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Visualization

MMSegmentation 1.x provides convenient ways for monitoring training status or visualizing data and model predictions.

Training status Monitor

MMSegmentation 1.x uses TensorBoard to monitor training status.

TensorBoard Configuration

Install TensorBoard following official instructions e.g.

pip install tensorboardX
pip install future tensorboard

Add TensorboardVisBackend in vis_backend of visualizer in default_runtime.py config file:

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

Examining scalars in TensorBoard

Launch training experiment e.g.

python tools/train.py configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py --work-dir work_dir/test_visual

Find the vis_data path of work_dir after starting training, for example, the vis_data path of this particular test is as follows:

work_dirs/test_visual/20220810_115248/vis_data

The scalar file in vis_data path includes learning rate, losses and data_time etc, also record metrics results and you can refer logging tutorial in mmengine to log custom data. The tensorboard visualization results are executed with the following command:

tensorboard --logdir work_dirs/test_visual/20220810_115248/vis_data

Data and Results visualization

MMSegmentation provides SegVisualizationHook that can render segmentation masks of ground truth and prediction. Users can modify default_hooks at each schedule_x.py config file.

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:

default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='SegVisualizationHook', draw=True, interval=1))

After launch training experiment, visualization results will be stored in the local folder in validation loop, or when launch evaluation a model on one dataset, the prediction results will be store in the local. The stored results of the local visualization are kept in vis_image under $WORK_DIRS/vis_data, e.g.:

work_dirs/test_visual/20220810_115248/vis_data/vis_image

In addition, if TensorboardVisBackend is add in vis_backends, like above, we can also run the following command to view them in TensorBoard:

tensorboard --logdir work_dirs/test_visual/20220810_115248/vis_data

If you would like to know more visualization usage, you can refer to visualization tutorial in mmengie.

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