my_dcgan.py

#Deep convolutional GANs

#Importing the Libraries
from _future_ import print_function
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
from torch.autograd import Variable

#Setting the hyper-parameters
batchSize=64
imageSize=64

#Creating the transformations
transform = transforms.Compose([transforms.Scale(imageSize), transforms.ToTensor() , transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),])

# Loading the dataset
dataset = dset.CIFAR10(root = './data', download = True, transform = transform) # We download the training set in the ./data folder and we apply the previous transformations on each image.
dataloader = torch.utils.data.DataLoader(dataset, batch_size = batchSize, shuffle = True, num_workers = 2) # We use dataLoader to get the images of the training set batch by batch.

# Defining the weights_init function that takes as input a neural network m and that will initialize all its weights.
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)

#Defining the Generator

class G(nn.Module):
    def _init__(self):
        super(G, self).__init__()#to activate inheritance
        self.main = nn.Sequential(
                nn.ConvTranspose2d(100,512,4,1,0, bias=False),
                nn.BatchNorm2d(512),
                nn.ReLU(True),
                nn.ConvTranspose2d(512,256,4,2,1,bias=False),
                nn.BatchNorm2d(256),
                nn.ReLU(True),
                nn.ConvTranspose2d(256,128,4,2,1,bias=False),
                nn.BatchNorm2d(128),
                nn.ReLU(True),
                nn.ConvTranspose2d(128,64,4,2,1,bias=False),
                nn.BatchNorm2d(64),
                nn.ReLU(True),              
                nn.ConvTranspose2d(64,3,4,2,1,bias=False),
                nn.Tanh()        #output value -1 to +1  
                )
        
    def forward(self, input):#input=noise
        output = self.main(input)
        return output
        
#creating the generator
netG = G()
netG.apply(weights_init)
        
#Defining the Discriminator
class D(nn.Module):
    
    def _init__(self):
        super(D,self).__init__()
        self.main = nn.Sequential(
                nn.Conv2d(3,64,4,2,1,bias=False),
                nn.LeakyReLU(0.2, inplace=True),
                
                nn.Conv2d(64,128,4,2,1,bias=False),
                nn.BatchNorm2d(128),
                nn.LeakyReLU(0.2,inplace=True),
                
                nn.Conv2d(128,256,4,2,1,bias=False),
                nn.BatchNorm2d(256),
                nn.LeakyReLU(0.2,inplace=True),
                
                nn.Conv2d(256,512,4,2,1,bias=False),
                nn.BatchNorm2d(512),
                nn.LeakyReLU(0.2,inplace=True),
                
                nn.Conv2d(512,1,4,1,0,bias=False),
                nn.Sigmoid()          #returns value between 0 to 1
                )
        
    def forward(self,input):#input=img created by generator
        output = self.main(input)
        return output.view(-1)#flattening the result of convolution so all the elements along output are along the same direction ,dimension coressponds to batch size
    
    
#Creating the descriptor
netD = D()
netD.apply(weights_init)


#Training the DCGANs
criterion = nn.BCELoss()
optimizerD = optim.Adam(netD.parameters(), lr=0.002, betas=(0.5,0.999))
optimizerG = optim.Adam(netG.parameters(), lr=0.002, betas=(0.5,0.999))

for epoch in range(25):
    for i, data in enumerate(dataloader, 0):#data=mini batch compose of real imgaes & labels=dc
        
        #1st Step: updating the wt.s of the neural network of the discriminator
        
        netD.zero_grad()
        
        #Training the discriminator with a real image of the dataset
        real, _ = data#real=tensor of image
        input = Variable(real)
        target = Variable(torch.ones(input.size()[0]))
        output = netD(input)
        errD_real = criterion(output,target)
        
        #Training the discriminator with a fake image generated by the generator
        noise = Variable(torch.randn(input.size()[0], 100, 1, 1))
        fake = netG(noise)
        target = Variable(torch.zeros(input.size()[0]))
        output = netD(fake.detach())
        errD_fake = criterion(output,target)
        
        #Bacpropogating the total error
        errD = errD_real + errD_fake
        errD.backward()
        optimizerD.step()
        
        
        #2nd Step:Updating the wts of the neural network of the generator
        
        netG.zero_grad()
        target = Variable(torch.zeros(input.size()[0]))
        output = netD(fake)
        errG = criterion(output,target)
        errG.backward()
        optimizerG.step()
        
        #3rd Step:Printing the loses and saving the real images and the genrated images
        print('[%d/%d][%d/%d]  Loss_D: %.4f  Loss_G: %.4f'% (epoch,25,i,len(dataloader),errD.data[0],errG.data[0]))
        if i % 100 == 0:
            vutils.save_image(real , '%s/real_samples.png' % "./results",normalize=True)
            fake = netG(noise)
            vutils.save_image(fake.data , '%s/fake_samples_epoch_%03d.png' % ("./results",epoch),normalize=True)
            #8hrs