class_docstr: Head of CentripetalNet: Pursuing High-quality Keypoint Pairs for Object
  Detection.
  
  CentripetalHead inherits from :class:`CornerHead`. It removes the
  embedding branch and adds guiding shift and centripetal shift branches.
  More details can be found in the `paper
  <https://arxiv.org/abs/2003.09119>`_ .
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      num_feat_levels (int): Levels of feature from the previous module.
          2 for HourglassNet-104 and 1 for HourglassNet-52. HourglassNet-104
          outputs the final feature and intermediate supervision feature and
          HourglassNet-52 only outputs the final feature. Defaults to 2.
      corner_emb_channels (int): Channel of embedding vector. Defaults to 1.
      train_cfg (:obj:`ConfigDict` or dict, optional): Training config.
          Useless in CornerHead, but we keep this variable for
          SingleStageDetector.
      test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
          CornerHead.
      loss_heatmap (:obj:`ConfigDict` or dict): Config of corner heatmap
          loss. Defaults to GaussianFocalLoss.
      loss_embedding (:obj:`ConfigDict` or dict): Config of corner embedding
          loss. Defaults to AssociativeEmbeddingLoss.
      loss_offset (:obj:`ConfigDict` or dict): Config of corner offset loss.
          Defaults to SmoothL1Loss.
      loss_guiding_shift (:obj:`ConfigDict` or dict): Config of
          guiding shift loss. Defaults to SmoothL1Loss.
      loss_centripetal_shift (:obj:`ConfigDict` or dict): Config of
          centripetal shift loss. Defaults to SmoothL1Loss.
     init_cfg (:obj:`ConfigDict` or dict, optional): the config to control
         the initialization.
class_docstr_tok: ["Head","of","CentripetalNet",":","Pursuing","High-quality","Keypoint","Pairs","for","Object","Detection",".","CentripetalHead","inherits","from",":","class",":","`","CornerHead","`",".","It","removes","the","embedding","branch","and","adds","guiding","shift","and","centripetal","shift","branches",".","More","details","can","be","found","in","the","`","paper","<","https",":","\/\/arxiv.org\/abs\/2003.09119",">","`","_",".","Args",":","num_classes","(","int",")",":","Number","of","categories","excluding","the","background","category",".","in_channels","(","int",")",":","Number","of","channels","in","the","input","feature","map",".","num_feat_levels","(","int",")",":","Levels","of","feature","from","the","previous","module",".","2","for","HourglassNet-104","and","1","for","HourglassNet-52",".","HourglassNet-104","outputs","the","final","feature","and","intermediate","supervision","feature","and","HourglassNet-52","only","outputs","the","final","feature",".","Defaults","to","2.","corner_emb_channels","(","int",")",":","Channel","of","embedding","vector",".","Defaults","to","1.","train_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",",","optional",")",":","Training","config",".","Useless","in","CornerHead",",","but","we","keep","this","variable","for","SingleStageDetector",".","test_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",",","optional",")",":","Testing","config","of","CornerHead",".","loss_heatmap","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","Config","of","corner","heatmap","loss",".","Defaults","to","GaussianFocalLoss",".","loss_embedding","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","Config","of","corner","embedding","loss",".","Defaults","to","AssociativeEmbeddingLoss",".","loss_offset","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","Config","of","corner","offset","loss",".","Defaults","to","SmoothL1Loss",".","loss_guiding_shift","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","Config","of","guiding","shift","loss",".","Defaults","to","SmoothL1Loss",".","loss_centripetal_shift","(",":","obj",":","`","ConfigDict","`","or","dict",")",":","Config","of","centripetal","shift","loss",".","Defaults","to","SmoothL1Loss",".","init_cfg","(",":","obj",":","`","ConfigDict","`","or","dict",",","optional",")",":","the","config","to","control","the","initialization","."]
docstr: Calculate the loss based on the features extracted by the detection
  head.
  
