Merge branch 'main' into user/azouitine/2025-04-24-hot-fix-ci

Co-authored-by: imstevenpmwork <steven.palma@huggingface.co>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>

lerobot/common/policies/act/modeling_act.py

@@ -142,37 +142,6 @@ class ACTPolicy(PreTrainedPolicy):
             self._action_queue.extend(actions.transpose(0, 1))
         return self._action_queue.popleft()
 
-    @torch.no_grad
-    def predict_chunk(self, batch: dict[str, Tensor]) -> Tensor:
-        """Predict a chunk of actions given environment observations.
-
-        This method returns the raw chunk of actions predicted by the model without
-        any queue management or action consumption logic.
-
-        Args:
-            batch: A dictionary of observation tensors.
-
-        Returns:
-            A tensor of shape (batch_size, chunk_size, action_dim) containing predicted actions.
-        """
-        self.eval()
-
-        batch = self.normalize_inputs(batch)
-        if self.config.image_features:
-            batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            batch["observation.images"] = [batch[key] for key in self.config.image_features]
-
-        # If we are using temporal ensembling
-        if self.config.temporal_ensemble_coeff is not None:
-            actions = self.model(batch)[0]  # (batch_size, chunk_size, action_dim)
-            actions = self.unnormalize_outputs({"action": actions})["action"]
-            return actions
-
-        # Standard action prediction
-        actions = self.model(batch)[0]
-        actions = self.unnormalize_outputs({"action": actions})["action"]
-        return actions
-
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)

pyproject.toml

@@ -49,7 +49,7 @@ dependencies = [
     "datasets>=2.19.0",
     "deepdiff>=7.0.1",
     "diffusers>=0.27.2",
-    "draccus>=0.10.0",
+    "draccus==0.10.0",
     "einops>=0.8.0",
     "flask>=3.0.3",
     "gdown>=5.1.0",

tests/optim/test_schedulers.py

@@ -37,7 +37,6 @@ def test_diffuser_scheduler(optimizer):
         "base_lrs": [0.001],
         "last_epoch": 1,
         "lr_lambdas": [None],
-        "verbose": False,
     }
     assert scheduler.state_dict() == expected_state_dict
 
@@ -56,7 +55,6 @@ def test_vqbet_scheduler(optimizer):
         "base_lrs": [0.001],
         "last_epoch": 1,
         "lr_lambdas": [None],
-        "verbose": False,
     }
     assert scheduler.state_dict() == expected_state_dict
 
@@ -77,7 +75,6 @@ def test_cosine_decay_with_warmup_scheduler(optimizer):
         "base_lrs": [0.001],
         "last_epoch": 1,
         "lr_lambdas": [None],
-        "verbose": False,
     }
     assert scheduler.state_dict() == expected_state_dict