Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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import pytest
from unittest import mock
import os
import unittest
import tempfile
import numpy as np
from mlagents.tf_utils import tf
from mlagents.trainers.saver.tf_saver import TFSaver
from mlagents.trainers import __version__
from mlagents.trainers.settings import TrainerSettings
from mlagents.trainers.policy.tf_policy import TFPolicy
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.tests.test_nn_policy import create_policy_mock
from mlagents.trainers.ppo.optimizer_tf import PPOOptimizer
def test_register(tmp_path):
trainer_params = TrainerSettings()
saver = TFSaver(trainer_params, tmp_path)
opt = mock.Mock(spec=PPOOptimizer)
saver.register(opt)
assert saver.policy is None
trainer_params = TrainerSettings()
policy = create_policy_mock(trainer_params)
saver.register(policy)
assert saver.policy is not None
class ModelVersionTest(unittest.TestCase):
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)
saver = TFSaver(trainer_params, mock_path)
saver.register(policy)
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
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_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)
saver = TFSaver(trainer_params, path1)
saver.register(policy)
saver.initialize_or_load(policy)
policy.set_step(2000)
mock_brain_name = "MockBrain"
saver.save_checkpoint(mock_brain_name, 2000)
assert len(os.listdir(tmp_path)) > 0
# Try load from this path
saver = TFSaver(trainer_params, path1, load=True)
policy2 = create_policy_mock(trainer_params)
saver.register(policy2)
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
saver = TFSaver(trainer_params, path2)
policy3 = create_policy_mock(trainer_params)
saver.register(policy3)
saver.initialize_or_load(policy3)
_compare_two_policies(policy2, policy3)
# Assert that the steps are 0.
assert policy3.get_current_step() == 0
def _compare_two_policies(policy1: TFPolicy, policy2: TFPolicy) -> None:
"""
Make sure two policies have the same output for the same input.
"""
decision_step, _ = mb.create_steps_from_behavior_spec(
policy1.behavior_spec, num_agents=1
)
run_out1 = policy1.evaluate(decision_step, list(decision_step.agent_id))
run_out2 = policy2.evaluate(decision_step, list(decision_step.agent_id))
np.testing.assert_array_equal(run_out2["log_probs"], run_out1["log_probs"])
@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
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()
saver = TFSaver(trainer_params, model_path)
saver.register(policy)
saver.save_checkpoint("Mock_Brain", 100)
assert os.path.isfile(model_path + "/Mock_Brain-100.nn")