class_docstr: Base class for single-stage detectors.
  
  Single-stage detectors directly and densely predict bounding boxes on the
  output features of the backbone+neck.
class_docstr_tok: ["Base","class","for","single-stage","detectors",".","Single-stage","detectors","directly","and","densely","predict","bounding","boxes","on","the","output","features","of","the","backbone+neck","."]
docstr: Network forward process. Usually includes backbone, neck and head
  forward without any post-processing.
  
   Args:
      batch_inputs (Tensor): Inputs with shape (N, C, H, W).
      batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
          the meta information of each image and corresponding
          annotations.
  
  Returns:
      tuple[list]: A tuple of features from ``bbox_head`` forward.
docstr_tok: ["Network","forward","process",".","Usually","includes","backbone",",","neck","and","head","forward","without","any","post-processing",".","Args",":","batch_inputs","(","Tensor",")",":","Inputs","with","shape","(","N",",","C",",","H",",","W",")",".","batch_data_samples","(","list","[",":","obj",":","`","DetDataSample","`","]",")",":","Each","item","contains","the","meta","information","of","each","image","and","corresponding","annotations",".","Returns",":","tuple","[","list","]",":","A","tuple","of","features","from","``","bbox_head","``","forward","."]
code: def _forward(
      self,
      batch_inputs: Tensor,
      batch_data_samples: OptSampleList = None,
  ) -> Tuple[List[Tensor]]:
      """Network forward process. Usually includes backbone, neck and head
      forward without any post-processing.
  
       Args:
          batch_inputs (Tensor): Inputs with shape (N, C, H, W).
          batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
              the meta information of each image and corresponding
              annotations.
  
      Returns:
          tuple[list]: A tuple of features from ``bbox_head`` forward.
      """
      x = self.extract_feat(batch_inputs)
      results = self.bbox_head.forward(x)
      return results
  
file_path: mmdetection/mmdet/models/detectors/single_stage.py
imports: from typing import List, Tuple, Union
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList, SampleList
  from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
  from .base import BaseDetector
label_desc: Use image node_id 5 for calling the SingleStageDetector obj's underlying member method code with example usage: obj._forward(batch_inputs, batch_data_samples) and returns: results