retrain_official.py

# Copyright 2015 The TensorFlow 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.
# ==============================================================================
r"""Simple transfer learning with Inception v3 or Mobilenet models.
With support for TensorBoard.
This example shows how to take a Inception v3 or Mobilenet model trained on
ImageNet images, and train a new top layer that can recognize other classes of
images.
The top layer receives as input a 2048-dimensional vector (1001-dimensional for
Mobilenet) for each image. We train a softmax layer on top of this
representation. Assuming the softmax layer contains N labels, this corresponds
to learning N + 2048*N (or 1001*N)	model parameters corresponding to the
learned biases and weights.
Here's an example, which assumes you have a folder containing class-named
subfolders, each full of images for each label. The example folder flower_photos
should have a structure like this:
~/flower_photos/daisy/photo1.jpg
~/flower_photos/daisy/photo2.jpg
...
~/flower_photos/rose/anotherphoto77.jpg
...
~/flower_photos/sunflower/somepicture.jpg
The subfolder names are important, since they define what label is applied to
each image, but the filenames themselves don't matter. Once your images are
prepared, you can run the training with a command like this:
```bash
bazel build tensorflow/examples/image_retraining:retrain && \
bazel-bin/tensorflow/examples/image_retraining/retrain \
	--image_dir ~/flower_photos
```
Or, if you have a pip installation of tensorflow, `retrain.py` can be run
without bazel:
```bash
python tensorflow/examples/image_retraining/retrain.py \
	--image_dir ~/flower_photos
```
You can replace the image_dir argument with any folder containing subfolders of
images. The label for each image is taken from the name of the subfolder it's
in.
This produces a new model file that can be loaded and run by any TensorFlow
program, for example the label_image sample code.
By default this script will use the high accuracy, but comparatively large and
slow Inception v3 model architecture. It's recommended that you start with this
to validate that you have gathered good training data, but if you want to deploy
on resource-limited platforms, you can try the `--architecture` flag with a
Mobilenet model. For example:
```bash
python tensorflow/examples/image_retraining/retrain.py \
	--image_dir ~/flower_photos --architecture mobilenet_1.0_224
```
There are 32 different Mobilenet models to choose from, with a variety of file
size and latency options. The first number can be '1.0', '0.75', '0.50', or
'0.25' to control the size, and the second controls the input image size, either
'224', '192', '160', or '128', with smaller sizes running faster. See
https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
for more information on Mobilenet.
To use with TensorBoard:
By default, this script will log summaries to /tmp/retrain_logs directory
Visualize the summaries with this command:
tensorboard --logdir /tmp/retrain_logs
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from knx.util.logging import Timing
import argparse
from datetime import datetime
import hashlib
import os.path
import random
import re
import sys
import tarfile

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

from tensorflow.python.framework import graph_util
from tensorflow.python.framework import tensor_shape
from tensorflow.python.platform import gfile
from tensorflow.python.util import compat

FLAGS = None

# These are all parameters that are tied to the particular model architecture
# we're using for Inception v3. These include things like tensor names and their
# sizes. If you want to adapt this script to work with another model, you will
# need to update these to reflect the values in the network you're using.
MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1	# ~134M


def get_image_path(image_lists, label_name, index, image_dir, category):
	""""Returns a path to an image for a label at the given index.
	Args:
	image_lists: Dictionary of training images for each label.
	label_name: Label string we want to get an image for.
      index: Int offset of the image we want. This will be moduloed by the
      available number of images for the label, so it can be arbitrarily large.
	image_dir: Root folder string of the subfolders containing the training
	images.
	category: Name string of set to pull images from - training, testing, or
	validation.
	Returns:
	File system path string to an image that meets the requested parameters.
	"""
	if label_name not in image_lists:
		tf.logging.fatal('Label does not exist %s.', label_name)
	label_lists = image_lists[label_name]
	if category not in label_lists:
		tf.logging.fatal('Category does not exist %s.', category)
	category_list = label_lists[category]
	if not category_list:
		tf.logging.fatal('Label %s has no images in the category %s.',
						 label_name, category)

	full_path = category_list[index]
	# sub_dir = label_lists['dir']
	# full_path = os.path.join(image_dir, sub_dir, base_name)
	return full_path


def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
						category, architecture):
	""""Returns a path to a bottleneck file for a label at the given index.
	Args:
	image_lists: Dictionary of training images for each label.
	label_name: Label string we want to get an image for.
	index: Integer offset of the image we want. This will be moduloed by the
	available number of images for the label, so it can be arbitrarily large.
	bottleneck_dir: Folder string holding cached files of bottleneck values.
	category: Name string of set to pull images from - training, testing, or
	validation.
	architecture: The name of the model architecture.
	Returns:
	File system path string to an image that meets the requested parameters.
	"""
	# return get_image_path(image_lists, label_name, index, bottleneck_dir,
	#						 category) + '.txt'
	full_path = image_lists[label_name][category][index]
	base_name = os.path.basename(full_path)
	return os.path.join(bottleneck_dir, label_name, base_name) + '.txt'

