摘要.
# 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_queueclass 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contentcontent_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, 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))
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, 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
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, 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
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
作者简介：wedo 实验君, 数据分析师；热爱生活，热爱写作
赞 赏 作 者
推荐阅读
**点击下方阅读原文加入 ****社区会员 **
点赞鼓励一下