这是机器未来的第1篇文章

原文首发地址:https://blog.csdn.net/RobotFutures/article/details/124745966

CSDN话题挑战赛第1期
活动详情地址：https://marketing.csdn.net/p/bb5081d88a77db8d6ef45bb7b6ef3d7f
参赛话题：自动驾驶技术学习记录
话题描述：自动驾驶是当前最火热的技术之一，吸引了无数的开发者与学习者融入其中。然而，自动驾驶技术是系统硬件平台与人工智能、物联网、大数据、云计算等新一代信息技术深度融合的产物，具有知识新、内容杂、难度深、缺少系统教程等特点，让许多开发者眼花缭乱。
本话题通过记录分享自动驾驶相关技术，为大家提供相互学习与交流的平台。话题分享与讨论的技术点包括不限于：自动驾驶算法、自动驾驶系统基础架构、智能驾驶交互技术、虚拟仿真、自动化测试、无人系统与车辆平台、自动驾驶计算平台与传感器等。

1. 概述

物体检测是自动驾驶感知层的重要基础，障碍物检测、行人检测、交通标志检测、车道线检测…等都基于物体检测理论来实现，物体检测的模型框架较多，本文着重描述tensorflow object detection api。

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。

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：

本文描述了基于Tensorflow2.x Object Detection API构建自定义物体检测器的保姆级教程，详细地描述了代码框架结构、数据集的标准方法，标注文件的数据处理、模型流水线的配置、模型的训练、评估、推理全流程。

最终的测试效果如下：标注物体的位置、物体的类型及置信度。

2. 组织工程文档结构

• 创建父目录
  创建tensorflow文件夹，将下载的object detection api源码models目录拷贝到tensorflow目录下，结构如下：

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

• 创建工作区

cd tensorflow;mkdir training_demo ;cd training_demo;

创建完毕后的文档组织结构如下：

TensorFlow/├─ models/│  ├─ community/│  ├─ official/│  ├─ orbit/│  ├─ research/│  └─ ...└─ workspace/   └─ training_demo/

• 项目目录

mkdir annotations exported-models images/ images/test/ images/train/ models/ pre_trained_models/;touch README.md

创建完毕后的项目结构如下

training_demo/├─ annotations/# annotations存标签映射文件和转换后的TFRecord文件├─ exported_models/# 存放训练完毕后导出的模型文件├─ images/# 存放原始图像数据文件│  ├─ test/# 存放评估图像数据集和标注文件集│  └─ train/# 存放训练图像数据集和标注文件集├─ models/# 存放训练中的pipline.config、模型数据、tensorboard事件数据├─ pre_trained_models/# 存放下载的预训练模型└─ README.md# 工程说明文档

3. 标注数据集

• 标注工具labelImg
  • 项目地址：传送门
  • 下载地址：windows版本
• 标注示例
• 
• 标注后的xml文件统一存放到training_demo/images目录下
• 划分训练、评估数据集
  首先创建一个公共目录tensorflow/scripts/preprocessing用于存放脚本，偏于将来复用

mkdir tensorflow/scriptsmkdir tensorflow/scripts/preprocessing

创建完成后的目录结构如下：

tensorflow/├─ models/│  ├─ community/│  ├─ official/│  ├─ orbit/│  ├─ research/│  └─ ...├─ scripts/│  └─ preprocessing/└─ workspace/   └─ training_demo/

在tensorflow/scripts/preprocessing目录下添加训练集划分脚本partition_dataset.py，脚本内容如下：

""" usage: partition_dataset.py [-h] [-i IMAGEDIR] [-o OUTPUTDIR] [-r RATIO] [-x]Partition dataset of images into training and testing setsoptional arguments:  -h, --help            show this help message and exit  -i IMAGEDIR, --imageDir IMAGEDIR                        Path to the folder where the image dataset is stored. If not specified, the CWD will be used.  -o OUTPUTDIR, --outputDir OUTPUTDIR                        Path to the output folder where the train and test dirs should be created. Defaults to the same directory as IMAGEDIR.  -r RATIO, --ratio RATIO                        The ratio of the number of test images over the total number of images. The default is 0.1.  -x, --xml             Set this flag if you want the xml annotation files to be processed and copied over."""import osimport refrom shutil import copyfileimport argparseimport mathimport randomdef iterate_dir(source, dest, ratio, copy_xml):    source = source.replace('\\', '/')    dest = dest.replace('\\', '/')    train_dir = os.path.join(dest, 'train')    test_dir = os.path.join(dest, 'test')    if not os.path.exists(train_dir):        os.makedirs(train_dir)    if not os.path.exists(test_dir):        os.makedirs(test_dir)    images = [f for f in os.listdir(source)              if re.search(r'([a-zA-Z0-9\s_\\.\-\(\):])+(" />

