Error when generating tf_record: AttributeError: module 'tensorflow' has no attribute 'app'
Error when generating tf_record: AttributeError: module 'tensorflow' has no attribute 'app'
我正在尝试使用 Tensorflow 按照 this 教程创建对象检测算法。基本上，当我尝试生成 tfrecord 并将其放入我的数据文件夹时，出现错误。详情如下。作为旁注，我正在使用 Python 3.7.8.
在使用 Labelimg 软件标记我的图像后，我在我的桌面目录中创建了三个名为“数据”、“图像”和“训练”的文件夹。在图像文件夹中，有两个名为“测试”和“训练”。以 PascalVOC 格式（.xml 文件输出）标记我的图像后，我将图像分别移动到“测试”和“训练”文件夹中。
我首先使用以下代码将 xml 文件转换为 csv 文件，并将其保存到我的目录中 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，训练样本格式正确。
现在，使用以下代码，我需要使用以下代码为训练文件和测试文件生成 tf_record。我只有一个class，“杂草”，已在下面进行了编辑。 python 文件保存为 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/ 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'
我该如何解决这个问题，以便我可以创建 tfrecord 文件直接放在我的“数据”文件夹中？
tensorflow.app 在最新的 tensorflow
中不可用
尝试替换
flags = tf.app.flags 和 flags = tf.compat.v1.flags（行号 14）
writer = tf.python_io.TFRecordWriter(FLAGS.output_path) 和 writer = tf.io.TFRecordWriter(FLAGS.output_path)（行号 77）
tf.app.run() 和 tf.compat.v1.app.run()（最后一行）
我正在尝试使用 Tensorflow 按照 this 教程创建对象检测算法。基本上，当我尝试生成 tfrecord 并将其放入我的数据文件夹时，出现错误。详情如下。作为旁注，我正在使用 Python 3.7.8.
在使用 Labelimg 软件标记我的图像后，我在我的桌面目录中创建了三个名为“数据”、“图像”和“训练”的文件夹。在图像文件夹中，有两个名为“测试”和“训练”。以 PascalVOC 格式（.xml 文件输出）标记我的图像后，我将图像分别移动到“测试”和“训练”文件夹中。
我首先使用以下代码将 xml 文件转换为 csv 文件，并将其保存到我的目录中 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，训练样本格式正确。
现在，使用以下代码，我需要使用以下代码为训练文件和测试文件生成 tf_record。我只有一个class，“杂草”，已在下面进行了编辑。 python 文件保存为 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/ 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'
我该如何解决这个问题，以便我可以创建 tfrecord 文件直接放在我的“数据”文件夹中？
tensorflow.app 在最新的 tensorflow
尝试替换
flags = tf.app.flags 和 flags = tf.compat.v1.flags（行号 14）
writer = tf.python_io.TFRecordWriter(FLAGS.output_path) 和 writer = tf.io.TFRecordWriter(FLAGS.output_path)（行号 77）
tf.app.run() 和 tf.compat.v1.app.run()（最后一行）