Yihanhu
commented
1 week ago Hi @happy-yan , thank you for your interest. The content related to this part hasn't been released yet. We are currently optimizing the entire pipeline in terms of both efficiency and performance. At the moment, I can't guarantee a specific time for the code release, as we are somewhat short on manpower.
The documentation doesn't give any indication of how NuPlan is tested, so we'll assume it's by running the simulate_model_for_world_model_reactive_agents.sh file.
SAVE_DIR='/home/public/data/nuPlan-v1.1/data/test/0831simulation' MODEL_PATH="/home/public/data/ckpt/epoch_llama_sm.ckpt"
EXPERIMENT="tmp"
export NUPLAN_MAPS_ROOT="/home/public/data/maps" export NUPLAN_DATA_ROOT="/home/public/data/nuPlan-v1.1/splits/test/" export NUPLAN_HYDRA_CONFIG_PATH=/root/workspace/GUMP/nuplan_extent/planning/script/config export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=$PWD:$PYTHONPATH
python nuplan_extent/planning/script/run_simulation.py \ experiment_name=$EXPERIMENT \ group=$SAVE_DIR \ +simulation='closed_loop_reactive_agents' \ planner=ml_planner \ planner.ml_planner.model_config='${model}' \ planner.ml_planner.checkpoint_path=$MODEL_PATH \ common_cfg=gump \ common_cfg.input_cfg.input_channel_indexes=[0,1,2,3,4,5] \ common_cfg.output_cfg.trajectory_steps=16 \ common_cfg.output_cfg.time_horizon=8. \ scenario_builder=nuplan \ scenario_filter=nuplan_challenge_scenarios \ scenario_builder.data_root=$NUPLAN_DATA_ROOT \ scenario_builder.scenario_mapping.subsample_ratio_override=0.5 \ model=policy_structured_plan_model_raster_naive \ model.feature_builders.0.subsample_ratio_override=0.5 \ scenario_filter.timestamp_threshold_s=15 \ number_of_gpus_allocated_per_simulation=1.0 \ scenario_builder.map_root=$NUPLAN_MAPS_ROOT \ scenario_filter.num_scenarios_per_type=1 \ ~callback.simulation_nuboard_video_callback \ simulation_history_buffer_duration=6.0 \ worker.threads_per_node=8 \ max_callback_workers=8 \ observation=world_model_agents_observation \ callback.simulation_feature_video_callback.visualize_all_scenarios=True \ scenario_filter.limit_total_scenarios=1 \
The result was File "/usr/local/lib/python3.9/dist-packages/hydra/_internal/defaults_list.py", line 773, in config_not_found_error raise MissingConfigException( hydra.errors.MissingConfigException: In 'horizon_simulation': Could not load 'callback/simulation_nuboard_video_callback'.
Config search path: provider=hydra, path=pkg://hydra.conf provider=main, path=file:///root/workspace/GUMP/nuplan_extent/planning/script/config provider=hydra.searchpath in main, path=pkg://nuplan.planning.script.config.common provider=hydra.searchpath in main, path=pkg://nuplan.planning.script.config.simulation provider=hydra.searchpath in main, path=pkg://nuplan_extent.planning.script.config.common provider=hydra.searchpath in main, path=pkg://nuplan_extent.planning.script.experiments provider=hydra.searchpath in main, path=pkg://nuplan_extent.planning.script.config.simulation provider=hydra.searchpath in main, path=file://root/workspace/GUMP/nuplan_extent/planning/simulation/callback provider=schema, path=structured://
However，we make sure the file is under the corresponding path. Besides, we sincerely hope to see your Downstream Task: Reinforcement Learning