class_docstr: Performs input / target transformation before feeding the data to a GeneralizedRCNN
  model.
  
  The transformations it perform are:
      - input normalization (mean subtraction and std division)
      - input / target resizing to match min_size / max_size
  
  It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
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docstr: 对网络的预测结果进行后处理（主要将bboxes还原到原图像尺度上）
  Args:
      result: list(dict), 网络的预测结果, len(result) == batch_size
      image_shapes: list(torch.Size), 图像预处理缩放后的尺寸, len(image_shapes) == batch_size
      original_image_sizes: list(torch.Size), 图像的原始尺寸, len(original_image_sizes) == batch_size
  
  Returns:
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code: def postprocess(
      self,
      result,  # type: List[Dict[str, Tensor]]
      image_shapes,  # type: List[Tuple[int, int]]
      original_image_sizes,  # type: List[Tuple[int, int]]
  ):
      # type: (...) -> List[Dict[str, Tensor]]
      """
      对网络的预测结果进行后处理（主要将bboxes还原到原图像尺度上）
      Args:
          result: list(dict), 网络的预测结果, len(result) == batch_size
          image_shapes: list(torch.Size), 图像预处理缩放后的尺寸, len(image_shapes) == batch_size
          original_image_sizes: list(torch.Size), 图像的原始尺寸, len(original_image_sizes) == batch_size
  
      Returns:
  
      """
      if self.training:
          return result
  
      # 遍历每张图片的预测信息，将boxes信息还原回原尺度
      for i, (pred, im_s, o_im_s) in enumerate(
          zip(result, image_shapes, original_image_sizes)
      ):
          boxes = pred["boxes"]
          boxes = resize_boxes(boxes, im_s, o_im_s)  # 将bboxes缩放回原图像尺度上
          result[i]["boxes"] = boxes
      return result
  
imports: import math
  from typing import List, Tuple, Dict, Optional
  import torch
  from torch import nn, Tensor
  import torchvision
  from .image_list import ImageList
label_desc: Use image node_id 8 for calling the GeneralizedRCNNTransform obj's underlying member method code with example usage: obj.postprocess(result, image_shapes, original_image_sizes) and returns: result, result