def __init__(self, path, is_train=True, min_size=600, max_size=1000): self.path = path self.is_train = is_train if self.is_train: self.db = VOCBboxDataset(path) self.tsf = Transform(min_size, max_size) else: self.db = VOCBboxDataset(path, split='val', use_difficult=True)
def __init__(self, opt): self.opt = opt if opt.data == 'voc': self.db = VOCBboxDataset(opt.voc_data_dir) else: self.db = CITYBboxDataset(opt.city_data_dir, train=True) self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt): self.opt = opt if opt.data == 'voc': self.db = VOCBboxDataset(opt.voc_data_dir) elif opt.data == 's3d': self.db = S3DBboxDataset(opt.s3d_data_dir) self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, split='test', use_difficult=True): self.opt = opt if opt.data == 'voc': self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult) else: self.db = CITYBboxDataset(opt.city_data_dir, train=False)
def __init__(self, opt, split='test', use_difficult=True): #add use_difficult self.opt = opt self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult)
def __init__(self, opt): self.opt = opt if opt.data == 'cityperson': self.db = CityPersonTrainset( img_path=opt.cityperson_data_dir + '/leftImg8bit/train', annotation_path=opt.cityperson_data_dir + '/gtBboxCityPersons/train') else: self.db = VOCBboxDataset(opt.voc_data_dir) self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, split='test', use_difficult=True): self.opt = opt if self.opt.data == 'markers': self.db = MarkersDataset(opt.data_dir) elif self.opt.data == 'voc': self.db = VOCBboxDataset(opt.data_dir) else: raise Exception('database type not recognised: {}'.format( self.opt.data)) self.label_names = self.db.get_label_names()
def __init__(self, opt, split='test', use_difficult=True): self.opt = opt if opt.data == 'cityperson': self.db = CityPersonTestset( img_path=opt.cityperson_data_dir + '/leftImg8bit/val', annotation_path=opt.cityperson_data_dir + '/gtBboxCityPersons/val') else: self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult)
def __init__(self, opt): self.opt = opt if self.opt.data == 'markers': self.db = MarkersDataset(opt.data_dir) elif self.opt.data == 'voc': self.db = VOCBboxDataset(opt.data_dir) else: raise Exception('database type not recognised: {}'.format( self.opt.data)) self.tsf = Transform(opt.min_size, opt.max_size) self.label_names = self.db.get_label_names()
def __init__(self, configurations): self.configurations = configurations self.db = VOCBboxDataset(configurations.voc_data_dir) self.tsf = Transform(configurations.min_size, configurations.max_size)
def __init__(self, configurations, split='val', use_difficult=True): self.configurations = configurations self.db = VOCBboxDataset(configurations.voc_data_dir, split=split, use_difficult=use_difficult)
def __init__(self, opt, split='test', use_difficult=True): self.opt = opt self.db = VOCBboxDataset('/data2/dechunwang/dataset/', split=split, use_difficult=use_difficult)
def __init__(self, opt, id_file=None, img_dir=None, anno_dir=None, use_diff=True): self.opt = opt self.db = VOCBboxDataset(opt.voc_data_dir, id_file=id_file, img_dir=img_dir, anno_dir=anno_dir, use_difficult=use_diff) self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, use_difficult=True, id_file=None, img_dir=None, anno_dir=None): self.opt = opt self.db = VOCBboxDataset(opt.voc_data_dir, id_file=id_file, img_dir=img_dir, anno_dir=anno_dir, use_difficult=use_difficult)
def __init__(self, opt, split='test_small_32', use_difficult=True): self.opt = opt self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult, small_obj=True)
def __init__(self, opt): self.opt = opt self.db = VOCBboxDataset(opt.voc_data_dir, split='trainval_small_32', small_obj=True) self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, split='test', use_difficult=True): self.opt = opt self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult) self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self): self.db = VOCBboxDataset(voc_data_dir) self.tsf = Transform(min_size=600, max_size=1000)
def __init__(self, opt): self.opt = opt self.db = VOCBboxDataset(self.opt.test_data_dir)
def __init__(self, opt): self.opt = opt self.db = VOCBboxDataset(opt.train_data_dir) self.tsf = Transform()
def __init__(self): self.db = VOCBboxDataset( 'D:/pyworks/FasterRCNN/data/testset/VOCdevkit/VOC2007', 'test', True)
def __init__(self): self.db = VOCBboxDataset( 'D:/pyworks/FasterRCNN/data/VOCdevkit/VOC2007', 'trainval') self.transform = Transform()
def __init__(self, opt): self.opt = opt self.db = VOCBboxDataset(opt.voc_data_dir) self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt): self.opt = opt #预设参数 self.db = VOCBboxDataset(opt.voc_data_dir) #用预设参数创建数据集类 self.tsf = Transform(opt.min_size, opt.max_size) #实例化预处理类