def __init__(self, options):
        if options.gpus[0] >= 0:
            try:
                self.ctx = mx.gpu()
                _ = nd.zeros((1,), ctx=self.ctx)
            except mx.base.MXNetError:
                print("No GPU available. Use CPU instead.")
                self.ctx = mx.cpu()
        else:
            self.ctx = mx.cpu()

        print("Creating model...")
        self.model = create_model(options.arch, options.heads, options.head_conv, ctx = self.ctx)
        if options.load_model_path != '':
            self.model = load_model(self.model, options.load_model_path, ctx = self.ctx)

        self.mean = np.array(options.mean, dtype=np.float32).reshape(1, 1, 3)
        self.std = np.array(options.std, dtype=np.float32).reshape(1, 1, 3)
        self.max_per_image = 100
        self.num_classes = options.num_classes
        self.scales = options.test_scales
        self.opt = options
        self.pause = True 

def __init__(self, opt):
    if opt.gpus[0] >= 0:
      opt.device = torch.device('cuda')
    else:
      opt.device = torch.device('cpu')
    
    print('Creating model...')
    self.model = create_model(opt.arch, opt.heads, opt.head_conv)
    self.model = load_model(self.model, opt.load_model)
    self.model = self.model.to(opt.device)
    self.model.eval()

    self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3)
    self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3)
    self.max_per_image = 100
    self.num_classes = opt.num_classes
    self.scales = opt.test_scales
    self.opt = opt
    self.pause = True 

def __init__(self, opt):
    if opt.gpus[0] >= 0:
      opt.device = torch.device('cuda')
    else:
      opt.device = torch.device('cpu')
    
    print('Creating model...')
    self.model = create_model(opt.arch, opt.heads, opt.head_conv)
    self.model = load_model(self.model, opt.load_model)
    self.model = self.model.to(opt.device)
    self.model.eval()

    self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3)
    self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3)
    self.max_per_image = 100
    self.num_classes = opt.num_classes
    self.scales = opt.test_scales
    self.opt = opt
    self.pause = True 

def __init__(self, opt):
    if opt.gpus[0] >= 0:
      opt.device = torch.device('cuda')
    else:
      opt.device = torch.device('cpu')
    
    print('Creating model...')
    self.model = create_model(opt.arch, opt.heads, opt.head_conv)
    self.model = load_model(self.model, opt.load_model)
    self.model = self.model.to(opt.device)
    self.model.eval()

    self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3)
    self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3)
    self.max_per_image = 100
    self.num_classes = opt.num_classes
    self.scales = opt.test_scales
    self.opt = opt
    self.pause = True 

def __init__(self, cfg):
    
        print('Creating model...')
        self.model = create_model(cfg.MODEL.NAME, cfg.MODEL.HEAD_CONV, cfg)
        self.model = load_model(self.model, cfg.TEST.MODEL_PATH)
        self.model = self.model.to(torch.device('cuda'))
        self.model.eval()

        self.mean = np.array(cfg.DATASET.MEAN, dtype=np.float32).reshape(1, 1, 3)
        self.std = np.array(cfg.DATASET.STD, dtype=np.float32).reshape(1, 1, 3)
        self.max_per_image = 100
        self.num_classes = cfg.MODEL.NUM_CLASSES
        self.scales = cfg.TEST.TEST_SCALES
        self.cfg = cfg
        self.pause = True