(fix): linter

.pre-commit-config.yaml

@@ -46,8 +46,6 @@ repos:
     rev: v3.20.0
     hooks:
     -   id: pyupgrade
-        # Exclude generated protobuf files
-        exclude: '^(.*_pb2_grpc\.py|.*_pb2\.py$)'
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.11.11
     hooks:

lerobot/common/motors/motors_bus.py

@@ -795,7 +795,6 @@ class MotorsBus(abc.ABC):
                 norm = ((bounded_val - min_) / (max_ - min_)) * 100
                 normalized_values[id_] = 100 - norm if drive_mode else norm
             elif self.motors[motor].norm_mode is MotorNormMode.DEGREE:
-                homing_offset = self.calibration[motor].homing_offset
                 resolution = self.model_resolution_table[self.motors[motor].model]
                 if drive_mode:
                     val *= -1
@@ -830,16 +829,14 @@ class MotorsBus(abc.ABC):
                 bounded_val = min(100.0, max(0.0, val))
                 unnormalized_values[id_] = int((bounded_val / 100) * (max_ - min_) + min_)
             elif self.motors[motor].norm_mode is MotorNormMode.DEGREE:
-                homing_offset = self.calibration[motor].homing_offset
                 resolution = self.model_resolution_table[self.motors[motor].model]
-                # middle_pos = homing_offset + resolution // 2
                 middle_pos = int((max_ + min_) / 2)
                 unnormalized_values[id_] = int((val / 180) * resolution // 2) + middle_pos
                 if drive_mode:
                     unnormalized_values[id_] *= -1
 
-                if unnormalized_values[id_] < 0:
+                # if unnormalized_values[id_] < 0:
-                    breakpoint()
+                #     breakpoint()
             else:
                 # TODO(aliberts): degree mode
                 raise NotImplementedError

lerobot/common/robots/so100_follower_end_effector/so100_follower_end_effector.py

@@ -131,7 +131,7 @@ class SO100FollowerEndEffector(SO100Follower):
         if self.current_joint_pos is None:
             # Read current joint positions
             current_joint_pos = self.bus.sync_read("Present_Position")
-            self.current_joint_pos = np.array([current_joint_pos[name] for name in self.bus.motors.keys()])
+            self.current_joint_pos = np.array([current_joint_pos[name] for name in self.bus.motors])
 
         # Calculate current end-effector position using forward kinematics
         if self.current_ee_pos is None:

lerobot/common/transport/services_pb2.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # NO CHECKED-IN PROTOBUF GENCODE
 # source: lerobot/common/transport/services.proto

lerobot/common/transport/services_pb2_grpc.py

@@ -25,7 +25,7 @@ if _version_not_supported:
     )
 
 
-class LearnerServiceStub(object):
+class LearnerServiceStub:
     """LearnerService: the Actor calls this to push transitions.
     The Learner implements this service.
     """
@@ -63,7 +63,7 @@ class LearnerServiceStub(object):
                 _registered_method=True)
 
 
-class LearnerServiceServicer(object):
+class LearnerServiceServicer:
     """LearnerService: the Actor calls this to push transitions.
     The Learner implements this service.
     """
@@ -135,7 +135,7 @@ def add_LearnerServiceServicer_to_server(servicer, server):
 
 
  # This class is part of an EXPERIMENTAL API.
-class LearnerService(object):
+class LearnerService:
     """LearnerService: the Actor calls this to push transitions.
     The Learner implements this service.
     """

pyproject.toml

@@ -137,6 +137,9 @@ exclude = [
 [tool.ruff.lint]
 select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
 
+[tool.ruff.lint.per-file-ignores]
+"__init__.py" = ["F401", "F403"]
+
 [tool.bandit]
 exclude_dirs = [
     "tests",

tests/datasets/test_datasets.py

@@ -72,9 +72,7 @@ def test_same_attributes_defined(tmp_path, lerobot_dataset_factory):
     # Instantiate both ways
     robot = make_robot("koch", mock=True)
     root_create = tmp_path / "create"
-    dataset_create = LeRobotDataset.create(
+    dataset_create = LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=30, robot=robot, root=root_create)
-        repo_id=DUMMY_REPO_ID, fps=30, robot=robot, root=root_create
-    )
 
     root_init = tmp_path / "init"
     dataset_init = lerobot_dataset_factory(root=root_init)
@@ -126,9 +124,7 @@ def test_add_frame_extra_feature(tmp_path, empty_lerobot_dataset_factory):
     with pytest.raises(
         ValueError, match="Feature mismatch in `frame` dictionary:\nExtra features: {'extra'}\n"
     ):
-        dataset.add_frame(
+        dataset.add_frame({"state": torch.randn(1), "task": "Dummy task", "extra": "dummy_extra"})
-            {"state": torch.randn(1), "task": "Dummy task", "extra": "dummy_extra"}
-        )
 
 
 def test_add_frame_wrong_type(tmp_path, empty_lerobot_dataset_factory):
@@ -137,9 +133,7 @@ def test_add_frame_wrong_type(tmp_path, empty_lerobot_dataset_factory):
     with pytest.raises(
         ValueError, match="The feature 'state' of dtype 'float16' is not of the expected dtype 'float32'.\n"
     ):
-        dataset.add_frame(
+        dataset.add_frame({"state": torch.randn(1, dtype=torch.float16), "task": "Dummy task"})
-            {"state": torch.randn(1, dtype=torch.float16), "task": "Dummy task"}
-        )
 
 
 def test_add_frame_wrong_shape(tmp_path, empty_lerobot_dataset_factory):
@@ -147,9 +141,7 @@ def test_add_frame_wrong_shape(tmp_path, empty_lerobot_dataset_factory):
     dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
     with pytest.raises(
         ValueError,
-        match=re.escape(
+        match=re.escape("The feature 'state' of shape '(1,)' does not have the expected shape '(2,)'.\n"),
-            "The feature 'state' of shape '(1,)' does not have the expected shape '(2,)'.\n"
-        ),
     ):
         dataset.add_frame({"state": torch.randn(1), "task": "Dummy task"})
 
@@ -171,9 +163,7 @@ def test_add_frame_wrong_shape_torch_ndim_0(tmp_path, empty_lerobot_dataset_fact
     dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
     with pytest.raises(
         ValueError,
-        match=re.escape(
+        match=re.escape("The feature 'state' of shape '()' does not have the expected shape '(1,)'.\n"),
-            "The feature 'state' of shape '()' does not have the expected shape '(1,)'.\n"
-        ),
     ):
         dataset.add_frame({"state": torch.tensor(1.0), "task": "Dummy task"})
 
@@ -467,9 +457,7 @@ def test_flatten_unflatten_dict():
     d = unflatten_dict(flatten_dict(d))
 
     # test equality between nested dicts
-    assert json.dumps(original_d, sort_keys=True) == json.dumps(d, sort_keys=True), (
+    assert json.dumps(original_d, sort_keys=True) == json.dumps(d, sort_keys=True), f"{original_d} != {d}"
-        f"{original_d} != {d}"
-    )
 
 
 @pytest.mark.parametrize(
@@ -523,13 +511,7 @@ def test_backward_compatibility(repo_id):
     load_and_compare(i + 1)
 
     # test 2 frames at the middle of first episode
-    i = int(
+    i = int((dataset.episode_data_index["to"][0].item() - dataset.episode_data_index["from"][0].item()) / 2)
-        (
-            dataset.episode_data_index["to"][0].item()
-            - dataset.episode_data_index["from"][0].item()
-        )
-        / 2
-    )
     load_and_compare(i)
     load_and_compare(i + 1)