Source code for

import collections
import os

import six

from chainer import reporter as reporter_module
from import extension as extension_module
from import trigger as trigger_module

class _ExtensionEntry(object):

    def __init__(self, extension, priority, trigger, invoke_before_training):
        self.extension = extension
        self.trigger = trigger
        self.invoke_before_training = invoke_before_training
        self.priority = priority

[docs]class Trainer(object): """The standard training loop in Chainer. Trainer is an implementation of a training loop. Users can invoke the training by calling the :meth:`run` method. Each iteration of the training loop proceeds as follows. - Update of the parameters. It includes the mini-batch loading, forward and backward computations, and an execution of the update formula. These are all done by the update object held by the trainer. - Invocation of trainer extensions in the descending order of their priorities. A trigger object is attached to each extension, and it decides at each iteration whether the extension should be executed. Trigger objects are callable objects that take the trainer object as the argument and return a boolean value indicating whether the extension should be called or not. Extensions are callable objects that take the trainer object as the argument. There are two ways to define custom extensions: inheriting the :class:`Extension` class, and decorating functions by :func:`make_extension`. See :class:`Extension` for more details on custom extensions. Users can register extensions to the trainer by calling the :meth:`extend` method, where some configurations can be added. - Trigger object, which is also explained above. In most cases, :class:`IntervalTrigger` is used, in which case users can simply specify a tuple of the interval length and its unit, like ``(1000, 'iteration')`` or ``(1, 'epoch')``. - The order of execution of extensions is determined by their priorities. Extensions of higher priorities are invoked earlier. There are three standard values for the priorities: - ``PRIORITY_WRITER``. This is the priority for extensions that write some records to the :attr:`observation` dictionary. It includes cases that the extension directly adds values to the observation dictionary, or the extension uses the :func:`` function to report values to the observation dictionary. - ``PRIORITY_EDITOR``. This is the priority for extensions that edit the :attr:`observation` dictionary based on already reported values. - ``PRIORITY_READER``. This is the priority for extensions that only read records from the :attr:`observation` dictionary. This is also suitable for extensions that do not use the :attr:`observation` dictionary at all. - Extensions with ``invoke_before_training`` flag on are also invoked at the beginning of the training loop. Extensions that update the training status (e.g., changing learning rates) should have this flag to be ``True`` to ensure that resume of the training loop correctly recovers the training status. The current state of the trainer object and objects handled by the trainer can be serialized through the standard serialization protocol of Chainer. It enables us to easily suspend and resume the training loop. .. note:: The serialization does not recover everything of the training loop. It only recovers the states which change over the training (e.g. parameters, optimizer states, the batch iterator state, extension states, etc.). You must initialize the objects correctly before deserializing the states. On the other hand, it means that users can change the settings on deserialization. For example, the exit condition can be changed on the deserialization, so users can train the model for some iterations, suspend it, and then resume it with larger number of total iterations. During the training, it also creates a :class:`~chainer.Reporter` object to store observed values on each update. For each iteration, it creates a fresh observation dictionary and stores it in the :attr:`observation` attribute. Links of the target model of each optimizer are registered to the reporter object as observers, where the name of each observer is constructed as the format ``<optimizer name><link name>``. The link name is given by the :meth:`chainer.Link.namedlink` method, which represents the path to each link in the hierarchy. Other observers can be registered by accessing the reporter object via the :attr:`reporter` attribute. The default trainer is `plain`, i.e., it does not contain any extensions. Args: updater ( Updater object. It defines how to update the models. stop_trigger: Trigger that determines when to stop the training loop. If it is not callable, it is passed to :class:`IntervalTrigger`. Attributes: updater: The updater object for this trainer. stop_trigger: Trigger that determines when to stop the training loop. The training loop stops at the iteration on which this trigger returns ``True``. observation: Observation of values made at the last update. See the :class:`Reporter` class for details. out: Output directory. reporter: Reporter object to report observed values. """ def __init__(self, updater, stop_trigger=None, out='result'): self.updater = updater self.stop_trigger = trigger_module.get_trigger(stop_trigger) self.observation = {} self.out = out reporter = reporter_module.Reporter() for name, optimizer in six.iteritems(updater.get_all_optimizers()): reporter.add_observer(name, reporter.add_observers( name, self.reporter = reporter self._done = False self._extensions = collections.OrderedDict() updater.connect_trainer(self)
