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Add YOLOv8 support

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This commit is contained in:
Marcos Luciano
2023-01-27 15:56:00 -03:00
parent f1cd701247
commit f9c7a4dfca
59 changed files with 3260 additions and 2763 deletions
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215
nvdsinfer_custom_impl_Yolo/layers/activation_layer.cpp
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@@ -5,118 +5,107 @@
#include "activation_layer.h"
nvinfer1::ITensor* activationLayer(
int layerIdx,
std::string activation,
nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
#include <cassert>
#include <iostream>
if (activation == "linear")
{
output = input;
}
else if (activation == "relu")
{
nvinfer1::IActivationLayer* relu = network->addActivation(*input, nvinfer1::ActivationType::kRELU);
assert(relu != nullptr);
std::string reluLayerName = "relu_" + std::to_string(layerIdx);
relu->setName(reluLayerName.c_str());
output = relu->getOutput(0);
}
else if (activation == "sigmoid" || activation == "logistic")
{
nvinfer1::IActivationLayer* sigmoid = network->addActivation(*input, nvinfer1::ActivationType::kSIGMOID);
assert(sigmoid != nullptr);
std::string sigmoidLayerName = "sigmoid_" + std::to_string(layerIdx);
sigmoid->setName(sigmoidLayerName.c_str());
output = sigmoid->getOutput(0);
}
else if (activation == "tanh")
{
nvinfer1::IActivationLayer* tanh = network->addActivation(*input, nvinfer1::ActivationType::kTANH);
assert(tanh != nullptr);
std::string tanhLayerName = "tanh_" + std::to_string(layerIdx);
tanh->setName(tanhLayerName.c_str());
output = tanh->getOutput(0);
}
else if (activation == "leaky")
{
nvinfer1::IActivationLayer* leaky = network->addActivation(*input, nvinfer1::ActivationType::kLEAKY_RELU);
assert(leaky != nullptr);
std::string leakyLayerName = "leaky_" + std::to_string(layerIdx);
leaky->setName(leakyLayerName.c_str());
leaky->setAlpha(0.1);
output = leaky->getOutput(0);
}
else if (activation == "softplus")
{
nvinfer1::IActivationLayer* softplus = network->addActivation(*input, nvinfer1::ActivationType::kSOFTPLUS);
assert(softplus != nullptr);
std::string softplusLayerName = "softplus_" + std::to_string(layerIdx);
softplus->setName(softplusLayerName.c_str());
output = softplus->getOutput(0);
}
else if (activation == "mish")
{
nvinfer1::IActivationLayer* softplus = network->addActivation(*input, nvinfer1::ActivationType::kSOFTPLUS);
assert(softplus != nullptr);
std::string softplusLayerName = "softplus_" + std::to_string(layerIdx);
softplus->setName(softplusLayerName.c_str());
nvinfer1::IActivationLayer* tanh = network->addActivation(*softplus->getOutput(0), nvinfer1::ActivationType::kTANH);
assert(tanh != nullptr);
std::string tanhLayerName = "tanh_" + std::to_string(layerIdx);
tanh->setName(tanhLayerName.c_str());
nvinfer1::IElementWiseLayer* mish
= network->addElementWise(*input, *tanh->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);
assert(mish != nullptr);
std::string mishLayerName = "mish_" + std::to_string(layerIdx);
mish->setName(mishLayerName.c_str());
output = mish->getOutput(0);
}
else if (activation == "silu" || activation == "swish")
{
nvinfer1::IActivationLayer* sigmoid = network->addActivation(*input, nvinfer1::ActivationType::kSIGMOID);
assert(sigmoid != nullptr);
std::string sigmoidLayerName = "sigmoid_" + std::to_string(layerIdx);
sigmoid->setName(sigmoidLayerName.c_str());
nvinfer1::IElementWiseLayer* silu
= network->addElementWise(*input, *sigmoid->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);
assert(silu != nullptr);
std::string siluLayerName = "silu_" + std::to_string(layerIdx);
silu->setName(siluLayerName.c_str());
output = silu->getOutput(0);
}
else if (activation == "hardsigmoid")
{
nvinfer1::IActivationLayer* hardsigmoid = network->addActivation(*input, nvinfer1::ActivationType::kHARD_SIGMOID);
assert(hardsigmoid != nullptr);
std::string hardsigmoidLayerName = "hardsigmoid_" + std::to_string(layerIdx);
hardsigmoid->setName(hardsigmoidLayerName.c_str());
hardsigmoid->setAlpha(1.0 / 6.0);
hardsigmoid->setBeta(0.5);
output = hardsigmoid->getOutput(0);
}
else if (activation == "hardswish")
{
nvinfer1::IActivationLayer* hardsigmoid = network->addActivation(*input, nvinfer1::ActivationType::kHARD_SIGMOID);
assert(hardsigmoid != nullptr);
std::string hardsigmoidLayerName = "hardsigmoid_" + std::to_string(layerIdx);
hardsigmoid->setName(hardsigmoidLayerName.c_str());
hardsigmoid->setAlpha(1.0 / 6.0);
hardsigmoid->setBeta(0.5);
nvinfer1::IElementWiseLayer* hardswish
= network->addElementWise(*input, *hardsigmoid->getOutput(0), nvinfer1::ElementWiseOperation::kPROD);
assert(hardswish != nullptr);
std::string hardswishLayerName = "hardswish_" + std::to_string(layerIdx);
hardswish->setName(hardswishLayerName.c_str());
