yolo3plug fix
This commit is contained in:
Francesco Gatti
@@ -74,6 +74,9 @@ target_link_libraries(test_yolo_224 tkDNN)
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add_executable(test_yolo_berkeley tests/yolo_berkeley/yolo_berkeley.cpp)
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target_link_libraries(test_yolo_berkeley tkDNN)
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add_executable(test_yolo3_voc tests/yolo3_voc/yolo3_voc.cpp)
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target_link_libraries(test_yolo3_voc tkDNN)
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add_executable(test_yolo3_berkeley tests/yolo3_berkeley/yolo3_berkeley.cpp)
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target_link_libraries(test_yolo3_berkeley tkDNN)
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################################################################################
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+1
-1
@@ -24,7 +24,7 @@ int main(int argc, char *argv[]) {
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signal(SIGINT, sig_handler);
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tk::dnn::Yolo3Detection yolo;
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yolo.init("./");
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yolo.init("yolo3_berkeley");
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gRun = true;
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@@ -9,7 +9,7 @@
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <tkDNN/tkdnn.h>
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#include "tkdnn.h"
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namespace tk { namespace dnn {
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@@ -31,10 +31,10 @@ class Yolo3Detection {
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cv::Mat bgr[3];
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public:
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static const int classes = 10;
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static const int num = 3;
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int classes = 10;
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int num = 3;
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float thresh = 0.3;
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cv::Scalar colors[classes];
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cv::Scalar colors[256];
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// this is filled with results
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std::vector<tk::dnn::box> detected;
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+8
-1
@@ -360,7 +360,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Yolo *l) {
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//std::cout<<"convert Yolo\n";
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//std::cout<<"New plugin YOLO\n";
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IPlugin *plugin = new YoloRT(l->classes, l->num);
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IPlugin *plugin = new YoloRT(l->classes, l->num, l);
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IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
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checkNULL(lRT);
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return lRT;
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@@ -396,6 +396,7 @@ bool NetworkRT::serialize(const char *filename) {
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class PluginFactory : IPluginFactory
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{
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public:
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virtual IPlugin* createPlugin(const char* layerName, const void* serialData, size_t serialLength) {
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const char * buf = reinterpret_cast<const char*>(serialData);
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@@ -440,6 +441,12 @@ public:
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r->c = readBUF<int>(buf);
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r->h = readBUF<int>(buf);
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r->w = readBUF<int>(buf);
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for(int i=0; i<r->num; i++)
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r->mask[i] = readBUF<dnnType>(buf);
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for(int i=0; i<3*2*r->num; i++)
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r->bias[i] = readBUF<dnnType>(buf);
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std::cout<<"YOLO: "<<r->c<<" "<<r->h<<" "<<r->w<<"\n";
