# Visualization

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

<!-- TOC -->

- [Visualization](#visualization)
  - [How visualization is implemented](#how-visualization-is-implemented)
  - [What Visualization do in MMSelfsup](#what-visualization-do-in-mmselfsup)
  - [Use Different Storage Backends](#use-different-storage-backends)
  - [Customize Visualization](#customize-visualization)
  - [Visualize Datasets](#visualize-datasets)
  - [Visualize t-SNE](#visualize-t-sne)
  - [Visualize Low-level Feature Reconstruction](#visualize-low-level-feature-reconstruction)

<!-- /TOC -->

## How visualization is implemented

It is recommended to learn the basic concept of visualization in [engine.md](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/design/visualization.md).

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

<div align="center">
<img src="https://user-images.githubusercontent.com/17425982/163327736-f7cb3b16-ef07-46bc-982a-3cc7495e6c82.png" width="800" />
</div>

## 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()](https://github.com/open-mmlab/mmengine/blob/main/mmengine/hooks/logger_hook.py#L150) in the default hook `LoggerHook` will be called, and use `add_scalars` in different backends, as follows:

```python
...
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()`](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/mmselfsup/visualization/selfsup_visualizer.py#L151) is impleted in [`SelfSupVisualizer`](mmselfsup.visualization.SelfSupVisualizer), and it is mainly used in [browse_dataset.py](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/analysis_tools/browse_dataset.py) for browsing dataset. More tutorial is in [analysis_tools.md](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**

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

**Tensorboard**

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

E.g.

<div align="center">
<img src="https://user-images.githubusercontent.com/36138628/199388357-5d1cc7b4-07b8-41b1-ac66-12ec8ef009da.png" width="400" />
</div>

**Wandb**

```python
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.

E.g.

<div align="center">
<img src="https://user-images.githubusercontent.com/36138628/199388643-288cf83f-0faa-4f34-a5d0-bf53c7bb3e08.png" width="600" />
</div>

## 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](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/tutorials/visualization.md) 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.

```shell
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:

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

An example of visualization:

<div align="center">
<img src="https://user-images.githubusercontent.com/36138628/199387454-219e6f6c-fbb7-43bb-b319-61d3e6266abc.png" width="600" />
</div>

- 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.

```shell
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](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/analysis_tools/visualize_tsne.py)

An example of command:

```shell
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`:

<div align="center">
<img src="https://user-images.githubusercontent.com/36138628/207305086-91df298c-0eb7-4254-9c5b-ba711644501b.png" width="250" />
<img src="https://user-images.githubusercontent.com/36138628/207305333-59af4747-1e9c-4f85-a57d-c7e5d132a6e5.png" width="250" />
</div>

## 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.

```shell
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](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/analysis_tools/visualize_reconstruction.py)

An example:

```shell
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:

<div align="center">
<img src="https://user-images.githubusercontent.com/36138628/200465826-83f316ed-5a46-46a9-b665-784b5332d348.jpg" width="800" />
</div>

Results of SimMIM:

<div align="center">
<img src="https://user-images.githubusercontent.com/36138628/200466133-b77bc9af-224b-4810-863c-eed81ddd1afa.jpg" width="800" />
</div>

Results of MaskFeat:

<div align="center">
<img src="https://user-images.githubusercontent.com/36138628/200465876-7e7dcb6f-5e8d-4d80-b300-9e1847cb975f.jpg" width="800" />
</div>