diff --git a/IS-Net/Inference.py b/IS-Net/Inference.py
index 3e6fae8..0b2907d 100644
--- a/IS-Net/Inference.py
+++ b/IS-Net/Inference.py
@@ -30,23 +30,24 @@ if __name__ == "__main__":
         net.load_state_dict(torch.load(model_path,map_location="cpu"))
     net.eval()
     im_list = glob(dataset_path+"/*.jpg")+glob(dataset_path+"/*.JPG")+glob(dataset_path+"/*.jpeg")+glob(dataset_path+"/*.JPEG")+glob(dataset_path+"/*.png")+glob(dataset_path+"/*.PNG")+glob(dataset_path+"/*.bmp")+glob(dataset_path+"/*.BMP")+glob(dataset_path+"/*.tiff")+glob(dataset_path+"/*.TIFF")
-    for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
-        print("im_path: ", im_path)
-        im = io.imread(im_path)
-        if len(im.shape) < 3:
-            im = im[:, :, np.newaxis]
-        im_shp=im.shape[0:2]
-        im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
-        im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear").type(torch.uint8)
-        image = torch.divide(im_tensor,255.0)
-        image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
+    with torch.no_grad():
+        for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
+            print("im_path: ", im_path)
+            im = io.imread(im_path)
+            if len(im.shape) < 3:
+                im = im[:, :, np.newaxis]
+            im_shp=im.shape[0:2]
+            im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
+            im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear").type(torch.uint8)
+            image = torch.divide(im_tensor,255.0)
+            image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
 
-        if torch.cuda.is_available():
-            image=image.cuda()
-        result=net(image)
-        result=torch.squeeze(F.upsample(result[0][0],im_shp,mode='bilinear'),0)
-        ma = torch.max(result)
-        mi = torch.min(result)
-        result = (result-mi)/(ma-mi)
-        im_name=im_path.split('/')[-1].split('.')[0]
-        io.imsave(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8))
+            if torch.cuda.is_available():
+                image=image.cuda()
+            result=net(image)
+            result=torch.squeeze(F.upsample(result[0][0],im_shp,mode='bilinear'),0)
+            ma = torch.max(result)
+            mi = torch.min(result)
+            result = (result-mi)/(ma-mi)
+            im_name=im_path.split('/')[-1].split('.')[0]
+            io.imsave(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8))