add some comments

cotracker/predictor.py

@@ -17,6 +17,7 @@ class CoTrackerPredictor(torch.nn.Module):
         self.support_grid_size = 6
         model = build_cotracker(checkpoint)
         self.interp_shape = model.model_resolution
+        print(self.interp_shape)
         self.model = model
         self.model.eval()
 
@@ -103,12 +104,16 @@ class CoTrackerPredictor(torch.nn.Module):
         B, T, C, H, W = video.shape
 
         video = video.reshape(B * T, C, H, W)
+        # ? what is interpolate?
+        # 将video插值成interp_shape?
         video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear", align_corners=True)
         video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
 
         if queries is not None:
-            B, N, D = queries.shape
+            B, N, D = queries.shape # batch_size, number of points, (t,x,y)
             assert D == 3
+            # query 缩放到( interp_shape - 1 ) / (W - 1)
+            # 插完值之后缩放
             queries = queries.clone()
             queries[:, :, 1:] *= queries.new_tensor(
                 [
@@ -116,6 +121,7 @@ class CoTrackerPredictor(torch.nn.Module):
                     (self.interp_shape[0] - 1) / (H - 1),
                 ]
             )
+        # 生成grid
         elif grid_size > 0:
             grid_pts = get_points_on_a_grid(grid_size, self.interp_shape, device=video.device)
             if segm_mask is not None:
@@ -131,6 +137,8 @@ class CoTrackerPredictor(torch.nn.Module):
                 dim=2,
             ).repeat(B, 1, 1)
 
+        # 添加支持点
+
         if add_support_grid:
             grid_pts = get_points_on_a_grid(
                 self.support_grid_size, self.interp_shape, device=video.device