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import argparse
import math
import os
import torch
from torch import optim
from torch.nn import functional as F
from torchvision import transforms
from PIL import Image
from tqdm import tqdm
import lpips
from model import Generator
def noise_regularize(noises):
loss = 0
for noise in noises:
size = noise.shape[2]
while True:
loss = (
loss
+ (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2)
+ (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2)
)
if size <= 8:
break
noise = noise.reshape([1, 1, size // 2, 2, size // 2, 2])
noise = noise.mean([3, 5])
size //= 2
return loss
def noise_normalize_(noises):
for noise in noises:
mean = noise.mean()
std = noise.std()
noise.data.add_(-mean).div_(std)
def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
lr_ramp = min(1, (1 - t) / rampdown)
lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
lr_ramp = lr_ramp * min(1, t / rampup)
return initial_lr * lr_ramp
def latent_noise(latent, strength):
noise = torch.randn_like(latent) * strength
return latent + noise
def make_image(tensor):
return (
tensor.detach()
.clamp_(min=-1, max=1)
.add(1)
.div_(2)
.mul(255)
.type(torch.uint8)
.permute(0, 2, 3, 1)
.to('cpu')
.numpy()
)
if __name__ == '__main__':
device = 'cuda'
parser = argparse.ArgumentParser()
parser.add_argument('--ckpt', type=str, required=True)
parser.add_argument('--size', type=int, default=256)
parser.add_argument('--lr_rampup', type=float, default=0.05)
parser.add_argument('--lr_rampdown', type=float, default=0.25)
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--noise', type=float, default=0.05)
parser.add_argument('--noise_ramp', type=float, default=0.75)
parser.add_argument('--step', type=int, default=1000)
parser.add_argument('--noise_regularize', type=float, default=1e5)
parser.add_argument('--mse', type=float, default=0)
parser.add_argument('--w_plus', action='store_true')
parser.add_argument('files', metavar='FILES', nargs='+')
args = parser.parse_args()
n_mean_latent = 10000
resize = min(args.size, 256)
transform = transforms.Compose(
[
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')
)
noises = g_ema.make_noise()
latent_in = latent_mean.detach().clone().unsqueeze(0).repeat(2, 1)
if args.w_plus:
latent_in = latent_in.unsqueeze(1).repeat(1, g_ema.n_latent, 1)
latent_in.requires_grad = True
for noise in noises:
noise.requires_grad = True
optimizer = optim.Adam([latent_in] + noises, lr=args.lr)
pbar = tqdm(range(args.step))
latent_path = []
for i in pbar:
t = i / args.step
lr = get_lr(t, args.lr)
optimizer.param_groups[0]['lr'] = lr
noise_strength = latent_std * args.noise * max(0, 1 - t / args.noise_ramp) ** 2
latent_n = latent_noise(latent_in, noise_strength.item())
img_gen, _ = g_ema([latent_n], input_is_latent=True, noise=noises)
batch, channel, height, width = img_gen.shape
if height > 256:
factor = height // 256
img_gen = img_gen.reshape(
batch, channel, height // factor, factor, width // factor, factor
)
img_gen = img_gen.mean([3, 5])
p_loss = percept(img_gen, imgs).sum()
n_loss = noise_regularize(noises)
mse_loss = F.mse_loss(img_gen, imgs)
loss = p_loss + args.noise_regularize * n_loss + args.mse * mse_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
noise_normalize_(noises)
if (i + 1) % 100 == 0:
latent_path.append(latent_in.detach().clone())
pbar.set_description(
(
f'perceptual: {p_loss.item():.4f}; noise regularize: {n_loss.item():.4f};'
f' mse: {mse_loss.item():.4f}; lr: {lr:.4f}'
)
)
result_file = {'noises': noises}
img_gen, _ = g_ema([latent_path[-1]], input_is_latent=True, noise=noises)
filename = os.path.splitext(os.path.basename(args.files[0]))[0] + '.pt'
img_ar = make_image(img_gen)
for i, input_name in enumerate(args.files):
result_file[input_name] = {'img': img_gen[i], 'latent': latent_in[i]}
img_name = os.path.splitext(os.path.basename(input_name))[0] + '-project.png'
pil_img = Image.fromarray(img_ar[i])
pil_img.save(img_name)
torch.save(result_file, filename)
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