output = hardswish->getOutput(0);
}
else
{
std::cerr << "Activation not supported: " << activation << std::endl;
std::abort();
}
return output;
nvinfer1::ITensor*
activationLayer(int layerIdx, std::string activation, nvinfer1::ITensor* input, nvinfer1::INetworkDefinition* network,
std::string layerName)
{
nvinfer1::ITensor* output;
if (activation == "linear")
output = input;
else if (activation == "relu") {
nvinfer1::IActivationLayer* relu = network->addActivation(*input, nvinfer1::ActivationType::kRELU);
assert(relu != nullptr);
std::string reluLayerName = "relu_" + layerName + std::to_string(layerIdx);
relu->setName(reluLayerName.c_str());
output = relu->getOutput(0);
}
else if (activation == "sigmoid" || activation == "logistic") {
nvinfer1::IActivationLayer* sigmoid = network->addActivation(*input, nvinfer1::ActivationType::kSIGMOID);
assert(sigmoid != nullptr);
std::string sigmoidLayerName = "sigmoid_" + layerName + std::to_string(layerIdx);
sigmoid->setName(sigmoidLayerName.c_str());
output = sigmoid->getOutput(0);
}
else if (activation == "tanh") {
nvinfer1::IActivationLayer* tanh = network->addActivation(*input, nvinfer1::ActivationType::kTANH);
assert(tanh != nullptr);
std::string tanhLayerName = "tanh_" + layerName + std::to_string(layerIdx);
tanh->setName(tanhLayerName.c_str());
output = tanh->getOutput(0);
}
else if (activation == "leaky") {
nvinfer1::IActivationLayer* leaky = network->addActivation(*input, nvinfer1::ActivationType::kLEAKY_RELU);
assert(leaky != nullptr);
std::string leakyLayerName = "leaky_" + layerName + std::to_string(layerIdx);
leaky->setName(leakyLayerName.c_str());
leaky->setAlpha(0.1);
output = leaky->getOutput(0);
}
else if (activation == "softplus") {
nvinfer1::IActivationLayer* softplus = network->addActivation(*input, nvinfer1::ActivationType::kSOFTPLUS);
assert(softplus != nullptr);
std::string softplusLayerName = "softplus_" + layerName + std::to_string(layerIdx);
softplus->setName(softplusLayerName.c_str());
output = softplus->getOutput(0);
}
else if (activation == "mish") {
nvinfer1::IActivationLayer* softplus = network->addActivation(*input, nvinfer1::ActivationType::kSOFTPLUS);
assert(softplus != nullptr);
std::string softplusLayerName = "softplus_" + layerName + std::to_string(layerIdx);
softplus->setName(softplusLayerName.c_str());
nvinfer1::IActivationLayer* tanh = network->addActivation(*softplus->getOutput(0), nvinfer1::ActivationType::kTANH);
assert(tanh != nullptr);
std::string tanhLayerName = "tanh_" + layerName + std::to_string(layerIdx);
tanh->setName(tanhLayerName.c_str());
nvinfer1::IElementWiseLayer* mish = network->addElementWise(*input, *tanh->getOutput(0),
nvinfer1::ElementWiseOperation::kPROD);
assert(mish != nullptr);
std::string mishLayerName = "mish_" + layerName + std::to_string(layerIdx);
mish->setName(mishLayerName.c_str());
output = mish->getOutput(0);
}
else if (activation == "silu" || activation == "swish") {
nvinfer1::IActivationLayer* sigmoid = network->addActivation(*input, nvinfer1::ActivationType::kSIGMOID);
assert(sigmoid != nullptr);
std::string sigmoidLayerName = "sigmoid_" + layerName + std::to_string(layerIdx);
sigmoid->setName(sigmoidLayerName.c_str());
nvinfer1::IElementWiseLayer* silu = network->addElementWise(*input, *sigmoid->getOutput(0),
nvinfer1::ElementWiseOperation::kPROD);
assert(silu != nullptr);
std::string siluLayerName = "silu_" + layerName + std::to_string(layerIdx);
silu->setName(siluLayerName.c_str());
output = silu->getOutput(0);
}
else if (activation == "hardsigmoid") {
nvinfer1::IActivationLayer* hardsigmoid = network->addActivation(*input, nvinfer1::ActivationType::kHARD_SIGMOID);
assert(hardsigmoid != nullptr);
std::string hardsigmoidLayerName = "hardsigmoid_" + layerName + std::to_string(layerIdx);
hardsigmoid->setName(hardsigmoidLayerName.c_str());
hardsigmoid->setAlpha(1.0 / 6.0);
hardsigmoid->setBeta(0.5);
output = hardsigmoid->getOutput(0);
}
else if (activation == "hardswish") {
nvinfer1::IActivationLayer* hardsigmoid = network->addActivation(*input, nvinfer1::ActivationType::kHARD_SIGMOID);
assert(hardsigmoid != nullptr);
std::string hardsigmoidLayerName = "hardsigmoid_" + layerName + std::to_string(layerIdx);
hardsigmoid->setName(hardsigmoidLayerName.c_str());
hardsigmoid->setAlpha(1.0 / 6.0);
hardsigmoid->setBeta(0.5);
nvinfer1::IElementWiseLayer* hardswish = network->addElementWise(*input, *hardsigmoid->getOutput(0),
nvinfer1::ElementWiseOperation::kPROD);
assert(hardswish != nullptr);
std::string hardswishLayerName = "hardswish_" + layerName + std::to_string(layerIdx);
hardswish->setName(hardswishLayerName.c_str());
output = hardswish->getOutput(0);
}
else {
std::cerr << "Activation not supported: " << activation << std::endl;
assert(0);
}
return output;
}

