def run_test(cfg, model, distributed, motion_specific=False): if distributed: model = model.module torch.cuda.empty_cache() # TODO check if it helps iou_types = ("bbox", ) if cfg.MODEL.MASK_ON: iou_types = iou_types + ("segm", ) if cfg.MODEL.KEYPOINT_ON: iou_types = iou_types + ("keypoints", ) output_folders = [None] * len(cfg.DATASETS.TEST) dataset_names = cfg.DATASETS.TEST if cfg.OUTPUT_DIR: for idx, dataset_name in enumerate(dataset_names): output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name) mkdir(output_folder) output_folders[idx] = output_folder data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed) for output_folder, dataset_name, data_loader_val in zip( output_folders, dataset_names, data_loaders_val): inference( cfg, model, data_loader_val, dataset_name=dataset_name, iou_types=iou_types, motion_specific=motion_specific, box_only=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY, bbox_aug=cfg.TEST.BBOX_AUG.ENABLED, device=cfg.MODEL.DEVICE, expected_results=cfg.TEST.EXPECTED_RESULTS, expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL, output_folder=output_folder, ) synchronize()
def main(): parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference") parser.add_argument( '--launcher', choices=['pytorch', 'mpi'], default='pytorch', help='job launcher') parser.add_argument( "--config-file", default="/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml", metavar="FILE", help="path to config file", ) parser.add_argument("--local_rank", type=int, default=0) parser.add_argument( "--ckpt", help="The path to the checkpoint for test, default is the latest checkpoint.", default=None, ) parser.add_argument( "--motion-specific", "-ms", action="store_true", help="if True, evaluate motion-specific iou" ) parser.add_argument("--master_port", "-mp", type=str, default='29999') parser.add_argument( "opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() if args.launcher == "pytorch": num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1 elif args.launcher == "mpi": num_gpus = int(os.environ["OMPI_COMM_WORLD_SIZE"]) if "OMPI_COMM_WORLD_SIZE" in os.environ else 1 else: num_gpus = 1 distributed = num_gpus > 1 if distributed: init_dist(args.launcher, args=args) synchronize() BASE_CONFIG = "configs/BASE_RCNN_{}gpu.yaml".format(num_gpus) cfg.merge_from_file(BASE_CONFIG) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() save_dir = "" logger = setup_logger("mega_core", save_dir, get_rank()) logger.info("Using {} GPUs".format(num_gpus)) logger.info(cfg) logger.info("Collecting env info (might take some time)") logger.info("\n" + collect_env_info()) model = build_detection_model(cfg) model.to(cfg.MODEL.DEVICE) # Initialize mixed-precision if necessary use_mixed_precision = cfg.DTYPE == 'float16' amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE) output_dir = cfg.OUTPUT_DIR checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir) ckpt = cfg.MODEL.WEIGHT if args.ckpt is None else args.ckpt _ = checkpointer.load(ckpt, use_latest=args.ckpt is None, flownet=None) iou_types = ("bbox",) if cfg.MODEL.MASK_ON: iou_types = iou_types + ("segm",) if cfg.MODEL.KEYPOINT_ON: iou_types = iou_types + ("keypoints",) output_folders = [None] * len(cfg.DATASETS.TEST) dataset_names = cfg.DATASETS.TEST if cfg.OUTPUT_DIR: for idx, dataset_name in enumerate(dataset_names): output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name) mkdir(output_folder) output_folders[idx] = output_folder data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed) for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val): inference( cfg, model, data_loader_val, dataset_name=dataset_name, iou_types=iou_types, motion_specific=args.motion_specific, box_only=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY, bbox_aug=cfg.TEST.BBOX_AUG.ENABLED, device=cfg.MODEL.DEVICE, expected_results=cfg.TEST.EXPECTED_RESULTS, expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL, output_folder=output_folder, ) synchronize()
def main(): parser = argparse.ArgumentParser( description="PyTorch Video Object Detection Training") parser.add_argument( "--config-file", default="", metavar="FILE", help="path to config file", type=str, ) parser.add_argument('--launcher', choices=['pytorch', 'mpi'], default='pytorch', help='job launcher') parser.add_argument("--local_rank", type=int, default=0) parser.add_argument( "--skip-test", dest="skip_test", help="Do not test the final model", action="store_true", ) parser.add_argument("--master_port", "-mp", type=str, default='29999') parser.add_argument("--save_name", default="", help="Where to store the log", type=str) parser.add_argument( "opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() num_gpus = int( os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1 args.distributed = num_gpus > 1 if args.distributed: init_dist(args.launcher, args=args) synchronize() # this is similar to the behavior of detectron2, which I think is a nice option. BASE_CONFIG = "configs/BASE_RCNN_{}gpu.yaml".format(num_gpus) cfg.merge_from_file(BASE_CONFIG) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) output_dir = cfg.OUTPUT_DIR if output_dir: mkdir(output_dir) cfg.freeze() logger = setup_logger("mega_core", output_dir, get_rank()) logger.info("Using {} GPUs".format(num_gpus)) logger.info(args) logger.info("Collecting env info (might take some time)") logger.info("\n" + collect_env_info()) logger.info("Loaded configuration file {}".format(args.config_file)) with open(args.config_file, "r") as cf: config_str = "\n" + cf.read() logger.info(config_str) logger.info("Running with config:\n{}".format(cfg)) output_config_path = os.path.join(cfg.OUTPUT_DIR, 'config.yml') logger.info("Saving config into: {}".format(output_config_path)) # save overloaded model config in the output directory save_config(cfg, output_config_path) if args.launcher == "mpi": args.local_rank = ompi_local_rank() model = train(cfg, args.local_rank, args.distributed) if not args.skip_test: run_test(cfg, model, args.distributed)
def main(): parser = argparse.ArgumentParser( description="PyTorch Object Detection Inference") parser.add_argument( "--config-file", default= "/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml", metavar="FILE", help="path to config file", ) parser.add_argument( "--prediction-folder", help="The path to the prediction file to be evaluated.", default=None, ) parser.add_argument("--motion-specific", "-ms", action="store_true", help="if True, evaluate motion-specific iou") parser.add_argument( "opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() BASE_CONFIG = "configs/BASE_RCNN_1gpu.yaml" cfg.merge_from_file(BASE_CONFIG) cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.freeze() save_dir = "" logger = setup_logger("mega_core", save_dir, 0) iou_types = ("bbox", ) if cfg.MODEL.MASK_ON: iou_types = iou_types + ("segm", ) if cfg.MODEL.KEYPOINT_ON: iou_types = iou_types + ("keypoints", ) output_folders = [None] * len(cfg.DATASETS.TEST) dataset_names = cfg.DATASETS.TEST if args.prediction_folder: for idx, dataset_name in enumerate(dataset_names): output_folder = os.path.join(args.prediction_folder, "inference", dataset_name) mkdir(output_folder) output_folders[idx] = output_folder data_loaders_val = make_data_loader(cfg, is_train=False) for output_folder, dataset_name, data_loader_val in zip( output_folders, dataset_names, data_loaders_val): inference_no_model( data_loader_val, iou_types=iou_types, motion_specific=args.motion_specific, box_only=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY, expected_results=cfg.TEST.EXPECTED_RESULTS, expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL, output_folder=output_folder, )