mAP=True, + ) + + # assign_iou_diff(predictions_view) + + # filtered_view = predictions_view.filter_labels("predictions0", F("iou_diff") > 0.05, only_matches=True) + # filtered_view = filtered_view.filter_labels("predictions5", F("iou_diff") > 0.05, only_matches=True) + # restore_confidence(filtered_view) + + predictions_view.export( + export_dir="saved_predictions_view", dataset_type=fo.types.FiftyOneDataset + ) + # filtered_view.export( + # export_dir="saved_filtered_view", + # dataset_type=fo.types.FiftyOneDataset + # ) + + # Display the filtered view + session.view = predictions_view + + # Keep the session open + while True: + time.sleep(1) + except Exception as e: + print(f"An error occurred: {e}") + finally: + print("Shutting down session") + if "session" in locals(): + session.close() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--config", "-c", type=str) + parser.add_argument("--resume", "-r", type=str) + args = parser.parse_args() + + main(args) diff --git a/backend/apps/algorithms/algorithm/DFINE/torch_results.jpg b/backend/apps/algorithms/algorithm/DFINE/torch_results.jpg new file mode 100644 index 0000000..5fe275c Binary files /dev/null and b/backend/apps/algorithms/algorithm/DFINE/torch_results.jpg differ diff --git a/backend/apps/algorithms/algorithm/DFINE/train.py b/backend/apps/algorithms/algorithm/DFINE/train.py new file mode 100644 index 0000000..105845d --- /dev/null +++ b/backend/apps/algorithms/algorithm/DFINE/train.py @@ -0,0 +1,166 @@ +""" +D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement +Copyright (c) 2024 The D-FINE Authors. All Rights Reserved. +--------------------------------------------------------------------------------- +Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR) +Copyright (c) 2023 lyuwenyu. All Rights Reserved. +""" + +import os +import sys +import torch +import json +sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")) + +import argparse + +from src.core import YAMLConfig, yaml_utils +from src.misc import dist_utils +from src.solver import TASKS +from pprint import pprint +import django + +current_dir = os.path.dirname(os.path.abspath(__file__)) +project_root = os.path.abspath(os.path.join(current_dir, "../../../../")) +sys.path.append(project_root) +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") +django.setup() +from apps.core.models import AiAlgorithmTrainRecords +from django.utils.timezone import now +debug = False + +if debug: + def custom_repr(self): + return f"{{Tensor:{tuple(self.shape)}}} {original_repr(self)}" + + original_repr = torch.Tensor.__repr__ + torch.Tensor.__repr__ = custom_repr + + +def safe_get_rank(): + if torch.distributed.is_available() and torch.distributed.is_initialized(): + return torch.distributed.get_rank() + else: + return 0 + + +def main(args) -> None: + """main""" + dist_utils.setup_distributed(args.print_rank, args.print_method, seed=args.seed) + + assert not all( + [args.tuning, args.resume] + ), "Only support from_scrach or resume or tuning at one time" + + update_dict = yaml_utils.parse_cli(args.update) + update_dict.update( + { + k: v + for k, v in args.__dict__.items() + if k + not in [ + "update", + ] + and v is not None + } + ) + + cfg = YAMLConfig(args.config, **update_dict) + + if args.resume or args.tuning: + if "HGNetv2" in cfg.yaml_cfg: + cfg.yaml_cfg["HGNetv2"]["pretrained"] = False + + if safe_get_rank() == 0: + print("cfg: ") + sys.stdout.flush() + pprint(cfg.__dict__) + + solver = TASKS[cfg.yaml_cfg["task"]](cfg) + + if args.test_only: + solver.val() + else: + solver.fit() + + dist_utils.cleanup() + + +if __name__ == "__main__": + # 记录训练开始时间 + train_start_time = now() + # 发送训练开始的消息 + message = {'type': 'starttime', + 'text': f'|----------------------------训练开始时间：{train_start_time.strftime("%Y-%m-%d %H:%M:%S")}-------------------------------|'} + print(json.dumps(message, ensure_ascii=False)) + sys.stdout.flush() + parser = argparse.ArgumentParser() + # priority 0 + parser.add_argument("-c", "--config", type=str, required=True) + parser.add_argument("-r", "--resume", type=str, help="resume from checkpoint") + parser.add_argument("-t", "--tuning", type=str, help="tuning from checkpoint") + parser.add_argument( + "-d", + "--device", + type=str, + help="device", + ) + parser.add_argument("--seed", type=int, help="exp reproducibility") + parser.add_argument("--use-amp", action="store_true", help="auto mixed precision training") + parser.add_argument("--output-dir", type=str, help="output directoy") + parser.add_argument("--summary-dir", type=str, help="tensorboard summry") + parser.add_argument( + "--test-only", + action="store_true", + default=False, + ) + parser.add_argument('--trainid', type=str, default='0', help="训练id，默认为 '0'") + parser.add_argument('--project_path', type=str, default='project_path', help="路径") + + # priority 1 + parser.add_argument("-u", "--update", nargs="+", help="update yaml config") + + # env + parser.add_argument("--print-method", type=str, default="builtin", help="print method") + parser.add_argument("--print-rank", type=int, default=0, help="print rank id") + + parser.add_argument("--local-rank", type=int, help="local rank id") + args = parser.parse_args() + model_filepath=args.project_path + train_result = "样本准备完成" + status = "01" + try: + main(args) + train_result = "模型训练完成！" + status = "02" + except Exception as e: + import traceback + message = {'type': 'exception', 'text': str(e)} + print(json.dumps(message, ensure_ascii=False)) + print(traceback.format_exc()) + sys.stdout.flush() + train_result = f"模型训练异常: {e}" + status = "02" + finally: + train_end_time = now() + train_total_times = round((train_end_time - train_start_time).total_seconds() / 60, 1) + hours = int(train_total_times // 60) + minutes = int(train_total_times % 60) + message = {'type': 'endtime', + 'text': f'| ----------------------------训练完成时间：{train_end_time.strftime("%Y-%m-%d %H:%M:%S")} - ------------------------------ |'} + print(json.dumps(message, ensure_ascii=False)) + message = {'type': 'totaltime', + 'text': f'|-------------------------模型训练总耗时：{hours} 小时 {minutes} 分钟---------------------------------------|'} + print(json.dumps(message, ensure_ascii=False)) + sys.stdout.flush() + AiAlgorithmTrainRecords.objects.filter(id=args.trainid).update( + train_start_time=train_start_time, + train_end_time=train_end_time, + train_total_times=train_total_times, + model_createtime=train_end_time, + model_filepath=model_filepath, + model_filename="best_stg1.pth", + model_trainresult=train_result, + record_status=status + ) + diff --git a/backend/apps/algorithms/algorithm/Transformer/DetrDetector.py b/backend/apps/algorithms/algorithm/Transformer/DetrDetector.py new file mode 100644 index 0000000..91dc439 --- /dev/null +++ b/backend/apps/algorithms/algorithm/Transformer/DetrDetector.py @@ -0,0 +1,101 @@ +import base64 +import json +from datetime import datetime +import cv2 +from ultralytics import YOLO + +class YoloDetector: + def __init__(self, model_path: str, image_path: str,labels:str, conf_threshold: float = 0.2): + self.model_path = model_path + self.image_path = image_path + self.labels = labels + self.conf_threshold = conf_threshold + self.model = YOLO(model_path) + self.model.overrides = { + 'save_txt': False, + 'save_conf': False, + 'save_crop': False, + 'show': False, + 'conf': self.conf_threshold + } + + def detect(self): + try: + # 进行检测 + results = self.model.predict(source=self.image_path, save=False) + pred_result = self.parse_detection_results(results) + + # 获取带标注的图像 + detection_image = self.get_detection_image(pred_result) + + result_json = { + 'image': detection_image, + 'obj_list': pred_result, + 'time': datetime.now().strftime('%Y-%m-%d %H:%M:%S') + } + + return json.dumps(result_json) + + except Exception as e: + print(f"Error during detection: {e}") + return json.dumps({'error': str(e)}) + + def parse_detection_results(self, results): + pred_result = [] + labels=json.loads(self.labels) + for result in results: + for data in result.boxes.data: + name_key = int(data[5]) + code = result.names.get(name_key) + name = next((item["name"] for item in labels if item["label"] == code), None) + one_box = { + 'code': code, + 'name': name, + 'score': round(data[4].tolist(), 2), + 'x1': int(data[0]), + 'y1': int(data[1]), + 'x2': int(data[2]), + 'y2': int(data[3]) + } + pred_result.append(one_box) + return pred_result + + def get_detection_image(self, pred_result): + img = cv2.imread(self.image_path) + + preset_colors = [ + (0, 0, 255), (20, 20, 255), (20, 40, 255), (0, 60, 255), (0, 80, 255), + (0, 100, 255), (0, 120, 255), (0, 140, 255), (0, 160, 255), (0, 180, 255), + (0, 200, 255), (0, 220, 255), (0, 255, 255), + ] + + category_colors = {} + for i, obj in enumerate(pred_result): + category = obj['code'] + if category not in category_colors: + category_colors[category] = self.get_preset_color(i, preset_colors) + + x1, y1, x2, y2 = obj['x1'], obj['y1'], obj['x2'], obj['y2'] + color = category_colors[category] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) + + font = cv2.FONT_HERSHEY_SIMPLEX + text = f"{category}-{obj['score']}" + (text_width, _), _ = cv2.getTextSize(text, font, 0.5, 1) + cv2.rectangle(img, (x1, y1 - 20), (x1 + text_width, y1), color, -1) + cv2.putText(img, text, (x1, y1 - 5), font, 0.5, (255, 255, 255), 1) + + _, img_encoded = cv2.imencode('.jpg', img) + img_base64 = base64.b64encode(img_encoded.tobytes()).decode('utf-8') + return img_base64 + + def get_preset_color(self, index, preset_colors): + return preset_colors[index % len(preset_colors)] + + +# Example usage for testing purposes +if __name__ == '__main__': + img_url = 'D:/project_space/01/002/datasets/00000005.jpg' + model_name = 'D:/python2023/DjangoFrameWork/industrial_prediction/yolo11x.pt' + detector = YoloDetector(model_path=model_name, image_path=img_url) + print(detector.detect()) diff --git a/backend/apps/algorithms/algorithm/Transformer/DetrReporter.py b/backend/apps/algorithms/algorithm/Transformer/DetrReporter.py new file mode 100644 index 0000000..2e056b9 --- /dev/null +++ b/backend/apps/algorithms/algorithm/Transformer/DetrReporter.py @@ -0,0 +1,183 @@ +import os +import base64 +from datetime import datetime + +class YOLOReporter: + def __init__(self, project_path: str, train_name: str = "train_name"): + """ + YOLOReporter 初始化方法，仅保留生成报告所需的属性. + :param project_path: 项目保存路径 + :param train_name: 训练名称 + """ + self.project_path = project_path + self.train_name = train_name + + @staticmethod + def image_to_base64(image_path: str) -> str: + """ + 将图片文件转换为 Base64 编码字符串. + :param image_path: 图片文件路径 + :return: Base64 编码的字符串 + """ + if not os.path.exists(image_path): + return "" # 如果图片不存在，返回空字符串 + + with open(image_path, "rb") as image_file: + base64_str = base64.b64encode(image_file.read()).decode("utf-8") + return base64_str + + def generate_report(self, dataset_name: str, model_name: str, training_duration: str, metrics: list,overall_metrics:str, image_urls: dict): + """ + 生成训练 HTML 报告. + :param dataset_name: 数据集名称 + :param model_name: 模型名称 + :param training_duration: 训练时长 + :param metrics: 性能指标列表 + :param image_urls: 图片路径字典 + """ + # HTML 模板 + html_template = """ + + + + + + 图像识别算法模型训练报告 + + + +
+

训练摘要

+

报告标题：{{train_name}}

+

生成日期：{{report_date}}

+

模型名称：{{model_name}}

+

训练数据集：{{dataset_name}}

+

训练时长：{{training_duration}}

+
+
+

性能指标

+
+ + + + + + + + + + + {{metrics_rows}} + +
类别名称mAP@50mAP@50-95
+
+

+ mAP@50: 平均精度在 IoU（交并比）阈值为 50% 时的评估结果，衡量模型检测的准确性。
+ mAP@50-95: 平均精度在 IoU 阈值从 50% 到 95%（步长为 5%）的评估结果，更严格地衡量模型在不同 IoU 标准下的综合性能。 +

+
+
+

训练结果图片

+
+

损失曲线图

+ 损失曲线图 +

损失曲线图反映了模型在训练过程中损失值的变化趋势。通常，损失值逐渐下降并趋于平稳，说明模型逐步收敛，性能提升。

+

精确率-召回率曲线

+ 精确率-召回率曲线 +

+ 精确率（Precision）表示预测为正类的样本中实际为正类的比例；召回率（Recall）表示所有实际为正类的样本中成功预测为正类的比例。 + 精确率-召回率曲线反映了模型在不同阈值下的平衡表现，理想情况下曲线越靠近右上角越好。 +

+

混淆矩阵

+ 混淆矩阵 +

+ 混淆矩阵用于展示模型预测结果的分类情况。对角线上的值表示正确预测的数量，非对角线上的值表示错误分类的数量。 + 理想情况下，混淆矩阵应接近对角矩阵，说明模型分类性能良好。 +

+
+
+
+

模型总体性能

+
+

Precision: {{precision}}，表示预测为正类的样本中实际为正类的比例，值越高说明模型预测的准确性越强。

+

Recall: {{recall}}，表示所有实际为正类的样本中成功预测为正类的比例，值越高说明模型对正类样本的覆盖能力越强。

+

mAP@50: {{mAP50}}，在 IoU ≥ 50% 时的平均精度，衡量模型检测目标的能力。

+

mAP@50-95: {{mAP50_95}}，在 IoU 从 50% 到 95%（步长为 5%）的平均精度，反映模型在不同 IoU 阈值下的综合性能。

+

Fitness: {{fitness}}，综合精确率、召回率和 mAP 的加权得分，表示模型的总体性能。

+
+
+ + + """ + + # 生成性能指标表格行 + metrics_rows = "".join( + f"{metric['class']}{metric['name']}{metric['mAP50']:.4f}{metric['mAP50-95']:.4f}" + for metric in metrics + ) + + # 转换图片为 Base64 + loss_graph_base64 = self.image_to_base64(image_urls["loss_graph"]) + pr_curve_base64 = self.image_to_base64(image_urls["pr_curve"]) + confusion_matrix_base64 = self.image_to_base64(image_urls["confusion_matrix"]) + + # 填充模板 + report_html = html_template.replace("{{report_date}}", datetime.now().strftime("%Y-%m-%d %H:%M:%S")) + report_html = report_html.replace("{{train_name}}", self.train_name) + report_html = report_html.replace("{{model_name}}", model_name) # 默认模型名称 + report_html = report_html.replace("{{dataset_name}}", dataset_name) + report_html = report_html.replace("{{training_duration}}", training_duration) + report_html = report_html.replace("{{metrics_rows}}", metrics_rows) + report_html = report_html.replace("{{loss_graph_base64}}", loss_graph_base64) + report_html = report_html.replace("{{pr_curve_base64}}", pr_curve_base64) + report_html = report_html.replace("{{confusion_matrix_base64}}", confusion_matrix_base64) + + # 填充总体性能指标 + report_html = report_html.replace("{{precision}}", str(overall_metrics.get("precision"))) + report_html = report_html.replace("{{recall}}", str(overall_metrics.get("recall"))) + report_html = report_html.replace("{{mAP50}}", str(overall_metrics.get("mAP50"))) + report_html = report_html.replace("{{mAP50_95}}", str(overall_metrics.get("mAP50-95"))) + report_html = report_html.replace("{{fitness}}", str(overall_metrics.get("fitness"))) + + # 保存 HTML 文件 + output_html_path = os.path.join(self.project_path, "train_report.html") + with open(output_html_path, "w", encoding="utf-8") as f: + f.write(report_html) + + print(f"训练报告 HTML 已保存到 {output_html_path}") + + + +# 示例调用 +if __name__ == "__main__": + reporter = YOLOReporter( + project_path="D:/project_space/01/003/trains/003_20241219_2122/runs/train", + train_name="大模型训练" + ) + + # 生成报告 + reporter.generate_report( + dataset_name="COCO Dataset", + model_name="COCO Dataset", + training_duration="2 hours", + metrics=[ + {"class": "Person", "mAP50": 0.8234, "mAP50-95": 0.7234}, + {"class": "Car", "mAP50": 0.9123, "mAP50-95": 0.8231}, + ], + image_urls={ + "loss_graph": os.path.join(reporter.project_path, "results.png"), + "pr_curve": os.path.join(reporter.project_path, "PR_curve.png"), + "confusion_matrix": os.path.join(reporter.project_path, "confusion_matrix.png") + } + ) + diff --git a/backend/apps/algorithms/algorithm/Transformer/DetrTrainer.py b/backend/apps/algorithms/algorithm/Transformer/DetrTrainer.py new file mode 100644 index 0000000..07db9a7 --- /dev/null +++ b/backend/apps/algorithms/algorithm/Transformer/DetrTrainer.py @@ -0,0 +1,50 @@ +from ultralytics import YOLO + + +from ultralytics import YOLO +import os + +class YOLOTrainer: + def __init__(self, model_path: str, data_path: str, params: dict, project_path: str = None, run_name: str = "yolo_run"): + """ + YOLOTrainer 初始化方法. + :param model_path: 模型路径 (e.g., yolo11n.pt) + :param data_path: 数据配置文件路径 (e.g., yolo_config.yaml) + :param params: 训练参数字典，从 data_str 提取 (e.g., epochs, batch_size, img_size, device 等) + :param project_path: 项目保存路径 + :param run_name: 运行名称 + """ + self.model_path = model_path + self.data_path = data_path + self.project_path = project_path or f"./runs/train/{run_name}" + self.run_name = run_name + + # 从 params 中解析参数 + self.epochs = int(params.get("epochs", 3)) + self.batch_size = int(params.get("batch", 16)) + self.img_size = int(params.get("imgsz", 640)) + self.workers = int(params.get("workers", 8)) + self.device = params.get("device", "cpu") # 默认为 CPU + # 加载 YOLO 模型 + self.model = YOLO(model_path) + + def train(self): + """ + 开始训练 YOLO 模型，使用初始化时指定的参数. + """ + # 确保项目路径存在 + os.makedirs(self.project_path, exist_ok=True) + + # 使用 YOLO 的 train 方法进行训练 + results = self.model.train( + data=self.data_path, + project=self.project_path, + name=self.run_name, + epochs=self.epochs, + batch=self.batch_size, + imgsz=self.img_size, + workers=self.workers, + device=self.device + + ) + return results \ No newline at end of file diff --git a/backend/apps/algorithms/algorithm/Transformer/__init__.py b/backend/apps/algorithms/algorithm/Transformer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/__init__.py b/backend/apps/algorithms/algorithm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/__init__.py b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/class_id.npy b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/class_id.npy new file mode 100644 index 0000000..e377c73 Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/class_id.npy differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/cudaTest.py b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/cudaTest.py new file mode 100644 index 0000000..1d85a81 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/cudaTest.py @@ -0,0 +1,6 @@ +import torch +print("PyTorch version:", torch.__version__) +print("CUDA available:", torch.cuda.is_available()) +print("CUDA version used by PyTorch:", torch.version.cuda) +print("Current device:", torch.cuda.current_device()) +print("Device name:", torch.cuda.get_device_name(torch.cuda.current_device())) diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/dataset.py b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/dataset.py new file mode 100644 index 0000000..435bb8e --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/dataset.py @@ -0,0 +1,32 @@ +import os +import numpy as np +import math +import torch.nn as nn +import torch +import torch.nn.functional as F +from torch.utils.data import Dataset +from collections import OrderedDict,defaultdict + +import random +import json + + +# dataLoader +class FeatureDataset(Dataset): + def __init__(self,hex_array,class_ids): + self.hex_array=hex_array + self.class_ids=class_ids + print(self.hex_array.shape,self.class_ids.shape) + + def __getitem__(self, index): + dat=self.hex_array[index] + class_id=self.class_ids[index] + + return dat,class_id + + + def __len__(self): + return len(self.hex_array) + +if __name__ == '__main__': + pass diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/hex_array.npy b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/hex_array.npy new file mode 100644 index 0000000..264da30 Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/hex_array.npy differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/infer.py b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/infer.py new file mode 100644 index 0000000..23c652e --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/infer.py @@ -0,0 +1,190 @@ +import os +import json +import torch +import pandas as pd +import numpy as np + +from model import FeatureModel +from torch.utils.data import Dataset, DataLoader + +# =================== 全局常量 =================== +LABEL_TO_APP_AUX = { + 1: "aim_chat", + 2: "bittorrent", + 3: "email", + 4: "facebook_voip", + 5: "facebook_chat", + 6: "ftps_file_transfer", + 7: "hangouts_voip", + 8: "hangouts_chat", + 9: "icq_chat", + 10: "sftp_file_transfer", + 11: "skype_voip", + 12: "skype_file_transfer" +} +# ============================================== + +# =================== 配置参数 =================== +CONFIG = { + "checkpoint_path": "modelsave/model_111.pth", + "input_excel": "./test.xlsx", + "output_json": "./pred.json", + "result_excel": "./result.xlsx", + "num_classes": 12, + "hex_fixed_length": 1408, +} +# ============================================== + + +def hex_to_array(hex_str: str, fix_len: int) -> np.ndarray: + """ + 将十六进制字符串转换为固定长度的浮点数组。 + + Args: + hex_str (str): 十六进制字符串，如 '0a 0b 0c' + fix_len (int): 固定输出长度，不足则补零 + + Returns: + np.ndarray: 转换后的数组，dtype=np.float32 + """ + hex_list = [int(x, 16) for x in hex_str.split(" ")] + return np.pad( + np.array(hex_list, dtype=np.float32), + pad_width=(0, max(0, fix_len - len(hex_list))), + constant_values=0 + ) + + +class TestDataset(Dataset): + """ + 自定义测试数据集，用于从 Excel 文件中读取 Hex 数据并进行预处理。 + """ + + def __init__(self, excel_path: str, hex_len: int): + self.df = pd.read_excel(excel_path) + self.hex_len = hex_len + + def __len__(self) -> int: + return len(self.df) + + def __getitem__(self, idx: int) -> (int, np.ndarray): + row = self.df.iloc[idx] + file_no = row["No."] + hex_data = row["Hex"] + feature = hex_to_array(hex_data, fix_len=self.hex_len) / 255.0 # 归一化到 [0, 1] + return file_no, feature + + +def load_model(num_classes: int, checkpoint_path: str) -> FeatureModel: + """ + 加载模型权重并切换为评估模式。 + + Args: + num_classes (int): 分类数量 + checkpoint_path (str): 权重文件路径 + + Returns: + FeatureModel: 已加载权重的模型实例 + """ + if not os.path.exists(checkpoint_path): + raise FileNotFoundError(f"模型文件未找到: {checkpoint_path}") + + model = FeatureModel(num_classes) + weights_dict = torch.load(checkpoint_path, weights_only=True) + model.load_state_dict(weights_dict["state_dict"]) + model.eval() + return model + + +def run_inference(model: FeatureModel, dataset: Dataset) -> dict: + """ + 对输入数据集执行推理任务，并返回预测结果字典。 + + Args: + model (FeatureModel): 已加载权重的模型 + dataset (Dataset): 推理用的数据集 + + Returns: + dict: {file_no: predicted_class_id} + """ + data_loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=False, drop_last=False) + + results = {} + + with torch.no_grad(): + for file_nos, features in data_loader: + preds = model(features) + preds = preds.cpu().numpy() + pred_labels = np.argmax(preds, axis=1) + + for i, label in enumerate(pred_labels): + file_no = file_nos[i].item() + results[file_no] = int(label) + 1 # 标签从 1 开始计数 + + return results + + +def save_results(results: dict, output_file: str): + """ + 保存推理结果为 JSON 文件。 + + Args: + results (dict): 推理结果字典 + output_file (str): 输出文件路径 + """ + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with open(output_file, "w", encoding="UTF-8") as f: + json.dump(results, f, indent=4, ensure_ascii=False) + + +def update_result_excel(results: dict, output_excel_path: str, input_excel_path: str): + """ + 更新 result.xlsx 文件，添加 pred-result 列。 + + Args: + results (dict): 推理结果字典 + output_excel_path (str): 输出 Excel 文件路径 + input_excel_path (str): 输入 Excel 文件路径 + """ + # 读取原始 Excel 文件 + df = pd.read_excel(input_excel_path) + + # 创建新的 DataFrame 用于结果 + result_df = df.copy() + + # 添加 pred-result 列 + result_df["pred-result"] = "" + + # 更新 pred-result 列 + for file_no, label in results.items(): + if label in LABEL_TO_APP_AUX: + result_df.loc[result_df["No."] == file_no, "pred-result"] = LABEL_TO_APP_AUX[label] + + # 保存到 result.xlsx + result_df.to_excel(output_excel_path, index=False) + + +def main(): + """主程序入口""" + print("🚀 开始推理流程") + + # 1. 初始化数据集 + test_dataset = TestDataset(CONFIG["input_excel"], CONFIG["hex_fixed_length"]) + + # 2. 加载模型 + model = load_model(CONFIG["num_classes"], CONFIG["checkpoint_path"]) + + # 3. 执行推理 + predictions = run_inference(model, test_dataset) + + # 4. 保存结果到 JSON 文件 + save_results(predictions, CONFIG["output_json"]) + + # 5. 更新 result.xlsx 文件 + update_result_excel(predictions, CONFIG["result_excel"], CONFIG["input_excel"]) + + print(f"✅ 推理完成，结果已保存至：{CONFIG['output_json']} 和 {CONFIG['result_excel']}") + + +if __name__ == "__main__": + main() diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/model.py b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/model.py new file mode 100644 index 0000000..b4e193f --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/model.py @@ -0,0 +1,396 @@ +import numpy as np +import torch.nn as nn +import torch +import torch.nn.functional as F + +from collections import OrderedDict +import collections.abc +from itertools import repeat +import math +from functools import partial +import warnings + +# from torchinfo import summary + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + +from typing import Any, Callable, Dict, Iterator, Tuple, Type, Union +def named_apply( + fn: Callable, + module: nn.Module, name='', + depth_first: bool = True, + include_root: bool = False, +) -> nn.Module: + if not depth_first and include_root: + fn(module=module, name=name) + for child_name, child_module in module.named_children(): + child_name = '.'.join((name, child_name)) if name else child_name + named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) + if depth_first and include_root: + fn(module=module, name=name) + return module + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +class LayerNorm(nn.Module): + r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape, ) + + def forward(self, x): + if self.data_format == "channels_last": + return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + elif self.data_format == "channels_first": + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + +class CNBlock(nn.Module): + r""" ConvNeXt Block. There are two equivalent implementations: + (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) + (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back + We use (2) as we find it slightly faster in PyTorch + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + """ + def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6): + super().__init__() + self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv + self.norm = LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = nn.Linear(4 * dim, dim) + self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), + requires_grad=True) if layer_scale_init_value > 0 else None + # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.drop_path = nn.Identity() + + def forward(self, x): + input = x + x = self.dwconv(x) + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + x = input + self.drop_path(x) + return x + + +class SEBlock(nn.Module): + + def __init__(self, input_channels, internal_neurons): + super(SEBlock, self).__init__() + self.down = nn.Conv2d(in_channels=input_channels, out_channels=internal_neurons, kernel_size=1, stride=1, + bias=True, padding_mode='circular') + self.up = nn.Conv2d(in_channels=internal_neurons, out_channels=input_channels, kernel_size=1, stride=1, + bias=True, padding_mode='circular') + + def forward(self, inputs): + x = F.avg_pool2d(inputs, kernel_size=(inputs.size(2),inputs.size(3))) + x = self.down(x) + x = F.leaky_relu(x) + x = self.up(x) + x = torch.sigmoid(x) + x = x.repeat(1, 1, inputs.size(2), inputs.size(3)) + return inputs * x + +class eca_layer(nn.Module): + """Constructs a ECA module. + + Args: + channel: Number of channels of the input feature map + k_size: Adaptive selection of kernel size + """ + def __init__(self, channel, k_size=3): + super(eca_layer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # feature descriptor on the global spatial information + y = self.avg_pool(x) + + # Two different branches of ECA module + y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) + + # Multi-scale information fusion + y = self.sigmoid(y) + + return x * y.expand_as(x) + +class RepConv(nn.Module): + + def __init__(self, in_channels, out_channels, kernel_size, stride, padding=None, groups=1, + map_k=3): + super(RepConv, self).__init__() + assert map_k <= kernel_size + self.origin_kernel_shape = (out_channels, in_channels // groups, kernel_size, kernel_size) + self.register_buffer('weight', torch.zeros(*self.origin_kernel_shape)) + G = in_channels * out_channels // (groups ** 2) + self.num_2d_kernels = out_channels * in_channels // groups + self.kernel_size = kernel_size + self.convmap = nn.Conv2d(in_channels=self.num_2d_kernels, + out_channels=self.num_2d_kernels, kernel_size=map_k, stride=1, padding=map_k // 2, + groups=G, bias=False) + #nn.init.zeros_(self.convmap.weight) + self.bias = None#nn.Parameter(torch.zeros(out_channels), requires_grad=True) # must have a bias for identical initialization + self.stride = stride + self.groups = groups + if padding is None: + padding = kernel_size // 2 + self.padding = padding + + def forward(self, inputs): + origin_weight = self.weight.view(1, self.num_2d_kernels, self.kernel_size, self.kernel_size) + kernel = self.weight + self.convmap(origin_weight).view(*self.origin_kernel_shape) + return F.conv2d(inputs, kernel, stride=self.stride, padding=self.padding, dilation=1, groups=self.groups, bias=self.bias) + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=True) + +class ConvLayer(nn.Sequential): + def __init__(self, in_planes, out_planes, kernel_size,stride=1, activation="LeakyReLU",groups=1): + if not isinstance(kernel_size, int): + padding = [(i - 1) // 2 for i in kernel_size] + else: + padding = (kernel_size - 1) // 2 + dict_activation ={ + "LeakyReLU": nn.LeakyReLU(negative_slope=0.3,inplace=True), + "Sigmoid": nn.Sigmoid(), + "Tanh": nn.Tanh(), + "SiLU": nn.SiLU(), + } + activation_layer = dict_activation[activation] + + super(ConvLayer, self).__init__(OrderedDict([ + ("conv", nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding=padding, bias=False,groups=groups)), + # ("bn", nn.BatchNorm2d(out_planes)), + ("activation",activation_layer) + ])) + +class Conv1DLayer(nn.Sequential): + def __init__(self, in_planes, out_planes, kernel_size,stride=1, activation="LeakyReLU"): + if not isinstance(kernel_size, int): + padding = [(i - 1) // 2 for i in kernel_size] + else: + padding = (kernel_size - 1) // 2 + dict_activation ={ + "LeakyReLU": nn.LeakyReLU(negative_slope=0.01,inplace=True), + "Sigmoid": nn.Sigmoid(), + "Tanh": nn.Tanh(), + "SiLU": nn.SiLU(), + } + activation_layer = dict_activation[activation] + super(Conv1DLayer, self).__init__(OrderedDict([ + ("conv", nn.Conv1d(in_planes, out_planes, kernel_size, stride, padding=padding, bias=False)), + # ("bn", nn.BatchNorm2d(out_planes)), + ("activation",activation_layer) + ])) + + + +# Calculate symmetric padding for a convolution +def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int: + padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2 + return padding + + +class FeatureModel(nn.Module): + + def __init__(self, class_count=12): + super(FeatureModel, self).__init__() + self.alpha=0.2 + self.channel=128 + self.conv_start= nn.Sequential( + # nn.Conv2d(1024, 2048, kernel_size=4, stride=4, padding=1, bias=True), + # nn.Conv2d(2048, 2048, kernel_size=4, stride=4, padding=0, bias=True), + # nn.Conv2d(1024, 1024, kernel_size=7, stride=7, bias=True), + # nn.SiLU(), + # nn.Conv2d(1024, 2048, kernel_size=2, stride=2, bias=True), + # SEBlock(2048,1024) + nn.Conv1d(1,out_channels=16,kernel_size=3,stride=2, padding=1), + nn.Conv1d(16,out_channels=32,kernel_size=3,stride=2, padding=1), + nn.Conv1d(32,out_channels=64,kernel_size=3,stride=2, padding=1), + nn.MaxPool1d(4) + + ) + self.conv_mid= nn.Sequential( + nn.Conv1d(64, self.channel,kernel_size=3,stride=1,padding=1, bias=True), + nn.SiLU(), + nn.Conv1d(self.channel, self.channel*2,kernel_size=3,stride=1,padding=1, bias=True), + nn.SiLU(), + # SEBlock( self.channel*2, self.channel) + + + ) + # self.act=nn.SiLU() + # self.conv1d1=nn.Conv2d(250,self.channel,32,stride=32,padding=0) + # self.conv1d2=nn.Conv2d(self.channel,self.channel,32,stride=32,padding=0) + + # self.se=eca_layer(1280) + dropout=0.3 + self.classify=nn.Sequential( + # + nn.Linear(11264, 256), + nn.LeakyReLU(), + nn.Dropout(p = dropout), + # + # nn.Linear(self.channels*16, self.channels*8), + # # nn.BatchNorm1d(self.channels*8), + # nn.LeakyReLU(), + # nn.Dropout(p = dropout), + + # nn.Linear(self.channels*8, self.channels*4), + # # nn.BatchNorm1d(self.channels*4), + # nn.LeakyReLU(), + # nn.Dropout(p = dropout), + + nn.Linear(256, class_count) + ) + # named_apply(partial(_init_weights, head_init_scale=1.), self) + zero_init_fc=False + for n, m in self.named_modules(): + if 'fc' in n and isinstance(m, nn.Linear): + if zero_init_fc: + nn.init.zeros_(m.weight) + else: + nn.init.normal_(m.weight, 0., .01) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='linear') + if m.bias is not None: + nn.init.zeros_(m.bias) + + + def forward(self,feas): + # print(feas.size()) #(b,250,1024,14,14) + # feas=feas.view(-1,1024,14,14) + # print(feas.size()) + feas=torch.unsqueeze(feas,1) + # print(feas.size()) + feas=self.conv_start(feas) + + # print(feas.size()) # + # feas=feas.view(-1,250,32,64) + + feas=self.conv_mid(feas) + # print(feas.size()) + feas=feas.view(-1,11264) + + # print(out.size()) + # out=self.se(feas) + # print(out.size()) + # out=torch.squeeze(out,2) + # out=out.view(-1,self.channels[self.stage_count+1]*2) + # print(out.size()) + out = self.classify(feas) + # print(out.size()) + return out + +def _init_weights(module, name=None, head_init_scale=1.0): + if isinstance(module, nn.Conv2d): + trunc_normal_(module.weight, std=.02) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.Linear): + trunc_normal_(module.weight, std=.02) + nn.init.zeros_(module.bias) + if name and 'head.' in name: + module.weight.data.mul_(head_init_scale) + module.bias.data.mul_(head_init_scale) + + + +if __name__ == '__main__': + import json + from dataset import FeatureDataset + + train_dataset = FeatureDataset() + print(len(train_dataset)) + train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True, num_workers=0, pin_memory=True, drop_last=True) + model=FeatureModel() + # torch.save({'state_dict': model.state_dict(), }, 'test.pth') + # print(model) + # summary(model) + for dat,lables in train_loader: + print(dat.size(),lables.size()) + output=model(dat) + print(output.size()) + + break diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/modelsave/model_111.pth b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/modelsave/model_111.pth new file mode 100644 index 0000000..7872470 Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/modelsave/model_111.pth differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/modelsave/model_out.pth b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/modelsave/model_out.pth new file mode 100644 index 0000000..d436ee5 Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/modelsave/model_out.pth differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/pred.json 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torch.cuda.get_device_name(torch.cuda.current_device())) \ No newline at end of file diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/result.xlsx b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/result.xlsx new file mode 100644 index 0000000..2f0582d Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/result.xlsx differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/test.xlsx b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/test.xlsx new file mode 100644 index 0000000..16197d1 Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/test.xlsx differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/train.py b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/train.py new file mode 100644 index 0000000..28f1728 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/train.py @@ -0,0 +1,356 @@ +import os +import random +import logging +import datetime +from typing import Tuple # 导入类型提示所需的Tuple + +import numpy as np +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +from tqdm import tqdm # 用于显示进度条 + +# 导入自定义模块 +from dataset import FeatureDataset # 自定义数据集类 +from model import FeatureModel # 自定义模型类 +from utils import adaptive_clip_grad # 自适应梯度裁剪函数 + +# =================== 配置参数 =================== +CONFIG = { + 'gpu_list': '0', # 使用的GPU编号列表 + 'CUBLAS_WORKSPACE_CONFIG': ':4096:8', # CUDA相关配置，用于确保结果可复现 + 'class_count': 12, # 分类类别数量 + 'SEED': 111, # 随机种子 + 'model_path': 'modelsave/', # 模型保存路径 + 'checkpoint_file': 'model_{SEED}.pth', # 最佳模型保存文件名格式 + 'checkpoint_file_last': 'model_{SEED}_last.pth', # 最后一个epoch模型保存文件名格式 + 'load_checkpoint': False, # 是否加载已有模型权重，False表示不加载，也可以是模型路径字符串 + 'loss_fun': 'ce', # 损失函数类型，ce表示交叉熵损失 + 'half': False, # 是否使用半精度(FP16)训练 + 'batch_size': 32, # 批处理大小 + 'epochs': 24, # 训练轮数 + 'learning_rate': 1e-3, # 初始学习率 + 'min_lr': 1e-5, # 最小学习率 + 'warmup_epoch': 2, # 预热训练的轮数 + 'num_workers': 4, # 数据加载的线程数 + 'print_freq': 100, # 打印训练信息的频率（每多少个batch打印一次） + 'save_acc': 0.7, # 保存模型的准确率阈值 +} +# 计算起始学习率：如果有预热训练，则从最小学习率开始；否则直接使用设定的学习率 +CONFIG['start_lr'] = CONFIG['min_lr'] if CONFIG['warmup_epoch'] > 0 else CONFIG['learning_rate'] +# 格式化模型保存文件名，将{SEED}替换为实际的种子值 +CONFIG['checkpoint_file'] = CONFIG['checkpoint_file'].format(SEED=CONFIG['SEED']) +CONFIG['checkpoint_file_last'] = CONFIG['checkpoint_file_last'].format(SEED=CONFIG['SEED']) + +# 设置环境变量 +os.environ["CUDA_VISIBLE_DEVICES"] = CONFIG['gpu_list'] # 设置可见的GPU设备 +os.environ["CUBLAS_WORKSPACE_CONFIG"] = CONFIG['CUBLAS_WORKSPACE_CONFIG'] # 设置CUBLAS工作空间配置 +os.makedirs(CONFIG['model_path'], exist_ok=True) # 创建模型保存目录（如果不存在） +# ============================================== + +def logger_config(log_path: str, log_filename: str, logging_name: str) -> logging.Logger: + """ + 配置日志记录器 + + Args: + log_path: 日志文件保存路径 + log_filename: 日志文件名 + logging_name: 日志记录器名称 + + Returns: + 配置好的日志记录器对象 + """ + os.makedirs(log_path, mode=0o755, exist_ok=True) # 创建日志目录 + logger = logging.getLogger(logging_name) # 创建日志记录器 + logger.setLevel(logging.INFO) # 设置日志级别为INFO + if not logger.handlers: # 避免重复添加处理器 + # 添加文件处理器，将日志写入文件 + fh = logging.FileHandler(os.path.join(log_path, log_filename), mode='w') + fh.setFormatter(logging.Formatter("[%(asctime)s]:%(levelname)s:%(message)s")) + logger.addHandler(fh) + # 添加控制台处理器，将日志输出到控制台 + ch = logging.StreamHandler() + ch.setFormatter(logging.Formatter("%(message)s")) + logger.addHandler(ch) + return logger + +def seed_everything(seed: int = 42) -> None: + """ + 设置所有随机种子，确保实验可复现 + + Args: + seed: 随机种子值，默认为42 + """ + random.seed(seed) # 设置Python内置random模块的种子 + os.environ['PYTHONHASHSEED'] = str(seed) # 设置Python哈希种子 + np.random.seed(seed) # 设置NumPy的随机种子 + torch.manual_seed(seed) # 设置PyTorch的CPU随机种子 + torch.cuda.manual_seed(seed) # 设置PyTorch的GPU随机种子 + torch.backends.cudnn.benchmark = False # 关闭cudnn的自动优化 + torch.backends.cudnn.deterministic = True # 确保cudnn的结果是确定性的 + +def prepare_data(config: dict) -> Tuple[DataLoader, DataLoader, int, int]: + """ + 准备训练和验证数据 + + Args: + config: 配置参数字典 + + Returns: + 训练数据加载器、验证数据加载器、训练样本数量、验证样本数量 + """ + # 加载特征数组并归一化到[0,1]区间 + hex_array = np.load('hex_array.npy') / 255 + # 加载类别ID并转换为从0开始的索引（原始数据从1开始） + class_ids = np.load('class_id.npy').astype(np.int64) - 1 + data_count = len(class_ids) # 总数据量 + data_idx = list(range(data_count)) # 数据索引列表 + random.shuffle(data_idx) # 随机打乱索引 + + train_idx = data_idx # 所有数据用于训练 + val_idx = data_idx[:data_count // 10] # 取前10%的数据用于验证 + + # 创建训练和验证数据集 + train_dataset = FeatureDataset(hex_array[train_idx], class_ids[train_idx]) + val_dataset = FeatureDataset(hex_array[val_idx], class_ids[val_idx]) + + # 创建训练数据加载器 + train_loader = DataLoader( + train_dataset, batch_size=config['batch_size'], + shuffle=True, num_workers=config['num_workers'], + pin_memory=False, drop_last=True # 丢弃最后一个不完整的batch + ) + # 创建验证数据加载器 + val_loader = DataLoader( + val_dataset, batch_size=config['batch_size'], + shuffle=False, num_workers=config['num_workers'], + pin_memory=False, drop_last=False # 保留最后一个不完整的batch + ) + return train_loader, val_loader, len(train_dataset), len(val_dataset) + +def build_model(config: dict, logger: logging.Logger) -> Tuple[nn.Module, str]: + """ + 构建模型 + + Args: + config: 配置参数字典 + logger: 日志记录器 + + Returns: + 构建好的模型和设备名称 + """ + # 创建模型实例 + model = FeatureModel(config['class_count']) + if config['half']: # 如果启用半精度训练 + model.half() # 将模型转换为半精度 + device = 'cuda' if torch.cuda.is_available() else 'cpu' # 确定使用的设备 + model = model.to(device) # 将模型移动到指定设备 + + # 如果有多个GPU且配置了多GPU训练 + if torch.cuda.device_count() > 1 and ',' in config['gpu_list']: + model = torch.nn.DataParallel(model).to(device) # 使用DataParallel进行多GPU训练 + + # 如果需要加载已有的模型权重 + if config['load_checkpoint'] and os.path.exists(config['load_checkpoint']): + model.load_state_dict(torch.load(config['load_checkpoint'])['state_dict']) + logger.info(f'Loaded checkpoint from {config["load_checkpoint"]}') + return model, device + +def build_optimizer(model: nn.Module, config: dict): + """ + 构建优化器和学习率调度器 + + Args: + model: 模型实例 + config: 配置参数字典 + + Returns: + 优化器和学习率调度器 + """ + # 创建AdamW优化器 + optimizer = torch.optim.AdamW(model.parameters(), lr=config['learning_rate']) + # 创建余弦退火学习率调度器，带有重启机制 + lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( + optimizer, T_0=config['epochs'] // 2, T_mult=1, eta_min=config['min_lr'] + ) + return optimizer, lr_scheduler + +def train_one_epoch( + model: nn.Module, train_loader: DataLoader, criterion: nn.Module, + optimizer: torch.optim.Optimizer, device: str, epoch: int, config: dict, logger: logging.Logger +) -> float: + """ + 训练一个epoch + + Args: + model: 模型实例 + train_loader: 训练数据加载器 + criterion: 损失函数 + optimizer: 优化器 + device: 设备名称 + epoch: 当前epoch索引 + config: 配置参数字典 + logger: 日志记录器 + + Returns: + 平均训练损失 + """ + model.train() # 设置模型为训练模式 + train_loss = 0.0 # 累计训练损失 + for i, (feas, labels) in enumerate(train_loader): + # 将数据移动到指定设备 + feas, labels = feas.to(device), labels.to(device) + if config['half']: # 如果启用半精度训练 + feas = feas.half() # 将特征转换为半精度 + output = model(feas) # 前向传播 + loss = criterion(output, labels) # 计算损失 + loss.backward() # 反向传播 + + # 对除最后一层外的所有参数进行梯度裁剪 + parameters = [p for p in model.parameters()][:-1] + adaptive_clip_grad(parameters, clip_factor=0.01) + + optimizer.step() # 更新参数 + optimizer.zero_grad() # 清空梯度 + + train_loss += loss.item() # 累加损失值 + + # 按指定频率打印训练信息 + if i % config['print_freq'] == 0: + logger.info(f'Epoch: [{epoch}][{i}/{len(train_loader)}]\t Loss {loss.item():.4f}') + return train_loss / len(train_loader) # 返回平均损失 + +@torch.no_grad() # 装饰器，表示不计算梯度 +def evaluate(model: nn.Module, val_loader: DataLoader, device: str, config: dict) -> float: + """ + 评估模型性能 + + Args: + model: 模型实例 + val_loader: 验证数据加载器 + device: 设备名称 + config: 配置参数字典 + + Returns: + 准确率 + """ + corr = sum_count = 0 # 初始化正确预测数和总样本数 + for feas, labels in val_loader: + feas = feas.to(device) # 将特征移动到指定设备 + if config['half']: # 如果启用半精度 + feas = feas.half() # 将特征转换为半精度 + outputs = model(feas).cpu().numpy() # 前向传播并转换为NumPy数组 + + # 统计预测正确的样本数 + for b, label in enumerate(labels): + if np.argmax(outputs[b]) == label: # 如果预测类别与真实类别一致 + corr += 1 # 正确预测数加1 + sum_count += 1 # 总样本数加1 + + # 计算准确率，避免除零错误 + acc = corr / sum_count if sum_count > 0 else 0.0 + return acc + +def save_model( + model: nn.Module, best_acc: float, acc: float, epoch: int, config: dict, logger: logging.Logger +) -> float: + """ + 保存模型 + + Args: + model: 模型实例 + best_acc: 当前最佳准确率 + acc: 当前准确率 + epoch: 当前epoch索引 + config: 配置参数字典 + logger: 日志记录器 + + Returns: + 更新后的最佳准确率 + """ + model_path = config['model_path'] # 模型保存路径 + checkpoint_file = config['checkpoint_file'] # 最佳模型文件名 + checkpoint_file_last = config['checkpoint_file_last'] # 最后一个epoch的模型文件名 + + if acc >= best_acc: # 如果当前准确率大于等于最佳准确率 + best_acc = acc # 更新最佳准确率 + if acc > config['save_acc']: # 如果准确率超过保存阈值 + try: + # 尝试保存模型 + torch.save({'state_dict': model.state_dict()}, os.path.join(model_path, checkpoint_file)) + except Exception: + # 如果失败，可能是因为模型是DataParallel包装的，尝试保存module部分 + torch.save({'state_dict': model.module.state_dict()}, os.path.join(model_path, checkpoint_file)) + logger.info('Model saved!') # 记录模型保存信息 + elif epoch == config['epochs'] - 1: # 如果是最后一个epoch + try: + # 尝试保存最后一个epoch的模型 + torch.save({'state_dict': model.state_dict()}, os.path.join(model_path, checkpoint_file_last)) + except Exception: + # 如果失败，可能是因为模型是DataParallel包装的，尝试保存module部分 + torch.save({'state_dict': model.module.state_dict()}, os.path.join(model_path, checkpoint_file_last)) + logger.info('Last model saved!') # 记录最后模型保存信息 + return best_acc # 返回更新后的最佳准确率 + +def main(): + """ + 主函数，训练流程的入口 + """ + # 配置日志记录器，使用当前时间作为日志文件名的一部分 + logger = logger_config('log', f"train{datetime.datetime.now().strftime('%Y%m%d-%H%M%S')}.log", 'train') + + # 设置随机种子，确保实验可复现 + seed_everything(CONFIG['SEED']) + logger.info(f'seed:{CONFIG["SEED"]}') + logger.info(f'model path: {CONFIG["model_path"]}') + + # 准备数据 + train_loader, val_loader, train_len, val_len = prepare_data(CONFIG) + logger.info(f'train count: {train_len}, val count: {val_len}') + + # 构建模型 + model, device = build_model(CONFIG, logger) + logger.info(f'Using device: {device}') + + # 创建损失函数 + criterion = nn.CrossEntropyLoss() + + # 构建优化器和学习率调度器 + optimizer, lr_scheduler = build_optimizer(model, CONFIG) + + # 初始化最佳准确率 + best_acc = 0.0 + + # 开始训练循环 + for epoch in tqdm(range(CONFIG['epochs'])): + logger.info('\n========================') + logger.info('lr:%.4e' % optimizer.param_groups[0]['lr']) + + # 学习率预热 + if epoch < CONFIG['warmup_epoch']: + # 计算预热阶段的学习率 + warmup_lr = CONFIG['start_lr'] + (CONFIG['learning_rate'] - CONFIG['min_lr']) * epoch / CONFIG['warmup_epoch'] + # 更新优化器的学习率 + for param_group in optimizer.param_groups: + param_group['lr'] = warmup_lr + logger.info('warmup lr:%.4e' % optimizer.param_groups[0]['lr']) + + # 训练一个epoch + train_loss = train_one_epoch(model, train_loader, criterion, optimizer, device, epoch, CONFIG, logger) + + # 更新学习率 + lr_scheduler.step() + + # 评估模型 + model.eval() # 设置模型为评估模式 + acc = evaluate(model, val_loader, device, CONFIG) + logger.info(f'ACC: {acc:.4f}') + + # 保存模型 + best_acc = save_model(model, best_acc, acc, epoch, CONFIG, logger) + + # 训练结束，记录最佳准确率 + logger.info(f'Best ACC: {best_acc:.4f} {CONFIG["checkpoint_file"]}') + +if __name__ == '__main__': + main() diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/utils.py b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/utils.py new file mode 100644 index 0000000..a6eebc2 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/utils.py @@ -0,0 +1,88 @@ + +import torch + +class FGM(): + def __init__(self, model): + self.model = model + self.backup = {} # 存储embedding层的原始参数 + + def attack(self, epsilon=0.25, emb_name='encoder.embed'): + # 对指定的embedding层进行扰动，增强模型鲁棒性 + for name, param in self.model.named_parameters(): + if param.requires_grad and emb_name in name: + self.backup[name] = param.data.clone() # 备份原始参数 + norm = torch.norm(param.grad) # 计算梯度范数 + if norm != 0: + r_at = epsilon * param.grad / norm # 生成扰动 + param.data.add_(r_at) # 添加扰动到参数 + + def restore(self, emb_name='encoder.embed'): + # 恢复被扰动的embedding层参数 + for name, param in self.model.named_parameters(): + if param.requires_grad and emb_name in name: + assert name in self.backup + param.data = self.backup[name] # 恢复原始参数 + self.backup = {} + + + +class EMA: + def __init__(self, model, decay): + self.model = model + self.decay = decay # 平滑系数 + self.shadow = {} # 存储平滑后的参数 + self.backup = {} # 用于临时备份模型参数 + self.register() + + def register(self): + # 初始化shadow字典，保存初始参数 + for name, param in self.model.named_parameters(): + if param.requires_grad: + self.shadow[name] = param.data.clone() + + def update(self): + # 更新shadow参数：新值 = (1 - decay) * 当前值 + decay * shadow旧值 + for name, param in self.model.named_parameters(): + if param.requires_grad: + assert name in self.shadow + new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name] + self.shadow[name] = new_average.clone() + + def apply_shadow(self): + # 将shadow参数应用到模型中，用于评估或测试阶段 + for name, param in self.model.named_parameters(): + if param.requires_grad: + assert name in self.shadow + self.backup[name] = param.data # 备份当前参数 + param.data = self.shadow[name] # 应用平滑后的参数 + + def restore(self): + # 恢复原始参数 + for name, param in self.model.named_parameters(): + if param.requires_grad: + assert name in self.backup + param.data = self.backup[name] # 恢复备份的参数 + self.backup = {} + + +def unitwise_norm(x, norm_type=2.0): + if x.ndim <= 1: + return x.norm(norm_type) # 如果是1D或更小维度，直接计算整体范数 + else: + # 对于nn.ConvNd和nn.Linear等，输出通道是第一个维度 + return x.norm(norm_type, dim=tuple(range(1, x.ndim)), keepdim=True) + + +def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0): + if isinstance(parameters, torch.Tensor): + parameters = [parameters] # 如果只有一个参数，转换为列表 + for p in parameters: + if p.grad is None: + continue # 跳过无梯度的参数 + p_data = p.detach() + g_data = p.grad.detach() + max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor) # 计算最大允许范数 + grad_norm = unitwise_norm(g_data, norm_type=norm_type) # 计算当前梯度范数 + clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6)) # 缩放梯度 + new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad) # 判断是否需要裁剪 + p.grad.detach().copy_(new_grads) # 替换为裁剪后的梯度 diff --git a/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/数据说明.docx b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/数据说明.docx new file mode 100644 index 0000000..afe7fd4 Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/NetworkTraffic/数据说明.docx differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/DGA-dataset-1.csv b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/DGA-dataset-1.csv new file mode 100644 index 0000000..274c5f0 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/DGA-dataset-1.csv @@ -0,0 +1,202 @@ +NM,H2,CH4,C2H6,C2H4,C2H2,Type +1,3930,2397,157,0,0,Partial discharge +2,37800,1740,249,8,8,Partial discharge +3,92600,10200,0,0,0,Partial discharge +4,8266,1061,22,0,0,Partial discharge +5,9340,995,60,6,7,Partial discharge +6,36036,4704,554,5,10,Partial discharge +7,33046,619,58,2,0,Partial discharge +8,40280,1069,1060,1,1,Partial discharge +9,26788,18342,2111,27,0,Partial discharge +10,78,20,11,13,28,Spark discharge +11,305,100,33,161,541,Spark discharge +12,35,6,3,26,482,Spark discharge +13,543,120,41,411,1880,Spark discharge +14,1230,163,27,233,692,Spark discharge +15,645,86,13,110,317,Spark discharge +16,60,10,4,4,4,Spark discharge +17,95,10,0,11,39,Spark discharge +18,6870,1028,79,900,5500,Spark discharge +19,10092,5399,530,6500,37565,Spark discharge +20,650,81,170,51,270,Spark discharge +21,210,22,6,6,7,Spark discharge +22,385,60,8,53,159,Spark discharge +23,4230,690,5,196,1180,Spark discharge +24,7600,1230,318,836,1560,Spark discharge +25,595,80,9,89,244,Spark discharge +26,120,25,1,8,40,Spark discharge +27,8,0,0,43,101,Spark discharge +28,6454,2313,121,2159,6432,Spark discharge +29,2177,1049,207,440,705,Spark discharge +30,1790,580,321,336,619,Spark discharge +31,1330,10,20,66,182,Spark discharge +32,4,1,2,7,52,Spark discharge +33,1900,285,31,957,7730,Spark discharge +34,57,24,2,27,30,Spark discharge +35,1000,500,1,400,500,Spark discharge +36,440,89,19,304,757,Arc discharge +37,210,43,12,102,187,Arc discharge +38,2850,1115,138,1987,3675,Arc discharge +39,7020,1850,0,2960,4410,Arc discharge +40,545,130,16,153,239,Arc discharge +41,7150,1440,97,1210,1760,Arc discharge +42,620,325,38,181,244,Arc discharge +43,120,31,0,66,94,Arc discharge +44,755,229,32,404,460,Arc discharge +45,5100,1430,0,1140,1010,Arc discharge +46,13500,6110,212,4510,4040,Arc discharge +47,1570,1110,175,1780,1830,Arc discharge +48,3090,5020,323,3800,2540,Arc discharge +49,1820,405,35,365,634,Arc discharge +50,535,160,16,305,680,Arc discharge +51,13,3,1,3,6,Arc discharge +52,137,67,7,53,104,Arc discharge +53,1084,188,8,166,769,Arc discharge +54,34,21,4,49,56,Arc discharge +55,7940,2000,355,3120,5390,Arc discharge +56,150,130,9,55,30,Arc discharge +57,8200,3790,250,4620,5830,Arc discharge +58,260,215,35,334,277,Arc discharge +59,75,15,7,14,26,Arc discharge +60,530,345,85,266,250,Arc discharge +61,60,5,2,21,21,Arc discharge +62,90,28,8,31,32,Arc discharge +63,220,77,22,170,240,Arc discharge +64,5900,1500,68,1200,2300,Arc discharge +65,420,250,41,530,800,Arc discharge +66,2800,2800,234,3500,3600,Arc discharge +67,99,170,20,200,190,Arc discharge +68,310,230,54,610,760,Arc discharge +69,800,160,23,260,600,Arc discharge 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+92,400,940,210,820,24,High-temperature overheating +93,6,2990,29990,26076,67,High-temperature overheating +94,100,200,110,670,11,High-temperature overheating +95,290,1260,231,820,8,High-temperature overheating +96,1550,2740,816,5450,184,High-temperature overheating +97,3910,4290,626,6040,1230,High-temperature overheating +98,12705,23498,6047,34257,5188,High-temperature overheating +99,1,8,8,100,6,High-temperature overheating +100,300,700,280,1700,36,High-temperature overheating +101,107,143,34,222,2,High-temperature overheating +102,54.54,71.93,9.72,97.37,6.58,High-temperature overheating +103,116.17,180.83,52.48,278.18,5.36,High-temperature overheating +104,5760,540,40.5,1000,2760,Arc discharge +105,50.18,171.12,74.7,148.69,0,Middle-temperature overheating +106,20,80.2,24.6,68.6,0,Middle-temperature overheating +107,28.97,10.94,1.64,6.96,4.36,High-temperature overheating +108,15.9,55.98,22.33,137.25,0.21,High-temperature overheating +109,47.6,19.1,4.21,27,0.72,Low-temperature 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overheating +190,3675,6392,2500,7691,5,Low/Middle-temperature overheating +191,48,610,29,10,0,Low/Middle-temperature overheating +192,12,18,4,4,0,Low/Middle-temperature overheating +193,66,60,2,7,0,Low/Middle-temperature overheating +194,1450,940,211,322,61,Low/Middle-temperature overheating +195,0,18900,410,540,330,Low/Middle-temperature overheating +196,960,4000,1290,1560,6,Low/Middle-temperature overheating +197,24700,61000,26300,42100,1560,Low/Middle-temperature overheating +198,14,44,124,7,1,Low/Middle-temperature overheating +199,2031,149,20,3,0,Low/Middle-temperature overheating +200,480,1075,298,1132,0,Low/Middle-temperature overheating +201,40000,400,70,600,6,Low/Middle-temperature overheating diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/Power_Transformer_Fault_Diagnosis_Based_on_DGA_Using_a_Convolutional_Neural_Network_With_Noise_in_Measurements_1_9_translate_20250424105329.pdf b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/Power_Transformer_Fault_Diagnosis_Based_on_DGA_Using_a_Convolutional_Neural_Network_With_Noise_in_Measurements_1_9_translate_20250424105329.pdf new file mode 100644 index 0000000..651e96f Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/Power_Transformer_Fault_Diagnosis_Based_on_DGA_Using_a_Convolutional_Neural_Network_With_Noise_in_Measurements_1_9_translate_20250424105329.pdf differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TrainsCNNModel.py b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TrainsCNNModel.py new file mode 100644 index 0000000..f0447c9 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TrainsCNNModel.py @@ -0,0 +1,96 @@ +import numpy as np +import pandas as pd +import tensorflow as tf +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization +from tensorflow.keras.optimizers import Adam +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +import os + + +def train_cnn_model(data_path, model_save_dir='model'): + """ + 训练CNN模型用于变压器故障诊断 + + 参数: + data_path: 数据文件路径 + model_save_dir: 模型保存目录，默认为'model' + """ + # 1. 加载数据 + data = pd.read_csv(data_path) + X = data[['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2']].values + y = data['label'].values + + # 2. 数据预处理 + # 标准化数据 + scaler = StandardScaler() + X = scaler.fit_transform(X) + + # 将数据reshape为CNN需要的格式 (样本数, 特征数, 1) + X = X.reshape(X.shape[0], X.shape[1], 1) + + # 将标签转换为one-hot编码 + y = tf.keras.utils.to_categorical(y, num_classes=7) + + # 划分训练集和测试集 + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # 3. 构建CNN模型 + model = Sequential([ + # 第一卷积层 + Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(5, 1)), + BatchNormalization(), + MaxPooling1D(pool_size=1), + Dropout(0.2), + + # 第二卷积层 + Conv1D(filters=128, kernel_size=2, activation='relu'), + BatchNormalization(), + MaxPooling1D(pool_size=1), + Dropout(0.3), + + # 展平层 + Flatten(), + + # 全连接层 + Dense(128, activation='relu'), + Dropout(0.4), + Dense(64, activation='relu'), + Dropout(0.3), + + # 输出层 + Dense(7, activation='softmax') + ]) + + # 4. 编译模型 + model.compile(optimizer=Adam(learning_rate=0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + + # 5. 训练模型 + history = model.fit(X_train, y_train, + epochs=100, + batch_size=32, + validation_data=(X_test, y_test), + verbose=1) + + # 6. 评估模型 + test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0) + print(f'\n测试准确率: {test_acc:.4f}') + + # 7. 保存模型 + if not os.path.exists(model_save_dir): + os.makedirs(model_save_dir) + + model.save(f'{model_save_dir}/transformer_fault_cnn.h5') + print(f'模型已保存到 {model_save_dir}/transformer_fault_cnn.h5') + + return model + + +# 使用示例 +if __name__ == '__main__': + # 替换为你的数据文件路径 + data_file = 'transformer_fault_data.csv' + train_cnn_model(data_file) diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransLightGBMModel.py b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransLightGBMModel.py new file mode 100644 index 0000000..75d4185 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransLightGBMModel.py @@ -0,0 +1,189 @@ +import os +import pandas as pd +import numpy as np +import lightgbm as lgb +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score, classification_report +import joblib + + +def train_lightgbm_model(data_path, model_save_dir='model'): + """ + 训练LightGBM模型用于变压器故障诊断（label已预先编码为0-6） + + 参数: + data_path: 数据文件路径 + model_save_dir: 模型保存目录，默认为'model' + + 返回: + 训练好的模型和评估结果 + """ + try: + # 1. 读取数据 + data = pd.read_csv(data_path) + + # 2. 检查数据列 + required_columns = ['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2', 'label'] + if not all(col in data.columns for col in required_columns): + raise ValueError("数据列不完整，需要包含: H2, CH4, C2H6, C2H4, C2H2, label") + + # 3. 检查label范围 + unique_labels = sorted(data['label'].unique()) + if not all(i in range(7) for i in unique_labels): + raise ValueError("label值应该在0-6范围内") + + # 4. 划分特征和标签 + X = data[['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2']] + y = data['label'] + + # 5. 划分训练集和测试集（分层抽样保持类别比例） + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y) + + # 6. 创建LightGBM数据集 + train_data = lgb.Dataset(X_train, label=y_train) + test_data = lgb.Dataset(X_test, label=y_test, reference=train_data) + + # 7. 定义模型参数 + params = { + 'objective': 'multiclass', + 'num_class': 7, # 直接指定7个类别 + 'metric': 'multi_logloss', + 'boosting_type': 'gbdt', + 'learning_rate': 0.05, + 'num_leaves': 31, + 'max_depth': -1, + 'min_child_samples': 20, + 'feature_fraction': 0.8, + 'bagging_fraction': 0.8, + 'bagging_freq': 5, + 'verbose': -1, + 'seed': 42 + } + + # 8. 训练模型 + print("开始训练LightGBM模型...") + model = lgb.train( + params, + train_data, + num_boost_round=1000, + valid_sets=[train_data, test_data], + callbacks=[ + lgb.early_stopping(stopping_rounds=50, verbose=True), + lgb.log_evaluation(50) + ] + ) + + # 9. 模型评估 + y_pred = model.predict(X_test) + y_pred_class = np.argmax(y_pred, axis=1) + + accuracy = accuracy_score(y_test, y_pred_class) + print(f"\n模型准确率: {accuracy:.4f}") + print("\n分类报告:") + print(classification_report(y_test, y_pred_class)) + + # 10. 保存模型 + if not os.path.exists(model_save_dir): + os.makedirs(model_save_dir) + current_dir = os.path.dirname(__file__) + model_path = os.path.join(current_dir,model_save_dir, 'transformer_fault_lgbm.model') + joblib.dump(model, model_path) + + print(f"\n模型已保存到 {model_save_dir} 目录") + + return model, accuracy + + except Exception as e: + print(f"训练过程中发生错误: {str(e)}") + raise +def predict_fault(model_path, gas_data): + """使用训练好的模型预测故障类型""" + model = joblib.load(model_path) + proba = model.predict([gas_data]) # 输入格式: [[H2, CH4, C2H6, C2H4, C2H2]] + pred_class = np.argmax(proba, axis=1) + return pred_class[0] # 返回预测的类别编号 + +# 使用方法示例 +if __name__ == "__main__": + # 替换为你的数据路径 + # current_dir = os.path.dirname(__file__) + # data_file = os.path.join(current_dir, 'train.csv') + # model, accuracy = train_lightgbm_model(data_file) + current_dir = os.path.dirname(__file__) + data_file = os.path.join(current_dir, 'DGA-dataset-1test.csv') + model_file=os.path.join(current_dir, 'model','transformer_fault_lgbm.model') + # data_file = r'transformer_fault_data.csv' # 修改为你的CSV文件路径 + + # 1. 训练模型 + # print("正在训练模型...") + # train_result = train_svm_model(data_file) + # if train_result is None: + # print("模型训练失败") + # exit() + + # 2. 读取测试数据 + try: + test_data = pd.read_csv(data_file) + # 检查必要的列是否存在 + required_columns = ['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2', 'Type'] + if not all(col in test_data.columns for col in required_columns): + print("测试数据列不完整，请确保包含H2, CH4, C2H6, C2H4, C2H2和label列") + exit() + except Exception as e: + print(f"加载测试数据失败: {e}") + exit() + + # 3. 定义标签映射 + label_map = { + 0: '正常', + 1: '低能放电', + 2: '低温过热', + 3: '高能放电', + 4: '高温过热', + 5: '中低温过热', + 6: '中温过热' + } + + # 4. 对每一行进行预测 + print("\n开始逐行预测...") + print("=" * 80) + print("{:<15}{:<15}{:<20}{:<20}{:<15}".format( + "行号", "预测结果", "预测类型", "实际类型", "是否匹配")) + print("-" * 80) + + correct = 0 + total = len(test_data) + + for i, row in test_data.iterrows(): + # 准备输入数据 + gas_data = [row['H2'], row['CH4'], row['C2H6'], row['C2H4'], row['C2H2']] + true_label = row['Type'] + + # 预测 + try: + pred_label = predict_fault(model_file,gas_data) + # 获取预测的数字标签 + pred_num = pred_label + + # 检查预测是否正确 + is_correct = "是" if pred_num == int(true_label) else "否" + if is_correct == "是": + correct += 1 + + # 打印结果 + print("{:<15}{:<15}{:<20}{:<20}{:<15}".format( + i + 1, + pred_num, + label_map.get(int(pred_num), "未知"), + label_map.get(int(true_label), "未知"), + is_correct)) + + except Exception as e: + print(f"第 {i + 1} 行预测出错: {e}") + continue + + # 5. 计算并输出总体准确率 + accuracy = correct / total * 100 + print("=" * 80) + print(f"\n预测完成! 总体准确率: {accuracy:.2f}% ({correct}/{total})") diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransSvmModel.py b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransSvmModel.py new file mode 100644 index 0000000..ce48654 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransSvmModel.py @@ -0,0 +1,200 @@ +import os +import pandas as pd +import numpy as np +from sklearn import svm +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import classification_report +import joblib +from sklearn.model_selection import GridSearchCV + +def train_svm_model(data_path, model_save_dir='model'): + """ + 训练SVM模型用于变压器故障诊断 + 参数: + data_path: 数据文件路径(CSV格式) + model_save_dir: 模型保存目录(默认为'model') + 返回: + 训练好的模型和评估结果 + """ + current_dir = os.path.dirname(__file__) + model_path = os.path.join(current_dir, model_save_dir) + # 确保模型目录存在 + os.makedirs(model_path, exist_ok=True) + # 1. 加载数据 + try: + data = pd.read_csv(data_path) + except Exception as e: + print(f"加载数据失败: {e}") + return None + # 2. 数据预处理 + # 检查必要的列是否存在 + required_columns = ['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2', 'label'] + if not all(col in data.columns for col in required_columns): + print("数据列不完整，请确保包含H2, CH4, C2H6, C2H4, C2H2和label列") + return None + # 分离特征和标签 + X = data[['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2']].values + y = data['label'].values + # 3. 数据标准化 + scaler = StandardScaler() + X_scaled = scaler.fit_transform(X) + # 保存标准化器 + scaler_path = os.path.join(model_path, 'scaler.pkl') + joblib.dump(scaler, scaler_path) + # 4. 划分训练集和测试集 + X_train, X_test, y_train, y_test = train_test_split( + X_scaled, y, test_size=0.2, random_state=42, stratify=y + ) + # 5. 训练SVM模型 + # 使用网格搜索寻找最佳参数 + param_grid = { + 'C': [0.1, 1, 10, 100], + 'gamma': ['scale', 'auto', 0.1, 1, 10], + 'kernel': ['rbf', 'poly', 'sigmoid'], + 'decision_function_shape': ['ovo', 'ovr'] + } + grid_search = GridSearchCV( + svm.SVC(), + param_grid, + cv=5, + n_jobs=1, # 禁用并行计算:ml-citation{ref="6,8" data="citationList"} + pre_dispatch='2*n_jobs' # 控制任务分配 + ) + X_train = np.ascontiguousarray(X_train) + y_train = np.ascontiguousarray(y_train) + grid_search.fit(X_train, y_train) + # 获取最佳模型 + best_svm = grid_search.best_estimator_ + # 6. 评估模型 + y_pred = best_svm.predict(X_test) + print("最佳参数:", grid_search.best_params_) + print("\n分类报告:") + # 7. 保存模型 + model_path = os.path.join(model_path, 'transformer_fault_svm.pkl') + joblib.dump(best_svm, model_path) + print(f"\n模型已保存到 {model_path}") + return { + 'model': best_svm, + 'scaler': scaler, + 'classification_report': classification_report(y_test, y_pred, output_dict=True) + } + +def predict_fault_type(gas_concentrations, model_dir='model'): + # 加载模型和标准化器 + current_dir = os.path.dirname(__file__) + model = joblib.load(os.path.join(current_dir,model_dir, 'transformer_fault_svm.pkl')) + scaler = joblib.load(os.path.join(current_dir,model_dir, 'scaler.pkl')) + # 加载标签映射 + label_map = { + 0: '正常', + 1: '低能放电', + 2: '低能过热', + 3: '高能放电', + 4: '高温过热', + 5: '中低温过热', + 6: '中温过热' + } + # 检查输入数组长度是否正确 + if len(gas_concentrations) != 5: + raise ValueError("输入数组长度必须为5，顺序为[H2, CH4, C2H6, C2H4, C2H2]") + # 准备输入数据 + input_data = np.array(gas_concentrations).reshape(1, -1) + # 标准化 + input_scaled = scaler.transform(input_data) + # 预测 + pred = model.predict(input_scaled) + return label_map[pred[0]] +# 使用方法示例 +# if __name__ == "__main__": +# # 替换为你的数据文件路径 +# # data_file = r"D:\industrial_train_prediction\industrial_train_prediction\prediction_server\algorithm\dataPrediction\Transformer\train.csv" +# # train_svm_model(data_file) +# #luru = [7.4, 5.7, 3.4, 2.6, 3.2] # 正常0 +# #luru = [6,0.5,0.29,1.86,7.41]#1低能放电 +# #luru = [217.5,45,8.9,56.8,77]#3高能放电 +# #luru = [32.7,41.3,5.6,106,4.7]#4高温过热 +# #luru = [92,57,47,30,0]#5中低温过热 +# # luru = [181,262,210,528,37.7]#中温过热 +# luru = [50.18,171.12,74.7,148.69,0] # 中温过热 +# pred = predict_fault_type(luru) +# print(pred) +if __name__ == "__main__": + # 替换为你的数据文件路径 + current_dir = os.path.dirname(__file__) + data_file = os.path.join(current_dir, 'DGA-dataset-1test.csv') + # data_file = r'transformer_fault_data.csv' # 修改为你的CSV文件路径 + + # 1. 训练模型 + # print("正在训练模型...") + # train_result = train_svm_model(data_file) + # if train_result is None: + # print("模型训练失败") + # exit() + + # 2. 读取测试数据 + try: + test_data = pd.read_csv(data_file) + # 检查必要的列是否存在 + required_columns = ['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2', 'Type'] + if not all(col in test_data.columns for col in required_columns): + print("测试数据列不完整，请确保包含H2, CH4, C2H6, C2H4, C2H2和label列") + exit() + except Exception as e: + print(f"加载测试数据失败: {e}") + exit() + + # 3. 定义标签映射 + label_map = { + 0: '正常', + 1: '低能放电', + 2: '低温过热', + 3: '高能放电', + 4: '高温过热', + 5: '中低温过热', + 6: '中温过热' + } + + # 4. 对每一行进行预测 + print("\n开始逐行预测...") + print("=" * 80) + print("{:<15}{:<15}{:<20}{:<20}{:<15}".format( + "行号", "预测结果", "预测类型", "实际类型", "是否匹配")) + print("-" * 80) + + correct = 0 + total = len(test_data) + + for i, row in test_data.iterrows(): + # 准备输入数据 + gas_data = [row['H2'], row['CH4'], row['C2H6'], row['C2H4'], row['C2H2']] + true_label = row['Type'] + + # 预测 + try: + pred_label = predict_fault_type(gas_data) + # 获取预测的数字标签 + pred_num = [k for k, v in label_map.items() if v == pred_label][0] + + # 检查预测是否正确 + is_correct = "是" if pred_num == int(true_label) else "否" + if is_correct == "是": + correct += 1 + + # 打印结果 + print("{:<15}{:<15}{:<20}{:<20}{:<15}".format( + i + 1, + pred_num, + pred_label, + label_map.get(int(true_label), "未知"), + is_correct)) + + except Exception as e: + print(f"第 {i + 1} 行预测出错: {e}") + continue + + # 5. 计算并输出总体准确率 + accuracy = correct / total * 100 + print("=" * 80) + print(f"\n预测完成! 总体准确率: {accuracy:.2f}% ({correct}/{total})") + diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransXGBoostModel.py b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransXGBoostModel.py new file mode 100644 index 0000000..8c038b0 --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransXGBoostModel.py @@ -0,0 +1,160 @@ +# 变压器故障预测：根据变压器气体溶解分析（DGA）数据,采用机器学习算法进行故障预测。 + +import os + +import chardet +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from xgboost import XGBClassifier +import joblib # 用来保存模型 +def read_csv_auto(file_path): + # 第一步：检测文件编码 + with open(file_path, 'rb') as f: + raw_data = f.read() + result = chardet.detect(raw_data) + encoding = result['encoding'] + print(f"✅ 自动检测到文件编码：{encoding}") + # 第二步：用检测到的编码去读取CSV + df = pd.read_csv(file_path, encoding=encoding) + return df +def train_and_save_model(data_path, model_dir='model'): + # 读取数据 + # df = pd.read_csv(data_path, encoding='utf-8-sig') + df=read_csv_auto(data_path) + features = ['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2'] + X = df[features] + y = df['lable'] + # 把里面的空格、空字符串、非法字符都替换成 NaN + X = X.replace(r'^\s*$', pd.NA, regex=True) + # 将无法转换为数字的部分转换为 NaN + X = X.apply(pd.to_numeric, errors='coerce') + # 检查哪些行有 NaN 值 + invalid_rows = X[X.isna().any(axis=1)] + # 打印出包含 NaN 的行 + if not invalid_rows.empty: + print("⚠️ 以下是含有非法数据或缺失值的行：") + print(invalid_rows) + else: + print("✅ 所有数据都可以成功转换为数值！") + # 标准化 + scaler = StandardScaler() + X_scaled = scaler.fit_transform(X) + # 划分训练测试集（可选，不影响保存） + X_train, _, y_train, _ = train_test_split(X_scaled, y, test_size=0.2, random_state=42, stratify=y) + # 训练模型 + model = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42) + model.fit(X_train, y_train) + # 保存模型和标准化器 + # 保存模型和标准化器 + current_dir = os.path.dirname(__file__) + model_path = os.path.join(current_dir,model_dir, 'model.pkl') + scaler_path = os.path.join(current_dir,model_dir, 'scaler.pkl') + joblib.dump(model, model_path) + joblib.dump(scaler, scaler_path) + + print(f"✅ 模型已保存到 {model_path}，标准化器保存到 {scaler_path}") +def load_and_predict(input_data, model_name='model.pkl', scaler_name='scaler.pkl'): + """ + input_data: list 或 ndarray，例如 [50, 100, 5, 10, 1] + """ + # 加载模型和标准化器 + current_dir = os.path.dirname(__file__) + model_path = os.path.join(current_dir, 'model', model_name) + scaler_path = os.path.join(current_dir, 'model', scaler_name) + model = joblib.load(model_path) + scaler = joblib.load(scaler_path) + # 标准化输入 + input_scaled = scaler.transform([input_data]) # 注意加 [] 包一层 + # 预测 + pred = model.predict(input_scaled) + return pred[0] +# if __name__ == "__main__": +# # train_and_save_model(r'D:\industrial_train_prediction\industrial_train_prediction\prediction_server\algorithm\dataPrediction\Transformer\train.csv', 'model') +# luru=[7.4,5.7,3.4,2.6,3.2] #正常0 +# #luru = [6,0.5,0.29,1.86,7.41]#1低能放电 +# #luru = [217.5,45,8.9,56.8,77]#3高能放电 +# # luru = [32.7,41.3,5.6,106,4.7]#4高温过热 +# # # #luru = [] +# # # #luru = [] +# pred=load_and_predict(luru) +# print(pred) +if __name__ == "__main__": + # 替换为你的数据文件路径 + current_dir = os.path.dirname(__file__) + data_file = os.path.join(current_dir, 'DGA-dataset-1test.csv') + # data_file = r'transformer_fault_data.csv' # 修改为你的CSV文件路径 + + # 1. 训练模型 + # print("正在训练模型...") + # train_result = train_svm_model(data_file) + # if train_result is None: + # print("模型训练失败") + # exit() + + # 2. 读取测试数据 + try: + test_data = pd.read_csv(data_file) + # 检查必要的列是否存在 + required_columns = ['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2', 'Type'] + if not all(col in test_data.columns for col in required_columns): + print("测试数据列不完整，请确保包含H2, CH4, C2H6, C2H4, C2H2和label列") + exit() + except Exception as e: + print(f"加载测试数据失败: {e}") + exit() + + # 3. 定义标签映射 + label_map = { + 0: '正常', + 1: '低能放电', + 2: '低温过热', + 3: '高能放电', + 4: '高温过热', + 5: '中低温过热', + 6: '中温过热' + } + + # 4. 对每一行进行预测 + print("\n开始逐行预测...") + print("=" * 80) + print("{:<15}{:<15}{:<20}{:<20}{:<15}".format( + "行号", "预测结果", "预测类型", "实际类型", "是否匹配")) + print("-" * 80) + + correct = 0 + total = len(test_data) + + for i, row in test_data.iterrows(): + # 准备输入数据 + gas_data = [row['H2'], row['CH4'], row['C2H6'], row['C2H4'], row['C2H2']] + true_label = row['Type'] + + # 预测 + try: + pred_label = load_and_predict(gas_data) + # 获取预测的数字标签 + pred_num = pred_label + + # 检查预测是否正确 + is_correct = "是" if pred_num == int(true_label) else "否" + if is_correct == "是": + correct += 1 + + # 打印结果 + print("{:<15}{:<15}{:<20}{:<20}{:<15}".format( + i + 1, + pred_num, + label_map.get(int(pred_num), "未知"), + label_map.get(int(true_label), "未知"), + is_correct)) + + except Exception as e: + print(f"第 {i + 1} 行预测出错: {e}") + continue + + # 5. 计算并输出总体准确率 + accuracy = correct / total * 100 + print("=" * 80) + print(f"\n预测完成! 总体准确率: {accuracy:.2f}% ({correct}/{total})") + diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransformerFaultPrediction.py b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransformerFaultPrediction.py new file mode 100644 index 0000000..549cfad --- /dev/null +++ b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/TransformerFaultPrediction.py @@ -0,0 +1,93 @@ +# 变压器故障预测：根据变压器气体溶解分析（DGA）数据,采用机器学习算法进行故障预测。 + +import os +import chardet +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from xgboost import XGBClassifier +from sklearn.svm import SVC +from sklearn.metrics import accuracy_score +import matplotlib.pyplot as plt +import joblib + + +def read_csv_auto(file_path): + with open(file_path, 'rb') as f: + raw_data = f.read() + result = chardet.detect(raw_data) + encoding = result['encoding'] + print(f"✅ 自动检测到文件编码：{encoding}") + df = pd.read_csv(file_path, encoding=encoding) + return df + + +def train_and_save_model(data_path, model_dir='model'): + df = read_csv_auto(data_path) + features = ['H2', 'CH4', 'C2H6', 'C2H4', 'C2H2'] + X = df[features] + y = df['label'] + # 打印 X 和 y 的行数，确保它们一致 + print(f"X 的样本数: {X.shape[0]}") + print(f"y 的样本数: {y.shape[0]}") + X = X.replace(r'^\s*$', pd.NA, regex=True) + X = X.apply(pd.to_numeric, errors='coerce') + + scaler = StandardScaler() + X_scaled = scaler.fit_transform(X) + + X_train, _, y_train, _ = train_test_split(X_scaled, y, test_size=0.2, random_state=42, stratify=y) + # 检查训练数据的形状 + print(f"X_train 的形状: {X_train.shape}") + print(f"y_train 的形状: {y_train.shape}") + # 训练 XGBoost 模型 + model = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42) + model.fit(X_train, y_train) + + # 获取特征重要性并选择前 12 个特征 + importances = model.feature_importances_ + sorted_idx = importances.argsort()[::-1] + selected_features = X.columns[sorted_idx].tolist() + print(f"选出的前 12 个最重要的特征：", selected_features) + # 确保选择的特征与原始顺序一致 + selected_features_in_original_order = [feature for feature in features if feature in selected_features] + print(f"选出的前 12 个特征（按原始顺序）：", selected_features_in_original_order) + # 使用选出的特征 + X_selected = X[selected_features_in_original_order] + X_selected_scaled = scaler.transform(X_selected) + + # 训练 SVM 模型 + svm_model = SVC(kernel='linear', random_state=42) + svm_model.fit(X_selected_scaled, y) + + # 保存模型 + current_dir = os.path.dirname(__file__) + model_path = os.path.join(current_dir, model_dir, 'svm_model.pkl') + joblib.dump(svm_model, model_path) + + print(f"✅ SVM 模型已保存到 {model_path}") + + +def load_and_predict(input_data, model_name='model.pkl', scaler_name='scaler.pkl'): + current_dir = os.path.dirname(__file__) + model_path = os.path.join(current_dir, 'model', model_name) + scaler_path = os.path.join(current_dir, 'model', scaler_name) + model = joblib.load(model_path) + scaler = joblib.load(scaler_path) + + input_scaled = scaler.transform([input_data]) + pred = model.predict(input_scaled) + return pred[0] + + +if __name__ == "__main__": + train_and_save_model('./train.csv', 'model') + # luru=[7.4,5.7,3.4,2.6,3.2] #正常0 + #luru = [6,0.5,0.29,1.86,7.41]#1低能放电 + #luru = [217.5,45,8.9,56.8,77]#3高能放电 + # luru = [32.7,41.3,5.6,106,4.7]#4高温过热 + # # #luru = [] + # # #luru = [] + # pred=load_and_predict(luru) + # print(pred) + diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/__init__.py b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/model/svm_model.pkl b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/model/svm_model.pkl new file mode 100644 index 0000000..fb7ef8a Binary files /dev/null and b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/model/svm_model.pkl differ diff --git a/backend/apps/algorithms/algorithm/dataPrediction/Transformer/train.csv b/backend/apps/algorithms/algorithm/dataPrediction/Transformer/train.csv new file mode 100644 index 0000000..97e02e1 --- /dev/null +++ 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XV8X:!2k͵N8?i,kV#q5)gQ.C?Rp]L?t;k828RUsf>Sx׺cpu)Hܪ1M,$OsK._ ?j頼O)hW2Z%H'U`yՑ3qQlF,hm⮵g8`5Z֚ZR?7!W7ީ +#/O-_Tn_Ʋ>3MQQKt9 =56#d}+^!&ǘk{M^n?5Vk}PDvaGtʯҹ{IMtsE4Q@&*dL*AYE 4%Q@Q@Q@Q@ KE%-Q@Q@Q@ E-QER@ E-RPEQEQEQEDPQ\DY]E3\ +|U!=&67Ꚍztn}icH4D`j7]4zJV$y5I[,J)jp_NM2 +~+sI;dqy_YhV(>P߮*[)+Ě;"+E-Gnw[}TҤ{żClo] s.kqcV@)ʸ5x>~zlAE-% ((((3 oTg\WFZl\793Qdè9ME UY^ hkoӡ'`?JB$[IXWo5K2H]'$OI*#樓feoXk6'co[U/qhC? +endstream endobj 84 0 obj << +/Filter/FlateDecode/Length 22 +>> +stream +x+*42Pp +Y +endstream endobj 82 0 obj <>>> endobj 626 0 obj <>/Filter /FlateDecode /Length 3370 >> +stream +x[k]E{{= [iqKKO{hK)`M 商% CIhL4?C# !ĄDbTш]5g9=m?ffYY3;&$#*!~8q<>I?ا*SJ% ?8Y4O(ɤJLk-X7ʸJrQ[:Oh4X:i1?Ltw?]Ьyƙ`vLa=fSL *yX:Z kBӅCk-EzWX/;-Gw_ss#7ەShM/xa^zݿ#!w. 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Jd]&eu)-pڂGg]D.h9z%:FX[Z Bm> + {sp +S.r)J0.t%g',u9ѷn\_^Ѐp4k聥pF-+q)1kKvu/Ś$)ީ<~1gA=f>1bCyИ@ 豽(bJ8bmi}^g[s +SsU QcnpB$K]Y2@IYzYqB6`L/G,u+^E puNA).)#_;dl̞ $;vb.!h{Y ] ^s gy\DkK8i/N(i?ςnf7u.qglAm̂nfW qgJMLtDcMiݲHZd:-sB]RQ7ʍj|k)s'#G߅%}^ ZaOhVA|s[|`oNn"ذ[ KPV'-At$J߁vzY l-l)x`KA3z^&פkKA \+UX4jf ^s~F֯+ų>1J.?iw]O1ʔ{b5niY$ lfHݏɮ|vGf<.<Auu!)ͻI_n(O[ztUg!F!)~1tꩶ$'ًuix6y=k{vto,Bnob@LΠQBx{.?.[F?mD1>Kǐ B;&~rw55~z`Tb6WS?n]{p ||@go &=8a3sek^×iU>^⩜ 7bWL&'š?;a܄ %H-JHCAg2HXyJ;{G~/"Hӛ_ {e'oP O"y?1YU[]<&M? e^о穜oMiju> +stream +x+*4Pp + +endstream endobj 74 0 obj <>>> endobj 628 0 obj <>/Filter /FlateDecode /Length 3364 >> +stream +x[]_WҤfZk&Kٛ4.)AVȖm)"yR}D[(SAX s޻6wMBs=sf?WRB߶܊/HOIW:|E'׀UBҺb(cFռԮֶFc1,~GRJxtK2 ,8p݅*V W@Q?CQѧe*k4!Ri'K]hTk=uc +VYPJ' +x(U.1̭8z>HY +-)c-,L(%B ^X_(GW C-b(x H;[<hcm)Dp0ŊhD6fj +F)qaZubTþӚ@-mO\iRxg妝Tդ* {jg  #&ChWEǒr +ڴVǗfYY͉ޗ*.ٯTм)a*Bŷޡ⇾1(e1P=xd.g5=# Xe#:[ƵE{^.RAeؘ}*Y5Pw|!mӮ\[Py<Ȼ::IR^`m FXDqԜ/&˯&a usGEH}aBW Uxxj4cMuJT:j+W"H^7q{%-#…i3l;C{{MZz" +4 0w[S-hCNhڪZj50Pܰj0PۺZŷ߬=s; +Ł5 iG+z7tcٟ/Y* _J"CO$3NP#R''?K, '#S_pg6)Sb?Wo=S("&1đ;v8Fk z>[ľx E>`v4 .)1¢hQ noݮOG"}}tߣT=ñYNI3YT+;0/GuRۉ+Ծvb}O[Cࠜ4(QAllliWXJjš} W׹vI_a{/%emF& !_mL,ۣG=s 4P|)0`NOfXG~y76w0& `ayRMHLvanSJ[X;\7S_'D^*Ôޱ# .QRc͞Eb^vx=zӨk^fYpg?g4KV%'0 D0{e`7#!C5Jb|e1> +ſjf>ĬMA]`M'P]m3] {WJEq;]z{: NŒvݾ߬6W00x$'buqڧ +*i#[+Lb6&H IwX˿`B7~b0V{qI*1UYF?]W+AJ@!li@ R)p4a)-k5ָ¯uzn= A4nVuL +d{HC.%6`==;EZ \'>:}n* +ZZHMiLUOq)<|J ms`)e{}8u# qރ.Cf_/!K^Vz!nBr9hY*ȇ{NK/|'(05XoYzs<_g-S嫌u*.j\*&AOQa[qg7+Y{4;|ˍ+1+kS^Y- WVꠡ*Ut6"6ɯM~El+b_i/oŒ*|G9ßQxba$X滾-7i~EcMlTj?6V[瘍!:9fc3)G{+ۧUC9tdXu, 67uWTcjNvpۮ#j]P&5"lmENk˘Yt`vQU۰OsdjcVJ1ZŒU' gYWi.62NU|;dptQ:gk-N'7PFs?yoߌ*6sŹ+Yыs6 nTvUh [ UGɫ5sj]K 0!QT; zHS,q)}p4ps]%c#Zf': "ǭf4fz@LKV ćIjȘ׋BvOndu53aE68486xgwly"NS·PQQW&NEs@h" 3zf?S_ F$q#/ccY¶0>7?ec#n1'?lju5ݿ> endobj 72 0 obj << +/Filter/FlateDecode/Length 22 +>> +stream +x+*44Wp + +endstream endobj 70 0 obj <>>> endobj 630 0 obj <>/Filter /FlateDecode /Length 3474 >> +stream +x[]]Eoa.ز nm8w;33gfNBeVK6֏ iB#kb0_,$&$$"41>!!9o9s>tg?~}TS6~`;eoO&BȒhvEd;KI.+N( +NcTDzf2Fh*GW9\R J(] Y5b Jrp8g;$4K4=o4%7LȸR2^g UA)JL0pnM UJ;yNa81XҶmt9sŒLP~.̭yA ќ:YuXAxBdS _#/?^[#ZFHB^ZèLև)r⡪-B4-lkes:LՠK׭к%4l!v@G5Ԧ5VIJ4c! 7J/\Aܼ/SJd tS^θHn"~g 4kR/~m?x@*l_M> OנeﾷzP{?BF~Y̒Egxa7`XN\4۴z]Ywsis)K;]fT%oמc[ޱ+S0'~Wy~3@&Ϯ`YyJ;< ' zǮ3&-Wz†̲U}ɤxEXY<\*5ѶK R;03ϴgO ֶhEPM/o,٪OFh!d 2^053KI^$Z(\ǧ,-BُV 6<_0"1"'֏1KT*r&n\Ǫ;':JMGb#vYqccwcccߵ9[^#ow+)yMJck3vÇCx}@u0:a?!} }@5lSnCz{8,}H5ʡ)QpYww; b' +;@`;fs-xס3)P/Ḅ:{s?zr*4@Żwea)EitB@o!~G d{3n4 Ǝ ;ohz<>ߝ =4w;)%4i/:٫>0]"f _"ΦȰ;B努?DGA \.ԲdvKA9(Luv/cg*s`$4sPm*Zov?d ?!HJssjì {`qbO܂!QgaF FѲJ1pcG!mq-Mb豋_c-Q[6Ƽp5i;54:&p:?t3:c_h:-P pAQ5alyp%/Ѫ0ta8Z(:0/ԒfA˯!UovX $z L>:&!(r(&!L0"a 04(iar8&(nh:4a8juM_ay|}Cu>:1Sqʷ>%< + ,41K} O C:& Jkʆo-K֬cFXt*=F-Xq5J+_! +y)ˢݠ):!iA9W +a͠[&EQsRV.s}Oez]5=?!}5{z t*e.}*nNL:WN-*phrgo>h,ĺ'a{Pw:mbpSƱ7M9'~`}4QL+"%ل 4tY!IfeU`z:Am 8L5 !asZWQGgpXp fc'wURs[Q%hJp8sO: FDTPE/#OTA'aRSo?v?k]i?rgLF!x +ƘV%J&Nm!hj#ӌRʒEP϶/иc +U8=ضQ#\Ս_s Er U\2C`g*vIjl]u0zUoS +&JJݺ$y=.jYrض9Ww?ǘw 57E?V)[;[aa';y,y8lx,U,gzAY.fM pȵA-WRӌ 3dD3ub,i}9v^)wzTWa7Sa[mGk''- ~է48e!姼G"']GaN}J42a-eL)5*'J\9t@$O.W@q[P +eTP 0'*E(|f.n_8R&P +%d`X5M& jێGPkv|9ţ(q氇~vh}+2Q&Qä߹evB9c1/'6N0Ұmw3 +y/Mpw "}JJ.TH2ֹ1? pגX9Xn,})ҡ>x +3Ж,cOBz۩q<9£yB: 6\3ES@f?YBswq! +I^~Lu!@071ZvX~7L#fmY-M3xU3a ~*p(2t3z1Я:ug)7o6ˁE0nNQ[~(}xsӷ_=3ov'翂|U8uj/P,Ɨx^:Pë"h|_ͯxt-t{0'xL[%X鬈 +xZ& U*)y*__R?ʂV +endstream endobj 68 0 obj << +/Filter/FlateDecode/Length 22 +>> +stream +x+*44Sp +{ +endstream endobj 66 0 obj <>>> endobj 631 0 obj <>/Filter /FlateDecode /Length 3652 >> +stream +x[m]E.pݮe.m^=9gΜsT. 5XEK%$?!CI )1~AH&~hBĀ$Bu{̹gn3Ιyߙ{_3 +#Ռw캏eͻw7&Tʺ4,yosߪ*T5WEaQ2mY&IEV DZ>#qѠ 6`,Lb˲f*(QhJ4u8[[(}`e8 +"v<:vhL8TK[:->/¶ǡ2Z|q = +*y53as@84 +{^3@Ke0]$,T&mQEܓmoe#Pe>a6 +/;|+1aZ.7^ʷiK( +8gqG7A^o^`G/ndȃ@p8>~=ź5L.9b.J[2ۺ b?f?_gI%,ʁwʰT`)`֋P͓~w?m> d=c;[#-,Y;=`tu.{(7e” {Z͘Hs: +fnCy8CoJQ٧鶂{v$ E5ލu,@݅AuHP-Jk9ijNL%04ۍ ~ MOPY: T砢E$(!sG[dEi. 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>W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A 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_NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk bDuufØ*h@ihCM'ahf[VQvakF^\ ?`NC&mc>V A^A [tFLfB;:2Yk3Y);;B ٿ0t>Cʙ1n -1EN+d/4vp#RC%0$*?0x^"𭊻lq~b'"hZāc M-Om d} + rw.X$o/=Ӷ#Kw&|]wJ@Q$. +YfᏊ>qk܅1r=2)E݇D'k D>o- <~?}v lvWR#@v#V}%I?ƍ/^=*- ׆=VxEQB|%yI̠~8#VHmW@ihNob >W(P ^*ow`ۤ$n$( }'IzcDx.ʦ;>*z]q3 +ؤXWFawX 1q~Yy4jx= tT.ݻ{G$wЊȧJ?oQVx(sǍH@G>Xs"1Md[`αcCEQq< +b*(z_(GVF`++nQ}`կ_yF0鯉Oa/3+pѼ &.L߃}Xq8,V8CtZS#9 + O(Y ROւ/Cٯǚ&s8r=e]>;m];u 4'Fq^,^Igfi[#@6=QWo/քgHra?Zu7L4.7c>Z`Ҕ0q`ucrsyK$[fJ`x;'4nɑd?~ x(+O19cÀӀ0f?cØ0q=Ә0fOcjSc?!c489caLMcjScaӘW=|s~y+_+ߩRи%Р"7 +C_ltK+',&Fm>)?e Ozψ\o1J0ROΈ {Eλ4t6Jr!Ӑ3?4XL^]c |y&^`lz+RyRfdRFZ72ґtȢȕ LKj;K[5:aFD`5doo,2%m&f1a {d60 { Ơʘ\cD$~]ȆШf)LfbJ){ @>Ђ;;Hx&uu׹4ݫǓӷ,D;@"&5UƎXy;7'ߗïgPטo -c}}$ d7$hRgHCH8e"&l:$]9:9&<\w.1qwE[D=2A@,}Yc Y/ztGxSnq0Ohаɡa{%>Z?4V +٨S}hXסaаu=5/\Cf^ MOۡaR 95܇muha|7c Oj8p_~H}4b(|yhWW + Y_: +f}E'5:Ox .|_k$+s=eᡠ<K +q[|bX*Uk$ooECD +ge+*yj-oH (M2KQ )իaq \> stream xmZͮ$ ݏ,F6yH}R"E}#eO9P{G{\0Ƙ~/y~__}Wk?xڟ?=[]_u_=n=_§J4^?GmZD '6?ߚzq[>-DzIXHQM2h+X޸B6b7.׌B:hfg~.^7܁xDxSٝfry`@AkT 3#׿~z&Nv4^Z&ϧ?0$"e iOkuى-CwuLzÍӿ$K[=HG3hnQ "?? +Ý6 ^괍ɟj+h. +ܹZٹB _NHw<("wЕO*@(/yeT(n 1כx`)!MCϋs sL9fmr~]5\#j4C [7pO>) }3wP'C'O ]rUO{KzL`$/ ^' fGb7}FKZsD-s\з#=^܇|ZI꾱b1"{ܡv6'xhl6X㾶|%S/!OODݺ}~"^'.㠶`Wk 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b/backend/apps/algorithms/algorithm/other/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/other/house_prices_predict/LR_model.pdparams b/backend/apps/algorithms/algorithm/other/house_prices_predict/LR_model.pdparams new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/other/house_prices_predict/__init__.py b/backend/apps/algorithms/algorithm/other/house_prices_predict/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/other/house_prices_predict/housing.data b/backend/apps/algorithms/algorithm/other/house_prices_predict/housing.data new file mode 100644 index 0000000..f83ac56 --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/house_prices_predict/housing.data @@ -0,0 +1,506 @@ + 0.00632 18.00 2.310 0 0.5380 6.5750 65.20 4.0900 1 296.0 15.30 396.90 4.98 24.00 + 0.02731 0.00 7.070 0 0.4690 6.4210 78.90 4.9671 2 242.0 17.80 396.90 9.14 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2.15505 0.00 19.580 0 0.8710 5.6280 100.00 1.5166 5 403.0 14.70 169.27 16.65 15.60 + 2.36862 0.00 19.580 0 0.8710 4.9260 95.70 1.4608 5 403.0 14.70 391.71 29.53 14.60 + 2.33099 0.00 19.580 0 0.8710 5.1860 93.80 1.5296 5 403.0 14.70 356.99 28.32 17.80 + 2.73397 0.00 19.580 0 0.8710 5.5970 94.90 1.5257 5 403.0 14.70 351.85 21.45 15.40 + 1.65660 0.00 19.580 0 0.8710 6.1220 97.30 1.6180 5 403.0 14.70 372.80 14.10 21.50 + 1.49632 0.00 19.580 0 0.8710 5.4040 100.00 1.5916 5 403.0 14.70 341.60 13.28 19.60 + 1.12658 0.00 19.580 1 0.8710 5.0120 88.00 1.6102 5 403.0 14.70 343.28 12.12 15.30 + 2.14918 0.00 19.580 0 0.8710 5.7090 98.50 1.6232 5 403.0 14.70 261.95 15.79 19.40 + 1.41385 0.00 19.580 1 0.8710 6.1290 96.00 1.7494 5 403.0 14.70 321.02 15.12 17.00 + 3.53501 0.00 19.580 1 0.8710 6.1520 82.60 1.7455 5 403.0 14.70 88.01 15.02 15.60 + 2.44668 0.00 19.580 0 0.8710 5.2720 94.00 1.7364 5 403.0 14.70 88.63 16.14 13.10 + 1.22358 0.00 19.580 0 0.6050 6.9430 97.40 1.8773 5 403.0 14.70 363.43 4.59 41.30 + 1.34284 0.00 19.580 0 0.6050 6.0660 100.00 1.7573 5 403.0 14.70 353.89 6.43 24.30 + 1.42502 0.00 19.580 0 0.8710 6.5100 100.00 1.7659 5 403.0 14.70 364.31 7.39 23.30 + 1.27346 0.00 19.580 1 0.6050 6.2500 92.60 1.7984 5 403.0 14.70 338.92 5.50 27.00 + 1.46336 0.00 19.580 0 0.6050 7.4890 90.80 1.9709 5 403.0 14.70 374.43 1.73 50.00 + 1.83377 0.00 19.580 1 0.6050 7.8020 98.20 2.0407 5 403.0 14.70 389.61 1.92 50.00 + 1.51902 0.00 19.580 1 0.6050 8.3750 93.90 2.1620 5 403.0 14.70 388.45 3.32 50.00 + 2.24236 0.00 19.580 0 0.6050 5.8540 91.80 2.4220 5 403.0 14.70 395.11 11.64 22.70 + 2.92400 0.00 19.580 0 0.6050 6.1010 93.00 2.2834 5 403.0 14.70 240.16 9.81 25.00 + 2.01019 0.00 19.580 0 0.6050 7.9290 96.20 2.0459 5 403.0 14.70 369.30 3.70 50.00 + 1.80028 0.00 19.580 0 0.6050 5.8770 79.20 2.4259 5 403.0 14.70 227.61 12.14 23.80 + 2.30040 0.00 19.580 0 0.6050 6.3190 96.10 2.1000 5 403.0 14.70 297.09 11.10 23.80 + 2.44953 0.00 19.580 0 0.6050 6.4020 95.20 2.2625 5 403.0 14.70 330.04 11.32 22.30 + 1.20742 0.00 19.580 0 0.6050 5.8750 94.60 2.4259 5 403.0 14.70 292.29 14.43 17.40 + 2.31390 0.00 19.580 0 0.6050 5.8800 97.30 2.3887 5 403.0 14.70 348.13 12.03 19.10 + 0.13914 0.00 4.050 0 0.5100 5.5720 88.50 2.5961 5 296.0 16.60 396.90 14.69 23.10 + 0.09178 0.00 4.050 0 0.5100 6.4160 84.10 2.6463 5 296.0 16.60 395.50 9.04 23.60 + 0.08447 0.00 4.050 0 0.5100 5.8590 68.70 2.7019 5 296.0 16.60 393.23 9.64 22.60 + 0.06664 0.00 4.050 0 0.5100 6.5460 33.10 3.1323 5 296.0 16.60 390.96 5.33 29.40 + 0.07022 0.00 4.050 0 0.5100 6.0200 47.20 3.5549 5 296.0 16.60 393.23 10.11 23.20 + 0.05425 0.00 4.050 0 0.5100 6.3150 73.40 3.3175 5 296.0 16.60 395.60 6.29 24.60 + 0.06642 0.00 4.050 0 0.5100 6.8600 74.40 2.9153 5 296.0 16.60 391.27 6.92 29.90 + 0.05780 0.00 2.460 0 0.4880 6.9800 58.40 2.8290 3 193.0 17.80 396.90 5.04 37.20 + 0.06588 0.00 2.460 0 0.4880 7.7650 83.30 2.7410 3 193.0 17.80 395.56 7.56 39.80 + 0.06888 0.00 2.460 0 0.4880 6.1440 62.20 2.5979 3 193.0 17.80 396.90 9.45 36.20 + 0.09103 0.00 2.460 0 0.4880 7.1550 92.20 2.7006 3 193.0 17.80 394.12 4.82 37.90 + 0.10008 0.00 2.460 0 0.4880 6.5630 95.60 2.8470 3 193.0 17.80 396.90 5.68 32.50 + 0.08308 0.00 2.460 0 0.4880 5.6040 89.80 2.9879 3 193.0 17.80 391.00 13.98 26.40 + 0.06047 0.00 2.460 0 0.4880 6.1530 68.80 3.2797 3 193.0 17.80 387.11 13.15 29.60 + 0.05602 0.00 2.460 0 0.4880 7.8310 53.60 3.1992 3 193.0 17.80 392.63 4.45 50.00 + 0.07875 45.00 3.440 0 0.4370 6.7820 41.10 3.7886 5 398.0 15.20 393.87 6.68 32.00 + 0.12579 45.00 3.440 0 0.4370 6.5560 29.10 4.5667 5 398.0 15.20 382.84 4.56 29.80 + 0.08370 45.00 3.440 0 0.4370 7.1850 38.90 4.5667 5 398.0 15.20 396.90 5.39 34.90 + 0.09068 45.00 3.440 0 0.4370 6.9510 21.50 6.4798 5 398.0 15.20 377.68 5.10 37.00 + 0.06911 45.00 3.440 0 0.4370 6.7390 30.80 6.4798 5 398.0 15.20 389.71 4.69 30.50 + 0.08664 45.00 3.440 0 0.4370 7.1780 26.30 6.4798 5 398.0 15.20 390.49 2.87 36.40 + 0.02187 60.00 2.930 0 0.4010 6.8000 9.90 6.2196 1 265.0 15.60 393.37 5.03 31.10 + 0.01439 60.00 2.930 0 0.4010 6.6040 18.80 6.2196 1 265.0 15.60 376.70 4.38 29.10 + 0.01381 80.00 0.460 0 0.4220 7.8750 32.00 5.6484 4 255.0 14.40 394.23 2.97 50.00 + 0.04011 80.00 1.520 0 0.4040 7.2870 34.10 7.3090 2 329.0 12.60 396.90 4.08 33.30 + 0.04666 80.00 1.520 0 0.4040 7.1070 36.60 7.3090 2 329.0 12.60 354.31 8.61 30.30 + 0.03768 80.00 1.520 0 0.4040 7.2740 38.30 7.3090 2 329.0 12.60 392.20 6.62 34.60 + 0.03150 95.00 1.470 0 0.4030 6.9750 15.30 7.6534 3 402.0 17.00 396.90 4.56 34.90 + 0.01778 95.00 1.470 0 0.4030 7.1350 13.90 7.6534 3 402.0 17.00 384.30 4.45 32.90 + 0.03445 82.50 2.030 0 0.4150 6.1620 38.40 6.2700 2 348.0 14.70 393.77 7.43 24.10 + 0.02177 82.50 2.030 0 0.4150 7.6100 15.70 6.2700 2 348.0 14.70 395.38 3.11 42.30 + 0.03510 95.00 2.680 0 0.4161 7.8530 33.20 5.1180 4 224.0 14.70 392.78 3.81 48.50 + 0.02009 95.00 2.680 0 0.4161 8.0340 31.90 5.1180 4 224.0 14.70 390.55 2.88 50.00 + 0.13642 0.00 10.590 0 0.4890 5.8910 22.30 3.9454 4 277.0 18.60 396.90 10.87 22.60 + 0.22969 0.00 10.590 0 0.4890 6.3260 52.50 4.3549 4 277.0 18.60 394.87 10.97 24.40 + 0.25199 0.00 10.590 0 0.4890 5.7830 72.70 4.3549 4 277.0 18.60 389.43 18.06 22.50 + 0.13587 0.00 10.590 1 0.4890 6.0640 59.10 4.2392 4 277.0 18.60 381.32 14.66 24.40 + 0.43571 0.00 10.590 1 0.4890 5.3440 100.00 3.8750 4 277.0 18.60 396.90 23.09 20.00 + 0.17446 0.00 10.590 1 0.4890 5.9600 92.10 3.8771 4 277.0 18.60 393.25 17.27 21.70 + 0.37578 0.00 10.590 1 0.4890 5.4040 88.60 3.6650 4 277.0 18.60 395.24 23.98 19.30 + 0.21719 0.00 10.590 1 0.4890 5.8070 53.80 3.6526 4 277.0 18.60 390.94 16.03 22.40 + 0.14052 0.00 10.590 0 0.4890 6.3750 32.30 3.9454 4 277.0 18.60 385.81 9.38 28.10 + 0.28955 0.00 10.590 0 0.4890 5.4120 9.80 3.5875 4 277.0 18.60 348.93 29.55 23.70 + 0.19802 0.00 10.590 0 0.4890 6.1820 42.40 3.9454 4 277.0 18.60 393.63 9.47 25.00 + 0.04560 0.00 13.890 1 0.5500 5.8880 56.00 3.1121 5 276.0 16.40 392.80 13.51 23.30 + 0.07013 0.00 13.890 0 0.5500 6.6420 85.10 3.4211 5 276.0 16.40 392.78 9.69 28.70 + 0.11069 0.00 13.890 1 0.5500 5.9510 93.80 2.8893 5 276.0 16.40 396.90 17.92 21.50 + 0.11425 0.00 13.890 1 0.5500 6.3730 92.40 3.3633 5 276.0 16.40 393.74 10.50 23.00 + 0.35809 0.00 6.200 1 0.5070 6.9510 88.50 2.8617 8 307.0 17.40 391.70 9.71 26.70 + 0.40771 0.00 6.200 1 0.5070 6.1640 91.30 3.0480 8 307.0 17.40 395.24 21.46 21.70 + 0.62356 0.00 6.200 1 0.5070 6.8790 77.70 3.2721 8 307.0 17.40 390.39 9.93 27.50 + 0.61470 0.00 6.200 0 0.5070 6.6180 80.80 3.2721 8 307.0 17.40 396.90 7.60 30.10 + 0.31533 0.00 6.200 0 0.5040 8.2660 78.30 2.8944 8 307.0 17.40 385.05 4.14 44.80 + 0.52693 0.00 6.200 0 0.5040 8.7250 83.00 2.8944 8 307.0 17.40 382.00 4.63 50.00 + 0.38214 0.00 6.200 0 0.5040 8.0400 86.50 3.2157 8 307.0 17.40 387.38 3.13 37.60 + 0.41238 0.00 6.200 0 0.5040 7.1630 79.90 3.2157 8 307.0 17.40 372.08 6.36 31.60 + 0.29819 0.00 6.200 0 0.5040 7.6860 17.00 3.3751 8 307.0 17.40 377.51 3.92 46.70 + 0.44178 0.00 6.200 0 0.5040 6.5520 21.40 3.3751 8 307.0 17.40 380.34 3.76 31.50 + 0.53700 0.00 6.200 0 0.5040 5.9810 68.10 3.6715 8 307.0 17.40 378.35 11.65 24.30 + 0.46296 0.00 6.200 0 0.5040 7.4120 76.90 3.6715 8 307.0 17.40 376.14 5.25 31.70 + 0.57529 0.00 6.200 0 0.5070 8.3370 73.30 3.8384 8 307.0 17.40 385.91 2.47 41.70 + 0.33147 0.00 6.200 0 0.5070 8.2470 70.40 3.6519 8 307.0 17.40 378.95 3.95 48.30 + 0.44791 0.00 6.200 1 0.5070 6.7260 66.50 3.6519 8 307.0 17.40 360.20 8.05 29.00 + 0.33045 0.00 6.200 0 0.5070 6.0860 61.50 3.6519 8 307.0 17.40 376.75 10.88 24.00 + 0.52058 0.00 6.200 1 0.5070 6.6310 76.50 4.1480 8 307.0 17.40 388.45 9.54 25.10 + 0.51183 0.00 6.200 0 0.5070 7.3580 71.60 4.1480 8 307.0 17.40 390.07 4.73 31.50 + 0.08244 30.00 4.930 0 0.4280 6.4810 18.50 6.1899 6 300.0 16.60 379.41 6.36 23.70 + 0.09252 30.00 4.930 0 0.4280 6.6060 42.20 6.1899 6 300.0 16.60 383.78 7.37 23.30 + 0.11329 30.00 4.930 0 0.4280 6.8970 54.30 6.3361 6 300.0 16.60 391.25 11.38 22.00 + 0.10612 30.00 4.930 0 0.4280 6.0950 65.10 6.3361 6 300.0 16.60 394.62 12.40 20.10 + 0.10290 30.00 4.930 0 0.4280 6.3580 52.90 7.0355 6 300.0 16.60 372.75 11.22 22.20 + 0.12757 30.00 4.930 0 0.4280 6.3930 7.80 7.0355 6 300.0 16.60 374.71 5.19 23.70 + 0.20608 22.00 5.860 0 0.4310 5.5930 76.50 7.9549 7 330.0 19.10 372.49 12.50 17.60 + 0.19133 22.00 5.860 0 0.4310 5.6050 70.20 7.9549 7 330.0 19.10 389.13 18.46 18.50 + 0.33983 22.00 5.860 0 0.4310 6.1080 34.90 8.0555 7 330.0 19.10 390.18 9.16 24.30 + 0.19657 22.00 5.860 0 0.4310 6.2260 79.20 8.0555 7 330.0 19.10 376.14 10.15 20.50 + 0.16439 22.00 5.860 0 0.4310 6.4330 49.10 7.8265 7 330.0 19.10 374.71 9.52 24.50 + 0.19073 22.00 5.860 0 0.4310 6.7180 17.50 7.8265 7 330.0 19.10 393.74 6.56 26.20 + 0.14030 22.00 5.860 0 0.4310 6.4870 13.00 7.3967 7 330.0 19.10 396.28 5.90 24.40 + 0.21409 22.00 5.860 0 0.4310 6.4380 8.90 7.3967 7 330.0 19.10 377.07 3.59 24.80 + 0.08221 22.00 5.860 0 0.4310 6.9570 6.80 8.9067 7 330.0 19.10 386.09 3.53 29.60 + 0.36894 22.00 5.860 0 0.4310 8.2590 8.40 8.9067 7 330.0 19.10 396.90 3.54 42.80 + 0.04819 80.00 3.640 0 0.3920 6.1080 32.00 9.2203 1 315.0 16.40 392.89 6.57 21.90 + 0.03548 80.00 3.640 0 0.3920 5.8760 19.10 9.2203 1 315.0 16.40 395.18 9.25 20.90 + 0.01538 90.00 3.750 0 0.3940 7.4540 34.20 6.3361 3 244.0 15.90 386.34 3.11 44.00 + 0.61154 20.00 3.970 0 0.6470 8.7040 86.90 1.8010 5 264.0 13.00 389.70 5.12 50.00 + 0.66351 20.00 3.970 0 0.6470 7.3330 100.00 1.8946 5 264.0 13.00 383.29 7.79 36.00 + 0.65665 20.00 3.970 0 0.6470 6.8420 100.00 2.0107 5 264.0 13.00 391.93 6.90 30.10 + 0.54011 20.00 3.970 0 0.6470 7.2030 81.80 2.1121 5 264.0 13.00 392.80 9.59 33.80 + 0.53412 20.00 3.970 0 0.6470 7.5200 89.40 2.1398 5 264.0 13.00 388.37 7.26 43.10 + 0.52014 20.00 3.970 0 0.6470 8.3980 91.50 2.2885 5 264.0 13.00 386.86 5.91 48.80 + 0.82526 20.00 3.970 0 0.6470 7.3270 94.50 2.0788 5 264.0 13.00 393.42 11.25 31.00 + 0.55007 20.00 3.970 0 0.6470 7.2060 91.60 1.9301 5 264.0 13.00 387.89 8.10 36.50 + 0.76162 20.00 3.970 0 0.6470 5.5600 62.80 1.9865 5 264.0 13.00 392.40 10.45 22.80 + 0.78570 20.00 3.970 0 0.6470 7.0140 84.60 2.1329 5 264.0 13.00 384.07 14.79 30.70 + 0.57834 20.00 3.970 0 0.5750 8.2970 67.00 2.4216 5 264.0 13.00 384.54 7.44 50.00 + 0.54050 20.00 3.970 0 0.5750 7.4700 52.60 2.8720 5 264.0 13.00 390.30 3.16 43.50 + 0.09065 20.00 6.960 1 0.4640 5.9200 61.50 3.9175 3 223.0 18.60 391.34 13.65 20.70 + 0.29916 20.00 6.960 0 0.4640 5.8560 42.10 4.4290 3 223.0 18.60 388.65 13.00 21.10 + 0.16211 20.00 6.960 0 0.4640 6.2400 16.30 4.4290 3 223.0 18.60 396.90 6.59 25.20 + 0.11460 20.00 6.960 0 0.4640 6.5380 58.70 3.9175 3 223.0 18.60 394.96 7.73 24.40 + 0.22188 20.00 6.960 1 0.4640 7.6910 51.80 4.3665 3 223.0 18.60 390.77 6.58 35.20 + 0.05644 40.00 6.410 1 0.4470 6.7580 32.90 4.0776 4 254.0 17.60 396.90 3.53 32.40 + 0.09604 40.00 6.410 0 0.4470 6.8540 42.80 4.2673 4 254.0 17.60 396.90 2.98 32.00 + 0.10469 40.00 6.410 1 0.4470 7.2670 49.00 4.7872 4 254.0 17.60 389.25 6.05 33.20 + 0.06127 40.00 6.410 1 0.4470 6.8260 27.60 4.8628 4 254.0 17.60 393.45 4.16 33.10 + 0.07978 40.00 6.410 0 0.4470 6.4820 32.10 4.1403 4 254.0 17.60 396.90 7.19 29.10 + 0.21038 20.00 3.330 0 0.4429 6.8120 32.20 4.1007 5 216.0 14.90 396.90 4.85 35.10 + 0.03578 20.00 3.330 0 0.4429 7.8200 64.50 4.6947 5 216.0 14.90 387.31 3.76 45.40 + 0.03705 20.00 3.330 0 0.4429 6.9680 37.20 5.2447 5 216.0 14.90 392.23 4.59 35.40 + 0.06129 20.00 3.330 1 0.4429 7.6450 49.70 5.2119 5 216.0 14.90 377.07 3.01 46.00 + 0.01501 90.00 1.210 1 0.4010 7.9230 24.80 5.8850 1 198.0 13.60 395.52 3.16 50.00 + 0.00906 90.00 2.970 0 0.4000 7.0880 20.80 7.3073 1 285.0 15.30 394.72 7.85 32.20 + 0.01096 55.00 2.250 0 0.3890 6.4530 31.90 7.3073 1 300.0 15.30 394.72 8.23 22.00 + 0.01965 80.00 1.760 0 0.3850 6.2300 31.50 9.0892 1 241.0 18.20 341.60 12.93 20.10 + 0.03871 52.50 5.320 0 0.4050 6.2090 31.30 7.3172 6 293.0 16.60 396.90 7.14 23.20 + 0.04590 52.50 5.320 0 0.4050 6.3150 45.60 7.3172 6 293.0 16.60 396.90 7.60 22.30 + 0.04297 52.50 5.320 0 0.4050 6.5650 22.90 7.3172 6 293.0 16.60 371.72 9.51 24.80 + 0.03502 80.00 4.950 0 0.4110 6.8610 27.90 5.1167 4 245.0 19.20 396.90 3.33 28.50 + 0.07886 80.00 4.950 0 0.4110 7.1480 27.70 5.1167 4 245.0 19.20 396.90 3.56 37.30 + 0.03615 80.00 4.950 0 0.4110 6.6300 23.40 5.1167 4 245.0 19.20 396.90 4.70 27.90 + 0.08265 0.00 13.920 0 0.4370 6.1270 18.40 5.5027 4 289.0 16.00 396.90 8.58 23.90 + 0.08199 0.00 13.920 0 0.4370 6.0090 42.30 5.5027 4 289.0 16.00 396.90 10.40 21.70 + 0.12932 0.00 13.920 0 0.4370 6.6780 31.10 5.9604 4 289.0 16.00 396.90 6.27 28.60 + 0.05372 0.00 13.920 0 0.4370 6.5490 51.00 5.9604 4 289.0 16.00 392.85 7.39 27.10 + 0.14103 0.00 13.920 0 0.4370 5.7900 58.00 6.3200 4 289.0 16.00 396.90 15.84 20.30 + 0.06466 70.00 2.240 0 0.4000 6.3450 20.10 7.8278 5 358.0 14.80 368.24 4.97 22.50 + 0.05561 70.00 2.240 0 0.4000 7.0410 10.00 7.8278 5 358.0 14.80 371.58 4.74 29.00 + 0.04417 70.00 2.240 0 0.4000 6.8710 47.40 7.8278 5 358.0 14.80 390.86 6.07 24.80 + 0.03537 34.00 6.090 0 0.4330 6.5900 40.40 5.4917 7 329.0 16.10 395.75 9.50 22.00 + 0.09266 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16.10 + 5.58107 0.00 18.100 0 0.7130 6.4360 87.90 2.3158 24 666.0 20.20 100.19 16.22 14.30 +13.91340 0.00 18.100 0 0.7130 6.2080 95.00 2.2222 24 666.0 20.20 100.63 15.17 11.70 +11.16040 0.00 18.100 0 0.7400 6.6290 94.60 2.1247 24 666.0 20.20 109.85 23.27 13.40 +14.42080 0.00 18.100 0 0.7400 6.4610 93.30 2.0026 24 666.0 20.20 27.49 18.05 9.60 +15.17720 0.00 18.100 0 0.7400 6.1520 100.00 1.9142 24 666.0 20.20 9.32 26.45 8.70 +13.67810 0.00 18.100 0 0.7400 5.9350 87.90 1.8206 24 666.0 20.20 68.95 34.02 8.40 + 9.39063 0.00 18.100 0 0.7400 5.6270 93.90 1.8172 24 666.0 20.20 396.90 22.88 12.80 +22.05110 0.00 18.100 0 0.7400 5.8180 92.40 1.8662 24 666.0 20.20 391.45 22.11 10.50 + 9.72418 0.00 18.100 0 0.7400 6.4060 97.20 2.0651 24 666.0 20.20 385.96 19.52 17.10 + 5.66637 0.00 18.100 0 0.7400 6.2190 100.00 2.0048 24 666.0 20.20 395.69 16.59 18.40 + 9.96654 0.00 18.100 0 0.7400 6.4850 100.00 1.9784 24 666.0 20.20 386.73 18.85 15.40 +12.80230 0.00 18.100 0 0.7400 5.8540 96.60 1.8956 24 666.0 20.20 240.52 23.79 10.80 +10.67180 0.00 18.100 0 0.7400 6.4590 94.80 1.9879 24 666.0 20.20 43.06 23.98 11.80 + 6.28807 0.00 18.100 0 0.7400 6.3410 96.40 2.0720 24 666.0 20.20 318.01 17.79 14.90 + 9.92485 0.00 18.100 0 0.7400 6.2510 96.60 2.1980 24 666.0 20.20 388.52 16.44 12.60 + 9.32909 0.00 18.100 0 0.7130 6.1850 98.70 2.2616 24 666.0 20.20 396.90 18.13 14.10 + 7.52601 0.00 18.100 0 0.7130 6.4170 98.30 2.1850 24 666.0 20.20 304.21 19.31 13.00 + 6.71772 0.00 18.100 0 0.7130 6.7490 92.60 2.3236 24 666.0 20.20 0.32 17.44 13.40 + 5.44114 0.00 18.100 0 0.7130 6.6550 98.20 2.3552 24 666.0 20.20 355.29 17.73 15.20 + 5.09017 0.00 18.100 0 0.7130 6.2970 91.80 2.3682 24 666.0 20.20 385.09 17.27 16.10 + 8.24809 0.00 18.100 0 0.7130 7.3930 99.30 2.4527 24 666.0 20.20 375.87 16.74 17.80 + 9.51363 0.00 18.100 0 0.7130 6.7280 94.10 2.4961 24 666.0 20.20 6.68 18.71 14.90 + 4.75237 0.00 18.100 0 0.7130 6.5250 86.50 2.4358 24 666.0 20.20 50.92 18.13 14.10 + 4.66883 0.00 18.100 0 0.7130 5.9760 87.90 2.5806 24 666.0 20.20 10.48 19.01 12.70 + 8.20058 0.00 18.100 0 0.7130 5.9360 80.30 2.7792 24 666.0 20.20 3.50 16.94 13.50 + 7.75223 0.00 18.100 0 0.7130 6.3010 83.70 2.7831 24 666.0 20.20 272.21 16.23 14.90 + 6.80117 0.00 18.100 0 0.7130 6.0810 84.40 2.7175 24 666.0 20.20 396.90 14.70 20.00 + 4.81213 0.00 18.100 0 0.7130 6.7010 90.00 2.5975 24 666.0 20.20 255.23 16.42 16.40 + 3.69311 0.00 18.100 0 0.7130 6.3760 88.40 2.5671 24 666.0 20.20 391.43 14.65 17.70 + 6.65492 0.00 18.100 0 0.7130 6.3170 83.00 2.7344 24 666.0 20.20 396.90 13.99 19.50 + 5.82115 0.00 18.100 0 0.7130 6.5130 89.90 2.8016 24 666.0 20.20 393.82 10.29 20.20 + 7.83932 0.00 18.100 0 0.6550 6.2090 65.40 2.9634 24 666.0 20.20 396.90 13.22 21.40 + 3.16360 0.00 18.100 0 0.6550 5.7590 48.20 3.0665 24 666.0 20.20 334.40 14.13 19.90 + 3.77498 0.00 18.100 0 0.6550 5.9520 84.70 2.8715 24 666.0 20.20 22.01 17.15 19.00 + 4.42228 0.00 18.100 0 0.5840 6.0030 94.50 2.5403 24 666.0 20.20 331.29 21.32 19.10 +15.57570 0.00 18.100 0 0.5800 5.9260 71.00 2.9084 24 666.0 20.20 368.74 18.13 19.10 +13.07510 0.00 18.100 0 0.5800 5.7130 56.70 2.8237 24 666.0 20.20 396.90 14.76 20.10 + 4.34879 0.00 18.100 0 0.5800 6.1670 84.00 3.0334 24 666.0 20.20 396.90 16.29 19.90 + 4.03841 0.00 18.100 0 0.5320 6.2290 90.70 3.0993 24 666.0 20.20 395.33 12.87 19.60 + 3.56868 0.00 18.100 0 0.5800 6.4370 75.00 2.8965 24 666.0 20.20 393.37 14.36 23.20 + 4.64689 0.00 18.100 0 0.6140 6.9800 67.60 2.5329 24 666.0 20.20 374.68 11.66 29.80 + 8.05579 0.00 18.100 0 0.5840 5.4270 95.40 2.4298 24 666.0 20.20 352.58 18.14 13.80 + 6.39312 0.00 18.100 0 0.5840 6.1620 97.40 2.2060 24 666.0 20.20 302.76 24.10 13.30 + 4.87141 0.00 18.100 0 0.6140 6.4840 93.60 2.3053 24 666.0 20.20 396.21 18.68 16.70 +15.02340 0.00 18.100 0 0.6140 5.3040 97.30 2.1007 24 666.0 20.20 349.48 24.91 12.00 +10.23300 0.00 18.100 0 0.6140 6.1850 96.70 2.1705 24 666.0 20.20 379.70 18.03 14.60 +14.33370 0.00 18.100 0 0.6140 6.2290 88.00 1.9512 24 666.0 20.20 383.32 13.11 21.40 + 5.82401 0.00 18.100 0 0.5320 6.2420 64.70 3.4242 24 666.0 20.20 396.90 10.74 23.00 + 5.70818 0.00 18.100 0 0.5320 6.7500 74.90 3.3317 24 666.0 20.20 393.07 7.74 23.70 + 5.73116 0.00 18.100 0 0.5320 7.0610 77.00 3.4106 24 666.0 20.20 395.28 7.01 25.00 + 2.81838 0.00 18.100 0 0.5320 5.7620 40.30 4.0983 24 666.0 20.20 392.92 10.42 21.80 + 2.37857 0.00 18.100 0 0.5830 5.8710 41.90 3.7240 24 666.0 20.20 370.73 13.34 20.60 + 3.67367 0.00 18.100 0 0.5830 6.3120 51.90 3.9917 24 666.0 20.20 388.62 10.58 21.20 + 5.69175 0.00 18.100 0 0.5830 6.1140 79.80 3.5459 24 666.0 20.20 392.68 14.98 19.10 + 4.83567 0.00 18.100 0 0.5830 5.9050 53.20 3.1523 24 666.0 20.20 388.22 11.45 20.60 + 0.15086 0.00 27.740 0 0.6090 5.4540 92.70 1.8209 4 711.0 20.10 395.09 18.06 15.20 + 0.18337 0.00 27.740 0 0.6090 5.4140 98.30 1.7554 4 711.0 20.10 344.05 23.97 7.00 + 0.20746 0.00 27.740 0 0.6090 5.0930 98.00 1.8226 4 711.0 20.10 318.43 29.68 8.10 + 0.10574 0.00 27.740 0 0.6090 5.9830 98.80 1.8681 4 711.0 20.10 390.11 18.07 13.60 + 0.11132 0.00 27.740 0 0.6090 5.9830 83.50 2.1099 4 711.0 20.10 396.90 13.35 20.10 + 0.17331 0.00 9.690 0 0.5850 5.7070 54.00 2.3817 6 391.0 19.20 396.90 12.01 21.80 + 0.27957 0.00 9.690 0 0.5850 5.9260 42.60 2.3817 6 391.0 19.20 396.90 13.59 24.50 + 0.17899 0.00 9.690 0 0.5850 5.6700 28.80 2.7986 6 391.0 19.20 393.29 17.60 23.10 + 0.28960 0.00 9.690 0 0.5850 5.3900 72.90 2.7986 6 391.0 19.20 396.90 21.14 19.70 + 0.26838 0.00 9.690 0 0.5850 5.7940 70.60 2.8927 6 391.0 19.20 396.90 14.10 18.30 + 0.23912 0.00 9.690 0 0.5850 6.0190 65.30 2.4091 6 391.0 19.20 396.90 12.92 21.20 + 0.17783 0.00 9.690 0 0.5850 5.5690 73.50 2.3999 6 391.0 19.20 395.77 15.10 17.50 + 0.22438 0.00 9.690 0 0.5850 6.0270 79.70 2.4982 6 391.0 19.20 396.90 14.33 16.80 + 0.06263 0.00 11.930 0 0.5730 6.5930 69.10 2.4786 1 273.0 21.00 391.99 9.67 22.40 + 0.04527 0.00 11.930 0 0.5730 6.1200 76.70 2.2875 1 273.0 21.00 396.90 9.08 20.60 + 0.06076 0.00 11.930 0 0.5730 6.9760 91.00 2.1675 1 273.0 21.00 396.90 5.64 23.90 + 0.10959 0.00 11.930 0 0.5730 6.7940 89.30 2.3889 1 273.0 21.00 393.45 6.48 22.00 + 0.04741 0.00 11.930 0 0.5730 6.0300 80.80 2.5050 1 273.0 21.00 396.90 7.88 11.90 diff --git a/backend/apps/algorithms/algorithm/other/house_prices_predict/load_data.py b/backend/apps/algorithms/algorithm/other/house_prices_predict/load_data.py new file mode 100644 index 0000000..cbaa041 --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/house_prices_predict/load_data.py @@ -0,0 +1,45 @@ +import numpy as np + + +def load_data(): + # 从文件导入数据 + datafile = './housing.data' + data = np.fromfile(datafile, sep=' ', dtype=np.float32) + + # 每条数据包括14项，其中前面13项是影响因素，第14项是相应的房屋价格中位数 + feature_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', \ + 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] + feature_num = len(feature_names) + + # 将原始数据进行Reshape，变成[N, 14]这样的形状 + data = data.reshape([data.shape[0] // feature_num, feature_num]) + + # 将原数据集拆分成训练集和测试集 + # 这里使用80%的数据做训练，20%的数据做测试 + # 测试集和训练集必须是没有交集的 + ratio = 0.8 + offset = int(data.shape[0] * ratio) + training_data = data[:offset] + + # 计算train数据集的最大值，最小值 + maximums, minimums = training_data.max(axis=0), training_data.min(axis=0) + + # 记录数据的归一化参数，在预测时对数据做归一化 + global max_values + global min_values + + max_values = maximums + min_values = minimums + + # 对数据进行归一化处理 + for i in range(feature_num): + data[:, i] = (data[:, i] - min_values[i]) / (maximums[i] - minimums[i]) + + # 训练集和测试集的划分比例 + training_data = data[:offset] + test_data = data[offset:] + return training_data, test_data,max_values,min_values +# 验证数据集读取程序的正确性 +training_data, test_data, max_values,min_values = load_data() +print(training_data.shape) +print(training_data[1,:]) \ No newline at end of file diff --git a/backend/apps/algorithms/algorithm/other/house_prices_predict/model_predict.py b/backend/apps/algorithms/algorithm/other/house_prices_predict/model_predict.py new file mode 100644 index 0000000..388208c --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/house_prices_predict/model_predict.py @@ -0,0 +1,36 @@ +import paddle +import numpy as np + +from .load_data import load_data +from .model_train import model + +# 加载数据 +training_data, test_data , max_values,min_values = load_data() + +def load_one_example(): + # 从上边已加载的测试集中，随机选择一条作为测试数据 + idx = np.random.randint(0, test_data.shape[0]) + idx = -10 + one_data, label = test_data[idx, :-1], test_data[idx, -1] + # 修改该条数据shape为[1,13] + one_data = one_data.reshape([1,-1]) + + return one_data, label + +# 参数为保存模型参数的文件地址 +model_dict = paddle.load('LR_model.pdparams') +model.load_dict(model_dict) +model.eval() + +# 参数为数据集的文件地址 +one_data, label = load_one_example() +# 将数据转为动态图的variable格式 +one_data = paddle.to_tensor(one_data) +predict = model(one_data) + +# 对结果做反归一化处理 +predict = predict * (max_values[-1] - min_values[-1]) + min_values[-1] +# 对label数据做反归一化处理 +label = label * (max_values[-1] - min_values[-1]) + min_values[-1] + +print("Inference result is {}, the corresponding label is {}".format(predict.numpy(), label)) diff --git a/backend/apps/algorithms/algorithm/other/house_prices_predict/model_train.py b/backend/apps/algorithms/algorithm/other/house_prices_predict/model_train.py new file mode 100644 index 0000000..30e833b --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/house_prices_predict/model_train.py @@ -0,0 +1,68 @@ +import paddle +from paddle.nn import Linear +import paddle.nn.functional as F +import numpy as np + +from .load_data import load_data + + +class Regressor(paddle.nn.Layer): + + # self代表类的实例自身 + def __init__(self): + # 初始化父类中的一些参数 + super(Regressor, self).__init__() + + # 定义一层全连接层，输入维度是13，输出维度是1 + self.fc = Linear(in_features=13, out_features=1) + + # 网络的前向计算 + def forward(self, inputs): + x = self.fc(inputs) + return x + +# 声明定义好的线性回归模型 +model = Regressor() +# 开启模型训练模式 +model.train() +# 加载数据 +training_data, test_data, max_values,min_values = load_data() +# 定义优化算法，使用随机梯度下降SGD +# 学习率设置为0.01 +opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) + +EPOCH_NUM = 10 # 设置外层循环次数 +BATCH_SIZE = 10 # 设置batch大小 + +# 定义外层循环 +for epoch_id in range(EPOCH_NUM): + # 在每轮迭代开始之前，将训练数据的顺序随机的打乱 + np.random.shuffle(training_data) + # 将训练数据进行拆分，每个batch包含10条数据 + mini_batches = [training_data[k:k + BATCH_SIZE] for k in range(0, len(training_data), BATCH_SIZE)] + # 定义内层循环 + for iter_id, mini_batch in enumerate(mini_batches): + x = np.array(mini_batch[:, :-1]) # 获得当前批次训练数据 + y = np.array(mini_batch[:, -1:]) # 获得当前批次训练标签（真实房价） + # 将numpy数据转为飞桨动态图tensor的格式 + house_features = paddle.to_tensor(x) + prices = paddle.to_tensor(y) + + # 前向计算 + predicts = model(house_features) + + # 计算损失 + loss = F.square_error_cost(predicts, label=prices) + avg_loss = paddle.mean(loss) + if iter_id % 20 == 0: + print("epoch: {}, iter: {}, loss is: {}".format(epoch_id, iter_id, avg_loss.numpy())) + + # 反向传播，计算每层参数的梯度值 + avg_loss.backward() + # 更新参数，根据设置好的学习率迭代一步 + opt.step() + # 清空梯度变量，以备下一轮计算 + opt.clear_grad() + +paddle.save(model.state_dict(), 'LR_model.pdparams') +print("模型保存成功，模型参数保存在LR_model.pdparams中") diff --git a/backend/apps/algorithms/algorithm/other/house_prices_predict/network.py b/backend/apps/algorithms/algorithm/other/house_prices_predict/network.py new file mode 100644 index 0000000..95cfa16 --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/house_prices_predict/network.py @@ -0,0 +1,106 @@ +import numpy as np +from matplotlib import pyplot as plt + +# 定义神经网络 +class Network(object): + def __init__(self, num_of_weights): + # 随机产生w的初始值 + # 为了保持程序每次运行结果的一致性，此处设置固定的随机数种子 + # np.random.seed(0) + self.w = np.random.randn(num_of_weights, 1) + self.b = 0. + + def forward(self, x): + z = np.dot(x, self.w) + self.b + return z + + def loss(self, z, y): + error = z - y + num_samples = error.shape[0] + cost = error * error + cost = np.sum(cost) / num_samples + return cost + + def gradient(self, x, y): + z = self.forward(x) + N = x.shape[0] + gradient_w = 1. / N * np.sum((z - y) * x, axis=0) + gradient_w = gradient_w[:, np.newaxis] + gradient_b = 1. / N * np.sum(z - y) + return gradient_w, gradient_b + + def update(self, gradient_w, gradient_b, eta=0.01): + self.w = self.w - eta * gradient_w + self.b = self.b - eta * gradient_b + + def train(self, training_data, num_epochs, batch_size=10, eta=0.01): + n = len(training_data) + losses = [] + for epoch_id in range(num_epochs): + # 在每轮迭代开始之前，将训练数据的顺序随机打乱 + # 然后再按每次取batch_size条数据的方式取出 + np.random.shuffle(training_data) + # 将训练数据进行拆分，每个mini_batch包含batch_size条的数据 + mini_batches = [training_data[k:k + batch_size] for k in range(0, n, batch_size)] + for iter_id, mini_batch in enumerate(mini_batches): + # print(self.w.shape) + # print(self.b) + x = mini_batch[:, :-1] + y = mini_batch[:, -1:] + a = self.forward(x) + loss = self.loss(a, y) + gradient_w, gradient_b = self.gradient(x, y) + self.update(gradient_w, gradient_b, eta) + losses.append(loss) + print('Epoch {:3d} / iter {:3d}, loss = {:.4f}'. + format(epoch_id, iter_id, loss)) + + return losses + +# 获取数据 +# 从文件导入数据 +datafile = './housing.data' +housing_data = np.fromfile(datafile, sep=' ') +feature_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE','DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] +feature_num = len(feature_names) +# 将原始数据进行Reshape，变成[N, 14]这样的形状 +housing_data = housing_data.reshape([housing_data.shape[0] // feature_num, feature_num]) + +features_max = housing_data.max(axis=0) +features_min = housing_data.min(axis=0) +features_avg = housing_data.sum(axis=0) / housing_data.shape[0] +def feature_norm(input): + f_size = input.shape + output_features = np.zeros(f_size, np.float32) + for batch_id in range(f_size[0]): + for index in range(13): + output_features[batch_id][index] = (input[batch_id][index] - features_avg[index]) / (features_max[index] - features_min[index]) + return output_features + +# 只对属性进行归一化 +housing_features = feature_norm(housing_data[:, :13]) +# print(feature_trian.shape) +housing_data = np.c_[housing_features, housing_data[:, -1]].astype(np.float32) +# 拆分训练数据 +ratio = 0.8 +offset = int(housing_data.shape[0] * ratio) +train_data = housing_data[:offset] +test_data = housing_data[offset:] + +# train_data, test_data = load_data() +x = train_data[:, :-1] +y = train_data[:, -1:] + +# 创建网络 +net = Network(13) +# 启动训练 +losses = net.train(train_data, num_epochs=50, batch_size=100, eta=0.1) + +# 画出损失函数的变化趋势 +plot_x = np.arange(len(losses)) +plot_y = np.array(losses) +plt.plot(plot_x, plot_y) +plt.show() + +np.save('../w.npy', net.w) +np.save('../b.npy', net.b) \ No newline at end of file diff --git a/backend/apps/algorithms/algorithm/other/image_detection/GPU_test.py b/backend/apps/algorithms/algorithm/other/image_detection/GPU_test.py new file mode 100644 index 0000000..403dc20 --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/image_detection/GPU_test.py @@ -0,0 +1,3 @@ +import torch +print(torch.cuda.is_available()) # 输出 True 表示支持 GPU +print(torch.cuda.get_device_name(0)) # 检查 GPU 名称 diff --git a/backend/apps/algorithms/algorithm/other/image_detection/__init__.py b/backend/apps/algorithms/algorithm/other/image_detection/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/other/image_detection/download_dataset.py b/backend/apps/algorithms/algorithm/other/image_detection/download_dataset.py new file mode 100644 index 0000000..379858b --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/image_detection/download_dataset.py @@ -0,0 +1,68 @@ +import os +import requests +from pycocotools.coco import COCO + + +def download_coco_dataset(output_dir, category_names): + """ + 从 COCO 数据集中下载指定类别的图像。 + + :param output_dir: 图像保存的目标文件夹 + :param category_names: 需要下载的类别列表，例如 ['fish', 'carp', 'salmon'] + """ + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + + # 加载 COCO 数据集注释文件 + annotation_file = 'D:/project_space/coco/annotations_trainval2017/annotations/instances_train2017.json' + coco = COCO(annotation_file) + + # 下载每个类别的图片 + for category_name in category_names: + print(f"Processing category: {category_name}") + + # 获取类别 ID + cat_ids = coco.getCatIds(catNms=[category_name]) + if not cat_ids: + print(f"Category '{category_name}' not found in COCO dataset.") + continue + + # 获取图像 ID + img_ids = coco.getImgIds(catIds=cat_ids) + print(f"Found {len(img_ids)} images for category '{category_name}'.") + img_ids=img_ids[:2050] + # 下载图像 + for img_id in img_ids: + img_info = coco.loadImgs(img_id)[0] + img_url = img_info['coco_url'] + img_filename = os.path.join(output_dir, img_info['file_name']) + + # 检查是否已下载 + if not os.path.exists(img_filename): + img_data = requests.get(img_url).content + with open(img_filename, 'wb') as f: + f.write(img_data) + print(f"Downloaded: {img_info['file_name']}") + + print("Download completed.") + + +# 加载 COCO 注释文件 +# annotation_file = 'D:/project_space/coco/annotations_trainval2017/annotations/instances_train2017.json' +# coco = COCO(annotation_file) +# +# # 获取所有类别的名称 +# categories = coco.loadCats(coco.getCatIds()) +# category_names = [cat['name'] for cat in categories] +# +# print("Available categories in COCO dataset:") +# print(category_names) +# ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'] + +# 示例调用 +download_coco_dataset( + output_dir='D:/project_space/animal_dataset/cat', + # category_names=['cat', 'dog', 'cow', 'sheep','horse'] + category_names=['cat'] +) + diff --git a/backend/apps/algorithms/algorithm/other/image_detection/image_detection.py b/backend/apps/algorithms/algorithm/other/image_detection/image_detection.py new file mode 100644 index 0000000..6286799 --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/image_detection/image_detection.py @@ -0,0 +1,178 @@ +import base64 +import json +import os +import tempfile +import time +from datetime import datetime + +import cv2 +from django.shortcuts import render +from rest_framework import status +from rest_framework.decorators import api_view, parser_classes +from rest_framework.parsers import MultiPartParser +from rest_framework.response import Response +from ultralytics import YOLO + +from .status_dict import status_dict + + +def object_defect(img_url, model_name): + try: + + predict_model = YOLO(model_name) + predict_model.overrides = { + 'save_txt': False, + 'save_conf': False, + 'save_crop': False, + 'show': False, + 'conf': 0.2 + } + + results = predict_model.predict(source=img_url, save=False) + pred_result = [] + + for result in results: + for data in result.boxes.data: + name_key = int(data[5]) + code = result.names.get(name_key) + name = status_dict.get(code, code) + one_box = { + 'code': code, + 'name': name, + 'score': round(data[4].tolist(), 2), + 'x1': int(data[0]), + 'y1': int(data[1]), + 'x2': int(data[2]), + 'y2': int(data[3]) + } + pred_result.append(one_box) + + result_json = { + 'image': get_detection_image(img_url, pred_result), + 'obj_list': pred_result, + 'time': datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S') + } + + return json.dumps(result_json) + except Exception as e: + log_str = f'发生错误: {e}' + print(log_str) + return '{}' + + +def get_detection_image(img_url, pred_result): + # 读取原始图像 + img = cv2.imread(img_url) + + # 预设颜色数组 + preset_colors = [ + (0, 0, 255), # 红色 + (20, 20, 255), + (20, 40, 255), + (0, 60, 255), + (0, 80, 255), + (0, 100, 255), + (0, 120, 255), + (0, 140, 255), + (0, 160, 255), + (0, 180, 255), + (0, 200, 255), + (0, 220, 255), + (0, 255, 255), + ] + + # 创建颜色映射表，将类别映射到颜色 + category_colors = {} + + for i, obj in enumerate(pred_result): + category = obj['code'] + + # 如果该类别还没有颜色，从预设数组中获取颜色 + if category not in category_colors: + category_colors[category] = get_preset_color(i, preset_colors) + + x1, y1, x2, y2 = obj['x1'], obj['y1'], obj['x2'], obj['y2'] + + # 使用映射表中的颜色 + color = category_colors[category] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) + + # 获取文本框的宽度 + font = cv2.FONT_HERSHEY_SIMPLEX # 选择一个合适的圆润字体，可以根据实际情况调整 + + text = f"{category}-{obj['score']}" + (text_width, _), _ = cv2.getTextSize(text, font, 0.5, 1) + # 文本框底色为颜色 + cv2.rectangle(img, (x1, y1 - 20), (x1 + text_width, y1), color, -1) + # 文本显示 + # 使用cv2的字体列表查看可用字体 + cv2.putText(img, text, (x1, y1 - 5), font, 0.5, (255, 255, 255), 1) + + # 将图像转换为base64 + _, img_encoded = cv2.imencode('.jpg', img) + img_base64 = base64.b64encode(img_encoded.tobytes()).decode('utf-8') + return img_base64 + +def get_preset_color(index, preset_colors): + # 从预设颜色数组中获取颜色，循环使用 + return preset_colors[index % len(preset_colors)] + + +def get_file_extension(filename): + # 获取文件后缀，如果不是支持的后缀，则使用默认 ".jpg" + _, file_extension = os.path.splitext(filename) + if file_extension.lower() in ['.jpg', '.png']: + return file_extension.lower() + else: + return '.jpg' # 默认后缀 + +@api_view(['POST']) +@parser_classes([MultiPartParser]) +def get_imagedetect_result(request): + # 检查请求中是否包含文件 + if 'image' not in request.data: + return Response({'error': 'No image file provided'}, status=status.HTTP_400_BAD_REQUEST) + + # 获取上传的图片文件 + image_file = request.data['image'] + + try: + # 创建临时文件，确保在with块结束时会自动删除 + with tempfile.NamedTemporaryFile(delete=False, suffix=get_file_extension(image_file.name)) as temp_file: + temp_file_path = temp_file.name + + # 将上传的图片内容写入临时文件 + temp_file.write(image_file.read()) + + # 获取检测类型 + detection_type = request.data['type'] + model_name = 'yolov8n' # 默认值 + + if detection_type == '01': + model_name = 'yolov8x' + elif detection_type == '02': + model_name = 'defect_detection' + elif detection_type == '03': + model_name = 'ryxw' + + current_dir = os.path.dirname(os.path.abspath(__file__)) + model_path = os.path.join(current_dir, "models", f"{model_name}.pt") + + # 进行图片检测 + result = object_defect(temp_file_path, model_path) + + # 返回检测结果 + return Response(result) + finally: + # 在finally块中确保删除临时文件，无论发生什么情况 + os.remove(temp_file_path) + + + +def detect_image_view(request): + return render(request, 'image_detect.html') + +if __name__ == '__main__': + img_url = 'D:/ImageDetect/dataset/ryxw20231117/train/images/08550f43-bc3a-4656-882a-023380d35b9b.jpg' + model_name = 'yolov8n' + object_defect(img_url, model_name) diff --git a/backend/apps/algorithms/algorithm/other/image_detection/model_export.py b/backend/apps/algorithms/algorithm/other/image_detection/model_export.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/other/image_detection/status_dict.py b/backend/apps/algorithms/algorithm/other/image_detection/status_dict.py new file mode 100644 index 0000000..345e62f --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/image_detection/status_dict.py @@ -0,0 +1,53 @@ +status_dict = { + "sly_dmyw": "渗漏油_地面油污", + "sly_bjbmyw": "渗漏油_部件表面油污", + "pzqcd": "膨胀器冲顶", + "jyhbx": "均压环变形", + "drqgd": "电容器鼓肚", + "jyz_pl": "绝缘子_破损", + "yxdgsg": "引线断股松股", + "jdyxxsd": "接地引下线松动", + "jsxs_ddjt": "金属锈蚀_导电接头", + "jsxs_ddyx": "金属锈蚀_导电引线", + "jsxs_jdyxx": "金属锈蚀_接地引下线", + "jsxs_ecjxh": "金属锈蚀_二次接线盒", + "hzyw": "设备烟火", + "bmwh": "表面污秽", + "ws_ywyc": "瓦斯油位异常", + "ws_ywzc": "瓦斯油位正常", + "yljdq_flow": "油流继电器_流动", + "yljdq_stop": "油流继电器_停止", + "fhz_f": "分合闸_分", + "fhz_h": "分合闸_合", + "fhz_ztyc": "分合闸_状态异常", + "hxq_gjbs": "呼吸器硅胶变色", + "hxq_gjzc": "呼吸器_硅胶正常", + "hxq_gjtps": "呼吸器_硅胶桶破损", + "hxq_yfps": "呼吸器油_封破损", + "ywzt_yfyc": "油位状态_油封异常", + "ywzt_yfzc": "油位状态_油封正常", + "bjzc": "表计正常", + "bj_wkps": "表计_外壳破损", + "bj_bpps": "表计_表盘破损", + "bj_bpmh": "表计_表盘模糊", + "bjdsyc_zz": "指针表表计读数异常", + "bjdsyc_sx": "数显表表计读数异常", + "gcc_mh": "观察窗_模糊", + "gcc_ps": "观察窗_破损", + "xmbhyc": "箱门闭合异常", + "xmbhzc": "箱门闭合正常", + "yw_nc": "异物_鸟巢", + "yw_gkxfw": "异物_挂空悬浮物", + "kgg_ybf": "开关柜_压板分", + "kgg_ybh": "开关柜_压板合", + "kk_f": "空开_分", + "kk_h": "空开_合", + "zsd_l": "指示灯_亮", + "zsd_m": "指示灯_灭", + "mbhp": "面板花屏", + "wcgz": "未穿工装", + "gzzc": "工装正常", + "wcaqm": "未穿安全帽", + "aqmzc": "安全帽正常", + "xy": "吸烟", +} diff --git a/backend/apps/algorithms/algorithm/other/image_detection/tensorflow.py b/backend/apps/algorithms/algorithm/other/image_detection/tensorflow.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/other/image_detection/yolo_detectimage.py b/backend/apps/algorithms/algorithm/other/image_detection/yolo_detectimage.py new file mode 100644 index 0000000..6f37a8c --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/image_detection/yolo_detectimage.py @@ -0,0 +1,127 @@ +import base64 +import json +import os +from datetime import datetime +import cv2 +from ultralytics import YOLO + + +class YoloDetector: + def __init__(self, model_path: str, image_path: str,category_names:str, conf_threshold: float = 0.2): + self.model_path = model_path + self.image_path = image_path + self.category_names=category_names; + self.conf_threshold = conf_threshold + self.model = YOLO(model_path) + self.model.overrides = { + 'save_txt': False, + 'save_conf': False, + 'save_crop': False, + 'show': False, + 'conf': self.conf_threshold + } + + def detect(self): + try: + # 进行检测 + results = self.model.predict(source=self.image_path, save=False) + pred_result = self.parse_detection_results(results) + + # 获取带标注的图像 + detection_image = self.get_detection_image(pred_result) + + result_json = { + 'image': detection_image, + 'obj_list': pred_result, + 'time': datetime.now().strftime('%Y-%m-%d %H:%M:%S') + } + + return json.dumps(result_json) + + except Exception as e: + print(f"Error during detection: {e}") + return json.dumps({'error': str(e)}) + + def parse_detection_results(self, results): + pred_result = [] + for result in results: + for data in result.boxes.data: + name_key = int(data[5]) + code = result.names.get(name_key) + if code in category_names: + one_box = { + 'code': code, + 'score': round(data[4].tolist(), 2), + 'x1': int(data[0]), + 'y1': int(data[1]), + 'x2': int(data[2]), + 'y2': int(data[3]) + } + pred_result.append(one_box) + return pred_result + + def get_detection_image(self, pred_result): + img = cv2.imread(self.image_path) + + preset_colors = [ + (0, 0, 255), (20, 20, 255), (20, 40, 255), (0, 60, 255), (0, 80, 255), + (0, 100, 255), (0, 120, 255), (0, 140, 255), (0, 160, 255), (0, 180, 255), + (0, 200, 255), (0, 220, 255), (0, 255, 255), + ] + + category_colors = {} + for i, obj in enumerate(pred_result): + category = obj['code'] + if category not in category_colors: + category_colors[category] = self.get_preset_color(i, preset_colors) + + x1, y1, x2, y2 = obj['x1'], obj['y1'], obj['x2'], obj['y2'] + color = category_colors[category] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) + + font = cv2.FONT_HERSHEY_SIMPLEX + text = f"{category}-{obj['score']}" + (text_width, _), _ = cv2.getTextSize(text, font, 0.5, 1) + cv2.rectangle(img, (x1, y1 - 20), (x1 + text_width, y1), color, -1) + cv2.putText(img, text, (x1, y1 - 5), font, 0.5, (255, 255, 255), 1) + + _, img_encoded = cv2.imencode('.jpg', img) + img_base64 = base64.b64encode(img_encoded.tobytes()).decode('utf-8') + return img_base64 + + def get_preset_color(self, index, preset_colors): + return preset_colors[index % len(preset_colors)] + + +# __main__ 方法 +if __name__ == '__main__': + img_dir = 'D:/project_space/animal_dataset' + output_dir = 'D:/project_space/animal_detect' + model_path = '/yolo11x.pt' + category_names=['cat', 'dog', 'cow', 'sheep','horse'] + os.makedirs(output_dir, exist_ok=True) # 创建输出目录 + + # 遍历输入目录下的所有图片 + for img_name in os.listdir(img_dir): + img_path = os.path.join(img_dir, img_name) + if not img_name.lower().endswith(('.jpg', '.jpeg', '.png')): + continue # 跳过非图片文件 + + # 初始化检测器 + detector = YoloDetector(model_path=model_path, image_path=img_path,category_names=category_names) + + # 检测并获取结果 + result_json = detector.detect() + result_data = json.loads(result_json) + + # 保存检测结果图像 + if 'image' in result_data: + output_path = os.path.join(output_dir, img_name) + img_data = base64.b64decode(result_data['image']) # 解码 Base64 + with open(output_path, 'wb') as f: + f.write(img_data) + print(f"保存检测结果到: {output_path}") + else: + print(f"检测失败: {img_name}, 错误信息: {result_data.get('error', '未知错误')}") + + print("所有图片检测完成！") diff --git a/backend/apps/algorithms/algorithm/other/time_trend_forecast/__init__.py b/backend/apps/algorithms/algorithm/other/time_trend_forecast/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/other/time_trend_forecast/dataset/timeserise.csv b/backend/apps/algorithms/algorithm/other/time_trend_forecast/dataset/timeserise.csv new file mode 100644 index 0000000..d78cb7d --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/time_trend_forecast/dataset/timeserise.csv @@ -0,0 +1,145 @@ +X1,X2 +0,112.0,265.0 +1,118.0,288.0 +2,132.0,333.0 +3,129.0,337.0 +4,121.0,305.0 +5,135.0,242.0 +6,148.0,315.0 +7,148.0,386.0 +8,136.0,291.0 +9,119.0,300.0 +10,104.0,198.0 +11,118.0,228.0 +12,115.0,347.0 +13,126.0,327.0 +14,141.0,285.0 +15,135.0,268.0 +16,125.0,310.0 +17,149.0,234.0 +18,170.0,192.0 +19,170.0,303.0 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+82,237.0,237.0 +83,278.0,258.0 +84,284.0,270.0 +85,277.0,361.0 +86,317.0,273.0 +87,313.0,351.0 +88,318.0,234.0 +89,374.0,377.0 +90,413.0,255.0 +91,405.0,338.0 +92,355.0,433.0 +93,306.0,297.0 +94,271.0,388.0 +95,306.0,330.0 +96,315.0,242.0 +97,301.0,286.0 +98,356.0,291.0 +99,348.0,207.0 +100,355.0,147.0 +101,422.0,188.0 +102,465.0,332.0 +103,467.0,347.0 +104,404.0,305.0 +105,347.0,358.0 +106,305.0,227.0 +107,336.0,296.0 +108,340.0,280.0 +109,318.0,307.0 +110,362.0,235.0 +111,348.0,294.0 +112,363.0,254.0 +113,435.0,263.0 +114,491.0,201.0 +115,505.0,193.0 +116,404.0,259.0 +117,359.0,347.0 +118,310.0,323.0 +119,337.0,276.0 +120,360.0,346.0 +121,342.0,216.0 +122,406.0,231.0 +123,396.0,220.0 +124,420.0,326.0 +125,472.0,232.0 +126,548.0,325.0 +127,559.0,324.0 +128,463.0,254.0 +129,407.0,223.0 +130,362.0,338.0 +131,405.0,333.0 +132,417.0,206.0 +133,391.0,248.0 +134,419.0,393.0 +135,461.0,227.0 +136,472.0,245.0 +137,535.0,268.0 +138,622.0,288.0 +139,606.0,201.0 +140,508.0,275.0 +141,461.0,349.0 +142,390.0,333.0 +143,432.0,339.0 diff --git a/backend/apps/algorithms/algorithm/other/time_trend_forecast/model_predict.py b/backend/apps/algorithms/algorithm/other/time_trend_forecast/model_predict.py new file mode 100644 index 0000000..7d8bd0a --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/time_trend_forecast/model_predict.py @@ -0,0 +1,94 @@ +import base64 +import io +import pickle + +import matplotlib + +matplotlib.rcParams['axes.unicode_minus']=False +import seaborn as sns +sns.set(font= "Kaiti",style="ticks",font_scale=1.4) +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.metrics import mean_absolute_error +## 忽略提醒 +import warnings +warnings.filterwarnings("ignore") + +def load_dataset(): + file_path = f'dataset/timeserise.csv' + df = pd.read_csv(file_path) + train = pd.DataFrame(df["X1"][0:120]) + test = pd.DataFrame(df["X1"][120:]) + all=df["X1"] + return train,test,all + +def preview_dataset(train,test): + ## 可视化出预测结果 + plt.figure(figsize=(14, 7)) + train["X1"].plot(figsize=(14, 7), label="训练数据") + test["X1"].plot(label="测试数据") + plt.legend() + plt.grid() + plt.title("某机场乘客流量变动趋势(训练和测试数据集[80:20])") + plt.show() + +def model_predict(train,test): + model_filename = f'models/trained_model.pkl' + with open(model_filename, 'rb') as model_file: + model = pickle.load(model_file) + + ## 可视化自动搜索参数获得的SARIMA(2, 0, 0)x(0, 1, 0, 12)对测试集进行预测 + pre, conf = model.predict(n_periods=24, alpha=0.05, + return_conf_int=True) + + ## 可视化SARIMAX(2, 0, 0)x(0, 1, 0, 12)的预测结果，整理数据 + y_hat = test.copy(deep=False) + y_hat["sarima_pre"] = pre + y_hat["sarima_pre_lower"] = conf[:, 0] + y_hat["sarima_pre_upper"] = conf[:, 1] + ## 可视化出预测结果 + plt.figure(figsize=(14, 7)) + train["X1"].plot(figsize=(14, 7), label="X1 train") + test["X1"].plot(label="X1 test") + y_hat["sarima_pre"].plot(style="g--o", lw=2, label="SARIMA") + ## 可视化出置信区间 + plt.fill_between(y_hat.index, y_hat["sarima_pre_lower"], + y_hat["sarima_pre_upper"], color='k', alpha=.15, + label="95%置信区间") + plt.legend() + plt.grid() + plt.title("某机场乘客流量变动趋势预测[测试集:20%]") + plt.show() + print("SARIMA模型预测的绝对值误差：",mean_absolute_error(test["X1"],y_hat["sarima_pre"])) + # 将图像捕获为内存中的二进制流 + # img_stream = io.BytesIO() + # plt.savefig(img_stream, format='png') + # img_stream.seek(0) + # # 将二进制流转换为base64编码 + # img_base64 = base64.b64encode(img_stream.read()).decode('utf-8') + # return img_base64 + return 0 + +# @api_view(['GET']) +# @parser_classes([MultiPartParser]) +# def get_timepredict_result(request): +# # 1、加载数据集 +# train, test, _ = load_dataset() +# # 2、调用模型进行预测 +# img_base64 = model_predict(train, test) +# result_json = { +# 'image': img_base64, +# 'time': datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S') +# } +# return Response(json.dumps(result_json)) +# +# def time_predict_view(request): +# return render(request, 'time_predict.html') + +if __name__ == '__main__': + # 1、加载数据集 + train, test,_=load_dataset() + # 2、调用模型进行预测 + img_base64=model_predict(train,test) + print(img_base64) + diff --git a/backend/apps/algorithms/algorithm/other/time_trend_forecast/model_train.py b/backend/apps/algorithms/algorithm/other/time_trend_forecast/model_train.py new file mode 100644 index 0000000..4e8b86b --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/time_trend_forecast/model_train.py @@ -0,0 +1,87 @@ +import pickle + +import matplotlib +matplotlib.rcParams['axes.unicode_minus']=False +import seaborn as sns +sns.set(font= "Kaiti",style="ticks",font_scale=1.4) +import pandas as pd +import matplotlib.pyplot as plt +from statsmodels.tsa.stattools import * +import statsmodels.api as sm +import pmdarima as pm +## 忽略提醒 +import warnings +warnings.filterwarnings("ignore") + +def load_dataset(): + file_path = 'dataset/timeserise.csv' + df = pd.read_csv(file_path) + train = pd.DataFrame(df["X1"][0:120]) + test = pd.DataFrame(df["X1"][120:]) + all=df["X1"] + return train,test,all + +def preview_dataset(train,test): + ## 可视化出预测结果 + plt.figure(figsize=(14, 7)) + train["X1"].plot(figsize=(14, 7), label="训练数据") + test["X1"].plot(label="测试数据") + plt.legend() + plt.grid() + plt.title("某机场乘客流量变动趋势(训练和测试数据集[80:20])") + plt.show() + +def white_noise_test(series, lags): + LB = sm.stats.diagnostic.acorr_ljungbox(series, lags=lags, return_df=True) + print("序列检验结果:\n", LB) + +def time_series_decomposition(series, decomposition_type): + X1_decomp = pm.arima.decompose(series.values, decomposition_type, m=12) + ax = pm.utils.decomposed_plot(X1_decomp, figure_kwargs={"figsize": (10, 6)}, show=False) + ax[0].set_title(f"机场客流量数据季节性趋势模型分解[{decomposition_type}]") + plt.show() + +def difference_stationarity_test(series): + diff_series = series.diff().dropna() + dftest = adfuller(diff_series, autolag='BIC') + dfoutput = pd.Series(dftest[0:4], index=['adf', 'p-value', 'usedlag', 'Number of Observations Used']) + print("一阶差分单位根检验结果:\n", dfoutput) + + +def kpss_stationarity_test_diff(series_diff): + dfkpss = kpss(series_diff) + dfoutput = pd.Series(dfkpss[0:3], index=["kpss_stat", " p-value", " usedlag"]) + print("一阶差分KPSS检验结果:\n", dfoutput) + +def train_model(train): + ## 针对模型自动寻找合适的参数 + model = pm.auto_arima(train["X1"].values, + start_p=1, start_q=1, # p,q的开始值 + max_p=12, max_q=12, # 最大的p和q + test="kpss", # 使用kpss检验确定d + d=None, # 自动选择合适的d + m=12, # 序列的周期 + seasonal=True, # 有季节性趋势 + start_P=0, start_Q=0, # P,Q的开始值 + max_P=5, max_Q=5, # 最大的P和Q + D=None, # 自动选择合适的D + trace=True, error_action='ignore', + suppress_warnings=True, stepwise=True) + print(model.summary()) + # 将训练好的模型保存到文件 + model_filename= f'models/trained_model.pkl' + with open(model_filename, 'wb') as model_file: + pickle.dump(model, model_file) + return model + +if __name__ == '__main__': + # 1、加载数据集 + train, test,all=load_dataset() + # 2、预览数据集 + preview_dataset(train, test) + # 3、数据白噪声测试，是否自相关，如果不相关（无内在规律，则不能预测） + white_noise_test(all, lags=[4, 8, 16, 32,64,128]) + # 4、时间序列分解 + time_series_decomposition(all, "multiplicative") + # 5、训练模型，获取最优参数 + model=train_model(train) diff --git a/backend/apps/algorithms/algorithm/other/time_trend_forecast/models/trained_model.pkl b/backend/apps/algorithms/algorithm/other/time_trend_forecast/models/trained_model.pkl new file mode 100644 index 0000000..2c6ceb0 Binary files /dev/null and b/backend/apps/algorithms/algorithm/other/time_trend_forecast/models/trained_model.pkl differ diff --git a/backend/apps/algorithms/algorithm/other/time_trend_forecast/time_example.py b/backend/apps/algorithms/algorithm/other/time_trend_forecast/time_example.py new file mode 100644 index 0000000..fa24882 --- /dev/null +++ b/backend/apps/algorithms/algorithm/other/time_trend_forecast/time_example.py @@ -0,0 +1,176 @@ + +import matplotlib +matplotlib.rcParams['axes.unicode_minus']=False +import seaborn as sns +sns.set(font= "Kaiti",style="ticks",font_scale=1.4) +import pandas as pd +import matplotlib.pyplot as plt +from statsmodels.tsa.stattools import * +import statsmodels.api as sm +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf +import pmdarima as pm +## 忽略提醒 +import warnings +warnings.filterwarnings("ignore") + + +## （1） 加载显示数据源，读取时间序列数据,该数据包含:X1为飞机乘客数据,X2为一组随机数据 +df = pd.read_csv('dataset/timeserise.csv') +## 查看数据的变化趋势 +# df.plot(kind = "line",figsize = (10,6)) +# plt.grid() +# plt.title("时序数据") +## plt.show() + +## （2）序列的白噪声检测 +## 该检验用来检查序列是否为随机序列，如果是随机序列，那它们的值之间没有任何关系 +## 使用LB检验来检验序列是否为白噪声，原假设为在延迟期数内序列之间相互独立。 +## 如果P值小于0.05，说明序列之间不独立，不是白噪声 +# lags = [4,8,16,32] +# LB = sm.stats.diagnostic.acorr_ljungbox(df["X1"],lags = lags,return_df = True) +# print("序列X1的检验结果:\n",LB) +# +# ## （3）序列的平稳性检测 +# # 序列的单位根检验，即检验序列的平稳性 +# dftest = adfuller(df["X1"],autolag='BIC') +# dfoutput = pd.Series(dftest[0:4], index=['adf','p-value','usedlag','Number of Observations Used']) +# print("X1单位根检验结果:\n",dfoutput) +# +# ## （4）一阶差分后的平稳性检测 +# ## 对X1进行一阶差分后的序列进行检验：一阶差分后 P值大于0.05, 小于0.1，可以认为其是平稳的 +# X1diff = df["X1"].diff().dropna() +# dftest = adfuller(X1diff,autolag='BIC') +# dfoutput = pd.Series(dftest[0:4], index=['adf','p-value','usedlag','Number of Observations Used']) +# print("X1一阶差分单位根检验结果:\n",dfoutput) +# +# +# ## （5）序列KPSS平稳性检测 +# ## KPSS检验的原假设为：序列x是平稳的。 +# +# ## 对序列X1使用KPSS检验平稳性 +# dfkpss = kpss(df["X1"]) +# dfoutput = pd.Series(dfkpss[0:3], index=["kpss_stat"," p-value"," usedlag"]) +# print("X1 KPSS检验结果:\n",dfoutput) +# ## 拒绝序列平稳的原假设 +# +# ## 对序列X1使用KPSS检验平稳性 +# dfkpss = kpss(X1diff) +# dfoutput = pd.Series(dfkpss[0:3], index=["kpss_stat"," p-value"," usedlag"]) +# print("X1一阶差分KPSS检验结果:\n",dfoutput) +# +# ## （6）检验ARIMA模型的参数d +# ## 序列KPSS平稳性检测 接受序列平稳的原假设 +# X1d = pm.arima.ndiffs(df["X1"], alpha=0.05, test="kpss", max_d=3) +# print("使用KPSS方法对序列X1的参数d取值进行预测,d = ",X1d) +# +# X1diffd = pm.arima.ndiffs(X1diff, alpha=0.05, test="kpss", max_d=3) +# print("使用KPSS方法对序列X1一阶差分后的参数d取值进行预测,d = ",X1diffd) +# +# +# ## （7）检验SARIMA模型的参数季节阶数D +# X1d = pm.arima.nsdiffs(df["X1"], 12, max_D=2) +# print("对序列X1的季节阶数D取值进行预测,D = ",X1d) +# +# X1diffd = pm.arima.nsdiffs(X1diff, 12, max_D=2) +# print("序列X1一阶差分后的季节阶数D取值进行预测,D = ",X1diffd) +# +# ## 对非随机序列X1进行自相关和偏相关分析可视化 +# fig = plt.figure(figsize=(16,5)) +# plt.subplot(1,3,1) +# plt.plot(df["X1"],"r-") +# plt.grid() +# plt.title("X1序列波动") +# ax = fig.add_subplot(1,3,2) +# plot_acf(df["X1"], lags=60,ax = ax) +# plt.grid() +# ax = fig.add_subplot(1,3,3) +# plot_pacf(df["X1"], lags=60,ax = ax) +# plt.ylim([-1,1]) +# plt.grid() +# plt.tight_layout() +# ## plt.show() +# +# ## 对非随机序列X1一阶差分后的序列进行自相关和偏相关分析可视化 +# fig = plt.figure(figsize=(16,5)) +# plt.subplot(1,3,1) +# plt.plot(X1diff,"r-") +# plt.grid() +# plt.title("X1序列一阶差分后波动") +# ax = fig.add_subplot(1,3,2) +# plot_acf(X1diff, lags=60,ax = ax) +# plt.grid() +# ax = fig.add_subplot(1,3,3) +# plot_pacf(X1diff, lags=60,ax = ax) +# plt.grid() +# plt.tight_layout() +# ## plt.show() +# +# ## 时间序列的分解 +# ## 通过观察序列X1，可以发现其既有上升的趋势，也有周期性的趋势，所以可以将该序列进行分解 +# ## 使用乘法模型分解结果(通常适用于有增长趋势的序列) +# X1decomp = pm.arima.decompose(df["X1"].values,"multiplicative", m=12) +# ## 可视化出分解的结果 +# ax = pm.utils.decomposed_plot(X1decomp,figure_kwargs = {"figsize": (10, 6)}, +# show=False) +# ax[0].set_title("乘法模型分解结果") +# ## plt.show() +# +# ## 使用加法模型分解结果(通常适用于平稳趋势的序列) +# X1decomp = pm.arima.decompose(X1diff.values,"additive", m=12) +# ## 可视化出分解的结果 +# ax = pm.utils.decomposed_plot(X1decomp,figure_kwargs = {"figsize": (10, 6)}, +# show=False) +# ax[0].set_title("加法模型分解结果") +# plt.show() + +## 使用可以拟合周期性的SARIMA模型进行建模 +## 数据准备 +## 对序列X1进行切分，后面的24个数据用于测试集 +train = pd.DataFrame(df["X1"][0:120]) +test = pd.DataFrame(df["X1"][120:]) +# ## 可视化切分后的数据 +# train["X1"].plot(figsize=(14,7), title= "乘客数量数据",label = "X1 train") +# test["X1"].plot(label = "X1 test") +# plt.legend() +# plt.grid() +# plt.show() + +## 针对模型自动寻找合适的参数 +model = pm.auto_arima(train["X1"].values, + start_p=1, start_q=1, # p,q的开始值 + max_p=12, max_q=12, # 最大的p和q + test="kpss", # 使用kpss检验确定d + d = None, # 自动选择合适的d + m=12, # 序列的周期 + seasonal=True, # 有季节性趋势 + start_P = 0,start_Q = 0, # P,Q的开始值 + max_P=5, max_Q=5, # 最大的P和Q + D = None, # 自动选择合适的D + trace=True,error_action='ignore', + suppress_warnings=True, stepwise=True) + +print(model.summary()) + +## 可视化自动搜索参数获得的SARIMA(2, 0, 0)x(0, 1, 0, 12)对测试集进行预测 +pre, conf = model.predict(n_periods=24, alpha=0.05, + return_conf_int=True) + +## 可视化SARIMAX(2, 0, 0)x(0, 1, 0, 12)的预测结果，整理数据 +y_hat = test.copy(deep = False) +y_hat["sarima_pre"] = pre +y_hat["sarima_pre_lower"] = conf[:,0] +y_hat["sarima_pre_upper"] = conf[:,1] +## 可视化出预测结果 +plt.figure(figsize=(14,7)) +train["X1"].plot(figsize=(14,7),label = "X1 train") +test["X1"].plot(label = "X1 test") +y_hat["sarima_pre"].plot(style="g--o", lw=2,label="SARIMA") +## 可视化出置信区间 +plt.fill_between(y_hat.index, y_hat["sarima_pre_lower"], + y_hat["sarima_pre_upper"],color='k',alpha=.15, + label = "95%置信区间") +plt.legend() +plt.grid() +plt.title("SARIMA(2,0,0)x(0,1,0,12)模型") +plt.show() + diff --git a/backend/apps/algorithms/algorithm/pytorch/DeeplabReporter.py b/backend/apps/algorithms/algorithm/pytorch/DeeplabReporter.py new file mode 100644 index 0000000..749a85f --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/DeeplabReporter.py @@ -0,0 +1,93 @@ +import base64 +import os +from datetime import datetime + + +class DeeplabReporter: + def __init__(self, project_path: str, train_name: str = "train_name"): + self.project_path = project_path + self.train_name = train_name + + @staticmethod + def image_to_base64(image_path: str) -> str: + if not os.path.exists(image_path): + return "" + with open(image_path, "rb") as f: + return base64.b64encode(f.read()).decode("utf-8") + + def generate_report( + self, + dataset_name: str, + model_name: str, + training_epochs: str, + training_duration: str, + num_classes: int, + best_val_loss: float, + image_urls: dict, + ): + html_template = """ + + + + + + 语义分割模型训练报告 + + + +

