5 分钟掌握 Python 中的 Hook 钩子函数
1. 什么是Hook
what is hook ？钩子hook，顾名思义，可以理解是一个挂钩，作用是有需要的时候挂一个东西上去。具体的解释是：钩子函数是把我们自己实现的hook函数在某一时刻挂接到目标挂载点上。 hook函数的作用举个例子，hook的概念在windows桌面软件开发很常见，特别是各种事件触发的机制; 比如C++的MFC程序中，要监听鼠标左键按下的时间，MFC提供了一个onLeftKeyDown的钩子函数。很显然，MFC框架并没有为我们实现onLeftKeyDown具体的操作，只是为我们提供一个钩子，当我们需要处理的时候，只要去重写这个函数，把我们需要操作挂载在这个钩子里，如果我们不挂载，MFC事件触发机制中执行的就是空的操作。
hook函数是程序中预定义好的函数，这个函数处于原有程序流程当中（暴露一个钩子出来） 我们需要再在有流程中钩子定义的函数块中实现某个具体的细节，需要把我们的实现，挂接或者注册（register）到钩子里，使得hook函数对目标可用 hook 是一种编程机制，和具体的语言没有直接的关系 如果从设计模式上看，hook模式是模板方法的扩展 钩子只有注册的时候，才会使用，所以原有程序的流程中，没有注册或挂载时，执行的是空（即没有执行任何操作）
2. hook实现例子
需要再插入队列前，对数据进行筛选 input_filter_fn插入队列 insert_queue
class ContentStash(object):
'''
content stash for online operation
pipeline is
1. input_filter: filter some contents, no use to user
2. insert_queue(redis or other broker): insert useful content to queue
'''
def __init__(self):
self.input_filter_fn = None
self.broker = []
def register_input_filter_hook(self, input_filter_fn):
'''
register input filter function, parameter is content dict
Args:
input_filter_fn: input filter function
Returns:
'''
self.input_filter_fn = input_filter_fn
def insert_queue(self, content):
'''
insert content to queue
Args:
content: dict
Returns:
'''
self.broker.append(content)
def input_pipeline(self, content, use=False):
'''
pipeline of input for content stash
Args:
use: is use, defaul False
content: dict
Returns:
'''
if not use:
return
# input filter
if self.input_filter_fn:
_filter = self.input_filter_fn(content)
# insert to queue
if not _filter:
self.insert_queue(content)
# test
## 实现一个你所需要的钩子实现：比如如果content 包含time就过滤掉，否则插入队列
def input_filter_hook(content):
'''
test input filter hook
Args:
content: dict
Returns: None or content
'''
if content.get('time') is None:
return
else:
return content
# 原有程序
content = {'filename': 'test.jpg', 'b64_file': '#test', 'data': {'result': 'cat', 'probility': 0.9}}
content_stash = ContentStash('audit', work_dir='')
# 挂上钩子函数， 可以有各种不同钩子函数的实现，但是要主要函数输入输出必须保持原有程序中一致，比如这里是content
content_stash.register_input_filter_hook(input_filter_hook)
# 执行流程
content_stash.input_pipeline(content)
3. hook在开源框架中的应用
3.1 keras
开始训练 训练一个epoch前 训练一个batch前 训练一个batch后 训练一个epoch后 评估验证集 结束训练
训练一个epoch后我们要保存下训练的模型，在结束训练时用最好的模型执行下测试集的效果等等。@keras_export('keras.callbacks.Callback')class Callback(object): '''Abstract base class used to build new callbacks.
Attributes: params: Dict. Training parameters (eg. verbosity, batch size, number of epochs...). model: Instance of `keras.models.Model`. Reference of the model being trained.
The `logs` dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings). '''
def __init__(self): self.validation_data = None # pylint: disable=g-missing-from-attributes self.model = None # Whether this Callback should only run on the chief worker in a # Multi-Worker setting. # TODO(omalleyt): Make this attr public once solution is stable. self._chief_worker_only = None self._supports_tf_logs = False
def set_params(self, params): self.params = params
def set_model(self, model): self.model = model
@doc_controls.for_subclass_implementers @generic_utils.default def on_batch_begin(self, batch, logs=None): '''A backwards compatibility alias for `on_train_batch_begin`.'''
