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Add Mobilenetv2 SSD Lite post and preprocessing, add mobilenet demo

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Signed-off-by: xavier <micaelaverucchi@gmail.com>
This commit is contained in:
xavier
2020-02-26 17:42:20 +01:00
parent 110bf56dc4
commit 3eb079dd13
16 changed files with 1156 additions and 202 deletions
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CMakeLists.txt
+3
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@@ -121,6 +121,9 @@ target_link_libraries(yolo3_demo tkDNN)
add_executable(centernet_demo demo/demo/demo_centernet.cpp)
target_link_libraries(centernet_demo tkDNN)
add_executable(mobilenet_demo demo/demo/demo_mobilenet.cpp)
target_link_libraries(mobilenet_demo tkDNN)
add_executable(map_demo demo/demo/map.cpp)
target_link_libraries(map_demo tkDNN)
demo/demo/demo_mobilenet.cpp
+93
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@@ -0,0 +1,93 @@
#include <iostream>
#include <signal.h>
#include <stdlib.h> /* srand, rand */
#include <unistd.h>
#include <mutex>
#include "utils.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "MobilenetDetection.h"
bool gRun;
bool SAVE_RESULT = false;
void sig_handler(int signo)
{
std::cout << "request gateway stop\n";
gRun = false;
}
int main(int argc, char *argv[])
{
std::cout << "detection\n";
signal(SIGINT, sig_handler);
char *net = "mobilenetv2ssd.rt";
if (argc > 1)
net = argv[1];
char *input = "../demo/yolo_test.mp4";
if (argc > 2)
input = argv[2];
tk::dnn::MobilenetDetection mbnet;
mbnet.init(net);
gRun = true;
cv::VideoCapture cap(input);
if (!cap.isOpened())
gRun = false;
else
std::cout << "camera started\n";
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::VideoWriter::fourcc('M', 'P', '4', 'V'), 30, cv::Size(w, h));
}
cv::Mat frame;
cv::Mat dnn_input;
cv::namedWindow("detection", cv::WINDOW_NORMAL);
while (gRun)
{
cap >> frame;
if (!frame.data)
{
break;
}
// this will be resized to the net format
dnn_input = frame.clone();
// TODO: async infer
mbnet.update(dnn_input);
// draw dets
frame = mbnet.draw();
cv::imshow("detection", frame);
cv::waitKey(1);
if (SAVE_RESULT)
resultVideo << frame;
}
std::cout << "detection end\n";
std::cout << COL_GREENB << "\n\nTime stats:\n";
std::cout << "Min: " << *std::min_element(mbnet.stats.begin(), mbnet.stats.end()) << " ms\n";
std::cout << "Max: " << *std::max_element(mbnet.stats.begin(), mbnet.stats.end()) << " ms\n";
double mean = 0;
for (int i = 0; i < mbnet.stats.size(); i++)
mean += mbnet.stats[i];
mean /= mbnet.stats.size();
std::cout << "Avg: " << mean << " ms\n"
<< COL_END;
return 0;
}
include/evaluation.h
+1 -1
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@@ -1,5 +1,5 @@
#ifndef EVALUATION_H
#define EVALUATION_H_H
#define EVALUATION_H
#include <iostream>
#include <vector>
include/tkDNN/Layer.h
+25 -2
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@@ -17,6 +17,7 @@ enum layerType_t {
LAYER_ACTIVATION_CRELU,
LAYER_ACTIVATION_LEAKY,
LAYER_FLATTEN,
LAYER_RESHAPE,
LAYER_MULADD,
LAYER_POOLING,
LAYER_SOFTMAX,
@@ -62,6 +63,7 @@ public:
case LAYER_ACTIVATION_CRELU: return "ActivationCReLU";
case LAYER_ACTIVATION_LEAKY: return "ActivationLeaky";
case LAYER_FLATTEN: return "Flatten";
case LAYER_RESHAPE: return "Reshape";
case LAYER_MULADD: return "MulAdd";
case LAYER_POOLING: return "Pooling";
case LAYER_SOFTMAX: return "Softmax";
@@ -265,6 +267,20 @@ public:
virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
};
/**
Reshape layer
*/
class Reshape : public Layer {
public:
Reshape(Network *net, dataDim_t new_dim, bool final=false);
virtual ~Reshape();
virtual layerType_t getLayerType() { return LAYER_RESHAPE; };
virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
};
/**
MulAdd layer
@@ -329,11 +345,13 @@ protected:
class Softmax : public Layer {
public:
Softmax(Network *net);
Softmax(Network *net, const tk::dnn::dataDim_t* dim=nullptr, bool final=false, const cudnnSoftmaxMode_t mode=CUDNN_SOFTMAX_MODE_CHANNEL);
virtual ~Softmax();
virtual layerType_t getLayerType() { return LAYER_SOFTMAX; };
virtual dnnType* infer(dataDim_t &dim, dnnType* srcData);
dataDim_t dim;
cudnnSoftmaxMode_t mode;
};
/**
@@ -343,7 +361,7 @@ public:
class Route : public Layer {
public:
Route(Network *net, Layer **layers, int layers_n);
Route(Network *net, Layer **layers, int layers_n, bool final=false);
virtual ~Route();
virtual layerType_t getLayerType() { return LAYER_ROUTE; };
@@ -410,6 +428,11 @@ struct box {
int cl;
float x, y, w, h;
float prob;
void print()
{
std::cout<<"x: "<<x<<"\ty: "<<y<<"\tw: "<<w<<"\th: "<<h<<"\tcl: "<<cl<<"\tprob: "<<prob<<std::endl;
}
};
struct sortable_bbox {
int index;
include/tkDNN/MobilenetDetection.h
+112
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@@ -0,0 +1,112 @@
#ifndef MOBILENETDETECTION_H
#define MOBILENETDETECTION_H
#include <iostream>
#include "tkdnn.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#define N_COORDS 4
struct SSDSpec
{
int feature_size = 0;
int shrinkage = 0;
int box_width = 0;
int box_height = 0;
int ratio1 = 0;
int ratio2 = 0;
SSDSpec() {}
SSDSpec(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2) : feature_size(feature_size), shrinkage(shrinkage), box_width(box_width), box_height(box_height),
ratio1(ratio1), ratio2(ratio2) {}
void setAll(int feature_size, int shrinkage, int box_width, int box_height, int ratio1, int ratio2)
{
this->feature_size = feature_size;
this->shrinkage = shrinkage;
this->box_width = box_width;
this->box_height = box_height;
this->ratio1 = ratio1;
this->ratio2 = ratio2;
}
void print()
{
std::cout << "fsize: " << feature_size << "\tshrinkage: " << shrinkage << "\t box W:" << box_width << "\tbox H: " << box_height << "\t x ratio:" << ratio1 << "\t y ratio:" << ratio2 << std::endl;
}
};
namespace tk
{
namespace dnn
{
class MobilenetDetection
{
private:
tk::dnn::NetworkRT *netRT = nullptr;
int classes = 21;
float iou_threshold = 0.45;
float center_variance = 0.1;
float size_variance = 0.2;
float conf_thresh = 0.4;
int input_h = 300;
int input_w = 300;
int image_size = 300;
float *priors = nullptr;
int n_priors = 0;
cv::Mat origImg;
cv::Mat bgr[3];
float *input, *input_d;
float *locations_h, *confidences_h;
tk::dnn::dataDim_t dim;
dnnType *conf;
dnnType *loc;
float __colors[6][3] = {{1, 0, 1}, {0, 0, 1}, {0, 1, 1}, {0, 1, 0}, {1, 1, 0}, {1, 0, 0}};
int baseline = 0;
float fontScale = 0.5;
int thickness = 2;
void generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp = true);
void convert_locatios_to_boxes_and_center(float *priors, const int n_priors, float *locations, const float center_variance, const float size_variance);
float iou(const tk::dnn::box &a, const tk::dnn::box &b);
std::vector<tk::dnn::box> postprocess(float *locations, float *confidences, const int n_values, const float threshold, const int n_classes, const float iou_thresh, const int width, const int height);
float get_color2(int c, int x, int max);
cv::Scalar colors[256];
std::vector<std::string> voc_class_name;
public:
// keep track of inference times (ms)
std::vector<double> stats;
std::vector<tk::dnn::box> detected;
MobilenetDetection() {}
~MobilenetDetection() {}
void init(std::string tensor_path);
cv::Mat draw();
void update(cv::Mat &img);
};
