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Python json_dataset_evaluator.evaluate_masks方法代码示例

本文整理汇总了Python中datasets.json_dataset_evaluator.evaluate_masks方法的典型用法代码示例。如果您正苦于以下问题:Python json_dataset_evaluator.evaluate_masks方法的具体用法?Python json_dataset_evaluator.evaluate_masks怎么用?Python json_dataset_evaluator.evaluate_masks使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在datasets.json_dataset_evaluator的用法示例。


在下文中一共展示了json_dataset_evaluator.evaluate_masks方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: evaluate_all

# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_masks [as 别名]
def evaluate_all(
    dataset, all_boxes, all_segms, all_keyps, output_dir, use_matlab=False
):
    """Evaluate "all" tasks, where "all" includes box detection, instance
    segmentation, and keypoint detection.
    """
    all_results = evaluate_boxes(
        dataset, all_boxes, output_dir, use_matlab=use_matlab
    )
    logger.info('Evaluating bounding boxes is done!')
    if cfg.MODEL.MASK_ON:
        results = evaluate_masks(dataset, all_boxes, all_segms, output_dir)
        all_results[dataset.name].update(results[dataset.name])
        logger.info('Evaluating segmentations is done!')
    if cfg.MODEL.KEYPOINTS_ON:
        results = evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir)
        all_results[dataset.name].update(results[dataset.name])
        logger.info('Evaluating keypoints is done!')
    return all_results 
开发者ID:roytseng-tw,项目名称:Detectron.pytorch,代码行数:21,代码来源:task_evaluation.py

示例2: evaluate_all

# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_masks [as 别名]
def evaluate_all(
    dataset, all_boxes, all_segms, all_keyps, output_dir, use_matlab=False
):
    """Evaluate "all" tasks, where "all" includes box detection, instance
    segmentation.
    """
    all_results = evaluate_boxes(
        dataset, all_boxes, output_dir, use_matlab=use_matlab
    )
    logger.info('Evaluating bounding boxes is done!')
    if cfg.MODEL.MASK_ON:
        results = evaluate_masks(dataset, all_boxes, all_segms, output_dir)
        all_results[dataset.name].update(results[dataset.name])
        logger.info('Evaluating segmentations is done!')
    
    return all_results 
开发者ID:jz462,项目名称:Large-Scale-VRD.pytorch,代码行数:18,代码来源:task_evaluation.py

示例3: evaluate_masks

# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_masks [as 别名]
def evaluate_masks(dataset, all_boxes, all_segms, output_dir):
    """Evaluate instance segmentation."""
    logger.info('Evaluating segmentations')
    not_comp = not cfg.TEST.COMPETITION_MODE
    if _use_json_dataset_evaluator(dataset):
        coco_eval = json_dataset_evaluator.evaluate_masks(
            dataset,
            all_boxes,
            all_segms,
            output_dir,
            use_salt=not_comp,
            cleanup=not_comp
        )
        mask_results = _coco_eval_to_mask_results(coco_eval)
    else:
        raise NotImplementedError(
            'No evaluator for dataset: {}'.format(dataset.name)
        )
    return OrderedDict([(dataset.name, mask_results)]) 
开发者ID:jz462,项目名称:Large-Scale-VRD.pytorch,代码行数:21,代码来源:task_evaluation.py

示例4: evaluate_masks

# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_masks [as 别名]
def evaluate_masks(dataset, all_boxes, all_segms, output_dir):
    """Evaluate instance segmentation."""
    logger.info('Evaluating segmentations')
    not_comp = not cfg.TEST.COMPETITION_MODE
    if _use_json_dataset_evaluator(dataset):
        coco_eval = json_dataset_evaluator.evaluate_masks(
            dataset,
            all_boxes,
            all_segms,
            output_dir,
            use_salt=not_comp,
            cleanup=not_comp
        )
        mask_results = _coco_eval_to_mask_results(coco_eval)
    elif _use_cityscapes_evaluator(dataset):
        cs_eval = cs_json_dataset_evaluator.evaluate_masks(
            dataset,
            all_boxes,
            all_segms,
            output_dir,
            use_salt=not_comp,
            cleanup=not_comp
        )
        mask_results = _cs_eval_to_mask_results(cs_eval)
    else:
        raise NotImplementedError(
            'No evaluator for dataset: {}'.format(dataset.name)
        )
    return OrderedDict([(dataset.name, mask_results)]) 
开发者ID:roytseng-tw,项目名称:Detectron.pytorch,代码行数:31,代码来源:task_evaluation.py

示例5: evaluate_masks

# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_masks [as 别名]
def evaluate_masks(dataset, all_boxes, all_segms, output_dir):
    """Evaluate instance segmentation."""
    logger.info('Evaluating segmentations')
    not_comp = not cfg.TEST.COMPETITION_MODE
    if _use_json_dataset_evaluator(dataset):
        coco_eval = json_dataset_evaluator.evaluate_masks(
            dataset,
            all_boxes,
            all_segms,
            output_dir,
            use_salt=not_comp,
            cleanup=not_comp
        )
        mask_results = _coco_eval_to_mask_results(coco_eval)
    elif _use_cityscapes_evaluator(dataset):
        cs_eval = cs_json_dataset_evaluator.evaluate_masks(
            dataset,
            all_boxes,
            all_segms,
            output_dir,
            use_salt=not_comp,
            cleanup=not_comp
        )
        mask_results = _cs_eval_to_mask_results(cs_eval)
    elif _use_no_evaluator(dataset):
        mask_results = _empty_mask_results()
    else:
        raise NotImplementedError(
            'No evaluator for dataset: {}'.format(dataset.name)
        )
    return OrderedDict([(dataset.name, mask_results)]) 
开发者ID:ronghanghu,项目名称:seg_every_thing,代码行数:33,代码来源:task_evaluation.py

示例6: evaluate_all

# 需要导入模块: from datasets import json_dataset_evaluator [as 别名]
# 或者: from datasets.json_dataset_evaluator import evaluate_masks [as 别名]
def evaluate_all(
    dataset, all_boxes, all_segms, all_keyps, all_hois, all_keyps_vcoco, output_dir, use_matlab=False
):
    """Evaluate "all" tasks, where "all" includes box detection, instance
    segmentation, and keypoint detection.
    """
    all_results = evaluate_boxes(
        dataset, all_boxes, output_dir, use_matlab=use_matlab
    )
    logger.info('Evaluating bounding boxes is done!')
    if cfg.MODEL.MASK_ON:
        results = evaluate_masks(dataset, all_boxes, all_segms, output_dir)
        all_results[dataset.name].update(results[dataset.name])
        logger.info('Evaluating segmentations is done!')
    if cfg.MODEL.KEYPOINTS_ON:
        results = evaluate_keypoints(dataset, all_boxes, all_keyps, output_dir)
        all_results[dataset.name].update(results[dataset.name])
        logger.info('Evaluating keypoints is done!')
    if cfg.MODEL.VCOCO_ON:
        results = evaluate_hoi_vcoco(dataset, all_hois, output_dir)
        #all_results[dataset.name].update(results[dataset.name])
        # if cfg.VCOCO.KEYPOINTS_ON:
            # results = evaluate_keypoints(dataset, all_boxes, all_keyps_vcoco, output_dir)
            # all_results[dataset.name].update(results[dataset.name])
        logger.info('Evaluating hois is done!')
    return all_results 
开发者ID:bobwan1995,项目名称:PMFNet,代码行数:28,代码来源:task_evaluation.py


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