5分钟掌握Python中的Hook钩子函数，让你学Python开发不在难！

1. 什么是Hook

经常会听到钩子函数(hook function)这个概念，最近在看目标检测开源框架mmdetection，里面也出现大量Hook的编程方式，那到底什么是hook？hook的作用是什么？

• 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。

从上面可知

• 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）

本文用python来解释hook的实现方式，并展示在开源项目中hook的应用案例。hook函数和我们常听到另外一个名称：回调函数（callback function）功能是类似的，可以按照同种模式来理解。

2. hook实现例子

据我所知，hook函数最常使用在某种流程处理当中。这个流程往往有很多步骤。hook函数常常挂载在这些步骤中，为增加额外的一些操作，提供灵活性。

下面举一个简单的例子，这个例子的目的是实现一个通用往队列中插入内容的功能。流程步骤有2个

• 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

在深度学习训练流程中，hook函数体现的淋漓尽致。

一个训练过程（不包括数据准备），会轮询多次训练集，每次称为一个epoch，每个epoch又分为多个batch来训练。流程先后拆解成：

• 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

这些步骤是穿插在训练一个batch数据的过程中，这些可以理解成是钩子函数，我们可能需要在这些钩子函数中实现一些定制化的东西，比如在训练一个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中是通过各种回调函数来实现钩子hook功能的。这里放一个callback的父类，定制时只要继承这个父类，实现你过关注的钩子就可以了。

@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, logslogs=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, logslogs=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))

这些钩子的原始程序是在模型训练流程中的

keras源码位置： tensorflow\python\keras\engine\training.py

部分摘录如下(## I am hook)：

# 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,              verboseverbose=verbose,              epochsepochs=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',                  epochepoch_num=epoch,                  stepstep_num=step,                  batch_sizebatch_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

mmdetection是一个目标检测的开源框架，集成了许多不同的目标检测深度学习算法（pytorch版），如faster-rcnn, fpn, retianet等。里面也大量使用了hook，暴露给应用实现流程中具体部分。

详见https://github.com/open-mmlab/mmdetection

这里看一个训练的调用例子(摘录)（https://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,          optimizeroptimizer=optimizer,          work_dir=cfg.work_dir,          loggerlogger=logger,          metameta=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_cfghook_cfg = hook_cfg.copy()              priority = hook_cfg.pop('priority', 'NORMAL')              hook = build_from_cfg(hook_cfg, HOOKS)              runner.register_hook(hook, prioritypriority=priority)

4. 总结

本文介绍了hook的概念和应用，并给出了python的实现细则。希望对比有帮助。总结如下：

• 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