@doc_controls.for_subclass_implementers @generic_utils.default def on_batch_end(self, batch, logs=None): '''A backwards compatibility alias for `on_train_batch_end`.'''
@doc_controls.for_subclass_implementers def on_epoch_begin(self, epoch, logs=None): '''Called at the start of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
Arguments: epoch: Integer, index of epoch. logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. '''
@doc_controls.for_subclass_implementers def on_epoch_end(self, epoch, logs=None): '''Called at the end of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
Arguments: epoch: Integer, index of epoch. logs: Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with `val_`. '''
@doc_controls.for_subclass_implementers @generic_utils.default def on_train_batch_begin(self, batch, logs=None): '''Called at the beginning of a training batch in `fit` methods.
Subclasses should override for any actions to run.
Arguments: batch: Integer, index of batch within the current epoch. logs: Dict, contains the return value of `model.train_step`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. ''' # For backwards compatibility. self.on_batch_begin(batch, logs=logs)
@doc_controls.for_subclass_implementers @generic_utils.default def on_train_batch_end(self, batch, logs=None): '''Called at the end of a training batch in `fit` methods.
Subclasses should override for any actions to run.
Arguments: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. ''' # For backwards compatibility. self.on_batch_end(batch, logs=logs)
@doc_controls.for_subclass_implementers @generic_utils.default def on_test_batch_begin(self, batch, logs=None): '''Called at the beginning of a batch in `evaluate` methods.
Also called at the beginning of a validation batch in the `fit` methods, if validation data is provided.
Subclasses should override for any actions to run.
Arguments: batch: Integer, index of batch within the current epoch. logs: Dict, contains the return value of `model.test_step`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. '''
@doc_controls.for_subclass_implementers @generic_utils.default def on_test_batch_end(self, batch, logs=None): '''Called at the end of a batch in `evaluate` methods.
Also called at the end of a validation batch in the `fit` methods, if validation data is provided.
Subclasses should override for any actions to run.
Arguments: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. '''
@doc_controls.for_subclass_implementers @generic_utils.default def on_predict_batch_begin(self, batch, logs=None): '''Called at the beginning of a batch in `predict` methods.
Subclasses should override for any actions to run.
Arguments: batch: Integer, index of batch within the current epoch. logs: Dict, contains the return value of `model.predict_step`, it typically returns a dict with a key 'outputs' containing the model's outputs. '''
@doc_controls.for_subclass_implementers @generic_utils.default def on_predict_batch_end(self, batch, logs=None): '''Called at the end of a batch in `predict` methods.
Subclasses should override for any actions to run.
Arguments: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. '''
@doc_controls.for_subclass_implementers def on_train_begin(self, logs=None): '''Called at the beginning of training.
Subclasses should override for any actions to run.
Arguments: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. '''
@doc_controls.for_subclass_implementers def on_train_end(self, logs=None): '''Called at the end of training.
Subclasses should override for any actions to run.
Arguments: logs: Dict. Currently the output of the last call to `on_epoch_end()` is passed to this argument for this method but that may change in the future. '''
@doc_controls.for_subclass_implementers def on_test_begin(self, logs=None): '''Called at the beginning of evaluation or validation.
Subclasses should override for any actions to run.
Arguments: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. '''
@doc_controls.for_subclass_implementers def on_test_end(self, logs=None): '''Called at the end of evaluation or validation.
Subclasses should override for any actions to run.
Arguments: logs: Dict. Currently the output of the last call to `on_test_batch_end()` is passed to this argument for this method but that may change in the future. '''
@doc_controls.for_subclass_implementers def on_predict_begin(self, logs=None): '''Called at the beginning of prediction.
Subclasses should override for any actions to run.
Arguments: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. '''
@doc_controls.for_subclass_implementers def on_predict_end(self, logs=None): '''Called at the end of prediction.
