| #include "mgr.hpp" |
|
|
| #include <madrona/macros.hpp> |
| #include <madrona/py/bindings.hpp> |
|
|
| #include <nanobind/stl/string.h> |
| #include <nanobind/stl/vector.h> |
|
|
| namespace nb = nanobind; |
|
|
| namespace madrona_gpudrive |
| { |
|
|
| |
| |
| NB_MODULE(madrona_gpudrive, m) |
| { |
| |
| |
| madrona::py::setupMadronaSubmodule(m); |
|
|
| |
| m.attr("kMaxAgentCount") = consts::kMaxAgentCount; |
| m.attr("kMaxRoadEntityCount") = consts::kMaxRoadEntityCount; |
| m.attr("kMaxAgentMapObservationsCount") = consts::kMaxAgentMapObservationsCount; |
| m.attr("episodeLen") = consts::episodeLen; |
| m.attr("numLidarSamples") = consts::numLidarSamples; |
| m.attr("vehicleScale") = consts::vehicleLengthScale; |
|
|
| |
| nb::enum_<RewardType>(m, "RewardType") |
| .value("DistanceBased", RewardType::DistanceBased) |
| .value("OnGoalAchieved", RewardType::OnGoalAchieved) |
| .value("Dense", RewardType::Dense); |
|
|
| |
| nb::class_<RewardParams>(m, "RewardParams") |
| .def(nb::init<>()) |
| .def_rw("rewardType", &RewardParams::rewardType) |
| .def_rw("distanceToGoalThreshold", &RewardParams::distanceToGoalThreshold) |
| .def_rw("distanceToExpertThreshold", &RewardParams::distanceToExpertThreshold); |
|
|
| nb::enum_<FindRoadObservationsWith>(m, "FindRoadObservationsWith") |
| .value("KNearestEntitiesWithRadiusFiltering", FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering) |
| .value("AllEntitiesWithRadiusFiltering", FindRoadObservationsWith::AllEntitiesWithRadiusFiltering); |
|
|
| |
| nb::class_<Parameters>(m, "Parameters") |
| .def(nb::init<>()) |
| .def_rw("polylineReductionThreshold", &Parameters::polylineReductionThreshold) |
| .def_rw("observationRadius", &Parameters::observationRadius) |
| .def_rw("rewardParams", &Parameters::rewardParams) |
| .def_rw("collisionBehaviour", &Parameters::collisionBehaviour) |
| .def_rw("maxNumControlledAgents", &Parameters::maxNumControlledAgents) |
| .def_rw("IgnoreNonVehicles", &Parameters::IgnoreNonVehicles) |
| .def_rw("roadObservationAlgorithm", &Parameters::roadObservationAlgorithm) |
| .def_rw("initOnlyValidAgentsAtFirstStep", &Parameters::initOnlyValidAgentsAtFirstStep) |
| .def_rw("dynamicsModel", &Parameters::dynamicsModel) |
| .def_rw("enableLidar", &Parameters::enableLidar) |
| .def_rw("disableClassicalObs", &Parameters::disableClassicalObs) |
| .def_rw("isStaticAgentControlled", &Parameters::isStaticAgentControlled) |
| .def_rw("readFromTracksToPredict", &Parameters::readFromTracksToPredict); |
|
|
| |
| nb::enum_<CollisionBehaviour>(m, "CollisionBehaviour") |
| .value("AgentStop", CollisionBehaviour::AgentStop) |
| .value("AgentRemoved", CollisionBehaviour::AgentRemoved) |
| .value("Ignore", CollisionBehaviour::Ignore); |
|
|
| nb::enum_<DynamicsModel>(m, "DynamicsModel") |
| .value("Classic", DynamicsModel::Classic) |
| .value("InvertibleBicycle", DynamicsModel::InvertibleBicycle) |
| .value("DeltaLocal", DynamicsModel::DeltaLocal) |
| .value("State", DynamicsModel::State); |
|
|
| nb::enum_<EntityType>(m, "EntityType") |
| .value("_None", EntityType::None) |
| .value("RoadEdge", EntityType::RoadEdge) |
| .value("RoadLine", EntityType::RoadLine) |
| .value("RoadLane", EntityType::RoadLane) |
