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Source code for pytorch_lightning.callbacks.model_checkpoint

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Model Checkpointing
===================

Automatically save model checkpoints during training.

"""
import logging
import os
import re
import time
import warnings
from copy import deepcopy
from datetime import timedelta
from typing import Any, Dict, Optional, Set
from weakref import proxy

import numpy as np
import torch
import yaml
from lightning_utilities.core.rank_zero import WarningCache
from torch import Tensor

import pytorch_lightning as pl
from lightning_lite.utilities.cloud_io import get_filesystem
from lightning_lite.utilities.types import _PATH
from pytorch_lightning.callbacks import Checkpoint
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_warn
from pytorch_lightning.utilities.types import STEP_OUTPUT

log = logging.getLogger(__name__)
warning_cache = WarningCache()


[docs]class ModelCheckpoint(Checkpoint): r""" Save the model periodically by monitoring a quantity. Every metric logged with :meth:`~pytorch_lightning.core.module.log` or :meth:`~pytorch_lightning.core.module.log_dict` in LightningModule is a candidate for the monitor key. For more information, see :ref:`checkpointing`. After training finishes, use :attr:`best_model_path` to retrieve the path to the best checkpoint file and :attr:`best_model_score` to retrieve its score. Args: dirpath: directory to save the model file. Example:: # custom path # saves a file like: my/path/epoch=0-step=10.ckpt >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/') By default, dirpath is ``None`` and will be set at runtime to the location specified by :class:`~pytorch_lightning.trainer.trainer.Trainer`'s :paramref:`~pytorch_lightning.trainer.trainer.Trainer.default_root_dir` argument, and if the Trainer uses a logger, the path will also contain logger name and version. filename: checkpoint filename. Can contain named formatting options to be auto-filled. Example:: # save any arbitrary metrics like `val_loss`, etc. in name # saves a file like: my/path/epoch=2-val_loss=0.02-other_metric=0.03.ckpt >>> checkpoint_callback = ModelCheckpoint( ... dirpath='my/path', ... filename='{epoch}-{val_loss:.2f}-{other_metric:.2f}' ... ) By default, filename is ``None`` and will be set to ``'{epoch}-{step}'``. monitor: quantity to monitor. By default it is ``None`` which saves a checkpoint only for the last epoch. verbose: verbosity mode. Default: ``False``. save_last: When ``True``, saves an exact copy of the checkpoint to a file `last.ckpt` whenever a checkpoint file gets saved. This allows accessing the latest checkpoint in a deterministic manner. Default: ``None``. save_top_k: if ``save_top_k == k``, the best k models according to the quantity monitored will be saved. if ``save_top_k == 0``, no models are saved. if ``save_top_k == -1``, all models are saved. Please note that the monitors are checked every ``every_n_epochs`` epochs. if ``save_top_k >= 2`` and the callback is called multiple times inside an epoch, the name of the saved file will be appended with a version count starting with ``v1``. mode: one of {min, max}. If ``save_top_k != 0``, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For ``'val_acc'``, this should be ``'max'``, for ``'val_loss'`` this should be ``'min'``, etc. auto_insert_metric_name: When ``True``, the checkpoints filenames will contain the metric name. For example, ``filename='checkpoint_{epoch:02d}-{acc:02.0f}`` with epoch ``1`` and acc ``1.12`` will resolve to ``checkpoint_epoch=01-acc=01.ckpt``. Is useful to set it to ``False`` when metric names contain ``/`` as this will result in extra folders. For example, ``filename='epoch={epoch}-step={step}-val_acc={val/acc:.2f}', auto_insert_metric_name=False`` save_weights_only: if ``True``, then only the model's weights will be saved. Otherwise, the optimizer states, lr-scheduler states, etc are added in the checkpoint too. every_n_train_steps: Number of training steps between checkpoints. If ``every_n_train_steps == None or every_n_train_steps == 0``, we skip saving during training. To disable, set ``every_n_train_steps = 0``. This value must be ``None`` or non-negative. This must be mutually exclusive with ``train_time_interval`` and ``every_n_epochs``. train_time_interval: Checkpoints are monitored at the specified time interval. For all practical purposes, this cannot be smaller than the amount of time it takes to process a single training batch. This is not guaranteed to execute at the exact time specified, but should be close. This must be mutually exclusive with ``every_n_train_steps`` and ``every_n_epochs``. every_n_epochs: Number of epochs between checkpoints. This value must be ``None`` or non-negative. To disable saving top-k checkpoints, set ``every_n_epochs = 0``. This argument does not impact the saving of ``save_last=True`` checkpoints. If all of ``every_n_epochs``, ``every_n_train_steps`` and ``train_time_interval`` are ``None``, we save a checkpoint at the end of every epoch (equivalent to ``every_n_epochs = 1``). If ``every_n_epochs == None`` and either ``every_n_train_steps != None`` or ``train_time_interval != None``, saving at the end of each epoch is disabled (equivalent to ``every_n_epochs = 0``). This must be mutually exclusive with ``every_n_train_steps`` and ``train_time_interval``. Setting both ``ModelCheckpoint(..., every_n_epochs=V, save_on_train_epoch_end=False)`` and ``Trainer(max_epochs=N, check_val_every_n_epoch=M)`` will only save checkpoints at epochs 0 < E <= N where both values for ``every_n_epochs`` and ``check_val_every_n_epoch`` evenly divide E. save_on_train_epoch_end: Whether to run checkpointing at the end of the training epoch. If this is ``False``, then the check runs at the end of the validation. Note: For extra customization, ModelCheckpoint includes the following attributes: - ``CHECKPOINT_JOIN_CHAR = "-"`` - ``CHECKPOINT_NAME_LAST = "last"`` - ``FILE_EXTENSION = ".ckpt"`` - ``STARTING_VERSION = 1`` For example, you can change the default last checkpoint name by doing ``checkpoint_callback.CHECKPOINT_NAME_LAST = "{epoch}-last"`` If you want to checkpoint every N hours, every M train batches, and/or every K val epochs, then you should create multiple ``ModelCheckpoint`` callbacks. If the checkpoint's ``dirpath`` changed from what it was before while resuming the training, only ``best_model_path`` will be reloaded and a warning will be issued. Raises: MisconfigurationException: If ``save_top_k`` is smaller than ``-1``, if ``monitor`` is ``None`` and ``save_top_k`` is none of ``None``, ``-1``, and ``0``, or if ``mode`` is none of ``"min"`` or ``"max"``. ValueError: If ``trainer.save_checkpoint`` is ``None``. Example:: >>> from pytorch_lightning import Trainer >>> from pytorch_lightning.callbacks import ModelCheckpoint # saves checkpoints to 'my/path/' at every epoch >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/') >>> trainer = Trainer(callbacks=[checkpoint_callback]) # save epoch and val_loss in name # saves a file like: my/path/sample-mnist-epoch=02-val_loss=0.32.ckpt >>> checkpoint_callback = ModelCheckpoint( ... monitor='val_loss', ... dirpath='my/path/', ... filename='sample-mnist-{epoch:02d}-{val_loss:.2f}' ... ) # save epoch and val_loss in name, but specify the formatting yourself (e.g. to avoid problems with Tensorboard # or Neptune, due to the presence of characters like '=' or '/') # saves a file like: my/path/sample-mnist-epoch02-val_loss0.32.ckpt >>> checkpoint_callback = ModelCheckpoint( ... monitor='val/loss', ... dirpath='my/path/', ... filename='sample-mnist-epoch{epoch:02d}-val_loss{val/loss:.2f}', ... auto_insert_metric_name=False ... ) # retrieve the best checkpoint after training checkpoint_callback = ModelCheckpoint(dirpath='my/path/') trainer = Trainer(callbacks=[checkpoint_callback]) model = ... trainer.fit(model) checkpoint_callback.best_model_path .. tip:: Saving and restoring multiple checkpoint