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Commit eb7389e4 authored by Martin Wicke's avatar Martin Wicke
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Merge pull request #5 from tensorflow/inception 

added inception model
parents 1ecaf090 83ee52cc
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.gitmodules 0 → 100644
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[submodule "tensorflow"]
path = tensorflow
url = https://github.com/tensorflow/tensorflow.git
WORKSPACE 0 → 100644
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local_repository(
name = "tf",
path = __workspace_dir__ + "/tensorflow",
)
load('//tensorflow/tensorflow:workspace.bzl', 'tf_workspace')
tf_workspace("tensorflow/")
# grpc expects //external:protobuf_clib and //external:protobuf_compiler
# to point to the protobuf's compiler library.
bind(
name = "protobuf_clib",
actual = "@tf//google/protobuf:protoc_lib",
)
bind(
name = "protobuf_compiler",
actual = "@tf//google/protobuf:protoc_lib",
)
git_repository(
name = "grpc",
commit = "73979f4",
init_submodules = True,
remote = "https://github.com/grpc/grpc.git",
)
# protobuf expects //external:grpc_cpp_plugin to point to grpc's
# C++ plugin code generator.
bind(
name = "grpc_cpp_plugin",
actual = "@grpc//:grpc_cpp_plugin",
)
bind(
name = "grpc_lib",
actual = "@grpc//:grpc++_unsecure",
)
inception/BUILD 0 → 100644
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# Description:
# Example TensorFlow models for ImageNet.
package(default_visibility = [":internal"])
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
package_group(
name = "internal",
packages = ["//inception/..."],
)
py_library(
name = "dataset",
srcs = [
"dataset.py",
],
deps = [
"@tf//tensorflow:tensorflow_py",
],
)
py_library(
name = "imagenet_data",
srcs = [
"imagenet_data.py",
],
deps = [
":dataset",
],
)
py_library(
name = "flowers_data",
srcs = [
"flowers_data.py",
],
deps = [
":dataset",
],
)
py_library(
name = "image_processing",
srcs = [
"image_processing.py",
],
)
py_library(
name = "inception",
srcs = [
"inception_model.py",
],
deps = [
"@tf//tensorflow:tensorflow_py",
":dataset",
"//inception/slim",
],
)
py_binary(
name = "imagenet_eval",
srcs = [
"imagenet_eval.py",
],
deps = [
":imagenet_data",
":inception_eval",
],
)
py_binary(
name = "flowers_eval",
srcs = [
"flowers_eval.py",
],
deps = [
":flowers_data",
":inception_eval",
],
)
py_library(
name = "inception_eval",
srcs = [
"inception_eval.py",
],
deps = [
"@tf//tensorflow:tensorflow_py",
":image_processing",
":inception",
],
)
py_binary(
name = "imagenet_train",
srcs = [
"imagenet_train.py",
],
deps = [
":imagenet_data",
":inception_train",
],
)
py_binary(
name = "flowers_train",
srcs = [
"flowers_train.py",
],
deps = [
":flowers_data",
":inception_train",
],
)
py_library(
name = "inception_train",
srcs = [
"inception_train.py",
],
deps = [
"@tf//tensorflow:tensorflow_py",
":image_processing",
":inception",
],
)
py_binary(
name = "build_image_data",
srcs = ["data/build_image_data.py"],
deps = [
"@tf//tensorflow:tensorflow_py",
],
)
sh_binary(
name = "download_and_preprocess_flowers",
srcs = ["data/download_and_preprocess_flowers.sh"],
data = [
":build_image_data",
],
)
sh_binary(
name = "download_and_preprocess_imagenet",
srcs = ["data/download_and_preprocess_imagenet.sh"],
data = [
"data/download_imagenet.sh",
"data/imagenet_2012_validation_synset_labels.txt",
"data/imagenet_lsvrc_2015_synsets.txt",
"data/imagenet_metadata.txt",
"data/preprocess_imagenet_validation_data.py",
"data/process_bounding_boxes.py",
":build_imagenet_data",
],
)
py_binary(
name = "build_imagenet_data",
srcs = ["data/build_imagenet_data.py"],
deps = [
"@tf//tensorflow:tensorflow_py",
],
)
filegroup(
name = "srcs",
srcs = glob(
[
"**/*.py",
"BUILD",
],
),
)
inception/README.md 0 → 100644
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inception/data/build_image_data.py 0 → 100644
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# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Converts image data to TFRecords file format with Example protos.
The image data set is expected to reside in JPEG files located in the
following directory structure.
data_dir/label_0/image0.jpeg
data_dir/label_0/image1.jpg
...
data_dir/label_1/weird-image.jpeg
data_dir/label_1/my-image.jpeg
...
where the sub-sirectory is the unique label associated with these images.
This TensorFlow script converts the training and evaluation data into
a sharded data set consisting of TFRecord files
train_directory/train-00000-of-01024
train_directory/train-00001-of-01024
...
train_directory/train-00127-of-01024
and
validation_directory/validation-00000-of-00128
validation_directory/validation-00001-of-00128
...
validation_directory/validation-00127-of-00128
where we have selected 1024 and 128 shards for each data set. Each record
within the TFRecord file is a serialized Example proto. The Example proto
contains the following fields:
image/encoded: string containing JPEG encoded image in RGB colorspace
image/height: integer, image height in pixels
image/width: integer, image width in pixels
image/colorspace: string, specifying the colorspace, always 'RGB'
image/channels: integer, specifying the number of channels, always 3
image/format: string, specifying the format, always'JPEG'
image/filename: string containing the basename of the image file
e.g. 'n01440764_10026.JPEG' or 'ILSVRC2012_val_00000293.JPEG'
image/class/label: integer specifying the index in a classification layer.
The label ranges from [0, num_labels] where 0 is unused and left as
the background class.
image/class/text: string specifying the human-readable version of the label
e.g. 'dog'
If you data set involves bounding boxes, please look at build_imagenet_data.py.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os
import random
import sys
import threading
import numpy as np
import tensorflow as tf
tf.app.flags.DEFINE_string('train_directory', '/tmp/',
'Training data directory')
tf.app.flags.DEFINE_string('validation_directory', '/tmp/',
'Validation data directory')
tf.app.flags.DEFINE_string('output_directory', '/tmp/',
'Output data directory')
tf.app.flags.DEFINE_integer('train_shards', 2,
'Number of shards in training TFRecord files.')
tf.app.flags.DEFINE_integer('validation_shards', 2,
'Number of shards in validation TFRecord files.')
tf.app.flags.DEFINE_integer('num_threads', 2,
'Number of threads to preprocess the images.')
