Fix ci pytorch cuda (#1946)

* (bugfix): fix python-lint CI

* Try to improve python lint

* try pytorch suggested previous version 1.12 w/ cuda 11.3

Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>

Author: aclegg3

.circleci/config.yml

@@ -123,7 +123,8 @@ commands:
                 . activate habitat
                 # For whatever reason we have to install pytorch first. If it isn't
                 # it installs the 1.4 cpuonly version. Which is no good.
-                conda install -q -y pytorch torchvision cudatoolkit=11.3 -c pytorch
+                #conda install -q -y pytorch=1.12 torchvision pytorch-cuda=11.3 -c pytorch -c nvidia
+                conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
                 conda install -q -y -c conda-forge ninja numpy pytest pytest-cov ccache hypothesis pytest-mock
                 pip install pytest-sugar pytest-xdist pytest-benchmark opencv-python cython mock
               fi
@@ -184,13 +185,6 @@ jobs:
       - run:
           name: setup
           command: |
-              # Updated llvm to v 10
-              sudo apt-get update
-              sudo apt-get install llvm-10
-              cd /usr/bin
-              sudo rm  llvm-config
-              sudo ln -s llvm-config-10 llvm-config
-              cd ~/project/
               pip install -U pip
               pip install -U --prefer-binary \
                 black \

.pre-commit-config.yaml

@@ -68,15 +68,15 @@ repos:
         exclude: docs/
 
 -   repo: https://github.com/pycqa/flake8
-    rev: 4.0.1
+    rev: 5.0.4
     hooks:
     -   id: flake8
         exclude: docs/
         additional_dependencies: &flake8_dependencies
         - flake8-bugbear==22.1.11
         - flake8-builtins==1.5.3
         - flake8-comprehensions==3.10.0
-        - flake8-return==1.1.3
+        - flake8-return==1.2.0
         - flake8-simplify==0.17.0
 
 -   repo: https://github.com/asottile/yesqa

examples/fairmotion_interface.py

@@ -221,7 +221,7 @@ def save_metadata(self, file: str):
                     break
 
                 # we are no longer overwriting a file
-                elif "/" not in file:
+                if "/" not in file:
                     # file is not a file path, we need to aim it at our directory
                     if ".json" not in file:
                         # add file type
@@ -422,14 +422,14 @@ def convert_CMUamass_single_pose(
                 raise KeyError(
                     "Error: pose data does not have a transform for that joint name"
                 )
-            elif joint_type not in [phy.JointType.Spherical]:
+            if joint_type not in [phy.JointType.Spherical]:
                 raise NotImplementedError(
                     f"Error: {joint_type} is not a supported joint type"
                 )
-            else:
-                T = pose.get_transform(pose_joint_index, local=True)
-                if joint_type == phy.JointType.Spherical:
-                    Q, _ = conversions.T2Qp(T)
+
+            T = pose.get_transform(pose_joint_index, local=True)
+            if joint_type == phy.JointType.Spherical:
+                Q, _ = conversions.T2Qp(T)
 
             new_pose += list(Q)
 

setup.cfg

@@ -27,7 +27,7 @@ ignore =
 	A003,
 	C401,C402,C408,
 	SIM105,SIM113,SIM9
-	R504,
+	R504,R505,
 	W503
 per-file-ignores =
 	*/__init__.py:F401

src_python/habitat_sim/nav/greedy_geodesic_follower.py

@@ -163,8 +163,7 @@ def next_action_along(self, goal_pos: np.ndarray) -> Any:
 
         if next_act == GreedyFollowerCodes.ERROR:
             raise errors.GreedyFollowerError()
-        else:
-            return self.action_mapping[next_act]
+        return self.action_mapping[next_act]
 
     def find_path(self, goal_pos: np.ndarray) -> List[Any]:
         r"""Finds the sequence actions that greedily follow the geodesic

src_python/habitat_sim/registry.py

@@ -75,8 +75,8 @@ def _wrapper(controller: Type[SceneNodeControl]):
 
         if controller is None:
             return _wrapper
-        else:
-            return _wrapper(controller)
+
+        return _wrapper(controller)
 
     @classmethod
     def register_noise_model(
@@ -104,8 +104,8 @@ def _wrapper(noise_model: Type[SensorNoiseModel]):
 
         if noise_model is None:
             return _wrapper
-        else:
-            return _wrapper(noise_model)
+
+        return _wrapper(noise_model)
 
