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Compatibility whether yolov8 fuse or not #1579

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Compatibility whether yolov8 fuse or not #1579

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81c5c83
Compatibility whether yolov8 fuse or not
saladjay Sep 11, 2024
cc4d8ac
Compatibility whether yolov8 fuse or not
saladjay Sep 11, 2024
9ec9925
Merge branch 'master' of https://github.com/wang-xinyu/tensorrtx
saladjay Nov 27, 2024
909e42f
Merge branch 'master' of https://github.com/saladjay/tensorrtx
saladjay Nov 27, 2024
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24 changes: 19 additions & 5 deletions yolov8/src/block.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -80,17 +80,31 @@ nvinfer1::IElementWiseLayer* convBnSiLU(nvinfer1::INetworkDefinition* network,
std::map<std::string, nvinfer1::Weights> weightMap, nvinfer1::ITensor& input,
int ch, int k, int s, int p, std::string lname) {
nvinfer1::Weights bias_empty{nvinfer1::DataType::kFLOAT, nullptr, 0};
nvinfer1::IConvolutionLayer* conv =
network->addConvolutionNd(input, ch, nvinfer1::DimsHW{k, k}, weightMap[lname + ".conv.weight"], bias_empty);
nvinfer1::IConvolutionLayer* conv{nullptr};
std::string bias_name{lname + ".conv.bias"};
// Compatibility whether there is a bias or not
// fuse conv+nb into conv, the new conv layer will have bias, and bn will disappear
if(weightMap.find(bias_name) == weightMap.end()){
conv = network->addConvolutionNd(input, ch, nvinfer1::DimsHW{k, k}, weightMap[lname + ".conv.weight"], bias_empty);
}else{
conv = network->addConvolutionNd(input, ch, nvinfer1::DimsHW{k, k}, weightMap[lname + ".conv.weight"], weightMap[bias_name]);
}
assert(conv);
conv->setStrideNd(nvinfer1::DimsHW{s, s});
conv->setPaddingNd(nvinfer1::DimsHW{p, p});

nvinfer1::IScaleLayer* bn = addBatchNorm2d(network, weightMap, *conv->getOutput(0), lname + ".bn", 1e-3);
// Compatibility whether there is a bn or not
nvinfer1::ILayer *layer{nullptr};
if(weightMap.find(std::string{lname + ".bn.weight"}) != weightMap.end()){
layer = addBatchNorm2d(network, weightMap, *conv->getOutput(0), lname + ".bn", 1e-3);
}else{
layer = conv;
}
nvinfer1::IActivationLayer* sigmoid = network->addActivation(*layer->getOutput(0), nvinfer1::ActivationType::kSIGMOID);

nvinfer1::IActivationLayer* sigmoid = network->addActivation(*bn->getOutput(0), nvinfer1::ActivationType::kSIGMOID);
nvinfer1::IElementWiseLayer* ew =
network->addElementWise(*bn->getOutput(0), *sigmoid->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);
network->addElementWise(*layer->getOutput(0), *sigmoid->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);

assert(ew);
return ew;
}
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