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
|
15天前
|
机器学习/深度学习 人工智能 算法
鸟类识别系统Python+卷积神经网络算法+深度学习+人工智能+TensorFlow+ResNet50算法模型+图像识别
鸟类识别系统。本系统采用Python作为主要开发语言,通过使用加利福利亚大学开源的200种鸟类图像作为数据集。使用TensorFlow搭建ResNet50卷积神经网络算法模型,然后进行模型的迭代训练,得到一个识别精度较高的模型,然后在保存为本地的H5格式文件。在使用Django开发Web网页端操作界面,实现用户上传一张鸟类图像,识别其名称。
60 12
鸟类识别系统Python+卷积神经网络算法+深度学习+人工智能+TensorFlow+ResNet50算法模型+图像识别
|
29天前
|
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!
|
27天前
|
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!
|
2月前
|
API UED 开发者
如何在Uno Platform中轻松实现流畅动画效果——从基础到优化,全方位打造用户友好的动态交互体验!
【8月更文挑战第31天】在开发跨平台应用时,确保用户界面流畅且具吸引力至关重要。Uno Platform 作为多端统一的开发框架,不仅支持跨系统应用开发,还能通过优化实现流畅动画,增强用户体验。本文探讨了Uno Platform中实现流畅动画的多个方面,包括动画基础、性能优化、实践技巧及问题排查,帮助开发者掌握具体优化策略,提升应用质量与用户满意度。通过合理利用故事板、减少布局复杂性、使用硬件加速等技术,结合异步方法与预设缓存技巧,开发者能够创建美观且流畅的动画效果。
57 0
|
2月前
|
C# 开发者 前端开发
揭秘混合开发新趋势:Uno Platform携手Blazor,教你一步到位实现跨平台应用,代码复用不再是梦!
【8月更文挑战第31天】随着前端技术的发展,混合开发日益受到开发者青睐。本文详述了如何结合.NET生态下的两大框架——Uno Platform与Blazor,进行高效混合开发。Uno Platform基于WebAssembly和WebGL技术,支持跨平台应用构建;Blazor则让C#成为可能的前端开发语言,实现了客户端与服务器端逻辑共享。二者结合不仅提升了代码复用率与跨平台能力,还简化了项目维护并增强了Web应用性能。文中提供了从环境搭建到示例代码的具体步骤,并展示了如何创建一个简单的计数器应用,帮助读者快速上手混合开发。
45 0
|
2月前
|
UED 存储 数据管理
深度解析 Uno Platform 离线状态处理技巧:从网络检测到本地存储同步,全方位提升跨平台应用在无网环境下的用户体验与数据管理策略
【8月更文挑战第31天】处理离线状态下的用户体验是现代应用开发的关键。本文通过在线笔记应用案例,介绍如何使用 Uno Platform 优雅地应对离线状态。首先,利用 `NetworkInformation` 类检测网络状态;其次,使用 SQLite 实现离线存储;然后,在网络恢复时同步数据;最后,通过 UI 反馈提升用户体验。
48 0
|
2月前
|
开发者 算法 虚拟化
惊爆!Uno Platform 调试与性能分析终极攻略,从工具运用到代码优化,带你攻克开发难题成就完美应用
【8月更文挑战第31天】在 Uno Platform 中,调试可通过 Visual Studio 设置断点和逐步执行代码实现,同时浏览器开发者工具有助于 Web 版本调试。性能分析则利用 Visual Studio 的性能分析器检查 CPU 和内存使用情况,还可通过记录时间戳进行简单分析。优化性能涉及代码逻辑优化、资源管理和用户界面简化,综合利用平台提供的工具和技术,确保应用高效稳定运行。
40 0
|
17天前
|
机器学习/深度学习 数据挖掘 TensorFlow
解锁Python数据分析新技能,TensorFlow&PyTorch双引擎驱动深度学习实战盛宴
在数据驱动时代,Python凭借简洁的语法和强大的库支持,成为数据分析与机器学习的首选语言。Pandas和NumPy是Python数据分析的基础,前者提供高效的数据处理工具,后者则支持科学计算。TensorFlow与PyTorch作为深度学习领域的两大框架,助力数据科学家构建复杂神经网络,挖掘数据深层价值。通过Python打下的坚实基础,结合TensorFlow和PyTorch的强大功能,我们能在数据科学领域探索无限可能,解决复杂问题并推动科研进步。
38 0
|
25天前
|
机器学习/深度学习 数据挖掘 TensorFlow
从数据小白到AI专家:Python数据分析与TensorFlow/PyTorch深度学习的蜕变之路
【9月更文挑战第10天】从数据新手成长为AI专家,需先掌握Python基础语法,并学会使用NumPy和Pandas进行数据分析。接着,通过Matplotlib和Seaborn实现数据可视化,最后利用TensorFlow或PyTorch探索深度学习。这一过程涉及从数据清洗、可视化到构建神经网络的多个步骤,每一步都需不断实践与学习。借助Python的强大功能及各类库的支持,你能逐步解锁数据的深层价值。
46 0
|
2月前
|
持续交付 测试技术 jenkins
JSF 邂逅持续集成,紧跟技术热点潮流,开启高效开发之旅,引发开发者强烈情感共鸣
【8月更文挑战第31天】在快速发展的软件开发领域,JavaServer Faces(JSF)这一强大的Java Web应用框架与持续集成(CI)结合,可显著提升开发效率及软件质量。持续集成通过频繁的代码集成及自动化构建测试,实现快速反馈、高质量代码、加强团队协作及简化部署流程。以Jenkins为例,配合Maven或Gradle,可轻松搭建JSF项目的CI环境,通过JUnit和Selenium编写自动化测试,确保每次构建的稳定性和正确性。
44 0
下一篇
无影云桌面