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| 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 modules.bsrgan_model_arch import RRDBNet | |
| from modules.paths import models_path | |
| class UpscalerBSRGAN(modules.upscaler.Upscaler): | |
| def __init__(self, dirname): | |
| self.name = "BSRGAN" | |
| self.model_path = os.path.join(models_path, self.name) | |
| self.model_name = "BSRGAN 4x" | |
| self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth" | |
| self.user_path = dirname | |
| super().__init__() | |
| model_paths = self.find_models(ext_filter=[".pt", ".pth"]) | |
| scalers = [] | |
| if len(model_paths) == 0: | |
| scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4) | |
| scalers.append(scaler_data) | |
| for file in model_paths: | |
| if "http" in file: | |
| name = self.model_name | |
| else: | |
| name = modelloader.friendly_name(file) | |
| try: | |
| scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) | |
| scalers.append(scaler_data) | |
| except Exception: | |
| print(f"Error loading BSRGAN model: {file}", file=sys.stderr) | |
| print(traceback.format_exc(), file=sys.stderr) | |
| 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 | |
| model.to(devices.device_bsrgan) | |
| torch.cuda.empty_cache() | |
| 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(devices.device_bsrgan) | |
| 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): | |
| 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(filename) or filename is None: | |
| print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr) | |
| return None | |
| model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network | |
| model.load_state_dict(torch.load(filename), strict=True) | |
| model.eval() | |
| for k, v in model.named_parameters(): | |
| v.requires_grad = False | |
| return model | |