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Delete realesrgan.py
Browse files- realesrgan.py +0 -56
realesrgan.py
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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_sr import *
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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(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
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def load_weights(self, model_path):
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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(lr_image, patch_size=patches_size,
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padding_size=padding)
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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(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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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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sr_img = Image.fromarray(sr_img)
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return sr_img
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