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Add yolo4-csp for crowdhuman dataset, add shelfnet for coco-stuff dataset, fix minor in demo.cpp

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2021-11-29 17:33:57 +01:00
parent 9cbac460bc
commit 04de9908a6
6 changed files with 1614 additions and 1 deletions
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CMakeLists.txt
+3
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@@ -140,6 +140,9 @@ target_link_libraries(test_shelfnet_berkeley tkDNN)
add_executable(test_shelfnet_mapillary tests/shelfnet/shelfnet_mapillary.cpp)
target_link_libraries(test_shelfnet_mapillary tkDNN)
add_executable(test_shelfnet_coco tests/shelfnet/shelfnet_coco.cpp)
target_link_libraries(test_shelfnet_coco tkDNN)
# DEMOS
add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
target_link_libraries(test_rtinference tkDNN)
demo/demo/demo.cpp
+1 -1
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@@ -26,7 +26,7 @@ int main(int argc, char *argv[]) {
std::string config_file = "..\\..\\..\\demo\\demoConfig.yaml";
#endif
if(argc > 1)
config_file = config_file[1];
config_file = argv[1];
YAML::Node conf = YAMLloadConf(config_file);
if(!conf)
tests/darknet/cfg/yolo4-csp_crowd.cfg
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File diff suppressed because it is too large Load Diff
tests/darknet/names/crowdhuman.names
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@@ -0,0 +1,2 @@
person
head
tests/darknet/yolo4-csp_crowd.cpp
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@@ -0,0 +1,34 @@
#include<iostream>
#include<vector>
#include "tkdnn.h"
#include "test.h"
#include "DarknetParser.h"
int main() {
std::string bin_path = "yolo4-csp_crowd";
std::vector<std::string> input_bins = {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "/debug/layer144_out.bin",
bin_path + "/debug/layer159_out.bin",
bin_path + "/debug/layer174_out.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = std::string(TKDNN_PATH) + "/tests/darknet/cfg/yolo4-csp_crowd.cfg";
std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/crowdhuman.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/RKWfWNmWXfJigsK/download");
// parse darknet network
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
net->print();
//convert network to tensorRT
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
}
tests/shelfnet/shelfnet_coco.cpp
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@@ -0,0 +1,295 @@
#include <iostream>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "tkdnn.h"
#include "NetworkViz.h"
const char *input_bin = "shelfnet_coco/debug/input.bin";
const char *backbone[] = {
"shelfnet_coco/layers/backbone-conv1.bin",
"shelfnet_coco/layers/backbone-layer1-0-conv1.bin",
"shelfnet_coco/layers/backbone-layer1-0-conv2.bin",
"shelfnet_coco/layers/backbone-layer1-1-conv1.bin",
"shelfnet_coco/layers/backbone-layer1-1-conv2.bin",
"shelfnet_coco/layers/backbone-layer2-0-conv1.bin",
"shelfnet_coco/layers/backbone-layer2-0-conv2.bin",
"shelfnet_coco/layers/backbone-layer2-0-downsample-0.bin",
"shelfnet_coco/layers/backbone-layer2-1-conv1.bin",
"shelfnet_coco/layers/backbone-layer2-1-conv2.bin",
"shelfnet_coco/layers/backbone-layer3-0-conv1.bin",
"shelfnet_coco/layers/backbone-layer3-0-conv2.bin",
"shelfnet_coco/layers/backbone-layer3-0-downsample-0.bin",
"shelfnet_coco/layers/backbone-layer3-1-conv1.bin",
"shelfnet_coco/layers/backbone-layer3-1-conv2.bin",
"shelfnet_coco/layers/backbone-layer4-0-conv1.bin",
"shelfnet_coco/layers/backbone-layer4-0-conv2.bin",
"shelfnet_coco/layers/backbone-layer4-0-downsample-0.bin",
"shelfnet_coco/layers/backbone-layer4-1-conv1.bin",
"shelfnet_coco/layers/backbone-layer4-1-conv2.bin"};
const char *conv_out[] = {
"shelfnet_coco/layers/conv_out-conv-conv.bin",
"shelfnet_coco/layers/conv_out-conv_out.bin",
"shelfnet_coco/layers/conv_out16-conv-conv.bin",
"shelfnet_coco/layers/conv_out16-conv_out.bin",
"shelfnet_coco/layers/conv_out32-conv-conv.bin",
"shelfnet_coco/layers/conv_out32-conv_out.bin"
};
const char *decoder[] = {
"shelfnet_coco/layers/decoder-bottom-conv1.bin",
"shelfnet_coco/layers/decoder-bottom-conv12.bin",
"shelfnet_coco/layers/decoder-up_conv_list-0-conv-conv.bin",
"shelfnet_coco/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet_coco/layers/decoder-up_dense_list-0-conv.bin",
"shelfnet_coco/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet_coco/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet_coco/layers/decoder-up_dense_list-1-conv.bin"
};
const char *ladder[] = {
"shelfnet_coco/layers/ladder-inconv-conv1.bin",
"shelfnet_coco/layers/ladder-inconv-conv12.bin",
"shelfnet_coco/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet_coco/layers/ladder-down_module_list-0-conv12.bin",
"shelfnet_coco/layers/ladder-down_conv_list-0.bin",
"shelfnet_coco/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet_coco/layers/ladder-down_module_list-1-conv12.bin",
