Source code for pytorch_lightning.callbacks.model_checkpoint

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

import numpy as np
import torch
import yaml

from pytorch_lightning import _logger as log
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities import 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.warnings import WarningCache

warning_cache = WarningCache()

[docs]class ModelCheckpoint(Callback): r""" Save the model after every epoch by monitoring a quantity. 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 {auto, 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. In `auto` mode, the direction is automatically inferred from the name of the monitored quantity. .. warning:: Setting ``mode='auto'`` has been deprecated in v1.1 and will be removed in v1.3. save_weights_only: if ``True``, then only the model's weights will be saved (``model.save_weights(filepath)``), else the full model is saved (````). period: Interval (number of epochs) between checkpoints. prefix: A string to put at the beginning of checkpoint filename. .. warning:: This argument has been deprecated in v1.1 and will be removed in v1.3 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"``, ``"max"``, and ``"auto"``. 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}' ... ) # 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 = "auto", period: int = 1, prefix: str = "", ): 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.period = period self._last_global_step_saved = -1 self.prefix = prefix 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.save_function = None self.warned_result_obj = False if prefix: rank_zero_warn( 'Argument `prefix` is deprecated in v1.1 and will be removed in v1.3.' ' Please prepend your prefix in `filename` instead.', DeprecationWarning ) self.__init_monitor_mode(monitor, mode) self.__init_ckpt_dir(dirpath, filename, save_top_k) self.__validate_init_configuration()
[docs] def on_pretrain_routine_start(self, trainer, pl_module): """ 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_validation_end(self, trainer, pl_module): """ checkpoints can be saved at the end of the val loop """ self.save_checkpoint(trainer, pl_module)
[docs] def on_save_checkpoint(self, trainer, pl_module, 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, callback_state: Dict[str, Any]): 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_module): """ Performs the main logic around saving a checkpoint. This method runs on all ranks, it is the responsibility of `self.save_function` to handle correct behaviour in distributed training, i.e., saving only on rank 0. """ epoch = trainer.current_epoch global_step = trainer.global_step if ( trainer.fast_dev_run # disable checkpointing with fast_dev_run or self.save_top_k == 0 # no models are saved or self.period < 1 # no models are saved or (epoch + 1) % self.period # skip epoch or trainer.running_sanity_check # don't save anything during sanity check or self._last_global_step_saved == global_step # already saved at the last step ): return 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) # callback supports multiple simultaneous modes # here we call each mode sequentially # Mode 1: save all checkpoints OR only the top k if self.save_top_k: self._save_top_k_checkpoints(trainer, pl_module, monitor_candidates) # Mode 2: save the last checkpoint self._save_last_checkpoint(trainer, pl_module, monitor_candidates)
def __validate_init_configuration(self): 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.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, monitor=None) is a redundant configuration.' ' You can save the last checkpoint with ModelCheckpoint(save_top_k=None, monitor=None).' ) def __init_ckpt_dir(self, dirpath, filename, save_top_k): 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: Union[str, None] = dirpath or None self.filename = filename or None def __init_monitor_mode(self, monitor, mode): torch_inf = torch.tensor(np.Inf) mode_dict = { "min": (torch_inf, "min"), "max": (-torch_inf, "max"), } if mode not in mode_dict and mode != 'auto': raise MisconfigurationException(f"`mode` can be auto, {', '.join(mode_dict.keys())}, got {mode}") # TODO: Update with MisconfigurationException when auto mode is removed in v1.3 if mode == 'auto': rank_zero_warn( "mode='auto' is deprecated in v1.1 and will be removed in v1.3." " Default value for mode with be 'min' in v1.3.", DeprecationWarning ) _condition = monitor is not None and ("acc" in monitor or monitor.startswith("fmeasure")) mode_dict['auto'] = ((-torch_inf, "max") if _condition else (torch_inf, "min")) self.kth_value, self.mode = mode_dict[mode] @rank_zero_only def _del_model(self, filepath: str): if self._fs.exists(filepath): self._fs.rm(filepath) log.debug(f"Removed checkpoint: {filepath}") def _save_model(self, filepath: str, trainer, pl_module): # Todo: required argument `pl_module` is not used # 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 if self.save_function is not None: self.save_function(filepath, self.save_weights_only) else: raise ValueError(".save_function() not set") def check_monitor_top_k(self, current) -> 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] return