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目录
目录
TF-slim的测试
数据集制作
数据集验证
数据集注册（引入）
注册数据集
修改slim/train_image_classifier.py文件，进行训练。
TF-slim的测试
运行以下命令，测试tf.contrib.slim模块是否已正确安装。从TF_MODELS/research/slim目录下运行：
python -c "import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once"数据集制作
创建目录slim/dataset/cvd，该目录下raw_data文件夹存放下载好的猫狗大战图片，而创建好的tfrecord文件直接存放在cvd目录下。
新建数据集制作文件download_and_convert_cvd_v1_0.py，该文件直接从download_and_convert_flowers.py文件修改而来。代码如下：
r"""Downloads and converts cat_vs_dog data to TFRecords of TF-Example protos.This module downloads the cat_vs_dog data, uncompresses it, reads the files
that make up the cat_vs_dog data and creates two TFRecord datasets: one for train
and one for test. Each TFRecord dataset is comprised of a set of TF-Example
protocol buffers, each of which contain a single image and label.The script should take about a minute to run."""from __future__ import absolute_import
from __future__ import division
from __future__ import print_functionimport math
import os
import random
import sysimport tensorflow as tffrom datasets import dataset_utils# 数据集原始图片的下载网址，本代码使用预先下载好的图片，因此这个字段不用。
_DATA_URL = 'http://url/to/download/xxx.tgz'# 验证集的图片数，由于猫狗大战数据集的train文件夹共有25000张图片，这里取其中0.3作为验证集。
_NUM_VALIDATION = 7500# Seed for repeatability.
_RANDOM_SEED = 0# 指定数据集分成几个tfrecord文件存放
_NUM_SHARDS = 5# 这个ImageReader类，主要提供两个方法，从gfile读取的*.jpg文件数据解码成图片的tensor数据
class ImageReader(object):"""Helper class that provides TensorFlow image coding utilities."""def __init__(self):# 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 read_image_dims(self, sess, image_data):image = self.decode_jpeg(sess, image_data)return image.shape[0], image.shape[1]def decode_jpeg(self, sess, image_data):image = sess.run(self._decode_jpeg,feed_dict={self._decode_jpeg_data: image_data})assert len(image.shape) == 3assert image.shape[2] == 3return image# 从提供的dataset_dir数据集文件夹读取所有的文件和类别，返回文件的相对路径./dataset_dir/raw_data/*.jpg。
# 默认原始图片存放于dataset_dir/raw_data文件夹下
def _get_filenames_and_classes(dataset_dir):"""Returns a list of filenames and inferred class names.Args:dataset_dir: A directory containing a set of subdirectories representingclass names. Each subdirectory should contain PNG or JPG encoded images.Returns:A list of image file paths, relative to `dataset_dir` and the list ofsubdirectories, representing class names."""cvd_root = os.path.join(dataset_dir, 'raw_data')directories = []class_names = []photo_filenames = []for filename in os.listdir(cvd_root):path = os.path.join(cvd_root, filename)if os.path.isfile(path):photo_filenames.append(path)class_names = ['cat', 'dog']return photo_filenames, class_names# 生成tfrecord文件的存储名，返回其相对dataset_dir的路径
def _get_dataset_filename(dataset_dir, split_name, shard_id):output_filename = 'cat_vs_dog_%s_%05d-of-%05d.tfrecord' % (split_name, shard_id, _NUM_SHARDS)return os.path.join(dataset_dir, output_filename)# 传入分割名'train'/'validation'，相应的文件相对路径的列表，类名到类id的映射字典，tfrecord存放的目录dataset_dir
def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir):"""Converts the given filenames to a TFRecord dataset.Args:split_name: The name of the dataset, either 'train' or 'validation'.filenames: A list of absolute paths to png or jpg images.class_names_to_ids: A dictionary from class names (strings) to ids(integers).dataset_dir: The directory where the converted datasets are stored."""assert split_name in ['train', 'validation']num_per_shard = int(math.ceil(len(filenames) / float(_NUM_SHARDS)))with tf.Graph().as_default():image_reader = ImageReader()with tf.Session('') as sess:for shard_id in range(_NUM_SHARDS):output_filename = _get_dataset_filename(dataset_dir, split_name, shard_id)with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:start_ndx = shard_id * num_per_shardend_ndx = min((shard_id+1) * num_per_shard, len(filenames))for i in range(start_ndx, end_ndx):sys.stdout.write('\r>> Converting image %d/%d shard %d' % (i+1, len(filenames), shard_id))sys.stdout.flush()# Read the filename:image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()height, width = image_reader.read_image_dims(sess, image_data)# 根据文件名中是以cat还是dog开始，判断其类别名# class_name = os.path.basename(os.path.dirname(filenames[i]))fname = os.path.basename(filenames[i])class_name = fname.split('.')[0]class_id = class_names_to_ids[class_name]print('\r>> File %s of class_name: %s and class_id: %d will be write into tfrecord.'%(filenames[i], class_name, class_id))example = dataset_utils.image_to_tfexample(image_data, b'jpg', height, width, class_id)tfrecord_writer.write(example.SerializeToString())sys.stdout.write('\n')sys.stdout.flush()def _clean_up_temporary_files(dataset_dir):"""Removes temporary files used to create the dataset.Args:dataset_dir: The directory where the temporary files are stored."""filename = _DATA_URL.split('/')[-1]filepath = os.path.join(dataset_dir, filename)tf.gfile.Remove(filepath)tmp_dir = os.path.join(dataset_dir, 'flower_photos')tf.gfile.DeleteRecursively(tmp_dir)def _dataset_exists(dataset_dir):for split_name in ['train', 'validation']:for shard_id in range(_NUM_SHARDS):output_filename = _get_dataset_filename(dataset_dir, split_name, shard_id)if not tf.gfile.Exists(output_filename):return Falsereturn Truedef run(dataset_dir):"""Runs the download and conversion operation.Args:dataset_dir: The dataset directory where the dataset is stored."""if not tf.gfile.Exists(dataset_dir):tf.gfile.MakeDirs(dataset_dir)if _dataset_exists(dataset_dir):print('Dataset files already exist. Exiting without re-creating them.')return# dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)class_names_to_ids = dict(zip(class_names, range(len(class_names))))# Divide into train and test:random.seed(_RANDOM_SEED)random.shuffle(photo_filenames)_NUM_VALIDATION = int(0.3*(len(photo_filenames))) # 这里按0.3分配训练和验证数training_filenames = photo_filenames[_NUM_VALIDATION:]validation_filenames = photo_filenames[:_NUM_VALIDATION]# First, convert the training and validation sets._convert_dataset('train', training_filenames, class_names_to_ids,dataset_dir)_convert_dataset('validation', validation_filenames, class_names_to_ids,dataset_dir)# Finally, write the labels file:labels_to_class_names = dict(zip(range(len(class_names)), class_names))dataset_utils.write_label_file(labels_to_class_names, dataset_dir)# _clean_up_temporary_files(dataset_dir)print('\nFinished converting the cat_vs_dog dataset!')if __name__ == "__main__":run('cvd')
在datasets目录下运行download_and_convert_flowers.py文件，将在slim/datasets/cvd目录下生成5个train-*.tfrecord和5个validation-*.tfrecord文件。
数据集验证
注意，在上一步数据集制作过程中，
_convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir)该函数调用了一个函数：
example = dataset_utils.image_to_tfexample(image_data, b'jpg', height, width, class_id)tfrecord_writer.write(example.SerializeToString())该函数的定义为：
def image_to_tfexample(image_data, image_format, height, width, class_id):return tf.train.Example(features=tf.train.Features(feature={'image/encoded': bytes_feature(image_data),'image/format': bytes_feature(image_format),'image/class/label': int64_feature(class_id),'image/height': int64_feature(height),'image/width': int64_feature(width),}))该函数明确了tfrecord中每一条tfexample记录的键值签名。后续在数据读取过程中，也应当按照此键值进行解码。
TODO：读取tfrecord，解码tfexample，并重新显示图片及其类别，以验证tfrecord文件的正确性。
数据集注册（引入）
在slim/datasets/目录下新建cvd_v1_0.py（可从flowers.py修改而来）。代码如下：
"""Provides data for the flowers dataset.The dataset scripts used to create the dataset can be found at:
tensorflow/models/research/slim/datasets/download_and_convert_flowers.py
