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')])
#print(val_dataset.classes)
#('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
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() == 'pedestrian':
lst_dataset = LstDetection('train_val.lst',root=os.path.expanduser('.'))
print(len(lst_dataset))
first_img = lst_dataset[0][0]
print(first_img.shape)
print(lst_dataset[0][1])
train_dataset = LstDetection('train.lst',root=os.path.expanduser('.'))
val_dataset = LstDetection('val.lst',root=os.path.expanduser('.'))
classs = ('pedestrian',)
val_metric = VOC07MApMetric(iou_thresh=0.5,class_names=classs)
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
if 0:
train_dataset = gdata.VOCDetection(root='E:/dataset/VOCdevkit',
splits=[(2007, 'trainval'),
(2012, 'trainval')])
val_dataset = gdata.VOCDetection(root='E:/dataset/VOCdevkit',
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
else:
voc_root = 'G:/MSDataset/' #layout same with VOC07
train_dataset = gdata.MSDetection(root=voc_root,
splits=[(2007, 'trainval')])
val_dataset = gdata.MSDetection(root=voc_root,
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))
if args.mixup:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
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,
os.path.join(args.logdir, 'eval'),
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if cfg.TRAIN.MODE_MIXUP:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(root=args.data_path,
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(root=args.data_path,
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_train2014', use_crowd=False)
train_dataset = gdata.COCODetection(root=args.data_path, splits='instances_train2017')
val_dataset = gdata.COCODetection(root=args.data_path, splits='instances_val2017', skip_empty=False)
val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True)
elif dataset.lower() == 'rec':
train_dataset = gdata.RecordFileDetection(os.path.join(args.data_path, 'pikachu_train.rec'))
val_dataset = gdata.RecordFileDetection(os.path.join(args.data_path, 'pikachu_train.rec'))
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=rec_classes)
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
if args.mixup:
from gluoncv.data.mixup import MixupDetection
train_dataset = MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric