def test_initialize_invalid_trainer_raises_exception(BrainParametersMock,
dummy_bad_config):
run_id = "testrun"
output_path = "results_dir"
keep_checkpoints = 1
train_model = True
load_model = False
seed = 11
bad_config = dummy_bad_config
BrainParametersMock.return_value.brain_name = "testbrain"
external_brains = {"testbrain": BrainParametersMock()}
with pytest.raises(TrainerConfigError):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=bad_config,
run_id=run_id,
output_path=output_path,
keep_checkpoints=keep_checkpoints,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for brain_name, brain_parameters in external_brains.items():
trainers[brain_name] = trainer_factory.generate(
brain_parameters.brain_name)
# Test no trainer specified
del bad_config["default"]["trainer"]
with pytest.raises(TrainerConfigError):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=bad_config,
run_id=run_id,
output_path=output_path,
keep_checkpoints=keep_checkpoints,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for brain_name, brain_parameters in external_brains.items():
trainers[brain_name] = trainer_factory.generate(
brain_parameters.brain_name)
# Test BC trainer specified
bad_config["default"]["trainer"] = "offline_bc"
with pytest.raises(UnityTrainerException):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=bad_config,
run_id=run_id,
output_path=output_path,
keep_checkpoints=keep_checkpoints,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for brain_name, brain_parameters in external_brains.items():
trainers[brain_name] = trainer_factory.generate(
brain_parameters.brain_name)
def test_raise_if_no_config_for_brain(dummy_config):
"""
Make sure the trainer setup raises a friendlier exception if both "default" and the brain name
are missing from the config.
"""
brain_name = "testbrain"
bad_config = {"some_other_brain": dummy_config["default"]}
brain_parameters = BrainParameters(
brain_name=brain_name,
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
trainer_factory = trainer_util.TrainerFactory(
trainer_config=bad_config,
summaries_dir="test_dir",
run_id="testrun",
model_path="model_dir",
keep_checkpoints=1,
train_model=True,
load_model=False,
seed=42,
)
with pytest.raises(TrainerConfigError):
trainer_factory.generate(brain_parameters)
def test_handles_no_default_section(dummy_config):
"""
Make sure the trainer setup handles a missing "default" in the config.
"""
brain_name = "testbrain"
no_default_config = {brain_name: dummy_config["default"]}
brain_parameters = BrainParameters(
brain_name=brain_name,
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
trainer_factory = trainer_util.TrainerFactory(
trainer_config=no_default_config,
summaries_dir="test_dir",
run_id="testrun",
model_path="model_dir",
keep_checkpoints=1,
train_model=True,
load_model=False,
seed=42,
)
trainer_factory.generate(brain_parameters.brain_name)
def test_initialize_invalid_trainer_raises_exception(BrainParametersMock,
dummy_bad_config):
summaries_dir = "test_dir"
run_id = "testrun"
model_path = "model_dir"
keep_checkpoints = 1
train_model = True
load_model = False
seed = 11
bad_config = dummy_bad_config
BrainParametersMock.return_value.brain_name = "testbrain"
external_brains = {"testbrain": BrainParametersMock()}
with pytest.raises(TrainerConfigError):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=bad_config,
summaries_dir=summaries_dir,
run_id=run_id,
model_path=model_path,
keep_checkpoints=keep_checkpoints,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for brain_name, brain_parameters in external_brains.items():
trainers[brain_name] = trainer_factory.generate(
brain_parameters.brain_name)
def test_initialize_trainer_parameters_override_defaults(
BrainParametersMock, dummy_config_with_override):
summaries_dir = "test_dir"
run_id = "testrun"
model_path = "model_dir"
keep_checkpoints = 1
train_model = True
load_model = False
seed = 11
expected_reward_buff_cap = 1
base_config = dummy_config_with_override
expected_config = base_config["default"]
expected_config["summary_path"] = f"{run_id}_testbrain"
expected_config["model_path"] = model_path + "/testbrain"
expected_config["keep_checkpoints"] = keep_checkpoints
# Override value from specific brain config
expected_config["normalize"] = False
brain_params_mock = BrainParametersMock()
BrainParametersMock.return_value.brain_name = "testbrain"
external_brains = {"testbrain": brain_params_mock}
def mock_constructor(
self,
brain,
reward_buff_cap,
trainer_parameters,
training,
load,
seed,
run_id,
multi_gpu,
):
assert brain == brain_params_mock
assert trainer_parameters == expected_config
assert reward_buff_cap == expected_reward_buff_cap
assert training == train_model
assert load == load_model
assert seed == seed
assert run_id == run_id
assert multi_gpu == multi_gpu
with patch.object(PPOTrainer, "__init__", mock_constructor):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=base_config,
summaries_dir=summaries_dir,
run_id=run_id,
model_path=model_path,
keep_checkpoints=keep_checkpoints,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for _, brain_parameters in external_brains.items():
trainers["testbrain"] = trainer_factory.generate(brain_parameters)
assert "testbrain" in trainers
assert isinstance(trainers["testbrain"], PPOTrainer)
def test_handles_no_config_provided():
"""
Make sure the trainer setup handles no configs provided at all.
