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