src/transformers/models/yolos/convert_yolos_to_pytorch.py/convert_state_dict
def convert_state_dict(orig_state_dict: dict, model: YolosForObjectDetection) -> dict:
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)

        if "qkv" in key:
            key_split = key.split(".")
            layer_num = int(key_split[2])
            dim = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
            if "weight" in key:
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.weight"] = val[:dim, :]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.weight"] = val[
                    dim : dim * 2, :
                ]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.bias"] = val[:dim]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.bias"] = val[-dim:]
        else:
            orig_state_dict[rename_key(key)] = val

    return orig_state_dict