json.dump(vars(args), open(os.path.join(args.save_path, 'args.json'), 'w'))
if args.use_visdom:
import viz
viz.setup(args.server, args.port, env=args.exp_name, use_tanh=True)
else:
viz = MockVisdom()
encoder = SNEncoder if args.use_sn else Encoder
train_loader, test_loader, shape = load_cifar(args.dataset_loc,
args.batch_size,
args.test_batch_size)
gan = Decoder(shape, args.gen_h_size, args.z_size, True, nn.ReLU(True),
4).cuda()
discriminator = encoder(shape, args.disc_h_size, 1, True,
nn.LeakyReLU(0.1, True), 4).cuda()
gan.apply(weights_init)
discriminator.apply(weights_init)
weight_clip = weight_cliping(0.05)
generator_optimizer = optim.Adam(gan.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2))
discriminator_optimizer = optim.Adam(discriminator.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2))
t = 0
for i in range(201):
print('Epoch: %s' % i)
gan.train()
discriminator.train()
elif classname.find("Conv1d") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
elif classname.find("BatchNorm1d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
# initialize generator and discriminator weights and device
if USE_CUDA:
print("Using", torch.cuda.device_count(), "GPUs")
gen_net = torch.nn.DataParallel(gen_net)
gen_net.to(device)
gen_net.apply(weights_init_normal)
dis_net = torch.nn.DataParallel(dis_net)
dis_net.to(device)
dis_net.apply(weights_init_normal)
if opt.netG != '':
gen_net.load_state_dict(
torch.load(
opt.netG,
map_location=lambda storage, location: storage)['state_dict'])
resume_epoch = torch.load(opt.netG)['epoch']
#print('G loaded')
if opt.netD != '':
dis_net.load_state_dict(
torch.load(
opt.netD,
