DCGAN-CelebA-PyTorch-CPP

DCGAN Implementation (on CelebA dataset) using PyTorch C++ Frontend API (Libtorch)

• Training Code location: src/main.cpp
• Generator and Discriminator Definition: include/network.hpp
• Dataset Class: include/dataset.hpp and src/dataset.cpp
• Tested on Libtorch Version: Stable 1.4.0 (cxx11 ABI) with and without CUDA (10.1), Linux, OpenCV 4.1.0

How is this different from dcgan sample of PyTorch?

1. This loads a custom dataset (which is not in the dataset class of PyTorch) - CelebA.
2. Since some users prefer using Sequential Modules, so this example uses Sequential Module.

Utility Functions (to visualize images & create animation), and architecture is inherited from the PyTorch Example on DCGAN (https://github.com/pytorch/examples/blob/master/cpp/dcgan/).

Please note that this is in no way targeted to achieve a certain accuracy, but only focuses on creating an example template for DCGAN using Libtorch on CelebA Dataset.

Steps to Follow

Note: This project requires OpenCV built from source. Make sure you also have Pillow (to save animation), NumPy, Matplotlib for running files in utils/ folder.

1. Create a build directory: mkdir build/
2. Change the directory path (absolute path) to libtorch in CMakeLists.txt file. Then configure using CMake: cmake ..
3. Create an output directory (in the build directory) to store the results & save checkpoints: mkdir output/
4. Build your project: make
5. Execute: ./bin/example/
6. The saved checkpoints and outputs will be at output/ directory
7. To visualize, go back to the main directory: cd ../
8. Execute: python3 utils/display_samples.py
9. The outputs will be stores in build/output/output_images/ directory
10. If you want to make an animation, run: python3 utils/visualize.py and it will save the animation for you in build/output/output_animation/ directory

Blog

Find more about DCGAN on my blogs here:

1. https://krshrimali.github.io/DCGAN-using-PyTorch-CPP/
2. https://krshrimali.github.io/DCGAN-using-PyTorch-CPP-Part-2/

Results

This is the output from random noise (batch of) images after ~10 epochs of training:

Happy learning!

TODOs

1. #9
2. #8
3. #6

Contribution

Note: We believe, contributing to an open source project is the best way to learn. To help others learn, we decided to add open source label to the issues we think can be resolved by others. This will benefit us both.

If you are eager to contribute to this project, pick up an issue with open source label. Before you start working on it, make sure you create an issue and cc to any of the main contributors (@krshrimali / @HimanshuSinghGH), and let us know that you are interested. We do not want to waste your efforts in case someone else is already working on it. Thank you!