} // namespace dnn
} // namespace tk
#endif /*MOBILENETDETECTION_H*/
include/tkDNN/NetworkRT.h
+4
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@@ -34,6 +34,8 @@ using namespace nvinfer1;
#include "pluginsRT/ResizeLayerRT.h"
//#include "pluginsRT/Int8Calibrator.h"
#include "pluginsRT/DeformableConvRT.h"
#include "pluginsRT/FlattenConcatRT.h"
#include "pluginsRT/ReshapeRT.h"
class PluginFactory : IPluginFactory
{
@@ -83,6 +85,8 @@ public:
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Pooling *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Softmax *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Route *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Flatten *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Reshape *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Reorg *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Region *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Shortcut *l);
include/tkDNN/pluginsRT/FlattenConcatRT.h
+76
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@@ -0,0 +1,76 @@
#include<cassert>
class FlattenConcatRT : public IPlugin {
public:
FlattenConcatRT() {
stat = cublasCreate(&handle);
if (stat != CUBLAS_STATUS_SUCCESS) {
printf ("CUBLAS initialization failed\n");
return;
}
}
~FlattenConcatRT(){
}
int getNbOutputs() const override {
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
return DimsCHW{ inputs[0].d[0] * inputs[0].d[1] * inputs[0].d[2], 1, 1};
}
void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
assert(nbOutputs == 1 && nbInputs ==1);
rows = inputDims[0].d[0];
cols = inputDims[0].d[1] * inputDims[0].d[2];
c = inputDims[0].d[0] * inputDims[0].d[1] * inputDims[0].d[2];
h = 1;
w = 1;
}
int initialize() override {
return 0;
}
virtual void terminate() override {
checkERROR(cublasDestroy(handle));
}
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 {
dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
checkCuda( cudaMemcpy(dstData, srcData, rows*cols*sizeof(dnnType), cudaMemcpyDeviceToDevice));
float const alpha(1.0);
float const beta(0.0);
checkERROR( cublasSgeam( handle, CUBLAS_OP_T, CUBLAS_OP_N, rows, cols, &alpha, srcData, cols, &beta, srcData, rows, dstData, rows ));
return 0;
}
virtual size_t getSerializationSize() override {
return 5*sizeof(int);
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
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);
}
int c, h, w;
int rows, cols;
cublasStatus_t stat;
cublasHandle_t handle;
};
include/tkDNN/pluginsRT/ReshapeRT.h
+61
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@@ -0,0 +1,61 @@
#include<cassert>
class ReshapeRT : public IPlugin {
public:
ReshapeRT(dataDim_t new_dim) {
n = new_dim.n;
c = new_dim.c;
h = new_dim.h;
w = new_dim.w;
}
~ReshapeRT(){
}
int getNbOutputs() const override {
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
return DimsCHW{ c,h,w};
}
void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
}
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 {
dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
checkCuda( cudaMemcpy(dstData, srcData, c*h*w*sizeof(dnnType), cudaMemcpyDeviceToDevice));
return 0;
}
virtual size_t getSerializationSize() override {
return 4*sizeof(int);
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
tk::dnn::writeBUF(buf, n);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
}
int n, c, h, w;
};
include/tkDNN/pluginsRT/SoftmaxRT.h
+64
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@@ -0,0 +1,64 @@
#include<cassert>
class SoftmaxRT : public IPlugin {
public:
SoftmaxRT(const tk::dnn::dataDim_t* dim) {
assert(dim != nullptr);
this->dim.n = dim->n;
this->dim.c = dim->c;
this->dim.h = dim->h;
this->dim.w = dim->w;
this->dim.l = dim->l;
}
~SoftmaxRT(){
}
int getNbOutputs() const override {
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
return DimsNCHW{this->dim.n,this->dim.c,this->dim.h,this->dim.w };
}
void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
}
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 {
dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
return 0;
}
virtual size_t getSerializationSize() override {
return 5*sizeof(int);
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
tk::dnn::writeBUF(buf, this->dim.n);
tk::dnn::writeBUF(buf, this->dim.c);
tk::dnn::writeBUF(buf, this->dim.h);
tk::dnn::writeBUF(buf, this->dim.w);
tk::dnn::writeBUF(buf, this->dim.l);
}
dataDim_t dim;
};
src/CenternetDetection.cpp
+3 -3
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@@ -226,7 +226,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
sz_old = sz;
cv::cuda::GpuMat im_Orig;
im_Orig = cv::cuda::GpuMat(imageORIG);
cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
// cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
checkCuda( cudaDeviceSynchronize() );
sz = imageF1_d.size();
@@ -235,7 +235,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
// cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
checkCuda( cudaDeviceSynchronize() );
end_t = std::chrono::steady_clock::now();
std::cout << " TIME warpAffine: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
@@ -248,7 +248,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
step_t = end_t;
dim2 = dim;
cv::cuda::split(imageF1_d,bgr);//split source
// cv::cuda::split(imageF1_d,bgr);//split source
end_t = std::chrono::steady_clock::now();
std::cout << " TIME split: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
step_t = end_t;
src/MobilenetDetection.cpp
+330
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@@ -0,0 +1,330 @@
#include "MobilenetDetection.h"
bool boxProbCmp(const tk::dnn::box &a, const tk::dnn::box &b)
{
return (a.prob > b.prob);
}
namespace tk
{
namespace dnn
{
void MobilenetDetection::generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp)
{
n_priors = 0;
for (int i = 0; i < n_specs; i++)
{
n_priors += specs[i].feature_size * specs[i].feature_size * 6;
}
// std::cout<<"n priors: "<<n_priors<<std::endl;
// std::cout<<"n priors: "<<n_specs<<std::endl;
priors = (float *)malloc(N_COORDS * n_priors * sizeof(float));
int i_prio = 0;
float scale, x_center, y_center, h, w, size, ratio;
int min, max;
for (int i = 0; i < n_specs; i++)
{
scale = (float)image_size / (float)specs[i].shrinkage;
min = specs[i].box_height > specs[i].box_width ? specs[i].box_width : specs[i].box_height;
max = specs[i].box_height < specs[i].box_width ? specs[i].box_width : specs[i].box_height;
for (int j = 0; j < specs[i].feature_size; j++)
{
for (int k = 0; k < specs[i].feature_size; k++)
{
//small sized square box
size = min;
x_center = (k + 0.5f) / scale;
y_center = (j + 0.5f) / scale;
h = w = (float)size / (float)image_size;
priors[i_prio * N_COORDS + 0] = x_center;
priors[i_prio * N_COORDS + 1] = y_center;
priors[i_prio * N_COORDS + 2] = w;
priors[i_prio * N_COORDS + 3] = h;
++i_prio;
//big sized square box
size = sqrt(max * min);
h = w = (float)size / (float)image_size;
priors[i_prio * N_COORDS + 0] = x_center;
priors[i_prio * N_COORDS + 1] = y_center;
priors[i_prio * N_COORDS + 2] = w;
priors[i_prio * N_COORDS + 3] = h;
++i_prio;
//change h/w ratio of the small sized box
size = min;
h = w = size / (float)image_size;
ratio = sqrt(specs[i].ratio1);
priors[i_prio * N_COORDS + 0] = x_center;
