Source code for sleepless.utils.checkpointer

# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later

from __future__ import annotations

import logging
import os

import torch

logger = logging.getLogger(__name__)




class Checkpointer:
    """A simple pytorch checkpointer.

    :param model: Network model, eventually loaded from a checkpointed
        file
    :param optimizer: Optimizer
    :param scheduler: Learning rate scheduler
    :param path: Directory where to save checkpoints.
    """

    def __init__(
        self,
        model: torch.nn.Module,
        optimizer: torch.optim.Optimizer = None,
        scheduler: torch.optim._LRScheduler = None,
        path: str = ".",
    ):
        self.model = model
        self.optimizer = optimizer
        self.scheduler = scheduler
        self.path = os.path.realpath(path)



    def save(self, name, **kwargs):
        data = {}
        data["model"] = self.model.state_dict()
        if self.optimizer is not None:
            data["optimizer"] = self.optimizer.state_dict()
        if self.scheduler is not None:
            data["scheduler"] = self.scheduler.state_dict()
        data.update(kwargs)

        name = f"{name}.pth"
        outf = os.path.join(self.path, name)
        logger.info(f"Saving checkpoint to {outf}")
        torch.save(data, outf)
        with open(self._last_checkpoint_filename, "w") as f:
            f.write(name)




    def load(self, f: str = None):
        """Loads model, optimizer and scheduler from file.

        :param f: Name of a file (absolute or relative to ``self.path``), that
            contains the checkpoint data to load into the model, and optionally
            into the optimizer and the scheduler.  If not specified, loads data
            from current path.
        """

        if f is None:
            f = self.last_checkpoint()

        if f is None:
            # no checkpoint could be found
            logger.warning("No checkpoint found (and none passed)")
            return {}

        # loads file data into memory
        logger.info(f"Loading checkpoint from {f}...")
        checkpoint = torch.load(f, map_location=torch.device("cpu"))

        # converts model entry to model parameters
        self.model.load_state_dict(checkpoint.pop("model"))

        if self.optimizer is not None:
            self.optimizer.load_state_dict(checkpoint.pop("optimizer"))
        if self.scheduler is not None:
            self.scheduler.load_state_dict(checkpoint.pop("scheduler"))

        return checkpoint


    @property
    def _last_checkpoint_filename(self):
        return os.path.join(self.path, "last_checkpoint")



    def has_checkpoint(self):
        return os.path.exists(self._last_checkpoint_filename)




    def last_checkpoint(self):
        if self.has_checkpoint():
            with open(self._last_checkpoint_filename) as fobj:
                return os.path.join(self.path, fobj.read().strip())
        return None