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mmselfsup/docs/en/user_guides/visualization.md

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Visualization

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

How visualization is implemented

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

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 section Visualize Datasets

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')

E.g.

Wandb

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

E.g.

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.

Visualize Datasets

tools/misc/browse_dataset.py helps the user to browse a mmselfsup dataset (transformed images) visually, or save the image to a designated directory.

python tools/misc/browse_dataset.py ${CONFIG} [-h] [--skip-type ${SKIP_TYPE[SKIP_TYPE...]}] [--output-dir ${OUTPUT_DIR}] [--not-show] [--show-interval ${SHOW_INTERVAL}]

An example:

python tools/misc/browse_dataset.py configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py

An example of visualization:

  • The left two pictures are images from contrastive learning data pipeline.
  • The right one is a masked image.

Visualize t-SNE

We provide an off-the-shelf tool to visualize the quality of image representations by t-SNE.

python tools/analysis_tools/visualize_tsne.py ${CONFIG_FILE} --checkpoint ${CKPT_PATH} --work-dir ${WORK_DIR} [optional arguments]

Arguments:

  • CONFIG_FILE: config file for t-SNE, which listed in the directory configs/tsne/
  • CKPT_PATH: the path or link of the model's checkpoint.
  • WORK_DIR: the directory to save the results of visualization.
  • [optional arguments]: for optional arguments, you can refer to visualize_tsne.py

An example of command:

python ./tools/analysis_tools/visualize_tsne.py \
    configs/tsne/resnet50_imagenet.py \
    --checkpoint https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/mocov2_resnet50_8xb32-coslr-200e_in1k_20220825-b6d23c86.pth \
    --work-dir  ./work_dirs/tsne/mocov2/ \
    --max-num-class 100

An example of visualization, left is from MoCoV2_ResNet50 and right is from MAE_ViT-base:

Visualize Low-level Feature Reconstruction

We provide several reconstruction visualization for listed algorithms:

  • MAE
  • SimMIM
  • MaskFeat

Users can run command below to visualize the reconstruction.

python tools/analysis_tools/visualize_reconstruction.py ${CONFIG_FILE} \
    --checkpoint ${CKPT_PATH} \
    --img-path ${IMAGE_PATH} \
    --out-file ${OUTPUT_PATH}

Arguments:

  • CONFIG_FILE: config file for the pre-trained model.
  • CKPT_PATH: the path of model's checkpoint.
  • IMAGE_PATH: the input image path.
  • OUTPUT_PATH: the output image path, including 4 sub-images.
  • [optional arguments]: for optional arguments, you can refer to visualize_reconstruction.py

An example:

python tools/analysis_tools/visualize_reconstruction.py configs/selfsup/mae/mae_vit-huge-p16_8xb512-amp-coslr-1600e_in1k.py \
    --checkpoint https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k_20220916-ff848775.pth \
    --img-path data/imagenet/val/ILSVRC2012_val_00000003.JPEG \
    --out-file test_mae.jpg \
    --norm-pix


# As for SimMIM, it generates the mask in data pipeline, thus we use '--use-vis-pipeline' to apply 'vis_pipeline' defined in config instead of the pipeline defined in script.
python tools/analysis_tools/visualize_reconstruction.py configs/selfsup/simmim/simmim_swin-large_16xb128-amp-coslr-800e_in1k-192.py \
    --checkpoint https://download.openmmlab.com/mmselfsup/1.x/simmim/simmim_swin-large_16xb128-amp-coslr-800e_in1k-192/simmim_swin-large_16xb128-amp-coslr-800e_in1k-192_20220916-4ad216d3.pth \
    --img-path data/imagenet/val/ILSVRC2012_val_00000003.JPEG \
    --out-file test_simmim.jpg \
    --use-vis-pipeline

Results of MAE:

Results of SimMIM:

Results of MaskFeat:

Visualize Shape Bias

Shape bias measures how a model relies the shapes, compared to texture, to sense the semantics in images. For more details, we recommend interested readers to this paper. MMSelfSup provide an off-the-shelf toolbox to obtain the shape bias of a classification model. You can following these steps below:

Prepare the dataset

First you should download the cue-conflict to data folder, and then unzip this dataset. After that, you data folder should have the following structure:

data
├──cue-conflict
|      |──airplane
|      |──bear
|      ...
|      |── truck

Modify the config for classification

Replace the original test_dataloader and test_evaluation with following configurations

test_pipeline = [...] # copy existing test transforms here
test_dataloader = dict(
    dataset=dict(
        type='CustomDataset',
        data_root='data/cue-conflict',
        pipeline=test_pipeline,
        _delete_=True),
    drop_last=False)
test_evaluator = dict(
    type='mmselfsup.ShapeBiasMetric',
    _delete_=True,
    csv_dir='directory/to/save/the/csv/file',
    model_name='your_model_name')

Please note you should make custom modifications to the csv_dir and model_name. You can follow the toy example here to make custom modification to your evaluation.

Inference your model with above modified config file

Then you should inferece your model on the cue-conflict dataset with the your modified config files.

# For Slurm
GPUS_PER_NODE=1 GPUS=1 bash tools/benchmarks/classification/mim_slurm_test.sh $partition $config $checkpoint

# For PyTorch
GPUS=1 bash tools/benchmarks/classification/mim_dist_test.sh $config $checkpoint

After that, you should obtain a csv file, named cue-conflict_model-name_session-1.csv. Besides this file, you should also download these csv files to the csv_dir.

Plot shape bias

Then we can start to plot the shape bias

python tools/analysis_tools/visualize_shape_bias.py --csv-dir $CVS_DIR --result-dir $CSV_DIR --colors $RGB --markers o --plotting-names $YOU_MODEL_NAME --model-names $YOU_MODEL_NAME
  • --csv-dir, the same directory to save these csv files
  • --colors, should be the RGB values, formatted in R G B, e.g. 100 100 100, and can be multiple RGB values, if you want to plot the shape bias of several models
  • --plotting-names, the name of the legend in the shape bias figure, and you can set it as your model name. If you want to plot several models, plotting_names can be multiple values
  • --model-names, should be the same name specified in your config, and can be multiple names if you want to plot the shape bias of several models

Please note, every three values for --colors corresponds to one value for --model-names. After all of above steps, you are expected to obtain the following figure.