     @classmethod
     def register_pose_extractor(
@@ -133,8 +133,8 @@ def _wrapper(pose_extractor: Type[PoseExtractor]):
 
         if pose_extractor is None:
             return _wrapper
-        else:
-            return _wrapper(pose_extractor)
+
+        return _wrapper(pose_extractor)
 
     @classmethod
     def _get_impl(cls, _type, name: str):

src_python/habitat_sim/sensors/noise_models/no_noise_model.py

@@ -32,7 +32,6 @@ def apply(
     ) -> Union[ndarray, "torch.Tensor"]:
         if isinstance(x, np.ndarray):
             return x.copy()
-        elif torch is not None and torch.is_tensor(x):
+        if torch is not None and torch.is_tensor(x):
             return x.clone()
-        else:
-            return x
+        return x

src_python/habitat_sim/sensors/noise_models/redwood_depth_noise_model.py

@@ -40,8 +40,8 @@ def _undistort(x, y, z, model):
 
     if f < 1e-5:
         return 0.0
-    else:
-        return z / f
+
+    return z / f
 
 
 @numba.jit(nopython=True, parallel=True, fastmath=True)
@@ -134,13 +134,12 @@ def simulate(self, gt_depth: Union[ndarray, "Tensor"]) -> Union[ndarray, "Tensor
         if cuda_enabled:
             if isinstance(gt_depth, np.ndarray):
                 return self._impl.simulate_from_cpu(gt_depth)
-            else:
-                noisy_depth = torch.empty_like(gt_depth)
-                rows, cols = gt_depth.size()
-                self._impl.simulate_from_gpu(
-                    gt_depth.data_ptr(), rows, cols, noisy_depth.data_ptr()  # type: ignore[attr-defined]
-                )
-                return noisy_depth
+            noisy_depth = torch.empty_like(gt_depth)
+            rows, cols = gt_depth.size()
+            self._impl.simulate_from_gpu(
+                gt_depth.data_ptr(), rows, cols, noisy_depth.data_ptr()  # type: ignore[attr-defined]
+            )
+            return noisy_depth
         else:
             return self._impl.simulate(gt_depth)
 

src_python/habitat_sim/simulator.py

@@ -649,76 +649,76 @@ def draw_observation(self) -> None:
             # do nothing in draw observation, get_observation will be called after this
             # run the simulation there
             return
-        else:
-            assert self._sim.renderer is not None
-            # see if the sensor is attached to a scene graph, otherwise it is invalid,
-            # and cannot make any observation
-            if not self._sensor_object.object:
-                raise habitat_sim.errors.InvalidAttachedObject(
-                    "Sensor observation requested but sensor is invalid.\
+
+        assert self._sim.renderer is not None
+        # see if the sensor is attached to a scene graph, otherwise it is invalid,
+        # and cannot make any observation
+        if not self._sensor_object.object:
+            raise habitat_sim.errors.InvalidAttachedObject(
+                "Sensor observation requested but sensor is invalid.\
                     (has it been detached from a scene node?)"
-                )
-            self._sim.renderer.draw(self._sensor_object, self._sim)
+            )
+        self._sim.renderer.draw(self._sensor_object, self._sim)
 
     def _draw_observation_async(self) -> None:
         if self._spec.sensor_type == SensorType.AUDIO:
             # do nothing in draw observation, get_observation will be called after this
             # run the simulation there
             return
-        else:
-            assert self._sim.renderer is not None
-            if (
-                self._spec.sensor_type == SensorType.SEMANTIC
-                and self._sim.get_active_scene_graph()
-                is not self._sim.get_active_semantic_scene_graph()
-            ):
-                raise RuntimeError(
-                    "Async drawing doesn't support semantic rendering when there are multiple scene graphs"
-                )
-            # TODO: sync this path with renderer changes as above (render from sensor object)
 
