Conditional Generative Adversarial Networks

In the previous chapter, images were randomly generated without respect to classes. Here we shall generated Images of specific classes While Conditional GANs are complex, torchfusion makes this super easy, all you have to do is state the num_classes in the Generator, Discriminator and in the Learner

Use classes in Generator and Discriminator

G = StandardGenerator(output_size=(1,32,32),latent_size=128,num_classes=10)
D = StandardProjectionDiscriminator(input_size=(1,32,32),apply_sigmoid=False,num_classes=10)

Define num_classes in Learner

if __name__ == "__main__":
    learner.train(dataset,num_classes=10,gen_optimizer=g_optim,disc_optimizer=d_optim,save_outputs_interval=500,model_dir="./fashion-gan",latent_size=128,num_epochs=50,batch_log=False)

And thats it ! The full code is below

Putting it all Together

from torchfusion.gan.learners import *
from torchfusion.gan.applications import StandardGenerator,StandardProjectionDiscriminator
from torch.optim import Adam
from torchfusion.datasets import fashionmnist_loader
import torch.cuda as cuda
import torch.nn as nn

G = StandardGenerator(output_size=(1,32,32),latent_size=128,num_classes=10)
D = StandardProjectionDiscriminator(input_size=(1,32,32),apply_sigmoid=False,num_classes=10)

if cuda.is_available():
    G = nn.DataParallel(G.cuda())
    D = nn.DataParallel(D.cuda())

g_optim = Adam(G.parameters(),lr=0.0002,betas=(0.5,0.999))
d_optim = Adam(D.parameters(),lr=0.0002,betas=(0.5,0.999))

dataset = fashionmnist_loader(size=32,batch_size=64)

learner = RStandardGanLearner(G,D)

if __name__ == "__main__":
    learner.train(dataset,num_classes=10,gen_optimizer=g_optim,disc_optimizer=d_optim,save_outputs_interval=500,model_dir="./fashion-gan",latent_size=128,num_epochs=50,batch_log=False)

After just 17 epochs, this produces