diff --git a/dataset.py b/dataset.py
index 4f01a31..79aa621 100644
--- a/dataset.py
+++ b/dataset.py
@@ -26,41 +26,32 @@ class VimeoDataset(Dataset):
         self.train_data = []
         self.flow_data = []
         self.val_data = []
-        for i in range(100):
-            f = np.load('dataset/{}.npz'.format(i))
-            if i < 80:
-                self.train_data.append(f['i0i1gt'])
-                self.flow_data.append(f['ft0ft1'])
-            else:
-                self.val_data.append(f['i0i1gt'])
         if self.dataset_name == 'train':
             self.meta_data = self.train_data
         else:
             self.meta_data = self.val_data
         self.nr_sample = len(self.meta_data)        
 
-    def aug(self, img0, gt, img1, flow_gt, h, w):
+    def aug(self, img0, gt, img1, h, w):
         ih, iw, _ = img0.shape
         x = np.random.randint(0, ih - h + 1)
         y = np.random.randint(0, iw - w + 1)
         img0 = img0[x:x+h, y:y+w, :]
         img1 = img1[x:x+h, y:y+w, :]
         gt = gt[x:x+h, y:y+w, :]
-        flow_gt = flow_gt[x:x+h, y:y+w, :]
-        return img0, gt, img1, flow_gt
+        return img0, gt, img1
 
     def getimg(self, index):
         data = self.meta_data[index]
         img0 = data[0:3].transpose(1, 2, 0)
         img1 = data[3:6].transpose(1, 2, 0)
         gt = data[6:9].transpose(1, 2, 0)
-        flow_gt = (self.flow_data[index]).transpose(1, 2, 0)
-        return img0, gt, img1, flow_gt
+        return img0, gt, img1
             
     def __getitem__(self, index):        
-        img0, gt, img1, flow_gt = self.getimg(index)
+        img0, gt, img1 = self.getimg(index)
         if self.dataset_name == 'train':
-            img0, gt, img1, flow_gt = self.aug(img0, gt, img1, flow_gt, 224, 224)
+            img0, gt, img1 = self.aug(img0, gt, img1, 224, 224)
             if random.uniform(0, 1) < 0.5:
                 img0 = img0[:, :, ::-1]
                 img1 = img1[:, :, ::-1]
@@ -69,23 +60,15 @@ class VimeoDataset(Dataset):
                 img0 = img0[::-1]
                 img1 = img1[::-1]
                 gt = gt[::-1]
-                flow_gt = flow_gt[::-1]
-                flow_gt = np.concatenate((flow_gt[:, :, 0:1], -flow_gt[:, :, 1:2], flow_gt[:, :, 2:3], -flow_gt[:, :, 3:4]), 2)
             if random.uniform(0, 1) < 0.5:
                 img0 = img0[:, ::-1]
                 img1 = img1[:, ::-1]
                 gt = gt[:, ::-1]
-                flow_gt = flow_gt[:, ::-1]
-                flow_gt = np.concatenate((-flow_gt[:, :, 0:1], flow_gt[:, :, 1:2], -flow_gt[:, :, 2:3], flow_gt[:, :, 3:4]), 2)
             if random.uniform(0, 1) < 0.5:
                 tmp = img1
                 img1 = img0
                 img0 = tmp
-                flow_gt = np.concatenate((flow_gt[:, :, 2:4], flow_gt[:, :, 0:2]), 2)
-        else:
-            flow_gt = np.zeros((256, 448, 4))
-        flow_gt = torch.from_numpy(flow_gt.copy()).permute(2, 0, 1)
         img0 = torch.from_numpy(img0.copy()).permute(2, 0, 1)
         img1 = torch.from_numpy(img1.copy()).permute(2, 0, 1)
         gt = torch.from_numpy(gt.copy()).permute(2, 0, 1)
-        return torch.cat((img0, img1, gt), 0), flow_gt
+        return torch.cat((img0, img1, gt), 0)