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139 行
4.2 KiB
139 行
4.2 KiB
from unittest.mock import MagicMock, patch
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import pytest
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from mlagents.torch_utils import torch
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from mlagents.trainers.trainer_controller import TrainerController
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from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
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from mlagents.trainers.ghost.controller import GhostController
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@pytest.fixture
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def basic_trainer_controller():
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trainer_factory_mock = MagicMock()
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trainer_factory_mock.ghost_controller = GhostController()
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return TrainerController(
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trainer_factory=trainer_factory_mock,
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output_path="test_model_path",
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run_id="test_run_id",
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param_manager=EnvironmentParameterManager(),
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train=True,
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training_seed=99,
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)
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@patch("numpy.random.seed")
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@patch.object(torch, "manual_seed")
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def test_initialization_seed(numpy_random_seed, torch_set_seed):
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seed = 27
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trainer_factory_mock = MagicMock()
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trainer_factory_mock.ghost_controller = GhostController()
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TrainerController(
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trainer_factory=trainer_factory_mock,
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output_path="",
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run_id="1",
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param_manager=None,
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train=True,
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training_seed=seed,
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)
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numpy_random_seed.assert_called_with(seed)
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torch_set_seed.assert_called_with(seed)
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@pytest.fixture
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def trainer_controller_with_start_learning_mocks(basic_trainer_controller):
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trainer_mock = MagicMock()
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trainer_mock.get_step = 0
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trainer_mock.get_max_steps = 5
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trainer_mock.should_still_train = True
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trainer_mock.parameters = {"some": "parameter"}
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trainer_mock.write_tensorboard_text = MagicMock()
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tc = basic_trainer_controller
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tc.trainers = {"testbrain": trainer_mock}
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tc.advance = MagicMock()
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tc.trainers["testbrain"].get_step = 0
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def take_step_sideeffect(env):
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tc.trainers["testbrain"].get_step += 1
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if (
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not tc.trainers["testbrain"].get_step
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<= tc.trainers["testbrain"].get_max_steps
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):
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tc.trainers["testbrain"].should_still_train = False
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if tc.trainers["testbrain"].get_step > 10:
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raise KeyboardInterrupt
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return 1
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tc.advance.side_effect = take_step_sideeffect
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tc._save_models = MagicMock()
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return tc, trainer_mock
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def test_start_learning_trains_forever_if_no_train_model(
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trainer_controller_with_start_learning_mocks
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):
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tc, trainer_mock = trainer_controller_with_start_learning_mocks
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tc.train_model = False
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env_mock = MagicMock()
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env_mock.close = MagicMock()
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env_mock.reset = MagicMock()
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env_mock.training_behaviors = MagicMock()
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tc.start_learning(env_mock)
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env_mock.reset.assert_called_once()
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assert tc.advance.call_count == 11
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tc._save_models.assert_not_called()
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def test_start_learning_trains_until_max_steps_then_saves(
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trainer_controller_with_start_learning_mocks
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):
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tc, trainer_mock = trainer_controller_with_start_learning_mocks
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brain_info_mock = MagicMock()
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env_mock = MagicMock()
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env_mock.close = MagicMock()
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env_mock.reset = MagicMock(return_value=brain_info_mock)
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env_mock.training_behaviors = MagicMock()
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tc.start_learning(env_mock)
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env_mock.reset.assert_called_once()
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assert tc.advance.call_count == trainer_mock.get_max_steps + 1
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tc._save_models.assert_called_once()
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@pytest.fixture
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def trainer_controller_with_take_step_mocks(basic_trainer_controller):
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trainer_mock = MagicMock()
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trainer_mock.get_step = 0
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trainer_mock.get_max_steps = 5
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trainer_mock.parameters = {"some": "parameter"}
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trainer_mock.write_tensorboard_text = MagicMock()
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tc = basic_trainer_controller
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tc.trainers = {"testbrain": trainer_mock}
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tc.managers = {"testbrain": MagicMock()}
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return tc, trainer_mock
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def test_advance_adds_experiences_to_trainer_and_trains(
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trainer_controller_with_take_step_mocks
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):
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tc, trainer_mock = trainer_controller_with_take_step_mocks
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brain_name = "testbrain"
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env_mock = MagicMock()
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tc.brain_name_to_identifier[brain_name].add(brain_name)
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tc.advance(env_mock)
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env_mock.reset.assert_not_called()
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env_mock.get_steps.assert_called_once()
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env_mock.process_steps.assert_called_once()
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# May have been called many times due to thread
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trainer_mock.advance.call_count > 0
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