def create_model_saver(framework: str, trainer_settings: TrainerSettings,
model_path: str, load: bool) -> BaseModelSaver:
if framework == FrameworkType.PYTORCH:
model_saver = TorchModelSaver( # type: ignore
trainer_settings, model_path, load)
else:
model_saver = TFModelSaver( # type: ignore
trainer_settings, model_path, load)
return model_saver
def test_version_compare(self):
# Test write_stats
with self.assertLogs("mlagents.trainers", level="WARNING") as cm:
trainer_params = TrainerSettings()
mock_path = tempfile.mkdtemp()
policy = create_policy_mock(trainer_params)
model_saver = TFModelSaver(trainer_params, mock_path)
model_saver.register(policy)
model_saver._check_model_version(
"0.0.0") # This is not the right version for sure
# Assert that 1 warning has been thrown with incorrect version
assert len(cm.output) == 1
model_saver._check_model_version(
__version__) # This should be the right version
# Assert that no additional warnings have been thrown wth correct ver
assert len(cm.output) == 1
def test_register(tmp_path):
trainer_params = TrainerSettings()
model_saver = TFModelSaver(trainer_params, tmp_path)
opt = mock.Mock(spec=PPOOptimizer)
model_saver.register(opt)
assert model_saver.policy is None
trainer_params = TrainerSettings()
policy = create_policy_mock(trainer_params)
model_saver.register(policy)
assert model_saver.policy is not None
def test_checkpoint_conversion(tmpdir, rnn, visual, discrete):
tf.reset_default_graph()
dummy_config = TrainerSettings()
model_path = os.path.join(tmpdir, "Mock_Brain")
policy = create_policy_mock(
dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
trainer_params = TrainerSettings()
model_saver = TFModelSaver(trainer_params, model_path)
model_saver.register(policy)
model_saver.save_checkpoint("Mock_Brain", 100)
assert os.path.isfile(model_path + "/Mock_Brain-100.nn")
def test_load_save(tmp_path):
path1 = os.path.join(tmp_path, "runid1")
path2 = os.path.join(tmp_path, "runid2")
trainer_params = TrainerSettings()
policy = create_policy_mock(trainer_params)
model_saver = TFModelSaver(trainer_params, path1)
model_saver.register(policy)
model_saver.initialize_or_load(policy)
policy.set_step(2000)
mock_brain_name = "MockBrain"
model_saver.save_checkpoint(mock_brain_name, 2000)
assert len(os.listdir(tmp_path)) > 0
# Try load from this path
model_saver = TFModelSaver(trainer_params, path1, load=True)
policy2 = create_policy_mock(trainer_params)
model_saver.register(policy2)
model_saver.initialize_or_load(policy2)
_compare_two_policies(policy, policy2)
assert policy2.get_current_step() == 2000
# Try initialize from path 1
trainer_params.init_path = path1
model_saver = TFModelSaver(trainer_params, path2)
policy3 = create_policy_mock(trainer_params)
model_saver.register(policy3)
model_saver.initialize_or_load(policy3)
_compare_two_policies(policy2, policy3)
# Assert that the steps are 0.
assert policy3.get_current_step() == 0
def test_normalizer_after_load(tmp_path):
behavior_spec = mb.setup_test_behavior_specs(use_discrete=True,
use_visual=False,
vector_action_space=[2],
vector_obs_space=1)
time_horizon = 6
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1, )],
action_space=[2],
)
# Change half of the obs to 0
for i in range(3):
trajectory.steps[i].obs[0] = np.zeros(1, dtype=np.float32)
trainer_params = TrainerSettings(network_settings=NetworkSettings(
normalize=True))
policy = TFPolicy(0, behavior_spec, trainer_params)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy.sess.run([
policy.normalization_steps, policy.running_mean,
policy.running_variance
])
assert steps == 6
assert mean[0] == 0.5
assert variance[0] / steps == pytest.approx(0.25, abs=0.01)
# Save ckpt and load into another policy
path1 = os.path.join(tmp_path, "runid1")
model_saver = TFModelSaver(trainer_params, path1)
model_saver.register(policy)
mock_brain_name = "MockBrain"
model_saver.save_checkpoint(mock_brain_name, 6)
assert len(os.listdir(tmp_path)) > 0
policy1 = TFPolicy(0, behavior_spec, trainer_params)
model_saver = TFModelSaver(trainer_params, path1, load=True)
model_saver.register(policy1)
model_saver.initialize_or_load(policy1)
# Make another update to new policy, this time with all 1's
time_horizon = 10
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1, )],
action_space=[2],
)
trajectory_buffer = trajectory.to_agentbuffer()
policy1.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy1.sess.run([
policy1.normalization_steps, policy1.running_mean,
policy1.running_variance
])
assert steps == 16
assert mean[0] == 0.8125
assert variance[0] / steps == pytest.approx(0.152, abs=0.01)