Repository: adept-thu/MomAD Branch: main Commit: da7539578bc1 Files: 1700 Total size: 46.0 MB Directory structure: gitextract_ijrumtl_/ ├── .gitignore ├── LICENSE ├── README.md ├── close_loop/ │ ├── SparseDrive_MomAD/ │ │ ├── adzoo/ │ │ │ └── sparsedrive/ │ │ │ ├── configs/ │ │ │ │ ├── momad_small_b2d_stage1.py │ │ │ │ ├── momad_small_b2d_stage2_multiplan.py │ │ │ │ ├── momad_small_b2d_stage2_singleplan.py │ │ │ │ ├── sparsedrive_small_b2d_stage1.py │ │ │ │ ├── sparsedrive_small_b2d_stage2_cmd_singleplan.py │ │ │ │ └── sparsedrive_small_b2d_stage2_targetpoint_multiplan.py │ │ │ ├── dist_train.sh │ │ │ ├── scripts/ │ │ │ │ ├── create_data.sh │ │ │ │ ├── kmeans.sh │ │ │ │ ├── test.sh │ │ │ │ ├── test_roboAD.sh │ │ │ │ ├── train.sh │ │ │ │ ├── train_6s.sh │ │ │ │ ├── train_roboAD.sh │ │ │ │ └── visualize.sh │ │ │ ├── tools/ │ │ │ │ ├── benchmark.py │ │ │ │ ├── data_converter/ │ │ │ │ │ ├── B2D_converter.py │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── nuscenes_converter.py │ │ │ │ │ ├── nuscenes_converter_1_10.py │ │ │ │ │ ├── nuscenes_converter_6s.py │ │ │ │ │ └── nuscenes_converter_hrad_planing_scene.py │ │ │ │ ├── dist_test.sh │ │ │ │ ├── dist_train.sh │ │ │ │ ├── fuse_conv_bn.py │ │ │ │ ├── kmeans/ │ │ │ │ │ ├── kmeans_det.py │ │ │ │ │ ├── kmeans_map.py │ │ │ │ │ ├── kmeans_motion.py │ │ │ │ │ └── kmeans_plan.py │ │ │ │ ├── test.py │ │ │ │ ├── train.py │ │ │ │ ├── train_single.py │ │ │ │ └── visualization/ │ │ │ │ ├── bev_render.py │ │ │ │ └── cam_render.py │ │ │ └── train.py │ │ ├── leaderboard/ │ │ │ ├── .pylintrc │ │ │ ├── CHANGELOG.md │ │ │ ├── LICENSE │ │ │ ├── README.md │ │ │ ├── docs/ │ │ │ │ ├── Gemfile │ │ │ │ ├── LICENSE │ │ │ │ ├── README.md │ │ │ │ ├── _config.yml │ │ │ │ ├── _includes/ │ │ │ │ │ ├── footer.html │ │ │ │ │ ├── google-analytics.html │ │ │ │ │ ├── head.html │ │ │ │ │ ├── navbar.html │ │ │ │ │ ├── read_time.html │ │ │ │ │ └── scripts.html │ │ │ │ ├── _layouts/ │ │ │ │ │ ├── default.html │ │ │ │ │ ├── home.html │ │ │ │ │ ├── page.html │ │ │ │ │ └── post.html │ │ │ │ ├── _sass/ │ │ │ │ │ └── styles.scss │ │ │ │ ├── about.html │ │ │ │ ├── assets/ │ │ │ │ │ ├── main.scss │ │ │ │ │ ├── scripts.js │ │ │ │ │ └── vendor/ │ │ │ │ │ ├── bootstrap/ │ │ │ │ │ │ ├── css/ │ │ │ │ │ │ │ └── bootstrap.css │ │ │ │ │ │ └── js/ │ │ │ │ │ │ ├── bootstrap.bundle.js │ │ │ │ │ │ └── bootstrap.js │ │ │ │ │ ├── fontawesome-free/ │ │ │ │ │ │ ├── LICENSE.txt │ │ │ │ │ │ ├── css/ │ │ │ │ │ │ │ ├── all.css │ │ │ │ │ │ │ ├── brands.css │ │ │ │ │ │ │ ├── fontawesome.css │ │ │ │ │ │ │ ├── regular.css │ │ │ │ │ │ │ ├── solid.css │ │ │ │ │ │ │ ├── svg-with-js.css │ │ │ │ │ │ │ └── v4-shims.css │ │ │ │ │ │ ├── js/ │ │ │ │ │ │ │ ├── all.js │ │ │ │ │ │ │ ├── brands.js │ │ │ │ │ │ │ ├── conflict-detection.js │ │ │ │ │ │ │ ├── fontawesome.js │ │ │ │ │ │ │ ├── regular.js │ │ │ │ │ │ │ ├── solid.js │ │ │ │ │ │ │ └── v4-shims.js │ │ │ │ │ │ ├── less/ │ │ │ │ │ │ │ ├── _animated.less │ │ │ │ │ │ │ ├── _bordered-pulled.less │ │ │ │ │ │ │ ├── _core.less │ │ │ │ │ │ │ ├── _fixed-width.less │ │ │ │ │ │ │ ├── _icons.less │ │ │ │ │ │ │ ├── _larger.less │ │ │ │ │ │ │ ├── _list.less │ │ │ │ │ │ │ ├── _mixins.less │ │ │ │ │ │ │ ├── _rotated-flipped.less │ │ │ │ │ │ │ ├── _screen-reader.less │ │ │ │ │ │ │ ├── _shims.less │ │ │ │ │ │ │ ├── _stacked.less │ │ │ │ │ │ │ ├── _variables.less │ │ │ │ │ │ │ ├── brands.less │ │ │ │ │ │ │ ├── fontawesome.less │ │ │ │ │ │ │ ├── regular.less │ │ │ │ │ │ │ ├── solid.less │ │ │ │ │ │ │ └── v4-shims.less │ │ │ │ │ │ └── scss/ │ │ │ │ │ │ ├── _animated.scss │ │ │ │ │ │ ├── _bordered-pulled.scss │ │ │ │ │ │ ├── _core.scss │ │ │ │ │ │ ├── _fixed-width.scss │ │ │ │ │ │ ├── _icons.scss │ │ │ │ │ │ ├── _larger.scss │ │ │ │ │ │ ├── _list.scss │ │ │ │ │ │ ├── _mixins.scss │ │ │ │ │ │ ├── _rotated-flipped.scss │ │ │ │ │ │ ├── _screen-reader.scss │ │ │ │ │ │ ├── _shims.scss │ │ │ │ │ │ ├── _stacked.scss │ │ │ │ │ │ ├── _variables.scss │ │ │ │ │ │ ├── brands.scss │ │ │ │ │ │ ├── fontawesome.scss │ │ │ │ │ │ ├── regular.scss │ │ │ │ │ │ ├── solid.scss │ │ │ │ │ │ └── v4-shims.scss │ │ │ │ │ ├── jquery/ │ │ │ │ │ │ ├── jquery.js │ │ │ │ │ │ └── jquery.slim.js │ │ │ │ │ └── startbootstrap-clean-blog/ │ │ │ │ │ ├── js/ │ │ │ │ │ │ └── jqBootstrapValidation.js │ │ │ │ │ └── scss/ │ │ │ │ │ ├── _bootstrap-overrides.scss │ │ │ │ │ ├── _contact.scss │ │ │ │ │ ├── _footer.scss │ │ │ │ │ ├── _global.scss │ │ │ │ │ ├── _masthead.scss │ │ │ │ │ ├── _mixins.scss │ │ │ │ │ ├── _navbar.scss │ │ │ │ │ ├── _post.scss │ │ │ │ │ ├── _variables.scss │ │ │ │ │ └── clean-blog.scss │ │ │ │ ├── contact.html │ │ │ │ ├── get_started.html │ │ │ │ ├── gulpfile.js │ │ │ │ ├── index.html │ │ │ │ ├── jekyll-theme-clean-blog.gemspec │ │ │ │ ├── leaderboard.html │ │ │ │ ├── posts/ │ │ │ │ │ └── index.html │ │ │ │ └── submit.html │ │ │ ├── leaderboard/ │ │ │ │ ├── __init__.py │ │ │ │ ├── autoagents/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── agent_wrapper.py │ │ │ │ │ ├── autonomous_agent.py │ │ │ │ │ ├── dummy_agent.py │ │ │ │ │ ├── human_agent.py │ │ │ │ │ ├── human_agent_config.txt │ │ │ │ │ ├── npc_agent.py │ │ │ │ │ ├── ros1_agent.py │ │ │ │ │ ├── ros2_agent.py │ │ │ │ │ └── ros_base_agent.py │ │ │ │ ├── envs/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── sensor_interface.py │ │ │ │ ├── leaderboard_evaluator.py │ │ │ │ ├── scenarios/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── route_scenario.py │ │ │ │ │ └── scenario_manager.py │ │ │ │ └── utils/ │ │ │ │ ├── __init__.py │ │ │ │ ├── checkpoint_tools.py │ │ │ │ ├── parked_vehicles.py │ │ │ │ ├── result_writer.py │ │ │ │ ├── route_indexer.py │ │ │ │ ├── route_manipulation.py │ │ │ │ ├── route_parser.py │ │ │ │ └── statistics_manager.py │ │ │ ├── requirements.txt │ │ │ ├── run_leaderboard.sh │ │ │ ├── scripts/ │ │ │ │ ├── Dockerfile.master │ │ │ │ ├── Dockerfile.ros │ │ │ │ ├── agent_entrypoint.sh │ │ │ │ ├── code_check_and_formatting.sh │ │ │ │ ├── make_docker.sh │ │ │ │ ├── manage_scenarios.py │ │ │ │ ├── merge_statistics.py │ │ │ │ ├── pretty_print_json.py │ │ │ │ ├── route_creator.py │ │ │ │ ├── route_displayer.py │ │ │ │ ├── route_summarizer.py │ │ │ │ ├── run_evaluation.sh │ │ │ │ ├── run_evaluation_debug.sh │ │ │ │ ├── run_evaluation_multi_admlp.sh │ │ │ │ ├── run_evaluation_multi_tcp.sh │ │ │ │ ├── run_evaluation_multi_uniad.sh │ │ │ │ ├── run_evaluation_multi_uniad_tiny.sh │ │ │ │ ├── run_evaluation_multi_vad.sh │ │ │ │ ├── run_evalutaion_multi_sparsedrive.sh │ │ │ │ ├── scenario_creator.py │ │ │ │ ├── scenario_orderer.py │ │ │ │ └── weather_creator.py │ │ │ └── team_code/ │ │ │ ├── pid_controller.py │ │ │ ├── planner.py │ │ │ ├── sparsedrive_b2d_agent.py │ │ │ ├── uniad_b2d_agent.py │ │ │ ├── vad_b2d_agent.py │ │ │ └── vad_b2d_agent_visualize.py │ │ ├── mmdet3d_plugin/ │ │ │ ├── __init__.py │ │ │ ├── apis/ │ │ │ │ ├── __init__.py │ │ │ │ ├── mmdet_train.py │ │ │ │ ├── test.py │ │ │ │ └── train.py │ │ │ ├── core/ │ │ │ │ ├── box3d.py │ │ │ │ └── evaluation/ │ │ │ │ ├── __init__.py │ │ │ │ └── eval_hooks.py │ │ │ ├── datasets/ │ │ │ │ ├── __init__.py │ │ │ │ ├── b2d_3d_dataset.py │ │ │ │ ├── builder.py │ │ │ │ ├── evaluation/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── detection/ │ │ │ │ │ │ └── nuscenes_styled_eval_utils.py │ │ │ │ │ ├── map/ │ │ │ │ │ │ ├── AP.py │ │ │ │ │ │ ├── distance.py │ │ │ │ │ │ └── vector_eval.py │ │ │ │ │ ├── motion/ │ │ │ │ │ │ ├── motion_eval_uniad.py │ │ │ │ │ │ └── motion_utils.py │ │ │ │ │ └── planning/ │ │ │ │ │ └── planning_eval.py │ │ │ │ ├── map_utils/ │ │ │ │ │ ├── nuscmap_extractor.py │ │ │ │ │ └── utils.py │ │ │ │ ├── nuscenes_3d_dataset.py │ │ │ │ ├── pipelines/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── augment.py │ │ │ │ │ ├── loading.py │ │ │ │ │ ├── transform.py │ │ │ │ │ └── vectorize.py │ │ │ │ ├── samplers/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── distributed_sampler.py │ │ │ │ │ ├── group_in_batch_sampler.py │ │ │ │ │ ├── group_sampler.py │ │ │ │ │ └── sampler.py │ │ │ │ └── utils.py │ │ │ ├── models/ │ │ │ │ ├── MomAD.py │ │ │ │ ├── MomAD_head.py │ │ │ │ ├── __init__.py │ │ │ │ ├── attention.py │ │ │ │ ├── base_target.py │ │ │ │ ├── blocks.py │ │ │ │ ├── detection3d/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── decoder.py │ │ │ │ │ ├── detection3d_blocks.py │ │ │ │ │ ├── detection3d_head.py │ │ │ │ │ ├── losses.py │ │ │ │ │ └── target.py │ │ │ │ ├── grid_mask.py │ │ │ │ ├── instance_bank.py │ │ │ │ ├── map/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── decoder.py │ │ │ │ │ ├── loss.py │ │ │ │ │ ├── map_blocks.py │ │ │ │ │ ├── match_cost.py │ │ │ │ │ └── target.py │ │ │ │ ├── motion/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── decoder.py │ │ │ │ │ ├── instance_queue.py │ │ │ │ │ ├── motion_blocks.py │ │ │ │ │ ├── motion_planning_cls_head.py │ │ │ │ │ ├── motion_planning_head.py │ │ │ │ │ ├── motion_planning_head_MomAD.py │ │ │ │ │ └── target.py │ │ │ │ ├── sparsedrive.py │ │ │ │ └── sparsedrive_head.py │ │ │ └── ops/ │ │ │ ├── __init__.py │ │ │ ├── deformable_aggregation.py │ │ │ ├── setup.py │ │ │ └── src/ │ │ │ ├── deformable_aggregation.cpp │ │ │ └── deformable_aggregation_cuda.cu │ │ ├── requirement.txt │ │ ├── scenario_runner/ │ │ │ ├── .pylintrc │ │ │ ├── .readthedocs.yml │ │ │ ├── CARLA_VER │ │ │ ├── Dockerfile │ │ │ ├── Docs/ │ │ │ │ ├── CHANGELOG.md │ │ │ │ ├── CODE_OF_CONDUCT.md │ │ │ │ ├── CONTRIBUTING.md │ │ │ │ ├── FAQ.md │ │ │ │ ├── agent_evaluation.md │ │ │ │ ├── coding_standard.md │ │ │ │ ├── creating_new_scenario.md │ │ │ │ ├── extra.css │ │ │ │ ├── getting_scenariorunner.md │ │ │ │ ├── getting_started.md │ │ │ │ ├── index.md │ │ │ │ ├── list_of_scenarios.md │ │ │ │ ├── metrics_module.md │ │ │ │ ├── openscenario_support.md │ │ │ │ ├── requirements.txt │ │ │ │ └── ros_agent.md │ │ │ ├── Jenkinsfile │ │ │ ├── LICENSE │ │ │ ├── README.md │ │ │ ├── manual_control.py │ │ │ ├── metrics_manager.py │ │ │ ├── mkdocs.yml │ │ │ ├── no_rendering_mode.py │ │ │ ├── requirements.txt │ │ │ ├── scenario_runner.py │ │ │ └── srunner/ │ │ │ ├── __init__.py │ │ │ ├── autoagents/ │ │ │ │ ├── __init__.py │ │ │ │ ├── agent_wrapper.py │ │ │ │ ├── autonomous_agent.py │ │ │ │ ├── dummy_agent.py │ │ │ │ ├── human_agent.py │ │ │ │ ├── human_agent_config.txt │ │ │ │ ├── npc_agent.py │ │ │ │ ├── ros_agent.py │ │ │ │ └── sensor_interface.py │ │ │ ├── examples/ │ │ │ │ ├── ActorFlow.xml │ │ │ │ ├── CatalogExample.xosc │ │ │ │ ├── ChangeLane.xml │ │ │ │ ├── ChangingWeather.xosc │ │ │ │ ├── ControlLoss.xml │ │ │ │ ├── CutIn.xml │ │ │ │ ├── CyclistCrossing.xosc │ │ │ │ ├── FollowLeadingVehicle.xml │ │ │ │ ├── FollowLeadingVehicle.xosc │ │ │ │ ├── FreeRide.xml │ │ │ │ ├── HighwayCutIn.xml │ │ │ │ ├── IntersectionCollisionAvoidance.xosc │ │ │ │ ├── LaneChangeSimple.xosc │ │ │ │ ├── LeadingVehicle.xml │ │ │ │ ├── NoSignalJunction.xml │ │ │ │ ├── ObjectCrossing.xml │ │ │ │ ├── OppositeDirection.xml │ │ │ │ ├── OscControllerExample.xosc │ │ │ │ ├── PedestrianCrossingFront.xosc │ │ │ │ ├── RouteObstacles.xml │ │ │ │ ├── RunningRedLight.xml │ │ │ │ ├── SignalizedJunctionLeftTurn.xml │ │ │ │ ├── SignalizedJunctionRightTurn.xml │ │ │ │ ├── Slalom.xosc │ │ │ │ ├── VehicleOpensDoor.xml │ │ │ │ ├── VehicleTurning.xml │ │ │ │ └── catalogs/ │ │ │ │ ├── ControllerCatalog.xosc │ │ │ │ ├── EnvironmentCatalog.xosc │ │ │ │ ├── ManeuverCatalog.xosc │ │ │ │ ├── MiscObjectCatalog.xosc │ │ │ │ ├── PedestrianCatalog.xosc │ │ │ │ └── VehicleCatalog.xosc │ │ │ ├── metrics/ │ │ │ │ ├── examples/ │ │ │ │ │ ├── basic_metric.py │ │ │ │ │ ├── criteria_filter.py │ │ │ │ │ ├── distance_between_vehicles.py │ │ │ │ │ └── distance_to_lane_center.py │ │ │ │ └── tools/ │ │ │ │ ├── metrics_log.py │ │ │ │ └── metrics_parser.py │ │ │ ├── openscenario/ │ │ │ │ ├── 0.9.x/ │ │ │ │ │ ├── OpenSCENARIO_Catalog.xsd │ │ │ │ │ ├── OpenSCENARIO_TypeDefs.xsd │ │ │ │ │ ├── OpenSCENARIO_v0.9.1.xsd │ │ │ │ │ └── migration0_9_1to1_0.xslt │ │ │ │ └── OpenSCENARIO.xsd │ │ │ ├── scenarioconfigs/ │ │ │ │ ├── __init__.py │ │ │ │ ├── openscenario_configuration.py │ │ │ │ ├── route_scenario_configuration.py │ │ │ │ └── scenario_configuration.py │ │ │ ├── scenariomanager/ │ │ │ │ ├── __init__.py │ │ │ │ ├── actorcontrols/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── actor_control.py │ │ │ │ │ ├── basic_control.py │ │ │ │ │ ├── carla_autopilot.py │ │ │ │ │ ├── external_control.py │ │ │ │ │ ├── npc_vehicle_control.py │ │ │ │ │ ├── pedestrian_control.py │ │ │ │ │ ├── simple_vehicle_control.py │ │ │ │ │ ├── vehicle_longitudinal_control.py │ │ │ │ │ └── visualizer.py │ │ │ │ ├── carla_data_provider.py │ │ │ │ ├── lights_sim.py │ │ │ │ ├── result_writer.py │ │ │ │ ├── scenario_manager.py │ │ │ │ ├── scenarioatomics/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── atomic_behaviors.py │ │ │ │ │ ├── atomic_criteria.py │ │ │ │ │ └── atomic_trigger_conditions.py │ │ │ │ ├── timer.py │ │ │ │ ├── traffic_events.py │ │ │ │ ├── watchdog.py │ │ │ │ └── weather_sim.py │ │ │ ├── scenarios/ │ │ │ │ ├── __init__.py │ │ │ │ ├── actor_flow.py │ │ │ │ ├── background_activity.py │ │ │ │ ├── background_activity_parametrizer.py │ │ │ │ ├── basic_scenario.py │ │ │ │ ├── blocked_intersection.py │ │ │ │ ├── change_lane.py │ │ │ │ ├── construction_crash_vehicle.py │ │ │ │ ├── control_loss.py │ │ │ │ ├── cross_bicycle_flow.py │ │ │ │ ├── cut_in.py │ │ │ │ ├── cut_in_with_static_vehicle.py │ │ │ │ ├── follow_leading_vehicle.py │ │ │ │ ├── freeride.py │ │ │ │ ├── green_traffic_light.py │ │ │ │ ├── hard_break.py │ │ │ │ ├── highway_cut_in.py │ │ │ │ ├── invading_turn.py │ │ │ │ ├── left_turn_enter_flow.py │ │ │ │ ├── maneuver_opposite_direction.py │ │ │ │ ├── no_signal_junction_crossing.py │ │ │ │ ├── object_crash_intersection.py │ │ │ │ ├── object_crash_vehicle.py │ │ │ │ ├── open_scenario.py │ │ │ │ ├── opposite_vehicle_taking_priority.py │ │ │ │ ├── other_leading_vehicle.py │ │ │ │ ├── parking_cut_in.py │ │ │ │ ├── parking_exit.py │ │ │ │ ├── pedestrian_crossing.py │ │ │ │ ├── route_obstacles.py │ │ │ │ ├── route_scenario.py │ │ │ │ ├── sequentially_lane_change.py │ │ │ │ ├── signalized_junction_left_turn.py │ │ │ │ ├── signalized_junction_right_turn.py │ │ │ │ ├── t_junction.py │ │ │ │ ├── vanilla_turn.py │ │ │ │ ├── vehicle_opens_door.py │ │ │ │ └── yield_to_emergency_vehicle.py │ │ │ ├── tests/ │ │ │ │ ├── __init__.py │ │ │ │ ├── carla_mocks/ │ │ │ │ │ ├── README.md │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── agents/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── navigation/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── basic_agent.py │ │ │ │ │ │ │ ├── behavior_agent.py │ │ │ │ │ │ │ ├── behavior_types.py │ │ │ │ │ │ │ ├── controller.py │ │ │ │ │ │ │ ├── global_route_planner.py │ │ │ │ │ │ │ └── local_planner.py │ │ │ │ │ │ └── tools/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── misc.py │ │ │ │ │ └── carla.py │ │ │ │ └── test_xosc_load.py │ │ │ ├── tools/ │ │ │ │ ├── __init__.py │ │ │ │ ├── background_manager.py │ │ │ │ ├── openscenario_parser.py │ │ │ │ ├── py_trees_port.py │ │ │ │ ├── route_manipulation.py │ │ │ │ ├── route_parser.py │ │ │ │ ├── scenario_helper.py │ │ │ │ └── scenario_parser.py │ │ │ └── utilities/ │ │ │ └── code_check_and_formatting.sh │ │ └── tools/ │ │ ├── ability_benchmark.py │ │ ├── check_carla.md │ │ ├── clean_carla.sh │ │ ├── data_collect.py │ │ ├── download_mini.sh │ │ ├── efficiency_smoothness_benchmark.py │ │ ├── gen_hdmap.py │ │ ├── generate_video.py │ │ ├── merge_route_json.py │ │ ├── split_xml.py │ │ ├── utils.py │ │ └── visualize.py │ ├── VAD_MomAD/ │ │ ├── Bench2DriveZoo/ │ │ │ ├── adzoo/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bevformer/ │ │ │ │ │ ├── analysis_tools/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── analyze_logs.py │ │ │ │ │ │ ├── benchmark.py │ │ │ │ │ │ ├── get_params.py │ │ │ │ │ │ └── visual.py │ │ │ │ │ ├── apis/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── mmdet_train.py │ │ │ │ │ │ ├── test.py │ │ │ │ │ │ └── train.py │ │ │ │ │ ├── configs/ │ │ │ │ │ │ ├── _base_/ │ │ │ │ │ │ │ ├── datasets/ │ │ │ │ │ │ │ │ ├── coco_instance.py │ │ │ │ │ │ │ │ ├── kitti-3d-3class.py │ │ │ │ │ │ │ │ ├── kitti-3d-car.py │ │ │ │ │ │ │ │ ├── lyft-3d.py │ │ │ │ │ │ │ │ ├── nuim_instance.py │ │ │ │ │ │ │ │ ├── nus-3d.py │ │ │ │ │ │ │ │ ├── nus-mono3d.py │ │ │ │ │ │ │ │ ├── range100_lyft-3d.py │ │ │ │ │ │ │ │ ├── s3dis-3d-5class.py │ │ │ │ │ │ │ │ ├── s3dis_seg-3d-13class.py │ │ │ │ │ │ │ │ ├── scannet-3d-18class.py │ │ │ │ │ │ │ │ ├── scannet_seg-3d-20class.py │ │ │ │ │ │ │ │ ├── sunrgbd-3d-10class.py │ │ │ │ │ │ │ │ ├── waymoD5-3d-3class.py │ │ │ │ │ │ │ │ └── waymoD5-3d-car.py │ │ │ │ │ │ │ ├── default_runtime.py │ │ │ │ │ │ │ ├── models/ │ │ │ │ │ │ │ │ ├── 3dssd.py │ │ │ │ │ │ │ │ ├── cascade_mask_rcnn_r50_fpn.py │ │ │ │ │ │ │ │ ├── centerpoint_01voxel_second_secfpn_nus.py │ │ │ │ │ │ │ │ ├── centerpoint_02pillar_second_secfpn_nus.py │ │ │ │ │ │ │ │ ├── fcos3d.py │ │ │ │ │ │ │ │ ├── groupfree3d.py │ │ │ │ │ │ │ │ ├── h3dnet.py │ │ │ │ │ │ │ │ ├── hv_pointpillars_fpn_lyft.py │ │ │ │ │ │ │ │ ├── hv_pointpillars_fpn_nus.py │ │ │ │ │ │ │ │ ├── hv_pointpillars_fpn_range100_lyft.py │ │ │ │ │ │ │ │ ├── hv_pointpillars_secfpn_kitti.py │ │ │ │ │ │ │ │ ├── hv_pointpillars_secfpn_waymo.py │ │ │ │ │ │ │ │ ├── hv_second_secfpn_kitti.py │ │ │ │ │ │ │ │ ├── hv_second_secfpn_waymo.py │ │ │ │ │ │ │ │ ├── imvotenet_image.py │ │ │ │ │ │ │ │ ├── mask_rcnn_r50_fpn.py │ │ │ │ │ │ │ │ ├── paconv_cuda_ssg.py │ │ │ │ │ │ │ │ ├── paconv_ssg.py │ │ │ │ │ │ │ │ ├── parta2.py │ │ │ │ │ │ │ │ ├── pointnet2_msg.py │ │ │ │ │ │ │ │ ├── pointnet2_ssg.py │ │ │ │ │ │ │ │ └── votenet.py │ │ │ │ │ │ │ └── schedules/ │ │ │ │ │ │ │ ├── cosine.py │ │ │ │ │ │ │ ├── cyclic_20e.py │ │ │ │ │ │ │ ├── cyclic_40e.py │ │ │ │ │ │ │ ├── mmdet_schedule_1x.py │ │ │ │ │ │ │ ├── schedule_2x.py │ │ │ │ │ │ │ ├── schedule_3x.py │ │ │ │ │ │ │ ├── seg_cosine_150e.py │ │ │ │ │ │ │ ├── seg_cosine_200e.py │ │ │ │ │ │ │ └── seg_cosine_50e.py │ │ │ │ │ │ ├── bevformer/ │ │ │ │ │ │ │ ├── bevformer_base.py │ │ │ │ │ │ │ ├── bevformer_base_b2d.py │ │ │ │ │ │ │ ├── bevformer_tiny.py │ │ │ │ │ │ │ └── bevformer_tiny_b2d.py │ │ │ │ │ │ ├── bevformer_fp16/ │ │ │ │ │ │ │ └── bevformer_tiny_fp16.py │ │ │ │ │ │ ├── bevformerv2/ │ │ │ │ │ │ │ ├── bevformerv2-r50-t1-24ep.py │ │ │ │ │ │ │ ├── bevformerv2-r50-t1-48ep.py │ │ │ │ │ │ │ ├── bevformerv2-r50-t1-base-24ep.py │ │ │ │ │ │ │ ├── bevformerv2-r50-t1-base-48ep.py │ │ │ │ │ │ │ ├── bevformerv2-r50-t2-24ep.py │ │ │ │ │ │ │ ├── bevformerv2-r50-t2-48ep.py │ │ │ │ │ │ │ └── bevformerv2-r50-t8-24ep.py │ │ │ │ │ │ └── datasets/ │ │ │ │ │ │ ├── custom_lyft-3d.py │ │ │ │ │ │ ├── custom_nus-3d.py │ │ │ │ │ │ └── custom_waymo-3d.py │ │ │ │ │ ├── create_data.py │ │ │ │ │ ├── data_converter/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── create_gt_database.py │ │ │ │ │ │ ├── indoor_converter.py │ │ │ │ │ │ ├── kitti_converter.py │ │ │ │ │ │ ├── kitti_data_utils.py │ │ │ │ │ │ ├── lyft_converter.py │ │ │ │ │ │ ├── lyft_data_fixer.py │ │ │ │ │ │ ├── nuimage_converter.py │ │ │ │ │ │ ├── nuscenes_converter.py │ │ │ │ │ │ ├── s3dis_data_utils.py │ │ │ │ │ │ ├── scannet_data_utils.py │ │ │ │ │ │ ├── sunrgbd_data_utils.py │ │ │ │ │ │ └── waymo_converter.py │ │ │ │ │ ├── dist_test.sh │ │ │ │ │ ├── dist_train.sh │ │ │ │ │ ├── fp16/ │ │ │ │ │ │ ├── dist_train.sh │ │ │ │ │ │ └── train.py │ │ │ │ │ ├── misc/ │ │ │ │ │ │ ├── browse_dataset.py │ │ │ │ │ │ ├── print_config.py │ │ │ │ │ │ └── visualize_results.py │ │ │ │ │ ├── mmdet3d_plugin/ │ │ │ │ │ │ ├── bevformer/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── apis/ │ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ │ ├── mmdet_train.py │ │ │ │ │ │ │ │ ├── test.py │ │ │ │ │ │ │ │ └── train.py │ │ │ │ │ │ │ └── hooks/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ └── custom_hooks.py │ │ │ │ │ │ ├── dd3d/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── datasets/ │ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ │ ├── nuscenes.py │ │ │ │ │ │ │ │ └── transform_utils.py │ │ │ │ │ │ │ ├── layers/ │ │ │ │ │ │ │ │ ├── iou_loss.py │ │ │ │ │ │ │ │ ├── normalization.py │ │ │ │ │ │ │ │ └── smooth_l1_loss.py │ │ │ │ │ │ │ ├── modeling/ │ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ │ ├── core.py │ │ │ │ │ │ │ │ ├── disentangled_box3d_loss.py │ │ │ │ │ │ │ │ ├── fcos2d.py │ │ │ │ │ │ │ │ ├── fcos3d.py │ │ │ │ │ │ │ │ ├── nuscenes_dd3d.py │ │ │ │ │ │ │ │ └── prepare_targets.py │ │ │ │ │ │ │ ├── structures/ │ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ │ ├── boxes3d.py │ │ │ │ │ │ │ │ ├── image_list.py │ │ │ │ │ │ │ │ ├── pose.py │ │ │ │ │ │ │ │ └── transform3d.py │ │ │ │ │ │ │ └── utils/ │ │ │ │ │ │ │ ├── comm.py │ │ │ │ │ │ │ ├── geometry.py │ │ │ │ │ │ │ ├── tasks.py │ │ │ │ │ │ │ ├── tensor2d.py │ │ │ │ │ │ │ └── visualization.py │ │ │ │ │ │ └── models/ │ │ │ │ │ │ └── hooks/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── hooks.py │ │ │ │ │ ├── model_converters/ │ │ │ │ │ │ ├── convert_votenet_checkpoints.py │ │ │ │ │ │ ├── publish_model.py │ │ │ │ │ │ └── regnet2mmdet.py │ │ │ │ │ ├── test.py │ │ │ │ │ └── train.py │ │ │ │ ├── sparsedrive/ │ │ │ │ │ ├── configs/ │ │ │ │ │ │ ├── sparsedrive_small_b2d_stage1.py │ │ │ │ │ │ ├── sparsedrive_small_b2d_stage2.py │ │ │ │ │ │ ├── sparsedrive_small_stage1.py │ │ │ │ │ │ └── sparsedrive_small_stage2.py │ │ │ │ │ ├── dist_train.sh │ │ │ │ │ ├── scripts/ │ │ │ │ │ │ ├── create_data.sh │ │ │ │ │ │ ├── kmeans.sh │ │ │ │ │ │ ├── test.sh │ │ │ │ │ │ ├── test_roboAD.sh │ │ │ │ │ │ ├── train.sh │ │ │ │ │ │ ├── train_6s.sh │ │ │ │ │ │ ├── train_roboAD.sh │ │ │ │ │ │ └── visualize.sh │ │ │ │ │ ├── tools/ │ │ │ │ │ │ ├── benchmark.py │ │ │ │ │ │ ├── data_converter/ │ │ │ │ │ │ │ ├── B2D_converter.py │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── nuscenes_converter.py │ │ │ │ │ │ │ ├── nuscenes_converter_1_10.py │ │ │ │ │ │ │ ├── nuscenes_converter_6s.py │ │ │ │ │ │ │ └── nuscenes_converter_hrad_planing_scene.py │ │ │ │ │ │ ├── dist_test.sh │ │ │ │ │ │ ├── dist_train.sh │ │ │ │ │ │ ├── fuse_conv_bn.py │ │ │ │ │ │ ├── kmeans/ │ │ │ │ │ │ │ ├── kmeans_det.py │ │ │ │ │ │ │ ├── kmeans_map.py │ │ │ │ │ │ │ ├── kmeans_motion.py │ │ │ │ │ │ │ └── kmeans_plan.py │ │ │ │ │ │ ├── test.py │ │ │ │ │ │ ├── train.py │ │ │ │ │ │ ├── train_single.py │ │ │ │ │ │ └── visualization/ │ │ │ │ │ │ ├── bev_render.py │ │ │ │ │ │ └── cam_render.py │ │ │ │ │ └── train.py │ │ │ │ ├── uniad/ │ │ │ │ │ ├── analysis_tools/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── analyze_logs.py │ │ │ │ │ │ ├── benchmark.py │ │ │ │ │ │ └── visualize/ │ │ │ │ │ │ ├── render/ │ │ │ │ │ │ │ ├── base_render.py │ │ │ │ │ │ │ ├── bev_render.py │ │ │ │ │ │ │ └── cam_render.py │ │ │ │ │ │ ├── run.py │ │ │ │ │ │ └── utils.py │ │ │ │ │ ├── configs/ │ │ │ │ │ │ ├── _base_/ │ │ │ │ │ │ │ ├── datasets/ │ │ │ │ │ │ │ │ └── nus-3d.py │ │ │ │ │ │ │ └── default_runtime.py │ │ │ │ │ │ ├── stage1_track_map/ │ │ │ │ │ │ │ ├── base_track_map.py │ │ │ │ │ │ │ ├── base_track_map_b2d.py │ │ │ │ │ │ │ └── tiny_track_map_b2d.py │ │ │ │ │ │ └── stage2_e2e/ │ │ │ │ │ │ ├── base_e2e.py │ │ │ │ │ │ ├── base_e2e_b2d.py │ │ │ │ │ │ └── tiny_e2e_b2d.py │ │ │ │ │ ├── data_converter/ │ │ │ │ │ │ ├── create_data.py │ │ │ │ │ │ ├── uniad_create_data.sh │ │ │ │ │ │ └── uniad_nuscenes_converter.py │ │ │ │ │ ├── test.py │ │ │ │ │ ├── test_utils.py │ │ │ │ │ ├── train.py │ │ │ │ │ ├── uniad_dist_eval.sh │ │ │ │ │ ├── uniad_dist_train.sh │ │ │ │ │ └── uniad_vis_result.sh │ │ │ │ └── vad/ │ │ │ │ ├── analysis_tools/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── analyze_logs.py │ │ │ │ │ ├── benchmark.py │ │ │ │ │ ├── get_flops.py │ │ │ │ │ ├── get_params.py │ │ │ │ │ └── visualization.py │ │ │ │ ├── apis/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── mmdet_train.py │ │ │ │ │ ├── test.py │ │ │ │ │ └── train.py │ │ │ │ ├── configs/ │ │ │ │ │ ├── VAD/ │ │ │ │ │ │ ├── MomAD_base_e2e_b2d.py │ │ │ │ │ │ ├── VAD_base_e2e.py │ │ │ │ │ │ ├── VAD_base_e2e_b2d.py │ │ │ │ │ │ └── VAD_tiny_e2e.py │ │ │ │ │ ├── _base_/ │ │ │ │ │ │ ├── datasets/ │ │ │ │ │ │ │ ├── coco_instance.py │ │ │ │ │ │ │ ├── kitti-3d-3class.py │ │ │ │ │ │ │ ├── kitti-3d-car.py │ │ │ │ │ │ │ ├── lyft-3d.py │ │ │ │ │ │ │ ├── nuim_instance.py │ │ │ │ │ │ │ ├── nus-3d.py │ │ │ │ │ │ │ ├── nus-mono3d.py │ │ │ │ │ │ │ ├── range100_lyft-3d.py │ │ │ │ │ │ │ ├── s3dis-3d-5class.py │ │ │ │ │ │ │ ├── s3dis_seg-3d-13class.py │ │ │ │ │ │ │ ├── scannet-3d-18class.py │ │ │ │ │ │ │ ├── scannet_seg-3d-20class.py │ │ │ │ │ │ │ ├── sunrgbd-3d-10class.py │ │ │ │ │ │ │ ├── waymoD5-3d-3class.py │ │ │ │ │ │ │ └── waymoD5-3d-car.py │ │ │ │ │ │ ├── default_runtime.py │ │ │ │ │ │ ├── models/ │ │ │ │ │ │ │ ├── 3dssd.py │ │ │ │ │ │ │ ├── cascade_mask_rcnn_r50_fpn.py │ │ │ │ │ │ │ ├── centerpoint_01voxel_second_secfpn_nus.py │ │ │ │ │ │ │ ├── centerpoint_02pillar_second_secfpn_nus.py │ │ │ │ │ │ │ ├── fcos3d.py │ │ │ │ │ │ │ ├── groupfree3d.py │ │ │ │ │ │ │ ├── h3dnet.py │ │ │ │ │ │ │ ├── hv_pointpillars_fpn_lyft.py │ │ │ │ │ │ │ ├── hv_pointpillars_fpn_nus.py │ │ │ │ │ │ │ ├── hv_pointpillars_fpn_range100_lyft.py │ │ │ │ │ │ │ ├── hv_pointpillars_secfpn_kitti.py │ │ │ │ │ │ │ ├── hv_pointpillars_secfpn_waymo.py │ │ │ │ │ │ │ ├── hv_second_secfpn_kitti.py │ │ │ │ │ │ │ ├── hv_second_secfpn_waymo.py │ │ │ │ │ │ │ ├── imvotenet_image.py │ │ │ │ │ │ │ ├── mask_rcnn_r50_fpn.py │ │ │ │ │ │ │ ├── paconv_cuda_ssg.py │ │ │ │ │ │ │ ├── paconv_ssg.py │ │ │ │ │ │ │ ├── parta2.py │ │ │ │ │ │ │ ├── pointnet2_msg.py │ │ │ │ │ │ │ ├── pointnet2_ssg.py │ │ │ │ │ │ │ └── votenet.py │ │ │ │ │ │ └── schedules/ │ │ │ │ │ │ ├── cosine.py │ │ │ │ │ │ ├── cyclic_20e.py │ │ │ │ │ │ ├── cyclic_40e.py │ │ │ │ │ │ ├── mmdet_schedule_1x.py │ │ │ │ │ │ ├── schedule_2x.py │ │ │ │ │ │ ├── schedule_3x.py │ │ │ │ │ │ ├── seg_cosine_150e.py │ │ │ │ │ │ ├── seg_cosine_200e.py │ │ │ │ │ │ └── seg_cosine_50e.py │ │ │ │ │ └── datasets/ │ │ │ │ │ ├── custom_lyft-3d.py │ │ │ │ │ ├── custom_nus-3d.py │ │ │ │ │ └── custom_waymo-3d.py │ │ │ │ ├── create_data.py │ │ │ │ ├── data_converter/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── create_gt_database.py │ │ │ │ │ └── vad_nuscenes_converter.py │ │ │ │ ├── dist_test.sh │ │ │ │ ├── dist_train.sh │ │ │ │ ├── dist_train_momad.sh │ │ │ │ ├── misc/ │ │ │ │ │ ├── browse_dataset.py │ │ │ │ │ ├── fuse_conv_bn.py │ │ │ │ │ ├── print_config.py │ │ │ │ │ └── visualize_results.py │ │ │ │ ├── model_converters/ │ │ │ │ │ ├── convert_votenet_checkpoints.py │ │ │ │ │ ├── publish_model.py │ │ │ │ │ └── regnet2mmdet.py │ │ │ │ ├── test.py │ │ │ │ ├── train.py │ │ │ │ └── train_momad.py │ │ │ ├── docs/ │ │ │ │ ├── CONVERT_GUIDE.md │ │ │ │ ├── DATA_PREP.md │ │ │ │ ├── EVAL_IN_CARLA.md │ │ │ │ ├── INSTALL.md │ │ │ │ └── TRAIN_EVAL.md │ │ │ ├── mmcv/ │ │ │ │ ├── __init__.py │ │ │ │ ├── core/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── anchor/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── anchor_3d_generator.py │ │ │ │ │ │ ├── anchor_generator.py │ │ │ │ │ │ ├── builder.py │ │ │ │ │ │ ├── point_generator.py │ │ │ │ │ │ └── utils.py │ │ │ │ │ ├── bbox/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── assigners/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── assign_result.py │ │ │ │ │ │ │ ├── base_assigner.py │ │ │ │ │ │ │ ├── hungarian_assigner.py │ │ │ │ │ │ │ ├── hungarian_assigner_3d.py │ │ │ │ │ │ │ ├── hungarian_assigner_3d_track.py │ │ │ │ │ │ │ └── map_hungarian_assigner_3d.py │ │ │ │ │ │ ├── box_np_ops.py │ │ │ │ │ │ ├── builder.py │ │ │ │ │ │ ├── coder/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── base_bbox_coder.py │ │ │ │ │ │ │ ├── detr3d_track_coder.py │ │ │ │ │ │ │ ├── fut_nms_free_coder.py │ │ │ │ │ │ │ ├── map_nms_free_coder.py │ │ │ │ │ │ │ └── nms_free_coder.py │ │ │ │ │ │ ├── iou_calculators/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── builder.py │ │ │ │ │ │ │ ├── iou2d_calculator.py │ │ │ │ │ │ │ └── iou3d_calculator.py │ │ │ │ │ │ ├── match_costs/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── builder.py │ │ │ │ │ │ │ └── match_cost.py │ │ │ │ │ │ ├── samplers/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── base_sampler.py │ │ │ │ │ │ │ ├── pseudo_sampler.py │ │ │ │ │ │ │ └── sampling_result.py │ │ │ │ │ │ ├── structures/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── base_box3d.py │ │ │ │ │ │ │ ├── box_3d_mode.py │ │ │ │ │ │ │ ├── cam_box3d.py │ │ │ │ │ │ │ ├── coord_3d_mode.py │ │ │ │ │ │ │ ├── depth_box3d.py │ │ │ │ │ │ │ ├── lidar_box3d.py │ │ │ │ │ │ │ ├── nuscenes_box.py │ │ │ │ │ │ │ └── utils.py │ │ │ │ │ │ ├── transforms.py │ │ │ │ │ │ └── util.py │ │ │ │ │ ├── evaluation/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── bbox_overlaps.py │ │ │ │ │ │ ├── class_names.py │ │ │ │ │ │ ├── eval_hooks.py │ │ │ │ │ │ ├── indoor_eval.py │ │ │ │ │ │ ├── kitti_utils/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── eval.py │ │ │ │ │ │ │ └── rotate_iou.py │ │ │ │ │ │ ├── lyft_eval.py │ │ │ │ │ │ ├── mean_ap.py │ │ │ │ │ │ ├── metric_motion.py │ │ │ │ │ │ ├── metrics.py │ │ │ │ │ │ ├── recall.py │ │ │ │ │ │ ├── seg_eval.py │ │ │ │ │ │ └── waymo_utils/ │ │ │ │ │ │ └── prediction_kitti_to_waymo.py │ │ │ │ │ ├── mask/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── mask_target.py │ │ │ │ │ │ ├── structures.py │ │ │ │ │ │ └── utils.py │ │ │ │ │ ├── points/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── base_points.py │ │ │ │ │ │ ├── cam_points.py │ │ │ │ │ │ ├── depth_points.py │ │ │ │ │ │ └── lidar_points.py │ │ │ │ │ ├── post_processing/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── bbox_nms.py │ │ │ │ │ │ ├── box3d_nms.py │ │ │ │ │ │ └── merge_augs.py │ │ │ │ │ ├── utils/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── dist_utils.py │ │ │ │ │ │ ├── gaussian.py │ │ │ │ │ │ └── misc.py │ │ │ │ │ ├── visualization/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── image.py │ │ │ │ │ ├── visualizer/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── image_vis.py │ │ │ │ │ │ ├── open3d_vis.py │ │ │ │ │ │ └── show_result.py │ │ │ │ │ └── voxel/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── builder.py │ │ │ │ │ └── voxel_generator.py │ │ │ │ ├── datasets/ │ │ │ │ │ ├── B2D_dataset.py │ │ │ │ │ ├── B2D_e2e_dataset.py │ │ │ │ │ ├── B2D_sparsedrive_dataset.py │ │ │ │ │ ├── B2D_vad_dataset.py │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── api_wrappers/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── coco_api.py │ │ │ │ │ ├── builder.py │ │ │ │ │ ├── coco.py │ │ │ │ │ ├── custom.py │ │ │ │ │ ├── custom_3d.py │ │ │ │ │ ├── custom_nuscenes_dataset.py │ │ │ │ │ ├── custom_nuscenes_dataset_v2.py │ │ │ │ │ ├── data_utils/ │ │ │ │ │ │ ├── data_utils.py │ │ │ │ │ │ ├── rasterize.py │ │ │ │ │ │ ├── trajectory_api.py │ │ │ │ │ │ └── vector_map.py │ │ │ │ │ ├── dataset_wrappers.py │ │ │ │ │ ├── dd3d_nuscenes_dataset.py │ │ │ │ │ ├── eval_utils/ │ │ │ │ │ │ ├── eval_utils.py │ │ │ │ │ │ ├── map_api.py │ │ │ │ │ │ ├── metric_utils.py │ │ │ │ │ │ ├── nuscenes_eval.py │ │ │ │ │ │ └── nuscenes_eval_motion.py │ │ │ │ │ ├── lyft_dataset.py │ │ │ │ │ ├── map_utils/ │ │ │ │ │ │ ├── mean_ap.py │ │ │ │ │ │ ├── struct.py │ │ │ │ │ │ ├── tpfp.py │ │ │ │ │ │ └── tpfp_chamfer.py │ │ │ │ │ ├── nuscenes_dataset.py │ │ │ │ │ ├── nuscenes_e2e_dataset.py │ │ │ │ │ ├── nuscenes_eval.py │ │ │ │ │ ├── nuscenes_mono_dataset.py │ │ │ │ │ ├── nuscenes_styled_eval_utils.py │ │ │ │ │ ├── nuscenes_vad_dataset.py │ │ │ │ │ ├── nuscnes_eval.py │ │ │ │ │ ├── pipelines/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── augment.py │ │ │ │ │ │ ├── compose.py │ │ │ │ │ │ ├── data_augment_utils.py │ │ │ │ │ │ ├── formating.py │ │ │ │ │ │ ├── loading.py │ │ │ │ │ │ ├── occflow_label.py │ │ │ │ │ │ ├── test_time_aug.py │ │ │ │ │ │ ├── transforms.py │ │ │ │ │ │ ├── transforms_3d.py │ │ │ │ │ │ └── vectorize.py │ │ │ │ │ ├── prepare_B2D.py │ │ │ │ │ ├── samplers/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── distributed_sampler.py │ │ │ │ │ │ ├── group_sampler.py │ │ │ │ │ │ └── sampler.py │ │ │ │ │ ├── utils.py │ │ │ │ │ ├── vad_custom_nuscenes_eval.py │ │ │ │ │ └── vis_utils.py │ │ │ │ ├── fileio/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── file_client.py │ │ │ │ │ ├── handlers/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── base.py │ │ │ │ │ │ ├── json_handler.py │ │ │ │ │ │ └── pickle_handler.py │ │ │ │ │ ├── io.py │ │ │ │ │ └── parse.py │ │ │ │ ├── image/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── colorspace.py │ │ │ │ │ ├── geometric.py │ │ │ │ │ ├── io.py │ │ │ │ │ ├── misc.py │ │ │ │ │ └── photometric.py │ │ │ │ ├── layers/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── aspp.py │ │ │ │ │ ├── batch_norm.py │ │ │ │ │ ├── blocks.py │ │ │ │ │ ├── csrc/ │ │ │ │ │ │ ├── README.md │ │ │ │ │ │ ├── ROIAlignRotated/ │ │ │ │ │ │ │ ├── ROIAlignRotated.h │ │ │ │ │ │ │ ├── ROIAlignRotated_cpu.cpp │ │ │ │ │ │ │ └── ROIAlignRotated_cuda.cu │ │ │ │ │ │ ├── box_iou_rotated/ │ │ │ │ │ │ │ ├── box_iou_rotated.h │ │ │ │ │ │ │ ├── box_iou_rotated_cpu.cpp │ │ │ │ │ │ │ ├── box_iou_rotated_cuda.cu │ │ │ │ │ │ │ └── box_iou_rotated_utils.h │ │ │ │ │ │ ├── cocoeval/ │ │ │ │ │ │ │ ├── cocoeval.cpp │ │ │ │ │ │ │ └── cocoeval.h │ │ │ │ │ │ ├── cuda_version.cu │ │ │ │ │ │ ├── deformable/ │ │ │ │ │ │ │ ├── deform_conv.h │ │ │ │ │ │ │ ├── deform_conv_cuda.cu │ │ │ │ │ │ │ └── deform_conv_cuda_kernel.cu │ │ │ │ │ │ ├── nms_rotated/ │ │ │ │ │ │ │ ├── nms_rotated.h │ │ │ │ │ │ │ ├── nms_rotated_cpu.cpp │ │ │ │ │ │ │ └── nms_rotated_cuda.cu │ │ │ │ │ │ └── vision.cpp │ │ │ │ │ ├── deform_conv.py │ │ │ │ │ ├── losses.py │ │ │ │ │ ├── mask_ops.py │ │ │ │ │ ├── nms.py │ │ │ │ │ ├── roi_align.py │ │ │ │ │ ├── roi_align_rotated.py │ │ │ │ │ ├── rotated_boxes.py │ │ │ │ │ ├── shape_spec.py │ │ │ │ │ └── wrappers.py │ │ │ │ ├── losses/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── dice_loss.py │ │ │ │ │ ├── focal_loss.py │ │ │ │ │ ├── fvcore_smooth_l1_loss.py │ │ │ │ │ ├── occflow_loss.py │ │ │ │ │ ├── planning_loss.py │ │ │ │ │ ├── track_loss.py │ │ │ │ │ └── traj_loss.py │ │ │ │ ├── metrics/ │ │ │ │ │ ├── classification.py │ │ │ │ │ ├── compositional.py │ │ │ │ │ ├── distributed.py │ │ │ │ │ ├── metric.py │ │ │ │ │ ├── reduction.py │ │ │ │ │ └── utils.py │ │ │ │ ├── modeling/ │ │ │ │ │ └── postprocessing.py │ │ │ │ ├── models/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── backbones/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── base_module.py │ │ │ │ │ │ ├── resnet.py │ │ │ │ │ │ ├── vgg.py │ │ │ │ │ │ └── vovnet.py │ │ │ │ │ ├── bricks/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── activation.py │ │ │ │ │ │ ├── conv.py │ │ │ │ │ │ ├── conv_module.py │ │ │ │ │ │ ├── drop.py │ │ │ │ │ │ ├── norm.py │ │ │ │ │ │ ├── padding.py │ │ │ │ │ │ ├── plugin.py │ │ │ │ │ │ ├── registry.py │ │ │ │ │ │ ├── transformer.py │ │ │ │ │ │ └── wrappers.py │ │ │ │ │ ├── builder.py │ │ │ │ │ ├── dense_heads/ │ │ │ │ │ │ ├── MomAD_head.py │ │ │ │ │ │ ├── VAD_head.py │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── anchor3d_head.py │ │ │ │ │ │ ├── anchor_free_head.py │ │ │ │ │ │ ├── anchor_head.py │ │ │ │ │ │ ├── base_dense_head.py │ │ │ │ │ │ ├── bev_head.py │ │ │ │ │ │ ├── bevformer_head.py │ │ │ │ │ │ ├── dense_test_mixins.py │ │ │ │ │ │ ├── detr_head.py │ │ │ │ │ │ ├── free_anchor3d_head.py │ │ │ │ │ │ ├── ga_rpn_head.py │ │ │ │ │ │ ├── guided_anchor_head.py │ │ │ │ │ │ ├── motion_head.py │ │ │ │ │ │ ├── motion_head_plugin/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── base_motion_head.py │ │ │ │ │ │ │ ├── modules.py │ │ │ │ │ │ │ ├── motion_deformable_attn.py │ │ │ │ │ │ │ ├── motion_optimization.py │ │ │ │ │ │ │ └── motion_utils.py │ │ │ │ │ │ ├── occ_head.py │ │ │ │ │ │ ├── occ_head_plugin/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── metrics.py │ │ │ │ │ │ │ ├── modules.py │ │ │ │ │ │ │ └── utils.py │ │ │ │ │ │ ├── panseg_head.py │ │ │ │ │ │ ├── planning_head.py │ │ │ │ │ │ ├── planning_head_plugin/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── collision_optimization.py │ │ │ │ │ │ │ ├── metric_stp3.py │ │ │ │ │ │ │ └── planning_metrics.py │ │ │ │ │ │ ├── rpn_head.py │ │ │ │ │ │ ├── seg_head_plugin/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── seg_assigner.py │ │ │ │ │ │ │ ├── seg_deformable_transformer.py │ │ │ │ │ │ │ ├── seg_detr_head.py │ │ │ │ │ │ │ ├── seg_mask_head.py │ │ │ │ │ │ │ └── seg_utils.py │ │ │ │ │ │ ├── track_head.py │ │ │ │ │ │ ├── track_head_plugin/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── modules.py │ │ │ │ │ │ │ ├── track_instance.py │ │ │ │ │ │ │ └── tracker.py │ │ │ │ │ │ └── train_mixins.py │ │ │ │ │ ├── detectors/ │ │ │ │ │ │ ├── MomAD.py │ │ │ │ │ │ ├── SpMomAD.py │ │ │ │ │ │ ├── VAD.py │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── base.py │ │ │ │ │ │ ├── bevformer.py │ │ │ │ │ │ ├── bevformerV2.py │ │ │ │ │ │ ├── bevformer_fp16.py │ │ │ │ │ │ ├── mvx_two_stage.py │ │ │ │ │ │ ├── single_stage.py │ │ │ │ │ │ ├── single_stage_mono3d.py │ │ │ │ │ │ ├── sparsedrive.py │ │ │ │ │ │ ├── uniad_e2e.py │ │ │ │ │ │ └── uniad_track.py │ │ │ │ │ ├── losses/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── focal_loss.py │ │ │ │ │ │ ├── iou_loss.py │ │ │ │ │ │ ├── smooth_l1_loss.py │ │ │ │ │ │ └── utils.py │ │ │ │ │ ├── modules/ │ │ │ │ │ │ ├── VAD_transformer.py │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── custom_base_transformer_layer.py │ │ │ │ │ │ ├── decoder.py │ │ │ │ │ │ ├── encoder.py │ │ │ │ │ │ ├── group_attention.py │ │ │ │ │ │ ├── multi_scale_deformable_attn_function.py │ │ │ │ │ │ ├── spatial_cross_attention.py │ │ │ │ │ │ ├── temporal_self_attention.py │ │ │ │ │ │ ├── transformer.py │ │ │ │ │ │ ├── transformerV2.py │ │ │ │ │ │ └── vote_module.py │ │ │ │ │ ├── necks/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── fpn.py │ │ │ │ │ ├── opt/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── adamw.py │ │ │ │ │ ├── roi_heads/ │ │ │ │ │ │ └── mask_heads/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── fused_semantic_head.py │ │ │ │ │ ├── segmentors/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── base.py │ │ │ │ │ ├── utils/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── builder.py │ │ │ │ │ │ ├── functional.py │ │ │ │ │ │ ├── fuse_conv_bn.py │ │ │ │ │ │ ├── grid_mask.py │ │ │ │ │ │ ├── positional_encoding.py │ │ │ │ │ │ ├── res_layer.py │ │ │ │ │ │ ├── transformer.py │ │ │ │ │ │ └── weight_init.py │ │ │ │ │ └── vad_utils/ │ │ │ │ │ ├── CD_loss.py │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── map_utils.py │ │ │ │ │ ├── plan_loss.py │ │ │ │ │ └── traj_lr_warmup.py │ │ │ │ ├── ops/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── csrc/ │ │ │ │ │ │ ├── common/ │ │ │ │ │ │ │ ├── box_iou_rotated_utils.hpp │ │ │ │ │ │ │ ├── cuda/ │ │ │ │ │ │ │ │ ├── assign_score_withk_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── ball_query_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── bbox_overlaps_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── border_align_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── box_iou_rotated_cuda.cuh │ │ │ │ │ │ │ │ ├── carafe_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── carafe_naive_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── common_cuda_helper.hpp │ │ │ │ │ │ │ │ ├── correlation_cuda.cuh │ │ │ │ │ │ │ │ ├── deform_conv_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── deform_roi_pool_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── furthest_point_sample_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── gather_points_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── group_points_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── iou3d_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── knn_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── masked_conv2d_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── modulated_deform_conv_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── ms_deform_attn_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── nms_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── nms_rotated_cuda.cuh │ │ │ │ │ │ │ │ ├── points_in_boxes_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── psamask_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── roi_align_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── roi_align_rotated_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── roi_pool_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── roiaware_pool3d_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── roipoint_pool3d_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── scatter_points_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── sigmoid_focal_loss_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── softmax_focal_loss_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── sync_bn_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── three_interpolate_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── three_nn_cuda_kernel.cuh │ │ │ │ │ │ │ │ ├── tin_shift_cuda_kernel.cuh │ │ │ │ │ │ │ │ └── voxelization_cuda_kernel.cuh │ │ │ │ │ │ │ ├── pytorch_cpp_helper.hpp │ │ │ │ │ │ │ ├── pytorch_cuda_helper.hpp │ │ │ │ │ │ │ └── pytorch_device_registry.hpp │ │ │ │ │ │ └── pytorch/ │ │ │ │ │ │ ├── assign_score_withk.cpp │ │ │ │ │ │ ├── ball_query.cpp │ │ │ │ │ │ ├── bbox_overlaps.cpp │ │ │ │ │ │ ├── border_align.cpp │ │ │ │ │ │ ├── box_iou_rotated.cpp │ │ │ │ │ │ ├── carafe.cpp │ │ │ │ │ │ ├── carafe_naive.cpp │ │ │ │ │ │ ├── contour_expand.cpp │ │ │ │ │ │ ├── corner_pool.cpp │ │ │ │ │ │ ├── correlation.cpp │ │ │ │ │ │ ├── cpu/ │ │ │ │ │ │ │ ├── box_iou_rotated.cpp │ │ │ │ │ │ │ ├── deform_conv.cpp │ │ │ │ │ │ │ ├── modulated_deform_conv.cpp │ │ │ │ │ │ │ ├── nms.cpp │ │ │ │ │ │ │ ├── nms_rotated.cpp │ │ │ │ │ │ │ ├── pixel_group.cpp │ │ │ │ │ │ │ ├── points_in_boxes.cpp │ │ │ │ │ │ │ ├── psamask.cpp │ │ │ │ │ │ │ ├── roi_align.cpp │ │ │ │ │ │ │ ├── roi_align_rotated.cpp │ │ │ │ │ │ │ └── voxelization.cpp │ │ │ │ │ │ ├── cuda/ │ │ │ │ │ │ │ ├── assign_score_withk_cuda.cu │ │ │ │ │ │ │ ├── ball_query_cuda.cu │ │ │ │ │ │ │ ├── bbox_overlaps_cuda.cu │ │ │ │ │ │ │ ├── border_align_cuda.cu │ │ │ │ │ │ │ ├── box_iou_rotated_cuda.cu │ │ │ │ │ │ │ ├── carafe_cuda.cu │ │ │ │ │ │ │ ├── carafe_naive_cuda.cu │ │ │ │ │ │ │ ├── correlation_cuda.cu │ │ │ │ │ │ │ ├── cudabind.cpp │ │ │ │ │ │ │ ├── deform_conv_cuda.cu │ │ │ │ │ │ │ ├── deform_roi_pool_cuda.cu │ │ │ │ │ │ │ ├── focal_loss_cuda.cu │ │ │ │ │ │ │ ├── furthest_point_sample_cuda.cu │ │ │ │ │ │ │ ├── fused_bias_leakyrelu_cuda.cu │ │ │ │ │ │ │ ├── gather_points_cuda.cu │ │ │ │ │ │ │ ├── group_points_cuda.cu │ │ │ │ │ │ │ ├── iou3d_cuda.cu │ │ │ │ │ │ │ ├── knn_cuda.cu │ │ │ │ │ │ │ ├── masked_conv2d_cuda.cu │ │ │ │ │ │ │ ├── modulated_deform_conv_cuda.cu │ │ │ │ │ │ │ ├── ms_deform_attn_cuda.cu │ │ │ │ │ │ │ ├── nms_cuda.cu │ │ │ │ │ │ │ ├── nms_rotated_cuda.cu │ │ │ │ │ │ │ ├── points_in_boxes_cuda.cu │ │ │ │ │ │ │ ├── psamask_cuda.cu │ │ │ │ │ │ │ ├── roi_align_cuda.cu │ │ │ │ │ │ │ ├── roi_align_rotated_cuda.cu │ │ │ │ │ │ │ ├── roi_pool_cuda.cu │ │ │ │ │ │ │ ├── roiaware_pool3d_cuda.cu │ │ │ │ │ │ │ ├── roipoint_pool3d_cuda.cu │ │ │ │ │ │ │ ├── scatter_points_cuda.cu │ │ │ │ │ │ │ ├── sync_bn_cuda.cu │ │ │ │ │ │ │ ├── three_interpolate_cuda.cu │ │ │ │ │ │ │ ├── three_nn_cuda.cu │ │ │ │ │ │ │ ├── tin_shift_cuda.cu │ │ │ │ │ │ │ ├── upfirdn2d_kernel.cu │ │ │ │ │ │ │ └── voxelization_cuda.cu │ │ │ │ │ │ ├── deform_conv.cpp │ │ │ │ │ │ ├── deform_roi_pool.cpp │ │ │ │ │ │ ├── focal_loss.cpp │ │ │ │ │ │ ├── furthest_point_sample.cpp │ │ │ │ │ │ ├── fused_bias_leakyrelu.cpp │ │ │ │ │ │ ├── gather_points.cpp │ │ │ │ │ │ ├── group_points.cpp │ │ │ │ │ │ ├── info.cpp │ │ │ │ │ │ ├── iou3d.cpp │ │ │ │ │ │ ├── knn.cpp │ │ │ │ │ │ ├── masked_conv2d.cpp │ │ │ │ │ │ ├── modulated_deform_conv.cpp │ │ │ │ │ │ ├── ms_deform_attn.cpp │ │ │ │ │ │ ├── nms.cpp │ │ │ │ │ │ ├── nms_rotated.cpp │ │ │ │ │ │ ├── pixel_group.cpp │ │ │ │ │ │ ├── points_in_boxes.cpp │ │ │ │ │ │ ├── psamask.cpp │ │ │ │ │ │ ├── pybind.cpp │ │ │ │ │ │ ├── roi_align.cpp │ │ │ │ │ │ ├── roi_align_rotated.cpp │ │ │ │ │ │ ├── roi_pool.cpp │ │ │ │ │ │ ├── roiaware_pool3d.cpp │ │ │ │ │ │ ├── roipoint_pool3d.cpp │ │ │ │ │ │ ├── scatter_points.cpp │ │ │ │ │ │ ├── sync_bn.cpp │ │ │ │ │ │ ├── three_interpolate.cpp │ │ │ │ │ │ ├── three_nn.cpp │ │ │ │ │ │ ├── tin_shift.cpp │ │ │ │ │ │ ├── upfirdn2d.cpp │ │ │ │ │ │ └── voxelization.cpp │ │ │ │ │ ├── deform_conv.py │ │ │ │ │ ├── focal_loss.py │ │ │ │ │ ├── iou3d.py │ │ │ │ │ ├── iou3d_det/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── iou3d_utils.py │ │ │ │ │ │ └── src/ │ │ │ │ │ │ ├── iou3d.cpp │ │ │ │ │ │ └── iou3d_kernel.cu │ │ │ │ │ ├── masked_conv.py │ │ │ │ │ ├── modulated_deform_conv.py │ │ │ │ │ ├── multi_scale_deform_attn.py │ │ │ │ │ ├── nms.py │ │ │ │ │ ├── roi_align.py │ │ │ │ │ ├── roiaware_pool3d/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── points_in_boxes.py │ │ │ │ │ │ ├── roiaware_pool3d.py │ │ │ │ │ │ └── src/ │ │ │ │ │ │ ├── points_in_boxes_cpu.cpp │ │ │ │ │ │ ├── points_in_boxes_cuda.cu │ │ │ │ │ │ ├── roiaware_pool3d.cpp │ │ │ │ │ │ └── roiaware_pool3d_kernel.cu │ │ │ │ │ └── voxelize.py │ │ │ │ ├── optims/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── adamw.py │ │ │ │ │ └── optimizer.py │ │ │ │ ├── parallel/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── collate.py │ │ │ │ │ ├── data_container.py │ │ │ │ │ ├── registry.py │ │ │ │ │ └── utils.py │ │ │ │ ├── runner/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── base_runner.py │ │ │ │ │ ├── builder.py │ │ │ │ │ ├── epoch_based_runner.py │ │ │ │ │ └── hooks/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── checkpoint.py │ │ │ │ │ ├── evaluation.py │ │ │ │ │ ├── hook.py │ │ │ │ │ ├── iter_timer.py │ │ │ │ │ ├── logger/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── base.py │ │ │ │ │ │ ├── tensorboard.py │ │ │ │ │ │ └── text.py │ │ │ │ │ ├── lr_updater.py │ │ │ │ │ ├── optimizer.py │ │ │ │ │ ├── sampler_seed.py │ │ │ │ │ └── vad_hooks.py │ │ │ │ ├── structures/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── boxes.py │ │ │ │ │ ├── image_list.py │ │ │ │ │ ├── instances.py │ │ │ │ │ ├── keypoints.py │ │ │ │ │ ├── masks.py │ │ │ │ │ └── rotated_boxes.py │ │ │ │ └── utils/ │ │ │ │ ├── __init__.py │ │ │ │ ├── bricks.py │ │ │ │ ├── checkpoint.py │ │ │ │ ├── collect_env.py │ │ │ │ ├── config.py │ │ │ │ ├── contextmanagers.py │ │ │ │ ├── ext_loader.py │ │ │ │ ├── fp16_utils.py │ │ │ │ ├── grid_mask.py │ │ │ │ ├── hub.py │ │ │ │ ├── log_buffer.py │ │ │ │ ├── logger.py │ │ │ │ ├── logging.py │ │ │ │ ├── memory.py │ │ │ │ ├── misc.py │ │ │ │ ├── path.py │ │ │ │ ├── position_embedding.py │ │ │ │ ├── priority.py │ │ │ │ ├── progressbar.py │ │ │ │ ├── registry.py │ │ │ │ ├── runner_utils.py │ │ │ │ ├── timer.py │ │ │ │ ├── util_mixins.py │ │ │ │ ├── version_utils.py │ │ │ │ └── visual.py │ │ │ ├── requirements.txt │ │ │ └── team_code/ │ │ │ ├── pid_controller.py │ │ │ ├── planner.py │ │ │ ├── sparsedrive_b2d_agent.py │ │ │ ├── uniad_b2d_agent.py │ │ │ ├── vad_b2d_agent.py │ │ │ └── vad_b2d_agent_visualize.py │ │ ├── docs/ │ │ │ ├── anno.md │ │ │ ├── bench2drive_base_1000.json │ │ │ ├── bench2drive_full+sup_13638.json │ │ │ └── bench2drive_mini_10.json │ │ ├── leaderboard/ │ │ │ ├── .pylintrc │ │ │ ├── CHANGELOG.md │ │ │ ├── LICENSE │ │ │ ├── README.md │ │ │ ├── docs/ │ │ │ │ ├── Gemfile │ │ │ │ ├── LICENSE │ │ │ │ ├── README.md │ │ │ │ ├── _config.yml │ │ │ │ ├── _includes/ │ │ │ │ │ ├── footer.html │ │ │ │ │ ├── google-analytics.html │ │ │ │ │ ├── head.html │ │ │ │ │ ├── navbar.html │ │ │ │ │ ├── read_time.html │ │ │ │ │ └── scripts.html │ │ │ │ ├── _layouts/ │ │ │ │ │ ├── default.html │ │ │ │ │ ├── home.html │ │ │ │ │ ├── page.html │ │ │ │ │ └── post.html │ │ │ │ ├── _sass/ │ │ │ │ │ └── styles.scss │ │ │ │ ├── about.html │ │ │ │ ├── assets/ │ │ │ │ │ ├── main.scss │ │ │ │ │ ├── scripts.js │ │ │ │ │ └── vendor/ │ │ │ │ │ ├── bootstrap/ │ │ │ │ │ │ ├── css/ │ │ │ │ │ │ │ └── bootstrap.css │ │ │ │ │ │ └── js/ │ │ │ │ │ │ ├── bootstrap.bundle.js │ │ │ │ │ │ └── bootstrap.js │ │ │ │ │ ├── fontawesome-free/ │ │ │ │ │ │ ├── LICENSE.txt │ │ │ │ │ │ ├── css/ │ │ │ │ │ │ │ ├── all.css │ │ │ │ │ │ │ ├── brands.css │ │ │ │ │ │ │ ├── fontawesome.css │ │ │ │ │ │ │ ├── regular.css │ │ │ │ │ │ │ ├── solid.css │ │ │ │ │ │ │ ├── svg-with-js.css │ │ │ │ │ │ │ └── v4-shims.css │ │ │ │ │ │ ├── js/ │ │ │ │ │ │ │ ├── all.js │ │ │ │ │ │ │ ├── brands.js │ │ │ │ │ │ │ ├── conflict-detection.js │ │ │ │ │ │ │ ├── fontawesome.js │ │ │ │ │ │ │ ├── regular.js │ │ │ │ │ │ │ ├── solid.js │ │ │ │ │ │ │ └── v4-shims.js │ │ │ │ │ │ ├── less/ │ │ │ │ │ │ │ ├── _animated.less │ │ │ │ │ │ │ ├── _bordered-pulled.less │ │ │ │ │ │ │ ├── _core.less │ │ │ │ │ │ │ ├── _fixed-width.less │ │ │ │ │ │ │ ├── _icons.less │ │ │ │ │ │ │ ├── _larger.less │ │ │ │ │ │ │ ├── _list.less │ │ │ │ │ │ │ ├── _mixins.less │ │ │ │ │ │ │ ├── _rotated-flipped.less │ │ │ │ │ │ │ ├── _screen-reader.less │ │ │ │ │ │ │ ├── _shims.less │ │ │ │ │ │ │ ├── _stacked.less │ │ │ │ │ │ │ ├── _variables.less │ │ │ │ │ │ │ ├── brands.less │ │ │ │ │ │ │ ├── fontawesome.less │ │ │ │ │ │ │ ├── regular.less │ │ │ │ │ │ │ ├── solid.less │ │ │ │ │ │ │ └── v4-shims.less │ │ │ │ │ │ └── scss/ │ │ │ │ │ │ ├── _animated.scss │ │ │ │ │ │ ├── _bordered-pulled.scss │ │ │ │ │ │ ├── _core.scss │ │ │ │ │ │ ├── _fixed-width.scss │ │ │ │ │ │ ├── _icons.scss │ │ │ │ │ │ ├── _larger.scss │ │ │ │ │ │ ├── _list.scss │ │ │ │ │ │ ├── _mixins.scss │ │ │ │ │ │ ├── _rotated-flipped.scss │ │ │ │ │ │ ├── _screen-reader.scss │ │ │ │ │ │ ├── _shims.scss │ │ │ │ │ │ ├── _stacked.scss │ │ │ │ │ │ ├── _variables.scss │ │ │ │ │ │ ├── brands.scss │ │ │ │ │ │ ├── fontawesome.scss │ │ │ │ │ │ ├── regular.scss │ │ │ │ │ │ ├── solid.scss │ │ │ │ │ │ └── v4-shims.scss │ │ │ │ │ ├── jquery/ │ │ │ │ │ │ ├── jquery.js │ │ │ │ │ │ └── jquery.slim.js │ │ │ │ │ └── startbootstrap-clean-blog/ │ │ │ │ │ ├── js/ │ │ │ │ │ │ └── jqBootstrapValidation.js │ │ │ │ │ └── scss/ │ │ │ │ │ ├── _bootstrap-overrides.scss │ │ │ │ │ ├── _contact.scss │ │ │ │ │ ├── _footer.scss │ │ │ │ │ ├── _global.scss │ │ │ │ │ ├── _masthead.scss │ │ │ │ │ ├── _mixins.scss │ │ │ │ │ ├── _navbar.scss │ │ │ │ │ ├── _post.scss │ │ │ │ │ ├── _variables.scss │ │ │ │ │ └── clean-blog.scss │ │ │ │ ├── contact.html │ │ │ │ ├── get_started.html │ │ │ │ ├── gulpfile.js │ │ │ │ ├── index.html │ │ │ │ ├── jekyll-theme-clean-blog.gemspec │ │ │ │ ├── leaderboard.html │ │ │ │ ├── posts/ │ │ │ │ │ └── index.html │ │ │ │ └── submit.html │ │ │ ├── leaderboard/ │ │ │ │ ├── __init__.py │ │ │ │ ├── autoagents/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── agent_wrapper.py │ │ │ │ │ ├── autonomous_agent.py │ │ │ │ │ ├── dummy_agent.py │ │ │ │ │ ├── human_agent.py │ │ │ │ │ ├── human_agent_config.txt │ │ │ │ │ ├── npc_agent.py │ │ │ │ │ ├── ros1_agent.py │ │ │ │ │ ├── ros2_agent.py │ │ │ │ │ └── ros_base_agent.py │ │ │ │ ├── envs/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── sensor_interface.py │ │ │ │ ├── leaderboard_evaluator.py │ │ │ │ ├── scenarios/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── route_scenario.py │ │ │ │ │ └── scenario_manager.py │ │ │ │ └── utils/ │ │ │ │ ├── __init__.py │ │ │ │ ├── checkpoint_tools.py │ │ │ │ ├── parked_vehicles.py │ │ │ │ ├── result_writer.py │ │ │ │ ├── route_indexer.py │ │ │ │ ├── route_manipulation.py │ │ │ │ ├── route_parser.py │ │ │ │ └── statistics_manager.py │ │ │ ├── requirements.txt │ │ │ ├── run_leaderboard.sh │ │ │ ├── scripts/ │ │ │ │ ├── Dockerfile.master │ │ │ │ ├── Dockerfile.ros │ │ │ │ ├── agent_entrypoint.sh │ │ │ │ ├── code_check_and_formatting.sh │ │ │ │ ├── make_docker.sh │ │ │ │ ├── manage_scenarios.py │ │ │ │ ├── merge_statistics.py │ │ │ │ ├── pretty_print_json.py │ │ │ │ ├── route_creator.py │ │ │ │ ├── route_displayer.py │ │ │ │ ├── route_summarizer.py │ │ │ │ ├── run_evaluation.sh │ │ │ │ ├── run_evaluation_debug.sh │ │ │ │ ├── run_evaluation_multi_admlp.sh │ │ │ │ ├── run_evaluation_multi_tcp.sh │ │ │ │ ├── run_evaluation_multi_uniad.sh │ │ │ │ ├── run_evaluation_multi_uniad_tiny.sh │ │ │ │ ├── run_evaluation_multi_vad.sh │ │ │ │ ├── run_evalutaion_multi_sparsedrive.sh │ │ │ │ ├── scenario_creator.py │ │ │ │ ├── scenario_orderer.py │ │ │ │ └── weather_creator.py │ │ │ └── team_code/ │ │ │ ├── pid_controller.py │ │ │ ├── planner.py │ │ │ ├── uniad_b2d_agent.py │ │ │ ├── vad_b2d_agent.py │ │ │ └── vad_b2d_agent_visualize.py │ │ ├── scenario_runner/ │ │ │ ├── .pylintrc │ │ │ ├── .readthedocs.yml │ │ │ ├── CARLA_VER │ │ │ ├── Dockerfile │ │ │ ├── Docs/ │ │ │ │ ├── CHANGELOG.md │ │ │ │ ├── CODE_OF_CONDUCT.md │ │ │ │ ├── CONTRIBUTING.md │ │ │ │ ├── FAQ.md │ │ │ │ ├── agent_evaluation.md │ │ │ │ ├── coding_standard.md │ │ │ │ ├── creating_new_scenario.md │ │ │ │ ├── extra.css │ │ │ │ ├── getting_scenariorunner.md │ │ │ │ ├── getting_started.md │ │ │ │ ├── index.md │ │ │ │ ├── list_of_scenarios.md │ │ │ │ ├── metrics_module.md │ │ │ │ ├── openscenario_support.md │ │ │ │ ├── requirements.txt │ │ │ │ └── ros_agent.md │ │ │ ├── Jenkinsfile │ │ │ ├── LICENSE │ │ │ ├── README.md │ │ │ ├── manual_control.py │ │ │ ├── metrics_manager.py │ │ │ ├── mkdocs.yml │ │ │ ├── no_rendering_mode.py │ │ │ ├── requirements.txt │ │ │ ├── scenario_runner.py │ │ │ └── srunner/ │ │ │ ├── __init__.py │ │ │ ├── autoagents/ │ │ │ │ ├── __init__.py │ │ │ │ ├── agent_wrapper.py │ │ │ │ ├── autonomous_agent.py │ │ │ │ ├── dummy_agent.py │ │ │ │ ├── human_agent.py │ │ │ │ ├── human_agent_config.txt │ │ │ │ ├── npc_agent.py │ │ │ │ ├── ros_agent.py │ │ │ │ └── sensor_interface.py │ │ │ ├── examples/ │ │ │ │ ├── ActorFlow.xml │ │ │ │ ├── CatalogExample.xosc │ │ │ │ ├── ChangeLane.xml │ │ │ │ ├── ChangingWeather.xosc │ │ │ │ ├── ControlLoss.xml │ │ │ │ ├── CutIn.xml │ │ │ │ ├── CyclistCrossing.xosc │ │ │ │ ├── FollowLeadingVehicle.xml │ │ │ │ ├── FollowLeadingVehicle.xosc │ │ │ │ ├── FreeRide.xml │ │ │ │ ├── HighwayCutIn.xml │ │ │ │ ├── IntersectionCollisionAvoidance.xosc │ │ │ │ ├── LaneChangeSimple.xosc │ │ │ │ ├── LeadingVehicle.xml │ │ │ │ ├── NoSignalJunction.xml │ │ │ │ ├── ObjectCrossing.xml │ │ │ │ ├── OppositeDirection.xml │ │ │ │ ├── OscControllerExample.xosc │ │ │ │ ├── PedestrianCrossingFront.xosc │ │ │ │ ├── RouteObstacles.xml │ │ │ │ ├── RunningRedLight.xml │ │ │ │ ├── SignalizedJunctionLeftTurn.xml │ │ │ │ ├── SignalizedJunctionRightTurn.xml │ │ │ │ ├── Slalom.xosc │ │ │ │ ├── VehicleOpensDoor.xml │ │ │ │ ├── VehicleTurning.xml │ │ │ │ └── catalogs/ │ │ │ │ ├── ControllerCatalog.xosc │ │ │ │ ├── EnvironmentCatalog.xosc │ │ │ │ ├── ManeuverCatalog.xosc │ │ │ │ ├── MiscObjectCatalog.xosc │ │ │ │ ├── PedestrianCatalog.xosc │ │ │ │ └── VehicleCatalog.xosc │ │ │ ├── metrics/ │ │ │ │ ├── examples/ │ │ │ │ │ ├── basic_metric.py │ │ │ │ │ ├── criteria_filter.py │ │ │ │ │ ├── distance_between_vehicles.py │ │ │ │ │ └── distance_to_lane_center.py │ │ │ │ └── tools/ │ │ │ │ ├── metrics_log.py │ │ │ │ └── metrics_parser.py │ │ │ ├── openscenario/ │ │ │ │ ├── 0.9.x/ │ │ │ │ │ ├── OpenSCENARIO_Catalog.xsd │ │ │ │ │ ├── OpenSCENARIO_TypeDefs.xsd │ │ │ │ │ ├── OpenSCENARIO_v0.9.1.xsd │ │ │ │ │ └── migration0_9_1to1_0.xslt │ │ │ │ └── OpenSCENARIO.xsd │ │ │ ├── scenarioconfigs/ │ │ │ │ ├── __init__.py │ │ │ │ ├── openscenario_configuration.py │ │ │ │ ├── route_scenario_configuration.py │ │ │ │ └── scenario_configuration.py │ │ │ ├── scenariomanager/ │ │ │ │ ├── __init__.py │ │ │ │ ├── actorcontrols/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── actor_control.py │ │ │ │ │ ├── basic_control.py │ │ │ │ │ ├── carla_autopilot.py │ │ │ │ │ ├── external_control.py │ │ │ │ │ ├── npc_vehicle_control.py │ │ │ │ │ ├── pedestrian_control.py │ │ │ │ │ ├── simple_vehicle_control.py │ │ │ │ │ ├── vehicle_longitudinal_control.py │ │ │ │ │ └── visualizer.py │ │ │ │ ├── carla_data_provider.py │ │ │ │ ├── lights_sim.py │ │ │ │ ├── result_writer.py │ │ │ │ ├── scenario_manager.py │ │ │ │ ├── scenarioatomics/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── atomic_behaviors.py │ │ │ │ │ ├── atomic_criteria.py │ │ │ │ │ └── atomic_trigger_conditions.py │ │ │ │ ├── timer.py │ │ │ │ ├── traffic_events.py │ │ │ │ ├── watchdog.py │ │ │ │ └── weather_sim.py │ │ │ ├── scenarios/ │ │ │ │ ├── __init__.py │ │ │ │ ├── actor_flow.py │ │ │ │ ├── background_activity.py │ │ │ │ ├── background_activity_parametrizer.py │ │ │ │ ├── basic_scenario.py │ │ │ │ ├── blocked_intersection.py │ │ │ │ ├── change_lane.py │ │ │ │ ├── construction_crash_vehicle.py │ │ │ │ ├── control_loss.py │ │ │ │ ├── cross_bicycle_flow.py │ │ │ │ ├── cut_in.py │ │ │ │ ├── cut_in_with_static_vehicle.py │ │ │ │ ├── follow_leading_vehicle.py │ │ │ │ ├── freeride.py │ │ │ │ ├── green_traffic_light.py │ │ │ │ ├── hard_break.py │ │ │ │ ├── highway_cut_in.py │ │ │ │ ├── invading_turn.py │ │ │ │ ├── left_turn_enter_flow.py │ │ │ │ ├── maneuver_opposite_direction.py │ │ │ │ ├── no_signal_junction_crossing.py │ │ │ │ ├── object_crash_intersection.py │ │ │ │ ├── object_crash_vehicle.py │ │ │ │ ├── open_scenario.py │ │ │ │ ├── opposite_vehicle_taking_priority.py │ │ │ │ ├── other_leading_vehicle.py │ │ │ │ ├── parking_cut_in.py │ │ │ │ ├── parking_exit.py │ │ │ │ ├── pedestrian_crossing.py │ │ │ │ ├── route_obstacles.py │ │ │ │ ├── route_scenario.py │ │ │ │ ├── sequentially_lane_change.py │ │ │ │ ├── signalized_junction_left_turn.py │ │ │ │ ├── signalized_junction_right_turn.py │ │ │ │ ├── t_junction.py │ │ │ │ ├── vanilla_turn.py │ │ │ │ ├── vehicle_opens_door.py │ │ │ │ └── yield_to_emergency_vehicle.py │ │ │ ├── tests/ │ │ │ │ ├── __init__.py │ │ │ │ ├── carla_mocks/ │ │ │ │ │ ├── README.md │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── agents/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ ├── navigation/ │ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ │ ├── basic_agent.py │ │ │ │ │ │ │ ├── behavior_agent.py │ │ │ │ │ │ │ ├── behavior_types.py │ │ │ │ │ │ │ ├── controller.py │ │ │ │ │ │ │ ├── global_route_planner.py │ │ │ │ │ │ │ └── local_planner.py │ │ │ │ │ │ └── tools/ │ │ │ │ │ │ ├── __init__.py │ │ │ │ │ │ └── misc.py │ │ │ │ │ └── carla.py │ │ │ │ └── test_xosc_load.py │ │ │ ├── tools/ │ │ │ │ ├── __init__.py │ │ │ │ ├── background_manager.py │ │ │ │ ├── openscenario_parser.py │ │ │ │ ├── py_trees_port.py │ │ │ │ ├── route_manipulation.py │ │ │ │ ├── route_parser.py │ │ │ │ ├── scenario_helper.py │ │ │ │ └── scenario_parser.py │ │ │ └── utilities/ │ │ │ └── code_check_and_formatting.sh │ │ └── tools/ │ │ ├── ability_benchmark.py │ │ ├── check_carla.md │ │ ├── clean_carla.sh │ │ ├── data_collect.py │ │ ├── download_mini.sh │ │ ├── efficiency_smoothness_benchmark.py │ │ ├── gen_hdmap.py │ │ ├── generate_video.py │ │ ├── merge_route_json.py │ │ ├── split_xml.py │ │ ├── utils.py │ │ └── visualize.py │ └── quick_start.md └── open_loop/ ├── docs/ │ └── quick_start.md ├── nuscenes_infos_val_hrad_planing_scene.pkl ├── projects/ │ ├── configs/ │ │ ├── MomAD_small_stage1_roboAD.py │ │ ├── MomAD_small_stage2_roboAD.py │ │ ├── MomAD_small_stage2_roboAD_6s.py │ │ ├── MomAD_small_trainval_1_10_stage1_test.py │ │ ├── sparsedrive_small_stage1.py │ │ ├── sparsedrive_small_stage1_roboAD.py │ │ ├── sparsedrive_small_stage2.py │ │ ├── sparsedrive_small_stage2_6s.py │ │ └── sparsedrive_small_trainval_1_10_stage1_test.py │ └── mmdet3d_plugin/ │ ├── __init__.py │ ├── apis/ │ │ ├── __init__.py │ │ ├── mmdet_train.py │ │ ├── test.py │ │ └── train.py │ ├── core/ │ │ ├── box3d.py │ │ └── evaluation/ │ │ ├── __init__.py │ │ └── eval_hooks.py │ ├── datasets/ │ │ ├── __init__.py │ │ ├── builder.py │ │ ├── evaluation/ │ │ │ ├── __init__.py │ │ │ ├── map/ │ │ │ │ ├── AP.py │ │ │ │ ├── distance.py │ │ │ │ └── vector_eval.py │ │ │ ├── motion/ │ │ │ │ ├── motion_eval_uniad.py │ │ │ │ └── motion_utils.py │ │ │ └── planning/ │ │ │ ├── planning_eval.py │ │ │ ├── planning_eval_roboAD.py │ │ │ └── planning_eval_roboAD_6s.py │ │ ├── map_utils/ │ │ │ ├── nuscmap_extractor.py │ │ │ └── utils.py │ │ ├── nuscenes_3d_dataset.py │ │ ├── nuscenes_3d_dataset_roboAD.py │ │ ├── nuscenes_3d_dataset_roboAD_6s.py │ │ ├── pipelines/ │ │ │ ├── __init__.py │ │ │ ├── augment.py │ │ │ ├── loading.py │ │ │ ├── transform.py │ │ │ └── vectorize.py │ │ ├── samplers/ │ │ │ ├── __init__.py │ │ │ ├── distributed_sampler.py │ │ │ ├── group_in_batch_sampler.py │ │ │ ├── group_sampler.py │ │ │ └── sampler.py │ │ └── utils.py │ ├── models/ │ │ ├── __init__.py │ │ ├── attention.py │ │ ├── base_target.py │ │ ├── blocks.py │ │ ├── detection3d/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ ├── detection3d_blocks.py │ │ │ ├── detection3d_head.py │ │ │ ├── detection3d_head_roboAD.py │ │ │ ├── feature_enhance.py │ │ │ ├── losses.py │ │ │ └── target.py │ │ ├── grid_mask.py │ │ ├── instance_bank.py │ │ ├── map/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ ├── loss.py │ │ │ ├── map_blocks.py │ │ │ ├── match_cost.py │ │ │ └── target.py │ │ ├── motion/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ ├── instance_queue.py │ │ │ ├── motion_blocks.py │ │ │ ├── motion_planning_head.py │ │ │ ├── motion_planning_head_roboAD.py │ │ │ ├── motion_planning_head_roboAD_6s.py │ │ │ ├── next_token_prediction.py │ │ │ └── target.py │ │ ├── sparsedrive.py │ │ └── sparsedrive_head.py │ └── ops/ │ ├── __init__.py │ ├── deformable_aggregation.py │ ├── setup.py │ └── src/ │ ├── deformable_aggregation.cpp │ └── deformable_aggregation_cuda.cu ├── requirement.txt ├── scripts/ │ ├── create_data.sh │ ├── kmeans.sh │ ├── test.sh │ ├── test_roboAD.sh │ ├── train.sh │ ├── train_6s.sh │ ├── train_roboAD.sh │ └── visualize.sh ├── test.py ├── tools/ │ ├── benchmark.py │ ├── data_converter/ │ │ ├── __init__.py │ │ ├── nuscenes_converter.py │ │ ├── nuscenes_converter_1_10.py │ │ ├── nuscenes_converter_6s.py │ │ └── nuscenes_converter_hrad_planing_scene.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── fuse_conv_bn.py │ ├── kmeans/ │ │ ├── kmeans_det.py │ │ ├── kmeans_map.py │ │ ├── kmeans_motion.py │ │ └── kmeans_plan.py │ ├── test.py │ ├── train.py │ ├── train_single.py │ └── visualization/ │ ├── bev_render.py │ ├── cam_render.py │ └── visualize.py └── visualize.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *.pyc *.npy *.pth *.whl *.swp data/ ckpt/ work_dirs*/ dist_test/ vis/ val/ lib/ *.egg-info build/ __pycache__/ *.so job_scripts/ temp_ops/ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2024 swc-17 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # [CVPR2025] Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
This is the official repository of [**MomAD**](https://arxiv.org/abs/2503.03125). :fire: Our work has been accepted by CVPR 2025!
