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
|
3月前
|
自然语言处理 安全 API
API First:模型驱动的阿里云API保障体系
本文介绍了阿里云在API设计和管理方面的最佳实践。首先,通过API First和模型驱动的方式确保API的安全、稳定和效率。其次,分享了阿里云内部如何使用CloudSpec IDL语言及配套工具保障API质量,并实现自动化生成多语言SDK等工具。接着,描述了API从设计到上线的完整生命周期,包括规范校验、企业级能力接入、测试和发布等环节。最后,展望了未来,强调了持续提升API质量和开源CloudSpec IDL的重要性,以促进社区共建更好的API生态。
|
13天前
|
人工智能 算法 安全
OpenRouter 推出百万 token 上下文 AI 模型!Quasar Alpha:提供完全免费的 API 服务,同时支持联网搜索和多模态交互
Quasar Alpha 是 OpenRouter 推出的预发布 AI 模型,具备百万级 token 上下文处理能力,在代码生成、指令遵循和低延迟响应方面表现卓越,同时支持联网搜索和多模态交互。
124 1
OpenRouter 推出百万 token 上下文 AI 模型!Quasar Alpha:提供完全免费的 API 服务,同时支持联网搜索和多模态交互
|
1月前
|
人工智能 自然语言处理 API
零门槛,即刻拥有DeepSeek-R1满血版——调用API及部署各尺寸模型
本文介绍了如何利用阿里云技术快速部署和使用DeepSeek系列模型,涵盖满血版API调用和云端部署两种方案。DeepSeek在数学、代码和自然语言处理等复杂任务中表现出色,支持私有化部署和企业级加密,确保数据安全。通过详细的步骤和代码示例,帮助开发者轻松上手,提升工作效率和模型性能。解决方案链接:[阿里云DeepSeek方案](https://www.aliyun.com/solution/tech-solution/deepseek-r1-for-platforms?utm_content=g_1000401616)。
零门槛,即刻拥有DeepSeek-R1满血版——调用API及部署各尺寸模型
|
1月前
|
人工智能 物联网 API
又又又上新啦!魔搭免费模型推理API支持DeepSeek-R1,Qwen2.5-VL,Flux.1 dev及Lora等
又又又上新啦!魔搭免费模型推理API支持DeepSeek-R1,Qwen2.5-VL,Flux.1 dev及Lora等
158 7
|
3月前
|
机器学习/深度学习 人工智能 安全
GLM-Zero:智谱AI推出与 OpenAI-o1-Preview 旗鼓相当的深度推理模型,开放在线免费使用和API调用
GLM-Zero 是智谱AI推出的深度推理模型,专注于提升数理逻辑、代码编写和复杂问题解决能力,支持多模态输入与完整推理过程输出。
325 24
GLM-Zero:智谱AI推出与 OpenAI-o1-Preview 旗鼓相当的深度推理模型,开放在线免费使用和API调用
|
1月前
|
人工智能 测试技术 API
Ollama本地模型部署+API接口调试超详细指南
本文介绍了如何使用Ollama工具下载并部署AI大模型(如DeepSeek-R1、Llama 3.2等)。首先,访问Ollama的官方GitHub页面下载适合系统的版本并安装。接着,在终端输入`ollama`命令验证安装是否成功。然后,通过命令如`ollama run Llama3.2`下载所需的AI模型。下载完成后,可以在控制台与AI模型进行对话,或通过快捷键`control+d`结束会话。为了更方便地与AI互动,可以安装GUI或Web界面。此外,Ollama还提供了API接口,默认支持API调用,用户可以通过Apifox等工具调试这些API。
|
4月前
|
存储 人工智能 API
AgentScope:阿里开源多智能体低代码开发平台,支持一键导出源码、多种模型API和本地模型部署
AgentScope是阿里巴巴集团开源的多智能体开发平台,旨在帮助开发者轻松构建和部署多智能体应用。该平台提供分布式支持,内置多种模型API和本地模型部署选项,支持多模态数据处理。
1072 4
AgentScope:阿里开源多智能体低代码开发平台,支持一键导出源码、多种模型API和本地模型部署
|
程序员 API
091030 T 焦点在外,框架API设计
框架的设计和API的设计,同样应该有客户服务意识,焦点在外。这时,可以使用TDD的方式先对API的设计进行规定,比较方便程序员间交流。到后期也可用于测试。
902 0
|
20天前
|
JSON 数据挖掘 API
1688API最新指南:商品详情接口接入与应用
本指南介绍1688商品详情接口的接入与应用,该接口可获取商品标题、价格、规格、库存等详细信息,适用于电商平台开发、数据分析等场景。接口通过商品唯一标识查询,支持HTTP GET/POST请求,返回JSON格式数据,助力开发者高效利用1688海量商品资源。
|
20天前
|
JSON 数据挖掘 API
京东API接口最新指南:店铺所有商品接口的接入与使用
本文介绍京东店铺商品数据接口的应用与功能。通过该接口,商家可自动化获取店铺内所有商品的详细信息,包括基本信息、销售数据及库存状态等,为营销策略制定提供数据支持。此接口采用HTTP请求(GET/POST),需携带店铺ID和授权令牌等参数,返回JSON格式数据,便于解析处理。这对于电商运营、数据分析及竞品研究具有重要价值。

热门文章

最新文章