Added Spectral Norm as a comparison

.gitignore

@@ -15,7 +15,6 @@ iccv21
 
 # data
 data
-stats
 *.png
 _cache_*
 figures

Readme.md

@@ -1,11 +1,6 @@
-# Gradient Normalization for Generative Adversarial Networks
-
-Yi-Lun Wu, Hong-Han Shuai, Zhi-Rui Tam, Hong-Yu Chiu
-
-Paper: [https://arxiv.org/abs/2109.02235](https://arxiv.org/abs/2109.02235)
-
-This is the official implementation of Gradient Normalized GAN (GN-GAN).
-
+## NOTE
+THIS IS A FORK OF A FORK TO TEST FOR SN 
+THE FILES ARE IN SN_TESTING
 ## Requirements
 - Python 3.8.9
 - Python packages
@@ -116,44 +111,3 @@ All the reported values (Inception Score and FID) in our paper are calculated by
         --eval \
         --save path/to/generated/images
     ```
-
-## How to integrate Gradient Normalization into your work?
-The function `normalize_gradient` is implemented based on `torch.autograd` module, which can easily normalize your forward propagation of discriminator by updating a single line.
-```python
-from torch.nn import BCEWithLogitsLoss
-from models.gradnorm import normalize_gradient
-
-net_D = ...     # discriminator
-net_G = ...     # generator
-loss_fn = BCEWithLogitsLoss()
-
-# Update discriminator
-x_real = ...                                    # real data
-x_fake = net_G(torch.randn(64, 3, 32, 32))      # fake data
-pred_real = normalize_gradient(net_D, x_real)   # net_D(x_real)
-pred_fake = normalize_gradient(net_D, x_fake)   # net_D(x_fake)
-loss_real = loss_fn(pred_real, torch.ones_like(pred_real))
-loss_fake = loss_fn(pred_fake, torch.zeros_like(pred_fake))
-(loss_real + loss_fake).backward()              # backward propagation
-...
-
-# Update generator
-x_fake = net_G(torch.randn(64, 3, 32, 32))      # fake data
-pred_fake = normalize_gradient(net_D, x_fake)   # net_D(x_fake)
-loss_fake = loss_fn(pred_fake, torch.ones_like(pred_fake))
-loss.backward()                                 # backward propagation
-...
-
-```
-
-## Citation
-If you find our work is relevant to your research, please cite:
-```
-@InProceedings{GNGAN_2021_ICCV,
-    author = {Yi-Lun Wu, Hong-Han Shuai, Zhi Rui Tam, Hong-Yu Chiu},
-    title = {Gradient Normalization for Generative Adversarial Networks},
-    booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
-    month = {Oct},
-    year = {2021}
-}
-```

config/GN-GAN-CR_CIFAR10_BIGGAN.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=125000
 --batch_size_D=50
 --batch_size_G=50
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0001
 --betas=0.0

config/GN-GAN-CR_CIFAR10_CNN.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=200000
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0002
 --n_dis=1

config/GN-GAN-CR_CIFAR10_RES.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=0
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0004
 --lr_G=0.0002
 --n_dis=5

config/GN-GAN-CR_STL10_CNN.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=200000
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0002
 --n_dis=1

config/GN-GAN-CR_STL10_CNN_MODULE.txt

@@ -0,0 +1,25 @@
+--dataset=stl10.48
+--arch=dcgan.48
+--loss=bce
+--total_steps=200000
+--lr_decay_start=200000
+--batch_size_D=64
+--batch_size_G=128
+--num_workers=10
+--lr_D=0.0002
+--lr_G=0.0002
+--n_dis=1
+--z_dim=128
+--cr=5
+--n_classes=1
+
+--ema_decay=0.9999
+--ema_start=0
+
+--sample_step=500
+--sample_size=64
+--eval_step=5000
+--save_step=20000
+--num_images=50000
+--fid_stats=./stats/stl10.unlabeled.48.npz
+--logdir=./logs/GN-GAN-CR_STL10_CNN_0

config/GN-GAN-CR_STL10_RES.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=0
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0004
 --lr_G=0.0002
 --n_dis=5

config/GN-GAN_CELEBAHQ128_RES.txt

@@ -4,7 +4,7 @@
 --batch_size_D=64
 --batch_size_G=128
 --accumulation=1
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0002
 --n_dis=5

config/GN-GAN_CELEBAHQ256_RES.txt

@@ -4,7 +4,7 @@
 --batch_size_D=64
 --batch_size_G=128
 --accumulation=1
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0002
 --n_dis=5

config/GN-GAN_CHURCH256_RES.txt

@@ -4,7 +4,7 @@
 --batch_size_D=64
 --batch_size_G=128
 --accumulation=1
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0002
 --n_dis=5

config/GN-GAN_CIFAR10_BIGGAN.txt

@@ -1,8 +1,8 @@
 --dataset=cifar10.32
 --arch=biggan.32
 --loss=hinge
---total_steps=125000
---lr_decay_start=125000
+--total_steps=75000
+--lr_decay_start=75000
 --batch_size_D=50
 --batch_size_G=50
 --num_workers=8

config/GN-GAN_CIFAR10_BIGGAN_MODULE.txt

@@ -0,0 +1,27 @@
+--dataset=cifar10.32
+--arch=biggan.32
+--loss=hinge
+--total_steps=75000
+--lr_decay_start=75000
+--batch_size_D=50
+--batch_size_G=50
+--num_workers=8
+--lr_D=0.0002
+--lr_G=0.0001
+--betas=0.0
+--betas=0.999
+--n_dis=4
+--z_dim=128
+--cr=0
+--n_classes=10
+
+--ema_decay=0.9999
+--ema_start=1000
+
+--sample_step=500
+--sample_size=64
+--eval_step=5000
+--save_step=20000
+--num_images=50000
+--fid_stats=./stats/cifar10.train.npz
+--logdir=./logs/GN-GAN_CIFAR10_BIGGAN_MODULE_0

config/GN-GAN_CIFAR10_CNN.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=200000
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0002
 --n_dis=1

config/GN-GAN_CIFAR10_CNN_MODULE.txt

@@ -0,0 +1,25 @@
+--dataset=cifar10.32
+--arch=dcgan.32
+--loss=bce
+--total_steps=200000
+--lr_decay_start=200000
+--batch_size_D=64
+--batch_size_G=128
+--num_workers=10
+--lr_D=0.0002
+--lr_G=0.0002
+--n_dis=1
+--z_dim=128
+--cr=0
+--n_classes=1
+
+--ema_decay=0.9999
+--ema_start=0
+
+--sample_step=500
+--sample_size=64
+--eval_step=5000
+--save_step=20000
+--num_images=50000
+--fid_stats=./stats/cifar10.train.npz
+--logdir=./logs/GN-GAN_CIFAR10_CNN_MODULE_0

config/GN-GAN_CIFAR10_RES.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=0
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0004
 --lr_G=0.0002
 --n_dis=5

config/GN-GAN_STL10_CNN.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=200000
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0002
 --lr_G=0.0002
 --n_dis=1

config/GN-GAN_STL10_CNN_MODULE.txt

@@ -0,0 +1,25 @@
+--dataset=stl10.48
+--arch=dcgan.48
+--loss=bce
+--total_steps=200000
+--lr_decay_start=200000
+--batch_size_D=64
+--batch_size_G=128
+--num_workers=10
+--lr_D=0.0002
+--lr_G=0.0002
+--n_dis=1
+--z_dim=128
+--cr=0
+--n_classes=1
+
+--ema_decay=0.9999
+--ema_start=0
+
+--sample_step=500
+--sample_size=64
+--eval_step=5000
+--save_step=20000
+--num_images=50000
+--fid_stats=./stats/stl10.unlabeled.48.npz
+--logdir=./logs/GN-GAN_STL10_CNN_MODULE_0

config/GN-GAN_STL10_RES.txt

@@ -5,7 +5,7 @@
 --lr_decay_start=0
 --batch_size_D=64
 --batch_size_G=128
---num_workers=8
+--num_workers=10
 --lr_D=0.0004
 --lr_G=0.0002
 --n_dis=5

generated/GN-GAN_CIFAR10_CNN_0/Results/results.txt

@@ -0,0 +1 @@
+IS:  7.683(0.082), FID:  21.932