JittorVis is an open-source library for understanding the inner workings of Jittor models by visually illustrating their dataflow graphs.

Deep neural networks have achieved breakthrough performance in many tasks such as image recognition, detection, segmentation, generation, etc. However, the development of high-quality deep models typically relies on a substantial amount of trial and error, as there is still no clear understanding of when and why a deep model works. Also, the complexity of the deep neural network architecture brings difficulties to debugging and modifying the model. JittorVis facilitates the visualization of the dataflow graph of the deep neural network at different levels, which brings users a deeper understanding of the dataflow graph from the whole to the part to debug and modify the model more effectively.

JittorVis provides the visualization and tooling needed for machine learning experimentation:

• Displaying the hierarchical structure of the model dataflow graph
• Visualizing the dataflow graph at different levels (ops and layers)
• Profiling Jittor programs

Features to be supported in the future:

• Tracking and visualizing metrics such as loss and accuracy
• Viewing line charts of weights, biases, or other tensors as they change over time
• And much more

Related Links:

• Jittor
• Jittor Website

JittorVis need python version >= 3.7.

pip install jittorvis
or
pip3 install jittorvis

1. run backend

cd backend
python server.py

2. run frontend

cd frontend
yarn
yarn start

3. generate doc

# frontend
cd frontend
yarn styleguide:build
# backend
cd ..
pdoc backend/ -o doc --html --force

Towards Better Analysis of Deep Convolutional Neural Networks

@article {
    liu2017convolutional,
    author={Liu, Mengchen and Shi, Jiaxin and Li, Zhen and Li, Chongxuan and Zhu, Jun and Liu, Shixia},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    title={Towards Better Analysis of Deep Convolutional Neural Networks},
    year={2017},
    volume={23},
    number={1},
    pages={91-100}
}

Analyzing the Training Processes of Deep Generative Models

@article {
    liu2018generative,
    author={Liu, Mengchen and Shi, Jiaxin and Cao, Kelei and Zhu, Jun and Liu, Shixia},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    title={Analyzing the Training Processes of Deep Generative Models},
    year={2018},
    volume={24},
    number={1},
    pages={77-87}
}

Analyzing the Noise Robustness of Deep Neural Networks

@article {
    cao2021robustness,
    author={Cao, Kelei and Liu, Mengchen and Su, Hang and Wu, Jing and Zhu, Jun and Liu, Shixia},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    title={Analyzing the Noise Robustness of Deep Neural Networks},
    year={2021},
    volume={27},
    number={7},
    pages={3289-3304}
}

JittorVis is currently maintained by the THUVIS Group. If you are also interested in JittorVis and want to improve it, Please join us!

JittorVis is Apache 2.0 licensed, as found in the LICENSE.txt file.