| import os.path |
| import sys |
| import traceback |
|
|
| import PIL.Image |
| import numpy as np |
| import torch |
| from basicsr.utils.download_util import load_file_from_url |
|
|
| import modules.upscaler |
| from modules import devices, modelloader |
| from scunet_model_arch import SCUNet as net |
|
|
|
|
| class UpscalerScuNET(modules.upscaler.Upscaler): |
| def __init__(self, dirname): |
| self.name = "ScuNET" |
| self.model_name = "ScuNET GAN" |
| self.model_name2 = "ScuNET PSNR" |
| self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" |
| self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth" |
| self.user_path = dirname |
| super().__init__() |
| model_paths = self.find_models(ext_filter=[".pth"]) |
| scalers = [] |
| add_model2 = True |
| for file in model_paths: |
| if "http" in file: |
| name = self.model_name |
| else: |
| name = modelloader.friendly_name(file) |
| if name == self.model_name2 or file == self.model_url2: |
| add_model2 = False |
| try: |
| scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) |
| scalers.append(scaler_data) |
| except Exception: |
| print(f"Error loading ScuNET model: {file}", file=sys.stderr) |
| print(traceback.format_exc(), file=sys.stderr) |
| if add_model2: |
| scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) |
| scalers.append(scaler_data2) |
| self.scalers = scalers |
|
|
| def do_upscale(self, img: PIL.Image, selected_file): |
| torch.cuda.empty_cache() |
|
|
| model = self.load_model(selected_file) |
| if model is None: |
| return img |
|
|
| device = devices.get_device_for('scunet') |
| img = np.array(img) |
| img = img[:, :, ::-1] |
| img = np.moveaxis(img, 2, 0) / 255 |
| img = torch.from_numpy(img).float() |
| img = img.unsqueeze(0).to(device) |
|
|
| with torch.no_grad(): |
| output = model(img) |
| output = output.squeeze().float().cpu().clamp_(0, 1).numpy() |
| output = 255. * np.moveaxis(output, 0, 2) |
| output = output.astype(np.uint8) |
| output = output[:, :, ::-1] |
| torch.cuda.empty_cache() |
| return PIL.Image.fromarray(output, 'RGB') |
|
|
| def load_model(self, path: str): |
| device = devices.get_device_for('scunet') |
| if "http" in path: |
| filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, |
| progress=True) |
| else: |
| filename = path |
| if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None: |
| print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr) |
| return None |
|
|
| model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) |
| model.load_state_dict(torch.load(filename), strict=True) |
| model.eval() |
| for k, v in model.named_parameters(): |
| v.requires_grad = False |
| model = model.to(device) |
|
|
| return model |
|
|
|
|