10
nvdsinfer_custom_impl_Yolo/layers/activation_layer.h
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@@ -6,15 +6,11 @@
#ifndef __ACTIVATION_LAYER_H__
#define __ACTIVATION_LAYER_H__
#include <cassert>
#include <iostream>
#include <string>
#include "NvInfer.h"
nvinfer1::ITensor* activationLayer(
int layerIdx,
std::string activation,
nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
nvinfer1::ITensor* activationLayer(int layerIdx, std::string activation, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network, std::string layerName = "");
#endif

166
nvdsinfer_custom_impl_Yolo/layers/batchnorm_layer.cpp
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@@ -3,108 +3,94 @@
* https://www.github.com/marcoslucianops
*/
#include <math.h>
#include "batchnorm_layer.h"
nvinfer1::ITensor* batchnormLayer(
int layerIdx,
std::map<std::string, std::string>& block,
std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights,
int& weightPtr,
std::string weightsType,
float eps,
nvinfer1::ITensor* input,
#include <cassert>
#include <math.h>
nvinfer1::ITensor*
batchnormLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "batchnorm");
assert(block.find("filters") != block.end());
assert(block.at("type") == "batchnorm");
assert(block.find("filters") != block.end());
int filters = std::stoi(block.at("filters"));
std::string activation = block.at("activation");
int filters = std::stoi(block.at("filters"));
std::string activation = block.at("activation");
std::vector<float> bnBiases;
std::vector<float> bnWeights;
std::vector<float> bnRunningMean;
std::vector<float> bnRunningVar;
std::vector<float> bnBiases;
std::vector<float> bnWeights;
std::vector<float> bnRunningMean;
std::vector<float> bnRunningVar;
if (weightsType == "weights")
{
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++;
}
if (weightsType == "weights") {
for (int i = 0; i < filters; ++i) {
bnBiases.push_back(weights[weightPtr]);
++weightPtr;
}
else
{
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++;
}
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;
}
}
else {
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;
}
}
int 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)
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)
scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
scale.values = scaleWt;
float* powerWt = new float[size];
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);
int 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)
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)
scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
scale.values = scaleWt;
float* powerWt = new float[size];
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);
nvinfer1::IScaleLayer* batchnorm = network->addScale(*input, nvinfer1::ScaleMode::kCHANNEL, shift, scale, power);
assert(batchnorm != nullptr);
std::string batchnormLayerName = "batchnorm_" + std::to_string(layerIdx);
batchnorm->setName(batchnormLayerName.c_str());
output = batchnorm->getOutput(0);
nvinfer1::IScaleLayer* batchnorm = network->addScale(*input, nvinfer1::ScaleMode::kCHANNEL, shift, scale, power);
assert(batchnorm != nullptr);
std::string batchnormLayerName = "batchnorm_" + std::to_string(layerIdx);
batchnorm->setName(batchnormLayerName.c_str());
output = batchnorm->getOutput(0);
output = activationLayer(layerIdx, activation, output, network);
assert(output != nullptr);
output = activationLayer(layerIdx, activation, output, network);
assert(output != nullptr);
return output;
return output;
}

11
nvdsinfer_custom_impl_Yolo/layers/batchnorm_layer.h
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@@ -13,15 +13,8 @@
#include "activation_layer.h"
nvinfer1::ITensor* batchnormLayer(
int layerIdx,
std::map<std::string, std::string>& block,
std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights,
int& weightPtr,
std::string weightsType,
float eps,
nvinfer1::ITensor* input,
nvinfer1::ITensor* batchnormLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

82
nvdsinfer_custom_impl_Yolo/layers/c2f_layer.cpp Normal file
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@@ -0,0 +1,82 @@
/*
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#include "c2f_layer.h"
#include <cassert>
#include "convolutional_layer.h"
nvinfer1::ITensor*
c2fLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
assert(block.at("type") == "c2f");
assert(block.find("n") != block.end());
assert(block.find("shortcut") != block.end());
assert(block.find("filters") != block.end());
int n = std::stoi(block.at("n"));
bool shortcut = (block.at("shortcut") == "1");
int filters = std::stoi(block.at("filters"));
nvinfer1::Dims inputDims = input->getDimensions();
nvinfer1::ISliceLayer* sliceLt = network->addSlice(*input,nvinfer1::Dims{3, {0, 0, 0}},
nvinfer1::Dims{3, {inputDims.d[0] / 2, inputDims.d[1], inputDims.d[2]}}, nvinfer1::Dims{3, {1, 1, 1}});
assert(sliceLt != nullptr);
std::string sliceLtLayerName = "slice_lt_" + std::to_string(layerIdx);
sliceLt->setName(sliceLtLayerName.c_str());
nvinfer1::ITensor* lt = sliceLt->getOutput(0);
nvinfer1::ISliceLayer* sliceRb = network->addSlice(*input,nvinfer1::Dims{3, {inputDims.d[0] / 2, 0, 0}},
nvinfer1::Dims{3, {inputDims.d[0] / 2, inputDims.d[1], inputDims.d[2]}}, nvinfer1::Dims{3, {1, 1, 1}});
assert(sliceRb != nullptr);
std::string sliceRbLayerName = "slice_rb_" + std::to_string(layerIdx);
sliceRb->setName(sliceRbLayerName.c_str());
nvinfer1::ITensor* rb = sliceRb->getOutput(0);
std::vector<nvinfer1::ITensor*> concatInputs;
concatInputs.push_back(lt);
concatInputs.push_back(rb);
output = rb;
for (int i = 0; i < n; ++i) {
std::string cv1MlayerName = "c2f_1_" + std::to_string(i + 1) + "_";
nvinfer1::ITensor* cv1M = convolutionalLayer(layerIdx, block, weights, trtWeights, weightPtr, weightsType, filters, eps,
output, network, cv1MlayerName);
assert(cv1M != nullptr);
std::string cv2MlayerName = "c2f_2_" + std::to_string(i + 1) + "_";
nvinfer1::ITensor* cv2M = convolutionalLayer(layerIdx, block, weights, trtWeights, weightPtr, weightsType, filters, eps,
cv1M, network, cv2MlayerName);
assert(cv2M != nullptr);
if (shortcut) {
nvinfer1::IElementWiseLayer* ew = network->addElementWise(*rb, *cv2M, nvinfer1::ElementWiseOperation::kSUM);
assert(ew != nullptr);
std::string ewLayerName = "shortcut_c2f_" + std::to_string(i + 1) + "_" + std::to_string(layerIdx);
ew->setName(ewLayerName.c_str());
output = ew->getOutput(0);
concatInputs.push_back(output);
}
else {
output = cv2M;
concatInputs.push_back(output);
}
}
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "route_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(0);
output = concat->getOutput(0);
return output;
}

18
nvdsinfer_custom_impl_Yolo/layers/c2f_layer.h Normal file
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@@ -0,0 +1,18 @@
/*
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#ifndef __C2F_LAYER_H__
#define __C2F_LAYER_H__
#include <map>
#include <vector>
#include "NvInfer.h"
nvinfer1::ITensor* c2fLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, std::string weightsType, float eps, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