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return r;
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}
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@@ -21,14 +21,14 @@ bool Yolo3Detection::init(std::string tensor_folder) {
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}
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//convert network to tensorRT
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std::cout<<(tensor_folder + "/yolo3_berkeley.rt").c_str()<<"\n";
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netRT = new tk::dnn::NetworkRT(NULL, (tensor_folder + "/yolo3_berkeley.rt").c_str() );
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std::cout<<(tensor_folder + ".rt").c_str()<<"\n";
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netRT = new tk::dnn::NetworkRT(NULL, (tensor_folder + ".rt").c_str() );
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yolo[0] = new tk::dnn::Yolo(nullptr, classes, num, (tensor_folder + "/yolo3_0.bin").c_str() ); // yolo without input and bias
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yolo[0] = new tk::dnn::Yolo(nullptr, classes, num, (tensor_folder + "_0.bin").c_str() ); // yolo without input and bias
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yolo[0]->input_dim = yolo[0]->output_dim = tk::dnn::dataDim_t(1, 45, 10, 17);
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yolo[1] = new tk::dnn::Yolo(nullptr, classes, num, (tensor_folder + "/yolo3_1.bin").c_str() ); // yolo without input and bias
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yolo[1] = new tk::dnn::Yolo(nullptr, classes, num, (tensor_folder + "_1.bin").c_str() ); // yolo without input and bias
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yolo[1]->input_dim = yolo[1]->output_dim = tk::dnn::dataDim_t(1, 45, 20, 34);
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yolo[2] = new tk::dnn::Yolo(nullptr, classes, num, (tensor_folder + "/yolo3_2.bin").c_str() ); // yolo without input and bias
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yolo[2] = new tk::dnn::Yolo(nullptr, classes, num, (tensor_folder + "_2.bin").c_str() ); // yolo without input and bias
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yolo[2]->input_dim = yolo[2]->output_dim = tk::dnn::dataDim_t(1, 45, 40, 68);
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dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
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@@ -3,11 +3,20 @@
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class YoloRT : public IPlugin {
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public:
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YoloRT(int classes, int num) {
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YoloRT(int classes, int num, tk::dnn::Yolo *yolo = nullptr) {
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this->classes = classes;
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this->num = num;
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mask = new dnnType[num];
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bias = new dnnType[num*3*2];
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if(yolo != nullptr) {
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memcpy(mask, yolo->mask_h, sizeof(dnnType)*num);
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memcpy(bias, yolo->bias_h, sizeof(dnnType)*num*3*2);
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}
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}
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~YoloRT(){
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@@ -63,7 +72,7 @@ public:
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virtual size_t getSerializationSize() override {
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return 5*sizeof(int);
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return 5*sizeof(int) + num*sizeof(dnnType) + num*3*2*sizeof(dnnType);
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}
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virtual void serialize(void* buffer) override {
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@@ -73,11 +82,18 @@ public:
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tk::dnn::writeBUF(buf, c);
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tk::dnn::writeBUF(buf, h);
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tk::dnn::writeBUF(buf, w);
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for(int i=0; i<num; i++)
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tk::dnn::writeBUF(buf, mask[i]);
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for(int i=0; i<3*2*num; i++)
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tk::dnn::writeBUF(buf, bias[i]);
