File size: 2,718 Bytes
9897e20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import madrona_gpudrive
import pytest
import torch
@pytest.fixture(scope="module")
def sim_init():
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 = 2 # we are going to use the second vehicle as the controlled vehicle
params.IgnoreNonVehicles = True
params.dynamicsModel = madrona_gpudrive.DynamicsModel.InvertibleBicycle
sim = madrona_gpudrive.SimManager(
exec_mode=madrona_gpudrive.madrona.ExecMode.CPU,
gpu_id=0,
scenes=["tests/pytest_data/test.json"],
params=params
)
return sim
def test_forward_inverse_dynamics(sim_init):
sim = sim_init
valid_agent_idx = 1
done_tensor = sim.done_tensor().to_torch()
expert_trajectory_tensor = sim.expert_trajectory_tensor().to_torch()
action_tensor = sim.action_tensor().to_torch()
absolute_obs = sim.absolute_self_observation_tensor().to_torch()
self_obs = sim.self_observation_tensor().to_torch()
actions = torch.zeros_like(action_tensor)
traj = expert_trajectory_tensor[:,valid_agent_idx].squeeze()
pos = traj[:2*91].view(91,2)
vel = traj[2*91:4*91].view(91,2)
headings = traj[4*91:5*91].view(91,1)
invActions = traj[6*91:16*91].view(91,10)
print('invActions', invActions[:2])
position = absolute_obs[0,valid_agent_idx,:2]
heading = absolute_obs[0,valid_agent_idx,7]
speed = self_obs[0, valid_agent_idx, 0]
assert torch.allclose(position, pos[0], atol=1e-2), f"Position mismatch: {position} vs {pos[0]}"
assert pytest.approx(heading.item(), abs=1e-2) == headings[0].item(), f"Heading mismatch: {heading.item()} vs {headings[0].item()}"
assert pytest.approx(speed.item(), abs=1e-2) == torch.norm(vel[0]).item(), f"Speed mismatch: {speed.item()} vs {torch.norm(vel[0]).item()}"
actions[:,valid_agent_idx,:3] = invActions[0,:3]
action_tensor.copy_(actions)
sim.step()
assert torch.allclose(position, pos[1], atol=2e-2), f"Position mismatch: {position} vs {pos[1]} at step {1}"
assert pytest.approx(heading.item(), abs=3e-3) == headings[1].item(), f"Heading mismatch: {heading.item()} vs {headings[1].item()}"
assert pytest.approx(speed.item(), abs=1e-3) == torch.norm(vel[1]).item(), f"Speed mismatch: {speed.item()} vs {torch.norm(vel[1]).item()}" |