def create_model_graph(model_info):
	""""Creates a graph from saved GraphDef file and returns a Graph object.
	Args:
	model_info: Dictionary containing information about the model architecture.
	Returns:
	Graph holding the trained Inception network, and various tensors we'll be
	manipulating.
	"""
	with tf.Graph().as_default() as graph:
		model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name'])
		with gfile.FastGFile(model_path, 'rb') as f:
			graph_def = tf.GraphDef()
			graph_def.ParseFromString(f.read())
			bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
				graph_def,
				name='',
				return_elements=[
					model_info['bottleneck_tensor_name'],
					model_info['resized_input_tensor_name'],
				]))
	return graph, bottleneck_tensor, resized_input_tensor

def run_bottleneck_on_image(sess, image_data, image_data_tensor,
							decoded_image_tensor, resized_input_tensor,
							bottleneck_tensor):
	"""Runs inference on an image to extract the 'bottleneck' summary layer.
	Args:
	sess: Current active TensorFlow Session.
	image_data: String of raw JPEG data.
	image_data_tensor: Input data layer in the graph.
	decoded_image_tensor: Output of initial image resizing and preprocessing.
	resized_input_tensor: The input node of the recognition graph.
	bottleneck_tensor: Layer before the final softmax.
	Returns:
	Numpy array of bottleneck values.
	"""
	# First decode the JPEG image, resize it, and rescale the pixel values.
	resized_input_values = sess.run(decoded_image_tensor,
									{image_data_tensor: image_data})
	# Then run it through the recognition network.
	bottleneck_values = sess.run(bottleneck_tensor,
								 {resized_input_tensor: resized_input_values})
	bottleneck_values = np.squeeze(bottleneck_values)
	return bottleneck_values

from collections import defaultdict
categories_list = ['training', 'validation', 'testing']

def create_image_lists(categories_dir):
	"""Builds a list of training images from the file system.
	Analyzes the sub folders in the image directory, splits them into stable
	training, testing, and validation sets, and returns a data structure
	describing the lists of images for each label and their paths.
	Args:
	image_dir: String path to a folder containing subfolders of images.
	testing_percentage: Integer percentage of the images to reserve for tests.
	validation_percentage: Integer percentage of images reserved for validation.
	Returns:
	A dictionary containing an entry for each label subfolder, with images split
	into training, testing, and validation sets within each label.
	"""
	assert type(categories_dir)==dict
	classes_dir_lists = []
	for each_category_name, each_category_dir in categories_dir.items():
		if not gfile.Exists(each_category_dir):
			tf.logging.error("Image directory '" + each_category_dir + "' not found.")
			return None
		temp = [x[0] for x in os.walk(each_category_dir)]
		del temp[0]
		classes_dir_lists.append(temp)

	# TODO:

	images_path_list = defaultdict(list)
	true_labels_list = defaultdict(list)
	classes_dir_list = defaultdict(set)

	for each_category_name, each_category_dir in categories_dir.items():
		classes_dir_list = [x[0] for x in os.walk(each_category_dir)]
		# The root directory comes first, so skip it.
		del classes_dir_list[0]

		print(classes_dir_list)
		for each_class_dir in classes_dir_list:
			extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
			file_list = []
			class_true_name = os.path.basename(each_class_dir)
			tf.logging.info("Looking for images in '" + class_true_name + "'")
			for extension in extensions:
				file_glob = os.path.join(each_category_dir, class_true_name, '*.' + extension)
				file_list.extend(gfile.Glob(file_glob))
			if not file_list:
				tf.logging.warning('No files found')
				continue
			if len(file_list) < 20:
				tf.logging.warning(
					'WARNING: Folder has less than 20 images, which may cause issues.')
			elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:
				tf.logging.warning(
					'WARNING: Folder {} has more than {} images. Some images will '
					'never be selected.'.format(class_true_name, MAX_NUM_IMAGES_PER_CLASS))
			# label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())
			true_label = class_true_name

			images_path_list[each_category_name].extend(file_list)
			true_labels_list[each_category_name].extend([true_label] * len(file_list))

	return images_path_list, true_labels_list

def maybe_download_and_extract(data_url):
	"""Download and extract model tar file.
	If the pretrained model we're using doesn't already exist, this function
	downloads it from the TensorFlow.org website and unpacks it into a directory.
	Args:
	data_url: Web location of the tar file containing the pretrained model.
	"""
	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)
		tf.logging.info('Successfully downloaded', filename, statinfo.st_size,
						'bytes.')
	tarfile.open(filepath, 'r:gz').extractall(dest_directory)