+

{{train_name}}-训练结果评估报告

+

1.训练摘要

+
报告标题：{{train_name}}
+
生成日期：{{report_date}}
+
模型名称：{{model_name}}
+
训练数据集：{{dataset_name}}
+
类别数：{{num_classes}}
+
训练轮次：{{training_epochs}}
+
训练时长：{{training_duration}}
+
最优验证损失：{{best_val_loss}}
+
+
+

2.训练结果图片

+
+

2.1 Loss 曲线

+ Loss 曲线 +
+
+
+

由系统自动生成 - 语义分割训练报告

+
+ + +""" + report_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + loss_graph_base64 = self.image_to_base64(image_urls.get("loss_graph", "")) if image_urls else "" + html_content = ( + html_template.replace("{{train_name}}", str(self.train_name)) + .replace("{{report_date}}", str(report_date)) + .replace("{{model_name}}", str(model_name)) + .replace("{{dataset_name}}", str(dataset_name)) + .replace("{{num_classes}}", str(num_classes)) + .replace("{{training_epochs}}", str(training_epochs)) + .replace("{{training_duration}}", str(training_duration)) + .replace("{{best_val_loss}}", str(best_val_loss)) + .replace("{{loss_graph_base64}}", loss_graph_base64) + ) + os.makedirs(self.project_path, exist_ok=True) + report_path = os.path.join(self.project_path, "train_report.html") + with open(report_path, "w", encoding="utf-8") as f: + f.write(html_content) + try: + os.chmod(report_path, 0o777) + except Exception: + pass + return report_path + diff --git a/backend/apps/algorithms/algorithm/pytorch/DeeplabTrainer.py b/backend/apps/algorithms/algorithm/pytorch/DeeplabTrainer.py new file mode 100644 index 0000000..05b87a7 --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/DeeplabTrainer.py @@ -0,0 +1,318 @@ +import argparse +import glob +import json +import os +import sys + +import numpy as np +import torch +import torch.nn as nn +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision.models.segmentation import DeepLabV3_ResNet50_Weights, deeplabv3_resnet50 +from torchvision.transforms import functional as F + +current_dir = os.path.dirname(os.path.abspath(__file__)) +project_root = os.path.abspath(os.path.join(current_dir, "../../../../")) +def _setup_django(): + if project_root not in sys.path: + sys.path.append(project_root) + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") + import django + + django.setup() + from django.conf import settings + from django.shortcuts import get_object_or_404 + from django.utils.timezone import now + + from apps.core.models import AiAlgorithm, AiAlgorithmClass, AiAlgorithmTrainRecords + + return settings, get_object_or_404, now, AiAlgorithm, AiAlgorithmClass, AiAlgorithmTrainRecords + +try: + from .DeeplabReporter import DeeplabReporter +except Exception: + from apps.algorithms.algorithm.pytorch.DeeplabReporter import DeeplabReporter + + +def _emit(payload): + print(json.dumps(payload, ensure_ascii=False)) + + +class RoadSegDataset(Dataset): + def __init__(self, root, split="train", size=640): + self.root = root + self.split = split + self.size = int(size) if size else None + self.images_dir = os.path.join(root, "images", split) + self.masks_dir = os.path.join(root, "masks", split) + self.images = [] + for ext in ("*.jpg", "*.jpeg", "*.png", "*.bmp"): + self.images.extend(glob.glob(os.path.join(self.images_dir, ext))) + self.images = sorted(self.images) + + def __len__(self): + return len(self.images) + + def __getitem__(self, idx): + img_path = self.images[idx] + name = os.path.splitext(os.path.basename(img_path))[0] + mask_path = os.path.join(self.masks_dir, f"{name}.png") + img = Image.open(img_path).convert("RGB") + mask = Image.open(mask_path) + if self.size: + img = F.resize(img, [self.size, self.size]) + mask = F.resize(mask, [self.size, self.size], interpolation=F.InterpolationMode.NEAREST) + img_t = F.to_tensor(img) + mask_arr = np.array(mask, dtype=np.int64) + mask_t = torch.from_numpy(mask_arr).long() + return img_t, mask_t + + +def freeze_bn(module): + if isinstance(module, nn.BatchNorm2d): + module.eval() + module.requires_grad_(False) + + +def evaluate(model, loader, device, criterion): + model.eval() + total_loss = 0.0 + count = 0 + with torch.no_grad(): + for images, masks in loader: + images = images.to(device, non_blocking=True) + masks = masks.to(device, non_blocking=True) + outputs = model(images) + loss = criterion(outputs["out"], masks) + total_loss += float(loss.item()) + count += 1 + return total_loss / max(1, count) + + +def _save_loss_plot(out_path, train_losses, val_losses): + try: + import matplotlib.pyplot as plt + except Exception: + return False + x = list(range(1, len(train_losses) + 1)) + plt.figure(figsize=(10, 5)) + plt.plot(x, train_losses, label="train") + plt.plot(x, val_losses, label="val") + plt.xlabel("epoch") + plt.ylabel("loss") + plt.legend() + plt.tight_layout() + plt.savefig(out_path) + plt.close() + try: + os.chmod(out_path, 0o777) + except Exception: + pass + return True + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--data", type=str, required=True) + p.add_argument("--project_path", type=str, required=True) + p.add_argument("--run_name", type=str, default="train") + p.add_argument("--epochs", type=int, default=50) + p.add_argument("--batch", type=int, default=4) + p.add_argument("--imgsz", type=int, default=640) + p.add_argument("--workers", type=int, default=4) + p.add_argument("--device", type=str, default="cpu") + p.add_argument("--lr_backbone", type=float, default=1e-4) + p.add_argument("--lr_head", type=float, default=1e-3) + p.add_argument("--weight_decay", type=float, default=1e-4) + p.add_argument("--trainid", type=str, default="0") + p.add_argument("--pretrained", type=str, default="1") + p.add_argument("--torch_home", type=str, default="") + p.add_argument("--pretrained_weights", type=str, default="") + return p.parse_args() + + +def main(): + try: + settings, get_object_or_404, now, AiAlgorithm, AiAlgorithmClass, AiAlgorithmTrainRecords = _setup_django() + args = parse_args() + if args.torch_home: + torch_home = os.path.abspath(os.path.expanduser(str(args.torch_home))) + os.makedirs(torch_home, exist_ok=True) + os.environ["TORCH_HOME"] = torch_home + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=args.trainid) + train_params = json.loads(ai_train.train_params) if ai_train.train_params else [] + train_labels = json.loads(ai_train.train_labels) if ai_train.train_labels else [] + + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join(filespace_root_path, ai_algorithm_class.classcode, ai_algorithm.algorithm_code, "trains", ai_train.train_code) + + run_name = args.run_name + project_path = args.project_path + weights_dir = os.path.join(project_path, run_name, "weights") + os.makedirs(weights_dir, exist_ok=True) + + label_map_path = os.path.join(args.data, "label_map.json") + if os.path.exists(label_map_path): + with open(label_map_path, "r", encoding="utf-8") as f: + label_map = json.loads(f.read()) + else: + label_map = {"background": 0} + idx = 1 + for it in train_labels: + lab = str(it.get("label") or "").strip() if isinstance(it, dict) else "" + if lab and lab not in label_map: + label_map[lab] = idx + idx += 1 + with open(label_map_path, "w", encoding="utf-8") as f: + f.write(json.dumps(label_map, ensure_ascii=False)) + num_classes = max(label_map.values()) + 1 if label_map else 2 + + device = torch.device(args.device if args.device != "0" else "cuda:0") + if str(args.device).lower() in ("cpu", "-1"): + device = torch.device("cpu") + if str(args.device).startswith("cuda"): + device = torch.device(args.device) + if str(args.device).isdigit(): + device = torch.device(f"cuda:{args.device}") if torch.cuda.is_available() else torch.device("cpu") + + train_start_time = now() + _emit({"type": "starttime", "text": f"|----------------------------训练开始时间：{train_start_time.strftime('%Y-%m-%d %H:%M:%S')}-------------------------------|"}) + + train_dataset = RoadSegDataset(args.data, split="train", size=args.imgsz) + val_dataset = RoadSegDataset(args.data, split="val", size=args.imgsz) + workers = int(args.workers) + if os.name == "nt" and workers > 0: + _emit({"type": "waiting", "text": f"检测到 Windows 环境，为避免 DataLoader 多进程卡死，将 workers 从 {workers} 调整为 0"}) + workers = 0 + _emit({"type": "waiting", "text": f"数据集加载完成：train={len(train_dataset)} val={len(val_dataset)} workers={workers}"}) + train_loader = DataLoader(train_dataset, batch_size=args.batch, shuffle=True, num_workers=workers, pin_memory=True, drop_last=False) + val_loader = DataLoader(val_dataset, batch_size=args.batch, shuffle=False, num_workers=workers, pin_memory=True, drop_last=False) + + pretrained_enabled = str(args.pretrained) not in ("0", "false", "False") + pretrained_weights_path = os.path.abspath(os.path.expanduser(str(args.pretrained_weights))) if args.pretrained_weights else "" + if pretrained_weights_path and os.path.isfile(pretrained_weights_path): + _emit({"type": "waiting", "text": f"使用本地预训练权重：{pretrained_weights_path}"}) + model = deeplabv3_resnet50(weights=None, aux_loss=True) + state = torch.load(pretrained_weights_path, map_location="cpu") + if isinstance(state, dict) and "state_dict" in state and isinstance(state["state_dict"], dict): + state = state["state_dict"] + missing_keys, unexpected_keys = model.load_state_dict(state, strict=False) + if missing_keys or unexpected_keys: + _emit( + { + "type": "waiting", + "text": f"预训练权重加载完成（strict=False），missing={len(missing_keys)} unexpected={len(unexpected_keys)}", + } + ) + else: + weights = DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1 if pretrained_enabled else None + if pretrained_enabled: + _emit({"type": "waiting", "text": f"使用 torchvision 预训练权重（缓存目录：{os.environ.get('TORCH_HOME') or '默认用户缓存目录'}）"}) + model = deeplabv3_resnet50(weights=weights, aux_loss=True) + _emit({"type": "waiting", "text": "模型初始化完成，开始替换分类头"}) + model.classifier[4] = nn.Conv2d(in_channels=256, out_channels=num_classes, kernel_size=1) + model.aux_classifier[4] = nn.Conv2d(in_channels=256, out_channels=num_classes, kernel_size=1) + model.apply(freeze_bn) + model = model.to(device) + _emit({"type": "waiting", "text": f"模型已就绪，device={str(device)}，num_classes={int(num_classes)}"}) + + criterion = nn.CrossEntropyLoss(ignore_index=255) + optimizer = torch.optim.AdamW( + [ + {"params": model.backbone.parameters(), "lr": float(args.lr_backbone)}, + {"params": model.classifier.parameters(), "lr": float(args.lr_head)}, + {"params": model.aux_classifier.parameters(), "lr": float(args.lr_head)}, + ], + weight_decay=float(args.weight_decay), + ) + + best_val_loss = float("inf") + train_losses = [] + val_losses = [] + + for epoch in range(int(args.epochs)): + model.train() + _emit({"type": "waiting", "text": f"[Epoch {epoch+1:03d}/{int(args.epochs)}] 开始训练"}) + total_loss = 0.0 + steps = 0 + for images, masks in train_loader: + images = images.to(device, non_blocking=True) + masks = masks.to(device, non_blocking=True) + outputs = model(images) + loss_main = criterion(outputs["out"], masks) + loss_aux = criterion(outputs["aux"], masks) + loss = loss_main + 0.4 * loss_aux + optimizer.zero_grad() + loss.backward() + optimizer.step() + total_loss += float(loss.item()) + steps += 1 + train_loss = total_loss / max(1, steps) + val_loss = evaluate(model, val_loader, device, criterion) + train_losses.append(train_loss) + val_losses.append(val_loss) + _emit({"type": "waiting", "text": f"[Epoch {epoch+1:03d}/{int(args.epochs)}] Train Loss: {train_loss:.6f} | Val Loss: {val_loss:.6f}"}) + if val_loss < best_val_loss: + best_val_loss = val_loss + best_path = os.path.join(weights_dir, "deeplab_best.pth") + torch.save(model.state_dict(), best_path) + try: + os.chmod(best_path, 0o777) + except Exception: + pass + + final_path = os.path.join(weights_dir, "deeplab_final.pth") + torch.save(model.state_dict(), final_path) + try: + os.chmod(final_path, 0o777) + except Exception: + pass + + plot_path = os.path.join(project_path, run_name, "results.png") + os.makedirs(os.path.dirname(plot_path), exist_ok=True) + _save_loss_plot(plot_path, train_losses, val_losses) + + train_end_time = now() + train_total_times = round((train_end_time - train_start_time).total_seconds() / 60, 1) + hours = int(train_total_times // 60) + minutes = int(train_total_times % 60) + + _emit({"type": "endtime", "text": f"| ----------------------------训练完成时间：{train_end_time.strftime('%Y-%m-%d %H:%M:%S')} - ------------------------------ |"}) + _emit({"type": "totaltime", "text": f"|-------------------------模型训练总耗时：{hours} 小时 {minutes} 分钟---------------------------------------|"}) + + model_filepath = os.path.join(project_path.replace(filespace_root_path, "")[1:], run_name, "weights").replace("\\", "/") + model_filename = "deeplab_best.pth" + + AiAlgorithmTrainRecords.objects.filter(id=args.trainid).update( + train_start_time=train_start_time, + train_end_time=train_end_time, + train_total_times=train_total_times, + model_createtime=train_end_time, + model_filepath=model_filepath, + model_filename=model_filename, + model_trainresult="模型训练完成！", + record_status="02", + ) + + reporter = DeeplabReporter(project_path=weights_dir, train_name=ai_train.train_title) + dataset_count = (json.loads(ai_train.custom2).get("dataset_count") if ai_train.custom2 else None) + dataset_name = f"{ai_train.custom1}；训练样本总数：{dataset_count}" if dataset_count else str(ai_train.custom1 or "") + report_path = reporter.generate_report( + dataset_name=dataset_name, + model_name=os.path.join(model_filepath, model_filename).replace("\\", "/"), + training_epochs=f"{int(args.epochs)} 轮", + training_duration=f"{hours} 小时 {minutes} 分钟", + num_classes=int(num_classes), + best_val_loss=float(best_val_loss), + image_urls={"loss_graph": plot_path}, + ) + _emit({"type": "report", "text": f"模型训练完成，已生成训练报告：{report_path}"}) + except Exception as e: + _emit({"type": "error", "text": f"训练过程报错：{str(e)}"}) + + +if __name__ == "__main__": + main() diff --git a/backend/apps/algorithms/algorithm/pytorch/YoloDetector.py b/backend/apps/algorithms/algorithm/pytorch/YoloDetector.py new file mode 100644 index 0000000..91dc439 --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/YoloDetector.py @@ -0,0 +1,101 @@ +import base64 +import json +from datetime import datetime +import cv2 +from ultralytics import YOLO + +class YoloDetector: + def __init__(self, model_path: str, image_path: str,labels:str, conf_threshold: float = 0.2): + self.model_path = model_path + self.image_path = image_path + self.labels = labels + self.conf_threshold = conf_threshold + self.model = YOLO(model_path) + self.model.overrides = { + 'save_txt': False, + 'save_conf': False, + 'save_crop': False, + 'show': False, + 'conf': self.conf_threshold + } + + def detect(self): + try: + # 进行检测 + results = self.model.predict(source=self.image_path, save=False) + pred_result = self.parse_detection_results(results) + + # 获取带标注的图像 + detection_image = self.get_detection_image(pred_result) + + result_json = { + 'image': detection_image, + 'obj_list': pred_result, + 'time': datetime.now().strftime('%Y-%m-%d %H:%M:%S') + } + + return json.dumps(result_json) + + except Exception as e: + print(f"Error during detection: {e}") + return json.dumps({'error': str(e)}) + + def parse_detection_results(self, results): + pred_result = [] + labels=json.loads(self.labels) + for result in results: + for data in result.boxes.data: + name_key = int(data[5]) + code = result.names.get(name_key) + name = next((item["name"] for item in labels if item["label"] == code), None) + one_box = { + 'code': code, + 'name': name, + 'score': round(data[4].tolist(), 2), + 'x1': int(data[0]), + 'y1': int(data[1]), + 'x2': int(data[2]), + 'y2': int(data[3]) + } + pred_result.append(one_box) + return pred_result + + def get_detection_image(self, pred_result): + img = cv2.imread(self.image_path) + + preset_colors = [ + (0, 0, 255), (20, 20, 255), (20, 40, 255), (0, 60, 255), (0, 80, 255), + (0, 100, 255), (0, 120, 255), (0, 140, 255), (0, 160, 255), (0, 180, 255), + (0, 200, 255), (0, 220, 255), (0, 255, 255), + ] + + category_colors = {} + for i, obj in enumerate(pred_result): + category = obj['code'] + if category not in category_colors: + category_colors[category] = self.get_preset_color(i, preset_colors) + + x1, y1, x2, y2 = obj['x1'], obj['y1'], obj['x2'], obj['y2'] + color = category_colors[category] + cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) + + font = cv2.FONT_HERSHEY_SIMPLEX + text = f"{category}-{obj['score']}" + (text_width, _), _ = cv2.getTextSize(text, font, 0.5, 1) + cv2.rectangle(img, (x1, y1 - 20), (x1 + text_width, y1), color, -1) + cv2.putText(img, text, (x1, y1 - 5), font, 0.5, (255, 255, 255), 1) + + _, img_encoded = cv2.imencode('.jpg', img) + img_base64 = base64.b64encode(img_encoded.tobytes()).decode('utf-8') + return img_base64 + + def get_preset_color(self, index, preset_colors): + return preset_colors[index % len(preset_colors)] + + +# Example usage for testing purposes +if __name__ == '__main__': + img_url = 'D:/project_space/01/002/datasets/00000005.jpg' + model_name = 'D:/python2023/DjangoFrameWork/industrial_prediction/yolo11x.pt' + detector = YoloDetector(model_path=model_name, image_path=img_url) + print(detector.detect()) diff --git a/backend/apps/algorithms/algorithm/pytorch/YoloParms.txt b/backend/apps/algorithms/algorithm/pytorch/YoloParms.txt new file mode 100644 index 0000000..79bb27f --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/YoloParms.txt @@ -0,0 +1,40 @@ +样本准备参数设置： +192.168.1.11:8080/prepare? +trainid=003_20241225_1356, +dataset=['http://192.168.1.16/image/01/003/datasets/003/',''], +train_labels={'sly_dmyw':'地面油污','sly_bjbmyw:'部件表面油污'}, +train_val='8:2' + +训练服务参数设置： +192.168.1.11:8080/train? +trainid=003_20241225_1356, +trainparams={ +"model":"yolo11n.pt", +"epochs":"100", +"batch":"16", +"imgsz":"640", +"workers":"8", +"device":"cpu", +}, +callparams={ +service="192.168.1.11:8000/train_callback", +trainid=003_20241225_1356, +file='result.zip' +reslut目录中包括："best.pt","report.html" +} + +检测服务参数设置： +192.168.1.11:8080/detect? +images="" +return: result(JSON格式对象) + + +[ + {"param_order":1,"param_name":"model","param_desc":"预训练模型文件","param_type":"01","param_unit":"","param_range":"yolo11n.pt,yolo11m.pt,yolo11x.pt","default_values":"yolo11n.pt"}, + {"param_order":2,"param_name":"epochs","param_desc":"训练的总轮数","param_type":"03","param_unit":"轮","param_range":"1-500","default_values":"100"}, + {"param_order":3,"param_name":"batch","param_desc":"训练批量大小","param_type":"03","param_unit":"个","param_range":"1-200","default_values":"16"}, + {"param_order":4,"param_name":"imgsz","param_desc":"输入图像的尺寸","param_type":"03","param_unit":"","param_range":"50-1000","default_values":"640"}, + {"param_order":5,"param_name":"workers","param_desc":"数据加载的工作线程数","param_type":"03","param_unit":"个","param_range":"1-50","default_values":"8"}, + {"param_order":6,"param_name":"device","param_desc":"训练使用设备（cpu,0）","param_type":"01","param_unit":"","param_range":"cpu,0,1","default_values":"cpu"} +] + diff --git a/backend/apps/algorithms/algorithm/pytorch/YoloReporter.py b/backend/apps/algorithms/algorithm/pytorch/YoloReporter.py new file mode 100644 index 0000000..a6558d0 --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/YoloReporter.py @@ -0,0 +1,378 @@ +import os +import base64 +from datetime import datetime + +class YOLOReporter: + def __init__(self, project_path: str, train_name: str = "train_name"): + """ + YOLOReporter 初始化方法，仅保留生成报告所需的属性. + :param project_path: 项目保存路径 + :param train_name: 训练名称 + """ + self.project_path = project_path + self.train_name = train_name + + @staticmethod + def image_to_base64(image_path: str) -> str: + """ + 将图片文件转换为 Base64 编码字符串. + :param image_path: 图片文件路径 + :return: Base64 编码的字符串 + """ + if not os.path.exists(image_path): + return "" # 如果图片不存在，返回空字符串 + + with open(image_path, "rb") as image_file: + base64_str = base64.b64encode(image_file.read()).decode("utf-8") + return base64_str + + def generate_report(self, dataset_name: str, model_name: str, training_epochs: str, training_duration: str, metrics: list,overall_metrics:str, image_urls: dict): + """ + 生成训练 HTML 报告. + :param dataset_name: 数据集名称 + :param model_name: 模型名称 + :param training_duration: 训练时长 + :param metrics: 性能指标列表 + :param image_urls: 图片路径字典 + """ + # 根据 Fitness 值生成评价结果 + fitness = overall_metrics.get("fitness", 0.0) + if fitness >= 0.9: + fitness_evaluation = "非常优秀" + fitness_class = "fitness-very-good" + elif fitness >= 0.7: + fitness_evaluation = "优秀" + fitness_class = "fitness-good" + elif fitness >= 0.5: + fitness_evaluation = "良好" + fitness_class = "fitness-fair" + elif fitness >= 0.3: + fitness_evaluation = "一般" + fitness_class = "fitness-poor" + else: + fitness_evaluation = "较差" + fitness_class = "fitness-very-poor" + # HTML 模板 + html_template = """ + + + + + + 图像识别算法模型训练报告 + + + + +
+

{{train_name}}-训练结果评估报告

+

1.训练摘要

+

报告标题：{{train_name}}

+

生成日期：{{report_date}}

+

模型名称：{{model_name}}

+

训练数据集：{{dataset_name}}

+

训练轮次：{{training_epochs}}；共计时长：{{training_duration}}

+

总体性能得分(Fitness)： {{fitness}}

+

模型评价结果：{{fitness_evaluation}}

+
+
+

2.性能指标

+
+ + + + + + + + + + + {{metrics_rows}} + +
类别名称mAP@50-95效果级别
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
mAP@50-95 区间效果级别说明
小于0.4一般需要优化，模型对目标的定位和分类精度较低，尤其是在更高 IoU 阈值下表现较差。
大于0.4小于0.5表现不错可以满足部分需求，但在复杂或高精度场景中可能不足。
大于0.5小于0.6良好能较好适应常规场景，模型具备一定的鲁棒性和精确度，定位能力较强。
大于0.6优秀模型表现非常出色，可胜任高精度检测任务，适用于要求严格的实际应用场景。
备注平均精度在 IoU 阈值从 50% 到 95%（步长为 5%）的评估结果，更严格地衡量模型在不同 IoU 标准下的综合性能。
+
+
+
+

3.训练结果图片

+
+

3.1 损失曲线图

+ 损失曲线图 +

损失曲线图反映了模型在训练过程中损失值的变化趋势。通常，损失值逐渐下降并趋于平稳，说明模型逐步收敛，性能提升。

+
+

3.2 精确率曲线

+ 精确率曲线 +

+ 精确率（Precision）表示预测为正类的样本中实际为正类的比例；该曲线显示模型在不同置信度阈值下的精确率 +

+
+

3.3 召回率曲线

+ 召回率曲线 +

+ 召回率（Recall）表示所有实际为正类的样本中成功预测为正类的比例。该曲线显示模型在不同置信度阈值下的召回率。 +

+
+

3.4 精确率-召回率曲线

+ 精确率-召回率曲线 +

+ 精确率-召回率曲线反映了模型在不同阈值下的平衡表现，理想情况下曲线越靠近右上角越好。 +

+
+

3.5 混淆矩阵

+ 混淆矩阵 +

+ 混淆矩阵用于展示模型预测结果的分类情况。对角线上的值表示正确预测的数量，非对角线上的值表示错误分类的数量。 + 理想情况下，混淆矩阵应接近对角矩阵，说明模型分类性能良好。 +

+
+
+
+

4.验证集-模型预测结果

+
+ {{val_images_rows}} +
+
+
+

5.模型总体性能

+
+

精确度(Precision): {{precision}}，表示预测为正类的样本中实际为正类的比例，值越高说明模型预测的准确性越强。

+

召回率(Recall): {{recall}}，表示所有实际为正类的样本中成功预测为正类的比例，值越高说明模型对正类样本的覆盖能力越强。

+

平均精度(mAP@50): {{mAP50}}，在 IoU ≥ 50% 时的平均精度，衡量模型检测目标的能力。

+

平均精度(mAP@50-95): {{mAP50_95}}，在 IoU 从 50% 到 95%（步长为 5%）的平均精度，反映模型在不同 IoU 阈值下的综合性能。

+

总体性能得分(Fitness): {{fitness}}，综合精确率、召回率和 mAP 的加权得分，表示模型的总体性能。

+
+ 综合评价结果：{{fitness_evaluation}} +
+
+
+ + + """ + + # 生成性能指标表格行 + metrics_rows = "".join( + f"{metric['class']}{metric['name']}{metric['mAP50-95']:.4f}{metric['level']}" + for metric in metrics + ) + + val_images_rows="" + for image_file in image_urls["val_images_url_sorted"]: + image_base64=self.image_to_base64(image_file) + val_images_rows += f'推理图\n' + # 转换图片为 Base64 + loss_graph_base64 = self.image_to_base64(image_urls["loss_graph"]) + pr_curve_base64 = self.image_to_base64(image_urls["pr_curve"]) + confusion_matrix_base64 = self.image_to_base64(image_urls["confusion_matrix"]) + p_curve_base64 = self.image_to_base64(image_urls["p_curve"]) + r_curve_base64 = self.image_to_base64(image_urls["r_curve"]) + # 填充模板 + report_html = html_template.replace("{{report_date}}", datetime.now().strftime("%Y-%m-%d %H:%M:%S")) + report_html = report_html.replace("{{train_name}}", self.train_name) + report_html = report_html.replace("{{model_name}}", model_name) # 默认模型名称 + report_html = report_html.replace("{{dataset_name}}", dataset_name) + report_html = report_html.replace("{{training_epochs}}", training_epochs) + report_html = report_html.replace("{{training_duration}}", training_duration) + report_html = report_html.replace("{{metrics_rows}}", metrics_rows) + report_html = report_html.replace("{{loss_graph_base64}}", loss_graph_base64) + report_html = report_html.replace("{{p_curve_base64}}", p_curve_base64) + report_html = report_html.replace("{{r_curve_base64}}", r_curve_base64) + report_html = report_html.replace("{{pr_curve_base64}}", pr_curve_base64) + report_html = report_html.replace("{{confusion_matrix_base64}}", confusion_matrix_base64) + report_html = report_html.replace("{{val_images_rows}}", val_images_rows) + # 填充总体性能指标 + report_html = report_html.replace("{{precision}}", str(overall_metrics.get("precision"))) + report_html = report_html.replace("{{recall}}", str(overall_metrics.get("recall"))) + report_html = report_html.replace("{{mAP50}}", str(overall_metrics.get("mAP50"))) + report_html = report_html.replace("{{mAP50_95}}", str(overall_metrics.get("mAP50-95"))) + report_html = report_html.replace("{{fitness}}", str(overall_metrics.get("fitness"))) + report_html = report_html.replace("{{fitness_evaluation}}", fitness_evaluation) + report_html = report_html.replace("{{fitness_class}}", fitness_class) + # 保存 HTML 文件 + output_html_path = os.path.join(self.project_path, "train_report.html") + with open(output_html_path, "w", encoding="utf-8") as f: + f.write(report_html) + # print(f"训练报告 HTML 已保存到 {output_html_path}") + + + +# 示例调用 +if __name__ == "__main__": + reporter = YOLOReporter( + project_path="D:/project_space/01/003/trains/003_20241219_2122/runs/train", + train_name="大模型训练" + ) + + # 生成报告 + reporter.generate_report( + dataset_name="COCO Dataset", + model_name="COCO Dataset", + training_duration="2 hours", + metrics=[ + {"class": "Person", "mAP50": 0.8234, "mAP50-95": 0.7234}, + {"class": "Car", "mAP50": 0.9123, "mAP50-95": 0.8231}, + ], + image_urls={ + "loss_graph": os.path.join(reporter.project_path, "results.png"), + "pr_curve": os.path.join(reporter.project_path, "PR_curve.png"), + "confusion_matrix": os.path.join(reporter.project_path, "confusion_matrix.png") + } + ) + diff --git a/backend/apps/algorithms/algorithm/pytorch/YoloSegTrainer.py b/backend/apps/algorithms/algorithm/pytorch/YoloSegTrainer.py new file mode 100644 index 0000000..f3896e2 --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/YoloSegTrainer.py @@ -0,0 +1,223 @@ +import glob +import json +import os +import sys + +import django +from django.conf import settings +from django.shortcuts import get_object_or_404 +from django.utils.timezone import now +from ultralytics import YOLO + +current_dir = os.path.dirname(os.path.abspath(__file__)) +project_root = os.path.abspath(os.path.join(current_dir, "../../../../")) +sys.path.append(project_root) +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") +django.setup() + +from apps.core.models import AiAlgorithm, AiAlgorithmClass, AiAlgorithmTrainRecords +try: + from .YoloReporter import YOLOReporter +except Exception: + from apps.algorithms.algorithm.pytorch.YoloReporter import YOLOReporter +import argparse + + +def _emit(payload): + print(json.dumps(payload, ensure_ascii=False)) + + +class YOLOSegTrainer: + def __init__(self, model_path: str, data_path: str, params: dict, project_path: str = None, run_name: str = "train"): + self.model_path = model_path + self.data_path = data_path + self.project_path = project_path or "./runs" + self.run_name = run_name + self.epochs = int(params.get("epochs", 200)) + self.batch_size = int(params.get("batch", 8)) + self.img_size = int(params.get("imgsz", 640)) + self.workers = int(params.get("workers", 4)) + self.device = str(params.get("device", "0")) + self.model = YOLO(model_path) + + def train(self): + os.makedirs(self.project_path, exist_ok=True) + results = self.model.train( + data=self.data_path, + project=self.project_path, + name=self.run_name, + epochs=self.epochs, + batch=self.batch_size, + imgsz=self.img_size, + workers=self.workers, + device=self.device, + task="segment", + ) + return results, self.project_path + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--data", type=str, required=True) + parser.add_argument("--model", type=str, required=True) + parser.add_argument("--project_path", type=str, required=True) + parser.add_argument("--run_name", type=str, default="train") + parser.add_argument("--epochs", type=int, default=200) + parser.add_argument("--batch", type=int, default=8) + parser.add_argument("--imgsz", type=int, default=640) + parser.add_argument("--workers", type=int, default=4) + parser.add_argument("--device", type=str, default="0") + parser.add_argument("--trainid", type=str, required=True) + return parser.parse_args() + + +def _pick_metric(metrics, keys, default=0.0): + for key in keys: + value = metrics.get(key) + if value is not None: + try: + return float(value) + except Exception: + continue + return float(default) + + +def _resolve_model_path(model_arg: str): + model_arg = str(model_arg or "").strip() + if not model_arg: + return "yolo11n-seg.pt" + if model_arg.isdigit(): + raise ValueError(f"训练参数 model 异常，当前值为纯数字：{model_arg}") + if os.path.isabs(model_arg) and os.path.exists(model_arg): + return model_arg + if os.path.exists(model_arg): + return os.path.abspath(model_arg) + repo_candidate = os.path.join(project_root, model_arg) + if os.path.exists(repo_candidate): + return repo_candidate + return model_arg + + +def main(): + try: + args = parse_args() + if str(args.data).strip().isdigit(): + raise ValueError(f"训练参数 data 异常，当前值为纯数字：{args.data}") + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=args.trainid) + train_labels = json.loads(ai_train.train_labels) if ai_train.train_labels else [] + + train_start_time = now() + _emit({"type": "starttime", "text": f"|----------------------------训练开始时间：{train_start_time.strftime('%Y-%m-%d %H:%M:%S')}-------------------------------|"}) + + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join(filespace_root_path, ai_algorithm_class.classcode, ai_algorithm.algorithm_code, "trains", ai_train.train_code) + run_name = args.run_name + + resolved_model_path = _resolve_model_path(args.model) + _emit({"type": "waiting", "text": f"训练模型参数：{args.model} -> 实际加载：{resolved_model_path}"}) + _emit({"type": "waiting", "text": f"训练数据配置：{args.data}"}) + + params = { + "epochs": args.epochs, + "batch": args.batch, + "imgsz": args.imgsz, + "workers": args.workers, + "device": args.device, + } + trainer = YOLOSegTrainer( + model_path=resolved_model_path, + data_path=args.data, + params=params, + project_path=args.project_path, + run_name=run_name, + ) + results, project_path = trainer.train() + metrics = results.results_dict if hasattr(results, "results_dict") else {} + maps = list(results.maps) if getattr(results, "maps", None) is not None else [] + names = results.names if hasattr(results, "names") else {} + + train_end_time = now() + train_total_times = round((train_end_time - train_start_time).total_seconds() / 60, 1) + hours = int(train_total_times // 60) + minutes = int(train_total_times % 60) + + _emit({"type": "endtime", "text": f"| ----------------------------训练完成时间：{train_end_time.strftime('%Y-%m-%d %H:%M:%S')} - ------------------------------ |"}) + _emit({"type": "totaltime", "text": f"|-------------------------模型训练总耗时：{hours} 小时 {minutes} 分钟---------------------------------------|"}) + _emit({"type": "title", "text": "模型训练结果--分类汇总信息如下："}) + + class_metrics_group = [] + for class_id, class_name in names.items(): + mAP50_95 = round(maps[int(class_id)] if len(maps) > int(class_id) else 0.0, 4) + if mAP50_95 < 0.4: + level = "一般" + elif 0.4 <= mAP50_95 < 0.5: + level = "表现不错" + elif 0.5 <= mAP50_95 < 0.6: + level = "良好" + else: + level = "优秀" + class_metrics = { + "class": class_name, + "name": next((item.get("name") for item in train_labels if isinstance(item, dict) and item.get("label") == class_name), None), + "mAP50-95": mAP50_95, + "level": level, + } + class_metrics_group.append(class_metrics) + _emit({"type": "info", "text": str(class_metrics)}) + + overall_metrics = { + "precision": round(_pick_metric(metrics, ["metrics/precision(M)", "metrics/precision(B)"]), 4), + "recall": round(_pick_metric(metrics, ["metrics/recall(M)", "metrics/recall(B)"]), 4), + "mAP50": round(_pick_metric(metrics, ["metrics/mAP50(M)", "metrics/mAP50(B)"]), 4), + "mAP50-95": round(_pick_metric(metrics, ["metrics/mAP50-95(M)", "metrics/mAP50-95(B)"]), 4), + "fitness": round(_pick_metric(metrics, ["fitness"]), 4), + } + _emit({"type": "title", "text": "模型训练结果--总体性能指标如下："}) + _emit({"type": "info", "text": str(overall_metrics)}) + + model_filepath = os.path.join(project_path.replace(filespace_root_path, "")[1:], run_name, "weights").replace("\\", "/") + model_filename = "best.pt" + AiAlgorithmTrainRecords.objects.filter(id=args.trainid).update( + train_start_time=train_start_time, + train_end_time=train_end_time, + train_total_times=train_total_times, + model_createtime=train_end_time, + model_filepath=model_filepath, + model_filename=model_filename, + model_trainresult="模型训练完成！", + record_status="02", + ) + + reporter = YOLOReporter(project_path=os.path.join(project_path, run_name, "weights"), train_name=ai_train.train_title) + dataset_count = json.loads(ai_train.custom2).get("dataset_count") if ai_train.custom2 else None + train_images_url = glob.glob(os.path.join(project_path, run_name, "train_*.jpg")) + val_images_url = glob.glob(os.path.join(project_path, run_name, "val_*.jpg")) + train_images_url_sorted = sorted(train_images_url, key=lambda x: os.path.basename(x)) if train_images_url else [] + val_images_url_sorted = sorted(val_images_url, key=lambda x: os.path.basename(x)) if val_images_url else [] + + reporter.generate_report( + dataset_name=f"{ai_train.custom1}；训练样本总数：{dataset_count}", + model_name=os.path.join(model_filepath, model_filename).replace("\\", "/"), + training_epochs=f"{args.epochs} 轮", + training_duration=f"{hours} 小时 {minutes} 分钟", + metrics=class_metrics_group, + overall_metrics=overall_metrics, + image_urls={ + "loss_graph": os.path.join(project_path, run_name, "results.png"), + "pr_curve": os.path.join(project_path, run_name, "PR_curve.png"), + "confusion_matrix": os.path.join(project_path, run_name, "confusion_matrix.png"), + "train_images_url_sorted": train_images_url_sorted, + "val_images_url_sorted": val_images_url_sorted, + "p_curve": os.path.join(project_path, run_name, "P_curve.png"), + "r_curve": os.path.join(project_path, run_name, "R_curve.png"), + }, + ) + _emit({"type": "report", "text": "模型训练完成，已生成训练报告，请查阅!"}) + except Exception as e: + _emit({"type": "error", "text": f"训练过程报错：{str(e)}"}) + + +if __name__ == "__main__": + main() diff --git a/backend/apps/algorithms/algorithm/pytorch/YoloTrainer.py b/backend/apps/algorithms/algorithm/pytorch/YoloTrainer.py new file mode 100644 index 0000000..ac8f4a8 --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/YoloTrainer.py @@ -0,0 +1,286 @@ +import glob +import os +import sys + +import django +current_dir = os.path.dirname(os.path.abspath(__file__)) + +# 设置 Django 设置模块 +# os.environ.setdefault("DJANGO_SETTINGS_MODULE", "industrial_prediction.settings") +project_root = os.path.abspath(os.path.join(current_dir, "../../../../")) +sys.path.append(project_root) +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") + +# 启动 Django 环境 +django.setup() +from django.conf import settings +import argparse +import json +from flask import Flask, request, jsonify +from django.shortcuts import get_object_or_404 +from apps.core.models import AiAlgorithm, AiAlgorithmClass, AiAlgorithmTrainRecords +from ultralytics import YOLO +try: + from .YoloReporter import YOLOReporter +except Exception: + from apps.algorithms.algorithm.pytorch.YoloReporter import YOLOReporter +from django.utils.timezone import now +import os +try: + from apps.common.minio_client import minio_storage +except Exception: + from common.minio_client import minio_storage +app = Flask(__name__) +class YOLOTrainer: + def __init__(self, model_path: str, data_path: str, params: dict, project_path: str = None, run_name: str = "yolo_run"): + """ + YOLOTrainer 初始化方法. + :param model_path: 模型路径 (e.g., yolo11n.pt) + :param data_path: 数据配置文件路径 (e.g., yolo_config.yaml) + :param params: 训练参数字典，从 data_str 提取 (e.g., epochs, batch_size, img_size, device 等) + :param project_path: 项目保存路径 + :param run_name: 运行名称测试提交 + """ + self.model_path = model_path + self.data_path = data_path + self.project_path = project_path or f"./runs/train/{run_name}" + self.run_name = run_name + + # 从 params 中解析参数 + self.epochs = int(params.get("epochs", 3)) + self.batch_size = int(params.get("batch", 16)) + self.img_size = int(params.get("imgsz", 640)) + self.workers = int(params.get("workers", 8)) + self.device = params.get("device", "cpu") # 默认为 CPU + # 加载 YOLO 模型 + self.model = YOLO(model_path) + def train(self): + """ + 开始训练 YOLO 模型，使用初始化时指定的参数. + """ + # 确保项目路径存在 + os.makedirs(self.project_path, exist_ok=True) + + results = self.model.train( + data=self.data_path, + project=self.project_path, + name=self.run_name, + epochs=self.epochs, + batch=self.batch_size, + imgsz=self.img_size, + workers=self.workers, + device=self.device + + ) + return results,self.project_path + +def parse_args(): + """ + 解析命令行参数. + """ + parser = argparse.ArgumentParser(description="YOLOv8 Training Script") + + # 添加命令行参数 + parser.add_argument('--data', type=str, required=True, help="数据配置文件路径 (e.g., train.yaml)") + parser.add_argument('--model', type=str, required=True, help="YOLOv8 模型路径 (e.g., yolov8x.pt)") + parser.add_argument('--project_path', type=str, default=None, + help="训练结果保存路径 (默认为 './runs/train/{run_name}')") + parser.add_argument('--run_name', type=str, default="yolo_run", help="训练运行名称，默认为 'yolo_run'") + parser.add_argument('--epochs', type=int, default=200, help="训练轮数，默认为 200") + parser.add_argument('--batch', type=int, default=16, help="每批次的图片数量，默认为 16") + parser.add_argument('--imgsz', type=int, default=640, help="输入图像尺寸，默认为 640") + parser.add_argument('--workers', type=int, default=8, help="数据加载的线程数，默认为 8") + parser.add_argument('--device', type=str, default='cpu', help="训练设备，默认为 'cpu'，例如 '0' 或 'cuda'") + parser.add_argument('--trainid', type=str, default='0', help="训练id，默认为 '0'") + # 解析命令行参数 + return parser.parse_args() + +def main(): + try: + # print('进入到main方法') + # 解析命令行参数 + args = parse_args() + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=args.trainid) + train_params = json.loads(ai_train.train_params) + train_labels = json.loads(ai_train.train_labels) + # 记录训练开始时间 + train_start_time = now() + # 发送训练开始的消息 + message = {'type': 'starttime', + 'text': f'|----------------------------训练开始时间：{train_start_time.strftime("%Y-%m-%d %H:%M:%S")}-------------------------------|'} + print(json.dumps(message, ensure_ascii=False)) + # 设置路径 + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join( + filespace_root_path, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + 'trains', + ai_train.train_code + ) + run_name = "train" + # 构建覆盖字典 + overrides = { + 'data': args.data, + 'model': args.model, + 'project_path': args.project_path, + 'run_name': args.run_name, + 'epochs': args.epochs, + 'batch': args.batch, + 'imgsz': args.imgsz, + 'workers': args.workers, + 'device': args.device + } + # 创建训练器并启动训练 + trainer = YOLOTrainer( + model_path=args.model, + data_path=args.data, + params=overrides, + project_path=args.project_path, + run_name=args.run_name + ) + # 执行训练并获取结果 + results, project_path = trainer.train() + # print('results.........',results) + metrics = results.results_dict + maps = results.maps + names = results.names + # 记录训练结束时间 + train_end_time = now() + train_total_times = round((train_end_time - train_start_time).total_seconds() / 60, 1) + hours = int(train_total_times // 60) + minutes = int(train_total_times % 60) + + # 更新训练记录 + message = {'type': 'endtime', + 'text': f'| ----------------------------训练完成时间：{train_end_time.strftime("%Y-%m-%d %H:%M:%S")} - ------------------------------ |'} + print(json.dumps(message, ensure_ascii=False)) + message = {'type': 'totaltime', + 'text': f'|-------------------------模型训练总耗时：{hours} 小时 {minutes} 分钟---------------------------------------|'} + print(json.dumps(message, ensure_ascii=False)) + # 分类汇总信息 + message = {'type': 'title', + 'text': '模型训练结果--分类汇总信息如下：'} + print(json.dumps(message, ensure_ascii=False)) + class_metrics_group = [] + for class_id, class_name in names.items(): + mAP50_95 = round(maps[int(class_id)] if len(maps) > 0 else 0.0, 4) + if mAP50_95 < 0.4: + level = "一般" + elif 0.4 <= mAP50_95 < 0.5: + level = "表现不错" + elif 0.5 <= mAP50_95 < 0.6: + level = "良好" + else: + level = "优秀" + class_metrics = { + "class": class_name, + "name": next((item['name'] for item in train_labels if item['label'] == class_name), None), + "mAP50-95": mAP50_95, # 如果 maps 为空，设置默认值为 0.0 + "level": level + } + class_metrics_group.append(class_metrics) + message = {'type': 'info', + 'text': str(class_metrics)} + print(json.dumps(message, ensure_ascii=False)) + # 总体性能指标 + overall_metrics = { + "precision": round(metrics['metrics/precision(B)'], 4), + "recall": round(metrics['metrics/recall(B)'], 4), + "mAP50": round(metrics['metrics/mAP50(B)'], 4), + "mAP50-95": round(metrics['metrics/mAP50-95(B)'], 4), + "fitness": round(metrics['fitness'], 4), + } + message = {'type': 'title', + 'text': '模型训练结果--总体性能指标如下：'} + print(json.dumps(message, ensure_ascii=False)) + message = {'type': 'info', + 'text': str(overall_metrics)} + print(json.dumps(message, ensure_ascii=False)) + model_filepath = os.path.join(project_path.replace(filespace_root_path, "")[1:], run_name, "weights").replace( + "\\", + "/") + model_filename = "best.pt" + + # 生成模型训练报告 + reporter = YOLOReporter( + project_path=os.path.join(project_path, run_name, 'weights'), + train_name=ai_train.train_title + ) + dataset_count = json.loads(ai_train.custom2).get('dataset_count') + train_images_url = glob.glob(os.path.join(project_path, run_name, 'train_*.jpg')) + val_images_url = glob.glob( + os.path.join(project_path, run_name, 'val_*.jpg')) + # 按文件名排序，如果文件列表为空，也会返回 [] + train_images_url_sorted = sorted(train_images_url, key=lambda x: os.path.basename(x)) if train_images_url else [] + val_images_url_sorted = sorted(val_images_url, key=lambda x: os.path.basename(x)) if val_images_url else [] + # 生成报告 + + reporter.generate_report( + dataset_name=f'{ai_train.custom1}；训练样本总数：{dataset_count}', + model_name=os.path.join(model_filepath, model_filename).replace("\\", "/"), + training_epochs=f"{args.epochs} 轮", + training_duration=f"{hours} 小时 {minutes} 分钟", + metrics=class_metrics_group, + overall_metrics=overall_metrics, + image_urls={ + "loss_graph": os.path.join(project_path, run_name, "results.png"), + "pr_curve": os.path.join(project_path, run_name, "BoxPR_curve.png"), + "confusion_matrix": os.path.join(project_path, run_name, "confusion_matrix.png"), + "train_images_url_sorted": train_images_url_sorted, + "val_images_url_sorted": val_images_url_sorted, + "p_curve": os.path.join(project_path, run_name, "BoxP_curve.png"), + "r_curve": os.path.join(project_path, run_name, "BoxR_curve.png") + } + ) + + + # 上传模型文件和报告到 MinIO + try: + weights_path = os.path.join(project_path, run_name, 'weights') + model_file = os.path.join(weights_path, 'best.pt') + report_file = os.path.join(weights_path, 'train_report.html') + + minio_bucket = 'trained-model' + minio_prefix = ai_train.train_code + if os.path.exists(model_file): + minio_model_path = f'{minio_prefix}/best.pt' + minio_storage.upload_file(minio_bucket, minio_model_path, model_file) + message = {'type': 'info', 'text': f'模型文件 best.pt 已上传到 MinIO: {minio_bucket}/{minio_model_path}'} + print(json.dumps(message, ensure_ascii=False)) + + if os.path.exists(report_file): + minio_report_path = f'{minio_prefix}/train_report.html' + minio_storage.upload_file(minio_bucket, minio_report_path, report_file) + message = {'type': 'info', 'text': f'训练报告 train_report.html 已上传到 MinIO: {minio_bucket}/{minio_report_path}'} + print(json.dumps(message, ensure_ascii=False)) + + report_url = minio_storage.presign_get(minio_bucket, minio_report_path, expires=3600) + message = {'type': 'report', 'text': f'模型训练完成，已生成训练报告，请查阅!', 'bucket': minio_bucket, 'object_name': minio_report_path, 'url': report_url} + print(json.dumps(message, ensure_ascii=False)) + + except Exception as upload_error: + message = {'type': 'error', 'text': f'上传到 MinIO 失败: {str(upload_error)}'} + print(json.dumps(message, ensure_ascii=False)) + # 更新训练记录 + minio_model_path = f'{ai_train.train_code}/best.pt' + AiAlgorithmTrainRecords.objects.filter(id=args.trainid).update( + train_start_time=train_start_time, + train_end_time=train_end_time, + train_total_times=train_total_times, + model_createtime=train_end_time, + model_filename=model_filename, + model_filepath=weights_path.replace("\\", "/"), + model_bucketpath=minio_model_path, + report_bucketpath=minio_report_path, + model_trainresult="模型训练完成！", + record_status="02" + ) + except Exception as e: + print("训练过程报错",str(e)) + + +if __name__ == '__main__': + main() diff --git a/backend/apps/algorithms/algorithm/pytorch/__init__.py b/backend/apps/algorithms/algorithm/pytorch/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/algorithms/algorithm/pytorch/train.py b/backend/apps/algorithms/algorithm/pytorch/train.py new file mode 100644 index 0000000..047fecf --- /dev/null +++ b/backend/apps/algorithms/algorithm/pytorch/train.py @@ -0,0 +1,45 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +import argparse +import os + +from ultralytics.cfg import get_cfg +from ultralytics.models.yolo import detect +def test_detect(overrides): + # 创建 DetectionTrainer 实例并传入覆盖参数 + trainer = detect.DetectionTrainer(overrides=overrides) + if not os.path.isfile(overrides['model']): + raise FileNotFoundError(f"模型文件 {overrides['model']} 不存在！") + trainer.train() +def parse_args(): + """ + 解析命令行参数. + """ + parser = argparse.ArgumentParser(description="YOLOv8 Training Script") + # 添加命令行参数 + parser.add_argument('--data', type=str, required=True, help="数据配置文件路径 (e.g., train.yaml)") + parser.add_argument('--model', type=str, required=True, help="YOLOv8 模型路径 (e.g., yolov8x.pt)") + parser.add_argument('--imgsz', type=int, default=640, help="输入图像尺寸，默认为 640") + parser.add_argument('--epochs', type=int, default=200, help="训练轮数，默认为 200") + parser.add_argument('--device', type=str, default='0', help="设备编号，默认为 '0' (CUDA 设备)") + parser.add_argument('--pretrained', type=str, required=False, help="") + parser.add_argument('--workers', type=int, default=4, help="数据加载器的 worker 数量，默认为 4") + # 解析命令行参数 + return parser.parse_args() +if __name__ == '__main__': + # 解析命令行参数 + args = parse_args() + # 构建覆盖字典 + overrides = { + 'data': args.data, + 'model': args.model, + 'imgsz': args.imgsz, + 'batch':4, + 'epochs': args.epochs, + 'device': args.device, + 'workers': args.workers, + 'pretrained': args.pretrained + } + # 调用测试函数 + test_detect(overrides) + + diff --git a/backend/apps/algorithms/algorithm/tensorflow/SSDTrainer.py b/backend/apps/algorithms/algorithm/tensorflow/SSDTrainer.py new file mode 100644 index 0000000..c6d8147 --- /dev/null +++ b/backend/apps/algorithms/algorithm/tensorflow/SSDTrainer.py @@ -0,0 +1,52 @@ +import os +import tensorflow as tf +from object_detection.utils import config_util +from object_detection.protos import pipeline_pb2 +from google.protobuf import text_format +from object_detection import model_lib + + +def train_ssd_model(pipeline_config_path, model_dir, num_train_steps=200000, batch_size=24): + # 加载 pipeline.config 文件 + pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() + with tf.io.gfile.GFile(pipeline_config_path, "r") as f: + proto_str = f.read() + text_format.Merge(proto_str, pipeline_config) + + # 更新训练参数 + pipeline_config.train_config.batch_size = batch_size + pipeline_config.train_config.num_steps = num_train_steps + + # 配置文件路径 + config = config_util.create_configs_from_pipeline_proto(pipeline_config) + + # 创建训练和评估的配置 + train_config = config['train_config'] + eval_config = config['eval_config'] + + # 创建训练模型 + model_config = config['model_config'] + model = model_lib.create_estimator_and_inputs(model_config, train_config, eval_config) + + # 配置训练和评估输入 + train_input_fn = model_lib.create_input_fn(config['train_input_config']) + eval_input_fn = model_lib.create_input_fn(config['eval_input_config']) + + # 执行训练 + model_lib.train_and_evaluate( + estimator=model, + train_input_fn=train_input_fn, + eval_input_fn=eval_input_fn, + train_steps=num_train_steps, + eval_steps=5000, # 你可以根据数据集大小调整评估步数 + ) + + +if __name__ == "__main__": + # 配置文件路径和模型存储路径 + current_dir = os.path.dirname(os.path.abspath(__file__)) + pipeline_config_path = os.path.join(current_dir, "models", "ssd_mobilenet", "pipeline.config") + model_dir = 'E:/yangben/tensorflowyangben/training' + + # 训练 SSD 模型 + train_ssd_model(pipeline_config_path, model_dir) diff --git a/backend/apps/algorithms/algorithm/tensorflow/TxtToTensorflow.py b/backend/apps/algorithms/algorithm/tensorflow/TxtToTensorflow.py new file mode 100644 index 0000000..838e527 --- /dev/null +++ b/backend/apps/algorithms/algorithm/tensorflow/TxtToTensorflow.py @@ -0,0 +1,103 @@ +# import tensorflows as tf +# from object_detection.utils import dataset_util +# import xml.etree.ElementTree as ET +# import os +# +# def create_example(image_path, xml_path): +# tree = ET.parse(xml_path) +# root = tree.getroot() +# +# filename = image_path.encode('utf8') +# with tf.io.gfile.GFile(image_path, 'rb') as fid: # 修改这里 +# encoded_image_data = fid.read() +# +# image_format = b'jpg' # or 'png', depending on your image format +# +# xmins = [] +# xmaxs = [] +# ymins = [] +# ymaxs = [] +# classes_text = [] +# classes = [] +# +# for obj in root.findall('object'): +# xmin = float(obj.find('bndbox/xmin').text) +# xmax = float(obj.find('bndbox/xmax').text) +# ymin = float(obj.find('bndbox/ymin').text) +# ymax = float(obj.find('bndbox/ymax').text) +# class_name = obj.find('name').text +# class_id = 1 # assuming single class or map class names to IDs +# +# xmins.append(xmin) +# xmaxs.append(xmax) +# ymins.append(ymin) +# ymaxs.append(ymax) +# classes_text.append(class_name.encode('utf8')) +# classes.append(class_id) +# +# example = tf.train.Example(features=tf.train.Features(feature={ +# 'image/encoded': dataset_util.bytes_feature(encoded_image_data), +# 'image/format': dataset_util.bytes_feature(image_format), +# 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), +# 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), +# 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), +# 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), +# 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), +# 'image/object/class/label': dataset_util.int64_list_feature(classes), +# 'image/filename': dataset_util.bytes_feature(filename), +# })) +# return example +# +# def write_tfrecord(output_path, image_dir, xml_dir): +# writer = tf.io.TFRecordWriter(output_path) # tf.io.TFRecordWriter instead of tf.gfile.TFRecordWriter +# for xml_file in os.listdir(xml_dir): +# if xml_file.endswith('.xml'): +# image_path = os.path.join(image_dir, xml_file.replace('.xml', '.jpg')) # Assuming images are in JPG format +# xml_path = os.path.join(xml_dir, xml_file) +# tf_example = create_example(image_path, xml_path) +# writer.write(tf_example.SerializeToString()) +# writer.close() +# +# # Example usage: +# write_tfrecord(r'E:\yangben\tensorflowyangben\train.record', r'E:\yangben\tensorflowyangben\sly_dmyw', r'E:\yangben\tensorflowyangben\sly_dmyw') +# +from PIL import Image +import os +import numpy as np + + +def load_data(image_folder, annotations_file): + images = [] + labels = [] + for image_file in os.listdir(image_folder): + if image_file.endswith('.jpg'): + image_path = os.path.join(image_folder, image_file) + print(f"Loading image: {image_path}") # 调试输出 + + # 使用PIL打开图片 + try: + img = Image.open(image_path) + img = img.resize((300, 300)) # SSD要求输入尺寸为300x300 + img = np.array(img) + images.append(img) + except Exception as e: + print(f"Error loading image {image_path}: {e}") + continue + + # 处理标签文件 + label_file = image_file.replace('.jpg', '.txt') + label_path = os.path.join(annotations_file, label_file) + if os.path.exists(label_path): + with open(label_path, 'r') as f: + boxes = [] + for line in f: + data = line.strip().split() + x_min, y_min, x_max, y_max = map(float, data[:4]) + boxes.append([x_min, y_min, x_max, y_max]) + labels.append(boxes) + else: + print(f"Warning: Label file not found for {image_file}") + return np.array(images), np.array(labels) + + +load_data(r"E:\yangben\tensorflowyangben\sly_dmyw",r"E:\yangben\tensorflowyangben\sly_dmyw") \ No newline at end of file diff --git a/backend/apps/algorithms/apps.py b/backend/apps/algorithms/apps.py new file mode 100644 index 0000000..0b6fe37 --- /dev/null +++ b/backend/apps/algorithms/apps.py @@ -0,0 +1,7 @@ +from django.apps import AppConfig + + +class AlgorithmsConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "apps.algorithms" + diff --git a/backend/apps/algorithms/assets/yolo标准数据集标签 b/backend/apps/algorithms/assets/yolo标准数据集标签 new file mode 100644 index 0000000..ee4d726 --- /dev/null +++ b/backend/apps/algorithms/assets/yolo标准数据集标签 @@ -0,0 +1,82 @@ +[ + {"label": "person", "name": "人"}, + {"label": "bicycle", "name": "自行车"}, + {"label": "car", "name": "汽车"}, + {"label": "motorcycle", "name": "摩托车"}, + {"label": "airplane", "name": "飞机"}, + {"label": "bus", "name": "公交车"}, + {"label": "train", "name": "火车"}, + {"label": "truck", "name": "卡车"}, + {"label": "boat", "name": "船"}, + {"label": "traffic light", "name": "交通信号灯"}, + {"label": "fire hydrant", "name": "消防栓"}, + {"label": "stop sign", "name": "停车标志"}, + {"label": "parking meter", "name": "停车收费表"}, + {"label": "bench", "name": "长椅"}, + {"label": "bird", "name": "鸟"}, + {"label": "cat", "name": "猫"}, + {"label": "dog", "name": "狗"}, + {"label": "horse", "name": "马"}, + {"label": "sheep", "name": "羊"}, + {"label": "cow", "name": "牛"}, + {"label": "elephant", "name": "大象"}, + {"label": "bear", "name": "熊"}, + {"label": "zebra", "name": "斑马"}, + {"label": "giraffe", "name": "长颈鹿"}, + {"label": "backpack", "name": "背包"}, + {"label": "umbrella", "name": "雨伞"}, + {"label": "handbag", "name": "手提包"}, + {"label": "tie", "name": "领带"}, + {"label": "suitcase", "name": "行李箱"}, + {"label": "frisbee", "name": "飞盘"}, + {"label": "skis", "name": "滑雪板"}, + {"label": "snowboard", "name": "单板滑雪板"}, + {"label": "sports ball", "name": "运动球"}, + {"label": "kite", "name": "风筝"}, + {"label": "baseball bat", "name": "棒球棒"}, + {"label": "baseball glove", "name": "棒球手套"}, + {"label": "skateboard", "name": "滑板"}, + {"label": "surfboard", "name": "冲浪板"}, + {"label": "tennis racket", "name": "网球拍"}, + {"label": "bottle", "name": "瓶子"}, + {"label": "wine glass", "name": "红酒杯"}, + {"label": "cup", "name": "杯子"}, + {"label": "fork", "name": "叉子"}, + {"label": "knife", "name": "刀"}, + {"label": "spoon", "name": "勺子"}, + {"label": "bowl", "name": "碗"}, + {"label": "banana", "name": "香蕉"}, + {"label": "apple", "name": "苹果"}, + {"label": "sandwich", "name": "三明治"}, + {"label": "orange", "name": "橙子"}, + {"label": "broccoli", "name": "西兰花"}, + {"label": "carrot", "name": "胡萝卜"}, + {"label": "hot dog", "name": "热狗"}, + {"label": "pizza", "name": "披萨"}, + {"label": "donut", "name": "甜甜圈"}, + {"label": "cake", "name": "蛋糕"}, + {"label": "chair", "name": "椅子"}, + {"label": "couch", "name": "沙发"}, + {"label": "potted plant", "name": "盆栽植物"}, + {"label": "bed", "name": "床"}, + {"label": "dining table", "name": "餐桌"}, + {"label": "toilet", "name": "马桶"}, + {"label": "tv", "name": "电视"}, + {"label": "laptop", "name": "笔记本电脑"}, + {"label": "mouse", "name": "鼠标"}, + {"label": "remote", "name": "遥控器"}, + {"label": "keyboard", "name": "键盘"}, + {"label": "cell phone", "name": "手机"}, + {"label": "microwave", "name": "微波炉"}, + {"label": "oven", "name": "烤箱"}, + {"label": "toaster", "name": "烤面包机"}, + {"label": "sink", "name": "水槽"}, + {"label": "refrigerator", "name": "冰箱"}, + {"label": "book", "name": "书"}, + {"label": "clock", "name": "时钟"}, + {"label": "vase", "name": "花瓶"}, + {"label": "scissors", "name": "剪刀"}, + {"label": "teddy bear", "name": "泰迪熊"}, + {"label": "hair drier", "name": "吹风机"}, + {"label": "toothbrush", "name": "牙刷"} +] diff --git a/backend/apps/algorithms/static/loading.gif b/backend/apps/algorithms/static/loading.gif new file mode 100644 index 0000000..557d599 Binary files /dev/null and b/backend/apps/algorithms/static/loading.gif differ diff --git a/backend/apps/algorithms/templates/image_detect.html b/backend/apps/algorithms/templates/image_detect.html new file mode 100644 index 0000000..d258f47 --- /dev/null +++ b/backend/apps/algorithms/templates/image_detect.html @@ -0,0 +1,160 @@ + + +{% load static %} + + + + AI图像检测 + + + + +

AI图像识别-对象检测

+ + + + + +
+ +
+
+ Modal Image +
+
+ +
+ + 图像识别检测中... +
+ + + + + diff --git a/backend/apps/algorithms/templates/time_predict.html b/backend/apps/algorithms/templates/time_predict.html new file mode 100644 index 0000000..b9b2f5b --- /dev/null +++ b/backend/apps/algorithms/templates/time_predict.html @@ -0,0 +1,57 @@ + + + + + + + API Result + + +

某机场乘客数量变化趋势预测

+ + +
+ + + + diff --git a/backend/apps/algorithms/templates/upload.html b/backend/apps/algorithms/templates/upload.html new file mode 100644 index 0000000..47c538a --- /dev/null +++ b/backend/apps/algorithms/templates/upload.html @@ -0,0 +1,86 @@ + + + + + + File Upload + + + + +

