Spaces:
Sleeping
Sleeping
File size: 15,270 Bytes
5769ee4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 |
"""evaluate_prediction_planning_stack.py --load_from <wandb ID> --seed <seed>
--num_episodes <num_episodes> --risk_level <a list of risk-levels>
--num_samples <a list of numbers of prediction samples>
This script loads a trained predictor from <wand ID>, runs a batch of open-loop MPC evaluations
(i.e., without replanning) using <num_episodes> episodes while varying risk-levels and numbers of
prediction samples. Results are stored in scripts/logs/planner_eval/run-<wandb ID>_<seed> as a
collection of pickle files.
"""
import argparse
import os
import pickle
from time import time_ns
from typing import List, Tuple
import sys
import torch
from mmcv import Config
from pytorch_lightning import seed_everything
from tqdm import trange
from risk_biased.mpc_planner.planner import MPCPlanner, MPCPlannerParams
from risk_biased.predictors.biased_predictor import LitTrajectoryPredictor
from risk_biased.scene_dataset.loaders import SceneDataLoaders
from risk_biased.scene_dataset.scene import RandomScene, RandomSceneParams
from risk_biased.utils.callbacks import get_fast_slow_scenes
from risk_biased.utils.load_model import load_from_config, config_argparse
from risk_biased.utils.planner_utils import (
evaluate_control_sequence,
get_interaction_cost,
AbstractState,
to_state,
)
def evaluate_main(
load_from: str,
seed: int,
num_episodes: int,
risk_level_list: List[float],
num_prediction_samples_list: List[int],
):
print(f"Risk-sensitivity level(s) to test: {risk_level_list}")
print(f"Number(s) of prediction samples to test: {num_prediction_samples_list} ")
save_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"logs",
"planner_eval",
f"run-{load_from}_{seed}",
)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cfg, planner = get_cfg_and_planner(load_from=load_from)
if not planner.solver.params.mean_warm_start == False:
print(
"switching to mean_warm_start = False for open-loop evaluation (i.e. without re-planning)"
)
planner.solver.params.mean_warm_start = False
for scene_type in [
"safer_slow",
"safer_fast",
]:
print(f"\nTesting {scene_type} scenes")
seed_everything(seed)
(
scene,
ado_state_history_batch,
ado_state_future_batch,
) = get_scene_and_ado_trajectory(
cfg, scene_type=scene_type, num_episodes=num_episodes
)
(
ego_state_history,
ego_state_target_trajectory,
) = get_ego_state_history_and_target_trajectory(cfg, scene)
for stack_risk_level in risk_level_list:
print(f" Risk_level: {stack_risk_level}")
file_name = f"{scene_type}_no_policy_opt_risk_level_{stack_risk_level}"
print(f" {file_name}")
stats_dict_no_policy_opt = evaluate_prediction_planning_stack(
planner,
ado_state_history_batch,
ado_state_future_batch,
ego_state_history,
ego_state_target_trajectory,
optimize_policy=False,
stack_risk_level=stack_risk_level,
risk_in_predictor=False,
)
with open(os.path.join(save_dir, file_name + ".pkl"), "wb") as f:
pickle.dump(stats_dict_no_policy_opt, f)
for num_prediction_samples in num_prediction_samples_list:
file_name = f"{scene_type}_{num_prediction_samples}_samples_risk_level_{stack_risk_level}"
if stack_risk_level == 0.0:
print(f" {file_name}")
stats_dict_risk_neutral = evaluate_prediction_planning_stack(
planner,
ado_state_history_batch,
ado_state_future_batch,
ego_state_history,
ego_state_target_trajectory,
optimize_policy=True,
stack_risk_level=stack_risk_level,
risk_in_predictor=False,
num_prediction_samples=num_prediction_samples,
)
with open(os.path.join(save_dir, file_name + ".pkl"), "wb") as f:
pickle.dump(stats_dict_risk_neutral, f)
else:
file_name_in_predictor = file_name + "_in_predictor"
print(f" {file_name_in_predictor}")
stats_dict_risk_in_predictor = evaluate_prediction_planning_stack(
planner,
ado_state_history_batch,
ado_state_future_batch,
ego_state_history,
ego_state_target_trajectory,
optimize_policy=True,
stack_risk_level=stack_risk_level,
risk_in_predictor=True,
