def test_database_runner_python_behavior(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=2)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
safe_dist_params = ParameterServer(log_if_default=True)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount",
"safe_dist_lon": {
"type": "EvaluatorDynamicSafeDist",
"params": safe_dist_params
},
"safe_dist_lat": {
"type": "EvaluatorStaticSafeDist",
"params": safe_dist_params
}
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2,
"safe_dist_lon": lambda x: x
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {"python_behavior": PythonDistanceBehavior(params)}
benchmark_runner = BenchmarkRunner(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=5,
deepcopy=False)
benchmark_runner.clear_checkpoint_dir()
result = benchmark_runner.run()
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
1 * 2 * 2) # 2 Behaviors * 2 Serialize Scenarios * 1 scenario sets
result.load_benchmark_configs()
groups = result.get_evaluation_groups()
self.assertEqual(set(groups), set(["behavior", "scen_set"]))
def test_database2(self):
# last params must be only passed for testing purposes not for release!
dbs = DatabaseSerializer(test_scenarios=1,
test_world_steps=10,
num_serialize_scenarios=1)
test_result = dbs.process("data/data2/database2")
self.assertEqual(test_result, 3)
dbs.release(version="0.0.1")
def test_database_multiprocessing_history(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=2)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantVelocity(params)
}
benchmark_runner = BenchmarkRunnerMP(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=10)
rst = benchmark_runner.run(maintain_history=True)
self.assertEqual(len(rst.get_histories()), 2 * 2 * 2)
rst = benchmark_runner.run_benchmark_config(3,
viewer=None,
maintain_history=True)
scenario_history = rst.get_histories()[3]
print(scenario_history)
params = ParameterServer()
viewer = MPViewer(params=params,
x_range=[5060, 5160],
y_range=[5070, 5150],
use_world_bounds=True)
viewer.drawWorld(world=scenario_history[1].GetWorldState(),
eval_agent_ids=scenario_history[1].eval_agent_ids)
viewer.show(block=True)
def test_database_multiprocessing_runner(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=5)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
test_python_params = ParameterServer(log_if_default=True)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount",
"test_evaluator": {
"type": "TestPythonEvaluator",
"params": test_python_params
}
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantAcceleration(params)
}
EvaluationConfig.AddEvaluationModule(
"bark.benchmark.tests.test_evaluator")
benchmark_runner = BenchmarkRunnerMP(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=10)
benchmark_runner.clear_checkpoint_dir()
result = benchmark_runner.run()
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
20) # 2 Behaviors * 5 Serialize Scenarios * 2 scenario sets
def test_database_multiprocessing_runner(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=5)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantVelocity(params)
}
benchmark_runner = BenchmarkRunnerMP(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=10)
result = benchmark_runner.run()
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
20) # 2 Behaviors * 5 Serialize Scenarios * 2 scenario sets
params2 = ParameterServer()
viewer = MPViewer(params=params2,
x_range=[5060, 5160],
y_range=[5070, 5150],
use_world_bounds=True)
rst = benchmark_runner.run_benchmark_config(10, viewer=viewer)
def test_database_runner_checkpoint(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=10)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantAcceleration(params)
}
benchmark_runner = BenchmarkRunner(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=20,
checkpoint_dir="checkpoints1/")
benchmark_runner.clear_checkpoint_dir()
# one run after 30 steps benchmark dumped
result = benchmark_runner.run(checkpoint_every=30)
