Python exporter._build_detection_graph方法代码示例

# 需要导入模块: from object_detection import exporter [as 别名]
# 或者: from object_detection.exporter import _build_detection_graph [as 别名]
def test_write_graph_and_checkpoint(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)
    output_directory = os.path.join(tmp_dir, 'output')
    model_path = os.path.join(output_directory, 'model.ckpt')
    meta_graph_path = model_path + '.meta'
    tf.gfile.MakeDirs(output_directory)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(
          add_detection_keypoints=True, 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)
      exporter._build_detection_graph(
          input_type='tf_example',
          detection_model=detection_model,
          input_shape=None,
          output_collection_name='inference_op',
          graph_hook_fn=None)
      saver = tf.train.Saver()
      input_saver_def = saver.as_saver_def()
      exporter.write_graph_and_checkpoint(
          inference_graph_def=tf.get_default_graph().as_graph_def(),
          model_path=model_path,
          input_saver_def=input_saver_def,
          trained_checkpoint_prefix=trained_checkpoint_prefix)

    tf_example_np = np.hstack([self._create_tf_example(
        np.ones((4, 4, 3)).astype(np.uint8))] * 2)
    with tf.Graph().as_default() as od_graph:
      with self.test_session(graph=od_graph) as sess:
        new_saver = tf.train.import_meta_graph(meta_graph_path)
        new_saver.restore(sess, model_path)

        tf_example = od_graph.get_tensor_by_name('tf_example:0')
        boxes = od_graph.get_tensor_by_name('detection_boxes:0')
        scores = od_graph.get_tensor_by_name('detection_scores:0')
        classes = od_graph.get_tensor_by_name('detection_classes:0')
        keypoints = od_graph.get_tensor_by_name('detection_keypoints:0')
        masks = od_graph.get_tensor_by_name('detection_masks:0')
        num_detections = od_graph.get_tensor_by_name('num_detections:0')
        (boxes_np, scores_np, classes_np, keypoints_np, masks_np,
         num_detections_np) = sess.run(
             [boxes, scores, classes, keypoints, 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(keypoints_np, np.arange(48).reshape([2, 2, 6, 2]))
        self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4]))
        self.assertAllClose(num_detections_np, [2, 1]) 

# 需要导入模块: from object_detection import exporter [as 别名]
# 或者: from object_detection.exporter import _build_detection_graph [as 别名]
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]) 

# 需要导入模块: from object_detection import exporter [as 别名]
# 或者: from object_detection.exporter import _build_detection_graph [as 别名]
def test_write_graph_and_checkpoint(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)
    output_directory = os.path.join(tmp_dir, 'output')
    model_path = os.path.join(output_directory, 'model.ckpt')
    meta_graph_path = model_path + '.meta'
    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)
      exporter._build_detection_graph(
          input_type='tf_example',
          detection_model=detection_model,
          input_shape=None,
          output_collection_name='inference_op',
          graph_hook_fn=None)
      saver = tf.train.Saver()
      input_saver_def = saver.as_saver_def()
      exporter.write_graph_and_checkpoint(
          inference_graph_def=tf.get_default_graph().as_graph_def(),
          model_path=model_path,
          input_saver_def=input_saver_def,
          trained_checkpoint_prefix=trained_checkpoint_prefix)

    tf_example_np = np.hstack([self._create_tf_example(
        np.ones((4, 4, 3)).astype(np.uint8))] * 2)
    with tf.Graph().as_default() as od_graph:
      with self.test_session(graph=od_graph) as sess:
        new_saver = tf.train.import_meta_graph(meta_graph_path)
        new_saver.restore(sess, model_path)

        tf_example = od_graph.get_tensor_by_name('tf_example:0')
        boxes = od_graph.get_tensor_by_name('detection_boxes:0')
        scores = od_graph.get_tensor_by_name('detection_scores:0')
        classes = od_graph.get_tensor_by_name('detection_classes:0')
        masks = od_graph.get_tensor_by_name('detection_masks:0')
        num_detections = od_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])