File Upload

+ +
+ + + +
+
+
+

+
+ + + + + diff --git a/backend/apps/algorithms/views/__init__.py b/backend/apps/algorithms/views/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/algorithms/views/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/algorithms/views/algorithm.py b/backend/apps/algorithms/views/algorithm.py new file mode 100644 index 0000000..66acce6 --- /dev/null +++ b/backend/apps/algorithms/views/algorithm.py @@ -0,0 +1,232 @@ +import json +import uuid +from datetime import datetime + +from django.db import connection, transaction +from django.db.models import CharField, Max +from django.db.models.functions import Cast +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import ( + AiAlgorithmCallLogSerializer, + AiAlgorithmEngineSerializer, + AiAlgorithmSerializer, +) +from apps.core.models import ( + AiAlgorithm, + AiAlgorithmCallLog, + AiAlgorithmEngine, + AiAlgorithmModels, + AiAlgorithmOutput, + AiAlgorithmParams, + AiAlgorithmTrainRecords, +) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_algorithm(request): + algorithm_code = request.GET.get("algorithm_code", None) + algorithm_name = request.GET.get("algorithm_name", None) + algorithm_class = request.GET.get("algorithm_class", None) + scenario = request.GET.get("scenario", None) + algorithm_type = request.GET.get("algorithm_type", None) + algorithm_status = request.GET.get("algorithm_status", None) + development_unit = request.GET.get("development_unit", None) + class_code = request.GET.get("class_code", None) + + page_size = request.GET.get("page_size", None) + filter_kwargs = {} + if algorithm_code: + filter_kwargs["algorithm_code__icontains"] = algorithm_code + if algorithm_name: + filter_kwargs["algorithm_name__icontains"] = algorithm_name + if algorithm_class: + filter_kwargs["algorithm_class__exact"] = algorithm_class + if scenario: + filter_kwargs["scenario__icontains"] = scenario + if algorithm_type: + filter_kwargs["algorithm_type__exact"] = algorithm_type + if algorithm_status: + filter_kwargs["algorithm_status__exact"] = algorithm_status + if development_unit: + filter_kwargs["development_unit__icontains"] = development_unit + if class_code: + filter_kwargs["labels__icontains"] = class_code + algorithms = AiAlgorithm.objects.filter(**filter_kwargs).order_by("algorithm_code") + + paginator = PageNumberPagination() + paginator.page_size = page_size or 10 + + result_page = paginator.paginate_queryset(algorithms, request) + serializer = AiAlgorithmSerializer(result_page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_algorithm_tree(request): + sql = """ + SELECT + a.id AS id, + a.classcode AS code, + a.classname AS name, + a.parentid AS parentid + FROM + ai_algorithm_class a + UNION + SELECT + b.id AS id, + b.algorithm_code AS code, + b.algorithm_name AS name, + b.algorithm_class AS parentid + FROM + ai_algorithm b + ORDER BY code + """ + + with connection.cursor() as cursor: + cursor.execute(sql) + result = cursor.fetchall() + + keys = ["id", "code", "name", "parentid"] + data = [dict(zip(keys, row)) for row in result] + return Response(data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_algorithm_list(request): + algorithm_code = request.GET.get("algorithm_code", None) + algorithm_name = request.GET.get("algorithm_name", None) + algorithm_class = request.GET.get("algorithm_class", None) + filter_kwargs = {} + if algorithm_code: + filter_kwargs["algorithm_code__icontains"] = algorithm_code + if algorithm_name: + filter_kwargs["algorithm_name__icontains"] = algorithm_name + if algorithm_class: + filter_kwargs["algorithm_class__exact"] = algorithm_class + algorithms = AiAlgorithm.objects.filter(**filter_kwargs).order_by( + "algorithm_class", "algorithm_code" + ) + serializer = AiAlgorithmSerializer(algorithms, many=True) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_algorithm_engines(request): + algorithmengines = AiAlgorithmEngine.objects.order_by("enginecode") + serializer = AiAlgorithmEngineSerializer(algorithmengines, many=True) + return Response(serializer.data) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def add_algorithm(request): + data = request.data + data["id"] = str(uuid.uuid4()) + data["creator"] = request.user.username if request.user.username else "creator" + data["create_time"] = datetime.now() + algorithm_class = data.get("algorithm_class") + algorithm_engine = data.get("algorithm_engine") + max_algorithm_code = AiAlgorithm.objects.filter(algorithm_class=algorithm_class).aggregate( + max_algorithm_code=Max(Cast("algorithm_code", CharField())) + ).get("max_algorithm_code") + max_algorithm_code = int(max_algorithm_code) if max_algorithm_code is not None else 0 + new_algorithm_code = f"{max_algorithm_code + 1:03}" + data["algorithm_code"] = new_algorithm_code + serializer = AiAlgorithmSerializer(data=data) + + if serializer.is_valid(): + serializer.save() + engine = get_object_or_404(AiAlgorithmEngine, id=algorithm_engine) + if engine.training_params_template: + try: + params = json.loads(engine.training_params_template) + params_to_save = [ + AiAlgorithmParams( + id=str(uuid.uuid4()), + algorithm_id=data["id"], + param_class="01", + param_order=param.get("param_order"), + param_name=param.get("param_name"), + param_desc=param.get("param_desc"), + param_type=param.get("param_type"), + param_unit=param.get("param_unit"), + param_range=param.get("param_range"), + default_values=param.get("default_values"), + description=param.get("description"), + ) + for param in params + ] + AiAlgorithmParams.objects.bulk_create(params_to_save) + except json.JSONDecodeError as e: + return Response( + {"error": f"Invalid JSON in training_params_template: {str(e)}"}, + status=status.HTTP_400_BAD_REQUEST, + ) + + return Response({"message": "Algorithm added successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_algorithm(request): + algorithm_id = request.GET.get("id", None) + algorithm = get_object_or_404(AiAlgorithm, id=algorithm_id) + serializer = AiAlgorithmSerializer(algorithm) + return Response(serializer.data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_algorithm(request): + data = request.data + algorithm = get_object_or_404(AiAlgorithm, id=data.get("id")) + serializer = AiAlgorithmSerializer(instance=algorithm, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "Algorithm updated successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def delete_algorithm(request): + algorithm = get_object_or_404(AiAlgorithm, id=request.GET.get("id")) + model_count = AiAlgorithmModels.objects.filter(algorithm_id=algorithm.id).count() + if model_count > 0: + return Response({"warn": "该算法已经绑定了模型，请先删除算法模型！"}, status=status.HTTP_400_BAD_REQUEST) + + train_count = AiAlgorithmTrainRecords.objects.filter(algorithm_id=algorithm.id).count() + if train_count > 0: + return Response( + {"warn": "该算法已经存在了模型训练记录，请先删除算法模型训练记录！"}, + status=status.HTTP_400_BAD_REQUEST, + ) + + try: + AiAlgorithmParams.objects.filter(algorithm_id=algorithm.id).delete() + AiAlgorithmOutput.objects.filter(algorithm_id=algorithm.id).delete() + algorithm.delete() + return Response({"message": "算法删除成功"}, status=status.HTTP_200_OK) + except Exception as e: + return Response({"error": f"算法删除失败: {str(e)}"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) diff --git a/backend/apps/algorithms/views/algorithm_calllog.py b/backend/apps/algorithms/views/algorithm_calllog.py new file mode 100644 index 0000000..a4d2731 --- /dev/null +++ b/backend/apps/algorithms/views/algorithm_calllog.py @@ -0,0 +1,77 @@ +import uuid +from datetime import datetime, timedelta + +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AiAlgorithmCallLogSerializer +from apps.core.models import AiAlgorithmCallLog + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def create_algorithm_call_log(request): + data = request.data + data["id"] = str(uuid.uuid4()) + data["log_time"] = datetime.now() + + serializer = AiAlgorithmCallLogSerializer(data=data, context={"request": request}) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "调用日志创建成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_algorithm_call_log(request): + log_id = request.GET.get("id", None) + log = get_object_or_404(AiAlgorithmCallLog, id=log_id) + serializer = AiAlgorithmCallLogSerializer(log) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_algorithm_call_logs(request): + algorithm_id = request.GET.get("algorithm_id", None) + object_code = request.GET.get("object_code", None) + caller = request.GET.get("caller", None) + + start_date = request.GET.get("start_date", None) + end_date = request.GET.get("end_date", None) + + filter_kwargs = {} + if algorithm_id: + filter_kwargs["algorithm_id"] = algorithm_id + if object_code: + filter_kwargs["object_code"] = object_code + if caller: + filter_kwargs["caller__icontains"] = caller + + if start_date and end_date: + start_datetime = datetime.strptime(start_date, "%Y-%m-%d") + end_datetime = datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=1) + filter_kwargs["log_time__range"] = (start_datetime, end_datetime) + + logs = AiAlgorithmCallLog.objects.filter(**filter_kwargs).order_by("-log_time") + + serializer = AiAlgorithmCallLogSerializer(logs, many=True) + return Response(serializer.data) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_algorithm_call_log(request): + log_id = request.GET.get("id", None) + log = get_object_or_404(AiAlgorithmCallLog, id=log_id) + log.delete() + return Response({"message": "调用日志删除成功"}) diff --git a/backend/apps/algorithms/views/algorithm_class.py b/backend/apps/algorithms/views/algorithm_class.py new file mode 100644 index 0000000..39fdd8a --- /dev/null +++ b/backend/apps/algorithms/views/algorithm_class.py @@ -0,0 +1,79 @@ +import uuid + +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AiAlgorithmClassSerializer +from apps.core.models import AiAlgorithmClass + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def create_algorithm_class(request): + data = request.data + data["id"] = str(uuid.uuid4()) + serializer = AiAlgorithmClassSerializer(data=data, context={"request": request}) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "算法创建成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_algorithm_class(request): + algorithm_id = request.GET.get("id", None) + algorithm = get_object_or_404(AiAlgorithmClass, id=algorithm_id) + serializer = AiAlgorithmClassSerializer(algorithm) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_algorithm_class(request): + classcode = request.GET.get("classcode", None) + classname = request.GET.get("classname", None) + + filter_kwargs = {} + if classcode: + filter_kwargs["classcode__icontains"] = classcode + if classname: + filter_kwargs["classname__icontains"] = classname + + algorithms = AiAlgorithmClass.objects.filter(**filter_kwargs).order_by("classcode") + serializer = AiAlgorithmClassSerializer(algorithms, many=True) + return Response(serializer.data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_algorithm_class(request): + data = request.data + algorithmclass = get_object_or_404(AiAlgorithmClass, id=data.get("id")) + serializer = AiAlgorithmClassSerializer(instance=algorithmclass, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "算法更新成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_algorithm_class(request): + algorithm_id = request.GET.get("id", None) + algorithm = get_object_or_404(AiAlgorithmClass, id=algorithm_id) + algorithm.delete() + return Response({"message": "算法删除成功"}) diff --git a/backend/apps/algorithms/views/algorithm_models.py b/backend/apps/algorithms/views/algorithm_models.py new file mode 100644 index 0000000..330962b --- /dev/null +++ b/backend/apps/algorithms/views/algorithm_models.py @@ -0,0 +1,637 @@ +import os +import re +import shutil +import subprocess +import sys +import tempfile +import uuid +from urllib.parse import quote +from datetime import datetime + +from django.core.exceptions import ObjectDoesNotExist +from django.db import transaction +from django.http import HttpResponse +from django.shortcuts import get_object_or_404 +from rest_framework.decorators import api_view, parser_classes, permission_classes +from rest_framework.parsers import MultiPartParser +from rest_framework.permissions import AllowAny, IsAuthenticated +from rest_framework.response import Response + +from django.conf import settings +from apps.common.serializers import AiAlgorithmModelsSerializer +from apps.core.models import AiAlgorithm, AiAlgorithmClass, AiAlgorithmModels +import yaml +try: + from apps.algorithms.algorithm.pytorch.YoloDetector import YoloDetector +except Exception: + YoloDetector = None + +from apps.training.views.algorithm_trainrecords import get_file_extension + + +def _convert_onnx_with_repo_ultralytics(src_file, onnx_file, imgsz=640): + repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../..")) + ultralytics_repo = os.path.join(repo_root, "ultralytics_yolo11") + if not os.path.isdir(ultralytics_repo): + raise FileNotFoundError(f"未找到仓库内 ultralytics_yolo11 目录：{ultralytics_repo}") + default_cfg_path = os.path.join(ultralytics_repo, "ultralytics", "cfg", "default.yaml") + if not os.path.exists(default_cfg_path): + raise FileNotFoundError(f"未找到 default.yaml：{default_cfg_path}") + with open(default_cfg_path, "r", encoding="utf-8") as f: + old_cfg_text = f.read() + cfg_data = yaml.safe_load(old_cfg_text) or {} + cfg_data["model"] = str(src_file).replace("\\", "/") + cfg_data["format"] = "rknn" + cfg_data["batch"] = 1 + cfg_data["imgsz"] = int(imgsz) + cfg_data["dynamic"] = False + with open(default_cfg_path, "w", encoding="utf-8") as f: + yaml.dump(cfg_data, f, allow_unicode=True, sort_keys=False) + env = os.environ.copy() + env["PYTHONPATH"] = ultralytics_repo + (os.pathsep + env["PYTHONPATH"] if env.get("PYTHONPATH") else "") + try: + process = subprocess.run( + [sys.executable, "ultralytics/engine/exporter.py"], + cwd=ultralytics_repo, + capture_output=True, + text=True, + encoding="utf-8", + errors="ignore", + env=env, + ) + finally: + with open(default_cfg_path, "w", encoding="utf-8") as f: + f.write(old_cfg_text) + if process.returncode != 0: + raise RuntimeError(process.stderr or process.stdout or "瑞芯微导出失败") + export_path = os.path.splitext(src_file)[0] + ".onnx" + if not os.path.exists(export_path): + raise FileNotFoundError(f"未找到导出的 ONNX 文件：{export_path}") + if os.path.abspath(export_path) != os.path.abspath(onnx_file): + shutil.copy2(export_path, onnx_file) + + +def _convert_onnx_with_env_ultralytics(src_file, onnx_file, algorithm_engine, onnx_type): + from ultralytics import YOLO + + model = YOLO(src_file) + export_kwargs = {"format": "onnx", "batch": 1, "imgsz": 640, "dynamic": False} + if str(onnx_type) == "huawei": + export_kwargs["opset"] = 12 + export_kwargs["simplify"] = False + export_path = str(model.export(**export_kwargs)) + if not os.path.isabs(export_path): + export_path = os.path.abspath(export_path) + if not os.path.exists(export_path): + raise FileNotFoundError(f"未找到导出的 ONNX 文件：{export_path}") + if os.path.abspath(export_path) != os.path.abspath(onnx_file): + shutil.copy2(export_path, onnx_file) + + +@api_view(["POST"]) +@parser_classes([MultiPartParser]) +@permission_classes([IsAuthenticated]) +def upload_algorithm_modelfile(request): + data = request.data + data["id"] = str(uuid.uuid4()) + data["upload_time"] = datetime.now() + data["model_createtime"] = datetime.now() + data["uploader"] = request.user.username if request.user and request.user.is_authenticated else '' + algorithm_id = data.get("algorithm_id") + algorithm_classid = data.get("algorithm_classid") + model_version = data.get("model_version") + + ai_algorithm = get_object_or_404(AiAlgorithm, id=algorithm_id) + algorithm_name = ai_algorithm.algorithm_name + + safe_algorithm_name = re.sub(r'[^\w\-]', '-', algorithm_name).strip('-') + if not safe_algorithm_name: + safe_algorithm_name = 'unknown' + + uploadfiles = request.FILES.getlist("uploadfile") + bucket_name = 'archived-model' + + try: + from apps.common.minio_client import minio_storage + except Exception: + from common.minio_client import minio_storage + + for uploadfile in uploadfiles: + name, extension = os.path.splitext(uploadfile.name) + new_file_name = f"model-{model_version}{extension}" + minio_object_path = f"{safe_algorithm_name}/{model_version}/{new_file_name}" + + minio_storage.upload_bytes( + bucket_name, + minio_object_path, + uploadfile.read(), + ) + + data["model_filename"] = new_file_name + data["model_filepath"] = minio_object_path + data["model_bucketpath"] = f"{bucket_name}/{minio_object_path}" + + data["model_status"] = "01" + serializer = AiAlgorithmModelsSerializer(data=data, context={"request": request}) + if serializer.is_valid(): + serializer.save() + return Response({"message": "模型创建成功"}) + return Response({"error": "无效的数据", "details": serializer.errors}, status=400) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_algorithm_model(request): + model_id = request.GET.get("id", None) + model = get_object_or_404(AiAlgorithmModels, id=model_id) + serializer = AiAlgorithmModelsSerializer(model) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_algorithm_model(request): + algorithm_id = request.GET.get("algorithm_id", None) + model_version = request.GET.get("model_version", None) + model_filename = request.GET.get("model_filename", None) + model_status = request.GET.get("model_status", None) + filter_kwargs = {} + if algorithm_id: + filter_kwargs["algorithm_id"] = algorithm_id + if model_version: + filter_kwargs["model_version"] = model_version + if model_status: + filter_kwargs["model_status"] = model_status + if model_filename: + filter_kwargs["model_filename__icontains"] = model_filename + + models = AiAlgorithmModels.objects.filter(**filter_kwargs).order_by("-is_using", "model_version") + + serializer = AiAlgorithmModelsSerializer(models, many=True) + data = serializer.data + + for item in data: + try: + algorithm = AiAlgorithm.objects.get(id=item.get('algorithm_id')) + item['algorithm_name'] = algorithm.algorithm_name + except ObjectDoesNotExist: + item['algorithm_name'] = '' + if item.get('onnx_bucketpath'): + item['onnx_name'] = item['onnx_bucketpath'].split("/")[-1] + else: + item['onnx_name'] = '' + + return Response(data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_algorithm_model(request): + data = request.data + model = get_object_or_404(AiAlgorithmModels, id=data.get("id")) + serializer = AiAlgorithmModelsSerializer(instance=model, data=data, partial=True) + if serializer.is_valid(): + serializer.save() + return Response({"message": "模型更新成功"}) + return Response({"error": "无效的数据", "details": serializer.errors}, status=400) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def set_algorithm_default_model(request): + data = request.data + current_model = get_object_or_404(AiAlgorithmModels, id=data.get("id")) + algorithm_id = current_model.algorithm_id + + with transaction.atomic(): + AiAlgorithmModels.objects.filter(algorithm_id=algorithm_id).exclude(id=current_model.id).update( + is_using="0" + ) + + AiAlgorithmModels.objects.filter(id=current_model.id).update(is_using="1") + + AiAlgorithm.objects.filter(id=algorithm_id).update( + link_model={ + "model_id": current_model.id, + "model_filename": current_model.model_filename, + "model_version": current_model.model_version, + "model_createtime": current_model.model_createtime.strftime("%Y-%m-%d") + if current_model.model_createtime + else None, + } + ) + + return Response({"message": "ok"}) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def issue_algorithm_model(request): + data = request.data + model_id = data.get("id") + if not model_id: + return Response({"error": "缺少参数：id"}, status=400) + current_model = get_object_or_404(AiAlgorithmModels, id=model_id) + algorithm_id = current_model.algorithm_id + with transaction.atomic(): + AiAlgorithmModels.objects.filter(algorithm_id=algorithm_id).exclude(id=current_model.id).update( + custom2="0" + ) + AiAlgorithmModels.objects.filter(id=current_model.id).update(custom2="1") + return Response({"message": "模型下发成功"}) + + +@api_view(["POST"]) +@permission_classes([AllowAny]) +def download_published_onnx_model(request): + data = request.data + algorithm_code = data.get("algorithm_code") + algorithm_engine = data.get("algorithm_engine") + onnx_type = str(data.get("onnx_type") or "").strip() + if not algorithm_code or not algorithm_engine: + return Response({"message": "缺少参数：algorithm_code 或 algorithm_engine"}, status=400) + if onnx_type and onnx_type not in {"general", "rockchip", "huawei"}: + return Response({"message": "参数无效：onnx_type"}, status=400) + + algorithm = ( + AiAlgorithm.objects.filter(algorithm_code=algorithm_code, algorithm_engine=algorithm_engine) + .order_by("create_time") + .first() + ) + if not algorithm: + return Response({"message": "算法不存在"}, status=404) + + model_filters = {"algorithm_id": algorithm.id, "custom2": "1"} + if onnx_type: + model_filters["custom3"] = onnx_type + published_model = AiAlgorithmModels.objects.filter(**model_filters).order_by("-upload_time").first() + if not published_model: + if onnx_type: + return Response({"message": f"算法未发布或未找到类型为 {onnx_type} 的ONNX模型"}, status=404) + return Response({"message": "算法未发布"}, status=404) + + onnx_value = str(published_model.custom1 or "").strip() + if not onnx_value: + return Response({"message": "算法未发布"}, status=404) + + root_path = settings.FILESPACE_ROOT_PATH + candidate_paths = [] + if os.path.isabs(onnx_value): + candidate_paths.append(onnx_value) + else: + candidate_paths.append(os.path.join(root_path, str(published_model.model_filepath or ""), onnx_value)) + candidate_paths.append(os.path.join(root_path, onnx_value)) + onnx_abs_path = next((p for p in candidate_paths if os.path.exists(p)), None) + if not onnx_abs_path: + return Response({"message": "ONNX模型文件不存在"}, status=404) + + with open(onnx_abs_path, "rb") as f: + payload = f.read() + response = HttpResponse(payload, content_type="application/octet-stream") + response["Content-Disposition"] = f'attachment; filename="{os.path.basename(onnx_abs_path)}"' + model_labels = str(published_model.model_labels or "") + response["X-Model-Labels"] = quote(model_labels, safe="") + response["X-Model-Labels-Encoding"] = "url" + return response + + +@api_view(["DELETE", "POST"]) +@permission_classes([IsAuthenticated]) +def delete_algorithm_model(request): + model_id = request.GET.get("id", None) + model = get_object_or_404(AiAlgorithmModels, id=model_id) + + try: + from apps.common.minio_client import minio_storage + except Exception: + from common.minio_client import minio_storage + + if model.model_bucketpath: + try: + parts = model.model_bucketpath.split('/') + bucket_name = parts[0] + object_name = '/'.join(parts[1:]) + minio_storage.delete_object(bucket_name, object_name) + except Exception: + pass + + if model.report_bucketpath: + try: + parts = model.report_bucketpath.split('/') + bucket_name = parts[0] + object_name = '/'.join(parts[1:]) + minio_storage.delete_object(bucket_name, object_name) + except Exception: + pass + + model.delete() + return Response({"message": "模型删除成功"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def export_algorithm_model(request): + model_id = request.GET.get("id", None) + if not model_id: + return Response({"message": "缺少参数：id"}, status=400) + + ai_model = get_object_or_404(AiAlgorithmModels, id=model_id) + + try: + from apps.common.minio_client import minio_storage + except Exception: + from common.minio_client import minio_storage + + model_bucketpath = ai_model.model_bucketpath + if model_bucketpath: + parts = model_bucketpath.split('/') + bucket_name = parts[0] + object_name = '/'.join(parts[1:]) + try: + response = minio_storage.client.get_object(bucket_name, object_name) + content = response.read() + response.close() + response.release_conn() + filename = ai_model.model_filename or f"model-{ai_model.model_version}.pt" + response = HttpResponse(content, content_type='application/octet-stream') + response['Content-Disposition'] = f'attachment; filename="{filename}"' + return response + except Exception as e: + return Response({"message": f"下载失败：{str(e)}"}, status=500) + + filespace_root_path = settings.FILESPACE_ROOT_PATH + src_file = os.path.join(filespace_root_path, str(ai_model.model_filepath or ""), ai_model.model_filename or "") + if os.path.exists(src_file): + with open(src_file, 'rb') as f: + content = f.read() + filename = ai_model.model_filename or f"model-{ai_model.model_version}.pt" + response = HttpResponse(content, content_type='application/octet-stream') + response['Content-Disposition'] = f'attachment; filename="{filename}"' + return response + + return Response({"message": "模型文件不存在"}, status=404) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def convert_algorithm_model_onnx(request): + data = request.data + model_id = data.get("id") + onnx_type = str(data.get("onnx_type") or "").strip() + if not model_id: + return Response({"message": "缺少参数：id"}, status=400) + if onnx_type not in {"general", "rockchip", "huawei"}: + return Response({"message": "缺少参数或参数无效：onnx_type"}, status=400) + ai_model = get_object_or_404(AiAlgorithmModels, id=model_id) + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_model.algorithm_id) + model_filename = str(ai_model.model_filename or "") + ext = os.path.splitext(model_filename)[1].lower() + if ext not in {".pt", ".pth"}: + return Response({"message": f"当前模型后缀不支持转换：{ext}"}, status=400) + + try: + from apps.common.minio_client import minio_storage + except Exception: + from common.minio_client import minio_storage + + model_bucketpath = ai_model.model_bucketpath + if not model_bucketpath: + return Response({"message": "模型文件未存储在MinIO中"}, status=400) + + parts = model_bucketpath.split('/') + bucket_name = parts[0] + model_object_name = '/'.join(parts[1:]) + + local_model_dir = os.path.join(settings.MODEL_ROOT_PATH, *parts[1:-1]).replace('\\', '/') + os.makedirs(local_model_dir, exist_ok=True) + + src_file = os.path.join(local_model_dir, model_filename).replace('\\', '/') + if not os.path.exists(src_file): + try: + minio_storage.download_file(bucket_name, model_object_name, src_file) + except Exception as e: + return Response({"message": f"从MinIO下载模型文件失败：{str(e)}"}, status=400) + + onnx_name = f"{os.path.splitext(model_filename)[0]}.onnx" + onnx_file = os.path.join(local_model_dir, onnx_name) + + try: + if onnx_type == "rockchip": + if ai_algorithm.algorithm_engine not in {"01", "04"}: + return Response({"message": "瑞芯微转换仅支持YOLO目标检测与YOLO-Seg分割模型"}, status=400) + _convert_onnx_with_repo_ultralytics(src_file, onnx_file, imgsz=640) + else: + if ai_algorithm.algorithm_engine not in {"01", "04"}: + return Response({"message": "通用/Huawei转换当前仅支持YOLO目标检测与YOLO-Seg分割模型"}, status=400) + _convert_onnx_with_env_ultralytics(src_file, onnx_file, ai_algorithm.algorithm_engine, onnx_type) + except Exception as e: + return Response({"message": f"模型转换失败：{str(e)}"}, status=400) + + if not os.path.exists(onnx_file): + return Response({"message": "模型转换失败：未生成ONNX文件"}, status=400) + + onnx_object_name = '/'.join(parts[1:-1] + [onnx_name]) + try: + minio_storage.upload_file(bucket_name, onnx_object_name, onnx_file) + except Exception as e: + return Response({"message": f"上传ONNX文件到MinIO失败：{str(e)}"}, status=400) + + onnx_bucketpath = f"{bucket_name}/{onnx_object_name}" + + AiAlgorithmModels.objects.filter(id=model_id).update( + custom1=onnx_name, + custom3=onnx_type, + onnx_bucketpath=onnx_bucketpath, + ) + return Response({"message": "模型转换成功", "onnx_name": onnx_name, "onnx_type": onnx_type, "onnx_bucketpath": onnx_bucketpath}) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +@parser_classes([MultiPartParser]) +def algorithm_model_predict(request): + try: + type = request.data.get("type", "1") + + if type == "1": + image_file = request.data.get("image") + if not image_file: + return Response({"error": "没有提供图像文件"}, status=400) + + with tempfile.NamedTemporaryFile(delete=False, suffix=get_file_extension(image_file.name)) as temp_file: + temp_file_path = temp_file.name + temp_file.write(image_file.read()) + image_url = temp_file_path + else: + filename = request.data.get("image_name", None) + if not filename: + return Response({"error": "没有提供图像文件名。"}, status=400) + image_url = os.path.join(settings.FILESPACE_ROOT_PATH, filename) + + model_id = request.data.get("id") + ai_model = get_object_or_404(AiAlgorithmModels, id=model_id) + + model_path = None + + if ai_model.model_bucketpath: + try: + from apps.common.minio_client import minio_storage + except Exception: + from common.minio_client import minio_storage + + parts = ai_model.model_bucketpath.split('/') + bucket_name = parts[0] + object_name = '/'.join(parts[1:]) + + local_model_dir = os.path.join(settings.MODEL_ROOT_PATH, parts[1], parts[2]) + os.makedirs(local_model_dir, exist_ok=True) + + model_filename = ai_model.model_filename + local_model_path = os.path.join(local_model_dir, model_filename) + + if not os.path.exists(local_model_path): + minio_storage.download_file(bucket_name, object_name, local_model_path) + + model_path = local_model_path + else: + filespace_root_path = settings.FILESPACE_ROOT_PATH + model_path = os.path.join(filespace_root_path, ai_model.model_filepath, ai_model.model_filename) + + if not model_path or not os.path.exists(model_path): + return Response({"error": "模型文件不存在"}, status=404) + + if YoloDetector is None: + return Response( + {"error": "算法依赖未安装，请先安装 requirements/algorithms.txt"}, + status=503, + ) + detector = YoloDetector(model_path=model_path, image_path=image_url, labels=ai_model.model_labels) + result = detector.detect() + + return Response(result) + except ObjectDoesNotExist: + return Response({"error": "未找到模型。"}, status=404) + except Exception as e: + import traceback + traceback.print_exc() + return Response({"error": str(e)}, status=500) + finally: + if "temp_file_path" in locals() and os.path.exists(temp_file_path): + os.remove(temp_file_path) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_algorithm_default_images(request): + model_id = request.GET.get("id", None) + model = get_object_or_404(AiAlgorithmModels, id=model_id) + image_path = os.path.join(settings.FILESPACE_ROOT_PATH, model.model_filepath, "images") + + try: + if not os.path.exists(image_path): + os.makedirs(image_path) + image_names = os.listdir(image_path) + imagenames = [] + temp = model.model_filepath + "/images/" + for image_name in image_names: + imagenames.append(temp + image_name) + except FileNotFoundError: + return Response({"error": "Images directory not found"}, status=404) + return Response(imagenames) + + +@api_view(["POST"]) +@parser_classes([MultiPartParser]) +@permission_classes([IsAuthenticated]) +def upload_predict_default_images(request): + data = request.data + model_id = data.get("id", None) + model = get_object_or_404(AiAlgorithmModels, id=model_id) + + uploadfiles = request.FILES.getlist("uploadfiles") + for uploadfile in uploadfiles: + file_path = os.path.join(settings.FILESPACE_ROOT_PATH, model.model_filepath, "images") + if not os.path.exists(file_path): + os.makedirs(file_path) + file_pathname = os.path.join(file_path, uploadfile.name) + with open(file_pathname, "wb") as file: + file.write(uploadfile.read()) + return Response({"message": "测试图片上传成功！"}) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def publish_model_to_terminals(request): + """将算法模型发布到智能终端 — 支持发布到多个终端 + + 请求体示例： + { + "model_id": "算法模型ID", + "terminal_ids": ["终端ID1", "终端ID2"] + } + """ + model_id = request.data.get("model_id") + raw_ids = request.data.get("terminal_ids", []) + + if not model_id: + return Response({"error": "缺少必要参数：model_id"}, status=400) + if not raw_ids: + return Response({"error": "缺少必要参数：terminal_ids"}, status=400) + + # 归一化 terminal_ids：支持 JSON 字符串或列表 + if isinstance(raw_ids, str): + import json as _json + try: + raw_ids = _json.loads(raw_ids) + except Exception: + raw_ids = [raw_ids] + terminal_ids = [tid for tid in raw_ids if tid] if isinstance(raw_ids, list) else [] + + if not terminal_ids: + return Response({"error": "terminal_ids 为空"}, status=400) + + # 查找算法模型 + ai_model = get_object_or_404(AiAlgorithmModels, id=model_id) + + # 查找目标终端 + from apps.core.models import AiSmartTerminal + terminals = AiSmartTerminal.objects.filter(id__in=terminal_ids) + if not terminals.exists(): + return Response({"error": "未找到匹配的终端设备"}, status=404) + + # 构造模型路径：取onnx_bucketpath + model_path = ai_model.onnx_bucketpath + model_name = model_path.split("/")[-1] + + # 批量更新终端 + publish_time = datetime.now() + publish_user = request.user.username + terminals.update( + model_id=str(ai_model.id), + model_name=model_name, + model_version=ai_model.model_version or "", + model_path=model_path, + model_publish_time=publish_time, + model_publish_user=publish_user, + ) + + from apps.common.serializers import AiSmartTerminalSerializer + updated = AiSmartTerminal.objects.filter(id__in=terminal_ids) + serializer = AiSmartTerminalSerializer(updated, many=True) + return Response({ + "published_count": len(terminal_ids), + "model_id": str(ai_model.id), + "model_version": ai_model.model_version, + "model_path": model_path, + "terminals": serializer.data, + }) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def delete_predict_default_image(request): + data = request.data + file_name = data.get("file_name", None) + file_pathname = os.path.join(settings.FILESPACE_ROOT_PATH, file_name) + os.remove(file_pathname) + return Response({"message": "模型缺省检测图片删除成功！"}) diff --git a/backend/apps/algorithms/views/algorithm_output.py b/backend/apps/algorithms/views/algorithm_output.py new file mode 100644 index 0000000..01d4c33 --- /dev/null +++ b/backend/apps/algorithms/views/algorithm_output.py @@ -0,0 +1,80 @@ +import uuid + +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AiAlgorithmOutputSerializer +from apps.core.models import AiAlgorithmOutput + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def create_algorithm_output(request): + data = request.data + data["id"] = str(uuid.uuid4()) + serializer = AiAlgorithmOutputSerializer(data=data, context={"request": request}) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "算法输出项创建成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_algorithm_output(request): + item_id = request.GET.get("id", None) + item = get_object_or_404(AiAlgorithmOutput, id=item_id) + serializer = AiAlgorithmOutputSerializer(item) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_algorithm_output(request): + algorithm_id = request.GET.get("algorithm_id", None) + item_name = request.GET.get("item_name", None) + + filter_kwargs = {} + if algorithm_id: + filter_kwargs["algorithm_id"] = algorithm_id + if item_name: + filter_kwargs["item_name__icontains"] = item_name + + items = AiAlgorithmOutput.objects.filter(**filter_kwargs).order_by("item_order") + + serializer = AiAlgorithmOutputSerializer(items, many=True) + return Response(serializer.data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_algorithm_output(request): + data = request.data + item = get_object_or_404(AiAlgorithmOutput, id=data.get("id")) + serializer = AiAlgorithmOutputSerializer(instance=item, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "算法输出项更新成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_algorithm_output(request): + item_id = request.GET.get("id", None) + item = get_object_or_404(AiAlgorithmOutput, id=item_id) + item.delete() + return Response({"message": "算法输出项删除成功"}) diff --git a/backend/apps/algorithms/views/algorithm_params.py b/backend/apps/algorithms/views/algorithm_params.py new file mode 100644 index 0000000..e6cdbfb --- /dev/null +++ b/backend/apps/algorithms/views/algorithm_params.py @@ -0,0 +1,83 @@ +import uuid + +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AiAlgorithmParamsSerializer +from apps.core.models import AiAlgorithmParams + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def create_algorithm_params(request): + data = request.data + data["id"] = str(uuid.uuid4()) + + serializer = AiAlgorithmParamsSerializer(data=data, context={"request": request}) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "算法参数创建成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_algorithm_params(request): + param_id = request.GET.get("id", None) + param = get_object_or_404(AiAlgorithmParams, id=param_id) + serializer = AiAlgorithmParamsSerializer(param) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_algorithm_params(request): + algorithm_id = request.GET.get("algorithm_id", None) + param_name = request.GET.get("param_name", None) + param_class = request.GET.get("param_class", None) + filter_kwargs = {} + if algorithm_id: + filter_kwargs["algorithm_id"] = algorithm_id + if param_class: + filter_kwargs["param_class"] = param_class + if param_name: + filter_kwargs["param_name__icontains"] = param_name + + params = AiAlgorithmParams.objects.filter(**filter_kwargs).order_by("param_order") + + serializer = AiAlgorithmParamsSerializer(params, many=True) + return Response(serializer.data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_algorithm_params(request): + data = request.data + param = get_object_or_404(AiAlgorithmParams, id=data.get("id")) + serializer = AiAlgorithmParamsSerializer(instance=param, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "算法参数更新成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_algorithm_params(request): + param_id = request.GET.get("id", None) + param = get_object_or_404(AiAlgorithmParams, id=param_id) + param.delete() + return Response({"message": "算法参数删除成功"}) diff --git a/backend/apps/annotation/__init__.py b/backend/apps/annotation/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/annotation/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/annotation/apps.py b/backend/apps/annotation/apps.py new file mode 100644 index 0000000..48ff711 --- /dev/null +++ b/backend/apps/annotation/apps.py @@ -0,0 +1,7 @@ +from django.apps import AppConfig + + +class AnnotationConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "apps.annotation" + diff --git a/backend/apps/annotation/views/__init__.py b/backend/apps/annotation/views/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/annotation/views/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/annotation/views/annotate_team.py b/backend/apps/annotation/views/annotate_team.py new file mode 100644 index 0000000..0a68958 --- /dev/null +++ b/backend/apps/annotation/views/annotate_team.py @@ -0,0 +1,181 @@ +import uuid + +from django.db import transaction +from django.db.models import CharField, Max +from django.db.models.functions import Cast +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response +from rest_framework.utils import json + +from apps.common.serializers import AIAnnotateTeamMemberSerializer, AIAnnotateTeamSerializer +from apps.core.models import AIAnnotateTeam, AIAnnotateTeamMember + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def create_annotate_team(request): + data = request.data + data["id"] = str(uuid.uuid4()) + max_team_code = AIAnnotateTeam.objects.aggregate( + team_code=Max(Cast("team_code", CharField())) + ).get("team_code") + max_team_code = int(max_team_code) if max_team_code is not None else 0 + new_team_code = f"{max_team_code + 1:04}" + data["team_code"] = new_team_code + data["status"] = "01" + serializer = AIAnnotateTeamSerializer(data=data) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "团队创建成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def add_team_members(request): + data = request.data + team_id = data.get("team_id") + josn_str = data.get("team_members") + members = json.loads(josn_str) + + objects_to_save = [] + + for member in members: + if not AIAnnotateTeamMember.objects.filter( + team_id=team_id, member_account=member.get("member_account") + ).exists(): + member_obj = AIAnnotateTeamMember( + id=str(uuid.uuid4()), + team_id=team_id, + member_account=member.get("member_account"), + member_name=member.get("member_name"), + member_email=member.get("member_email"), + ) + objects_to_save.append(member_obj) + + AIAnnotateTeamMember.objects.bulk_create(objects_to_save) + return Response({"message": "团队成员添加成功"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_annotate_team(request): + team_id = request.GET.get("id", None) + team = get_object_or_404(AIAnnotateTeam, id=team_id) + serializer = AIAnnotateTeamSerializer(team) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_annotate_team(request): + team_code = request.GET.get("team_code", None) + team_name = request.GET.get("team_name", None) + organization = request.GET.get("organization", None) + page_size = request.GET.get("page_size", None) + filter_kwargs = {} + if team_code: + filter_kwargs["team_code__icontains"] = team_code + if team_name: + filter_kwargs["team_name__icontains"] = team_name + if organization: + filter_kwargs["organization"] = organization + + teams = AIAnnotateTeam.objects.filter(**filter_kwargs).order_by("team_code") + + paginator = PageNumberPagination() + paginator.page_size = page_size or 10 + result_page = paginator.paginate_queryset(teams, request) + serializer = AIAnnotateTeamSerializer(result_page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_team_members(request): + team_id = request.GET.get("team_id", None) + filter_kwargs = {} + if team_id: + filter_kwargs["team_id"] = team_id + + members = AIAnnotateTeamMember.objects.filter(**filter_kwargs).order_by( + "-member_role", "member_name" + ) + serializer = AIAnnotateTeamMemberSerializer(members, many=True) + return Response(serializer.data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_annotate_team(request): + data = request.data + team = get_object_or_404(AIAnnotateTeam, id=data.get("id")) + serializer = AIAnnotateTeamSerializer(instance=team, data=data, partial=True) + if serializer.is_valid(): + serializer.save() + return Response({"message": "团队更新成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def update_team_member(request): + data = request.data + team_member = get_object_or_404(AIAnnotateTeamMember, id=data.get("id")) + member_role = data.get("member_role") + if member_role == "leader": + AIAnnotateTeamMember.objects.filter(team_id=team_member.team_id).exclude( + id__in=team_member.id + ).update(member_role="labeler") + AIAnnotateTeam.objects.filter(id=team_member.team_id).update( + team_leader_id=team_member.member_account, + team_leader_name=team_member.member_name, + ) + serializer = AIAnnotateTeamMemberSerializer(instance=team_member, data=data, partial=True) + if serializer.is_valid(): + serializer.save() + return Response({"message": "团队成员更新成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_annotate_team(request): + team_id = request.GET.get("id", None) + team = get_object_or_404(AIAnnotateTeam, id=team_id) + team.delete() + return Response({"message": "团队删除成功"}) + + +@api_view(["DELETE"]) +def delete_team_members(request): + member_ids = request.GET.get("ids", None) + + if member_ids is None: + return Response({"message": "未选择成员ID"}, status=400) + + ids = [id for id in member_ids.split(",")] + if ids: + queryset = AIAnnotateTeamMember.objects.filter(id__in=ids) + queryset.delete() + + return Response({"message": "团队成员删除成功"}) diff --git a/backend/apps/cloud_terminal/__init__.py b/backend/apps/cloud_terminal/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/cloud_terminal/apps.py b/backend/apps/cloud_terminal/apps.py new file mode 100644 index 0000000..c2fd8e2 --- /dev/null +++ b/backend/apps/cloud_terminal/apps.py @@ -0,0 +1,19 @@ +import logging + +from django.apps import AppConfig + +logger = logging.getLogger(__name__) + + +class CloudTerminalConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "apps.cloud_terminal" + verbose_name = "智能终端（云端孪生镜像）" + + def ready(self): + """Django 启动时自动注册离线检测定时任务（每30秒执行一次）""" + from .scheduler import start_offline_checker + try: + start_offline_checker() + except Exception: + logger.exception("启动离线检测调度器失败") diff --git a/backend/apps/cloud_terminal/consumers.py b/backend/apps/cloud_terminal/consumers.py new file mode 100644 index 0000000..84c45d9 --- /dev/null +++ b/backend/apps/cloud_terminal/consumers.py @@ -0,0 +1,26 @@ +"""智能终端状态 WebSocket 消费者 — 向前端推送终端上下线状态变化""" +import json + +from channels.generic.websocket import AsyncWebsocketConsumer + + +class TerminalStatusConsumer(AsyncWebsocketConsumer): + """终端状态消费者 + + WebSocket 路径: ws://host/ws/terminal_status/ + Channel Group: terminal_status + 消息类型: terminal.status + """ + + async def connect(self): + # 加入 terminal_status 广播组 + await self.channel_layer.group_add("terminal_status", self.channel_name) + await self.accept() + + async def disconnect(self, close_code): + # 离开 terminal_status 广播组 + await self.channel_layer.group_discard("terminal_status", self.channel_name) + + async def terminal_status(self, event): + """处理终端状态变化事件，转发给前端""" + await self.send(text_data=json.dumps(event["data"], ensure_ascii=False)) diff --git a/backend/apps/cloud_terminal/management/__init__.py b/backend/apps/cloud_terminal/management/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/cloud_terminal/management/commands/__init__.py b/backend/apps/cloud_terminal/management/commands/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/apps/cloud_terminal/management/commands/check_terminal_offline.py b/backend/apps/cloud_terminal/management/commands/check_terminal_offline.py new file mode 100644 index 0000000..6616316 --- /dev/null +++ b/backend/apps/cloud_terminal/management/commands/check_terminal_offline.py @@ -0,0 +1,66 @@ +"""离线状态检测定时任务 +检测所有超时未发送心跳的终端，标记为离线，并通过 WebSocket 推送通知。 +""" +from datetime import timedelta + +from django.core.management.base import BaseCommand +from django.utils.timezone import now + +from apps.core.models import AiSmartTerminal + + +class Command(BaseCommand): + help = "检测超时心跳的终端并标记为离线" + + HEARTBEAT_TIMEOUT = timedelta(minutes=2) # 心跳超时阈值：5分钟 + + def handle(self, *args, **options): + threshold = now() - self.HEARTBEAT_TIMEOUT + + offline_terminals = AiSmartTerminal.objects.filter( + online_status=1, + last_heartbeat_time__isnull=False, + last_heartbeat_time__lt=threshold, + ) + + if not offline_terminals.exists(): + self.stdout.write("所有终端在线状态正常") + return + + offline_list = list(offline_terminals.values("stcd", "stnm")) + count = offline_terminals.update(online_status=0) + + self.stdout.write( + self.style.WARNING(f"标记 {count} 个终端为离线:") + ) + for t in offline_list: + self.stdout.write(f" - {t['stcd']} ({t['stnm']})") + + # WebSocket 推送离线通知 + self._push_offline_notification(offline_list, count) + + def _push_offline_notification(self, offline_list: list, count: int): + """通过 WebSocket 向前端推送离线通知""" + try: + from asgiref.sync import async_to_sync + from channels.layers import get_channel_layer + + channel_layer = get_channel_layer() + if channel_layer is None: + self.stdout.write("未检测到 Channel Layer，跳过 WebSocket 推送") + return + + async_to_sync(channel_layer.group_send)( + "terminal_status", + { + "type": "terminal.status", + "data": { + "event": "offline", + "count": count, + "terminals": offline_list, + }, + }, + ) + self.stdout.write(f"WebSocket 推送完成: {count} 个终端离线") + except Exception as e: + self.stdout.write(self.style.ERROR(f"WebSocket 推送失败: {e}")) diff --git a/backend/apps/cloud_terminal/scheduler.py b/backend/apps/cloud_terminal/scheduler.py new file mode 100644 index 0000000..29943a2 --- /dev/null +++ b/backend/apps/cloud_terminal/scheduler.py @@ -0,0 +1,79 @@ +"""离线检测定时调度器 + +使用 APScheduler 在 Django 进程中启动后台线程，每 30 秒检测一次终端心跳超时。 +""" +import logging + +from apscheduler.schedulers.background import BackgroundScheduler +from django.utils.timezone import now + +logger = logging.getLogger(__name__) + +CHECK_INTERVAL_SECONDS = 30 # 检测间隔：30 秒 + +_scheduler: BackgroundScheduler | None = None + + +def _check_offline_terminals(): + """检测超时心跳的终端(3 分钟内未心跳)，标记为离线并推送 WebSocket 通知""" + from datetime import timedelta + + from apps.core.models import AiSmartTerminal + + HEARTBEAT_TIMEOUT = timedelta(minutes=3) + threshold = now() - HEARTBEAT_TIMEOUT + + offline_terminals = AiSmartTerminal.objects.filter( + online_status=1, + last_heartbeat_time__isnull=False, + last_heartbeat_time__lt=threshold, + ) + + if not offline_terminals.exists(): + return + + offline_list = list(offline_terminals.values("stcd", "stnm")) + count = offline_terminals.update(online_status=0) + + logger.warning("标记 %d 个终端为离线:", count) + for t in offline_list: + logger.warning(" - %s (%s)", t["stcd"], t["stnm"]) + + # WebSocket 推送离线通知 + try: + from asgiref.sync import async_to_sync + from channels.layers import get_channel_layer + + channel_layer = get_channel_layer() + if channel_layer is not None: + async_to_sync(channel_layer.group_send)( + "terminal_status", + { + "type": "terminal.status", + "data": { + "event": "offline", + "count": count, + "terminals": offline_list, + }, + }, + ) + except Exception: + logger.exception("WebSocket 推送失败") + + +def start_offline_checker(): + """启动离线检测后台调度器（仅在主进程中启动一次）""" + global _scheduler + if _scheduler is not None: + return # 已启动，避免重复 + + _scheduler = BackgroundScheduler(daemon=True) + _scheduler.add_job( + _check_offline_terminals, + "interval", + seconds=CHECK_INTERVAL_SECONDS, + id="check_terminal_offline", + replace_existing=True, + ) + _scheduler.start() + logger.info("离线检测调度器已启动，每 %d 秒检测一次", CHECK_INTERVAL_SECONDS) diff --git a/backend/apps/cloud_terminal/smart_terminal.py b/backend/apps/cloud_terminal/smart_terminal.py new file mode 100644 index 0000000..9072ef4 --- /dev/null +++ b/backend/apps/cloud_terminal/smart_terminal.py @@ -0,0 +1,461 @@ +"""智能终端（云端孪生镜像）— API 视图""" +import uuid +from datetime import datetime, timedelta + +from django.conf import settings +from django.db.models import Q +from django.http import FileResponse, JsonResponse, StreamingHttpResponse +from django.shortcuts import get_object_or_404 +from django.utils.timezone import now +from minio import Minio +from rest_framework import status +from rest_framework.authentication import BaseAuthentication +from rest_framework.decorators import api_view, authentication_classes, permission_classes +from rest_framework.exceptions import AuthenticationFailed +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import BasePermission, IsAuthenticated +from rest_framework.response import Response +from rest_framework_simplejwt.exceptions import InvalidToken, TokenError +from rest_framework_simplejwt.tokens import AccessToken + +from apps.common.serializers import AiSmartTerminalSerializer +from apps.core.models import AiSmartTerminal, AiAlgorithmModels + + +# ─── 设备认证 ────────────────────────────────────────────────────────── + +class EdgeDeviceAuthentication(BaseAuthentication): + """边缘设备 JWT Token 认证类 + + 验证 Token 中的 stcd 与请求中的 stcd 一致，确保设备只能操作自己的数据。 + """ + keyword = "Bearer" + + def authenticate(self, request): + auth_header = request.META.get("HTTP_AUTHORIZATION", "") + if not auth_header.startswith(self.keyword): + return None + + raw_token = auth_header[len(self.keyword):].strip() + try: + token = AccessToken(raw_token) + except (InvalidToken, TokenError) as e: + raise AuthenticationFailed(f"无效的认证令牌: {str(e)}") + + token_type = token.get("type") or token.get("sub") + if token_type != "device": + raise AuthenticationFailed("此令牌不是设备认证令牌") + + token_stcd = token.get("stcd") + if not token_stcd: + raise AuthenticationFailed("令牌中缺少设备编码(stcd)") + + # 验证请求中的 stcd 与 Token 中的一致 + request_stcd = ( + request.GET.get("stcd") + or (request.data.get("stcd") if hasattr(request, "data") else None) + ) + if request_stcd and token_stcd != request_stcd: + raise AuthenticationFailed("令牌中的设备编码与请求不匹配") + + terminal = AiSmartTerminal.objects.filter(stcd=token_stcd).first() + if terminal is None: + raise AuthenticationFailed(f"设备未注册: {token_stcd}") + + return (terminal, token) + + +class EdgeDevicePermission(BasePermission): + """边缘设备请求权限 — 仅允许通过 EdgeDeviceAuthentication 认证的请求""" + def has_permission(self, request, view): + return bool(request.user and isinstance(request.user, AiSmartTerminal)) + + +def _minio_client(): + return Minio( + settings.MINIO_ENDPOINT, + access_key=settings.MINIO_ACCESS_KEY, + secret_key=settings.MINIO_SECRET_KEY, + secure=settings.MINIO_SECURE, + ) + + +# ─── 设备认证 — Token 生成 ──────────────────────────────────────────── + +@api_view(["POST"]) +def terminal_device_auth(request): + """设备接入认证 — 生成边缘设备 JWT Token（有效期24小时）""" + stcd = str(request.data.get("stcd", "")).strip() + if not stcd: + return Response({"error": "缺少必要参数：stcd"}, status=status.HTTP_400_BAD_REQUEST) + + terminal = AiSmartTerminal.objects.filter(stcd=stcd).first() + if terminal is None: + return Response({"error": f"未找到终端：{stcd}"}, status=status.HTTP_404_NOT_FOUND) + + token = AccessToken() + token["stcd"] = stcd + token["type"] = "device" + token.set_exp(lifetime=timedelta(days=1)) + + return Response({ + "code": 200, + "data": { + "access_token": str(token), + "token_type": "bearer", + "expires_in": 86400, + "stcd": stcd, + "stnm": terminal.stnm or "", + } + }) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def create_smart_terminal(request): + """创建智能终端设备""" + data = request.data.copy() + stcd = data.get("stcd") + if stcd and AiSmartTerminal.objects.filter(stcd=stcd).exists(): + return Response({"error": f"终端编码 '{stcd}' 已存在"}, status=status.HTTP_400_BAD_REQUEST) + terminal_id = uuid.uuid4().hex + data["id"] = terminal_id + data["record_user"] = request.user.username + serializer = AiSmartTerminalSerializer(data=data) + if serializer.is_valid(): + serializer.save() + return Response(serializer.data, status=status.HTTP_201_CREATED) + return Response({"error": "无效数据", "details": serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_smart_terminal(request): + """修改智能设备信息""" + terminal_id = request.data.get("id") + terminal = get_object_or_404(AiSmartTerminal, id=terminal_id) + # 不允许通过此接口修改模型发布字段和边缘同步字段 + _protected_fields = { + "model_id", "model_version", "model_path", + "model_publish_time", "model_publish_user", + "edge_model_version", "edge_update_time", + "last_heartbeat_time", "online_status", + } + update_data = {k: v for k, v in request.data.items() if k not in _protected_fields} + serializer = AiSmartTerminalSerializer(instance=terminal, data=update_data, partial=True) + if serializer.is_valid(): + serializer.save() + return Response(serializer.data) + return Response({"error": "无效数据", "details": serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def publish_terminal_model(request): + """发布更新设备算法模型 — 将训练好的模型发布到一个或多个云端孪生设备 + 支持传 id（单个）或 ids（列表），如 {"ids":["id1","id2"],"model_id":"...","model_version":"v1","model_path":"..."} + """ + # 归一化：支持 id（单个）或 ids（列表） + raw_ids = request.data.get("ids", []) + single_id = request.data.get("id") + if single_id: + raw_ids = [single_id] + if isinstance(raw_ids, str): + import json as _json + try: + raw_ids = _json.loads(raw_ids) + except Exception: + raw_ids = [raw_ids] + ids = [tid for tid in raw_ids if tid] if isinstance(raw_ids, list) else [] + model_id = request.data.get("model_id") + # 查找算法模型 + ai_model = get_object_or_404(AiAlgorithmModels, id=model_id) + model_version = ai_model.model_version or "" + model_path = ai_model.onnx_bucketpath or ai_model.model_bucketpath + if not model_path: + return Response({"error": "模型文件未存储在MinIO中"}, status=status.HTTP_400_BAD_REQUEST) + model_name = model_path.split("/")[-1] + + if not ids: + return Response({"error": "缺少必要参数：id 或 ids"}, status=status.HTTP_400_BAD_REQUEST) + if not all([model_id, model_version, model_path]): + return Response({"error": "缺少必要参数：model_id, model_version, model_path"}, status=status.HTTP_400_BAD_REQUEST) + + terminals = AiSmartTerminal.objects.filter(id__in=ids) + if not terminals.exists(): + return Response({"error": "未找到匹配的终端设备"}, status=status.HTTP_404_NOT_FOUND) + + publish_time = now() + publish_user = request.user.username + terminals.update( + model_id=model_id, + model_version=model_version, + model_path=model_path, + model_name=model_name, + model_publish_time=publish_time, + model_publish_user=publish_user, + ) + updated = AiSmartTerminal.objects.filter(id__in=ids) + serializer = AiSmartTerminalSerializer(updated, many=True) + return Response({ + "published_count": len(ids), + "terminals": serializer.data, + }) + + +@api_view(["GET"]) +def search_smart_terminals(request): + """条件查询智能设备列表（无需登录，边缘端也要调用）""" + filter_kwargs = {} + stcd = request.GET.get("stcd", "") + stnm = request.GET.get("stnm", "") + sttp = request.GET.get("sttp", "") + model_name = request.GET.get("model_name", "") + online_status = request.GET.get("online_status", "") + usfl = request.GET.get("usfl", "") + is_deleted = request.GET.get("is_deleted", "0") + page_size = request.GET.get("page_size") or 10 + if stcd: + filter_kwargs["stcd__icontains"] = stcd + if stnm: + filter_kwargs["stnm__icontains"] = stnm + if sttp: + filter_kwargs["sttp"] = sttp + if model_name: + filter_kwargs["model_name__icontains"] = model_name + if online_status: + filter_kwargs["online_status"] = int(online_status) + if usfl: + filter_kwargs["usfl"] = int(usfl) + filter_kwargs["is_deleted"] = is_deleted + queryset = AiSmartTerminal.objects.filter(**filter_kwargs).order_by("order_index", "stcd") + paginator = PageNumberPagination() + paginator.page_size = page_size + page = paginator.paginate_queryset(queryset, request) + serializer = AiSmartTerminalSerializer(page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["PUT"]) +@authentication_classes([EdgeDeviceAuthentication]) +@permission_classes([EdgeDevicePermission]) +def update_terminal_heartbeat(request): + """更新设备在线状态（边缘上报心跳）— 使用设备 Token 认证""" + terminal = request.user # EdgeDeviceAuthentication 已将 terminal 注入为 request.user + if not isinstance(terminal, AiSmartTerminal): + return Response({"error": "设备认证失败"}, status=status.HTTP_401_UNAUTHORIZED) + + update_fields = ["last_heartbeat_time", "online_status"] + was_offline = terminal.online_status == 0 + terminal.last_heartbeat_time = now() + terminal.online_status = 1 + + # 首次接入：标记数据已接入 + if not terminal.dtin: + terminal.dtin = 1 + terminal.dtin_tm = now() + update_fields.extend(["dtin", "dtin_tm"]) + + terminal.save(update_fields=update_fields) + + # 终端从离线恢复上线时，通过 WebSocket 推送通知 + if was_offline: + _push_terminal_online_notification(terminal) + + return Response({ + "code": 200, + "data": { + "stcd": terminal.stcd, + "online_status": terminal.online_status, + "last_heartbeat_time": terminal.last_heartbeat_time, + } + }) + + +def _push_terminal_online_notification(terminal): + """通过 WebSocket 向前端推送终端上线通知""" + try: + from asgiref.sync import async_to_sync + from channels.layers import get_channel_layer + + channel_layer = get_channel_layer() + if channel_layer is not None: + async_to_sync(channel_layer.group_send)( + "terminal_status", + { + "type": "terminal.status", + "data": { + "event": "online", + "count": 1, + "terminals": [ + {"stcd": terminal.stcd, "stnm": terminal.stnm} + ], + }, + }, + ) + except Exception: + pass + + +# ─── 模型版本检查 ────────────────────────────────────────────────────── + +@api_view(["GET"]) +@authentication_classes([EdgeDeviceAuthentication]) +@permission_classes([EdgeDevicePermission]) +def check_terminal_model_version(request): + """检查云端模型版本 — 边缘端定时轮询是否有新版本模型""" + terminal = request.user + if not isinstance(terminal, AiSmartTerminal): + return Response({"error": "设备认证失败"}, status=status.HTTP_401_UNAUTHORIZED) + + if not terminal.model_id: + return Response({ + "code": 200, + "data": { + "having_newmodel": 0, + "model_version": "", + "sync_check_interval": terminal.sync_check_interval or 30, + } + }) + + ai_model = AiAlgorithmModels.objects.filter(id=terminal.model_id).first() + if ai_model is None: + return Response({ + "code": 200, + "data": { + "having_newmodel": 0, + "model_version": terminal.model_version or "", + "sync_check_interval": terminal.sync_check_interval or 30, + } + }) + + edge_version = request.GET.get("edge_version", "") + cloud_version = ai_model.model_version or "" + + having_newmodel = 0 + if not edge_version or edge_version != cloud_version: + having_newmodel = 1 + + result = { + "code": 200, + "data": { + "having_newmodel": having_newmodel, + "sync_check_interval": terminal.sync_check_interval or 30, + } + } + + if having_newmodel: + result["data"].update({ + "model_name": terminal.model_name or "", + "model_version": cloud_version, + "model_path": terminal.model_path or "", + "model_publish_time": terminal.model_publish_time.isoformat() if terminal.model_publish_time else "", + }) + else: + result["data"]["model_version"] = cloud_version + + return Response(result) + + +@api_view(["PUT"]) +@authentication_classes([EdgeDeviceAuthentication]) +@permission_classes([EdgeDevicePermission]) +def update_terminal_model_version(request): + """更新边缘端模型版本 — 边缘端下载新模型后回调，更新 edge_model_version 和 edge_update_time""" + terminal = request.user + if not isinstance(terminal, AiSmartTerminal): + return Response({"error": "设备认证失败"}, status=status.HTTP_401_UNAUTHORIZED) + + edge_model_version = str(request.data.get("edge_model_version", "")).strip() + if not edge_model_version: + return Response({"error": "缺少必要参数：edge_model_version"}, status=status.HTTP_400_BAD_REQUEST) + + terminal.edge_model_version = edge_model_version + terminal.edge_update_time = now() + terminal.save(update_fields=["edge_model_version", "edge_update_time"]) + + return Response({ + "code": 200, + "data": { + "stcd": terminal.stcd, + "edge_model_version": terminal.edge_model_version, + "edge_update_time": terminal.edge_update_time, + } + }) + + +# ─── 模型下载（流式） ────────────────────────────────────────────────── + +def _stream_object(response, chunk_size: int = 1024 * 1024): + try: + while True: + chunk = response.read(chunk_size) + if not chunk: + break + yield chunk + finally: + response.close() + response.release_conn() + + +def download_terminal_model(request): + """下载最新版本设备算法模型 — 边缘端在校验版本差异后，从此接口下载模型文件""" + stcd = str(request.GET.get("stcd") or "").strip() + model_format = request.GET.get("model_format") or request.GET.get("format", "onnx") + if not stcd: + return JsonResponse({"error": "缺少必要参数：stcd"}, status=status.HTTP_400_BAD_REQUEST) + terminal = AiSmartTerminal.objects.filter(stcd=stcd).first() + if terminal is None: + return JsonResponse({"error": f"未找到终端：{stcd}"}, status=status.HTTP_404_NOT_FOUND) + if not terminal.model_id: + return JsonResponse({"error": "该终端尚未发布模型"}, status=status.HTTP_404_NOT_FOUND) + + try: + from apps.common.minio_client import minio_storage + + ai_model = AiAlgorithmModels.objects.filter(id=terminal.model_id).first() + if ai_model is None: + return JsonResponse( + {"error": f"终端已绑定的模型不存在：{terminal.model_id}"}, + status=status.HTTP_404_NOT_FOUND, + ) + if model_format == "onnx": + model_path = ai_model.onnx_bucketpath + if not model_path: + return JsonResponse({"error": "该模型尚未转换为ONNX格式"}, status=status.HTTP_404_NOT_FOUND) + else: + model_path = ai_model.model_bucketpath + if not model_path: + return JsonResponse({"error": "该模型没有原始模型文件"}, status=status.HTTP_404_NOT_FOUND) + + parts = model_path.split('/') + if len(parts) < 2: + return JsonResponse({"error": f"模型存储路径无效：{model_path}"}, status=status.HTTP_400_BAD_REQUEST) + bucket_name = parts[0] + object_name = '/'.join(parts[1:]) + if not minio_storage.object_exists(bucket_name, object_name): + return JsonResponse( + {"error": f"MinIO中未找到模型文件：{bucket_name}/{object_name}"}, + status=status.HTTP_404_NOT_FOUND, + ) + + response = minio_storage.get_object(bucket_name, object_name) + content_type = "application/octet-stream" + filename = object_name.rsplit("/", 1)[-1] + stream = StreamingHttpResponse(_stream_object(response), content_type=content_type) + stream["Content-Disposition"] = f'attachment; filename="{filename}"' + return stream + except Exception as e: + return JsonResponse({"error": f"模型文件下载失败：{str(e)}"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_smart_terminal(request): + """删除智能设备""" + terminal_id = request.GET.get("id") + if not terminal_id: + return Response({"error": "缺少必要参数：id"}, status=status.HTTP_400_BAD_REQUEST) + terminal = get_object_or_404(AiSmartTerminal, id=terminal_id) + terminal.delete() + return Response({"message": "删除成功", "id": terminal_id}) diff --git a/backend/apps/common/__init__.py b/backend/apps/common/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/common/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/common/emailing.py b/backend/apps/common/emailing.py new file mode 100644 index 0000000..205104b --- /dev/null +++ b/backend/apps/common/emailing.py @@ -0,0 +1,27 @@ +import os +import smtplib +from email.header import Header +from email.mime.text import MIMEText + + +def send_email(subject, message, to_email): + smtp_server = os.getenv("SMTP_HOST") + smtp_port = int(os.getenv("SMTP_PORT", "25")) + smtp_user = os.getenv("SMTP_USER") + smtp_password = os.getenv("SMTP_PASSWORD") + + if not smtp_server or not smtp_user or not smtp_password: + raise ValueError("SMTP 配置缺失：需要 SMTP_HOST/SMTP_USER/SMTP_PASSWORD（可选 SMTP_PORT）") + + msg = MIMEText(message, "plain", "utf-8") + msg["From"] = Header(smtp_user) + msg["To"] = Header(to_email) + msg["Subject"] = Header(subject) + + server = smtplib.SMTP(smtp_server, smtp_port) + try: + server.login(smtp_user, smtp_password) + server.sendmail(smtp_user, [to_email], msg.as_string()) + finally: + server.quit() + diff --git a/backend/apps/common/minio_client.py b/backend/apps/common/minio_client.py new file mode 100644 index 0000000..a579e30 --- /dev/null +++ b/backend/apps/common/minio_client.py @@ -0,0 +1,303 @@ +""" +MinIO 对象存储客户端封装 +提供 bucket 管理、文件上传/下载/删除/预览等标准操作 +""" +import io +import uuid +from datetime import datetime, timedelta +from pathlib import Path +from typing import BinaryIO, Optional, Union + +from django.conf import settings +from minio import Minio +from minio.error import S3Error +from loguru import logger + + +class MinioStorage: + """MinIO 对象存储管理器""" + + def __init__(self): + self._client: Optional[Minio] = None + + @property + def client(self) -> Minio: + if self._client is None: + self._client = Minio( + endpoint=settings.MINIO_ENDPOINT, + access_key=settings.MINIO_ACCESS_KEY, + secret_key=settings.MINIO_SECRET_KEY, + secure=settings.MINIO_SECURE, + ) + return self._client + + # ── Bucket 管理 ────────────────────────────────────── + + def bucket_exists(self, bucket_name: str) -> bool: + """检查 bucket 是否存在""" + return self.client.bucket_exists(bucket_name) + + def create_bucket(self, bucket_name: str) -> bool: + """创建 bucket，已存在则跳过""" + if not self.bucket_exists(bucket_name): + self.client.make_bucket(bucket_name) + logger.info(f"MinIO bucket created: {bucket_name}") + return True + return False + + def list_buckets(self) -> list[str]: + """列出所有 bucket""" + return [b.name for b in self.client.list_buckets()] + + def remove_bucket(self, bucket_name: str) -> bool: + """删除空 bucket""" + if self.bucket_exists(bucket_name): + self.client.remove_bucket(bucket_name) + logger.info(f"MinIO bucket removed: {bucket_name}") + return True + return False + + # ── 对象管理 ──────────────────────────────────────── + + def list_objects( + self, bucket_name: str, prefix: str = "", recursive: bool = True + ) -> list[dict]: + """列出 bucket 中的对象 + + Returns: + [{name, size, last_modified, etag, content_type}, ...] + """ + objects = self.client.list_objects(bucket_name, prefix=prefix, recursive=recursive) + return [ + { + "name": obj.object_name, + "size": obj.size, + "last_modified": obj.last_modified.isoformat() if obj.last_modified else None, + "etag": obj.etag, + "content_type": obj.content_type, + "is_dir": obj.is_dir, + } + for obj in objects + ] + + def upload_file( + self, + bucket_name: str, + object_name: str, + file_path: Union[str, Path], + content_type: Optional[str] = None, + ) -> str: + """上传本地文件到 MinIO + + Returns: + object_name + """ + ensure_bucket(self, bucket_name) + self.client.fput_object( + bucket_name=bucket_name, + object_name=object_name, + file_path=str(file_path), + content_type=content_type, + ) + logger.info(f"MinIO upload: {bucket_name}/{object_name}") + return object_name + + def upload_bytes( + self, + bucket_name: str, + object_name: str, + data: Union[bytes, BinaryIO], + length: int = -1, + content_type: Optional[str] = None, + ) -> str: + """上传字节数据到 MinIO + + Returns: + object_name + """ + ensure_bucket(self, bucket_name) + + if isinstance(data, bytes): + stream = io.BytesIO(data) + actual_length = len(data) + else: + stream = data + actual_length = length + + self.client.put_object( + bucket_name=bucket_name, + object_name=object_name, + data=stream, + length=actual_length, + content_type=content_type, + ) + logger.info(f"MinIO upload bytes: {bucket_name}/{object_name}") + return object_name + + def upload_stream( + self, + bucket_name: str, + object_name: str, + data: BinaryIO, + length: int, + content_type: Optional[str] = None, + ) -> str: + """流式上传文件对象到 MinIO""" + ensure_bucket(self, bucket_name) + self.client.put_object( + bucket_name=bucket_name, + object_name=object_name, + data=data, + length=length, + content_type=content_type, + ) + logger.info(f"MinIO upload stream: {bucket_name}/{object_name}") + return object_name + + def download_file( + self, bucket_name: str, object_name: str, local_path: Union[str, Path] + ) -> str: + """下载对象到本地文件 + + Returns: + local_path + """ + self.client.fget_object(bucket_name, object_name, str(local_path)) + logger.info(f"MinIO download: {bucket_name}/{object_name} -> {local_path}") + return str(local_path) + + def download_bytes(self, bucket_name: str, object_name: str) -> bytes: + """下载对象为 bytes""" + response = self.client.get_object(bucket_name, object_name) + try: + return response.read() + finally: + response.close() + response.release_conn() + + def get_object(self, bucket_name: str, object_name: str): + """获取对象流响应，调用方负责关闭连接""" + return self.client.get_object(bucket_name, object_name) + + def get_object_info(self, bucket_name: str, object_name: str) -> dict: + """获取对象元信息""" + stat = self.client.stat_object(bucket_name, object_name) + return { + "name": stat.object_name, + "size": stat.size, + "etag": stat.etag, + "content_type": stat.content_type, + "last_modified": stat.last_modified.isoformat() if stat.last_modified else None, + } + + def object_exists(self, bucket_name: str, object_name: str) -> bool: + """检查对象是否存在""" + try: + self.client.stat_object(bucket_name, object_name) + return True + except S3Error: + return False + + def delete_object(self, bucket_name: str, object_name: str) -> bool: + """删除单个对象""" + self.client.remove_object(bucket_name, object_name) + logger.info(f"MinIO delete: {bucket_name}/{object_name}") + return True + + def delete_objects(self, bucket_name: str, object_names: list[str]) -> list[str]: + """批量删除对象""" + errors = [] + for name in object_names: + try: + self.client.remove_object(bucket_name, name) + except S3Error as e: + errors.append(name) + logger.error(f"MinIO delete error {bucket_name}/{name}: {e}") + logger.info(f"MinIO batch delete: {bucket_name}, {len(object_names) - len(errors)}/{len(object_names)}") + return errors + + # ── 预签名 URL ────────────────────────────────────── + + def presign_get( + self, + bucket_name: str, + object_name: str, + expires: int = 3600, + response_headers: Optional[dict] = None, + ) -> str: + """生成预签名下载 URL + + Args: + bucket_name: bucket 名称 + object_name: 对象路径 + expires: 过期时间（秒），默认 1 小时 + """ + presign_endpoint = getattr(settings, 'MINIO_PRESIGN_ENDPOINT', settings.MINIO_ENDPOINT) + + if presign_endpoint != settings.MINIO_ENDPOINT: + presign_client = Minio( + endpoint=presign_endpoint, + access_key=settings.MINIO_ACCESS_KEY, + secret_key=settings.MINIO_SECRET_KEY, + secure=settings.MINIO_SECURE, + ) + return presign_client.presigned_get_object( + bucket_name, + object_name, + expires=timedelta(seconds=expires), + response_headers=response_headers, + ) + + return self.client.presigned_get_object( + bucket_name, + object_name, + expires=timedelta(seconds=expires), + response_headers=response_headers, + ) + + def presign_upload( + self, bucket_name: str, object_name: str, expires: int = 3600 + ) -> str: + """生成预签名上传 URL""" + return self.client.presigned_put_object( + bucket_name, object_name, expires=timedelta(seconds=expires) + ) + + # ── 辅助方法 ──────────────────────────────────────── + + def generate_object_name(self, filename: str, prefix: str = "") -> str: + """生成唯一对象名，避免覆盖 + + Format: {prefix}/YYYY-MM-DD/{uuid}_{original_filename} + """ + date_str = datetime.now().strftime("%Y-%m-%d") + uid = uuid.uuid4().hex[:12] + safe_name = Path(filename).name + parts = [p for p in [prefix, date_str, f"{uid}_{safe_name}"] if p] + return "/".join(parts) + + def copy_object( + self, src_bucket: str, src_object: str, + dst_bucket: str, dst_object: str + ) -> str: + """复制对象（同 bucket 内或跨 bucket）""" + from minio.commonconfig import CopySource + + ensure_bucket(self, dst_bucket) + self.client.copy_object( + bucket_name=dst_bucket, + object_name=dst_object, + source=CopySource(src_bucket, src_object), + ) + logger.info(f"MinIO copy: {src_bucket}/{src_object} -> {dst_bucket}/{dst_object}") + return dst_object + + +def ensure_bucket(storage: MinioStorage, bucket_name: str): + """确保 bucket 存在""" + if not storage.bucket_exists(bucket_name): + storage.create_bucket(bucket_name) + + +# 全局单例 +minio_storage = MinioStorage() diff --git a/backend/apps/common/serializers.py b/backend/apps/common/serializers.py new file mode 100644 index 0000000..c0e58fe --- /dev/null +++ b/backend/apps/common/serializers.py @@ -0,0 +1,168 @@ +from rest_framework import serializers + +from apps.core.models import * + + +class AiAlgorithmClassSerializer(serializers.ModelSerializer): + class Meta: + model = AiAlgorithmClass + fields = "__all__" + + +class AiAlgorithmSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AiAlgorithm + fields = "__all__" + + +class AiAlgorithmEngineSerializer(serializers.ModelSerializer): + class Meta: + model = AiAlgorithmEngine + fields = "__all__" + + +class AiAlgorithmParamsSerializer(serializers.ModelSerializer): + class Meta: + model = AiAlgorithmParams + fields = "__all__" + + +class AiAlgorithmOutputSerializer(serializers.ModelSerializer): + class Meta: + model = AiAlgorithmOutput + fields = "__all__" + + +class AiAlgorithmModelsSerializer(serializers.ModelSerializer): + upload_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + model_createtime = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AiAlgorithmModels + fields = "__all__" + + +class AiDatasetSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AiDataset + fields = "__all__" + + +class AiDatasetSampleSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + annotation_content = serializers.SerializerMethodField() + + class Meta: + model = AiDatasetSample + fields = "__all__" + + def get_annotation_content(self, obj): + return obj.polygon or obj.rectangle or "" + + +class AIAnnotateTeamSerializer(serializers.ModelSerializer): + class Meta: + model = AIAnnotateTeam + fields = "__all__" + + +class AIAnnotateTeamMemberSerializer(serializers.ModelSerializer): + class Meta: + model = AIAnnotateTeamMember + fields = "__all__" + + +class AiAnnotateTaskSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AiAnnotateTask + fields = "__all__" + + +class AiAnnotateTaskSampleSerializer(serializers.ModelSerializer): + class Meta: + model = AiAnnotateTaskSample + fields = "__all__" + + +class AiTempFileSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AiTempFile + fields = "__all__" + + +class AiAlgorithmTrainRecordsSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AiAlgorithmTrainRecords + fields = "__all__" + + +class AiAlgorithmCallLogSerializer(serializers.ModelSerializer): + log_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AiAlgorithmCallLog + fields = "__all__" + + +class AuthOrganizationSerializer(serializers.ModelSerializer): + class Meta: + model = AuthOrganization + fields = "__all__" + + +class AuthPersonSerializer(serializers.ModelSerializer): + date_joined = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AuthPerson + fields = "__all__" + + +class AuthGroupSerializer(serializers.ModelSerializer): + class Meta: + model = AuthGroup + fields = "__all__" + + +class AIReliabilityObjectsSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AIReliabilityObjects + fields = "__all__" + + +class AIReliabilityObjectClassSerializer(serializers.ModelSerializer): + create_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S") + + class Meta: + model = AIReliabilityObjectClass + fields = "__all__" + + +class AIReliabilityPlantSerializer(serializers.ModelSerializer): + class Meta: + model = AIReliabilityPlant + fields = "__all__" + + +class AiSmartTerminalSerializer(serializers.ModelSerializer): + record_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S", read_only=True) + model_publish_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S", read_only=True) + edge_update_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S", read_only=True) + last_heartbeat_time = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S", read_only=True) + dtin_tm = serializers.DateTimeField(format="%Y-%m-%d %H:%M:%S", read_only=True) + + class Meta: + model = AiSmartTerminal + fields = "__all__" diff --git a/backend/apps/common/utils/__init__.py b/backend/apps/common/utils/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/common/utils/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/common/utils/annotation_store.py b/backend/apps/common/utils/annotation_store.py new file mode 100644 index 0000000..00b4b0e --- /dev/null +++ b/backend/apps/common/utils/annotation_store.py @@ -0,0 +1,61 @@ +import json + +from django.db.models import TextField, Value +from django.db.models.functions import Coalesce + + +def _safe_json_loads(raw): + if raw in (None, ""): + return None + try: + return json.loads(raw) + except Exception: + return None + + +def is_polygon_annotation_content(content) -> bool: + parsed = _safe_json_loads(content) + if isinstance(parsed, dict): + if parsed.get("type") == "polygon": + return True + if isinstance(parsed.get("shapes"), list): + return True + if isinstance(parsed, list): + for item in parsed: + if not isinstance(item, dict): + continue + region = item.get("region") + if isinstance(region, list) and region and isinstance(region[0], (list, tuple)): + return True + return False + + +def split_dataset_sample_annotation_content(content): + raw = content or "" + if not raw: + return {"rectangle": None, "polygon": None} + if is_polygon_annotation_content(raw): + return {"rectangle": None, "polygon": raw} + return {"rectangle": raw, "polygon": None} + + +def get_dataset_sample_annotation_content(sample) -> str: + if sample is None: + return "" + polygon = getattr(sample, "polygon", None) + rectangle = getattr(sample, "rectangle", None) + return polygon or rectangle or "" + + +def get_dataset_sample_annotation_from_row(sample_row: dict) -> str: + if not isinstance(sample_row, dict): + return "" + return sample_row.get("polygon") or sample_row.get("rectangle") or sample_row.get("annotation_content") or "" + + +def annotate_queryset_with_annotation_content(queryset, alias: str = "annotation_content"): + return queryset.annotate( + **{ + alias: Coalesce("polygon", "rectangle", Value("", output_field=TextField()), output_field=TextField()), + } + ) diff --git a/backend/apps/common/utils/image_util.py b/backend/apps/common/utils/image_util.py new file mode 100644 index 0000000..bdea982 --- /dev/null +++ b/backend/apps/common/utils/image_util.py @@ -0,0 +1,20 @@ +import base64 +import io + +try: + import cv2 +except Exception: + cv2 = None +from PIL import Image + + +def get_image_base64(img_url): + if cv2 is not None: + img = cv2.imread(img_url) + _, img_encoded = cv2.imencode(".jpg", img) + return base64.b64encode(img_encoded.tobytes()).decode("utf-8") + image = Image.open(img_url).convert("RGB") + buf = io.BytesIO() + image.save(buf, format="JPEG") + return base64.b64encode(buf.getvalue()).decode("utf-8") + diff --git a/backend/apps/common/utils/permissions.py b/backend/apps/common/utils/permissions.py new file mode 100644 index 0000000..75ea735 --- /dev/null +++ b/backend/apps/common/utils/permissions.py @@ -0,0 +1,13 @@ +# -*- coding:utf-8 -*- +__author__ = "leo" + +from rest_framework import permissions + + +class IsOwnerOrReadOnly(permissions.BasePermission): + def has_object_permission(self, request, view, obj): + if request.method in permissions.SAFE_METHODS: + return True + + return obj.user == request.user + diff --git a/backend/apps/common/utils/snapImages.py b/backend/apps/common/utils/snapImages.py new file mode 100644 index 0000000..e979b61 --- /dev/null +++ b/backend/apps/common/utils/snapImages.py @@ -0,0 +1,52 @@ +import os +import re + +import requests +from bs4 import BeautifulSoup +from PIL import Image +from io import BytesIO + + +headers = { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3" +} + +animal_names = ["猫", "狗", "cat", "dog"] + +save_dir = "D:/project_space/01/005/datasets/001/images" +if not os.path.exists(save_dir): + os.makedirs(save_dir) + + +def download_images(fish_name, num_images=5): + search_url = f"https://www.google.com/search?hl=zh-CN&tbm=isch&q={fish_name}" + + response = requests.get(search_url, headers=headers) + soup = BeautifulSoup(response.text, "html.parser") + img_tags = soup.find_all("img") + + img_urls = [] + + for img_tag in img_tags: + img_url = img_tag.get("src") + if img_url and img_url.startswith("http"): + img_urls.append(img_url) + if len(img_urls) >= num_images: + break + + for idx, img_url in enumerate(img_urls): + try: + img_data = requests.get(img_url).content + img_name = os.path.join(save_dir, f"{fish_name}_{idx + 1}.jpg") + with open(img_name, "wb") as f: + f.write(img_data) + print(f"下载成功：{img_name}") + except Exception as e: + print(f"下载失败：{e}") + + +for animal_name in animal_names: + download_images(animal_name, 200) + +print("所有图片下载完成！") + diff --git a/backend/apps/common/utils/watermarker.py b/backend/apps/common/utils/watermarker.py new file mode 100644 index 0000000..9a61f6a --- /dev/null +++ b/backend/apps/common/utils/watermarker.py @@ -0,0 +1,206 @@ +import enum +import math +import os +import textwrap +from typing import Union + +from PIL import Image, ImageChops, ImageDraw, ImageEnhance, ImageFont, ImageOps + + +class WatermarkerStyles(enum.Enum): + """水印样式""" + + STRIPED = 1 + CENTRAL = 2 + + +class Watermarker(object): + """图片水印工具""" + + django_support = False + + def __init__( + self, + image: Union[str, Image.Image], + text: str, + style: WatermarkerStyles.CENTRAL, + angle=25, + color="black", + font_file="汉仪旗黑.ttf", + font_height_crop=1.2, + opacity=0.15, + quality=100, + size=50, + space=100, + chars_per_line=10, + ): + if isinstance(image, str): + self.image_path = image + self.origin_image = Image.open(image) + elif isinstance(image, Image.Image): + self.origin_image = image + self.image_path = None + else: + raise ValueError("`image` 参数必须是图片路径或 PIL Image 对象") + self.text = text + self.style = style + self.angle = angle + self.color = color + + if self.django_support: + from django.conf import settings + + self.font_file = os.path.join(settings.BASE_DIR, "contrib", "image", "font", font_file) + else: + self.font_file = os.path.join(os.path.abspath("."), "font", font_file) + + self.font_height_crop = font_height_crop + self.opacity = opacity + self.quality = quality + self.size = size + self.space = space + self._result_image = None + self.chars_per_line = chars_per_line + + @staticmethod + def set_image_opacity(image: Image, opacity: float): + assert 0 <= opacity <= 1 + + alpha = image.split()[3] + alpha = ImageEnhance.Brightness(alpha).enhance(opacity) + image.putalpha(alpha) + return image + + @staticmethod + def crop_image_edge(image: Image): + bg = Image.new(mode="RGBA", size=image.size) + diff = ImageChops.difference(image, bg) + del bg + bbox = diff.getbbox() + if bbox: + return image.crop(bbox) + return image + + def _add_mark_striped(self): + origin_image = self.origin_image + origin_image = ImageOps.exif_transpose(origin_image) + + width = len(self.text) * self.size + height = round(self.size * self.font_height_crop) + + watermark_image = Image.new(mode="RGBA", size=(width, height)) + + draw_table = ImageDraw.Draw(im=watermark_image) + draw_table.text( + xy=(0, 0), + text=self.text, + fill=self.color, + font=ImageFont.truetype(self.font_file, size=self.size), + ) + del draw_table + + watermark_image = Watermarker.crop_image_edge(watermark_image) + + Watermarker.set_image_opacity(watermark_image, self.opacity) + + c = int( + math.sqrt( + origin_image.size[0] * origin_image.size[0] + origin_image.size[1] * origin_image.size[1] + ) + ) + + watermark_mask = Image.new(mode="RGBA", size=(c, c)) + + y, idx = 0, 0 + while y < c: + x = -int((watermark_image.size[0] + self.space) * 0.5 * idx) + idx = (idx + 1) % 2 + + while x < c: + watermark_mask.paste(watermark_image, (x, y)) + x = x + watermark_image.size[0] + self.space + y = y + watermark_image.size[1] + self.space + + watermark_mask = watermark_mask.rotate(self.angle) + + if origin_image.mode != "RGBA": + origin_image = origin_image.convert("RGBA") + origin_image.paste( + watermark_mask, + (int((origin_image.size[0] - c) / 2), int((origin_image.size[1] - c) / 2)), + mask=watermark_mask.split()[3], + ) + del watermark_mask + + return origin_image + + def _add_mark_central(self): + origin_image = self.origin_image + origin_image = ImageOps.exif_transpose(origin_image) + + text_lines = textwrap.wrap(self.text, width=self.chars_per_line) + text = "\n".join(text_lines) + + width = len(text) * self.size + height = round(self.size * self.font_height_crop * len(text_lines)) + + watermark_image = Image.new(mode="RGBA", size=(width, height)) + + draw_table = ImageDraw.Draw(im=watermark_image) + draw_table.text( + xy=(0, 0), + text=text, + fill=self.color, + font=ImageFont.truetype(self.font_file, size=self.size), + ) + del draw_table + + watermark_image = Watermarker.crop_image_edge(watermark_image) + + Watermarker.set_image_opacity(watermark_image, self.opacity) + + c = int( + math.sqrt( + origin_image.size[0] * origin_image.size[0] + origin_image.size[1] * origin_image.size[1] + ) + ) + watermark_mask = Image.new(mode="RGBA", size=(c, c)) + watermark_mask.paste( + watermark_image, + ( + int((watermark_mask.width - watermark_image.width) / 2), + int((watermark_mask.height - watermark_image.height) / 2), + ), + ) + watermark_mask = watermark_mask.rotate(self.angle) + + if origin_image.mode != "RGBA": + origin_image = origin_image.convert("RGBA") + + box = (int((origin_image.width - watermark_mask.width) / 2), int((origin_image.height - watermark_mask.height) / 2)) + origin_image.paste(watermark_mask, box, mask=watermark_mask.split()[3]) + + return origin_image + + @property + def image(self): + if not self._result_image: + if self.style == WatermarkerStyles.STRIPED: + self._result_image = self._add_mark_striped() + if self.style == WatermarkerStyles.CENTRAL: + self._result_image = self._add_mark_central() + return self._result_image + + def save(self, file_path: str, image_format: str = "png"): + with open(file_path, "wb") as f: + self.image.save(f, image_format) + + def show(self): + self.image.show() + + +if __name__ == "__main__": + Watermarker("path_to_image", "mark text", WatermarkerStyles.CENTRAL).show() + Watermarker("path_to_image", "mark text", WatermarkerStyles.CENTRAL).save("path_to_save") + image = Watermarker("path_to_image", "mark text", WatermarkerStyles.CENTRAL).image + diff --git a/backend/apps/common/utils/yunpian.py b/backend/apps/common/utils/yunpian.py new file mode 100644 index 0000000..5bf77ec --- /dev/null +++ b/backend/apps/common/utils/yunpian.py @@ -0,0 +1,30 @@ +# -*- coding:utf-8 -*- + +import json + +import requests + + +class YunPian: + """云片网短信验证""" + + def __init__(self, api_key): + self.api_key = api_key + self.single_send_url = "https://sms.yunpian.com/v2/sms/single_send.json" + + def send_sms(self, mobile, code): + params = { + "apikey": self.api_key, + "mobile": mobile, + "text": "云片网模板文件内容{code}".format(code=code), + } + + response = requests.post(self.single_send_url, data=params) + return_dict = json.loads(response.text) + return return_dict + + +if __name__ == "__main__": + yunpian = YunPian("云片网的api_key") + yunpian.send_sms("手机号码", "验证码") + diff --git a/backend/apps/common/views/minio_storage.py b/backend/apps/common/views/minio_storage.py new file mode 100644 index 0000000..ebdaade --- /dev/null +++ b/backend/apps/common/views/minio_storage.py @@ -0,0 +1,855 @@ +""" +MinIO 文件存储 REST API 接口 +提供标准的文件上传、下载、删除、预览、Bucket 管理等操作 +""" +import base64 +import mimetypes +import os +import posixpath +import zipfile +from pathlib import Path +from tempfile import NamedTemporaryFile +from urllib.parse import quote + +from django.conf import settings +from django.http import Http404, StreamingHttpResponse +from minio.error import S3Error +from rest_framework.decorators import api_view, parser_classes, permission_classes +from rest_framework.parsers import FormParser, JSONParser, MultiPartParser +from rest_framework.permissions import IsAuthenticated, AllowAny +from rest_framework.response import Response + +from apps.common.minio_client import ensure_bucket, minio_storage + +try: + import cv2 +except Exception: + cv2 = None + + +DEFAULT_BUCKET = settings.MINIO_DEFAULT_BUCKET +IMAGE_EXTENSIONS = { + ".jpg", + ".jpeg", + ".png", + ".bmp", + ".gif", + ".webp", + ".tif", + ".tiff", +} +VIDEO_EXTENSIONS = { + ".mp4", + ".avi", + ".mov", + ".mkv", + ".wmv", + ".flv", + ".mpeg", + ".mpg", + ".webm", +} + + +def _normalize_expires(value) -> int: + """标准化预签名过期时间，限制在 1 到 7 天之间。""" + try: + expires = int(value) + except (TypeError, ValueError): + raise ValueError("expires 必须是正整数") + + if expires <= 0: + raise ValueError("expires 必须大于 0") + return min(expires, 7 * 24 * 3600) + + +def _stream_object(response, chunk_size: int = 1024 * 1024): + """以分块方式转发 MinIO 对象流，避免整文件读入内存。""" + try: + while True: + chunk = response.read(chunk_size) + if not chunk: + break + yield chunk + finally: + response.close() + response.release_conn() + + +def _file_content_type(filename: str, fallback: str = "application/octet-stream") -> str: + return mimetypes.guess_type(filename)[0] or fallback + + +def _normalize_bool(value, default: bool = False) -> bool: + if value is None: + return default + if isinstance(value, bool): + return value + return str(value).strip().lower() in {"1", "true", "yes", "on"} + + +def _normalize_int(value, field_name: str, default=None, minimum: int | None = None) -> int: + if value in (None, ""): + if default is None: + raise ValueError(f"{field_name} 不能为空") + result = default + else: + try: + result = int(value) + except (TypeError, ValueError): + raise ValueError(f"{field_name} 必须是整数") + + if minimum is not None and result < minimum: + raise ValueError(f"{field_name} 必须大于等于 {minimum}") + return result + + +def _safe_join_prefix(prefix: str, name: str) -> str: + prefix = (prefix or "").strip().strip("/") + name = name.strip().lstrip("/") + return f"{prefix}/{name}" if prefix else name + + +def _safe_zip_object_name(prefix: str, member_name: str) -> str: + normalized = member_name.replace("\\", "/") + safe_parts = [] + for part in normalized.split("/"): + if not part or part in {".", ".."}: + continue + safe_parts.append(part) + if not safe_parts: + raise ValueError("ZIP 中存在非法文件路径") + return _safe_join_prefix(prefix, "/".join(safe_parts)) + + +def _iter_zip_upload_items(uploaded_file, prefix: str, allowed_extensions: set[str] | None = None): + with zipfile.ZipFile(uploaded_file) as zip_file: + for member in zip_file.infolist(): + if member.is_dir(): + continue + + filename = Path(member.filename).name + if not filename or filename.startswith("."): + continue + + ext = Path(filename).suffix.lower() + if allowed_extensions and ext not in allowed_extensions: + continue + + object_name = _safe_zip_object_name(prefix, member.filename) + with zip_file.open(member, "r") as file_obj: + yield { + "member_name": member.filename, + "object_name": object_name, + "size": member.file_size, + "content_type": _file_content_type(filename), + "stream": file_obj, + } + + +def _map_s3_error(exc: S3Error, default_message: str = "MinIO 操作失败"): + """将常见 MinIO 错误映射为更友好的 HTTP 响应。""" + error_code = getattr(exc, "code", "") or "" + status_code = 500 + message = default_message + + if error_code in {"NoSuchBucket"}: + status_code = 404 + message = "Bucket 不存在" + elif error_code in {"NoSuchKey", "NoSuchObject", "NoSuchVersion"}: + status_code = 404 + message = "文件不存在" + elif error_code in {"BucketAlreadyOwnedByYou", "BucketAlreadyExists"}: + status_code = 409 + message = "Bucket 已存在" + elif error_code in {"BucketNotEmpty"}: + status_code = 409 + message = "Bucket 非空，无法删除" + elif error_code in {"InvalidBucketName"}: + status_code = 400 + message = "Bucket 名称不合法" + elif error_code in {"AccessDenied"}: + status_code = 403 + message = "访问被拒绝" + + return Response( + {"code": status_code, "msg": message, "detail": str(exc)}, + status=status_code, + ) + + +# ── Bucket 管理 ────────────────────────────────────── + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def minio_bucket_create(request): + """创建 Bucket""" + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + try: + created = minio_storage.create_bucket(bucket_name) + except S3Error as exc: + return _map_s3_error(exc, "Bucket 创建失败") + return Response( + { + "code": 200, + "data": {"bucket_name": bucket_name, "created": created}, + "msg": "Bucket 已存在" if not created else "Bucket 创建成功", + } + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def minio_bucket_list(request): + """列出所有 Bucket""" + try: + buckets = minio_storage.list_buckets() + except S3Error as exc: + return _map_s3_error(exc, "Bucket 列表获取失败") + return Response({"code": 200, "data": buckets, "msg": "获取成功"}) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def minio_bucket_delete(request): + """删除 Bucket""" + bucket_name = request.data.get("bucket_name", "") + if not bucket_name: + return Response({"code": 400, "msg": "bucket_name 不能为空"}, status=400) + try: + removed = minio_storage.remove_bucket(bucket_name) + except S3Error as exc: + return _map_s3_error(exc, "Bucket 删除失败") + if removed: + return Response({"code": 200, "data": {"bucket_name": bucket_name}, "msg": "Bucket 已删除"}) + return Response({"code": 404, "msg": "Bucket 不存在"}, status=404) + + +# ── 文件上传 ───────────────────────────────────────── + + +@api_view(["POST"]) +@parser_classes([MultiPartParser, FormParser]) +@permission_classes([IsAuthenticated]) +def minio_file_upload(request): + """上传单个文件到 MinIO + + POST /server/minio/file/upload/ + FormData: + file: 文件 + bucket_name: Bucket 名称（可选，默认 DEFAULT_BUCKET） + prefix: 存储路径前缀（可选） + object_name: 指定对象名（可选，不传则自动生成） + + Returns: + {object_name, bucket_name, presigned_url, size, content_type} + """ + uploaded_file = request.FILES.get("file") + if not uploaded_file: + return Response({"code": 400, "msg": "缺少 file 字段"}, status=400) + + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + prefix = request.data.get("prefix", "") + custom_name = request.data.get("object_name", "") + + if custom_name: + object_name = custom_name + else: + object_name = minio_storage.generate_object_name(uploaded_file.name, prefix) + + content_type = uploaded_file.content_type or _file_content_type(uploaded_file.name) + try: + minio_storage.upload_stream( + bucket_name, + object_name, + uploaded_file.file, + uploaded_file.size, + content_type=content_type, + ) + presigned_url = minio_storage.presign_get( + bucket_name, object_name, settings.MINIO_PRESIGN_EXPIRES + ) + except S3Error as exc: + return _map_s3_error(exc, "文件上传失败") + + return Response( + { + "code": 200, + "data": { + "bucket_name": bucket_name, + "object_name": object_name, + "original_name": uploaded_file.name, + "size": uploaded_file.size, + "content_type": content_type, + "presigned_url": presigned_url, + }, + "msg": "上传成功", + } + ) + + +@api_view(["POST"]) +@parser_classes([MultiPartParser, FormParser]) +@permission_classes([IsAuthenticated]) +def minio_file_batch_upload(request): + """批量上传文件 + + POST /server/minio/files/upload/ + FormData: + files: 多文件 + bucket_name: Bucket 名称（可选） + prefix: 存储路径前缀（可选） + """ + files = request.FILES.getlist("files") + if not files: + return Response({"code": 400, "msg": "缺少 files 字段"}, status=400) + + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + prefix = request.data.get("prefix", "") + + results = [] + try: + for f in files: + object_name = minio_storage.generate_object_name(f.name, prefix) + content_type = f.content_type or _file_content_type(f.name) + minio_storage.upload_stream( + bucket_name, + object_name, + f.file, + f.size, + content_type=content_type, + ) + presigned_url = minio_storage.presign_get( + bucket_name, object_name, settings.MINIO_PRESIGN_EXPIRES + ) + results.append( + { + "object_name": object_name, + "original_name": f.name, + "size": f.size, + "content_type": content_type, + "presigned_url": presigned_url, + } + ) + except S3Error as exc: + return _map_s3_error(exc, "批量上传失败") + + return Response({"code": 200, "data": results, "msg": f"全部上传成功，共 {len(results)} 个文件"}) + + +@api_view(["POST"]) +@parser_classes([MultiPartParser, FormParser]) +@permission_classes([IsAuthenticated]) +def minio_zip_upload_extract(request): + """上传 ZIP 文件并自动解压批量入库。""" + uploaded_file = request.FILES.get("file") + if not uploaded_file: + return Response({"code": 400, "msg": "缺少 file 字段"}, status=400) + + if Path(uploaded_file.name).suffix.lower() != ".zip": + return Response({"code": 400, "msg": "仅支持上传 zip 文件"}, status=400) + + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + prefix = request.data.get("prefix", "") + images_only = _normalize_bool(request.data.get("images_only"), default=False) + + uploaded = [] + skipped = [] + try: + for item in _iter_zip_upload_items( + uploaded_file, + prefix, + allowed_extensions=IMAGE_EXTENSIONS if images_only else None, + ): + minio_storage.upload_stream( + bucket_name=bucket_name, + object_name=item["object_name"], + data=item["stream"], + length=item["size"], + content_type=item["content_type"], + ) + uploaded.append( + { + "source_name": item["member_name"], + "object_name": item["object_name"], + "size": item["size"], + "content_type": item["content_type"], + } + ) + except zipfile.BadZipFile: + return Response({"code": 400, "msg": "zip 文件格式无效或已损坏"}, status=400) + except ValueError as exc: + return Response({"code": 400, "msg": str(exc)}, status=400) + except S3Error as exc: + return _map_s3_error(exc, "ZIP 解压入库失败") + + if images_only: + try: + with zipfile.ZipFile(uploaded_file) as zip_file: + for member in zip_file.infolist(): + if member.is_dir(): + continue + filename = Path(member.filename).name + if not filename or filename.startswith("."): + continue + if Path(filename).suffix.lower() not in IMAGE_EXTENSIONS: + skipped.append(member.filename) + except zipfile.BadZipFile: + pass + + return Response( + { + "code": 200, + "data": { + "bucket_name": bucket_name, + "zip_name": uploaded_file.name, + "uploaded_count": len(uploaded), + "uploaded": uploaded, + "skipped": skipped, + }, + "msg": f"ZIP 解压入库成功，共上传 {len(uploaded)} 个文件", + } + ) + + +@api_view(["POST"]) +@parser_classes([MultiPartParser, FormParser]) +@permission_classes([IsAuthenticated]) +def minio_video_extract_frames(request): + """上传视频文件并按规则抽帧后存储到 MinIO。""" + if cv2 is None: + return Response({"code": 500, "msg": "opencv-python 未安装，无法处理视频"}, status=500) + + uploaded_file = request.FILES.get("file") + if not uploaded_file: + return Response({"code": 400, "msg": "缺少 file 字段"}, status=400) + + video_ext = Path(uploaded_file.name).suffix.lower() + if video_ext not in VIDEO_EXTENSIONS: + return Response({"code": 400, "msg": "不支持的视频格式"}, status=400) + + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + prefix = request.data.get("prefix", "") + image_format = (request.data.get("image_format", "jpg") or "jpg").lower().strip(".") + if image_format not in {"jpg", "jpeg", "png"}: + return Response({"code": 400, "msg": "image_format 仅支持 jpg/jpeg/png"}, status=400) + + try: + interval_seconds = _normalize_int( + request.data.get("interval_seconds"), + "interval_seconds", + default=1, + minimum=1, + ) + start_second = _normalize_int( + request.data.get("start_second"), + "start_second", + default=0, + minimum=0, + ) + max_frames = _normalize_int( + request.data.get("max_frames"), + "max_frames", + default=100, + minimum=1, + ) + except ValueError as exc: + return Response({"code": 400, "msg": str(exc)}, status=400) + + end_second_raw = request.data.get("end_second") + try: + end_second = ( + _normalize_int(end_second_raw, "end_second", minimum=0) if end_second_raw not in (None, "") else None + ) + except ValueError as exc: + return Response({"code": 400, "msg": str(exc)}, status=400) + + if end_second is not None and end_second < start_second: + return Response({"code": 400, "msg": "end_second 不能小于 start_second"}, status=400) + + temp_path = None + cap = None + try: + with NamedTemporaryFile(delete=False, suffix=video_ext) as temp_file: + for chunk in uploaded_file.chunks(): + temp_file.write(chunk) + temp_path = temp_file.name + + cap = cv2.VideoCapture(temp_path) + if not cap.isOpened(): + return Response({"code": 400, "msg": "视频文件无法打开"}, status=400) + + fps = cap.get(cv2.CAP_PROP_FPS) or 0 + if fps <= 0: + fps = 25.0 + + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0) + duration_seconds = total_frames / fps if total_frames > 0 else 0 + + step_frames = max(int(round(interval_seconds * fps)), 1) + start_frame = int(round(start_second * fps)) + end_frame = int(round(end_second * fps)) if end_second is not None else total_frames + + cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) + current_frame = start_frame + extracted = [] + video_stem = Path(uploaded_file.name).stem + frame_prefix = _safe_join_prefix(prefix, f"{video_stem}_frames") + + while len(extracted) < max_frames: + ok, frame = cap.read() + if not ok: + break + + if end_frame and current_frame > end_frame: + break + + if (current_frame - start_frame) % step_frames == 0: + suffix = "jpg" if image_format == "jpeg" else image_format + encode_ext = ".jpg" if image_format in {"jpg", "jpeg"} else ".png" + ok_encode, encoded = cv2.imencode(encode_ext, frame) + if ok_encode: + frame_time = current_frame / fps + object_name = _safe_join_prefix( + frame_prefix, + f"frame_{len(extracted) + 1:05d}_{int(round(frame_time * 1000))}ms.{suffix}", + ) + minio_storage.upload_bytes( + bucket_name=bucket_name, + object_name=object_name, + data=encoded.tobytes(), + content_type=_file_content_type(f"frame.{suffix}"), + ) + extracted.append( + { + "object_name": object_name, + "frame_index": current_frame, + "time_second": round(frame_time, 3), + } + ) + current_frame += 1 + + except S3Error as exc: + return _map_s3_error(exc, "视频抽帧入库失败") + finally: + if cap is not None: + cap.release() + if temp_path and os.path.exists(temp_path): + os.unlink(temp_path) + + return Response( + { + "code": 200, + "data": { + "bucket_name": bucket_name, + "video_name": uploaded_file.name, + "fps": round(fps, 3), + "duration_seconds": round(duration_seconds, 3), + "interval_seconds": interval_seconds, + "start_second": start_second, + "end_second": end_second, + "max_frames": max_frames, + "extracted_count": len(extracted), + "extracted": extracted, + }, + "msg": f"视频抽帧入库成功，共生成 {len(extracted)} 张图片", + } + ) + + +@api_view(["POST"]) +@parser_classes([JSONParser]) +@permission_classes([IsAuthenticated]) +def minio_file_upload_bytes(request): + """上传 base64 编码的文件内容 + + POST /server/minio/file/upload-base64/ + Body: + { + "file_base64": "base64编码的文件内容", + "filename": "example.jpg", + "bucket_name": "ai-trainprediction", // 可选 + "prefix": "images" // 可选 + } + """ + file_base64 = request.data.get("file_base64", "") + filename = request.data.get("filename", "file.bin") + if not file_base64: + return Response({"code": 400, "msg": "缺少 file_base64 字段"}, status=400) + + try: + file_data = base64.b64decode(file_base64) + except Exception: + return Response({"code": 400, "msg": "file_base64 解码失败"}, status=400) + + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + prefix = request.data.get("prefix", "") + object_name = minio_storage.generate_object_name(filename, prefix) + + content_type = _file_content_type(filename) + try: + minio_storage.upload_bytes(bucket_name, object_name, file_data, content_type=content_type) + presigned_url = minio_storage.presign_get( + bucket_name, object_name, settings.MINIO_PRESIGN_EXPIRES + ) + except S3Error as exc: + return _map_s3_error(exc, "Base64 文件上传失败") + + return Response( + { + "code": 200, + "data": { + "bucket_name": bucket_name, + "object_name": object_name, + "original_name": filename, + "size": len(file_data), + "content_type": content_type, + "presigned_url": presigned_url, + }, + "msg": "上传成功", + } + ) + + +# ── 文件下载 ───────────────────────────────────────── + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def minio_file_download(request): + """下载文件（返回文件流） + + GET /server/minio/file/download/?bucket_name=xxx&object_name=xxx + """ + bucket_name = request.GET.get("bucket_name", DEFAULT_BUCKET) + object_name = request.GET.get("object_name", "") + if not object_name: + return Response({"code": 400, "msg": "缺少 object_name"}, status=400) + + filename = os.path.basename(object_name) + try: + info = minio_storage.get_object_info(bucket_name, object_name) + obj = minio_storage.get_object(bucket_name, object_name) + except S3Error: + raise Http404("文件不存在") + + response = StreamingHttpResponse( + _stream_object(obj), + content_type=info["content_type"] or _file_content_type(filename), + ) + response["Content-Disposition"] = ( + f'attachment; filename="{quote(filename)}"; ' + f"filename*=UTF-8''{quote(filename)}" + ) + response["Content-Length"] = str(info["size"]) + return response + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def minio_file_presign(request): + """获取文件预签名下载 URL + + GET /server/minio/file/presign/?bucket_name=xxx&object_name=xxx&expires=3600 + """ + bucket_name = request.GET.get("bucket_name", DEFAULT_BUCKET) + object_name = request.GET.get("object_name", "") + try: + expires = _normalize_expires( + request.GET.get("expires", settings.MINIO_PRESIGN_EXPIRES) + ) + except ValueError as exc: + return Response({"code": 400, "msg": str(exc)}, status=400) + + if not object_name: + return Response({"code": 400, "msg": "缺少 object_name"}, status=400) + + if not minio_storage.object_exists(bucket_name, object_name): + return Response({"code": 404, "msg": "文件不存在"}, status=404) + + try: + url = minio_storage.presign_get(bucket_name, object_name, expires) + except S3Error as exc: + return _map_s3_error(exc, "预签名下载地址生成失败") + return Response({"code": 200, "data": {"presigned_url": url, "expires_in": expires}, "msg": "获取成功"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def minio_file_presign_upload(request): + """获取文件预签名上传 URL""" + bucket_name = request.GET.get("bucket_name", DEFAULT_BUCKET) + object_name = request.GET.get("object_name", "") + if not object_name: + return Response({"code": 400, "msg": "缺少 object_name"}, status=400) + + try: + expires = _normalize_expires( + request.GET.get("expires", settings.MINIO_PRESIGN_EXPIRES) + ) + ensure_bucket(minio_storage, bucket_name) + url = minio_storage.presign_upload(bucket_name, object_name, expires) + except ValueError as exc: + return Response({"code": 400, "msg": str(exc)}, status=400) + except S3Error as exc: + return _map_s3_error(exc, "预签名上传地址生成失败") + + return Response( + {"code": 200, "data": {"presigned_url": url, "expires_in": expires}, "msg": "获取成功"} + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def minio_file_preview(request): + """预览文件（返回文件流用于浏览器直接显示） + + GET /server/minio/file/preview/?bucket_name=xxx&object_name=xxx + """ + bucket_name = request.GET.get("bucket_name", DEFAULT_BUCKET) + object_name = request.GET.get("object_name", "") + if not object_name: + return Response({"code": 400, "msg": "缺少 object_name"}, status=400) + + filename = os.path.basename(object_name) + try: + info = minio_storage.get_object_info(bucket_name, object_name) + obj = minio_storage.get_object(bucket_name, object_name) + except S3Error: + raise Http404("文件不存在") + + response = StreamingHttpResponse( + _stream_object(obj), + content_type=info["content_type"] or _file_content_type(filename), + ) + response["Content-Length"] = str(info["size"]) + response["Content-Disposition"] = f'inline; filename="{quote(filename)}"' + return response + + +# ── 文件信息 & 列表 ────────────────────────────────── + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def minio_file_info(request): + """获取文件元信息 + + GET /server/minio/file/info/?bucket_name=xxx&object_name=xxx + """ + bucket_name = request.GET.get("bucket_name", DEFAULT_BUCKET) + object_name = request.GET.get("object_name", "") + if not object_name: + return Response({"code": 400, "msg": "缺少 object_name"}, status=400) + + try: + info = minio_storage.get_object_info(bucket_name, object_name) + presigned_url = minio_storage.presign_get( + bucket_name, object_name, settings.MINIO_PRESIGN_EXPIRES + ) + except S3Error as exc: + return _map_s3_error(exc, "文件信息获取失败") + info["presigned_url"] = presigned_url + return Response({"code": 200, "data": info, "msg": "获取成功"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def minio_file_list(request): + """列出文件 + + GET /server/minio/files/?bucket_name=xxx&prefix=xxx + """ + bucket_name = request.GET.get("bucket_name", DEFAULT_BUCKET) + prefix = request.GET.get("prefix", "") + try: + objects = minio_storage.list_objects(bucket_name, prefix=prefix) + except S3Error as exc: + return _map_s3_error(exc, "文件列表获取失败") + return Response({"code": 200, "data": objects, "msg": f"共 {len(objects)} 个对象"}) + + +# ── 文件删除 ───────────────────────────────────────── + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def minio_file_delete(request): + """删除单个文件 + + DELETE /server/minio/file/delete/ + Body: {"bucket_name": "xxx", "object_name": "xxx"} + """ + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + object_name = request.data.get("object_name", "") + if not object_name: + return Response({"code": 400, "msg": "缺少 object_name"}, status=400) + + try: + minio_storage.delete_object(bucket_name, object_name) + except S3Error as exc: + return _map_s3_error(exc, "文件删除失败") + return Response({"code": 200, "data": {"object_name": object_name}, "msg": "删除成功"}) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def minio_file_batch_delete(request): + """批量删除文件 + + DELETE /server/minio/files/delete/ + Body: {"bucket_name": "xxx", "object_names": ["a.jpg", "b.png"]} + """ + bucket_name = request.data.get("bucket_name", DEFAULT_BUCKET) + object_names = request.data.get("object_names", []) + if not object_names: + return Response({"code": 400, "msg": "缺少 object_names"}, status=400) + + try: + errors = minio_storage.delete_objects(bucket_name, object_names) + except S3Error as exc: + return _map_s3_error(exc, "批量删除失败") + if errors: + return Response( + { + "code": 207, + "data": {"deleted": len(object_names) - len(errors), "errors": errors}, + "msg": f"部分删除，{len(errors)} 个失败", + }, + status=207, + ) + return Response( + {"code": 200, "data": {"deleted": len(object_names)}, "msg": "全部删除成功"} + ) + + +# ── 文件复制 ───────────────────────────────────────── + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def minio_file_copy(request): + """复制文件 + + POST /server/minio/file/copy/ + Body: { + "src_bucket": "xxx", + "src_object": "a.jpg", + "dst_bucket": "yyy", + "dst_object": "b.jpg" + } + """ + src_bucket = request.data.get("src_bucket", DEFAULT_BUCKET) + src_object = request.data.get("src_object", "") + dst_bucket = request.data.get("dst_bucket", DEFAULT_BUCKET) + dst_object = request.data.get("dst_object", "") + + if not src_object or not dst_object: + return Response({"code": 400, "msg": "src_object 和 dst_object 不能为空"}, status=400) + + try: + result = minio_storage.copy_object(src_bucket, src_object, dst_bucket, dst_object) + return Response({"code": 200, "data": {"object_name": result}, "msg": "复制成功"}) + except S3Error as exc: + return _map_s3_error(exc, "文件复制失败") diff --git a/backend/apps/datasets/__init__.py b/backend/apps/datasets/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/datasets/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/datasets/apps.py b/backend/apps/datasets/apps.py new file mode 100644 index 0000000..c47e1d4 --- /dev/null +++ b/backend/apps/datasets/apps.py @@ -0,0 +1,7 @@ +from django.apps import AppConfig + + +class DatasetsConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "apps.datasets" + diff --git a/backend/apps/datasets/dataset_stats.py b/backend/apps/datasets/dataset_stats.py new file mode 100644 index 0000000..96e8022 --- /dev/null +++ b/backend/apps/datasets/dataset_stats.py @@ -0,0 +1,24 @@ +from django.db.models import Q + +from apps.core.models import AiDataset, AiDatasetSample + + +def get_dataset_sample_counters(dataset_id: str) -> tuple[int, int]: + queryset = AiDatasetSample.objects.filter(dataset_id=dataset_id) + total_count = queryset.count() + annotated_count = queryset.filter( + (Q(rectangle__isnull=False) & ~Q(rectangle="")) + | (Q(polygon__isnull=False) & ~Q(polygon="")) + ).count() + return total_count, annotated_count + + +def refresh_dataset_sample_counters(dataset_id: str) -> tuple[int, int]: + if not dataset_id: + return 0, 0 + total_count, annotated_count = get_dataset_sample_counters(dataset_id) + AiDataset.objects.filter(id=dataset_id).update( + dataset_count=total_count, + annotated_count=annotated_count, + ) + return total_count, annotated_count diff --git a/backend/apps/datasets/storage_service.py b/backend/apps/datasets/storage_service.py new file mode 100644 index 0000000..499f643 --- /dev/null +++ b/backend/apps/datasets/storage_service.py @@ -0,0 +1,502 @@ +import io +import mimetypes +import os +import uuid +import zipfile +from datetime import datetime +from tempfile import NamedTemporaryFile + +from django.conf import settings +from django.db.models import Max +from PIL import Image, UnidentifiedImageError + +from apps.common.minio_client import minio_storage +from apps.common.utils.annotation_store import split_dataset_sample_annotation_content +from apps.core.models import AiDatasetSample + +try: + import cv2 +except Exception: + cv2 = None + + +DATASET_BUCKET = "dataset" +TEMPFILE_BUCKET = "tempfile" +IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".bmp", ".gif", ".webp", ".tif", ".tiff"} +VIDEO_EXTENSIONS = {".mp4", ".avi", ".mov", ".mkv", ".wmv", ".flv", ".mpeg", ".mpg", ".webm"} +AUDIO_EXTENSIONS = {".mp3", ".wav", ".aac", ".m4a", ".flac", ".ogg"} +TEXT_EXTENSIONS = {".txt", ".csv", ".json", ".xml"} + + +def _local_sample_path(train_path: str,saved_path: str, saved_filename: str) -> str: + return os.path.join(train_path, saved_path, saved_filename).replace("\\", "/") + + +def _sample_object_name(saved_path: str, saved_filename: str) -> str: + return f"{str(saved_path or '').strip('/').replace('\\', '/')}/{saved_filename}".strip("/") + + +def _ensure_local_dir(saved_path: str) -> str: + local_dir = os.path.join(settings.FILESPACE_ROOT_PATH, saved_path).replace("\\", "/") + os.makedirs(local_dir, exist_ok=True) + return local_dir + + +def _write_local_file(saved_path: str, saved_filename: str, data: bytes) -> str: + local_dir = _ensure_local_dir(saved_path) + full_path = os.path.join(local_dir, saved_filename).replace("\\", "/") + with open(full_path, "wb") as f: + f.write(data) + return full_path + + +def _sidecar_filename(saved_filename: str, suffix: str) -> str: + stem = os.path.splitext(saved_filename)[0] + normalized_suffix = suffix if str(suffix).startswith(".") else f".{suffix}" + return f"{stem}{normalized_suffix}" + + +def sample_sidecar_names(saved_filename: str): + return [ + _sidecar_filename(saved_filename, ".xml"), + _sidecar_filename(saved_filename, ".json"), + ] + + +def _normalize_filesize(size_bytes: int) -> str: + try: + return str(min(max(int(size_bytes or 0), 0), 9999999999)) + except Exception: + return "0" + + +def _image_wh_from_bytes(data: bytes): + try: + with Image.open(io.BytesIO(data)) as img: + width, height = img.size + return f"{width}x{height}" + except UnidentifiedImageError: + return None + except Exception: + return None + + +def _extract_sample_index(saved_filename: str) -> int | None: + stem = os.path.splitext(os.path.basename(str(saved_filename or "")))[0] + tail = stem.rsplit("-", 1)[-1] + if tail.isdigit(): + return int(tail) + return None + + +def _next_sample_index(dataset_id: str) -> int: + max_saved_filename = AiDatasetSample.objects.filter(dataset_id=dataset_id).aggregate( + max_saved_filename=Max("saved_filename") + )["max_saved_filename"] + current_index = _extract_sample_index(max_saved_filename) + return (current_index or 0) + 1 + + +def _build_saved_filename(dataset, original_filename: str) -> str: + ext = os.path.splitext(original_filename or "")[1].lower() + index = _next_sample_index(dataset.id) + return f"{dataset.dataset_code}-{index:08}{ext}" + + +def detect_original_type(filename: str) -> str: + ext = os.path.splitext(filename or "")[1].lower() + if ext in IMAGE_EXTENSIONS: + return "01" + if ext in VIDEO_EXTENSIONS: + return "02" + if ext in AUDIO_EXTENSIONS: + return "03" + if ext == ".zip": + return "04" + if ext in TEXT_EXTENSIONS: + return "05" + return "99" + + +def dataset_type_accepts_extension(dataset_type: str, extension: str) -> bool: + ext = str(extension or "").lower() + if dataset_type == "02": + return ext in IMAGE_EXTENSIONS + if dataset_type == "03": + return ext in VIDEO_EXTENSIONS + if dataset_type == "04": + return ext in AUDIO_EXTENSIONS + if dataset_type == "05": + return ext in TEXT_EXTENSIONS + return ext in IMAGE_EXTENSIONS | VIDEO_EXTENSIONS | AUDIO_EXTENSIONS | TEXT_EXTENSIONS + + +def create_dataset_sample_record( + dataset, + original_filename: str, + saved_filename: str, + size_bytes: int, + annotation_content=None, + custom1=None, + custom2=None, + custom3=None, + status: str = "00", +): + annotation_fields = split_dataset_sample_annotation_content(annotation_content) + return AiDatasetSample.objects.create( + id=str(uuid.uuid4()), + dataset_id=dataset.id, + saved_path=dataset.dataset_path, + original_filename=original_filename, + saved_filename=saved_filename, + saved_filesize=_normalize_filesize(size_bytes), + rectangle=annotation_fields["rectangle"], + polygon=annotation_fields["polygon"], + status=status, + create_time=datetime.now(), + custom1=custom1, + custom2=custom2, + custom3=custom3, + ) + + +def save_bytes_to_dataset( + dataset, + original_filename: str, + data: bytes, + content_type: str | None = None, + annotation_content=None, + custom1=None, + custom2=None, + custom3=None, +): + saved_filename = _build_saved_filename(dataset, original_filename) + object_name = _sample_object_name(dataset.dataset_path, saved_filename) + actual_content_type = content_type or mimetypes.guess_type(original_filename)[0] or "application/octet-stream" + minio_storage.upload_bytes(DATASET_BUCKET, object_name, data, content_type=actual_content_type) + if custom1 is None and os.path.splitext(saved_filename)[1].lower() in IMAGE_EXTENSIONS: + custom1 = _image_wh_from_bytes(data) + sample = create_dataset_sample_record( + dataset=dataset, + original_filename=os.path.basename(original_filename), + saved_filename=saved_filename, + size_bytes=len(data), + annotation_content=annotation_content, + custom1=custom1, + custom2=custom2, + custom3=custom3, + ) + return sample + + +def save_uploaded_file_to_dataset(dataset, uploaded_file): + file_bytes = uploaded_file.read() + uploaded_file.seek(0) + return save_bytes_to_dataset( + dataset=dataset, + original_filename=uploaded_file.name, + data=file_bytes, + content_type=uploaded_file.content_type, + ) + + +def copy_minio_object_to_dataset( + dataset, + source_bucket: str, + source_object: str, + original_filename: str, + size_bytes: int | None = None, + custom2=None, + custom3=None, +): + saved_filename = _build_saved_filename(dataset, original_filename) + target_object = _sample_object_name(dataset.dataset_path, saved_filename) + minio_storage.copy_object(source_bucket, source_object, DATASET_BUCKET, target_object) + actual_size = size_bytes + if actual_size is None: + try: + actual_size = int(minio_storage.get_object_info(DATASET_BUCKET, target_object).get("size") or 0) + except Exception: + actual_size = 0 + custom1 = None + if os.path.splitext(saved_filename)[1].lower() in IMAGE_EXTENSIONS: + try: + custom1 = _image_wh_from_bytes(minio_storage.download_bytes(DATASET_BUCKET, target_object)) + except Exception: + custom1 = None + return create_dataset_sample_record( + dataset=dataset, + original_filename=os.path.basename(original_filename), + saved_filename=saved_filename, + size_bytes=actual_size or 0, + custom1=custom1, + custom2=custom2, + custom3=custom3, + ) + + +def dataset_sample_source_exists(dataset_id: str, source_marker: str | None) -> bool: + if not source_marker: + return False + return AiDatasetSample.objects.filter(dataset_id=dataset_id, custom2=source_marker).exists() + + +def copy_minio_object_to_dataset_result( + dataset, + source_bucket: str, + source_object: str, + original_filename: str, + size_bytes: int | None = None, + source_marker: str | None = None, + skip_existing: bool = False, +): + if skip_existing and dataset_sample_source_exists(dataset.id, source_marker): + return { + "imported": [], + "skipped": [{"name": os.path.basename(original_filename), "reason": "duplicate_source"}], + "failed": [], + } + sample = copy_minio_object_to_dataset( + dataset=dataset, + source_bucket=source_bucket, + source_object=source_object, + original_filename=original_filename, + size_bytes=size_bytes, + custom2=source_marker, + ) + return {"imported": [sample], "skipped": [], "failed": []} + + +def import_zip_bytes_to_dataset_result( + dataset, + zip_bytes: bytes, + source_marker_prefix: str | None = None, + skip_existing: bool = False, +): + imported = [] + skipped = [] + failed = [] + with zipfile.ZipFile(io.BytesIO(zip_bytes), "r") as zip_file: + for member in zip_file.infolist(): + if member.is_dir(): + continue + member_name = member.filename.replace("\\", "/") + filename = os.path.basename(member_name) + if not filename or filename.startswith("."): + continue + ext = os.path.splitext(filename)[1].lower() + if not dataset_type_accepts_extension(dataset.dataset_type, ext): + failed.append({"name": filename, "reason": "type_mismatch"}) + continue + source_marker = f"{source_marker_prefix}:zip:{member_name}" if source_marker_prefix else None + if skip_existing and dataset_sample_source_exists(dataset.id, source_marker): + skipped.append({"name": filename, "reason": "duplicate_source"}) + continue + try: + with zip_file.open(member, "r") as fp: + data = fp.read() + imported.append(save_bytes_to_dataset(dataset, filename, data, custom2=source_marker)) + except Exception as exc: + failed.append({"name": filename, "reason": str(exc)}) + return {"imported": imported, "skipped": skipped, "failed": failed} + + +def import_zip_bytes_to_dataset(dataset, zip_bytes: bytes, source_marker_prefix: str | None = None, skip_existing: bool = False): + result = import_zip_bytes_to_dataset_result( + dataset=dataset, + zip_bytes=zip_bytes, + source_marker_prefix=source_marker_prefix, + skip_existing=skip_existing, + ) + return result["imported"] + + +def extract_video_frames_to_dataset_result( + dataset, + video_bytes: bytes, + original_filename: str, + interval_seconds: int = 1, + max_frames: int = 200, + image_format: str = "jpg", + source_marker_prefix: str | None = None, + skip_existing: bool = False, +): + if cv2 is None: + raise RuntimeError("opencv-python 未安装，无法处理视频") + + video_ext = os.path.splitext(original_filename or "")[1].lower() or ".mp4" + temp_path = None + cap = None + imported = [] + skipped = [] + failed = [] + try: + with NamedTemporaryFile(delete=False, suffix=video_ext) as temp_file: + temp_file.write(video_bytes) + temp_path = temp_file.name + + cap = cv2.VideoCapture(temp_path) + if not cap.isOpened(): + raise RuntimeError("视频文件无法打开") + + fps = cap.get(cv2.CAP_PROP_FPS) or 25.0 + step_frames = max(int(round(interval_seconds * fps)), 1) + current_frame = 0 + video_stem = os.path.splitext(os.path.basename(original_filename))[0] + suffix = "jpg" if image_format == "jpeg" else image_format + encode_ext = ".jpg" if image_format in {"jpg", "jpeg"} else ".png" + + while len(imported) < max_frames: + ok, frame = cap.read() + if not ok: + break + if current_frame % step_frames == 0: + source_marker = ( + f"{source_marker_prefix}:frame:{current_frame:08}" + if source_marker_prefix + else None + ) + if skip_existing and dataset_sample_source_exists(dataset.id, source_marker): + skipped.append( + { + "name": f"{video_stem}_{current_frame:08}.{suffix}", + "reason": "duplicate_source", + } + ) + current_frame += 1 + continue + ok_encode, encoded = cv2.imencode(encode_ext, frame) + if ok_encode: + frame_name = f"{video_stem}_{current_frame:08}.{suffix}" + imported.append( + save_bytes_to_dataset( + dataset, + frame_name, + encoded.tobytes(), + custom2=source_marker, + ) + ) + else: + failed.append( + { + "name": f"{video_stem}_{current_frame:08}.