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| ''' | |
| @paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) | |
| @author: yangxy ([email protected]) | |
| ''' | |
| import torch | |
| import os | |
| import cv2 | |
| import glob | |
| import numpy as np | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from torchvision import transforms, utils | |
| from gpen_model import FullGenerator | |
| class FaceGAN(object): | |
| def __init__(self, base_dir='./', size=512, model=None, channel_multiplier=2, narrow=1, key=None, is_norm=True, device='cuda'): | |
| self.mfile = os.path.join(base_dir, 'weights', model+'.pth') | |
| self.n_mlp = 8 | |
| self.device = device | |
| self.is_norm = is_norm | |
| self.resolution = size | |
| self.key = key | |
| self.load_model(channel_multiplier, narrow) | |
| def load_model(self, channel_multiplier=2, narrow=1): | |
| self.model = FullGenerator(self.resolution, 512, self.n_mlp, channel_multiplier, narrow=narrow, device=self.device) | |
| pretrained_dict = torch.load(self.mfile, map_location=torch.device('cpu')) | |
| if self.key is not None: pretrained_dict = pretrained_dict[self.key] | |
| self.model.load_state_dict(pretrained_dict) | |
| self.model.to(self.device) | |
| self.model.eval() | |
| def process(self, img): | |
| img = cv2.resize(img, (self.resolution, self.resolution)) | |
| img_t = self.img2tensor(img) | |
| with torch.no_grad(): | |
| out, __ = self.model(img_t) | |
| out = self.tensor2img(out) | |
| return out | |
| def img2tensor(self, img): | |
| img_t = torch.from_numpy(img).to(self.device)/255. | |
| if self.is_norm: | |
| img_t = (img_t - 0.5) / 0.5 | |
| img_t = img_t.permute(2, 0, 1).unsqueeze(0).flip(1) # BGR->RGB | |
| return img_t | |
| def tensor2img(self, img_t, pmax=255.0, imtype=np.uint8): | |
| if self.is_norm: | |
| img_t = img_t * 0.5 + 0.5 | |
| img_t = img_t.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR | |
| img_np = np.clip(img_t.float().cpu().numpy(), 0, 1) * pmax | |
| return img_np.astype(imtype) | |