'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:
args.lr = {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003}
args.batch = {
