CI, fix pytest etc.

.github/workflows/lint.yaml

@@ -5,16 +5,16 @@ on: [push, pull_request]
 jobs:
  lint-format:
  runs-on: ubuntu-latest
- container: 
+ container:
  image: python:3.10-slim
 
  steps:
- - uses: actions/checkout@v2
- - name: Install Ruff, Black, and isort
- run: pip install ruff "black[jupyter]" isort
- - name: Check imports order with isort
- run: isort . --check-only
- - name: Check Python code formatting with Black
- run: black --check .
- - name: Run Ruff linter
- run: ruff .
+  - uses: actions/checkout@v2
+  - name: Install Ruff, Black, and isort
+  run: pip install ruff "black[jupyter]" isort
+  - name: Check imports order with isort
+  run: isort . --check-only
+  - name: Check Python code formatting with Black
+  run: black --check .
+  - name: Run Ruff linter
+  run: ruff .

.github/workflows/test.yaml

@@ -0,0 +1,20 @@
+name: Python package testing
+
+on: [push, pull_request]
+
+jobs:
+ build:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python
+ uses: actions/setup-python@v2
+ with:
+ python-version: "3.10"
+ - name: Install system dependencies
+ run: sudo apt-get update && sudo apt-get install -y libeigen3-dev
+ - name: Install dependencies
+ run: python -m pip install --upgrade pip && python -m pip install -e .
+ - name: Run tests
+ run: pytest

.gitignore

@@ -128,11 +128,6 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
-# Images
-.images
-*.png
-*.gif
-
 # VScode
 .vscode
 .vscode/settings.json
@@ -149,4 +144,5 @@ data/*
 
 # results
 *.csv
-*.pdf
+*.pdf
+*.drawio.bkp

pyproject.toml

@@ -28,6 +28,7 @@ dependencies = [
  "ipykernel>=6.26.0",
  "black[jupyter]>=23.10.1",
  "nb-clean>=3.1.0",
+ "pytest-xdist[psutil]>=3.3.1",
 ]
 requires-python = ">= 3.9, < 3.11"
 readme = "README.md"

src/common/state.py

@@ -262,11 +262,3 @@ def __new__(cls, log_weight, gaussian):
  obj = np.asarray((log_weight, gaussian), dtype=dtype).view(cls)
  # Finally, we must return the newly created object:
  return obj
-
-
-if __name__ == "__main__":
- a = Gaussian(mean=[2.0, 2.0, 3.0], covariance=np.eye(3), ndim=3)
- b = WeightedGaussian(log_weight=0.03)
- import pdb
-
- pdb.set_trace()

src/common/vectorized_gaussian_logpdf.py

@@ -47,8 +47,8 @@ def vectorized_gaussian_logpdf(X, means, covariances):
  results = vectorized_gaussian_logpdf(X, means, covariances)
  fast_time = time.time() - fast_start
 
- print("Reference time:", ref_time)
- print("Vectorized time:", fast_time)
- print("Speedup:", ref_time / fast_time)
+ print("Reference time:", ref_time) # noqa: T201
+ print("Vectorized time:", fast_time) # noqa: T201
+ print("Speedup:", ref_time / fast_time) # noqa: T201
 
  assert np.allclose(results, refs)

src/evaluation_runners/nuscenes/evaluate.py

@@ -92,7 +92,7 @@ def __init__(
 
  # Load data.
  if verbose:
- print("Initializing nuScenes tracking evaluation")
+ print("Initializing nuScenes tracking evaluation") # noqa: T201
  pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, TrackingBox, verbose=verbose)
  gt_boxes = load_gt(nusc, self.eval_set, TrackingBox, verbose=verbose)
  # assert set(pred_boxes.sample_tokens) == set(
@@ -105,10 +105,10 @@ def __init__(
 
