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tkDNN windows 10 support #218

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perseusdg merged 21 commits from master into master 2021-04-24 18:55:57 +02:00
Conversation 6 Commits 21 Files Changed 42
+1807 -126
42 changed files with 1807 additions and 126 deletions
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.gitignore
+7 -1
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@@ -12,5 +12,11 @@ build/
*.hdf5
*.pk
*.table
cmake-build-release/
demo/COCO_val2017
demo/BDD100K_val
demo/BDD100K_val
/.vs
cmake-build-minsizerel/*
scripts/COCO_val2017/*
scripts/COCO_val2017.zip
scripts/all_labels.txt
CMakeLists.txt
+12 -4
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@@ -2,7 +2,14 @@ cmake_minimum_required(VERSION 3.5)
project (tkDNN)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fPIC -Wno-deprecated-declarations -Wno-unused-variable")
if(UNIX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fPIC -Wno-deprecated-declarations -Wno-unused-variable ")
endif()
if(WIN32)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_FLAGS "/O2 /FS /EHsc")
set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
endif(WIN32)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/tkDNN)
# project specific flags
@@ -18,7 +25,7 @@ add_definitions(-DTKDNN_PATH="${CMAKE_CURRENT_SOURCE_DIR}")
find_package(CUDA 9.0 REQUIRED)
SET(CUDA_SEPARABLE_COMPILATION ON)
#set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch=sm_30 --compiler-options '-fPIC'")
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32 -arch=sm_61 )
find_package(CUDNN REQUIRED)
include_directories(${CUDNN_INCLUDE_DIR})
@@ -28,6 +35,7 @@ include_directories(${CUDNN_INCLUDE_DIR})
file(GLOB tkdnn_CUSRC "src/kernels/*.cu" "src/sorting.cu")
cuda_include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${CUDNN_INCLUDE_DIRS})
cuda_add_library(kernels SHARED ${tkdnn_CUSRC})
target_link_libraries(kernels ${CUDA_CUBLAS_LIBRARIES})
#-------------------------------------------------------------------------------
@@ -40,7 +48,7 @@ find_package(OpenCV REQUIRED)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
# gives problems in cross-compiling, probably malformed cmake config
#find_package(yaml-cpp REQUIRED)
find_package(yaml-cpp REQUIRED)
#-------------------------------------------------------------------------------
# Build Libraries
@@ -48,7 +56,7 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
file(GLOB tkdnn_SRC "src/*.cpp")
set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES} ${OpenCV_LIBS} yaml-cpp)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${OPENCV_INCLUDE_DIRS} ${NVINFER_INCLUDES})
add_library(tkDNN SHARED ${tkdnn_SRC})
target_link_libraries(tkDNN ${tkdnn_LIBS})
Issues.md
+1
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@@ -0,0 +1 @@
1)error C2131 @ Yolo3Detection.cpp(97) -> expression doesnt evaluate to a constant caused to read of variable outside its lifetime
README.md
+96
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@@ -80,6 +80,14 @@ Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
- [mAP demo](#map-demo)
- [Existing tests and supported networks](#existing-tests-and-supported-networks)
- [References](#references)
- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
- [Dependencies-Windows](#dependencies-windows)
- [Compiling tkDNN on Windows](#compiling-tkdnn-on-windows)
- [Run the demo on Windows](#run-the-demo-on-windows)
- [FP16 inference windows](#fp16-inference-windows)
- [INT8 inference windows](#int8-inference-windows)
- [Known issues with tkDNN on Windows](#known-issues-with-tkdnn-on-windows)
@@ -355,6 +363,94 @@ This demo also creates a json file named ```net_name_COCO_res.json``` containing
| yolo4tiny | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 672x672 | [weights](https://cloud.hipert.unimore.it/s/BLPpiAigZJLorQD/download) |
### tkDNN on Windows 10 (experimental)
### Dependencies-Windows
This branch should work on every NVIDIA GPU supported in windows with the following dependencies:
* WINDOWS 10 1803 or HIGHER
* CUDA 10.0 (Recommended CUDA 11.2 )
* CUDNN 7.6 (Recommended CUDNN 8.1.1 )
* TENSORRT 6.0.1 (Recommended TENSORRT 7.2.3.4 )
* OPENCV 3.4 (Recommended OPENCV 4.2.0 )
* MSVC 16.7
* YAML-CPP
* EIGEN3
* 7ZIP (ADD TO PATH)
* NINJA 1.10
All the above mentioned dependencies except 7ZIP can be installed using Microsoft's [VCPKG](https://github.com/microsoft/vcpkg.git) .
After bootstrapping VCPKG the dependencies can be built and installed using the following command :
```
opencv4(normal) - vcpkg.exe install opencv4[tbb,jpeg,tiff,opengl,openmp,png,ffmpeg,eigen]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
opencv4(cuda) - vcpkg.exe install opencv4[cuda,nonfree,contrib,eigen,tbb,jpeg,tiff,opengl,openmp,png,ffmpeg]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
```
To build opencv4 with cuda and cudnn version corresponding to your cuda version,vcpkg's cudnn portfile needs to be modified by adding ```$ENV{CUDA_PATH}``` at lines 16 and 17 in the portfile.cmake
After VCPKG finishes building and installing all the packages delete C:\temp_vcpkg_build and add C:\opt\x64-windows\bin and C:\opt\x64-windows\debug\bin to path
### Compiling tkDNN on Windows
tkDNN is built with cmake(3.15+) on windows along with ninja.Msbuild and NMake Makefiles are drastically slower when compiling the library compared to windows
```
git clone https://github.com/ceccocats/tkDNN.git
cd tkdnn-windows
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -G"Ninja" ..
