def main(): # Get args from command line args = get_args_from_command_line() if args.gpu_id is not None: cfg.CONST.DEVICE = args.gpu_id if args.weights is not None: cfg.CONST.WEIGHTS = args.weights # Print config print('Use config:') pprint(cfg) # Set GPU to use os.environ["CUDA_VISIBLE_DEVICES"] = cfg.CONST.DEVICE # Start train/test process if not args.test and not args.inference: train_net_new(cfg) else: if 'WEIGHTS' not in cfg.CONST or not os.path.exists(cfg.CONST.WEIGHTS): logging.error('Please specify the file path of checkpoint.') sys.exit(2) if args.test: test_net_new(cfg) else: inference_net(cfg)
def inference(self): self.makeCurrent() pc_points = inference_net(cfg, upload_image=False) GM.base_point_cloud = PointCloud(pc_points) GM.base_point_cloud.set_color_according_camera_pos( camera_pos=[1.5, 1.5, 0.0]) self.update()
def main(): # Get args from command line args = get_args_from_command_line() if args.gpu_id is not None: cfg.CONST.DEVICE = args.gpu_id if args.weights is not None: cfg.CONST.WEIGHTS = args.weights # Print config print('Use config:') pprint(cfg) # f_runner.write(str(cfg)) # Set GPU to use os.environ["CUDA_VISIBLE_DEVICES"] = cfg.CONST.DEVICE # Start train/test process if not args.test and not args.inference: train_net(cfg) else: ''' if 'WEIGHTS' not in cfg.CONST or not os.path.exists(cfg.CONST.WEIGHTS): logging.error('Please specify the file path of checkpoint.') sys.exit(2) ''' if 'WEIGHTS' not in cfg.CONST: logging.error('Please specify the file path of checkpoint.1') sys.exit(2) if not os.path.exists(cfg.CONST.WEIGHTS): logging.error('Please specify the file path of checkpoint.2') sys.exit(2) if args.test: # test_net(cfg) path = '/raid/wuruihai/GRNet_FILES/tb_log' test_writer = SummaryWriter(path) test_net(cfg, test_writer=test_writer) if args.test_KITTI: path = '/raid/wuruihai/GRNet_FILES/tb_log' test_writer = SummaryWriter(path) test_net_KITTI(cfg, test_writer=test_writer) else: inference_net(cfg)
def main(): # Get args from command line args = get_args_from_command_line() # Read the experimental config exec(compile(open(args.cfg_file, "rb").read(), args.cfg_file, 'exec')) cfg = locals()['__C'] pprint(cfg) # Parse runtime arguments if args.gpu_id is not None: os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id if not args.randomize: random.seed(cfg.CONST.RNG_SEED) np.random.seed(cfg.CONST.RNG_SEED) torch.manual_seed(cfg.CONST.RNG_SEED) torch.cuda.manual_seed(cfg.CONST.RNG_SEED) torch.cuda.manual_seed_all(cfg.CONST.RNG_SEED) # References: https://pytorch.org/docs/stable/notes/randomness.html torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False if args.exp_name is not None: cfg.CONST.EXP_NAME = args.exp_name if args.weights is not None: cfg.CONST.WEIGHTS = args.weights # Start train/test process if not args.test and not args.inference: # Make sure cfg.TRAIN.NETWORK in ['RMNet', 'TinyFlowNet'] if cfg.TRAIN.NETWORK not in ['RMNet', 'TinyFlowNet']: logging.error( 'Please make sure cfg.TRAIN.NETWORK in ["RMNet", "TinyFlowNet"].' ) sys.exit(1) train_net(cfg) else: if 'WEIGHTS' not in cfg.CONST or not os.path.exists(cfg.CONST.WEIGHTS): logging.error('Please specify the file path of checkpoint.') sys.exit(2) if args.test: test_net(cfg) else: inference_net(cfg)