lee-jinhee / self-diagnosing-gan

Code for Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks

Self-Diagnosing GAN

Code for Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks

Setup

This setting requires CUDA 11. However, you can still use your own environment by installing requirements including PyTorch and Torchvision.

1. Install conda environment and activate it

conda env create -f environment.yml
conda activate torchenv

2. Install local package

pip install -e diagan-pkg

Train for CIFAR-10 & CelebA

Phase 1

1. Original GAN training

python train_mimicry_phase1.py --exp_name [exp name] --dataset [dataset] --root [dataset root] --loss_type [loss type] --seed [seed] --model [model]  --gpu [gpu id]  --save_logit_after [logit record start step] --stop_save_logit_after [logit record stop step]

Example command

To compare with original GAN, we train for 50k (75k) steps in total while our method uses checkpoint at 40k (60k) steps in phase 2, for CIFAR-10 (CelebA).

• CIFAR-10

python train_mimicry_phase1.py --exp_name cifar10-phase1 --dataset cifar10 --root ./dataset/cifar10 --loss_type ns --seed 1 --model sngan  --gpu 0  --save_logit_after 35000 --stop_save_logit_after 40000

• CelebA

python train_mimicry_phase1.py --exp_name celeba-phase1 --dataset celeba --root ./dataset/celeba --loss_type ns --seed 1 --model sngan  --gpu 0  --save_logit_after 55000 --stop_save_logit_after 60000

Downloading CelebA dataset might took very long. We recommend direct downloading from this website.

2. Top-k training

python train_mimicry_phase1.py --exp_name celeba-topk --dataset celeba --root ./dataset/celeba --loss_type ns --seed 1 --model sngan  --gpu 0  --save_logit_after 55000 --stop_save_logit_after 60000 --topk

Phase 2

1. Dia-GAN (Ours)

• CIFAR-10

python train_mimicry_phase2.py --gpu 0 --exp_name cifar10-phase2 --resample_score ldr_conf_0.3_ratio_50 --baseline_exp_name cifar10-phase1 --seed 1 --p1_step 40000 --dataset cifar10 --root ./dataset/cifar10  --loss_type ns  --num_steps 50000 --model sngan

• CelebA

python train_mimicry_phase2.py --gpu 0 --exp_name celeba-phase2 --resample_score ldr_conf_5.0_ratio_50 --baseline_exp_name celeba-phase1 --seed 1 --p1_step 60000 --dataset celeba --root ./dataset/celeba  --loss_type ns  --num_steps 75000 --model sngan

2. GOLD Reweight

python train_mimicry_phase2.py --gpu 0 --exp_name celeba-gold --baseline_exp_name celeba-phase1 --seed 1 --p1_step 60000 --dataset celeba --root ./dataset/celeba  --loss_type ns  --num_steps 75000 --model sngan --gold

Eval

1. Original GAN, GOLD, Top-k

Without DRS

python eval_gan.py --gpu 0 --exp_name celeba-phase1 --loss_type ns --netG_ckpt_step 75000 --dataset celeba --seed 1

With DRS

python eval_gan_drs.py --gpu 0 --exp_name celeba-phase1 --loss_type ns --netG_ckpt_step 75000 --dataset celeba --seed 1 --use_original_netD

2. Dia-GAN (Ours)

python eval_gan_drs.py --gpu 0 --exp_name celeba-phase1-diagan --loss_type ns --netG_ckpt_step 75000 --dataset celeba --seed 1

Train for Colored-MNIST

Phase 1

1. Original GAN training

python train_mimicry_color_mnist_phase1.py --gpu 0 --exp_name rd0.99-n10000-mnist_dcgan-bs64-loss_ns --model mnist_dcgan --major_ratio 0.99 --num_data 10000 --batch_size 64 --loss_type ns

2. PacGAN (Used packing of 2)

python train_mimicry_color_mnist_phase1.py --gpu 0 --exp_name rd0.99-n10000-mnist_dcgan-bs64-loss_ns-pack2 --model mnist_dcgan --major_ratio 0.99 --num_data 10000 --batch_size 64 --loss_type ns --num_pack 2

