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.PersonDetection(root=COCO_ROOT_DIR,
splits=('person_train2014',
'person_train2017'),
use_crowd=False)
val_dataset = gdata.PersonDetection(root=COCO_ROOT_DIR,
splits='person_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)
return train_dataset, val_dataset, val_metric
def get_dataset(dataset, args):
# load training and validation images
if dataset.lower == 'voc':
train_dataset = gdata.VOCDetection(root=args.dataset_root + "/voc",
splits=[(2007, 'trainval'),
(2012, 'trainval')])
val_dataset = gdata.VOCDetection(root=args.dataset_root + "/voc",
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
# if class_names is provided, will print out AP for each class
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))
# will print out AP for each class
if args.val_interval == 1:
# args.val_interval: 进行验证集测试的循环间隔
# 如果进行测试很慢的话， 需要将该值改大一些，以加快训练
args.val_interval = 10
else:
raise NotImplementedError(
"dataset: {} not implemented".format(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(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:
train_dataset = petVOC(splits=[(2019, 'train_val')])
val_dataset = train_dataset
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
return train_dataset, val_dataset, val_metric
def __init__(self):
super().__init__()
self.train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
self.val_dataset = gdata.VOCDetection(splits=[(2007, 'test')])
self.val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=self.val_dataset.classes)
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':
train_dataset = gdata.VOCDetection(
root='/home/users/chenxin.lu/VOCdevkit/VOCdevkit',
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/stuff_annotations_trainval2017",
splits='stuff_train2017')
val_dataset = gdata.COCODetection(
root=args.dataset_root + "/coco/stuff_annotations_trainval2017",
splits='stuff_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
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(root=args.dataset_root,
splits=[(2007, 'trainval'),
(2012, 'trainval')])
val_dataset = gdata.VOCDetection(root=args.dataset_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')
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
elif dataset.lower() == 'tt100k':
train_dataset = gdata.TT100KDetection(root=args.dataset_root,
splits='train')
val_dataset = None
val_metric = None
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(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() 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, 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
def get_dataset(dataset):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(
splits=[(2007, 'test')])
else:
raise NotImplementedError('Dataset: {} not implemented.'.format(dataset))
return train_dataset, val_dataset
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)
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return train_dataset, val_dataset, val_metric
def get_dataset():
train_dataset = gdata.VOCDetection(
root='H:\\Self_study\\DeepLearing\\4 MXNet\\Pre-modeul\\data\\VOC',
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(
root='H:\\Self_study\\DeepLearing\\4 MXNet\\Pre-modeul\\data\\VOC',
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
return train_dataset, val_dataset, val_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):
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(
splits=[(2007, 'test')])
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
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
def get_data_set(train_root='./VOCData/train_val/VOCdevkit', test_root='./VOCData/test/VOCdevkit'):
"""
get PASCAL-VOC train & val & test dataset
:return:
train_dataset :
val_dataset :
use test data as valid dataset
"""
train_dataset = gdata.VOCDetection(root=train_root, splits=[(2007, 'trainval')])
val_dataset = gdata.VOCDetection(root=test_root, splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes)
return (train_dataset, val_dataset, val_metric)
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':
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
elif dataset.lower() == 'nzrc':
# The classes for the dataset need to be reset after net is loaded to prevent a classes mismatch errors when loading net.
gdata.COCODetection.CLASSES = classes
print("train_efficirntdet.py-50 get_dataset CLASSES=",
gdata.COCODetection.CLASSES)
train_dataset = gdata.COCODetection(root=args.dataset_root +
"/NZRC/ML4DR_v2",
splits='coco_export2_train')
val_dataset = gdata.COCODetection(root=args.dataset_root +
"/NZRC/ML4DR_v2",
splits='coco_export2_val',
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))
if args.num_samples < 0:
args.num_samples = len(train_dataset)
return train_dataset, val_dataset, val_metric
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]
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 = 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
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.PersonDetection(root=COCO_ROOT_DIR, splits='person_train2017', skip_empty=False)
#val_dataset = gdata.COCODetection(root=COCO_ROOT_DIR, splits='instances_val2017', skip_empty=False)
val_dataset = gdata.PersonDetection(root=COCO_ROOT_DIR, splits='person_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
def _load_data(self):
assert len(self.ishape) == 4
N, C, H, W = self.ishape
assert C == 3
val_dataset = gdata.VOCDetection(root=path.join(
self.root_dir, 'VOCdevkit'),
splits=[('2007', 'test')])
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
self.data = gluon.data.DataLoader(val_dataset.transform(
YOLO3DefaultValTransform(W, H)),
N,
False,
batchify_fn=val_batchify_fn,
last_batch='discard',
num_workers=30)
def load_voc(batch_size, input_size=416, **kwargs):
fname = "VOCtest_06-Nov-2007.tar"
root_dir = download_files("voc", [fname], **kwargs)
extract_file(os.path.join(root_dir, fname), root_dir)
width, height = input_size, input_size
val_dataset = gdata.VOCDetection(root=os.path.join(root_dir, 'VOCdevkit'),
splits=[('2007', 'test')])
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
val_loader = gluon.data.DataLoader(
val_dataset.transform(YOLO3DefaultValTransform(width, height)),
batch_size,
False,
batchify_fn=val_batchify_fn,
last_batch='keep',
num_workers=30)
return val_loader
def _load_data(self):
""" Customized _load_data method introduction.
VOC dataset only support layout of NCHW and the number of channels must be 3, i.e. (batch_size, 3, input_size, input_size).
The validation dataset will be created by Pascal *VOC detection Dataset* and use YOLO3DefaultValTransform as data preprocess function.
"""
assert len(self.ishape) == 4
N, C, H, W = self.ishape
assert C == 3
val_dataset = gdata.VOCDetection(
root=path.join(self.root_dir, 'VOCdevkit'),
splits=[('2007', 'test')])
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
self.data = gluon.data.DataLoader(
val_dataset.transform(YOLO3DefaultValTransform(W, H)),
N, False, batchify_fn=val_batchify_fn,
last_batch='discard', num_workers=30)
def get_dataset(dataset, data_shape):
if dataset.lower() == 'voc':
val_dataset = gdata.VOCDetection(root=args.dataset_root,
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))
elif dataset.lower() == 'tt100k':
val_dataset = gdata.TT100KDetection(root=args.dataset_root,
splits='test',
preload_label=False)
val_metric = None
else:
raise NotImplementedError(
'Dataset: {} not implemented.'.format(dataset))
return val_dataset, val_metric
from gluoncv import data, utils
from matplotlib import pyplot as plt
train_dataset = data.VOCDetection(splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = data.VOCDetection(splits=[(2007, 'test')])
print('Num of training images:', len(train_dataset))
print('Num of validation images:', len(val_dataset))
train_image, train_label = train_dataset[52]
print('Image size (height, width, RGB):', train_image.shape)
bounding_boxes = train_label[:, :4]
print('Num of objects:', bounding_boxes.shape[0])
print('Bounding boxes (num_boxes, x_min, y_min, x_max, y_max):\n',
bounding_boxes)
class_ids = train_label[:, 4:5]
print('Class IDs (num_boxes, ):\n', class_ids)
utils.viz.plot_bbox(train_image.asnumpy(), bounding_boxes, scores=None,
labels=class_ids, class_names=train_dataset.classes)
plt.show()
def get_dataset(dataset, args):
if dataset.lower() == 'voc':
train_dataset = gdata.VOCDetection(
splits=[(2007, 'trainval'), (2012, 'trainval')])
