Tensorflow2.0 TypeError：在将xml文件转换为记录文件时需要一个类似字节的对象，而不是'str'

如何解决Tensorflow2.0 TypeError：在将xml文件转换为记录文件时需要一个类似字节的对象，而不是'str'

所以我按照 this tutorial 在 macOS 上构建我的自定义 Tensorflow 对象检测模型，一切都很顺利，直到我尝试将 xml 文件转换为记录文件。我使用了脚本generate_tfrecord.py，但是出现了错误，它说：

TypeError: a bytes-like object is required,not 'str'

Python 版本是 3.7，仅供参考。

在 generate_tfrecord.py 内部：

 """ Sample TensorFlow XML-to-TFRecord converter

usage: generate_tfrecord.py [-h] [-x XML_DIR] [-l LABELS_PATH] [-o OUTPUT_PATH] [-i IMAGE_DIR] [-c CSV_PATH]

optional arguments:
  -h,--help            show this help message and exit
  -x XML_DIR,--xml_dir XML_DIR
                        Path to the folder where the input .xml files are stored.
  -l LABELS_PATH,--labels_path LABELS_PATH
                        Path to the labels (.pbtxt) file.
  -o OUTPUT_PATH,--output_path OUTPUT_PATH
                        Path of output TFRecord (.record) file.
  -i IMAGE_DIR,--image_dir IMAGE_DIR
                        Path to the folder where the input image files are stored. Defaults to the same directory as XML_DIR.
  -c CSV_PATH,--csv_path CSV_PATH
                        Path of output .csv file. If none provided,then no file will be written.
"""

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

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)
import tensorflow.compat.v1 as tf
from PIL import Image
from object_detection.utils import dataset_util,label_map_util
from collections import namedtuple

# Initiate argument parser
parser = argparse.ArgumentParser(
    description="Sample TensorFlow XML-to-TFRecord converter")
parser.add_argument("-x","--xml_dir",help="Path to the folder where the input .xml files are stored.",type=str)
parser.add_argument("-l","--labels_path",help="Path to the labels (.pbtxt) file.",type=str)
parser.add_argument("-o","--output_path",help="Path of output TFRecord (.record) file.",type=str)
parser.add_argument("-i","--image_dir",help="Path to the folder where the input image files are stored. "
                         "Defaults to the same directory as XML_DIR.",type=str,default=None)
parser.add_argument("-c","--csv_path",help="Path of output .csv file. If none provided,then no file will be "
                         "written.",default=None)

args = parser.parse_args()

if args.image_dir is None:
    args.image_dir = args.xml_dir

label_map = label_map_util.load_labelmap(args.labels_path)
label_map_dict = label_map_util.get_label_map_dict(label_map)


def xml_to_csv(path):
    """Iterates through all .xml files (generated by labelImg) in a given directory and combines
    them in a single Pandas dataframe.

    Parameters:
    ----------
    path : str
        The path containing the .xml files
    Returns
    -------
    Pandas DataFrame
        The produced dataframe
    """

    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 class_text_to_int(row_label):
    return label_map_dict[row_label]


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.io.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.io.TFRecordWriter(args.output_path)
    path = os.path.join(args.image_dir)
    examples = xml_to_csv(args.xml_dir)
    grouped = split(examples,'filename')
    for group in grouped:
        tf_example = create_tf_example(group,path)
        writer.write(tf_example.SerializeToString())
    writer.close()
    print('Successfully created the TFRecord file: {}'.format(args.output_path))
    if args.csv_path is not None:
        examples.to_csv(args.csv_path,index=None)
        print('Successfully created the CSV file: {}'.format(args.csv_path))


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

我如何使用代码：

# Create train data:
python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/train -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/train.record

# Create test data:
python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/test -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/test.record

# For example
# python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/train -l C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/train.record
# python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/test -l C:/Users/sglvladi/Documents/Tensorflow2/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/test.record

真的需要你们的帮助，提前致谢:)