make_folder(genImages)
# Plot some training images
real_batch = next(iter(dataloader))
fig = plt.figure(figsize=(8, 8))
plt.axis("off")
plt.title("Training Images")
plt.imshow(
np.transpose(
vutils.make_grid(real_batch[0][0:64], padding=2, normalize=True),
(1, 2, 0)))
plt.show()
fig.savefig('genImages/trainImages-ts{}.png'.format(int(time.time())))
#Discriminator
discriminator = Discriminator.DNet(ngpu).to(device)
# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
discriminator = nn.DataParallel(discriminator, list(range(ngpu)))
#-------------------------------------#
#Generator
#Generating images from noise
generator = Generator.GNet(ngpu).to(device)
# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
generator = nn.DataParallel(generator, list(range(ngpu)))
gimageList = []
fixedNoise = torch.randn(8, 100, 1, 1, device=device)
