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9897e20 | 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 | #!/usr/bin/env python
"""Benchmark GPUDrive NuScenes environment throughput without PPO training."""
from __future__ import annotations
import argparse
import os
import time
from pathlib import Path
import torch
from gpudrive.env.config import EnvConfig, RenderConfig
from gpudrive.env.dataset import SceneDataLoader
from gpudrive.env.env_torch import GPUDriveTorchEnv
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", default="data/processed/nuscenes")
parser.add_argument("--num-worlds", type=int, default=75)
parser.add_argument("--dataset-size", type=int, default=10000)
parser.add_argument("--steps", type=int, default=200)
parser.add_argument("--warmup-steps", type=int, default=20)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--device", default="cuda")
parser.add_argument("--obs-radius", type=float, default=50.0)
parser.add_argument("--polyline-reduction-threshold", type=float, default=0.1)
parser.add_argument("--max-controlled-agents", type=int, default=64)
parser.add_argument("--remove-non-vehicles", type=int, default=1)
parser.add_argument("--init-mode", default="all_valid")
parser.add_argument("--action-mode", choices=["zero", "random"], default="zero")
return parser.parse_args()
def sync_if_cuda(device: str) -> None:
if device == "cuda":
torch.cuda.synchronize()
def main() -> None:
args = parse_args()
torch.manual_seed(args.seed)
project_root = Path(__file__).resolve().parents[1]
data_dir = (project_root / args.data_dir).resolve()
os.chdir(project_root / "gpudrive")
device = args.device
if device == "cuda" and not torch.cuda.is_available():
device = "cpu"
data_loader = SceneDataLoader(
root=str(data_dir),
batch_size=args.num_worlds,
dataset_size=args.dataset_size,
sample_with_replacement=True,
shuffle=True,
seed=args.seed,
)
env_config = EnvConfig(
ego_state=True,
road_map_obs=True,
partner_obs=True,
norm_obs=True,
reward_type="weighted_combination",
dynamics_model="classic",
collision_behavior="ignore",
remove_non_vehicles=bool(args.remove_non_vehicles),
obs_radius=args.obs_radius,
polyline_reduction_threshold=args.polyline_reduction_threshold,
init_mode=args.init_mode,
)
env = GPUDriveTorchEnv(
config=env_config,
render_config=RenderConfig(render_3d=False),
data_loader=data_loader,
max_cont_agents=args.max_controlled_agents,
device=device,
)
control_mask = env.cont_agent_mask.clone()
num_controlled = int(control_mask.sum().item())
total_slots = env.num_worlds * env.max_agent_count
print(
"[bench] env ready:",
f"device={device}",
f"num_worlds={env.num_worlds}",
f"max_agent_count={env.max_agent_count}",
f"controlled_agents={num_controlled}",
f"obs_radius={args.obs_radius}",
f"polyline_reduction_threshold={args.polyline_reduction_threshold}",
flush=True,
)
generator = torch.Generator(device=device)
generator.manual_seed(args.seed)
def make_actions() -> torch.Tensor:
if args.action_mode == "random":
return torch.randint(
low=0,
high=env.action_space.n,
size=(env.num_worlds, env.max_agent_count),
generator=generator,
device=device,
)
return torch.zeros(
(env.num_worlds, env.max_agent_count),
dtype=torch.int64,
device=device,
)
for _ in range(args.warmup_steps):
env.step_dynamics(make_actions())
_ = env.get_obs(control_mask)
_ = env.get_rewards()
_ = env.get_infos()
if bool(env.get_dones().all().item()):
env.reset()
sync_if_cuda(device)
start = time.perf_counter()
measured_steps = 0
for _ in range(args.steps):
env.step_dynamics(make_actions())
_ = env.get_obs(control_mask)
_ = env.get_rewards()
_ = env.get_infos()
measured_steps += 1
if bool(env.get_dones().all().item()):
env.reset()
sync_if_cuda(device)
elapsed = time.perf_counter() - start
sim_steps_per_sec = measured_steps / elapsed
controlled_sps = measured_steps * num_controlled / elapsed
padded_sps = measured_steps * total_slots / elapsed
print(
"[bench] result:",
f"elapsed_sec={elapsed:.3f}",
f"sim_steps={measured_steps}",
f"sim_steps_per_sec={sim_steps_per_sec:.2f}",
f"controlled_agent_sps={controlled_sps:.2f}",
f"padded_agent_sps={padded_sps:.2f}",
flush=True,
)
env.close()
if __name__ == "__main__":
main()
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