Описание
Я пытаюсь развернуть один из предварительно подготовленных Модели обнаружения объектов TensorFlow (faster_rcnn_inception_v2_coco_2018_01_28
) в службе TF. Я выполняю следующие шаги:
- Клонировать https://github.com/tensorflow/models
- Загрузите предварительно обученную модель контрольной точки
- Экспортируйте Inception, как описано здесь с помощью этой команды. . Примечание. Я меняю
input_type
наencoded_image_string_tensor
, чтобы мне не нужно было прикреплять входной тензор для десериализации входной строки в API. - Преобразуйте Inception из предыдущего шага в обслуживаемый с помощью кода ниже
- Запустите TF Serving, указав
model_base_path
на папку, созданную на предыдущем шаге.
Проблема
При запросе конечной точки API predict
происходит сбой, похоже, что модель не была правильно инициализирована:
{ "error": "Attempting to use uninitialized value SecondStageFeatureExtractor/InceptionV2/Mixed_5c/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean\\n\\t [[Node: SecondStageFeatureExtractor/InceptionV2/Mixed_5c/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean/read = Identity[T=DT_FLOAT, _output_shapes=[[128]], _device=\\"/job:localhost/replica:0/task:0/device:CPU:0\\"](SecondStageFeatureExtractor/InceptionV2/Mixed_5c/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean)]]" }
Действительно, журнал обслуживания TF предупредил The specified SavedModel has no variables; no checkpoints were restored.
(см. Приложение к Шагу 5).
Что мне не хватает на этапе экспорта модели? Из более старых выпусков (this и this), я вижу, это может быть связано с тем, что на шаге (3) переменные замораживаются как константы в сохраненной модели. Так ли это?
Приложение к Шагу 3 - Начало реэкспорта
python object_detection/export_inference_graph.py \
--input_type encoded_image_string_tensor \
--pipeline_config_path ${MODEL_DIR}/pipeline.config \
--trained_checkpoint_prefix ${MODEL_DIR}/model.ckpt \
--inference_graph_path ${MODEL_DIR} \
--export_as_saved_model=True \
--write_inference_graph=True \
--output_directory ${OUTPUT_DIR}
Эта команда производит эту модель
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['inputs'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: encoded_image_string_tensor:0
The given SavedModel SignatureDef contains the following output(s):
outputs['detection_boxes'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 100, 4)
name: detection_boxes:0
outputs['detection_classes'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 100)
name: detection_classes:0
outputs['detection_scores'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 100)
name: detection_scores:0
outputs['num_detections'] tensor_info:
dtype: DT_FLOAT
shape: (-1)
name: num_detections:0
Method name is: tensorflow/serving/predict
Приложение к шагу 4 - Сохраненная модель в доступную для просмотра
import os
import shutil
import tensorflow as tf
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/inception_train',
"""Directory where to read training checkpoints.""")
tf.app.flags.DEFINE_string('output_dir', '/tmp/faster_rcnn_inception_v2_coco_2018_01_28-export/',
"""Directory where to export inference model.""")
tf.app.flags.DEFINE_integer('model_version', 1,
"""Version number of the model.""")
tf.app.flags.DEFINE_string('summaries_dir', '/tmp/tensorboard_data',
"""Directory where to store tensorboard data.""")
FLAGS = tf.app.flags.FLAGS
def main(_):
with tf.Graph().as_default() as graph:
saver = tf.train.import_meta_graph(meta_graph_or_file=os.path.join(FLAGS.checkpoint_dir, 'model.ckpt.meta'))
with tf.Session(graph=graph) as sess:
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.checkpoint_dir))
# (re-)create export directory
export_path = os.path.join(
tf.compat.as_bytes(FLAGS.output_dir),
tf.compat.as_bytes(str(FLAGS.model_version)))
if os.path.exists(export_path):
shutil.rmtree(export_path)
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
print("tf.global_variables()")
print(sess.run(tf.global_variables()))
print("tf.local_variables()")
print(sess.run(tf.local_variables()))
# create model builder
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
predict_inputs_tensor_info = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('encoded_image_string_tensor:0'))
boxes_output_tensor_info = tf.saved_model.utils.build_tensor_info(graph.get_tensor_by_name('detection_boxes:0'))
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
'images': predict_inputs_tensor_info
},
outputs={
'classes': boxes_output_tensor_info
},
method_name=tf.saved_model.signature_constants.