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
40) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
# check twice first, merging from checkpoints
merged_result = BenchmarkRunner.merge_checkpoint_benchmark_results(
checkpoint_dir="checkpoints1/")
df = merged_result.get_data_frame()
self.assertEqual(len(df.index), 40)
def test_database_from_local_release(self):
# release the database
dbs = DatabaseSerializer(test_scenarios=1,
test_world_steps=10,
num_serialize_scenarios=1)
dbs.process("data/database1")
local_release_filename = dbs.release(version="0.0.1")
# then reload to test correct parsing
db = BenchmarkDatabase(database_root=local_release_filename)
scenario_generation, _, set_parameters = db.get_scenario_generator(
scenario_set_id=1)
self.assertEqual(db.get_num_scenario_sets(), 2)
self.assertEqual(set_parameters["Test1"], 200)
self.assertEqual(set_parameters["Test2"], 0.5)
db_filtered = db.apply_filter("40")
self.assertEqual(db_filtered.get_num_scenario_sets(), 1)
for scenario_generation, _, params in db:
print(scenario_generation)
def test_database_runner(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=2)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantVelocity(params)
}
benchmark_runner = BenchmarkRunner(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=5)
result = benchmark_runner.run()
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
2 * 2 * 2) # 2 Behaviors * 2 Serialize Scenarios * 1 scenario sets
groups = result.get_evaluation_groups()
self.assertEqual(set(groups), set(["behavior", "scen_set"]))
def main():
args = configure_args()
if is_local:
dir_prefix = ""
else:
dir_prefix="hy-iqn-lfd-full-beliefs-exp.runfiles/hythe/"
logging.info(f"Executing job: {args.jobname}")
logging.info(f"Experiment server at: {os.getcwd()}")
params = ParameterServer(filename=os.path.join(dir_prefix, params_file),
log_if_default=True)
params = configure_params(params, seed=args.jobname)
experiment_id = params["Experiment"]["random_seed"]
params_filename = os.path.join(params["Experiment"]["dir"], "params_{}.json".format(experiment_id))
params_behavior_filename = os.path.join(params["Experiment"]["dir"], "behavior_params_{}.json".format(experiment_id))
params_behavior = ParameterServer(filename=os.path.join(dir_prefix, "configuration/params/1D_desired_gap_no_prior.json"),
log_if_default=True)
params_behavior.Save(filename=params_behavior_filename)
splits = 2
behavior_space = BehaviorSpace(params_behavior)
hypothesis_set, hypothesis_params = behavior_space.create_hypothesis_set_fixed_split(split=splits)
observer = BeliefObserver(params, hypothesis_set, splits=splits)
behavior = BehaviorDiscreteMacroActionsML(params)
evaluator = GoalReached(params)
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
# extract params and save experiment parameters
params["ML"]["BaseAgent"]["SummaryPath"] = os.path.join(params["Experiment"]["dir"], "agent/summaries")
params["ML"]["BaseAgent"]["CheckpointPath"] = os.path.join(params["Experiment"]["dir"], "agent/checkpoints")
params.Save(filename=params_filename)
logging.info('-' * 60)
logging.info("Writing params to :{}".format(params_filename))
logging.info('-' * 60)
# database creation
dbs1 = DatabaseSerializer(test_scenarios=1, test_world_steps=2,
num_serialize_scenarios=num_demo_scenarios)
dbs1.process(os.path.join(dir_prefix, "configuration/database"),
filter_sets="**/**/interaction_merging_light_dense_1D.json")
local_release_filename = dbs1.release(version="lfd_offline")
db1 = BenchmarkDatabase(database_root=local_release_filename)
scenario_generator1, _, _ = db1.get_scenario_generator(0)
env = HyDiscreteHighway(params=params,
scenario_generation=scenario_generator1,
behavior=behavior,
evaluator=evaluator,
observer=observer,
viewer=viewer,
render=False)
scenario, _ = scenario_generator1.get_next_scenario()
world = scenario.GetWorldState()
observer.Reset(world)
assert env.action_space._n == 8, "Action Space is incorrect!"
