mirror of https://github.com/open-mmlab/mmcv.git
75 lines
1.6 KiB
Markdown
75 lines
1.6 KiB
Markdown
## Utils
|
|
|
|
### 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.
|
|
|
|
|
|
|
|
There is another method `track_parallel_progress`, which wraps multiprocessing and
|
|
progress visualization.
|
|
|
|
```python
|
|
mmcv.track_parallel_progress(func, tasks, 8) # 8 workers
|
|
```
|
|
|
|
|
|
|
|
If you want to iterate or enumerate a list of items and track the progress, `track_iter_progress`
|
|
is a good choice. It will display a progress bar to tell the progress and ETA.
|
|
|
|
```python
|
|
import mmcv
|
|
|
|
tasks = [item_1, item_2, ..., item_n]
|
|
|
|
for task in mmcv.track_iter_progress(tasks):
|
|
# do something like print
|
|
print(task)
|
|
|
|
for i, task in enumerate(mmcv.track_iter_progress(tasks)):
|
|
# do something like print
|
|
print(i)
|
|
print(task)
|
|
```
|
|
|
|
### Timer
|
|
|
|
It is convenient 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())
|
|
```
|