default="sample_strip_config.yaml")
args = parser.parse_args()
args.latent = 512
args.n_mlp = 8
yaml_config = {}
with open(args.config, 'r') as stream:
try:
yaml_config = yaml.load(stream)
except yaml.YAMLError as exc:
print(exc)
g_ema = Generator(args.size,
args.latent,
args.n_mlp,
channel_multiplier=args.channel_multiplier).to(device)
new_state_dict = g_ema.state_dict()
checkpoint = torch.load(args.ckpt)
ext_state_dict = torch.load(args.ckpt)['g_ema']
new_state_dict.update(ext_state_dict)
g_ema.load_state_dict(new_state_dict)
g_ema.eval()
g_ema.to(device)
if args.truncation < 1:
with torch.no_grad():
mean_latent = g_ema.mean_latent(args.truncation_mean)
else:
mean_latent = None
transforms.Resize(resize),
transforms.CenterCrop(resize),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
)
imgs = []
for imgfile in args.files:
img = transform(Image.open(imgfile).convert('RGB'))
imgs.append(img)
imgs = torch.stack(imgs, 0).to(device)
g_ema = Generator(args.size, 512, 8)
g_ema.load_state_dict(torch.load(args.ckpt)['g_ema'], strict=False)
g_ema.eval()
g_ema = g_ema.to(device)
with torch.no_grad():
noise_sample = torch.randn(n_mean_latent, 512, device=device)
latent_out = g_ema.style(noise_sample)
latent_mean = latent_out.mean(0)
latent_std = ((latent_out - latent_mean).pow(2).sum() / n_mean_latent) ** 0.5
percept = lpips.PerceptualLoss(
model='net-lin', net='vgg', use_gpu=device.startswith('cuda')
)