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- CMakeLists.txt opencv cuda contrib autodetect

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- Updated Docker to cuda-11.3+cudnn-8.2.1+TensorRT-8.0.34,Ubuntu to 20.04 and OpenCV to 4.5.4
- Updated OpenCV4 to 4.5.4 in install_OpenCV4.sh
- Updated README.md
This commit is contained in:
perseusdg
2021-11-10 12:30:57 +05:30
parent 744396fb0e
commit c0e2097397
7 changed files with 55 additions and 44 deletions
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CMakeLists.txt
+5
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@@ -107,8 +107,13 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
if(ENABLE_OPENCV_CUDA_CONTRIB) if(ENABLE_OPENCV_CUDA_CONTRIB)
if (OpenCV_FOUND) if (OpenCV_FOUND)
find_package(OpenCV COMPONENTS cudawarping cudaarithm) find_package(OpenCV COMPONENTS cudawarping cudaarithm)
if(OpenCV_cudawarping_FOUND AND OpenCV_cudaarithm_FOUND)
add_compile_definitions(OPENCV_CUDACONTRIB) add_compile_definitions(OPENCV_CUDACONTRIB)
message("OpenCV Cuda Contrib modules found") message("OpenCV Cuda Contrib modules found")
else()
message("OpenCV Cuda Contrib modules not found")
set(ENABLE_OPENCV_CUDA_CONTRIB OFF)
endif()
endif() endif()
endif() endif()
# if(OpenCV_CUDA_VERSION) # if(OpenCV_CUDA_VERSION)
README.md
+12 -12
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@@ -17,10 +17,9 @@ If you use tkDNN in your research, please cite the [following paper](https://iee
} }
``` ```
### What's new (20 July 2021) ### What's new (November 2021)
- [x] Support to sematic segmentation [README](docs/README_seg.md) - [x] Support to sematic segmentation on cuda 11+ [README](docs/README_seg.md)
- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md) - [x] Support to TensorRT8
- [ ] Support to TensorRT8 (WIP)
## FPS Results ## FPS Results
Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on
@@ -75,17 +74,17 @@ Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
- [Workflow](#workflow) - [Workflow](#workflow)
- [Exporting weights](#exporting-weights) - [Exporting weights](#exporting-weights)
- [Run the demos](#run-the-demos) - [Run the demos](#run-the-demos)
- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental) - [tkDNN on Windows 10 or Windows 11](#tkdnn-on-windows-10-or-windows-11)
- [Existing tests and supported networks](#existing-tests-and-supported-networks) - [Existing tests and supported networks](#existing-tests-and-supported-networks)
- [References](#references) - [References](#references)
## Dependencies ## Dependencies
This branch works on every NVIDIA GPU that supports the following (latest tested) dependencies: This branch works on every NVIDIA GPU that supports the following (latest tested) dependencies:
* CUDA 11.0 (or >= 10) [the segmentation only works with CUDA 10 for now] * CUDA 11.3 (or >= 10.2) [the segmentation only works with CUDA 10 for now]
* cuDNN 8.0.4 (or >= 7.3) * cuDNN 8.2.1 (or >= 8.0.4)
* TensorRT 7.2.0 (or >=5) * TensorRT 8.0.3 (or >=7.2)
* OpenCV 4.5.2 (or >=4) * OpenCV 4.5.4 (or >=4)
* cmake 3.21 (or >= 3.15) * cmake 3.21 (or >= 3.15)
* yaml-cpp 0.5.2 * yaml-cpp 0.5.2
* eigen3 3.3.4 * eigen3 3.3.4
@@ -101,7 +100,8 @@ To compile and install OpenCV4 with contrib us the script ```install_OpenCV4.sh`
``` ```
bash scripts/install_OpenCV4.sh bash scripts/install_OpenCV4.sh
``` ```
When using openCV not compiled with contrib, comment the definition of OPENCV_CUDACONTRIBCONTRIB in include/tkDNN/DetectionNN.h. When commented, the preprocessing of the networks is computed on the CPU, otherwise on the GPU. In the latter case some milliseconds are saved in the end-to-end latency. If you have OpenCV compiled with cuda and contrib and want to use it with tkDNN pass ```ENABLE_OPENCV_CUDA_CONTRIB=ON``` flag when compiling tkDBB
. If the flag is not passed,the preprocessing of the networks is computed on the CPU, otherwise on the GPU. In the latter case some milliseconds are saved in the end-to-end latency.
## How to compile this repo ## How to compile this repo
Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (3.15 or above). Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (3.15 or above).
@@ -137,9 +137,9 @@ For specific details on how to run:
![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif) ![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
## tkDNN on Windows 10/11 (experimental) ## tkDNN on Windows 10 or Windows 11
For specific details on how to run tkDNN on Windows 10 see [HERE](./docs/windows.md). For specific details on how to run tkDNN on Windows 10/11 see [HERE](./docs/windows.md).
## Existing tests and supported networks ## Existing tests and supported networks
docker/Dockerfile.base
+16 -16
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@@ -1,16 +1,14 @@
FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 FROM nvidia/cuda:11.3.1-devel-ubuntu20.04
LABEL maintainer "Francesco Gatti" LABEL maintainer "Francesco Gatti"
ENV DEBIAN_FRONTEND=noninteractive
ADD nv-tensorrt-repo-ubuntu1804-cuda10.2-trt7.0.0.11-ga-20191216_1-1_amd64.deb /tmp/trt.deb RUN apt-get update && apt-get install libcudnn8-dev=8.2.1.32-1+cuda11.3 libcudnn8=8.2.1.32-1+cuda11.3 libnvinfer-dev=8.0.3-1+cuda11.3 libnvinfer8=8.0.3-1+cuda11.3
RUN apt-get update && dpkg -i /tmp/trt.deb && rm /tmp/trt.deb && apt-get update RUN DEBIAN_FRONTEND=noninteractive apt-get update && apt install -y git wget libeigen3-dev libyaml-cpp-dev gcc-9 g++-9 libopengl-dev libgl-dev
RUN apt install -y libnvinfer7=7.0.0-1+cuda10.2 libnvinfer-dev=7.0.0-1+cuda10.2
RUN DEBIAN_FRONTEND=noninteractive apt install -y git wget libeigen3-dev libyaml-cpp-dev
RUN cd /tmp && \ RUN cd /tmp && \
wget https://github.com/Kitware/CMake/releases/download/v3.17.3/cmake-3.17.3-Linux-x86_64.sh && \ wget https://github.com/Kitware/CMake/releases/download/v3.21.4/cmake-3.21.4-Linux-x86_64.sh && \
chmod +x cmake-3.17.3-Linux-x86_64.sh && \ chmod +x cmake-3.21.4-Linux-x86_64.sh && \
./cmake-3.17.3-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \ ./cmake-3.21.4-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \
rm ./cmake-3.17.3-Linux-x86_64.sh rm ./cmake-3.21.4-Linux-x86_64.sh
ENV NVIDIA_VISIBLE_DEVICES all
RUN echo "INSTALL OPENCV" RUN echo "INSTALL OPENCV"
RUN apt-get install -y build-essential \ RUN apt-get install -y build-essential \
unzip \ unzip \
@@ -26,20 +24,21 @@ RUN apt-get install -y build-essential \
libx264-dev \ libx264-dev \
libgtk-3-dev \ libgtk-3-dev \
libatlas-base-dev \ libatlas-base-dev \
gfortran \ gfortran-9 \
libtbb-dev \
libgstreamer1.0-dev \ libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \ libgstreamer-plugins-base1.0-dev \
libdc1394-22-dev \ libdc1394-22-dev \
libavresample-dev libavresample-dev
RUN cd && wget https://github.com/opencv/opencv/archive/4.3.0.tar.gz && tar -xf 4.3.0.tar.gz && rm *.tar.gz RUN cd && wget https://github.com/opencv/opencv/archive/4.5.4.tar.gz && tar -xf 4.5.4.tar.gz && rm *.tar.gz
RUN cd && wget https://github.com/opencv/opencv_contrib/archive/4.3.0.tar.gz && tar -xf 4.3.0.tar.gz && rm *.tar.gz RUN cd && wget https://github.com/opencv/opencv_contrib/archive/4.5.4.tar.gz && tar -xf 4.5.4.tar.gz && rm *.tar.gz
RUN cd && \ RUN cd && \
cd opencv-4.3.0 && mkdir build && cd build && \ cd opencv-4.5.4 && mkdir build && cd build && \
cmake -D CMAKE_BUILD_TYPE=RELEASE \ cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \ -D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_PYTHON_EXAMPLES=OFF \ -D INSTALL_PYTHON_EXAMPLES=OFF \
-D INSTALL_C_EXAMPLES=OFF \ -D INSTALL_C_EXAMPLES=OFF \
-D OPENCV_EXTRA_MODULES_PATH='~/opencv_contrib-4.3.0/modules' \ -D OPENCV_EXTRA_MODULES_PATH='~/opencv_contrib-4.5.4/modules' \
-D BUILD_EXAMPLES=OFF \ -D BUILD_EXAMPLES=OFF \
-D WITH_CUDA=ON \ -D WITH_CUDA=ON \
-D CUDA_ARCH_BIN=7.2 \ -D CUDA_ARCH_BIN=7.2 \
@@ -47,6 +46,7 @@ RUN cd && \
-D ENABLE_FAST_MATH=ON \ -D ENABLE_FAST_MATH=ON \
-D CUDA_FAST_MATH=ON \ -D CUDA_FAST_MATH=ON \
-D WITH_CUBLAS=ON \ -D WITH_CUBLAS=ON \
-D WITH_OPENMP=ON \
-D WITH_LIBV4L=ON \ -D WITH_LIBV4L=ON \
-D WITH_GSTREAMER=ON \ -D WITH_GSTREAMER=ON \
-D WITH_GSTREAMER_0_10=OFF \ -D WITH_GSTREAMER_0_10=OFF \
docs/demo.md
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@@ -32,7 +32,7 @@ make
Once you have successfully created your rt file, run the demo(yolo) : Once you have successfully created your rt file, run the demo(yolo) :
``` ```
./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/cococ.names ./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/coco.names
``` ```
To run demo for mobilenet and centernet for the created rt file : To run demo for mobilenet and centernet for the created rt file :
@@ -67,9 +67,9 @@ N.B. By default it is used FP32 inference
To run the demo with FP16 inference follow these steps (example with yolov3): To run the demo with FP16 inference follow these steps (example with yolov3):
``` ```
export TKDNN_MODE=FP16 # set the half floating point optimization export TKDNN_MODE=FP16 # set the half floating point optimization
rm yolo3_fp16.rt # be sure to delete(or move) old tensorRT files rm yolo4_fp16.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow) ./test_yolo4 # run the yolo test (is slow)
./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y ./demo yolo4_fp16.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/coco.names
``` ```
N.B. Using FP16 inference will lead to some errors in the results (first or second decimal). N.B. Using FP16 inference will lead to some errors in the results (first or second decimal).
@@ -92,9 +92,9 @@ Then a complete example using yolo3 and COCO dataset would be:
export TKDNN_MODE=INT8 export TKDNN_MODE=INT8
export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
rm yolo3_int8.rt # be sure to delete(or move) old tensorRT files rm yolo4_int8.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow) ./test_yolo4 # run the yolo test (is slow)
./demo yolo3_int8.rt ../demo/yolo_test.mp4 y ./demo yolo4_int8.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/coco.names
``` ```
N.B. N.B.
docs/windows.md
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@@ -13,7 +13,7 @@
### Dependencies-Windows ### Dependencies-Windows
This branch should work on every NVIDIA GPU supported in windows with the following dependencies: This branch should work on every NVIDIA GPU supported in windows with the following dependencies:
* WINDOWS 10 1803 or HIGHER * WINDOWS 10 1803/WINDOWS 11 or HIGHER
* CUDA 11.2 * CUDA 11.2
* CUDNN 8.1.1 * CUDNN 8.1.1
* TENSORRT 7.2.3 * TENSORRT 7.2.3
scripts/install_OpenCV4.sh
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@@ -27,10 +27,14 @@ sudo apt-get install -y build-essential \
libgstreamer1.0-dev \ libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \ libgstreamer-plugins-base1.0-dev \
libdc1394-22-dev \ libdc1394-22-dev \
libavresample-dev libavresample-dev \
libtbb-dev \
git clone https://github.com/opencv/opencv.git git clone https://github.com/opencv/opencv.git
cd opencv && git checkout 4.5.4 && cd ..
git clone https://github.com/opencv/opencv_contrib.git git clone https://github.com/opencv/opencv_contrib.git
cd opencv_contrib && git checkout 4.5.4 && cd ..
python3 -m venv opencv4 python3 -m venv opencv4
source opencv4/bin/activate source opencv4/bin/activate
@@ -56,6 +60,8 @@ cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D WITH_GSTREAMER=ON \ -D WITH_GSTREAMER=ON \
-D WITH_GSTREAMER_0_10=OFF \ -D WITH_GSTREAMER_0_10=OFF \
-D WITH_TBB=ON \ -D WITH_TBB=ON \
-D WITH_OPENGL=ON \
-D WITH_VULKAN=ON \
../ ../
make -j4 make -j4
tests/centernet/dla34_cnet/dla34_cnet.cpp
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@@ -492,7 +492,7 @@ int main()
// } // }
//convert network to tensorRT //convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34_cnet")); tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(&net, net.getNetworkRTName("dla34_cnet"));
tk::dnn::dataDim_t dim1 = dim; //input dim tk::dnn::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30); printCenteredTitle(" CUDNN inference ", '=', 30);
@@ -509,7 +509,7 @@ int main()
{ {
dim2.print(); dim2.print();
TKDNN_TSTART TKDNN_TSTART
netRT.infer(dim2, data); netRT->infer(dim2, data);
TKDNN_TSTOP TKDNN_TSTOP
dim2.print(); dim2.print();
} }
@@ -528,7 +528,7 @@ int main()
dnnType *cudnn_out, *rt_out; dnnType *cudnn_out, *rt_out;
cudnn_out = outs[i]->dstData; cudnn_out = outs[i]->dstData;
rt_out = (dnnType *)netRT.buffersRT[i+out_count]; rt_out = (dnnType *)netRT->buffersRT[i+out_count];
// there is the maxpool. It isn't an output but it is necessary for the process section // there is the maxpool. It isn't an output but it is necessary for the process section
if(i==0) if(i==0)
out_count ++; out_count ++;
@@ -540,6 +540,6 @@ int main()
std::cout<<"CUDNN vs TRT "; std::cout<<"CUDNN vs TRT ";
ret_cudnn_tensorrt |= checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT; ret_cudnn_tensorrt |= checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
} }
netRT.destroy(); netRT->destroy();
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt; return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
} }