Skip to content

Latest commit

 

History

History
148 lines (114 loc) · 4.72 KB

README.md

File metadata and controls

148 lines (114 loc) · 4.72 KB
Raw

Visual Concepts Tokenization

This is the official PyTorch implementation of NeurIPS 2022 paper: Visual Concepts Tokenization Arxiv | OpenReview.

Visual Concepts Tokenization
Tao Yang, Yuwang Wang, Yan Lu, Nanning Zheng
arXiv preprint arXiv:2102.10303
NeurIPS 2022

requirements

A suitable conda environment named vct can be created and activated with:

conda env create -f environment.yaml
conda activate vct

Usage

from models.visual_concept_tokenizor import VCT_Decoder, VCT_Encoder
import torch
vct_enc = VCT_Encoder(z_index_dim = 20)
vct_dec = VCT_Decoder(index_num = 256, z_index_dim=256, ce_loss=True)
vct_enc.cuda()
vct_dec.cuda()
x = torch.randn((32,256,256)).cuda()
z = vct_enc(x)
x_hat = vct_dec(z)

Datasets

Download datasets for training VCT (Shapes3D as an example)

Manully set the "get_args.data_dir = /my/datasets/path" in each gin-config, e.g., shapes3d_ce.gin. Or run the following script for setting dataset path.

python set_dataset_dir.py --datasets /path/to/your/datasets

Dataset path structure:

/path/to/your/datasets/
├──shapes3d/3dshapes.h5
├──mpi_toy/mpi3d_toy.npz
├──...

VQVAE as Image Tokenizor

Train VQVAE

main_vqvae.py script conducts the training of VQVAE, we take the pretrained VQVAE as the image tokenizor and detokenizor. A VQVAE can be trained with the following commend:

python train_vqvae.py --dataset {dataset} --model vqvae --data-dir /path/to/your/datasets/ --epochs 200

dataset can be {cars3d, shapes3d, mpi3d, clevr}. The training setting is the same with VQ-VAE. As an example,

python train_vqvae.py --dataset shapes3d --model vqvae --data-dir /path/to/your/datasets/ --epochs 200

Train VCT

main_vct.py is the main script to train VCT autoencoder with pretrained VQ-VAE. After the VQ-VAE is trained we set the checkpoint path of the pretrained model as follows: "get_args.data_dir = /my/checkpoint/path" in each gin-config, e.g., shapes3d_shared.gin.

python main_vct.py --config {dataset}_ce.gin

For scene decomposition

If we train a VQ-VAE based VCT model for scene decomposition, the training command is following:

python train_vqvae.py --dataset clevr --model vqvae --data-dir /path/to/your/datasets/ --epochs 200

python main_vct.py --config clevr_ce.gin

main_vct_mask.py is the script to train VCT autoencoder with a mask based decoder (see discriptions in the main paper).

python main_vct_mask.py --config clevr_ce.gin

CLIP as Image Tokenizor

main_vct_clip.py script conducts the training of VCT with CLIP image encoder as the Image Tokenizor.

python main_vct_clip.py --config shapes3d_ce.gin

Pretrained GAN as Image Detokenizor

We follow DisCo to adopt different pretrained gan as the Image Detokenizor. Specifically, for the following datasets we use the following commands, respectively.

  • For dataset Cat/Church
python main_gan_based.py --config {church/cat}_ce.gin
  • For dataset FFHQ
python main_ffhq_gan_based.py --config ffhq_low_ce.gin
  • For dataset ImageNet
python main_biggan_based.py --config imgnet_ce.gin

Visualization Results

For VCT with VQVAE as Image Tokenizor

For VCT with Pretrained GAN as Image Detokenizor

Acknowlegement

Note that this project is built upon VQ-VAE and DisCo and Perceiver. The eveluation code is built upon disentanglement_lib.

Citation

@article{yang2022visual,
  title={Visual Concepts Tokenization},
  author={Yang, Tao and Wang, Yuwang and Lu, Yan and Zheng, Nanning},
  journal={NeurIPS},
  year={2022}
}