Failed to build the samples using VS2015

Failed to build the samples using VS2015

Hello, everyone

 

I have successfully built the samples using the VS2017.

But when I build the samples using the VS2015, I use this command:    build_samples_msvc.bat VS2015

 I get this error:

Searching Visual Studio 2015...
Creating Visual Studio 14 2015 x64 files in C:\Users\itl-ocr\Documents\Intel\OpenVINO\inference_engine_samples_build...
-- Selecting Windows SDK version 10.0.17134.0 to target Windows 10.0.14393.
-- The C compiler identification is unknown
-- The CXX compiler identification is unknown
CMake Error at CMakeLists.txt:7 (project):
  No CMAKE_C_COMPILER could be found.

CMake Error at CMakeLists.txt:7 (project):
  No CMAKE_CXX_COMPILER could be found.

-- Configuring incomplete, errors occurred!
See also "C:/Users/itl-ocr/Documents/Intel/OpenVINO/inference_engine_samples_build/CMakeFiles/CMakeOutput.log".
See also "C:/Users/itl-ocr/Documents/Intel/OpenVINO/inference_engine_samples_build/CMakeFiles/CMakeError.log"

The following lines are from CMakeError.log:

Link:
  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64\link.exe /ERRORREPORT:QUEUE /OUT:".\CompilerIdC.exe" /INCREMENTAL:NO /NOLOGO kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /MANIFEST /MANIFESTUAC:"level='asInvoker' uiAccess='false'" /manifest:embed /PDB:".\CompilerIdC.pdb" /SUBSYSTEM:CONSOLE /TLBID:1 /DYNAMICBASE /NXCOMPAT /IMPLIB:".\CompilerIdC.lib" /MACHINE:X64 Debug\CMakeCCompilerId.obj
LINK : fatal error LNK1158: 无法运行“C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64\rc.exe” [C:\Users\itl-ocr\Documents\Intel\OpenVINO\inference_engine_samples_build\CMakeFiles\3.12.2\CompilerIdC\CompilerIdC.vcxproj]
已完成生成项目“C:\Users\itl-ocr\Documents\Intel\OpenVINO\inference_engine_samples_build\CMakeFiles\3.12.2\CompilerIdC\CompilerIdC.vcxproj”(默认目标)的操作 - 失败。

生成失败。

“C:\Users\itl-ocr\Documents\Intel\OpenVINO\inference_engine_samples_build\CMakeFiles\3.12.2\CompilerIdC\CompilerIdC.vcxproj”(默认目标) (1) ->
(Link 目标) -> 
  LINK : fatal error LNK1158: 无法运行“C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64\rc.exe” [C:\Users\itl-ocr\Documents\Intel\OpenVINO\inference_engine_samples_build\CMakeFiles\3.12.2\CompilerIdC\CompilerIdC.vcxproj]

 

Does anyone know how to solve this problem?thank you!

10 posts / 0 new
Last post
For more complete information about compiler optimizations, see our Optimization Notice.

It looks like you missed installing the c++ compiler into visual studio 2015 I would go to the Visual studio page to see how to add it after installation. 

 

Hello,Kenneth Craft

I have installed C++ compiler into visual studio 2015. And I searched this problem on Internet, someone say this error occurred because of the installing of visual studio 2017. However, I tried some solutions from Internet, and can't solve this problem.

I would check other boards for information, but it does sound like a visual studio/cmake issues.You may need to load cmake on it's own and configure it to find compiler. 

 

Hello,Kenneth Craft

 

I have loaded cmake from VS2015 command prompt and configured to successfully built the samples.

But when I ran this test command:

object_detection_demo.exe -i <path_to_image>/inputImage.bmp -m <path_to_model>/faster-rcnn.xml -d CPU

I met a problem about object_detection_demo.exe:

"object_detection_demo.exe stops working!" 

the screen shoot  about the error is shown in the attachments.

 

Attachments: 

AttachmentSize
Downloadimage/png 捕获.PNG9.57 KB

Dear zhang, chunyan,

If it wouldn't be too inconvenient for you please upgrade to Visual 2017 Community Edition and download OpenVino 2019 R2 which was just released today. In the latest package object_detection_demo.exe is no longer there. Please see This forum post for details. OpenVino should build on VS 2015 but it's pretty old. My guess is that you didn't install MSBuild dependencies .OpenVino actually even supports Visual Studio 2019 now.

Hope it helps,

Thanks,

Shubha

 

Dear Shubha,

 

I have download OpenVINO 2019 R2, and I re-generated the IR files.

But when I use this command:

object_detection_demo_faster_rcnn -i .\inputimg.bmp -m .\frozen_inference_graph.xml -d CPU

I get the following error:

[ INFO ] Loading network files:
        .\frozen_inference_graph.xml
        .\frozen_inference_graph.bin
[ ERROR ] Can't find output layer named bbox_pred

Do you know how to solve this problem?thank you!

 

Best regards,

Zhang Chunyan

Dear Zhang Chunyan,

When you do a help on object_detection_demo_faster_rcnn.exe -h, You get these as (some of) the options :

  -bbox_name "<string>"     Optional. The name of output box prediction layer. Default value is "bbox_pred" 
  -proposal_name "<string>" Optional. The name of output proposal layer. Default value is "proposal"
  -prob_name "<string>"     Optional. The name of output probability layer. Default value is "cls_prob"
  -p_msg                    Optional. Enables messages from a plugin

I assume that you have in fact converted a faster_rcnn model from the Model Optimizer Tensorflow Models Supported List, and that you followed the Model Optimizer Tensorflow Object Detection API Instructions .

If so, please look at your generated *.xml file and find out the actual name for the output layer and pass it in to --box_name. Check for --proposal_name as well in the *.xml file. Right now it's a bit awkward because R2 documentation is not quite ready for this demo. R2 documentation should be fully available in a week or so.

Why not add  -p_msg  as well to see if you can get more information about the error.

Hope it helps,

Thanks,

Shubha

Dear Shubha,

 

Thanks for your advise. 

I thought I have found the actual names from *.xml file for the three parameters (-bbox_name ,  -proposal_name , -prob_name).

I found the actual names according to the inputs of  last layer <layer id="181" name="detection_output">. and the demo runs successfully. 

However, I got the totally wrong outputs:

[181,4] element, prob = 1.99557e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[182,4] element, prob = 1.90589e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[183,4] element, prob = 1.55295e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[184,4] element, prob = 1.3824e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[185,4] element, prob = 1.2354e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[186,4] element, prob = 1.10734e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[187,4] element, prob = 1.06737e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[188,4] element, prob = 1.01295e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[189,4] element, prob = 1.00889e-07    (0,-2147483648)-(0,-2147483648) batch id : 0
[190,4] element, prob = 9.33275e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[191,4] element, prob = 8.55716e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[192,4] element, prob = 8.54856e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[193,4] element, prob = 8.44165e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[194,4] element, prob = 8.05001e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[195,4] element, prob = 8.00926e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[196,4] element, prob = 7.44765e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[197,4] element, prob = 7.15444e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[198,4] element, prob = 6.95467e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[199,4] element, prob = 6.13289e-08    (0,-2147483648)-(0,-2147483648) batch id : 0
[ INFO ] Image out_0.bmp created!
[ INFO ] Execution successful

[ INFO ] This demo is an API example, for any performance measurements please use the dedicated benchmark_app tool from the openVINO toolkit

 

It seems that on object was detected. Do you know the reason of the wrong results?

 

Best regards,

Zhang Chunyan

Dear zhang, chunyan,

I believe I've answered your faster rcnn questions in your latest IDZ post . I explained why in that post you should really use object_detection_sample_ssd instead of object_detection_demo_faster_rcnn . I'm dreadfully sorry that this has been so confusing but I explained it well in that other post.

Thanks for your patience !

Shubha

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