问题不大，代码有点长，求大神解答：tensorflow生成tfrecord文件运行不下去

Question

运行结果如下：
完整代码如下：

 #验证集数量
_NUM_TEST = 100
#随机种子
_RANDOM_SEED = 0
#数据块
_NUM_SHARDS = 3
#数据集路径
DATASET_DIR = "C:/Users/ASUS/TF实战（炼石成金）/8-对谷歌inception-v3模型从头开始训练/slim/images/"
#标签文件名字
LABELS_FILENAME = r"C:\Users\ASUS\TF实战（炼石成金）\8-对谷歌inception-v3模型从头开始训练\slim\images\labels"

#定义tfrecord文件的路径+名字
def _get_dataset_filename(dataset_dir, split_name, shard_id):
    output_filename = 'image_%s_%05d-of-%05d.tfrecord' % (split_name, shard_id, _NUM_SHARDS)
    return os.path.join(dataset_dir, output_filename)

#判断tfrecord文件是否存在
def _dataset_exists(dataset_dir):
    for split_name in ['train', 'test']:
        for shard_id in range(_NUM_SHARDS):
            #定义tfrecord文件的路径+名字
            output_filename = _get_dataset_filename(dataset_dir, split_name, shard_id)
        if not tf.gfile.Exists(output_filename):
            return False
    return True

#获取所有文件以及分类
def _get_filenames_and_classes(dataset_dir):
    #数据所在路径目录
    directories = []
    #分类名称
    class_names = []
    for filename in os.listdir(dataset_dir): #os.listdir(dataset_dir)列出给出的路径下所有的文件夹或者文件名的名字
        #合并文件路径
        path = os.path.join(dataset_dir, filename)
        #判断该路径是否为目录
        if os.path.isdir(path):
            #加入数据目录
            directories.append(path)
            #加入类别名称
            class_names.append(filename)

    photo_filenames = []
    #循环每个分类的文件夹
    for directory in directories:
        for filename in os.listdir(directory):
            path = os.path.join(directory, filename)
            #把图片的路径加入图片列表
            photo_filenames.append(path)

    return photo_filenames, class_names

def int64_feature(values):
    if not isinstance(values, (tuple, list)):
        values = [values]
    return tf.train.Feature(int64_list=tf.train.Int64List(value=values))

def bytes_feature(values):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))

def image_to_tfexample(image_data, image_format, class_id):
    #Abstract base class for protocol messages.
    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),
    }))

def write_label_file(labels_to_class_names, dataset_dir,filename=LABELS_FILENAME):
    labels_filename = os.path.join(dataset_dir, filename)
    with tf.gfile.Open(labels_filename, 'w') as f:
        for label in labels_to_class_names:
            class_name = labels_to_class_names[label]
            f.write('%d:%s\n' % (label, class_name))

#把数据转为TFRecord格式
def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir):
    assert split_name in ['train', 'test']
    #计算每个数据块有多少数据
    num_per_shard = int(len(filenames) / _NUM_SHARDS)
    with tf.Graph().as_default():
        with tf.Session() as sess:
            for shard_id in range(_NUM_SHARDS):
                #定义tfrecord文件的路径+名字
                output_filename = _get_dataset_filename(dataset_dir, split_name, shard_id)
                with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
                    #每一个数据块开始的位置
                    start_ndx = shard_id * num_per_shard
                    #每一个数据块最后的位置
                    end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
                    for i in range(start_ndx, end_ndx):
                        try:
                            sys.stdout.write('\r>> Converting image %d/%d shard %d' % (i+1, len(filenames), shard_id))
                            sys.stdout.flush()
                            #读取图片
                            image_data = tf.gfile.FastGFile(filenames[i], 'r').read()
                            #获得图片的类别名称
                            #os.path.dirname(filenames[i])输出filenames[i]所在的绝对路径
                            #os.path.basename(path),返回path最后的文件名,这里是类别名称
                            class_name = os.path.basename(os.path.dirname(filenames[i]))
                            #找到类别名称对应的id
                            class_id = class_names_to_ids[class_name]
                            #生成tfrecord文件
                            example = image_to_tfexample(image_data, b'jpg', class_id)
                            tfrecord_writer.write(example.SerializeToString())
                        except IOError as e:
                            print("Could not read:",filenames[i])
                            print("Error:",e)
                            print("Skip it\n")

    sys.stdout.write('\n')
    sys.stdout.flush()


if __name__ == '__main__':
    #判断tfrecord文件是否存在
    if _dataset_exists(DATASET_DIR):
        print('tfcecord文件已存在')
    else:
        #获得所有图片以及分类
        photo_filenames, class_names = _get_filenames_and_classes(DATASET_DIR)
        #把分类转为字典格式，类似于{'house': 3, 'flower': 1, 'plane': 4, 'guitar': 2, 'animal': 0}
        class_names_to_ids = dict(zip(class_names, range(len(class_names))))

        #把数据切分为训练集和测试集
        random.seed(_RANDOM_SEED)
        random.shuffle(photo_filenames)
        training_filenames = photo_filenames[_NUM_TEST:]
        testing_filenames = photo_filenames[:_NUM_TEST]

        #数据转换
        _convert_dataset('train', training_filenames, class_names_to_ids, DATASET_DIR)
        _convert_dataset('test', testing_filenames, class_names_to_ids, DATASET_DIR)

        #输出labels文件
        labels_to_class_names = dict(zip(range(len(class_names)), class_names))
        write_label_file(labels_to_class_names, DATASET_DIR)

Answer 1

https://blog.csdn.net/qq_21033779/article/details/78948900