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
作者简介：wedo实验君, 数据分析师；热爱生活，热爱写作
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