# The labels file contains a list of valid labels are held in this file.
# Assumes that the file contains entries as such:
# dog
# cat
# flower
# where each line corresponds to a label. We map each label contained in
# the file to an integer corresponding to the line number starting from 0.
tf.app.flags.DEFINE_string('labels_file', '', 'Labels file')
FLAGS = tf.app.flags.FLAGS
def _int64_feature(value):
"""Wrapper for inserting int64 features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _convert_to_example(filename, image_buffer, label, text, height, width):
"""Build an Example proto for an example.
Args:
filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
label: integer, identifier for the ground truth for the network
text: string, unique human-readable, e.g. 'dog'
height: integer, image height in pixels
width: integer, image width in pixels
Returns:
Example proto
"""
colorspace = 'RGB'
channels = 3
image_format = 'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': _int64_feature(height),
'image/width': _int64_feature(width),
'image/colorspace': _bytes_feature(colorspace),
'image/channels': _int64_feature(channels),
'image/class/label': _int64_feature(label),
'image/class/text': _bytes_feature(text),
'image/format': _bytes_feature(image_format),
'image/filename': _bytes_feature(os.path.basename(filename)),
'image/encoded': _bytes_feature(image_buffer)}))
return example
class ImageCoder(object):
"""Helper class that provides TensorFlow image coding utilities."""
def __init__(self):
# Create a single Session to run all image coding calls.
self._sess = tf.Session()
# Initializes function that converts PNG to JPEG data.
self._png_data = tf.placeholder(dtype=tf.string)
image = tf.image.decode_png(self._png_data, channels=3)
self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
# Initializes function that decodes RGB JPEG data.
self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
def png_to_jpeg(self, image_data):
return self._sess.run(self._png_to_jpeg,
feed_dict={self._png_data: image_data})
def decode_jpeg(self, image_data):
image = self._sess.run(self._decode_jpeg,
feed_dict={self._decode_jpeg_data: image_data})
assert len(image.shape) == 3
assert image.shape[2] == 3
return image
def _is_png(filename):
"""Determine if a file contains a PNG format image.
Args:
filename: string, path of the image file.
Returns:
boolean indicating if the image is a PNG.
"""
return '.png' in filename
def _process_image(filename, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
image_data = tf.gfile.FastGFile(filename, 'r').read()
# Convert any PNG to JPEG's for consistency.
if _is_png(filename):
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
# Decode the RGB JPEG.
image = coder.decode_jpeg(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
texts, labels, num_shards):
"""Processes and saves list of images as TFRecord in 1 thread.
Args:
coder: instance of ImageCoder to provide TensorFlow image coding utils.
thread_index: integer, unique batch to run index is within [0, len(ranges)).
ranges: list of pairs of integers specifying ranges of each batches to
analyze in parallel.
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
texts: list of strings; each string is human readable, e.g. 'dog'
labels: list of integer; each integer identifies the ground truth
num_shards: integer number of shards for this data set.
"""
# Each thread produces N shards where N = int(num_shards / num_threads).
# For instance, if num_shards = 128, and the num_threads = 2, then the first
# thread would produce shards [0, 64).
num_threads = len(ranges)
assert not num_shards % num_threads
num_shards_per_batch = int(num_shards / num_threads)
shard_ranges = np.linspace(ranges[thread_index][0],
ranges[thread_index][1],
num_shards_per_batch + 1).astype(int)
num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
counter = 0
for s in xrange(num_shards_per_batch):
# Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
shard = thread_index * num_shards_per_batch + s
output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)
output_file = os.path.join(FLAGS.output_directory, output_filename)
writer = tf.python_io.TFRecordWriter(output_file)
shard_counter = 0
files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
text = texts[i]
image_buffer, height, width = _process_image(filename, coder)
example = _convert_to_example(filename, image_buffer, label,
text, height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
if not counter % 1000:
print('%s [thread %d]: Processed %d of %d images in thread batch.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush()
print('%s [thread %d]: Wrote %d images to %s' %
(datetime.now(), thread_index, shard_counter, output_file))
sys.stdout.flush()
shard_counter = 0
print('%s [thread %d]: Wrote %d images to %d shards.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush()
def _process_image_files(name, filenames, texts, labels, num_shards):
"""Process and save list of images as TFRecord of Example protos.
Args:
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
texts: list of strings; each string is human readable, e.g. 'dog'
labels: list of integer; each integer identifies the ground truth
num_shards: integer number of shards for this data set.
"""
assert len(filenames) == len(texts)
assert len(filenames) == len(labels)
# Break all images into batches with a [ranges[i][0], ranges[i][1]].
spacing = np.linspace(0, len(filenames), FLAGS.num_threads + 1).astype(np.int)
ranges = []
threads = []
for i in xrange(len(spacing) - 1):
ranges.append([spacing[i], spacing[i+1]])
# Launch a thread for each batch.
print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges))
sys.stdout.flush()
# Create a mechanism for monitoring when all threads are finished.
coord = tf.train.Coordinator()
# Create a generic TensorFlow-based utility for converting all image codings.
coder = ImageCoder()
threads = []
for thread_index in xrange(len(ranges)):
args = (coder, thread_index, ranges, name, filenames,
texts, labels, num_shards)
t = threading.Thread(target=_process_image_files_batch, args=args)
t.start()
threads.append(t)
# Wait for all the threads to terminate.
coord.join(threads)
print('%s: Finished writing all %d images in data set.' %
(datetime.now(), len(filenames)))
sys.stdout.flush()
def _find_image_files(data_dir, labels_file):
"""Build a list of all images files and labels in the data set.
Args:
data_dir: string, path to the root directory of images.
Assumes that the image data set resides in JPEG files located in
the following directory structure.
data_dir/dog/another-image.JPEG
data_dir/dog/my-image.jpg
where 'dog' is the label associated with these images.
labels_file: string, path to the labels file.
The list of valid labels are held in this file. Assumes that the file
contains entries as such:
dog
cat
flower
where each line corresponds to a label. We map each label contained in
the file to an integer starting with the integer 0 corresponding to the
label contained in the first line.
Returns:
filenames: list of strings; each string is a path to an image file.
texts: list of strings; each string is the class, e.g. 'dog'
labels: list of integer; each integer identifies the ground truth.
"""
print('Determining list of input files and labels from %s.' % data_dir)
unique_labels = [l.strip() for l in tf.gfile.FastGFile(
labels_file, 'r').readlines()]
labels = []
filenames = []
texts = []
# Leave label index 0 empty as a background class.
label_index = 1
# Construct the list of JPEG files and labels.
for text in unique_labels:
jpeg_file_path = '%s/%s/*' % (data_dir, text)
matching_files = tf.gfile.Glob(jpeg_file_path)
labels.extend([label_index] * len(matching_files))
texts.extend([text] * len(matching_files))
filenames.extend(matching_files)
if not label_index % 100:
print('Finished finding files in %d of %d classes.' % (
label_index, len(labels)))
label_index += 1
# Shuffle the ordering of all image files in order to guarantee
# random ordering of the images with respect to label in the
# saved TFRecord files. Make the randomization repeatable.
shuffled_index = range(len(filenames))
random.seed(12345)
random.shuffle(shuffled_index)
filenames = [filenames[i] for i in shuffled_index]
texts = [texts[i] for i in shuffled_index]
labels = [labels[i] for i in shuffled_index]
print('Found %d JPEG files across %d labels inside %s.' %
(len(filenames), len(unique_labels), data_dir))
return filenames, texts, labels
def _process_dataset(name, directory, num_shards, labels_file):
"""Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
num_shards: integer number of shards for this data set.
labels_file: string, path to the labels file.
