def __init__(self,
target=None,
condition=None,
writer=None,
filename='snapshot_iter_{.updater.iteration}',
snapshot_on_error=False,
n_retains=-1,
autoload=False,
**kwargs):
if condition is None:
condition = _always_true
if writer is None:
writer = snapshot_writers.SimpleWriter()
if 'num_retain' in kwargs:
warnings.warn(
'Argument `num_retain` is deprecated. '
'Please use `n_retains` instead', DeprecationWarning)
n_retains = kwargs['num_retain']
self._target = target
self.filename = filename
self.condition = condition
self.writer = writer
self._snapshot_on_error = snapshot_on_error
self.n_retains = n_retains
self.autoload = autoload
def test_call(self):
target = mock.MagicMock()
w = snapshot_writers.SimpleWriter()
w.save = mock.MagicMock()
with utils.tempdir() as tempd:
w('myfile.dat', tempd, target)
assert w.save.call_count == 1
def __init__(self,
target=None,
condition=None,
writer=None,
filename='snapshot_iter_{.updater.iteration}',
snapshot_on_error=False):
if condition is None:
condition = _always_true
if writer is None:
writer = snapshot_writers.SimpleWriter()
self._target = target
self.filename = filename
self.condition = condition
self.writer = writer
self._snapshot_on_error = snapshot_on_error
def snapshot_object(target, filename, savefun=npz.save_npz, **kwargs):
"""Returns a trainer extension to take snapshots of a given object.
This extension serializes the given object and saves it to the output
directory.
This extension is called once per epoch by default. To take a
snapshot at a different interval, a trigger object specifying the
required interval can be passed along with this extension
to the `extend()` method of the trainer.
The default priority is -100, which is lower than that of most
built-in extensions.
Args:
target: Object to serialize.
filename (str): Name of the file into which the object is serialized.
It can be a format string, where the trainer object is passed to
the :meth:`str.format` method. For example,
``'snapshot_{.updater.iteration}'`` is converted to
``'snapshot_10000'`` at the 10,000th iteration.
savefun: Function to save the object. It takes two arguments: the
output file path and the object to serialize.
snapshot_on_error (bool): Whether to take a snapshot in case trainer
loop has been failed.
Returns:
Snapshot extension object.
.. seealso::
- :meth:`chainer.training.extensions.snapshot`
"""
snapshot_on_error = argument.parse_kwargs(kwargs,
('snapshot_on_error', False))
argument.assert_kwargs_empty(kwargs)
return _Snapshot(target=target,
writer=snapshot_writers.SimpleWriter(savefun=savefun),
filename=filename,
snapshot_on_error=snapshot_on_error)
def gradient_snapshot(savefun=None,
filename='snapshot_iter_{.updater.iteration}',
**kwargs):
target, condition, writer, snapshot_on_error, model = argument.parse_kwargs(
kwargs, ('target', None), ('condition', None), ('writer', None),
('snapshot_on_error', False), ('model', None))
argument.assert_kwargs_empty(kwargs)
if savefun is not None and writer is not None:
raise TypeError(
'savefun and writer arguments cannot be specified together.')
if writer is None:
if savefun is None:
savefun = npz.save_npz
writer = snapshot_writers.SimpleWriter(savefun=savefun)
return GradientSnapshot(target=target,
condition=condition,
writer=writer,
filename=filename,
snapshot_on_error=snapshot_on_error,
model=model)
def snapshot(savefun=None,
filename='snapshot_iter_{.updater.iteration}',
**kwargs):
"""snapshot(savefun=None, filename='snapshot_iter_{.updater.iteration}', \
*, target=None, condition=None, writer=None, snapshot_on_error=False, \
n_retains=-1, autoload=False)
Returns a trainer extension to take snapshots of the trainer.
This extension serializes the trainer object and saves it to the output
directory. It is used to support resuming the training loop from the saved
state.
This extension is called once per epoch by default. To take a
snapshot at a different interval, a trigger object specifying the
required interval can be passed along with this extension
to the `extend()` method of the trainer.
The default priority is -100, which is lower than that of most
built-in extensions.
