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#!/usr/bin/python
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# Copyright 2016 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Converts image data to TFRecords file format with Example protos.
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The image data set is expected to reside in JPEG files located in the
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following directory structure.
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data_dir/label_0/image0.jpeg
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data_dir/label_0/image1.jpg
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...
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data_dir/label_1/weird-image.jpeg
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data_dir/label_1/my-image.jpeg
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...
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where the sub-directory is the unique label associated with these images.
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This TensorFlow script converts the training and evaluation data into
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a sharded data set consisting of TFRecord files
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train_directory/train-00000-of-01024
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train_directory/train-00001-of-01024
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...
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train_directory/train-01023-of-01024
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and
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validation_directory/validation-00000-of-00128
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validation_directory/validation-00001-of-00128
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...
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validation_directory/validation-00127-of-00128
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where we have selected 1024 and 128 shards for each data set. Each record
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within the TFRecord file is a serialized Example proto. The Example proto
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contains the following fields:
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image/encoded: string containing JPEG encoded image in RGB colorspace
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image/height: integer, image height in pixels
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image/width: integer, image width in pixels
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image/colorspace: string, specifying the colorspace, always 'RGB'
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image/channels: integer, specifying the number of channels, always 3
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image/format: string, specifying the format, always 'JPEG'
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image/filename: string containing the basename of the image file
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e.g. 'n01440764_10026.JPEG' or 'ILSVRC2012_val_00000293.JPEG'
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image/class/label: integer specifying the index in a classification layer.
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The label ranges from [0, num_labels] where 0 is unused and left as
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the background class.
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image/class/text: string specifying the human-readable version of the label
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e.g. 'dog'
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If your data set involves bounding boxes, please look at build_imagenet_data.py.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from datetime import datetime
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import os
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import random
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import sys
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import threading
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import numpy as np
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import tensorflow as tf
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tf.app.flags.DEFINE_string('train_directory', '/cifar100/train',
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'Training data directory')
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tf.app.flags.DEFINE_string('validation_directory', '/cifar100/val',
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'Validation data directory')
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tf.app.flags.DEFINE_string('output_directory', '/cifar100/tfrecords/',
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'Output data directory')
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tf.app.flags.DEFINE_integer('train_shards', 2,
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'Number of shards in training TFRecord files.')
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tf.app.flags.DEFINE_integer('validation_shards', 2,
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'Number of shards in validation TFRecord files.')
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tf.app.flags.DEFINE_integer('num_threads', 2,
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'Number of threads to preprocess the images.')
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# The labels file contains a list of valid labels are held in this file.
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# Assumes that the file contains entries as such:
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# dog
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# cat
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# flower
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# where each line corresponds to a label. We map each label contained in
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# the file to an integer corresponding to the line number starting from 0.
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tf.app.flags.DEFINE_string('labels_file', '/cifar100/labels.txt', 'Labels file')
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FLAGS = tf.app.flags.FLAGS
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def _int64_feature(value):
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"""Wrapper for inserting int64 features into Example proto."""
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if not isinstance(value, list):
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value = [value]
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return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
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def _bytes_feature(value):
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"""Wrapper for inserting bytes features into Example proto."""
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return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
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def _convert_to_example(filename, image_buffer, label, text, height, width):
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"""Build an Example proto for an example.
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Args:
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filename: string, path to an image file, e.g., '/path/to/example.JPG'
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image_buffer: string, JPEG encoding of RGB image
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label: integer, identifier for the ground truth for the network
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text: string, unique human-readable, e.g. 'dog'
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height: integer, image height in pixels
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width: integer, image width in pixels
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Returns:
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Example proto
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"""
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colorspace = 'RGB'
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channels = 3
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image_format = 'JPEG'
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example = tf.train.Example(features=tf.train.Features(feature={
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'image/height': _int64_feature(height),
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'image/width': _int64_feature(width),
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'image/colorspace': _bytes_feature(tf.compat.as_bytes(colorspace)),
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'image/channels': _int64_feature(channels),
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'image/class/label': _int64_feature(label),
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'image/class/text': _bytes_feature(tf.compat.as_bytes(text)),
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'image/format': _bytes_feature(tf.compat.as_bytes(image_format)),
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'image/filename': _bytes_feature(tf.compat.as_bytes(os.path.basename(filename))),
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'image/encoded': _bytes_feature(tf.compat.as_bytes(image_buffer))}))
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return example
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class ImageCoder(object):
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"""Helper class that provides TensorFlow image coding utilities."""