run(args, params, env, db=db1)
params.Save(params_filename)
logging.info('-' * 60)
logging.info("Writing params to :{}".format(params_filename))
logging.info('-' * 60)
def run(args, params, env, exp_exists=False, db=None):
agent = None
demonstrations = None
exp = None
columns = ['pos_x', 'pos_y', 'orientation', 'velocity', 'action', 'is_demo']
# add an eval criteria and generate demonstrations
if is_generate_demonstrations:
logging.info("Generating Demonstrations")
eval_criteria = {"goal_reached" : lambda x : x}
demo_behavior, mcts_params = generate_uct_hypothesis_behavior()
demonstrations = generate_demonstrations(params, env, eval_criteria, demo_behavior, db=db)
logging.info(f"Total demonstrations generated {len(demonstrations)}")
ego_world_states = unpack_demo_states(demonstrations, env._observer)
save_transitions(params, ego_world_states, columns=columns,
filename="demonstrations/demo_dataframe")
print("Training off", env._observer._world_x_range, env._observer._world_y_range)
if is_train_on_demonstrations:
# if demonstrations were not generated in this run
if not is_generate_demonstrations:
collector, demonstrations = unpack_load_demonstrations(demo_dir_default)
else:
if args.demodir is None:
collector, demonstrations = unpack_load_demonstrations(params["Experiment"]["dir"])
else:
collector, demonstrations = unpack_load_demonstrations(args.demodir)
if params["ML"]["BaseAgent"]["Multi_step"] is not None:
multistep_capacity = capacity + params["ML"]["BaseAgent"]["Multi_step"] - 1
if multistep_capacity < len(demonstrations):
demonstrations = demonstrations[-multistep_capacity:]
logging.info(f"Pruned number of demonstrations {len(demonstrations)}")
else:
logging.info("Number of demonstrations under capacity requested, using full demonstrations")
logging.info(f"Loaded number of demonstrations {len(demonstrations)}")
if is_local:
# Assign steps by args
params["ML"]["DemonstratorAgent"]["Agent"]["online_gradient_update_steps"] = args.grad_update_steps
# Assign capacity by length of demonstrations
params["ML"]["BaseAgent"]["MemorySize"] = len(demonstrations)
params["ML"]["DemonstratorAgent"]["Buffer"]["demo_ratio"] = 1.0
logging.info(f"Capacity configured {len(demonstrations)}")
agent = configure_agent(params, env)
exp = Experiment(params=params, agent=agent, dump_scenario_interval=25000)
exp.run(demonstrator=True, demonstrations=demonstrations,
num_episodes=num_episodes, learn_only=True)
if is_train_mixed_experiences:
assert agent is not None
params["ML"]["DemonstratorAgent"]["Buffer"]["demo_ratio"] = 0.25
params["ML"]["BaseAgent"]["Update_interval"] = 4
agent.reset_params(params)
agent.reset_training_variables(is_online_demo=is_train_mixed_experiences)
if is_local:
dir_prefix = ""
else:
dir_prefix="hy-iqn-lfd-full-beliefs-exp.runfiles/hythe/"
# database creation
dbs2 = DatabaseSerializer(test_scenarios=1, test_world_steps=2,
num_serialize_scenarios=num_scenarios)
dbs2.process(os.path.join(dir_prefix, "configuration/database"),
filter_sets="**/**/interaction_merging_light_dense_1D.json")
local_release_filename = dbs2.release(version="test_online")
db2 = BenchmarkDatabase(database_root=local_release_filename)
scenario_generator2, _, _ = db2.get_scenario_generator(0)
agent._env.scenario_generation = scenario_generator2
exp.run(demonstrator=True, demonstrations=None, num_episodes=num_episodes)
ego_world_states = extract_learned_states(agent.memory, env._observer)
save_transitions(params, ego_world_states, columns=columns,
filename="demonstrations/learned_dataframe")
print("Training on", env._observer._world_x_range, env._observer._world_y_range)
def main():
map_filename = "external/bark_ml_project/bark_ml/environments/blueprints/highway/city_highway_straight.xodr"
params = ParameterServer(filename=os.path.join(
exp_root, "params_iqn_pre_exp_2sdi64.json"),
log_if_default=True)
params_behavior = ParameterServer(filename=os.path.join(
exp_root, "behavior_params_iqn_pre_exp_2sdi64.json"))
behavior = BehaviorDiscreteMacroActionsML(params)
evaluator = GoalReached(params)