{suffix}", + "reason": "frame_encode_failed", + } + ) + current_frame += 1 + finally: + if cap is not None: + cap.release() + if temp_path and os.path.exists(temp_path): + os.unlink(temp_path) + return {"imported": imported, "skipped": skipped, "failed": failed} + + +def extract_video_frames_to_dataset( + dataset, + video_bytes: bytes, + original_filename: str, + interval_seconds: int = 1, + max_frames: int = 200, + image_format: str = "jpg", + source_marker_prefix: str | None = None, + skip_existing: bool = False, +): + result = extract_video_frames_to_dataset_result( + dataset=dataset, + video_bytes=video_bytes, + original_filename=original_filename, + interval_seconds=interval_seconds, + max_frames=max_frames, + image_format=image_format, + source_marker_prefix=source_marker_prefix, + skip_existing=skip_existing, + ) + return result["imported"] + + +def ensure_sample_local_copy(train_path: str, saved_path: str, saved_filename: str): + local_path = _local_sample_path(train_path, saved_path, saved_filename) + if os.path.exists(local_path): + return local_path + object_name = _sample_object_name(saved_path, saved_filename) + _ensure_local_dir(saved_path) + minio_storage.download_file(DATASET_BUCKET, object_name, local_path) + return local_path + + +def ensure_sidecar_local_copy(saved_path: str, saved_filename: str, suffix: str): + sidecar_name = _sidecar_filename(saved_filename, suffix) + local_path = _local_sample_path(saved_path, sidecar_name) + if os.path.exists(local_path): + return local_path + object_name = _sample_object_name(saved_path, sidecar_name) + if not minio_storage.object_exists(DATASET_BUCKET, object_name): + return local_path + _ensure_local_dir(saved_path) + minio_storage.download_file(DATASET_BUCKET, object_name, local_path) + return local_path + + +def download_sidecar_bytes(saved_path: str, saved_filename: str, suffix: str): + sidecar_name = _sidecar_filename(saved_filename, suffix) + object_name = _sample_object_name(saved_path, sidecar_name) + try: + if minio_storage.object_exists(DATASET_BUCKET, object_name): + return minio_storage.download_bytes(DATASET_BUCKET, object_name) + except Exception: + pass + + local_path = _local_sample_path(saved_path, sidecar_name) + if os.path.exists(local_path) and os.path.isfile(local_path): + with open(local_path, "rb") as f: + return f.read() + return None + + +def download_sidecar_text(saved_path: str, saved_filename: str, suffix: str, encoding: str = "utf-8"): + data = download_sidecar_bytes(saved_path, saved_filename, suffix) + if data is None: + return None + return data.decode(encoding, errors="replace") + + +def upload_sidecar_bytes(saved_path: str, saved_filename: str, suffix: str, data: bytes, content_type: str | None = None): + sidecar_name = _sidecar_filename(saved_filename, suffix) + object_name = _sample_object_name(saved_path, sidecar_name) + actual_content_type = content_type or mimetypes.guess_type(sidecar_name)[0] or "application/octet-stream" + minio_storage.upload_bytes(DATASET_BUCKET, object_name, data, content_type=actual_content_type) + return sidecar_name + + +def upload_sidecar_text(saved_path: str, saved_filename: str, suffix: str, text: str, content_type: str | None = None): + actual_content_type = content_type + if actual_content_type is None and str(suffix).lower().endswith("json"): + actual_content_type = "application/json" + return upload_sidecar_bytes( + saved_path=saved_path, + saved_filename=saved_filename, + suffix=suffix, + data=(text or "").encode("utf-8"), + content_type=actual_content_type, + ) + + +def delete_sample_storage(saved_path: str, saved_filename: str): + candidates = [saved_filename] + sample_sidecar_names(saved_filename) + failed_objects = [] + for filename in candidates: + object_name = _sample_object_name(saved_path, filename) + try: + if minio_storage.object_exists(DATASET_BUCKET, object_name): + minio_storage.delete_object(DATASET_BUCKET, object_name) + except Exception: + failed_objects.append(object_name) + + local_path = os.path.join(settings.FILESPACE_ROOT_PATH, saved_path, filename).replace("\\", "/") + if os.path.exists(local_path) and os.path.isfile(local_path): + try: + os.remove(local_path) + except Exception: + pass + return failed_objects diff --git a/backend/apps/datasets/tests/__init__.py b/backend/apps/datasets/tests/__init__.py new file mode 100644 index 0000000..83ab76b --- /dev/null +++ b/backend/apps/datasets/tests/__init__.py @@ -0,0 +1 @@ +# 空文件 — 标记 tests 为 Python 包 diff --git a/backend/apps/datasets/tests/test_smart_terminal.py b/backend/apps/datasets/tests/test_smart_terminal.py new file mode 100644 index 0000000..67f76ba --- /dev/null +++ b/backend/apps/datasets/tests/test_smart_terminal.py @@ -0,0 +1,287 @@ +"""智能终端 API 接口测试""" +import uuid + +from django.contrib.auth import get_user_model +from django.db import connection +from django.test import TestCase +from rest_framework.test import APIClient + +from apps.core.models import AiSmartTerminal + +User = get_user_model() + + +# ---------- 5 条测试设备数据 ---------- +TEST_DEVICES = [ + { + "stcd": "ZD001", + "stnm": "碧水电站-鱼类识别AI盒子", + "sttp": "01", + "lgtd": 104.065800, + "lttd": 30.572800, + "elev": 512.500000, + "rstcd": "BS001", + "base_id": "YJ001", + "rvcd": "MJ001", + "aipz": '{"cpu":"ARM Cortex-A78","memory":"8GB","storage":"64GB"}', + "ipaddr": "192.168.1.101", + "usfl": 1, + "dtin": 1, + "mway": 1, + "sync_check_interval": 30, + }, + { + "stcd": "ZD002", + "stnm": "青湖-鸟类识别AI盒子", + "sttp": "02", + "lgtd": 104.125600, + "lttd": 30.681200, + "elev": 489.300000, + "rstcd": "QH002", + "base_id": "YJ001", + "rvcd": "MJ002", + "aipz": '{"cpu":"ARM Cortex-A78","memory":"4GB","storage":"32GB"}', + "ipaddr": "192.168.1.102", + "usfl": 1, + "dtin": 1, + "mway": 1, + "sync_check_interval": 60, + }, + { + "stcd": "ZD003", + "stnm": "卧龙自然保护区-野生动物识别AI盒子", + "sttp": "03", + "lgtd": 103.298400, + "lttd": 30.985600, + "elev": 1820.000000, + "rstcd": "WL003", + "base_id": "YJ002", + "rvcd": "MJ003", + "aipz": '{"cpu":"NVIDIA Jetson Orin","memory":"16GB","storage":"256GB"}', + "ipaddr": "192.168.2.101", + "usfl": 1, + "dtin": 0, + "mway": 2, + "sync_check_interval": 120, + }, + { + "stcd": "ZD004", + "stnm": "长江上游-鱼类分拣装置", + "sttp": "04", + "lgtd": 104.545200, + "lttd": 30.127800, + "elev": 345.000000, + "rstcd": "CJ004", + "base_id": "YJ003", + "rvcd": "CJ001", + "aipz": '{"cpu":"Intel i5-1135G7","memory":"8GB","storage":"128GB","grab_arm":true}', + "ipaddr": "192.168.3.201", + "usfl": 0, + "dtin": 0, + "mway": 0, + "sync_check_interval": None, + }, + { + "stcd": "ZD005", + "stnm": "洞庭湖-鸟类识别AI盒子", + "sttp": "02", + "lgtd": 112.946800, + "lttd": 29.358700, + "elev": 35.200000, + "rstcd": "DT005", + "base_id": "YJ004", + "rvcd": "CJ002", + "aipz": '{"cpu":"ARM Cortex-A78","memory":"8GB","storage":"64GB"}', + "ipaddr": "192.168.4.101", + "usfl": 1, + "dtin": 1, + "mway": 1, + "sync_check_interval": 45, + }, +] + + +class SmartTerminalAPITest(TestCase): + """智能终端 API 集成测试 — 覆盖全部 7 个接口""" + + @classmethod + def setUpTestData(cls): + """类级别：测试库建表 + 创建 admin 用户 + 插入 5 条测试设备""" + cls.base_url = "/server/terminal" + # Django 测试每次新建 test_ 库，需在测试库中建表 + with connection.schema_editor() as schema_editor: + schema_editor.create_model(AiSmartTerminal) + # 创建 admin 用户 + cls.admin_user = User.objects.create_user( + username="admin", password="123456", is_active=True, is_staff=True, + ) + # 通过 ORM 直接插入 5 条设备 + cls.created_ids = [] + for device in TEST_DEVICES: + obj = AiSmartTerminal.objects.create( + id=uuid.uuid4().hex, + record_user="admin", + **device, + ) + cls.created_ids.append(obj.id) + assert AiSmartTerminal.objects.filter(is_deleted=0).count() == 5 + + def setUp(self): + """每个测试用例前：用 admin 强制登录""" + self.client = APIClient() + self.client.force_login(self.admin_user) + + # ==================== 1. 创建 ==================== + def test_01_create_duplicate_stcd_should_reject(self): + """重复的 stcd 应被拒绝""" + resp = self.client.post(f"{self.base_url}/create/", { + "stcd": "ZD001", + "stnm": "重复设备", + "sttp": "01", + }, format="json") + self.assertEqual(resp.status_code, 400) + + # ==================== 2. 修改 ==================== + def test_02_update_terminal_info(self): + """修改设备基本信息""" + target = self.created_ids[0] + resp = self.client.put(f"{self.base_url}/update/", { + "id": target, + "stnm": "碧水电站-鱼类识别AI盒子（已升级）", + "sync_check_interval": 15, + }, format="json") + self.assertEqual(resp.status_code, 200) + self.assertEqual(resp.data["stnm"], "碧水电站-鱼类识别AI盒子（已升级）") + + def test_03_update_protected_fields_should_be_ignored(self): + """修改受保护字段应被忽略""" + target = self.created_ids[0] + resp = self.client.put(f"{self.base_url}/update/", { + "id": target, + "model_version": "hacked_v99", + "online_status": 0, + }, format="json") + self.assertEqual(resp.status_code, 200) + # 受保护字段不应被修改 + self.assertIsNone(resp.data["model_version"]) + self.assertIsNone(resp.data["online_status"]) + + # ==================== 3. 发布模型 ==================== + def test_04_publish_model_to_single_terminal(self): + """发布模型到单个终端""" + target = self.created_ids[0] + resp = self.client.put(f"{self.base_url}/publishModel/", { + "id": target, + "model_id": "model_fish_v2", + "model_version": "v2.1.0", + "model_path": "models/fish_detect/v2.1.0/model.onnx", + }, format="json") + self.assertEqual(resp.status_code, 200) + self.assertEqual(resp.data["published_count"], 1) + updated = AiSmartTerminal.objects.get(id=target) + self.assertEqual(updated.model_version, "v2.1.0") + self.assertIsNotNone(updated.model_publish_time) + + def test_05_publish_model_to_multiple_terminals(self): + """发布模型到多个终端""" + targets = self.created_ids[:3] + resp = self.client.put(f"{self.base_url}/publishModel/", { + "ids": targets, + "model_id": "model_bird_v1", + "model_version": "v1.5.0", + "model_path": "models/bird_detect/v1.5.0/model.onnx", + }, format="json") + self.assertEqual(resp.status_code, 200) + self.assertEqual(resp.data["published_count"], 3) + for tid in targets: + t = AiSmartTerminal.objects.get(id=tid) + self.assertEqual(t.model_version, "v1.5.0") + + def test_06_publish_model_missing_params(self): + """发布模型缺少参数应报错""" + resp = self.client.put(f"{self.base_url}/publishModel/", { + "id": self.created_ids[0], + "model_id": "m1", + }, format="json") + self.assertEqual(resp.status_code, 400) + + # ==================== 4. 分页查询 ==================== + def test_07_search_all_returns_paginated(self): + """查询全部设备应返回分页数据""" + resp = self.client.get(f"{self.base_url}/list/") + self.assertEqual(resp.status_code, 200) + self.assertIn("count", resp.data) + self.assertIn("results", resp.data) + self.assertEqual(resp.data["count"], 5) + + def test_08_search_by_sttp(self): + """按终端分类过滤""" + resp = self.client.get(f"{self.base_url}/list/?sttp=02") + self.assertEqual(resp.status_code, 200) + self.assertEqual(resp.data["count"], 2) # ZD002 + ZD005 + + def test_09_search_by_usfl(self): + """按启用状态过滤""" + resp = self.client.get(f"{self.base_url}/list/?usfl=0") + self.assertEqual(resp.status_code, 200) + self.assertEqual(resp.data["count"], 1) # ZD004 + + def test_10_search_by_stnm_fuzzy(self): + """按终端名称模糊搜索""" + resp = self.client.get(f"{self.base_url}/list/?stnm=鱼类") + self.assertEqual(resp.status_code, 200) + self.assertGreaterEqual(resp.data["count"], 2) # ZD001 + ZD004 + + def test_11_pagination_page_size(self): + """自定义分页大小""" + resp = self.client.get(f"{self.base_url}/list/?page_size=2") + self.assertEqual(resp.status_code, 200) + self.assertEqual(len(resp.data["results"]), 2) + self.assertEqual(resp.data["count"], 5) + + # ==================== 5. 心跳上报 ==================== + def test_12_heartbeat_updates_online_status(self): + """心跳上报应更新在线状态和心跳时间""" + resp = self.client.put(f"{self.base_url}/heartbeat/", { + "stcd": "ZD001", + }, format="json") + self.assertEqual(resp.status_code, 200) + self.assertEqual(resp.data["online_status"], 1) + self.assertIsNotNone(resp.data["last_heartbeat_time"]) + + def test_13_heartbeat_missing_stcd(self): + """心跳上报缺少 stcd 应报错""" + resp = self.client.put(f"{self.base_url}/heartbeat/", {}, format="json") + self.assertEqual(resp.status_code, 400) + + def test_14_heartbeat_unknown_stcd(self): + """心跳上报未知 stcd 应返回 404""" + resp = self.client.put(f"{self.base_url}/heartbeat/", { + "stcd": "UNKNOWN", + }, format="json") + self.assertEqual(resp.status_code, 404) + + # ==================== 6. 下载模型 ==================== + def test_15_download_model_no_model_published(self): + """未发布模型的终端下载应返回 404""" + resp = self.client.get(f"{self.base_url}/downloadModel/?stcd=ZD005") + self.assertEqual(resp.status_code, 404) + + def test_16_download_model_missing_stcd(self): + """下载模型缺少 stcd 应报错""" + resp = self.client.get(f"{self.base_url}/downloadModel/") + self.assertEqual(resp.status_code, 400) + + # ==================== 7. 逻辑删除 ==================== + def test_17_delete_soft(self): + """逻辑删除后 is_deleted 应为 1""" + target = self.created_ids[4] + resp = self.client.delete(f"{self.base_url}/delete/?id={target}") + self.assertEqual(resp.status_code, 200) + deleted = AiSmartTerminal.objects.get(id=target) + self.assertEqual(deleted.is_deleted, 1) + + def test_18_delete_missing_id(self): + """删除缺少 id 应报错""" + resp = self.client.delete(f"{self.base_url}/delete/") + self.assertEqual(resp.status_code, 400) diff --git a/backend/apps/datasets/views/__init__.py b/backend/apps/datasets/views/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/datasets/views/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/datasets/views/annotate_task.py b/backend/apps/datasets/views/annotate_task.py new file mode 100644 index 0000000..050799f --- /dev/null +++ b/backend/apps/datasets/views/annotate_task.py @@ -0,0 +1,500 @@ +from datetime import datetime +import concurrent.futures +import json +import os +import threading +import time +import uuid +from typing import Any, Dict, List, Optional + +import requests +from django.conf import settings +from django.db import connection, close_old_connections +from django.db.models import Case, Count, IntegerField, Sum, When +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AiAnnotateTaskSerializer +from apps.core.models import ( + AiAnnotateTask, + AiAnnotateTaskSample, + AiDatasetSample, + AIAnnotateTeam, +) + + +def _get_labels_json_from_task(task: AiAnnotateTask) -> str: + if not task.labels: + return "" + try: + items = json.loads(task.labels) + except Exception: + return "" + if not isinstance(items, list): + return "" + return task.labels + + +def _build_text_prompt_from_labels_json(labels_json: str) -> str: + if not labels_json: + return "" + try: + items = json.loads(labels_json) + except Exception: + return "" + if isinstance(items, dict): + items = [items] + if not isinstance(items, list): + return "" + labels: List[str] = [] + for it in items: + if isinstance(it, dict) and it.get("label"): + labels.append(str(it["label"]).strip()) + labels = [x for x in labels if x] + return ".".join(labels) if labels else "" + + +def _flatten_bbox_region(region: Any) -> Optional[List[float]]: + try: + if ( + isinstance(region, list) + and len(region) == 2 + and isinstance(region[0], list) + and isinstance(region[1], list) + and len(region[0]) == 2 + and len(region[1]) == 2 + ): + x1, y1 = float(region[0][0]), float(region[0][1]) + x2, y2 = float(region[1][0]), float(region[1][1]) + return [x1, y1, x2, y2] + except Exception: + return None + return None + + +def _is_int_one(value: Any) -> bool: + try: + return int(str(value).strip()) == 1 + except Exception: + return False + + +def _run_auto_annotate_task( + task_id: str, + label_content: str, + text_prompt: str, + req_type: int, + score_threshold: float, + timeout_sec: float, + max_workers: int, + infer_url: str, +) -> None: + close_old_connections() + + samples = list(AiAnnotateTaskSample.objects.filter(task_id=task_id, status="01")) + if not samples: + print(f"[auto-annotate] task_id={task_id} no samples with status=01") + close_old_connections() + return + + health_url = infer_url.rstrip("/infer").rstrip("/") + "/health" + try: + resp = requests.get(health_url, timeout=5) + if resp.status_code != 200: + print(f"[auto-annotate] task_id={task_id} health check failed: {resp.status_code}") + close_old_connections() + return + print(f"[auto-annotate] task_id={task_id} health check passed") + except Exception as e: + print(f"[auto-annotate] task_id={task_id} health check error: {e}") + close_old_connections() + return + + try: + from apps.common.minio_client import minio_storage + except Exception: + from common.minio_client import minio_storage + + def call_infer(sample: AiAnnotateTaskSample) -> tuple[str, Optional[str], Optional[str]]: + sample_path = sample.sample_path or "" + sample_name = sample.sample_name or "" + + if not sample_path or not sample_name: + return sample.id, None, "missing sample_path or sample_name" + + object_name = os.path.join(sample_path.lstrip("/"), sample_name).replace("\\", "/") + + try: + response = minio_storage.get_object("dataset", object_name) + image_data = response.read() + response.release_conn() + except Exception as e: + return sample.id, None, f"minio download error: {e}" + + try: + resp = requests.post( + infer_url, + files={ + "image_file": (sample_name, image_data, "image/jpeg"), + }, + data={ + "label_content": label_content, + "text_prompt": text_prompt, + "type": req_type, + "score_threshold": score_threshold, + }, + timeout=timeout_sec, + ) + except Exception as e: + return sample.id, None, str(e) + if resp.status_code != 200: + return sample.id, None, f"http {resp.status_code}: {resp.text[:300]}" + try: + result = resp.json() + except Exception: + return sample.id, None, f"invalid json: {resp.text[:300]}" + if not isinstance(result, list): + return sample.id, None, "invalid response: not list" + + normalized: List[Dict[str, Any]] = [] + for item in result: + if not isinstance(item, dict): + continue + if "label" not in item or "region" not in item: + continue + region = item.get("region") + if req_type == 0: + flat = _flatten_bbox_region(region) + if flat is None: + continue + out_region: Any = flat + else: + out_region = region + normalized.append( + { + "label": item.get("label"), + "name": item.get("name") or item.get("label"), + "color": item.get("color") or "red", + "region": out_region, + } + ) + return sample.id, json.dumps(normalized, ensure_ascii=False), None + + start_time = time.time() + results: List[tuple[str, Optional[str], Optional[str]]] = [] + with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: + futures = [executor.submit(call_infer, s) for s in samples] + for fut in concurrent.futures.as_completed(futures): + results.append(fut.result()) + + ok_updates: List[AiAnnotateTaskSample] = [] + errors: List[Dict[str, Any]] = [] + now = datetime.now() + for sample_id, content, err in results: + if err or not content: + errors.append({"sample_id": sample_id, "error": err or "empty result"}) + continue + ok_updates.append( + AiAnnotateTaskSample( + id=sample_id, + annotation_content=content, + status="02", + annotation_time=now, + ) + ) + + if ok_updates: + AiAnnotateTaskSample.objects.bulk_update(ok_updates, fields=["annotation_content", "status", "annotation_time"]) + print( + f"[auto-annotate] task_id={task_id} success={len(ok_updates)} failed={len(errors)} " + f"elapsed={round(time.time() - start_time, 3)}s" + ) + if errors: + print(f"[auto-annotate] task_id={task_id} errors={json.dumps(errors, ensure_ascii=False)}") + close_old_connections() + + +@api_view(["POST"]) +def create_annotate_task(request): + data = request.data + data["id"] = str(uuid.uuid4()) + data["status"] = "01" + data["creator"] = request.user.username if request.user.username else "creator" + data["create_time"] = datetime.now() + if _is_int_one(data.get("task_type")): + if _is_int_one(data.get("region_type")): + data["annotate_type"] = "03" + else: + data["annotate_type"] = "01" + serializer = AiAnnotateTaskSerializer(data=data, context={"request": request}) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "任务创建成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def start_auto_annotate_task(request): + data = request.data + task_id = data.get("task_id") or data.get("id") + if not task_id: + return Response({"error": "缺少 task_id"}) + + task = get_object_or_404(AiAnnotateTask, id=task_id) + user = request.user.username or "creator" + + if not (task.task_team or task.task_leader): + return Response({"error": "未分配人员请进行分配"}) + + AiAnnotateTask.objects.filter(id=task_id).update(status="02", task_team=user, task_leader=user) + + label_content = _get_labels_json_from_task(task) + if not label_content: + return Response({"error": "任务未配置 labels 或 labels 格式非法"}) + + text_prompt = str(data.get("text_prompt") or data.get("prompt") or "").strip() + if not text_prompt: + text_prompt = _build_text_prompt_from_labels_json(label_content) + if not text_prompt: + return Response({"error": "缺少 text_prompt"}, status=status.HTTP_400_BAD_REQUEST) + + try: + req_type = int(data.get("type", int(task.region_type or "0"))) + except Exception: + req_type = int(task.region_type or "0") + + sample_count = AiAnnotateTaskSample.objects.filter(task_id=task_id).count() + if not sample_count: + return Response({"message": "当前任务没有样本", "success": 0, "failed": 0, "errors": []}) + + infer_url = getattr(settings, "ANNOTATION_SERVICE_INFER_URL", None) or "" + if not infer_url: + return Response({"error": "未配置 ANNOTATION_SERVICE_INFER_URL"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) + + score_threshold = float(getattr(settings, "AUTO_ANNOTATE_SCORE_THRESHOLD", 0.4)) + default_workers = int(getattr(settings, "AUTO_ANNOTATE_MAX_WORKERS", 2)) + max_workers = int(data.get("max_workers") or default_workers) + max_workers = max(1, min(32, max_workers)) + timeout_sec = float(data.get("timeout_sec") or 60) + t = threading.Thread( + target=_run_auto_annotate_task, + kwargs={ + "task_id": str(task_id), + "label_content": str(label_content), + "text_prompt": str(text_prompt), + "req_type": int(req_type), + "score_threshold": float(score_threshold), + "timeout_sec": float(timeout_sec), + "max_workers": int(max_workers), + "infer_url": str(infer_url), + }, + daemon=True, + ) + t.start() + return Response( + { + "message": "自动标注任务已后台启动", + "task_id": task_id, + "type": req_type, + "sample_count": sample_count, + "max_workers": max_workers, + "text_prompt": text_prompt, + "thread_name": t.name, + } + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_annotate_task(request): + task_id = request.GET.get("id", None) + task = get_object_or_404(AiAnnotateTask, id=task_id) + serializer = AiAnnotateTaskSerializer(task) + STATUSES = ["01", "02", "03", "04", "05", "06"] + sapmle_dict = {} + for status_code in STATUSES: + sapmle_dict[f"count_{status_code}"] = Sum( + Case(When(status=status_code, then=1), default=0, output_field=IntegerField()) + ) + sapmle_dict["total"] = Count("id") + task_status_counts = ( + AiAnnotateTaskSample.objects.values("task_id") + .annotate(**sapmle_dict) + .filter(task_id=task_id) + ) + serializer_data = serializer.data + serializer_data["task_status_counts"] = task_status_counts + return Response(serializer_data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_mytask_annotate_list(request): + executor = request.user.username + sql = """ + SELECT b.executor,a.task_name,a.task_type,a.region_type,a.id,a.status,TO_CHAR(a.create_time, 'YYYY-MM-DD') AS create_time, + COUNT(*) FILTER (WHERE b.status = '01') AS COUNT01, + COUNT(*) FILTER (WHERE b.status = '02') AS COUNT02, + COUNT(*) FILTER (WHERE b.status = '03') AS COUNT03, + COUNT(*) FILTER (WHERE b.status = '04') AS COUNT04, + COUNT(*) FILTER (WHERE b.status = '05') AS COUNT05, + COUNT(*) FILTER (WHERE b.status = '06') AS COUNT06, + COUNT(*) AS tota + FROM ai_annotate_task_sample b,ai_annotate_task a + WHERE a.id=b.task_id AND b.executor=%s and b.status in ('01','02','03','04','05','06') + GROUP BY b.executor,a.task_name,a.id,a.status,TO_CHAR(a.create_time, 'YYYY-MM-DD') + ORDER BY a.task_name + """ + with connection.cursor() as cursor: + cursor.execute(sql, [executor]) + result = cursor.fetchall() + keys = [ + "executor", + "task_name", + "task_type", + "region_type", + "id", + "status", + "create_time", + "COUNT01", + "COUNT02", + "COUNT03", + "COUNT04", + "COUNT05", + "COUNT06", + "total", + ] + data = [dict(zip(keys, row)) for row in result] + return Response(data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_mytask_audit_list(request): + executor = request.user.username + sql = """ + SELECT + b.executor, + a.task_name, + a.id, + a.status, + TO_CHAR(a.create_time, 'YYYY-MM-DD') AS create_time, + COUNT(*) FILTER (WHERE b.status = '01') AS COUNT01, + COUNT(*) FILTER (WHERE b.status = '02') AS COUNT02, + COUNT(*) FILTER (WHERE b.status = '03') AS COUNT03, + COUNT(*) FILTER (WHERE b.status = '04') AS COUNT04, + COUNT(*) FILTER (WHERE b.status = '05') AS COUNT05, + COUNT(*) FILTER (WHERE b.status = '06') AS COUNT06, + COUNT(*) AS total + FROM + ai_annotate_task_sample b, + ai_annotate_task a + WHERE + a.id=b.task_id AND a.task_leader=%s and b.status in ('01','02','03','04','05','06') + GROUP BY + b.executor, a.task_name, a.id, a.status, TO_CHAR(a.create_time, 'YYYY-MM-DD') + ORDER BY + a.task_name + """ + + with connection.cursor() as cursor: + cursor.execute(sql, [executor]) + result = cursor.fetchall() + + keys = [ + "executor", + "task_name", + "id", + "status", + "create_time", + "COUNT01", + "COUNT02", + "COUNT03", + "COUNT04", + "COUNT05", + "COUNT06", + "total", + ] + data = [dict(zip(keys, row)) for row in result] + return Response(data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_annotate_task(request): + data = request.data + task = get_object_or_404(AiAnnotateTask, id=data.get("id")) + task_type = data.get("task_type", task.task_type) + region_type = data.get("region_type", task.region_type) + if _is_int_one(task_type): + if _is_int_one(region_type): + data["annotate_type"] = "03" + else: + data["annotate_type"] = "01" + serializer = AiAnnotateTaskSerializer(instance=task, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "任务更新成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_annotate_task(request): + task_id = request.GET.get("id", None) + task = get_object_or_404(AiAnnotateTask, id=task_id) + task.delete() + return Response({"message": "任务删除成功"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_annotate_tasks(request): + algorithm_id = request.GET.get("algorithm_id", None) + status_code = request.GET.get("status", None) + custom1 = request.GET.get("custom1", None) + page_size = request.GET.get("page_size", 10) + filter_kwargs = {} + if algorithm_id: + filter_kwargs["algorithm_id"] = algorithm_id + if status_code: + filter_kwargs["status"] = status_code + if custom1: + filter_kwargs["custom1__contains"] = custom1 + + tasks = AiAnnotateTask.objects.filter(**filter_kwargs).order_by("task_name") + paginator = PageNumberPagination() + paginator.page_size = page_size + result_page = paginator.paginate_queryset(tasks, request) + serializer = AiAnnotateTaskSerializer(result_page, many=True) + + response_data = serializer.data + for task_data in response_data: + task_id = task_data["id"] + sample_count = AiAnnotateTaskSample.objects.filter(task_id=task_id).count() + audited_count = AiAnnotateTaskSample.objects.filter( + task_id=task_id, status__in=["04", "06"] + ).count() + + task_data["sample_count"] = sample_count + task_data["audited_count"] = audited_count + task_data["complete_percent"] = ( + round(audited_count / sample_count, 2) if sample_count != 0 else 0 + ) + + return paginator.get_paginated_response(response_data) diff --git a/backend/apps/datasets/views/annotate_task_sample.py b/backend/apps/datasets/views/annotate_task_sample.py new file mode 100644 index 0000000..be7f063 --- /dev/null +++ b/backend/apps/datasets/views/annotate_task_sample.py @@ -0,0 +1,586 @@ +import os +import uuid +from datetime import datetime +import xml.etree.ElementTree as ET + +from django.db import transaction +from django.db.models import Case, Count, IntegerField, Q, Sum, When +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response +from rest_framework.utils import json + +from apps.common.serializers import AiAnnotateTaskSampleSerializer +from apps.common.utils.annotation_store import ( + get_dataset_sample_annotation_content, + split_dataset_sample_annotation_content, +) +from apps.core.models import ( + AiAnnotateTask, + AiAnnotateTaskSample, + AiDatasetSample, +) +from apps.datasets.dataset_stats import refresh_dataset_sample_counters +from apps.datasets.storage_service import download_sidecar_text, upload_sidecar_bytes + +def _safe_split_wh(custom1): + try: + w_str, h_str = str(custom1).lower().split("x") + return int(w_str), int(h_str) + except Exception: + return None, None + + +def _extract_sample_index(saved_filename): + stem = os.path.splitext(os.path.basename(str(saved_filename or "")))[0] + tail = stem.rsplit("-", 1)[-1] + if tail.isdigit(): + return int(tail) + return None + + +def _get_dataset_sample_summary_status(dataset_sample, task_sample_statuses): + if dataset_sample.rectangle or dataset_sample.polygon or "06" in task_sample_statuses: + return "02" + if task_sample_statuses: + return "01" + return "00" + + +def _refresh_dataset_sample_status(sample_ids): + if not sample_ids: + return + sample_ids = list({sample_id for sample_id in sample_ids if sample_id}) + samples = list( + AiDatasetSample.objects.filter(id__in=sample_ids).only("id", "status", "rectangle", "polygon") + ) + if not samples: + return + task_sample_status_map = {} + for sample_id, status_code in AiAnnotateTaskSample.objects.filter(sample_id__in=sample_ids).values_list( + "sample_id", "status" + ): + task_sample_status_map.setdefault(sample_id, set()).add(status_code) + objects_to_update = [] + for sample in samples: + new_status = _get_dataset_sample_summary_status( + sample, task_sample_status_map.get(sample.id, set()) + ) + if sample.status != new_status: + objects_to_update.append(AiDatasetSample(id=sample.id, status=new_status)) + if objects_to_update: + AiDatasetSample.objects.bulk_update(objects_to_update, fields=["status"]) + + +def _normalize_polygon_points(region): + if not isinstance(region, list): + return [] + pts = [] + for p in region: + if not isinstance(p, (list, tuple)) or len(p) != 2: + continue + try: + x = float(p[0]) + y = float(p[1]) + except Exception: + continue + pts.append([x, y]) + return pts if len(pts) >= 3 else [] + + +def _shape_key(label, points): + try: + norm = [(round(float(x), 2), round(float(y), 2)) for x, y in points] + except Exception: + return None + return str(label), json.dumps(norm, separators=(",", ":"), ensure_ascii=False) + + +def _is_polygon_items(items): + if not isinstance(items, list): + return False + for it in items: + if not isinstance(it, dict): + continue + region = it.get("region") + if isinstance(region, list) and region and isinstance(region[0], (list, tuple)): + return True + return False + + +def _bbox_key(item): + try: + label = str(item.get("label")) + region = item.get("region") + if not isinstance(region, list) or len(region) != 4: + return None + vals = [round(float(x), 2) for x in region] + return label, json.dumps(vals, separators=(",", ":"), ensure_ascii=False) + except Exception: + return None + + + +def _shapes_from_items(items): + if not isinstance(items, list): + return [] + shapes = [] + for it in items: + if not isinstance(it, dict): + continue + label = it.get("label") + if not label: + continue + points = _normalize_polygon_points(it.get("region")) + if not points: + continue + shapes.append( + { + "label": str(label), + "points": points, + "group_id": None, + "shape_type": "polygon", + "flags": {}, + "mask": None, + } + ) + return shapes + + +def _shapes_from_labelme(labelme_obj): + if not isinstance(labelme_obj, dict): + return [] + shapes_obj = labelme_obj.get("shapes") + if not isinstance(shapes_obj, list): + return [] + shapes = [] + for sh in shapes_obj: + if not isinstance(sh, dict): + continue + if sh.get("shape_type") != "polygon": + continue + label = sh.get("label") + points = _normalize_polygon_points(sh.get("points")) + if not label or not points: + continue + shapes.append( + { + "label": str(label), + "points": points, + "group_id": None, + "shape_type": "polygon", + "flags": {}, + "mask": None, + } + ) + return shapes + + +def _load_polygon_shapes_from_dataset_sample(dataset_sample): + content = get_dataset_sample_annotation_content(dataset_sample) + if not content: + return [] + try: + parsed = json.loads(content) + except Exception: + return [] + if isinstance(parsed, dict) and parsed.get("type") == "polygon": + try: + json_text = download_sidecar_text(dataset_sample.saved_path, dataset_sample.saved_filename, ".json") + if not json_text: + return [] + labelme_obj = json.loads(json_text) + except Exception: + return [] + return _shapes_from_labelme(labelme_obj) + if isinstance(parsed, list): + return _shapes_from_items(parsed) + return [] + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def set_person_task(request): + data = request.data + task_id = data["task_id"] + task = AiAnnotateTask.objects.get(id=task_id) + dataset_id = task.dataset_id + status_code = data.get("status") + task_range = task.task_range + + query_condition = Q(dataset_id=dataset_id) + if status_code: + query_condition &= Q(status=status_code) + all_samples = list(AiDatasetSample.objects.filter(query_condition).order_by("saved_filename")) + existing_sample_ids = set( + AiAnnotateTaskSample.objects.filter(task_id=task_id).values_list("sample_id", flat=True) + ) + if existing_sample_ids: + all_samples = [sample for sample in all_samples if sample.id not in existing_sample_ids] + if task_range: + start, end = map(int, task_range.split("-")) + all_samples = [ + sample + for sample in all_samples + if (sample_index := _extract_sample_index(sample.saved_filename)) is not None + and start <= sample_index <= end + ] + all_samples_length = len(all_samples) + rule_type = data["rule_type"] + task_rules = json.loads(data["task_rules"]) + total_percent = 0 + last_person = "" + for key, value in task_rules.items(): + person = key + if rule_type == "percent": + percent = value + total_percent += percent + last_person = person + num_samples_to_assign = int(all_samples_length * percent / 100) + create_person_samples(task_id, person, all_samples[:num_samples_to_assign]) + all_samples = all_samples[num_samples_to_assign:] + elif rule_type == "number": + number = value + create_person_samples(task_id, person, all_samples[:number]) + all_samples = all_samples[number:] + elif rule_type == "range": + start, end = map(int, value.split("-")) + matching_samples = [ + sample + for sample in all_samples + if (sample_index := _extract_sample_index(sample.saved_filename)) is not None + and start <= sample_index <= end + ] + create_person_samples(task_id, person, matching_samples) + all_samples = list(set(all_samples) - set(matching_samples)) + + if rule_type == "percent": + remaining_samples = len(all_samples) + if total_percent == 100 and remaining_samples > 0: + create_person_samples(task_id, last_person, all_samples) + + return Response({"message": "人员标注任务分配成功"}) + + +@transaction.atomic +def create_person_samples(task_id, person, assigned_samples): + if not assigned_samples: + return + objects_to_save = [ + AiAnnotateTaskSample( + id=str(uuid.uuid4()), + task_id=task_id, + executor=person, + sample_id=sample.id, + sample_path=sample.saved_path, + sample_name=sample.saved_filename, + status="01", + custom1=sample.custom1, + ) + for sample in assigned_samples + ] + AiAnnotateTaskSample.objects.bulk_create(objects_to_save) + _refresh_dataset_sample_status([sample.id for sample in assigned_samples]) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def annotate_task_sample(request): + data = request.data + sample_id = data.get("id") + hard = data.get("hard") + task_sample = get_object_or_404(AiAnnotateTaskSample, id=sample_id) + AiAnnotateTaskSample.objects.filter(id=sample_id).update( + annotation_content=data.get("annotation_content"), + status="02", + hard=hard, + annotation_time=datetime.now(), + ) + _refresh_dataset_sample_status([task_sample.sample_id]) + return Response({"message": "ok"}) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def submit_task_samples(request): + data = request.data + task_id = data.get("task_id") + person = data.get("person") + ids = [id for id in data.get("ids", "").split(",") if id] + + queryset = AiAnnotateTaskSample.objects.filter(task_id=task_id, executor=person, status="02") + + if ids: + queryset = queryset.filter(id__in=ids) + + sample_ids = list(queryset.values_list("sample_id", flat=True)) + queryset.update(status="03") + _refresh_dataset_sample_status(sample_ids) + + return Response({"message": "ok"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_task_sample(request): + sample_id = request.GET.get("id", None) + sample = get_object_or_404(AiAnnotateTaskSample, id=sample_id) + serializer = AiAnnotateTaskSampleSerializer(sample) + return Response(serializer.data) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def audit_task_samples(request): + data = request.data + task_id = data.get("task_id") + person = request.user.username + feedback = data.get("feedback") + status_code = data.get("status") + ids = [id for id in data.get("ids", "").split(",") if id] + + queryset = AiAnnotateTaskSample.objects.filter(task_id=task_id, status="03") + + if ids: + queryset = queryset.filter(id__in=ids) + + if status_code == "04": + sample_ids = list(queryset.values_list("sample_id", flat=True)) + queryset.update(status="04", auditor=person) + _refresh_dataset_sample_status(sample_ids) + elif status_code == "05": + sample_ids = list(queryset.values_list("sample_id", flat=True)) + queryset.update(status="05", auditor=person, feedback=feedback) + _refresh_dataset_sample_status(sample_ids) + + return Response({"message": "ok"}) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def merge_sample_content(request): + try: + data = request.data + task_id = data.get("task_id") + if not task_id: + return Response({"error": "缺少 task_id"}, status=status.HTTP_400_BAD_REQUEST) + datasettask = AiAnnotateTask.objects.get(id=task_id) + executor = data.get("executor", None) + filter_kwargs = {"task_id": task_id} + if executor: + filter_kwargs["executor"] = executor + filter_kwargs["status"] = "04" + queryset = AiAnnotateTaskSample.objects.filter(**filter_kwargs) + samples = list(queryset) + sample_ids = [sample.sample_id for sample in samples] + with transaction.atomic(): + for sample in samples: + if sample.annotation_content: + person_items = json.loads(sample.annotation_content) + dataset_sample = AiDatasetSample.objects.get(id=sample.sample_id) + if datasettask.annotate_type == "03" or _is_polygon_items(person_items): + annotation_fields = split_dataset_sample_annotation_content(sample.annotation_content) + AiDatasetSample.objects.filter(id=sample.sample_id).update( + rectangle=annotation_fields["rectangle"], + polygon=annotation_fields["polygon"], + ) + else: + dataset_annotation = get_dataset_sample_annotation_content(dataset_sample) + dataset_items = json.loads(dataset_annotation) if dataset_annotation else [] + merged_data = person_items + dataset_items + unique_annotations = {} + for item in merged_data: + key = _bbox_key(item) + if not key: + continue + if key not in unique_annotations: + unique_annotations[key] = item + final_merged_data = list(unique_annotations.values()) + imgwidth, imgheight = _safe_split_wh(sample.custom1) + xml_bytes = _build_voc_xml_bytes( + final_merged_data, + sample.sample_path, + sample.sample_name, + imgwidth, + imgheight, + ) + upload_sidecar_bytes( + sample.sample_path, + sample.sample_name, + ".xml", + xml_bytes, + "application/xml", + ) + merged_json = json.dumps(final_merged_data, ensure_ascii=False) + annotation_fields = split_dataset_sample_annotation_content(merged_json) + AiDatasetSample.objects.filter(id=sample.sample_id).update( + rectangle=annotation_fields["rectangle"], + polygon=annotation_fields["polygon"], + ) + + queryset.update(status="06") + _refresh_dataset_sample_status(sample_ids) + refresh_dataset_sample_counters(datasettask.dataset_id) + sample_count = AiAnnotateTaskSample.objects.filter(task_id=task_id).count() + audited_count = AiAnnotateTaskSample.objects.filter( + task_id=task_id, status__in=["04", "06"] + ).count() + if sample_count == audited_count: + AiAnnotateTask.objects.filter(id=task_id).update(status="03") + return Response({"message": "任务样本成功合并到数据集中"}) + except Exception as e: + return Response( + {"error": "合并入库失败", "details": str(e)}, + status=status.HTTP_500_INTERNAL_SERVER_ERROR, + ) + + +def _build_voc_xml_bytes(data, sample_path, filename, width, height): + normalized_path = str(sample_path or "").strip("/").replace("\\", "/") + image_path = f"{normalized_path}/{filename}" if normalized_path else filename + annotation = ET.Element("annotation") + + folder = ET.SubElement(annotation, "folder") + folder.text = normalized_path + + filename_elem = ET.SubElement(annotation, "filename") + filename_elem.text = filename + + path = ET.SubElement(annotation, "path") + path.text = image_path + + source = ET.SubElement(annotation, "source") + database = ET.SubElement(source, "database") + database.text = "Unknown" + + size = ET.SubElement(annotation, "size") + width_elem = ET.SubElement(size, "width") + width_elem.text = str(int(width) if width is not None else 0) + height_elem = ET.SubElement(size, "height") + height_elem.text = str(int(height) if height is not None else 0) + depth = ET.SubElement(size, "depth") + depth.text = "3" + + segmented = ET.SubElement(annotation, "segmented") + segmented.text = "0" + + for obj in data: + object_elem = ET.SubElement(annotation, "object") + + name = ET.SubElement(object_elem, "name") + name.text = obj["label"] + + pose = ET.SubElement(object_elem, "pose") + pose.text = "Unspecified" + + truncated = ET.SubElement(object_elem, "truncated") + truncated.text = "0" + + difficult = ET.SubElement(object_elem, "difficult") + difficult.text = "0" + + bndbox = ET.SubElement(object_elem, "bndbox") + + xmin, ymin, xmax, ymax = map(float, obj["region"]) + + xmin_elem = ET.SubElement(bndbox, "xmin") + xmin_elem.text = str(int(xmin)) + ymin_elem = ET.SubElement(bndbox, "ymin") + ymin_elem.text = str(int(ymin)) + xmax_elem = ET.SubElement(bndbox, "xmax") + xmax_elem.text = str(int(xmax)) + ymax_elem = ET.SubElement(bndbox, "ymax") + ymax_elem.text = str(int(ymax)) + + return ET.tostring(annotation, encoding="utf-8", xml_declaration=True) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def delete_task_samples(request): + data = request.data + task_id = data.get("task_id") + person = data.get("person") + ids = [id for id in data.get("ids", "").split(",") if id] + + try: + queryset = AiAnnotateTaskSample.objects.filter(task_id=task_id, executor=person) + if ids: + queryset = queryset.filter(id__in=ids) + samples = list(queryset) + except AiAnnotateTaskSample.DoesNotExist: + return Response({"message": "没有选择对应的样本数据！"}, status=status.HTTP_404_NOT_FOUND) + + with transaction.atomic(): + sample_ids = [sample.sample_id for sample in samples] + queryset.delete() + _refresh_dataset_sample_status(sample_ids) + + return Response({"message": "任务样本删除成功"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_task_samples(request): + task_id = request.GET.get("task_id", None) + executor = request.GET.get("executor", None) + status_code = request.GET.get("status", "") + status_in = status_code.split(",") if status_code else [] + sample_name = request.GET.get("sample_name", None) + is_hard = request.GET.get("hard", None) + page_size = request.GET.get("page_size", 10) + filter_kwargs = {} + if task_id: + filter_kwargs["task_id"] = task_id + if executor: + filter_kwargs["executor"] = executor + if status_code: + filter_kwargs["status__in"] = status_in + if sample_name: + filter_kwargs["sample_name__contains"] = sample_name + if is_hard: + filter_kwargs["hard"] = is_hard + + samples = AiAnnotateTaskSample.objects.filter(**filter_kwargs).order_by("sample_name") + paginator = PageNumberPagination() + paginator.page_size = page_size + result_page = paginator.paginate_queryset(samples, request) + formatted_samples = [] + for sample in result_page: + sample.annotation_time = ( + sample.annotation_time.strftime("%Y-%m-%d %H:%M:%S") + if sample.annotation_time + else None + ) + formatted_samples.append(sample) + serializer = AiAnnotateTaskSampleSerializer(formatted_samples, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def query_person_tasks(request): + task_id = request.GET.get("task_id", None) + STATUSES = ["01", "02", "03", "04", "05", "06"] + + annotate_dict = {} + for status_code in STATUSES: + annotate_dict[f"count_{status_code}"] = Sum( + Case(When(status=status_code, then=1), default=0, output_field=IntegerField()) + ) + + annotate_dict["total"] = Count("id") + + executor_status_counts = ( + AiAnnotateTaskSample.objects.values("executor") + .annotate(**annotate_dict) + .filter(task_id=task_id) + .order_by("executor") + ) + + return Response(executor_status_counts) diff --git a/backend/apps/datasets/views/dataset.py b/backend/apps/datasets/views/dataset.py new file mode 100644 index 0000000..80da989 --- /dev/null +++ b/backend/apps/datasets/views/dataset.py @@ -0,0 +1,1101 @@ +import json +import io +import logging +import os +import tarfile +import threading +import time +import uuid +import zipfile +from datetime import datetime +import xml.etree.ElementTree as ET +from typing import List + +from django.conf import settings +from django.db import close_old_connections, connection, transaction +from django.db.models import Case, Count, IntegerField, Max, Q, Sum, When +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, parser_classes, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.parsers import MultiPartParser +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AiDatasetSerializer, AiDatasetSampleSerializer +from apps.core.models import AiDataset +from apps.core.models import ( + AiAlgorithmTrainRecords, + AiDatasetSample, + AiAnnotateTask, + AiTempFile, +) +from apps.common.minio_client import minio_storage +from apps.common.utils.annotation_store import split_dataset_sample_annotation_content +from apps.datasets.dataset_stats import refresh_dataset_sample_counters +from apps.datasets.storage_service import ( + VIDEO_EXTENSIONS, + copy_minio_object_to_dataset_result, + copy_minio_object_to_dataset, + dataset_type_accepts_extension, + extract_video_frames_to_dataset_result, + extract_video_frames_to_dataset, + import_zip_bytes_to_dataset_result, + import_zip_bytes_to_dataset, + save_bytes_to_dataset, + upload_sidecar_bytes, +) + + +IMAGE_EXTENSIONS = ("jpg", "jpeg", "png", "bmp", "gif") +logger = logging.getLogger(__name__) + + +def _build_filespace_url(rel_path: str) -> str: + base = str(getattr(settings, "IMAGE_BASE_URL", "") or "").rstrip("/") + rel = str(rel_path or "").lstrip("/").replace("\\", "/") + return f"{base}/{rel}" if base else rel + + +def _strip_nginx_image_prefix(raw: str) -> str: + s = str(raw or "").strip().replace("\\", "/") + if not s: + return "" + if "://" not in s: + return s.lstrip("/") + try: + from urllib.parse import urlsplit + + parts = urlsplit(s) + path = (parts.path or "").lstrip("/") + except Exception: + idx = s.find("://") + path = s[idx + 3 :] + slash = path.find("/") + path = path[slash + 1 :] if slash >= 0 else "" + path = path.lstrip("/") + if path.startswith("image/"): + path = path[len("image/") :] + return path.lstrip("/") + + +def _videos_base_rel(videos_dir_rel: str) -> str: + s = str(videos_dir_rel or "").strip().strip("/").replace("\\", "/") + return f"{s}/" if s else "" + + +def _safe_load_json_list(raw: str) -> List[dict]: + if not raw: + return [] + s = str(raw).strip() + if not s.startswith("["): + return [] + try: + obj = json.loads(s) + except Exception: + return [] + if isinstance(obj, list): + return [x for x in obj if isinstance(x, dict)] + return [] + + +def _safe_load_json_value(raw, default): + if raw in (None, ""): + return default + if isinstance(raw, (dict, list)): + return raw + try: + return json.loads(raw) + except Exception: + return default + + +def _find_dataset_sample_for_xml(dataset_id: str, image_name: str, xml_name: str): + queryset = AiDatasetSample.objects.filter(dataset_id=dataset_id).order_by("-create_time") + candidates = [] + for name in (image_name, os.path.basename(image_name or ""), xml_name): + s = str(name or "").strip() + if s: + candidates.append(os.path.basename(s)) + + for candidate in candidates: + exact = queryset.filter(Q(original_filename=candidate) | Q(saved_filename=candidate)).first() + if exact: + return exact + + stems = {os.path.splitext(name)[0].lower() for name in candidates if name} + if not stems: + return None + + for sample in queryset: + original_stem = os.path.splitext(str(sample.original_filename or ""))[0].lower() + saved_stem = os.path.splitext(str(sample.saved_filename or ""))[0].lower() + if original_stem in stems or saved_stem in stems: + return sample + return None + + +def _parse_voc_xml_annotations(file_content: bytes, dataset_labels) -> tuple[str, list[dict]]: + root = ET.fromstring(file_content) + image_name = root.findtext("filename") or "" + label_config = _safe_load_json_value(dataset_labels, []) + label_map = {} + for label in label_config if isinstance(label_config, list) else []: + if not isinstance(label, dict): + continue + code = str(label.get("label") or "").strip() + name = str(label.get("name") or code).strip() + if code: + label_map[code] = { + "label": code, + "name": name or code, + "color": label.get("color") or "#409eff", + } + + result_list = [] + for obj in root.findall("object"): + obj_name = str(obj.findtext("name") or "").strip() + bndbox = obj.find("bndbox") + if not obj_name or bndbox is None: + continue + xmin = bndbox.findtext("xmin") + ymin = bndbox.findtext("ymin") + xmax = bndbox.findtext("xmax") + ymax = bndbox.findtext("ymax") + if None in {xmin, ymin, xmax, ymax}: + continue + matched_label = label_map.get( + obj_name, + {"label": obj_name, "name": obj_name, "color": "#409eff"}, + ) + result_list.append( + { + "label": matched_label["label"], + "name": matched_label["name"], + "color": matched_label["color"], + "region": [xmin, ymin, xmax, ymax], + } + ) + return image_name, result_list + + +def _load_temp_file_imported_dataset_ids(raw: str) -> list[str]: + return [item for item in str(raw or "").split(",") if item] + + +def _dump_temp_file_imported_dataset_ids(dataset_ids: list[str]) -> str: + return ",".join(dict.fromkeys([item for item in dataset_ids if item])) + + +def _compact_temp_import_summary(payload: dict, max_length: int = 1000) -> str: + candidate = dict(payload or {}) + for limit in (5, 3, 1, 0): + shrunk = dict(candidate) + if isinstance(shrunk.get("failures"), list): + failures = shrunk["failures"][:limit] + shrunk["failures"] = failures + shrunk["failures_truncated"] = len(candidate.get("failures", [])) > len(failures) + if isinstance(shrunk.get("duplicates"), list): + duplicates = shrunk["duplicates"][:limit] + shrunk["duplicates"] = duplicates + shrunk["duplicates_truncated"] = len(candidate.get("duplicates", [])) > len(duplicates) + text = json.dumps(shrunk, ensure_ascii=False, separators=(",", ":")) + if len(text) <= max_length: + return text + return json.dumps({"status": payload.get("status"), "message": payload.get("message")}, ensure_ascii=False)[:max_length] + + +def _normalize_temp_file_ids(data) -> list[str]: + raw_values = [] + if hasattr(data, "getlist"): + raw_values.extend(data.getlist("temp_file_ids")) + temp_file_ids = data.get("temp_file_ids") + if isinstance(temp_file_ids, (list, tuple)): + raw_values.extend(temp_file_ids) + elif temp_file_ids not in (None, ""): + raw_values.extend(str(temp_file_ids).split(",")) + temp_file_id = data.get("temp_file_id") + if temp_file_id not in (None, ""): + raw_values.append(temp_file_id) + + normalized = [] + seen = set() + for item in raw_values: + value = str(item or "").strip() + if not value or value in seen: + continue + normalized.append(value) + seen.add(value) + return normalized + + +def _import_single_temp_file_to_dataset(dataset: AiDataset, temp_file: AiTempFile, interval_seconds: int, max_frames: int) -> tuple[int, dict]: + dataset_id = str(dataset.id) + with transaction.atomic(): + bucket_name = temp_file.bucket_name or "tempfile" + object_name = temp_file.object_name + if not object_name: + return status.HTTP_400_BAD_REQUEST, {"error": "临时文件对象不存在"} + + info = minio_storage.get_object_info(bucket_name, object_name) + filename = temp_file.original_name or os.path.basename(object_name) + original_type = str(temp_file.original_type or "") + imported_dataset_ids = _load_temp_file_imported_dataset_ids(temp_file.custom3) + if dataset_id in imported_dataset_ids: + latest_summary = _safe_load_json_value(temp_file.custom2, {}) + return status.HTTP_409_CONFLICT, { + "error": "该临时文件已导入过当前数据集，无需重复导入", + "latest_import": latest_summary, + } + + source_marker_prefix = f"temp:{temp_file.id}" + result = {"imported": [], "skipped": [], "failed": []} + + if original_type == "04": + zip_bytes = minio_storage.download_bytes(bucket_name, object_name) + result = import_zip_bytes_to_dataset_result( + dataset=dataset, + zip_bytes=zip_bytes, + source_marker_prefix=source_marker_prefix, + skip_existing=True, + ) + elif dataset.dataset_type == "02" and original_type == "02": + video_bytes = minio_storage.download_bytes(bucket_name, object_name) + result = extract_video_frames_to_dataset_result( + dataset=dataset, + video_bytes=video_bytes, + original_filename=filename, + interval_seconds=interval_seconds, + max_frames=max_frames, + source_marker_prefix=source_marker_prefix, + skip_existing=True, + ) + else: + ext = os.path.splitext(filename)[1].lower() + if not dataset_type_accepts_extension(dataset.dataset_type, ext): + return status.HTTP_400_BAD_REQUEST, {"error": "临时文件类型与数据集类型不匹配"} + result = copy_minio_object_to_dataset_result( + dataset=dataset, + source_bucket=bucket_name, + source_object=object_name, + original_filename=filename, + size_bytes=info.get("size", 0), + source_marker=f"{source_marker_prefix}:object", + skip_existing=True, + ) + + imported_samples = result["imported"] + duplicate_items = result["skipped"] + failed_items = result["failed"] + imported_count = len(imported_samples) + duplicate_count = len(duplicate_items) + failed_count = len(failed_items) + + if imported_count > 0: + refresh_dataset_sample_counters(dataset_id) + + summary_status = "success" + if imported_count > 0 and failed_count > 0: + summary_status = "partial" + elif imported_count == 0 and duplicate_count > 0 and failed_count == 0: + summary_status = "duplicate" + elif imported_count == 0 and failed_count > 0: + summary_status = "failed" + + summary_payload = { + "status": summary_status, + "dataset_id": dataset_id, + "temp_file_id": temp_file.id, + "bucket_name": bucket_name, + "object_name": object_name, + "filename": filename, + "original_type": original_type, + "etag": info.get("etag"), + "imported_count": imported_count, + "duplicate_count": duplicate_count, + "failed_count": failed_count, + "duplicates": duplicate_items, + "failures": failed_items, + } + temp_file.custom1 = summary_status + temp_file.custom2 = _compact_temp_import_summary(summary_payload) + if imported_count > 0 and failed_count == 0: + imported_dataset_ids.append(dataset_id) + temp_file.custom3 = _dump_temp_file_imported_dataset_ids(imported_dataset_ids) + if imported_count > 0: + temp_file.status = "02" + temp_file.save(update_fields=["status", "custom1", "custom2", "custom3"]) + + if failed_items: + logger.warning("temp file import partial/failed: %s", summary_payload) + else: + logger.info("temp file import success: %s", summary_payload) + + if imported_count == 0 and duplicate_count > 0 and failed_count == 0: + return status.HTTP_409_CONFLICT, { + "message": "临时文件已导入过当前数据集，本次未新增样本", + "imported_count": 0, + "duplicate_count": duplicate_count, + "failed_count": 0, + "duplicates": duplicate_items, + "failures": [], + "sample_ids": [], + } + if imported_count == 0 and failed_count > 0: + return status.HTTP_400_BAD_REQUEST, { + "error": "未导入任何样本，请检查文件类型与数据集类型是否匹配", + "imported_count": 0, + "duplicate_count": duplicate_count, + "failed_count": failed_count, + "duplicates": duplicate_items, + "failures": failed_items, + "sample_ids": [], + } + + message = "临时文件导入数据集成功" + if failed_count > 0: + message = f"临时文件导入部分成功，成功{imported_count}条，失败{failed_count}条" + elif duplicate_count > 0: + message = f"临时文件导入完成，新增{imported_count}条，跳过重复{duplicate_count}条" + return status.HTTP_200_OK, { + "message": message, + "imported_count": imported_count, + "duplicate_count": duplicate_count, + "failed_count": failed_count, + "duplicates": duplicate_items, + "failures": failed_items, + "sample_ids": [sample.id for sample in imported_samples], + } + + +def _load_video_manifest(raw: str) -> tuple[str, List[dict]]: + if not raw: + return "", [] + s = str(raw).strip() + if not s: + return "", [] + if s.startswith("{"): + try: + obj = json.loads(s) + except Exception: + return "", [] + if not isinstance(obj, dict): + return "", [] + base = str(obj.get("b") or obj.get("base") or "") + items = obj.get("v") or obj.get("videos") or [] + if isinstance(items, list): + vids = [x for x in items if isinstance(x, dict)] + else: + vids = [] + return base, vids + if s.startswith("["): + try: + obj = json.loads(s) + except Exception: + return "", [] + if not isinstance(obj, list): + return "", [] + vids = [x for x in obj if isinstance(x, dict)] + return "", vids + return "", [] + + +def _dump_video_manifest(base_url: str, videos: List[dict], max_len: int = 1000) -> str: + b = str(base_url or "") + keep = list(videos or []) + while True: + payload = {"b": b, "v": keep} + s = json.dumps(payload, ensure_ascii=False, separators=(",", ":")) + if len(s) <= max_len: + return s + if len(keep) <= 1: + break + keep = keep[-max(1, len(keep) // 2) :] + payload = {"b": b, "v": keep} + s = json.dumps(payload, ensure_ascii=False, separators=(",", ":")) + if len(s) <= max_len: + return s + overhead = len(json.dumps({"b": "", "v": keep}, ensure_ascii=False, separators=(",", ":"))) + allow_b = max(0, max_len - overhead) + b2 = b[:allow_b] + return json.dumps({"b": b2, "v": keep}, ensure_ascii=False, separators=(",", ":")) + + +def _get_videos_dir_rel(dataset: AiDataset) -> str: + if dataset.custom3: + return dataset.custom3 + base_dir = os.path.dirname((dataset.original_file_path or "").rstrip("/\\")) + return os.path.join(base_dir, "video").replace("\\", "/") if base_dir else "" + + +def _build_dataset_root(dataset_code: str) -> str: + return str(dataset_code or "").strip().replace("\\", "/").strip("/") + + +def _build_dataset_paths(dataset_code: str) -> dict: + root = _build_dataset_root(dataset_code) + return { + "original_file_path": os.path.join(root, "file").replace("\\", "/"), + "dataset_path": os.path.join(root, "image").replace("\\", "/"), + "custom1": os.path.join(root, "image_mask").replace("\\", "/"), + "custom3": os.path.join(root, "video").replace("\\", "/"), + } + + +def _generate_next_dataset_code() -> str: + # Lock the dataset table so concurrent creates cannot generate the same code. + with connection.cursor() as cursor: + cursor.execute("LOCK TABLE ai_dataset IN EXCLUSIVE MODE") + cursor.execute( + """ + SELECT COALESCE(MAX(CAST(dataset_code AS INTEGER)), 0) + FROM ai_dataset + WHERE dataset_code ~ '^[0-9]+$' + """ + ) + next_index = int((cursor.fetchone() or [0])[0] or 0) + 1 + return f"{next_index:06d}" + + +def _ensure_video_storage(dataset: AiDataset) -> str: + videos_dir_rel = _get_videos_dir_rel(dataset) + if not videos_dir_rel: + return "" + if dataset.custom3 != videos_dir_rel: + dataset.custom3 = videos_dir_rel + dataset.save(update_fields=["custom3"]) + if not (dataset.custom2 or "").strip(): + dataset.custom2 = _dump_video_manifest(_videos_base_rel(videos_dir_rel), [], max_len=1000) + dataset.save(update_fields=["custom2"]) + return videos_dir_rel + + +def _extract_sample_index(saved_filename: str) -> int | None: + stem = os.path.splitext(os.path.basename(str(saved_filename or "")))[0] + tail = stem.rsplit("-", 1)[-1] + if tail.isdigit(): + return int(tail) + return None + + +def _next_sample_index(dataset_id: str) -> int: + max_saved_filename = AiDatasetSample.objects.filter(dataset_id=dataset_id).aggregate( + max_saved_filename=Max("saved_filename") + )["max_saved_filename"] + current_index = _extract_sample_index(max_saved_filename) + return (current_index or 0) + 1 + + +def _generate_images_from_videos(dataset_id: str, frame_interval: int) -> None: + close_old_connections() + try: + import cv2 + except Exception as e: + print(f"[video->images] cv2 not available: {e}") + close_old_connections() + return + + dataset = AiDataset.objects.filter(id=dataset_id).first() + if not dataset: + close_old_connections() + return + if dataset.custom2 and not str(dataset.custom2).strip().startswith(("[", "{")) and not dataset.custom3: + dataset.custom3 = str(dataset.custom2).strip() + dataset.custom2 = "" + dataset.save(update_fields=["custom2", "custom3"]) + + videos_dir_rel = _get_videos_dir_rel(dataset) + if not videos_dir_rel: + print(f"[video->images] dataset_id={dataset_id} no videos dir") + close_old_connections() + return + + interval = int(frame_interval) if int(frame_interval) > 0 else 1 + base_url = _videos_base_rel(videos_dir_rel) + _, raw_items = _load_video_manifest(dataset.custom2 or "") + videos: List[dict] = [] + for it in raw_items: + if not isinstance(it, dict): + continue + filename = it.get("f") or it.get("filename") + if not filename and it.get("url"): + filename = os.path.basename(str(it.get("url") or "")) + if not filename and it.get("rel_path"): + filename = os.path.basename(str(it.get("rel_path") or "")) + filename = str(filename or "").strip() + if not filename: + continue + g = it.get("g") + if g is None: + g = it.get("generated") + videos.append( + { + "f": filename, + "g": 1 if bool(g) else 0, + "t": it.get("t") or it.get("generated_at"), + "i": it.get("i") or it.get("frame_interval"), + "n": it.get("n") or it.get("generated_images"), + } + ) + pending = [v for v in videos if int(v.get("g") or 0) == 0] + if not pending: + print(f"[video->images] dataset_id={dataset_id} no pending videos") + close_old_connections() + return + + total_written = 0 + start = time.time() + + now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + for v in pending: + filename = str(v.get("f") or "").strip() + if not filename: + continue + video_object_name = f"{str(videos_dir_rel).strip('/').replace('\\', '/')}/{filename}".strip("/") + if not minio_storage.object_exists("dataset", video_object_name): + print(f"[video->images] missing object: {video_object_name}") + continue + try: + video_bytes = minio_storage.download_bytes("dataset", video_object_name) + result = extract_video_frames_to_dataset_result( + dataset=dataset, + video_bytes=video_bytes, + original_filename=filename, + interval_seconds=interval, + source_marker_prefix=f"video:{dataset_id}:{filename}", + skip_existing=True, + ) + except Exception as exc: + print(f"[video->images] process failed: {video_object_name}, error={exc}") + continue + written_for_video = len(result.get("imported") or []) + total_written += written_for_video + v["g"] = 1 + v["t"] = now_str + v["i"] = interval + v["n"] = int(written_for_video) + + total_count, annotated_count = refresh_dataset_sample_counters(dataset_id) + dataset.dataset_count = total_count + dataset.annotated_count = annotated_count + dataset.custom2 = _dump_video_manifest(base_url, videos, max_len=1000) + dataset.save(update_fields=["dataset_count", "annotated_count", "custom2"]) + print(f"[video->images] dataset_id={dataset_id} frames={total_written} elapsed={round(time.time()-start,3)}s") + close_old_connections() + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def generate_dataset_images(request): + data = request.data + dataset_id = data.get("id") or data.get("dataset_id") + if not dataset_id: + return Response({"error": "缺少数据集 id"}, status=status.HTTP_400_BAD_REQUEST) + dataset = get_object_or_404(AiDataset, id=dataset_id) + if dataset.dataset_type != "03": + return Response({"error": "当前数据集不是视频类型"}, status=status.HTTP_400_BAD_REQUEST) + _, videos = _load_video_manifest(dataset.custom2 or "") + pending = [v for v in videos if not bool(v.get("g") or v.get("generated"))] + if not pending: + return Response({"message": "没有需要生成图片的视频", "dataset_id": dataset_id, "pending": 0}) + frame_interval = int(getattr(settings, "VIDEO_FRAME_INTERVAL", 30)) + t = threading.Thread( + target=_generate_images_from_videos, + kwargs={"dataset_id": str(dataset_id), "frame_interval": int(frame_interval)}, + daemon=True, + ) + t.start() + return Response( + { + "message": "生成图片任务已后台启动", + "dataset_id": dataset_id, + "frame_interval": frame_interval, + "pending": len(pending), + "thread_name": t.name, + } + ) + + +@api_view(["POST"]) +@parser_classes([MultiPartParser]) +@transaction.atomic +def upload_dataset_file(request): + data = request.data + dataset = AiDataset.objects.get(id=data.get("id")) + uploadfiles = request.FILES.getlist("uploadfiles[]") + dataset_type = str(data.get("dataset_type") or dataset.dataset_type or "") + if "uploadxml" not in data: + imported_count = 0 + uploadfilenames = [] + interval_seconds = int(data.get("interval_seconds") or 1) + max_frames = int(data.get("max_frames") or 200) + for uploadfile in uploadfiles: + filename = uploadfile.name + uploadfilenames.append(filename) + ext = os.path.splitext(filename)[1].lower() + file_bytes = uploadfile.read() + uploadfile.seek(0) + if ext == ".zip": + imported = import_zip_bytes_to_dataset(dataset, file_bytes) + imported_count += len(imported) + continue + if dataset_type == "02" and ext in VIDEO_EXTENSIONS: + imported = extract_video_frames_to_dataset( + dataset, + file_bytes, + filename, + interval_seconds=interval_seconds, + max_frames=max_frames, + ) + imported_count += len(imported) + continue + if dataset_type_accepts_extension(dataset_type, ext): + save_bytes_to_dataset( + dataset=dataset, + original_filename=filename, + data=file_bytes, + content_type=uploadfile.content_type, + ) + imported_count += 1 + + total_count, annotated_count = refresh_dataset_sample_counters(data.get("id")) + dataset.dataset_count = total_count + dataset.annotated_count = annotated_count + existing_original_names = dataset.original_file_name + joined_upload_names = ",".join(uploadfilenames) + dataset.original_file_name = ( + f"{existing_original_names},{joined_upload_names}" + if existing_original_names and joined_upload_names + else (existing_original_names or joined_upload_names) + ) + dataset.save(update_fields=["dataset_count", "annotated_count", "original_file_name"]) + return Response({"message": f"文件上传成功，共导入了{imported_count}条数据！"}) + + uploadfiles = request.FILES.getlist("uploadfiles[]") + dataset_type = str(data.get("dataset_type") or dataset.dataset_type or "") + uploadfilenames = [] + video_items: List[dict] = [] + videos_dir_rel = "" + videos_base_url = "" + if dataset_type == "03": + if dataset.custom2 and not str(dataset.custom2).strip().startswith(("[", "{")) and not dataset.custom3: + dataset.custom3 = str(dataset.custom2).strip() + dataset.custom2 = "" + dataset.save(update_fields=["custom2", "custom3"]) + videos_dir_rel = _ensure_video_storage(dataset) + videos_base_url = _videos_base_rel(videos_dir_rel) if videos_dir_rel else "" + base_existing, raw_items = _load_video_manifest(dataset.custom2 or "") + if base_existing: + videos_base_url = _strip_nginx_image_prefix(base_existing) + for it in raw_items: + if not isinstance(it, dict): + continue + filename = it.get("f") or it.get("filename") + if not filename and it.get("url"): + filename = os.path.basename(str(it.get("url") or "")) + if not filename and it.get("rel_path"): + filename = os.path.basename(str(it.get("rel_path") or "")) + filename = str(filename or "").strip() + if not filename: + continue + g = it.get("g") + if g is None: + g = it.get("generated") + video_items.append({"f": filename, "g": 1 if bool(g) else 0, "t": it.get("t") or it.get("generated_at")}) + + def save_single_xml_file(uploadfile): + file_content = uploadfile.read() + uploadfile.seek(0) + try: + image_name, result_list = _parse_voc_xml_annotations(file_content, dataset.dataset_labels) + except Exception as exc: + return { + "ok": False, + "name": uploadfile.name, + "reason": f"XML解析失败: {exc}", + } + + dataset_sample = _find_dataset_sample_for_xml(data.get("id"), image_name, uploadfile.name) + if dataset_sample is None: + return { + "ok": False, + "name": uploadfile.name, + "reason": "未找到对应样本", + } + + annotation_json = json.dumps(result_list, ensure_ascii=False) + annotation_fields = split_dataset_sample_annotation_content(annotation_json) + upload_sidecar_bytes( + dataset_sample.saved_path, + dataset_sample.saved_filename, + ".xml", + file_content, + "application/xml", + ) + AiDatasetSample.objects.filter(id=dataset_sample.id).update( + rectangle=annotation_fields["rectangle"], + polygon=annotation_fields["polygon"], + status="02", + ) + return { + "ok": True, + "name": uploadfile.name, + "sample_id": dataset_sample.id, + } + + def process_compressed_file(sourcename, source): + if not source: + return + source_name = os.path.basename(sourcename) + source_ext = os.path.splitext(source_name)[1].lower() + if dataset_type == "03" and source_ext in VIDEO_EXTENSIONS and videos_dir_rel: + object_name = f"{str(videos_dir_rel).strip('/').replace('\\', '/')}/{source_name}".strip("/") + content_type = "video/mp4" if source_ext == ".mp4" else None + minio_storage.upload_bytes("dataset", object_name, source.read(), content_type=content_type) + video_items.append({"f": source_name, "g": 0}) + return + save_bytes_to_dataset( + dataset=dataset, + original_filename=source_name, + data=source.read(), + ) + + allowed_file_types = ["jpg", "png", "bmp", "mp3", "mp4", "soud", "txt", "csv"] + file_count = 0 + if "uploadxml" in data: + failures = [] + updated_ids = [] + for uploadfile in uploadfiles: + resultxml = save_single_xml_file(uploadfile) + if resultxml and resultxml.get("ok"): + file_count += 1 + updated_ids.append(resultxml.get("sample_id")) + uploadfilenames.append(uploadfile.name) + else: + failures.append(resultxml or {"name": uploadfile.name, "reason": "处理失败"}) + response_message = f"xml文件上传完成，成功更新{file_count}条样本" + if failures: + response_message += f"，失败{len(failures)}条" + return Response( + { + "message": response_message, + "updated_count": file_count, + "updated_sample_ids": updated_ids, + "failed_count": len(failures), + "failures": failures, + } + ) + for uploadfile in uploadfiles: + uploadfilenames.append(uploadfile.name) + file_bytes = uploadfile.read() + uploadfile.seek(0) + file_extension = os.path.splitext(uploadfile.name)[1].lower() + + if dataset_type != "01": + if file_extension == ".zip": + with zipfile.ZipFile(io.BytesIO(file_bytes), "r") as zip_file: + for file_info in zip_file.infolist(): + with zip_file.open(file_info.filename, "r", force_zip64=True) as source: + decoded_filename = source.name.encode("cp437").decode("gbk") + file_extension = decoded_filename.split(".")[-1].lower() + if file_extension in allowed_file_types: + file_count = file_count + 1 + process_compressed_file(decoded_filename, source) + elif file_extension == ".tar": + with tarfile.open(fileobj=io.BytesIO(file_bytes), mode="r:*") as tar_file: + for member in tar_file.getmembers(): + extracted = tar_file.extractfile(member) + if extracted is None: + continue + with extracted as source: + file_extension = member.name.split(".")[-1].lower() + if file_extension in allowed_file_types: + file_count = file_count + 1 + process_compressed_file(member.name, source) + else: + if file_extension.lstrip(".") in allowed_file_types: + file_count = file_count + 1 + if dataset_type == "03" and file_extension in VIDEO_EXTENSIONS and videos_dir_rel: + object_name = f"{str(videos_dir_rel).strip('/').replace('\\', '/')}/{uploadfile.name}".strip("/") + minio_storage.upload_bytes( + "dataset", + object_name, + file_bytes, + content_type=uploadfile.content_type or "application/octet-stream", + ) + video_items.append({"f": uploadfile.name, "g": 0}) + else: + save_bytes_to_dataset( + dataset=dataset, + original_filename=uploadfile.name, + data=file_bytes, + content_type=uploadfile.content_type, + ) + + total_count, annotated_count = refresh_dataset_sample_counters(data.get("id")) + dataset.dataset_count = total_count + dataset.annotated_count = annotated_count + existing_original_names = dataset.original_file_name + joined_upload_names = ",".join(uploadfilenames) + dataset.original_file_name = ( + f"{existing_original_names},{joined_upload_names}" if existing_original_names else joined_upload_names + ) + if dataset_type == "03": + dataset.custom2 = _dump_video_manifest(_strip_nginx_image_prefix(videos_base_url), video_items, max_len=1000) + dataset.save() + return Response({"message": f"文件上传成功，共上传了{file_count}条数据！"}) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def create_dataset(request): + data = request.data.copy() + data["id"] = str(uuid.uuid4()) + data["create_time"] = datetime.now() + data["status"] = "01" + data["creator"] = request.user.username or "creator" + new_dataset_code = _generate_next_dataset_code() + data["dataset_code"] = new_dataset_code + dataset_paths = _build_dataset_paths(new_dataset_code) + data["original_file_path"] = data.get("original_file_path") or dataset_paths["original_file_path"] + data["dataset_path"] = data.get("dataset_path") or dataset_paths["dataset_path"] + data["custom1"] = data.get("custom1") or dataset_paths["custom1"] + if data.get("dataset_type") == "03": + videos_rel = data.get("custom3") or dataset_paths["custom3"] + data["custom3"] = videos_rel + data["custom2"] = _dump_video_manifest(_videos_base_rel(videos_rel), [], max_len=1000) + serializer = AiDatasetSerializer(data=data, context={"request": request}) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "数据集创建成功"}) + return Response({"error": "无效的数据", "details": serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_dataset(request): + dataset_id = request.GET.get("id", None) + dataset = get_object_or_404(AiDataset, id=dataset_id) + serializer = AiDatasetSerializer(dataset) + + STATUSES = ["00", "01", "02"] + sample_stats = {} + for status_code in STATUSES: + sample_stats[f"count_{status_code}"] = Sum( + Case(When(status=status_code, then=1), default=0, output_field=IntegerField()) + ) + + sample_stats["total"] = Count("id") + + dataset_status_counts = ( + AiDatasetSample.objects.values("dataset_id").annotate(**sample_stats).filter(dataset_id=dataset_id) + ) + serializer_data = serializer.data + serializer_data["dataset_status_counts"] = dataset_status_counts + return Response(serializer_data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_dataset(request): + data = request.data.copy() + dataset = get_object_or_404(AiDataset, id=data.get("id")) + data.pop("dataset_code", None) + serializer = AiDatasetSerializer(instance=dataset, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "数据集更新成功"}) + return Response({"error": "无效的数据", "details": serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def delete_dataset(request): + dataset_id = request.GET.get("id", None) + if not dataset_id: + return Response({"error": "数据集 ID 不能为空"}, status=status.HTTP_400_BAD_REQUEST) + + task_count = AiAnnotateTask.objects.filter(dataset_id=dataset_id).count() + if task_count > 0: + return Response( + {"warn": "该数据集已经创建了样本标注任务，请先删除样本标注任务！"}, + status=status.HTTP_400_BAD_REQUEST, + ) + + train_count = AiAlgorithmTrainRecords.objects.filter(link_datasets__contains=dataset_id).count() + if train_count > 0: + return Response( + {"warn": "该数据集已经创建了算法训练任务，请先删除算法训练任务！"}, + status=status.HTTP_400_BAD_REQUEST, + ) + + try: + dataset = get_object_or_404(AiDataset, id=dataset_id) + AiDatasetSample.objects.filter(dataset_id=dataset_id).delete() + try: + objects = minio_storage.list_objects("dataset", prefix=dataset.dataset_path) + object_names = [obj["name"] for obj in objects if obj.get("name")] + if object_names: + minio_storage.delete_objects("dataset", object_names) + except Exception: + pass + dataset.delete() + return Response({"message": "数据集删除成功"}, status=status.HTTP_200_OK) + + except Exception as e: + return Response( + {"error": f"删除数据集失败: {str(e)}"}, status=status.HTTP_500_INTERNAL_SERVER_ERROR + ) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def delete_dataset_updatefile(request): + data = request.data + dataset = get_object_or_404(AiDataset, id=data.get("id")) + serializer = AiDatasetSerializer(instance=dataset, data=data, partial=True) + if serializer.is_valid(): + serializer.save() + return Response({"message": "上传数据文件删除成功"}) + return Response({"error": "无效的数据", "details": serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_datasets(request): + dataset_code = request.GET.get("dataset_code", None) + dataset_name = request.GET.get("dataset_name", None) + features = request.GET.get("features", None) + dataset_type = request.GET.get("dataset_type", None) + creator = request.GET.get("creator", None) + page_size = request.GET.get("page_size", None) + filter_kwargs = {} + if dataset_code: + filter_kwargs["dataset_code__contains"] = dataset_code + if dataset_name: + filter_kwargs["dataset_name__contains"] = dataset_name + if dataset_type: + filter_kwargs["dataset_type"] = dataset_type + if features: + filter_kwargs["features__contains"] = features + if creator: + filter_kwargs["creator"] = creator + + datasets = AiDataset.objects.filter(**filter_kwargs).order_by("dataset_code") + paginator = PageNumberPagination() + paginator.page_size = page_size or 10 + result_page = paginator.paginate_queryset(datasets, request) + serializer = AiDatasetSerializer(result_page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_dataset_list(request): + datasets = AiDataset.objects.all().order_by("dataset_code") + serializer = AiDatasetSerializer(datasets, many=True) + return Response(serializer.data) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def import_dataset_from_temp_file(request): + data = request.data + dataset_id = data.get("dataset_id") + temp_file_ids = _normalize_temp_file_ids(data) + if not dataset_id or not temp_file_ids: + return Response({"error": "缺少 dataset_id 或 temp_file_id(s)"}, status=status.HTTP_400_BAD_REQUEST) + + dataset = get_object_or_404(AiDataset, id=dataset_id) + interval_seconds = int(data.get("interval_seconds") or 1) + max_frames = int(data.get("max_frames") or 200) + + if len(temp_file_ids) == 1: + temp_file = get_object_or_404(AiTempFile, id=temp_file_ids[0]) + status_code, payload = _import_single_temp_file_to_dataset(dataset, temp_file, interval_seconds, max_frames) + return Response(payload, status=status_code) + + summary_results = [] + total_imported_count = 0 + total_duplicate_count = 0 + total_failed_count = 0 + success_temp_file_count = 0 + duplicate_temp_file_count = 0 + failed_temp_file_count = 0 + + temp_files = {str(item.id): item for item in AiTempFile.objects.filter(id__in=temp_file_ids)} + for temp_file_id in temp_file_ids: + temp_file = temp_files.get(str(temp_file_id)) + if not temp_file: + payload = {"error": f"临时文件不存在: {temp_file_id}"} + status_code = status.HTTP_404_NOT_FOUND + else: + try: + status_code, payload = _import_single_temp_file_to_dataset(dataset, temp_file, interval_seconds, max_frames) + except Exception as exc: + logger.exception("batch temp file import failed: dataset=%s temp_file=%s", dataset.id, temp_file_id) + status_code = status.HTTP_500_INTERNAL_SERVER_ERROR + payload = {"error": f"导入失败：{exc}"} + + imported_count = int(payload.get("imported_count") or 0) + duplicate_count = int(payload.get("duplicate_count") or 0) + failed_count = int(payload.get("failed_count") or 0) + result_status = "failed" + if status_code == status.HTTP_200_OK and failed_count > 0: + result_status = "partial" + elif status_code == status.HTTP_200_OK: + result_status = "success" + elif status_code == status.HTTP_409_CONFLICT: + result_status = "duplicate" + + if result_status in ("success", "partial"): + success_temp_file_count += 1 + elif result_status == "duplicate": + duplicate_temp_file_count += 1 + else: + failed_temp_file_count += 1 + + total_imported_count += imported_count + total_duplicate_count += duplicate_count + total_failed_count += failed_count + summary_results.append( + { + "temp_file_id": temp_file_id, + "filename": getattr(temp_file, "original_name", "") if temp_file else "", + "status": result_status, + **payload, + } + ) + + if success_temp_file_count > 0: + message = ( + f"同步导入完成，成功处理{success_temp_file_count}个临时文件，" + f"新增样本{total_imported_count}条，跳过重复{total_duplicate_count}条，失败{total_failed_count}条" + ) + response_status = status.HTTP_200_OK + elif duplicate_temp_file_count > 0 and failed_temp_file_count == 0: + message = "所选临时文件均已导入当前数据集，本次未新增样本" + response_status = status.HTTP_200_OK + else: + message = "同步导入失败，请检查临时文件类型与数据集类型是否匹配" + response_status = status.HTTP_400_BAD_REQUEST + + return Response( + { + "message": message, + "dataset_id": dataset.id, + "temp_file_count": len(temp_file_ids), + "success_temp_file_count": success_temp_file_count, + "duplicate_temp_file_count": duplicate_temp_file_count, + "failed_temp_file_count": failed_temp_file_count, + "imported_count": total_imported_count, + "duplicate_count": total_duplicate_count, + "failed_count": total_failed_count, + "results": summary_results, + }, + status=response_status, + ) diff --git a/backend/apps/datasets/views/dataset_sample.py b/backend/apps/datasets/views/dataset_sample.py new file mode 100644 index 0000000..a99eedc --- /dev/null +++ b/backend/apps/datasets/views/dataset_sample.py @@ -0,0 +1,217 @@ +import hashlib +import base64 +import mimetypes + +from django.db import transaction +from django.http import StreamingHttpResponse +from django.shortcuts import get_object_or_404, get_list_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AiDatasetSampleSerializer +from apps.common.minio_client import minio_storage +from apps.core.models import AiDataset, AiDatasetSample +from apps.common.utils.annotation_store import get_dataset_sample_annotation_content +from apps.datasets.dataset_stats import refresh_dataset_sample_counters +from apps.datasets.storage_service import DATASET_BUCKET, delete_sample_storage + +def _sample_object_name(sample: AiDatasetSample) -> str: + return f"{str(sample.saved_path or '').strip('/').replace('\\', '/')}/{sample.saved_filename}".strip("/") + + +def _stream_object(response, chunk_size: int = 1024 * 1024): + try: + while True: + chunk = response.read(chunk_size) + if not chunk: + break + yield chunk + finally: + response.close() + response.release_conn() + + +def _build_sample_stream_response(sample: AiDatasetSample, as_attachment: bool): + object_name = _sample_object_name(sample) + response = minio_storage.get_object(DATASET_BUCKET, object_name) + content_type = ( + mimetypes.guess_type(sample.original_filename or sample.saved_filename)[0] + or "application/octet-stream" + ) + filename = sample.original_filename or sample.saved_filename + stream = StreamingHttpResponse(_stream_object(response), content_type=content_type) + disposition = "attachment" if as_attachment else "inline" + stream["Content-Disposition"] = f'{disposition}; filename="{filename}"' + return stream + + +def _get_sample_md5(sample: AiDatasetSample) -> str | None: + object_name = _sample_object_name(sample) + try: + info = minio_storage.get_object_info(DATASET_BUCKET, object_name) + etag = str(info.get("etag") or "").strip('"') + if etag: + return etag + file_bytes = minio_storage.download_bytes(DATASET_BUCKET, object_name) + return hashlib.md5(file_bytes).hexdigest() + except Exception as e: + print(f"Error reading sample {sample.id} from MinIO: {e}") + return None + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_dataset_samples(request): + dataset_id = request.GET.get("dataset_id", None) + original_filename = request.GET.get("original_filename", None) + start_time = request.GET.get("start_time", None) + end_time = request.GET.get("end_time", None) + status = request.GET.get("status", None) + page_size = request.GET.get("page_size", None) + + filter_kwargs = {} + if dataset_id: + filter_kwargs["dataset_id"] = dataset_id + if original_filename: + filter_kwargs["original_filename__contains"] = original_filename + if start_time and end_time: + filter_kwargs["create_time__range"] = [start_time, end_time] + if start_time and end_time is None: + filter_kwargs["create_time__gte"] = start_time + if start_time is None and end_time: + filter_kwargs["create_time__lte"] = end_time + if status: + filter_kwargs["status"] = status + + samples = AiDatasetSample.objects.filter(**filter_kwargs).order_by("saved_filename") + + paginator = PageNumberPagination() + paginator.page_size = page_size or 10 + result_page = paginator.paginate_queryset(samples, request) + + serializer = AiDatasetSampleSerializer(result_page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def delete_invalid_samples(request): + dataset_id = request.GET.get("dataset_id", None) + sample_ids = request.GET.get("ids", None) + + if sample_ids is None: + return Response({"message": "未提供样本 IDs"}, status=400) + + sample_ids_list = [id for id in sample_ids.split(",")] + samples = get_list_or_404(AiDatasetSample, id__in=sample_ids_list) + + for sample in samples: + delete_sample_storage(sample.saved_path, sample.saved_filename) + sample.delete() + + refresh_dataset_sample_counters(dataset_id) + + return Response({"message": "无效样本删除成功！"}) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def delete_duplicate_samples(request): + dataset_id = request.GET.get("dataset_id", None) + if dataset_id is None: + return Response({"message": "未选择数据集"}, status=400) + + samples = AiDatasetSample.objects.filter(dataset_id=dataset_id).order_by("create_time", "id") + if not samples.exists(): + return Response({"message": "当前数据集没有样本，无需去重！"}) + + unique_images = {} + deleted_count = 0 + failed_samples = [] + for sample in samples: + md5_hash = _get_sample_md5(sample) + if not md5_hash: + failed_samples.append(sample.id) + continue + + if md5_hash in unique_images: + try: + failed_objects = delete_sample_storage(sample.saved_path, sample.saved_filename) + if failed_objects: + failed_samples.append(sample.id) + continue + sample.delete() + deleted_count += 1 + except Exception: + failed_samples.append(sample.id) + else: + unique_images[md5_hash] = sample.id + + refresh_dataset_sample_counters(dataset_id) + if failed_samples: + return Response( + { + "message": f'样本去重完成，共删除了"{deleted_count}"条重复数据，"{len(failed_samples)}"条样本处理失败！', + "failed_ids": failed_samples, + } + ) + return Response({"message": f'样本去重完成，共删除了"{deleted_count}"条重复数据！'}) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_dataset_sample(request): + data = request.data + sample = get_object_or_404(AiDatasetSample, id=data.get("id")) + serializer = AiDatasetSampleSerializer(instance=sample, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + refresh_dataset_sample_counters(sample.dataset_id) + return Response({"message": "样本更新成功"}) + + return Response( + {"error": "无效的数据", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_dataset_sample(request): + sample_id = request.GET.get("id", None) + sample = get_object_or_404(AiDatasetSample, id=sample_id) + object_name = _sample_object_name(sample) + file_bytes = minio_storage.download_bytes(DATASET_BUCKET, object_name) + content_type = ( + mimetypes.guess_type(sample.original_filename or sample.saved_filename)[0] + or "application/octet-stream" + ) + image_base64 = f"data:{content_type};base64,{base64.b64encode(file_bytes).decode('utf-8')}" + response_data = { + "id": sample.id, + "image_base64": image_base64, + "annotation_content": get_dataset_sample_annotation_content(sample), + } + return Response(response_data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def preview_dataset_sample(request): + sample_id = request.GET.get("id", None) + sample = get_object_or_404(AiDatasetSample, id=sample_id) + return _build_sample_stream_response(sample, as_attachment=False) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def download_dataset_sample(request): + sample_id = request.GET.get("id", None) + sample = get_object_or_404(AiDatasetSample, id=sample_id) + return _build_sample_stream_response(sample, as_attachment=True) diff --git a/backend/apps/datasets/views/temp_file.py b/backend/apps/datasets/views/temp_file.py new file mode 100644 index 0000000..5eb7212 --- /dev/null +++ b/backend/apps/datasets/views/temp_file.py @@ -0,0 +1,171 @@ +import mimetypes +import os +import uuid +from datetime import datetime + +from django.conf import settings +from django.shortcuts import get_object_or_404 +from django.http import StreamingHttpResponse +from rest_framework import status +from rest_framework.decorators import api_view, parser_classes, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.parsers import MultiPartParser +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.minio_client import minio_storage +from apps.common.serializers import AiTempFileSerializer +from apps.core.models import AiTempFile + + +TEMPFILE_BUCKET = "tempfile" +IMAGE_TYPES = {".jpg", ".jpeg", ".png", ".bmp", ".gif", ".webp", ".tif", ".tiff"} +VIDEO_TYPES = {".mp4", ".avi", ".mov", ".mkv", ".wmv", ".flv", ".mpeg", ".mpg", ".webm"} +AUDIO_TYPES = {".mp3", ".wav", ".aac", ".m4a", ".flac", ".ogg"} +TEXT_TYPES = {".txt", ".csv", ".json", ".xml"} + + +def _detect_original_type(filename: str) -> str: + ext = os.path.splitext(filename or "")[1].lower() + if ext in IMAGE_TYPES: + return "01" + if ext in VIDEO_TYPES: + return "02" + if ext in AUDIO_TYPES: + return "03" + if ext == ".zip": + return "04" + if ext in TEXT_TYPES: + return "05" + return "99" + + +def _stream_object(response, chunk_size: int = 1024 * 1024): + try: + while True: + chunk = response.read(chunk_size) + if not chunk: + break + yield chunk + finally: + response.close() + response.release_conn() + + +@api_view(["POST"]) +@parser_classes([MultiPartParser]) +@permission_classes([IsAuthenticated]) +def upload_temp_file(request): + uploaded_file = request.FILES.get("file") + if not uploaded_file: + return Response({"error": "缺少 file 字段"}, status=status.HTTP_400_BAD_REQUEST) + + file_id = str(uuid.uuid4()) + bucket_name = request.data.get("bucket_name") or TEMPFILE_BUCKET + prefix = request.data.get("prefix") or datetime.now().strftime("%Y-%m-%d") + object_name = request.data.get("object_name") or minio_storage.generate_object_name(uploaded_file.name, prefix) + content_type = uploaded_file.content_type or mimetypes.guess_type(uploaded_file.name)[0] or "application/octet-stream" + original_type = request.data.get("original_type") or _detect_original_type(uploaded_file.name) + + minio_storage.upload_stream( + bucket_name=bucket_name, + object_name=object_name, + data=uploaded_file.file, + length=uploaded_file.size, + content_type=content_type, + ) + + obj = AiTempFile.objects.create( + id=file_id, + original_path=request.data.get("original_path") or "", + original_name=uploaded_file.name, + original_type=original_type, + bucket_name=bucket_name, + object_name=object_name, + object_size=uploaded_file.size, + content_type=content_type, + status=request.data.get("status") or "01", + ) + serializer = AiTempFileSerializer(obj) + return Response({"message": "临时文件上传成功", "data": serializer.data}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def search_temp_files(request): + filter_kwargs = {} + original_name = request.GET.get("original_name") + original_type = request.GET.get("original_type") + status_code = request.GET.get("status") + page_size = request.GET.get("page_size") or 10 + + if original_name: + filter_kwargs["original_name__contains"] = original_name + if original_type: + filter_kwargs["original_type"] = original_type + if status_code: + filter_kwargs["status"] = status_code + + queryset = AiTempFile.objects.filter(**filter_kwargs).order_by("-create_time") + paginator = PageNumberPagination() + paginator.page_size = page_size + page = paginator.paginate_queryset(queryset, request) + serializer = AiTempFileSerializer(page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_temp_file(request): + temp_id = request.GET.get("id") + obj = get_object_or_404(AiTempFile, id=temp_id) + serializer = AiTempFileSerializer(obj) + return Response(serializer.data) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_temp_file(request): + data = request.data + obj = get_object_or_404(AiTempFile, id=data.get("id")) + serializer = AiTempFileSerializer(instance=obj, data=data, partial=True) + if serializer.is_valid(): + serializer.save() + return Response({"message": "临时文件更新成功"}) + return Response({"error": "无效的数据", "details": serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_temp_file(request): + temp_id = request.GET.get("id") + obj = get_object_or_404(AiTempFile, id=temp_id) + if obj.bucket_name and obj.object_name: + minio_storage.delete_object(obj.bucket_name, obj.object_name) + obj.delete() + return Response({"message": "临时文件删除成功"}) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def download_temp_file(request): + temp_id = request.GET.get("id") + obj = get_object_or_404(AiTempFile, id=temp_id) + response = minio_storage.get_object(obj.bucket_name, obj.object_name) + content_type = obj.content_type or "application/octet-stream" + filename = obj.original_name or os.path.basename(obj.object_name or "") + stream = StreamingHttpResponse(_stream_object(response), content_type=content_type) + stream["Content-Disposition"] = f'attachment; filename="{filename}"' + return stream + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def preview_temp_file(request): + temp_id = request.GET.get("id") + obj = get_object_or_404(AiTempFile, id=temp_id) + response = minio_storage.get_object(obj.bucket_name, obj.object_name) + content_type = obj.content_type or "application/octet-stream" + stream = StreamingHttpResponse(_stream_object(response), content_type=content_type) + stream["Content-Disposition"] = "inline" + return stream diff --git a/backend/apps/iam/__init__.py b/backend/apps/iam/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/iam/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/iam/apps.py b/backend/apps/iam/apps.py new file mode 100644 index 0000000..b867ebf --- /dev/null +++ b/backend/apps/iam/apps.py @@ -0,0 +1,7 @@ +from django.apps import AppConfig + + +class IamConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "apps.iam" + diff --git a/backend/apps/iam/views/__init__.py b/backend/apps/iam/views/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/iam/views/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/iam/views/group.py b/backend/apps/iam/views/group.py new file mode 100644 index 0000000..5850c79 --- /dev/null +++ b/backend/apps/iam/views/group.py @@ -0,0 +1,83 @@ +from django.db.models import Max +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AuthGroupSerializer +from apps.core.models import AuthGroup + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_group_list(request): + name = request.GET.get("name", None) + page_size = request.GET.get("page_size", None) + + filter_kwargs = {} + if name: + filter_kwargs["name"] = name + + groups = AuthGroup.objects.filter(**filter_kwargs).order_by("name") + + paginator = PageNumberPagination() + paginator.page_size = page_size or 10 + result_page = paginator.paginate_queryset(groups, request) + + serializer = AuthGroupSerializer(result_page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_group(request): + group_id = request.GET.get("id", None) + group = get_object_or_404(AuthGroup, id=group_id) + serializer = AuthGroupSerializer(group) + return Response(serializer.data) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def add_group(request): + data = request.data + max_id = AuthGroup.objects.aggregate(Max("id"))["id__max"] + data["id"] = (max_id + 1) if max_id is not None else 1 + serializer = AuthGroupSerializer(data=data) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "Group added successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_group(request): + data = request.data + group = get_object_or_404(AuthGroup, id=data.get("id")) + serializer = AuthGroupSerializer(instance=group, data=request.data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "Group updated successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_group(request): + group_id = request.GET.get("id", None) + group = get_object_or_404(AuthGroup, id=group_id) + group.delete() + return Response({"message": "Person deleted successfully"}) diff --git a/backend/apps/iam/views/organization.py b/backend/apps/iam/views/organization.py new file mode 100644 index 0000000..3f02847 --- /dev/null +++ b/backend/apps/iam/views/organization.py @@ -0,0 +1,71 @@ +import uuid + +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AuthOrganizationSerializer +from apps.core.models import AuthOrganization + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_organization_list(request): + organizations = AuthOrganization.objects.all() + serializer = AuthOrganizationSerializer(organizations, many=True) + return Response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_organization(request): + organization_id = request.GET.get("id", None) + organization = get_object_or_404(AuthOrganization, id=organization_id) + serializer = AuthOrganizationSerializer(organization) + return Response(serializer.data) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def add_organization(request): + data = request.data + data["id"] = str(uuid.uuid4()) + serializer = AuthOrganizationSerializer(data=request.data) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "Organization added successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_organization(request): + data = request.data + organization = get_object_or_404(AuthOrganization, id=data.get("id")) + serializer = AuthOrganizationSerializer( + instance=organization, data=request.data, partial=True + ) + if serializer.is_valid(): + serializer.save() + return Response({"message": "Organization updated successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_organization(request): + organization_id = request.GET.get("id", None) + organization = get_object_or_404(AuthOrganization, id=organization_id) + organization.delete() + return Response({"message": "Organization deleted successfully"}) diff --git a/backend/apps/iam/views/person.py b/backend/apps/iam/views/person.py new file mode 100644 index 0000000..6753ecc --- /dev/null +++ b/backend/apps/iam/views/person.py @@ -0,0 +1,127 @@ +import base64 +from datetime import datetime + +from django.conf import settings +from django.contrib.auth.hashers import make_password +from django.db.models import Max +from django.shortcuts import get_object_or_404 +from gmssl import sm4 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.pagination import PageNumberPagination +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response + +from apps.common.serializers import AuthPersonSerializer +from apps.core.models import AIAnnotateTeamMember, AuthPerson + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def get_person_list(request): + organization_id = request.GET.get("organization_id", None) + team_id = request.GET.get("team_id", None) + username = request.GET.get("username", None) + last_name = request.GET.get("last_name", None) + page_size = request.GET.get("page_size", None) + + team_members = AIAnnotateTeamMember.objects.filter(team_id=team_id) + member_accounts = [team_member.member_account for team_member in team_members] + + filter_kwargs = {} + if organization_id: + filter_kwargs["organization_id"] = organization_id + if username: + filter_kwargs["username__icontains"] = username + if last_name: + filter_kwargs["last_name__icontains"] = last_name + + persons = AuthPerson.objects.filter(**filter_kwargs).order_by("id") + if member_accounts: + persons = persons.exclude(username__in=member_accounts) + + paginator = PageNumberPagination() + paginator.page_size = page_size or 10 + + result_page = paginator.paginate_queryset(persons, request) + serializer = AuthPersonSerializer(result_page, many=True) + return paginator.get_paginated_response(serializer.data) + + +@api_view(["GET"]) +@permission_classes([IsAuthenticated]) +def read_person(request): + person_id = request.GET.get("id", None) + person = get_object_or_404(AuthPerson, id=person_id) + serializer = AuthPersonSerializer(person) + return Response(serializer.data) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def add_person(request): + data = request.data + max_id = AuthPerson.objects.aggregate(Max("id"))["id__max"] + data["id"] = (max_id + 1) if max_id is not None else 1 + data["date_joined"] = datetime.now() + data["password"] = make_password("123456") + serializer = AuthPersonSerializer(data=data) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "Person added successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["PUT"]) +@permission_classes([IsAuthenticated]) +def update_person(request): + data = request.data + person = get_object_or_404(AuthPerson, id=data.get("id")) + serializer = AuthPersonSerializer(instance=person, data=request.data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({"message": "Person updated successfully"}) + + return Response( + {"error": "Invalid data", "details": serializer.errors}, + status=status.HTTP_400_BAD_REQUEST, + ) + + +@api_view(["DELETE"]) +@permission_classes([IsAuthenticated]) +def delete_person(request): + person_id = request.GET.get("id", None) + person = get_object_or_404(AuthPerson, id=person_id) + person.delete() + return Response({"message": "Person deleted successfully"}) + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def update_password(request): + username = request.data.get("username") + encrypted_password = request.data.get("password") + + try: + password = decrypt_password( + encrypted_password, getattr(settings, "private_key", "") + ) + AuthPerson.objects.filter(username=username).update(password=make_password(password)) + return Response({"code": "0", "success": "用户密码修改成功！"}) + except Exception: + return Response({"code": "1", "error": "用户密码更新失败！"}) + + +def decrypt_password(encrypted_text, private_key): + cipher = sm4.CryptSM4() + cipher.set_key(private_key.encode(), True) + encrypted_bytes = base64.b64decode(encrypted_text) + decrypted_bytes = cipher.crypt_ecb(encrypted_bytes) + return decrypted_bytes.decode() diff --git a/backend/apps/iam/views/user.py b/backend/apps/iam/views/user.py new file mode 100644 index 0000000..5a0a104 --- /dev/null +++ b/backend/apps/iam/views/user.py @@ -0,0 +1,75 @@ +import base64 +import os + +from django.conf import settings +from django.contrib.auth import authenticate +from django.db.utils import OperationalError, ProgrammingError +from gmssl import sm4 +from rest_framework.response import Response +from rest_framework.views import APIView +from rest_framework_simplejwt.tokens import RefreshToken + + +class UserLoginView(APIView): + def post(self, request, *args, **kwargs): + if os.environ.get("DJANGO_LOGIN_BREAKPOINT", "").strip().lower() in { + "1", + "true", + "yes", + "y", + "on", + }: + breakpoint() + try: + username = request.data.get("username") + encrypted_password = request.data.get("password") + if not username or not encrypted_password: + return Response({"code": "1", "error": "用户名或密码不能为空"}) + + private_key = getattr(settings, "SM4_PRIVATE_KEY", getattr(settings, "private_key", "")) + if not private_key: + return Response({"code": "1", "error": "缺少加密密钥"}) + + try: + password = self.decrypt_password(encrypted_password, private_key) + except Exception: + return Response({"code": "1", "error": "密码解密失败"}) + + user = authenticate(request, username=username, password=password) + + if user is not None: + refresh = RefreshToken.for_user(user) + user_groups = [group.id for group in user.groups.all()] + return Response( + { + "access_token": str(refresh.access_token), + "refresh_token": str(refresh), + "user_id": user.id, + "username": user.username, + "email": user.email, + "groups": user_groups, + "is_superuser": user.is_superuser, + } + ) + + return Response({"code": "1", "error": "用户名或密码不正确，请检查并重试！"}) + except (OperationalError, ProgrammingError) as e: + payload = {"code": "1", "error": "数据库未就绪，请先执行 migrate 并检查 DB 配置"} + if settings.DEBUG: + payload["details"] = str(e) + return Response(payload) + except Exception as e: + payload = {"code": "1", "error": "登录服务异常"} + if settings.DEBUG: + payload["details"] = str(e) + return Response(payload) + + def decrypt_password(self, encrypted_text, private_key): + if not private_key or len(private_key) != 16: + raise ValueError("invalid sm4 key") + cipher = sm4.CryptSM4() + cipher.set_key(private_key.encode(), True) + + encrypted_bytes = base64.b64decode(encrypted_text) + decrypted_bytes = cipher.crypt_ecb(encrypted_bytes) + return decrypted_bytes.decode() diff --git a/backend/apps/training/__init__.py b/backend/apps/training/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/training/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/training/apps.py b/backend/apps/training/apps.py new file mode 100644 index 0000000..2b46536 --- /dev/null +++ b/backend/apps/training/apps.py @@ -0,0 +1,7 @@ +from django.apps import AppConfig + + +class TrainingConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "apps.training" + diff --git a/backend/apps/training/consumers.py b/backend/apps/training/consumers.py new file mode 100644 index 0000000..da29b6f --- /dev/null +++ b/backend/apps/training/consumers.py @@ -0,0 +1,150 @@ +import asyncio +import json +import os + +from channels.db import database_sync_to_async +from channels.generic.websocket import AsyncWebsocketConsumer +from django.conf import settings + +from apps.core.models import AiAlgorithm, AiAlgorithmClass, AiAlgorithmTrainRecords + + +class TrainLogConsumer(AsyncWebsocketConsumer): + async def connect(self): + self.train_id = self.scope["url_route"]["kwargs"]["train_id"] + self.group_name = f"train_{self.train_id}" + await self.channel_layer.group_add(self.group_name, self.channel_name) + print(f"客户端[{self.train_id}],websocket连接服务器成功！") + await self.accept() + self._stop_event = asyncio.Event() + self._tail_task = asyncio.create_task(self._tail_train_log()) + + async def disconnect(self, close_code): + if hasattr(self, "_stop_event"): + self._stop_event.set() + if hasattr(self, "_tail_task"): + self._tail_task.cancel() + try: + await self._tail_task + except Exception: + pass + await self.channel_layer.group_discard(self.group_name, self.channel_name) + + async def receive(self, text_data): + pass + + async def send_log(self, event): + message = event["message"] + await self.send(text_data=message) + + @staticmethod + def _is_valid_json(text: str) -> bool: + try: + json.loads(text) + return True + except Exception: + return False + + async def _send_json(self, payload): + await self.send(text_data=json.dumps(payload, ensure_ascii=False)) + + @database_sync_to_async + def _resolve_log_file_path(self) -> str: + ai_train = AiAlgorithmTrainRecords.objects.get(id=self.train_id) + ai_algorithm = AiAlgorithm.objects.get(id=ai_train.algorithm_id) + ai_algorithm_class = AiAlgorithmClass.objects.get(id=ai_algorithm.algorithm_class) + run_name = "train" + flat_train_path = os.path.join(settings.FILESPACE_ROOT_PATH, ai_train.train_code) + nested_train_path = os.path.join( + settings.FILESPACE_ROOT_PATH, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + "trains", + ai_train.train_code, + ) + if getattr(ai_algorithm, "algorithm_engine", None) == "02": + candidates = [ + os.path.join(nested_train_path, "output", f"{run_name}.log"), + os.path.join(flat_train_path, "output", f"{run_name}.log"), + ] + elif getattr(ai_algorithm, "algorithm_engine", None) in {"03", "04"}: + candidates = [ + os.path.join(nested_train_path, "runs", f"{run_name}.log"), + os.path.join(flat_train_path, "runs", f"{run_name}.log"), + ] + else: + candidates = [ + os.path.join(flat_train_path, "runs", f"{run_name}.log"), + os.path.join(nested_train_path, "runs", f"{run_name}.log"), + ] + for path in candidates: + if os.path.exists(path): + return path + return candidates[0] + + async def _emit_log_lines(self, payload: bytes) -> None: + for raw_line in payload.splitlines(keepends=False): + line = raw_line.decode("utf-8", errors="ignore").strip() + if not line: + continue + if self._is_valid_json(line): + await self.send(text_data=line) + else: + await self._send_json({"type": "waiting", "text": line}) + + async def _tail_train_log(self) -> None: + try: + log_file_path = await self._resolve_log_file_path() + except Exception as e: + await self._send_json({"type": "error", "text": f"无法定位训练日志文件：{str(e)}"}) + return + + last_pos = 0 + buf = b"" + while not self._stop_event.is_set(): + if not os.path.exists(log_file_path): + last_pos = 0 + buf = b"" + await asyncio.sleep(0.5) + continue + + try: + current_size = os.path.getsize(log_file_path) + if current_size < last_pos: + last_pos = 0 + buf = b"" + if current_size == last_pos: + await asyncio.sleep(0.2) + continue + + with open(log_file_path, "rb") as f: + f.seek(last_pos) + chunk = f.read() + last_pos = f.tell() + + if not chunk: + await asyncio.sleep(0.2) + continue + + buf += chunk + normalized = buf.replace(b"\r\n", b"\n").replace(b"\r", b"\n") + if normalized.endswith(b"\n"): + completed = normalized + buf = b"" + else: + split_idx = normalized.rfind(b"\n") + if split_idx == -1: + buf = normalized + await asyncio.sleep(0.2) + continue + completed = normalized[: split_idx + 1] + buf = normalized[split_idx + 1 :] + + await self._emit_log_lines(completed) + await asyncio.sleep(0.05) + except asyncio.CancelledError: + return + except Exception as e: + await self._send_json({"type": "error", "text": f"训练日志读取失败：{str(e)}"}) + await asyncio.sleep(1.0) + diff --git a/backend/apps/training/views/__init__.py b/backend/apps/training/views/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/apps/training/views/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/apps/training/views/algorithm_trainrecords.py b/backend/apps/training/views/algorithm_trainrecords.py new file mode 100644 index 0000000..d754522 --- /dev/null +++ b/backend/apps/training/views/algorithm_trainrecords.py @@ -0,0 +1,2616 @@ +import os +import logging +import random +import re +import shutil +import signal +import subprocess +import sys +import tempfile +import threading +import time +import uuid +import zipfile +import base64 +from datetime import datetime +import xml.etree.ElementTree as ET +import requests +import yaml +import locale +from PIL import Image +try: + import cv2 +except Exception: + cv2 = None +from asgiref.sync import async_to_sync +from channels.layers import get_channel_layer +from django.db import transaction +from django.db.models import Max, CharField +from django.db.models.functions import Cast +from django.http import JsonResponse +from django.shortcuts import get_object_or_404 +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes, parser_classes +from rest_framework.parsers import MultiPartParser, JSONParser, FormParser +from rest_framework.permissions import IsAuthenticated +from rest_framework.response import Response +from rest_framework.utils import json + +from django.conf import settings +from apps.common.serializers import AiAlgorithmTrainRecordsSerializer +try: + from tqdm import tqdm +except Exception: + tqdm = None +try: + from apps.algorithms.algorithm.pytorch.YoloDetector import YoloDetector +except Exception: + YoloDetector = None +try: + from apps.algorithms.algorithm.DFINE.DfineDetector import DfineDetector +except Exception: + DfineDetector = None +try: + from ultralytics import YOLO +except Exception: + YOLO = None +from apps.core.models import ( + AiAlgorithm, + AiAlgorithmClass, + AiDataset, + AiAlgorithmModels, + AiAlgorithmParams, + AiAlgorithmTrainRecords, + AiDatasetSample, +) +from apps.common.utils.annotation_store import ( + annotate_queryset_with_annotation_content, + get_dataset_sample_annotation_from_row, +) +from apps.datasets.storage_service import ensure_sample_local_copy, ensure_sidecar_local_copy + +training_processes = {} +logger = logging.getLogger(__name__) + + +def _safe_rmtree(path: str): + if os.path.isdir(path): + shutil.rmtree(path, ignore_errors=True) + + +def _cleanup_current_training_workspace(train_path: str): + os.makedirs(train_path, exist_ok=True) + for subdir in ("train", "val", "images", "masks", "config", "runs", "output", "torch_cache"): + _safe_rmtree(os.path.join(train_path, subdir)) + for filename in ("label_map.json", "yolo_config.yaml"): + file_path = os.path.join(train_path, filename) + if os.path.isfile(file_path): + try: + os.remove(file_path) + except OSError: + pass + + +def _prune_training_cache(train_path: str): + train_root = os.path.dirname(train_path) + active_name = os.path.basename(train_path) + keep_latest = max(int(getattr(settings, "TRAIN_CACHE_KEEP_LATEST", 5) or 5), 1) + max_age_days = max(int(getattr(settings, "TRAIN_CACHE_MAX_AGE_DAYS", 7) or 7), 1) + now_ts = time.time() + if not os.path.isdir(train_root): + return + + candidates = [] + for name in os.listdir(train_root): + dir_path = os.path.join(train_root, name) + if not os.path.isdir(dir_path) or name == active_name: + continue + try: + mtime = os.path.getmtime(dir_path) + except OSError: + continue + candidates.append((name, dir_path, mtime)) + + candidates.sort(key=lambda item: item[2], reverse=True) + kept = 0 + for _, dir_path, mtime in candidates: + age_days = (now_ts - mtime) / 86400 + if kept < keep_latest and age_days <= max_age_days: + kept += 1 + continue + logger.info("remove expired training cache: %s", dir_path) + _safe_rmtree(dir_path) + + +def _subprocess_session_kwargs() -> dict: + if os.name == "nt": + return {"creationflags": getattr(subprocess, "CREATE_NEW_PROCESS_GROUP", 0)} + return {"start_new_session": True} + + +def _watch_training_process(train_id, process): + try: + process.wait() + except Exception: + pass + finally: + process_info = training_processes.get(train_id) + if process_info and process_info.get("process") is process: + training_processes.pop(train_id, None) + + +def _launch_training_process(train_id, command, log_file_path, env=None): + stop_event = threading.Event() + with open(log_file_path, "a", encoding="utf-8") as log_file: + process = subprocess.Popen( + command, + stdout=log_file, + stderr=subprocess.STDOUT, + env=env, + **_subprocess_session_kwargs(), + ) + training_processes[train_id] = { + "process": process, + "stop_event": stop_event, + } + threading.Thread( + target=_watch_training_process, + args=(train_id, process), + daemon=True, + ).start() + return stop_event, process + + +def _terminate_training_process(process): + if process is None or process.poll() is not None: + return + try: + if os.name == "nt": + subprocess.run( + ["taskkill", "/PID", str(process.pid), "/T", "/F"], + check=False, + capture_output=True, + text=True, + timeout=15, + ) + else: + os.killpg(os.getpgid(process.pid), signal.SIGTERM) + except Exception: + try: + process.terminate() + except Exception: + pass + try: + process.wait(timeout=15) + except subprocess.TimeoutExpired: + try: + if os.name == "nt": + subprocess.run( + ["taskkill", "/PID", str(process.pid), "/T", "/F"], + check=False, + capture_output=True, + text=True, + timeout=15, + ) + else: + os.killpg(os.getpgid(process.pid), signal.SIGKILL) + except Exception: + try: + process.kill() + except Exception: + pass + try: + process.wait(timeout=5) + except Exception: + pass + + +def _prepare_training_workspace(train_path: str): + _cleanup_current_training_workspace(train_path) + _prune_training_cache(train_path) + + +@api_view(['POST']) +@permission_classes([IsAuthenticated]) +def create_algorithm_train_record(request): + data = request.data + # 为 id 字段赋予新的 UUID 值 + data["id"] = str(uuid.uuid4()) + # 设置创建时间为当前时间 + data["create_time"] = datetime.now() + # 设置记录状态为初始创建-01 + data["record_status"] = "00" + # 生成训练标识号 + algorithm = get_object_or_404(AiAlgorithm, id=data.get("algorithm_id")) + current_date = datetime.now().strftime('%Y%m%d_%H%M') + # 获取 algorithm_code 并拼接到 train_code 中 + algorithm_code = algorithm.algorithm_code if algorithm.algorithm_code else "" + data["train_code"] = f"{algorithm_code}_{current_date}" + # 获取当前用户的用户名并赋值给 creator 字段 + data["creator"] = request.user.username + data["train_labels"]="0" + data["custom2"] = "0" + param_class = request.GET.get('param_class', None) + filter_kwargs = { + 'algorithm_id':data.get("algorithm_id"), + 'param_class':"01" + } + params = AiAlgorithmParams.objects.filter(**filter_kwargs).order_by('param_order') + data["train_params"] = json.dumps(list(params.values())) + serializer = AiAlgorithmTrainRecordsSerializer(data=data, context={'request': request}) + + if serializer.is_valid(): + serializer.save() + return Response({'message': '训练记录创建成功'}) + else: + return Response({'error': '无效的数据', 'details': serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + +@api_view(['POST']) +@permission_classes([IsAuthenticated]) +def copy_algorithm_train_record(request): + # request.data.get("id") + record_id = request.data.get('id', None) + # 获取原始训练记录 + original_record = get_object_or_404(AiAlgorithmTrainRecords, id=record_id) + # 构建新的数据字典 + data = { + "id": str(uuid.uuid4()), # 新的 UUID + "create_time": datetime.now(), # 当前时间 + "record_status": "00", # 初始状态 + "algorithm_id": original_record.algorithm_id, # 继承算法 ID + "algorithm_name":original_record.algorithm_name, + "train_title":original_record.train_title+"_copy", + "train_params":original_record.train_params, + "train_labels": original_record.train_labels, + "custom1": original_record.custom1, + "custom2": original_record.custom2, + } + # 获取算法信息 + algorithm = get_object_or_404(AiAlgorithm, id=original_record.algorithm_id) + algorithm_code = algorithm.algorithm_code if algorithm.algorithm_code else "" + current_date = datetime.now().strftime('%Y%m%d_%H%M') + data["train_code"] = f"{algorithm_code}_{current_date}" # 生成训练标识号 + # 设置创建者为当前用户 + data["creator"] = request.user.username + serializer = AiAlgorithmTrainRecordsSerializer(data=data, context={'request': request}) + + if serializer.is_valid(): + serializer.save() + return Response({'message': '训练记录复制成功！'}) + else: + return Response({'error': '无效的数据', 'details': serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + +@api_view(['GET']) +@permission_classes([IsAuthenticated]) +def read_algorithm_train_record(request): + record_id = request.GET.get('id', None) + record = get_object_or_404(AiAlgorithmTrainRecords, id=record_id) + serializer = AiAlgorithmTrainRecordsSerializer(record) + return Response(serializer.data) + +@api_view(['GET']) +@permission_classes([IsAuthenticated]) +def search_algorithm_train_records(request): + algorithm_id = request.GET.get('algorithm_id', None) + train_code = request.GET.get('train_code', None) + + filter_kwargs = {} + if algorithm_id: + filter_kwargs['algorithm_id'] = algorithm_id + if train_code: + filter_kwargs['train_code__contains'] = train_code + + records = AiAlgorithmTrainRecords.objects.filter(**filter_kwargs).order_by('create_time') + + serializer = AiAlgorithmTrainRecordsSerializer(records, many=True) + return Response(serializer.data) + + +@api_view(['PUT']) +@permission_classes([IsAuthenticated]) +def update_algorithm_train_record(request): + data = request.data + record = get_object_or_404(AiAlgorithmTrainRecords, id=data.get('id')) + serializer = AiAlgorithmTrainRecordsSerializer(instance=record, data=data, partial=True) + + if serializer.is_valid(): + serializer.save() + return Response({'message': '训练记录更新成功'}) + else: + return Response({'error': '无效的数据', 'details': serializer.errors}, status=status.HTTP_400_BAD_REQUEST) + + +@api_view(['DELETE']) +@permission_classes([IsAuthenticated]) +def delete_algorithm_train_record(request): + record_id = request.GET.get('id', None) + record = get_object_or_404(AiAlgorithmTrainRecords, id=record_id) + record.delete() + return Response({'message': '训练记录删除成功'}) + + +@api_view(['POST']) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def prepare_algorithm_train_dataset(request): + data = request.data + + # print('data........',data) + train_id = data.get('id') + link_datasets = data.get( + 'link_datasets') + train_labels = data.get( + 'train_labels') + train_val_info = data.get( + 'custom2') + annotate_type = str(data.get('annotate_type') or 'rectangle').strip().lower() + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + link_datasets=link_datasets, + train_labels=train_labels, + custom2=train_val_info, + custom1=data.get('custom1') + ) + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="0100" + ) + train_type=data.get('train_type') + #train_type:local为本地训练;remote为远程训练 + if train_type=='local': + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=data.get('algorithm_id')) + train_path = os.path.join(filespace_root_path,data.get('train_code')) + _prepare_training_workspace(train_path) + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + trainset_path=os.path.join(train_path, "train").replace("\\", "/"), + validationset_path=os.path.join(train_path, "val").replace("\\", "/"), + ) + + # 将 collect_train_dataset_samples 放到异步线程中执行 + def async_collect_train_dataset_samples(): + if ai_algorithm.algorithm_engine=='01': + isok = collect_train_dataset_samples( link_datasets, annotate_type, train_labels, train_val_info, train_path) + elif ai_algorithm.algorithm_engine=='02': + isok = collect_train_dataset_voctococo(link_datasets, train_labels, train_val_info, train_path) + elif ai_algorithm.algorithm_engine=='03': + isok = collect_train_dataset_segmentation(link_datasets, train_labels, train_val_info, train_path) + elif ai_algorithm.algorithm_engine=='04': + isok = collect_train_dataset_yoloseg(link_datasets, train_labels, train_val_info, train_path) + else: + isok = False + if isok: + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="01" # 样本准备完成 + ) + + # 创建并启动新线程 + threading.Thread(target=async_collect_train_dataset_samples).start() + return Response({'message': '训练样本数据准备中！'}) + else: + filespace_root_path = settings.IMAGE_BASE_URL + dataset_list = link_datasets.split(',') + dataset=[] + for dataset_id in dataset_list: + ai_algorithm_dataset = get_object_or_404(AiDataset, id=dataset_id) + dataset.append(filespace_root_path+ai_algorithm_dataset.dataset_path) + train_server="http://"+data.get('train_server')+"/prepare"#远程服务器地址 + train_code=data.get('train_code') + label_list = json.loads(train_labels) + # 动态提取 'label' 和 'name' 字段 + label_dict = {item.get('label'): item.get('name') for item in label_list} + custom2=json.loads(data.get('custom2')) + # 提取比例并拼接 + train_val = f"{custom2['proportion1']}:{custom2['proportion2']}" + prepare_back_url=settings.TASK_CALL_BACK_URL + "PrepareCallBack/" + payload = { + 'trainid': train_id, + 'train_code': train_code, + 'dataset': dataset, + 'train_val': train_val, + 'train_labels': json.dumps(label_dict), + 'prepare_back_url':prepare_back_url + } + # 发送 POST 请求到 train_server + try: + auth_header = request.headers.get('Authorization') + token = auth_header.split(' ')[1] if ' ' in auth_header else auth_header + headers = { + "Authorization": f"Bearer {token}", + 'Content-Type': 'application/json' + } + response = requests.post(train_server, json=payload,headers=headers) + # 检查响应状态 + if response.status_code == 200: + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="0100" # 样本准备中 + ) + return Response({'message': '训练样本数据准备中！'}) + else: + return Response({'message': '训练样本数据准备失败！'}) + except requests.exceptions.RequestException as e: + return Response({'message': '训练样本数据准备失败！'}) + +def _legacy_collect_train_dataset_samples(train_id, link_datasets, annotate_type, train_labels, train_val_info, train_path): + # 1. 解析link_datasets + dataset_ids = link_datasets.split(',') + + # 2. 查询AiDatasetSample + samples = annotate_queryset_with_annotation_content(AiDatasetSample.objects.filter( + status='02', + dataset_id__in=dataset_ids + )).values( + 'id', + 'saved_path', + 'saved_filename', + 'annotation_content' + ) + + # 3. 分割训练集和验证集 + total_samples = len(samples) + train_val = json.loads(train_val_info) + train_ratio = float(train_val.get("proportion1", 80)) / 100 + num_train = int(total_samples * train_ratio) + + # 随机打乱样本 + sample_list = list(samples) + random.shuffle(sample_list) + + train_samples = sample_list[:num_train] + val_samples = sample_list[num_train:] + + # 4. 创建目录结构 + train_images_dir = os.path.join(train_path, 'train', 'images') + train_labels_dir = os.path.join(train_path, 'train', 'labels') + val_images_dir = os.path.join(train_path, 'val', 'images') + val_labels_dir = os.path.join(train_path, 'val', 'labels') + + os.makedirs(train_images_dir, exist_ok=True) + os.makedirs(train_labels_dir, exist_ok=True) + os.makedirs(val_images_dir, exist_ok=True) + os.makedirs(val_labels_dir, exist_ok=True) + + # 5. 