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_sr import * 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): 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)).cpu().clamp_(0, 1) 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