让你轻松掌握 Python 中的 Hook 钩子函数

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, 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

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