feat(annotation): 自动标注任务支持非图像类型数据集（TEXT/AUDIO/VIDEO）

移除自动标注任务创建流程中的 IMAGE-only 限制，使 TEXT、AUDIO、VIDEO
类型数据集均可用于自动标注任务。

- 新增数据库迁移：t_dm_auto_annotation_tasks 表添加 dataset_type 列
- 后端 schema/API/service 全链路传递 dataset_type
- Worker 动态构建 sample key（image/text/audio/video）和输出目录
- 前端移除数据集类型校验，下拉框显示数据集类型标识
- 输出数据集继承源数据集类型，不再硬编码为 IMAGE
- 保持向后兼容：默认值为 IMAGE，worker 有元数据回退和目录 fallback

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

frontend/src/pages/DataAnnotation/OperatorCreate/CreateTask.tsx

@@ -6,7 +6,7 @@ import { ArrowLeft } from "lucide-react";
 import { Link, useNavigate } from "react-router";
 import { queryDatasetsUsingGet } from "@/pages/DataManagement/dataset.api";
 import { mapDataset } from "@/pages/DataManagement/dataset.const";
-import { Dataset, DatasetType } from "@/pages/DataManagement/dataset.model";
+import { Dataset } from "@/pages/DataManagement/dataset.model";
 import { createAnnotationOperatorTaskUsingPost } from "../annotation.api";
 import { useCreateStepTwo } from "./hooks/useCreateStepTwo";
 import PipelinePreview from "./components/PipelinePreview";
@@ -85,11 +85,6 @@ export default function AnnotationOperatorTaskCreate() {
     try {
       if (currentStep === 1) {
         await form.validateFields();
-
-        if (selectedDataset?.datasetType !== DatasetType.IMAGE) {
-          message.error("自动标注算子编排当前仅支持图片数据集");
-          return;
-        }
       }
       setCurrentStep((prev) => Math.min(prev + 1, 2));
     } catch {
@@ -109,11 +104,6 @@ export default function AnnotationOperatorTaskCreate() {
         return;
       }
 
-      if (selectedDataset?.datasetType !== DatasetType.IMAGE) {
-        message.error("自动标注算子编排当前仅支持图片数据集");
-        return;
-      }
-
       const outputDatasetName = values.outputDatasetName?.trim();
       const pipeline = selectedOperators.map((operator, index) => {
         const overrides = {
@@ -200,10 +190,10 @@ export default function AnnotationOperatorTaskCreate() {
                 label="选择数据集"
                 name="datasetId"
                 rules={[{ required: true, message: "请选择数据集" }]}
-                extra="自动标注算子编排当前仅支持图片数据集"
+                extra="请选择用于自动标注的数据集"
               >
                 <Select
-                  placeholder="请选择图片数据集"
+                  placeholder="请选择数据集"
                   optionFilterProp="label"
                   options={datasets.map((dataset) => ({
                     label: (
@@ -215,12 +205,11 @@ export default function AnnotationOperatorTaskCreate() {
                           {dataset.name}
                         </div>
                         <div className="text-xs text-gray-500">
-                          {dataset?.fileCount} 文件 • {dataset.size}
+                          {dataset.datasetType} &bull; {dataset?.fileCount} 文件 &bull; {dataset.size}
                         </div>
                       </div>
                     ),
                     value: dataset.id,
-                    disabled: dataset.datasetType !== DatasetType.IMAGE,
                   }))}
                 />
               </Form.Item>

runtime/datamate-python/app/db/models/annotation_management.py

@@ -210,6 +210,10 @@ class AutoAnnotationTask(Base):
     dataset_name = Column(
         String(255), nullable=True, comment="数据集名称（冗余字段，方便查询）"
     )
+    dataset_type = Column(
+        String(50), nullable=False, default="IMAGE",
+        comment="数据集类型: IMAGE/TEXT/AUDIO/VIDEO",
+    )
     created_by = Column(String(255), nullable=True, comment="任务创建人")
     config = Column(JSON, nullable=False, comment="任务配置（模型规模、置信度等）")
     file_ids = Column(

runtime/datamate-python/app/module/annotation/interface/auto.py

@@ -85,23 +85,28 @@ async def _create_task_internal(
     await assert_dataset_access(db, normalized_request.dataset_id, user_context)
     # 尝试获取数据集名称和总量用于冗余字段
     dataset_name = None
+    dataset_type = "IMAGE"
     total_images = len(normalized_request.file_ids) if normalized_request.file_ids else 0
     try:
         dm_client = DatasetManagementService(db)
         dataset = await dm_client.get_dataset(normalized_request.dataset_id)
         if dataset is not None:
             dataset_name = dataset.name
+            dataset_type = getattr(dataset, "datasetType", None) or "IMAGE"
             if not normalized_request.file_ids:
                 total_images = getattr(dataset, "fileCount", 0) or 0
     except Exception as e:  # pragma: no cover - 容错
         logger.warning("Failed to fetch dataset summary for annotation task: %s", e)
 
