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why generator batch_size will multiply two？
Thank you！！

Why is the in_channels and output_channels in ResGenerator and ResDiscriminator should be set to the same instead of gradually decreasing or increasing

Hi, I learned SNGAN (https://arxiv.org/pdf/1802.05957.pdf) with your code.
Assume that "Table 5: ResNet architectures for STL-10 dataset." is referred to the code above, I don't know why there is an "optimized block" whereas I cannot find it within all the paper.
So could you tell me what it is and where I can learn it? THX a lot!

Why is ResSNGAN's FID performance so different? Tf is officially 27, Torch is 18

Thank you sooooo much! :)

HI, I want to test the neural networks below:

And I tried to change the model files like below:

[class Generator(nn.Module):
def init(self, z_dim, M):
super(Generator, self).init()
self.z_dim = z_dim
self.main = nn.Sequential(
nn.ConvTranspose2d(self.z_dim, 256, M, 1, 0, bias=False), # 4, 4
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.ConvTranspose2d(64, 3, 4, stride=2, padding=1, bias=False),
nn.Tanh()
)

def forward(self, z):
    return self.main(z.view(-1, self.z_dim, 1, 1))

class Discriminator(nn.Module):
def init(self, M):
super(Discriminator, self).init()

    self.main = nn.Sequential(
        # 32
        nn.Conv2d(3, 32, 5, 2, 2, bias=False),
        nn.LeakyReLU(0.2, inplace=True),
        # 16
        nn.Conv2d(32, 64, 5, 2, 2, bias=False),
        nn.LeakyReLU(0.2, inplace=True),
        nn.BatchNorm2d(64),
        # 8
        nn.Conv2d(64, 128, 5, 2, 2, bias=False),
        nn.LeakyReLU(0.2, inplace=True),
        nn.BatchNorm2d(128),
        # 4
        nn.Conv2d(128, 10, 5, 2, 2, bias=False), #128*2*2
        nn.ReLU(True),

    )

    self.linear = nn.Linear(M // 16 * M // 16 * 10, 1)

def forward(self, x):
    x = self.main(x)
    x = torch.flatten(x, start_dim=1)
    x = self.linear(x)
    return x

class Generator32(Generator):
def init(self, z_dim):
super().init(z_dim, M=3)

class Generator48(Generator):
def init(self, z_dim):
super().init(z_dim, M=4)

class Discriminator32(Discriminator):
def init(self):
super().init(M=16)

class Discriminator48(Discriminator):
def init(self):
super().init(M=48)]

it seems I have to change the M and other parameters . How can I do? Thank you in advance

everything = np.fromfile(f, dtype=np.uint8)

numpy.core._exceptions.MemoryError: Unable to allocate 2.57 GiB for an array with shape (2764800000,) and data type uint8

HI!!
When I want to use stl10 dataset error happended.....
I have changed the file like below

flags.DEFINE_enum('dataset', 'stl10', ['cifar10', 'stl10'], "dataset")
flags.DEFINE_string('fid_cache', './stats/stl10.unlabeled.48.npz', 'FID cache')

可是它說沒有內存的樣子。。我該怎麼辦。。求指點。。
謝謝您

The link you gaving about cifar10_stats.npz could not be downloaded

My experiment on stl10 doesn't seem to work very well

Hello, thanks for creating this repo and I found it very helpful for me to play with basic GAN structures. I tried to replicate the reported performance by running the code with the following setups

python wgan.py
python wgan.py --arch=cnn32 --logdir=./logs/WGAN_CIFAR10_CNN32/
python wgangp.py

I got FID plot as follows. The FID of WGAN on CIFAR10 is ~80, which is much larger than the reported 33.27. The FID on WGAN(RES) is even worse. I was wondering how I could replicate the 33 FID. Also I am pretty confused how ResNet is performing worse than CNN. It'd be helpful if you can give any hints. Thanks!

Hi! I am wandering what is the effect of adding uniform noise?

Hi! Dear Yi-Lun
I want to epoch 200 times or 500 times because 10k or 50k will cost a long time to run.
I changed the sample_step=50 and total_steps=200
should I change num_images=50000 as num_images = 50? I guess it should be smaller than epoch times? or it doens't matter ?
Thank you in advance:)

--arch=cnn32
--batch_size=128
--dataset=cifar10
--fid_cache=./stats/cifar10.train.npz
--logdir=./logs/SNGAN_CIFAR10_CNN
--loss=hinge
--lr_D=0.0002
--lr_G=0.0002
--n_dis=1
--num_images=50
--record
--sample_step=50
--sample_size=64
--seed=0
--total_steps=500
--z_dim=100