[Docs] Translate two docs (#653)
* translate * translate * Update visualization.md * Update analysis_tools.md * Update visualization.md * Update analysis_tools.md * Update visualization.md * Update visualization.md * Update visualization.md * Update visualization.md * Update visualization.md * Update analysis_tools.md * Update visualization.md * Update analysis_tools.md * Update visualization.md * update Co-authored-by: fangyixiao18 <fangyx18@hotmail.com>pull/661/head
JetstramSam
GitHub
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@ -22,7 +22,7 @@ Visualization can give an intuitive interpretation of the performance of the mod
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## How visualization is implemented
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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).
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It is recommended to learn the basic concept of visualization in [documentation](https://github.com/open-mmlab/mmengine/blob/main/docs/en/design/visualization.md).
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OpenMMLab 2.0 introduces the visualization object `Visualizer` and several visualization backends `VisBackend`. The diagram below shows the relationship between `Visualizer` and `VisBackend`,
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@ -49,7 +49,7 @@ def after_train_iter(...):
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(2) Browse dataset
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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)
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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 section [Visualize Datasets](#visualize-datasets)
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## Use Different Storage Backends
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@ -83,8 +83,6 @@ visualizer = dict(
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type='SelfSupVisualizer', vis_backends=vis_backends, name='visualizer')
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```
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Note that when multiple visualization backends exist for `vis_backends`, only `WandbVisBackend` is valid.
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E.g.
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<div align="center">
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@ -93,7 +91,7 @@ E.g.
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## Customize Visualization
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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.
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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/en/advanced_tutorials/visualization.md) in MMEngine.
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## Visualize Datasets
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@ -277,13 +275,12 @@ Then we can start to plot the shape bias
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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
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```
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- csv-dir, the same directory to save these csv files
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- 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
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- `--csv-dir`, the same directory to save these csv files
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- `--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
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to plot the shape bias of several models
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- plotting-names, the name of the legend in the shape bias figure, and you can set it as your model name. If you want
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- `--plotting-names`, the name of the legend in the shape bias figure, and you can set it as your model name. If you want
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to plot several models, plotting_names can be multiple values
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- model-names, should be the same name specified in your config, and can be multiple names if you want to plot the
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shape bias of several models
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- `--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
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Please note, every three values for `--colors` corresponds to one value for `--model-names`. After all of above steps, you
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are expected to obtain the following figure.
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@ -1 +1,91 @@
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# Analysis tools
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# 分析工具
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<!-- TOC -->
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- [分析工具](#分析工具)
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- [统计参数量](#统计参数量)
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- [发布模型](#发布模型)
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- [结果复现](#结果复现)
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- [日志分析](#日志分析)
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## 统计参数量
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```shell
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python tools/analysis_tools/count_parameters.py ${CONFIG_FILE}
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```
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一个例子如下：
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```shell
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python tools/analysis_tools/count_parameters.py configs/selfsup/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k.py
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```
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## 发布模型
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发布模型之前，你可能是想：
