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Visualization

Visualization can give an intuitive interpretation of the performance of the model.

Visualization

How visualization is implemented

It is recommended to learn the basic concept of visualization in engine.md.

OpenMMLab 2.0 introduces the visualization object Visualizer and several visualization backends VisBackend. The diagram below shows the relationship between Visualizer and VisBackend,

What Visualization do in MMSelfsup

(1) Save training data using different storage backends

The backends in MMEngine includes LocalVisBackend, TensorboardVisBackend and WandbVisBackend .

During training, after_train_iter() in the default hook LoggerHook will be called, and use add_scalars in different backends, as follows:

...
def after_train_iter(...):
    ...
    runner.visualizer.add_scalars(
        tag, step=runner.iter + 1, file_path=self.json_log_path)
...

(2) Browse dataset

The function add_datasample() is impleted in SelfSupVisualizer, and it is mainly used in browse_dataset.py for browsing dataset. More tutorial is in analysis_tools.md

Use different storage backends

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:

Local

vis_backends = [dict(type='LocalVisBackend')]

Tensorboard

vis_backends = [dict(type='TensorboardVisBackend')]
visualizer = dict(
    type='SelfSupVisualizer', vis_backends=vis_backends, name='visualizer')

Wandb

vis_backends = [dict(type='WandbVisBackend')]
visualizer = dict(
    type='SelfSupVisualizer', vis_backends=vis_backends, name='visualizer')

Note that when multiple visualization backends exist for vis_backends, only WandbVisBackend is valid.

Customize Visualization

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 in MMEngine.

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