ninja -j4
```
### Run the demo on Windows
This example uses yolo4_tiny.\
To run the object detection file create .rt file bu running:
```
.\test_yolo4tiny.exe
```
Once the rt file has been successfully create,run the demo using the following command:
```
.\demo.exe yolo4tiny_fp32.rt ..\demo\yolo_test.mp4 y
```
For general info on more demo paramters,check Run the demo section on top
To run the test_all_tests.sh on windows,use git bash or msys2
### FP16 inference windows
This is an untested feature on windows.To run the object detection demo with FP16 interference follow the below steps(example with yolo4tiny):
```
set TKDNN_MODE=FP16
del /f yolo4tiny_fp16.rt
.\test_yolo4tiny.exe
.\demo.exe yolo4tiny_fp16.rt ..\demo\yolo_test.mp4
```
### INT8 inference windows
To run object detection demo with INT8 (example with yolo4tiny):
```
set TKDNN_MODE=INT8
set TKDNN_CALIB_LABEL_PATH=..\demo\COCO_val2017\all_labels.txt
set TKDNN_CALIB_IMG_PATH=..\demo\COCO_val2017\all_images.txt
del /f yolo4tiny_int8.rt # be sure to delete(or move) old tensorRT files
.\test_yolo4tiny.exe # run the yolo test (is slow)
.\demo.exe yolo4tiny_int8.rt ..\demo\yolo_test.mp4 y
```
### Known issues with tkDNN on Windows
Mobilenet and Centernet demos work properly only when built with msvc 16.7 in Release Mode,when built in debug mode for the mentioned networks one might encounter opencv assert errors
All Darknet models work properly with demo using MSVC version(16.7-16.9)
It is recommended to use Nvidia Driver(465+),Cuda unknown errors have been observed when using older drivers on pascal(SM 61) devices.
## References
demo/demo/demo.cpp
+9 -4
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@@ -1,7 +1,7 @@
#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
#include <unistd.h>
//#include <unistd.h>
#include <mutex>
#include "CenternetDetection.h"
@@ -22,10 +22,15 @@ int main(int argc, char *argv[]) {
signal(SIGINT, sig_handler);
std::string net = "yolo3_berkeley.rt";
std::string net = "yolo4tiny_fp32.rt";
if(argc > 1)
net = argv[1];
std::string input = "../demo/yolo_test.mp4";
#ifdef __linux__
std::string input = "../demo/yolo_test.mp4";
#elif _WIN32
std::string input = "..\\..\\..\\demo\\yolo_test.mp4";
#endif
if(argc > 2)
input = argv[2];
char ntype = 'y';
@@ -131,7 +136,7 @@ int main(int argc, char *argv[]) {
double mean = 0;
std::cout<<COL_GREENB<<"\n\nTime stats:\n";
std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
std::cout<<"Min: "<<*std::min_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
std::cout<<"Max: "<<*std::max_element(detNN->stats.begin(), detNN->stats.end())/n_batch<<" ms\n";
for(int i=0; i<detNN->stats.size(); i++) mean += detNN->stats[i]; mean /= detNN->stats.size();
std::cout<<"Avg: "<<mean/n_batch<<" ms\t"<<1000/(mean/n_batch)<<" FPS\n"<<COL_END;
demo/demo/map.cpp
+3
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@@ -2,7 +2,10 @@
#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
#ifdef __linux__
#include <unistd.h>
#endif
#include <mutex>
#include "utils.h"
include/tkDNN/DetectionNN.h
+4 -2
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@@ -4,7 +4,10 @@
#include <iostream>
#include <signal.h>
#include <stdlib.h>
#ifdef __linux__
#include <unistd.h>
#endif
#include <mutex>
#include "utils.h"
@@ -14,7 +17,7 @@
#include "tkdnn.h"
// #define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
//#define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
#ifdef OPENCV_CUDACONTRIB
#include <opencv2/cudawarping.hpp>
@@ -150,7 +153,6 @@ class DetectionNN {
int x0, w, x1, y0, h, y1;
int objClass;
std::string det_class;
int baseline = 0;
float font_scale = 0.5;
int thickness = 2;
include/tkDNN/ImuOdom.h
+7
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@@ -1,7 +1,14 @@
#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
#ifdef __linux__
#include <unistd.h>
#elif _WIN32
#define _USE_MATH_DEFINES
#include <math.h>
#endif
#include <mutex>
#include <Eigen/Dense>
#include "utils.h"
include/tkDNN/Int8BatchStream.h
+4 -1
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@@ -11,8 +11,11 @@
#include <fstream>
#include <iomanip>
#include <signal.h>
#include <stdlib.h>
#include <stdlib.h>
#ifdef __linux__
#include <unistd.h>
#endif
#include <mutex>
#include "NvInfer.h"
include/tkDNN/Layer.h
+6 -1
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@@ -19,6 +19,7 @@ enum layerType_t {
LAYER_ACTIVATION_CRELU,
LAYER_ACTIVATION_LEAKY,
LAYER_ACTIVATION_MISH,
LAYER_ACTIVATION_LOGISTIC,
LAYER_FLATTEN,
LAYER_RESHAPE,
LAYER_MULADD,
@@ -68,6 +69,7 @@ public:
case LAYER_ACTIVATION_CRELU: return "ActivationCReLU";
case LAYER_ACTIVATION_LEAKY: return "ActivationLeaky";
case LAYER_ACTIVATION_MISH: return "ActivationMish";
case LAYER_ACTIVATION_LOGISTIC: return "ActivationLogistic";
case LAYER_FLATTEN: return "Flatten";
case LAYER_RESHAPE: return "Reshape";
case LAYER_MULADD: return "MulAdd";
@@ -212,7 +214,8 @@ public:
typedef enum {
ACTIVATION_ELU = 100,
ACTIVATION_LEAKY = 101,
ACTIVATION_MISH = 102
ACTIVATION_MISH = 102,
ACTIVATION_LOGISTIC = 103
} tkdnnActivationMode_t;
/**
@@ -233,6 +236,8 @@ public:
return LAYER_ACTIVATION_LEAKY;