  Args:
      tl_heats (list[Tensor]): Top-left corner heatmaps for each level
          with shape (N, num_classes, H, W).
      br_heats (list[Tensor]): Bottom-right corner heatmaps for each
          level with shape (N, num_classes, H, W).
      tl_offs (list[Tensor]): Top-left corner offsets for each level
          with shape (N, corner_offset_channels, H, W).
      br_offs (list[Tensor]): Bottom-right corner offsets for each level
          with shape (N, corner_offset_channels, H, W).
      tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
          level with shape (N, guiding_shift_channels, H, W).
      br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
          each level with shape (N, guiding_shift_channels, H, W).
      tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
          for each level with shape (N, centripetal_shift_channels, H,
          W).
      br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
          shifts for each level with shape (N,
          centripetal_shift_channels, H, W).
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance. It usually includes ``bboxes`` and ``labels``
          attributes.
      batch_img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
          Specify which bounding boxes can be ignored when computing
          the loss.
  
  Returns:
      dict[str, Tensor]: A dictionary of loss components. Containing the
      following losses:
  
          - det_loss (list[Tensor]): Corner keypoint losses of all
            feature levels.
          - off_loss (list[Tensor]): Corner offset losses of all feature
            levels.
          - guiding_loss (list[Tensor]): Guiding shift losses of all
            feature levels.
          - centripetal_loss (list[Tensor]): Centripetal shift losses of
            all feature levels.
docstr_tok: ["Calculate","the","loss","based","on","the","features","extracted","by","the","detection","head",".","Args",":","tl_heats","(","list","[","Tensor","]",")",":","Top-left","corner","heatmaps","for","each","level","with","shape","(","N",",","num_classes",",","H",",","W",")",".","br_heats","(","list","[","Tensor","]",")",":","Bottom-right","corner","heatmaps","for","each","level","with","shape","(","N",",","num_classes",",","H",",","W",")",".","tl_offs","(","list","[","Tensor","]",")",":","Top-left","corner","offsets","for","each","level","with","shape","(","N",",","corner_offset_channels",",","H",",","W",")",".","br_offs","(","list","[","Tensor","]",")",":","Bottom-right","corner","offsets","for","each","level","with","shape","(","N",",","corner_offset_channels",",","H",",","W",")",".","tl_guiding_shifts","(","list","[","Tensor","]",")",":","Top-left","guiding","shifts","for","each","level","with","shape","(","N",",","guiding_shift_channels",",","H",",","W",")",".","br_guiding_shifts","(","list","[","Tensor","]",")",":","Bottom-right","guiding","shifts","for","each","level","with","shape","(","N",",","guiding_shift_channels",",","H",",","W",")",".","tl_centripetal_shifts","(","list","[","Tensor","]",")",":","Top-left","centripetal","shifts","for","each","level","with","shape","(","N",",","centripetal_shift_channels",",","H",",","W",")",".","br_centripetal_shifts","(","list","[","Tensor","]",")",":","Bottom-right","centripetal","shifts","for","each","level","with","shape","(","N",",","centripetal_shift_channels",",","H",",","W",")",".","batch_gt_instances","(","list","[",":","obj",":","`","InstanceData","`","]",")",":","Batch","of","gt_instance",".","It","usually","includes","``","bboxes","``","and","``","labels","``","attributes",".","batch_img_metas","(","list","[","dict","]",")",":","Meta","information","of","each","image",",","e.g.",",","image","size",",","scaling","factor",",","etc",".","batch_gt_instances_ignore","(","list","[",":","obj",":","`","InstanceData","`","]",",","optional",")",":","Specify","which","bounding","boxes","can","be","ignored","when","computing","the","loss",".","Returns",":","dict","[","str",",","Tensor","]",":","A","dictionary","of","loss","components",".","Containing","the","following","losses",":","-","det_loss","(","list","[","Tensor","]",")",":","Corner","keypoint","losses","of","all","feature","levels",".","-","off_loss","(","list","[","Tensor","]",")",":","Corner","offset","losses","of","all","feature","levels",".","-","guiding_loss","(","list","[","Tensor","]",")",":","Guiding","shift","losses","of","all","feature","levels",".","-","centripetal_loss","(","list","[","Tensor","]",")",":","Centripetal","shift","losses","of","all","feature","levels","."]
code: def loss_by_feat(
      self,
      tl_heats: List[Tensor],
      br_heats: List[Tensor],
      tl_offs: List[Tensor],
      br_offs: List[Tensor],
      tl_guiding_shifts: List[Tensor],
      br_guiding_shifts: List[Tensor],
      tl_centripetal_shifts: List[Tensor],
      br_centripetal_shifts: List[Tensor],
      batch_gt_instances: InstanceList,
      batch_img_metas: List[dict],
      batch_gt_instances_ignore: OptInstanceList = None,
  ) -> dict:
      """Calculate the loss based on the features extracted by the detection
      head.
  