• 注意事项
在使用脚本之前，请确认已经安装了pandas，使用脚本确认是否安装，如下命令显示版本号即为安装：

root@cc58e655b170# python -c "import pandas as pd;print(pd.__version__)"1.4.1

未安装时，可以按照当前环境安装

conda install pandas # Anaconda                     # orpip install pandas   # pip

• 脚本调用格式

# Create train data:python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/train -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/train.record# Create test data:python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/test -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/test.record

• 使用脚本转换train、test数据集

# For exampleroot@cc58e655b170:/home/zhou/tensorflow/workspace/scripts/preprocessing# python generate_tfrecord.py -x ../../training_demo/images/train/ -l ../../training_demo/annotations/label_map.pbtxt -o ../../training_demo/annotations/train.recordSuccessfully created the TFRecord file: ../../training_demo/annotations/train.recordroot@cc58e655b170:/home/zhou/tensorflow/workspace/scripts/preprocessing# python generate_tfrecord.py -x ../../training_demo/images/test/ -l ../../training_demo/annotations/label_map.pbtxt -o ../../training_demo/annotations/test.recordSuccessfully created the TFRecord file: ../../training_demo/annotations/test.record

脚本执行完毕后，在training_demo/annotations目录下会生成train.record和test.record文件

root@cc58e655b170:/home/zhou/tensorflow/workspace/scripts/preprocessing# ls ../../training_demo/annotations/label_map.pbtxt  test.record  train.record

6. 配置训练流水线

6.1 下载预训练模型

使用wget命令行下载预训练detection模型

wget -c http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz

如果提示找不到wget命令，使用apt工具安装

apt install wget

特别注意：此处下载的模型为detection模型，pipline.config中的fine_tune_checkpoint_type:配置为"detection"才有效，否则会报错

• TF2 Detection zoo：传送门
• TF2 Classification Zoo:传送门

6.2 解压缩预训练模型

使用tar命令tar zxvf ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz解压预训练模型

root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo/pre_trained_models# tar zxvf ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/checkpointssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0.indexssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/pipeline.configssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/saved_model.pbssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/assets/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/variables/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/variables/variables.index

解压后预训练模型在training_demo/pre_trained_models目录下：

root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo/pre_trained_models# lsssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8

预训练模型的目录结构如下

training_demo/├─ ...├─ pre_trained_models/│  └─ ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/│     ├─ checkpoint/│     ├─ saved_model/│     └─ pipeline.config└─ ...

6.3 配置预训练模型

• 拷贝流水线文件到指定目录
在training_demo/models目录下创建my_ssd_resnet50_v1_fpn目录，将流水线配置文件training_demo/pre_trained_models/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/pipeline.config 拷贝到该目录下，拷贝后的目录结构如下

  training_demo/├─ ...├─ models/│  └─ my_ssd_resnet50_v1_fpn/│     └─ pipeline.config└─ ...

• 配置pipline.config
相关的配置项及描述如下代码所示：

model {  ssd {    num_classes: 2     # num_classes为自定义对象检测器所检测的物体分类总数，此处为2    image_resizer {      fixed_shape_resizer {        height: 640        width: 640      }    }......train_config {  batch_size: 8             # batch_size依赖可用的内存，可根据需要添加或减少，且至少保证大于样本数  data_augmentation_options {    random_horizontal_flip {    }  }  ......   # fine_tune_checkpoint为预训练模型的文件路径配置  fine_tune_checkpoint: "pre_trained_models/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0"  num_steps: 2000         # num_steps为训练的epochs的数量  startup_delay_steps: 0.0  replicas_to_aggregate: 8  max_number_of_boxes: 100  unpad_groundtruth_tensors: false  fine_tune_checkpoint_type: "detection"    # 因为要训练完整的检测模型，这里填写detection，而不是classification  use_bfloat16: false                       # 使用TPU训练时置为true  fine_tune_checkpoint_version: V2}train_input_reader {  label_map_path: "annotations/label_map.pbtxt" # 配置标签映射文件  tf_record_input_reader {    input_path: "annotations/train.record"      # 配置待训练的训练集TFRecord 文件  }}......eval_input_reader {  label_map_path: "annotations/label_map.pbtxt" # 配置标签映射文件  shuffle: false  num_epochs: 1  tf_record_input_reader {    input_path: "annotations/test.record"        # 配置待训练的测试集TFRecord 文件     }}