49
nvdsinfer_custom_impl_Yolo/layers/channels_layer.cpp
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@@ -5,33 +5,32 @@
#include "channels_layer.h"
nvinfer1::ITensor* channelsLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* implicitTensor,
nvinfer1::INetworkDefinition* network)
#include <cassert>
nvinfer1::ITensor*
channelsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::ITensor* implicitTensor, nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "shift_channels" || block.at("type") == "control_channels");
assert(block.at("type") == "shift_channels" || block.at("type") == "control_channels");
if (block.at("type") == "shift_channels") {
nvinfer1::IElementWiseLayer* shift
= network->addElementWise(*input, *implicitTensor, nvinfer1::ElementWiseOperation::kSUM);
assert(shift != nullptr);
std::string shiftLayerName = "shift_channels_" + std::to_string(layerIdx);
shift->setName(shiftLayerName.c_str());
output = shift->getOutput(0);
}
else if (block.at("type") == "control_channels") {
nvinfer1::IElementWiseLayer* control
= network->addElementWise(*input, *implicitTensor, nvinfer1::ElementWiseOperation::kPROD);
assert(control != nullptr);
std::string controlLayerName = "control_channels_" + std::to_string(layerIdx);
control->setName(controlLayerName.c_str());
output = control->getOutput(0);
}
if (block.at("type") == "shift_channels") {
nvinfer1::IElementWiseLayer* shift = network->addElementWise(*input, *implicitTensor,
nvinfer1::ElementWiseOperation::kSUM);
assert(shift != nullptr);
std::string shiftLayerName = "shift_channels_" + std::to_string(layerIdx);
shift->setName(shiftLayerName.c_str());
output = shift->getOutput(0);
}
else if (block.at("type") == "control_channels") {
nvinfer1::IElementWiseLayer* control = network->addElementWise(*input, *implicitTensor,
nvinfer1::ElementWiseOperation::kPROD);
assert(control != nullptr);
std::string controlLayerName = "control_channels_" + std::to_string(layerIdx);
control->setName(controlLayerName.c_str());
output = control->getOutput(0);
}
return output;
return output;
}

9
nvdsinfer_custom_impl_Yolo/layers/channels_layer.h
View File

@@ -7,15 +7,10 @@
#define __CHANNELS_LAYER_H__
#include <map>
#include <cassert>
#include "NvInfer.h"
nvinfer1::ITensor* channelsLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* implicitTensor,
nvinfer1::INetworkDefinition* network);
nvinfer1::ITensor* channelsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::ITensor* implicitTensor, nvinfer1::INetworkDefinition* network);
#endif

32
nvdsinfer_custom_impl_Yolo/layers/cls_layer.cpp
View File

@@ -5,25 +5,25 @@
#include "cls_layer.h"
nvinfer1::ITensor* clsLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
#include <cassert>
nvinfer1::ITensor*
clsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "cls");
assert(block.at("type") == "cls");
nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*input);
assert(shuffle != nullptr);
std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
shuffle->setName(shuffleLayerName.c_str());
nvinfer1::Permutation permutation;
permutation.order[0] = 1;
permutation.order[1] = 0;
shuffle->setFirstTranspose(permutation);
output = shuffle->getOutput(0);
nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*input);
assert(shuffle != nullptr);
std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
shuffle->setName(shuffleLayerName.c_str());
nvinfer1::Permutation permutation;
permutation.order[0] = 1;
permutation.order[1] = 0;
shuffle->setFirstTranspose(permutation);
output = shuffle->getOutput(0);
return output;
return output;
}

6
nvdsinfer_custom_impl_Yolo/layers/cls_layer.h
View File

@@ -7,14 +7,10 @@
#define __CLS_LAYER_H__
#include <map>
#include <cassert>
#include "NvInfer.h"
nvinfer1::ITensor* clsLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* clsLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

379
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.cpp
View File

@@ -3,224 +3,197 @@
* https://www.github.com/marcoslucianops
*/
#include <math.h>
#include "convolutional_layer.h"
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)
#include <cassert>
#include <math.h>
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)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "convolutional");
assert(block.find("filters") != block.end());
assert(block.find("pad") != block.end());
assert(block.find("size") != block.end());
assert(block.find("stride") != block.end());
assert(block.at("type") == "convolutional" || block.at("type") == "c2f");
assert(block.find("filters") != block.end());
assert(block.find("pad") != block.end());
assert(block.find("size") != block.end());
assert(block.find("stride") != block.end());
int filters = std::stoi(block.at("filters"));
int padding = std::stoi(block.at("pad"));
int kernelSize = std::stoi(block.at("size"));
int stride = std::stoi(block.at("stride"));
std::string activation = block.at("activation");
int bias = filters;
int filters = std::stoi(block.at("filters"));
int padding = std::stoi(block.at("pad"));
int kernelSize = std::stoi(block.at("size"));
int stride = std::stoi(block.at("stride"));
std::string activation = block.at("activation");
int bias = filters;
bool batchNormalize = false;
if (block.find("batch_normalize") != block.end())
{
bias = 0;
batchNormalize = (block.at("batch_normalize") == "1");
}
bool batchNormalize = false;
if (block.find("batch_normalize") != block.end()) {
bias = 0;
batchNormalize = (block.at("batch_normalize") == "1");
}
int groups = 1;
if (block.find("groups") != block.end())
groups = std::stoi(block.at("groups"));
int groups = 1;
if (block.find("groups") != block.end())
groups = std::stoi(block.at("groups"));
if (block.find("bias") != block.end())
bias = std::stoi(block.at("bias"));
if (block.find("bias") != block.end())
bias = std::stoi(block.at("bias"));
int pad;
if (padding)
pad = (kernelSize - 1) / 2;
else
pad = 0;
int pad;
if (padding)
pad = (kernelSize - 1) / 2;
else
pad = 0;
int size = filters * inputChannels * kernelSize * kernelSize / groups;
std::vector<float> bnBiases;
std::vector<float> bnWeights;
std::vector<float> bnRunningMean;
std::vector<float> bnRunningVar;
nvinfer1::Weights convWt{nvinfer1::DataType::kFLOAT, nullptr, size};
nvinfer1::Weights convBias{nvinfer1::DataType::kFLOAT, nullptr, bias};
int size = filters * inputChannels * kernelSize * kernelSize / groups;
std::vector<float> bnBiases;
std::vector<float> bnWeights;
std::vector<float> bnRunningMean;
std::vector<float> bnRunningVar;
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)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convWt.values = val;
trtWeights.push_back(convWt);
}
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 = 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);
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) {
val[i] = weights[weightPtr];
++weightPtr;
}
convWt.values = val;
trtWeights.push_back(convWt);
}
else
{
if (batchNormalize == false)
{
float* 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) {
val = new float[filters];
for (int i = 0; i < filters; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convBias.values = val;
trtWeights.push_back(convBias);
}
}
else
{
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] + eps));
weightPtr++;
}
trtWeights.push_back(convWt);
if (bias != 0)
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 = 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}}, convWt, convBias);
assert(conv != nullptr);
std::string convLayerName = "conv_" + 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())
conv->setNbGroups(groups);
output = conv->getOutput(0);
if (batchNormalize == true)
{
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)
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)
scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
scale.values = scaleWt;
float* powerWt = new float[size];
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);
nvinfer1::IScaleLayer* batchnorm = network->addScale(*output, nvinfer1::ScaleMode::kCHANNEL, shift, scale, power);
assert(batchnorm != nullptr);
std::string batchnormLayerName = "batchnorm_" + std::to_string(layerIdx);
batchnorm->setName(batchnormLayerName.c_str());
output = batchnorm->getOutput(0);
}
else {
if (batchNormalize == false) {
float* 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) {
val = new float[filters];
for (int i = 0; i < filters; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convBias.values = val;
trtWeights.push_back(convBias);
}
}
else {
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] + eps));
++weightPtr;
}
trtWeights.push_back(convWt);
if (bias != 0)
trtWeights.push_back(convBias);
}
}
output = activationLayer(layerIdx, activation, output, network);
assert(output != nullptr);
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}});
return output;
if (block.find("groups") != block.end())
conv->setNbGroups(groups);
output = conv->getOutput(0);
if (batchNormalize == true) {
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)
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)
scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
scale.values = scaleWt;
float* powerWt = new float[size];
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);
nvinfer1::IScaleLayer* batchnorm = network->addScale(*output, nvinfer1::ScaleMode::kCHANNEL, shift, scale, power);
assert(batchnorm != nullptr);
std::string batchnormLayerName = "batchnorm_" + layerName + std::to_string(layerIdx);
batchnorm->setName(batchnormLayerName.c_str());
output = batchnorm->getOutput(0);
}
output = activationLayer(layerIdx, activation, output, network, layerName);
assert(output != nullptr);
return output;
}

