生成tf记录时的Tensorflow类型错误

Question

这是我的 tensorflow 生成 tf 记录的一部分

def class_text_to_int(row_label):
    if row_label == "110kmh": 
        return 1
    else:
        return 0

但是我得到了这个错误

 File "/usr/local/lib/python3.5/dist-packages/object_detection-0.1-py3.5.egg/object_detection/utils/dataset_util.py", line 34, in bytes_list_feature
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))

有人知道这个错误吗？我在网上搜索了一整天，但仍然没有得到任何解决方案。

Answer 1

如果您需要其他（工作）generate_tfrecord.py 文件：

"""
Usage:
# Create train data:
python generate_tfrecord.py --label_map=<PATH_TO_LABEL_MAP_FILE> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/
train_labels.csv --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/train.record

# Create test data:
python generate_tfrecord.py --label_map=<PATH_TO_LABEL_MAP_FILE> --csv_input=<PATH_TO_ANNOTATIONS_FOLDER>/
test_labels.csv --output_path=<PATH_TO_ANNOTATIONS_FOLDER>/test.record
"""
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
import sys

sys.path.append("../../models/research")
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('img_path', '', 'Path to images')
flags.DEFINE_string('label_map', '', 'Path to label map (.pbtxt) file')

# if your image has more labels input them as
# flags.DEFINE_string('label0', '', 'Name of class[0] label')
# flags.DEFINE_string('label1', '', 'Name of class[1] label')
# and so on.

FLAGS = flags.FLAGS


def class_text_to_int(row_label):
    for label_id, label_name in get_label_info():
        if row_label == label_name:
            return label_id
    return 0


def get_label_info():
    """
    Generate label info from label map (.pbtxt) file
    :return: id, name
    """
    label_info = []
    with open(FLAGS.label_map) as fp:
        for _, line in enumerate(fp):
            if "id" in line:
                label_id = int(line.split(":")[1])
                label_info.append(label_id)
            elif "name" in line:
                label_name = line.split(":")[1].strip().replace("'", "")
                label_info.append(label_name)

    for i in range(0, len(label_info), 2):
        yield label_info[i:i + 2]


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'

    # check if the image format is matching with your images.
    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.img_path)
    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()