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物体检测快速入门系列(4)-TensorFlow 2.x Object Detection API快速安装手册

  • 作者简介：关注AIoT领域的嵌入式开发一枚，追逐未来时代的脉搏，记录路途中的技术成长
  • 本文由机器未来原创，首发在CSDN
  • 订阅专栏：物体检测快速入门系列
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文章目录

1. 概述

tensorflow object detection api一个框架，它可以很容易地构建、训练和部署对象检测模型，并且是一个提供了众多基于COCO数据集、Kitti数据集、Open Images数据集、AVA v2.1数据集和iNaturalist物种检测数据集上提供预先训练的对象检测模型集合。

sphx_glr_plot_object_detection_saved_model_002

tensorflow object detection api是目前最主流的目标检测框架之一，主流的目标检测模型如图所示：
snipaste20220513_094828

2. 预置条件

为了顺利按照本手册安装tensroflow object detection api，请参考Windows部署Docker GPU深度学习开发环境安装必备的工具。

若自行创建安装条件，请确保已经满足以下条件

  • 支持python3.8以上版本
  • 支持cuda、cudnn（可选）
  • 支持git

本手册使用docker运行环境。

3. 安装步骤

3.1 Docker环境

3.1.1 启动docker

启动docker桌面客户端，如图所示：
1

3.1.2 启动容器

在windows平台可以启动命令行工具或者windows terminal工具(App Store下载)，这里使用terminal工具。

输入以下命令，查看当前存在的images列表

PS C:\Users\xxxxx> docker images
REPOSITORY               TAG                 IMAGE ID       CREATED       SIZE
docker/getting-started   latest              bd9a9f733898   5 weeks ago   28.8MB
tensorflow/tensorflow    2.8.0-gpu-jupyter   cc9a9ae2a5af   6 weeks ago   5.99GB

可以看到之前安装的tensorflow-2.8.0-gpu-jupyter镜像，现在基于这个镜像启动容器

docker run --gpus all -itd -v e:/dockerdir/docker_work/:/home/zhou/ -p 8888:8888 -p 6006:6006 --ipc=host cc9a9ae2a5af  jupyter notebook --no-browser --ip=0.0.0.0 --allow-root --NotebookApp.token= --notebook-dir='/home/zhou/'

命令释义：
docker run：表示基于镜像启动容器
–gpus all：不加此选项,nvidia-smi命令会不可用
-i: 交互式操作。
-t: 终端。
-d:后台运行，需要使用【docker exec -it 容器id /bin/bash】进入容器
-v e:/dockerdir/docker_work/:/home/zhou/:将windows平台的e:/dockerdir/docker_work/目录映射到docker的ubuntu系统的/home/zhou/目录下，实现windows平台和docker系统的文件共享
-p 8888:8888 -p 6006:6006：表示将windows系统的8888、6006端口映射到docker的8888、6006端口，这两个端口分别为jupyter notebook和tensorboard的访问端口
–ipc=host:用于多个容器之间的通讯
cc9a9ae2a5af:tensorflow-2.8.0-gpu-jupyter镜像的IMAGE ID
jupyter notebook --no-browser --ip=0.0.0.0 --allow-root --NotebookApp.token= --notebook-dir=‘/home/zhou/’: docker开机启动命令，这里启动jupyter

3.1.3 使用vscode访问docker container

启动vscode后，选择docker工具栏，在启动的容器上，右键选择附着到VsCode
2

3.1.4 更换docker容器ubuntu系统的安装源为国内源

在vscode软件界面上，选择【文件】-【打开文件夹】，选择根目录【/】，找到【/etc/apt/sources.list】,将ubuntu的安装源全部切换为aliyun源，具体操作为：将【archive.ubuntu.com】修改为【mirrors.aliyun.com】即可，修改后如下：

# See http://help.ubuntu.com/community/UpgradeNotes for how to upgrade to
# newer versions of the distribution.
deb http://mirrors.aliyun.com/ubuntu/ focal main restricted
# deb-src http://mirrors.aliyun.com/ubuntu/ focal main restricted

## Major bug fix updates produced after the final release of the
## distribution.
deb http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted
# deb-src http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted

## N.B. software from this repository is ENTIRELY UNSUPPORTED by the Ubuntu
## team. Also, please note that software in universe WILL NOT receive any
## review or updates from the Ubuntu security team.
deb http://mirrors.aliyun.com/ubuntu/ focal universe
# deb-src http://mirrors.aliyun.com/ubuntu/ focal universe
deb http://mirrors.aliyun.com/ubuntu/ focal-updates universe
# deb-src http://mirrors.aliyun.com/ubuntu/ focal-updates universe

## N.B. software from this repository is ENTIRELY UNSUPPORTED by the Ubuntu
## team, and may not be under a free licence. Please satisfy yourself as to
## your rights to use the software. Also, please note that software in
## multiverse WILL NOT receive any review or updates from the Ubuntu
## security team.
deb http://mirrors.aliyun.com/ubuntu/ focal multiverse
# deb-src http://mirrors.aliyun.com/ubuntu/ focal multiverse
deb http://mirrors.aliyun.com/ubuntu/ focal-updates multiverse
# deb-src http://mirrors.aliyun.com/ubuntu/ focal-updates multiverse

