Problems On Deploying the Segmentation model trained on TLT-3.0

[zieganwes]

Hello,

I have gone through the process of training a Segmentation model - MaskRCNN using the coco-2017 data-set in TLT-3.0, and trained the model with the default configuration provided in NVIDIA TLT-3.0. I get errors deploying the model trained using the default data-set.

Now i am trying to make a custom Segmentation model created from custom data-set.

I have annotated the data using the Intel CVAT and exported it into TFrecord which is used to feed into the config file for the training process.

My Questions :

1. Is there any constrains that i have to deal with before changing the training steps parameter ?

2. Can it take both JPEG and PNG as data-set formats ?

3. How to deploy the model into deepstream or what is the application format that is for Segmentation,

Is it the deepstream-mrcnn-app or the deepstream-segmentation-test ?

4. Is it mandatory for the data-set to have a BG (BackGround) class for an both scemantic & instance segmentation model ?

5. After a Successful model generation using Default/Custom Data-set and trying to deploy the model under Deepstream with the config file similar to shown here https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-the-transfer-learning-toolkit/, I end up getting errors such as

a) While trying to run the model using the deepstream-segmentation-test application

0:00:00.298115171 563 0x561db309c630 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1715> [UID = 1]: Trying to create engine from model files
ERROR: …/nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: UffParser: Validator error: generate_detections: Unsupported operation _GenerateDetection_TRT
parseModel: Failed to parse UFF model
ERROR: tlt/tlt_decode.cpp:274 failed to build network since parsing model errors.
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:797 Failed to create network using custom network creation function
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:862 Failed to get cuda engine from custom library API
0:00:00.691056241 563 0x561db309c630 ERROR nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger: NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1735> [UID = 1]: build engine file failed
Segmentation fault (core dumped)

b) While trying to run the model using the deepstream-app as shown in the
https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-the-transfer-learning-toolkit/

Quitting
ERROR from src_bin_muxer: Output width not set
Debug info: gstnvstreammux.c(2283): gst_nvstreammux_change_state (): /GstPipeline:pipeline/GstBin:multi_src_bin/GstNvStreamMux:src_bin_muxer
App run failed

Note :
The Errors where same for both models with the provided default dataset and an custom dataset. While training ,the process didn’t get any errors, the custom dataset is a single class dataset.

[Morganh]

Hi,
For 1) , please see Transfer Learning Toolkit — Transfer Learning Toolkit 3.0 documentation
For 2), please see Transfer Learning Toolkit — Transfer Learning Toolkit 3.0 documentation , png is not supported yet.
For 3), please refer to MaskRCNN — Transfer Learning Toolkit 3.0 documentation , the reference application is installed as deepstream-app. Also, you can use deepstream-mrcnn-test to run the MaskRCNN model. See the README under $DS_TOP/source/apps/sample_apps/deepstream-mrcnn-test/ .
For 4), The background class is needed to set when you config the training spec. But it is needed for config of deepstream deployment. See MaskRCNN — Transfer Learning Toolkit 3.0 documentation
For 5 (a), For ERROR: …/nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: UffParser: Validator error: generate_detections: Unsupported operation _GenerateDetection_TRT parseModel: Failed to parse UFF model,
please refer to MaskRCNN — Transfer Learning Toolkit 3.0 documentation
For 5 (b) , please double check , and refer to MaskRCNN — Transfer Learning Toolkit 3.0 documentation

[Morganh]

Please note that I update above description. The background class is needed to set when you config the training spec.

[zieganwes]

Can you elaborate how to over come the error i am facing, because both the model trained with the default coco-2017 data-set and the custom data-set throws the same error while deploying in deepstream.

For deployment, Im using the Nvidia Deepstream Docker container in a x64 pc

Hardware specs are:

i9 9th gen CPU
RTX 2080Ti

Software Version:

ubuntu 18.04 OS
cuda 11.2
driver version 460.32.03
docker 20.10.6

Docker image Version:

nvcr.io/nvidia/deepstream:5.0.1-20.09-triton

The error while deploying the model is

For ERROR: …/nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: UffParser: Validator error: generate_detections: Unsupported operation _GenerateDetection_TRT parseModel: Failed to parse UFF model,

aslo

On reading this Nvidia dev blog (https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-the-transfer-learning-toolkit/)

they have managed to deploy the etlt file without using the OSS plugins and TLT-converter .

[Morganh]

@zieganwes

Unsupported operation _GenerateDetection_TRT

It is needed to build OSS plugins. See MaskRCNN — Transfer Learning Toolkit 3.0 documentation

More, for 5.0.1-20.09-devel deepstream container, please refer to the successful case from MaskRCNN on Xavier - UffParser: Validator error Unsupported operation _GenerateDetection_TRT - #13 by tlalexander

[zieganwes]

I have tried to build the OSS plugins … but the process of “make” command never gets finished.

[ 0%] Creating directories for ‘third_party.protobuf’
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– File already exists but no hash specified (use URL_HASH):
** file=‘/home/deeplearning/TensorRT/build/third_party.protobuf/src/protobuf-cpp-3.0.0.tar.gz’**
Old file will be removed and new file downloaded from URL.
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– Retry after 60 seconds (attempt #5) …
– Using src=‘https://github.com/google/protobuf/releases/download/v3.0.0/protobuf-cpp-3.0.0.tar.gz’
CMake Error at third_party.protobuf-stamp/download-third_party.protobuf.cmake:159 (message):
** Each download failed!**

** error: downloading ‘https://github.com/google/protobuf/releases/download/v3.0.0/protobuf-cpp-3.0.0.tar.gz’ failed**
** status_code: 1**
** status_string: “Unsupported protocol”**
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** status_code: 1**
** status_string: “Unsupported protocol”**
** log:**
** — LOG BEGIN —**
** Protocol “https” not supported or disabled in libcurl**

** Closing connection -1**

---

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** error: downloading ‘https://github.com/google/protobuf/releases/download/v3.0.0/protobuf-cpp-3.0.0.tar.gz’ failed**
** status_code: 1**
** status_string: “Unsupported protocol”**
** log:**
** — LOG BEGIN —**
** Protocol “https” not supported or disabled in libcurl**

** Closing connection -1**

---

** — LOG END —**
** error: downloading ‘https://github.com/google/protobuf/releases/download/v3.0.0/protobuf-cpp-3.0.0.tar.gz’ failed**
** status_code: 1**
** status_string: “Unsupported protocol”**
** log:**
** — LOG BEGIN —**
** Protocol “https” not supported or disabled in libcurl**

** Closing connection -1**

---

** — LOG END —**
** error: downloading ‘https://github.com/google/protobuf/releases/download/v3.0.0/protobuf-cpp-3.0.0.tar.gz’ failed**
** status_code: 1**
** status_string: “Unsupported protocol”**
** log:**
** — LOG BEGIN —**
** Protocol “https” not supported or disabled in libcurl**

** Closing connection -1**

---

** — LOG END —**

---

---

CMakeFiles/third_party.protobuf.dir/build.make:89: recipe for target ‘third_party.protobuf/src/third_party.protobuf-stamp/third_party.protobuf-download’ failed
make[2]: *** [third_party.protobuf/src/third_party.protobuf-stamp/third_party.protobuf-download] Error 1
CMakeFiles/Makefile2:136: recipe for target ‘CMakeFiles/third_party.protobuf.dir/all’ failed
make[1]: *** [CMakeFiles/third_party.protobuf.dir/all] Error 2
Makefile:151: recipe for target ‘all’ failed
make: *** [all] Error 2

It seems to a have problem to download the protobuff archive ,…But using the same system with the same network provided while make command. I can download the protobuff by pasting the URL in a browser (or) by using the wget & curl.

Have tried to build the OSS plugins numerous times and every time it gets to this result of failing to download protobuff.

Also,

The config files for the pretrained PeopleSegNet in TLT NGC (https://ngc.nvidia.com/catalog/models/nvidia:tlt_peoplesegnet) is not yet added, Adding it would be more helpful on deploying the Segmentation models and trying-out the pretrained segmentation model.

[Morganh]

For below unavailable link, please refer to Missing config files for PeopleSegNet in NGC

@zieganwes

The config files for the pretrained PeopleSegNet in TLT NGC (https://ngc.nvidia.com/catalog/models/nvidia:tlt_peoplesegnet) is not yet added, Adding it would be more helpful on deploying the Segmentation models and trying-out the pretrained segmentation model.

[Morganh]

For your OSS plugin build error, please refer to How to export model using tlt-converter for Jetson Nano - #34 by Morganh

If you meet issue as compile error · Issue #78 · NVIDIA/TensorRT · GitHub, please use below workaround.
…

[zieganwes]

The previous issue of curl has be successfully solved.

I am now facing a issue related to the GPU compatibility.

The GPU i am using is 2080Ti with about 7.5 compatibility score.

using -DGPU_ARCHS=75 i ran into this problem:

**[ 0%] Creating directories for ‘third_party.protobuf’
[ 0%] Performing download step (download, verify and extract) for ‘third_party.protobuf’
– File already exists but no hash specified (use URL_HASH):
file=‘/home/deeplearning/TensorRT/build/third_party.protobuf/src/protobuf-cpp-3.0.0.tar.gz’
Old file will be removed and new file downloaded from URL.
– Downloading…
dst=‘/home/deeplearning/TensorRT/build/third_party.protobuf/src/protobuf-cpp-3.0.0.tar.gz’
timeout=‘none’
– Using src=‘https://github.com/google/protobuf/releases/download/v3.0.0/protobuf-cpp-3.0.0.tar.gz’
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The file
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is listed in
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but there not exists. The file will not be installed.
Call Stack (most recent call first):
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This warning is for project developers. Use -Wno-dev to suppress it.

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– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/port.h
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– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/shared_ptr.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/singleton.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/status.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/stl_util.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/stringpiece.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/template_util.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/stubs/type_traits.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/text_format.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/timestamp.pb.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/type.pb.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/unknown_field_set.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/util/field_comparator.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/util/field_mask_util.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/util/json_util.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/util/message_differencer.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/util/time_util.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/util/type_resolver.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/util/type_resolver_util.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/wire_format.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/wire_format_lite.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/wire_format_lite_inl.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/wrappers.pb.h
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/descriptor.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/any.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/api.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/duration.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/empty.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/field_mask.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/source_context.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/struct.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/timestamp.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/type.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/wrappers.proto
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/include/google/protobuf/compiler/plugin.proto
– Installing: /home/deeplearning/TensorRT/build/third_party.protobuf/lib/cmake/protobuf/protobuf-targets.cmake
– Installing: /home/deeplearning/TensorRT/build/third_party.protobuf/lib/cmake/protobuf/protobuf-targets-noconfig.cmake
– Up-to-date: /home/deeplearning/TensorRT/build/third_party.protobuf/lib/cmake/protobuf
– Installing: /home/deeplearning/TensorRT/build/third_party.protobuf/lib/cmake/protobuf/protobuf-config-version.cmake
– Installing: /home/deeplearning/TensorRT/build/third_party.protobuf/lib/cmake/protobuf/protobuf-config.cmake
– Installing: /home/deeplearning/TensorRT/build/third_party.protobuf/lib/cmake/protobuf/protobuf-module.cmake
– Installing: /home/deeplearning/TensorRT/build/third_party.protobuf/lib/cmake/protobuf/protobuf-options.cmake
[ 2%] Completed ‘third_party.protobuf’
[ 2%] Built target third_party.protobuf
[ 2%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/nmsPlugin/nmsPlugin.cpp.o
[ 2%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/normalizePlugin/normalizePlugin.cpp.o
[ 3%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/priorBoxPlugin/priorBoxPlugin.cpp.o
[ 3%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/reorgPlugin/reorgPlugin.cpp.o
[ 3%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/gridAnchorPlugin/gridAnchorPlugin.cpp.o
[ 3%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/regionPlugin/regionPlugin.cpp.o
[ 4%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/nvFasterRCNN/nvFasterRCNNPlugin.cpp.o
[ 4%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/batchedNMSPlugin/batchedNMSInference.cpp.o
[ 4%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/batchedNMSPlugin/batchedNMSPlugin.cpp.o
[ 5%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/flattenConcat/flattenConcat.cpp.o
[ 5%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/cropAndResizePlugin/cropAndResizePlugin.cpp.o
[ 5%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/proposalPlugin/proposalPlugin.cpp.o
[ 7%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/batchTilePlugin/batchTilePlugin.cpp.o
[ 7%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/detectionLayerPlugin/detectionLayerPlugin.cpp.o
[ 7%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/proposalLayerPlugin/proposalLayerPlugin.cpp.o
[ 7%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp.o
[ 8%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/generateDetectionPlugin/generateDetectionPlugin.cpp.o
[ 8%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/multilevelProposeROI/multilevelProposeROIPlugin.cpp.o
/home/deeplearning/TensorRT/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp: In member function ‘virtual int nvinfer1::plugin::MultilevelProposeROI::enqueue(int, const void* const*, void**,
void*, cudaStream_t)’:
/home/deeplearning/TensorRT/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp:408:44: warning: pointer of type ‘void *’ used in arithmetic [-Wpointer-arith]
mParam, proposal_ws, workspace + kernel_workspace_offset,
^~~~~~~~~~~~~~~
/home/deeplearning/TensorRT/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp:425:27: warning: pointer of type ‘void *’ used in arithmetic [-Wpointer-arith]
workspace + kernel_workspace_offset,
^~~~~~~~~~~~~~~
[ 8%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp.o
[ 9%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/resizeNearestPlugin/resizeNearestPlugin.cpp.o
[ 9%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/specialSlicePlugin/specialSlicePlugin.cpp.o
[ 9%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp.o
[ 9%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/common/kernels/kernel.cpp.o
[ 10%] Building CXX object plugin/CMakeFiles/nvinfer_plugin.dir/common/nmsHelper.cpp.o
[ 10%] Building CUDA object plugin/CMakeFiles/nvinfer_plugin.dir/batchedNMSPlugin/gatherNMSOutputs.cu.o
nvcc fatal : Unsupported gpu architecture ‘compute_75’
plugin/CMakeFiles/nvinfer_plugin.dir/build.make:374: recipe for target ‘plugin/CMakeFiles/nvinfer_plugin.dir/batchedNMSPlugin/gatherNMSOutputs.cu.o’ failed
make[2]: *** [plugin/CMakeFiles/nvinfer_plugin.dir/batchedNMSPlugin/gatherNMSOutputs.cu.o] Error 1
CMakeFiles/Makefile2:259: recipe for target ‘plugin/CMakeFiles/nvinfer_plugin.dir/all’ failed
make[1]: *** [plugin/CMakeFiles/nvinfer_plugin.dir/all] Error 2
Makefile:151: recipe for target ‘all’ failed
make: *** [all] Error 2

**

at the end it says

nvcc fatal : Unsupported gpu architecture ‘compute_75’
plugin/CMakeFiles/nvinfer_plugin.dir/build.make:374: recipe for target ‘plugin/CMakeFiles/nvinfer_plugin.dir/batchedNMSPlugin/gatherNMSOutputs.cu.o’ failed
make[2]: *** [plugin/CMakeFiles/nvinfer_plugin.dir/batchedNMSPlugin/gatherNMSOutputs.cu.o] Error 1
CMakeFiles/Makefile2:259: recipe for target ‘plugin/CMakeFiles/nvinfer_plugin.dir/all’ failed
make[1]: *** [plugin/CMakeFiles/nvinfer_plugin.dir/all] Error 2
Makefile:151: recipe for target ‘all’ failed
make: *** [all] Error 2

I have cleaned using make clean and tried several times.

[Morganh]

@zieganwes

Hardware specs are:

i9 9th gen CPU
RTX 2080Ti

Software Version:

ubuntu 18.04 OS
cuda 11.2
driver version 460.32.03
docker 20.10.6

Docker image Version:

nvcr.io/nvidia/deepstream:5.0.1-20.09-triton

Where did you try to build the TRT OSS plugin? Is above configuration?
Which branch of TRT OSS did you sync?

[zieganwes]

Yes, its the same Configuration i have mentioned in the starting.
2080Ti coupled with a i9 9th gen, running on Ubuntu 18.04.5.

For building TRT OSS, I’m following the instructions provided in the metropolis TLT Documentation.

TRT release 7.0 from (https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/instance_segmentation/mask_rcnn.html#tensorrt-open-source-software-oss)

[zieganwes]

While waiting for your reply,

I also had tried making OSS plugins with the Git Version of 7.2.3.4 and this time while cmake ,I got some errors…

Building for TensorRT version: 7.2.3, library version: 7
– The CXX compiler identification is GNU 7.5.0
– The CUDA compiler identification is NVIDIA 9.1.85
– Check for working CXX compiler: /usr/bin/g++
– Check for working CXX compiler: /usr/bin/g++ – works
– Detecting CXX compiler ABI info
– Detecting CXX compiler ABI info - done
– Detecting CXX compile features
– Detecting CXX compile features - done
– Check for working CUDA compiler: /usr/bin/nvcc
– Check for working CUDA compiler: /usr/bin/nvcc – works
– Detecting CUDA compiler ABI info
– Detecting CUDA compiler ABI info - done
– Targeting TRT Platform: x86_64
– CUDA version set to 11.1
– cuDNN version set to 8.0
– Protobuf version set to 3.0.0
– Looking for C++ include pthread.h
– Looking for C++ include pthread.h - found
– Looking for pthread_create
– Looking for pthread_create - not found
– Looking for pthread_create in pthreads
– Looking for pthread_create in pthreads - not found
– Looking for pthread_create in pthread
– Looking for pthread_create in pthread - found
– Found Threads: TRUE
– Found PkgConfig: /usr/bin/pkg-config (found version “0.29.1”)
– Checking for one of the modules ‘zlib’
– Found CUDA: /usr (found suitable version “11.1”, minimum required is “11.1”)
– Using libprotobuf /home/deeplearning/TensorRT/build/third_party.protobuf/lib/libprotobuf.a
– ========================= Importing and creating target nvinfer ==========================
– Looking for library nvinfer
– Library that was found /usr/lib/x86_64-linux-gnu/libnvinfer.so
– ==========================================================================================
– ========================= Importing and creating target nvuffparser ==========================
– Looking for library nvparsers
– Library that was found /usr/lib/x86_64-linux-gnu/libnvparsers.so
– ==========================================================================================
– GPU_ARCHS defined as 75. Generating CUDA code for SM 75
– Protobuf proto/trtcaffe.proto → proto/trtcaffe.pb.cc proto/trtcaffe.pb.h
– /home/deeplearning/TensorRT/build/parsers/caffe
– The C compiler identification is GNU 7.5.0
– Check for working C compiler: /usr/bin/gcc
– Check for working C compiler: /usr/bin/gcc – works
– Detecting C compiler ABI info
– Detecting C compiler ABI info - done
– Detecting C compile features
– Detecting C compile features - done
– Build type not set - defaulting to Release
Generated: /home/deeplearning/TensorRT/build/parsers/onnx/third_party/onnx/onnx/onnx_onnx2trt_onnx-ml.proto
Generated: /home/deeplearning/TensorRT/build/parsers/onnx/third_party/onnx/onnx/onnx-operators_onnx2trt_onnx-ml.proto

– ******** Summary ********
– CMake version : 3.13.5
– CMake command : /usr/local/bin/cmake
– System : Linux
– C++ compiler : /usr/bin/g++
– C++ compiler version : 7.5.0
– CXX flags : -Wno-deprecated-declarations -DBUILD_SYSTEM=cmake_oss -Wall -Wno-deprecated-declarations -Wno-unused-function -Wnon-virtual-dtor
– Build type : Release
– Compile definitions : _PROTOBUF_INSTALL_DIR=/home/deeplearning/TensorRT/build;ONNX_NAMESPACE=onnx2trt_onnx
– CMAKE_PREFIX_PATH :
– CMAKE_INSTALL_PREFIX : /usr/lib/aarch64-linux-gnu/…
– CMAKE_MODULE_PATH :

– ONNX version : 1.6.0
– ONNX NAMESPACE : onnx2trt_onnx
– ONNX_BUILD_TESTS : OFF
– ONNX_BUILD_BENCHMARKS : OFF
– ONNX_USE_LITE_PROTO : OFF
– ONNXIFI_DUMMY_BACKEND : OFF
– ONNXIFI_ENABLE_EXT : OFF

– Protobuf compiler :
– Protobuf includes :
– Protobuf libraries :
– BUILD_ONNX_PYTHON : OFF
– Found TensorRT headers at /home/deeplearning/TensorRT/include
– Find TensorRT libs at /usr/lib/x86_64-linux-gnu/libnvinfer.so;/usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so;/usr/lib/x86_64-linux-gnu/libmyelin.so
– Found TENSORRT: /home/deeplearning/TensorRT/include
– Adding new sample: sample_algorithm_selector
– - Parsers Used: caffe
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_char_rnn
– - Parsers Used: uff;caffe;onnx
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_dynamic_reshape
– - Parsers Used: onnx
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_fasterRCNN
– - Parsers Used: caffe
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: sample_googlenet
– - Parsers Used: caffe
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_int8
– - Parsers Used: caffe
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: sample_int8_api
– - Parsers Used: onnx
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_mlp
– - Parsers Used: caffe
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_mnist
– - Parsers Used: caffe
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_mnist_api
– - Parsers Used: caffe
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_movielens
– - Parsers Used: uff
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_movielens_mps
– - Parsers Used: uff
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_nmt
– - Parsers Used: none
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_onnx_mnist
– - Parsers Used: onnx
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_plugin
– - Parsers Used: caffe
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: sample_reformat_free_io
– - Parsers Used: caffe
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_ssd
– - Parsers Used: caffe
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: sample_uff_fasterRCNN
– - Parsers Used: uff
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: sample_uff_maskRCNN
– - Parsers Used: uff
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: sample_uff_mnist
– - Parsers Used: uff
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_uff_plugin_v2_ext
– - Parsers Used: uff
– - InferPlugin Used: OFF
– - Licensing: opensource
– Adding new sample: sample_uff_ssd
– - Parsers Used: uff
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: sample_onnx_mnist_coord_conv_ac
– - Parsers Used: onnx
– - InferPlugin Used: ON
– - Licensing: opensource
– Adding new sample: trtexec
– - Parsers Used: caffe;uff;onnx
– - InferPlugin Used: OFF
– - Licensing: opensource
CMake Error: The following variables are used in this project, but they are set to NOTFOUND.
Please set them or make sure they are set and tested correctly in the CMake files:
CUBLASLT_LIB
linked by target “nvinfer_plugin” in directory /home/deeplearning/TensorRT/plugin

– Configuring incomplete, errors occurred!
See also “/home/deeplearning/TensorRT/build/CMakeFiles/CMakeOutput.log”.
See also “/home/deeplearning/TensorRT/build/CMakeFiles/CMakeError.log”.

aslo i have attached the log files for convenience

CMakeError.log (3.0 KB) CMakeOutput.log (75.1 KB)

Waiting for your reply.

Note: I’am trying these methods to deploy(into nvidia deepstream) a made MRCNN model trained in TLT with custom dataset.

[Morganh]

In a GTX 1080Ti host PC, I pull the docker you mentioned. And finally I can build the TRT OSS plugin successfully inside it.

$ docker pull nvcr.io/nvidia/deepstream:5.0.1-20.09-triton

$ docker run --runtime=nvidia -it nvcr.io/nvidia/deepstream:5.0.1-20.09-triton /bin/bash
Then, follow https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/tree/master/TRT-OSS/x86 to generate plugin.

Please double check.

[zieganwes]

So the solution is to pull the docker image nvcr.io/nvidia/deepstream:5.0.1-20.09-triton and, build the OSS plugins inside the docker.

Did i get it right ?

[Morganh]

@zieganwes

Software Version:

ubuntu 18.04 OS
cuda 11.2
driver version 460.32.03
docker 20.10.6

Docker image Version:

nvcr.io/nvidia/deepstream:5.0.1-20.09-triton

I just want to reproduce your error with the same environment. You mentioned as above.
So, where did you build the OSS plugin on your side?

[zieganwes]

I tried building the OSS Plugins outside the docker.

I have been using the docker container to only deploy models.

So, I planned to build the pugins outside the container and use it on the MCRNN model deployment to check the model for its performance and accuracy.

Did i miss anything?

[Morganh]

If you plan to use the docker container to deploy models, please build OSS plugin inside the docker.
The same, see MaskRCNN — Transfer Learning Toolkit 3.0 documentation , if the deployment platform is x86 with an NVIDIA GPU, follow the TensorRT OSS on x86 instructions. On the other hand, if your deployment is on NVIDIA Jetson platform, follow the TensorRT OSS on Jetson (ARM64) instructions.

[zieganwes]

Thanks a lot, It sure helped !.

Also,

For mrcnn in the deepstream TLT pretrained samples section “/opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models”

the README file uses wget to download the model from NVIDIA-AI-IOT github page.

mkdir -p …/…/models/tlt_pretrained_models/mrcnn&&
wget https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/master/models/mrcnn/mask_rcnn_resnet50.etlt?raw=true
-O …/…/models/tlt_pretrained_models/mrcnn/mask_rcnn_resnet50.etlt &&
wget https://raw.githubusercontent.com/NVIDIA-AI-IOT/deepstream_tlt_apps/master/models/mrcnn/cal.bin
-O …/…/models/tlt_pretrained_models/mrcnn/cal.bin

But instead of the model being downloaded, I am getting a ERROR 404: Not Found.

Could you help me with that !

[Morganh]

@zieganwes

wget https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/master/models/mrcnn/mask_rcnn_resnet50.etlt?raw=true

I will sync with internal team for this issue.
For workaround, could you please download it from the step in GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream ?

cd deepstream_tlt_apps/
wget https://nvidia.box.com/shared/static/i1cer4s3ox4v8svbfkuj5js8yqm3yazo.zip -O models.zip
unzip models.zip
rm models.zip

[Morganh]

For readme in DS5.1, it already tells end user to download the models mentioned in the GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream
But in readme of DS5.0, the links are not correct.

In a word, please download the models files in GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream