| import os |
| import torch |
| from torch.nn import functional as F |
| from PIL import Image |
| import numpy as np |
| import cv2 |
| from huggingface_hub import hf_hub_url, hf_hub_download, cached_download |
|
|
| 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: |
| 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']) |
| htr = hf_hub_download(repo_id=config['repo_id'], cache_dir=cache_dir, local_dir=cache_dir, |
| filename=config['filename']) |
| print(htr) |
| |
| 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): |
| torch.autocast(device_type=self.device.type) |
| 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 |