7. 训练模型

7.1 拷贝脚本到工程目录

拷贝models/research/object_detection/model_main_tf2.py到training_demo目录

root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo# cp ../../models/research/object_detection/model_main_tf2.py .root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo# lsREADME.md  annotations  exported-models  images  model_main_tf2.py  models  pre_trained_models

7.2 训练模型

python model_main_tf2.py --model_dir=models/ssd_mobilenet_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config

训练输出如下所示：

2022-03-18 16:31:18.185503: W tensorflow/core/common_runtime/bfc_allocator.cc:275] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.05GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.INFO:tensorflow:Step 100 per-step time 0.690sI0318 16:31:54.968108 140508069689152 model_lib_v2.py:705] Step 100 per-step time 0.690sINFO:tensorflow:{'Loss/classification_loss': 0.5467333, 'Loss/localization_loss': 0.62460774, 'Loss/regularization_loss': 0.37178832, 'Loss/total_loss': 1.5431294, 'learning_rate': 0.014666351}I0318 16:31:54.968450 140508069689152 model_lib_v2.py:708] {'Loss/classification_loss': 0.5467333, 'Loss/localization_loss': 0.62460774, 'Loss/regularization_loss': 0.37178832, 'Loss/total_loss': 1.5431294, 'learning_rate': 0.014666351}

7.2.1 训练模型过程中遇到的问题：

6.2.1.1 ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

• 解决办法：
  • 查看numpy、pycocotools版本

  root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo# python -c "import numpy as np;print(np.__version__)"  1.21.5  root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo# python -m pip show pycocotools   Name: pycocotools  Version: 2.0.5  Summary: Official APIs for the MS-COCO dataset  Home-page: None  Author: None  Author-email: None  License: UNKNOWN  Location: /usr/local/lib/python3.8/dist-packages  Requires: cython, matplotlib, setuptools  Required-by: tf-models-official, object-detection