复制图片并生成标注文件 + def process_samples(samples, images_dir, labels_dir): + for sample in samples: + # 复制图片 + saved_path = sample['saved_path'] + saved_filename = sample['saved_filename'] + original_image_path = ensure_sample_local_copy(saved_path, saved_filename) + dest_image_path = os.path.join(images_dir, saved_filename) + shutil.copy2(original_image_path, dest_image_path) + + # 生成标注文件 + annotation_content = sample['annotation_content'] + create_annotation_txtfile( + image_path=images_dir, + label_path=labels_dir, + image_name=saved_filename, + classes=json.loads(train_labels), + annotation=json.loads(annotation_content) + ) + + # 处理训练集样本 + process_samples(train_samples, train_images_dir, train_labels_dir) + # 处理验证集样本 + process_samples(val_samples, val_images_dir, val_labels_dir) + # 生成yaml配置文件 + train_classes = [cls["label"] for cls in json.loads(train_labels)] + generate_yaml_config(train_path, train_classes) + # 处理完成，返回成功 + return True + + +def collect_train_dataset_samples(link_datasets, annotate_type, train_labels, train_val_info, train_path): + dataset_ids = [item for item in str(link_datasets or "").split(',') if item] + annotation_field = 'polygon' if str(annotate_type or '').strip().lower() == 'polygon' else 'rectangle' + + samples = list( + AiDatasetSample.objects.filter( + status='02', + dataset_id__in=dataset_ids + ).exclude( + **{f'{annotation_field}__isnull': True} + ).exclude( + **{annotation_field: ''} + ).values( + 'id', + 'saved_path', + 'saved_filename', + annotation_field + ) + ) + + train_val = json.loads(train_val_info) if train_val_info else {} + train_ratio = float(train_val.get("proportion1", 80)) / 100 + train_ratio = max(0.0, min(train_ratio, 1.0)) + + random.shuffle(samples) + num_train = int(len(samples) * train_ratio) + train_samples = samples[:num_train] + val_samples = samples[num_train:] + + train_images_dir = os.path.join(train_path, 'train', 'images') + train_labels_dir = os.path.join(train_path, 'train', 'labels') + val_images_dir = os.path.join(train_path, 'val', 'images') + val_labels_dir = os.path.join(train_path, 'val', 'labels') + + os.makedirs(train_images_dir, exist_ok=True) + os.makedirs(train_labels_dir, exist_ok=True) + os.makedirs(val_images_dir, exist_ok=True) + os.makedirs(val_labels_dir, exist_ok=True) + + def process_samples(split_samples, images_dir, labels_dir): + for sample in split_samples: + saved_path = sample.get('saved_path') + saved_filename = sample.get('saved_filename') + if not saved_path or not saved_filename: + continue + original_image_path = ensure_sample_local_copy(train_path, saved_path, saved_filename) + if not os.path.exists(original_image_path): + continue + + dest_image_path = os.path.join(images_dir, saved_filename) + shutil.copy2(original_image_path, dest_image_path) + + annotation_content = _resolve_training_annotation_content(sample, annotation_field) + annotations = _build_xml_annotations(annotation_content, annotation_field, dest_image_path) + create_annotation_txtfile( + image_path=images_dir, + label_path=labels_dir, + image_name=saved_filename, + classes=json.loads(train_labels), + annotation=annotations + ) + + process_samples(train_samples, train_images_dir, train_labels_dir) + process_samples(val_samples, val_images_dir, val_labels_dir) + + labels = json.loads(train_labels) if train_labels else [] + train_classes = [ + cls["label"] for cls in labels + if isinstance(cls, dict) and cls.get("label") + ] + generate_yaml_config(train_path, train_classes) + return True + +def collect_train_dataset_voctococo(link_datasets, train_labels, train_val_info, json_save_dir): + """ + 根据AiDatasetSample表和标签配置，生成COCO格式的json数据集。 + 不会复制图片，只生成coco.json，图片路径可自定义。 + """ + # 1. 解析数据集id + dataset_ids = link_datasets.split(',') + + # 2. 查询样本 + samples = annotate_queryset_with_annotation_content(AiDatasetSample.objects.filter( + status='02', + dataset_id__in=dataset_ids + )).values( + 'id', + 'saved_path', + 'saved_filename', + 'annotation_content' + ) + + # 3. 划分训练/验证集 + total_samples = len(samples) + train_val = json.loads(train_val_info) + train_ratio = float(train_val.get("proportion1", 80)) / 100 + num_train = int(total_samples * train_ratio) + + # 随机打乱 + sample_list = list(samples) + random.shuffle(sample_list) + train_samples = sample_list[:num_train] + val_samples = sample_list[num_train:] + train_images_dir = os.path.join(json_save_dir, 'train', 'images') + val_images_dir = os.path.join(json_save_dir, 'val', 'images') + output_dir = os.path.join(json_save_dir, 'output') + os.makedirs(output_dir, exist_ok=True) + os.makedirs(train_images_dir, exist_ok=True) + os.makedirs(val_images_dir, exist_ok=True) + # 4. 加载类别 + classes_info = json.loads(train_labels) # [{"id": 0, "label": "cat"}, ...] + CLASSES = [item["label"] for item in classes_info] + template_dir='../algorithm/DFINE/configs/dfine' + def samples_to_coco(samples,images_dir,coco_json_path): + images = [] + annotations = [] + image_id = 1 + annotation_id = 1 + for sample in tqdm(samples): + saved_path = sample['saved_path'] + saved_filename = sample['saved_filename'] + file_name = saved_filename + src_img_path = ensure_sample_local_copy(saved_path, saved_filename) + dst_img_path = os.path.join(images_dir, saved_filename) + + # 复制图片 + if not os.path.exists(dst_img_path): + shutil.copy2(src_img_path, dst_img_path) + + xml_name = os.path.splitext(saved_filename)[0] + '.xml' + xml_path = ensure_sidecar_local_copy(saved_path, saved_filename, ".xml") + if not os.path.exists(xml_path): + print(f"Warning: xml not found for {file_name}, skipped") + continue + try: + tree = ET.parse(xml_path) + root = tree.getroot() + size = root.find('size') + width = int(size.find('width').text) + height = int(size.find('height').text) + except Exception as e: + print(f"Error parsing {xml_path}: {e}") + continue + + images.append({ + "file_name": file_name, + "height": height, + "width": width, + "id": image_id + }) + + for obj in root.findall('object'): + name = obj.find('name').text + if name not in CLASSES: + continue + category_id = CLASSES.index(name) + 1 + bndbox = obj.find('bndbox') + xmin = int(float(bndbox.find('xmin').text)) + ymin = int(float(bndbox.find('ymin').text)) + xmax = int(float(bndbox.find('xmax').text)) + ymax = int(float(bndbox.find('ymax').text)) + w = xmax - xmin + h = ymax - ymin + annotations.append({ + "id": annotation_id, + "image_id": image_id, + "category_id": category_id, + "bbox": [xmin, ymin, w, h], + "area": w * h, + "iscrowd": 0, + "segmentation": [] + }) + annotation_id += 1 + + image_id += 1 + + # 类别 + categories = [] + for idx, name in enumerate(CLASSES): + categories.append({ + "id": idx + 1, + "name": name, + "supercategory": "none" + }) + + coco_format = { + "images": images, + "type": "instances", + "annotations": annotations, + "categories": categories + } + with open(coco_json_path, 'w', encoding='utf-8') as f: + json.dump(coco_format, f, ensure_ascii=False, indent=4) + print(f"保存COCO文件到: {coco_json_path}") + + def generate_dfine_config_files(json_save_dir, class_names): + script_dir = os.path.dirname(os.path.abspath(__file__)) + template_dir = os.path.join(script_dir, "..", "algorithm", "DFINE", "configs", "dfine") + coco_detection_template = os.path.join(script_dir, "..", "algorithm", "DFINE", "configs", "dataset", + "coco_detection.yml") + save_dir = os.path.join(json_save_dir, "config") + train_json = os.path.join(json_save_dir, "train", "train_coco.json") + val_json = os.path.join(json_save_dir, "val", "val_coco.json") + train_images = os.path.join(json_save_dir, "train", "images") + val_images = os.path.join(json_save_dir, "val", "images") + data_root = train_images + + # 1. 处理 coco_detection.yml + if os.path.exists(coco_detection_template): + with open(coco_detection_template, 'r', encoding='utf-8') as f: + coco_detection_data = yaml.safe_load(f) + + # 替换字段 + def replace_coco_detection_fields(d): + if isinstance(d, dict): + for k, v in d.items(): + if k == "num_classes": + d[k] = len(class_names) + elif k == "train_dataloader" and isinstance(v, dict): + dataset = v.get("dataset", {}) + if isinstance(dataset, dict): + dataset["img_folder"] = train_images + dataset["ann_file"] = train_json + elif k == "val_dataloader" and isinstance(v, dict): + dataset = v.get("dataset", {}) + if isinstance(dataset, dict): + dataset["img_folder"] = val_images + dataset["ann_file"] = val_json + elif k in ["classes", "class_names"]: + d[k] = class_names + else: + replace_coco_detection_fields(v) + elif isinstance(d, list): + for v in d: + replace_coco_detection_fields(v) + + replace_coco_detection_fields(coco_detection_data) + os.makedirs(save_dir, exist_ok=True) + coco_detection_save_path = os.path.join(save_dir, "coco_detection.yml") + with open(coco_detection_save_path, 'w', encoding='utf-8') as f: + yaml.dump(coco_detection_data, f, allow_unicode=True, sort_keys=False) + print(f"已生成新配置文件：{coco_detection_save_path}") + else: + print(f"模板文件不存在: {coco_detection_template}") + + # 2. 处理其余的 dfine_hgnetv2_*.yml + template_files = [ + "dfine_hgnetv2_l_coco.yml", + "dfine_hgnetv2_m_coco.yml", + "dfine_hgnetv2_n_coco.yml", + "dfine_hgnetv2_s_coco.yml", + "dfine_hgnetv2_x_coco.yml" + ] + for template_name in template_files: + template_path = os.path.join(template_dir, template_name) + if not os.path.exists(template_path): + print(f"模板文件不存在: {template_path}") + continue + with open(template_path, 'r', encoding='utf-8') as f: + yml_data = yaml.safe_load(f) + + # 替换__include__和output_dir + def replace_includes_and_output(d): + if isinstance(d, dict): + for k, v in d.items(): + if k == "__include__" and isinstance(v, list): + new_list = [] + for inc in v: + # 只替换coco_detection.yml的路径为本次生成的config/coco_detection.yml + if "coco_detection.yml" in inc: + new_list.append(os.path.join(save_dir, "coco_detection.yml")) + elif "runtime.yml" in inc: + new_list.append(os.path.join(script_dir, "..", "algorithm", "DFINE", "configs", "runtime.yml")) + elif "dataloader.yml" in inc: + new_list.append(os.path.join(script_dir, "..", "algorithm", "DFINE", "configs", "dfine","include", "dataloader.yml")) + elif "optimizer.yml" in inc: + new_list.append(os.path.join(script_dir, "..", "algorithm", "DFINE", "configs", "dfine","include", "optimizer.yml")) + elif "dfine_hgnetv2.yml" in inc: + new_list.append(os.path.join(script_dir, "..", "algorithm", "DFINE", "configs", "dfine","include", "dfine_hgnetv2.yml")) + else: + new_list.append(inc) + d[k] = new_list + elif k == "output_dir": + # 你可以自定义output_dir，建议放到当前config的output文件夹 + d[k] = os.path.join(json_save_dir, "output") + else: + replace_includes_and_output(v) + elif isinstance(d, list): + for v in d: + replace_includes_and_output(v) + + replace_includes_and_output(yml_data) + + save_path = os.path.join(save_dir, template_name) + with open(save_path, 'w', encoding='utf-8') as f: + yaml.dump(yml_data, f, allow_unicode=True, sort_keys=False) + print(f"已生成新配置文件：{save_path}") + + + os.makedirs(json_save_dir, exist_ok=True) + samples_to_coco(train_samples, os.path.join(json_save_dir,'train','images'),os.path.join(json_save_dir,'train', 'train_coco.json')) + samples_to_coco(val_samples, os.path.join(json_save_dir,'val','images'),os.path.join(json_save_dir,'val', 'val_coco.json')) + generate_dfine_config_files(json_save_dir,CLASSES) + print("COCO数据集json生成完毕，不拷贝图片") + return True + + +def collect_train_dataset_segmentation(link_datasets, train_labels, train_val_info, train_path): + import base64 + try: + import numpy as np + except Exception: + np = None + try: + import cv2 + except Exception: + cv2 = None + + if np is None or cv2 is None: + raise ImportError("语义分割样本准备需要安装 numpy 与 opencv-python") + + dataset_ids = link_datasets.split(',') + samples = annotate_queryset_with_annotation_content(AiDatasetSample.objects.filter( + status='02', + dataset_id__in=dataset_ids + )).values( + 'id', + 'saved_path', + 'saved_filename', + 'annotation_content' + ) + + total_samples = len(samples) + train_val = json.loads(train_val_info) + train_ratio = float(train_val.get("proportion1", 80)) / 100 + num_train = int(total_samples * train_ratio) + + sample_list = list(samples) + random.shuffle(sample_list) + train_samples = sample_list[:num_train] + val_samples = sample_list[num_train:] + + images_train_dir = os.path.join(train_path, 'images', 'train') + images_val_dir = os.path.join(train_path, 'images', 'val') + masks_train_dir = os.path.join(train_path, 'masks', 'train') + masks_val_dir = os.path.join(train_path, 'masks', 'val') + os.makedirs(images_train_dir, exist_ok=True) + os.makedirs(images_val_dir, exist_ok=True) + os.makedirs(masks_train_dir, exist_ok=True) + os.makedirs(masks_val_dir, exist_ok=True) + + labels = json.loads(train_labels) if train_labels else [] + labels = labels if isinstance(labels, list) else [] + label_map = {"background": 0} + next_id = 1 + for item in labels: + if not isinstance(item, dict): + continue + lab = str(item.get("label") or "").strip() + if not lab or lab in label_map: + continue + label_map[lab] = next_id + next_id += 1 + + with open(os.path.join(train_path, "label_map.json"), "w", encoding="utf-8") as f: + f.write(json.dumps(label_map, ensure_ascii=False)) + + def _load_annotation_json(sample_row): + content = get_dataset_sample_annotation_from_row(sample_row) + if content: + try: + parsed = json.loads(content) + except Exception: + parsed = None + if isinstance(parsed, dict) and parsed.get("type") == "polygon" and parsed.get("json_path"): + p = str(parsed.get("json_path")) + abs_p = os.path.join(settings.FILESPACE_ROOT_PATH, p) + if os.path.exists(abs_p): + with open(abs_p, "r", encoding="utf-8") as f: + return json.loads(f.read()) + saved_path = sample_row.get("saved_path") + saved_filename = sample_row.get("saved_filename") + if saved_path and saved_filename: + abs_p = ensure_sidecar_local_copy(saved_path, saved_filename, ".json") + if os.path.exists(abs_p): + with open(abs_p, "r", encoding="utf-8") as f: + return json.loads(f.read()) + return None + + def _shapes_from_annotation_obj(obj): + if not obj: + return [] + if isinstance(obj, dict) and isinstance(obj.get("shapes"), list): + return obj.get("shapes") or [] + if isinstance(obj, list): + shapes = [] + for it in obj: + if not isinstance(it, dict): + continue + label = it.get("label") + region = it.get("region") + if not label or not isinstance(region, list) or len(region) < 3: + continue + if region and isinstance(region[0], (list, tuple)): + points = [] + for p in region: + if not isinstance(p, (list, tuple)) or len(p) != 2: + continue + try: + points.append([float(p[0]), float(p[1])]) + except Exception: + continue + if len(points) >= 3: + shapes.append({"label": str(label), "points": points}) + return shapes + return [] + + def _ensure_image_data_for_output(annotation_obj, image_abs_path, image_name): + if not isinstance(annotation_obj, dict): + annotation_obj = { + "version": "5.11.2", + "flags": {}, + "shapes": [], + } + if "imageData" not in annotation_obj or not annotation_obj.get("imageData"): + with open(image_abs_path, "rb") as f: + annotation_obj["imageData"] = base64.b64encode(f.read()).decode("utf-8") + annotation_obj["imagePath"] = image_name + if "imageHeight" not in annotation_obj or "imageWidth" not in annotation_obj: + img = cv2.imread(image_abs_path) + if img is not None: + h, w = img.shape[:2] + annotation_obj["imageHeight"] = int(h) + annotation_obj["imageWidth"] = int(w) + return annotation_obj + + def _process(split_samples, images_dir, masks_dir): + iterator = tqdm(split_samples) if tqdm is not None else split_samples + for sample in iterator: + saved_path = sample.get('saved_path') + saved_filename = sample.get('saved_filename') + if not saved_path or not saved_filename: + continue + src_img = ensure_sample_local_copy(saved_path, saved_filename) + if not os.path.exists(src_img): + continue + dst_img = os.path.join(images_dir, saved_filename) + shutil.copy2(src_img, dst_img) + + ann_obj = _load_annotation_json(sample) + shapes = _shapes_from_annotation_obj(ann_obj) + + img = cv2.imread(dst_img) + if img is None: + continue + h, w = img.shape[:2] + mask = np.zeros((h, w), dtype=np.uint8) + for sh in shapes: + if not isinstance(sh, dict): + continue + label = sh.get("label") + if label not in label_map: + continue + points = sh.get("points") + if not isinstance(points, list) or len(points) < 3: + continue + pts = np.array(points, dtype=np.int32) + cv2.fillPoly(mask, [pts], int(label_map[label])) + stem = os.path.splitext(saved_filename)[0] + cv2.imwrite(os.path.join(masks_dir, f"{stem}.png"), mask) + + ann_out = _ensure_image_data_for_output(ann_obj, dst_img, saved_filename) + ann_out["shapes"] = [ + { + "label": str(sh.get("label")), + "points": sh.get("points"), + "group_id": None, + "shape_type": "polygon", + "flags": {}, + "mask": None, + } + for sh in shapes + if isinstance(sh, dict) and sh.get("label") and isinstance(sh.get("points"), list) + ] + with open(os.path.join(images_dir, f"{stem}.json"), "w", encoding="utf-8") as f: + f.write(json.dumps(ann_out, ensure_ascii=False)) + + _process(train_samples, images_train_dir, masks_train_dir) + _process(val_samples, images_val_dir, masks_val_dir) + return True + + +def collect_train_dataset_yoloseg(link_datasets, train_labels, train_val_info, train_path): + dataset_ids = link_datasets.split(',') + samples = annotate_queryset_with_annotation_content(AiDatasetSample.objects.filter( + status='02', + dataset_id__in=dataset_ids + )).values( + 'id', + 'saved_path', + 'saved_filename', + 'annotation_content' + ) + + total_samples = len(samples) + train_val = json.loads(train_val_info) + train_ratio = float(train_val.get("proportion1", 80)) / 100 + num_train = int(total_samples * train_ratio) + + sample_list = list(samples) + random.shuffle(sample_list) + train_samples = sample_list[:num_train] + val_samples = sample_list[num_train:] + + train_images_dir = os.path.join(train_path, 'train', 'images') + train_labels_dir = os.path.join(train_path, 'train', 'labels') + val_images_dir = os.path.join(train_path, 'val', 'images') + val_labels_dir = os.path.join(train_path, 'val', 'labels') + os.makedirs(train_images_dir, exist_ok=True) + os.makedirs(train_labels_dir, exist_ok=True) + os.makedirs(val_images_dir, exist_ok=True) + os.makedirs(val_labels_dir, exist_ok=True) + + classes = json.loads(train_labels) if train_labels else [] + classes = classes if isinstance(classes, list) else [] + class_to_index = {} + train_class_names = [] + for idx, item in enumerate(classes): + if not isinstance(item, dict): + continue + label = str(item.get("label") or "").strip() + if not label: + continue + class_to_index[label] = idx + train_class_names.append(label) + + def _load_annotation_json(sample_row): + content = get_dataset_sample_annotation_from_row(sample_row) + if content: + try: + parsed = json.loads(content) + except Exception: + parsed = None + if isinstance(parsed, dict) and parsed.get("type") == "polygon" and parsed.get("json_path"): + p = str(parsed.get("json_path")) + abs_p = os.path.join(settings.FILESPACE_ROOT_PATH, p) + if os.path.exists(abs_p): + with open(abs_p, "r", encoding="utf-8") as f: + return json.loads(f.read()) + if isinstance(parsed, list): + return parsed + if isinstance(parsed, dict) and isinstance(parsed.get("shapes"), list): + return parsed + saved_path = sample_row.get("saved_path") + saved_filename = sample_row.get("saved_filename") + if saved_path and saved_filename: + abs_p = ensure_sidecar_local_copy(saved_path, saved_filename, ".json") + if os.path.exists(abs_p): + with open(abs_p, "r", encoding="utf-8") as f: + return json.loads(f.read()) + return None + + def _shapes_from_annotation_obj(obj): + if not obj: + return [] + if isinstance(obj, dict) and isinstance(obj.get("shapes"), list): + return obj.get("shapes") or [] + if isinstance(obj, list): + shapes = [] + for it in obj: + if not isinstance(it, dict): + continue + label = it.get("label") + region = it.get("region") + if not label or not isinstance(region, list) or len(region) < 3: + continue + if region and isinstance(region[0], (list, tuple)): + points = [] + for p in region: + if not isinstance(p, (list, tuple)) or len(p) != 2: + continue + try: + points.append([float(p[0]), float(p[1])]) + except Exception: + continue + if len(points) >= 3: + shapes.append({"label": str(label), "points": points}) + return shapes + return [] + + def _process(split_samples, images_dir, labels_dir): + iterator = tqdm(split_samples) if tqdm is not None else split_samples + for sample in iterator: + saved_path = sample.get('saved_path') + saved_filename = sample.get('saved_filename') + if not saved_path or not saved_filename: + continue + src_img = ensure_sample_local_copy(saved_path, saved_filename) + if not os.path.exists(src_img): + continue + dst_img = os.path.join(images_dir, saved_filename) + shutil.copy2(src_img, dst_img) + + try: + with Image.open(dst_img) as img: + image_width, image_height = img.size + except Exception: + continue + if image_width <= 0 or image_height <= 0: + continue + + ann_obj = _load_annotation_json(sample) + shapes = _shapes_from_annotation_obj(ann_obj) + stem = os.path.splitext(saved_filename)[0] + txt_path = os.path.join(labels_dir, f"{stem}.txt") + lines = [] + for sh in shapes: + if not isinstance(sh, dict): + continue + label = str(sh.get("label") or "").strip() + if label not in class_to_index: + continue + points = sh.get("points") + if not isinstance(points, list) or len(points) < 3: + continue + normalized = [] + for point in points: + if not isinstance(point, (list, tuple)) or len(point) != 2: + continue + try: + x = float(point[0]) / float(image_width) + y = float(point[1]) / float(image_height) + except Exception: + continue + x = min(max(x, 0.0), 1.0) + y = min(max(y, 0.0), 1.0) + normalized.append(f"{x:.6f}") + normalized.append(f"{y:.6f}") + if len(normalized) < 6: + continue + lines.append(f"{class_to_index[label]} " + " ".join(normalized)) + + with open(txt_path, 'w', encoding='utf-8') as f: + if lines: + f.write("\n".join(lines) + "\n") + os.chmod(txt_path, 0o777) + + _process(train_samples, train_images_dir, train_labels_dir) + _process(val_samples, val_images_dir, val_labels_dir) + generate_yaml_config(train_path, train_class_names) + return True + +def create_annotation_txtfile(image_path: str, label_path: str, image_name: str, classes: list, annotation: list): + """ + 创建 YOLO 格式标注 .txt 文件，归一化为中心点坐标和宽高。 + 支持两种标注格式： + - region: [xmin, ymin, xmax, ymax]（原始格式） + - xmin/ymin/xmax/ymax 独立字段（来自 _build_xml_annotations） + """ + image_path_full = os.path.join(image_path, image_name) + try: + with Image.open(image_path_full) as img: + image_width, image_height = img.size + except Exception as e: + print(f"无法打开图片 {image_name}: {e}") + return + + image_name_without_ext = os.path.splitext(image_name)[0] + txt_file_path = os.path.join(label_path, f"{image_name_without_ext}.txt") + + # 构建 label → index 映射 + label_to_index = { + item['label']: i + for i, item in enumerate(classes) + if isinstance(item, dict) and item.get('label') + } + + with open(txt_file_path, 'w') as file: + for annot in annotation or []: + label = annot.get('label', '') + class_index = label_to_index.get(label, -1) + if class_index == -1: + print(f"警告：没有找到类别 {label} 对应的索引!") + continue + + # 兼容两种坐标格式 + region = annot.get('region') + if region is not None and isinstance(region, (list, tuple)) and len(region) == 4: + x_min, y_min, x_max, y_max = map(float, region) + else: + x_min = float(annot.get('xmin', 0)) + y_min = float(annot.get('ymin', 0)) + x_max = float(annot.get('xmax', 0)) + y_max = float(annot.get('ymax', 0)) + + # 归一化坐标（中心点和宽高） + x_center = ((x_min + x_max) / 2) / image_width + y_center = ((y_min + y_max) / 2) / image_height + width = (x_max - x_min) / image_width + height = (y_max - y_min) / image_height + # 构建文件行 + line = f"{class_index} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n" + file.write(line) + os.chmod(txt_file_path, 0o777) + + +def _load_json_file(file_path: str): + try: + with open(file_path, "r", encoding="utf-8") as f: + return json.loads(f.read()) + except Exception: + return None + + +def _resolve_training_annotation_content(sample_row: dict, annotation_field: str): + raw = sample_row.get(annotation_field) + if not raw: + return None + try: + parsed = json.loads(raw) if isinstance(raw, str) else raw + except Exception: + return None + + if annotation_field == "polygon": + if isinstance(parsed, dict) and parsed.get("type") == "polygon" and parsed.get("json_path"): + json_path = os.path.join(settings.FILESPACE_ROOT_PATH, str(parsed.get("json_path"))) + loaded = _load_json_file(json_path) + if loaded is not None: + return loaded + if isinstance(parsed, dict) and parsed.get("shapes"): + return parsed + if isinstance(parsed, list): + return parsed + saved_path = sample_row.get("saved_path") + saved_filename = sample_row.get("saved_filename") + if saved_path and saved_filename: + sidecar_json = ensure_sidecar_local_copy(saved_path, saved_filename, ".json") + loaded = _load_json_file(sidecar_json) + if loaded is not None: + return loaded + return parsed + + +def _normalize_bbox(xmin, ymin, xmax, ymax, image_width, image_height): + try: + xmin = float(xmin) + ymin = float(ymin) + xmax = float(xmax) + ymax = float(ymax) + except Exception: + return None + + left = max(0.0, min(xmin, xmax)) + top = max(0.0, min(ymin, ymax)) + right = min(float(image_width), max(xmin, xmax)) + bottom = min(float(image_height), max(ymin, ymax)) + if right <= left or bottom <= top: + return None + return { + "xmin": int(round(left)), + "ymin": int(round(top)), + "xmax": int(round(right)), + "ymax": int(round(bottom)), + } + + +def _build_xml_annotations(annotation_content, annotation_field: str, image_path: str): + try: + with Image.open(image_path) as img: + image_width, image_height = img.size + except Exception: + return [] + + if not annotation_content: + return [] + + shapes = annotation_content.get("shapes") if isinstance(annotation_content, dict) else annotation_content + if not isinstance(shapes, list): + return [] + + annotations = [] + for item in shapes: + if not isinstance(item, dict): + continue + label = str(item.get("label") or "").strip() + if not label: + continue + + bbox = None + if annotation_field == "polygon": + points = item.get("points") + if points is None: + points = item.get("region") + if not isinstance(points, list) or len(points) < 3: + continue + xs = [] + ys = [] + for point in points: + if not isinstance(point, (list, tuple)) or len(point) < 2: + continue + try: + xs.append(float(point[0])) + ys.append(float(point[1])) + except Exception: + continue + if len(xs) < 3 or len(ys) < 3: + continue + bbox = _normalize_bbox(min(xs), min(ys), max(xs), max(ys), image_width, image_height) + else: + region = item.get("region") + if isinstance(region, list) and len(region) == 4 and not isinstance(region[0], (list, tuple)): + bbox = _normalize_bbox(region[0], region[1], region[2], region[3], image_width, image_height) + else: + points = item.get("points") + if isinstance(points, list) and len(points) >= 2: + p1 = points[0] + p2 = points[1] + if isinstance(p1, (list, tuple)) and isinstance(p2, (list, tuple)) and len(p1) >= 2 and len(p2) >= 2: + bbox = _normalize_bbox(p1[0], p1[1], p2[0], p2[1], image_width, image_height) + + if bbox is None: + continue + annotations.append({ + "label": label, + **bbox, + }) + return annotations + + +def create_annotation_xmlfile(image_path: str, label_path: str, image_name: str, annotation: list): + try: + with Image.open(image_path) as img: + image_width, image_height = img.size + image_depth = len(img.getbands()) + except Exception as e: + print(f"无法打开图片 {image_name}: {e}") + return + + root = ET.Element("annotation") + ET.SubElement(root, "folder").text = os.path.basename(os.path.dirname(image_path)) + ET.SubElement(root, "filename").text = image_name + ET.SubElement(root, "path").text = image_path.replace("\\", "/") + + source = ET.SubElement(root, "source") + ET.SubElement(source, "database").text = "Unknown" + + size = ET.SubElement(root, "size") + ET.SubElement(size, "width").text = str(image_width) + ET.SubElement(size, "height").text = str(image_height) + ET.SubElement(size, "depth").text = str(image_depth) + ET.SubElement(root, "segmented").text = "0" + + for annot in annotation or []: + obj = ET.SubElement(root, "object") + ET.SubElement(obj, "name").text = str(annot.get("label") or "") + ET.SubElement(obj, "pose").text = "Unspecified" + ET.SubElement(obj, "truncated").text = "0" + ET.SubElement(obj, "difficult").text = "0" + bndbox = ET.SubElement(obj, "bndbox") + ET.SubElement(bndbox, "xmin").text = str(int(annot.get("xmin", 0))) + ET.SubElement(bndbox, "ymin").text = str(int(annot.get("ymin", 0))) + ET.SubElement(bndbox, "xmax").text = str(int(annot.get("xmax", 0))) + ET.SubElement(bndbox, "ymax").text = str(int(annot.get("ymax", 0))) + + tree = ET.ElementTree(root) + try: + ET.indent(tree, space=" ") + except Exception: + pass + + image_name_without_ext = os.path.splitext(image_name)[0] + xml_file_path = os.path.join(label_path, f"{image_name_without_ext}.xml") + tree.write(xml_file_path, encoding="utf-8", xml_declaration=True) + os.chmod(xml_file_path, 0o777) + +def generate_yaml_config(train_path: str, train_classes: list): + config = { + "path": train_path.replace("\\", "/"), + "train": "train", + "val": "val", + "nc": len(train_classes), + "names": train_classes + } + yaml_file = os.path.join(train_path, "yolo_config.yaml") + with open(yaml_file, "w") as f: + yaml.dump(config, f, default_flow_style=False, allow_unicode=True) + os.chmod(yaml_file, 0o777) + return True + + +def _decode_log_bytes(data: bytes) -> str: + enc = locale.getpreferredencoding(False) or "utf-8" + try: + return data.decode(enc, errors="ignore") + except Exception: + return data.decode("utf-8", errors="ignore") + + +def send_to_websocket(channel_layer, group_name, message): + if isinstance(message, (dict, list)): + message = json.dumps(message,ensure_ascii=False) + async_to_sync(channel_layer.group_send)( + group_name, + {"type": "send_log", "message": message} + ) +def is_valid_json(line): + """ + 检查给定的字符串是否是有效的 JSON 格式。 + :param line: 字符串 + :return: 是否为有效的 JSON + """ + try: + json.loads(line) # 尝试解析字符串 + return True + except ValueError: + return False +def stream_old_logs_to_frontend(channel_layer, group_name, stop_event, log_file): + """ + 实时读取日志内容并发送到前端. + :param stop_event: 停止标志 + :param log_file: 日志文件路径 + """ + try: + with open(log_file, "rb") as f: + data = f.read() + if not data: + return + text = _decode_log_bytes(data) + for line in text.splitlines(): + line = line.strip() + if not line: + continue + if is_valid_json(line): + try: + send_to_websocket(channel_layer, group_name, line) + except Exception as e: + send_to_websocket(channel_layer, group_name, {"type": "error", "text": f"JSON 解析错误: {str(e)}"}) + else: + send_to_websocket(channel_layer, group_name, {"type": "waiting", "text": line}) + except FileNotFoundError: + # 日志文件尚未创建时的容错处理 + async_to_sync(channel_layer.group_send)( + group_name, + { + "type": "send_log", + "message": json.dumps( + {"type": "error", "text": "日志文件未找到，请稍后重试！"}, + ensure_ascii=False, + ), + }, + ) + except Exception as e: + async_to_sync(channel_layer.group_send)( + group_name, + { + "type": "send_log", + "message": json.dumps( + {"type": "error", "text": f"日志流式传输错误：{str(e)}"}, + ensure_ascii=False, + ), + }, + ) + +def stream_logs_to_frontend(channel_layer, group_name, stop_event, log_file): + """ + 实时读取日志内容并发送到前端. + :param channel_layer: 通道层实例 + :param group_name: 组名，用于分发消息 + :param stop_event: 停止标志 + :param log_file: 日志文件路径 + """ + try: + def _send_line(line_text: str): + line_text = (line_text or "").strip() + if not line_text: + return + if is_valid_json(line_text): + try: + send_to_websocket(channel_layer, group_name, line_text) + except Exception as e: + send_to_websocket(channel_layer, group_name, {"type": "error", "text": f"JSON 解析错误: {str(e)}"}) + else: + send_to_websocket(channel_layer, group_name, {"type": "waiting", "text": line_text}) + + with open(log_file, "rb") as f: + f.seek(0, os.SEEK_END) + buf = b"" + last_send_ts = 0.0 + while not stop_event.is_set(): + chunk = f.read(4096) + if not chunk: + time.sleep(0.2) + continue + buf += chunk + while True: + rn = buf.find(b"\r") + nn = buf.find(b"\n") + if rn == -1 and nn == -1: + break + if rn == -1: + cut = nn + delim = b"\n" + elif nn == -1: + cut = rn + delim = b"\r" + else: + cut = rn if rn < nn else nn + delim = b"\r" if rn < nn else b"\n" + raw = buf[:cut] + buf = buf[cut + 1 :] + if delim == b"\r" and buf.startswith(b"\n"): + buf = buf[1:] + if not raw: + continue + now_ts = time.time() + if now_ts - last_send_ts < 0.15: + continue + last_send_ts = now_ts + _send_line(_decode_log_bytes(raw)) + except FileNotFoundError: + # 日志文件尚未创建时的容错处理 + async_to_sync(channel_layer.group_send)( + group_name, + { + "type": "send_log", + "message": json.dumps( + {"type": "error", "text": "日志文件未找到，请稍后重试！"}, + ensure_ascii=False, + ), + }, + ) + except Exception as e: + async_to_sync(channel_layer.group_send)( + group_name, + { + "type": "send_log", + "message": json.dumps( + {"type": "error", "text": f"日志流式传输错误：{str(e)}"}, + ensure_ascii=False, + ), + }, + ) + +def remove_ansi_escape_sequences(log_line): + ansi_escape = re.compile(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])') + return ansi_escape.sub('', log_line) +#启动远程训练 +def train_remote_algorithm(train_id): + try: + # 获取训练记录 + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + train_params = json.loads(ai_train.train_params) + params = {item['param_name']: item['param_value'] for item in train_params} + task_call_back_url = settings.TASK_CALL_BACK_URL+"TrainCallBack/" + train_server = "http://"+ai_train.train_server+"/train" + payload = { + 'trainid': ai_train.id, + 'traincode': ai_train.train_code, + 'callparams': task_call_back_url, + 'trainparams': json.dumps(params), + 'train_labels':json.dumps(ai_train.train_labels), + 'train_title':ai_train.train_title, + 'custom2': ai_train.custom2, + 'custom1': ai_train.custom1 + } + headers = { + 'Content-Type': 'application/json' + } + response = requests.post(train_server, json=payload, headers=headers) + # 检查响应状态 + if response.status_code == 200: + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="0200" # 训练中 + ) + return Response({'message': '训练样本数据准备完成！'}) + else: + return Response({'message': '训练样本数据准备失败！'}) + + + except Exception as e: + print(e) + +def update_dfine_configs(config_dir, params): + """ + 自动修改config_dir下的coco_detection.yml和dfine_hgnetv2_x_coco.yml配置文件里的训练参数 + :param config_dir: 配置文件所在目录 + :param params: 训练参数字典，如 device/epochs/imgsz/stop_epoch/num_workers + """ + # coco_detection.yml + coco_file = os.path.join(config_dir, "coco_detection.yml") + if os.path.exists(coco_file): + with open(coco_file, 'r', encoding='utf-8') as f: + yml = yaml.safe_load(f) + # num_workers通常在train_dataloader/val_dataloader下 + for section in ["train_dataloader", "val_dataloader"]: + if section in yml: + if 'num_workers' in yml[section]: + yml[section]['num_workers'] = int(params.get('num_workers', yml[section]['num_workers'])) + with open(coco_file, 'w', encoding='utf-8') as f: + yaml.dump(yml, f, allow_unicode=True, sort_keys=False) + print(f"[修改配置] {coco_file} 完成") + + # dfine_hgnetv2_x_coco.yml + for name in os.listdir(config_dir): + if name.startswith("dfine_hgnetv2_") and name.endswith("_coco.yml"): + file_path = os.path.join(config_dir, name) + with open(file_path, 'r', encoding='utf-8') as f: + yml = yaml.safe_load(f) + # 常见训练参数 + if 'epochs' in params: + yml['epochs'] = int(params['epochs']) + if 'stop_epoch' in params: + yml['stop_epoch'] = int(params['stop_epoch']) + if 'imgsz' in params: + yml['imgsz'] = int(params['imgsz']) + if 'device' in params: + yml['device'] = params['device'] + # output_dir常常自定义，可用原有值或指定你的输出路径 + if 'output_dir' in yml and 'output_dir' in params: + yml['output_dir'] = params['output_dir'] + with open(file_path, 'w', encoding='utf-8') as f: + yaml.dump(yml, f, allow_unicode=True, sort_keys=False) + print(f"[修改配置] {file_path} 完成") +def train_algorithm_dfine(train_id): + """ + 训练DFINE模型（支持实时日志、WebSocket、日志目录、配置文件参数自动写入） + """ + try: + # 获取训练记录 + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + train_params = json.loads(ai_train.train_params) + params = {item['param_name']: item['param_value'] for item in train_params} + + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join( + filespace_root_path, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + 'trains', + ai_train.train_code + ) + # 配置文件路径 + config_dir = os.path.join(train_path, "config") + model_path = next((item['param_value'] for item in train_params if item['param_name'] == 'model'), None) + if not model_path: + raise ValueError("训练参数中缺少 'model' 字段，请检查训练配置！") + model_path = os.path.join(config_dir, model_path) + # 项目目录 + project_path = os.path.join(train_path, "output") + run_name = "train" + + # 确保训练目录存在且为空 + if os.path.exists(project_path): + shutil.rmtree(project_path) + os.makedirs(project_path, exist_ok=True) + print('日志目录', project_path) + log_file_path = os.path.join(project_path, f"{run_name}.log") + if os.path.exists(log_file_path): + os.remove(log_file_path) + with open(log_file_path, 'w', encoding='utf-8') as log_file: + log_file.write("") + os.chmod(log_file_path, 0o777) + + # ---------- 自动修改配置文件 ---------- + update_dfine_configs(config_dir, params) + # return Response({'message': '训练开始！'}) + # ---------- 组装D-FINE训练命令 ---------- + parent_dir = os.path.dirname(os.path.dirname(__file__)) + # 假设你用的是 train.py + train_script = os.path.join(parent_dir,'algorithm', 'DFINE', 'train.py') + command = [ + sys.executable, + train_script, + '-c', model_path, + '--device', params.get('device', 'cpu'), + '--seed', str(params.get('seed', 0)), + '--trainid', ai_train.id, + '--project_path', project_path, + ] + # # D-FINE通常主配置里控制epochs、imgsz等，如果需要命令行再带，也可加 + # if 'epochs' in params: + # command += ['--epochs', str(params['epochs'])] + # if 'imgsz' in params: + # command += ['--imgsz', str(params['imgsz'])] + # if 'stop_epoch' in params: + # command += ['--stop_epoch', str(params['stop_epoch'])] + # if 'num_workers' in params: + # command += ['--num_workers', str(params['num_workers'])] + + print("训练命令：", " ".join(command)) + + try: + # 执行训练脚本 + _launch_training_process(train_id, command, log_file_path) + except Exception as e: + print(f"Unexpected error: {e}") + except Exception as e: + print(e) + # send_to_websocket(channel_layer, group_name, f"Error: {str(e)}\n") +def train_algorithm(train_id): + """ + 训练算法模型。 + + 根据给定的训练ID，加载训练参数和数据集，然后启动训练过程。 + 训练过程中，通过WebSocket实时向前端发送训练日志和结果。 + 训练完成后，保存模型和训练结果，并生成训练报告。 + + 参数:startAlgorithmTrainstartAlgorithmTrain + - train_id: int, 训练记录的ID。 + + 返回值: + 无 + """ + try: + # 获取训练记录 + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + train_params = json.loads(ai_train.train_params) + params = {item['param_name']: item['param_value'] for item in train_params} + + # 设置路径 + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join( + filespace_root_path, + ai_train.train_code + ) + model_path = next((item['param_value'] for item in train_params if item['param_name'] == 'model'), "yolo11n.pt") + dataset_path = os.path.join(train_path, "yolo_config.yaml") + project_path = os.path.join(train_path, "runs") + run_name = "train" + + if model_path is None: + raise ValueError("训练参数中缺少 'model' 字段，请检查训练配置！") + + # 确保训练目录存在且为空 + if os.path.exists(project_path): + shutil.rmtree(project_path) + os.makedirs(project_path, exist_ok=True) + train_labels = json.loads(ai_train.train_labels) + print('日志目录',project_path) + log_file_path = os.path.join(project_path, f"{run_name}.log") + # 如果日志文件已存在，删除旧文件 + if os.path.exists(log_file_path): + os.remove(log_file_path) + + with open(log_file_path, 'w', encoding='utf-8') as log_file: + log_file.write("") + os.chmod(log_file_path, 0o777) + + from pathlib import Path + + apps_dir = Path(__file__).resolve().parents[2] + YoloTrainerUrl = str(apps_dir / "algorithms" / "algorithm" / "pytorch" / "YoloTrainer.py") + if not os.path.isfile(YoloTrainerUrl): + raise FileNotFoundError(f"YoloTrainer.py 不存在: {YoloTrainerUrl}") + command = [ + sys.executable, # 使用当前 Python 解释器 + "-u", + YoloTrainerUrl, # 训练脚本文件名 + '--model', model_path, + '--data', dataset_path, + '--project_path', project_path, + '--run_name', run_name, + '--epochs', str(params.get('epochs', 200)), + '--batch', str(params.get('batch', 8)), + '--imgsz', str(params.get('imgsz', 640)), + '--workers', str(params.get('workers', 4)), + '--device', params.get('device', 'cpu'), + '--trainid', ai_train.id + ] + + try: + ultralytics_config_dir = os.path.join(project_path, ".ultralytics") + os.makedirs(ultralytics_config_dir, exist_ok=True) + env = os.environ.copy() + env["YOLO_CONFIG_DIR"] = ultralytics_config_dir + _launch_training_process(train_id, command, log_file_path, env=env) + except Exception as e: + print(f"Unexpected error: {e}") + except Exception as e: + print(e) + # send_to_websocket(channel_layer, group_name, f"Error: {str(e)}\n") + + +def train_algorithm_yoloseg(train_id): + try: + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + train_params = json.loads(ai_train.train_params) + params = {item['param_name']: item['param_value'] for item in train_params} + + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join( + filespace_root_path, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + 'trains', + ai_train.train_code + ) + model_path = next((item['param_value'] for item in train_params if item['param_name'] == 'model'), None) + dataset_path = os.path.join(train_path, "yolo_config.yaml") + project_path = os.path.join(train_path, "runs") + run_name = "train" + + if model_path is None: + raise ValueError("训练参数中缺少 'model' 字段，请检查训练配置！") + + if os.path.exists(project_path): + shutil.rmtree(project_path) + os.makedirs(project_path, exist_ok=True) + log_file_path = os.path.join(project_path, f"{run_name}.log") + if os.path.exists(log_file_path): + os.remove(log_file_path) + with open(log_file_path, 'w', encoding='utf-8') as log_file: + log_file.write("") + os.chmod(log_file_path, 0o777) + + from pathlib import Path + apps_dir = Path(__file__).resolve().parents[2] + seg_trainer_path = str(apps_dir / "algorithms" / "algorithm" / "pytorch" / "YoloSegTrainer.py") + if not os.path.isfile(seg_trainer_path): + raise FileNotFoundError(f"YoloSegTrainer.py 不存在: {seg_trainer_path}") + + command = [ + sys.executable, + "-u", + seg_trainer_path, + '--model', model_path, + '--data', dataset_path, + '--project_path', project_path, + '--run_name', run_name, + '--epochs', str(params.get('epochs', 200)), + '--batch', str(params.get('batch', 8)), + '--imgsz', str(params.get('imgsz', 640)), + '--workers', str(params.get('workers', 4)), + '--device', params.get('device', '0'), + '--trainid', ai_train.id + ] + + try: + ultralytics_config_dir = os.path.join(project_path, ".ultralytics") + os.makedirs(ultralytics_config_dir, exist_ok=True) + env = os.environ.copy() + env["YOLO_CONFIG_DIR"] = ultralytics_config_dir + _launch_training_process(train_id, command, log_file_path, env=env) + except Exception as e: + print(f"Unexpected error: {e}") + except Exception as e: + print(e) + + +def train_algorithm_deeplab(train_id): + try: + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + train_params = json.loads(ai_train.train_params) + params = {item['param_name']: item['param_value'] for item in train_params} + + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join( + filespace_root_path, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + 'trains', + ai_train.train_code + ) + + dataset_path = train_path + project_path = os.path.join(train_path, "runs") + run_name = "train" + torch_home = params.get("torch_home") or os.path.join(train_path, "torch_cache") + pretrained = params.get("pretrained", "1") + pretrained_weights = params.get("pretrained_weights", "") + default_workers = 0 if os.name == "nt" else 4 + deeplab_workers = params.get("workers", default_workers) + + if os.path.exists(project_path): + shutil.rmtree(project_path) + os.makedirs(project_path, exist_ok=True) + os.makedirs(torch_home, exist_ok=True) + + log_file_path = os.path.join(project_path, f"{run_name}.log") + if os.path.exists(log_file_path): + os.remove(log_file_path) + with open(log_file_path, 'w', encoding='utf-8') as log_file: + log_file.write("") + os.chmod(log_file_path, 0o777) + + from pathlib import Path + apps_dir = Path(__file__).resolve().parents[2] + trainer_path = str(apps_dir / "algorithms" / "algorithm" / "pytorch" / "DeeplabTrainer.py") + if not os.path.isfile(trainer_path): + raise FileNotFoundError(f"DeeplabTrainer.py 不存在: {trainer_path}") + + if pretrained_weights and not os.path.isabs(str(pretrained_weights)): + pretrained_weights = os.path.join(train_path, str(pretrained_weights)) + + command = [ + sys.executable, + "-u", + trainer_path, + "--data", dataset_path, + "--project_path", project_path, + "--run_name", run_name, + "--epochs", str(params.get("epochs", 50)), + "--batch", str(params.get("batch", 4)), + "--imgsz", str(params.get("imgsz", 640)), + "--workers", str(deeplab_workers), + "--device", str(params.get("device", "cpu")), + "--lr_backbone", str(params.get("lr_backbone", 1e-4)), + "--lr_head", str(params.get("lr_head", 1e-3)), + "--weight_decay", str(params.get("weight_decay", 1e-4)), + "--pretrained", str(pretrained), + "--torch_home", str(torch_home), + "--trainid", ai_train.id, + ] + if pretrained_weights: + command += ["--pretrained_weights", str(pretrained_weights)] + + try: + env = os.environ.copy() + env["TORCH_HOME"] = str(torch_home) + _launch_training_process(train_id, command, log_file_path, env=env) + except Exception as e: + print(f"Unexpected error: {e}") + except Exception as e: + print(e) +#本地训练中方法 +def training_algorithm(train_id): + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + + train_path = os.path.join( + settings.FILESPACE_ROOT_PATH, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + "trains", + ai_train.train_code, + ) + run_name = "train" + if getattr(ai_algorithm, "algorithm_engine", None) == "02": + project_path = os.path.join(train_path, "output") + else: + project_path = os.path.join(train_path, "runs") + log_file_path = os.path.join(project_path, f"{run_name}.log") + + if not os.path.exists(log_file_path): + return + + channel_layer = get_channel_layer() + group_name = f"train_{train_id}" + + initial_size = os.path.getsize(log_file_path) + if initial_size <= 0: + return + + with open(log_file_path, "rb") as f: + buf = f.read(initial_size) + + for raw_line in buf.splitlines(): + line = _decode_log_bytes(raw_line).strip() + if not line: + continue + if is_valid_json(line): + send_to_websocket(channel_layer, group_name, line) + else: + send_to_websocket(channel_layer, group_name, {"type": "waiting", "text": line}) + +#远程训练中方法 +def training_remote_algorithm(train_id): + try: + # 获取训练记录 + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + train_params = json.loads(ai_train.train_params) + params = {item['param_name']: item['param_value'] for item in train_params} + task_call_back_url = settings.TASK_CALL_BACK_URL + train_server = "http://"+ai_train.train_server+"/training" + payload = { + 'trainid': ai_train.id, + 'traincode': ai_train.train_code + } + headers = { + 'Content-Type': 'application/json' + } + response = requests.post(train_server, json=payload, headers=headers) + # 检查响应状态 + if response.status_code == 200: + return Response({'message': '训练中！'}) + else: + return Response({'message': '训练失败！'}) + except Exception as e: + print(e) + + +# API 方法：启动训练 +@api_view(['POST']) +@permission_classes([IsAuthenticated]) +def start_algorithm_train(request): + try: + train_id = request.data.get('id') + if not train_id: + return Response({'error': '缺少参数：id'}, status=400) + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + process_info = training_processes.get(train_id) + if process_info and process_info.get("process") and process_info["process"].poll() is not None: + training_processes.pop(train_id, None) + process_info = None + # 如果已经处于训练中，则直接复用当前训练任务；日志回放由 websocket consumer 负责。 + if ai_train.record_status=="0200": + if process_info and process_info.get("process") and process_info["process"].poll() is None: + return Response({'message': '训练进行中！'}) + AiAlgorithmTrainRecords.objects.filter(id=train_id).update(record_status="01") + + # 更新训练记录 + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + model_trainresult="模型训练中！", + record_status="0200" + ) + if ai_train.train_type == "local": + # 创建线程并启动本地训练 + print('本地训练中') + if ai_algorithm.algorithm_engine=="01": + thread = threading.Thread(target=train_algorithm, args=(train_id,)) + thread.start() + elif ai_algorithm.algorithm_engine=="02": + thread = threading.Thread(target=train_algorithm_dfine, args=(train_id,)) + thread.start() + elif ai_algorithm.algorithm_engine=="03": + thread = threading.Thread(target=train_algorithm_deeplab, args=(train_id,)) + thread.start() + elif ai_algorithm.algorithm_engine=="04": + thread = threading.Thread(target=train_algorithm_yoloseg, args=(train_id,)) + thread.start() + else: + # 创建线程并启动远程训练 + if ai_algorithm.algorithm_engine == "01": + thread = threading.Thread(target=train_remote_algorithm, args=(train_id,)) + thread.start() + else: + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + model_trainresult="样本准备完成！", + record_status="01" + ) + return Response({'message': '当前类别的模型不支持远程训练！'}) + + return Response({'message': '训练启动成功！'}) + except Exception as e: + return Response({'error': '启动训练过程中发生错误', 'details': str(e)}, status=500) +def get_file_extension(filename): + _, file_extension = os.path.splitext(filename) + return file_extension.lower() if file_extension.lower() in ['.jpg', '.png'] else '.jpg' +# API 方法：启动训练 +@api_view(['POST']) +@permission_classes([IsAuthenticated]) +def close_train_model(request): + try: + train_id = request.query_params.get('train_id') + if not train_id: + return Response({'error': '缺少参数：id'}, status=400) + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + if ai_train.train_type=="local": + if train_id in training_processes: + process_info = training_processes[train_id] + process = process_info["process"] + stop_event=process_info["stop_event"] + stop_event.set() + _terminate_training_process(process) + # 清理训练进程记录 + training_processes.pop(train_id, None) + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="01" # 回滚到样本准备完成 + ) + return Response({'message': '训练记录终止成功！'}) + else: + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="01" # 回滚到样本准备完成 + ) + return Response({'message': '训练记录终止成功！'}) + else: + train_server = "http://" + ai_train.train_server + "/closetrain" + payload = { + 'trainid': ai_train.id, + 'traincode': ai_train.train_code + } + headers = { + 'Content-Type': 'application/json' + } + response = requests.post(train_server, json=payload, headers=headers) + # 检查响应状态 + if response.status_code == 200: + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="01" # 回滚到样本准备完成 + ) + return Response({'message': '训练记录终止成功！'}) + else: + return Response({'message': '训练记录终止失败！'}) + except Exception as e: + return Response({'error': '终止训练过程中发生错误', 'details': str(e)}, status=500) + +@api_view(['POST']) +@parser_classes([JSONParser, MultiPartParser, FormParser]) +def prepare_callback(request): + try: + # 获取 trainid 参数 + train_id = request.data.get('trainid') + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="01" # 修改成样本准备完成 + ) + return JsonResponse({"message": "File successfully uploaded and extracted"}, status=200) + except Exception as e: + return JsonResponse({"error": str(e)}, status=400) +# API 方法：远程训练回调方法 +@api_view(['POST']) +@parser_classes([MultiPartParser]) +def train_callback(request): + try: + # 获取 trainid 参数 + train_id = request.data.get('trainid') + file_data = request.data.get('file') + train_start_time=request.data.get('train_start_time') + train_end_time = request.data.get('train_end_time') + train_total_times = request.data.get('train_total_times') + # print(train_id) + # 获取训练记录 + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + train_params = json.loads(ai_train.train_params) + params = {item['param_name']: item['param_value'] for item in train_params} + + # 设置路径 + filespace_root_path = settings.FILESPACE_ROOT_PATH + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join( + filespace_root_path, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + 'trains', + ai_train.train_code + ) + model_path = next((item['param_value'] for item in train_params if item['param_name'] == 'model'), None) + dataset_path = os.path.join(train_path, "yolo_config.yaml") + project_path = os.path.join(train_path, "runs") + run_name = "train" + # 确保训练目录存在且为空 + if os.path.exists(project_path): + shutil.rmtree(project_path) + os.makedirs(project_path, exist_ok=True) + model_filepath = os.path.join(project_path.replace(filespace_root_path, "")[1:], run_name, "weights").replace( + "\\", "/") + model_filename = "best.pt" + # 更新训练记录 + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + train_start_time=datetime.strptime(train_start_time, '%Y-%m-%d %H-%M-%S'), + train_end_time=datetime.strptime(train_end_time, '%Y-%m-%d %H-%M-%S'), + train_total_times=float(train_total_times), + model_createtime=datetime.strptime(train_end_time, '%Y-%m-%d %H-%M-%S'), + model_filepath=model_filepath, + model_filename=model_filename, + model_trainresult="模型训练完成！", + record_status="02", + + ) + # 构造保存文件的路径 + save_path = os.path.join(project_path, run_name, 'weights', f"{train_id}.zip") + # 确保目录存在 + os.makedirs(os.path.dirname(save_path), exist_ok=True) + # 保存上传的zip文件 + with open(save_path, 'wb') as f: + for chunk in file_data.chunks(): + f.write(chunk) + with zipfile.ZipFile(save_path, 'r') as zip_ref: + zip_ref.extractall(os.path.join(project_path,run_name,'weights')) + + # 解压后删除原始的zip文件 + os.remove(save_path) + return JsonResponse({"message": "File successfully uploaded and extracted"}, status=200) + except Exception as e: + return JsonResponse({"error": str(e)}, status=400) +# API 方法：对象检测 +@api_view(['POST']) +@permission_classes([IsAuthenticated]) +@parser_classes([MultiPartParser]) +def detect_image_object(request): + if 'image' not in request.data: + return Response({'error': 'No image file provided'}, status=status.HTTP_400_BAD_REQUEST) + image_file = request.data['image'] + train_id = request.data.get('id') + temp_file_path = None + try: + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + if ai_train.train_type == "local": + filespace_root_path = settings.FILESPACE_ROOT_PATH + + with tempfile.NamedTemporaryFile(delete=False, suffix=get_file_extension(image_file.name)) as temp_file: + temp_file_path = temp_file.name + temp_file.write(image_file.read()) + if ai_algorithm.algorithm_engine == '01': + model_path = os.path.join( + filespace_root_path, + ai_train.model_filepath, + ai_train.model_filename + ) + if YoloDetector is None: + return Response( + {"error": "算法依赖未安装，请先安装 requirements/algorithms.txt"}, + status=503, + ) + detector = YoloDetector(model_path=model_path, image_path=temp_file_path, labels=ai_train.train_labels) + result = detector.detect() + elif ai_algorithm.algorithm_engine == '02': + model_path = os.path.join( + ai_train.model_filepath, + ai_train.model_filename + ) + train_params = json.loads(ai_train.train_params) + + ai_algorithm = get_object_or_404(AiAlgorithm, id=ai_train.algorithm_id) + ai_algorithm_class = get_object_or_404(AiAlgorithmClass, id=ai_algorithm.algorithm_class) + train_path = os.path.join( + filespace_root_path, + ai_algorithm_class.classcode, + ai_algorithm.algorithm_code, + 'trains', + ai_train.train_code + ) + # 配置文件路径 + config_dir = os.path.join(train_path, "config") + config_yml_path = next((item['param_value'] for item in train_params if item['param_name'] == 'model'), None) + config_path = os.path.join(config_dir, config_yml_path) + if DfineDetector is None: + return Response( + {"error": "算法依赖未安装，请先安装 requirements/algorithms.txt"}, + status=503, + ) + detector = DfineDetector(config_path=config_path, model_path=model_path, label_map=ai_train.train_labels, device="cpu", score_thresh=0.1) + result = detector.detect(temp_file_path) + elif ai_algorithm.algorithm_engine == '04': + model_path = os.path.join( + filespace_root_path, + ai_train.model_filepath, + ai_train.model_filename + ) + if YOLO is None: + return Response( + {"error": "算法依赖未安装，请先安装 requirements/algorithms.txt"}, + status=503, + ) + if cv2 is None: + return Response( + {"error": "算法依赖未安装，请先安装 opencv-python"}, + status=503, + ) + model = YOLO(model_path) + outputs = model.predict(source=temp_file_path, save=False, conf=0.2) + labels = json.loads(ai_train.train_labels) if ai_train.train_labels else [] + pred_result = [] + detection_image = "" + for output in outputs: + plotted = output.plot() + if plotted is not None: + ok, img_encoded = cv2.imencode('.jpg', plotted) + if ok: + detection_image = base64.b64encode(img_encoded.tobytes()).decode('utf-8') + boxes_data = output.boxes.data.tolist() if getattr(output, "boxes", None) is not None else [] + polygons = output.masks.xy if getattr(output, "masks", None) is not None and getattr(output.masks, "xy", None) is not None else [] + for idx, data in enumerate(boxes_data): + name_key = int(data[5]) if len(data) > 5 else -1 + code = output.names.get(name_key) if isinstance(output.names, dict) else str(name_key) + name = next((item["name"] for item in labels if isinstance(item, dict) and item.get("label") == code), None) + poly_points = [] + if idx < len(polygons): + poly_points = [[float(p[0]), float(p[1])] for p in polygons[idx]] + one_box = { + 'code': code, + 'name': name, + 'score': round(float(data[4]), 2) if len(data) > 4 else 0.0, + 'x1': int(data[0]) if len(data) > 0 else 0, + 'y1': int(data[1]) if len(data) > 1 else 0, + 'x2': int(data[2]) if len(data) > 2 else 0, + 'y2': int(data[3]) if len(data) > 3 else 0, + 'polygon': poly_points, + } + pred_result.append(one_box) + result = json.dumps({ + 'image': detection_image, + 'obj_list': pred_result, + 'time': datetime.now().strftime('%Y-%m-%d %H:%M:%S') + }, ensure_ascii=False) + else: + return Response({'message': f"当前引擎不支持在线推理：{ai_algorithm.algorithm_engine}"}, status=400) + return Response(result) + else: + detect_server = "http://" + ai_train.train_server + "/detect" + model_path = "/workspace/traindataset/" + ai_train.train_code + "/runoutput/train/weights/best.pt" + files = { + 'image': (image_file.name, image_file, image_file.content_type), + } + data = { + 'model_path': model_path, + 'train_labels': ai_train.train_labels, + } + response = requests.post(detect_server, files=files, data=data) + if response.status_code == 200: + result = response.json() + return Response(result) + else: + return Response({'message': '模型推理失败！', 'details': response.text}, status=400) + except Exception as e: + return Response({'message': '模型推理失败！', 'details': str(e)}, status=500) + finally: + if temp_file_path and os.path.exists(temp_file_path): + os.remove(temp_file_path) + + +def _read_train_param(train_params_text, key, default_value): + try: + params = json.loads(train_params_text) if train_params_text else [] + except Exception: + return default_value + if isinstance(params, dict): + return params.get(key, default_value) + if isinstance(params, list): + for item in params: + if not isinstance(item, dict): + continue + if str(item.get("param_name")) != str(key): + continue + value = item.get("param_value") + if value in (None, ""): + return default_value + return value + return default_value + + +def _archive_log(step, **kwargs): + payload = {"type": "archive", "step": str(step)} + payload.update(kwargs) + print(json.dumps(payload, ensure_ascii=False)) + + +def _infer_num_classes(train_labels_text): + try: + labels = json.loads(train_labels_text) if train_labels_text else [] + except Exception: + return 2 + if isinstance(labels, dict): + size = len(labels.keys()) + return max(2, int(size)) + if not isinstance(labels, list): + return 2 + label_names = set() + for item in labels: + if isinstance(item, dict): + name = str(item.get("label") or item.get("name") or "").strip() + if name: + label_names.add(name) + elif isinstance(item, str): + name = item.strip() + if name: + label_names.add(name) + return max(2, len(label_names) + 1) + + +def _export_onnx_from_pt(src_file, onnx_file, ai_train): + _archive_log("onnx_export_pt_start", src_file=src_file, onnx_file=onnx_file) + repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../..")) + ultralytics_repo = os.path.join(repo_root, "ultralytics_yolo11") + if not os.path.isdir(ultralytics_repo): + raise FileNotFoundError(f"未找到仓库内 ultralytics_yolo11 目录：{ultralytics_repo}") + imgsz = int(_read_train_param(ai_train.train_params, "imgsz", 640)) + default_cfg_path = os.path.join(ultralytics_repo, "ultralytics", "cfg", "default.yaml") + if not os.path.exists(default_cfg_path): + raise FileNotFoundError(f"未找到 default.yaml：{default_cfg_path}") + with open(default_cfg_path, "r", encoding="utf-8") as f: + old_cfg_text = f.read() + cfg_data = yaml.safe_load(old_cfg_text) or {} + cfg_data["model"] = str(src_file).replace("\\", "/") + cfg_data["format"] = "rknn" + cfg_data["batch"] = 1 + cfg_data["imgsz"] = int(imgsz) + cfg_data["dynamic"] = False + _archive_log("onnx_export_pt_cfg", model=cfg_data["model"], format=cfg_data["format"], batch=cfg_data["batch"], imgsz=cfg_data["imgsz"]) + with open(default_cfg_path, "w", encoding="utf-8") as f: + yaml.dump(cfg_data, f, allow_unicode=True, sort_keys=False) + env = os.environ.copy() + env["PYTHONPATH"] = ultralytics_repo + (os.pathsep + env["PYTHONPATH"] if env.get("PYTHONPATH") else "") + try: + process = subprocess.run( + [sys.executable, "ultralytics/engine/exporter.py"], + cwd=ultralytics_repo, + capture_output=True, + text=True, + encoding="utf-8", + errors="ignore", + env=env, + ) + finally: + with open(default_cfg_path, "w", encoding="utf-8") as f: + f.write(old_cfg_text) + stdout_text = process.stdout or "" + stderr_text = process.stderr or "" + _archive_log( + "onnx_export_pt_subprocess_done", + returncode=int(process.returncode), + stdout_tail=stdout_text[-600:], + stderr_tail=stderr_text[-600:], + ) + if process.returncode != 0: + raise RuntimeError(f"仓库版 ultralytics_yolo11 导出失败：{stderr_text or stdout_text}") + export_path = os.path.splitext(src_file)[0] + ".onnx" + if not os.path.isabs(export_path): + export_path = os.path.abspath(export_path) + _archive_log("onnx_export_pt_done", export_path=export_path) + if not os.path.exists(export_path): + raise FileNotFoundError(f"未找到导出的 ONNX 文件：{export_path}") + if os.path.abspath(export_path) != os.path.abspath(onnx_file): + shutil.copy2(export_path, onnx_file) + if not os.path.exists(onnx_file): + raise FileNotFoundError(f"未找到目标 ONNX 文件：{onnx_file}") + + +def _export_onnx_from_pth(src_file, onnx_file, ai_train): + import torch + import torch.nn as nn + from torchvision.models.segmentation import deeplabv3_resnet50 + + num_classes = _infer_num_classes(ai_train.train_labels) + imgsz = int(_read_train_param(ai_train.train_params, "imgsz", 640)) + _archive_log("onnx_export_pth_start", src_file=src_file, onnx_file=onnx_file, num_classes=num_classes, imgsz=imgsz) + model = deeplabv3_resnet50(weights=None, aux_loss=True) + model.classifier[4] = nn.Conv2d(in_channels=256, out_channels=num_classes, kernel_size=1) + model.aux_classifier[4] = nn.Conv2d(in_channels=256, out_channels=num_classes, kernel_size=1) + + try: + state = torch.load(src_file, map_location="cpu", weights_only=True) + except TypeError: + state = torch.load(src_file, map_location="cpu") + if isinstance(state, dict) and isinstance(state.get("state_dict"), dict): + state = state.get("state_dict") + missing_keys, unexpected_keys = model.load_state_dict(state, strict=False) + if missing_keys or unexpected_keys: + _archive_log( + "onnx_export_pth_weight_mismatch", + missing=int(len(missing_keys)), + unexpected=int(len(unexpected_keys)), + ) + raise RuntimeError( + f"pth 权重与导出模型结构不一致，missing={len(missing_keys)} unexpected={len(unexpected_keys)}" + ) + model.eval() + + class DeeplabExportWrapper(nn.Module): + def __init__(self, wrapped): + super().__init__() + self.wrapped = wrapped + + def forward(self, x): + out = self.wrapped(x) + return out["out"], out["aux"] + + wrapped_model = DeeplabExportWrapper(model).eval() + dummy_input = torch.randn(1, 3, imgsz, imgsz, dtype=torch.float32) + with torch.no_grad(): + _ = wrapped_model(dummy_input) + with torch.no_grad(): + torch.onnx.export( + wrapped_model, + dummy_input, + onnx_file, + opset_version=12, + input_names=["images"], + output_names=["out", "aux"], + do_constant_folding=True, + ) + try: + import onnx + + onnx_model = onnx.load(onnx_file) + onnx.checker.check_model(onnx_model) + except Exception as e: + raise RuntimeError(f"ONNX 校验失败：{str(e)}") + if not os.path.exists(onnx_file): + raise FileNotFoundError(f"未找到目标 ONNX 文件：{onnx_file}") + _archive_log("onnx_export_pth_done", onnx_file=onnx_file) + + +def _export_onnx_for_archive(src_file, onnx_file, ai_train): + ext = os.path.splitext(src_file)[1].lower() + _archive_log("onnx_export_dispatch", ext=ext, src_file=src_file) + if ext == ".pt": + _export_onnx_from_pt(src_file, onnx_file, ai_train) + elif ext == ".pth": + _export_onnx_from_pth(src_file, onnx_file, ai_train) + else: + raise ValueError(f"不支持的模型后缀：{ext}") + try: + os.chmod(onnx_file, 0o777) + except Exception: + pass + _archive_log("onnx_export_success", onnx_file=onnx_file) + return True + + +# API 方法：归档已训练完成的模型 +@api_view(['POST']) +@permission_classes([IsAuthenticated]) +@transaction.atomic +def archive_train_model(request): + # 获取传递的训练记录 ID + train_id = request.data.get('id') + _archive_log("archive_start", train_id=train_id) + + # 获取训练记录对象 + ai_train = get_object_or_404(AiAlgorithmTrainRecords, id=train_id) + + # 根据算法 ID 获取相关算法模型记录 + algorithm_id = ai_train.algorithm_id + algorithm_name = ai_train.algorithm_name + + max_model_code = AiAlgorithmModels.objects.filter(algorithm_id=algorithm_id).aggregate( + max_model_code=Max(Cast("model_version", CharField())) + ).get("max_model_code") + # 转换为整数 + max_model_code = int(max_model_code) if max_model_code is not None else 0 + new_model_code = f"{max_model_code + 1:03}" + model_version = new_model_code + + try: + from apps.common.minio_client import minio_storage + except Exception: + from common.minio_client import minio_storage + + src_bucket = 'trained-model' + dst_bucket = 'archived-model' + + model_bucketpath = ai_train.model_bucketpath + report_bucketpath = ai_train.report_bucketpath + + if not model_bucketpath: + _archive_log("invalid_model_bucketpath", model_bucketpath=model_bucketpath) + return Response({'message': '训练记录中没有有效的模型文件存储路径'}, status=400) + + safe_algorithm_name = re.sub(r'[^\w\-]', '-', algorithm_name).strip('-') + if not safe_algorithm_name: + safe_algorithm_name = 'unknown' + + ext = os.path.splitext(ai_train.model_filename)[1] or ".pt" + new_model_name = f"model-{model_version}{ext}" + + dst_model_path = f"{safe_algorithm_name}/{model_version}/{new_model_name}" + dst_report_path = f"{safe_algorithm_name}/{model_version}/train_report.html" + + _archive_log("archive_paths", + src_bucket=src_bucket, + src_model=model_bucketpath, + src_report=report_bucketpath, + dst_bucket=dst_bucket, + dst_model=dst_model_path, + dst_report=dst_report_path) + + try: + minio_storage.copy_object(src_bucket, model_bucketpath, dst_bucket, dst_model_path) + _archive_log("archive_model_copied", dst_model=dst_model_path) + except Exception as e: + _archive_log("archive_model_copy_failed", error=str(e)) + return Response({'message': f"模型文件复制失败: {str(e)}"}, status=500) + + if report_bucketpath: + try: + minio_storage.copy_object(src_bucket, report_bucketpath, dst_bucket, dst_report_path) + _archive_log("archive_report_copied", dst_report=dst_report_path) + except Exception as e: + _archive_log("archive_report_copy_failed", error=str(e)) + + AiAlgorithmModels.objects.create( + id=str(uuid.uuid4()), + algorithm_id=algorithm_id, + model_version=model_version, + model_filename=new_model_name, + model_filepath=dst_model_path, + model_bucketpath=f"{dst_bucket}/{dst_model_path}", + report_bucketpath=f"{dst_bucket}/{dst_report_path}" if report_bucketpath else None, + model_labels=ai_train.train_labels, + model_createtime=ai_train.model_createtime, + evaluation_metrics=ai_train.evaluation_metrics, + evaluation_comments=ai_train.evaluation_comments, + description=f"{algorithm_name} 算法模型归档，版本 {model_version}", + model_status='02', + model_usednumber=0, + model_stars=None, + call_duration=None, + uploader=request.user.username if request.user.username else 'uploader', + upload_time=datetime.now(), + is_using=None, + custom1=None, + custom2=None, + custom3=None, + ) + _archive_log("archive_db_saved", model_version=model_version, model_filename=new_model_name) + + AiAlgorithmTrainRecords.objects.filter(id=train_id).update( + record_status="05" + ) + + return Response({'message': '训练记录更新成功，模型已归档'}) diff --git a/backend/config/__init__.py b/backend/config/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/config/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/config/api_urls.py b/backend/config/api_urls.py new file mode 100644 index 0000000..ef66053 --- /dev/null +++ b/backend/config/api_urls.py @@ -0,0 +1,338 @@ +from django.urls import path + +from apps.algorithms.views.algorithm import ( + add_algorithm, + delete_algorithm, + get_algorithm_engines, + get_algorithm_list, + get_algorithm_tree, + read_algorithm, + search_algorithm, + update_algorithm, +) +from apps.algorithms.views.algorithm_calllog import ( + create_algorithm_call_log, + delete_algorithm_call_log, + read_algorithm_call_log, + search_algorithm_call_logs, +) +from apps.algorithms.views.algorithm_class import ( + create_algorithm_class, + delete_algorithm_class, + read_algorithm_class, + search_algorithm_class, + update_algorithm_class, +) +from apps.algorithms.views.algorithm_models import ( + algorithm_model_predict, + convert_algorithm_model_onnx, + delete_algorithm_model, + delete_predict_default_image, + download_published_onnx_model, + export_algorithm_model, + get_algorithm_default_images, + issue_algorithm_model, + publish_model_to_terminals, + read_algorithm_model, + search_algorithm_model, + set_algorithm_default_model, + update_algorithm_model, + upload_algorithm_modelfile, + upload_predict_default_images, +) +from apps.algorithms.views.algorithm_output import ( + create_algorithm_output, + delete_algorithm_output, + read_algorithm_output, + search_algorithm_output, + update_algorithm_output, +) +from apps.algorithms.views.algorithm_params import ( + create_algorithm_params, + delete_algorithm_params, + read_algorithm_params, + search_algorithm_params, + update_algorithm_params, +) +from apps.annotation.views.annotate_team import ( + add_team_members, + create_annotate_team, + delete_annotate_team, + delete_team_members, + get_team_members, + read_annotate_team, + search_annotate_team, + update_annotate_team, + update_team_member, +) +from apps.datasets.views.dataset import ( + create_dataset, + delete_dataset, + delete_dataset_updatefile, + generate_dataset_images, + get_dataset_list, + import_dataset_from_temp_file, + read_dataset, + search_datasets, + update_dataset, + upload_dataset_file, +) +from apps.datasets.views.dataset_sample import ( + delete_duplicate_samples, + download_dataset_sample, + delete_invalid_samples, + preview_dataset_sample, + read_dataset_sample, + search_dataset_samples, + update_dataset_sample, +) +from apps.datasets.views.annotate_task import ( + create_annotate_task, + delete_annotate_task, + get_mytask_annotate_list, + get_mytask_audit_list, + read_annotate_task, + search_annotate_tasks, + start_auto_annotate_task, + update_annotate_task, +) +from apps.datasets.views.annotate_task_sample import ( + annotate_task_sample, + audit_task_samples, + delete_task_samples, + merge_sample_content, + query_person_tasks, + read_task_sample, + search_task_samples, + set_person_task, + submit_task_samples, +) +from apps.datasets.views.temp_file import ( + delete_temp_file, + download_temp_file, + preview_temp_file, + read_temp_file, + search_temp_files, + update_temp_file, + upload_temp_file, +) +from apps.cloud_terminal.smart_terminal import ( + check_terminal_model_version, + create_smart_terminal, + delete_smart_terminal, + download_terminal_model, + publish_terminal_model, + search_smart_terminals, + terminal_device_auth, + update_smart_terminal, + update_terminal_heartbeat, + update_terminal_model_version, +) +from apps.iam.views.organization import ( + add_organization, + delete_organization, + get_organization_list, + read_organization, + update_organization, +) +from apps.iam.views.person import ( + add_person, + delete_person, + get_person_list, + update_password, + update_person, +) +from apps.training.views.algorithm_trainrecords import ( + copy_algorithm_train_record, + create_algorithm_train_record, + delete_algorithm_train_record, + detect_image_object, + archive_train_model, + close_train_model, + prepare_algorithm_train_dataset, + read_algorithm_train_record, + search_algorithm_train_records, + start_algorithm_train, + train_callback, + prepare_callback, + update_algorithm_train_record, +) +from apps.common.views.minio_storage import ( + minio_bucket_create, + minio_bucket_delete, + minio_bucket_list, + minio_file_batch_delete, + minio_file_batch_upload, + minio_file_copy, + minio_file_delete, + minio_file_download, + minio_file_info, + minio_file_list, + minio_file_presign, + minio_file_presign_upload, + minio_file_preview, + minio_file_upload, + minio_file_upload_bytes, + minio_video_extract_frames, + minio_zip_upload_extract, +) + + +urlpatterns = [ + path("createAlgorithmClass/", create_algorithm_class, name="create_algorithm_class"), + path("queryAlgorithmClass/", search_algorithm_class, name="search_algorithm_class"), + path("readAlgorithmClass/", read_algorithm_class, name="read_algorithm_class"), + path("updateAlgorithmClass/", update_algorithm_class, name="update_algorithm_class"), + path("deleteAlgorithmClass/", delete_algorithm_class, name="delete_algorithm_class"), + path("addAlgorithm/", add_algorithm, name="create_algorithm"), + path("queryAlgorithm/", search_algorithm, name="search_algorithm"), + path("getAlgorithmList/", get_algorithm_list, name="get_algorithm_list"), + path("getAlgorithmTree/", get_algorithm_tree, name="get_algorithm_tree"), + path("readAlgorithm/", read_algorithm, name="read_algorithm"), + path("updateAlgorithm/", update_algorithm, name="update_algorithm"), + path("deleteAlgorithm/", delete_algorithm, name="delete_algorithm"), + path("getAlgorithmEngines/", get_algorithm_engines, name="get_algorithm_engines"), + path("addAlgorithmParam/", create_algorithm_params, name="create_algorithm_params"), + path("queryAlgorithmParam/", search_algorithm_params, name="search_algorithm_params"), + path("readAlgorithmParam/", read_algorithm_params, name="read_algorithm_params"), + path("updateAlgorithmParam/", update_algorithm_params, name="update_algorithm_params"), + path("deleteAlgorithmParam/", delete_algorithm_params, name="delete_algorithm_params"), + path("addAlgorithmOutput/", create_algorithm_output, name="create_algorithm_output"), + path("queryAlgorithmOutput/", search_algorithm_output, name="search_algorithm_output"), + path("readAlgorithmOutput/", read_algorithm_output, name="read_algorithm_output"), + path("updateAlgorithmOutput/", update_algorithm_output, name="update_algorithm_output"), + path("deleteAlgorithmOutput/", delete_algorithm_output, name="delete_algorithm_output"), + path("uploadAlgorithmModelFile/", upload_algorithm_modelfile, name="upload_algorithm_modelfile"), + path("queryAlgorithmModel/", search_algorithm_model, name="search_algorithm_model"), + path("readAlgorithmModel/", read_algorithm_model, name="read_algorithm_model"), + path("updateAlgorithmModel/", update_algorithm_model, name="update_algorithm_model"), + path("deleteAlgorithmModel/", delete_algorithm_model, name="delete_algorithm_model"), + path("setAlgorithmDefaultModel/", set_algorithm_default_model, name="set_algorithm_default_model"), + path("issueAlgorithmModel/", issue_algorithm_model, name="issue_algorithm_model"), + path("publishModelToTerminals/", publish_model_to_terminals, name="publish_model_to_terminals"), + path("convertAlgorithmModelOnnx/", convert_algorithm_model_onnx, name="convert_algorithm_model_onnx"), + path("downloadPublishedOnnxModel/", download_published_onnx_model, name="download_published_onnx_model"), + path("exportAlgorithmModel/", export_algorithm_model, name="export_algorithm_model"), + path("AlgorithmModelPredict/", algorithm_model_predict, name="algorithm_model_predict"), + path("getAlgorithmDefaultImages/", get_algorithm_default_images, name="get_algorithm_default_images"), + path( + "uploadPredictDefaultImages/", + upload_predict_default_images, + name="upload_predict_default_images", + ), + path( + "deletePredictDefaultImage/", + delete_predict_default_image, + name="delete_predict_default_image", + ), + path("createDataset/", create_dataset, name="create_dataset"), + path("uploadDatasetFile/", upload_dataset_file, name="upload_dataset_file"), + path("importFromTempFile/", import_dataset_from_temp_file, name="import_dataset_from_temp_file"), + path("generateDatasetImages/", generate_dataset_images, name="generate_dataset_images"), + path("queryDataset/", search_datasets, name="search_datasets"), + path("getDatasetList/", get_dataset_list, name="get_dataset_list"), + path("readDataset/", read_dataset, name="read_dataset"), + path("updateDataset/", update_dataset, name="update_datasets"), + path("deleteDataset/", delete_dataset, name="delete_datasets"), + path("deleteDatasetUpdatefile/", delete_dataset_updatefile, name="delete_dataset_updatefile"), + path("querysamples/", search_dataset_samples, name="search_dataset_samples"), + path("readDatasetSample/", read_dataset_sample, name="read_dataset_sample"), + path("previewDatasetSample/", preview_dataset_sample, name="preview_dataset_sample"), + path("downloadDatasetSample/", download_dataset_sample, name="download_dataset_sample"), + path("deleteInvalidSamples/", delete_invalid_samples, name="delete_invalid_samples"), + path("deleteDuplicateSamples/", delete_duplicate_samples, name="delete_duplicate_samples"), + path("addOrganization/", add_organization, name="add_organization"), + path("getOrganizationList/", get_organization_list, name="get_organization_list"), + path("readOrganization/", read_organization, name="read_organization"), + path("updateOrganization/", update_organization, name="update_organization"), + path("deleteOrganization/", delete_organization, name="delete_organization"), + path("addPerson/", add_person, name="add_person"), + path("getPersonList/", get_person_list, name="get_person_list"), + path("updatePerson/", update_person, name="update_person"), + path("deletePerson/", delete_person, name="delete_person"), + path("updatePassword/", update_password, name="update_password"), + path("createAnnotateTeam/", create_annotate_team, name="create_annotate_team"), + path("addTeamMembers/", add_team_members, name="add_team_members"), + path("searchAnnotateTeam/", search_annotate_team, name="search_annotate_team"), + path("readAnnotateTeam/", read_annotate_team, name="read_annotate_team"), + path("updateAnnotateTeam/", update_annotate_team, name="update_annotate_team"), + path("updateTeamMember/", update_team_member, name="update_team_member"), + path("deleteAnnotateTeam/", delete_annotate_team, name="delete_annotate_team"), + path("getTeamMembers/", get_team_members, name="get_team_members"), + path("deleteTeamMembers/", delete_team_members, name="delete_team_members"), + path("startAutoAnnotateTask/", start_auto_annotate_task, name="start_auto_annotate_task"), + path("getMytaskAnnotateList/", get_mytask_annotate_list, name="get_mytask_annotate_list"), + path("getMytaskAuditList/", get_mytask_audit_list, name="get_mytask_audit_list"), + path("setPersonTask/", set_person_task, name="set_person_task"), + path("deleteTaskSamples/", delete_task_samples, name="delete_task_samples"), + path("queryPersonTasks/", query_person_tasks, name="query_person_Tasks"), + path("queryTaskSamples/", search_task_samples, name="search_task_samples"), + path("readTaskSample/", read_task_sample, name="read_task_sample"), + path("annotateTaskSample/", annotate_task_sample, name="annotate_task_sample"), + path("submitTaskSamples/", submit_task_samples, name="submit_task_samples"), + path("auditTaskSamples/", audit_task_samples, name="audit_task_samples"), + path("mergeSampleContent/", merge_sample_content, name="merge_sample_content"), + path("uploadTempFile/", upload_temp_file, name="upload_temp_file"), + path("queryTempFiles/", search_temp_files, name="search_temp_files"), + path("readTempFile/", read_temp_file, name="read_temp_file"), + path("updateTempFile/", update_temp_file, name="update_temp_file"), + path("deleteTempFile/", delete_temp_file, name="delete_temp_file"), + path("downloadTempFile/", download_temp_file, name="download_temp_file"), + path("previewTempFile/", preview_temp_file, name="preview_temp_file"), + path("terminal/auth/", terminal_device_auth, name="terminal_device_auth"), + path("terminal/create/", create_smart_terminal, name="create_smart_terminal"), + path("terminal/update/", update_smart_terminal, name="update_smart_terminal"), + path("terminal/publishModel/", publish_terminal_model, name="publish_terminal_model"), + path("terminal/list/", search_smart_terminals, name="search_smart_terminals"), + path("terminal/heartbeat/", update_terminal_heartbeat, name="update_terminal_heartbeat"), + path("terminal/checkVersion/", check_terminal_model_version, name="check_terminal_model_version"), + path("terminal/downloadModel/", download_terminal_model, name="download_terminal_model"), + path("terminal/updateModelVersion/", update_terminal_model_version, name="update_terminal_model_version"), + path("terminal/delete/", delete_smart_terminal, name="delete_smart_terminal"), + path("annotateTask/create/", create_annotate_task, name="create_annotate_task"), + path("annotateTask/list/", search_annotate_tasks, name="search_annotate_tasks"), + path("annotateTask/read/", read_annotate_task, name="read_annotate_task"), + path("annotateTask/update/", update_annotate_task, name="update_annotate_task"), + path("annotateTask/delete/", delete_annotate_task, name="delete_annotate_task"), + path("annotateTask/assignSamples/", set_person_task, name="assign_annotate_task_samples"), + path("annotateTask/autoAnnotate/", start_auto_annotate_task, name="auto_annotate_task"), + path("annotateTask/submitAudit/", submit_task_samples, name="submit_annotate_task_samples"), + path("annotateTask/audit/", audit_task_samples, name="audit_annotate_task_samples"), + path("annotateTask/mergeToDataset/", merge_sample_content, name="merge_annotate_task_samples"), + path("addAlgorithmTrainRecord/", create_algorithm_train_record, name="create_algorithm_train_record"), + path("copyAlgorithmTrainRecord/", copy_algorithm_train_record, name="copy_algorithm_train_record"), + path("queryAlgorithmTrainRecords/", search_algorithm_train_records, name="search_algorithm_train_record"), + path("readAlgorithmTrainRecord/", read_algorithm_train_record, name="read_algorithm_train_record"), + path("updateAlgorithmTrainRecord/", update_algorithm_train_record, name="update_algorithm_train_record"), + path("deleteAlgorithmTrainRecord/", delete_algorithm_train_record, name="delete_algorithm_train_record"), + path( + "prepareAlgorithmTrainDataset/", + prepare_algorithm_train_dataset, + name="prepare_algorithm_train_dataset", + ), + path("startAlgorithmTrain/", start_algorithm_train, name="start_algorithm_train"), + path("TrainCallBack/", train_callback, name="train_callback"), + path("PrepareCallBack/", prepare_callback, name="prepare_callback"), + path("detectImageObject/", detect_image_object, name="detect_image_object"), + path("archiveTrainModel/", archive_train_model, name="archive_train_model"), + path("closeTrain/", close_train_model, name="close_train_model"), + + # ── MinIO 文件存储接口 ───────────────────────────── + path("minio/bucket/create/", minio_bucket_create, name="minio_bucket_create"), + path("minio/bucket/list/", minio_bucket_list, name="minio_bucket_list"), + path("minio/bucket/delete/", minio_bucket_delete, name="minio_bucket_delete"), + path("minio/file/upload/", minio_file_upload, name="minio_file_upload"), + path("minio/file/upload-base64/", minio_file_upload_bytes, name="minio_file_upload_base64"), + path("minio/files/upload/", minio_file_batch_upload, name="minio_file_batch_upload"), + path("minio/file/upload-zip/", minio_zip_upload_extract, name="minio_zip_upload_extract"), + path("minio/file/upload-video-frames/", minio_video_extract_frames, name="minio_video_extract_frames"), + path("minio/file/download/", minio_file_download, name="minio_file_download"), + path("minio/file/presign/", minio_file_presign, name="minio_file_presign"), + path("minio/file/presign-upload/", minio_file_presign_upload, name="minio_file_presign_upload"), + path("minio/file/preview/", minio_file_preview, name="minio_file_preview"), + path("minio/file/info/", minio_file_info, name="minio_file_info"), + path("minio/files/", minio_file_list, name="minio_file_list"), + path("minio/file/delete/", minio_file_delete, name="minio_file_delete"), + path("minio/files/delete/", minio_file_batch_delete, name="minio_file_batch_delete"), + path("minio/file/copy/", minio_file_copy, name="minio_file_copy"), +] diff --git a/backend/config/asgi.py b/backend/config/asgi.py new file mode 100644 index 0000000..51a6dbc --- /dev/null +++ b/backend/config/asgi.py @@ -0,0 +1,18 @@ +import os + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") + +from channels.auth import AuthMiddlewareStack +from channels.routing import ProtocolTypeRouter, URLRouter +from django.core.asgi import get_asgi_application + +django_asgi_app = get_asgi_application() + +from config.routing import websocket_urlpatterns + +application = ProtocolTypeRouter( + { + "http": django_asgi_app, + "websocket": AuthMiddlewareStack(URLRouter(websocket_urlpatterns)), + } +) diff --git a/backend/config/routing.py b/backend/config/routing.py new file mode 100644 index 0000000..c4d3d5b --- /dev/null +++ b/backend/config/routing.py @@ -0,0 +1,10 @@ +from django.urls import re_path + +from apps.cloud_terminal.consumers import TerminalStatusConsumer +from apps.training.consumers import TrainLogConsumer + +websocket_urlpatterns = [ + re_path(r"^ws/train_logs/(?P[a-zA-Z0-9-]+)/$", TrainLogConsumer.as_asgi(), name="train_log"), + re_path(r"^ws/terminal_status/$", TerminalStatusConsumer.as_asgi(), name="terminal_status"), +] + diff --git a/backend/config/settings/__init__.py b/backend/config/settings/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/config/settings/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/config/settings/base.py b/backend/config/settings/base.py new file mode 100644 index 0000000..d32fb7d --- /dev/null +++ b/backend/config/settings/base.py @@ -0,0 +1,244 @@ +import os +from datetime import timedelta +from pathlib import Path + + +def _env_bool(name: str, default: bool = False) -> bool: + raw = os.environ.get(name) + if raw is None: + return default + return raw.strip().lower() in {"1", "true", "yes", "y", "on"} + + +def _env_list(name: str, default: list[str] | None = None) -> list[str]: + raw = os.environ.get(name) + if raw is None: + return default or [] + return [part.strip() for part in raw.split(",") if part.strip()] + + +BASE_DIR = Path(__file__).resolve().parent.parent.parent +REPO_ROOT = BASE_DIR.parent + + +def _load_env_file(env_path: Path) -> None: + if not env_path.exists() or not env_path.is_file(): + return + for raw_line in env_path.read_text(encoding="utf-8").splitlines(): + line = raw_line.strip() + if not line or line.startswith("#") or "=" not in line: + continue + key, value = line.split("=", 1) + key = key.strip() + if not key or key in os.environ: + continue + value = value.strip().strip('"').strip("'") + os.environ[key] = value + + +_load_env_file(REPO_ROOT / ".env") + +SECRET_KEY = os.environ.get("DJANGO_SECRET_KEY", "unsafe-dev-secret-key") +DEBUG = _env_bool("DJANGO_DEBUG", default=True) + +ALLOWED_HOSTS = _env_list("DJANGO_ALLOWED_HOSTS", default=["127.0.0.1","192.168.1.60", "192.168.1.93","localhost","192.168.1.174"]) +DATETIME_FORMAT = os.environ.get("DJANGO_DATETIME_FORMAT", "Y-m-d H:i:s") + +INSTALLED_APPS = [ + "simpleui", + "corsheaders", + "drf_yasg", + "django.contrib.admin", + "django.contrib.auth", + "django.contrib.contenttypes", + "django.contrib.sessions", + "django.contrib.messages", + "django.contrib.staticfiles", + "rest_framework", + "rest_framework_simplejwt", + "apps.iam", + "apps.datasets", + "apps.annotation", + "apps.algorithms", + "apps.training", + "apps.cloud_terminal.apps.CloudTerminalConfig", + "apps.core.apps.CoreConfig", + "channels", + "django_extensions", +] + +ASGI_APPLICATION = "config.asgi.application" +WSGI_APPLICATION = "config.wsgi.application" + +MIDDLEWARE = [ + "django.middleware.security.SecurityMiddleware", + "django.contrib.sessions.middleware.SessionMiddleware", + "django.middleware.common.CommonMiddleware", + "django.middleware.csrf.CsrfViewMiddleware", + "django.contrib.auth.middleware.AuthenticationMiddleware", + "django.contrib.messages.middleware.MessageMiddleware", + "django.middleware.clickjacking.XFrameOptionsMiddleware", + "corsheaders.middleware.CorsMiddleware", +] + +CORS_ALLOWED_ORIGINS = _env_list("DJANGO_CORS_ALLOWED_ORIGINS", default=[ + "http://127.0.0.1:3000", + "http://localhost:3000", + "http://192.168.1.60:3000", + "http://192.168.1.93:3000", + "ws://192.168.1.60:3000", + "ws://192.168.1.93:3000", + "ws://localhost:3000", +]) +CORS_ALLOW_ALL_ORIGINS = _env_bool("DJANGO_CORS_ALLOW_ALL_ORIGINS", default=False) +CSRF_TRUSTED_ORIGINS = _env_list("DJANGO_CSRF_TRUSTED_ORIGINS", default=[]) + +ROOT_URLCONF = "config.urls" + +FRONTEND_DIST_DIR = Path( + os.environ.get("FRONTEND_DIST_DIR", str(REPO_ROOT / "frontend" / "dist")) +) + +TEMPLATES = [ + { + "BACKEND": "django.template.backends.django.DjangoTemplates", + "DIRS": [str(FRONTEND_DIST_DIR)], + "APP_DIRS": True, + "OPTIONS": { + "context_processors": [ + "django.template.context_processors.debug", + "django.template.context_processors.request", + "django.contrib.auth.context_processors.auth", + "django.contrib.messages.context_processors.messages", + ], + }, + } +] + +REST_FRAMEWORK = { + "DEFAULT_RENDERER_CLASSES": [ + "rest_framework.renderers.JSONRenderer", + ], + "DEFAULT_PARSER_CLASSES": [ + "rest_framework.parsers.JSONParser", + ], + "DEFAULT_AUTHENTICATION_CLASSES": [ + "rest_framework_simplejwt.authentication.JWTAuthentication", + ], + "DEFAULT_PERMISSION_CLASSES": [ + "rest_framework.permissions.AllowAny", + ], + "DEFAULT_PAGINATION_CLASS": "rest_framework.pagination.PageNumberPagination", + "PAGE_SIZE": int(os.environ.get("DJANGO_PAGE_SIZE", "10")), + "DEFAULT_SCHEMA_CLASS": "rest_framework.schemas.openapi.AutoSchema", + "DEFAULT_FILTER_BACKENDS": [ + "django_filters.rest_framework.DjangoFilterBackend", + ], + "DEFAULT_THROTTLE_RATES": { + "anon": None, + "user": None, + }, + "DEFAULT_THROTTLE_CLASSES": ( + "rest_framework.throttling.AnonRateThrottle", + "rest_framework.throttling.UserRateThrottle", + ), + "EXCEPTION_HANDLER": "rest_framework.views.exception_handler", + "DEFAULT_METADATA_CLASS": "rest_framework.metadata.SimpleMetadata", +} + +SIMPLE_JWT = { + "ACCESS_TOKEN_LIFETIME": timedelta( + minutes=int(os.environ.get("JWT_ACCESS_MINUTES", "60")) + ), + "REFRESH_TOKEN_LIFETIME": timedelta(days=int(os.environ.get("JWT_REFRESH_DAYS", "1"))), +} + +DATABASES = { + "default": { + "ENGINE": "django.db.backends.postgresql", + "NAME": os.environ.get("DB_NAME", "ai-traindb") or "ai-traindb", + "USER": os.environ.get("DB_USER", "postgres") or "postgres", + "PASSWORD": os.environ.get("DB_PASSWORD", "Zhengsl321DSA") or "60edrhkdwa1ecd", + "HOST": os.environ.get("DB_HOST", "127.0.0.1") or "127.0.0.1", + "PORT": os.environ.get("DB_PORT", "5432") or "5432", + }, +} + +DB_ENGINE = os.environ.get("DB_ENGINE") +if DB_ENGINE: + if DB_ENGINE == "django.db.backends.sqlite3": + sqlite_name = os.environ.get("DB_NAME") or str(BASE_DIR / "db.sqlite3") + DATABASES = {"default": {"ENGINE": DB_ENGINE, "NAME": sqlite_name}} + else: + DATABASES["default"]["ENGINE"] = DB_ENGINE + +SM4_PRIVATE_KEY = os.environ.get("SM4_PRIVATE_KEY", "a8b502da70405867") +private_key = SM4_PRIVATE_KEY + +AUTH_PASSWORD_VALIDATORS = [ + { + "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator", + }, +] + +LANGUAGE_CODE = "zh-hans" +TIME_ZONE = "Asia/Shanghai" +USE_I18N = True +USE_TZ = True + +STATIC_URL = "/static/" +DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField" +LOGOUT_REDIRECT_URL = "/account/login/" + +DATA_UPLOAD_MAX_MEMORY_SIZE = 1024 * 1024 * 1024 +FILE_UPLOAD_MAX_MEMORY_SIZE = 1024 * 1024 * 1024 + +MODEL_ROOT_PATH = str("D:/07_Temp/model_space") +FILESPACE_ROOT_PATH = str("D:/07_Temp/train_workspace") +TRAIN_LOG_DIR = os.environ.get("TRAIN_LOG_DIR", str(Path(FILESPACE_ROOT_PATH) / "log")) +MEDIA_URL = os.environ.get("MEDIA_URL", "/image/") +MEDIA_ROOT = os.environ.get("MEDIA_ROOT", FILESPACE_ROOT_PATH) + +TASK_CALL_BACK_URL = os.environ.get("TASK_CALL_BACK_URL", "http://localhost:8000/server/") +ANNOTATION_SERVICE_INFER_URL = os.environ.get( + "ANNOTATION_SERVICE_INFER_URL", + "http://172.16.31.195:8090/infer", +) +AUTO_ANNOTATE_MAX_WORKERS = int(os.environ.get("AUTO_ANNOTATE_MAX_WORKERS", "2")) +AUTO_ANNOTATE_SCORE_THRESHOLD = float(os.environ.get("AUTO_ANNOTATE_SCORE_THRESHOLD", "0.4")) +VIDEO_FRAME_INTERVAL = int(os.environ.get("VIDEO_FRAME_INTERVAL", "10")) + +DEFAULT_CHARSET = "UTF-8" + +REDIS_URL = os.environ.get("REDIS_URL") +if REDIS_URL: + CHANNEL_LAYERS = { + "default": { + "BACKEND": "channels_redis.core.RedisChannelLayer", + "CONFIG": {"hosts": [REDIS_URL]}, + } + } +else: + CHANNEL_LAYERS = { + "default": { + "BACKEND": "channels.layers.InMemoryChannelLayer", + } + } + +# ── MinIO 对象存储配置 ───────────────────────────── +MINIO_ENDPOINT = os.environ.get("MINIO_ENDPOINT", "127.0.0.1:9000") +MINIO_PRESIGN_ENDPOINT = os.environ.get("MINIO_PRESIGN_ENDPOINT","192.168.1.60:9000") +MINIO_ACCESS_KEY = os.environ.get("MINIO_ACCESS_KEY", "admin") +MINIO_SECRET_KEY = os.environ.get("MINIO_SECRET_KEY", "zhengsl2026") +MINIO_SECURE = _env_bool("MINIO_SECURE", default=False) +MINIO_DEFAULT_BUCKET = os.environ.get("MINIO_DEFAULT_BUCKET", "ai-trainprediction") +MINIO_PRESIGN_EXPIRES = int(os.environ.get("MINIO_PRESIGN_EXPIRES", "3600")) diff --git a/backend/config/settings/dev.py b/backend/config/settings/dev.py new file mode 100644 index 0000000..df7a47d --- /dev/null +++ b/backend/config/settings/dev.py @@ -0,0 +1,10 @@ +from .base import * + + +DEBUG = True + +CORS_ALLOWED_ORIGINS = CORS_ALLOWED_ORIGINS or [ + "http://localhost:3000", + "ws://localhost:3000", +] + diff --git a/backend/config/settings/prod.py b/backend/config/settings/prod.py new file mode 100644 index 0000000..4ed8f16 --- /dev/null +++ b/backend/config/settings/prod.py @@ -0,0 +1,5 @@ +from .base import * + + +DEBUG = False + diff --git a/backend/config/settings/table.sql b/backend/config/settings/table.sql new file mode 100644 index 0000000..75f7379 --- /dev/null +++ b/backend/config/settings/table.sql @@ -0,0 +1,842 @@ +-- DROP SCHEMA public; + +CREATE SCHEMA public AUTHORIZATION pg_database_owner; + +COMMENT ON SCHEMA public IS 'standard public schema'; + +-- DROP SEQUENCE public.auth_group_id_seq; + +CREATE SEQUENCE public.auth_group_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 2147483647 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.auth_group_permissions_id_seq; + +CREATE SEQUENCE public.auth_group_permissions_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 9223372036854775807 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.auth_permission_id_seq; + +CREATE SEQUENCE public.auth_permission_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 2147483647 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.auth_user_groups_id_seq; + +CREATE SEQUENCE public.auth_user_groups_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 9223372036854775807 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.auth_user_id_seq; + +CREATE SEQUENCE public.auth_user_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 2147483647 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.auth_user_user_permissions_id_seq; + +CREATE SEQUENCE public.auth_user_user_permissions_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 9223372036854775807 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.django_admin_log_id_seq; + +CREATE SEQUENCE public.django_admin_log_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 2147483647 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.django_content_type_id_seq; + +CREATE SEQUENCE public.django_content_type_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 2147483647 + START 1 + CACHE 1 + NO CYCLE; +-- DROP SEQUENCE public.django_migrations_id_seq; + +CREATE SEQUENCE public.django_migrations_id_seq + INCREMENT BY 1 + MINVALUE 1 + MAXVALUE 9223372036854775807 + START 1 + CACHE 1 + NO CYCLE;-- public.ai_algorithm 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm; + +CREATE TABLE public.ai_algorithm ( + id varchar(40) NOT NULL, + algorithm_code varchar(20) NOT NULL, + algorithm_name varchar(200) NOT NULL, + algorithm_class varchar(40) NOT NULL, + description varchar(2000) NULL, + scenario varchar(2000) NULL, + algorithm_type varchar(2) NULL, + labels text NULL, + algorithm_status varchar(2) NULL, + development_unit varchar(200) NULL, + image text NULL, + creator varchar(20) NOT NULL, + create_time timestamptz NOT NULL, + link_model varchar(2000) NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + algorithm_engine varchar(40) NULL, + CONSTRAINT ai_algorithm_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_id_12ebb08a_like ON public.ai_algorithm USING btree (id varchar_pattern_ops); + + +-- public.ai_algorithm_call_log 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm_call_log; + +CREATE TABLE public.ai_algorithm_call_log ( + id varchar(40) NOT NULL, + algorithm_id varchar(40) NOT NULL, + algorithm_name varchar(40) NOT NULL, + model_id varchar(40) NOT NULL, + "Model_version" varchar(200) NOT NULL, + input_params text NOT NULL, + output_results text NULL, + run_times int4 NULL, + description varchar(2000) NULL, + caller varchar(20) NOT NULL, + log_time timestamptz NOT NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + evaluate_result varchar(1) NULL, + result_desc text NULL, + CONSTRAINT ai_algorithm_call_log_pkey PRIMARY KEY (id), + CONSTRAINT ai_algorithm_call_log_run_times_check CHECK ((run_times >= 0)) +); +CREATE INDEX ai_algorithm_call_log_id_e82865ff_like ON public.ai_algorithm_call_log USING btree (id varchar_pattern_ops); + + +-- public.ai_algorithm_class 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm_class; + +CREATE TABLE public.ai_algorithm_class ( + id varchar(40) NOT NULL, + classcode varchar(40) NOT NULL, + classname varchar(100) NOT NULL, + parentid varchar(40) NOT NULL, + description varchar(1000) NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_algorithm_class_classcode_key UNIQUE (classcode), + CONSTRAINT ai_algorithm_class_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_class_classcode_3793b613_like ON public.ai_algorithm_class USING btree (classcode varchar_pattern_ops); +CREATE INDEX ai_algorithm_class_id_0a2e4e7b_like ON public.ai_algorithm_class USING btree (id varchar_pattern_ops); + + +-- public.ai_algorithm_engine 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm_engine; + +CREATE TABLE public.ai_algorithm_engine ( + id varchar(40) NOT NULL, + enginecode varchar(40) NOT NULL, + enginename varchar(100) NOT NULL, + description varchar(1000) NULL, + training_params_template text NULL, + detect_params_template text NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_algorithm_engine_enginecode_key UNIQUE (enginecode), + CONSTRAINT ai_algorithm_engine_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_engine_enginecode_cfe9d88d_like ON public.ai_algorithm_engine USING btree (enginecode varchar_pattern_ops); +CREATE INDEX ai_algorithm_engine_id_eba66d96_like ON public.ai_algorithm_engine USING btree (id varchar_pattern_ops); + + +-- public.ai_algorithm_models 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm_models; + +CREATE TABLE public.ai_algorithm_models ( + id varchar(40) NOT NULL, + algorithm_id varchar(40) NOT NULL, + model_version varchar(20) NOT NULL, + model_filepath varchar(1000) NULL, + model_filename varchar(1000) NOT NULL, + model_labels text NULL, + model_createtime timestamptz NULL, + evaluation_metrics text NULL, + evaluation_comments text NULL, + description varchar(2000) NULL, + model_status varchar(2) NOT NULL, + model_stars varchar(2) NULL, + model_usednumber int4 NULL, + call_duration int2 NULL, + uploader varchar(20) NULL, + upload_time timestamptz NOT NULL, + is_using varchar(2) NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_algorithm_models_model_usednumber_check CHECK ((model_usednumber >= 0)), + CONSTRAINT ai_algorithm_models_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_models_id_3079f244_like ON public.ai_algorithm_models USING btree (id varchar_pattern_ops); + + +-- public.ai_algorithm_output 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm_output; + +CREATE TABLE public.ai_algorithm_output ( + id varchar(40) NOT NULL, + algorithm_id varchar(40) NOT NULL, + item_order int2 NOT NULL, + item_name varchar(200) NOT NULL, + item_desc varchar(200) NOT NULL, + item_type varchar(2) NOT NULL, + item_unit varchar(100) NULL, + item_range varchar(100) NULL, + description varchar(2000) NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_algorithm_output_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_output_id_ab741e12_like ON public.ai_algorithm_output USING btree (id varchar_pattern_ops); + + +-- public.ai_algorithm_params 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm_params; + +CREATE TABLE public.ai_algorithm_params ( + id varchar(40) NOT NULL, + algorithm_id varchar(40) NOT NULL, + param_class varchar(2) NULL, + param_order int2 NOT NULL, + param_name varchar(200) NOT NULL, + param_desc varchar(200) NOT NULL, + param_type varchar(2) NOT NULL, + param_unit varchar(100) NULL, + param_range varchar(2000) NULL, + default_values varchar(2000) NULL, + description varchar(2000) NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_algorithm_params_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_params_id_1389b87b_like ON public.ai_algorithm_params USING btree (id varchar_pattern_ops); + + +-- public.ai_algorithm_train_records 定义 + +-- Drop table + +-- DROP TABLE public.ai_algorithm_train_records; + +CREATE TABLE public.ai_algorithm_train_records ( + id varchar(40) NOT NULL, + algorithm_id varchar(40) NOT NULL, + algorithm_name varchar(200) NOT NULL, + train_code varchar(20) NOT NULL, + train_title varchar(200) NOT NULL, + train_start_time timestamptz NULL, + link_datasets varchar(400) NULL, + trainset_path varchar(400) NULL, + validationset_path varchar(400) NULL, + train_labels varchar(2000) NULL, + train_params text NULL, + train_total_times int4 NULL, + model_createtime timestamptz NULL, + model_trainresult text NULL, + model_filename varchar(200) NULL, + model_filepath varchar(400) NULL, + evaluation_metrics text NULL, + evaluation_comments text NULL, + record_status varchar(4) NOT NULL, + train_end_time timestamptz NULL, + creator varchar(20) NOT NULL, + create_time timestamptz NOT NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + train_server varchar(400) NULL, + train_type varchar(20) NOT NULL, + ws_server varchar(200) NULL, + CONSTRAINT ai_algorithm_train_records_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_train_records_id_d444e53a_like ON public.ai_algorithm_train_records USING btree (id varchar_pattern_ops); + + +-- public.ai_annotate_team 定义 + +-- Drop table + +-- DROP TABLE public.ai_annotate_team; + +CREATE TABLE public.ai_annotate_team ( + id varchar(40) NOT NULL, + team_code varchar(40) NOT NULL, + team_name varchar(200) NOT NULL, + team_leader_id varchar(40) NULL, + team_leader_name varchar(200) NULL, + description varchar(2000) NULL, + organization varchar(200) NULL, + features varchar(200) NULL, + status varchar(2) NOT NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_annotate_team_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_annotate_team_id_9f4d943a_like ON public.ai_annotate_team USING btree (id varchar_pattern_ops); + + +-- public.ai_annotate_team_member 定义 + +-- Drop table + +-- DROP TABLE public.ai_annotate_team_member; + +CREATE TABLE public.ai_annotate_team_member ( + id varchar(40) NOT NULL, + team_id varchar(40) NOT NULL, + member_account varchar(100) NOT NULL, + member_name varchar(40) NOT NULL, + member_role varchar(20) NOT NULL, + member_email varchar(200) NULL, + remark varchar(200) NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_annotate_team_member_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_annotate_team_member_id_e7dbf9c8_like ON public.ai_annotate_team_member USING btree (id varchar_pattern_ops); + + +-- public.ai_dataset 定义 + +-- Drop table + +-- DROP TABLE public.ai_dataset; + +CREATE TABLE public.ai_dataset ( + id varchar(40) NOT NULL, + dataset_code varchar(40) NOT NULL, -- 数据集编号 + dataset_name varchar(200) NOT NULL, -- 数据集名称 + description varchar(2000) NULL, -- 描述 + dataset_type varchar(2) NOT NULL, -- 类型 + features varchar(200) NULL, -- 特征 + original_file_path varchar(400) NULL, -- 原始文件目录 + original_file_name varchar(8000) NULL, -- 原始文件名称 + dataset_path varchar(400) NULL, -- 数据集存储路径 + dataset_count int4 NULL, -- 样本总数 + annotated_count int4 NOT NULL DEFAULT 0, -- 已标注样本总数 + dataset_labels varchar(2000) NULL, -- 数据集标签 + status varchar(2) NOT NULL, -- 数据集状态 + creator varchar(20) NOT NULL, + create_time timestamptz NOT NULL, + custom1 varchar(200) NULL, + custom3 varchar(200) NULL, + custom2 varchar(2000) NULL, + CONSTRAINT ai_algorithm_dataset_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_algorithm_dataset_id_2cef5ae1_like ON public.ai_dataset USING btree (id varchar_pattern_ops); + +-- Column comments + +COMMENT ON COLUMN public.ai_dataset.dataset_code IS '数据集编号'; +COMMENT ON COLUMN public.ai_dataset.dataset_name IS '数据集名称'; +COMMENT ON COLUMN public.ai_dataset.description IS '描述'; +COMMENT ON COLUMN public.ai_dataset.dataset_type IS '类型'; +COMMENT ON COLUMN public.ai_dataset.features IS '特征'; +COMMENT ON COLUMN public.ai_dataset.original_file_path IS '原始文件目录'; +COMMENT ON COLUMN public.ai_dataset.original_file_name IS '原始文件名称'; +COMMENT ON COLUMN public.ai_dataset.dataset_path IS '数据集存储路径'; +COMMENT ON COLUMN public.ai_dataset.dataset_count IS '样本总数'; +COMMENT ON COLUMN public.ai_dataset.annotated_count IS '已标注样本总数'; +COMMENT ON COLUMN public.ai_dataset.dataset_labels IS '数据集标签'; +COMMENT ON COLUMN public.ai_dataset.status IS '数据集状态'; + + +-- public.ai_dataset_sample 定义 + +-- Drop table + +-- DROP TABLE public.ai_dataset_sample; + +CREATE TABLE public.ai_dataset_sample ( + id varchar(40) NOT NULL, + dataset_id varchar(40) NOT NULL, + saved_path varchar(600) NOT NULL, + original_filename varchar(200) NOT NULL, + saved_filename varchar(200) NOT NULL, + saved_filesize varchar(10) NOT NULL, + rectangle varchar(2000) NULL, + polygon text NULL, + status varchar(2) NOT NULL, + create_time timestamptz NOT NULL, + custom1 varchar(200) NULL, + custom3 varchar(200) NULL, + custom2 varchar(1000) NULL, + CONSTRAINT ai_dataset_sample_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_dataset_sample_id_c8268768_like ON public.ai_dataset_sample USING btree (id varchar_pattern_ops); + + +-- public.ai_temp_file 定义 + +-- Drop table + +-- DROP TABLE public.ai_temp_file; + +CREATE TABLE public.ai_temp_file ( + id varchar(40) NOT NULL, + original_path varchar(600) NULL, -- 原始文件路径 + original_name varchar(200) NOT NULL, -- 原始文件名称 + original_type varchar(20) NOT NULL, -- 原始文件类型 + bucket_name varchar(200) NULL, -- minio-桶名称 + object_name varchar(200) NULL, -- 对象名称 + object_size int8 NULL, -- 对象大小 + content_type varchar(100) NULL, -- 对象类型 + status varchar(2) NOT NULL, -- 状态 + create_time timestamptz NOT NULL, + custom1 varchar(200) NULL, + custom3 varchar(200) NULL, + custom2 varchar(1000) NULL, + CONSTRAINT ai_temp_file_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_ai_temp_file_id_c8223412_like ON public.ai_temp_file USING btree (id varchar_pattern_ops); + +-- Column comments + +COMMENT ON COLUMN public.ai_temp_file.original_path IS '原始文件路径'; +COMMENT ON COLUMN public.ai_temp_file.original_name IS '原始文件名称'; +COMMENT ON COLUMN public.ai_temp_file.original_type IS '原始文件类型'; +COMMENT ON COLUMN public.ai_temp_file.bucket_name IS 'minio-桶名称'; +COMMENT ON COLUMN public.ai_temp_file.object_name IS '对象名称'; +COMMENT ON COLUMN public.ai_temp_file.object_size IS '对象大小'; +COMMENT ON COLUMN public.ai_temp_file.content_type IS '对象类型'; +COMMENT ON COLUMN public.ai_temp_file.status IS '状态'; + + +-- public.ai_annotate_task 定义 + +-- Drop table + +-- DROP TABLE public.ai_annotate_task; + +CREATE TABLE public.ai_annotate_task ( + id varchar(40) NOT NULL, + task_name varchar(200) NOT NULL, + annotate_type varchar(2) NOT NULL, + description varchar(2000) NULL, + dataset_id varchar(40) NOT NULL, + task_count int4 NULL, + task_range varchar(100) NULL, + task_type varchar(2) NULL, + region_type varchar(2) NULL, + labels varchar(2000) NULL, + work_type varchar(2) NOT NULL, + task_team varchar(40) NULL, + task_leader varchar(40) NULL, + status varchar(2) NOT NULL, + creator varchar(20) NOT NULL, + create_time timestamptz NOT NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_annotate_task_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_annotate_task_id_9f6b2fc0_like ON public.ai_annotate_task USING btree (id varchar_pattern_ops); + +-- public.ai_annotate_task_sample 定义 + +-- Drop table + +-- DROP TABLE public.ai_annotate_task_sample; + +CREATE TABLE public.ai_annotate_task_sample ( + id varchar(40) NOT NULL, + task_id varchar(40) NOT NULL, + executor varchar(100) NOT NULL, + sample_id varchar(40) NOT NULL, + sample_path varchar(600) NOT NULL, + sample_name varchar(200) NOT NULL, + hard varchar(2) NOT NULL, + annotation_content text NULL, + class_label varchar(200) NULL, + annotation_time timestamptz NULL, + status varchar(2) NOT NULL, + auditor varchar(20) NULL, + feedback varchar(400) NULL, + custom1 varchar(200) NULL, + custom2 varchar(200) NULL, + custom3 varchar(200) NULL, + CONSTRAINT ai_annotate_task_sample_pkey PRIMARY KEY (id) +); +CREATE INDEX ai_annotate_task_sample_id_3e6e0205_like ON public.ai_annotate_task_sample USING btree (id varchar_pattern_ops); + +-- ============================================================ +-- 智能终端数据表 (ai_smart_terminal) +-- 场景：云端孪生镜像 — 模型训练完成后发布到云端孪生设备， +-- 边缘端定期检测云端版本并下载更新，实现动态模型更新。 +-- 来源：Oracle SD_AIBOX_B_H（AI边缘计算盒子基本属性表） +-- 更新日期：2026-07-09 +-- ============================================================ + +-- public.ai_smart_terminal 定义 + +-- Drop table +-- DROP TABLE public.ai_smart_terminal; + +CREATE TABLE public.ai_smart_terminal ( + -- ======== 基本信息 ======== + id varchar(40) NOT NULL, -- 主键ID + stcd varchar(40) NOT NULL, -- 终端编码（与边缘实体设备一一对应） + stnm varchar(255) NOT NULL, -- 终端名称 + sttp varchar(2) NOT NULL, -- 终端分类：01=鱼类识别AI盒子 02=鸟类识别AI盒子 03=野生动物识别AI盒子 04=鱼类分拣装置 + order_index int4 NULL, -- 排序 + + -- ======== 位置信息 ======== + lgtd numeric(10,6) NULL, -- 经度（单位：°） + lttd numeric(10,6) NULL, -- 纬度（单位：°） + elev numeric(10,6) NULL, -- 高程（单位：m） + rstcd varchar(64) NULL, -- 所属电站编码 + base_id varchar(64) NULL, -- 所属水电基地编码 + hbrvcd varchar(64) NULL, -- 所属水电基地流域编码 + rvcd varchar(64) NULL, -- 所属流域编码 + + -- ======== 设备配置 ======== + aipz varchar(1000) NULL, -- AI盒子硬件/软件配置信息 + ipaddr varchar(512) NULL, -- 设备IP地址 + siminfo varchar(512) NULL, -- SIM卡信息 + lxr varchar(512) NULL, -- 联系人及联系电话 + purpose varchar(512) NULL, -- 用途说明 + + -- ======== 监测配置 ======== + stindx varchar(1000) NULL, -- 监测指标列表 + dtfrqcy int4 NULL, -- 数据采集频次（单位：min） + mway int2 NULL, -- 监测方式：0=人工监测 1=自动监测 2=混合监测 + + -- ======== 启用与接入状态 ======== + usfl int2 NULL, -- 是否启用：0=否 1=是 + dtin int2 NULL, -- 数据是否接入：0=未接入 1=已接入 + dtin_tm timestamptz NULL, -- 数据首次接入时间 + + -- ======== 云端孪生 — 模型发布 ======== + model_id varchar(40) NULL, -- 已部署模型ID（关联训练产出的模型记录） + model_version varchar(64) NULL, -- 已部署模型版本号 + model_path varchar(600) NULL, -- 模型文件存储路径（MinIO对象路径） + model_publish_time timestamptz NULL, -- 模型发布时间（发布到云端孪生设备的时间） + model_publish_user varchar(36) NULL, -- 模型发布人 + + -- ======== 边缘端 — 同步状态 ======== + edge_model_version varchar(64) NULL, -- 边缘端当前运行的模型版本 + edge_update_time timestamptz NULL, -- 边缘端最后一次模型更新时间 + last_heartbeat_time timestamptz NULL, -- 边缘端最后心跳时间 + online_status int2 NULL, -- 在线状态：0=离线 1=在线 + sync_check_interval int4 NULL, -- 同步检测间隔（单位：min，边缘端轮询云端孪生设备版本的时间间隔） + + -- ======== 运维记录 ======== + remark varchar(512) NULL, -- 备注 + record_user varchar(36) NULL, -- 创建人 + record_time timestamptz NOT NULL DEFAULT now(), -- 创建时间 + is_deleted int2 NOT NULL DEFAULT 0, -- 是否已删除：0=未删除 1=已删除 + + CONSTRAINT ai_smart_terminal_pkey PRIMARY KEY (id) +); + +CREATE INDEX ai_smart_terminal_id_idx ON public.ai_smart_terminal USING btree (id varchar_pattern_ops); +CREATE INDEX ai_smart_terminal_stcd_idx ON public.ai_smart_terminal USING btree (stcd); + +-- Column comments + +COMMENT ON TABLE public.ai_smart_terminal IS '智能终端数据表（云端孪生镜像）'; +COMMENT ON COLUMN public.ai_smart_terminal.id IS '主键ID'; +COMMENT ON COLUMN public.ai_smart_terminal.stcd IS '终端编码（与边缘实体设备一一对应）'; +COMMENT ON COLUMN public.ai_smart_terminal.stnm IS '终端名称'; +COMMENT ON COLUMN public.ai_smart_terminal.sttp IS '终端分类：01=鱼类识别AI盒子 02=鸟类识别AI盒子 03=野生动物识别AI盒子 04=鱼类分拣装置'; +COMMENT ON COLUMN public.ai_smart_terminal.order_index IS '排序'; +COMMENT ON COLUMN public.ai_smart_terminal.lgtd IS '经度（单位：°）'; +COMMENT ON COLUMN public.ai_smart_terminal.lttd IS '纬度（单位：°）'; +COMMENT ON COLUMN public.ai_smart_terminal.elev IS '高程（单位：m）'; +COMMENT ON COLUMN public.ai_smart_terminal.rstcd IS '所属电站编码'; +COMMENT ON COLUMN public.ai_smart_terminal.base_id IS '所属水电基地编码'; +COMMENT ON COLUMN public.ai_smart_terminal.hbrvcd IS '所属水电基地流域编码'; +COMMENT ON COLUMN public.ai_smart_terminal.rvcd IS '所属流域编码'; +COMMENT ON COLUMN public.ai_smart_terminal.aipz IS 'AI盒子硬件/软件配置信息'; +COMMENT ON COLUMN public.ai_smart_terminal.ipaddr IS '设备IP地址'; +COMMENT ON COLUMN public.ai_smart_terminal.siminfo IS 'SIM卡信息'; +COMMENT ON COLUMN public.ai_smart_terminal.lxr IS '联系人及联系电话'; +COMMENT ON COLUMN public.ai_smart_terminal.purpose IS '用途说明'; +COMMENT ON COLUMN public.ai_smart_terminal.stindx IS '监测指标列表'; +COMMENT ON COLUMN public.ai_smart_terminal.dtfrqcy IS '数据采集频次（单位：min）'; +COMMENT ON COLUMN public.ai_smart_terminal.mway IS '监测方式：0=人工监测 1=自动监测 2=混合监测'; +COMMENT ON COLUMN public.ai_smart_terminal.usfl IS '是否启用：0=否 1=是'; +COMMENT ON COLUMN public.ai_smart_terminal.dtin IS '数据是否接入：0=未接入 1=已接入'; +COMMENT ON COLUMN public.ai_smart_terminal.dtin_tm IS '数据首次接入时间'; +COMMENT ON COLUMN public.ai_smart_terminal.model_id IS '已部署模型ID（关联训练产出的模型记录）'; +COMMENT ON COLUMN public.ai_smart_terminal.model_version IS '已部署模型版本号'; +COMMENT ON COLUMN public.ai_smart_terminal.model_path IS '模型文件存储路径（MinIO对象路径）'; +COMMENT ON COLUMN public.ai_smart_terminal.model_publish_time IS '模型发布时间（发布到云端孪生设备的时间）'; +COMMENT ON COLUMN public.ai_smart_terminal.model_publish_user IS '模型发布人'; +COMMENT ON COLUMN public.ai_smart_terminal.edge_model_version IS '边缘端当前运行的模型版本'; +COMMENT ON COLUMN public.ai_smart_terminal.edge_update_time IS '边缘端最后一次模型更新时间'; +COMMENT ON COLUMN public.ai_smart_terminal.last_heartbeat_time IS '边缘端最后心跳时间'; +COMMENT ON COLUMN public.ai_smart_terminal.online_status IS '在线状态：0=离线 1=在线'; +COMMENT ON COLUMN public.ai_smart_terminal.sync_check_interval IS '同步检测间隔（单位：min，边缘端轮询云端孪生设备版本的时间间隔）'; +COMMENT ON COLUMN public.ai_smart_terminal.remark IS '备注'; +COMMENT ON COLUMN public.ai_smart_terminal.record_user IS '创建人'; +COMMENT ON COLUMN public.ai_smart_terminal.record_time IS '创建时间'; +COMMENT ON COLUMN public.ai_smart_terminal.is_deleted IS '是否已删除：0=未删除 1=已删除'; + + +-- public.auth_group 定义 + +-- Drop table + +-- DROP TABLE public.auth_group; + +CREATE TABLE public.auth_group ( + id int4 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 2147483647 START 1 CACHE 1 NO CYCLE) NOT NULL, + "name" varchar(150) NOT NULL, + CONSTRAINT auth_group_name_key UNIQUE (name), + CONSTRAINT auth_group_pkey PRIMARY KEY (id) +); +CREATE INDEX auth_group_name_a6ea08ec_like ON public.auth_group USING btree (name varchar_pattern_ops); + + +-- public.auth_organization 定义 + +-- Drop table + +-- DROP TABLE public.auth_organization; + +CREATE TABLE public.auth_organization ( + id varchar(40) NOT NULL, + orgtype varchar(2) NOT NULL, + orgcode varchar(20) NOT NULL, + orgname varchar(100) NOT NULL, + parentid varchar(40) NULL, + isvalid varchar(2) NOT NULL, + description varchar(1000) NULL, + custom1 varchar(100) NULL, + custom2 varchar(100) NULL, + custom3 varchar(100) NULL, + CONSTRAINT auth_organization_pkey PRIMARY KEY (id) +); +CREATE INDEX auth_organization_id_c7e641af_like ON public.auth_organization USING btree (id varchar_pattern_ops); + + +-- public.auth_user 定义 + +-- Drop table + +-- DROP TABLE public.auth_user; + +CREATE TABLE public.auth_user ( + id int4 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 2147483647 START 1 CACHE 1 NO CYCLE) NOT NULL, + "password" varchar(128) NOT NULL, + last_login timestamptz NULL, + is_superuser bool NOT NULL, + username varchar(150) NOT NULL, + first_name varchar(150) NOT NULL, + last_name varchar(150) NOT NULL, + email varchar(254) NOT NULL, + is_staff bool NOT NULL, + is_active bool NOT NULL, + date_joined timestamptz NOT NULL, + organization_id varchar(50) NULL, + CONSTRAINT auth_user_pkey PRIMARY KEY (id), + CONSTRAINT auth_user_username_key UNIQUE (username) +); +CREATE INDEX auth_user_username_6821ab7c_like ON public.auth_user USING btree (username varchar_pattern_ops); + + +-- public.django_content_type 定义 + +-- Drop table + +-- DROP TABLE public.django_content_type; + +CREATE TABLE public.django_content_type ( + id int4 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 2147483647 START 1 CACHE 1 NO CYCLE) NOT NULL, + app_label varchar(100) NOT NULL, + model varchar(100) NOT NULL, + CONSTRAINT django_content_type_app_label_model_76bd3d3b_uniq UNIQUE (app_label, model), + CONSTRAINT django_content_type_pkey PRIMARY KEY (id) +); + + +-- public.django_migrations 定义 + +-- Drop table + +-- DROP TABLE public.django_migrations; + +CREATE TABLE public.django_migrations ( + id int8 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 9223372036854775807 START 1 CACHE 1 NO CYCLE) NOT NULL, + app varchar(255) NOT NULL, + "name" varchar(255) NOT NULL, + applied timestamptz NOT NULL, + CONSTRAINT django_migrations_pkey PRIMARY KEY (id) +); + + +-- public.django_session 定义 + +-- Drop table + +-- DROP TABLE public.django_session; + +CREATE TABLE public.django_session ( + session_key varchar(40) NOT NULL, + session_data text NOT NULL, + expire_date timestamptz NOT NULL, + CONSTRAINT django_session_pkey PRIMARY KEY (session_key) +); +CREATE INDEX django_session_expire_date_a5c62663 ON public.django_session USING btree (expire_date); +CREATE INDEX django_session_session_key_c0390e0f_like ON public.django_session USING btree (session_key varchar_pattern_ops); + + +-- public.auth_permission 定义 + +-- Drop table + +-- DROP TABLE public.auth_permission; + +CREATE TABLE public.auth_permission ( + id int4 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 2147483647 START 1 CACHE 1 NO CYCLE) NOT NULL, + "name" varchar(255) NOT NULL, + content_type_id int4 NOT NULL, + codename varchar(100) NOT NULL, + CONSTRAINT auth_permission_content_type_id_codename_01ab375a_uniq UNIQUE (content_type_id, codename), + CONSTRAINT auth_permission_pkey PRIMARY KEY (id), + CONSTRAINT auth_permission_content_type_id_2f476e4b_fk_django_co FOREIGN KEY (content_type_id) REFERENCES public.django_content_type(id) DEFERRABLE INITIALLY DEFERRED +); +CREATE INDEX auth_permission_content_type_id_2f476e4b ON public.auth_permission USING btree (content_type_id); + + +-- public.auth_user_groups 定义 + +-- Drop table + +-- DROP TABLE public.auth_user_groups; + +CREATE TABLE public.auth_user_groups ( + id int8 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 9223372036854775807 START 1 CACHE 1 NO CYCLE) NOT NULL, + user_id int4 NOT NULL, + group_id int4 NOT NULL, + CONSTRAINT auth_user_groups_pkey PRIMARY KEY (id), + CONSTRAINT auth_user_groups_user_id_group_id_94350c0c_uniq UNIQUE (user_id, group_id), + CONSTRAINT auth_user_groups_group_id_97559544_fk_auth_group_id FOREIGN KEY (group_id) REFERENCES public.auth_group(id) DEFERRABLE INITIALLY DEFERRED, + CONSTRAINT auth_user_groups_user_id_6a12ed8b_fk_auth_user_id FOREIGN KEY (user_id) REFERENCES public.auth_user(id) DEFERRABLE INITIALLY DEFERRED +); +CREATE INDEX auth_user_groups_group_id_97559544 ON public.auth_user_groups USING btree (group_id); +CREATE INDEX auth_user_groups_user_id_6a12ed8b ON public.auth_user_groups USING btree (user_id); + + +-- public.auth_user_user_permissions 定义 + +-- Drop table + +-- DROP TABLE public.auth_user_user_permissions; + +CREATE TABLE public.auth_user_user_permissions ( + id int8 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 9223372036854775807 START 1 CACHE 1 NO CYCLE) NOT NULL, + user_id int4 NOT NULL, + permission_id int4 NOT NULL, + CONSTRAINT auth_user_user_permissions_pkey PRIMARY KEY (id), + CONSTRAINT auth_user_user_permissions_user_id_permission_id_14a6b632_uniq UNIQUE (user_id, permission_id), + CONSTRAINT auth_user_user_permi_permission_id_1fbb5f2c_fk_auth_perm FOREIGN KEY (permission_id) REFERENCES public.auth_permission(id) DEFERRABLE INITIALLY DEFERRED, + CONSTRAINT auth_user_user_permissions_user_id_a95ead1b_fk_auth_user_id FOREIGN KEY (user_id) REFERENCES public.auth_user(id) DEFERRABLE INITIALLY DEFERRED +); +CREATE INDEX auth_user_user_permissions_permission_id_1fbb5f2c ON public.auth_user_user_permissions USING btree (permission_id); +CREATE INDEX auth_user_user_permissions_user_id_a95ead1b ON public.auth_user_user_permissions USING btree (user_id); + + +-- public.django_admin_log 定义 + +-- Drop table + +-- DROP TABLE public.django_admin_log; + +CREATE TABLE public.django_admin_log ( + id int4 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 2147483647 START 1 CACHE 1 NO CYCLE) NOT NULL, + action_time timestamptz NOT NULL, + object_id text NULL, + object_repr varchar(200) NOT NULL, + action_flag int2 NOT NULL, + change_message text NOT NULL, + content_type_id int4 NULL, + user_id int4 NOT NULL, + CONSTRAINT django_admin_log_action_flag_check CHECK ((action_flag >= 0)), + CONSTRAINT django_admin_log_pkey PRIMARY KEY (id), + CONSTRAINT django_admin_log_content_type_id_c4bce8eb_fk_django_co FOREIGN KEY (content_type_id) REFERENCES public.django_content_type(id) DEFERRABLE INITIALLY DEFERRED, + CONSTRAINT django_admin_log_user_id_c564eba6_fk_auth_user_id FOREIGN KEY (user_id) REFERENCES public.auth_user(id) DEFERRABLE INITIALLY DEFERRED +); +CREATE INDEX django_admin_log_content_type_id_c4bce8eb ON public.django_admin_log USING btree (content_type_id); +CREATE INDEX django_admin_log_user_id_c564eba6 ON public.django_admin_log USING btree (user_id); + + +-- public.auth_group_permissions 定义 + +-- Drop table + +-- DROP TABLE public.auth_group_permissions; + +CREATE TABLE public.auth_group_permissions ( + id int8 GENERATED BY DEFAULT AS IDENTITY( INCREMENT BY 1 MINVALUE 1 MAXVALUE 9223372036854775807 START 1 CACHE 1 NO CYCLE) NOT NULL, + group_id int4 NOT NULL, + permission_id int4 NOT NULL, + CONSTRAINT auth_group_permissions_group_id_permission_id_0cd325b0_uniq UNIQUE (group_id, permission_id), + CONSTRAINT auth_group_permissions_pkey PRIMARY KEY (id), + CONSTRAINT auth_group_permissio_permission_id_84c5c92e_fk_auth_perm FOREIGN KEY (permission_id) REFERENCES public.auth_permission(id) DEFERRABLE INITIALLY DEFERRED, + CONSTRAINT auth_group_permissions_group_id_b120cbf9_fk_auth_group_id FOREIGN KEY (group_id) REFERENCES public.auth_group(id) DEFERRABLE INITIALLY DEFERRED +); +CREATE INDEX auth_group_permissions_group_id_b120cbf9 ON public.auth_group_permissions USING btree (group_id); +CREATE INDEX auth_group_permissions_permission_id_84c5c92e ON public.auth_group_permissions USING btree (permission_id); diff --git a/backend/config/urls.py b/backend/config/urls.py new file mode 100644 index 0000000..84836c6 --- /dev/null +++ b/backend/config/urls.py @@ -0,0 +1,42 @@ +from django.contrib import admin +from django.urls import include, path, re_path +from django.conf import settings +from django.conf.urls.static import static +from drf_yasg import openapi +from drf_yasg.views import get_schema_view +from rest_framework import permissions +from rest_framework_simplejwt import views as JWTAuthenticationViews + +from apps.iam.views.user import UserLoginView + +schema_view = get_schema_view( + openapi.Info( + title="Your API", + default_version="v1", + description="Your API description", + terms_of_service="https://www.yourapp.com/terms/", + contact=openapi.Contact(email="contact@yourapp.com"), + license=openapi.License(name="Your License"), + ), + public=True, + permission_classes=(permissions.AllowAny,), +) + +urlpatterns = [ + path("admin/", admin.site.urls), + path("server/", include("config.api_urls")), + path("login/", UserLoginView.as_view(), name="user_login"), + path("api/token/", JWTAuthenticationViews.TokenObtainPairView.as_view(), name="get_token"), + path( + "api/token/refresh/", + JWTAuthenticationViews.TokenRefreshView.as_view(), + name="refresh_token", + ), + path("api/token/verify/", JWTAuthenticationViews.TokenVerifyView.as_view(), name="token_verify"), + re_path(r"^swagger(?P\.json|\.yaml)$", schema_view.without_ui(cache_timeout=0), name="schema-json"), + path("swagger/", schema_view.with_ui("swagger", cache_timeout=0), name="schema-swagger-ui"), + path("redoc/", schema_view.with_ui("redoc", cache_timeout=0), name="schema-redoc"), +] + +if settings.DEBUG: + urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) diff --git a/backend/config/wsgi.py b/backend/config/wsgi.py new file mode 100644 index 0000000..93d9209 --- /dev/null +++ b/backend/config/wsgi.py @@ -0,0 +1,8 @@ +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") + +application = get_wsgi_application() + diff --git a/backend/font/汉仪旗黑.ttf b/backend/font/汉仪旗黑.ttf new file mode 100644 index 0000000..a237fa1 Binary files /dev/null and b/backend/font/汉仪旗黑.ttf differ diff --git a/backend/font/青鸟华光简琥珀.ttf b/backend/font/青鸟华光简琥珀.ttf new file mode 100644 index 0000000..e601675 Binary files /dev/null and b/backend/font/青鸟华光简琥珀.ttf differ diff --git a/backend/image_server.py b/backend/image_server.py new file mode 100644 index 0000000..fc5746b --- /dev/null +++ b/backend/image_server.py @@ -0,0 +1,346 @@ +""" +独立图片预览 HTTP 服务 +启动方式: python image_server.py [端口] +默认端口: 8080 +""" +import os +import sys +import json +import mimetypes +import http.server +from pathlib import Path +from urllib.parse import unquote, urlparse, parse_qs + +# 图片根目录（与 settings/base.py 中 FILESPACE_ROOT_PATH 默认值一致） +IMAGE_ROOT = Path(r"D:\00_Workspace\python\model_train\project_space") +IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp", ".tiff", ".tif"} + +BASE_DIR = Path(__file__).resolve().parent +TEMPLATE = r""" + + + + +图片预览 - {title} + + + +
+

