backup wip

lerobot/common/policies/act/modeling_act.py

@@ -161,6 +161,9 @@ class ActionChunkingTransformerPolicy(nn.Module):
         if self.cfg.n_action_steps is not None:
             self._action_queue = deque([], maxlen=self.cfg.n_action_steps)
 
+    def forward(self, batch: dict[str, Tensor], **_) -> Tensor:
+        return self.select_action(self, batch)
+
     @torch.no_grad
     def select_action(self, batch: dict[str, Tensor], **_) -> Tensor:
         """Select a single action given environment observations.
@@ -172,23 +175,17 @@ class ActionChunkingTransformerPolicy(nn.Module):
         if len(self._action_queue) == 0:
             # `select_actions` returns a (batch_size, n_action_steps, *) tensor, but the queue effectively has
             # shape (n_action_steps, batch_size, *), hence the transpose.
-            self._action_queue.extend(self.select_actions(batch).transpose(0, 1))
+            self._action_queue.extend(self._select_actions(batch).transpose(0, 1))
         return self._action_queue.popleft()
 
     @torch.no_grad
-    def select_actions(self, batch: dict[str, Tensor]) -> Tensor:
+    def _select_actions(self, batch: dict[str, Tensor]) -> Tensor:
         """Use the action chunking transformer to generate a sequence of actions."""
         self.eval()
         self._preprocess_batch(batch, add_obs_steps_dim=True)
-
         action = self.forward(batch, return_loss=False)
-
         return action[: self.cfg.n_action_steps]
 
-    def __call__(self, *args, **kwargs) -> dict:
-        # TODO(alexander-soare): Temporary bridge until we know what to do about the `update` method.
-        return self.update(*args, **kwargs)
-
     def _preprocess_batch(
         self, batch: dict[str, Tensor], add_obs_steps_dim: bool = False
     ) -> dict[str, Tensor]:
@@ -216,9 +213,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
         # Note: no squeeze is required for "observation.images.top" because then we'd have to unsqueeze to get
         # the image index dimension.
 
-    def update(self, batch, **_) -> dict:
-        """Run the model in train mode, compute the loss, and do an optimization step."""
-        start_time = time.time()
+    def compute_loss(self, batch, **_) -> float:
         self._preprocess_batch(batch)
 
         self.train()
@@ -230,6 +225,12 @@ class ActionChunkingTransformerPolicy(nn.Module):
         assert batch_size % num_slices == 0
 
         loss = self.forward(batch, return_loss=True)["loss"]
+        return loss
+
+    def update(self, batch, **_) -> dict:
+        """Run the model in train mode, compute the loss, and do an optimization step."""
+        start_time = time.time()
+        loss = self.compute_loss(batch)
         loss.backward()
 
         grad_norm = torch.nn.utils.clip_grad_norm_(

lerobot/scripts/train.py

@@ -251,7 +251,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
         for key in batch:
             batch[key] = batch[key].to(cfg.device, non_blocking=True)
 
-        train_info = policy(batch, step=step)
+        train_info = policy.update(batch, step=step)
 
         # TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
         if step % cfg.log_freq == 0:
@@ -313,7 +313,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
             for key in batch:
                 batch[key] = batch[key].to(cfg.device, non_blocking=True)
 
-            train_info = policy(batch, step)
+            train_info = policy.update(batch, step)
 
             if step % cfg.log_freq == 0:
                 log_train_info(logger, train_info, step, cfg, online_dataset, is_offline)