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Project description
PyTorchFI
Background • Usage • Code • Contributing • Contributors • License
Background
PyTorchFI is a runtime perturbation tool for deep neural networks (DNNs), implemented for the popular PyTorch deep learning platform. PyTorchFI enables users to perform perturbation on weights or neurons of a DNN during runtime. It is extremely versatile for dependability and reliability research, with applications including resiliency analysis of classification networks, resiliency analysis of object detection networks, analysis of models robust to adversarial attacks, training resilient models, and for DNN interpertability.
For example, this is an object detection network before a fault injection:
This is the same object detection network after a fault injection:
Download on PyPI here, or take a look at our documentation at pytorchfi.github.io.
Usage
Installing
From Pip
Install using pip install pytorchfi
From Source
Download this repository into your project folder.
Importing
Import the entire package:
import pytorchfi
Import a specific module:
from pytorchfi import core
Code
Structure
The main source code of PyTorchFI is held in pytorchfi
, which carries both core
and util
implementations.
Formatting
All python code is formatted with black.
Contributing
Before contributing, please refer to our contributing guidelines.
Contributors
- Sarita V. Adve (UIUC)
- Neeraj Aggarwal (UIUC)
- Christopher W. Fletcher (UIUC)
- Siva Kumar Sastry Hari (NVIDIA)
- Abdulrahman Mahmoud (UIUC)
- Alex Nobbe (UIUC)
Funding Sources
This project was funded in part by the Applications Driving Architectures (ADA) Research Center, a JUMP Center co-sponsored by SRC and DARPA, and in collaboration with NVIDIA Research.
License
NCSA License. Copyright © 2020 RSim Research Group.
Project details
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