class_docstr: Base class for two-stage detectors.
  
  Two-stage detectors typically consisting of a region proposal network and a
  task-specific regression head.
class_docstr_tok: ["Base","class","for","two-stage","detectors",".","Two-stage","detectors","typically","consisting","of","a","region","proposal","network","and","a","task-specific","regression","head","."]
docstr: Used for computing network flops.
  
  See `mmdetection/tools/get_flops.py`
docstr_tok: ["Used","for","computing","network","flops",".","See","`","mmdetection\/tools\/get_flops.py","`"]
code: def forward_dummy(self, img):
      """Used for computing network flops.
  
      See `mmdetection/tools/get_flops.py`
      """
      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(img.device)
      # roi_head
      roi_outs = self.roi_head.forward_dummy(x, proposals)
      outs = outs + (roi_outs,)
      return outs
  
file_path: insightface/detection/scrfd/mmdet/models/detectors/two_stage.py
imports: import torch
  import torch.nn
  from ..builder import DETECTORS, build_backbone, build_head, build_neck
  from .base import BaseDetector
label_desc: Use image node_id 6 for calling the TwoStageDetector obj's underlying member method code with example usage: obj.forward_dummy(img) and returns: outs