Python inputs.create_train_input_fn方法代码示例

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def setUp(self):
    super(CheckpointV2Test, self).setUp()

    self._model = SimpleModel()
    tf.keras.backend.set_value(self._model.weight, np.ones(10) * 42)
    ckpt = tf.train.Checkpoint(model=self._model)

    self._test_dir = tf.test.get_temp_dir()
    self._ckpt_path = ckpt.save(os.path.join(self._test_dir, 'ckpt'))
    tf.keras.backend.set_value(self._model.weight, np.ones(10))

    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=_get_config_kwarg_overrides())
    self._train_input_fn = inputs.create_train_input_fn(
        configs['train_config'],
        configs['train_input_config'],
        configs['model']) 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_faster_rcnn_resnet50_train_input(self):
    """Tests the training input function for FasterRcnnResnet50."""
    configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
    model_config = configs['model']
    model_config.faster_rcnn.num_classes = 37
    train_input_fn = inputs.create_train_input_fn(
        configs['train_config'], configs['train_input_config'], model_config)
    features, labels = _make_initializable_iterator(train_input_fn()).get_next()

    self.assertAllEqual([1, None, None, 3],
                        features[fields.InputDataFields.image].shape.as_list())
    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
    self.assertAllEqual([1],
                        features[inputs.HASH_KEY].shape.as_list())
    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
    self.assertAllEqual(
        [1, 100, 4],
        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
    self.assertAllEqual(
        [1, 100, model_config.faster_rcnn.num_classes],
        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_classes].dtype)
    self.assertAllEqual(
        [1, 100, model_config.faster_rcnn.num_classes],
        labels[fields.InputDataFields.groundtruth_confidences].shape.as_list())
    self.assertEqual(
        tf.float32,
        labels[fields.InputDataFields.groundtruth_confidences].dtype)
    self.assertAllEqual(
        [1, 100],
        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_weights].dtype) 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_error_with_bad_train_config(self):
    """Tests that a TypeError is raised with improper train config."""
    configs = _get_configs_for_model('ssd_inception_v2_pets')
    configs['model'].ssd.num_classes = 37
    train_input_fn = inputs.create_train_input_fn(
        train_config=configs['eval_config'],  # Expecting `TrainConfig`.
        train_input_config=configs['train_input_config'],
        model_config=configs['model'])
    with self.assertRaises(TypeError):
      train_input_fn() 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_error_with_bad_train_input_config(self):
    """Tests that a TypeError is raised with improper train input config."""
    configs = _get_configs_for_model('ssd_inception_v2_pets')
    configs['model'].ssd.num_classes = 37
    train_input_fn = inputs.create_train_input_fn(
        train_config=configs['train_config'],
        train_input_config=configs['model'],  # Expecting `InputReader`.
        model_config=configs['model'])
    with self.assertRaises(TypeError):
      train_input_fn() 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_error_with_bad_train_model_config(self):
    """Tests that a TypeError is raised with improper train model config."""
    configs = _get_configs_for_model('ssd_inception_v2_pets')
    configs['model'].ssd.num_classes = 37
    train_input_fn = inputs.create_train_input_fn(
        train_config=configs['train_config'],
        train_input_config=configs['train_input_config'],
        model_config=configs['train_config'])  # Expecting `DetectionModel`.
    with self.assertRaises(TypeError):
      train_input_fn() 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_faster_rcnn_resnet50_train_input(self):
    """Tests the training input function for FasterRcnnResnet50."""
    configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
    configs['train_config'].unpad_groundtruth_tensors = True
    model_config = configs['model']
    model_config.faster_rcnn.num_classes = 37
    train_input_fn = inputs.create_train_input_fn(
        configs['train_config'], configs['train_input_config'], model_config)
    features, labels = train_input_fn()

    self.assertAllEqual([1, None, None, 3],
                        features[fields.InputDataFields.image].shape.as_list())
    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
    self.assertAllEqual([1],
                        features[inputs.HASH_KEY].shape.as_list())
    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
    self.assertAllEqual(
        [1, 50, 4],
        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
    self.assertAllEqual(
        [1, 50, model_config.faster_rcnn.num_classes],
        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_classes].dtype)
    self.assertAllEqual(
        [1, 50],
        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_weights].dtype) 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_ssd_inceptionV2_train_input(self):
    """Tests the training input function for SSDInceptionV2."""
    configs = _get_configs_for_model('ssd_inception_v2_pets')
    model_config = configs['model']
    model_config.ssd.num_classes = 37
    batch_size = configs['train_config'].batch_size
    train_input_fn = inputs.create_train_input_fn(
        configs['train_config'], configs['train_input_config'], model_config)
    features, labels = train_input_fn()