if is_belief_observer:
splits = 2
behavior_space = BehaviorSpace(params_behavior)
hypothesis_set, hypothesis_params = behavior_space.create_hypothesis_set_fixed_split(
split=splits)
observer = BeliefObserver(params, hypothesis_set, splits=splits)
else:
observer = NearestAgentsObserver(params)
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
# database creation
dbs1 = DatabaseSerializer(test_scenarios=1,
test_world_steps=2,
num_serialize_scenarios=1)
dbs1.process(os.path.join("", "configuration/database"),
filter_sets="**/**/interaction_merging_light_dense_1D.json")
local_release_filename = dbs1.release(version="lfd_offline")
db1 = BenchmarkDatabase(database_root=local_release_filename)
scenario_generator1, _, _ = db1.get_scenario_generator(0)
env = HyDiscreteHighway(params=params,
scenario_generation=scenario_generator1,
map_filename=map_filename,
behavior=behavior,
evaluator=evaluator,
observer=observer,
viewer=viewer,
render=False)
scenario, _ = scenario_generator1.get_next_scenario()
world = scenario.GetWorldState()
observer.Reset(world)
env.reset()
agent = pick_agent(exp_root, env, params)
ego_world_states = []
memory = agent.memory
learned_states = memory["state"]
actions = memory['action']
is_demos = memory['is_demo']
for state, action, is_demo in zip(learned_states, actions, is_demos):
ego_state = np.zeros((observer._len_ego_state + 1))
ego_nn_input_state = deepcopy(state[0:observer._len_ego_state])
ego_state[1:] = ego_nn_input_state
reverted_observed_state = observer.rev_observe_for_ego_vehicle(
ego_state)
ego_world_states.append(
(reverted_observed_state[int(StateDefinition.X_POSITION)],
reverted_observed_state[int(StateDefinition.Y_POSITION)],
reverted_observed_state[int(StateDefinition.THETA_POSITION)],
reverted_observed_state[int(StateDefinition.VEL_POSITION)],
action[0], is_demo[0]))
df = pd.DataFrame(ego_world_states,
columns=[
'pos_x', 'pos_y', 'orientation', 'velocity',
'action', 'is_demo'
])
print(df.head(10))
if not os.path.exists(os.path.join(exp_root, "demonstrations")):
os.makedirs(os.path.join(exp_root, "demonstrations"))
df.to_pickle(os.path.join(exp_root, "demonstrations", "learned_dataframe"))
return
def main():
print("Experiment server at :", os.getcwd())
args = configure_args()
#load exp params
exp_dir = args.output_dir
params_filename = glob.glob(os.path.join(exp_dir, "params_[!behavior]*"))
params = ParameterServer(filename=params_filename[0])
params.load(fn=params_filename[0])
params["ML"]["BaseAgent"]["SummaryPath"] = os.path.join(exp_dir, "agent/summaries")
params["ML"]["BaseAgent"]["CheckpointPath"] = os.path.join(exp_dir, "agent/checkpoints")
splits = 8
behavior_params_filename = glob.glob(os.path.join(exp_dir, "behavior_params*"))
if behavior_params_filename:
params_behavior = ParameterServer(filename=behavior_params_filename[0])
else:
params_behavior = ParameterServer(filename="configuration/params/1D_desired_gap_no_prior.json")
behavior_space = configure_behavior_space(params_behavior)
hypothesis_set, hypothesis_params = behavior_space.create_hypothesis_set_fixed_split(split=splits)
observer = BeliefObserver(params, hypothesis_set, splits=splits)
behavior = BehaviorDiscreteMacroActionsML(params_behavior)
evaluator = GoalReached(params)
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
# database creation
dir_prefix = ""
dbs = DatabaseSerializer(test_scenarios=2, test_world_steps=2,
num_serialize_scenarios=10)
dbs.process(os.path.join(dir_prefix, "configuration/database"), filter_sets="**/**/interaction_merging_light_dense_1D.json")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
scenario_generator, _, _ = db.get_scenario_generator(0)
video_renderer = VideoRenderer(renderer=viewer, world_step_time=0.2)
env = HyDiscreteHighway(params=params,
scenario_generation=scenario_generator,
behavior=behavior,
evaluator=evaluator,
observer=observer,
viewer=video_renderer,
render=is_local)
# non-agent evaluation mode
num_steps = 100
num_samples = params_behavior["BehaviorSpace"]["Hypothesis"]["BehaviorHypothesisIDM"]["NumSamples"]