monitor_op(current, self.best_k_models[self.kth_best_model_path]).item() @classmethod def _format_checkpoint_name( cls, filename: Optional[str], epoch: int, step: int, metrics: Dict[str, Any], prefix: str = "", ) -> 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: metrics.update({"epoch": epoch, 'step': step}) for group in groups: name = group[1:] 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, epoch: int, step: int, metrics: Dict[str, Any], 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(0, 1, metrics={})) 'epoch=0.ckpt' >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch:03d}') >>> os.path.basename(ckpt.format_checkpoint_name(5, 2, metrics={})) 'epoch=005.ckpt' >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch}-{val_loss:.2f}') >>> os.path.basename(ckpt.format_checkpoint_name(2, 3, metrics=dict(val_loss=0.123456))) 'epoch=2-val_loss=0.12.ckpt' >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{missing:d}') >>> os.path.basename(ckpt.format_checkpoint_name(0, 4, metrics={})) 'missing=0.ckpt' >>> ckpt = ModelCheckpoint(filename='{step}') >>> os.path.basename(ckpt.format_checkpoint_name(0, 0, {})) 'step=0.ckpt' """ filename = self._format_checkpoint_name(self.filename, epoch, step, metrics, prefix=self.prefix) 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): """ 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}" ) version, name = trainer.training_type_plugin.broadcast((version, ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints") else: ckpt_path = os.path.join(trainer.weights_save_path, "checkpoints") 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): 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): 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}', tensor) in the LightningModule?" ) raise MisconfigurationException(m) def _get_metric_interpolated_filepath_name( self, ckpt_name_metrics: Dict[str, Any], epoch: int, step: int, trainer, del_filepath: Optional[str] = None, ) -> str: filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics) version_cnt = self.STARTING_VERSION while self.file_exists(filepath, trainer) and filepath != del_filepath: filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics, ver=version_cnt) version_cnt += 1 return filepath def _monitor_candidates(self, trainer): monitor_candidates = deepcopy(trainer.logger_connector.callback_metrics) monitor_candidates.update(step=trainer.global_step, epoch=trainer.current_epoch) return monitor_candidates def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics): should_save_last = self.monitor is None or self.save_last if not should_save_last: return # when user ALSO asked for the 'last.ckpt' change the name if self.save_last: last_filepath = self._format_checkpoint_name( self.CHECKPOINT_NAME_LAST, trainer.current_epoch, trainer.global_step, ckpt_name_metrics, prefix=self.prefix ) last_filepath = os.path.join(self.dirpath, f"{last_filepath}{self.FILE_EXTENSION}") else: last_filepath = self._get_metric_interpolated_filepath_name( ckpt_name_metrics, trainer.current_epoch, trainer.global_step, trainer, ) if trainer.training_type_plugin.rpc_enabled: # RPCPlugin manages saving all model states trainer.training_type_plugin.rpc_save_model(self._save_model, last_filepath, trainer, pl_module) else: self._save_model(last_filepath, trainer, pl_module) if ( self.last_model_path and self.last_model_path != last_filepath and (self.save_top_k != -1 or self.save_last) and trainer.is_global_zero ): self._del_model(self.last_model_path) self.last_model_path = last_filepath if self.monitor is None: self.best_model_path = self.last_model_path def _save_top_k_checkpoints(self, trainer, pl_module, metrics): current = metrics.get(self.monitor) epoch = metrics.get("epoch") step = metrics.get("step") # when `val_loss` is being logged and no ModelCheckpoint is being provided # `val_loss` will be selected for monitor and need to be reduced to # prevent processes divergence # TODO: Move this logic to logger_connector. This also needs to be fixed for any # other monitor logged value which aren't produced from a Metric. if self.monitor == "val_loss": current = trainer.training_type_plugin.reduce(current, reduce_op="mean") if self.check_monitor_top_k(current): self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics) elif self.verbose: rank_zero_info(f"Epoch {epoch:d}, step {step:d}: {self.monitor} was not in top {self.save_top_k}") def _is_valid_monitor_key(self, metrics): return self.monitor in metrics or len(metrics) == 0 def _update_best_and_save( self, current: torch.Tensor, epoch: int, step: int, trainer, pl_module, ckpt_name_metrics ): 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(ckpt_name_metrics, epoch, step, 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: 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(filepath, trainer, pl_module) 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): """ 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) -> 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) exists = trainer.training_type_plugin.broadcast(exists) return exists

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