"""
filenames, texts, labels = _find_image_files(directory, labels_file)
_process_image_files(name, filenames, texts, labels, num_shards)
def main(unused_argv):
assert not FLAGS.train_shards % FLAGS.num_threads, (
'Please make the FLAGS.num_threads commensurate with FLAGS.train_shards')
assert not FLAGS.validation_shards % FLAGS.num_threads, (
'Please make the FLAGS.num_threads commensurate with '
'FLAGS.validation_shards')
print('Saving results to %s' % FLAGS.output_directory)
# Run it!
_process_dataset('validation', FLAGS.validation_directory,
FLAGS.validation_shards, FLAGS.labels_file)
_process_dataset('train', FLAGS.train_directory,
FLAGS.train_shards, FLAGS.labels_file)
if __name__ == '__main__':
tf.app.run()
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inception/data/download_and_preprocess_flowers.sh 0 → 100755
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#!/bin/bash
# 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.
# ==============================================================================
# Script to download and preprocess the flowers data set. This data set
# provides a demonstration for how to perform fine-tuning (i.e. tranfer
# learning) from one model to a new data set.
#
# This script provides a demonstration for how to prepare an arbitrary
# data set for training an Inception v3 model.
#
# We demonstrate this with the flowers data set which consists of images
# of labeled flower images from 5 classes:
#
# daisy, dandelion, roses, sunflowers, tulips
#
# The final output of this script are sharded TFRecord files containing
# serialized Example protocol buffers. See build_image_data.py for
# details of how the Example protocol buffer contains image data.
#
# usage:
# ./download_and_preprocess_flowers.sh [data-dir]
set -e
if [ -z "$1" ]; then
echo "usage download_and_preprocess_flowers.sh [data dir]"
exit
fi
# Create the output and temporary directories.
DATA_DIR="${1%/}"
SCRATCH_DIR="${DATA_DIR}/raw-data/"
mkdir -p "${DATA_DIR}"
mkdir -p "${SCRATCH_DIR}"
WORK_DIR="$0.runfiles/inception"
# Download the flowers data.
DATA_URL="http://download.tensorflow.org/example_images/flower_photos.tgz"
CURRENT_DIR=$(pwd)
cd "${DATA_DIR}"
TARBALL="flower_photos.tgz"
if [ ! -f ${TARBALL} ]; then
echo "Downloading flower data set."
wget -O ${TARBALL} "${DATA_URL}"
else
echo "Skipping download of flower data."
fi
# Note the locations of the train and validation data.
TRAIN_DIRECTORY="${SCRATCH_DIR}train/"
VALIDATION_DIRECTORY="${SCRATCH_DIR}validation/"
# Expands the data into the flower_photos/ directory and rename it as the
# train directory.
tar xf flower_photos.tgz
rm -rf "${TRAIN_DIRECTORY}" "${VALIDATION_DIRECTORY}"
mv flower_photos "${TRAIN_DIRECTORY}"
# Generate a list of 5 labels: daisy, dandelion, roses, sunflowers, tulips
LABELS_FILE="${SCRATCH_DIR}/labels.txt"
ls -1 "${TRAIN_DIRECTORY}" | grep -v 'LICENSE' | sed 's/\///' | sort > "${LABELS_FILE}"
# Generate the validation data set.
while read LABEL; do
VALIDATION_DIR_FOR_LABEL="${VALIDATION_DIRECTORY}${LABEL}"
TRAIN_DIR_FOR_LABEL="${TRAIN_DIRECTORY}${LABEL}"
# Move the first randomly selected 100 images to the validation set.
mkdir -p "${VALIDATION_DIR_FOR_LABEL}"
VALIDATION_IMAGES=$(ls -1 "${TRAIN_DIR_FOR_LABEL}" | shuf | head -100)
for IMAGE in ${VALIDATION_IMAGES}; do
mv -f "${TRAIN_DIRECTORY}${LABEL}/${IMAGE}" "${VALIDATION_DIR_FOR_LABEL}"
done
done < "${LABELS_FILE}"
# Build the TFRecords version of the image data.
cd "${CURRENT_DIR}"
BUILD_SCRIPT="${WORK_DIR}/build_image_data"
OUTPUT_DIRECTORY="${DATA_DIR}"
"${BUILD_SCRIPT}" \
--train_directory="${TRAIN_DIRECTORY}" \
--validation_directory="${VALIDATION_DIRECTORY}" \
--output_directory="${OUTPUT_DIRECTORY}" \
--labels_file="${LABELS_FILE}"
inception/data/download_and_preprocess_imagenet.sh 0 → 100755
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#!/bin/bash
# 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.
# ==============================================================================
# Script to download and preprocess ImageNet Challenge 2012
# training and validation data set.
#
# The final output of this script are sharded TFRecord files containing
# serialized Example protocol buffers. See build_imagenet_data.py for
# details of how the Example protocol buffers contain the ImageNet data.
#
# The final output of this script appears as such:
#
# data_dir/train-00000-of-01024
# data_dir/train-00001-of-01024
# ...
# data_dir/train-00127-of-01024
#
# and
#
# data_dir/validation-00000-of-00128
# data_dir/validation-00001-of-00128
# ...
# data_dir/validation-00127-of-00128
#
# Note that this script may take several hours to run to completion. The
# conversion of the ImageNet data to TFRecords alone takes 2-3 hours depending
# on the speed of your machine. Please be patient.
#
# **IMPORTANT**
# To download the raw images, the user must create an account with image-net.org
# and generate a username and access_key. The latter two are required for
# downloading the raw images.
#
# usage:
# ./download_and_preprocess_imagenet.sh [data-dir]
set -e
if [ -z "$1" ]; then
echo "usage download_and_preprocess_imagenet.sh [data dir]"
exit
fi
# Create the output and temporary directories.
DATA_DIR="${1%/}"
SCRATCH_DIR="${DATA_DIR}/raw-data/"
mkdir -p "${DATA_DIR}"
mkdir -p "${SCRATCH_DIR}"
WORK_DIR="$0.runfiles/inception"
# Download the ImageNet data.
LABELS_FILE="${WORK_DIR}/data/imagenet_lsvrc_2015_synsets.txt"
DOWNLOAD_SCRIPT="${WORK_DIR}/data/download_imagenet.sh"
"${DOWNLOAD_SCRIPT}" "${SCRATCH_DIR}" "${LABELS_FILE}"
# Note the locations of the train and validation data.
TRAIN_DIRECTORY="${SCRATCH_DIR}train/"
VALIDATION_DIRECTORY="${SCRATCH_DIR}validation/"
# Preprocess the validation data by moving the images into the appropriate
# sub-directory based on the label (synset) of the image.
echo "Organizing the validation data into sub-directories."
