imksuma/export_model.py

## export_model.py
#!/usr/bin/env python
#
# Copyright 2016 The Open Images Authors. 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.
# ==============================================================================
#
# This script takes an Inception v3 checkpoint, runs the classifier
# on the image and prints top(n) predictions in the human-readable form.
# Example:
#     $ wget -O /tmp/cat.jpg https://farm6.staticflickr.com/5470/9372235876_d7d69f1790_b.jpg
#     $ ./tools/classify.py /tmp/cat.jpg
#     5723: /m/0jbk - animal (score = 0.94)
#     3473: /m/04rky - mammal (score = 0.93)
#     4605: /m/09686 - vertebrate (score = 0.91)
#     1261: /m/01yrx - cat (score = 0.90)
#     3981: /m/068hy - pet (score = 0.87)
#     841: /m/01l7qd - whiskers (score = 0.83)
#     2430: /m/0307l - cat-like mammal (score = 0.78)
#     4349: /m/07k6w8 - small to medium-sized cats (score = 0.75)
#     2537: /m/035qhg - fauna (score = 0.47)
#     1776: /m/02cqfm - close-up (score = 0.45)
#
# Make sure to download the ANN weights and support data with:
# $ ./tools/download_data.sh

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import math
import sys
import os.path

import numpy as np
import tensorflow as tf

from tensorflow.contrib.slim.python.slim.nets import inception
from tensorflow.python.framework import ops
from tensorflow.python.training import saver as tf_saver
from tensorflow.python.training import supervisor
from tensorflow.python.saved_model import tag_constants, signature_constants
from tensorflow.python.saved_model.signature_def_utils_impl import build_signature_def, predict_signature_def
from tensorflow.python.saved_model import builder

from tensorflow.python.ops.gen_image_ops import *
from tensorflow.python.ops.image_ops_impl import *

import time

slim = tf.contrib.slim
FLAGS = None

def PreprocessImage(image, central_fraction=0.875):
    """Load and preprocess an image.

    Args:
        image: a tf.string tensor with an JPEG-encoded image.
        central_fraction: do a central crop with the specified
            fraction of image covered.
    Returns:
        An ops.Tensor that produces the preprocessed image.
    """

    # Decode Jpeg data and convert to float.
    channels = 3
    image = tf.image.decode_image(image, channels=channels)
    image = tf.cast(image, tf.float32)

    image = tf.image.central_crop(image, central_fraction=central_fraction)
    # Make into a 4D tensor by setting a 'batch size' of 1.
    image = tf.expand_dims(image, [0])
    image = tf.image.resize_bilinear(image,
                                                                 [FLAGS.image_size, FLAGS.image_size],
                                                                 align_corners=False)
    image.set_shape([1,FLAGS.image_size,FLAGS.image_size,channels])

    # Center the image about 128.0 (which is done during training) and normalize.
    image = tf.multiply(image, 1.0/127.5)
    return tf.subtract(image, 1.0)

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    from tensorflow.python.framework.graph_util import convert_variables_to_constants
    graph = session.graph
    with graph.as_default():
        freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
        output_names = output_names or []
        output_names += [v.op.name for v in tf.global_variables()]
        input_graph_def = graph.as_graph_def()
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = convert_variables_to_constants(session, input_graph_def,
                                                      output_names, freeze_var_names)
        return frozen_graph

def LoadLabelMaps(num_classes, labelmap_path, dict_path):
    """Load index->mid and mid->display name maps.

    Args:
        labelmap_path: path to the file with the list of mids, describing predictions.
        dict_path: path to the dict.csv that translates from mids to display names.
    Returns:
        labelmap: an index to mid list
        label_dict: mid to display name dictionary
    """
    labelmap = [line.rstrip() for line in tf.gfile.GFile(labelmap_path).readlines()]
    if len(labelmap) != num_classes:
        tf.logging.fatal(
                "Label map loaded from {} contains {} lines while the number of classes is {}".format(
                        labelmap_path, len(labelmap), num_classes))
        sys.exit(1)

    label_dict = {}
    for line in tf.gfile.GFile(dict_path).readlines():
        words = [word.strip(' "\n') for word in line.split(',', 1)]
        label_dict[words[0]] = words[1]

    return labelmap, label_dict

def main(args):
    if not os.path.exists(FLAGS.checkpoint):
        tf.logging.fatal(
                'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
                FLAGS.checkpoint)

