Python training._TrainingExecutor函数代码示例

  def test_invalid_estimator(self):
    invalid_estimator = object()
    train_spec = training.TrainSpec(input_fn=lambda: 1)
    eval_spec = training.EvalSpec(input_fn=lambda: 1)

    with self.assertRaisesRegexp(TypeError, _INVALID_ESTIMATOR_MSG):
      training._TrainingExecutor(invalid_estimator, train_spec, eval_spec)

  def test_invalid_eval_spec(self):
    estimator = estimator_lib.Estimator(model_fn=lambda features: features)
    train_spec = training.TrainSpec(input_fn=lambda: 1)
    invalid_eval_spec = object()

    with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_SPEC_MSG):
      training._TrainingExecutor(estimator, train_spec, invalid_eval_spec)

  def test_fail_with_empty_task_type(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    mock_train_spec = test.mock.Mock(spec=training.TrainSpec)
    mock_eval_spec = test.mock.Mock(spec=training.EvalSpec)

    mock_est.config = test.mock.PropertyMock(spec=run_config_lib.RunConfig)
    mock_est.config.cluster_spec = {'gs': 'dummy'}
    mock_est.config.master = 'grpc://...'
    mock_est.config.task_type = ''
    mock_est.config.task_id = 2

    with self.assertRaisesRegexp(RuntimeError,
                                 _INVALID_CONFIG_FOR_STD_SERVER_MSG):
      training._TrainingExecutor(mock_est, mock_train_spec,
                                 mock_eval_spec).run_ps()

  def test_runs_in_a_loop_until_max_steps(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
    mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn

    mock_est.times_export_was_called = 0
    def export(estimator, *args, **kwargs):
      del args, kwargs
      estimator.times_export_was_called += 1

    exporter = test.mock.PropertyMock(spec=exporter_lib.Exporter)
    exporter.name = 'see_how_many_times_export_is_called'
    exporter.export = export

    train_spec = training.TrainSpec(
        input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()])
    eval_spec = training.EvalSpec(
        input_fn=lambda: 1,
        hooks=[_FakeHook()],
        throttle_secs=100,
        exporters=exporter)
    # should be called 3 times.
    mock_est.evaluate.side_effect = [{
        _GLOBAL_STEP_KEY: train_spec.max_steps - 100
    }, {
        _GLOBAL_STEP_KEY: train_spec.max_steps - 50
    }, {
        _GLOBAL_STEP_KEY: train_spec.max_steps
    }]

    executor = training._TrainingExecutor(mock_est, train_spec, eval_spec)
    executor.run_local()

    self.assertEqual(3, mock_est.train.call_count)
    self.assertEqual(3, mock_est.evaluate.call_count)
    self.assertEqual(3, mock_est.times_export_was_called)

  def test_evaluate_multiple_times(self):
    training_max_step = 200

    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    mock_est.model_dir = compat.as_bytes(test.get_temp_dir())
    mock_est.evaluate.side_effect = [
        {_GLOBAL_STEP_KEY: training_max_step // 2},
        {_GLOBAL_STEP_KEY: training_max_step}
    ]
    mock_est.latest_checkpoint.side_effect = ['path_1', 'path_2']

    mock_train_spec = test.mock.Mock(spec=training.TrainSpec)
    mock_train_spec.max_steps = training_max_step

    mock_est.times_export_fn_was_called = 0
    def export_fn(estimator, *args, **kwargs):
      del args, kwargs
      estimator.times_export_fn_was_called += 1

    export_strategy = export_strategy_lib.ExportStrategy(
        name='see_whether_export_fn_is_called', export_fn=export_fn)

    eval_spec = training.EvalSpec(
        input_fn=lambda: 1,
        delay_secs=0,
        throttle_secs=0,
        export_strategies=export_strategy)

    executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec)
    executor.run_evaluator()

    self.assertEqual(2, mock_est.evaluate.call_count)
    self.assertEqual(2, mock_est.times_export_fn_was_called)

  def test_skip_evaluation_due_to_ckpt(self):
    training_max_step = 200
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    mock_est.evaluate.side_effect = [
        {_GLOBAL_STEP_KEY: training_max_step // 2},
        {_GLOBAL_STEP_KEY: training_max_step}
    ]
    mock_train_spec = test.mock.Mock(spec=training.TrainSpec)
    mock_train_spec.max_steps = training_max_step

    self._set_up_mock_est_to_train_and_evaluate_once(mock_est, mock_train_spec)

    # First two items are invalid, next two items are same.
    mock_est.latest_checkpoint.side_effect = [
        None, '', 'same', 'same', 'path_2'
    ]

    eval_spec = training.EvalSpec(
        input_fn=lambda: 1, start_delay_secs=0, throttle_secs=0)

    executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec)
    with test.mock.patch.object(logging, 'warning') as mock_log:
      executor.run_evaluator()

    # Three checkpoint paths are invalid.
    self.assertEqual(5, mock_est.latest_checkpoint.call_count)
    self.assertEqual(2, mock_est.evaluate.call_count)