PREPROCESS_VAL_SCRIPT="${WORK_DIR}/data/preprocess_imagenet_validation_data.py"
VAL_LABELS_FILE="${WORK_DIR}/data/imagenet_2012_validation_synset_labels.txt"
"${PREPROCESS_VAL_SCRIPT}" "${VALIDATION_DIRECTORY}" "${VAL_LABELS_FILE}"
# Convert the XML files for bounding box annotations into a single CSV.
echo "Extracting bounding box information from XML."
BOUNDING_BOX_SCRIPT="${WORK_DIR}/data/process_bounding_boxes.py"
BOUNDING_BOX_FILE="${SCRATCH_DIR}/imagenet_2012_bounding_boxes.csv"
BOUNDING_BOX_DIR="${SCRATCH_DIR}bounding_boxes/"
"${BOUNDING_BOX_SCRIPT}" "${BOUNDING_BOX_DIR}" "${LABELS_FILE}" \
| sort >"${BOUNDING_BOX_FILE}"
echo "Finished downloading and preprocessing the ImageNet data."
# Build the TFRecords version of the ImageNet data.
BUILD_SCRIPT="${WORK_DIR}/build_imagenet_data"
OUTPUT_DIRECTORY="${DATA_DIR}"
IMAGENET_METADATA_FILE="${WORK_DIR}/data/imagenet_metadata.txt"
"${BUILD_SCRIPT}" \
--train_directory="${TRAIN_DIRECTORY}" \
--validation_directory="${VALIDATION_DIRECTORY}" \
--output_directory="${OUTPUT_DIRECTORY}" \
--imagenet_metadata_file="${IMAGENET_METADATA_FILE}" \
--labels_file="${LABELS_FILE}" \
--bounding_box_file="${BOUNDING_BOX_FILE}"
inception/data/download_imagenet.sh 0 → 100755
View file @ eb7389e4
#!/bin/bash
# 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.
# ==============================================================================
# Script to download ImageNet Challenge 2012 training and validation data set.
#
# Downloads and decompresses raw images and bounding boxes.
#
# **IMPORTANT**
# To download the raw images, the user must create an account with image-net.org
# and generate a username and access_key. The latter two are required for
# downloading the raw images.
#
# usage:
# ./download_imagenet.sh [dirname]
set -e
if [ "x$IMAGENET_ACCESS_KEY" == x -o "x$IMAGENET_USERNAME" == x ]; then
cat <<END
In order to download the imagenet data, you have to create an account with
image-net.org. This will get you a username and an access key. You can set the
IMAGENET_USERNAME and IMAGENET_ACCESS_KEY environment variables, or you can
enter the credentials here.
END
read -p "Username: " IMAGENET_USERNAME
read -p "Access key: " IMAGENET_ACCESS_KEY
fi
OUTDIR="${1:-./imagenet-data}"
SYNSETS_FILE="${2:-./synsets.txt}"
SYNSETS_FILE="${PWD}/${SYNSETS_FILE}"
echo "Saving downloaded files to $OUTDIR"
mkdir -p "${OUTDIR}"
CURRENT_DIR=$(pwd)
BBOX_DIR="${OUTDIR}bounding_boxes"
mkdir -p "${BBOX_DIR}"
cd "${OUTDIR}"
# Download and process all of the ImageNet bounding boxes.
BASE_URL="http://www.image-net.org/challenges/LSVRC/2012/nonpub"
# See here for details: http://www.image-net.org/download-bboxes
BOUNDING_BOX_ANNOTATIONS="${BASE_URL}/ILSVRC2012_bbox_train_v2.tar.gz"
BBOX_TAR_BALL="${BBOX_DIR}/annotations.tar.gz"
echo "Downloading bounding box annotations."
wget "${BOUNDING_BOX_ANNOTATIONS}" -O "${BBOX_TAR_BALL}"
echo "Uncompressing bounding box annotations ..."
tar xzf "${BBOX_TAR_BALL}" -C "${BBOX_DIR}"
LABELS_ANNOTATED="${BBOX_DIR}/*"
NUM_XML=$(ls -1 ${LABELS_ANNOTATED} | wc -l)
echo "Identified ${NUM_XML} bounding box annotations."
# Download and uncompress all images from the ImageNet 2012 validation dataset.
VALIDATION_TARBALL="ILSVRC2012_img_val.tar"
OUTPUT_PATH="${OUTDIR}validation/"
mkdir -p "${OUTPUT_PATH}"
cd "${OUTDIR}/.."
echo "Downloading ${VALIDATION_TARBALL} to ${OUTPUT_PATH}."
wget -nd -c "${BASE_URL}/${VALIDATION_TARBALL}"
tar xf "${VALIDATION_TARBALL}" -C "${OUTPUT_PATH}"
# Download all images from the ImageNet 2012 train dataset.
TRAIN_TARBALL="ILSVRC2012_img_train.tar"
OUTPUT_PATH="${OUTDIR}train/"
mkdir -p "${OUTPUT_PATH}"
cd "${OUTDIR}/.."
echo "Downloading ${TRAIN_TARBALL} to ${OUTPUT_PATH}."
wget -nd -c "${BASE_URL}/${TRAIN_TARBALL}"
# Un-compress the individual tar-files within the train tar-file.
echo "Uncompressing individual train tar-balls in the training data."
while read SYNSET; do
echo "Processing: ${SYNSET}"
# Create a directory and delete anything there.
mkdir -p "${OUTPUT_PATH}/${SYNSET}"
rm -rf "${OUTPUT_PATH}/${SYNSET}/*"
# Uncompress into the directory.
tar xf "${TRAIN_TARBALL}" "${SYNSET}.tar"
tar xf "${SYNSET}.tar" -C "${OUTPUT_PATH}/${SYNSET}/"
rm -f "${SYNSET}.tar"
echo "Finished processing: ${SYNSET}"
done < "${SYNSETS_FILE}"
inception/data/imagenet_2012_validation_synset_labels.txt 0 → 100644
View file @ eb7389e4
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inception/data/imagenet_lsvrc_2015_synsets.txt 0 → 100644
View file @ eb7389e4
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n13037406
n13040303
n13044778
n13052670
n13054560
n13133613
n15075141
inception/data/imagenet_metadata.txt 0 → 100644
View file @ eb7389e4
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inception/data/preprocess_imagenet_validation_data.py 0 → 100755
View file @ eb7389e4
#!/usr/bin/python
# 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.
# ==============================================================================
"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
Associate the ImageNet 2012 Challenge validation data set with labels.
