【发布时间】：2021-03-02 13:39:59
【问题描述】：
关注this tutorial 我卡在.xml to .record conversion。
事实上，当我使用以下查询时：
C:\XXXX\scripts\processing>python generate_tfrecord.py -x C:/XXXX/workspace/training_demo/images/train -l C:/XXXX/training_demo/annotations/label_map.pbtxt -o C:/XXXX/workspace/training_demo/annotations/train.record
这确实返回了我：
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 107: invalid start byte
.xml 是这样的：
<annotation>
<folder>train</folder>
<filename>XXXX.PNG</filename>
<path>C:\XXXX\workspace\training_demo\images\train\XXXX.PNG</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>93</width>
<height>66</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>XXXX</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>1</xmin>
<ymin>1</ymin>
<xmax>93</xmax>
<ymax>66</ymax>
</bndbox>
</object>
</annotation>
而且代码和教程中的完全一样：
""" 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.",
type=str, 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.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(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()
还有 label_map.pbtxt 文件
item {
id: 21
name: 'XXXX'
}
item {
id: 31
name: 'XXXX'
}
item {
id: 41
name: 'XXXX'
}
完整的控制台返回：
C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\numpy\.libs\libopenblas.JPIJNSWNNAN3CE6LLI5FWSPHUT2VXMTH.gfortran-win_amd64.dll
C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\numpy\.libs\libopenblas.QVLO2T66WEPI7JZ63PS3HMOHFEY472BC.gfortran-win_amd64.dll
warnings.warn("loaded more than 1 DLL from .libs:"
Traceback (most recent call last):
File "generate_tfrecord.py", line 61, in <module>
label_map = label_map_util.load_labelmap(args.labels_path)
File "C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\utils\label_map_util.py", line 168, in load_labelmap
label_map_string = fid.read()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 117, in read
self._preread_check()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 79, in _preread_check
self._read_buf = _pywrap_file_io.BufferedInputStream(
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 107: invalid start byte
此处编辑 label_map
item {
id: 21
name: '2Carreau'
}
item {
id: 31
name: '3Carreau'
}
item {
id: 41
name: '4Carreau'
}
item {
id: 51
name: '5Carreau'
}
item {
id: 61
name: '6Carreau'
}
item {
id: 71
name: '7Carreau'
}
item {
id: 81
name: '8Carreau'
}
item {
id: 91
name: '9Carreau'
}
item {
id: 101
name: '10Carreau'
}
item {
id: 111
name: '11Carreau'
}
item {
id: 121
name: '12Carreau'
}
item {
id: 131
name: '13Carreau'
}
item {
id: 141
name: '14Carreau'
}
item {
id: 22
name: '2Coeur'
}
item {
id: 32
name: '3Coeur'
}
item {
id: 42
name: '4Coeur'
}
item {
id: 52
name: '5Coeur'
}
item {
id: 62
name: '6Coeur'
}
item {
id: 72
name: '7Coeur'
}
item {
id: 82
name: '8Coeur'
}
item {
id: 92
name: '9Coeur'
}
item {
id: 102
name: '10Coeur'
}
item {
id: 112
name: '11Coeur'
}
item {
id: 122
name: '12Coeur'
}
item {
id: 132
name: '13Coeur'
}
item {
id: 142
name: '14Coeur'
}
item {
id: 23
name: '2Trefle'
}
item {
id: 33
name: '3Trefle'
}
item {
id: 43
name: '4Trefle'
}
item {
id: 53
name: '5Trefle'
}
item {
id: 63
name: '6Trefle'
}
item {
id: 73
name: '7Trefle'
}
item {
id: 83
name: '8Trefle'
}
item {
id: 93
name: '9Trefle'
}
item {
id: 103
name: '10Trefle'
}
item {
id: 113
name: '11Trefle'
}
item {
id: 123
name: '12Trefle'
}
item {
id: 133
name: '13Trefle'
}
item {
id: 143
name: '14Trefle'
}
item {
id: 24
name: '2Pic'
}
item {
id: 34
name: '3Pic'
}
item {
id: 44
name: '4Pic'
}
item {
id: 54
name: '5Pic'
}
item {
id: 64
name: '6Pic'
}
item {
id: 74
name: '7Pic'
}
item {
id: 84
name: '8Pic'
}
item {
id: 94
name: '9Pic'
}
item {
id: 104
name: '10Pic'
}
item {
id: 114
name: '11Pic'
}
item {
id: 124
name: '12Pic'
}
item {
id: 134
name: '13Pic'
}
item {
id: 144
name: '14Pic'
}
现在我使用这个查询：
C:\####\workspace\training_demo>python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config
开始很好，但是最后抛出了同样的问题，我检查了pipeline.config 和model_main_tf2，但你的回答没有纠正这个......你有什么想法吗？
