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.
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Model Checkpointing

Automatically save model checkpoints during training.

import logging
import os
import re
from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Union

import numpy as np
import torch
import yaml

import pytorch_lightning as pl
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities import rank_zero_deprecation, rank_zero_info, rank_zero_only, rank_zero_warn
from pytorch_lightning.utilities.cloud_io import get_filesystem
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.types import _METRIC, STEP_OUTPUT
from pytorch_lightning.utilities.warnings import WarningCache

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

[docs]class ModelCheckpoint(Callback): r""" Save the model periodically by monitoring a quantity. Every metric logged with :meth:`~pytorch_lightning.core.lightning.log` or :meth:`~pytorch_lightning.core.lightning.log_dict` in LightningModule is a candidate for the monitor key. For more information, see :ref:`weights_loading`. 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` or :paramref:`~pytorch_lightning.trainer.trainer.Trainer.weights_save_path` arguments, 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``, always saves the model at the end of the epoch to a file `last.ckpt`. 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 ``period`` 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. save_weights_only: if ``True``, then only the model's weights will be saved (``model.save_weights(filepath)``), else the full model is saved (````). 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`` non-negative. This must be mutually exclusive with ``every_n_val_epochs``. every_n_val_epochs: Number of validation epochs between checkpoints. If ``every_n_val_epochs == None or every_n_val_epochs == 0``, we skip saving on validation end To disable, set ``every_n_val_epochs = 0``. This value must be ``None`` or non-negative. This must be mutually exclusive with ``every_n_train_steps``. Setting both ``ModelCheckpoint(..., every_n_val_epochs=V)`` 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_val_epochs`` and ``check_val_every_n_epoch`` evenly divide E. period: Interval (number of epochs) between checkpoints. .. warning:: This argument has been deprecated in v1.3 and will be removed in v1.5. Use ``every_n_val_epochs`` instead. 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"`` Raises: MisconfigurationException: If ``save_top_k`` is neither ``None`` nor more than or equal to ``-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 = ... checkpoint_callback.best_model_path """ CHECKPOINT_JOIN_CHAR = "-" CHECKPOINT_NAME_LAST = "last" FILE_EXTENSION = ".ckpt" STARTING_VERSION = 1 def __init__( self, dirpath: Optional[Union[str, Path]] = None, filename: Optional[str] = None, monitor: Optional[str] = None, verbose: bool = False, save_last: Optional[bool] = None, save_top_k: Optional[int] = None, save_weights_only: bool = False, mode: str = "min", auto_insert_metric_name: bool = True, every_n_train_steps: Optional[int] = None, every_n_val_epochs: Optional[int] = None, period: Optional[int] = 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._last_global_step_saved = -1 self.current_score = None self.best_k_models = {} self.kth_best_model_path = "" self.best_model_score = None self.best_model_path = "" self.last_model_path = "" self.__init_monitor_mode(mode) self.__init_ckpt_dir(dirpath, filename, save_top_k) self.__init_triggers(every_n_train_steps, every_n_val_epochs, period) self.__validate_init_configuration() self._save_function = None
[docs] def on_pretrain_routine_start(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None: """ When pretrain routine starts we build the ckpt dir on the fly """ self.__resolve_ckpt_dir(trainer) self._save_function = trainer.save_checkpoint
[docs] def on_train_batch_end( self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule', outputs: STEP_OUTPUT, batch: Any, batch_idx: int, dataloader_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 step = trainer.global_step skip_batch = self._every_n_train_steps < 1 or ((step + 1) % self._every_n_train_steps != 0) if skip_batch: return self.save_checkpoint(trainer)
