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
def get_dataset(dataset, mixup=False, tclass=None):
if dataset.lower() == 'real':
train_dataset = RealDataset(mode='train')
val_dataset = RealDataset(mode='test')
elif dataset.lower() == 'real_with_grasp':
train_dataset = RealGraspDataset(mode='train')
val_dataset = RealGraspDataset(mode='test')
elif dataset.lower() == 'synth_spec':
train_dataset = SpecSynthDataset(tclass=tclass, root=pjoin(cfg.dataset_folder, 'synth_small_bg'), mode='all')
val_dataset = SpecRealDataset(tclass=tclass, mode='all')
elif dataset.lower() == 'synth_small_printer':
train_dataset = SynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), mode='all')
val_dataset = RealDataset(mode='test')
elif dataset.lower() == 'synth_part2':
train_dataset = SpecSynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), tclass='part2', mode='all')
val_dataset = SpecRealDataset(mode='all', tclass='part2')
elif dataset.lower() == 'synth_part3':
train_dataset = SpecSynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), tclass='part3', mode='all')
val_dataset = SpecRealDataset(mode='all', tclass='part3')
elif dataset.lower() == 'synth_dosing_nozzle':
train_dataset = SpecSynthDataset(root=pjoin(cfg.dataset_folder, 'synth_small_printer'), tclass='part3', mode='all')
val_dataset = SpecRealDataset(mode='all', tclass='dosing_nozzle')
elif dataset.split('_')[0] == 'synth':
train_dataset = SynthDataset(root=pjoin(cfg.dataset_folder, dataset), mode='all')
val_dataset = RealDataset(mode='test')
val_metric = VOCMApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
if mixup:
from gluoncv.data.mixup import detection
train_dataset = detection.MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = VOCLike(
root="/content/drive/My Drive/Research/Dataset_conversion/Dataset/",
splits=[(2007, 'train')])
val_dataset = VOCLike(
root="/content/drive/My Drive/Research/Dataset_conversion/Dataset/",
splits=[(2007, 'validation')])
print(train_dataset.classes)
print(val_dataset.classes)
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
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
pass
# 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='/home/xcq/PycharmProjects/datasets/coco/',
splits='instances_train2017',
use_crowd=False)
val_dataset = gdata.COCODetection(
root='/home/xcq/PycharmProjects/datasets/coco/',
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
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
pass
elif dataset.lower() == 'coco':
train_dataset = gdata.COCOInstance(root='/home/xcq/PycharmProjects/datasets/coco/',splits='instances_train2017')
val_dataset = gdata.COCOInstance(root='/home/xcq/PycharmProjects/datasets/coco/',splits='instances_val2017', skip_empty=False)
val_metric = COCOInstanceMetric(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
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() == 'voc_tiny':
# need to download the dataset and specify the path to store the dataset in
# root = os.path.expanduser('~/.mxnet/datasets/')
# filename_zip = ag.download('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip', path=root)
# filename = ag.unzip(filename_zip, root=root)
# data_root = os.path.join(root, filename)
train_dataset = gdata.CustomVOCDetectionBase(classes=('motorbike', ),
root=args.dataset_root +
'tiny_motorbike',
splits=[('', 'trainval')])
val_dataset = gdata.CustomVOCDetectionBase(classes=('motorbike', ),
root=args.dataset_root +
'tiny_motorbike',
splits=[('', '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,
os.path.join(
args.logdir,
args.save_prefix + '_eval'),
cleanup=True)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
if args.train.mixup:
from gluoncv.data.mixup import detection
train_dataset = detection.MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(args):
dataset = args.dataset
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)
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.mixup import detection
train_dataset = detection.MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = VOCLike(root='clothes_data',
splits=((2018, 'train'), ))
print(train_dataset)
val_dataset = VOCLike(root='clothes_data', splits=((2018, '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 detection
train_dataset = detection.MixupDetection(train_dataset)
return train_dataset, val_dataset, val_metric