callbacks at the same time is supported under variation in the following arguments: *monitor, mode, every_n_train_steps, every_n_epochs, train_time_interval* Read more: :ref:`Persisting Callback State <extensions/callbacks_state:save callback state>` """ CHECKPOINT_JOIN_CHAR = "-" CHECKPOINT_NAME_LAST = "last" FILE_EXTENSION = ".ckpt" STARTING_VERSION = 1 def __init__( self, dirpath: Optional[_PATH] = None, filename: Optional[str] = None, monitor: Optional[str] = None, verbose: bool = False, save_last: Optional[bool] = None, save_top_k: int = 1, save_weights_only: bool = False, mode: str = "min", auto_insert_metric_name: bool = True, every_n_train_steps: Optional[int] = None, train_time_interval: Optional[timedelta] = None, every_n_epochs: Optional[int] = None, save_on_train_epoch_end: Optional[bool] = None, ): super().__init__() self.monitor = monitor self.verbose = verbose self.save_last = save_last self.save_top_k = save_top_k self.save_weights_only = save_weights_only self.auto_insert_metric_name = auto_insert_metric_name self._save_on_train_epoch_end = save_on_train_epoch_end self._last_global_step_saved = 0 # no need to save when no steps were taken self._last_time_checked: Optional[float] = None self.current_score: Optional[Tensor] = None self.best_k_models: Dict[str, Tensor] = {} self.kth_best_model_path = "" self.best_model_score: Optional[Tensor] = None self.best_model_path = "" self.last_model_path = "" self.kth_value: Tensor self.dirpath: Optional[_PATH] self.__init_monitor_mode(mode) self.__init_ckpt_dir(dirpath, filename) self.__init_triggers(every_n_train_steps, every_n_epochs, train_time_interval) self.__validate_init_configuration() @property def state_key(self) -> str: return self._generate_state_key( monitor=self.monitor, mode=self.mode, every_n_train_steps=self._every_n_train_steps, every_n_epochs=self._every_n_epochs, train_time_interval=self._train_time_interval, )
[docs] def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None: dirpath = self.__resolve_ckpt_dir(trainer) dirpath = trainer.strategy.broadcast(dirpath) self.dirpath = dirpath if trainer.is_global_zero and stage == "fit": self.__warn_if_dir_not_empty(self.dirpath)
[docs] def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: self._last_time_checked = time.monotonic()
[docs] def on_train_batch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int, ) -> None: """Save checkpoint on train batch end if we meet the criteria for `every_n_train_steps`""" if self._should_skip_saving_checkpoint(trainer): return skip_batch = self._every_n_train_steps < 1 or (trainer.global_step % self._every_n_train_steps != 0) train_time_interval = self._train_time_interval skip_time = True now = time.monotonic() if train_time_interval: prev_time_check = self._last_time_checked skip_time = prev_time_check is None or (now - prev_time_check) < train_time_interval.total_seconds() # in case we have time differences across ranks # broadcast the decision on whether to checkpoint from rank 0 to avoid possible hangs skip_time = trainer.strategy.broadcast(skip_time) if skip_batch and skip_time: return if not skip_time: self._last_time_checked = now monitor_candidates = self._monitor_candidates(trainer) self._save_topk_checkpoint(trainer, monitor_candidates) self._save_last_checkpoint(trainer, monitor_candidates)
[docs] def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: """Save a checkpoint at the end of the training epoch.""" if not self._should_skip_saving_checkpoint(trainer) and self._should_save_on_train_epoch_end(trainer): monitor_candidates = self._monitor_candidates(trainer) if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0: self._save_topk_checkpoint(trainer, monitor_candidates) self._save_last_checkpoint(trainer, monitor_candidates)
[docs] def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: """Save a checkpoint at the end of the validation stage.""" if not self._should_skip_saving_checkpoint(trainer) and not self._should_save_on_train_epoch_end(trainer): monitor_candidates = self._monitor_candidates(trainer) if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0: self._save_topk_checkpoint(trainer, monitor_candidates) self._save_last_checkpoint(trainer, monitor_candidates)
