Python data.VOCDetection方法代碼示例

本文整理匯總了Python中gluoncv.data.VOCDetection方法的典型用法代碼示例。如果您正苦於以下問題：Python data.VOCDetection方法的具體用法？Python data.VOCDetection怎麽用？Python data.VOCDetection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gluoncv.data的用法示例。

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

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:dmlc，項目名稱:dgl，代碼行數:25，代碼來源:train_faster_rcnn.py

示例2: test_pascal_voc_detection

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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]

開發者ID:dmlc，項目名稱:gluon-cv，代碼行數:20，代碼來源:test_data_datasets.py

示例3: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:dmlc，項目名稱:gluon-cv，代碼行數:23，代碼來源:train_center_net.py

示例4: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:dmlc，項目名稱:gluon-cv，代碼行數:21，代碼來源:train_ssd.py

示例5: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:dmlc，項目名稱:gluon-cv，代碼行數:26，代碼來源:train_faster_rcnn.py

示例6: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:Angzz，項目名稱:panoptic-fpn-gluon，代碼行數:21，代碼來源:train_ssd.py

示例7: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:Angzz，項目名稱:panoptic-fpn-gluon，代碼行數:23，代碼來源:train_yolo3.py

示例8: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:dmlc，項目名稱:gluon-cv，代碼行數:14，代碼來源:eval_center_net.py

示例9: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:dmlc，項目名稱:gluon-cv，代碼行數:14，代碼來源:eval_ssd.py

示例10: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:dmlc，項目名稱:gluon-cv，代碼行數:14，代碼來源:eval_faster_rcnn.py

示例11: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:zzdang，項目名稱:cascade_rcnn_gluon，代碼行數:16，代碼來源:train_cascade_rfcn.py

示例12: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:zzdang，項目名稱:cascade_rcnn_gluon，代碼行數:14，代碼來源:eval_cascade_rfcn_mAP.py

示例13: get_dataset

# 需要導入模塊: from gluoncv import data [as 別名]
# 或者: from gluoncv.data import VOCDetection [as 別名]
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

開發者ID:zzdang，項目名稱:cascade_rcnn_gluon，代碼行數:16，代碼來源:train_faster_rcnn.py

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