+    resolved_dataset_type = normalized_request.dataset_type or dataset_type
+
     return await service.create_task(
         db,
         normalized_request,
         user_context=user_context,
         dataset_name=dataset_name,
         total_images=total_images,
+        dataset_type=resolved_dataset_type,
     )
 
 

runtime/datamate-python/app/module/annotation/schema/auto.py

@@ -75,6 +75,11 @@ class CreateAutoAnnotationTaskRequest(BaseModel):
 
     name: str = Field(..., min_length=1, max_length=255, description="任务名称")
     dataset_id: str = Field(..., alias="datasetId", description="数据集ID")
+    dataset_type: Optional[str] = Field(
+        default=None,
+        alias="datasetType",
+        description="数据集类型: IMAGE/TEXT/AUDIO/VIDEO（不传时由后端自动获取）",
+    )
     config: Optional[AutoAnnotationConfig] = Field(
         default=None,
         description="兼容旧版 YOLO 任务配置",
@@ -120,6 +125,7 @@ class AutoAnnotationTaskResponse(BaseModel):
     name: str = Field(..., description="任务名称")
     dataset_id: str = Field(..., alias="datasetId", description="数据集ID")
     dataset_name: Optional[str] = Field(None, alias="datasetName", description="数据集名称")
+    dataset_type: Optional[str] = Field(None, alias="datasetType", description="数据集类型: IMAGE/TEXT/AUDIO/VIDEO")
     task_mode: Optional[str] = Field(None, alias="taskMode", description="任务模式")
     executor_type: Optional[str] = Field(None, alias="executorType", description="执行器类型")
     pipeline: Optional[List[Dict[str, Any]]] = Field(None, description="算子编排定义")

runtime/datamate-python/app/module/annotation/service/auto.py

@@ -141,6 +141,7 @@ class AutoAnnotationTaskService:
         user_context: RequestUserContext,
         dataset_name: Optional[str] = None,
         total_images: int = 0,
+        dataset_type: str = "IMAGE",
     ) -> AutoAnnotationTaskResponse:
         """创建自动标注任务，初始状态为 pending。
 
@@ -170,6 +171,7 @@ class AutoAnnotationTaskService:
             name=request.name,
             dataset_id=request.dataset_id,
             dataset_name=dataset_name,
+            dataset_type=dataset_type,
             created_by=user_context.user_id,
             config=normalized_config,
             task_mode=request.task_mode,

runtime/python-executor/datamate/auto_annotation_worker.py

@@ -175,7 +175,7 @@ def _fetch_pending_task() -> Optional[Dict[str, Any]]:
 
     query_sql = text(
         """
-        SELECT id, name, dataset_id, dataset_name, created_by,
+        SELECT id, name, dataset_id, dataset_name, dataset_type, created_by,
                config, file_ids, pipeline,
                task_mode, executor_type,
                status, stop_requested, run_token,
@@ -521,6 +521,35 @@ def _count_detections(sample: Dict[str, Any]) -> int:
         return 0
 
 
+# ---------------------------------------------------------------------------
+# 数据集类型 → sample key / 输出子目录 映射
+# ---------------------------------------------------------------------------
+
+DATASET_TYPE_SAMPLE_KEY: Dict[str, str] = {
+    "IMAGE": "image",
+    "TEXT": "text",
+    "AUDIO": "audio",
+    "VIDEO": "video",
+}
+
+DATASET_TYPE_DATA_DIR: Dict[str, str] = {
+    "IMAGE": "images",
+    "TEXT": "data",
+    "AUDIO": "data",
+    "VIDEO": "data",
+}
+
+
+def _get_sample_key(dataset_type: str) -> str:
+    """根据数据集类型返回 sample dict 中主数据对应的 key。"""
+    return DATASET_TYPE_SAMPLE_KEY.get(dataset_type.upper(), "image")
+
+
+def _get_data_dir_name(dataset_type: str) -> str:
+    """根据数据集类型返回输出子目录名。"""
+    return DATASET_TYPE_DATA_DIR.get(dataset_type.upper(), "images")
+
+
 def _get_operator_whitelist() -> Optional[set[str]]:
     """获取灰度白名单；返回 None 表示放开全部。"""
 
