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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)
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