| .value("CrossWalk", EntityType::CrossWalk) |
| .value("SpeedBump", EntityType::SpeedBump) |
| .value("StopSign", EntityType::StopSign) |
| .value("Vehicle", EntityType::Vehicle) |
| .value("Pedestrian", EntityType::Pedestrian) |
| .value("Cyclist", EntityType::Cyclist) |
| .value("Padding", EntityType::Padding) |
| .value("NumTypes", EntityType::NumTypes); |
|
|
| |
| nb::class_<Manager>(m, "SimManager") |
| .def( |
| "__init__", [](Manager *self, madrona::py::PyExecMode exec_mode, int64_t gpu_id, std::vector<std::string> scenes, Parameters params, bool enable_batch_renderer, uint32_t batch_render_view_width, uint32_t batch_render_view_height) |
| { new (self) Manager(Manager::Config{ |
| .execMode = exec_mode, |
| .gpuID = (int)gpu_id, |
| .scenes = scenes, |
| .params = params, |
| .enableBatchRenderer = enable_batch_renderer, |
| .batchRenderViewWidth = batch_render_view_width, |
| .batchRenderViewHeight = batch_render_view_height});}, |
| nb::arg("exec_mode"), |
| nb::arg("gpu_id"), |
| nb::arg("scenes"), |
| nb::arg("params"), |
| nb::arg("enable_batch_renderer") = false, |
| nb::arg("batch_render_view_width") = 64, |
| nb::arg("batch_render_view_height") = 64) |
| .def("step", &Manager::step) |
| .def("reset", &Manager::reset) |
| .def("action_tensor", &Manager::actionTensor) |
| .def("reward_tensor", &Manager::rewardTensor) |
| .def("done_tensor", &Manager::doneTensor) |
| .def("self_observation_tensor", &Manager::selfObservationTensor) |
| .def("map_observation_tensor", &Manager::mapObservationTensor) |
| .def("partner_observations_tensor", &Manager::partnerObservationsTensor) |
| .def("lidar_tensor", &Manager::lidarTensor) |
| .def("steps_remaining_tensor", &Manager::stepsRemainingTensor) |
| .def("shape_tensor", &Manager::shapeTensor) |
| .def("controlled_state_tensor", &Manager::controlledStateTensor) |
| .def("agent_roadmap_tensor", &Manager::agentMapObservationsTensor) |
| .def("absolute_self_observation_tensor", |
| &Manager::absoluteSelfObservationTensor) |
| .def("bev_observation_tensor", &Manager::bevObservationTensor) |
| .def("valid_state_tensor", &Manager::validStateTensor) |
| .def("info_tensor", &Manager::infoTensor) |
| .def("rgb_tensor", &Manager::rgbTensor) |
| .def("depth_tensor", &Manager::depthTensor) |
| .def("response_type_tensor", &Manager::responseTypeTensor) |
| .def("expert_trajectory_tensor", &Manager::expertTrajectoryTensor) |
| .def("set_maps", &Manager::setMaps) |
| .def("world_means_tensor", &Manager::worldMeansTensor) |
| .def("metadata_tensor", &Manager::metadataTensor) |
| .def("map_name_tensor", &Manager::mapNameTensor) |
| .def("deleteAgents", [](Manager &self, nb::dict py_agents_to_delete) { |
| std::unordered_map<int32_t, std::vector<int32_t>> agents_to_delete; |
|
|
| |
| for (auto item : py_agents_to_delete) { |
| int32_t key = nb::cast<int32_t>(item.first); |
| std::vector<int32_t> value = nb::cast<std::vector<int32_t>>(item.second); |
| agents_to_delete[key] = value; |
| } |
|
|
| self.deleteAgents(agents_to_delete); |
| }) |
| .def("deleted_agents_tensor", &Manager::deletedAgentsTensor) |
| .def("map_name_tensor", &Manager::mapNameTensor) |
| .def("scenario_id_tensor", &Manager::scenarioIdTensor); |
| } |
|
|
| } |
|
|
