Skip to content

Latest commit

 

History

History
119 lines (84 loc) · 3.75 KB

README.md

File metadata and controls

119 lines (84 loc) · 3.75 KB
Raw

SCAM! Transferring humans between images with Semantic Cross Attention Modulation

Official PyTorch implementation of "SCAM! Transferring humans between images with Semantic Cross Attention Modulation", ECCV 2022.

Arxiv | Website

Bibtex

If you happen to find this code or method useful in your research please cite this paper with

@article{dufour2022scam,
  title={SCAM! Transferring humans between images with Semantic Cross Attention Modulation},
  author={Nicolas Dufour, David Picard, Vicky Kalogeiton},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2022}
}

Installation guide

To install this repo follow the following step:

  1. Install Anaconda or MiniConda
  2. Run conda create -n scam python=3.9.12
  3. Activate scam: conda activate scam
  4. Install pytorch 1.12 and torchvision 0.13 that match your device:
    • For cpu:
    conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cpuonly -c pytorch
    • For cuda 11.6:
    conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.6 -c pytorch -c conda-forge
  5. Install dependencies:
conda env update --file environment.yml

Prepare the data

Download CelebAHQ

Download link: Dataset provided and processed by SEAN authors. Unzip in the datasets folder. Then run:

python preprocess_fid_features.py dataset=CelebAHQ

to run on GPU run:

python preprocess_fid_features.py dataset=CelebAHQ compnode=single-gpu

Add a new dataset

If you want to add a new dataset you need to follow the same directory structure as the other datasets:

2 folders: train and test. Each folders contains 4 sub-folders: images, labels, vis and stats.

  • images: contains the rgb images. can be jpg or png.
  • labels: the segmentation labels. needs to be png where each pixels takes value between 0 and num_labels.
  • vis: rgb visualization of the labels
  • stats: Inception statistics of the train dataset. To do so, initialize a dataloader with the train dataloader

Create a config file for the dataset in configs/dataset (the name of the dataset should be the name of the config file)

and run:

python preprocess_fid_features.py dataset=dataset_name compnode=(cpu or single-gpu)

Run SCAM

Our code needs to be logged in wandb. Make sure to be logged in to wandb before starting. To run our code you just need to do:

python train.py

By default the code will be runned on cpu. If you want to run it on gpu you can do:

python train.py compnode=single-gpu

Other compute configs exists in configs/compnode. If none suit your needs you can easily create one thanks to the modularity of hydra.

In this paper, the main compute config we use is the 4 gpus (NVIDIA V100 32g with a total batch size of 32, 8 per GPUs) config called 1-node-cluster.

By default, our method scam will be runned. If you want to try one of the baselines we compare to, you can run

python train.py model=sean

We include implementations for SEAN, SPADE, INADE, CLADE and GroupDNET.

To swap datasets, you can do:

python train.py dataset=CelebAHQ

we support 3 datasets, CelebAHQ, idesigner and ADE20K.

If you want to run different experiments, make sure to change experiment_name_comp or experiment_name when running. Otherwise or code will restart the training from the checkpointed weights for the experiment with the same name.

Pretrained models

Coming soon