def main(): parse_args() rospy.init_node('yolact_ros', anonymous=True) if args.config is not None: set_cfg(args.config) if args.config is None: model_path = SavePath.from_str(args.trained_model) # TODO: Bad practice? Probably want to do a name lookup instead. args.config = model_path.model_name + '_config' print('Config not specified. Parsed %s from the file name.\n' % args.config) set_cfg(args.config) if args.detect: cfg.eval_mask_branch = False if args.dataset is not None: set_dataset(args.dataset) with torch.no_grad(): if not os.path.exists('results'): os.makedirs('results') if args.cuda: cudnn.benchmark = True cudnn.fastest = True torch.set_default_tensor_type('torch.cuda.FloatTensor') else: torch.set_default_tensor_type('torch.FloatTensor') if args.resume and not args.display: with open(args.ap_data_file, 'rb') as f: ap_data = pickle.load(f) calc_map(ap_data) exit() print('Loading model...', end='') net = Yolact() net.load_weights(args.trained_model) net.eval() print(' Done.') if args.cuda: net = net.cuda() net.detect.use_fast_nms = True cfg.mask_proto_debug = False detect_ = DetectImg(net) try: rospy.spin() except KeyboardInterrupt: print("Shutting down") cv2.destroyAllWindows()
elif args.trained_model == 'latest': args.trained_model = SavePath.get_latest('weights/', cfg.name) if args.config is None: model_path = SavePath.from_str(args.trained_model) # TODO: Bad practice? Probably want to do a name lookup instead. args.config = model_path.model_name + '_config' print('Config not specified. Parsed %s from the file name.\n' % args.config) set_cfg(args.config) if args.detect: cfg.eval_mask_branch = False if args.dataset is not None: set_dataset(args.dataset) with torch.no_grad(): if not os.path.exists('results'): os.makedirs('results') if args.cuda: cudnn.benchmark = True cudnn.fastest = True torch.set_default_tensor_type('torch.cuda.FloatTensor') else: torch.set_default_tensor_type('torch.FloatTensor') if args.resume and not args.display: with open(args.ap_data_file, 'rb') as f: ap_data = pickle.load(f)