fix obs_projector to rgb_feature_projector, replace notimplementederror to assert

lerobot/common/policies/vqbet/modeling_vqbet.py

@@ -262,7 +262,7 @@ class VQBeTModel(nn.Module):
                 config.output_shapes["action"][0], 
                 hidden_channels=[self.config.gpt_input_dim]
             )
-        self.obs_projector = MLP(
+        self.rgb_feature_projector = MLP(
                 self.rgb_encoder.feature_dim, 
                 hidden_channels=[self.config.gpt_input_dim]
             )
@@ -288,12 +288,11 @@ class VQBeTModel(nn.Module):
         
         # image observation feature, state feature, and action query token are grouped together with the same timestpe to form a group, which is listed in order to be entered into GPT sequentially.
         observation_feature = torch.cat([
-                torch.unsqueeze(self.obs_projector(img_features), dim=2), 
+                torch.unsqueeze(self.rgb_feature_projector(img_features), dim=2), 
                 torch.unsqueeze(self.state_projector(batch["observation.state"]), dim=2), 
                 self._action_token.repeat(batch_size, n_obs_steps, 1, 1)
             ], dim=-2).view(batch_size, -1, self.config.gpt_input_dim)
-        if img_features.shape[1] != n_obs_steps:
-            raise NotImplementedError
+        assert img_features.shape[1] == n_obs_steps, "The number of input image feature tokens should be same with n_obs_steps"
         len_additional_action_token = self.config.n_action_pred_token-1
         action_token = self._action_token.repeat(batch_size, len_additional_action_token, 1)
 
@@ -577,7 +576,7 @@ class VQBeTOptimizer:
                 {"params": policy.vqbet.state_projector.parameters()}
             )
         self.bet_optimizer1.add_param_group(
-                {"params": policy.vqbet.obs_projector.parameters()}
+                {"params": policy.vqbet.rgb_feature_projector.parameters()}
             )
 
         self.bet_optimizer2 = torch.optim.AdamW(