Skip to content

ermongroup/fairgen

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
src
 
 
 
 
 
 

Fair Generative Modeling via Weak Supervision

This repo contains a reference implementation for fairgen as described in the paper:

Fair Generative Modeling via Weak Supervision
Kristy Choi*, Aditya Grover*, Trisha Singh, Rui Shu, Stefano Ermon
International Conference on Machine Learning (ICML), 2020.
Paper: https://arxiv.org/abs/1910.12008

1) Data setup:

(a) Download the CelebA dataset here (http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) into the data/ directory (if elsewhere, note the path for step b). Of the download links provided, choose Align&Cropped Images and download Img/img_align_celeba/ folder, Anno/list_attr_celeba.txt, and Eval/list_eval_partition.txt to data/.

(b) Preprocess the CelebA dataset for faster training:

python3 preprocess_celeba.py --data_dir=/path/to/downloaded/dataset/celeba/ --out_dir=../data --partition=train

You should run this script for --partition=[train, val, test] to cache all the necessary data. The preprocessed files will then be saved in data/.

To split the data for multiple attributes, check notebooks/multi-attribute data and unbiased FID splits.ipynb.

2) Pre-train attribute classifier

For a single-attribute:

python3 train_attribute_clf.py celeba ./results/attr_clf

For multiple attributes, add the --multi=True flag.

python3 train_attribute_clf.py celeba ./results/multi_clf -- multi=True

Then, the trained attribute classifier will be saved in ./results/attr_clf (./results/multi_clf) and will be used for downstream evaluation for generative model training. Note the path where these classifiers are saved, as they will be needed for GAN training + evaluation.

3) Pre-train density ratio classifier

The density ratio classifier should be trained for the appropriate bias and perc setting, which can be adjusted in the script below:

python3 get_density_ratios.py celeba celeba --perc=[0.1, 0.25, 0.5, 1.0] --bias=[90_10, 80_20, multi]

Note that the best density ratio classifier will be saved in its corresponding directory under ./data/.

4) Pre-compute unbiased FID scores:

We have provided both (a) biased and (b) unbiased FID statistics in the fid_stats/ directory.

(a) fid_stats/fid_stats_celeba.npz contains the original activations from the entire CelebA dataset, as in: https://github.com/ajbrock/BigGAN-PyTorch

(b) fid_stats/unbiased_all_gender_fid_stats.npz contains activations from the entire CelebA dataset, where the gender attribute (female, male) are balanced.

(c) fid_stats/unbiased_all_multi_fid_stats.npz contains activations from the entire CelebA dataset, where the 4 attribute classes (black hair, other hair, female, male) are balanced.

These pre-computed FID statistics are for model checkpointing (during GAN training) and downstream evaluation of sample quality only, and should be substituted for other statistics when using a different dataset/attribute splits.

5) Train generative model (BigGAN)

A sample script to train the model can be found in scripts/:

bash run_celeba_90_10_perc1.0_impweight.sh

You should add different arguments for different model configurations. For example: (a) for the multi-attribute setting, append --multi 1 (b) for the equi-weighted baseline, append --reweight 0 (c) for the conditional baseline, append --conditional 1 --y 1 --reweight 0 (d) for the importance-weighted model, append --reweight 1 --alpha 1.0

Note the argument for --name_suffix my_experiment, as you will need it for sampling and computing FID scores.

6) Sample from trained model

A sample script to sample from the (trained) model can be found in scripts/:

bash sample_celeba_90_10_perc1.0_impweight.sh

You can either append the argument --load_weights name_of_weights to load a specific set of weights, or pass in the --name_suffix my_experiment argument for the script to find the most recent checkpoint with the best FID.

7) Compute FID scores

To compute FID scores after running the sampling script, (using the original Tensorflow implementation), run the following: python3 fast_fid.py my_experiment --multi=[True,False] --n_replicates=10

This code assumes that there are 10 sets (n_replicates) of 10K samples generated from the model (as per sample.py), and will evaluate the samples on both (a) the original FID scores and (b) unbiased FID scores (as per Step #4). my_experiment refers to the --name_suffix my_experiment parameter from Step 5.

References

If you find this work useful in your research, please consider citing the following paper:

@article{grover2019fair,
  title={Fair Generative Modeling via Weak Supervision},
  author={Grover, Aditya and Choi, Kristy and Singh, Trisha and Shu, Rui and Ermon, Stefano},
  journal={arXiv preprint arXiv:1910.12008},
  year={2019}
}

About

Fair Generative Modeling via Weak Supervision

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published