Python model_hparams.create_hparams方法代碼示例

本文整理匯總了Python中object_detection.model_hparams.create_hparams方法的典型用法代碼示例。如果您正苦於以下問題：Python model_hparams.create_hparams方法的具體用法？Python model_hparams.create_hparams怎麽用？Python model_hparams.create_hparams使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.model_hparams的用法示例。

在下文中一共展示了model_hparams.create_hparams方法的12個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: _assert_model_fn_for_predict

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def _assert_model_fn_for_predict(self, configs):
    model_config = configs['model']

    with tf.Graph().as_default():
      features, _ = _make_initializable_iterator(
          inputs.create_eval_input_fn(configs['eval_config'],
                                      configs['eval_input_config'],
                                      configs['model'])()).get_next()
      detection_model_fn = functools.partial(
          model_builder.build, model_config=model_config, is_training=False)

      hparams = model_hparams.create_hparams(
          hparams_overrides='load_pretrained=false')

      model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
      estimator_spec = model_fn(features, None, tf.estimator.ModeKeys.PREDICT)

      self.assertIsNone(estimator_spec.loss)
      self.assertIsNone(estimator_spec.train_op)
      self.assertIsNotNone(estimator_spec.predictions)
      self.assertIsNotNone(estimator_spec.export_outputs)
      self.assertIn(tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
                    estimator_spec.export_outputs)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:25，代碼來源:model_lib_test.py

示例2: test_create_estimator_and_inputs

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_create_estimator_and_inputs(self):
    """Tests that Estimator and input function are constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    self.assertIsInstance(estimator, tf.estimator.Estimator)
    self.assertEqual(20, train_steps)
    self.assertIn('train_input_fn', train_and_eval_dict)
    self.assertIn('eval_input_fns', train_and_eval_dict)
    self.assertIn('eval_on_train_input_fn', train_and_eval_dict)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:21，代碼來源:model_lib_test.py

示例3: test_create_tpu_estimator_and_inputs

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_create_tpu_estimator_and_inputs(self):
    """Tests that number of train/eval defaults to config values."""

    run_config = tpu_config.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        use_tpu_estimator=True)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']

    self.assertIsInstance(estimator, tpu_estimator.TPUEstimator)
    self.assertEqual(20, train_steps)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:21，代碼來源:model_lib_test.py

示例4: _assert_outputs_for_predict

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def _assert_outputs_for_predict(self, configs):
    model_config = configs['model']

    with tf.Graph().as_default():
      features, _ = inputs.create_eval_input_fn(
          configs['eval_config'],
          configs['eval_input_config'],
          configs['model'])()
      detection_model_fn = functools.partial(
          model_builder.build, model_config=model_config, is_training=False)

      hparams = model_hparams.create_hparams(
          hparams_overrides='load_pretrained=false')

      model_fn = model.create_model_fn(detection_model_fn, configs, hparams)
      estimator_spec = model_fn(features, None, tf.estimator.ModeKeys.PREDICT)

      self.assertIsNone(estimator_spec.loss)
      self.assertIsNone(estimator_spec.train_op)
      self.assertIsNotNone(estimator_spec.predictions)
      self.assertIsNotNone(estimator_spec.export_outputs)
      self.assertIn(tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
                    estimator_spec.export_outputs)

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:25，代碼來源:model_test.py

示例5: _assert_model_fn_for_predict

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def _assert_model_fn_for_predict(self, configs):
    model_config = configs['model']

    with tf.Graph().as_default():
      features, _ = inputs.create_eval_input_fn(
          configs['eval_config'],
          configs['eval_input_config'],
          configs['model'])()
      detection_model_fn = functools.partial(
          model_builder.build, model_config=model_config, is_training=False)

      hparams = model_hparams.create_hparams(
          hparams_overrides='load_pretrained=false')

      model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
      estimator_spec = model_fn(features, None, tf.estimator.ModeKeys.PREDICT)

      self.assertIsNone(estimator_spec.loss)
      self.assertIsNone(estimator_spec.train_op)
      self.assertIsNotNone(estimator_spec.predictions)
      self.assertIsNotNone(estimator_spec.export_outputs)
      self.assertIn(tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
                    estimator_spec.export_outputs)

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:25，代碼來源:model_lib_test.py

示例6: test_create_estimator_and_inputs

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_create_estimator_and_inputs(self):
    """Tests that Estimator and input function are constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    eval_steps = 10
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        eval_steps=eval_steps)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    eval_steps = train_and_eval_dict['eval_steps']
    self.assertIsInstance(estimator, tf.estimator.Estimator)
    self.assertEqual(20, train_steps)
    self.assertEqual(10, eval_steps)
    self.assertIn('train_input_fn', train_and_eval_dict)
    self.assertIn('eval_input_fn', train_and_eval_dict)
    self.assertIn('eval_on_train_input_fn', train_and_eval_dict)