      Args:
          tl_heats (list[Tensor]): Top-left corner heatmaps for each level
              with shape (N, num_classes, H, W).
          br_heats (list[Tensor]): Bottom-right corner heatmaps for each
              level with shape (N, num_classes, H, W).
          tl_offs (list[Tensor]): Top-left corner offsets for each level
              with shape (N, corner_offset_channels, H, W).
          br_offs (list[Tensor]): Bottom-right corner offsets for each level
              with shape (N, corner_offset_channels, H, W).
          tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
              level with shape (N, guiding_shift_channels, H, W).
          br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
              each level with shape (N, guiding_shift_channels, H, W).
          tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
              for each level with shape (N, centripetal_shift_channels, H,
              W).
          br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
              shifts for each level with shape (N,
              centripetal_shift_channels, H, W).
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance. It usually includes ``bboxes`` and ``labels``
              attributes.
          batch_img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
              Specify which bounding boxes can be ignored when computing
              the loss.
  
      Returns:
          dict[str, Tensor]: A dictionary of loss components. Containing the
          following losses:
  
              - det_loss (list[Tensor]): Corner keypoint losses of all
                feature levels.
              - off_loss (list[Tensor]): Corner offset losses of all feature
                levels.
              - guiding_loss (list[Tensor]): Guiding shift losses of all
                feature levels.
              - centripetal_loss (list[Tensor]): Centripetal shift losses of
                all feature levels.
      """
      gt_bboxes = [
          gt_instances.bboxes for gt_instances in batch_gt_instances
      ]
      gt_labels = [
          gt_instances.labels for gt_instances in batch_gt_instances
      ]
  
      targets = self.get_targets(
          gt_bboxes,
          gt_labels,
          tl_heats[-1].shape,
          batch_img_metas[0]["batch_input_shape"],
          with_corner_emb=self.with_corner_emb,
          with_guiding_shift=True,
          with_centripetal_shift=True,
      )
      mlvl_targets = [targets for _ in range(self.num_feat_levels)]
      [
          det_losses,
          off_losses,
          guiding_losses,
          centripetal_losses,
      ] = multi_apply(
          self.loss_by_feat_single,
          tl_heats,
          br_heats,
          tl_offs,
          br_offs,
          tl_guiding_shifts,
          br_guiding_shifts,
          tl_centripetal_shifts,
          br_centripetal_shifts,
          mlvl_targets,
      )
      loss_dict = dict(
          det_loss=det_losses,
          off_loss=off_losses,
          guiding_loss=guiding_losses,
          centripetal_loss=centripetal_losses,
      )
      return loss_dict
  
imports: from typing import List, Optional, Tuple
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmcv.ops import DeformConv2d
  from mmengine.model import normal_init
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, InstanceList, OptInstanceList, OptMultiConfig
  from ..utils import multi_apply
  from .corner_head import CornerHead
label_desc: Use image node_id 6 for calling the CentripetalHead obj's underlying member method code with example usage: obj.loss_by_feat(tl_heats, br_heats, tl_offs, br_offs, tl_guiding_shifts, br_guiding_shifts, tl_centripetal_shifts, br_centripetal_shifts, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore) and returns: loss_dict