14
nvdsinfer_custom_impl_Yolo/layers/convolutional_layer.h
View File

@@ -13,16 +13,8 @@
#include "activation_layer.h"
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);
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 = "");
#endif

196
nvdsinfer_custom_impl_Yolo/layers/detect_v8_layer.cpp Normal file
View File

@@ -0,0 +1,196 @@
/*
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#include "detect_v8_layer.h"
#include <cassert>
nvinfer1::ITensor*
detectV8Layer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
assert(block.at("type") == "detect_v8");
assert(block.find("num") != block.end());
assert(block.find("classes") != block.end());
int num = std::stoi(block.at("num"));
int classes = std::stoi(block.at("classes"));
int reg_max = num / 4;
nvinfer1::Dims inputDims = input->getDimensions();
nvinfer1::ISliceLayer* sliceBox = network->addSlice(*input, nvinfer1::Dims{2, {0, 0}},
nvinfer1::Dims{2, {num, inputDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceBox != nullptr);
std::string sliceBoxLayerName = "slice_box_" + std::to_string(layerIdx);
sliceBox->setName(sliceBoxLayerName.c_str());
nvinfer1::ITensor* box = sliceBox->getOutput(0);
nvinfer1::ISliceLayer* sliceCls = network->addSlice(*input, nvinfer1::Dims{2, {num, 0}},
nvinfer1::Dims{2, {classes, inputDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceCls != nullptr);
std::string sliceClsLayerName = "slice_cls_" + std::to_string(layerIdx);
sliceCls->setName(sliceClsLayerName.c_str());
nvinfer1::ITensor* cls = sliceCls->getOutput(0);
nvinfer1::IShuffleLayer* shuffle1Box = network->addShuffle(*box);
assert(shuffle1Box != nullptr);
std::string shuffle1BoxLayerName = "shuffle1_box_" + std::to_string(layerIdx);
shuffle1Box->setName(shuffle1BoxLayerName.c_str());
nvinfer1::Dims reshape1Dims = {3, {4, reg_max, inputDims.d[1]}};
shuffle1Box->setReshapeDimensions(reshape1Dims);
nvinfer1::Permutation permutation1;
permutation1.order[0] = 1;
permutation1.order[1] = 0;
permutation1.order[2] = 2;
shuffle1Box->setSecondTranspose(permutation1);
box = shuffle1Box->getOutput(0);
nvinfer1::ISoftMaxLayer* softmax = network->addSoftMax(*box);
assert(softmax != nullptr);
std::string softmaxLayerName = "softmax_box_" + std::to_string(layerIdx);
softmax->setName(softmaxLayerName.c_str());
softmax->setAxes(1 << 0);
box = softmax->getOutput(0);
nvinfer1::Weights dflWt {nvinfer1::DataType::kFLOAT, nullptr, reg_max};
float* val = new float[reg_max];
for (int i = 0; i < reg_max; ++i) {
val[i] = i;
}
dflWt.values = val;
nvinfer1::IConvolutionLayer* conv = network->addConvolutionNd(*box, 1, nvinfer1::Dims{2, {1, 1}}, dflWt,
nvinfer1::Weights{});
assert(conv != nullptr);
std::string convLayerName = "conv_box_" + std::to_string(layerIdx);
conv->setName(convLayerName.c_str());
conv->setStrideNd(nvinfer1::Dims{2, {1, 1}});
conv->setPaddingNd(nvinfer1::Dims{2, {0, 0}});
box = conv->getOutput(0);
nvinfer1::IShuffleLayer* shuffle2Box = network->addShuffle(*box);
assert(shuffle2Box != nullptr);
std::string shuffle2BoxLayerName = "shuffle2_box_" + std::to_string(layerIdx);
shuffle2Box->setName(shuffle2BoxLayerName.c_str());
nvinfer1::Dims reshape2Dims = {2, {4, inputDims.d[1]}};
shuffle2Box->setReshapeDimensions(reshape2Dims);
box = shuffle2Box->getOutput(0);
nvinfer1::Dims shuffle2BoxDims = box->getDimensions();
nvinfer1::ISliceLayer* sliceLtBox = network->addSlice(*box, nvinfer1::Dims{2, {0, 0}},
nvinfer1::Dims{2, {2, shuffle2BoxDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceLtBox != nullptr);
std::string sliceLtBoxLayerName = "slice_lt_box_" + std::to_string(layerIdx);
sliceLtBox->setName(sliceLtBoxLayerName.c_str());
nvinfer1::ITensor* lt = sliceLtBox->getOutput(0);
nvinfer1::ISliceLayer* sliceRbBox = network->addSlice(*box, nvinfer1::Dims{2, {2, 0}},
nvinfer1::Dims{2, {2, shuffle2BoxDims.d[1]}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceRbBox != nullptr);
std::string sliceRbBoxLayerName = "slice_rb_box_" + std::to_string(layerIdx);
sliceRbBox->setName(sliceRbBoxLayerName.c_str());
nvinfer1::ITensor* rb = sliceRbBox->getOutput(0);
int channels = 2 * shuffle2BoxDims.d[1];
nvinfer1::Weights anchorPointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
val = new float[channels];
for (int i = 0; i < channels; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
anchorPointsWt.values = val;
trtWeights.push_back(anchorPointsWt);
nvinfer1::IConstantLayer* anchorPoints = network->addConstant(nvinfer1::Dims{2, {2, shuffle2BoxDims.d[1]}},
anchorPointsWt);
assert(anchorPoints != nullptr);
std::string anchorPointsLayerName = "anchor_points_" + std::to_string(layerIdx);
anchorPoints->setName(anchorPointsLayerName.c_str());
nvinfer1::ITensor* anchorPointsTensor = anchorPoints->getOutput(0);
nvinfer1::IElementWiseLayer* x1y1 = network->addElementWise(*anchorPointsTensor, *lt,
nvinfer1::ElementWiseOperation::kSUB);
assert(x1y1 != nullptr);
std::string x1y1LayerName = "x1y1_" + std::to_string(layerIdx);
