本文整理汇总了Python中datasets.voc_dataset_evaluator.evaluate_boxes方法的典型用法代码示例。如果您正苦于以下问题：Python voc_dataset_evaluator.evaluate_boxes方法的具体用法？Python voc_dataset_evaluator.evaluate_boxes怎么用？Python voc_dataset_evaluator.evaluate_boxes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类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 voc_dataset_evaluator [as 别名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [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 示例2: evaluate_boxes
# 需要导入模块: from datasets import voc_dataset_evaluator [as 别名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 别名]
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)]) 示例3: evaluate_boxes
# 需要导入模块: from datasets import voc_dataset_evaluator [as 别名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 别名]
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)]) 示例4: evaluate_all
# 需要导入模块: from datasets import voc_dataset_evaluator [as 别名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 别名]
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 示例5: evaluate_boxes
# 需要导入模块: from datasets import voc_dataset_evaluator [as 别名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 别名]
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)]) 示例6: evaluate_boxes
# 需要导入模块: from datasets import voc_dataset_evaluator [as 别名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [as 别名]
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)]) 示例7: evaluate_all
# 需要导入模块: from datasets import voc_dataset_evaluator [as 别名]
# 或者: from datasets.voc_dataset_evaluator import evaluate_boxes [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