[Runner](../tutorials/runner.md) will produce a lot of logs during the running process, such as loss, iteration time, learning rate, etc. MMEngine implements a flexible logging system that allows us to choose different types of log statistical methods when configuring the runner. It could help us set/get the recorded log at any location in the code.
## Flexible Logging System
Logging system is configured by passing a [LogProcessor](mmengine.logging.LogProcessor) to the runner. If no log processor is passed, the runner will use the default log processor, which is equivalent to:
- Learning rate (`lr`): The learning rate of the last iteration.
- Time:
-`time`: The averaged time for infernce of the last `window_size` iterations.
-`data_time`: The averaged time for loading data of the last `window_size` iterations.
-`eta`: The estimated time of arrival to finish the training.
- Loss: The averaged loss output by model of the last `window_size` iterations.
```{note}
`window_size=10` by default.
The significant digits(`num_digits`) of the log is 4 by default.
Output the value of all custom logsthe at last iteration by default.
```
```{warnning}
log_processor outputs the epoch based log by default(`by_epoch=True`). To get an expected log matched with the `train_cfg`, we should set the same value for `by_epoch` in `train_cfg` and `log_processor`.
```
Based on the rules above, the code snippet will count the average value of the `loss1` and `loss2` every 10 iterations.
If we want to count the global average value of `loss1`, we can set `custom_cfg` like this:
`data_src` means the original loss name, `method_name` means the statistic method, `window_size` means the window size of the statistic method. Since we want to count the global average value of `loss1`, we set `window_size` to `global`.
Currently, MMEngine supports the following statistical methods:
- int number: The window size of the statistic method.
-`global`: Equivalent to `window_size=cur_iteration`.
-`epoch`: Equivalent to `window_size=len(dataloader)`.
If we want to statistic the average value of `loss1` of the last 10 iterations, and also want to statistic the global average value of `loss1`. We need to set `log_name` additionally:
More examples can be found in [log_processor](mmengine.logging.LogProcessor).
## Customize log
The logging system could not only log the `loss`, `lr`, .etc but also collect and output the custom log. For example, if we want to statistic the intermediate `loss`:
```python
from mmengine.logging import MessageHub
class ToyModel(BaseModel):
def __init__(self) -> None:
super().__init__()
self.linear = nn.Linear(1, 1)
def forward(self, img, label, mode):
feat = self.linear(img)
loss_tmp = (feat - label).abs()
loss = loss_tmp.pow(2)
message_hub = MessageHub.get_current_instance()
# update the intermediate `loss_tmp` in the message hub
The custom log will be recorded by updating the [messagehub](mmengine.logging.MessageHub):
1. Calling `MessageHub.get_current_instance()` to get the message of runner
2. Calling `MessageHub.update_scalar` to update the custom log. The first argument means the log name with the mode prefix(`train/val/test`). The output log will only retain the log name without the mode prefix.
3. Configure statistic method of `loss_tmp` in `log_processor`. If it is not configured, only the latest value of `loss_tmp` will be logged.
## Export the debug log
Set `log_level=DEBUG` for runner, and the debug log will be exported to the `work_dir`:
08/21 18:16:22 - mmengine - DEBUG - Get class `LocalVisBackend` from "vis_backend" registry in "mmengine"
08/21 18:16:22 - mmengine - DEBUG - An `LocalVisBackend` instance is built from registry, its implementation can be found in mmengine.visualization.vis_backend
08/21 18:16:22 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
08/21 18:16:22 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, its implementation can be found in mmengine.hooks.runtime_info_hook
08/21 18:16:22 - mmengine - DEBUG - Get class `IterTimerHook` from "hook" registry in "mmengine"
...
```
Besides, logs of different ranks will be saved in `debug` mode if you are training your model with the shared storage. The hierarchy of the log is as follows:
```text
./tmp
├── tmp.log
├── tmp_rank1.log
├── tmp_rank2.log
├── tmp_rank3.log
├── tmp_rank4.log
├── tmp_rank5.log
├── tmp_rank6.log
└── tmp_rank7.log
...
└── tmp_rank63.log
```
The log of Multiple machine with independent storage:
