1. 什么是Hook
經常會聽到鉤子函式(hook function)這個概念，最近在看目標檢測開源框架mmdetection，里面也出現大量Hook的編程方式，那到底什么是hook？hook的作用是什么？
what is hook ？鉤子hook，顧名思義，可以理解是一個掛鉤，作用是有需要的時候掛一個東西上去，具體的解釋是：鉤子函式是把我們自己實作的hook函式在某一時刻掛接到目標掛載點上，
hook函式的作用 舉個例子，hook的概念在windows桌面軟體開發很常見，特別是各種事件觸發的機制; 比如C++的MFC程式中，要監聽滑鼠左鍵按下的時間，MFC提供了一個onLeftKeyDown的鉤子函式，很顯然，MFC框架并沒有為我們實作onLeftKeyDown具體的操作，只是為我們提供一個鉤子，當我們需要處理的時候，只要去重寫這個函式，把我們需要操作掛載在這個鉤子里，如果我們不掛載，MFC事件觸發機制中執行的就是空的操作，
從上面可知
hook函式是程式中預定義好的函式，這個函式處于原有程式流程當中（暴露一個鉤子出來）
我們需要再在有流程中鉤子定義的函式塊中實作某個具體的細節，需要把我們的實作，掛接或者注冊（register）到鉤子里，使得hook函式對目標可用
hook 是一種編程機制，和具體的語言沒有直接的關系
如果從設計模式上看，hook模式是模板方法的擴展
鉤子只有注冊的時候，才會使用，所以原有程式的流程中，沒有注冊或掛載時，執行的是空（即沒有執行任何操作）
本文用python來解釋hook的實作方式，并展示在開源專案中hook的應用案例，hook函式和我們常聽到另外一個名稱：回呼函式（callback function）功能是類似的，可以按照同種模式來理解，
2. hook實作例子
據我所知，hook函式最常使用在某種流程處理當中，這個流程往往有很多步驟，hook函式常常掛載在這些步驟中，為增加額外的一些操作，提供靈活性，
下面舉一個簡單的例子，這個例子的目的是實作一個通用往佇列中插入內容的功能，流程步驟有2個
需要再插入佇列前，對資料進行篩選
input_filter_fn插入佇列
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
## 實作一個你所需要的鉤子實作：比如如果content 包含time就過濾掉，否則插入佇列
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
# 原有程式
content = {'filename': 'test.jpg', 'b64_file': "#test", 'data': {"result": "cat", "probility": 0.9}}
content_stash = ContentStash('audit', work_dir='')
# 掛上鉤子函式， 可以有各種不同鉤子函式的實作，但是要主要函式輸入輸出必須保持原有程式中一致，比如這里是content
content_stash.register_input_filter_hook(input_filter_hook)
# 執行流程
content_stash.input_pipeline(content)
3. hook在開源框架中的應用
3.1 keras
在深度學習訓練流程中，hook函式體現的淋漓盡致，
一個訓練程序（不包括資料準備），會輪詢多次訓練集，每次稱為一個epoch，每個epoch又分為多個batch來訓練，流程先后拆解成：
開始訓練
訓練一個epoch前
訓練一個batch前
訓練一個batch后
訓練一個epoch后
評估驗證集
結束訓練
這些步驟是穿插在訓練一個batch資料的程序中，這些可以理解成是鉤子函式，我們可能需要在這些鉤子函式中實作一些定制化的東西，比如在訓練一個epoch后我們要保存下訓練的模型，在結束訓練時用最好的模型執行下測驗集的效果等等，
keras中是通過各種回呼函式來實作鉤子hook功能的，這里放一個callback的父類，定制時只要繼承這個父類，實作你過關注的鉤子就可以了，
@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))
這些鉤子的原始程式是在模型訓練流程中的
keras原始碼位置： tensorflow\python\keras\engine\training.py
部分摘錄如下(## 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是一個目標檢測的開源框架，集成了許多不同的目標檢測深度學習演算法（pytorch版），如faster-rcnn, fpn, retianet等，里面也大量使用了hook，暴露給應用實作流程中具體部分，
詳見https://github.com/open-mmlab/mmdetection
這里看一個訓練的呼叫例子(摘錄)（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. 總結
本文介紹了hook的概念和應用，并給出了python的實作細則，希望對比有幫助，總結如下：
hook函式是流程中預定義好的一個步驟，沒有實作
掛載或者注冊時， 流程執行就會執行這個鉤子函式
回呼函式和hook函式功能上是一致的
hook設計方式帶來靈活性，如果流程中有一個步驟，你想讓呼叫方來實作，你可以用hook函式
作者簡介：wedo實驗君, 資料分析師；熱愛生活，熱愛寫作
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