def ensure_dir_exists(dir_name):
	"""Makes sure the folder exists on disk.
	Args:
	dir_name: Path string to the folder we want to create.
	"""
	if not os.path.exists(dir_name):
		os.makedirs(dir_name)


bottleneck_path_2_bottleneck_values = {}


def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
							 image_dir, category, sess, jpeg_data_tensor,
							 decoded_image_tensor, resized_input_tensor,
							 bottleneck_tensor):
	"""Create a single bottleneck file."""
	tf.logging.info('Creating bottleneck at ' + bottleneck_path)
	image_path = get_image_path(image_lists, label_name, index,
								image_dir, category)

	if not gfile.Exists(image_path):
		tf.logging.fatal('File does not exist %s', image_path)
	image_data = gfile.FastGFile(image_path, 'rb').read()
	try:
		bottleneck_values = run_bottleneck_on_image(
			sess, image_data, jpeg_data_tensor, decoded_image_tensor,
			resized_input_tensor, bottleneck_tensor)
	except Exception as e:
		raise RuntimeError('Error during processing file %s (%s)' % (image_path,
																	 str(e)))
	bottleneck_string = ','.join(str(x) for x in bottleneck_values)
	with open(bottleneck_path, 'w') as bottleneck_file:
		bottleneck_file.write(bottleneck_string)


def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
							 category, bottleneck_dir, jpeg_data_tensor,
							 decoded_image_tensor, resized_input_tensor,
							 bottleneck_tensor, architecture):
	"""Retrieves or calculates bottleneck values for an image.
	If a cached version of the bottleneck data exists on-disk, return that,
	otherwise calculate the data and save it to disk for future use.
	Args:
	sess: The current active TensorFlow Session.
	image_lists: Dictionary of training images for each label.
	label_name: Label string we want to get an image for.
	index: Integer offset of the image we want. This will be modulo-ed by the
	available number of images for the label, so it can be arbitrarily large.
	image_dir: Root folder string of the subfolders containing the training
	images.
	category: Name string of which set to pull images from - training, testing,
	or validation.
	bottleneck_dir: Folder string holding cached files of bottleneck values.
	jpeg_data_tensor: The tensor to feed loaded jpeg data into.
	decoded_image_tensor: The output of decoding and resizing the image.
	resized_input_tensor: The input node of the recognition graph.
	bottleneck_tensor: The output tensor for the bottleneck values.
	architecture: The name of the model architecture.
	Returns:
	Numpy array of values produced by the bottleneck layer for the image.
	"""
	# label_lists = image_lists[label_name]
	# sub_dir = label_lists['dir']
	sub_dir_path = os.path.join(bottleneck_dir, label_name)
	ensure_dir_exists(sub_dir_path)
	bottleneck_path = get_bottleneck_path(image_lists, label_name, index,
											bottleneck_dir, category, architecture)
	if not os.path.exists(bottleneck_path):
		create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
								 image_dir, category, sess, jpeg_data_tensor,
								 decoded_image_tensor, resized_input_tensor,
								 bottleneck_tensor)
	with open(bottleneck_path, 'r') as bottleneck_file:
		bottleneck_string = bottleneck_file.read()
	did_hit_error = False
	try:
		bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
	except ValueError:
		tf.logging.warning('Invalid float found, recreating bottleneck')
		did_hit_error = True
	if did_hit_error:
		create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
								 image_dir, category, sess, jpeg_data_tensor,
								 decoded_image_tensor, resized_input_tensor,
								 bottleneck_tensor)
		with open(bottleneck_path, 'r') as bottleneck_file:
			bottleneck_string = bottleneck_file.read()
		# Allow exceptions to propagate here, since they shouldn't happen after a
		# fresh creation
		bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
	return bottleneck_values

bottlenecks_data_list = defaultdict(list)
bottlenecks_label_list = defaultdict(list)

def cache_bottlenecks(sess, image_lists, image_dir_dict, bottleneck_dir,
						jpeg_data_tensor, decoded_image_tensor,
						resized_input_tensor, bottleneck_tensor, architecture):
	"""Ensures all the training, testing, and validation bottlenecks are cached.
	Because we're likely to read the same image multiple times (if there are no
	distortions applied during training) it can speed things up a lot if we
	calculate the bottleneck layer values once for each image during
	preprocessing, and then just read those cached values repeatedly during
	training. Here we go through all the images we've found, calculate those
	values, and save them off.
	Args:
	sess: The current active TensorFlow Session.
	image_lists: Dictionary of training images for each label.
	image_dir: Root folder string of the subfolders containing the training
	images.
	bottleneck_dir: Folder string holding cached files of bottleneck values.
	jpeg_data_tensor: Input tensor for jpeg data from file.
	decoded_image_tensor: The output of decoding and resizing the image.
	resized_input_tensor: The input node of the recognition graph.
	bottleneck_tensor: The penultimate output layer of the graph.
	architecture: The name of the model architecture.
	Returns:
	Nothing.
	"""
	with Timing('Creating bottleneck files'):
		how_many_bottlenecks = 0
		ensure_dir_exists(bottleneck_dir)
		class_count = len(image_lists.keys())
		for label_name, label_lists in image_lists.items():
			for category in ['training', 'testing', 'validation']:
				category_list = label_lists[category]
				image_dir = image_dir_dict[category]
				for index, unused_base_name in enumerate(category_list):
					bottleneck = get_or_create_bottleneck(
						sess, image_lists, label_name, index, image_dir, category,
						bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
						resized_input_tensor, bottleneck_tensor, architecture)

