diff --git a/main_test_swinir.py b/main_test_swinir.py
index f06b5f39d..cebcd8805 100644
--- a/main_test_swinir.py
+++ b/main_test_swinir.py
@@ -10,6 +10,9 @@
 from models.network_swinir import SwinIR as net
 from utils import util_calculate_psnr_ssim as util
 
+def dbg(tag, arr):
+    print(f'{tag}: shape={arr.shape}, dtype={arr.dtype}, '
+          f'range=({arr.min():.3f}, {arr.max():.3f})')
 
 def main():
     parser = argparse.ArgumentParser()
@@ -24,10 +27,13 @@ def main():
     parser.add_argument('--large_model', action='store_true', help='use large model, only provided for real image sr')
     parser.add_argument('--model_path', type=str,
                         default='model_zoo/swinir/001_classicalSR_DIV2K_s48w8_SwinIR-M_x2.pth')
+
+    parser.add_argument('--endoscope_data', type=bool, default=True, help='use endoscope data, which you need to pass in LR and HR folder where image with same name will be paired')
     parser.add_argument('--folder_lq', type=str, default=None, help='input low-quality test image folder')
     parser.add_argument('--folder_gt', type=str, default=None, help='input ground-truth test image folder')
     parser.add_argument('--tile', type=int, default=None, help='Tile size, None for no tile during testing (testing as a whole)')
     parser.add_argument('--tile_overlap', type=int, default=32, help='Overlapping of different tiles')
+    parser.add_argument('--save_dir', type=str, default=None, help='output directory')
     args = parser.parse_args()
 
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -47,6 +53,9 @@ def main():
 
     # setup folder and path
     folder, save_dir, border, window_size = setup(args)
+    if args.endoscope_data:
+        hq_folder = folder[1]
+        folder = folder[0]
     os.makedirs(save_dir, exist_ok=True)
     test_results = OrderedDict()
     test_results['psnr'] = []
@@ -59,14 +68,25 @@ def main():
 
     for idx, path in enumerate(sorted(glob.glob(os.path.join(folder, '*')))):
         # read image
-        imgname, img_lq, img_gt = get_image_pair(args, path)  # image to HWC-BGR, float32
+        if not args.endoscope_data:
+            imgname, img_lq, img_gt = get_image_pair(args, path)  # image to HWC-BGR, float32
+        else: 
+            imgname = path.split('/')[-1]
+            img_lq = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.
+            if args.folder_gt:
+                img_gt_path = os.path.join(hq_folder, imgname)
+                if os.path.exists(img_gt_path):
+                    img_gt = cv2.imread(img_gt_path, cv2.IMREAD_COLOR).astype(np.float32) / 255.
+                else:
+                    img_gt = None
+
         img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]], (2, 0, 1))  # HCW-BGR to CHW-RGB
         img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(device)  # CHW-RGB to NCHW-RGB
 
         # inference
         with torch.no_grad():
             # pad input image to be a multiple of window_size
-            _, _, h_old, w_old = img_lq.size()
+            _, _, h_old, w_old = img_lq.size()          
             h_pad = (h_old // window_size + 1) * window_size - h_old
             w_pad = (w_old // window_size + 1) * window_size - w_old
             img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
@@ -194,31 +214,45 @@ def define_model(args):
 
 def setup(args):
     # 001 classical image sr/ 002 lightweight image sr
+    save_dir = args.save_dir
     if args.task in ['classical_sr', 'lightweight_sr']:
-        save_dir = f'results/swinir_{args.task}_x{args.scale}'
-        folder = args.folder_gt
+        if not args.save_dir:
+            save_dir = f'results/swinir_{args.task}_x{args.scale}'
+        if args.endoscope_data:
+            folder = [args.folder_lq, args.folder_gt]
+        else: 
+            folder = args.folder_gt
         border = args.scale
         window_size = 8
 
     # 003 real-world image sr
     elif args.task in ['real_sr']:
-        save_dir = f'results/swinir_{args.task}_x{args.scale}'
+        if not args.save_dir:
+            save_dir = f'results/swinir_{args.task}_x{args.scale}'
         if args.large_model:
             save_dir += '_large'
-        folder = args.folder_lq
+        if args.endoscope_data:
+            folder = [args.folder_lq, args.folder_gt]
+        else: 
+            folder = args.folder_lq
         border = 0
         window_size = 8
 
     # 004 grayscale image denoising/ 005 color image denoising
     elif args.task in ['gray_dn', 'color_dn']:
-        save_dir = f'results/swinir_{args.task}_noise{args.noise}'
-        folder = args.folder_gt
+        if not args.save_dir:
+            save_dir = f'results/swinir_{args.task}_noise{args.noise}'
+        if args.endoscope_data:
+            folder = [args.folder_lq, args.folder_gt]
+        else: 
+            folder = args.folder_gt
         border = 0
         window_size = 8
 
     # 006 JPEG compression artifact reduction
     elif args.task in ['jpeg_car', 'color_jpeg_car']:
-        save_dir = f'results/swinir_{args.task}_jpeg{args.jpeg}'
+        if not args.save_dir:
+            save_dir = f'results/swinir_{args.task}_jpeg{args.jpeg}'
         folder = args.folder_gt
         border = 0
         window_size = 7