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d439dc1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | # Visualization
## 1 Overall Design
Visualization provides an intuitive explanation of the training and testing process of the deep learning model. In OpenMMLab, we expect the visualization module to meet the following requirements:
- Provides rich out-of-the-box features that can meet most computer vision visualization tasks.
- Versatile, expandable, and can be customized easily
- Able to visualize at anywhere in the training and testing process.
- Unified APIs for all OpenMMLab libraries, which is convenient for users to understand and use.
Based on the above requirements, we proposed the `Visualizer` and various `VisBackend` such as `LocalVisBackend`, `WandbVisBackend`, and `TensorboardVisBackend` in OpenMMLab 2.0. The visualizer could not only visualize the image data, but also things like configurations, scalars, and model structure.
- For convenience, the APIs provided by the `Visualizer` implement the drawing and storage functions. As an internal property of `Visualizer`, `VisBackend` will be called by `Visualizer` to write data to different backends.
- Considering that you may want to write data to multiple backends after drawing, `Visualizer` can be configured with multiple backends. When the user calls the storage API of the `Visualizer`, it will traverse and call all the specified APIs of `VisBackend` internally.
The UML diagram of the two is as follows.
<div align="center">
<img src="https://user-images.githubusercontent.com/17425982/163327736-f7cb3b16-ef07-46bc-982a-3cc7495e6c82.png" >
</div>
## 2 Visualizer
The external interface of `Visualizer` can be divided into three categories.
1. Drawing APIs
- [draw_bboxes](mmengine.visualization.Visualizer.draw_bboxes) draws a single or multiple bounding boxes
- [draw_points](mmengine.visualization.Visualizer.draw_points) draws a single or multiple points
- [draw_texts](mmengine.visualization.Visualizer.draw_texts) draws a single or multiple text boxes
- [draw_lines](mmengine.visualization.Visualizer.lines) draws a single or multiple line segments
- [draw_circles](mmengine.visualization.Visualizer.draw_circles) draws a single or multiple circles
- [draw_polygons](mmengine.visualization.Visualizer.draw_polygons) draws a single or multiple polygons
- [draw_binary_masks](mmengine.visualization.Visualizer.draw_binary_mask) draws single or multiple binary masks
- [draw_featmap](mmengine.visualization.Visualizer.draw_featmap) draws feature map (**static method**)
The above APIs can be called in a chain except for `draw_featmap` because the image size may change after this method is called. To avoid confusion, `draw_featmap` is a static method.
2. Storage APIs
- [add_config](mmengine.visualization.writer.BaseWriter.add_config) writes configuration to a specific storage backend
- [add_graph](mmengine.visualization.writer.BaseWriter.add_graph) writes model graph to a specific storage backend
- [add_image](mmengine.visualization.writer.BaseWriter.add_image) writes image to a specific storage backend
- [add_scalar](mmengine.visualization.writer.BaseWriter.add_scalar) writes scalar to a specific storage backend
- [add_scalars](mmengine.visualization.writer.BaseWriter.add_scalars) writes multiple scalars to a specific storage backend at once
- [add_datasample](mmengine.visualization.writer.BaseWriter.add_datasample) the abstract interface for each repositories to draw data sample
Interfaces beginning with the `add` prefix represent storage APIs. \[datasample\] (`./data_element.md`)is the unified interface of each downstream repository in the OpenMMLab 2.0, and `add_datasample` can process the data sample directly .
3. Other APIs
- [set_image](mmengine.visualization.Visualizer.set_image) sets the original image data, the default input image format is RGB
- [get_image](mmengine.visualization.Visualizer.get_image) gets the image data in Numpy format after drawing, the default output format is RGB
- [show](mmengine.visualization.Visualizer.show) for visualization
- [get_backend](mmengine.visualization.Visualizer.get_backend) gets a specific storage backend by name
- [close](mmengine.visualization.Visualizer.close) closes all resources, including `VisBackend`
For more details, you can refer to [Visualizer Tutorial](../tutorials/visualization.md).
## 3 VisBackend
After drawing, the drawn data can be stored in multiple visualization storage backends. To unify the interfaces, MMEngine provides an abstract class, `BaseVisBackend`, and some commonly used backends such as `LocalVisBackend`, `WandbVisBackend`, and `TensorboardVisBackend`.
The main interfaces and properties of `BaseVisBackend` are as follows:
- [add_config](mmengine.visualization.vis_backend.BaseVisBackend.add_config) writes configuration to a specific storage backend
- [add_graph](mmengine.visualization.vis_backend.BaseVisBackend.add_graph) writes model graph to a specific backend
- [add_image](mmengine.visualization.vis_backend.BaseVisBackend.add_image) writes image to a specific backend
- [add_scalar](mmengine.visualization.vis_backend.BaseVisBackend.add_scalar) writes scalar to a specific backend
- [add_scalars](mmengine.visualization.vis_backend.BaseVisBackend.add_scalars) writes multiple scalars to a specific backend at once
- [close](mmengine.visualization.vis_backend.BaseVisBackend.close) closes the resource that has been opened
- [experiment](mmengine.visualization.vis_backend.BaseVisBackend.experiment) writes backend objects, such as WandB objects and Tensorboard objects
`BaseVisBackend` defines five common data writing interfaces. Some writing backends are very powerful, such as WandB, which could write tables and videos. Users can directly obtain the `experiment` object for such needs and then call native APIs of the corresponding backend. `LocalVisBackend`, `WandbVisBackend`, and `TensorboardVisBackend` are all inherited from `BaseVisBackend` and implement corresponding storage functions according to their features. Users can also customize `BaseVisBackend` to extend the storage backends and implement custom storage requirements.
For more details, you can refer to [Storage Backend Tutorial](../advanced_tutorials//visualization.md).
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