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])
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])