diff --git a/app.py b/app.py
index 3aefae9..1247f6b 100644
--- a/app.py
+++ b/app.py
@@ -81,7 +81,7 @@ def get_frames_from_video(video_input, play_state):
         video_path:str
         timestamp:float64
     Return 
-        [[0:nearest_frame-1], [nearest_frame+1], nearest_frame]
+        [[0:nearest_frame], [nearest_frame:], nearest_frame]
     """
     video_path = video_input
     timestamp = play_state[1] - play_state[0]
diff --git a/tracker/base_tracker.py b/tracker/base_tracker.py
index 318a6af..c6c5223 100644
--- a/tracker/base_tracker.py
+++ b/tracker/base_tracker.py
@@ -9,9 +9,16 @@ import torch
 import yaml
 from model.network import XMem
 from inference.inference_core import InferenceCore
+from inference.data.mask_mapper import MaskMapper
+
 # for data transormation
 from torchvision import transforms
 from dataset.range_transform import im_normalization
+import torch.nn.functional as F
+
+import sys
+sys.path.insert(0, sys.path[0]+"/../")
+from tools.painter import mask_painter
 
 
 class BaseTracker:
@@ -32,7 +39,18 @@ class BaseTracker:
             transforms.ToTensor(),
             im_normalization,
         ])
+        self.mapper = MaskMapper()
+        self.device = device
 
+    @torch.no_grad()
+    def resize_mask(self, mask):
+        # mask transform is applied AFTER mapper, so we need to post-process it in eval.py
+        h, w = mask.shape[-2:]
+        min_hw = min(h, w)
+        return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)), 
+                    mode='nearest')
+
+    @torch.no_grad()
     def track(self, frames, first_frame_annotation):
         """
         Input: 
@@ -40,14 +58,30 @@ class BaseTracker:
         first_frame_annotation: numpy array: H, W
 
         Output:
-        masks: numpy arrays: T, H, W
+        masks: numpy arrays: H, W
         """
-        # data transformation
-        for frame in frames:
-            frame = self.im_transform(frame)
+        vid_length = len(frames)
+        masks = []
 
-        # tracking
-        
+        for ti, frame in enumerate(frames):
+            # convert to tensor
+            frame_tensor = self.im_transform(frame).to(self.device)
+            if ti == 0:
+                mask, labels = self.mapper.convert_mask(first_frame_annotation)
+                mask = torch.Tensor(mask).to(self.device)
+                self.tracker.set_all_labels(list(self.mapper.remappings.values()))
+            else:
+                mask = None
+                labels = None
+            
+            # track one frame
+            prob = self.tracker.step(frame_tensor, mask, labels, end=(ti==vid_length-1))
+            # convert to mask
+            out_mask = torch.argmax(prob, dim=0)
+            out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)
+            masks.append(out_mask)
+
+        return np.stack(masks, 0)
 
 
 if __name__ == '__main__':
@@ -56,20 +90,27 @@ if __name__ == '__main__':
     video_path_list.sort()
     # first frame
     first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/dance-twirl/00000.png'
-
     # load frames
-    frames = ["test_confict"]
+    frames = []
     for video_path in video_path_list:
         frames.append(np.array(Image.open(video_path).convert('RGB')))
     frames = np.stack(frames, 0)    # N, H, W, C
-
     # load first frame annotation
     first_frame_annotation = np.array(Image.open(first_frame_path).convert('P'))    # H, W, C
 
+    # ----------------------------------------------------------
     # initalise tracker
-    device = 'cuda:0'
+    # ----------------------------------------------------------
+    device = 'cuda:1'
     XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'
-    tracker = BaseTracker('cuda:0', XMEM_checkpoint)
+    tracker = BaseTracker(device, XMEM_checkpoint)
     
     # track anything given in the first frame annotation
-    tracker.track(frames, first_frame_annotation)
+    masks = tracker.track(frames, first_frame_annotation)
+
+    # save
+    for ti, (frame, mask) in enumerate(zip(frames, masks)):
+        painted_image = mask_painter(frame, mask)
+        # save
+        painted_image = Image.fromarray(painted_image)
+        painted_image.save(f'/ssd1/gaomingqi/results/TrackA/{ti:05d}.png')