x1y1->setName(x1y1LayerName.c_str());
nvinfer1::ITensor* x1y1Tensor = x1y1->getOutput(0);
nvinfer1::IElementWiseLayer* x2y2 = network->addElementWise(*rb, *anchorPointsTensor,
nvinfer1::ElementWiseOperation::kSUM);
assert(x2y2 != nullptr);
std::string x2y2LayerName = "x2y2_" + std::to_string(layerIdx);
x2y2->setName(x2y2LayerName.c_str());
nvinfer1::ITensor* x2y2Tensor = x2y2->getOutput(0);
std::vector<nvinfer1::ITensor*> concatBoxInputs;
concatBoxInputs.push_back(x1y1Tensor);
concatBoxInputs.push_back(x2y2Tensor);
nvinfer1::IConcatenationLayer* concatBox = network->addConcatenation(concatBoxInputs.data(), concatBoxInputs.size());
assert(concatBox != nullptr);
std::string concatBoxLayerName = "concat_box_" + std::to_string(layerIdx);
concatBox->setName(concatBoxLayerName.c_str());
concatBox->setAxis(0);
box = concatBox->getOutput(0);
channels = shuffle2BoxDims.d[1];
nvinfer1::Weights stridePointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
val = new float[channels];
for (int i = 0; i < channels; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
stridePointsWt.values = val;
trtWeights.push_back(stridePointsWt);
nvinfer1::IConstantLayer* stridePoints = network->addConstant(nvinfer1::Dims{2, {1, shuffle2BoxDims.d[1]}},
stridePointsWt);
assert(stridePoints != nullptr);
std::string stridePointsLayerName = "stride_points_" + std::to_string(layerIdx);
stridePoints->setName(stridePointsLayerName.c_str());
nvinfer1::ITensor* stridePointsTensor = stridePoints->getOutput(0);
nvinfer1::IElementWiseLayer* pred = network->addElementWise(*box, *stridePointsTensor,
nvinfer1::ElementWiseOperation::kPROD);
assert(pred != nullptr);
std::string predLayerName = "pred_" + std::to_string(layerIdx);
pred->setName(predLayerName.c_str());
box = pred->getOutput(0);
nvinfer1::IActivationLayer* sigmoid = network->addActivation(*cls, nvinfer1::ActivationType::kSIGMOID);
assert(sigmoid != nullptr);
std::string sigmoidLayerName = "sigmoid_cls_" + std::to_string(layerIdx);
sigmoid->setName(sigmoidLayerName.c_str());
cls = sigmoid->getOutput(0);
std::vector<nvinfer1::ITensor*> concatInputs;
concatInputs.push_back(box);
concatInputs.push_back(cls);
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "concat_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(0);
output = concat->getOutput(0);
nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*output);
assert(shuffle != nullptr);
std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
shuffle->setName(shuffleLayerName.c_str());
nvinfer1::Permutation permutation2;
permutation2.order[0] = 1;
permutation2.order[1] = 0;
shuffle->setFirstTranspose(permutation2);
output = shuffle->getOutput(0);
return output;
}

18
nvdsinfer_custom_impl_Yolo/layers/detect_v8_layer.h Normal file
View File

@@ -0,0 +1,18 @@
/*
* Created by Marcos Luciano
* https://www.github.com/marcoslucianops
*/
#ifndef __DETECT_V8_LAYER_H__
#define __DETECT_V8_LAYER_H__
#include <map>
#include <vector>
#include "NvInfer.h"
nvinfer1::ITensor* detectV8Layer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

49
nvdsinfer_custom_impl_Yolo/layers/implicit_layer.cpp
View File

@@ -5,37 +5,34 @@
#include "implicit_layer.h"
nvinfer1::ITensor* implicitLayer(
int layerIdx,
std::map<std::string, std::string>& block,
std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights,
int& weightPtr,
nvinfer1::INetworkDefinition* network)
#include <cassert>
nvinfer1::ITensor*
implicitLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "implicit_add" || block.at("type") == "implicit_mul");
assert(block.find("filters") != block.end());
assert(block.at("type") == "implicit_add" || block.at("type") == "implicit_mul");
assert(block.find("filters") != block.end());
int filters = std::stoi(block.at("filters"));
int filters = std::stoi(block.at("filters"));
nvinfer1::Weights convWt{nvinfer1::DataType::kFLOAT, nullptr, filters};
nvinfer1::Weights convWt {nvinfer1::DataType::kFLOAT, nullptr, filters};
float* val = new float[filters];
for (int i = 0; i < filters; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
convWt.values = val;
trtWeights.push_back(convWt);
float* val = new float[filters];
for (int i = 0; i < filters; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
convWt.values = val;
trtWeights.push_back(convWt);
nvinfer1::IConstantLayer* implicit = network->addConstant(nvinfer1::Dims{3, {filters, 1, 1}}, convWt);
assert(implicit != nullptr);
std::string implicitLayerName = block.at("type") + "_" + std::to_string(layerIdx);
implicit->setName(implicitLayerName.c_str());
output = implicit->getOutput(0);
nvinfer1::IConstantLayer* implicit = network->addConstant(nvinfer1::Dims{3, {filters, 1, 1}}, convWt);
assert(implicit != nullptr);
std::string implicitLayerName = block.at("type") + "_" + std::to_string(layerIdx);
implicit->setName(implicitLayerName.c_str());
output = implicit->getOutput(0);
return output;
return output;
}