					bottlenecks_data_list[category].append(bottleneck)
					one_hot_label = np.zeros(class_count, dtype=np.float32)
					label_index = list(image_lists.keys()).index(label_name)
					one_hot_label[label_index] = 1.0
					bottlenecks_label_list[category].append(one_hot_label)

					how_many_bottlenecks += 1

	tf.logging.info(str(how_many_bottlenecks) + ' bottleneck files created.')

# def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
# 									bottleneck_dir, image_dir, jpeg_data_tensor,
# 									decoded_image_tensor, resized_input_tensor,
# 									bottleneck_tensor, architecture):
# 	"""Retrieves bottleneck values for cached images.
# 	If no distortions are being applied, this function can retrieve the cached
# 	bottleneck values directly from disk for images. It picks a random set of
# 	images from the specified category.
# 	Args:
# 	sess: Current TensorFlow Session.
# 	image_lists: Dictionary of training images for each label.
# 	how_many: If positive, a random sample of this size will be chosen.
# 	If negative, all bottlenecks will be retrieved.
# 	category: Name string of which set to pull from - training, testing, or
# 	validation.
# 	bottleneck_dir: Folder string holding cached files of bottleneck values.
# 	image_dir: Root folder string of the subfolders containing the training
# 	images.
# 	jpeg_data_tensor: The layer to feed jpeg image data into.
# 	decoded_image_tensor: The output of decoding and resizing the image.
# 	resized_input_tensor: The input node of the recognition graph.
# 	bottleneck_tensor: The bottleneck output layer of the CNN graph.
# 	architecture: The name of the model architecture.
# 	Returns:
# 	List of bottleneck arrays, their corresponding ground truths, and the
# 	relevant filenames.
# 	"""
# 	class_count = len(image_lists.keys())
# 	bottlenecks = []
# 	ground_truths = []
# 	filenames = []
# 	if how_many >= 0:
# 		# Retrieve a random sample of bottlenecks.
# 		for unused_i in range(how_many):
# 			label_index = random.randrange(class_count)
# 			label_name = list(image_lists.keys())[label_index]
# 			image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
# 			image_name = get_image_path(image_lists, label_name, image_index,
# 										image_dir, category)
# 			bottleneck = get_or_create_bottleneck(
# 				sess, image_lists, label_name, image_index, image_dir, category,
# 				bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
# 				resized_input_tensor, bottleneck_tensor, architecture)
# 			ground_truth = np.zeros(class_count, dtype=np.float32)
# 			ground_truth[label_index] = 1.0
# 			bottlenecks.append(bottleneck)
# 			ground_truths.append(ground_truth)
# 			filenames.append(image_name)
# 	else:
# 		# Retrieve all bottlenecks.
# 		for label_index, label_name in enumerate(image_lists.keys()):
# 			for image_index, image_name in enumerate(
# 					image_lists[label_name][category]):
# 				image_name = get_image_path(image_lists, label_name, image_index,
# 											image_dir, category)
# 				bottleneck = get_or_create_bottleneck(
# 					sess, image_lists, label_name, image_index, image_dir, category,
# 					bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
# 					resized_input_tensor, bottleneck_tensor, architecture)
# 				ground_truth = np.zeros(class_count, dtype=np.float32)
# 				ground_truth[label_index] = 1.0
# 				bottlenecks.append(bottleneck)
# 				ground_truths.append(ground_truth)
# 				filenames.append(image_name)
# 	return bottlenecks, ground_truths, filenames

