add inference codes

inference_realesrgan.py

@@ -0,0 +1,68 @@
+import argparse
+import cv2
+import glob
+import numpy as np
+import os
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from torch.nn import functional as F
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth')
+    parser.add_argument('--scale', type=int, default=4)
+    parser.add_argument('--input', type=str, default='inputs', help='input image or folder')
+    args = parser.parse_args()
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    # set up model
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=args.scale)
+    loadnet = torch.load(args.model_path)
+    model.load_state_dict(loadnet['params_ema'], strict=True)
+    model.eval()
+    model = model.to(device)
+
+    os.makedirs('results/', exist_ok=True)
+    for idx, path in enumerate(sorted(glob.glob(os.path.join(args.input, '*')))):
+        imgname = os.path.splitext(os.path.basename(path))[0]
+        print('Testing', idx, imgname)
+        # read image
+        img = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.
+        img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
+        img = img.unsqueeze(0).to(device)
+
+        if args.scale == 2:
+            mod_scale = 2
+        elif args.scale == 1:
+            mod_scale = 4
+        else:
+            mod_scale = None
+        if mod_scale is not None:
+            h_pad, w_pad = 0, 0
+            _, _, h, w = img.size()
+            if (h % mod_scale != 0):
+                h_pad = (mod_scale - h % mod_scale)
+            if (w % mod_scale != 0):
+                w_pad = (mod_scale - w % mod_scale)
+            img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect')
+
+        try:
+            # inference
+            with torch.no_grad():
+                output = model(img)
+            # remove extra pad
+            if mod_scale is not None:
+                _, _, h, w = output.size()
+                output = output[:, :, 0:h - h_pad, 0:w - w_pad]
+            # save image
+            output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+            output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
+            output = (output * 255.0).round().astype(np.uint8)
+            cv2.imwrite(f'results/{imgname}_RealESRGAN.png', output)
+        except Exception as error:
+            print('Error', error)
+
+
+if __name__ == '__main__':
+    main()