val314159/EXAMPLE.md

## class_image.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os.path
import re
import sys
import tarfile

import numpy as np
from six.moves import urllib
import tensorflow as tf

FLAGS = None

# pylint: disable=line-too-long
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
# pylint: enable=line-too-long


class NodeLookup(object):
  """Converts integer node ID's to human readable labels."""

  def __init__(self,
               label_lookup_path=None,
               uid_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

  def load(self, label_lookup_path, uid_lookup_path):
    """Loads a human readable English name for each softmax node.

    Args:
      label_lookup_path: string UID to integer node ID.
      uid_lookup_path: string UID to human-readable string.

    Returns:
      dict from integer node ID to human-readable string.
    """
    if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal('File does not exist %s', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal('File does not exist %s', label_lookup_path)

    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'[n\d]*[ \S,]*')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string

    # Loads mapping from string UID to integer node ID.
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith('  target_class:'):
        target_class = int(line.split(': ')[1])
      if line.startswith('  target_class_string:'):
        target_class_string = line.split(': ')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]

    # Loads the final mapping of integer node ID to human-readable string
    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal('Failed to locate: %s', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name

    return node_id_to_name

  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]


def create_graph():
  """Creates a graph from saved GraphDef file and returns a saver."""
  # Creates graph from saved graph_def.pb.
  with tf.gfile.FastGFile(os.path.join(
      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')


def run_inference_on_image(image):
  """Runs inference on an image.

  Args:
    image: Image file name.

  Returns:
    Nothing
  """
  if not tf.gfile.Exists(image):
    tf.logging.fatal('File does not exist %s', image)
  image_data = tf.gfile.FastGFile(image, 'rb').read()

  # Creates graph from saved GraphDef.
  create_graph()

  with tf.Session() as sess:
    # Some useful tensors:
    # 'softmax:0': A tensor containing the normalized prediction across
    #   1000 labels.
    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048
    #   float description of the image.
    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
    #   encoding of the image.
    # Runs the softmax tensor by feeding the image_data as input to the graph.
    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
    predictions = sess.run(softmax_tensor,
                           {'DecodeJpeg/contents:0': image_data})
    predictions = np.squeeze(predictions)

    # Creates node ID --> English string lookup.
    node_lookup = NodeLookup()

    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
    for node_id in top_k:
      human_string = node_lookup.id_to_string(node_id)
      score = predictions[node_id]
      print('%s (score = %.5f)' % (human_string, score))


def maybe_download_and_extract():
  """Download and extract model tar file."""
  dest_directory = FLAGS.model_dir
  if not os.path.exists(dest_directory):
    os.makedirs(dest_directory)
  filename = DATA_URL.split('/')[-1]
  filepath = os.path.join(dest_directory, filename)
  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
  tarfile.open(filepath, 'r:gz').extractall(dest_directory)


def main(_):
  maybe_download_and_extract()
  image = (FLAGS.image_file if FLAGS.image_file else
           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
  run_inference_on_image(image)


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  # classify_image_graph_def.pb:
  #   Binary representation of the GraphDef protocol buffer.
  # imagenet_synset_to_human_label_map.txt:
  #   Map from synset ID to a human readable string.
  # imagenet_2012_challenge_label_map_proto.pbtxt:
  #   Text representation of a protocol buffer mapping a label to synset ID.
  parser.add_argument(
      '--model_dir',
      type=str,
      default='/tmp/imagenet',
      help="""\
      Path to classify_image_graph_def.pb,
      imagenet_synset_to_human_label_map.txt, and
      imagenet_2012_challenge_label_map_proto.pbtxt.\
      """
  )
  parser.add_argument(
      '--image_file',
      type=str,
      default='',
      help='Absolute path to image file.'
  )
  parser.add_argument(
      '--num_top_predictions',
      type=int,
      default=5,
      help='Display this many predictions.'
  )
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

