Python detector.build_detection_model方法代碼示例

本文整理匯總了Python中maskrcnn_benchmark.modeling.detector.build_detection_model方法的典型用法代碼示例。如果您正苦於以下問題：Python detector.build_detection_model方法的具體用法？Python detector.build_detection_model怎麽用？Python detector.build_detection_model使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類maskrcnn_benchmark.modeling.detector的用法示例。

在下文中一共展示了detector.build_detection_model方法的13個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: init

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
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

開發者ID:Xiangyu-CAS，項目名稱:R2CNN.pytorch，代碼行數:27，代碼來源:inference_engine.py

示例2: init

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        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(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # 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
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim

開發者ID:Res2Net，項目名稱:Res2Net-maskrcnn，代碼行數:33，代碼來源:predictor.py

示例3: init

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
        weight_loading = None
    ):
        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(cfg.MODEL.WEIGHT)
        
        if weight_loading:
            print('Loading weight from {}.'.format(weight_loading))
            _ = checkpointer._load_model(torch.load(weight_loading))
        
        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # 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
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim

開發者ID:megvii-model，項目名稱:DetNAS，代碼行數:38，代碼來源:predictor.py

示例4: create_model

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def create_model(cfg, device):
    cfg = copy.deepcopy(cfg)
    cfg.freeze()
    model = build_detection_model(cfg)
    model = model.to(device)
    return model

開發者ID:megvii-model，項目名稱:DetNAS，代碼行數:8，代碼來源:test_detectors.py

示例5: init

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
    ):
        self.color_list = colormap()
        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(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # 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 = torch.tensor(confidence_threshold)
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim

開發者ID:ChenJoya，項目名稱:sampling-free，代碼行數:34，代碼來源:predictor.py

示例6: init

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        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

        checkpointer = DetectronCheckpointer(cfg, self.model)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.checkpointer = checkpointer
        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # 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
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim

        self.CATEGORIES = COCO_CATEGORIES if cfg.DATASETS.TEST[0][:4] == 'coco' else VOC_CATEGORIES

開發者ID:zhangxiaosong18，項目名稱:FreeAnchor，代碼行數:35，代碼來源:predictor.py

示例7: init

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        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

        checkpointer = DetectronCheckpointer(cfg, self.model)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # 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
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim

開發者ID:chengyangfu，項目名稱:retinamask，代碼行數:32，代碼來源:predictor.py

示例8: train

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
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

開發者ID:Res2Net，項目名稱:Res2Net-maskrcnn，代碼行數:50，代碼來源:train_net.py

示例9: train

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
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)
    arguments["iteration"] = 0

    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

開發者ID:Xiangyu-CAS，項目名稱:R2CNN.pytorch，代碼行數:51，代碼來源:train_net.py

示例10: init

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def __init__(
        self,
        model,
        CATEGORIES,
        dataset,
        confidence_threshold=0.5,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
    ):
        if model == 'faster':
            config_file = "faster-retina/configs/e2e_faster_rcnn_R_50_FPN_1x_{}_test.yaml".format(dataset)
        if model == 'retinanet':
            config_file = 'faster-retina/configs/retinanet_R-50-FPN_1x-{}.yaml'.format(dataset)
        
        if model == 'maskrcnn':
            config_file = 'faster-retina/configs/e2e_mask_rcnn_R_50_FPN_1x-{}.yaml'.format(dataset)
        cfg.merge_from_file(config_file)
        self.cfg = cfg.clone()
        self.CATEGORIES = CATEGORIES
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device('cuda')
        self.model.to(self.device)
        self.min_image_size = min_image_size
        self.feat_extractor = FeatureExtractorFromBoxes(self.model)
        save_dir = cfg.OUTPUT_DIR
        checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # 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
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim

開發者ID:simaiden，項目名稱:Clothing-Detection，代碼行數:44，代碼來源:DetectronModels.py

示例11: train

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
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(  # clw note:創建數據集
        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

開發者ID:clw5180，項目名稱:remote_sensing_object_detection_2019，代碼行數:53，代碼來源:train_net.py

示例12: train

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
def train(cfg, local_rank, distributed):
    model = build_detection_model(cfg)
    device = torch.device(cfg.MODEL.DEVICE)
    model.to(device)
    if cfg.SOLVER.ENABLE_FP16:
        model.half()

    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
    )
    print(cfg.MODEL.WEIGHT)
    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,
        cfg
    )

    return model

開發者ID:HRNet，項目名稱:HRNet-MaskRCNN-Benchmark，代碼行數:53，代碼來源:train_net.py

示例13: train

# 需要導入模塊: from maskrcnn_benchmark.modeling import detector [as 別名]
# 或者: from maskrcnn_benchmark.modeling.detector import build_detection_model [as 別名]
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.deprecated.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

開發者ID:chengyangfu，項目名稱:retinamask，代碼行數:50，代碼來源:train_net.py

注：本文中的maskrcnn_benchmark.modeling.detector.build_detection_model方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。

示例1: __init__

示例2: __init__

示例3: __init__