-            # see if the sensor is attached to a scene graph, otherwise it is invalid,
-            # and cannot make any observation
-            if not self._sensor_object.object:
-                raise habitat_sim.errors.InvalidAttachedObject(
-                    "Sensor observation requested but sensor is invalid.\
-                    (has it been detached from a scene node?)"
-                )
+        assert self._sim.renderer is not None
+        if (
+            self._spec.sensor_type == SensorType.SEMANTIC
+            and self._sim.get_active_scene_graph()
+            is not self._sim.get_active_semantic_scene_graph()
+        ):
+            raise RuntimeError(
+                "Async drawing doesn't support semantic rendering when there are multiple scene graphs"
+            )
+        # TODO: sync this path with renderer changes as above (render from sensor object)
+
+        # see if the sensor is attached to a scene graph, otherwise it is invalid,
+        # and cannot make any observation
+        if not self._sensor_object.object:
+            raise habitat_sim.errors.InvalidAttachedObject(
+                "Sensor observation requested but sensor is invalid.\
+                (has it been detached from a scene node?)"
+            )
 
-            # get the correct scene graph based on application
-            if self._spec.sensor_type == SensorType.SEMANTIC:
-                if self._sim.semantic_scene is None:
-                    raise RuntimeError(
-                        "SemanticSensor observation requested but no SemanticScene is loaded"
-                    )
-                scene = self._sim.get_active_semantic_scene_graph()
-            else:  # SensorType is DEPTH or any other type
-                scene = self._sim.get_active_scene_graph()
+        # get the correct scene graph based on application
+        if self._spec.sensor_type == SensorType.SEMANTIC:
+            if self._sim.semantic_scene is None:
+                raise RuntimeError(
+                    "SemanticSensor observation requested but no SemanticScene is loaded"
+                )
+            scene = self._sim.get_active_semantic_scene_graph()
+        else:  # SensorType is DEPTH or any other type
+            scene = self._sim.get_active_scene_graph()
 
-            # now, connect the agent to the root node of the current scene graph
+        # now, connect the agent to the root node of the current scene graph
 
-            # sanity check is not needed on agent:
-            # because if a sensor is attached to a scene graph,
-            # it implies the agent is attached to the same scene graph
-            # (it assumes backend simulator will guarantee it.)
+        # sanity check is not needed on agent:
+        # because if a sensor is attached to a scene graph,
+        # it implies the agent is attached to the same scene graph
+        # (it assumes backend simulator will guarantee it.)
 
-            agent_node = self._agent.scene_node
-            agent_node.parent = scene.get_root_node()
+        agent_node = self._agent.scene_node
+        agent_node.parent = scene.get_root_node()
 
-            # get the correct scene graph based on application
-            if self._spec.sensor_type == SensorType.SEMANTIC:
-                scene = self._sim.get_active_semantic_scene_graph()
-            else:  # SensorType is DEPTH or any other type
-                scene = self._sim.get_active_scene_graph()
+        # get the correct scene graph based on application
+        if self._spec.sensor_type == SensorType.SEMANTIC:
+            scene = self._sim.get_active_semantic_scene_graph()
+        else:  # SensorType is DEPTH or any other type
+            scene = self._sim.get_active_scene_graph()
 
-            render_flags = habitat_sim.gfx.Camera.Flags.NONE
+        render_flags = habitat_sim.gfx.Camera.Flags.NONE
 
-            if self._sim.frustum_culling:
-                render_flags |= habitat_sim.gfx.Camera.Flags.FRUSTUM_CULLING
+        if self._sim.frustum_culling:
+            render_flags |= habitat_sim.gfx.Camera.Flags.FRUSTUM_CULLING
 
-            self._sim.renderer.enqueue_async_draw_job(
-                self._sensor_object, scene, self.view, render_flags
-            )
+        self._sim.renderer.enqueue_async_draw_job(
+            self._sensor_object, scene, self.view, render_flags
+        )
 
     def get_observation(self) -> Union[ndarray, "Tensor"]:
         if self._spec.sensor_type == SensorType.AUDIO:

src_python/habitat_sim/utils/data/data_structures.py

@@ -14,11 +14,11 @@ def __init__(self, capacity=1000):
     def __getitem__(self, key):
         if not self.__contains__(key):
             raise KeyError("Key {} not in extractor cache".format(key))
-        else:
-            # Accessing the data should move it to front of cache
-            k, data = self._order.pop(key)
-            self._order[key] = (k, data)
-            return data
+
+        # Accessing the data should move it to front of cache
+        k, data = self._order.pop(key)
+        self._order[key] = (k, data)
+        return data
 
     def __contains__(self, key):
         return key in self._order