remove more PPO tests

ml-agents/mlagents/trainers/tests/test_ppo.py

 
 from mlagents.trainers.ppo.trainer import PPOTrainer, discount_rewards
 from mlagents.trainers.common.nn_policy import NNPolicy
-from mlagents.trainers.models import EncoderType, LearningModel
-from mlagents.trainers.exception import UnityTrainerException
-from mlagents.trainers.brain import BrainParameters, CameraResolution
+from mlagents.trainers.brain import BrainParameters
 from mlagents.trainers.agent_processor import AgentManagerQueue
 from mlagents.trainers.tests import mock_brain as mb
 from mlagents.trainers.tests.mock_brain import make_brain_parameters
     policy = mock.Mock()
     with pytest.raises(RuntimeError):
         trainer.add_policy(brain_params, policy)
-
-
-def test_normalization(dummy_config):
-    brain_params = BrainParameters(
-        brain_name="test_brain",
-        vector_observation_space_size=1,
-        camera_resolutions=[],
-        vector_action_space_size=[2],
-        vector_action_descriptions=[],
-        vector_action_space_type=0,
-    )
-    dummy_config["summary_path"] = "./summaries/test_trainer_summary"
-    dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
-    trainer = PPOTrainer(
-        brain_params.brain_name, 0, dummy_config, True, False, 0, "0", False
-    )
-    time_horizon = 6
-    trajectory = make_fake_trajectory(
-        length=time_horizon,
-        max_step_complete=True,
-        vec_obs_size=1,
-        num_vis_obs=0,
-        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 = trainer.create_policy(brain_params)
-    trainer.add_policy(brain_params.brain_name, policy)
-
-    trainer._process_trajectory(trajectory)
-
-    # Check that the running mean and variance is correct
-    steps, mean, variance = trainer.policy.sess.run(
-        [
-            trainer.policy.normalization_steps,
-            trainer.policy.running_mean,
-            trainer.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,
-        vec_obs_size=1,
-        num_vis_obs=0,
-        action_space=[2],
-    )
-    trainer._process_trajectory(trajectory)
-
-    # Check that the running mean and variance is correct
-    steps, mean, variance = trainer.policy.sess.run(
-        [
-            trainer.policy.normalization_steps,
-            trainer.policy.running_mean,
-            trainer.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(LearningModel.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType)
-
-    for encoder_type in EncoderType:
-        with tf.Graph().as_default():
-            good_size = LearningModel.MIN_RESOLUTION_FOR_ENCODER[encoder_type]
-            good_res = CameraResolution(
-                width=good_size, height=good_size, num_channels=3
-            )
-            LearningModel._check_resolution_for_encoder(good_res, encoder_type)
-            vis_input = LearningModel.create_visual_input(
-                good_res, "test_min_visual_size"
-            )
-            enc_func = LearningModel.get_encoder_for_type(encoder_type)
-            enc_func(vis_input, 32, LearningModel.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 = LearningModel.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1
-                bad_res = CameraResolution(
-                    width=bad_size, height=bad_size, num_channels=3
-                )
-
-                with pytest.raises(UnityTrainerException):
-                    # Make sure we'd hit a friendly error during model setup time.
-                    LearningModel._check_resolution_for_encoder(bad_res, encoder_type)
-
-                vis_input = LearningModel.create_visual_input(
-                    bad_res, "test_min_visual_size"
-                )
-                enc_func = LearningModel.get_encoder_for_type(encoder_type)
-                enc_func(vis_input, 32, LearningModel.swish, 1, "test", False)
 
 
 if __name__ == "__main__":