priors[i_prio * N_COORDS + 1] = y_center;
priors[i_prio * N_COORDS + 2] = w * ratio;
priors[i_prio * N_COORDS + 3] = h / ratio;
++i_prio;
priors[i_prio * N_COORDS + 0] = x_center;
priors[i_prio * N_COORDS + 1] = y_center;
priors[i_prio * N_COORDS + 2] = w / ratio;
priors[i_prio * N_COORDS + 3] = h * ratio;
++i_prio;
ratio = sqrt(specs[i].ratio2);
priors[i_prio * N_COORDS + 0] = x_center;
priors[i_prio * N_COORDS + 1] = y_center;
priors[i_prio * N_COORDS + 2] = w * ratio;
priors[i_prio * N_COORDS + 3] = h / ratio;
++i_prio;
priors[i_prio * N_COORDS + 0] = x_center;
priors[i_prio * N_COORDS + 1] = y_center;
priors[i_prio * N_COORDS + 2] = w / ratio;
priors[i_prio * N_COORDS + 3] = h * ratio;
++i_prio;
}
}
}
if (clamp)
{
for (int i = 0; i < n_priors * N_COORDS; i++)
{
priors[i] = priors[i] > 1.0f ? 1.0f : priors[i];
priors[i] = priors[i] < 0.0f ? 0.0f : priors[i];
// std::cout<<priors[i]<<" ";
// if((i+1)%4 == 0)
// std::cout<< i/4 <<" " <<std::endl;
}
}
}
void MobilenetDetection::convert_locatios_to_boxes_and_center(float *priors, const int n_priors, float *locations, const float center_variance, const float size_variance)
{
float cur_x, cur_y;
for (int i = 0; i < n_priors; i++)
{
locations[i * N_COORDS + 0] = locations[i * N_COORDS + 0] * center_variance * priors[i * N_COORDS + 2] + priors[i * N_COORDS + 0];
locations[i * N_COORDS + 1] = locations[i * N_COORDS + 1] * center_variance * priors[i * N_COORDS + 3] + priors[i * N_COORDS + 1];
locations[i * N_COORDS + 2] = exp(locations[i * N_COORDS + 2] * size_variance) * priors[i * N_COORDS + 2];
locations[i * N_COORDS + 3] = exp(locations[i * N_COORDS + 3] * size_variance) * priors[i * N_COORDS + 3];
cur_x = locations[i * N_COORDS + 0];
cur_y = locations[i * N_COORDS + 1];
locations[i * N_COORDS + 0] = cur_x - locations[i * N_COORDS + 2] / 2;
locations[i * N_COORDS + 1] = cur_y - locations[i * N_COORDS + 3] / 2;
locations[i * N_COORDS + 2] = cur_x + locations[i * N_COORDS + 2] / 2;
locations[i * N_COORDS + 3] = cur_y + locations[i * N_COORDS + 3] / 2;
// std::cout<<locations[i*N_COORDS + 0]<<" "<<locations[i*N_COORDS + 1]<<" "<<locations[i*N_COORDS + 2]<<" "<<locations[i*N_COORDS + 3]<<" "<<std::endl;
}
}
float MobilenetDetection::iou(const tk::dnn::box &a, const tk::dnn::box &b)
{
float max_x = a.x > b.x ? a.x : b.x;
float max_y = a.y > b.y ? a.y : b.y;
float min_w = a.w < b.w ? a.w : b.w;
float min_h = a.h < b.h ? a.h : b.h;
float ao_w = min_w - max_x > 0 ? min_w - max_x : 0;
float ao_h = min_h - max_y > 0 ? min_h - max_y : 0;
// std::cout<<" ao w: "<<ao_w<<" ao h: "<<ao_h<<std::endl;
float area_overlap = ao_w * ao_h;
float area_0_w = a.w - a.x > 0 ? a.w - a.x : 0;
float area_0_h = a.h - a.y > 0 ? a.h - a.y : 0;
float area_1_w = b.w - b.x > 0 ? b.w - b.x : 0;
float area_1_h = b.h - b.y > 0 ? b.h - b.y : 0;
float area_0 = area_0_h * area_0_w;
float area_1 = area_1_h * area_1_w;
// std::cout<<" area_overlap : "<<area_overlap<<" area_0: "<<area_0<<" area_1: "<<area_1<<std::endl;
float iou = area_overlap / (area_0 + area_1 - area_overlap + 1e-5);
return iou;
}
std::vector<tk::dnn::box> MobilenetDetection::postprocess(float *locations, float *confidences, const int n_values, const float threshold, const int n_classes, const float iou_thresh, const int width, const int height)
{
float *conf_per_class;
std::vector<tk::dnn::box> detections;
for (int i = 1; i < n_classes; i++)
{
conf_per_class = &confidences[i * n_values];
std::vector<tk::dnn::box> boxes;
for (int j = 0; j < n_values; j++)
{
if (conf_per_class[j] > threshold)
{
tk::dnn::box b;
b.cl = i;
b.prob = conf_per_class[j];
b.x = locations[j * N_COORDS + 0];
b.y = locations[j * N_COORDS + 1];
b.w = locations[j * N_COORDS + 2];
b.h = locations[j * N_COORDS + 3];
boxes.push_back(b);
}
}
std::sort(boxes.begin(), boxes.end(), boxProbCmp);
// for(auto b:boxes)
// b.print();
std::vector<tk::dnn::box> remaining;
while (boxes.size() > 0)
{
remaining.clear();
tk::dnn::box b;
b.cl = boxes[0].cl;
b.prob = boxes[0].prob;
b.x = boxes[0].x * width;
b.y = boxes[0].y * height;
b.w = boxes[0].w * width;
b.h = boxes[0].h * height;
detections.push_back(b);
for (size_t j = 1; j < boxes.size(); j++)
{
if (iou(boxes[0], boxes[j]) <= iou_thresh)
{
remaining.push_back(boxes[j]);
}
}
boxes = remaining;
}
}
// std::cout<<"picked"<<std::endl;
// for(auto b:detections)
// b.print();
return detections;
}
float MobilenetDetection::get_color2(int c, int x, int max)
{
float ratio = ((float)x / max) * 5;
int i = floor(ratio);
int j = ceil(ratio);
ratio -= i;
float r = (1 - ratio) * __colors[i % 6][c % 3] + ratio * __colors[j % 6][c % 3];
//printf("%f\n", r);
return r;
}
void MobilenetDetection::init(std::string tensor_path)
{
const int n_SSDSpec = 6;
SSDSpec specs[6];
specs[0].setAll(19, 16, 60, 105, 2, 3);
specs[1].setAll(10, 32, 105, 150, 2, 3);
specs[2].setAll(5, 64, 150, 195, 2, 3);
specs[3].setAll(3, 100, 195, 240, 2, 3);
specs[4].setAll(2, 150, 240, 285, 2, 3);
specs[5].setAll(1, 300, 285, 330, 2, 3);
generate_ssd_priors(specs, n_SSDSpec);
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str());
checkCuda(cudaMallocHost(&input, sizeof(dnnType) * netRT->input_dim.tot()));
checkCuda(cudaMalloc(&input_d, sizeof(dnnType) * netRT->input_dim.tot()));
locations_h = (float *)malloc(N_COORDS * n_priors * sizeof(float));
confidences_h = (float *)malloc(n_priors * classes * sizeof(float));
dim = tk::dnn::dataDim_t(1, 3, input_w, input_h, 1);
for (int c = 0; c < classes; c++)
{
int offset = c * 123457 % classes;
float r = get_color2(2, offset, classes);
float g = get_color2(1, offset, classes);
float b = get_color2(0, offset, classes);
colors[c] = cv::Scalar(int(255.0 * b), int(255.0 * g), int(255.0 * r));
}
const char *voc_class_name_[] = {
"BACKGROUND", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
"car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike",
"person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
voc_class_name = std::vector<std::string>(voc_class_name_, std::end(voc_class_name_));
}
cv::Mat MobilenetDetection::draw()
{
tk::dnn::box b;
for (size_t i = 0; i < detected.size(); i++)
{
b = detected[i];
std::string det_class = voc_class_name[b.cl];
cv::rectangle(origImg, cv::Point(b.x, b.y), cv::Point(b.w, b.h), colors[b.cl], 2);
// draw label
cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
cv::rectangle(origImg, cv::Point(b.x, b.y), cv::Point((b.x + textSize.width - 2), (b.y - textSize.height - 2)), colors[b.cl], -1);
cv::putText(origImg, det_class, cv::Point(b.x, (b.y - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
}
return origImg;
}
void MobilenetDetection::update(cv::Mat &img)
{
TIMER_START
detected.clear();
//save origin image
origImg = img;
cv::Size sz = origImg.size();
//resize image, remove mean, divide by std
cv::Mat frame_resize, frame_nomean, frame_scaled;
resize(origImg, frame_resize, cv::Size(netRT->input_dim.w, netRT->input_dim.h));
frame_resize.convertTo(frame_nomean, CV_32FC3, 1, -127);
frame_nomean.convertTo(frame_scaled, CV_32FC3, 1 / 128.0, 0);
//copy image into tensor and copy it into GPU
cv::split(frame_scaled, bgr);
for (int i = 0; i < netRT->input_dim.c; i++)
{
int idx = i * frame_scaled.rows * frame_scaled.cols;
