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centerline_generation.py
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centerline_generation.py
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import os
import gc
import pickle
import numpy as np
import torch
from copy import deepcopy
from shapely.geometry import Point
import time
from nuplan.common.actor_state.state_representation import StateSE2
from nuplan.common.maps.nuplan_map.map_factory import get_maps_api
from transformer4planning.preprocess.pdm_vectorize import (get_discrete_centerline,
get_drivable_area_map,
load_route_dicts,
route_roadblock_correction,
get_starting_lane,
convert_absolute_to_relative_se2_array,
PDMPath,
)
MAP_API=dict()
for map in ['sg-one-north', 'us-ma-boston', 'us-nv-las-vegas-strip', 'us-pa-pittsburgh-hazelwood']:
MAP_API[map] = get_maps_api(
map_root="/public/MARS/datasets/nuPlan/nuplan-maps-v1.1",
# map_root="/localdata_ssd/nuplan/nuplan-maps-v1.1",
map_version="nuplan-maps-v1.0",
map_name=map
)
def get_centerline(sample, split, data_path,
map_radius=50, centerline_samples=120, centerline_interval=1.0,
frame_rate=20, past_seconds=2, frame_frequency_rate=2):
"""
Args:
sample: the data unit in datasets, include file_name, frame_id and map etc.
"""
filename = sample["file_name"]
map = sample["map"]
frame_id = sample["frame_id"]
route_ids = sample["route_ids"]
if isinstance(frame_id, torch.Tensor):
frame_id = frame_id.item()
if isinstance(route_ids, torch.Tensor):
route_ids = route_ids.tolist()
pickle_path = os.path.join(data_path, f"{split}", f"{map}", f"{filename}.pkl")
if os.path.exists(pickle_path):
with open(pickle_path, "rb") as f:
data_dic = pickle.load(f)
if 'agent_dic' in data_dic:
agent_dic = data_dic["agent_dic"]
elif 'agent' in data_dic:
agent_dic = data_dic['agent']
else:
raise ValueError(f'cannot find agent_dic or agent in pickle file, keys: {data_dic.keys()}')
else:
print(f"Error: cannot load {filename} from {data_path} with {map}")
return None
map_api = MAP_API[map]
# convert ego poses to nuplan format (x, y, heading)
ego_poses = deepcopy(agent_dic["ego"]["pose"][(frame_id - past_seconds * frame_rate) // frame_frequency_rate:frame_id // frame_frequency_rate, :])
ego_shape = agent_dic["ego"]["shape"][0]
nuplan_ego_poses = [StateSE2(x=ego_pose[0], y=ego_pose[1], heading=ego_pose[-1]) for ego_pose in ego_poses]
anchor_ego_pose = nuplan_ego_poses[-1]
# build drivable area map and extract centerline
drivable_area_map = get_drivable_area_map(map_api, ego_poses[-1], map_radius=map_radius)
# compute centerlines
# _, init_route_dict = load_route_dicts(route_ids, map_api)
# gc.collect()
# gc.disable()
# route_ids = route_roadblock_correction(ego_poses[-1], map_api, init_route_dict)
route_lane_dict, route_block_dict = load_route_dicts(route_ids, map_api)
# e2_time = time.time()
# print("time to load corrected routes:", e2_time - e1_time)
current_lane = get_starting_lane(ego_poses[-1], drivable_area_map, route_lane_dict, ego_shape)
# e5_time = time.time()
# print("time to get lane:", e5_time - e4_time)
centerline = PDMPath(get_discrete_centerline(current_lane, route_block_dict, route_lane_dict))
current_progress = centerline.project(Point(*anchor_ego_pose.array))
centerline_progress_values = (
np.arange(centerline_samples, dtype=np.float64) * centerline_interval + current_progress
)
planner_centerline = convert_absolute_to_relative_se2_array(
anchor_ego_pose,
centerline.interpolate(centerline_progress_values, as_array=True),
)
return planner_centerline
def centerline_map(sample, split, data_path):
try:
centerline = get_centerline(sample, split, data_path)
sample["centerline"] = centerline
except:
print("Error: routes is incorrect")
sample["centerline"] = None
return sample
if __name__ == "__main__":
import multiprocessing as mp
import datasets
from datasets.arrow_dataset import _concatenate_map_style_datasets
from datasets import Dataset
from functools import partial
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--data_path", type=str, default="/localdata_ssd/nuplan/online_float32_opt")
# parser.add_argument("--data_path", type=str, default="/public/MARS/datasets/nuPlanCache/online_float32_opt")
parser.add_argument("--split", type=str, default="train")
parser.add_argument("--cache_dir", type=str, default="/localdata_ssd/nuplan/centerline")
parser.add_argument("--dataset_name", type=str, default="train")
parser.add_argument("--num_proc", type=int, default=40)
parser.add_argument("--start_id", type=int, default=0)
parser.add_argument("--end_id", type=int, default=1)
parser.add_argument("--method", type=str, default="map", help="choose from ['map', 'mp', 'sequential'], map is recommended")
args = parser.parse_args()
data_path = args.data_path
# root = "/localdata_ssd/nuplan/online_float32_opt/index/val/"
root = os.path.join(data_path, "index", args.split)
subset_dirs = os.listdir(root)
alldatasets = list()
for i, subset_dir in enumerate(subset_dirs):
if i >= args.start_id and i < args.end_id:
print(f"loading {subset_dir}")
dataset = datasets.load_from_disk(os.path.join(root, subset_dir))
alldatasets.append(dataset)
dataset = _concatenate_map_style_datasets(alldatasets)
print(dataset)
def yield_centerline(shards):
for shard in shards:
# filename, map, frame_id, route_id = filenames[shard], maps[shard], frame_ids[shard], route_ids[shard]
# centerline_dic = get_centerline(filename, map, frame_id, route_id, args.split, data_path)
sample = dataset[shard]
centerline_dic = get_centerline(sample, args.split, data_path)
yield centerline_dic
indices = range(len(dataset))
print("begin to generate dataset, length is", len(indices))
# dataset map
if args.method == "map":
func = partial(centerline_map, split=args.split, data_path=data_path)
dataset = dataset.map(func, num_proc=args.num_proc)
dataset.save_to_disk(os.path.join(args.cache_dir, args.dataset_name))
# multiprocessing
elif args.method == "mp":
func = partial(get_centerline, split="train", data_path=data_path)
with mp.Pool(processes=40) as pool:
result = list(tqdm(pool.imap(func, dataset), total=len(indices)))
elif args.method == "sequential":
for i in tqdm(indices):
try:
centerline = get_centerline(dataset[i], args.split, data_path)
except:
print(f"Error: cannot load from {data_path} with {map}")