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''' |
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@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) |
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@author: yangxy (yangtao9009@gmail.com) |
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''' |
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import torch |
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import os |
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import cv2 |
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import glob |
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import numpy as np |
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from torch import nn |
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import torch.nn.functional as F |
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from torchvision import transforms, utils |
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from model import FullGenerator |
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import torch |
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class FaceGAN(object): |
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def __init__(self, base_dir='./', size=512, model=None, channel_multiplier=2, narrow=1, is_norm=True): |
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self.mfile = os.path.join(base_dir, 'weights', model+'.pth') |
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self.n_mlp = 8 |
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self.is_norm = is_norm |
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self.resolution = size |
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self.load_model(channel_multiplier, narrow) |
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def load_model(self, channel_multiplier=2, narrow=1): |
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self.model = FullGenerator(self.resolution, 512, self.n_mlp, channel_multiplier, narrow=narrow).cuda() |
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pretrained_dict = torch.load(self.mfile) |
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self.model.load_state_dict(pretrained_dict) |
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self.model.eval() |
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def process(self, img): |
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img = cv2.resize(img, (self.resolution, self.resolution)) |
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img_t = self.img2tensor(img) |
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with torch.no_grad(): |
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out, __ = self.model(img_t) |
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out = self.tensor2img(out) |
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return out |
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def img2tensor(self, img): |
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img_t = torch.from_numpy(img).cuda()/255. |
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if self.is_norm: |
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img_t = (img_t - 0.5) / 0.5 |
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img_t = img_t.permute(2, 0, 1).unsqueeze(0).flip(1) |
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return img_t |
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def tensor2img(self, img_t, pmax=255.0, imtype=np.uint8): |
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if self.is_norm: |
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img_t = img_t * 0.5 + 0.5 |
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img_t = img_t.squeeze(0).permute(1, 2, 0).flip(2) |
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img_np = np.clip(img_t.float().cpu().numpy(), 0, 1) * pmax |
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return img_np.astype(imtype) |
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