Python object_detection.exporter.freeze_graph_with_def_protos() Examples

The following are 5 code examples of object_detection.exporter.freeze_graph_with_def_protos(). 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: generate_embedding_data_tf1_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def _export_saved_model(self):
    tmp_dir = self.get_temp_dir()
    checkpoint_path = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path)
    output_directory = os.path.join(tmp_dir, 'output')
    saved_model_path = os.path.join(output_directory, 'saved_model')
    tf.io.gfile.makedirs(output_directory)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(num_classes=5)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      detection_model = model_builder.build(pipeline_config.model,
                                            is_training=False)
      outputs, placeholder_tensor = exporter.build_detection_graph(
          input_type='tf_example',
          detection_model=detection_model,
          input_shape=None,
          output_collection_name='inference_op',
          graph_hook_fn=None)
      output_node_names = ','.join(outputs.keys())
      saver = tf.train.Saver()
      input_saver_def = saver.as_saver_def()
      frozen_graph_def = exporter.freeze_graph_with_def_protos(
          input_graph_def=tf.get_default_graph().as_graph_def(),
          input_saver_def=input_saver_def,
          input_checkpoint=checkpoint_path,
          output_node_names=output_node_names,
          restore_op_name='save/restore_all',
          filename_tensor_name='save/Const:0',
          output_graph='',
          clear_devices=True,
          initializer_nodes='')
      exporter.write_saved_model(
          saved_model_path=saved_model_path,
          frozen_graph_def=frozen_graph_def,
          inputs=placeholder_tensor,
          outputs=outputs)
      return saved_model_path
Example #2
Source File: generate_detection_data_tf1_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def _export_saved_model(self):
    tmp_dir = self.get_temp_dir()
    checkpoint_path = os.path.join(tmp_dir, 'model.ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path)
    output_directory = os.path.join(tmp_dir, 'output')
    saved_model_path = os.path.join(output_directory, 'saved_model')
    tf.io.gfile.makedirs(output_directory)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(num_classes=5)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      detection_model = model_builder.build(pipeline_config.model,
                                            is_training=False)
      outputs, placeholder_tensor = exporter.build_detection_graph(
          input_type='tf_example',
          detection_model=detection_model,
          input_shape=None,
          output_collection_name='inference_op',
          graph_hook_fn=None)
      output_node_names = ','.join(outputs.keys())
      saver = tf.train.Saver()
      input_saver_def = saver.as_saver_def()
      frozen_graph_def = exporter.freeze_graph_with_def_protos(
          input_graph_def=tf.get_default_graph().as_graph_def(),
          input_saver_def=input_saver_def,
          input_checkpoint=checkpoint_path,
          output_node_names=output_node_names,
          restore_op_name='save/restore_all',
          filename_tensor_name='save/Const:0',
          output_graph='',
          clear_devices=True,
          initializer_nodes='')
      exporter.write_saved_model(
          saved_model_path=saved_model_path,
          frozen_graph_def=frozen_graph_def,
          inputs=placeholder_tensor,
          outputs=outputs)
      return saved_model_path
Example #3
Source File: exporter_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 4 votes vote down vote up 
def test_write_frozen_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=True)
    output_directory = os.path.join(tmp_dir, 'output')
    inference_graph_path = os.path.join(output_directory,
                                        'frozen_inference_graph.pb')
    tf.gfile.MakeDirs(output_directory)
    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()
      pipeline_config.eval_config.use_moving_averages = False
      detection_model = model_builder.build(pipeline_config.model,
                                            is_training=False)
      outputs, _ = exporter._build_detection_graph(
          input_type='tf_example',
          detection_model=detection_model,
          input_shape=None,
          output_collection_name='inference_op',
          graph_hook_fn=None)
      output_node_names = ','.join(outputs.keys())
      saver = tf.train.Saver()
      input_saver_def = saver.as_saver_def()
      frozen_graph_def = exporter.freeze_graph_with_def_protos(
          input_graph_def=tf.get_default_graph().as_graph_def(),
          input_saver_def=input_saver_def,
          input_checkpoint=trained_checkpoint_prefix,
          output_node_names=output_node_names,
          restore_op_name='save/restore_all',
          filename_tensor_name='save/Const:0',
          clear_devices=True,
          initializer_nodes='')
      exporter.write_frozen_graph(inference_graph_path, frozen_graph_def)

    inference_graph = self._load_inference_graph(inference_graph_path)
    tf_example_np = np.expand_dims(self._create_tf_example(
        np.ones((4, 4, 3)).astype(np.uint8)), axis=0)
    with self.test_session(graph=inference_graph) as sess:
      tf_example = inference_graph.get_tensor_by_name('tf_example:0')
      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
      scores = inference_graph.get_tensor_by_name('detection_scores:0')
      classes = inference_graph.get_tensor_by_name('detection_classes:0')
      masks = inference_graph.get_tensor_by_name('detection_masks:0')
      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
      (boxes_np, scores_np, classes_np, masks_np, num_detections_np) = sess.run(
          [boxes, scores, classes, masks, num_detections],
          feed_dict={tf_example: tf_example_np})
      self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
                                      [0.5, 0.5, 0.8, 0.8]],
                                     [[0.5, 0.5, 1.0, 1.0],
                                      [0.0, 0.0, 0.0, 0.0]]])
      self.assertAllClose(scores_np, [[0.7, 0.6],
                                      [0.9, 0.0]])
      self.assertAllClose(classes_np, [[1, 2],
                                       [2, 1]])
      self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
      self.assertAllClose(num_detections_np, [2, 1])
Example #4
Source File: exporter_test.py    From Gun-Detector with Apache License 2.0 4 votes vote down vote up 
def test_write_frozen_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=True)
    output_directory = os.path.join(tmp_dir, 'output')
    inference_graph_path = os.path.join(output_directory,
                                        'frozen_inference_graph.pb')
    tf.gfile.MakeDirs(output_directory)
    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()
      pipeline_config.eval_config.use_moving_averages = False
      detection_model = model_builder.build(pipeline_config.model,
                                            is_training=False)
      outputs, _ = exporter._build_detection_graph(
          input_type='tf_example',
          detection_model=detection_model,
          input_shape=None,
          output_collection_name='inference_op',
          graph_hook_fn=None)
      output_node_names = ','.join(outputs.keys())
      saver = tf.train.Saver()
      input_saver_def = saver.as_saver_def()
      frozen_graph_def = exporter.freeze_graph_with_def_protos(
          input_graph_def=tf.get_default_graph().as_graph_def(),
          input_saver_def=input_saver_def,
          input_checkpoint=trained_checkpoint_prefix,
          output_node_names=output_node_names,
          restore_op_name='save/restore_all',
          filename_tensor_name='save/Const:0',
          clear_devices=True,
          initializer_nodes='')
      exporter.write_frozen_graph(inference_graph_path, frozen_graph_def)

    inference_graph = self._load_inference_graph(inference_graph_path)
    tf_example_np = np.expand_dims(self._create_tf_example(
        np.ones((4, 4, 3)).astype(np.uint8)), axis=0)
    with self.test_session(graph=inference_graph) as sess:
      tf_example = inference_graph.get_tensor_by_name('tf_example:0')
      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
      scores = inference_graph.get_tensor_by_name('detection_scores:0')
      classes = inference_graph.get_tensor_by_name('detection_classes:0')
      masks = inference_graph.get_tensor_by_name('detection_masks:0')
      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
      (boxes_np, scores_np, classes_np, masks_np, num_detections_np) = sess.run(
          [boxes, scores, classes, masks, num_detections],
          feed_dict={tf_example: tf_example_np})
      self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
                                      [0.5, 0.5, 0.8, 0.8]],
                                     [[0.5, 0.5, 1.0, 1.0],
                                      [0.0, 0.0, 0.0, 0.0]]])
      self.assertAllClose(scores_np, [[0.7, 0.6],
                                      [0.9, 0.0]])
      self.assertAllClose(classes_np, [[1, 2],
                                       [2, 1]])
      self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
      self.assertAllClose(num_detections_np, [2, 1])
Example #5
Source File: exporter_test.py    From ros_tensorflow with Apache License 2.0 4 votes vote down vote up 
def test_write_frozen_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=True)
    output_directory = os.path.join(tmp_dir, 'output')
    inference_graph_path = os.path.join(output_directory,
                                        'frozen_inference_graph.pb')
    tf.gfile.MakeDirs(output_directory)
    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()
      pipeline_config.eval_config.use_moving_averages = False
      detection_model = model_builder.build(pipeline_config.model,
                                            is_training=False)
      outputs, _ = exporter._build_detection_graph(
          input_type='tf_example',
          detection_model=detection_model,
          input_shape=None,
          output_collection_name='inference_op',
          graph_hook_fn=None)
      output_node_names = ','.join(outputs.keys())
      saver = tf.train.Saver()
      input_saver_def = saver.as_saver_def()
      frozen_graph_def = exporter.freeze_graph_with_def_protos(
          input_graph_def=tf.get_default_graph().as_graph_def(),
          input_saver_def=input_saver_def,
          input_checkpoint=trained_checkpoint_prefix,
          output_node_names=output_node_names,
          restore_op_name='save/restore_all',
          filename_tensor_name='save/Const:0',
          clear_devices=True,
          initializer_nodes='')
      exporter.write_frozen_graph(inference_graph_path, frozen_graph_def)

    inference_graph = self._load_inference_graph(inference_graph_path)
    tf_example_np = np.expand_dims(self._create_tf_example(
        np.ones((4, 4, 3)).astype(np.uint8)), axis=0)
    with self.test_session(graph=inference_graph) as sess:
      tf_example = inference_graph.get_tensor_by_name('tf_example:0')
      boxes = inference_graph.get_tensor_by_name('detection_boxes:0')
      scores = inference_graph.get_tensor_by_name('detection_scores:0')
      classes = inference_graph.get_tensor_by_name('detection_classes:0')
      masks = inference_graph.get_tensor_by_name('detection_masks:0')
      num_detections = inference_graph.get_tensor_by_name('num_detections:0')
      (boxes_np, scores_np, classes_np, masks_np, num_detections_np) = sess.run(
          [boxes, scores, classes, masks, num_detections],
          feed_dict={tf_example: tf_example_np})
      self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5],
                                      [0.5, 0.5, 0.8, 0.8]],
                                     [[0.5, 0.5, 1.0, 1.0],
                                      [0.0, 0.0, 0.0, 0.0]]])
      self.assertAllClose(scores_np, [[0.7, 0.6],
                                      [0.9, 0.0]])
      self.assertAllClose(classes_np, [[1, 2],
                                       [2, 1]])
      self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
      self.assertAllClose(num_detections_np, [2, 1])