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lightweight_separable.py
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lightweight_separable.py
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import argparse
from datetime import datetime
import configparser
import matplotlib.pyplot as plt
import torch
import numpy as np
from scipy.spatial import distance
from tqdm.auto import trange
from timeit import default_timer as timer
from multiprocessing import Process
import itertools
import birdseye.utils
import birdseye.sensor
import birdseye.actions
import birdseye.state
import birdseye.env
from birdseye.utils import tracking_metrics_separable, targets_found
from birdseye.planners.light_mcts import LightMCTS
from birdseye.planners.lavapilot import LAVAPilot
from birdseye.planners.repp import REPP
def main(config=None, config_path=None):
n_simulations = 100
max_iterations = 400
reward_func = lambda pf: pf.weight_entropy # lambda *args, **kwargs: None
r_min = 10
horizon = 1 # 8
min_bound = 0.82
min_std_dev = 35
num_particles = 3000 # 3000
default_config = {
"native_plot": "false",
"make_gif": "false",
"n_targets": "2",
"antenna_type": "logp",
"planner_method": "lightweight",
"target_speed": "0.5",
"sensor_speed": "1.0",
"power_tx": "26.0",
"directivity_tx": "1.0",
"freq": "5.7e9",
"fading_sigma": "8.0",
"threshold": "-120",
"mcts_depth": "3",
"mcts_c": "20.0",
"mcts_simulations": "100",
"mcts_n_downsample": "400",
}
if config and config_path:
raise ValueError("config and config_path cannot both be defined")
if config_path:
config = configparser.ConfigParser()
config.read(config_path)
config = config["lightweight"]
elif config is None:
config = default_config
local_plot = config.get("native_plot", default_config["native_plot"]).lower()
make_gif = config.get("make_gif", default_config["make_gif"]).lower()
n_targets = int(config.get("n_targets", default_config["n_targets"]))
antenna_type = config.get("antenna_type", default_config["antenna_type"])
planner_method = config.get("planner_method", default_config["planner_method"])
experiment_name = config.get("experiment_name", planner_method)
target_speed = float(config.get("target_speed", default_config["target_speed"]))
sensor_speed = float(config.get("sensor_speed", default_config["sensor_speed"]))
power_tx = config.get("power_tx", default_config["power_tx"])
power_tx = [float(x) for x in power_tx.split(",")]
if len(power_tx) == 1:
power_tx = [power_tx[0] for _ in range(n_targets)]
directivity_tx = config.get("directivity_tx", default_config["directivity_tx"])
directivity_tx = [float(x) for x in directivity_tx.split(",")]
if len(directivity_tx) == 1:
directivity_tx = [directivity_tx[0] for _ in range(n_targets)]
freq = config.get("freq", default_config["freq"])
freq = [float(x) for x in freq.split(",")]
if len(freq) == 1:
freq = [freq[0] for _ in range(n_targets)]
fading_sigma = float(config.get("fading_sigma", default_config["fading_sigma"]))
threshold = float(config.get("threshold", default_config["threshold"]))
depth = int(config.get("depth", default_config["mcts_depth"]))
c = float(config.get("c", default_config["mcts_c"]))
mcts_simulations = int(
config.get("mcts_simulations", default_config["mcts_simulations"])
)
n_downsample = int(config.get("n_downsample", default_config["mcts_n_downsample"]))
# Sensor
if antenna_type in ["directional", "yagi", "logp"]:
antenna_filename = "radiation_pattern_yagi_5.csv"
elif antenna_type in ["omni", "omnidirectional"]:
antenna_filename = "radiation_pattern_monopole.csv"
# BirdsEye
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
) # pylint: disable=no-member
def run_simulation():
global_start_time = datetime.utcnow().timestamp()
results = birdseye.utils.Results(
experiment_name=experiment_name,