## N.B. software from this repository may not have been tested as
## extensively as that contained in the main release, although it includes
## newer versions of some applications which may provide useful features.
## Also, please note that software in backports WILL NOT receive any review
## or updates from the Ubuntu security team.
deb http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse
# deb-src http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse

## Uncomment the following two lines to add software from Canonical's
## 'partner' repository.
## This software is not part of Ubuntu, but is offered by Canonical and the
## respective vendors as a service to Ubuntu users.
# deb http://archive.canonical.com/ubuntu focal partner
# deb-src http://archive.canonical.com/ubuntu focal partner

deb http://security.ubuntu.com/ubuntu/ focal-security main restricted
# deb-src http://security.ubuntu.com/ubuntu/ focal-security main restricted
deb http://security.ubuntu.com/ubuntu/ focal-security universe
# deb-src http://security.ubuntu.com/ubuntu/ focal-security universe
deb http://security.ubuntu.com/ubuntu/ focal-security multiverse
# deb-src http://security.ubuntu.com/ubuntu/ focal-security multiverse
  • 执行如下命令，更新配置
apt-get update;apt-get -f install; apt-get upgrade
  • 更多aliyun的源配置访问：阿里云安装源传送门

3.1.5 验证GPU是否加载成功（在电脑有Nvidia显卡的情况下）

  • 输入nvidia-smi查看GPU使用情况，nvcc -V查询cuda版本
root@cc58e655b170:/home/zhou# nvidia-smi
Tue Mar 22 15:08:57 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.85       Driver Version: 472.47       CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   48C    P8     9W /  N/A |    153MiB /  6144MiB |    ERR!      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
root@cc58e655b170:/home/zhou# nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_14_21:12:58_PST_2021
Cuda compilation tools, release 11.2, V11.2.152
Build cuda_11.2.r11.2/compiler.29618528_0

从nvcc -V的日志，可以看出cuda版本为11.2

  • 输入以下命令，查询cuDNN版本
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

输出结果如下：

root@cc58e655b170:/usr# python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
2022-03-22 15:26:13.281719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3951 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
tf.Tensor(-2613.715, shape=(), dtype=float32)

从输出日志，可以看到GPU：NVIDIA GeForce GTX 1660 Ti已经加载到docker，cuDNN版本为7.5

3.2 Windows开发环境

同Docker环境，验证cuda和cuDNN安装情况。

3.3 下载tensorflow object detection api项目源码

  • 在home/zhou目录下创建tensorflow的目录
cd /home/zhou; mkdir tensorflow; cd tensorflow
  • 下载源码
git clone https://github.com/tensorflow/models.git

下载完毕后，默认文件名名称为models-master, 将文件名重命名为models,保持文件名和平台一致

mv models-matser models

如果网速不好，直接下载zip压缩包吧
3下载完毕后的文档结构如图所示：

tensorflow/
└─ models/
   ├─ community/
   ├─ official/
   ├─ orbit/
   ├─ research/
   └── ...

3.4 安装配置protobuf

Tensorflow对象检测API使用Protobufs来配置模型和训练参数。在使用框架之前，必须下载并编译Protobuf库。

  • 回到用户目录
cd /home/zhou
  • 下载protobuf 这里下载的已经预编译好的protobuf
wget -c https://github.com/protocolbuffers/protobuf/releases/download/v3.19.4/protoc-3.19.4-linux-x86_64.zip
  • 解压 先执行mkdir protoc-3.19.4创建目录，然后执行unzip protoc-3.19.4-linux-x86_64.zip -d protoc-3.19.4/解压到制定目录protoc-3.19.4
root@cc58e655b170:/home/zhou# mkdir protoc-3.19.4
root@cc58e655b170:/home/zhou# unzip protoc-3.19.4-linux-x86_64.zip -d protoc-3.19.4/
Archive:  protoc-3.19.4-linux-x86_64.zip
   creating: protoc-3.19.4/include/
   creating: protoc-3.19.4/include/google/
   creating: protoc-3.19.4/include/google/protobuf/
  inflating: protoc-3.19.4/include/google/protobuf/wrappers.proto  
  inflating: protoc-3.19.4/include/google/protobuf/source_context.proto  
  inflating: protoc-3.19.4/include/google/protobuf/struct.proto  
  inflating: protoc-3.19.4/include/google/protobuf/any.proto  
  inflating: protoc-3.19.4/include/google/protobuf/api.proto  
  inflating: protoc-3.19.4/include/google/protobuf/descriptor.proto  
   creating: protoc-3.19.4/include/google/protobuf/compiler/
  inflating: protoc-3.19.4/include/google/protobuf/compiler/plugin.proto  
  inflating: protoc-3.19.4/include/google/protobuf/timestamp.proto  
  inflating: protoc-3.19.4/include/google/protobuf/field_mask.proto  
  inflating: protoc-3.19.4/include/google/protobuf/empty.proto  
  inflating: protoc-3.19.4/include/google/protobuf/duration.proto  
  inflating: protoc-3.19.4/include/google/protobuf/type.proto  
   creating: protoc-3.19.4/bin/
  inflating: protoc-3.19.4/bin/protoc  
  inflating: protoc-3.19.4/readme.txt
  • 配置protoc 在~/.bashrc文件的末尾添加如下代码
export PATH=$PATH:/home/zhou/protoc-3.19.4/bin

