CommutativeLieGroupVAE-Pytorch

Code for our paper Commutative Lie Group VAE for Disentanglement Learning.

Abstract

We view disentanglement learning as discovering an underlying structure that equivariantly reflects the factorized variations shown in data. Traditionally, such a structure is fixed to be a vector space with data variations represented by translations along individual latent dimensions. We argue this simple structure is suboptimal since it requires the model to learn to discard the properties (e.g. different scales of changes, different levels of abstractness) of data variations, which is an extra work than equivariance learning. Instead, we propose to encode the data variations with groups, a structure not only can equivariantly represent variations, but can also be adaptively optimized to preserve the properties of data variations. Considering it is hard to conduct training on group structures, we focus on Lie groups and adopt a parameterization using Lie algebra. Based on the parameterization, some disentanglement learning constraints are naturally derived. A simple model named Commutative Lie Group VAE is introduced to realize the group-based disentanglement learning. Experiments show that our model can effectively learn disentangled representations without supervision, and can achieve state-of-the-art performance without extra constraints.

Conference Poster

Video Presentation

Requirements

python == 3.6.12
Numpy == 1.19.2
pytorch == 1.7.1
tensorboard == 2.3.0
tqdm == 4.54.1
h5py == 2.8.0
Our code is based on this repository, thus has the same following structure:
- datasets: Stores PyTorch datasets and code to initialise them.
- logger: Stores tensorboard logging and image generation code.
- metrics: Stores all disentanglement metrics.
- models: Stores all models used.
- main.py: Defines the command line args to run training and executes the trainer.
- trainer.py: Sets up parameters for training, optimisers etc.
- training_loop.py: Defines the model independent training logic. We use Anaconda for package management.

Preparing datasets

DSprites The training code will download the dataset (dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz) and convert it to img.npy file automatically (see datasets/dsprites.py). Just specify where (a directory) to store the downloaded dataset with --data-path flag.

3DShapes Download 3dshapes.h5 file from here into a directory. Use --data-path to specify the directory during training.

Training

Pretrained checkpoints can be found here.

DSprites:

forward=0.2
hes=40
rec=0.1
com=0
for i in 0 1 2 3 4 5 6 7 8 9
do
    CUDA_VISIBLE_DEVICES=0 \
    python main.py \
        --model=lie_group \
        --epochs=70 \
        --batch-size=256 \
        --latents 10 \
        --learning-rate=1e-4 \
        --subgroup_sizes_ls '[100]' \
        --subspace_sizes_ls '[10]' \
        --lie_alg_init_scale 0.001 \
        --hy_hes ${hes} \
        --hy_rec ${rec} \
        --hy_commute ${com} \
        --forward_eg_prob=${forward} \
        --recons_loss_type=bce \
        --log-path=/path/to/results/lie_group_dsp_1mul100_rec${rec}_com${com}_hes${hes}_for${forward}_init0001 \
        --data-path=/path/to/dsprites_dir \
        --dataset=dsprites \
        --eval_data_path=/path/to/dsprites_dir \
        --eval_dataset=dsprites \
        --split=0
done

3DShapes:

forward=0.2
hes=20
rec=0.1
com=0
for i in 0 1 2 3 4 5 6 7 8 9
do
    CUDA_VISIBLE_DEVICES=1 \
    python main.py \
        --model=lie_group \
        --epochs=70 \
        --batch-size=256 \
        --latents 10 \
        --learning-rate=1e-4 \
        --subgroup_sizes_ls '[400]' \
        --subspace_sizes_ls '[10]' \
        --lie_alg_init_scale 0.001 \
        --hy_hes ${hes} \
        --hy_rec ${rec} \
        --hy_commute ${com} \
        --forward_eg_prob=${forward} \
        --recons_loss_type=l2 \
        --log-path=/path/to/results/CommutativeLieGroupVAE-Pytorch/lie_group_3ds_1mul400_rec${rec}_com${com}_hes${hes}_for${forward}_init0001 \
        --data-path=/path/to/3dshapes_dir \
        --dataset=shapes3d \
        --eval_data_path=/path/to/3dshapes_dir \
        --eval_dataset=shapes3d \
        --split=0
done

Recorded values and visualizations can be explored with tensorboard.

Collect results

To collect results (metrics with mean and std) in a directory of multiple models, we can run:

python collect_results.py \
    --in_dir /path/to/model_results \
    --result_file /path/to/model_results/aggregated_results.csv \
    --config_variables '[model, dataset, latents, subgroup_sizes_ls, subspace_sizes_ls, lie_alg_init_scale, hy_rec, hy_commute, hy_hes, forward_eg_prob, recons_loss_type]'

Evaluation

To evaluate a checkpoint, run code (an example, where the --log-path contains the target checkpoint):

forward=0.2
hes=20
rec=0.1
com=0
for i in 0
do
    CUDA_VISIBLE_DEVICES=1 \
    python main.py \
        --evaluate=True \
        --model=lie_group \
        --epochs=70 \
        --batch-size=256 \
        --latents 10 \
        --learning-rate=1e-4 \
        --subgroup_sizes_ls '[400]' \
        --subspace_sizes_ls '[10]' \
        --lie_alg_init_scale 0.001 \
        --hy_hes ${hes} \
        --hy_rec ${rec} \
        --hy_commute ${com} \
        --forward_eg_prob=${forward} \
        --recons_loss_type=l2 \
        --log-path=/path/to/results/CommutativeLieGroupVAE-Pytorch/lie_group_3ds_1mul400_rec${rec}_com${com}_hes${hes}_for${forward}_init0001/version_${i} \
        --data-path=/path/to/3dshapes_dir \
        --dataset=shapes3d \
        --eval_data_path=/path/to/3dshapes_dir \
        --eval_dataset=shapes3d \
        --split=0
done

Citation

@inproceedings{Xinqi_liegroupvae_icml21,
author={Xinqi Zhu and Chang Xu and Dacheng Tao},
title={Commutative Lie Group VAE for Disentanglement Learning},
booktitle={ICML},
year={2021}
}

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