global_start_time=global_start_time,
config=config,
)
if (local_plot == "true") or (make_gif == "true"):
fig = plt.figure(figsize=(14, 10), dpi=100)
ax = fig.subplots()
fig.set_tight_layout(True)
sensor = birdseye.sensor.SingleRSSISeparable(
antenna_filename=antenna_filename,
power_tx=power_tx,
directivity_tx=directivity_tx,
freq=freq,
n_targets=n_targets,
fading_sigma=fading_sigma,
)
actions = birdseye.actions.BaselineActions(sensor_speed=sensor_speed)
# actions.print_action_info()
state = birdseye.state.RFMultiState(
n_targets=n_targets,
target_speed=target_speed,
sensor_speed=sensor_speed,
reward=reward_func,
simulated=True,
)
env = birdseye.env.RFMultiSeparableEnv(
sensor=sensor,
actions=actions,
state=state,
simulated=True,
num_particles=num_particles,
)
env.reset()
target_selections = {t for t in range(n_targets)}
if planner_method == "repp": # REPP
planner = REPP(
env, min_std_dev, r_min, horizon, min_bound, target_selections
)
elif planner_method == "lavapilot": # LAVAPilot
planner = LAVAPilot(env, min_std_dev, r_min, horizon, min_bound)
elif planner_method == "mcts": # MCTS
planner = LightMCTS(
env,
depth=depth,
c=c,
simulations=mcts_simulations,
n_downsample=n_downsample,
)
else:
raise Exception
control_actions = []
for i in trange(max_iterations, desc="Time steps"):
if i % horizon == 0:
if targets_found(env, min_std_dev):
# all objects localized
control_action = None
break
else:
plan_start_time = timer()
control_action = planner.get_action()
plan_end_time = timer()
control_actions.extend(control_action)
action = control_actions[i]
# print(f"{action=}")
(env_obs, reward, _, info) = env.step(action)
if (local_plot == "true") or (make_gif == "true"):
results.live_plot(
env=env, time_step=i, fig=fig, ax=ax, data={}, separable=True
)
(
r_error,
theta_error,
heading_error,
centroid_distance_error,
rmse,
mae,
) = tracking_metrics_separable(
env.state.target_state, env.get_all_particles()
)
utc_time = datetime.utcnow().timestamp()
# results.data_to_npy(env.get_all_particles(), "particles", utc_time)
### save results
data = {
"time": utc_time,
"target": env.state.target_state,
"sensor": env.state.sensor_state,
"action": action,
"observation": info["observation"],
"std_dev_cartesian": env.get_particle_std_dev_cartesian(),
"std_dev_polar": env.get_particle_std_dev_polar(),
"r_err": r_error,
"theta_err": theta_error,
"heading_err": heading_error,
"centroid_distance_err": centroid_distance_error,
"rmse": rmse,
"mae": mae,
"plan_time": plan_end_time - plan_start_time,
}
results.data_to_json(data)
if make_gif == "true":
results.save_gif("tracking")
if (local_plot == "true") or (make_gif == "true"):
plt.close(fig)
for i in trange(n_simulations, desc="Experiments"):
run_simulation()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--batch_mode", action="store_true")
parser.add_argument(
"--config_path", type=str, default="lightweight_separable_config.ini"
)
args = parser.parse_args()
if args.batch_mode:
procs = []
n_targets = [4, 8]
target_speeds = [0.1, 0.5, 1]
sensor_speeds = [1, 2, 3]
planner_methods = ["repp", "lavapilot"] # "mcts"
fading_sigmas = [5, 10]
for conf in list(
itertools.product(
n_targets, target_speeds, sensor_speeds, planner_methods, fading_sigmas
)
):
n_target, target_speed, sensor_speed, planner_method, fading_sigma = conf
config = {
"experiment_name": f"{planner_method}_{target_speed}targetspeed_{sensor_speed}sensorspeed_{n_target}targets_{fading_sigma}fading",
"n_targets": str(n_target),
"target_speed": str(target_speed),
"sensor_speed": str(sensor_speed),
"planner_method": planner_method,
"fading_sigma": str(fading_sigma),
}
proc = Process(target=main, kwargs=({"config": config}))
procs.append(proc)
proc.start()
# complete the processes
for proc in procs:
proc.join()
else:
main(config_path=args.config_path)