## EXAMPLE.md

      
    Raw
  

              EXAMPLE.md
            
          
    Tensorflow example

cd $HOME
mkdir workspace
cd workspace
git clone http://github.com/tensorflow/tensorflow
git clone http://github.com/tensorflow/models
cd tensorflow/tensorflow/models/image/imagenet
python classify_image.py

  
## EXAMPLE1.md

      
    Raw
  

              EXAMPLE1.md
            
          
    Grab the flowers data set

mkdir $HOME
cd $HOME
curl -O http://download.tensorflow.org/example_images/flower_photos.tgz
tar xzf flower_photos.tgz
Grab the cars data set

mkdir -p $HOME/cars
cd $HOME/cars
wget http://imagenet.stanford.edu/internal/car196/cars_train.tgz
wget http://imagenet.stanford.edu/internal/car196/cars_test.tgz
wget http://ai.stanford.edu/~jkrause/cars/car_devkit.tgz
wget http://imagenet.stanford.edu/internal/car196/cars_annos.mat
wget http://imagenet.stanford.edu/internal/car196/cars_test_annos_withlabels.mat
tar xvf cars_train.tgz
tar xvf cars_test.tgz
tar xvf car_devkit.tgz
pip install scipy
pip3 install scipy

  
## EXAMPLE2.md

      
    Raw
  

              EXAMPLE2.md
            
          
    Example 2 - let's pick some flowers

Grab the flowers data set

mkdir $HOME/p
cd $HOME/p
curl -O http://download.tensorflow.org/example_images/flower_photos.tgz
tar xzf flower_photos.tgz
make the c++ image retrainer

cd ~/workspace3/tensorflow
bazel build -c opt --copt=-mavx tensorflow/examples/image_retraining:retrain
do the retraining!


what outputs does this have?

bazel-bin/tensorflow/examples/image_retraining/retrain --image_dir ~/p/flower_photos
Artifacts are:

/tmp/bottleneck
/tmp/retrain_logs

bazel build tensorflow/examples/label_image:label_image && \
 bazel-bin/tensorflow/examples/label_image/label_image \
--graph=/tmp/output_graph.pb --labels=/tmp/output_labels.txt \
--output_layer=final_result \
--image=$HOME/p/flower_photos/daisy/21652746_cc379e0eea_m.jpg

  
## EXAMPLE3.md

      
    Raw
  

              EXAMPLE3.md
            
          
    Build the C++ retrainer:
bazel build -c opt --copt=-mavx tensorflow/examples/image_retraining:retrain
Build the C++ labeller:
bazel build tensorflow/examples/label_image:label_image

bazel build -c opt --copt=-mavx tensorflow/examples/label_image:label_image
For C++ retraining (flowers):
PFX=/tmp/flower
bazel-bin/tensorflow/examples/image_retraining/retrain \
    --image_dir /home/ubuntu/flower_photos \
    --summaries_dir=$PFX/retrain_logs \
    --bottleneck_dir=$PFX/bottlenecks \
    --output_graph=$PFX/output_graph.pb \
    --output_labels=$PFX/output_labels.txt \
    --final_tensor_name=final_result
For C++ labelling (flowers):
PFX=/tmp/flower
bazel-bin/tensorflow/examples/label_image/label_image \
    --graph=/path/output_graph.pb --labels=/path/output_labels.txt \
    --output_layer=final_result \
    --image=/path/to/test/image

  
## EXAMPLE4.md

      
    Raw
  

              EXAMPLE4.md
            
          
    Let's try it all in python

training for flowers

PFX=/tmp/flower
python tensorflow/examples/image_retraining/retrain.py --image_dir /home/ubuntu/flower_photos --summaries_dir=$PFX/retrain_logs --bottleneck_dir=$PFX/bottlenecks --output_graph=$PFX
/output_graph.pb --output_labels=$PFX/output_labels.txt --final_tensor_name=final_result
classify for flowers

PFX=/tmp/flower
python label_image.py $1 $PFX/output_graph.pb $PFX/output_labels.txt
training for cars