# def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
# 									bottleneck_dir, image_dir, jpeg_data_tensor,
# 									decoded_image_tensor, resized_input_tensor,
# 									bottleneck_tensor, architecture):
#
#

def get_random_distorted_bottlenecks(
		sess, image_lists, how_many, category, image_dir, input_jpeg_tensor,
		distorted_image, resized_input_tensor, bottleneck_tensor):
	"""Retrieves bottleneck values for training images, after distortions.
	If we're training with distortions like crops, scales, or flips, we have to
	recalculate the full model for every image, and so we can't use cached
	bottleneck values. Instead we find random images for the requested category,
	run them through the distortion graph, and then the full graph to get the
	bottleneck results for each.
	Args:
	sess: Current TensorFlow Session.
	image_lists: Dictionary of training images for each label.
	how_many: The integer number of bottleneck values to return.
	category: Name string of which set of images to fetch - training, testing,
	or validation.
	image_dir: Root folder string of the subfolders containing the training
	images.
	input_jpeg_tensor: The input layer we feed the image data to.
	distorted_image: The output node of the distortion graph.
	resized_input_tensor: The input node of the recognition graph.
	bottleneck_tensor: The bottleneck output layer of the CNN graph.
	Returns:
	List of bottleneck arrays and their corresponding ground truths.
	"""
	class_count = len(image_lists.keys())
	bottlenecks = []
	ground_truths = []
	for unused_i in range(how_many):
		label_index = random.randrange(class_count)
		label_name = list(image_lists.keys())[label_index]
		image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
		image_path = get_image_path(image_lists, label_name, image_index, image_dir,
									category)
		if not gfile.Exists(image_path):
			tf.logging.fatal('File does not exist %s', image_path)
		jpeg_data = gfile.FastGFile(image_path, 'rb').read()
		# Note that we materialize the distorted_image_data as a numpy array before
		# sending running inference on the image. This involves 2 memory copies and
		# might be optimized in other implementations.
		distorted_image_data = sess.run(distorted_image,
										{input_jpeg_tensor: jpeg_data})
		bottleneck_values = sess.run(bottleneck_tensor,
									 {resized_input_tensor: distorted_image_data})
		bottleneck_values = np.squeeze(bottleneck_values)
		ground_truth = np.zeros(class_count, dtype=np.float32)
		ground_truth[label_index] = 1.0
		bottlenecks.append(bottleneck_values)
		ground_truths.append(ground_truth)
	return bottlenecks, ground_truths

def should_distort_images(flip_left_right, random_crop, random_scale,
							random_brightness):
	"""Whether any distortions are enabled, from the input flags.
	Args:
	flip_left_right: Boolean whether to randomly mirror images horizontally.
	random_crop: Integer percentage setting the total margin used around the
	crop box.
	random_scale: Integer percentage of how much to vary the scale by.
	random_brightness: Integer range to randomly multiply the pixel values by.
	Returns:
	Boolean value indicating whether any distortions should be applied.
	"""
	return (flip_left_right or (random_crop != 0) or (random_scale != 0) or
			(random_brightness != 0))


def add_input_distortions(flip_left_right, random_crop, random_scale,
							random_brightness, input_width, input_height,
							input_depth, input_mean, input_std):
	"""Creates the operations to apply the specified distortions.
	During training it can help to improve the results if we run the images
	through simple distortions like crops, scales, and flips. These reflect the
	kind of variations we expect in the real world, and so can help train the
	model to cope with natural data more effectively. Here we take the supplied
	parameters and construct a network of operations to apply them to an image.
	Cropping
	~~~~~~~~
	Cropping is done by placing a bounding box at a random position in the full
	image. The cropping parameter controls the size of that box relative to the
	input image. If it's zero, then the box is the same size as the input and no
	cropping is performed. If the value is 50%, then the crop box will be half the
	width and height of the input. In a diagram it looks like this:
	<			 width				 >
	+---------------------+
	|										 |
	|	 width - crop%		 |
	|		<			>				 |
	|		+------+				 |
	|		|			|				 |
	|		|			|				 |
	|		|			|				 |
	|		+------+				 |
	|										 |
	|										 |
	+---------------------+
	Scaling
	~~~~~~~
	Scaling is a lot like cropping, except that the bounding box is always
	centered and its size varies randomly within the given range. For example if
	the scale percentage is zero, then the bounding box is the same size as the
	input and no scaling is applied. If it's 50%, then the bounding box will be in
	a random range between half the width and height and full size.
	Args:
	flip_left_right: Boolean whether to randomly mirror images horizontally.
	random_crop: Integer percentage setting the total margin used around the
	crop box.
	random_scale: Integer percentage of how much to vary the scale by.
	random_brightness: Integer range to randomly multiply the pixel values by.
	graph.
	input_width: Horizontal size of expected input image to model.
	input_height: Vertical size of expected input image to model.
	input_depth: How many channels the expected input image should have.
	input_mean: Pixel value that should be zero in the image for the graph.
	input_std: How much to divide the pixel values by before recognition.
	Returns:
	The jpeg input layer and the distorted result tensor.
	"""