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}
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int c, h, w;
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int classes, num;
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dnnType *mask;
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dnnType *bias;
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int entry_index(int batch, int location, int entry, int batchSize) {
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int n = location / (w*h);
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int loc = location % (w*h);
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@@ -372,11 +372,11 @@ int main() {
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std::cout<<"copyng layer config to this folder\n";
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std::string cmd;
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cmd = "cp " + std::string(g82_bin) + " yolo3_0.bin";
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cmd = "cp " + std::string(g82_bin) + " yolo3_berkeley_0.bin";
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std::cout<<cmd<<"\n"; system(cmd.c_str());
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cmd = "cp " + std::string(g94_bin) + " yolo3_1.bin";
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cmd = "cp " + std::string(g94_bin) + " yolo3_berkeley_1.bin";
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std::cout<<cmd<<"\n"; system(cmd.c_str());
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cmd = "cp " + std::string(g106_bin) + " yolo3_2.bin";
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cmd = "cp " + std::string(g106_bin) + " yolo3_berkeley_2.bin";
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std::cout<<cmd<<"\n"; system(cmd.c_str());
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return 0;
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}
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@@ -0,0 +1,382 @@
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#include<iostream>
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#include "tkdnn.h"
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const char *input_bin = "../tests/yolo3_voc/layers/input.bin";
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const char *c0_bin = "../tests/yolo3_voc/layers/c0.bin";
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const char *c1_bin = "../tests/yolo3_voc/layers/c1.bin";
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const char *c2_bin = "../tests/yolo3_voc/layers/c2.bin";
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const char *c3_bin = "../tests/yolo3_voc/layers/c3.bin";
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const char *c5_bin = "../tests/yolo3_voc/layers/c5.bin";
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const char *c6_bin = "../tests/yolo3_voc/layers/c6.bin";
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const char *c7_bin = "../tests/yolo3_voc/layers/c7.bin";
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const char *c9_bin = "../tests/yolo3_voc/layers/c9.bin";
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const char *c10_bin = "../tests/yolo3_voc/layers/c10.bin";
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const char *c12_bin = "../tests/yolo3_voc/layers/c12.bin";
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const char *c13_bin = "../tests/yolo3_voc/layers/c13.bin";
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const char *c14_bin = "../tests/yolo3_voc/layers/c14.bin";
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const char *c16_bin = "../tests/yolo3_voc/layers/c16.bin";
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const char *c17_bin = "../tests/yolo3_voc/layers/c17.bin";
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const char *c19_bin = "../tests/yolo3_voc/layers/c19.bin";
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const char *c20_bin = "../tests/yolo3_voc/layers/c20.bin";
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const char *c22_bin = "../tests/yolo3_voc/layers/c22.bin";
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const char *c23_bin = "../tests/yolo3_voc/layers/c23.bin";
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const char *c25_bin = "../tests/yolo3_voc/layers/c25.bin";
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const char *c26_bin = "../tests/yolo3_voc/layers/c26.bin";
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const char *c28_bin = "../tests/yolo3_voc/layers/c28.bin";
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const char *c29_bin = "../tests/yolo3_voc/layers/c29.bin";
