Pytorch implementation of Probabilistic Semantic Inpainting with Pixel Constrained CNNs (2018).
This repo contains an implementation of Pixel Constrained CNN, a framework for performing probabilistic inpainting of images with arbitrary occlusions. It also includes all code to reproduce the experiments in the paper as well as the weights of the trained models.
For a TensorFlow implementation, see this repo.
The attributes of the model can be set in the config.json file. To train the model, run
python main.py config.json
This will also save the trained model and log various information as training progresses. Examples of config.json files are available in the trained_models directory.
To generate images with a trained model use main_generate.py. As an example, the following command generates 64 completions for images 73 and 84 in the MNIST dataset by conditioning on the top 14 rows. The model used to generate the completions is the trained MNIST model included in this repo and the results are saved to the mnist_inpaintings folder.
python main_generate.py -n mnist_inpaintings -m trained_models/mnist -t generation -i 73 84 -to 14 -ns 64
For a full list of options, run python main_generate.py --help. Note that if you do not have the MNIST dataset on your machine it will be automatically downloaded when running the above command. The CelebA dataset will have to be manually downloaded (see the Data sources section). If you already have the datasets downloaded, you can change the paths in utils/dataloaders.py to point to the correct folders on your machine.
The trained models referenced in the paper are included in the trained_models folder. You can use the main_generate.py script to generate image completions (and other plots) with these models.
The MNIST dataset can be automatically downloaded using torchvision. The CelebA dataset can be found here.
If you find this work useful in your research, please cite using:
@article{dupont2018probabilistic,
title={Probabilistic Semantic Inpainting with Pixel Constrained CNNs},
author={Dupont, Emilien and Suresha, Suhas},
journal={arXiv preprint arXiv:1810.03728},
year={2018}
}