@@ -584,7 +613,7 @@ def _load_dataset_meta(dataset_id: str) -> Optional[Dict[str, Any]]:
 
     sql = text(
         """
-        SELECT id, name, parent_dataset_id, path
+        SELECT id, name, parent_dataset_id, path, dataset_type
         FROM t_dm_datasets
         WHERE id = :dataset_id
         """
@@ -638,11 +667,12 @@ def _load_files_by_ids(file_ids: List[str]) -> List[Tuple[str, str, str]]:
         return [(str(r[0]), str(r[1]), str(r[2])) for r in rows]
 
 
-def _ensure_output_dir(output_dir: str) -> str:
+def _ensure_output_dir(output_dir: str, dataset_type: str = "IMAGE") -> str:
-    """确保输出目录及其 images/、annotations/ 子目录存在。"""
+    """确保输出目录及其数据/annotations 子目录存在。"""
 
     os.makedirs(output_dir, exist_ok=True)
-    os.makedirs(os.path.join(output_dir, "images"), exist_ok=True)
+    data_dir_name = _get_data_dir_name(dataset_type)
+    os.makedirs(os.path.join(output_dir, data_dir_name), exist_ok=True)
     os.makedirs(os.path.join(output_dir, "annotations"), exist_ok=True)
     return output_dir
 
@@ -651,6 +681,7 @@ def _create_output_dataset(
     source_dataset_id: str,
     source_dataset_name: str,
     output_dataset_name: str,
+    dataset_type: str = "IMAGE",
 ) -> Tuple[str, str]:
     """为自动标注结果创建一个新的数据集并返回 (dataset_id, path)。"""
 
@@ -673,7 +704,7 @@ def _create_output_dataset(
         "parent_dataset_id": parent_dataset_id,
         "name": output_dataset_name,
         "description": description,
-        "dataset_type": "IMAGE",
+        "dataset_type": dataset_type,
         "path": output_dir,
         "status": "ACTIVE",
     }
@@ -690,31 +721,38 @@ def _register_output_dataset(
     output_dir: str,
     output_dataset_name: str,
     total_images: int,
+    dataset_type: str = "IMAGE",
 ) -> None:
     """将自动标注结果注册到新建的数据集。"""
 
-    images_dir = os.path.join(output_dir, "images")
+    data_dir_name = _get_data_dir_name(dataset_type)
-    if not os.path.isdir(images_dir):
+    data_dir = os.path.join(output_dir, data_dir_name)
+    # 兼容旧任务和 IMAGE 算子（它们写入 images/ 目录）
+    if not os.path.isdir(data_dir):
+        fallback_dir = os.path.join(output_dir, "images")
+        if os.path.isdir(fallback_dir):
+            data_dir = fallback_dir
+        else:
             logger.warning(
-            "Auto-annotation images directory not found for task {}: {}",
+                "Auto-annotation data directory not found for task {}: {}",
                 task_id,
-            images_dir,
+                data_dir,
             )
             return
 
-    image_files: List[Tuple[str, str, int]] = []
+    data_files: List[Tuple[str, str, int]] = []
     annotation_files: List[Tuple[str, str, int]] = []
     total_size = 0
 
-    for file_name in sorted(os.listdir(images_dir)):
+    for file_name in sorted(os.listdir(data_dir)):
-        file_path = os.path.join(images_dir, file_name)
+        file_path = os.path.join(data_dir, file_name)
         if not os.path.isfile(file_path):
             continue
         try:
             file_size = os.path.getsize(file_path)
         except OSError:
             file_size = 0
-        image_files.append((file_name, file_path, int(file_size)))
+        data_files.append((file_name, file_path, int(file_size)))
         total_size += int(file_size)
 
     annotations_dir = os.path.join(output_dir, "annotations")
@@ -730,11 +768,11 @@ def _register_output_dataset(
             annotation_files.append((file_name, file_path, int(file_size)))
             total_size += int(file_size)
 
-    if not image_files:
+    if not data_files:
         logger.warning(
-            "No image files found in auto-annotation output for task {}: {}",
+            "No data files found in auto-annotation output for task {}: {}",
             task_id,
-            images_dir,
+            data_dir,
         )
         return
 