PFX=/tmp/cars
echo SOMEHOW CREATE THE ~/car_photos directory here
python tensorflow/examples/image_retraining/retrain.py --image_dir /home/ubuntu/car_photos \
  --summaries_dir=$PFX/retrain_logs --bottleneck_dir=$PFX/bottlenecks \
  --output_graph=$PFX/output_graph.pb --output_labels=$PFX/output_labels.txt \
  --final_tensor_name=final_result
classify for cars

PFX=/tmp/cars
python label_image.py $1 $PFX/output_graph.pb $PFX/output_labels.txt

  
## extract.py
#!/usr/bin/env python3
import os, sys
from scipy.io import loadmat

def makedirs(x):
    try:
        os.makedirs(x)
    except:
        pass

def main():
    print("EXTRACT")
    makedirs('train')
    makedirs('test')
    x = loadmat('devkit/cars_meta.mat')
    y = loadmat('devkit/cars_test_annos.mat')
    z = loadmat('devkit/cars_train_annos.mat')
    w = loadmat('cars_test_annos_withlabels.mat')
    class_names = [z[0] for z in x['class_names'][0]]
    class_dict = {}
    for n, name in enumerate(class_names):
        class_dict[name] = n
        makedirs('train/'+name)
        makedirs('test/'+name)

    f = open('class_names.txt','w')
    f.write('\n'.join(class_names))
    f.close()

    tests  = [(_[4][0][0],_[5][0]) for _ in w['annotations'][0]]
    trains = [(_[4][0][0],_[5][0]) for _ in z['annotations'][0]]

    for class_no, short_name in tests:
        class_name = class_names[class_no-1]
        os.link('cars_test/'+short_name,
                'test/'+class_name+'/'+short_name)
        pass
    for class_no, short_name in trains:
        class_name = class_names[class_no-1]
        os.link('cars_train/'+short_name,
                'train/'+class_name+'/'+short_name)
        pass

if __name__=='__main__': main()

## label_image.py
import os, sys
import numpy as np
import tensorflow as tf

class NodeLookup:
    def __init__(self, labelsFullPath):
        with open(labelsFullPath, 'rb') as f:
            self.lines = f.readlines()
            self.labels = [str(w).replace("\n", "") for w in self.lines]
    def id_to_string(self, node_id):
        return self.labels[node_id]

def run_inference_on_image(
        image = '/home/ubuntu/flower_photos/daisy/15029936576_8d6f96c72c_n.jpg',
        modelFullPath = '/tmp/flower/output_graph.pb',
        labelsFullPath = '/tmp/flower/output_labels.txt',
        #finalLayer='softmax'+':0'):
        finalLayer='final_result'):

    if not tf.gfile.Exists(image):
        tf.logging.fatal('File does not exist %s', image)
        return

    image_data = tf.gfile.FastGFile(image, 'rb').read()

    # Creates graph from saved graph_def.pb.
    with tf.gfile.FastGFile(modelFullPath, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')

    with tf.Session() as sess:
        softmax_tensor = sess.graph.get_tensor_by_name(finalLayer+':0')
        predictions = sess.run(softmax_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)

        # Creates node ID --> English string lookup.
        node_lookup = NodeLookup(labelsFullPath)

        top_k = predictions.argsort()[-5:][::-1]  # Getting top 5 predictions
        for node_id in top_k:
            human_string = node_lookup.id_to_string(node_id)
            score = predictions[node_id]
            print('%s (score = %.5f)' % (human_string, score))

if __name__ == '__main__': run_inference_on_image(*sys.argv[1:])

## node_lookup_1.py
class NodeLookup:
    def __init__(self, labelsFullPath):
        with open(labelsFullPath, 'rb') as f:
            self.lines = f.readlines()
            self.labels = [str(w).replace("\n", "") for w in self.lines]
    def id_to_string(self, node_id):
        return self.labels[node_id]

## node_lookup_2.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os, sys, re, tensorflow as tf

class NodeLookup(object):
  """Converts integer node ID's to human readable labels."""

  def __init__(self,
               label_lookup_path=None,
               uid_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

  def load(self, label_lookup_path, uid_lookup_path):
    """Loads a human readable English name for each softmax node.

