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This commit is contained in:
Marcos Luciano
2023-05-19 03:05:43 -03:00
parent 68f762d5bd
commit 07feae9509
86 changed files with 1523 additions and 5223 deletions
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141
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.cpp
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@@ -10,8 +10,8 @@
nvinfer1::ITensor*
convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, int& inputChannels, float eps,
nvinfer1::ITensor* input, nvinfer1::INetworkDefinition* network, std::string layerName)
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, int& inputChannels, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network, std::string layerName)
{
nvinfer1::ITensor* output;
@@ -58,117 +58,60 @@ convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std:
nvinfer1::Weights convWt {nvinfer1::DataType::kFLOAT, nullptr, size};
nvinfer1::Weights convBias {nvinfer1::DataType::kFLOAT, nullptr, bias};
if (weightsType == "weights") {
if (batchNormalize == false) {
float* val;
if (bias != 0) {
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) {
if (batchNormalize == false) {
float* val;
if (bias != 0) {
val = new float[filters];
for (int i = 0; i < filters; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convWt.values = val;
trtWeights.push_back(convWt);
convBias.values = val;
trtWeights.push_back(convBias);
}
else {
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;
if (bias != 0) {
val = new float[filters];
for (int i = 0; i < filters; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convBias.values = val;
}
val = new float[size];
for (int i = 0; i < size; ++i) {
val = new float[size];
for (int i = 0; i < size; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convWt.values = val;
trtWeights.push_back(convWt);
if (bias != 0)
trtWeights.push_back(convBias);
}
convWt.values = val;
trtWeights.push_back(convWt);
}
else {
if (batchNormalize == false) {
float* val = new float[size];
for (int i = 0; i < size; ++i) {
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;
if (bias != 0) {
val = new float[filters];
for (int i = 0; i < filters; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convWt.values = val;
trtWeights.push_back(convWt);
if (bias != 0) {
val = new float[filters];
for (int i = 0; i < filters; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convBias.values = val;
trtWeights.push_back(convBias);
}
convBias.values = val;
}
else {
float* val = new float[size];
for (int i = 0; i < size; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convWt.values = val;
if (bias != 0) {
val = new float[filters];
for (int i = 0; i < filters; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convBias.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] + eps));
++weightPtr;
}
trtWeights.push_back(convWt);
if (bias != 0)
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);
if (bias != 0)
trtWeights.push_back(convBias);
}
nvinfer1::IConvolutionLayer* conv = network->addConvolutionNd(*input, filters, nvinfer1::Dims{2, {kernelSize, kernelSize}},