else if (act_mode == ACTIVATION_MISH)
return LAYER_ACTIVATION_MISH;
else if (act_mode == ACTIVATION_LOGISTIC)
return LAYER_ACTIVATION_LOGISTIC;
else
return LAYER_ACTIVATION;
};
include/tkDNN/NetworkRT.h
+6
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@@ -6,6 +6,7 @@
#include "Network.h"
#include "Layer.h"
#include "NvInfer.h"
#include <memory>
namespace tk { namespace dnn {
@@ -26,6 +27,7 @@ using namespace nvinfer1;
#include "pluginsRT/ActivationLeakyRT.h"
#include "pluginsRT/ActivationReLUCeilingRT.h"
#include "pluginsRT/ActivationMishRT.h"
#include "pluginsRT/ActivationLogisticRT.h"
#include "pluginsRT/ReorgRT.h"
#include "pluginsRT/RegionRT.h"
#include "pluginsRT/RouteRT.h"
@@ -59,6 +61,7 @@ public:
#if NV_TENSORRT_MAJOR >= 6
nvinfer1::IBuilderConfig *configRT;
#endif
nvinfer1::ICudaEngine *engineRT;
nvinfer1::IExecutionContext *contextRT;
@@ -114,6 +117,9 @@ public:
bool serialize(const char *filename);
bool deserialize(const char *filename);
};
}}
include/tkDNN/pluginsRT/ActivationLeakyRT.h
+2 -1
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@@ -52,8 +52,9 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, size);
assert(buf == a + getSerializationSize());
}
int size;
include/tkDNN/pluginsRT/ActivationLogisticRT.h
+60
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@@ -0,0 +1,60 @@
#include<cassert>
#include "../kernels.h"
class ActivationLogisticRT : public IPlugin {
public:
ActivationLogisticRT() {
}
~ActivationLogisticRT(){
}
int getNbOutputs() const override {
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
return inputs[0];
}
void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
size = 1;
for(int i=0; i<outputDims[0].nbDims; i++)
size *= outputDims[0].d[i];
}
int initialize() override {
return 0;
}
virtual void terminate() override {
}
virtual size_t getWorkspaceSize(int maxBatchSize) const override {
return 0;
}
virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {
activationLOGISTICForward((dnnType*)reinterpret_cast<const dnnType*>(inputs[0]),
reinterpret_cast<dnnType*>(outputs[0]), batchSize*size, stream);
return 0;
}
virtual size_t getSerializationSize() override {
return 1*sizeof(int);
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
tk::dnn::writeBUF(buf, size);
}
int size;
};
include/tkDNN/pluginsRT/ActivationMishRT.h
+2 -1
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@@ -52,8 +52,9 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, size);
assert(buf == a + getSerializationSize());
}
int size;
include/tkDNN/pluginsRT/ActivationReLUCeilingRT.h
+2 -1
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@@ -51,9 +51,10 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, ceiling);
tk::dnn::writeBUF(buf, size);
assert(buf = a + getSerializationSize());
}
include/tkDNN/pluginsRT/ActivationSigmoidRT.h
+2 -1
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@@ -52,8 +52,9 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, size);
assert(buf == a + getSerializationSize());
}
int size;
include/tkDNN/pluginsRT/DeformableConvRT.h
+2 -1
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@@ -116,7 +116,7 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, chunk_dim);
tk::dnn::writeBUF(buf, kh);
tk::dnn::writeBUF(buf, kw);
@@ -163,6 +163,7 @@ public:
for(int i=0; i<dim_ones; i++)
tk::dnn::writeBUF(buf, aus[i]);
free(aus);
assert(buf == a + getSerializationSize());
}
cublasStatus_t stat;
include/tkDNN/pluginsRT/FlattenConcatRT.h
+2 -1
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@@ -65,12 +65,13 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a = buf;
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
tk::dnn::writeBUF(buf, rows);
tk::dnn::writeBUF(buf, cols);
assert(buf == a + getSerializationSize());
}
int c, h, w;
include/tkDNN/pluginsRT/MaxPoolingFixedSizeRT.h
+2 -1
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@@ -55,7 +55,7 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, this->c);
tk::dnn::writeBUF(buf, this->h);
@@ -65,6 +65,7 @@ public:
tk::dnn::writeBUF(buf, this->stride_W);
tk::dnn::writeBUF(buf, this->winSize);
tk::dnn::writeBUF(buf, this->padding);
assert(buf == a + getSerializationSize());
}
int n, c, h, w;
include/tkDNN/pluginsRT/RegionRT.h
+2 -1
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@@ -73,13 +73,14 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, classes);
tk::dnn::writeBUF(buf, coords);
tk::dnn::writeBUF(buf, num);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
assert(buf == a + getSerializationSize());
}
int c, h, w;
include/tkDNN/pluginsRT/ReorgRT.h
+2 -1
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@@ -52,11 +52,12 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, stride);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
assert(buf == a + getSerializationSize());
}
int c, h, w, stride;
include/tkDNN/pluginsRT/ReshapeRT.h
+2 -1
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@@ -50,11 +50,12 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a = buf;
tk::dnn::writeBUF(buf, n);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
assert(buf == a + getSerializationSize());
}
int n, c, h, w;