## Abstract
End-to-end autonomous driving frameworks facilitate seamless integration of perception and planning but often rely on one-shot trajectory prediction, lacking temporal consistency and long-horizon awareness. This limitation can lead to unstable control, undesirable shifts, and vulnerability to occlusions in single-frame perception. In this work, we propose the Momentum-Aware Driving (MomAD) framework to address these issues by introducing trajectory momentum and perception momentum to stabilize and refine trajectory prediction. MomAD consists of two key components: (1) Topological Trajectory Matching (TTM), which uses Hausdorff Distance to align predictions with prior paths and ensure temporal coherence, and (2) Momentum Planning Interactor (MPI), which cross-attends the planning query with historical spatial-temporal context. Additionally, an encoder-decoder module introduces feature perturbations to increase robustness against perception noise. To quantify planning stability, we propose the Trajectory Prediction Consistency (TPC) metric, showing that MomAD achieves long-term consistency (>3s) on the nuScenes dataset. We further curate the challenging Turning-nuScenes validation set, focused on turning scenarios, where MomAD surpasses state-of-the-art methods, highlighting its enhanced stability and responsiveness in dynamic driving conditions.
:fire: Contributions: * **Momentum Planning Concept.** We propose the concept of momentum planning in multi-modal trajectory planning, drawing an analogy to human driving behavior. We provide theoretical evidence to demonstrate the effectiveness of our momentum planning in addressing temporal consistency in end-to-end autonomous driving * **MomAD Framework.** We propose MomAD, an end-to-end autonomous driving framework that employs momentum planning. It optimizes current trajectory planning by integrating historical planning guidance, significantly improving trajectory consistency and stability in autonomous driving. * **Turning NuScenes Validation Dataset.** We create the Turning-nuScenes val dataset, derived from the nuScenes full validation dataset. This new dataset focuses on turning scenarios, providing a specialized benchmark for evaluating the performance of autonomous driving systems in complex driving situations. * **Trajectory Prediction Consistency (TPC) Metric.** We introduce the TPC metric to quantitatively assess the consistency of trajectory predictions in existing end-to-end autonomous driving methods, addressing a critical gap in the evaluation of trajectory planning. * **Performance Evaluation.** Through extensive experiments on the nuScenes dataset, we demonstrate that MomAD significantly outperforms SOTA methods in terms of trajectory consistency and stability, highlighting its effectiveness in tackling challenges within autonomous driving planning. We evaluated the results of long trajectory predictions, specifically at 4, 5, and 6 seconds, which are critical for ensuring the stability of autonomous driving systems.
## Method

The overall architecture of MomAD. MomAD, as a multi-model trajectory end-to-end autonomous driving method, first encodes multi-view images into feature maps, then learns a sparse scene representation through sparse perception, and finally performs a momentum-guided motion planner to accomplish the planning task. The momentum planning module integrates historical planning to inform current planning, effectively addressing the issue of maximum score deviation in multi-modal trajectories.
## Results in paper ### Open-loop mertics - Planning results on [nuScenes](https://github.com/nutonomy/nuscenes-devkit). - MomAD 3s stage2: [ckpt](https://huggingface.co/ZI-YING/MomAD_nuScenes_stage2_planing_3s) | Method | Backbone | L2 (m) 1s | L2 (m) 2s | L2 (m) 3s | L2 (m) Avg | Col. (%) 1s | Col. (%) 2s | Col. (%) 3s | Col. (%) Avg | TPC (m) 1s | TPC (m) 2s | TPC (m) 3s | TPC (m) Avg | FPS | | :---: | :---:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: | | UniAD | ResNet101 | 0.45 | 0.70 | 1.04 |0.73 | 0.62 | 0.58 | 0.63 | 0.61 |0.41 | 0.68 | 0.97 | 0.68 |1.8 (A100)| SparseDrive |ResNet50| **0.29** | 0.58 | 0.96 | 0.61 | **0.01** | **0.05** | 0.18 | **0.08** | **0.30** | 0.57 | 0.85 | 0.57 | **9.0 (4090)**| **MomAD (Ours)** | ResNet50 | 0.31 | **0.57** | **0.91** | **0.60** | **0.01** | **0.05** | **0.22** | 0.09 | **0.30** | **0.53** | **0.78** | **0.54** | 7.8 (4090) | - Planning results for long trajectory prediction on [nuScenes](https://github.com/nutonomy/nuscenes-devkit). We train 10 epochs on 6s trajectories and test on 6s trajectories. | Method | L2 (m) 4s | L2 (m) 5s | L2 (m) 6s | Col. (%) 4s | Col. (%) 5s | Col. (%) 6s | TPC (m) 4s | TPC (m) 5s | TPC (m) 6s | | :---: | :---:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | SparseDrive | 1.75|2.32|2.95| 0.87| 1.54| 2.33| 1.33| 1.66| 1.99| **MomAD (Ours)** | **1.67**| **1.98**| **2.45**| **0.83**| **1.43**| **2.13**| **1.19**| **1.45**| **1.61**| - Planning results on the Turning-nuScenes validation dataset [Turning-nuScenes ](/open_loop/nuscenes_infos_val_hrad_planing_scene.pkl). We train 10 epochs on 6s trajectories and test on 6s trajectories. | Method |L2 (m) 1s | L2 (m) 2s | L2 (m) 3s | Col. (%) 1s | Col. (%) 2s | Col. (%) 3s | TPC (m) 1s | TPC (m) 2s | TPC (m) 3s | | :---: | :---:| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | SparseDrive |0.35 | 0.77 | 1.46 | 0.86 | 0.04 | 0.17 | 0.98 | 0.40 | 0.34 | 0.70 | 1.33 | 0.79| **MomAD (Ours)** | 0.33| 0.70| 1.24| 0.76| 0.03| 0.13| 0.79| 0.32| 0.32| 0.54| 1.05| 0.63| ### Close-loop mertics (**weight** and **pkl**) - Open-loop and Closed-loop Results of E2E-AD Methods in Bench2Drive (V0.0.3)} under base training set. `mmt' denotes the extension of VAD on Multi-modal Trajectory. * denotes our re-implementation. The metircs momad used follows [Bench2Drive](https://github.com/Thinklab-SJTU/Bench2Drive) - The **weight(stage-1)**, **data pkl** and**kmenas** of MomAD in Bench2Drive:[**MomAD**](https://pan.baidu.com/s/1qBVdpXUohfveU8au9ShAyg?pwd=u36f)
Method Open-loop Metric Closed-loop Metric
Avg. L2 ↓ DS ↑ SR(%) ↑ Effi ↑ Comf ↑
VAD 0.91 42.35 15.00 157.94 46.01
VAD mmt* 0.89 42.87 15.91 158.12 47.22
Our MomAD (Euclidean) 0.84 46.12 17.45 173.35 50.98
Our MomAD 0.85 45.35 17.44 162.09 49.34
SparcDrive* 0.87 44.54 16.71 170.21 48.63
Our MomAD (Euclidean) 0.84 46.12 17.45 173.35 50.98
Our MomAD 0.82 47.91 18.11 174.91 51.20
### Close_loop Vis

### Robustness evaluation - Robustness analysis on [nuScenes-C](https://github.com/thu-ml/3D_Corruptions_AD)
Setting Method Detection Tracking Mapping Motion Planning
mAP ↑ NDS ↑ AMOTA ↑ mAP ↑ mADE ↓ L2 ↓ Col. ↓ TPC ↓
Clean SparseDrive 0.418 0.525 0.386 55.1 0.62 0.61 0.08 0.57
Clean Our MomAD 0.423 0.531 0.391 55.9 0.61 0.60 0.09 0.54
Snow SparseDrive 0.091 0.111 0.102 16.0 0.98 0.88 0.32 0.82
Snow Our MomAD 0.154 0.173 0.166 20.9 0.76 0.73 0.16 0.68
Fog SparseDrive 0.141 0.159 0.154 18.8 0.91 0.86 0.41 0.80
Fog Our MomAD 0.197 0.197 0.206 24.9 0.73 0.71 0.18 0.67
Rain SparseDrive 0.128 0.140 0.193 19.4 0.97 0.93 0.46 0.92
Rain Our MomAD 0.207 0.213 0.266 25.2 0.76 0.71 0.21 0.71
## Trajectory Prediction Consistency (TPC) metric To evaluate the planning stability of MomAD, we propose a new [Trajectory Prediction Consistency (TPC) metric](/open_loop/projects/mmdet3d_plugin/datasets/evaluation/planning/planning_eval_roboAD_6s.py) to measure consistency between predicted and historical trajectories. ## How to generate a 6s nuScenes trajectory dataset？ ``` python tools/data_converter/nuscenes_converter_6s.py nuscenes \ --root-path ./data/nuscenes \ --canbus ./data/nuscenes \ --out-dir ./data/infos/ \ --extra-tag nuscenes \ --version v1.0 ``` ## Quick Start [Quick Start for Open_loop](open_loop/docs/quick_start.md) [Quick start for Close_loop](close_loop/quick_start.md) ## Citation If you find MomAD is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry. ``` @article{song2025momad, title={Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving}, author={Ziying Song and Caiyan Jia and Lin Liu and Hongyu Pan and Yongchang Zhang and Junming Wang and Xingyu Zhang and Shaoqing Xu and Lei Yang and Yadan Luo}, year={2025}, eprint={2503.03125}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2503.03125}, } ``` ## Acknowledgement - [SparseDrive](https://github.com/swc-17/SparseDrive) - [UniAD](https://github.com/OpenDriveLab/UniAD) - [VAD](https://github.com/hustvl/VAD) - [mmdet3d](https://github.com/open-mmlab/mmdetection3d) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/configs/momad_small_b2d_stage1.py ================================================ # ================ base config =================== plugin = True plugin_dir = "mmdet3d_plugin/" dist_params = dict(backend="nccl") log_level = "INFO" work_dir = None total_batch_size = 64 num_gpus = 8 batch_size = total_batch_size // num_gpus num_iters_per_epoch = int(234769 // (num_gpus * batch_size)) num_epochs = 10 checkpoint_epoch_interval = 2 checkpoint_config = dict( interval=num_iters_per_epoch * checkpoint_epoch_interval ) log_config = dict( interval=51, hooks=[ dict(type="TextLoggerHook", by_epoch=False), dict(type="TensorboardLoggerHook"), ], ) load_from = None resume_from = None workflow = [("train", 1)] fp16 = dict(loss_scale=32.0) input_shape = (704, 384) # ================== model ======================== class_names = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others', ] map_class_names = [ 'Broken', 'Solid', 'SolidSolid', # 'Center', # 'TrafficLight', # 'StopSign', ] NameMapping = { #=================vehicle================= # bicycle 'vehicle.bh.crossbike': 'bicycle', "vehicle.diamondback.century": 'bicycle', "vehicle.gazelle.omafiets": 'bicycle', # car "vehicle.chevrolet.impala": 'car', "vehicle.dodge.charger_2020": 'car', "vehicle.dodge.charger_police": 'car', "vehicle.dodge.charger_police_2020": 'car', "vehicle.lincoln.mkz_2017": 'car', "vehicle.lincoln.mkz_2020": 'car', "vehicle.mini.cooper_s_2021": 'car', "vehicle.mercedes.coupe_2020": 'car', "vehicle.ford.mustang": 'car', "vehicle.nissan.patrol_2021": 'car', "vehicle.audi.tt": 'car', "vehicle.audi.etron": 'car', "vehicle.ford.crown": 'car', "vehicle.ford.mustang": 'car', "vehicle.tesla.model3": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": 'car', # bus # van "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": "van", "vehicle.ford.ambulance": "van", # truck "vehicle.carlamotors.firetruck": 'truck', #========================================= #=================traffic sign============ # traffic.speed_limit "traffic.speed_limit.30": 'traffic_sign', "traffic.speed_limit.40": 'traffic_sign', "traffic.speed_limit.50": 'traffic_sign', "traffic.speed_limit.60": 'traffic_sign', "traffic.speed_limit.90": 'traffic_sign', "traffic.speed_limit.120": 'traffic_sign', "traffic.stop": 'traffic_sign', "traffic.yield": 'traffic_sign', "traffic.traffic_light": 'traffic_light', #========================================= #===================Construction=========== "static.prop.warningconstruction" : 'traffic_cone', "static.prop.warningaccident": 'traffic_cone', "static.prop.trafficwarning": "traffic_cone", #===================Construction=========== "static.prop.constructioncone": 'traffic_cone', #=================pedestrian============== "walker.pedestrian.0001": 'pedestrian', "walker.pedestrian.0004": 'pedestrian', "walker.pedestrian.0005": 'pedestrian', "walker.pedestrian.0007": 'pedestrian', "walker.pedestrian.0013": 'pedestrian', "walker.pedestrian.0014": 'pedestrian', "walker.pedestrian.0017": 'pedestrian', "walker.pedestrian.0018": 'pedestrian', "walker.pedestrian.0019": 'pedestrian', "walker.pedestrian.0020": 'pedestrian', "walker.pedestrian.0022": 'pedestrian', "walker.pedestrian.0025": 'pedestrian', "walker.pedestrian.0035": 'pedestrian', "walker.pedestrian.0041": 'pedestrian', "walker.pedestrian.0046": 'pedestrian', "walker.pedestrian.0047": 'pedestrian', # ========================================== "static.prop.dirtdebris01": 'others', "static.prop.dirtdebris02": 'others', } num_classes = len(class_names) num_map_classes = len(map_class_names) roi_size = (30, 60) num_sample = 20 fut_ts = 6 fut_mode = 6 ego_fut_ts = 6 ego_fut_mode = 6 num_cmd = 6 queue_length = 4 # history + current embed_dims = 256 num_groups = 8 num_decoder = 6 num_single_frame_decoder = 1 num_single_frame_decoder_map = 1 use_deformable_func = True # mmdet3d_plugin/ops/setup.py needs to be executed strides = [4, 8, 16, 32] num_levels = len(strides) num_depth_layers = 3 drop_out = 0.1 temporal = True temporal_map = True decouple_attn = True decouple_attn_map = False decouple_attn_motion = True with_quality_estimation = True task_config = dict( with_det=True, with_map=True, with_motion_plan=False, ) model = dict( type="MomAD", use_grid_mask=True, use_deformable_func=use_deformable_func, img_backbone=dict( type="ResNet", depth=50, num_stages=4, frozen_stages=-1, norm_eval=False, style="pytorch", with_cp=True, out_indices=(0, 1, 2, 3), norm_cfg=dict(type="BN", requires_grad=True), pretrained="ckpt/resnet50-19c8e357.pth", ), img_neck=dict( type="FPN", num_outs=num_levels, start_level=0, out_channels=embed_dims, add_extra_convs="on_output", relu_before_extra_convs=True, in_channels=[256, 512, 1024, 2048], ), depth_branch=dict( # for auxiliary supervision only type="DenseDepthNet", embed_dims=embed_dims, num_depth_layers=num_depth_layers, loss_weight=0.2, ), head=dict( type="SparseDriveHead", task_config=task_config, det_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn, instance_bank=dict( type="InstanceBank", num_anchor=900, embed_dims=embed_dims, anchor="data/kmeans/kmeans_det_900.npy", anchor_handler=dict(type="SparseBox3DKeyPointsGenerator"), num_temp_instances=600 if temporal else -1, confidence_decay=0.9, feat_grad=False, ), anchor_encoder=dict( type="SparseBox3DEncoder", vel_dims=3, embed_dims=[128, 32, 32, 64] if decouple_attn else 256, mode="cat" if decouple_attn else "add", output_fc=not decouple_attn, in_loops=1, out_loops=4 if decouple_attn else 2, ), num_single_frame_decoder=num_single_frame_decoder, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder) )[2:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparseBox3DKeyPointsGenerator", num_learnable_pts=6, fix_scale=[ [0, 0, 0], [0.45, 0, 0], [-0.45, 0, 0], [0, 0.45, 0], [0, -0.45, 0], [0, 0, 0.45], [0, 0, -0.45], ], ), ), refine_layer=dict( type="SparseBox3DRefinementModule", embed_dims=embed_dims, num_cls=num_classes, refine_yaw=True, with_quality_estimation=with_quality_estimation, ), sampler=dict( type="SparseBox3DTarget", num_dn_groups=0, num_temp_dn_groups=0, dn_noise_scale=[2.0] * 3 + [0.5] * 7, max_dn_gt=32, add_neg_dn=True, cls_weight=2.0, box_weight=0.25, reg_weights=[2.0] * 3 + [0.5] * 3 + [0.0] * 4, cls_wise_reg_weights={ class_names.index("traffic_cone"): [ 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, ], }, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0, ), loss_reg=dict( type="SparseBox3DLoss", loss_box=dict(type="L1Loss", loss_weight=0.25), loss_centerness=dict(type="CrossEntropyLoss", use_sigmoid=True), loss_yawness=dict(type="GaussianFocalLoss"), # cls_allow_reverse=[class_names.index("barrier")], ), decoder=dict(type="SparseBox3DDecoder"), reg_weights=[2.0] * 3 + [1.0] * 7, ), map_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn_map, instance_bank=dict( type="InstanceBank", num_anchor=100, embed_dims=embed_dims, anchor="data/kmeans/kmeans_map_100.npy", anchor_handler=dict(type="SparsePoint3DKeyPointsGenerator"), num_temp_instances=0 if temporal_map else -1, confidence_decay=0.9, feat_grad=True, ), anchor_encoder=dict( type="SparsePoint3DEncoder", embed_dims=embed_dims, num_sample=num_sample, ), num_single_frame_decoder=num_single_frame_decoder_map, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder_map + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder_map) )[:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal_map else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparsePoint3DKeyPointsGenerator", embed_dims=embed_dims, num_sample=num_sample, num_learnable_pts=3, fix_height=(0, 0.25, -0.25, 0.5, -0.5), ground_height=-1.84, # ground height in lidar frame ), ), refine_layer=dict( type="SparsePoint3DRefinementModule", embed_dims=embed_dims, num_sample=num_sample, num_cls=num_map_classes, ), sampler=dict( type="SparsePoint3DTarget", assigner=dict( type='HungarianLinesAssigner', cost=dict( type='MapQueriesCost', cls_cost=dict(type='FocalLossCost', weight=1.0), reg_cost=dict(type='LinesL1Cost', weight=10.0, beta=0.01, permute=True), ), ), num_cls=num_map_classes, num_sample=num_sample, roi_size=roi_size, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_reg=dict( type="SparseLineLoss", loss_line=dict( type='LinesL1Loss', loss_weight=10.0, beta=0.01, ), num_sample=num_sample, roi_size=roi_size, ), decoder=dict( type="SparsePoint3DDecoder", score_threshold=0.5, ), reg_weights=[1.0] * 40, gt_cls_key="gt_map_labels", gt_reg_key="gt_map_pts", gt_id_key="map_instance_id", with_instance_id=False, task_prefix='map', ), motion_plan_head=dict( type='MomADMotionPlanningHead', fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, motion_anchor=f'data/kmeans/kmeans_motion_{fut_mode}.npy', plan_anchor=f'data/kmeans/kmeans_plan_{ego_fut_mode}.npy', embed_dims=embed_dims, decouple_attn=decouple_attn_motion, instance_queue=dict( type="InstanceQueue", embed_dims=embed_dims, queue_length=queue_length, tracking_threshold=0.2, feature_map_scale=(input_shape[1]/strides[-1], input_shape[0]/strides[-1]), ), operation_order=( [ "temp_gnn", "gnn", "norm", "cross_gnn", "norm", "ffn", "norm", ] * 3 + [ "refine", ] ), temp_graph_model=dict( type="MultiheadAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), cross_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 2, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), refine_layer=dict( type="MotionPlanningRefinementModule", embed_dims=embed_dims, fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, ), motion_sampler=dict( type="MotionTarget", ), motion_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.2 ), motion_loss_reg=dict(type='L1Loss', loss_weight=0.2), planning_sampler=dict( type="PlanningTarget", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, ), plan_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.5, ), plan_loss_reg=dict(type='L1Loss', loss_weight=1.0), plan_loss_status=dict(type='L1Loss', loss_weight=1.0), motion_decoder=dict(type="SparseBox3DMotionDecoder"), planning_decoder=dict( type="HierarchicalPlanningDecoder", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_rescore=True, ), num_det=50, num_map=10, ), ), ) # ================== data ======================== dataset_type = "B2D3DDataset" data_root = "data/bench2drive/" anno_root = "data/infos/" file_client_args = dict(backend="disk") img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) train_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict( type="DenseDepthMapGenerator", downsample=strides[:num_depth_layers], ), dict(type="BBoxRotation"), dict(type="PhotoMetricDistortionMultiViewImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=False, normalize=False, sample_num=num_sample, permute=True, ), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", "gt_depth", "focal", "gt_bboxes_3d", "gt_labels_3d", 'gt_map_labels', 'gt_map_pts', 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', 'ego_status', 'ego_his_trajs', ], meta_keys=["T_global", "T_global_inv", "timestamp", "instance_id"], ), ] test_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", 'ego_status', 'gt_ego_fut_cmd', 'ego_his_trajs', ], meta_keys=["T_global", "T_global_inv", "timestamp"], ), ] eval_pipeline = [ dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=True, normalize=False, ), dict( type='Collect', keys=[ 'vectors', "gt_bboxes_3d", "gt_labels_3d", 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', # 'fut_boxes' ], meta_keys=['token', 'timestamp'] ), ] input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=False, ) data_basic_config = dict( type=dataset_type, data_root=data_root, classes=class_names, map_classes=map_class_names, name_mapping=NameMapping, modality=input_modality, sample_interval=5, past_frames=6, future_frames=6, ) eval_config = dict( **data_basic_config, ann_file=anno_root + 'b2d_infos_val.pkl', pipeline=eval_pipeline, test_mode=True, ) data_aug_conf = { "resize_lim": (0.40, 0.47), "final_dim": input_shape[::-1], "bot_pct_lim": (0.0, 0.0), "rot_lim": (-5.4, 5.4), "H": 900, "W": 1600, "rand_flip": True, "rot3d_range": [0, 0], } data = dict( samples_per_gpu=batch_size, workers_per_gpu=batch_size, train=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_train.pkl", pipeline=train_pipeline, test_mode=False, data_aug_conf=data_aug_conf, with_seq_flag=True, sequences_split_num=5, keep_consistent_seq_aug=True, ), val=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), test=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), ) # ================== training ======================== optimizer = dict( type="AdamW", lr=3e-4, weight_decay=0.001, paramwise_cfg=dict( custom_keys={ "img_backbone": dict(lr_mult=0.1), } ), ) optimizer_config = dict(grad_clip=dict(max_norm=25, norm_type=2)) lr_config = dict( policy="CosineAnnealing", warmup="linear", warmup_iters=500, warmup_ratio=1.0 / 3, min_lr_ratio=1e-3, ) runner = dict( type="IterBasedRunner", max_iters=num_iters_per_epoch * num_epochs, ) # ================== eval ======================== eval_mode = dict( with_det=True, with_tracking=False, with_map=False, with_motion=False, with_planning=False, tracking_threshold=0.2, motion_threshhold=0.2, ) evaluation = dict( interval=num_iters_per_epoch*checkpoint_epoch_interval, eval_mode=eval_mode, ) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/configs/momad_small_b2d_stage2_multiplan.py ================================================ # ================ base config =================== version = "mini" version = "base" length = {'base': 234769, 'mini': 1933} plugin = True plugin_dir = "mmdet3d_plugin/" dist_params = dict(backend="nccl") log_level = "INFO" work_dir = None total_batch_size = 48 num_gpus = 8 batch_size = total_batch_size // num_gpus num_iters_per_epoch = int(length[version] // (num_gpus * batch_size)) num_epochs = 6 checkpoint_epoch_interval = 2 checkpoint_config = dict( interval=num_iters_per_epoch * checkpoint_epoch_interval ) log_config = dict( interval=51, hooks=[ dict(type="TextLoggerHook", by_epoch=False), dict(type="TensorboardLoggerHook"), ], ) load_from = None resume_from = None workflow = [("train", 1)] fp16 = dict(loss_scale=32.0) input_shape = (704, 384) # ================== model ======================== class_names = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others', ] map_class_names = [ 'Broken', 'Solid', 'SolidSolid', # 'Center', # 'TrafficLight', # 'StopSign', ] NameMapping = { #=================vehicle================= # bicycle 'vehicle.bh.crossbike': 'bicycle', "vehicle.diamondback.century": 'bicycle', "vehicle.gazelle.omafiets": 'bicycle', # car "vehicle.chevrolet.impala": 'car', "vehicle.dodge.charger_2020": 'car', "vehicle.dodge.charger_police": 'car', "vehicle.dodge.charger_police_2020": 'car', "vehicle.lincoln.mkz_2017": 'car', "vehicle.lincoln.mkz_2020": 'car', "vehicle.mini.cooper_s_2021": 'car', "vehicle.mercedes.coupe_2020": 'car', "vehicle.ford.mustang": 'car', "vehicle.nissan.patrol_2021": 'car', "vehicle.audi.tt": 'car', "vehicle.audi.etron": 'car', "vehicle.ford.crown": 'car', "vehicle.ford.mustang": 'car', "vehicle.tesla.model3": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": 'car', # bus # van "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": "van", "vehicle.ford.ambulance": "van", # truck "vehicle.carlamotors.firetruck": 'truck', #========================================= #=================traffic sign============ # traffic.speed_limit "traffic.speed_limit.30": 'traffic_sign', "traffic.speed_limit.40": 'traffic_sign', "traffic.speed_limit.50": 'traffic_sign', "traffic.speed_limit.60": 'traffic_sign', "traffic.speed_limit.90": 'traffic_sign', "traffic.speed_limit.120": 'traffic_sign', "traffic.stop": 'traffic_sign', "traffic.yield": 'traffic_sign', "traffic.traffic_light": 'traffic_light', #========================================= #===================Construction=========== "static.prop.warningconstruction" : 'traffic_cone', "static.prop.warningaccident": 'traffic_cone', "static.prop.trafficwarning": "traffic_cone", #===================Construction=========== "static.prop.constructioncone": 'traffic_cone', #=================pedestrian============== "walker.pedestrian.0001": 'pedestrian', "walker.pedestrian.0004": 'pedestrian', "walker.pedestrian.0005": 'pedestrian', "walker.pedestrian.0007": 'pedestrian', "walker.pedestrian.0013": 'pedestrian', "walker.pedestrian.0014": 'pedestrian', "walker.pedestrian.0017": 'pedestrian', "walker.pedestrian.0018": 'pedestrian', "walker.pedestrian.0019": 'pedestrian', "walker.pedestrian.0020": 'pedestrian', "walker.pedestrian.0022": 'pedestrian', "walker.pedestrian.0025": 'pedestrian', "walker.pedestrian.0035": 'pedestrian', "walker.pedestrian.0041": 'pedestrian', "walker.pedestrian.0046": 'pedestrian', "walker.pedestrian.0047": 'pedestrian', # ========================================== "static.prop.dirtdebris01": 'others', "static.prop.dirtdebris02": 'others', } num_classes = len(class_names) num_map_classes = len(map_class_names) roi_size = (30, 60) num_sample = 20 fut_ts = 6 fut_mode = 6 ego_fut_ts = 6 ego_fut_mode = 6 num_cmd = 1 queue_length = 4 # history + current embed_dims = 256 num_groups = 8 num_decoder = 6 num_single_frame_decoder = 1 num_single_frame_decoder_map = 1 use_deformable_func = True # mmdet3d_plugin/ops/setup.py needs to be executed strides = [4, 8, 16, 32] num_levels = len(strides) num_depth_layers = 3 drop_out = 0.1 temporal = True temporal_map = True decouple_attn = True decouple_attn_map = False decouple_attn_motion = True with_quality_estimation = True task_config = dict( with_det=True, with_map=True, with_motion_plan=True, ) model = dict( type="MomAD", use_grid_mask=True, use_deformable_func=use_deformable_func, img_backbone=dict( type="ResNet", depth=50, num_stages=4, frozen_stages=-1, norm_eval=False, style="pytorch", with_cp=True, out_indices=(0, 1, 2, 3), norm_cfg=dict(type="BN", requires_grad=True), pretrained="ckpt/resnet50-19c8e357.pth", ), img_neck=dict( type="FPN", num_outs=num_levels, start_level=0, out_channels=embed_dims, add_extra_convs="on_output", relu_before_extra_convs=True, in_channels=[256, 512, 1024, 2048], ), # depth_branch=dict( # for auxiliary supervision only # type="DenseDepthNet", # embed_dims=embed_dims, # num_depth_layers=num_depth_layers, # loss_weight=0.2, # ), head=dict( type="SparseDriveHead", task_config=task_config, det_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn, instance_bank=dict( type="InstanceBank", num_anchor=900, embed_dims=embed_dims, anchor="data/kmeans/kmeans_det_900.npy", anchor_handler=dict(type="SparseBox3DKeyPointsGenerator"), num_temp_instances=600 if temporal else -1, confidence_decay=0.9, feat_grad=False, ), anchor_encoder=dict( type="SparseBox3DEncoder", vel_dims=3, embed_dims=[128, 32, 32, 64] if decouple_attn else 256, mode="cat" if decouple_attn else "add", output_fc=not decouple_attn, in_loops=1, out_loops=4 if decouple_attn else 2, ), num_single_frame_decoder=num_single_frame_decoder, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder) )[2:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparseBox3DKeyPointsGenerator", num_learnable_pts=6, fix_scale=[ [0, 0, 0], [0.45, 0, 0], [-0.45, 0, 0], [0, 0.45, 0], [0, -0.45, 0], [0, 0, 0.45], [0, 0, -0.45], ], ), ), refine_layer=dict( type="SparseBox3DRefinementModule", embed_dims=embed_dims, num_cls=num_classes, refine_yaw=True, with_quality_estimation=with_quality_estimation, ), sampler=dict( type="SparseBox3DTarget", num_dn_groups=0, num_temp_dn_groups=0, dn_noise_scale=[2.0] * 3 + [0.5] * 7, max_dn_gt=32, add_neg_dn=True, cls_weight=2.0, box_weight=0.25, reg_weights=[2.0] * 3 + [0.5] * 3 + [0.0] * 4, cls_wise_reg_weights={ class_names.index("traffic_cone"): [ 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, ], }, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0, ), loss_reg=dict( type="SparseBox3DLoss", loss_box=dict(type="L1Loss", loss_weight=0.25), loss_centerness=dict(type="CrossEntropyLoss", use_sigmoid=True), loss_yawness=dict(type="GaussianFocalLoss"), # cls_allow_reverse=[class_names.index("barrier")], ), decoder=dict(type="SparseBox3DDecoder"), reg_weights=[2.0] * 3 + [1.0] * 7, ), map_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn_map, instance_bank=dict( type="InstanceBank", num_anchor=100, embed_dims=embed_dims, anchor="data/kmeans/kmeans_map_100.npy", anchor_handler=dict(type="SparsePoint3DKeyPointsGenerator"), num_temp_instances=0 if temporal_map else -1, confidence_decay=0.9, feat_grad=True, ), anchor_encoder=dict( type="SparsePoint3DEncoder", embed_dims=embed_dims, num_sample=num_sample, ), num_single_frame_decoder=num_single_frame_decoder_map, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder_map + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder_map) )[:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal_map else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparsePoint3DKeyPointsGenerator", embed_dims=embed_dims, num_sample=num_sample, num_learnable_pts=3, fix_height=(0, 0.25, -0.25, 0.5, -0.5), ground_height=-1.84, # ground height in lidar frame ), ), refine_layer=dict( type="SparsePoint3DRefinementModule", embed_dims=embed_dims, num_sample=num_sample, num_cls=num_map_classes, ), sampler=dict( type="SparsePoint3DTarget", assigner=dict( type='HungarianLinesAssigner', cost=dict( type='MapQueriesCost', cls_cost=dict(type='FocalLossCost', weight=1.0), reg_cost=dict(type='LinesL1Cost', weight=10.0, beta=0.01, permute=True), ), ), num_cls=num_map_classes, num_sample=num_sample, roi_size=roi_size, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_reg=dict( type="SparseLineLoss", loss_line=dict( type='LinesL1Loss', loss_weight=10.0, beta=0.01, ), num_sample=num_sample, roi_size=roi_size, ), decoder=dict( type="SparsePoint3DDecoder", score_threshold=0.5, ), reg_weights=[1.0] * 40, gt_cls_key="gt_map_labels", gt_reg_key="gt_map_pts", gt_id_key="map_instance_id", with_instance_id=False, task_prefix='map', ), motion_plan_head=dict( type='MomADMotionPlanningHead', fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, motion_anchor=f'data/kmeans/kmeans_motion_{fut_mode}.npy', plan_anchor=None, # plan_anchor=f'data/kmeans/kmeans_plan_{ego_fut_mode}_b2d.npy', embed_dims=embed_dims, decouple_attn=decouple_attn_motion, instance_queue=dict( type="InstanceQueue", embed_dims=embed_dims, queue_length=queue_length, frame_rate=1, tracking_threshold=0.2, feature_map_scale=(input_shape[1]/strides[-1], input_shape[0]/strides[-1]), use_ego_status=False, use_tp=['near',], ), operation_order=( [ "temp_gnn", "gnn", "norm", "cross_gnn", "norm", "ffn", "norm", ] * 3 + [ "refine", ] ), temp_graph_model=dict( type="MultiheadAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), cross_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 2, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), refine_layer=dict( type="MotionPlanningRefinementModule", embed_dims=embed_dims, fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_gru=False, ), motion_sampler=dict( type="MotionTarget", ), motion_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.2 ), motion_loss_reg=dict(type='L1Loss', loss_weight=0.2), planning_sampler=dict( type="PlanningTarget", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, ), plan_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.5, ), plan_loss_reg=dict(type='L1Loss', loss_weight=1.0), plan_loss_status=dict(type='L1Loss', loss_weight=1.0), motion_decoder=dict(type="SparseBox3DMotionDecoder"), planning_decoder=dict( type="HierarchicalPlanningDecoder", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_rescore=True, ), num_det=50, num_map=10, ), ), ) # ================== data ======================== dataset_type = "B2D3DDataset" data_root = "data/bench2drive/" anno_root = "data/infos/" if version == 'base' else "data/infos/mini/" file_client_args = dict(backend="disk") img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) train_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), # dict( # type="DenseDepthMapGenerator", # downsample=strides[:num_depth_layers], # ), dict(type="BBoxRotation"), dict(type="PhotoMetricDistortionMultiViewImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=False, normalize=False, sample_num=num_sample, permute=True, ), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", # "gt_depth", "focal", "gt_bboxes_3d", "gt_labels_3d", 'gt_map_labels', 'gt_map_pts', 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', 'ego_status', #'ego_his_trajs', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp", "instance_id"], ), ] test_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", # # 'ego_status', #'ego_his_trajs', 'gt_ego_fut_cmd', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp"], ), ] eval_pipeline = [ dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=True, normalize=False, ), dict( type='Collect', keys=[ 'vectors', "gt_bboxes_3d", "gt_labels_3d", 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', # 'fut_boxes' ], meta_keys=['token', 'timestamp'] ), ] input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=False, ) data_basic_config = dict( type=dataset_type, data_root=data_root, classes=class_names, map_classes=map_class_names, name_mapping=NameMapping, modality=input_modality, sample_interval=5, past_frames=6, future_frames=6, use_cmd=num_cmd>1, ) eval_config = dict( **data_basic_config, ann_file=anno_root + 'b2d_infos_val.pkl', pipeline=eval_pipeline, test_mode=True, ) data_aug_conf = { "resize_lim": (0.40, 0.47), "final_dim": input_shape[::-1], "bot_pct_lim": (0.0, 0.0), "rot_lim": (-5.4, 5.4), "H": 900, "W": 1600, "rand_flip": True, "rot3d_range": [0, 0], } data = dict( samples_per_gpu=batch_size, workers_per_gpu=batch_size, train=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_train.pkl", pipeline=train_pipeline, test_mode=False, data_aug_conf=data_aug_conf, with_seq_flag=True, sequences_split_num=5, keep_consistent_seq_aug=True, ), val=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), test=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), ) # ================== training ======================== optimizer = dict( type="AdamW", lr=2e-4, weight_decay=0.001, paramwise_cfg=dict( custom_keys={ "img_backbone": dict(lr_mult=0.1), } ), ) optimizer_config = dict(grad_clip=dict(max_norm=25, norm_type=2)) lr_config = dict( policy="CosineAnnealing", warmup="linear", warmup_iters=500, warmup_ratio=1.0 / 3, min_lr_ratio=1e-3, ) runner = dict( type="IterBasedRunner", max_iters=num_iters_per_epoch * num_epochs, ) # ================== eval ======================== eval_mode = dict( with_det=True, with_tracking=False, with_map=False, with_motion=False, with_planning=True, tracking_threshold=0.2, motion_threshhold=0.2, ) evaluation = dict( interval=num_iters_per_epoch*checkpoint_epoch_interval, eval_mode=eval_mode, ) # ================== pretrained model ======================== # load_from = 'http://svcspawner.bcloud-beijing.hobot.cc/user/homespace/wenchao.sun/plat_gpu/sparsedrive_small_b2d_stage1_20e-20240903-225131.954333/output/work_dirs/latest.pth' load_from = "ckpt/sparsedrive_small_b2d_stage1.pth" ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/configs/momad_small_b2d_stage2_singleplan.py ================================================ # ================ base config =================== version = "mini" version = "base" length = {'base': 234769, 'mini': 1933} plugin = True plugin_dir = "mmdet3d_plugin/" dist_params = dict(backend="nccl") log_level = "INFO" work_dir = None total_batch_size = 48 num_gpus = 8 batch_size = total_batch_size // num_gpus num_iters_per_epoch = int(length[version] // (num_gpus * batch_size)) num_epochs = 6 checkpoint_epoch_interval = 2 checkpoint_config = dict( interval=num_iters_per_epoch * checkpoint_epoch_interval ) log_config = dict( interval=51, hooks=[ dict(type="TextLoggerHook", by_epoch=False), dict(type="TensorboardLoggerHook"), ], ) load_from = None resume_from = None workflow = [("train", 1)] fp16 = dict(loss_scale=32.0) input_shape = (704, 384) # ================== model ======================== class_names = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others', ] map_class_names = [ 'Broken', 'Solid', 'SolidSolid', # 'Center', # 'TrafficLight', # 'StopSign', ] NameMapping = { #=================vehicle================= # bicycle 'vehicle.bh.crossbike': 'bicycle', "vehicle.diamondback.century": 'bicycle', "vehicle.gazelle.omafiets": 'bicycle', # car "vehicle.chevrolet.impala": 'car', "vehicle.dodge.charger_2020": 'car', "vehicle.dodge.charger_police": 'car', "vehicle.dodge.charger_police_2020": 'car', "vehicle.lincoln.mkz_2017": 'car', "vehicle.lincoln.mkz_2020": 'car', "vehicle.mini.cooper_s_2021": 'car', "vehicle.mercedes.coupe_2020": 'car', "vehicle.ford.mustang": 'car', "vehicle.nissan.patrol_2021": 'car', "vehicle.audi.tt": 'car', "vehicle.audi.etron": 'car', "vehicle.ford.crown": 'car', "vehicle.ford.mustang": 'car', "vehicle.tesla.model3": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": 'car', # bus # van "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": "van", "vehicle.ford.ambulance": "van", # truck "vehicle.carlamotors.firetruck": 'truck', #========================================= #=================traffic sign============ # traffic.speed_limit "traffic.speed_limit.30": 'traffic_sign', "traffic.speed_limit.40": 'traffic_sign', "traffic.speed_limit.50": 'traffic_sign', "traffic.speed_limit.60": 'traffic_sign', "traffic.speed_limit.90": 'traffic_sign', "traffic.speed_limit.120": 'traffic_sign', "traffic.stop": 'traffic_sign', "traffic.yield": 'traffic_sign', "traffic.traffic_light": 'traffic_light', #========================================= #===================Construction=========== "static.prop.warningconstruction" : 'traffic_cone', "static.prop.warningaccident": 'traffic_cone', "static.prop.trafficwarning": "traffic_cone", #===================Construction=========== "static.prop.constructioncone": 'traffic_cone', #=================pedestrian============== "walker.pedestrian.0001": 'pedestrian', "walker.pedestrian.0004": 'pedestrian', "walker.pedestrian.0005": 'pedestrian', "walker.pedestrian.0007": 'pedestrian', "walker.pedestrian.0013": 'pedestrian', "walker.pedestrian.0014": 'pedestrian', "walker.pedestrian.0017": 'pedestrian', "walker.pedestrian.0018": 'pedestrian', "walker.pedestrian.0019": 'pedestrian', "walker.pedestrian.0020": 'pedestrian', "walker.pedestrian.0022": 'pedestrian', "walker.pedestrian.0025": 'pedestrian', "walker.pedestrian.0035": 'pedestrian', "walker.pedestrian.0041": 'pedestrian', "walker.pedestrian.0046": 'pedestrian', "walker.pedestrian.0047": 'pedestrian', # ========================================== "static.prop.dirtdebris01": 'others', "static.prop.dirtdebris02": 'others', } num_classes = len(class_names) num_map_classes = len(map_class_names) roi_size = (30, 60) num_sample = 20 fut_ts = 6 fut_mode = 6 ego_fut_ts = 6 ego_fut_mode = 6 num_cmd = 1 queue_length = 4 # history + current embed_dims = 256 num_groups = 8 num_decoder = 6 num_single_frame_decoder = 1 num_single_frame_decoder_map = 1 use_deformable_func = True # mmdet3d_plugin/ops/setup.py needs to be executed strides = [4, 8, 16, 32] num_levels = len(strides) num_depth_layers = 3 drop_out = 0.1 temporal = True temporal_map = True decouple_attn = True decouple_attn_map = False decouple_attn_motion = True with_quality_estimation = True task_config = dict( with_det=True, with_map=True, with_motion_plan=True, ) model = dict( type="MomAD", use_grid_mask=True, use_deformable_func=use_deformable_func, img_backbone=dict( type="ResNet", depth=50, num_stages=4, frozen_stages=-1, norm_eval=False, style="pytorch", with_cp=True, out_indices=(0, 1, 2, 3), norm_cfg=dict(type="BN", requires_grad=True), pretrained="ckpt/resnet50-19c8e357.pth", ), img_neck=dict( type="FPN", num_outs=num_levels, start_level=0, out_channels=embed_dims, add_extra_convs="on_output", relu_before_extra_convs=True, in_channels=[256, 512, 1024, 2048], ), # depth_branch=dict( # for auxiliary supervision only # type="DenseDepthNet", # embed_dims=embed_dims, # num_depth_layers=num_depth_layers, # loss_weight=0.2, # ), head=dict( type="SparseDriveHead", task_config=task_config, det_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn, instance_bank=dict( type="InstanceBank", num_anchor=900, embed_dims=embed_dims, anchor="data/kmeans/kmeans_det_900.npy", anchor_handler=dict(type="SparseBox3DKeyPointsGenerator"), num_temp_instances=600 if temporal else -1, confidence_decay=0.9, feat_grad=False, ), anchor_encoder=dict( type="SparseBox3DEncoder", vel_dims=3, embed_dims=[128, 32, 32, 64] if decouple_attn else 256, mode="cat" if decouple_attn else "add", output_fc=not decouple_attn, in_loops=1, out_loops=4 if decouple_attn else 2, ), num_single_frame_decoder=num_single_frame_decoder, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder) )[2:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparseBox3DKeyPointsGenerator", num_learnable_pts=6, fix_scale=[ [0, 0, 0], [0.45, 0, 0], [-0.45, 0, 0], [0, 0.45, 0], [0, -0.45, 0], [0, 0, 0.45], [0, 0, -0.45], ], ), ), refine_layer=dict( type="SparseBox3DRefinementModule", embed_dims=embed_dims, num_cls=num_classes, refine_yaw=True, with_quality_estimation=with_quality_estimation, ), sampler=dict( type="SparseBox3DTarget", num_dn_groups=0, num_temp_dn_groups=0, dn_noise_scale=[2.0] * 3 + [0.5] * 7, max_dn_gt=32, add_neg_dn=True, cls_weight=2.0, box_weight=0.25, reg_weights=[2.0] * 3 + [0.5] * 3 + [0.0] * 4, cls_wise_reg_weights={ class_names.index("traffic_cone"): [ 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, ], }, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0, ), loss_reg=dict( type="SparseBox3DLoss", loss_box=dict(type="L1Loss", loss_weight=0.25), loss_centerness=dict(type="CrossEntropyLoss", use_sigmoid=True), loss_yawness=dict(type="GaussianFocalLoss"), # cls_allow_reverse=[class_names.index("barrier")], ), decoder=dict(type="SparseBox3DDecoder"), reg_weights=[2.0] * 3 + [1.0] * 7, ), map_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn_map, instance_bank=dict( type="InstanceBank", num_anchor=100, embed_dims=embed_dims, anchor="data/kmeans/kmeans_map_100.npy", anchor_handler=dict(type="SparsePoint3DKeyPointsGenerator"), num_temp_instances=0 if temporal_map else -1, confidence_decay=0.9, feat_grad=True, ), anchor_encoder=dict( type="SparsePoint3DEncoder", embed_dims=embed_dims, num_sample=num_sample, ), num_single_frame_decoder=num_single_frame_decoder_map, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder_map + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder_map) )[:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal_map else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparsePoint3DKeyPointsGenerator", embed_dims=embed_dims, num_sample=num_sample, num_learnable_pts=3, fix_height=(0, 0.25, -0.25, 0.5, -0.5), ground_height=-1.84, # ground height in lidar frame ), ), refine_layer=dict( type="SparsePoint3DRefinementModule", embed_dims=embed_dims, num_sample=num_sample, num_cls=num_map_classes, ), sampler=dict( type="SparsePoint3DTarget", assigner=dict( type='HungarianLinesAssigner', cost=dict( type='MapQueriesCost', cls_cost=dict(type='FocalLossCost', weight=1.0), reg_cost=dict(type='LinesL1Cost', weight=10.0, beta=0.01, permute=True), ), ), num_cls=num_map_classes, num_sample=num_sample, roi_size=roi_size, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_reg=dict( type="SparseLineLoss", loss_line=dict( type='LinesL1Loss', loss_weight=10.0, beta=0.01, ), num_sample=num_sample, roi_size=roi_size, ), decoder=dict( type="SparsePoint3DDecoder", score_threshold=0.5, ), reg_weights=[1.0] * 40, gt_cls_key="gt_map_labels", gt_reg_key="gt_map_pts", gt_id_key="map_instance_id", with_instance_id=False, task_prefix='map', ), motion_plan_head=dict( type='MomADMotionPlanningHead', fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, motion_anchor=f'data/kmeans/kmeans_motion_{fut_mode}.npy', plan_anchor=None, # plan_anchor=f'data/kmeans/kmeans_plan_{ego_fut_mode}_b2d.npy', embed_dims=embed_dims, decouple_attn=decouple_attn_motion, instance_queue=dict( type="InstanceQueue", embed_dims=embed_dims, queue_length=queue_length, frame_rate=1, tracking_threshold=0.2, feature_map_scale=(input_shape[1]/strides[-1], input_shape[0]/strides[-1]), use_ego_status=False, use_tp=['near',], ), operation_order=( [ "temp_gnn", "gnn", "norm", "cross_gnn", "norm", "ffn", "norm", ] * 3 + [ "refine", ] ), temp_graph_model=dict( type="MultiheadAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), cross_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 2, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), refine_layer=dict( type="MotionPlanningRefinementModule", embed_dims=embed_dims, fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_gru=False, ), motion_sampler=dict( type="MotionTarget", ), motion_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.2 ), motion_loss_reg=dict(type='L1Loss', loss_weight=0.2), planning_sampler=dict( type="PlanningTarget", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, ), plan_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.5, ), plan_loss_reg=dict(type='L1Loss', loss_weight=1.0), plan_loss_status=dict(type='L1Loss', loss_weight=1.0), motion_decoder=dict(type="SparseBox3DMotionDecoder"), planning_decoder=dict( type="HierarchicalPlanningDecoder", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_rescore=True, ), num_det=50, num_map=10, ), ), ) # ================== data ======================== dataset_type = "B2D3DDataset" data_root = "data/bench2drive/" anno_root = "data/infos/" if version == 'base' else "data/infos/mini/" file_client_args = dict(backend="disk") img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) train_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), # dict( # type="DenseDepthMapGenerator", # downsample=strides[:num_depth_layers], # ), dict(type="BBoxRotation"), dict(type="PhotoMetricDistortionMultiViewImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=False, normalize=False, sample_num=num_sample, permute=True, ), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", # "gt_depth", "focal", "gt_bboxes_3d", "gt_labels_3d", 'gt_map_labels', 'gt_map_pts', 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', 'ego_status', 'ego_his_trajs', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp", "instance_id"], ), ] test_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", 'ego_status', 'ego_his_trajs', 'gt_ego_fut_cmd', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp"], ), ] eval_pipeline = [ dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=True, normalize=False, ), dict( type='Collect', keys=[ 'vectors', "gt_bboxes_3d", "gt_labels_3d", 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', # 'fut_boxes' ], meta_keys=['token', 'timestamp'] ), ] input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=False, ) data_basic_config = dict( type=dataset_type, data_root=data_root, classes=class_names, map_classes=map_class_names, name_mapping=NameMapping, modality=input_modality, sample_interval=5, past_frames=6, future_frames=6, use_cmd=num_cmd>1, ) eval_config = dict( **data_basic_config, ann_file=anno_root + 'b2d_infos_val.pkl', pipeline=eval_pipeline, test_mode=True, ) data_aug_conf = { "resize_lim": (0.40, 0.47), "final_dim": input_shape[::-1], "bot_pct_lim": (0.0, 0.0), "rot_lim": (-5.4, 5.4), "H": 900, "W": 1600, "rand_flip": True, "rot3d_range": [0, 0], } data = dict( samples_per_gpu=batch_size, workers_per_gpu=batch_size, train=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_train.pkl", pipeline=train_pipeline, test_mode=False, data_aug_conf=data_aug_conf, with_seq_flag=True, sequences_split_num=5, keep_consistent_seq_aug=True, ), val=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), test=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), ) # ================== training ======================== optimizer = dict( type="AdamW", lr=2e-4, weight_decay=0.001, paramwise_cfg=dict( custom_keys={ "img_backbone": dict(lr_mult=0.1), } ), ) optimizer_config = dict(grad_clip=dict(max_norm=25, norm_type=2)) lr_config = dict( policy="CosineAnnealing", warmup="linear", warmup_iters=500, warmup_ratio=1.0 / 3, min_lr_ratio=1e-3, ) runner = dict( type="IterBasedRunner", max_iters=num_iters_per_epoch * num_epochs, ) # ================== eval ======================== eval_mode = dict( with_det=True, with_tracking=False, with_map=False, with_motion=False, with_planning=True, tracking_threshold=0.2, motion_threshhold=0.2, ) evaluation = dict( interval=num_iters_per_epoch*checkpoint_epoch_interval, eval_mode=eval_mode, ) # ================== pretrained model ======================== # load_from = 'http://svcspawner.bcloud-beijing.hobot.cc/user/homespace/wenchao.sun/plat_gpu/sparsedrive_small_b2d_stage1_20e-20240903-225131.954333/output/work_dirs/latest.pth' load_from = "ckpt/sparsedrive_small_b2d_stage1.pth" ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/configs/sparsedrive_small_b2d_stage1.py ================================================ # ================ base config =================== plugin = True plugin_dir = "mmdet3d_plugin/" dist_params = dict(backend="nccl") log_level = "INFO" work_dir = None total_batch_size = 64 num_gpus = 8 batch_size = total_batch_size // num_gpus num_iters_per_epoch = int(234769 // (num_gpus * batch_size)) num_epochs = 10 checkpoint_epoch_interval = 2 checkpoint_config = dict( interval=num_iters_per_epoch * checkpoint_epoch_interval ) log_config = dict( interval=51, hooks=[ dict(type="TextLoggerHook", by_epoch=False), dict(type="TensorboardLoggerHook"), ], ) load_from = None resume_from = None workflow = [("train", 1)] fp16 = dict(loss_scale=32.0) input_shape = (704, 384) # ================== model ======================== class_names = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others', ] map_class_names = [ 'Broken', 'Solid', 'SolidSolid', # 'Center', # 'TrafficLight', # 'StopSign', ] NameMapping = { #=================vehicle================= # bicycle 'vehicle.bh.crossbike': 'bicycle', "vehicle.diamondback.century": 'bicycle', "vehicle.gazelle.omafiets": 'bicycle', # car "vehicle.chevrolet.impala": 'car', "vehicle.dodge.charger_2020": 'car', "vehicle.dodge.charger_police": 'car', "vehicle.dodge.charger_police_2020": 'car', "vehicle.lincoln.mkz_2017": 'car', "vehicle.lincoln.mkz_2020": 'car', "vehicle.mini.cooper_s_2021": 'car', "vehicle.mercedes.coupe_2020": 'car', "vehicle.ford.mustang": 'car', "vehicle.nissan.patrol_2021": 'car', "vehicle.audi.tt": 'car', "vehicle.audi.etron": 'car', "vehicle.ford.crown": 'car', "vehicle.ford.mustang": 'car', "vehicle.tesla.model3": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": 'car', # bus # van "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": "van", "vehicle.ford.ambulance": "van", # truck "vehicle.carlamotors.firetruck": 'truck', #========================================= #=================traffic sign============ # traffic.speed_limit "traffic.speed_limit.30": 'traffic_sign', "traffic.speed_limit.40": 'traffic_sign', "traffic.speed_limit.50": 'traffic_sign', "traffic.speed_limit.60": 'traffic_sign', "traffic.speed_limit.90": 'traffic_sign', "traffic.speed_limit.120": 'traffic_sign', "traffic.stop": 'traffic_sign', "traffic.yield": 'traffic_sign', "traffic.traffic_light": 'traffic_light', #========================================= #===================Construction=========== "static.prop.warningconstruction" : 'traffic_cone', "static.prop.warningaccident": 'traffic_cone', "static.prop.trafficwarning": "traffic_cone", #===================Construction=========== "static.prop.constructioncone": 'traffic_cone', #=================pedestrian============== "walker.pedestrian.0001": 'pedestrian', "walker.pedestrian.0004": 'pedestrian', "walker.pedestrian.0005": 'pedestrian', "walker.pedestrian.0007": 'pedestrian', "walker.pedestrian.0013": 'pedestrian', "walker.pedestrian.0014": 'pedestrian', "walker.pedestrian.0017": 'pedestrian', "walker.pedestrian.0018": 'pedestrian', "walker.pedestrian.0019": 'pedestrian', "walker.pedestrian.0020": 'pedestrian', "walker.pedestrian.0022": 'pedestrian', "walker.pedestrian.0025": 'pedestrian', "walker.pedestrian.0035": 'pedestrian', "walker.pedestrian.0041": 'pedestrian', "walker.pedestrian.0046": 'pedestrian', "walker.pedestrian.0047": 'pedestrian', # ========================================== "static.prop.dirtdebris01": 'others', "static.prop.dirtdebris02": 'others', } num_classes = len(class_names) num_map_classes = len(map_class_names) roi_size = (30, 60) num_sample = 20 fut_ts = 6 fut_mode = 6 ego_fut_ts = 6 ego_fut_mode = 6 num_cmd = 6 queue_length = 4 # history + current embed_dims = 256 num_groups = 8 num_decoder = 6 num_single_frame_decoder = 1 num_single_frame_decoder_map = 1 use_deformable_func = True # mmdet3d_plugin/ops/setup.py needs to be executed strides = [4, 8, 16, 32] num_levels = len(strides) num_depth_layers = 3 drop_out = 0.1 temporal = True temporal_map = True decouple_attn = True decouple_attn_map = False decouple_attn_motion = True with_quality_estimation = True task_config = dict( with_det=True, with_map=True, with_motion_plan=False, ) model = dict( type="SparseDrive", use_grid_mask=True, use_deformable_func=use_deformable_func, img_backbone=dict( type="ResNet", depth=50, num_stages=4, frozen_stages=-1, norm_eval=False, style="pytorch", with_cp=True, out_indices=(0, 1, 2, 3), norm_cfg=dict(type="BN", requires_grad=True), pretrained="ckpt/resnet50-19c8e357.pth", ), img_neck=dict( type="FPN", num_outs=num_levels, start_level=0, out_channels=embed_dims, add_extra_convs="on_output", relu_before_extra_convs=True, in_channels=[256, 512, 1024, 2048], ), depth_branch=dict( # for auxiliary supervision only type="DenseDepthNet", embed_dims=embed_dims, num_depth_layers=num_depth_layers, loss_weight=0.2, ), head=dict( type="SparseDriveHead", task_config=task_config, det_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn, instance_bank=dict( type="InstanceBank", num_anchor=900, embed_dims=embed_dims, anchor="data/kmeans/kmeans_det_900.npy", anchor_handler=dict(type="SparseBox3DKeyPointsGenerator"), num_temp_instances=600 if temporal else -1, confidence_decay=0.9, feat_grad=False, ), anchor_encoder=dict( type="SparseBox3DEncoder", vel_dims=3, embed_dims=[128, 32, 32, 64] if decouple_attn else 256, mode="cat" if decouple_attn else "add", output_fc=not decouple_attn, in_loops=1, out_loops=4 if decouple_attn else 2, ), num_single_frame_decoder=num_single_frame_decoder, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder) )[2:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparseBox3DKeyPointsGenerator", num_learnable_pts=6, fix_scale=[ [0, 0, 0], [0.45, 0, 0], [-0.45, 0, 0], [0, 0.45, 0], [0, -0.45, 0], [0, 0, 0.45], [0, 0, -0.45], ], ), ), refine_layer=dict( type="SparseBox3DRefinementModule", embed_dims=embed_dims, num_cls=num_classes, refine_yaw=True, with_quality_estimation=with_quality_estimation, ), sampler=dict( type="SparseBox3DTarget", num_dn_groups=0, num_temp_dn_groups=0, dn_noise_scale=[2.0] * 3 + [0.5] * 7, max_dn_gt=32, add_neg_dn=True, cls_weight=2.0, box_weight=0.25, reg_weights=[2.0] * 3 + [0.5] * 3 + [0.0] * 4, cls_wise_reg_weights={ class_names.index("traffic_cone"): [ 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, ], }, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0, ), loss_reg=dict( type="SparseBox3DLoss", loss_box=dict(type="L1Loss", loss_weight=0.25), loss_centerness=dict(type="CrossEntropyLoss", use_sigmoid=True), loss_yawness=dict(type="GaussianFocalLoss"), # cls_allow_reverse=[class_names.index("barrier")], ), decoder=dict(type="SparseBox3DDecoder"), reg_weights=[2.0] * 3 + [1.0] * 7, ), map_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn_map, instance_bank=dict( type="InstanceBank", num_anchor=100, embed_dims=embed_dims, anchor="data/kmeans/kmeans_map_100.npy", anchor_handler=dict(type="SparsePoint3DKeyPointsGenerator"), num_temp_instances=0 if temporal_map else -1, confidence_decay=0.9, feat_grad=True, ), anchor_encoder=dict( type="SparsePoint3DEncoder", embed_dims=embed_dims, num_sample=num_sample, ), num_single_frame_decoder=num_single_frame_decoder_map, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder_map + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder_map) )[:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal_map else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparsePoint3DKeyPointsGenerator", embed_dims=embed_dims, num_sample=num_sample, num_learnable_pts=3, fix_height=(0, 0.25, -0.25, 0.5, -0.5), ground_height=-1.84, # ground height in lidar frame ), ), refine_layer=dict( type="SparsePoint3DRefinementModule", embed_dims=embed_dims, num_sample=num_sample, num_cls=num_map_classes, ), sampler=dict( type="SparsePoint3DTarget", assigner=dict( type='HungarianLinesAssigner', cost=dict( type='MapQueriesCost', cls_cost=dict(type='FocalLossCost', weight=1.0), reg_cost=dict(type='LinesL1Cost', weight=10.0, beta=0.01, permute=True), ), ), num_cls=num_map_classes, num_sample=num_sample, roi_size=roi_size, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_reg=dict( type="SparseLineLoss", loss_line=dict( type='LinesL1Loss', loss_weight=10.0, beta=0.01, ), num_sample=num_sample, roi_size=roi_size, ), decoder=dict( type="SparsePoint3DDecoder", score_threshold=0.5, ), reg_weights=[1.0] * 40, gt_cls_key="gt_map_labels", gt_reg_key="gt_map_pts", gt_id_key="map_instance_id", with_instance_id=False, task_prefix='map', ), motion_plan_head=dict( type='MotionPlanningHead', fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, motion_anchor=f'data/kmeans/kmeans_motion_{fut_mode}.npy', plan_anchor=f'data/kmeans/kmeans_plan_{ego_fut_mode}.npy', embed_dims=embed_dims, decouple_attn=decouple_attn_motion, instance_queue=dict( type="InstanceQueue", embed_dims=embed_dims, queue_length=queue_length, tracking_threshold=0.2, feature_map_scale=(input_shape[1]/strides[-1], input_shape[0]/strides[-1]), ), operation_order=( [ "temp_gnn", "gnn", "norm", "cross_gnn", "norm", "ffn", "norm", ] * 3 + [ "refine", ] ), temp_graph_model=dict( type="MultiheadAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), cross_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 2, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), refine_layer=dict( type="MotionPlanningRefinementModule", embed_dims=embed_dims, fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, ), motion_sampler=dict( type="MotionTarget", ), motion_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.2 ), motion_loss_reg=dict(type='L1Loss', loss_weight=0.2), planning_sampler=dict( type="PlanningTarget", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, ), plan_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.5, ), plan_loss_reg=dict(type='L1Loss', loss_weight=1.0), plan_loss_status=dict(type='L1Loss', loss_weight=1.0), motion_decoder=dict(type="SparseBox3DMotionDecoder"), planning_decoder=dict( type="HierarchicalPlanningDecoder", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_rescore=True, ), num_det=50, num_map=10, ), ), ) # ================== data ======================== dataset_type = "B2D3DDataset" data_root = "data/bench2drive/" anno_root = "data/infos/" file_client_args = dict(backend="disk") img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) train_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict( type="DenseDepthMapGenerator", downsample=strides[:num_depth_layers], ), dict(type="BBoxRotation"), dict(type="PhotoMetricDistortionMultiViewImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=False, normalize=False, sample_num=num_sample, permute=True, ), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", "gt_depth", "focal", "gt_bboxes_3d", "gt_labels_3d", 'gt_map_labels', 'gt_map_pts', 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', 'ego_status', ], meta_keys=["T_global", "T_global_inv", "timestamp", "instance_id"], ), ] test_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", 'ego_status', 'gt_ego_fut_cmd', ], meta_keys=["T_global", "T_global_inv", "timestamp"], ), ] eval_pipeline = [ dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=True, normalize=False, ), dict( type='Collect', keys=[ 'vectors', "gt_bboxes_3d", "gt_labels_3d", 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', # 'fut_boxes' ], meta_keys=['token', 'timestamp'] ), ] input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=False, ) data_basic_config = dict( type=dataset_type, data_root=data_root, classes=class_names, map_classes=map_class_names, name_mapping=NameMapping, modality=input_modality, sample_interval=5, past_frames=2, future_frames=6, ) eval_config = dict( **data_basic_config, ann_file=anno_root + 'b2d_infos_val.pkl', pipeline=eval_pipeline, test_mode=True, ) data_aug_conf = { "resize_lim": (0.40, 0.47), "final_dim": input_shape[::-1], "bot_pct_lim": (0.0, 0.0), "rot_lim": (-5.4, 5.4), "H": 900, "W": 1600, "rand_flip": True, "rot3d_range": [0, 0], } data = dict( samples_per_gpu=batch_size, workers_per_gpu=batch_size, train=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_train.pkl", pipeline=train_pipeline, test_mode=False, data_aug_conf=data_aug_conf, with_seq_flag=True, sequences_split_num=5, keep_consistent_seq_aug=True, ), val=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), test=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), ) # ================== training ======================== optimizer = dict( type="AdamW", lr=3e-4, weight_decay=0.001, paramwise_cfg=dict( custom_keys={ "img_backbone": dict(lr_mult=0.1), } ), ) optimizer_config = dict(grad_clip=dict(max_norm=25, norm_type=2)) lr_config = dict( policy="CosineAnnealing", warmup="linear", warmup_iters=500, warmup_ratio=1.0 / 3, min_lr_ratio=1e-3, ) runner = dict( type="IterBasedRunner", max_iters=num_iters_per_epoch * num_epochs, ) # ================== eval ======================== eval_mode = dict( with_det=True, with_tracking=False, with_map=False, with_motion=False, with_planning=False, tracking_threshold=0.2, motion_threshhold=0.2, ) evaluation = dict( interval=num_iters_per_epoch*checkpoint_epoch_interval, eval_mode=eval_mode, ) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/configs/sparsedrive_small_b2d_stage2_cmd_singleplan.py ================================================ # ================ base config =================== version = "mini" version = "base" length = {'base': 234769, 'mini': 1933} plugin = True plugin_dir = "mmdet3d_plugin/" dist_params = dict(backend="nccl") log_level = "INFO" work_dir = None total_batch_size = 16 num_gpus = 8 batch_size = total_batch_size // num_gpus num_iters_per_epoch = int(length[version] // (num_gpus * batch_size)) num_epochs = 10 checkpoint_epoch_interval = 2 checkpoint_config = dict( interval=num_iters_per_epoch * checkpoint_epoch_interval ) log_config = dict( interval=51, hooks=[ dict(type="TextLoggerHook", by_epoch=False), dict(type="TensorboardLoggerHook"), ], ) load_from = None resume_from = None workflow = [("train", 1)] fp16 = dict(loss_scale=32.0) input_shape = (704, 384) # ================== model ======================== class_names = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others', ] map_class_names = [ 'Broken', 'Solid', 'SolidSolid', # 'Center', # 'TrafficLight', # 'StopSign', ] NameMapping = { #=================vehicle================= # bicycle 'vehicle.bh.crossbike': 'bicycle', "vehicle.diamondback.century": 'bicycle', "vehicle.gazelle.omafiets": 'bicycle', # car "vehicle.chevrolet.impala": 'car', "vehicle.dodge.charger_2020": 'car', "vehicle.dodge.charger_police": 'car', "vehicle.dodge.charger_police_2020": 'car', "vehicle.lincoln.mkz_2017": 'car', "vehicle.lincoln.mkz_2020": 'car', "vehicle.mini.cooper_s_2021": 'car', "vehicle.mercedes.coupe_2020": 'car', "vehicle.ford.mustang": 'car', "vehicle.nissan.patrol_2021": 'car', "vehicle.audi.tt": 'car', "vehicle.audi.etron": 'car', "vehicle.ford.crown": 'car', "vehicle.ford.mustang": 'car', "vehicle.tesla.model3": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": 'car', # bus # van "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": "van", "vehicle.ford.ambulance": "van", # truck "vehicle.carlamotors.firetruck": 'truck', #========================================= #=================traffic sign============ # traffic.speed_limit "traffic.speed_limit.30": 'traffic_sign', "traffic.speed_limit.40": 'traffic_sign', "traffic.speed_limit.50": 'traffic_sign', "traffic.speed_limit.60": 'traffic_sign', "traffic.speed_limit.90": 'traffic_sign', "traffic.speed_limit.120": 'traffic_sign', "traffic.stop": 'traffic_sign', "traffic.yield": 'traffic_sign', "traffic.traffic_light": 'traffic_light', #========================================= #===================Construction=========== "static.prop.warningconstruction" : 'traffic_cone', "static.prop.warningaccident": 'traffic_cone', "static.prop.trafficwarning": "traffic_cone", #===================Construction=========== "static.prop.constructioncone": 'traffic_cone', #=================pedestrian============== "walker.pedestrian.0001": 'pedestrian', "walker.pedestrian.0004": 'pedestrian', "walker.pedestrian.0005": 'pedestrian', "walker.pedestrian.0007": 'pedestrian', "walker.pedestrian.0013": 'pedestrian', "walker.pedestrian.0014": 'pedestrian', "walker.pedestrian.0017": 'pedestrian', "walker.pedestrian.0018": 'pedestrian', "walker.pedestrian.0019": 'pedestrian', "walker.pedestrian.0020": 'pedestrian', "walker.pedestrian.0022": 'pedestrian', "walker.pedestrian.0025": 'pedestrian', "walker.pedestrian.0035": 'pedestrian', "walker.pedestrian.0041": 'pedestrian', "walker.pedestrian.0046": 'pedestrian', "walker.pedestrian.0047": 'pedestrian', # ========================================== "static.prop.dirtdebris01": 'others', "static.prop.dirtdebris02": 'others', } num_classes = len(class_names) num_map_classes = len(map_class_names) roi_size = (30, 60) num_sample = 20 fut_ts = 6 fut_mode = 6 ego_fut_ts = 6 ego_fut_mode = 6 num_cmd = 1 queue_length = 4 # history + current embed_dims = 256 num_groups = 8 num_decoder = 6 num_single_frame_decoder = 1 num_single_frame_decoder_map = 1 use_deformable_func = True # mmdet3d_plugin/ops/setup.py needs to be executed strides = [4, 8, 16, 32] num_levels = len(strides) num_depth_layers = 3 drop_out = 0.1 temporal = True temporal_map = True decouple_attn = True decouple_attn_map = False decouple_attn_motion = True with_quality_estimation = True task_config = dict( with_det=True, with_map=True, with_motion_plan=True, ) model = dict( type="SparseDrive", use_grid_mask=True, use_deformable_func=use_deformable_func, img_backbone=dict( type="ResNet", depth=50, num_stages=4, frozen_stages=-1, norm_eval=False, style="pytorch", with_cp=True, out_indices=(0, 1, 2, 3), norm_cfg=dict(type="BN", requires_grad=True), pretrained="ckpt/resnet50-19c8e357.pth", ), img_neck=dict( type="FPN", num_outs=num_levels, start_level=0, out_channels=embed_dims, add_extra_convs="on_output", relu_before_extra_convs=True, in_channels=[256, 512, 1024, 2048], ), # depth_branch=dict( # for auxiliary supervision only # type="DenseDepthNet", # embed_dims=embed_dims, # num_depth_layers=num_depth_layers, # loss_weight=0.2, # ), head=dict( type="SparseDriveHead", task_config=task_config, det_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn, instance_bank=dict( type="InstanceBank", num_anchor=900, embed_dims=embed_dims, anchor="data/kmeans/kmeans_det_900.npy", anchor_handler=dict(type="SparseBox3DKeyPointsGenerator"), num_temp_instances=600 if temporal else -1, confidence_decay=0.9, feat_grad=False, ), anchor_encoder=dict( type="SparseBox3DEncoder", vel_dims=3, embed_dims=[128, 32, 32, 64] if decouple_attn else 256, mode="cat" if decouple_attn else "add", output_fc=not decouple_attn, in_loops=1, out_loops=4 if decouple_attn else 2, ), num_single_frame_decoder=num_single_frame_decoder, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder) )[2:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparseBox3DKeyPointsGenerator", num_learnable_pts=6, fix_scale=[ [0, 0, 0], [0.45, 0, 0], [-0.45, 0, 0], [0, 0.45, 0], [0, -0.45, 0], [0, 0, 0.45], [0, 0, -0.45], ], ), ), refine_layer=dict( type="SparseBox3DRefinementModule", embed_dims=embed_dims, num_cls=num_classes, refine_yaw=True, with_quality_estimation=with_quality_estimation, ), sampler=dict( type="SparseBox3DTarget", num_dn_groups=0, num_temp_dn_groups=0, dn_noise_scale=[2.0] * 3 + [0.5] * 7, max_dn_gt=32, add_neg_dn=True, cls_weight=2.0, box_weight=0.25, reg_weights=[2.0] * 3 + [0.5] * 3 + [0.0] * 4, cls_wise_reg_weights={ class_names.index("traffic_cone"): [ 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, ], }, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0, ), loss_reg=dict( type="SparseBox3DLoss", loss_box=dict(type="L1Loss", loss_weight=0.25), loss_centerness=dict(type="CrossEntropyLoss", use_sigmoid=True), loss_yawness=dict(type="GaussianFocalLoss"), # cls_allow_reverse=[class_names.index("barrier")], ), decoder=dict(type="SparseBox3DDecoder"), reg_weights=[2.0] * 3 + [1.0] * 7, ), map_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn_map, instance_bank=dict( type="InstanceBank", num_anchor=100, embed_dims=embed_dims, anchor="data/kmeans/kmeans_map_100.npy", anchor_handler=dict(type="SparsePoint3DKeyPointsGenerator"), num_temp_instances=0 if temporal_map else -1, confidence_decay=0.9, feat_grad=True, ), anchor_encoder=dict( type="SparsePoint3DEncoder", embed_dims=embed_dims, num_sample=num_sample, ), num_single_frame_decoder=num_single_frame_decoder_map, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder_map + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder_map) )[:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal_map else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparsePoint3DKeyPointsGenerator", embed_dims=embed_dims, num_sample=num_sample, num_learnable_pts=3, fix_height=(0, 0.25, -0.25, 0.5, -0.5), ground_height=-1.84, # ground height in lidar frame ), ), refine_layer=dict( type="SparsePoint3DRefinementModule", embed_dims=embed_dims, num_sample=num_sample, num_cls=num_map_classes, ), sampler=dict( type="SparsePoint3DTarget", assigner=dict( type='HungarianLinesAssigner', cost=dict( type='MapQueriesCost', cls_cost=dict(type='FocalLossCost', weight=1.0), reg_cost=dict(type='LinesL1Cost', weight=10.0, beta=0.01, permute=True), ), ), num_cls=num_map_classes, num_sample=num_sample, roi_size=roi_size, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_reg=dict( type="SparseLineLoss", loss_line=dict( type='LinesL1Loss', loss_weight=10.0, beta=0.01, ), num_sample=num_sample, roi_size=roi_size, ), decoder=dict( type="SparsePoint3DDecoder", score_threshold=0.5, ), reg_weights=[1.0] * 40, gt_cls_key="gt_map_labels", gt_reg_key="gt_map_pts", gt_id_key="map_instance_id", with_instance_id=False, task_prefix='map', ), motion_plan_head=dict( type='MotionPlanningHead', fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, motion_anchor=f'data/kmeans/kmeans_motion_{fut_mode}.npy', plan_anchor=None, #plan_anchor=f'data/kmeans/kmeans_plan_{ego_fut_mode}.npy', embed_dims=embed_dims, decouple_attn=decouple_attn_motion, instance_queue=dict( type="InstanceQueue", embed_dims=embed_dims, queue_length=queue_length, frame_rate=1, tracking_threshold=0.2, feature_map_scale=(input_shape[1]/strides[-1], input_shape[0]/strides[-1]), use_ego_status=False, use_tp=['near',], ), operation_order=( [ "temp_gnn", "gnn", "norm", "cross_gnn", "norm", "ffn", "norm", ] * 3 + [ "refine", ] ), temp_graph_model=dict( type="MultiheadAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), cross_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 2, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), refine_layer=dict( type="MotionPlanningRefinementModule", embed_dims=embed_dims, fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_gru=False, ), motion_sampler=dict( type="MotionTarget", ), motion_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.2 ), motion_loss_reg=dict(type='L1Loss', loss_weight=0.2), planning_sampler=dict( type="PlanningTarget", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, ), plan_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.5, ), plan_loss_reg=dict(type='L1Loss', loss_weight=1.0), plan_loss_status=dict(type='L1Loss', loss_weight=1.0), motion_decoder=dict(type="SparseBox3DMotionDecoder"), planning_decoder=dict( type="HierarchicalPlanningDecoder", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_rescore=True, ), num_det=50, num_map=10, ), ), ) # ================== data ======================== dataset_type = "B2D3DDataset" data_root = "data/bench2drive/" anno_root = "data/infos/" if version == 'base' else "data/infos/mini/" file_client_args = dict(backend="disk") img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) train_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), # dict( # type="DenseDepthMapGenerator", # downsample=strides[:num_depth_layers], # ), dict(type="BBoxRotation"), dict(type="PhotoMetricDistortionMultiViewImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=False, normalize=False, sample_num=num_sample, permute=True, ), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", # "gt_depth", "focal", "gt_bboxes_3d", "gt_labels_3d", 'gt_map_labels', 'gt_map_pts', 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', 'ego_status', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp", "instance_id"], ), ] test_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", 'ego_status', 'gt_ego_fut_cmd', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp"], ), ] eval_pipeline = [ dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=True, normalize=False, ), dict( type='Collect', keys=[ 'vectors', "gt_bboxes_3d", "gt_labels_3d", 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', # 'fut_boxes' ], meta_keys=['token', 'timestamp'] ), ] input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=False, ) data_basic_config = dict( type=dataset_type, data_root=data_root, classes=class_names, map_classes=map_class_names, name_mapping=NameMapping, modality=input_modality, sample_interval=5, past_frames=2, future_frames=6, use_cmd=num_cmd>1, ) eval_config = dict( **data_basic_config, ann_file=anno_root + 'b2d_infos_val.pkl', pipeline=eval_pipeline, test_mode=True, ) data_aug_conf = { "resize_lim": (0.40, 0.47), "final_dim": input_shape[::-1], "bot_pct_lim": (0.0, 0.0), "rot_lim": (-5.4, 5.4), "H": 900, "W": 1600, "rand_flip": True, "rot3d_range": [0, 0], } data = dict( samples_per_gpu=batch_size, workers_per_gpu=batch_size, train=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_train.pkl", pipeline=train_pipeline, test_mode=False, data_aug_conf=data_aug_conf, with_seq_flag=True, sequences_split_num=5, keep_consistent_seq_aug=True, ), val=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), test=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), ) # ================== training ======================== optimizer = dict( type="AdamW", lr=2e-4, weight_decay=0.001, paramwise_cfg=dict( custom_keys={ "img_backbone": dict(lr_mult=0.1), } ), ) optimizer_config = dict(grad_clip=dict(max_norm=25, norm_type=2)) lr_config = dict( policy="CosineAnnealing", warmup="linear", warmup_iters=500, warmup_ratio=1.0 / 3, min_lr_ratio=1e-3, ) runner = dict( type="IterBasedRunner", max_iters=num_iters_per_epoch * num_epochs, ) # ================== eval ======================== eval_mode = dict( with_det=True, with_tracking=False, with_map=False, with_motion=False, with_planning=True, tracking_threshold=0.2, motion_threshhold=0.2, ) evaluation = dict( interval=num_iters_per_epoch*checkpoint_epoch_interval, eval_mode=eval_mode, ) # ================== pretrained model ======================== # load_from = 'http://svcspawner.bcloud-beijing.hobot.cc/user/homespace/wenchao.sun/plat_gpu/sparsedrive_small_b2d_stage1_20e-20240903-225131.954333/output/work_dirs/latest.pth' load_from = "ckpt/sparsedrive_small_b2d_stage1.pth" ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/configs/sparsedrive_small_b2d_stage2_targetpoint_multiplan.py ================================================ # ================ base config =================== version = "mini" version = "base" length = {'base': 234769, 'mini': 1933} plugin = True plugin_dir = "mmdet3d_plugin/" dist_params = dict(backend="nccl") log_level = "INFO" work_dir = None total_batch_size = 16 num_gpus = 8 batch_size = total_batch_size // num_gpus num_iters_per_epoch = int(length[version] // (num_gpus * batch_size)) num_epochs = 10 checkpoint_epoch_interval = 2 checkpoint_config = dict( interval=num_iters_per_epoch * checkpoint_epoch_interval ) log_config = dict( interval=51, hooks=[ dict(type="TextLoggerHook", by_epoch=False), dict(type="TensorboardLoggerHook"), ], ) load_from = None resume_from = None workflow = [("train", 1)] fp16 = dict(loss_scale=32.0) input_shape = (704, 384) # ================== model ======================== class_names = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others', ] map_class_names = [ 'Broken', 'Solid', 'SolidSolid', # 'Center', # 'TrafficLight', # 'StopSign', ] NameMapping = { #=================vehicle================= # bicycle 'vehicle.bh.crossbike': 'bicycle', "vehicle.diamondback.century": 'bicycle', "vehicle.gazelle.omafiets": 'bicycle', # car "vehicle.chevrolet.impala": 'car', "vehicle.dodge.charger_2020": 'car', "vehicle.dodge.charger_police": 'car', "vehicle.dodge.charger_police_2020": 'car', "vehicle.lincoln.mkz_2017": 'car', "vehicle.lincoln.mkz_2020": 'car', "vehicle.mini.cooper_s_2021": 'car', "vehicle.mercedes.coupe_2020": 'car', "vehicle.ford.mustang": 'car', "vehicle.nissan.patrol_2021": 'car', "vehicle.audi.tt": 'car', "vehicle.audi.etron": 'car', "vehicle.ford.crown": 'car', "vehicle.ford.mustang": 'car', "vehicle.tesla.model3": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": 'car', # bus # van "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": "van", "vehicle.ford.ambulance": "van", # truck "vehicle.carlamotors.firetruck": 'truck', #========================================= #=================traffic sign============ # traffic.speed_limit "traffic.speed_limit.30": 'traffic_sign', "traffic.speed_limit.40": 'traffic_sign', "traffic.speed_limit.50": 'traffic_sign', "traffic.speed_limit.60": 'traffic_sign', "traffic.speed_limit.90": 'traffic_sign', "traffic.speed_limit.120": 'traffic_sign', "traffic.stop": 'traffic_sign', "traffic.yield": 'traffic_sign', "traffic.traffic_light": 'traffic_light', #========================================= #===================Construction=========== "static.prop.warningconstruction" : 'traffic_cone', "static.prop.warningaccident": 'traffic_cone', "static.prop.trafficwarning": "traffic_cone", #===================Construction=========== "static.prop.constructioncone": 'traffic_cone', #=================pedestrian============== "walker.pedestrian.0001": 'pedestrian', "walker.pedestrian.0004": 'pedestrian', "walker.pedestrian.0005": 'pedestrian', "walker.pedestrian.0007": 'pedestrian', "walker.pedestrian.0013": 'pedestrian', "walker.pedestrian.0014": 'pedestrian', "walker.pedestrian.0017": 'pedestrian', "walker.pedestrian.0018": 'pedestrian', "walker.pedestrian.0019": 'pedestrian', "walker.pedestrian.0020": 'pedestrian', "walker.pedestrian.0022": 'pedestrian', "walker.pedestrian.0025": 'pedestrian', "walker.pedestrian.0035": 'pedestrian', "walker.pedestrian.0041": 'pedestrian', "walker.pedestrian.0046": 'pedestrian', "walker.pedestrian.0047": 'pedestrian', # ========================================== "static.prop.dirtdebris01": 'others', "static.prop.dirtdebris02": 'others', } num_classes = len(class_names) num_map_classes = len(map_class_names) roi_size = (30, 60) num_sample = 20 fut_ts = 6 fut_mode = 6 ego_fut_ts = 6 ego_fut_mode = 6 num_cmd = 1 queue_length = 4 # history + current embed_dims = 256 num_groups = 8 num_decoder = 6 num_single_frame_decoder = 1 num_single_frame_decoder_map = 1 use_deformable_func = True # mmdet3d_plugin/ops/setup.py needs to be executed strides = [4, 8, 16, 32] num_levels = len(strides) num_depth_layers = 3 drop_out = 0.1 temporal = True temporal_map = True decouple_attn = True decouple_attn_map = False decouple_attn_motion = True with_quality_estimation = True task_config = dict( with_det=True, with_map=True, with_motion_plan=True, ) model = dict( type="SparseDrive", use_grid_mask=True, use_deformable_func=use_deformable_func, img_backbone=dict( type="ResNet", depth=50, num_stages=4, frozen_stages=-1, norm_eval=False, style="pytorch", with_cp=True, out_indices=(0, 1, 2, 3), norm_cfg=dict(type="BN", requires_grad=True), pretrained="ckpt/resnet50-19c8e357.pth", ), img_neck=dict( type="FPN", num_outs=num_levels, start_level=0, out_channels=embed_dims, add_extra_convs="on_output", relu_before_extra_convs=True, in_channels=[256, 512, 1024, 2048], ), # depth_branch=dict( # for auxiliary supervision only # type="DenseDepthNet", # embed_dims=embed_dims, # num_depth_layers=num_depth_layers, # loss_weight=0.2, # ), head=dict( type="SparseDriveHead", task_config=task_config, det_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn, instance_bank=dict( type="InstanceBank", num_anchor=900, embed_dims=embed_dims, anchor="data/kmeans/kmeans_det_900.npy", anchor_handler=dict(type="SparseBox3DKeyPointsGenerator"), num_temp_instances=600 if temporal else -1, confidence_decay=0.9, feat_grad=False, ), anchor_encoder=dict( type="SparseBox3DEncoder", vel_dims=3, embed_dims=[128, 32, 32, 64] if decouple_attn else 256, mode="cat" if decouple_attn else "add", output_fc=not decouple_attn, in_loops=1, out_loops=4 if decouple_attn else 2, ), num_single_frame_decoder=num_single_frame_decoder, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder) )[2:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparseBox3DKeyPointsGenerator", num_learnable_pts=6, fix_scale=[ [0, 0, 0], [0.45, 0, 0], [-0.45, 0, 0], [0, 0.45, 0], [0, -0.45, 0], [0, 0, 0.45], [0, 0, -0.45], ], ), ), refine_layer=dict( type="SparseBox3DRefinementModule", embed_dims=embed_dims, num_cls=num_classes, refine_yaw=True, with_quality_estimation=with_quality_estimation, ), sampler=dict( type="SparseBox3DTarget", num_dn_groups=0, num_temp_dn_groups=0, dn_noise_scale=[2.0] * 3 + [0.5] * 7, max_dn_gt=32, add_neg_dn=True, cls_weight=2.0, box_weight=0.25, reg_weights=[2.0] * 3 + [0.5] * 3 + [0.0] * 4, cls_wise_reg_weights={ class_names.index("traffic_cone"): [ 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, ], }, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=2.0, ), loss_reg=dict( type="SparseBox3DLoss", loss_box=dict(type="L1Loss", loss_weight=0.25), loss_centerness=dict(type="CrossEntropyLoss", use_sigmoid=True), loss_yawness=dict(type="GaussianFocalLoss"), # cls_allow_reverse=[class_names.index("barrier")], ), decoder=dict(type="SparseBox3DDecoder"), reg_weights=[2.0] * 3 + [1.0] * 7, ), map_head=dict( type="Sparse4DHead", cls_threshold_to_reg=0.05, decouple_attn=decouple_attn_map, instance_bank=dict( type="InstanceBank", num_anchor=100, embed_dims=embed_dims, anchor="data/kmeans/kmeans_map_100.npy", anchor_handler=dict(type="SparsePoint3DKeyPointsGenerator"), num_temp_instances=0 if temporal_map else -1, confidence_decay=0.9, feat_grad=True, ), anchor_encoder=dict( type="SparsePoint3DEncoder", embed_dims=embed_dims, num_sample=num_sample, ), num_single_frame_decoder=num_single_frame_decoder_map, operation_order=( [ "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * num_single_frame_decoder_map + [ "temp_gnn", "gnn", "norm", "deformable", "ffn", "norm", "refine", ] * (num_decoder - num_single_frame_decoder_map) )[:], temp_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ) if temporal_map else None, graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_map else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims * 2, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 4, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), deformable_model=dict( type="DeformableFeatureAggregation", embed_dims=embed_dims, num_groups=num_groups, num_levels=num_levels, num_cams=6, attn_drop=0.15, use_deformable_func=use_deformable_func, use_camera_embed=True, residual_mode="cat", kps_generator=dict( type="SparsePoint3DKeyPointsGenerator", embed_dims=embed_dims, num_sample=num_sample, num_learnable_pts=3, fix_height=(0, 0.25, -0.25, 0.5, -0.5), ground_height=-1.84, # ground height in lidar frame ), ), refine_layer=dict( type="SparsePoint3DRefinementModule", embed_dims=embed_dims, num_sample=num_sample, num_cls=num_map_classes, ), sampler=dict( type="SparsePoint3DTarget", assigner=dict( type='HungarianLinesAssigner', cost=dict( type='MapQueriesCost', cls_cost=dict(type='FocalLossCost', weight=1.0), reg_cost=dict(type='LinesL1Cost', weight=10.0, beta=0.01, permute=True), ), ), num_cls=num_map_classes, num_sample=num_sample, roi_size=roi_size, ), loss_cls=dict( type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, ), loss_reg=dict( type="SparseLineLoss", loss_line=dict( type='LinesL1Loss', loss_weight=10.0, beta=0.01, ), num_sample=num_sample, roi_size=roi_size, ), decoder=dict( type="SparsePoint3DDecoder", score_threshold=0.5, ), reg_weights=[1.0] * 40, gt_cls_key="gt_map_labels", gt_reg_key="gt_map_pts", gt_id_key="map_instance_id", with_instance_id=False, task_prefix='map', ), motion_plan_head=dict( type='MotionPlanningHead', fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, motion_anchor=f'data/kmeans/kmeans_motion_{fut_mode}.npy', plan_anchor=None, # plan_anchor=f'data/kmeans/kmeans_plan_{ego_fut_mode}_b2d.npy', embed_dims=embed_dims, decouple_attn=decouple_attn_motion, instance_queue=dict( type="InstanceQueue", embed_dims=embed_dims, queue_length=queue_length, frame_rate=1, tracking_threshold=0.2, feature_map_scale=(input_shape[1]/strides[-1], input_shape[0]/strides[-1]), use_ego_status=False, use_tp=['near',], ), operation_order=( [ "temp_gnn", "gnn", "norm", "cross_gnn", "norm", "ffn", "norm", ] * 3 + [ "refine", ] ), temp_graph_model=dict( type="MultiheadAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims if not decouple_attn_motion else embed_dims * 2, num_heads=num_groups, batch_first=True, dropout=drop_out, ), cross_graph_model=dict( type="MultiheadFlashAttention", embed_dims=embed_dims, num_heads=num_groups, batch_first=True, dropout=drop_out, ), norm_layer=dict(type="LN", normalized_shape=embed_dims), ffn=dict( type="AsymmetricFFN", in_channels=embed_dims, pre_norm=dict(type="LN"), embed_dims=embed_dims, feedforward_channels=embed_dims * 2, num_fcs=2, ffn_drop=drop_out, act_cfg=dict(type="ReLU", inplace=True), ), refine_layer=dict( type="MotionPlanningRefinementModule", embed_dims=embed_dims, fut_ts=fut_ts, fut_mode=fut_mode, ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_gru=False, ), motion_sampler=dict( type="MotionTarget", ), motion_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.2 ), motion_loss_reg=dict(type='L1Loss', loss_weight=0.2), planning_sampler=dict( type="PlanningTarget", ego_fut_ts=ego_fut_ts,#6 ego_fut_mode=ego_fut_mode,#6 num_cmd=num_cmd,#1 ), plan_loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.5, ), plan_loss_reg=dict(type='L1Loss', loss_weight=1.0), plan_loss_status=dict(type='L1Loss', loss_weight=1.0), motion_decoder=dict(type="SparseBox3DMotionDecoder"), planning_decoder=dict( type="HierarchicalPlanningDecoder", ego_fut_ts=ego_fut_ts, ego_fut_mode=ego_fut_mode, num_cmd=num_cmd, use_rescore=True, ), num_det=50, num_map=10, ), ), ) # ================== data ======================== dataset_type = "B2D3DDataset" data_root = "data/bench2drive/" anno_root = "data/infos/" if version == 'base' else "data/infos/mini/" file_client_args = dict(backend="disk") img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True ) train_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), # dict( # type="DenseDepthMapGenerator", # downsample=strides[:num_depth_layers], # ), dict(type="BBoxRotation"), dict(type="PhotoMetricDistortionMultiViewImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=False, normalize=False, sample_num=num_sample, permute=True, ), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", # "gt_depth", "focal", "gt_bboxes_3d", "gt_labels_3d", 'gt_map_labels', 'gt_map_pts', 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', 'ego_status', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp", "instance_id"], ), ] test_pipeline = [ dict(type="LoadMultiViewImageFromFiles", to_float32=True), dict(type="ResizeCropFlipImage"), dict(type="NormalizeMultiviewImage", **img_norm_cfg), dict(type="NuScenesSparse4DAdaptor"), dict( type="Collect", keys=[ "img", "timestamp", "projection_mat", "image_wh", 'ego_status', 'gt_ego_fut_cmd', 'tp_near', 'tp_far', ], meta_keys=["T_global", "T_global_inv", "timestamp"], ), ] eval_pipeline = [ dict( type="CircleObjectRangeFilter", class_dist_thred=[55] * len(class_names), ), dict(type="InstanceNameFilter", classes=class_names), dict( type='VectorizeMap', roi_size=roi_size, simplify=True, normalize=False, ), dict( type='Collect', keys=[ 'vectors', "gt_bboxes_3d", "gt_labels_3d", 'gt_agent_fut_trajs', 'gt_agent_fut_masks', 'gt_ego_fut_trajs', 'gt_ego_fut_masks', 'gt_ego_fut_cmd', # 'fut_boxes' ], meta_keys=['token', 'timestamp'] ), ] input_modality = dict( use_lidar=False, use_camera=True, use_radar=False, use_map=False, use_external=False, ) data_basic_config = dict( type=dataset_type, data_root=data_root, classes=class_names, map_classes=map_class_names, name_mapping=NameMapping, modality=input_modality, sample_interval=5, past_frames=2, future_frames=6, use_cmd=num_cmd>1, ) eval_config = dict( **data_basic_config, ann_file=anno_root + 'b2d_infos_val.pkl', pipeline=eval_pipeline, test_mode=True, ) data_aug_conf = { "resize_lim": (0.40, 0.47), "final_dim": input_shape[::-1], "bot_pct_lim": (0.0, 0.0), "rot_lim": (-5.4, 5.4), "H": 900, "W": 1600, "rand_flip": True, "rot3d_range": [0, 0], } data = dict( samples_per_gpu=batch_size, workers_per_gpu=batch_size, train=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_train.pkl", pipeline=train_pipeline, test_mode=False, data_aug_conf=data_aug_conf, with_seq_flag=True, sequences_split_num=5, keep_consistent_seq_aug=True, ), val=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), test=dict( **data_basic_config, ann_file=anno_root + "b2d_infos_val.pkl", pipeline=test_pipeline, data_aug_conf=data_aug_conf, test_mode=True, eval_config=eval_config, ), ) # ================== training ======================== optimizer = dict( type="AdamW", lr=2e-4, weight_decay=0.001, paramwise_cfg=dict( custom_keys={ "img_backbone": dict(lr_mult=0.1), } ), ) optimizer_config = dict(grad_clip=dict(max_norm=25, norm_type=2)) lr_config = dict( policy="CosineAnnealing", warmup="linear", warmup_iters=500, warmup_ratio=1.0 / 3, min_lr_ratio=1e-3, ) runner = dict( type="IterBasedRunner", max_iters=num_iters_per_epoch * num_epochs, ) # ================== eval ======================== eval_mode = dict( with_det=True, with_tracking=False, with_map=False, with_motion=False, with_planning=True, tracking_threshold=0.2, motion_threshhold=0.2, ) evaluation = dict( interval=num_iters_per_epoch*checkpoint_epoch_interval, eval_mode=eval_mode, ) # ================== pretrained model ======================== # load_from = 'http://svcspawner.bcloud-beijing.hobot.cc/user/homespace/wenchao.sun/plat_gpu/sparsedrive_small_b2d_stage1_20e-20240903-225131.954333/output/work_dirs/latest.pth' load_from = "ckpt/sparsedrive_small_b2d_stage1.pth" ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/dist_train.sh ================================================ #!