import pytest import os import unittest import tempfile import numpy as np from mlagents.model_serialization import SerializationSettings from mlagents.tf_utils import tf from mlagents.trainers.policy.tf_policy import TFPolicy from mlagents.trainers.tf.models import ModelUtils, Tensor3DShape from mlagents.trainers.exception import UnityTrainerException from mlagents.trainers.tests import mock_brain as mb from mlagents.trainers.settings import TrainerSettings, NetworkSettings, EncoderType from mlagents.trainers.tests.test_trajectory import make_fake_trajectory from mlagents.trainers import __version__ VECTOR_ACTION_SPACE = 2 VECTOR_OBS_SPACE = 8 DISCRETE_ACTION_SPACE = [3, 3, 3, 2] BUFFER_INIT_SAMPLES = 32 NUM_AGENTS = 12 def create_policy_mock( dummy_config: TrainerSettings, use_rnn: bool = False, use_discrete: bool = True, use_visual: bool = False, model_path: str = "", load: bool = False, seed: int = 0, ) -> TFPolicy: mock_spec = mb.setup_test_behavior_specs( use_discrete, use_visual, vector_action_space=DISCRETE_ACTION_SPACE if use_discrete else VECTOR_ACTION_SPACE, vector_obs_space=VECTOR_OBS_SPACE, ) trainer_settings = dummy_config trainer_settings.keep_checkpoints = 3 trainer_settings.network_settings.memory = ( NetworkSettings.MemorySettings() if use_rnn else None ) policy = TFPolicy( seed, mock_spec, trainer_settings, model_path=model_path, load=load ) return policy 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_path=path1) policy.initialize_or_load() policy._set_step(2000) mock_brain_name = "MockBrain" checkpoint_path = f"{policy.model_path}/{mock_brain_name}-2000" serialization_settings = SerializationSettings(policy.model_path, mock_brain_name) policy.checkpoint(checkpoint_path, serialization_settings) assert len(os.listdir(tmp_path)) > 0 # Try load from this path policy2 = create_policy_mock(trainer_params, model_path=path1, load=True, seed=1) policy2.initialize_or_load() _compare_two_policies(policy, policy2) assert policy2.get_current_step() == 2000 # Try initialize from path 1 trainer_params.output_path = path2 trainer_params.init_path = path1 policy3 = create_policy_mock(trainer_params, model_path=path1, load=False, seed=2) policy3.initialize_or_load() _compare_two_policies(policy2, policy3) # Assert that the steps are 0. assert policy3.get_current_step() == 0 class ModelVersionTest(unittest.TestCase): def test_version_compare(self): # Test write_stats with self.assertLogs("mlagents.trainers", level="WARNING") as cm: path1 = tempfile.mkdtemp() trainer_params = TrainerSettings() policy = create_policy_mock(trainer_params, model_path=path1) policy.initialize_or_load() policy._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 policy._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 _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_policy_evaluate(rnn, visual, discrete): # Test evaluate tf.reset_default_graph() policy = create_policy_mock( TrainerSettings(), use_rnn=rnn, use_discrete=discrete, use_visual=visual ) decision_step, terminal_step = mb.create_steps_from_behavior_spec( policy.behavior_spec, num_agents=NUM_AGENTS ) run_out = policy.evaluate(decision_step, list(decision_step.agent_id)) if discrete: run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE)) else: assert run_out["action"].shape == (NUM_AGENTS, VECTOR_ACTION_SPACE) def test_normalization(): 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) policy = TFPolicy( 0, behavior_spec, TrainerSettings(network_settings=NetworkSettings(normalize=True)), "testdir", False, ) 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 # Note: variance is divided by number of steps, and initialized to 1 to avoid # divide by 0. The right answer is 0.25 assert (variance[0] - 1) / steps == 0.25 # Make another update, 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() 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 == 16 assert mean[0] == 0.8125 assert (variance[0] - 1) / steps == pytest.approx(0.152, abs=0.01) def test_min_visual_size(): # Make sure each EncoderType has an entry in MIS_RESOLUTION_FOR_ENCODER assert set(ModelUtils.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType) for encoder_type in EncoderType: with tf.Graph().as_default(): good_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] good_res = Tensor3DShape(width=good_size, height=good_size, num_channels=3) vis_input = ModelUtils.create_visual_input(good_res, "test_min_visual_size") ModelUtils._check_resolution_for_encoder(vis_input, encoder_type) enc_func = ModelUtils.get_encoder_for_type(encoder_type) enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False) # Anything under the min size should raise an exception. If not, decrease the min size! with pytest.raises(Exception): with tf.Graph().as_default(): bad_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1 bad_res = Tensor3DShape(width=bad_size, height=bad_size, num_channels=3) vis_input = ModelUtils.create_visual_input( bad_res, "test_min_visual_size" ) with pytest.raises(UnityTrainerException): # Make sure we'd hit a friendly error during model setup time. ModelUtils._check_resolution_for_encoder(vis_input, encoder_type) enc_func = ModelUtils.get_encoder_for_type(encoder_type) enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False) if __name__ == "__main__": pytest.main()