mmr/tkDNN
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Merge with master, works with Jetpack 4.3

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Signed-off-by: xavier <micaelaverucchi@gmail.com>
This commit is contained in:
xavier
2020-01-15 19:24:19 +01:00
parent 23a1365dc4 c32a0be257
commit 0f0c8c28e1
10 changed files with 59 additions and 35 deletions
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CMakeLists.txt
+3 -9
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@@ -18,17 +18,11 @@ find_package(CUDA 9.0 REQUIRED)
SET(CUDA_SEPARABLE_COMPILATION ON)
#set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch=sm_30 --compiler-options '-fPIC'")
# compile Discovery only if TensorRT is installed
find_library(NVINFER NAMES nvinfer)
if(NVINFER STREQUAL "NVINFER-NOTFOUND")
set(NVINFER_INCLUDES "/usr/local/nvidia/tensorrt/include/")
link_directories(/usr/local/nvidia/tensorrt/targets/x86_64-linux-gnu/lib/
/usr/local/cuda/targets/x86_64-linux/lib/)
endif()
find_package(CUDNN REQUIRED)
# compile
file(GLOB tkdnn_CUSRC "src/kernels/*.cu")
cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${NVINFER_INCLUDES})
cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${CUDNN_INCLUDE_DIRS})
cuda_add_library(kernels SHARED ${tkdnn_CUSRC})
@@ -43,7 +37,7 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
# Build Libraries
#-------------------------------------------------------------------------------
file(GLOB tkdnn_SRC "src/*.cpp")
set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} -lcudnn -lnvinfer ${OpenCV_LIBS})
set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES} ${OpenCV_LIBS})
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -std=c++11")
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${OPENCV_INCLUDE_DIRS} ${NVINFER_INCLUDES})
README.md
+4 -5
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@@ -3,9 +3,10 @@ tkDNN is a Deep Neural Network library built with cuDNN primitives specifically
The main scope is to do high performance inference on already trained models.
this branch actually work on every NVIDIA GPU that support the dependencies:
* CUDA 9
* CUDNN 7.105
* TENSORRT 4.02
* CUDA 10.0
* CUDNN 7.603
* TENSORRT 6.01
* OPENCV 4.1
## Workflow
The recommended workflow follow these step:
@@ -48,5 +49,3 @@ this will genereate a yolo3_berkeley.rt file that can be used for live detection
./yolo3_demo # launch detection on a demo video
./yolo3_demo yolo3_berkeley.rt /dev/video0 # launch detection on device 0
```
cmake/FindCUDNN.cmake
+33
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@@ -0,0 +1,33 @@
# Find the header files
find_path(CUDNN_INCLUDE_DIR
${CMAKE_SYSROOT}/usr/local/include
${CMAKE_SYSROOT}/usr/include
/usr/local/nvidia/tensorrt/include/
NO_DEFAULT_PATH
)
set(OLD_ROOT ${CMAKE_FIND_ROOT_PATH})
list(APPEND CMAKE_FIND_ROOT_PATH /)
list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.7)
list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.5)
find_library(CUDNN_LIB
NAMES cudnn
PATHS
/usr/local/driveworks/targets/${CMAKE_SYSTEM_PROCESSOR}-Linux/lib
/usr/lib/${CMAKE_SYSTEM_PROCESSOR}-linux-gnu/
NO_DEFAULT_PATH
)
find_library(CUDNN_NVLIB
NAMES "nvinfer"
PATHS
/usr/local/driveworks/targets/${CMAKE_SYSTEM_PROCESSOR}-Linux/lib
/usr/lib/${CMAKE_SYSTEM_PROCESSOR}-linux-gnu/
NO_DEFAULT_PATH
)
set(CMAKE_FIND_ROOT_PATH ${OLD_ROOT})
set(CUDNN_LIBRARIES ${CUDNN_LIB} ${CUDNN_NVLIB})