num_prediction_samples=num_prediction_samples,
)
with open(
os.path.join(save_dir, file_name_in_predictor + ".pkl"), "wb"
) as f:
pickle.dump(stats_dict_risk_in_predictor, f)
file_name_in_planner = file_name + "_in_planner"
print(f" {file_name_in_planner}")
stats_dict_risk_in_planner = evaluate_prediction_planning_stack(
planner,
ado_state_history_batch,
ado_state_future_batch,
ego_state_history,
ego_state_target_trajectory,
optimize_policy=True,
stack_risk_level=stack_risk_level,
risk_in_predictor=False,
num_prediction_samples=num_prediction_samples,
)
with open(
os.path.join(save_dir, file_name_in_planner + ".pkl"), "wb"
) as f:
pickle.dump(stats_dict_risk_in_planner, f)
def evaluate_prediction_planning_stack(
planner: MPCPlanner,
ado_state_history_batch: AbstractState,
ado_state_future_batch: AbstractState,
ego_state_history: AbstractState,
ego_state_target_trajectory: AbstractState,
optimize_policy: bool = True,
stack_risk_level: float = 0.0,
risk_in_predictor: bool = False,
num_prediction_samples: int = 128,
num_prediction_samples_for_policy_eval: int = 4096,
) -> dict:
assert planner.solver.params.mean_warm_start == False
if risk_in_predictor:
predictor_risk_level, planner_risk_level = stack_risk_level, 0.0
else:
predictor_risk_level, planner_risk_level = 0.0, stack_risk_level
stats_dict = {
"stack_risk_level": stack_risk_level,
"predictor_risk_level": predictor_risk_level,
"planner_risk_level": planner_risk_level,
}
num_episodes = ado_state_history_batch.shape[0]
assert num_episodes == ado_state_future_batch.shape[0]
for episode_id in trange(num_episodes, desc="episodes", leave=False):
ado_state_history = ado_state_history_batch[episode_id]
ado_state_future = ado_state_future_batch[episode_id]
(
ado_state_future_samples_for_policy_eval,
sample_weights,
) = planner.solver.sample_prediction(
planner.predictor,
ado_state_history,
planner.normalizer,
ego_state_history=ego_state_history,
ego_state_future=ego_state_target_trajectory,
num_prediction_samples=num_prediction_samples_for_policy_eval,
risk_level=0.0,
)
if optimize_policy:
start = time_ns()
solver_info = planner.solver.solve(
planner.predictor,
ego_state_history,
ego_state_target_trajectory,
ado_state_history,
planner.normalizer,
num_prediction_samples=num_prediction_samples,
verbose=False,
risk_level=stack_risk_level,
resample_prediction=False,
risk_in_predictor=risk_in_predictor,
)
end = time_ns()
computation_time_ms = (end - start) * 1e-6
else:
planner.solver.reset()
computation_time_ms = 0.0
solver_info = None
interaction_cost_gt = get_ground_truth_interaction_cost(
planner, ado_state_future, ego_state_history
)
interaction_risk, tracking_cost = evaluate_control_sequence(
planner.solver.control_sequence,
planner.solver.dynamics_model,
ego_state_history,
ego_state_target_trajectory,
ado_state_future_samples_for_policy_eval,
sample_weights,
planner.solver.interaction_cost,
planner.solver.tracking_cost,
risk_level=stack_risk_level,
risk_estimator=planner.solver.risk_estimator,
)
stats_dict_this_run = {
"computation_time_ms": computation_time_ms,
"interaction_cost_ground_truth": interaction_cost_gt,
"interaction_risk": interaction_risk,
"tracking_cost": tracking_cost,
"control_sequence": planner.solver.control_sequence,
"solver_info": solver_info,
"ado_unbiased_predictions": ado_state_future_samples_for_policy_eval.position.detach()
.cpu()
.numpy(),
"sample_weights": sample_weights.detach().cpu().numpy(),
"ado_position_future": ado_state_future.position.detach().cpu().numpy(),
"ado_position_history": ado_state_history.position.detach().cpu().numpy(),
}
stats_dict[episode_id] = stats_dict_this_run
return stats_dict
def get_cfg_and_predictor() -> Tuple[Config, LitTrajectoryPredictor]:
config_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"..",
"..",
"risk_biased",
"config",
"learning_config.py",
)
cfg = config_argparse(config_path)
predictor, _, cfg = load_from_config(cfg)
return cfg, predictor
def get_cfg_and_planner(load_from: str) -> Tuple[Config, MPCPlanner]:
planner_config_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"..",
"..",
"risk_biased",
"config",
"planning_config.py",
)
planner_cfg = Config.fromfile(planner_config_path)
cfg, predictor = get_cfg_and_predictor()
# joint_dict = {**dict(cfg), **dict(planner_cfg)}
# assert joint_dict == {
# **dict(planner_cfg),
# **dict(cfg),
# }, f"some of the entries conflict between {cfg.filename} and {planner_cfg.filename}"
# joint_cfg = Config(joint_dict)
cfg.update(planner_cfg)
planner_params = MPCPlannerParams.from_config(cfg)
normalizer = SceneDataLoaders.normalize_trajectory
planner = MPCPlanner(planner_params, predictor, normalizer)
return cfg, planner
def get_scene_and_ado_trajectory(
cfg: Config, scene_type: str, num_episodes: int
) -> Tuple[RandomScene, torch.Tensor, torch.Tensor]:
scene_params = RandomSceneParams.from_config(cfg)
safer_fast_scene, safer_slow_scene = get_fast_slow_scenes(
scene_params, num_episodes
)
assert scene_type in ["safer_fast", "safer_slow"]
if scene_type == "safer_fast":
scene = safer_fast_scene
elif scene_type == "safer_slow":
scene = safer_slow_scene
else:
raise ValueError(f"unknown scene type {scene_type}")
ado_trajectory = torch.from_numpy(
scene.get_pedestrians_trajectories().astype("float32")
)
ado_position_history = to_state(ado_trajectory[..., : cfg.num_steps, :], cfg.dt)
ado_position_future = to_state(ado_trajectory[..., cfg.num_steps :, :], cfg.dt)
return scene, ado_position_history, ado_position_future
def get_ego_state_history_and_target_trajectory(
cfg: Config, scene: RandomScene
) -> Tuple[torch.Tensor, torch.Tensor]:
ego_state_traj = to_state(
torch.from_numpy(
scene.get_ego_ref_trajectory(cfg.sample_times).astype("float32")
),
cfg.dt,
)
ego_state_history = ego_state_traj[0, :, : cfg.num_steps]
ego_state_target_trajectory = ego_state_traj[0, :, cfg.num_steps :]
return ego_state_history, ego_state_target_trajectory
def get_ground_truth_interaction_cost(
planner: MPCPlanner,
ado_state_future: AbstractState, # (num_agents, num_steps_future)
ego_state_history: AbstractState, # (1, 1, num_steps)
) -> float:
ego_state_future = planner.solver.dynamics_model.simulate(
ego_state_history[..., -1], planner.solver.control_sequence.unsqueeze(0)
)
interaction_cost = get_interaction_cost(
ego_state_future,
ado_state_future,
planner.solver.interaction_cost,
)
return interaction_cost.item()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="evaluate prediciton-planning stack using safer_fast and safer_slow scenes"
)
parser.add_argument(
"--load_from",
type=str,
required=True,
help="WandB ID to load trained predictor from",
)
parser.add_argument(
"--seed",
type=int,
default=0,
help="Random seed for evaluation",
)
parser.add_argument(
"--num_episodes",
type=int,
default=100,
help="Number of episodes",
)
parser.add_argument(
"--risk_level",
type=float,
nargs="+",
help="Risk-sensitivity level(s) to test",
default=[0.95, 1],
)
parser.add_argument(
"--num_samples",
type=int,
nargs="+",
help="Number(s) of prediction samples to test",
default=[1, 4, 16, 64, 256, 1024],
)
parser.add_argument(
"--force_config",
action="store_true",
help="""Use this flag to force the use of the local config file
when loading a model from a checkpoint. Otherwise the checkpoint config file is used.
In any case the parameters can be overwritten with an argparse argument.""",
)
parser.add_argument(
"--load_last",
action="store_true",
help="""Use this flag to force the use of the last checkpoint instead of the best one
when loading a model.""",
)
args = parser.parse_args()
# Args will be re-parsed, this keeps only the arguments that are compatible with the second parser.
keep_list = ["--load_from", "--seed", "--load_last", "--force_config"]
sys.argv = [ss for s in sys.argv for ss in s.split("=")]
sys.argv = [
sys.argv[i]
for i in range(len(sys.argv))
if sys.argv[i] in keep_list or sys.argv[i - 1] in keep_list or i == 0
]
evaluate_main(
args.load_from, args.seed, args.num_episodes, args.risk_level, args.num_samples
)
|