Python voc_dataset_evaluator.evaluate_boxes方法代碼示例

本文整理匯總了Python中datasets.voc_dataset_evaluator.evaluate_boxes方法的典型用法代碼示例。如果您正苦於以下問題：Python voc_dataset_evaluator.evaluate_boxes方法的具體用法？Python voc_dataset_evaluator.evaluate_boxes怎麽用？Python voc_dataset_evaluator.evaluate_boxes使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類datasets.voc_dataset_evaluator的用法示例。

在下文中一共展示了voc_dataset_evaluator.evaluate_boxes方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: evaluate_all

# 需要導入模塊: from datasets import voc_dataset_evaluator [as 別名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 別名]
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

開發者ID:roytseng-tw，項目名稱:Detectron.pytorch，代碼行數:21，代碼來源:task_evaluation.py

示例2: evaluate_boxes

# 需要導入模塊: from datasets import voc_dataset_evaluator [as 別名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 別名]
def evaluate_boxes(dataset, all_boxes, output_dir, test_corloc=False, use_matlab=False):
    """Evaluate bounding box detection."""
    logger.info('Evaluating detections')
    not_comp = not cfg.TEST.COMPETITION_MODE
    if _use_json_dataset_evaluator(dataset):
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_voc_evaluator(dataset):
        # For VOC, always use salt and always cleanup because results are
        # written to the shared VOCdevkit results directory
        voc_eval = voc_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, test_corloc=test_corloc,
            use_matlab=use_matlab
        )
        box_results = _voc_eval_to_box_results(voc_eval)
    else:
        raise NotImplementedError(
            'No evaluator for dataset: {}'.format(dataset.name)
        )
    return OrderedDict([(dataset.name, box_results)])

開發者ID:ppengtang，項目名稱:pcl.pytorch，代碼行數:24，代碼來源:task_evaluation.py

示例3: evaluate_boxes

# 需要導入模塊: from datasets import voc_dataset_evaluator [as 別名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 別名]
def evaluate_boxes(dataset, all_boxes, output_dir, use_matlab=False):
    """Evaluate bounding box detection."""
    logger.info('Evaluating detections')
    not_comp = not cfg.TEST.COMPETITION_MODE
    if _use_json_dataset_evaluator(dataset):
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_cityscapes_evaluator(dataset):
        logger.warn('Cityscapes bbox evaluated using COCO metrics/conversions')
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_voc_evaluator(dataset):
        # For VOC, always use salt and always cleanup because results are
        # written to the shared VOCdevkit results directory
        voc_eval = voc_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_matlab=use_matlab
        )
        box_results = _voc_eval_to_box_results(voc_eval)
    else:
        raise NotImplementedError(
            'No evaluator for dataset: {}'.format(dataset.name)
        )
    return OrderedDict([(dataset.name, box_results)])

開發者ID:roytseng-tw，項目名稱:Detectron.pytorch，代碼行數:29，代碼來源:task_evaluation.py

示例4: evaluate_all

# 需要導入模塊: from datasets import voc_dataset_evaluator [as 別名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 別名]
def evaluate_all(
    dataset, all_boxes, output_dir, test_corloc=False, 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, test_corloc=test_corloc,
        use_matlab=use_matlab
    )
    logger.info('Evaluating bounding boxes is done!')
    return all_results

開發者ID:ppengtang，項目名稱:pcl.pytorch，代碼行數:14，代碼來源:task_evaluation.py

示例5: evaluate_boxes

# 需要導入模塊: from datasets import voc_dataset_evaluator [as 別名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 別名]
def evaluate_boxes(dataset, all_boxes, output_dir, use_matlab=False):
    """Evaluate bounding box detection."""
    logger.info('Evaluating detections')
    not_comp = not cfg.TEST.COMPETITION_MODE
    if _use_json_dataset_evaluator(dataset):
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_cityscapes_evaluator(dataset):
        logger.warn('Cityscapes bbox evaluated using COCO metrics/conversions')
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_vg_evaluator(dataset):
        logger.warn('Visual Genome bbox evaluated using COCO metrics/conversions')
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_voc_evaluator(dataset):
        # For VOC, always use salt and always cleanup because results are
        # written to the shared VOCdevkit results directory
        voc_eval = voc_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_matlab=use_matlab
        )
        box_results = _voc_eval_to_box_results(voc_eval)
    else:
        raise NotImplementedError(
            'No evaluator for dataset: {}'.format(dataset.name)
        )
    return OrderedDict([(dataset.name, box_results)])

開發者ID:ruotianluo，項目名稱:Context-aware-ZSR，代碼行數:35，代碼來源:task_evaluation.py

示例6: evaluate_boxes

# 需要導入模塊: from datasets import voc_dataset_evaluator [as 別名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 別名]
def evaluate_boxes(dataset, all_boxes, output_dir, use_matlab=False):
    """Evaluate bounding box detection."""
    logger.info('Evaluating detections')
    not_comp = not cfg.TEST.COMPETITION_MODE
    if _use_json_dataset_evaluator(dataset):
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_cityscapes_evaluator(dataset):
        logger.warn('Cityscapes bbox evaluated using COCO metrics/conversions')
        coco_eval = json_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_salt=not_comp, cleanup=not_comp
        )
        box_results = _coco_eval_to_box_results(coco_eval)
    elif _use_voc_evaluator(dataset):
        # For VOC, always use salt and always cleanup because results are
        # written to the shared VOCdevkit results directory
        voc_eval = voc_dataset_evaluator.evaluate_boxes(
            dataset, all_boxes, output_dir, use_matlab=use_matlab
        )
        box_results = _voc_eval_to_box_results(voc_eval)
    elif _use_no_evaluator(dataset):
        box_results = _empty_box_results()
    else:
        raise NotImplementedError(
            'No evaluator for dataset: {}'.format(dataset.name)
        )
    return OrderedDict([(dataset.name, box_results)])

開發者ID:ronghanghu，項目名稱:seg_every_thing，代碼行數:31，代碼來源:task_evaluation.py

示例7: evaluate_all

# 需要導入模塊: from datasets import voc_dataset_evaluator [as 別名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 別名]
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

開發者ID:bobwan1995，項目名稱:PMFNet，代碼行數:28，代碼來源:task_evaluation.py

注：本文中的datasets.voc_dataset_evaluator.evaluate_boxes方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。