A graduation design project: Collected GAN's improved models, and innovation introduces Contrastive Learning to help GAN improve the generated image effect.
This work adopts the evaluation index calculation method of IS and FID in Project(https://github.com/w86763777/pytorch-inception-score-fid)
- Python 3
- matplotlib
- tensorboardX
- torchsummary
- dataclasses
- numpy
- Pillow
- scipy
- torch
- torchvision
- tqdm
- typing-extensions
- tensorboard
pip install -r requirements.txt-
Dataset
- Cifar-10 dataset
-
- if you have download this weight file, you can add it to
data/weight/inceptionv3_weight_name, - or you can discard
arguments: --inception_weight_pathwhen you train model(the default value of this arguments isNone)
- if you have download this weight file, you can add it to
-
cifar-10 fid stats
CUDA_VISIBLE_DEVICES=0 \
python calc_stats.py --output=cifar_10_fid_stats.npz --use_torch --data_path=your_cifar-10_dataset_pathCUDA_VISIBLE_DEVICES=0 \
python train.py --n_d=1 --gpu=True --data_save_root=output --inception_weight_path=data/weight/inceptionv3_weight_name --ms_file_name=cifar_10_fid_stats.npz --experiment_name=dcgan_cnn --total_steps=100000 --latent_dim=100 --mode='dcgan' --model='dcgan_cnn' --batch_size=128CUDA_VISIBLE_DEVICES=0 \
python train.py --n_d=1 --gpu=True --data_save_root=output --inception_weight_path=data/weight/inceptionv3_weight_name --ms_file_name=cifar_10_fid_stats.npz --experiment_name=wgan_gp_cnn --total_steps=100000 --latent_dim=100 --mode='wgan' --model='wgan_gp_cnn' --batch_size=128 --b1=0 --b2=0.9CUDA_VISIBLE_DEVICES=0 \
python train.py --n_d=5 --gpu=True --data_save_root=output --inception_weight_path=data/weight/pt_inception-2015-12-05-6726825d.pth --ms_file_name=cifar_10_fid_stats.npz --experiment_name=wgan_gp_resnet --total_steps=100000 --latent_dim=128 --mode='wgan' --model='wgan_gp_resnet' --batch_size=64 --b1=0 --b2=0.9CUDA_VISIBLE_DEVICES=0 \
python train.py --n_d=5 --gpu=True --data_save_root=output --weight_path=/root/wbw/zhangl/dataset/InceptionV3/pt_inception-2015-12-05-6726825d.pth --data_path=/root/wbw/zhangl/dataset/cifar10 --ms_file_name=m1s1_np.npz --experiment_name=con_gan_5_27 --total_steps=100000 --latent_dim=128 --mode='wgan' --model='con_gan' --batch_size=64 --b1=0 --b2=0.9 --t=0.5