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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
+7 -2
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@@ -107,8 +107,13 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
if(ENABLE_OPENCV_CUDA_CONTRIB)
if (OpenCV_FOUND)
find_package(OpenCV COMPONENTS cudawarping cudaarithm)
add_compile_definitions(OPENCV_CUDACONTRIB)
message("OpenCV Cuda Contrib modules found")
if(OpenCV_cudawarping_FOUND AND OpenCV_cudaarithm_FOUND)
add_compile_definitions(OPENCV_CUDACONTRIB)
message("OpenCV Cuda Contrib modules found")
else()
message("OpenCV Cuda Contrib modules not found")
set(ENABLE_OPENCV_CUDA_CONTRIB OFF)
endif()
endif()
endif()
# 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)
- [x] Support to sematic segmentation [README](docs/README_seg.md)
- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md)
- [ ] Support to TensorRT8 (WIP)
### What's new (November 2021)
- [x] Support to sematic segmentation on cuda 11+ [README](docs/README_seg.md)
- [x] Support to TensorRT8
## FPS Results
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)
- [Exporting weights](#exporting-weights)
- [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)
- [References](#references)
## 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]
* cuDNN 8.0.4 (or >= 7.3)
* TensorRT 7.2.0 (or >=5)
* OpenCV 4.5.2 (or >=4)
* CUDA 11.3 (or >= 10.2) [the segmentation only works with CUDA 10 for now]
* cuDNN 8.2.1 (or >= 8.0.4)
* TensorRT 8.0.3 (or >=7.2)
* OpenCV 4.5.4 (or >=4)
* cmake 3.21 (or >= 3.15)
* yaml-cpp 0.5.2
* 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
```
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
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)
## 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
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"
ADD nv-tensorrt-repo-ubuntu1804-cuda10.2-trt7.0.0.11-ga-20191216_1-1_amd64.deb /tmp/trt.deb
RUN apt-get update && dpkg -i /tmp/trt.deb && rm /tmp/trt.deb && apt-get update
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
ENV DEBIAN_FRONTEND=noninteractive
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 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 cd /tmp && \
wget https://github.com/Kitware/CMake/releases/download/v3.17.3/cmake-3.17.3-Linux-x86_64.sh && \
chmod +x cmake-3.17.3-Linux-x86_64.sh && \
./cmake-3.17.3-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \
rm ./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-3.21.4-Linux-x86_64.sh && \
./cmake-3.21.4-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \
rm ./cmake-3.21.4-Linux-x86_64.sh
ENV NVIDIA_VISIBLE_DEVICES all
RUN echo "INSTALL OPENCV"
RUN apt-get install -y build-essential \
unzip \
@@ -26,20 +24,21 @@ RUN apt-get install -y build-essential \
libx264-dev \
libgtk-3-dev \
libatlas-base-dev \
gfortran \
gfortran-9 \
libtbb-dev \
libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \
libdc1394-22-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_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/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/4.5.4.tar.gz && tar -xf 4.5.4.tar.gz && rm *.tar.gz
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 \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_PYTHON_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 WITH_CUDA=ON \
-D CUDA_ARCH_BIN=7.2 \
@@ -47,6 +46,7 @@ RUN cd && \
-D ENABLE_FAST_MATH=ON \
-D CUDA_FAST_MATH=ON \
-D WITH_CUBLAS=ON \
-D WITH_OPENMP=ON \
-D WITH_LIBV4L=ON \
-D WITH_GSTREAMER=ON \
-D WITH_GSTREAMER_0_10=OFF \
docs/demo.md
+7 -7
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@@ -32,7 +32,7 @@ make
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 :
@@ -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):
```
export TKDNN_MODE=FP16 # set the half floating point optimization
rm yolo3_fp16.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow)
./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y
rm yolo4_fp16.rt # be sure to delete(or move) old tensorRT files
./test_yolo4 # run the yolo test (is slow)
./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).
@@ -92,9 +92,9 @@ Then a complete example using yolo3 and COCO dataset would be:
export TKDNN_MODE=INT8
export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.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
./test_yolo3 # run the yolo test (is slow)
./demo yolo3_int8.rt ../demo/yolo_test.mp4 y
rm yolo4_int8.rt # be sure to delete(or move) old tensorRT files
./test_yolo4 # run the yolo test (is slow)
./demo yolo4_int8.rt ../demo/yolo_test.mp4 y 80 ../tests/darknet/cfg/yolo4.cfg ../tests/darknet/names/coco.names
```
N.B.
docs/windows.md
+1 -1
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@@ -13,7 +13,7 @@
### Dependencies-Windows
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
* CUDNN 8.1.1
* TENSORRT 7.2.3
scripts/install_OpenCV4.sh
+8 -2
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@@ -27,17 +27,21 @@ sudo apt-get install -y build-essential \
libgstreamer1.0-dev \
libgstreamer-plugins-base1.0-dev \
libdc1394-22-dev \
libavresample-dev
libavresample-dev \
libtbb-dev \
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
cd opencv_contrib && git checkout 4.5.4 && cd ..
python3 -m venv opencv4
source opencv4/bin/activate
pip install wheel
pip install numpy
cd opencv && mkdir build && cd build
cd opencv && mkdir build && cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
@@ -56,6 +60,8 @@ cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D WITH_GSTREAMER=ON \
-D WITH_GSTREAMER_0_10=OFF \
-D WITH_TBB=ON \
-D WITH_OPENGL=ON \
-D WITH_VULKAN=ON \
../
make -j4
tests/centernet/dla34_cnet/dla34_cnet.cpp
+4 -4
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@@ -492,7 +492,7 @@ int main()
// }
//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
printCenteredTitle(" CUDNN inference ", '=', 30);
@@ -509,7 +509,7 @@ int main()
{
dim2.print();
TKDNN_TSTART
netRT.infer(dim2, data);
netRT->infer(dim2, data);
TKDNN_TSTOP
dim2.print();
}
@@ -528,7 +528,7 @@ int main()
dnnType *cudnn_out, *rt_out;
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
if(i==0)
out_count ++;
@@ -540,6 +540,6 @@ int main()
std::cout<<"CUDNN vs TRT ";
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;
}