import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import cv2
import random
import math
import argparse
import torch
from torch.utils import data
from torch.nn import functional as F
from torch import autograd
from torch.nn import init
import torchvision.transforms as transforms
from model.stylegan.op import conv2d_gradfix
from model.encoder.encoders.psp_encoders import GradualStyleEncoder
from model.encoder.align_all_parallel import get_landmark
def visualize(img_arr, dpi):
plt.figure(figsize=(10,10),dpi=dpi)
plt.imshow(((img_arr.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8))
plt.axis('off')
plt.show()
def save_image(img, filename):
tmp = ((img.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)
cv2.imwrite(filename, cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR))
def load_image(filename):
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
])
img = Image.open(filename)
img = transform(img)
return img.unsqueeze(dim=0)
def data_sampler(dataset, shuffle, distributed):
if distributed:
return data.distributed.DistributedSampler(dataset, shuffle=shuffle)
if shuffle:
return data.RandomSampler(dataset)
else:
return data.SequentialSampler(dataset)
def requires_grad(model, flag=True):
for p in model.parameters():
p.requires_grad = flag
def accumulate(model1, model2, decay=0.999):
par1 = dict(model1.named_parameters())
par2 = dict(model2.named_parameters())
for k in par1.keys():
par1[k].data.mul_(decay).add_(par2[k].data, alpha=1 - decay)
def sample_data(loader):
while True:
for batch in loader:
yield batch
def d_logistic_loss(real_pred, fake_pred):
real_loss = F.softplus(-real_pred)
fake_loss = F.softplus(fake_pred)
return real_loss.mean() + fake_loss.mean()
def d_r1_loss(real_pred, real_img):
with conv2d_gradfix.no_weight_gradients():
grad_real, = autograd.grad(
outputs=real_pred.sum(), inputs=real_img, create_graph=True
)
grad_penalty = grad_real.pow(2).reshape(grad_real.shape[0], -1).sum(1).mean()
return grad_penalty
def g_nonsaturating_loss(fake_pred):
loss = F.softplus(-fake_pred).mean()
return loss
def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01):
noise = torch.randn_like(fake_img) / math.sqrt(
fake_img.shape[2] * fake_img.shape[3]
)
grad, = autograd.grad(
outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True
)
path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1))
path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length)
path_penalty = (path_lengths - path_mean).pow(2).mean()
return path_penalty, path_mean.detach(), path_lengths
def make_noise(batch, latent_dim, n_noise, device):
if n_noise == 1:
return torch.randn(batch, latent_dim, device=device)
noises = torch.randn(n_noise, batch, latent_dim, device=device).unbind(0)
return noises
def mixing_noise(batch, latent_dim, prob, device):
if prob > 0 and random.random() < prob:
return make_noise(batch, latent_dim, 2, device)
else:
return [make_noise(batch, latent_dim, 1, device)]
def set_grad_none(model, targets):
for n, p in model.named_parameters():
if n in targets:
p.grad = None
def weights_init(m):
classname = m.__class__.__name__
if classname.find('BatchNorm2d') != -1:
if hasattr(m, 'weight') and m.weight is not None:
init.normal_(m.weight.data, 1.0, 0.02)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
def load_psp_standalone(checkpoint_path, device='cuda'):
ckpt = torch.load(checkpoint_path, map_location='cpu')
opts = ckpt['opts']
if 'output_size' not in opts:
opts['output_size'] = 1024
opts['n_styles'] = int(math.log(opts['output_size'], 2)) * 2 - 2
opts = argparse.Namespace(**opts)
psp = GradualStyleEncoder(50, 'ir_se', opts)
psp_dict = {k.replace('encoder.', ''): v for k, v in ckpt['state_dict'].items() if k.startswith('encoder.')}
psp.load_state_dict(psp_dict)
psp.eval()
psp = psp.to(device)
latent_avg = ckpt['latent_avg'].to(device)
def add_latent_avg(model, inputs, outputs):
return outputs + latent_avg.repeat(outputs.shape[0], 1, 1)
psp.register_forward_hook(add_latent_avg)
return psp
def get_video_crop_parameter(filepath, predictor, padding=[200,200,200,200]):
if type(filepath) == str:
img = dlib.load_rgb_image(filepath)
else:
img = filepath
lm = get_landmark(img, predictor)
if lm is None:
return None
lm_chin = lm[0 : 17] # left-right
lm_eyebrow_left = lm[17 : 22] # left-right
lm_eyebrow_right = lm[22 : 27] # left-right
lm_nose = lm[27 : 31] # top-down
lm_nostrils = lm[31 : 36] # top-down
lm_eye_left = lm[36 : 42] # left-clockwise
lm_eye_right = lm[42 : 48] # left-clockwise
lm_mouth_outer = lm[48 : 60] # left-clockwise
lm_mouth_inner = lm[60 : 68] # left-clockwise
scale = 64. / (np.mean(lm_eye_right[:,0])-np.mean(lm_eye_left[:,0]))
center = ((np.mean(lm_eye_right, axis=0)+np.mean(lm_eye_left, axis=0)) / 2) * scale
h, w = round(img.shape[0] * scale), round(img.shape[1] * scale)
left = max(round(center[0] - padding[0]), 0) // 8 * 8
right = min(round(center[0] + padding[1]), w) // 8 * 8
top = max(round(center[1] - padding[2]), 0) // 8 * 8
bottom = min(round(center[1] + padding[3]), h) // 8 * 8
return h,w,top,bottom,left,right,scale
def tensor2cv2(img):
tmp = ((img.cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)
return cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR)
# get parameters from the stylegan and mark them with their layers
def gather_params(G):
params = dict(
[(res, {}) for res in range(18)] + [("others", {})]
)
for n, p in sorted(list(G.named_buffers()) + list(G.named_parameters())):
if n.startswith("convs"):
layer = int(n.split(".")[1]) + 1
params[layer][n] = p
elif n.startswith("to_rgbs"):
layer = int(n.split(".")[1]) * 2 + 3
params[layer][n] = p
elif n.startswith("conv1"):
params[0][n] = p
elif n.startswith("to_rgb1"):
params[1][n] = p
else:
params["others"][n] = p
return params
# blend the ffhq stylegan model and the finetuned model for toonify
# see ``Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains''
def blend_models(G_low, G_high, weight=[1]*7+[0]*11):
params_low = gather_params(G_low)
params_high = gather_params(G_high)
for res in range(18):
for n, p in params_high[res].items():
params_high[res][n] = params_high[res][n] * (1-weight[res]) + params_low[res][n] * weight[res]
state_dict = {}
for _, p in params_high.items():
state_dict.update(p)
return state_dict