/usr/bin/env bash CONFIG=$1 GPUS=$2 PORT=${PORT:-28512} PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} --deterministic ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/create_data.sh ================================================ export PYTHONPATH="$(dirname $0)/..":$PYTHONPATH #python tools/data_converter/B2D_converter.py nuscenes \ ## --root-path ./data/nuscenes \ # --canbus ./data/nuscenes \ # --out-dir ./data/infos/ \ # --extra-tag nuscenes \ # --version v1.0 python adzoo/sparsedrive/tools/data_converter/B2D_converter.py ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/kmeans.sh ================================================ python tools/kmeans/kmeans_det.py python tools/kmeans/kmeans_map.py python tools/kmeans/kmeans_motion.py python tools/kmeans/kmeans_plan.py ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/test.sh ================================================ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7\ bash ./tools/dist_test.sh \ projects/configs/sparsedrive_small_stage2.py \ work_dirs/sparsedrive_small_stage2/iter_5860.pth \ 8 \ --deterministic \ --eval bbox # --result_file ./work_dirs/sparsedrive_small_stage2/results.pkl ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/test_roboAD.sh ================================================ # bash ./tools/dist_test.sh \ # projects/configs/sparsedrive_small_stage2.py \ # ckpt/sparsedrive_stage2.pth \ # 8 \ # --deterministic \ # --ev/data/songziying/workspace/SparseDrive/scriptsal bbox # # --result_file ./work_dirs/sparsedrive_small_stage2/results.pkl bash ./tools/dist_test.sh \ projects/configs/sparsedrive_small_stage2_roboAD_6s.py \ work_dirs/sparsedrive_small_stage2_roboAD_6s/iter_5860.pth\ 1 \ --deterministic \ --eval bbox # --result_file ./work_dirs/sparsedrive_small_stage2_roboAD/results.pkl ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/train.sh ================================================ ## stage1 # bash ./tools/dist_train.sh \ # projects/configs/sparsedrive_small_trainval_1_10_stage1_test.py \ # 1 \ # --deterministic # stage2 bash ./adzoo/sparsedrive/tools/dist_train.sh \ ./adzoo/sparsedrive/configs/sparsedrive_small_stage2.py \ 8 \ --deterministic ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/train_6s.sh ================================================ ## stage1 # bash ./tools/dist_train.sh \ # projects/configs/sparsedrive_small_trainval_1_10_stage1_test.py \ # 1 \ # --deterministic # stage2 bash ./tools/dist_train.sh \ projects/configs/sparsedrive_small_stage2_6s.py \ 8 \ --deterministic ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/train_roboAD.sh ================================================ ## stage1 # bash ./tools/dist_train.sh \ # projects/configs/sparsedrive_small_stage1_roboAD.py \ # 1 \ # --deterministic ## stage2 # bash ./tools/dist_train.sh \ # projects/configs/sparsedrive_small_stage2_roboAD.py \ # 8 \ # --deterministic bash ./tools/dist_train.sh \ projects/configs/sparsedrive_small_stage2_roboAD_6s.py \ 8 \ --deterministic ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/scripts/visualize.sh ================================================ export PYTHONPATH="$(dirname $0)/..":$PYTHONPATH python tools/visualization/visualize.py \ projects/configs/sparsedrive_small_stage2.py \ --result-path work_dirs/sparsedrive_small_stage2/results.pkl ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/benchmark.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import argparse import time import torch from mmcv import Config from mmcv.parallel import MMDataParallel from mmcv.runner import load_checkpoint, wrap_fp16_model import sys sys.path.append('.') from projects.mmdet3d_plugin.datasets.builder import build_dataloader from projects.mmdet3d_plugin.datasets import custom_build_dataset from mmdet.models import build_detector from mmcv.cnn.utils.flops_counter import add_flops_counting_methods from mmcv.parallel import scatter def parse_args(): parser = argparse.ArgumentParser(description='MMDet benchmark a model') parser.add_argument('config', help='test config file path') parser.add_argument('--checkpoint', default=None, help='checkpoint file') parser.add_argument('--samples', default=1000, help='samples to benchmark') parser.add_argument( '--log-interval', default=50, help='interval of logging') parser.add_argument( '--fuse-conv-bn', action='store_true', help='Whether to fuse conv and bn, this will slightly increase' 'the inference speed') args = parser.parse_args() return args def get_max_memory(model): device = getattr(model, 'output_device', None) mem = torch.cuda.max_memory_allocated(device=device) mem_mb = torch.tensor([mem / (1024 * 1024)], dtype=torch.int, device=device) return mem_mb.item() def main(): args = parse_args() get_flops_params(args) get_mem_fps(args) def get_mem_fps(args): cfg = Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True # build the dataloader # TODO: support multiple images per gpu (only minor changes are needed) print(cfg.data.test) dataset = custom_build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, samples_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=False, shuffle=False) # build the model and load checkpoint cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) if args.checkpoint is not None: load_checkpoint(model, args.checkpoint, map_location='cpu') # if args.fuse_conv_bn: # model = fuse_module(model) model = MMDataParallel(model, device_ids=[0]) model.eval() # the first several iterations may be very slow so skip them num_warmup = 5 pure_inf_time = 0 # benchmark with several samples and take the average max_memory = 0 for i, data in enumerate(data_loader): # torch.cuda.synchronize() with torch.no_grad(): start_time = time.perf_counter() model(return_loss=False, rescale=True, **data) torch.cuda.synchronize() elapsed = time.perf_counter() - start_time max_memory = max(max_memory, get_max_memory(model)) if i >= num_warmup: pure_inf_time += elapsed if (i + 1) % args.log_interval == 0: fps = (i + 1 - num_warmup) / pure_inf_time print(f'Done image [{i + 1:<3}/ {args.samples}], ' f'fps: {fps:.1f} img / s, ' f"gpu mem: {max_memory} M") if (i + 1) == args.samples: pure_inf_time += elapsed fps = (i + 1 - num_warmup) / pure_inf_time print(f'Overall fps: {fps:.1f} img / s') break def get_flops_params(args): gpu_id = 0 cfg = Config.fromfile(args.config) dataset = custom_build_dataset(cfg.data.val) dataloader = build_dataloader( dataset, samples_per_gpu=1, workers_per_gpu=0, dist=False, shuffle=False, ) data_iter = dataloader.__iter__() data = next(data_iter) data = scatter(data, [gpu_id])[0] cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) if args.checkpoint is not None: load_checkpoint(model, args.checkpoint, map_location='cpu') model = model.cuda(gpu_id) model.eval() bilinear_flops = 11 num_key_pts_det = ( cfg.model["head"]['det_head']["deformable_model"]["kps_generator"]["num_learnable_pts"] + len(cfg.model["head"]['det_head']["deformable_model"]["kps_generator"]["fix_scale"]) ) deformable_agg_flops_det = ( cfg.num_decoder * cfg.embed_dims * cfg.num_levels * cfg.model["head"]['det_head']["instance_bank"]["num_anchor"] * cfg.model["head"]['det_head']["deformable_model"]["num_cams"] * num_key_pts_det * bilinear_flops ) num_key_pts_map = ( cfg.model["head"]['map_head']["deformable_model"]["kps_generator"]["num_learnable_pts"] + len(cfg.model["head"]['map_head']["deformable_model"]["kps_generator"]["fix_height"]) ) * cfg.model["head"]['map_head']["deformable_model"]["kps_generator"]["num_sample"] deformable_agg_flops_map = ( cfg.num_decoder * cfg.embed_dims * cfg.num_levels * cfg.model["head"]['map_head']["instance_bank"]["num_anchor"] * cfg.model["head"]['map_head']["deformable_model"]["num_cams"] * num_key_pts_map * bilinear_flops ) deformable_agg_flops = deformable_agg_flops_det + deformable_agg_flops_map for module in ["total", "img_backbone", "img_neck", "head"]: if module != "total": flops_model = add_flops_counting_methods(getattr(model, module)) else: flops_model = add_flops_counting_methods(model) flops_model.eval() flops_model.start_flops_count() if module == "img_backbone": flops_model(data["img"].flatten(0, 1)) elif module == "img_neck": flops_model(model.img_backbone(data["img"].flatten(0, 1))) elif module == "head": flops_model(model.extract_feat(data["img"], metas=data), data) else: flops_model(**data) flops_count, params_count = flops_model.compute_average_flops_cost() flops_count *= flops_model.__batch_counter__ flops_model.stop_flops_count() if module == "head" or module == "total": flops_count += deformable_agg_flops if module == "total": total_flops = flops_count total_params = params_count print( f"{module:<13} complexity: " f"FLOPs={flops_count/ 10.**9:>8.4f} G / {flops_count/total_flops*100:>6.2f}%, " f"Params={params_count/10**6:>8.4f} M / {params_count/total_params*100:>6.2f}%." ) if __name__ == '__main__': main() ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/data_converter/B2D_converter.py ================================================ import os from os.path import join import gzip, json, pickle import numpy as np from pyquaternion import Quaternion from tqdm import tqdm import copy # from vis_utils import calculate_cube_vertices,calculate_occlusion_stats,edges,DIS_CAR_SAVE import cv2 import multiprocessing import argparse from shapely.geometry import LineString # All data in the Bench2Drive dataset are in the left-handed coordinate system. # This code converts all coordinate systems (world coordinate system, vehicle coordinate system, # camera coordinate system, and lidar coordinate system) to the right-handed coordinate system # consistent with the nuscenes dataset. DATAROOT = 'data/bench2drive' MAP_ROOT = 'data/bench2drive/maps' OUT_DIR = 'data/infos' # split_file = 'data/bench2drive/bench2drive_mini_train_val_split.json' split_file = 'data/splits/bench2drive_base_train_val_split.json' MAP_CLASSES = [ 'Broken', 'Solid', 'SolidSolid', 'Center', 'TrafficLight', 'StopSign', ] point_cloud_range = [-15.0, -30.0, -2.0, 15.0, 30.0, 2.0] MAX_DISTANCE = 75 # Filter bounding boxes that are too far from the vehicle FILTER_Z_SHRESHOLD = 10 # Filter bounding boxes that are too high/low from the vehicle FILTER_INVISINLE = True # Filter bounding boxes based on visibility NUM_VISIBLE_SHRESHOLD = 1 # Filter bounding boxes with fewer visible vertices than this value NUM_OUTPOINT_SHRESHOLD = 7 # Filter bounding boxes where the number of vertices outside the frame is greater than this value in all cameras CAMERAS = ['CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'] CAMERA_TO_FOLDER_MAP = {'CAM_FRONT':'rgb_front', 'CAM_FRONT_LEFT':'rgb_front_left', 'CAM_FRONT_RIGHT':'rgb_front_right', 'CAM_BACK':'rgb_back', 'CAM_BACK_LEFT':'rgb_back_left', 'CAM_BACK_RIGHT':'rgb_back_right'} stand_to_ue4_rotate = np.array([[ 0, 0, 1, 0], [ 1, 0, 0, 0], [ 0,-1, 0, 0], [ 0, 0, 0, 1]]) lidar_to_righthand_ego = np.array([[ 0, 1, 0, 0], [ -1, 0, 0, 0], [ 0, 0, 1, 0], [ 0, 0, 0, 1]]) lefthand_ego_to_lidar = np.array([[ 0, 1, 0, 0], [ 1, 0, 0, 0], [ 0, 0, 1, 0], [ 0, 0, 0, 1]]) left2right = np.eye(4) left2right[1,1] = -1 WINDOW_HEIGHT = 900 WINDOW_WIDTH = 1600 def point_in_canvas_hw(pos): """Return true if point is in canvas""" if (pos[0] >= 0) and (pos[0] < WINDOW_HEIGHT) and (pos[1] >= 0) and (pos[1] < WINDOW_WIDTH): return True return False def calculate_cube_vertices(center, extent): cx, cy, cz = center x, y, z = extent vertices = [ (cx + x, cy + y, cz + z), (cx + x, cy + y, cz - z), (cx + x, cy - y, cz + z), (cx + x, cy - y, cz - z), (cx - x, cy + y, cz + z), (cx - x, cy + y, cz - z), (cx - x, cy - y, cz + z), (cx - x, cy - y, cz - z) ] return vertices def calculate_occlusion_stats(bbox_points, depth, depth_map, max_render_depth): """ Draws each vertex in vertices_pos2d if it is in front of the camera The color is based on whether the object is occluded or not. Returns the number of visible vertices and the number of vertices outside the camera. """ num_visible_vertices = 0 num_invisible_vertices = 0 num_vertices_outside_camera = 0 points = [] for i in range(len(bbox_points)): x_2d = bbox_points[i][0] y_2d = bbox_points[i][1] point_depth = depth[i] # if the point is in front of the camera but not too far away if max_render_depth > point_depth > 0 and point_in_canvas_hw((y_2d, x_2d)): #is_occluded_v = point_is_occluded_vectorized([[y_2d, x_2d]], point_depth, depth_map) is_occluded = point_is_occluded( (y_2d, x_2d), point_depth, depth_map) if is_occluded: vertex_color = (0,0,255) # bgr, red num_invisible_vertices += 1 else: num_visible_vertices += 1 vertex_color = (0,255,0) # bgr, green points.append((x_2d, y_2d, vertex_color)) else: num_vertices_outside_camera += 1 return num_visible_vertices, num_invisible_vertices, num_vertices_outside_camera, points def point_is_occluded(point, vertex_depth, depth_map): """ Checks whether or not the four pixels directly around the given point has less depth than the given vertex depth If True, this means that the point is occluded. """ y, x = map(int, point) from itertools import product neigbours = product((1, -1), repeat=2) is_occluded = [] for dy, dx in neigbours: if point_in_canvas_hw((dy+y, dx+x)): # If the depth map says the pixel is closer to the camera than the actual vertex if depth_map[y+dy, x+dx] < vertex_depth: is_occluded.append(True) else: is_occluded.append(False) # Only say point is occluded if all four neighbours are closer to camera than vertex return all(is_occluded) def apply_trans(vec,world2ego): vec = np.concatenate((vec,np.array([1]))) t = world2ego @ vec return t[0:3] def get_pose_matrix(dic): new_matrix = np.zeros((4,4)) new_matrix[0:3,0:3] = Quaternion(axis=[0, 0, 1], radians=dic['theta']-np.pi/2).rotation_matrix new_matrix[0,3] = dic['x'] new_matrix[1,3] = dic['y'] new_matrix[3,3] = 1 return new_matrix def get_npc2world(npc): for key in ['world2vehicle','world2ego','world2sign','world2ped']: if key in npc.keys(): npc2world = np.linalg.inv(np.array(npc[key])) yaw_from_matrix = np.arctan2(npc2world[1,0], npc2world[0,0]) yaw = npc['rotation'][-1] / 180 * np.pi if abs(yaw-yaw_from_matrix)> 0.01: npc2world[0:3,0:3] = Quaternion(axis=[0, 0, 1], radians=yaw).rotation_matrix npc2world = left2right @ npc2world @ left2right return npc2world npc2world = np.eye(4) npc2world[0:3,0:3] = Quaternion(axis=[0, 0, 1], radians=npc['rotation'][-1]/180*np.pi).rotation_matrix npc2world[0:3,3] = np.array(npc['location']) return left2right @ npc2world @ left2right def get_global_trigger_vertex(center,extent,yaw_in_degree): x,y = center[0],-center[1] dx,dy = extent[0],extent[1] yaw_in_radians = -yaw_in_degree/180*np.pi vertex_in_self = np.array([[ dx, dy], [-dx, dy], [-dx,-dy], [ dx,-dy]]) rotate_matrix = np.array([[np.cos(yaw_in_radians),-np.sin(yaw_in_radians)], [np.sin(yaw_in_radians), np.cos(yaw_in_radians)]]) rotated_vertex = (rotate_matrix @ vertex_in_self.T).T vertex_in_global = np.array([[x,y]]).repeat(4,axis=0) + rotated_vertex return vertex_in_global def get_image_point(loc, K, w2c): point = np.array([loc[0], loc[1], loc[2], 1]) point_camera = np.dot(w2c, point) point_camera = point_camera[0:3] depth = point_camera[2] point_img = np.dot(K, point_camera) point_img[0] /= point_img[2] point_img[1] /= point_img[2] return point_img[0:2], depth def get_action(index): Discrete_Actions_DICT = { 0: (0, 0, 1, False), 1: (0.7, -0.5, 0, False), 2: (0.7, -0.3, 0, False), 3: (0.7, -0.2, 0, False), 4: (0.7, -0.1, 0, False), 5: (0.7, 0, 0, False), 6: (0.7, 0.1, 0, False), 7: (0.7, 0.2, 0, False), 8: (0.7, 0.3, 0, False), 9: (0.7, 0.5, 0, False), 10: (0.3, -0.7, 0, False), 11: (0.3, -0.5, 0, False), 12: (0.3, -0.3, 0, False), 13: (0.3, -0.2, 0, False), 14: (0.3, -0.1, 0, False), 15: (0.3, 0, 0, False), 16: (0.3, 0.1, 0, False), 17: (0.3, 0.2, 0, False), 18: (0.3, 0.3, 0, False), 19: (0.3, 0.5, 0, False), 20: (0.3, 0.7, 0, False), 21: (0, -1, 0, False), 22: (0, -0.6, 0, False), 23: (0, -0.3, 0, False), 24: (0, -0.1, 0, False), 25: (1, 0, 0, False), 26: (0, 0.1, 0, False), 27: (0, 0.3, 0, False), 28: (0, 0.6, 0, False), 29: (0, 1.0, 0, False), 30: (0.5, -0.5, 0, True), 31: (0.5, -0.3, 0, True), 32: (0.5, -0.2, 0, True), 33: (0.5, -0.1, 0, True), 34: (0.5, 0, 0, True), 35: (0.5, 0.1, 0, True), 36: (0.5, 0.2, 0, True), 37: (0.5, 0.3, 0, True), 38: (0.5, 0.5, 0, True), } throttle, steer, brake, reverse = Discrete_Actions_DICT[index] return throttle, steer, brake def gengrate_map(map_root): map_infos = {} for file_name in os.listdir(map_root): if '.npz' in file_name: map_info = dict(np.load(join(map_root,file_name), allow_pickle=True)['arr']) town_name = file_name.split('_')[0] map_infos[town_name] = {} lane_points = [] lane_types = [] lane_sample_points = [] trigger_volumes_points = [] trigger_volumes_types = [] trigger_volumes_sample_points = [] for road_id, road in map_info.items(): for lane_id, lane in road.items(): if lane_id == 'Trigger_Volumes': for single_trigger_volume in lane: points = np.array(single_trigger_volume['Points']) points[:,1] *= -1 #left2right trigger_volumes_points.append(points) trigger_volumes_sample_points.append(points.mean(axis=0)) trigger_volumes_types.append(single_trigger_volume['Type']) else: for single_lane in lane: points = np.array([raw_point[0] for raw_point in single_lane['Points']]) points[:,1] *= -1 lane_points.append(points) lane_types.append(single_lane['Type']) lane_lenth = points.shape[0] if lane_lenth % 50 != 0: devide_points = [50*i for i in range(lane_lenth//50+1)] else: devide_points = [50*i for i in range(lane_lenth//50)] devide_points.append(lane_lenth-1) lane_sample_points_tmp = points[devide_points] lane_sample_points.append(lane_sample_points_tmp) map_infos[town_name]['lane_points'] = lane_points map_infos[town_name]['lane_sample_points'] = lane_sample_points map_infos[town_name]['lane_types'] = lane_types map_infos[town_name]['trigger_volumes_points'] = trigger_volumes_points map_infos[town_name]['trigger_volumes_sample_points'] = trigger_volumes_sample_points map_infos[town_name]['trigger_volumes_types'] = trigger_volumes_types with open(join(OUT_DIR,'b2d_map_infos.pkl'),'wb') as f: pickle.dump(map_infos,f) return map_infos def preprocess(folder_list,idx,tmp_dir,train_or_val,map_infos): data_root = DATAROOT cameras = CAMERAS final_data = [] if idx == 0: folders = tqdm(folder_list) else: folders = folder_list for folder_name in folders: print(folder_name) folder_path = join(data_root, folder_name) last_position_dict = {} for ann_name in sorted(os.listdir(join(folder_path,'anno')),key= lambda x: int(x.split('.')[0])): if idx == 0: print(ann_name) position_dict = {} frame_data = {} cam_gray_depth = {} with gzip.open(join(folder_path,'anno',ann_name), 'rt', encoding='utf-8') as gz_file: anno = json.load(gz_file) frame_data['folder'] = folder_name frame_data['town_name'] = folder_name.split('/')[1].split('_')[1] frame_data['command_far_xy'] = np.array([anno['x_command_far'],-anno['y_command_far']]) frame_data['command_far'] = anno['command_far'] frame_data['command_near_xy'] = np.array([anno['x_command_near'],-anno['y_command_near']]) frame_data['command_near'] = anno['command_near'] frame_data['frame_idx'] = int(ann_name.split('.')[0]) frame_data['timestamp'] = int(ann_name.split('.')[0]) / 10 * 1e6 # consistent with nusc frame_data['token'] = folder_name + '_' + str(int(ann_name.split('.')[0])).zfill(4) frame_data['ego_yaw'] = -np.nan_to_num(anno['theta'],nan=np.pi)+np.pi/2 frame_data['ego_translation'] = np.array([anno['x'],-anno['y'],0]) frame_data['ego_vel'] = np.array([anno['speed'],0,0]) frame_data['ego_accel'] = np.array([anno['acceleration'][0],-anno['acceleration'][1],anno['acceleration'][2]]) frame_data['ego_rotation_rate'] = -np.array(anno['angular_velocity']) frame_data['ego_size'] = np.array([anno['bounding_boxes'][0]['extent'][1],anno['bounding_boxes'][0]['extent'][0],anno['bounding_boxes'][0]['extent'][2]])*2 world2ego = left2right @ anno['bounding_boxes'][0]['world2ego'] @ left2right frame_data['world2ego'] = world2ego if frame_data['frame_idx'] == 0: expert_file_path = join(folder_path,'expert_assessment','-0001.npz') else: expert_file_path = join(folder_path,'expert_assessment',str(frame_data['frame_idx']-1).zfill(5)+'.npz') expert_data = np.load(expert_file_path,allow_pickle=True)['arr_0'] action_id = expert_data[-1] # value = expert_data[-2] # expert_feature = expert_data[:-2] throttle, steer, brake = get_action(action_id) frame_data['brake'] = brake frame_data['throttle'] = throttle frame_data['steer'] = steer #frame_data['action_id'] = action_id #frame_data['value'] = value #frame_data['expert_feature'] = expert_feature ###get sensor infos### sensor_infos = {} for cam in CAMERAS: sensor_infos[cam] = {} sensor_infos[cam]['cam2ego'] = left2right @ np.array(anno['sensors'][cam]['cam2ego']) @ stand_to_ue4_rotate sensor_infos[cam]['intrinsic'] = np.array(anno['sensors'][cam]['intrinsic']) sensor_infos[cam]['world2cam'] = np.linalg.inv(stand_to_ue4_rotate) @ np.array(anno['sensors'][cam]['world2cam']) @left2right sensor_infos[cam]['data_path'] = join(folder_name,'camera',CAMERA_TO_FOLDER_MAP[cam],ann_name.split('.')[0]+'.jpg') cam_gray_depth[cam] = cv2.imread(join(data_root,sensor_infos[cam]['data_path']).replace('rgb_','depth_').replace('.jpg','.png'))[:,:,0] sensor_infos['LIDAR_TOP'] = {} sensor_infos['LIDAR_TOP']['lidar2ego'] = left2right @ np.array(anno['sensors']['LIDAR_TOP']['lidar2ego']) @ left2right @ lidar_to_righthand_ego world2lidar = lefthand_ego_to_lidar @ np.array(anno['sensors']['LIDAR_TOP']['world2lidar']) @ left2right sensor_infos['LIDAR_TOP']['world2lidar'] = world2lidar frame_data['sensors'] = sensor_infos map_annos = get_map_anno(frame_data, map_infos) frame_data["map_annos"] = map_annos ###get bounding_boxes infos### gt_boxes = [] gt_names = [] gt_ids = [] num_points_list = [] npc2world_list = [] for npc in anno['bounding_boxes']: if npc['class'] == 'ego_vehicle': continue if npc['distance'] > MAX_DISTANCE: continue if abs(npc['location'][2] - anno['bounding_boxes'][0]['location'][2]) > FILTER_Z_SHRESHOLD: continue center = np.array([npc['center'][0],-npc['center'][1],npc['center'][2]]) # left hand -> right hand # extent = np.array([npc['extent'][1],npc['extent'][0],npc['extent'][2]]) # lwh -> wlh extent = np.array([npc['extent'][0],npc['extent'][1],npc['extent'][2]]) # lwh position_dict[npc['id']] = center local_center = apply_trans(center, world2lidar) size = extent * 2 if 'world2vehicle' in npc.keys(): world2vehicle = left2right @ np.array(npc['world2vehicle'])@left2right vehicle2lidar = world2lidar @ np.linalg.inv(world2vehicle) yaw_local = np.arctan2(vehicle2lidar[1,0], vehicle2lidar[0,0]) else: yaw_local = -npc['rotation'][-1]/180*np.pi - frame_data['ego_yaw'] +np.pi / 2 # yaw_local_in_lidar_box = -yaw_local - np.pi / 2 yaw_local_in_lidar_box = yaw_local while yaw_local < -np.pi: yaw_local += 2*np.pi while yaw_local > np.pi: yaw_local -= 2*np.pi if 'speed' in npc.keys(): if 'vehicle' in npc['class']: # only vehicles have correct speed speed = npc['speed'] else: if npc['id'] in last_position_dict.keys(): #calculate speed for other object speed = np.linalg.norm((center-last_position_dict[npc['id']])[0:2]) * 10 else: speed = 0 else: speed = 0 if 'num_points' in npc.keys(): num_points = npc['num_points'] else: num_points = -1 npc2world = get_npc2world(npc) speed_x = speed * np.cos(yaw_local) speed_y = speed * np.sin(yaw_local) ###fliter_bounding_boxes### if FILTER_INVISINLE: valid = False box2lidar = np.eye(4) box2lidar[0:3,0:3] = Quaternion(axis=[0, 0, 1], radians=yaw_local).rotation_matrix box2lidar[0:3,3] = local_center lidar2box = np.linalg.inv(box2lidar) raw_verts = calculate_cube_vertices(local_center,extent) verts = [] for raw_vert in raw_verts: tmp = np.dot(lidar2box, [raw_vert[0], raw_vert[1], raw_vert[2],1]) tmp[0:3] += local_center verts.append(tmp.tolist()[:-1]) for cam in cameras: lidar2cam = np.linalg.inv(frame_data['sensors'][cam]['cam2ego']) @ sensor_infos['LIDAR_TOP']['lidar2ego'] test_points = [] test_depth = [] for vert in verts: point, depth = get_image_point(vert, frame_data['sensors'][cam]['intrinsic'], lidar2cam) if depth > 0: test_points.append(point) test_depth.append(depth) num_visible_vertices, num_invisible_vertices, num_vertices_outside_camera, colored_points = calculate_occlusion_stats(np.array(test_points), np.array(test_depth), cam_gray_depth[cam], max_render_depth=MAX_DISTANCE) if num_visible_vertices>NUM_VISIBLE_SHRESHOLD and num_vertices_outside_camerapoint_cloud_range[0]) & (points_in_lidar[:,0]point_cloud_range[1]) & (points_in_lidar[:,1] 1: label = MAP_CLASSES.index(lane_types[idx]) line = LineString(points_in_lidar_range).simplify(0.2, preserve_topology=True) line = np.array(line.coords) map_anno[label].append(line) for idx in range(len(trigger_volumes_points)): if not trigger_volumes_types[idx] in MAP_CLASSES: continue points = trigger_volumes_points[idx] points = np.concatenate([points,np.ones((points.shape[0],1))],axis=-1) points_in_lidar = (world2lidar @ points.T).T mask = (points_in_lidar[:,0]>point_cloud_range[0]) & (points_in_lidar[:,0]point_cloud_range[1]) & (points_in_lidar[:,1] 0 and utime - utimes[i-1] < utimes[i] - utime): i -= 1 return i def geom2anno(map_geoms): MAP_CLASSES = ( 'ped_crossing', 'divider', 'boundary', ) vectors = {} for cls, geom_list in map_geoms.items(): if cls in MAP_CLASSES: label = MAP_CLASSES.index(cls) vectors[label] = [] for geom in geom_list: line = np.array(geom.coords) vectors[label].append(line) return vectors def create_nuscenes_infos(root_path, out_path, can_bus_root_path, info_prefix, version='v1.0-trainval', max_sweeps=10, roi_size=(30, 60),): """Create info file of nuscene dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of the data root. info_prefix (str): Prefix of the info file to be generated. version (str): Version of the data. Default: 'v1.0-trainval' max_sweeps (int): Max number of sweeps. Default: 10 """ print(version, root_path) nusc = NuScenes(version=version, dataroot=root_path, verbose=True) nusc_map_extractor = NuscMapExtractor(root_path, roi_size) nusc_can_bus = NuScenesCanBus(dataroot=can_bus_root_path) from nuscenes.utils import splits available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini'] assert version in available_vers if version == 'v1.0-trainval': train_scenes = splits.train val_scenes = splits.val elif version == 'v1.0-test': train_scenes = splits.test val_scenes = [] elif version == 'v1.0-mini': train_scenes = splits.mini_train val_scenes = splits.mini_val out_path = osp.join(out_path, 'mini') else: raise ValueError('unknown') os.makedirs(out_path, exist_ok=True) # filter existing scenes. available_scenes = get_available_scenes(nusc) available_scene_names = [s['name'] for s in available_scenes] train_scenes = list( filter(lambda x: x in available_scene_names, train_scenes)) val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes)) train_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in train_scenes ]) val_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in val_scenes ]) test = 'test' in version if test: print('test scene: {}'.format(len(train_scenes))) else: print('train scene: {}, val scene: {}'.format( len(train_scenes), len(val_scenes))) train_nusc_infos, val_nusc_infos = _fill_trainval_infos( nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test, max_sweeps=max_sweeps) metadata = dict(version=version) if test: print('test sample: {}'.format(len(train_nusc_infos))) data = dict(infos=train_nusc_infos, metadata=metadata) info_path = osp.join(out_path, '{}_infos_test.pkl'.format(info_prefix)) mmcv.dump(data, info_path) else: print('train sample: {}, val sample: {}'.format( len(train_nusc_infos), len(val_nusc_infos))) data = dict(infos=train_nusc_infos, metadata=metadata) info_path = osp.join(out_path, '{}_infos_train.pkl'.format(info_prefix)) mmcv.dump(data, info_path) data['infos'] = val_nusc_infos info_val_path = osp.join(out_path, '{}_infos_val.pkl'.format(info_prefix)) mmcv.dump(data, info_val_path) def get_available_scenes(nusc): """Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation. Args: nusc (class): Dataset class in the nuScenes dataset. Returns: available_scenes (list[dict]): List of basic information for the available scenes. """ available_scenes = [] print('total scene num: {}'.format(len(nusc.scene))) for scene in nusc.scene: scene_token = scene['token'] scene_rec = nusc.get('scene', scene_token) sample_rec = nusc.get('sample', scene_rec['first_sample_token']) sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP']) has_more_frames = True scene_not_exist = False while has_more_frames: lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token']) lidar_path = str(lidar_path) if os.getcwd() in lidar_path: # path from lyftdataset is absolute path lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1] # relative path if not mmcv.is_filepath(lidar_path): scene_not_exist = True break else: break if scene_not_exist: continue available_scenes.append(scene) print('exist scene num: {}'.format(len(available_scenes))) return available_scenes def _fill_trainval_infos(nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test=False, max_sweeps=10, fut_ts=12, ego_fut_ts=6): """Generate the train/val infos from the raw data. Args: nusc (:obj:`NuScenes`): Dataset class in the nuScenes dataset. train_scenes (list[str]): Basic information of training scenes. val_scenes (list[str]): Basic information of validation scenes. test (bool): Whether use the test mode. In the test mode, no annotations can be accessed. Default: False. max_sweeps (int): Max number of sweeps. Default: 10. Returns: tuple[list[dict]]: Information of training set and validation set that will be saved to the info file. """ train_nusc_infos = [] val_nusc_infos = [] cat2idx = {} for idx, dic in enumerate(nusc.category): cat2idx[dic['name']] = idx predict_helper = PredictHelper(nusc) for sample in mmcv.track_iter_progress(nusc.sample): map_location = nusc.get('log', nusc.get('scene', sample['scene_token'])['log_token'])['location'] lidar_token = sample['data']['LIDAR_TOP'] sd_rec = nusc.get('sample_data', lidar_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) lidar_path, boxes, _ = nusc.get_sample_data(lidar_token) mmcv.check_file_exist(lidar_path) info = { 'lidar_path': lidar_path, 'token': sample['token'], 'sweeps': [], 'cams': dict(), 'scene_token': sample['scene_token'], 'lidar2ego_translation': cs_record['translation'], 'lidar2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sample['timestamp'], 'map_location': map_location, } l2e_r = info['lidar2ego_rotation'] l2e_t = info['lidar2ego_translation'] e2g_r = info['ego2global_rotation'] e2g_t = info['ego2global_translation'] l2e_r_mat = Quaternion(l2e_r).rotation_matrix e2g_r_mat = Quaternion(e2g_r).rotation_matrix # extract map annos lidar2ego = np.eye(4) lidar2ego[:3, :3] = Quaternion( info["lidar2ego_rotation"] ).rotation_matrix lidar2ego[:3, 3] = np.array(info["lidar2ego_translation"]) ego2global = np.eye(4) ego2global[:3, :3] = Quaternion( info["ego2global_rotation"] ).rotation_matrix ego2global[:3, 3] = np.array(info["ego2global_translation"]) lidar2global = ego2global @ lidar2ego translation = list(lidar2global[:3, 3]) rotation = list(Quaternion(matrix=lidar2global).q) map_geoms = nusc_map_extractor.get_map_geom(map_location, translation, rotation) map_annos = geom2anno(map_geoms) info['map_annos'] = map_annos # obtain 6 image's information per frame camera_types = [ 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT', ] for cam in camera_types: cam_token = sample['data'][cam] cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token) cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam) cam_info.update(cam_intrinsic=cam_intrinsic) info['cams'].update({cam: cam_info}) # obtain sweeps for a single key-frame sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP']) sweeps = [] while len(sweeps) < max_sweeps: if not sd_rec['prev'] == '': sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, 'lidar') sweeps.append(sweep) sd_rec = nusc.get('sample_data', sd_rec['prev']) else: break info['sweeps'] = sweeps # obtain annotation if not test: # object detection annos: boxes (locs, dims, yaw, velocity), names and valid flags annotations = [ nusc.get('sample_annotation', token) for token in sample['anns'] ] locs = np.array([b.center for b in boxes]).reshape(-1, 3) dims = np.array([b.wlh for b in boxes]).reshape(-1, 3) rots = np.array([b.orientation.yaw_pitch_roll[0] for b in boxes]).reshape(-1, 1) velocity = np.array( [nusc.box_velocity(token)[:2] for token in sample['anns']]) # convert velo from global to lidar for i in range(len(boxes)): velo = np.array([*velocity[i], 0.0]) velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv( l2e_r_mat).T velocity[i] = velo[:2] names = [b.name for b in boxes] for i in range(len(names)): if names[i] in NameMapping: names[i] = NameMapping[names[i]] names = np.array(names) valid_flag = np.array( [(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0 for anno in annotations], dtype=bool).reshape(-1) ## TODO update valid flag for tracking # we need to convert box size to # the format of our lidar coordinate system # which is x_size, y_size, z_size (corresponding to l, w, h) gt_boxes = np.concatenate([locs, dims[:, [1, 0, 2]], rots], axis=1) assert len(gt_boxes) == len( annotations), f'{len(gt_boxes)}, {len(annotations)}' # object tracking annos: instance_ids instance_inds = [nusc.getind('instance', anno['instance_token']) for anno in annotations] # motion prediction annos: future trajectories offset in lidar frame and valid mask num_box = len(boxes) gt_fut_trajs = np.zeros((num_box, fut_ts, 2)) gt_fut_masks = np.zeros((num_box, fut_ts)) for i, anno in enumerate(annotations): instance_token = anno['instance_token'] fut_traj_local = predict_helper.get_future_for_agent( instance_token, sample['token'], seconds=fut_ts/2, in_agent_frame=True ) if fut_traj_local.shape[0] > 0: box = boxes[i] trans = box.center rot = Quaternion(matrix=box.rotation_matrix) fut_traj_scene = convert_local_coords_to_global(fut_traj_local, trans, rot) valid_step = fut_traj_scene.shape[0] gt_fut_trajs[i, 0] = fut_traj_scene[0] - box.center[:2] gt_fut_trajs[i, 1:valid_step] = fut_traj_scene[1:] - fut_traj_scene[:-1] gt_fut_masks[i, :valid_step] = 1 # motion planning annos: future trajectories offset in lidar frame and valid mask ego_fut_trajs = np.zeros((ego_fut_ts + 1, 3)) ego_fut_masks = np.zeros((ego_fut_ts + 1)) sample_cur = sample ego_status = get_ego_status(nusc, nusc_can_bus, sample_cur) for i in range(ego_fut_ts + 1): pose_mat = get_global_sensor_pose(sample_cur, nusc) ego_fut_trajs[i] = pose_mat[:3, 3] ego_fut_masks[i] = 1 if sample_cur['next'] == '': ego_fut_trajs[i+1:] = ego_fut_trajs[i] break else: sample_cur = nusc.get('sample', sample_cur['next']) # global to ego ego_fut_trajs = ego_fut_trajs - np.array(pose_record['translation']) rot_mat = Quaternion(pose_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # ego to lidar ego_fut_trajs = ego_fut_trajs - np.array(cs_record['translation']) rot_mat = Quaternion(cs_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # drive command according to final fut step offset if ego_fut_trajs[-1][0] >= 2: command = np.array([1, 0, 0]) # Turn Right elif ego_fut_trajs[-1][0] <= -2: command = np.array([0, 1, 0]) # Turn Left else: command = np.array([0, 0, 1]) # Go Straight # get offset ego_fut_trajs = ego_fut_trajs[1:] - ego_fut_trajs[:-1] info['gt_boxes'] = gt_boxes info['gt_names'] = names info['gt_velocity'] = velocity.reshape(-1, 2) info['num_lidar_pts'] = np.array( [a['num_lidar_pts'] for a in annotations]) info['num_radar_pts'] = np.array( [a['num_radar_pts'] for a in annotations]) info['valid_flag'] = valid_flag info['instance_inds'] = instance_inds info['gt_agent_fut_trajs'] = gt_fut_trajs.astype(np.float32) info['gt_agent_fut_masks'] = gt_fut_masks.astype(np.float32) info['gt_ego_fut_trajs'] = ego_fut_trajs[:, :2].astype(np.float32) info['gt_ego_fut_masks'] = ego_fut_masks[1:].astype(np.float32) info['gt_ego_fut_cmd'] = command.astype(np.float32) info['ego_status'] = ego_status if sample['scene_token'] in train_scenes: train_nusc_infos.append(info) else: val_nusc_infos.append(info) return train_nusc_infos, val_nusc_infos def get_ego_status(nusc, nusc_can_bus, sample): ego_status = [] ref_scene = nusc.get("scene", sample['scene_token']) try: pose_msgs = nusc_can_bus.get_messages(ref_scene['name'],'pose') steer_msgs = nusc_can_bus.get_messages(ref_scene['name'], 'steeranglefeedback') pose_uts = [msg['utime'] for msg in pose_msgs] steer_uts = [msg['utime'] for msg in steer_msgs] ref_utime = sample['timestamp'] pose_index = locate_message(pose_uts, ref_utime) pose_data = pose_msgs[pose_index] steer_index = locate_message(steer_uts, ref_utime) steer_data = steer_msgs[steer_index] ego_status.extend(pose_data["accel"]) # acceleration in ego vehicle frame, m/s/s ego_status.extend(pose_data["rotation_rate"]) # angular velocity in ego vehicle frame, rad/s ego_status.extend(pose_data["vel"]) # velocity in ego vehicle frame, m/s ego_status.append(steer_data["value"]) # steering angle, positive: left turn, negative: right turn except: ego_status = [0] * 10 return np.array(ego_status).astype(np.float32) def get_global_sensor_pose(rec, nusc): lidar_sample_data = nusc.get('sample_data', rec['data']['LIDAR_TOP']) pose_record = nusc.get("ego_pose", lidar_sample_data["ego_pose_token"]) cs_record = nusc.get("calibrated_sensor", lidar_sample_data["calibrated_sensor_token"]) ego2global = transform_matrix(pose_record["translation"], Quaternion(pose_record["rotation"]), inverse=False) sensor2ego = transform_matrix(cs_record["translation"], Quaternion(cs_record["rotation"]), inverse=False) pose = ego2global.dot(sensor2ego) return pose def obtain_sensor2top(nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type='lidar'): """Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. sensor_token (str): Sample data token corresponding to the specific sensor type. l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3). l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego in shape (3, 3). e2g_t (np.ndarray): Translation from ego to global in shape (1, 3). e2g_r_mat (np.ndarray): Rotation matrix from ego to global in shape (3, 3). sensor_type (str): Sensor to calibrate. Default: 'lidar'. Returns: sweep (dict): Sweep information after transformation. """ sd_rec = nusc.get('sample_data', sensor_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) data_path = str(nusc.get_sample_data_path(sd_rec['token'])) if os.getcwd() in data_path: # path from lyftdataset is absolute path data_path = data_path.split(f'{os.getcwd()}/')[-1] # relative path sweep = { 'data_path': data_path, 'type': sensor_type, 'sample_data_token': sd_rec['token'], 'sensor2ego_translation': cs_record['translation'], 'sensor2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sd_rec['timestamp'] } l2e_r_s = sweep['sensor2ego_rotation'] l2e_t_s = sweep['sensor2ego_translation'] e2g_r_s = sweep['ego2global_rotation'] e2g_t_s = sweep['ego2global_translation'] # obtain the RT from sensor to Top LiDAR # sweep->ego->global->ego'->lidar l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T ) + l2e_t @ np.linalg.inv(l2e_r_mat).T sweep['sensor2lidar_rotation'] = R.T # points @ R.T + T sweep['sensor2lidar_translation'] = T return sweep def nuscenes_data_prep(root_path, can_bus_root_path, info_prefix, version, dataset_name, out_dir, max_sweeps=10): """Prepare data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth database. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. version (str): Dataset version. dataset_name (str): The dataset class name. out_dir (str): Output directory of the groundtruth database info. max_sweeps (int): Number of input consecutive frames. Default: 10 """ create_nuscenes_infos( root_path, out_dir, can_bus_root_path, info_prefix, version=version, max_sweeps=max_sweeps) parser = argparse.ArgumentParser(description='Data converter arg parser') parser.add_argument('dataset', metavar='kitti', help='name of the dataset') parser.add_argument( '--root-path', type=str, default='./data/kitti', help='specify the root path of dataset') parser.add_argument( '--canbus', type=str, default='./data', help='specify the root path of nuScenes canbus') parser.add_argument( '--version', type=str, default='v1.0', required=False, help='specify the dataset version, no need for kitti') parser.add_argument( '--max-sweeps', type=int, default=10, required=False, help='specify sweeps of lidar per example') parser.add_argument( '--out-dir', type=str, default='./data/kitti', required='False', help='name of info pkl') parser.add_argument('--extra-tag', type=str, default='kitti') parser.add_argument( '--workers', type=int, default=4, help='number of threads to be used') args = parser.parse_args() if __name__ == '__main__': if args.dataset == 'nuscenes' and args.version != 'v1.0-mini': train_version = f'{args.version}-trainval' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=train_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) test_version = f'{args.version}-test' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=test_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) elif args.dataset == 'nuscenes' and args.version == 'v1.0-mini': train_version = f'{args.version}' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=train_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/data_converter/nuscenes_converter_1_10.py ================================================ import os import math import copy import argparse from os import path as osp from collections import OrderedDict from typing import List, Tuple, Union import numpy as np from pyquaternion import Quaternion from shapely.geometry import MultiPoint, box import mmcv from nuscenes.nuscenes import NuScenes from nuscenes.can_bus.can_bus_api import NuScenesCanBus from nuscenes.utils.geometry_utils import transform_matrix from nuscenes.utils.data_classes import Box from nuscenes.utils.geometry_utils import view_points from nuscenes.prediction import PredictHelper, convert_local_coords_to_global from projects.mmdet3d_plugin.datasets.map_utils.nuscmap_extractor import NuscMapExtractor NameMapping = { "movable_object.barrier": "barrier", "vehicle.bicycle": "bicycle", "vehicle.bus.bendy": "bus", "vehicle.bus.rigid": "bus", "vehicle.car": "car", "vehicle.construction": "construction_vehicle", "vehicle.motorcycle": "motorcycle", "human.pedestrian.adult": "pedestrian", "human.pedestrian.child": "pedestrian", "human.pedestrian.construction_worker": "pedestrian", "human.pedestrian.police_officer": "pedestrian", "movable_object.trafficcone": "traffic_cone", "vehicle.trailer": "trailer", "vehicle.truck": "truck", } def quart_to_rpy(qua): x, y, z, w = qua roll = math.atan2(2 * (w * x + y * z), 1 - 2 * (x * x + y * y)) pitch = math.asin(2 * (w * y - x * z)) yaw = math.atan2(2 * (w * z + x * y), 1 - 2 * (z * z + y * y)) return roll, pitch, yaw def locate_message(utimes, utime): i = np.searchsorted(utimes, utime) if i == len(utimes) or (i > 0 and utime - utimes[i-1] < utimes[i] - utime): i -= 1 return i def geom2anno(map_geoms): MAP_CLASSES = ( 'ped_crossing', 'divider', 'boundary', ) vectors = {} for cls, geom_list in map_geoms.items(): if cls in MAP_CLASSES: label = MAP_CLASSES.index(cls) vectors[label] = [] for geom in geom_list: line = np.array(geom.coords) vectors[label].append(line) return vectors def create_nuscenes_infos(root_path, out_path, can_bus_root_path, info_prefix, version='v1.0-trainval', max_sweeps=10, roi_size=(30, 60),): """Create info file of nuscene dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of the data root. info_prefix (str): Prefix of the info file to be generated. version (str): Version of the data. Default: 'v1.0-trainval' max_sweeps (int): Max number of sweeps. Default: 10 """ print(version, root_path) nusc = NuScenes(version=version, dataroot=root_path, verbose=True) nusc_map_extractor = NuscMapExtractor(root_path, roi_size) nusc_can_bus = NuScenesCanBus(dataroot=can_bus_root_path) from nuscenes.utils import splits available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini'] assert version in available_vers if version == 'v1.0-trainval': train_scenes = splits.train import random random.shuffle(train_scenes) train_scenes = train_scenes[:int(len(train_scenes)*0.1)] # 0.2 为 1/5；0.5为 1/2 以此类推 val_scenes = splits.val elif version == 'v1.0-test': train_scenes = splits.test val_scenes = [] elif version == 'v1.0-mini': train_scenes = splits.mini_train val_scenes = splits.mini_val out_path = osp.join(out_path, 'mini') else: raise ValueError('unknown') os.makedirs(out_path, exist_ok=True) # filter existing scenes. available_scenes = get_available_scenes(nusc) available_scene_names = [s['name'] for s in available_scenes] train_scenes = list( filter(lambda x: x in available_scene_names, train_scenes)) val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes)) train_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in train_scenes ]) val_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in val_scenes ]) # import pdb; pdb.set_trace() test = 'test' in version if test: print('test scene: {}'.format(len(train_scenes))) else: print('train scene: {}, val scene: {}'.format( len(train_scenes), len(val_scenes))) train_nusc_infos, val_nusc_infos = _fill_trainval_infos( nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test, max_sweeps=max_sweeps) metadata = dict(version=version) if test: pass # print('test sample: {}'.format(len(train_nusc_infos))) # data = dict(infos=train_nusc_infos, metadata=metadata) # info_path = osp.join(out_path, # '{}_infos_test.pkl'.format(info_prefix)) # mmcv.dump(data, info_path) else: print('train sample: {}, val sample: {}'.format( len(train_nusc_infos), len(val_nusc_infos))) data = dict(infos=train_nusc_infos, metadata=metadata) info_path = osp.join(out_path, '{}_infos_1_10_train.pkl'.format(info_prefix)) mmcv.dump(data, info_path) # data['infos'] = val_nusc_infos # info_val_path = osp.join(out_path, # '{}_infos_val.pkl'.format(info_prefix)) # mmcv.dump(data, info_val_path) def get_available_scenes(nusc): """Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation. Args: nusc (class): Dataset class in the nuScenes dataset. Returns: available_scenes (list[dict]): List of basic information for the available scenes. """ available_scenes = [] print('total scene num: {}'.format(len(nusc.scene))) for scene in nusc.scene: scene_token = scene['token'] scene_rec = nusc.get('scene', scene_token) sample_rec = nusc.get('sample', scene_rec['first_sample_token']) sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP']) has_more_frames = True scene_not_exist = False while has_more_frames: lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token']) lidar_path = str(lidar_path) if os.getcwd() in lidar_path: # path from lyftdataset is absolute path lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1] # relative path if not mmcv.is_filepath(lidar_path): scene_not_exist = True break else: break if scene_not_exist: continue available_scenes.append(scene) print('exist scene num: {}'.format(len(available_scenes))) return available_scenes def _fill_trainval_infos(nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test=False, max_sweeps=10, fut_ts=12, ego_fut_ts=6): """Generate the train/val infos from the raw data. Args: nusc (:obj:`NuScenes`): Dataset class in the nuScenes dataset. train_scenes (list[str]): Basic information of training scenes. val_scenes (list[str]): Basic information of validation scenes. test (bool): Whether use the test mode. In the test mode, no annotations can be accessed. Default: False. max_sweeps (int): Max number of sweeps. Default: 10. Returns: tuple[list[dict]]: Information of training set and validation set that will be saved to the info file. """ train_nusc_infos = [] val_nusc_infos = [] cat2idx = {} for idx, dic in enumerate(nusc.category): cat2idx[dic['name']] = idx # import pdb; pdb.set_trace() predict_helper = PredictHelper(nusc) trainval_samples=[] for sample in mmcv.track_iter_progress(nusc.sample): if sample['scene_token'] in train_scenes: trainval_samples.append(sample) # import pdb; pdb.set_trace() for sample in mmcv.track_iter_progress(trainval_samples): map_location = nusc.get('log', nusc.get('scene', sample['scene_token'])['log_token'])['location'] lidar_token = sample['data']['LIDAR_TOP'] sd_rec = nusc.get('sample_data', lidar_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) lidar_path, boxes, _ = nusc.get_sample_data(lidar_token) mmcv.check_file_exist(lidar_path) info = { 'lidar_path': lidar_path, 'token': sample['token'], 'sweeps': [], 'cams': dict(), 'scene_token': sample['scene_token'], 'lidar2ego_translation': cs_record['translation'], 'lidar2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sample['timestamp'], 'map_location': map_location, } l2e_r = info['lidar2ego_rotation'] l2e_t = info['lidar2ego_translation'] e2g_r = info['ego2global_rotation'] e2g_t = info['ego2global_translation'] l2e_r_mat = Quaternion(l2e_r).rotation_matrix e2g_r_mat = Quaternion(e2g_r).rotation_matrix # extract map annos lidar2ego = np.eye(4) lidar2ego[:3, :3] = Quaternion( info["lidar2ego_rotation"] ).rotation_matrix lidar2ego[:3, 3] = np.array(info["lidar2ego_translation"]) ego2global = np.eye(4) ego2global[:3, :3] = Quaternion( info["ego2global_rotation"] ).rotation_matrix ego2global[:3, 3] = np.array(info["ego2global_translation"]) lidar2global = ego2global @ lidar2ego translation = list(lidar2global[:3, 3]) rotation = list(Quaternion(matrix=lidar2global).q) map_geoms = nusc_map_extractor.get_map_geom(map_location, translation, rotation) map_annos = geom2anno(map_geoms) info['map_annos'] = map_annos # obtain 6 image's information per frame camera_types = [ 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT', ] for cam in camera_types: cam_token = sample['data'][cam] cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token) cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam) cam_info.update(cam_intrinsic=cam_intrinsic) info['cams'].update({cam: cam_info}) # obtain sweeps for a single key-frame sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP']) sweeps = [] while len(sweeps) < max_sweeps: if not sd_rec['prev'] == '': sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, 'lidar') sweeps.append(sweep) sd_rec = nusc.get('sample_data', sd_rec['prev']) else: break info['sweeps'] = sweeps # obtain annotation if not test: # object detection annos: boxes (locs, dims, yaw, velocity), names and valid flags annotations = [ nusc.get('sample_annotation', token) for token in sample['anns'] ] locs = np.array([b.center for b in boxes]).reshape(-1, 3) dims = np.array([b.wlh for b in boxes]).reshape(-1, 3) rots = np.array([b.orientation.yaw_pitch_roll[0] for b in boxes]).reshape(-1, 1) velocity = np.array( [nusc.box_velocity(token)[:2] for token in sample['anns']]) # convert velo from global to lidar for i in range(len(boxes)): velo = np.array([*velocity[i], 0.0]) velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv( l2e_r_mat).T velocity[i] = velo[:2] names = [b.name for b in boxes] for i in range(len(names)): if names[i] in NameMapping: names[i] = NameMapping[names[i]] names = np.array(names) valid_flag = np.array( [(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0 for anno in annotations], dtype=bool).reshape(-1) ## TODO update valid flag for tracking # we need to convert box size to # the format of our lidar coordinate system # which is x_size, y_size, z_size (corresponding to l, w, h) gt_boxes = np.concatenate([locs, dims[:, [1, 0, 2]], rots], axis=1) assert len(gt_boxes) == len( annotations), f'{len(gt_boxes)}, {len(annotations)}' # object tracking annos: instance_ids instance_inds = [nusc.getind('instance', anno['instance_token']) for anno in annotations] # motion prediction annos: future trajectories offset in lidar frame and valid mask num_box = len(boxes) gt_fut_trajs = np.zeros((num_box, fut_ts, 2)) gt_fut_masks = np.zeros((num_box, fut_ts)) for i, anno in enumerate(annotations): instance_token = anno['instance_token'] fut_traj_local = predict_helper.get_future_for_agent( instance_token, sample['token'], seconds=fut_ts/2, in_agent_frame=True ) if fut_traj_local.shape[0] > 0: box = boxes[i] trans = box.center rot = Quaternion(matrix=box.rotation_matrix) fut_traj_scene = convert_local_coords_to_global(fut_traj_local, trans, rot) valid_step = fut_traj_scene.shape[0] gt_fut_trajs[i, 0] = fut_traj_scene[0] - box.center[:2] gt_fut_trajs[i, 1:valid_step] = fut_traj_scene[1:] - fut_traj_scene[:-1] gt_fut_masks[i, :valid_step] = 1 # motion planning annos: future trajectories offset in lidar frame and valid mask ego_fut_trajs = np.zeros((ego_fut_ts + 1, 3)) ego_fut_masks = np.zeros((ego_fut_ts + 1)) sample_cur = sample ego_status = get_ego_status(nusc, nusc_can_bus, sample_cur) for i in range(ego_fut_ts + 1): pose_mat = get_global_sensor_pose(sample_cur, nusc) ego_fut_trajs[i] = pose_mat[:3, 3] ego_fut_masks[i] = 1 if sample_cur['next'] == '': ego_fut_trajs[i+1:] = ego_fut_trajs[i] break else: sample_cur = nusc.get('sample', sample_cur['next']) # global to ego ego_fut_trajs = ego_fut_trajs - np.array(pose_record['translation']) rot_mat = Quaternion(pose_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # ego to lidar ego_fut_trajs = ego_fut_trajs - np.array(cs_record['translation']) rot_mat = Quaternion(cs_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # drive command according to final fut step offset if ego_fut_trajs[-1][0] >= 2: command = np.array([1, 0, 0]) # Turn Right elif ego_fut_trajs[-1][0] <= -2: command = np.array([0, 1, 0]) # Turn Left else: command = np.array([0, 0, 1]) # Go Straight # get offset ego_fut_trajs = ego_fut_trajs[1:] - ego_fut_trajs[:-1] info['gt_boxes'] = gt_boxes info['gt_names'] = names info['gt_velocity'] = velocity.reshape(-1, 2) info['num_lidar_pts'] = np.array( [a['num_lidar_pts'] for a in annotations]) info['num_radar_pts'] = np.array( [a['num_radar_pts'] for a in annotations]) info['valid_flag'] = valid_flag info['instance_inds'] = instance_inds info['gt_agent_fut_trajs'] = gt_fut_trajs.astype(np.float32) info['gt_agent_fut_masks'] = gt_fut_masks.astype(np.float32) info['gt_ego_fut_trajs'] = ego_fut_trajs[:, :2].astype(np.float32) info['gt_ego_fut_masks'] = ego_fut_masks[1:].astype(np.float32) info['gt_ego_fut_cmd'] = command.astype(np.float32) info['ego_status'] = ego_status if sample['scene_token'] in train_scenes: train_nusc_infos.append(info) else: val_nusc_infos.append(info) return train_nusc_infos, val_nusc_infos def get_ego_status(nusc, nusc_can_bus, sample): ego_status = [] ref_scene = nusc.get("scene", sample['scene_token']) try: pose_msgs = nusc_can_bus.get_messages(ref_scene['name'],'pose') steer_msgs = nusc_can_bus.get_messages(ref_scene['name'], 'steeranglefeedback') pose_uts = [msg['utime'] for msg in pose_msgs] steer_uts = [msg['utime'] for msg in steer_msgs] ref_utime = sample['timestamp'] pose_index = locate_message(pose_uts, ref_utime) pose_data = pose_msgs[pose_index] steer_index = locate_message(steer_uts, ref_utime) steer_data = steer_msgs[steer_index] ego_status.extend(pose_data["accel"]) # acceleration in ego vehicle frame, m/s/s ego_status.extend(pose_data["rotation_rate"]) # angular velocity in ego vehicle frame, rad/s ego_status.extend(pose_data["vel"]) # velocity in ego vehicle frame, m/s ego_status.append(steer_data["value"]) # steering angle, positive: left turn, negative: right turn except: ego_status = [0] * 10 return np.array(ego_status).astype(np.float32) def get_global_sensor_pose(rec, nusc): lidar_sample_data = nusc.get('sample_data', rec['data']['LIDAR_TOP']) pose_record = nusc.get("ego_pose", lidar_sample_data["ego_pose_token"]) cs_record = nusc.get("calibrated_sensor", lidar_sample_data["calibrated_sensor_token"]) ego2global = transform_matrix(pose_record["translation"], Quaternion(pose_record["rotation"]), inverse=False) sensor2ego = transform_matrix(cs_record["translation"], Quaternion(cs_record["rotation"]), inverse=False) pose = ego2global.dot(sensor2ego) return pose def obtain_sensor2top(nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type='lidar'): """Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. sensor_token (str): Sample data token corresponding to the specific sensor type. l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3). l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego in shape (3, 3). e2g_t (np.ndarray): Translation from ego to global in shape (1, 3). e2g_r_mat (np.ndarray): Rotation matrix from ego to global in shape (3, 3). sensor_type (str): Sensor to calibrate. Default: 'lidar'. Returns: sweep (dict): Sweep information after transformation. """ sd_rec = nusc.get('sample_data', sensor_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) data_path = str(nusc.get_sample_data_path(sd_rec['token'])) if os.getcwd() in data_path: # path from lyftdataset is absolute path data_path = data_path.split(f'{os.getcwd()}/')[-1] # relative path sweep = { 'data_path': data_path, 'type': sensor_type, 'sample_data_token': sd_rec['token'], 'sensor2ego_translation': cs_record['translation'], 'sensor2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sd_rec['timestamp'] } l2e_r_s = sweep['sensor2ego_rotation'] l2e_t_s = sweep['sensor2ego_translation'] e2g_r_s = sweep['ego2global_rotation'] e2g_t_s = sweep['ego2global_translation'] # obtain the RT from sensor to Top LiDAR # sweep->ego->global->ego'->lidar l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T ) + l2e_t @ np.linalg.inv(l2e_r_mat).T sweep['sensor2lidar_rotation'] = R.T # points @ R.T + T sweep['sensor2lidar_translation'] = T return sweep def nuscenes_data_prep(root_path, can_bus_root_path, info_prefix, version, dataset_name, out_dir, max_sweeps=10): """Prepare data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth database. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. version (str): Dataset version. dataset_name (str): The dataset class name. out_dir (str): Output directory of the groundtruth database info. max_sweeps (int): Number of input consecutive frames. Default: 10 """ create_nuscenes_infos( root_path, out_dir, can_bus_root_path, info_prefix, version=version, max_sweeps=max_sweeps) parser = argparse.ArgumentParser(description='Data converter arg parser') parser.add_argument('dataset', metavar='kitti', help='name of the dataset') parser.add_argument( '--root-path', type=str, default='./data/kitti', help='specify the root path of dataset') parser.add_argument( '--canbus', type=str, default='./data', help='specify the root path of nuScenes canbus') parser.add_argument( '--version', type=str, default='v1.0', required=False, help='specify the dataset version, no need for kitti') parser.add_argument( '--max-sweeps', type=int, default=10, required=False, help='specify sweeps of lidar per example') parser.add_argument( '--out-dir', type=str, default='./data/kitti', required='False', help='name of info pkl') parser.add_argument('--extra-tag', type=str, default='kitti') parser.add_argument( '--workers', type=int, default=4, help='number of threads to be used') args = parser.parse_args() if __name__ == '__main__': if args.dataset == 'nuscenes' and args.version != 'v1.0-mini': train_version = f'{args.version}-trainval' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=train_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/data_converter/nuscenes_converter_6s.py ================================================ import os import math import copy import argparse from os import path as osp from collections import OrderedDict from typing import List, Tuple, Union import numpy as np from pyquaternion import Quaternion from shapely.geometry import MultiPoint, box import mmcv from nuscenes.nuscenes import NuScenes from nuscenes.can_bus.can_bus_api import NuScenesCanBus from nuscenes.utils.geometry_utils import transform_matrix from nuscenes.utils.data_classes import Box from nuscenes.utils.geometry_utils import view_points from nuscenes.prediction import PredictHelper, convert_local_coords_to_global from projects.mmdet3d_plugin.datasets.map_utils.nuscmap_extractor import NuscMapExtractor NameMapping = { "movable_object.barrier": "barrier", "vehicle.bicycle": "bicycle", "vehicle.bus.bendy": "bus", "vehicle.bus.rigid": "bus", "vehicle.car": "car", "vehicle.construction": "construction_vehicle", "vehicle.motorcycle": "motorcycle", "human.pedestrian.adult": "pedestrian", "human.pedestrian.child": "pedestrian", "human.pedestrian.construction_worker": "pedestrian", "human.pedestrian.police_officer": "pedestrian", "movable_object.trafficcone": "traffic_cone", "vehicle.trailer": "trailer", "vehicle.truck": "truck", } def quart_to_rpy(qua): x, y, z, w = qua roll = math.atan2(2 * (w * x + y * z), 1 - 2 * (x * x + y * y)) pitch = math.asin(2 * (w * y - x * z)) yaw = math.atan2(2 * (w * z + x * y), 1 - 2 * (z * z + y * y)) return roll, pitch, yaw def locate_message(utimes, utime): i = np.searchsorted(utimes, utime) if i == len(utimes) or (i > 0 and utime - utimes[i-1] < utimes[i] - utime): i -= 1 return i def geom2anno(map_geoms): MAP_CLASSES = ( 'ped_crossing', 'divider', 'boundary', ) vectors = {} for cls, geom_list in map_geoms.items(): if cls in MAP_CLASSES: label = MAP_CLASSES.index(cls) vectors[label] = [] for geom in geom_list: line = np.array(geom.coords) vectors[label].append(line) return vectors def create_nuscenes_infos(root_path, out_path, can_bus_root_path, info_prefix, version='v1.0-trainval', max_sweeps=10, roi_size=(30, 60),): """Create info file of nuscene dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of the data root. info_prefix (str): Prefix of the info file to be generated. version (str): Version of the data. Default: 'v1.0-trainval' max_sweeps (int): Max number of sweeps. Default: 10 """ print(version, root_path) nusc = NuScenes(version=version, dataroot=root_path, verbose=True) nusc_map_extractor = NuscMapExtractor(root_path, roi_size) nusc_can_bus = NuScenesCanBus(dataroot=can_bus_root_path) from nuscenes.utils import splits available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini'] assert version in available_vers if version == 'v1.0-trainval': train_scenes = splits.train val_scenes = splits.val elif version == 'v1.0-test': train_scenes = splits.test val_scenes = [] elif version == 'v1.0-mini': train_scenes = splits.mini_train val_scenes = splits.mini_val out_path = osp.join(out_path, 'mini') else: raise ValueError('unknown') os.makedirs(out_path, exist_ok=True) # filter existing scenes. available_scenes = get_available_scenes(nusc) available_scene_names = [s['name'] for s in available_scenes] train_scenes = list( filter(lambda x: x in available_scene_names, train_scenes)) val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes)) train_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in train_scenes ]) val_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in val_scenes ]) test = 'test' in version if test: print('test scene: {}'.format(len(train_scenes))) else: print('train scene: {}, val scene: {}'.format( len(train_scenes), len(val_scenes))) train_nusc_infos, val_nusc_infos = _fill_trainval_infos( nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test, max_sweeps=max_sweeps) metadata = dict(version=version) if test: print('test sample: {}'.format(len(train_nusc_infos))) data = dict(infos=train_nusc_infos, metadata=metadata) info_path = osp.join(out_path, '{}_infos_test_6s.pkl'.format(info_prefix)) mmcv.dump(data, info_path) else: print('train sample: {}, val sample: {}'.format( len(train_nusc_infos), len(val_nusc_infos))) data = dict(infos=train_nusc_infos, metadata=metadata) info_path = osp.join(out_path, '{}_infos_train_6s.pkl'.format(info_prefix)) mmcv.dump(data, info_path) data['infos'] = val_nusc_infos info_val_path = osp.join(out_path, '{}_infos_val_6s.pkl'.format(info_prefix)) mmcv.dump(data, info_val_path) def get_available_scenes(nusc): """Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation. Args: nusc (class): Dataset class in the nuScenes dataset. Returns: available_scenes (list[dict]): List of basic information for the available scenes. """ available_scenes = [] print('total scene num: {}'.format(len(nusc.scene))) for scene in nusc.scene: scene_token = scene['token'] scene_rec = nusc.get('scene', scene_token) sample_rec = nusc.get('sample', scene_rec['first_sample_token']) sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP']) has_more_frames = True scene_not_exist = False while has_more_frames: lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token']) lidar_path = str(lidar_path) if os.getcwd() in lidar_path: # path from lyftdataset is absolute path lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1] # relative path if not mmcv.is_filepath(lidar_path): scene_not_exist = True break else: break if scene_not_exist: continue available_scenes.append(scene) print('exist scene num: {}'.format(len(available_scenes))) return available_scenes def _fill_trainval_infos(nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test=False, max_sweeps=10, fut_ts=12, ego_fut_ts=12): """Generate the train/val infos from the raw data. Args: nusc (:obj:`NuScenes`): Dataset class in the nuScenes dataset. train_scenes (list[str]): Basic information of training scenes. val_scenes (list[str]): Basic information of validation scenes. test (bool): Whether use the test mode. In the test mode, no annotations can be accessed. Default: False. max_sweeps (int): Max number of sweeps. Default: 10. Returns: tuple[list[dict]]: Information of training set and validation set that will be saved to the info file. """ train_nusc_infos = [] val_nusc_infos = [] cat2idx = {} for idx, dic in enumerate(nusc.category): cat2idx[dic['name']] = idx predict_helper = PredictHelper(nusc) for sample in mmcv.track_iter_progress(nusc.sample): map_location = nusc.get('log', nusc.get('scene', sample['scene_token'])['log_token'])['location'] lidar_token = sample['data']['LIDAR_TOP'] sd_rec = nusc.get('sample_data', lidar_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) lidar_path, boxes, _ = nusc.get_sample_data(lidar_token) mmcv.check_file_exist(lidar_path) info = { 'lidar_path': lidar_path, 'token': sample['token'], 'sweeps': [], 'cams': dict(), 'scene_token': sample['scene_token'], 'lidar2ego_translation': cs_record['translation'], 'lidar2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sample['timestamp'], 'map_location': map_location, } l2e_r = info['lidar2ego_rotation'] l2e_t = info['lidar2ego_translation'] e2g_r = info['ego2global_rotation'] e2g_t = info['ego2global_translation'] l2e_r_mat = Quaternion(l2e_r).rotation_matrix e2g_r_mat = Quaternion(e2g_r).rotation_matrix # extract map annos lidar2ego = np.eye(4) lidar2ego[:3, :3] = Quaternion( info["lidar2ego_rotation"] ).rotation_matrix lidar2ego[:3, 3] = np.array(info["lidar2ego_translation"]) ego2global = np.eye(4) ego2global[:3, :3] = Quaternion( info["ego2global_rotation"] ).rotation_matrix ego2global[:3, 3] = np.array(info["ego2global_translation"]) lidar2global = ego2global @ lidar2ego translation = list(lidar2global[:3, 3]) rotation = list(Quaternion(matrix=lidar2global).q) map_geoms = nusc_map_extractor.get_map_geom(map_location, translation, rotation) map_annos = geom2anno(map_geoms) info['map_annos'] = map_annos # obtain 6 image's information per frame camera_types = [ 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT', ] for cam in camera_types: cam_token = sample['data'][cam] cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token) cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam) cam_info.update(cam_intrinsic=cam_intrinsic) info['cams'].update({cam: cam_info}) # obtain sweeps for a single key-frame sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP']) sweeps = [] while len(sweeps) < max_sweeps: if not sd_rec['prev'] == '': sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, 'lidar') sweeps.append(sweep) sd_rec = nusc.get('sample_data', sd_rec['prev']) else: break info['sweeps'] = sweeps # obtain annotation if not test: # object detection annos: boxes (locs, dims, yaw, velocity), names and valid flags annotations = [ nusc.get('sample_annotation', token) for token in sample['anns'] ] locs = np.array([b.center for b in boxes]).reshape(-1, 3) dims = np.array([b.wlh for b in boxes]).reshape(-1, 3) rots = np.array([b.orientation.yaw_pitch_roll[0] for b in boxes]).reshape(-1, 1) velocity = np.array( [nusc.box_velocity(token)[:2] for token in sample['anns']]) # convert velo from global to lidar for i in range(len(boxes)): velo = np.array([*velocity[i], 0.0]) velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv( l2e_r_mat).T velocity[i] = velo[:2] names = [b.name for b in boxes] for i in range(len(names)): if names[i] in NameMapping: names[i] = NameMapping[names[i]] names = np.array(names) valid_flag = np.array( [(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0 for anno in annotations], dtype=bool).reshape(-1) ## TODO update valid flag for tracking # we need to convert box size to # the format of our lidar coordinate system # which is x_size, y_size, z_size (corresponding to l, w, h) gt_boxes = np.concatenate([locs, dims[:, [1, 0, 2]], rots], axis=1) assert len(gt_boxes) == len( annotations), f'{len(gt_boxes)}, {len(annotations)}' # object tracking annos: instance_ids instance_inds = [nusc.getind('instance', anno['instance_token']) for anno in annotations] # motion prediction annos: future trajectories offset in lidar frame and valid mask num_box = len(boxes) gt_fut_trajs = np.zeros((num_box, fut_ts, 2)) gt_fut_masks = np.zeros((num_box, fut_ts)) for i, anno in enumerate(annotations): instance_token = anno['instance_token'] fut_traj_local = predict_helper.get_future_for_agent( instance_token, sample['token'], seconds=fut_ts/2, in_agent_frame=True ) if fut_traj_local.shape[0] > 0: box = boxes[i] trans = box.center rot = Quaternion(matrix=box.rotation_matrix) fut_traj_scene = convert_local_coords_to_global(fut_traj_local, trans, rot) valid_step = fut_traj_scene.shape[0] gt_fut_trajs[i, 0] = fut_traj_scene[0] - box.center[:2] gt_fut_trajs[i, 1:valid_step] = fut_traj_scene[1:] - fut_traj_scene[:-1] gt_fut_masks[i, :valid_step] = 1 # motion planning annos: future trajectories offset in lidar frame and valid mask ego_fut_trajs = np.zeros((ego_fut_ts + 1, 3)) ego_fut_masks = np.zeros((ego_fut_ts + 1)) sample_cur = sample ego_status = get_ego_status(nusc, nusc_can_bus, sample_cur) for i in range(ego_fut_ts + 1): pose_mat = get_global_sensor_pose(sample_cur, nusc) ego_fut_trajs[i] = pose_mat[:3, 3] ego_fut_masks[i] = 1 if sample_cur['next'] == '': ego_fut_trajs[i+1:] = ego_fut_trajs[i] break else: sample_cur = nusc.get('sample', sample_cur['next']) # global to ego ego_fut_trajs = ego_fut_trajs - np.array(pose_record['translation']) rot_mat = Quaternion(pose_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # ego to lidar ego_fut_trajs = ego_fut_trajs - np.array(cs_record['translation']) rot_mat = Quaternion(cs_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # drive command according to final fut step offset if ego_fut_trajs[-1][0] >= 2: command = np.array([1, 0, 0]) # Turn Right elif ego_fut_trajs[-1][0] <= -2: command = np.array([0, 1, 0]) # Turn Left else: command = np.array([0, 0, 1]) # Go Straight # get offset ego_fut_trajs = ego_fut_trajs[1:] - ego_fut_trajs[:-1] info['gt_boxes'] = gt_boxes info['gt_names'] = names info['gt_velocity'] = velocity.reshape(-1, 2) info['num_lidar_pts'] = np.array( [a['num_lidar_pts'] for a in annotations]) info['num_radar_pts'] = np.array( [a['num_radar_pts'] for a in annotations]) info['valid_flag'] = valid_flag info['instance_inds'] = instance_inds info['gt_agent_fut_trajs'] = gt_fut_trajs.astype(np.float32) info['gt_agent_fut_masks'] = gt_fut_masks.astype(np.float32) info['gt_ego_fut_trajs'] = ego_fut_trajs[:, :2].astype(np.float32) info['gt_ego_fut_masks'] = ego_fut_masks[1:].astype(np.float32) info['gt_ego_fut_cmd'] = command.astype(np.float32) info['ego_status'] = ego_status if sample['scene_token'] in train_scenes: train_nusc_infos.append(info) else: val_nusc_infos.append(info) return train_nusc_infos, val_nusc_infos def get_ego_status(nusc, nusc_can_bus, sample): ego_status = [] ref_scene = nusc.get("scene", sample['scene_token']) try: pose_msgs = nusc_can_bus.get_messages(ref_scene['name'],'pose') steer_msgs = nusc_can_bus.get_messages(ref_scene['name'], 'steeranglefeedback') pose_uts = [msg['utime'] for msg in pose_msgs] steer_uts = [msg['utime'] for msg in steer_msgs] ref_utime = sample['timestamp'] pose_index = locate_message(pose_uts, ref_utime) pose_data = pose_msgs[pose_index] steer_index = locate_message(steer_uts, ref_utime) steer_data = steer_msgs[steer_index] ego_status.extend(pose_data["accel"]) # acceleration in ego vehicle frame, m/s/s ego_status.extend(pose_data["rotation_rate"]) # angular velocity in ego vehicle frame, rad/s ego_status.extend(pose_data["vel"]) # velocity in ego vehicle frame, m/s ego_status.append(steer_data["value"]) # steering angle, positive: left turn, negative: right turn except: ego_status = [0] * 10 return np.array(ego_status).astype(np.float32) def get_global_sensor_pose(rec, nusc): lidar_sample_data = nusc.get('sample_data', rec['data']['LIDAR_TOP']) pose_record = nusc.get("ego_pose", lidar_sample_data["ego_pose_token"]) cs_record = nusc.get("calibrated_sensor", lidar_sample_data["calibrated_sensor_token"]) ego2global = transform_matrix(pose_record["translation"], Quaternion(pose_record["rotation"]), inverse=False) sensor2ego = transform_matrix(cs_record["translation"], Quaternion(cs_record["rotation"]), inverse=False) pose = ego2global.dot(sensor2ego) return pose def obtain_sensor2top(nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type='lidar'): """Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. sensor_token (str): Sample data token corresponding to the specific sensor type. l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3). l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego in shape (3, 3). e2g_t (np.ndarray): Translation from ego to global in shape (1, 3). e2g_r_mat (np.ndarray): Rotation matrix from ego to global in shape (3, 3). sensor_type (str): Sensor to calibrate. Default: 'lidar'. Returns: sweep (dict): Sweep information after transformation. """ sd_rec = nusc.get('sample_data', sensor_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) data_path = str(nusc.get_sample_data_path(sd_rec['token'])) if os.getcwd() in data_path: # path from lyftdataset is absolute path data_path = data_path.split(f'{os.getcwd()}/')[-1] # relative path sweep = { 'data_path': data_path, 'type': sensor_type, 'sample_data_token': sd_rec['token'], 'sensor2ego_translation': cs_record['translation'], 'sensor2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sd_rec['timestamp'] } l2e_r_s = sweep['sensor2ego_rotation'] l2e_t_s = sweep['sensor2ego_translation'] e2g_r_s = sweep['ego2global_rotation'] e2g_t_s = sweep['ego2global_translation'] # obtain the RT from sensor to Top LiDAR # sweep->ego->global->ego'->lidar l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T ) + l2e_t @ np.linalg.inv(l2e_r_mat).T sweep['sensor2lidar_rotation'] = R.T # points @ R.T + T sweep['sensor2lidar_translation'] = T return sweep def nuscenes_data_prep(root_path, can_bus_root_path, info_prefix, version, dataset_name, out_dir, max_sweeps=10): """Prepare data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth database. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. version (str): Dataset version. dataset_name (str): The dataset class name. out_dir (str): Output directory of the groundtruth database info. max_sweeps (int): Number of input consecutive frames. Default: 10 """ create_nuscenes_infos( root_path, out_dir, can_bus_root_path, info_prefix, version=version, max_sweeps=max_sweeps) parser = argparse.ArgumentParser(description='Data converter arg parser') parser.add_argument('dataset', metavar='kitti', help='name of the dataset') parser.add_argument( '--root-path', type=str, default='./data/kitti', help='specify the root path of dataset') parser.add_argument( '--canbus', type=str, default='./data', help='specify the root path of nuScenes canbus') parser.add_argument( '--version', type=str, default='v1.0', required=False, help='specify the dataset version, no need for kitti') parser.add_argument( '--max-sweeps', type=int, default=10, required=False, help='specify sweeps of lidar per example') parser.add_argument( '--out-dir', type=str, default='./data/kitti', required='False', help='name of info pkl') parser.add_argument('--extra-tag', type=str, default='kitti') parser.add_argument( '--workers', type=int, default=4, help='number of threads to be used') args = parser.parse_args() if __name__ == '__main__': if args.dataset == 'nuscenes' and args.version != 'v1.0-mini': train_version = f'{args.version}-trainval' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=train_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) test_version = f'{args.version}-test' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=test_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) elif args.dataset == 'nuscenes' and args.version == 'v1.0-mini': train_version = f'{args.version}' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=train_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/data_converter/nuscenes_converter_hrad_planing_scene.py ================================================ import os import math import copy import argparse from os import path as osp from collections import OrderedDict from typing import List, Tuple, Union import numpy as np from pyquaternion import Quaternion from shapely.geometry import MultiPoint, box import mmcv from nuscenes.nuscenes import NuScenes from nuscenes.can_bus.can_bus_api import NuScenesCanBus from nuscenes.utils.geometry_utils import transform_matrix from nuscenes.utils.data_classes import Box from nuscenes.utils.geometry_utils import view_points from nuscenes.prediction import PredictHelper, convert_local_coords_to_global from projects.mmdet3d_plugin.datasets.map_utils.nuscmap_extractor import NuscMapExtractor NameMapping = { "movable_object.barrier": "barrier", "vehicle.bicycle": "bicycle", "vehicle.bus.bendy": "bus", "vehicle.bus.rigid": "bus", "vehicle.car": "car", "vehicle.construction": "construction_vehicle", "vehicle.motorcycle": "motorcycle", "human.pedestrian.adult": "pedestrian", "human.pedestrian.child": "pedestrian", "human.pedestrian.construction_worker": "pedestrian", "human.pedestrian.police_officer": "pedestrian", "movable_object.trafficcone": "traffic_cone", "vehicle.trailer": "trailer", "vehicle.truck": "truck", } def quart_to_rpy(qua): x, y, z, w = qua roll = math.atan2(2 * (w * x + y * z), 1 - 2 * (x * x + y * y)) pitch = math.asin(2 * (w * y - x * z)) yaw = math.atan2(2 * (w * z + x * y), 1 - 2 * (z * z + y * y)) return roll, pitch, yaw def locate_message(utimes, utime): i = np.searchsorted(utimes, utime) if i == len(utimes) or (i > 0 and utime - utimes[i-1] < utimes[i] - utime): i -= 1 return i def geom2anno(map_geoms): MAP_CLASSES = ( 'ped_crossing', 'divider', 'boundary', ) vectors = {} for cls, geom_list in map_geoms.items(): if cls in MAP_CLASSES: label = MAP_CLASSES.index(cls) vectors[label] = [] for geom in geom_list: line = np.array(geom.coords) vectors[label].append(line) return vectors def create_nuscenes_infos(root_path, out_path, can_bus_root_path, info_prefix, version='v1.0-trainval', max_sweeps=10, roi_size=(30, 60),): """Create info file of nuscene dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of the data root. info_prefix (str): Prefix of the info file to be generated. version (str): Version of the data. Default: 'v1.0-trainval' max_sweeps (int): Max number of sweeps. Default: 10 """ print(version, root_path) nusc = NuScenes(version=version, dataroot=root_path, verbose=True) nusc_map_extractor = NuscMapExtractor(root_path, roi_size) nusc_can_bus = NuScenesCanBus(dataroot=can_bus_root_path) from nuscenes.utils import splits available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini'] assert version in available_vers if version == 'v1.0-trainval': train_scenes = splits.train import random random.shuffle(train_scenes) train_scenes = train_scenes[:int(len(train_scenes)*0.1)] # 0.2 为 1/5；0.5为 1/2 以此类推 # import pdb; pdb.set_trace() val_scenes = splits.val val_scenes = splits.val elif version == 'v1.0-test': train_scenes = splits.test val_scenes = [] elif version == 'v1.0-mini': train_scenes = splits.mini_train val_scenes = splits.mini_val out_path = osp.join(out_path, 'mini') else: raise ValueError('unknown') os.makedirs(out_path, exist_ok=True) # filter existing scenes. available_scenes = get_available_scenes(nusc) available_scene_names = [s['name'] for s in available_scenes] train_scenes = list( filter(lambda x: x in available_scene_names, train_scenes)) val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes)) # import pdb; pdb.set_trace() train_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in train_scenes ]) val_scenes = set([ available_scenes[available_scene_names.index(s)]['token'] for s in val_scenes ]) val_scenes = set(['a178a1b5415f45c08d179bd2cacdf284', 'e005041f659c47e194cd5b18ea6fc346', 'e8099a6136804f3bb9b38ff94d98eb64', 'b789de07180846cc972118ee6d1fb027', '080a52cb8f59489b9cddc7b721808088', 'ed242d80ccb34b139aaf9ab89859332e', '325cef682f064c55a255f2625c533b75', '2f56eb47c64f43df8902d9f88aa8a019', '7210f928860043b5a7e0d3dd4b3e80ff', 'f97bf749746c4c3a8ad9f1c11eab6444', 'cba3ddd5c3664a43b6a08e586e094900', 'd29527ec841045d18d04a933e7a0afd2', 'c4df079d260241ff8015218e29b42ea7', '7052d21b95fc4bae8761b8d9524f3e42', '01e4fcbe6e49483293ce45727152b36e', '19d97841d6f64eba9f6eb9b6e8c257dc', 'fcc020250f884397965ba00c1d9ad9e6']) test = 'test' in version if test: print('test scene: {}'.format(len(train_scenes))) else: print('train scene: {}, val scene: {}'.format( len(train_scenes), len(val_scenes))) train_nusc_infos, val_nusc_infos = _fill_trainval_infos( nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test, max_sweeps=max_sweeps) metadata = dict(version=version) if test: pass else: print('train sample: {}, val sample: {}'.format( len(train_nusc_infos), len(val_nusc_infos))) data = dict(infos=train_nusc_infos, metadata=metadata) data['infos'] = val_nusc_infos info_val_path = osp.join(out_path, '{}_infos_val_hrad_planing_scene.pkl'.format(info_prefix)) mmcv.dump(data, info_val_path) def get_available_scenes(nusc): """Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation. Args: nusc (class): Dataset class in the nuScenes dataset. Returns: available_scenes (list[dict]): List of basic information for the available scenes. """ available_scenes = [] print('total scene num: {}'.format(len(nusc.scene))) for scene in nusc.scene: scene_token = scene['token'] scene_rec = nusc.get('scene', scene_token) sample_rec = nusc.get('sample', scene_rec['first_sample_token']) sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP']) has_more_frames = True scene_not_exist = False while has_more_frames: lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token']) lidar_path = str(lidar_path) if os.getcwd() in lidar_path: # path from lyftdataset is absolute path lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1] # relative path if not mmcv.is_filepath(lidar_path): scene_not_exist = True break else: break if scene_not_exist: continue available_scenes.append(scene) print('exist scene num: {}'.format(len(available_scenes))) return available_scenes def _fill_trainval_infos(nusc, nusc_map_extractor, nusc_can_bus, train_scenes, val_scenes, test=False, max_sweeps=10, fut_ts=12, ego_fut_ts=6): """Generate the train/val infos from the raw data. Args: nusc (:obj:`NuScenes`): Dataset class in the nuScenes dataset. train_scenes (list[str]): Basic information of training scenes. val_scenes (list[str]): Basic information of validation scenes. test (bool): Whether use the test mode. In the test mode, no annotations can be accessed. Default: False. max_sweeps (int): Max number of sweeps. Default: 10. Returns: tuple[list[dict]]: Information of training set and validation set that will be saved to the info file. """ train_nusc_infos = [] val_nusc_infos = [] cat2idx = {} for idx, dic in enumerate(nusc.category): cat2idx[dic['name']] = idx predict_helper = PredictHelper(nusc) trainval_samples=[] for sample in mmcv.track_iter_progress(nusc.sample): if sample['scene_token'] in val_scenes : trainval_samples.append(sample) # import pdb; pdb.set_trace() for sample in mmcv.track_iter_progress(trainval_samples): map_location = nusc.get('log', nusc.get('scene', sample['scene_token'])['log_token'])['location'] lidar_token = sample['data']['LIDAR_TOP'] sd_rec = nusc.get('sample_data', lidar_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) lidar_path, boxes, _ = nusc.get_sample_data(lidar_token) mmcv.check_file_exist(lidar_path) info = { 'lidar_path': lidar_path, 'token': sample['token'], 'sweeps': [], 'cams': dict(), 'scene_token': sample['scene_token'], 'lidar2ego_translation': cs_record['translation'], 'lidar2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sample['timestamp'], 'map_location': map_location, } l2e_r = info['lidar2ego_rotation'] l2e_t = info['lidar2ego_translation'] e2g_r = info['ego2global_rotation'] e2g_t = info['ego2global_translation'] l2e_r_mat = Quaternion(l2e_r).rotation_matrix e2g_r_mat = Quaternion(e2g_r).rotation_matrix # extract map annos lidar2ego = np.eye(4) lidar2ego[:3, :3] = Quaternion( info["lidar2ego_rotation"] ).rotation_matrix lidar2ego[:3, 3] = np.array(info["lidar2ego_translation"]) ego2global = np.eye(4) ego2global[:3, :3] = Quaternion( info["ego2global_rotation"] ).rotation_matrix ego2global[:3, 3] = np.array(info["ego2global_translation"]) lidar2global = ego2global @ lidar2ego translation = list(lidar2global[:3, 3]) rotation = list(Quaternion(matrix=lidar2global).q) map_geoms = nusc_map_extractor.get_map_geom(map_location, translation, rotation) map_annos = geom2anno(map_geoms) info['map_annos'] = map_annos # obtain 6 image's information per frame camera_types = [ 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT', ] for cam in camera_types: cam_token = sample['data'][cam] cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token) cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam) cam_info.update(cam_intrinsic=cam_intrinsic) info['cams'].update({cam: cam_info}) # obtain sweeps for a single key-frame sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP']) sweeps = [] while len(sweeps) < max_sweeps: if not sd_rec['prev'] == '': sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, 'lidar') sweeps.append(sweep) sd_rec = nusc.get('sample_data', sd_rec['prev']) else: break info['sweeps'] = sweeps # obtain annotation if not test: # object detection annos: boxes (locs, dims, yaw, velocity), names and valid flags annotations = [ nusc.get('sample_annotation', token) for token in sample['anns'] ] locs = np.array([b.center for b in boxes]).reshape(-1, 3) dims = np.array([b.wlh for b in boxes]).reshape(-1, 3) rots = np.array([b.orientation.yaw_pitch_roll[0] for b in boxes]).reshape(-1, 1) velocity = np.array( [nusc.box_velocity(token)[:2] for token in sample['anns']]) # convert velo from global to lidar for i in range(len(boxes)): velo = np.array([*velocity[i], 0.0]) velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv( l2e_r_mat).T velocity[i] = velo[:2] names = [b.name for b in boxes] for i in range(len(names)): if names[i] in NameMapping: names[i] = NameMapping[names[i]] names = np.array(names) valid_flag = np.array( [(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0 for anno in annotations], dtype=bool).reshape(-1) ## TODO update valid flag for tracking # we need to convert box size to # the format of our lidar coordinate system # which is x_size, y_size, z_size (corresponding to l, w, h) gt_boxes = np.concatenate([locs, dims[:, [1, 0, 2]], rots], axis=1) assert len(gt_boxes) == len( annotations), f'{len(gt_boxes)}, {len(annotations)}' # object tracking annos: instance_ids instance_inds = [nusc.getind('instance', anno['instance_token']) for anno in annotations] # motion prediction annos: future trajectories offset in lidar frame and valid mask num_box = len(boxes) gt_fut_trajs = np.zeros((num_box, fut_ts, 2)) gt_fut_masks = np.zeros((num_box, fut_ts)) for i, anno in enumerate(annotations): instance_token = anno['instance_token'] fut_traj_local = predict_helper.get_future_for_agent( instance_token, sample['token'], seconds=fut_ts/2, in_agent_frame=True ) if fut_traj_local.shape[0] > 0: box = boxes[i] trans = box.center rot = Quaternion(matrix=box.rotation_matrix) fut_traj_scene = convert_local_coords_to_global(fut_traj_local, trans, rot) valid_step = fut_traj_scene.shape[0] gt_fut_trajs[i, 0] = fut_traj_scene[0] - box.center[:2] gt_fut_trajs[i, 1:valid_step] = fut_traj_scene[1:] - fut_traj_scene[:-1] gt_fut_masks[i, :valid_step] = 1 # motion planning annos: future trajectories offset in lidar frame and valid mask ego_fut_trajs = np.zeros((ego_fut_ts + 1, 3)) ego_fut_masks = np.zeros((ego_fut_ts + 1)) sample_cur = sample ego_status = get_ego_status(nusc, nusc_can_bus, sample_cur) for i in range(ego_fut_ts + 1): pose_mat = get_global_sensor_pose(sample_cur, nusc) ego_fut_trajs[i] = pose_mat[:3, 3] ego_fut_masks[i] = 1 if sample_cur['next'] == '': ego_fut_trajs[i+1:] = ego_fut_trajs[i] break else: sample_cur = nusc.get('sample', sample_cur['next']) # global to ego ego_fut_trajs = ego_fut_trajs - np.array(pose_record['translation']) rot_mat = Quaternion(pose_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # ego to lidar ego_fut_trajs = ego_fut_trajs - np.array(cs_record['translation']) rot_mat = Quaternion(cs_record['rotation']).inverse.rotation_matrix ego_fut_trajs = np.dot(rot_mat, ego_fut_trajs.T).T # drive command according to final fut step offset if ego_fut_trajs[-1][0] >= 2: command = np.array([1, 0, 0]) # Turn Right elif ego_fut_trajs[-1][0] <= -2: command = np.array([0, 1, 0]) # Turn Left else: command = np.array([0, 0, 1]) # Go Straight # get offset ego_fut_trajs = ego_fut_trajs[1:] - ego_fut_trajs[:-1] info['gt_boxes'] = gt_boxes info['gt_names'] = names info['gt_velocity'] = velocity.reshape(-1, 2) info['num_lidar_pts'] = np.array( [a['num_lidar_pts'] for a in annotations]) info['num_radar_pts'] = np.array( [a['num_radar_pts'] for a in annotations]) info['valid_flag'] = valid_flag info['instance_inds'] = instance_inds info['gt_agent_fut_trajs'] = gt_fut_trajs.astype(np.float32) info['gt_agent_fut_masks'] = gt_fut_masks.astype(np.float32) info['gt_ego_fut_trajs'] = ego_fut_trajs[:, :2].astype(np.float32) info['gt_ego_fut_masks'] = ego_fut_masks[1:].astype(np.float32) info['gt_ego_fut_cmd'] = command.astype(np.float32) info['ego_status'] = ego_status if sample['scene_token'] in train_scenes: train_nusc_infos.append(info) else: val_nusc_infos.append(info) return train_nusc_infos, val_nusc_infos def get_ego_status(nusc, nusc_can_bus, sample): ego_status = [] ref_scene = nusc.get("scene", sample['scene_token']) try: pose_msgs = nusc_can_bus.get_messages(ref_scene['name'],'pose') steer_msgs = nusc_can_bus.get_messages(ref_scene['name'], 'steeranglefeedback') pose_uts = [msg['utime'] for msg in pose_msgs] steer_uts = [msg['utime'] for msg in steer_msgs] ref_utime = sample['timestamp'] pose_index = locate_message(pose_uts, ref_utime) pose_data = pose_msgs[pose_index] steer_index = locate_message(steer_uts, ref_utime) steer_data = steer_msgs[steer_index] ego_status.extend(pose_data["accel"]) # acceleration in ego vehicle frame, m/s/s ego_status.extend(pose_data["rotation_rate"]) # angular velocity in ego vehicle frame, rad/s ego_status.extend(pose_data["vel"]) # velocity in ego vehicle frame, m/s ego_status.append(steer_data["value"]) # steering angle, positive: left turn, negative: right turn except: ego_status = [0] * 10 return np.array(ego_status).astype(np.float32) def get_global_sensor_pose(rec, nusc): lidar_sample_data = nusc.get('sample_data', rec['data']['LIDAR_TOP']) pose_record = nusc.get("ego_pose", lidar_sample_data["ego_pose_token"]) cs_record = nusc.get("calibrated_sensor", lidar_sample_data["calibrated_sensor_token"]) ego2global = transform_matrix(pose_record["translation"], Quaternion(pose_record["rotation"]), inverse=False) sensor2ego = transform_matrix(cs_record["translation"], Quaternion(cs_record["rotation"]), inverse=False) pose = ego2global.dot(sensor2ego) return pose def obtain_sensor2top(nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type='lidar'): """Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. sensor_token (str): Sample data token corresponding to the specific sensor type. l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3). l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego in shape (3, 3). e2g_t (np.ndarray): Translation from ego to global in shape (1, 3). e2g_r_mat (np.ndarray): Rotation matrix from ego to global in shape (3, 3). sensor_type (str): Sensor to calibrate. Default: 'lidar'. Returns: sweep (dict): Sweep information after transformation. """ sd_rec = nusc.get('sample_data', sensor_token) cs_record = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token']) pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token']) data_path = str(nusc.get_sample_data_path(sd_rec['token'])) if os.getcwd() in data_path: # path from lyftdataset is absolute path data_path = data_path.split(f'{os.getcwd()}/')[-1] # relative path sweep = { 'data_path': data_path, 'type': sensor_type, 'sample_data_token': sd_rec['token'], 'sensor2ego_translation': cs_record['translation'], 'sensor2ego_rotation': cs_record['rotation'], 'ego2global_translation': pose_record['translation'], 'ego2global_rotation': pose_record['rotation'], 'timestamp': sd_rec['timestamp'] } l2e_r_s = sweep['sensor2ego_rotation'] l2e_t_s = sweep['sensor2ego_translation'] e2g_r_s = sweep['ego2global_rotation'] e2g_t_s = sweep['ego2global_translation'] # obtain the RT from sensor to Top LiDAR # sweep->ego->global->ego'->lidar l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ ( np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T) T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T ) + l2e_t @ np.linalg.inv(l2e_r_mat).T sweep['sensor2lidar_rotation'] = R.T # points @ R.T + T sweep['sensor2lidar_translation'] = T return sweep def nuscenes_data_prep(root_path, can_bus_root_path, info_prefix, version, dataset_name, out_dir, max_sweeps=10): """Prepare data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth database. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. version (str): Dataset version. dataset_name (str): The dataset class name. out_dir (str): Output directory of the groundtruth database info. max_sweeps (int): Number of input consecutive frames. Default: 10 """ create_nuscenes_infos( root_path, out_dir, can_bus_root_path, info_prefix, version=version, max_sweeps=max_sweeps) parser = argparse.ArgumentParser(description='Data converter arg parser') parser.add_argument('dataset', metavar='kitti', help='name of the dataset') parser.add_argument( '--root-path', type=str, default='./data/kitti', help='specify the root path of dataset') parser.add_argument( '--canbus', type=str, default='./data', help='specify the root path of nuScenes canbus') parser.add_argument( '--version', type=str, default='v1.0', required=False, help='specify the dataset version, no need for kitti') parser.add_argument( '--max-sweeps', type=int, default=10, required=False, help='specify sweeps of lidar per example') parser.add_argument( '--out-dir', type=str, default='./data/kitti', required='False', help='name of info pkl') parser.add_argument('--extra-tag', type=str, default='kitti') parser.add_argument( '--workers', type=int, default=4, help='number of threads to be used') args = parser.parse_args() if __name__ == '__main__': if args.dataset == 'nuscenes' and args.version != 'v1.0-mini': train_version = f'{args.version}-trainval' nuscenes_data_prep( root_path=args.root_path, can_bus_root_path=args.canbus, info_prefix=args.extra_tag, version=train_version, dataset_name='NuScenesDataset', out_dir=args.out_dir, max_sweeps=args.max_sweeps) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/dist_test.sh ================================================ #!/usr/bin/env bash CONFIG=$1 CHECKPOINT=$2 GPUS=$3 PORT=${PORT:-29611} PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ python3 -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4} ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/dist_train.sh ================================================ #!/usr/bin/env bash CONFIG=$1 GPUS=$2 PORT=${PORT:-28651} PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ python3 -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/fuse_conv_bn.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import argparse import torch from mmcv.runner import save_checkpoint from torch import nn as nn from mmdet3d.apis import init_model def fuse_conv_bn(conv, bn): """During inference, the functionary of batch norm layers is turned off but only the mean and var alone channels are used, which exposes the chance to fuse it with the preceding conv layers to save computations and simplify network structures.""" conv_w = conv.weight conv_b = conv.bias if conv.bias is not None else torch.zeros_like( bn.running_mean) factor = bn.weight / torch.sqrt(bn.running_var + bn.eps) conv.weight = nn.Parameter(conv_w * factor.reshape([conv.out_channels, 1, 1, 1])) conv.bias = nn.Parameter((conv_b - bn.running_mean) * factor + bn.bias) return conv def fuse_module(m): last_conv = None last_conv_name = None for name, child in m.named_children(): if isinstance(child, (nn.BatchNorm2d, nn.SyncBatchNorm)): if last_conv is None: # only fuse BN that is after Conv continue fused_conv = fuse_conv_bn(last_conv, child) m._modules[last_conv_name] = fused_conv # To reduce changes, set BN as Identity instead of deleting it. m._modules[name] = nn.Identity() last_conv = None elif isinstance(child, nn.Conv2d): last_conv = child last_conv_name = name else: fuse_module(child) return m def parse_args(): parser = argparse.ArgumentParser( description='fuse Conv and BN layers in a model') parser.add_argument('config', help='config file path') parser.add_argument('checkpoint', help='checkpoint file path') parser.add_argument('out', help='output path of the converted model') args = parser.parse_args() return args def main(): args = parse_args() # build the model from a config file and a checkpoint file model = init_model(args.config, args.checkpoint) # fuse conv and bn layers of the model fused_model = fuse_module(model) save_checkpoint(fused_model, args.out) if __name__ == '__main__': main() ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/kmeans/kmeans_det.py ================================================ import os import pickle from tqdm import tqdm import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans import mmcv os.makedirs('data/kmeans', exist_ok=True) os.makedirs('vis/kmeans', exist_ok=True) K = 900 DIS_THRESH = 55 fp = 'data/infos_sparsedrive/b2d_infos_train.pkl' data = mmcv.load(fp) #import pdb;pdb.set_trace() #data_infos = list(sorted(data["infos"], key=lambda e: e["timestamp"])) data_infos = sorted(data ,key=lambda e: e["timestamp"]) center = [] for idx in tqdm(range(len(data_infos))): boxes = data_infos[idx]['gt_boxes'][:,:3] if len(boxes) == 0: continue distance = np.linalg.norm(boxes[:, :2], axis=1) center.append(boxes[distance < DIS_THRESH]) center = np.concatenate(center, axis=0) print("start clustering, may take a few minutes.") cluster = KMeans(n_clusters=K).fit(center).cluster_centers_ plt.scatter(cluster[:,0], cluster[:,1]) plt.savefig(f'vis/kmeans/det_anchor_{K}', bbox_inches='tight') others = np.array([1,1,1,1,0,0,0,0])[np.newaxis].repeat(K, axis=0) cluster = np.concatenate([cluster, others], axis=1) np.save(f'data/kmeans/kmeans_det_{K}.npy', cluster) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/kmeans/kmeans_map.py ================================================ import os import pickle from tqdm import tqdm import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans import mmcv K = 100 num_sample = 20 fp = 'data/infos_sparsedrive/b2d_infos_train.pkl' data = mmcv.load(fp) #data_infos = list(sorted(data["infos"], key=lambda e: e["timestamp"])) import pdb;pdb.set_trace() data_infos = sorted(data ,key=lambda e: e["timestamp"]) center = [] for idx in tqdm(range(len(data_infos))): for cls, geoms in data_infos[idx]["map_annos"].items(): for geom in geoms: center.append(geom.mean(axis=0)) center = np.stack(center, axis=0) center = KMeans(n_clusters=K).fit(center).cluster_centers_ delta_y = np.linspace(-4, 4, num_sample) delta_x = np.zeros([num_sample]) delta = np.stack([delta_x, delta_y], axis=-1) vecs = center[:, np.newaxis] + delta[np.newaxis] for i in range(K): x = vecs[i, :, 0] y = vecs[i, :, 1] plt.plot(x, y, linewidth=1, marker='o', linestyle='-', markersize=2) plt.savefig(f'vis/kmeans/map_anchor_{K}', bbox_inches='tight') np.save(f'data/kmeans/kmeans_map_{K}.npy', vecs) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/kmeans/kmeans_motion.py ================================================ import os import pickle from tqdm import tqdm import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans import mmcv CLASSES = [ 'car', 'van', 'truck', 'bicycle', 'traffic_sign', 'traffic_cone', 'traffic_light', 'pedestrian', 'others', ] NameMapping = { #=================vehicle================= # bicycle 'vehicle.bh.crossbike': 'bicycle', "vehicle.diamondback.century": 'bicycle', "vehicle.gazelle.omafiets": 'bicycle', # car "vehicle.audi.etron": 'car', "vehicle.chevrolet.impala": 'car', "vehicle.dodge.charger_2020": 'car', "vehicle.dodge.charger_police": 'car', "vehicle.dodge.charger_police_2020": 'car', "vehicle.lincoln.mkz_2017": 'car', "vehicle.lincoln.mkz_2020": 'car', "vehicle.mini.cooper_s_2021": 'car', "vehicle.mercedes.coupe_2020": 'car', "vehicle.ford.mustang": 'car', "vehicle.nissan.patrol_2021": 'car', "vehicle.audi.tt": 'car', "vehicle.audi.etron": 'car', "vehicle.ford.crown": 'car', "vehicle.ford.mustang": 'car', "vehicle.tesla.model3": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/FordCrown/SM_FordCrown_parked.SM_FordCrown_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Charger/SM_ChargerParked.SM_ChargerParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Lincoln/SM_LincolnParked.SM_LincolnParked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/MercedesCCC/SM_MercedesCCC_Parked.SM_MercedesCCC_Parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/Mini2021/SM_Mini2021_parked.SM_Mini2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/NissanPatrol2021/SM_NissanPatrol2021_parked.SM_NissanPatrol2021_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/TeslaM3/SM_TeslaM3_parked.SM_TeslaM3_parked": 'car', "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": 'car', # bus # van "/Game/Carla/Static/Car/4Wheeled/ParkedVehicles/VolkswagenT2/SM_VolkswagenT2_2021_Parked.SM_VolkswagenT2_2021_Parked": "van", "vehicle.ford.ambulance": "van", # truck "vehicle.carlamotors.firetruck": 'truck', #========================================= #=================traffic sign============ # traffic.speed_limit "traffic.speed_limit.30": 'traffic_sign', "traffic.speed_limit.40": 'traffic_sign', "traffic.speed_limit.50": 'traffic_sign', "traffic.speed_limit.60": 'traffic_sign', "traffic.speed_limit.90": 'traffic_sign', "traffic.speed_limit.120": 'traffic_sign', "traffic.stop": 'traffic_sign', "traffic.yield": 'traffic_sign', "traffic.traffic_light": 'traffic_light', #========================================= #===================Construction=========== "static.prop.warningconstruction" : 'traffic_cone', "static.prop.warningaccident": 'traffic_cone', "static.prop.trafficwarning": "traffic_cone", #===================Construction=========== "static.prop.constructioncone": 'traffic_cone', #=================pedestrian============== "walker.pedestrian.0001": 'pedestrian', "walker.pedestrian.0003": 'pedestrian', "walker.pedestrian.0004": 'pedestrian', "walker.pedestrian.0005": 'pedestrian', "walker.pedestrian.0007": 'pedestrian', "walker.pedestrian.0010": 'pedestrian', "walker.pedestrian.0013": 'pedestrian', "walker.pedestrian.0014": 'pedestrian', "walker.pedestrian.0015": 'pedestrian', "walker.pedestrian.0016": 'pedestrian', "walker.pedestrian.0017": 'pedestrian', "walker.pedestrian.0018": 'pedestrian', "walker.pedestrian.0019": 'pedestrian', "walker.pedestrian.0020": 'pedestrian', "walker.pedestrian.0021": 'pedestrian', "walker.pedestrian.0022": 'pedestrian', "walker.pedestrian.0025": 'pedestrian', "walker.pedestrian.0027": 'pedestrian', "walker.pedestrian.0030": 'pedestrian', "walker.pedestrian.0031": 'pedestrian', "walker.pedestrian.0032": 'pedestrian', "walker.pedestrian.0034": 'pedestrian', "walker.pedestrian.0035": 'pedestrian', "walker.pedestrian.0041": 'pedestrian', "walker.pedestrian.0042": 'pedestrian', "walker.pedestrian.0046": 'pedestrian', "walker.pedestrian.0047": 'pedestrian', # ========================================== "static.prop.dirtdebris01": 'others', "static.prop.dirtdebris02": 'others', } def get_fut_agent(idx, sample_rate, frames, data_infos): adj_idx_list = range(idx,idx+(frames+1)*sample_rate,sample_rate) cur_frame = data_infos[idx] cur_boxes = cur_frame['gt_boxes'].copy() box_ids = cur_frame['gt_ids'] future_track = np.zeros((len(box_ids),frames+1,2)) future_mask = np.zeros((len(box_ids),frames+1)) world2lidar_lidar_cur = cur_frame['sensors']['LIDAR_TOP']['world2lidar'] for i in range(len(box_ids)): box_id = box_ids[i] cur_box2lidar = world2lidar_lidar_cur @ cur_frame['npc2world'][i] cur_xy = cur_box2lidar[0:2,3] for j in range(len(adj_idx_list)): adj_idx = adj_idx_list[j] if adj_idx < 0 or adj_idx >= len(data_infos): break adj_frame = data_infos[adj_idx] if adj_frame['folder'] != cur_frame ['folder']: break if len(np.where(adj_frame['gt_ids']==box_id)[0])==0: break assert len(np.where(adj_frame['gt_ids']==box_id)[0]) == 1 , np.where(adj_frame['gt_ids']==box_id)[0] adj_idx = np.where(adj_frame['gt_ids']==box_id)[0][0] adj_box2lidar = world2lidar_lidar_cur @ adj_frame['npc2world'][adj_idx] adj_xy = adj_box2lidar[0:2,3] if j > 0: last_xy = future_track[i,j-1,:] distance = np.linalg.norm(last_xy - adj_xy) if distance > 10: break future_track[i,j,:] = adj_xy future_mask[i,j] = 1 future_track_offset = future_track[:,1:,:] - future_track[:,:-1,:] future_mask_offset = future_mask[:,1:] future_track_offset[future_mask_offset==0] = 0 return future_track_offset.astype(np.float32), future_mask_offset.astype(np.float32) def lidar2agent(trajs_offset, boxes): origin = np.zeros((trajs_offset.shape[0], 1, 2), dtype=np.float32) trajs_offset = np.concatenate([origin, trajs_offset], axis=1) trajs = trajs_offset.cumsum(axis=1) yaws = - boxes[:, 6] rot_sin = np.sin(yaws) rot_cos = np.cos(yaws) rot_mat_T = np.stack( [ np.stack([rot_cos, rot_sin]), np.stack([-rot_sin, rot_cos]), ] ) trajs_new = np.einsum('aij,jka->aik', trajs, rot_mat_T) trajs_new = trajs_new[:, 1:] return trajs_new K = 6 DIS_THRESH = 65 fp = 'data/infos_sparsedrive/b2d_infos_train.pkl' data = mmcv.load(fp) #import pdb;pdb.set_trace() data_infos = data #sorted(data, key=lambda e: e["timestamp"]) intention = dict() for i in range(len(CLASSES)): intention[i] = [] for idx in tqdm(range(len(data_infos))): info = data_infos[idx] boxes = info['gt_boxes'] names = info['gt_names'] for name_i in range(len(names)): names[name_i] = NameMapping[names[name_i]] #import pdb;pdb.set_trace() gt_agent_fut_trajs, gt_agent_fut_masks = get_fut_agent(idx, 5, 6, data_infos) fut_masks = gt_agent_fut_masks trajs = gt_agent_fut_trajs x = gt_agent_fut_trajs[:,0,0] y = gt_agent_fut_trajs[:,0,1] result = np.sqrt(x**2 + y**2) #import pdb;pdb.set_trace() velos = result labels = [] for cat in names: if cat in CLASSES: labels.append(CLASSES.index(cat)) else: labels.append(-1) labels = np.array(labels) if len(boxes) == 0: continue #import pdb;pdb.set_trace() for i in range(len(CLASSES)): #import pdb;pdb.set_trace() cls_mask = (labels == i) box_cls = boxes[cls_mask] fut_masks_cls = fut_masks[cls_mask] trajs_cls = trajs[cls_mask] velos_cls = velos[cls_mask] distance = np.linalg.norm(box_cls[:, :2], axis=1) mask = np.logical_and( fut_masks_cls.sum(axis=1) == 6, distance < DIS_THRESH, ) trajs_cls = trajs_cls[mask] box_cls = box_cls[mask] velos_cls = velos_cls[mask] trajs_agent = lidar2agent(trajs_cls, box_cls) if trajs_agent.shape[0] == 0: continue intention[i].append(trajs_agent) #import pdb;pdb.set_trace() clusters = [] for i in range(len(CLASSES)): intention_cls = np.concatenate(intention[i], axis=0).reshape(-1, 12) if intention_cls.shape[0] < K: continue cluster = KMeans(n_clusters=K).fit(intention_cls).cluster_centers_ cluster = cluster.reshape(-1, 6, 2) clusters.append(cluster) for j in range(K): plt.scatter(cluster[j, :, 0], cluster[j, :,1]) plt.savefig(f'vis/kmeans/motion_intention_{CLASSES[i]}_{K}', bbox_inches='tight') plt.close() clusters = np.stack(clusters, axis=0) np.save(f'data/kmeans/kmeans_motion_{K}.npy', clusters) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/kmeans/kmeans_plan.py ================================================ import os import pickle from tqdm import tqdm import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans import mmcv K = 6 fp = './data/infos/b2d_infos_val.pkl' data = mmcv.load(fp) data_infos = list(sorted(data, key=lambda e: e["timestamp"])) navi_trajs = [[], [], [], [], [], []] def get_ego_trajs(idx,sample_rate,past_frames,future_frames,data_infos): #import pdb;pdb.set_trace() # idx = 128386 adj_idx_list = range(idx-past_frames*sample_rate,idx+(future_frames+1)*sample_rate,sample_rate) cur_frame = data_infos[idx] full_adj_track = np.zeros((past_frames+future_frames+1,2)) full_adj_adj_mask = np.zeros(past_frames+future_frames+1) world2lidar_lidar_cur = cur_frame['sensors']['LIDAR_TOP']['world2lidar'] for j in range(len(adj_idx_list)): adj_idx = adj_idx_list[j] if adj_idx <0 or adj_idx>=len(data_infos): break adj_frame = data_infos[adj_idx] if adj_frame['folder'] != cur_frame ['folder']: break world2lidar_ego_adj = adj_frame['sensors']['LIDAR_TOP']['world2lidar'] adj2cur_lidar = world2lidar_lidar_cur @ np.linalg.inv(world2lidar_ego_adj) xy = adj2cur_lidar[0:2,3] full_adj_track[j,0:2] = xy full_adj_adj_mask[j] = 1 offset_track = full_adj_track[1:] - full_adj_track[:-1] for j in range(past_frames-1,-1,-1): if full_adj_adj_mask[j] == 0: offset_track[j] = offset_track[j+1] for j in range(past_frames,past_frames+future_frames,1): if full_adj_adj_mask[j+1] == 0 : offset_track[j] = 0 #command = self.command2hot(cur_frame['command_near']) return offset_track[past_frames:].copy() def lidar2agent(trajs_offset, boxes): origin = np.zeros((trajs_offset.shape[0], 1, 2), dtype=np.float32) trajs_offset = np.concatenate([origin, trajs_offset], axis=1) trajs = trajs_offset.cumsum(axis=1) yaws = - boxes[:, 6] rot_sin = np.sin(yaws) rot_cos = np.cos(yaws) rot_mat_T = np.stack( [ np.stack([rot_cos, rot_sin]), np.stack([-rot_sin, rot_cos]), ] ) trajs_new = np.einsum('aij,jka->aik', trajs, rot_mat_T) trajs_new = trajs_new[:, 1:] return trajs_new sum_turn = 0 for idx in tqdm(range(len(data_infos))): info = data_infos[idx] plan_traj = get_ego_trajs(idx, 5, 6, 6, data_infos) #plan_traj = info['gt_ego_fut_trajs'].cumsum(axis=-2) #plan_mask = info['gt_ego_fut_masks'] #import pdb;pdb.set_trace() cmd = info['command_near']#.astype(np.int32) if cmd == 1 or cmd == 2 or cmd == 5 or cmd == 6: print(cmd) sum_turn = sum_turn + 1 #cmd = cmd.argmax(axis=-1) #if not plan_mask.sum() == 6: # continue navi_trajs[cmd-1].append(plan_traj) import pdb;pdb.set_trace() clusters = [] import pdb;pdb.set_trace() for trajs in navi_trajs: trajs = np.concatenate(trajs, axis=0).reshape(-1, 12) cluster = KMeans(n_clusters=K).fit(trajs).cluster_centers_ cluster = cluster.reshape(-1, 6, 2) clusters.append(cluster) for j in range(K): plt.scatter(cluster[j, :, 0], cluster[j, :,1]) plt.savefig(f'vis/kmeans/plan_{K}', bbox_inches='tight') plt.close() clusters = np.stack(clusters, axis=0) np.save(f'data/kmeans/kmeans_plan_{K}.npy', clusters) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/test.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import argparse import mmcv import os from os import path as osp import torch import warnings from mmcv import Config, DictAction from mmcv.cnn import fuse_conv_bn from mmcv.parallel import MMDataParallel, MMDistributedDataParallel from mmcv.runner import ( get_dist_info, init_dist, load_checkpoint, wrap_fp16_model, ) from mmdet.apis import single_gpu_test, multi_gpu_test, set_random_seed from mmdet.datasets import replace_ImageToTensor, build_dataset from mmdet.datasets import build_dataloader as build_dataloader_origin from mmdet.models import build_detector from projects.mmdet3d_plugin.datasets.builder import build_dataloader from projects.mmdet3d_plugin.apis.test import custom_multi_gpu_test def parse_args(): parser = argparse.ArgumentParser( description="MMDet test (and eval) a model" ) parser.add_argument("config", help="test config file path") parser.add_argument("checkpoint", help="checkpoint file") parser.add_argument("--out", help="output result file in pickle format") parser.add_argument( "--fuse-conv-bn", action="store_true", help="Whether to fuse conv and bn, this will slightly increase" "the inference speed", ) parser.add_argument( "--format-only", action="store_true", help="Format the output results without perform evaluation. It is" "useful when you want to format the result to a specific format and " "submit it to the test server", ) parser.add_argument( "--eval", type=str, nargs="+", help='evaluation metrics, which depends on the dataset, e.g., "bbox",' ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC', ) parser.add_argument("--show", action="store_true", help="show results") parser.add_argument( "--show-dir", help="directory where results will be saved" ) parser.add_argument( "--gpu-collect", action="store_true", help="whether to use gpu to collect results.", ) parser.add_argument( "--tmpdir", help="tmp directory used for collecting results from multiple " "workers, available when gpu-collect is not specified", ) parser.add_argument("--seed", type=int, default=0, help="random seed") parser.add_argument( "--deterministic", action="store_true", help="whether to set deterministic options for CUDNN backend.", ) parser.add_argument( "--cfg-options", nargs="+", action=DictAction, help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file. If the value to " 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' "Note that the quotation marks are necessary and that no white space " "is allowed.", ) parser.add_argument( "--options", nargs="+", action=DictAction, help="custom options for evaluation, the key-value pair in xxx=yyy " "format will be kwargs for dataset.evaluate() function (deprecate), " "change to --eval-options instead.", ) parser.add_argument( "--eval-options", nargs="+", action=DictAction, help="custom options for evaluation, the key-value pair in xxx=yyy " "format will be kwargs for dataset.evaluate() function", ) parser.add_argument( "--launcher", choices=["none", "pytorch", "slurm", "mpi"], default="none", help="job launcher", ) parser.add_argument("--local_rank", type=int, default=0) parser.add_argument("--result_file", type=str, default=None) parser.add_argument("--show_only", action="store_true") args = parser.parse_args() if "LOCAL_RANK" not in os.environ: os.environ["LOCAL_RANK"] = str(args.local_rank) if args.options and args.eval_options: raise ValueError( "--options and --eval-options cannot be both specified, " "--options is deprecated in favor of --eval-options" ) if args.options: warnings.warn("--options is deprecated in favor of --eval-options") args.eval_options = args.options return args def main(): args = parse_args() assert ( args.out or args.eval or args.format_only or args.show or args.show_dir ), ( "Please specify at least one operation (save/eval/format/show the " 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"' ) if args.eval and args.format_only: raise ValueError("--eval and --format_only cannot be both specified") if args.out is not None and not args.out.endswith((".pkl", ".pickle")): raise ValueError("The output file must be a pkl file.") cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get("custom_imports", None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg["custom_imports"]) # import modules from plguin/xx, registry will be updated if hasattr(cfg, "plugin"): if cfg.plugin: import importlib if hasattr(cfg, "plugin_dir"): plugin_dir = cfg.plugin_dir _module_dir = os.path.dirname(plugin_dir) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) else: # import dir is the dirpath for the config file _module_dir = os.path.dirname(args.config) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) # set cudnn_benchmark if cfg.get("cudnn_benchmark", False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None # in case the test dataset is concatenated samples_per_gpu = 1 if isinstance(cfg.data.test, dict): cfg.data.test.test_mode = True samples_per_gpu = cfg.data.test.pop("samples_per_gpu", 1) if samples_per_gpu > 1: # Replace 'ImageToTensor' to 'DefaultFormatBundle' cfg.data.test.pipeline = replace_ImageToTensor( cfg.data.test.pipeline ) elif isinstance(cfg.data.test, list): for ds_cfg in cfg.data.test: ds_cfg.test_mode = True samples_per_gpu = max( [ds_cfg.pop("samples_per_gpu", 1) for ds_cfg in cfg.data.test] ) if samples_per_gpu > 1: for ds_cfg in cfg.data.test: ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline) # init distributed env first, since logger depends on the dist info. if args.launcher == "none": distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) # set random seeds if args.seed is not None: set_random_seed(args.seed, deterministic=args.deterministic) # set work dir if cfg.get('work_dir', None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) cfg.data.test.work_dir = cfg.work_dir print('work_dir: ',cfg.work_dir) # build the dataloader dataset = build_dataset(cfg.data.test) import pdb; if distributed: data_loader = build_dataloader( dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False, nonshuffler_sampler=dict(type="DistributedSampler"), ) else: data_loader = build_dataloader_origin( dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, dist=distributed, shuffle=False, ) # build the model and load checkpoint cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get("test_cfg")) # model = build_model(cfg.model, test_cfg=cfg.get("test_cfg")) fp16_cfg = cfg.get("fp16", None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu") if args.fuse_conv_bn: model = fuse_conv_bn(model) # old versions did not save class info in checkpoints, this walkaround is # for backward compatibility if "CLASSES" in checkpoint.get("meta", {}): model.CLASSES = checkpoint["meta"]["CLASSES"] else: model.CLASSES = dataset.CLASSES # palette for visualization in segmentation tasks if "PALETTE" in checkpoint.get("meta", {}): model.PALETTE = checkpoint["meta"]["PALETTE"] elif hasattr(dataset, "PALETTE"): # segmentation dataset has `PALETTE` attribute model.PALETTE = dataset.PALETTE if args.result_file is not None: # outputs = torch.load(args.result_file) outputs = mmcv.load(args.result_file) elif not distributed: model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, ) outputs = custom_multi_gpu_test( model, data_loader, args.tmpdir, args.gpu_collect ) rank, _ = get_dist_info() if rank == 0: if args.out: print(f"\nwriting results to {args.out}") mmcv.dump(outputs, args.out) kwargs = {} if args.eval_options is None else args.eval_options if args.show_only: eval_kwargs = cfg.get("evaluation", {}).copy() # hard-code way to remove EvalHook args for key in [ "interval", "tmpdir", "start", "gpu_collect", "save_best", "rule", ]: eval_kwargs.pop(key, None) eval_kwargs.update(kwargs) dataset.show(outputs, show=True, **eval_kwargs) elif args.format_only: dataset.format_results(outputs, **kwargs) elif args.eval: eval_kwargs = cfg.get("evaluation", {}).copy() # hard-code way to remove EvalHook args for key in [ "interval", "tmpdir", "start", "gpu_collect", "save_best", "rule", ]: eval_kwargs.pop(key, None) eval_kwargs.update(dict(metric=args.eval, **kwargs)) print(eval_kwargs) results_dict = dataset.evaluate(outputs, **eval_kwargs) print(results_dict) if __name__ == "__main__": torch.multiprocessing.set_start_method( "fork" ) # use fork workers_per_gpu can be > 1 main() ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/train.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. from __future__ import division import sys import os print(sys.executable, os.path.abspath(__file__)) # import init_paths # for conda pkgs submitting method import argparse import copy import mmcv import time import torch import warnings from mmcv import Config, DictAction from mmcv.runner import get_dist_info, init_dist from os import path as osp from mmdet import __version__ as mmdet_version from mmdet.apis import train_detector from mmdet.datasets import build_dataset from mmdet.models import build_detector from mmdet.utils import collect_env, get_root_logger from mmdet.apis import set_random_seed from torch import distributed as dist from datetime import timedelta import cv2 cv2.setNumThreads(8) def parse_args(): parser = argparse.ArgumentParser(description="Train a detector") parser.add_argument("config", help="train config file path") parser.add_argument("--work-dir", help="the dir to save logs and models") parser.add_argument( "--resume-from", help="the checkpoint file to resume from" ) parser.add_argument( "--no-validate", action="store_true", help="whether not to evaluate the checkpoint during training", ) group_gpus = parser.add_mutually_exclusive_group() group_gpus.add_argument( "--gpus", type=int, help="number of gpus to use " "(only applicable to non-distributed training)", ) group_gpus.add_argument( "--gpu-ids", type=int, nargs="+", help="ids of gpus to use " "(only applicable to non-distributed training)", ) parser.add_argument("--seed", type=int, default=0, help="random seed") parser.add_argument( "--deterministic", action="store_true", help="whether to set deterministic options for CUDNN backend.", ) parser.add_argument( "--options", nargs="+", action=DictAction, help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file (deprecate), " "change to --cfg-options instead.", ) parser.add_argument( "--cfg-options", nargs="+", action=DictAction, help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file. If the value to " 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' "Note that the quotation marks are necessary and that no white space " "is allowed.", ) parser.add_argument( "--dist-url", type=str, default="auto", help="dist url for init process, such as tcp://localhost:8000", ) parser.add_argument("--gpus-per-machine", type=int, default=8) parser.add_argument( "--launcher", choices=["none", "pytorch", "slurm", "mpi", "mpi_nccl"], default="none", help="job launcher", ) parser.add_argument("--local_rank", type=int, default=0) parser.add_argument( "--autoscale-lr", action="store_true", help="automatically scale lr with the number of gpus", ) args = parser.parse_args() if "LOCAL_RANK" not in os.environ: os.environ["LOCAL_RANK"] = str(args.local_rank) if args.options and args.cfg_options: raise ValueError( "--options and --cfg-options cannot be both specified, " "--options is deprecated in favor of --cfg-options" ) if args.options: warnings.warn("--options is deprecated in favor of --cfg-options") args.cfg_options = args.options return args def main(): args = parse_args() cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get("custom_imports", None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg["custom_imports"]) # import modules from plguin/xx, registry will be updated if hasattr(cfg, "plugin"): if cfg.plugin: import importlib if hasattr(cfg, "plugin_dir"): plugin_dir = cfg.plugin_dir _module_dir = os.path.dirname(plugin_dir) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) else: # import dir is the dirpath for the config file _module_dir = os.path.dirname(args.config) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) from projects.mmdet3d_plugin.apis.train import custom_train_model # set cudnn_benchmark if cfg.get("cudnn_benchmark", False): torch.backends.cudnn.benchmark = True # work_dir is determined in this priority: CLI > segment in file > filename if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get("work_dir", None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join( "./work_dirs", osp.splitext(osp.basename(args.config))[0] ) if args.resume_from is not None: cfg.resume_from = args.resume_from if args.gpu_ids is not None: cfg.gpu_ids = args.gpu_ids else: cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) if args.autoscale_lr: # apply the linear scaling rule (https://arxiv.org/abs/1706.02677) cfg.optimizer["lr"] = cfg.optimizer["lr"] * len(cfg.gpu_ids) / 8 # init distributed env first, since logger depends on the dist info. if args.launcher == "none": distributed = False elif args.launcher == "mpi_nccl": distributed = True import mpi4py.MPI as MPI comm = MPI.COMM_WORLD mpi_local_rank = comm.Get_rank() mpi_world_size = comm.Get_size() print( "MPI local_rank=%d, world_size=%d" % (mpi_local_rank, mpi_world_size) ) # num_gpus = torch.cuda.device_count() device_ids_on_machines = list(range(args.gpus_per_machine)) str_ids = list(map(str, device_ids_on_machines)) os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str_ids) torch.cuda.set_device(mpi_local_rank % args.gpus_per_machine) dist.init_process_group( backend="nccl", init_method=args.dist_url, world_size=mpi_world_size, rank=mpi_local_rank, timeout=timedelta(seconds=3600), ) cfg.gpu_ids = range(mpi_world_size) print("cfg.gpu_ids:", cfg.gpu_ids) else: distributed = True init_dist( args.launcher, timeout=timedelta(seconds=3600), **cfg.dist_params ) # re-set gpu_ids with distributed training mode _, world_size = get_dist_info() cfg.gpu_ids = range(world_size) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # dump config cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) # init the logger before other steps timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) log_file = osp.join(cfg.work_dir, f"{timestamp}.log") # specify logger name, if we still use 'mmdet', the output info will be # filtered and won't be saved in the log_file # TODO: ugly workaround to judge whether we are training det or seg model logger = get_root_logger( log_file=log_file, log_level=cfg.log_level ) # init the meta dict to record some important information such as # environment info and seed, which will be logged meta = dict() # log env info env_info_dict = collect_env() env_info = "\n".join([(f"{k}: {v}") for k, v in env_info_dict.items()]) dash_line = "-" * 60 + "\n" logger.info( "Environment info:\n" + dash_line + env_info + "\n" + dash_line ) meta["env_info"] = env_info meta["config"] = cfg.pretty_text # log some basic info logger.info(f"Distributed training: {distributed}") logger.info(f"Config:\n{cfg.pretty_text}") # set random seeds if args.seed is not None: logger.info( f"Set random seed to {args.seed}, " f"deterministic: {args.deterministic}" ) set_random_seed(args.seed, deterministic=args.deterministic) cfg.seed = args.seed meta["seed"] = args.seed meta["exp_name"] = osp.basename(args.config) model = build_detector( cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg") ) model.init_weights() logger.info(f"Model:\n{model}") cfg.data.train.work_dir = cfg.work_dir cfg.data.val.work_dir = cfg.work_dir # print("hhhhhhhhhhhhhhh") print(cfg.data.train) datasets = [build_dataset(cfg.data.train)] if len(cfg.workflow) == 2: val_dataset = copy.deepcopy(cfg.data.val) # in case we use a dataset wrapper if "dataset" in cfg.data.train: val_dataset.pipeline = cfg.data.train.dataset.pipeline else: val_dataset.pipeline = cfg.data.train.pipeline # set test_mode=False here in deep copied config # which do not affect AP/AR calculation later # refer to https://mmdetection3d.readthedocs.io/en/latest/tutorials/customize_runtime.html#customize-workflow # noqa val_dataset.test_mode = False datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmdet version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict( mmdet_version=mmdet_version, config=cfg.pretty_text, CLASSES=datasets[0].CLASSES, ) # add an attribute for visualization convenience model.CLASSES = datasets[0].CLASSES if hasattr(cfg, "plugin"): custom_train_model( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta, ) else: train_detector( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta, ) if __name__ == "__main__": torch.multiprocessing.set_start_method( "fork" ) # use fork workers_per_gpu can be > 1 main() ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/train_single.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. from __future__ import division import sys import os print(sys.executable, os.path.abspath(__file__)) # import init_paths # for conda pkgs submitting method # for single gpu debug BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(ROOT_DIR) ############### import argparse import copy import mmcv import time import torch import warnings from mmcv import Config, DictAction from mmcv.runner import get_dist_info, init_dist from os import path as osp from mmdet import __version__ as mmdet_version from mmdet.apis import train_detector from mmdet.datasets import build_dataset from mmdet.models import build_detector from mmdet.utils import collect_env, get_root_logger from mmdet.apis import set_random_seed from torch import distributed as dist from datetime import timedelta import cv2 cv2.setNumThreads(8) def parse_args(): parser = argparse.ArgumentParser(description="Train a detector") parser.add_argument("config", help="train config file path") parser.add_argument("--work-dir", help="the dir to save logs and models") parser.add_argument( "--resume-from", help="the checkpoint file to resume from" ) parser.add_argument( "--no-validate", action="store_true", help="whether not to evaluate the checkpoint during training", ) group_gpus = parser.add_mutually_exclusive_group() group_gpus.add_argument( "--gpus", type=int, help="number of gpus to use " "(only applicable to non-distributed training)", ) group_gpus.add_argument( "--gpu-ids", type=int, nargs="+", help="ids of gpus to use " "(only applicable to non-distributed training)", ) parser.add_argument("--seed", type=int, default=0, help="random seed") parser.add_argument( "--deterministic", action="store_true", help="whether to set deterministic options for CUDNN backend.", ) parser.add_argument( "--options", nargs="+", action=DictAction, help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file (deprecate), " "change to --cfg-options instead.", ) parser.add_argument( "--cfg-options", nargs="+", action=DictAction, help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file. If the value to " 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' "Note that the quotation marks are necessary and that no white space " "is allowed.", ) parser.add_argument( "--dist-url", type=str, default="auto", help="dist url for init process, such as tcp://localhost:8000", ) parser.add_argument("--gpus-per-machine", type=int, default=8) parser.add_argument( "--launcher", choices=["none", "pytorch", "slurm", "mpi", "mpi_nccl"], default="none", help="job launcher", ) parser.add_argument("--local_rank", type=int, default=0) parser.add_argument( "--autoscale-lr", action="store_true", help="automatically scale lr with the number of gpus", ) args = parser.parse_args() if "LOCAL_RANK" not in os.environ: os.environ["LOCAL_RANK"] = str(args.local_rank) if args.options and args.cfg_options: raise ValueError( "--options and --cfg-options cannot be both specified, " "--options is deprecated in favor of --cfg-options" ) if args.options: warnings.warn("--options is deprecated in favor of --cfg-options") args.cfg_options = args.options return args def main(): args = parse_args() cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get("custom_imports", None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg["custom_imports"]) # import modules from plguin/xx, registry will be updated if hasattr(cfg, "plugin"): if cfg.plugin: import importlib if hasattr(cfg, "plugin_dir"): plugin_dir = cfg.plugin_dir _module_dir = os.path.dirname(plugin_dir) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) else: # import dir is the dirpath for the config file _module_dir = os.path.dirname(args.config) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) from projects.mmdet3d_plugin.apis.train import custom_train_model # set cudnn_benchmark if cfg.get("cudnn_benchmark", False): torch.backends.cudnn.benchmark = True # work_dir is determined in this priority: CLI > segment in file > filename if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get("work_dir", None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join( "./work_dirs", osp.splitext(osp.basename(args.config))[0] ) if args.resume_from is not None: cfg.resume_from = args.resume_from if args.gpu_ids is not None: cfg.gpu_ids = args.gpu_ids else: cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) if args.autoscale_lr: # apply the linear scaling rule (https://arxiv.org/abs/1706.02677) cfg.optimizer["lr"] = cfg.optimizer["lr"] * len(cfg.gpu_ids) / 8 # init distributed env first, since logger depends on the dist info. if args.launcher == "none": distributed = False elif args.launcher == "mpi_nccl": distributed = True import mpi4py.MPI as MPI comm = MPI.COMM_WORLD mpi_local_rank = comm.Get_rank() mpi_world_size = comm.Get_size() print( "MPI local_rank=%d, world_size=%d" % (mpi_local_rank, mpi_world_size) ) # num_gpus = torch.cuda.device_count() device_ids_on_machines = list(range(args.gpus_per_machine)) str_ids = list(map(str, device_ids_on_machines)) os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str_ids) torch.cuda.set_device(mpi_local_rank % args.gpus_per_machine) dist.init_process_group( backend="nccl", init_method=args.dist_url, world_size=mpi_world_size, rank=mpi_local_rank, timeout=timedelta(seconds=3600), ) cfg.gpu_ids = range(mpi_world_size) print("cfg.gpu_ids:", cfg.gpu_ids) else: distributed = True init_dist( args.launcher, timeout=timedelta(seconds=3600), **cfg.dist_params ) # re-set gpu_ids with distributed training mode _, world_size = get_dist_info() cfg.gpu_ids = range(world_size) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # dump config cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) # init the logger before other steps timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) log_file = osp.join(cfg.work_dir, f"{timestamp}.log") # specify logger name, if we still use 'mmdet', the output info will be # filtered and won't be saved in the log_file # TODO: ugly workaround to judge whether we are training det or seg model logger = get_root_logger( log_file=log_file, log_level=cfg.log_level ) # init the meta dict to record some important information such as # environment info and seed, which will be logged meta = dict() # log env info env_info_dict = collect_env() env_info = "\n".join([(f"{k}: {v}") for k, v in env_info_dict.items()]) dash_line = "-" * 60 + "\n" logger.info( "Environment info:\n" + dash_line + env_info + "\n" + dash_line ) meta["env_info"] = env_info meta["config"] = cfg.pretty_text # log some basic info logger.info(f"Distributed training: {distributed}") logger.info(f"Config:\n{cfg.pretty_text}") # set random seeds if args.seed is not None: logger.info( f"Set random seed to {args.seed}, " f"deterministic: {args.deterministic}" ) set_random_seed(args.seed, deterministic=args.deterministic) cfg.seed = args.seed meta["seed"] = args.seed meta["exp_name"] = osp.basename(args.config) model = build_detector( cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg") ) model.init_weights() logger.info(f"Model:\n{model}") cfg.data.train.work_dir = cfg.work_dir cfg.data.val.work_dir = cfg.work_dir datasets = [build_dataset(cfg.data.train)] if len(cfg.workflow) == 2: val_dataset = copy.deepcopy(cfg.data.val) # in case we use a dataset wrapper if "dataset" in cfg.data.train: val_dataset.pipeline = cfg.data.train.dataset.pipeline else: val_dataset.pipeline = cfg.data.train.pipeline # set test_mode=False here in deep copied config # which do not affect AP/AR calculation later # refer to https://mmdetection3d.readthedocs.io/en/latest/tutorials/customize_runtime.html#customize-workflow # noqa val_dataset.test_mode = False datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmdet version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict( mmdet_version=mmdet_version, config=cfg.pretty_text, CLASSES=datasets[0].CLASSES, ) # add an attribute for visualization convenience model.CLASSES = datasets[0].CLASSES if hasattr(cfg, "plugin"): custom_train_model( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta, ) else: train_detector( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta, ) if __name__ == "__main__": torch.multiprocessing.set_start_method( "fork" ) # use fork workers_per_gpu can be > 1 main() ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/visualization/bev_render.py ================================================ import os import numpy as np import cv2 import matplotlib import matplotlib.pyplot as plt #from .projects.mmdet3d_plugin.datasets.utils import box3d_to_corners def box3d_to_corners(box3d): if isinstance(box3d, torch.Tensor): box3d = box3d.detach().cpu().numpy() corners_norm = np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1) corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]] # use relative origin [0.5, 0.5, 0] corners_norm = corners_norm - np.array([0.5, 0.5, 0.5]) corners = box3d[:, None, [W, L, H]] * corners_norm.reshape([1, 8, 3]) # rotate around z axis rot_cos = np.cos(box3d[:, YAW]) rot_sin = np.sin(box3d[:, YAW]) rot_mat = np.tile(np.eye(3)[None], (box3d.shape[0], 1, 1)) rot_mat[:, 0, 0] = rot_cos rot_mat[:, 0, 1] = -rot_sin rot_mat[:, 1, 0] = rot_sin rot_mat[:, 1, 1] = rot_cos corners = (rot_mat[:, None] @ corners[..., None]).squeeze(axis=-1) corners += box3d[:, None, :3] return corners CMD_LIST = ['Turn Right', 'Turn Left', 'Go Straight'] COLOR_VECTORS = ['cornflowerblue', 'royalblue', 'slategrey'] SCORE_THRESH = 0.3 MAP_SCORE_THRESH = 0.3 color_mapping = np.asarray([ [0, 0, 0], [255, 179, 0], [128, 62, 117], [255, 104, 0], [166, 189, 215], [193, 0, 32], [206, 162, 98], [129, 112, 102], [0, 125, 52], [246, 118, 142], [0, 83, 138], [255, 122, 92], [83, 55, 122], [255, 142, 0], [179, 40, 81], [244, 200, 0], [127, 24, 13], [147, 170, 0], [89, 51, 21], [241, 58, 19], [35, 44, 22], [112, 224, 255], [70, 184, 160], [153, 0, 255], [71, 255, 0], [255, 0, 163], [255, 204, 0], [0, 255, 235], [255, 0, 235], [255, 0, 122], [255, 245, 0], [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 255, 204], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [0, 255, 184], [0, 92, 255], [184, 255, 0], [255, 214, 0], [25, 194, 194], [92, 0, 255], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], [250, 10, 15], [20, 255, 0], ]) / 255 class BEVRender: def __init__( self, plot_choices, out_dir, xlim = 40, ylim = 40, ): self.plot_choices = plot_choices self.xlim = xlim self.ylim = ylim self.gt_dir = os.path.join(out_dir, "bev_gt") self.pred_dir = os.path.join(out_dir, "bev_pred") os.makedirs(self.gt_dir, exist_ok=True) os.makedirs(self.pred_dir, exist_ok=True) def reset_canvas(self): plt.close() self.fig, self.axes = plt.subplots(1, 1, figsize=(20, 20)) self.axes.set_xlim(- self.xlim, self.xlim) self.axes.set_ylim(- self.ylim, self.ylim) self.axes.axis('off') def render( self, data, result, index, ): self.reset_canvas() self.draw_detection_gt(data) self.draw_motion_gt(data) self.draw_map_gt(data) self.draw_planning_gt(data) self._render_sdc_car() self._render_command(data) self._render_legend() save_path_gt = os.path.join(self.gt_dir, str(index).zfill(4) + '.jpg') self.save_fig(save_path_gt) self.reset_canvas() self.draw_detection_pred(result) self.draw_track_pred(result) self.draw_motion_pred(result) self.draw_map_pred(result) self.draw_planning_pred(data, result) self._render_sdc_car() self._render_command(data) self._render_legend() save_path_pred = os.path.join(self.pred_dir, str(index).zfill(4) + '.jpg') self.save_fig(save_path_pred) return save_path_gt, save_path_pred def save_fig(self, filename): plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) plt.margins(0, 0) plt.savefig(filename) def draw_detection_gt(self, data): if not self.plot_choices['det']: return for i in range(data['gt_labels_3d'].shape[0]): label = data['gt_labels_3d'][i] if label == -1: continue color = color_mapping[i % len(color_mapping)] # draw corners corners = box3d_to_corners(data['gt_bboxes_3d'])[i, [0, 3, 7, 4, 0]] x = corners[:, 0] y = corners[:, 1] self.axes.plot(x, y, color=color, linewidth=3, linestyle='-') # draw line to indicate forward direction forward_center = np.mean(corners[2:4], axis=0) center = np.mean(corners[0:4], axis=0) x = [forward_center[0], center[0]] y = [forward_center[1], center[1]] self.axes.plot(x, y, color=color, linewidth=3, linestyle='-') def draw_detection_pred(self, result): if not (self.plot_choices['draw_pred'] and self.plot_choices['det'] and "boxes_3d" in result): return bboxes = result['boxes_3d'] for i in range(result['labels_3d'].shape[0]): score = result['scores_3d'][i] if score < SCORE_THRESH: continue color = color_mapping[result['instance_ids'][i] % len(color_mapping)] # draw corners corners = box3d_to_corners(bboxes)[i, [0, 3, 7, 4, 0]] x = corners[:, 0] y = corners[:, 1] self.axes.plot(x, y, color=color, linewidth=3, linestyle='-') # draw line to indicate forward direction forward_center = np.mean(corners[2:4], axis=0) center = np.mean(corners[0:4], axis=0) x = [forward_center[0], center[0]] y = [forward_center[1], center[1]] self.axes.plot(x, y, color=color, linewidth=3, linestyle='-') def draw_track_pred(self, result): if not (self.plot_choices['draw_pred'] and self.plot_choices['track'] and "anchor_queue" in result): return temp_bboxes = result["anchor_queue"] period = result["period"] bboxes = result['boxes_3d'] for i in range(result['labels_3d'].shape[0]): score = result['scores_3d'][i] if score < SCORE_THRESH: continue color = color_mapping[result['instance_ids'][i] % len(color_mapping)] center = bboxes[i, :3] centers = [center] for j in range(period[i]): # draw corners corners = box3d_to_corners(temp_bboxes[:, -1-j])[i, [0, 3, 7, 4, 0]] x = corners[:, 0] y = corners[:, 1] self.axes.plot(x, y, color=color, linewidth=2, linestyle='-') # draw line to indicate forward direction forward_center = np.mean(corners[2:4], axis=0) center = np.mean(corners[0:4], axis=0) x = [forward_center[0], center[0]] y = [forward_center[1], center[1]] self.axes.plot(x, y, color=color, linewidth=2, linestyle='-') centers.append(center) centers = np.stack(centers) xs = centers[:, 0] ys = centers[:, 1] self.axes.plot(xs, ys, color=color, linewidth=2, linestyle='-') def draw_motion_gt(self, data): if not self.plot_choices['motion']: return for i in range(data['gt_labels_3d'].shape[0]): label = data['gt_labels_3d'][i] if label == -1: continue color = color_mapping[i % len(color_mapping)] vehicle_id_list = [0, 1, 2, 3, 4, 6, 7] if label in vehicle_id_list: dot_size = 150 else: dot_size = 25 center = data['gt_bboxes_3d'][i, :2] masks = data['gt_agent_fut_masks'][i].astype(bool) if masks[0] == 0: continue trajs = data['gt_agent_fut_trajs'][i][masks] trajs = trajs.cumsum(axis=0) + center trajs = np.concatenate([center.reshape(1, 2), trajs], axis=0) self._render_traj(trajs, traj_score=1.0, colormap='winter', dot_size=dot_size) def draw_motion_pred(self, result, top_k=3): if not (self.plot_choices['draw_pred'] and self.plot_choices['motion'] and "trajs_3d" in result): return bboxes = result['boxes_3d'] labels = result['labels_3d'] for i in range(result['labels_3d'].shape[0]): score = result['scores_3d'][i] if score < SCORE_THRESH: continue label = labels[i] vehicle_id_list = [0, 1, 2, 3, 4, 6, 7] if label in vehicle_id_list: dot_size = 150 else: dot_size = 25 traj_score = result['trajs_score'][i].numpy() traj = result['trajs_3d'][i].numpy() num_modes = len(traj_score) center = bboxes[i, :2][None, None].repeat(num_modes, 1, 1).numpy() traj = np.concatenate([center, traj], axis=1) sorted_ind = np.argsort(traj_score)[::-1] sorted_traj = traj[sorted_ind, :, :2] sorted_score = traj_score[sorted_ind] norm_score = np.exp(sorted_score[0]) for j in range(top_k - 1, -1, -1): viz_traj = sorted_traj[j] traj_score = np.exp(sorted_score[j])/norm_score self._render_traj(viz_traj, traj_score=traj_score, colormap='winter', dot_size=dot_size) def draw_map_gt(self, data): if not self.plot_choices['map']: return vectors = data['map_infos'] for label, vector_list in vectors.items(): color = COLOR_VECTORS[label] for vector in vector_list: pts = vector[:, :2] x = np.array([pt[0] for pt in pts]) y = np.array([pt[1] for pt in pts]) self.axes.plot(x, y, color=color, linewidth=3, marker='o', linestyle='-', markersize=7) def draw_map_pred(self, result): if not (self.plot_choices['draw_pred'] and self.plot_choices['map'] and "vectors" in result): return for i in range(result['scores'].shape[0]): score = result['scores'][i] if score < MAP_SCORE_THRESH: continue color = COLOR_VECTORS[result['labels'][i]] pts = result['vectors'][i] x = pts[:, 0] y = pts[:, 1] plt.plot(x, y, color=color, linewidth=3, marker='o', linestyle='-', markersize=7) def draw_planning_gt(self, data): if not self.plot_choices['planning']: return # draw planning gt masks = data['gt_ego_fut_masks'].astype(bool) if masks[0] != 0: plan_traj = data['gt_ego_fut_trajs'][masks] cmd = data['gt_ego_fut_cmd'] plan_traj[abs(plan_traj) < 0.01] = 0.0 plan_traj = plan_traj.cumsum(axis=0) plan_traj = np.concatenate((np.zeros((1, plan_traj.shape[1])), plan_traj), axis=0) self._render_traj(plan_traj, traj_score=1.0, colormap='autumn', dot_size=50) def draw_planning_pred(self, data, result, top_k=3): if not (self.plot_choices['draw_pred'] and self.plot_choices['planning'] and "planning" in result): return if self.plot_choices['track'] and "ego_anchor_queue" in result: ego_temp_bboxes = result["ego_anchor_queue"] ego_period = result["ego_period"] for j in range(ego_period[0]): # draw corners corners = box3d_to_corners(ego_temp_bboxes[:, -1-j])[0, [0, 3, 7, 4, 0]] x = corners[:, 0] y = corners[:, 1] self.axes.plot(x, y, color='mediumseagreen', linewidth=2, linestyle='-') # draw line to indicate forward direction forward_center = np.mean(corners[2:4], axis=0) center = np.mean(corners[0:4], axis=0) x = [forward_center[0], center[0]] y = [forward_center[1], center[1]] self.axes.plot(x, y, color='mediumseagreen', linewidth=2, linestyle='-') # import ipdb; ipdb.set_trace() plan_trajs = result['planning'].cpu().numpy() num_cmd = len(CMD_LIST) num_mode = plan_trajs.shape[1] plan_trajs = np.concatenate((np.zeros((num_cmd, num_mode, 1, 2)), plan_trajs), axis=2) plan_score = result['planning_score'].cpu().numpy() cmd = data['gt_ego_fut_cmd'].argmax() plan_trajs = plan_trajs[cmd] plan_score = plan_score[cmd] sorted_ind = np.argsort(plan_score)[::-1] sorted_traj = plan_trajs[sorted_ind, :, :2] sorted_score = plan_score[sorted_ind] norm_score = np.exp(sorted_score[0]) for j in range(top_k - 1, -1, -1): viz_traj = sorted_traj[j] traj_score = np.exp(sorted_score[j]) / norm_score self._render_traj(viz_traj, traj_score=traj_score, colormap='autumn', dot_size=50) def _render_traj( self, future_traj, traj_score=1, colormap='winter', points_per_step=20, dot_size=25 ): total_steps = (len(future_traj) - 1) * points_per_step + 1 dot_colors = matplotlib.colormaps[colormap]( np.linspace(0, 1, total_steps))[:, :3] dot_colors = dot_colors * traj_score + \ (1 - traj_score) * np.ones_like(dot_colors) total_xy = np.zeros((total_steps, 2)) for i in range(total_steps - 1): unit_vec = future_traj[i // points_per_step + 1] - future_traj[i // points_per_step] total_xy[i] = (i / points_per_step - i // points_per_step) * \ unit_vec + future_traj[i // points_per_step] total_xy[-1] = future_traj[-1] self.axes.scatter( total_xy[:, 0], total_xy[:, 1], c=dot_colors, s=dot_size) def _render_sdc_car(self): sdc_car_png = cv2.imread('resources/sdc_car.png') sdc_car_png = cv2.cvtColor(sdc_car_png, cv2.COLOR_BGR2RGB) im = self.axes.imshow(sdc_car_png, extent=(-1, 1, -2, 2)) im.set_zorder(2) def _render_legend(self): legend = cv2.imread('resources/legend.png') legend = cv2.cvtColor(legend, cv2.COLOR_BGR2RGB) self.axes.imshow(legend, extent=(15, 40, -40, -30)) def _render_command(self, data): cmd = data['gt_ego_fut_cmd'].argmax() self.axes.text(-38, -38, CMD_LIST[cmd], fontsize=60) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/tools/visualization/cam_render.py ================================================ import os import numpy as np import cv2 from PIL import Image import matplotlib import matplotlib.pyplot as plt from pyquaternion import Quaternion from nuscenes.utils.data_classes import Box as NuScenesBox from nuscenes.utils.geometry_utils import view_points, box_in_image, BoxVisibility, transform_matrix from tools.visualization.bev_render import ( color_mapping, SCORE_THRESH, MAP_SCORE_THRESH, CMD_LIST ) CAM_NAMES_NUSC = [ 'CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_BACK_RIGHT', 'CAM_BACK', 'CAM_BACK_LEFT', ] CAM_NAMES_NUSC_converter = [ 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT', ] class CamRender: def __init__( self, plot_choices, out_dir, ): self.plot_choices = plot_choices self.pred_dir = os.path.join(out_dir, "cam_pred") os.makedirs(self.pred_dir, exist_ok=True) def reset_canvas(self): plt.close() plt.gca().set_axis_off() plt.axis('off') self.fig, self.axes = plt.subplots(2, 3, figsize=(160 /3 , 20)) plt.tight_layout() def render( self, data, result, index, ): self.reset_canvas() self.render_image_data(data, index) self.draw_detection_pred(data, result) self.draw_motion_pred(data, result) self.draw_planning_pred(data, result) save_path = os.path.join(self.pred_dir, str(index).zfill(4) + '.jpg') self.save_fig(save_path) return save_path def load_image(self, data_path, cam): """Update the axis of the plot with the provided image.""" image = np.array(Image.open(data_path)) font = cv2.FONT_HERSHEY_SIMPLEX org = (50, 60) fontScale = 2 color = (0, 0, 0) thickness = 4 return cv2.putText(image, cam, org, font, fontScale, color, thickness, cv2.LINE_AA) def update_image(self, image, index, cam): """Render image data for each camera.""" ax = self.get_axis(index) ax.imshow(image) plt.axis('off') ax.axis('off') ax.grid(False) def get_axis(self, index): """Retrieve the corresponding axis based on the index.""" return self.axes[index//3, index % 3] def save_fig(self, filename): plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) plt.margins(0, 0) plt.savefig(filename) def render_image_data(self, data, index): """Load and annotate image based on the provided path.""" for i, cam in enumerate(CAM_NAMES_NUSC): idx = CAM_NAMES_NUSC_converter.index(cam) img_path = data['img_filename'][idx] image = self.load_image(img_path, cam) self.update_image(image, i, cam) def draw_detection_pred(self, data, result): if not (self.plot_choices['draw_pred'] and self.plot_choices['det'] and "boxes_3d" in result): return bboxes = result['boxes_3d'].numpy() for j, cam in enumerate(CAM_NAMES_NUSC): idx = CAM_NAMES_NUSC_converter.index(cam) cam_intrinsic = data['cam_intrinsic'][idx] lidar2cam = data['lidar2cam'] extrinsic = lidar2cam[idx] trans = extrinsic[3, :3] rot = Quaternion(matrix=extrinsic[:3, :3]).inverse imsize = (1600, 900) for i in range(result['labels_3d'].shape[0]): score = result['scores_3d'][i] if score < SCORE_THRESH: continue color = color_mapping[result['instance_ids'][i] % len(color_mapping)] center = bboxes[i, 0 : 3] box_dims = bboxes[i, 3 : 6] nusc_dims = box_dims[..., [1, 0, 2]] quat = Quaternion(axis=[0, 0, 1], radians=bboxes[i, 6]) box = NuScenesBox( center, nusc_dims, quat ) box.rotate(rot) box.translate(trans) if box_in_image(box, cam_intrinsic, imsize): box.render( self.axes[j // 3, j % 3], view=cam_intrinsic, normalize=True, colors=(color, color, color), linewidth=4, ) self.axes[j//3, j % 3].set_xlim(0, imsize[0]) self.axes[j//3, j % 3].set_ylim(imsize[1], 0) def draw_motion_pred(self, data, result, points_per_step=10): if not (self.plot_choices['draw_pred'] and self.plot_choices['motion'] and "trajs_3d" in result): return bboxes = result['boxes_3d'].numpy() for j, cam in enumerate(CAM_NAMES_NUSC): idx = CAM_NAMES_NUSC_converter.index(cam) cam_intrinsic = data['cam_intrinsic'][idx] lidar2cam = data['lidar2cam'] extrinsic = lidar2cam[idx] trans = extrinsic[3, :3] rot = Quaternion(matrix=extrinsic[:3, :3]).inverse imsize = (1600, 900) for i in range(result['labels_3d'].shape[0]): score = result['scores_3d'][i] if score < SCORE_THRESH: continue color = color_mapping[result['instance_ids'][i] % len(color_mapping)] traj_score = result['trajs_score'][i].numpy() traj = result['trajs_3d'][i].numpy() mode_idx = traj_score.argmax() traj = traj[mode_idx] origin = bboxes[i, :2][None] traj = np.concatenate([origin, traj], axis=0) traj_expand = np.ones((traj.shape[0], 1)) traj_expand[:] = bboxes[i, 2] - bboxes[i, 5] / 2 traj = np.concatenate([traj, traj_expand], axis=1) center = bboxes[i, 0 : 3] box_dims = bboxes[i, 3 : 6] nusc_dims = box_dims[..., [1, 0, 2]] quat = Quaternion(axis=[0, 0, 1], radians=bboxes[i, 6]) box = NuScenesBox( center, nusc_dims, quat ) box.rotate(rot) box.translate(trans) if not box_in_image(box, cam_intrinsic, imsize): continue traj_points = traj @ extrinsic[:3, :3] + trans self._render_traj(traj_points, cam_intrinsic, j, color=color, s=15) def draw_planning_pred(self, data, result): if not (self.plot_choices['draw_pred'] and self.plot_choices['planning'] and "planning" in result): return # for j, cam in enumerate(CAM_NAMES_NUSC[1]): # idx = CAM_NAMES_NUSC_converter.index(cam) # cam_intrinsic = data['cam_intrinsic'][idx] # lidar2cam = data['lidar2cam'] # extrinsic = lidar2cam[idx] # trans = extrinsic[3, :3] # rot = Quaternion(matrix=extrinsic[:3, :3]).inverse # imsize = (1600, 900) # plan_trajs = result['planning'][0].cpu().numpy() # plan_trajs = plan_trajs.reshape(3, -1, 6, 2) # num_cmd = len(CMD_LIST) # num_mode = plan_trajs.shape[1] # plan_trajs = np.concatenate((np.zeros((num_cmd, num_mode, 1, 2)), plan_trajs), axis=2) # plan_trajs = plan_trajs.cumsum(axis=-2) # plan_score = result['planning_score'][0].cpu().numpy() # plan_score = plan_score.reshape(3, -1) # cmd = data['gt_ego_fut_cmd'].argmax() # plan_trajs = plan_trajs[cmd] # plan_score = plan_score[cmd] # mode_idx = plan_score.argmax() # plan_traj = plan_trajs[mode_idx] # traj_expand = np.ones((plan_traj.shape[0], 1)) * -2 # # traj_expand[:] = bboxes[i, 2] - bboxes[i, 5] / 2 # plan_traj = np.concatenate([plan_traj, traj_expand], axis=1) # traj_points = plan_traj @ extrinsic[:3, :3] + trans # self._render_traj(traj_points, cam_intrinsic, j) idx = 0 ## front camera cam_intrinsic = data['cam_intrinsic'][idx] lidar2cam = data['lidar2cam'] extrinsic = lidar2cam[idx] trans = extrinsic[3, :3] rot = Quaternion(matrix=extrinsic[:3, :3]).inverse # plan_trajs = result['planning'][0].cpu().numpy() # plan_trajs = plan_trajs.reshape(3, -1, 6, 2) # num_cmd = len(CMD_LIST) # num_mode = plan_trajs.shape[1] # plan_trajs = np.concatenate((np.zeros((num_cmd, num_mode, 1, 2)), plan_trajs), axis=2) # plan_trajs = plan_trajs.cumsum(axis=-2) # plan_score = result['planning_score'][0].cpu().numpy() # plan_score = plan_score.reshape(3, -1) # cmd = data['gt_ego_fut_cmd'].argmax() # plan_trajs = plan_trajs[cmd] # plan_score = plan_score[cmd] # mode_idx = plan_score.argmax() # plan_traj = plan_trajs[mode_idx] plan_traj = result["final_planning"] plan_traj = np.concatenate((np.zeros((1, 2)), plan_traj), axis=0) traj_expand = np.ones((plan_traj.shape[0], 1)) * -1.8 plan_traj = np.concatenate([plan_traj, traj_expand], axis=1) traj_points = plan_traj @ extrinsic[:3, :3] + trans self._render_traj(traj_points, cam_intrinsic, j=1) def _render_traj(self, traj_points, cam_intrinsic, j, color=(1, 0.5, 0), s=150, points_per_step=10): total_steps = (len(traj_points)-1) * points_per_step + 1 total_xy = np.zeros((total_steps, 3)) for k in range(total_steps-1): unit_vec = traj_points[k//points_per_step + 1] - traj_points[k//points_per_step] total_xy[k] = (k/points_per_step - k//points_per_step) * \ unit_vec + traj_points[k//points_per_step] in_range_mask = total_xy[:, 2] > 0.1 traj_points = view_points( total_xy.T, cam_intrinsic, normalize=True)[:2, :] traj_points = traj_points[:2, in_range_mask] self.axes[j // 3, j % 3].scatter(traj_points[0], traj_points[1], color=color, s=s) ================================================ FILE: close_loop/SparseDrive_MomAD/adzoo/sparsedrive/train.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. from __future__ import division import sys import os print(sys.executable, os.path.abspath(__file__)) # import init_paths # for conda pkgs submitting method import argparse import copy import mmcv import time import torch import warnings from mmcv import Config, DictAction from mmcv.runner import get_dist_info, init_dist from os import path as osp from mmdet import __version__ as mmdet_version from mmdet.apis import train_detector from mmdet.datasets import build_dataset from mmdet.models import build_detector from mmdet.utils import collect_env, get_root_logger from mmdet.apis import set_random_seed from torch import distributed as dist from datetime import timedelta import cv2 cv2.setNumThreads(8) def parse_args(): parser = argparse.ArgumentParser(description="Train a detector") parser.add_argument("config", help="train config file path") parser.add_argument("--work-dir", help="the dir to save logs and models") parser.add_argument( "--resume-from", help="the checkpoint file to resume from" ) parser.add_argument( "--no-validate", action="store_true", help="whether not to evaluate the checkpoint during training", ) group_gpus = parser.add_mutually_exclusive_group() group_gpus.add_argument( "--gpus", type=int, help="number of gpus to use " "(only applicable to non-distributed training)", ) group_gpus.add_argument( "--gpu-ids", type=int, nargs="+", help="ids of gpus to use " "(only applicable to non-distributed training)", ) parser.add_argument("--seed", type=int, default=0, help="random seed") parser.add_argument( "--deterministic", action="store_true", help="whether to set deterministic options for CUDNN backend.", ) parser.add_argument( "--options", nargs="+", action=DictAction, help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file (deprecate), " "change to --cfg-options instead.", ) parser.add_argument( "--cfg-options", nargs="+", action=DictAction, help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file. If the value to " 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' "Note that the quotation marks are necessary and that no white space " "is allowed.", ) parser.add_argument( "--dist-url", type=str, default="auto", help="dist url for init process, such as tcp://localhost:8000", ) parser.add_argument("--gpus-per-machine", type=int, default=8) parser.add_argument( "--launcher", choices=["none", "pytorch", "slurm", "mpi", "mpi_nccl"], default="none", help="job launcher", ) parser.add_argument("--local_rank", type=int, default=0) parser.add_argument( "--autoscale-lr", action="store_true", help="automatically scale lr with the number of gpus", ) args = parser.parse_args() if "LOCAL_RANK" not in os.environ: os.environ["LOCAL_RANK"] = str(args.local_rank) if args.options and args.cfg_options: raise ValueError( "--options and --cfg-options cannot be both specified, " "--options is deprecated in favor of --cfg-options" ) if args.options: warnings.warn("--options is deprecated in favor of --cfg-options") args.cfg_options = args.options return args def main(): args = parse_args() cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # import modules from string list. if cfg.get("custom_imports", None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg["custom_imports"]) # import modules from plguin/xx, registry will be updated if hasattr(cfg, "plugin"): if cfg.plugin: import importlib if hasattr(cfg, "plugin_dir"): plugin_dir = cfg.plugin_dir _module_dir = os.path.dirname(plugin_dir) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) else: # import dir is the dirpath for the config file _module_dir = os.path.dirname(args.config) _module_dir = _module_dir.split("/") _module_path = _module_dir[0] for m in _module_dir[1:]: _module_path = _module_path + "." + m print(_module_path) plg_lib = importlib.import_module(_module_path) from mmdet3d_plugin.apis.train import custom_train_model # set cudnn_benchmark if cfg.get("cudnn_benchmark", False): torch.backends.cudnn.benchmark = True # work_dir is determined in this priority: CLI > segment in file > filename if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get("work_dir", None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join( "./work_dirs", osp.splitext(osp.basename(args.config))[0] ) if args.resume_from is not None: cfg.resume_from = args.resume_from if args.gpu_ids is not None: cfg.gpu_ids = args.gpu_ids else: cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) if args.autoscale_lr: # apply the linear scaling rule (https://arxiv.org/abs/1706.02677) cfg.optimizer["lr"] = cfg.optimizer["lr"] * len(cfg.gpu_ids) / 8 # init distributed env first, since logger depends on the dist info. if args.launcher == "none": distributed = False elif args.launcher == "mpi_nccl": distributed = True import mpi4py.MPI as MPI comm = MPI.COMM_WORLD mpi_local_rank = comm.Get_rank() mpi_world_size = comm.Get_size() print( "MPI local_rank=%d, world_size=%d" % (mpi_local_rank, mpi_world_size) ) # num_gpus = torch.cuda.device_count() device_ids_on_machines = list(range(args.gpus_per_machine)) str_ids = list(map(str, device_ids_on_machines)) os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str_ids) torch.cuda.set_device(mpi_local_rank % args.gpus_per_machine) dist.init_process_group( backend="nccl", init_method=args.dist_url, world_size=mpi_world_size, rank=mpi_local_rank, timeout=timedelta(seconds=3600), ) cfg.gpu_ids = range(mpi_world_size) print("cfg.gpu_ids:", cfg.gpu_ids) else: distributed = True init_dist( args.launcher, timeout=timedelta(seconds=3600), **cfg.dist_params ) # re-set gpu_ids with distributed training mode _, world_size = get_dist_info() cfg.gpu_ids = range(world_size) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # dump config cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) # init the logger before other steps timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) log_file = osp.join(cfg.work_dir, f"{timestamp}.log") # specify logger name, if we still use 'mmdet', the output info will be # filtered and won't be saved in the log_file # TODO: ugly workaround to judge whether we are training det or seg model logger = get_root_logger( log_file=log_file, log_level=cfg.log_level ) # init the meta dict to record some important information such as # environment info and seed, which will be logged meta = dict() # log env info env_info_dict = collect_env() env_info = "\n".join([(f"{k}: {v}") for k, v in env_info_dict.items()]) dash_line = "-" * 60 + "\n" logger.info( "Environment info:\n" + dash_line + env_info + "\n" + dash_line ) meta["env_info"] = env_info meta["config"] = cfg.pretty_text # log some basic info logger.info(f"Distributed training: {distributed}") logger.info(f"Config:\n{cfg.pretty_text}") # set random seeds if args.seed is not None: logger.info( f"Set random seed to {args.seed}, " f"deterministic: {args.deterministic}" ) set_random_seed(args.seed, deterministic=args.deterministic) cfg.seed = args.seed meta["seed"] = args.seed meta["exp_name"] = osp.basename(args.config) model = build_detector( cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg") ) model.init_weights() logger.info(f"Model:\n{model}") cfg.data.train.work_dir = cfg.work_dir cfg.data.val.work_dir = cfg.work_dir # print("hhhhhhhhhhhhhhh") print(cfg.data.train) datasets = [build_dataset(cfg.data.train)] if len(cfg.workflow) == 2: val_dataset = copy.deepcopy(cfg.data.val) # in case we use a dataset wrapper if "dataset" in cfg.data.train: val_dataset.pipeline = cfg.data.train.dataset.pipeline else: val_dataset.pipeline = cfg.data.train.pipeline # set test_mode=False here in deep copied config # which do not affect AP/AR calculation later # refer to https://mmdetection3d.readthedocs.io/en/latest/tutorials/customize_runtime.html#customize-workflow # noqa val_dataset.test_mode = False datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmdet version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict( mmdet_version=mmdet_version, config=cfg.pretty_text, CLASSES=datasets[0].CLASSES, ) # add an attribute for visualization convenience model.CLASSES = datasets[0].CLASSES if hasattr(cfg, "plugin"): custom_train_model( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta, ) else: train_detector( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta, ) if __name__ == "__main__": torch.multiprocessing.set_start_method( "fork", force=True ) # use fork workers_per_gpu can be > 1 main() ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/.pylintrc ================================================ [MESSAGES CONTROL] max-line-length=120 disable=no-self-use,anomalous-backslash-in-string,too-many-arguments,too-few-public-methods,too-many-instance-attributes,redefined-variable-type,unused-argument,wildcard-import,unused-wildcard-import,bare-except,broad-except,bad-continuation,too-many-lines,too-many-branches,locally-disabled,too-many-locals,too-many-statements,duplicate-code,too-many-nested-blocks,fixme ignored-modules=carla,carla.command variable-rgx=[a-z0-9_]{1,40}$ function-rgx=[a-z0-9_]{1,40}$ extension-pkg-whitelist=cv2,pygame,numpy ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/CHANGELOG.md ================================================ ## Latest changes * Added support to ROS agents, which are meant to inherit from the new `ROS1Agent` and `ROS2Agent` agents. The old `RosAgent` has been deleted. * Improved the format of the printed runtime information * Added optional side mirrors to the hyman agent * Improved the performance of the routes by initializing the scenarios during runtime * Improved result writer output, in the same wasy as ScenarioRunner's one. * Improved the initialization and cleanup of the Leaderboard. * Improved the robustness of the resuming functionality * Improved the example Dockerfile and added new example for ROS based agents * Added new utility scripts: - merge_statistics.py: join two or more json results into one - route_creator.py: simplifies the creation of new routes - route_displayer.py: parse and debug route xml files from inside the simulator - route_summarizer.py: parses the route xml file into a table. - scenario_creator.py: uses the spectator and terminal inputs to automatically add scenarios to a route - scenario_orderer.py: modifies the scenarios part of the route file to be ordered according to their route's position - weather_creator.py: gets the current simulation's weather in the route format for easy copy. * The `StatisticsManager` class has been remade to add robustness, remove unneeded complexity and hardcoded values. Its interaction with other classes has remained unchanged * Routes have had the same changed as the ones in ScenarioRunner * Added parked vehicles to the routes. These are parsed from a file with all their possible positions. * Added new arguments to the leaderboard: - `routes-subset` allows to run part of the routes. Use `-` to run groups of routes (i.e `0-4`), `,` to run specific routes (i.e `1,6,8,14`), or a combination of the two (i.e `0-2,5,8-10`). - `debug-checkpoint` defines the endpoint of the live results, created when the `debug` argument is 2 or higher. * Added support for traffic manager hybrid mode. * Added a new attribute to the global statistics, *scores_std_dev*, which calculates the standard deviation of the scores done throughout the simulation. * Fixed bug causing the global infractions to not be correctly calculated * Creating stable version for the CARLA online leaderboard * Initial creation of the repository ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/LICENSE ================================================ MIT License Copyright (c) 2019 CARLA Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/README.md ================================================ The main goal of the CARLA Autonomous Driving Leaderboard is to evaluate the driving proficiency of autonomous agents in realistic traffic situations. The leaderboard serves as an open platform for the community to perform fair and reproducible evaluations, simplifying the comparison between different approaches. Autonomous agents have to drive through a set of predefined routes. For each route, agents are initialized at a starting point and have to drive to a destination point. The agents will be provided with a description of the route. Routes will happen in a variety of areas, including freeways, urban scenes, and residential districts. Agents will face multiple traffic situations based in the NHTSA typology, such as: * Lane merging * Lane changing * Negotiations at traffic intersections * Negotiations at roundabouts * Handling traffic lights and traffic signs * Coping with pedestrians, cyclists and other elements The user can change the weather of the simulation, allowing the evaluation of the agent in a variety of weather conditions, including daylight scenes, sunset, rain, fog, and night, among others. More information can be found [here](https://leaderboard.carla.org/) ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/Gemfile ================================================ source "https://rubygems.org" gem "jekyll", "~> 3.8.5" group :jekyll_plugins do gem "jekyll-feed", "~> 0.6" gem "jekyll-paginate", "~> 1.1.0" gem "jekyll-sitemap" end # Windows does not include zoneinfo files, so bundle the tzinfo-data gem gem "tzinfo-data", platforms: [:mingw, :mswin, :x64_mingw, :jruby] # Performance-booster for watching directories on Windows gem "wdm", "~> 0.1.0" if Gem.win_platform? ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/LICENSE ================================================ The MIT License (MIT) Copyright (c) 2013-2019 Blackrock Digital LLC Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/README.md ================================================ ## Welcome to GitHub Pages You can use the [editor on GitHub](https://github.com/carla-simulator/leaderboard/edit/master/README.md) to maintain and preview the content for your website in Markdown files. Whenever you commit to this repository, GitHub Pages will run [Jekyll](https://jekyllrb.com/) to rebuild the pages in your site, from the content in your Markdown files. ### Markdown Markdown is a lightweight and easy-to-use syntax for styling your writing. It includes conventions for ```markdown Syntax highlighted code block # Header 1 ## Header 2 ### Header 3 - Bulleted - List 1. Numbered 2. List **Bold** and _Italic_ and `Code` text [Link](url) and ![Image](src) ``` For more details see [GitHub Flavored Markdown](https://guides.github.com/features/mastering-markdown/). ### Jekyll Themes Your Pages site will use the layout and styles from the Jekyll theme you have selected in your [repository settings](https://github.com/carla-simulator/leaderboard/settings). The name of this theme is saved in the Jekyll `_config.yml` configuration file. ### Support or Contact Having trouble with Pages? Check out our [documentation](https://help.github.com/categories/github-pages-basics/) or [contact support](https://github.com/contact) and we’ll help you sort it out. ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_config.yml ================================================ title: CARLA Leaderboard email: carla.simulator@gmail.com description: CARLA Autonomous Driving competition author: CARLA Team baseurl: "" url: https://leaderboard.carla.org/ # Social Profiles github_username: carla-simulator twitter_username: carlasimulator discord_invite: 8kqACuC youtube_channel: UC1llP9ekCwt8nEJzMJBQekg twitter: username: carlasimulator social: name: CARLA Simulator links: - https://github.com/carla-simulator/carla - https://twitter.com/carlasimulator - https://discord.gg/8kqACuC - https://www.youtube.com/channel/UC1llP9ekCwt8nEJzMJBQekg # Build settings markdown: kramdown paginate: 5 paginate_path: "/posts/page:num/" plugins: - jekyll-feed - jekyll-paginate - jekyll-sitemap ## Uncomment this line to silently generate a sitemaps.org compliant sitemap for your Jekyll site ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_includes/footer.html ================================================
    {% if site.email %}