.. note::
This extension first writes the serialized object to a temporary file
and then rename it to the target file name. Thus, if the program stops
right before the renaming, the temporary file might be left in the
output directory.
Args:
savefun: Function to save the trainer. It takes two arguments: the
output file path and the trainer object.
It is :meth:`chainer.serializers.save_npz` by default.
If ``writer`` is specified, this argument must be ``None``.
filename (str): Name of the file into which the trainer is serialized.
It can be a format string, where the trainer object is passed to
the :meth:`str.format` method.
target: Object to serialize. If it is not specified, it will
be the trainer object.
condition: Condition object. It must be a callable object that returns
boolean without any arguments. If it returns ``True``, the snapshot
will be done.
If not, it will be skipped. The default is a function that always
returns ``True``.
writer: Writer object.
It must be a callable object.
See below for the list of built-in writers.
If ``savefun`` is other than ``None``, this argument must be
``None``. In that case, a
:class:`~chainer.training.extensions.snapshot_writers.SimpleWriter`
object instantiated with specified ``savefun`` argument will be
used.
snapshot_on_error (bool): Whether to take a snapshot in case trainer
loop has been failed.
n_retains (int): Number of snapshot files to retain
through the cleanup. Must be a positive integer for any cleanup to
take place. Automatic deletion of old snapshots only works when the
filename is string.
num_retain (int): Same as ``n_retains`` (deprecated).
autoload (bool): With this enabled, the extension
automatically finds the latest snapshot and loads the data
to the target. Automatic loading only works when the
filename is a string. It is assumed that snapshots are generated
by :func:`chainer.serializers.save_npz` .
Returns:
Snapshot extension object.
.. testcode::
:hide:
from chainer import training
class Model(chainer.Link):
def __call__(self, x):
return x
train_iter = chainer.iterators.SerialIterator([], 1)
optimizer = optimizers.SGD().setup(Model())
updater = training.updaters.StandardUpdater(
train_iter, optimizer, device=0)
trainer = training.Trainer(updater)
.. admonition:: Using asynchronous writers
By specifying ``writer`` argument, writing operations can be made
asynchronous, hiding I/O overhead of snapshots.
>>> from chainer.training import extensions
>>> writer = extensions.snapshot_writers.ProcessWriter()
>>> trainer.extend(extensions.snapshot(writer=writer), \
trigger=(1, 'epoch'))
To change the format, such as npz or hdf5, you can pass a saving
function as ``savefun`` argument of the writer.
>>> from chainer.training import extensions
>>> from chainer import serializers
>>> writer = extensions.snapshot_writers.ProcessWriter(
... savefun=serializers.save_npz)
>>> trainer.extend(extensions.snapshot(writer=writer), \
trigger=(1, 'epoch'))
This is the list of built-in snapshot writers.
- :class:`chainer.training.extensions.snapshot_writers.SimpleWriter`
- :class:`chainer.training.extensions.snapshot_writers.ThreadWriter`
- :class:`chainer.training.extensions.snapshot_writers.ProcessWriter`
- :class:`chainer.training.extensions.snapshot_writers.\
ThreadQueueWriter`
- :class:`chainer.training.extensions.snapshot_writers.\
ProcessQueueWriter`
.. seealso::
- :meth:`chainer.training.extensions.snapshot_object`
"""
if 'num_retain' in kwargs:
warnings.warn(
'Argument `num_retain` is deprecated. '
'Please use `n_retains` instead', DeprecationWarning)
kwargs['n_retains'] = kwargs.pop('num_retain')
target, condition, writer, snapshot_on_error, n_retains,\
autoload = argument.parse_kwargs(
kwargs,
('target', None), ('condition', None), ('writer', None),
('snapshot_on_error', False), ('n_retains', -1),
('autoload', False))
argument.assert_kwargs_empty(kwargs)
if savefun is not None and writer is not None:
raise TypeError(
'savefun and writer arguments cannot be specified together.')
if writer is None:
if savefun is None:
savefun = npz.save_npz
writer = snapshot_writers.SimpleWriter(savefun=savefun)
return _Snapshot(target=target,
condition=condition,
writer=writer,
filename=filename,
snapshot_on_error=snapshot_on_error,
n_retains=n_retains,
autoload=autoload)
