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>

.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

src_python/habitat_sim/utils/datasets_download.py

@@ -416,10 +416,9 @@ def prompt_yes_no(message):
  answer = input("(y|n): ")
  if answer.lower() == "y":
  return True
- elif answer.lower() == "n":
+ if answer.lower() == "n":
  return False
- else:
- print("Invalid answer...")
+ print("Invalid answer...")
 
 
 def get_version_dir(uid, data_path):