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version used for the paper submission
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@whoenig
whoenig committed Sep 16, 2023
1 parent 68bd2ba commit d8842b4
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Showing 3 changed files with 238 additions and 128 deletions.
72 changes: 42 additions & 30 deletions scripts/benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,7 +175,7 @@ def run_checker(filename_env, filename_result, filename_log):
"--result_file", filename_result,
"--env_file", filename_env,
"--models_base_path" , "../deps/dynoplan/dynobench/models/",
"--goal_tol" , "2",
"--goal_tol" , "999",
"--u_bound_tol", "0.101",
"--col_tol", "0.02"]
print(subprocess.list2cmdline(cmd))
Expand Down Expand Up @@ -322,48 +322,60 @@ def execute_task(task: ExecutionTask):
# # visualize: reference trajectory from the geometric planner, output of controller tracking the ref traj
run_visualizer("../deps/dynoplan/dynobench/envs/quad3d_payload/benchmark_envs/" + task.env ,result_folder / "init_guess.yaml", result_folder / "trajectory_geom.yaml", result_folder / "trajectory_geom.html")

if task.alg == "extraiter":

result_folder = results_path / task.instance / "opt" / "{:03d}".format(task.trial)
previous_result = result_folder / "output.trajopt.yaml"
for i in range(1, 20):
result_folder_i = result_folder / "iter{:02d}".format(i)
result_folder_i.mkdir(parents=True, exist_ok=False)
run_opt(previous_result, str(result_folder / "env_inflated.yaml"), str(result_folder_i), task.timelimit_opt, t_weight="3.0", t_ref="0.8")
previous_result = result_folder_i / "output.trajopt.yaml"


except:
traceback.print_exc()

def main():
parallel = True
instances = [
{ "name": "empty_2robots", "models_path": "point_2.yaml"},
{ "name": "empty_3robots", "models_path": "point_3.yaml"},
{ "name": "empty_4robots", "models_path": "point_4.yaml"},
{ "name": "empty_5robots", "models_path": "point_5.yaml"},
{ "name": "empty_6robots", "models_path": "point_6.yaml"},

{ "name": "forest_2robots", "models_path": "point_2.yaml"},
{ "name": "forest_3robots", "models_path": "point_3.yaml"},
# { "name": "empty_2robots", "models_path": "point_2.yaml"},
# { "name": "empty_3robots", "models_path": "point_3.yaml"},
# { "name": "empty_4robots", "models_path": "point_4.yaml"},
# { "name": "empty_5robots", "models_path": "point_5.yaml"},
# { "name": "empty_6robots", "models_path": "point_6.yaml"},

# { "name": "forest_2robots", "models_path": "point_2.yaml"},
# { "name": "forest_3robots", "models_path": "point_3.yaml"},
{ "name": "forest_4robots", "models_path": "point_4.yaml"},
{ "name": "forest_5robots", "models_path": "point_5.yaml"},
{ "name": "forest_6robots", "models_path": "point_6.yaml"},
# { "name": "forest_5robots", "models_path": "point_5.yaml"},
# { "name": "forest_6robots", "models_path": "point_6.yaml"},

# { "name": "maze_2robots", "models_path": "point_2.yaml"},
# { "name": "maze_3robots", "models_path": "point_3.yaml"},
# { "name": "maze_4robots", "models_path": "point_4.yaml"},
# { "name": "maze_5robots", "models_path": "point_5.yaml"},
# { "name": "maze_6robots", "models_path": "point_6.yaml"},
# # { "name": "maze_2robots", "models_path": "point_2.yaml"},
# # { "name": "maze_3robots", "models_path": "point_3.yaml"},
# # { "name": "maze_4robots", "models_path": "point_4.yaml"},
# # { "name": "maze_5robots", "models_path": "point_5.yaml"},
# # { "name": "maze_6robots", "models_path": "point_6.yaml"},

{ "name": "window_2robots", "models_path": "point_2.yaml"},
{ "name": "window_3robots", "models_path": "point_3.yaml"},
{ "name": "window_4robots", "models_path": "point_4.yaml"},
{ "name": "window_5robots", "models_path": "point_5.yaml"},
{ "name": "window_6robots", "models_path": "point_6.yaml"},
# { "name": "window_2robots", "models_path": "point_2.yaml"},
# { "name": "window_3robots", "models_path": "point_3.yaml"},
# { "name": "window_4robots", "models_path": "point_4.yaml"},
# { "name": "window_5robots", "models_path": "point_5.yaml"},
# { "name": "window_6robots", "models_path": "point_6.yaml"},

{ "name": "empty_5robots_uniform", "models_path": "point_5.yaml"},
{ "name": "forest_4robots_uniform", "models_path": "point_4.yaml"},
{ "name": "window_3robots_uniform", "models_path": "point_3.yaml"},
# { "name": "empty_5robots_uniform", "models_path": "point_5.yaml"},
# { "name": "forest_4robots_uniform", "models_path": "point_4.yaml"},
# { "name": "window_3robots_uniform", "models_path": "point_3.yaml"},

]
algs = [
"payload",
"geom",
"opt",
# "payload",
# "geom",
# "opt",
"extraiter",
]
trials = 1
# trials = 3
trials = [0]
timelimit_geom = 300
timelimit_opt = 15*60
max_cpus = 32 # limit the number of CPUs due to high memory usage
Expand All @@ -375,7 +387,7 @@ def main():
# "geom" is implicitly executed with "opt", so don't execute here
if alg == "geom" and "opt" in algs:
continue
for trial in range(trials):
for trial in trials:
tasks.append(ExecutionTask(instance["name"], env, instance["models_path"], alg, trial, timelimit_geom, timelimit_opt))

if parallel and len(tasks) > 1:
Expand All @@ -387,7 +399,7 @@ def main():
else:
for task in tasks:
execute_task(task)
trials_ = ["00"+str(i) for i in range(trials)]
trials_ = ["00"+str(i) for i in trials]
compute_errors([instance["name"] for instance in instances], algs, trials_)

paper_tables.write_table1(Path("../results"), trials_)
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