Python config.get_cfg方法代码示例

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def setup_cfg(config_file, weights_file=None, config_opts=[], confidence_threshold=None, cpu=False):
    # load config from file and command-line arguments
    cfg = get_cfg()
    cfg.merge_from_file(config_file)
    cfg.merge_from_list(config_opts)

    if confidence_threshold is not None:
        # Set score_threshold for builtin models
        cfg.MODEL.RETINANET.SCORE_THRESH_TEST = confidence_threshold
        cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = confidence_threshold
        cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = confidence_threshold

    if weights_file is not None:
        cfg.MODEL.WEIGHTS = weights_file

    if cpu or not torch.cuda.is_available():
        cfg.MODEL.DEVICE = "cpu"

    cfg.freeze()

    return cfg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def test_apply_rotated_boxes(self):
        np.random.seed(125)
        cfg = get_cfg()
        is_train = True
        augs = detection_utils.build_augmentation(cfg, is_train)
        image = np.random.rand(200, 300)
        image, transforms = T.apply_augmentations(augs, image)
        image_shape = image.shape[:2]  # h, w
        assert image_shape == (800, 1200)
        annotation = {"bbox": [179, 97, 62, 40, -56]}

        boxes = np.array([annotation["bbox"]], dtype=np.float64)  # boxes.shape = (1, 5)
        transformed_bbox = transforms.apply_rotated_box(boxes)[0]

        expected_bbox = np.array([484, 388, 248, 160, 56], dtype=np.float64)
        err_msg = "transformed_bbox = {}, expected {}".format(transformed_bbox, expected_bbox)
        assert np.allclose(transformed_bbox, expected_bbox), err_msg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def testInitWithCfg(self):
        cfg = get_cfg()
        cfg.ARG1 = 1
        cfg.ARG2 = 2
        cfg.ARG3 = 3
        _ = _TestClassA(cfg)
        _ = _TestClassB(cfg, input_shape="shape")
        _ = _TestClassC(cfg, input_shape="shape")
        _ = _TestClassD(cfg, input_shape="shape")
        _ = _LegacySubClass(cfg, input_shape="shape")
        _ = _NewSubClassNewInit(cfg, input_shape="shape")
        _ = _LegacySubClassNotCfg(cfg, input_shape="shape")
        with self.assertRaises(TypeError):
            # disallow forwarding positional args to __init__ since it's prone to errors
            _ = _TestClassD(cfg, "shape")

        # call with kwargs instead
        _ = _TestClassA(cfg=cfg)
        _ = _TestClassB(cfg=cfg, input_shape="shape")
        _ = _TestClassC(cfg=cfg, input_shape="shape")
        _ = _TestClassD(cfg=cfg, input_shape="shape")
        _ = _LegacySubClass(cfg=cfg, input_shape="shape")
        _ = _NewSubClassNewInit(cfg=cfg, input_shape="shape")
        _ = _LegacySubClassNotCfg(config=cfg, input_shape="shape") 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def testInitWithCfgOverwrite(self):
        cfg = get_cfg()
        cfg.ARG1 = 1
        cfg.ARG2 = 999  # wrong config
        with self.assertRaises(AssertionError):
            _ = _TestClassA(cfg, arg3=3)

        # overwrite arg2 with correct config later:
        _ = _TestClassA(cfg, arg2=2, arg3=3)
        _ = _TestClassB(cfg, input_shape="shape", arg2=2, arg3=3)
        _ = _TestClassC(cfg, input_shape="shape", arg2=2, arg3=3)
        _ = _TestClassD(cfg, input_shape="shape", arg2=2, arg3=3)

