thebears/deepstream_yolo
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Added YOLOv5 6.0 native support

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unknown
2021-12-09 15:44:17 -03:00
parent dcc44b730c
commit bfd9268a31
8 changed files with 688 additions and 80 deletions
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139
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.cpp
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@@ -12,6 +12,7 @@ nvinfer1::ILayer* convolutionalLayer(
std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights,
int& weightPtr,
std::string weightsType,
int& inputChannels,
nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
@@ -56,57 +57,111 @@ nvinfer1::ILayer* convolutionalLayer(
nvinfer1::Weights convWt{nvinfer1::DataType::kFLOAT, nullptr, size};
nvinfer1::Weights convBias{nvinfer1::DataType::kFLOAT, nullptr, bias};
if (batchNormalize == false)
{
float* val = new float[filters];
for (int i = 0; i < filters; ++i)
if (weightsType == "weights") {
if (batchNormalize == false)
{
val[i] = weights[weightPtr];
weightPtr++;
float* val = new float[filters];
for (int i = 0; i < filters; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convBias.values = val;
trtWeights.push_back(convBias);
val = new float[size];
for (int i = 0; i < size; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convWt.values = val;
trtWeights.push_back(convWt);
}
convBias.values = val;
trtWeights.push_back(convBias);
val = new float[size];
for (int i = 0; i < size; ++i)
else
{
val[i] = weights[weightPtr];
weightPtr++;
for (int i = 0; i < filters; ++i)
{
bnBiases.push_back(weights[weightPtr]);
weightPtr++;
}
for (int i = 0; i < filters; ++i)
{
bnWeights.push_back(weights[weightPtr]);
weightPtr++;
}
for (int i = 0; i < filters; ++i)
{
bnRunningMean.push_back(weights[weightPtr]);
weightPtr++;
}
for (int i = 0; i < filters; ++i)
{
bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5));
weightPtr++;
}
float* val = new float[size];
for (int i = 0; i < size; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convWt.values = val;
trtWeights.push_back(convWt);
trtWeights.push_back(convBias);
}
convWt.values = val;
trtWeights.push_back(convWt);
}
else
{
for (int i = 0; i < filters; ++i)
else {
if (batchNormalize == false)
{
bnBiases.push_back(weights[weightPtr]);
weightPtr++;
float* val = new float[size];
for (int i = 0; i < size; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convWt.values = val;
trtWeights.push_back(convWt);
val = new float[filters];
for (int i = 0; i < filters; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convBias.values = val;
trtWeights.push_back(convBias);
}
for (int i = 0; i < filters; ++i)
else
{
bnWeights.push_back(weights[weightPtr]);
weightPtr++;
float* val = new float[size];
for (int i = 0; i < size; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convWt.values = val;
for (int i = 0; i < filters; ++i)
{
bnWeights.push_back(weights[weightPtr]);
weightPtr++;
}
for (int i = 0; i < filters; ++i)
{
bnBiases.push_back(weights[weightPtr]);
weightPtr++;
}
for (int i = 0; i < filters; ++i)
{
bnRunningMean.push_back(weights[weightPtr]);
weightPtr++;
}
for (int i = 0; i < filters; ++i)
{
bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5));
weightPtr++;
}
trtWeights.push_back(convWt);
trtWeights.push_back(convBias);
}
for (int i = 0; i < filters; ++i)
{
bnRunningMean.push_back(weights[weightPtr]);
weightPtr++;
}
for (int i = 0; i < filters; ++i)
{
bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5));
weightPtr++;
}
float* val = new float[size];
for (int i = 0; i < size; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convWt.values = val;
trtWeights.push_back(convWt);
trtWeights.push_back(convBias);
}
nvinfer1::IConvolutionLayer* conv = network->addConvolution(