本文整理汇总了Python中maskrcnn_benchmark.data.datasets.PascalVOCDataset方法的典型用法代码示例。如果您正苦于以下问题：Python datasets.PascalVOCDataset方法的具体用法？Python datasets.PascalVOCDataset怎么用？Python datasets.PascalVOCDataset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maskrcnn_benchmark.data.datasets的用法示例。
在下文中一共展示了datasets.PascalVOCDataset方法的4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
# 需要导入模块: from maskrcnn_benchmark.data import datasets [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets import PascalVOCDataset [as 别名]
def evaluate(dataset, predictions, output_folder, **kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: output folder, to save evaluation files or results.
**kwargs: other args.
Returns:
evaluation result
"""
args = dict(
dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
)
if isinstance(dataset, datasets.COCODataset):
return coco_evaluation(**args)
elif isinstance(dataset, datasets.PascalVOCDataset):
return voc_evaluation(**args)
else:
dataset_name = dataset.__class__.__name__
raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name)) 示例2: evaluate
# 需要导入模块: from maskrcnn_benchmark.data import datasets [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets import PascalVOCDataset [as 别名]
def evaluate(dataset, predictions, output_folder, **kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: output folder, to save evaluation files or results.
**kwargs: other args.
Returns:
evaluation result
"""
args = dict(
dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
)
if isinstance(dataset, datasets.COCODataset):
return coco_evaluation(**args)
if isinstance(dataset, datasets.ModaNetDataset):
return coco_evaluation(**args)
elif isinstance(dataset, datasets.PascalVOCDataset):
return voc_evaluation(**args)
else:
dataset_name = dataset.__class__.__name__
raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name)) 示例3: evaluate
# 需要导入模块: from maskrcnn_benchmark.data import datasets [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets import PascalVOCDataset [as 别名]
def evaluate(dataset, predictions, output_folder, **kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: output folder, to save evaluation files or results.
**kwargs: other args.
Returns:
evaluation result
"""
args = dict(
dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
)
if isinstance(dataset, datasets.COCODataset):
return coco_evaluation(**args)
elif isinstance(dataset, datasets.PascalVOCDataset):
return voc_evaluation(**args)
elif isinstance(dataset, datasets.AbstractDataset):
return abs_cityscapes_evaluation(**args)
else:
dataset_name = dataset.__class__.__name__
raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name)) 示例4: evaluate
# 需要导入模块: from maskrcnn_benchmark.data import datasets [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets import PascalVOCDataset [as 别名]
def evaluate(dataset, predictions, output_folder, evaluate_method='', # add by hui evaluate_method
**kwargs):
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
prediction results for one image.
output_folder: output folder, to save evaluation files or results.
**kwargs: other args.
evaluate_method: 'coco' or 'voc' or ''(determine by dataset type)
Returns:
evaluation result
"""
args = dict(
dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
)
# changed by hui ####################################################
if len(evaluate_method) == 0:
if isinstance(dataset, datasets.COCODataset):
args.pop('voc_iou_ths')
return coco_evaluation(**args)
elif isinstance(dataset, datasets.PascalVOCDataset):
return voc_evaluation(**args)
else:
dataset_name = dataset.__class__.__name__
raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))
else:
evaluate_method = evaluate_method.lower()
if evaluate_method == 'voc':
return voc_evaluation(**args)
elif evaluate_method == 'coco':
return coco_evaluation(**args)
else:
raise NotImplementedError("Unsupported evaluate method {}.".format(evaluate_method))
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