#!/usr/bin/env python3 # Copyright (c) 2019 Intel Labs. # authors: German Ros (german.ros@intel.com) # # This work is licensed under the terms of the MIT license. # For a copy, see . """ This is a benchmarking script for CARLA. It serves to analyze the performance of CARLA in different scenarios and conditions, for both sensors and traffic. Please, make sure you install the following dependencies: * python -m pip install -U py-cpuinfo * python -m pip install psutil * python -m pip install python-tr * python -m pip install gpuinfo """ # @todo Include this file in the Pylint checks. # pylint: skip-file import sys if sys.version_info[0] < 3: print('This script is only available for Python 3') sys.exit(1) from tr import tr import argparse import cpuinfo import glob import math import numpy as np import os import psutil import pygame import shutil import GPUtil import threading import time import logging try: sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % ( sys.version_info.major, sys.version_info.minor, 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0]) except IndexError: pass import carla # ====================================================================================================================== # -- Global variables. So sorry... ------------------------------------------------------------------------------------- # ====================================================================================================================== sensors_callback = [] def define_weather(): list_weather = [] if args.tm: weather00 = { 'parameter' : carla.WeatherParameters.ClearNoon, 'name': 'ClearNoon'} list_weather.append(weather00) else: weather00 = { 'parameter' : carla.WeatherParameters.ClearNoon, 'name' : 'ClearNoon'} weather01 = { 'parameter' : carla.WeatherParameters.CloudyNoon, 'name' : 'CloudyNoon'} weather02 = { 'parameter' : carla.WeatherParameters.SoftRainSunset, 'name' : 'SoftRainSunset'} list_weather.append(weather00) list_weather.append(weather01) list_weather.append(weather02) return list_weather def define_sensors(): list_sensor_specs = [] if args.tm: sensors00 = [{'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0, 'width': 300, 'height': 200, 'fov': 100, 'label': '1. cam-300x200'}] list_sensor_specs.append(sensors00) else: sensors00 = [{'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0, 'width': 300, 'height': 200, 'fov': 100, 'label': '1. cam-300x200'}] sensors01 = [{'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0, 'width': 800, 'height': 600, 'fov': 100, 'label': '2. cam-800x600'}] sensors02 = [{'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0, 'width': 1900, 'height': 1080, 'fov': 100, 'label': '3. cam-1900x1080'}] sensors03 = [{'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0, 'width': 300, 'height': 200, 'fov': 100, 'label': '4. cam-300x200'}, {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0, 'width': 300, 'height': 200, 'fov': 100, 'label': 'cam-300x200'}, ] sensors04 = [{'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0, 'label': '5. LIDAR'}] list_sensor_specs.append(sensors00) list_sensor_specs.append(sensors01) list_sensor_specs.append(sensors02) list_sensor_specs.append(sensors03) list_sensor_specs.append(sensors04) return list_sensor_specs def define_environments(): list_env_specs = [] if args.tm: env00 = {'vehicles': 10, 'walkers': 0} env01 = {'vehicles': 50, 'walkers': 50} env02 = {'vehicles': 250, 'walkers': 0} env03 = {'vehicles': 150, 'walkers': 50} list_env_specs.append(env00) list_env_specs.append(env01) list_env_specs.append(env02) list_env_specs.append(env03) else: env00 = {'vehicles': 1, 'walkers': 0} list_env_specs.append(env00) return list_env_specs class CallBack(object): def __init__(self): self._lock = threading.Lock() self._pygame_clock = pygame.time.Clock() self._current_fps = 0 def __call__(self, data): self._pygame_clock.tick() self._current_fps = self._pygame_clock.get_fps() def get_fps(self): with self._lock: return self._current_fps def create_environment(world, sensors, n_vehicles, n_walkers, spawn_points, client): global sensors_callback sensors_ret = [] blueprint_library = world.get_blueprint_library() # setup sensors for sensor_spec in sensors: bp = blueprint_library.find(sensor_spec['type']) if sensor_spec['type'].startswith('sensor.camera'): bp.set_attribute('image_size_x', str(sensor_spec['width'])) bp.set_attribute('image_size_y', str(sensor_spec['height'])) bp.set_attribute('fov', str(sensor_spec['fov'])) sensor_location = carla.Location( x=sensor_spec['x'], y=sensor_spec['y'], z=sensor_spec['z']) sensor_rotation = carla.Rotation( pitch=sensor_spec['pitch'], roll=sensor_spec['roll'], yaw=sensor_spec['yaw']) elif