print("Steps, samples, splits", num_steps, num_samples, splits)
step = 1
env.reset()
threshold = observer.is_enabled_threshold
discretize = observer.is_discretize
beliefs_df = pd.DataFrame(columns=["Step", "Action", "Agent", "Beliefs", "HyNum"])
beliefs_orig_df = pd.DataFrame(columns=["Step", "Action", "Agent", "Beliefs", "HyNum"])
while step <= num_steps:
action = 5 #np.random.randint(0, behavior.action_space.n)
next_state, reward, done, info = env.step(action)
for agent, beliefs in observer.beliefs.items():
beliefs = np.asarray(beliefs)
oring = deepcopy(beliefs)
for i, belief in enumerate(oring):
beliefs_orig_df = beliefs_orig_df.append({"Step": step, "Action": action, "Agent": agent, "Beliefs": belief, "HyNum": i}, ignore_index=True)
if discretize:
beliefs = observer.discretize_beliefs(beliefs)
if threshold:
beliefs = observer.threshold_beliefs(beliefs)
for i, belief in enumerate(beliefs):
beliefs_df = beliefs_df.append({"Step": step, "Action": action, "Agent": agent, "Beliefs": belief, "HyNum": i}, ignore_index=True)
step += 1
suffix = "switch"
if threshold:
suffix += "_threshold"
if discretize:
suffix += "_discretize"
beliefs_data_filename = "beliefs_{}_{}_{}".format(splits, num_samples, num_steps)
beliefs_data_filename += suffix
print(beliefs_data_filename)
beliefs_df.to_pickle(os.path.join(str(Path.home()), "master_thesis/code/hythe-src/beliefs_data/", beliefs_data_filename))
beliefs_data_filename = "orig_beliefs_{}_{}_{}".format(splits, num_samples, num_steps)
beliefs_data_filename += suffix
print(beliefs_data_filename)
beliefs_orig_df.to_pickle(os.path.join(str(Path.home()), "master_thesis/code/hythe-src/beliefs_data/", beliefs_data_filename))
video_filename = os.path.join(str(Path.home()), "master_thesis/code/hythe-src/beliefs_data/", "video_{}".format(num_samples))
print(video_filename)
video_filename += suffix
print(video_filename)
video_renderer.export_video(filename=video_filename)
return
def main():
args = configure_args()
if is_local:
dir_prefix = ""
else:
dir_prefix = "hy-iqn-exp.runfiles/hythe/"
print("Executing job :", args.jobname)
print("Experiment server at :", os.getcwd())
params = ParameterServer(filename=os.path.join(
dir_prefix, "configuration/params/iqn_params.json"),
log_if_default=True)
params = configure_params(params, seed=args.jobname)
experiment_id = params["Experiment"]["random_seed"]
params_filename = os.path.join(params["Experiment"]["dir"],
"params_{}.json".format(experiment_id))
# check if exp exists and handle preemption
exp_exists = check_if_exp_exists(params)
if exp_exists:
print("Loading existing experiment from: {}".format(
args.jobname, (params["Experiment"]["dir"])))
if os.path.isfile(params_filename):
params = ParameterServer(filename=params_filename,
log_if_default=True)
else:
Path(params["Experiment"]["dir"]).mkdir(parents=True, exist_ok=True)
behavior = BehaviorDiscreteMacroActionsML(params)
evaluator = GoalReached(params)
observer = NearestAgentsObserver(params)
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
# extract params and save experiment parameters
params["ML"]["BaseAgent"]["SummaryPath"] = os.path.join(
params["Experiment"]["dir"], "agent/summaries")
params["ML"]["BaseAgent"]["CheckpointPath"] = os.path.join(
params["Experiment"]["dir"], "agent/checkpoints")
params.Save(filename=params_filename)
logging.info('-' * 60)
logging.info("Writing params to :{}".format(params_filename))
logging.info('-' * 60)
# database creation
dbs = DatabaseSerializer(test_scenarios=2,
test_world_steps=2,
num_serialize_scenarios=num_scenarios)
dbs.process(os.path.join(dir_prefix, "configuration/database"),
filter_sets="**/**/interaction_merging_light_dense_1D.json")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
scenario_generator, _, _ = db.get_scenario_generator(0)
env = HyDiscreteHighway(params=params,
scenario_generation=scenario_generator,
behavior=behavior,
evaluator=evaluator,
observer=observer,
viewer=viewer,
render=False)
assert env.action_space._n == 8, "Action Space is incorrect!"