	jpeg_data = tf.placeholder(tf.string, name='DistortJPGInput')
	decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
	decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)
	decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
	margin_scale = 1.0 + (random_crop / 100.0)
	resize_scale = 1.0 + (random_scale / 100.0)
	margin_scale_value = tf.constant(margin_scale)
	resize_scale_value = tf.random_uniform(tensor_shape.scalar(),
											 minval=1.0,
											 maxval=resize_scale)
	scale_value = tf.multiply(margin_scale_value, resize_scale_value)
	precrop_width = tf.multiply(scale_value, input_width)
	precrop_height = tf.multiply(scale_value, input_height)
	precrop_shape = tf.stack([precrop_height, precrop_width])
	precrop_shape_as_int = tf.cast(precrop_shape, dtype=tf.int32)
	precropped_image = tf.image.resize_bilinear(decoded_image_4d,
												precrop_shape_as_int)
	precropped_image_3d = tf.squeeze(precropped_image, squeeze_dims=[0])
	cropped_image = tf.random_crop(precropped_image_3d,
									 [input_height, input_width, input_depth])
	if flip_left_right:
		flipped_image = tf.image.random_flip_left_right(cropped_image)
	else:
		flipped_image = cropped_image
	brightness_min = 1.0 - (random_brightness / 100.0)
	brightness_max = 1.0 + (random_brightness / 100.0)
	brightness_value = tf.random_uniform(tensor_shape.scalar(),
										 minval=brightness_min,
										 maxval=brightness_max)
	brightened_image = tf.multiply(flipped_image, brightness_value)
	offset_image = tf.subtract(brightened_image, input_mean)
	mul_image = tf.multiply(offset_image, 1.0 / input_std)
	distort_result = tf.expand_dims(mul_image, 0, name='DistortResult')
	return jpeg_data, distort_result


def variable_summaries(var):
	"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
	with tf.name_scope('summaries'):
		mean = tf.reduce_mean(var)
		tf.summary.scalar('mean', mean)
		with tf.name_scope('stddev'):
			stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
		tf.summary.scalar('stddev', stddev)
		tf.summary.scalar('max', tf.reduce_max(var))
		tf.summary.scalar('min', tf.reduce_min(var))
		tf.summary.histogram('histogram', var)

def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, bottleneck_tensor_size):
	"""Adds a new softmax and fully-connected layer for training.
	We need to retrain the top layer to identify our new classes, so this function
	adds the right operations to the graph, along with some variables to hold the
	weights, and then sets up all the gradients for the backward pass.
	The set up for the softmax and fully-connected layers is based on:
	https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
	Args:
	class_count: Integer of how many categories of things we're trying to
	recognize.
	final_tensor_name: Name string for the new final node that produces results.
	bottleneck_tensor: The output of the main CNN graph.
	bottleneck_tensor_size: How many entries in the bottleneck vector.
	Returns:
	The tensors for the training and cross entropy results, and tensors for the
	bottleneck input and ground truth input.
	"""
	with tf.name_scope('input'):
		bottleneck_input = tf.placeholder_with_default(
			bottleneck_tensor,
			shape=[None, bottleneck_tensor_size],
			name='BottleneckInputPlaceholder')

		ground_truth_input = tf.placeholder(tf.float32,
											[None, class_count],
											name='GroundTruthInput')

	# Organizing the following ops as `final_training_ops` so they're easier
	# to see in TensorBoard
	layer_name = 'final_training_ops'
	with tf.name_scope(layer_name):
		with tf.name_scope('weights'):
			initial_value = tf.truncated_normal(
				[bottleneck_tensor_size, class_count], stddev=0.001)

			layer_weights = tf.Variable(initial_value, name='final_weights')

			variable_summaries(layer_weights)
		with tf.name_scope('biases'):
			layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
			variable_summaries(layer_biases)
		with tf.name_scope('Wx_plus_b'):
			logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
			tf.summary.histogram('pre_activations', logits)

	final_tensor = tf.nn.softmax(logits, name=final_tensor_name)
	tf.summary.histogram('activations', final_tensor)

	with tf.name_scope('cross_entropy'):
		cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
			labels=ground_truth_input, logits=logits)
		with tf.name_scope('total'):
			cross_entropy_mean = tf.reduce_mean(cross_entropy)
	tf.summary.scalar('cross_entropy', cross_entropy_mean)

	with tf.name_scope('train'):
		optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
		train_step = optimizer.minimize(cross_entropy_mean)

	return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input,
			final_tensor)


def add_evaluation_step(result_tensor, ground_truth_tensor):
	"""Inserts the operations we need to evaluate the accuracy of our results.
	Args:
	result_tensor: The new final node that produces results.
	ground_truth_tensor: The node we feed ground truth data
	into.
	Returns:
	Tuple of (evaluation step, predicted_labels).
	"""
	with tf.name_scope('accuracy'):
		with tf.name_scope('correct_prediction'):
			prediction = tf.argmax(result_tensor, 1)
			correct_prediction = tf.equal(
				prediction, tf.argmax(ground_truth_tensor, 1))
		with tf.name_scope('accuracy'):
			evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
	tf.summary.scalar('accuracy', evaluation_step)
	return evaluation_step, prediction