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- 把模型权重转换为 CPU 张量。
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- 删除优化器相关状态。
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- 计算检查点文件的哈希值并把哈希 ID 加到文件名上。
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```shell
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python tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
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```
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例子如下：
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```shell
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python tools/model_converters/publish_model.py YOUR/PATH/epoch_100.pth YOUR/PATH/epoch_100_output.pth
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```
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## 结果复现
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想让你的结果完全可以复现的话，训练最终模型时请设置 `--cfg-options randomness.deterministic=True` 。值得一提的是，这会关掉 `torch.backends.cudnn.benchmark` 并降低训练速度。
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## 日志分析
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`tools/analysis_tools/analyze_logs.py` 用训练日志文件画损失/学习率曲线。首先 `pip install seaborn` 安装依赖库。
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]
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```
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<div align="center">
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<img src="https://raw.githubusercontent.com/open-mmlab/mmdetection/master/resources/loss_curve.png" width="400" />
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</div>
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例子如下:
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- 画部分运行过程中分类的损失函数图像。
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_dense --legend loss_dense
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```
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- 画部分运行过程中分类和倒退的损失函数图像并存到 pdf 文件里。
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_dense loss_single --out losses.pdf
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```
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- 在同一张图内，比较两次训练的损失。
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys loss --legend run1 run2
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```
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- 计算平均训练速度。
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```shell
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python tools/analysis_tools/analyze_logs.py cal_train_time log.json [--include-outliers]
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```
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输出应该像下面这样：
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```text
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-----Analyze train time of work_dirs/some_exp/20190611_192040.log.json-----
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slowest epoch 11, average time is 1.2024
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fastest epoch 1, average time is 1.1909
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time std over epochs is 0.0028
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average iter time: 1.1959 s/iter
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```
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@ -1 +1,282 @@
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# Visualization
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# 可视化
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可视化能直观反映模型性能表现。
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<!-- TOC -->
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- [可视化](#可视化)
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- [如何实现可视化](#如何实现可视化)
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- [MMSelfSup 的可视化做什么](#mmselfsup-的可视化做什么)
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- [用不同的存储后端](#用不同的存储后端)
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- [定制化的可视化](#定制化的可视化)
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- [数据集可视化](#数据集可视化)
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- [t-SNE 可视化](#t-sne-可视化)
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- [可视化低级特征重建](#可视化低级特征重建)
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- [可视化 shape bias](#可视化-shape-bias)
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- [准备数据集](#准备数据集)
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- [为分类调整配置](#为分类调整配置)
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- [用上述调整过的配置文件推理模型](#用上述调整过的配置文件推理模型)
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- [画出 shape bias](#画出-shape-bias)
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<!-- /TOC -->
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## 如何实现可视化
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建议先学习 [文档](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/design/visualization.md) 里关于可视化的基本概念。
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OpenMMLab 2.0 引入可视化对象 `Visualizer` 和一些可视化后端 `VisBackend` 。如下图表展示了 `Visualizer` 和 `VisBackend` 的关系。
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<div align="center">
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<img src="https://user-images.githubusercontent.com/17425982/163327736-f7cb3b16-ef07-46bc-982a-3cc7495e6c82.png" width="800" />
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</div>
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## MMSelfSup 的可视化做什么
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(1) 用不同的存储后端存训练数据
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MMEngine 的后端包括 `LocalVisBackend`, `TensorboardVisBackend` 和 `WandbVisBackend`。
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在训练过程中，默认钩子 `LoggerHook` 中的 [after_train_iter()](https://github.com/open-mmlab/mmengine/blob/main/mmengine/hooks/logger_hook.py#L150) 会被调用，并且会在不同后端中用到 `add_scalars`，例如：
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```python
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...
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def after_train_iter(...):
