Exporting cifar10 model from checkpoint file to tensorflow serving - tensorflow

Question

I tried to modify the inception_export.py for CIFAR10 model, but I get the errors:
raise type(e)(node_def, op, message) tensorflow.python.framework.errors.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [18,384] rhs shape= [2304,384]
[[Node: save/Assign_5 = Assign[T=DT_FLOAT, _class=["loc:#local3/weights"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/cpu:0"](local3/weights, save/restore_slice_5)]] Caused by op u'save/Assign_5', defined at:
I am still very new to tensorflow, any help is much appreciated, thanks
EDIT1: here is my code. I haven't installed tensorflow serving so the related block is commented out. I also change the image_size to 24 to fit the CIFAR10 model.
# 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.
# ==============================================================================
#!/usr/bin/env python2.7
"""Modified for CIFAR10 model from https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/inception_export.py
"""
import os.path
import sys
# This is a placeholder for a Google-internal import.
import tensorflow as tf
from tensorflow.models.image.cifar10 import cifar10
#from inception import inception_model
#from tensorflow_serving.session_bundle import exporter
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train',
"""Directory where to read training checkpoints.""")
tf.app.flags.DEFINE_string('export_dir', '/tmp/cifar10_export',
"""Directory where to export inference model.""")
tf.app.flags.DEFINE_integer('image_size', 24,
"""Needs to provide same value as in training.""")
FLAGS = tf.app.flags.FLAGS
NUM_CLASSES = 10
NUM_TOP_CLASSES = 2
WORKING_DIR = os.path.dirname(os.path.realpath(__file__))
SYNSET_FILE = os.path.join(WORKING_DIR, 'imagenet_lsvrc_2015_synsets.txt')
METADATA_FILE = os.path.join(WORKING_DIR, 'imagenet_metadata.txt')
def export():
"""can be deleted if my simply define the constant string manually below?
# Create index->synset mapping
synsets = []
with open(SYNSET_FILE) as f:
synsets = f.read().splitlines()
# Create synset->metadata mapping
texts = {}
with open(METADATA_FILE) as f:
for line in f.read().splitlines():
parts = line.split('\t')
assert len(parts) == 2
texts[parts[0]] = parts[1]
"""
with tf.Graph().as_default():
# Build inference model.
# Please refer to Tensorflow inception model for details.
# Input transformation.
# TODO(b/27776734): Add batching support.
jpegs = tf.placeholder(tf.string, shape=(1))
image_buffer = tf.squeeze(jpegs, [0])
# Decode the string as an RGB JPEG.
# Note that the resulting image contains an unknown height and width
# that is set dynamically by decode_jpeg. In other words, the height
# and width of image is unknown at compile-time.
image = tf.image.decode_jpeg(image_buffer, channels=3)
# After this point, all image pixels reside in [0,1)
# until the very end, when they're rescaled to (-1, 1). The various
# adjust_* ops all require this range for dtype float.
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# Crop the central region of the image with an area containing 87.5% of
# the original image.
image = tf.image.central_crop(image, central_fraction=0.875)
# Resize the image to the original height and width.
image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear(image,
[FLAGS.image_size, FLAGS.image_size],
align_corners=False)
image = tf.squeeze(image, [0])
# Finally, rescale to [-1,1] instead of [0, 1)
image = tf.sub(image, 0.5)
image = tf.mul(image, 2.0)
images = tf.expand_dims(image, 0)
# Run inference.
logits = cifar10.inference(images)
# Transform output to topK result.
values, indices = tf.nn.top_k(logits, NUM_TOP_CLASSES)
# Create a constant string Tensor where the i'th element is
# the human readable class description for the i'th index.
# Note that the 0th index is an unused background class
# (see inception model definition code).
class_descriptions = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
# for s in synsets:
# class_descriptions.append(texts[s])
class_tensor = tf.constant(class_descriptions)
classes = tf.contrib.lookup.index_to_string(tf.to_int64(indices),
mapping=class_tensor)
# Restore variables from training checkpoint.
variable_averages = tf.train.ExponentialMovingAverage(
cifar10.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
# Restore variables from training checkpoints.
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/imagenet_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
print('Successfully loaded model from %s at step=%s.' %
(ckpt.model_checkpoint_path, global_step))
else:
print('No checkpoint file found at %s' % FLAGS.checkpoint_dir)
return
""" Not exporting yet because I haven't installed tensorflow serving
# Export inference model.
init_op = tf.group(tf.initialize_all_tables(), name='init_op')
model_exporter = exporter.Exporter(saver)
signature = exporter.classification_signature(
input_tensor=jpegs, classes_tensor=classes, scores_tensor=values)
model_exporter.init(default_graph_signature=signature, init_op=init_op)
model_exporter.export(FLAGS.export_dir, tf.constant(global_step), sess)
print('Successfully exported model to %s' % FLAGS.export_dir)
"""
def main(unused_argv=None):
export()
if __name__ == '__main__':
tf.app.run()