[docs] def on_validation_end(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None: """ Save a checkpoint at the end of the validation stage. """ skip = ( self._should_skip_saving_checkpoint(trainer) or self._every_n_val_epochs < 1 or (trainer.current_epoch + 1) % self._every_n_val_epochs != 0 ) if skip: return self.save_checkpoint(trainer)
[docs] def on_save_checkpoint( self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule', checkpoint: Dict[str, Any], ) -> 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 }
[docs] def on_load_checkpoint( self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule', callback_state: Dict[str, Any] ) -> None: self.best_model_score = callback_state["best_model_score"] self.best_model_path = callback_state["best_model_path"]
[docs] def save_checkpoint(self, trainer: 'pl.Trainer', unused: Optional['pl.LightningModule'] = None) -> None: """ Performs the main logic around saving a checkpoint. This method runs on all ranks. It is the responsibility of `trainer.save_checkpoint` to correctly handle the behaviour in distributed training, i.e., saving only on rank 0 for data parallel use cases. """ if unused is not None: rank_zero_deprecation( "`ModelCheckpoint.save_checkpoint` signature has changed in v1.3. The `pl_module` parameter" " has been removed. Support for the old signature will be removed in v1.5" ) epoch = trainer.current_epoch global_step = trainer.global_step self._add_backward_monitor_support(trainer) self._validate_monitor_key(trainer) # track epoch when ckpt was last checked self._last_global_step_saved = global_step # what can be monitored monitor_candidates = self._monitor_candidates(trainer, epoch=epoch, step=global_step) # callback supports multiple simultaneous modes # here we call each mode sequentially # Mode 1: save the top k checkpoints self._save_top_k_checkpoint(trainer, monitor_candidates) # Mode 2: save monitor=None checkpoints self._save_none_monitor_checkpoint(trainer, monitor_candidates) # Mode 3: save last checkpoints self._save_last_checkpoint(trainer, monitor_candidates)
def _should_skip_saving_checkpoint(self, trainer: 'pl.Trainer') -> bool: from pytorch_lightning.trainer.states import TrainerFn return ( 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 __validate_init_configuration(self) -> None: if self.save_top_k is not None and self.save_top_k < -1: raise MisconfigurationException(f'Invalid value for save_top_k={self.save_top_k}. Must be None or >= -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_val_epochs < 0: raise MisconfigurationException( f'Invalid value for every_n_val_epochs={self._every_n_val_epochs}. Must be >= 0' ) if self._every_n_train_steps > 0 and self._every_n_val_epochs > 0: raise MisconfigurationException( f'Invalid values for every_n_train_steps={self._every_n_train_steps}' ' and every_n_val_epochs={self._every_n_val_epochs}.' ' Both cannot be enabled at the same time.' ) if self.monitor is None: # None: save last epoch, -1: save all epochs, 0: nothing is saved if self.save_top_k not in (None, -1, 0): 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_last: rank_zero_warn( 'ModelCheckpoint(save_last=True, save_top_k=None, monitor=None) is a redundant configuration.' ' You can save the last checkpoint with ModelCheckpoint(save_top_k=None, monitor=None).' ) 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[Union[str, Path]], filename: Optional[str], save_top_k: Optional[int], ) -> None: self._fs = get_filesystem(str(dirpath) if dirpath else '') if ( save_top_k is not None and save_top_k > 0 and dirpath is not None and self._fs.isdir(dirpath) and len( > 0 ): rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.") 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_val_epochs: Optional[int], period: Optional[int] ) -> None: # Default to running once after each validation epoch if neither # every_n_train_steps nor every_n_val_epochs is set if every_n_train_steps is None and every_n_val_epochs is None: self._every_n_val_epochs = 1 self._every_n_train_steps = 0 log.debug("Both every_n_train_steps and every_n_val_epochs are not set. Setting every_n_val_epochs=1") else: self._every_n_val_epochs = every_n_val_epochs or 0 self._every_n_train_steps = every_n_train_steps or 0 # period takes precedence over every_n_val_epochs for backwards compatibility if period is not None: rank_zero_deprecation( 'Argument `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.' ' Please