Shortcuts

Tutorial 7: Customize Runtime Settings

In this tutorial, we will introduce some methods about how to customize workflow and hooks when running your own settings for the project.

Customize Workflow

Workflow is a list of (phase, duration) to specify the running order and duration. The meaning of “duration” depends on the runner’s type.

For example, we use epoch-based runner by default, and the “duration” means how many epochs the phase to be executed in a cycle. Usually, we only want to execute training phase, just use the following config.

workflow = [('train', 1)]

Sometimes we may want to check some metrics (e.g. loss, accuracy) about the model on the validate set. In such case, we can set the workflow as

[('train', 1), ('val', 1)]

so that 1 epoch for training and 1 epoch for validation will be run iteratively.

By default, we recommend using EvalHook to do evaluation after the training epoch, but you can still use val workflow as an alternative.

Note

  1. The parameters of model will not be updated during the val epoch.

  2. Keyword max_epochs in the config only controls the number of training epochs and will not affect the validation workflow.

  3. Workflows [('train', 1), ('val', 1)] and [('train', 1)] will not change the behavior of EvalHook because EvalHook is called by after_train_epoch and validation workflow only affect hooks that are called through after_val_epoch. Therefore, the only difference between [('train', 1), ('val', 1)] and [('train', 1)] is that the runner will calculate losses on the validation set after each training epoch.

Hooks

The hook mechanism is widely used in the OpenMMLab open-source algorithm library. Combined with the Runner, the entire life cycle of the training process can be managed easily. You can learn more about the hook through related article.

Hooks only work after being registered into the runner. At present, hooks are mainly divided into two categories:

  • default training hooks

The default training hooks are registered by the runner by default. Generally, they are hooks for some basic functions, and have a certain priority, you don’t need to modify the priority.

  • custom hooks

The custom hooks are registered through custom_hooks. Generally, they are hooks with enhanced functions. The priority needs to be specified in the configuration file. If you do not specify the priority of the hook, it will be set to ‘NORMAL’ by default.

Priority list

Level Value
HIGHEST 0
VERY_HIGH 10
HIGH 30
ABOVE_NORMAL 40
NORMAL(default) 50
BELOW_NORMAL 60
LOW 70
VERY_LOW 90
LOWEST 100

The priority determines the execution order of the hooks. Before training, the log will print out the execution order of the hooks at each stage to facilitate debugging.

default training hooks

Some common hooks are not registered through custom_hooks, they are

Hooks Priority
LrUpdaterHook VERY_HIGH (10)
MomentumUpdaterHook HIGH (30)
OptimizerHook ABOVE_NORMAL (40)
CheckpointHook NORMAL (50)
IterTimerHook LOW (70)
EvalHook LOW (70)
LoggerHook(s) VERY_LOW (90)

OptimizerHook, MomentumUpdaterHook and LrUpdaterHook have been introduced in sehedule strategy. IterTimerHook is used to record elapsed time and does not support modification.

Here we reveal how to customize CheckpointHook, LoggerHooks, and EvalHook.

CheckpointHook

The MMCV runner will use checkpoint_config to initialize CheckpointHook.

checkpoint_config = dict(interval=1)

We could set max_keep_ckpts to save only a small number of checkpoints or decide whether to store state dict of optimizer by save_optimizer. More details of the arguments are here

LoggerHooks

The log_config wraps multiple logger hooks and enables to set intervals. Now MMCV supports TextLoggerHook, WandbLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, DvcliveLoggerHook and TensorboardLoggerHook. The detailed usages can be found in the doc.

log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook')
    ])

EvalHook

The config of evaluation will be used to initialize the EvalHook.

The EvalHook has some reserved keys, such as interval, save_best and start, and the other arguments such as metrics will be passed to the dataset.evaluate()

evaluation = dict(interval=1, metric='accuracy', metric_options={'topk': (1, )})

You can save the model weight when the best verification result is obtained by modifying the parameter save_best:

# "auto" means automatically select the metrics to compare.
# You can also use a specific key like "accuracy_top-1".
evaluation = dict(interval=1, save_best="auto", metric='accuracy', metric_options={'topk': (1, )})

When running some large experiments, you can skip the validation step at the beginning of training by modifying the parameter start as below:

evaluation = dict(interval=1, start=200, metric='accuracy', metric_options={'topk': (1, )})

This indicates that, before the 200th epoch, evaluations would not be executed. Since the 200th epoch, evaluations would be executed after the training process.

Note

In the default configuration files of MMClassification, the evaluation field is generally placed in the datasets configs.

Use other implemented hooks

Some hooks have been already implemented in MMCV and MMClassification, they are:

If the hook is already implemented in MMCV, you can directly modify the config to use the hook as below

mmcv_hooks = [
    dict(type='MMCVHook', a=a_value, b=b_value, priority='NORMAL')
]

such as using EMAHook, interval is 100 iters:

custom_hooks = [
    dict(type='EMAHook', interval=100, priority='HIGH')
]

Customize self-implemented hooks

1. Implement a new hook

Here we give an example of creating a new hook in MMClassification and using it in training.

from mmcv.runner import HOOKS, Hook


@HOOKS.register_module()
class MyHook(Hook):

    def __init__(self, a, b):
        pass

    def before_run(self, runner):
        pass

    def after_run(self, runner):
        pass

    def before_epoch(self, runner):
        pass

    def after_epoch(self, runner):
        pass

    def before_iter(self, runner):
        pass

    def after_iter(self, runner):
        pass

Depending on the functionality of the hook, the users need to specify what the hook will do at each stage of the training in before_run, after_run, before_epoch, after_epoch, before_iter, and after_iter.

2. Register the new hook

Then we need to make MyHook imported. Assuming the file is in mmcls/core/utils/my_hook.py there are two ways to do that:

  • Modify mmcls/core/utils/__init__.py to import it.

    The newly defined module should be imported in mmcls/core/utils/__init__.py so that the registry will find the new module and add it:

from .my_hook import MyHook
  • Use custom_imports in the config to manually import it

custom_imports = dict(imports=['mmcls.core.utils.my_hook'], allow_failed_imports=False)

3. Modify the config

custom_hooks = [
    dict(type='MyHook', a=a_value, b=b_value)
]

You can also set the priority of the hook as below:

custom_hooks = [
    dict(type='MyHook', a=a_value, b=b_value, priority='ABOVE_NORMAL')
]

By default, the hook’s priority is set as NORMAL during registration.

FAQ

1. resume_from and load_from and init_cfg.Pretrained

  • load_from : only imports model weights, which is mainly used to load pre-trained or trained models;

  • resume_from : not only import model weights, but also optimizer information, current epoch information, mainly used to continue training from the checkpoint.

  • init_cfg.Pretrained : Load weights during weight initialization, and you can specify which module to load. This is usually used when fine-tuning a model, refer to Tutorial 2: Fine-tune Models.

Read the Docs v: latest
Versions
master
latest
stable
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.