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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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | #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
{
// This file creates the python bindings used by the learning code.
// Refer to the nanobind documentation for more details on these functions.
NB_MODULE(madrona_gpudrive, m)
{
// Each simulator has a madrona submodule that includes base types
// like madrona::py::Tensor and madrona::py::PyExecMode.
madrona::py::setupMadronaSubmodule(m);
// Add bindings for constants defined in src/consts.hpp
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;
// Define RewardType enum
nb::enum_<RewardType>(m, "RewardType")
.value("DistanceBased", RewardType::DistanceBased)
.value("OnGoalAchieved", RewardType::OnGoalAchieved)
.value("Dense", RewardType::Dense);
// Define RewardParams class
nb::class_<RewardParams>(m, "RewardParams")
.def(nb::init<>()) // Default constructor
.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);
// Define Parameters class
nb::class_<Parameters>(m, "Parameters")
.def(nb::init<>()) // Default constructor
.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);
// Define CollisionBehaviour enum
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);
// Bindings for Manager class
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;
// Convert Python dict to C++ unordered_map
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);
}
}
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