39 lines
1.7 KiB
Markdown
39 lines
1.7 KiB
Markdown
# Visualization
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MMSegmentation provides segmentation visualization hook, used to visualize validation and testing process prediction results.
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## Usage
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Users can modify `default_hooks` at each `schedule_x.py` config file.
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For exsample, In `_base_/schedules/schedule_20k.py`, modify the `SegVisualizationHook` configuration, set `draw` to `True` to enable the storage of network inference results, `interval` indicates the sampling interval of the prediction results, and when set to 1, each inference result of the network will be saved. `interval` is set to 50 by default:
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```python
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default_hooks = dict(
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timer=dict(type='IterTimerHook'),
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logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
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param_scheduler=dict(type='ParamSchedulerHook'),
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checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
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sampler_seed=dict(type='DistSamplerSeedHook'),
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visualization=dict(type='SegVisualizationHook', draw=True, interval=1))
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vis_backends = [dict(type='LocalVisBackend'),
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dict(type='TensorboardVisBackend')]
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visualizer = dict(
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type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer')
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```
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View visualization results in a local folder or use tensorboard.
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Find the `vis_data` path of `work_dir` after starting training or testing, for example, the vis_data path of a particular test is as follows:
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```shell
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work_dirs/test_visual/20220810_115248/vis_data
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```
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The stored results of the local visualization are kept in `vis_image` under `vis_data`, while the tensorboard visualization results are executed with the following command:
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```shell
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tensorboard --logdir work_dirs/test_visual/20220810_115248/vis_data
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```
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