mirror of https://github.com/open-mmlab/mmcv.git
69 lines
1.5 KiB
Markdown
69 lines
1.5 KiB
Markdown
## 辅助函数
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### 进度条
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如果你想跟踪函数批处理任务的进度，可以使用 `track_progress` 。它能以进度条的形式展示任务的完成情况以及剩余任务所需的时间（内部实现为for循环）。
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```python
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import mmcv
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def func(item):
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# 执行相关操作
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pass
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tasks = [item_1, item_2, ..., item_n]
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mmcv.track_progress(func, tasks)
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```
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效果如下
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如果你想可视化多进程任务的进度，你可以使用 `track_parallel_progress` 。
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```python
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mmcv.track_parallel_progress(func, tasks, 8) # 8 workers
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```
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如果你想要迭代或枚举数据列表并可视化进度,你可以使用 `track_iter_progress` 。
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```python
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import mmcv
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tasks = [item_1, item_2, ..., item_n]
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for task in mmcv.track_iter_progress(tasks):
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# do something like print
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print(task)
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for i, task in enumerate(mmcv.track_iter_progress(tasks)):
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# do something like print
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print(i)
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print(task)
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```
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### 计时器
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mmcv提供的 `Timer` 可以很方便地计算代码块的执行时间。
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```python
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import time
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with mmcv.Timer():
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# simulate some code block
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time.sleep(1)
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```
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你也可以使用 `since_start()` 和 `since_last_check()` 。前者返回计时器启动后的运行时长，后者返回最近一次查看计时器后的运行时长。
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```python
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timer = mmcv.Timer()
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# code block 1 here
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print(timer.since_start())
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# code block 2 here
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print(timer.since_last_check())
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print(timer.since_start())
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```
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