[docs] def extend(self, extension, name=None, trigger=None, priority=None, invoke_before_training=None): """Registers an extension to the trainer. :class:`Extension` is a callable object which is called after each update unless the corresponding trigger object decides to skip the iteration. The order of execution is determined by priorities: extensions with higher priorities are called earlier in each iteration. Extensions with the same priority are invoked in the order of registrations. If two or more extensions with the same name are registered, suffixes are added to the names of the second to last extensions. The suffix is ``_N`` where N is the ordinal of the extensions. See :class:`Extension` for the interface of extensions. Args: extension: Extension to register. name (str): Name of the extension. If it is omitted, the ``default_name`` attribute of the extension is used instead. Note that the name would be suffixed by an ordinal in case of duplicated names as explained above. trigger (tuple or Trigger): Trigger object that determines when to invoke the extension. If it is ``None``, ``extension.trigger`` is used instead. If the trigger is not callable, it is passed to :class:`IntervalTrigger` to build an interval trigger. priority (int): Invocation priority of the extension. Extensions are invoked in the descending order of priorities in each iteration. If this is ``None``, ``extension.priority`` is used instead. invoke_before_training (bool): If ``True``, the extension is also invoked just before entering the training loop. If this ``None``, ``extension.invoke_before_training`` is used instead. This option is mainly used for extensions that alter the training configuration (e.g., learning rates); in such a case, resuming from snapshots require the call of extension to recover the configuration before any updates. """ if name is None: name = getattr(extension, 'name', None) if name is None: name = getattr(extension, 'default_name', None) if name is None: raise TypeError('name is not given for the extension') if name == 'training': raise ValueError( 'the name "training" is prohibited as an extension name') if trigger is None: trigger = getattr(extension, 'trigger', None) trigger = trigger_module.get_trigger(trigger) if priority is None: priority = getattr( extension, 'priority', extension_module.PRIORITY_READER) if invoke_before_training is None: invoke_before_training = getattr( extension, 'invoke_before_training', False) modified_name = name ordinal = 0 while modified_name in self._extensions: ordinal += 1 modified_name = '%s_%d' % (name, ordinal) = modified_name self._extensions[modified_name] = _ExtensionEntry( extension, priority, trigger, invoke_before_training)
[docs] def get_extension(self, name): """Returns the extension of a given name. Args: name (str): Name of the extension. Returns: Extension. """ extensions = self._extensions if name in extensions: return extensions[name].extension else: raise ValueError('extension %s not found' % name)
[docs] def run(self): """Executes the training loop. This method is the core of ``Trainer``. It executes the whole loop of training the models. Note that this method cannot run multiple times for one trainer object. """ if self._done: raise RuntimeError('cannot run training loop multiple times') try: os.makedirs(self.out) except OSError: pass # sort extensions by priorities extension_order = sorted( self._extensions.keys(), key=lambda name: self._extensions[name].priority, reverse=True) extensions = [(name, self._extensions[name]) for name in extension_order] # invoke extensions before the loop for _, entry in extensions: if entry.invoke_before_training: entry.extension(self) update = self.updater.update reporter = self.reporter stop_trigger = self.stop_trigger # main training loop try: while not stop_trigger(self): self.observation = {} with reporter.scope(self.observation): update() for name, entry in extensions: if entry.trigger(self): entry.extension(self) finally: for _, entry in extensions: finalize = entry.extension.finalize if finalize: finalize() self.updater.finalize() self._done = True
def serialize(self, serializer): self.updater.serialize(serializer['updater']) if hasattr(self.stop_trigger, 'serialize'): self.stop_trigger.serialize(serializer['stop_trigger']) s = serializer['extensions'] t = serializer['extension_triggers'] for name, entry in six.iteritems(self._extensions): if hasattr(entry.extension, 'serialize'): entry.extension.serialize(s[name]) if hasattr(entry.trigger, 'serialize'): entry.trigger.serialize(t[name])