run_individual_migration.py

import argparse
import logging
import os

import numpy as np

from config import (
    MOCK_SIM_NAME,
    SIMULATOR_NAMES,
    NUM_CONTROL_NODES,
    MAX_ANGLE,
    NUM_SAMPLED_POINTS,
    AGENT_TYPES,
)
from envs.beamng.config import MAP_SIZE
from factories import make_test_generator, make_env, make_agent
from global_log import GlobalLog
from test_generators.mapelites.config import (
    FEATURE_COMBINATIONS,
    TURNS_COUNT_FEATURE_NAME,
    CURVATURE_FEATURE_NAME,
)
from test_generators.mapelites.mapelites import MapElites
from utils.randomness import set_random_seed
from utils.report_utils import load_individual_report

parser = argparse.ArgumentParser()
parser.add_argument(
    "--folder", help="Path of the folder where the logs are", type=str, default="logs"
)
parser.add_argument(
    "--filepath",
    help="Paths of the folders where the reports are",
    type=str,
    required=True,
)
# run arguments
parser.add_argument(
    "--env-name",
    help="Should be the name of the third simulator",
    type=str,
    choices=SIMULATOR_NAMES,
)
parser.add_argument(
    "--donkey-exe-path",
    help="Path to the donkey simulator executor",
    type=str,
    default=None,
)
parser.add_argument(
    "--udacity-exe-path",
    help="Path to the udacity simulator executor",
    type=str,
    default=None,
)
parser.add_argument(
    "--beamng-user-path", help="Beamng user path", type=str, default=None
)
parser.add_argument(
    "--beamng-home-path", help="Beamng home path", type=str, default=None
)
parser.add_argument("--seed", help="Random seed", type=int, default=-1)
parser.add_argument(
    "--add-to-port", help="Modify default simulator port", type=int, default=-1
)
parser.add_argument(
    "--headless", help="Headless simulation", action="store_true", default=False
)
parser.add_argument(
    "--agent-type", help="Agent type", type=str, choices=AGENT_TYPES, default="random"
)
parser.add_argument(
    "--test-generator",
    help="Which test generator to use",
    type=str,
    choices=["random"],
    default="random",
)
parser.add_argument(
    "--model-path",
    help="Path to agent model with extension (only if agent_type == 'supervised')",
    type=str,
    default=None,
)
parser.add_argument(
    "--predict-throttle",
    help="Predict steering and throttle. Model to load must have been trained using an output dimension of 2",
    action="store_true",
    default=False,
)
parser.add_argument(
    "--feature-combination",
    help="Feature combination",
    type=str,
    choices=FEATURE_COMBINATIONS,
    default="{}-{}".format(TURNS_COUNT_FEATURE_NAME, CURVATURE_FEATURE_NAME),
)
parser.add_argument(
    "--collect-images",
    help="Collect images during execution",
    action="store_true",
    default=False,
)
# cyclegan options
parser.add_argument(
    "--cyclegan-experiment-name",
    type=str,
    default=None,
    help="name of the experiment. It decides where to store samples and models",
)
parser.add_argument(
    "--gpu-ids",
    type=str,
    default="-1",
    help="gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU",
)
parser.add_argument(
    "--cyclegan-checkpoints-dir", type=str, default=None, help="models are saved here"
)
parser.add_argument(
    "--cyclegan-epoch",
    type=str,
    default=-1,
    help="which epoch to load? set to latest to use latest cached model",
)

args = parser.parse_args()

if __name__ == "__main__":

    logg = GlobalLog("run_individual_migration")

    # load probability map by default
    report = load_individual_report(filepath=os.path.join(args.folder, args.filepath))
    individuals = [
        individual
        for feature_bin in report.keys()
        for individual in report[feature_bin]
    ]

    if args.seed == -1:
        args.seed = np.random.randint(2**30 - 1)

    set_random_seed(seed=args.seed)

    test_generator = make_test_generator(
        generator_name=args.test_generator,
        map_size=MAP_SIZE,
        simulator_name=args.env_name,
        agent_type=args.agent_type,
        num_control_nodes=NUM_CONTROL_NODES,
        max_angle=MAX_ANGLE,
        num_spline_nodes=NUM_SAMPLED_POINTS,
    )

    env = make_env(
        simulator_name=args.env_name,
        seed=args.seed,
        port=args.add_to_port,
        test_generator=test_generator,
        donkey_exe_path=args.donkey_exe_path,
        udacity_exe_path=args.udacity_exe_path,
        beamng_home=args.beamng_home_path,
        beamng_user=args.beamng_user_path,
        headless=args.headless,
        beamng_autopilot=args.agent_type == "autopilot",
        cyclegan_experiment_name=args.cyclegan_experiment_name,
        gpu_ids=args.gpu_ids,
        cyclegan_checkpoints_dir=args.cyclegan_checkpoints_dir,
        cyclegan_epoch=args.cyclegan_epoch,
    )
    agent = make_agent(
        env_name=args.env_name,
        env=env,
        model_path=args.model_path,
        agent_type=args.agent_type,
        predict_throttle=args.predict_throttle,
        fake_images=args.cyclegan_experiment_name is not None
        and args.cyclegan_checkpoints_dir is not None
        and args.cyclegan_epoch != -1,
    )

    logg.info("Disabling Shapely logs")
    for id in ["shapely.geos"]:
        l = logging.getLogger(id)
        l.setLevel(logging.CRITICAL)
        l.disabled = True

    mapelites = MapElites(
        env=env,
        env_name=args.env_name,
        agent=agent,
        filepath=args.folder,
        min_angle=0,
        max_angle=1,  # to pass the assertion
        mutation_extent=0,
        population_size=0,
        mock_evaluator=args.env_name == MOCK_SIM_NAME,
        iteration_runtime=0,
        test_generator=test_generator,
        individual_migration=True,
        feature_combination=args.feature_combination,
        port=args.add_to_port,
        donkey_exe_path=args.donkey_exe_path,
        udacity_exe_path=args.udacity_exe_path,
        beamng_home=args.beamng_home_path,
        beamng_user=args.beamng_user_path,
        headless=args.headless,
        collect_images=args.collect_images,
        cyclegan_experiment_name=args.cyclegan_experiment_name,
        gpu_ids=args.gpu_ids,
        cyclegan_checkpoints_dir=args.cyclegan_checkpoints_dir,
        cyclegan_epoch=args.cyclegan_epoch,
    )
    # load probability map by default
    mapelites.execute_individuals_and_place_in_map(
        individuals=individuals, occupation_map=False
    )