Python data.VOCDetection方法代码示例

本文整理汇总了Python中gluoncv.data.VOCDetection方法的典型用法代码示例。如果您正苦于以下问题：Python data.VOCDetection方法的具体用法？Python data.VOCDetection怎么用？Python data.VOCDetection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gluoncv.data的用法示例。

在下文中一共展示了data.VOCDetection方法的13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: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False)
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    elif dataset.lower() == 'visualgenome':
        train_dataset = VGObject(root=os.path.join('~', '.mxnet', 'datasets', 'visualgenome'),
                                 splits='detections_train', use_crowd=False)
        val_dataset = VGObject(root=os.path.join('~', '.mxnet', 'datasets', 'visualgenome'),
                               splits='detections_val', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    if args.mixup:
        from gluoncv.data.mixup import detection
        train_dataset = detection.MixupDetection(train_dataset)
    return train_dataset, val_dataset, val_metric

开发者ID:dmlc，项目名称:dgl，代码行数:25，代码来源:train_faster_rcnn.py

示例2: test_pascal_voc_detection

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def test_pascal_voc_detection():
    if not osp.isdir(osp.expanduser('~/.mxnet/datasets/voc')):
        return

    train = data.VOCDetection(splits=((2007, 'trainval'), (2012, 'trainval')))
    name = str(train)
    val = data.VOCDetection(splits=((2007, 'test'), ))
    name = str(val)

    assert train.classes == val.classes

    for _ in range(10):
        index = np.random.randint(0, len(train))
        _ = train[index]

    for _ in range(10):
        index = np.random.randint(0, len(val))
        _ = val[index]

开发者ID:dmlc，项目名称:gluon-cv，代码行数:20，代码来源:test_data_datasets.py

示例3: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco", splits='instances_train2017')
        val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco", splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(args.data_shape, args.data_shape), post_affine=get_post_transform)
        # coco validation is slow, consider increase the validation interval
        if args.val_interval == 1:
            args.val_interval = 10
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    if args.num_samples < 0:
        args.num_samples = len(train_dataset)
    return train_dataset, val_dataset, val_metric

开发者ID:dmlc，项目名称:gluon-cv，代码行数:23，代码来源:train_center_net.py

示例4: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(root=args.dataset_root + "/coco", splits='instances_train2017')
        val_dataset = gdata.COCODetection(root=args.dataset_root + "/coco", splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(args.data_shape, args.data_shape))
        # coco validation is slow, consider increase the validation interval
        if args.val_interval == 1:
            args.val_interval = 10
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return train_dataset, val_dataset, val_metric

开发者ID:dmlc，项目名称:gluon-cv，代码行数:21，代码来源:train_ssd.py

示例5: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() in ['clipart', 'comic', 'watercolor']:
        root = os.path.join('~', '.mxnet', 'datasets', dataset.lower())
        train_dataset = gdata.CustomVOCDetection(root=root, splits=[('', 'train')],
                                                 generate_classes=True)
        val_dataset = gdata.CustomVOCDetection(root=root, splits=[('', 'test')],
                                               generate_classes=True)
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False)
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    if args.mixup:
        from gluoncv.data.mixup import detection
        train_dataset = detection.MixupDetection(train_dataset)
    return train_dataset, val_dataset, val_metric

开发者ID:dmlc，项目名称:gluon-cv，代码行数:26，代码来源:train_faster_rcnn.py

示例6: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017')
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(args.data_shape, args.data_shape))
        # coco validation is slow, consider increase the validation interval
        if args.val_interval == 1:
            args.val_interval = 10
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return train_dataset, val_dataset, val_metric

开发者ID:Angzz，项目名称:panoptic-fpn-gluon，代码行数:21，代码来源:train_ssd.py

示例7: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False)
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(args.data_shape, args.data_shape))
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    if args.num_samples < 0:
        args.num_samples = len(train_dataset)
    if args.mixup:
        from gluoncv.data import MixupDetection
        train_dataset = MixupDetection(train_dataset)
    return train_dataset, val_dataset, val_metric

开发者ID:Angzz，项目名称:panoptic-fpn-gluon，代码行数:23，代码来源:train_yolo3.py

示例8: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, data_shape):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(data_shape, data_shape), post_affine=get_post_transform)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric

开发者ID:dmlc，项目名称:gluon-cv，代码行数:14，代码来源:eval_center_net.py

示例9: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, data_shape):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(
            val_dataset, args.save_prefix + '_eval', cleanup=True,
            data_shape=(data_shape, data_shape))
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric

开发者ID:dmlc，项目名称:gluon-cv，代码行数:14，代码来源:eval_ssd.py

示例10: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval',
                                         cleanup=not args.save_json)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric

开发者ID:dmlc，项目名称:gluon-cv，代码行数:14，代码来源:eval_faster_rcnn.py

示例11: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017')
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return train_dataset, val_dataset, val_metric

开发者ID:zzdang，项目名称:cascade_rcnn_gluon，代码行数:16，代码来源:train_cascade_rfcn.py

示例12: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.75, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval',
                                         cleanup=not args.save_json)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return val_dataset, val_metric

开发者ID:zzdang，项目名称:cascade_rcnn_gluon，代码行数:14，代码来源:eval_cascade_rfcn_mAP.py

示例13: get_dataset

# 需要导入模块: from gluoncv import data [as 别名]
# 或者: from gluoncv.data import VOCDetection [as 别名]
def get_dataset(dataset, args):
    if dataset.lower() == 'voc':
        train_dataset = gdata.VOCDetection(
            splits=[(2007, 'trainval'), (2012, 'trainval')])
        val_dataset = gdata.VOCDetection(
            splits=[(2007, 'test')])
        val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
    elif dataset.lower() == 'coco':
        train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False)
        val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False)
        val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
    else:
        raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
    return train_dataset, val_dataset, val_metric

开发者ID:zzdang，项目名称:cascade_rcnn_gluon，代码行数:16，代码来源:train_faster_rcnn.py

注：本文中的gluoncv.data.VOCDetection方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。