    Args:
      label_lookup_path: string UID to integer node ID.
      uid_lookup_path: string UID to human-readable string.

    Returns:
      dict from integer node ID to human-readable string.
    """
    if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal('File does not exist %s', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal('File does not exist %s', label_lookup_path)

    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'[n\d]*[ \S,]*')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string

    # Loads mapping from string UID to integer node ID.
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith('  target_class:'):
        target_class = int(line.split(': ')[1])
      if line.startswith('  target_class_string:'):
        target_class_string = line.split(': ')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]

    # Loads the final mapping of integer node ID to human-readable string
    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal('Failed to locate: %s', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name

    return node_id_to_name

  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]
	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import argparse
	import os.path
	import re
	import sys
	import tarfile

	import numpy as np
	from six.moves import urllib
	import tensorflow as tf

	FLAGS = None

	# pylint: disable=line-too-long
	DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
	# pylint: enable=line-too-long


	class NodeLookup(object):
	"""Converts integer node ID's to human readable labels."""

	def __init__(self,
	label_lookup_path=None,
	uid_lookup_path=None):
	if not label_lookup_path:
	label_lookup_path = os.path.join(
	FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
	if not uid_lookup_path:
	uid_lookup_path = os.path.join(
	FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
	self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

	def load(self, label_lookup_path, uid_lookup_path):
	"""Loads a human readable English name for each softmax node.

	Args:
	label_lookup_path: string UID to integer node ID.
	uid_lookup_path: string UID to human-readable string.

	Returns:
	dict from integer node ID to human-readable string.
	"""
	if not tf.gfile.Exists(uid_lookup_path):
	tf.logging.fatal('File does not exist %s', uid_lookup_path)
	if not tf.gfile.Exists(label_lookup_path):
	tf.logging.fatal('File does not exist %s', label_lookup_path)

	# Loads mapping from string UID to human-readable string
	proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
	uid_to_human = {}
	p = re.compile(r'[n\d][ \S,]')
	for line in proto_as_ascii_lines:
	parsed_items = p.findall(line)
	uid = parsed_items[0]
	human_string = parsed_items[2]
	uid_to_human[uid] = human_string

	# Loads mapping from string UID to integer node ID.
	node_id_to_uid = {}
	proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
	for line in proto_as_ascii:
	if line.startswith(' target_class:'):
	target_class = int(line.split(': ')[1])
	if line.startswith(' target_class_string:'):
	target_class_string = line.split(': ')[1]
	node_id_to_uid[target_class] = target_class_string[1:-2]

	# Loads the final mapping of integer node ID to human-readable string
	node_id_to_name = {}
	for key, val in node_id_to_uid.items():
	if val not in uid_to_human:
	tf.logging.fatal('Failed to locate: %s', val)
	name = uid_to_human[val]
	node_id_to_name[key] = name

	return node_id_to_name

	def id_to_string(self, node_id):
	if node_id not in self.node_lookup:
	return ''
	return self.node_lookup[node_id]


	def create_graph():
	"""Creates a graph from saved GraphDef file and returns a saver."""
	# Creates graph from saved graph_def.pb.
	with tf.gfile.FastGFile(os.path.join(
	FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
	graph_def = tf.GraphDef()
	graph_def.ParseFromString(f.read())
	_ = tf.import_graph_def(graph_def, name='')


	def run_inference_on_image(image):
	"""Runs inference on an image.

	Args:
	image: Image file name.

