Upload realesrgan.py

realesrgan.py

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+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)
+        
+    @torch.cuda.amp.autocast()
+    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)).clamp_(0, 1).cpu()
+        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