use `every_n_val_epochs` instead.' ) self._every_n_val_epochs = period self._period = self._every_n_val_epochs @property def period(self) -> Optional[int]: rank_zero_deprecation( 'Property `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.' ' Please use `every_n_val_epochs` instead.' ) return self._period @period.setter def period(self, value: Optional[int]) -> None: rank_zero_deprecation( 'Property `period` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.' ' Please use `every_n_val_epochs` instead.' ) self._period = value @property def save_function(self) -> Optional[Callable]: rank_zero_deprecation( 'Property `save_function` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.' ' Please use `trainer.save_checkpoint` instead.' ) return self._save_function @save_function.setter def save_function(self, value: Optional[Callable]) -> None: rank_zero_deprecation( 'Property `save_function` in `ModelCheckpoint` is deprecated in v1.3 and will be removed in v1.5.' ' Please use `trainer.save_checkpoint` instead.' ) self._save_function = value @rank_zero_only def _del_model(self, filepath: str) -> None: if self._fs.exists(filepath): self._fs.rm(filepath) log.debug(f"Removed checkpoint: {filepath}") def _save_model(self, trainer: 'pl.Trainer', filepath: str) -> None: if trainer.training_type_plugin.rpc_enabled: # RPCPlugin manages saving all model states # TODO: the rpc plugin should wrap trainer.save_checkpoint # instead of us having to do it here manually trainer.training_type_plugin.rpc_save_model(trainer, self._do_save, filepath) else: self._do_save(trainer, filepath) def _do_save(self, trainer: 'pl.Trainer', filepath: str) -> None: # in debugging, track when we save checkpoints trainer.dev_debugger.track_checkpointing_history(filepath) # make paths if trainer.is_global_zero: self._fs.makedirs(os.path.dirname(filepath), exist_ok=True) # delegate the saving to the trainer trainer.save_checkpoint(filepath, self.save_weights_only) def check_monitor_top_k(self, trainer: 'pl.Trainer', current: Optional[torch.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 if not isinstance(current, torch.Tensor): rank_zero_warn( f"{current} is supposed to be a `torch.Tensor`. Saving checkpoint may not work correctly." f" HINT: check the value of {self.monitor} in your validation loop", RuntimeWarning, ) current = torch.tensor(current) monitor_op = {"min":, "max":}[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.training_type_plugin.reduce_boolean_decision(should_update_best_and_save) return should_update_best_and_save @classmethod def _format_checkpoint_name( cls, filename: Optional[str], metrics: Dict[str, _METRIC], 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) if name not in metrics: metrics[name] = 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, _METRIC], 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' >>> 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 = self._format_checkpoint_name( self.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') -> None: """ Determines model checkpoint save directory at runtime. References attributes from the trainer's logger to determine where to save checkpoints. The base path for saving weights is set in this priority: 1. Checkpoint callback's path (if passed in) 2. The default_root_dir from trainer if trainer has no logger 3. The weights_save_path from trainer, if user provides it 4. User provided weights_saved_path The base path gets extended with logger name and version (if these are available) and subfolder "checkpoints". """ # Todo: required argument `pl_module` is not used if self.dirpath is not None: return # short circuit if trainer.logger is not None: if trainer.weights_save_path != trainer.default_root_dir: # the user has changed weights_save_path, it overrides anything save_dir = trainer.weights_save_path else: save_dir = trainer.logger.save_dir or trainer.default_root_dir version = ( trainer.logger.version if isinstance(trainer.logger.version, str) else f"version_{trainer.logger.version}" ) ckpt_path = os.path.join(save_dir, str(, version, "checkpoints") else: ckpt_path = os.path.join(trainer.weights_save_path, "checkpoints") ckpt_path = trainer.training_type_plugin.broadcast(ckpt_path) self.dirpath = ckpt_path if not trainer.fast_dev_run and trainer.is_global_zero: self._fs.makedirs(self.dirpath, exist_ok=True) def _add_backward_monitor_support(self, trainer: 'pl.Trainer') -> None: metrics = trainer.logger_connector.callback_metrics deprecation_warning = False if self.monitor is None and 'val_loss' in metrics: self.monitor = 'val_loss' deprecation_warning = True if self.save_top_k is None and self.monitor is not None: # TODO: Remove `Optional` from `save_top_k` when this is deleted in v1.4 self.save_top_k = 1 if deprecation_warning: warning_cache.warn( "Relying on `self.log('val_loss', ...)