run(params, env, exp_exists)
params.Save(params_filename)
logging.info('-' * 60)
logging.info("Writing params to :{}".format(params_filename))
logging.info('-' * 60)
def test_database_runner_checkpoint(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=10)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantVelocity(params)
}
benchmark_runner = BenchmarkRunner(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=20,
checkpoint_dir="checkpoints1/")
# one run after 30 steps benchmark dumped
result = benchmark_runner.run(checkpoint_every=30)
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
40) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
# check twice first, merging from checkpoints
merged_result = BenchmarkRunner.merge_checkpoint_benchmark_results(
checkpoint_dir="checkpoints1/")
df = merged_result.get_data_frame()
self.assertEqual(len(df.index), 30)
# second load merged results
self.assertTrue(
os.path.exists(os.path.join("checkpoints1/merged_results.ckpnt")))
merged_result = BenchmarkRunner.merge_checkpoint_benchmark_results(
checkpoint_dir="checkpoints1/")
df = merged_result.get_data_frame()
self.assertEqual(len(df.index), 30)
configs_to_run = BenchmarkRunner.get_configs_to_run(
benchmark_runner.configs_to_run, merged_result)
self.assertEqual(len(configs_to_run), 10)
benchmark_runner2 = BenchmarkRunner(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=1,
checkpoint_dir="checkpoints1/",
merge_existing=True)
result = benchmark_runner2.run(checkpoint_every=7)
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
40) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
# check if results maintained in existing result dump, 30 from previous run + 7 after new checkpoint
merged_result = BenchmarkRunner.merge_checkpoint_benchmark_results(
checkpoint_dir="checkpoints1/")
df = merged_result.get_data_frame()
self.assertEqual(len(df.index), 37)
def test_database_multiprocessing_runner_checkpoint(self):
dbs = DatabaseSerializer(test_scenarios=1,
test_world_steps=2,
num_serialize_scenarios=10)
dbs.process("data/database1")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 2
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantVelocity(params)
}
benchmark_runner = BenchmarkRunnerMP(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=10,
num_cpus=4,
checkpoint_dir="checkpoints2/",
merge_existing=False)
benchmark_runner.clear_checkpoint_dir()
# one run after 30 steps benchmark dumped
result = benchmark_runner.run(checkpoint_every=3)
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
40) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
merged_result = BenchmarkRunner.merge_checkpoint_benchmark_results(
checkpoint_dir="checkpoints2/")
df = merged_result.get_data_frame()
self.assertEqual(len(df.index), 4 * 9)
# self.assertEqual(len(merged_result.get_histories()), 4*9)
self.assertEqual(len(merged_result.get_benchmark_configs()), 4 * 9)
configs_to_run = BenchmarkRunner.get_configs_to_run(
benchmark_runner.configs_to_run, merged_result)
self.assertEqual(len(configs_to_run), 4)
ray.shutdown()
benchmark_runner2 = BenchmarkRunnerMP(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=1,
checkpoint_dir="checkpoints2/",
merge_existing=True)
result = benchmark_runner2.run(checkpoint_every=1)
df = result.get_data_frame()
print(df)
self.assertEqual(
len(df.index),
40) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
# check if existing result is incorporated for mergin result
merged_result = BenchmarkRunner.merge_checkpoint_benchmark_results(
checkpoint_dir="checkpoints2/")
df = merged_result.get_data_frame()
self.assertEqual(len(df.index), 40)
env = Runtime(0.2, viewer, scenario_generator, render=True)
env.reset()
env._world.agents[
env._scenario._eval_agent_ids[0]].behavior_model = ml_behavior
print(ml_behavior)
for _ in range(0, 50):
env.step()
# to find database files
os.chdir("../benchmark_database/")
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=10)
dbs.process("database")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {"collision": lambda x: x, "max_steps": lambda x: x > 31}
behaviors_tested = {"bark_ml": ml_behavior}
benchmark_runner = BenchmarkRunner(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
agent_dir = os.path.join(exp_dir, 'agent')
return IQNAgent(params=params, env=env, agent_save_dir=agent_dir, checkpoint_load=checkpoint)
def resume_experiment(params, num_episodes, agent):
exp = Experiment(params=params, agent=agent)
exp.resume(num_episodes)
args = configure_args()
exp_dir = args.exp_dir
num_episodes = args.num_episodes
params_filename, params = load_params(exp_dir)
dbs = DatabaseSerializer(test_scenarios=2, test_world_steps=20, num_serialize_scenarios=num_scenarios)
dbs.process("configuration/database", filter_sets="**/**/interaction_merging_light_dense_1D.json")
local_release_filename = dbs.release(version="resume")
db = BenchmarkDatabase(database_root=local_release_filename)
scenario_generator, _, _ = db.get_scenario_generator(0)
# load belief observer specifics
if is_belief_observer:
splits = 2
behavior_params_filename = glob.glob(os.path.join(exp_dir, "behavior_params*"))[0]
params_behavior = ParameterServer(filename=behavior_params_filename, log_if_default=True)
behavior_space = BehaviorSpace(params_behavior)
hypothesis_set, hypothesis_params = behavior_space.create_hypothesis_set_fixed_split(split=splits)
observer = BeliefObserver(params, hypothesis_set, splits=splits)
behavior = BehaviorDiscreteMacroActionsML(params_behavior)
def test_database_run_and_analyze(self):
dbs = DatabaseSerializer(test_scenarios=4,
test_world_steps=5,
num_serialize_scenarios=10)
dbs.process("database")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
evaluators = {
"success": "EvaluatorGoalReached",
"collision": "EvaluatorCollisionEgoAgent",
"max_steps": "EvaluatorStepCount"
}
terminal_when = {
"collision": lambda x: x,
"max_steps": lambda x: x > 5
}
params = ParameterServer(
) # only for evaluated agents not passed to scenario!
