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/")
# 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" : BehaviorConstantAcceleration(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)
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)
