Python model_lib.create_model_fn方法代碼示例

本文整理匯總了Python中object_detection.model_lib.create_model_fn方法的典型用法代碼示例。如果您正苦於以下問題：Python model_lib.create_model_fn方法的具體用法？Python model_lib.create_model_fn怎麽用？Python model_lib.create_model_fn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_lib的用法示例。

在下文中一共展示了model_lib.create_model_fn方法的5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_lib [as 別名]
# 或者: from object_detection.model_lib import create_model_fn [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: _assert_model_fn_for_predict

# 需要導入模塊: from object_detection import model_lib [as 別名]
# 或者: from object_detection.model_lib import create_model_fn [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3: _assert_model_fn_for_train_eval

# 需要導入模塊: from object_detection import model_lib [as 別名]
# 或者: from object_detection.model_lib import create_model_fn [as 別名]
def _assert_model_fn_for_train_eval(self, configs, mode,
                                      class_agnostic=False):
    model_config = configs['model']
    train_config = configs['train_config']
    with tf.Graph().as_default():
      if mode == 'train':
        features, labels = _make_initializable_iterator(
            inputs.create_train_input_fn(configs['train_config'],
                                         configs['train_input_config'],
                                         configs['model'])()).get_next()
        model_mode = tf.estimator.ModeKeys.TRAIN
        batch_size = train_config.batch_size
      elif mode == 'eval':
        features, labels = _make_initializable_iterator(
            inputs.create_eval_input_fn(configs['eval_config'],
                                        configs['eval_input_config'],
                                        configs['model'])()).get_next()
        model_mode = tf.estimator.ModeKeys.EVAL
        batch_size = 1
      elif mode == 'eval_on_train':
        features, labels = _make_initializable_iterator(
            inputs.create_eval_input_fn(configs['eval_config'],
                                        configs['train_input_config'],
                                        configs['model'])()).get_next()
        model_mode = tf.estimator.ModeKeys.EVAL
        batch_size = 1

      detection_model_fn = functools.partial(
          model_builder.build, model_config=model_config, is_training=True)

      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, labels, model_mode)

      self.assertIsNotNone(estimator_spec.loss)
      self.assertIsNotNone(estimator_spec.predictions)
      if mode == 'eval' or mode == 'eval_on_train':
        if class_agnostic:
          self.assertNotIn('detection_classes', estimator_spec.predictions)
        else:
          detection_classes = estimator_spec.predictions['detection_classes']
          self.assertEqual(batch_size, detection_classes.shape.as_list()[0])
          self.assertEqual(tf.float32, detection_classes.dtype)
        detection_boxes = estimator_spec.predictions['detection_boxes']
        detection_scores = estimator_spec.predictions['detection_scores']
        num_detections = estimator_spec.predictions['num_detections']
        self.assertEqual(batch_size, detection_boxes.shape.as_list()[0])
        self.assertEqual(tf.float32, detection_boxes.dtype)
        self.assertEqual(batch_size, detection_scores.shape.as_list()[0])
        self.assertEqual(tf.float32, detection_scores.dtype)
        self.assertEqual(tf.float32, num_detections.dtype)
        if mode == 'eval':
          self.assertIn('Detections_Left_Groundtruth_Right/0',
                        estimator_spec.eval_metric_ops)
      if model_mode == tf.estimator.ModeKeys.TRAIN:
        self.assertIsNotNone(estimator_spec.train_op)
      return estimator_spec

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:61，代碼來源:model_lib_test.py

示例4: _assert_model_fn_for_train_eval

# 需要導入模塊: from object_detection import model_lib [as 別名]
# 或者: from object_detection.model_lib import create_model_fn [as 別名]
def _assert_model_fn_for_train_eval(self, configs, mode,
                                      class_agnostic=False):
    model_config = configs['model']
    train_config = configs['train_config']
    with tf.Graph().as_default():
      if mode == 'train':
        features, labels = inputs.create_train_input_fn(
            configs['train_config'],
            configs['train_input_config'],
            configs['model'])()
        model_mode = tf.estimator.ModeKeys.TRAIN
        batch_size = train_config.batch_size
      elif mode == 'eval':
        features, labels = inputs.create_eval_input_fn(
            configs['eval_config'],
            configs['eval_input_config'],
            configs['model'])()
        model_mode = tf.estimator.ModeKeys.EVAL
        batch_size = 1
      elif mode == 'eval_on_train':
        features, labels = inputs.create_eval_input_fn(
            configs['eval_config'],
            configs['train_input_config'],
            configs['model'])()
        model_mode = tf.estimator.ModeKeys.EVAL
        batch_size = 1

      detection_model_fn = functools.partial(
          model_builder.build, model_config=model_config, is_training=True)

