生成tf_record时出错：AttributeError：模块'tensorflow'没有属性'app'

问题描述

我正在尝试根据this教程使用Tensorflow创建对象检测算法。基本上，当我尝试生成tfrecord并将其放在我的数据文件夹中时，出现错误。详细信息如下。附带说明，我正在使用 Python 3.7.8 。

使用Labelimg软件标记图像后，我在桌面目录中创建了三个文件夹，分别为“数据”，“图像”和“培训”。在图像文件夹中，有两个子文件夹，称为“测试”。和“火车”。以PascalVOC格式标记图像（.xml文件输出）后，我分别将图像移到“测试”和“火车”文件夹中。

我首先使用以下代码将xml文件转换为csv文件，这些代码另存为xml_to_csv.py：

import os
import glob
import pandas as pd
import xml.etree.ElementTree as ET


def xml_to_csv(path):
    xml_list = []
    for xml_file in glob.glob(path + '/*.xml'):
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for member in root.findall('object'):
            value = (root.find('filename').text,int(root.find('size')[0].text),int(root.find('size')[1].text),member[0].text,int(member[4][0].text),int(member[4][1].text),int(member[4][2].text),int(member[4][3].text)
                     )
            xml_list.append(value)
    column_name = ['filename','width','height','class','xmin','ymin','xmax','ymax']
    xml_df = pd.DataFrame(xml_list,columns=column_name)
    return xml_df


def main():
    for directory in ['train','test']:
        image_path = os.path.join(os.getcwd(),'images/{}'.format(directory))
        xml_df = xml_to_csv(image_path)
        xml_df.to_csv('data/{}_labels.csv'.format(directory),index=None)
        print('Successfully converted xml to csv.')


main()

运行anaconda提示命令python xml_to_csv.py在我的“数据”文件夹中生成两个CSV文件，并且训练样本的格式正确。

现在，使用以下代码，我需要使用以下代码为火车和测试文件生成tf_record。我只有一类“杂草”，下面进行了编辑。 python文件另存为generate_tfrecord.py。

from __future__ import division
from __future__ import print_function
from __future__ import absolute_import

import os
import io
import pandas as pd
import tensorflow as tf

from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple,OrderedDict

flags = tf.app.flags
flags.DEFINE_string('csv_input','','Path to the CSV input')
flags.DEFINE_string('output_path','Path to output TFRecord')
flags.DEFINE_string('image_dir','Path to images')
FLAGS = flags.FLAGS


# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'weed':
        return 1
    else:
        None


def split(df,group):
    data = namedtuple('data',['filename','object'])
    gb = df.groupby(group)
    return [data(filename,gb.get_group(x)) for filename,x in zip(gb.groups.keys(),gb.groups)]


def create_tf_example(group,path):
    with tf.gfile.GFile(os.path.join(path,'{}'.format(group.filename)),'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width,height = image.size

    filename = group.filename.encode('utf8')
    image_format = b'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []

    for index,row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        classes.append(class_text_to_int(row['class']))

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),'image/width': dataset_util.int64_feature(width),'image/filename': dataset_util.bytes_feature(filename),'image/source_id': dataset_util.bytes_feature(filename),'image/encoded': dataset_util.bytes_feature(encoded_jpg),'image/format': dataset_util.bytes_feature(image_format),'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),'image/object/class/text': dataset_util.bytes_list_feature(classes_text),'image/object/class/label': dataset_util.int64_list_feature(classes),}))
    return tf_example


def main(_):
    writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
    path = os.path.join(FLAGS.image_dir)
    examples = pd.read_csv(FLAGS.csv_input)
    grouped = split(examples,'filename')
    for group in grouped:
        tf_example = create_tf_example(group,path)
        writer.write(tf_example.SerializeToString())

    writer.close()
    output_path = os.path.join(os.getcwd(),FLAGS.output_path)
    print('Successfully created the TFRecords: {}'.format(output_path))


if __name__ == '__main__':
    tf.app.run()

在anaconda命令提示符中，运行命令python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=data/test.record --image_dir=images/会产生以下错误：

2020-10-15 11:20:43.224624: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-10-15 11:20:43.226712: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
  File "generate_tfrecord.py",line 22,in <module>
    flags = tf.app.flags
AttributeError: module 'tensorflow' has no attribute 'app'

如何解决此问题，以便可以创建tfrecord文件直接放在“数据”文件夹中？

解决方法

tensorflow.app在最新的张量流中不可用

尝试替换

flags = tf.app.flags和flags = tf.compat.v1.flags（第14行）

writer = tf.python_io.TFRecordWriter(FLAGS.output_path)和writer = tf.io.TFRecordWriter(FLAGS.output_path)（第77行）

tf.app.run()和tf.compat.v1.app.run()（最后一行）