Python object_detection.exporter.replace_variable_values_with_moving_averages() Examples

The following are 15 code examples of object_detection.exporter.replace_variable_values_with_moving_averages(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.exporter , or try the search function .
Example #1
Source File: exporter_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #2
Source File: exporter_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #3
Source File: exporter_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #4
Source File: exporter_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs)
      fake_model.postprocess(predictions)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #5
Source File: exporter_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #6
Source File: exporter_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #7
Source File: exporter_test.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #8
Source File: exporter_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #9
Source File: exporter_test.py    From Elphas with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
        tmp_dir = self.get_temp_dir()
        trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
        new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
        self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                              use_moving_averages=True)
        graph = tf.Graph()
        with graph.as_default():
            fake_model = FakeModel()
            preprocessed_inputs, true_image_shapes = fake_model.preprocess(
                tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
            predictions = fake_model.predict(
                preprocessed_inputs, true_image_shapes)
            fake_model.postprocess(predictions, true_image_shapes)
            exporter.replace_variable_values_with_moving_averages(
                graph, trained_checkpoint_prefix, new_checkpoint_prefix)

        expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
        variables_in_old_ckpt = self._get_variables_in_checkpoint(
            trained_checkpoint_prefix)
        self.assertIn('conv2d/bias/ExponentialMovingAverage',
                      variables_in_old_ckpt)
        self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                      variables_in_old_ckpt)
        variables_in_new_ckpt = self._get_variables_in_checkpoint(
            new_checkpoint_prefix)
        self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
        self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                         variables_in_new_ckpt)
        self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                         variables_in_new_ckpt)
Example #10
Source File: exporter_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs)
      fake_model.postprocess(predictions)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #11
Source File: exporter_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs)
      fake_model.postprocess(predictions)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #12
Source File: exporter_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #13
Source File: exporter_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #14
Source File: exporter_tf1_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)
Example #15
Source File: exporter_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def test_replace_variable_values_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    new_checkpoint_prefix = os.path.join(tmp_dir, 'new.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    graph = tf.Graph()
    with graph.as_default():
      fake_model = FakeModel()
      preprocessed_inputs, true_image_shapes = fake_model.preprocess(
          tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3]))
      predictions = fake_model.predict(preprocessed_inputs, true_image_shapes)
      fake_model.postprocess(predictions, true_image_shapes)
      exporter.replace_variable_values_with_moving_averages(
          graph, trained_checkpoint_prefix, new_checkpoint_prefix)

    expected_variables = set(['conv2d/bias', 'conv2d/kernel'])
    variables_in_old_ckpt = self._get_variables_in_checkpoint(
        trained_checkpoint_prefix)
    self.assertIn('conv2d/bias/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    self.assertIn('conv2d/kernel/ExponentialMovingAverage',
                  variables_in_old_ckpt)
    variables_in_new_ckpt = self._get_variables_in_checkpoint(
        new_checkpoint_prefix)
    self.assertTrue(expected_variables.issubset(variables_in_new_ckpt))
    self.assertNotIn('conv2d/bias/ExponentialMovingAverage',
                     variables_in_new_ckpt)
    self.assertNotIn('conv2d/kernel/ExponentialMovingAverage',
                     variables_in_new_ckpt)