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const char *c31_bin = "../tests/yolo3_voc/layers/c31.bin";
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const char *c32_bin = "../tests/yolo3_voc/layers/c32.bin";
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const char *c34_bin = "../tests/yolo3_voc/layers/c34.bin";
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const char *c35_bin = "../tests/yolo3_voc/layers/c35.bin";
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const char *c37_bin = "../tests/yolo3_voc/layers/c37.bin";
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const char *c38_bin = "../tests/yolo3_voc/layers/c38.bin";
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const char *c39_bin = "../tests/yolo3_voc/layers/c39.bin";
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const char *c41_bin = "../tests/yolo3_voc/layers/c41.bin";
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const char *c42_bin = "../tests/yolo3_voc/layers/c42.bin";
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const char *c44_bin = "../tests/yolo3_voc/layers/c44.bin";
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const char *c45_bin = "../tests/yolo3_voc/layers/c45.bin";
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const char *c47_bin = "../tests/yolo3_voc/layers/c47.bin";
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const char *c48_bin = "../tests/yolo3_voc/layers/c48.bin";
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const char *c50_bin = "../tests/yolo3_voc/layers/c50.bin";
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const char *c51_bin = "../tests/yolo3_voc/layers/c51.bin";
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const char *c53_bin = "../tests/yolo3_voc/layers/c53.bin";
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const char *c54_bin = "../tests/yolo3_voc/layers/c54.bin";
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const char *c56_bin = "../tests/yolo3_voc/layers/c56.bin";
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const char *c57_bin = "../tests/yolo3_voc/layers/c57.bin";
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const char *c59_bin = "../tests/yolo3_voc/layers/c59.bin";
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const char *c60_bin = "../tests/yolo3_voc/layers/c60.bin";
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const char *c62_bin = "../tests/yolo3_voc/layers/c62.bin";
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const char *c63_bin = "../tests/yolo3_voc/layers/c63.bin";
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const char *c64_bin = "../tests/yolo3_voc/layers/c64.bin";
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const char *c66_bin = "../tests/yolo3_voc/layers/c66.bin";
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const char *c67_bin = "../tests/yolo3_voc/layers/c67.bin";
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const char *c69_bin = "../tests/yolo3_voc/layers/c69.bin";
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const char *c70_bin = "../tests/yolo3_voc/layers/c70.bin";
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const char *c72_bin = "../tests/yolo3_voc/layers/c72.bin";
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const char *c73_bin = "../tests/yolo3_voc/layers/c73.bin";
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const char *c75_bin = "../tests/yolo3_voc/layers/c75.bin";
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const char *c76_bin = "../tests/yolo3_voc/layers/c76.bin";
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const char *c77_bin = "../tests/yolo3_voc/layers/c77.bin";
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const char *c78_bin = "../tests/yolo3_voc/layers/c78.bin";
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const char *c79_bin = "../tests/yolo3_voc/layers/c79.bin";
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const char *c80_bin = "../tests/yolo3_voc/layers/c80.bin";
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const char *c81_bin = "../tests/yolo3_voc/layers/c81.bin";
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const char *g82_bin = "../tests/yolo3_voc/layers/g82.bin";
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const char *c84_bin = "../tests/yolo3_voc/layers/c84.bin";
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const char *c87_bin = "../tests/yolo3_voc/layers/c87.bin";
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const char *c88_bin = "../tests/yolo3_voc/layers/c88.bin";
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const char *c89_bin = "../tests/yolo3_voc/layers/c89.bin";
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const char *c90_bin = "../tests/yolo3_voc/layers/c90.bin";