"""from __future__ import absolute_import
from __future__ import division
from __future__ import print_functionimport os
import tensorflow as tffrom datasets import dataset_utilsslim = tf.contrib.slim_FILE_PATTERN = 'cat_vs_dog_%s_*.tfrecord'SPLITS_TO_SIZES = {'train': 17500, 'validation': 7500}_NUM_CLASSES = 2_ITEMS_TO_DESCRIPTIONS = {'image': 'A color image of varying size.','label': 'A single integer between 0 and 4',
}def get_split(split_name, dataset_dir, file_pattern=None, reader=None):"""Gets a dataset tuple with instructions for reading flowers.Args:split_name: A train/validation split name.dataset_dir: The base directory of the dataset sources.file_pattern: The file pattern to use when matching the dataset sources.It is assumed that the pattern contains a '%s' string so that the splitname can be inserted.reader: The TensorFlow reader type.Returns:A `Dataset` namedtuple.Raises:ValueError: if `split_name` is not a valid train/validation split."""if split_name not in SPLITS_TO_SIZES:raise ValueError('split name %s was not recognized.' % split_name)# if not file_pattern:# file_pattern = _FILE_PATTERNfile_pattern = _FILE_PATTERNfile_pattern = os.path.join(dataset_dir, file_pattern % split_name)# Allowing None in the signature so that dataset_factory can use the default.if reader is None:reader = tf.TFRecordReaderkeys_to_features = {'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),'image/class/label': tf.FixedLenFeature([], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),}items_to_handlers = {'image': slim.tfexample_decoder.Image(),'label': slim.tfexample_decoder.Tensor('image/class/label'),}decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)labels_to_names = Noneif dataset_utils.has_labels(dataset_dir):labels_to_names = dataset_utils.read_label_file(dataset_dir)return slim.dataset.Dataset(data_sources=file_pattern,reader=reader,decoder=decoder,num_samples=SPLITS_TO_SIZES[split_name],items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,num_classes=_NUM_CLASSES,labels_to_names=labels_to_names)
该文件主要实现了一个函数，该函数返回一个slim.dataset.Dataset()类，供调用。
def get_split(split_name, dataset_dir, file_pattern=None, reader=None)注册数据集
主要修改dataset_factory.py文件，添加cvd数据集从代码到文件的路线。dataset_name='cvd'，并映射到相应的程序数据集slim.dataset.Dataset()生成文件cvd_v1_0.py。
"""A factory-pattern class which returns classification image/label pairs."""from __future__ import absolute_import
from __future__ import division
from __future__ import print_functionfrom datasets import cifar10
from datasets import flowers
from datasets import imagenet
from datasets import mnist
from datasets import cvd_v1_0datasets_map = {'cifar10': cifar10,'flowers': flowers,'imagenet': imagenet,'mnist': mnist,'cvd': cvd_v1_0,
}def get_dataset(name, split_name, dataset_dir, file_pattern=None, reader=None):"""Given a dataset name and a split_name returns a Dataset.Args:name: String, the name of the dataset.split_name: A train/test split name.dataset_dir: The directory where the dataset files are stored.file_pattern: The file pattern to use for matching the dataset source files.reader: The subclass of tf.ReaderBase. If left as `None`, then the defaultreader defined by each dataset is used.Returns:A `Dataset` class.Raises:ValueError: If the dataset `name` is unknown."""if name not in datasets_map:raise ValueError('Name of dataset unknown %s' % name)return datasets_map[name].get_split(split_name,dataset_dir,file_pattern,reader)
该文件主要实现了函数：
def get_dataset(name, split_name, dataset_dir, file_pattern=None, reader=None)实现从dataset_name和split_name到slim.dataset.Dataset()的映射。至此，数据集准备完毕。
修改slim/train_image_classifier.py文件，进行训练。
主要修改
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