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
from weakref import proxy
import numpy as np
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities.cloud_io import get_filesystem
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.logger import _name, _version
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
from pytorch_lightning.utilities.types import _METRIC, _PATH, 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:`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` 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``, 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.
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, save_on_train_epoch_end*
Read more: :ref:`Persisting 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 = 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)
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,
save_on_train_epoch_end=self._save_on_train_epoch_end,
)
[docs] def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
self.__resolve_ckpt_dir(trainer)
if trainer.is_global_zero and stage == "fit":
self.__warn_if_dir_not_empty(self.dirpath)
# NOTE: setting these attributes needs to happen as early as possible BEFORE reloading callback states,
# because the attributes are part of the state_key which needs to be fully defined before reloading.
if self._save_on_train_epoch_end is None:
# if the user runs validation multiple times per training epoch or multiple training epochs without
# validation, then we run after validation instead of on train epoch end
self._save_on_train_epoch_end = trainer.val_check_interval == 1.0 and trainer.check_val_every_n_epoch == 1
[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._save_on_train_epoch_end:
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._save_on_train_epoch_end:
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"]
[docs] def save_checkpoint(self, trainer: "pl.Trainer") -> None: # pragma: no-cover
"""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.
"""
rank_zero_deprecation(
f"`{self.__class__.__name__}.save_checkpoint()` was deprecated in v1.6 and will be removed in v1.8."
" Instead, you can use `trainer.save_checkpoint()` to manually save a checkpoint."
)
monitor_candidates = self._monitor_candidates(trainer)
self._save_topk_checkpoint(trainer, monitor_candidates)
self._save_last_checkpoint(trainer, monitor_candidates)
def _save_topk_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, _METRIC]) -> 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 (
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 < -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[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
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(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], 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") -> 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 (deprecated)
4. User provided weights_saved_path
The base path gets extended with logger name and version (if these are available)
and subfolder "checkpoints".
"""
if self.dirpath is not None:
return # short circuit
# TODO: Remove weights_save_path logic here in v1.8
if trainer.loggers:
if trainer._weights_save_path_internal != trainer.default_root_dir:
# the user has changed weights_save_path, it overrides anything
save_dir = trainer._weights_save_path_internal
elif len(trainer.loggers) == 1:
save_dir = trainer.logger.save_dir or trainer.default_root_dir
else:
save_dir = trainer.default_root_dir
name = _name(trainer.loggers)
version = _version(trainer.loggers)
version = version if isinstance(version, str) else f"version_{version}"
ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints")
else:
ckpt_path = os.path.join(trainer._weights_save_path_internal, "checkpoints")
ckpt_path = trainer.strategy.broadcast(ckpt_path)
self.dirpath = ckpt_path
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, _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") -> Dict[str, _METRIC]:
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, torch.Tensor) else torch.tensor(trainer.current_epoch)
)
step = monitor_candidates.get("step")
monitor_candidates["step"] = step.int() if isinstance(step, torch.Tensor) else torch.tensor(trainer.global_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(monitor_candidates, self.CHECKPOINT_NAME_LAST)
# 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, _METRIC]) -> None:
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["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, _METRIC]) -> 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: 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"), 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)
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["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:
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)
