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Improved docs for callbacks (#1370)

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* improved docs for callbacks

* class references

* make doctest pass

* doctests

* fix lines too long

* fix line too long

* fix permission error in doctest

* Apply suggestions from code review

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* fix doctest

* fix default

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
This commit is contained in:
Adrian Wälchli
2020-04-05 09:38:52 +00:00
committed by GitHub
co-authored by Jirka Borovec
parent 22bedf9b57
commit 1f2da71069
6 changed files with 88 additions and 72 deletions
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pytorch_lightning/callbacks/model_checkpoint.py
+31 -25
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@@ -3,6 +3,7 @@ Model Checkpointing
===================
Automatically save model checkpoints during training.
"""
import os
@@ -26,18 +27,19 @@ class ModelCheckpoint(Callback):
Example::
# no path
ModelCheckpoint()
# saves like /my/path/epoch_0.ckpt
# custom path
# saves a file like: my/path/epoch_0.ckpt
>>> checkpoint_callback = ModelCheckpoint('my/path/')
# save any arbitrary metrics like and val_loss, etc in name
ModelCheckpoint(filepath='/my/path/{epoch}-{val_loss:.2f}-{other_metric:.2f}')
# saves file like: /my/path/epoch=2-val_loss=0.2_other_metric=0.3.ckpt
# save any arbitrary metrics like `val_loss`, etc. in name
# saves a file like: my/path/epoch=2-val_loss=0.2_other_metric=0.3.ckpt
>>> checkpoint_callback = ModelCheckpoint(
... filepath='my/path/{epoch}-{val_loss:.2f}-{other_metric:.2f}'
... )
monitor (str): quantity to monitor.
verbose (bool): verbosity mode, False or True.
save_top_k (int): if `save_top_k == k`,
monitor: quantity to monitor.
verbose: verbosity mode. Default: ``False``.
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.
@@ -46,7 +48,7 @@ class ModelCheckpoint(Callback):
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 `v0`.
mode (str): one of {auto, min, max}.
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
@@ -54,26 +56,29 @@ class ModelCheckpoint(Callback):
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.
save_weights_only (bool): if True, then only the model's weights will be
saved (`model.save_weights(filepath)`), else the full model
is saved (`model.save(filepath)`).
period (int): Interval (number of epochs) between checkpoints.
save_weights_only: if ``True``, then only the model's weights will be
saved (``model.save_weights(filepath)``), else the full model
is saved (``model.save(filepath)``).
period: Interval (number of epochs) between checkpoints.
Example::
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import ModelCheckpoint
# saves checkpoints to my_path whenever 'val_loss' has a new min
checkpoint_callback = ModelCheckpoint(filepath='my_path')
Trainer(checkpoint_callback=checkpoint_callback)
# saves checkpoints to 'my/path/' whenever 'val_loss' has a new min
>>> checkpoint_callback = ModelCheckpoint(filepath='my/path/')
>>> trainer = Trainer(checkpoint_callback=checkpoint_callback)
# save epoch and val_loss in name
ModelCheckpoint(filepath='/my/path/here/sample-mnist_{epoch:02d}-{val_loss:.2f}')
# saves file like: /my/path/here/sample-mnist_epoch=02_val_loss=0.32.ckpt
# saves a file like: my/path/sample-mnist_epoch=02_val_loss=0.32.ckpt
>>> checkpoint_callback = ModelCheckpoint(
... filepath='my/path/sample-mnist_{epoch:02d}-{val_loss:.2f}'
... )
"""
def __init__(self, filepath, monitor: str = 'val_loss', verbose: bool = False,
def __init__(self, filepath: str, monitor: str = 'val_loss', verbose: bool = False,
save_top_k: int = 1, save_weights_only: bool = False,
mode: str = 'auto', period: int = 1, prefix: str = ''):
super().__init__()
@@ -137,9 +142,10 @@ class ModelCheckpoint(Callback):
return self.monitor_op(current, self.best_k_models[self.kth_best_model])
def format_checkpoint_name(self, epoch, metrics, ver=None):
"""Generate a filename according define template.
"""Generate a filename according to the defined template.
Example::
Examples:
>>> tmpdir = os.path.dirname(__file__)
>>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch}'))
>>> os.path.basename(ckpt.format_checkpoint_name(0, {}))