# Tutorial 5: Customize Runtime Settings

- [Tutorial 5: Customize Runtime Settings](#tutorial-5-customize-runtime-settings)
  - [Customize Workflow](#customize-workflow)
  - [Hooks](#hooks)
    - [default training hooks](#default-training-hooks)
      - [CheckpointHook](#checkpointhook)
      - [LoggerHooks](#loggerhooks)
      - [EvalHook](#evalhook)
  - [Use other implemented hooks](#use-other-implemented-hooks)
  - [Customize self-implemented hooks](#customize-self-implemented-hooks)
    - [1. Implement a new hook](#1-implement-a-new-hook)
    - [2. Import the new hook](#2-import-the-new-hook)
    - [3. Modify the config](#3-modify-the-config)

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.

```python
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

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

so we will run training and valiation for one epoch iteratively.

By default, we recommend using `EvalHook` to do evaluation after the training epoch.

## Hooks

The hook mechanism is widely used in the OpenMMLab open-source algorithm library. Inserted in the `Runner`, the entire life cycle of the training process can be managed easily. You can learn more about the hook through [related article](https://www.calltutors.com/blog/what-is-hook/).

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

- default training hooks

Those hooks are registered by the runner by default. Generally, they fulfill some basic functions, and have default 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 [schedule strategy](./4_schedule.md). `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`](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/hooks/checkpoint.py).

```python
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](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.CheckpointHook)

#### 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](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook).

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

#### EvalHook

The config of `evaluation` will be used to initialize the [`EvalHook`](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/core/evaluation/eval_hooks.py).

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()`

```python
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`:

```python
# "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-scale experiments, you can skip the validation step at the beginning of training by modifying the parameter `start` as below:

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

This indicates that, during the first 200 epochs, evaluation will not be executed. From the 200th epoch, evaluation will be executed after the training process.

## Use other implemented hooks

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

- [EMAHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/ema.py)
- [SyncBuffersHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/sync_buffer.py)
- [EmptyCacheHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/memory.py)
- [ProfilerHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/profiler.py)
- ......

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

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

such as using `EMAHook`, interval is 100 iters:

```python
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 MMSelfSup.

```python
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 your intention of this hook, you need to implement different functionalities in `before_run`, `after_run`, `before_epoch`, `after_epoch`, `before_iter`, and `after_iter`.

### 2. Import the new hook

Then we need to ensure `MyHook` imported. Assuming `MyHook` is in `mmselfsup/core/hooks/my_hook.py`, there are two ways to import it:

- Modify `mmselfsup/core/hooks/__init__.py` as below

```python
from .my_hook import MyHook

__all__ = [..., MyHook, ...]
```

- Use `custom_imports` in the config to manually import it

```python
custom_imports = dict(imports=['mmselfsup.core.hooks.my_hook'], allow_failed_imports=False)
```

### 3. Modify the config

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

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

```python
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.