    g = tf.Graph()
    with g.as_default():
        input_image = tf.placeholder(tf.string,name='input_byte_image')
        processed_image = PreprocessImage(input_image)

        init_op = tf.group(tf.global_variables_initializer(),
                   tf.local_variables_initializer(),
                   tf.tables_initializer())
        with slim.arg_scope(inception.inception_v3_arg_scope()):
            logits, end_points = inception.inception_v3(
                processed_image, num_classes=FLAGS.num_classes, is_training=False)

        predictions = end_points['multi_predictions'] = tf.nn.sigmoid(
            logits, name='multi_predictions')
        out_pooling = tf.identity(end_points['PreLogits'],name='bottleneck')
        #model_file_path = '/home/ilham/Documents/python/dataset/tools/inceptionv3.pb'
        #with tf.gfile.FastGFile(model_file_path, 'rb') as f:
        #    model_data = f.read()
        #graph_def = tf.GraphDef()
        #graph_def.ParseFromString(model_data)
        #_ = tf.import_graph_def(graph_def, name='')
        with tf.Session() as sess:
            saver = tf_saver.Saver()

            saver.restore(sess, FLAGS.checkpoint)


            frozen_graph = freeze_session(sess, output_names=['bottleneck'])
            _ = tf.import_graph_def(frozen_graph, name='')
            #tf.train.write_graph(frozen_graph, '/home/ilham/Documents/python/dataset/tools/', 'inceptionv3.pb')
            with tf.gfile.GFile('/home/ilham/Documents/python/dataset/tools/inceptionv3.pb', "wb") as f:
                f.write(frozen_graph.SerializeToString())
            #ls_op = sess.graph.get_operations()
            #for op in ls_op:
            #    print(op.values())
            ls_t = []
            input_image = sess.graph.get_tensor_by_name('input_byte_image:0')
            for iii in range(2):
                for image_path in FLAGS.image_path:
                    if not os.path.exists(image_path):
                        tf.logging.fatal('Input image does not exist %s', FLAGS.image_path[0])
                    img_data = tf.gfile.FastGFile(image_path, "rb").read()
                    print(image_path)
                    start_time = time.time()
                    predictions_eval = np.squeeze(sess.run(predictions,
                                                                                         {input_image: img_data}))
                    bottle_feat = np.squeeze(sess.run(sess.graph.get_tensor_by_name('bottleneck:0'),
                                                                                         {input_image: img_data}))
                    print(len(bottle_feat))
                    # your code
                    elapsed_time = time.time() - start_time
                    print('done in {}'.format(elapsed_time))
                    ls_t.append(elapsed_time)
                    # Print top(n) results
                    labelmap, label_dict = LoadLabelMaps(FLAGS.num_classes, FLAGS.labelmap, FLAGS.dict)

                    top_k = predictions_eval.argsort()[-FLAGS.n:][::-1]
                    for idx in top_k:
                        mid = labelmap[idx]
                        display_name = label_dict.get(mid, 'unknown')
                        score = predictions_eval[idx]
                        print('{}: {} - {} (score = {:.2f})'.format(idx, mid, display_name, score))
                    print()
            print('mean time {}'.format(np.mean(ls_t)))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--checkpoint', type=str, default='data/2016_08/model.ckpt',
                                            help='Checkpoint to run inference on.')
    parser.add_argument('--labelmap', type=str, default='data/2016_08/labelmap.txt',
                                            help='Label map that translates from index to mid.')
    parser.add_argument('--dict', type=str, default='dict.csv',
                                            help='Path to a dict.csv that translates from mid to a display name.')
    parser.add_argument('--image_size', type=int, default=299,
                                            help='Image size to run inference on.')
    parser.add_argument('--num_classes', type=int, default=6012,
                                            help='Number of output classes.')
    parser.add_argument('--n', type=int, default=10,
                                            help='Number of top predictions to print.')
    parser.add_argument('image_path', nargs='+', default='')
    FLAGS = parser.parse_args()
    tf.app.run()
	#!/usr/bin/env python
	#
	# Copyright 2016 The Open Images Authors. 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.
	# ==============================================================================
	#
	# This script takes an Inception v3 checkpoint, runs the classifier
	# on the image and prints top(n) predictions in the human-readable form.
	# Example:
	# $ wget -O /tmp/cat.jpg https://farm6.staticflickr.com/5470/9372235876_d7d69f1790_b.jpg
	# $ ./tools/classify.py /tmp/cat.jpg
	# 5723: /m/0jbk - animal (score = 0.94)
	# 3473: /m/04rky - mammal (score = 0.93)
	# 4605: /m/09686 - vertebrate (score = 0.91)
	# 1261: /m/01yrx - cat (score = 0.90)
	# 3981: /m/068hy - pet (score = 0.87)
	# 841: /m/01l7qd - whiskers (score = 0.83)
	# 2430: /m/0307l - cat-like mammal (score = 0.78)
	# 4349: /m/07k6w8 - small to medium-sized cats (score = 0.75)
	# 2537: /m/035qhg - fauna (score = 0.47)
	# 1776: /m/02cqfm - close-up (score = 0.45)
	#
	# Make sure to download the ANN weights and support data with:
	# $ ./tools/download_data.sh

