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.

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

To load and use configs

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.

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 convinient 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())