10
nvdsinfer_custom_impl_Yolo/layers/implicit_layer.h
View File

@@ -8,16 +8,10 @@
#include <map>
#include <vector>
#include <cassert>
#include "NvInfer.h"
nvinfer1::ITensor* implicitLayer(
int layerIdx,
std::map<std::string, std::string>& block,
std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights,
int& weightPtr,
nvinfer1::INetworkDefinition* network);
nvinfer1::ITensor* implicitLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::INetworkDefinition* network);
#endif

79
nvdsinfer_custom_impl_Yolo/layers/pooling_layer.cpp
View File

@@ -5,53 +5,50 @@
#include "pooling_layer.h"
nvinfer1::ITensor* poolingLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
#include <cassert>
#include <iostream>
nvinfer1::ITensor*
poolingLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "maxpool" || block.at("type") == "avgpool");
assert(block.at("type") == "maxpool" || block.at("type") == "avgpool");
if (block.at("type") == "maxpool")
{
assert(block.find("size") != block.end());
assert(block.find("stride") != block.end());
if (block.at("type") == "maxpool") {
assert(block.find("size") != block.end());
assert(block.find("stride") != block.end());
int size = std::stoi(block.at("size"));
int stride = std::stoi(block.at("stride"));
int size = std::stoi(block.at("size"));
int stride = std::stoi(block.at("stride"));
nvinfer1::IPoolingLayer* maxpool
= network->addPoolingNd(*input, nvinfer1::PoolingType::kMAX, nvinfer1::Dims{2, {size, size}});
assert(maxpool != nullptr);
std::string maxpoolLayerName = "maxpool_" + std::to_string(layerIdx);
maxpool->setName(maxpoolLayerName.c_str());
maxpool->setStrideNd(nvinfer1::Dims{2, {stride, stride}});
maxpool->setPaddingNd(nvinfer1::Dims{2, {(size - 1) / 2, (size - 1) / 2}});
if (size == 2 && stride == 1)
{
maxpool->setPrePadding(nvinfer1::Dims{2, {0, 0}});
maxpool->setPostPadding(nvinfer1::Dims{2, {1, 1}});
}
output = maxpool->getOutput(0);
}
else if (block.at("type") == "avgpool")
{
nvinfer1::Dims inputDims = input->getDimensions();
nvinfer1::IPoolingLayer* avgpool = network->addPoolingNd(
*input, nvinfer1::PoolingType::kAVERAGE, nvinfer1::Dims{2, {inputDims.d[1], inputDims.d[2]}});
assert(avgpool != nullptr);
std::string avgpoolLayerName = "avgpool_" + std::to_string(layerIdx);
avgpool->setName(avgpoolLayerName.c_str());
output = avgpool->getOutput(0);
}
else
{
std::cerr << "Pooling not supported: " << block.at("type") << std::endl;
std::abort();
nvinfer1::IPoolingLayer* maxpool = network->addPoolingNd(*input, nvinfer1::PoolingType::kMAX,
nvinfer1::Dims{2, {size, size}});
assert(maxpool != nullptr);
std::string maxpoolLayerName = "maxpool_" + std::to_string(layerIdx);
maxpool->setName(maxpoolLayerName.c_str());
maxpool->setStrideNd(nvinfer1::Dims{2, {stride, stride}});
maxpool->setPaddingNd(nvinfer1::Dims{2, {(size - 1) / 2, (size - 1) / 2}});
if (size == 2 && stride == 1) {
maxpool->setPrePadding(nvinfer1::Dims{2, {0, 0}});
maxpool->setPostPadding(nvinfer1::Dims{2, {1, 1}});
}
output = maxpool->getOutput(0);
}
else if (block.at("type") == "avgpool") {
nvinfer1::Dims inputDims = input->getDimensions();
nvinfer1::IPoolingLayer* avgpool = network->addPoolingNd(*input, nvinfer1::PoolingType::kAVERAGE,
nvinfer1::Dims{2, {inputDims.d[1], inputDims.d[2]}});
assert(avgpool != nullptr);
std::string avgpoolLayerName = "avgpool_" + std::to_string(layerIdx);
avgpool->setName(avgpoolLayerName.c_str());
output = avgpool->getOutput(0);
}
else {
std::cerr << "Pooling not supported: " << block.at("type") << std::endl;
assert(0);
}
return output;
return output;
}

7
nvdsinfer_custom_impl_Yolo/layers/pooling_layer.h
View File

@@ -7,15 +7,10 @@
#define __POOLING_LAYER_H__
#include <map>
#include <cassert>
#include <iostream>
#include "NvInfer.h"
nvinfer1::ITensor* poolingLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* poolingLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

