from typing import List from utils.data_types import ModalityType, TaskType, TaskResult from utils.base_processor import BaseModalityProcessor class ImageProcessor(BaseModalityProcessor): """图像模态处理器""" def __init__(self, modality: ModalityType, dataset_dir: str, pred_json_file: str): super().__init__(modality, dataset_dir, pred_json_file) def process_1(self): return [] def process_comprehension(self) -> List[TaskResult]: """处理图像理解类任务 需要返回一个TaskResult列表,每个TaskResult包含: - task_name: 任务名称,例如 "image_classification", "object_detection" 等 - metric: 评估指标,例如 "accuracy", "mAP" 等 - score: 评估分数 - task_type: 默认为 TaskType.COMPREHENSION,不需要指定 示例格式: return [ TaskResult( task_name="image_classification", metric="accuracy", score=0.95 ), TaskResult( task_name="object_detection", metric="mAP", score=0.82 ) ] """ return [] def process_generation(self) -> List[TaskResult]: """处理图像生成类任务 需要返回一个TaskResult列表,每个TaskResult包含: - task_name: 任务名称,例如 "image_generation", "image_editing" 等 - metric: 评估指标,例如 "FID", "IS" 等 - score: 评估分数 - task_type: 需要指定为 TaskType.GENERATION 示例格式: return [ TaskResult( task_name="image_generation", metric="FID", score=15.2, task_type=TaskType.GENERATION ), TaskResult( task_name="image_editing", metric="PSNR", score=28.5, task_type=TaskType.GENERATION ) ] """ return [] # 使用示例 if __name__ == "__main__": processor = ImageProcessor(ModalityType.IMAGE, "") # 测试理解任务 print("\n理解类任务结果:") for task in processor.process_comprehension(): print(f"任务: {task.task_name}") print(f"指标: {task.metric}") print(f"分数: {task.score}") print("-" * 20) # 测试生成任务 print("\n生成类任务结果:") for task in processor.process_generation(): print(f"任务: {task.task_name}") print(f"指标: {task.metric}") print(f"分数: {task.score}") print("-" * 20)