        # call with kwargs cfg=cfg instead
        _ = _TestClassA(cfg=cfg, arg2=2, arg3=3)
        _ = _TestClassB(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
        _ = _TestClassC(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
        _ = _TestClassD(cfg=cfg, input_shape="shape", arg2=2, arg3=3) 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def _test_model(self, config_path, device="cpu"):
        # requires extra dependencies
        from detectron2.export import Caffe2Model, add_export_config, export_caffe2_model

        cfg = get_cfg()
        cfg.merge_from_file(model_zoo.get_config_file(config_path))
        cfg = add_export_config(cfg)
        cfg.MODEL.DEVICE = device

        model = build_model(cfg)
        DetectionCheckpointer(model).load(model_zoo.get_checkpoint_url(config_path))

        inputs = [{"image": self._get_test_image()}]
        c2_model = export_caffe2_model(cfg, model, copy.deepcopy(inputs))

        with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
            c2_model.save_protobuf(d)
            c2_model.save_graph(os.path.join(d, "test.svg"), inputs=copy.deepcopy(inputs))
            c2_model = Caffe2Model.load_protobuf(d)
        c2_model(inputs)[0]["instances"] 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def test_apply_rotated_boxes(self):
        np.random.seed(125)
        cfg = get_cfg()
        is_train = True
        transform_gen = detection_utils.build_transform_gen(cfg, is_train)
        image = np.random.rand(200, 300)
        image, transforms = T.apply_transform_gens(transform_gen, image)
        image_shape = image.shape[:2]  # h, w
        assert image_shape == (800, 1200)
        annotation = {"bbox": [179, 97, 62, 40, -56]}

        boxes = np.array([annotation["bbox"]], dtype=np.float64)  # boxes.shape = (1, 5)
        transformed_bbox = transforms.apply_rotated_box(boxes)[0]

        expected_bbox = np.array([484, 388, 248, 160, 56], dtype=np.float64)
        err_msg = "transformed_bbox = {}, expected {}".format(transformed_bbox, expected_bbox)
        assert np.allclose(transformed_bbox, expected_bbox), err_msg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def setup_cfg(args):
    # load config from file and command-line arguments
    cfg = get_cfg()
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    # Set score_threshold for builtin models
    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
    cfg.freeze()
    return cfg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    add_pointrend_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    return cfg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    add_tridentnet_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    return cfg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def setup_config(
        cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
    ):
        cfg = get_cfg()
        add_densepose_config(cfg)
        cfg.merge_from_file(config_fpath)
        cfg.merge_from_list(args.opts)
        if opts:
            cfg.merge_from_list(opts)
        cfg.MODEL.WEIGHTS = model_fpath
        cfg.freeze()
        return cfg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def _get_model_config(config_file):
    """
    Load and return the configuration from the specified file (relative to the base configuration
    directory)
    """
    cfg = get_cfg()
    add_dataset_category_config(cfg)
    add_densepose_config(cfg)
    path = os.path.join(_get_base_config_dir(), config_file)
    cfg.merge_from_file(path)
    if not torch.cuda.is_available():
        cfg.MODEL_DEVICE = "cpu"
    return cfg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    add_tensormask_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    return cfg 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def get_model_zoo(config_path):
    """
    Like model_zoo.get, but do not load any weights (even pretrained)
    """
    cfg_file = model_zoo.get_config_file(config_path)
    cfg = get_cfg()
    cfg.merge_from_file(cfg_file)
    if not torch.cuda.is_available():
        cfg.MODEL.DEVICE = "cpu"
    return build_model(cfg) 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def test_upgrade_downgrade_consistency(self):
        cfg = get_cfg()
        # check that custom is preserved
        cfg.USER_CUSTOM = 1

        down = downgrade_config(cfg, to_version=0)
        up = upgrade_config(down)
        self.assertTrue(up == cfg) 

# 需要导入模块: from detectron2 import config [as 别名]
# 或者: from detectron2.config import get_cfg [as 别名]
def test_auto_upgrade(self):
        cfg = get_cfg()
        latest_ver = cfg.VERSION
        cfg.USER_CUSTOM = 1

        self._merge_cfg_str(cfg, _V0_CFG)

        self.assertEqual(cfg.MODEL.RPN.HEAD_NAME, "TEST")
        self.assertEqual(cfg.VERSION, latest_ver)