behaviors_tested = {
"IDM": BehaviorIDMClassic(params),
"Const": BehaviorConstantVelocity(params)
}
benchmark_runner = BenchmarkRunnerMP(benchmark_database=db,
evaluators=evaluators,
terminal_when=terminal_when,
behaviors=behaviors_tested,
log_eval_avg_every=10)
result = benchmark_runner.run(maintain_history=True)
result.dump(os.path.join("./benchmark_results.pickle"))
result_loaded = BenchmarkResult.load(
os.path.join("./benchmark_results.pickle"))
params2 = ParameterServer()
fig = plt.figure(figsize=[10, 10])
viewer = MPViewer(params=params2,
center=[5112, 5165],
y_length=120,
enforce_y_length=True,
axis=fig.gca())
analyzer = BenchmarkAnalyzer(benchmark_result=result_loaded)
configs = analyzer.find_configs(criteria={
"behavior": lambda x: x == "IDM",
"success": lambda x: not x
})
configs_const = analyzer.find_configs(criteria={
"behavior": lambda x: x == "Const",
"success": lambda x: not x
})
#analyzer.visualize(configs_idx_list = configs,
# viewer = viewer, real_time_factor=10, fontsize=12)
plt.close(fig)
fig, (ax1, ax2) = plt.subplots(1, 2)
viewer1 = MPViewer(params=params2,
center=[5112, 5165],
y_length=120,
enforce_y_length=True,
axis=ax1)
viewer2 = MPViewer(params=params2,
center=[5112, 5165],
y_length=120,
enforce_y_length=True,
axis=ax2)
analyzer.visualize(configs_idx_list=[configs[1:3], configs_const[1:3]],
viewer=[viewer1, viewer2],
viewer_names=["IDM", "ConstVelocity"],
real_time_factor=1,
fontsize=12)
def main(version, github_token, delete):
dbs = DatabaseSerializer(test_scenarios=1, test_world_steps=10)
dbs.process(DATABASE_ROOT)
dbs.release(version=version, github_token=github_token, delete=delete)
def main():
args = configure_args()
if is_local:
dir_prefix = ""
else:
dir_prefix = "hy-x-iqn-beliefs.runfiles/hythe/"
print("Experiment server at :", os.getcwd())
params = ParameterServer(filename=os.path.join(
dir_prefix, "configuration/params/iqn_params.json"))
params = configure_params(params, seed=args.jobname)
experiment_id = params["Experiment"]["random_seed"]
params_filename = os.path.join(params["Experiment"]["dir"],
"params_{}.json".format(experiment_id))
params_behavior_filename = os.path.join(
params["Experiment"]["dir"],
"behavior_params_{}.json".format(experiment_id))
# check if exp exists and handle preemption
exp_exists = check_if_exp_exists(params)
if exp_exists:
print("Loading existing experiment from: {}".format(
args.jobname, (params["Experiment"]["dir"])))
if os.path.isfile(params_filename):
params = ParameterServer(filename=params_filename,
log_if_default=True)
if os.path.isfile(params_behavior_filename):
params_behavior = ParameterServer(
filename=params_behavior_filename, log_if_default=True)
else:
Path(params["Experiment"]["dir"]).mkdir(parents=True, exist_ok=True)
params["ML"]["BaseAgent"]["SummaryPath"] = os.path.join(
params["Experiment"]["dir"], "agent/summaries")
params["ML"]["BaseAgent"]["CheckpointPath"] = os.path.join(
params["Experiment"]["dir"], "agent/checkpoints")
params_behavior = ParameterServer(filename=os.path.join(
dir_prefix, "configuration/params/1D_desired_gap_no_prior.json"),
log_if_default=True)
params.Save(filename=params_filename)
params_behavior.Save(filename=params_behavior_filename)
# configure belief observer
splits = 2
behavior_space = configure_behavior_space(params_behavior)