def save_graph_to_file(sess, graph, graph_file_name):
	output_graph_def = graph_util.convert_variables_to_constants(
		sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
	with gfile.FastGFile(graph_file_name, 'wb') as f:
		f.write(output_graph_def.SerializeToString())
	return


def prepare_file_system():
	# Setup the directory we'll write summaries to for TensorBoard
	if tf.gfile.Exists(FLAGS.summaries_dir):
		tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
	tf.gfile.MakeDirs(FLAGS.summaries_dir)
	if FLAGS.intermediate_store_frequency > 0:
		ensure_dir_exists(FLAGS.intermediate_output_graphs_dir)
	return


def create_model_info(architecture):
	"""Given the name of a model architecture, returns information about it.
	There are different base image recognition pretrained models that can be
	retrained using transfer learning, and this function translates from the name
	of a model to the attributes that are needed to download and train with it.
	Args:
	architecture: Name of a model architecture.
	Returns:
	Dictionary of information about the model, or None if the name isn't
	recognized
	Raises:
	ValueError: If architecture name is unknown.
	"""
	architecture = architecture.lower()
	if architecture == 'inception_v3':
		# pylint: disable=line-too-long
		data_url = 'http://download.tensorflow.org/models/image/imagenet/pretrain_models-2015-12-05.tgz'
		# pylint: enable=line-too-long
		bottleneck_tensor_name = 'pool_3/_reshape:0'
		bottleneck_tensor_size = 2048
		input_width = 299
		input_height = 299
		input_depth = 3
		resized_input_tensor_name = 'Mul:0'
		model_file_name = 'classify_image_graph_def.pb'
		input_mean = 128
		input_std = 128
	elif architecture.startswith('mobilenet_'):
		parts = architecture.split('_')
		if len(parts) != 3 and len(parts) != 4:
			tf.logging.error("Couldn't understand architecture name '%s'",
							 architecture)
			return None
		version_string = parts[1]
		if (version_string != '1.0' and version_string != '0.75' and
					version_string != '0.50' and version_string != '0.25'):
			tf.logging.error(
				""""The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25', but found '%s' for architecture '%s'""",
				version_string, architecture)
			return None
		size_string = parts[2]
		if (size_string != '224' and size_string != '192' and
					size_string != '160' and size_string != '128'):
			tf.logging.error(
				"""The Mobilenet input size should be '224', '192', '160', or '128', but found '%s' for architecture '%s'""",
				size_string, architecture)
			return None
		if len(parts) == 3:
			is_quantized = False
		else:
			if parts[3] != 'quantized':
				tf.logging.error(
					"Couldn't understand architecture suffix '%s' for '%s'", parts[3],
					architecture)
				return None
			is_quantized = True
		data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
		data_url += version_string + '_' + size_string + '_frozen.tgz'
		bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
		bottleneck_tensor_size = 1001
		input_width = int(size_string)
		input_height = int(size_string)
		input_depth = 3
		resized_input_tensor_name = 'input:0'
		if is_quantized:
			model_base_name = 'quantized_graph.pb'
		else:
			model_base_name = 'frozen_graph.pb'
		model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string
		model_file_name = os.path.join(model_dir_name, model_base_name)
		input_mean = 127.5
		input_std = 127.5
	else:
		tf.logging.error("Couldn't understand architecture name '%s'", architecture)
		raise ValueError('Unknown architecture', architecture)

	return {
		'data_url': data_url,
		'bottleneck_tensor_name': bottleneck_tensor_name,
		'bottleneck_tensor_size': bottleneck_tensor_size,
		'input_width': input_width,
		'input_height': input_height,
		'input_depth': input_depth,
		'resized_input_tensor_name': resized_input_tensor_name,
		'model_file_name': model_file_name,
		'input_mean': input_mean,
		'input_std': input_std,
	}

def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
						input_std):
	"""Adds operations that perform JPEG decoding and resizing to the graph..
	Args:
	input_width: Desired width of the image fed into the recognizer graph.
	input_height: Desired width of the image fed into the recognizer graph.
	input_depth: Desired channels of the image fed into the recognizer graph.
	input_mean: Pixel value that should be zero in the image for the graph.
	input_std: How much to divide the pixel values by before recognition.
	Returns:
	Tensors for the node to feed JPEG data into, and the output of the
		preprocessing steps.
	"""
	jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput')
	decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
	decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)
	decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
	resize_shape = tf.stack([input_height, input_width])
	resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32)
	resized_image = tf.image.resize_bilinear(decoded_image_4d,
											 resize_shape_as_int)
	offset_image = tf.subtract(resized_image, input_mean)
	mul_image = tf.multiply(offset_image, 1.0 / input_std)
	return jpeg_data, mul_image

