mmr/tkDNN
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yolo3 ok

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This commit is contained in:
Francesco Gatti
2019-01-04 22:28:10 +01:00
parent 2e8d0b1002
commit 88097a3774
7 changed files with 139 additions and 23 deletions
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tests/yolo3_berkeley/yolo3_berkeley.cpp
+33 -15
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@@ -77,7 +77,11 @@ const char *c102_bin = "../tests/yolo3_berkeley/layers/c102.bin";
const char *c103_bin = "../tests/yolo3_berkeley/layers/c103.bin";
const char *c104_bin = "../tests/yolo3_berkeley/layers/c104.bin";
const char *c105_bin = "../tests/yolo3_berkeley/layers/c105.bin";
const char *output_bin = "../tests/yolo3_berkeley/debug/layer93_out.bin";
const char *output_bins[3] = {
"../tests/yolo3_berkeley/debug/layer82_out.bin",
"../tests/yolo3_berkeley/debug/layer94_out.bin",
"../tests/yolo3_berkeley/debug/layer106_out.bin"
};
int main() {
@@ -234,7 +238,7 @@ int main() {
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, 45, 1, 1, 1, 1, 0, 0, c81_bin, false);
tk::dnn::Yolo y82 (&net, 10, 3);
tk::dnn::Yolo yolo0 (&net, 10, 3);
tk::dnn::Layer *m83_layers[1] = { &a79 };
tk::dnn::Route m83 (&net, m83_layers, 1);
@@ -243,7 +247,7 @@ int main() {
tk::dnn::Upsample u85 (&net, 2);
tk::dnn::Layer *m86_layers[2] = { &u85, &s61 };
tk::dnn::Route m86 (&net, m86_layers, 1); // ROUTE ERROR IN RT INFERENCE
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);
@@ -254,10 +258,11 @@ int main() {
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, 45, 1, 1, 1, 1, 0, 0, c93_bin, false);
tk::dnn::Yolo y94 (&net, 10, 3);
tk::dnn::Yolo yolo1 (&net, 10, 3);
tk::dnn::Layer *m95_layers[1] = { &a91 };
tk::dnn::Route m95 (&net, m95_layers, 1);
@@ -281,7 +286,7 @@ int main() {
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, 45, 1, 1, 1, 1, 0, 0, c105_bin, false);
tk::dnn::Yolo y106 (&net, 10, 3);
tk::dnn::Yolo yolo2 (&net, 10, 3);
// Load input
dnnType *data;
@@ -294,32 +299,45 @@ int main() {
//convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, "yolo3_berkeley.rt");
dnnType *out_data, *out_data2; // cudnn output, tensorRT output
// 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
out_data = net.infer(dim1, data);
net.infer(dim1, data);
TIMER_STOP
dim1.print();
}
cudnn_out[0] = yolo0.dstData;
cudnn_out[1] = yolo1.dstData;
cudnn_out[2] = yolo2.dstData;
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
TIMER_START
out_data2 = netRT.infer(dim2, data);
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];
printCenteredTitle(" CHECK RESULTS ", '=', 30);
dnnType *out, *out_h;
int out_dim = net.getOutputDim().tot();
//readBinaryFile(output_bin, out_dim, &out_h, &out);
//std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
//std::cout<<"TRT vs correct"; checkResult(out_dim, out_data2, out);
std::cout<<"CUDNN vs TRT "; checkResult(out_dim, out_data, out_data2);
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]);
}
return 0;
}