include/tkDNN/pluginsRT/ResizeLayerRT.h
+2 -1
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@@ -52,7 +52,7 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, o_c);
tk::dnn::writeBUF(buf, o_h);
@@ -61,6 +61,7 @@ public:
tk::dnn::writeBUF(buf, i_c);
tk::dnn::writeBUF(buf, i_h);
tk::dnn::writeBUF(buf, i_w);
assert(buf == a + getSerializationSize());
}
int i_c, i_h, i_w, o_c, o_h, o_w;
include/tkDNN/pluginsRT/RouteRT.h
+2 -1
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@@ -75,7 +75,7 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, groups);
tk::dnn::writeBUF(buf, group_id);
tk::dnn::writeBUF(buf, in);
@@ -85,6 +85,7 @@ public:
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
assert(buf == a + getSerializationSize());
}
static const int MAX_INPUTS = 4;
include/tkDNN/pluginsRT/ShortcutRT.h
+2 -1
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@@ -59,13 +59,14 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, bc);
tk::dnn::writeBUF(buf, bh);
tk::dnn::writeBUF(buf, bw);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
assert(buf == a + getSerializationSize());
}
include/tkDNN/pluginsRT/UpsampleRT.h
+2 -1
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@@ -54,11 +54,12 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, stride);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
assert(buf == a + getSerializationSize());
}
int c, h, w, stride;
include/tkDNN/pluginsRT/YoloRT.h
+36 -28
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@@ -64,20 +64,23 @@ public:
checkCuda( cudaMemcpyAsync(dstData, srcData, batchSize*c*h*w*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));
for (int b = 0; b < batchSize; ++b){
for(int n = 0; n < n_masks; ++n){
int index = entry_index(b, n*w*h, 0);
if (new_coords == 1)
activationLOGISTICForward(srcData + index, dstData + index, 4*w*h, stream); //x,y,w,h
else
activationLOGISTICForward(srcData + index, dstData + index, 2*w*h, stream); //x,y
if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
index = entry_index(b, n*w*h, 4);
activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*w*h, stream);
}
}
for (int b = 0; b < batchSize; ++b){
for(int n = 0; n < n_masks; ++n){
int index = entry_index(b, n*w*h, 0);
if (new_coords == 1){
if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
}
else{
activationLOGISTICForward(srcData + index, dstData + index, 2*w*h, stream); //x,y
if (this->scaleXY != 1) scalAdd(dstData + index, 2 * w*h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
index = entry_index(b, n*w*h, 4);
activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*w*h, stream);
}
}
}
//std::cout<<"YOLO END\n";
return 0;
@@ -89,21 +92,25 @@ public:
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
tk::dnn::writeBUF(buf, classes);
tk::dnn::writeBUF(buf, num);
tk::dnn::writeBUF(buf, n_masks);
tk::dnn::writeBUF(buf, scaleXY);
tk::dnn::writeBUF(buf, nms_thresh);
tk::dnn::writeBUF(buf, nms_kind);
tk::dnn::writeBUF(buf, new_coords);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
for(int i=0; i<n_masks; i++)
tk::dnn::writeBUF(buf, mask[i]);
for(int i=0; i<n_masks*2*num; i++)
tk::dnn::writeBUF(buf, bias[i]);
char *buf = reinterpret_cast<char*>(buffer),*a=buf;
tk::dnn::writeBUF(buf, classes); std::cout << "Classes :" << classes << std::endl;
tk::dnn::writeBUF(buf, num); std::cout << "Num : " << num << std::endl;
tk::dnn::writeBUF(buf, n_masks); std::cout << "N_Masks" << n_masks << std::endl;
tk::dnn::writeBUF(buf, scaleXY); std::cout << "ScaleXY :" << scaleXY << std::endl;
tk::dnn::writeBUF(buf, nms_thresh); std::cout << "nms_thresh :" << nms_thresh << std::endl;
tk::dnn::writeBUF(buf, nms_kind); std::cout << "nms_kind : " << nms_kind << std::endl;
tk::dnn::writeBUF(buf, new_coords); std::cout << "new_coords : " << new_coords << std::endl;
tk::dnn::writeBUF(buf, c); std::cout << "C : " << c << std::endl;
tk::dnn::writeBUF(buf, h); std::cout << "H : " << h << std::endl;
tk::dnn::writeBUF(buf, w); std::cout << "C : " << c << std::endl;
for (int i = 0; i < n_masks; i++)
{
tk::dnn::writeBUF(buf, mask[i]); std::cout << "mask[i] : " << mask[i] << std::endl;
}
for (int i = 0; i < n_masks * 2 * num; i++)
{
tk::dnn::writeBUF(buf, bias[i]); std::cout << "bias[i] : " << bias[i] << std::endl;
}
// save classes names
for(int i=0; i<classes; i++) {
@@ -113,6 +120,7 @@ public:
tk::dnn::writeBUF(buf, tmp[j]);
}
}
assert(buf == a + getSerializationSize());
}
int c, h, w;
include/tkDNN/test.h
+4 -3
View File
@@ -29,7 +29,8 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
readBinaryFile(input_bins[0], net->input_dim.tot(), &input_h, &data);
// outputs
dnnType *cudnn_out[outputs.size()], *rt_out[outputs.size()];
//dnnType *cudnn_out[outputs.size()], *rt_out[outputs.size()];
std::vector<dnnType *> cudnn_out,rt_out;
tk::dnn::dataDim_t dim1 = net->input_dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30); {
@@ -39,7 +40,7 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
TKDNN_TSTOP
dim1.print();
}
for(int i=0; i<outputs.size(); i++) cudnn_out[i] = outputs[i]->dstData;
for(int i=0; i<outputs.size(); i++) cudnn_out.push_back(outputs[i]->dstData);
if(netRT != nullptr) {