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...
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runner.visualizer.add_scalars(
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tag, step=runner.iter + 1, file_path=self.json_log_path)
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...
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```
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(2) 浏览数据集
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[`add_datasample()`](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/mmselfsup/visualization/selfsup_visualizer.py#L151) 函数位于 [`SelfSupVisualizer`](mmselfsup.visualization.SelfSupVisualizer), 常用于在 [browse_dataset.py](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/analysis_tools/browse_dataset.py) 中浏览数据集。更多细节可以参考 [数据集可视化](#数据集可视化)。
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## 用不同的存储后端
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如果想用不同的存储后端( Wandb, Tensorboard, 或者远程窗口里常规的后端)，像以下这样改配置文件的 `vis_backends` 就行了：
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**Local**
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```python
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vis_backends = [dict(type='LocalVisBackend')]
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```
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**Tensorboard**
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```python
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vis_backends = [dict(type='TensorboardVisBackend')]
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visualizer = dict(
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type='SelfSupVisualizer', vis_backends=vis_backends, name='visualizer')
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```
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例如
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/199388357-5d1cc7b4-07b8-41b1-ac66-12ec8ef009da.png" width="400" />
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</div>
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**Wandb**
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```python
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vis_backends = [dict(type='WandbVisBackend')]
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visualizer = dict(
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type='SelfSupVisualizer', vis_backends=vis_backends, name='visualizer')
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```
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例如：
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/199388643-288cf83f-0faa-4f34-a5d0-bf53c7bb3e08.png" width="600" />
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</div>
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## 定制化的可视化
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定制化可视化就像定制化其他组成部分那样。想定制化 `Visualizer`, `VisBackend` 或者 `VisualizationHook` 的话可以参考 MMEngine 里的 [可视化文档](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/advanced_tutorials/visualization.md)
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## 数据集可视化
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`tools/misc/browse_dataset.py` 帮助用户可视化浏览 MMSelfSup 数据集，或者也可以把图像存到指定的目录里。
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```shell
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python tools/misc/browse_dataset.py ${CONFIG} [-h] [--skip-type ${SKIP_TYPE[SKIP_TYPE...]}] [--output-dir ${OUTPUT_DIR}] [--not-show] [--show-interval ${SHOW_INTERVAL}]
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```
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例子如下：
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```shell
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python tools/misc/browse_dataset.py configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py
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```
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一个可视化的例子如下：
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/199387454-219e6f6c-fbb7-43bb-b319-61d3e6266abc.png" width="600" />
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</div>
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- 左边两张图来自对比学习数据流。
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- 右边那张图是添加了掩码的图像。
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## t-SNE 可视化
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我们提供可视化 t-SNE 展示图片表征的现成工具。
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```shell
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python tools/analysis_tools/visualize_tsne.py ${CONFIG_FILE} --checkpoint ${CKPT_PATH} --work-dir ${WORK_DIR} [optional arguments]
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```
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参数:
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- `CONFIG_FILE`: 位于 `configs/tsne/` 中的 t-SNE 的配置文件。
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- `CKPT_PATH`: 模型检查点的目录或链接。
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- `WORK_DIR`: 拿来存可视化结果的目录。
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- `[optional arguments]`: 可选项，可以参考 [visualize_tsne.py](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/analysis_tools/visualize_tsne.py)
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一个命令示例如下：
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```shell
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python ./tools/analysis_tools/visualize_tsne.py \
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configs/tsne/resnet50_imagenet.py \
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--checkpoint https://download.openmmlab.com/mmselfsup/1.x/mocov2/mocov2_resnet50_8xb32-coslr-200e_in1k/mocov2_resnet50_8xb32-coslr-200e_in1k_20220825-b6d23c86.pth \
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--work-dir ./work_dirs/tsne/mocov2/ \
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--max-num-class 100
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```
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下面是可视化的例子,左边来自 `MoCoV2_ResNet50`，右边来自 `MAE_ViT-base`:
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/207305086-91df298c-0eb7-4254-9c5b-ba711644501b.png" width="250" />
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<img src="https://user-images.githubusercontent.com/36138628/207305333-59af4747-1e9c-4f85-a57d-c7e5d132a6e5.png" width="250" />
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</div>
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## 可视化低级特征重建
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我们提供如下算法的重建可视化：
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- MAE
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- SimMIM
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- MaskFeat
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用户可以通过如下命令可视化重建。
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```shell
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python tools/analysis_tools/visualize_reconstruction.py ${CONFIG_FILE} \
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--checkpoint ${CKPT_PATH} \
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--img-path ${IMAGE_PATH} \
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--out-file ${OUTPUT_PATH}
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```
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参数:
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- `CONFIG_FILE`: 预训练模型配置文件。
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- `CKPT_PATH`: 模型检查点的路径。
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- `IMAGE_PATH`: 输入图像的路径。
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- `OUTPUT_PATH`: 输出图像的路径，包含４个子图。
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- `[optional arguments]`: for optional arguments, 您可以参考 [visualize_reconstruction.py](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/analysis_tools/visualize_reconstruction.py) 了解可选参数。
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例子如下:
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```shell
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python tools/analysis_tools/visualize_reconstruction.py configs/selfsup/mae/mae_vit-huge-p16_8xb512-amp-coslr-1600e_in1k.py \
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--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 \
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--img-path data/imagenet/val/ILSVRC2012_val_00000003.JPEG \
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--out-file test_mae.jpg \
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--norm-pix
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# SimMIM 在数据流里生成掩码，所以我们不用脚本里定义好的管道而用 '--use-vis-pipeline' 来应用配置里定义的 'vis_pipeline'
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python tools/analysis_tools/visualize_reconstruction.py configs/selfsup/simmim/simmim_swin-large_16xb128-amp-coslr-800e_in1k-192.py \
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--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 \
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--img-path data/imagenet/val/ILSVRC2012_val_00000003.JPEG \
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--out-file test_simmim.jpg \
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--use-vis-pipeline
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```
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MAE 结果如下:
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<div align="center">
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<img src="https://user-images.githubusercontent.com/36138628/200465826-83f316ed-5a46-46a9-b665-784b5332d348.jpg" width="800" />
|
||||
</div>
|
||||
|
||||
SimMIM 结果如下:
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/36138628/200466133-b77bc9af-224b-4810-863c-eed81ddd1afa.jpg" width="800" />
|
||||
</div>
|
||||
|
||||
MaskFeat 结果如下:
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/36138628/200465876-7e7dcb6f-5e8d-4d80-b300-9e1847cb975f.jpg" width="800" />
|
||||
</div>
|
||||
|
||||
## 可视化 shape bias
|
||||
|
||||
shape bias 衡量在感知图像特征的过程中，与纹理相比，模型依赖 shape 的程度。感兴趣的话可以参考 [paper](https://arxiv.org/abs/2106.07411) 了解更多信息。 MMSelfSup 提供一个现有的用于得到分类模型 shape bias 的工具箱。可以按以下步骤来做：
|
||||
|
||||
### 准备数据集
|
||||
|
||||
首先把 [cue-conflict](https://github.com/bethgelab/model-vs-human/releases/download/v0.1/cue-conflict.tar.gz) 下载到 `data` 文件夹里,然后解压数据集。然后，您的 `data` 文件夹的结构应该像这样：
|
||||
|
||||
```text
|
||||
data
|
||||
├──cue-conflict
|
||||
| |──airplane
|
||||
| |──bear
|
||||
| ...
|
||||
| |── truck
|
||||
```
|
||||
|
||||
### 为分类调整配置
|
||||
|
||||
用以下配置代替原来的 test_dataloader 和 test_evaluation
|
||||
|
||||
```python
|
||||
test_dataloader = dict(
|
||||
dataset=dict(
|
||||
type='CustomDataset',
|
||||
data_root='data/cue-conflict',
|
||||
_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')
|
||||
```
|
||||
|
||||
请记得自己修改一下 `csv_dir` 和 `model_name`。
|
||||
|
||||
### 用上述调整过的配置文件推理模型
|
||||
|
||||
然后您需要做的是用调整过的配置文件在 `cue-conflict` 数据集上推理模型。
|
||||
|
||||
```shell
|
||||
# For Slurm
|
||||
GPUS_PER_NODE=1 GPUS=1 bash tools/benchmarks/classification/mim_slurm_test.sh $partition $config $checkpoint
|
||||
```
|
||||
|
||||
```shell
|
||||
# For PyTorch
|
||||
GPUS=1 bash tools/benchmarks/classification/mim_dist_test.sh $config $checkpoint
|
||||
```
|
||||
|
||||
在这之后，可以获得名为 `cue-conflict_model-name_session-1.csv` 的 csv 文件。除了这个文件之外，您应该下载 [csv 文件](https://github.com/bethgelab/model-vs-human/tree/master/raw-data/cue-conflict) 到对应的 `csv_dir`。
|
||||
|
||||
### 画出 shape bias
|
||||
|
||||
然后我们就可以开始画出 shape bias 了。
|
||||
|
||||
```shell
|
||||
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`, 相同目录下，用于存储 csv 文件。
|
||||
- `--colors`, 应为以 RGB 为格式的 RGB 值,比如 100 100 100,如果您想画若干模型的 shape bias 的话多个RGB值也行。
|
||||
- `--plotting-names`, 偏好形状里图例的名称，您可将之设为模型名字。如果您想画若干模型的 shape bias 的话名字设多个值也行。
|
||||
- `--model-names`，应该跟配置文件里的一样，如果您想画若干模型的 shape bias 的话多个名字也行。
|
||||
|
||||
请注意，每三个 `--colors` 对应一个 `--model-names` 。上面步骤做完后您会得到如下图像：
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/30762564/208357938-c744d3c3-7e08-468e-82b7-fc5f1804da59.png" width="400" />
|
||||
</div>
|
||||
|
|
|
|||
Loading…
Reference in New Issue