docstr: Calculate losses from a batch of inputs and data samples.
  
  Args:
      batch_inputs (Tensor): Input images of shape (N, C, H, W).
          These should usually be mean centered and std scaled.
      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:
      dict: A dictionary of loss components
docstr_tok: ["Calculate","losses","from","a","batch","of","inputs","and","data","samples",".","Args",":","batch_inputs","(","Tensor",")",":","Input","images","of","shape","(","N",",","C",",","H",",","W",")",".","These","should","usually","be","mean","centered","and","std","scaled",".","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",":","dict",":","A","dictionary","of","loss","components"]
code: def loss(
      self, batch_inputs: Tensor, batch_data_samples: SampleList
  ) -> dict:
      """Calculate losses from a batch of inputs and data samples.
  
      Args:
          batch_inputs (Tensor): Input images of shape (N, C, H, W).
              These should usually be mean centered and std scaled.
          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:
          dict: A dictionary of loss components
      """
  
      x = self.extract_feat(batch_inputs)
      losses = dict()
  
      # RPN forward and loss
      if self.with_rpn:
          proposal_cfg = self.train_cfg.get(
              "rpn_proposal", self.test_cfg.rpn
          )
          rpn_data_samples = copy.deepcopy(batch_data_samples)
          # set cat_id of gt_labels to 0 in RPN
          for data_sample in rpn_data_samples:
              data_sample.gt_instances.labels = torch.zeros_like(
                  data_sample.gt_instances.labels
              )
  
          rpn_losses, rpn_results_list = self.rpn_head.loss_and_predict(
              x, rpn_data_samples, proposal_cfg=proposal_cfg
          )
  
          # avoid get same name with roi_head loss
          keys = rpn_losses.keys()
          for key in list(keys):
              if "loss" in key and "rpn" not in key:
                  rpn_losses[f"rpn_{key}"] = rpn_losses.pop(key)
          losses.update(rpn_losses)
          # if not hasattr(batch_data_samples[0].gt_instances, 'bboxes'):
          #     losses.update({k: v * 0 for k, v in rpn_losses.items()})
          # else:
          #     losses.update(rpn_losses)
      else:
          assert (
              batch_data_samples[0].get("proposals", None) is not None
          )
          # use pre-defined proposals in InstanceData for the second stage
          # to extract ROI features.
          rpn_results_list = [
              data_sample.proposals
              for data_sample in batch_data_samples
          ]
  
      roi_losses = self.roi_head.loss(
          x, rpn_results_list, batch_data_samples
      )
  
      losses.update(roi_losses)
  
      return losses
  
imports: import copy
  from typing import List, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.logging import print_log
  from torch import Tensor
  from mmdet.datasets import LVISV1Dataset
  from mmdet.models.detectors.cascade_rcnn import CascadeRCNN
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
label_desc: Use image node_id 2 for calling the Detic obj's underlying member method code with example usage: obj.loss(batch_inputs, batch_data_samples) and returns: losses