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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) |