Subclasses should override for any actions to run.
Arguments: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. '''
def _implements_train_batch_hooks(self): '''Determines if this Callback should be called for each train batch.''' return (not generic_utils.is_default(self.on_batch_begin) or not generic_utils.is_default(self.on_batch_end) or not generic_utils.is_default(self.on_train_batch_begin) or not generic_utils.is_default(self.on_train_batch_end))keras源码位置： tensorflow\python\keras\engine\training.py
# Container that configures and calls `tf.keras.Callback`s.
if not isinstance(callbacks, callbacks_module.CallbackList):
callbacks = callbacks_module.CallbackList(
callbacks,
add_history=True,
add_progbar=verbose != 0,
model=self,
verbose=verbose,
epochs=epochs,
steps=data_handler.inferred_steps)
## I am hook
callbacks.on_train_begin()
training_logs = None
# Handle fault-tolerance for multi-worker.
# TODO(omalleyt): Fix the ordering issues that mean this has to
# happen after `callbacks.on_train_begin`.
data_handler._initial_epoch = ( # pylint: disable=protected-access
self._maybe_load_initial_epoch_from_ckpt(initial_epoch))
for epoch, iterator in data_handler.enumerate_epochs():
self.reset_metrics()
callbacks.on_epoch_begin(epoch)
with data_handler.catch_stop_iteration():
for step in data_handler.steps():
with trace.Trace(
'TraceContext',
graph_type='train',
epoch_num=epoch,
step_num=step,
batch_size=batch_size):
## I am hook
callbacks.on_train_batch_begin(step)
tmp_logs = train_function(iterator)
if data_handler.should_sync:
context.async_wait()
logs = tmp_logs # No error, now safe to assign to logs.
end_step = step + data_handler.step_increment
callbacks.on_train_batch_end(end_step, logs)
epoch_logs = copy.copy(logs)
# Run validation.
## I am hook
callbacks.on_epoch_end(epoch, epoch_logs)
3.2 mmdetection
https://github.com/open-mmlab/mmdetectionhttps://github.com/open-mmlab/mmdetection/blob/5d592154cca589c5113e8aadc8798bbc73630d98/mmdet/apis/train.py）def train_detector(model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None): logger = get_root_logger(cfg.log_level)
# prepare data loaders
# put model on gpus
# build runner optimizer = build_optimizer(model, cfg.optimizer) runner = EpochBasedRunner( model, optimizer=optimizer, work_dir=cfg.work_dir, logger=logger, meta=meta) # an ugly workaround to make .log and .log.json filenames the same runner.timestamp = timestamp
# fp16 setting # register hooks runner.register_training_hooks(cfg.lr_config, optimizer_config, cfg.checkpoint_config, cfg.log_config, cfg.get('momentum_config', None)) if distributed: runner.register_hook(DistSamplerSeedHook())
# register eval hooks if validate: # Support batch_size > 1 in validation eval_cfg = cfg.get('evaluation', {}) eval_hook = DistEvalHook if distributed else EvalHook runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
# user-defined hooks if cfg.get('custom_hooks', None): custom_hooks = cfg.custom_hooks assert isinstance(custom_hooks, list), \ f'custom_hooks expect list type, but got {type(custom_hooks)}' for hook_cfg in cfg.custom_hooks: assert isinstance(hook_cfg, dict), \ 'Each item in custom_hooks expects dict type, but got ' \ f'{type(hook_cfg)}' hook_cfg = hook_cfg.copy() priority = hook_cfg.pop('priority', 'NORMAL') hook = build_from_cfg(hook_cfg, HOOKS) runner.register_hook(hook, priority=priority)4. 总结
hook函数是流程中预定义好的一个步骤，没有实现 挂载或者注册时， 流程执行就会执行这个钩子函数 回调函数和hook函数功能上是一致的 hook设计方式带来灵活性，如果流程中有一个步骤，你想让调用方来实现，你可以用hook函数
作者简介：wedo实验君, 数据分析师；热爱生活，热爱写作