sensor_spec['type'].startswith('sensor.lidar'): bp.set_attribute('range', '200') bp.set_attribute('rotation_frequency', '10') bp.set_attribute('channels', '32') bp.set_attribute('upper_fov', '15') bp.set_attribute('lower_fov', '-30') bp.set_attribute('points_per_second', '500000') sensor_location = carla.Location( x=sensor_spec['x'], y=sensor_spec['y'], z=sensor_spec['z']) sensor_rotation = carla.Rotation( pitch=sensor_spec['pitch'], roll=sensor_spec['roll'], yaw=sensor_spec['yaw']) elif sensor_spec['type'].startswith('sensor.other.gnss'): sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'], z=sensor_spec['z']) sensor_rotation = carla.Rotation() # create sensor sensor_transform = carla.Transform(sensor_location, sensor_rotation) sensor = world.spawn_actor(bp, sensor_transform) # add callbacks sc = CallBack() sensor.listen(sc) sensors_callback.append(sc) sensors_ret.append(sensor) vehicles_list = [] walkers_list = [] all_id = [] blueprint = world.get_blueprint_library().filter('vehicle.audi.a2')[0] walker_bp = world.get_blueprint_library().filter("walker.pedestrian.0001")[0] # @todo cannot import these directly. SpawnActor = carla.command.SpawnActor SetAutopilot = carla.command.SetAutopilot FutureActor = carla.command.FutureActor # -------------- # Spawn vehicles # -------------- batch = [] for num, transform in enumerate(spawn_points): if num >= n_vehicles: break blueprint.set_attribute('role_name', 'autopilot') batch.append(SpawnActor(blueprint, transform).then(SetAutopilot(FutureActor, True))) for response in client.apply_batch_sync(batch, False): if response.error: logging.error(response.error) else: vehicles_list.append(response.actor_id) # ------------- # Spawn Walkers # ------------- # some settings percentagePedestriansRunning = 0.0 # how many pedestrians will run percentagePedestriansCrossing = 0.0 # how many pedestrians will walk through the road # 1. take all the random locations to spawn spawn_points = [] for i in range(n_walkers): spawn_point = carla.Transform() loc = world.get_random_location_from_navigation() if (loc != None): spawn_point.location = loc spawn_points.append(spawn_point) # 2. we spawn the walker object batch = [] walker_speed = [] for spawn_point in spawn_points: # set as not invincible if walker_bp.has_attribute('is_invincible'): walker_bp.set_attribute('is_invincible', 'false') # set the max speed if walker_bp.has_attribute('speed'): # walking walker_speed.append(walker_bp.get_attribute('speed').recommended_values[1]) else: print("Walker has no speed") walker_speed.append(0.0) batch.append(SpawnActor(walker_bp, spawn_point)) results = client.apply_batch_sync(batch, True) walker_speed2 = [] for i in range(len(results)): if results[i].error: logging.error(results[i].error) else: walkers_list.append({"id": results[i].actor_id}) walker_speed2.append(walker_speed[i]) walker_speed = walker_speed2 # 3. we spawn the walker controller batch = [] walker_controller_bp = world.get_blueprint_library().find('controller.ai.walker') for i in range(len(walkers_list)): batch.append(SpawnActor(walker_controller_bp, carla.Transform(), walkers_list[i]["id"])) results = client.apply_batch_sync(batch, True) for i in range(len(results)): if results[i].error: logging.error(results[i].error) else: walkers_list[i]["con"] = results[i].actor_id # 4. we put altogether the walkers and controllers id to get the objects from their id for i in range(len(walkers_list)): all_id.append(walkers_list[i]["con"]) all_id.append(walkers_list[i]["id"]) all_actors = world.get_actors(all_id) # wait for a tick to ensure client receives the last transform of the walkers we have just created world.wait_for_tick() # 5. initialize each controller and set target to walk to (list is [controler, actor, controller, actor ...]) # set how many pedestrians can cross the road world.set_pedestrians_cross_factor(percentagePedestriansCrossing) for i in range(0, len(all_id), 2): # start walker all_actors[i].start() # set walk to random point all_actors[i].go_to_location(world.get_random_location_from_navigation()) # max speed all_actors[i].set_max_speed(float(walker_speed[int(i/2)])) print('Spawned %d vehicles and %d walkers.' % (len(vehicles_list), len(walkers_list))) return vehicles_list, walkers_list, all_id, all_actors, sensors_ret # ====================================================================================================================== # -- Benchmarking functions -------------------------------------------------------------------------------------------- # ====================================================================================================================== def run_benchmark(world, sensors, n_vehicles, n_walkers, client, debug=False): global sensors_callback spawn_points = world.get_map().get_spawn_points() n = min(n_vehicles, len(spawn_points)) list_fps = [] sensor_list = None vehicles_list, walkers_list, all_id, all_actors, sensors_ret = create_environment(world, sensors, n, n_walkers, spawn_points, client) if sensors_ret: sensor_list = sensors_ret ticks = 0 while ticks < int(args.ticks): _ = world.wait_for_tick() if debug: print("== Samples {} / {}".format(ticks + 1, args.ticks)) min_fps = float('inf') for sc in sensors_callback: fps = sc.get_fps() if fps < min_fps: min_fps = fps if math.isinf(min_fps): min_fps = 0 list_fps.append(min_fps) ticks += 1 for sensor in sensor_list: sensor.stop() sensor.destroy() sensors_callback.clear() print('Destroying %d vehicles.