Python exporter.export_inference_graph方法代碼示例

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_frozen_graph(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=False)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_model_with_all_output_nodes(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=False)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(add_detection_masks=True)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path)
    inference_graph = self._load_inference_graph(inference_graph_path)
    with self.test_session(graph=inference_graph):
      inference_graph.get_tensor_by_name('image_tensor:0')
      inference_graph.get_tensor_by_name('detection_boxes:0')
      inference_graph.get_tensor_by_name('detection_scores:0')
      inference_graph.get_tensor_by_name('detection_classes:0')
      inference_graph.get_tensor_by_name('detection_masks:0')
      inference_graph.get_tensor_by_name('num_detections:0') 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_model_with_detection_only_nodes(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=False)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(add_detection_masks=False)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path)
    inference_graph = self._load_inference_graph(inference_graph_path)
    with self.test_session(graph=inference_graph):
      inference_graph.get_tensor_by_name('image_tensor:0')
      inference_graph.get_tensor_by_name('detection_boxes:0')
      inference_graph.get_tensor_by_name('detection_scores:0')
      inference_graph.get_tensor_by_name('detection_classes:0')
      inference_graph.get_tensor_by_name('num_detections:0')
      with self.assertRaises(KeyError):
        inference_graph.get_tensor_by_name('detection_masks:0') 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_frozen_graph(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=False)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(num_classes=1)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_frozen_graph_with_moving_averages(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=True)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(num_classes=1)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = True
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def main(_):
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
    text_format.Merge(f.read(), pipeline_config)
  text_format.Merge(FLAGS.config_override, pipeline_config)
  if FLAGS.input_shape:
    input_shape = [
        int(dim) if dim != '-1' else None
        for dim in FLAGS.input_shape.split(',')
    ]
  else:
    input_shape = None
  exporter.export_inference_graph(
      FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix,
      FLAGS.output_directory, input_shape=input_shape,
      write_inference_graph=FLAGS.write_inference_graph) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_graph_with_image_tensor_input(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=False)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      output_directory = os.path.join(tmp_dir, 'output')
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory)
      self.assertTrue(os.path.exists(os.path.join(
          output_directory, 'saved_model', 'saved_model.pb'))) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_write_inference_graph(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=False)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      output_directory = os.path.join(tmp_dir, 'output')
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory,
          write_inference_graph=True)
      self.assertTrue(os.path.exists(os.path.join(
          output_directory, 'inference_graph.pbtxt'))) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_graph_with_tf_example_input(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=False)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      output_directory = os.path.join(tmp_dir, 'output')
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='tf_example',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory)
      self.assertTrue(os.path.exists(os.path.join(
          output_directory, 'saved_model', 'saved_model.pb'))) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_graph_with_moving_averages(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    output_directory = os.path.join(tmp_dir, 'output')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = True
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory)
      self.assertTrue(os.path.exists(os.path.join(
          output_directory, 'saved_model', 'saved_model.pb')))
    expected_variables = set(['conv2d/bias', 'conv2d/kernel', 'global_step'])
    actual_variables = set(
        [var_name for var_name, _ in tf.train.list_variables(output_directory)])
    self.assertTrue(expected_variables.issubset(actual_variables)) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_graph_saves_pipeline_file(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    output_directory = os.path.join(tmp_dir, 'output')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory)
      expected_pipeline_path = os.path.join(
          output_directory, 'pipeline.config')
      self.assertTrue(os.path.exists(expected_pipeline_path))

      written_pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      with tf.gfile.GFile(expected_pipeline_path, 'r') as f:
        proto_str = f.read()
        text_format.Merge(proto_str, written_pipeline_config)
        self.assertProtoEquals(pipeline_config, written_pipeline_config) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_graph_with_encoded_image_string_input(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=False)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      output_directory = os.path.join(tmp_dir, 'output')
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='encoded_image_string_tensor',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory)
      self.assertTrue(os.path.exists(os.path.join(
          output_directory, 'saved_model', 'saved_model.pb'))) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_model_with_all_output_nodes(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=True)
    output_directory = os.path.join(tmp_dir, 'output')
    inference_graph_path = os.path.join(output_directory,
                                        'frozen_inference_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(add_detection_masks=True)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory)
    inference_graph = self._load_inference_graph(inference_graph_path)
    with self.test_session(graph=inference_graph):
      inference_graph.get_tensor_by_name('image_tensor:0')
      inference_graph.get_tensor_by_name('detection_boxes:0')
      inference_graph.get_tensor_by_name('detection_scores:0')
      inference_graph.get_tensor_by_name('detection_classes:0')
      inference_graph.get_tensor_by_name('detection_masks:0')
      inference_graph.get_tensor_by_name('num_detections:0') 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_graph_with_image_tensor_input(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=False)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      output_directory = os.path.join(tmp_dir, 'output')
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory) 

# 需要導入模塊: from object_detection import exporter [as 別名]
# 或者: from object_detection.exporter import export_inference_graph [as 別名]
def test_export_graph_with_tf_example_input(self):
    tmp_dir = self.get_temp_dir()
    trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(trained_checkpoint_prefix,
                                          use_moving_averages=False)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      output_directory = os.path.join(tmp_dir, 'output')
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='tf_example',
          pipeline_config=pipeline_config,
          trained_checkpoint_prefix=trained_checkpoint_prefix,
          output_directory=output_directory)