    # Two warning logs are expected (last warning time is reset after a
    # successuful evaluation)
    self.assertEqual(2, mock_log.call_count)

  def test_train_with_train_spec(self, mock_server, unused_mock_sleep):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    mock_est.config = self._run_config
    train_spec = training.TrainSpec(
        input_fn=lambda: 1, max_steps=2, hooks=[_FakeHook()])
    mock_eval_spec = test.mock.Mock(spec=training.EvalSpec)
    mock_server_instance = mock_server.return_value

    executor = training._TrainingExecutor(mock_est, train_spec, mock_eval_spec)
    self._run_task(executor)

    mock_server.assert_called_with(
        mock_est.config.cluster_spec,
        job_name=mock_est.config.task_type,
        task_index=mock_est.config.task_id,
        config=test.mock.ANY,
        start=False)

    self.assertTrue(mock_server_instance.start.called)

    mock_est.train.assert_called_with(input_fn=train_spec.input_fn,
                                      max_steps=train_spec.max_steps,
                                      hooks=train_spec.hooks,
                                      saving_listeners=test.mock.ANY)
    mock_est.evaluate.assert_not_called()
    mock_est.export_savedmodel.assert_not_called()

  def testRequiredArgumentsSet(self):
    estimator = estimator_lib.Estimator(model_fn=lambda features: features)
    train_spec = training.TrainSpec(input_fn=lambda: 1)
    eval_spec = training.EvalSpec(input_fn=lambda: 1)

    executor = training._TrainingExecutor(estimator, train_spec, eval_spec)
    self.assertEqual(estimator, executor.estimator)

  def test_that_export_is_called(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    mock_train_spec = test.mock.Mock(spec=training.TrainSpec)
    self._set_up_mock_est_to_train_and_evaluate_once(mock_est, mock_train_spec)

    def export(estimator, *args, **kwargs):
      del args, kwargs
      estimator.export_was_called = True

    exporter = test.mock.PropertyMock(spec=exporter_lib.Exporter)
    exporter.name = 'see_whether_export_is_called'
    exporter.export = export

    eval_spec = training.EvalSpec(
        input_fn=lambda: 1,
        steps=2,
        start_delay_secs=0,
        throttle_secs=0,
        exporters=exporter)

    executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec)
    executor.run_evaluator()

    # Verify that export was called on the right estimator.
    self.assertTrue(mock_est.export_was_called)

  def test_evaluate_multiple_times(self):
    training_max_step = 200

    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    mock_est.model_dir = compat.as_bytes(test.get_temp_dir())
    mock_est.evaluate.side_effect = [
        {_GLOBAL_STEP_KEY: training_max_step // 2},
        {_GLOBAL_STEP_KEY: training_max_step}
    ]
    mock_est.latest_checkpoint.side_effect = ['path_1', 'path_2']

    mock_train_spec = test.mock.Mock(spec=training.TrainSpec)
    mock_train_spec.max_steps = training_max_step

    exporter = test.mock.PropertyMock(spec=exporter_lib.Exporter)
    exporter.name = 'see_how_many_times_export_is_called'

    eval_spec = training.EvalSpec(
        input_fn=lambda: 1,
        start_delay_secs=0,
        throttle_secs=0,
        exporters=exporter)

    executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec)
    executor.run_evaluator()

    self.assertEqual(2, mock_est.evaluate.call_count)
    self.assertEqual(2, exporter.export.call_count)

  def test_that_export_is_called_with_run_local(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    mock_train_spec = test.mock.Mock(spec=training.TrainSpec)
    mock_train_spec.max_steps = 200
    mock_est.evaluate.return_value = {
        _GLOBAL_STEP_KEY: mock_train_spec.max_steps
    }
    # _validate_hooks would have made sure that train_spec.hooks is [], when
    # None were passed.
    mock_train_spec.hooks = []

    def export(estimator, *args, **kwargs):
      del args, kwargs
      estimator.export_was_called = True

    exporter = test.mock.PropertyMock(spec=exporter_lib.Exporter)
    exporter.name = 'see_whether_export_is_called'
    exporter.export = export

    eval_spec = training.EvalSpec(
        input_fn=lambda: 1,
        steps=2,
        start_delay_secs=0,
        throttle_secs=213,
        exporters=exporter)

    executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec)
    executor.run_local()

    self.assertTrue(mock_est.export_was_called)

  def test_errors_out_if_throttle_secs_is_zero(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    train_spec = training.TrainSpec(input_fn=lambda: 1)
    eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=0)

    executor = training._TrainingExecutor(mock_est, train_spec, eval_spec)
    with self.assertRaisesRegexp(ValueError, 'throttle_secs'):
      executor.run_local()

  def test_errors_out_if_evaluate_returns_non_dict(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    train_spec = training.TrainSpec(input_fn=lambda: 1)
    eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123)
    mock_est.evaluate.return_value = 123

    executor = training._TrainingExecutor(mock_est, train_spec, eval_spec)
    with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_RESULT_TYPE_ERR):
      executor.run_local()

  def test_errors_out_if_evaluate_returns_dict_without_global_step(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    train_spec = training.TrainSpec(input_fn=lambda: 1)
    eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123)
    mock_est.evaluate.return_value = {'loss': 123}

    executor = training._TrainingExecutor(mock_est, train_spec, eval_spec)
    with self.assertRaisesRegexp(RuntimeError,
                                 _MISSING_GLOBAL_STEP_IN_EVAL_RESULT_ERR):
      executor.run_local()

  def test_errors_out_if_evaluate_returns_empty_dict(self):
    mock_est = test.mock.Mock(spec=estimator_lib.Estimator)
    train_spec = training.TrainSpec(input_fn=lambda: 1)
    eval_spec = training.EvalSpec(input_fn=(lambda: 1),
                                  start_delay_secs=0, throttle_secs=0)
    mock_est.evaluate.return_value = {}

    executor = training._TrainingExecutor(mock_est, train_spec, eval_spec)
    with self.assertRaisesRegexp(RuntimeError, _INVALID_EMPTY_EVAL_RESULT_ERR):
      executor.run_evaluator()