mmr/tkDNN
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Merge branch 'cnet' of https://github.com/ceccocats/tkDNN into cnet

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nvidia
2019-12-20 15:27:55 +01:00
parent 854a4c316a d889ed385d
commit f0ea2c027f
12 changed files with 253 additions and 187 deletions
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src/NetworkRT.cpp
+52 -1
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@@ -75,7 +75,7 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
input = Ilay->getOutput(0);
input->setName( (l->getLayerName() + std::to_string(i) + "_out").c_str() );
if(l->getLayerType() == LAYER_YOLO)
if(l->getLayerType() == LAYER_YOLO || l->final)
networkRT->markOutput(*input);
tensors[l] = input;
}
@@ -182,6 +182,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
return convert_layer(input, (Yolo*) l);
if(type == LAYER_UPSAMPLE)
return convert_layer(input, (Upsample*) l);
if(type == LAYER_DEFORMCONV2D)
return convert_layer(input, (DeformConv2d*) l);
std::cout<<l->getLayerName()<<"\n";
FatalError("Layer not implemented in tensorRT");
@@ -254,6 +256,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
lRTconv->setPadding(DimsHW{l->paddingH, l->paddingW});
lRT = (ILayer*) lRTconv;
Dims d = lRTconv->getOutput(0)->getDimensions();
std::cout<<"DECONV: "<<d.d[0]<<" "<<d.d[1]<<" "<<d.d[2]<<" "<<d.d[3]<<"\n";
}
checkNULL(lRT);
@@ -421,6 +425,53 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Upsample *l) {
return lRT;
}
ILayer* NetworkRT::convert_layer(ITensor *input, DeformConv2d *l) {
//std::cout<<"convert DEFORMABLE\n";
ILayer *preconv = convert_layer(input, l->preconv);
ITensor **inputs = new ITensor*[2];
inputs[0] = input;
inputs[1] = preconv->getOutput(0);
//std::cout<<"New plugin DEFORMABLE\n";
IPlugin *plugin = new DeformableConvRT(l);
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
// batchnorm
void *bias_b, *power_b, *mean_b, *variance_b, *scales_b;
if(dtRT == DataType::kHALF) {
bias_b = l->bias16_h;
power_b = l->power16_h;
mean_b = l->mean16_h;
variance_b = l->variance16_h;
scales_b = l->scales16_h;
} else {
bias_b = l->bias_h;
power_b = l->power_h;
mean_b = l->mean_h;
variance_b = l->variance_h;
scales_b = l->scales_h;
}
Weights power{dtRT, power_b, l->outputs};
Weights shift{dtRT, mean_b, l->outputs};
Weights scale{dtRT, variance_b, l->outputs};
std::cout<<lRT->getNbOutputs()<<std::endl;
IScaleLayer *lRT2 = networkRT->addScale(*lRT->getOutput(0), ScaleMode::kCHANNEL,
shift, scale, power);
checkNULL(lRT2);
Weights shift2{dtRT, bias_b, l->outputs};
Weights scale2{dtRT, scales_b, l->outputs};
IScaleLayer *lRT3 = networkRT->addScale(*lRT2->getOutput(0), ScaleMode::kCHANNEL,
shift2, scale2, power);
checkNULL(lRT3);
return lRT3;
}
bool NetworkRT::serialize(const char *filename) {
std::ofstream p(filename);