def test_moving_variables_properly_loaded_from_a_checkpoint(self):
batch_size = 32
dataset_name = 'fsns'
images_placeholder, endpoints = demo_inference.create_model(batch_size,
dataset_name)
image_path_pattern = 'testdata/fsns_train_%02d.png'
images_data = demo_inference.load_images(image_path_pattern, batch_size,
dataset_name)
tensor_name = 'AttentionOcr_v1/conv_tower_fn/INCE/InceptionV3/Conv2d_2a_3x3/BatchNorm/moving_mean'
moving_mean_tf = tf.get_default_graph().get_tensor_by_name(
tensor_name + ':0')
reader = tf.train.NewCheckpointReader(_CHECKPOINT)
moving_mean_expected = reader.get_tensor(tensor_name)
session_creator = monitored_session.ChiefSessionCreator(
checkpoint_filename_with_path=_CHECKPOINT)
with monitored_session.MonitoredSession(
session_creator=session_creator) as sess:
moving_mean_np = sess.run(moving_mean_tf,
feed_dict={images_placeholder: images_data})
self.assertAllEqual(moving_mean_expected, moving_mean_np)
def test_moving_variables_properly_loaded_from_a_checkpoint(self):
batch_size = 32
dataset_name = 'fsns'
images_placeholder, endpoints = demo_inference.create_model(batch_size,
dataset_name)
image_path_pattern = 'testdata/fsns_train_%02d.png'
images_data = demo_inference.load_images(image_path_pattern, batch_size,
dataset_name)
tensor_name = 'AttentionOcr_v1/conv_tower_fn/INCE/InceptionV3/Conv2d_2a_3x3/BatchNorm/moving_mean'
moving_mean_tf = tf.get_default_graph().get_tensor_by_name(
tensor_name + ':0')
reader = tf.train.NewCheckpointReader(_CHECKPOINT)
moving_mean_expected = reader.get_tensor(tensor_name)
session_creator = monitored_session.ChiefSessionCreator(
checkpoint_filename_with_path=_CHECKPOINT)
with monitored_session.MonitoredSession(
session_creator=session_creator) as sess:
moving_mean_np = sess.run(moving_mean_tf,
feed_dict={images_placeholder: images_data})
self.assertAllEqual(moving_mean_expected, moving_mean_np)