###### Definition of variables ######
# Networks
netG_A2B = Generator(opt.input_nc, opt.output_nc)
netG_B2A = Generator(opt.output_nc, opt.input_nc)
netD_A = Discriminator(opt.input_nc)
netD_B = Discriminator(opt.output_nc)
if opt.cuda:
netG_A2B.cuda()
netG_B2A.cuda()
netD_A.cuda()
netD_B.cuda()
netG_A2B.apply(weights_init_normal)
netG_B2A.apply(weights_init_normal)
netD_A.apply(weights_init_normal)
netD_B.apply(weights_init_normal)
# Lossess
criterion_GAN = torch.nn.MSELoss()
criterion_cycle = torch.nn.L1Loss()
criterion_identity = torch.nn.L1Loss()
##
criterion_classification = torch.nn.BCELoss()
# Optimizers & LR schedulers
optimizer_G = torch.optim.Adam(itertools.chain(netG_A2B.parameters(),
netG_B2A.parameters()),
lr=opt.lr,
betas=(0.5, 0.999))
device = torch.device("cuda:0" if opt.cuda else "cpu")
ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)
generator = Generator(nz, nc, ngf, opt.imageSize, ngpu).to(device)
generator.apply(weights_init)
if opt.generator != "":
generator.load_state_dict(torch.load(opt.generator))
print(generator)
discriminator = Discriminator(nc, ndf, opt.imageSize, ngpu).to(device)
discriminator.apply(weights_init)
if opt.discriminator != "":
discriminator.load_state_dict(torch.load(opt.discriminator))
print(discriminator)
# setup optimizer
optimizerD = optim.Adam(
discriminator.parameters(), lr=opt.lr_d, betas=(opt.beta1, 0.999)
)
optimizerG = optim.Adam(generator.parameters(), lr=opt.lr_g, betas=(opt.beta1, 0.999))
fixed_noise = (
torch.from_numpy(truncated_noise_sample(batch_size=64, dim_z=nz, truncation=0.4))
.view(64, nz, 1, 1)
.to(device)
)