message("-- Found CUDNN: " ${CUDNN_LIB})
message("-- Found NVINFER: " ${CUDNN_NVLIB})
set(CUDNN_FOUND true)
cmake/tkDNNConfig.cmake
+5 -11
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@@ -4,27 +4,21 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} --std=c++11 -fPIC")
find_package(CUDA REQUIRED)
find_package(OpenCV REQUIRED)
find_library(NVINFER NAMES nvinfer)
if(NVINFER STREQUAL "NVINFER-NOTFOUND")
set(NVINFER_INCLUDES "/usr/local/nvidia/tensorrt/include/")
link_directories(/usr/local/nvidia/tensorrt/targets/x86_64-linux-gnu/lib/
/usr/local/cuda/targets/x86_64-linux/lib/)
endif()
find_package(CUDNN REQUIRED)
set(tkDNN_INCLUDE_DIRS
${CUDA_INCLUDE_DIRS}
${OPENCV_INCLUDE_DIRS}
${NVINFER_INCLUDES}
${CUDNN_INCLUDE_DIRS}
)
set(tkDNN_LIBRARIES
tkDNN
kernels
${CUDA_LIBRARIES}
${CUDA_CUBLAS_LIBRARIES}
-lcudnn
-lnvinfer
${OpenCV_LIBS}
${CUDA_CUBLAS_LIBRARIES}
${CUDNN_LIBRARIES}
${OpenCV_LIBS}
)
set(tkDNN_FOUND true)
demo/demo/demo.cpp
+4 -3
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@@ -7,6 +7,7 @@
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "Yolo3Detection.h"
@@ -46,9 +47,9 @@ int main(int argc, char *argv[]) {
cv::VideoWriter resultVideo;
if(SAVE_RESULT) {
int w = cap.get(CV_CAP_PROP_FRAME_WIDTH);
int h = cap.get(CV_CAP_PROP_FRAME_HEIGHT);
resultVideo.open("result.mp4", CV_FOURCC('M','P','4','V'), 30, cv::Size(w, h));
int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
}
cv::Mat frame;
include/tkDNN/Layer.h
+2
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@@ -26,6 +26,8 @@ enum layerType_t {
LAYER_YOLO
};
#define TKDNN_BN_MIN_EPSILON 1e-5
/**
Simple layer Father class
*/
src/Conv2d.cpp
+1 -1
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@@ -113,7 +113,7 @@ void Conv2d::inferCUDNN(dnnType* srcData, bool back) {
dstTensorDesc, dstData, dstTensorDesc,
dstData, biasTensorDesc, //same tensor descriptor as bias
scales_d, bias_d, mean_d, variance_d,
CUDNN_BN_MIN_EPSILON) );
TKDNN_BN_MIN_EPSILON) );
}
}
src/DeformConv2d.cpp
+1 -1
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@@ -124,7 +124,7 @@ dnnType* DeformConv2d::infer(dataDim_t &dim, dnnType* srcData) {
dstTensorDesc, dstData, dstTensorDesc,
dstData, biasTensorDesc, //same tensor descriptor as bias
scales_d, bias_d, mean_d, variance_d,
CUDNN_BN_MIN_EPSILON) );
TKDNN_BN_MIN_EPSILON) );
}
//update data dimensions
src/LayerWgs.cpp
+1 -1
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@@ -35,7 +35,7 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
seek += outputs;
readBinaryFile(weights_path.c_str(), outputs, &variance_h, &variance_d, seek, net->dontLoadWeights);
float eps = CUDNN_BN_MIN_EPSILON;
float eps = TKDNN_BN_MIN_EPSILON;
power_h = new dnnType[outputs];
for(int i=0; i<outputs; i++) power_h[i] = 1.0f;
src/Network.cpp
+5 -4
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@@ -18,10 +18,6 @@ Network::Network(dataDim_t input_dim) {
dataType = CUDNN_DATA_FLOAT;
tensorFormat = CUDNN_TENSOR_NCHW;
dontLoadWeights = false;
checkCUDNN( cudnnCreate(&cudnnHandle) );
checkERROR( cublasCreate(&cublasHandle) );
num_layers = 0;
fp16 = false;
@@ -39,6 +35,11 @@ Network::Network(dataDim_t input_dim) {
std::cout<<COL_REDB<<"!! FP16 INERENCE ENABLED !!"<<COL_END<<"\n";
if(dla)
std::cout<<COL_GREENB<<"!! DLA INERENCE ENABLED !!"<<COL_END<<"\n";
checkCUDNN( cudnnCreate(&cudnnHandle) );
checkERROR( cublasCreate(&cublasHandle) );
}
Network::~Network() {