The raw ImageNet validation data set is expected to reside in JPEG files
located in the following directory structure.
data_dir/ILSVRC2012_val_00000001.JPEG
data_dir/ILSVRC2012_val_00000002.JPEG
...
data_dir/ILSVRC2012_val_00050000.JPEG
This script moves the files into a directory structure like such:
data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
...
where 'n01440764' is the unique synset label associated with
these images.
This directory reorganization requires a mapping from validation image
number (i.e. suffix of the original file) to the associated label. This
is provided in the ImageNet development kit via a Matlab file.
In order to make life easier and divorce ourselves from Matlab, we instead
supply a custom text file that provides this mapping for us.
Sample usage:
./preprocess_imagenet_validation_data.py ILSVRC2012_img_val \
imagenet_2012_validation_synset_labels.txt
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path
import sys
if __name__ == '__main__':
if len(sys.argv) < 3:
print('Invalid usage\n'
'usage: preprocess_imagenet_validation_data.py '
'<validation data dir> <validation labels file>')
sys.exit(-1)
data_dir = sys.argv[1]
validation_labels_file = sys.argv[2]
# Read in the 50000 synsets associated with the validation data set.
labels = [l.strip() for l in open(validation_labels_file).readlines()]
unique_labels = set(labels)
# Make all sub-directories in the validation data dir.
for label in unique_labels:
labeled_data_dir = os.path.join(data_dir, label)
os.makedirs(labeled_data_dir)
# Move all of the image to the appropriate sub-directory.
for i in xrange(len(labels)):
basename = 'ILSVRC2012_val_000%.5d.JPEG' % (i + 1)
original_filename = os.path.join(data_dir, basename)
if not os.path.exists(original_filename):
print('Failed to find: ' % original_filename)
sys.exit(-1)
new_filename = os.path.join(data_dir, labels[i], basename)
os.rename(original_filename, new_filename)
inception/data/process_bounding_boxes.py 0 → 100755
View file @ eb7389e4
#!/usr/bin/python
# 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.
# ==============================================================================
"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
This script is called as
process_bounding_boxes.py <dir> [synsets-file]
Where <dir> is a directory containing the downloaded and unpacked bounding box
data. If [synsets-file] is supplied, then only the bounding boxes whose
synstes are contained within this file are returned. Note that the
[synsets-file] file contains synset ids, one per line.
The script dumps out a CSV text file in which each line contains an entry.
n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
The entry can be read as:
<JPEG file name>, <xmin>, <ymin>, <xmax>, <ymax>
The bounding box for <JPEG file name> contains two points (xmin, ymin) and
(xmax, ymax) specifying the lower-left corner and upper-right corner of a
bounding box in *relative* coordinates.
The user supplies a directory where the XML files reside. The directory
structure in the directory <dir> is assumed to look like this:
<dir>/nXXXXXXXX/nXXXXXXXX_YYYY.xml
Each XML file contains a bounding box annotation. The script:
(1) Parses the XML file and extracts the filename, label and bounding box info.
(2) The bounding box is specified in the XML files as integer (xmin, ymin) and
(xmax, ymax) *relative* to image size displayed to the human annotator. The
size of the image displayed to the human annotator is stored in the XML file
as integer (height, width).
Note that the displayed size will differ from the actual size of the image
downloaded from image-net.org. To make the bounding box annotation useable,
we convert bounding box to floating point numbers relative to displayed
height and width of the image.
Note that each XML file might contain N bounding box annotations.
Note that the points are all clamped at a range of [0.0, 1.0] because some
human annotations extend outside the range of the supplied image.
See details here: http://image-net.org/download-bboxes
(3) By default, the script outputs all valid bounding boxes. If a
[synsets-file] is supplied, only the subset of bounding boxes associated
with those synsets are outputted. Importantly, one can supply a list of
synsets in the ImageNet Challenge and output the list of bounding boxes
associated with the training images of the ILSVRC.
We use these bounding boxes to inform the random distortion of images
supplied to the network.
If you run this script successfully, you will see the following output
to stderr:
> Finished processing 544546 XML files.
> Skipped 0 XML files not in ImageNet Challenge.
> Skipped 0 bounding boxes not in ImageNet Challenge.
> Wrote 615299 bounding boxes from 544546 annotated images.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import glob
import os.path
import sys
import xml.etree.ElementTree as ET
class BoundingBox(object):
pass
def GetItem(name, root, index=0):
count = 0
for item in root.iter(name):
if count == index:
return item.text
count += 1
# Failed to find "index" occurrence of item.
return -1
def GetInt(name, root, index=0):
return int(GetItem(name, root, index))
def FindNumberBoundingBoxes(root):
index = 0
while True:
if GetInt('xmin', root, index) == -1:
break
index += 1
return index
def ProcessXMLAnnotation(xml_file):
"""Process a single XML file containing a bounding box."""
# pylint: disable=broad-except
try:
tree = ET.parse(xml_file)
except Exception:
print('Failed to parse: ' + xml_file, file=sys.stderr)
return None
# pylint: enable=broad-except
root = tree.getroot()
num_boxes = FindNumberBoundingBoxes(root)
boxes = []
for index in xrange(num_boxes):
box = BoundingBox()
# Grab the 'index' annotation.
box.xmin = GetInt('xmin', root, index)
box.ymin = GetInt('ymin', root, index)
box.xmax = GetInt('xmax', root, index)
box.ymax = GetInt('ymax', root, index)
box.width = GetInt('width', root)
box.height = GetInt('height', root)
box.filename = GetItem('filename', root) + '.JPEG'
box.label = GetItem('name', root)
xmin = float(box.xmin) / float(box.width)
xmax = float(box.xmax) / float(box.width)
ymin = float(box.ymin) / float(box.height)
ymax = float(box.ymax) / float(box.height)