\n' % len(vehicles_list)) client.apply_batch([carla.command.DestroyActor(x) for x in vehicles_list]) # stop walker controllers (list is [controller, actor, controller, actor ...]) for i in range(0, len(all_id), 2): all_actors[i].stop() print('\ndestroying %d walkers' % len(walkers_list)) client.apply_batch([carla.command.DestroyActor(x) for x in all_id]) return list_fps def compute_mean_std(list_values): np_values = np.array(list_values) mean = np.mean(np_values) std = np.std(np_values) return mean, std def serialize_records(records, system_specs, filename): with open(filename, 'w+') as fd: s = "| Town | Sensors | Weather | # of Vehicles | # of Walkers | Samples | Mean FPS | Std FPS |\n" s += "| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |\n" fd.write(s) for sensor_key in sorted(records.keys()): list_records = records[sensor_key] for record in list_records: s = "| {} | {} | {} | {} | {} | {} | {:03.2f} | {:03.2f} |\n".format(record['town'], record['sensors'], record['weather'], record['n_vehicles'], record['n_walkers'], record['samples'], record['fps_mean'], record['fps_std']) fd.write(s) s = "\n| Global mean FPS | Global std FPS |\n" s += "| **{:03.2f}** | **{:03.2f}** |\n".format(*get_total(records)) fd.write(s) s = "Table: {}.\n".format(system_specs) fd.write(s) def get_total(records): record_vals = [item for sublist in records.values() for item in sublist] total_mean_fps = sum([r['fps_mean'] for r in record_vals]) / len(record_vals) total_mean_std = sum([r['fps_std'] for r in record_vals]) / len(record_vals) return total_mean_fps, total_mean_std def get_system_specs(): str_system = "" cpu_info = cpuinfo.get_cpu_info() str_system += "CPU {} {}. ".format(cpu_info['brand'], cpu_info['family']) memory_info = psutil.virtual_memory() str_system += "{:03.2f} GB RAM memory. ".format(memory_info.total / (1024 * 1024 * 1024)) nvidia_cmd = shutil.which("nvidia-smi") if nvidia_cmd: str_system += "GPU " gpu_info = GPUtil.getGPUs() for gpu in gpu_info: str_system += "{} ".format(gpu.name) return str_system def main(args): client = carla.Client(args.host, int(args.port)) client.set_timeout(60.0) pygame.init() records = {} maps = [m.replace('/Game/Carla/Maps/', '') for m in client.get_available_maps()] for town in sorted(maps): world = client.load_world(town) # set to async mode settings = world.get_settings() settings.synchronous_mode = False settings.fixed_delta_seconds = None world.apply_settings(settings) # spectator pointing to the sky to reduce rendering impact spectator = world.get_spectator() spectator.set_transform(carla.Transform(carla.Location(z=500), carla.Rotation(pitch=90))) for weather in define_weather(): world.set_weather(weather["parameter"]) for env in define_environments(): for sensors in define_sensors(): list_fps = run_benchmark(world, sensors, env["vehicles"], env["walkers"], client) mean, std = compute_mean_std(list_fps) sensor_str = "" for sensor in sensors: sensor_str += (sensor['label'] + " ") record = { 'town': town, 'sensors': sensor_str, 'weather': weather["name"], 'n_vehicles': env["vehicles"], 'n_walkers': env["walkers"], 'samples': args.ticks, 'fps_mean': mean, 'fps_std': std } env_str = str(env["vehicles"]) + str(env["walkers"]) if env_str not in records: records[env_str] = [] records[env_str].append(record) print(record) system_specs = get_system_specs() serialize_records(records, system_specs, args.file) pygame.quit() if __name__ == '__main__': description = "Benchmark CARLA performance in your platform for different towns and sensor or traffic configurations.\n" parser = argparse.ArgumentParser(description=description) parser.add_argument('--host', default='localhost', help='IP of the host server (default: localhost)') parser.add_argument('--port', default='2000', help='TCP port to listen to (default: 2000)') parser.add_argument('--file', type=str, help='Write results into a txt file', default="benchmark.md") parser.add_argument('--tm', action='store_true', help='Switch to traffic manager benchmark') parser.add_argument('--ticks', default=100, help='Number of ticks for each scenario (default: 100)') args = parser.parse_args() main(args)