class_docstr: Implementation of `Per-Pixel Classification is
  NOT All You Need for Semantic Segmentation
  <https://arxiv.org/pdf/2107.06278>`_.
class_docstr_tok: ["Implementation","of","`","Per-Pixel","Classification","is","NOT","All","You","Need","for","Semantic","Segmentation","<","https",":","\/\/arxiv.org\/pdf\/2107.06278",">","`","_","."]
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`]): The batch
          data samples. It usually includes information such
          as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
  
  Returns:
      tuple[List[Tensor]]: A tuple of features from ``panoptic_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","`","]",")",":","The","batch","data","samples",".","It","usually","includes","information","such","as","`","gt_instance","`","or","`","gt_panoptic_seg","`","or","`","gt_sem_seg","`",".","Returns",":","tuple","[","List","[","Tensor","]","]",":","A","tuple","of","features","from","``","panoptic_head","``","forward","."]
code: def _forward(
      self, batch_inputs: Tensor, batch_data_samples: SampleList
  ) -> 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`]): The batch
              data samples. It usually includes information such
              as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
  
      Returns:
          tuple[List[Tensor]]: A tuple of features from ``panoptic_head``
          forward.
      """
      feats = self.extract_feat(batch_inputs)
      results = self.panoptic_head.forward(feats, batch_data_samples)
      return results
  
file_path: mmdetection/mmdet/models/detectors/maskformer.py
imports: from typing import Dict, List, Tuple
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
  from .single_stage import SingleStageDetector
label_desc: Use image node_id 5 for calling the MaskFormer obj's underlying member method code with example usage: obj._forward(batch_inputs, batch_data_samples) and returns: results