# Some images contain bounding box annotations that
# extend outside of the supplied image. See, e.g.
# n03127925/n03127925_147.xml
# Additionally, for some bounding boxes, the min > max
# or the box is entirely outside of the image.
min_x = min(xmin, xmax)
max_x = max(xmin, xmax)
box.xmin_scaled = min(max(min_x, 0.0), 1.0)
box.xmax_scaled = min(max(max_x, 0.0), 1.0)
min_y = min(ymin, ymax)
max_y = max(ymin, ymax)
box.ymin_scaled = min(max(min_y, 0.0), 1.0)
box.ymax_scaled = min(max(max_y, 0.0), 1.0)
boxes.append(box)
return boxes
if __name__ == '__main__':
if len(sys.argv) < 2 or len(sys.argv) > 3:
print('Invalid usage\n'
'usage: process_bounding_boxes.py <dir> [synsets-file]',
file=sys.stderr)
sys.exit(-1)
xml_files = glob.glob(sys.argv[1] + '/*/*.xml')
print('Identified %d XML files in %s' % (len(xml_files), sys.argv[1]),
file=sys.stderr)
if len(sys.argv) == 3:
labels = set([l.strip() for l in open(sys.argv[2]).readlines()])
print('Identified %d synset IDs in %s' % (len(labels), sys.argv[2]),
file=sys.stderr)
else:
labels = None
skipped_boxes = 0
skipped_files = 0
saved_boxes = 0
saved_files = 0
for file_index, one_file in enumerate(xml_files):
# Example: <...>/n06470073/n00141669_6790.xml
label = os.path.basename(os.path.dirname(one_file))
# Determine if the annotation is from an ImageNet Challenge label.
if labels is not None and label not in labels:
skipped_files += 1
continue
bboxes = ProcessXMLAnnotation(one_file)
assert bboxes is not None, 'No bounding boxes found in ' + one_file
found_box = False
for bbox in bboxes:
if labels is not None:
if bbox.label != label:
# Note: There is a slight bug in the bounding box annotation data.
# Many of the dog labels have the human label 'Scottish_deerhound'
# instead of the synset ID 'n02092002' in the bbox.label field. As a
# simple hack to overcome this issue, we only exclude bbox labels
# *which are synset ID's* that do not match original synset label for
# the XML file.
if bbox.label in labels:
skipped_boxes += 1
continue
# Guard against improperly specified boxes.
if (bbox.xmin_scaled >= bbox.xmax_scaled or
bbox.ymin_scaled >= bbox.ymax_scaled):
skipped_boxes += 1
continue
# Note bbox.filename occasionally contains '%s' in the name. This is
# data set noise that is fixed by just using the basename of the XML file.
image_filename = os.path.splitext(os.path.basename(one_file))[0]
print('%s.JPEG,%.4f,%.4f,%.4f,%.4f' %
(image_filename,
bbox.xmin_scaled, bbox.ymin_scaled,
bbox.xmax_scaled, bbox.ymax_scaled))
saved_boxes += 1
found_box = True
if found_box:
saved_files += 1
else:
skipped_files += 1
if not file_index % 5000:
print('--> processed %d of %d XML files.' %
(file_index + 1, len(xml_files)),
file=sys.stderr)
print('--> skipped %d boxes and %d XML files.' %
(skipped_boxes, skipped_files), file=sys.stderr)
print('Finished processing %d XML files.' % len(xml_files), file=sys.stderr)
print('Skipped %d XML files not in ImageNet Challenge.' % skipped_files,
file=sys.stderr)
print('Skipped %d bounding boxes not in ImageNet Challenge.' % skipped_boxes,
file=sys.stderr)
print('Wrote %d bounding boxes from %d annotated images.' %
(saved_boxes, saved_files),
file=sys.stderr)
print('Finished.', file=sys.stderr)
inception/dataset.py 0 → 100644
View file @ eb7389e4
# 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.
# ==============================================================================
"""Small library that points to a data set.
Methods of Data class:
data_files: Returns a python list of all (sharded) data set files.
num_examples_per_epoch: Returns the number of examples in the data set.
num_classes: Returns the number of classes in the data set.
reader: Return a reader for a single entry from the data set.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from abc import ABCMeta
from abc import abstractmethod
import os
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
# Basic model parameters.
tf.app.flags.DEFINE_string('data_dir', '/tmp/mydata',
"""Path to the processed data, i.e. """
"""TFRecord of Example protos.""")
class Dataset(object):
"""A simple class for handling data sets."""
__metaclass__ = ABCMeta
def __init__(self, name, subset):
"""Initialize dataset using a subset and the path to the data."""
assert subset in self.available_subsets(), self.available_subsets()
self.name = name
self.subset = subset
@abstractmethod
def num_classes(self):
"""Returns the number of classes in the data set."""
pass
# return 10
@abstractmethod
def num_examples_per_epoch(self):
"""Returns the number of examples in the data subset."""
pass
# if self.subset == 'train':
# return 10000
# if self.subset == 'validation':
# return 1000
@abstractmethod
def download_message(self):
"""Prints a download message for the Dataset."""
pass
def available_subsets(self):
"""Returns the list of available subsets."""
return ['train', 'validation']
def data_files(self):
"""Returns a python list of all (sharded) data subset files.
Returns:
python list of all (sharded) data set files.
Raises:
ValueError: if there are not data_files matching the subset.
"""
tf_record_pattern = os.path.join(FLAGS.data_dir, '%s-*' % self.subset)
data_files = tf.gfile.Glob(tf_record_pattern)
if not data_files:
print('No files found for dataset %s/%s at %s' % (self.name,
self.subset,
FLAGS.data_dir))
self.download_message()
exit(-1)
return data_files
def reader(self):
"""Return a reader for a single entry from the data set.
See io_ops.py for details of Reader class.
Returns:
Reader object that reads the data set.
"""
return tf.TFRecordReader()
inception/flowers_data.py 0 → 100644
View file @ eb7389e4
# 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.
# ==============================================================================
"""Small library that points to the flowers data set.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from inception.dataset import Dataset
class FlowersData(Dataset):
"""Flowers data set."""
def __init__(self, subset):
super(FlowersData, self).__init__('Flowers', subset)
def num_classes(self):
"""Returns the number of classes in the data set."""
return 5
def num_examples_per_epoch(self):
"""Returns the number of examples in the data subset."""
if self.subset == 'train':
return 3170
if self.subset == 'validation':
return 500
def download_message(self):
"""Instruction to download and extract the tarball from Flowers website."""
print('Failed to find any Flowers %s files'% self.subset)
print('')
print('If you have already downloaded and processed the data, then make '
'sure to set --data_dir to point to the directory containing the '
'location of the sharded TFRecords.\n')
print('Please see README.md for instructions on how to build '
'the flowers dataset using download_and_preprocess_flowers.\n')
inception/flowers_eval.py 0 → 100644
View file @ eb7389e4
# 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.
# ==============================================================================
"""A binary to evaluate Inception on the flowers data set.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from inception import inception_eval
from inception.flowers_data import FlowersData
FLAGS = tf.app.flags.FLAGS
def main(unused_argv=None):
dataset = FlowersData(subset=FLAGS.subset)
assert dataset.data_files()
if tf.gfile.Exists(FLAGS.eval_dir):
tf.gfile.DeleteRecursively(FLAGS.eval_dir)
tf.gfile.MakeDirs(FLAGS.eval_dir)
inception_eval.evaluate(dataset)
if __name__ == '__main__':
tf.app.run()
inception/flowers_train.py 0 → 100644
View file @ eb7389e4
# 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.
# ==============================================================================
"""A binary to train Inception on the flowers data set.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from inception import inception_train
from inception.flowers_data import FlowersData
FLAGS = tf.app.flags.FLAGS
def main(_):
dataset = FlowersData(subset=FLAGS.subset)
assert dataset.data_files()
if tf.gfile.Exists(FLAGS.train_dir):
tf.gfile.DeleteRecursively(FLAGS.train_dir)
tf.gfile.MakeDirs(FLAGS.train_dir)
inception_train.train(dataset)
if __name__ == '__main__':
tf.app.run()
inception/image_processing.py 0 → 100644
View file @ eb7389e4
# 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.
# ==============================================================================
"""Read and preprocess image data.