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const char *c91_bin = "../tests/yolo3_voc/layers/c91.bin";
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const char *c92_bin = "../tests/yolo3_voc/layers/c92.bin";
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const char *c93_bin = "../tests/yolo3_voc/layers/c93.bin";
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const char *g94_bin = "../tests/yolo3_voc/layers/g94.bin";
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const char *c96_bin = "../tests/yolo3_voc/layers/c96.bin";
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const char *c99_bin = "../tests/yolo3_voc/layers/c99.bin";
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const char *c100_bin = "../tests/yolo3_voc/layers/c100.bin";
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const char *c101_bin = "../tests/yolo3_voc/layers/c101.bin";
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const char *c102_bin = "../tests/yolo3_voc/layers/c102.bin";
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const char *c103_bin = "../tests/yolo3_voc/layers/c103.bin";
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const char *c104_bin = "../tests/yolo3_voc/layers/c104.bin";
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const char *c105_bin = "../tests/yolo3_voc/layers/c105.bin";
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const char *g106_bin = "../tests/yolo3_voc/layers/g106.bin";
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const char *output_bins[3] = {
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"../tests/yolo3_voc/debug/layer82_out.bin",
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"../tests/yolo3_voc/debug/layer94_out.bin",
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"../tests/yolo3_voc/debug/layer106_out.bin"
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};
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int main() {
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
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tk::dnn::Network net(dim);
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tk::dnn::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
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tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c1 (&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
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tk::dnn::Activation a1 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c2 (&net, 32, 1, 1, 1, 1, 0, 0, c2_bin, true);
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tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c3 (&net, 64, 3, 3, 1, 1, 1, 1, c3_bin, true);
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tk::dnn::Activation a3 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s4 (&net, &a1);
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tk::dnn::Conv2d c5 (&net, 128, 3, 3, 2, 2, 1, 1, c5_bin, true);
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tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c6 (&net, 64, 1, 1, 1, 1, 0, 0, c6_bin, true);
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tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c7 (&net, 128, 3, 3, 1, 1, 1, 1, c7_bin, true);
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tk::dnn::Activation a7 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s8 (&net, &a5);
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tk::dnn::Conv2d c9 (&net, 64, 1, 1, 1, 1, 0, 0, c9_bin, true);
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tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c10 (&net, 128, 3, 3, 1, 1, 1, 1, c10_bin, true);
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tk::dnn::Activation a10 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s11 (&net, &s8);
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tk::dnn::Conv2d c12 (&net, 256, 3, 3, 2, 2, 1, 1, c12_bin, true);
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tk::dnn::Activation a12 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c13 (&net, 128, 1, 1, 1, 1, 0, 0, c13_bin, true);
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tk::dnn::Activation a13 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c14 (&net, 256, 3, 3, 1, 1, 1, 1, c14_bin, true);
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tk::dnn::Activation a14 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s15 (&net, &a12);
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tk::dnn::Conv2d c16 (&net, 128, 1, 1, 1, 1, 0, 0, c16_bin, true);
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tk::dnn::Activation a16 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c17 (&net, 256, 3, 3, 1, 1, 1, 1, c17_bin, true);