tk::dnn::dataDim_t dim2 = net->input_dim;
@@ -50,7 +51,7 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
TKDNN_TSTOP
dim2.print();
}
for(int i=0; i<outputs.size(); i++) rt_out[i] = (dnnType*)netRT->buffersRT[i+1];
for(int i=0; i<outputs.size(); i++) rt_out.push_back((dnnType*)netRT->buffersRT[i+1]);
}
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
include/tkDNN/utils.h
+13
View File
@@ -12,8 +12,12 @@
#include <cublas_v2.h>
#include <cudnn.h>
#ifdef __linux__
#include <unistd.h>
#endif
#include <ios>
#include <chrono>
#define dnnType float
@@ -39,6 +43,7 @@
#define TKDNN_VERBOSE 0
// Simple Timer
#ifdef __linux__
#define TKDNN_TSTART timespec start, end; \
clock_gettime(CLOCK_MONOTONIC, &start);
@@ -48,6 +53,14 @@
if(show) std::cout<<col<<"Time:"<<std::setw(16)<<t_ns<<" ms\n"<<COL_END;
#define TKDNN_TSTOP TKDNN_TSTOP_C(COL_CYANB, TKDNN_VERBOSE)
#elif _WIN32
#define TKDNN_TSTART auto start = std::chrono::high_resolution_clock::now();
#define TKDNN_TSTOP auto stop = std::chrono::high_resolution_clock::now(); \
std::chrono::duration<double> duration = stop -start; \
auto time_ms = std::chrono::duration_cast<std::chrono::milliseconds>(duration);\
double t_ns = time_ms.count();
#endif
/********************************************************
* Prints the error message, and exits
scripts/download_validation.py
+39
View File
@@ -0,0 +1,39 @@
import os
import urllib.request as dowReq
import zipfile
val = input("Enter BDD or COCO :")
if(val == "COCO"):
url = "https://cloud.hipert.unimore.it/s/LNxBDk4wzqXPL8c/download"
lib = "..\demo\COCO_val2017"
lib_zip = "COCO_val2017.zip"
elif(val == "BDD"):
url = "https://cloud.hipert.unimore.it/s/bikqk3FzCq2tg4D/download"
lib = "..\demo\BDD100k_val"
lib_zip = "BDD100k_val.zip"
dowReq.urlretrieve(url,lib_zip)
with zipfile.ZipFile(lib_zip,'r') as zip_ref:
zip_ref.extractall(lib)
labelFolder = lib + "\labels"
imageFolder = lib + "\images"
file1 = open(".\\..\\demo\\all_labels.txt","a")
path1 = os.path.realpath(labelFolder)
for file in os.listdir(labelFolder):
valTemp = path1 + "\\" + file
valTemp = valTemp + '\n'
file1.write(valTemp)
file1.close()
file2 = open(".\\..\\demo\\all_images.txt","a")
path2 = os.path.realpath(imageFolder)
for file in os.listdir(imageFolder):
pathtemp = path2 + "\\" + file
pathtemp = pathtemp + '\n'
file2.write(pathtemp)
file2.close()
print("Completed")
src/Activation.cpp
+5 -1
View File
@@ -52,7 +52,11 @@ dnnType* Activation::infer(dataDim_t &dim, dnnType* srcData) {
else if(act_mode == ACTIVATION_MISH) {
activationMishForward(srcData, dstData, dim.tot());
} else {
}
else if(act_mode == ACTIVATION_LOGISTIC) {
activationLOGISTICForward(srcData, dstData, dim.tot());
}else {
dnnType alpha = dnnType(1);
dnnType beta = dnnType(0);
checkCUDNN( cudnnActivationForward(net->cudnnHandle,
src/DarknetParser.cpp
+1
View File
@@ -187,6 +187,7 @@ namespace tk { namespace dnn {
if(f.activation == "relu") act = tkdnnActivationMode_t(CUDNN_ACTIVATION_RELU);
else if(f.activation == "leaky") act = tk::dnn::ACTIVATION_LEAKY;
else if(f.activation == "mish") act = tk::dnn::ACTIVATION_MISH;
else if(f.activation == "logistic") act = tk::dnn::ACTIVATION_LOGISTIC;
else { FatalError("activation not supported: " + f.activation); }
netLayers[netLayers.size()-1] = new tk::dnn::Activation(net, act);
};
src/LSTM.cpp
+9 -4
View File
@@ -87,17 +87,22 @@ LSTM::LSTM( Network *net, int hiddensize, bool returnSeq, std::string fname_weig
checkCUDNN(cudnnCreateRNNDescriptor(&rnnDesc));
#if CUDNN_MAJOR > 7
checkCUDNN(cudnnSetRNNDescriptor_v6(net->cudnnHandle,
checkCUDNN(cudnnSetRNNDescriptor_v6(net->cudnnHandle,rnnDesc, stateSize, numLayers, dropoutDesc,
cudnnRNNInputMode_t::CUDNN_LINEAR_INPUT,
//(bidirectional ? cudnnDirectionMode_t::CUDNN_BIDIRECTIONAL : cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL),
cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL,
cudnnRNNMode_t::CUDNN_LSTM,
cudnnRNNAlgo_t::CUDNN_RNN_ALGO_STANDARD,
net->dataType));
#else
checkCUDNN(cudnnSetRNNDescriptor(net->cudnnHandle,
#endif
rnnDesc, stateSize, numLayers, dropoutDesc,
checkCUDNN(cudnnSetRNNDescriptor(net->cudnnHandle,rnnDesc, stateSize, numLayers, dropoutDesc,
cudnnRNNInputMode_t::CUDNN_LINEAR_INPUT,
//(bidirectional ? cudnnDirectionMode_t::CUDNN_BIDIRECTIONAL : cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL),
cudnnDirectionMode_t::CUDNN_UNIDIRECTIONAL,
cudnnRNNMode_t::CUDNN_LSTM,
cudnnRNNAlgo_t::CUDNN_RNN_ALGO_STANDARD,
net->dataType));
#endif
// Get temp space sizes
src/NetworkRT.cpp
+89 -37
View File
@@ -139,7 +139,8 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
#if NV_TENSORRT_MAJOR >= 6
engineRT = builderRT->buildEngineWithConfig(*networkRT, *configRT);
#else
engineRT = builderRT->buildCudaEngine(*networkRT);
//engineRT = builderRT->buildCudaEngine(*networkRT);
engineRT = std::shared_ptr<nvinfer1::ICudaEngine>(builderRT->buildCudaEngine(*networkRT));
#endif
if(engineRT == nullptr)
FatalError("cloud not build cuda engine")
@@ -226,7 +227,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
return convert_layer(input, (Conv2d*) l);
if(type == LAYER_POOLING)
return convert_layer(input, (Pooling*) l);
