mmr/tkDNN
Archived
1
0
Fork 0
Code Issues 46 Pull Requests 12 Releases 4 Activity

Merge branch 'master' of https://github.com/ceccocats/tkDNN

Browse Source
This commit is contained in:
marco montes
2020-07-24 13:19:49 +02:00
parent 9c6122a456 f4970d1e6f
commit 67402848b1
7 changed files with 125 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+19 -8
View File
@@ -1,9 +1,9 @@
# tkDNN
tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier and several discrete GPU.
tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier, Nano and several discrete GPUs.
The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training.
If you use tkDNN in your research, please cite one of the following papers. For use in commercial solutions, write at gattifrancesco@hotmail.it or refer to https://hipert.unimore.it/ .
If you use tkDNN in your research, please cite one of the following papers. For use in commercial solutions, write at gattifrancesco@hotmail.it and micaela.verucchi@unimore.it or refer to https://hipert.unimore.it/ .
```
Accepted paper @ IRC 2020, will soon be published.
@@ -175,15 +175,25 @@ All models from darknet are now parsed directly from cfg, you still need to expo
mish
</details>
## Run the demo
## Run the demo
This is an example using yolov4.
To run the an object detection demo follow these steps (example with yolov3):
To run the an object detection first create the .rt file by running:
```
rm yolo3_fp32.rt # be sure to delete(or move) old tensorRT files
./test_yolo3 # run the yolo test (is slow)
./demo yolo3_fp32.rt ../demo/yolo_test.mp4 y
rm yolo4_fp32.rt # be sure to delete(or move) old tensorRT files
./test_yolo4 # run the yolo test (is slow)
```
In general the demo program takes 4 parameters:
If you get problems in the creation, try to check the error activating the debug of TensorRT in this way:
```
cmake .. -DDEBUG=True
make
```
Once you have succesfully created your rt file, run the demo:
```
./demo yolo4_fp32.rt ../demo/yolo_test.mp4 y
```
In general the demo program takes 6 parameters:
```
./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag>
```
@@ -197,6 +207,7 @@ where
N.b. By default it is used FP32 inference
![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
### FP16 inference
docker/Dockerfile
+7
View File
@@ -0,0 +1,7 @@
FROM ceccocats/tkdnn:latest
LABEL maintainer "Francesco Gatti"
RUN cd && git clone https://github.com/ceccocats/tkDNN.git && cd tkDNN && mkdir build && cd build \
&& cmake .. && make -j12
docker/Dockerfile.base
+57
View File
@@ -0,0 +1,57 @@
FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04
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
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
RUN echo "INSTALL OPENCV"
RUN apt-get install -y build-essential \
unzip \
pkg-config \
libjpeg-dev \
libpng-dev \
libtiff-dev \
libavcodec-dev \
libavformat-dev \
libswscale-dev \
libv4l-dev \
libxvidcore-dev \
libx264-dev \
libgtk-3-dev \
libatlas-base-dev \
gfortran \
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 && \
cd opencv-4.3.0 && 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 BUILD_EXAMPLES=OFF \
-D WITH_CUDA=ON \
-D CUDA_ARCH_BIN=7.2 \
-D CUDA_ARCH_PTX="" \
-D ENABLE_FAST_MATH=ON \
-D CUDA_FAST_MATH=ON \
-D WITH_CUBLAS=ON \
-D WITH_LIBV4L=ON \
-D WITH_GSTREAMER=ON \
-D WITH_GSTREAMER_0_10=OFF \
-D WITH_TBB=ON \
../ && make -j12 && make install
RUN apt clean
docker/README.md
+21
View File
@@ -0,0 +1,21 @@
# Use the prebuilt image
```
# build image
docker build -t tkdnn:build -f Dockerfile .
```
# Build Base Docker image
```
# make nvidia docker working
# follow this guide: https://github.com/NVIDIA/nvidia-docker
# dowload tensorrt
# from: https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.0/7.0.0.11/local_repo/nv-tensorrt-repo-ubuntu1804-cuda10.2-trt7.0.0.11-ga-20191216_1-1_amd64.deb
# build image
docker build -t ceccocats/tkdnn:latest -f Dockerfile.base .