` to set the ModelCheckpoint monitor is deprecated in v1.2" " and will be removed in v1.4. Please, create your own `mc = ModelCheckpoint(monitor='your_monitor')`" " and use it as `Trainer(callbacks=[mc])`.", DeprecationWarning ) def _validate_monitor_key(self, trainer: 'pl.Trainer') -> None: metrics = trainer.logger_connector.callback_metrics # validate metric if self.monitor is not None and not self._is_valid_monitor_key(metrics): m = ( f"ModelCheckpoint(monitor='{self.monitor}') not found in the returned metrics:" f" {list(metrics.keys())}. " f"HINT: Did you call self.log('{self.monitor}', value) in the LightningModule?" ) raise MisconfigurationException(m) def _get_metric_interpolated_filepath_name( self, monitor_candidates: Dict[str, _METRIC], 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', epoch: int, step: int) -> Dict[str, _METRIC]: monitor_candidates = deepcopy(trainer.logger_connector.callback_metrics) monitor_candidates.update(epoch=epoch, step=step) return monitor_candidates def _save_last_checkpoint(self, trainer: 'pl.Trainer', monitor_candidates: Dict[str, _METRIC]) -> None: if not self.save_last: return filepath = self._format_checkpoint_name(self.CHECKPOINT_NAME_LAST, monitor_candidates) filepath = os.path.join(self.dirpath, f"{filepath}{self.FILE_EXTENSION}") self._save_model(trainer, filepath) if self.last_model_path and self.last_model_path != filepath and trainer.is_global_zero: self._del_model(self.last_model_path) self.last_model_path = filepath def _save_top_k_checkpoint(self, trainer: 'pl.Trainer', monitor_candidates: Dict[str, _METRIC]) -> None: if self.monitor is None or self.save_top_k == 0: return current = monitor_candidates.get(self.monitor) if self.check_monitor_top_k(trainer, current): self._update_best_and_save(current, trainer, monitor_candidates) elif self.verbose: epoch = monitor_candidates.get("epoch") step = monitor_candidates.get("step") rank_zero_info(f"Epoch {epoch:d}, global step {step:d}: {self.monitor} was not in top {self.save_top_k}") def _save_none_monitor_checkpoint(self, trainer: 'pl.Trainer', monitor_candidates: Dict[str, _METRIC]) -> None: if self.monitor is not None or self.save_top_k == 0: return filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer) self._save_model(trainer, filepath) if ( self.save_top_k is None and self.best_model_path and self.best_model_path != filepath and trainer.is_global_zero ): self._del_model(self.best_model_path) self.best_model_path = filepath def _is_valid_monitor_key(self, metrics: Dict[str, _METRIC]) -> bool: return self.monitor in metrics or len(metrics) == 0 def _update_best_and_save( self, current: torch.Tensor, trainer: 'pl.Trainer', monitor_candidates: Dict[str, _METRIC] ) -> 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, torch.Tensor) and torch.isnan(current): current = torch.tensor(float('inf' if self.mode == "min" else '-inf')) 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) 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) self.best_model_score = self.best_k_models[self.best_model_path] if self.verbose: epoch = monitor_candidates.get("epoch") step = monitor_candidates.get("step") rank_zero_info( f"Epoch {epoch:d}, global step {step:d}: {self.monitor} reached {current:0.5f}" f' (best {self.best_model_score:0.5f}), saving model to "{filepath}" as top {k}' ) self._save_model(trainer, filepath) if del_filepath is not None and filepath != del_filepath: self._del_model(del_filepath)
[docs] def to_yaml(self, filepath: Optional[Union[str, 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: filepath = os.path.join(self.dirpath, "best_k_models.yaml") with, "w") as fp: yaml.dump(best_k, fp)
[docs] def file_exists(self, filepath: Union[str, 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.training_type_plugin.broadcast(exists)

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