How to use the mmdet.models.builder function in mmdet

def __init__(self,
                 backbone,
                 neck,
                 rpn_head,
                 train_cfg,
                 test_cfg,
                 pretrained=None):
        super(RPN, self).__init__()
        self.backbone = builder.build_backbone(backbone)
        self.neck = builder.build_neck(neck) if neck is not None else None
        self.rpn_head = builder.build_head(rpn_head)
        self.train_cfg = train_cfg
        self.test_cfg = test_cfg
        self.init_weights(pretrained=pretrained)

def __init__(self,
                 num_stages,
                 backbone,
                 semantic_roi_extractor=None,
                 semantic_head=None,
                 semantic_fusion=('bbox', 'mask'),
                 interleaved=True,
                 mask_info_flow=True,
                 **kwargs):
        super(HybridTaskCascade, self).__init__(num_stages, backbone, **kwargs)
        assert self.with_bbox and self.with_mask
        assert not self.with_shared_head  # shared head not supported
        if semantic_head is not None:
            self.semantic_roi_extractor = builder.build_roi_extractor(
                semantic_roi_extractor)
            self.semantic_head = builder.build_head(semantic_head)

        self.semantic_fusion = semantic_fusion
        self.interleaved = interleaved
        self.mask_info_flow = mask_info_flow

neck=None,
                 shared_head=None,
                 rpn_head=None,
                 bbox_roi_extractor=None,
                 bbox_head=None,
                 mask_roi_extractor=None,
                 mask_head=None,
                 train_cfg=None,
                 test_cfg=None,
                 pretrained=None):
        assert bbox_roi_extractor is not None
        assert bbox_head is not None
        super(CascadeRCNN, self).__init__()

        self.num_stages = num_stages
        self.backbone = builder.build_backbone(backbone)

        if neck is not None:
            self.neck = builder.build_neck(neck)

        if rpn_head is not None:
            self.rpn_head = builder.build_head(rpn_head)

        if shared_head is not None:
            self.shared_head = builder.build_shared_head(shared_head)

        if bbox_head is not None:
            self.bbox_roi_extractor = nn.ModuleList()
            self.bbox_head = nn.ModuleList()
            if not isinstance(bbox_roi_extractor, list):
                bbox_roi_extractor = [
                    bbox_roi_extractor for _ in range(num_stages)

shared_head=shared_head,
            rpn_head=rpn_head,
            bbox_roi_extractor=bbox_roi_extractor,
            bbox_head=bbox_head,
            train_cfg=train_cfg,
            test_cfg=test_cfg,
            pretrained=pretrained)

        if grid_roi_extractor is not None:
            self.grid_roi_extractor = builder.build_roi_extractor(
                grid_roi_extractor)
            self.share_roi_extractor = False
        else:
            self.share_roi_extractor = True
            self.grid_roi_extractor = self.bbox_roi_extractor
        self.grid_head = builder.build_head(grid_head)

        self.init_extra_weights()

def __init__(self,
                 backbone,
                 neck=None,
                 shared_head=None,
                 rpn_head=None,
                 bbox_roi_extractor=None,
                 bbox_head=None,
                 mask_roi_extractor=None,
                 mask_head=None,
                 train_cfg=None,
                 test_cfg=None,
                 pretrained=None):
        super(TwoStageDetector, self).__init__()
        self.backbone = builder.build_backbone(backbone)

        if neck is not None:
            self.neck = builder.build_neck(neck)

        if shared_head is not None:
            self.shared_head = builder.build_shared_head(shared_head)

        if rpn_head is not None:
            self.rpn_head = builder.build_head(rpn_head)

        if bbox_head is not None:
            self.bbox_roi_extractor = builder.build_roi_extractor(
                bbox_roi_extractor)
            self.bbox_head = builder.build_head(bbox_head)

        if mask_head is not None:

