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Shelfnet works on tensorRT (shortcut need to be fixed)

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-06-23 12:50:24 +02:00
parent 6fd261f628
commit 94e558003d
9 changed files with 75 additions and 100 deletions
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tests/shelfnet/shelfnet.cpp
+37 -74
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@@ -6,8 +6,6 @@
#include "NetworkViz.h"
const char *output_bin1 = "shelfnet/debug/classification_headers-5.bin";
const char *output_bin2 = "shelfnet/debug/regression_headers-5.bin";
const char *input_bin = "shelfnet/debug/input.bin";
const char *backbone[] = {
@@ -95,14 +93,14 @@ int main()
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);
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);
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);
@@ -113,7 +111,7 @@ int main()
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);
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);
@@ -121,7 +119,7 @@ int main()
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);
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);
@@ -133,7 +131,7 @@ int main()
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);
features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
}
//DECODER
@@ -142,7 +140,7 @@ int main()
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);
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);
@@ -153,7 +151,7 @@ int main()
//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);
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);
@@ -168,7 +166,7 @@ int main()
//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);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
up_out.push_back(last);
}
@@ -176,7 +174,7 @@ int main()
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);
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);
@@ -186,7 +184,7 @@ int main()
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);
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);
@@ -197,7 +195,7 @@ int main()
}
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);
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);
@@ -208,7 +206,7 @@ int main()
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);
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);
@@ -223,7 +221,7 @@ int main()
// //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);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
up_out.push_back(last);
}
@@ -231,29 +229,26 @@ int main()
// 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);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, 19, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
/*up_out[i] =*/ new tk::dnn::Resize(&net, 19, net.input_dim.h, net.input_dim.w, true);
// /*up_out[i] =*/ new tk::dnn::Resize(&net, 19, net.input_dim.h, net.input_dim.w, true);
// }
new tk::dnn::Softmax(&net);
// new tk::dnn::Softmax(&net);
const char *output_bin = "shelfnet/debug/fofmaf.bin";
const char *output_bin = "shelfnet/debug/conv_out-conv_out.bin";
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
std::cout<<"Input:"<<std::endl;
// printDeviceVector(64, data, true);
//print network model
net.print();
// // convert network to tensorRT
// tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet"));
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet"));
tk::dnn::dataDim_t dim1 = dim; //input dim
dnnType *cudnn_out = nullptr;
@@ -266,67 +261,35 @@ int main()
dim1.print();
}
// 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);
{
dim2.print();
TKDNN_TSTART
netRT.infer(dim2, data);
TKDNN_TSTOP
dim2.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];
// 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);
printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
dnnType *out1, *out1_h;
int odim1 = dim1.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
printDeviceVector(64, out1);
// dnnType *out2, *out2_h;
// int odim2 = out_dim2.tot();
// readBinaryFile(output_bin2, odim2, &out2_h, &out2);
// int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
std::cout << "CUDNN vs correct" << std::endl;
checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
// std::cout << "TRT vs correct" << std::endl;
// checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
// ret_tensorrt |= checkResult(odim2, rt_out2, out2) == 0 ? 0 : ERROR_TENSORRT;
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_out1, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// ret_cudnn_tensorrt |= checkResult(odim2, cudnn_out2, rt_out2) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// 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;
// ret_cudnn_tensorrt |= checkResult(conf->output_dim.tot(), conf->dstData, rt_out3) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// ret_cudnn_tensorrt |= checkResult(loc->output_dim.tot(), loc->dstData, rt_out4) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
// return ret_cudnn | ret_tensorrt | ret_cudnn_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;
}