TensorFlow Object Detection API训练时UnicodeDecodeError问题求助

问题场景 #

使用TensorFlow Object Detection API训练模型时触发UnicodeDecodeError，搜索网络方案未解决，同时需要相关操作教学视频。

错误详情 #

File "C:\Users\berat\anaconda3\envs\testTensorflow\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 77, in _preread_check
    self._read_buf = _pywrap_file_io.BufferedInputStream(
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xfd in position 118: invalid start byte 

我的generate_tfrecord.py代码 #

"""
Usage:
  # From tensorflow/models/
  # Create train data:
  python generate_tfrecord.py --csv_input=images/train_labels.csv --image_dir=images/train --output_path=train.record

  # Create test data:
  python generate_tfrecord.py --csv_input=images/test_labels.csv  --image_dir=images/test --output_path=test.record
"""
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import

import os
import io
import pandas as pd

from tensorflow.python.framework.versions import VERSION
if VERSION >= "2.0.0a0":
    import tensorflow.compat.v1 as tf
else:
    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('image_dir', '', 'Path to the image directory')
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
FLAGS = flags.FLAGS


# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'person':
        return 1
    elif row_label == 'other':
        return 2
    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(os.getcwd(), 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()  

解决方案 #

可能的原因及修复步骤 #

• CSV文件编码不匹配
  错误大概率来自读取CSV时的编码冲突，若CSV是GBK或其他非UTF-8编码，会触发解码失败。
  修复：修改pd.read_csv指定编码，比如：

  examples = pd.read_csv(FLAGS.csv_input, encoding='gbk')
  

  或者用记事本打开CSV，另存为UTF-8编码格式。

• 图片文件名含特殊字符
  部分图片文件名包含非UTF-8兼容的特殊字符，导致编码转换出错。
  修复：检查所有图片文件名，替换特殊字符；或修改文件名编码逻辑：

  filename = group.filename.encode('utf8', errors='ignore')
  

• TensorFlow文件读取API兼容问题
  兼容v1的TensorFlow版本在文件读取上可能存在编码处理bug。
  修复：替换tf.gfile.GFile为Python原生文件读取：

  with open(os.path.join(path, group.filename), 'rb') as fid:
      encoded_jpg = fid.read()
  

操作教学视频建议 #

可搜索“TensorFlow Object Detection API 从标注到训练完整流程”，找到国内平台上的实操教学视频，内容涵盖数据集准备、TFRecord生成、模型配置与训练的完整步骤。

内容的提问来源于stack exchange，提问作者Berat