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tk::dnn::Activation a17 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s18 (&net, &s15);
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tk::dnn::Conv2d c19 (&net, 128, 1, 1, 1, 1, 0, 0, c19_bin, true);
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tk::dnn::Activation a19 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c20 (&net, 256, 3, 3, 1, 1, 1, 1, c20_bin, true);
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tk::dnn::Activation a20 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s21 (&net, &s18);
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tk::dnn::Conv2d c22 (&net, 128, 1, 1, 1, 1, 0, 0, c22_bin, true);
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tk::dnn::Activation a22 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c23 (&net, 256, 3, 3, 1, 1, 1, 1, c23_bin, true);
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tk::dnn::Activation a23 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s24 (&net, &s21);
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tk::dnn::Conv2d c25 (&net, 128, 1, 1, 1, 1, 0, 0, c25_bin, true);
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tk::dnn::Activation a25 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Conv2d c26 (&net, 256, 3, 3, 1, 1, 1, 1, c26_bin, true);
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tk::dnn::Activation a26 (&net, tk::dnn::ACTIVATION_LEAKY);
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tk::dnn::Shortcut s27 (&net, &s24);
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tk::dnn::Conv2d c28 (&net, 128, 1, 1, 1, 1, 0, 0, c28_bin, true);
|
||||
tk::dnn::Activation a28 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c29 (&net, 256, 3, 3, 1, 1, 1, 1, c29_bin, true);
|
||||
tk::dnn::Activation a29 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s30 (&net, &s27);
|
||||
tk::dnn::Conv2d c31 (&net, 128, 1, 1, 1, 1, 0, 0, c31_bin, true);
|
||||
tk::dnn::Activation a31 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c32 (&net, 256, 3, 3, 1, 1, 1, 1, c32_bin, true);
|
||||
tk::dnn::Activation a32 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s33 (&net, &s30);
|
||||
tk::dnn::Conv2d c34 (&net, 128, 1, 1, 1, 1, 0, 0, c34_bin, true);
|
||||
tk::dnn::Activation a34 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c35 (&net, 256, 3, 3, 1, 1, 1, 1, c35_bin, true);
|
||||
tk::dnn::Activation a35 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s36 (&net, &s33);
|
||||
|
||||
tk::dnn::Conv2d c37 (&net, 512, 3, 3, 2, 2, 1, 1, c37_bin, true);
|
||||
tk::dnn::Activation a37 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c38 (&net, 256, 1, 1, 1, 1, 0, 0, c38_bin, true);
|
||||
tk::dnn::Activation a38 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c39 (&net, 512, 3, 3, 1, 1, 1, 1, c39_bin, true);
|
||||
tk::dnn::Activation a39 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s40 (&net, &a37);
|
||||
|
||||
tk::dnn::Conv2d c41 (&net, 256, 1, 1, 1, 1, 0, 0, c41_bin, true);
|
||||
tk::dnn::Activation a41 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c42 (&net, 512, 3, 3, 1, 1, 1, 1, c42_bin, true);
|
||||
tk::dnn::Activation a42 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s43 (&net, &s40);
|
||||
tk::dnn::Conv2d c44 (&net, 256, 1, 1, 1, 1, 0, 0, c44_bin, true);
|
||||
tk::dnn::Activation a44 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c45 (&net, 512, 3, 3, 1, 1, 1, 1, c45_bin, true);
|
||||
tk::dnn::Activation a45 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s46 (&net, &s43);
|
||||
tk::dnn::Conv2d c47 (&net, 256, 1, 1, 1, 1, 0, 0, c47_bin, true);
|
||||
tk::dnn::Activation a47 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c48 (&net, 512, 3, 3, 1, 1, 1, 1, c48_bin, true);
|
||||
tk::dnn::Activation a48 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s49 (&net, &s46);
|
||||
tk::dnn::Conv2d c50 (&net, 256, 1, 1, 1, 1, 0, 0, c50_bin, true);
|
||||
tk::dnn::Activation a50 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c51 (&net, 512, 3, 3, 1, 1, 1, 1, c51_bin, true);
|
||||
tk::dnn::Activation a51 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s52 (&net, &s49);
|
||||
tk::dnn::Conv2d c53 (&net, 256, 1, 1, 1, 1, 0, 0, c53_bin, true);
|
||||
tk::dnn::Activation a53 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c54 (&net, 512, 3, 3, 1, 1, 1, 1, c54_bin, true);
|
||||
tk::dnn::Activation a54 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s55 (&net, &s52);
|
||||
tk::dnn::Conv2d c56 (&net, 256, 1, 1, 1, 1, 0, 0, c56_bin, true);
|
||||
tk::dnn::Activation a56 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c57 (&net, 512, 3, 3, 1, 1, 1, 1, c57_bin, true);
|
||||
tk::dnn::Activation a57 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s58 (&net, &s55);
