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CenterNet TensorRT works. TensorRT serialization not yet implemented

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Signed-oof-by: Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
Davide Sapienza
2019-12-20 11:05:03 +01:00
parent e99b353d8b
commit d889ed385d
9 changed files with 167 additions and 35 deletions
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tests/resnet101_cnet/resnet101_cnet.cpp
+13 -13
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@@ -172,9 +172,9 @@ const char *reg_conv2_bin = "../tests/resnet101_cnet/layers/reg-2.bin";
const char *fc_bin = "../tests/resnet101_cnet/layers/fc.bin";
const char *output_bin[]={
"../tests/resnet101_cnet/debug/hm.bin",
"../tests/resnet101_cnet/debug/wh.bin",
"../tests/resnet101_cnet/debug/reg.bin"};
"../tests/resnet101_cnet/debug/hm.bin",
"../tests/resnet101_cnet/debug/wh.bin",
"../tests/resnet101_cnet/debug/reg.bin"};
int main()
{
@@ -317,17 +317,17 @@ int main()
tk::dnn::Layer *route_1_0_layers[1] = { layer2_deconv1_relu };
tk::dnn::Conv2d *hm_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false);
tk::dnn::Activation *hm_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 80, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false);
tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 80, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false, false, true);
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *wh_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false);
tk::dnn::Activation *wh_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false);
tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false, false, true);
tk::dnn::Route *route_2_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *reg_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false);
tk::dnn::Activation *reg_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false);
tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false, false, true);
// Load input
dnnType *data;
@@ -339,7 +339,7 @@ int main()
net.print();
//convert network to tensorRT
// tk::dnn::NetworkRT netRT(&net, "resnet101_cnet.rt");
tk::dnn::NetworkRT netRT(&net, "resnet101_cnet.rt");
tk::dnn::dataDim_t dim1 = dim; //input dim
@@ -354,7 +354,7 @@ int main()
// printDeviceVector(64, cudnn_out, true);
/* tk::dnn::dataDim_t dim2 = dim;
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
@@ -363,10 +363,9 @@ int main()
TIMER_STOP
dim2.print();
}
rt_out = (dnnType *)netRT.buffersRT[1];
*/
tk::dnn::Conv2d *outs[3] = { hm, wh, reg };
tk::dnn::Layer *outs[3] = { hm, wh, reg };
for(int i=0; i<3; i++) {
printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30);
@@ -382,14 +381,15 @@ int main()
dnnType *cudnn_out, *rt_out;
cudnn_out = outs[i]->dstData;
rt_out = (dnnType *)netRT.buffersRT[i+1];
std::cout << "CUDNN vs correct";
checkResult(odim, cudnn_out, out);
/* std::cout << "TRT vs correct";
std::cout << "TRT vs correct";
checkResult(odim, rt_out, out);
std::cout << "CUDNN vs TRT ";
checkResult(odim, cudnn_out, rt_out);*/
checkResult(odim, cudnn_out, rt_out);
}
return 0;
}