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) @torch.cuda.amp.autocast() 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