|
||||
tk::dnn::Conv2d c59 (&net, 256, 1, 1, 1, 1, 0, 0, c59_bin, true);
|
||||
tk::dnn::Activation a59 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c60 (&net, 512, 3, 3, 1, 1, 1, 1, c60_bin, true);
|
||||
tk::dnn::Activation a60 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s61 (&net, &s58);
|
||||
|
||||
tk::dnn::Conv2d c62 (&net,1024, 3, 3, 2, 2, 1, 1, c62_bin, true);
|
||||
tk::dnn::Activation a62 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c63 (&net, 512, 1, 1, 1, 1, 0, 0, c63_bin, true);
|
||||
tk::dnn::Activation a63 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c64 (&net,1024, 3, 3, 1, 1, 1, 1, c64_bin, true);
|
||||
tk::dnn::Activation a64 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s65 (&net, &a62);
|
||||
|
||||
tk::dnn::Conv2d c66 (&net, 512, 1, 1, 1, 1, 0, 0, c66_bin, true);
|
||||
tk::dnn::Activation a66 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c67 (&net,1024, 3, 3, 1, 1, 1, 1, c67_bin, true);
|
||||
tk::dnn::Activation a67 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s68 (&net, &s65);
|
||||
|
||||
tk::dnn::Conv2d c69 (&net, 512, 1, 1, 1, 1, 0, 0, c69_bin, true);
|
||||
tk::dnn::Activation a69 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c70 (&net,1024, 3, 3, 1, 1, 1, 1, c70_bin, true);
|
||||
tk::dnn::Activation a70 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s71 (&net, &s68);
|
||||
|
||||
tk::dnn::Conv2d c72 (&net, 512, 1, 1, 1, 1, 0, 0, c72_bin, true);
|
||||
tk::dnn::Activation a72 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c73 (&net,1024, 3, 3, 1, 1, 1, 1, c73_bin, true);
|
||||
tk::dnn::Activation a73 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Shortcut s74 (&net, &s71);
|
||||
|
||||
tk::dnn::Conv2d c75 (&net, 512, 1, 1, 1, 1, 0, 0, c75_bin, true);
|
||||
tk::dnn::Activation a75 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c76 (&net,1024, 3, 3, 1, 1, 1, 1, c76_bin, true);
|
||||
tk::dnn::Activation a76 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c77 (&net, 512, 1, 1, 1, 1, 0, 0, c77_bin, true);
|
||||
tk::dnn::Activation a77 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c78 (&net,1024, 3, 3, 1, 1, 1, 1, c78_bin, true);
|
||||
tk::dnn::Activation a78 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c79 (&net, 512, 1, 1, 1, 1, 0, 0, c79_bin, true);
|
||||
tk::dnn::Activation a79 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c80 (&net,1024, 3, 3, 1, 1, 1, 1, c80_bin, true);
|
||||
tk::dnn::Activation a80 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c81 (&net, 75, 1, 1, 1, 1, 0, 0, c81_bin, false);
|
||||
tk::dnn::Yolo yolo0 (&net, 20, 3, g82_bin);
|
||||
|
||||
tk::dnn::Layer *m83_layers[1] = { &a79 };
|
||||
tk::dnn::Route m83 (&net, m83_layers, 1);
|
||||
tk::dnn::Conv2d c84 (&net, 256, 1, 1, 1, 1, 0, 0, c84_bin, true);
|
||||
tk::dnn::Activation a84 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Upsample u85 (&net, 2);
|
||||
|
||||
tk::dnn::Layer *m86_layers[2] = { &u85, &s61 };
|
||||
tk::dnn::Route m86 (&net, m86_layers, 2);
|
||||
tk::dnn::Conv2d c87 (&net, 256, 1, 1, 1, 1, 0, 0, c87_bin, true);
|
||||
tk::dnn::Activation a87 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c88 (&net, 512, 3, 3, 1, 1, 1, 1, c88_bin, true);
|
||||
tk::dnn::Activation a88 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c89 (&net, 256, 1, 1, 1, 1, 0, 0, c89_bin, true);
|
||||
tk::dnn::Activation a89 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c90 (&net, 512, 3, 3, 1, 1, 1, 1, c90_bin, true);
|
||||
tk::dnn::Activation a90 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c91 (&net, 256, 1, 1, 1, 1, 0, 0, c91_bin, true);
|
||||
tk::dnn::Activation a91 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
|
||||
tk::dnn::Conv2d c92 (&net, 512, 3, 3, 1, 1, 1, 1, c92_bin, true);
|
||||
tk::dnn::Activation a92 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c93 (&net, 75, 1, 1, 1, 1, 0, 0, c93_bin, false);
|
||||
tk::dnn::Yolo yolo1 (&net, 20, 3, g94_bin);
|
||||
|
||||
tk::dnn::Layer *m95_layers[1] = { &a91 };
|
||||
tk::dnn::Route m95 (&net, m95_layers, 1);
|
||||
tk::dnn::Conv2d c96 (&net, 128, 1, 1, 1, 1, 0, 0, c96_bin, true);
|
||||
tk::dnn::Activation a96 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Upsample u97 (&net, 2);
|
||||
|
||||
tk::dnn::Layer *m98_layers[2] = { &u97, &s36 };
|
||||
tk::dnn::Route m98 (&net, m98_layers, 2);
|
||||
tk::dnn::Conv2d c99 (&net, 128, 1, 1, 1, 1, 0, 0, c99_bin, true);
|
||||
tk::dnn::Activation a99 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c100 (&net, 256, 3, 3, 1, 1, 1, 1, c100_bin, true);
|
||||
tk::dnn::Activation a100 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c101 (&net, 128, 1, 1, 1, 1, 0, 0, c101_bin, true);
|