"shelfnet_coco/layers/ladder-down_conv_list-1.bin",
"shelfnet_coco/layers/ladder-bottom-conv1.bin",
"shelfnet_coco/layers/ladder-bottom-conv12.bin",
"shelfnet_coco/layers/ladder-up_conv_list-0-conv-conv.bin",
"shelfnet_coco/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet_coco/layers/ladder-up_dense_list-0-conv.bin",
"shelfnet_coco/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet_coco/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet_coco/layers/ladder-up_dense_list-1-conv.bin"};
const char *trans[] = {
"shelfnet_coco/layers/trans1-conv.bin",
"shelfnet_coco/layers/trans2-conv.bin",
"shelfnet_coco/layers/trans3-conv.bin"};
int main()
{
downloadWeightsifDoNotExist(input_bin, "shelfnet_coco", "https://cloud.hipert.unimore.it/s/KfQ9fGJQsgzNbiW/download");
int classes = 183;
// Network layout
tk::dnn::dataDim_t dim(1, 3, 1024, 1024, 1);
tk::dnn::Network net(dim);
int bi = 0, di = 0, li = 0, ci = 0;
new tk::dnn::Conv2d(&net, 64, 7, 7, 2, 2, 3, 3, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
for(int i=0; i<2; ++i){
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
}
std::vector<tk::dnn::Layer*> features;
for(int i=0;i<3;++i){
int out_channel = pow(2,7+i);
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
new tk::dnn::Shortcut(&net, bn2);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
features.push_back(last);
}
for(int i=0; i<features.size(); ++i){
new tk::dnn::Route(&net, &features[i], 1);
int out_channel = pow(2,6+i);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, trans[i], true);
features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
}
//DECODER
last = features[2];
std::vector<tk::dnn::Layer*> up_out;
//bottom
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
up_out.push_back(last);
for(int i=0; i<2; ++i){
int out_channel = pow(2,7-i);
//up-conv
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, decoder[di++], true);
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Shortcut(&net, act, true);
//interpolate
new tk::dnn::Resize(&net, 1,2,2);
new tk::dnn::Shortcut(&net, features[1-i]);
//up-dense
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
up_out.push_back(last);
}
//LADDER
std::vector<tk::dnn::Layer*> down_out;
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Shortcut(&net, last);
new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
for(int i=0; i<2;++i){
int out_channel = pow(2,6+i);
tk::dnn::Layer* l_last = new tk::dnn::Shortcut(&net, up_out[2-i]);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Shortcut(&net, l_last);
l_last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
down_out.push_back(l_last);
new tk::dnn::Conv2d (&net, out_channel*2, 3, 3, 2, 2, 1, 1, ladder[li++], false);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.0f); //should be ReLU
}
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
up_out.clear();
up_out.push_back(last);
for(int i=0; i<2; ++i){
int out_channel = pow(2,7-i);
//up-conv
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, ladder[li++], true);
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Shortcut(&net, act, true);
//interpolate
new tk::dnn::Resize(&net, 1,2,2);
new tk::dnn::Shortcut(&net, down_out[1-i]);
// //up-dense
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
up_out.push_back(last);
}
// for(int i=2;i>=0;--i){
// new tk::dnn::Route(&net, &up_out[i], 1);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, conv_out[ci++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, classes, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
/*up_out[i] =*/ new tk::dnn::Resize(&net, classes, net.input_dim.h, net.input_dim.w, true, tk::dnn::ResizeMode_t::LINEAR);
// // }
new tk::dnn::Softmax(&net);
const char *output_bin = "shelfnet_coco/debug/softmax.bin";
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
std::cout<<"Input:"<<std::endl;
//print network model
net.print();
// // convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet_coco"));
tk::dnn::dataDim_t dim1 = dim; //input dim
dnnType *cudnn_out = nullptr;
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TKDNN_TSTART
cudnn_out = net.infer(dim1, data);
TKDNN_TSTOP
dim1.print();
}
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TKDNN_TSTART
netRT.infer(dim2, data);
TKDNN_TSTOP
dim2.print();
}
dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
dnnType *out1, *out1_h;
int odim1 = dim1.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
// std::cout << "CUDNN vs correct" << std::endl;
// ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
std::cout << "TRT vs correct" << std::endl;
ret_tensorrt |=checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
std::cout << "CUDNN vs TRT " << std::endl;
ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
cv::imwrite("test.png", viz);
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}