def num_samples(images_list, category):
	res = 0
	for label_name, label_list in images_list.items():
		res += len(label_list[category])
	return res

import time
from tensorflow.python.platform import tf_logging as logging

# def create_image_lists(image_dir, testing_percentage, validation_percentage):
#	 """Builds a list of training images from the file system.
#	 Analyzes the sub folders in the image directory, splits them into stable
#	 training, testing, and validation sets, and returns a data structure
#	 describing the lists of images for each label and their paths.
#	 Args:
#		 image_dir: String path to a folder containing subfolders of images.
#		 testing_percentage: Integer percentage of the images to reserve for tests.
#		 validation_percentage: Integer percentage of images reserved for validation.
#	 Returns:
#		 A dictionary containing an entry for each label subfolder, with images split
#		 into training, testing, and validation sets within each label.
#	 """
#	 if not gfile.Exists(image_dir):
#		 tf.logging.error("Image directory '" + image_dir + "' not found.")
#		 return None
#	 result = {}
#	 sub_dirs = [x[0] for x in gfile.Walk(image_dir)]
#	 # The root directory comes first, so skip it.
#	 is_root_dir = True
#	 for sub_dir in sub_dirs:
#		 if is_root_dir:
#			 is_root_dir = False
#			 continue
#		 extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
#		 file_list = []
#		 dir_name = os.path.basename(sub_dir)
#		 if dir_name == image_dir:
#			 continue
#		 tf.logging.info("Looking for images in '" + dir_name + "'")
#		 for extension in extensions:
#			 file_glob = os.path.join(image_dir, dir_name, '*.' + extension)
#			 file_list.extend(gfile.Glob(file_glob))
#		 if not file_list:
#			 tf.logging.warning('No files found')
#			 continue
#		 if len(file_list) < 20:
#			 tf.logging.warning(
#				 'WARNING: Folder has less than 20 images, which may cause issues.')
#		 elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:
#			 tf.logging.warning(
#				 'WARNING: Folder {} has more than {} images. Some images will '
#				 'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS))
#		 label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())
#		 training_images = []
#		 testing_images = []
#		 validation_images = []
#
#		 for file_name in file_list:
#			 base_name = os.path.basename(file_name)
#			 # We want to ignore anything after '_nohash_' in the file name when
#			 # deciding which set to put an image in, the data set creator has a way o
#
#			 # grouping photos that are close variations of each other. For example
#			 # this is used in the plant disease data set to group multiple pictures of
#			 # the same leaf.
#			 hash_name = re.sub(r'_nohash_.*$', '', file_name)
#			 # This looks a bit magical, but we need to decide whether this file should
#			 # go into the training, testing, or validation sets, and we want to keep
#			 # existing files in the same set even if more files are subsequently
#			 # added.
#			 # To do that, we need a stable way of deciding based on just the file name
#			 # itself, so we do a hash of that and then use that to generate a
#			 # probability value that we use to assign it.
#			 hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest()
#			 percentage_hash = ((int(hash_name_hashed, 16) %
#													 (MAX_NUM_IMAGES_PER_CLASS + 1)) *
#													(100.0 / MAX_NUM_IMAGES_PER_CLASS))
#			 if percentage_hash < validation_percentage:
#				 validation_images.append(base_name)
#			 elif percentage_hash < (testing_percentage + validation_percentage):
#				 testing_images.append(base_name)
#			 else:
#				 training_images.append(base_name)
#
#		 result[label_name] = {
#			 'dir': dir_name,
#			 'training': training_images,
#			 'testing': testing_images,
#			 'validation': validation_images,
#		 }
#	 print(label_name)
#	 return result


# def train_one_step(sess, train_op, global_step):
# 	'''
# 	Simply runs a session for the three arguments provided and gives a logging on the time elapsed for each global step
# 	'''
# 	# Check the time for each sess run
# 	start_time = time.time()
# 	total_loss, global_step_count = sess.run([train_op, global_step])
# 	time_elapsed = time.time() - start_time
#
# 	# Run the logging to print some results
# 	logging.info('global step %s: loss: %.4f (%.2f sec/step)', global_step_count, total_loss, time_elapsed)
#
# 	return total_loss, global_step_count


# # Store intermediate results
# intermediate_frequency = FLAGS.intermediate_store_frequency
#
# if (intermediate_frequency > 0 and (i % intermediate_frequency == 0)
# 	and i > 0):
# 	intermediate_file_name = (FLAGS.intermediate_output_graphs_dir +
# 							  'intermediate_' + str(i) + '.pb')
# 	tf.logging.info('Save intermediate result to : ' +
# 					intermediate_file_name)
# 	save_graph_to_file(sess, graph, intermediate_file_name)