shared_head=None,
                 pretrained=None):
        assert grid_head is not None
        super(GridRCNN, self).__init__(
            backbone=backbone,
            neck=neck,
            shared_head=shared_head,
            rpn_head=rpn_head,
            bbox_roi_extractor=bbox_roi_extractor,
            bbox_head=bbox_head,
            train_cfg=train_cfg,
            test_cfg=test_cfg,
            pretrained=pretrained)

        if grid_roi_extractor is not None:
            self.grid_roi_extractor = builder.build_roi_extractor(
                grid_roi_extractor)
            self.share_roi_extractor = False
        else:
            self.share_roi_extractor = True
            self.grid_roi_extractor = self.bbox_roi_extractor
        self.grid_head = builder.build_head(grid_head)

        self.init_extra_weights()

def __init__(self,
                 num_stages,
                 backbone,
                 semantic_roi_extractor=None,
                 semantic_head=None,
                 semantic_fusion=('bbox', 'mask'),
                 interleaved=True,
                 mask_info_flow=True,
                 **kwargs):
        super(HybridTaskCascade, self).__init__(num_stages, backbone, **kwargs)
        assert self.with_bbox and self.with_mask
        assert not self.with_shared_head  # shared head not supported
        if semantic_head is not None:
            self.semantic_roi_extractor = builder.build_roi_extractor(
                semantic_roi_extractor)
            self.semantic_head = builder.build_head(semantic_head)

        self.semantic_fusion = semantic_fusion
        self.interleaved = interleaved
        self.mask_info_flow = mask_info_flow

def __init__(self,
                 backbone,
                 neck=None,
                 bbox_head=None,
                 train_cfg=None,
                 test_cfg=None,
                 pretrained=None):
        super(SingleStageDetector, self).__init__()
        self.backbone = builder.build_backbone(backbone)
        if neck is not None:
            self.neck = builder.build_neck(neck)
        self.bbox_head = builder.build_head(bbox_head)
        self.train_cfg = train_cfg
        self.test_cfg = test_cfg
        self.init_weights(pretrained=pretrained)

]
            if not isinstance(bbox_head, list):
                bbox_head = [bbox_head for _ in range(num_stages)]
            assert len(bbox_roi_extractor) == len(bbox_head) == self.num_stages
            for roi_extractor, head in zip(bbox_roi_extractor, bbox_head):
                self.bbox_roi_extractor.append(
                    builder.build_roi_extractor(roi_extractor))
                self.bbox_head.append(builder.build_head(head))

        if mask_head is not None:
            self.mask_head = nn.ModuleList()
            if not isinstance(mask_head, list):
                mask_head = [mask_head for _ in range(num_stages)]
            assert len(mask_head) == self.num_stages
            for head in mask_head:
                self.mask_head.append(builder.build_head(head))
            if mask_roi_extractor is not None:
                self.share_roi_extractor = False
                self.mask_roi_extractor = nn.ModuleList()
                if not isinstance(mask_roi_extractor, list):
                    mask_roi_extractor = [
                        mask_roi_extractor for _ in range(num_stages)
                    ]
                assert len(mask_roi_extractor) == self.num_stages
                for roi_extractor in mask_roi_extractor:
                    self.mask_roi_extractor.append(
                        builder.build_roi_extractor(roi_extractor))
            else:
                self.share_roi_extractor = True
                self.mask_roi_extractor = self.bbox_roi_extractor

        self.train_cfg = train_cfg

def __init__(self,
                 backbone,
                 neck,
                 rpn_head,
                 train_cfg,
                 test_cfg,
                 pretrained=None):
        super(RPN, self).__init__()
        self.backbone = builder.build_backbone(backbone)
        self.neck = builder.build_neck(neck) if neck is not None else None
        self.rpn_head = builder.build_head(rpn_head)
        self.train_cfg = train_cfg
        self.test_cfg = test_cfg
        self.init_weights(pretrained=pretrained)