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Copyright © {{ site.author }} {{ 'now' | date: "%Y" }}

================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_includes/google-analytics.html ================================================ ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_includes/head.html ================================================ {% if page.title %}{{ page.title | escape }} - {{ site.title | escape }} {% else %}{{ site.title | escape }}{% endif %} ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_includes/navbar.html ================================================ ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_includes/read_time.html ================================================ {% assign words = include.content | number_of_words %} {% if words < 270 %} 1 min {% else %} {{ words | divided_by:135 }} mins {% endif %} read ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_includes/scripts.html ================================================ {% if page.url contains 'contact' %} {% endif %} ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_layouts/default.html ================================================ {% include head.html %} {% include navbar.html %} {{ content }} {% include footer.html %} {% include scripts.html %} {% include google-analytics.html %} ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_layouts/home.html ================================================ --- layout: default --- {% if page.background %}
{% else %}
{% endif %}
{{ content }}
================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_layouts/page.html ================================================ --- layout: default --- {% if page.background %}
{% else %}
{% endif %}

{{ page.title }}

{% if page.description %} {{ page.description }} {% endif %}
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================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/_layouts/post.html ================================================ --- layout: default --- {% if page.background %}
{% else %}
{% endif %}

{{ page.title }}

{% if page.subtitle %}

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{% endif %} Posted by {% if page.author %}{{ page.author }}{% else %}{{ site.author }}{% endif %} on {{ page.date | date: '%B %d, %Y' }} · {% include read_time.html content=page.content %}
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================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/assets/main.scss ================================================ --- # Only the main Sass file needs front matter (the dashes are enough) --- @import "styles"; ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/assets/scripts.js ================================================ $(function () { $('[data-toggle="tooltip"]').tooltip() }) ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/assets/vendor/bootstrap/css/bootstrap.css ================================================ /*! * Bootstrap v4.3.1 (https://getbootstrap.com/) * Copyright 2011-2019 The Bootstrap Authors * Copyright 2011-2019 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) */ :root { --blue: #007bff; --indigo: #6610f2; --purple: #6f42c1; --pink: #e83e8c; --red: #dc3545; --orange: #fd7e14; 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} .col-sm-9 { -ms-flex: 0 0 75%; flex: 0 0 75%; max-width: 75%; } .col-sm-10 { -ms-flex: 0 0 83.333333%; flex: 0 0 83.333333%; max-width: 83.333333%; } .col-sm-11 { -ms-flex: 0 0 91.666667%; flex: 0 0 91.666667%; max-width: 91.666667%; } .col-sm-12 { -ms-flex: 0 0 100%; flex: 0 0 100%; max-width: 100%; } .order-sm-first { -ms-flex-order: -1; order: -1; } .order-sm-last { -ms-flex-order: 13; order: 13; } .order-sm-0 { -ms-flex-order: 0; order: 0; } .order-sm-1 { -ms-flex-order: 1; order: 1; } .order-sm-2 { -ms-flex-order: 2; order: 2; } .order-sm-3 { -ms-flex-order: 3; order: 3; } .order-sm-4 { -ms-flex-order: 4; order: 4; } .order-sm-5 { -ms-flex-order: 5; order: 5; } .order-sm-6 { -ms-flex-order: 6; order: 6; } .order-sm-7 { -ms-flex-order: 7; order: 7; } .order-sm-8 { -ms-flex-order: 8; order: 8; } .order-sm-9 { -ms-flex-order: 9; order: 9; } .order-sm-10 { -ms-flex-order: 10; order: 10; } .order-sm-11 { -ms-flex-order: 11; order: 11; } .order-sm-12 { -ms-flex-order: 12; order: 12; } .offset-sm-0 { margin-left: 0; } .offset-sm-1 { margin-left: 8.333333%; } .offset-sm-2 { margin-left: 16.666667%; } .offset-sm-3 { margin-left: 25%; } .offset-sm-4 { margin-left: 33.333333%; } .offset-sm-5 { margin-left: 41.666667%; } .offset-sm-6 { margin-left: 50%; } .offset-sm-7 { margin-left: 58.333333%; } .offset-sm-8 { margin-left: 66.666667%; } .offset-sm-9 { margin-left: 75%; } .offset-sm-10 { margin-left: 83.333333%; } .offset-sm-11 { margin-left: 91.666667%; } } @media (min-width: 768px) { .col-md { -ms-flex-preferred-size: 0; flex-basis: 0; -ms-flex-positive: 1; flex-grow: 1; max-width: 100%; } .col-md-auto { -ms-flex: 0 0 auto; flex: 0 0 auto; width: auto; max-width: 100%; } .col-md-1 { -ms-flex: 0 0 8.333333%; flex: 0 0 8.333333%; max-width: 8.333333%; } .col-md-2 { -ms-flex: 0 0 16.666667%; flex: 0 0 16.666667%; max-width: 16.666667%; } .col-md-3 { -ms-flex: 0 0 25%; flex: 0 0 25%; max-width: 25%; } .col-md-4 { -ms-flex: 0 0 33.333333%; flex: 0 0 33.333333%; max-width: 33.333333%; } .col-md-5 { -ms-flex: 0 0 41.666667%; flex: 0 0 41.666667%; max-width: 41.666667%; } .col-md-6 { -ms-flex: 0 0 50%; flex: 0 0 50%; max-width: 50%; } .col-md-7 { -ms-flex: 0 0 58.333333%; flex: 0 0 58.333333%; max-width: 58.333333%; } .col-md-8 { -ms-flex: 0 0 66.666667%; flex: 0 0 66.666667%; max-width: 66.666667%; } .col-md-9 { -ms-flex: 0 0 75%; flex: 0 0 75%; max-width: 75%; } .col-md-10 { -ms-flex: 0 0 83.333333%; flex: 0 0 83.333333%; max-width: 83.333333%; } .col-md-11 { -ms-flex: 0 0 91.666667%; flex: 0 0 91.666667%; max-width: 91.666667%; } .col-md-12 { -ms-flex: 0 0 100%; flex: 0 0 100%; max-width: 100%; } .order-md-first { -ms-flex-order: -1; order: -1; } .order-md-last { -ms-flex-order: 13; order: 13; } .order-md-0 { -ms-flex-order: 0; order: 0; } .order-md-1 { -ms-flex-order: 1; order: 1; } .order-md-2 { -ms-flex-order: 2; order: 2; } .order-md-3 { -ms-flex-order: 3; order: 3; } .order-md-4 { -ms-flex-order: 4; order: 4; } .order-md-5 { -ms-flex-order: 5; order: 5; } .order-md-6 { -ms-flex-order: 6; order: 6; } .order-md-7 { -ms-flex-order: 7; order: 7; } .order-md-8 { -ms-flex-order: 8; order: 8; } .order-md-9 { -ms-flex-order: 9; order: 9; } .order-md-10 { -ms-flex-order: 10; order: 10; } .order-md-11 { -ms-flex-order: 11; order: 11; } .order-md-12 { -ms-flex-order: 12; order: 12; } .offset-md-0 { margin-left: 0; } .offset-md-1 { margin-left: 8.333333%; } .offset-md-2 { margin-left: 16.666667%; } .offset-md-3 { margin-left: 25%; } .offset-md-4 { margin-left: 33.333333%; } .offset-md-5 { margin-left: 41.666667%; } .offset-md-6 { margin-left: 50%; } .offset-md-7 { margin-left: 58.333333%; } .offset-md-8 { margin-left: 66.666667%; } .offset-md-9 { margin-left: 75%; } .offset-md-10 { margin-left: 83.333333%; } .offset-md-11 { margin-left: 91.666667%; } } @media (min-width: 992px) { .col-lg { -ms-flex-preferred-size: 0; flex-basis: 0; -ms-flex-positive: 1; flex-grow: 1; max-width: 100%; } .col-lg-auto { -ms-flex: 0 0 auto; flex: 0 0 auto; width: auto; max-width: 100%; } .col-lg-1 { -ms-flex: 0 0 8.333333%; flex: 0 0 8.333333%; max-width: 8.333333%; } .col-lg-2 { -ms-flex: 0 0 16.666667%; flex: 0 0 16.666667%; max-width: 16.666667%; } .col-lg-3 { -ms-flex: 0 0 25%; flex: 0 0 25%; max-width: 25%; } .col-lg-4 { -ms-flex: 0 0 33.333333%; flex: 0 0 33.333333%; max-width: 33.333333%; } .col-lg-5 { -ms-flex: 0 0 41.666667%; flex: 0 0 41.666667%; max-width: 41.666667%; } .col-lg-6 { -ms-flex: 0 0 50%; flex: 0 0 50%; max-width: 50%; } .col-lg-7 { -ms-flex: 0 0 58.333333%; flex: 0 0 58.333333%; max-width: 58.333333%; } .col-lg-8 { -ms-flex: 0 0 66.666667%; flex: 0 0 66.666667%; max-width: 66.666667%; } .col-lg-9 { -ms-flex: 0 0 75%; flex: 0 0 75%; max-width: 75%; } .col-lg-10 { -ms-flex: 0 0 83.333333%; flex: 0 0 83.333333%; max-width: 83.333333%; } .col-lg-11 { -ms-flex: 0 0 91.666667%; flex: 0 0 91.666667%; max-width: 91.666667%; } .col-lg-12 { -ms-flex: 0 0 100%; flex: 0 0 100%; max-width: 100%; } .order-lg-first { -ms-flex-order: -1; order: -1; } .order-lg-last { -ms-flex-order: 13; order: 13; } .order-lg-0 { -ms-flex-order: 0; order: 0; } .order-lg-1 { -ms-flex-order: 1; order: 1; } .order-lg-2 { -ms-flex-order: 2; order: 2; } .order-lg-3 { -ms-flex-order: 3; order: 3; } .order-lg-4 { -ms-flex-order: 4; order: 4; } .order-lg-5 { -ms-flex-order: 5; order: 5; } .order-lg-6 { -ms-flex-order: 6; order: 6; } .order-lg-7 { -ms-flex-order: 7; order: 7; } .order-lg-8 { -ms-flex-order: 8; order: 8; } .order-lg-9 { -ms-flex-order: 9; order: 9; } .order-lg-10 { -ms-flex-order: 10; order: 10; } .order-lg-11 { -ms-flex-order: 11; order: 11; } .order-lg-12 { -ms-flex-order: 12; order: 12; } .offset-lg-0 { margin-left: 0; } .offset-lg-1 { margin-left: 8.333333%; } .offset-lg-2 { margin-left: 16.666667%; } .offset-lg-3 { margin-left: 25%; } .offset-lg-4 { margin-left: 33.333333%; } .offset-lg-5 { margin-left: 41.666667%; } .offset-lg-6 { margin-left: 50%; } .offset-lg-7 { margin-left: 58.333333%; } .offset-lg-8 { margin-left: 66.666667%; } .offset-lg-9 { margin-left: 75%; } .offset-lg-10 { margin-left: 83.333333%; } .offset-lg-11 { margin-left: 91.666667%; } } @media (min-width: 1200px) { .col-xl { -ms-flex-preferred-size: 0; flex-basis: 0; -ms-flex-positive: 1; flex-grow: 1; max-width: 100%; } .col-xl-auto { -ms-flex: 0 0 auto; flex: 0 0 auto; width: auto; max-width: 100%; } .col-xl-1 { -ms-flex: 0 0 8.333333%; flex: 0 0 8.333333%; max-width: 8.333333%; } .col-xl-2 { -ms-flex: 0 0 16.666667%; flex: 0 0 16.666667%; max-width: 16.666667%; } .col-xl-3 { -ms-flex: 0 0 25%; flex: 0 0 25%; max-width: 25%; } .col-xl-4 { -ms-flex: 0 0 33.333333%; flex: 0 0 33.333333%; max-width: 33.333333%; } .col-xl-5 { -ms-flex: 0 0 41.666667%; flex: 0 0 41.666667%; max-width: 41.666667%; } .col-xl-6 { -ms-flex: 0 0 50%; flex: 0 0 50%; max-width: 50%; } .col-xl-7 { -ms-flex: 0 0 58.333333%; flex: 0 0 58.333333%; max-width: 58.333333%; } .col-xl-8 { -ms-flex: 0 0 66.666667%; flex: 0 0 66.666667%; max-width: 66.666667%; } .col-xl-9 { -ms-flex: 0 0 75%; flex: 0 0 75%; max-width: 75%; } .col-xl-10 { -ms-flex: 0 0 83.333333%; flex: 0 0 83.333333%; max-width: 83.333333%; } .col-xl-11 { -ms-flex: 0 0 91.666667%; flex: 0 0 91.666667%; max-width: 91.666667%; } .col-xl-12 { -ms-flex: 0 0 100%; flex: 0 0 100%; max-width: 100%; } .order-xl-first { -ms-flex-order: -1; order: -1; } .order-xl-last { -ms-flex-order: 13; order: 13; } .order-xl-0 { -ms-flex-order: 0; order: 0; } .order-xl-1 { -ms-flex-order: 1; order: 1; } .order-xl-2 { -ms-flex-order: 2; order: 2; } .order-xl-3 { -ms-flex-order: 3; order: 3; } .order-xl-4 { -ms-flex-order: 4; order: 4; } .order-xl-5 { -ms-flex-order: 5; order: 5; } .order-xl-6 { -ms-flex-order: 6; order: 6; } .order-xl-7 { -ms-flex-order: 7; order: 7; } .order-xl-8 { -ms-flex-order: 8; order: 8; } .order-xl-9 { -ms-flex-order: 9; order: 9; } .order-xl-10 { -ms-flex-order: 10; order: 10; } .order-xl-11 { -ms-flex-order: 11; order: 11; } .order-xl-12 { -ms-flex-order: 12; order: 12; } .offset-xl-0 { margin-left: 0; } .offset-xl-1 { margin-left: 8.333333%; } .offset-xl-2 { margin-left: 16.666667%; } .offset-xl-3 { margin-left: 25%; } .offset-xl-4 { margin-left: 33.333333%; } .offset-xl-5 { margin-left: 41.666667%; } .offset-xl-6 { margin-left: 50%; } .offset-xl-7 { margin-left: 58.333333%; } .offset-xl-8 { margin-left: 66.666667%; } .offset-xl-9 { margin-left: 75%; } .offset-xl-10 { margin-left: 83.333333%; } .offset-xl-11 { margin-left: 91.666667%; } } .table { width: 100%; margin-bottom: 1rem; color: #212529; } .table th, .table td { padding: 0.75rem; vertical-align: top; border-top: 1px solid #dee2e6; } .table thead th { vertical-align: bottom; border-bottom: 2px solid #dee2e6; } .table tbody + tbody { border-top: 2px solid #dee2e6; } .table-sm th, .table-sm td { padding: 0.3rem; } .table-bordered { border: 1px solid #dee2e6; } .table-bordered th, .table-bordered td { border: 1px solid #dee2e6; } .table-bordered thead th, .table-bordered thead td { border-bottom-width: 2px; } .table-borderless th, .table-borderless td, .table-borderless thead th, .table-borderless tbody + tbody { border: 0; } .table-striped tbody tr:nth-of-type(odd) { background-color: rgba(0, 0, 0, 0.05); } .table-hover tbody tr:hover { color: #212529; background-color: rgba(0, 0, 0, 0.075); } .table-primary, .table-primary > th, .table-primary > td { background-color: #b8daff; } .table-primary th, .table-primary td, .table-primary thead th, .table-primary tbody + tbody { border-color: #7abaff; } .table-hover .table-primary:hover { background-color: #9fcdff; } .table-hover .table-primary:hover > td, .table-hover .table-primary:hover > th { background-color: #9fcdff; } .table-secondary, .table-secondary > th, .table-secondary > td { background-color: #d6d8db; } .table-secondary th, .table-secondary td, .table-secondary thead th, .table-secondary tbody + tbody { border-color: #b3b7bb; } .table-hover .table-secondary:hover { background-color: #c8cbcf; } .table-hover .table-secondary:hover > td, .table-hover .table-secondary:hover > th { background-color: #c8cbcf; } .table-success, .table-success > th, .table-success > td { background-color: #c3e6cb; } .table-success th, .table-success td, .table-success thead th, .table-success tbody + tbody { border-color: #8fd19e; } .table-hover .table-success:hover { background-color: #b1dfbb; } .table-hover .table-success:hover > td, .table-hover .table-success:hover > th { background-color: #b1dfbb; } .table-info, .table-info > th, .table-info > td { background-color: #bee5eb; } .table-info th, .table-info td, .table-info thead th, .table-info tbody + tbody { border-color: #86cfda; } .table-hover .table-info:hover { background-color: #abdde5; } .table-hover .table-info:hover > td, .table-hover .table-info:hover > th { background-color: #abdde5; } .table-warning, .table-warning > th, .table-warning > td { background-color: #ffeeba; } .table-warning th, .table-warning td, .table-warning thead th, .table-warning tbody + tbody { border-color: #ffdf7e; } .table-hover .table-warning:hover { background-color: #ffe8a1; } .table-hover .table-warning:hover > td, .table-hover .table-warning:hover > th { background-color: #ffe8a1; } .table-danger, .table-danger > th, .table-danger > td { background-color: #f5c6cb; } .table-danger th, .table-danger td, .table-danger thead th, .table-danger tbody + tbody { border-color: #ed969e; } .table-hover .table-danger:hover { background-color: #f1b0b7; } .table-hover .table-danger:hover > td, .table-hover .table-danger:hover > th { background-color: #f1b0b7; } .table-light, .table-light > th, .table-light > td { background-color: #fdfdfe; } .table-light th, .table-light td, .table-light thead th, .table-light tbody + tbody { border-color: #fbfcfc; } .table-hover .table-light:hover { background-color: #ececf6; } .table-hover .table-light:hover > td, .table-hover .table-light:hover > th { background-color: #ececf6; } .table-dark, .table-dark > th, .table-dark > td { background-color: #c6c8ca; } .table-dark th, .table-dark td, .table-dark thead th, .table-dark tbody + tbody { border-color: #95999c; } .table-hover .table-dark:hover { background-color: #b9bbbe; } .table-hover .table-dark:hover > td, .table-hover .table-dark:hover > th { background-color: #b9bbbe; } .table-active, .table-active > th, .table-active > td { background-color: rgba(0, 0, 0, 0.075); } .table-hover .table-active:hover { background-color: rgba(0, 0, 0, 0.075); } .table-hover .table-active:hover > td, .table-hover .table-active:hover > th { background-color: rgba(0, 0, 0, 0.075); } .table .thead-dark th { color: #fff; background-color: #343a40; border-color: #454d55; } .table .thead-light th { color: #495057; background-color: #e9ecef; border-color: #dee2e6; } .table-dark { color: #fff; background-color: #343a40; } .table-dark th, .table-dark td, .table-dark thead th { border-color: #454d55; } .table-dark.table-bordered { border: 0; } .table-dark.table-striped tbody tr:nth-of-type(odd) { background-color: rgba(255, 255, 255, 0.05); } .table-dark.table-hover tbody tr:hover { color: #fff; background-color: rgba(255, 255, 255, 0.075); } @media (max-width: 575.98px) { .table-responsive-sm { display: block; width: 100%; overflow-x: auto; -webkit-overflow-scrolling: touch; } .table-responsive-sm > .table-bordered { border: 0; } } @media (max-width: 767.98px) { .table-responsive-md { display: block; width: 100%; overflow-x: auto; -webkit-overflow-scrolling: touch; } .table-responsive-md > .table-bordered { border: 0; } } @media (max-width: 991.98px) { .table-responsive-lg { display: block; width: 100%; overflow-x: auto; -webkit-overflow-scrolling: touch; } .table-responsive-lg > .table-bordered { border: 0; } } @media (max-width: 1199.98px) { .table-responsive-xl { display: block; width: 100%; overflow-x: auto; -webkit-overflow-scrolling: touch; } .table-responsive-xl > .table-bordered { border: 0; } } .table-responsive { display: block; width: 100%; overflow-x: auto; -webkit-overflow-scrolling: touch; } .table-responsive > .table-bordered { border: 0; } .form-control { display: block; width: 100%; height: calc(1.5em + 0.75rem + 2px); padding: 0.375rem 0.75rem; font-size: 1rem; font-weight: 400; line-height: 1.5; color: #495057; background-color: #fff; background-clip: padding-box; border: 1px solid #ced4da; border-radius: 0.25rem; transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; } @media (prefers-reduced-motion: reduce) { .form-control { transition: none; } } .form-control::-ms-expand { background-color: transparent; border: 0; } .form-control:focus { color: #495057; background-color: #fff; border-color: #80bdff; outline: 0; box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .form-control::-webkit-input-placeholder { color: #6c757d; opacity: 1; } .form-control::-moz-placeholder { color: #6c757d; opacity: 1; } .form-control:-ms-input-placeholder { color: #6c757d; opacity: 1; } .form-control::-ms-input-placeholder { color: #6c757d; opacity: 1; } .form-control::placeholder { color: #6c757d; opacity: 1; } .form-control:disabled, .form-control[readonly] { background-color: #e9ecef; opacity: 1; } select.form-control:focus::-ms-value { color: #495057; background-color: #fff; } .form-control-file, .form-control-range { display: block; width: 100%; } .col-form-label { padding-top: calc(0.375rem + 1px); padding-bottom: calc(0.375rem + 1px); margin-bottom: 0; font-size: inherit; line-height: 1.5; } .col-form-label-lg { padding-top: calc(0.5rem + 1px); padding-bottom: calc(0.5rem + 1px); font-size: 1.25rem; line-height: 1.5; } .col-form-label-sm { padding-top: calc(0.25rem + 1px); padding-bottom: calc(0.25rem + 1px); font-size: 0.875rem; line-height: 1.5; } .form-control-plaintext { display: block; width: 100%; padding-top: 0.375rem; padding-bottom: 0.375rem; margin-bottom: 0; line-height: 1.5; color: #212529; background-color: transparent; border: solid transparent; border-width: 1px 0; } .form-control-plaintext.form-control-sm, .form-control-plaintext.form-control-lg { padding-right: 0; padding-left: 0; } .form-control-sm { height: calc(1.5em + 0.5rem + 2px); padding: 0.25rem 0.5rem; font-size: 0.875rem; line-height: 1.5; border-radius: 0.2rem; } .form-control-lg { height: calc(1.5em + 1rem + 2px); padding: 0.5rem 1rem; font-size: 1.25rem; line-height: 1.5; border-radius: 0.3rem; } select.form-control[size], select.form-control[multiple] { height: auto; } textarea.form-control { height: auto; } .form-group { margin-bottom: 1rem; } .form-text { display: block; margin-top: 0.25rem; } .form-row { display: -ms-flexbox; display: flex; -ms-flex-wrap: wrap; flex-wrap: wrap; margin-right: -5px; margin-left: -5px; } .form-row > .col, .form-row > [class*="col-"] { padding-right: 5px; padding-left: 5px; } .form-check { position: relative; display: block; padding-left: 1.25rem; } .form-check-input { position: absolute; margin-top: 0.3rem; margin-left: -1.25rem; } .form-check-input:disabled ~ .form-check-label { color: #6c757d; } .form-check-label { margin-bottom: 0; } .form-check-inline { display: -ms-inline-flexbox; display: inline-flex; -ms-flex-align: center; align-items: center; padding-left: 0; margin-right: 0.75rem; } .form-check-inline .form-check-input { position: static; margin-top: 0; margin-right: 0.3125rem; margin-left: 0; } .valid-feedback { display: none; width: 100%; margin-top: 0.25rem; font-size: 80%; color: #28a745; } .valid-tooltip { position: absolute; top: 100%; z-index: 5; display: none; max-width: 100%; padding: 0.25rem 0.5rem; margin-top: .1rem; font-size: 0.875rem; line-height: 1.5; color: #fff; background-color: rgba(40, 167, 69, 0.9); border-radius: 0.25rem; } .was-validated .form-control:valid, .form-control.is-valid { border-color: #28a745; padding-right: calc(1.5em + 0.75rem); background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%2328a745' d='M2.3 6.73L.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e"); background-repeat: no-repeat; background-position: center right calc(0.375em + 0.1875rem); background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); } .was-validated .form-control:valid:focus, .form-control.is-valid:focus { border-color: #28a745; box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); } .was-validated .form-control:valid ~ .valid-feedback, .was-validated .form-control:valid ~ .valid-tooltip, .form-control.is-valid ~ .valid-feedback, .form-control.is-valid ~ .valid-tooltip { display: block; } .was-validated textarea.form-control:valid, textarea.form-control.is-valid { padding-right: calc(1.5em + 0.75rem); background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem); } .was-validated .custom-select:valid, .custom-select.is-valid { border-color: #28a745; padding-right: calc((1em + 0.75rem) * 3 / 4 + 1.75rem); background: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 5'%3e%3cpath fill='%23343a40' d='M2 0L0 2h4zm0 5L0 3h4z'/%3e%3c/svg%3e") no-repeat right 0.75rem center/8px 10px, url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%2328a745' d='M2.3 6.73L.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e") #fff no-repeat center right 1.75rem/calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); } .was-validated .custom-select:valid:focus, .custom-select.is-valid:focus { border-color: #28a745; box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); } .was-validated .custom-select:valid ~ .valid-feedback, .was-validated .custom-select:valid ~ .valid-tooltip, .custom-select.is-valid ~ .valid-feedback, .custom-select.is-valid ~ .valid-tooltip { display: block; } .was-validated .form-control-file:valid ~ .valid-feedback, .was-validated .form-control-file:valid ~ .valid-tooltip, .form-control-file.is-valid ~ .valid-feedback, .form-control-file.is-valid ~ .valid-tooltip { display: block; } .was-validated .form-check-input:valid ~ .form-check-label, .form-check-input.is-valid ~ .form-check-label { color: #28a745; } .was-validated .form-check-input:valid ~ .valid-feedback, .was-validated .form-check-input:valid ~ .valid-tooltip, .form-check-input.is-valid ~ .valid-feedback, .form-check-input.is-valid ~ .valid-tooltip { display: block; } .was-validated .custom-control-input:valid ~ .custom-control-label, .custom-control-input.is-valid ~ .custom-control-label { color: #28a745; } .was-validated .custom-control-input:valid ~ .custom-control-label::before, .custom-control-input.is-valid ~ .custom-control-label::before { border-color: #28a745; } .was-validated .custom-control-input:valid ~ .valid-feedback, .was-validated .custom-control-input:valid ~ .valid-tooltip, .custom-control-input.is-valid ~ .valid-feedback, .custom-control-input.is-valid ~ .valid-tooltip { display: block; } .was-validated .custom-control-input:valid:checked ~ .custom-control-label::before, .custom-control-input.is-valid:checked ~ .custom-control-label::before { border-color: #34ce57; background-color: #34ce57; } .was-validated .custom-control-input:valid:focus ~ .custom-control-label::before, .custom-control-input.is-valid:focus ~ .custom-control-label::before { box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); } .was-validated .custom-control-input:valid:focus:not(:checked) ~ .custom-control-label::before, .custom-control-input.is-valid:focus:not(:checked) ~ .custom-control-label::before { border-color: #28a745; } .was-validated .custom-file-input:valid ~ .custom-file-label, .custom-file-input.is-valid ~ .custom-file-label { border-color: #28a745; } .was-validated .custom-file-input:valid ~ .valid-feedback, .was-validated .custom-file-input:valid ~ .valid-tooltip, .custom-file-input.is-valid ~ .valid-feedback, .custom-file-input.is-valid ~ .valid-tooltip { display: block; } .was-validated .custom-file-input:valid:focus ~ .custom-file-label, .custom-file-input.is-valid:focus ~ .custom-file-label { border-color: #28a745; box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); } .invalid-feedback { display: none; width: 100%; margin-top: 0.25rem; font-size: 80%; color: #dc3545; } .invalid-tooltip { position: absolute; top: 100%; z-index: 5; display: none; max-width: 100%; padding: 0.25rem 0.5rem; margin-top: .1rem; font-size: 0.875rem; line-height: 1.5; color: #fff; background-color: rgba(220, 53, 69, 0.9); border-radius: 0.25rem; } .was-validated .form-control:invalid, .form-control.is-invalid { border-color: #dc3545; padding-right: calc(1.5em + 0.75rem); background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' fill='%23dc3545' viewBox='-2 -2 7 7'%3e%3cpath stroke='%23dc3545' d='M0 0l3 3m0-3L0 3'/%3e%3ccircle r='.5'/%3e%3ccircle cx='3' r='.5'/%3e%3ccircle cy='3' r='.5'/%3e%3ccircle cx='3' cy='3' r='.5'/%3e%3c/svg%3E"); background-repeat: no-repeat; background-position: center right calc(0.375em + 0.1875rem); background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); } .was-validated .form-control:invalid:focus, .form-control.is-invalid:focus { border-color: #dc3545; box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); } .was-validated .form-control:invalid ~ .invalid-feedback, .was-validated .form-control:invalid ~ .invalid-tooltip, .form-control.is-invalid ~ .invalid-feedback, .form-control.is-invalid ~ .invalid-tooltip { display: block; } .was-validated textarea.form-control:invalid, textarea.form-control.is-invalid { padding-right: calc(1.5em + 0.75rem); background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem); } .was-validated .custom-select:invalid, .custom-select.is-invalid { border-color: #dc3545; padding-right: calc((1em + 0.75rem) * 3 / 4 + 1.75rem); background: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 5'%3e%3cpath fill='%23343a40' d='M2 0L0 2h4zm0 5L0 3h4z'/%3e%3c/svg%3e") no-repeat right 0.75rem center/8px 10px, url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' fill='%23dc3545' viewBox='-2 -2 7 7'%3e%3cpath stroke='%23dc3545' d='M0 0l3 3m0-3L0 3'/%3e%3ccircle r='.5'/%3e%3ccircle cx='3' r='.5'/%3e%3ccircle cy='3' r='.5'/%3e%3ccircle cx='3' cy='3' r='.5'/%3e%3c/svg%3E") #fff no-repeat center right 1.75rem/calc(0.75em + 0.375rem) calc(0.75em + 0.375rem); } .was-validated .custom-select:invalid:focus, .custom-select.is-invalid:focus { border-color: #dc3545; box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); } .was-validated .custom-select:invalid ~ .invalid-feedback, .was-validated .custom-select:invalid ~ .invalid-tooltip, .custom-select.is-invalid ~ .invalid-feedback, .custom-select.is-invalid ~ .invalid-tooltip { display: block; } .was-validated .form-control-file:invalid ~ .invalid-feedback, .was-validated .form-control-file:invalid ~ .invalid-tooltip, .form-control-file.is-invalid ~ .invalid-feedback, .form-control-file.is-invalid ~ .invalid-tooltip { display: block; } .was-validated .form-check-input:invalid ~ .form-check-label, .form-check-input.is-invalid ~ .form-check-label { color: #dc3545; } .was-validated .form-check-input:invalid ~ .invalid-feedback, .was-validated .form-check-input:invalid ~ .invalid-tooltip, .form-check-input.is-invalid ~ .invalid-feedback, .form-check-input.is-invalid ~ .invalid-tooltip { display: block; } .was-validated .custom-control-input:invalid ~ .custom-control-label, .custom-control-input.is-invalid ~ .custom-control-label { color: #dc3545; } .was-validated .custom-control-input:invalid ~ .custom-control-label::before, .custom-control-input.is-invalid ~ .custom-control-label::before { border-color: #dc3545; } .was-validated .custom-control-input:invalid ~ .invalid-feedback, .was-validated .custom-control-input:invalid ~ .invalid-tooltip, .custom-control-input.is-invalid ~ .invalid-feedback, .custom-control-input.is-invalid ~ .invalid-tooltip { display: block; } .was-validated .custom-control-input:invalid:checked ~ .custom-control-label::before, .custom-control-input.is-invalid:checked ~ .custom-control-label::before { border-color: #e4606d; background-color: #e4606d; } .was-validated .custom-control-input:invalid:focus ~ .custom-control-label::before, .custom-control-input.is-invalid:focus ~ .custom-control-label::before { box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); } .was-validated .custom-control-input:invalid:focus:not(:checked) ~ .custom-control-label::before, .custom-control-input.is-invalid:focus:not(:checked) ~ .custom-control-label::before { border-color: #dc3545; } .was-validated .custom-file-input:invalid ~ .custom-file-label, .custom-file-input.is-invalid ~ .custom-file-label { border-color: #dc3545; } .was-validated .custom-file-input:invalid ~ .invalid-feedback, .was-validated .custom-file-input:invalid ~ .invalid-tooltip, .custom-file-input.is-invalid ~ .invalid-feedback, .custom-file-input.is-invalid ~ .invalid-tooltip { display: block; } .was-validated .custom-file-input:invalid:focus ~ .custom-file-label, .custom-file-input.is-invalid:focus ~ .custom-file-label { border-color: #dc3545; box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); } .form-inline { display: -ms-flexbox; display: flex; -ms-flex-flow: row wrap; flex-flow: row wrap; -ms-flex-align: center; align-items: center; } .form-inline .form-check { width: 100%; } @media (min-width: 576px) { .form-inline label { display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; -ms-flex-pack: center; justify-content: center; margin-bottom: 0; } .form-inline .form-group { display: -ms-flexbox; display: flex; -ms-flex: 0 0 auto; flex: 0 0 auto; -ms-flex-flow: row wrap; flex-flow: row wrap; -ms-flex-align: center; align-items: center; margin-bottom: 0; } .form-inline .form-control { display: inline-block; width: auto; vertical-align: middle; } .form-inline .form-control-plaintext { display: inline-block; } .form-inline .input-group, .form-inline .custom-select { width: auto; } .form-inline .form-check { display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; -ms-flex-pack: center; justify-content: center; width: auto; padding-left: 0; } .form-inline .form-check-input { position: relative; -ms-flex-negative: 0; flex-shrink: 0; margin-top: 0; margin-right: 0.25rem; margin-left: 0; } .form-inline .custom-control { -ms-flex-align: center; align-items: center; -ms-flex-pack: center; justify-content: center; } .form-inline .custom-control-label { margin-bottom: 0; } } .btn { display: inline-block; font-weight: 400; color: #212529; text-align: center; vertical-align: middle; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; background-color: transparent; border: 1px solid transparent; padding: 0.375rem 0.75rem; font-size: 1rem; line-height: 1.5; border-radius: 0.25rem; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; } @media (prefers-reduced-motion: reduce) { .btn { transition: none; } } .btn:hover { color: #212529; text-decoration: none; } .btn:focus, .btn.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .btn.disabled, .btn:disabled { opacity: 0.65; } a.btn.disabled, fieldset:disabled a.btn { pointer-events: none; } .btn-primary { color: #fff; background-color: #007bff; border-color: #007bff; } .btn-primary:hover { color: #fff; background-color: #0069d9; border-color: #0062cc; } .btn-primary:focus, .btn-primary.focus { box-shadow: 0 0 0 0.2rem rgba(38, 143, 255, 0.5); } .btn-primary.disabled, .btn-primary:disabled { color: #fff; background-color: #007bff; border-color: #007bff; } .btn-primary:not(:disabled):not(.disabled):active, .btn-primary:not(:disabled):not(.disabled).active, .show > .btn-primary.dropdown-toggle { color: #fff; background-color: #0062cc; border-color: #005cbf; } .btn-primary:not(:disabled):not(.disabled):active:focus, .btn-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-primary.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(38, 143, 255, 0.5); } .btn-secondary { color: #fff; background-color: #6c757d; border-color: #6c757d; } .btn-secondary:hover { color: #fff; background-color: #5a6268; border-color: #545b62; } .btn-secondary:focus, .btn-secondary.focus { box-shadow: 0 0 0 0.2rem rgba(130, 138, 145, 0.5); } .btn-secondary.disabled, .btn-secondary:disabled { color: #fff; background-color: #6c757d; border-color: #6c757d; } .btn-secondary:not(:disabled):not(.disabled):active, .btn-secondary:not(:disabled):not(.disabled).active, .show > .btn-secondary.dropdown-toggle { color: #fff; background-color: #545b62; border-color: #4e555b; } .btn-secondary:not(:disabled):not(.disabled):active:focus, .btn-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-secondary.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(130, 138, 145, 0.5); } .btn-success { color: #fff; background-color: #28a745; border-color: #28a745; } .btn-success:hover { color: #fff; background-color: #218838; border-color: #1e7e34; } .btn-success:focus, .btn-success.focus { box-shadow: 0 0 0 0.2rem rgba(72, 180, 97, 0.5); } .btn-success.disabled, .btn-success:disabled { color: #fff; background-color: #28a745; border-color: #28a745; } .btn-success:not(:disabled):not(.disabled):active, .btn-success:not(:disabled):not(.disabled).active, .show > .btn-success.dropdown-toggle { color: #fff; background-color: #1e7e34; border-color: #1c7430; } .btn-success:not(:disabled):not(.disabled):active:focus, .btn-success:not(:disabled):not(.disabled).active:focus, .show > .btn-success.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(72, 180, 97, 0.5); } .btn-info { color: #fff; background-color: #17a2b8; border-color: #17a2b8; } .btn-info:hover { color: #fff; background-color: #138496; border-color: #117a8b; } .btn-info:focus, .btn-info.focus { box-shadow: 0 0 0 0.2rem rgba(58, 176, 195, 0.5); } .btn-info.disabled, .btn-info:disabled { color: #fff; background-color: #17a2b8; border-color: #17a2b8; } .btn-info:not(:disabled):not(.disabled):active, .btn-info:not(:disabled):not(.disabled).active, .show > .btn-info.dropdown-toggle { color: #fff; background-color: #117a8b; border-color: #10707f; } .btn-info:not(:disabled):not(.disabled):active:focus, .btn-info:not(:disabled):not(.disabled).active:focus, .show > .btn-info.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(58, 176, 195, 0.5); } .btn-warning { color: #212529; background-color: #ffc107; border-color: #ffc107; } .btn-warning:hover { color: #212529; background-color: #e0a800; border-color: #d39e00; } .btn-warning:focus, .btn-warning.focus { box-shadow: 0 0 0 0.2rem rgba(222, 170, 12, 0.5); } .btn-warning.disabled, .btn-warning:disabled { color: #212529; background-color: #ffc107; border-color: #ffc107; } .btn-warning:not(:disabled):not(.disabled):active, .btn-warning:not(:disabled):not(.disabled).active, .show > .btn-warning.dropdown-toggle { color: #212529; background-color: #d39e00; border-color: #c69500; } .btn-warning:not(:disabled):not(.disabled):active:focus, .btn-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-warning.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(222, 170, 12, 0.5); } .btn-danger { color: #fff; background-color: #dc3545; border-color: #dc3545; } .btn-danger:hover { color: #fff; background-color: #c82333; border-color: #bd2130; } .btn-danger:focus, .btn-danger.focus { box-shadow: 0 0 0 0.2rem rgba(225, 83, 97, 0.5); } .btn-danger.disabled, .btn-danger:disabled { color: #fff; background-color: #dc3545; border-color: #dc3545; } .btn-danger:not(:disabled):not(.disabled):active, .btn-danger:not(:disabled):not(.disabled).active, .show > .btn-danger.dropdown-toggle { color: #fff; background-color: #bd2130; border-color: #b21f2d; } .btn-danger:not(:disabled):not(.disabled):active:focus, .btn-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-danger.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(225, 83, 97, 0.5); } .btn-light { color: #212529; background-color: #f8f9fa; border-color: #f8f9fa; } .btn-light:hover { color: #212529; background-color: #e2e6ea; border-color: #dae0e5; } .btn-light:focus, .btn-light.focus { box-shadow: 0 0 0 0.2rem rgba(216, 217, 219, 0.5); } .btn-light.disabled, .btn-light:disabled { color: #212529; background-color: #f8f9fa; border-color: #f8f9fa; } .btn-light:not(:disabled):not(.disabled):active, .btn-light:not(:disabled):not(.disabled).active, .show > .btn-light.dropdown-toggle { color: #212529; background-color: #dae0e5; border-color: #d3d9df; } .btn-light:not(:disabled):not(.disabled):active:focus, .btn-light:not(:disabled):not(.disabled).active:focus, .show > .btn-light.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(216, 217, 219, 0.5); } .btn-dark { color: #fff; background-color: #343a40; border-color: #343a40; } .btn-dark:hover { color: #fff; background-color: #23272b; border-color: #1d2124; } .btn-dark:focus, .btn-dark.focus { box-shadow: 0 0 0 0.2rem rgba(82, 88, 93, 0.5); } .btn-dark.disabled, .btn-dark:disabled { color: #fff; background-color: #343a40; border-color: #343a40; } .btn-dark:not(:disabled):not(.disabled):active, .btn-dark:not(:disabled):not(.disabled).active, .show > .btn-dark.dropdown-toggle { color: #fff; background-color: #1d2124; border-color: #171a1d; } .btn-dark:not(:disabled):not(.disabled):active:focus, .btn-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-dark.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(82, 88, 93, 0.5); } .btn-outline-primary { color: #007bff; border-color: #007bff; } .btn-outline-primary:hover { color: #fff; background-color: #007bff; border-color: #007bff; } .btn-outline-primary:focus, .btn-outline-primary.focus { box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); } .btn-outline-primary.disabled, .btn-outline-primary:disabled { color: #007bff; background-color: transparent; } .btn-outline-primary:not(:disabled):not(.disabled):active, .btn-outline-primary:not(:disabled):not(.disabled).active, .show > .btn-outline-primary.dropdown-toggle { color: #fff; background-color: #007bff; border-color: #007bff; } .btn-outline-primary:not(:disabled):not(.disabled):active:focus, .btn-outline-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-primary.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); } .btn-outline-secondary { color: #6c757d; border-color: #6c757d; } .btn-outline-secondary:hover { color: #fff; background-color: #6c757d; border-color: #6c757d; } .btn-outline-secondary:focus, .btn-outline-secondary.focus { box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); } .btn-outline-secondary.disabled, .btn-outline-secondary:disabled { color: #6c757d; background-color: transparent; } .btn-outline-secondary:not(:disabled):not(.disabled):active, .btn-outline-secondary:not(:disabled):not(.disabled).active, .show > .btn-outline-secondary.dropdown-toggle { color: #fff; background-color: #6c757d; border-color: #6c757d; } .btn-outline-secondary:not(:disabled):not(.disabled):active:focus, .btn-outline-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-secondary.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); } .btn-outline-success { color: #28a745; border-color: #28a745; } .btn-outline-success:hover { color: #fff; background-color: #28a745; border-color: #28a745; } .btn-outline-success:focus, .btn-outline-success.focus { box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); } .btn-outline-success.disabled, .btn-outline-success:disabled { color: #28a745; background-color: transparent; } .btn-outline-success:not(:disabled):not(.disabled):active, .btn-outline-success:not(:disabled):not(.disabled).active, .show > .btn-outline-success.dropdown-toggle { color: #fff; background-color: #28a745; border-color: #28a745; } .btn-outline-success:not(:disabled):not(.disabled):active:focus, .btn-outline-success:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-success.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); } .btn-outline-info { color: #17a2b8; border-color: #17a2b8; } .btn-outline-info:hover { color: #fff; background-color: #17a2b8; border-color: #17a2b8; } .btn-outline-info:focus, .btn-outline-info.focus { box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); } .btn-outline-info.disabled, .btn-outline-info:disabled { color: #17a2b8; background-color: transparent; } .btn-outline-info:not(:disabled):not(.disabled):active, .btn-outline-info:not(:disabled):not(.disabled).active, .show > .btn-outline-info.dropdown-toggle { color: #fff; background-color: #17a2b8; border-color: #17a2b8; } .btn-outline-info:not(:disabled):not(.disabled):active:focus, .btn-outline-info:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-info.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); } .btn-outline-warning { color: #ffc107; border-color: #ffc107; } .btn-outline-warning:hover { color: #212529; background-color: #ffc107; border-color: #ffc107; } .btn-outline-warning:focus, .btn-outline-warning.focus { box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); } .btn-outline-warning.disabled, .btn-outline-warning:disabled { color: #ffc107; background-color: transparent; } .btn-outline-warning:not(:disabled):not(.disabled):active, .btn-outline-warning:not(:disabled):not(.disabled).active, .show > .btn-outline-warning.dropdown-toggle { color: #212529; background-color: #ffc107; border-color: #ffc107; } .btn-outline-warning:not(:disabled):not(.disabled):active:focus, .btn-outline-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-warning.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); } .btn-outline-danger { color: #dc3545; border-color: #dc3545; } .btn-outline-danger:hover { color: #fff; background-color: #dc3545; border-color: #dc3545; } .btn-outline-danger:focus, .btn-outline-danger.focus { box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); } .btn-outline-danger.disabled, .btn-outline-danger:disabled { color: #dc3545; background-color: transparent; } .btn-outline-danger:not(:disabled):not(.disabled):active, .btn-outline-danger:not(:disabled):not(.disabled).active, .show > .btn-outline-danger.dropdown-toggle { color: #fff; background-color: #dc3545; border-color: #dc3545; } .btn-outline-danger:not(:disabled):not(.disabled):active:focus, .btn-outline-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-danger.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); } .btn-outline-light { color: #f8f9fa; border-color: #f8f9fa; } .btn-outline-light:hover { color: #212529; background-color: #f8f9fa; border-color: #f8f9fa; } .btn-outline-light:focus, .btn-outline-light.focus { box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); } .btn-outline-light.disabled, .btn-outline-light:disabled { color: #f8f9fa; background-color: transparent; } .btn-outline-light:not(:disabled):not(.disabled):active, .btn-outline-light:not(:disabled):not(.disabled).active, .show > .btn-outline-light.dropdown-toggle { color: #212529; background-color: #f8f9fa; border-color: #f8f9fa; } .btn-outline-light:not(:disabled):not(.disabled):active:focus, .btn-outline-light:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-light.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); } .btn-outline-dark { color: #343a40; border-color: #343a40; } .btn-outline-dark:hover { color: #fff; background-color: #343a40; border-color: #343a40; } .btn-outline-dark:focus, .btn-outline-dark.focus { box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); } .btn-outline-dark.disabled, .btn-outline-dark:disabled { color: #343a40; background-color: transparent; } .btn-outline-dark:not(:disabled):not(.disabled):active, .btn-outline-dark:not(:disabled):not(.disabled).active, .show > .btn-outline-dark.dropdown-toggle { color: #fff; background-color: #343a40; border-color: #343a40; } .btn-outline-dark:not(:disabled):not(.disabled):active:focus, .btn-outline-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-dark.dropdown-toggle:focus { box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); } .btn-link { font-weight: 400; color: #007bff; text-decoration: none; } .btn-link:hover { color: #0056b3; text-decoration: underline; } .btn-link:focus, .btn-link.focus { text-decoration: underline; box-shadow: none; } .btn-link:disabled, .btn-link.disabled { color: #6c757d; pointer-events: none; } .btn-lg, .btn-group-lg > .btn { padding: 0.5rem 1rem; font-size: 1.25rem; line-height: 1.5; border-radius: 0.3rem; } .btn-sm, .btn-group-sm > .btn { padding: 0.25rem 0.5rem; font-size: 0.875rem; line-height: 1.5; border-radius: 0.2rem; } .btn-block { display: block; width: 100%; } .btn-block + .btn-block { margin-top: 0.5rem; } input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { width: 100%; } .fade { transition: opacity 0.15s linear; } @media (prefers-reduced-motion: reduce) { .fade { transition: none; } } .fade:not(.show) { opacity: 0; } .collapse:not(.show) { display: none; } .collapsing { position: relative; height: 0; overflow: hidden; transition: height 0.35s ease; } @media (prefers-reduced-motion: reduce) { .collapsing { transition: none; } } .dropup, .dropright, .dropdown, .dropleft { position: relative; } .dropdown-toggle { white-space: nowrap; } .dropdown-toggle::after { display: inline-block; margin-left: 0.255em; vertical-align: 0.255em; content: ""; border-top: 0.3em solid; border-right: 0.3em solid transparent; border-bottom: 0; border-left: 0.3em solid transparent; } .dropdown-toggle:empty::after { margin-left: 0; } .dropdown-menu { position: absolute; top: 100%; left: 0; z-index: 1000; display: none; float: left; min-width: 10rem; padding: 0.5rem 0; margin: 0.125rem 0 0; font-size: 1rem; color: #212529; text-align: left; list-style: none; background-color: #fff; background-clip: padding-box; border: 1px solid rgba(0, 0, 0, 0.15); border-radius: 0.25rem; } .dropdown-menu-left { right: auto; left: 0; } .dropdown-menu-right { right: 0; left: auto; } @media (min-width: 576px) { .dropdown-menu-sm-left { right: auto; left: 0; } .dropdown-menu-sm-right { right: 0; left: auto; } } @media (min-width: 768px) { .dropdown-menu-md-left { right: auto; left: 0; } .dropdown-menu-md-right { right: 0; left: auto; } } @media (min-width: 992px) { .dropdown-menu-lg-left { right: auto; left: 0; } .dropdown-menu-lg-right { right: 0; left: auto; } } @media (min-width: 1200px) { .dropdown-menu-xl-left { right: auto; left: 0; } .dropdown-menu-xl-right { right: 0; left: auto; } } .dropup .dropdown-menu { top: auto; bottom: 100%; margin-top: 0; margin-bottom: 0.125rem; } .dropup .dropdown-toggle::after { display: inline-block; margin-left: 0.255em; vertical-align: 0.255em; content: ""; border-top: 0; border-right: 0.3em solid transparent; border-bottom: 0.3em solid; border-left: 0.3em solid transparent; } .dropup .dropdown-toggle:empty::after { margin-left: 0; } .dropright .dropdown-menu { top: 0; right: auto; left: 100%; margin-top: 0; margin-left: 0.125rem; } .dropright .dropdown-toggle::after { display: inline-block; margin-left: 0.255em; vertical-align: 0.255em; content: ""; border-top: 0.3em solid transparent; border-right: 0; border-bottom: 0.3em solid transparent; border-left: 0.3em solid; } .dropright .dropdown-toggle:empty::after { margin-left: 0; } .dropright .dropdown-toggle::after { vertical-align: 0; } .dropleft .dropdown-menu { top: 0; right: 100%; left: auto; margin-top: 0; margin-right: 0.125rem; } .dropleft .dropdown-toggle::after { display: inline-block; margin-left: 0.255em; vertical-align: 0.255em; content: ""; } .dropleft .dropdown-toggle::after { display: none; } .dropleft .dropdown-toggle::before { display: inline-block; margin-right: 0.255em; vertical-align: 0.255em; content: ""; border-top: 0.3em solid transparent; border-right: 0.3em solid; border-bottom: 0.3em solid transparent; } .dropleft .dropdown-toggle:empty::after { margin-left: 0; } .dropleft .dropdown-toggle::before { vertical-align: 0; } .dropdown-menu[x-placement^="top"], .dropdown-menu[x-placement^="right"], .dropdown-menu[x-placement^="bottom"], .dropdown-menu[x-placement^="left"] { right: auto; bottom: auto; } .dropdown-divider { height: 0; margin: 0.5rem 0; overflow: hidden; border-top: 1px solid #e9ecef; } .dropdown-item { display: block; width: 100%; padding: 0.25rem 1.5rem; clear: both; font-weight: 400; color: #212529; text-align: inherit; white-space: nowrap; background-color: transparent; border: 0; } .dropdown-item:hover, .dropdown-item:focus { color: #16181b; text-decoration: none; background-color: #f8f9fa; } .dropdown-item.active, .dropdown-item:active { color: #fff; text-decoration: none; background-color: #007bff; } .dropdown-item.disabled, .dropdown-item:disabled { color: #6c757d; pointer-events: none; background-color: transparent; } .dropdown-menu.show { display: block; } .dropdown-header { display: block; padding: 0.5rem 1.5rem; margin-bottom: 0; font-size: 0.875rem; color: #6c757d; white-space: nowrap; } .dropdown-item-text { display: block; padding: 0.25rem 1.5rem; color: #212529; } .btn-group, .btn-group-vertical { position: relative; display: -ms-inline-flexbox; display: inline-flex; vertical-align: middle; } .btn-group > .btn, .btn-group-vertical > .btn { position: relative; -ms-flex: 1 1 auto; flex: 1 1 auto; } .btn-group > .btn:hover, .btn-group-vertical > .btn:hover { z-index: 1; } .btn-group > .btn:focus, .btn-group > .btn:active, .btn-group > .btn.active, .btn-group-vertical > .btn:focus, .btn-group-vertical > .btn:active, .btn-group-vertical > .btn.active { z-index: 1; } .btn-toolbar { display: -ms-flexbox; display: flex; -ms-flex-wrap: wrap; flex-wrap: wrap; -ms-flex-pack: start; justify-content: flex-start; } .btn-toolbar .input-group { width: auto; } .btn-group > .btn:not(:first-child), .btn-group > .btn-group:not(:first-child) { margin-left: -1px; } .btn-group > .btn:not(:last-child):not(.dropdown-toggle), .btn-group > .btn-group:not(:last-child) > .btn { border-top-right-radius: 0; border-bottom-right-radius: 0; } .btn-group > .btn:not(:first-child), .btn-group > .btn-group:not(:first-child) > .btn { border-top-left-radius: 0; border-bottom-left-radius: 0; } .dropdown-toggle-split { padding-right: 0.5625rem; padding-left: 0.5625rem; } .dropdown-toggle-split::after, .dropup .dropdown-toggle-split::after, .dropright .dropdown-toggle-split::after { margin-left: 0; } .dropleft .dropdown-toggle-split::before { margin-right: 0; } .btn-sm + .dropdown-toggle-split, .btn-group-sm > .btn + .dropdown-toggle-split { padding-right: 0.375rem; padding-left: 0.375rem; } .btn-lg + .dropdown-toggle-split, .btn-group-lg > .btn + .dropdown-toggle-split { padding-right: 0.75rem; padding-left: 0.75rem; } .btn-group-vertical { -ms-flex-direction: column; flex-direction: column; -ms-flex-align: start; align-items: flex-start; -ms-flex-pack: center; justify-content: center; } .btn-group-vertical > .btn, .btn-group-vertical > .btn-group { width: 100%; } .btn-group-vertical > .btn:not(:first-child), .btn-group-vertical > .btn-group:not(:first-child) { margin-top: -1px; } .btn-group-vertical > .btn:not(:last-child):not(.dropdown-toggle), .btn-group-vertical > .btn-group:not(:last-child) > .btn { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn:not(:first-child), .btn-group-vertical > .btn-group:not(:first-child) > .btn { border-top-left-radius: 0; border-top-right-radius: 0; } .btn-group-toggle > .btn, .btn-group-toggle > .btn-group > .btn { margin-bottom: 0; } .btn-group-toggle > .btn input[type="radio"], .btn-group-toggle > .btn input[type="checkbox"], .btn-group-toggle > .btn-group > .btn input[type="radio"], .btn-group-toggle > .btn-group > .btn input[type="checkbox"] { position: absolute; clip: rect(0, 0, 0, 0); pointer-events: none; } .input-group { position: relative; display: -ms-flexbox; display: flex; -ms-flex-wrap: wrap; flex-wrap: wrap; -ms-flex-align: stretch; align-items: stretch; width: 100%; } .input-group > .form-control, .input-group > .form-control-plaintext, .input-group > .custom-select, .input-group > .custom-file { position: relative; -ms-flex: 1 1 auto; flex: 1 1 auto; width: 1%; margin-bottom: 0; } .input-group > .form-control + .form-control, .input-group > .form-control + .custom-select, .input-group > .form-control + .custom-file, .input-group > .form-control-plaintext + .form-control, .input-group > .form-control-plaintext + .custom-select, .input-group > .form-control-plaintext + .custom-file, .input-group > .custom-select + .form-control, .input-group > .custom-select + .custom-select, .input-group > .custom-select + .custom-file, .input-group > .custom-file + .form-control, .input-group > .custom-file + .custom-select, .input-group > .custom-file + .custom-file { margin-left: -1px; } .input-group > .form-control:focus, .input-group > .custom-select:focus, .input-group > .custom-file .custom-file-input:focus ~ .custom-file-label { z-index: 3; } .input-group > .custom-file .custom-file-input:focus { z-index: 4; } .input-group > .form-control:not(:last-child), .input-group > .custom-select:not(:last-child) { border-top-right-radius: 0; border-bottom-right-radius: 0; } .input-group > .form-control:not(:first-child), .input-group > .custom-select:not(:first-child) { border-top-left-radius: 0; border-bottom-left-radius: 0; } .input-group > .custom-file { display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; } .input-group > .custom-file:not(:last-child) .custom-file-label, .input-group > .custom-file:not(:last-child) .custom-file-label::after { border-top-right-radius: 0; border-bottom-right-radius: 0; } .input-group > .custom-file:not(:first-child) .custom-file-label { border-top-left-radius: 0; border-bottom-left-radius: 0; } .input-group-prepend, .input-group-append { display: -ms-flexbox; display: flex; } .input-group-prepend .btn, .input-group-append .btn { position: relative; z-index: 2; } .input-group-prepend .btn:focus, .input-group-append .btn:focus { z-index: 3; } .input-group-prepend .btn + .btn, .input-group-prepend .btn + .input-group-text, .input-group-prepend .input-group-text + .input-group-text, .input-group-prepend .input-group-text + .btn, .input-group-append .btn + .btn, .input-group-append .btn + .input-group-text, .input-group-append .input-group-text + .input-group-text, .input-group-append .input-group-text + .btn { margin-left: -1px; } .input-group-prepend { margin-right: -1px; } .input-group-append { margin-left: -1px; } .input-group-text { display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; padding: 0.375rem 0.75rem; margin-bottom: 0; font-size: 1rem; font-weight: 400; line-height: 1.5; color: #495057; text-align: center; white-space: nowrap; background-color: #e9ecef; border: 1px solid #ced4da; border-radius: 0.25rem; } .input-group-text input[type="radio"], .input-group-text input[type="checkbox"] { margin-top: 0; } .input-group-lg > .form-control:not(textarea), .input-group-lg > .custom-select { height: calc(1.5em + 1rem + 2px); } .input-group-lg > .form-control, .input-group-lg > .custom-select, .input-group-lg > .input-group-prepend > .input-group-text, .input-group-lg > .input-group-append > .input-group-text, .input-group-lg > .input-group-prepend > .btn, .input-group-lg > .input-group-append > .btn { padding: 0.5rem 1rem; font-size: 1.25rem; line-height: 1.5; border-radius: 0.3rem; } .input-group-sm > .form-control:not(textarea), .input-group-sm > .custom-select { height: calc(1.5em + 0.5rem + 2px); } .input-group-sm > .form-control, .input-group-sm > .custom-select, .input-group-sm > .input-group-prepend > .input-group-text, .input-group-sm > .input-group-append > .input-group-text, .input-group-sm > .input-group-prepend > .btn, .input-group-sm > .input-group-append > .btn { padding: 0.25rem 0.5rem; font-size: 0.875rem; line-height: 1.5; border-radius: 0.2rem; } .input-group-lg > .custom-select, .input-group-sm > .custom-select { padding-right: 1.75rem; } .input-group > .input-group-prepend > .btn, .input-group > .input-group-prepend > .input-group-text, .input-group > .input-group-append:not(:last-child) > .btn, .input-group > .input-group-append:not(:last-child) > .input-group-text, .input-group > .input-group-append:last-child > .btn:not(:last-child):not(.dropdown-toggle), .input-group > .input-group-append:last-child > .input-group-text:not(:last-child) { border-top-right-radius: 0; border-bottom-right-radius: 0; } .input-group > .input-group-append > .btn, .input-group > .input-group-append > .input-group-text, .input-group > .input-group-prepend:not(:first-child) > .btn, .input-group > .input-group-prepend:not(:first-child) > .input-group-text, .input-group > .input-group-prepend:first-child > .btn:not(:first-child), .input-group > .input-group-prepend:first-child > .input-group-text:not(:first-child) { border-top-left-radius: 0; border-bottom-left-radius: 0; } .custom-control { position: relative; display: block; min-height: 1.5rem; padding-left: 1.5rem; } .custom-control-inline { display: -ms-inline-flexbox; display: inline-flex; margin-right: 1rem; } .custom-control-input { position: absolute; z-index: -1; opacity: 0; } .custom-control-input:checked ~ .custom-control-label::before { color: #fff; border-color: #007bff; background-color: #007bff; } .custom-control-input:focus ~ .custom-control-label::before { box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .custom-control-input:focus:not(:checked) ~ .custom-control-label::before { border-color: #80bdff; } .custom-control-input:not(:disabled):active ~ .custom-control-label::before { color: #fff; background-color: #b3d7ff; border-color: #b3d7ff; } .custom-control-input:disabled ~ .custom-control-label { color: #6c757d; } .custom-control-input:disabled ~ .custom-control-label::before { background-color: #e9ecef; } .custom-control-label { position: relative; margin-bottom: 0; vertical-align: top; } .custom-control-label::before { position: absolute; top: 0.25rem; left: -1.5rem; display: block; width: 1rem; height: 1rem; pointer-events: none; content: ""; background-color: #fff; border: #adb5bd solid 1px; } .custom-control-label::after { position: absolute; top: 0.25rem; left: -1.5rem; display: block; width: 1rem; height: 1rem; content: ""; background: no-repeat 50% / 50% 50%; } .custom-checkbox .custom-control-label::before { border-radius: 0.25rem; } .custom-checkbox .custom-control-input:checked ~ .custom-control-label::after { background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%23fff' d='M6.564.75l-3.59 3.612-1.538-1.55L0 4.26 2.974 7.25 8 2.193z'/%3e%3c/svg%3e"); } .custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::before { border-color: #007bff; background-color: #007bff; } .custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::after { background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 4'%3e%3cpath stroke='%23fff' d='M0 2h4'/%3e%3c/svg%3e"); } .custom-checkbox .custom-control-input:disabled:checked ~ .custom-control-label::before { background-color: rgba(0, 123, 255, 0.5); } .custom-checkbox .custom-control-input:disabled:indeterminate ~ .custom-control-label::before { background-color: rgba(0, 123, 255, 0.5); } .custom-radio .custom-control-label::before { border-radius: 50%; } .custom-radio .custom-control-input:checked ~ .custom-control-label::after { background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23fff'/%3e%3c/svg%3e"); } .custom-radio .custom-control-input:disabled:checked ~ .custom-control-label::before { background-color: rgba(0, 123, 255, 0.5); } .custom-switch { padding-left: 2.25rem; } .custom-switch .custom-control-label::before { left: -2.25rem; width: 1.75rem; pointer-events: all; border-radius: 0.5rem; } .custom-switch .custom-control-label::after { top: calc(0.25rem + 2px); left: calc(-2.25rem + 2px); width: calc(1rem - 4px); height: calc(1rem - 4px); background-color: #adb5bd; border-radius: 0.5rem; transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-transform 0.15s ease-in-out; transition: transform 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; transition: transform 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-transform 0.15s ease-in-out; } @media (prefers-reduced-motion: reduce) { .custom-switch .custom-control-label::after { transition: none; } } .custom-switch .custom-control-input:checked ~ .custom-control-label::after { background-color: #fff; -webkit-transform: translateX(0.75rem); transform: translateX(0.75rem); } .custom-switch .custom-control-input:disabled:checked ~ .custom-control-label::before { background-color: rgba(0, 123, 255, 0.5); } .custom-select { display: inline-block; width: 100%; height: calc(1.5em + 0.75rem + 2px); padding: 0.375rem 1.75rem 0.375rem 0.75rem; font-size: 1rem; font-weight: 400; line-height: 1.5; color: #495057; vertical-align: middle; background: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 5'%3e%3cpath fill='%23343a40' d='M2 0L0 2h4zm0 5L0 3h4z'/%3e%3c/svg%3e") no-repeat right 0.75rem center/8px 10px; background-color: #fff; border: 1px solid #ced4da; border-radius: 0.25rem; -webkit-appearance: none; -moz-appearance: none; appearance: none; } .custom-select:focus { border-color: #80bdff; outline: 0; box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .custom-select:focus::-ms-value { color: #495057; background-color: #fff; } .custom-select[multiple], .custom-select[size]:not([size="1"]) { height: auto; padding-right: 0.75rem; background-image: none; } .custom-select:disabled { color: #6c757d; background-color: #e9ecef; } .custom-select::-ms-expand { display: none; } .custom-select-sm { height: calc(1.5em + 0.5rem + 2px); padding-top: 0.25rem; padding-bottom: 0.25rem; padding-left: 0.5rem; font-size: 0.875rem; } .custom-select-lg { height: calc(1.5em + 1rem + 2px); padding-top: 0.5rem; padding-bottom: 0.5rem; padding-left: 1rem; font-size: 1.25rem; } .custom-file { position: relative; display: inline-block; width: 100%; height: calc(1.5em + 0.75rem + 2px); margin-bottom: 0; } .custom-file-input { position: relative; z-index: 2; width: 100%; height: calc(1.5em + 0.75rem + 2px); margin: 0; opacity: 0; } .custom-file-input:focus ~ .custom-file-label { border-color: #80bdff; box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .custom-file-input:disabled ~ .custom-file-label { background-color: #e9ecef; } .custom-file-input:lang(en) ~ .custom-file-label::after { content: "Browse"; } .custom-file-input ~ .custom-file-label[data-browse]::after { content: attr(data-browse); } .custom-file-label { position: absolute; top: 0; right: 0; left: 0; z-index: 1; height: calc(1.5em + 0.75rem + 2px); padding: 0.375rem 0.75rem; font-weight: 400; line-height: 1.5; color: #495057; background-color: #fff; border: 1px solid #ced4da; border-radius: 0.25rem; } .custom-file-label::after { position: absolute; top: 0; right: 0; bottom: 0; z-index: 3; display: block; height: calc(1.5em + 0.75rem); padding: 0.375rem 0.75rem; line-height: 1.5; color: #495057; content: "Browse"; background-color: #e9ecef; border-left: inherit; border-radius: 0 0.25rem 0.25rem 0; } .custom-range { width: 100%; height: calc(1rem + 0.4rem); padding: 0; background-color: transparent; -webkit-appearance: none; -moz-appearance: none; appearance: none; } .custom-range:focus { outline: none; } .custom-range:focus::-webkit-slider-thumb { box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .custom-range:focus::-moz-range-thumb { box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .custom-range:focus::-ms-thumb { box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .custom-range::-moz-focus-outer { border: 0; } .custom-range::-webkit-slider-thumb { width: 1rem; height: 1rem; margin-top: -0.25rem; background-color: #007bff; border: 0; border-radius: 1rem; transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -webkit-appearance: none; appearance: none; } @media (prefers-reduced-motion: reduce) { .custom-range::-webkit-slider-thumb { transition: none; } } .custom-range::-webkit-slider-thumb:active { background-color: #b3d7ff; } .custom-range::-webkit-slider-runnable-track { width: 100%; height: 0.5rem; color: transparent; cursor: pointer; background-color: #dee2e6; border-color: transparent; border-radius: 1rem; } .custom-range::-moz-range-thumb { width: 1rem; height: 1rem; background-color: #007bff; border: 0; border-radius: 1rem; transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; -moz-appearance: none; appearance: none; } @media (prefers-reduced-motion: reduce) { .custom-range::-moz-range-thumb { transition: none; } } .custom-range::-moz-range-thumb:active { background-color: #b3d7ff; } .custom-range::-moz-range-track { width: 100%; height: 0.5rem; color: transparent; cursor: pointer; background-color: #dee2e6; border-color: transparent; border-radius: 1rem; } .custom-range::-ms-thumb { width: 1rem; height: 1rem; margin-top: 0; margin-right: 0.2rem; margin-left: 0.2rem; background-color: #007bff; border: 0; border-radius: 1rem; transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; appearance: none; } @media (prefers-reduced-motion: reduce) { .custom-range::-ms-thumb { transition: none; } } .custom-range::-ms-thumb:active { background-color: #b3d7ff; } .custom-range::-ms-track { width: 100%; height: 0.5rem; color: transparent; cursor: pointer; background-color: transparent; border-color: transparent; border-width: 0.5rem; } .custom-range::-ms-fill-lower { background-color: #dee2e6; border-radius: 1rem; } .custom-range::-ms-fill-upper { margin-right: 15px; background-color: #dee2e6; border-radius: 1rem; } .custom-range:disabled::-webkit-slider-thumb { background-color: #adb5bd; } .custom-range:disabled::-webkit-slider-runnable-track { cursor: default; } .custom-range:disabled::-moz-range-thumb { background-color: #adb5bd; } .custom-range:disabled::-moz-range-track { cursor: default; } .custom-range:disabled::-ms-thumb { background-color: #adb5bd; } .custom-control-label::before, .custom-file-label, .custom-select { transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; } @media (prefers-reduced-motion: reduce) { .custom-control-label::before, .custom-file-label, .custom-select { transition: none; } } .nav { display: -ms-flexbox; display: flex; -ms-flex-wrap: wrap; flex-wrap: wrap; padding-left: 0; margin-bottom: 0; list-style: none; } .nav-link { display: block; padding: 0.5rem 1rem; } .nav-link:hover, .nav-link:focus { text-decoration: none; } .nav-link.disabled { color: #6c757d; pointer-events: none; cursor: default; } .nav-tabs { border-bottom: 1px solid #dee2e6; } .nav-tabs .nav-item { margin-bottom: -1px; } .nav-tabs .nav-link { border: 1px solid transparent; border-top-left-radius: 0.25rem; border-top-right-radius: 0.25rem; } .nav-tabs .nav-link:hover, .nav-tabs .nav-link:focus { border-color: #e9ecef #e9ecef #dee2e6; } .nav-tabs .nav-link.disabled { color: #6c757d; background-color: transparent; border-color: transparent; } .nav-tabs .nav-link.active, .nav-tabs .nav-item.show .nav-link { color: #495057; background-color: #fff; border-color: #dee2e6 #dee2e6 #fff; } .nav-tabs .dropdown-menu { margin-top: -1px; border-top-left-radius: 0; border-top-right-radius: 0; } .nav-pills .nav-link { border-radius: 0.25rem; } .nav-pills .nav-link.active, .nav-pills .show > .nav-link { color: #fff; background-color: #007bff; } .nav-fill .nav-item { -ms-flex: 1 1 auto; flex: 1 1 auto; text-align: center; } .nav-justified .nav-item { -ms-flex-preferred-size: 0; flex-basis: 0; -ms-flex-positive: 1; flex-grow: 1; text-align: center; } .tab-content > .tab-pane { display: none; } .tab-content > .active { display: block; } .navbar { position: relative; display: -ms-flexbox; display: flex; -ms-flex-wrap: wrap; flex-wrap: wrap; -ms-flex-align: center; align-items: center; -ms-flex-pack: justify; justify-content: space-between; padding: 0.5rem 1rem; } .navbar > .container, .navbar > .container-fluid { display: -ms-flexbox; display: flex; -ms-flex-wrap: wrap; flex-wrap: wrap; -ms-flex-align: center; align-items: center; -ms-flex-pack: justify; justify-content: space-between; } .navbar-brand { display: inline-block; padding-top: 0.3125rem; padding-bottom: 0.3125rem; margin-right: 1rem; font-size: 1.25rem; line-height: inherit; white-space: nowrap; } .navbar-brand:hover, .navbar-brand:focus { text-decoration: none; } .navbar-nav { display: -ms-flexbox; display: flex; -ms-flex-direction: column; flex-direction: column; padding-left: 0; margin-bottom: 0; list-style: none; } .navbar-nav .nav-link { padding-right: 0; padding-left: 0; } .navbar-nav .dropdown-menu { position: static; float: none; } .navbar-text { display: inline-block; padding-top: 0.5rem; padding-bottom: 0.5rem; } .navbar-collapse { -ms-flex-preferred-size: 100%; flex-basis: 100%; -ms-flex-positive: 1; flex-grow: 1; -ms-flex-align: center; align-items: center; } .navbar-toggler { padding: 0.25rem 0.75rem; font-size: 1.25rem; line-height: 1; background-color: transparent; border: 1px solid transparent; border-radius: 0.25rem; } .navbar-toggler:hover, .navbar-toggler:focus { text-decoration: none; } .navbar-toggler-icon { display: inline-block; width: 1.5em; height: 1.5em; vertical-align: middle; content: ""; background: no-repeat center center; background-size: 100% 100%; } @media (max-width: 575.98px) { .navbar-expand-sm > .container, .navbar-expand-sm > .container-fluid { padding-right: 0; padding-left: 0; } } @media (min-width: 576px) { .navbar-expand-sm { -ms-flex-flow: row nowrap; flex-flow: row nowrap; -ms-flex-pack: start; justify-content: flex-start; } .navbar-expand-sm .navbar-nav { -ms-flex-direction: row; flex-direction: row; } .navbar-expand-sm .navbar-nav .dropdown-menu { position: absolute; } .navbar-expand-sm .navbar-nav .nav-link { padding-right: 0.5rem; padding-left: 0.5rem; } .navbar-expand-sm > .container, .navbar-expand-sm > .container-fluid { -ms-flex-wrap: nowrap; flex-wrap: nowrap; } .navbar-expand-sm .navbar-collapse { display: -ms-flexbox !important; display: flex !important; -ms-flex-preferred-size: auto; flex-basis: auto; } .navbar-expand-sm .navbar-toggler { display: none; } } @media (max-width: 767.98px) { .navbar-expand-md > .container, .navbar-expand-md > .container-fluid { padding-right: 0; padding-left: 0; } } @media (min-width: 768px) { .navbar-expand-md { -ms-flex-flow: row nowrap; flex-flow: row nowrap; -ms-flex-pack: start; justify-content: flex-start; } .navbar-expand-md .navbar-nav { -ms-flex-direction: row; flex-direction: row; } .navbar-expand-md .navbar-nav .dropdown-menu { position: absolute; } .navbar-expand-md .navbar-nav .nav-link { padding-right: 0.5rem; padding-left: 0.5rem; } .navbar-expand-md > .container, .navbar-expand-md > .container-fluid { -ms-flex-wrap: nowrap; flex-wrap: nowrap; } .navbar-expand-md .navbar-collapse { display: -ms-flexbox !important; display: flex !important; -ms-flex-preferred-size: auto; flex-basis: auto; } .navbar-expand-md .navbar-toggler { display: none; } } @media (max-width: 991.98px) { .navbar-expand-lg > .container, .navbar-expand-lg > .container-fluid { padding-right: 0; padding-left: 0; } } @media (min-width: 992px) { .navbar-expand-lg { -ms-flex-flow: row nowrap; flex-flow: row nowrap; -ms-flex-pack: start; justify-content: flex-start; } .navbar-expand-lg .navbar-nav { -ms-flex-direction: row; flex-direction: row; } .navbar-expand-lg .navbar-nav .dropdown-menu { position: absolute; } .navbar-expand-lg .navbar-nav .nav-link { padding-right: 0.5rem; padding-left: 0.5rem; } .navbar-expand-lg > .container, .navbar-expand-lg > .container-fluid { -ms-flex-wrap: nowrap; flex-wrap: nowrap; } .navbar-expand-lg .navbar-collapse { display: -ms-flexbox !important; display: flex !important; -ms-flex-preferred-size: auto; flex-basis: auto; } .navbar-expand-lg .navbar-toggler { display: none; } } @media (max-width: 1199.98px) { .navbar-expand-xl > .container, .navbar-expand-xl > .container-fluid { padding-right: 0; padding-left: 0; } } @media (min-width: 1200px) { .navbar-expand-xl { -ms-flex-flow: row nowrap; flex-flow: row nowrap; -ms-flex-pack: start; justify-content: flex-start; } .navbar-expand-xl .navbar-nav { -ms-flex-direction: row; flex-direction: row; } .navbar-expand-xl .navbar-nav .dropdown-menu { position: absolute; } .navbar-expand-xl .navbar-nav .nav-link { padding-right: 0.5rem; padding-left: 0.5rem; } .navbar-expand-xl > .container, .navbar-expand-xl > .container-fluid { -ms-flex-wrap: nowrap; flex-wrap: nowrap; } .navbar-expand-xl .navbar-collapse { display: -ms-flexbox !important; display: flex !important; -ms-flex-preferred-size: auto; flex-basis: auto; } .navbar-expand-xl .navbar-toggler { display: none; } } .navbar-expand { -ms-flex-flow: row nowrap; flex-flow: row nowrap; -ms-flex-pack: start; justify-content: flex-start; } .navbar-expand > .container, .navbar-expand > .container-fluid { padding-right: 0; padding-left: 0; } .navbar-expand .navbar-nav { -ms-flex-direction: row; flex-direction: row; } .navbar-expand .navbar-nav .dropdown-menu { position: absolute; } .navbar-expand .navbar-nav .nav-link { padding-right: 0.5rem; padding-left: 0.5rem; } .navbar-expand > .container, .navbar-expand > .container-fluid { -ms-flex-wrap: nowrap; flex-wrap: nowrap; } .navbar-expand .navbar-collapse { display: -ms-flexbox !important; display: flex !important; -ms-flex-preferred-size: auto; flex-basis: auto; } .navbar-expand .navbar-toggler { display: none; } .navbar-light .navbar-brand { color: rgba(0, 0, 0, 0.9); } .navbar-light .navbar-brand:hover, .navbar-light .navbar-brand:focus { color: rgba(0, 0, 0, 0.9); } .navbar-light .navbar-nav .nav-link { color: rgba(0, 0, 0, 0.5); } .navbar-light .navbar-nav .nav-link:hover, .navbar-light .navbar-nav .nav-link:focus { color: rgba(0, 0, 0, 0.7); } .navbar-light .navbar-nav .nav-link.disabled { color: rgba(0, 0, 0, 0.3); } .navbar-light .navbar-nav .show > .nav-link, .navbar-light .navbar-nav .active > .nav-link, .navbar-light .navbar-nav .nav-link.show, .navbar-light .navbar-nav .nav-link.active { color: rgba(0, 0, 0, 0.9); } .navbar-light .navbar-toggler { color: rgba(0, 0, 0, 0.5); border-color: rgba(0, 0, 0, 0.1); } .navbar-light .navbar-toggler-icon { background-image: url("data:image/svg+xml,%3csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3e%3cpath stroke='rgba(0, 0, 0, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e"); } .navbar-light .navbar-text { color: rgba(0, 0, 0, 0.5); } .navbar-light .navbar-text a { color: rgba(0, 0, 0, 0.9); } .navbar-light .navbar-text a:hover, .navbar-light .navbar-text a:focus { color: rgba(0, 0, 0, 0.9); } .navbar-dark .navbar-brand { color: #fff; } .navbar-dark .navbar-brand:hover, .navbar-dark .navbar-brand:focus { color: #fff; } .navbar-dark .navbar-nav .nav-link { color: rgba(255, 255, 255, 0.5); } .navbar-dark .navbar-nav .nav-link:hover, .navbar-dark .navbar-nav .nav-link:focus { color: rgba(255, 255, 255, 0.75); } .navbar-dark .navbar-nav .nav-link.disabled { color: rgba(255, 255, 255, 0.25); } .navbar-dark .navbar-nav .show > .nav-link, .navbar-dark .navbar-nav .active > .nav-link, .navbar-dark .navbar-nav .nav-link.show, .navbar-dark .navbar-nav .nav-link.active { color: #fff; } .navbar-dark .navbar-toggler { color: rgba(255, 255, 255, 0.5); border-color: rgba(255, 255, 255, 0.1); } .navbar-dark .navbar-toggler-icon { background-image: url("data:image/svg+xml,%3csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3e%3cpath stroke='rgba(255, 255, 255, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e"); } .navbar-dark .navbar-text { color: rgba(255, 255, 255, 0.5); } .navbar-dark .navbar-text a { color: #fff; } .navbar-dark .navbar-text a:hover, .navbar-dark .navbar-text a:focus { color: #fff; } .card { position: relative; display: -ms-flexbox; display: flex; -ms-flex-direction: column; flex-direction: column; min-width: 0; word-wrap: break-word; background-color: #fff; background-clip: border-box; border: 1px solid rgba(0, 0, 0, 0.125); border-radius: 0.25rem; } .card > hr { margin-right: 0; margin-left: 0; } .card > .list-group:first-child .list-group-item:first-child { border-top-left-radius: 0.25rem; border-top-right-radius: 0.25rem; } .card > .list-group:last-child .list-group-item:last-child { border-bottom-right-radius: 0.25rem; border-bottom-left-radius: 0.25rem; } .card-body { -ms-flex: 1 1 auto; flex: 1 1 auto; padding: 1.25rem; } .card-title { margin-bottom: 0.75rem; } .card-subtitle { margin-top: -0.375rem; margin-bottom: 0; } .card-text:last-child { margin-bottom: 0; } .card-link:hover { text-decoration: none; } .card-link + .card-link { margin-left: 1.25rem; } .card-header { padding: 0.75rem 1.25rem; margin-bottom: 0; background-color: rgba(0, 0, 0, 0.03); border-bottom: 1px solid rgba(0, 0, 0, 0.125); } .card-header:first-child { border-radius: calc(0.25rem - 1px) calc(0.25rem - 1px) 0 0; } .card-header + .list-group .list-group-item:first-child { border-top: 0; } .card-footer { padding: 0.75rem 1.25rem; background-color: rgba(0, 0, 0, 0.03); border-top: 1px solid rgba(0, 0, 0, 0.125); } .card-footer:last-child { border-radius: 0 0 calc(0.25rem - 1px) calc(0.25rem - 1px); } .card-header-tabs { margin-right: -0.625rem; margin-bottom: -0.75rem; margin-left: -0.625rem; border-bottom: 0; } .card-header-pills { margin-right: -0.625rem; margin-left: -0.625rem; } .card-img-overlay { position: absolute; top: 0; right: 0; bottom: 0; left: 0; padding: 1.25rem; } .card-img { width: 100%; border-radius: calc(0.25rem - 1px); } .card-img-top { width: 100%; border-top-left-radius: calc(0.25rem - 1px); border-top-right-radius: calc(0.25rem - 1px); } .card-img-bottom { width: 100%; border-bottom-right-radius: calc(0.25rem - 1px); border-bottom-left-radius: calc(0.25rem - 1px); } .card-deck { display: -ms-flexbox; display: flex; -ms-flex-direction: column; flex-direction: column; } .card-deck .card { margin-bottom: 15px; } @media (min-width: 576px) { .card-deck { -ms-flex-flow: row wrap; flex-flow: row wrap; margin-right: -15px; margin-left: -15px; } .card-deck .card { display: -ms-flexbox; display: flex; -ms-flex: 1 0 0%; flex: 1 0 0%; -ms-flex-direction: column; flex-direction: column; margin-right: 15px; margin-bottom: 0; margin-left: 15px; } } .card-group { display: -ms-flexbox; display: flex; -ms-flex-direction: column; flex-direction: column; } .card-group > .card { margin-bottom: 15px; } @media (min-width: 576px) { .card-group { -ms-flex-flow: row wrap; flex-flow: row wrap; } .card-group > .card { -ms-flex: 1 0 0%; flex: 1 0 0%; margin-bottom: 0; } .card-group > .card + .card { margin-left: 0; border-left: 0; } .card-group > .card:not(:last-child) { border-top-right-radius: 0; border-bottom-right-radius: 0; } .card-group > .card:not(:last-child) .card-img-top, .card-group > .card:not(:last-child) .card-header { border-top-right-radius: 0; } .card-group > .card:not(:last-child) .card-img-bottom, .card-group > .card:not(:last-child) .card-footer { border-bottom-right-radius: 0; } .card-group > .card:not(:first-child) { border-top-left-radius: 0; border-bottom-left-radius: 0; } .card-group > .card:not(:first-child) .card-img-top, .card-group > .card:not(:first-child) .card-header { border-top-left-radius: 0; } .card-group > .card:not(:first-child) .card-img-bottom, .card-group > .card:not(:first-child) .card-footer { border-bottom-left-radius: 0; } } .card-columns .card { margin-bottom: 0.75rem; } @media (min-width: 576px) { .card-columns { -webkit-column-count: 3; -moz-column-count: 3; column-count: 3; -webkit-column-gap: 1.25rem; -moz-column-gap: 1.25rem; column-gap: 1.25rem; orphans: 1; widows: 1; } .card-columns .card { display: inline-block; width: 100%; } } .accordion > .card { overflow: hidden; } .accordion > .card:not(:first-of-type) .card-header:first-child { border-radius: 0; } .accordion > .card:not(:first-of-type):not(:last-of-type) { border-bottom: 0; border-radius: 0; } .accordion > .card:first-of-type { border-bottom: 0; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .accordion > .card:last-of-type { border-top-left-radius: 0; border-top-right-radius: 0; } .accordion > .card .card-header { margin-bottom: -1px; } .breadcrumb { display: -ms-flexbox; display: flex; -ms-flex-wrap: wrap; flex-wrap: wrap; padding: 0.75rem 1rem; margin-bottom: 1rem; list-style: none; background-color: #e9ecef; border-radius: 0.25rem; } .breadcrumb-item + .breadcrumb-item { padding-left: 0.5rem; } .breadcrumb-item + .breadcrumb-item::before { display: inline-block; padding-right: 0.5rem; color: #6c757d; content: "/"; } .breadcrumb-item + .breadcrumb-item:hover::before { text-decoration: underline; } .breadcrumb-item + .breadcrumb-item:hover::before { text-decoration: none; } .breadcrumb-item.active { color: #6c757d; } .pagination { display: -ms-flexbox; display: flex; padding-left: 0; list-style: none; border-radius: 0.25rem; } .page-link { position: relative; display: block; padding: 0.5rem 0.75rem; margin-left: -1px; line-height: 1.25; color: #007bff; background-color: #fff; border: 1px solid #dee2e6; } .page-link:hover { z-index: 2; color: #0056b3; text-decoration: none; background-color: #e9ecef; border-color: #dee2e6; } .page-link:focus { z-index: 2; outline: 0; box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); } .page-item:first-child .page-link { margin-left: 0; border-top-left-radius: 0.25rem; border-bottom-left-radius: 0.25rem; } .page-item:last-child .page-link { border-top-right-radius: 0.25rem; border-bottom-right-radius: 0.25rem; } .page-item.active .page-link { z-index: 1; color: #fff; background-color: #007bff; border-color: #007bff; } .page-item.disabled .page-link { color: #6c757d; pointer-events: none; cursor: auto; background-color: #fff; border-color: #dee2e6; } .pagination-lg .page-link { padding: 0.75rem 1.5rem; font-size: 1.25rem; line-height: 1.5; } .pagination-lg .page-item:first-child .page-link { border-top-left-radius: 0.3rem; border-bottom-left-radius: 0.3rem; } .pagination-lg .page-item:last-child .page-link { border-top-right-radius: 0.3rem; border-bottom-right-radius: 0.3rem; } .pagination-sm .page-link { padding: 0.25rem 0.5rem; font-size: 0.875rem; line-height: 1.5; } .pagination-sm .page-item:first-child .page-link { border-top-left-radius: 0.2rem; border-bottom-left-radius: 0.2rem; } .pagination-sm .page-item:last-child .page-link { border-top-right-radius: 0.2rem; border-bottom-right-radius: 0.2rem; } .badge { display: inline-block; padding: 0.25em 0.4em; font-size: 75%; font-weight: 700; line-height: 1; text-align: center; white-space: nowrap; vertical-align: baseline; border-radius: 0.25rem; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; } @media (prefers-reduced-motion: reduce) { .badge { transition: none; } } a.badge:hover, a.badge:focus { text-decoration: none; } .badge:empty { display: none; } .btn .badge { position: relative; top: -1px; } .badge-pill { padding-right: 0.6em; padding-left: 0.6em; border-radius: 10rem; } .badge-primary { color: #fff; background-color: #007bff; } a.badge-primary:hover, a.badge-primary:focus { color: #fff; background-color: #0062cc; } a.badge-primary:focus, a.badge-primary.