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train_dcgan.py
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# the code for DCGAN was sourced from https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
import argparse
from dcgan.dcgan import *
def trainDCGAN(args):
manualSeed = 999
random.seed(manualSeed)
torch.manual_seed(manualSeed)
dataroot = "./data/%s_preprocessed/train"%args.dataset
save_path= "./trained_models/%s/dcgan"%args.dataset
workers = 2
batch_size = args.batchsize
image_size = args.size
nz = 100
num_epochs = args.epochs
lr = args.lr
beta1 = 0.5
ngpu = 1
dataset = dset.ImageFolder(root=dataroot,
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=True, num_workers=workers)
device = args.device
real_batch = next(iter(dataloader))
netG = Generator(ngpu).to(device)
netG.apply(weights_init)
if os.path.exists(save_path+"/dcgan_G.pt"):
netG.load_state_dict(torch.load(save_path+"/dcgan_G.pt"))
print("Loading weights of G")
netD = Discriminator(ngpu).to(device)
netD.apply(weights_init)
if os.path.exists(save_path+"/dcgan_D.pt"):
netD.load_state_dict(torch.load(save_path+"/dcgan_D.pt"))
print("Loading weights of D")
criterion = nn.BCELoss()
fixed_noise = torch.randn(64, nz, 1, 1, device=device)
real_label = 1
fake_label = 0
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
img_list = []
G_losses = []
D_losses = []
iters = 0
print("Starting Training Loop...")
for epoch in range(num_epochs):
for i, data in enumerate(dataloader, 0):
netD.zero_grad()
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, device=device)
output = netD(real_cpu).view(-1)
errD_real = criterion(output, label)
errD_real.backward()
D_x = output.mean().item()
noise = torch.randn(b_size, nz, 1, 1, device=device)
fake = netG(noise)
label.fill_(fake_label)
output = netD(fake.detach()).view(-1)
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.mean().item()
errD = errD_real + errD_fake
optimizerD.step()
netG.zero_grad()
label.fill_(real_label)
output = netD(fake).view(-1)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.mean().item()
optimizerG.step()
# Output training stats
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(dataloader),
errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
G_losses.append(errG.item())
D_losses.append(errD.item())
if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
img_list.append(vutils.make_grid(fake, padding=2, normalize=True))
iters += 1
if i%5:
torch.save(netG.state_dict(), save_path+"/dcgan_G.pt")
torch.save(netD.state_dict(), save_path+"/dcgan_D.pt")
plt.figure(figsize=(10,5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses,label="G")
plt.plot(D_losses,label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()
fig = plt.figure(figsize=(8,8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)
HTML(ani.to_jshtml())
real_batch = next(iter(dataloader))
# Plot the real images
plt.figure(figsize=(15,15))
plt.subplot(1,2,1)
plt.axis("off")
plt.title("Real Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=5, normalize=True).cpu(),(1,2,0)))
# Plot the fake images from the last epoch
plt.subplot(1,2,2)
plt.axis("off")
plt.title("Fake Images")
plt.imshow(np.transpose(img_list[-1],(1,2,0)))
plt.show()
torch.save(netG.state_dict(), save_path+"/dcgan_G.pt")
torch.save(netD.state_dict(), save_path+"/dcgan_D.pt")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='train glow network')
parser.add_argument('-dataset',type=str,help='the dataset to train the model on', default='celeba')
parser.add_argument('-batchsize',type=bool,help='batch size for training',default=6)
parser.add_argument('-size',type=int,help='images will be resized to this dimension',default=64)
parser.add_argument('-lr',type=float,help='learning rate for training',default=0.0002)
parser.add_argument('-epochs',type=int,help='epochs to train for',default=500)
parser.add_argument('-device',type=str,help='device to use',default="cuda")
args = parser.parse_args()
trainDCGAN(args)