[docs] def state_dict(self) -> Dict[str, Any]: return { "monitor": self.monitor, "best_model_score": self.best_model_score, "best_model_path": self.best_model_path, "current_score": self.current_score, "dirpath": self.dirpath, "best_k_models": self.best_k_models, "kth_best_model_path": self.kth_best_model_path, "kth_value": self.kth_value, "last_model_path": self.last_model_path, }
[docs] def load_state_dict(self, state_dict: Dict[str, Any]) -> None: dirpath_from_ckpt = state_dict.get("dirpath", self.dirpath) if self.dirpath == dirpath_from_ckpt: self.best_model_score = state_dict["best_model_score"] self.kth_best_model_path = state_dict.get("kth_best_model_path", self.kth_best_model_path) self.kth_value = state_dict.get("kth_value", self.kth_value) self.best_k_models = state_dict.get("best_k_models", self.best_k_models) self.last_model_path = state_dict.get("last_model_path", self.last_model_path) else: warnings.warn( f"The dirpath has changed from {dirpath_from_ckpt!r} to {self.dirpath!r}," " therefore `best_model_score`, `kth_best_model_path`, `kth_value`, `last_model_path` and" " `best_k_models` won't be reloaded. Only `best_model_path` will be reloaded." ) self.best_model_path = state_dict["best_model_path"]
def _save_topk_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None: if self.save_top_k == 0: return # validate metric if self.monitor is not None: if self.monitor not in monitor_candidates: m = ( f"`ModelCheckpoint(monitor={self.monitor!r})` could not find the monitored key in the returned" f" metrics: {list(monitor_candidates)}." f" HINT: Did you call `log({self.monitor!r}, value)` in the `LightningModule`?" ) if trainer.fit_loop.epoch_loop.val_loop._has_run: raise MisconfigurationException(m) warning_cache.warn(m) self._save_monitor_checkpoint(trainer, monitor_candidates) else: self._save_none_monitor_checkpoint(trainer, monitor_candidates) def _save_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None: trainer.save_checkpoint(filepath, self.save_weights_only) self._last_global_step_saved = trainer.global_step # notify loggers if trainer.is_global_zero: for logger in trainer.loggers: logger.after_save_checkpoint(proxy(self)) def _should_skip_saving_checkpoint(self, trainer: "pl.Trainer") -> bool: from pytorch_lightning.trainer.states import TrainerFn return ( bool(trainer.fast_dev_run) # disable checkpointing with fast_dev_run or trainer.state.fn != TrainerFn.FITTING # don't save anything during non-fit or trainer.sanity_checking # don't save anything during sanity check or self._last_global_step_saved == trainer.global_step # already saved at the last step ) def _should_save_on_train_epoch_end(self, trainer: "pl.Trainer") -> bool: if self._save_on_train_epoch_end is not None: return self._save_on_train_epoch_end # if `check_val_every_n_epoch != 1`, we can't say when the validation dataloader will be loaded # so let's not enforce saving at every training epoch end if trainer.check_val_every_n_epoch != 1: return False # no validation means save on train epoch end if sum(trainer.num_val_batches) == 0: return True # if the user runs validation multiple times per training epoch, then we run after validation # instead of on train epoch end return trainer.val_check_interval == 1.0 def __validate_init_configuration(self) -> None: if self.save_top_k < -1: raise MisconfigurationException(f"Invalid value for save_top_k={self.save_top_k}. Must be >= -1") if self._every_n_train_steps < 0: raise MisconfigurationException( f"Invalid value for every_n_train_steps={self._every_n_train_steps}. Must be >= 0" ) if self._every_n_epochs < 0: raise MisconfigurationException(f"Invalid value for every_n_epochs={self._every_n_epochs}. Must be >= 0") every_n_train_steps_triggered = self._every_n_train_steps >= 1 