||||
tk::dnn::Activation a101 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c102 (&net, 256, 3, 3, 1, 1, 1, 1, c102_bin, true);
|
||||
tk::dnn::Activation a102 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c103 (&net, 128, 1, 1, 1, 1, 0, 0, c103_bin, true);
|
||||
tk::dnn::Activation a103 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
|
||||
tk::dnn::Conv2d c104 (&net, 256, 3, 3, 1, 1, 1, 1, c104_bin, true);
|
||||
tk::dnn::Activation a104 (&net, tk::dnn::ACTIVATION_LEAKY);
|
||||
tk::dnn::Conv2d c105 (&net, 75, 1, 1, 1, 1, 0, 0, c105_bin, false);
|
||||
tk::dnn::Yolo yolo2 (&net, 20, 3, g106_bin);
|
||||
|
||||
// Load input
|
||||
dnnType *data;
|
||||
dnnType *input_h;
|
||||
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
|
||||
|
||||
//print network model
|
||||
net.print();
|
||||
|
||||
//convert network to tensorRT
|
||||
tk::dnn::NetworkRT netRT(&net, "yolo3_voc.rt");
|
||||
|
||||
// the network have 3 outputs
|
||||
tk::dnn::dataDim_t out_dim[3];
|
||||
out_dim[0] = yolo0.output_dim;
|
||||
out_dim[1] = yolo1.output_dim;
|
||||
out_dim[2] = yolo2.output_dim;
|
||||
dnnType *cudnn_out[3], *rt_out[3];
|
||||
|
||||
tk::dnn::dataDim_t dim1 = dim; //input dim
|
||||
printCenteredTitle(" CUDNN inference ", '=', 30); {
|
||||
dim1.print();
|
||||
TIMER_START
|
||||
net.infer(dim1, data);
|
||||
TIMER_STOP
|
||||
dim1.print();
|
||||
}
|
||||
cudnn_out[0] = yolo0.dstData;
|
||||
cudnn_out[1] = yolo1.dstData;
|
||||
cudnn_out[2] = yolo2.dstData;
|
||||
|
||||
printCenteredTitle(" compute detections ", '=', 30);
|
||||
TIMER_START
|
||||
int ndets = 0;
|
||||
int classes = yolo0.classes;
|
||||
tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
|
||||
yolo0.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, net.input_dim.w, net.input_dim.h, 0.5);
|
||||
yolo1.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, net.input_dim.w, net.input_dim.h, 0.5);
|
||||
yolo2.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, net.input_dim.w, net.input_dim.h, 0.5);
|
||||
tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
|
||||
|
||||
for(int j=0; j<ndets; j++) {
|
||||
tk::dnn::Yolo::box b = dets[j].bbox;
|
||||
int x0 = (b.x-b.w/2.);
|
||||
int x1 = (b.x+b.w/2.);
|
||||
int y0 = (b.y-b.h/2.);
|
||||
int y1 = (b.y+b.h/2.);
|
||||
|
||||
int cl = 0;
|
||||
for(int c = 0; c < classes; ++c){
|
||||
float prob = dets[j].prob[c];
|
||||
if(prob > 0)
|
||||
cl = c;
|
||||
}
|
||||
std::cout<<cl<<": "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
|
||||
}
|
||||
TIMER_STOP
|
||||
|
||||
tk::dnn::dataDim_t dim2 = dim;
|
||||
printCenteredTitle(" TENSORRT inference ", '=', 30); {
|
||||
dim2.print();
|
||||
TIMER_START
|
||||
netRT.infer(dim2, data);
|
||||
TIMER_STOP
|
||||
dim2.print();
|
||||
}
|
||||
rt_out[0] = (dnnType*)netRT.buffersRT[1];
|
||||
rt_out[1] = (dnnType*)netRT.buffersRT[2];
|
||||
rt_out[2] = (dnnType*)netRT.buffersRT[3];
|
||||
|
||||
for(int i=0; i<3; i++) {
|
||||
printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
|
||||
dnnType *out, *out_h;
|
||||
int odim = out_dim[i].tot();
|
||||
readBinaryFile(output_bins[i], odim, &out_h, &out);
|
||||
std::cout<<"CUDNN vs correct"; checkResult(odim, cudnn_out[i], out);
|
||||
std::cout<<"TRT vs correct"; checkResult(odim, rt_out[i], out);
|
||||
std::cout<<"CUDNN vs TRT "; checkResult(odim, cudnn_out[i], rt_out[i]);
|
||||
}
|
||||
|
||||
std::cout<<"copyng layer config to this folder\n";
|
||||
std::string cmd;
|
||||
cmd = "cp " + std::string(g82_bin) + " yolo3_voc_0.bin";
|
||||
std::cout<<cmd<<"\n"; system(cmd.c_str());
|
||||
cmd = "cp " + std::string(g94_bin) + " yolo3_voc_1.bin";
|
||||
std::cout<<cmd<<"\n"; system(cmd.c_str());
|
||||
cmd = "cp " + std::string(g106_bin) + " yolo3_voc_2.bin";
|
||||
std::cout<<cmd<<"\n"; system(cmd.c_str());
|
||||
return 0;
|
||||
}
|
||||
@@ -0,0 +1,785 @@
|
||||
[net]
|
||||
# Testing
|
||||
batch=1
|
||||
subdivisions=1
|
||||
# Training
|
||||
# batch=64
|
||||
# subdivisions=16
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 50200
|
||||
policy=steps
|
||||
steps=40000,45000
|
||||
scales=.1,.1
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=75
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=20
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .5
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=75
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=20
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .5
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=75
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=20
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .5
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
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Reference in New Issue
Block a user