update.py

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"""
Update the CDFs parameters of a trained model.

To be called on a model checkpoint after training. This will update the internal
CDFs related buffers required for entropy coding.
"""
import hashlib
import yaml
import os 

from pathlib import Path
from typing import Dict

import torch

from compressai.models.compass import (
    JointAutoregressiveHierarchicalPriors,
    MeanScaleHyperprior,
    ScaleHyperprior,
)
from compressai.zoo import load_state_dict
from compressai.zoo.image import model_architectures as zoo_models


def sha256_file(filepath: Path, len_hash_prefix: int = 8) -> str:
    # from pytorch github repo
    sha256 = hashlib.sha256()
    with filepath.open("rb") as f:
        while True:
            buf = f.read(8192)
            if len(buf) == 0:
                break
            sha256.update(buf)
    digest = sha256.hexdigest()

    return digest[:len_hash_prefix]


def load_checkpoint(filepath: Path) -> Dict[str, torch.Tensor]:
    checkpoint = torch.load(filepath, map_location="cpu")

    if "network" in checkpoint:
        state_dict = checkpoint["network"]
    elif "state_dict" in checkpoint:
        state_dict = checkpoint["state_dict"]
    else:
        state_dict = checkpoint

    state_dict = load_state_dict(state_dict)
    return state_dict


description = """
Export a trained model to a new checkpoint with an updated CDFs parameters and a
hash prefix, so that it can be loaded later via `load_state_dict_from_url`.
""".strip()

models = {
    "jarhp": JointAutoregressiveHierarchicalPriors,
    "scale-hyperprior": ScaleHyperprior,
}
models.update(zoo_models)


def main(cfg):
    checkpoint_path = os.path.join('checkpoints', 'lambda_' + str(cfg['lmbda']), 'best_model.pth.tar')
    filepath = Path(checkpoint_path).resolve()
    if not filepath.is_file():
        raise RuntimeError(f'"{filepath}" is not a valid file.')

    state_dict = load_checkpoint(filepath)

    model_cls_or_entrypoint_base = models[cfg['CompModel']['BL']]
    model_cls_or_entrypoint_res = models[cfg['CompModel']['EL']]

    if not isinstance(model_cls_or_entrypoint_base, type):
        model_cls = model_cls_or_entrypoint_base()
        model_res_cls = model_cls_or_entrypoint_res()
    else:
        model_cls = model_cls_or_entrypoint_base
        model_res_cls = model_cls_or_entrypoint_res

    net = model_cls.from_state_dict(state_dict['base_state_dict'])
    net_res = model_res_cls.from_state_dict(state_dict['residual_state_dict'])

    net.update(force=True)
    net_res.update(force=True)

    state_dict['base_state_dict'] = net.state_dict()
    state_dict['residual_state_dict'] = net_res.state_dict()

    filename = filepath
    while filename.suffixes:
        filename = Path(filename.stem)

    ext = "".join(filepath.suffixes[:2])

    filepath_update = f"{filepath}"[:-len(ext)] + "_updated" + f"{ext}"
    torch.save(state_dict, filepath_update)

if __name__ == "__main__":
    with open('configs/cfg_eval.yaml') as f:
        cfg = yaml.safe_load(f)

    main(cfg)