code: def main():
      args = parse_args()
  
      # D2 expects contiguous classes, so we need to remap the 92 classes from DETR
      # fmt: off
      coco_idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
                  27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51,
                  52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77,
                  78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91]
      # fmt: on
  
      coco_idx = np.array(coco_idx)
  
      if args.source_model.startswith("https"):
          checkpoint = torch.hub.load_state_dict_from_url(
              args.source_model, map_location="cpu", check_hash=True
          )
      else:
          checkpoint = torch.load(args.source_model, map_location="cpu")
      model_to_convert = checkpoint["model"]
  
      model_converted = {}
      for k in model_to_convert.keys():
          old_k = k
          if "backbone" in k:
              k = k.replace("backbone.0.body.", "")
              if "layer" not in k:
                  k = "stem." + k
              for t in [1, 2, 3, 4]:
                  k = k.replace(f"layer{t}", f"res{t + 1}")
              for t in [1, 2, 3]:
                  k = k.replace(f"bn{t}", f"conv{t}.norm")
              k = k.replace("downsample.0", "shortcut")
              k = k.replace("downsample.1", "shortcut.norm")
              k = "backbone.0.backbone." + k
          k = "detr." + k
          print(old_k, "->", k)
          if "class_embed" in old_k:
              v = model_to_convert[old_k].detach()
              if v.shape[0] == 92:
                  shape_old = v.shape
                  model_converted[k] = v[coco_idx]
                  print(
                      "Head conversion: changing shape from {} to {}".format(
                          shape_old, model_converted[k].shape
                      )
                  )
                  continue
          model_converted[k] = model_to_convert[old_k].detach()
  
      model_to_save = {"model": model_converted}
      torch.save(model_to_save, args.output_model)
  
imports: import json
  import argparse
  import numpy
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types