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import argparse
	import math
	import sys
	import os.path

	import numpy as np
	import tensorflow as tf

	from tensorflow.contrib.slim.python.slim.nets import inception
	from tensorflow.python.framework import ops
	from tensorflow.python.training import saver as tf_saver
	from tensorflow.python.training import supervisor
	from tensorflow.python.saved_model import tag_constants, signature_constants
	from tensorflow.python.saved_model.signature_def_utils_impl import build_signature_def, predict_signature_def
	from tensorflow.python.saved_model import builder

	from tensorflow.python.ops.gen_image_ops import *
	from tensorflow.python.ops.image_ops_impl import *

	import time

	slim = tf.contrib.slim
	FLAGS = None

	def PreprocessImage(image, central_fraction=0.875):
	"""Load and preprocess an image.

	Args:
	image: a tf.string tensor with an JPEG-encoded image.
	central_fraction: do a central crop with the specified
	fraction of image covered.
	Returns:
	An ops.Tensor that produces the preprocessed image.
	"""

	# Decode Jpeg data and convert to float.
	channels = 3
	image = tf.image.decode_image(image, channels=channels)
	image = tf.cast(image, tf.float32)

	image = tf.image.central_crop(image, central_fraction=central_fraction)
	# Make into a 4D tensor by setting a 'batch size' of 1.
	image = tf.expand_dims(image, [0])
	image = tf.image.resize_bilinear(image,
	[FLAGS.image_size, FLAGS.image_size],
	align_corners=False)
	image.set_shape([1,FLAGS.image_size,FLAGS.image_size,channels])

	# Center the image about 128.0 (which is done during training) and normalize.
	image = tf.multiply(image, 1.0/127.5)
	return tf.subtract(image, 1.0)

	def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
	"""
	Freezes the state of a session into a pruned computation graph.

	Creates a new computation graph where variable nodes are replaced by
	constants taking their current value in the session. The new graph will be
	pruned so subgraphs that are not necessary to compute the requested
	outputs are removed.
	@param session The TensorFlow session to be frozen.
	@param keep_var_names A list of variable names that should not be frozen,
	or None to freeze all the variables in the graph.
	@param output_names Names of the relevant graph outputs.
	@param clear_devices Remove the device directives from the graph for better portability.
	@return The frozen graph definition.
	"""
	from tensorflow.python.framework.graph_util import convert_variables_to_constants
	graph = session.graph
	with graph.as_default():
	freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
	output_names = output_names or []
	output_names += [v.op.name for v in tf.global_variables()]
	input_graph_def = graph.as_graph_def()
	if clear_devices:
	for node in input_graph_def.node:
	node.device = ""
	frozen_graph = convert_variables_to_constants(session, input_graph_def,
	output_names, freeze_var_names)
	return frozen_graph

	def LoadLabelMaps(num_classes, labelmap_path, dict_path):
	"""Load index->mid and mid->display name maps.

