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projector.py
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import argparse
import math
import os
import torch
import torch.nn as nn
from torch import optim
from torch.nn import functional as F
import pickle
from torchvision import transforms
from PIL import Image
from tqdm import tqdm
import lpips
from model import Generator
from op import fused_leaky_relu
import numpy as np
from util import *
import torchvision
def gaussian_loss(v):
# [B, 9088]
loss = (v-gt_mean) @ gt_cov_inv @ (v-gt_mean).transpose(1,0)
return loss.mean()
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('--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=0.05)
parser.add_argument('--noise', type=float, default=0.1)
parser.add_argument('--noise_ramp', type=float, default=0.75)
parser.add_argument('--step', type=int, default=3000)
parser.add_argument('--noise_regularize', type=float, default=1e5)
parser.add_argument('--n_mean_latent', type=int, default=10000)
parser.add_argument('files', metavar='FILES', nargs='+')
args = parser.parse_args()
out_dir = './inversion_codes'
os.makedirs(out_dir, exist_ok=True)
n_mean_latent = args.n_mean_latent
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)
ensure_checkpoint_exists('face.pt')
g_ema.load_state_dict(torch.load('face.pt')['g_ema'], strict=False)
g_ema = g_ema.to(device).eval()
with torch.no_grad():
latent_mean = g_ema.mean_latent(50000)
latent_in = list2style(latent_mean)
# get gaussian stats
if not os.path.isfile('inversion_stats.npz'):
with torch.no_grad():
source = list2style(g_ema.get_latent(torch.randn([10000, 512]).cuda())).cpu().numpy()
gt_mean = source.mean(0)
gt_cov = np.cov(source, rowvar=False)
# We show that style space follows gaussian distribution
# An extension from this work https://arxiv.org/abs/2009.06529
np.savez('inversion_stats.npz', mean=gt_mean, cov=gt_cov)
data = np.load('inversion_stats.npz')
gt_mean = torch.tensor(data['mean']).cuda().view(1,-1).float()
gt_cov_inv = torch.tensor(data['cov']).cuda()
# Only take diagonals
mask = torch.eye(*gt_cov_inv.size()).cuda()
gt_cov_inv = torch.inverse(gt_cov_inv*mask).float()
percept = lpips.LPIPS(net='vgg', spatial=True).to(device)
latent_in.requires_grad = True
optimizer = optim.Adam([latent_in], lr=args.lr, betas=(0.9, 0.999))
pbar = tqdm(range(args.step))
latent_path = []
for i in pbar:
t = i / args.step
lr = get_lr(t, args.lr)
latent_n = latent_in
img_gen, _ = g_ema(style2list(latent_n))
batch, channel, height, width = img_gen.shape
if height > 256:
img_gen = F.interpolate(img_gen, size=(256,256), mode='area')
p_loss = 20*percept(img_gen, imgs).mean()
mse_loss = 1*F.mse_loss(img_gen, imgs)
g_loss = 1e-3*gaussian_loss(latent_n)
loss = p_loss + mse_loss + g_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0:
latent_path.append(latent_in.detach().clone())
pbar.set_description(
(
f'perceptual: {p_loss.item():.4f};'
f' mse: {mse_loss.item():.4f}; gaussian: {g_loss.item():.4f} lr: {lr:.4f}'
)
)
result_file = {}
latent_path.append(latent_in.detach().clone())
img_gen, _ = g_ema(style2list(latent_path[-1]))
filename = f'{out_dir}/{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['latent'] = latent_in[i]
torch.save(result_file, filename)