if(type == LAYER_ACTIVATION || type == LAYER_ACTIVATION_CRELU || type == LAYER_ACTIVATION_LEAKY || type == LAYER_ACTIVATION_MISH)
if(type == LAYER_ACTIVATION || type == LAYER_ACTIVATION_CRELU || type == LAYER_ACTIVATION_LEAKY || type == LAYER_ACTIVATION_MISH || type == LAYER_ACTIVATION_LOGISTIC)
return convert_layer(input, (Activation*) l);
if(type == LAYER_SOFTMAX)
return convert_layer(input, (Softmax*) l);
@@ -421,6 +422,12 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Activation *l) {
checkNULL(lRT);
return lRT;
}
else if(l->act_mode == ACTIVATION_LOGISTIC) {
IPlugin *plugin = new ActivationLogisticRT();
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
return lRT;
}
else {
FatalError("this Activation mode is not yet implemented");
return NULL;
@@ -561,7 +568,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, DeformConv2d *l) {
IPluginLayer *lRT = networkRT->addPlugin(inputs, 2, *plugin);
checkNULL(lRT);
lRT->setName( ("Deformable" + std::to_string(l->id)).c_str() );
delete(inputs);
delete[](inputs);
// batchnorm
void *bias_b, *power_b, *mean_b, *variance_b, *scales_b;
if(dtRT == DataType::kHALF) {
@@ -638,7 +645,7 @@ bool NetworkRT::deserialize(const char *filename) {
IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialData, size_t serialLength) {
const char * buf = reinterpret_cast<const char*>(serialData);
const char * buf = reinterpret_cast<const char*>(serialData),*bufCheck = buf;
std::string name(layerName);
//std::cout<<name<<std::endl;
@@ -646,35 +653,48 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
if(name.find("ActivationLeaky") == 0) {
ActivationLeakyRT *a = new ActivationLeakyRT();
a->size = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return a;
}
if(name.find("ActivationMish") == 0) {
ActivationMishRT *a = new ActivationMishRT();
a->size = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return a;
}
if(name.find("ActivationLogistic") == 0) {
ActivationLogisticRT *a = new ActivationLogisticRT();
a->size = readBUF<int>(buf);
return a;
}
if(name.find("ActivationCReLU") == 0) {
ActivationReLUCeiling *a = new ActivationReLUCeiling(readBUF<float>(buf));
float activationReluTemp = readBUF<float>(buf);
ActivationReLUCeiling* a = new ActivationReLUCeiling(activationReluTemp);
a->size = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return a;
}
if(name.find("Region") == 0) {
RegionRT *r = new RegionRT(readBUF<int>(buf), //classes
readBUF<int>(buf), //coords
readBUF<int>(buf)); //num
int classesTemp = readBUF<int>(buf);
int coordsTemp = readBUF<int>(buf);
int numTemp = readBUF<int>(buf);
RegionRT* r = new RegionRT(classesTemp, coordsTemp, numTemp);
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return r;
}
if(name.find("Reorg") == 0) {
ReorgRT *r = new ReorgRT(readBUF<int>(buf)); //stride
int strideTemp = readBUF<int>(buf);
ReorgRT *r = new ReorgRT(strideTemp);
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return r;
}
@@ -690,27 +710,34 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
return r;
assert(buf == bufCheck + serialLength);
}
if(name.find("Pooling") == 0) {
MaxPoolFixedSizeRT *r = new MaxPoolFixedSizeRT( readBUF<int>(buf), //c
readBUF<int>(buf), //h
readBUF<int>(buf), //w
readBUF<int>(buf), //n
readBUF<int>(buf), //strideH
readBUF<int>(buf), //strideW
readBUF<int>(buf), //winSize
readBUF<int>(buf)); //padding
int cTemp = readBUF<int>(buf);
int hTemp = readBUF<int>(buf);
int wTemp = readBUF<int>(buf);
int nTemp = readBUF<int>(buf);
int strideHTemp = readBUF<int>(buf);
int strideWTemp = readBUF<int>(buf);
int winSizeTemp = readBUF<int>(buf);
int paddingTemp = readBUF<int>(buf);
MaxPoolFixedSizeRT* r = new MaxPoolFixedSizeRT(cTemp, hTemp, wTemp, nTemp, strideHTemp, strideWTemp, winSizeTemp, paddingTemp);
assert(buf == bufCheck + serialLength);
return r;
}
if(name.find("Resize") == 0) {
ResizeLayerRT *r = new ResizeLayerRT(readBUF<int>(buf), //o_c
readBUF<int>(buf), //o_h
readBUF<int>(buf)); //o_w
int o_cTemp = readBUF<int>(buf);
int o_hTemp = readBUF<int>(buf);
int o_wTemp = readBUF<int>(buf);
ResizeLayerRT* r = new ResizeLayerRT(o_cTemp, o_hTemp, o_wTemp);
r->i_c = readBUF<int>(buf);
r->i_h = readBUF<int>(buf);
r->i_w = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return r;
}
@@ -721,6 +748,7 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
r->w = readBUF<int>(buf);
r->rows = readBUF<int>(buf);
r->cols = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return r;
}
@@ -732,20 +760,25 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
new_dim.h = readBUF<int>(buf);
new_dim.w = readBUF<int>(buf);
ReshapeRT *r = new ReshapeRT(new_dim);
assert(buf == bufCheck + serialLength);
return r;
}
if(name.find("Yolo") == 0) {
YoloRT *r = new YoloRT(readBUF<int>(buf), //classes
readBUF<int>(buf), //num
nullptr, //yolo
readBUF<int>(buf), //n_masks
readBUF<float>(buf), //scale_xy
readBUF<float>(buf), //nms_thresh
readBUF<int>(buf), //nms_kind
readBUF<int>(buf) //new_coords
);
int classes_temp = readBUF<int>(buf);
int num_temp = readBUF<int>(buf);
int n_masks_temp = readBUF<int>(buf);
float scale_xy_temp = readBUF<float>(buf);
float nms_thresh_temp = readBUF<float>(buf);