Phase 2

1. Dia-GAN (Ours)

python train_mimicry_color_mnist_phase2.py --gpu 0 --exp_name rd0.99-mnist_dcgan-phase2 --baseline_exp_name rd0.99-n10000-mnist_dcgan-bs64-loss_ns --model mnist_dcgan --major_ratio 0.99 --p1_step 15000 --resample_score ldr_conf_1.0_ratio_50 --batch_size 64 --loss_type ns --use_eval_logits 0

2. GOLD

python train_mimicry_color_mnist_phase2_gold.py --gpu 0 --exp_name rd0.99-mnist_dcgan-phase2-gold --baseline_exp_name rd0.99-n10000-mnist_dcgan-bs64-loss_ns --model mnist_dcgan --major_ratio 0.99 --p1_step 15000  --batch_size 64 --loss_type ns

Eval for Colored-MNIST

We calculate Reconstruction Error (RE) score for Colored-MNIST.

1. Train CAE and calculate RE

python train_cae.py --exp_name rd0.99-mnist_dcgan-phase2 --netG_step 20000 --dataset color_mnist --model mnist_dcgan --root ./dataset/colour_mnist --num_data 10000 --major_ratio 0.99 --gpu 4 --loss_type ns

Then, we measure the difference of RE scores between baseline and our method for green samples.

2. Evaluation

python eval_ae_score.py --resample_exp_path ./exp_results/rd0.99-mnist_dcgan-phase2 --baseline_exp_path ./exp_results/rd0.99-n10000-mnist_dcgan-bs64-loss_ns --major_ratio 0.99

Train - MNIST-FMNIST

1. Phase 1

python train_mimicry_mnist_fmnist_phase1.py --exp_name fmnist-0.9-dcgan-seed1-phase1 --loss_type ns --model mnist_dcgan --gpu 2 --seed 1 --major_ratio 0.9 --num_data 60000

2. Phase 2

python train_mimicry_mnist_fmnist_phase2.py --exp_name fmnist-0.9-dcgan-seed1-phase2  --baseline_exp_name fmnist-0.9-dcgan-seed1-phase1 --loss_type ns --model mnist_dcgan --gpu 0 --seed 3 --major_ratio 0.9 --num_data 60000 --resample_score ldr_conf_5.0_ratio_50 --num_steps 20000 --p1_step 15000 --use_eval_logits 1

Eval - MNIST-FMNIST

python eval_ae_score.py -d mnist_fmnist -r ./dataset/mnist_fmnist --baseline_exp_path exp_results/mf0.9-n60000-mnist_dcgan-bs64-loss_ns-seed1-inclusive --resample_exp_path exp_results/mf0.9-n60000-mnist_dcgan-bs64-loss_ns-seed1-inclusive --resample_score ldr_conf_3.0_ratio_50_beta_1.0 --use_loss --major_ratio 0.9 --num_data 60000 --seed 1 --name inclusive

StyleGAN2

We use the implementation of https://github.com/rosinality/stylegan2-pytorch . All the commands should be executed inside the stylegan2 directory.

1. Prepare data Downlaod FFHQ dataset from https://github.com/NVlabs/ffhq-dataset Then, convert to LMDB format.

python prepare_data.py --out ./dataset/ffhq/lmdb_256.mdb --size 256 --path ./dataset/ffhq

2. Train - Phase 1

python -m torch.distributed.launch --nproc_per_node=4 --master_port=15694 train_ffhq.py --root ./dataset/ffhq/lmdb_256.mdb --batch 4 --dataset ffhq --exp_name ffhq-seed1 --seed 1

3. Train - Phase 2

python -m torch.distributed.launch --nproc_per_node=4 --master_port=15694 train_ffhq_phase2.py --root ./dataset/ffhq/lmdb_256.mdb --batch 4 --dataset ffhq  --exp_name ffhq-phase2-seed1 --baseline_exp_name ffhq-seed1 --seed 1 --resample_score ldr_conf_3.0_ratio_50

4. Evaluate

python eval_gan_drs.py -d ffhq -r ./dataset/ffhq/lmdb_256.mdb --exp_name stylegan2-ffhq-phase2 --model stylegan2 --seed 1 --netG_ckpt_step 250000 --gpu 0