memcpy((void *)&input[idx], (void *)bgr[i].data, frame_scaled.rows * frame_scaled.cols * sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_d, input, netRT->input_dim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
//do inference
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TIMER_START
netRT->infer(dim2, input_d);
TIMER_STOP
dim2.print();
}
//get confidences and locations
conf = (dnnType *)netRT->buffersRT[3];
loc = (dnnType *)netRT->buffersRT[4];
checkCuda(cudaMemcpy(confidences_h, conf, n_priors * classes * sizeof(float), cudaMemcpyDeviceToHost));
checkCuda(cudaMemcpy(locations_h, loc, N_COORDS * n_priors * sizeof(float), cudaMemcpyDeviceToHost));
//postprocess
convert_locatios_to_boxes_and_center(priors, n_priors, locations_h, center_variance, size_variance);
detected = postprocess(locations_h, confidences_h, n_priors, conf_thresh, classes, iou_threshold, sz.width, sz.height);
TIMER_STOP
stats.push_back(t_ns);
}
} // namespace dnn
} // namespace tk
src/NetworkRT.cpp
+40
View File
@@ -179,6 +179,10 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
return convert_layer(input, (Softmax*) l);
if(type == LAYER_ROUTE)
return convert_layer(input, (Route*) l);
if(type == LAYER_FLATTEN)
return convert_layer(input, (Flatten*) l);
if(type == LAYER_RESHAPE)
return convert_layer(input, (Reshape*) l);
if(type == LAYER_REORG)
return convert_layer(input, (Reorg*) l);
if(type == LAYER_REGION)
@@ -389,6 +393,24 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Route *l) {
return lRT;
}
ILayer* NetworkRT::convert_layer(ITensor *input, Flatten *l) {
IPlugin *plugin = new FlattenConcatRT();
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
return lRT;
}
ILayer* NetworkRT::convert_layer(ITensor *input, Reshape *l) {
// std::cout<<"convert Reshape\n";
l->output_dim.print();
IPlugin *plugin = new ReshapeRT(l->output_dim);
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
return lRT;
}
ILayer* NetworkRT::convert_layer(ITensor *input, Reorg *l) {
//std::cout<<"convert Reorg\n";
@@ -597,6 +619,24 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
return r;
}
if(name.find("Flatten") == 0) {
FlattenConcatRT *r = new FlattenConcatRT();
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
r->rows = readBUF<int>(buf);
r->cols = readBUF<int>(buf);
return r;
}
if(name.find("Reshape") == 0) {
dataDim_t new_dim(readBUF<int>(buf), readBUF<int>(buf),readBUF<int>(buf), readBUF<int>(buf));
ReshapeRT *r = new ReshapeRT(new_dim);
return r;
}
if(name.find("Yolo") == 0) {
YoloRT *r = new YoloRT(readBUF<int>(buf), //classes
readBUF<int>(buf), //num
src/Reshape.cpp
+37
View File
@@ -0,0 +1,37 @@
#include <iostream>
#include "Layer.h"
#include "kernels.h"
namespace tk { namespace dnn {
Reshape::Reshape(Network *net, dataDim_t new_dim, bool final) : Layer(net, final) {
checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(dnnType)) );
output_dim.n = new_dim.n;
output_dim.c = new_dim.c;
output_dim.h = new_dim.h;
output_dim.w = new_dim.w;
output_dim.l = new_dim.l;
}
Reshape::~Reshape() {
checkCuda( cudaFree(dstData) );
}
dnnType* Reshape::infer(dataDim_t &dim, dnnType* srcData) {
//transpose per channel
checkCuda( cudaMemcpy(dstData, srcData, dim.n*dim.c*dim.h*dim.w*sizeof(dnnType), cudaMemcpyDeviceToDevice));
//update data dimensions
dim = output_dim;
return dstData;
}
}}
src/Route.cpp
+1 -1
View File
@@ -5,7 +5,7 @@
namespace tk { namespace dnn {
Route::Route(Network *net, Layer **layers, int layers_n) : Layer(net) {
Route::Route(Network *net, Layer **layers, int layers_n, bool final) : Layer(net, final) {
this->layers_n = layers_n;
if(layers_n > MAX_INPUT_LAYERS)
src/Softmax.cpp
+26 -8
View File
@@ -5,22 +5,40 @@
namespace tk { namespace dnn {
Softmax::Softmax(Network *net) : Layer(net) {
Softmax::Softmax(Network *net, const tk::dnn::dataDim_t* dim, bool final, const cudnnSoftmaxMode_t mode) : Layer(net, final) {
checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(dnnType)) );
this->mode = mode;
if(dim == nullptr)
{
this->dim.n= input_dim.n;
this->dim.c= input_dim.c;
this->dim.h= input_dim.h;
this->dim.w= input_dim.w;
this->dim.l= input_dim.l;
}
else
{
this->dim.n= dim->n;
this->dim.c= dim->c;
this->dim.h= dim->h;
this->dim.w= dim->w;
this->dim.l= dim->l;
}
checkCUDNN( cudnnSetTensor4dDescriptor(srcTensorDesc,
net->tensorFormat,
net->dataType,
input_dim.n*input_dim.l,
input_dim.c,
input_dim.h, input_dim.w) );
this->dim.n*this->dim.l,
this->dim.c,
this->dim.h, this->dim.w) );
checkCUDNN( cudnnSetTensor4dDescriptor(dstTensorDesc,
net->tensorFormat,
net->dataType,
input_dim.n*input_dim.l,
input_dim.c,
input_dim.h, input_dim.w) );
this->dim.n*this->dim.l,
this->dim.c,
this->dim.h, this->dim.w) );
}
Softmax::~Softmax() {
@@ -34,7 +52,7 @@ dnnType* Softmax::infer(dataDim_t &dim, dnnType* srcData) {
dnnType beta = dnnType(0);
checkCUDNN( cudnnSoftmaxForward(net->cudnnHandle,
CUDNN_SOFTMAX_ACCURATE ,
CUDNN_SOFTMAX_MODE_CHANNEL,
this->mode,
&alpha,
srcTensorDesc,
srcData,
tests/mobilenetv2ssd/mobilenetv2ssd.cpp
+280 -187
View File
@@ -1,139 +1,143 @@
#include <iostream>
#include "tkdnn.h"
const char *output_bin = "../tests/mobilenetv2ssd/debug/regression_headers-5.bin";
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/imgproc/imgproc.hpp>
const char *output_bin1 = "../tests/mobilenetv2ssd/debug/classification_headers-5.bin";
const char *output_bin2 = "../tests/mobilenetv2ssd/debug/regression_headers-5.bin";
const char *input_bin = "../tests/mobilenetv2ssd/debug/input.bin";
const char *conv0_bin = "../tests/mobilenetv2ssd/layers/base_net-0-0.bin";
const char *inverted_residual1[]={
"../tests/mobilenetv2ssd/layers/base_net-1-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-1-conv-3.bin"};
const char *inverted_residual2[]={
"../tests/mobilenetv2ssd/layers/base_net-2-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-2-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-2-conv-6.bin"};
const char *inverted_residual3[]={
"../tests/mobilenetv2ssd/layers/base_net-3-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-3-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-3-conv-6.bin"};
const char *inverted_residual4[]={
"../tests/mobilenetv2ssd/layers/base_net-4-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-4-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-4-conv-6.bin"};
const char *inverted_residual5[]={
"../tests/mobilenetv2ssd/layers/base_net-5-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-5-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-5-conv-6.bin"};
const char *inverted_residual6[]={
"../tests/mobilenetv2ssd/layers/base_net-6-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-6-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-6-conv-6.bin"};
const char *inverted_residual7[]={
"../tests/mobilenetv2ssd/layers/base_net-7-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-7-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-7-conv-6.bin"};
const char *inverted_residual8[]={
"../tests/mobilenetv2ssd/layers/base_net-8-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-8-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-8-conv-6.bin"};
const char *inverted_residual9[]={
"../tests/mobilenetv2ssd/layers/base_net-9-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-9-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-9-conv-6.bin"};