Image processing occurs on a single image at a time. Image are read and
preprocessed in pararllel across mulitple threads. The resulting images
are concatenated together to form a single batch for training or evaluation.
-- Provide processed image data for a network:
inputs: Construct batches of evaluation examples of images.
distorted_inputs: Construct batches of training examples of images.
batch_inputs: Construct batches of training or evaluation examples of images.
-- Data processing:
parse_example_proto: Parses an Example proto containing a training example
of an image.
-- Image decoding:
decode_jpeg: Decode a JPEG encoded string into a 3-D float32 Tensor.
-- Image preprocessing:
image_preprocessing: Decode and preprocess one image for evaluation or training
distort_image: Distort one image for training a network.
eval_image: Prepare one image for evaluation.
distort_color: Distort the color in one image for training.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 32,
"""Number of images to process in a batch.""")
tf.app.flags.DEFINE_integer('image_size', 299,
"""Provide square images of this size.""")
tf.app.flags.DEFINE_integer('num_preprocess_threads', 4,
"""Number of preprocessing threads per tower. """
"""Please make this a multiple of 4.""")
# Images are preprocessed asynchronously using multiple threads specifed by
# --num_preprocss_threads and the resulting processed images are stored in a
# random shuffling queue. The shuffling queue dequeues --batch_size images
# for processing on a given Inception tower. A larger shuffling queue guarantees
# better mixing across examples within a batch and results in slightly higher
# predictive performance in a trained model. Empirically,
# --input_queue_memory_factor=16 works well. A value of 16 implies a queue size
# of 1024*16 images. Assuming RGB 299x299 images, this implies a queue size of
# 16GB. If the machine is memory limited, then decrease this factor to
# decrease the CPU memory footprint, accordingly.
tf.app.flags.DEFINE_integer('input_queue_memory_factor', 16,
"""Size of the queue of preprocessed images. """
"""Default is ideal but try smaller values, e.g. """
"""4, 2 or 1, if host memory is constrained. See """
"""comments in code for more details.""")
def inputs(dataset, batch_size=None, num_preprocess_threads=None):
"""Generate batches of ImageNet images for evaluation.
Use this function as the inputs for evaluating a network.
Note that some (minimal) image preprocessing occurs during evaluation
including central cropping and resizing of the image to fit the network.
Args:
dataset: instance of Dataset class specifying the dataset.
batch_size: integer, number of examples in batch
num_preprocess_threads: integer, total number of preprocessing threads but
None defaults to FLAGS.num_preprocess_threads.
Returns:
images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
image_size, 3].
labels: 1-D integer Tensor of [FLAGS.batch_size].
"""
if not batch_size:
batch_size = FLAGS.batch_size
# Force all input processing onto CPU in order to reserve the GPU for
# the forward inference and back-propagation.
with tf.device('/cpu:0'):
images, labels = batch_inputs(
dataset, batch_size, train=False,
num_preprocess_threads=num_preprocess_threads)
return images, labels
def distorted_inputs(dataset, batch_size=None, num_preprocess_threads=None):
"""Generate batches of distorted versions of ImageNet images.
Use this function as the inputs for training a network.
Distorting images provides a useful technique for augmenting the data
set during training in order to make the network invariant to aspects
of the image that do not effect the label.
Args:
dataset: instance of Dataset class specifying the dataset.
batch_size: integer, number of examples in batch
num_preprocess_threads: integer, total number of preprocessing threads but
None defaults to FLAGS.num_preprocess_threads.
Returns:
images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
FLAGS.image_size, 3].
labels: 1-D integer Tensor of [batch_size].
"""
if not batch_size:
batch_size = FLAGS.batch_size
# Force all input processing onto CPU in order to reserve the GPU for
# the forward inference and back-propagation.
with tf.device('/cpu:0'):
images, labels = batch_inputs(
dataset, batch_size, train=True,
num_preprocess_threads=num_preprocess_threads)
return images, labels
def decode_jpeg(image_buffer, scope=None):
"""Decode a JPEG string into one 3-D float image Tensor.
Args:
image_buffer: scalar string Tensor.
scope: Optional scope for op_scope.
Returns:
3-D float Tensor with values ranging from [0, 1).
"""
with tf.op_scope([image_buffer], scope, 'decode_jpeg'):
# 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)
return image
def distort_color(image, thread_id=0, scope=None):
"""Distort the color of the image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
Args:
image: Tensor containing single image.
thread_id: preprocessing thread ID.
scope: Optional scope for op_scope.
Returns:
color-distorted image
"""
with tf.op_scope([image], scope, 'distort_color'):
color_ordering = thread_id % 2
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
# The random_* ops do not necessarily clamp.
image = tf.clip_by_value(image, 0.0, 1.0)
return image
def distort_image(image, height, width, bbox, thread_id=0, scope=None):
"""Distort one image for training a network.
Distorting images provides a useful technique for augmenting the data
set during training in order to make the network invariant to aspects
of the image that do not effect the label.
Args:
image: 3-D float Tensor of image
height: integer
width: integer
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged
as [ymin, xmin, ymax, xmax].
thread_id: integer indicating the preprocessing thread.
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of distorted image used for training.
"""
with tf.op_scope([image, height, width, bbox], scope, 'distort_image'):
# Each bounding box has shape [1, num_boxes, box coords] and
# the coordinates are ordered [ymin, xmin, ymax, xmax].
# Display the bounding box in the first thread only.
if not thread_id:
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
bbox)
tf.image_summary('image_with_bounding_boxes', image_with_box)
# A large fraction of image datasets contain a human-annotated bounding
# box delineating the region of the image containing the object of interest.
# We choose to create a new bounding box for the object which is a randomly
# distorted version of the human-annotated bounding box that obeys an allowed
# range of aspect ratios, sizes and overlap with the human-annotated
# bounding box. If no box is supplied, then we assume the bounding box is
# the entire image.
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
tf.shape(image),
bounding_boxes=bbox,
min_object_covered=0.1,
aspect_ratio_range=[0.75, 1.33],
area_range=[0.05, 1.0],
max_attempts=100,
use_image_if_no_bounding_boxes=True)
bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box
if not thread_id:
image_with_distorted_box = tf.image.draw_bounding_boxes(
tf.expand_dims(image, 0), distort_bbox)
tf.image_summary('images_with_distorted_bounding_box',
image_with_distorted_box)
# Crop the image to the specified bounding box.
distorted_image = tf.slice(image, bbox_begin, bbox_size)
# This resizing operation may distort the images because the aspect
# ratio is not respected. We select a resize method in a round robin
# fashion based on the thread number.
# Note that ResizeMethod contains 4 enumerated resizing methods.
resize_method = thread_id % 4
distorted_image = tf.image.resize_images(distorted_image, height, width,
resize_method)
# Restore the shape since the dynamic slice based upon the bbox_size loses
# the third dimension.
distorted_image.set_shape([height, width, 3])
if not thread_id:
tf.image_summary('cropped_resized_image',
tf.expand_dims(distorted_image, 0))
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
# Randomly distort the colors.
distorted_image = distort_color(distorted_image, thread_id)
if not thread_id:
tf.image_summary('final_distorted_image',
tf.expand_dims(distorted_image, 0))
return distorted_image
def eval_image(image, height, width, scope=None):
"""Prepare one image for evaluation.
