| #include "mgr.hpp" |
| #include "consts.hpp" |
| #include "types.hpp" |
|
|
| #include <algorithm> |
| #include <cstdio> |
| #include <chrono> |
| #include <string> |
| #include <filesystem> |
| #include <fstream> |
| #include <random> |
| #include <vector> |
|
|
| using namespace madrona; |
|
|
| int main(int argc, char *argv[]) |
| { |
| using namespace madrona_gpudrive; |
|
|
| if (argc < 3) { |
| fprintf(stderr, "%s TYPE NUM_STEPS [--rand-actions]\n", argv[0]); |
| return -1; |
| } |
| std::string type(argv[1]); |
|
|
| ExecMode exec_mode; |
| if (type == "CPU") { |
| exec_mode = ExecMode::CPU; |
| } else if (type == "CUDA") { |
| exec_mode = ExecMode::CUDA; |
| } else { |
| fprintf(stderr, "Invalid ExecMode\n"); |
| return -1; |
| } |
|
|
| uint64_t num_steps = std::stoul(argv[2]); |
| std::vector<std::string> scenes = { |
| "../data/processed/examples/tfrecord-00000-of-00150_78.json", |
| "../data/processed/examples/tfrecord-00043-of-00150_223.json", |
| "../data/processed/examples/tfrecord-00149-of-00150_111.json" |
| }; |
| uint64_t num_worlds = scenes.size(); |
|
|
| bool rand_actions = false; |
| if (argc >= 4) { |
| if (std::string(argv[3]) == "--rand-actions") { |
| rand_actions = true; |
| } |
| } |
|
|
| Manager mgr({ |
| .execMode = exec_mode, |
| .gpuID = 0, |
| .scenes = scenes, |
| .params = { |
| .polylineReductionThreshold = 1.0, |
| .observationRadius = 100.0, |
| .rewardParams = { |
| .rewardType = RewardType::DistanceBased, |
| .distanceToGoalThreshold = 0.5, |
| .distanceToExpertThreshold = 0.5 |
| }, |
| .maxNumControlledAgents = 0, |
| } |
| }); |
|
|
| std::random_device rd; |
| std::mt19937 rand_gen(rd()); |
| std::uniform_real_distribution<float> acc_gen(-3.0,2.0); |
| std::uniform_real_distribution<float> steer_gen(-0.7,0.7); |
|
|
| auto action_printer = mgr.actionTensor().makePrinter(); |
| auto self_printer = mgr.selfObservationTensor().makePrinter(); |
| auto partner_obs_printer = mgr.partnerObservationsTensor().makePrinter(); |
| auto map_obs_printer = mgr.mapObservationTensor().makePrinter(); |
| auto shapePrinter = mgr.shapeTensor().makePrinter(); |
| auto rewardPrinter = mgr.rewardTensor().makePrinter(); |
| auto donePrinter = mgr.doneTensor().makePrinter(); |
| auto controlledStatePrinter = mgr.controlledStateTensor().makePrinter(); |
| auto agent_map_obs_printer = mgr.agentMapObservationsTensor().makePrinter(); |
| auto info_printer = mgr.infoTensor().makePrinter(); |
| auto means_printer = mgr.worldMeansTensor().makePrinter(); |
| auto metadata_printer = mgr.metadataTensor().makePrinter(); |
|
|
| auto printObs = [&]() { |
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| |
| |
|
|
| metadata_printer.print(); |
| }; |
|
|
| auto worldToShape = |
| mgr.getShapeTensorFromDeviceMemory(); |
|
|
| const auto start = std::chrono::steady_clock::now(); |
| for (CountT i = 0; i < (CountT)num_steps; i++) { |
| if (rand_actions) { |
| for (CountT j = 0; j < (CountT)num_worlds; j++) { |
| auto agentCount = worldToShape.at(j).agentEntityCount; |
| for (CountT k = 0; k < agentCount; k++) { |
| float acc = acc_gen(rand_gen); |
| float steer = steer_gen(rand_gen); |
| float head = 0; |
|
|
| mgr.setAction(j, k, acc, steer, head); |
| } |
| } |
| } |
| mgr.step(); |
| } |
| const auto end = std::chrono::steady_clock::now(); |
| const std::chrono::duration<double> elapsed = end - start; |
| printObs(); |
|
|
| float fps = (double)num_steps * (double)num_worlds / elapsed.count(); |
| printf("FPS %f\n", fps); |
|
|
| uint64_t totalAgentCount{0}; |
| for (CountT j = 0; j < (CountT)num_worlds; j++) { |
| auto agentCount = worldToShape.at(j).agentEntityCount; |
| totalAgentCount += agentCount; |
| } |
|
|
| float fpsNormalized = fps * totalAgentCount; |
| printf("Agent-Normalized FPS %f\n", fpsNormalized); |
| } |
|
|