执行如下命令，使其生效

source ~/.bashrc

执行

echo $PATH

查看是否生效

root@cc58e655b170:/home/zhou/protoc-3.19.4/bin# echo $PATH
/home/zhou/protoc-3.19.4/bin:/home/zhou/protoc-3.19.4/bin:/home/zhou/protoc-3.19.4/bin:/root/.vscode-server/bin/c722ca6c7eed3d7987c0d5c3df5c45f6b15e77d1/bin/remote-cli:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/home/zhou/protoc-3.19.4/bin

可以看到protoc的安装目录

/home/zhou/protoc-3.19.4/bin

已经添加到PATH了。

3.5 将proto后缀文件转换为python可识别格式

  • 切换目录
cd /home/zhou/tensorflow/models/research/
  • 查看转换前的目录文件列表
 ls object_detection/protos/

4

  • 转换proto文件格式为python可识别序列化文件
protoc object_detection/protos/*.proto --python_out=.
  • 转换后，如下所示
 ls object_detection/protos/

5

3.6 安装coco api

从TensorFlow 2.x开始, pycocotools包被列为对象检测API的依赖项。理想情况下，这个包应该在安装对象检测API时安装，如下面安装对象检测API一节所述，但是由于各种原因，安装可能会失败，因此更简单的方法是提前安装这个包，在这种情况下，稍后的安装将被跳过。

pip install cython
pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

默认指标是基于Pascal VOC评估中使用的那些指标。要使用COCO对象检测指标，在配置文件的eval_config消息中添加

metrics_set: "coco_detection_metrics"

。要使用COCO实例分割度量，在配置文件的eval_config消息中添加

metrics_set: "coco_mask_metrics"

。

3.7 安装object detection api

  • 当前的工作路径应为
root@cc58e655b170:/home/zhou/tensorflow/models/research# pwd
/home/zhou/tensorflow/models/research
  • 安装object detection api
cp object_detection/packages/tf2/setup.py .
python -m pip install --use-feature=2020-resolver .

安装过程会持续一段时间，安装完毕后，可以执行如下代码，测试安装是否完成。

python object_detection/builders/model_builder_tf2_test.py

输出如下：

......
I0322 16:48:09.677789 140205126002496 efficientnet_model.py:144] round_filter input=192 output=384
I0322 16:48:10.876914 140205126002496 efficientnet_model.py:144] round_filter input=192 output=384
I0322 16:48:10.877072 140205126002496 efficientnet_model.py:144] round_filter input=320 output=640
I0322 16:48:11.294571 140205126002496 efficientnet_model.py:144] round_filter input=1280 output=2560
I0322 16:48:11.337533 140205126002496 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 33.12s
I0322 16:48:11.521103 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 33.12s
[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
I0322 16:48:11.532667 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
I0322 16:48:11.535152 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
I0322 16:48:11.535965 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto
[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
I0322 16:48:11.539124 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[ RUN      ] ModelBuilderTF2Test.test_session
[  SKIPPED ] ModelBuilderTF2Test.test_session
[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
I0322 16:48:11.542018 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
I0322 16:48:11.543226 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
I0322 16:48:11.545147 140205126002496 test_util.py:2373] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran 24 tests in 42.982s

OK (skipped=1)

看到结果为OK,则表示安装成功，接下来就可以开始物体检测之旅了。

《物体检测快速入门系列》快速导航：

  • 物体检测快速入门系列(1)-基于Tensorflow2.x Object Detection API构建自定义物体检测器
  • 物体检测快速入门系列(2)-Windows部署GPU深度学习开发环境
  • 物体检测快速入门系列(3)-Windows部署Docker GPU深度学习开发环境
  • 作者简介：关注AIoT领域的嵌入式开发一枚，追逐未来时代的脉搏，记录路途中的技术成长
  • 本文由机器未来原创，首发在CSDN
  • 订阅专栏：物体检测快速入门系列
  • 面向人群：具备深度学习理论基础的学生或初级开发者
  • 专栏计划：接下来会逐步发布跨入人工智能的系列博文，尽情期待 - 14天快速入门Python系列- 快速入门Python数据科学系列- 人工智能开发环境搭建系列- 机器学习系列- 物体检测快速入门系列- 自动驾驶物体检测系列- …
  • 💖如果觉得博主的文章还不错的话，请点赞👍+收藏⭐️+留言📝支持一下博主哦🤞
标签： tensorflow 目标检测 人工智能
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