windows 10 安装TensorFlow object detection 及训练

﹏ヽ暗。殇╰゛Y 2023-07-18 06:52 112阅读 0赞

基本的环境已经准备好了（[https://blog.csdn.net/TYtangyan/article/details/105057503][https_blog.csdn.net_TYtangyan_article_details_105057503]），安装检测训练环境

1.材料准备（这篇文章是以前的tf1.的版本，之后会安装一下tf2.0的，tf2.x的函数改动太大，好多删掉了，安装这个问题太多，网上说1.15就有这样的情况，还是用低一点的吧）：

[https://github.com/protocolbuffers/protobuf/releases/][https_github.com_protocolbuffers_protobuf_releases]

[https://github.com/tensorflow/models][https_github.com_tensorflow_models]

2.models-master\\research文件夹下cmd，复制解压后protoc.exe的完整路径

xxx/xxx/x/protoc.exe object_detection/protos/*.proto --python_out=.

正常情况无任何输出

![20200419170005439.png][]

before

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70][]

after， 生成py文件成功 ！![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 1][]

**3、添加环境变量（可以跳过）**

在Anaconda\\Lib\\site-packages添加一个路径文件，如tensorflow\_model.pth，必须以.pth为后缀，写上你要加入的模块文件所在的目录名称，如下图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 2][]

**4、运行models/research下的setup.py**

python setup.py build
    python setup.py install

**5、安装完成测试**

在models/research下运行如下命令：

python object\_detection/builders/model\_builder\_test.py

输出如下，安装完成

![20200419171040661.png][]

# ModuleNotFoundError: No module named 'nets' #

1删除\*/research/slim下的BUILD文件

![202004191711159.png][]

2运行命令：  
在\*/research/slim下运行命令

python setup.py build
    python setup.py install

遇到的问题：在训练模型的时候，显示使用GPU而且显存也占用正常，但是gpu的使用率一直为0，训练几步以后卡死不动，测试的时候一切正常。

本人这个问题的解决方式：应该是系统的显卡驱动版本太高，和cuda版本不匹配，把英伟达显卡驱动降低以后一切变正常

\-----------------------------------------------------------------------------------------------------------------------------------------------------------------

二、训练：

本人将需要的文件复制到了工程内，以后如果需要训练其他模型，直接更换数据集，无需 修改其他太多的东西

**1.下载预训练重**：[https://github.com/tensorflow/models/blob/master/research/object\_detection/g3doc/detection\_model\_zoo.md][https_github.com_tensorflow_models_blob_master_research_object_detection_g3doc_detection_model_zoo.md]

选择自己要使用的模型，是要速度还是精度，下载。。。

**2. 数据集准备**，准备好自己的数据集，labelimg标记，我使用的voc数据集的格式

下图是训练所需的类别标识，讲这个pascal\_label\_map文件里面的类别改为自己的，格式不变，复制该文件修改

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 3][] 下图为将数据集转化为record格式的文件，路径可能和你自己的数据集不同，稍作更改![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 4][]

** 数据准备完成后，训练所需完整的数据**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 5][]

我的数据集格式如上，使用的转化py文件修改后如下，如果你的数据集和我的一样，可以直接使用，修改其中的set，及保存的record名称，分别转化训练集以及验证集

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================
    
    r"""Convert raw PASCAL dataset to TFRecord for object_detection.
    
    Example usage:
        python object_detection/dataset_tools/create_pascal_tf_record.py \
            --data_dir=/home/user/VOCdevkit \
            --year=VOC2012 \
            --output_path=/home/user/pascal.record
    """
    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import hashlib
    import io
    import logging
    import os
    
    from lxml import etree
    import PIL.Image
    import tensorflow as tf
    
    from object_detection.utils import dataset_util
    from object_detection.utils import label_map_util
    from warnings import simplefilter
    simplefilter(action='ignore', category=FutureWarning)
    # import warnings
    # warnings.filterwarnings("ignore")
    # os.environ["TF_CPP_MIN_LOG_LEVEL"]='3'
    flags = tf.app.flags
    flags.DEFINE_string('data_dir', './data/VOCdevkit/', 'Root directory to raw PASCAL VOC dataset.')
    flags.DEFINE_string('set', 'train', 'Convert training set, validation set or '
                        'merged set.')
    flags.DEFINE_string('annotations_dir', 'Annotations',
                        '(Relative) path to annotations directory.')
    flags.DEFINE_string('year', 'VOC2007', 'Desired challenge year.')
    flags.DEFINE_string('output_path', './data/train.record', 'Path to output TFRecord')
    flags.DEFINE_string('label_map_path', './data/pascal_label_map.pbtxt',
                        'Path to label map proto')
    flags.DEFINE_boolean('ignore_difficult_instances', False, 'Whether to ignore '
                         'difficult instances')
    FLAGS = flags.FLAGS
    
    SETS = ['train', 'val', 'trainval', 'test']
    YEARS = ['VOC2007', 'VOC2012', 'merged']
    
    
    def dict_to_tf_example(data,
                           dataset_directory,
                           label_map_dict,
                           ignore_difficult_instances=False,
                           image_subdirectory='JPEGImages'):
      """Convert XML derived dict to tf.Example proto.
    
      Notice that this function normalizes the bounding box coordinates provided
      by the raw data.
    
      Args:
        data: dict holding PASCAL XML fields for a single image (obtained by
          running dataset_util.recursive_parse_xml_to_dict)
        dataset_directory: Path to root directory holding PASCAL dataset
        label_map_dict: A map from string label names to integers ids.
        ignore_difficult_instances: Whether to skip difficult instances in the
          dataset  (default: False).
        image_subdirectory: String specifying subdirectory within the
          PASCAL dataset directory holding the actual image data.
    
      Returns:
        example: The converted tf.Example.
    
      Raises:
        ValueError: if the image pointed to by data['filename'] is not a valid JPEG
      """
      img_path = os.path.join( "VOC2007", image_subdirectory, data['filename'])
      full_path = os.path.join(dataset_directory, img_path)
      with tf.gfile.GFile(full_path, 'rb') as fid:
        encoded_jpg = fid.read()
      encoded_jpg_io = io.BytesIO(encoded_jpg)
      image = PIL.Image.open(encoded_jpg_io)
      # if image.format != 'JPEG':
      #   raise ValueError('Image format not JPEG')
      key = hashlib.sha256(encoded_jpg).hexdigest()
    
      width = int(data['size']['width'])
      height = int(data['size']['height'])
    
      xmin = []
      ymin = []
      xmax = []
      ymax = []
      classes = []
      classes_text = []
      truncated = []
      poses = []
      difficult_obj = []
      if 'object' in data:
        for obj in data['object']:
          difficult = bool(int(obj['difficult']))
          if ignore_difficult_instances and difficult:
            continue
    
          difficult_obj.append(int(difficult))
    
          xmin.append(float(obj['bndbox']['xmin']) / width)
          ymin.append(float(obj['bndbox']['ymin']) / height)
          xmax.append(float(obj['bndbox']['xmax']) / width)
          ymax.append(float(obj['bndbox']['ymax']) / height)
          classes_text.append(obj['name'].encode('utf8'))
          classes.append(label_map_dict[obj['name']])
          truncated.append(int(obj['truncated']))
          poses.append(obj['pose'].encode('utf8'))
    
      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(
              data['filename'].encode('utf8')),
          'image/source_id': dataset_util.bytes_feature(
              data['filename'].encode('utf8')),
          'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),
          'image/encoded': dataset_util.bytes_feature(encoded_jpg),
          'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')),
          'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),
          'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),
          'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),
          'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),
          'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
          'image/object/class/label': dataset_util.int64_list_feature(classes),
          'image/object/difficult': dataset_util.int64_list_feature(difficult_obj),
          'image/object/truncated': dataset_util.int64_list_feature(truncated),
          'image/object/view': dataset_util.bytes_list_feature(poses),
      }))
      return example
    
    
    def main(_):
      if FLAGS.set not in SETS:
        raise ValueError('set must be in : {}'.format(SETS))
      if FLAGS.year not in YEARS:
        raise ValueError('year must be in : {}'.format(YEARS))
    
      data_dir = FLAGS.data_dir
      years = ['VOC2007', 'VOC2012']
      if FLAGS.year != 'merged':
        years = [FLAGS.year]
    
      writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
    
      label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path)
    
