| import collections |
| import madrona_gpudrive |
| import pytest |
|
|
| @pytest.fixture(scope="module") |
| def simulation_results(): |
| |
| reward_params = madrona_gpudrive.RewardParams() |
| reward_params.rewardType = madrona_gpudrive.RewardType.DistanceBased |
| reward_params.distanceToGoalThreshold = 1.0 |
| reward_params.distanceToExpertThreshold = 1.0 |
|
|
| |
| params = madrona_gpudrive.Parameters() |
| params.polylineReductionThreshold = 0.5 |
| params.observationRadius = 10.0 |
| params.collisionBehaviour = madrona_gpudrive.CollisionBehaviour.AgentStop |
| params.rewardParams = reward_params |
| params.maxNumControlledAgents = 0 |
| params.IgnoreNonVehicles = True |
| params.isStaticAgentControlled = False |
| |
| sim = madrona_gpudrive.SimManager( |
| exec_mode=madrona_gpudrive.madrona.ExecMode.CPU, |
| gpu_id=0, |
| scenes=["tests/pytest_data/test.json"], |
| params=params, |
| enable_batch_renderer=False, |
| batch_render_view_width=1024, |
| batch_render_view_height=1024 |
| ) |
|
|
| done = sim.done_tensor().to_torch() |
|
|
| while not done.all(): |
| sim.step() |
| |
| return sim |
|
|
| def test_goal_reaching(simulation_results): |
| sim = simulation_results |
| info, shape = sim.info_tensor().to_torch(), sim.shape_tensor().to_torch() |
| info_valid = info[info[:, :, -1] == float(madrona_gpudrive.EntityType.Vehicle)] |
| goal_reached = info_valid[:, -2].sum().item() |
| assert goal_reached == shape[:, 0].sum().item() |
| print("Test passed!") |
|
|
| def test_collision_rate(simulation_results): |
| sim = simulation_results |
| info = sim.info_tensor().to_torch() |
|
|
| info_valid = info[info[:, :, -1] == float(madrona_gpudrive.EntityType.Vehicle)] |
| collisions = info_valid[:, :3].sum().item() |
| try: |
| assert collisions == 0 |
| except AssertionError: |
| print("Assertion failed! Info tensor:") |
| print(info_valid) |
| raise |
| print("Test passed!") |
|
|