const char *inverted_residual10[]={
"../tests/mobilenetv2ssd/layers/base_net-10-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-10-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-10-conv-6.bin"};
const char *inverted_residual11[]={
"../tests/mobilenetv2ssd/layers/base_net-11-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-11-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-11-conv-6.bin"};
const char *inverted_residual12[]={
"../tests/mobilenetv2ssd/layers/base_net-12-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-12-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-12-conv-6.bin"};
const char *inverted_residual13[]={
"../tests/mobilenetv2ssd/layers/base_net-13-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-13-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-13-conv-6.bin"};
const char *inverted_residual14[]={
"../tests/mobilenetv2ssd/layers/base_net-14-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-14-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-14-conv-6.bin"};
const char *inverted_residual15[]={
"../tests/mobilenetv2ssd/layers/base_net-15-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-15-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-15-conv-6.bin"};
const char *inverted_residual16[]={
"../tests/mobilenetv2ssd/layers/base_net-16-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-16-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-16-conv-6.bin"};
const char *inverted_residual17[]={
"../tests/mobilenetv2ssd/layers/base_net-17-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-17-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-17-conv-6.bin"};
const char *inverted_residual1[] = {
"../tests/mobilenetv2ssd/layers/base_net-1-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-1-conv-3.bin"};
const char *inverted_residual2[] = {
"../tests/mobilenetv2ssd/layers/base_net-2-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-2-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-2-conv-6.bin"};
const char *inverted_residual3[] = {
"../tests/mobilenetv2ssd/layers/base_net-3-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-3-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-3-conv-6.bin"};
const char *inverted_residual4[] = {
"../tests/mobilenetv2ssd/layers/base_net-4-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-4-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-4-conv-6.bin"};
const char *inverted_residual5[] = {
"../tests/mobilenetv2ssd/layers/base_net-5-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-5-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-5-conv-6.bin"};
const char *inverted_residual6[] = {
"../tests/mobilenetv2ssd/layers/base_net-6-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-6-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-6-conv-6.bin"};
const char *inverted_residual7[] = {
"../tests/mobilenetv2ssd/layers/base_net-7-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-7-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-7-conv-6.bin"};
const char *inverted_residual8[] = {
"../tests/mobilenetv2ssd/layers/base_net-8-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-8-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-8-conv-6.bin"};
const char *inverted_residual9[] = {
"../tests/mobilenetv2ssd/layers/base_net-9-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-9-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-9-conv-6.bin"};
const char *inverted_residual10[] = {
"../tests/mobilenetv2ssd/layers/base_net-10-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-10-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-10-conv-6.bin"};
const char *inverted_residual11[] = {
"../tests/mobilenetv2ssd/layers/base_net-11-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-11-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-11-conv-6.bin"};
const char *inverted_residual12[] = {
"../tests/mobilenetv2ssd/layers/base_net-12-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-12-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-12-conv-6.bin"};
const char *inverted_residual13[] = {
"../tests/mobilenetv2ssd/layers/base_net-13-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-13-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-13-conv-6.bin"};
const char *inverted_residual14[] = {
"../tests/mobilenetv2ssd/layers/base_net-14-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-14-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-14-conv-6.bin"};
const char *inverted_residual15[] = {
"../tests/mobilenetv2ssd/layers/base_net-15-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-15-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-15-conv-6.bin"};
const char *inverted_residual16[] = {
"../tests/mobilenetv2ssd/layers/base_net-16-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-16-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-16-conv-6.bin"};
const char *inverted_residual17[] = {
"../tests/mobilenetv2ssd/layers/base_net-17-conv-0.bin",
"../tests/mobilenetv2ssd/layers/base_net-17-conv-3.bin",
"../tests/mobilenetv2ssd/layers/base_net-17-conv-6.bin"};
const char *conv18 = "../tests/mobilenetv2ssd/layers/base_net-18-0.bin";
const char *extras0[]={
"../tests/mobilenetv2ssd/layers/extras-0-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-0-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-0-conv-6.bin"};
const char *extras1[]={
"../tests/mobilenetv2ssd/layers/extras-1-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-1-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-1-conv-6.bin"};
const char *extras2[]={
"../tests/mobilenetv2ssd/layers/extras-2-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-2-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-2-conv-6.bin"};
const char *extras3[]={
"../tests/mobilenetv2ssd/layers/extras-3-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-3-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-3-conv-6.bin"};
const char *extras0[] = {
"../tests/mobilenetv2ssd/layers/extras-0-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-0-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-0-conv-6.bin"};
const char *extras1[] = {
"../tests/mobilenetv2ssd/layers/extras-1-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-1-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-1-conv-6.bin"};
const char *extras2[] = {
"../tests/mobilenetv2ssd/layers/extras-2-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-2-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-2-conv-6.bin"};
const char *extras3[] = {
"../tests/mobilenetv2ssd/layers/extras-3-conv-0.bin",
"../tests/mobilenetv2ssd/layers/extras-3-conv-3.bin",
"../tests/mobilenetv2ssd/layers/extras-3-conv-6.bin"};
const char *classification_header0[]={
"../tests/mobilenetv2ssd/layers/classification_headers-0-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-0-3.bin"};
const char *classification_header1[]={
"../tests/mobilenetv2ssd/layers/classification_headers-1-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-1-3.bin"};
const char *classification_header2[]={