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def __init__(self):
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# Create a single Session to run all image coding calls.
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self._sess = tf.Session()
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# Initializes function that converts PNG to JPEG data.
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self._png_data = tf.placeholder(dtype=tf.string)
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image = tf.image.decode_png(self._png_data, channels=3)
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self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
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# Initializes function that decodes RGB JPEG data.
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self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
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self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
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def png_to_jpeg(self, image_data):
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return self._sess.run(self._png_to_jpeg,
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feed_dict={self._png_data: image_data})
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def decode_jpeg(self, image_data):
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image = self._sess.run(self._decode_jpeg,
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feed_dict={self._decode_jpeg_data: image_data})
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assert len(image.shape) == 3
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assert image.shape[2] == 3
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return image
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def _is_png(filename):
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"""Determine if a file contains a PNG format image.
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Args:
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filename: string, path of the image file.
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Returns:
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boolean indicating if the image is a PNG.
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"""
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return filename.endswith('.png')
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def _process_image(filename, coder):
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"""Process a single image file.
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Args:
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filename: string, path to an image file e.g., '/path/to/example.JPG'.
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coder: instance of ImageCoder to provide TensorFlow image coding utils.
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Returns:
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image_buffer: string, JPEG encoding of RGB image.
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height: integer, image height in pixels.
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width: integer, image width in pixels.
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"""
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# Read the image file.
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with tf.gfile.FastGFile(filename, 'rb') as f:
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image_data = f.read()
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# Convert any PNG to JPEG's for consistency.
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if _is_png(filename):
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print('Converting PNG to JPEG for %s' % filename)
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image_data = coder.png_to_jpeg(image_data)
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# Decode the RGB JPEG.
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image = coder.decode_jpeg(image_data)
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# Check that image converted to RGB
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assert len(image.shape) == 3
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height = image.shape[0]
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width = image.shape[1]
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assert image.shape[2] == 3
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return image_data, height, width
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def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
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texts, labels, num_shards):
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"""Processes and saves list of images as TFRecord in 1 thread.
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Args:
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coder: instance of ImageCoder to provide TensorFlow image coding utils.
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thread_index: integer, unique batch to run index is within [0, len(ranges)).
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ranges: list of pairs of integers specifying ranges of each batches to
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analyze in parallel.
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name: string, unique identifier specifying the data set
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filenames: list of strings; each string is a path to an image file
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texts: list of strings; each string is human readable, e.g. 'dog'
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labels: list of integer; each integer identifies the ground truth
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num_shards: integer number of shards for this data set.
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"""
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# Each thread produces N shards where N = int(num_shards / num_threads).
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# For instance, if num_shards = 128, and the num_threads = 2, then the first
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# thread would produce shards [0, 64).
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num_threads = len(ranges)
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assert not num_shards % num_threads
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num_shards_per_batch = int(num_shards / num_threads)
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shard_ranges = np.linspace(ranges[thread_index][0],
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ranges[thread_index][1],
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num_shards_per_batch + 1).astype(int)
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num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
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counter = 0
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for s in range(num_shards_per_batch):
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# Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
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shard = thread_index * num_shards_per_batch + s
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output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)
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output_file = os.path.join(FLAGS.output_directory, output_filename)
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writer = tf.python_io.TFRecordWriter(output_file)
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shard_counter = 0
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files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
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for i in files_in_shard:
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filename = filenames[i]
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label = labels[i]
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text = texts[i]
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try:
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image_buffer, height, width = _process_image(filename, coder)
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except Exception as e:
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print(e)
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print('SKIPPED: Unexpected error while decoding %s.' % filename)
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continue
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example = _convert_to_example(filename, image_buffer, label,
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text, height, width)
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writer.write(example.SerializeToString())
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shard_counter += 1
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counter += 1
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if not counter % 1000:
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print('%s [thread %d]: Processed %d of %d images in thread batch.' %
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(datetime.now(), thread_index, counter, num_files_in_thread))
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sys.stdout.flush()
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writer.close()
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print('%s [thread %d]: Wrote %d images to %s' %
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(datetime.now(), thread_index, shard_counter, output_file))
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sys.stdout.flush()
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shard_counter = 0
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print('%s [thread %d]: Wrote %d images to %d shards.' %
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(datetime.now(), thread_index, counter, num_files_in_thread))
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sys.stdout.flush()
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def _process_image_files(name, filenames, texts, labels, num_shards):
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"""Process and save list of images as TFRecord of Example protos.
