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explainable-cnn

(★ 235)

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

파일 탐색기

최종 버전 다운로드 (.zip)

- - test_workflow.yml
- - dog.jpg
  - tiger_shark.jpeg
- - 0-resnet18-gradcam-layer4-tiger_shark.png
  - 0-resnet18-guided-tiger_shark.png
  - 0-resnet18-guided_gradcam-layer4-tiger_shark.png
  - cam_and_guided_compare.png
  - explainable-cnn-output.png
  - test_guided_gradcam.png
- .DS_Store
- architecture.png
- dog.jpg
- imagenet_class_labels.pkl
- synset_words.txt
- explainable_cnn_usage.ipynb
- - .DS_Store
  - __init__.py
- conftest.py
.all-contributorsrc
.gitignore
CONTRIBUTING.md
LICENSE
pyproject.toml
README.md
requirements.txt
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/ashutosh1919/explainable-cnn

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd explainable-cnn

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install explainable-cnn

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install explainable-cnn

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# Explainable CNNs
[![Torch Version](https://img.shields.io/badge/torch>=1.10.0-61DAFB.svg?style=flat-square)](https://raw.githubusercontent.com/ashutosh1919/explainable-cnn/main/) [![Torchvision Version](https://img.shields.io/badge/torchvision>=0.2.2-yellow.svg?style=flat-square)](https://raw.githubusercontent.com/ashutosh1919/explainable-cnn/main/) [![Python Version](https://img.shields.io/badge/python->=3.6-blue.svg?style=flat-square)](https://raw.githubusercontent.com/ashutosh1919/explainable-cnn/main/) [![test workflow](https://github.com/ashutosh1919/explainable-cnn/blob/main/.github/workflows/test_workflow.yml/badge.svg)](https://raw.githubusercontent.com/ashutosh1919/explainable-cnn/main/) [![Price](https://img.shields.io/badge/price-free-ff69b4.svg?style=flat-square)](https://raw.githubusercontent.com/ashutosh1919/explainable-cnn/main/) [![Maintained](https://img.shields.io/badge/maintained-yes-green.svg?style=flat-square)](https://raw.githubusercontent.com/ashutosh1919/explainable-cnn/main/)

**📦 Flexible visualization package for generating layer-wise explanations for CNNs.**

It is a common notion that a Deep Learning model is considered as a black box. Working towards this problem, this project provides flexible and easy to use `pip` package `explainable-cnn` that will help you to create visualization for any `torch` based CNN model. Note that it uses one of the data centric approach. This project focusses on making the internal working of the Neural layers more transparent. In order to do so, `explainable-cnn` is a plug & play component that visualizes the layers based on on their gradients and builds different representations including Saliency Map, Guided BackPropagation, Grad CAM and Guided Grad CAM.

## Architechture

<p align="center">
<img src = "https://github.com/ashutosh1919/explainable-cnn/blob/main/data/architecture.png"></img>
</p>
:star: Star us on GitHub — it helps!

## Usage

Install the package

```bash
pip install explainable-cnn
```

To create visualizations, create an instance of `CNNExplainer`.

```python
from explainable_cnn import CNNExplainer

x_cnn = CNNExplainer(...)
```

The following method calls returns `numpy` arrays corresponding to image for different types of visualizations.

```python
saliency_map = x_cnn.get_saliency_map(...)

grad_cam = x_cnn.get_grad_cam(...)

guided_grad_cam = x_cnn.get_guided_grad_cam(...)
```

<p>To see full list of arguments and their usage for all methods, please refer to <a href="https://github.com/ashutosh1919/explainable-cnn/blob/main/src/explainable_cnn/explainers/cnn_explainer.py">this file</a></p>
<p>You may want to look at example usage in the <a href="https://github.com/ashutosh1919/explainable-cnn/blob/main/examples/explainable_cnn_usage.ipynb">example notebook</a>.</p>

## Output
<p>Below is a comparison of the visualization generated between GradCam and GuidedGradCam </p>

<p align="center"> 
    <img src="https://github.com/ashutosh1919/explainable-cnn/blob/main/data/outputs/explainable-cnn-output.png" align="center" height="1000px"></img>
</p>

## Contributors ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

<table>
  <tr>
    <td align="center"><a href="https://github.com/ashutosh1919"><img src="https://avatars.githubusercontent.com/u/20843596?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Ashutosh Hathidara</b></sub></a><br /><a href="https://github.com/ashutosh1919/explainable-cnn/commits?author=ashutosh1919" title="Code">💻</a> <a href="#design-ashutosh1919" title="Design">🎨</a> <a href="#research-ashutosh1919" title="Research">🔬</a> <a href="#maintenance-ashutosh1919" title="Maintenance">🚧</a> <a href="#tutorial-ashutosh1919" title="Tutorials">✅</a> <a href="https://github.com/ashutosh1919/explainable-cnn/commits?author=ashutosh1919" title="Tests">⚠️</a></td>
    <td align="center"><a href="https://github.com/L-Pandey"><img src="https://avatars.githubusercontent.com/u/90662028?v=4?s=100" width="100px;" alt=""/><br /><sub><b>Lalit Pandey</b></sub></a><br /><a href="#research-L-Pandey" title="Research">🔬</a> <a href="https://github.com/ashutosh1919/explainable-cnn/commits?author=L-Pandey" title="Documentation">📖</a></td>
  </tr>
</table>

This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!

## References

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://arxiv.org/pdf/1610.02391.pdf)
- [Grad CAM demonstrations in PyTorch](https://github.com/kazuto1011/grad-cam-pytorch)