How to use the mmdet.models.registry.DETECTORS.register_module function in mmdet

import torch
import torch.nn.functional as F

from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner,
                        build_sampler, merge_aug_bboxes, merge_aug_masks,
                        multiclass_nms)
from .. import builder
from ..registry import DETECTORS
from .cascade_rcnn import CascadeRCNN


@DETECTORS.register_module
class HybridTaskCascade(CascadeRCNN):

    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(

import torch
import torch.nn as nn

from .base import BaseDetector
from .test_mixins import RPNTestMixin, BBoxTestMixin, MaskTestMixin
from .. import builder
from ..registry import DETECTORS
from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler

import ipdb

# 注意多继承的继承顺序，是广度优先，通过__mro__内置方法可以查看
# 但是后面几个都没有构造函数
@DETECTORS.register_module
class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
                       MaskTestMixin):

    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__()

import torch

from mmdet.core import bbox2roi, build_assigner, build_sampler
from ..registry import DETECTORS
from .two_stage import TwoStageDetector


@DETECTORS.register_module
class DoubleHeadRCNN(TwoStageDetector):

    def __init__(self, reg_roi_scale_factor, **kwargs):
        super().__init__(**kwargs)
        self.reg_roi_scale_factor = reg_roi_scale_factor

    def forward_dummy(self, img):
        outs = ()
        # backbone
        x = self.extract_feat(img)
        # rpn
        if self.with_rpn:
            rpn_outs = self.rpn_head(x)
            outs = outs + (rpn_outs, )
        proposals = torch.randn(1000, 4).to(device=img.device)
        # bbox head

from __future__ import division

import torch
import torch.nn as nn

from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner,
                        build_sampler, merge_aug_bboxes, merge_aug_masks,
                        multiclass_nms)
from .. import builder
from ..registry import DETECTORS
from .base import BaseDetector
from .test_mixins import RPNTestMixin


@DETECTORS.register_module
class CascadeRCNN(BaseDetector, RPNTestMixin):

    def __init__(self,
                 num_stages,
                 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):
        assert bbox_roi_extractor is not None

from ..registry import DETECTORS
from .two_stage import TwoStageDetector


@DETECTORS.register_module
class FasterRCNN(TwoStageDetector):

    def __init__(self,
                 backbone,
                 rpn_head,
                 bbox_roi_extractor,
                 bbox_head,
                 train_cfg,
                 test_cfg,
                 neck=None,
                 shared_head=None,
                 pretrained=None):
        super(FasterRCNN, self).__init__(
            backbone=backbone,
            neck=neck,
            shared_head=shared_head,

from ..registry import DETECTORS
from .single_stage import SingleStageDetector


@DETECTORS.register_module
class FCOS(SingleStageDetector):

    def __init__(self,
                 backbone,
                 neck,
                 bbox_head,
                 train_cfg=None,
                 test_cfg=None,
                 pretrained=None):
        super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg,
                                   test_cfg, pretrained)

import torch

from mmdet.core import bbox2roi, build_assigner, build_sampler
from ..registry import DETECTORS
from .two_stage import TwoStageDetector


@DETECTORS.register_module
class DoubleHeadRCNN(TwoStageDetector):

    def __init__(self, reg_roi_scale_factor, **kwargs):
        super().__init__(**kwargs)
        self.reg_roi_scale_factor = reg_roi_scale_factor

    def forward_dummy(self, img):
        outs = ()
        # backbone
        x = self.extract_feat(img)
        # rpn
        if self.with_rpn:
            rpn_outs = self.rpn_head(x)
            outs = outs + (rpn_outs, )
        proposals = torch.randn(1000, 4).cuda()
        # bbox head

import torch

from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from .. import builder
from ..registry import DETECTORS
from .two_stage import TwoStageDetector


@DETECTORS.register_module
class GridRCNN(TwoStageDetector):
    """Grid R-CNN.

    This detector is the implementation of:
    - Grid R-CNN (https://arxiv.org/abs/1811.12030)
    - Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/1906.05688)
    """

    def __init__(self,
                 backbone,
                 rpn_head,
                 bbox_roi_extractor,
                 bbox_head,
                 grid_roi_extractor,
                 grid_head,
                 train_cfg,

from ..registry import DETECTORS
from .single_stage import SingleStageDetector

import pdb


@DETECTORS.register_module
class FSAF(SingleStageDetector):

    def __init__(self,
                 backbone,
                 neck,
                 bbox_head,
                 train_cfg=None,
                 test_cfg=None,
                 pretrained=None):
        super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg,
                                        test_cfg, pretrained)
        
    def forward_train(self,
                      img,
                      img_metas,
                      gt_bboxes,

from ..registry import DETECTORS
from .single_stage import SingleStageDetector


@DETECTORS.register_module
class RetinaNet(SingleStageDetector):

    def __init__(self,
                 backbone,
                 neck,
                 bbox_head,
                 train_cfg=None,
                 test_cfg=None,
                 pretrained=None):
        super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg,
                                        test_cfg, pretrained)