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Args:
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name: string, unique identifier specifying the data set
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filenames: list of strings; each string is a path to an image file
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texts: list of strings; each string is human readable, e.g. 'dog'
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labels: list of integer; each integer identifies the ground truth
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num_shards: integer number of shards for this data set.
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"""
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assert len(filenames) == len(texts)
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assert len(filenames) == len(labels)
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# Break all images into batches with a [ranges[i][0], ranges[i][1]].
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spacing = np.linspace(0, len(filenames), FLAGS.num_threads + 1).astype(np.int)
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ranges = []
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for i in range(len(spacing) - 1):
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ranges.append([spacing[i], spacing[i + 1]])
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# Launch a thread for each batch.
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print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges))
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sys.stdout.flush()
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# Create a mechanism for monitoring when all threads are finished.
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coord = tf.train.Coordinator()
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# Create a generic TensorFlow-based utility for converting all image codings.
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coder = ImageCoder()
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threads = []
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for thread_index in range(len(ranges)):
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args = (coder, thread_index, ranges, name, filenames,
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texts, labels, num_shards)
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t = threading.Thread(target=_process_image_files_batch, args=args)
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t.start()
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threads.append(t)
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# Wait for all the threads to terminate.
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coord.join(threads)
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print('%s: Finished writing all %d images in data set.' %
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(datetime.now(), len(filenames)))
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sys.stdout.flush()
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def _find_image_files(data_dir, labels_file):
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"""Build a list of all images files and labels in the data set.
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Args:
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data_dir: string, path to the root directory of images.
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Assumes that the image data set resides in JPEG files located in
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the following directory structure.
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data_dir/dog/another-image.JPEG
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data_dir/dog/my-image.jpg
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where 'dog' is the label associated with these images.
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labels_file: string, path to the labels file.
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The list of valid labels are held in this file. Assumes that the file
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contains entries as such:
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dog
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cat
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flower
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where each line corresponds to a label. We map each label contained in
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the file to an integer starting with the integer 0 corresponding to the
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label contained in the first line.
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Returns:
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filenames: list of strings; each string is a path to an image file.
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texts: list of strings; each string is the class, e.g. 'dog'
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labels: list of integer; each integer identifies the ground truth.
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"""
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print('Determining list of input files and labels from %s.' % data_dir)
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unique_labels = [l.strip() for l in tf.gfile.FastGFile(
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labels_file, 'r').readlines()]
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labels = []
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filenames = []
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texts = []
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# Leave label index 0 empty as a background class.
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label_index = 1
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# Construct the list of JPEG files and labels.
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for text in unique_labels:
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jpeg_file_path = '%s/%s/*' % (data_dir, text)
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matching_files = tf.gfile.Glob(jpeg_file_path)
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labels.extend([label_index] * len(matching_files))
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texts.extend([text] * len(matching_files))
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filenames.extend(matching_files)
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if not label_index % 100:
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print('Finished finding files in %d of %d classes.' % (
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label_index, len(labels)))
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label_index += 1
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# Shuffle the ordering of all image files in order to guarantee
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# random ordering of the images with respect to label in the
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# saved TFRecord files. Make the randomization repeatable.
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shuffled_index = list(range(len(filenames)))
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random.seed(12345)
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random.shuffle(shuffled_index)
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filenames = [filenames[i] for i in shuffled_index]
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texts = [texts[i] for i in shuffled_index]
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labels = [labels[i] for i in shuffled_index]
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print('Found %d JPEG files across %d labels inside %s.' %
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(len(filenames), len(unique_labels), data_dir))
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return filenames, texts, labels
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def _process_dataset(name, directory, num_shards, labels_file):
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|
|
"""Process a complete data set and save it as a TFRecord.
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|
|
|
Args:
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|
|
name: string, unique identifier specifying the data set.
|
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|
|
directory: string, root path to the data set.
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|
|
num_shards: integer number of shards for this data set.
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|
|
labels_file: string, path to the labels file.
|
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|
|
|
"""
|
|
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|
|
filenames, texts, labels = _find_image_files(directory, labels_file)
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|
|
_process_image_files(name, filenames, texts, labels, num_shards)
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|
|
def main(unused_argv):
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|
|
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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