"""
brain_name = "testbrain"
no_default_config = RunOptions().behaviors
trainer_factory = trainer_util.TrainerFactory(
trainer_config=no_default_config,
output_path="output_path",
train_model=True,
load_model=False,
seed=42,
)
trainer_factory.generate(brain_name)
def test_initialize_ppo_trainer(BrainParametersMock, dummy_config):
brain_params_mock = BrainParametersMock()
BrainParametersMock.return_value.brain_name = "testbrain"
external_brains = {"testbrain": BrainParametersMock()}
summaries_dir = "test_dir"
run_id = "testrun"
model_path = "model_dir"
keep_checkpoints = 1
train_model = True
load_model = False
seed = 11
expected_reward_buff_cap = 1
base_config = dummy_config
expected_config = base_config["default"]
expected_config["summary_path"] = f"{run_id}_testbrain"
expected_config["model_path"] = model_path + "/testbrain"
expected_config["keep_checkpoints"] = keep_checkpoints
def mock_constructor(self, brain, reward_buff_cap, trainer_parameters,
training, load, seed, run_id):
assert brain == brain_params_mock.brain_name
assert trainer_parameters == expected_config
assert reward_buff_cap == expected_reward_buff_cap
assert training == train_model
assert load == load_model
assert seed == seed
assert run_id == run_id
with patch.object(PPOTrainer, "__init__", mock_constructor):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=base_config,
summaries_dir=summaries_dir,
run_id=run_id,
model_path=model_path,
keep_checkpoints=keep_checkpoints,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for brain_name, brain_parameters in external_brains.items():
trainers[brain_name] = trainer_factory.generate(
brain_parameters.brain_name)
assert "testbrain" in trainers
assert isinstance(trainers["testbrain"], PPOTrainer)
def test_initialize_ppo_trainer(BrainParametersMock, dummy_config):
brain_params_mock = BrainParametersMock()
BrainParametersMock.return_value.brain_name = "testbrain"
external_brains = {"testbrain": BrainParametersMock()}
output_path = "results_dir"
train_model = True
load_model = False
seed = 11
expected_reward_buff_cap = 1
base_config = dummy_config.behaviors
expected_config = PPO_CONFIG
def mock_constructor(
self,
brain,
reward_buff_cap,
trainer_settings,
training,
load,
seed,
artifact_path,
):
assert brain == brain_params_mock.brain_name
assert trainer_settings == expected_config
assert reward_buff_cap == expected_reward_buff_cap
assert training == train_model
assert load == load_model
assert seed == seed
assert artifact_path == os.path.join(output_path, brain_name)
with patch.object(PPOTrainer, "__init__", mock_constructor):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=base_config,
output_path=output_path,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for brain_name, brain_parameters in external_brains.items():
trainers[brain_name] = trainer_factory.generate(
brain_parameters.brain_name)
assert "testbrain" in trainers
assert isinstance(trainers["testbrain"], PPOTrainer)
def test_initialize_ppo_trainer(BehaviorSpecMock, dummy_config):
brain_name = "testbrain"
training_behaviors = {"testbrain": BehaviorSpecMock()}
output_path = "results_dir"
train_model = True
load_model = False
seed = 11
expected_reward_buff_cap = 1
base_config = dummy_config.behaviors
expected_config = ppo_dummy_config()
def mock_constructor(
self,
brain,
reward_buff_cap,
trainer_settings,
training,
load,
seed,
artifact_path,
):
assert brain == brain_name
assert trainer_settings == expected_config
assert reward_buff_cap == expected_reward_buff_cap
assert training == train_model
assert load == load_model
assert seed == seed
assert artifact_path == os.path.join(output_path, brain_name)
with patch.object(PPOTrainer, "__init__", mock_constructor):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=base_config,
output_path=output_path,
train_model=train_model,
load_model=load_model,
seed=seed,
param_manager=EnvironmentParameterManager(),
)
trainers = {}
for brain_name in training_behaviors.keys():
trainers[brain_name] = trainer_factory.generate(brain_name)
assert "testbrain" in trainers
assert isinstance(trainers["testbrain"], PPOTrainer)
def test_handles_no_config_provided(BrainParametersMock):
"""
Make sure the trainer setup handles no configs provided at all.
"""
brain_name = "testbrain"
no_default_config = RunOptions().behaviors
brain_parameters = BrainParameters(
brain_name=brain_name,
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
trainer_factory = trainer_util.TrainerFactory(
trainer_config=no_default_config,
output_path="output_path",
train_model=True,
load_model=False,
seed=42,
)
trainer_factory.generate(brain_parameters.brain_name)
