Delete realesrgan.py

realesrgan.py

@@ -1,56 +0,0 @@
-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