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darknet parser tested

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This commit is contained in:
Francesco Gatti
2020-06-01 14:55:08 +02:00
parent d2e2669b6d
commit 18794d52c2
21 changed files with 112 additions and 30 deletions
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include/tkDNN/DarknetParser.h
+34 -13
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@@ -22,6 +22,7 @@ namespace tk { namespace dnn {
int classes = 20;
int num = 1;
int pad = 0;
int coords = 4;
float scale_xy = 1;
std::vector<int> layers;
std::string activation = "linear";
@@ -102,9 +103,11 @@ namespace tk { namespace dnn {
fields.classes = std::stoi(value);
else if(name.find("num") != std::string::npos)
fields.num = std::stoi(value);
else if(name.find("coords") != std::string::npos)
fields.coords = std::stoi(value);
else if(name.find("groups") != std::string::npos)
fields.groups = std::stoi(value);
else if(name.find("scale_xy") != std::string::npos)
else if(name.find("scale_x_y") != std::string::npos)
fields.scale_xy = std::stof(value);
else if(name.find("from") != std::string::npos)
fields.layers.push_back(std::stof(value));
@@ -135,23 +138,25 @@ namespace tk { namespace dnn {
if(f.pad == 1) {
f.padding_x = f.padding_y = f.size_x /2;
}
std::cout<<"Add layer: "<<f.type<<"\n";
if(f.type == "convolutional") {
std::string wgs = wgs_path + "/c" + std::to_string(netLayers.size()) + ".bin";
printf("%d (%d,%d) (%d,%d) (%d,%d) %s %d %d\n", f.filters, f.size_x, f.size_y, f.stride_x, f.stride_y, f.padding_x, f.padding_y, wgs.c_str(), f.batch_normalize, f.groups);
tk::dnn::Conv2d *l= new tk::dnn::Conv2d(net, f.filters, f.size_x, f.size_y, f.stride_x,
f.stride_y, f.padding_x, f.padding_y, wgs, f.batch_normalize, false, f.groups);
if(f.activation != "linear") {
tkdnnActivationMode_t act;
if(f.activation == "relu") act = tkdnnActivationMode_t(CUDNN_ACTIVATION_RELU);
else if(f.activation == "leaky") act = tk::dnn::ACTIVATION_LEAKY;
else if(f.activation == "mish") act = tk::dnn::ACTIVATION_MISH;
else { FatalError("activation not supported: " + f.activation); }
netLayers.push_back(new tk::dnn::Activation(net, act));
} else {
netLayers.push_back(l);
}
netLayers.push_back(l);
} else if(f.type == "maxpool") {
if(f.stride_x == 1 && f.stride_y == 1)
netLayers.push_back(new tk::dnn::Pooling(net, f.size_x, f.size_y, f.stride_x, f.stride_y,
f.padding_x, f.padding_y, tk::dnn::POOLING_MAX_FIXEDSIZE));
else
netLayers.push_back(new tk::dnn::Pooling(net, f.size_x, f.size_y, f.stride_x, f.stride_y,
f.padding_x, f.padding_y, tk::dnn::POOLING_MAX));
} else if(f.type == "avgpool") {
netLayers.push_back(new tk::dnn::Pooling(net, f.size_x, f.size_y, f.stride_x, f.stride_y,
f.padding_x, f.padding_y, tk::dnn::POOLING_AVERAGE));
} else if(f.type == "shortcut") {
if(f.layers.size() != 1) FatalError("no layers to shortcut\n");
int layerIdx = f.layers[0];
@@ -177,6 +182,12 @@ namespace tk { namespace dnn {
}
netLayers.push_back(new tk::dnn::Route(net, layers.data(), layers.size()));
} else if(f.type == "reorg") {
netLayers.push_back(new tk::dnn::Reorg(net, f.stride_x));
} else if(f.type == "region") {
netLayers.push_back(new tk::dnn::Region(net, f.classes, f.coords, f.num));
} else if(f.type == "yolo") {
std::string wgs = wgs_path + "/g" + std::to_string(netLayers.size()) + ".bin";
printf("%d %d %s %d %f\n", f.classes, f.num/f.n_mask, wgs.c_str(), f.n_mask, f.scale_xy);
@@ -189,6 +200,16 @@ namespace tk { namespace dnn {
} else{
FatalError("layer not supported: " + f.type);
}
// add activation
if(netLayers.size() > 0 && f.activation != "linear") {
tkdnnActivationMode_t act;
if(f.activation == "relu") act = tkdnnActivationMode_t(CUDNN_ACTIVATION_RELU);
else if(f.activation == "leaky") act = tk::dnn::ACTIVATION_LEAKY;
else if(f.activation == "mish") act = tk::dnn::ACTIVATION_MISH;
else { FatalError("activation not supported: " + f.activation); }
netLayers[netLayers.size()-1] = new tk::dnn::Activation(net, act);
};
}
std::vector<std::string> darknetReadNames(const std::string& names_file){
@@ -244,7 +265,7 @@ namespace tk { namespace dnn {
// new type
//std::cout<<"type: "<<type<<"\n";
fields = darknetFields_t();
fields = darknetFields_t(); // reset to default
fields.type = type;
continue;
}
include/tkDNN/Network.h
+1
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@@ -40,6 +40,7 @@ class Network {
public:
Network(dataDim_t input_dim);
virtual ~Network();
void releaseLayers();
/**
Do inferece for every added layer
include/tkDNN/test.h
+5
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@@ -8,6 +8,11 @@ int testInference(std::vector<std::string> input_bins, std::vector<std::string>
if(net->layers[i]->final)
outputs.push_back(net->layers[i]);
}
// no final layers, set last as output
if(outputs.size() == 0) {
outputs.push_back(net->layers[net->num_layers-1]);
}
// check input
if(input_bins.size() != 1) {
scripts/test_all_tests.sh
+2 -1
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@@ -78,9 +78,10 @@ do
test_net yolo3_512
test_net yolo3tiny
test_net csresnext50-panet-spp
#test_net csresnext50-panet-spp_berkeley
test_net mobilenetv2ssd
test_net yolo3tiny_512
test_net yolo2tiny
#test_net yolo2tiny
test_net mobilenetv2ssd512
test_net mnist
test_net yolo2
src/Network.cpp
+6 -2
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@@ -59,12 +59,16 @@ Network::Network(dataDim_t input_dim) {
}
Network::~Network() {
for(int i=0; i<num_layers; i++)
delete layers[i];
checkCUDNN( cudnnDestroy(cudnnHandle) );
checkERROR( cublasDestroy(cublasHandle) );
}
void Network::releaseLayers() {