int nms_kind_temp = readBUF<int>(buf);
int new_coords_temp = readBUF<int>(buf);
YoloRT *r = new YoloRT(classes_temp,num_temp,nullptr,n_masks_temp,scale_xy_temp,nms_thresh_temp,nms_kind_temp,new_coords_temp);
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
@@ -762,36 +795,54 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
tmp[j] = readBUF<char>(buf);
r->classesNames[i] = std::string(tmp);
}
assert(buf == bufCheck + serialLength);
yolos[n_yolos++] = r;
return r;
}
if(name.find("Upsample") == 0) {
UpsampleRT *r = new UpsampleRT(readBUF<int>(buf)); //stride
int strideTemp = readBUF<int>(buf);
UpsampleRT* r = new UpsampleRT(strideTemp);
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return r;
}
if(name.find("Route") == 0) {
RouteRT *r = new RouteRT(readBUF<int>(buf),readBUF<int>(buf));
int groupsTemp = readBUF<int>(buf);
int group_idTemp = readBUF<int>(buf);
RouteRT* r = new RouteRT(groupsTemp, group_idTemp);
r->in = readBUF<int>(buf);
for(int i=0; i<RouteRT::MAX_INPUTS; i++)
r->c_in[i] = readBUF<int>(buf);
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
assert(buf == bufCheck + serialLength);
return r;
}
if(name.find("Deformable") == 0) {
DeformableConvRT *r = new DeformableConvRT(readBUF<int>(buf), readBUF<int>(buf), readBUF<int>(buf),
readBUF<int>(buf), readBUF<int>(buf), readBUF<int>(buf),
readBUF<int>(buf), readBUF<int>(buf),
readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),
readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),readBUF<int>(buf),
nullptr);
int chuck_dimTemp = readBUF<int>(buf);
int khTemp = readBUF<int>(buf);
int kwTemp = readBUF<int>(buf);
int shTemp = readBUF<int>(buf);
int swTemp = readBUF<int>(buf);
int phTemp = readBUF<int>(buf);
int pwTemp = readBUF<int>(buf);
int deformableGroupTemp = readBUF<int>(buf);
int i_nTemp = readBUF<int>(buf);
int i_cTemp = readBUF<int>(buf);
int i_hTemp = readBUF<int>(buf);
int i_wTemp = readBUF<int>(buf);
int o_nTemp = readBUF<int>(buf);
int o_cTemp = readBUF<int>(buf);
int o_hTemp = readBUF<int>(buf);
int o_wTemp = readBUF<int>(buf);
DeformableConvRT* r = new DeformableConvRT(chuck_dimTemp, khTemp, kwTemp, shTemp, swTemp, phTemp, pwTemp, deformableGroupTemp, i_nTemp, i_cTemp, i_hTemp, i_wTemp, o_nTemp, o_cTemp, o_hTemp, o_wTemp, nullptr);
dnnType *aus = new dnnType[r->chunk_dim*2];
for(int i=0; i<r->chunk_dim*2; i++)
aus[i] = readBUF<dnnType>(buf);
@@ -822,6 +873,7 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
aus[i] = readBUF<dnnType>(buf);
checkCuda( cudaMemcpy(r->ones_d2, aus, sizeof(dnnType)*r->dim_ones, cudaMemcpyHostToDevice) );
free(aus);
assert(buf == bufCheck + serialLength);
return r;
}
src/Yolo.cpp
+17 -13
View File
@@ -9,6 +9,7 @@
#include "Layer.h"
#include "kernels.h"
namespace tk { namespace dnn {
Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights, int n_masks, float scale_xy, double nms_thresh, nmsKind_t nsm_kind, int new_coords) :
@@ -72,8 +73,8 @@ Yolo::box get_yolo_box(float *x, float *biases, int n, int index, int i, int j,
b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
}
else{
b.x = (i + x[index + 0 * stride] * 2 - 0.5) / lw;
b.y = (j + x[index + 1 * stride] * 2 - 0.5) / lh;
b.x = (i + x[index + 0 * stride] ) / lw;
b.y = (j + x[index + 1 * stride] ) / lh;
b.w = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w;
b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h;
}
@@ -87,15 +88,18 @@ dnnType* Yolo::infer(dataDim_t &dim, dnnType* srcData) {
for (int b = 0; b < dim.n; ++b){
for(int n = 0; n < n_masks; ++n){
int index = entry_index(b, n*dim.w*dim.h, 0, classes, input_dim, output_dim);
if (new_coords == 1)
activationLOGISTICForward(srcData + index, dstData + index, 4*dim.w*dim.h);
else
std::cout<<"new_coords"<<new_coords<<std::endl;
if (new_coords == 1){
if (this->scaleXY != 1) scalAdd(dstData + index, 2 * dim.w*dim.h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
}
else{
activationLOGISTICForward(srcData + index, dstData + index, 2*dim.w*dim.h);
if (this->scaleXY != 1) scalAdd(dstData + index, 2 * dim.w*dim.h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
index = entry_index(b, n*dim.w*dim.h, 4, classes, input_dim, output_dim);
activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*dim.w*dim.h);
if (this->scaleXY != 1) scalAdd(dstData + index, 2 * dim.w*dim.h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
index = entry_index(b, n*dim.w*dim.h, 4, classes, input_dim, output_dim);
activationLOGISTICForward(srcData + index, dstData + index, (1+classes)*dim.w*dim.h);
}
}
}
@@ -209,10 +213,10 @@ float yolo_box_iou(Yolo::box a, Yolo::box b)
}
void box_c(const Yolo::box a, const Yolo::box b, float& top, float& bot, float& left, float& right) {
top = std::min(a.y - a.h / 2, b.y - b.h / 2);
bot = std::max(a.y + a.h / 2, b.y + b.h / 2);
left = std::min(a.x - a.w / 2, b.x - b.w / 2);
right = std::max(a.x + a.w / 2, b.x + b.w / 2);
top = (std::min)(a.y - a.h / 2, b.y - b.h / 2);
bot = (std::max)(a.y + a.h / 2, b.y + b.h / 2);
left = (std::min)(a.x - a.w / 2, b.x - b.w / 2);
right = (std::max)(a.x + a.w / 2, b.x + b.w / 2);
}
// https://github.com/Zzh-tju/DIoU-darknet
src/Yolo3Detection.cpp
+4 -3
View File
