My Tensorflow Версия: 2.1.0
My Python Версия: 3.7.0
Здравствуйте, я следую этому уроку Здесь
Я нахожусь на этапе, когда мне нужно сгенерировать TFRecord.
Однако версия тензорного потока, которую он использует в видео, старше моей, и он выдает ошибку, говорящую, что нет атрибута с именем app .
Я попытался использовать import tensorflow.compat.v1 as tf
и изменить tf.app.run() на tf.compat.v1.app.run()
Но это только приводит к ошибке, сообщающей
Taceback (most recent call last):
File "generate_tfrecord.py", line 106, in <module>
tf.compat.v1.app.run()
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\app.py", line 299, in run
_run_main(main, args)
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\app.py", line 250, in _run_main
sys.exit(main(argv))
File "generate_tfrecord.py", line 97, in main
tf_example = create_tf_example(group, path)
File "generate_tfrecord.py", line 86, in create_tf_example
'image/object/class/label': dataset_util.int64_list_feature(classes),
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\object_detection\utils\dataset_util.py", line 26, in int64_list_feature
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
TypeError: None has type NoneType, but expected one of: int, long
Я не уверен, что происходит e_e
Вот скрипт python, который я использую для генерации TFRecord
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import io
import pandas as pd
import tensorflow.compat.v1 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 == 'C':
return 1
elif row_label == 'CH':
return 2
elif row_label == 'T':
return 3
elif row_label == 'TH':
return 4
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.compat.v1.app.run()
UPDATE 1 I добавили tf.disable_v2_behavior() и удалось получить 2 файла, созданные после запуска кода, train и test.record
и эта ошибка
Instructions for updating:
non-resource variables are not supported in the long term
Traceback (most recent call last):
File "generate_tfrecord.py", line 99, in <module>
tf.app.run()
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\app.py", line 299, in run
_run_main(main, args)
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\absl\app.py", line 250, in _run_main
sys.exit(main(argv))
File "generate_tfrecord.py", line 90, in main
tf_example = create_tf_example(group, path)
File "generate_tfrecord.py", line 79, in create_tf_example
'image/object/class/label': dataset_util.int64_list_feature(classes),
File "C:\Users\otter\AppData\Local\Programs\Python\Python37\lib\site-packages\object_detection\utils\dataset_util.py", line 26, in int64_list_feature
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
TypeError: None has type NoneType, but expected one of: int, long