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DeepStream 7.1 + Fixes + New model output format

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This commit is contained in:
Marcos Luciano
2024-11-07 11:25:17 -03:00
parent bca9e59d07
commit b451b036b2
75 changed files with 2383 additions and 1113 deletions
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49
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.cpp
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@@ -15,7 +15,7 @@ convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std:
{
nvinfer1::ITensor* output;
assert(block.at("type") == "convolutional" || block.at("type") == "c2f");
assert(block.at("type") == "conv" || block.at("type") == "convolutional");
assert(block.find("filters") != block.end());
assert(block.find("pad") != block.end());
assert(block.find("size") != block.end());
@@ -28,27 +28,35 @@ convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std:
std::string activation = block.at("activation");
int bias = filters;
bool batchNormalize = false;
int batchNormalize = 0;
float eps = 1.0e-5;
if (block.find("batch_normalize") != block.end()) {
bias = 0;
batchNormalize = (block.at("batch_normalize") == "1");
if (block.find("eps") != block.end()) {
eps = std::stof(block.at("eps"));
}
}
if (block.find("bias") != block.end()) {
bias = std::stoi(block.at("bias"));
if (bias == 1)
if (bias == 1) {
bias = filters;
}
}
int groups = 1;
if (block.find("groups") != block.end())
if (block.find("groups") != block.end()) {
groups = std::stoi(block.at("groups"));
}
int pad;
if (padding)
if (padding) {
pad = (kernelSize - 1) / 2;
else
}
else {
pad = 0;
}
int size = filters * inputChannels * kernelSize * kernelSize / groups;
std::vector<float> bnBiases;
@@ -58,7 +66,7 @@ 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 (batchNormalize == false) {
if (batchNormalize == 0) {
float* val;
if (bias != 0) {
val = new float[filters];
@@ -91,7 +99,7 @@ convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std:
++weightPtr;
}
for (int i = 0; i < filters; ++i) {
bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5));
bnRunningVar.push_back(sqrt(weights[weightPtr] + eps));
++weightPtr;
}
float* val;
@@ -110,40 +118,49 @@ convolutionalLayer(int layerIdx, std::map<std::string, std::string>& block, std:
}
convWt.values = val;
trtWeights.push_back(convWt);
if (bias != 0)
if (bias != 0) {
trtWeights.push_back(convBias);
}
}
nvinfer1::IConvolutionLayer* conv = network->addConvolutionNd(*input, filters, nvinfer1::Dims{2, {kernelSize, kernelSize}},
convWt, convBias);
nvinfer1::IConvolutionLayer* conv = network->addConvolutionNd(*input, filters,
nvinfer1::Dims{2, {kernelSize, kernelSize}}, convWt, convBias);
assert(conv != nullptr);
std::string convLayerName = "conv_" + layerName + std::to_string(layerIdx);
conv->setName(convLayerName.c_str());
conv->setStrideNd(nvinfer1::Dims{2, {stride, stride}});
conv->setPaddingNd(nvinfer1::Dims{2, {pad, pad}});
if (block.find("groups") != block.end())
if (block.find("groups") != block.end()) {
conv->setNbGroups(groups);
}
output = conv->getOutput(0);
if (batchNormalize == true) {
if (batchNormalize == 1) {
size = filters;
nvinfer1::Weights shift {nvinfer1::DataType::kFLOAT, nullptr, size};
nvinfer1::Weights scale {nvinfer1::DataType::kFLOAT, nullptr, size};
nvinfer1::Weights power {nvinfer1::DataType::kFLOAT, nullptr, size};
float* shiftWt = new float[size];
for (int i = 0; i < size; ++i)
for (int i = 0; i < size; ++i) {
shiftWt[i] = bnBiases.at(i) - ((bnRunningMean.at(i) * bnWeights.at(i)) / bnRunningVar.at(i));
}
shift.values = shiftWt;
float* scaleWt = new float[size];
for (int i = 0; i < size; ++i)
for (int i = 0; i < size; ++i) {
scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
}
scale.values = scaleWt;
float* powerWt = new float[size];
for (int i = 0; i < size; ++i)
for (int i = 0; i < size; ++i) {
powerWt[i] = 1.0;
}
power.values = powerWt;
trtWeights.push_back(shift);
trtWeights.push_back(scale);
trtWeights.push_back(power);