code: 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
  
  
  @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__()
          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:
              if mask_roi_extractor is not None:
                  self.mask_roi_extractor = builder.build_roi_extractor(
                      mask_roi_extractor)
                  self.share_roi_extractor = False
              else:
                  self.share_roi_extractor = True
                  self.mask_roi_extractor = self.bbox_roi_extractor
              self.mask_head = builder.build_head(mask_head)
  
          self.train_cfg = train_cfg
          self.test_cfg = test_cfg
  
          self.init_weights(pretrained=pretrained)
  
      @property
      def with_rpn(self):
          return hasattr(self, 'rpn_head') and self.rpn_head is not None
  
      def init_weights(self, pretrained=None):
          super(TwoStageDetector, self).init_weights(pretrained)
          self.backbone.init_weights(pretrained=pretrained)
          if self.with_neck:
              if isinstance(self.neck, nn.Sequential):
                  for m in self.neck:
                      m.init_weights()
              else:
                  self.neck.init_weights()
          if self.with_shared_head:
              self.shared_head.init_weights(pretrained=pretrained)
          if self.with_rpn:
              self.rpn_head.init_weights()
          if self.with_bbox:
              self.bbox_roi_extractor.init_weights()
              self.bbox_head.init_weights()
          if self.with_mask:
              self.mask_head.init_weights()
              if not self.share_roi_extractor:
                  self.mask_roi_extractor.init_weights()
  
      def extract_feat(self, img):
          x = self.backbone(img)
          if self.with_neck:
              x = self.neck(x)
          return x
  
      def forward_train(self,
                        img,
                        img_meta,
                        gt_bboxes,
                        gt_labels,
                        gt_bboxes_ignore=None,
                        gt_masks=None,
                        proposals=None):
          x = self.extract_feat(img)
  
          losses = dict()
  
          # RPN forward and loss
          if self.with_rpn:
              rpn_outs = self.rpn_head(x)
              rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta,
                                            self.train_cfg.rpn)
              rpn_losses = self.rpn_head.loss(
                  *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
              losses.update(rpn_losses)
  
              proposal_cfg = self.train_cfg.get('rpn_proposal',
                                                self.test_cfg.rpn)
              proposal_inputs = rpn_outs + (img_meta, proposal_cfg)
              proposal_list = self.rpn_head.get_bboxes(*proposal_inputs)
          else:
              proposal_list = proposals
  
          # assign gts and sample proposals
          if self.with_bbox or self.with_mask:
              bbox_assigner = build_assigner(self.train_cfg.rcnn.assigner)
              bbox_sampler = build_sampler(
                  self.train_cfg.rcnn.sampler, context=self)
              num_imgs = img.size(0)
              if gt_bboxes_ignore is None:
                  gt_bboxes_ignore = [None for _ in range(num_imgs)]
              sampling_results = []
              for i in range(num_imgs):
                  assign_result = bbox_assigner.assign(proposal_list[i],
                                                       gt_bboxes[i],
                                                       gt_bboxes_ignore[i],
                                                       gt_labels[i])
                  sampling_result = bbox_sampler.sample(
                      assign_result,
                      proposal_list[i],
                      gt_bboxes[i],
                      gt_labels[i],
                      feats=[lvl_feat[i][None] for lvl_feat in x])
                  sampling_results.append(sampling_result)
  
          # bbox head forward and loss
          if self.with_bbox:
              rois = bbox2roi([res.bboxes for res in sampling_results])
              # TODO: a more flexible way to decide which feature maps to use
              bbox_feats = self.bbox_roi_extractor(
                  x[:self.bbox_roi_extractor.num_inputs], rois)
              if self.with_shared_head:
                  bbox_feats = self.shared_head(bbox_feats)
              cls_score, bbox_pred = self.bbox_head(bbox_feats)
  
              bbox_targets = self.bbox_head.get_target(sampling_results,
                                                       gt_bboxes, gt_labels,
                                                       self.train_cfg.rcnn)
              loss_bbox = self.bbox_head.loss(cls_score, bbox_pred,
                                              *bbox_targets)
              losses.update(loss_bbox)
  
          # mask head forward and loss
          if self.with_mask:
              if not self.share_roi_extractor:
                  pos_rois = bbox2roi(
                      [res.pos_bboxes for res in sampling_results])
                  mask_feats = self.mask_roi_extractor(
                      x[:self.mask_roi_extractor.num_inputs], pos_rois)
                  if self.with_shared_head:
                      mask_feats = self.shared_head(mask_feats)
              else:
                  pos_inds = []
                  device = bbox_feats.device
                  for res in sampling_results:
                      pos_inds.append(
                          torch.ones(
                              res.pos_bboxes.shape[0],
                              device=device,
                              dtype=torch.uint8))
                      pos_inds.append(
                          torch.zeros(
                              res.neg_bboxes.shape[0],
                              device=device,
                              dtype=torch.uint8))
                  pos_inds = torch.cat(pos_inds)
                  mask_feats = bbox_feats[pos_inds]
              mask_pred = self.mask_head(mask_feats)
  
              mask_targets = self.mask_head.get_target(sampling_results,
                                                       gt_masks,
                                                       self.train_cfg.rcnn)
              pos_labels = torch.cat(
                  [res.pos_gt_labels for res in sampling_results])
              loss_mask = self.mask_head.loss(mask_pred, mask_targets,
                                              pos_labels)
              losses.update(loss_mask)
  
          return losses
  
      def simple_test(self, img, img_meta, proposals=None, rescale=False):
          """Test without augmentation."""
          assert self.with_bbox, "Bbox head must be implemented."
  
          x = self.extract_feat(img)
  
          proposal_list = self.simple_test_rpn(
              x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
  
          det_bboxes, det_labels = self.simple_test_bboxes(
              x, img_meta, proposal_list, self.test_cfg.rcnn, rescale=rescale)
          bbox_results = bbox2result(det_bboxes, det_labels,
                                     self.bbox_head.num_classes)
  
          if not self.with_mask:
              return bbox_results
          else:
              segm_results = self.simple_test_mask(
                  x, img_meta, det_bboxes, det_labels, rescale=rescale)
              return bbox_results, segm_results
  
      def aug_test(self, imgs, img_metas, rescale=False):
          """Test with augmentations.
  
          If rescale is False, then returned bboxes and masks will fit the scale
          of imgs[0].
          """
          # recompute feats to save memory
          proposal_list = self.aug_test_rpn(
              self.extract_feats(imgs), img_metas, self.test_cfg.rpn)
          det_bboxes, det_labels = self.aug_test_bboxes(
              self.extract_feats(imgs), img_metas, proposal_list,
              self.test_cfg.rcnn)
  
          if rescale:
              _det_bboxes = det_bboxes
          else:
              _det_bboxes = det_bboxes.clone()
              _det_bboxes[:, :4] *= img_metas[0][0]['scale_factor']
          bbox_results = bbox2result(_det_bboxes, det_labels,
                                     self.bbox_head.num_classes)
  
          # det_bboxes always keep the original scale
          if self.with_mask:
              segm_results = self.aug_test_mask(
                  self.extract_feats(imgs), img_metas, det_bboxes, det_labels)
              return bbox_results, segm_results
          else:
              return bbox_results
  
apis: torch.zeros torch.ones torch.cat