  # Filter boxes (distance, points per box, etc.).
  if verbose:
- print("Filtering tracks")
+ print("Filtering tracks") # noqa: T201
  pred_boxes = filter_eval_boxes(nusc, pred_boxes, self.cfg.class_range, verbose=verbose)
  if verbose:
- print("Filtering ground truth tracks")
+ print("Filtering ground truth tracks") # noqa: T201
  gt_boxes = filter_eval_boxes(nusc, gt_boxes, self.cfg.class_range, verbose=verbose)
 
  self.sample_tokens = gt_boxes.sample_tokens
@@ -129,7 +129,7 @@ def evaluate(self) -> Tuple[TrackingMetrics, TrackingMetricDataList]:
  # Step 1: Accumulate metric data for all classes and distance thresholds.
  # -----------------------------------
  if self.verbose:
- print("Accumulating metric data...")
+ print("Accumulating metric data...") # noqa: T201
  metric_data_list = TrackingMetricDataList()
 
  def accumulate_class(curr_class_name):
@@ -156,7 +156,7 @@ def accumulate_class(curr_class_name):
  # Step 2: Aggregate metrics from the metric data.
  # -----------------------------------
  if self.verbose:
- print("Calculating metrics...")
+ print("Calculating metrics...") # noqa: T201
  for class_name in self.cfg.class_names:
  # Find best MOTA to determine threshold to pick for traditional metrics.
  # If multiple thresholds have the same value, pick the one with the highest recall.
@@ -200,7 +200,7 @@ def render(self, md_list: TrackingMetricDataList) -> None:
  :param md_list: TrackingMetricDataList instance.
  """
  if self.verbose:
- print("Rendering curves")
+ print("Rendering curves") # noqa: T201
 
  def savepath(name):
  return os.path.join(self.plot_dir, name + ".pdf")
@@ -223,7 +223,7 @@ def main(self, render_curves: bool = True) -> Dict[str, Any]:
 
  # Dump the metric data, meta and metrics to disk.
  if self.verbose:
- print("Saving metrics to: %s" % self.output_dir)
+ print("Saving metrics to: %s" % self.output_dir) # noqa: T201
  metrics_summary = metrics.serialize()
  metrics_summary["meta"] = self.meta.copy()
  with open(os.path.join(self.output_dir, "metrics_summary.json"), "w") as f:

src/evaluation_runners/nuscenes/nuscenes_eval.py

@@ -96,7 +96,6 @@ def run_tracking_evaluation(self):
  results = {}
 
  for scene_name in self.evaluation_set:
- print(f"scene name: {scene_name}")
  scene = [scene for scene in self.nuscenes_helper.scene if scene["name"] == scene_name][0]
  scene_token = scene["token"]
  results.update(self.process_scene(scene_token))
@@ -245,10 +244,7 @@ def get_measurements_for_one_sample(self, token):
 
  # get object pose
  pos_x, pos_y, pos_z = detection.translation
- quaternion = Quaternion(detection.rotation)
- yaw = quaternion.angle if quaternion.axis[2] > 0 else -quaternion.angle
- print(yaw)
-
+ _quaternion = Quaternion(detection.rotation)
  object_size = detection.size
  detection_confidence = detection.detection_score
 
@@ -258,7 +254,6 @@ def get_measurements_for_one_sample(self, token):
  metadata=ObjectMetadata(object_class=obj_type, size=object_size, confidence=detection_confidence),
  )
  if detection.detection_name == "car" and detection.detection_score > 0.3:
- print(detection.detection_name, detection.detection_score)
  measurements.append(object_detection) # get only x,y position
 
  observations = ObservationList(measurements)

src/run.py

@@ -13,7 +13,6 @@
 from src.metrics import GOSPA
 from src.motion_models import MotionModel
 from src.simulator import MeasurementData, ObjectData
-from src.utils.get_path import get_images_dir
 from src.utils.plotting import setup_ax
 from src.utils.visualizer.common.plot_series import (
  OBJECT_COLORS,
@@ -256,11 +255,6 @@ def run_tracker(
  tracker_P_G: float = 0.999,
 ):
  ground_truth, env_sensor_model, object_data, meas_data = generate_environment(object_configs, total_time, env_motion_model, env_meas_model, env_P_D, env_lambda_c, env_range_c)
- # ground_truth = GroundTruthConfig(object_configs, total_time)
- # env_sensor_model = SensorModelConfig(env_P_D, env_lambda_c, env_range_c)
- # object_data = ObjectData(ground_truth, env_motion_model, if_noisy=False)
- # meas_data_gen = MeasurementData(object_data, env_sensor_model, env_meas_model)
- # meas_data = [next(meas_data_gen) for _ in range(total_time)]
 
  tracker_meas_model = env_meas_model if tracker_meas_model is None else tracker_meas_model
  tracker_motion_model = env_motion_model if tracker_motion_model is None else tracker_motion_model

src/utils/get_path.py

@@ -17,14 +17,14 @@ def get_data_dir():
  return os.path.join(get_project_dir(), "data")
 