2021-03-03 09:53:43.878440: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-03-03 09:53:48.745301: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-03-03 09:53:48.749824: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-03-03 09:53:48.779768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:1c:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
2021-03-03 09:53:48.786205: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-03-03 09:53:48.800110: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-03-03 09:53:48.803731: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-03-03 09:53:48.812755: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-03-03 09:53:48.822516: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-03-03 09:53:48.837930: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-03-03 09:53:48.851302: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-03-03 09:53:48.856177: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-03-03 09:53:48.860712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-03-03 09:53:48.863378: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-03-03 09:53:48.873474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:1c:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
2021-03-03 09:53:48.881298: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-03-03 09:53:48.884006: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-03-03 09:53:48.887551: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-03-03 09:53:48.891894: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-03-03 09:53:48.895372: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-03-03 09:53:48.898176: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-03-03 09:53:48.903001: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-03-03 09:53:48.906421: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-03-03 09:53:48.910388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-03-03 09:53:49.506138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-03-03 09:53:49.509246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2021-03-03 09:53:49.511875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2021-03-03 09:53:49.513745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6278 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:1c:00.0, compute capability: 6.1)
2021-03-03 09:53:49.521636: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0303 09:53:49.527721 12968 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
Traceback (most recent call last):
File "model_main_tf2.py", line 113, in <module>
tf.compat.v1.app.run()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\absl\app.py", line 303, in run
_run_main(main, args)
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\absl\app.py", line 251, in _run_main
sys.exit(main(argv))
File "model_main_tf2.py", line 104, in main
model_lib_v2.train_loop(
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\model_lib_v2.py", line 474, in train_loop
configs = get_configs_from_pipeline_file(
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\utils\config_util.py", line 138, in get_configs_from_pipeline_file
proto_str = f.read()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 117, in read
self._preread_check()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 79, in _preread_check
self._read_buf = _pywrap_file_io.BufferedInputStream(
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 102: invalid start byte
【问题讨论】：
-
错误提示您的 XML 文件包含非 utf-8 编码的数据。不幸的是，您没有向我们展示 xml 文件的字节 127 附近存在什么。更糟糕的是，您没有具体给出完整的错误消息，代码中的哪一行引发了错误。我们需要来帮助您。
-
@SergeBallesta 我怎么知道 xml 文件的 127 字节附近有什么 ecist？它有很多小的 .xml 就像示例中的那个（总是相同的结构），我的 python 查询的目标是合并它们并构建一个 .record 文件我猜
-
至少你应该给出完整的错误消息带有引发错误的行。
-
@SergeBallesta 我添加了所有我拥有的...我在哪里可以找到更多信息？我正在尝试使用
tensorflow，但是这个错误是抛出的，我不知道在哪里可以找到为什么会产生这个问题......所以我应该在哪里搜索，因为我提供了所有的 consol 输出......请感谢您的帮助 -
阅读完整的错误信息后，我认为问题出在 label_map 文件中。它可能包含非 ASCII 字符，并且不是 utf8 编码的。
标签： python xml tensorflow