Python cfg.OUTPUT_DIR属性代码示例

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def __init__(
        self,
        cfg,
        weights,
        confidence_threshold=0.5,
        min_image_size=224,
    ):
        self.cfg = cfg.clone()
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device(cfg.MODEL.DEVICE)
        self.model.to(self.device)
        self.min_image_size = min_image_size

        save_dir = cfg.OUTPUT_DIR
        checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
        _ = checkpointer.load(weights)

        self.transforms = self.build_transform()

        # used to make colors for each class
        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])

        self.cpu_device = torch.device("cpu")
        self.confidence_threshold = confidence_threshold 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def _inference(self, cand):
        # bn_statistic
        parent_conn, child_conn = mp.Pipe()
        args = dict({"local_rank": 0, "distributed": False})
        mp.spawn(
            bn_statistic, nprocs=self.ngpus_per_node,
            args=(self.ngpus_per_node, cfg, args, cand, child_conn))
        salt = parent_conn.recv()

        # fitness
        parent_conn, child_conn = mp.Pipe()
        args = dict({"local_rank": 0, "distributed": False})
        mp.spawn(
            fitness, nprocs=self.ngpus_per_node,
            args=(self.ngpus_per_node, cfg, args, cand, salt, child_conn))

        if os.path.isfile(os.path.join(cfg.OUTPUT_DIR, salt+".pth")):
            os.remove(os.path.join(cfg.OUTPUT_DIR, salt+".pth"))

        return parent_conn.recv() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def __init__(self,cfg,*,refresh=False):

        self.log_dir=cfg.OUTPUT_DIR
        self.checkpoint_name=os.path.join(self.log_dir,'checkpoint.brainpkl')

        self.refresh=refresh

        self.tester = TestClient()
        self.tester.connect()

        self.model = GeneralizedRCNN(cfg)
        self.complexity_info=Complexity()

        self.memory=[]
        self.candidates=[]
        self.vis_dict={}
        self.keep_top_k = {config.select_num:[],50:[]}
        self.epoch=0 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def main():
    parser=argparse.ArgumentParser()
    parser.add_argument('-r','--refresh',action='store_true')
    parser.add_argument(
        "--config-file",
        default="",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    args=parser.parse_args()
    refresh=args.refresh

    t = time.time()

    cfg.merge_from_file(args.config_file)
    cfg.OUTPUT_DIR = config.log_dir
    cfg.freeze()
    trainer=EvolutionTrainer(cfg,refresh=refresh)

    trainer.train()
    print('total searching time = {:.2f} hours'.format((time.time()-t)/3600)) 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
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.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() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def 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",)
    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=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() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
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.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() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def 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",)
    output_folders = [None] * len(cfg.DATASETS.TEST)
    if cfg.OUTPUT_DIR:
        dataset_names = cfg.DATASETS.TEST
        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, data_loader_val in zip(output_folders, data_loaders_val):
        inference(
            model,
            data_loader_val,
            iou_types=iou_types,
            #box_only=cfg.MODEL.RPN_ONLY,
            box_only=False if cfg.RETINANET.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() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def 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.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() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
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, mode=0, resolution=None, is_train=False, is_distributed=distributed)
    for loader in data_loaders_val:
        loader.collate_fn.special_deal = False
    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.RETINANET_ON or cfg.MODEL.DENSEBOX_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() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def 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",)
    output_folders = [None] * len(cfg.DATASETS.TEST)
    if cfg.OUTPUT_DIR:
        dataset_names = cfg.DATASETS.TEST
        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, data_loader_val in zip(output_folders, data_loaders_val):
        inference(
            model,
            data_loader_val,
            iou_types=iou_types,
            box_only=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,
            maskiou_on=cfg.MODEL.MASKIOU_ON
        )
        synchronize() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
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.FCOS_ON or cfg.MODEL.RETINANET_ON or cfg.MODEL.GAU_ON else cfg.MODEL.RPN_ONLY,  # changed for fcos
            device=cfg.MODEL.DEVICE,
            expected_results=cfg.TEST.EXPECTED_RESULTS,
            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
            output_folder=output_folder,
            ignore_uncertain=cfg.TEST.IGNORE_UNCERTAIN,
            use_iod_for_ignore=cfg.TEST.USE_IOD_FOR_IGNORE,
            eval_standard=cfg.TEST.COCO_EVALUATE_STANDARD,
            use_last_prediction=cfg.TEST.DEBUG.USE_LAST_PREDICTION,
            evaluate_method=cfg.TEST.EVALUATE_METHOD,
            voc_iou_ths=cfg.TEST.VOC_IOU_THS,
            gt_file={'merge': cfg.TEST.MERGE_GT_FILE,
                     'sub': DatasetCatalog.DATA_DIR + '/' + DatasetCatalog.DATASETS[dataset_name]["ann_file"]},
            use_ignore_attr=cfg.TEST.USE_IGNORE_ATTR
        )
        synchronize()

# ################################################ add by hui ################################################# 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
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.FCOS_ON or cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,  # changed for fcos
            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() 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def train(cfg, local_rank, distributed):
    model = build_detection_model(cfg)
    device = torch.device(cfg.MODEL.DEVICE)
    model.to(device)

    optimizer = make_optimizer(cfg, model)
    scheduler = make_lr_scheduler(cfg, optimizer)

    if distributed:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[local_rank], output_device=local_rank,
            # this should be removed if we update BatchNorm stats
            broadcast_buffers=False,
        )

    arguments = {}
    arguments["iteration"] = 0

    output_dir = cfg.OUTPUT_DIR

    save_to_disk = get_rank() == 0
    checkpointer = DetectronCheckpointer(
        cfg, model, optimizer, scheduler, output_dir, save_to_disk
    )
    extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
    arguments.update(extra_checkpoint_data)

    data_loader = make_data_loader(
        cfg,
        is_train=True,
        is_distributed=distributed,
        start_iter=arguments["iteration"],
    )

    checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD

    do_train(
        model,
        data_loader,
        optimizer,
        scheduler,
        checkpointer,
        device,
        checkpoint_period,
        arguments,
    )

    return model 

# 需要导入模块: from maskrcnn_benchmark.config import cfg [as 别名]
# 或者: from maskrcnn_benchmark.config.cfg import OUTPUT_DIR [as 别名]
def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--config-file",
        default="",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    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(
        "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:
        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()

    output_dir = cfg.OUTPUT_DIR
    if output_dir:
        mkdir(output_dir)

    logger = setup_logger("maskrcnn_benchmark", 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))

    model = train(cfg, args.local_rank, args.distributed)

    if not args.skip_test:
        run_test(cfg, model, args.distributed)