def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[('sbdche', 'train' + '_' + str(args.deg) + '_bboxwh')])
if args.val_2012 == True:
val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche',
'val_2012_bboxwh')])
else:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val' + '_' + str(args.deg) + '_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(
iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco_pretrain':
train_dataset = gdata.coco_pretrain_Detection(
splits=[('_coco_20', 'train' + '_' + str(args.deg) + '_bboxwh')])
if args.val_2012 == True:
val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche',
'val_2012_bboxwh')])
else:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val' + '_' + str(args.deg) + '_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(
iou_thresh=0.5, class_names=val_dataset.classes)
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, val_polygon_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
if args.val_2012 == True:
train_dataset = gdata.VOCDetection(
splits=[('sbdche', 'train_voc2012_bboxwh')])
val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche',
'val_2012_bboxwh')])
else:
train_dataset = gdata.VOCDetection(splits=[('sbdche',
'train' + '_' + '8' +
'_bboxwh')])
val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche',
'val' + '_' + '8' +
'_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.7,
class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(
iou_thresh=0.7, class_names=val_dataset.classes)
elif dataset.lower() == 'coco_pretrain':
train_dataset = gdata.coco_pretrain_Detection(
splits=[('_coco_20', 'train' + '_' + '8' + '_bboxwh')])
if args.val_2012 == True:
val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche',
'val_2012_bboxwh')])
else:
val_dataset = gdata.VOC_Val_Detection(splits=[('sbdche',
'val' + '_' + '8' +
'_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.7,
class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(
iou_thresh=0.7, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
train_dataset = gdata.cocoDetection(
root='/home/tutian/dataset/coco_to_voc/train',
subfolder='./bases_50_xml_each_' + 'var')
val_dataset = gdata.cocoDetection(
root='/home/tutian/dataset/coco_to_voc/val',
subfolder='./bases_50_xml_' + 'raw_coef')
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
# val_polygon_metric = New07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes, root='/home/tutian/dataset/coco_to_voc/val/')
val_polygon_metric = None
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, val_polygon_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
if args.val_voc2012:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val_2012_bboxwh')])
else:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val'+'_'+'8'+'_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
if args.val_voc2012:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val_2012_bboxwh')])
else:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val' + '_' + str(args.deg) + '_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
return val_dataset, val_metric, val_polygon_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
if args.val_voc2012:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val_2012_bboxwh')])
else:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val'+'_'+'8'+'_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
val_dataset = gdata.cocoDetection(root='/home/tutian/dataset/coco_to_voc/val', subfolder='./bases_50_xml_'+'raw_coef')
val_metric = VOC07MApMetric(iou_thresh=0.75, class_names=val_dataset.classes)
val_polygon_metric = New07PolygonMApMetric(iou_thresh=0.75, class_names=val_dataset.classes, root='/home/tutian/dataset/coco_to_voc/val/')
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
return val_dataset, val_metric, val_polygon_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
if args.val_voc2012:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val_2012_bboxwh')])
else:
val_dataset = gdata.VOC_Val_Detection(
splits=[('sbdche', 'val'+'_'+'8'+'_bboxwh')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
val_polygon_metric = VOC07PolygonMApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
elif dataset.lower() == 'coco':
val_dataset = COCOInstance(root='/home/tutian/dataset/', skip_empty=False)
val_metric = COCOInstanceMetric(val_dataset, 'test_cocoapi', method='var')
val_polygon_metric = None
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
return val_dataset, val_metric, val_polygon_metric