/
invert_gan.py
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/
invert_gan.py
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import os
import argparse
import numpy as np
from PIL import Image
from tqdm import tqdm
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from lpips import LPIPS
from model import Generator
parser = argparse.ArgumentParser('Invertor')
# training params
parser.add_argument('--expname', type=str, default='exp1', help='experiment name')
parser.add_argument('--expdir', type=str, default='exps', help='dirs of experiments')
#parser.add_argument('--imagename', type=str, required=True, help='input image name')
parser.add_argument('--stylegan2_path', type=str, default='data/stylegan2-ffhq-config-f.pt', help='path of pretrianed stylegan model')
parser.add_argument('--iter_num', type=int, default=4002, help='iteration steps')
parser.add_argument('--learning_rate', type=float, default=1e-1, help='learning rate')
parser.add_argument('--image_dir', type=str, default='data/', help='path to inverted images')
parser.add_argument('--latent_dir', type=str, default='latent/', help='path to inverted images')
args = parser.parse_args()
def preprocess(images, channel_order='RGB'):
"""Preprocesses the input images if needed.
This function assumes the input numpy array is with shape [batch_size,
height, width, channel]. Here, `channel = 3` for color image and
`channel = 1` for grayscale image. The returned images are with shape
[batch_size, channel, height, width].
NOTE: The channel order of input images is always assumed as `RGB`.
Args:
images: The raw inputs with dtype `numpy.uint8` and range [0, 255].
Returns:
The preprocessed images with dtype `numpy.float32` and range
[-1, 1].
"""
# input : numpy, np.uint8, 0~255, RGB, BHWC
# output : numpy, np.float32, -1~1, RGB, BCHW
image_channels = 3
max_val = 1.0
min_val = -1.0
if image_channels == 3 and channel_order == 'BGR':
images = images[:, :, :, ::-1]
images = images / 255.0 * (max_val - min_val) + min_val
images = images.astype(np.float32).transpose(0, 3, 1, 2)
return images
class Invertor():
def __init__(self,options):
self.options = options
self.Up = nn.Upsample(256)
self.device = torch.device('cuda')
self.exppath = os.path.join(self.options.expdir, self.options.expname)
os.makedirs(self.exppath, exist_ok=True)
# load stylegan2
self.G = Generator(256, 512, 8, channel_multiplier=2
).to(self.device)
ckpt_source = torch.load(args.stylegan2_path, map_location=lambda storage, loc: storage)
self.G.load_state_dict(ckpt_source["g_ema"], strict=False)
with torch.no_grad():
self.find_avg_latent()
self.avg_latent = self.avg_latent.view(1, 1, -1).clone()
self.avg_latent = self.avg_latent.repeat(1, self.G.n_latent, 1)
# setup image transform
self.image_transforms = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor()])
# setup criterion
self.lpips_criterion = LPIPS(net='vgg').to(self.device).eval()
self.MSE_criterion = nn.MSELoss().to(self.device)
def find_avg_latent(self,):
self.avg_latent = torch.randn((5120,512)).to(self.device)
with torch.no_grad():
self.avg_latent = torch.mean(self.G.style(self.avg_latent), dim=0, keepdim=True)
def write_summaries(self, results, step):
for k in results.items():
if 'loss' in k:
self.logger.add_scalar(f'{k}', results[k], step)
elif 'image' in k:
self.logger.add_images(f'{k}', results[k], step)
return
def read_image(self, imagename):
image = cv2.imread(imagename)
image_target = torch.from_numpy(preprocess(image[np.newaxis, :], channel_order='BGR')).cuda()
image_target = self.Up(image_target)
return image_target
def tensor2numpy(self, images):
""" we assume the shape of image is (1, C, H, W), and it's a cuda pytorch tensor
"""
images = torch.clamp(images.detach(), min=-1, max=1)
images = ((images+1)/2)*255
images = images.permute(0,2,3,1).detach().cpu().numpy().astype('uint8')
return images
def initial_latentcode(self, latent_type):
if latent_type == 'randn':
return torch.randn((1,18,512)).to(self.device)
elif latent_type == 'zero':
return torch.zeros((1,18,512)).to(self.device)
elif latent_type == 'mean':
return torch.from_numpy(np.load('data/mean_latent.npy')).float().to(self.device).unsqueeze(0)
elif latent_type == 'mean_ckpt':
return self.mean_latent
else:
raise NotImplementedError
def run(self,):
image_list = os.listdir(args.image_dir)
for i in range(len(image_list)):
print(args.image_dir + image_list[i])
image = self.read_image(args.image_dir + image_list[i])
self.avg_latent_sub = self.avg_latent.clone()
self.avg_latent_sub.requires_grad = True
optimizer = torch.optim.Adam([self.avg_latent_sub], lr=self.options.learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1000, gamma=0.5)
for step in tqdm(range(self.options.iter_num)):
decoded_image, _ = self.G([self.avg_latent_sub], input_is_latent=True, return_latents=True, randomize_noise=False)
lpipsloss = self.lpips_criterion(decoded_image, image)
mseloss = self.MSE_criterion(decoded_image, image)
loss = lpipsloss + mseloss
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
dataitems = {'lpipsloss': lpipsloss,
'mseloss': mseloss}
if step % 500 == 0 or step == self.options.iter_num:
self.write_summaries(dataitems, step)
#print(step)
decoded_image_np = self.tensor2numpy(decoded_image)
decoded_image_np = Image.fromarray(decoded_image_np[0])
decoded_image_np.save(args.latent_dir + f'images/{i}.png')
np.save(args.latent_dir + f'latent/{i}_latentcode.npy', self.avg_latent_sub.detach().cpu().numpy())
print('Finished')
if __name__ == '__main__':
options = parser.parse_args()
invertor = Invertor(options)
invertor.run()