	Returns:
	Nothing
	"""
	if not tf.gfile.Exists(image):
	tf.logging.fatal('File does not exist %s', image)
	image_data = tf.gfile.FastGFile(image, 'rb').read()

	# Creates graph from saved GraphDef.
	create_graph()

	with tf.Session() as sess:
	# Some useful tensors:
	# 'softmax:0': A tensor containing the normalized prediction across
	# 1000 labels.
	# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
	# float description of the image.
	# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
	# encoding of the image.
	# Runs the softmax tensor by feeding the image_data as input to the graph.
	softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
	predictions = sess.run(softmax_tensor,
	{'DecodeJpeg/contents:0': image_data})
	predictions = np.squeeze(predictions)

	# Creates node ID --> English string lookup.
	node_lookup = NodeLookup()

	top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
	for node_id in top_k:
	human_string = node_lookup.id_to_string(node_id)
	score = predictions[node_id]
	print('%s (score = %.5f)' % (human_string, score))


	def maybe_download_and_extract():
	"""Download and extract model tar file."""
	dest_directory = FLAGS.model_dir
	if not os.path.exists(dest_directory):
	os.makedirs(dest_directory)
	filename = DATA_URL.split('/')[-1]
	filepath = os.path.join(dest_directory, filename)
	if not os.path.exists(filepath):
	def _progress(count, block_size, total_size):
	sys.stdout.write('\r>> Downloading %s %.1f%%' % (
	filename, float(count * block_size) / float(total_size) * 100.0))
	sys.stdout.flush()
	filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
	print()
	statinfo = os.stat(filepath)
	print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
	tarfile.open(filepath, 'r:gz').extractall(dest_directory)


	def main(_):
	maybe_download_and_extract()
	image = (FLAGS.image_file if FLAGS.image_file else
	os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
	run_inference_on_image(image)


	if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	# classify_image_graph_def.pb:
	# Binary representation of the GraphDef protocol buffer.
	# imagenet_synset_to_human_label_map.txt:
	# Map from synset ID to a human readable string.
	# imagenet_2012_challenge_label_map_proto.pbtxt:
	# Text representation of a protocol buffer mapping a label to synset ID.
	parser.add_argument(
	'--model_dir',
	type=str,
	default='/tmp/imagenet',
	help="""\
	Path to classify_image_graph_def.pb,
	imagenet_synset_to_human_label_map.txt, and
	imagenet_2012_challenge_label_map_proto.pbtxt.\
	"""
	)
	parser.add_argument(
	'--image_file',
	type=str,
	default='',
	help='Absolute path to image file.'
	)
	parser.add_argument(
	'--num_top_predictions',
	type=int,
	default=5,
	help='Display this many predictions.'
	)
	FLAGS, unparsed = parser.parse_known_args()
	tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
	#!/usr/bin/env python3
	import os, sys
	from scipy.io import loadmat

	def makedirs(x):
	try:
	os.makedirs(x)
	except:
	pass

	def main():
	print("EXTRACT")
	makedirs('train')
	makedirs('test')
	x = loadmat('devkit/cars_meta.mat')
	y = loadmat('devkit/cars_test_annos.mat')
	z = loadmat('devkit/cars_train_annos.mat')
	w = loadmat('cars_test_annos_withlabels.mat')
	class_names = [z[0] for z in x['class_names'][0]]
	class_dict = {}
	for n, name in enumerate(class_names):
	class_dict[name] = n
	makedirs('train/'+name)
	makedirs('test/'+name)

	f = open('class_names.txt','w')
	f.write('\n'.join(class_names))
	f.close()

	tests = [(_[4][0][0],_[5][0]) for _ in w['annotations'][0]]
	trains = [(_[4][0][0],_[5][0]) for _ in z['annotations'][0]]

	for class_no, short_name in tests:
	class_name = class_names[class_no-1]
	os.link('cars_test/'+short_name,
	'test/'+class_name+'/'+short_name)
	pass
	for class_no, short_name in trains:
	class_name = class_names[class_no-1]
	os.link('cars_train/'+short_name,
	'train/'+class_name+'/'+short_name)
	pass

	if __name__=='__main__': main()
	class NodeLookup:
	def __init__(self, labelsFullPath):
	with open(labelsFullPath, 'rb') as f:
	self.lines = f.readlines()
	self.labels = [str(w).replace("\n", "") for w in self.lines]
	def id_to_string(self, node_id):
	return self.labels[node_id]