remove gpt_num_obs_mode

lerobot/common/policies/vqbet/configuration_vqbet.py

@@ -51,7 +51,6 @@ class VQBeTConfig:
         gpt_n_layer: Number of layers of GPT
         gpt_n_head: Number of headers of GPT
         gpt_hidden_dim: Size of hidden dimensions of GPT
-        gpt_num_obs_mode: Number of different observation modes. (e.g., PushT env: {state, image observation}, thus 2.)
         dropout: Dropout rate for GPT
         mlp_hidden_dim: Size of hidden dimensions of offset header / bin prediction headers parts of VQ-BeT
         offset_loss_weight:  A constant that is multiplied to the offset loss
@@ -109,7 +108,6 @@ class VQBeTConfig:
     gpt_n_layer: int = 8
     gpt_n_head: int = 8
     gpt_hidden_dim: int = 512
-    gpt_num_obs_mode: int = 2
     dropout: float = 0.1
     mlp_hidden_dim: int = 1024
     offset_loss_weight: float = 10000.

lerobot/common/policies/vqbet/modeling_vqbet.py

@@ -314,7 +314,8 @@ class VQBeTModel(nn.Module):
         
         # get action features (pass through GPT)
         features = self.policy(input_tokens)
-        historical_act_pred_index = np.arange(0, n_obs_steps) * (self.config.gpt_num_obs_mode+1) + self.config.gpt_num_obs_mode
+        # len(self.config.input_shapes) is the number of different observation modes. this line gets the index of action prompt tokens.
+        historical_act_pred_index = np.arange(0, n_obs_steps) * (len(self.config.input_shapes)+1) + len(self.config.input_shapes)
 
         # only extract the output tokens at the position of action query
         features = torch.cat([

lerobot/configs/policy/vqbet.yaml

@@ -96,7 +96,6 @@ policy:
   gpt_n_layer: 8
   gpt_n_head: 8
   gpt_hidden_dim: 512
-  gpt_num_obs_mode: 2
   dropout: 0.1
   mlp_hidden_dim: 1024
   offset_loss_weight: 10000.