Args:
image: 3-D float Tensor
height: integer
width: integer
scope: Optional scope for op_scope.
Returns:
3-D float Tensor of prepared image.
"""
with tf.op_scope([image, height, width], scope, 'eval_image'):
# 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, [height, width],
align_corners=False)
image = tf.squeeze(image, [0])
return image
def image_preprocessing(image_buffer, bbox, train, thread_id=0):
"""Decode and preprocess one image for evaluation or training.
Args:
image_buffer: JPEG encoded string Tensor
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged as
[ymin, xmin, ymax, xmax].
train: boolean
thread_id: integer indicating preprocessing thread
Returns:
3-D float Tensor containing an appropriately scaled image
Raises:
ValueError: if user does not provide bounding box
"""
if bbox is None:
raise ValueError('Please supply a bounding box.')
image = decode_jpeg(image_buffer)
height = FLAGS.image_size
width = FLAGS.image_size
if train:
image = distort_image(image, height, width, bbox, thread_id)
else:
image = eval_image(image, height, width)
# Finally, rescale to [-1,1] instead of [0, 1)
image = tf.sub(image, 0.5)
image = tf.mul(image, 2.0)
return image
def parse_example_proto(example_serialized):
"""Parses an Example proto containing a training example of an image.
The output of the build_image_data.py image preprocessing script is a dataset
containing serialized Example protocol buffers. Each Example proto contains
the following fields:
image/height: 462
image/width: 581
image/colorspace: 'RGB'
image/channels: 3
image/class/label: 615
image/class/synset: 'n03623198'
image/class/text: 'knee pad'
image/object/bbox/xmin: 0.1
image/object/bbox/xmax: 0.9
image/object/bbox/ymin: 0.2
image/object/bbox/ymax: 0.6
image/object/bbox/label: 615
image/format: 'JPEG'
image/filename: 'ILSVRC2012_val_00041207.JPEG'
image/encoded: <JPEG encoded string>
Args:
example_serialized: scalar Tensor tf.string containing a serialized
Example protocol buffer.
Returns:
image_buffer: Tensor tf.string containing the contents of a JPEG file.
label: Tensor tf.int32 containing the label.
bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
where each coordinate is [0, 1) and the coordinates are arranged as
[ymin, xmin, ymax, xmax].
text: Tensor tf.string containing the human-readable label.
"""
# Dense features in Example proto.
feature_map = {
'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
default_value=''),
'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,
default_value=-1),
'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
default_value=''),
}
sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
# Sparse features in Example proto.
feature_map.update(
{k: sparse_float32 for k in ['image/object/bbox/xmin',
'image/object/bbox/ymin',
'image/object/bbox/xmax',
'image/object/bbox/ymax']})
features = tf.parse_single_example(example_serialized, feature_map)
label = tf.cast(features['image/class/label'], dtype=tf.int32)
xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)
ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0)
xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0)
ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0)
# Note that we impose an ordering of (y, x) just to make life difficult.
bbox = tf.concat(0, [ymin, xmin, ymax, xmax])
# Force the variable number of bounding boxes into the shape
# [1, num_boxes, coords].
bbox = tf.expand_dims(bbox, 0)
bbox = tf.transpose(bbox, [0, 2, 1])
return features['image/encoded'], label, bbox, features['image/class/text']
def batch_inputs(dataset, batch_size, train, num_preprocess_threads=None):
"""Contruct batches of training or evaluation examples from the image dataset.
Args:
dataset: instance of Dataset class specifying the dataset.
See dataset.py for details.
batch_size: integer
train: boolean
num_preprocess_threads: integer, total number of preprocessing threads
Returns:
images: 4-D float Tensor of a batch of images
labels: 1-D integer Tensor of [batch_size].
Raises:
ValueError: if data is not found
"""
with tf.name_scope('batch_processing'):
data_files = dataset.data_files()
if data_files is None:
raise ValueError('No data files found for this dataset')
filename_queue = tf.train.string_input_producer(data_files, capacity=16)
if num_preprocess_threads is None:
num_preprocess_threads = FLAGS.num_preprocess_threads
if num_preprocess_threads % 4:
raise ValueError('Please make num_preprocess_threads a multiple '
'of 4 (%d % 4 != 0).', num_preprocess_threads)
# Create a subgraph with its own reader (but sharing the
# filename_queue) for each preprocessing thread.
images_and_labels = []
for thread_id in range(num_preprocess_threads):
reader = dataset.reader()
_, example_serialized = reader.read(filename_queue)
# Parse a serialized Example proto to extract the image and metadata.
image_buffer, label_index, bbox, _ = parse_example_proto(
example_serialized)
image = image_preprocessing(image_buffer, bbox, train, thread_id)
images_and_labels.append([image, label_index])
# Approximate number of examples per shard.
examples_per_shard = 1024
# Size the random shuffle queue to balance between good global
# mixing (more examples) and memory use (fewer examples).
# 1 image uses 299*299*3*4 bytes = 1MB
# The default input_queue_memory_factor is 16 implying a shuffling queue
# size: examples_per_shard * 16 * 1MB = 17.6GB
min_queue_examples = examples_per_shard * FLAGS.input_queue_memory_factor
# Create a queue that produces the examples in batches after shuffling.
if train:
images, label_index_batch = tf.train.shuffle_batch_join(
images_and_labels,
batch_size=batch_size,
capacity=min_queue_examples + 3 * batch_size,
min_after_dequeue=min_queue_examples)
else:
images, label_index_batch = tf.train.batch_join(
images_and_labels,
batch_size=batch_size,
capacity=min_queue_examples + 3 * batch_size)
# Reshape images into these desired dimensions.
height = FLAGS.image_size
width = FLAGS.image_size
depth = 3
images = tf.cast(images, tf.float32)
images = tf.reshape(images, shape=[batch_size, height, width, depth])
# Display the training images in the visualizer.
tf.image_summary('images', images)
return images, tf.reshape(label_index_batch, [batch_size])
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