every_n_epochs_triggered = self._every_n_epochs >= 1 train_time_interval_triggered = self._train_time_interval is not None if every_n_train_steps_triggered + every_n_epochs_triggered + train_time_interval_triggered > 1: raise MisconfigurationException( f"Combination of parameters every_n_train_steps={self._every_n_train_steps}, " f"every_n_epochs={self._every_n_epochs} and train_time_interval={self._train_time_interval} " "should be mutually exclusive." ) if self.monitor is None: # -1: save all epochs, 0: nothing is saved, 1: save last epoch if self.save_top_k not in (-1, 0, 1): raise MisconfigurationException( f"ModelCheckpoint(save_top_k={self.save_top_k}, monitor=None) is not a valid" " configuration. No quantity for top_k to track." ) if self.save_top_k == -1 and self.save_last: rank_zero_info( "ModelCheckpoint(save_last=True, save_top_k=-1, monitor=None)" " will duplicate the last checkpoint saved." ) def __init_ckpt_dir(self, dirpath: Optional[_PATH], filename: Optional[str]) -> None: self._fs = get_filesystem(dirpath if dirpath else "") if dirpath and self._fs.protocol == "file": dirpath = os.path.realpath(dirpath) self.dirpath = dirpath self.filename = filename def __init_monitor_mode(self, mode: str) -> None: torch_inf = torch.tensor(np.Inf) mode_dict = {"min": (torch_inf, "min"), "max": (-torch_inf, "max")} if mode not in mode_dict: raise MisconfigurationException(f"`mode` can be {', '.join(mode_dict.keys())} but got {mode}") self.kth_value, self.mode = mode_dict[mode] def __init_triggers( self, every_n_train_steps: Optional[int], every_n_epochs: Optional[int], train_time_interval: Optional[timedelta], ) -> None: # Default to running once after each validation epoch if neither # every_n_train_steps nor every_n_epochs is set if every_n_train_steps is None and every_n_epochs is None and train_time_interval is None: every_n_epochs = 1 every_n_train_steps = 0 log.debug("Both every_n_train_steps and every_n_epochs are not set. Setting every_n_epochs=1") else: every_n_epochs = every_n_epochs or 0 every_n_train_steps = every_n_train_steps or 0 self._train_time_interval: Optional[timedelta] = train_time_interval self._every_n_epochs: int = every_n_epochs self._every_n_train_steps: int = every_n_train_steps @property def every_n_epochs(self) -> Optional[int]: return self._every_n_epochs def check_monitor_top_k(self, trainer: "pl.Trainer", current: Optional[Tensor] = None) -> bool: if current is None: return False if self.save_top_k == -1: return True less_than_k_models = len(self.best_k_models) < self.save_top_k if less_than_k_models: return True monitor_op = {"min": torch.lt, "max": torch.gt}[self.mode] should_update_best_and_save = monitor_op(current, self.best_k_models[self.kth_best_model_path]) # If using multiple devices, make sure all processes are unanimous on the decision. should_update_best_and_save = trainer.strategy.reduce_boolean_decision(bool(should_update_best_and_save)) return should_update_best_and_save @classmethod def _format_checkpoint_name( cls, filename: Optional[str], metrics: Dict[str, Tensor], prefix: str = "", auto_insert_metric_name: bool = True, ) -> str: if not filename: # filename is not set, use default name filename = "{epoch}" + cls.CHECKPOINT_JOIN_CHAR + "{step}" # check and parse user passed keys in the string groups = re.findall(r"(\{.*?)[:\}]", filename) if len(groups) >= 0: for group in groups: name = group[1:] if auto_insert_metric_name: filename = filename.replace(group, name + "={" + name) # support for dots: https://stackoverflow.com/a/7934969 filename = filename.replace(group, f"{{0[{name}]") if name not in metrics: metrics[name] = torch.tensor(0) filename = filename.format(metrics) if prefix: filename = cls.CHECKPOINT_JOIN_CHAR.join([prefix, filename]) return filename