    self.assertAllEqual([batch_size, 300, 300, 3],
                        features[fields.InputDataFields.image].shape.as_list())
    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
    self.assertAllEqual([batch_size],
                        features[inputs.HASH_KEY].shape.as_list())
    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
    self.assertAllEqual(
        [batch_size],
        labels[fields.InputDataFields.num_groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.int32,
                     labels[fields.InputDataFields.num_groundtruth_boxes].dtype)
    self.assertAllEqual(
        [batch_size, 50, 4],
        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
    self.assertAllEqual(
        [batch_size, 50, model_config.ssd.num_classes],
        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_classes].dtype)
    self.assertAllEqual(
        [batch_size, 50],
        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_weights].dtype) 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_faster_rcnn_resnet50_train_input(self):
    """Tests the training input function for FasterRcnnResnet50."""
    configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
    configs['train_config'].unpad_groundtruth_tensors = True
    model_config = configs['model']
    model_config.faster_rcnn.num_classes = 37
    train_input_fn = inputs.create_train_input_fn(
        configs['train_config'], configs['train_input_config'], model_config)
    features, labels = train_input_fn()

    self.assertAllEqual([None, None, 3],
                        features[fields.InputDataFields.image].shape.as_list())
    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
    self.assertAllEqual([],
                        features[inputs.HASH_KEY].shape.as_list())
    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
    self.assertAllEqual(
        [None, 4],
        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
    self.assertAllEqual(
        [None, model_config.faster_rcnn.num_classes],
        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_classes].dtype)
    self.assertAllEqual(
        [None],
        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_weights].dtype) 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_faster_rcnn_resnet50_train_input(self):
    """Tests the training input function for FasterRcnnResnet50."""
    configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
    model_config = configs['model']
    model_config.faster_rcnn.num_classes = 37
    train_input_fn = inputs.create_train_input_fn(
        configs['train_config'], configs['train_input_config'], model_config)
    features, labels = _make_initializable_iterator(train_input_fn()).get_next()

    self.assertAllEqual([1, None, None, 3],
                        features[fields.InputDataFields.image].shape.as_list())
    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
    self.assertAllEqual([1],
                        features[inputs.HASH_KEY].shape.as_list())
    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
    self.assertAllEqual(
        [1, 100, 4],
        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
    self.assertAllEqual(
        [1, 100, model_config.faster_rcnn.num_classes],
        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_classes].dtype)
    self.assertAllEqual(
        [1, 100],
        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_weights].dtype) 

# 需要导入模块: from object_detection import inputs [as 别名]
# 或者: from object_detection.inputs import create_train_input_fn [as 别名]
def test_ssd_inceptionV2_train_input(self):
    """Tests the training input function for SSDInceptionV2."""
    configs = _get_configs_for_model('ssd_inception_v2_pets')
    model_config = configs['model']
    model_config.ssd.num_classes = 37
    batch_size = configs['train_config'].batch_size
    train_input_fn = inputs.create_train_input_fn(
        configs['train_config'], configs['train_input_config'], model_config)
    features, labels = _make_initializable_iterator(train_input_fn()).get_next()

    self.assertAllEqual([batch_size, 300, 300, 3],
                        features[fields.InputDataFields.image].shape.as_list())
    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
    self.assertAllEqual([batch_size],
                        features[inputs.HASH_KEY].shape.as_list())
    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
    self.assertAllEqual(
        [batch_size],
        labels[fields.InputDataFields.num_groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.int32,
                     labels[fields.InputDataFields.num_groundtruth_boxes].dtype)
    self.assertAllEqual(
        [batch_size, 100, 4],
        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_boxes].dtype)
    self.assertAllEqual(
        [batch_size, 100, model_config.ssd.num_classes],
        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_classes].dtype)
    self.assertAllEqual(
        [batch_size, 100],
        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
    self.assertEqual(tf.float32,
                     labels[fields.InputDataFields.groundtruth_weights].dtype)