	Args:
	labelmap_path: path to the file with the list of mids, describing predictions.
	dict_path: path to the dict.csv that translates from mids to display names.
	Returns:
	labelmap: an index to mid list
	label_dict: mid to display name dictionary
	"""
	labelmap = [line.rstrip() for line in tf.gfile.GFile(labelmap_path).readlines()]
	if len(labelmap) != num_classes:
	tf.logging.fatal(
	"Label map loaded from {} contains {} lines while the number of classes is {}".format(
	labelmap_path, len(labelmap), num_classes))
	sys.exit(1)

	label_dict = {}
	for line in tf.gfile.GFile(dict_path).readlines():
	words = [word.strip(' "\n') for word in line.split(',', 1)]
	label_dict[words[0]] = words[1]

	return labelmap, label_dict

	def main(args):
	if not os.path.exists(FLAGS.checkpoint):
	tf.logging.fatal(
	'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
	FLAGS.checkpoint)

	g = tf.Graph()
	with g.as_default():
	input_image = tf.placeholder(tf.string,name='input_byte_image')
	processed_image = PreprocessImage(input_image)

	init_op = tf.group(tf.global_variables_initializer(),
	tf.local_variables_initializer(),
	tf.tables_initializer())
	with slim.arg_scope(inception.inception_v3_arg_scope()):
	logits, end_points = inception.inception_v3(
	processed_image, num_classes=FLAGS.num_classes, is_training=False)

	predictions = end_points['multi_predictions'] = tf.nn.sigmoid(
	logits, name='multi_predictions')
	out_pooling = tf.identity(end_points['PreLogits'],name='bottleneck')
	#model_file_path = '/home/ilham/Documents/python/dataset/tools/inceptionv3.pb'
	#with tf.gfile.FastGFile(model_file_path, 'rb') as f:
	# model_data = f.read()
	#graph_def = tf.GraphDef()
	#graph_def.ParseFromString(model_data)
	#_ = tf.import_graph_def(graph_def, name='')
	with tf.Session() as sess:
	saver = tf_saver.Saver()

	saver.restore(sess, FLAGS.checkpoint)


	frozen_graph = freeze_session(sess, output_names=['bottleneck'])
	_ = tf.import_graph_def(frozen_graph, name='')
	#tf.train.write_graph(frozen_graph, '/home/ilham/Documents/python/dataset/tools/', 'inceptionv3.pb')
	with tf.gfile.GFile('/home/ilham/Documents/python/dataset/tools/inceptionv3.pb', "wb") as f:
	f.write(frozen_graph.SerializeToString())
	#ls_op = sess.graph.get_operations()
	#for op in ls_op:
	# print(op.values())
	ls_t = []
	input_image = sess.graph.get_tensor_by_name('input_byte_image:0')
	for iii in range(2):
	for image_path in FLAGS.image_path:
	if not os.path.exists(image_path):
	tf.logging.fatal('Input image does not exist %s', FLAGS.image_path[0])
	img_data = tf.gfile.FastGFile(image_path, "rb").read()
	print(image_path)
	start_time = time.time()
	predictions_eval = np.squeeze(sess.run(predictions,
	{input_image: img_data}))
	bottle_feat = np.squeeze(sess.run(sess.graph.get_tensor_by_name('bottleneck:0'),
	{input_image: img_data}))
	print(len(bottle_feat))
	# your code
	elapsed_time = time.time() - start_time
	print('done in {}'.format(elapsed_time))
	ls_t.append(elapsed_time)
	# Print top(n) results
	labelmap, label_dict = LoadLabelMaps(FLAGS.num_classes, FLAGS.labelmap, FLAGS.dict)

	top_k = predictions_eval.argsort()[-FLAGS.n:][::-1]
	for idx in top_k:
	mid = labelmap[idx]
	display_name = label_dict.get(mid, 'unknown')
	score = predictions_eval[idx]
	print('{}: {} - {} (score = {:.2f})'.format(idx, mid, display_name, score))
	print()
	print('mean time {}'.format(np.mean(ls_t)))

	if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--checkpoint', type=str, default='data/2016_08/model.ckpt',
	help='Checkpoint to run inference on.')
	parser.add_argument('--labelmap', type=str, default='data/2016_08/labelmap.txt',
	help='Label map that translates from index to mid.')
	parser.add_argument('--dict', type=str, default='dict.csv',
	help='Path to a dict.csv that translates from mid to a display name.')
	parser.add_argument('--image_size', type=int, default=299,
	help='Image size to run inference on.')
	parser.add_argument('--num_classes', type=int, default=6012,
	help='Number of output classes.')
	parser.add_argument('--n', type=int, default=10,
	help='Number of top predictions to print.')
	parser.add_argument('image_path', nargs='+', default='')
	FLAGS = parser.parse_args()
	tf.app.run()