| """ |
| Extract model state dict from trainer checkpoint, either "model" or "ema_model", |
| and store it in a new checkpoint file with corresponding suffix and model config. |
| """ |
|
|
| import torch |
| import argparse |
| from omegaconf import OmegaConf |
|
|
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
| parser.add_argument("ckpt_path", type=str, help="Path to the checkpoint file") |
| parser.add_argument( |
| "--prefix", type=str, default="ema_model", help="Prefix for state dict (e.g., 'model' or 'ema_model')" |
| ) |
| args = parser.parse_args() |
|
|
| ckpt = torch.load(args.ckpt_path, map_location="cpu") |
|
|
| |
| clean_state_dict = {} |
| for k, v in ckpt["state_dict"].items(): |
| if k.startswith(args.prefix + "."): |
| new_k = k[len(args.prefix) + 1 :] |
| clean_state_dict[new_k] = v |
| print(f"Extracted {len(clean_state_dict):,} parameters with prefix '{args.prefix}'") |
|
|
| |
| config = ckpt["hyper_parameters"]["model"] |
| print("Extracted model config:") |
| print(OmegaConf.to_yaml(config)) |
|
|
| |
| new_fp = args.ckpt_path.replace(".ckpt", f"_{args.prefix}.ckpt") |
| torch.save({"state_dict": clean_state_dict, "config": config}, new_fp) |
| print(f"Saved extracted checkpoint to {new_fp}") |
|
|