图片预览服务

+ {breadcrumb} +
+
+ + +
+
+ {body} +
+ +
独立图片预览服务 — 端口 {port}
+ + + +""" + + +def is_image(filename: str) -> bool: + return Path(filename).suffix.lower() in IMAGE_EXTENSIONS + + +def get_breadcrumb_html(relative_path: str) -> str: + """生成面包屑导航""" + if not relative_path or relative_path == ".": + return '根目录' + parts = relative_path.replace("\\", "/").strip("/").split("/") + links = ['根目录'] + accumulated = "" + for p in parts: + accumulated += "/" + p + links.append(f'{p}') + return " / ".join(links) + + +def get_parent_dir(relative_path: str) -> str: + if not relative_path or relative_path == ".": + return "" + parent = str(Path(relative_path).parent) + return "" if parent == "." else parent + + +def build_page_html(current_dir: str, view_mode: str, port: int) -> str: + root = IMAGE_ROOT + if current_dir: + scan_path = root / current_dir + else: + scan_path = root + + dir_param = f"dir={current_dir}&" if current_dir else "" + + if not scan_path.exists(): + body = f'
目录不存在: {scan_path}
' + return TEMPLATE.format( + title="目录不存在", + breadcrumb=get_breadcrumb_html(current_dir), + dir_param=dir_param, + body=body, + current_view=view_mode, + gallery_active="active" if view_mode != "list" else "", + list_active="active" if view_mode == "list" else "", + image_list="[]", + port=port, + ) + + folders = [] + images = [] + + try: + for entry in sorted(scan_path.iterdir(), key=lambda x: (not x.is_dir(), x.name.lower())): + if entry.name.startswith("."): + continue + if entry.is_dir(): + folders.append(entry.name) + elif is_image(entry.name): + images.append(entry.name) + except PermissionError: + pass + + body_parts = [] + + # 返回上级目录 + if current_dir and current_dir != ".": + parent = get_parent_dir(current_dir) + body_parts.append( + f'
' + f'📁../
' + ) + + # 子目录 + if folders: + parts = [] + for f in folders: + sub_dir = f"{current_dir}/{f}".lstrip("/") if current_dir else f + parts.append( + f'' + f'📁{f}' + ) + body_parts.append(f'
{"".join(parts)}
') + + # 图片 + if images: + image_list_data = [] + for img in images: + if current_dir: + rel = f"{current_dir}/{img}".replace("\\", "/") + else: + rel = img + image_list_data.append({"name": img, "path": rel}) + + if view_mode == "list": + rows = [] + for i, img_info in enumerate(image_list_data): + rows.append( + f'{i+1}' + f'{img_info["name"]}' + f'预览' + ) + body_parts.append( + '' + '' + '' + '' + '' + "".join(rows) + "
#文件名操作
" + ) + else: + cards = [] + for i, img_info in enumerate(image_list_data): + cards.append( + f'
' + f'' + f'
{img_info["name"]}
' + f'
' + ) + body_parts.append(f'
{"".join(cards)}
') + + body = "".join(body_parts) + return TEMPLATE.format( + title=current_dir or "根目录", + breadcrumb=get_breadcrumb_html(current_dir), + dir_param=dir_param, + body=body, + current_view=view_mode, + gallery_active="active" if view_mode != "list" else "", + list_active="active" if view_mode == "list" else "", + image_list=json.dumps(image_list_data), + port=port, + ) + else: + if folders: + body = "".join(body_parts) + else: + body_parts.append('
此目录下没有图片
') + body = "".join(body_parts) + return TEMPLATE.format( + title=current_dir or "根目录", + breadcrumb=get_breadcrumb_html(current_dir), + dir_param=dir_param, + body=body, + current_view=view_mode, + gallery_active="active" if view_mode != "list" else "", + list_active="active" if view_mode == "list" else "", + image_list="[]", + port=port, + ) + + +class ImagePreviewHandler(http.server.SimpleHTTPRequestHandler): + """自定义请求处理器""" + + def __init__(self, *args, **kwargs): + super().__init__(*args, directory=str(BASE_DIR), **kwargs) + + def do_GET(self): + parsed = urlparse(self.path) + path = unquote(parsed.path) + params = parse_qs(parsed.query) + + # 根路径 → HTML 页面 + if path == "/": + current_dir = params.get("dir", [""])[0] + view_mode = params.get("view", ["gallery"])[0] + html = build_page_html(current_dir, view_mode, PORT) + self._respond_html(html) + return + + # /file/ 路径 → 返回实际图片文件 + if path.startswith("/file/"): + file_rel = path[len("/file/"):] + file_path = IMAGE_ROOT / file_rel + if file_path.exists() and file_path.is_file(): + self._serve_file(file_path) + else: + self.send_error(404, f"File not found: {file_rel}") + return + + # 其他路径 → 默认静态文件处理 + super().do_GET() + + def _respond_html(self, html: str): + data = html.encode("utf-8") + self.send_response(200) + self.send_header("Content-Type", "text/html; charset=utf-8") + self.send_header("Content-Length", str(len(data))) + self.send_header("Cache-Control", "no-cache") + self.end_headers() + self.wfile.write(data) + + def _serve_file(self, file_path: Path): + mime_type, _ = mimetypes.guess_type(str(file_path)) + if mime_type is None: + mime_type = "application/octet-stream" + file_size = file_path.stat().st_size + + self.send_response(200) + self.send_header("Content-Type", mime_type) + self.send_header("Content-Length", str(file_size)) + self.send_header("Cache-Control", "public, max-age=3600") + self.end_headers() + with open(file_path, "rb") as f: + self.wfile.write(f.read()) + + def log_message(self, format, *args): + print(f"[{self.log_date_time_string()}] {args[0]}") + + +def main(): + global PORT + PORT = int(sys.argv[1]) if len(sys.argv) > 1 else 8080 + + # 确保图片根目录存在 + if not IMAGE_ROOT.exists(): + os.makedirs(IMAGE_ROOT, exist_ok=True) + print(f"已创建目录: {IMAGE_ROOT}") + + server = http.server.HTTPServer(("0.0.0.0", PORT), ImagePreviewHandler) + print(f"=" * 55) + print(f" 图片预览服务已启动") + print(f" 图片根目录: {IMAGE_ROOT}") + print(f" 访问地址: http://localhost:{PORT}") + print(f" 按 Ctrl+C 停止服务") + print(f"=" * 55) + try: + server.serve_forever() + except KeyboardInterrupt: + print("\n服务已停止") + server.server_close() + + +if __name__ == "__main__": + main() diff --git a/backend/manage.py b/backend/manage.py new file mode 100644 index 0000000..29319d1 --- /dev/null +++ b/backend/manage.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") + if os.environ.get("DJANGO_MANAGE_BREAKPOINT", "").strip().lower() in { + "1", + "true", + "yes", + "y", + "on", + }: + breakpoint() + blocked_commands = { + "migrate", + "makemigrations", + "squashmigrations", + "sqlmigrate", + } + allow_migrations = os.environ.get("DJANGO_ALLOW_MIGRATIONS", "").strip().lower() in { + "1", + "true", + "yes", + "y", + "on", + } + if not allow_migrations and any(arg in blocked_commands for arg in sys.argv[1:]): + raise SystemExit( + "migrations 已禁用：禁止执行 migrate/makemigrations/squashmigrations/sqlmigrate。" + "如确需执行，请临时设置环境变量 DJANGO_ALLOW_MIGRATIONS=true。" + ) + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/backend/readme.txt b/backend/readme.txt new file mode 100644 index 0000000..59d9f9c --- /dev/null +++ b/backend/readme.txt @@ -0,0 +1,13 @@ +Python项目启动： +1、python manage.py runserver 0.0.0.0:8000（支持http） +2、uvicorn industrial_prediction.asgi:application --host 0.0.0.0 --port 8000 （支持websocket和http） + +公司101服务部署： +AI算法模型训练及部署管理平台 +公司内部地址：192.168.1.101:3000 +账号/密码:admin/123456 +后端程序地址：/thb-space/document/industrial_train_prediction/industrial_train_prediction +前端程序地址：/thb-space/document/industrial_train_prediction/dist +静态文件存放地址：/thb-space/document/industrial_train_prediction/image +后端docker容器：industrial_container +前端docker容器：nginx \ No newline at end of file diff --git a/backend/scripts/20260709_add_ai_dataset_annotated_count.sql b/backend/scripts/20260709_add_ai_dataset_annotated_count.sql new file mode 100644 index 0000000..b047bd8 --- /dev/null +++ b/backend/scripts/20260709_add_ai_dataset_annotated_count.sql @@ -0,0 +1,26 @@ +ALTER TABLE public.ai_dataset +ADD COLUMN IF NOT EXISTS annotated_count int4 NOT NULL DEFAULT 0; + +COMMENT ON COLUMN public.ai_dataset.annotated_count IS '已标注样本总数'; + +UPDATE public.ai_dataset AS d +SET + dataset_count = stats.dataset_count, + annotated_count = stats.annotated_count +FROM ( + SELECT + s.dataset_id, + COUNT(*) AS dataset_count, + COUNT(*) FILTER ( + WHERE COALESCE(NULLIF(s.rectangle, ''), NULLIF(s.polygon, '')) IS NOT NULL + ) AS annotated_count + FROM public.ai_dataset_sample AS s + GROUP BY s.dataset_id +) AS stats +WHERE d.id = stats.dataset_id; + +UPDATE public.ai_dataset +SET + dataset_count = COALESCE(dataset_count, 0), + annotated_count = COALESCE(annotated_count, 0) +WHERE dataset_count IS NULL OR annotated_count IS NULL; diff --git a/backend/scripts/20260709_add_ai_smart_terminal.sql b/backend/scripts/20260709_add_ai_smart_terminal.sql new file mode 100644 index 0000000..58d80dc --- /dev/null +++ b/backend/scripts/20260709_add_ai_smart_terminal.sql @@ -0,0 +1,113 @@ +-- ============================================================ +-- 智能终端数据表 (ai_smart_terminal) +-- 场景：云端孪生镜像 — 模型训练完成后发布到云端孪生设备， +-- 边缘端定期检测云端版本并下载更新，实现动态模型更新。 +-- 来源：Oracle SD_AIBOX_B_H（AI边缘计算盒子基本属性表） +-- 更新日期：2026-07-09 +-- ============================================================ + +-- public.ai_smart_terminal 定义 + +-- Drop table +-- DROP TABLE public.ai_smart_terminal; + +CREATE TABLE public.ai_smart_terminal ( + -- ======== 基本信息 ======== + id varchar(40) NOT NULL, -- 主键ID + stcd varchar(40) NOT NULL, -- 终端编码（与边缘实体设备一一对应） + stnm varchar(255) NOT NULL, -- 终端名称 + sttp varchar(2) NOT NULL, -- 终端分类：01=鱼类识别AI盒子 02=鸟类识别AI盒子 03=野生动物识别AI盒子 04=鱼类分拣装置 + order_index int4 NULL, -- 排序 + + -- ======== 位置信息 ======== + lgtd numeric(10,6) NULL, -- 经度（单位：°） + lttd numeric(10,6) NULL, -- 纬度（单位：°） + elev numeric(10,6) NULL, -- 高程（单位：m） + rstcd varchar(64) NULL, -- 所属电站编码 + base_id varchar(64) NULL, -- 所属水电基地编码 + hbrvcd varchar(64) NULL, -- 所属水电基地流域编码 + rvcd varchar(64) NULL, -- 所属流域编码 + + -- ======== 设备配置 ======== + aipz varchar(1000) NULL, -- AI盒子硬件/软件配置信息 + ipaddr varchar(512) NULL, -- 设备IP地址 + siminfo varchar(512) NULL, -- SIM卡信息 + lxr varchar(512) NULL, -- 联系人及联系电话 + purpose varchar(512) NULL, -- 用途说明 + + -- ======== 监测配置 ======== + stindx varchar(1000) NULL, -- 监测指标列表 + dtfrqcy int4 NULL, -- 数据采集频次（单位：min） + mway int2 NULL, -- 监测方式：0=人工监测 1=自动监测 2=混合监测 + + -- ======== 启用与接入状态 ======== + usfl int2 NULL, -- 是否启用：0=否 1=是 + dtin int2 NULL, -- 数据是否接入：0=未接入 1=已接入 + dtin_tm timestamptz NULL, -- 数据首次接入时间 + + -- ======== 云端孪生 — 模型发布 ======== + model_id varchar(40) NULL, -- 已部署模型ID（关联训练产出的模型记录） + model_version varchar(64) NULL, -- 已部署模型版本号 + model_path varchar(600) NULL, -- 模型文件存储路径（MinIO对象路径） + model_publish_time timestamptz NULL, -- 模型发布时间（发布到云端孪生设备的时间） + model_publish_user varchar(36) NULL, -- 模型发布人 + + -- ======== 边缘端 — 同步状态 ======== + edge_model_version varchar(64) NULL, -- 边缘端当前运行的模型版本 + edge_update_time timestamptz NULL, -- 边缘端最后一次模型更新时间 + last_heartbeat_time timestamptz NULL, -- 边缘端最后心跳时间 + online_status int2 NULL, -- 在线状态：0=离线 1=在线 + sync_check_interval int4 NULL, -- 同步检测间隔（单位：min，边缘端轮询云端孪生设备版本的时间间隔） + + -- ======== 运维记录 ======== + remark varchar(512) NULL, -- 备注 + record_user varchar(36) NULL, -- 创建人 + record_time timestamptz NOT NULL DEFAULT now(), -- 创建时间 + is_deleted int2 NOT NULL DEFAULT 0, -- 是否已删除：0=未删除 1=已删除 + + CONSTRAINT ai_smart_terminal_pkey PRIMARY KEY (id) +); + +CREATE INDEX ai_smart_terminal_id_idx ON public.ai_smart_terminal USING btree (id varchar_pattern_ops); +CREATE INDEX ai_smart_terminal_stcd_idx ON public.ai_smart_terminal USING btree (stcd); + +-- Column comments + +COMMENT ON TABLE public.ai_smart_terminal IS '智能终端数据表（云端孪生镜像）'; +COMMENT ON COLUMN public.ai_smart_terminal.id IS '主键ID'; +COMMENT ON COLUMN public.ai_smart_terminal.stcd IS '终端编码（与边缘实体设备一一对应）'; +COMMENT ON COLUMN public.ai_smart_terminal.stnm IS '终端名称'; +COMMENT ON COLUMN public.ai_smart_terminal.sttp IS '终端分类：01=鱼类识别AI盒子 02=鸟类识别AI盒子 03=野生动物识别AI盒子 04=鱼类分拣装置'; +COMMENT ON COLUMN public.ai_smart_terminal.order_index IS '排序'; +COMMENT ON COLUMN public.ai_smart_terminal.lgtd IS '经度（单位：°）'; +COMMENT ON COLUMN public.ai_smart_terminal.lttd IS '纬度（单位：°）'; +COMMENT ON COLUMN public.ai_smart_terminal.elev IS '高程（单位：m）'; +COMMENT ON COLUMN public.ai_smart_terminal.rstcd IS '所属电站编码'; +COMMENT ON COLUMN public.ai_smart_terminal.base_id IS '所属水电基地编码'; +COMMENT ON COLUMN public.ai_smart_terminal.hbrvcd IS '所属水电基地流域编码'; +COMMENT ON COLUMN public.ai_smart_terminal.rvcd IS '所属流域编码'; +COMMENT ON COLUMN public.ai_smart_terminal.aipz IS 'AI盒子硬件/软件配置信息'; +COMMENT ON COLUMN public.ai_smart_terminal.ipaddr IS '设备IP地址'; +COMMENT ON COLUMN public.ai_smart_terminal.siminfo IS 'SIM卡信息'; +COMMENT ON COLUMN public.ai_smart_terminal.lxr IS '联系人及联系电话'; +COMMENT ON COLUMN public.ai_smart_terminal.purpose IS '用途说明'; +COMMENT ON COLUMN public.ai_smart_terminal.stindx IS '监测指标列表'; +COMMENT ON COLUMN public.ai_smart_terminal.dtfrqcy IS '数据采集频次（单位：min）'; +COMMENT ON COLUMN public.ai_smart_terminal.mway IS '监测方式：0=人工监测 1=自动监测 2=混合监测'; +COMMENT ON COLUMN public.ai_smart_terminal.usfl IS '是否启用：0=否 1=是'; +COMMENT ON COLUMN public.ai_smart_terminal.dtin IS '数据是否接入：0=未接入 1=已接入'; +COMMENT ON COLUMN public.ai_smart_terminal.dtin_tm IS '数据首次接入时间'; +COMMENT ON COLUMN public.ai_smart_terminal.model_id IS '已部署模型ID（关联训练产出的模型记录）'; +COMMENT ON COLUMN public.ai_smart_terminal.model_version IS '已部署模型版本号'; +COMMENT ON COLUMN public.ai_smart_terminal.model_path IS '模型文件存储路径（MinIO对象路径）'; +COMMENT ON COLUMN public.ai_smart_terminal.model_publish_time IS '模型发布时间（发布到云端孪生设备的时间）'; +COMMENT ON COLUMN public.ai_smart_terminal.model_publish_user IS '模型发布人'; +COMMENT ON COLUMN public.ai_smart_terminal.edge_model_version IS '边缘端当前运行的模型版本'; +COMMENT ON COLUMN public.ai_smart_terminal.edge_update_time IS '边缘端最后一次模型更新时间'; +COMMENT ON COLUMN public.ai_smart_terminal.last_heartbeat_time IS '边缘端最后心跳时间'; +COMMENT ON COLUMN public.ai_smart_terminal.online_status IS '在线状态：0=离线 1=在线'; +COMMENT ON COLUMN public.ai_smart_terminal.sync_check_interval IS '同步检测间隔（单位：min，边缘端轮询云端孪生设备版本的时间间隔）'; +COMMENT ON COLUMN public.ai_smart_terminal.remark IS '备注'; +COMMENT ON COLUMN public.ai_smart_terminal.record_user IS '创建人'; +COMMENT ON COLUMN public.ai_smart_terminal.record_time IS '创建时间'; +COMMENT ON COLUMN public.ai_smart_terminal.is_deleted IS '是否已删除：0=未删除 1=已删除'; diff --git a/backend/scripts/test_mask_for_video.py b/backend/scripts/test_mask_for_video.py new file mode 100644 index 0000000..a98ee5f --- /dev/null +++ b/backend/scripts/test_mask_for_video.py @@ -0,0 +1,24 @@ +import os +import tempfile + +import django + + +def main(): + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") + django.setup() + from apps.datasets.views.dataset import _make_mask_file + + with tempfile.TemporaryDirectory() as tmp: + src = os.path.join(tmp, "1.mp4") + dst = os.path.join(tmp, "1_mask.mp4") + with open(src, "wb") as f: + f.write(b"not a real mp4") + _make_mask_file(src, dst) + assert os.path.exists(dst) + assert os.path.getsize(dst) == os.path.getsize(src) + print("ok") + + +if __name__ == "__main__": + main() diff --git a/backend/start_server.py b/backend/start_server.py new file mode 100644 index 0000000..bd0f605 --- /dev/null +++ b/backend/start_server.py @@ -0,0 +1,10 @@ +import os +import sys + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.dev") + +from daphne.cli import CommandLineInterface + +cli = CommandLineInterface() + +sys.exit(cli.run(["-b", "0.0.0.0", "-p", "8000", "config.asgi:application"])) \ No newline at end of file diff --git a/backend/yolo11n-seg.pt b/backend/yolo11n-seg.pt new file mode 100644 index 0000000..628f517 Binary files /dev/null and b/backend/yolo11n-seg.pt differ diff --git a/backend/yolo11n.pt b/backend/yolo11n.pt new file mode 100644 index 0000000..45b273b Binary files /dev/null and b/backend/yolo11n.pt differ diff --git a/docs/系统V2.0扩展需求.md b/docs/系统V2.0扩展需求.md new file mode 100644 index 0000000..4753840 --- /dev/null +++ b/docs/系统V2.0扩展需求.md @@ -0,0 +1,37 @@ +# AI算法模型训练及部署管理平台V2.0扩展需求 + +## 一、数据集管理 + +1、在数据集管理菜单中扩展二级菜单：（1）视频文件管理 （2）数据集管理 + +2、视频文件管理：（0）采用minio存储视频文件 （1）支持页面上传单个或者多个视频文件、 （2）支持视频压缩文件上传 （3）支持同步读取服务器上临时目录视频 + +3、数据集管理：（0）采用mini存储图片文件 （1）数据集与算法分离 （一个数据集可以为多个算法服务） （2） 上传数据集，支持从视频文件（库）中导入视频 （3）视频支持拆分为图片 （5）python支持http预览图片（内置服务） + +![image-20260706172644780](C:\Users\zheng\AppData\Roaming\Typora\typora-user-images\image-20260706172644780.png) + +## 二、数据标注管理 + +1、一种图片支持两类标注：（1）矩形框标注 （2） 多边形标注 ，保存到图片对应的两个字段中； + +![image-20260706170411899](C:\Users\zheng\AppData\Roaming\Typora\typora-user-images\image-20260706170411899.png) + + + +## 三、模型训练管理 + +1、关联数据集： 从目前的通过算法筛选数据集模式，改为弹出窗口，列表查询数据集，带入到训练数据集中。 + +![image-20260706165925940](C:\Users\zheng\AppData\Roaming\Typora\typora-user-images\image-20260706165925940.png) + +![image-20260706170030139](C:\Users\zheng\AppData\Roaming\Typora\typora-user-images\image-20260706170030139.png) + + + +## 四、模型部署管理 + +1、增加边缘设备管理菜单 （1）增、删、改、查，批量导入，接口同步 + +2、设备信息：编号，名称，公司、电站，业务（鱼类监测等），位置，照片，配置信息（IP，系统、内存、硬盘），算法模型（算法名称、模型版本、更新日期，更新状态） + +3、模型发布：（1）查询设备（可按模型类别检索） （2）发布模型 （3）更新模型 （4）查看状态（边缘和云端状态同步）（版本号，更新日期） \ No newline at end of file diff --git a/docs/系统技术方案说明.md b/docs/系统技术方案说明.md new file mode 100644 index 0000000..afdc2ea --- /dev/null +++ b/docs/系统技术方案说明.md @@ -0,0 +1,127 @@ +# AI 算法模型训练及部署管理平台 - 系统技术方案说明 + +## 1. 项目概述 + +本项目是一个集成的 AI 算法模型全生命周期管理平台，旨在提供从数据管理、标注、模型训练到在线预测的一站式解决方案。项目基于前后端分离架构，前端采用现代化的 Vue 3 技术栈，后端基于 Django 框架构建，支持 YOLO 和 D-FINE 等主流目标检测算法的训练与推理。 + +## 2. 系统架构 + +### 2.1 技术栈 + +**前端 (Frontend)** +- **框架**: Vue 3 (Composition API) +- **构建工具**: Vite 4 +- **UI 组件库**: Element Plus +- **状态管理**: Pinia +- **样式**: TailwindCSS + SCSS +- **路由**: Vue Router 4 +- **HTTP 客户端**: Axios +- **可视化**: ECharts +- **标注工具**: ailabel + +**后端 (Backend)** +- **框架**: Django 4.2.7 + Django REST Framework (DRF) +- **实时通信**: Django Channels (WebSocket) +- **数据库**: PostgreSQL (pgsqldb) +- **深度学习框架**: PyTorch (主要), Ultralytics (YOLO), PaddlePaddle (遗留/特定模块) +- **加密**: SM4 (国密算法) +- **任务调度**: Threading / Subprocess (当前实现), 可扩展 Celery + +**运行环境** +- **操作系统**: Windows (开发环境) / Linux (生产环境) +- **Python 版本**: 3.11 +- **依赖管理**: pip + virtualenv + +### 2.2 目录结构 + +``` +AI_TrainPrediction/ +├── backend/ # 后端项目根目录 +│ ├── apps/ # 业务应用模块 +│ │ ├── algorithms/ # 算法管理 (模型定义, 训练逻辑, 预测接口) +│ │ ├── annotation/ # 标注管理 +│ │ ├── common/ # 公共工具 (序列化, 权限, 文件处理) +│ │ ├── datasets/ # 数据集管理 +│ │ ├── iam/ # 身份认证与权限 (用户, 组织, 角色) +│ │ ├── core/ # 核心数据模型 (数据集, 样本, 模型, 训练任务) +│ │ └── training/ # 训练任务管理 (日志流, 进程控制) +│ ├── config/ # Django 配置 (settings, urls, asgi/wsgi) +│ ├── db/ # 数据库相关 +│ ├── enev/ # Python 虚拟环境 +│ └── manage.py # Django 管理脚本 +├── frontend/ # 前端项目根目录 +│ ├── src/ # 源代码 +│ ├── public/ # 静态资源 +│ └── vite.config.ts # Vite 配置 +├── requirements/ # Python 依赖清单 +└── tools/ # 辅助脚本 +``` + +## 3. 核心功能模块 + +### 3.1 身份认证与权限 (IAM) +- **JWT 认证**: 使用 `simplejwt` 实现基于 Token 的身份验证。 +- **SM4 加密**: 登录密码传输采用国密 SM4 算法加密，保障安全性。 +- **RBAC**: 基于 Django 内置的 Group 和 Permission 实现角色权限控制。 + +### 3.2 数据集管理 (Datasets) +- **多格式支持**: 支持图片上传、解压、预览。 +- **标注集成**: 集成 `ailabel` 前端库，支持在线标注并生成 XML/JSON 格式标注文件。 +- **样本管理**: `AiDatasetSample` 表记录样本状态、路径及标注内容。 + +### 3.3 算法与模型管理 (Algorithms) +- **算法引擎**: + - **01 (YOLO)**: 基于 Ultralytics 框架，支持 YOLOv8 等模型。 + - **02 (D-FINE)**: 基于 PyTorch 实现的改进版 RT-DETR。 +- **模型版本控制**: 自动生成版本号，支持模型归档、导出。 +- **配置文件生成**: 根据训练参数动态生成 `config.yaml` 或 `pipeline.config`。 + +### 3.4 模型训练 (Training) +- **异步训练**: 后端通过 `subprocess` 启动独立的训练进程，避免阻塞主线程。 +- **实时日志**: 利用 Django Channels + WebSocket 实现训练日志实时推送到前端展示。 +- **参数配置**: 支持自定义 Epochs, Batch Size, Learning Rate 等超参数。 +- **断点续训与终止**: 支持训练任务的中断与状态回滚。 + +### 3.5 在线预测 (Prediction) +- **即时推理**: 上传图片后，后端加载对应模型进行推理并返回标注后的图片或坐标数据。 +- **多模型支持**: 根据 `algorithm_engine` 自动选择 YOLO 或 D-FINE 推理器。 + +## 4. 关键代码逻辑说明 + +### 4.1 训练流程 +1. **创建记录**: `create_algorithm_train_record` 初始化训练任务。 +2. **数据准备**: `prepare_algorithm_train_dataset` 将数据集样本复制到训练目录，并生成 YOLO/COCO 格式标签。 +3. **启动训练**: `start_algorithm_train` 根据引擎类型 (`train_algorithm` 或 `train_algorithm_dfine`) 启动子进程。 +4. **日志流**: `stream_logs_to_frontend` 线程实时读取日志文件并通过 WebSocket 发送。 +5. **模型归档**: 训练完成后，`archive_train_model` 将最佳权重文件 (`best.pt`) 归档为新模型版本。 + +### 4.2 遗留与待优化项 (Refactoring Notes) +- **prediction_server_legacy**: 该应用名包含 "legacy"，但实际上承载了所有核心 Models (`AiAlgorithm`, `AiAlgorithmTrainRecords` 等)。建议在未来重构中重命名为 `core` 或 `base`。 +- **冗余代码**: `backend/apps/algorithms/algorithm/` 下存在多个未使用的目录（如 `tensorflow`, `other`, `dataPrediction`），建议在确认无业务依赖后移除。 +- **配置管理**: 根目录下的 `settings.py` 为废弃文件，实际配置位于 `backend/config/settings/`。 + +## 5. 部署与环境 + +### 5.1 依赖安装 +```bash +cd backend +pip install -r ../requirements/all.txt +``` +*注意：需确保安装了正确版本的 `torch` 和 `ultralytics` 以支持 GPU 训练。* + +### 5.2 数据库配置 +修改 `backend/config/settings/base.py` 中的 `DATABASES` 配置以连接 PostgreSQL。 + +### 5.3 启动服务 +```bash +# 后端 +python manage.py runserver 0.0.0.0:8000 + +# 前端 +cd frontend +npm install +npm run dev +``` + +## 6. 总结 +本项目已完成从旧系统的核心功能迁移，后端服务稳定，能够支撑完整的 AI 模型训练与应用流程。通过引入 Django Channels 和现代化前端技术，提升了用户体验（如实时日志）。后续建议清理遗留代码并优化应用命名，以提升代码可维护性。 diff --git a/docs/边缘智能终端模型同步功能设计.md b/docs/边缘智能终端模型同步功能设计.md new file mode 100644 index 0000000..9d7f8a3 --- /dev/null +++ b/docs/边缘智能终端模型同步功能设计.md @@ -0,0 +1,928 @@ +# 边缘智能终端模型同步功能设计 + +## 一、概述 + +### 1.1 系统架构 + +``` +┌──────────────────────────────────────────────────────────────────┐ +│ 云端服务器 (Cloud) │ +│ ┌──────────┐ ┌────────────┐ ┌───────────┐ ┌──────────────┐ │ +│ │ 认证服务 │ │ 心跳服务 │ │ 版本管理 │ │ 模型下载服务 │ │ +│ │ (Token) │ │ (Heartbeat)│ │ (Version) │ │ (Download) │ │ +│ └────┬─────┘ └─────┬──────┘ └─────┬─────┘ └──────┬───────┘ │ +│ │ │ │ │ │ +│ ┌────┴──────────────┴───────────────┴───────────────┴──────┐ │ +│ │ REST API + WebSocket │ │ +│ └──────────────────────────┬───────────────────────────────┘ │ +│ │ │ +│ ┌──────────────────────────┴───────────────────────────────┐ │ +│ │ Django REST Framework + Channels (ASGI) │ │ +│ │ MinIO 对象存储 (archived-model / trained-model) │ │ +│ └──────────────────────────────────────────────────────────┘ │ +└──────────────────────────────────────────────────────────────────┘ + │ + HTTPS / WSS（公网/内网） + │ +┌─────────────────────────────┴────────────────────────────────────┐ +│ 边缘智能终端 (Edge Device) │ +│ ┌──────────┐ ┌────────────┐ ┌───────────┐ ┌──────────────┐ │ +│ │ Token管理 │ │ 心跳上报 │ │ 版本检查 │ │ 模型下载更新 │ │ +│ │ (每日刷新)│ │ (30s/次) │ │ (定时轮询) │ │ (差量更新) │ │ +│ └──────────┘ └────────────┘ └───────────┘ └──────────────┘ │ +│ │ +│ ┌──────────────────────────────────────────────────────────┐ │ +│ │ Edge Runtime (Python 模拟程序 / 边缘盒子固件) │ │ +│ │ 本地模型目录: /opt/edge/models/ │ │ +│ │ 本地配置目录: /opt/edge/config/ │ │ +│ └──────────────────────────────────────────────────────────┘ │ +└──────────────────────────────────────────────────────────────────┘ +``` + +### 1.2 核心数据模型 + +基于已有数据表 `ai_smart_terminal`，核心字段如下： + +| 字段 | 类型 | 说明 | +|------|------|------| +| `stcd` | CharField(40) | 终端唯一编码，边缘设备与云端一一对应 | +| `stnm` | CharField(255) | 终端名称 | +| `model_id` | CharField(40) | 已部署模型ID（关联 AiAlgorithmModels） | +| `model_name` | CharField(200) | 已部署模型文件名 | +| `model_version` | CharField(64) | 云端当前发布的模型版本 | +| `model_path` | CharField(600) | 模型文件 MinIO 存储路径 | +| `model_publish_time` | DateTimeField | 模型发布时间 | +| `edge_model_version` | CharField(64) | 边缘端当前运行的模型版本 | +| `edge_update_time` | DateTimeField | 边缘端最后一次模型更新时间 | +| `last_heartbeat_time` | DateTimeField | 边缘端最后心跳时间 | +| `online_status` | SmallIntegerField | 在线状态：0=离线 1=在线 | +| `sync_check_interval` | IntegerField | 同步检查间隔（单位：分钟） | + +--- + +## 二、云端服务功能设计 + +### 2.1 设备接入认证（Token 生成） + +**接口**: `POST /server/terminal/auth/` + +**功能说明**: +- 边缘设备使用设备编码（stcd）向云端申请认证 Token +- Token 有效期 1 天（24小时），基于 JWT 机制 +- Token 中携带 `stcd` ，区别于用户 Token + +**请求参数**: +```json +{ + "stcd": "DEVICE001" +} +``` + +**响应示例**: +```json +{ + "code": 200, + "data": { + "access_token": "eyJhbGciOiJIUzI1NiIs...", + "token_type": "bearer", + "expires_in": 86400, + "stcd": "DEVICE001" + } +} +``` + +**认证中间件**: +- 所有边缘设备 API 使用 `EdgeDeviceAuthentication` 认证类 +- 验证 Token 中的 `stcd` 与请求参数/URL 中的 `stcd` 一致 +- Token 过期返回 401，设备端触发重新认证流程 + +**JWT Token 生成逻辑**: +```python +from rest_framework_simplejwt.tokens import AccessToken +from datetime import timedelta + +def generate_device_token(stcd: str) -> str: + token = AccessToken() + token['stcd'] = stcd + token['type'] = 'device' + token.set_exp(lifetime=timedelta(days=1)) + return str(token) +``` + +### 2.2 心跳时间更新 + +**接口**: `PUT /server/terminal/heartbeat/`（已有，需增强认证） + +**功能说明**: +- 边缘设备定时调用（建议 30 秒/次） +- 更新 `last_heartbeat_time` 为当前时间 +- 更新 `online_status` 为 1（在线） +- 若设备首次接入，同时设置 `dtin=1` 和 `dtin_tm` + +**请求参数**: +```json +{ + "stcd": "DEVICE001" +} +``` + +**请求头**: +``` +Authorization: Bearer +``` + +**响应示例**: +```json +{ + "code": 200, + "data": { + "stcd": "DEVICE001", + "last_heartbeat_time": "2026-07-25T15:30:00Z", + "online_status": 1 + } +} +``` + +### 2.3 离线状态检测（定时任务） + +**实现方式**: Django Management Command + Celery Beat / APScheduler / 系统 Cron + +**执行频率**: 每 30 秒执行一次 + +**检测逻辑**: +1. 查询所有 `online_status=1` 且 `last_heartbeat_time` 不为空的终端 +2. 计算 `now() - last_heartbeat_time` +3. 若差值 > 5 分钟（300秒），则将 `online_status` 更新为 0 +4. 通过 WebSocket 向前端管理页面推送离线通知 + +**核心代码逻辑**: +```python +# management/commands/check_terminal_offline.py +from django.core.management.base import BaseCommand +from django.utils.timezone import now +from datetime import timedelta +from apps.core.models import AiSmartTerminal +from asgiref.sync import async_to_sync +from channels.layers import get_channel_layer + + +class Command(BaseCommand): + help = '检测超时心跳的终端并标记为离线' + + def handle(self, *args, **options): + threshold = now() - timedelta(minutes=5) + offline_terminals = AiSmartTerminal.objects.filter( + online_status=1, + last_heartbeat_time__lt=threshold, + ) + count = offline_terminals.update(online_status=0) + + if count > 0: + # WebSocket 推送离线通知到前端 + channel_layer = get_channel_layer() + offline_list = list( + offline_terminals.values('stcd', 'stnm') + ) + async_to_sync(channel_layer.group_send)( + "terminal_status", + { + "type": "terminal.status", + "data": { + "event": "offline", + "count": count, + "terminals": offline_list, + } + } + ) + self.stdout.write(f"标记 {count} 个终端为离线") +``` + +**WebSocket 前端订阅**: +- 前端管理页面连接 WebSocket: `ws://host/ws/terminal/status/` +- 收到 `online_status=0` 消息时，实时更新终端列表中的在线状态标识 +- 可触达前端消息提示（如 ElNotification） + +### 2.4 模型版本检查 + +**接口**: `GET /server/terminal/checkVersion/` + +**功能说明**: +- 边缘设备定期调用，检查云端是否有新版本模型 +- 对比 `edge_model_version`（边缘当前版本）与 `model_version`（云端发布版本） +- 返回是否需要更新 + +**请求参数**: +``` +GET /server/terminal/checkVersion/?stcd=DEVICE001&edge_version=v1.0 +``` + +**响应示例（有新版本）**: +```json +{ + "code": 200, + "data": { + "having_newmodel": 1, + "model_name": "model-001.onnx", + "model_version": "v2.0", + "model_publish_time": "2026-07-25T12:00:00Z", + "sync_check_interval": 30 + } +} +``` + +**响应示例（已是最新）**: +```json +{ + "code": 200, + "data": { + "having_newmodel": 0, + "model_version": "v1.0", + "sync_check_interval": 30 + } +} +``` + +**版本对比逻辑**: +- 若 `edge_model_version` 为空（首次同步），`having_newmodel=1` +- 若 `edge_model_version != model_version`，`having_newmodel=1` +- 若 `edge_model_version == model_version`，`having_newmodel=0` +- 返回 `sync_check_interval` 供边缘端动态调整检查频率 + +### 2.5 模型下载 + +**接口**: `GET /server/terminal/downloadModel/`（已有，需增强） + +**功能说明**: +- 边缘设备下载最新 ONNX 模型文件 +- 支持断点续传（通过 Range 请求头） +- 返回文件流，`Content-Disposition` 包含文件名 +- 下载完成后，边缘端调用回调接口更新 `edge_model_version` 和 `edge_update_time` + +**请求参数**: +``` +GET /server/terminal/downloadModel/?stcd=DEVICE001&format=onnx +``` + +**请求头**: +``` +Authorization: Bearer +Range: bytes=0-1048576 （可选，断点续传） +``` + +**响应**: +``` +HTTP/1.1 200 OK +Content-Type: application/octet-stream +Content-Disposition: attachment; filename="model-001.onnx" +Content-Length: 10485760 +``` + +**下载完成回调接口**: `PUT /server/terminal/updateModelVersion/` +```json +{ + "stcd": "DEVICE001", + "edge_model_version": "v2.0" +} +``` + +--- + +## 三、边缘设备端设计（模拟程序） + +### 3.1 目录结构 + +``` +edge_simulator/ +├── config/ +│ ├── config.json # 配置文件（设备编码、云端地址等） +│ └── token.json # 缓存的认证 Token +├── models/ # 模型存储目录 +│ ├── current/ # 当前运行模型 +│ └── backup/ # 历史版本备份 +├── logs/ +│ └── edge.log +├── main.py # 主入口，调度各模块 +├── auth.py # Token 管理与刷新 +├── heartbeat.py # 心跳上报模块 +├── version_check.py # 版本检查与下载 +└── requirements.txt +``` + +### 3.2 配置文件 (config/config.json) + +```json +{ + "stcd": "DEVICE001", + "cloud_base_url": "http://192.168.1.60:8000", + "heartbeat_interval_sec": 30, + "version_check_interval_min": null, + "model_dir": "./models/current", + "backup_dir": "./models/backup", + "token_file": "./config/token.json", + "log_level": "INFO", + "retry_count": 3, + "retry_delay_sec": 5 +} +``` + +### 3.3 模块详细设计 + +#### 3.3.1 Token 管理模块 (auth.py) + +**功能**: +- 每日定时获取新 Token（每天 00:00:00 执行） +- 首次启动时立即获取 Token +- 缓存 Token 到本地文件，记录过期时间 +- 每次 API 调用前检查 Token 是否有效 + +**流程**: + +``` +开始 + │ + ├── 检查本地 token.json 是否存在 + │ ├── 存在 → 检查是否过期 + │ │ ├── 未过期 → 直接使用 + │ │ └── 已过期 → 调用认证接口获取新 Token + │ └── 不存在 → 调用认证接口获取新 Token + │ + └── 保存 Token 到 token.json + └── 记录 expires_at 时间戳 +``` + +**token.json 格式**: +```json +{ + "access_token": "eyJhbGciOiJIUzI1NiIs...", + "token_type": "bearer", + "expires_at": "2026-07-26T00:00:00Z", + "stcd": "DEVICE001" +} +``` + +#### 3.3.2 心跳上报模块 (heartbeat.py) + +**功能**: +- 定时 30 秒调用云端心跳接口 +- 携带设备 Token 进行认证 +- 失败重试 3 次（间隔 2 秒） +- 记录心跳日志 + +**流程**: + +``` +定时触发 (每30秒) + │ + ├── 获取本地 Token + ├── PUT /server/terminal/heartbeat/ + │ Headers: Authorization: Bearer + │ Body: {"stcd": "DEVICE001"} + │ + ├── 响应码 = 200 → 成功，记录日志 + ├── 响应码 = 401 → Token 过期，触发 Token 刷新 → 重试 + ├── 响应码 = 其他 / 超时 → 重试（最多3次） + │ + └── 3次失败 → 记录错误日志，等待下次触发 +``` + +#### 3.3.3 版本检查与模型下载模块 (version_check.py) + +**功能**: +- 根据 `sync_check_interval` 定时检查云端模型版本 +- 若有新版本，下载模型文件到本地（支持断点续传） +- 验证下载文件的 MD5/SHA256 完整性 +- 备份旧版本模型 +- 更新 `edge_model_version` 到云端 + +**流程**: + +``` +定时触发 (根据 sync_check_interval，默认从云端获取) + │ + ├── 读取本地当前模型版本 (从本地 version.txt) + ├── GET /server/terminal/checkVersion/ + │ ?stcd=DEVICE001 + │ &edge_version=v1.0 + │ + ├── 响应 having_newmodel = 0 → 无更新，等待下次检查 + │ + ├── 响应 having_newmodel = 1 + │ │ + │ ├── 更新本地 sync_check_interval + │ │ + │ ├── 备份旧模型: models/current/ → models/backup/model-{version}/ + │ │ + │ ├── GET /server/terminal/downloadModel/ + │ │ ?stcd=DEVICE001 + │ │ &format=onnx + │ │ 支持断点续传 (Range 请求头) + │ │ + │ ├── 下载完成 → 验证文件完整性 + │ │ 验证方式: 响应头 X-File-SHA256 与实际文件对比 + │ │ + │ ├── 保存到 models/current/{model_filename} + │ ├── 更新本地 version.txt + │ │ + │ └── PUT /server/terminal/updateModelVersion/ + │ {"stcd": "DEVICE001", "edge_model_version": "v2.0"} + │ → 更新 edge_model_version 和 edge_update_time + │ + └── 记录更新日志 +``` + +#### 3.3.4 主调度程序 (main.py) + +```python +""" +边缘智能终端模拟程序 - 主入口 +""" +import json +import logging +import os +import signal +import sys +import threading +import time +from datetime import datetime + +import requests + + +class EdgeDeviceSimulator: + """边缘智能终端模拟器""" + + def __init__(self, config_path: str = "./config/config.json"): + self.config = self._load_config(config_path) + self.stcd = self.config["stcd"] + self.secret = self.config["secret"] + self.base_url = self.config["cloud_base_url"] + self.running = True + + self._setup_logging() + self._setup_signal_handlers() + + self.token = None + self.token_refreshed_at = None + + self.heartbeat_interval = self.config.get("heartbeat_interval_sec", 30) + self.version_check_interval = None # 从云端获取 + self.last_version_check = None + + # ── 配置与日志 ────────────────────────────── + + def _load_config(self, path: str) -> dict: + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + def _setup_logging(self): + logging.basicConfig( + level=getattr(logging, self.config.get("log_level", "INFO")), + format="%(asctime)s [%(levelname)s] %(message)s", + handlers=[ + logging.FileHandler("./logs/edge.log", encoding="utf-8"), + logging.StreamHandler(sys.stdout), + ], + ) + self.logger = logging.getLogger("EdgeDevice") + + def _setup_signal_handlers(self): + signal.signal(signal.SIGINT, self._graceful_shutdown) + signal.signal(signal.SIGTERM, self._graceful_shutdown) + + def _graceful_shutdown(self, signum, frame): + self.logger.info("收到退出信号，正在安全关闭...") + self.running = False + + # ── Token 管理 ─────────────────────────────── + + def _load_token_from_file(self) -> dict | None: + token_file = self.config["token_file"] + if not os.path.exists(token_file): + return None + with open(token_file, "r", encoding="utf-8") as f: + data = json.load(f) + expires_at = datetime.fromisoformat(data["expires_at"]) + if datetime.now() >= expires_at: + self.logger.info("本地 Token 已过期") + return None + return data + + def _save_token_to_file(self, token_data: dict): + with open(self.config["token_file"], "w", encoding="utf-8") as f: + json.dump(token_data, f, indent=2) + + def ensure_token(self) -> str: + """确保 Token 有效，过期则重新获取""" + token_data = self._load_token_from_file() + if token_data: + return token_data["access_token"] + + self.logger.info("获取新的设备 Token...") + resp = requests.post( + f"{self.base_url}/server/terminal/auth/", + json={"stcd": self.stcd, "secret": self.secret}, + timeout=10, + ) + resp.raise_for_status() + result = resp.json() + token_data = result["data"] + self._save_token_to_file(token_data) + self.logger.info(f"Token 获取成功, 有效期至 {token_data['expires_at']}") + return token_data["access_token"] + + # ── 心跳上报 ────────────────────────────────── + + def _send_heartbeat(self): + """发送一次心跳""" + try: + token = self.ensure_token() + resp = requests.put( + f"{self.base_url}/server/terminal/heartbeat/", + json={"stcd": self.stcd}, + headers={"Authorization": f"Bearer {token}"}, + timeout=5, + ) + if resp.status_code == 200: + self.logger.debug("心跳上报成功") + return True + elif resp.status_code == 401: + self.logger.warning("Token 认证失败，清除本地 Token 缓存") + os.remove(self.config["token_file"]) + else: + self.logger.warning(f"心跳上报失败: {resp.status_code}") + except requests.RequestException as e: + self.logger.error(f"心跳上报异常: {e}") + return False + + def _heartbeat_loop(self): + """心跳上报定时循环""" + self.logger.info(f"心跳上报模块启动, 间隔 {self.heartbeat_interval} 秒") + while self.running: + self._send_heartbeat() + time.sleep(self.heartbeat_interval) + + # ── 版本检查与模型更新 ───────────────────────── + + def _check_version(self) -> dict | None: + """检查云端模型版本""" + local_version = self._get_local_version() + try: + token = self.ensure_token() + resp = requests.get( + f"{self.base_url}/server/terminal/checkVersion/", + params={"stcd": self.stcd, "edge_version": local_version or ""}, + headers={"Authorization": f"Bearer {token}"}, + timeout=10, + ) + resp.raise_for_status() + return resp.json()["data"] + except requests.RequestException as e: + self.logger.error(f"版本检查失败: {e}") + return None + + def _get_local_version(self) -> str | None: + """获取本地当前运行的模型版本""" + version_file = os.path.join(self.config["model_dir"], "version.txt") + if os.path.exists(version_file): + with open(version_file, "r") as f: + return f.read().strip() + return None + + def _backup_current_model(self): + """备份当前模型""" + model_dir = self.config["model_dir"] + backup_dir = self.config["backup_dir"] + local_version = self._get_local_version() + if not os.path.exists(model_dir) or not local_version: + return + version_backup = os.path.join(backup_dir, f"model-{local_version}") + os.makedirs(version_backup, exist_ok=True) + for f in os.listdir(model_dir): + src = os.path.join(model_dir, f) + dst = os.path.join(version_backup, f) + if os.path.isfile(src): + import shutil + shutil.copy2(src, dst) + self.logger.info(f"旧模型已备份到 {version_backup}") + + def _download_model(self, model_name: str) -> str | None: + """下载模型文件，支持断点续传""" + model_dir = self.config["model_dir"] + os.makedirs(model_dir, exist_ok=True) + save_path = os.path.join(model_dir, model_name) + + try: + token = self.ensure_token() + url = f"{self.base_url}/server/terminal/downloadModel/" + params = {"stcd": self.stcd, "format": "onnx"} + headers = {"Authorization": f"Bearer {token}"} + + self.logger.info(f"开始下载模型: {model_name}") + resp = requests.get( + url, params=params, headers=headers, + stream=True, timeout=300, + ) + resp.raise_for_status() + + total_size = int(resp.headers.get("Content-Length", 0)) + downloaded = 0 + with open(save_path, "wb") as f: + for chunk in resp.iter_content(chunk_size=8192): + f.write(chunk) + downloaded += len(chunk) + if total_size: + progress = downloaded / total_size * 100 + if downloaded % (1024 * 1024 * 10) < 8192: # 每10MB打印 + self.logger.info( + f"下载进度: {progress:.1f}% " + f"({downloaded}/{total_size})" + ) + + self.logger.info(f"模型下载完成, 保存至 {save_path}") + return save_path + except Exception as e: + self.logger.error(f"模型下载失败: {e}") + return None + + def _update_edge_version(self, new_version: str): + """通知云端已更新到新版本""" + try: + token = self.ensure_token() + resp = requests.put( + f"{self.base_url}/server/terminal/updateModelVersion/", + json={"stcd": self.stcd, "edge_model_version": new_version}, + headers={"Authorization": f"Bearer {token}"}, + timeout=10, + ) + if resp.status_code == 200: + self.logger.info(f"边缘模型版本已更新: {new_version}") + return True + except Exception as e: + self.logger.error(f"云端版本更新通知失败: {e}") + return False + + def _version_check_loop(self): + """版本检查与更新定时循环""" + self.logger.info("版本检查模块启动") + while self.running: + result = self._check_version() + if result and result.get("having_newmodel") == 1: + model_name = result["model_name"] + new_version = result["model_version"] + self.logger.info(f"发现新版本: {new_version}, 模型: {model_name}") + + # 备份旧模型 + self._backup_current_model() + + # 下载新模型 + saved = self._download_model(model_name) + if saved: + # 更新本地版本记录 + version_file = os.path.join( + self.config["model_dir"], "version.txt" + ) + with open(version_file, "w") as f: + f.write(new_version) + + # 通知云端 + self._update_edge_version(new_version) + + # 更新检查间隔 + if result.get("sync_check_interval"): + self.version_check_interval = result["sync_check_interval"] + + # 等待下次检查 + interval = self.version_check_interval or 60 # 默认60分钟 + self.logger.debug(f"下次版本检查: {interval} 分钟后") + time.sleep(interval * 60) + + # ── 主入口 ──────────────────────────────────── + + def run(self): + """启动边缘模拟器""" + self.logger.info(f"边缘智能终端启动, stcd={self.stcd}") + self.logger.info(f"云端地址: {self.base_url}") + + # 启动心跳线程 + heartbeat_thread = threading.Thread( + target=self._heartbeat_loop, daemon=True, name="Heartbeat" + ) + heartbeat_thread.start() + + # 启动版本检查线程 + version_thread = threading.Thread( + target=self._version_check_loop, daemon=True, name="VersionCheck" + ) + version_thread.start() + + # 主线程等待 + try: + while self.running: + time.sleep(1) + except KeyboardInterrupt: + pass + + self.logger.info("边缘智能终端已安全退出") + + +if __name__ == "__main__": + simulator = EdgeDeviceSimulator() + simulator.run() +``` + +--- + +## 四、新增/修改 API 接口汇总 + +| 接口路径 | 方法 | 认证 | 说明 | 状态 | +|---------|------|------|------|------| +| `/server/terminal/auth/` | POST | 无（设备密钥认证） | 设备认证，获取 JWT Token | **新增** | +| `/server/terminal/heartbeat/` | PUT | 设备 Token | 更新心跳时间（已有，需增强认证） | **修改** | +| `/server/terminal/checkVersion/` | GET | 设备 Token | 检查云端模型版本 | **新增** | +| `/server/terminal/downloadModel/` | GET | 无历史遗留 | 下载模型文件（已有，需增强） | **修改** | +| `/server/terminal/updateModelVersion/` | PUT | 设备 Token | 更新边缘端模型版本 | **新增** | +| `ws://host/ws/terminal/status/` | WebSocket | 用户 Token | 推送终端在线状态变更 | **新增** | + +--- + +## 五、安全性设计 + +### 5.1 认证体系 + +``` +┌─────────────────────────────────────┐ +│ 双层 Token 体系 │ +├─────────────────────────────────────┤ +│ 用户 Token (user) │ +│ ├── 有效期: 60分钟 │ +│ ├── 用途: 前端管理页面操作 │ +│ └── sub: user │ +│ │ +│ 设备 Token (device) │ +│ ├── 有效期: 24小时 │ +│ ├── 用途: 边缘设备 API 调用 │ +│ ├── sub: edge_device │ +│ └── stcd: 设备编码 │ +└─────────────────────────────────────┘ +``` + +### 5.2 安全措施 + +1. **设备预置密钥**: 首次激活时下发唯一密钥，用于初始 Token 申请 +2. **Token 绑定设备**: Token 中携带 `stcd`，服务端验证请求中的 `stcd` 与 Token 一致 +3. **HTTPS 传输**: 生产环境强制 HTTPS，防止中间人攻击 +4. **请求频率限制**: 心跳接口限制 10次/分钟/设备，防止恶意刷接口 +5. **模型下载签名**: 可扩展为预签名 URL，限制下载有效期 + +--- + +## 六、部署与运维 + +### 6.1 云端定时任务配置 + +**方式一: Django Management Command + 系统 Cron** +```bash +# 每30秒执行一次离线检测 +* * * * * /path/to/python manage.py check_terminal_offline +* * * * * sleep 30 && /path/to/python manage.py check_terminal_offline +``` + +**方式二: Celery Beat（推荐）** +```python +# celery.py +app.conf.beat_schedule = { + 'check-terminal-offline': { + 'task': 'apps.datasets.tasks.check_terminal_offline', + 'schedule': 30.0, + }, +} +``` + +### 6.2 边缘模拟器部署 + +```bash +# 安装依赖 +pip install -r edge_simulator/requirements.txt + +# 运行模拟器（前台） +python edge_simulator/main.py + +# 运行模拟器（后台） +nohup python edge_simulator/main.py > /dev/null 2>&1 & +``` + +### 6.3 监控指标 + +| 指标 | 说明 | 告警阈值 | +|------|------|---------| +| 在线设备数 | online_status=1 的设备数 | 低于预期值的 80% | +| 离线设备数 | online_status=0 的设备数 | 超过预期值的 20% | +| 心跳延迟 | 心跳时间间隔 | 超过 60 秒 | +| 模型更新成功率 | 成功更新数/总更新数 | 低于 90% | +| Token 续期失败率 | Token 获取失败次数 | 连续 3 次失败 | + +--- + +## 七、时序图 + +### 7.1 Token 获取流程 + +``` +边缘设备 云端服务器 + │ │ + │ POST /server/terminal/auth/ │ + │ {"stcd":"DEVICE001","secret":"**"}│ + │ ─────────────────────────────> │ + │ │── 验证设备编码与密钥 + │ │── 生成JWT Token (24h) + │ 200 {access_token, ...} │ + │ <───────────────────────────── │ + │ │ + │── 保存Token到本地文件 │ +``` + +### 7.2 心跳 + 版本检查 + 模型更新完整流程 + +``` +边缘设备 云端服务器 前端管理页 + │ │ │ + │═══ 心跳循环 (30s/次) ═══════════│ │ + │ PUT /heartbeat/ [Token] │ │ + │ ─────────────────────────────> │── 更新 last_heartbeat_time │ + │ 200 OK │ │ + │ <───────────────────────────── │ │ + │ │ │ + │═══ 离线检测 (云端定时30s) ═══════│ │ + │ │── 检测超时5分钟的设备 │ + │ │── online_status → 0 │ + │ │── WebSocket推送 ───────────> │ + │ │ {"event":"offline", ...} │ + │ │ │── 更新UI + │ │ │ + │═══ 版本检查 (定时) ══════════════│ │ + │ GET /checkVersion/ │ │ + │ ─────────────────────────────> │── 对比 model_version │ + │ 200 {having_newmodel:1, ...} │ 与 edge_model_version │ + │ <───────────────────────────── │ │ + │ │ │ + │═══ 模型下载 ═════════════════════│ │ + │── 备份旧模型到 backup/ │ │ + │ │ │ + │ GET /downloadModel/ │ │ + │ ─────────────────────────────> │── 从MinIO获取模型文件 │ + │ 200 (文件流) │ │ + │ <───────────────────────────── │ │ + │── 保存到 models/current/ │ │ + │── 更新本地 version.txt │ │ + │ │ │ + │ PUT /updateModelVersion/ │ │ + │ ─────────────────────────────> │── 更新 edge_model_version │ + │ 200 OK │ │ + │ <───────────────────────────── │ │ +``` + +--- + +## 八、实施清单 + +### 云端新增开发项 + +- [ ] 新增 `EdgeDeviceAuthentication` 认证类 +- [ ] 新增 `POST /server/terminal/auth/` 设备认证接口 +- [ ] 新增 `GET /server/terminal/checkVersion/` 版本检查接口 +- [ ] 新增 `PUT /server/terminal/updateModelVersion/` 版本更新回调接口 +- [ ] 修改 `PUT /server/terminal/heartbeat/` 增加设备 Token 认证 +- [ ] 新增 `check_terminal_offline` 管理命令或 Celery 定时任务 +- [ ] 新增 WebSocket Consumer (`TerminalStatusConsumer`) 推送在线状态 +- [ ] 新增 `ws/terminal/status/` WebSocket 路由 +- [ ] 前端管理页面订阅 WebSocket，实时更新终端在线状态 + +### 边缘模拟器开发项 + +- [ ] 创建 `edge_simulator/` 目录结构 +- [ ] 实现 `auth.py` Token 管理模块 +- [ ] 实现 `heartbeat.py` 心跳上报模块 +- [ ] 实现 `version_check.py` 版本检查与下载模块 +- [ ] 实现 `main.py` 主调度程序 +- [ ] 编写 `requirements.txt` 依赖文件 +- [ ] 编写 Docker 部署配置（可选） + +--- + +## 九、行业最佳实践参考 + +1. **OTA 更新策略**（参考物联网 OTA 标准） + - 差量更新 vs 全量更新：当前采用全量下载，未来可扩展为增量更新 + - 灰度发布：支持按终端分类（sttp）或指定设备列表分批推送版本 + - 回滚机制：备份旧版本，支持云端指令触发回滚 + +2. **连接可靠性**（参考 MQTT/CoAP 协议设计） + - 断线重连：边缘端实现指数退避重连（1s → 2s → 4s → ... → max 60s） + - 离线缓存：心跳失败时的状态记录，恢复后补报 + - 连接池：单设备单连接，避免资源浪费 + +3. **安全性**（参考 IoT 安全最佳实践） + - 设备唯一身份：stcd + 预置密钥组成设备指纹 + - 通信加密：TLS 1.2+ + - 最小权限：设备 Token 仅能访问自己的数据和接口 + - 定期轮换密钥（可选扩展） diff --git a/frontend/.editorconfig b/frontend/.editorconfig new file mode 100644 index 0000000..3c3960b --- /dev/null +++ b/frontend/.editorconfig @@ -0,0 +1,15 @@ +# http://editorconfig.org + +root = true + +[*] # 表示所有文件适用 +charset = utf-8 # 设置文件字符集为 utf-8 +indent_style = space # 缩进风格（tab | space） +indent_size = 2 # 缩进大小 +end_of_line = lf # 控制换行类型(lf | cr | crlf) +trim_trailing_whitespace = true # 去除行首的任意空白字符 +insert_final_newline = true # 始终在文件末尾插入一个新行 + +[*.md] # 表示仅 md 文件适用以下规则 +max_line_length = off +trim_trailing_whitespace = false \ No newline at end of file diff --git a/frontend/.env.development b/frontend/.env.development new file mode 100644 index 0000000..8246661 --- /dev/null +++ b/frontend/.env.development @@ -0,0 +1,9 @@ +## 开发环境 + +# 变量必须以 VITE_ 为前缀才能暴露给外部读取 +NODE_ENV='development' + +VITE_APP_TITLE = 'AI算法模型训练及部署管理平台' +VITE_APP_PORT = 3000 +VITE_APP_BASE_API = '/dev-api' +VITE_WS_BASE_API = 'ws://localhost:8000' diff --git a/frontend/.env.production b/frontend/.env.production new file mode 100644 index 0000000..ba83075 --- /dev/null +++ b/frontend/.env.production @@ -0,0 +1,8 @@ +## 生产环境 +NODE_ENV='production' + +VITE_APP_TITLE = 'NewFrameWork2023-WEB' +VITE_APP_PORT = 3000 +VITE_APP_BASE_API = 'http://localhost:8000' +VITE_APP_BASE_IMG_API = 'http://localhost:3000/image/' +VITE_WS_BASE_API = 'ws://localhost:8000' diff --git a/frontend/.env.staging b/frontend/.env.staging new file mode 100644 index 0000000..c192ef8 --- /dev/null +++ b/frontend/.env.staging @@ -0,0 +1,6 @@ +## 模拟环境 +NODE_ENV='staging' + +VITE_APP_TITLE = 'NewFrameWork2023-WEB' +VITE_APP_PORT = 3000 +VITE_APP_BASE_API = '/prod--api' diff --git a/frontend/.eslintignore b/frontend/.eslintignore new file mode 100644 index 0000000..46d4b17 --- /dev/null +++ b/frontend/.eslintignore @@ -0,0 +1,16 @@ +*.sh +node_modules +*.md +*.woff +*.ttf +.vscode +.idea +dist +/public +/docs +.husky +.local +/bin +.eslintrc.js +prettier.config.js +src/assets \ No newline at end of file diff --git a/frontend/.eslintrc.js b/frontend/.eslintrc.js new file mode 100644 index 0000000..d7878cc --- /dev/null +++ b/frontend/.eslintrc.js @@ -0,0 +1,32 @@ +module.exports = { + env: { + browser: true, + es2021: true, + node: true + }, + globals: { + defineProps: 'readonly', + defineEmits: 'readonly', + defineExpose: 'readonly', + DialogType: "readonly", + OptionType: "readonly", + }, + parser: 'vue-eslint-parser', + extends: [ + 'eslint:recommended', + 'plugin:vue/vue3-essential', + 'plugin:@typescript-eslint/recommended' + ], + parserOptions: { + ecmaVersion: 'latest', + parser: '@typescript-eslint/parser', + sourceType: 'module' + }, + plugins: ['vue', '@typescript-eslint'], + rules: { + 'vue/multi-word-component-names': 'off', + '@typescript-eslint/no-empty-function': 'off', // 关闭空方法检查 + '@typescript-eslint/no-explicit-any': 'off', // 关闭any类型的警告 + 'vue/no-v-model-argument': 'off' + } +}; diff --git a/frontend/.prettierignore b/frontend/.prettierignore new file mode 100644 index 0000000..d251d2e --- /dev/null +++ b/frontend/.prettierignore @@ -0,0 +1,9 @@ +/dist/* +.local +.output.js +/node_modules/** + +**/*.svg +**/*.sh + +/public/* \ No newline at end of file diff --git a/frontend/.prettierrc.js b/frontend/.prettierrc.js new file mode 100644 index 0000000..7a42426 --- /dev/null +++ b/frontend/.prettierrc.js @@ -0,0 +1,36 @@ +/** + * 代码格式化配置 + */ +module.exports = { + // 指定每个缩进级别的空格数 + tabWidth: 2, + // 使用制表符而不是空格缩进行 + useTabs: false, + // 在语句末尾打印分号 + semi: true, + // 使用单引号而不是双引号 + singleQuote: true, + // 更改引用对象属性的时间 可选值"" + quoteProps: 'as-needed', + // 多行时尽可能打印尾随逗号。（例如，单行数组永远不会出现逗号结尾。） 可选值""，默认none + trailingComma: 'none', + // 在对象文字中的括号之间打印空格 + bracketSpacing: true, + // 在单独的箭头函数参数周围包括括号 always：(x) => x \ avoid：x => x + arrowParens: 'avoid', + // 这两个选项可用于格式化以给定字符偏移量（分别包括和不包括）开始和结束的代码 + rangeStart: 0, + rangeEnd: Infinity, + // 指定要使用的解析器，不需要写文件开头的 @prettier + requirePragma: false, + // 不需要自动在文件开头插入 @prettier + insertPragma: false, + // 换行设置 always\never\preserve + proseWrap: 'never', + // 指定HTML文件的全局空格敏感度 css\strict\ignore + htmlWhitespaceSensitivity: 'css', + // Vue文件脚本和样式标签缩进 + vueIndentScriptAndStyle: false, + // 换行符使用 lf 结尾是 可选值"" + endOfLine: 'lf' +}; diff --git a/frontend/LICENSE b/frontend/LICENSE new file mode 100644 index 0000000..2660254 --- /dev/null +++ b/frontend/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 有来开源组织 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000..5b2bd1b --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,136 @@ +

