mmcv/docs/en/understand_mmcv/utils.md

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.

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.

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.

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.

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.

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