TF之TFOD-API:基于tensorflow框架利用TFOD-API脚本文件将YoloV3训练好的.ckpt模型文件转换为推理时采用的.pb文件

简介: TF之TFOD-API:基于tensorflow框架利用TFOD-API脚本文件将YoloV3训练好的.ckpt模型文件转换为推理时采用的.pb文件

导出前后文件结果

image.png


输出结果记录

Instructions for updating:

keep_dims is deprecated, use keepdims instead

W0929 20:40:36.003197  1396 tf_logging.py:125] From F:\File_Python\Python_example\models-master\research\object_detection\predictors\heads\box_head.py:93: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.

Instructions for updating:

keep_dims is deprecated, use keepdims instead

WARNING:tensorflow:From F:\File_Python\Python_example\models-master\research\object_detection\exporter.py:280: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.

Instructions for updating:

Please switch to tf.train.get_or_create_global_step

W0929 20:40:37.104074  1396 tf_logging.py:125] From F:\File_Python\Python_example\models-master\research\object_detection\exporter.py:280: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.

Instructions for updating:

Please switch to tf.train.get_or_create_global_step

WARNING:tensorflow:From F:\File_Python\Python_example\models-master\research\object_detection\exporter.py:434: print_model_analysis (from tensorflow.contrib.tfprof.model_analyzer) is deprecated and will be removed after 2018-01-01.

Instructions for updating:

Use `tf.profiler.profile(graph, run_meta, op_log, cmd, options)`. Build `options` with `tf.profiler.ProfileOptionBuilder`. See README.md for details

W0929 20:40:37.111633  1396 tf_logging.py:125] From F:\File_Python\Python_example\models-master\research\object_detection\exporter.py:434: print_model_analysis (from tensorflow.contrib.tfprof.model_analyzer) is deprecated and will be removed after 2018-01-01.

Instructions for updating:

Use `tf.profiler.profile(graph, run_meta, op_log, cmd, options)`. Build `options` with `tf.profiler.ProfileOptionBuilder`. See README.md for details

568 ops no flops stats due to incomplete shapes.

Parsing Inputs...

Incomplete shape.

=========================Options=============================

-max_depth                  10000

-min_bytes                  0

-min_peak_bytes             0

-min_residual_bytes         0

-min_output_bytes           0

-min_micros                 0

-min_accelerator_micros     0

-min_cpu_micros             0

-min_params                 0

-min_float_ops              0

-min_occurrence             0

-step                       -1

-order_by                   name

-account_type_regexes       _trainable_variables

-start_name_regexes         .*

-trim_name_regexes          .*BatchNorm.*

-show_name_regexes          .*

-hide_name_regexes

-account_displayed_op_only  true

-select                     params

-output                     stdout:

==================Model Analysis Report======================

Incomplete shape.

Doc:

scope: The nodes in the model graph are organized by their names, which is hierarchical like filesystem.

param: Number of parameters (in the Variable).

Profile:

node name | # parameters

_TFProfRoot (--/59.45m params)

 Conv (--/5.01m params)

   Conv/biases (512, 512/512 params)

   Conv/weights (3x3x1088x512, 5.01m/5.01m params)

 FirstStageBoxPredictor (--/36.94k params)

   FirstStageBoxPredictor/BoxEncodingPredictor (--/24.62k params)

     FirstStageBoxPredictor/BoxEncodingPredictor/biases (48, 48/48 params)

     FirstStageBoxPredictor/BoxEncodingPredictor/weights (1x1x512x48, 24.58k/24.58k params)

   FirstStageBoxPredictor/ClassPredictor (--/12.31k params)

     FirstStageBoxPredictor/ClassPredictor/biases (24, 24/24 params)

     FirstStageBoxPredictor/ClassPredictor/weights (1x1x512x24, 12.29k/12.29k params)

 FirstStageFeatureExtractor (--/26.84m params)

   FirstStageFeatureExtractor/InceptionResnetV2 (--/26.84m params)

     FirstStageFeatureExtractor/InceptionResnetV2/Conv2d_1a_3x3 (--/864 params)

       FirstStageFeatureExtractor/InceptionResnetV2/Conv2d_1a_3x3/BatchNorm (--/0 params)

…………

           SecondStageFeatureExtractor/InceptionResnetV2/Repeat/block8_9/Conv2d_1x1/biases (2080, 2.08k/2.08k params)

           SecondStageFeatureExtractor/InceptionResnetV2/Repeat/block8_9/Conv2d_1x1/weights (1x1x448x2080, 931.84k/931.84k params)

======================End of Report==========================

568 ops no flops stats due to incomplete shapes.

Parsing Inputs...

Incomplete shape.

=========================Options=============================

-max_depth                  10000

-min_bytes                  0

-min_peak_bytes             0

-min_residual_bytes         0

-min_output_bytes           0

-min_micros                 0

-min_accelerator_micros     0

-min_cpu_micros             0

-min_params                 0

-min_float_ops              1

-min_occurrence             0

-step                       -1

-order_by                   float_ops

-account_type_regexes       .*

-start_name_regexes         .*

-trim_name_regexes          .*BatchNorm.*,.*Initializer.*,.*Regularizer.*,.*BiasAdd.*

-show_name_regexes          .*

-hide_name_regexes

-account_displayed_op_only  true

-select                     float_ops

-output                     stdout:

==================Model Analysis Report======================

Incomplete shape.

Doc:

scope: The nodes in the model graph are organized by their names, which is hierarchical like filesystem.

flops: Number of float operations. Note: Please read the implementation for the math behind it.

Profile:

node name | # float_ops

_TFProfRoot (--/3.42k flops)

 map_1/while/mul_3 (300/300 flops)

 map_1/while/mul_2 (300/300 flops)

 map_1/while/mul_1 (300/300 flops)

 map_1/while/mul (300/300 flops)

 map/while/ToNormalizedCoordinates/Scale/mul_3 (300/300 flops)

 map/while/ToNormalizedCoordinates/Scale/mul_2 (300/300 flops)

 map/while/ToNormalizedCoordinates/Scale/mul_1 (300/300 flops)

 map/while/ToNormalizedCoordinates/Scale/mul (300/300 flops)

 GridAnchorGenerator/mul (12/12 flops)

 GridAnchorGenerator/truediv (12/12 flops)

 GridAnchorGenerator/mul_2 (12/12 flops)

 GridAnchorGenerator/mul_1 (12/12 flops)

 FirstStageFeatureExtractor/InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/required_space_to_batch_paddings/add (2/2 flops)

 FirstStageFeatureExtractor/InceptionResnetV2/InceptionResnetV2/Repeat_1/block17_8/Conv2d_1x1/required_space_to_batch_paddings/add (2/2 flops)

 ……

 BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/SortByField/Equal (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/SortByField_1/Equal (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_1 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_2 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_3 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_4 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_5 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater_6 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_1 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_10 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_11 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_12 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_13 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_2 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_3 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_4 (1/1 flops)

 GridAnchorGenerator/add_3 (1/1 flops)

 GridAnchorGenerator/add_4 (1/1 flops)

 GridAnchorGenerator/assert_equal/Equal (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/Less (1/1 flops)

 Decode/transpose_1/sub (1/1 flops)

 Decode/transpose/sub (1/1 flops)

 GridAnchorGenerator/mul_7 (1/1 flops)

 GridAnchorGenerator/mul_8 (1/1 flops)

 Decode/get_center_coordinates_and_sizes/transpose/sub (1/1 flops)

 GridAnchorGenerator/zeros/Less (1/1 flops)

 Preprocessor/map/while/Less (1/1 flops)

 Preprocessor/map/while/Less_1 (1/1 flops)

 Preprocessor/map/while/ResizeToRange/Greater (1/1 flops)

 Preprocessor/map/while/ResizeToRange/Maximum (1/1 flops)

 Preprocessor/map/while/ResizeToRange/Minimum (1/1 flops)

 Preprocessor/map/while/ResizeToRange/mul (1/1 flops)

 Preprocessor/map/while/ResizeToRange/mul_1 (1/1 flops)

 Preprocessor/map/while/ResizeToRange/mul_2 (1/1 flops)

 Preprocessor/map/while/ResizeToRange/mul_3 (1/1 flops)

 Preprocessor/map/while/ResizeToRange/truediv (1/1 flops)

 Preprocessor/map/while/ResizeToRange/truediv_1 (1/1 flops)

 Preprocessor/map/while/add (1/1 flops)

 Preprocessor/map/while/add_1 (1/1 flops)

 SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/Less (1/1 flops)

 SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/Less_1 (1/1 flops)

 SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/sub (1/1 flops)

 ……

 SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/sub_9 (1/1 flops)

 SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Greater (1/1 flops)

 

……

SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_9 (1/1 flops)

 SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/add (1/1 flops)

 SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/add_1 (1/1 flops)

 SecondStagePostprocessor/Decode/get_center_coordinates_and_sizes/transpose/sub (1/1 flops)

 SecondStagePostprocessor/Decode/transpose/sub (1/1 flops)

 SecondStagePostprocessor/Decode/transpose_1/sub (1/1 flops)

 map/while/Less (1/1 flops)

 map/while/Less_1 (1/1 flops)

 BatchMultiClassNonMaxSuppression/ones/Less (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/add_1 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/add (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_9 (1/1 flops)

 map/while/ToNormalizedCoordinates/truediv (1/1 flops)

 map/while/ToNormalizedCoordinates/truediv_1 (1/1 flops)

 map/while/add (1/1 flops)

 map/while/add_1 (1/1 flops)

 map_1/while/Less (1/1 flops)

 map_1/while/Less_1 (1/1 flops)

 map_1/while/add (1/1 flops)

 map_1/while/add_1 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_8 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_7 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_6 (1/1 flops)

 BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_5 (1/1 flops)

 mul (1/1 flops)

======================End of Report==========================

2018-09-29 20:40:47.042684:


相关文章
|
11天前
|
监控 Java 应用服务中间件
高级java面试---spring.factories文件的解析源码API机制
【11月更文挑战第20天】Spring Boot是一个用于快速构建基于Spring框架的应用程序的开源框架。它通过自动配置、起步依赖和内嵌服务器等特性,极大地简化了Spring应用的开发和部署过程。本文将深入探讨Spring Boot的背景历史、业务场景、功能点以及底层原理,并通过Java代码手写模拟Spring Boot的启动过程,特别是spring.factories文件的解析源码API机制。
39 2
|
2月前
|
人工智能 Serverless API
一键服务化:从魔搭开源模型到OpenAI API服务
在多样化大模型的背后,OpenAI得益于在领域的先发优势,其API接口今天也成为了业界的一个事实标准。
一键服务化:从魔搭开源模型到OpenAI API服务
|
1月前
|
TensorFlow 算法框架/工具
Tensorflow学习笔记(二):各种tf类型的函数用法集合
这篇文章总结了TensorFlow中各种函数的用法,包括创建张量、设备管理、数据类型转换、随机数生成等基础知识。
37 0
|
2月前
|
Java API 开发者
【Java字节码操控新篇章】JDK 22类文件API预览:解锁Java底层的无限可能!
【9月更文挑战第6天】JDK 22的类文件API为Java开发者们打开了一扇通往Java底层世界的大门。通过这个API,我们可以更加深入地理解Java程序的工作原理,实现更加灵活和强大的功能。虽然目前它还处于预览版阶段,但我们已经可以预见其在未来Java开发中的重要地位。让我们共同期待Java字节码操控新篇章的到来!
|
2月前
|
Java API 开发者
【Java字节码的掌控者】JDK 22类文件API:解锁Java深层次的奥秘,赋能开发者无限可能!
【9月更文挑战第8天】JDK 22类文件API的引入,为Java开发者们打开了一扇通往Java字节码操控新世界的大门。通过这个API,我们可以更加深入地理解Java程序的底层行为,实现更加高效、可靠和创新的Java应用。虽然目前它还处于预览版阶段,但我们已经可以预见其在未来Java开发中的重要地位。让我们共同期待Java字节码操控新篇章的到来,并积极探索类文件API带来的无限可能!
|
3月前
|
机器学习/深度学习 API 算法框架/工具
【Tensorflow+keras】Keras API三种搭建神经网络的方式及以mnist举例实现
使用Keras API构建神经网络的三种方法:使用Sequential模型、使用函数式API以及通过继承Model类来自定义模型,并提供了基于MNIST数据集的示例代码。
57 12
|
3月前
|
UED 开发工具 iOS开发
Uno Platform大揭秘:如何在你的跨平台应用中,巧妙融入第三方库与服务,一键解锁无限可能,让应用功能飙升,用户体验爆棚!
【8月更文挑战第31天】Uno Platform 让开发者能用同一代码库打造 Windows、iOS、Android、macOS 甚至 Web 的多彩应用。本文介绍如何在 Uno Platform 中集成第三方库和服务,如 Mapbox 或 Google Maps 的 .NET SDK,以增强应用功能并提升用户体验。通过 NuGet 安装所需库,并在 XAML 页面中添加相应控件,即可实现地图等功能。尽管 Uno 平台减少了平台差异,但仍需关注版本兼容性和性能问题,确保应用在多平台上表现一致。掌握正确方法,让跨平台应用更出色。
54 0
|
3月前
|
SQL Shell API
python Django教程 之 模型(数据库)、自定义Field、数据表更改、QuerySet API
python Django教程 之 模型(数据库)、自定义Field、数据表更改、QuerySet API
|
3月前
|
API 算法框架/工具
【Tensorflow+keras】使用keras API保存模型权重、plot画loss损失函数、保存训练loss值
使用keras API保存模型权重、plot画loss损失函数、保存训练loss值
32 0
|
12天前
|
机器学习/深度学习 人工智能 算法
基于Python深度学习的【垃圾识别系统】实现~TensorFlow+人工智能+算法网络
垃圾识别分类系统。本系统采用Python作为主要编程语言,通过收集了5种常见的垃圾数据集('塑料', '玻璃', '纸张', '纸板', '金属'),然后基于TensorFlow搭建卷积神经网络算法模型,通过对图像数据集进行多轮迭代训练,最后得到一个识别精度较高的模型文件。然后使用Django搭建Web网页端可视化操作界面,实现用户在网页端上传一张垃圾图片识别其名称。
43 0
基于Python深度学习的【垃圾识别系统】实现~TensorFlow+人工智能+算法网络
下一篇
无影云桌面