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online_bspline_tokenizer.py

@@ -590,7 +590,6 @@ class BestTokenizer(torch.nn.Module, ProcessorMixin):
         if self.do_pad:
             all_params = _pad_params(all_params, self.out_seq_len)
 
-        print("1. Decode continous has compeltete: (for debug stop error)")
         return all_params
 
     def decode_continuous(self, all_params: Union[torch.Tensor, List[torch.Tensor]], 
@@ -629,7 +628,6 @@ class BestTokenizer(torch.nn.Module, ProcessorMixin):
             batch_size = all_params.shape[0] if isinstance(all_params, torch.Tensor) else len(all_params)
             all_trajs = torch.zeros((batch_size, self.in_seq_len, self.num_dof), dtype=torch.float32)
         
-        print("1). Decode continous has compeltete: (for debug stop error)")
         return all_trajs
 
     @torch.no_grad()
@@ -653,7 +651,6 @@ class BestTokenizer(torch.nn.Module, ProcessorMixin):
         
         # Rearrange: [batch, out_seq_len, num_t_dof] -> [batch, out_seq_len * num_t_dof]
         tokens = tokens.reshape(batch_size, -1).long().to(self.device)
-        print("1). Decode discrete has compeltete: (for debug stop error)")
         return tokens
     
     @torch.no_grad()
@@ -677,7 +674,6 @@ class BestTokenizer(torch.nn.Module, ProcessorMixin):
         target_length = self.in_seq_len if target_length is None else target_length
         all_trajs = self.decode_continuous(normalized_all_params, target_length)
         
-        print("2. Decode discrete has compeltete: (for debug stop error)")
         return all_trajs.to(self.device)
     
 if __name__ == "__main__":