class_docstr: Implement Detr
class_docstr_tok: ["Implement","Detr"]
docstr: Args:
      batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
          Each item in the list contains the inputs for one image.
          For now, each item in the list is a dict that contains:
  
          * image: Tensor, image in (C, H, W) format.
          * instances: Instances
  
          Other information that's included in the original dicts, such as:
  
          * "height", "width" (int): the output resolution of the model, used in inference.
            See :meth:`postprocess` for details.
  Returns:
      dict[str: Tensor]:
          mapping from a named loss to a tensor storing the loss. Used during training only.
docstr_tok: ["Args",":","batched_inputs",":","a","list",",","batched","outputs","of",":","class",":","`","DatasetMapper","`",".","Each","item","in","the","list","contains","the","inputs","for","one","image",".","For","now",",","each","item","in","the","list","is","a","dict","that","contains",":","*","image",":","Tensor",",","image","in","(","C",",","H",",","W",")","format",".","*","instances",":","Instances","Other","information","that","'s","included","in","the","original","dicts",",","such","as",":","*","``","height","''",",","``","width","''","(","int",")",":","the","output","resolution","of","the","model",",","used","in","inference",".","See",":","meth",":","`","postprocess","`","for","details",".","Returns",":","dict","[","str",":","Tensor","]",":","mapping","from","a","named","loss","to","a","tensor","storing","the","loss",".","Used","during","training","only","."]
code: def forward(self, batched_inputs):
      """
      Args:
          batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
              Each item in the list contains the inputs for one image.
              For now, each item in the list is a dict that contains:
  
              * image: Tensor, image in (C, H, W) format.
              * instances: Instances
  
              Other information that's included in the original dicts, such as:
  
              * "height", "width" (int): the output resolution of the model, used in inference.
                See :meth:`postprocess` for details.
      Returns:
          dict[str: Tensor]:
              mapping from a named loss to a tensor storing the loss. Used during training only.
      """
      images = self.preprocess_image(batched_inputs)
      output = self.detr(images)
  
      if self.training:
          gt_instances = [
              x["instances"].to(self.device) for x in batched_inputs
          ]
  
          targets = self.prepare_targets(gt_instances)
          loss_dict = self.criterion(output, targets)
          weight_dict = self.criterion.weight_dict
          for k in loss_dict.keys():
              if k in weight_dict:
                  loss_dict[k] *= weight_dict[k]
          return loss_dict
      else:
          box_cls = output["pred_logits"]
          box_pred = output["pred_boxes"]
          mask_pred = output["pred_masks"] if self.mask_on else None
          results = self.inference(
              box_cls, box_pred, mask_pred, images.image_sizes
          )
          processed_results = []
          for results_per_image, input_per_image, image_size in zip(
              results, batched_inputs, images.image_sizes
          ):
              height = input_per_image.get("height", image_size[0])
              width = input_per_image.get("width", image_size[1])
              r = detector_postprocess(results_per_image, height, width)
              processed_results.append({"instances": r})
          return processed_results
  
imports: import logging
  import math
  from typing import List
  import numpy
  import torch
  import torch.distributed
  import torch.nn.functional
  from scipy.optimize import linear_sum_assignment
  from torch import nn
  from detectron2.layers import ShapeSpec
  from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, detector_postprocess
  from detectron2.structures import Boxes, ImageList, Instances, BitMasks, PolygonMasks
  from detectron2.utils.logger import log_first_n
  from fvcore.nn import giou_loss, smooth_l1_loss
  from models.backbone import Joiner
  from models.detr import DETR, SetCriterion
  from models.matcher import HungarianMatcher
  from models.position_encoding import PositionEmbeddingSine
  from models.transformer import Transformer
  from models.segmentation import DETRsegm, PostProcessPanoptic, PostProcessSegm
  from util.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
  from util.misc import NestedTensor
  from datasets.coco import convert_coco_poly_to_mask
label_desc: Use image node_id 2 for calling the Detr obj's underlying member method code with example usage: obj.forward(batched_inputs) and returns: loss_dict, processed_results