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); } .badge-secondary { color: #fff; background-color: #6c757d; } a.badge-secondary:hover, a.badge-secondary:focus { color: #fff; background-color: #545b62; } a.badge-secondary:focus, a.badge-secondary.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); } .badge-success { color: #fff; background-color: #28a745; } a.badge-success:hover, a.badge-success:focus { color: #fff; background-color: #1e7e34; } a.badge-success:focus, a.badge-success.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); } .badge-info { color: #fff; background-color: #17a2b8; } a.badge-info:hover, a.badge-info:focus { color: #fff; background-color: #117a8b; } a.badge-info:focus, a.badge-info.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); } .badge-warning { color: #212529; background-color: #ffc107; } a.badge-warning:hover, a.badge-warning:focus { color: #212529; background-color: #d39e00; } a.badge-warning:focus, a.badge-warning.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); } .badge-danger { color: #fff; background-color: #dc3545; } a.badge-danger:hover, a.badge-danger:focus { color: #fff; background-color: #bd2130; } a.badge-danger:focus, a.badge-danger.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); } .badge-light { color: #212529; background-color: #f8f9fa; } a.badge-light:hover, a.badge-light:focus { color: #212529; background-color: #dae0e5; } a.badge-light:focus, a.badge-light.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); } .badge-dark { color: #fff; background-color: #343a40; } a.badge-dark:hover, a.badge-dark:focus { color: #fff; background-color: #1d2124; } a.badge-dark:focus, a.badge-dark.focus { outline: 0; box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); } .jumbotron { padding: 2rem 1rem; margin-bottom: 2rem; background-color: #e9ecef; border-radius: 0.3rem; } @media (min-width: 576px) { .jumbotron { padding: 4rem 2rem; } } .jumbotron-fluid { padding-right: 0; padding-left: 0; border-radius: 0; } .alert { position: relative; padding: 0.75rem 1.25rem; margin-bottom: 1rem; border: 1px solid transparent; border-radius: 0.25rem; } .alert-heading { color: inherit; } .alert-link { font-weight: 700; } .alert-dismissible { padding-right: 4rem; } .alert-dismissible .close { position: absolute; top: 0; right: 0; padding: 0.75rem 1.25rem; color: inherit; } .alert-primary { color: #004085; background-color: #cce5ff; border-color: #b8daff; } .alert-primary hr { border-top-color: #9fcdff; } .alert-primary .alert-link { color: #002752; } .alert-secondary { color: #383d41; background-color: #e2e3e5; border-color: #d6d8db; } .alert-secondary hr { border-top-color: #c8cbcf; } .alert-secondary .alert-link { color: #202326; } .alert-success { color: #155724; background-color: #d4edda; border-color: #c3e6cb; } .alert-success hr { border-top-color: #b1dfbb; } .alert-success .alert-link { color: #0b2e13; } .alert-info { color: #0c5460; background-color: #d1ecf1; border-color: #bee5eb; } .alert-info hr { border-top-color: #abdde5; } .alert-info .alert-link { color: #062c33; } .alert-warning { color: #856404; background-color: #fff3cd; border-color: #ffeeba; } .alert-warning hr { border-top-color: #ffe8a1; } .alert-warning .alert-link { color: #533f03; } .alert-danger { color: #721c24; background-color: #f8d7da; border-color: #f5c6cb; } .alert-danger hr { border-top-color: #f1b0b7; } .alert-danger .alert-link { color: #491217; } .alert-light { color: #818182; background-color: #fefefe; border-color: #fdfdfe; } .alert-light hr { border-top-color: #ececf6; } .alert-light .alert-link { color: #686868; } .alert-dark { color: #1b1e21; background-color: #d6d8d9; border-color: #c6c8ca; } .alert-dark hr { border-top-color: #b9bbbe; } .alert-dark .alert-link { color: #040505; } @-webkit-keyframes progress-bar-stripes { from { background-position: 1rem 0; } to { background-position: 0 0; } } @keyframes progress-bar-stripes { from { background-position: 1rem 0; } to { background-position: 0 0; } } .progress { display: -ms-flexbox; display: flex; height: 1rem; overflow: hidden; font-size: 0.75rem; background-color: #e9ecef; border-radius: 0.25rem; } .progress-bar { display: -ms-flexbox; display: flex; -ms-flex-direction: column; flex-direction: column; -ms-flex-pack: center; justify-content: center; color: #fff; text-align: center; white-space: nowrap; background-color: #007bff; transition: width 0.6s ease; } @media (prefers-reduced-motion: reduce) { .progress-bar { transition: none; } } .progress-bar-striped { background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-size: 1rem 1rem; } .progress-bar-animated { -webkit-animation: progress-bar-stripes 1s linear infinite; animation: progress-bar-stripes 1s linear infinite; } @media (prefers-reduced-motion: reduce) { .progress-bar-animated { -webkit-animation: none; animation: none; } } .media { display: -ms-flexbox; display: flex; -ms-flex-align: start; align-items: flex-start; } .media-body { -ms-flex: 1; flex: 1; } .list-group { display: -ms-flexbox; display: flex; -ms-flex-direction: column; flex-direction: column; padding-left: 0; margin-bottom: 0; } .list-group-item-action { width: 100%; color: #495057; text-align: inherit; } .list-group-item-action:hover, .list-group-item-action:focus { z-index: 1; color: #495057; text-decoration: none; background-color: #f8f9fa; } .list-group-item-action:active { color: #212529; background-color: #e9ecef; } .list-group-item { position: relative; display: block; padding: 0.75rem 1.25rem; margin-bottom: -1px; background-color: #fff; border: 1px solid rgba(0, 0, 0, 0.125); } .list-group-item:first-child { border-top-left-radius: 0.25rem; border-top-right-radius: 0.25rem; } .list-group-item:last-child { margin-bottom: 0; border-bottom-right-radius: 0.25rem; border-bottom-left-radius: 0.25rem; } .list-group-item.disabled, .list-group-item:disabled { color: #6c757d; pointer-events: none; background-color: #fff; } .list-group-item.active { z-index: 2; color: #fff; background-color: #007bff; border-color: #007bff; } .list-group-horizontal { -ms-flex-direction: row; flex-direction: row; } .list-group-horizontal .list-group-item { margin-right: -1px; margin-bottom: 0; } .list-group-horizontal .list-group-item:first-child { border-top-left-radius: 0.25rem; border-bottom-left-radius: 0.25rem; border-top-right-radius: 0; } .list-group-horizontal .list-group-item:last-child { margin-right: 0; border-top-right-radius: 0.25rem; border-bottom-right-radius: 0.25rem; border-bottom-left-radius: 0; } @media (min-width: 576px) { .list-group-horizontal-sm { -ms-flex-direction: row; flex-direction: row; } .list-group-horizontal-sm .list-group-item { margin-right: -1px; margin-bottom: 0; } .list-group-horizontal-sm .list-group-item:first-child { border-top-left-radius: 0.25rem; border-bottom-left-radius: 0.25rem; border-top-right-radius: 0; } .list-group-horizontal-sm .list-group-item:last-child { margin-right: 0; border-top-right-radius: 0.25rem; border-bottom-right-radius: 0.25rem; border-bottom-left-radius: 0; } } @media (min-width: 768px) { .list-group-horizontal-md { -ms-flex-direction: row; flex-direction: row; } .list-group-horizontal-md .list-group-item { margin-right: -1px; margin-bottom: 0; } .list-group-horizontal-md .list-group-item:first-child { border-top-left-radius: 0.25rem; border-bottom-left-radius: 0.25rem; border-top-right-radius: 0; } .list-group-horizontal-md .list-group-item:last-child { margin-right: 0; border-top-right-radius: 0.25rem; border-bottom-right-radius: 0.25rem; border-bottom-left-radius: 0; } } @media (min-width: 992px) { .list-group-horizontal-lg { -ms-flex-direction: row; flex-direction: row; } .list-group-horizontal-lg .list-group-item { margin-right: -1px; margin-bottom: 0; } .list-group-horizontal-lg .list-group-item:first-child { border-top-left-radius: 0.25rem; border-bottom-left-radius: 0.25rem; border-top-right-radius: 0; } .list-group-horizontal-lg .list-group-item:last-child { margin-right: 0; border-top-right-radius: 0.25rem; border-bottom-right-radius: 0.25rem; border-bottom-left-radius: 0; } } @media (min-width: 1200px) { .list-group-horizontal-xl { -ms-flex-direction: row; flex-direction: row; } .list-group-horizontal-xl .list-group-item { margin-right: -1px; margin-bottom: 0; } .list-group-horizontal-xl .list-group-item:first-child { border-top-left-radius: 0.25rem; border-bottom-left-radius: 0.25rem; border-top-right-radius: 0; } .list-group-horizontal-xl .list-group-item:last-child { margin-right: 0; border-top-right-radius: 0.25rem; border-bottom-right-radius: 0.25rem; border-bottom-left-radius: 0; } } .list-group-flush .list-group-item { border-right: 0; border-left: 0; border-radius: 0; } .list-group-flush .list-group-item:last-child { margin-bottom: -1px; } .list-group-flush:first-child .list-group-item:first-child { border-top: 0; } .list-group-flush:last-child .list-group-item:last-child { margin-bottom: 0; border-bottom: 0; } .list-group-item-primary { color: #004085; background-color: #b8daff; } .list-group-item-primary.list-group-item-action:hover, .list-group-item-primary.list-group-item-action:focus { color: #004085; background-color: #9fcdff; } .list-group-item-primary.list-group-item-action.active { color: #fff; background-color: #004085; border-color: #004085; } .list-group-item-secondary { color: #383d41; background-color: #d6d8db; } .list-group-item-secondary.list-group-item-action:hover, .list-group-item-secondary.list-group-item-action:focus { color: #383d41; background-color: #c8cbcf; } .list-group-item-secondary.list-group-item-action.active { color: #fff; background-color: #383d41; border-color: #383d41; } .list-group-item-success { color: #155724; background-color: #c3e6cb; } .list-group-item-success.list-group-item-action:hover, .list-group-item-success.list-group-item-action:focus { color: #155724; background-color: #b1dfbb; } .list-group-item-success.list-group-item-action.active { color: #fff; background-color: #155724; border-color: #155724; } .list-group-item-info { color: #0c5460; background-color: #bee5eb; } .list-group-item-info.list-group-item-action:hover, .list-group-item-info.list-group-item-action:focus { color: #0c5460; background-color: #abdde5; } .list-group-item-info.list-group-item-action.active { color: #fff; background-color: #0c5460; border-color: #0c5460; } .list-group-item-warning { color: #856404; background-color: #ffeeba; } .list-group-item-warning.list-group-item-action:hover, .list-group-item-warning.list-group-item-action:focus { color: #856404; background-color: #ffe8a1; } .list-group-item-warning.list-group-item-action.active { color: #fff; background-color: #856404; border-color: #856404; } .list-group-item-danger { color: #721c24; background-color: #f5c6cb; } .list-group-item-danger.list-group-item-action:hover, .list-group-item-danger.list-group-item-action:focus { color: #721c24; background-color: #f1b0b7; } .list-group-item-danger.list-group-item-action.active { color: #fff; background-color: #721c24; border-color: #721c24; } .list-group-item-light { color: #818182; background-color: #fdfdfe; } .list-group-item-light.list-group-item-action:hover, .list-group-item-light.list-group-item-action:focus { color: #818182; background-color: #ececf6; } .list-group-item-light.list-group-item-action.active { color: #fff; background-color: #818182; border-color: #818182; } .list-group-item-dark { color: #1b1e21; background-color: #c6c8ca; } .list-group-item-dark.list-group-item-action:hover, .list-group-item-dark.list-group-item-action:focus { color: #1b1e21; background-color: #b9bbbe; } .list-group-item-dark.list-group-item-action.active { color: #fff; background-color: #1b1e21; border-color: #1b1e21; } .close { float: right; font-size: 1.5rem; font-weight: 700; line-height: 1; color: #000; text-shadow: 0 1px 0 #fff; opacity: .5; } .close:hover { color: #000; text-decoration: none; } .close:not(:disabled):not(.disabled):hover, .close:not(:disabled):not(.disabled):focus { opacity: .75; } button.close { padding: 0; background-color: transparent; border: 0; -webkit-appearance: none; -moz-appearance: none; appearance: none; } a.close.disabled { pointer-events: none; } .toast { max-width: 350px; overflow: hidden; font-size: 0.875rem; background-color: rgba(255, 255, 255, 0.85); background-clip: padding-box; border: 1px solid rgba(0, 0, 0, 0.1); box-shadow: 0 0.25rem 0.75rem rgba(0, 0, 0, 0.1); -webkit-backdrop-filter: blur(10px); backdrop-filter: blur(10px); opacity: 0; border-radius: 0.25rem; } .toast:not(:last-child) { margin-bottom: 0.75rem; } .toast.showing { opacity: 1; } .toast.show { display: block; opacity: 1; } .toast.hide { display: none; } .toast-header { display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; padding: 0.25rem 0.75rem; color: #6c757d; background-color: rgba(255, 255, 255, 0.85); background-clip: padding-box; border-bottom: 1px solid rgba(0, 0, 0, 0.05); } .toast-body { padding: 0.75rem; } .modal-open { overflow: hidden; } .modal-open .modal { overflow-x: hidden; overflow-y: auto; } .modal { position: fixed; top: 0; left: 0; z-index: 1050; display: none; width: 100%; height: 100%; overflow: hidden; outline: 0; } .modal-dialog { position: relative; width: auto; margin: 0.5rem; pointer-events: none; } .modal.fade .modal-dialog { transition: -webkit-transform 0.3s ease-out; transition: transform 0.3s ease-out; transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out; -webkit-transform: translate(0, -50px); transform: translate(0, -50px); } @media (prefers-reduced-motion: reduce) { .modal.fade .modal-dialog { transition: none; } } .modal.show .modal-dialog { -webkit-transform: none; transform: none; } .modal-dialog-scrollable { display: -ms-flexbox; display: flex; max-height: calc(100% - 1rem); } .modal-dialog-scrollable .modal-content { max-height: calc(100vh - 1rem); overflow: hidden; } .modal-dialog-scrollable .modal-header, .modal-dialog-scrollable .modal-footer { -ms-flex-negative: 0; flex-shrink: 0; } .modal-dialog-scrollable .modal-body { overflow-y: auto; } .modal-dialog-centered { display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; min-height: calc(100% - 1rem); } .modal-dialog-centered::before { display: block; height: calc(100vh - 1rem); content: ""; } .modal-dialog-centered.modal-dialog-scrollable { -ms-flex-direction: column; flex-direction: column; -ms-flex-pack: center; justify-content: center; height: 100%; } .modal-dialog-centered.modal-dialog-scrollable .modal-content { max-height: none; } .modal-dialog-centered.modal-dialog-scrollable::before { content: none; } .modal-content { position: relative; display: -ms-flexbox; display: flex; -ms-flex-direction: column; flex-direction: column; width: 100%; pointer-events: auto; background-color: #fff; background-clip: padding-box; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 0.3rem; outline: 0; } .modal-backdrop { position: fixed; top: 0; left: 0; z-index: 1040; width: 100vw; height: 100vh; background-color: #000; } .modal-backdrop.fade { opacity: 0; } .modal-backdrop.show { opacity: 0.5; } .modal-header { display: -ms-flexbox; display: flex; -ms-flex-align: start; align-items: flex-start; -ms-flex-pack: justify; justify-content: space-between; padding: 1rem 1rem; border-bottom: 1px solid #dee2e6; border-top-left-radius: 0.3rem; border-top-right-radius: 0.3rem; } .modal-header .close { padding: 1rem 1rem; margin: -1rem -1rem -1rem auto; } .modal-title { margin-bottom: 0; line-height: 1.5; } .modal-body { position: relative; -ms-flex: 1 1 auto; flex: 1 1 auto; padding: 1rem; } .modal-footer { display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; -ms-flex-pack: end; justify-content: flex-end; padding: 1rem; border-top: 1px solid #dee2e6; border-bottom-right-radius: 0.3rem; border-bottom-left-radius: 0.3rem; } .modal-footer > :not(:first-child) { margin-left: .25rem; } .modal-footer > :not(:last-child) { margin-right: .25rem; } .modal-scrollbar-measure { position: absolute; top: -9999px; width: 50px; height: 50px; overflow: scroll; } @media (min-width: 576px) { .modal-dialog { max-width: 500px; margin: 1.75rem auto; } .modal-dialog-scrollable { max-height: calc(100% - 3.5rem); } .modal-dialog-scrollable .modal-content { max-height: calc(100vh - 3.5rem); } .modal-dialog-centered { min-height: calc(100% - 3.5rem); } .modal-dialog-centered::before { height: calc(100vh - 3.5rem); } .modal-sm { max-width: 300px; } } @media (min-width: 992px) { .modal-lg, .modal-xl { max-width: 800px; } } @media (min-width: 1200px) { .modal-xl { max-width: 1140px; } } .tooltip { position: absolute; z-index: 1070; display: block; margin: 0; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; font-style: normal; font-weight: 400; line-height: 1.5; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; letter-spacing: normal; word-break: normal; word-spacing: normal; white-space: normal; line-break: auto; font-size: 0.875rem; word-wrap: break-word; opacity: 0; } .tooltip.show { opacity: 0.9; } .tooltip .arrow { position: absolute; display: block; width: 0.8rem; height: 0.4rem; } .tooltip .arrow::before { position: absolute; content: ""; border-color: transparent; border-style: solid; } .bs-tooltip-top, .bs-tooltip-auto[x-placement^="top"] { padding: 0.4rem 0; } .bs-tooltip-top .arrow, .bs-tooltip-auto[x-placement^="top"] .arrow { bottom: 0; } .bs-tooltip-top .arrow::before, .bs-tooltip-auto[x-placement^="top"] .arrow::before { top: 0; border-width: 0.4rem 0.4rem 0; border-top-color: #000; } .bs-tooltip-right, .bs-tooltip-auto[x-placement^="right"] { padding: 0 0.4rem; } .bs-tooltip-right .arrow, .bs-tooltip-auto[x-placement^="right"] .arrow { left: 0; width: 0.4rem; height: 0.8rem; } .bs-tooltip-right .arrow::before, .bs-tooltip-auto[x-placement^="right"] .arrow::before { right: 0; border-width: 0.4rem 0.4rem 0.4rem 0; border-right-color: #000; } .bs-tooltip-bottom, .bs-tooltip-auto[x-placement^="bottom"] { padding: 0.4rem 0; } .bs-tooltip-bottom .arrow, .bs-tooltip-auto[x-placement^="bottom"] .arrow { top: 0; } .bs-tooltip-bottom .arrow::before, .bs-tooltip-auto[x-placement^="bottom"] .arrow::before { bottom: 0; border-width: 0 0.4rem 0.4rem; border-bottom-color: #000; } .bs-tooltip-left, .bs-tooltip-auto[x-placement^="left"] { padding: 0 0.4rem; } .bs-tooltip-left .arrow, .bs-tooltip-auto[x-placement^="left"] .arrow { right: 0; width: 0.4rem; height: 0.8rem; } .bs-tooltip-left .arrow::before, .bs-tooltip-auto[x-placement^="left"] .arrow::before { left: 0; border-width: 0.4rem 0 0.4rem 0.4rem; border-left-color: #000; } .tooltip-inner { max-width: 200px; padding: 0.25rem 0.5rem; color: #fff; text-align: center; background-color: #000; border-radius: 0.25rem; } .popover { position: absolute; top: 0; left: 0; z-index: 1060; display: block; max-width: 276px; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; font-style: normal; font-weight: 400; line-height: 1.5; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; letter-spacing: normal; word-break: normal; word-spacing: normal; white-space: normal; line-break: auto; font-size: 0.875rem; word-wrap: break-word; background-color: #fff; background-clip: padding-box; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 0.3rem; } .popover .arrow { position: absolute; display: block; width: 1rem; height: 0.5rem; margin: 0 0.3rem; } .popover .arrow::before, .popover .arrow::after { position: absolute; display: block; content: ""; border-color: transparent; border-style: solid; } .bs-popover-top, .bs-popover-auto[x-placement^="top"] { margin-bottom: 0.5rem; } .bs-popover-top > .arrow, .bs-popover-auto[x-placement^="top"] > .arrow { bottom: calc((0.5rem + 1px) * -1); } .bs-popover-top > .arrow::before, .bs-popover-auto[x-placement^="top"] > .arrow::before { bottom: 0; border-width: 0.5rem 0.5rem 0; border-top-color: rgba(0, 0, 0, 0.25); } .bs-popover-top > .arrow::after, .bs-popover-auto[x-placement^="top"] > .arrow::after { bottom: 1px; border-width: 0.5rem 0.5rem 0; border-top-color: #fff; } .bs-popover-right, .bs-popover-auto[x-placement^="right"] { margin-left: 0.5rem; } .bs-popover-right > .arrow, .bs-popover-auto[x-placement^="right"] > .arrow { left: calc((0.5rem + 1px) * -1); width: 0.5rem; height: 1rem; margin: 0.3rem 0; } .bs-popover-right > .arrow::before, .bs-popover-auto[x-placement^="right"] > .arrow::before { left: 0; border-width: 0.5rem 0.5rem 0.5rem 0; border-right-color: rgba(0, 0, 0, 0.25); } .bs-popover-right > .arrow::after, .bs-popover-auto[x-placement^="right"] > .arrow::after { left: 1px; border-width: 0.5rem 0.5rem 0.5rem 0; border-right-color: #fff; } .bs-popover-bottom, .bs-popover-auto[x-placement^="bottom"] { margin-top: 0.5rem; } .bs-popover-bottom > .arrow, .bs-popover-auto[x-placement^="bottom"] > .arrow { top: calc((0.5rem + 1px) * -1); } .bs-popover-bottom > .arrow::before, .bs-popover-auto[x-placement^="bottom"] > .arrow::before { top: 0; border-width: 0 0.5rem 0.5rem 0.5rem; border-bottom-color: rgba(0, 0, 0, 0.25); } .bs-popover-bottom > .arrow::after, .bs-popover-auto[x-placement^="bottom"] > .arrow::after { top: 1px; border-width: 0 0.5rem 0.5rem 0.5rem; border-bottom-color: #fff; } .bs-popover-bottom .popover-header::before, .bs-popover-auto[x-placement^="bottom"] .popover-header::before { position: absolute; top: 0; left: 50%; display: block; width: 1rem; margin-left: -0.5rem; content: ""; border-bottom: 1px solid #f7f7f7; } .bs-popover-left, .bs-popover-auto[x-placement^="left"] { margin-right: 0.5rem; } .bs-popover-left > .arrow, .bs-popover-auto[x-placement^="left"] > .arrow { right: calc((0.5rem + 1px) * -1); width: 0.5rem; height: 1rem; margin: 0.3rem 0; } .bs-popover-left > .arrow::before, .bs-popover-auto[x-placement^="left"] > .arrow::before { right: 0; border-width: 0.5rem 0 0.5rem 0.5rem; border-left-color: rgba(0, 0, 0, 0.25); } .bs-popover-left > .arrow::after, .bs-popover-auto[x-placement^="left"] > .arrow::after { right: 1px; border-width: 0.5rem 0 0.5rem 0.5rem; border-left-color: #fff; } .popover-header { padding: 0.5rem 0.75rem; margin-bottom: 0; font-size: 1rem; background-color: #f7f7f7; border-bottom: 1px solid #ebebeb; border-top-left-radius: calc(0.3rem - 1px); border-top-right-radius: calc(0.3rem - 1px); } .popover-header:empty { display: none; } .popover-body { padding: 0.5rem 0.75rem; color: #212529; } .carousel { position: relative; } .carousel.pointer-event { -ms-touch-action: pan-y; touch-action: pan-y; } .carousel-inner { position: relative; width: 100%; overflow: hidden; } .carousel-inner::after { display: block; clear: both; content: ""; } .carousel-item { position: relative; display: none; float: left; width: 100%; margin-right: -100%; -webkit-backface-visibility: hidden; backface-visibility: hidden; transition: -webkit-transform 0.6s ease-in-out; transition: transform 0.6s ease-in-out; transition: transform 0.6s ease-in-out, -webkit-transform 0.6s ease-in-out; } @media (prefers-reduced-motion: reduce) { .carousel-item { transition: none; } } .carousel-item.active, .carousel-item-next, .carousel-item-prev { display: block; } .carousel-item-next:not(.carousel-item-left), .active.carousel-item-right { -webkit-transform: translateX(100%); transform: translateX(100%); } .carousel-item-prev:not(.carousel-item-right), .active.carousel-item-left { -webkit-transform: translateX(-100%); transform: translateX(-100%); } .carousel-fade .carousel-item { opacity: 0; transition-property: opacity; -webkit-transform: none; transform: none; } .carousel-fade .carousel-item.active, .carousel-fade .carousel-item-next.carousel-item-left, .carousel-fade .carousel-item-prev.carousel-item-right { z-index: 1; opacity: 1; } .carousel-fade .active.carousel-item-left, .carousel-fade .active.carousel-item-right { z-index: 0; opacity: 0; transition: 0s 0.6s opacity; } @media (prefers-reduced-motion: reduce) { .carousel-fade .active.carousel-item-left, .carousel-fade .active.carousel-item-right { transition: none; } } .carousel-control-prev, .carousel-control-next { position: absolute; top: 0; bottom: 0; z-index: 1; display: -ms-flexbox; display: flex; -ms-flex-align: center; align-items: center; -ms-flex-pack: center; justify-content: center; width: 15%; color: #fff; text-align: center; opacity: 0.5; transition: opacity 0.15s ease; } @media (prefers-reduced-motion: reduce) { .carousel-control-prev, .carousel-control-next { transition: none; } } .carousel-control-prev:hover, .carousel-control-prev:focus, .carousel-control-next:hover, .carousel-control-next:focus { color: #fff; text-decoration: none; outline: 0; opacity: 0.9; } .carousel-control-prev { left: 0; } .carousel-control-next { right: 0; } .carousel-control-prev-icon, .carousel-control-next-icon { display: inline-block; width: 20px; height: 20px; background: no-repeat 50% / 100% 100%; } .carousel-control-prev-icon { background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3e%3cpath d='M5.25 0l-4 4 4 4 1.5-1.5-2.5-2.5 2.5-2.5-1.5-1.5z'/%3e%3c/svg%3e"); } .carousel-control-next-icon { background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3e%3cpath d='M2.75 0l-1.5 1.5 2.5 2.5-2.5 2.5 1.5 1.5 4-4-4-4z'/%3e%3c/svg%3e"); } .carousel-indicators { position: absolute; right: 0; bottom: 0; left: 0; z-index: 15; display: -ms-flexbox; display: flex; -ms-flex-pack: center; justify-content: center; padding-left: 0; margin-right: 15%; margin-left: 15%; list-style: none; } .carousel-indicators li { box-sizing: content-box; -ms-flex: 0 1 auto; flex: 0 1 auto; width: 30px; height: 3px; margin-right: 3px; margin-left: 3px; text-indent: -999px; cursor: pointer; background-color: #fff; background-clip: padding-box; border-top: 10px solid transparent; border-bottom: 10px solid transparent; opacity: .5; transition: opacity 0.6s ease; } @media (prefers-reduced-motion: reduce) { .carousel-indicators li { transition: none; } } .carousel-indicators .active { opacity: 1; } .carousel-caption { position: absolute; right: 15%; bottom: 20px; left: 15%; z-index: 10; padding-top: 20px; padding-bottom: 20px; color: #fff; text-align: center; } @-webkit-keyframes spinner-border { to { -webkit-transform: rotate(360deg); transform: rotate(360deg); } } @keyframes spinner-border { to { -webkit-transform: rotate(360deg); transform: rotate(360deg); } } .spinner-border { display: inline-block; width: 2rem; height: 2rem; vertical-align: text-bottom; border: 0.25em solid currentColor; border-right-color: transparent; border-radius: 50%; -webkit-animation: spinner-border .75s linear infinite; animation: spinner-border .75s linear infinite; } .spinner-border-sm { width: 1rem; height: 1rem; border-width: 0.2em; } @-webkit-keyframes spinner-grow { 0% { -webkit-transform: scale(0); transform: scale(0); } 50% { opacity: 1; } } @keyframes spinner-grow { 0% { -webkit-transform: scale(0); transform: scale(0); } 50% { opacity: 1; } } .spinner-grow { display: inline-block; width: 2rem; height: 2rem; vertical-align: text-bottom; background-color: currentColor; border-radius: 50%; opacity: 0; -webkit-animation: spinner-grow .75s linear infinite; animation: spinner-grow .75s linear infinite; } .spinner-grow-sm { width: 1rem; height: 1rem; } .align-baseline { vertical-align: baseline !important; } .align-top { vertical-align: top !important; } .align-middle { vertical-align: middle !important; } .align-bottom { vertical-align: bottom !important; } .align-text-bottom { vertical-align: text-bottom !important; } .align-text-top { vertical-align: text-top !important; } .bg-primary { background-color: #007bff !important; } a.bg-primary:hover, a.bg-primary:focus, button.bg-primary:hover, button.bg-primary:focus { background-color: #0062cc !important; } .bg-secondary { background-color: #6c757d !important; } a.bg-secondary:hover, a.bg-secondary:focus, button.bg-secondary:hover, button.bg-secondary:focus { background-color: #545b62 !important; } .bg-success { background-color: #28a745 !important; } a.bg-success:hover, a.bg-success:focus, button.bg-success:hover, button.bg-success:focus { background-color: #1e7e34 !important; } .bg-info { background-color: #17a2b8 !important; } a.bg-info:hover, a.bg-info:focus, button.bg-info:hover, button.bg-info:focus { background-color: #117a8b !important; } .bg-warning { background-color: #ffc107 !important; } a.bg-warning:hover, a.bg-warning:focus, button.bg-warning:hover, button.bg-warning:focus { background-color: #d39e00 !important; } .bg-danger { background-color: #dc3545 !important; } a.bg-danger:hover, a.bg-danger:focus, button.bg-danger:hover, button.bg-danger:focus { background-color: #bd2130 !important; } .bg-light { background-color: #f8f9fa !important; } a.bg-light:hover, a.bg-light:focus, button.bg-light:hover, button.bg-light:focus { background-color: #dae0e5 !important; } .bg-dark { background-color: #343a40 !important; } a.bg-dark:hover, a.bg-dark:focus, button.bg-dark:hover, button.bg-dark:focus { background-color: #1d2124 !important; } .bg-white { background-color: #fff !important; } .bg-transparent { background-color: transparent !important; } .border { border: 1px solid #dee2e6 !important; } .border-top { border-top: 1px solid #dee2e6 !important; } .border-right { border-right: 1px solid #dee2e6 !important; } .border-bottom { border-bottom: 1px solid #dee2e6 !important; } .border-left { border-left: 1px solid #dee2e6 !important; } .border-0 { border: 0 !important; } .border-top-0 { border-top: 0 !important; } .border-right-0 { border-right: 0 !important; } .border-bottom-0 { border-bottom: 0 !important; } .border-left-0 { border-left: 0 !important; } .border-primary { border-color: #007bff !important; } .border-secondary { border-color: #6c757d !important; } .border-success { border-color: #28a745 !important; } .border-info { border-color: #17a2b8 !important; } .border-warning { border-color: #ffc107 !important; } .border-danger { border-color: #dc3545 !important; } .border-light { border-color: #f8f9fa !important; } .border-dark { border-color: #343a40 !important; } .border-white { border-color: #fff !important; } .rounded-sm { border-radius: 0.2rem !important; } .rounded { border-radius: 0.25rem !important; } .rounded-top { border-top-left-radius: 0.25rem !important; border-top-right-radius: 0.25rem !important; } .rounded-right { border-top-right-radius: 0.25rem !important; border-bottom-right-radius: 0.25rem !important; } .rounded-bottom { border-bottom-right-radius: 0.25rem !important; border-bottom-left-radius: 0.25rem !important; } .rounded-left { border-top-left-radius: 0.25rem !important; border-bottom-left-radius: 0.25rem !important; } .rounded-lg { border-radius: 0.3rem !important; } .rounded-circle { border-radius: 50% !important; } .rounded-pill { border-radius: 50rem !important; } .rounded-0 { border-radius: 0 !important; } .clearfix::after { display: block; clear: both; content: ""; } .d-none { display: none !important; } .d-inline { display: inline !important; } .d-inline-block { display: inline-block !important; } .d-block { display: block !important; } .d-table { display: table !important; } .d-table-row { display: table-row !important; } .d-table-cell { display: table-cell !important; } .d-flex { display: -ms-flexbox !important; display: flex !important; } .d-inline-flex { display: -ms-inline-flexbox !important; display: inline-flex !important; } @media (min-width: 576px) { .d-sm-none { display: none !important; } .d-sm-inline { display: inline !important; } .d-sm-inline-block { display: inline-block !important; } .d-sm-block { display: block !important; } .d-sm-table { display: table !important; } .d-sm-table-row { display: table-row !important; } .d-sm-table-cell { display: table-cell !important; } .d-sm-flex { display: -ms-flexbox !important; display: flex !important; } .d-sm-inline-flex { display: -ms-inline-flexbox !important; display: inline-flex !important; } } @media (min-width: 768px) { .d-md-none { display: none !important; } .d-md-inline { display: inline !important; } .d-md-inline-block { display: inline-block !important; } .d-md-block { display: block !important; } .d-md-table { display: table !important; } .d-md-table-row { display: table-row !important; } .d-md-table-cell { display: table-cell !important; } .d-md-flex { display: -ms-flexbox !important; display: flex !important; } .d-md-inline-flex { display: -ms-inline-flexbox !important; display: inline-flex !important; } } @media (min-width: 992px) { .d-lg-none { display: none !important; } .d-lg-inline { display: inline !important; } .d-lg-inline-block { display: inline-block !important; } .d-lg-block { display: block !important; } .d-lg-table { display: table !important; } .d-lg-table-row { display: table-row !important; } .d-lg-table-cell { display: table-cell !important; } .d-lg-flex { display: -ms-flexbox !important; display: flex !important; } .d-lg-inline-flex { display: -ms-inline-flexbox !important; display: inline-flex !important; } } @media (min-width: 1200px) { .d-xl-none { display: none !important; } .d-xl-inline { display: inline !important; } .d-xl-inline-block { display: inline-block !important; } .d-xl-block { display: block !important; } .d-xl-table { display: table !important; } .d-xl-table-row { display: table-row !important; } .d-xl-table-cell { display: table-cell !important; } .d-xl-flex { display: -ms-flexbox !important; display: flex !important; } .d-xl-inline-flex { display: -ms-inline-flexbox !important; display: inline-flex !important; } } @media print { .d-print-none { display: none !important; } .d-print-inline { display: inline !important; } .d-print-inline-block { display: inline-block !important; } .d-print-block { display: block !important; } .d-print-table { display: table !important; } .d-print-table-row { display: table-row !important; } .d-print-table-cell { display: table-cell !important; } .d-print-flex { display: -ms-flexbox !important; display: flex !important; } .d-print-inline-flex { display: -ms-inline-flexbox !important; display: inline-flex !important; } } .embed-responsive { position: relative; display: block; width: 100%; padding: 0; overflow: hidden; } .embed-responsive::before { display: block; content: ""; } .embed-responsive .embed-responsive-item, .embed-responsive iframe, .embed-responsive embed, .embed-responsive object, .embed-responsive video { position: absolute; top: 0; bottom: 0; left: 0; width: 100%; height: 100%; border: 0; } .embed-responsive-21by9::before { padding-top: 42.857143%; } .embed-responsive-16by9::before { padding-top: 56.25%; } .embed-responsive-4by3::before { padding-top: 75%; } .embed-responsive-1by1::before { padding-top: 100%; } .flex-row { -ms-flex-direction: row !important; flex-direction: row !important; } .flex-column { -ms-flex-direction: column !important; flex-direction: column !important; } .flex-row-reverse { -ms-flex-direction: row-reverse !important; flex-direction: row-reverse !important; } .flex-column-reverse { -ms-flex-direction: column-reverse !important; flex-direction: column-reverse !important; } .flex-wrap { -ms-flex-wrap: wrap !important; flex-wrap: wrap !important; } .flex-nowrap { -ms-flex-wrap: nowrap !important; flex-wrap: nowrap !important; } .flex-wrap-reverse { -ms-flex-wrap: wrap-reverse !important; flex-wrap: wrap-reverse !important; } .flex-fill { -ms-flex: 1 1 auto !important; flex: 1 1 auto !important; } .flex-grow-0 { -ms-flex-positive: 0 !important; flex-grow: 0 !important; } .flex-grow-1 { -ms-flex-positive: 1 !important; flex-grow: 1 !important; } .flex-shrink-0 { -ms-flex-negative: 0 !important; flex-shrink: 0 !important; } .flex-shrink-1 { -ms-flex-negative: 1 !important; flex-shrink: 1 !important; } .justify-content-start { -ms-flex-pack: start !important; justify-content: flex-start !important; } .justify-content-end { -ms-flex-pack: end !important; justify-content: flex-end !important; } .justify-content-center { -ms-flex-pack: center !important; justify-content: center !important; } .justify-content-between { -ms-flex-pack: justify !important; justify-content: space-between !important; } .justify-content-around { -ms-flex-pack: distribute !important; justify-content: space-around !important; } .align-items-start { -ms-flex-align: start !important; align-items: flex-start !important; } .align-items-end { -ms-flex-align: end !important; align-items: flex-end !important; } .align-items-center { -ms-flex-align: center !important; align-items: center !important; } .align-items-baseline { -ms-flex-align: baseline !important; align-items: baseline !important; } .align-items-stretch { -ms-flex-align: stretch !important; align-items: stretch !important; } .align-content-start { -ms-flex-line-pack: start !important; align-content: flex-start !important; } .align-content-end { -ms-flex-line-pack: end !important; align-content: flex-end !important; } .align-content-center { -ms-flex-line-pack: center !important; align-content: center !important; } .align-content-between { -ms-flex-line-pack: justify !important; align-content: space-between !important; } .align-content-around { -ms-flex-line-pack: distribute !important; align-content: space-around !important; } .align-content-stretch { -ms-flex-line-pack: stretch !important; align-content: stretch !important; } .align-self-auto { -ms-flex-item-align: auto !important; align-self: auto !important; } .align-self-start { -ms-flex-item-align: start !important; align-self: flex-start !important; } .align-self-end { -ms-flex-item-align: end !important; align-self: flex-end !important; } .align-self-center { -ms-flex-item-align: center !important; align-self: center !important; } .align-self-baseline { -ms-flex-item-align: baseline !important; align-self: baseline !important; } .align-self-stretch { -ms-flex-item-align: stretch !important; align-self: stretch !important; } @media (min-width: 576px) { .flex-sm-row { -ms-flex-direction: row !important; flex-direction: row !important; } .flex-sm-column { -ms-flex-direction: column !important; flex-direction: column !important; } .flex-sm-row-reverse { -ms-flex-direction: row-reverse !important; flex-direction: row-reverse !important; } .flex-sm-column-reverse { -ms-flex-direction: column-reverse !important; flex-direction: column-reverse !important; } .flex-sm-wrap { -ms-flex-wrap: wrap !important; flex-wrap: wrap !important; } .flex-sm-nowrap { -ms-flex-wrap: nowrap !important; flex-wrap: nowrap !important; } .flex-sm-wrap-reverse { -ms-flex-wrap: wrap-reverse !important; flex-wrap: wrap-reverse !important; } .flex-sm-fill { -ms-flex: 1 1 auto !important; flex: 1 1 auto !important; } .flex-sm-grow-0 { -ms-flex-positive: 0 !important; flex-grow: 0 !important; } .flex-sm-grow-1 { -ms-flex-positive: 1 !important; flex-grow: 1 !important; } .flex-sm-shrink-0 { -ms-flex-negative: 0 !important; flex-shrink: 0 !important; } .flex-sm-shrink-1 { -ms-flex-negative: 1 !important; flex-shrink: 1 !important; } .justify-content-sm-start { -ms-flex-pack: start !important; justify-content: flex-start !important; } .justify-content-sm-end { -ms-flex-pack: end !important; justify-content: flex-end !important; } .justify-content-sm-center { -ms-flex-pack: center !important; justify-content: center !important; } .justify-content-sm-between { -ms-flex-pack: justify !important; justify-content: space-between !important; } .justify-content-sm-around { -ms-flex-pack: distribute !important; justify-content: space-around !important; } .align-items-sm-start { -ms-flex-align: start !important; align-items: flex-start !important; } .align-items-sm-end { -ms-flex-align: end !important; align-items: flex-end !important; } .align-items-sm-center { -ms-flex-align: center !important; align-items: center !important; } .align-items-sm-baseline { -ms-flex-align: baseline !important; align-items: baseline !important; } .align-items-sm-stretch { -ms-flex-align: stretch !important; align-items: stretch !important; } .align-content-sm-start { -ms-flex-line-pack: start !important; align-content: flex-start !important; } .align-content-sm-end { -ms-flex-line-pack: end !important; align-content: flex-end !important; } .align-content-sm-center { -ms-flex-line-pack: center !important; align-content: center !important; } .align-content-sm-between { -ms-flex-line-pack: justify !important; align-content: space-between !important; } .align-content-sm-around { -ms-flex-line-pack: distribute !important; align-content: space-around !important; } .align-content-sm-stretch { -ms-flex-line-pack: stretch !important; align-content: stretch !important; } .align-self-sm-auto { -ms-flex-item-align: auto !important; align-self: auto !important; } .align-self-sm-start { -ms-flex-item-align: start !important; align-self: flex-start !important; } .align-self-sm-end { -ms-flex-item-align: end !important; align-self: flex-end !important; } .align-self-sm-center { -ms-flex-item-align: center !important; align-self: center !important; } .align-self-sm-baseline { -ms-flex-item-align: baseline !important; align-self: baseline !important; } .align-self-sm-stretch { -ms-flex-item-align: stretch !important; align-self: stretch !important; } } @media (min-width: 768px) { .flex-md-row { -ms-flex-direction: row !important; flex-direction: row !important; } .flex-md-column { -ms-flex-direction: column !important; flex-direction: column !important; } .flex-md-row-reverse { -ms-flex-direction: row-reverse !important; flex-direction: row-reverse !important; } .flex-md-column-reverse { -ms-flex-direction: column-reverse !important; flex-direction: column-reverse !important; } .flex-md-wrap { -ms-flex-wrap: wrap !important; flex-wrap: wrap !important; } .flex-md-nowrap { -ms-flex-wrap: nowrap !important; flex-wrap: nowrap !important; } .flex-md-wrap-reverse { -ms-flex-wrap: wrap-reverse !important; flex-wrap: wrap-reverse !important; } .flex-md-fill { -ms-flex: 1 1 auto !important; flex: 1 1 auto !important; } .flex-md-grow-0 { -ms-flex-positive: 0 !important; flex-grow: 0 !important; } .flex-md-grow-1 { -ms-flex-positive: 1 !important; flex-grow: 1 !important; } .flex-md-shrink-0 { -ms-flex-negative: 0 !important; flex-shrink: 0 !important; } .flex-md-shrink-1 { -ms-flex-negative: 1 !important; flex-shrink: 1 !important; } .justify-content-md-start { -ms-flex-pack: start !important; justify-content: flex-start !important; } .justify-content-md-end { -ms-flex-pack: end !important; justify-content: flex-end !important; } .justify-content-md-center { -ms-flex-pack: center !important; justify-content: center !important; } .justify-content-md-between { -ms-flex-pack: justify !important; justify-content: space-between !important; } .justify-content-md-around { -ms-flex-pack: distribute !important; justify-content: space-around !important; } .align-items-md-start { -ms-flex-align: start !important; align-items: flex-start !important; } .align-items-md-end { -ms-flex-align: end !important; align-items: flex-end !important; } .align-items-md-center { -ms-flex-align: center !important; align-items: center !important; } .align-items-md-baseline { -ms-flex-align: baseline !important; align-items: baseline !important; } .align-items-md-stretch { -ms-flex-align: stretch !important; align-items: stretch !important; } .align-content-md-start { -ms-flex-line-pack: start !important; align-content: flex-start !important; } .align-content-md-end { -ms-flex-line-pack: end !important; align-content: flex-end !important; } .align-content-md-center { -ms-flex-line-pack: center !important; align-content: center !important; } .align-content-md-between { -ms-flex-line-pack: justify !important; align-content: space-between !important; } .align-content-md-around { -ms-flex-line-pack: distribute !important; align-content: space-around !important; } .align-content-md-stretch { -ms-flex-line-pack: stretch !important; align-content: stretch !important; } .align-self-md-auto { -ms-flex-item-align: auto !important; align-self: auto !important; } .align-self-md-start { -ms-flex-item-align: start !important; align-self: flex-start !important; } .align-self-md-end { -ms-flex-item-align: end !important; align-self: flex-end !important; } .align-self-md-center { -ms-flex-item-align: center !important; align-self: center !important; } .align-self-md-baseline { -ms-flex-item-align: baseline !important; align-self: baseline !important; } .align-self-md-stretch { -ms-flex-item-align: stretch !important; align-self: stretch !important; } } @media (min-width: 992px) { .flex-lg-row { -ms-flex-direction: row !important; flex-direction: row !important; } .flex-lg-column { -ms-flex-direction: column !important; flex-direction: column !important; } .flex-lg-row-reverse { -ms-flex-direction: row-reverse !important; flex-direction: row-reverse !important; } .flex-lg-column-reverse { -ms-flex-direction: column-reverse !important; flex-direction: column-reverse !important; } .flex-lg-wrap { -ms-flex-wrap: wrap !important; flex-wrap: wrap !important; } .flex-lg-nowrap { -ms-flex-wrap: nowrap !important; flex-wrap: nowrap !important; } .flex-lg-wrap-reverse { -ms-flex-wrap: wrap-reverse !important; flex-wrap: wrap-reverse !important; } .flex-lg-fill { -ms-flex: 1 1 auto !important; flex: 1 1 auto !important; } .flex-lg-grow-0 { -ms-flex-positive: 0 !important; flex-grow: 0 !important; } .flex-lg-grow-1 { -ms-flex-positive: 1 !important; flex-grow: 1 !important; } .flex-lg-shrink-0 { -ms-flex-negative: 0 !important; flex-shrink: 0 !important; } .flex-lg-shrink-1 { -ms-flex-negative: 1 !important; flex-shrink: 1 !important; } .justify-content-lg-start { -ms-flex-pack: start !important; justify-content: flex-start !important; } .justify-content-lg-end { -ms-flex-pack: end !important; justify-content: flex-end !important; } .justify-content-lg-center { -ms-flex-pack: center !important; justify-content: center !important; } .justify-content-lg-between { -ms-flex-pack: justify !important; justify-content: space-between !important; } .justify-content-lg-around { -ms-flex-pack: distribute !important; justify-content: space-around !important; } .align-items-lg-start { -ms-flex-align: start !important; align-items: flex-start !important; } .align-items-lg-end { -ms-flex-align: end !important; align-items: flex-end !important; } .align-items-lg-center { -ms-flex-align: center !important; align-items: center !important; } .align-items-lg-baseline { -ms-flex-align: baseline !important; align-items: baseline !important; } .align-items-lg-stretch { -ms-flex-align: stretch !important; align-items: stretch !important; } .align-content-lg-start { -ms-flex-line-pack: start !important; align-content: flex-start !important; } .align-content-lg-end { -ms-flex-line-pack: end !important; align-content: flex-end !important; } .align-content-lg-center { -ms-flex-line-pack: center !important; align-content: center !important; } .align-content-lg-between { -ms-flex-line-pack: justify !important; align-content: space-between !important; } .align-content-lg-around { -ms-flex-line-pack: distribute !important; align-content: space-around !important; } .align-content-lg-stretch { -ms-flex-line-pack: stretch !important; align-content: stretch !important; } .align-self-lg-auto { -ms-flex-item-align: auto !important; align-self: auto !important; } .align-self-lg-start { -ms-flex-item-align: start !important; align-self: flex-start !important; } .align-self-lg-end { -ms-flex-item-align: end !important; align-self: flex-end !important; } .align-self-lg-center { -ms-flex-item-align: center !important; align-self: center !important; } .align-self-lg-baseline { -ms-flex-item-align: baseline !important; align-self: baseline !important; } .align-self-lg-stretch { -ms-flex-item-align: stretch !important; align-self: stretch !important; } } @media (min-width: 1200px) { .flex-xl-row { -ms-flex-direction: row !important; flex-direction: row !important; } .flex-xl-column { -ms-flex-direction: column !important; flex-direction: column !important; } .flex-xl-row-reverse { -ms-flex-direction: row-reverse !important; flex-direction: row-reverse !important; } .flex-xl-column-reverse { -ms-flex-direction: column-reverse !important; flex-direction: column-reverse !important; } .flex-xl-wrap { -ms-flex-wrap: wrap !important; flex-wrap: wrap !important; } .flex-xl-nowrap { -ms-flex-wrap: nowrap !important; flex-wrap: nowrap !important; } .flex-xl-wrap-reverse { -ms-flex-wrap: wrap-reverse !important; flex-wrap: wrap-reverse !important; } .flex-xl-fill { -ms-flex: 1 1 auto !important; flex: 1 1 auto !important; } .flex-xl-grow-0 { -ms-flex-positive: 0 !important; flex-grow: 0 !important; } .flex-xl-grow-1 { -ms-flex-positive: 1 !important; flex-grow: 1 !important; } .flex-xl-shrink-0 { -ms-flex-negative: 0 !important; flex-shrink: 0 !important; } .flex-xl-shrink-1 { -ms-flex-negative: 1 !important; flex-shrink: 1 !important; } .justify-content-xl-start { -ms-flex-pack: start !important; justify-content: flex-start !important; } .justify-content-xl-end { -ms-flex-pack: end !important; justify-content: flex-end !important; } .justify-content-xl-center { -ms-flex-pack: center !important; justify-content: center !important; } .justify-content-xl-between { -ms-flex-pack: justify !important; justify-content: space-between !important; } .justify-content-xl-around { -ms-flex-pack: distribute !important; justify-content: space-around !important; } .align-items-xl-start { -ms-flex-align: start !important; align-items: flex-start !important; } .align-items-xl-end { -ms-flex-align: end !important; align-items: flex-end !important; } .align-items-xl-center { -ms-flex-align: center !important; align-items: center !important; } .align-items-xl-baseline { -ms-flex-align: baseline !important; align-items: baseline !important; } .align-items-xl-stretch { -ms-flex-align: stretch !important; align-items: stretch !important; } .align-content-xl-start { -ms-flex-line-pack: start !important; align-content: flex-start !important; } .align-content-xl-end { -ms-flex-line-pack: end !important; align-content: flex-end !important; } .align-content-xl-center { -ms-flex-line-pack: center !important; align-content: center !important; } .align-content-xl-between { -ms-flex-line-pack: justify !important; align-content: space-between !important; } .align-content-xl-around { -ms-flex-line-pack: distribute !important; align-content: space-around !important; } .align-content-xl-stretch { -ms-flex-line-pack: stretch !important; align-content: stretch !important; } .align-self-xl-auto { -ms-flex-item-align: auto !important; align-self: auto !important; } .align-self-xl-start { -ms-flex-item-align: start !important; align-self: flex-start !important; } .align-self-xl-end { -ms-flex-item-align: end !important; align-self: flex-end !important; } .align-self-xl-center { -ms-flex-item-align: center !important; align-self: center !important; } .align-self-xl-baseline { -ms-flex-item-align: baseline !important; align-self: baseline !important; } .align-self-xl-stretch { -ms-flex-item-align: stretch !important; align-self: stretch !important; } } .float-left { float: left !important; } .float-right { float: right !important; } .float-none { float: none !important; } @media (min-width: 576px) { .float-sm-left { float: left !important; } .float-sm-right { float: right !important; } .float-sm-none { float: none !important; } } @media (min-width: 768px) { .float-md-left { float: left !important; } .float-md-right { float: right !important; } .float-md-none { float: none !important; } } @media (min-width: 992px) { .float-lg-left { float: left !important; } .float-lg-right { float: right !important; } .float-lg-none { float: none !important; } } @media (min-width: 1200px) { .float-xl-left { float: left !important; } .float-xl-right { float: right !important; } .float-xl-none { float: none !important; } } .overflow-auto { overflow: auto !important; } .overflow-hidden { overflow: hidden !important; } .position-static { position: static !important; } .position-relative { position: relative !important; } .position-absolute { position: absolute !important; } .position-fixed { position: fixed !important; } .position-sticky { position: -webkit-sticky !important; position: sticky !important; } .fixed-top { position: fixed; top: 0; right: 0; left: 0; z-index: 1030; } .fixed-bottom { position: fixed; right: 0; bottom: 0; left: 0; z-index: 1030; } @supports ((position: -webkit-sticky) or (position: sticky)) { .sticky-top { position: -webkit-sticky; position: sticky; top: 0; z-index: 1020; } } .sr-only { position: absolute; width: 1px; height: 1px; padding: 0; overflow: hidden; clip: rect(0, 0, 0, 0); white-space: nowrap; border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; overflow: visible; clip: auto; white-space: normal; } .shadow-sm { box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075) !important; } .shadow { box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15) !important; } .shadow-lg { box-shadow: 0 1rem 3rem rgba(0, 0, 0, 0.175) !important; } .shadow-none { box-shadow: none !important; } .w-25 { width: 25% !important; } .w-50 { width: 50% !important; } .w-75 { width: 75% !important; } .w-100 { width: 100% !important; } .w-auto { width: auto !important; } .h-25 { height: 25% !important; } .h-50 { height: 50% !important; } .h-75 { height: 75% !important; } .h-100 { height: 100% !important; } .h-auto { height: auto !important; } .mw-100 { max-width: 100% !important; } .mh-100 { max-height: 100% !important; } .min-vw-100 { min-width: 100vw !important; } .min-vh-100 { min-height: 100vh !important; } .vw-100 { width: 100vw !important; } .vh-100 { height: 100vh !important; } .stretched-link::after { position: absolute; top: 0; right: 0; bottom: 0; left: 0; z-index: 1; pointer-events: auto; content: ""; background-color: rgba(0, 0, 0, 0); } .m-0 { margin: 0 !important; } .mt-0, .my-0 { margin-top: 0 !important; } .mr-0, .mx-0 { margin-right: 0 !important; } .mb-0, .my-0 { margin-bottom: 0 !important; } .ml-0, .mx-0 { margin-left: 0 !important; } .m-1 { margin: 0.25rem !important; } .mt-1, .my-1 { margin-top: 0.25rem !important; } .mr-1, .mx-1 { margin-right: 0.25rem !important; } .mb-1, .my-1 { margin-bottom: 0.25rem !important; } .ml-1, .mx-1 { margin-left: 0.25rem !important; } .m-2 { margin: 0.5rem !important; } .mt-2, .my-2 { margin-top: 0.5rem !important; } .mr-2, .mx-2 { margin-right: 0.5rem !important; } .mb-2, .my-2 { margin-bottom: 0.5rem !important; } .ml-2, .mx-2 { margin-left: 0.5rem !important; } .m-3 { margin: 1rem !important; } .mt-3, .my-3 { margin-top: 1rem !important; } .mr-3, .mx-3 { margin-right: 1rem !important; } .mb-3, .my-3 { margin-bottom: 1rem !important; } .ml-3, .mx-3 { margin-left: 1rem !important; } .m-4 { margin: 1.5rem !important; } .mt-4, .my-4 { margin-top: 1.5rem !important; } .mr-4, .mx-4 { margin-right: 1.5rem !important; } .mb-4, .my-4 { margin-bottom: 1.5rem !important; } .ml-4, .mx-4 { margin-left: 1.5rem !important; } .m-5 { margin: 3rem !important; } .mt-5, .my-5 { margin-top: 3rem !important; } .mr-5, .mx-5 { margin-right: 3rem !important; } .mb-5, .my-5 { margin-bottom: 3rem !important; } .ml-5, .mx-5 { margin-left: 3rem !important; } .p-0 { padding: 0 !important; } .pt-0, .py-0 { padding-top: 0 !important; } .pr-0, .px-0 { padding-right: 0 !important; } .pb-0, .py-0 { padding-bottom: 0 !important; } .pl-0, .px-0 { padding-left: 0 !important; } .p-1 { padding: 0.25rem !important; } .pt-1, .py-1 { padding-top: 0.25rem !important; } .pr-1, .px-1 { padding-right: 0.25rem !important; } .pb-1, .py-1 { padding-bottom: 0.25rem !important; } .pl-1, .px-1 { padding-left: 0.25rem !important; } .p-2 { padding: 0.5rem !important; } .pt-2, .py-2 { padding-top: 0.5rem !important; } .pr-2, .px-2 { padding-right: 0.5rem !important; } .pb-2, .py-2 { padding-bottom: 0.5rem !important; } .pl-2, .px-2 { padding-left: 0.5rem !important; } .p-3 { padding: 1rem !important; } .pt-3, .py-3 { padding-top: 1rem !important; } .pr-3, .px-3 { padding-right: 1rem !important; } .pb-3, .py-3 { padding-bottom: 1rem !important; } .pl-3, .px-3 { padding-left: 1rem !important; } .p-4 { padding: 1.5rem !important; } .pt-4, .py-4 { padding-top: 1.5rem !important; } .pr-4, .px-4 { padding-right: 1.5rem !important; } .pb-4, .py-4 { padding-bottom: 1.5rem !important; } .pl-4, .px-4 { padding-left: 1.5rem !important; } .p-5 { padding: 3rem !important; } .pt-5, .py-5 { padding-top: 3rem !important; } .pr-5, .px-5 { padding-right: 3rem !important; } .pb-5, .py-5 { padding-bottom: 3rem !important; } .pl-5, .px-5 { padding-left: 3rem !important; } .m-n1 { margin: -0.25rem !important; } .mt-n1, .my-n1 { margin-top: -0.25rem !important; } .mr-n1, .mx-n1 { margin-right: -0.25rem !important; } .mb-n1, .my-n1 { margin-bottom: -0.25rem !important; } .ml-n1, .mx-n1 { margin-left: -0.25rem !important; } .m-n2 { margin: -0.5rem !important; } .mt-n2, .my-n2 { margin-top: -0.5rem !important; } .mr-n2, .mx-n2 { margin-right: -0.5rem !important; } .mb-n2, .my-n2 { margin-bottom: -0.5rem !important; } .ml-n2, .mx-n2 { margin-left: -0.5rem !important; } .m-n3 { margin: -1rem !important; } .mt-n3, .my-n3 { margin-top: -1rem !important; } .mr-n3, .mx-n3 { margin-right: -1rem !important; } .mb-n3, .my-n3 { margin-bottom: -1rem !important; } .ml-n3, .mx-n3 { margin-left: -1rem !important; } .m-n4 { margin: -1.5rem !important; } .mt-n4, .my-n4 { margin-top: -1.5rem !important; } .mr-n4, .mx-n4 { margin-right: -1.5rem !important; } .mb-n4, .my-n4 { margin-bottom: -1.5rem !important; } .ml-n4, .mx-n4 { margin-left: -1.5rem !important; } .m-n5 { margin: -3rem !important; } .mt-n5, .my-n5 { margin-top: -3rem !important; } .mr-n5, .mx-n5 { margin-right: -3rem !important; } .mb-n5, .my-n5 { margin-bottom: -3rem !important; } .ml-n5, .mx-n5 { margin-left: -3rem !important; } .m-auto { margin: auto !important; } .mt-auto, .my-auto { margin-top: auto !important; } .mr-auto, .mx-auto { margin-right: auto !important; } .mb-auto, .my-auto { margin-bottom: auto !important; } .ml-auto, .mx-auto { margin-left: auto !important; } @media (min-width: 576px) { .m-sm-0 { margin: 0 !important; } .mt-sm-0, .my-sm-0 { margin-top: 0 !important; } .mr-sm-0, .mx-sm-0 { margin-right: 0 !important; } .mb-sm-0, .my-sm-0 { margin-bottom: 0 !important; } .ml-sm-0, .mx-sm-0 { margin-left: 0 !important; } .m-sm-1 { margin: 0.25rem !important; } .mt-sm-1, .my-sm-1 { margin-top: 0.25rem !important; } .mr-sm-1, .mx-sm-1 { margin-right: 0.25rem !important; } .mb-sm-1, .my-sm-1 { margin-bottom: 0.25rem !important; } .ml-sm-1, .mx-sm-1 { margin-left: 0.25rem !important; } .m-sm-2 { margin: 0.5rem !important; } .mt-sm-2, .my-sm-2 { margin-top: 0.5rem !important; } .mr-sm-2, .mx-sm-2 { margin-right: 0.5rem !important; } .mb-sm-2, .my-sm-2 { margin-bottom: 0.5rem !important; } .ml-sm-2, .mx-sm-2 { margin-left: 0.5rem !important; } .m-sm-3 { margin: 1rem !important; } .mt-sm-3, .my-sm-3 { margin-top: 1rem !important; } .mr-sm-3, .mx-sm-3 { margin-right: 1rem !important; } .mb-sm-3, .my-sm-3 { margin-bottom: 1rem !important; } .ml-sm-3, .mx-sm-3 { margin-left: 1rem !important; } .m-sm-4 { margin: 1.5rem !important; } .mt-sm-4, .my-sm-4 { margin-top: 1.5rem !important; } .mr-sm-4, .mx-sm-4 { margin-right: 1.5rem !important; } .mb-sm-4, .my-sm-4 { margin-bottom: 1.5rem !important; } .ml-sm-4, .mx-sm-4 { margin-left: 1.5rem !important; } .m-sm-5 { margin: 3rem !important; } .mt-sm-5, .my-sm-5 { margin-top: 3rem !important; } .mr-sm-5, .mx-sm-5 { margin-right: 3rem !important; } .mb-sm-5, .my-sm-5 { margin-bottom: 3rem !important; } .ml-sm-5, .mx-sm-5 { margin-left: 3rem !important; } .p-sm-0 { padding: 0 !important; } .pt-sm-0, .py-sm-0 { padding-top: 0 !important; } .pr-sm-0, .px-sm-0 { padding-right: 0 !important; } .pb-sm-0, .py-sm-0 { padding-bottom: 0 !important; } .pl-sm-0, .px-sm-0 { padding-left: 0 !important; } .p-sm-1 { padding: 0.25rem !important; } .pt-sm-1, .py-sm-1 { padding-top: 0.25rem !important; } .pr-sm-1, .px-sm-1 { padding-right: 0.25rem !important; } .pb-sm-1, .py-sm-1 { padding-bottom: 0.25rem !important; } .pl-sm-1, .px-sm-1 { padding-left: 0.25rem !important; } .p-sm-2 { padding: 0.5rem !important; } .pt-sm-2, .py-sm-2 { padding-top: 0.5rem !important; } .pr-sm-2, .px-sm-2 { padding-right: 0.5rem !important; } .pb-sm-2, .py-sm-2 { padding-bottom: 0.5rem !important; } .pl-sm-2, .px-sm-2 { padding-left: 0.5rem !important; } .p-sm-3 { padding: 1rem !important; } .pt-sm-3, .py-sm-3 { padding-top: 1rem !important; } .pr-sm-3, .px-sm-3 { padding-right: 1rem !important; } .pb-sm-3, .py-sm-3 { padding-bottom: 1rem !important; } .pl-sm-3, .px-sm-3 { padding-left: 1rem !important; } .p-sm-4 { padding: 1.5rem !important; } .pt-sm-4, .py-sm-4 { padding-top: 1.5rem !important; } .pr-sm-4, .px-sm-4 { padding-right: 1.5rem !important; } .pb-sm-4, .py-sm-4 { padding-bottom: 1.5rem !important; } .pl-sm-4, .px-sm-4 { padding-left: 1.5rem !important; } .p-sm-5 { padding: 3rem !important; } .pt-sm-5, .py-sm-5 { padding-top: 3rem !important; } .pr-sm-5, .px-sm-5 { padding-right: 3rem !important; } .pb-sm-5, .py-sm-5 { padding-bottom: 3rem !important; } .pl-sm-5, .px-sm-5 { padding-left: 3rem !important; } .m-sm-n1 { margin: -0.25rem !important; } .mt-sm-n1, .my-sm-n1 { margin-top: -0.25rem !important; } .mr-sm-n1, .mx-sm-n1 { margin-right: -0.25rem !important; } .mb-sm-n1, .my-sm-n1 { margin-bottom: -0.25rem !important; } .ml-sm-n1, .mx-sm-n1 { margin-left: -0.25rem !important; } .m-sm-n2 { margin: -0.5rem !important; } .mt-sm-n2, .my-sm-n2 { margin-top: -0.5rem !important; } .mr-sm-n2, .mx-sm-n2 { margin-right: -0.5rem !important; } .mb-sm-n2, .my-sm-n2 { margin-bottom: -0.5rem !important; } .ml-sm-n2, .mx-sm-n2 { margin-left: -0.5rem !important; } .m-sm-n3 { margin: -1rem !important; } .mt-sm-n3, .my-sm-n3 { margin-top: -1rem !important; } .mr-sm-n3, .mx-sm-n3 { margin-right: -1rem !important; } .mb-sm-n3, .my-sm-n3 { margin-bottom: -1rem !important; } .ml-sm-n3, .mx-sm-n3 { margin-left: -1rem !important; } .m-sm-n4 { margin: -1.5rem !important; } .mt-sm-n4, .my-sm-n4 { margin-top: -1.5rem !important; } .mr-sm-n4, .mx-sm-n4 { margin-right: -1.5rem !important; } .mb-sm-n4, .my-sm-n4 { margin-bottom: -1.5rem !important; } .ml-sm-n4, .mx-sm-n4 { margin-left: -1.5rem !important; } .m-sm-n5 { margin: -3rem !important; } .mt-sm-n5, .my-sm-n5 { margin-top: -3rem !important; } .mr-sm-n5, .mx-sm-n5 { margin-right: -3rem !important; } .mb-sm-n5, .my-sm-n5 { margin-bottom: -3rem !important; } .ml-sm-n5, .mx-sm-n5 { margin-left: -3rem !important; } .m-sm-auto { margin: auto !important; } .mt-sm-auto, .my-sm-auto { margin-top: auto !important; } .mr-sm-auto, .mx-sm-auto { margin-right: auto !important; } .mb-sm-auto, .my-sm-auto { margin-bottom: auto !important; } .ml-sm-auto, .mx-sm-auto { margin-left: auto !important; } } @media (min-width: 768px) { .m-md-0 { margin: 0 !important; } .mt-md-0, .my-md-0 { margin-top: 0 !important; } .mr-md-0, .mx-md-0 { margin-right: 0 !important; } .mb-md-0, .my-md-0 { margin-bottom: 0 !important; } .ml-md-0, .mx-md-0 { margin-left: 0 !important; } .m-md-1 { margin: 0.25rem !important; } .mt-md-1, .my-md-1 { margin-top: 0.25rem !important; } .mr-md-1, .mx-md-1 { margin-right: 0.25rem !important; } .mb-md-1, .my-md-1 { margin-bottom: 0.25rem !important; } .ml-md-1, .mx-md-1 { margin-left: 0.25rem !important; } .m-md-2 { margin: 0.5rem !important; } .mt-md-2, .my-md-2 { margin-top: 0.5rem !important; } .mr-md-2, .mx-md-2 { margin-right: 0.5rem !important; } .mb-md-2, .my-md-2 { margin-bottom: 0.5rem !important; } .ml-md-2, .mx-md-2 { margin-left: 0.5rem !important; } .m-md-3 { margin: 1rem !important; } .mt-md-3, .my-md-3 { margin-top: 1rem !important; } .mr-md-3, .mx-md-3 { margin-right: 1rem !important; } .mb-md-3, .my-md-3 { margin-bottom: 1rem !important; } .ml-md-3, .mx-md-3 { margin-left: 1rem !important; } .m-md-4 { margin: 1.5rem !important; } .mt-md-4, .my-md-4 { margin-top: 1.5rem !important; } .mr-md-4, .mx-md-4 { margin-right: 1.5rem !important; } .mb-md-4, .my-md-4 { margin-bottom: 1.5rem !important; } .ml-md-4, .mx-md-4 { margin-left: 1.5rem !important; } .m-md-5 { margin: 3rem !important; } .mt-md-5, .my-md-5 { margin-top: 3rem !important; } .mr-md-5, .mx-md-5 { margin-right: 3rem !important; } .mb-md-5, .my-md-5 { margin-bottom: 3rem !important; } .ml-md-5, .mx-md-5 { margin-left: 3rem !important; } .p-md-0 { padding: 0 !important; } .pt-md-0, .py-md-0 { padding-top: 0 !important; } .pr-md-0, .px-md-0 { padding-right: 0 !important; } .pb-md-0, .py-md-0 { padding-bottom: 0 !important; } .pl-md-0, .px-md-0 { padding-left: 0 !important; } .p-md-1 { padding: 0.25rem !important; } .pt-md-1, .py-md-1 { padding-top: 0.25rem !important; } .pr-md-1, .px-md-1 { padding-right: 0.25rem !important; } .pb-md-1, .py-md-1 { padding-bottom: 0.25rem !important; } .pl-md-1, .px-md-1 { padding-left: 0.25rem !important; } .p-md-2 { padding: 0.5rem !important; } .pt-md-2, .py-md-2 { padding-top: 0.5rem !important; } .pr-md-2, .px-md-2 { padding-right: 0.5rem !important; } .pb-md-2, .py-md-2 { padding-bottom: 0.5rem !important; } .pl-md-2, .px-md-2 { padding-left: 0.5rem !important; } .p-md-3 { padding: 1rem !important; } .pt-md-3, .py-md-3 { padding-top: 1rem !important; } .pr-md-3, .px-md-3 { padding-right: 1rem !important; } .pb-md-3, .py-md-3 { padding-bottom: 1rem !important; } .pl-md-3, .px-md-3 { padding-left: 1rem !important; } .p-md-4 { padding: 1.5rem !important; } .pt-md-4, .py-md-4 { padding-top: 1.5rem !important; } .pr-md-4, .px-md-4 { padding-right: 1.5rem !important; } .pb-md-4, .py-md-4 { padding-bottom: 1.5rem !important; } .pl-md-4, .px-md-4 { padding-left: 1.5rem !important; } .p-md-5 { padding: 3rem !important; } .pt-md-5, .py-md-5 { padding-top: 3rem !important; } .pr-md-5, .px-md-5 { padding-right: 3rem !important; } .pb-md-5, .py-md-5 { padding-bottom: 3rem !important; } .pl-md-5, .px-md-5 { padding-left: 3rem !important; } .m-md-n1 { margin: -0.25rem !important; } .mt-md-n1, .my-md-n1 { margin-top: -0.25rem !important; } .mr-md-n1, .mx-md-n1 { margin-right: -0.25rem !important; } .mb-md-n1, .my-md-n1 { margin-bottom: -0.25rem !important; } .ml-md-n1, .mx-md-n1 { margin-left: -0.25rem !important; } .m-md-n2 { margin: -0.5rem !important; } .mt-md-n2, .my-md-n2 { margin-top: -0.5rem !important; } .mr-md-n2, .mx-md-n2 { margin-right: -0.5rem !important; } .mb-md-n2, .my-md-n2 { margin-bottom: -0.5rem !important; } .ml-md-n2, .mx-md-n2 { margin-left: -0.5rem !important; } .m-md-n3 { margin: -1rem !important; } .mt-md-n3, .my-md-n3 { margin-top: -1rem !important; } .mr-md-n3, .mx-md-n3 { margin-right: -1rem !important; } .mb-md-n3, .my-md-n3 { margin-bottom: -1rem !important; } .ml-md-n3, .mx-md-n3 { margin-left: -1rem !important; } .m-md-n4 { margin: -1.5rem !important; } .mt-md-n4, .my-md-n4 { margin-top: -1.5rem !important; } .mr-md-n4, .mx-md-n4 { margin-right: -1.5rem !important; } .mb-md-n4, .my-md-n4 { margin-bottom: -1.5rem !important; } .ml-md-n4, .mx-md-n4 { margin-left: -1.5rem !important; } .m-md-n5 { margin: -3rem !important; } .mt-md-n5, .my-md-n5 { margin-top: -3rem !important; } .mr-md-n5, .mx-md-n5 { margin-right: -3rem !important; } .mb-md-n5, .my-md-n5 { margin-bottom: -3rem !important; } .ml-md-n5, .mx-md-n5 { margin-left: -3rem !important; } .m-md-auto { margin: auto !important; } .mt-md-auto, .my-md-auto { margin-top: auto !important; } .mr-md-auto, .mx-md-auto { margin-right: auto !important; } .mb-md-auto, .my-md-auto { margin-bottom: auto !important; } .ml-md-auto, .mx-md-auto { margin-left: auto !important; } } @media (min-width: 992px) { .m-lg-0 { margin: 0 !important; } .mt-lg-0, .my-lg-0 { margin-top: 0 !important; } .mr-lg-0, .mx-lg-0 { margin-right: 0 !important; } .mb-lg-0, .my-lg-0 { margin-bottom: 0 !important; } .ml-lg-0, .mx-lg-0 { margin-left: 0 !important; } .m-lg-1 { margin: 0.25rem !important; } .mt-lg-1, .my-lg-1 { margin-top: 0.25rem !important; } .mr-lg-1, .mx-lg-1 { margin-right: 0.25rem !important; } .mb-lg-1, .my-lg-1 { margin-bottom: 0.25rem !important; } .ml-lg-1, .mx-lg-1 { margin-left: 0.25rem !important; } .m-lg-2 { margin: 0.5rem !important; } .mt-lg-2, .my-lg-2 { margin-top: 0.5rem !important; } .mr-lg-2, .mx-lg-2 { margin-right: 0.5rem !important; } .mb-lg-2, .my-lg-2 { margin-bottom: 0.5rem !important; } .ml-lg-2, .mx-lg-2 { margin-left: 0.5rem !important; } .m-lg-3 { margin: 1rem !important; } .mt-lg-3, .my-lg-3 { margin-top: 1rem !important; } .mr-lg-3, .mx-lg-3 { margin-right: 1rem !important; } .mb-lg-3, .my-lg-3 { margin-bottom: 1rem !important; } .ml-lg-3, .mx-lg-3 { margin-left: 1rem !important; } .m-lg-4 { margin: 1.5rem !important; } .mt-lg-4, .my-lg-4 { margin-top: 1.5rem !important; } .mr-lg-4, .mx-lg-4 { margin-right: 1.5rem !important; } .mb-lg-4, .my-lg-4 { margin-bottom: 1.5rem !important; } .ml-lg-4, .mx-lg-4 { margin-left: 1.5rem !important; } .m-lg-5 { margin: 3rem !important; } .mt-lg-5, .my-lg-5 { margin-top: 3rem !important; } .mr-lg-5, .mx-lg-5 { margin-right: 3rem !important; } .mb-lg-5, .my-lg-5 { margin-bottom: 3rem !important; } .ml-lg-5, .mx-lg-5 { margin-left: 3rem !important; } .p-lg-0 { padding: 0 !important; } .pt-lg-0, .py-lg-0 { padding-top: 0 !important; } .pr-lg-0, .px-lg-0 { padding-right: 0 !important; } .pb-lg-0, .py-lg-0 { padding-bottom: 0 !important; } .pl-lg-0, .px-lg-0 { padding-left: 0 !important; } .p-lg-1 { padding: 0.25rem !important; } .pt-lg-1, .py-lg-1 { padding-top: 0.25rem !important; } .pr-lg-1, .px-lg-1 { padding-right: 0.25rem !important; } .pb-lg-1, .py-lg-1 { padding-bottom: 0.25rem !important; } .pl-lg-1, .px-lg-1 { padding-left: 0.25rem !important; } .p-lg-2 { padding: 0.5rem !important; } .pt-lg-2, .py-lg-2 { padding-top: 0.5rem !important; } .pr-lg-2, .px-lg-2 { padding-right: 0.5rem !important; } .pb-lg-2, .py-lg-2 { padding-bottom: 0.5rem !important; } .pl-lg-2, .px-lg-2 { padding-left: 0.5rem !important; } .p-lg-3 { padding: 1rem !important; } .pt-lg-3, .py-lg-3 { padding-top: 1rem !important; } .pr-lg-3, .px-lg-3 { padding-right: 1rem !important; } .pb-lg-3, .py-lg-3 { padding-bottom: 1rem !important; } .pl-lg-3, .px-lg-3 { padding-left: 1rem !important; } .p-lg-4 { padding: 1.5rem !important; } .pt-lg-4, .py-lg-4 { padding-top: 1.5rem !important; } .pr-lg-4, .px-lg-4 { padding-right: 1.5rem !important; } .pb-lg-4, .py-lg-4 { padding-bottom: 1.5rem !important; } .pl-lg-4, .px-lg-4 { padding-left: 1.5rem !important; } .p-lg-5 { padding: 3rem !important; } .pt-lg-5, .py-lg-5 { padding-top: 3rem !important; } .pr-lg-5, .px-lg-5 { padding-right: 3rem !important; } .pb-lg-5, .py-lg-5 { padding-bottom: 3rem !important; } .pl-lg-5, .px-lg-5 { padding-left: 3rem !important; } .m-lg-n1 { margin: -0.25rem !important; } .mt-lg-n1, .my-lg-n1 { margin-top: -0.25rem !important; } .mr-lg-n1, .mx-lg-n1 { margin-right: -0.25rem !important; } .mb-lg-n1, .my-lg-n1 { margin-bottom: -0.25rem !important; } .ml-lg-n1, .mx-lg-n1 { margin-left: -0.25rem !important; } .m-lg-n2 { margin: -0.5rem !important; } .mt-lg-n2, .my-lg-n2 { margin-top: -0.5rem !important; } .mr-lg-n2, .mx-lg-n2 { margin-right: -0.5rem !important; } .mb-lg-n2, .my-lg-n2 { margin-bottom: -0.5rem !important; } .ml-lg-n2, .mx-lg-n2 { margin-left: -0.5rem !important; } .m-lg-n3 { margin: -1rem !important; } .mt-lg-n3, .my-lg-n3 { margin-top: -1rem !important; } .mr-lg-n3, .mx-lg-n3 { margin-right: -1rem !important; } .mb-lg-n3, .my-lg-n3 { margin-bottom: -1rem !important; } .ml-lg-n3, .mx-lg-n3 { margin-left: -1rem !important; } .m-lg-n4 { margin: -1.5rem !important; } .mt-lg-n4, .my-lg-n4 { margin-top: -1.5rem !important; } .mr-lg-n4, .mx-lg-n4 { margin-right: -1.5rem !important; } .mb-lg-n4, .my-lg-n4 { margin-bottom: -1.5rem !important; } .ml-lg-n4, .mx-lg-n4 { margin-left: -1.5rem !important; } .m-lg-n5 { margin: -3rem !important; } .mt-lg-n5, .my-lg-n5 { margin-top: -3rem !important; } .mr-lg-n5, .mx-lg-n5 { margin-right: -3rem !important; } .mb-lg-n5, .my-lg-n5 { margin-bottom: -3rem !important; } .ml-lg-n5, .mx-lg-n5 { margin-left: -3rem !important; } .m-lg-auto { margin: auto !important; } .mt-lg-auto, .my-lg-auto { margin-top: auto !important; } .mr-lg-auto, .mx-lg-auto { margin-right: auto !important; } .mb-lg-auto, .my-lg-auto { margin-bottom: auto !important; } .ml-lg-auto, .mx-lg-auto { margin-left: auto !important; } } @media (min-width: 1200px) { .m-xl-0 { margin: 0 !important; } .mt-xl-0, .my-xl-0 { margin-top: 0 !important; } .mr-xl-0, .mx-xl-0 { margin-right: 0 !important; } .mb-xl-0, .my-xl-0 { margin-bottom: 0 !important; } .ml-xl-0, .mx-xl-0 { margin-left: 0 !important; } .m-xl-1 { margin: 0.25rem !important; } .mt-xl-1, .my-xl-1 { margin-top: 0.25rem !important; } .mr-xl-1, .mx-xl-1 { margin-right: 0.25rem !important; } .mb-xl-1, .my-xl-1 { margin-bottom: 0.25rem !important; } .ml-xl-1, .mx-xl-1 { margin-left: 0.25rem !important; } .m-xl-2 { margin: 0.5rem !important; } .mt-xl-2, .my-xl-2 { margin-top: 0.5rem !important; } .mr-xl-2, .mx-xl-2 { margin-right: 0.5rem !important; } .mb-xl-2, .my-xl-2 { margin-bottom: 0.5rem !important; } .ml-xl-2, .mx-xl-2 { margin-left: 0.5rem !important; } .m-xl-3 { margin: 1rem !important; } .mt-xl-3, .my-xl-3 { margin-top: 1rem !important; } .mr-xl-3, .mx-xl-3 { margin-right: 1rem !important; } .mb-xl-3, .my-xl-3 { margin-bottom: 1rem !important; } .ml-xl-3, .mx-xl-3 { margin-left: 1rem !important; } .m-xl-4 { margin: 1.5rem !important; } .mt-xl-4, .my-xl-4 { margin-top: 1.5rem !important; } .mr-xl-4, .mx-xl-4 { margin-right: 1.5rem !important; } .mb-xl-4, .my-xl-4 { margin-bottom: 1.5rem !important; } .ml-xl-4, .mx-xl-4 { margin-left: 1.5rem !important; } .m-xl-5 { margin: 3rem !important; } .mt-xl-5, .my-xl-5 { margin-top: 3rem !important; } .mr-xl-5, .mx-xl-5 { margin-right: 3rem !important; } .mb-xl-5, .my-xl-5 { margin-bottom: 3rem !important; } .ml-xl-5, .mx-xl-5 { margin-left: 3rem !important; } .p-xl-0 { padding: 0 !important; } .pt-xl-0, .py-xl-0 { padding-top: 0 !important; } .pr-xl-0, .px-xl-0 { padding-right: 0 !important; } .pb-xl-0, .py-xl-0 { padding-bottom: 0 !important; } .pl-xl-0, .px-xl-0 { padding-left: 0 !important; } .p-xl-1 { padding: 0.25rem !important; } .pt-xl-1, .py-xl-1 { padding-top: 0.25rem !important; } .pr-xl-1, .px-xl-1 { padding-right: 0.25rem !important; } .pb-xl-1, .py-xl-1 { padding-bottom: 0.25rem !important; } .pl-xl-1, .px-xl-1 { padding-left: 0.25rem !important; } .p-xl-2 { padding: 0.5rem !important; } .pt-xl-2, .py-xl-2 { padding-top: 0.5rem !important; } .pr-xl-2, .px-xl-2 { padding-right: 0.5rem !important; } .pb-xl-2, .py-xl-2 { padding-bottom: 0.5rem !important; } .pl-xl-2, .px-xl-2 { padding-left: 0.5rem !important; } .p-xl-3 { padding: 1rem !important; } .pt-xl-3, .py-xl-3 { padding-top: 1rem !important; } .pr-xl-3, .px-xl-3 { padding-right: 1rem !important; } .pb-xl-3, .py-xl-3 { padding-bottom: 1rem !important; } .pl-xl-3, .px-xl-3 { padding-left: 1rem !important; } .p-xl-4 { padding: 1.5rem !important; } .pt-xl-4, .py-xl-4 { padding-top: 1.5rem !important; } .pr-xl-4, .px-xl-4 { padding-right: 1.5rem !important; } .pb-xl-4, .py-xl-4 { padding-bottom: 1.5rem !important; } .pl-xl-4, .px-xl-4 { padding-left: 1.5rem !important; } .p-xl-5 { padding: 3rem !important; } .pt-xl-5, .py-xl-5 { padding-top: 3rem !important; } .pr-xl-5, .px-xl-5 { padding-right: 3rem !important; } .pb-xl-5, .py-xl-5 { padding-bottom: 3rem !important; } .pl-xl-5, .px-xl-5 { padding-left: 3rem !important; } .m-xl-n1 { margin: -0.25rem !important; } .mt-xl-n1, .my-xl-n1 { margin-top: -0.25rem !important; } .mr-xl-n1, .mx-xl-n1 { margin-right: -0.25rem !important; } .mb-xl-n1, .my-xl-n1 { margin-bottom: -0.25rem !important; } .ml-xl-n1, .mx-xl-n1 { margin-left: -0.25rem !important; } .m-xl-n2 { margin: -0.5rem !important; } .mt-xl-n2, .my-xl-n2 { margin-top: -0.5rem !important; } .mr-xl-n2, .mx-xl-n2 { margin-right: -0.5rem !important; } .mb-xl-n2, .my-xl-n2 { margin-bottom: -0.5rem !important; } .ml-xl-n2, .mx-xl-n2 { margin-left: -0.5rem !important; } .m-xl-n3 { margin: -1rem !important; } .mt-xl-n3, .my-xl-n3 { margin-top: -1rem !important; } .mr-xl-n3, .mx-xl-n3 { margin-right: -1rem !important; } .mb-xl-n3, .my-xl-n3 { margin-bottom: -1rem !important; } .ml-xl-n3, .mx-xl-n3 { margin-left: -1rem !important; } .m-xl-n4 { margin: -1.5rem !important; } .mt-xl-n4, .my-xl-n4 { margin-top: -1.5rem !important; } .mr-xl-n4, .mx-xl-n4 { margin-right: -1.5rem !important; } .mb-xl-n4, .my-xl-n4 { margin-bottom: -1.5rem !important; } .ml-xl-n4, .mx-xl-n4 { margin-left: -1.5rem !important; } .m-xl-n5 { margin: -3rem !important; } .mt-xl-n5, .my-xl-n5 { margin-top: -3rem !important; } .mr-xl-n5, .mx-xl-n5 { margin-right: -3rem !important; } .mb-xl-n5, .my-xl-n5 { margin-bottom: -3rem !important; } .ml-xl-n5, .mx-xl-n5 { margin-left: -3rem !important; } .m-xl-auto { margin: auto !important; } .mt-xl-auto, .my-xl-auto { margin-top: auto !important; } .mr-xl-auto, .mx-xl-auto { margin-right: auto !important; } .mb-xl-auto, .my-xl-auto { margin-bottom: auto !important; } .ml-xl-auto, .mx-xl-auto { margin-left: auto !important; } } .text-monospace { font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace !important; } .text-justify { text-align: justify !important; } .text-wrap { white-space: normal !important; } .text-nowrap { white-space: nowrap !important; } .text-truncate { overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .text-left { text-align: left !important; } .text-right { text-align: right !important; } .text-center { text-align: center !important; } @media (min-width: 576px) { .text-sm-left { text-align: left !important; } .text-sm-right { text-align: right !important; } .text-sm-center { text-align: center !important; } } @media (min-width: 768px) { .text-md-left { text-align: left !important; } .text-md-right { text-align: right !important; } .text-md-center { text-align: center !important; } } @media (min-width: 992px) { .text-lg-left { text-align: left !important; } .text-lg-right { text-align: right !important; } .text-lg-center { text-align: center !important; } } @media (min-width: 1200px) { .text-xl-left { text-align: left !important; } .text-xl-right { text-align: right !important; } .text-xl-center { text-align: center !important; } } .text-lowercase { text-transform: lowercase !important; } .text-uppercase { text-transform: uppercase !important; } .text-capitalize { text-transform: capitalize !important; } .font-weight-light { font-weight: 300 !important; } .font-weight-lighter { font-weight: lighter !important; } .font-weight-normal { font-weight: 400 !important; } .font-weight-bold { font-weight: 700 !important; } .font-weight-bolder { font-weight: bolder !important; } .font-italic { font-style: italic !important; } .text-white { color: #fff !important; } .text-primary { color: #007bff !important; } a.text-primary:hover, a.text-primary:focus { color: #0056b3 !important; } .text-secondary { color: #6c757d !important; } a.text-secondary:hover, a.text-secondary:focus { color: #494f54 !important; } .text-success { color: #28a745 !important; } a.text-success:hover, a.text-success:focus { color: #19692c !important; } .text-info { color: #17a2b8 !important; } a.text-info:hover, a.text-info:focus { color: #0f6674 !important; } .text-warning { color: #ffc107 !important; } a.text-warning:hover, a.text-warning:focus { color: #ba8b00 !important; } .text-danger { color: #dc3545 !important; } a.text-danger:hover, a.text-danger:focus { color: #a71d2a !important; } .text-light { color: #f8f9fa !important; } a.text-light:hover, a.text-light:focus { color: #cbd3da !important; } .text-dark { color: #343a40 !important; } a.text-dark:hover, a.text-dark:focus { color: #121416 !important; } .text-body { color: #212529 !important; } .text-muted { color: #6c757d !important; } .text-black-50 { color: rgba(0, 0, 0, 0.5) !important; } .text-white-50 { color: rgba(255, 255, 255, 0.5) !important; } .text-hide { font: 0/0 a; color: transparent; text-shadow: none; background-color: transparent; border: 0; } .text-decoration-none { text-decoration: none !important; } .text-break { word-break: break-word !important; overflow-wrap: break-word !important; } .text-reset { color: inherit !important; } .visible { visibility: visible !important; } .invisible { visibility: hidden !important; } @media print { *, *::before, *::after { text-shadow: none !important; box-shadow: none !important; } a:not(.btn) { text-decoration: underline; } abbr[title]::after { content: " (" attr(title) ")"; } pre { white-space: pre-wrap !important; } pre, blockquote { border: 1px solid #adb5bd; page-break-inside: avoid; } thead { display: table-header-group; } tr, img { page-break-inside: avoid; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } @page { size: a3; } body { min-width: 992px !important; } .container { min-width: 992px !important; } .navbar { display: none; } .badge { border: 1px solid #000; } .table { border-collapse: collapse !important; } .table td, .table th { background-color: #fff !important; } .table-bordered th, .table-bordered td { border: 1px solid #dee2e6 !important; } .table-dark { color: inherit; } .table-dark th, .table-dark td, .table-dark thead th, .table-dark tbody + tbody { border-color: #dee2e6; } .table .thead-dark th { color: inherit; border-color: #dee2e6; } } /*# sourceMappingURL=bootstrap.css.map */ ================================================ FILE: close_loop/SparseDrive_MomAD/leaderboard/docs/assets/vendor/bootstrap/js/bootstrap.bundle.js ================================================ /*! * Bootstrap v4.3.1 (https://getbootstrap.com/) * Copyright 2011-2019 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) */ (function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('jquery')) : typeof define === 'function' && define.amd ? define(['exports', 'jquery'], factory) : (global = global || self, factory(global.bootstrap = {}, global.jQuery)); }(this, function (exports, $) { 'use strict'; $ = $ && $.hasOwnProperty('default') ? $['default'] : $; function _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } function _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; } function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; } function _objectSpread(target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i] != null ? arguments[i] : {}; var ownKeys = Object.keys(source); if (typeof Object.getOwnPropertySymbols === 'function') { ownKeys = ownKeys.concat(Object.getOwnPropertySymbols(source).filter(function (sym) { return Object.getOwnPropertyDescriptor(source, sym).enumerable; })); } ownKeys.forEach(function (key) { _defineProperty(target, key, source[key]); }); } return target; } function _inheritsLoose(subClass, superClass) { subClass.prototype = Object.create(superClass.prototype); subClass.prototype.constructor = subClass; subClass.