"../tests/mobilenetv2ssd/layers/classification_headers-2-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-2-3.bin"};
const char *classification_header3[]={
"../tests/mobilenetv2ssd/layers/classification_headers-3-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-3-3.bin"};
const char *classification_header4[]={
"../tests/mobilenetv2ssd/layers/classification_headers-4-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-4-3.bin"};
const char *classification_header0[] = {
"../tests/mobilenetv2ssd/layers/classification_headers-0-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-0-3.bin"};
const char *classification_header1[] = {
"../tests/mobilenetv2ssd/layers/classification_headers-1-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-1-3.bin"};
const char *classification_header2[] = {
"../tests/mobilenetv2ssd/layers/classification_headers-2-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-2-3.bin"};
const char *classification_header3[] = {
"../tests/mobilenetv2ssd/layers/classification_headers-3-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-3-3.bin"};
const char *classification_header4[] = {
"../tests/mobilenetv2ssd/layers/classification_headers-4-0.bin",
"../tests/mobilenetv2ssd/layers/classification_headers-4-3.bin"};
const char *classification_header5 = "../tests/mobilenetv2ssd/layers/classification_headers-5.bin";
const char *regression_header0[]={
"../tests/mobilenetv2ssd/layers/regression_headers-0-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-0-3.bin"};
const char *regression_header1[]={
"../tests/mobilenetv2ssd/layers/regression_headers-1-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-1-3.bin"};
const char *regression_header2[]={
"../tests/mobilenetv2ssd/layers/regression_headers-2-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-2-3.bin"};
const char *regression_header3[]={
"../tests/mobilenetv2ssd/layers/regression_headers-3-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-3-3.bin"};
const char *regression_header4[]={
"../tests/mobilenetv2ssd/layers/regression_headers-4-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-4-3.bin"};
const char *regression_header0[] = {
"../tests/mobilenetv2ssd/layers/regression_headers-0-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-0-3.bin"};
const char *regression_header1[] = {
"../tests/mobilenetv2ssd/layers/regression_headers-1-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-1-3.bin"};
const char *regression_header2[] = {
"../tests/mobilenetv2ssd/layers/regression_headers-2-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-2-3.bin"};
const char *regression_header3[] = {
"../tests/mobilenetv2ssd/layers/regression_headers-3-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-3-3.bin"};
const char *regression_header4[] = {
"../tests/mobilenetv2ssd/layers/regression_headers-4-0.bin",
"../tests/mobilenetv2ssd/layers/regression_headers-4-3.bin"};
const char *regression_header5 = "../tests/mobilenetv2ssd/layers/regression_headers-5.bin";
int main()
{
int classes = 21;
// Network layout
tk::dnn::dataDim_t dim(1, 3, 300, 300, 1);
@@ -143,11 +147,10 @@ int main()
tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
//Inverted Residual 1
tk::dnn::Conv2d conv2(&net, 32, 3, 3, 1, 1, 1, 1, inverted_residual1[0], true,false, false,32);
tk::dnn::Conv2d conv2(&net, 32, 3, 3, 1, 1, 1, 1, inverted_residual1[0], true, false, false, 32);
tk::dnn::Activation relu5(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d conv3(&net, 16, 1, 1, 1, 1, 0, 0, inverted_residual1[1], true);
//Inverted Residual 2
tk::dnn::Conv2d ir_2_conv1(&net, 96, 1, 1, 1, 1, 0, 0, inverted_residual2[0], true);
@@ -157,14 +160,14 @@ int main()
tk::dnn::Conv2d ir_2_conv3(&net, 24, 1, 1, 1, 1, 0, 0, inverted_residual2[2], true);
//Inverted Residual 3
tk::dnn::Layer *last = &ir_2_conv3;
tk::dnn::Layer *last = &ir_2_conv3;
tk::dnn::Conv2d ir_3_conv1(&net, 144, 1, 1, 1, 1, 0, 0, inverted_residual3[0], true);
tk::dnn::Activation relu_3_1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_3_conv2(&net, 144, 3, 3, 1, 1, 1, 1, inverted_residual3[1], true, false, false, 144);
tk::dnn::Activation relu_3_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_3_conv3(&net, 24, 1, 1, 1, 1, 0, 0, inverted_residual3[2], true);
tk::dnn::Shortcut s3_0 (&net, last);
tk::dnn::Shortcut s3_0(&net, last);
// //Inverted Residual 4
tk::dnn::Conv2d ir_4_conv1(&net, 144, 1, 1, 1, 1, 0, 0, inverted_residual4[0], true);
tk::dnn::Activation relu_4_1(&net, CUDNN_ACTIVATION_RELU);
@@ -179,8 +182,8 @@ int main()
tk::dnn::Conv2d ir_5_conv2(&net, 192, 3, 3, 1, 1, 1, 1, inverted_residual5[1], true, false, false, 192);
tk::dnn::Activation relu_5_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_5_conv3(&net, 32, 1, 1, 1, 1, 0, 0, inverted_residual5[2], true);
tk::dnn::Shortcut s5_0 (&net, last);
tk::dnn::Shortcut s5_0(&net, last);
// // // //Inverted Residual 6
last = &s5_0;
tk::dnn::Conv2d ir_6_conv1(&net, 192, 1, 1, 1, 1, 0, 0, inverted_residual6[0], true);
@@ -189,7 +192,7 @@ int main()
tk::dnn::Activation relu_6_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_6_conv3(&net, 32, 1, 1, 1, 1, 0, 0, inverted_residual6[2], true);
tk::dnn::Shortcut s6_0 (&net, last);
tk::dnn::Shortcut s6_0(&net, last);
//Inverted Residual 7
tk::dnn::Conv2d ir_7_conv1(&net, 192, 1, 1, 1, 1, 0, 0, inverted_residual7[0], true);
tk::dnn::Activation relu_7_1(&net, CUDNN_ACTIVATION_RELU);
@@ -204,8 +207,8 @@ int main()
tk::dnn::Conv2d ir_8_conv2(&net, 384, 3, 3, 1, 1, 1, 1, inverted_residual8[1], true, false, false, 384);
tk::dnn::Activation relu_8_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_8_conv3(&net, 64, 1, 1, 1, 1, 0, 0, inverted_residual8[2], true);
tk::dnn::Shortcut s8_0 (&net, last);
tk::dnn::Shortcut s8_0(&net, last);
//Inverted Residual 9
last = &s8_0;
tk::dnn::Conv2d ir_9_conv1(&net, 384, 1, 1, 1, 1, 0, 0, inverted_residual9[0], true);
@@ -214,7 +217,7 @@ int main()
tk::dnn::Activation relu_9_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_9_conv3(&net, 64, 1, 1, 1, 1, 0, 0, inverted_residual9[2], true);
tk::dnn::Shortcut s9_0 (&net, last);
tk::dnn::Shortcut s9_0(&net, last);
//Inverted Residual 10
last = &s9_0;
tk::dnn::Conv2d ir_10_conv1(&net, 384, 1, 1, 1, 1, 0, 0, inverted_residual10[0], true);
@@ -223,7 +226,7 @@ int main()
tk::dnn::Activation relu_10_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_10_conv3(&net, 64, 1, 1, 1, 1, 0, 0, inverted_residual10[2], true);
tk::dnn::Shortcut s10_0 (&net, last);
tk::dnn::Shortcut s10_0(&net, last);
//Inverted Residual 11
tk::dnn::Conv2d ir_11_conv1(&net, 384, 1, 1, 1, 1, 0, 0, inverted_residual11[0], true);
tk::dnn::Activation relu_11_1(&net, CUDNN_ACTIVATION_RELU);
@@ -239,7 +242,7 @@ int main()
tk::dnn::Activation relu_12_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_12_conv3(&net, 96, 1, 1, 1, 1, 0, 0, inverted_residual12[2], true);
tk::dnn::Shortcut s12_0 (&net, last);
tk::dnn::Shortcut s12_0(&net, last);
last = &s12_0;
//Inverted Residual 13
tk::dnn::Conv2d ir_13_conv1(&net, 576, 1, 1, 1, 1, 0, 0, inverted_residual13[0], true);
@@ -248,10 +251,10 @@ int main()
tk::dnn::Activation relu_13_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_13_conv3(&net, 96, 1, 1, 1, 1, 0, 0, inverted_residual13[2], true);