@@ -94,9 +94,10 @@ void Yolo3Detection::preprocess(cv::Mat &frame, const int bi){
void Yolo3Detection::postprocess(const int bi, const bool mAP){
//get yolo outputs
dnnType *rt_out[netRT->pluginFactory->n_yolos];
for(int i=0; i<netRT->pluginFactory->n_yolos; i++)
rt_out[i] = (dnnType*)netRT->buffersRT[i+1] + netRT->buffersDIM[i+1].tot()*bi;
std::vector<float *> rt_out;
//dnnType *rt_out[netRT->pluginFactory->n_yolos];
for(int i=0; i<netRT->pluginFactory->n_yolos; i++)
rt_out.push_back((dnnType*)netRT->buffersRT[i+1] + netRT->buffersDIM[i+1].tot()*bi);
float x_ratio = float(originalSize[bi].width) / float(netRT->input_dim.w);
float y_ratio = float(originalSize[bi].height) / float(netRT->input_dim.h);
src/kernels/deformable_conv.cu
+1 -1
View File
@@ -18,7 +18,7 @@ inline int GET_BLOCKS(const int N)
}
__device__ float dmcn_im2col_bilinear(const float *bottom_data, const int data_width,
__device__ __host__ float dmcn_im2col_bilinear(const float *bottom_data, const int data_width,
const int height, const int width, float h, float w) {
int h_low = floor(h);
int w_low = floor(w);
src/utils.cpp
+15 -2
View File
@@ -23,14 +23,23 @@ bool fileExist(const char *fname) {
void downloadWeightsifDoNotExist(const std::string& input_bin, const std::string& test_folder, const std::string& weights_url){
if(!fileExist(input_bin.c_str())){
std::string mkdir_cmd = "mkdir " + test_folder;
std::string wget_cmd = "wget " + weights_url + " -O " + test_folder + "/weights.zip";
std::string wget_cmd = "curl " + weights_url + " --output " + test_folder + "/weights.zip";
#ifdef __linux__
std::string unzip_cmd = "unzip " + test_folder + "/weights.zip -d" + test_folder;
std::string rm_cmd = "rm " + test_folder + "/weights.zip";
#elif _WIN32
std::string unzip_cmd = "7z x " + test_folder + "/weights.zip -o" + test_folder;
#endif
int err = 0;
err = system(mkdir_cmd.c_str());
err = system(wget_cmd.c_str());
err = system(unzip_cmd.c_str());
#ifdef __linux__
err = system(rm_cmd.c_str());
#endif
}
}
@@ -191,8 +200,12 @@ void getMemUsage(double& vm_usage_kb, double& resident_set_kb){
>> O >> itrealvalue >> starttime >> vsize >> rss;
stat_stream.close();
#ifdef __linux__
long page_size_kb = sysconf(_SC_PAGE_SIZE) / 1024; // in case x86-64 is configured to use 2MB pages
#elif _WIN32
long page_size_kb = 4096/1024;
#endif
vm_usage_kb = vsize / 1024.0;
resident_set_kb = rss * page_size_kb;
}
tests/darknet/cfg/yolo4-csp.cfg
+1279
View File
File diff suppressed because it is too large Load Diff
tests/darknet/cfg/yolo4x.cfg
+15 -6
View File
@@ -5,8 +5,8 @@
# Training
batch=64
subdivisions=8
width=672
height=672
width=640
height=640
channels=3
momentum=0.949
decay=0.0005
@@ -15,7 +15,7 @@ saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.00261
learning_rate=0.001
burn_in=1000
max_batches = 500500
policy=steps
@@ -26,6 +26,8 @@ mosaic=1
letter_box=1
#optimized_memory=1
[convolutional]
batch_normalize=1
filters=32
@@ -1131,6 +1133,7 @@ size=1
stride=1
pad=1
activation=mish
stopbackward=800
##########################
@@ -1147,7 +1150,7 @@ size=1
stride=1
pad=1
filters=255
activation=linear
activation=logistic
[yolo]
@@ -1156,6 +1159,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4
classes=80
num=9
jitter=.1
scale_x_y = 2.0
objectness_smooth=0
ignore_thresh = .7
truth_thresh = 1
@@ -1169,6 +1173,7 @@ iou_loss=ciou
nms_kind=diounms
beta_nms=0.6
new_coords=1
max_delta=5
[route]
layers = -4
@@ -1275,7 +1280,7 @@ size=1
stride=1
pad=1
filters=255
activation=linear
activation=logistic
[yolo]
@@ -1284,6 +1289,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4
classes=80
num=9
jitter=.1
scale_x_y = 2.0
objectness_smooth=1
ignore_thresh = .7
truth_thresh = 1
@@ -1297,6 +1303,7 @@ iou_loss=ciou
nms_kind=diounms
beta_nms=0.6
new_coords=1
max_delta=5
[route]
layers = -4
@@ -1403,7 +1410,7 @@ size=1
stride=1
pad=1
filters=255
activation=linear
activation=logistic
[yolo]
@@ -1412,6 +1419,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4
classes=80
num=9
jitter=.1
scale_x_y = 2.0
objectness_smooth=1
ignore_thresh = .7
truth_thresh = 1
@@ -1425,3 +1433,4 @@ iou_loss=ciou
nms_kind=diounms
beta_nms=0.6
new_coords=1
max_delta=2
tests/darknet/yolo4-csp.cpp
+36
View File
@@ -0,0 +1,36 @@
#include<iostream>
#include<vector>
#include "tkdnn.h"
#include "test.h"
#include "DarknetParser.h"
int main() {
std::string bin_path = "yolo4-csp";
std::vector<std::string> input_bins = {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "/debug/layer144_out.bin",
bin_path + "/debug/layer159_out.bin",
bin_path + "/debug/layer174_out.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-csp.cfg";
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download");
// parse darknet network
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
net->print();
//convert network to tensorRT
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
}
tests/darknet/yolo4x.cpp
+1 -1
View File
@@ -17,7 +17,7 @@ int main() {
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4x.cfg";
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/BLPpiAigZJLorQD/download");
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download");