__proto__ = superClass; } /** * -------------------------------------------------------------------------- * Bootstrap (v4.3.1): util.js * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * -------------------------------------------------------------------------- */ /** * ------------------------------------------------------------------------ * Private TransitionEnd Helpers * ------------------------------------------------------------------------ */ var TRANSITION_END = 'transitionend'; var MAX_UID = 1000000; var MILLISECONDS_MULTIPLIER = 1000; // Shoutout AngusCroll (https://goo.gl/pxwQGp) function toType(obj) { return {}.toString.call(obj).match(/\s([a-z]+)/i)[1].toLowerCase(); } function getSpecialTransitionEndEvent() { return { bindType: TRANSITION_END, delegateType: TRANSITION_END, handle: function handle(event) { if ($(event.target).is(this)) { return event.handleObj.handler.apply(this, arguments); // eslint-disable-line prefer-rest-params } return undefined; // eslint-disable-line no-undefined } }; } function transitionEndEmulator(duration) { var _this = this; var called = false; $(this).one(Util.TRANSITION_END, function () { called = true; }); setTimeout(function () { if (!called) { Util.triggerTransitionEnd(_this); } }, duration); return this; } function setTransitionEndSupport() { $.fn.emulateTransitionEnd = transitionEndEmulator; $.event.special[Util.TRANSITION_END] = getSpecialTransitionEndEvent(); } /** * -------------------------------------------------------------------------- * Public Util Api * -------------------------------------------------------------------------- */ var Util = { TRANSITION_END: 'bsTransitionEnd', getUID: function getUID(prefix) { do { // eslint-disable-next-line no-bitwise prefix += ~~(Math.random() * MAX_UID); // "~~" acts like a faster Math.floor() here } while (document.getElementById(prefix)); return prefix; }, getSelectorFromElement: function getSelectorFromElement(element) { var selector = element.getAttribute('data-target'); if (!selector || selector === '#') { var hrefAttr = element.getAttribute('href'); selector = hrefAttr && hrefAttr !== '#' ? hrefAttr.trim() : ''; } try { return document.querySelector(selector) ? selector : null; } catch (err) { return null; } }, getTransitionDurationFromElement: function getTransitionDurationFromElement(element) { if (!element) { return 0; } // Get transition-duration of the element var transitionDuration = $(element).css('transition-duration'); var transitionDelay = $(element).css('transition-delay'); var floatTransitionDuration = parseFloat(transitionDuration); var floatTransitionDelay = parseFloat(transitionDelay); // Return 0 if element or transition duration is not found if (!floatTransitionDuration && !floatTransitionDelay) { return 0; } // If multiple durations are defined, take the first transitionDuration = transitionDuration.split(',')[0]; transitionDelay = transitionDelay.split(',')[0]; return (parseFloat(transitionDuration) + parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER; }, reflow: function reflow(element) { return element.offsetHeight; }, triggerTransitionEnd: function triggerTransitionEnd(element) { $(element).trigger(TRANSITION_END); }, // TODO: Remove in v5 supportsTransitionEnd: function supportsTransitionEnd() { return Boolean(TRANSITION_END); }, isElement: function isElement(obj) { return (obj[0] || obj).nodeType; }, typeCheckConfig: function typeCheckConfig(componentName, config, configTypes) { for (var property in configTypes) { if (Object.prototype.hasOwnProperty.call(configTypes, property)) { var expectedTypes = configTypes[property]; var value = config[property]; var valueType = value && Util.isElement(value) ? 'element' : toType(value); if (!new RegExp(expectedTypes).test(valueType)) { throw new Error(componentName.toUpperCase() + ": " + ("Option \"" + property + "\" provided type \"" + valueType + "\" ") + ("but expected type \"" + expectedTypes + "\".")); } } } }, findShadowRoot: function findShadowRoot(element) { if (!document.documentElement.attachShadow) { return null; } // Can find the shadow root otherwise it'll return the document if (typeof element.getRootNode === 'function') { var root = element.getRootNode(); return root instanceof ShadowRoot ? root : null; } if (element instanceof ShadowRoot) { return element; } // when we don't find a shadow root if (!element.parentNode) { return null; } return Util.findShadowRoot(element.parentNode); } }; setTransitionEndSupport(); /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME = 'alert'; var VERSION = '4.3.1'; var DATA_KEY = 'bs.alert'; var EVENT_KEY = "." + DATA_KEY; var DATA_API_KEY = '.data-api'; var JQUERY_NO_CONFLICT = $.fn[NAME]; var Selector = { DISMISS: '[data-dismiss="alert"]' }; var Event = { CLOSE: "close" + EVENT_KEY, CLOSED: "closed" + EVENT_KEY, CLICK_DATA_API: "click" + EVENT_KEY + DATA_API_KEY }; var ClassName = { ALERT: 'alert', FADE: 'fade', SHOW: 'show' /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Alert = /*#__PURE__*/ function () { function Alert(element) { this._element = element; } // Getters var _proto = Alert.prototype; // Public _proto.close = function close(element) { var rootElement = this._element; if (element) { rootElement = this._getRootElement(element); } var customEvent = this._triggerCloseEvent(rootElement); if (customEvent.isDefaultPrevented()) { return; } this._removeElement(rootElement); }; _proto.dispose = function dispose() { $.removeData(this._element, DATA_KEY); this._element = null; } // Private ; _proto._getRootElement = function _getRootElement(element) { var selector = Util.getSelectorFromElement(element); var parent = false; if (selector) { parent = document.querySelector(selector); } if (!parent) { parent = $(element).closest("." + ClassName.ALERT)[0]; } return parent; }; _proto._triggerCloseEvent = function _triggerCloseEvent(element) { var closeEvent = $.Event(Event.CLOSE); $(element).trigger(closeEvent); return closeEvent; }; _proto._removeElement = function _removeElement(element) { var _this = this; $(element).removeClass(ClassName.SHOW); if (!$(element).hasClass(ClassName.FADE)) { this._destroyElement(element); return; } var transitionDuration = Util.getTransitionDurationFromElement(element); $(element).one(Util.TRANSITION_END, function (event) { return _this._destroyElement(element, event); }).emulateTransitionEnd(transitionDuration); }; _proto._destroyElement = function _destroyElement(element) { $(element).detach().trigger(Event.CLOSED).remove(); } // Static ; Alert._jQueryInterface = function _jQueryInterface(config) { return this.each(function () { var $element = $(this); var data = $element.data(DATA_KEY); if (!data) { data = new Alert(this); $element.data(DATA_KEY, data); } if (config === 'close') { data[config](this); } }); }; Alert._handleDismiss = function _handleDismiss(alertInstance) { return function (event) { if (event) { event.preventDefault(); } alertInstance.close(this); }; }; _createClass(Alert, null, [{ key: "VERSION", get: function get() { return VERSION; } }]); return Alert; }(); /** * ------------------------------------------------------------------------ * Data Api implementation * ------------------------------------------------------------------------ */ $(document).on(Event.CLICK_DATA_API, Selector.DISMISS, Alert._handleDismiss(new Alert())); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME] = Alert._jQueryInterface; $.fn[NAME].Constructor = Alert; $.fn[NAME].noConflict = function () { $.fn[NAME] = JQUERY_NO_CONFLICT; return Alert._jQueryInterface; }; /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$1 = 'button'; var VERSION$1 = '4.3.1'; var DATA_KEY$1 = 'bs.button'; var EVENT_KEY$1 = "." + DATA_KEY$1; var DATA_API_KEY$1 = '.data-api'; var JQUERY_NO_CONFLICT$1 = $.fn[NAME$1]; var ClassName$1 = { ACTIVE: 'active', BUTTON: 'btn', FOCUS: 'focus' }; var Selector$1 = { DATA_TOGGLE_CARROT: '[data-toggle^="button"]', DATA_TOGGLE: '[data-toggle="buttons"]', INPUT: 'input:not([type="hidden"])', ACTIVE: '.active', BUTTON: '.btn' }; var Event$1 = { CLICK_DATA_API: "click" + EVENT_KEY$1 + DATA_API_KEY$1, FOCUS_BLUR_DATA_API: "focus" + EVENT_KEY$1 + DATA_API_KEY$1 + " " + ("blur" + EVENT_KEY$1 + DATA_API_KEY$1) /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Button = /*#__PURE__*/ function () { function Button(element) { this._element = element; } // Getters var _proto = Button.prototype; // Public _proto.toggle = function toggle() { var triggerChangeEvent = true; var addAriaPressed = true; var rootElement = $(this._element).closest(Selector$1.DATA_TOGGLE)[0]; if (rootElement) { var input = this._element.querySelector(Selector$1.INPUT); if (input) { if (input.type === 'radio') { if (input.checked && this._element.classList.contains(ClassName$1.ACTIVE)) { triggerChangeEvent = false; } else { var activeElement = rootElement.querySelector(Selector$1.ACTIVE); if (activeElement) { $(activeElement).removeClass(ClassName$1.ACTIVE); } } } if (triggerChangeEvent) { if (input.hasAttribute('disabled') || rootElement.hasAttribute('disabled') || input.classList.contains('disabled') || rootElement.classList.contains('disabled')) { return; } input.checked = !this._element.classList.contains(ClassName$1.ACTIVE); $(input).trigger('change'); } input.focus(); addAriaPressed = false; } } if (addAriaPressed) { this._element.setAttribute('aria-pressed', !this._element.classList.contains(ClassName$1.ACTIVE)); } if (triggerChangeEvent) { $(this._element).toggleClass(ClassName$1.ACTIVE); } }; _proto.dispose = function dispose() { $.removeData(this._element, DATA_KEY$1); this._element = null; } // Static ; Button._jQueryInterface = function _jQueryInterface(config) { return this.each(function () { var data = $(this).data(DATA_KEY$1); if (!data) { data = new Button(this); $(this).data(DATA_KEY$1, data); } if (config === 'toggle') { data[config](); } }); }; _createClass(Button, null, [{ key: "VERSION", get: function get() { return VERSION$1; } }]); return Button; }(); /** * ------------------------------------------------------------------------ * Data Api implementation * ------------------------------------------------------------------------ */ $(document).on(Event$1.CLICK_DATA_API, Selector$1.DATA_TOGGLE_CARROT, function (event) { event.preventDefault(); var button = event.target; if (!$(button).hasClass(ClassName$1.BUTTON)) { button = $(button).closest(Selector$1.BUTTON); } Button._jQueryInterface.call($(button), 'toggle'); }).on(Event$1.FOCUS_BLUR_DATA_API, Selector$1.DATA_TOGGLE_CARROT, function (event) { var button = $(event.target).closest(Selector$1.BUTTON)[0]; $(button).toggleClass(ClassName$1.FOCUS, /^focus(in)?$/.test(event.type)); }); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME$1] = Button._jQueryInterface; $.fn[NAME$1].Constructor = Button; $.fn[NAME$1].noConflict = function () { $.fn[NAME$1] = JQUERY_NO_CONFLICT$1; return Button._jQueryInterface; }; /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$2 = 'carousel'; var VERSION$2 = '4.3.1'; var DATA_KEY$2 = 'bs.carousel'; var EVENT_KEY$2 = "." + DATA_KEY$2; var DATA_API_KEY$2 = '.data-api'; var JQUERY_NO_CONFLICT$2 = $.fn[NAME$2]; var ARROW_LEFT_KEYCODE = 37; // KeyboardEvent.which value for left arrow key var ARROW_RIGHT_KEYCODE = 39; // KeyboardEvent.which value for right arrow key var TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch var SWIPE_THRESHOLD = 40; var Default = { interval: 5000, keyboard: true, slide: false, pause: 'hover', wrap: true, touch: true }; var DefaultType = { interval: '(number|boolean)', keyboard: 'boolean', slide: '(boolean|string)', pause: '(string|boolean)', wrap: 'boolean', touch: 'boolean' }; var Direction = { NEXT: 'next', PREV: 'prev', LEFT: 'left', RIGHT: 'right' }; var Event$2 = { SLIDE: "slide" + EVENT_KEY$2, SLID: "slid" + EVENT_KEY$2, KEYDOWN: "keydown" + EVENT_KEY$2, MOUSEENTER: "mouseenter" + EVENT_KEY$2, MOUSELEAVE: "mouseleave" + EVENT_KEY$2, TOUCHSTART: "touchstart" + EVENT_KEY$2, TOUCHMOVE: "touchmove" + EVENT_KEY$2, TOUCHEND: "touchend" + EVENT_KEY$2, POINTERDOWN: "pointerdown" + EVENT_KEY$2, POINTERUP: "pointerup" + EVENT_KEY$2, DRAG_START: "dragstart" + EVENT_KEY$2, LOAD_DATA_API: "load" + EVENT_KEY$2 + DATA_API_KEY$2, CLICK_DATA_API: "click" + EVENT_KEY$2 + DATA_API_KEY$2 }; var ClassName$2 = { CAROUSEL: 'carousel', ACTIVE: 'active', SLIDE: 'slide', RIGHT: 'carousel-item-right', LEFT: 'carousel-item-left', NEXT: 'carousel-item-next', PREV: 'carousel-item-prev', ITEM: 'carousel-item', POINTER_EVENT: 'pointer-event' }; var Selector$2 = { ACTIVE: '.active', ACTIVE_ITEM: '.active.carousel-item', ITEM: '.carousel-item', ITEM_IMG: '.carousel-item img', NEXT_PREV: '.carousel-item-next, .carousel-item-prev', INDICATORS: '.carousel-indicators', DATA_SLIDE: '[data-slide], [data-slide-to]', DATA_RIDE: '[data-ride="carousel"]' }; var PointerType = { TOUCH: 'touch', PEN: 'pen' /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Carousel = /*#__PURE__*/ function () { function Carousel(element, config) { this._items = null; this._interval = null; this._activeElement = null; this._isPaused = false; this._isSliding = false; this.touchTimeout = null; this.touchStartX = 0; this.touchDeltaX = 0; this._config = this._getConfig(config); this._element = element; this._indicatorsElement = this._element.querySelector(Selector$2.INDICATORS); this._touchSupported = 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0; this._pointerEvent = Boolean(window.PointerEvent || window.MSPointerEvent); this._addEventListeners(); } // Getters var _proto = Carousel.prototype; // Public _proto.next = function next() { if (!this._isSliding) { this._slide(Direction.NEXT); } }; _proto.nextWhenVisible = function nextWhenVisible() { // Don't call next when the page isn't visible // or the carousel or its parent isn't visible if (!document.hidden && $(this._element).is(':visible') && $(this._element).css('visibility') !== 'hidden') { this.next(); } }; _proto.prev = function prev() { if (!this._isSliding) { this._slide(Direction.PREV); } }; _proto.pause = function pause(event) { if (!event) { this._isPaused = true; } if (this._element.querySelector(Selector$2.NEXT_PREV)) { Util.triggerTransitionEnd(this._element); this.cycle(true); } clearInterval(this._interval); this._interval = null; }; _proto.cycle = function cycle(event) { if (!event) { this._isPaused = false; } if (this._interval) { clearInterval(this._interval); this._interval = null; } if (this._config.interval && !this._isPaused) { this._interval = setInterval((document.visibilityState ? this.nextWhenVisible : this.next).bind(this), this._config.interval); } }; _proto.to = function to(index) { var _this = this; this._activeElement = this._element.querySelector(Selector$2.ACTIVE_ITEM); var activeIndex = this._getItemIndex(this._activeElement); if (index > this._items.length - 1 || index < 0) { return; } if (this._isSliding) { $(this._element).one(Event$2.SLID, function () { return _this.to(index); }); return; } if (activeIndex === index) { this.pause(); this.cycle(); return; } var direction = index > activeIndex ? Direction.NEXT : Direction.PREV; this._slide(direction, this._items[index]); }; _proto.dispose = function dispose() { $(this._element).off(EVENT_KEY$2); $.removeData(this._element, DATA_KEY$2); this._items = null; this._config = null; this._element = null; this._interval = null; this._isPaused = null; this._isSliding = null; this._activeElement = null; this._indicatorsElement = null; } // Private ; _proto._getConfig = function _getConfig(config) { config = _objectSpread({}, Default, config); Util.typeCheckConfig(NAME$2, config, DefaultType); return config; }; _proto._handleSwipe = function _handleSwipe() { var absDeltax = Math.abs(this.touchDeltaX); if (absDeltax <= SWIPE_THRESHOLD) { return; } var direction = absDeltax / this.touchDeltaX; // swipe left if (direction > 0) { this.prev(); } // swipe right if (direction < 0) { this.next(); } }; _proto._addEventListeners = function _addEventListeners() { var _this2 = this; if (this._config.keyboard) { $(this._element).on(Event$2.KEYDOWN, function (event) { return _this2._keydown(event); }); } if (this._config.pause === 'hover') { $(this._element).on(Event$2.MOUSEENTER, function (event) { return _this2.pause(event); }).on(Event$2.MOUSELEAVE, function (event) { return _this2.cycle(event); }); } if (this._config.touch) { this._addTouchEventListeners(); } }; _proto._addTouchEventListeners = function _addTouchEventListeners() { var _this3 = this; if (!this._touchSupported) { return; } var start = function start(event) { if (_this3._pointerEvent && PointerType[event.originalEvent.pointerType.toUpperCase()]) { _this3.touchStartX = event.originalEvent.clientX; } else if (!_this3._pointerEvent) { _this3.touchStartX = event.originalEvent.touches[0].clientX; } }; var move = function move(event) { // ensure swiping with one touch and not pinching if (event.originalEvent.touches && event.originalEvent.touches.length > 1) { _this3.touchDeltaX = 0; } else { _this3.touchDeltaX = event.originalEvent.touches[0].clientX - _this3.touchStartX; } }; var end = function end(event) { if (_this3._pointerEvent && PointerType[event.originalEvent.pointerType.toUpperCase()]) { _this3.touchDeltaX = event.originalEvent.clientX - _this3.touchStartX; } _this3._handleSwipe(); if (_this3._config.pause === 'hover') { // If it's a touch-enabled device, mouseenter/leave are fired as // part of the mouse compatibility events on first tap - the carousel // would stop cycling until user tapped out of it; // here, we listen for touchend, explicitly pause the carousel // (as if it's the second time we tap on it, mouseenter compat event // is NOT fired) and after a timeout (to allow for mouse compatibility // events to fire) we explicitly restart cycling _this3.pause(); if (_this3.touchTimeout) { clearTimeout(_this3.touchTimeout); } _this3.touchTimeout = setTimeout(function (event) { return _this3.cycle(event); }, TOUCHEVENT_COMPAT_WAIT + _this3._config.interval); } }; $(this._element.querySelectorAll(Selector$2.ITEM_IMG)).on(Event$2.DRAG_START, function (e) { return e.preventDefault(); }); if (this._pointerEvent) { $(this._element).on(Event$2.POINTERDOWN, function (event) { return start(event); }); $(this._element).on(Event$2.POINTERUP, function (event) { return end(event); }); this._element.classList.add(ClassName$2.POINTER_EVENT); } else { $(this._element).on(Event$2.TOUCHSTART, function (event) { return start(event); }); $(this._element).on(Event$2.TOUCHMOVE, function (event) { return move(event); }); $(this._element).on(Event$2.TOUCHEND, function (event) { return end(event); }); } }; _proto._keydown = function _keydown(event) { if (/input|textarea/i.test(event.target.tagName)) { return; } switch (event.which) { case ARROW_LEFT_KEYCODE: event.preventDefault(); this.prev(); break; case ARROW_RIGHT_KEYCODE: event.preventDefault(); this.next(); break; default: } }; _proto._getItemIndex = function _getItemIndex(element) { this._items = element && element.parentNode ? [].slice.call(element.parentNode.querySelectorAll(Selector$2.ITEM)) : []; return this._items.indexOf(element); }; _proto._getItemByDirection = function _getItemByDirection(direction, activeElement) { var isNextDirection = direction === Direction.NEXT; var isPrevDirection = direction === Direction.PREV; var activeIndex = this._getItemIndex(activeElement); var lastItemIndex = this._items.length - 1; var isGoingToWrap = isPrevDirection && activeIndex === 0 || isNextDirection && activeIndex === lastItemIndex; if (isGoingToWrap && !this._config.wrap) { return activeElement; } var delta = direction === Direction.PREV ? -1 : 1; var itemIndex = (activeIndex + delta) % this._items.length; return itemIndex === -1 ? this._items[this._items.length - 1] : this._items[itemIndex]; }; _proto._triggerSlideEvent = function _triggerSlideEvent(relatedTarget, eventDirectionName) { var targetIndex = this._getItemIndex(relatedTarget); var fromIndex = this._getItemIndex(this._element.querySelector(Selector$2.ACTIVE_ITEM)); var slideEvent = $.Event(Event$2.SLIDE, { relatedTarget: relatedTarget, direction: eventDirectionName, from: fromIndex, to: targetIndex }); $(this._element).trigger(slideEvent); return slideEvent; }; _proto._setActiveIndicatorElement = function _setActiveIndicatorElement(element) { if (this._indicatorsElement) { var indicators = [].slice.call(this._indicatorsElement.querySelectorAll(Selector$2.ACTIVE)); $(indicators).removeClass(ClassName$2.ACTIVE); var nextIndicator = this._indicatorsElement.children[this._getItemIndex(element)]; if (nextIndicator) { $(nextIndicator).addClass(ClassName$2.ACTIVE); } } }; _proto._slide = function _slide(direction, element) { var _this4 = this; var activeElement = this._element.querySelector(Selector$2.ACTIVE_ITEM); var activeElementIndex = this._getItemIndex(activeElement); var nextElement = element || activeElement && this._getItemByDirection(direction, activeElement); var nextElementIndex = this._getItemIndex(nextElement); var isCycling = Boolean(this._interval); var directionalClassName; var orderClassName; var eventDirectionName; if (direction === Direction.NEXT) { directionalClassName = ClassName$2.LEFT; orderClassName = ClassName$2.NEXT; eventDirectionName = Direction.LEFT; } else { directionalClassName = ClassName$2.RIGHT; orderClassName = ClassName$2.PREV; eventDirectionName = Direction.RIGHT; } if (nextElement && $(nextElement).hasClass(ClassName$2.ACTIVE)) { this._isSliding = false; return; } var slideEvent = this._triggerSlideEvent(nextElement, eventDirectionName); if (slideEvent.isDefaultPrevented()) { return; } if (!activeElement || !nextElement) { // Some weirdness is happening, so we bail return; } this._isSliding = true; if (isCycling) { this.pause(); } this._setActiveIndicatorElement(nextElement); var slidEvent = $.Event(Event$2.SLID, { relatedTarget: nextElement, direction: eventDirectionName, from: activeElementIndex, to: nextElementIndex }); if ($(this._element).hasClass(ClassName$2.SLIDE)) { $(nextElement).addClass(orderClassName); Util.reflow(nextElement); $(activeElement).addClass(directionalClassName); $(nextElement).addClass(directionalClassName); var nextElementInterval = parseInt(nextElement.getAttribute('data-interval'), 10); if (nextElementInterval) { this._config.defaultInterval = this._config.defaultInterval || this._config.interval; this._config.interval = nextElementInterval; } else { this._config.interval = this._config.defaultInterval || this._config.interval; } var transitionDuration = Util.getTransitionDurationFromElement(activeElement); $(activeElement).one(Util.TRANSITION_END, function () { $(nextElement).removeClass(directionalClassName + " " + orderClassName).addClass(ClassName$2.ACTIVE); $(activeElement).removeClass(ClassName$2.ACTIVE + " " + orderClassName + " " + directionalClassName); _this4._isSliding = false; setTimeout(function () { return $(_this4._element).trigger(slidEvent); }, 0); }).emulateTransitionEnd(transitionDuration); } else { $(activeElement).removeClass(ClassName$2.ACTIVE); $(nextElement).addClass(ClassName$2.ACTIVE); this._isSliding = false; $(this._element).trigger(slidEvent); } if (isCycling) { this.cycle(); } } // Static ; Carousel._jQueryInterface = function _jQueryInterface(config) { return this.each(function () { var data = $(this).data(DATA_KEY$2); var _config = _objectSpread({}, Default, $(this).data()); if (typeof config === 'object') { _config = _objectSpread({}, _config, config); } var action = typeof config === 'string' ? config : _config.slide; if (!data) { data = new Carousel(this, _config); $(this).data(DATA_KEY$2, data); } if (typeof config === 'number') { data.to(config); } else if (typeof action === 'string') { if (typeof data[action] === 'undefined') { throw new TypeError("No method named \"" + action + "\""); } data[action](); } else if (_config.interval && _config.ride) { data.pause(); data.cycle(); } }); }; Carousel._dataApiClickHandler = function _dataApiClickHandler(event) { var selector = Util.getSelectorFromElement(this); if (!selector) { return; } var target = $(selector)[0]; if (!target || !$(target).hasClass(ClassName$2.CAROUSEL)) { return; } var config = _objectSpread({}, $(target).data(), $(this).data()); var slideIndex = this.getAttribute('data-slide-to'); if (slideIndex) { config.interval = false; } Carousel._jQueryInterface.call($(target), config); if (slideIndex) { $(target).data(DATA_KEY$2).to(slideIndex); } event.preventDefault(); }; _createClass(Carousel, null, [{ key: "VERSION", get: function get() { return VERSION$2; } }, { key: "Default", get: function get() { return Default; } }]); return Carousel; }(); /** * ------------------------------------------------------------------------ * Data Api implementation * ------------------------------------------------------------------------ */ $(document).on(Event$2.CLICK_DATA_API, Selector$2.DATA_SLIDE, Carousel._dataApiClickHandler); $(window).on(Event$2.LOAD_DATA_API, function () { var carousels = [].slice.call(document.querySelectorAll(Selector$2.DATA_RIDE)); for (var i = 0, len = carousels.length; i < len; i++) { var $carousel = $(carousels[i]); Carousel._jQueryInterface.call($carousel, $carousel.data()); } }); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME$2] = Carousel._jQueryInterface; $.fn[NAME$2].Constructor = Carousel; $.fn[NAME$2].noConflict = function () { $.fn[NAME$2] = JQUERY_NO_CONFLICT$2; return Carousel._jQueryInterface; }; /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$3 = 'collapse'; var VERSION$3 = '4.3.1'; var DATA_KEY$3 = 'bs.collapse'; var EVENT_KEY$3 = "." + DATA_KEY$3; var DATA_API_KEY$3 = '.data-api'; var JQUERY_NO_CONFLICT$3 = $.fn[NAME$3]; var Default$1 = { toggle: true, parent: '' }; var DefaultType$1 = { toggle: 'boolean', parent: '(string|element)' }; var Event$3 = { SHOW: "show" + EVENT_KEY$3, SHOWN: "shown" + EVENT_KEY$3, HIDE: "hide" + EVENT_KEY$3, HIDDEN: "hidden" + EVENT_KEY$3, CLICK_DATA_API: "click" + EVENT_KEY$3 + DATA_API_KEY$3 }; var ClassName$3 = { SHOW: 'show', COLLAPSE: 'collapse', COLLAPSING: 'collapsing', COLLAPSED: 'collapsed' }; var Dimension = { WIDTH: 'width', HEIGHT: 'height' }; var Selector$3 = { ACTIVES: '.show, .collapsing', DATA_TOGGLE: '[data-toggle="collapse"]' /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Collapse = /*#__PURE__*/ function () { function Collapse(element, config) { this._isTransitioning = false; this._element = element; this._config = this._getConfig(config); this._triggerArray = [].slice.call(document.querySelectorAll("[data-toggle=\"collapse\"][href=\"#" + element.id + "\"]," + ("[data-toggle=\"collapse\"][data-target=\"#" + element.id + "\"]"))); var toggleList = [].slice.call(document.querySelectorAll(Selector$3.DATA_TOGGLE)); for (var i = 0, len = toggleList.length; i < len; i++) { var elem = toggleList[i]; var selector = Util.getSelectorFromElement(elem); var filterElement = [].slice.call(document.querySelectorAll(selector)).filter(function (foundElem) { return foundElem === element; }); if (selector !== null && filterElement.length > 0) { this._selector = selector; this._triggerArray.push(elem); } } this._parent = this._config.parent ? this._getParent() : null; if (!this._config.parent) { this._addAriaAndCollapsedClass(this._element, this._triggerArray); } if (this._config.toggle) { this.toggle(); } } // Getters var _proto = Collapse.prototype; // Public _proto.toggle = function toggle() { if ($(this._element).hasClass(ClassName$3.SHOW)) { this.hide(); } else { this.show(); } }; _proto.show = function show() { var _this = this; if (this._isTransitioning || $(this._element).hasClass(ClassName$3.SHOW)) { return; } var actives; var activesData; if (this._parent) { actives = [].slice.call(this._parent.querySelectorAll(Selector$3.ACTIVES)).filter(function (elem) { if (typeof _this._config.parent === 'string') { return elem.getAttribute('data-parent') === _this._config.parent; } return elem.classList.contains(ClassName$3.COLLAPSE); }); if (actives.length === 0) { actives = null; } } if (actives) { activesData = $(actives).not(this._selector).data(DATA_KEY$3); if (activesData && activesData._isTransitioning) { return; } } var startEvent = $.Event(Event$3.SHOW); $(this._element).trigger(startEvent); if (startEvent.isDefaultPrevented()) { return; } if (actives) { Collapse._jQueryInterface.call($(actives).not(this._selector), 'hide'); if (!activesData) { $(actives).data(DATA_KEY$3, null); } } var dimension = this._getDimension(); $(this._element).removeClass(ClassName$3.COLLAPSE).addClass(ClassName$3.COLLAPSING); this._element.style[dimension] = 0; if (this._triggerArray.length) { $(this._triggerArray).removeClass(ClassName$3.COLLAPSED).attr('aria-expanded', true); } this.setTransitioning(true); var complete = function complete() { $(_this._element).removeClass(ClassName$3.COLLAPSING).addClass(ClassName$3.COLLAPSE).addClass(ClassName$3.SHOW); _this._element.style[dimension] = ''; _this.setTransitioning(false); $(_this._element).trigger(Event$3.SHOWN); }; var capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1); var scrollSize = "scroll" + capitalizedDimension; var transitionDuration = Util.getTransitionDurationFromElement(this._element); $(this._element).one(Util.TRANSITION_END, complete).emulateTransitionEnd(transitionDuration); this._element.style[dimension] = this._element[scrollSize] + "px"; }; _proto.hide = function hide() { var _this2 = this; if (this._isTransitioning || !$(this._element).hasClass(ClassName$3.SHOW)) { return; } var startEvent = $.Event(Event$3.HIDE); $(this._element).trigger(startEvent); if (startEvent.isDefaultPrevented()) { return; } var dimension = this._getDimension(); this._element.style[dimension] = this._element.getBoundingClientRect()[dimension] + "px"; Util.reflow(this._element); $(this._element).addClass(ClassName$3.COLLAPSING).removeClass(ClassName$3.COLLAPSE).removeClass(ClassName$3.SHOW); var triggerArrayLength = this._triggerArray.length; if (triggerArrayLength > 0) { for (var i = 0; i < triggerArrayLength; i++) { var trigger = this._triggerArray[i]; var selector = Util.getSelectorFromElement(trigger); if (selector !== null) { var $elem = $([].slice.call(document.querySelectorAll(selector))); if (!$elem.hasClass(ClassName$3.SHOW)) { $(trigger).addClass(ClassName$3.COLLAPSED).attr('aria-expanded', false); } } } } this.setTransitioning(true); var complete = function complete() { _this2.setTransitioning(false); $(_this2._element).removeClass(ClassName$3.COLLAPSING).addClass(ClassName$3.COLLAPSE).trigger(Event$3.HIDDEN); }; this._element.style[dimension] = ''; var transitionDuration = Util.getTransitionDurationFromElement(this._element); $(this._element).one(Util.TRANSITION_END, complete).emulateTransitionEnd(transitionDuration); }; _proto.setTransitioning = function setTransitioning(isTransitioning) { this._isTransitioning = isTransitioning; }; _proto.dispose = function dispose() { $.removeData(this._element, DATA_KEY$3); this._config = null; this._parent = null; this._element = null; this._triggerArray = null; this._isTransitioning = null; } // Private ; _proto._getConfig = function _getConfig(config) { config = _objectSpread({}, Default$1, config); config.toggle = Boolean(config.toggle); // Coerce string values Util.typeCheckConfig(NAME$3, config, DefaultType$1); return config; }; _proto._getDimension = function _getDimension() { var hasWidth = $(this._element).hasClass(Dimension.WIDTH); return hasWidth ? Dimension.WIDTH : Dimension.HEIGHT; }; _proto._getParent = function _getParent() { var _this3 = this; var parent; if (Util.isElement(this._config.parent)) { parent = this._config.parent; // It's a jQuery object if (typeof this._config.parent.jquery !== 'undefined') { parent = this._config.parent[0]; } } else { parent = document.querySelector(this._config.parent); } var selector = "[data-toggle=\"collapse\"][data-parent=\"" + this._config.parent + "\"]"; var children = [].slice.call(parent.querySelectorAll(selector)); $(children).each(function (i, element) { _this3._addAriaAndCollapsedClass(Collapse._getTargetFromElement(element), [element]); }); return parent; }; _proto._addAriaAndCollapsedClass = function _addAriaAndCollapsedClass(element, triggerArray) { var isOpen = $(element).hasClass(ClassName$3.SHOW); if (triggerArray.length) { $(triggerArray).toggleClass(ClassName$3.COLLAPSED, !isOpen).attr('aria-expanded', isOpen); } } // Static ; Collapse._getTargetFromElement = function _getTargetFromElement(element) { var selector = Util.getSelectorFromElement(element); return selector ? document.querySelector(selector) : null; }; Collapse._jQueryInterface = function _jQueryInterface(config) { return this.each(function () { var $this = $(this); var data = $this.data(DATA_KEY$3); var _config = _objectSpread({}, Default$1, $this.data(), typeof config === 'object' && config ? config : {}); if (!data && _config.toggle && /show|hide/.test(config)) { _config.toggle = false; } if (!data) { data = new Collapse(this, _config); $this.data(DATA_KEY$3, data); } if (typeof config === 'string') { if (typeof data[config] === 'undefined') { throw new TypeError("No method named \"" + config + "\""); } data[config](); } }); }; _createClass(Collapse, null, [{ key: "VERSION", get: function get() { return VERSION$3; } }, { key: "Default", get: function get() { return Default$1; } }]); return Collapse; }(); /** * ------------------------------------------------------------------------ * Data Api implementation * ------------------------------------------------------------------------ */ $(document).on(Event$3.CLICK_DATA_API, Selector$3.DATA_TOGGLE, function (event) { // preventDefault only for elements (which change the URL) not inside the collapsible element if (event.currentTarget.tagName === 'A') { event.preventDefault(); } var $trigger = $(this); var selector = Util.getSelectorFromElement(this); var selectors = [].slice.call(document.querySelectorAll(selector)); $(selectors).each(function () { var $target = $(this); var data = $target.data(DATA_KEY$3); var config = data ? 'toggle' : $trigger.data(); Collapse._jQueryInterface.call($target, config); }); }); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME$3] = Collapse._jQueryInterface; $.fn[NAME$3].Constructor = Collapse; $.fn[NAME$3].noConflict = function () { $.fn[NAME$3] = JQUERY_NO_CONFLICT$3; return Collapse._jQueryInterface; }; /**! * @fileOverview Kickass library to create and place poppers near their reference elements. * @version 1.14.7 * @license * Copyright (c) 2016 Federico Zivolo and contributors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ var isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined'; var longerTimeoutBrowsers = ['Edge', 'Trident', 'Firefox']; var timeoutDuration = 0; for (var i = 0; i < longerTimeoutBrowsers.length; i += 1) { if (isBrowser && navigator.userAgent.indexOf(longerTimeoutBrowsers[i]) >= 0) { timeoutDuration = 1; break; } } function microtaskDebounce(fn) { var called = false; return function () { if (called) { return; } called = true; window.Promise.resolve().then(function () { called = false; fn(); }); }; } function taskDebounce(fn) { var scheduled = false; return function () { if (!scheduled) { scheduled = true; setTimeout(function () { scheduled = false; fn(); }, timeoutDuration); } }; } var supportsMicroTasks = isBrowser && window.Promise; /** * Create a debounced version of a method, that's asynchronously deferred * but called in the minimum time possible. * * @method * @memberof Popper.Utils * @argument {Function} fn * @returns {Function} */ var debounce = supportsMicroTasks ? microtaskDebounce : taskDebounce; /** * Check if the given variable is a function * @method * @memberof Popper.Utils * @argument {Any} functionToCheck - variable to check * @returns {Boolean} answer to: is a function? */ function isFunction(functionToCheck) { var getType = {}; return functionToCheck && getType.toString.call(functionToCheck) === '[object Function]'; } /** * Get CSS computed property of the given element * @method * @memberof Popper.Utils * @argument {Eement} element * @argument {String} property */ function getStyleComputedProperty(element, property) { if (element.nodeType !== 1) { return []; } // NOTE: 1 DOM access here var window = element.ownerDocument.defaultView; var css = window.getComputedStyle(element, null); return property ? css[property] : css; } /** * Returns the parentNode or the host of the element * @method * @memberof Popper.Utils * @argument {Element} element * @returns {Element} parent */ function getParentNode(element) { if (element.nodeName === 'HTML') { return element; } return element.parentNode || element.host; } /** * Returns the scrolling parent of the given element * @method * @memberof Popper.Utils * @argument {Element} element * @returns {Element} scroll parent */ function getScrollParent(element) { // Return body, `getScroll` will take care to get the correct `scrollTop` from it if (!element) { return document.body; } switch (element.nodeName) { case 'HTML': case 'BODY': return element.ownerDocument.body; case '#document': return element.body; } // Firefox want us to check `-x` and `-y` variations as well var _getStyleComputedProp = getStyleComputedProperty(element), overflow = _getStyleComputedProp.overflow, overflowX = _getStyleComputedProp.overflowX, overflowY = _getStyleComputedProp.overflowY; if (/(auto|scroll|overlay)/.test(overflow + overflowY + overflowX)) { return element; } return getScrollParent(getParentNode(element)); } var isIE11 = isBrowser && !!(window.MSInputMethodContext && document.documentMode); var isIE10 = isBrowser && /MSIE 10/.test(navigator.userAgent); /** * Determines if the browser is Internet Explorer * @method * @memberof Popper.Utils * @param {Number} version to check * @returns {Boolean} isIE */ function isIE(version) { if (version === 11) { return isIE11; } if (version === 10) { return isIE10; } return isIE11 || isIE10; } /** * Returns the offset parent of the given element * @method * @memberof Popper.Utils * @argument {Element} element * @returns {Element} offset parent */ function getOffsetParent(element) { if (!element) { return document.documentElement; } var noOffsetParent = isIE(10) ? document.body : null; // NOTE: 1 DOM access here var offsetParent = element.offsetParent || null; // Skip hidden elements which don't have an offsetParent while (offsetParent === noOffsetParent && element.nextElementSibling) { offsetParent = (element = element.nextElementSibling).offsetParent; } var nodeName = offsetParent && offsetParent.nodeName; if (!nodeName || nodeName === 'BODY' || nodeName === 'HTML') { return element ? element.ownerDocument.documentElement : document.documentElement; } // .offsetParent will return the closest TH, TD or TABLE in case // no offsetParent is present, I hate this job... if (['TH', 'TD', 'TABLE'].indexOf(offsetParent.nodeName) !== -1 && getStyleComputedProperty(offsetParent, 'position') === 'static') { return getOffsetParent(offsetParent); } return offsetParent; } function isOffsetContainer(element) { var nodeName = element.nodeName; if (nodeName === 'BODY') { return false; } return nodeName === 'HTML' || getOffsetParent(element.firstElementChild) === element; } /** * Finds the root node (document, shadowDOM root) of the given element * @method * @memberof Popper.Utils * @argument {Element} node * @returns {Element} root node */ function getRoot(node) { if (node.parentNode !== null) { return getRoot(node.parentNode); } return node; } /** * Finds the offset parent common to the two provided nodes * @method * @memberof Popper.Utils * @argument {Element} element1 * @argument {Element} element2 * @returns {Element} common offset parent */ function findCommonOffsetParent(element1, element2) { // This check is needed to avoid errors in case one of the elements isn't defined for any reason if (!element1 || !element1.nodeType || !element2 || !element2.nodeType) { return document.documentElement; } // Here we make sure to give as "start" the element that comes first in the DOM var order = element1.compareDocumentPosition(element2) & Node.DOCUMENT_POSITION_FOLLOWING; var start = order ? element1 : element2; var end = order ? element2 : element1; // Get common ancestor container var range = document.createRange(); range.setStart(start, 0); range.setEnd(end, 0); var commonAncestorContainer = range.commonAncestorContainer; // Both nodes are inside #document if (element1 !== commonAncestorContainer && element2 !== commonAncestorContainer || start.contains(end)) { if (isOffsetContainer(commonAncestorContainer)) { return commonAncestorContainer; } return getOffsetParent(commonAncestorContainer); } // one of the nodes is inside shadowDOM, find which one var element1root = getRoot(element1); if (element1root.host) { return findCommonOffsetParent(element1root.host, element2); } else { return findCommonOffsetParent(element1, getRoot(element2).host); } } /** * Gets the scroll value of the given element in the given side (top and left) * @method * @memberof Popper.Utils * @argument {Element} element * @argument {String} side `top` or `left` * @returns {number} amount of scrolled pixels */ function getScroll(element) { var side = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 'top'; var upperSide = side === 'top' ? 'scrollTop' : 'scrollLeft'; var nodeName = element.nodeName; if (nodeName === 'BODY' || nodeName === 'HTML') { var html = element.ownerDocument.documentElement; var scrollingElement = element.ownerDocument.scrollingElement || html; return scrollingElement[upperSide]; } return element[upperSide]; } /* * Sum or subtract the element scroll values (left and top) from a given rect object * @method * @memberof Popper.Utils * @param {Object} rect - Rect object you want to change * @param {HTMLElement} element - The element from the function reads the scroll values * @param {Boolean} subtract - set to true if you want to subtract the scroll values * @return {Object} rect - The modifier rect object */ function includeScroll(rect, element) { var subtract = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; var scrollTop = getScroll(element, 'top'); var scrollLeft = getScroll(element, 'left'); var modifier = subtract ? -1 : 1; rect.top += scrollTop * modifier; rect.bottom += scrollTop * modifier; rect.left += scrollLeft * modifier; rect.right += scrollLeft * modifier; return rect; } /* * Helper to detect borders of a given element * @method * @memberof Popper.Utils * @param {CSSStyleDeclaration} styles * Result of `getStyleComputedProperty` on the given element * @param {String} axis - `x` or `y` * @return {number} borders - The borders size of the given axis */ function getBordersSize(styles, axis) { var sideA = axis === 'x' ? 'Left' : 'Top'; var sideB = sideA === 'Left' ? 'Right' : 'Bottom'; return parseFloat(styles['border' + sideA + 'Width'], 10) + parseFloat(styles['border' + sideB + 'Width'], 10); } function getSize(axis, body, html, computedStyle) { return Math.max(body['offset' + axis], body['scroll' + axis], html['client' + axis], html['offset' + axis], html['scroll' + axis], isIE(10) ? parseInt(html['offset' + axis]) + parseInt(computedStyle['margin' + (axis === 'Height' ? 'Top' : 'Left')]) + parseInt(computedStyle['margin' + (axis === 'Height' ? 'Bottom' : 'Right')]) : 0); } function getWindowSizes(document) { var body = document.body; var html = document.documentElement; var computedStyle = isIE(10) && getComputedStyle(html); return { height: getSize('Height', body, html, computedStyle), width: getSize('Width', body, html, computedStyle) }; } var classCallCheck = function (instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }; var createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); var defineProperty = function (obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; }; var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; /** * Given element offsets, generate an output similar to getBoundingClientRect * @method * @memberof Popper.Utils * @argument {Object} offsets * @returns {Object} ClientRect like output */ function getClientRect(offsets) { return _extends({}, offsets, { right: offsets.left + offsets.width, bottom: offsets.top + offsets.height }); } /** * Get bounding client rect of given element * @method * @memberof Popper.Utils * @param {HTMLElement} element * @return {Object} client rect */ function getBoundingClientRect(element) { var rect = {}; // IE10 10 FIX: Please, don't ask, the element isn't // considered in DOM in some circumstances... // This isn't reproducible in IE10 compatibility mode of IE11 try { if (isIE(10)) { rect = element.getBoundingClientRect(); var scrollTop = getScroll(element, 'top'); var scrollLeft = getScroll(element, 'left'); rect.top += scrollTop; rect.left += scrollLeft; rect.bottom += scrollTop; rect.right += scrollLeft; } else { rect = element.getBoundingClientRect(); } } catch (e) {} var result = { left: rect.left, top: rect.top, width: rect.right - rect.left, height: rect.bottom - rect.top }; // subtract scrollbar size from sizes var sizes = element.nodeName === 'HTML' ? getWindowSizes(element.ownerDocument) : {}; var width = sizes.width || element.clientWidth || result.right - result.left; var height = sizes.height || element.clientHeight || result.bottom - result.top; var horizScrollbar = element.offsetWidth - width; var vertScrollbar = element.offsetHeight - height; // if an hypothetical scrollbar is detected, we must be sure it's not a `border` // we make this check conditional for performance reasons if (horizScrollbar || vertScrollbar) { var styles = getStyleComputedProperty(element); horizScrollbar -= getBordersSize(styles, 'x'); vertScrollbar -= getBordersSize(styles, 'y'); result.width -= horizScrollbar; result.height -= vertScrollbar; } return getClientRect(result); } function getOffsetRectRelativeToArbitraryNode(children, parent) { var fixedPosition = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; var isIE10 = isIE(10); var isHTML = parent.nodeName === 'HTML'; var childrenRect = getBoundingClientRect(children); var parentRect = getBoundingClientRect(parent); var scrollParent = getScrollParent(children); var styles = getStyleComputedProperty(parent); var borderTopWidth = parseFloat(styles.borderTopWidth, 10); var borderLeftWidth = parseFloat(styles.borderLeftWidth, 10); // In cases where the parent is fixed, we must ignore negative scroll in offset calc if (fixedPosition && isHTML) { parentRect.top = Math.max(parentRect.top, 0); parentRect.left = Math.max(parentRect.left, 0); } var offsets = getClientRect({ top: childrenRect.top - parentRect.top - borderTopWidth, left: childrenRect.left - parentRect.left - borderLeftWidth, width: childrenRect.width, height: childrenRect.height }); offsets.marginTop = 0; offsets.marginLeft = 0; // Subtract margins of documentElement in case it's being used as parent // we do this only on HTML because it's the only element that behaves // differently when margins are applied to it. The margins are included in // the box of the documentElement, in the other cases not. if (!isIE10 && isHTML) { var marginTop = parseFloat(styles.marginTop, 10); var marginLeft = parseFloat(styles.marginLeft, 10); offsets.top -= borderTopWidth - marginTop; offsets.bottom -= borderTopWidth - marginTop; offsets.left -= borderLeftWidth - marginLeft; offsets.right -= borderLeftWidth - marginLeft; // Attach marginTop and marginLeft because in some circumstances we may need them offsets.marginTop = marginTop; offsets.marginLeft = marginLeft; } if (isIE10 && !fixedPosition ? parent.contains(scrollParent) : parent === scrollParent && scrollParent.nodeName !== 'BODY') { offsets = includeScroll(offsets, parent); } return offsets; } function getViewportOffsetRectRelativeToArtbitraryNode(element) { var excludeScroll = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; var html = element.ownerDocument.documentElement; var relativeOffset = getOffsetRectRelativeToArbitraryNode(element, html); var width = Math.max(html.clientWidth, window.innerWidth || 0); var height = Math.max(html.clientHeight, window.innerHeight || 0); var scrollTop = !excludeScroll ? getScroll(html) : 0; var scrollLeft = !excludeScroll ? getScroll(html, 'left') : 0; var offset = { top: scrollTop - relativeOffset.top + relativeOffset.marginTop, left: scrollLeft - relativeOffset.left + relativeOffset.marginLeft, width: width, height: height }; return getClientRect(offset); } /** * Check if the given element is fixed or is inside a fixed parent * @method * @memberof Popper.Utils * @argument {Element} element * @argument {Element} customContainer * @returns {Boolean} answer to "isFixed?" */ function isFixed(element) { var nodeName = element.nodeName; if (nodeName === 'BODY' || nodeName === 'HTML') { return false; } if (getStyleComputedProperty(element, 'position') === 'fixed') { return true; } var parentNode = getParentNode(element); if (!parentNode) { return false; } return isFixed(parentNode); } /** * Finds the first parent of an element that has a transformed property defined * @method * @memberof Popper.Utils * @argument {Element} element * @returns {Element} first transformed parent or documentElement */ function getFixedPositionOffsetParent(element) { // This check is needed to avoid errors in case one of the elements isn't defined for any reason if (!element || !element.parentElement || isIE()) { return document.documentElement; } var el = element.parentElement; while (el && getStyleComputedProperty(el, 'transform') === 'none') { el = el.parentElement; } return el || document.documentElement; } /** * Computed the boundaries limits and return them * @method * @memberof Popper.Utils * @param {HTMLElement} popper * @param {HTMLElement} reference * @param {number} padding * @param {HTMLElement} boundariesElement - Element used to define the boundaries * @param {Boolean} fixedPosition - Is in fixed position mode * @returns {Object} Coordinates of the boundaries */ function getBoundaries(popper, reference, padding, boundariesElement) { var fixedPosition = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : false; // NOTE: 1 DOM access here var boundaries = { top: 0, left: 0 }; var offsetParent = fixedPosition ? getFixedPositionOffsetParent(popper) : findCommonOffsetParent(popper, reference); // Handle viewport case if (boundariesElement === 'viewport') { boundaries = getViewportOffsetRectRelativeToArtbitraryNode(offsetParent, fixedPosition); } else { // Handle other cases based on DOM element used as boundaries var boundariesNode = void 0; if (boundariesElement === 'scrollParent') { boundariesNode = getScrollParent(getParentNode(reference)); if (boundariesNode.nodeName === 'BODY') { boundariesNode = popper.ownerDocument.documentElement; } } else if (boundariesElement === 'window') { boundariesNode = popper.ownerDocument.documentElement; } else { boundariesNode = boundariesElement; } var offsets = getOffsetRectRelativeToArbitraryNode(boundariesNode, offsetParent, fixedPosition); // In case of HTML, we need a different computation if (boundariesNode.nodeName === 'HTML' && !isFixed(offsetParent)) { var _getWindowSizes = getWindowSizes(popper.ownerDocument), height = _getWindowSizes.height, width = _getWindowSizes.width; boundaries.top += offsets.top - offsets.marginTop; boundaries.bottom = height + offsets.top; boundaries.left += offsets.left - offsets.marginLeft; boundaries.right = width + offsets.left; } else { // for all the other DOM elements, this one is good boundaries = offsets; } } // Add paddings padding = padding || 0; var isPaddingNumber = typeof padding === 'number'; boundaries.left += isPaddingNumber ? padding : padding.left || 0; boundaries.top += isPaddingNumber ? padding : padding.top || 0; boundaries.right -= isPaddingNumber ? padding : padding.right || 0; boundaries.bottom -= isPaddingNumber ? padding : padding.bottom || 0; return boundaries; } function getArea(_ref) { var width = _ref.width, height = _ref.height; return width * height; } /** * Utility used to transform the `auto` placement to the placement with more * available space. * @method * @memberof Popper.Utils * @argument {Object} data - The data object generated by update method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function computeAutoPlacement(placement, refRect, popper, reference, boundariesElement) { var padding = arguments.length > 5 && arguments[5] !== undefined ? arguments[5] : 0; if (placement.indexOf('auto') === -1) { return placement; } var boundaries = getBoundaries(popper, reference, padding, boundariesElement); var rects = { top: { width: boundaries.width, height: refRect.top - boundaries.top }, right: { width: boundaries.right - refRect.right, height: boundaries.height }, bottom: { width: boundaries.width, height: boundaries.bottom - refRect.bottom }, left: { width: refRect.left - boundaries.left, height: boundaries.height } }; var sortedAreas = Object.keys(rects).map(function (key) { return _extends({ key: key }, rects[key], { area: getArea(rects[key]) }); }).sort(function (a, b) { return b.area - a.area; }); var filteredAreas = sortedAreas.filter(function (_ref2) { var width = _ref2.width, height = _ref2.height; return width >= popper.clientWidth && height >= popper.clientHeight; }); var computedPlacement = filteredAreas.length > 0 ? filteredAreas[0].key : sortedAreas[0].key; var variation = placement.split('-')[1]; return computedPlacement + (variation ? '-' + variation : ''); } /** * Get offsets to the reference element * @method * @memberof Popper.Utils * @param {Object} state * @param {Element} popper - the popper element * @param {Element} reference - the reference element (the popper will be relative to this) * @param {Element} fixedPosition - is in fixed position mode * @returns {Object} An object containing the offsets which will be applied to the popper */ function getReferenceOffsets(state, popper, reference) { var fixedPosition = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : null; var commonOffsetParent = fixedPosition ? getFixedPositionOffsetParent(popper) : findCommonOffsetParent(popper, reference); return getOffsetRectRelativeToArbitraryNode(reference, commonOffsetParent, fixedPosition); } /** * Get the outer sizes of the given element (offset size + margins) * @method * @memberof Popper.Utils * @argument {Element} element * @returns {Object} object containing width and height properties */ function getOuterSizes(element) { var window = element.ownerDocument.defaultView; var styles = window.getComputedStyle(element); var x = parseFloat(styles.marginTop || 0) + parseFloat(styles.marginBottom || 0); var y = parseFloat(styles.marginLeft || 0) + parseFloat(styles.marginRight || 0); var result = { width: element.offsetWidth + y, height: element.offsetHeight + x }; return result; } /** * Get the opposite placement of the given one * @method * @memberof Popper.Utils * @argument {String} placement * @returns {String} flipped placement */ function getOppositePlacement(placement) { var hash = { left: 'right', right: 'left', bottom: 'top', top: 'bottom' }; return placement.replace(/left|right|bottom|top/g, function (matched) { return hash[matched]; }); } /** * Get offsets to the popper * @method * @memberof Popper.Utils * @param {Object} position - CSS position the Popper will get applied * @param {HTMLElement} popper - the popper element * @param {Object} referenceOffsets - the reference offsets (the popper will be relative to this) * @param {String} placement - one of the valid placement options * @returns {Object} popperOffsets - An object containing the offsets which will be applied to the popper */ function getPopperOffsets(popper, referenceOffsets, placement) { placement = placement.split('-')[0]; // Get popper node sizes var popperRect = getOuterSizes(popper); // Add position, width and height to our offsets object var popperOffsets = { width: popperRect.width, height: popperRect.height }; // depending by the popper placement we have to compute its offsets slightly differently var isHoriz = ['right', 'left'].indexOf(placement) !== -1; var mainSide = isHoriz ? 'top' : 'left'; var secondarySide = isHoriz ? 'left' : 'top'; var measurement = isHoriz ? 'height' : 'width'; var secondaryMeasurement = !isHoriz ? 'height' : 'width'; popperOffsets[mainSide] = referenceOffsets[mainSide] + referenceOffsets[measurement] / 2 - popperRect[measurement] / 2; if (placement === secondarySide) { popperOffsets[secondarySide] = referenceOffsets[secondarySide] - popperRect[secondaryMeasurement]; } else { popperOffsets[secondarySide] = referenceOffsets[getOppositePlacement(secondarySide)]; } return popperOffsets; } /** * Mimics the `find` method of Array * @method * @memberof Popper.Utils * @argument {Array} arr * @argument prop * @argument value * @returns index or -1 */ function find(arr, check) { // use native find if supported if (Array.prototype.find) { return arr.find(check); } // use `filter` to obtain the same behavior of `find` return arr.filter(check)[0]; } /** * Return the index of the matching object * @method * @memberof Popper.Utils * @argument {Array} arr * @argument prop * @argument value * @returns index or -1 */ function findIndex(arr, prop, value) { // use native findIndex if supported if (Array.prototype.findIndex) { return arr.findIndex(function (cur) { return cur[prop] === value; }); } // use `find` + `indexOf` if `findIndex` isn't supported var match = find(arr, function (obj) { return obj[prop] === value; }); return arr.indexOf(match); } /** * Loop trough the list of modifiers and run them in order, * each of them will then edit the data object. * @method * @memberof Popper.Utils * @param {dataObject} data * @param {Array} modifiers * @param {String} ends - Optional modifier name used as stopper * @returns {dataObject} */ function runModifiers(modifiers, data, ends) { var modifiersToRun = ends === undefined ? modifiers : modifiers.slice(0, findIndex(modifiers, 'name', ends)); modifiersToRun.forEach(function (modifier) { if (modifier['function']) { // eslint-disable-line dot-notation console.warn('`modifier.function` is deprecated, use `modifier.fn`!'); } var fn = modifier['function'] || modifier.fn; // eslint-disable-line dot-notation if (modifier.enabled && isFunction(fn)) { // Add properties to offsets to make them a complete clientRect object // we do this before each modifier to make sure the previous one doesn't // mess with these values data.offsets.popper = getClientRect(data.offsets.popper); data.offsets.reference = getClientRect(data.offsets.reference); data = fn(data, modifier); } }); return data; } /** * Updates the position of the popper, computing the new offsets and applying * the new style.
* Prefer `scheduleUpdate` over `update` because of performance reasons. * @method * @memberof Popper */ function update() { // if popper is destroyed, don't perform any further update if (this.state.isDestroyed) { return; } var data = { instance: this, styles: {}, arrowStyles: {}, attributes: {}, flipped: false, offsets: {} }; // compute reference element offsets data.offsets.reference = getReferenceOffsets(this.state, this.popper, this.reference, this.options.positionFixed); // compute auto placement, store placement inside the data object, // modifiers will be able to edit `placement` if needed // and refer to originalPlacement to know the original value data.placement = computeAutoPlacement(this.options.placement, data.offsets.reference, this.popper, this.reference, this.options.modifiers.flip.boundariesElement, this.options.modifiers.flip.padding); // store the computed placement inside `originalPlacement` data.originalPlacement = data.placement; data.positionFixed = this.options.positionFixed; // compute the popper offsets data.offsets.popper = getPopperOffsets(this.popper, data.offsets.reference, data.placement); data.offsets.popper.position = this.options.positionFixed ? 'fixed' : 'absolute'; // run the modifiers data = runModifiers(this.modifiers, data); // the first `update` will call `onCreate` callback // the other ones will call `onUpdate` callback if (!this.state.isCreated) { this.state.isCreated = true; this.options.onCreate(data); } else { this.options.onUpdate(data); } } /** * Helper used to know if the given modifier is enabled. * @method * @memberof Popper.Utils * @returns {Boolean} */ function isModifierEnabled(modifiers, modifierName) { return modifiers.some(function (_ref) { var name = _ref.name, enabled = _ref.enabled; return enabled && name === modifierName; }); } /** * Get the prefixed supported property name * @method * @memberof Popper.Utils * @argument {String} property (camelCase) * @returns {String} prefixed property (camelCase or PascalCase, depending on the vendor prefix) */ function getSupportedPropertyName(property) { var prefixes = [false, 'ms', 'Webkit', 'Moz', 'O']; var upperProp = property.charAt(0).toUpperCase() + property.slice(1); for (var i = 0; i < prefixes.length; i++) { var prefix = prefixes[i]; var toCheck = prefix ? '' + prefix + upperProp : property; if (typeof document.body.style[toCheck] !== 'undefined') { return toCheck; } } return null; } /** * Destroys the popper. * @method * @memberof Popper */ function destroy() { this.state.isDestroyed = true; // touch DOM only if `applyStyle` modifier is enabled if (isModifierEnabled(this.modifiers, 'applyStyle')) { this.popper.removeAttribute('x-placement'); this.popper.style.position = ''; this.popper.style.top = ''; this.popper.style.left = ''; this.popper.style.right = ''; this.popper.style.bottom = ''; this.popper.style.willChange = ''; this.popper.style[getSupportedPropertyName('transform')] = ''; } this.disableEventListeners(); // remove the popper if user explicity asked for the deletion on destroy // do not use `remove` because IE11 doesn't support it if (this.options.removeOnDestroy) { this.popper.parentNode.removeChild(this.popper); } return this; } /** * Get the window associated with the element * @argument {Element} element * @returns {Window} */ function getWindow(element) { var ownerDocument = element.ownerDocument; return ownerDocument ? ownerDocument.defaultView : window; } function attachToScrollParents(scrollParent, event, callback, scrollParents) { var isBody = scrollParent.nodeName === 'BODY'; var target = isBody ? scrollParent.ownerDocument.defaultView : scrollParent; target.addEventListener(event, callback, { passive: true }); if (!isBody) { attachToScrollParents(getScrollParent(target.parentNode), event, callback, scrollParents); } scrollParents.push(target); } /** * Setup needed event listeners used to update the popper position * @method * @memberof Popper.Utils * @private */ function setupEventListeners(reference, options, state, updateBound) { // Resize event listener on window state.updateBound = updateBound; getWindow(reference).addEventListener('resize', state.updateBound, { passive: true }); // Scroll event listener on scroll parents var scrollElement = getScrollParent(reference); attachToScrollParents(scrollElement, 'scroll', state.updateBound, state.scrollParents); state.scrollElement = scrollElement; state.eventsEnabled = true; return state; } /** * It will add resize/scroll events and start recalculating * position of the popper element when they are triggered. * @method * @memberof Popper */ function enableEventListeners() { if (!this.state.eventsEnabled) { this.state = setupEventListeners(this.reference, this.options, this.state, this.scheduleUpdate); } } /** * Remove event listeners used to update the popper position * @method * @memberof Popper.Utils * @private */ function removeEventListeners(reference, state) { // Remove resize event listener on window getWindow(reference).removeEventListener('resize', state.updateBound); // Remove scroll event listener on scroll parents state.scrollParents.forEach(function (target) { target.removeEventListener('scroll', state.updateBound); }); // Reset state state.updateBound = null; state.scrollParents = []; state.scrollElement = null; state.eventsEnabled = false; return state; } /** * It will remove resize/scroll events and won't recalculate popper position * when they are triggered. It also won't trigger `onUpdate` callback anymore, * unless you call `update` method manually. * @method * @memberof Popper */ function disableEventListeners() { if (this.state.eventsEnabled) { cancelAnimationFrame(this.scheduleUpdate); this.state = removeEventListeners(this.reference, this.state); } } /** * Tells if a given input is a number * @method * @memberof Popper.Utils * @param {*} input to check * @return {Boolean} */ function isNumeric(n) { return n !== '' && !isNaN(parseFloat(n)) && isFinite(n); } /** * Set the style to the given popper * @method * @memberof Popper.Utils * @argument {Element} element - Element to apply the style to * @argument {Object} styles * Object with a list of properties and values which will be applied to the element */ function setStyles(element, styles) { Object.keys(styles).forEach(function (prop) { var unit = ''; // add unit if the value is numeric and is one of the following if (['width', 'height', 'top', 'right', 'bottom', 'left'].indexOf(prop) !== -1 && isNumeric(styles[prop])) { unit = 'px'; } element.style[prop] = styles[prop] + unit; }); } /** * Set the attributes to the given popper * @method * @memberof Popper.Utils * @argument {Element} element - Element to apply the attributes to * @argument {Object} styles * Object with a list of properties and values which will be applied to the element */ function setAttributes(element, attributes) { Object.keys(attributes).forEach(function (prop) { var value = attributes[prop]; if (value !== false) { element.setAttribute(prop, attributes[prop]); } else { element.removeAttribute(prop); } }); } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by `update` method * @argument {Object} data.styles - List of style properties - values to apply to popper element * @argument {Object} data.attributes - List of attribute properties - values to apply to popper element * @argument {Object} options - Modifiers configuration and options * @returns {Object} The same data object */ function applyStyle(data) { // any property present in `data.styles` will be applied to the popper, // in this way we can make the 3rd party modifiers add custom styles to it // Be aware, modifiers could override the properties defined in the previous // lines of this modifier! setStyles(data.instance.popper, data.styles); // any property present in `data.attributes` will be applied to the popper, // they will be set as HTML attributes of the element setAttributes(data.instance.popper, data.attributes); // if arrowElement is defined and arrowStyles has some properties if (data.arrowElement && Object.keys(data.arrowStyles).length) { setStyles(data.arrowElement, data.arrowStyles); } return data; } /** * Set the x-placement attribute before everything else because it could be used * to add margins to the popper margins needs to be calculated to get the * correct popper offsets. * @method * @memberof Popper.modifiers * @param {HTMLElement} reference - The reference element used to position the popper * @param {HTMLElement} popper - The HTML element used as popper * @param {Object} options - Popper.js options */ function applyStyleOnLoad(reference, popper, options, modifierOptions, state) { // compute reference element offsets var referenceOffsets = getReferenceOffsets(state, popper, reference, options.positionFixed); // compute auto placement, store placement inside the data object, // modifiers will be able to edit `placement` if needed // and refer to originalPlacement to know the original value var placement = computeAutoPlacement(options.placement, referenceOffsets, popper, reference, options.modifiers.flip.boundariesElement, options.modifiers.flip.padding); popper.setAttribute('x-placement', placement); // Apply `position` to popper before anything else because // without the position applied we can't guarantee correct computations setStyles(popper, { position: options.positionFixed ? 'fixed' : 'absolute' }); return options; } /** * @function * @memberof Popper.Utils * @argument {Object} data - The data object generated by `update` method * @argument {Boolean} shouldRound - If the offsets should be rounded at all * @returns {Object} The popper's position offsets rounded * * The tale of pixel-perfect positioning. It's still not 100% perfect, but as * good as it can be within reason. * Discussion here: https://github.com/FezVrasta/popper.js/pull/715 * * Low DPI screens cause a popper to be blurry if not using full pixels (Safari * as well on High DPI screens). * * Firefox prefers no rounding for positioning and does not have blurriness on * high DPI screens. * * Only horizontal placement and left/right values need to be considered. */ function getRoundedOffsets(data, shouldRound) { var _data$offsets = data.offsets, popper = _data$offsets.popper, reference = _data$offsets.reference; var round = Math.round, floor = Math.floor; var noRound = function noRound(v) { return v; }; var referenceWidth = round(reference.width); var popperWidth = round(popper.width); var isVertical = ['left', 'right'].indexOf(data.placement) !== -1; var isVariation = data.placement.indexOf('-') !== -1; var sameWidthParity = referenceWidth % 2 === popperWidth % 2; var bothOddWidth = referenceWidth % 2 === 1 && popperWidth % 2 === 1; var horizontalToInteger = !shouldRound ? noRound : isVertical || isVariation || sameWidthParity ? round : floor; var verticalToInteger = !shouldRound ? noRound : round; return { left: horizontalToInteger(bothOddWidth && !isVariation && shouldRound ? popper.left - 1 : popper.left), top: verticalToInteger(popper.top), bottom: verticalToInteger(popper.bottom), right: horizontalToInteger(popper.right) }; } var isFirefox = isBrowser && /Firefox/i.test(navigator.userAgent); /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by `update` method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function computeStyle(data, options) { var x = options.x, y = options.y; var popper = data.offsets.popper; // Remove this legacy support in Popper.js v2 var legacyGpuAccelerationOption = find(data.instance.modifiers, function (modifier) { return modifier.name === 'applyStyle'; }).gpuAcceleration; if (legacyGpuAccelerationOption !== undefined) { console.warn('WARNING: `gpuAcceleration` option moved to `computeStyle` modifier and will not be supported in future versions of Popper.js!'); } var gpuAcceleration = legacyGpuAccelerationOption !== undefined ? legacyGpuAccelerationOption : options.gpuAcceleration; var offsetParent = getOffsetParent(data.instance.popper); var offsetParentRect = getBoundingClientRect(offsetParent); // Styles var styles = { position: popper.position }; var offsets = getRoundedOffsets(data, window.devicePixelRatio < 2 || !isFirefox); var sideA = x === 'bottom' ? 'top' : 'bottom'; var sideB = y === 'right' ? 'left' : 'right'; // if gpuAcceleration is set to `true` and transform is supported, // we use `translate3d` to apply the position to the popper we // automatically use the supported prefixed version if needed var prefixedProperty = getSupportedPropertyName('transform'); // now, let's make a step back and look at this code closely (wtf?) // If the content of the popper grows once it's been positioned, it // may happen that the popper gets misplaced because of the new content // overflowing its reference element // To avoid this problem, we provide two options (x and y), which allow // the consumer to define the offset origin. // If we position a popper on top of a reference element, we can set // `x` to `top` to make the popper grow towards its top instead of // its bottom. var left = void 0, top = void 0; if (sideA === 'bottom') { // when offsetParent is the positioning is relative to the bottom of the screen (excluding the scrollbar) // and not the bottom of the html element if (offsetParent.nodeName === 'HTML') { top = -offsetParent.clientHeight + offsets.bottom; } else { top = -offsetParentRect.height + offsets.bottom; } } else { top = offsets.top; } if (sideB === 'right') { if (offsetParent.nodeName === 'HTML') { left = -offsetParent.clientWidth + offsets.right; } else { left = -offsetParentRect.width + offsets.right; } } else { left = offsets.left; } if (gpuAcceleration && prefixedProperty) { styles[prefixedProperty] = 'translate3d(' + left + 'px, ' + top + 'px, 0)'; styles[sideA] = 0; styles[sideB] = 0; styles.willChange = 'transform'; } else { // othwerise, we use the standard `top`, `left`, `bottom` and `right` properties var invertTop = sideA === 'bottom' ? -1 : 1; var invertLeft = sideB === 'right' ? -1 : 1; styles[sideA] = top * invertTop; styles[sideB] = left * invertLeft; styles.willChange = sideA + ', ' + sideB; } // Attributes var attributes = { 'x-placement': data.placement }; // Update `data` attributes, styles and arrowStyles data.attributes = _extends({}, attributes, data.attributes); data.styles = _extends({}, styles, data.styles); data.arrowStyles = _extends({}, data.offsets.arrow, data.arrowStyles); return data; } /** * Helper used to know if the given modifier depends from another one.
* It checks if the needed modifier is listed and enabled. * @method * @memberof Popper.Utils * @param {Array} modifiers - list of modifiers * @param {String} requestingName - name of requesting modifier * @param {String} requestedName - name of requested modifier * @returns {Boolean} */ function isModifierRequired(modifiers, requestingName, requestedName) { var requesting = find(modifiers, function (_ref) { var name = _ref.name; return name === requestingName; }); var isRequired = !!requesting && modifiers.some(function (modifier) { return modifier.name === requestedName && modifier.enabled && modifier.order < requesting.order; }); if (!isRequired) { var _requesting = '`' + requestingName + '`'; var requested = '`' + requestedName + '`'; console.warn(requested + ' modifier is required by ' + _requesting + ' modifier in order to work, be sure to include it before ' + _requesting + '!'); } return isRequired; } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by update method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function arrow(data, options) { var _data$offsets$arrow; // arrow depends on keepTogether in order to work if (!isModifierRequired(data.instance.modifiers, 'arrow', 'keepTogether')) { return data; } var arrowElement = options.element; // if arrowElement is a string, suppose it's a CSS selector if (typeof arrowElement === 'string') { arrowElement = data.instance.popper.querySelector(arrowElement); // if arrowElement is not found, don't run the modifier if (!arrowElement) { return data; } } else { // if the arrowElement isn't a query selector we must check that the // provided DOM node is child of its popper node if (!data.instance.popper.contains(arrowElement)) { console.warn('WARNING: `arrow.element` must be child of its popper element!'); return data; } } var placement = data.placement.split('-')[0]; var _data$offsets = data.offsets, popper = _data$offsets.popper, reference = _data$offsets.reference; var isVertical = ['left', 'right'].indexOf(placement) !== -1; var len = isVertical ? 'height' : 'width'; var sideCapitalized = isVertical ? 'Top' : 'Left'; var side = sideCapitalized.toLowerCase(); var altSide = isVertical ? 'left' : 'top'; var opSide = isVertical ? 'bottom' : 'right'; var arrowElementSize = getOuterSizes(arrowElement)[len]; // // extends keepTogether behavior making sure the popper and its // reference have enough pixels in conjunction // // top/left side if (reference[opSide] - arrowElementSize < popper[side]) { data.offsets.popper[side] -= popper[side] - (reference[opSide] - arrowElementSize); } // bottom/right side if (reference[side] + arrowElementSize > popper[opSide]) { data.offsets.popper[side] += reference[side] + arrowElementSize - popper[opSide]; } data.offsets.popper = getClientRect(data.offsets.popper); // compute center of the popper var center = reference[side] + reference[len] / 2 - arrowElementSize / 2; // Compute the sideValue using the updated popper offsets // take popper margin in account because we don't have this info available var css = getStyleComputedProperty(data.instance.popper); var popperMarginSide = parseFloat(css['margin' + sideCapitalized], 10); var popperBorderSide = parseFloat(css['border' + sideCapitalized + 'Width'], 10); var sideValue = center - data.offsets.popper[side] - popperMarginSide - popperBorderSide; // prevent arrowElement from being placed not contiguously to its popper sideValue = Math.max(Math.min(popper[len] - arrowElementSize, sideValue), 0); data.arrowElement = arrowElement; data.offsets.arrow = (_data$offsets$arrow = {}, defineProperty(_data$offsets$arrow, side, Math.round(sideValue)), defineProperty(_data$offsets$arrow, altSide, ''), _data$offsets$arrow); return data; } /** * Get the opposite placement variation of the given one * @method * @memberof Popper.Utils * @argument {String} placement variation * @returns {String} flipped placement variation */ function getOppositeVariation(variation) { if (variation === 'end') { return 'start'; } else if (variation === 'start') { return 'end'; } return variation; } /** * List of accepted placements to use as values of the `placement` option.
* Valid placements are: * - `auto` * - `top` * - `right` * - `bottom` * - `left` * * Each placement can have a variation from this list: * - `-start` * - `-end` * * Variations are interpreted easily if you think of them as the left to right * written languages. Horizontally (`top` and `bottom`), `start` is left and `end` * is right.
