Exemplo n.º 1
0
def run_test(cfg, model, distributed):
    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(
            model,
            data_loader_val,
            dataset_name=dataset_name,
            iou_types=iou_types,
            box_only=False if cfg.MODEL.ATSS_ON or cfg.MODEL.FCOS_ON
            or cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
            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()
Exemplo n.º 2
0
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("--local_rank", type=int, default=0)
    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
    distributed = num_gpus > 1

    if distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(backend="nccl",
                                             init_method="env://")
        synchronize()

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    save_dir = ""
    logger = setup_logger("atss_core", save_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info("cfg:\n", cfg)

    logger.info("Collecting env info ( It might take some time)")
    logger.info("\n" + collect_env_info())

    model = build_detection_model(cfg)
    model.to(cfg.MODEL.DEVICE)

    output_dir = cfg.OUTPUT_DIR
    checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
    _ = checkpointer.load(cfg.MODEL.WEIGHT)

    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(
            model,
            data_loader_val,
            dataset_name=dataset_name,
            iou_types=iou_types,
            box_only=False if cfg.MODEL.ATSS_ON or cfg.MODEL.FCOS_ON
            or cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
            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()