[Paper 3.5MB]  [arXiv]

• NVIDIA GPU + CUDA CuDNN
• Python 3.6
• PyTorch 1.0
• Please install dependencies by

pip install -r requirements.txt

• Clone this repo with its submodules

git clone --recurse-submodules -j8 https://github.com/azadis/SB-GAN.git
cd SB-GAN/SPADE/models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../../../

• To have access to the indoor images from the ADE20K dataset and their corresponding segmentation maps used in our paper:

cd SB-GAN
bash SBGAN/datasets/download_ade.sh
cd ..

cd SB-GAN/SBGAN/datasets
mkdir cityscapes
cd cityscapes

• Download and unzip leftImg8bit_trainvaltest.zip and gtFine_trainvaltest.zip from the Cityscapes webpage .

mv leftImg8bit_trainvaltest/leftImg8bit ./
mv gtFine_trainvaltest/gtFine ./

Cityscapes-25k

• In addition to the 5K portion already downloaded, download and unzip leftImg8bit_trainextra.zip. You can have access to the fine annotations of these 20K images we used in our paper by:

wget https://people.eecs.berkeley.edu/~sazadi/SBGAN/datasets/drn_d_105_000_test.tar.gz
tar -xzvf drn_d_105_000_test.tar.gz

These annotations are predicted by a DRN trained on the 5K fine-annotated portion of Cityscapes with 19 semantic categories. The new fine annotations of the 5K portion with 19 semantic classes can be also downloaded by:

wget https://people.eecs.berkeley.edu/~sazadi/SBGAN/datasets/gtFine_new.tar.gz
tar -xzvf gtFine_new.tar.gz
cd ../../../..

cd SB-GAN/SBGAN

• On each $dataset in ade_indoor, cityscapes, cityscapes_25k:

1. Semantic bottleneck synthesis:

bash SBGAN/scipts/$dataset/train_progressive_seg.sh

2. Semantic image synthesis:

cd ../SPADE
bash scripts/$dataset/train_spade.sh

3. Train the end2end SBGAN model:

cd ../SBGAN
bash SBGAN/scripts/$dataset/train_finetune_end2end.sh

• In the above script, set $pro_iter to the iteration number of the checkpoint saved from step 1 that you want to use before fine-tuning. Also, set $spade_epoch to the last epoch saved for SPADE from step 2.
• To visualize the training you have started in steps 1 and 3 on a ${date-time}, run the following commands. Then, open http://localhost:6006/ on your web browser.

cd SBGAN/logs/${date-time}
tensorboard --logdir=. --port=6006

To compute FID after training the end2end model, for each $dataset, do:

bash SBGAN/scripts/$dataset/test_finetune_end2end.sh

• In the above script, set $pro_iter and $spade_epoch to the appropriate checkpoints saved from your end2end training.

If you use this code, please cite our paper:

@article{azadi2019semantic,
  title={Semantic Bottleneck Scene Generation},
  author={Azadi, Samaneh and Tschannen, Michael and Tzeng, Eric and Gelly, Sylvain and Darrell, Trevor and Lucic, Mario},
  journal={arXiv preprint arXiv:1911.11357},
  year={2019}
}