@@ -759,7 +797,7 @@ def _register_output_dataset(
     with SQLManager.create_connect() as conn:
         added_count = 0
 
-        for file_name, file_path, file_size in image_files:
+        for file_name, file_path, file_size in data_files:
             ext = os.path.splitext(file_name)[1].lstrip(".").upper() or None
             logical_path = os.path.relpath(file_path, output_dir).replace("\\", "/")
             conn.execute(
@@ -811,7 +849,7 @@ def _register_output_dataset(
         "Registered auto-annotation output into dataset: dataset_id={}, name={}, added_files={}, added_size_bytes={}, task_id={}, output_dir={}",
         output_dataset_id,
         output_dataset_name,
-        len(image_files) + len(annotation_files),
+        len(data_files) + len(annotation_files),
         total_size,
         task_id,
         output_dir,
@@ -832,6 +870,13 @@ def _process_single_task(task: Dict[str, Any]) -> None:
     pipeline_raw = task.get("pipeline")
     selected_file_ids: Optional[List[str]] = task.get("file_ids") or None
 
+    # 解析数据集类型，兜底从数据集元数据获取
+    dataset_type = str(task.get("dataset_type") or "").upper() or "IMAGE"
+    if dataset_type == "IMAGE" and not task.get("dataset_type"):
+        source_meta = _load_dataset_meta(dataset_id)
+        if source_meta and source_meta.get("dataset_type"):
+            dataset_type = str(source_meta["dataset_type"]).upper()
+
     output_dataset_name = _get_output_dataset_name(
         task_id=task_id,
         dataset_id=dataset_id,
@@ -892,8 +937,9 @@ def _process_single_task(task: Dict[str, Any]) -> None:
         source_dataset_id=dataset_id,
         source_dataset_name=source_dataset_name,
         output_dataset_name=output_dataset_name,
+        dataset_type=dataset_type,
     )
-    output_dir = _ensure_output_dir(output_dir)
+    output_dir = _ensure_output_dir(output_dir, dataset_type=dataset_type)
 
     _update_task_status(
         task_id,
@@ -959,8 +1005,9 @@ def _process_single_task(task: Dict[str, Any]) -> None:
                 return
 
             try:
+                sample_key = _get_sample_key(dataset_type)
                 sample = {
-                    "image": file_path,
+                    sample_key: file_path,
                     "filename": file_name,
                 }
 
@@ -983,7 +1030,7 @@ def _process_single_task(task: Dict[str, Any]) -> None:
                 )
             except Exception as e:
                 logger.error(
-                    "Failed to process image for task {}: file_path={}, error={}",
+                    "Failed to process file for task {}: file_path={}, error={}",
                     task_id,
                     file_path,
                     e,
@@ -1021,6 +1068,7 @@ def _process_single_task(task: Dict[str, Any]) -> None:
                     output_dir=output_dir,
                     output_dataset_name=output_dataset_name,
                     total_images=total_images,
+                    dataset_type=dataset_type,
                 )
             except Exception as e:  # pragma: no cover - 防御性日志
                 logger.error(

scripts/db/data-annotation-multitype-migration.sql

@@ -0,0 +1,25 @@
+-- =============================================
+-- 自动标注任务支持多数据集类型迁移
+-- 为 t_dm_auto_annotation_tasks 表添加 dataset_type 列
+-- =============================================
+
+USE datamate;
+SET @db_name = DATABASE();
+
+-- 添加 dataset_type 列（IMAGE/TEXT/AUDIO/VIDEO），已有记录默认为 IMAGE
+SET @ddl = (
+    SELECT IF(
+        EXISTS(
+            SELECT 1
+            FROM information_schema.COLUMNS
+            WHERE TABLE_SCHEMA = @db_name
+              AND TABLE_NAME = 't_dm_auto_annotation_tasks'
+              AND COLUMN_NAME = 'dataset_type'
+        ),
+        'SELECT ''skip: column dataset_type already exists''',
+        'ALTER TABLE t_dm_auto_annotation_tasks ADD COLUMN dataset_type VARCHAR(50) NOT NULL DEFAULT ''IMAGE'' COMMENT ''数据集类型: IMAGE/TEXT/AUDIO/VIDEO'' AFTER dataset_name'
+    )
+);
+PREPARE stmt FROM @ddl;
+EXECUTE stmt;
+DEALLOCATE PREPARE stmt;