      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, labels, model_mode)

      self.assertIsNotNone(estimator_spec.loss)
      self.assertIsNotNone(estimator_spec.predictions)
      if class_agnostic:
        self.assertNotIn('detection_classes', estimator_spec.predictions)
      else:
        detection_classes = estimator_spec.predictions['detection_classes']
        self.assertEqual(batch_size, detection_classes.shape.as_list()[0])
        self.assertEqual(tf.float32, detection_classes.dtype)
      detection_boxes = estimator_spec.predictions['detection_boxes']
      detection_scores = estimator_spec.predictions['detection_scores']
      num_detections = estimator_spec.predictions['num_detections']
      self.assertEqual(batch_size, detection_boxes.shape.as_list()[0])
      self.assertEqual(tf.float32, detection_boxes.dtype)
      self.assertEqual(batch_size, detection_scores.shape.as_list()[0])
      self.assertEqual(tf.float32, detection_scores.dtype)
      self.assertEqual(tf.float32, num_detections.dtype)
      if model_mode == tf.estimator.ModeKeys.TRAIN:
        self.assertIsNotNone(estimator_spec.train_op)
      return estimator_spec

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:57，代碼來源:model_lib_test.py

示例5: _assert_model_fn_for_train_eval

# 需要導入模塊: from object_detection import model_lib [as 別名]
# 或者: from object_detection.model_lib import create_model_fn [as 別名]
def _assert_model_fn_for_train_eval(self, configs, mode,
                                      class_agnostic=False):
    model_config = configs['model']
    train_config = configs['train_config']
    with tf.Graph().as_default():
      if mode == 'train':
        features, labels = _make_initializable_iterator(
            inputs.create_train_input_fn(configs['train_config'],
                                         configs['train_input_config'],
                                         configs['model'])()).get_next()
        model_mode = tf.estimator.ModeKeys.TRAIN
        batch_size = train_config.batch_size
      elif mode == 'eval':
        features, labels = _make_initializable_iterator(
            inputs.create_eval_input_fn(configs['eval_config'],
                                        configs['eval_input_config'],
                                        configs['model'])()).get_next()
        model_mode = tf.estimator.ModeKeys.EVAL
        batch_size = 1
      elif mode == 'eval_on_train':
        features, labels = _make_initializable_iterator(
            inputs.create_eval_input_fn(configs['eval_config'],
                                        configs['train_input_config'],
                                        configs['model'])()).get_next()
        model_mode = tf.estimator.ModeKeys.EVAL
        batch_size = 1

      detection_model_fn = functools.partial(
          model_builder.build, model_config=model_config, is_training=True)

      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, labels, model_mode)

      self.assertIsNotNone(estimator_spec.loss)
      self.assertIsNotNone(estimator_spec.predictions)
      if mode == 'eval' or mode == 'eval_on_train':
        if class_agnostic:
          self.assertNotIn('detection_classes', estimator_spec.predictions)
        else:
          detection_classes = estimator_spec.predictions['detection_classes']
          self.assertEqual(batch_size, detection_classes.shape.as_list()[0])
          self.assertEqual(tf.float32, detection_classes.dtype)
        detection_boxes = estimator_spec.predictions['detection_boxes']
        detection_scores = estimator_spec.predictions['detection_scores']
        num_detections = estimator_spec.predictions['num_detections']
        self.assertEqual(batch_size, detection_boxes.shape.as_list()[0])
        self.assertEqual(tf.float32, detection_boxes.dtype)
        self.assertEqual(batch_size, detection_scores.shape.as_list()[0])
        self.assertEqual(tf.float32, detection_scores.dtype)
        self.assertEqual(tf.float32, num_detections.dtype)
      if model_mode == tf.estimator.ModeKeys.TRAIN:
        self.assertIsNotNone(estimator_spec.train_op)
      return estimator_spec

開發者ID:BMW-InnovationLab，項目名稱:BMW-TensorFlow-Training-GUI，代碼行數:58，代碼來源:model_lib_test.py

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