* Vertically (`left` and `right`), `start` is top and `end` is bottom. * * Some valid examples are: * - `top-end` (on top of reference, right aligned) * - `right-start` (on right of reference, top aligned) * - `bottom` (on bottom, centered) * - `auto-end` (on the side with more space available, alignment depends by placement) * * @static * @type {Array} * @enum {String} * @readonly * @method placements * @memberof Popper */ var placements = ['auto-start', 'auto', 'auto-end', 'top-start', 'top', 'top-end', 'right-start', 'right', 'right-end', 'bottom-end', 'bottom', 'bottom-start', 'left-end', 'left', 'left-start']; // Get rid of `auto` `auto-start` and `auto-end` var validPlacements = placements.slice(3); /** * Given an initial placement, returns all the subsequent placements * clockwise (or counter-clockwise). * * @method * @memberof Popper.Utils * @argument {String} placement - A valid placement (it accepts variations) * @argument {Boolean} counter - Set to true to walk the placements counterclockwise * @returns {Array} placements including their variations */ function clockwise(placement) { var counter = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; var index = validPlacements.indexOf(placement); var arr = validPlacements.slice(index + 1).concat(validPlacements.slice(0, index)); return counter ? arr.reverse() : arr; } var BEHAVIORS = { FLIP: 'flip', CLOCKWISE: 'clockwise', COUNTERCLOCKWISE: 'counterclockwise' }; /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by update method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function flip(data, options) { // if `inner` modifier is enabled, we can't use the `flip` modifier if (isModifierEnabled(data.instance.modifiers, 'inner')) { return data; } if (data.flipped && data.placement === data.originalPlacement) { // seems like flip is trying to loop, probably there's not enough space on any of the flippable sides return data; } var boundaries = getBoundaries(data.instance.popper, data.instance.reference, options.padding, options.boundariesElement, data.positionFixed); var placement = data.placement.split('-')[0]; var placementOpposite = getOppositePlacement(placement); var variation = data.placement.split('-')[1] || ''; var flipOrder = []; switch (options.behavior) { case BEHAVIORS.FLIP: flipOrder = [placement, placementOpposite]; break; case BEHAVIORS.CLOCKWISE: flipOrder = clockwise(placement); break; case BEHAVIORS.COUNTERCLOCKWISE: flipOrder = clockwise(placement, true); break; default: flipOrder = options.behavior; } flipOrder.forEach(function (step, index) { if (placement !== step || flipOrder.length === index + 1) { return data; } placement = data.placement.split('-')[0]; placementOpposite = getOppositePlacement(placement); var popperOffsets = data.offsets.popper; var refOffsets = data.offsets.reference; // using floor because the reference offsets may contain decimals we are not going to consider here var floor = Math.floor; var overlapsRef = placement === 'left' && floor(popperOffsets.right) > floor(refOffsets.left) || placement === 'right' && floor(popperOffsets.left) < floor(refOffsets.right) || placement === 'top' && floor(popperOffsets.bottom) > floor(refOffsets.top) || placement === 'bottom' && floor(popperOffsets.top) < floor(refOffsets.bottom); var overflowsLeft = floor(popperOffsets.left) < floor(boundaries.left); var overflowsRight = floor(popperOffsets.right) > floor(boundaries.right); var overflowsTop = floor(popperOffsets.top) < floor(boundaries.top); var overflowsBottom = floor(popperOffsets.bottom) > floor(boundaries.bottom); var overflowsBoundaries = placement === 'left' && overflowsLeft || placement === 'right' && overflowsRight || placement === 'top' && overflowsTop || placement === 'bottom' && overflowsBottom; // flip the variation if required var isVertical = ['top', 'bottom'].indexOf(placement) !== -1; var flippedVariation = !!options.flipVariations && (isVertical && variation === 'start' && overflowsLeft || isVertical && variation === 'end' && overflowsRight || !isVertical && variation === 'start' && overflowsTop || !isVertical && variation === 'end' && overflowsBottom); if (overlapsRef || overflowsBoundaries || flippedVariation) { // this boolean to detect any flip loop data.flipped = true; if (overlapsRef || overflowsBoundaries) { placement = flipOrder[index + 1]; } if (flippedVariation) { variation = getOppositeVariation(variation); } data.placement = placement + (variation ? '-' + variation : ''); // this object contains `position`, we want to preserve it along with // any additional property we may add in the future data.offsets.popper = _extends({}, data.offsets.popper, getPopperOffsets(data.instance.popper, data.offsets.reference, data.placement)); data = runModifiers(data.instance.modifiers, data, 'flip'); } }); return data; } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by update method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function keepTogether(data) { var _data$offsets = data.offsets, popper = _data$offsets.popper, reference = _data$offsets.reference; var placement = data.placement.split('-')[0]; var floor = Math.floor; var isVertical = ['top', 'bottom'].indexOf(placement) !== -1; var side = isVertical ? 'right' : 'bottom'; var opSide = isVertical ? 'left' : 'top'; var measurement = isVertical ? 'width' : 'height'; if (popper[side] < floor(reference[opSide])) { data.offsets.popper[opSide] = floor(reference[opSide]) - popper[measurement]; } if (popper[opSide] > floor(reference[side])) { data.offsets.popper[opSide] = floor(reference[side]); } return data; } /** * Converts a string containing value + unit into a px value number * @function * @memberof {modifiers~offset} * @private * @argument {String} str - Value + unit string * @argument {String} measurement - `height` or `width` * @argument {Object} popperOffsets * @argument {Object} referenceOffsets * @returns {Number|String} * Value in pixels, or original string if no values were extracted */ function toValue(str, measurement, popperOffsets, referenceOffsets) { // separate value from unit var split = str.match(/((?:\-|\+)?\d*\.?\d*)(.*)/); var value = +split[1]; var unit = split[2]; // If it's not a number it's an operator, I guess if (!value) { return str; } if (unit.indexOf('%') === 0) { var element = void 0; switch (unit) { case '%p': element = popperOffsets; break; case '%': case '%r': default: element = referenceOffsets; } var rect = getClientRect(element); return rect[measurement] / 100 * value; } else if (unit === 'vh' || unit === 'vw') { // if is a vh or vw, we calculate the size based on the viewport var size = void 0; if (unit === 'vh') { size = Math.max(document.documentElement.clientHeight, window.innerHeight || 0); } else { size = Math.max(document.documentElement.clientWidth, window.innerWidth || 0); } return size / 100 * value; } else { // if is an explicit pixel unit, we get rid of the unit and keep the value // if is an implicit unit, it's px, and we return just the value return value; } } /** * Parse an `offset` string to extrapolate `x` and `y` numeric offsets. * @function * @memberof {modifiers~offset} * @private * @argument {String} offset * @argument {Object} popperOffsets * @argument {Object} referenceOffsets * @argument {String} basePlacement * @returns {Array} a two cells array with x and y offsets in numbers */ function parseOffset(offset, popperOffsets, referenceOffsets, basePlacement) { var offsets = [0, 0]; // Use height if placement is left or right and index is 0 otherwise use width // in this way the first offset will use an axis and the second one // will use the other one var useHeight = ['right', 'left'].indexOf(basePlacement) !== -1; // Split the offset string to obtain a list of values and operands // The regex addresses values with the plus or minus sign in front (+10, -20, etc) var fragments = offset.split(/(\+|\-)/).map(function (frag) { return frag.trim(); }); // Detect if the offset string contains a pair of values or a single one // they could be separated by comma or space var divider = fragments.indexOf(find(fragments, function (frag) { return frag.search(/,|\s/) !== -1; })); if (fragments[divider] && fragments[divider].indexOf(',') === -1) { console.warn('Offsets separated by white space(s) are deprecated, use a comma (,) instead.'); } // If divider is found, we divide the list of values and operands to divide // them by ofset X and Y. var splitRegex = /\s*,\s*|\s+/; var ops = divider !== -1 ? [fragments.slice(0, divider).concat([fragments[divider].split(splitRegex)[0]]), [fragments[divider].split(splitRegex)[1]].concat(fragments.slice(divider + 1))] : [fragments]; // Convert the values with units to absolute pixels to allow our computations ops = ops.map(function (op, index) { // Most of the units rely on the orientation of the popper var measurement = (index === 1 ? !useHeight : useHeight) ? 'height' : 'width'; var mergeWithPrevious = false; return op // This aggregates any `+` or `-` sign that aren't considered operators // e.g.: 10 + +5 => [10, +, +5] .reduce(function (a, b) { if (a[a.length - 1] === '' && ['+', '-'].indexOf(b) !== -1) { a[a.length - 1] = b; mergeWithPrevious = true; return a; } else if (mergeWithPrevious) { a[a.length - 1] += b; mergeWithPrevious = false; return a; } else { return a.concat(b); } }, []) // Here we convert the string values into number values (in px) .map(function (str) { return toValue(str, measurement, popperOffsets, referenceOffsets); }); }); // Loop trough the offsets arrays and execute the operations ops.forEach(function (op, index) { op.forEach(function (frag, index2) { if (isNumeric(frag)) { offsets[index] += frag * (op[index2 - 1] === '-' ? -1 : 1); } }); }); return offsets; } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by update method * @argument {Object} options - Modifiers configuration and options * @argument {Number|String} options.offset=0 * The offset value as described in the modifier description * @returns {Object} The data object, properly modified */ function offset(data, _ref) { var offset = _ref.offset; var placement = data.placement, _data$offsets = data.offsets, popper = _data$offsets.popper, reference = _data$offsets.reference; var basePlacement = placement.split('-')[0]; var offsets = void 0; if (isNumeric(+offset)) { offsets = [+offset, 0]; } else { offsets = parseOffset(offset, popper, reference, basePlacement); } if (basePlacement === 'left') { popper.top += offsets[0]; popper.left -= offsets[1]; } else if (basePlacement === 'right') { popper.top += offsets[0]; popper.left += offsets[1]; } else if (basePlacement === 'top') { popper.left += offsets[0]; popper.top -= offsets[1]; } else if (basePlacement === 'bottom') { popper.left += offsets[0]; popper.top += offsets[1]; } data.popper = popper; return data; } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by `update` method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function preventOverflow(data, options) { var boundariesElement = options.boundariesElement || getOffsetParent(data.instance.popper); // If offsetParent is the reference element, we really want to // go one step up and use the next offsetParent as reference to // avoid to make this modifier completely useless and look like broken if (data.instance.reference === boundariesElement) { boundariesElement = getOffsetParent(boundariesElement); } // NOTE: DOM access here // resets the popper's position so that the document size can be calculated excluding // the size of the popper element itself var transformProp = getSupportedPropertyName('transform'); var popperStyles = data.instance.popper.style; // assignment to help minification var top = popperStyles.top, left = popperStyles.left, transform = popperStyles[transformProp]; popperStyles.top = ''; popperStyles.left = ''; popperStyles[transformProp] = ''; var boundaries = getBoundaries(data.instance.popper, data.instance.reference, options.padding, boundariesElement, data.positionFixed); // NOTE: DOM access here // restores the original style properties after the offsets have been computed popperStyles.top = top; popperStyles.left = left; popperStyles[transformProp] = transform; options.boundaries = boundaries; var order = options.priority; var popper = data.offsets.popper; var check = { primary: function primary(placement) { var value = popper[placement]; if (popper[placement] < boundaries[placement] && !options.escapeWithReference) { value = Math.max(popper[placement], boundaries[placement]); } return defineProperty({}, placement, value); }, secondary: function secondary(placement) { var mainSide = placement === 'right' ? 'left' : 'top'; var value = popper[mainSide]; if (popper[placement] > boundaries[placement] && !options.escapeWithReference) { value = Math.min(popper[mainSide], boundaries[placement] - (placement === 'right' ? popper.width : popper.height)); } return defineProperty({}, mainSide, value); } }; order.forEach(function (placement) { var side = ['left', 'top'].indexOf(placement) !== -1 ? 'primary' : 'secondary'; popper = _extends({}, popper, check[side](placement)); }); data.offsets.popper = popper; return data; } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by `update` method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function shift(data) { var placement = data.placement; var basePlacement = placement.split('-')[0]; var shiftvariation = placement.split('-')[1]; // if shift shiftvariation is specified, run the modifier if (shiftvariation) { var _data$offsets = data.offsets, reference = _data$offsets.reference, popper = _data$offsets.popper; var isVertical = ['bottom', 'top'].indexOf(basePlacement) !== -1; var side = isVertical ? 'left' : 'top'; var measurement = isVertical ? 'width' : 'height'; var shiftOffsets = { start: defineProperty({}, side, reference[side]), end: defineProperty({}, side, reference[side] + reference[measurement] - popper[measurement]) }; data.offsets.popper = _extends({}, popper, shiftOffsets[shiftvariation]); } return data; } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by update method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function hide(data) { if (!isModifierRequired(data.instance.modifiers, 'hide', 'preventOverflow')) { return data; } var refRect = data.offsets.reference; var bound = find(data.instance.modifiers, function (modifier) { return modifier.name === 'preventOverflow'; }).boundaries; if (refRect.bottom < bound.top || refRect.left > bound.right || refRect.top > bound.bottom || refRect.right < bound.left) { // Avoid unnecessary DOM access if visibility hasn't changed if (data.hide === true) { return data; } data.hide = true; data.attributes['x-out-of-boundaries'] = ''; } else { // Avoid unnecessary DOM access if visibility hasn't changed if (data.hide === false) { return data; } data.hide = false; data.attributes['x-out-of-boundaries'] = false; } return data; } /** * @function * @memberof Modifiers * @argument {Object} data - The data object generated by `update` method * @argument {Object} options - Modifiers configuration and options * @returns {Object} The data object, properly modified */ function inner(data) { var placement = data.placement; var basePlacement = placement.split('-')[0]; var _data$offsets = data.offsets, popper = _data$offsets.popper, reference = _data$offsets.reference; var isHoriz = ['left', 'right'].indexOf(basePlacement) !== -1; var subtractLength = ['top', 'left'].indexOf(basePlacement) === -1; popper[isHoriz ? 'left' : 'top'] = reference[basePlacement] - (subtractLength ? popper[isHoriz ? 'width' : 'height'] : 0); data.placement = getOppositePlacement(placement); data.offsets.popper = getClientRect(popper); return data; } /** * Modifier function, each modifier can have a function of this type assigned * to its `fn` property.
* These functions will be called on each update, this means that you must * make sure they are performant enough to avoid performance bottlenecks. * * @function ModifierFn * @argument {dataObject} data - The data object generated by `update` method * @argument {Object} options - Modifiers configuration and options * @returns {dataObject} The data object, properly modified */ /** * Modifiers are plugins used to alter the behavior of your poppers.
* Popper.js uses a set of 9 modifiers to provide all the basic functionalities * needed by the library. * * Usually you don't want to override the `order`, `fn` and `onLoad` props. * All the other properties are configurations that could be tweaked. * @namespace modifiers */ var modifiers = { /** * Modifier used to shift the popper on the start or end of its reference * element.
* It will read the variation of the `placement` property.
* It can be one either `-end` or `-start`. * @memberof modifiers * @inner */ shift: { /** @prop {number} order=100 - Index used to define the order of execution */ order: 100, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: shift }, /** * The `offset` modifier can shift your popper on both its axis. * * It accepts the following units: * - `px` or unit-less, interpreted as pixels * - `%` or `%r`, percentage relative to the length of the reference element * - `%p`, percentage relative to the length of the popper element * - `vw`, CSS viewport width unit * - `vh`, CSS viewport height unit * * For length is intended the main axis relative to the placement of the popper.
* This means that if the placement is `top` or `bottom`, the length will be the * `width`. In case of `left` or `right`, it will be the `height`. * * You can provide a single value (as `Number` or `String`), or a pair of values * as `String` divided by a comma or one (or more) white spaces.
* The latter is a deprecated method because it leads to confusion and will be * removed in v2.
* Additionally, it accepts additions and subtractions between different units. * Note that multiplications and divisions aren't supported. * * Valid examples are: * ``` * 10 * '10%' * '10, 10' * '10%, 10' * '10 + 10%' * '10 - 5vh + 3%' * '-10px + 5vh, 5px - 6%' * ``` * > **NB**: If you desire to apply offsets to your poppers in a way that may make them overlap * > with their reference element, unfortunately, you will have to disable the `flip` modifier. * > You can read more on this at this [issue](https://github.com/FezVrasta/popper.js/issues/373). * * @memberof modifiers * @inner */ offset: { /** @prop {number} order=200 - Index used to define the order of execution */ order: 200, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: offset, /** @prop {Number|String} offset=0 * The offset value as described in the modifier description */ offset: 0 }, /** * Modifier used to prevent the popper from being positioned outside the boundary. * * A scenario exists where the reference itself is not within the boundaries.
* We can say it has "escaped the boundaries" — or just "escaped".
* In this case we need to decide whether the popper should either: * * - detach from the reference and remain "trapped" in the boundaries, or * - if it should ignore the boundary and "escape with its reference" * * When `escapeWithReference` is set to`true` and reference is completely * outside its boundaries, the popper will overflow (or completely leave) * the boundaries in order to remain attached to the edge of the reference. * * @memberof modifiers * @inner */ preventOverflow: { /** @prop {number} order=300 - Index used to define the order of execution */ order: 300, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: preventOverflow, /** * @prop {Array} [priority=['left','right','top','bottom']] * Popper will try to prevent overflow following these priorities by default, * then, it could overflow on the left and on top of the `boundariesElement` */ priority: ['left', 'right', 'top', 'bottom'], /** * @prop {number} padding=5 * Amount of pixel used to define a minimum distance between the boundaries * and the popper. This makes sure the popper always has a little padding * between the edges of its container */ padding: 5, /** * @prop {String|HTMLElement} boundariesElement='scrollParent' * Boundaries used by the modifier. Can be `scrollParent`, `window`, * `viewport` or any DOM element. */ boundariesElement: 'scrollParent' }, /** * Modifier used to make sure the reference and its popper stay near each other * without leaving any gap between the two. Especially useful when the arrow is * enabled and you want to ensure that it points to its reference element. * It cares only about the first axis. You can still have poppers with margin * between the popper and its reference element. * @memberof modifiers * @inner */ keepTogether: { /** @prop {number} order=400 - Index used to define the order of execution */ order: 400, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: keepTogether }, /** * This modifier is used to move the `arrowElement` of the popper to make * sure it is positioned between the reference element and its popper element. * It will read the outer size of the `arrowElement` node to detect how many * pixels of conjunction are needed. * * It has no effect if no `arrowElement` is provided. * @memberof modifiers * @inner */ arrow: { /** @prop {number} order=500 - Index used to define the order of execution */ order: 500, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: arrow, /** @prop {String|HTMLElement} element='[x-arrow]' - Selector or node used as arrow */ element: '[x-arrow]' }, /** * Modifier used to flip the popper's placement when it starts to overlap its * reference element. * * Requires the `preventOverflow` modifier before it in order to work. * * **NOTE:** this modifier will interrupt the current update cycle and will * restart it if it detects the need to flip the placement. * @memberof modifiers * @inner */ flip: { /** @prop {number} order=600 - Index used to define the order of execution */ order: 600, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: flip, /** * @prop {String|Array} behavior='flip' * The behavior used to change the popper's placement. It can be one of * `flip`, `clockwise`, `counterclockwise` or an array with a list of valid * placements (with optional variations) */ behavior: 'flip', /** * @prop {number} padding=5 * The popper will flip if it hits the edges of the `boundariesElement` */ padding: 5, /** * @prop {String|HTMLElement} boundariesElement='viewport' * The element which will define the boundaries of the popper position. * The popper will never be placed outside of the defined boundaries * (except if `keepTogether` is enabled) */ boundariesElement: 'viewport' }, /** * Modifier used to make the popper flow toward the inner of the reference element. * By default, when this modifier is disabled, the popper will be placed outside * the reference element. * @memberof modifiers * @inner */ inner: { /** @prop {number} order=700 - Index used to define the order of execution */ order: 700, /** @prop {Boolean} enabled=false - Whether the modifier is enabled or not */ enabled: false, /** @prop {ModifierFn} */ fn: inner }, /** * Modifier used to hide the popper when its reference element is outside of the * popper boundaries. It will set a `x-out-of-boundaries` attribute which can * be used to hide with a CSS selector the popper when its reference is * out of boundaries. * * Requires the `preventOverflow` modifier before it in order to work. * @memberof modifiers * @inner */ hide: { /** @prop {number} order=800 - Index used to define the order of execution */ order: 800, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: hide }, /** * Computes the style that will be applied to the popper element to gets * properly positioned. * * Note that this modifier will not touch the DOM, it just prepares the styles * so that `applyStyle` modifier can apply it. This separation is useful * in case you need to replace `applyStyle` with a custom implementation. * * This modifier has `850` as `order` value to maintain backward compatibility * with previous versions of Popper.js. Expect the modifiers ordering method * to change in future major versions of the library. * * @memberof modifiers * @inner */ computeStyle: { /** @prop {number} order=850 - Index used to define the order of execution */ order: 850, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: computeStyle, /** * @prop {Boolean} gpuAcceleration=true * If true, it uses the CSS 3D transformation to position the popper. * Otherwise, it will use the `top` and `left` properties */ gpuAcceleration: true, /** * @prop {string} [x='bottom'] * Where to anchor the X axis (`bottom` or `top`). AKA X offset origin. * Change this if your popper should grow in a direction different from `bottom` */ x: 'bottom', /** * @prop {string} [x='left'] * Where to anchor the Y axis (`left` or `right`). AKA Y offset origin. * Change this if your popper should grow in a direction different from `right` */ y: 'right' }, /** * Applies the computed styles to the popper element. * * All the DOM manipulations are limited to this modifier. This is useful in case * you want to integrate Popper.js inside a framework or view library and you * want to delegate all the DOM manipulations to it. * * Note that if you disable this modifier, you must make sure the popper element * has its position set to `absolute` before Popper.js can do its work! * * Just disable this modifier and define your own to achieve the desired effect. * * @memberof modifiers * @inner */ applyStyle: { /** @prop {number} order=900 - Index used to define the order of execution */ order: 900, /** @prop {Boolean} enabled=true - Whether the modifier is enabled or not */ enabled: true, /** @prop {ModifierFn} */ fn: applyStyle, /** @prop {Function} */ onLoad: applyStyleOnLoad, /** * @deprecated since version 1.10.0, the property moved to `computeStyle` modifier * @prop {Boolean} gpuAcceleration=true * If true, it uses the CSS 3D transformation to position the popper. * Otherwise, it will use the `top` and `left` properties */ gpuAcceleration: undefined } }; /** * The `dataObject` is an object containing all the information used by Popper.js. * This object is passed to modifiers and to the `onCreate` and `onUpdate` callbacks. * @name dataObject * @property {Object} data.instance The Popper.js instance * @property {String} data.placement Placement applied to popper * @property {String} data.originalPlacement Placement originally defined on init * @property {Boolean} data.flipped True if popper has been flipped by flip modifier * @property {Boolean} data.hide True if the reference element is out of boundaries, useful to know when to hide the popper * @property {HTMLElement} data.arrowElement Node used as arrow by arrow modifier * @property {Object} data.styles Any CSS property defined here will be applied to the popper. It expects the JavaScript nomenclature (eg. `marginBottom`) * @property {Object} data.arrowStyles Any CSS property defined here will be applied to the popper arrow. It expects the JavaScript nomenclature (eg. `marginBottom`) * @property {Object} data.boundaries Offsets of the popper boundaries * @property {Object} data.offsets The measurements of popper, reference and arrow elements * @property {Object} data.offsets.popper `top`, `left`, `width`, `height` values * @property {Object} data.offsets.reference `top`, `left`, `width`, `height` values * @property {Object} data.offsets.arrow] `top` and `left` offsets, only one of them will be different from 0 */ /** * Default options provided to Popper.js constructor.
* These can be overridden using the `options` argument of Popper.js.
* To override an option, simply pass an object with the same * structure of the `options` object, as the 3rd argument. For example: * ``` * new Popper(ref, pop, { * modifiers: { * preventOverflow: { enabled: false } * } * }) * ``` * @type {Object} * @static * @memberof Popper */ var Defaults = { /** * Popper's placement. * @prop {Popper.placements} placement='bottom' */ placement: 'bottom', /** * Set this to true if you want popper to position it self in 'fixed' mode * @prop {Boolean} positionFixed=false */ positionFixed: false, /** * Whether events (resize, scroll) are initially enabled. * @prop {Boolean} eventsEnabled=true */ eventsEnabled: true, /** * Set to true if you want to automatically remove the popper when * you call the `destroy` method. * @prop {Boolean} removeOnDestroy=false */ removeOnDestroy: false, /** * Callback called when the popper is created.
* By default, it is set to no-op.
* Access Popper.js instance with `data.instance`. * @prop {onCreate} */ onCreate: function onCreate() {}, /** * Callback called when the popper is updated. This callback is not called * on the initialization/creation of the popper, but only on subsequent * updates.
* By default, it is set to no-op.
* Access Popper.js instance with `data.instance`. * @prop {onUpdate} */ onUpdate: function onUpdate() {}, /** * List of modifiers used to modify the offsets before they are applied to the popper. * They provide most of the functionalities of Popper.js. * @prop {modifiers} */ modifiers: modifiers }; /** * @callback onCreate * @param {dataObject} data */ /** * @callback onUpdate * @param {dataObject} data */ // Utils // Methods var Popper = function () { /** * Creates a new Popper.js instance. * @class Popper * @param {HTMLElement|referenceObject} reference - The reference element used to position the popper * @param {HTMLElement} popper - The HTML element used as the popper * @param {Object} options - Your custom options to override the ones defined in [Defaults](#defaults) * @return {Object} instance - The generated Popper.js instance */ function Popper(reference, popper) { var _this = this; var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; classCallCheck(this, Popper); this.scheduleUpdate = function () { return requestAnimationFrame(_this.update); }; // make update() debounced, so that it only runs at most once-per-tick this.update = debounce(this.update.bind(this)); // with {} we create a new object with the options inside it this.options = _extends({}, Popper.Defaults, options); // init state this.state = { isDestroyed: false, isCreated: false, scrollParents: [] }; // get reference and popper elements (allow jQuery wrappers) this.reference = reference && reference.jquery ? reference[0] : reference; this.popper = popper && popper.jquery ? popper[0] : popper; // Deep merge modifiers options this.options.modifiers = {}; Object.keys(_extends({}, Popper.Defaults.modifiers, options.modifiers)).forEach(function (name) { _this.options.modifiers[name] = _extends({}, Popper.Defaults.modifiers[name] || {}, options.modifiers ? options.modifiers[name] : {}); }); // Refactoring modifiers' list (Object => Array) this.modifiers = Object.keys(this.options.modifiers).map(function (name) { return _extends({ name: name }, _this.options.modifiers[name]); }) // sort the modifiers by order .sort(function (a, b) { return a.order - b.order; }); // modifiers have the ability to execute arbitrary code when Popper.js get inited // such code is executed in the same order of its modifier // they could add new properties to their options configuration // BE AWARE: don't add options to `options.modifiers.name` but to `modifierOptions`! this.modifiers.forEach(function (modifierOptions) { if (modifierOptions.enabled && isFunction(modifierOptions.onLoad)) { modifierOptions.onLoad(_this.reference, _this.popper, _this.options, modifierOptions, _this.state); } }); // fire the first update to position the popper in the right place this.update(); var eventsEnabled = this.options.eventsEnabled; if (eventsEnabled) { // setup event listeners, they will take care of update the position in specific situations this.enableEventListeners(); } this.state.eventsEnabled = eventsEnabled; } // We can't use class properties because they don't get listed in the // class prototype and break stuff like Sinon stubs createClass(Popper, [{ key: 'update', value: function update$$1() { return update.call(this); } }, { key: 'destroy', value: function destroy$$1() { return destroy.call(this); } }, { key: 'enableEventListeners', value: function enableEventListeners$$1() { return enableEventListeners.call(this); } }, { key: 'disableEventListeners', value: function disableEventListeners$$1() { return disableEventListeners.call(this); } /** * Schedules an update. It will run on the next UI update available. * @method scheduleUpdate * @memberof Popper */ /** * Collection of utilities useful when writing custom modifiers. * Starting from version 1.7, this method is available only if you * include `popper-utils.js` before `popper.js`. * * **DEPRECATION**: This way to access PopperUtils is deprecated * and will be removed in v2! Use the PopperUtils module directly instead. * Due to the high instability of the methods contained in Utils, we can't * guarantee them to follow semver. Use them at your own risk! * @static * @private * @type {Object} * @deprecated since version 1.8 * @member Utils * @memberof Popper */ }]); return Popper; }(); /** * The `referenceObject` is an object that provides an interface compatible with Popper.js * and lets you use it as replacement of a real DOM node.
* You can use this method to position a popper relatively to a set of coordinates * in case you don't have a DOM node to use as reference. * * ``` * new Popper(referenceObject, popperNode); * ``` * * NB: This feature isn't supported in Internet Explorer 10. * @name referenceObject * @property {Function} data.getBoundingClientRect * A function that returns a set of coordinates compatible with the native `getBoundingClientRect` method. * @property {number} data.clientWidth * An ES6 getter that will return the width of the virtual reference element. * @property {number} data.clientHeight * An ES6 getter that will return the height of the virtual reference element. */ Popper.Utils = (typeof window !== 'undefined' ? window : global).PopperUtils; Popper.placements = placements; Popper.Defaults = Defaults; /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$4 = 'dropdown'; var VERSION$4 = '4.3.1'; var DATA_KEY$4 = 'bs.dropdown'; var EVENT_KEY$4 = "." + DATA_KEY$4; var DATA_API_KEY$4 = '.data-api'; var JQUERY_NO_CONFLICT$4 = $.fn[NAME$4]; var ESCAPE_KEYCODE = 27; // KeyboardEvent.which value for Escape (Esc) key var SPACE_KEYCODE = 32; // KeyboardEvent.which value for space key var TAB_KEYCODE = 9; // KeyboardEvent.which value for tab key var ARROW_UP_KEYCODE = 38; // KeyboardEvent.which value for up arrow key var ARROW_DOWN_KEYCODE = 40; // KeyboardEvent.which value for down arrow key var RIGHT_MOUSE_BUTTON_WHICH = 3; // MouseEvent.which value for the right button (assuming a right-handed mouse) var REGEXP_KEYDOWN = new RegExp(ARROW_UP_KEYCODE + "|" + ARROW_DOWN_KEYCODE + "|" + ESCAPE_KEYCODE); var Event$4 = { HIDE: "hide" + EVENT_KEY$4, HIDDEN: "hidden" + EVENT_KEY$4, SHOW: "show" + EVENT_KEY$4, SHOWN: "shown" + EVENT_KEY$4, CLICK: "click" + EVENT_KEY$4, CLICK_DATA_API: "click" + EVENT_KEY$4 + DATA_API_KEY$4, KEYDOWN_DATA_API: "keydown" + EVENT_KEY$4 + DATA_API_KEY$4, KEYUP_DATA_API: "keyup" + EVENT_KEY$4 + DATA_API_KEY$4 }; var ClassName$4 = { DISABLED: 'disabled', SHOW: 'show', DROPUP: 'dropup', DROPRIGHT: 'dropright', DROPLEFT: 'dropleft', MENURIGHT: 'dropdown-menu-right', MENULEFT: 'dropdown-menu-left', POSITION_STATIC: 'position-static' }; var Selector$4 = { DATA_TOGGLE: '[data-toggle="dropdown"]', FORM_CHILD: '.dropdown form', MENU: '.dropdown-menu', NAVBAR_NAV: '.navbar-nav', VISIBLE_ITEMS: '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)' }; var AttachmentMap = { TOP: 'top-start', TOPEND: 'top-end', BOTTOM: 'bottom-start', BOTTOMEND: 'bottom-end', RIGHT: 'right-start', RIGHTEND: 'right-end', LEFT: 'left-start', LEFTEND: 'left-end' }; var Default$2 = { offset: 0, flip: true, boundary: 'scrollParent', reference: 'toggle', display: 'dynamic' }; var DefaultType$2 = { offset: '(number|string|function)', flip: 'boolean', boundary: '(string|element)', reference: '(string|element)', display: 'string' /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Dropdown = /*#__PURE__*/ function () { function Dropdown(element, config) { this._element = element; this._popper = null; this._config = this._getConfig(config); this._menu = this._getMenuElement(); this._inNavbar = this._detectNavbar(); this._addEventListeners(); } // Getters var _proto = Dropdown.prototype; // Public _proto.toggle = function toggle() { if (this._element.disabled || $(this._element).hasClass(ClassName$4.DISABLED)) { return; } var parent = Dropdown._getParentFromElement(this._element); var isActive = $(this._menu).hasClass(ClassName$4.SHOW); Dropdown._clearMenus(); if (isActive) { return; } var relatedTarget = { relatedTarget: this._element }; var showEvent = $.Event(Event$4.SHOW, relatedTarget); $(parent).trigger(showEvent); if (showEvent.isDefaultPrevented()) { return; } // Disable totally Popper.js for Dropdown in Navbar if (!this._inNavbar) { /** * Check for Popper dependency * Popper - https://popper.js.org */ if (typeof Popper === 'undefined') { throw new TypeError('Bootstrap\'s dropdowns require Popper.js (https://popper.js.org/)'); } var referenceElement = this._element; if (this._config.reference === 'parent') { referenceElement = parent; } else if (Util.isElement(this._config.reference)) { referenceElement = this._config.reference; // Check if it's jQuery element if (typeof this._config.reference.jquery !== 'undefined') { referenceElement = this._config.reference[0]; } } // If boundary is not `scrollParent`, then set position to `static` // to allow the menu to "escape" the scroll parent's boundaries // https://github.com/twbs/bootstrap/issues/24251 if (this._config.boundary !== 'scrollParent') { $(parent).addClass(ClassName$4.POSITION_STATIC); } this._popper = new Popper(referenceElement, this._menu, this._getPopperConfig()); } // If this is a touch-enabled device we add extra // empty mouseover listeners to the body's immediate children; // only needed because of broken event delegation on iOS // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html if ('ontouchstart' in document.documentElement && $(parent).closest(Selector$4.NAVBAR_NAV).length === 0) { $(document.body).children().on('mouseover', null, $.noop); } this._element.focus(); this._element.setAttribute('aria-expanded', true); $(this._menu).toggleClass(ClassName$4.SHOW); $(parent).toggleClass(ClassName$4.SHOW).trigger($.Event(Event$4.SHOWN, relatedTarget)); }; _proto.show = function show() { if (this._element.disabled || $(this._element).hasClass(ClassName$4.DISABLED) || $(this._menu).hasClass(ClassName$4.SHOW)) { return; } var relatedTarget = { relatedTarget: this._element }; var showEvent = $.Event(Event$4.SHOW, relatedTarget); var parent = Dropdown._getParentFromElement(this._element); $(parent).trigger(showEvent); if (showEvent.isDefaultPrevented()) { return; } $(this._menu).toggleClass(ClassName$4.SHOW); $(parent).toggleClass(ClassName$4.SHOW).trigger($.Event(Event$4.SHOWN, relatedTarget)); }; _proto.hide = function hide() { if (this._element.disabled || $(this._element).hasClass(ClassName$4.DISABLED) || !$(this._menu).hasClass(ClassName$4.SHOW)) { return; } var relatedTarget = { relatedTarget: this._element }; var hideEvent = $.Event(Event$4.HIDE, relatedTarget); var parent = Dropdown._getParentFromElement(this._element); $(parent).trigger(hideEvent); if (hideEvent.isDefaultPrevented()) { return; } $(this._menu).toggleClass(ClassName$4.SHOW); $(parent).toggleClass(ClassName$4.SHOW).trigger($.Event(Event$4.HIDDEN, relatedTarget)); }; _proto.dispose = function dispose() { $.removeData(this._element, DATA_KEY$4); $(this._element).off(EVENT_KEY$4); this._element = null; this._menu = null; if (this._popper !== null) { this._popper.destroy(); this._popper = null; } }; _proto.update = function update() { this._inNavbar = this._detectNavbar(); if (this._popper !== null) { this._popper.scheduleUpdate(); } } // Private ; _proto._addEventListeners = function _addEventListeners() { var _this = this; $(this._element).on(Event$4.CLICK, function (event) { event.preventDefault(); event.stopPropagation(); _this.toggle(); }); }; _proto._getConfig = function _getConfig(config) { config = _objectSpread({}, this.constructor.Default, $(this._element).data(), config); Util.typeCheckConfig(NAME$4, config, this.constructor.DefaultType); return config; }; _proto._getMenuElement = function _getMenuElement() { if (!this._menu) { var parent = Dropdown._getParentFromElement(this._element); if (parent) { this._menu = parent.querySelector(Selector$4.MENU); } } return this._menu; }; _proto._getPlacement = function _getPlacement() { var $parentDropdown = $(this._element.parentNode); var placement = AttachmentMap.BOTTOM; // Handle dropup if ($parentDropdown.hasClass(ClassName$4.DROPUP)) { placement = AttachmentMap.TOP; if ($(this._menu).hasClass(ClassName$4.MENURIGHT)) { placement = AttachmentMap.TOPEND; } } else if ($parentDropdown.hasClass(ClassName$4.DROPRIGHT)) { placement = AttachmentMap.RIGHT; } else if ($parentDropdown.hasClass(ClassName$4.DROPLEFT)) { placement = AttachmentMap.LEFT; } else if ($(this._menu).hasClass(ClassName$4.MENURIGHT)) { placement = AttachmentMap.BOTTOMEND; } return placement; }; _proto._detectNavbar = function _detectNavbar() { return $(this._element).closest('.navbar').length > 0; }; _proto._getOffset = function _getOffset() { var _this2 = this; var offset = {}; if (typeof this._config.offset === 'function') { offset.fn = function (data) { data.offsets = _objectSpread({}, data.offsets, _this2._config.offset(data.offsets, _this2._element) || {}); return data; }; } else { offset.offset = this._config.offset; } return offset; }; _proto._getPopperConfig = function _getPopperConfig() { var popperConfig = { placement: this._getPlacement(), modifiers: { offset: this._getOffset(), flip: { enabled: this._config.flip }, preventOverflow: { boundariesElement: this._config.boundary } } // Disable Popper.js if we have a static display }; if (this._config.display === 'static') { popperConfig.modifiers.applyStyle = { enabled: false }; } return popperConfig; } // Static ; Dropdown._jQueryInterface = function _jQueryInterface(config) { return this.each(function () { var data = $(this).data(DATA_KEY$4); var _config = typeof config === 'object' ? config : null; if (!data) { data = new Dropdown(this, _config); $(this).data(DATA_KEY$4, data); } if (typeof config === 'string') { if (typeof data[config] === 'undefined') { throw new TypeError("No method named \"" + config + "\""); } data[config](); } }); }; Dropdown._clearMenus = function _clearMenus(event) { if (event && (event.which === RIGHT_MOUSE_BUTTON_WHICH || event.type === 'keyup' && event.which !== TAB_KEYCODE)) { return; } var toggles = [].slice.call(document.querySelectorAll(Selector$4.DATA_TOGGLE)); for (var i = 0, len = toggles.length; i < len; i++) { var parent = Dropdown._getParentFromElement(toggles[i]); var context = $(toggles[i]).data(DATA_KEY$4); var relatedTarget = { relatedTarget: toggles[i] }; if (event && event.type === 'click') { relatedTarget.clickEvent = event; } if (!context) { continue; } var dropdownMenu = context._menu; if (!$(parent).hasClass(ClassName$4.SHOW)) { continue; } if (event && (event.type === 'click' && /input|textarea/i.test(event.target.tagName) || event.type === 'keyup' && event.which === TAB_KEYCODE) && $.contains(parent, event.target)) { continue; } var hideEvent = $.Event(Event$4.HIDE, relatedTarget); $(parent).trigger(hideEvent); if (hideEvent.isDefaultPrevented()) { continue; } // If this is a touch-enabled device we remove the extra // empty mouseover listeners we added for iOS support if ('ontouchstart' in document.documentElement) { $(document.body).children().off('mouseover', null, $.noop); } toggles[i].setAttribute('aria-expanded', 'false'); $(dropdownMenu).removeClass(ClassName$4.SHOW); $(parent).removeClass(ClassName$4.SHOW).trigger($.Event(Event$4.HIDDEN, relatedTarget)); } }; Dropdown._getParentFromElement = function _getParentFromElement(element) { var parent; var selector = Util.getSelectorFromElement(element); if (selector) { parent = document.querySelector(selector); } return parent || element.parentNode; } // eslint-disable-next-line complexity ; Dropdown._dataApiKeydownHandler = function _dataApiKeydownHandler(event) { // If not input/textarea: // - And not a key in REGEXP_KEYDOWN => not a dropdown command // If input/textarea: // - If space key => not a dropdown command // - If key is other than escape // - If key is not up or down => not a dropdown command // - If trigger inside the menu => not a dropdown command if (/input|textarea/i.test(event.target.tagName) ? event.which === SPACE_KEYCODE || event.which !== ESCAPE_KEYCODE && (event.which !== ARROW_DOWN_KEYCODE && event.which !== ARROW_UP_KEYCODE || $(event.target).closest(Selector$4.MENU).length) : !REGEXP_KEYDOWN.test(event.which)) { return; } event.preventDefault(); event.stopPropagation(); if (this.disabled || $(this).hasClass(ClassName$4.DISABLED)) { return; } var parent = Dropdown._getParentFromElement(this); var isActive = $(parent).hasClass(ClassName$4.SHOW); if (!isActive || isActive && (event.which === ESCAPE_KEYCODE || event.which === SPACE_KEYCODE)) { if (event.which === ESCAPE_KEYCODE) { var toggle = parent.querySelector(Selector$4.DATA_TOGGLE); $(toggle).trigger('focus'); } $(this).trigger('click'); return; } var items = [].slice.call(parent.querySelectorAll(Selector$4.VISIBLE_ITEMS)); if (items.length === 0) { return; } var index = items.indexOf(event.target); if (event.which === ARROW_UP_KEYCODE && index > 0) { // Up index--; } if (event.which === ARROW_DOWN_KEYCODE && index < items.length - 1) { // Down index++; } if (index < 0) { index = 0; } items[index].focus(); }; _createClass(Dropdown, null, [{ key: "VERSION", get: function get() { return VERSION$4; } }, { key: "Default", get: function get() { return Default$2; } }, { key: "DefaultType", get: function get() { return DefaultType$2; } }]); return Dropdown; }(); /** * ------------------------------------------------------------------------ * Data Api implementation * ------------------------------------------------------------------------ */ $(document).on(Event$4.KEYDOWN_DATA_API, Selector$4.DATA_TOGGLE, Dropdown._dataApiKeydownHandler).on(Event$4.KEYDOWN_DATA_API, Selector$4.MENU, Dropdown._dataApiKeydownHandler).on(Event$4.CLICK_DATA_API + " " + Event$4.KEYUP_DATA_API, Dropdown._clearMenus).on(Event$4.CLICK_DATA_API, Selector$4.DATA_TOGGLE, function (event) { event.preventDefault(); event.stopPropagation(); Dropdown._jQueryInterface.call($(this), 'toggle'); }).on(Event$4.CLICK_DATA_API, Selector$4.FORM_CHILD, function (e) { e.stopPropagation(); }); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME$4] = Dropdown._jQueryInterface; $.fn[NAME$4].Constructor = Dropdown; $.fn[NAME$4].noConflict = function () { $.fn[NAME$4] = JQUERY_NO_CONFLICT$4; return Dropdown._jQueryInterface; }; /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$5 = 'modal'; var VERSION$5 = '4.3.1'; var DATA_KEY$5 = 'bs.modal'; var EVENT_KEY$5 = "." + DATA_KEY$5; var DATA_API_KEY$5 = '.data-api'; var JQUERY_NO_CONFLICT$5 = $.fn[NAME$5]; var ESCAPE_KEYCODE$1 = 27; // KeyboardEvent.which value for Escape (Esc) key var Default$3 = { backdrop: true, keyboard: true, focus: true, show: true }; var DefaultType$3 = { backdrop: '(boolean|string)', keyboard: 'boolean', focus: 'boolean', show: 'boolean' }; var Event$5 = { HIDE: "hide" + EVENT_KEY$5, HIDDEN: "hidden" + EVENT_KEY$5, SHOW: "show" + EVENT_KEY$5, SHOWN: "shown" + EVENT_KEY$5, FOCUSIN: "focusin" + EVENT_KEY$5, RESIZE: "resize" + EVENT_KEY$5, CLICK_DISMISS: "click.dismiss" + EVENT_KEY$5, KEYDOWN_DISMISS: "keydown.dismiss" + EVENT_KEY$5, MOUSEUP_DISMISS: "mouseup.dismiss" + EVENT_KEY$5, MOUSEDOWN_DISMISS: "mousedown.dismiss" + EVENT_KEY$5, CLICK_DATA_API: "click" + EVENT_KEY$5 + DATA_API_KEY$5 }; var ClassName$5 = { SCROLLABLE: 'modal-dialog-scrollable', SCROLLBAR_MEASURER: 'modal-scrollbar-measure', BACKDROP: 'modal-backdrop', OPEN: 'modal-open', FADE: 'fade', SHOW: 'show' }; var Selector$5 = { DIALOG: '.modal-dialog', MODAL_BODY: '.modal-body', DATA_TOGGLE: '[data-toggle="modal"]', DATA_DISMISS: '[data-dismiss="modal"]', FIXED_CONTENT: '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top', STICKY_CONTENT: '.sticky-top' /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Modal = /*#__PURE__*/ function () { function Modal(element, config) { this._config = this._getConfig(config); this._element = element; this._dialog = element.querySelector(Selector$5.DIALOG); this._backdrop = null; this._isShown = false; this._isBodyOverflowing = false; this._ignoreBackdropClick = false; this._isTransitioning = false; this._scrollbarWidth = 0; } // Getters var _proto = Modal.prototype; // Public _proto.toggle = function toggle(relatedTarget) { return this._isShown ? this.hide() : this.show(relatedTarget); }; _proto.show = function show(relatedTarget) { var _this = this; if (this._isShown || this._isTransitioning) { return; } if ($(this._element).hasClass(ClassName$5.FADE)) { this._isTransitioning = true; } var showEvent = $.Event(Event$5.SHOW, { relatedTarget: relatedTarget }); $(this._element).trigger(showEvent); if (this._isShown || showEvent.isDefaultPrevented()) { return; } this._isShown = true; this._checkScrollbar(); this._setScrollbar(); this._adjustDialog(); this._setEscapeEvent(); this._setResizeEvent(); $(this._element).on(Event$5.CLICK_DISMISS, Selector$5.DATA_DISMISS, function (event) { return _this.hide(event); }); $(this._dialog).on(Event$5.MOUSEDOWN_DISMISS, function () { $(_this._element).one(Event$5.MOUSEUP_DISMISS, function (event) { if ($(event.target).is(_this._element)) { _this._ignoreBackdropClick = true; } }); }); this._showBackdrop(function () { return _this._showElement(relatedTarget); }); }; _proto.hide = function hide(event) { var _this2 = this; if (event) { event.preventDefault(); } if (!this._isShown || this._isTransitioning) { return; } var hideEvent = $.Event(Event$5.HIDE); $(this._element).trigger(hideEvent); if (!this._isShown || hideEvent.isDefaultPrevented()) { return; } this._isShown = false; var transition = $(this._element).hasClass(ClassName$5.FADE); if (transition) { this._isTransitioning = true; } this._setEscapeEvent(); this._setResizeEvent(); $(document).off(Event$5.FOCUSIN); $(this._element).removeClass(ClassName$5.SHOW); $(this._element).off(Event$5.CLICK_DISMISS); $(this._dialog).off(Event$5.MOUSEDOWN_DISMISS); if (transition) { var transitionDuration = Util.getTransitionDurationFromElement(this._element); $(this._element).one(Util.TRANSITION_END, function (event) { return _this2._hideModal(event); }).emulateTransitionEnd(transitionDuration); } else { this._hideModal(); } }; _proto.dispose = function dispose() { [window, this._element, this._dialog].forEach(function (htmlElement) { return $(htmlElement).off(EVENT_KEY$5); }); /** * `document` has 2 events `Event.FOCUSIN` and `Event.CLICK_DATA_API` * Do not move `document` in `htmlElements` array * It will remove `Event.CLICK_DATA_API` event that should remain */ $(document).off(Event$5.FOCUSIN); $.removeData(this._element, DATA_KEY$5); this._config = null; this._element = null; this._dialog = null; this._backdrop = null; this._isShown = null; this._isBodyOverflowing = null; this._ignoreBackdropClick = null; this._isTransitioning = null; this._scrollbarWidth = null; }; _proto.handleUpdate = function handleUpdate() { this._adjustDialog(); } // Private ; _proto._getConfig = function _getConfig(config) { config = _objectSpread({}, Default$3, config); Util.typeCheckConfig(NAME$5, config, DefaultType$3); return config; }; _proto._showElement = function _showElement(relatedTarget) { var _this3 = this; var transition = $(this._element).hasClass(ClassName$5.FADE); if (!this._element.parentNode || this._element.parentNode.nodeType !== Node.ELEMENT_NODE) { // Don't move modal's DOM position document.body.appendChild(this._element); } this._element.style.display = 'block'; this._element.removeAttribute('aria-hidden'); this._element.setAttribute('aria-modal', true); if ($(this._dialog).hasClass(ClassName$5.SCROLLABLE)) { this._dialog.querySelector(Selector$5.MODAL_BODY).scrollTop = 0; } else { this._element.scrollTop = 0; } if (transition) { Util.reflow(this._element); } $(this._element).addClass(ClassName$5.SHOW); if (this._config.focus) { this._enforceFocus(); } var shownEvent = $.Event(Event$5.SHOWN, { relatedTarget: relatedTarget }); var transitionComplete = function transitionComplete() { if (_this3._config.focus) { _this3._element.focus(); } _this3._isTransitioning = false; $(_this3._element).trigger(shownEvent); }; if (transition) { var transitionDuration = Util.getTransitionDurationFromElement(this._dialog); $(this._dialog).one(Util.TRANSITION_END, transitionComplete).emulateTransitionEnd(transitionDuration); } else { transitionComplete(); } }; _proto._enforceFocus = function _enforceFocus() { var _this4 = this; $(document).off(Event$5.FOCUSIN) // Guard against infinite focus loop .on(Event$5.FOCUSIN, function (event) { if (document !== event.target && _this4._element !== event.target && $(_this4._element).has(event.target).length === 0) { _this4._element.focus(); } }); }; _proto._setEscapeEvent = function _setEscapeEvent() { var _this5 = this; if (this._isShown && this._config.keyboard) { $(this._element).on(Event$5.KEYDOWN_DISMISS, function (event) { if (event.which === ESCAPE_KEYCODE$1) { event.preventDefault(); _this5.hide(); } }); } else if (!this._isShown) { $(this._element).off(Event$5.KEYDOWN_DISMISS); } }; _proto._setResizeEvent = function _setResizeEvent() { var _this6 = this; if (this._isShown) { $(window).on(Event$5.RESIZE, function (event) { return _this6.handleUpdate(event); }); } else { $(window).off(Event$5.RESIZE); } }; _proto._hideModal = function _hideModal() { var _this7 = this; this._element.style.display = 'none'; this._element.setAttribute('aria-hidden', true); this._element.removeAttribute('aria-modal'); this._isTransitioning = false; this._showBackdrop(function () { $(document.body).removeClass(ClassName$5.OPEN); _this7._resetAdjustments(); _this7._resetScrollbar(); $(_this7._element).trigger(Event$5.HIDDEN); }); }; _proto._removeBackdrop = function _removeBackdrop() { if (this._backdrop) { $(this._backdrop).remove(); this._backdrop = null; } }; _proto._showBackdrop = function _showBackdrop(callback) { var _this8 = this; var animate = $(this._element).hasClass(ClassName$5.FADE) ? ClassName$5.FADE : ''; if (this._isShown && this._config.backdrop) { this._backdrop = document.createElement('div'); this._backdrop.className = ClassName$5.BACKDROP; if (animate) { this._backdrop.classList.add(animate); } $(this._backdrop).appendTo(document.body); $(this._element).on(Event$5.CLICK_DISMISS, function (event) { if (_this8._ignoreBackdropClick) { _this8._ignoreBackdropClick = false; return; } if (event.target !== event.currentTarget) { return; } if (_this8._config.backdrop === 'static') { _this8._element.focus(); } else { _this8.hide(); } }); if (animate) { Util.reflow(this._backdrop); } $(this._backdrop).addClass(ClassName$5.SHOW); if (!callback) { return; } if (!animate) { callback(); return; } var backdropTransitionDuration = Util.getTransitionDurationFromElement(this._backdrop); $(this._backdrop).one(Util.TRANSITION_END, callback).emulateTransitionEnd(backdropTransitionDuration); } else if (!this._isShown && this._backdrop) { $(this._backdrop).removeClass(ClassName$5.SHOW); var callbackRemove = function callbackRemove() { _this8._removeBackdrop(); if (callback) { callback(); } }; if ($(this._element).hasClass(ClassName$5.FADE)) { var _backdropTransitionDuration = Util.getTransitionDurationFromElement(this._backdrop); $(this._backdrop).one(Util.TRANSITION_END, callbackRemove).emulateTransitionEnd(_backdropTransitionDuration); } else { callbackRemove(); } } else if (callback) { callback(); } } // ---------------------------------------------------------------------- // the following methods are used to handle overflowing modals // todo (fat): these should probably be refactored out of modal.js // ---------------------------------------------------------------------- ; _proto._adjustDialog = function _adjustDialog() { var isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight; if (!this._isBodyOverflowing && isModalOverflowing) { this._element.style.paddingLeft = this._scrollbarWidth + "px"; } if (this._isBodyOverflowing && !isModalOverflowing) { this._element.style.paddingRight = this._scrollbarWidth + "px"; } }; _proto._resetAdjustments = function _resetAdjustments() { this._element.style.paddingLeft = ''; this._element.style.paddingRight = ''; }; _proto._checkScrollbar = function _checkScrollbar() { var rect = document.body.getBoundingClientRect(); this._isBodyOverflowing = rect.left + rect.right < window.innerWidth; this._scrollbarWidth = this._getScrollbarWidth(); }; _proto._setScrollbar = function _setScrollbar() { var _this9 = this; if (this._isBodyOverflowing) { // Note: DOMNode.style.paddingRight returns the actual value or '' if not set // while $(DOMNode).css('padding-right') returns the calculated value or 0 if not set var fixedContent = [].slice.call(document.querySelectorAll(Selector$5.FIXED_CONTENT)); var stickyContent = [].slice.call(document.querySelectorAll(Selector$5.STICKY_CONTENT)); // Adjust fixed content padding $(fixedContent).each(function (index, element) { var actualPadding = element.style.paddingRight; var calculatedPadding = $(element).css('padding-right'); $(element).data('padding-right', actualPadding).css('padding-right', parseFloat(calculatedPadding) + _this9._scrollbarWidth + "px"); }); // Adjust sticky content margin $(stickyContent).each(function (index, element) { var actualMargin = element.style.marginRight; var calculatedMargin = $(element).css('margin-right'); $(element).data('margin-right', actualMargin).css('margin-right', parseFloat(calculatedMargin) - _this9._scrollbarWidth + "px"); }); // Adjust body padding var actualPadding = document.body.style.paddingRight; var calculatedPadding = $(document.body).css('padding-right'); $(document.body).data('padding-right', actualPadding).css('padding-right', parseFloat(calculatedPadding) + this._scrollbarWidth + "px"); } $(document.body).addClass(ClassName$5.OPEN); }; _proto._resetScrollbar = function _resetScrollbar() { // Restore fixed content padding var fixedContent = [].slice.call(document.querySelectorAll(Selector$5.FIXED_CONTENT)); $(fixedContent).each(function (index, element) { var padding = $(element).data('padding-right'); $(element).removeData('padding-right'); element.style.paddingRight = padding ? padding : ''; }); // Restore sticky content var elements = [].slice.call(document.querySelectorAll("" + Selector$5.STICKY_CONTENT)); $(elements).each(function (index, element) { var margin = $(element).data('margin-right'); if (typeof margin !== 'undefined') { $(element).css('margin-right', margin).removeData('margin-right'); } }); // Restore body padding var padding = $(document.body).data('padding-right'); $(document.body).removeData('padding-right'); document.body.style.paddingRight = padding ? padding : ''; }; _proto._getScrollbarWidth = function _getScrollbarWidth() { // thx d.walsh var scrollDiv = document.createElement('div'); scrollDiv.className = ClassName$5.SCROLLBAR_MEASURER; document.body.appendChild(scrollDiv); var scrollbarWidth = scrollDiv.getBoundingClientRect().width - scrollDiv.clientWidth; document.body.removeChild(scrollDiv); return scrollbarWidth; } // Static ; Modal._jQueryInterface = function _jQueryInterface(config, relatedTarget) { return this.each(function () { var data = $(this).data(DATA_KEY$5); var _config = _objectSpread({}, Default$3, $(this).data(), typeof config === 'object' && config ? config : {}); if (!data) { data = new Modal(this, _config); $(this).data(DATA_KEY$5, data); } if (typeof config === 'string') { if (typeof data[config] === 'undefined') { throw new TypeError("No method named \"" + config + "\""); } data[config](relatedTarget); } else if (_config.show) { data.show(relatedTarget); } }); }; _createClass(Modal, null, [{ key: "VERSION", get: function get() { return VERSION$5; } }, { key: "Default", get: function get() { return Default$3; } }]); return Modal; }(); /** * ------------------------------------------------------------------------ * Data Api implementation * ------------------------------------------------------------------------ */ $(document).on(Event$5.CLICK_DATA_API, Selector$5.DATA_TOGGLE, function (event) { var _this10 = this; var target; var selector = Util.getSelectorFromElement(this); if (selector) { target = document.querySelector(selector); } var config = $(target).data(DATA_KEY$5) ? 'toggle' : _objectSpread({}, $(target).data(), $(this).data()); if (this.tagName === 'A' || this.tagName === 'AREA') { event.preventDefault(); } var $target = $(target).one(Event$5.SHOW, function (showEvent) { if (showEvent.isDefaultPrevented()) { // Only register focus restorer if modal will actually get shown return; } $target.one(Event$5.HIDDEN, function () { if ($(_this10).is(':visible')) { _this10.focus(); } }); }); Modal._jQueryInterface.call($(target), config, this); }); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME$5] = Modal._jQueryInterface; $.fn[NAME$5].Constructor = Modal; $.fn[NAME$5].noConflict = function () { $.fn[NAME$5] = JQUERY_NO_CONFLICT$5; return Modal._jQueryInterface; }; /** * -------------------------------------------------------------------------- * Bootstrap (v4.3.1): tools/sanitizer.js * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) * -------------------------------------------------------------------------- */ var uriAttrs = ['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']; var ARIA_ATTRIBUTE_PATTERN = /^aria-[\w-]*$/i; var DefaultWhitelist = { // Global attributes allowed on any supplied element below. '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN], a: ['target', 'href', 'title', 'rel'], area: [], b: [], br: [], col: [], code: [], div: [], em: [], hr: [], h1: [], h2: [], h3: [], h4: [], h5: [], h6: [], i: [], img: ['src', 'alt', 'title', 'width', 'height'], li: [], ol: [], p: [], pre: [], s: [], small: [], span: [], sub: [], sup: [], strong: [], u: [], ul: [] /** * A pattern that recognizes a commonly useful subset of URLs that are safe. * * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts */ }; var SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file):|[^&:/?#]*(?:[/?#]|$))/gi; /** * A pattern that matches safe data URLs. Only matches image, video and audio types. * * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts */ var DATA_URL_PATTERN = /^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[a-z0-9+/]+=*$/i; function allowedAttribute(attr, allowedAttributeList) { var attrName = attr.nodeName.toLowerCase(); if (allowedAttributeList.indexOf(attrName) !== -1) { if (uriAttrs.indexOf(attrName) !== -1) { return Boolean(attr.nodeValue.match(SAFE_URL_PATTERN) || attr.nodeValue.match(DATA_URL_PATTERN)); } return true; } var regExp = allowedAttributeList.filter(function (attrRegex) { return attrRegex instanceof RegExp; }); // Check if a regular expression validates the attribute. for (var i = 0, l = regExp.length; i < l; i++) { if (attrName.match(regExp[i])) { return true; } } return false; } function sanitizeHtml(unsafeHtml, whiteList, sanitizeFn) { if (unsafeHtml.length === 0) { return unsafeHtml; } if (sanitizeFn && typeof sanitizeFn === 'function') { return sanitizeFn(unsafeHtml); } var domParser = new window.DOMParser(); var createdDocument = domParser.parseFromString(unsafeHtml, 'text/html'); var whitelistKeys = Object.keys(whiteList); var elements = [].slice.call(createdDocument.body.querySelectorAll('*')); var _loop = function _loop(i, len) { var el = elements[i]; var elName = el.nodeName.toLowerCase(); if (whitelistKeys.indexOf(el.nodeName.toLowerCase()) === -1) { el.parentNode.removeChild(el); return "continue"; } var attributeList = [].slice.call(el.attributes); var whitelistedAttributes = [].concat(whiteList['*'] || [], whiteList[elName] || []); attributeList.forEach(function (attr) { if (!allowedAttribute(attr, whitelistedAttributes)) { el.removeAttribute(attr.nodeName); } }); }; for (var i = 0, len = elements.length; i < len; i++) { var _ret = _loop(i, len); if (_ret === "continue") continue; } return createdDocument.body.innerHTML; } /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$6 = 'tooltip'; var VERSION$6 = '4.3.1'; var DATA_KEY$6 = 'bs.tooltip'; var EVENT_KEY$6 = "." + DATA_KEY$6; var JQUERY_NO_CONFLICT$6 = $.fn[NAME$6]; var CLASS_PREFIX = 'bs-tooltip'; var BSCLS_PREFIX_REGEX = new RegExp("(^|\\s)" + CLASS_PREFIX + "\\S+", 'g'); var DISALLOWED_ATTRIBUTES = ['sanitize', 'whiteList', 'sanitizeFn']; var DefaultType$4 = { animation: 'boolean', template: 'string', title: '(string|element|function)', trigger: 'string', delay: '(number|object)', html: 'boolean', selector: '(string|boolean)', placement: '(string|function)', offset: '(number|string|function)', container: '(string|element|boolean)', fallbackPlacement: '(string|array)', boundary: '(string|element)', sanitize: 'boolean', sanitizeFn: '(null|function)', whiteList: 'object' }; var AttachmentMap$1 = { AUTO: 'auto', TOP: 'top', RIGHT: 'right', BOTTOM: 'bottom', LEFT: 'left' }; var Default$4 = { animation: true, template: '', trigger: 'hover focus', title: '', delay: 0, html: false, selector: false, placement: 'top', offset: 0, container: false, fallbackPlacement: 'flip', boundary: 'scrollParent', sanitize: true, sanitizeFn: null, whiteList: DefaultWhitelist }; var HoverState = { SHOW: 'show', OUT: 'out' }; var Event$6 = { HIDE: "hide" + EVENT_KEY$6, HIDDEN: "hidden" + EVENT_KEY$6, SHOW: "show" + EVENT_KEY$6, SHOWN: "shown" + EVENT_KEY$6, INSERTED: "inserted" + EVENT_KEY$6, CLICK: "click" + EVENT_KEY$6, FOCUSIN: "focusin" + EVENT_KEY$6, FOCUSOUT: "focusout" + EVENT_KEY$6, MOUSEENTER: "mouseenter" + EVENT_KEY$6, MOUSELEAVE: "mouseleave" + EVENT_KEY$6 }; var ClassName$6 = { FADE: 'fade', SHOW: 'show' }; var Selector$6 = { TOOLTIP: '.tooltip', TOOLTIP_INNER: '.tooltip-inner', ARROW: '.arrow' }; var Trigger = { HOVER: 'hover', FOCUS: 'focus', CLICK: 'click', MANUAL: 'manual' /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Tooltip = /*#__PURE__*/ function () { function Tooltip(element, config) { /** * Check for Popper dependency * Popper - https://popper.js.org */ if (typeof Popper === 'undefined') { throw new TypeError('Bootstrap\'s tooltips require Popper.js (https://popper.js.org/)'); } // private this._isEnabled = true; this._timeout = 0; this._hoverState = ''; this._activeTrigger = {}; this._popper = null; // Protected this.element = element; this.config = this._getConfig(config); this.tip = null; this._setListeners(); } // Getters var _proto = Tooltip.prototype; // Public _proto.enable = function enable() { this._isEnabled = true; }; _proto.disable = function disable() { this._isEnabled = false; }; _proto.toggleEnabled = function toggleEnabled() { this._isEnabled = !this._isEnabled; }; _proto.toggle = function toggle(event) { if (!this._isEnabled) { return; } if (event) { var dataKey = this.constructor.DATA_KEY; var context = $(event.currentTarget).data(dataKey); if (!context) { context = new this.constructor(event.currentTarget, this._getDelegateConfig()); $(event.currentTarget).data(dataKey, context); } context._activeTrigger.click = !context._activeTrigger.click; if (context._isWithActiveTrigger()) { context._enter(null, context); } else { context._leave(null, context); } } else { if ($(this.getTipElement()).hasClass(ClassName$6.SHOW)) { this._leave(null, this); return; } this._enter(null, this); } }; _proto.dispose = function dispose() { clearTimeout(this._timeout); $.removeData(this.element, this.constructor.DATA_KEY); $(this.element).off(this.constructor.EVENT_KEY); $(this.element).closest('.modal').off('hide.bs.modal'); if (this.tip) { $(this.tip).remove(); } this._isEnabled = null; this._timeout = null; this._hoverState = null; this._activeTrigger = null; if (this._popper !== null) { this._popper.destroy(); } this._popper = null; this.element = null; this.config = null; this.tip = null; }; _proto.show = function show() { var _this = this; if ($(this.element).css('display') === 'none') { throw new Error('Please use show on visible elements'); } var showEvent = $.Event(this.constructor.Event.SHOW); if (this.isWithContent() && this._isEnabled) { $(this.element).trigger(showEvent); var shadowRoot = Util.findShadowRoot(this.element); var isInTheDom = $.contains(shadowRoot !== null ? shadowRoot : this.element.ownerDocument.documentElement, this.element); if (showEvent.isDefaultPrevented() || !isInTheDom) { return; } var tip = this.getTipElement(); var tipId = Util.getUID(this.constructor.NAME); tip.setAttribute('id', tipId); this.element.setAttribute('aria-describedby', tipId); this.setContent(); if (this.config.animation) { $(tip).addClass(ClassName$6.FADE); } var placement = typeof this.config.placement === 'function' ? this.config.placement.call(this, tip, this.element) : this.config.placement; var attachment = this._getAttachment(placement); this.addAttachmentClass(attachment); var container = this._getContainer(); $(tip).data(this.constructor.DATA_KEY, this); if (!$.contains(this.element.ownerDocument.documentElement, this.tip)) { $(tip).appendTo(container); } $(this.element).trigger(this.constructor.Event.INSERTED); this._popper = new Popper(this.element, tip, { placement: attachment, modifiers: { offset: this._getOffset(), flip: { behavior: this.config.fallbackPlacement }, arrow: { element: Selector$6.ARROW }, preventOverflow: { boundariesElement: this.config.boundary } }, onCreate: function onCreate(data) { if (data.originalPlacement !== data.placement) { _this._handlePopperPlacementChange(data); } }, onUpdate: function onUpdate(data) { return _this._handlePopperPlacementChange(data); } }); $(tip).addClass(ClassName$6.SHOW); // If this is a touch-enabled device we add extra // empty mouseover listeners to the body's immediate children; // only needed because of broken event delegation on iOS // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html if ('ontouchstart' in document.documentElement) { $(document.body).children().on('mouseover', null, $.noop); } var complete = function complete() { if (_this.config.animation) { _this._fixTransition(); } var prevHoverState = _this._hoverState; _this._hoverState = null; $(_this.element).trigger(_this.constructor.Event.SHOWN); if (prevHoverState === HoverState.OUT) { _this._leave(null, _this); } }; if ($(this.tip).hasClass(ClassName$6.FADE)) { var transitionDuration = Util.getTransitionDurationFromElement(this.tip); $(this.tip).one(Util.TRANSITION_END, complete).emulateTransitionEnd(transitionDuration); } else { complete(); } } }; _proto.hide = function hide(callback) { var _this2 = this; var tip = this.getTipElement(); var hideEvent = $.Event(this.constructor.Event.HIDE); var complete = function complete() { if (_this2._hoverState !== HoverState.SHOW && tip.parentNode) { tip.parentNode.removeChild(tip); } _this2._cleanTipClass(); _this2.element.removeAttribute('aria-describedby'); $(_this2.element).trigger(_this2.constructor.Event.HIDDEN); if (_this2._popper !== null) { _this2._popper.destroy(); } if (callback) { callback(); } }; $(this.element).trigger(hideEvent); if (hideEvent.isDefaultPrevented()) { return; } $(tip).removeClass(ClassName$6.SHOW); // If this is a touch-enabled device we remove the extra // empty mouseover listeners we added for iOS support if ('ontouchstart' in document.documentElement) { $(document.body).children().off('mouseover', null, $.noop); } this._activeTrigger[Trigger.CLICK] = false; this._activeTrigger[Trigger.FOCUS] = false; this._activeTrigger[Trigger.HOVER] = false; if ($(this.tip).hasClass(ClassName$6.FADE)) { var transitionDuration = Util.getTransitionDurationFromElement(tip); $(tip).one(Util.TRANSITION_END, complete).emulateTransitionEnd(transitionDuration); } else { complete(); } this._hoverState = ''; }; _proto.update = function update() { if (this._popper !== null) { this._popper.scheduleUpdate(); } } // Protected ; _proto.isWithContent = function isWithContent() { return Boolean(this.getTitle()); }; _proto.addAttachmentClass = function addAttachmentClass(attachment) { $(this.getTipElement()).addClass(CLASS_PREFIX + "-" + attachment); }; _proto.getTipElement = function getTipElement() { this.tip = this.tip || $(this.config.template)[0]; return this.tip; }; _proto.setContent = function setContent() { var tip = this.getTipElement(); this.setElementContent($(tip.querySelectorAll(Selector$6.TOOLTIP_INNER)), this.getTitle()); $(tip).removeClass(ClassName$6.FADE + " " + ClassName$6.SHOW); }; _proto.setElementContent = function setElementContent($element, content) { if (typeof content === 'object' && (content.nodeType || content.jquery)) { // Content is a DOM node or a jQuery if (this.config.html) { if (!$(content).parent().is($element)) { $element.empty().append(content); } } else { $element.text($(content).text()); } return; } if (this.config.html) { if (this.config.sanitize) { content = sanitizeHtml(content, this.config.whiteList, this.config.sanitizeFn); } $element.html(content); } else { $element.text(content); } }; _proto.getTitle = function getTitle() { var title = this.element.getAttribute('data-original-title'); if (!title) { title = typeof this.config.title === 'function' ? this.config.title.call(this.element) : this.config.title; } return title; } // Private ; _proto._getOffset = function _getOffset() { var _this3 = this; var offset = {}; if (typeof this.config.offset === 'function') { offset.fn = function (data) { data.offsets = _objectSpread({}, data.offsets, _this3.config.offset(data.offsets, _this3.element) || {}); return data; }; } else { offset.offset = this.config.offset; } return offset; }; _proto._getContainer = function _getContainer() { if (this.config.container === false) { return document.body; } if (Util.isElement(this.config.container)) { return $(this.config.container); } return $(document).find(this.config.container); }; _proto._getAttachment = function _getAttachment(placement) { return AttachmentMap$1[placement.toUpperCase()]; }; _proto._setListeners = function _setListeners() { var _this4 = this; var triggers = this.config.trigger.split(' '); triggers.forEach(function (trigger) { if (trigger === 'click') { $(_this4.element).on(_this4.constructor.Event.CLICK, _this4.config.selector, function (event) { return _this4.toggle(event); }); } else if (trigger !== Trigger.MANUAL) { var eventIn = trigger === Trigger.HOVER ? _this4.constructor.Event.MOUSEENTER : _this4.constructor.Event.FOCUSIN; var eventOut = trigger === Trigger.HOVER ? _this4.constructor.Event.MOUSELEAVE : _this4.constructor.Event.FOCUSOUT; $(_this4.element).on(eventIn, _this4.config.selector, function (event) { return _this4._enter(event); }).on(eventOut, _this4.config.selector, function (event) { return _this4._leave(event); }); } }); $(this.element).closest('.modal').on('hide.bs.modal', function () { if (_this4.element) { _this4.hide(); } }); if (this.config.selector) { this.config = _objectSpread({}, this.config, { trigger: 'manual', selector: '' }); } else { this._fixTitle(); } }; _proto._fixTitle = function _fixTitle() { var titleType = typeof this.element.getAttribute('data-original-title'); if (this.element.getAttribute('title') || titleType !== 'string') { this.element.setAttribute('data-original-title', this.element.getAttribute('title') || ''); this.element.setAttribute('title', ''); } }; _proto._enter = function _enter(event, context) { var dataKey = this.constructor.DATA_KEY; context = context || $(event.currentTarget).data(dataKey); if (!context) { context = new this.constructor(event.currentTarget, this._getDelegateConfig()); $(event.currentTarget).data(dataKey, context); } if (event) { context._activeTrigger[event.type === 'focusin' ? Trigger.FOCUS : Trigger.HOVER] = true; } if ($(context.getTipElement()).hasClass(ClassName$6.SHOW) || context._hoverState === HoverState.SHOW) { context._hoverState = HoverState.SHOW; return; } clearTimeout(context._timeout); context._hoverState = HoverState.SHOW; if (!context.config.delay || !context.config.delay.show) { context.show(); return; } context._timeout = setTimeout(function () { if (context._hoverState === HoverState.SHOW) { context.show(); } }, context.config.delay.show); }; _proto._leave = function _leave(event, context) { var dataKey = this.constructor.DATA_KEY; context = context || $(event.currentTarget).data(dataKey); if (!context) { context = new this.constructor(event.currentTarget, this._getDelegateConfig()); $(event.currentTarget).data(dataKey, context); } if (event) { context._activeTrigger[event.type === 'focusout' ? Trigger.FOCUS : Trigger.HOVER] = false; } if (context._isWithActiveTrigger()) { return; } clearTimeout(context._timeout); context._hoverState = HoverState.OUT; if (!context.config.delay || !context.config.delay.hide) { context.hide(); return; } context._timeout = setTimeout(function () { if (context._hoverState === HoverState.OUT) { context.hide(); } }, context.config.delay.hide); }; _proto._isWithActiveTrigger = function _isWithActiveTrigger() { for (var trigger in this._activeTrigger) { if (this._activeTrigger[trigger]) { return true; } } return false; }; _proto._getConfig = function _getConfig(config) { var dataAttributes = $(this.element).data(); Object.keys(dataAttributes).forEach(function (dataAttr) { if (DISALLOWED_ATTRIBUTES.indexOf(dataAttr) !== -1) { delete dataAttributes[dataAttr]; } }); config = _objectSpread({}, this.constructor.Default, dataAttributes, typeof config === 'object' && config ? config : {}); if (typeof config.delay === 'number') { config.delay = { show: config.delay, hide: config.delay }; } if (typeof config.title === 'number') { config.title = config.title.toString(); } if (typeof config.content === 'number') { config.content = config.content.toString(); } Util.typeCheckConfig(NAME$6, config, this.constructor.DefaultType); if (config.sanitize) { config.template = sanitizeHtml(config.template, config.whiteList, config.sanitizeFn); } return config; }; _proto._getDelegateConfig = function _getDelegateConfig() { var config = {}; if (this.config) { for (var key in this.config) { if (this.constructor.Default[key] !== this.config[key]) { config[key] = this.config[key]; } } } return config; }; _proto._cleanTipClass = function _cleanTipClass() { var $tip = $(this.getTipElement()); var tabClass = $tip.attr('class').match(BSCLS_PREFIX_REGEX); if (tabClass !== null && tabClass.length) { $tip.removeClass(tabClass.join('')); } }; _proto._handlePopperPlacementChange = function _handlePopperPlacementChange(popperData) { var popperInstance = popperData.instance; this.tip = popperInstance.popper; this._cleanTipClass(); this.addAttachmentClass(this._getAttachment(popperData.placement)); }; _proto._fixTransition = function _fixTransition() { var tip = this.getTipElement(); var initConfigAnimation = this.config.animation; if (tip.getAttribute('x-placement') !== null) { return; } $(tip).removeClass(ClassName$6.FADE); this.config.animation = false; this.hide(); this.show(); this.config.animation = initConfigAnimation; } // Static ; Tooltip._jQueryInterface = function _jQueryInterface(config) { return this.each(function () { var data = $(this).data(DATA_KEY$6); var _config = typeof config === 'object' && config; if (!data && /dispose|hide/.test(config)) { return; } if (!data) { data = new Tooltip(this, _config); $(this).data(DATA_KEY$6, data); } if (typeof config === 'string') { if (typeof data[config] === 'undefined') { throw new TypeError("No method named \"" + config + "\""); } data[config](); } }); }; _createClass(Tooltip, null, [{ key: "VERSION", get: function get() { return VERSION$6; } }, { key: "Default", get: function get() { return Default$4; } }, { key: "NAME", get: function get() { return NAME$6; } }, { key: "DATA_KEY", get: function get() { return DATA_KEY$6; } }, { key: "Event", get: function get() { return Event$6; } }, { key: "EVENT_KEY", get: function get() { return EVENT_KEY$6; } }, { key: "DefaultType", get: function get() { return DefaultType$4; } }]); return Tooltip; }(); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME$6] = Tooltip._jQueryInterface; $.fn[NAME$6].Constructor = Tooltip; $.fn[NAME$6].noConflict = function () { $.fn[NAME$6] = JQUERY_NO_CONFLICT$6; return Tooltip._jQueryInterface; }; /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$7 = 'popover'; var VERSION$7 = '4.3.1'; var DATA_KEY$7 = 'bs.popover'; var EVENT_KEY$7 = "." + DATA_KEY$7; var JQUERY_NO_CONFLICT$7 = $.fn[NAME$7]; var CLASS_PREFIX$1 = 'bs-popover'; var BSCLS_PREFIX_REGEX$1 = new RegExp("(^|\\s)" + CLASS_PREFIX$1 + "\\S+", 'g'); var Default$5 = _objectSpread({}, Tooltip.Default, { placement: 'right', trigger: 'click', content: '', template: '' }); var DefaultType$5 = _objectSpread({}, Tooltip.DefaultType, { content: '(string|element|function)' }); var ClassName$7 = { FADE: 'fade', SHOW: 'show' }; var Selector$7 = { TITLE: '.popover-header', CONTENT: '.popover-body' }; var Event$7 = { HIDE: "hide" + EVENT_KEY$7, HIDDEN: "hidden" + EVENT_KEY$7, SHOW: "show" + EVENT_KEY$7, SHOWN: "shown" + EVENT_KEY$7, INSERTED: "inserted" + EVENT_KEY$7, CLICK: "click" + EVENT_KEY$7, FOCUSIN: "focusin" + EVENT_KEY$7, FOCUSOUT: "focusout" + EVENT_KEY$7, MOUSEENTER: "mouseenter" + EVENT_KEY$7, MOUSELEAVE: "mouseleave" + EVENT_KEY$7 /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var Popover = /*#__PURE__*/ function (_Tooltip) { _inheritsLoose(Popover, _Tooltip); function Popover() { return _Tooltip.apply(this, arguments) || this; } var _proto = Popover.prototype; // Overrides _proto.isWithContent = function isWithContent() { return this.getTitle() || this._getContent(); }; _proto.addAttachmentClass = function addAttachmentClass(attachment) { $(this.getTipElement()).addClass(CLASS_PREFIX$1 + "-" + attachment); }; _proto.getTipElement = function getTipElement() { this.tip = this.tip || $(this.config.template)[0]; return this.tip; }; _proto.setContent = function setContent() { var $tip = $(this.getTipElement()); // We use append for html objects to maintain js events this.setElementContent($tip.find(Selector$7.TITLE), this.getTitle()); var content = this._getContent(); if (typeof content === 'function') { content = content.call(this.element); } this.setElementContent($tip.find(Selector$7.CONTENT), content); $tip.removeClass(ClassName$7.FADE + " " + ClassName$7.SHOW); } // Private ; _proto._getContent = function _getContent() { return this.element.getAttribute('data-content') || this.config.content; }; _proto._cleanTipClass = function _cleanTipClass() { var $tip = $(this.getTipElement()); var tabClass = $tip.attr('class').match(BSCLS_PREFIX_REGEX$1); if (tabClass !== null && tabClass.length > 0) { $tip.removeClass(tabClass.join('')); } } // Static ; Popover._jQueryInterface = function _jQueryInterface(config) { return this.each(function () { var data = $(this).data(DATA_KEY$7); var _config = typeof config === 'object' ? config : null; if (!data && /dispose|hide/.test(config)) { return; } if (!data) { data = new Popover(this, _config); $(this).data(DATA_KEY$7, data); } if (typeof config === 'string') { if (typeof data[config] === 'undefined') { throw new TypeError("No method named \"" + config + "\""); } data[config](); } }); }; _createClass(Popover, null, [{ key: "VERSION", // Getters get: function get() { return VERSION$7; } }, { key: "Default", get: function get() { return Default$5; } }, { key: "NAME", get: function get() { return NAME$7; } }, { key: "DATA_KEY", get: function get() { return DATA_KEY$7; } }, { key: "Event", get: function get() { return Event$7; } }, { key: "EVENT_KEY", get: function get() { return EVENT_KEY$7; } }, { key: "DefaultType", get: function get() { return DefaultType$5; } }]); return Popover; }(Tooltip); /** * ------------------------------------------------------------------------ * jQuery * ------------------------------------------------------------------------ */ $.fn[NAME$7] = Popover._jQueryInterface; $.fn[NAME$7].Constructor = Popover; $.fn[NAME$7].noConflict = function () { $.fn[NAME$7] = JQUERY_NO_CONFLICT$7; return Popover._jQueryInterface; }; /** * ------------------------------------------------------------------------ * Constants * ------------------------------------------------------------------------ */ var NAME$8 = 'scrollspy'; var VERSION$8 = '4.3.1'; var DATA_KEY$8 = 'bs.scrollspy'; var EVENT_KEY$8 = "." + DATA_KEY$8; var DATA_API_KEY$6 = '.data-api'; var JQUERY_NO_CONFLICT$8 = $.fn[NAME$8]; var Default$6 = { offset: 10, method: 'auto', target: '' }; var DefaultType$6 = { offset: 'number', method: 'string', target: '(string|element)' }; var Event$8 = { ACTIVATE: "activate" + EVENT_KEY$8, SCROLL: "scroll" + EVENT_KEY$8, LOAD_DATA_API: "load" + EVENT_KEY$8 + DATA_API_KEY$6 }; var ClassName$8 = { DROPDOWN_ITEM: 'dropdown-item', DROPDOWN_MENU: 'dropdown-menu', ACTIVE: 'active' }; var Selector$8 = { DATA_SPY: '[data-spy="scroll"]', ACTIVE: '.active', NAV_LIST_GROUP: '.nav, .list-group', NAV_LINKS: '.nav-link', NAV_ITEMS: '.nav-item', LIST_ITEMS: '.list-group-item', DROPDOWN: '.dropdown', DROPDOWN_ITEMS: '.dropdown-item', DROPDOWN_TOGGLE: '.dropdown-toggle' }; var OffsetMethod = { OFFSET: 'offset', POSITION: 'position' /** * ------------------------------------------------------------------------ * Class Definition * ------------------------------------------------------------------------ */ }; var ScrollSpy = /*#__PURE__*/ function () { function ScrollSpy(element, config) { var _this = this; this._element = element; this._scrollElement = element.tagName === 'BODY' ? window : element; this._config = this._getConfig(config); this._selector = this._config.target + " " + Selector$8.NAV_LINKS + "," + (this._config.target + " " + Selector$8.LIST_ITEMS + ",") + (this._config.target + " " + Selector$8.DROPDOWN_ITEMS); this._offsets = []; this._targets = []; this._activeTarget = null; this._scrollHeight = 0; $(this._scrollElement).on(Event$8.SCROLL, function (event) { return _this._process(event); }); this.refresh(); this._process(); } // Getters var _proto = ScrollSpy.prototype; // Public _proto.refresh = function refresh() { var _this2 = this; var autoMethod = this._scrollElement === this._scrollElement.window ? OffsetMethod.OFFSET : OffsetMethod.POSITION; var offsetMethod = this._config.method === 'auto' ? autoMethod : this._config.method; var offsetBase = offsetMethod === OffsetMethod.POSITION ? this._getScrollTop() : 0; this._offsets = []; this._targets = []; this._scrollHeight = this._getScrollHeight(); var targets = [].slice.call(document.querySelectorAll(this._selector)); targets.map(function (element) { var target; var targetSelector = Util.getSelectorFromElement(element); if (targetSelector) { target = document.querySelector(targetSelector); } if (target) { var targetBCR = target.getBoundingClientRect(); if (targetBCR.width || targetBCR.height) { // TODO (fat): remove sketch reliance on jQuery position/offset return [$(target)[offsetMethod]().top + offsetBase, targetSelector]; } } return null; }).filter(function (item) { return item; }).sort(function (a, b) { return a[0] - b[0]; }).forEach(function (item) { _this2._offsets.push(item[0]); _this2._targets.push(item[1]); }); }; _proto.dispose = function dispose() { $.removeData(this._element, DATA_KEY$8); $(this._scrollElement).off(EVENT_KEY$8); this._element = null; this._scrollElement = null; this._config = null; this._selector = null; this._offsets = null; this._targets = null; this._activeTarget = null; this._scrollHeight = null; } // Private ; _proto._getConfig = function _getConfig(config) { config = _objectSpread({}, Default$6, typeof config === 'object' && config ? config : {}); if (typeof config.target !== 'string') { var id = $(config.target).attr('id'); if (!id) { id = Util.getUID(NAME$8); $(config.target).attr('id', id); } config.target = "#" + id; } Util.typeCheckConfig(NAME$8, config, DefaultType$6); return config; }; _proto._getScrollTop = function _getScrollTop() { return this._scrollElement === window ? this._scrollElement.pageYOffset : this._scrollElement.scrollTop; }; _proto._getScrollHeight = function _getScrollHeight() { return this._scrollElement.scrollHeight || Math.max(document.body.scrollHeight, document.documentElement.scrollHeight); }; _proto._getOffsetHeight = function _getOffsetHeight() { return this._scrollElement === window ? window.innerHeight : this._scrollElement.getBoundingClientRect().height; }; _proto._process = function _process() { var scrollTop = this._getScrollTop() + this._config.offset; var scrollHeight = this._getScrollHeight(); var maxScroll = this._config.offset + scrollHeight - this._getOffsetHeight(); if (this._scrollHeight !== scrollHeight) { this.refresh(); } if (scrollTop >= maxScroll) { var target = this._targets[this._targets.length - 1]; if (this._activeTarget !== target) { this._activate(target); } return; } if (this._activeTarget && scrollTop < this._offsets[0] && this._offsets[0] > 0) { this._activeTarget = null; this._clear(); return; } var offsetLength = this._offsets.length; for (var i = offsetLength; i--;) { var isActiveTarget = this._activeTarget !== this._targets[i] && scrollTop >= this._offsets[i] && (typeof this._offsets[i + 1] === 'undefined' || scrollTop < this._offsets[i + 1]); if (isActiveTarget) { this._activate(this._targets[i]); } } }; _proto._activate = function _activate(target) { this._activeTarget = target; this._clear(); var queries = this._selector.split(',').map(function (selector) { return selector + "[data-target=\"" + target + "\"]," + selector + "[href=\"" + target + "\"]"; }); var $link = $([].slice.call(document.querySelectorAll(queries.join(',')))); if ($link.hasClass(ClassName$8.DROPDOWN_ITEM)) { $link.closest(Selector$8.DROPDOWN).find(Selector$8.DROPDOWN_TOGGLE).addClass(ClassName$8.ACTIVE); $link.addClass(ClassName$8.ACTIVE); } else { // Set triggered link as active $link.addClass(ClassName$8.ACTIVE); // Set triggered links parents as active // With both