help='output directory for samples')
parser.add_argument('--path',
type=str,
default='./stylegan-ffhq-1024px-new.params',
help='path to checkpoint file')
args = parser.parse_args()
if args.gpu_id == '-1':
device = mx.cpu()
else:
device = mx.gpu(int(args.gpu_id.strip()))
generator = StyledGenerator(code_dim=512)
generator.initialize()
generator.collect_params().reset_ctx(device)
generator.load_parameters(args.path, ctx=device)
mean_style = get_mean_style(generator, device)
step = int(math.log(args.size, 2)) - 2
imgs = sample(generator, step, mean_style, args.n_sample, device)
if not os.path.isdir(args.out_dir):
os.makedirs(args.out_dir)
for i in range(args.n_sample):
save_image(imgs[i],
os.path.join(args.out_dir, 'sample_{}.png'.format(i)),
normalize=True,
img_range=(-1, 1))
'learning_rate': args.lr_default,
'beta1': 0.0,
'beta2': 0.99
},
kvstore='local')
g_running = StyledGenerator(code_size)
g_running.initialize(ctx=mx.gpu(0))
g_running.collect_params().reset_ctx(mx.gpu(0))
requires_grad(g_running, False)
if args.ckpt_g:
g_running.load_params(args.ckpt_g_running,
ctx=mx.gpu(),
allow_missing=True)
generator.load_parameters(args.ckpt_g, ctx=context, allow_missing=True)
discriminator.load_parameters(args.ckpt_d,
ctx=context,
allow_missing=True)
accumulate(g_running, generator, 0)
transform = transforms.Compose([
transforms.RandomFlipLeftRight(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
])
dataset = MultiResolutionDataset(args.path, transform)
if args.sched:
