Tensorflow object detection API 训练自己的数据集

╰+攻爆jí腚メ 2022-04-17 00:42 224阅读 0赞

环境：win10

Anaconda3  tensorflow 1.9.0

【参考】[https://blog.csdn.net/dy\_guox/article/details/79111949][https_blog.csdn.net_dy_guox_article_details_79111949]

上篇运行了demo之后，打算训练自己的数据集，但是没有完全成功，不过反复弄了好几次后，这些步骤还是熟了的，把遇到的问题也贴出来，有人会的话帮我解答下

### 一、准备数据集 ###

数据集用 [LabelImg][] 标注会会生成相应的xml文件，具体不在详述，我是直接找了之前用过的一个车的数据集（300张做训练集，60张做测试集），但是这里除了原图跟xml文件外，还需要.cvs和.record文件，生成这两个文件的python代码如下（参考的博主有提供，这里我做个简单的注释，不懂可以问）

注：.cvs和.record文件这两个文件训练集train和测试集test都需要有，所以下面的代码里修改数据集路径就可以成相应的文件啦，也就是这些python脚本分别要操作训练集跟测试集，最后生成4个文件

1.生成.cvs文件python脚本

#生成.cvs文件pyhon脚本
    # -*- coding: utf-8 -*-
    """
    Created on Tue Jan 16 00:52:02 2018
    @author: Xiang Guo  #博主，感谢 
    将文件夹内所有XML文件的信息记录到CSV文件中
    """
     
    import os
    import glob
    import pandas as pd
    import xml.etree.ElementTree as ET
     
    os.chdir('D:\\RaniFile\\CarModelYHQ\\test')  #数据集路径
    path = 'D:\\RaniFile\\CarModelYHQ\\test'     #生成的.cvs文件路径
     
    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():
        image_path = path
        xml_df = xml_to_csv(image_path)
        xml_df.to_csv('tv_vehicle_labels.csv', index=None)  #第一个参数是生成的文件名
        print('Successfully converted xml to csv.')
     
     
    main()

2.生成.recored文件python脚本

代码里原博主非常用心的写了使用方法，我在这里在说一下吧

##  --csv_input=参数表示.cvs路径及文件名 --output_path=参数表示生成的文件名
    ##  
    python generate_tfrecord.py --csv_input=data/tv_vehicle_labels.csv  --output_path=train.record
    
      python generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=test.record

#生成.recored文件python脚本
    # -*- coding: utf-8 -*-
    """
    Created on Tue Jan 16 01:04:55 2018
    @author: Xiang Guo
    由CSV文件生成TFRecord文件
    """
     
    """
    Usage:
      # From tensorflow/models/
      # Create train data:
      python generate_tfrecord.py --csv_input=data/tv_vehicle_labels.csv  --output_path=train.record
      # Create test data:
      python generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=test.record
    """
     
     
     
    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
     
    os.chdir('D:\\RaniFile\\myproject\\tensorflow\\models\\research\\object_detection') #这个跟路径下面会用到还会再接一段，包括运行时候参数的路径的路径也是接着这个
     
    flags = tf.app.flags
    flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
    flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
    FLAGS = flags.FLAGS
     
     
    # TO-DO replace this with label map
    #注意将对应的label改成自己的类别！！！！！！！！！！
    def class_text_to_int(row_label):
        if row_label == 'sedan':
            return 1
        elif row_label == 'van':
            return 2
        elif row_label == 'SUV':
            return 3
        elif row_label == 'truck':
            return 4
        elif row_label == 'minibus':
            return 5
        elif row_label == 'hatchback':
            return 6
        elif row_label == 'tricycle':
            return 7
        elif row_label == 'bus':
            return 8
        else:
            0
     
     
    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(), 'myimages\\test')  #第二个参数，这个原图路径接着前面那个注释的路径的
        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()

这里面的路径如果有点纠结理不清地话，可以先是这运行下，找不到会报错，然后慢慢改。。。我就是这样的，其实python代码仔细看看也不难理解

3.建一个.pbtxt文件，里面写类别，我的文件名是tv\_vehicle\_detection.pbtxt

#这个id跟name，要跟上不一个生成.record脚本里面的那个类别一致
    item {
      id: 1
      name: 'sedan'
    }
    ...
    ...
    
    item {
      id: 8
      name: 'bus'
    }

好了数据集的准备到此就做完了

### 二、配置文件与模型 ###

先列一下文件夹，这些文件都是 object\_detection这个文件夹下，相信运行过demo的同学对这个文件的位置应该很熟悉了

-mydata/
    --test_labels.csv    #这些文件都在上一步准备好了
    --test.record
    --train_labels.csv
    --train.record
    --tv_vehicle_detection.pbtxt
    
    
    -myimages/
    --test/             #测试集图片
    ---testingimages.jpg
    --train/            #训练集图片
    ---testingimages.jpg
    
    
    -mytraining

接下来要配置模型文件了

1.下载所需预训练模型COCO-trained models

地址：[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]

我用的是ssd\_mobilenet\_v1\_coco\_2018\_01\_28.tar.gz所以下面配置都以它为例讲，下载下来后解压就行

2.把里面的model.ckpt、model.ckpt.data-00000-of-00001、model.ckpt.index这三个文件都放到mydata文件夹下

3.把pipeline.config文件放到mytraining文件夹下，里面一些参数要配置（之后有时间仔细读下这个配置文件，写个注释吧）

model {
      ssd {
        num_classes: 8    #类别个数，我的标签一共是有8类
        image_resizer {
          fixed_shape_resizer {
            height: 300
            width: 300
          }
        }
        feature_extractor {
          type: "ssd_mobilenet_v1"  #模型名称
          depth_multiplier: 1.0
          min_depth: 16
          conv_hyperparams {
            regularizer {
              l2_regularizer {
                weight: 3.99999989895e-05
              }
            }
            initializer {
              truncated_normal_initializer {
                mean: 0.0
                stddev: 0.0299999993294
              }
            }
            activation: RELU_6
            batch_norm {
              decay: 0.999700009823
              center: true
              scale: true
              epsilon: 0.0010000000475
              train: true
            }
          }
        }
        box_coder {
          faster_rcnn_box_coder {
            y_scale: 10.0
            x_scale: 10.0
            height_scale: 5.0
            width_scale: 5.0
          }
        }
        matcher {
          argmax_matcher {
            matched_threshold: 0.5
            unmatched_threshold: 0.5
            ignore_thresholds: false
            negatives_lower_than_unmatched: true
            force_match_for_each_row: true
          }
        }
        similarity_calculator {
          iou_similarity {
          }
        }
        box_predictor {
          convolutional_box_predictor {
            conv_hyperparams {
              regularizer {
                l2_regularizer {
                  weight: 3.99999989895e-05
                }
              }
              initializer {
                truncated_normal_initializer {
                  mean: 0.0
                  stddev: 0.0299999993294
                }
              }
              activation: RELU_6
              batch_norm {
                decay: 0.999700009823
                center: true
                scale: true
                epsilon: 0.0010000000475
                train: true
              }
            }
            min_depth: 0
            max_depth: 0
            num_layers_before_predictor: 0
            use_dropout: false
            dropout_keep_probability: 0.800000011921
            kernel_size: 1
            box_code_size: 4
            apply_sigmoid_to_scores: false
          }
        }
        anchor_generator {
          ssd_anchor_generator {
            num_layers: 6
            min_scale: 0.20000000298
            max_scale: 0.949999988079
            aspect_ratios: 1.0
            aspect_ratios: 2.0
            aspect_ratios: 0.5
            aspect_ratios: 3.0
            aspect_ratios: 0.333299994469
          }
        }
        post_processing {
          batch_non_max_suppression {
            score_threshold: 0.300000011921
            iou_threshold: 0.600000023842
            max_detections_per_class: 100
            max_total_detections: 100
          }
          score_converter: SIGMOID
        }
        normalize_loss_by_num_matches: true
        loss {
          localization_loss {
            weighted_smooth_l1 {
            }
          }
          classification_loss {
            weighted_sigmoid {
            }
          }
          hard_example_miner {
            num_hard_examples: 3000
            iou_threshold: 0.990000009537
            loss_type: CLASSIFICATION
            max_negatives_per_positive: 3
            min_negatives_per_image: 0
          }
          classification_weight: 1.0
          localization_weight: 1.0
        }
      }
    }
    train_config {                 #训练的一些配置
      batch_size: 1                #batch_size大小，改成了1，怕显存不足，硬件支持的话可以不改
      data_augmentation_options {
        random_horizontal_flip {
        }
      }
      data_augmentation_options {
        ssd_random_crop {
        }
      }
      optimizer {
        rms_prop_optimizer {
          learning_rate {
            exponential_decay_learning_rate {
              initial_learning_rate: 0.00400000018999
              decay_steps: 800720
              decay_factor: 0.949999988079
            }
          }
          momentum_optimizer_value: 0.899999976158
          decay: 0.899999976158
          epsilon: 1.0
        }
      }
      fine_tune_checkpoint: "mydata/model.ckpt" 
      from_detection_checkpoint: true
      num_steps: 200000     #训练迭代次数，当然运行的时候也可以写参数设置
    }
    train_input_reader {
      label_map_path: "mydata/tv_vehicle_detection.pbtxt"  #类别路径
      tf_record_input_reader {
        input_path: "mydata/train.record"   #训练集.record文件路径
      }
    }
    eval_config {
      num_examples: 8000
      max_evals: 10
      use_moving_averages: false
    }
    eval_input_reader {
      label_map_path: "mydata/tv_vehicle_detection.pbtxt" #类别路径，同上
      shuffle: false
      num_readers: 1
      tf_record_input_reader {
        input_path: "mydata/test.record"  #测试集.record文件路径
      }
    }

好了文件配置也完成了

### 三、训练模型 ###

Anaconda Prompt 定位到 *models\\research\\object\_detection*文件夹下，运行如下命令：

python model_main.py 
    --pipeline_config_path=mytraining/pipeline.config  #pipeline.config路径
    --model_dir=object_detection/mytraining            #生成模型的文件夹
    --num_train_steps=20000                            #训练20000步
    --num_eval_steps=1000                              #测试1000步
    --alsologtostderr

然后就开始训练了

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjczMTI0MQ_size_16_color_FFFFFF_t_70][]

tensorboard可以可视化训练过程，所以我也试了下， Anaconda Prompt 定位到 *models\\research\\object\_detection*文件夹下，运行如下命令

tensorboard --logdir='mytraining'  #这个文件夹就是存训练好的模型的那个文件夹

路径对的话就可以出来，关于tensordboard可以展示还有很多，不过要在代码里加上你需要统计的信息，我就简单看了下，有兴趣的可以仔细研究尝试

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjczMTI0MQ_size_16_color_FFFFFF_t_70 1][]

其实我训练到后面还是出了问题。。。。。（训练就很慢了，当时就只留了这张截图，不知道能不能明白问题的意思，有时间在尝试下。。。。）

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjczMTI0MQ_size_16_color_FFFFFF_t_70 2][]

[https_blog.csdn.net_dy_guox_article_details_79111949]: https://blog.csdn.net/dy_guox/article/details/79111949
[LabelImg]: https://github.com/tzutalin/labelImg
[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
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjczMTI0MQ_size_16_color_FFFFFF_t_70 1]: /images/20220417/f66dcb00720142b38178808e08cb1bad.png
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