tk::dnn::Shortcut s13_0 (&net, last);
tk::dnn::Shortcut s13_0(&net, last);
// //Inverted Residual 14
tk::dnn::Conv2d ir_14_conv1(&net, 576, 1, 1, 1, 1, 0, 0, inverted_residual14[0], true);
tk::dnn::Activation relu_14_1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Activation relu_14_1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_14_conv2(&net, 576, 3, 3, 2, 2, 1, 1, inverted_residual14[1], true, false, false, 576);
tk::dnn::Activation relu_14_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_14_conv3(&net, 160, 1, 1, 1, 1, 0, 0, inverted_residual14[2], true);
@@ -264,7 +267,7 @@ int main()
tk::dnn::Activation relu_15_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_15_conv3(&net, 160, 1, 1, 1, 1, 0, 0, inverted_residual15[2], true);
tk::dnn::Shortcut s15_0 (&net, last);
tk::dnn::Shortcut s15_0(&net, last);
//Inverted Residual 16
last = &s15_0;
tk::dnn::Conv2d ir_16_conv1(&net, 960, 1, 1, 1, 1, 0, 0, inverted_residual16[0], true);
@@ -273,7 +276,7 @@ int main()
tk::dnn::Activation relu_16_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d ir_16_conv3(&net, 160, 1, 1, 1, 1, 0, 0, inverted_residual16[2], true);
tk::dnn::Shortcut s16_0 (&net, last);
tk::dnn::Shortcut s16_0(&net, last);
//Inverted Residual 17
tk::dnn::Conv2d ir_17_conv1(&net, 960, 1, 1, 1, 1, 0, 0, inverted_residual17[0], true);
tk::dnn::Activation relu_17_1(&net, CUDNN_ACTIVATION_RELU);
@@ -284,7 +287,7 @@ int main()
//Conv 18
tk::dnn::Conv2d ir_18_conv1(&net, 1280, 1, 1, 1, 1, 0, 0, conv18, true);
tk::dnn::Activation relu_18_1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Layer * header_1[1] = {&relu_18_1};
tk::dnn::Layer *header_1[1] = {&relu_18_1};
// //extras Inverted Residual 0
tk::dnn::Conv2d e_0_conv1(&net, 256, 1, 1, 1, 1, 0, 0, extras0[0], true);
@@ -292,7 +295,7 @@ int main()
tk::dnn::Conv2d e_0_conv2(&net, 256, 3, 3, 2, 2, 1, 1, extras0[1], true, false, false, 256);
tk::dnn::Activation e_relu_0_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d e_0_conv3(&net, 512, 1, 1, 1, 1, 0, 0, extras0[2], true);
tk::dnn::Layer * header_2[1] = {&e_0_conv3};
tk::dnn::Layer *header_2[1] = {&e_0_conv3};
// //extras Inverted Residual 1
tk::dnn::Conv2d e_1_conv1(&net, 128, 1, 1, 1, 1, 0, 0, extras1[0], true);
@@ -300,15 +303,15 @@ int main()
tk::dnn::Conv2d e_1_conv2(&net, 128, 3, 3, 2, 2, 1, 1, extras1[1], true, false, false, 128);
tk::dnn::Activation e_relu_1_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d e_1_conv3(&net, 256, 1, 1, 1, 1, 0, 0, extras1[2], true);
tk::dnn::Layer * header_3[1] = {&e_1_conv3};
tk::dnn::Layer *header_3[1] = {&e_1_conv3};
//extras Inverted Residual 2
tk::dnn::Conv2d e_2_conv1(&net, 128, 1, 1, 1, 1, 0, 0, extras2[0], true);
tk::dnn::Activation e_relu_2_1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d e_2_conv2(&net, 128, 3, 3, 2, 2, 1, 1, extras2[1], true, false, false, 128);
tk::dnn::Activation e_relu_2_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d e_2_conv3(&net, 256, 1, 1, 1, 1, 0, 0, extras2[2], true);
tk::dnn::Layer * header_4[1] = {&e_2_conv3};
tk::dnn::Layer *header_4[1] = {&e_2_conv3};
//extras Inverted Residual 3
tk::dnn::Conv2d e_3_conv1(&net, 64, 1, 1, 1, 1, 0, 0, extras3[0], true);
@@ -316,79 +319,144 @@ int main()
tk::dnn::Conv2d e_3_conv2(&net, 64, 3, 3, 2, 2, 1, 1, extras3[1], true, false, false, 64);
tk::dnn::Activation e_relu_3_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d e_3_conv3(&net, 64, 1, 1, 1, 1, 0, 0, extras3[2], true);
tk::dnn::Layer * header_5[1] = {&e_3_conv3};
tk::dnn::Layer *header_5[1] = {&e_3_conv3};
// classification header 0
tk::dnn::Layer * header_0[1] = {&relu_14_1};
tk::dnn::Route rout_ch_0(&net, header_0, 1);
tk::dnn::Conv2d ch_0_conv1(&net, 576, 3, 3, 1, 1, 1, 1, classification_header0[0], true, false, false, 576,true);
tk::dnn::Activation ch_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Layer *header_0[1] = {&relu_14_1};
tk::dnn::Route rout_ch_0(&net, header_0, 1);
tk::dnn::Conv2d ch_0_conv1(&net, 576, 3, 3, 1, 1, 1, 1, classification_header0[0], true, false, false, 576, true);
tk::dnn::Activation ch_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d ch_0_conv2(&net, 126, 1, 1, 1, 1, 0, 0, classification_header0[1], false);
tk::dnn::Layer *conf0[1] = {&ch_0_conv2};
// // classification header 1
tk::dnn::Route rout_ch_1(&net, header_1, 1);
tk::dnn::Conv2d ch_1_conv1(&net, 1280, 3, 3, 1, 1, 1, 1, classification_header1[0], true, false, false, 1280,true);
tk::dnn::Activation ch_relu_1_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Route rout_ch_1(&net, header_1, 1);
tk::dnn::Conv2d ch_1_conv1(&net, 1280, 3, 3, 1, 1, 1, 1, classification_header1[0], true, false, false, 1280, true);
tk::dnn::Activation ch_relu_1_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d ch_1_conv2(&net, 126, 1, 1, 1, 1, 0, 0, classification_header1[1], false);
tk::dnn::Layer *conf1[1] = {&ch_1_conv2};
// //classification header 2
tk::dnn::Route rout_ch_2(&net, header_2, 1);
tk::dnn::Route rout_ch_2(&net, header_2, 1);
tk::dnn::Conv2d ch_2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, classification_header2[0], true, false, false, 512, true);
tk::dnn::Activation ch_relu_2_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation ch_relu_2_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d ch_2_conv2(&net, 126, 1, 1, 1, 1, 0, 0, classification_header2[1], false);
tk::dnn::Layer *conf2[1] = {&ch_2_conv2};
// //classification header 3
tk::dnn::Route rout_ch_3(&net, header_3, 1);
tk::dnn::Route rout_ch_3(&net, header_3, 1);
tk::dnn::Conv2d ch_3_conv1(&net, 256, 3, 3, 1, 1, 1, 1, classification_header3[0], true, false, false, 256, true);
tk::dnn::Activation ch_relu_3_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation ch_relu_3_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d ch_3_conv2(&net, 126, 1, 1, 1, 1, 0, 0, classification_header3[1], false);
tk::dnn::Layer *conf3[1] = {&ch_3_conv2};
// //classification header 4
tk::dnn::Route rout_ch_4(&net, header_4, 1);
tk::dnn::Route rout_ch_4(&net, header_4, 1);
tk::dnn::Conv2d ch_4_conv1(&net, 256, 3, 3, 1, 1, 1, 1, classification_header4[0], true, false, false, 256, true);
tk::dnn::Activation ch_relu_4_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation ch_relu_4_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d ch_4_conv2(&net, 126, 1, 1, 1, 1, 0, 0, classification_header4[1], false);
tk::dnn::Layer *conf4[1] = {&ch_4_conv2};
// //classification header 5
tk::dnn::Route rout_ch_5(&net, header_5, 1);
tk::dnn::Conv2d ch_5_conv(&net, 126, 1, 1, 1, 1, 0, 0, classification_header5, false);
tk::dnn::Route rout_ch_5(&net, header_5, 1);
tk::dnn::Conv2d ch_5_conv(&net, 126, 1, 1, 1, 1, 0, 0, classification_header5, false, false, true);
tk::dnn::Layer *conf5[1] = {&ch_5_conv};
//regression header 0
tk::dnn::Route rout_rh_0(&net, header_0, 1);
tk::dnn::Route rout_rh_0(&net, header_0, 1);
tk::dnn::Conv2d rh_0_conv1(&net, 576, 3, 3, 1, 1, 1, 1, regression_header0[0], true, false, false, 576, true);
tk::dnn::Activation rh_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation rh_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d rh_0_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header0[1], false);
tk::dnn::Layer *loc0[1] = {&rh_0_conv2};