      for year in years:
        logging.info('Reading from PASCAL %s dataset.', year)
        examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main',
                                      FLAGS.set + '.txt')
        annotations_dir = os.path.join(data_dir, year, FLAGS.annotations_dir)
        examples_list = dataset_util.read_examples_list(examples_path)
        for idx, example in enumerate(examples_list):
          if idx % 100 == 0:
            logging.info('On image %d of %d', idx, len(examples_list))
          path = os.path.join(annotations_dir, example + '.xml')
          with tf.gfile.GFile(path, 'r') as fid:
            xml_str = fid.read()
          xml = etree.fromstring(xml_str)
          data = dataset_util.recursive_parse_xml_to_dict(xml)['annotation']
    
          tf_example = dict_to_tf_example(data, FLAGS.data_dir, label_map_dict,
                                          FLAGS.ignore_difficult_instances)
          writer.write(tf_example.SerializeToString())
    
      writer.close()
    
    
    if __name__ == '__main__':
      tf.app.run()

**3.训练**

数据集准备好，接下来训练，训练的**train.p**y文件路径如下：复制到自己的工程（如果想要带eval的需要使用model\_main.py）

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 6][]

去下面的文件夹找到自己使用模型的config文件

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 7][]

修改config其中类别大小，图像大小也可以固定

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 8][]

训练步数，数据路径，下面还有两个数据的路径没截取全，但是在使用train.py文件的时候没有使用到val数据集

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 9][]

接下来就可以训练了，修改train.py中的两个参数， 输出权重路径 ，这个路径会自动 创建，且需要为空。下面的那个是刚刚设置好的参数路径

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 10][] 训练。。。。。。。。。。。。。 等待。。。。。。。。。。。。。。

电脑显卡不好的话，这个要等好久了。可以使用tensorboard --logdir=“你的ckptl路径”查看模型训练的loss 等

win10 电脑可以直接输入nvidia-smi 查看显卡使用

显卡使用如下，电脑嗡嗡嗡我选择放过我的笔记本。 如果出现gpu使用率为0时候见前面

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 11][]

**4.转换pb文件**

下图路径的py文件复制，到自己工程

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 12][]

修改其中三处，config路径，刚刚的训练得到的ckpt权重，以及输出pb文件夹的路径，也会自动创建

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 13][]

5.预测

object\_detection路径下的object\_detection\_tutorial.ipynb自己改改

网上好多，待续

[https_blog.csdn.net_TYtangyan_article_details_105057503]: https://blog.csdn.net/TYtangyan/article/details/105057503
[https_github.com_protocolbuffers_protobuf_releases]: https://github.com/protocolbuffers/protobuf/releases/
[https_github.com_tensorflow_models]: https://github.com/tensorflow/models
[20200419170005439.png]: /images/20230528/f6e5904654cc432783c6f96a21b607a6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70]: /images/20230528/6fc7b6d8689c4472ad904ada55922209.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 1]: /images/20230528/9e8f3435c2f5434093af33b07df15aac.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 2]: /images/20230528/6f98e72c89dc4688aa53ba3ea6072c8d.png
[20200419171040661.png]: /images/20230528/0b072b0d4b8a4d8da38d00a9af802e05.png
[202004191711159.png]: /images/20230528/bcfd8b008708424e970db19fdd665657.png
[https_github.com_tensorflow_models_blob_master_research_object_detection_g3doc_detection_model_zoo.md]: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/20200419175454943.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 4]: /images/20230528/8589ec8560014b6fa2ab9ff2ab913f10.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 5]: /images/20230528/240724629de741f083d121718c84eaa3.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 6]: /images/20230528/f2680e897b2c4f529ad11e708108b0d5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 7]: /images/20230528/2668429176a940c68cb4ae234e2b680d.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 8]: /images/20230528/7485a6f8ea68438da3bd37234369a6fd.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 9]: /images/20230528/94fd3493f9de4223ae7566001ef0a2a5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 10]: /images/20230528/fc625658486343b78278d0e409199beb.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 11]: /images/20230528/7907a6ba23944d1894f13f6f45c68dba.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 12]: /images/20230528/fb79f36eb2fd4221ae10ee6bd1c1b6d7.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1RZdGFuZ3lhbg_size_16_color_FFFFFF_t_70 13]: /images/20230528/f18e3ee886964f2c8f0f7a35e3d14e32.png