numpy的版本为1.21.5，pycocotools 的版本为2.0.5，将pycocotools 的版本降为2.0.1可以解决问题

pip install --upgrade pycocotools==2.0.1

7.2.1.2 模型输入输出访问错误: Input/output error [Op:SaveV2]

```I0320 12:47:20.331673 139707635332928 model_lib_v2.py:705] Step 1000 per-step time 0.433sINFO:tensorflow:{'Loss/classification_loss': 0.26997942, 'Loss/localization_loss': 0.30341092, 'Loss/regularization_loss': 2.2776012, 'Loss/total_loss': 2.8509917, 'learning_rate': 0.0266665}I0320 12:47:20.331962 139707635332928 model_lib_v2.py:708] {'Loss/classification_loss': 0.26997942, 'Loss/localization_loss': 0.30341092, 'Loss/regularization_loss': 2.2776012, 'Loss/total_loss': 2.8509917, 'learning_rate': 0.0266665}2022-03-20 12:47:23.322429: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at save_restore_v2_ops.cc:138 : UNKNOWN: models/my_ssd_resnet50_v1_fpn/ckpt-3_temp/part-00000-of-00001.data-00000-of-00001.tempstate1791880307736640246; Input/output errorTraceback (most recent call last):  File "model_main_tf2.py", line 115, in     tf.compat.v1.app.run()  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/app.py", line 36, in run    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)  File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 312, in run    _run_main(main, args)  File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 258, in _run_main    sys.exit(main(argv))  File "model_main_tf2.py", line 106, in main    model_lib_v2.train_loop(  File "/usr/local/lib/python3.8/dist-packages/object_detection/model_lib_v2.py", line 713, in train_loop    manager.save()  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_management.py", line 813, in save    save_path = self._checkpoint.write(prefix)  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py", line 2105, in write    output = self._saver.save(file_prefix=file_prefix, options=options)  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py", line 1262, in save    save_path, new_feed_additions = self._save_cached_when_graph_building(  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py", line 1206, in _save_cached_when_graph_building    save_op = saver.save(file_prefix, options=options)  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py", line 371, in save    return save_fn()  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py", line 345, in save_fn    sharded_saves.append(saver.save(shard_prefix, options))  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py", line 80, in save    return io_ops.save_v2(file_prefix, tensor_names, tensor_slices, tensors)  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1707, in save_v2    return save_v2_eager_fallback(  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1728, in save_v2_eager_fallback    _result = _execute.execute(b"SaveV2", 0, inputs=_inputs_flat, attrs=_attrs,  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 54, in quick_execute    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,tensorflow.python.framework.errors_impl.UnknownError: models/my_ssd_resnet50_v1_fpn/ckpt-3_temp/part-00000-of-00001.data-00000-of-00001.tempstate1791880307736640246; Input/output error [Op:SaveV2]```- 解决办法：- 重启电脑后解决。 

7.3 tensorboard查看训练过程曲线

执行命令tensorboard --logdir=training_demo/models/ssd_mobilenet_v1_fpn/train查看tensorboard面板

root@cc58e655b170:/home/zhou/tensorflow/workspace# tensorboard --logdir=training_demo/models/my_ssd_resnet50_v1_fpn/train2022-03-18 16:38:07.080715: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_nodeYour kernel may have been built without NUMA support.2022-03-18 16:38:07.100073: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_nodeYour kernel may have been built without NUMA support.2022-03-18 16:38:07.101614: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_nodeYour kernel may have been built without NUMA support.NOTE: Using experimental fast data loading logic. To disable, pass    "--load_fast=false" and report issues on GitHub. More details:    https://github.com/tensorflow/tensorboard/issues/4784Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_allTensorBoard 2.8.0 at http://localhost:6006/ (Press CTRL+C to quit)

• 打开浏览器，输入http://127.0.0.1:6006/#scalars，浏览器效果如图

注意事项：

• tensorboard: error: invalid choice: ‘Recognizer\logs’ (choose from ‘serve’, ‘dev’)
  • 命令的logdir的=号两侧不可以有空格 tensorboard --logdir=[train_path]

7.4 评估模型

root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo# python model_main_tf2.py --model_dir=models/ssd_mobilenet_v1_fpn --pipeline_config_path=models/ssd_mobilenet_v1_fpn/pipeline.config --checkpoint_dir=models/ssd_mobilenet_v1_fpn......2022-03-18 17:10:21.973191: 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: 7.5INFO:tensorflow:Reading unweighted datasets: ['annotations/test.record']I0318 17:10:22.271144 140242454390592 dataset_builder.py:163] Reading unweighted datasets: ['annotations/test.record']INFO:tensorflow:Reading record datasets for input file: ['annotations/test.record']I0318 17:10:22.272839 140242454390592 dataset_builder.py:80] Reading record datasets for input file: ['annotations/test.record']INFO:tensorflow:Number of filenames to read: 1I0318 17:10:22.273008 140242454390592 dataset_builder.py:81] Number of filenames to read: 1WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.W0318 17:10:22.273125 140242454390592 dataset_builder.py:87] num_readers has been reduced to 1 to match input file shards.......INFO:tensorflow:Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpnI0318 17:10:32.268154 140242454390592 checkpoint_utils.py:136] Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpnINFO:tensorflow:Found new checkpoint at models/my_ssd_resnet50_v1_fpn/ckpt-6I0318 17:10:32.275528 140242454390592 checkpoint_utils.py:145] Found new checkpoint at models/my_ssd_resnet50_v1_fpn/ckpt-6/usr/local/lib/python3.8/dist-packages/keras/backend.py:450: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model. ......INFO:tensorflow:Performing evaluation on 2 images.I0318 17:11:09.182368 140242454390592 coco_evaluation.py:293] Performing evaluation on 2 images.creating index...index created!INFO:tensorflow:Loading and preparing annotation results...I0318 17:11:09.182641 140242454390592 coco_tools.py:116] Loading and preparing annotation results...INFO:tensorflow:DONE (t=0.00s)I0318 17:11:09.182915 140242454390592 coco_tools.py:138] DONE (t=0.00s)creating index...index created!Running per image evaluation...Evaluate annotation type *bbox*DONE (t=0.02s).Accumulating evaluation results...DONE (t=0.01s). Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.275 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.538 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.167 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.276 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.250 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.350 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.550 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.550INFO:tensorflow:Eval metrics at step 1000I0318 17:11:09.223372 140242454390592 model_lib_v2.py:1015] Eval metrics at step 1000INFO:tensorflow:        + DetectionBoxes_Precision/mAP: 0.275370I0318 17:11:09.227210 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Precision/mAP: 0.275370INFO:tensorflow:        + DetectionBoxes_Precision/mAP@.50IOU: 0.538462I0318 17:11:09.231014 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Precision/mAP@.50IOU: 0.538462INFO:tensorflow:        + DetectionBoxes_Precision/mAP@.75IOU: 0.166667I0318 17:11:09.233924 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Precision/mAP@.75IOU: 0.166667INFO:tensorflow:        + DetectionBoxes_Precision/mAP (small): -1.000000I0318 17:11:09.238049 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Precision/mAP (small): -1.000000INFO:tensorflow:        + DetectionBoxes_Precision/mAP (medium): -1.000000I0318 17:11:09.241585 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Precision/mAP (medium): -1.000000INFO:tensorflow:        + DetectionBoxes_Precision/mAP (large): 0.275628I0318 17:11:09.245546 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Precision/mAP (large): 0.275628INFO:tensorflow:        + DetectionBoxes_Recall/AR@1: 0.250000I0318 17:11:09.250303 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Recall/AR@1: 0.250000INFO:tensorflow:        + DetectionBoxes_Recall/AR@10: 0.350000I0318 17:11:09.255020 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Recall/AR@10: 0.350000INFO:tensorflow:        + DetectionBoxes_Recall/AR@100: 0.550000I0318 17:11:09.266462 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Recall/AR@100: 0.550000INFO:tensorflow:        + DetectionBoxes_Recall/AR@100 (small): -1.000000I0318 17:11:09.269966 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Recall/AR@100 (small): -1.000000INFO:tensorflow:        + DetectionBoxes_Recall/AR@100 (medium): -1.000000I0318 17:11:09.272413 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Recall/AR@100 (medium): -1.000000INFO:tensorflow:        + DetectionBoxes_Recall/AR@100 (large): 0.550000I0318 17:11:09.275286 140242454390592 model_lib_v2.py:1018]     + DetectionBoxes_Recall/AR@100 (large): 0.550000INFO:tensorflow:        + Loss/localization_loss: 0.467712I0318 17:11:09.278475 140242454390592 model_lib_v2.py:1018]     + Loss/localization_loss: 0.467712INFO:tensorflow:        + Loss/classification_loss: 0.517050I0318 17:11:09.281271 140242454390592 model_lib_v2.py:1018]     + Loss/classification_loss: 0.517050INFO:tensorflow:        + Loss/regularization_loss: 0.373590I0318 17:11:09.284374 140242454390592 model_lib_v2.py:1018]     + Loss/regularization_loss: 0.373590INFO:tensorflow:        + Loss/total_loss: 1.358352I0318 17:11:09.287869 140242454390592 model_lib_v2.py:1018]     + Loss/total_loss: 1.358352

8. 导出模型

• 拷贝models/research/object_detection/exporter_main_v2.py脚本到training_demo目录下

root@cc58e655b170:/home/zhou/tensorflow/workspace/training_demo# cp ../../models/research/object_detection/exporter_main_v2.py .

• 执行导出

python exporter_main_v2.py \--input_type image_tensor \--pipeline_config_path models/ssd_mobilenet_v1_fpn/pipeline.config \--trained_checkpoint_dir models/ssd_mobilenet_v1_fpn \--output_directory "exported_models/my_model"

输出如下：

2022-03-18 17:31:40.560733: 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: 7.5WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.Instructions for updating:back_prop=False is deprecated. Consider using tf.stop_gradient instead.Instead of:results = tf.map_fn(fn, elems, back_prop=False)Use:results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))W0318 17:31:40.987179 139643789973312 deprecation.py:610] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.Instructions for updating:back_prop=False is deprecated. Consider using tf.stop_gradient instead.Instead of:results = tf.map_fn(fn, elems, back_prop=False)Use:results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))2022-03-18 17:32:03.258760: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.WARNING:tensorflow:Skipping full serialization of Keras layer