def convert_to_saved_model(model: BaseModel,
model_path: str,
version: str = None,
inputs: Optional[Dict] = None,
outputs: Optional[Dict] = None):
"""
Export model for tensorflow serving
Args:
model: Target model
model_path: The path to which the SavedModel will be stored.
version: The model version code, default timestamp
inputs: dict mapping string input names to tensors. These are added
to the SignatureDef as the inputs.
outputs: dict mapping string output names to tensors. These are added
to the SignatureDef as the outputs.
"""
pathlib.Path(model_path).mkdir(exist_ok=True, parents=True)
if version is None:
version = round(time.time())
export_path = os.path.join(model_path, str(version))
if inputs is None:
inputs = {i.name: i for i in model.tf_model.inputs}
if outputs is None:
outputs = {o.name: o for o in model.tf_model.outputs}
sess = keras.backend.get_session()
saved_model.simple_save(session=sess,
export_dir=export_path,
inputs=inputs,
outputs=outputs)
with open(os.path.join(export_path, 'model_info.json'), 'w') as f:
f.write(json.dumps(model.info(), indent=2, ensure_ascii=True))
f.close()
def _createSimpleSavedModel(self, shape):
"""Create a simple savedmodel on the fly."""
saved_model_dir = os.path.join(self.get_temp_dir(), "simple_savedmodel")
with session.Session() as sess:
in_tensor = array_ops.placeholder(shape=shape, dtype=dtypes.float32)
out_tensor = in_tensor + in_tensor
inputs = {"x": in_tensor}
outputs = {"y": out_tensor}
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
return saved_model_dir
def _createSimpleSavedModel(self, shape):
"""Create a simple savedmodel on the fly."""
saved_model_dir = os.path.join(self.get_temp_dir(),
"simple_savedmodel")
with session.Session() as sess:
in_tensor = array_ops.placeholder(shape=shape,
dtype=dtypes.float32)
out_tensor = in_tensor + in_tensor
inputs = {"x": in_tensor}
outputs = {"y": out_tensor}
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
return saved_model_dir
def model_export(model_name):
export_path = "pb_models/{}/1".format(model_name)
graph = tf.Graph()
saver = tf.train.import_meta_graph("./ckpt_models/{}/{}.ckpt.meta".format(
model_name, model_name),
graph=graph)
with tf.Session(graph=graph) as sess:
saver.restore(
sess,
tf.train.latest_checkpoint("./ckpt_models/{}".format(model_name)))
saved_model.simple_save(
session=sess,
export_dir=export_path,
inputs={"t": graph.get_operation_by_name('t').outputs[0]},
outputs={"z": graph.get_operation_by_name('z').outputs[0]})
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorflow.python import saved_model
export_path = "pb_models/lr/1"
graph = tf.Graph()
saver = tf.train.import_meta_graph("./model/lr.ckpt.meta", graph=graph)
with tf.Session(graph=graph) as sess:
saver.restore(sess, tf.train.latest_checkpoint("./model"))
saved_model.simple_save(
session=sess,
export_dir=export_path,
inputs={"x": graph.get_operation_by_name('x').outputs[0]},
outputs={
"y_pred":
graph.get_operation_by_name('inference/y_pred').outputs[0]
})
'''
> saved_model_cli show --dir 1 --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['x'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 3)
name: x:0
The given SavedModel SignatureDef contains the following output(s):
outputs['y_pred'] tensor_info:
# -*- coding: utf-8 -*-
# @Time : 2020/11/5 23:10
# @Author : Jclian91
# @File : single_ckpt_2_pb.py
# @Place : Yangpu, Shanghai
import tensorflow as tf
from tensorflow.python import saved_model
export_path = "pb_models/add/1"
graph = tf.Graph()
saver = tf.train.import_meta_graph("./ckpt_models/add/add.ckpt.meta",
graph=graph)
with tf.Session(graph=graph) as sess:
saver.restore(sess, tf.train.latest_checkpoint("./ckpt_models/add"))
saved_model.simple_save(
session=sess,
export_dir=export_path,
inputs={"t": graph.get_operation_by_name('t').outputs[0]},
outputs={"z": graph.get_operation_by_name('z').outputs[0]})
