diff --git a/codes/data/full_image_dataset.py b/codes/data/full_image_dataset.py
index cf2fe8a1..84893699 100644
--- a/codes/data/full_image_dataset.py
+++ b/codes/data/full_image_dataset.py
@@ -232,9 +232,14 @@ class FullImageDataset(data.Dataset):
         if self.paths_LQ:
             LQ_path = self.get_lq_path(index)
             img_lq_full = util.read_img(None, LQ_path, None)
-            img_lq_full = util.augment([img_lq_full], self.opt['use_flip'], self.opt['use_rot'])[0]
-            img_lq_full = self.get_square_image(img_lq_full)
-            img_LQ, lq_fullsize_ref, lq_mask, lq_center = self.pull_tile(img_lq_full, lq=True)
+            if self.opt['phase'] == 'train':
+                img_lq_full = util.augment([img_lq_full], self.opt['use_flip'], self.opt['use_rot'])[0]
+                img_lq_full = self.get_square_image(img_lq_full)
+                img_LQ, lq_fullsize_ref, lq_mask, lq_center = self.pull_tile(img_lq_full, lq=True)
+            else:
+                img_LQ, lq_fullsize_ref = img_lq_full, img_lq_full
+                lq_mask = np.ones(img_lq_full.shape[:2], dtype=lq_fullsize_ref.dtype)
+                lq_center = torch.tensor([img_lq_full.shape[0] // 2, img_lq_full.shape[1] // 2], dtype=torch.long)
         else:  # down-sampling on-the-fly
             # randomly scale during training
             if self.opt['phase'] == 'train':