Source code for catalyst.utils.checkpoint

from typing import Callable, Dict, Union
from collections import OrderedDict
import os
from pathlib import Path
import shutil

import torch

from catalyst.utils.distributed import get_nn_from_ddp_module
from catalyst.utils.misc import maybe_recursive_call

[docs]def pack_checkpoint( model=None, criterion=None, optimizer=None, scheduler=None, **kwargs ): """@TODO: Docs. Contribution is welcome.""" checkpoint = kwargs if isinstance(model, OrderedDict): raise NotImplementedError() else: model_module = get_nn_from_ddp_module(model) checkpoint["model_state_dict"] = maybe_recursive_call( model_module, "state_dict" ) for dict2save, name2save in zip( [criterion, optimizer, scheduler], ["criterion", "optimizer", "scheduler"], ): if dict2save is None: continue # @TODO refactor with maybe_recursive_call if isinstance(dict2save, dict): for key, value in dict2save.items(): if value is not None: state_dict2save = name2save + "_" + str(key) # checkpoint[name2save_] = value state_dict2save = state_dict2save + "_state_dict" checkpoint[state_dict2save] = value.state_dict() else: # checkpoint[name2save] = dict2save name2save = name2save + "_state_dict" checkpoint[name2save] = dict2save.state_dict() return checkpoint
[docs]def unpack_checkpoint( checkpoint, model=None, criterion=None, optimizer=None, scheduler=None ) -> None: """Load checkpoint from file and unpack the content to a model (if not None), criterion (if not None), optimizer (if not None), scheduler (if not None). Args: checkpoint: checkpoint to load model: model where should be updated state criterion: criterion where should be updated state optimizer: optimizer where should be updated state scheduler: scheduler where should be updated state """ if model is not None: model = get_nn_from_ddp_module(model) maybe_recursive_call( model, "load_state_dict", recursive_args=checkpoint["model_state_dict"], ) for dict2load, name2load in zip( [criterion, optimizer, scheduler], ["criterion", "optimizer", "scheduler"], ): if dict2load is None: continue if isinstance(dict2load, dict): for key, value in dict2load.items(): if value is not None: state_dict2load = f"{name2load}_{key}_state_dict" value.load_state_dict(checkpoint[state_dict2load]) else: name2load = f"{name2load}_state_dict" dict2load.load_state_dict(checkpoint[name2load])
[docs]def save_checkpoint( checkpoint: Dict, logdir: Union[Path, str], suffix: str, is_best: bool = False, is_last: bool = False, special_suffix: str = "", saver_fn: Callable =, ) -> Union[Path, str]: """Saving checkpoint to a file. Args: checkpoint: data to save. logdir: directory where checkpoint should be stored. suffix: checkpoint file name. is_best: if ``True`` then also will be generated best checkpoint file. is_last: if ``True`` then also will be generated last checkpoint file. special_suffix: suffix to use for saving best/last checkpoints. saver_fn: function to use for saving data to file, default is ```` Returns: path to saved checkpoint """ os.makedirs(logdir, exist_ok=True) filename = f"{logdir}/{suffix}.pth" saver_fn(checkpoint, filename) if is_best: shutil.copyfile(filename, f"{logdir}/best{special_suffix}.pth") if is_last: shutil.copyfile(filename, f"{logdir}/last{special_suffix}.pth") return filename
[docs]def load_checkpoint(filepath: str): """Load checkpoint from path. Args: filepath: checkpoint file to load Returns: checkpoint content """ checkpoint = torch.load( filepath, map_location=lambda storage, loc: storage ) return checkpoint
__all__ = [ "pack_checkpoint", "unpack_checkpoint", "save_checkpoint", "load_checkpoint", ]