[docs] def format_checkpoint_name( self, metrics: Dict[str, Tensor], filename: Optional[str] = None, ver: Optional[int] = None ) -> str: """Generate a filename according to the defined template. Example:: >>> tmpdir = os.path.dirname(__file__) >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch}') >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=0))) 'epoch=0.ckpt' >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch:03d}') >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=5))) 'epoch=005.ckpt' >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch}-{val_loss:.2f}') >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.123456))) 'epoch=2-val_loss=0.12.ckpt' >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.12), filename='{epoch:d}')) 'epoch=2.ckpt' >>> ckpt = ModelCheckpoint(dirpath=tmpdir, ... filename='epoch={epoch}-validation_loss={val_loss:.2f}', ... auto_insert_metric_name=False) >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.123456))) 'epoch=2-validation_loss=0.12.ckpt' >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{missing:d}') >>> os.path.basename(ckpt.format_checkpoint_name({})) 'missing=0.ckpt' >>> ckpt = ModelCheckpoint(filename='{step}') >>> os.path.basename(ckpt.format_checkpoint_name(dict(step=0))) 'step=0.ckpt' """ filename = filename or self.filename filename = self._format_checkpoint_name(filename, metrics, auto_insert_metric_name=self.auto_insert_metric_name) if ver is not None: filename = self.CHECKPOINT_JOIN_CHAR.join((filename, f"v{ver}")) ckpt_name = f"{filename}{self.FILE_EXTENSION}" return os.path.join(self.dirpath, ckpt_name) if self.dirpath else ckpt_name
def __resolve_ckpt_dir(self, trainer: "pl.Trainer") -> _PATH: """Determines model checkpoint save directory at runtime. Reference attributes from the trainer's logger to determine where to save checkpoints. The path for saving weights is set in this priority: 1. The ``ModelCheckpoint``'s ``dirpath`` if passed in 2. The ``Logger``'s ``log_dir`` if the trainer has loggers 3. The ``Trainer``'s ``default_root_dir`` if the trainer has no loggers The path gets extended with subdirectory "checkpoints". """ if self.dirpath is not None: # short circuit if dirpath was passed to ModelCheckpoint return self.dirpath if len(trainer.loggers) > 0: if trainer.loggers[0].save_dir is not None: save_dir = trainer.loggers[0].save_dir else: save_dir = trainer.default_root_dir name = trainer.loggers[0].name version = trainer.loggers[0].version version = version if isinstance(version, str) else f"version_{version}" ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints") else: # if no loggers, use default_root_dir ckpt_path = os.path.join(trainer.default_root_dir, "checkpoints") return ckpt_path def _find_last_checkpoints(self, trainer: "pl.Trainer") -> Set[str]: # find all checkpoints in the folder ckpt_path = self.__resolve_ckpt_dir(trainer) if self._fs.exists(ckpt_path): return { os.path.normpath(p) for p in self._fs.ls(ckpt_path, detail=False) if self.CHECKPOINT_NAME_LAST in os.path.split(p)[1] } return set() def __warn_if_dir_not_empty(self, dirpath: _PATH) -> None: if self.save_top_k != 0 and self._fs.isdir(dirpath) and len(self._fs.ls(dirpath)) > 0: rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.") def _get_metric_interpolated_filepath_name( self, monitor_candidates: Dict[str, Tensor], trainer: "pl.Trainer", del_filepath: Optional[str] = None ) -> str: filepath = self.format_checkpoint_name(monitor_candidates) version_cnt = self.STARTING_VERSION while self.file_exists(filepath, trainer) and filepath != del_filepath: filepath = self.format_checkpoint_name(monitor_candidates, ver=version_cnt) version_cnt += 1 return filepath def _monitor_candidates(self, trainer: "pl.Trainer") -> Dict[str, Tensor]: monitor_candidates = deepcopy(trainer.callback_metrics) # cast to int if necessary because `self.log("epoch", 123)` will convert it to float. if it's not a tensor # or does not exist we overwrite it as it's likely an error epoch = monitor_candidates.get("epoch") monitor_candidates["epoch"] = epoch.int() if isinstance(epoch, Tensor) else torch.tensor(trainer.current_epoch) step = monitor_candidates.get("step") monitor_candidates["step"] = step.int() if isinstance(step, Tensor) else