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| import os | |
| import torch | |
| import numpy as np | |
| from rrdbnet_arch import RRDBNet | |
| from torch.nn import functional as F | |
| class RealESRNet(object): | |
| def __init__(self, base_dir='./', model=None, scale=2, device='cuda'): | |
| self.base_dir = base_dir | |
| self.scale = scale | |
| self.device = device | |
| self.load_srmodel(base_dir, model) | |
| def load_srmodel(self, base_dir, model): | |
| self.srmodel = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=32, num_block=23, num_grow_ch=32, scale=self.scale) | |
| if model is None: | |
| loadnet = torch.load(os.path.join(self.base_dir, 'weights', 'rrdb_realesrnet_psnr.pth')) | |
| else: | |
| loadnet = torch.load(os.path.join(self.base_dir, 'weights', model+'.pth')) | |
| #print(loadnet['params_ema'].keys) | |
| self.srmodel.load_state_dict(loadnet['params_ema'], strict=True) | |
| self.srmodel.eval() | |
| self.srmodel = self.srmodel.to(self.device) | |
| def process(self, img): | |
| img = img.astype(np.float32) / 255. | |
| img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() | |
| img = img.unsqueeze(0).to(self.device) | |
| if self.scale == 2: | |
| mod_scale = 2 | |
| elif self.scale == 1: | |
| mod_scale = 4 | |
| else: | |
| mod_scale = None | |
| if mod_scale is not None: | |
| h_pad, w_pad = 0, 0 | |
| _, _, h, w = img.size() | |
| if (h % mod_scale != 0): | |
| h_pad = (mod_scale - h % mod_scale) | |
| if (w % mod_scale != 0): | |
| w_pad = (mod_scale - w % mod_scale) | |
| img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect') | |
| try: | |
| with torch.no_grad(): | |
| output = self.srmodel(img) | |
| # remove extra pad | |
| if mod_scale is not None: | |
| _, _, h, w = output.size() | |
| output = output[:, :, 0:h - h_pad, 0:w - w_pad] | |
| output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() | |
| output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) | |
| output = (output * 255.0).round().astype(np.uint8) | |
| return output | |
| except: | |
| return None |