def get_dataset(dataset, args):
if dataset.lower() == 'polar':
train_dataset = gdata.POLARDetection(split='train')
val_dataset = gdata.POLARDetection(split='val')
val_metric = VOCMApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif dataset.lower() == 'voca':
train_dataset = gdata.VOCAction(split='train')
val_dataset = gdata.VOCAction(split='val')
val_metric = VOCMApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
elif 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
def get_dataset(dataset, args):
if dataset.lower() == 'voca':
train_dataset = gdata.VOCAction(split='train', augment_box=True, load_box=True)
val_dataset = gdata.VOCAction(split='val', load_box=True)
val_metric = VOCMultiClsMApMetric(class_names=val_dataset.classes, ignore_label=-1, voc_action_type=True)
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voca':
val_dataset = gdata.VOCAction(split='test')
elif dataset.lower() == 'st40':
val_dataset = gdata.Stanford40Action(split='test')
elif dataset.lower() == 'hico':
val_dataset = gdata.HICOClassification(split='all',
preload_label=False)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return val_dataset