# run image
docker run -ti --gpus all --rm ceccocats/tkdnn:latest bash
```
include/tkDNN/Layer.h
+2 -2
View File
@@ -273,8 +273,8 @@ public:
protected:
cudnnFilterDescriptor_t filterDesc;
cudnnConvolutionDescriptor_t convDesc;
cudnnConvolutionFwdAlgo_t algo;
cudnnConvolutionBwdDataAlgo_t bwAlgo;
cudnnConvolutionFwdAlgoPerf_t algo;
cudnnConvolutionBwdDataAlgoPerf_t bwAlgo;
cudnnTensorDescriptor_t biasTensorDesc;
void initCUDNN(bool back = false);
src/Conv2d.cpp
+18 -13
View File
@@ -62,25 +62,30 @@ void Conv2d::initCUDNN(bool back) {
// init workspace
workSpace = NULL;
ws_sizeInBytes = 0;
int algo_count = 0;
if(back) {
checkCUDNN( cudnnGetConvolutionBackwardDataAlgorithm(net->cudnnHandle,
filterDesc, dstTensor, convDesc, srcTensor,
CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &bwAlgo) );
checkCUDNN( cudnnGetConvolutionBackwardDataAlgorithm_v7(net->cudnnHandle,
filterDesc, dstTensor, convDesc, srcTensor, 1, &algo_count, &bwAlgo) );
checkCUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(net->cudnnHandle,
filterDesc, dstTensor, convDesc, srcTensor,
bwAlgo, &ws_sizeInBytes));
filterDesc, dstTensor, convDesc, srcTensor,
bwAlgo.algo, &ws_sizeInBytes));
// invert tensors
srcTensorDesc = dstTensor;
dstTensorDesc = srcTensor;
} else {
checkCUDNN( cudnnGetConvolutionForwardAlgorithm(net->cudnnHandle,
srcTensor, filterDesc, convDesc, dstTensor,
CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algo) );
checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(net->cudnnHandle,
srcTensor, filterDesc, convDesc, dstTensor,
algo, &ws_sizeInBytes));
checkCUDNN( cudnnGetConvolutionForwardAlgorithm_v7(net->cudnnHandle,
srcTensor, filterDesc, convDesc, dstTensor,
1, &algo_count, &algo) );
checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(net->cudnnHandle,
srcTensor, filterDesc, convDesc, dstTensor,
algo.algo, &ws_sizeInBytes));
}
if(algo_count < 1)
FatalError("Cannot retrieve convolutional algo");
}
void Conv2d::inferCUDNN(dnnType* srcData, bool back) {
@@ -91,12 +96,12 @@ void Conv2d::inferCUDNN(dnnType* srcData, bool back) {
checkCUDNN(cudnnConvolutionBackwardData(net->cudnnHandle,
&alpha, filterDesc, data_d,
srcTensorDesc, srcData,
convDesc, bwAlgo, workSpace, ws_sizeInBytes,
convDesc, bwAlgo.algo, workSpace, ws_sizeInBytes,
&beta, dstTensorDesc, dstData));
} else {
checkCUDNN(cudnnConvolutionForward(net->cudnnHandle,
&alpha, srcTensorDesc, srcData, filterDesc,
data_d, convDesc, algo, workSpace, ws_sizeInBytes,
data_d, convDesc, algo.algo, workSpace, ws_sizeInBytes,
&beta, dstTensorDesc, dstData));
}
tests/darknet/csresnext50-panet-spp_berkeley.cpp
+1 -2
View File
@@ -17,8 +17,7 @@ int main() {
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/csresnext50-panet-spp_berkeley.cfg";
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/berkeley.names";
// FIXME: wrong weights
// downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s//download");
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/q82qHAtqpoaFYo5/download");
// parse darknet network
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);