PREDICT_METHOD_NAME
)
)
builder.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={
tf.saved_model.signature_constants.
DEFAULT_SERVING_SIGNATURE_DEF_KEY:
prediction_signature,
},
legacy_init_op=None)
builder.save(as_text=False)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/' + str(FLAGS.model_version),
sess.graph)
print("Successfully exported Faster RCNN Inception model version '{}' into '{}'".format(
FLAGS.model_version, FLAGS.output_dir))
if __name__ == '__main__':
tf.app.run()
Это приводит к следующему обслуживаемому:
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['images'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: encoded_image_string_tensor:0
The given SavedModel SignatureDef contains the following output(s):
outputs['classes'] tensor_info:
dtype: DT_FLOAT
shape: unknown_rank
name: detection_boxes:0
Method name is: tensorflow/serving/predict
Приложение к шагу 5 - журнал docker-compose для развертывания обслуживаемого
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.191941: I tensorflow_serving/model_servers/main.cc:153] Building single TensorFlow model file config: model_name: faster_rcnn_inception model_base_path: /tmp/faster_rcnn_inception_v2_coco_2018_01_28_string_input_version-export/
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.192341: I tensorflow_serving/model_servers/server_core.cc:459] Adding/updating models.
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.192465: I tensorflow_serving/model_servers/server_core.cc:514] (Re-)adding model: faster_rcnn_inception
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.195056: I tensorflow_serving/core/basic_manager.cc:716] Successfully reserved resources to load servable {name: faster_rcnn_inception version: 1}
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.195241: I tensorflow_serving/core/loader_harness.cc:66] Approving load for servable version {name: faster_rcnn_inception version: 1}
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.195404: I tensorflow_serving/core/loader_harness.cc:74] Loading servable version {name: faster_rcnn_inception version: 1}
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.195652: I external/org_tensorflow/tensorflow/contrib/session_bundle/bundle_shim.cc:360] Attempting to load native SavedModelBundle in bundle-shim from: /tmp/faster_rcnn_inception_v2_coco_2018_01_28_string_input_version-export/1
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.195829: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:242] Loading SavedModel with tags: { serve }; from: /tmp/faster_rcnn_inception_v2_coco_2018_01_28_string_input_version-export/1
tf-serving-base exited with code 0
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.313633: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.492904: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:161] Restoring SavedModel bundle.
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.493224: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:171] The specified SavedModel has no variables; no checkpoints were restored.
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.493329: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:196] Running LegacyInitOp on SavedModel bundle.
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.512745: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:291] SavedModel load for tags { serve }; Status: success. Took 316614 microseconds.
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.513085: I tensorflow_serving/servables/tensorflow/saved_model_warmup.cc:83] No warmup data file found at /tmp/faster_rcnn_inception_v2_coco_2018_01_28_string_input_version-export/1/assets.extra/tf_serving_warmup_requests
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.513927: I tensorflow_serving/core/loader_harness.cc:86] Successfully loaded servable version {name: faster_rcnn_inception version: 1}
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.516875: I tensorflow_serving/model_servers/main.cc:323] Running ModelServer at 0.0.0.0:8500 ...
tf-serving-faster_rcnn_inception | [warn] getaddrinfo: address family for nodename not supported
tf-serving-faster_rcnn_inception | 2018-08-16 16:05:28.518018: I tensorflow_serving/model_servers/main.cc:333] Exporting HTTP/REST API at:localhost:8501 ...
tf-serving-faster_rcnn_inception | [evhttp_server.cc : 235] RAW: Entering the event loop ...
Примечания:
- питон 2.7
- тензорный поток 1.10.0 на обоих этапах (3), (4) и (5). Устанавливается через пипс.