We provide a PyTorch implementation of MONet.

This project is built on top of the CycleGAN/pix2pix code written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang.

Note: The implementation is developed and tested on Python 3.7 and PyTorch 1.1.

• Linux or macOS (not tested)
• Python 3.7
• CPU or NVIDIA GPU + CUDA 10 + CuDNN

• Clone this repo:

git clone https://github.com/baudm/MONet-pytorch.git
cd MONet-pytorch

• Install [PyTorch](http://pytorch.org and) 1.1+ and other dependencies (e.g., torchvision, visdom and dominate).
  • For pip users, please type the command pip install -r requirements.txt.
  • For Conda users, we provide a installation script ./scripts/conda_deps.sh. Alternatively, you can create a new Conda environment using conda env create -f environment.yml.
  • For Docker users, we provide the pre-built Docker image and Dockerfile. Please refer to our Docker page.

• Download a MONet dataset (e.g. CLEVR):

wget -cN https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip

• To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.
• Train a model:

python train.py --dataroot ./datasets/CLEVR_v1.0 --name clevr_monet --model monet

To see more intermediate results, check out ./checkpoints/clevr_monet/web/index.html.

To generate a montage of the model outputs like the ones shown above:

./scripts/test_monet.sh
./scripts/generate_monet_montage.sh

• Download pretrained weights for CLEVR 64x64:

./scripts/download_monet_model.sh clevr