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* add visualization examples * refine docs * remove old version contents * update * update * fix lint
89 lines
3.3 KiB
Markdown
89 lines
3.3 KiB
Markdown
# Visualization
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Visualization can give an intuitive interpretation of the performance of the model.
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<!-- TOC -->
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- [Visualization](#visualization)
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- [How visualization is implemented](#how-visualization-is-implemented)
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- [What Visualization do in MMSelfsup](#what-visualization-do-in-mmselfsup)
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- [Use different storage backends](#use-different-storage-backends)
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- [Customize Visualization](#customize-visualization)
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<!-- /TOC -->
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## How visualization is implemented
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It is recommended to learn the basic concept of visualization in [engine.md](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/design/visualization.md).
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OpenMMLab 2.0 introduces the visualization object `Visualizer` and several visualization backends `VisBackend`. The diagram below shows the relationship between `Visualizer` and `VisBackend`,
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<div align="center">
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<img src="https://user-images.githubusercontent.com/17425982/163327736-f7cb3b16-ef07-46bc-982a-3cc7495e6c82.png" width="800" />
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</div>
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## What Visualization do in MMSelfsup
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(1) Save training data using different storage backends
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The backends in MMEngine includes `LocalVisBackend`, `TensorboardVisBackend` and `WandbVisBackend` .
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During training, [after_train_iter()](https://github.com/open-mmlab/mmengine/blob/main/mmengine/hooks/logger_hook.py#L150) in the default hook `LoggerHook` will be called, and use `add_scalars` in different backends, as follows:
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```python
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...
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def after_train_iter(...):
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...
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runner.visualizer.add_scalars(
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tag, step=runner.iter + 1, file_path=self.json_log_path)
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...
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```
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(2) Browse dataset
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The function [`add_datasample()`](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/mmselfsup/visualization/selfsup_visualizer.py#L151) is impleted in [`SelfSupVisualizer`](mmselfsup.visualization.SelfSupVisualizer), and it is mainly used in [browse_dataset.py](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/analysis_tools/browse_dataset.py) for browsing dataset. More tutorial is in [analysis_tools.md](analysis_tools.md)
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## Use different storage backends
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If you want to use a different backend (Wandb, Tensorboard, or a custom backend with a remote window), just change the `vis_backends` in the config, as follows:
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**Local**
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```python
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vis_backends = [dict(type='LocalVisBackend')]
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```
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**Tensorboard**
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```python
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vis_backends = [dict(type='TensorboardVisBackend')]
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visualizer = dict(
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type='SelfSupVisualizer', vis_backends=vis_backends, name='visualizer')
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```
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E.g.
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/199388357-5d1cc7b4-07b8-41b1-ac66-12ec8ef009da.png" width="400" />
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</div>
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**Wandb**
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```python
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vis_backends = [dict(type='WandbVisBackend')]
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visualizer = dict(
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type='SelfSupVisualizer', vis_backends=vis_backends, name='visualizer')
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```
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Note that when multiple visualization backends exist for `vis_backends`, only `WandbVisBackend` is valid.
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E.g.
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/199388643-288cf83f-0faa-4f34-a5d0-bf53c7bb3e08.png" width="600" />
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</div>
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## Customize Visualization
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The customization of the visualization is similar to other components. If you want to customize `Visualizer`, `VisBackend` or `VisualizationHook`, you can refer to [Visualization Doc](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/tutorials/visualization.md) in MMEngine.
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