// //regression header 1
tk::dnn::Route rout_rh_1(&net, header_1, 1);
tk::dnn::Route rout_rh_1(&net, header_1, 1);
tk::dnn::Conv2d rh_1_conv1(&net, 1280, 3, 3, 1, 1, 1, 1, regression_header1[0], true, false, false, 1280, true);
tk::dnn::Activation rh_relu_1_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation rh_relu_1_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d rh_1_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header1[1], false);
tk::dnn::Layer *loc1[1] = {&rh_1_conv2};
//regression header 2
tk::dnn::Route rout_rh_2(&net, header_2, 1);
tk::dnn::Route rout_rh_2(&net, header_2, 1);
tk::dnn::Conv2d rh_2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, regression_header2[0], true, false, false, 512, true);
tk::dnn::Activation rh_relu_2_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation rh_relu_2_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d rh_2_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header2[1], false);
tk::dnn::Layer *loc2[1] = {&rh_2_conv2};
//regression header 3
tk::dnn::Route rout_rh_3(&net, header_3, 1);
tk::dnn::Route rout_rh_3(&net, header_3, 1);
tk::dnn::Conv2d rh_3_conv1(&net, 256, 3, 3, 1, 1, 1, 1, regression_header3[0], true, false, false, 256, true);
tk::dnn::Activation rh_relu_3_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation rh_relu_3_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d rh_3_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header3[1], false);
tk::dnn::Layer *loc3[1] = {&rh_3_conv2};
//regression header 4
tk::dnn::Route rout_rh_4(&net, header_4, 1);
tk::dnn::Route rout_rh_4(&net, header_4, 1);
tk::dnn::Conv2d rh_4_conv1(&net, 256, 3, 3, 1, 1, 1, 1, regression_header4[0], true, false, false, 256, true);
tk::dnn::Activation rh_relu_4_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU,6);
tk::dnn::Activation rh_relu_4_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d rh_4_conv2(&net, 24, 1, 1, 1, 1, 0, 0, regression_header4[1], false);
tk::dnn::Layer *loc4[1] = {&rh_4_conv2};
//regression header 5
tk::dnn::Route rout_rh_5(&net, header_5, 1);
tk::dnn::Conv2d rh_5_conv(&net, 24, 1, 1, 1, 1, 0, 0, regression_header5, false);
tk::dnn::Route rout_rh_5(&net, header_5, 1);
tk::dnn::Conv2d rh_5_conv(&net, 24, 1, 1, 1, 1, 0, 0, regression_header5, false, false, true);
tk::dnn::Layer *loc5[1] = {&rh_5_conv};
//flatten confidence and flatten locations
last = &rh_5_conv;
//flatten all confidence
tk::dnn::Route r_conf_0(&net, conf0, 1);
tk::dnn::Flatten fl_c_0(&net);
tk::dnn::Route r_conf_1(&net, conf1, 1);
tk::dnn::Flatten fl_c_1(&net);
tk::dnn::Route r_conf_2(&net, conf2, 1);
tk::dnn::Flatten fl_c_2(&net);
tk::dnn::Route r_conf_3(&net, conf3, 1);
tk::dnn::Flatten fl_c_3(&net);
tk::dnn::Route r_conf_4(&net, conf4, 1);
tk::dnn::Flatten fl_c_4(&net);
tk::dnn::Route r_conf_5(&net, conf5, 1);
tk::dnn::Flatten fl_c_5(&net);
// //flatten all locations
tk::dnn::Route r_loc_0(&net, loc0, 1);
tk::dnn::Flatten fl_l_0(&net);
tk::dnn::Route r_loc_1(&net, loc1, 1);
tk::dnn::Flatten fl_l_1(&net);
tk::dnn::Route r_loc_2(&net, loc2, 1);
tk::dnn::Flatten fl_l_2(&net);
tk::dnn::Route r_loc_3(&net, loc3, 1);
tk::dnn::Flatten fl_l_3(&net);
tk::dnn::Route r_loc_4(&net, loc4, 1);
tk::dnn::Flatten fl_l_4(&net);
tk::dnn::Route r_loc_5(&net, loc5, 1);
tk::dnn::Flatten fl_l_5(&net);
// //concat confidence + softmax
tk::dnn::Layer *confidences[6] = {&fl_c_0, &fl_c_1, &fl_c_2, &fl_c_3, &fl_c_4, &fl_c_5};
tk::dnn::Route rout_conf(&net, confidences, 6);
tk::dnn::dataDim_t olddim_c = net.layers[net.num_layers - 1]->output_dim;
tk::dnn::dataDim_t dim_resh(1, olddim_c.c * olddim_c.h * olddim_c.w / classes, classes, 1, 1);
tk::dnn::Reshape reshape_conf1(&net, dim_resh);
tk::dnn::Flatten fl_l_6(&net);
tk::dnn::dataDim_t newdim_c(1, classes, olddim_c.c * olddim_c.h * olddim_c.w / classes, 1, 1);
tk::dnn::Reshape reshape_conf2(&net, newdim_c);
tk::dnn::Softmax sm_1(&net, &newdim_c, true);
// tk::dnn::Flatten fl_l_7(&net);
// tk::dnn::Reshape reshape_conf3(&net,dim_resh, true);
tk::dnn::Layer *conf = &sm_1;
//concat locations
tk::dnn::Layer *locations[6] = {&fl_l_0, &fl_l_1, &fl_l_2, &fl_l_3, &fl_l_4, &fl_l_5};
tk::dnn::Route rout_loc(&net, locations, 6);
tk::dnn::dataDim_t olddim_l = net.layers[net.num_layers - 1]->output_dim;
tk::dnn::dataDim_t newdim_l(1, olddim_l.c * olddim_l.h * olddim_l.w / 4, 1, 4, 1);
tk::dnn::Reshape reshape_loc(&net, newdim_l, true);
tk::dnn::Layer *loc = &reshape_loc;
// Load input
dnnType *data;
@@ -399,14 +467,9 @@ int main()
//print network model
net.print();
//convert network to tensorRT
// convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, "mobilenetv2ssd.rt");
tk::dnn::dataDim_t out_dim;
out_dim = net.layers[net.num_layers-1]->output_dim;
dnnType *cudnn_out, *rt_out;
tk::dnn::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30);
{
@@ -416,9 +479,11 @@ int main()
TIMER_STOP
dim1.print();
}
cudnn_out = net.layers[net.num_layers-1]->dstData;
printDeviceVector(64, cudnn_out, true);
dnnType *cudnn_out1 = conf5[0]->dstData;
tk::dnn::dataDim_t out_dim1 = conf5[0]->output_dim;
dnnType *cudnn_out2 = loc5[0]->dstData;
tk::dnn::dataDim_t out_dim2 = loc5[0]->output_dim;
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
@@ -429,20 +494,48 @@ int main()
TIMER_STOP
dim2.print();
}
rt_out = (dnnType *)netRT.buffersRT[1];
dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
dnnType *rt_out2 = (dnnType *)netRT.buffersRT[2];
dnnType *rt_out3 = (dnnType *)netRT.buffersRT[3];
dnnType *rt_out4 = (dnnType *)netRT.buffersRT[4];
printCenteredTitle(std::string(" RESNET CHECK RESULTS ").c_str(), '=', 30);
dnnType *out, *out_h;
int odim = out_dim.tot();
readBinaryFile(output_bin, odim, &out_h, &out);
std::cout << "CUDNN vs correct";
checkResult(odim, cudnn_out, out);
dnnType *out1, *out1_h;
int odim1 = out_dim1.tot();
readBinaryFile(output_bin1, odim1, &out1_h, &out1);
std::cout << "TRT vs correct";
checkResult(odim, rt_out, out);
std::cout << "CUDNN vs TRT ";
checkResult(odim, cudnn_out, rt_out);
dnnType *out2, *out2_h;
int odim2 = out_dim2.tot();
readBinaryFile(output_bin2, odim2, &out2_h, &out2);
std::cout << "CUDNN vs correct" << std::endl;
checkResult(odim1, cudnn_out1, out1);
checkResult(odim2, cudnn_out2, out2);
std::cout << "TRT vs correct" << std::endl;
checkResult(odim1, rt_out1, out1);
checkResult(odim2, rt_out2, out2);
std::cout << "CUDNN vs TRT " << std::endl;
checkResult(odim1, cudnn_out1, rt_out1);
checkResult(odim2, cudnn_out2, rt_out2);
std::cout << "---------------------------------------------------" << std::endl;
std::cout << "Confidence CUDNN" << std::endl;
printDeviceVector(64, conf->dstData, true);
std::cout << "Locations CUDNN" << std::endl;
printDeviceVector(64, loc->dstData, true);
std::cout << "---------------------------------------------------" << std::endl;
std::cout << "Confidence tensorRT" << std::endl;
printDeviceVector(64, rt_out3, true);
std::cout << "Locations tensorRT" << std::endl;
printDeviceVector(64, rt_out4, true);
std::cout << "---------------------------------------------------" << std::endl;
std::cout << "CUDNN vs TRT " << std::endl;
checkResult(conf->output_dim.tot(), conf->dstData, rt_out3);
checkResult(loc->output_dim.tot(), loc->dstData, rt_out4);
return 0;
}