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:25，代碼來源:model_lib_test.py

示例7: test_create_tpu_estimator_and_inputs

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_create_tpu_estimator_and_inputs(self):
    """Tests that number of train/eval defaults to config values."""

    run_config = tpu_config.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    eval_steps = 10
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        eval_steps=eval_steps,
        use_tpu_estimator=True)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    eval_steps = train_and_eval_dict['eval_steps']

    self.assertIsInstance(estimator, tpu_estimator.TPUEstimator)
    self.assertEqual(20, train_steps)
    self.assertEqual(10, eval_steps)

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:25，代碼來源:model_lib_test.py

示例8: _assert_outputs_for_predict

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def _assert_outputs_for_predict(self, configs):
        model_config = configs['model']

        with tf.Graph().as_default():
            features, _ = inputs.create_eval_input_fn(
                configs['eval_config'],
                configs['eval_input_config'],
                configs['model'])()
            detection_model_fn = functools.partial(
                model_builder.build, model_config=model_config, is_training=False)

            hparams = model_hparams.create_hparams(
                hparams_overrides='load_pretrained=false')

            model_fn = model.create_model_fn(
                detection_model_fn, configs, hparams)
            estimator_spec = model_fn(
                features, None, tf.estimator.ModeKeys.PREDICT)

            self.assertIsNone(estimator_spec.loss)
            self.assertIsNone(estimator_spec.train_op)
            self.assertIsNotNone(estimator_spec.predictions)
            self.assertIsNotNone(estimator_spec.export_outputs)
            self.assertIn(tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
                          estimator_spec.export_outputs)

開發者ID:scorelab，項目名稱:Elphas，代碼行數:27，代碼來源:model_test.py

示例9: test_create_estimator_and_inputs_sequence_example

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_create_estimator_and_inputs_sequence_example(self):
    """Tests that Estimator and input function are constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(
        MODEL_NAME_FOR_SEQUENCE_EXAMPLE_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    self.assertIsInstance(estimator, tf.estimator.Estimator)
    self.assertEqual(20, train_steps)
    self.assertIn('train_input_fn', train_and_eval_dict)
    self.assertIn('eval_input_fns', train_and_eval_dict)
    self.assertIn('eval_on_train_input_fn', train_and_eval_dict)

開發者ID:tensorflow，項目名稱:models，代碼行數:22，代碼來源:model_lib_tf1_test.py

示例10: test_create_tpu_estimator_and_inputs

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_create_tpu_estimator_and_inputs(self):
    """Tests that number of train/eval defaults to config values."""
    run_config = tf.estimator.tpu.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        use_tpu_estimator=True)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']

    self.assertIsInstance(estimator, tf.estimator.tpu.TPUEstimator)
    self.assertEqual(20, train_steps)

開發者ID:tensorflow，項目名稱:models，代碼行數:20，代碼來源:model_lib_tf1_test.py

示例11: test_create_train_and_eval_specs

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_create_train_and_eval_specs(self):
    """Tests that `TrainSpec` and `EvalSpec` is created correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps)
    train_input_fn = train_and_eval_dict['train_input_fn']
    eval_input_fns = train_and_eval_dict['eval_input_fns']
    eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
    predict_input_fn = train_and_eval_dict['predict_input_fn']
    train_steps = train_and_eval_dict['train_steps']

    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fns,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_on_train_data=True,
        final_exporter_name='exporter',
        eval_spec_names=['holdout'])
    self.assertEqual(train_steps, train_spec.max_steps)
    self.assertEqual(2, len(eval_specs))
    self.assertEqual(None, eval_specs[0].steps)
    self.assertEqual('holdout', eval_specs[0].name)
    self.assertEqual('exporter', eval_specs[0].exporters[0].name)
    self.assertEqual(None, eval_specs[1].steps)
    self.assertEqual('eval_on_train', eval_specs[1].name)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:36，代碼來源:model_lib_test.py

示例12: test_experiment

# 需要導入模塊: from object_detection import model_hparams [as 別名]
# 或者: from object_detection.model_hparams import create_hparams [as 別名]
def test_experiment(self):
    """Tests that the `Experiment` object is constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    experiment = model_lib.populate_experiment(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=10,
        eval_steps=20)
    self.assertEqual(10, experiment.train_steps)
    self.assertEqual(None, experiment.eval_steps)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:16，代碼來源:model_lib_test.py

注：本文中的object_detection.model_hparams.create_hparams方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。