for(int i=0; i<num_layers; i++)
delete layers[i];
num_layers = 0;
}
dnnType* Network::infer(dataDim_t &dim, dnnType* data) {
//do infer for every layer
src/Region.cpp
+1 -2
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@@ -12,8 +12,7 @@
namespace tk { namespace dnn {
Region::Region(Network *net, int classes, int coords, int num) :
Layer(net) {
Layer(net) {
this->classes = classes;
this->coords = coords;
this->num = num;
tests/darknet/cfg/yolo2tiny.cfg
+4 -4
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@@ -1,5 +1,5 @@
[net]
Training
# Training
batch=64
subdivisions=8
# Testing
@@ -90,9 +90,9 @@ stride=1
pad=1
activation=leaky
#[maxpool]
#size=2
#stride=1
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
tests/darknet/csresnext50-panet-spp.cpp
+1
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@@ -27,6 +27,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/csresnext50-panet-spp_berkeley.cpp
+3 -1
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@@ -5,7 +5,7 @@
#include "DarknetParser.h"
int main() {
std::string bin_path = "bdd-csresnext50-panet-spp";
std::string bin_path = "csresnext50-panet-spp_berkeley";
std::vector<std::string> input_bins = {
bin_path + "/layers/input.bin"
};
@@ -17,6 +17,7 @@ int main() {
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/csresnext50-panet-spp_berkeley.cfg";
std::string name_path = "../tests/darknet/names/berkeley.names";
// FIXME: wrong weights
// downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s//download");
// parse darknet network
@@ -27,6 +28,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo2.cpp
+2 -1
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@@ -10,7 +10,7 @@ int main() {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "layers/output.bin"
bin_path + "/layers/output.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/yolo2.cfg";
@@ -25,6 +25,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo2_voc.cpp
+1
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@@ -25,6 +25,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo2tiny.cpp
+4 -2
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@@ -10,12 +10,13 @@ int main() {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "layers/output.bin"
bin_path + "/layers/output.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/yolo2tiny.cfg";
std::string name_path = "../tests/darknet/names/coco.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/nf4PJ3k8bxBETwL/download");
// FIXME: wrong weights
//downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s//download");
// parse darknet network
tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
@@ -25,6 +26,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo3.cpp
+2 -1
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@@ -26,7 +26,8 @@ int main() {
//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);
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo3_512.cpp
+1
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@@ -27,6 +27,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo3_berkeley.cpp
+2 -1
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@@ -16,7 +16,7 @@ int main() {
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/yolo3_berkeley.cfg";
std::string name_path = "../tests/darknet/names/barkeley.names";
std::string name_path = "../tests/darknet/names/berkeley.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/o5cHa4AjTKS64oD/download");
// parse darknet network
@@ -27,6 +27,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo3_coco4.cpp
+1
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@@ -27,6 +27,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo3_flir.cpp
+1
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@@ -27,6 +27,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo3tiny.cpp
+3 -1
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@@ -10,7 +10,8 @@ int main() {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "debug/layer23_out.bin",
bin_path + "/debug/layer16_out.bin",
bin_path + "/debug/layer23_out.bin",
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/yolo3tiny.cfg";
@@ -25,6 +26,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo3tiny512.cpp → tests/darknet/yolo3tiny_512.cpp
+3 -1
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@@ -10,7 +10,8 @@ int main() {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "debug/layer23_out.bin",
bin_path + "/debug/layer16_out.bin",
bin_path + "/debug/layer23_out.bin",
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/yolo3tiny_512.cfg";
@@ -25,6 +26,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo4.cpp
+1
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@@ -27,6 +27,7 @@ int main() {
tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
int ret = testInference(input_bins, output_bins, net, netRT);
net->releaseLayers();
delete net;
delete netRT;
return ret;
tests/darknet/yolo4_berkeley.cpp
+34
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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_berkeley";
std::vector<std::string> input_bins = {
bin_path + "/layers/input.bin"
};
std::vector<std::string> output_bins = {
bin_path + "/debug/layer139_out.bin",
bin_path + "/debug/layer150_out.bin",
bin_path + "/debug/layer161_out.bin"
};
std::string wgs_path = bin_path + "/layers";
std::string cfg_path = "../tests/darknet/cfg/yolo4_berkeley.cfg";
std::string name_path = "../tests/darknet/names/berkeley.names";
downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s//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;
}