hypothesis_set, hypothesis_params = behavior_space.create_hypothesis_set_fixed_split(
split=splits)
observer = BeliefObserver(params, hypothesis_set, splits=splits)
behavior = BehaviorDiscreteMacroActionsML(params)
evaluator = GoalReached(params)
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
# database creation
dbs = DatabaseSerializer(test_scenarios=2,
test_world_steps=2,
num_serialize_scenarios=num_scenarios)
dbs.process(os.path.join(dir_prefix, "configuration/database"),
filter_sets="**/**/interaction_merging_mid_dense_1D_new.json")
local_release_filename = dbs.release(version="test")
db = BenchmarkDatabase(database_root=local_release_filename)
scenario_generator, _, _ = db.get_scenario_generator(scenario_set_id=0)
env = HyDiscreteHighway(params=params,
scenario_generation=scenario_generator,
behavior=behavior,
evaluator=evaluator,
observer=observer,
viewer=viewer,
render=is_local)
print('Observation/state space size', env.observation_space)
run(params, env, exp_exists)
params.Save(filename=params_filename)
logging.info('-' * 60)
logging.info("Writing params to :{}".format(params_filename))
logging.info('-' * 60)
params_behavior.Save(filename=params_behavior_filename)
logging.info('-' * 60)
logging.info(
"Writing behavior params to :{}".format(params_behavior_filename))
logging.info('-' * 60)
return
def main():
print("Experiment server at:", os.getcwd())
params = ParameterServer(
filename=
"hy-exp-run.runfiles/hythe/configuration/params/default_exp_runne_params.json"
)
params = configure_params(params)
num_scenarios = 5
random_seed = 0
behavior = BehaviorDiscreteMacroActionsML(params)
# evaluator = GoalReachedGuiding(params)
evaluator = GoalReached(params)
observer = NearestAgentsObserver(params)
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
experiment_id = params["Experiment"]["random_seed"]
params_filename = "./{}_default_exp_runner_params.json".format(
experiment_id)
print(params_filename)
params.Save(filename=params_filename)
# database creation
dbs = DatabaseSerializer(
test_scenarios=2, test_world_steps=2, num_serialize_scenarios=20
) # increase the number of serialize scenarios to 100
dbs.process("hy-exp-run.runfiles/hythe/configuration/database")
local_release_filename = dbs.release(version="test",
sub_dir="hy_bark_packaged_databases")
db = BenchmarkDatabase(database_root=local_release_filename)
# switch this to other generator to get other index
# scenario_generator, _, _ = db.get_scenario_generator(0)
scenario_generator, _, _ = db.get_scenario_generator(1)
#
# env = GymSingleAgentRuntime(ml_behavior = behavior,
# observer = observer,
# evaluator = evaluator,
# step_time=0.2,
# viewer=viewer,
# scenario_generator=scenario_generator,
# render=False)
env = HyDiscreteHighway(params=params,
scenario_generation=scenario_generator,
behavior=behavior,
evaluator=evaluator,
observer=observer,
viewer=viewer,
render=False)
run(params, env)
# video_renderer.drawWorld(env._world)
env._viewer.export_video("./test_video")
# from gym.envs.registration import register
# register(
# id='highway-v1',
# entry_point='bark_ml.environments.gym:DiscreteHighwayGym'
# )
# import gym
# env = gym.make("highway-v1")
# env.reset()
# actions = [5]*100
# print(actions)
# for action in actions:
# env.step(action)
# time.sleep(0.2)
return
