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style_transfer.py
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style_transfer.py
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import os
import numpy as np
import torch
from util import save_image, load_image
import argparse
from argparse import Namespace
from torchvision import transforms
from torch.nn import functional as F
import torchvision
from model.dualstylegan import DualStyleGAN
from model.encoder.psp import pSp
class TestOptions():
def __init__(self):
self.parser = argparse.ArgumentParser(description="Exemplar-Based Style Transfer")
self.parser.add_argument("--content", type=str, default='./data/content/081680.jpg', help="path of the content image")
self.parser.add_argument("--style", type=str, default='cartoon', help="target style type")
self.parser.add_argument("--style_id", type=int, default=53, help="the id of the style image")
self.parser.add_argument("--truncation", type=float, default=0.75, help="truncation for intrinsic style code (content)")
self.parser.add_argument("--weight", type=float, nargs=18, default=[0.75]*7+[1]*11, help="weight of the extrinsic style")
self.parser.add_argument("--name", type=str, default='cartoon_transfer', help="filename to save the generated images")
self.parser.add_argument("--preserve_color", action="store_true", help="preserve the color of the content image")
self.parser.add_argument("--model_path", type=str, default='./checkpoint/', help="path of the saved models")
self.parser.add_argument("--model_name", type=str, default='generator.pt', help="name of the saved dualstylegan")
self.parser.add_argument("--output_path", type=str, default='./output/', help="path of the output images")
self.parser.add_argument("--data_path", type=str, default='./data/', help="path of dataset")
self.parser.add_argument("--align_face", action="store_true", help="apply face alignment to the content image")
self.parser.add_argument("--exstyle_name", type=str, default=None, help="name of the extrinsic style codes")
def parse(self):
self.opt = self.parser.parse_args()
if self.opt.exstyle_name is None:
if os.path.exists(os.path.join(self.opt.model_path, self.opt.style, 'refined_exstyle_code.npy')):
self.opt.exstyle_name = 'refined_exstyle_code.npy'
else:
self.opt.exstyle_name = 'exstyle_code.npy'
args = vars(self.opt)
print('Load options')
for name, value in sorted(args.items()):
print('%s: %s' % (str(name), str(value)))
return self.opt
def run_alignment(args):
import dlib
from model.encoder.align_all_parallel import align_face
modelname = os.path.join(args.model_path, 'shape_predictor_68_face_landmarks.dat')
if not os.path.exists(modelname):
import wget, bz2
wget.download('http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2', modelname+'.bz2')
zipfile = bz2.BZ2File(modelname+'.bz2')
data = zipfile.read()
open(modelname, 'wb').write(data)
predictor = dlib.shape_predictor(modelname)
aligned_image = align_face(filepath=args.content, predictor=predictor)
return aligned_image
if __name__ == "__main__":
device = "cuda"
parser = TestOptions()
args = parser.parse()
print('*'*98)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
])
generator = DualStyleGAN(1024, 512, 8, 2, res_index=6).to(device)
generator.eval()
ckpt = torch.load(os.path.join(args.model_path, args.style, args.model_name))
generator.load_state_dict(ckpt["g_ema"])
model_path = os.path.join(args.model_path, 'encoder.pt')
ckpt = torch.load(model_path, map_location='cpu')
opts = ckpt['opts']
opts['checkpoint_path'] = model_path
opts = Namespace(**opts)
encoder = pSp(opts)
encoder.eval()
encoder.to(device)
exstyles = np.load(os.path.join(args.model_path, args.style, args.exstyle_name), allow_pickle='TRUE').item()
print('Load models successfully!')
with torch.no_grad():
viz = []
# load content image
if args.align_face:
I = transform(run_alignment(args)).unsqueeze(dim=0).to(device)
I = F.adaptive_avg_pool2d(I, 1024)
else:
I = load_image(args.content).to(device)
viz += [I]
# reconstructed content image and its intrinsic style code
img_rec, instyle = encoder(F.adaptive_avg_pool2d(I, 256), randomize_noise=False, return_latents=True,
z_plus_latent=True, return_z_plus_latent=True, resize=False)
img_rec = torch.clamp(img_rec.detach(), -1, 1)
viz += [img_rec]
stylename = list(exstyles.keys())[args.style_id]
latent = torch.tensor(exstyles[stylename]).to(device)
if args.preserve_color:
latent[:,7:18] = instyle[:,7:18]
# extrinsic styte code
exstyle = generator.generator.style(latent.reshape(latent.shape[0]*latent.shape[1], latent.shape[2])).reshape(latent.shape)
# load style image if it exists
S = None
if os.path.exists(os.path.join(args.data_path, args.style, 'images/train', stylename)):
S = load_image(os.path.join(args.data_path, args.style, 'images/train', stylename)).to(device)
viz += [S]
# style transfer
# input_is_latent: instyle is not in W space
# z_plus_latent: instyle is in Z+ space
# use_res: use extrinsic style path, or the style is not transferred
# interp_weights: weight vector for style combination of two paths
img_gen, _ = generator([instyle], exstyle, input_is_latent=False, z_plus_latent=True,
truncation=args.truncation, truncation_latent=0, use_res=True, interp_weights=args.weight)
img_gen = torch.clamp(img_gen.detach(), -1, 1)
viz += [img_gen]
print('Generate images successfully!')
save_name = args.name+'_%d_%s'%(args.style_id, os.path.basename(args.content).split('.')[0])
save_image(torchvision.utils.make_grid(F.adaptive_avg_pool2d(torch.cat(viz, dim=0), 256), 4, 2).cpu(),
os.path.join(args.output_path, save_name+'_overview.jpg'))
save_image(img_gen[0].cpu(), os.path.join(args.output_path, save_name+'.jpg'))
print('Save images successfully!')