diff --git a/data/dataset.py b/data/dataset.py
index 5600cd0..c717498 100644
--- a/data/dataset.py
+++ b/data/dataset.py
@@ -77,9 +77,9 @@ class TrackSynDataset(torchext.BaseDataset):
             ambs = []
             grads = []
             for tidx in track_ind:
-                imgs.append(np.load(os.path.join(sample_path, f'im{sidx}_{tidx}.npy')))
-                ambs.append(np.load(os.path.join(sample_path, f'ambient{sidx}_{tidx}.npy')))
-                grads.append(np.load(os.path.join(sample_path, f'grad{sidx}_{tidx}.npy')))
+                imgs.append(np.load(os.path.join(sample_path, f'im{sidx}_{tidx}.npy')), allow_pickle=True)
+                ambs.append(np.load(os.path.join(sample_path, f'ambient{sidx}_{tidx}.npy')), allow_pickle=True)
+                grads.append(np.load(os.path.join(sample_path, f'grad{sidx}_{tidx}.npy')), allow_pickle=True)
             ret[f'im{sidx}'] = np.stack(imgs, axis=0)
             ret[f'ambient{sidx}'] = np.stack(ambs, axis=0)
             ret[f'grad{sidx}'] = np.stack(grads, axis=0)
@@ -89,14 +89,14 @@ class TrackSynDataset(torchext.BaseDataset):
         R = []
         t = []
         for tidx in track_ind:
-            disps.append(np.load(os.path.join(sample_path, f'disp0_{tidx}.npy')))
-            R.append(np.load(os.path.join(sample_path, f'R_{tidx}.npy')))
-            t.append(np.load(os.path.join(sample_path, f't_{tidx}.npy')))
+            disps.append(np.load(os.path.join(sample_path, f'disp0_{tidx}.npy')), allow_pickle=True)
+            R.append(np.load(os.path.join(sample_path, f'R_{tidx}.npy')), allow_pickle=True)
+            t.append(np.load(os.path.join(sample_path, f't_{tidx}.npy')), allow_pickle=True)
         ret[f'disp0'] = np.stack(disps, axis=0)
         ret['R'] = np.stack(R, axis=0)
         ret['t'] = np.stack(t, axis=0)
 
-        blend_im = np.load(os.path.join(sample_path, 'blend_im.npy'))
+        blend_im = np.load(os.path.join(sample_path, 'blend_im.npy'), allow_pickle=True)
         ret['blend_im'] = blend_im.astype(np.float32)
 
         #### apply data augmentation at different scales seperately, only work for max_shift=0