mmcv/docs/utils.md

## Utils

### Config

`Config` class is used for manipulating config and config files. It supports
loading configs from multiple file formats including **python**, **json** and **yaml**.
It provides dict-like apis to get and set values.

Here is an example of the config file `test.py`.

```python
a = 1
b = {'b1': [0, 1, 2], 'b2': None}
c = (1, 2)
d = 'string'
```

To load and use configs

```python
cfg = Config.fromfile('test.py')
assert cfg.a == 1
assert cfg.b.b1 == [0, 1, 2]
cfg.c = None
assert cfg.c == None
```

### ProgressBar

If you want to apply a method to a list of items and track the progress, `track_progress`
is a good choice. It will display a progress bar to tell the progress and ETA.

```python
import mmcv

def func(item):
    # do something
    pass

tasks = [item_1, item_2, ..., item_n]

mmcv.track_progress(func, tasks)
```

The output is like the following.
![progress](_static/progress.gif)

There is another method `track_parallel_progress`, which wraps multiprocessing and
progress visualization.

```python
mmcv.track_parallel_progress(func, tasks, 8)  # 8 workers
```

![progress](_static/parallel_progress.gif)


### Timer

It is convinient to compute the runtime of a code block with `Timer`.

```python
import time

with mmcv.Timer():
    # simulate some code block
    time.sleep(1)
```

or try with `since_start()` and `since_last_check()`. This former can
return the runtime since the timer starts and the latter will return the time
since the last time checked.

```python
timer = mmcv.Timer()
# code block 1 here
print(timer.since_start())
# code block 2 here
print(timer.since_last_check())
print(timer.since_start())
```