 
-def get_images_dir(current_file=__file__, dir_name=".images"):
+def get_images_dir(current_file=__file__, dir_name="images"):
  current_dir = os.path.dirname(os.path.abspath(current_file))
  image_dir = os.path.join(current_dir, dir_name)
  os.makedirs(image_dir, exist_ok=True)
  return image_dir
 
 
-def delete_images_dir(current_file=__file__, dir_name=".images") -> None:
+def delete_images_dir(current_file=__file__, dir_name="images") -> None:
  current_dir = os.path.dirname(os.path.abspath(current_file))
  image_dir = os.path.join(current_dir, dir_name)
  try:

src/utils/profiler.py

@@ -68,17 +68,17 @@ def get_profile_data(self):
  for _i in range(1000):
  r = re.compile(r"^$")
 
- print(p.get_profile_data())
+ print(p.get_profile_data()) # noqa: T201
 
  try:
  with Profiler(enabled=True, contextstr="exception") as p:
  raise ValueError("Error")
  except ValueError:
- print(p.get_profile_data())
+ print(p.get_profile_data()) # noqa: T201
 
  profiling_enabled = False
  with Profiler(enabled=profiling_enabled, contextstr="not enabled") as p:
  for _i in range(1000):
  r = re.compile(r"^$")
 
- print(p.get_profile_data())
+ print(p.get_profile_data()) # noqa: T201

src/utils/vectorized_gaussian_logpdf.py

@@ -47,8 +47,8 @@ def vectorized_gaussian_logpdf(X, means, covariances):
  results = vectorized_gaussian_logpdf(X, means, covariances)
  fast_time = time.time() - fast_start
 
- print("Reference time:", ref_time)
- print("Vectorized time:", fast_time)
- print("Speedup:", ref_time / fast_time)
+ print("Reference time:", ref_time) # noqa: T201
+ print("Vectorized time:", fast_time) # noqa: T201
+ print("Speedup:", ref_time / fast_time) # noqa: T201
 
  assert np.allclose(results, refs)

tests.yml

@@ -0,0 +1,24 @@
+# .github/workflows/tests.yml
+name: Tests
+on: push
+jobs:
+ setup:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v2
+ - uses: actions/setup-python@v1
+ with:
+ python-version: 3.8
+ architecture: x64
+ - run: sudo apt -y install libeigen3-dev
+ - run: cd /usr/include && sudo ln -sf eigen3/Eigen Eigen
+ - run: sudo apt -y install ninja-build
+ - run: pip install nox==2020.12.31
+ - run: pip install poetry==1.1.6
+ tests:
+ - run: nox
+ - name: Archive code coverage results
+ uses: actions/upload-artifact@v2
+ with:
+ name: test-pmbm-plots
+ path: tests/PMBM/.images/*.png

tests/MOT/n_mot_test.py

@@ -3,13 +3,12 @@
 from src.motion_models import ConstantVelocityMotionModel
 from src.scenarios.initial_conditions import all_object_scenarios
 from src.trackers.n_object_trackers import GlobalNearestNeighboursTracker
-from tests.testing_trackers import ( # noqa
- env_clutter_rate,
- env_detection_probability,
- env_measurement_model,
- filepath_fixture,
- test_synthetic_scenario,
-)
+from tests.testing_trackers import test_synthetic_scenario # noqa: F401
+
+
+@pytest.fixture(scope="session")
+def _file_():
+ yield __file__
 
 
 @pytest.fixture(params=[x for x in all_object_scenarios if x.motion_model == ConstantVelocityMotionModel]) # and

tests/PHD/phd_test.py

@@ -6,12 +6,12 @@
 from src.scenarios.initial_conditions import all_object_scenarios
 from src.trackers.multiple_object_trackers.PHD import GMPHD
 
-from ..testing_trackers import ( # noqa
- env_clutter_rate,
- env_detection_probability,
- env_measurement_model,
- test_synthetic_scenario,
-)
+from ..testing_trackers import test_synthetic_scenario # noqa: F401
+
+
+@pytest.fixture(scope="session")
+def _file_():
+ yield __file__
 
 
 @pytest.fixture(params=[x for x in all_object_scenarios if x.motion_model == ConstantVelocityMotionModel])