onebitss/Real-ESRGAN

1import os2import torch3from torch.nn import functional as F4from PIL import Image5import numpy as np6import cv27from huggingface_hub import hf_hub_url, hf_hub_download, cached_download8 9from .rrdbnet_arch import RRDBNet10from .utils import pad_reflect, split_image_into_overlapping_patches, stich_together, \11    unpad_image12 13HF_MODELS = {14    2: dict(15        repo_id='sberbank-ai/Real-ESRGAN',16        filename='RealESRGAN_x2.pth',17    ),18    4: dict(19        repo_id='sberbank-ai/Real-ESRGAN',20        filename='RealESRGAN_x4.pth',21    ),22    8: dict(23        repo_id='sberbank-ai/Real-ESRGAN',24        filename='RealESRGAN_x8.pth',25    ),26}27 28 29class RealESRGAN:30    def __init__(self, device, scale=4):31        self.device = device32        self.scale = scale33        self.model = RRDBNet(34            num_in_ch=3, num_out_ch=3, num_feat=64,35            num_block=23, num_grow_ch=32, scale=scale36        )37 38    def load_weights(self, model_path, download=True):39        if not os.path.exists(model_path) and download:40            assert self.scale in [2, 4, 8], 'You can download models only with scales: 2, 4, 8'41            config = HF_MODELS[self.scale]42            cache_dir = os.path.dirname(model_path)43            local_filename = os.path.basename(model_path)44            config_file_url = hf_hub_url(repo_id=config['repo_id'], filename=config['filename'])45            htr = hf_hub_download(repo_id=config['repo_id'], cache_dir=cache_dir, local_dir=cache_dir,46                                  filename=config['filename'])47            print(htr)48            # cached_download(config_file_url, cache_dir=cache_dir, force_filename=local_filename)49            print('Weights downloaded to:', os.path.join(cache_dir, local_filename))50 51        loadnet = torch.load(model_path)52        if 'params' in loadnet:53            self.model.load_state_dict(loadnet['params'], strict=True)54        elif 'params_ema' in loadnet:55            self.model.load_state_dict(loadnet['params_ema'], strict=True)56        else:57            self.model.load_state_dict(loadnet, strict=True)58        self.model.eval()59        self.model.to(self.device)60 61    # @torch.cuda.amp.autocast()62    def predict(self, lr_image, batch_size=4, patches_size=192,63                padding=24, pad_size=15):64        torch.autocast(device_type=self.device.type)65        scale = self.scale66        device = self.device67        lr_image = np.array(lr_image)68        lr_image = pad_reflect(lr_image, pad_size)69 70        patches, p_shape = split_image_into_overlapping_patches(71            lr_image, patch_size=patches_size, padding_size=padding72        )73        img = torch.FloatTensor(patches / 255).permute((0, 3, 1, 2)).to(device).detach()74 75        with torch.no_grad():76            res = self.model(img[0:batch_size])77            for i in range(batch_size, img.shape[0], batch_size):78                res = torch.cat((res, self.model(img[i:i + batch_size])), 0)79 80        sr_image = res.permute((0, 2, 3, 1)).cpu().clamp_(0, 1)81        np_sr_image = sr_image.numpy()82 83        padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)84        scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)85        np_sr_image = stich_together(86            np_sr_image, padded_image_shape=padded_size_scaled,87            target_shape=scaled_image_shape, padding_size=padding * scale88        )89        sr_img = (np_sr_image * 255).astype(np.uint8)90        sr_img = unpad_image(sr_img, pad_size * scale)91        sr_img = Image.fromarray(sr_img)92 93        return sr_img