torch.tensor(trainer.global_step) return monitor_candidates def _save_last_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None: if not self.save_last: return filepath = self.format_checkpoint_name(monitor_candidates, self.CHECKPOINT_NAME_LAST) version_cnt = self.STARTING_VERSION while self.file_exists(filepath, trainer) and filepath != self.last_model_path: filepath = self.format_checkpoint_name(monitor_candidates, self.CHECKPOINT_NAME_LAST, ver=version_cnt) version_cnt += 1 # set the last model path before saving because it will be part of the state. previous, self.last_model_path = self.last_model_path, filepath self._save_checkpoint(trainer, filepath) if previous and previous != filepath: trainer.strategy.remove_checkpoint(previous) def _save_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None: assert self.monitor current = monitor_candidates.get(self.monitor) if self.check_monitor_top_k(trainer, current): assert current is not None self._update_best_and_save(current, trainer, monitor_candidates) elif self.verbose: epoch = monitor_candidates["epoch"] step = monitor_candidates["step"] rank_zero_info(f"Epoch {epoch:d}, global step {step:d}: {self.monitor!r} was not in top {self.save_top_k}") def _save_none_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None: filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer) # set the best model path before saving because it will be part of the state. previous, self.best_model_path = self.best_model_path, filepath self._save_checkpoint(trainer, filepath) if self.save_top_k == 1 and previous and previous != filepath: trainer.strategy.remove_checkpoint(previous) def _update_best_and_save( self, current: Tensor, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor] ) -> None: k = len(self.best_k_models) + 1 if self.save_top_k == -1 else self.save_top_k del_filepath = None if len(self.best_k_models) == k and k > 0: del_filepath = self.kth_best_model_path self.best_k_models.pop(del_filepath) # do not save nan, replace with +/- inf if isinstance(current, Tensor) and torch.isnan(current): current = torch.tensor(float("inf" if self.mode == "min" else "-inf"), device=current.device) filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer, del_filepath) # save the current score self.current_score = current self.best_k_models[filepath] = current if len(self.best_k_models) == k: # monitor dict has reached k elements _op = max if self.mode == "min" else min self.kth_best_model_path = _op(self.best_k_models, key=self.best_k_models.get) # type: ignore[arg-type] self.kth_value = self.best_k_models[self.kth_best_model_path] _op = min if self.mode == "min" else max self.best_model_path = _op(self.best_k_models, key=self.best_k_models.get) # type: ignore[arg-type] self.best_model_score = self.best_k_models[self.best_model_path] if self.verbose: epoch = monitor_candidates["epoch"] step = monitor_candidates["step"] rank_zero_info( f"Epoch {epoch:d}, global step {step:d}: {self.monitor!r} reached {current:0.5f}" f" (best {self.best_model_score:0.5f}), saving model to {filepath!r} as top {k}" ) self._save_checkpoint(trainer, filepath) if del_filepath is not None and filepath != del_filepath: trainer.strategy.remove_checkpoint(del_filepath)
[docs] def to_yaml(self, filepath: Optional[_PATH] = None) -> None: """Saves the `best_k_models` dict containing the checkpoint paths with the corresponding scores to a YAML file.""" best_k = {k: v.item() for k, v in self.best_k_models.items()} if filepath is None: assert self.dirpath filepath = os.path.join(self.dirpath, "best_k_models.yaml") with self._fs.open(filepath, "w") as fp: yaml.dump(best_k, fp)
[docs] def file_exists(self, filepath: _PATH, trainer: "pl.Trainer") -> bool: """Checks if a file exists on rank 0 and broadcasts the result to all other ranks, preventing the internal state to diverge between ranks.""" exists = self._fs.exists(filepath) return trainer.strategy.broadcast(exists)

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