content: # Modified from https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/utils/model_serialization.py
  from collections import OrderedDict
  import logging
  
  import torch
  
  
  def align_and_update_state_dicts(model_state_dict, loaded_state_dict, no_head):
      """
      Strategy: suppose that the models that we will create will have prefixes appended
      to each of its keys, for example due to an extra level of nesting that the original
      pre-trained weights from ImageNet won't contain. For example, model.state_dict()
      might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
      res2.conv1.weight. We thus want to match both parameters together.
      For that, we look for each model weight, look among all loaded keys if there is one
      that is a suffix of the current weight name, and use it if that's the case.
      If multiple matches exist, take the one with longest size
      of the corresponding name. For example, for the same model as before, the pretrained
      weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
      we want to match backbone[0].body.conv1.weight to conv1.weight, and
      backbone[0].body.res2.conv1.weight to res2.conv1.weight.
      """
      current_keys = sorted(list(model_state_dict.keys()))
      loaded_keys = sorted(list(loaded_state_dict.keys()))
      # get a matrix of string matches, where each (i, j) entry correspond to the size of the
      # loaded_key string, if it matches
      match_matrix = [
          len(j) if i.endswith(j) else 0 for i in current_keys for j in loaded_keys
      ]
      match_matrix = torch.as_tensor(match_matrix).view(
          len(current_keys), len(loaded_keys)
      )
      max_match_size, idxs = match_matrix.max(1)
      # remove indices that correspond to no-match
      idxs[max_match_size == 0] = -1
  
      # used for logging
      max_size = max([len(key) for key in current_keys]) if current_keys else 1
      max_size_loaded = max([len(key) for key in loaded_keys]) if loaded_keys else 1
      log_str_template = "{: <{}} loaded from {: <{}} of shape {}"
      logger = logging.getLogger(__name__)
      for idx_new, idx_old in enumerate(idxs.tolist()):
          if idx_old == -1:
              continue
          key = current_keys[idx_new]
          key_old = loaded_keys[idx_old]
  
          if no_head and key_old.startswith("roi_heads."):
              logger.info("{} will not be loaded.".format(key))
              continue
  
          model_state_dict[key] = loaded_state_dict[key_old]
          logger.info(
              log_str_template.format(
                  key,
                  max_size,
                  key_old,
                  max_size_loaded,
                  tuple(loaded_state_dict[key_old].shape),
              )
          )
  
  
  def strip_prefix_if_present(state_dict, prefix):
      keys = sorted(state_dict.keys())
      if not all(key.startswith(prefix) for key in keys):
          return state_dict
      stripped_state_dict = OrderedDict()
      for key, value in state_dict.items():
          stripped_state_dict[key.replace(prefix, "")] = value
      return stripped_state_dict
  
  
  def load_state_dict(model, loaded_state_dict, no_head):
      model_state_dict = model.state_dict()
      # if the state_dict comes from a model that was wrapped in a
      # DataParallel or DistributedDataParallel during serialization,
      # remove the "module" prefix before performing the matching
      loaded_state_dict = strip_prefix_if_present(loaded_state_dict, prefix="module.")
      align_and_update_state_dicts(model_state_dict, loaded_state_dict, no_head)
  
      # use strict loading
      model.load_state_dict(model_state_dict)
  