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| import os | |
| import torch | |
| from torch.nn import functional as F | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| from .rrdbnet_arch import RRDBNet | |
| from .utils import pad_reflect, split_image_into_overlapping_patches, stich_together, \ | |
| unpad_image | |
| HF_MODELS = { | |
| 2: dict( | |
| repo_id='sberbank-ai/Real-ESRGAN', | |
| filename='RealESRGAN_x2.pth', | |
| ), | |
| 4: dict( | |
| repo_id='sberbank-ai/Real-ESRGAN', | |
| filename='RealESRGAN_x4.pth', | |
| ), | |
| 8: dict( | |
| repo_id='sberbank-ai/Real-ESRGAN', | |
| filename='RealESRGAN_x8.pth', | |
| ), | |
| } | |
| class RealESRGAN: | |
| def __init__(self, device, scale=4): | |
| self.device = device | |
| self.scale = scale | |
| self.model = RRDBNet( | |
| num_in_ch=3, num_out_ch=3, num_feat=64, | |
| num_block=23, num_grow_ch=32, scale=scale | |
| ) | |
| def load_weights(self, model_path, download=True): | |
| if not os.path.exists(model_path) and download: | |
| from huggingface_hub import hf_hub_url, cached_download | |
| assert self.scale in [2,4,8], 'You can download models only with scales: 2, 4, 8' | |
| config = HF_MODELS[self.scale] | |
| cache_dir = os.path.dirname(model_path) | |
| local_filename = os.path.basename(model_path) | |
| config_file_url = hf_hub_url(repo_id=config['repo_id'], filename=config['filename']) | |
| cached_download(config_file_url, cache_dir=cache_dir, force_filename=local_filename) | |
| print('Weights downloaded to:', os.path.join(cache_dir, local_filename)) | |
| loadnet = torch.load(model_path) | |
| if 'params' in loadnet: | |
| self.model.load_state_dict(loadnet['params'], strict=True) | |
| elif 'params_ema' in loadnet: | |
| self.model.load_state_dict(loadnet['params_ema'], strict=True) | |
| else: | |
| self.model.load_state_dict(loadnet, strict=True) | |
| self.model.eval() | |
| self.model.to(self.device) | |
| def predict(self, lr_image, batch_size=4, patches_size=192, | |
| padding=24, pad_size=15): | |
| scale = self.scale | |
| device = self.device | |
| lr_image = np.array(lr_image) | |
| lr_image = pad_reflect(lr_image, pad_size) | |
| patches, p_shape = split_image_into_overlapping_patches( | |
| lr_image, patch_size=patches_size, padding_size=padding | |
| ) | |
| img = torch.FloatTensor(patches/255).permute((0,3,1,2)).to(device).detach() | |
| with torch.no_grad(): | |
| res = self.model(img[0:batch_size]) | |
| for i in range(batch_size, img.shape[0], batch_size): | |
| res = torch.cat((res, self.model(img[i:i+batch_size])), 0) | |
| sr_image = res.permute((0,2,3,1)).clamp_(0, 1).cpu() | |
| np_sr_image = sr_image.numpy() | |
| padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,) | |
| scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,) | |
| np_sr_image = stich_together( | |
| np_sr_image, padded_image_shape=padded_size_scaled, | |
| target_shape=scaled_image_shape, padding_size=padding * scale | |
| ) | |
| sr_img = (np_sr_image*255).astype(np.uint8) | |
| sr_img = unpad_image(sr_img, pad_size*scale) | |
| #sr_img = Image.fromarray(sr_img) | |
| return sr_img |