export_model.py
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# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities to export a model for batch prediction."""
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import utils as saved_model_utils
_TOP_PREDICTIONS_IN_OUTPUT = 20
class ModelExporter(object):
def __init__(self, frame_features, model, reader):
self.frame_features = frame_features
self.model = model
self.reader = reader
with tf.Graph().as_default() as graph:
self.inputs, self.outputs = self.build_inputs_and_outputs()
self.graph = graph
self.saver = tf.train.Saver(tf.trainable_variables(), sharded=True)
def export_model(self, model_dir, global_step_val, last_checkpoint):
"""Exports the model so that it can used for batch predictions."""
with self.graph.as_default():
with tf.Session() as session:
session.run(tf.global_variables_initializer())
self.saver.restore(session, last_checkpoint)
signature = signature_def_utils.build_signature_def(
inputs=self.inputs,
outputs=self.outputs,
method_name=signature_constants.PREDICT_METHOD_NAME)
signature_map = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
}
model_builder = saved_model_builder.SavedModelBuilder(model_dir)
model_builder.add_meta_graph_and_variables(
session,
tags=[tag_constants.SERVING],
signature_def_map=signature_map,
clear_devices=True)
model_builder.save()
def build_inputs_and_outputs(self):
if self.frame_features:
serialized_examples = tf.placeholder(tf.string, shape=(None,))
fn = lambda x: self.build_prediction_graph(x)
video_id_output, top_indices_output, top_predictions_output = (tf.map_fn(
fn, serialized_examples, dtype=(tf.string, tf.int32, tf.float32)))
else:
serialized_examples = tf.placeholder(tf.string, shape=(None,))
video_id_output, top_indices_output, top_predictions_output = (
self.build_prediction_graph(serialized_examples))
inputs = {
"example_bytes":
saved_model_utils.build_tensor_info(serialized_examples)
}
outputs = {
"video_id":
saved_model_utils.build_tensor_info(video_id_output),
"class_indexes":
saved_model_utils.build_tensor_info(top_indices_output),
"predictions":
saved_model_utils.build_tensor_info(top_predictions_output)
}
return inputs, outputs
def build_prediction_graph(self, serialized_examples):
input_data_dict = (
self.reader.prepare_serialized_examples(serialized_examples))
video_id = input_data_dict["video_ids"]
model_input_raw = input_data_dict["video_matrix"]
labels_batch = input_data_dict["labels"]
num_frames = input_data_dict["num_frames"]
feature_dim = len(model_input_raw.get_shape()) - 1
model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
with tf.variable_scope("tower"):
result = self.model.create_model(model_input,
num_frames=num_frames,
vocab_size=self.reader.num_classes,
labels=labels_batch,
is_training=False)
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
top_predictions, top_indices = tf.nn.top_k(predictions,
_TOP_PREDICTIONS_IN_OUTPUT)
return video_id, top_indices, top_predictions