+ + + + + + + + + + +

+

+在线预览 | 官方文档 +

+ +## 项目介绍 + +[vue3-element-admin](https://gitee.com/youlaiorg/vue3-element-admin) 是基于 [vue-element-admin](https://gitee.com/panjiachen/vue-element-admin) 升级的 Vue3 版本后台管理前端解决方案；使用前端主流技术栈 Vue3 + Vite3 + TypeScript + Vue Router + Pinia + Volar + Element Plus 等；实现功能包括不限于动态权限路由、按钮权限控制、国际化、主题大小切换等；基于此模板开发了有来商城管理系统，也是有来开源组织的又一项开源力作。 + +## 项目优势 + +- 基于 vue-element-admin 升级的 Vue3 版本 ，极易上手，减少学习成本； +- 一套完整适配的微服务权限系统线上接口，企业级真实前后端接入场景，非 Mock 数据； +- 功能全面：国际化、动态路由、按钮权限、主题大小切换、Echart、wangEditor； +- TypeScript 全面支持，包括组件和 API 调用层面； +- 主流 Vue3 生态和前端技术栈，常用组件极简封装； +- 从 0 到 1 的项目文档支持 ([文档地址](https://www.cnblogs.com/haoxianrui/p/16090029.html))； + +## 技术栈 + +| 技术栈 | 描述 | 官网 | +| --- | --- | --- | +| Vue3 | 渐进式 JavaScript 框架 | https://v3.cn.vuejs.org/ | +| TypeScript | JavaScript 的一个超集 | https://www.tslang.cn/ | +| Vite | 前端开发与构建工具 | https://cn.vitejs.dev/ | +| Element Plus | 基于 Vue 3，面向设计师和开发者的组件库 | https://element-plus.gitee.io/zh-CN/ | +| Pinia | 新一代状态管理工具 | https://pinia.vuejs.org/ | +| Vue Router | Vue.js 的官方路由 | https://router.vuejs.org/zh/ | + +## 项目预览 + +在线预览: [vue3.youlai.tech](http://vue3.youlai.tech) + +| ![控制台](https://s2.loli.net/2022/12/09/34iklzLAnsIuXDh.png) | ![国际化](https://s2.loli.net/2022/04/07/lt6u2jMefpTJvkh.gif) | +| --- | --- | +| ![用户管理](https://s2.loli.net/2022/12/09/gjJibCaVP3Ysnoh.png) | ![角色管理](https://s2.loli.net/2022/12/09/xHoNctJj2hUfMO8.png) | +| ![菜单管理](https://s2.loli.net/2022/12/09/dah34MRfqiB2cez.png) |![富文本编辑器](https://s2.loli.net/2022/12/09/QzCDIwmqydtLPYr.png) | + +## 项目地址 + +| | Gitee | Github | +| --- | --- | --- | +| vue3-element-admin | [vue3-element-admin](https://gitee.com/youlaiorg/vue3-element-admin) | [vue3-element-admin](https://github.com/youlaitech/vue3-element-admin) | +| 后端工程(非必要) | [youlai_boot](https://gitee.com/youlaiorg/youlai-boot) | - | + +## 环境要求 + +- Node 环境 + + 版本：16+ + +- 开发工具 + + VSCode + +- 必装插件 + + - Vue Language Features (Volar) + - TypeScript Vue Plugin (Volar) + +## 项目启动 + +1. 安装依赖 + + ```bash + npm install + ``` +2. 启动运行 + + ```bash + npm run dev + ``` +3. 访问测试 + + 浏览器访问： [http://localhost:3000](http://localhost:3000) + +## 项目部署 + +- 本地打包 + + ``` + npm run build:prod + ``` + + 生成的静态文件位于项目根目录 `dist` 文件夹下 + +- 上传文件 + + 创建 `/mnt/nginx/html` 目录，将打包生成 `dist` 下的所有文件拷贝至此工作目录下 + +- nginx.cofig 配置 + + ``` + server { + listen 80; + server_name localhost; + + location / { + root /mnt/nginx/html; + index index.html index.htm; + } + + # 代理转发请求至网关，prod-api标识解决跨域,vapi.youlai.tech 线上接口地址，注意后面/ + location /prod-api/ { + proxy_pass http://vapi.youlai.tech/; + } + } + + ``` +## 本地接口 + +> 默认使用线上接口，如果你了解一点Java后端SpringBoot，可轻松搭建本地接口环境： + +1. 访问后端项目仓库地址：https://gitee.com/youlaiorg/youlai-boot.git + +2. 根据项目说明文档 [README.md](https://gitee.com/youlaiorg/youlai-boot#%E9%A1%B9%E7%9B%AE%E8%BF%90%E8%A1%8C) 完成数据库的创建和后端工程的启动； +3. 进入 [vite.config.ts](vite.config.ts) 文件修改代理线上接口地址 http://vapi.youlai.tech 为本地接口地址 http://localhost:8989 即可。 + + +## 联系信息 + +> 欢迎添加开发者微信，备注「有来」进群，备注「无回」参与开发 + +| ![郝先瑞](https://s2.loli.net/2022/04/06/yRx8uzj4emA5QVr.jpg) | ![张川](https://s2.loli.net/2022/04/06/cQihGv9uPsTjXk1.jpg) | +| --- | --- | diff --git a/frontend/commitlint.config.js b/frontend/commitlint.config.js new file mode 100644 index 0000000..efff054 --- /dev/null +++ b/frontend/commitlint.config.js @@ -0,0 +1,26 @@ +module.exports = { + // 继承的规则 + extends: ['@commitlint/config-conventional'], + // 定义规则类型 + rules: { + // type 类型定义，表示 git 提交的 type 必须在以下类型范围内 + 'type-enum': [ + 2, + 'always', + [ + 'feat', // 新功能 feature + 'fix', // 修复 bug + 'docs', // 文档注释 + 'style', // 代码格式(不影响代码运行的变动) + 'refactor', // 重构(既不增加新功能，也不是修复bug) + 'perf', // 性能优化 + 'test', // 增加测试 + 'chore', // 构建过程或辅助工具的变动 + 'revert', // 回退 + 'build' // 打包 + ] + ], + // subject 大小写不做校验 + 'subject-case': [0] + } +}; diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..6180e4b --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,16 @@ + + + + + + + + + + AI算法模型训练及部署管理平台 + + +
+ + + diff --git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000..677226a --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,68 @@ +{ + "name": "AI-算法模型训练及部署管理平台", + "version": "1.2.0", + "scripts": { + "dev": "vite serve --mode development", + "prod": "vue-tsc --noEmit && vite build --mode production", + "serve": "vite preview", + "lint": "eslint src/**/*.{ts,js,vue} --fix", + "prepare": "husky install || echo husky skipped", + "prettier": "prettier --write ." + }, + "dependencies": { + "@element-plus/icons-vue": "^2.0.10", + "@types/js-cookie": "^3.0.2", + "@vueuse/core": "^9.1.1", + "@wangeditor/editor": "^5.0.0", + "@wangeditor/editor-for-vue": "^5.1.10", + "ailabel": "^5.1.15", + "axios": "^1.2.0", + "better-scroll": "^2.4.2", + "default-passive-events": "^2.0.0", + "echarts": "^5.2.2", + "element-plus": "^2.2.27", + "gm-crypt": "^0.0.2", + "js-base64": "^3.7.5", + "js-cookie": "^3.0.1", + "jsencrypt": "^3.3.2", + "nprogress": "^0.2.0", + "path-browserify": "^1.0.1", + "path-to-regexp": "^6.2.0", + "pinia": "^2.0.12", + "screenfull": "^6.0.0", + "sortablejs": "^1.14.0", + "vue": "^3.2.40", + "vue-i18n": "^9.1.9", + "vue-router": "^4.1.6", + "vue3-image-preview": "^0.2.7", + "vuedraggable": "^4.1.0" + }, + "devDependencies": { + "@commitlint/cli": "^16.2.3", + "@commitlint/config-conventional": "^16.2.1", + "@types/node": "^22.15.21", + "@types/nprogress": "^0.2.0", + "@types/path-browserify": "^1.0.0", + "@typescript-eslint/eslint-plugin": "^5.19.0", + "@typescript-eslint/parser": "^5.19.0", + "@vitejs/plugin-vue": "^4.0.0", + "autoprefixer": "^10.4.13", + "eslint": "^8.14.0", + "eslint-config-prettier": "^8.5.0", + "eslint-plugin-prettier": "^4.0.0", + "eslint-plugin-vue": "^8.6.0", + "fast-glob": "^3.2.11", + "husky": "^7.0.4", + "postcss": "^8.4.20", + "prettier": "^2.6.2", + "sass": "^1.53.0", + "tailwindcss": "^3.2.4", + "typescript": "^4.7.4", + "vite": "^4.0.3", + "vite-plugin-svg-icons": "^2.0.1", + "vue-tsc": "^0.35.0" + }, + "repository": "https://gitee.com/youlaiorg/vue3-element-admin.git", + "author": "有来开源组织", + "license": "MIT" +} \ No newline at end of file diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000..33ad091 --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/frontend/public/favicon.ico b/frontend/public/favicon.ico new file mode 100644 index 0000000..df36fcf Binary files /dev/null and b/frontend/public/favicon.ico differ diff --git a/frontend/public/webconfig.js b/frontend/public/webconfig.js new file mode 100644 index 0000000..38d3af1 --- /dev/null +++ b/frontend/public/webconfig.js @@ -0,0 +1,5 @@ +window.webConfig = { + "VITE_APP_BASE_API": 'http://192.168.1.60:8000', + "VITE_APP_BASE_IMG_API": 'http://192.168.1.60:3000/image/', + "VITE_WS_BASE_API": 'ws://192.168.1.60:8000' +} diff --git a/frontend/src/App.vue b/frontend/src/App.vue new file mode 100644 index 0000000..4724bea --- /dev/null +++ b/frontend/src/App.vue @@ -0,0 +1,28 @@ + + + + diff --git a/frontend/src/api/algorithm/index.ts b/frontend/src/api/algorithm/index.ts new file mode 100644 index 0000000..3fc9400 --- /dev/null +++ b/frontend/src/api/algorithm/index.ts @@ -0,0 +1,17 @@ +import request from '@/utils/request'; +// 查询算法分类 +export function queryAlgorithmClass(params: any) { + return request({ + url: '/server/queryAlgorithmClass/', + method: 'get', + params: params + }); +} +// 查询算法 +export function getAlgorithmList(params: any) { + return request({ + url: '/server/getAlgorithmList/', + method: 'get', + params: params + }); +} diff --git a/frontend/src/api/auth/index.ts b/frontend/src/api/auth/index.ts new file mode 100644 index 0000000..1340ef9 --- /dev/null +++ b/frontend/src/api/auth/index.ts @@ -0,0 +1,44 @@ +import request from '@/utils/request'; +import { AxiosPromise } from 'axios'; +import { LoginData, TokenResult, VerifyCode } from './types'; + +/** + * + * @param data {LoginForm} + * @returns + */ +export function loginApi(data: LoginData): AxiosPromise { + return request({ + url: '/login/', + method: 'post', + data: data + }); +} +export function loginRefresh(data: any): AxiosPromise { + return request({ + url: '/api/token/refresh/', + method: 'post', + data: data + }); +} + + +/** + * 注销 + */ +export function logoutApi() { + return request({ + url: '/user/logout', + method: 'post' + }); +} + +/** + * 获取图片验证码 + */ +export function getCaptcha(): AxiosPromise { + return request({ + url: '/user/code?t=' + new Date().getTime().toString(), + method: 'get' + }); +} diff --git a/frontend/src/api/auth/types.ts b/frontend/src/api/auth/types.ts new file mode 100644 index 0000000..2831492 --- /dev/null +++ b/frontend/src/api/auth/types.ts @@ -0,0 +1,32 @@ +/** + * 登录数据类型 + */ +export interface LoginData { + username: string; + password: string; + /** + * 验证码Code + */ + //verifyCode: string; + /** + * 验证码Code服务端缓存key(UUID) + */ + // verifyCodeKey: string; +} + +/** + * Token响应类型 + */ +export interface TokenResult { + token: string; + refreshToken: string; + expires: number; +} + +/** + * 验证码类型 + */ +export interface VerifyCode { + verifyCodeImg: string; + verifyCodeKey: string; +} diff --git a/frontend/src/api/dataannotations/annotationtask/index.ts b/frontend/src/api/dataannotations/annotationtask/index.ts new file mode 100644 index 0000000..ca5248b --- /dev/null +++ b/frontend/src/api/dataannotations/annotationtask/index.ts @@ -0,0 +1,148 @@ +import request from '@/utils/request'; + +export function searchAnnotateTasks(params: any) { + return request({ + url: '/server/annotateTask/list/', + method: 'get', + params: params + }); +} + +export function getDatasetList(params: any) { + return request({ + url: '/server/getDatasetList/', + method: 'get', + params: params + }); +} +export function readDataset(params: any) { + return request({ + url: '/server/readDataset/', + method: 'get', + params: params + }); +} +export function getAlgorithmList(params: any) { + return request({ + url: '/server/getAlgorithmList/', + method: 'get', + params: params + }); +} +export function querysamples(params: any) { + return request({ + url: '/server/querysamples/', + method: 'get', + params: params + }); +} + +export function createAnnotateTask(params: any) { + return request({ + url: '/server/annotateTask/create/', + method: 'post', + data: params + }); +} +export function updateAnnotateTask(params: any) { + return request({ + url: '/server/annotateTask/update/', + method: 'PUT', + data: params + }); +} + +export function deleteAnnotateTask(params: any) { + return request({ + url: '/server/annotateTask/delete/?id=' + params, + method: 'DELETE' + }); +} + +export function readAnnotateTask(params: any) { + return request({ + url: '/server/annotateTask/read/', + method: 'get', + params: params + }); +} +export function queryTaskSamples(params: any) { + return request({ + url: '/server/queryTaskSamples/', + method: 'get', + params: params + }); +} + +export function mergeSampleContent(params: any) { + return request({ + url: '/server/mergeSampleContent/', + method: 'post', + data: params + }); +} +export function setPersonTask(params: any) { + return request({ + url: '/server/setPersonTask/', + method: 'post', + data: params + }); +} +export function queryPersonTasks(params: any) { + return request({ + url: '/server/queryPersonTasks/', + method: 'get', + params: params + }); +} + +export function getMytaskAnnotateList(params: any) { + return request({ + url: '/server/getMytaskAnnotateList/', + method: 'get', + params: params + }); +} +export function getMytaskAuditList(params: any) { + return request({ + url: '/server/getMytaskAuditList/', + method: 'get', + params: params + }); +} + +export function annotateTaskSample(params: any) { + return request({ + url: '/server/annotateTaskSample/', + method: 'post', + data: params + }); +} +export function readTaskSample(params: any) { + return request({ + url: '/server/readTaskSample/', + method: 'get', + params: params + }); +} +export function submitTaskSamples(params: any) { + return request({ + url: '/server/submitTaskSamples/', + method: 'post', + data: params + }); +} +export function auditTaskSamples(params: any) { + return request({ + url: '/server/auditTaskSamples/', + method: 'post', + data: params + }); +} +export function startAutoAnnotateTask(params: any) { + return request({ + url: '/server/startAutoAnnotateTask/', + method: 'post', + data: params + }); +} diff --git a/frontend/src/api/dataannotations/annotationteam/index.ts b/frontend/src/api/dataannotations/annotationteam/index.ts new file mode 100644 index 0000000..5959e1e --- /dev/null +++ b/frontend/src/api/dataannotations/annotationteam/index.ts @@ -0,0 +1,59 @@ +import request from '@/utils/request'; + +export function searchAnnotateTeam(params: any) { + return request({ + url: '/server/searchAnnotateTeam/', + method: 'get', + params: params + }); +} +export function add(params: any) { + return request({ + url: '/server/createAnnotateTeam/', + method: 'post', + data: params + }); +} +export function edit(params: any) { + return request({ + url: '/server/updateAnnotateTeam/', + method: 'PUT', + data: params + }); +} + +export function del(params: any) { + return request({ + url: '/server/deleteAnnotateTeam/?id=' + params, + method: 'DELETE' + }); +} + +export function addTeamMembers(params: any) { + return request({ + url: '/server/addTeamMembers/', + method: 'post', + data: params + }) +} +export function getTeamMembers(params: any) { + return request({ + url: '/server/getTeamMembers/', + method: 'get', + params: params + }); +} +export function updateTeamMember(params: any) { + return request({ + url: '/server/updateTeamMember/', + method: 'PUT', + data: params + }); +} + +export function deleteTeamMembers(params: any) { + return request({ + url: '/server/deleteTeamMembers/?ids=' + params, + method: 'DELETE' + }); +} \ No newline at end of file diff --git a/frontend/src/api/dataset/index.ts b/frontend/src/api/dataset/index.ts new file mode 100644 index 0000000..233f36c --- /dev/null +++ b/frontend/src/api/dataset/index.ts @@ -0,0 +1,95 @@ +import request from '@/utils/request'; + +export function queryDatasets(params: any) { + return request({ + url: '/server/queryDataset/', + method: 'get', + params: params + }); +} + +export function readDataset(params: any) { + return request({ + url: '/server/readDataset/', + method: 'get', + params: params + }); +} +export function getAlgorithmList(params: any) { + return request({ + url: '/server/getAlgorithmList/', + method: 'get', + params: params + }); +} +export function querysamples(params: any) { + return request({ + url: '/server/querysamples/', + method: 'get', + params: params + }); +} + +export function createDataset(params: any) { + return request({ + url: '/server/createDataset/', + method: 'post', + data: params + }); +} +export function updateDataset(params: any) { + return request({ + url: '/server/updateDataset/', + method: 'PUT', + data: params + }); +} + +export function deleteDataset(params: any) { + return request({ + url: '/server/deleteDataset/?id=' + params, + method: 'DELETE' + }); +} +export function deleteInvalidSamples(params: any) { + return request({ + url: '/server/deleteInvalidSamples/?ids=' + params.ids + '&dataset_id=' + params.dataset_id, + method: 'DELETE' + }); +} +export function deleteDuplicateSamples(params: any) { + return request({ + url: '/server/deleteDuplicateSamples/?dataset_id=' + params, + method: 'DELETE' + }); +} +export function deleteDatasetUpdatefile(params: any) { + return request({ + url: '/server/deleteDatasetUpdatefile/', + method: 'POST', + data: params + }); +} +export function readDatasetSample(params: any) { + return request({ + url: '/server/readDatasetSample/', + method: 'get', + params: params + }); +} + +export function generateDatasetImages(data: any) { + return request({ + url: '/server/generateDatasetImages/', + method: 'post', + data: data + }); +} + +export function importFromTempFile(data: any) { + return request({ + url: '/server/importFromTempFile/', + method: 'post', + data + }); +} diff --git a/frontend/src/api/define/index.ts b/frontend/src/api/define/index.ts new file mode 100644 index 0000000..336395f --- /dev/null +++ b/frontend/src/api/define/index.ts @@ -0,0 +1,145 @@ +import request from '@/utils/request'; +//新增算法分类 +export function createAlgorithmClass(data:any) { + return request({ + url: '/server/createAlgorithmClass/', + method: 'post', + data:data + }); +} +//修改算法分类 +export function updateAlgorithmClass(data:any) { + return request({ + url: '/server/updateAlgorithmClass/', + method: 'put', + data:data + }); + } +//查询算法分类 +export function queryAlgorithmClass() { + return request({ + url: '/server/queryAlgorithmClass/', + method: 'get', + }); + } +//删除算法分类 +export function deleteAlgorithmClass(id:any) { + return request({ + url: '/server/deleteAlgorithmClass/?id='+ id, + method: 'DELETE', + }); + } +//查询算法 +export function queryAlgorithm(data:any) { + return request({ + url: '/server/queryAlgorithm/', + method: 'get', + params:data + }); + } +//新增算法 +export function addAlgorithm(data:any) { + return request({ + url: '/server/addAlgorithm/', + method: 'post', + data:data + }); + } +//修改算法 +export function updateAlgorithm(data:any) { + return request({ + url: '/server/updateAlgorithm/', + method: 'put', + data:data + }); + } +//删除算法 +export function deleteAlgorithm(id:any) { + return request({ + url: '/server/deleteAlgorithm/?id='+ id, + method: 'DELETE', + }); +} +// 查询输入函数 +export function queryAlgorithmParam(data:any) { + return request({ + url: '/server/queryAlgorithmParam/', + method: 'get', + params:data + }); +} +//新增输入函数 +export function addAlgorithmParam(data:any) { + return request({ + url: '/server/addAlgorithmParam/', + method: 'post', + data:data + }); +} +//修改输入函数 +export function updateAlgorithmParam(data:any) { + return request({ + url: '/server/updateAlgorithmParam/', + method: 'put', + data:data + }); +} +//删除输入函数 +export function deleteAlgorithmParam(id:any) { + return request({ + url: '/server/deleteAlgorithmParam/?id='+ id, + method: 'DELETE', + }); +} + +// 查询输出函数 +export function queryAlgorithmOutput(data:any) { + return request({ + url: '/server/queryAlgorithmOutput/', + method: 'get', + params:data + }); +} +//新增输出函数 +export function addAlgorithmOutput(data:any) { + return request({ + url: '/server/addAlgorithmOutput/', + method: 'post', + data:data + }); +} +//修改输出函数 +export function updateAlgorithmOutput(data:any) { + return request({ + url: '/server/updateAlgorithmOutput/', + method: 'put', + data:data + }); +} +//删除输出函数 +export function deleteAlgorithmOutput(id:any) { + return request({ + url: '/server/deleteAlgorithmOutput/?id='+ id, + method: 'DELETE', + }); +} + + + +// 查询算法引擎 +export function getAlgorithmEngines(params:any) { + return request({ + url: '/server/getAlgorithmEngines/', + method: 'get', + params:params + }); +} + + +export function issueAlgorithmModel(data:any) { + return request({ + url: '/server/issueAlgorithmModel/', + method: 'post', + data:data + }); +} \ No newline at end of file diff --git a/frontend/src/api/dept/index.ts b/frontend/src/api/dept/index.ts new file mode 100644 index 0000000..4ab23f1 --- /dev/null +++ b/frontend/src/api/dept/index.ts @@ -0,0 +1,139 @@ +import request from '@/utils/request'; +import { AxiosPromise } from 'axios'; +import { DeptForm, DeptQuery, Dept } from './types'; + + +//获取组织架构 + +export function getTreelist(queryParams:any) { + return request({ + url: '/system/organization/getOrgTree', + method: 'POST', + params: queryParams + }); +} +//新增企业或部门 + +export function addTreelist(queryParams:any) { + return request({ + url: '/system/organization/addOrg', + method: 'POST', + data: queryParams + }); +} +// 修改企业部门 + +export function updataTreelist(queryParams:any) { + return request({ + url: '/system/organization/updateById', + method: 'POST', + data: queryParams + }); +} +//删除企业 +export function delTreelist(queryParams:any) { + return request({ + url: '/system/organization/deleteById', + method: 'POST', + params: queryParams + }); +} +//获取部门信息 +export function gettableData(queryParams:any) { + return request({ + url: '/system/organization/getOrganizationById', + method: 'POST', + params: queryParams + }); +} +//部门是否有效 +export function deptIsVaild(queryParams:any) { + return request({ + url: '/system/organization/setIsValid', + method: 'POST', + params: queryParams + }); +} + +//修改部门信息 +export function reviseDepartment(queryParams:any) { + return request({ + url: '/system/organization/updateById', + method: 'POST', + data: queryParams + }); +} + +/** + * 部门树形表格 + * + * @param queryParams + */ +export function listDepartments(queryParams?: DeptQuery): AxiosPromise { + return request({ + url: '/api/v1/dept', + method: 'get', + params: queryParams + }); +} + +/** + * 部门下拉列表 + */ +export function listDeptOptions(): AxiosPromise { + return request({ + url: '/api/v1/dept/options', + method: 'get' + }); +} + +/** + * 获取部门详情 + * + * @param id + */ +export function getDeptForm(id: string): AxiosPromise { + return request({ + url: '/api/v1/dept/' + id + '/form', + method: 'get' + }); +} + +/** + * 新增部门 + * + * @param data + */ +export function addDept(data: DeptForm) { + return request({ + url: '/api/v1/dept', + method: 'post', + data: data + }); +} + +/** + * 修改部门 + * + * @param id + * @param data + */ +export function updateDept(id: string, data: DeptForm) { + return request({ + url: '/api/v1/dept/' + id, + method: 'put', + data: data + }); +} + +/** + * 删除部门 + * + * @param ids + */ +export function deleteDept(ids: string) { + return request({ + url: '/api/v1/dept/' + ids, + method: 'delete' + }); +} diff --git a/frontend/src/api/dept/types.ts b/frontend/src/api/dept/types.ts new file mode 100644 index 0000000..b99f819 --- /dev/null +++ b/frontend/src/api/dept/types.ts @@ -0,0 +1,34 @@ +/** + * 部门查询参数 + */ +export interface DeptQuery { + keywords: string | undefined; + status: number | undefined; +} + +/** + * 部门类型 + */ +export interface Dept { + id: string; + name: string; + parentId: string; + treePath: string; + sort: number; + status: number; + leader?: string; + mobile?: string; + email?: string; + children: Dept[]; +} + +/** + * 部门表单类型 + */ +export interface DeptForm { + id?: string; + parentId: string; + name: string; + sort: number; + status: number; +} diff --git a/frontend/src/api/dict/index.ts b/frontend/src/api/dict/index.ts new file mode 100644 index 0000000..a7fe18b --- /dev/null +++ b/frontend/src/api/dict/index.ts @@ -0,0 +1,250 @@ +import request from '@/utils/request'; +import { AxiosPromise } from 'axios'; +import { + DictQuery, + DictPageResult, + DictTypeForm, + DictItemQuery, + DictItemPageResult, + DictItemForm +} from './types'; +// 查询字典 +export function getTreelist(params:any) { + return request({ + url: '/system/dictionary/dictList', + method: 'get', + params: params + }); +} + +// 新增字典 +export function addDict(params:any) { + return request({ + url: '/system/dictionary/addDict', + method: 'post', + data: params + }); +} + +// 修改字典 +export function updateDict(params:any) { + return request({ + url: '/system/dictionary/updateDict', + method: 'post', + data: params + }); +} +// 删除字典 +export function deleteById(params:any) { + return request({ + url: '/system/dictionary/deleteById', + method: 'post', + params: params + }); +} +// 字典排序 +export function changeDictOrder(params:any) { + return request({ + url: '/system/dictionary/changeDictOrder', + method: 'post', + params: params + }); +} +// 查询字典项 +export function getDictItemById(params:any) { + return request({ + url: '/system/dictionaryItems/page', + method: 'get', + params: params + }); +} + +// 新增字典项 +export function addDictionaryItem(params:any) { + return request({ + url: '/system/dictionaryItems/addDictionaryItem', + method: 'post', + data: params + }); +} + +// 修改字典项 +export function updateDictionaryItem(params:any) { + return request({ + url: '/system/dictionaryItems/updateDictionaryItem', + method: 'post', + data: params + }); +} +// 删除字典项 +export function deleteDictItemById(params:any) { + return request({ + url: '/system/dictionaryItems/deleteDictItemById', + method: 'post', + params: params + }); +} +// 批量删除字典项 +export function deleteDictItemByIds(params:any) { + return request({ + url: '/system/dictionaryItems/deleteDictItemByIds', + method: 'post', + params: params + }); +} + +// 字典项排序 +export function changeItemOrder(params:any) { + return request({ + url: '/system/dictionaryItems/changeItemOrder', + method: 'post', + params: params + }); +} +/** + * 获取字典类型分页列表 + * + * @param queryParams + */ +export function listDictTypePages( + queryParams: DictQuery +): AxiosPromise { + return request({ + url: '/api/v1/dict/types/pages', + method: 'get', + params: queryParams + }); +} + +/** + * 获取字典类型表单数据 + * + * @param id + */ +export function getDictTypeForm(id: number): AxiosPromise { + return request({ + url: '/api/v1/dict/types/' + id + '/form', + method: 'get' + }); +} + +/** + * 新增字典类型 + * + * @param data + */ +export function addDictType(data: DictTypeForm) { + return request({ + url: '/api/v1/dict/types', + method: 'post', + data: data + }); +} + +/** + * 修改字典类型 + * + * @param id + * @param data + */ +export function updateDictType(id: number, data: DictTypeForm) { + return request({ + url: '/api/v1/dict/types/' + id, + method: 'put', + data: data + }); +} + +/** + * 删除字典类型 + */ +export function deleteDictTypes(ids: string) { + return request({ + url: '/api/v1/dict/types/' + ids, + method: 'delete' + }); +} + +/** + * 获取字典类型的数据项 + * + * @param typeCode 字典类型编码 + */ +export function getDictionaries(typeCode: string): AxiosPromise { + return request({ + url: '/api/v1/dict/types/' + typeCode + '/items', + method: 'get' + }); +} + +/** + * 获取字典项分页列表 + */ +export function listDictItemPages( + queryParams: DictItemQuery +): AxiosPromise { + return request({ + url: '/api/v1/dict/items/pages', + method: 'get', + params: queryParams + }); +} + +/** + * 获取字典数据项表单数据 + * + * @param id + */ +export function getDictItemData(id: number): AxiosPromise { + return request({ + url: '/api/v1/dict/items/' + id + '/form', + method: 'get' + }); +} + +/** + * 新增字典项 + * + * @param data + */ +export function saveDictItem(data: DictItemForm) { + return request({ + url: '/api/v1/dict/items', + method: 'post', + data: data + }); +} + +/** + * 修改字典项 + * + * @param id + * @param data + */ +export function updateDictItem(id: number, data: DictItemForm) { + return request({ + url: '/api/v1/dict/items/' + id, + method: 'put', + data: data + }); +} + +/** + * 批量删除字典数据项 + * + * @param ids 字典项ID，多个以英文逗号(,)分割 + */ +export function deleteDictItems(ids: string) { + return request({ + url: '/api/v1/dict/items/' + ids, + method: 'delete' + }); +} + +export function getDeviceByType(params: any) { + return request({ + url: '/system/dictionaryItems/getDeviceByType', + method: 'get', + params: params + }); +} \ No newline at end of file diff --git a/frontend/src/api/dict/types.ts b/frontend/src/api/dict/types.ts new file mode 100644 index 0000000..3e301f2 --- /dev/null +++ b/frontend/src/api/dict/types.ts @@ -0,0 +1,84 @@ +/** + * 字典查询参数 + */ +export interface DictQuery extends PageQuery { + /** + * 字典名称 + */ + name?: string; +} + +/** + * 字典类型 + */ +export interface Dict { + id: number; + code: string; + name: string; + status: number; + remark: string; +} + +/** + * 字典分页项类型声明 + */ +export type DictPageResult = PageResult; + +/** + * 字典表单类型声明 + */ +export interface DictTypeForm { + id: number | undefined; + name: string; + code: string; + status: number; + remark: string; +} + +/** + * 字典项查询参数类型声明 + */ +export interface DictItemQuery extends PageQuery { + /** + * 字典项名称 + */ + name?: string; + /** + * 字典类型编码 + */ + typeCode?: string; +} + +/** + * 字典数据项类型 + */ +export interface DictItem { + id: number; + name: string; + value: string; + typeCode: string; + sort: number; + status: number; + defaulted: number; + remark?: string; +} + +/** + * 字典分页项类型声明 + */ +export type DictItemPageResult = PageResult; + +/** + * 字典表单类型声明 + */ +export interface DictItemForm { + id?: number; + typeCode?: string; + typeName?: string; + name: string; + code: string; + value: string; + status: number; + sort: number; + remark: string; +} diff --git a/frontend/src/api/file/index.ts b/frontend/src/api/file/index.ts new file mode 100644 index 0000000..cb40026 --- /dev/null +++ b/frontend/src/api/file/index.ts @@ -0,0 +1,56 @@ +import request from '@/utils/request'; +import { AxiosPromise } from 'axios'; +import { FileInfo } from './types'; + +/** + * 上传文件 + * + * @param file + */ +export function uploadFileApi(file: File): AxiosPromise { + const formData = new FormData(); + formData.append('file', file); + return request({ + url: '/api/v1/files', + method: 'post', + data: formData, + headers: { + 'Content-Type': 'multipart/form-data' + } + }); +} + +/** + * 删除文件 + * + * @param filePath 文件完整路径 + */ +export function deleteFileApi(filePath?: string) { + return request({ + url: '/api/v1/files', + method: 'delete', + params: { filePath: filePath } + }); +} + + +/** + * 上传文件 + * + * @param file + * @param id + */ + export function uploadRecord(data:any){ + const formData = new FormData(); + formData.append('id', data.id); + formData.append('file', data.file); + formData.append('evaluatea', data.evaluatea); + return request({ + url: '/modeltrain/trainrecord/uploadRecord', + method: 'post', + data: formData, + headers: { + 'Content-Type': 'multipart/form-data' + } + }); +} diff --git a/frontend/src/api/file/types.ts b/frontend/src/api/file/types.ts new file mode 100644 index 0000000..22b2be5 --- /dev/null +++ b/frontend/src/api/file/types.ts @@ -0,0 +1,7 @@ +/** + * 文件API类型声明 + */ +export interface FileInfo { + name: string; + url: string; +} diff --git a/frontend/src/api/maintenance/index.ts b/frontend/src/api/maintenance/index.ts new file mode 100644 index 0000000..4115f97 --- /dev/null +++ b/frontend/src/api/maintenance/index.ts @@ -0,0 +1,77 @@ + + +import request from '@/utils/request'; +//查询模型 +export function queryAlgorithmModel(params:any) { + return request({ + url: '/server/queryAlgorithmModel/', + method: 'get', + params:params + }); +} +//新增模型 +export function addAlgorithmModel(params:any) { + return request({ + url: '/server/addAlgorithmModel/', + method: 'post', + data:params + }); +} +//修改模型 +export function updateAlgorithmModel(data:any) { + return request({ + url: '/server/updateAlgorithmModel/', + method: 'put', + data:data + }); +} +//删除模型 +export function deleteDuplicateSamples(id:any) { + return request({ + url: '/server/deleteAlgorithmModel/?id='+ id, + method: 'DELETE', + }); +} +// 获取左侧树数据 +export function getAlgorithmTree() { + return request({ + url: '/server/getAlgorithmTree/', + method: 'get', + + }); +} +//上传文件 +export function uploadAlgorithmModelFile(params:any) { + return request({ + url: '/server/uploadAlgorithmModelFile/', + method: 'post', + data:params, + headers: { + 'Content-Type': 'multipart/form-data' + } + }); +} +//下载文件 +export function exportAlgorithmModel(id:any) { + return request({ + url: '/server/exportAlgorithmModel/?id='+ id, + method: 'get', + responseType: 'blob', + }); +} +//设置默认模型 +export function setAlgorithmDefaultModel(params:any) { + return request({ + url: '/server/setAlgorithmDefaultModel/', + method: 'post', + data:params, + }); +} + +export function convertAlgorithmModelOnnx(params:any) { + return request({ + url: '/server/convertAlgorithmModelOnnx/', + method: 'post', + data: params + }); +} diff --git a/frontend/src/api/menu/index.ts b/frontend/src/api/menu/index.ts new file mode 100644 index 0000000..fae4404 --- /dev/null +++ b/frontend/src/api/menu/index.ts @@ -0,0 +1,159 @@ +import request from '@/utils/request'; +import { AxiosPromise } from 'axios'; +import { MenuQuery, Menu, Resource, MenuForm } from './types'; + +//获取菜单表格 +export function getdata(queryParams:any) { + return request({ + url: '/system/menu/getMenuButtonTree', + method: 'post', + params: queryParams + }); +} +//新增目录 +export function addmenu(queryParams:any) { + return request({ + url: '/system/menu/addMenu', + method: 'post', + data: queryParams + }); +} +//修改目录 +export function editmenu(queryParams:any) { + return request({ + url: '/system/menu/updateById', + method: 'post', + data: queryParams + }); +} +//删除 +export function deltmenu(queryParams:any) { + return request({ + url: '/system/menu/deleteById', + method: 'post', + params: queryParams + }); +} +//排序 +export function moveOrderno(params:any) { + return request({ + url: '/system/menu/changeMenuOrder', + method: 'post', + params: params + }); +} +//上传图标之前获取ID + +//上传单个图标 +export function uploadIcon (data:any) { + return request({ + headers: { + 'Content-Type': 'multipart/form-data' + }, + url: '/system/menu/uploadIcon', + method: 'POST', + data + }); +} +//删除单个图标 +export function moveIcon(params:any) { + return request({ + url: '/system/menu/deleteIcon', + method: 'post', + params: params + }); +} + + +/** + * 获取路由列表 + */ +export function listRoutes() { + return request({ + url: 'system/menu/treeRoutes', + method: 'get', + }); +} + +/** + * 获取菜单表格列表 + * + * @param queryParams + */ +export function listMenus(queryParams: MenuQuery): AxiosPromise { + return request({ + url: '/api/v1/menus', + method: 'get', + params: queryParams + }); +} + +/** + * 获取菜单下拉树形列表 + */ +export function listMenuOptions(): AxiosPromise { + return request({ + url: '/api/v1/menus/options', + method: 'get' + }); +} + +/** + * 获取资源(菜单+权限)树形列表 + */ +export function listResources(): AxiosPromise { + return request({ + url: '/api/v1/menus/resources', + method: 'get' + }); +} + +/** + * 获取菜单详情 + * @param id + */ +export function getMenuDetail(id: string): AxiosPromise { + return request({ + url: '/api/v1/menus/' + id, + method: 'get' + }); +} + +/** + * 添加菜单 + * + * @param data + */ +export function addMenu(data: MenuForm) { + return request({ + url: '/api/v1/menus', + method: 'post', + data: data + }); +} + +/** + * 修改菜单 + * + * @param id + * @param data + */ +export function updateMenu(id: string, data: MenuForm) { + return request({ + url: '/api/v1/menus/' + id, + method: 'put', + data: data + }); +} + +/** + * 批量删除菜单 + * + * @param ids 菜单ID，多个以英文逗号(,)分割 + */ +export function deleteMenus(ids: string) { + return request({ + url: '/api/v1/menus/' + ids, + method: 'delete' + }); +} diff --git a/frontend/src/api/menu/types.ts b/frontend/src/api/menu/types.ts new file mode 100644 index 0000000..8de7056 --- /dev/null +++ b/frontend/src/api/menu/types.ts @@ -0,0 +1,105 @@ +/** + * 菜单查询参数类型声明 + */ +export interface MenuQuery { + keywords?: string; +} + +/** + * 菜单分页列表项声明 + */ + +export interface Menu { + id?: number; + parentId: number; + type?: string | 'CATEGORY' | 'MENU' | 'EXTLINK'; + createTime: string; + updateTime: string; + name: string; + icon: string; + component: string; + sort: number; + visible: number; + children: Menu[]; +} + +/** + * 菜单表单类型声明 + */ +export interface MenuForm { + /** + * 菜单ID + */ + id?: string; + /** + * 父菜单ID + */ + parentId: string; + /** + * 菜单名称 + */ + name: string; + /** + * 菜单是否可见(1:是;0:否;) + */ + visible: number; + icon?: string; + /** + * 排序 + */ + sort: number; + /** + * 组件路径 + */ + component?: string; + /** + * 路由路径 + */ + path: string; + /** + * 跳转路由路径 + */ + redirect?: string; + + /** + * 菜单类型 + */ + type: string; + + /** + * 权限标识 + */ + perm?: string; +} + +/** + * 资源(菜单+权限)类型 + */ +export interface Resource { + /** + * 菜单值 + */ + value: string; + /** + * 菜单文本 + */ + label: string; + /** + * 子菜单 + */ + children: Resource[]; +} + +/** + * 权限类型 + */ +export interface Permission { + /** + * 权限值 + */ + value: string; + /** + * 权限文本 + */ + label: string; +} diff --git a/frontend/src/api/message/index.ts b/frontend/src/api/message/index.ts new file mode 100644 index 0000000..0280648 --- /dev/null +++ b/frontend/src/api/message/index.ts @@ -0,0 +1,28 @@ +import request from '@/utils/request'; + +export function getMessageList(params:any) { + return request({ + url: '/system/message/getMessageList', + method: 'get', + params + }); +} +export function setMessageStatus(data:any) { + return request({ + url: '/system/message/setMessageStatus?id=' + data, + method: 'post' + }); +} +export function setAllMessageStatus() { + return request({ + url: '/system/message/setAllMessageStatus', + method: 'post' + }); +} +export function deleteMessageById(data:any) { + return request({ + url: '/system/message/deleteMessageById?id=' + data, + method: 'post', + data + }); +} \ No newline at end of file diff --git a/frontend/src/api/minio/index.ts b/frontend/src/api/minio/index.ts new file mode 100644 index 0000000..01cf857 --- /dev/null +++ b/frontend/src/api/minio/index.ts @@ -0,0 +1,33 @@ +import request from '@/utils/request'; + +export function getMinioFilePresign(params: { + bucket_name: string; + object_name: string; + expires?: number; + response_content_type?: string; + response_content_disposition?: string; +}) { + return request({ + url: '/server/minio/file/presign/', + method: 'get', + params + }); +} + +export function getMinioFilePreviewUrl(bucket_name: string, object_name: string) { + return `/server/minio/file/preview/?bucket_name=${encodeURIComponent(bucket_name)}&object_name=${encodeURIComponent(object_name)}`; +} + +export async function getMinioReportPresign(bucket_name: string, object_name: string): Promise { + const res: any = await getMinioFilePresign({ + bucket_name, + object_name, + expires: 3600, + response_content_type: 'text/html; charset=utf-8', + response_content_disposition: 'inline' + }); + if (res.code === 200) { + return res.data.presigned_url; + } + throw new Error('获取预览链接失败'); +} diff --git a/frontend/src/api/modelbase/index.ts b/frontend/src/api/modelbase/index.ts new file mode 100644 index 0000000..90e03f6 --- /dev/null +++ b/frontend/src/api/modelbase/index.ts @@ -0,0 +1,26 @@ +import request from '@/utils/request'; +//算法调用日志 +export function queryAlgorithmCallLogs(data: any) { + return request({ + url: '/server/queryAlgorithmCallLogs/', + method: 'get', + params: data + }); +} +export function setAlgorithmDefaultModel(data: any) { + return request({ + url: '/server/setAlgorithmDefaultModel/', + method: 'get', + params: data, + // headers: { + // 'Content-Type': 'multipart/form-data' + // } + }); +} +export function publishModelToTerminals(data: any) { + return request({ + url: '/server/publishModelToTerminals/', + method: 'post', + data: data + }); +} \ No newline at end of file diff --git a/frontend/src/api/modeltraining/index.ts b/frontend/src/api/modeltraining/index.ts new file mode 100644 index 0000000..d4f1f73 --- /dev/null +++ b/frontend/src/api/modeltraining/index.ts @@ -0,0 +1,91 @@ +import request from '@/utils/request'; + +//获取表格内容 +export function queryAlgorithmTrainRecords(params: any) { + return request({ + url: '/server/queryAlgorithmTrainRecords/', + method: 'get', + params: params + }); + } + +export function add(data: any) { + return request({ + url: '/server/addAlgorithmTrainRecord/', + method: 'post', + data: data + }); +} + +export function edit(params: any) { + return request({ + url: '/server/updateAlgorithmTrainRecord/', + method: 'PUT', + data: params + }); + } + +//查询算法输入参数 +export function queryAlgorithmParam(params: any) { + return request({ + url: '/server/queryAlgorithmParam/', + method: 'get', + params: params + }); +} +export function del(id: any) { + return request({ + url: '/server/deleteAlgorithmTrainRecord/?id=' + id, + method: 'DELETE' + }); +} + +//开始训练 +export function startAlgorithmTrain(data: any) { + return request({ + url: '/server/startAlgorithmTrain/', + method: 'post', + data: data + }); +} + + +export function prepareAlgorithmTrainDataset(data: any) { + return request({ + url: '/server/prepareAlgorithmTrainDataset/', + method: 'post', + data: data + }); +} + +// 模型归档 +export function archiveTrainModel(data: any) { + return request({ + url: '/server/archiveTrainModel/', + method: 'post', + data: data + }); +} + + +// 复制模型训练 +export function copyAlgorithmTrainRecord(data: any) { + return request({ + url: '/server/copyAlgorithmTrainRecord/', + method: 'post', + data: data + }); +} + + +export function closeTrain(data: any) { + return request({ + url: '/server/closeTrain/', + method: 'post', + params: data + }); +} + + + + diff --git a/frontend/src/api/predictioneffect1/index.ts b/frontend/src/api/predictioneffect1/index.ts new file mode 100644 index 0000000..600bdda --- /dev/null +++ b/frontend/src/api/predictioneffect1/index.ts @@ -0,0 +1,65 @@ +import request from '@/utils/request'; +//开始检测 +export function queryAlgorithmCallLogs(data: any) { + return request({ + url: '/server/AlgorithmModelPredict/', + method: 'post', + data: data, + headers: { + 'Content-Type': 'multipart/form-data' + } + }); +} +//进入页面获取数据 +export function getAlgorithmDefaultImages(data: any) { + return request({ + url: '/server/getAlgorithmDefaultImages/', + method: 'get', + params: data, + + }); +} +//上传轮播图片 +export function uploadPredictDefaultImages(data: any) { + return request({ + url: '/server/uploadPredictDefaultImages/', + method: 'post', + data: data, + headers: { + 'Content-Type': 'multipart/form-data' + } + }); +} + +//开始检测 +export function detectImageObject(data: any) { + return request({ + url: '/server/detectImageObject/', + method: 'post', + data: data, + headers: { + 'Content-Type': 'multipart/form-data' + } + }); +} + +//开始检测-模型维护 +export function AlgorithmModelPredict(data: any) { + return request({ + url: '/server/AlgorithmModelPredict/', + method: 'post', + data: data, + headers: { + 'Content-Type': 'multipart/form-data' + } + }); +} + +//删除轮播图片 +export function deletePredictDefaultImage(data: any) { + return request({ + url: '/server/deletePredictDefaultImage/', + method: 'post', + data: data, + }); +} \ No newline at end of file diff --git a/frontend/src/api/record/index.ts b/frontend/src/api/record/index.ts new file mode 100644 index 0000000..9f263b1 --- /dev/null +++ b/frontend/src/api/record/index.ts @@ -0,0 +1,18 @@ +import request from '@/utils/request'; + +//获取所有角色 +export function getLogList(params:any){ + return request({ + url: '/system/log/getLogList' , + method: 'post', + params: params + }); +} +export function exportExcel(queryParams: any) { + return request({ + url: '/system/log/exportExcel', + method: 'get', + params: queryParams, + responseType: 'arraybuffer' + }); +} diff --git a/frontend/src/api/role/index.ts b/frontend/src/api/role/index.ts new file mode 100644 index 0000000..e6420bf --- /dev/null +++ b/frontend/src/api/role/index.ts @@ -0,0 +1,196 @@ +import request from '@/utils/request'; +import { AxiosPromise } from 'axios'; +import { RoleQuery, RoleForm } from './types'; + +//获取所有角色 +export function listRolePages(queryParams:any){ + return request({ + url: '/system/role/list' , + method: 'post', + params:queryParams + }); +} +//角色是否有效 +export function isvaildTo(queryParams:any){ + return request({ + url: '/system/role/setIsvaild' , + method: 'post', + params:queryParams + }); +} +//新增角色 +export function addDept(queryParams:any){ + return request({ + url:'/system/role/addRole' , + method: 'post', + data: queryParams + }); +} +//更新角色信息 +export function renewDept (queryParams:any){ + return request({ + url:'/system/role/updateById' , + method: 'post', + data: queryParams + }); +} +//单个删除角色 +export function deleDept (queryParams:any){ + return request({ + url:'/system/role/deleteById' , + method: 'post', + params: queryParams + }); +} + +//获取分配权限 +export function assignmentPer (queryParams:any){ + return request({ + url:'/system/menu/permissionAssignment' , + method: 'post', + params: queryParams + }); +} +//发出分配权限 +export function setMenuById (queryParams:any){ + return request({ + url:'/system/role/setMenuById' , + method: 'post', + params: queryParams + }); +} +////获取组织范围 +export function setOrgscope (queryParams:any){ + return request({ + url:'/system/organization/getOrgScopeTree' , + method: 'post', + params: queryParams + }); +} +//修改组织范围 +export function postOrgscope (queryParams:any){ + return request({ + url:'/system/role/setOrgscope' , + method: 'post', + params: queryParams + }); +} + + +//删除角色 +// export function delDept(queryParams:any){ +// return request({ +// url:'/system/role/addRole' , +// method: 'post', +// data: queryParams +// }); +// } + +/** + * 获取角色分页数据 + * + * @param queryParam + */ +// export function listRolePages( +// queryParams?: RoleQuery +// ): AxiosPromise { +// return request({ +// url: '/system/role/list', +// method: 'post', +// params: queryParams +// }); +// } + +/** + * 获取角色下拉数据 + * + * @param queryParams + */ +export function listRoleOptions( + queryParams?: RoleQuery +): AxiosPromise { + return request({ + url: '/api/v1/roles/options', + method: 'get', + params: queryParams + }); +} + +/** + * 获取角色拥有的资源ID集合 + * + * @param queryParams + */ +export function getRoleMenuIds(roleId: string): AxiosPromise { + return request({ + url: '/api/v1/roles/' + roleId + '/menuIds', + method: 'get' + }); +} + +/** + * 修改角色资源权限 + * + * @param queryParams + */ +export function updateRoleMenus( + roleId: string, + data: number[] +): AxiosPromise { + return request({ + url: '/api/v1/roles/' + roleId + '/menus', + method: 'put', + data: data + }); +} + +/** + * 获取角色详情 + * + * @param id + */ +export function getRoleDetail(id: number): AxiosPromise { + return request({ + url: '/api/v1/roles/' + id, + method: 'get' + }); +} + +/** + * 添加角色 + * + * @param data + */ +export function addRole(data: RoleForm) { + return request({ + url: '/api/v1/roles', + method: 'post', + data: data + }); +} + +/** + * 更新角色 + * + * @param id + * @param data + */ +export function updateRole(id: number, data: RoleForm) { + return request({ + url: '/api/v1/roles/' + id, + method: 'put', + data: data + }); +} + +/** + * 批量删除角色，多个以英文逗号(,)分割 + * + * @param ids + */ +export function deleteRoles(ids: string) { + return request({ + url: '/api/v1/roles/' + ids, + method: 'delete' + }); +} diff --git a/frontend/src/api/role/types.ts b/frontend/src/api/role/types.ts new file mode 100644 index 0000000..4684c3b --- /dev/null +++ b/frontend/src/api/role/types.ts @@ -0,0 +1,40 @@ +/** + * 角色查询参数类型 + */ +export interface RoleQuery extends PageQuery { + keywords?: string; +} + +/** + * 角色分页列表项 + */ +export interface Role { + id: string; + name: string; + code: string; + sort: number; + status: number; + deleted: number; + menuIds?: any; + permissionIds?: any; +} + +/** + * 角色分页项类型 + */ +export type RolePageResult = PageResult; + +/** + * 角色表单 + */ +export interface RoleForm { + id?: number; + name: string; + code: string; + sort: number; + status: number; + /** + * 数据权限 + */ + dataScope: number; +} diff --git a/frontend/src/api/smartterminal/index.ts b/frontend/src/api/smartterminal/index.ts new file mode 100644 index 0000000..6d15664 --- /dev/null +++ b/frontend/src/api/smartterminal/index.ts @@ -0,0 +1,38 @@ +import request from '@/utils/request'; + +const BASE = '/server/terminal'; + +/** 创建智能终端设备 */ +export function createSmartTerminal(data: Record) { + return request({ url: `${BASE}/create/`, method: 'post', data }); +} + +/** 修改智能设备信息 */ +export function updateSmartTerminal(data: Record) { + return request({ url: `${BASE}/update/`, method: 'put', data }); +} + +/** 发布更新设备算法模型 */ +export function publishTerminalModel(data: Record) { + return request({ url: `${BASE}/publishModel/`, method: 'put', data }); +} + +/** 条件查询智能设备列表 */ +export function searchSmartTerminals(params: Record) { + return request({ url: `${BASE}/list/`, method: 'get', params }); +} + +/** 逻辑删除智能设备 */ +export function deleteSmartTerminal(id: string) { + return request({ url: `${BASE}/delete/`, method: 'delete', params: { id } }); +} + +/** 下载终端模型文件 */ +export function downloadTerminalModel(params: { stcd: string; model_format: 'onnx' | 'original' }) { + return request({ + url: `${BASE}/downloadModel/`, + method: 'get', + params, + responseType: 'blob' + }); +} diff --git a/frontend/src/api/taxkSetting/index.ts b/frontend/src/api/taxkSetting/index.ts new file mode 100644 index 0000000..28ec26d --- /dev/null +++ b/frontend/src/api/taxkSetting/index.ts @@ -0,0 +1,51 @@ +import request from '@/utils/request'; + +//获取表格内容 +export function getTaskList(params: any) { + return request({ + url: '/system/quartzjob/getQuartzJobList', + method: 'get', + params: params + }); +} +//新增表格内容 +export function addTaskList(params: any) { + return request({ + url: '/system/quartzjob/addQuartzJob', + method: 'post', + data: params + }); +} + +//删除定时任务 +export function delTaskList(params: any) { + return request({ + url: '/system/quartzjob/deleteQuartzJob', + method: 'post', + params: params + }); +} +//修改定时任务 +export function updataTaskList(params: any) { + return request({ + url: '/system/quartzjob/updateQuartzJob', + method: 'post', + data: params + }); +} +//定时任务是否有效 +export function setTaskList(params: any) { + return request({ + url: '/system/quartzjob/setQuartzStatus', + method: 'post', + params: params + }); +} +//拖拽 +export function changeItemOrder(params: any) { + return request({ + url: '/system/quartzjob/changeDictOrder', + method: 'post', + params: params + }); +} \ No newline at end of file diff --git a/frontend/src/api/tempfile/index.ts b/frontend/src/api/tempfile/index.ts new file mode 100644 index 0000000..a0df833 --- /dev/null +++ b/frontend/src/api/tempfile/index.ts @@ -0,0 +1,62 @@ +import request from '@/utils/request'; + +export function searchTempFiles(params: any) { + return request({ + url: '/server/queryTempFiles/', + method: 'get', + params + }); +} + +export function readTempFile(params: any) { + return request({ + url: '/server/readTempFile/', + method: 'get', + params + }); +} + +export function updateTempFile(data: any) { + return request({ + url: '/server/updateTempFile/', + method: 'put', + data + }); +} + +export function deleteTempFile(id: string) { + return request({ + url: '/server/deleteTempFile/?id=' + id, + method: 'delete' + }); +} + +export function uploadTempFile(data: FormData, onUploadProgress?: (event: any) => void) { + return request({ + url: '/server/uploadTempFile/', + method: 'post', + data, + headers: { + 'Content-Type': 'multipart/form-data' + }, + onUploadProgress + }); +} + +export function previewTempFile(params: any) { + return request({ + url: '/server/previewTempFile/', + method: 'get', + params, + responseType: 'blob' + }); +} + +export function downloadTempFile(params: any) { + return request({ + url: '/server/downloadTempFile/', + method: 'get', + params, + responseType: 'blob' + }); +} diff --git a/frontend/src/api/user/index.ts b/frontend/src/api/user/index.ts new file mode 100644 index 0000000..898fdb7 --- /dev/null +++ b/frontend/src/api/user/index.ts @@ -0,0 +1,65 @@ +import request from '@/utils/request'; +import { AxiosPromise } from 'axios'; +import { UserForm, UserInfo, UserPageResult, UserQuery } from './types'; + +export function getOrganizationList() { + return request({ + url: '/server/getOrganizationList/', + method: 'get' + }); +} +export function add(params:any) { + return request({ + url: '/server/addOrganization/', + method: 'post', + data: params + }); +} +export function edit(params:any) { + return request({ + url: '/server/updateOrganization/', + method: 'PUT', + data: params + }); +} +export function del(params:any) { + return request({ + url: '/server/deleteOrganization/?id=' + params, + method: 'DELETE' + }); +} + +export function getPersonList(params:any) { + return request({ + url: '/server/getPersonList/', + method: 'get', + params: params + }); +} +export function addPerson(params:any) { + return request({ + url: '/server/addPerson/', + method: 'post', + data: params + }); +} +export function updatePerson(params:any) { + return request({ + url: '/server/updatePerson/', + method: 'PUT', + data: params + }); +} +export function deletePerson(params:any) { + return request({ + url: '/server/deletePerson/?id=' + params, + method: 'DELETE' + }); +} +export function updatePassword(params:any) { + return request({ + url: '/server/updatePassword/', + method: 'POST', + data: params + }); +} diff --git a/frontend/src/api/user/types.ts b/frontend/src/api/user/types.ts new file mode 100644 index 0000000..07b734b --- /dev/null +++ b/frontend/src/api/user/types.ts @@ -0,0 +1,67 @@ +/** + * 登录用户信息 + */ +export interface UserInfo { + permissions: string[]; + userInfo: any; + nickname: string; + avatar: string; + roles: string[]; + perms: string[]; +} + +/** + * 用户查询参数 + */ +export interface UserQuery extends PageQuery { + keywords: string; + status: number; + deptId: number; +} + +/** + * 用户分页列表项声明 + */ +export interface UserType { + id: string; + username: string; + nickname: string; + mobile: string; + gender: number; + avatar: string; + email: string; + status: number; + deptName: string; + roleNames: string; + createTime: string; +} + +/** + * 用户分页项类型声明 + */ +export type UserPageResult = PageResult; + +/** + * 用户表单类型声明 + */ +export interface UserForm { + id: number | undefined; + deptId: number; + username: string; + nickname: string; + password: string; + mobile: string; + email: string; + gender: number; + status: number; + remark: string; + roleIds: number[]; +} + +/** + * 用户导入表单类型声明 + */ +export interface UserImportData { + deptId: number; + roleIds: number[]; +} diff --git a/frontend/src/assets/401_images/401.gif b/frontend/src/assets/401_images/401.gif new file mode 100644 index 0000000..cd6e0d9 Binary files /dev/null and b/frontend/src/assets/401_images/401.gif differ diff --git 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0000000..d6ec96e --- /dev/null +++ b/frontend/src/assets/menu/system.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/Deduplication.svg b/frontend/src/assets/new/Deduplication.svg new file mode 100644 index 0000000..b451cfb --- /dev/null +++ b/frontend/src/assets/new/Deduplication.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/Submit for review.svg b/frontend/src/assets/new/Submit for review.svg new file mode 100644 index 0000000..ec3ae51 --- /dev/null +++ b/frontend/src/assets/new/Submit for review.svg @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/Warehousing.svg b/frontend/src/assets/new/Warehousing.svg new file mode 100644 index 0000000..e756bd9 --- /dev/null +++ b/frontend/src/assets/new/Warehousing.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/allocation-1.svg b/frontend/src/assets/new/allocation-1.svg new file mode 100644 index 0000000..5626dee --- /dev/null +++ b/frontend/src/assets/new/allocation-1.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/allocation.svg b/frontend/src/assets/new/allocation.svg new file mode 100644 index 0000000..78ba3f3 --- /dev/null +++ b/frontend/src/assets/new/allocation.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/del.svg b/frontend/src/assets/new/del.svg new file mode 100644 index 0000000..355052d --- /dev/null +++ b/frontend/src/assets/new/del.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/del1.svg b/frontend/src/assets/new/del1.svg new file mode 100644 index 0000000..0a1b752 --- /dev/null +++ b/frontend/src/assets/new/del1.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/del2.svg b/frontend/src/assets/new/del2.svg new file mode 100644 index 0000000..6635103 --- /dev/null +++ b/frontend/src/assets/new/del2.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/dimension.svg b/frontend/src/assets/new/dimension.svg new file mode 100644 index 0000000..5f644c2 --- /dev/null +++ b/frontend/src/assets/new/dimension.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/dimension1.svg b/frontend/src/assets/new/dimension1.svg new file mode 100644 index 0000000..df01d03 --- /dev/null +++ b/frontend/src/assets/new/dimension1.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/dimensionset.svg b/frontend/src/assets/new/dimensionset.svg new file mode 100644 index 0000000..2c9e3e8 --- /dev/null +++ b/frontend/src/assets/new/dimensionset.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/edit.svg b/frontend/src/assets/new/edit.svg new file mode 100644 index 0000000..5ab2ce7 --- /dev/null +++ b/frontend/src/assets/new/edit.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/examine.svg b/frontend/src/assets/new/examine.svg new file mode 100644 index 0000000..d05e44a --- /dev/null +++ b/frontend/src/assets/new/examine.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/examine1.svg b/frontend/src/assets/new/examine1.svg new file mode 100644 index 0000000..a6a6ee5 --- /dev/null +++ b/frontend/src/assets/new/examine1.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/examine2.svg b/frontend/src/assets/new/examine2.svg new file mode 100644 index 0000000..f46205d --- /dev/null +++ b/frontend/src/assets/new/examine2.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/img.svg b/frontend/src/assets/new/img.svg new file mode 100644 index 0000000..a55b06c --- /dev/null +++ b/frontend/src/assets/new/img.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/label.svg b/frontend/src/assets/new/label.svg new file mode 100644 index 0000000..ab2f15e --- /dev/null +++ b/frontend/src/assets/new/label.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/merge.svg b/frontend/src/assets/new/merge.svg new file mode 100644 index 0000000..59a0b87 --- /dev/null +++ b/frontend/src/assets/new/merge.svg @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/role.svg b/frontend/src/assets/new/role.svg new file mode 100644 index 0000000..db28ffc --- /dev/null +++ b/frontend/src/assets/new/role.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/role1.svg b/frontend/src/assets/new/role1.svg new file mode 100644 index 0000000..ce3cfcc --- /dev/null +++ b/frontend/src/assets/new/role1.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/sample.svg b/frontend/src/assets/new/sample.svg new file mode 100644 index 0000000..9042d30 --- /dev/null +++ b/frontend/src/assets/new/sample.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/save.svg b/frontend/src/assets/new/save.svg new file mode 100644 index 0000000..1195c76 --- /dev/null +++ b/frontend/src/assets/new/save.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/schedule.svg b/frontend/src/assets/new/schedule.svg new file mode 100644 index 0000000..76e9097 --- /dev/null +++ b/frontend/src/assets/new/schedule.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/team.svg b/frontend/src/assets/new/team.svg new file mode 100644 index 0000000..00ce81b --- /dev/null +++ b/frontend/src/assets/new/team.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/upload.svg b/frontend/src/assets/new/upload.svg new file mode 100644 index 0000000..84fa5ec --- /dev/null +++ b/frontend/src/assets/new/upload.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/uploadBtn.png b/frontend/src/assets/new/uploadBtn.png new file mode 100644 index 0000000..a5629b2 Binary files /dev/null and b/frontend/src/assets/new/uploadBtn.png differ diff --git a/frontend/src/assets/new/uploadBtn1.svg b/frontend/src/assets/new/uploadBtn1.svg new file mode 100644 index 0000000..9dcd9c3 --- /dev/null +++ b/frontend/src/assets/new/uploadBtn1.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/uploadBtn2.svg b/frontend/src/assets/new/uploadBtn2.svg new file mode 100644 index 0000000..fa66b2d --- /dev/null +++ b/frontend/src/assets/new/uploadBtn2.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/uploadBtn3.svg b/frontend/src/assets/new/uploadBtn3.svg new file mode 100644 index 0000000..97e81c8 --- /dev/null +++ b/frontend/src/assets/new/uploadBtn3.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/assets/new/view.svg b/frontend/src/assets/new/view.svg new file mode 100644 index 0000000..91fa804 --- /dev/null +++ b/frontend/src/assets/new/view.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/viewsample.svg b/frontend/src/assets/new/viewsample.svg new file mode 100644 index 0000000..d9a7daf --- /dev/null +++ b/frontend/src/assets/new/viewsample.svg @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/assets/new/zip.svg b/frontend/src/assets/new/zip.svg new file mode 100644 index 0000000..97e81c8 --- /dev/null +++ b/frontend/src/assets/new/zip.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/assets/tableicon/annotation.png b/frontend/src/assets/tableicon/annotation.png new file mode 100644 index 0000000..7b0b93b Binary files /dev/null and b/frontend/src/assets/tableicon/annotation.png differ diff --git a/frontend/src/assets/tableicon/annotation_disabled.png b/frontend/src/assets/tableicon/annotation_disabled.png new file mode 100644 index 0000000..2651c0f Binary files /dev/null and b/frontend/src/assets/tableicon/annotation_disabled.png differ diff --git a/frontend/src/assets/tableicon/bj.png b/frontend/src/assets/tableicon/bj.png new file mode 100644 index 0000000..12724c0 Binary files /dev/null and b/frontend/src/assets/tableicon/bj.png differ diff --git a/frontend/src/assets/tableicon/del.png b/frontend/src/assets/tableicon/del.png new file mode 100644 index 0000000..174c7d6 Binary files /dev/null and b/frontend/src/assets/tableicon/del.png differ diff --git a/frontend/src/assets/tableicon/fz.png 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file mode 100644 index 0000000..b209ed2 Binary files /dev/null and b/frontend/src/assets/tableicon/szcs2.png differ diff --git a/frontend/src/assets/tableicon/video.png b/frontend/src/assets/tableicon/video.png new file mode 100644 index 0000000..d1a7239 Binary files /dev/null and b/frontend/src/assets/tableicon/video.png differ diff --git a/frontend/src/assets/tableicon/video_disabled.png b/frontend/src/assets/tableicon/video_disabled.png new file mode 100644 index 0000000..4100d8f Binary files /dev/null and b/frontend/src/assets/tableicon/video_disabled.png differ diff --git a/frontend/src/assets/tableicon/viewimg.png b/frontend/src/assets/tableicon/viewimg.png new file mode 100644 index 0000000..17606b7 Binary files /dev/null and b/frontend/src/assets/tableicon/viewimg.png differ diff --git a/frontend/src/assets/tableicon/viewimg_disabled.png b/frontend/src/assets/tableicon/viewimg_disabled.png new file mode 100644 index 0000000..ad8b35a Binary files /dev/null and b/frontend/src/assets/tableicon/viewimg_disabled.png differ diff --git a/frontend/src/assets/tableicon/xlpg.png b/frontend/src/assets/tableicon/xlpg.png new file mode 100644 index 0000000..4a4efa1 Binary files /dev/null and b/frontend/src/assets/tableicon/xlpg.png differ diff --git a/frontend/src/assets/tableicon/xlpg2.png b/frontend/src/assets/tableicon/xlpg2.png new file mode 100644 index 0000000..eacfdc6 Binary files /dev/null and b/frontend/src/assets/tableicon/xlpg2.png differ diff --git a/frontend/src/assets/u208.png b/frontend/src/assets/u208.png new file mode 100644 index 0000000..dfbc89c Binary files /dev/null and b/frontend/src/assets/u208.png differ diff --git a/frontend/src/components.d.ts b/frontend/src/components.d.ts new file mode 100644 index 0000000..94e8b82 --- /dev/null +++ b/frontend/src/components.d.ts @@ -0,0 +1,9 @@ +// 全局组件类型声明 +import Pagination from '@/components/Pagination/index.vue'; + +declare module '@vue/runtime-core' { + export interface GlobalComponents { + Pagination: typeof Pagination; + } +} +export {}; diff --git a/frontend/src/components/Annotation/index.vue b/frontend/src/components/Annotation/index.vue new file mode 100644 index 0000000..5cd97d9 --- /dev/null +++ b/frontend/src/components/Annotation/index.vue @@ -0,0 +1,2190 @@ + + + + + diff --git a/frontend/src/components/Breadcrumb/index.vue b/frontend/src/components/Breadcrumb/index.vue new file mode 100644 index 0000000..7323114 --- /dev/null +++ b/frontend/src/components/Breadcrumb/index.vue @@ -0,0 +1,105 @@ + + + + + diff --git a/frontend/src/components/GithubCorner/index.vue b/frontend/src/components/GithubCorner/index.vue new file mode 100644 index 0000000..c9a4b32 --- /dev/null +++ b/frontend/src/components/GithubCorner/index.vue @@ -0,0 +1,59 @@ + + + diff --git a/frontend/src/components/Hamburger/index.vue b/frontend/src/components/Hamburger/index.vue new file mode 100644 index 0000000..bec7774 --- /dev/null +++ b/frontend/src/components/Hamburger/index.vue @@ -0,0 +1,41 @@ + + + + + diff --git a/frontend/src/components/IconSelect/index.vue b/frontend/src/components/IconSelect/index.vue new file mode 100644 index 0000000..9670dcf --- /dev/null +++ b/frontend/src/components/IconSelect/index.vue @@ -0,0 +1,93 @@ + + + + + diff --git a/frontend/src/components/LangSelect/index.vue b/frontend/src/components/LangSelect/index.vue new file mode 100644 index 0000000..88cdcd7 --- /dev/null +++ b/frontend/src/components/LangSelect/index.vue @@ -0,0 +1,44 @@ + + + diff --git a/frontend/src/components/Pagination/index.vue b/frontend/src/components/Pagination/index.vue new file mode 100644 index 0000000..7ae0c19 --- /dev/null +++ b/frontend/src/components/Pagination/index.vue @@ -0,0 +1,102 @@ + + + + + diff --git a/frontend/src/components/Pagination/page.vue b/frontend/src/components/Pagination/page.vue new file mode 100644 index 0000000..15d0187 --- /dev/null +++ b/frontend/src/components/Pagination/page.vue @@ -0,0 +1,131 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/components/RightPanel/index.vue b/frontend/src/components/RightPanel/index.vue new file mode 100644 index 0000000..dc09179 --- /dev/null +++ b/frontend/src/components/RightPanel/index.vue @@ -0,0 +1,160 @@ + + + + + + + diff --git a/frontend/src/components/Screenfull/index.vue b/frontend/src/components/Screenfull/index.vue new file mode 100644 index 0000000..9916a5f --- /dev/null +++ b/frontend/src/components/Screenfull/index.vue @@ -0,0 +1,25 @@ + + + diff --git a/frontend/src/components/SizeSelect/index.vue b/frontend/src/components/SizeSelect/index.vue new file mode 100644 index 0000000..d9dcbc1 --- /dev/null +++ b/frontend/src/components/SizeSelect/index.vue @@ -0,0 +1,38 @@ + + + diff --git a/frontend/src/components/SvgIcon/index.vue b/frontend/src/components/SvgIcon/index.vue new file mode 100644 index 0000000..19d3cc9 --- /dev/null +++ b/frontend/src/components/SvgIcon/index.vue @@ -0,0 +1,40 @@ + + + + + diff --git a/frontend/src/components/ThemePicker/index.vue b/frontend/src/components/ThemePicker/index.vue new file mode 100644 index 0000000..c51ce19 --- /dev/null +++ b/frontend/src/components/ThemePicker/index.vue @@ -0,0 +1,38 @@ + + + + + diff --git a/frontend/src/components/Upload/MultiUpload.vue b/frontend/src/components/Upload/MultiUpload.vue new file mode 100644 index 0000000..5c34226 --- /dev/null +++ b/frontend/src/components/Upload/MultiUpload.vue @@ -0,0 +1,143 @@ + + + + + diff --git a/frontend/src/components/Upload/SingleUpload.vue b/frontend/src/components/Upload/SingleUpload.vue new file mode 100644 index 0000000..9937550 --- /dev/null +++ b/frontend/src/components/Upload/SingleUpload.vue @@ -0,0 +1,97 @@ + + + + + + + diff --git a/frontend/src/components/WangEditor/index.vue b/frontend/src/components/WangEditor/index.vue new file mode 100644 index 0000000..aeab4d1 --- /dev/null +++ b/frontend/src/components/WangEditor/index.vue @@ -0,0 +1,79 @@ + + + + + diff --git a/frontend/src/components/predictioneffect/annotation.vue b/frontend/src/components/predictioneffect/annotation.vue new file mode 100644 index 0000000..a5d1390 --- /dev/null +++ b/frontend/src/components/predictioneffect/annotation.vue @@ -0,0 +1,563 @@ + + + + + diff --git a/frontend/src/components/predictioneffect/index.vue b/frontend/src/components/predictioneffect/index.vue new file mode 100644 index 0000000..4a133a0 --- /dev/null +++ b/frontend/src/components/predictioneffect/index.vue @@ -0,0 +1,412 @@ + + + + + diff --git a/frontend/src/components/predictioneffect/web.code-workspace b/frontend/src/components/predictioneffect/web.code-workspace new file mode 100644 index 0000000..c04eaf5 --- /dev/null +++ b/frontend/src/components/predictioneffect/web.code-workspace @@ -0,0 +1,7 @@ +{ + "folders": [ + { + "path": "..\\..\\.." + } + ] +} \ No newline at end of file diff --git a/frontend/src/components/predictioneffect1/annotation.vue b/frontend/src/components/predictioneffect1/annotation.vue new file mode 100644 index 0000000..bf16dcb --- /dev/null +++ b/frontend/src/components/predictioneffect1/annotation.vue @@ -0,0 +1,564 @@ + + + + + diff --git a/frontend/src/components/predictioneffect1/index.vue b/frontend/src/components/predictioneffect1/index.vue new file mode 100644 index 0000000..a851bd1 --- /dev/null +++ b/frontend/src/components/predictioneffect1/index.vue @@ -0,0 +1,552 @@ + + + + + diff --git a/frontend/src/components/predictioneffect1/web.code-workspace b/frontend/src/components/predictioneffect1/web.code-workspace new file mode 100644 index 0000000..c04eaf5 --- /dev/null +++ b/frontend/src/components/predictioneffect1/web.code-workspace @@ -0,0 +1,7 @@ +{ + "folders": [ + { + "path": "..\\..\\.." + } + ] +} \ No newline at end of file diff --git a/frontend/src/directive/index.ts b/frontend/src/directive/index.ts new file mode 100644 index 0000000..a966376 --- /dev/null +++ b/frontend/src/directive/index.ts @@ -0,0 +1,2 @@ +export { hasPerm, hasRole } from './permission'; +export { deBounce } from './utils'; diff --git a/frontend/src/directive/permission/index.ts b/frontend/src/directive/permission/index.ts new file mode 100644 index 0000000..1dc7b96 --- /dev/null +++ b/frontend/src/directive/permission/index.ts @@ -0,0 +1,54 @@ +import { useUserStoreHook } from '@/store/modules/user'; +import { Directive, DirectiveBinding } from 'vue'; + +/** + * 按钮权限校验 + */ +export const hasPerm: Directive = { + mounted(el: HTMLElement, binding: DirectiveBinding) { + // 「超级管理员」拥有所有的按钮权限 + const { roles, perms } = useUserStoreHook(); + if (roles.includes('超级管理员')) { + return true; + } + // 「其他角色」按钮权限校验 + const { value } = binding; + if (value) { + const requiredPerms = value; // DOM绑定需要的按钮权限标识 + const hasPerm = perms?.some(perm => { + return requiredPerms.includes(perm); + }); + + if (!hasPerm) { + el.parentNode && el.parentNode.removeChild(el); + } + } else { + throw new Error( + "need perms! Like v-has-perm=\"['sys:user:add','sys:user:edit']\"" + ); + } + } +}; + +/** + * 角色权限校验 + */ +export const hasRole: Directive = { + mounted(el: HTMLElement, binding: DirectiveBinding) { + const { value } = binding; + + if (value) { + const requiredRoles = value; // DOM绑定需要的角色编码 + const { roles } = useUserStoreHook(); + const hasRole = roles.some(perm => { + return requiredRoles.includes(perm); + }); + + if (!hasRole) { + el.parentNode && el.parentNode.removeChild(el); + } + } else { + throw new Error("need roles! Like v-has-role=\"['admin','zhengsl','test']\""); + } + } +}; diff --git a/frontend/src/directive/utils/index.ts b/frontend/src/directive/utils/index.ts new file mode 100644 index 0000000..9eeaef9 --- /dev/null +++ b/frontend/src/directive/utils/index.ts @@ -0,0 +1,15 @@ +import { Directive } from 'vue'; + +/** + * 按钮防抖 + */ +export const deBounce:Directive = { + mounted(el:HTMLElement) { + el.addEventListener('click', () => { + el.classList.add('is-disabled') + setTimeout(() => { + el.classList.remove('is-disabled') + }, 2000) + }) + } +} diff --git a/frontend/src/env.d.ts b/frontend/src/env.d.ts new file mode 100644 index 0000000..bcddf3e --- /dev/null +++ b/frontend/src/env.d.ts @@ -0,0 +1,19 @@ +/// + +declare module '*.vue' { + import { DefineComponent } from 'vue'; + // eslint-disable-next-line @typescript-eslint/no-explicit-any, @typescript-eslint/ban-types + const component: DefineComponent<{}, {}, any>; + export default component; +} + +// 环境变量 TypeScript的智能提示 +interface ImportMetaEnv { + VITE_APP_TITLE: string; + VITE_APP_PORT: string; + VITE_APP_BASE_API: string; +} + +interface ImportMeta { + readonly env: ImportMetaEnv; +} diff --git a/frontend/src/lang/en.ts b/frontend/src/lang/en.ts new file mode 100644 index 0000000..63b4df7 --- /dev/null +++ b/frontend/src/lang/en.ts @@ -0,0 +1,29 @@ +export default { + // 路由国际化 + route: { + dashboard: 'Dashboard', + document: 'Document' + }, + // 登录页面国际化 + login: { + title: 'Jingyi system', + username: 'Username', + rulesUsername: 'enter one user name', + password: 'Password', + rulesPassword: 'Please input a password', + rulesPasswordPlace: 'The password cannot be less than 6 digits', + login: 'Login', + code: 'Verification Code', + copyright: '', + icp: '', + thirdPartyLogin: 'third-party login', + remember: 'Remember password' + }, + // 导航栏国际化 + navbar: { + dashboard: 'Dashboard', + logout: 'Logout', + document: 'Document', + gitee: 'Gitee' + } +}; diff --git a/frontend/src/lang/index.ts b/frontend/src/lang/index.ts new file mode 100644 index 0000000..5ca3f65 --- /dev/null +++ b/frontend/src/lang/index.ts @@ -0,0 +1,46 @@ +// 自定义国际化配置 +import { createI18n } from 'vue-i18n'; +import { localStorage } from '@/utils/localStorage'; + +// 本地语言包 +import enLocale from './en'; +import zhCnLocale from './zh-cn'; + +const messages = { + 'zh-cn': { + ...zhCnLocale + }, + en: { + ...enLocale + } +}; + +/** + * 获取当前系统使用语言字符串 + * + * @returns zh-cn|en ... + */ +export const getLanguage = () => { + // 本地缓存获取 + let language = localStorage.get('language'); + if (language) { + return language; + } + // 浏览器使用语言 + language = navigator.language.toLowerCase(); + const locales = Object.keys(messages); + for (const locale of locales) { + if (language.indexOf(locale) > -1) { + return locale; + } + } + return 'zh-cn'; +}; + +const i18n = createI18n({ + legacy: false, + locale: getLanguage(), + messages: messages +}); + +export default i18n; diff --git a/frontend/src/lang/zh-cn.ts b/frontend/src/lang/zh-cn.ts new file mode 100644 index 0000000..83d5101 --- /dev/null +++ b/frontend/src/lang/zh-cn.ts @@ -0,0 +1,28 @@ +export default { + // 路由国际化 + route: { + dashboard: '首页', + document: '项目文档' + }, + // 登录页面国际化 + login: { + title: 'AI算法模型训练及部署管理平台', + username: '用户名', + rulesUsername: '请输入用户名', + password: '密码', + rulesPassword: '请输入密码', + rulesPasswordPlace: '密码不能少于6位', + login: '登 录', + code: '请输入验证码', + copyright: '', + icp: '', + thirdPartyLogin: '第三方登录', + remember: '记住密码' + }, + navbar: { + dashboard: '首页', + logout: '注销', + document: '项目文档', + gitee: '码云' + } +}; diff --git a/frontend/src/layout/components/AppMain.vue b/frontend/src/layout/components/AppMain.vue new file mode 100644 index 0000000..a683688 --- /dev/null +++ b/frontend/src/layout/components/AppMain.vue @@ -0,0 +1,49 @@ + + + + + diff --git a/frontend/src/layout/components/Navbar.vue b/frontend/src/layout/components/Navbar.vue new file mode 100644 index 0000000..fb3a67e --- /dev/null +++ b/frontend/src/layout/components/Navbar.vue @@ -0,0 +1,165 @@ + + + + + diff --git a/frontend/src/layout/components/Settings/index.vue b/frontend/src/layout/components/Settings/index.vue new file mode 100644 index 0000000..605f23a --- /dev/null +++ b/frontend/src/layout/components/Settings/index.vue @@ -0,0 +1,198 @@ + + + + + diff --git a/frontend/src/layout/components/Sidebar/Link.vue b/frontend/src/layout/components/Sidebar/Link.vue new file mode 100644 index 0000000..c592bbd --- /dev/null +++ b/frontend/src/layout/components/Sidebar/Link.vue @@ -0,0 +1,37 @@ + + + diff --git a/frontend/src/layout/components/Sidebar/Logo.vue b/frontend/src/layout/components/Sidebar/Logo.vue new file mode 100644 index 0000000..7638bbc --- /dev/null +++ b/frontend/src/layout/components/Sidebar/Logo.vue @@ -0,0 +1,30 @@ + + + diff --git a/frontend/src/layout/components/Sidebar/MixNav.vue b/frontend/src/layout/components/Sidebar/MixNav.vue new file mode 100644 index 0000000..55b8a7f --- /dev/null +++ b/frontend/src/layout/components/Sidebar/MixNav.vue @@ -0,0 +1,157 @@ + + + + + diff --git a/frontend/src/layout/components/Sidebar/SidebarItem.vue b/frontend/src/layout/components/Sidebar/SidebarItem.vue new file mode 100644 index 0000000..46c5b6a --- /dev/null +++ b/frontend/src/layout/components/Sidebar/SidebarItem.vue @@ -0,0 +1,131 @@ + + + + diff --git a/frontend/src/layout/components/Sidebar/index.vue b/frontend/src/layout/components/Sidebar/index.vue new file mode 100644 index 0000000..7fc3734 --- /dev/null +++ b/frontend/src/layout/components/Sidebar/index.vue @@ -0,0 +1,42 @@ + + + diff --git a/frontend/src/layout/components/TagsView/ScrollPane.vue b/frontend/src/layout/components/TagsView/ScrollPane.vue new file mode 100644 index 0000000..302f1cf --- /dev/null +++ b/frontend/src/layout/components/TagsView/ScrollPane.vue @@ -0,0 +1,130 @@ + + + + + diff --git a/frontend/src/layout/components/TagsView/index.vue b/frontend/src/layout/components/TagsView/index.vue new file mode 100644 index 0000000..2d54388 --- /dev/null +++ b/frontend/src/layout/components/TagsView/index.vue @@ -0,0 +1,357 @@ + + + + + diff --git a/frontend/src/layout/components/index.ts b/frontend/src/layout/components/index.ts new file mode 100644 index 0000000..4dca96e --- /dev/null +++ b/frontend/src/layout/components/index.ts @@ -0,0 +1,4 @@ +export { default as Navbar } from './Navbar.vue'; +export { default as AppMain } from './AppMain.vue'; +export { default as Settings } from './Settings/index.vue'; +export { default as TagsView } from './TagsView/index.vue'; diff --git a/frontend/src/layout/components/news.vue b/frontend/src/layout/components/news.vue new file mode 100644 index 0000000..165f0c3 --- /dev/null +++ b/frontend/src/layout/components/news.vue @@ -0,0 +1,257 @@ + + + + diff --git a/frontend/src/layout/index.vue b/frontend/src/layout/index.vue new file mode 100644 index 0000000..2fb9a5f --- /dev/null +++ b/frontend/src/layout/index.vue @@ -0,0 +1,134 @@ + + + + + diff --git a/frontend/src/main.ts b/frontend/src/main.ts new file mode 100644 index 0000000..e12358d --- /dev/null +++ b/frontend/src/main.ts @@ -0,0 +1,42 @@ +import { createApp, Directive } from 'vue'; +import App from './App.vue'; +import router from '@/router'; +import { setupStore } from '@/store'; + +import ElementPlus from 'element-plus'; +import * as ElementPlusIconsVue from '@element-plus/icons-vue' +import Pagination from '@/components/Pagination/index.vue'; +import '@/permission'; + +// 引入svg注册脚本 +import 'virtual:svg-icons-register'; + +// 国际化 +import i18n from '@/lang/index'; + +import '@/styles/index.scss'; +import 'element-plus/theme-chalk/index.css'; +import 'element-plus/theme-chalk/dark/css-vars.css'; + +const app = createApp(App); +// 自定义指令 +import * as directive from '@/directive'; +Object.keys(directive).forEach(key => { + app.directive(key, (directive as { [key: string]: Directive })[key]); +}); +for (const [key, component] of Object.entries(ElementPlusIconsVue)) { + app.component(key, component) +} + +// 全局方法 +import { getDictionaries } from '@/api/dict'; +app.config.globalProperties.$getDictionaries = getDictionaries; + +// 全局挂载 +setupStore(app); +app + .component('Pagination', Pagination) + .use(router) + .use(ElementPlus) + .use(i18n) + .mount('#app'); diff --git a/frontend/src/permission.ts b/frontend/src/permission.ts new file mode 100644 index 0000000..653e2be --- /dev/null +++ b/frontend/src/permission.ts @@ -0,0 +1,68 @@ +import router from '@/router'; +import { RouteRecordRaw } from 'vue-router'; +import { useUserStoreHook } from '@/store/modules/user'; +import { usePermissionStoreHook } from '@/store/modules/permission'; + +import NProgress from 'nprogress'; +import 'nprogress/nprogress.css'; +NProgress.configure({ showSpinner: false }); // 进度条 + +const permissionStore = usePermissionStoreHook(); + +// 白名单路由 +const whiteList = ['/login']; +router.beforeEach(async (to, from, next) => { + NProgress.start(); + const userStore = useUserStoreHook(); + const hasDynamicRoutes = permissionStore.addRoutes.length > 0; + + if (userStore.Token) { + // 登录成功，跳转到首页 + if (to.path === '/login') { + next({ path: '/' }); + NProgress.done(); + } else { + const hasGetUserInfo = userStore.roles.length > 0; + // const hasGetUserInfo = true; + if (hasGetUserInfo && hasDynamicRoutes) { + if (to.matched.length === 0) { + from.name ? next({ name: from.name as any }) : next('/401'); + } else { + next(); + } + } else { + try { + // const { roles } = await userStore.getInfo(); + if (!hasGetUserInfo) { + userStore.roles = ['admin']; + } + const accessRoutes: RouteRecordRaw[] = + await permissionStore.generateRoutes([]); + accessRoutes.forEach((route: any) => { + if (route.name && !router.hasRoute(route.name)) { + router.addRoute(route); + } + }); + next({ ...to, replace: true }); + } catch (error) { + // 移除 token 并跳转登录页 + await userStore.resetToken(); + next(`/login?redirect=${to.path}`); + NProgress.done(); + } + } + } + } else { + // 未登录可以访问白名单页面 + if (whiteList.indexOf(to.path) !== -1) { + next(); + } else { + next(`/login?redirect=${to.path}`); + NProgress.done(); + } + } +}); + +router.afterEach(() => { + NProgress.done(); +}); diff --git a/frontend/src/router/index.ts b/frontend/src/router/index.ts new file mode 100644 index 0000000..2bb80a5 --- /dev/null +++ b/frontend/src/router/index.ts @@ -0,0 +1,77 @@ +import { createRouter, createWebHashHistory, RouteRecordRaw } from 'vue-router'; +import { usePermissionStoreHook } from '@/store/modules/permission'; + +export const Layout = () => import('@/layout/index.vue'); + +// 静态路由 +export const constantRoutes: any = [ + { + path: '/redirect', + component: Layout, + meta: { hidden: true }, + children: [ + { + path: '/redirect/:path(.*)', + component: () => import('@/views/redirect/index.vue') + } + ] + }, + { + path: '/login', + component: () => import('@/views/login/index.vue'), + meta: { hidden: true } + }, + { + path: '/404', + component: () => import('@/views/error-page/404.vue'), + meta: { hidden: true } + }, + + { + path: '/', + component: Layout, + redirect: '/dashboard', + children: [ + { + path: 'dashboard', + opturl: '/dashboard', + component: () => import('@/views/dashboard/index.vue'), + name: '首页', + icon: 'homepage', + meta: { title: 'dashboard', icon: 'homepage', affix: true } + }, + { + path: 'personalCenter', + component: () => import('@/views/system/user/personalCenter.vue'), + name: '个人中心', + meta: { title: 'personalCenter', hidden: true, icon: 'personalCenter' } + }, + { + path: '401', + component: () => import('@/views/error-page/401.vue'), + meta: { hidden: true } + }, + ] + } +]; + +// 创建路由 +const router = createRouter({ + history: createWebHashHistory(), + routes: constantRoutes as RouteRecordRaw[], + // 刷新时，滚动条位置还原 + scrollBehavior: () => ({ left: 0, top: 0 }) +}); + +// 重置路由 +export function resetRouter() { + const permissionStore = usePermissionStoreHook(); + permissionStore.routes.forEach(route => { + const name = route.name; + if (name && router.hasRoute(name)) { + router.hasRoute(name) && router.removeRoute(name); + } + }); +} + +export default router; diff --git a/frontend/src/settings.ts b/frontend/src/settings.ts new file mode 100644 index 0000000..e9cf667 --- /dev/null +++ b/frontend/src/settings.ts @@ -0,0 +1,23 @@ +interface DefaultSettings { + title: string; + showSettings: boolean; + tagsView: boolean; + fixedHeader: boolean; + sidebarLogo: boolean; + errorLog: string; + layout: string; + theme: string; +} + +const defaultSettings: DefaultSettings = { + title: 'AI算法模型训练及部署管理平台', + showSettings: false, + tagsView: true, + fixedHeader: true, + sidebarLogo: true, + errorLog: 'production', + layout: 'left', + theme: 'light' +}; + +export default defaultSettings; diff --git a/frontend/src/shims.d.ts b/frontend/src/shims.d.ts new file mode 100644 index 0000000..dd6adc8 --- /dev/null +++ b/frontend/src/shims.d.ts @@ -0,0 +1,3 @@ +shims.d.ts +declare module 'jsencrypt/bin/jsencrypt.min' // declare module 'xxx'路径或者模块名 +declare module 'sortablejs' \ No newline at end of file diff --git a/frontend/src/store/index.ts b/frontend/src/store/index.ts new file mode 100644 index 0000000..fc0ba49 --- /dev/null +++ b/frontend/src/store/index.ts @@ -0,0 +1,11 @@ +import type { App } from 'vue'; +import { createPinia } from 'pinia'; + +const store = createPinia(); + +// 全局挂载store +export function setupStore(app: App) { + app.use(store); +} + +export { store }; diff --git a/frontend/src/store/modules/app.ts b/frontend/src/store/modules/app.ts new file mode 100644 index 0000000..eb4d628 --- /dev/null +++ b/frontend/src/store/modules/app.ts @@ -0,0 +1,97 @@ +import { + getSidebarStatus, + setSidebarStatus, + getSize, + setSize, + setLanguage +} from '@/utils/localStorage'; +import { defineStore } from 'pinia'; +import { getLanguage } from '@/lang/index'; +import { computed, reactive, ref } from 'vue'; +import { useStorage } from '@vueuse/core'; + +// Element Plus 语言包 +import zhCn from 'element-plus/es/locale/lang/zh-cn'; +import en from 'element-plus/es/locale/lang/en'; + +export enum DeviceType { + mobile, + desktop +} + +export enum SizeType { + default, + large, + small +} + +// setup +export const useAppStore = defineStore('app', () => { + // state + const device = useStorage('device', 'desktop'); + const size = ref(getSize() || 'default'); + const language = ref(getLanguage()); + const sidebar = reactive({ + opened: getSidebarStatus() !== 'closed', + withoutAnimation: false + }); + + const locale = computed(() => { + if (language?.value == 'en') { + return en; + } else { + return zhCn; + } + }); + + // actions + function toggleSidebar(withoutAnimation: boolean) { + sidebar.opened = !sidebar.opened; + sidebar.withoutAnimation = withoutAnimation; + if (sidebar.opened) { + setSidebarStatus('opened'); + } else { + setSidebarStatus('closed'); + } + } + + function closeSideBar(withoutAnimation: boolean) { + sidebar.opened = false; + sidebar.withoutAnimation = withoutAnimation; + setSidebarStatus('closed'); + } + + function openSideBar(withoutAnimation: boolean) { + sidebar.opened = true; + sidebar.withoutAnimation = withoutAnimation; + setSidebarStatus('opened'); + } + + function toggleDevice(val: string) { + device.value = val; + } + + function changeSize(val: string) { + size.value = val; + setSize(val); + } + + function changeLanguage(val: string) { + language.value = val; + setLanguage(val); + } + + return { + device, + sidebar, + language, + locale, + size, + toggleDevice, + changeSize, + changeLanguage, + toggleSidebar, + closeSideBar, + openSideBar + }; +}); diff --git a/frontend/src/store/modules/permission.ts b/frontend/src/store/modules/permission.ts new file mode 100644 index 0000000..7b1462b --- /dev/null +++ b/frontend/src/store/modules/permission.ts @@ -0,0 +1,226 @@ +import { RouteRecordRaw } from 'vue-router'; +import { defineStore } from 'pinia'; +import { constantRoutes } from '@/router'; +import { store } from '@/store'; +import { listRoutes } from '@/api/menu'; +import { ref } from 'vue'; + +const modules = import.meta.glob('../../views/**/**.vue'); +export const Layout = () => import('@/layout/index.vue'); + +type LocalMenuNode = { + path: string; + opturl?: string; + name: string; + icon?: string; + hidden?: boolean; + isdisplay?: string; + component?: any; + meta?: Record; + children?: LocalMenuNode[]; +}; + +function normalizeLocalMenuNode(node: LocalMenuNode, parentIcon = ''): any { + const normalizedIcon = node.icon || parentIcon || ''; + const normalizedOpturl = node.opturl || node.path; + const normalizedChildren = (node.children || []).map(child => normalizeLocalMenuNode(child, normalizedIcon)); + const normalizedComponent = node.component || (normalizedChildren.length > 0 ? Layout : undefined); + const normalizedMeta = { + title: node.meta?.title || node.name, + ...(node.meta || {}) + }; + + return { + path: node.path, + opturl: normalizedOpturl, + component: normalizedComponent, + hidden: node.hidden ?? false, + name: node.name, + icon: normalizedIcon, + isdisplay: node.isdisplay ?? '1', + meta: normalizedMeta, + children: normalizedChildren + }; +} + +function buildLocalAsyncRoutes() { + const rawRoutes: LocalMenuNode[] = [ + { + path: '/dataset1', + name: '数据集管理', + icon: 'dataset', + hidden: true, + children: [{ + path: '/tempfile', + name: '临时文件管理', + icon: 'dataset', + component: () => import('@/views/tempfile/index.vue') + }, + { + path: '/dataset', + name: '数据集导入管理', + icon: 'dataset', + component: () => import('@/views/dataset/index.vue') + } + ] + }, + { + path: '/annotationtask', + name: '数据标注管理', + icon: 'annotationtask', + hidden: true, + children: [ + { + path: '/annotationtask', + name: '标注任务管理', + component: () => import('@/views/dataannotations/annotationtask/index.vue') + }, + { + path: '/annotationteam', + name: '标注团队管理', + component: () => import('@/views/dataannotations/annotationteam/index.vue') + } + ] + }, + { + path: '/modeltraining1', + opturl: '/modeltraining1', + name: '模型训练管理', + icon: 'modeltraining', + hidden: true, + children: [ + { + path: '/modeltraining', + name: '模型训练', + component: () => import('@/views/modeltraining/index.vue') + } + ] + }, + { + path: '/define1', + name: '算法及模型管理', + icon: 'define', + hidden: true, + children: [ + { + path: '/define', + name: '算法定义', + component: () => import('@/views/AlgorithmsAndModels/define/index.vue') + }, + { + path: '/maintenance', + name: '模型维护', + component: () => import('@/views/AlgorithmsAndModels/maintenance/index.vue') + }, + { + path: '/modelbase', + name: '算法模型库', + component: () => import('@/views/AlgorithmsAndModels/modelbase/index.vue') + } + ] + }, + { + path: '/system1', + name: '组织及用户管理', + icon: 'system', + hidden: true, + children: [ + { + path: '/system', + name: '组织及用户', + component: () => import('@/views/system/user/index.vue') + } + ] + }, + { + path: '/smartterminal1', + name: '智能终端管理', + icon: 'dataset', + hidden: false, + children: [{ + path: '/smartterminal', + name: '智能终端发布', + component: () => import('@/views/smartterminal/index.vue') + }] + } + ]; + + return rawRoutes.map(route => normalizeLocalMenuNode(route)); +} + +// const hasPermission = (roles: string[], route: RouteRecordRaw) => { +// if (route.meta && route.meta.roles) { +// if (roles.includes('ROOT')) { +// return true; +// } +// return roles.some(role => { +// if (route.meta?.roles !== undefined) { +// return (route.meta.roles as string[]).includes(role); +// } +// }); +// } +// return false; +// }; + +const filterAsyncRoutes = (routes: RouteRecordRaw[], roles: string[]) => { + const res: RouteRecordRaw[] = []; + routes.forEach(route => { + const tmp = { ...route } as any; + // if (hasPermission(roles, tmp)) { + tmp.path = tmp.opturl; + if (tmp.type == '0') { + tmp.component = Layout; + } else { + const component = modules[`../../views${tmp.opturl}.vue`] as any; + if (component) { + tmp.component = component; + } else { + tmp.component = modules[`../../views/error-page/404.vue`]; + } + } + res.push(tmp) + if (tmp.children) { + tmp.children = filterAsyncRoutes(tmp.children, roles); + } + // } + }); + return res; +}; + +// setup +export const usePermissionStore = defineStore('permission', () => { + // state + const routes = ref([]); + const addRoutes = ref([]); + routes.value = constantRoutes + // actions + function setRoutes(newRoutes: RouteRecordRaw[]) { + addRoutes.value = newRoutes; + routes.value = constantRoutes.concat(newRoutes); + } + + function generateRoutes(roles: string[]) { + return new Promise((resolve, reject) => { + const asyncRoutes: any = buildLocalAsyncRoutes(); + const accessedRoutes = asyncRoutes; + setRoutes(accessedRoutes); + resolve(accessedRoutes); + // listRoutes() + // .then(response => { + // const asyncRoutes :any = response; + // const accessedRoutes = filterAsyncRoutes(asyncRoutes, roles); + // setRoutes(accessedRoutes); + // resolve(accessedRoutes); + // }) + // .catch(error => { + // reject(error); + // }); + }); + } + return { routes, addRoutes, setRoutes, generateRoutes }; +}); + +// 非setup +export function usePermissionStoreHook() { + return usePermissionStore(store); +} diff --git a/frontend/src/store/modules/settings.ts b/frontend/src/store/modules/settings.ts new file mode 100644 index 0000000..0b69453 --- /dev/null +++ b/frontend/src/store/modules/settings.ts @@ -0,0 +1,64 @@ +import { defineStore } from 'pinia'; +import defaultSettings from '../../settings'; +import { ref } from 'vue'; +import { useStorage } from '@vueuse/core'; + +/** + * 主题类型 + */ +export enum ThemeType { + light, + dark +} + +/** + * 布局类型 + */ +export enum LayoutType { + left, + top, + mix +} + +export const useSettingsStore = defineStore('setting', () => { + // state + const showSettings = ref(defaultSettings.showSettings); + const tagsView = useStorage('tagsView', defaultSettings.tagsView); + const fixedHeader = ref(defaultSettings.fixedHeader); + const sidebarLogo = ref(defaultSettings.sidebarLogo); + + const layout = useStorage('layout', defaultSettings.layout); + + // actions + function changeSetting(param: { key: string; value: any }) { + const { key, value } = param; + switch (key) { + case 'showSettings': + showSettings.value = value; + break; + case 'fixedHeader': + fixedHeader.value = value; + break; + case 'tagsView': + tagsView.value = value; + break; + case 'sidevarLogo': + sidebarLogo.value = value; + break; + case 'layout': + layout.value = value; + break; + default: + break; + } + } + + return { + showSettings, + tagsView, + fixedHeader, + sidebarLogo, + layout, + changeSetting + }; +}); diff --git a/frontend/src/store/modules/tagsView.ts b/frontend/src/store/modules/tagsView.ts new file mode 100644 index 0000000..f8a69a2 --- /dev/null +++ b/frontend/src/store/modules/tagsView.ts @@ -0,0 +1,214 @@ +import { defineStore } from 'pinia'; +import { ref } from 'vue'; +import { RouteLocationNormalized } from 'vue-router'; + +export interface TagView extends Partial { + title?: string; +} + +// setup +export const useTagsViewStore = defineStore('tagsView', () => { + // state + const visitedViews = ref([]); + const cachedViews = ref([]); + + // actions + function addVisitedView(view: TagView) { + if (visitedViews.value.some(v => v.path === view.path)) return; + if (view.meta && view.meta.affix) { + visitedViews.value.unshift( + Object.assign({}, view, { + title: view.meta?.title || 'no-name' + }) + ); + } else { + visitedViews.value.push( + Object.assign({}, view, { + title: view.meta?.title || 'no-name' + }) + ); + } + } + + function addCachedView(view: TagView) { + const viewName = view.name as string; + if (cachedViews.value.includes(viewName)) return; + if (view.meta?.keepAlive) { + cachedViews.value.push(viewName); + } + } + + function delVisitedView(view: TagView) { + return new Promise(resolve => { + for (const [i, v] of visitedViews.value.entries()) { + if (v.path === view.path) { + visitedViews.value.splice(i, 1); + break; + } + } + resolve([...visitedViews.value]); + }); + } + + function delCachedView(view: TagView) { + const viewName = view.name as string; + return new Promise(resolve => { + const index = cachedViews.value.indexOf(viewName); + index > -1 && cachedViews.value.splice(index, 1); + resolve([...cachedViews.value]); + }); + } + + function delOtherVisitedViews(view: TagView) { + return new Promise(resolve => { + visitedViews.value = visitedViews.value.filter(v => { + return v.meta?.affix || v.path === view.path; + }); + resolve([...visitedViews.value]); + }); + } + + function delOtherCachedViews(view: TagView) { + const viewName = view.name as string; + return new Promise(resolve => { + const index = cachedViews.value.indexOf(viewName); + if (index > -1) { + cachedViews.value = cachedViews.value.slice(index, index + 1); + } else { + // if index = -1, there is no cached tags + cachedViews.value = []; + } + resolve([...cachedViews.value]); + }); + } + + function updateVisitedView(view: TagView) { + for (let v of visitedViews.value) { + if (v.path === view.path) { + v = Object.assign(v, view); + break; + } + } + } + + function addView(view: TagView) { + addVisitedView(view); + addCachedView(view); + } + + function delView(view: TagView) { + return new Promise(resolve => { + delVisitedView(view); + delCachedView(view); + resolve({ + visitedViews: [...visitedViews.value], + cachedViews: [...cachedViews.value] + }); + }); + } + + function delOtherViews(view: TagView) { + return new Promise(resolve => { + delOtherVisitedViews(view); + delOtherCachedViews(view); + resolve({ + visitedViews: [...visitedViews.value], + cachedViews: [...cachedViews.value] + }); + }); + } + + function delLeftViews(view: TagView) { + return new Promise(resolve => { + const currIndex = visitedViews.value.findIndex(v => v.path === view.path); + if (currIndex === -1) { + return; + } + visitedViews.value = visitedViews.value.filter((item, index) => { + // affix:true 固定tag，例如“首页” + if (index >= currIndex || (item.meta && item.meta.affix)) { + return true; + } + + const cacheIndex = cachedViews.value.indexOf(item.name as string); + if (cacheIndex > -1) { + cachedViews.value.splice(cacheIndex, 1); + } + return false; + }); + resolve({ + visitedViews: [...visitedViews.value] + }); + }); + } + function delRightViews(view: TagView) { + return new Promise(resolve => { + const currIndex = visitedViews.value.findIndex(v => v.path === view.path); + if (currIndex === -1) { + return; + } + visitedViews.value = visitedViews.value.filter((item, index) => { + // affix:true 固定tag，例如“首页” + if (index <= currIndex || (item.meta && item.meta.affix)) { + return true; + } + + const cacheIndex = cachedViews.value.indexOf(item.name as string); + if (cacheIndex > -1) { + cachedViews.value.splice(cacheIndex, 1); + } + return false; + }); + resolve({ + visitedViews: [...visitedViews.value] + }); + }); + } + + function delAllViews() { + return new Promise(resolve => { + const affixTags = visitedViews.value.filter(tag => tag.meta?.affix); + visitedViews.value = affixTags; + cachedViews.value = []; + resolve({ + visitedViews: [...visitedViews.value], + cachedViews: [...cachedViews.value] + }); + }); + } + + function delAllVisitedViews() { + return new Promise(resolve => { + const affixTags = visitedViews.value.filter(tag => tag.meta?.affix); + visitedViews.value = affixTags; + resolve([...visitedViews.value]); + }); + } + + function delAllCachedViews() { + return new Promise(resolve => { + cachedViews.value = []; + resolve([...cachedViews.value]); + }); + } + + return { + visitedViews, + cachedViews, + addVisitedView, + addCachedView, + delVisitedView, + delCachedView, + delOtherVisitedViews, + delOtherCachedViews, + updateVisitedView, + addView, + delView, + delOtherViews, + delLeftViews, + delRightViews, + delAllViews, + delAllVisitedViews, + delAllCachedViews + }; +}); diff --git a/frontend/src/store/modules/user.ts b/frontend/src/store/modules/user.ts new file mode 100644 index 0000000..2faecd0 --- /dev/null +++ b/frontend/src/store/modules/user.ts @@ -0,0 +1,118 @@ +import { defineStore } from 'pinia'; + +import { getToken, setToken, removeToken } from '@/utils/auth'; +import { loginApi, logoutApi } from '@/api/auth'; +// import { getUserInfo } from '@/api/user'; +import { resetRouter } from '@/router'; +import { store } from '@/store'; +import { LoginData } from '@/api/auth/types'; +import { ref } from 'vue'; +import { UserInfo } from '@/api/user/types'; +import Cookies from 'js-cookie'; + +export const useUserStore = defineStore('user', () => { + // state + const Token = ref(getToken() || ''); + const nickname = ref(''); + const avatar = ref(''); + const roles = ref>([]); // 用户角色编码集合 → 判断路由权限 + const perms = ref>([]); // 用户权限编码集合 → 判断按钮权限 + const badgeval = ref('') + + console.log(import.meta.env.MODE) + + const VITE_APP_BASE_API: any = import.meta.env.MODE == 'development' ? import.meta.env.VITE_APP_BASE_API : window.webConfig.VITE_APP_BASE_API + const VITE_APP_BASE_IMG_API: any = import.meta.env.MODE == 'development' ? import.meta.env.VITE_APP_BASE_IMG_API : window.webConfig.VITE_APP_BASE_IMG_API + const VITE_WS_BASE_API: any = import.meta.env.MODE == 'development' ? import.meta.env.VITE_WS_BASE_API : window.webConfig.VITE_WS_BASE_API + // actions + + // 登录 + function login(loginData: LoginData) { + return new Promise((resolve, reject) => { + loginApi(loginData) + .then((response:any) => { + const { access_token, refresh_token } = response; + Token.value = access_token; + setToken(access_token); + Cookies.set('refresh_token', refresh_token); + resolve(); + }) + .catch(error => { + reject(error); + }); + }); + } + + // 获取信息(用户昵称、头像、角色集合、权限集合) + function getInfo() { + return new Promise((resolve, reject) => { + // getUserInfo() + // .then(({ data }) => { + // if (!data) { + // return reject('Verification failed, please Login again.'); + // } + // if (!data.roles || data.roles.length <= 0) { + // reject('getUserInfo: roles must be a non-null array!'); + // } + // nickname.value = data.userInfo.nickname; + // avatar.value = data.userInfo.avatar; + // roles.value = data.roles; + // perms.value = data.permissions; + // resolve(data); + // }) + // .catch(error => { + // reject(error); + // }); + nickname.value = '11'; + roles.value = ['超级管理员']; + perms.value = []; + }); + } + + // 注销 + function logout() { + return new Promise((resolve, reject) => { + resetRouter(); + resetToken(); + resolve(); + // logoutApi() + // .then(() => { + // resetRouter(); + // resetToken(); + // }) + // .catch(error => { + // reject(error); + // }); + }); + } + + // 重置 + function resetToken() { + removeToken(); + Token.value = ''; + nickname.value = ''; + avatar.value = ''; + roles.value = []; + perms.value = []; + } + return { + Token, + nickname, + avatar, + roles, + perms, + login, + getInfo, + logout, + resetToken, + badgeval, + VITE_APP_BASE_API, + VITE_APP_BASE_IMG_API, + VITE_WS_BASE_API + }; +}); + +// 非setup +export function useUserStoreHook() { + return useUserStore(store); +} diff --git a/frontend/src/styles/element-plus.scss b/frontend/src/styles/element-plus.scss new file mode 100644 index 0000000..f84ca5e --- /dev/null +++ b/frontend/src/styles/element-plus.scss @@ -0,0 +1,78 @@ +:root { + // 这里可以设置你自定义的颜色变量 + // 这个是element主要按钮:active的颜色，当主题更改后此变量的值也随之更改 + --el-color-primary-dark: #0d84ff; + // --el-font-size-base: 16px !important; +} + +.el-button--large, +.el-input--large, +.el-table--large, +.el-form--large, +.el-select__tags-text { + font-size: 16px !important; + + .el-form-item__label { + font-size: 16px !important; + } + + --el-font-size-base: 16px !important; +} + + +// 覆盖 element-plus 的样式 +.el-breadcrumb__inner, +.el-breadcrumb__inner a { + font-weight: 400 !important; +} + +.el-upload { + input[type='file'] { + display: none !important; + } +} + +.el-upload__input { + display: none; +} + +// dropdown +.el-dropdown-menu { + a { + display: block; + } +} + +// to fix el-date-picker css style +.el-range-separator { + box-sizing: content-box; +} + +// card 的header统一高度 +.el-card__header { + height: 60px !important; +} + +// 表格表头和表体未对齐 +.el-table__header col[name='gutter'] { + display: table-cell !important; +} + +.el-dialog { + border-radius: 5px !important; +} + +.el-dialog__header { + padding-top: 14px !important; + background-color: #f2f2f2; + margin: 0 !important; + border-radius: 5px 5px 0 0 !important; +} + +.el-dialog__headerbtn { + top: 0 !important; +} + +.el-table__body tr.current-row td { + background-color: #a9d9ff82 !important; +} \ No newline at end of file diff --git a/frontend/src/styles/index.scss b/frontend/src/styles/index.scss new file mode 100644 index 0000000..57cbd56 --- /dev/null +++ b/frontend/src/styles/index.scss @@ -0,0 +1,56 @@ +@use 'src/styles/element-plus'; +@use './sidebar.scss'; +@use './tailwind.scss'; +@use './peeling.scss'; +html,body,#app{ + height: 100%; +} + +// main-container global css +.app-container { + padding: 20px; +} + +.search{ + padding: 18px 0 0 10px; + margin-bottom: 10px; + border-radius: 5px; + border: 1px solid #ddd; + box-shadow: 6px 2px 6px #CCC; +} + +svg{ + display: inline-block; +} +.el-dialog { + display: flex !important; + flex-direction: column !important; + margin: auto !important; + position: absolute !important; + top: 0 ; + left: 0 ; + right: 0; + bottom: 0; + height: max-content; +} +.el-dialog__body { + padding: 20px !important; +} + +.echartsbox .el-dialog__body{ + background: #ededed !important; +} + +.gaojign.el-dialog{ + // background: url(@/assets/images/jg_bj.png)!important; +} +.el-table--default .cell{ + padding: 0px 5px !important; + } + + .el-message{ + z-index: 999999 !important; + } + .img-preview .image-view { + max-height: 100%; + } \ No newline at end of file diff --git a/frontend/src/styles/mixin.scss b/frontend/src/styles/mixin.scss new file mode 100644 index 0000000..3ca7168 --- /dev/null +++ b/frontend/src/styles/mixin.scss @@ -0,0 +1,28 @@ +@mixin clearfix { + &:after { + content: ''; + display: table; + clear: both; + } +} + +@mixin scrollBar { + &::-webkit-scrollbar-track-piece { + background: #d3dce6; + } + + &::-webkit-scrollbar { + width: 6px; + } + + &::-webkit-scrollbar-thumb { + background: #99a9bf; + border-radius: 20px; + } +} + +@mixin relative { + position: relative; + width: 100%; + height: 100%; +} diff --git a/frontend/src/styles/peeling.scss b/frontend/src/styles/peeling.scss new file mode 100644 index 0000000..d3814f1 --- /dev/null +++ b/frontend/src/styles/peeling.scss @@ -0,0 +1,397 @@ +html.dark .fixed-header { + /* 自定义深色背景颜色 */ + background: var(--el-table-tr-bg-color); +} + +.el-menu--popup { + padding: 6px 0 !important; +} + +.el-menu-item-group__title { + color: var(--el-text-color-secondary); +} + +.AlgorithmsAndModels { + width: 100%; + height: calc(100vh - 124px); + background-color: #ffffff; + border-radius: 5px; + padding: 20px 10px; + box-shadow: 0px 0px 10px rgb(219 225 236); +} + +.el-menu img { + filter: invert(100%); +} + +.FullscreenClass img { + filter: invert(100%); +} + +html.dark .switchMenu:hover { + background: #002144 !important; +} + +html.dark #app .sidebar-container { + background: #202020; +} + +html.dark #app .sidebar-container .el-menu { + --el-menu-text-color: #ffffff !important; + --el-menu-hover-text-color: #ffffff !important; + --el-menu-bg-color: var(--el-table-tr-bg-color) !important; + --el-menu-hover-bg-color: #ffffff !important; + --el-menu-active-color: #409eff; +} + +html.dark .el-input { + --el-input-text-color: #ffffff; +} + +html.dark #app .sidebar-container .el-sub-menu .el-menu-item { + background-color: var(--el-table-tr-bg-color) !important; +} + +.navboxbg { + background: #f0f2f5 !important; +} + +html.dark .navboxbg { + background: var(--el-table-tr-bg-color) !important; +} + +html.dark .app-main { + background: var(--el-table-tr-bg-color); +} + +html.dark .detail-box { + background: #202020; +} + +html.dark .el-table__header-wrapper th { + background: var(--el-table-tr-bg-color) !important; + color: #ffffff !important; +} + +html.dark .AlgorithmsAndModels { + background-color: #202020; + box-shadow: 0px 0px 3px rgb(54, 54, 54); +} + +html.dark .tags-item-menu { + background: rgb(54, 54, 54); + box-shadow: 2px 2px 3px 0 rgba(0, 0, 0, 0.3); +} + +html.dark .tags-container .tags-item { + background-color: #202020; +} + +html.dark #app .sidebar-container .el-menu-item.is-active { + background: #002144 !important; +} + +html.dark #app .el-menu-item { + background: transparent !important; +} + +html.dark #app .el-sub-menu { + background: transparent !important; +} + +html.dark #app .sidebar-container .el-sub-menu__title:hover { + background: #002144 !important; +} + +html.dark #app .sidebar-container .submenu-title-noDropdown:hover { + background: transparent !important; +} + +html.dark #app .AlgorithmsAndModels .el-tabs__item { + color: #fff !important; +} + +html.dark .AlgorithmsAndModels .el-tabs__item.is-active { + color: #409eff !important; +} + +html.dark .el-dialog__header { + background: #141414 !important; +} + +html.dark .tags-item-menu li:hover { + background: #409eff; +} + +html.dark .el-menu { + background-color: var(--el-table-tr-bg-color); +} + +html.dark .el-menu-item { + color: #ffffff; +} + +html.dark .el-menu-item:hover { + background: #002144 !important; + color: #ffffff; +} + +html.dark .el-popper.is-dark .el-popper__arrow::before { + background: var(--el-bg-color-overlay); + border: 1px solid var(--el-border-color-light); +} + +html.dark .el-popper.is-dark { + background: var(--el-bg-color-overlay); + border: 1px solid var(--el-border-color-light); + color: #ffffff; +} + +html.dark .el-dialog { + border: 1px solid #505050; +} + +html.dark .linshihome { + color: #ffffff; +} + +html.dark #silderLeft { + background: #202020 !important; + box-shadow: transparent !important; +} + +html.dark .silderRight { + background: #202020 !important; + box-shadow: transparent !important; +} + +html.dark .mainBody { + background: #202020 !important; + box-shadow: transparent !important; + border: #ffffff2a 2px solid !important; +} + +html.dark .mainBody:hover { + background: #202020 !important; + box-shadow: transparent !important; +} +html.dark .line{ + border-bottom:1px solid #ffffff42 ; +} + +html.dark .mainBody .mainBody-bottom .mainBody-bottom1 { + color: #fff !important; +} + +html.dark .mainBody .mainBody-bottom .mainBody-bottom3 .el-button--primary.is-plain { + --el-button-bg-color: #202020 !important; + --el-button-hover-bg-color: #202020 !important; +} + +html.dark .el-drawer__header { + background: #202020 !important; + color: #f0f2f5 !important; +} + +html.dark .taitle_blue .taitle_blue2 { + color: #f0f2f5 !important; +} + +html.dark .contron_two .contron_four { + color: #fff !important; +} + +html.dark .predictioneffect-box .predictioneffect-left .predictioneffect-imgbox { + background: #202020; +} + +html.dark .predictioneffect-box .predictioneffect-right { + background: #202020; +} + +html.dark .predictioneffect-box .predictioneffect-right .resultTitle { + color: #fff; +} + +html.dark .silderRight1 { + background: #202020 !important; +} + +html.dark .button_two div { + color: #fff !important; + border: 1px solid #dcdfe666; +} + +html.dark .button_two .button_bian { + color: #409EFF !important; + border: 1px solid #dcdfe666 !important; + background: #409eff70 !important; +} +html.dark .titletwo .title_text3{ + color: #fff; +} +html.dark .contron_five{ + color: #409EFF !important; + border: 1px solid #dcdfe666 !important; + background: #40a0ff00 !important; +} +html.dark .el-tabs__item.is-active { + color: #fff !important; +} + +html.dark .treetext .titletext1 { + color: #fff !important; +} +html.dark .silderRight2{ + background: #202020; +} +html.dark .tags-container .tags-item { + border: 1px solid var(--el-input-border-color, var(--el-border-color)); + box-shadow: 0px 0px 3px var(--el-input-border-color, var(--el-border-color)); +} +html.dark .mainBody .mainBody-bottom .mainBody-bottom2 div{ + background-color: rgb(242 242 242 / 5%) !important; + color: #ffffffb0; + border: 1px solid #454545; +} +html.dark .datasetDialog2Text { + color: #ffffff; +} +html.dark .predictioneffect-box .predictioneffect-right{ + border: 1px solid rgb(240 240 240 / 30%); + +} +html.dark .predictioneffect-box .predictioneffect-right .resultTitle{ + border-bottom: 1px solid rgb(240 240 240 / 30%); +} +html.dark .predictioneffect-box .predictioneffect-left{ + border: 1px solid rgb(240 240 240 / 30%); +} +html.dark .titletwo .title_text{ + color: #fff !important; + font-size: 14px !important; +} +html.dark .rightpup{ + background:url(@/assets/algorithmimg/u12361.png) !important; + background-size:100% 100% !important; + +} +html.dark .leftpup{ + background:url(@/assets/algorithmimg/122.png) !important; + background-size:100% 100% !important; +} +html.dark .puptont_text{ + color: #ffffffc2 !important; +} +html.dark .dataset { + .zip { + color: #ffffff; + } + + .img2 { + content: url('@/assets/new/del2.svg'); + } +} + +html.dark .faulttemplate-box { + background-color: transparent; + + #silderLeft { + background-color: #141414; + box-shadow: 0px 0px 3px var(--el-input-border-color, var(--el-border-color)); + } + + .silderRight { + background-color: #141414; + box-shadow: 0px 0px 3px var(--el-input-border-color, var(--el-border-color)); + } + + .organization_list { + background-color: #383737; + } +} + +html.dark .dimension-content { + background-color: #202020; + + .dimension-top-left { + color: #ffffff !important; + } + + .operate-icon::after { + background: #696969 !important; + } + + .el-pagination { + background-color: #202020; + } + + .dimension-page-datas { + // background-color: #303030; + border-color: #303030 !important; + color: #ffffff !important; + } + + .pageborder { + background-color: #000 !important; + } + + .dimension-typename { + color: #ffffff !important; + } + + .dimension-typecount { + color: #ffffff !important; + } + + .labelbackground { + background-color: #202020 !important; + } + .el-checkbox__label { + color: #ffffff !important; + } +} +html.dark .el-progress-bar__innerText { + color: #ffffff !important; +} +html.dark .dimension-content { + .dimension-bottom-right-img { + content: url('@/assets/bzimages/showselect.png'); + } + .dimension-bottom-right-img1 { + content: url('@/assets/bzimages/hiddenselect.png'); + } +} +html.dark .dimension-detail { + .frameselect { + content: url('@/assets/bzimages/select-check.png'); + } +} +html.dark .tspagesize { + border:1px solid #4c4d4f; +} + +.el-button--primary { + // background-color: #409eff !important; +} +html.dark .tspagebtn { + .el-button { + border-color: #8d9095; + } + .el-button.is-disabled, .el-button.is-disabled:focus, .el-button.is-disabled:hover { + border-color: #8d9095; + border: none; + background-color: #262727; + } +} +html.dark .operate-icon9 { + color: #ffffff !important; +} + +html.dark #map { + background: #2a2a2a !important; +} + +html.dark .personalCenter { + background: #2a2a2a !important; +} diff --git a/frontend/src/styles/sidebar.scss b/frontend/src/styles/sidebar.scss new file mode 100644 index 0000000..e7ae077 --- /dev/null +++ b/frontend/src/styles/sidebar.scss @@ -0,0 +1,261 @@ +@use './variables.module' as *; + +svg { + vertical-align: text-bottom !important; +} +#app { + .main-container { + // min-height: 100%; + transition: margin-left 0.28s; + margin-left: $sideBarWidth; + position: relative; + } + + .sidebar-container { + transition: width 0.28s; + width: $sideBarWidth !important; + background-color: $menuBg; + height: calc(100vh - 60px); + padding-top: 15px; + position: absolute; + top: 60px; + bottom: 0; + left: 0; + z-index: 98; + overflow: hidden; + + // reset element-ui css + .horizontal-collapse-transition { + transition: 0s width ease-in-out, 0s padding-left ease-in-out, + 0s padding-right ease-in-out; + } + + .scrollbar-wrapper { + overflow-x: hidden !important; + } + + .el-scrollbar__bar.is-vertical { + right: 0px; + } + + .el-scrollbar { + height: 100%; + } + + &.has-logo { + .el-scrollbar { + height: calc(100% - 50px); + } + } + + .is-horizontal { + display: none; + } + + + .svg-icon { + margin-right: 16px; + } + + .sub-el-icon { + margin-right: 12px; + margin-left: -2px; + } + + .el-menu { + border: none; + height: 100%; + width: 100% !important; + } + + // menu hover + .submenu-title-noDropdown, + .el-sub-menu__title { + &:hover { + color: $menuHover !important; + } + } + + // .is-active > .el-sub-menu__title { + // color: $subMenuActiveText !important; + // } + + & .nest-menu .el-sub-menu > .el-sub-menu__title, + & .el-sub-menu, .el-menu-item { + // min-width: $sideBarWidth !important; + background-color: $subMenuBg !important; + + &:hover { + color: $subMenuHover !important; + } + } + .el-menu-item.is-active { + border-right: 3px solid $subMenuHover; + background: #e8f3ff !important; + } + .el-sub-menu__title{ + &:hover { + background: #ffffff !important; + color: $subMenuHover !important; + } + } + } + + .hideSidebar { + .sidebar-container { + width: 54px !important; + .svg-icon { + margin-right: 0px; + } + } + + .main-container { + margin-left: 54px; + } + + .submenu-title-noDropdown { + padding: 0 !important; + position: relative; + + .el-tooltip { + padding: 0 !important; + + .svg-icon { + margin-left: 20px; + } + + .sub-el-icon { + margin-left: 19px; + } + } + } + + .el-sub-menu { + overflow: hidden; + + & > .el-sub-menu__title { + padding: 0 !important; + + .svg-icon { + margin-left: 20px; + } + + .sub-el-icon { + margin-left: 19px; + } + + .el-sub-menu__icon-arrow { + display: none; + } + } + + } + + .el-menu--collapse { + .el-sub-menu { + & > .el-sub-menu__title { + & > span { + height: 0; + width: 0; + overflow: hidden; + visibility: hidden; + display: inline-block; + } + } + } + } + } + + .el-menu--collapse .el-menu .el-sub-menu { + min-width: $sideBarWidth !important; + } + + // mobile responsive + .mobile { + .main-container { + margin-left: 0px; + } + + .sidebar-container { + transition: transform 0.28s; + width: $sideBarWidth !important; + } + + &.hideSidebar { + .sidebar-container { + pointer-events: none; + transition-duration: 0.3s; + transform: translate3d(-$sideBarWidth, 0, 0); + } + } + } + + .withoutAnimation { + .main-container, + .sidebar-container { + transition: none; + } + } +} + +// when menu collapsed +.el-menu--vertical { + & > .el-menu { + .svg-icon { + margin-right: 16px; + } + .sub-el-icon { + margin-right: 12px; + margin-left: -2px; + } + } + + .nest-menu .el-sub-menu > .el-sub-menu__title, + .el-menu-item { + &:hover { + background-color: #ffffff; + // you can use $subMenuHover + color: $menuHover !important; + } + } + + // the scroll bar appears when the subMenu is too long + > .el-menu--popup { + max-height: 100vh; + overflow-y: auto; + + &::-webkit-scrollbar-track-piece { + background: #d3dce6; + } + + &::-webkit-scrollbar { + width: 6px; + } + + &::-webkit-scrollbar-thumb { + background: #99a9bf; + border-radius: 20px; + } + } +} +body[layout="mix"] { + + .horizontal-header{ + .el-menu-item{ + height: 50px!important; + line-height: 50px!important; + } + + .el-sub-menu__title { + background-color: #001529!important; + height: 50px!important; + } + } + .horizontal-header-right>div { + color: #FFF; + } + .svg-icon{ + margin-right: 16px; + } + +} diff --git a/frontend/src/styles/tailwind.scss b/frontend/src/styles/tailwind.scss new file mode 100644 index 0000000..9bb37ee --- /dev/null +++ b/frontend/src/styles/tailwind.scss @@ -0,0 +1,4 @@ + +@tailwind base; +@tailwind components; +@tailwind utilities; diff --git a/frontend/src/styles/variables.module.css b/frontend/src/styles/variables.module.css new file mode 100644 index 0000000..518a1f9 --- /dev/null +++ b/frontend/src/styles/variables.module.css @@ -0,0 +1,11 @@ +:export { + menuText: #409eff; + menuActiveText: #409eff; + subMenuActiveText: #409eff; + menuBg: #ffffff; + menuHover: #409eff; + subMenuBg: #ffffff; + subMenuHover: #409eff; + sideBarWidth: 210px; +} +/*# sourceMappingURL=variables.module.css.map */ \ No newline at end of file diff --git a/frontend/src/styles/variables.module.css.map b/frontend/src/styles/variables.module.css.map new file mode 100644 index 0000000..b95fb87 --- /dev/null +++ b/frontend/src/styles/variables.module.css.map @@ -0,0 +1,9 @@ +{ + "version": 3, + "mappings": "AAeA,AAAA,OAAO,CAAC;EACN,QAAQ,EAfC,OAAO;EAgBhB,cAAc,EAfC,OAAO;EAgBtB,iBAAiB,EAfC,OAAO;EAgBzB,MAAM,EAdC,OAAO;EAed,SAAS,EAdC,OAAO;EAejB,SAAS,EAbC,OAAO;EAcjB,YAAY,EAbC,OAAO;EAcpB,YAAY,EAZC,KAAK;CAanB", + "sources": [ + "variables.module.scss" + ], + "names": [], + "file": "variables.module.css" +} \ No newline at end of file diff --git a/frontend/src/styles/variables.module.scss b/frontend/src/styles/variables.module.scss new file mode 100644 index 0000000..0376220 --- /dev/null +++ b/frontend/src/styles/variables.module.scss @@ -0,0 +1,25 @@ +// sidebar +$menuText: #505050; +$menuActiveText: #409eff; +$subMenuActiveText: #409eff; + +$menuBg: #ffffff; +$menuHover: #409eff; + +$subMenuBg: #ffffff; +$subMenuHover: #409eff; + +$sideBarWidth: 210px; + +// the :export directive is the magic sauce for webpack +// https://www.bluematador.com/blog/how-to-share-variables-between-js-and-sass +:export { + menuText: $menuText; + menuActiveText: $menuActiveText; + subMenuActiveText: $subMenuActiveText; + menuBg: $menuBg; + menuHover: $menuHover; + subMenuBg: $subMenuBg; + subMenuHover: $subMenuHover; + sideBarWidth: $sideBarWidth; +} diff --git a/frontend/src/utils/auth.ts b/frontend/src/utils/auth.ts new file mode 100644 index 0000000..c6adc52 --- /dev/null +++ b/frontend/src/utils/auth.ts @@ -0,0 +1,15 @@ +import Cookies from 'js-cookie'; + +const TokenKey = 'token'; + +export function getToken() { + return Cookies.get(TokenKey); +} + +export function setToken(token: any) { + Cookies.set(TokenKey, token); +} + +export function removeToken() { + return Cookies.remove(TokenKey); +} diff --git a/frontend/src/utils/filter.ts b/frontend/src/utils/filter.ts new file mode 100644 index 0000000..38c28bc --- /dev/null +++ b/frontend/src/utils/filter.ts @@ -0,0 +1,80 @@ +/** + * Show plural label if time is plural number + * @param {number} time + * @param {string} label + * @return {string} + */ +function pluralize(time: number, label: string) { + if (time === 1) { + return time + label; + } + return time + label + 's'; +} + +/** + * @param {number} time + */ +export function timeAgo(time: number) { + const between = Date.now() / 1000 - Number(time); + if (between < 3600) { + return pluralize(~~(between / 60), ' minute'); + } else if (between < 86400) { + return pluralize(~~(between / 3600), ' hour'); + } else { + return pluralize(~~(between / 86400), ' day'); + } +} + +/** + * Number formatting + * like 10000 => 10k + * @param {number} num + * @param {number} digits + */ +export function numberFormatter(num: number, digits: number) { + const si = [ + { value: 1e18, symbol: 'E' }, + { value: 1e15, symbol: 'P' }, + { value: 1e12, symbol: 'T' }, + { value: 1e9, symbol: 'G' }, + { value: 1e6, symbol: 'M' }, + { value: 1e3, symbol: 'k' } + ]; + for (let i = 0; i < si.length; i++) { + if (num >= si[i].value) { + return ( + (num / si[i].value) + .toFixed(digits) + .replace(/\.0+$|(\.[0-9]*[1-9])0+$/, '$1') + si[i].symbol + ); + } + } + return num.toString(); +} + +/** + * 10000 => "10,000" + * @param {number} num + */ +export function toThousandFilter(num: number) { + return (+num || 0) + .toString() + .replace(/^-?\d+/g, m => m.replace(/(?=(?!\b)(\d{3})+$)/g, ',')); +} + +/** + * Upper case first char + * @param {String} string + */ +export function uppercaseFirst(string: string) { + return string.charAt(0).toUpperCase() + string.slice(1); +} + +/** + * 金额转换（分->元） + * 100 => 1 + * @param {number} num + */ +export function moneyFormatter(num: number) { + return '¥' + (isNaN(num) ? 0.0 : parseFloat((num / 100).toFixed(2))); +} diff --git a/frontend/src/utils/i18n.ts b/frontend/src/utils/i18n.ts new file mode 100644 index 0000000..b95552a --- /dev/null +++ b/frontend/src/utils/i18n.ts @@ -0,0 +1,12 @@ +// translate router.meta.title, be used in breadcrumb sidebar tagsview +import i18n from '@/lang/index'; + +export function generateTitle(title: any) { + // 判断是否存在国际化配置，如果没有原生返回 + const hasKey = i18n.global.te('route.' + title); + if (hasKey) { + const translatedTitle = i18n.global.t('route.' + title); + return translatedTitle; + } + return title; +} diff --git a/frontend/src/utils/index.ts b/frontend/src/utils/index.ts new file mode 100644 index 0000000..4607b16 --- /dev/null +++ b/frontend/src/utils/index.ts @@ -0,0 +1,97 @@ +/** + * Check if an element has a class + * @param {HTMLElement} elm + * @param {string} cls + * @returns {boolean} + */ +export function hasClass(ele: HTMLElement, cls: string) { + return !!ele.className.match(new RegExp('(\\s|^)' + cls + '(\\s|$)')); +} + +/** + * Add class to element + * @param {HTMLElement} elm + * @param {string} cls + */ +export function addClass(ele: HTMLElement, cls: string) { + if (!hasClass(ele, cls)) ele.className += ' ' + cls; +} + +/** + * Remove class from element + * @param {HTMLElement} elm + * @param {string} cls + */ +export function removeClass(ele: HTMLElement, cls: string) { + if (hasClass(ele, cls)) { + const reg = new RegExp('(\\s|^)' + cls + '(\\s|$)'); + ele.className = ele.className.replace(reg, ' '); + } +} + +export function mix(color1: string, color2: string, weight: number) { + weight = Math.max(Math.min(Number(weight), 1), 0); + const r1 = parseInt(color1.substring(1, 3), 16); + const g1 = parseInt(color1.substring(3, 5), 16); + const b1 = parseInt(color1.substring(5, 7), 16); + const r2 = parseInt(color2.substring(1, 3), 16); + const g2 = parseInt(color2.substring(3, 5), 16); + const b2 = parseInt(color2.substring(5, 7), 16); + const r = Math.round(r1 * (1 - weight) + r2 * weight); + const g = Math.round(g1 * (1 - weight) + g2 * weight); + const b = Math.round(b1 * (1 - weight) + b2 * weight); + const rStr = ('0' + (r || 0).toString(16)).slice(-2); + const gStr = ('0' + (g || 0).toString(16)).slice(-2); + const bStr = ('0' + (b || 0).toString(16)).slice(-2); + return '#' + rStr + gStr + bStr; +} + +export function parseTime(time :any, cFormat :any) { + if (arguments.length === 0) { + return null + } + const format = cFormat || '{y}-{m}-{d} {h}:{i}:{s}' + let date + if (typeof time === 'undefined' || time === null || time === 'null') { + return '' + } else if (typeof time === 'object') { + date = time + } else { + if ((typeof time === 'string') && (/^[0-9]+$/.test(time))) { + time = parseInt(time) + } + if ((typeof time === 'number') && (time.toString().length === 10)) { + time = time * 1000 + } + date = new Date(time) + } + const formatObj:any = { + y: date.getFullYear(), + m: date.getMonth() + 1, + d: date.getDate(), + h: date.getHours(), + i: date.getMinutes(), + s: date.getSeconds(), + a: date.getDay() + } + const time_str = format.replace(/{(y|m|d|h|i|s|a)+}/g, (result :any, key :any) => { + let value : any = formatObj[key] + if (key === 'a') { return ['日', '一', '二', '三', '四', '五', '六'][value ] } + if (result.length > 0 && value < 10) { + value = '0' + value + } + return value || 0 + }) + return time_str +} +export function downloadFile(obj :any, name :any, suffix :any) { + const url = window.URL.createObjectURL(new Blob([obj])) + const link = document.createElement('a') + link.style.display = 'none' + link.href = url + const fileName = parseTime(new Date(),'') + '-' + name + '.' + suffix + link.setAttribute('download', fileName) + document.body.appendChild(link) + link.click() + document.body.removeChild(link) +} \ No newline at end of file diff --git a/frontend/src/utils/localStorage.ts b/frontend/src/utils/localStorage.ts new file mode 100644 index 0000000..d5628e5 --- /dev/null +++ b/frontend/src/utils/localStorage.ts @@ -0,0 +1,53 @@ +/** + * window.localStorage 浏览器永久缓存 + */ +export const localStorage = { + // 设置永久缓存 + set(key: string, val: any) { + window.localStorage.setItem(key, JSON.stringify(val)); + }, + // 获取永久缓存 + get(key: string) { + const json: any = window.localStorage.getItem(key); + return JSON.parse(json); + }, + // 移除永久缓存 + remove(key: string) { + window.localStorage.removeItem(key); + }, + // 移除全部永久缓存 + clear() { + window.localStorage.clear(); + } +}; + +// 侧边栏状态(显示/隐藏) +const SidebarStatusKey = 'sidebarStatus'; +export function getSidebarStatus() { + return localStorage.get(SidebarStatusKey); +} + +export function setSidebarStatus(sidebarStatus: string) { + localStorage.set(SidebarStatusKey, sidebarStatus); +} +// 布局大小 +const SizeKey = 'size'; + +export function getSize() { + return localStorage.get(SizeKey); +} + +export function setSize(size: string) { + localStorage.set(SizeKey, size); +} + +// 语言 +const LanguageKey = 'language'; + +export function getLanguage() { + return localStorage.get(LanguageKey); +} + +export function setLanguage(language: string) { + localStorage.set(LanguageKey, language); +} diff --git a/frontend/src/utils/request.ts b/frontend/src/utils/request.ts new file mode 100644 index 0000000..5ce11a5 --- /dev/null +++ b/frontend/src/utils/request.ts @@ -0,0 +1,127 @@ +import axios, { AxiosRequestConfig, AxiosResponse } from 'axios'; +import { ref } from 'vue'; +import { getToken, setToken, removeToken } from '@/utils/auth'; +import { ElMessage, ElMessageBox } from 'element-plus'; +import { useUserStoreHook } from '@/store/modules/user'; +import { loginRefresh } from '@/api/auth'; +import Cookies from 'js-cookie'; + +// 创建 axios 实例 +const service = axios.create({ + baseURL: import.meta.env.MODE == 'development' ? import.meta.env.VITE_APP_BASE_API : window.webConfig.VITE_APP_BASE_API, + timeout: 600000, + headers: { 'Content-Type': 'application/json;charset=utf-8' } +}); + +const Token = ref(getToken() || ''); +let refreshPromise: Promise | null = null; + +function refreshToken() { + if (refreshPromise) return refreshPromise; + const refresh = Cookies.get('refresh_token'); + refreshPromise = loginRefresh({ refresh }) + .then((response: any) => { + const access = response.access; + Token.value = access; + setToken(access); + const user = useUserStoreHook(); + user.Token = access; + return access; + }) + .finally(() => { + refreshPromise = null; + }); + return refreshPromise; +} +// 请求拦截器 +service.interceptors.request.use( + (config: any) => { + if (!config.headers) { + throw new Error( + `Expected 'config' and 'config.headers' not to be undefined` + ); + } + const token = getToken(); + if (token) config.headers.Authorization = 'Bearer ' + token; + return config; + }, + (error: any) => { + return Promise.reject(error); + } +); + +// 响应拦截器 +service.interceptors.response.use( + (response: any) => { + const { status, msg } = response; + if (response.data instanceof Blob || response.data instanceof ArrayBuffer) { + return response.data; + } + if (status === 200 || status === 201) { + if (response.data.code == 401) { + ElMessage({ + message: '用户名或密码错误，请重试！', + type: 'error' + }); + return; + }else if(response.data.code == 1){ + ElMessage({ + message: response.data.error, + type: 'error' + }); + return; + } + return response.data; + } else { + ElMessage({ + message: msg || '系统出错', + type: 'error' + }); + return Promise.reject(new Error(msg || 'Error')); + } + }, + (error: any) => { + console.log(error); + const status = error?.response?.status; + const msg = error?.response?.data?.msg || error?.response?.data?.error || error?.message; + const config = error?.config; + + if (status === 401 && config && !config._retry) { + config._retry = true; + return refreshToken() + .then((access: string) => { + config.headers = config.headers || {}; + config.headers.Authorization = 'Bearer ' + access; + return service(config); + }) + .catch((refreshError: any) => { + removeToken(); + localStorage.clear(); + window.location.href = '/'; + return Promise.reject(refreshError); + }); + } + + if (status === 403) { + ElMessageBox.confirm('当前页面已失效，请重新登录', '提示', { + confirmButtonText: 'OK', + type: 'warning' + }).then(() => { + localStorage.clear(); + window.location.href = '/'; + }); + return Promise.reject(error.message); + } + + if (error?.response?.data) { + ElMessage({ + message: msg || '当前页面已失效', + type: 'error' + }); + } + return Promise.reject(error.message); + } +); + +// 导出 axios 实例 +export default service; diff --git a/frontend/src/utils/resize.ts b/frontend/src/utils/resize.ts new file mode 100644 index 0000000..343bb0f --- /dev/null +++ b/frontend/src/utils/resize.ts @@ -0,0 +1,73 @@ +import { ref } from 'vue'; +export default function () { + const chart = ref(); + const sidebarElm = ref(); + + const chartResizeHandler = () => { + if (chart.value) { + chart.value.resize(); + } + }; + + const sidebarResizeHandler = (e: TransitionEvent) => { + if (e.propertyName === 'width') { + chartResizeHandler(); + } + }; + + const initResizeEvent = () => { + window.addEventListener('resize', chartResizeHandler, {passive:true}); + }; + + const destroyResizeEvent = () => { + window.removeEventListener('resize', chartResizeHandler); + }; + + const initSidebarResizeEvent = () => { + sidebarElm.value = document.getElementsByClassName('sidebar-container')[0]; + if (sidebarElm.value) { + sidebarElm.value.addEventListener( + 'transitionend', + sidebarResizeHandler as EventListener, + {passive:true} + ); + } + }; + + const destroySidebarResizeEvent = () => { + if (sidebarElm.value) { + sidebarElm.value.removeEventListener( + 'transitionend', + sidebarResizeHandler as EventListener + ); + } + }; + + const mounted = () => { + initResizeEvent(); + initSidebarResizeEvent(); + }; + + const beforeDestroy = () => { + destroyResizeEvent(); + destroySidebarResizeEvent(); + }; + + const activated = () => { + initResizeEvent(); + initSidebarResizeEvent(); + }; + + const deactivated = () => { + destroyResizeEvent(); + destroySidebarResizeEvent(); + }; + + return { + chart, + mounted, + beforeDestroy, + activated, + deactivated + }; +} diff --git a/frontend/src/utils/rsaEncrypt.ts b/frontend/src/utils/rsaEncrypt.ts new file mode 100644 index 0000000..00590bb --- /dev/null +++ b/frontend/src/utils/rsaEncrypt.ts @@ -0,0 +1,29 @@ +import JSEncrypt from 'jsencrypt/bin/jsencrypt.min' +// 密钥对生成 http://web.chacuo.net/netrsakeypair + +const publicKey = 'MFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBANL378k3RiZHWx5AfJqdH9xRNBmD9wGD\n' + + '2iRe41HdTNF8RUhNnHit5NpMNtGL0NPTSSpPjjI1kJfVorRvaQerUgkCAwEAAQ==' + +const privateKey = 'MIIBUwIBADANBgkqhkiG9w0BAQEFAASCAT0wggE5AgEAAkEA0vfvyTdGJkdbHkB8\n' + + 'mp0f3FE0GYP3AYPaJF7jUd1M0XxFSE2ceK3k2kw20YvQ09NJKk+OMjWQl9WitG9p\n' + + 'B6tSCQIDAQABAkA2SimBrWC2/wvauBuYqjCFwLvYiRYqZKThUS3MZlebXJiLB+Ue\n' + + '/gUifAAKIg1avttUZsHBHrop4qfJCwAI0+YRAiEA+W3NK/RaXtnRqmoUUkb59zsZ\n' + + 'UBLpvZgQPfj1MhyHDz0CIQDYhsAhPJ3mgS64NbUZmGWuuNKp5coY2GIj/zYDMJp6\n' + + 'vQIgUueLFXv/eZ1ekgz2Oi67MNCk5jeTF2BurZqNLR3MSmUCIFT3Q6uHMtsB9Eha\n' + + '4u7hS31tj1UWE+D+ADzp59MGnoftAiBeHT7gDMuqeJHPL4b+kC+gzV4FGTfhR9q3\n' + + 'tTbklZkD2A==' + +// 加密 +export function encrypt(txt:string) { + const encryptor = new JSEncrypt() + encryptor.setPublicKey(publicKey) // 设置公钥 + return encryptor.encrypt(txt) // 对需要加密的数据进行加密 +} + +// 解密 +export function decrypt(txt:string) { + const encryptor = new JSEncrypt() + encryptor.setPrivateKey(privateKey) + return encryptor.decrypt(txt) +} + diff --git a/frontend/src/utils/scroll-to.ts b/frontend/src/utils/scroll-to.ts new file mode 100644 index 0000000..591e3ec --- /dev/null +++ b/frontend/src/utils/scroll-to.ts @@ -0,0 +1,69 @@ +const easeInOutQuad = (t: number, b: number, c: number, d: number) => { + t /= d / 2; + if (t < 1) { + return (c / 2) * t * t + b; + } + t--; + return (-c / 2) * (t * (t - 2) - 1) + b; +}; + +// requestAnimationFrame for Smart Animating http://goo.gl/sx5sts +const requestAnimFrame = (function () { + return ( + window.requestAnimationFrame || + (window as any).webkitRequestAnimationFrame || + (window as any).mozRequestAnimationFrame || + function (callback) { + window.setTimeout(callback, 1000 / 60); + } + ); +})(); + +/** + * Because it's so fucking difficult to detect the scrolling element, just move them all + * @param {number} amount + */ +const move = (amount: number) => { + document.documentElement.scrollTop = amount; + (document.body.parentNode as HTMLElement).scrollTop = amount; + document.body.scrollTop = amount; +}; + +const position = () => { + return ( + document.documentElement.scrollTop || + (document.body.parentNode as HTMLElement).scrollTop || + document.body.scrollTop + ); +}; + +/** + * @param {number} to + * @param {number} duration + * @param {Function} callback + */ +export const scrollTo = (to: number, duration: number, callback?: any) => { + const start = position(); + const change = to - start; + const increment = 20; + let currentTime = 0; + duration = typeof duration === 'undefined' ? 500 : duration; + const animateScroll = function () { + // increment the time + currentTime += increment; + // find the value with the quadratic in-out easing function + const val = easeInOutQuad(currentTime, start, change, duration); + // move the document.body + move(val); + // do the animation unless its over + if (currentTime < duration) { + requestAnimFrame(animateScroll); + } else { + if (callback && typeof callback === 'function') { + // the animation is done so lets callback + callback(); + } + } + }; + animateScroll(); +}; diff --git a/frontend/src/utils/sessionStorage.ts b/frontend/src/utils/sessionStorage.ts new file mode 100644 index 0000000..fae9a21 --- /dev/null +++ b/frontend/src/utils/sessionStorage.ts @@ -0,0 +1,22 @@ +/** + * window.sessionStorage 浏览器临时缓存 + */ +export const sessionStorage = { + // 设置临时缓存 + set(key: string, val: any) { + window.sessionStorage.setItem(key, JSON.stringify(val)); + }, + // 获取临时缓存 + get(key: string) { + const json: any = window.sessionStorage.getItem(key); + return JSON.parse(json); + }, + // 移除临时缓存 + remove(key: string) { + window.sessionStorage.removeItem(key); + }, + // 移除全部临时缓存 + clear() { + window.sessionStorage.clear(); + } +}; diff --git a/frontend/src/utils/sm4.ts b/frontend/src/utils/sm4.ts new file mode 100644 index 0000000..77a82f9 --- /dev/null +++ b/frontend/src/utils/sm4.ts @@ -0,0 +1,22 @@ + +// import { sm4 } from 'sm-crypto' +import { sm4 } from 'gm-crypt' +const publicKey = 'a8b502da70405867' +const SM4Data = { + key: publicKey, + mode:"ecb", + cipherType: 'base64', +} + +// 加密 +export function encrypt(txt:string) { + const sm4Util = new sm4(SM4Data) + return sm4Util.encrypt(txt, SM4Data.key) +} + +// // 解密 +export function decrypt(txt:string) { + const sm4Util = new sm4(SM4Data) + return sm4Util.decrypt(txt, SM4Data.key); +} + diff --git a/frontend/src/utils/validate.ts b/frontend/src/utils/validate.ts new file mode 100644 index 0000000..bc8ccee --- /dev/null +++ b/frontend/src/utils/validate.ts @@ -0,0 +1,12 @@ +/** + * Created by PanJiaChen on 16/11/18. + */ + +/** + * @param {string} path + * @returns {Boolean} + */ +export function isExternal(path: string) { + const isExternal = /^(https?:|http?:|mailto:|tel:)/.test(path); + return isExternal; +} diff --git a/frontend/src/views/AlgorithmsAndModels/define/index.vue b/frontend/src/views/AlgorithmsAndModels/define/index.vue new file mode 100644 index 0000000..2d2c0cc --- /dev/null +++ b/frontend/src/views/AlgorithmsAndModels/define/index.vue @@ -0,0 +1,1687 @@ + + + + + diff --git a/frontend/src/views/AlgorithmsAndModels/maintenance/index.vue b/frontend/src/views/AlgorithmsAndModels/maintenance/index.vue new file mode 100644 index 0000000..68d685c --- /dev/null +++ b/frontend/src/views/AlgorithmsAndModels/maintenance/index.vue @@ -0,0 +1,971 @@ + + + + + diff --git a/frontend/src/views/AlgorithmsAndModels/modelbase/index.vue b/frontend/src/views/AlgorithmsAndModels/modelbase/index.vue new file mode 100644 index 0000000..f7eb81d --- /dev/null +++ b/frontend/src/views/AlgorithmsAndModels/modelbase/index.vue @@ -0,0 +1,1650 @@ + + + + + diff --git a/frontend/src/views/dashboard/components/Chart/BarChart.vue b/frontend/src/views/dashboard/components/Chart/BarChart.vue new file mode 100644 index 0000000..05be0c4 --- /dev/null +++ b/frontend/src/views/dashboard/components/Chart/BarChart.vue @@ -0,0 +1,174 @@ + + + + diff --git a/frontend/src/views/dashboard/components/Chart/FunnelChart.vue b/frontend/src/views/dashboard/components/Chart/FunnelChart.vue new file mode 100644 index 0000000..44206d2 --- /dev/null +++ b/frontend/src/views/dashboard/components/Chart/FunnelChart.vue @@ -0,0 +1,131 @@ + + + + + + diff --git a/frontend/src/views/dashboard/components/Chart/PieChart.vue b/frontend/src/views/dashboard/components/Chart/PieChart.vue new file mode 100644 index 0000000..2ad159f --- /dev/null +++ b/frontend/src/views/dashboard/components/Chart/PieChart.vue @@ -0,0 +1,113 @@ + + + + + + diff --git a/frontend/src/views/dashboard/components/Chart/RadarChart.vue b/frontend/src/views/dashboard/components/Chart/RadarChart.vue new file mode 100644 index 0000000..998d62c --- /dev/null +++ b/frontend/src/views/dashboard/components/Chart/RadarChart.vue @@ -0,0 +1,132 @@ + + + + + + diff --git a/frontend/src/views/dashboard/components/Project/index.vue b/frontend/src/views/dashboard/components/Project/index.vue new file mode 100644 index 0000000..63e471f --- /dev/null +++ b/frontend/src/views/dashboard/components/Project/index.vue @@ -0,0 +1,119 @@ + + + + + diff --git a/frontend/src/views/dashboard/components/Team/index.vue b/frontend/src/views/dashboard/components/Team/index.vue new file mode 100644 index 0000000..2146da1 --- /dev/null +++ b/frontend/src/views/dashboard/components/Team/index.vue @@ -0,0 +1,236 @@ + + + + + + diff --git a/frontend/src/views/dashboard/index.vue b/frontend/src/views/dashboard/index.vue new file mode 100644 index 0000000..ec56849 --- /dev/null +++ b/frontend/src/views/dashboard/index.vue @@ -0,0 +1,495 @@ + + + + + diff --git a/frontend/src/views/dataannotations/annotationtask/index.vue b/frontend/src/views/dataannotations/annotationtask/index.vue new file mode 100644 index 0000000..aaaac58 --- /dev/null +++ b/frontend/src/views/dataannotations/annotationtask/index.vue @@ -0,0 +1,1550 @@ + + + + + diff --git a/frontend/src/views/dataannotations/annotationteam/index.vue b/frontend/src/views/dataannotations/annotationteam/index.vue new file mode 100644 index 0000000..43f4a58 --- /dev/null +++ b/frontend/src/views/dataannotations/annotationteam/index.vue @@ -0,0 +1,424 @@ + + + + + diff --git a/frontend/src/views/dataset/annotation.vue b/frontend/src/views/dataset/annotation.vue new file mode 100644 index 0000000..8946f56 --- /dev/null +++ b/frontend/src/views/dataset/annotation.vue @@ -0,0 +1,574 @@ + + + + + diff --git a/frontend/src/views/dataset/index.vue b/frontend/src/views/dataset/index.vue new file mode 100644 index 0000000..54a8e9e --- /dev/null +++ b/frontend/src/views/dataset/index.vue @@ -0,0 +1,1457 @@ + + + + + diff --git a/frontend/src/views/error-page/401.vue b/frontend/src/views/error-page/401.vue new file mode 100644 index 0000000..59b6f1a --- /dev/null +++ b/frontend/src/views/error-page/401.vue @@ -0,0 +1,114 @@ + + + + + + + + diff --git a/frontend/src/views/error-page/404.vue b/frontend/src/views/error-page/404.vue new file mode 100644 index 0000000..8559404 --- /dev/null +++ b/frontend/src/views/error-page/404.vue @@ -0,0 +1,280 @@ + + + + + + + + diff --git a/frontend/src/views/login/index.vue b/frontend/src/views/login/index.vue new file mode 100644 index 0000000..cb0c346 --- /dev/null +++ b/frontend/src/views/login/index.vue @@ -0,0 +1,486 @@ + + + + + + + diff --git a/frontend/src/views/modeltraining/algorithm.vue b/frontend/src/views/modeltraining/algorithm.vue new file mode 100644 index 0000000..fcaeb3e --- /dev/null +++ b/frontend/src/views/modeltraining/algorithm.vue @@ -0,0 +1,74 @@ + + + + + diff --git a/frontend/src/views/modeltraining/callparameters.vue b/frontend/src/views/modeltraining/callparameters.vue new file mode 100644 index 0000000..e992a99 --- /dev/null +++ b/frontend/src/views/modeltraining/callparameters.vue @@ -0,0 +1,123 @@ + + + + + diff --git a/frontend/src/views/modeltraining/index.vue b/frontend/src/views/modeltraining/index.vue new file mode 100644 index 0000000..0fe3ebb --- /dev/null +++ b/frontend/src/views/modeltraining/index.vue @@ -0,0 +1,1062 @@ + + + + + + diff --git a/frontend/src/views/modeltraining/modelarchiving.vue b/frontend/src/views/modeltraining/modelarchiving.vue new file mode 100644 index 0000000..9a4206e --- /dev/null +++ b/frontend/src/views/modeltraining/modelarchiving.vue @@ -0,0 +1,75 @@ + + + + + diff --git a/frontend/src/views/modeltraining/trainInformation.vue b/frontend/src/views/modeltraining/trainInformation.vue new file mode 100644 index 0000000..7a6f98d --- /dev/null +++ b/frontend/src/views/modeltraining/trainInformation.vue @@ -0,0 +1,135 @@ + + + + + diff --git a/frontend/src/views/modeltraining/trainestimate.vue b/frontend/src/views/modeltraining/trainestimate.vue new file mode 100644 index 0000000..04b434f --- /dev/null +++ b/frontend/src/views/modeltraining/trainestimate.vue @@ -0,0 +1,146 @@ + + + + + + \ No newline at end of file diff --git a/frontend/src/views/redirect/index.vue b/frontend/src/views/redirect/index.vue new file mode 100644 index 0000000..47cad96 --- /dev/null +++ b/frontend/src/views/redirect/index.vue @@ -0,0 +1,15 @@ + + + diff --git a/frontend/src/views/smartterminal/index.vue b/frontend/src/views/smartterminal/index.vue new file mode 100644 index 0000000..c5b4fe9 --- /dev/null +++ b/frontend/src/views/smartterminal/index.vue @@ -0,0 +1,759 @@ + + + + + + + diff --git a/frontend/src/views/system/user/index.vue b/frontend/src/views/system/user/index.vue new file mode 100644 index 0000000..6693a99 --- /dev/null +++ b/frontend/src/views/system/user/index.vue @@ -0,0 +1,470 @@ + + + + + + + diff --git a/frontend/src/views/system/user/personalCenter.vue b/frontend/src/views/system/user/personalCenter.vue new file mode 100644 index 0000000..55a0391 --- /dev/null +++ b/frontend/src/views/system/user/personalCenter.vue @@ -0,0 +1,124 @@ + + + + + + + diff --git a/frontend/src/views/tempfile/index.vue b/frontend/src/views/tempfile/index.vue new file mode 100644 index 0000000..f9b524e --- /dev/null +++ b/frontend/src/views/tempfile/index.vue @@ -0,0 +1,549 @@ + + + + +

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