80
nvdsinfer_custom_impl_Yolo/layers/reduce_layer.cpp
View File

@@ -5,54 +5,50 @@
#include "reduce_layer.h"
nvinfer1::ITensor* reduceLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor*
reduceLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "reduce");
assert(block.find("mode") != block.end());
assert(block.find("axes") != block.end());
assert(block.at("type") == "reduce");
assert(block.find("mode") != block.end());
assert(block.find("axes") != block.end());
std::string mode = block.at("mode");
std::string mode = block.at("mode");
nvinfer1::ReduceOperation operation;
if (mode == "mean")
operation = nvinfer1::ReduceOperation::kAVG;
nvinfer1::ReduceOperation operation;
if (mode == "mean")
operation = nvinfer1::ReduceOperation::kAVG;
std::string strAxes = block.at("axes");
std::vector<int32_t> axes;
size_t lastPos = 0, pos = 0;
while ((pos = strAxes.find(',', lastPos)) != std::string::npos)
{
int vL = std::stoi(trim(strAxes.substr(lastPos, pos - lastPos)));
axes.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strAxes.length())
{
std::string lastV = trim(strAxes.substr(lastPos));
if (!lastV.empty())
axes.push_back(std::stoi(lastV));
}
assert(!axes.empty());
uint32_t axisMask = 0;
for (int axis : axes)
axisMask |= 1 << axis;
bool keepDims = false;
if (block.find("keep") != block.end())
keepDims = std::stoi(block.at("keep")) == 1 ? true : false;
std::string strAxes = block.at("axes");
std::vector<int32_t> axes;
size_t lastPos = 0, pos = 0;
while ((pos = strAxes.find(',', lastPos)) != std::string::npos) {
int vL = std::stoi(trim(strAxes.substr(lastPos, pos - lastPos)));
axes.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strAxes.length()) {
std::string lastV = trim(strAxes.substr(lastPos));
if (!lastV.empty())
axes.push_back(std::stoi(lastV));
}
assert(!axes.empty());
nvinfer1::IReduceLayer* reduce = network->addReduce(*input, operation, axisMask, keepDims);
assert(reduce != nullptr);
std::string reduceLayerName = "reduce_" + std::to_string(layerIdx);
reduce->setName(reduceLayerName.c_str());
output = reduce->getOutput(0);
uint32_t axisMask = 0;
for (int axis : axes)
axisMask |= 1 << axis;
return output;
bool keepDims = false;
if (block.find("keep") != block.end())
keepDims = std::stoi(block.at("keep")) == 1 ? true : false;
nvinfer1::IReduceLayer* reduce = network->addReduce(*input, operation, axisMask, keepDims);
assert(reduce != nullptr);
std::string reduceLayerName = "reduce_" + std::to_string(layerIdx);
reduce->setName(reduceLayerName.c_str());
output = reduce->getOutput(0);
return output;
}

6
nvdsinfer_custom_impl_Yolo/layers/reduce_layer.h
View File

@@ -6,13 +6,9 @@
#ifndef __REDUCE_LAYER_H__
#define __REDUCE_LAYER_H__
#include "NvInfer.h"
#include "../utils.h"
nvinfer1::ITensor* reduceLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* reduceLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

174
nvdsinfer_custom_impl_Yolo/layers/reg_layer.cpp
View File

@@ -5,109 +5,105 @@
#include "reg_layer.h"
nvinfer1::ITensor* regLayer(
int layerIdx,
std::map<std::string, std::string>& block,
std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights,
int& weightPtr,
nvinfer1::ITensor* input,
#include <cassert>
nvinfer1::ITensor*
regLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "reg");
assert(block.at("type") == "reg");
nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*input);
assert(shuffle != nullptr);
std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
shuffle->setName(shuffleLayerName.c_str());
nvinfer1::Permutation permutation;
permutation.order[0] = 1;
permutation.order[1] = 0;
shuffle->setFirstTranspose(permutation);
output = shuffle->getOutput(0);
nvinfer1::Dims shuffleDims = output->getDimensions();
nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*input);
assert(shuffle != nullptr);
std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
shuffle->setName(shuffleLayerName.c_str());
nvinfer1::Permutation permutation;
permutation.order[0] = 1;
permutation.order[1] = 0;
shuffle->setFirstTranspose(permutation);
output = shuffle->getOutput(0);
nvinfer1::Dims shuffleDims = output->getDimensions();
nvinfer1::ISliceLayer* sliceLt = network->addSlice(
*output, nvinfer1::Dims{2, {0, 0}}, nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceLt != nullptr);
std::string sliceLtLayerName = "slice_lt_" + std::to_string(layerIdx);
sliceLt->setName(sliceLtLayerName.c_str());
nvinfer1::ITensor* lt = sliceLt->getOutput(0);
nvinfer1::ISliceLayer* sliceLt = network->addSlice(*output, nvinfer1::Dims{2, {0, 0}},
nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceLt != nullptr);
std::string sliceLtLayerName = "slice_lt_" + std::to_string(layerIdx);
sliceLt->setName(sliceLtLayerName.c_str());
nvinfer1::ITensor* lt = sliceLt->getOutput(0);
nvinfer1::ISliceLayer* sliceRb = network->addSlice(
*output, nvinfer1::Dims{2, {0, 2}}, nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceRb != nullptr);
std::string sliceRbLayerName = "slice_rb_" + std::to_string(layerIdx);
sliceRb->setName(sliceRbLayerName.c_str());
nvinfer1::ITensor* rb = sliceRb->getOutput(0);
nvinfer1::ISliceLayer* sliceRb = network->addSlice(*output, nvinfer1::Dims{2, {0, 2}},
nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, nvinfer1::Dims{2, {1, 1}});
assert(sliceRb != nullptr);
std::string sliceRbLayerName = "slice_rb_" + std::to_string(layerIdx);
sliceRb->setName(sliceRbLayerName.c_str());
nvinfer1::ITensor* rb = sliceRb->getOutput(0);
int channels = shuffleDims.d[0] * 2;
nvinfer1::Weights anchorPointsWt{nvinfer1::DataType::kFLOAT, nullptr, channels};
float* val = new float[channels];
for (int i = 0; i < channels; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
anchorPointsWt.values = val;
trtWeights.push_back(anchorPointsWt);
int channels = shuffleDims.d[0] * 2;
nvinfer1::Weights anchorPointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
float* val = new float[channels];
for (int i = 0; i < channels; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
anchorPointsWt.values = val;
trtWeights.push_back(anchorPointsWt);
nvinfer1::IConstantLayer* anchorPoints = network->addConstant(nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, anchorPointsWt);
assert(anchorPoints != nullptr);
std::string anchorPointsLayerName = "anchor_points_" + std::to_string(layerIdx);
anchorPoints->setName(anchorPointsLayerName.c_str());
nvinfer1::ITensor* anchorPointsTensor = anchorPoints->getOutput(0);
nvinfer1::IConstantLayer* anchorPoints = network->addConstant(nvinfer1::Dims{2, {shuffleDims.d[0], 2}}, anchorPointsWt);
assert(anchorPoints != nullptr);
std::string anchorPointsLayerName = "anchor_points_" + std::to_string(layerIdx);
anchorPoints->setName(anchorPointsLayerName.c_str());
nvinfer1::ITensor* anchorPointsTensor = anchorPoints->getOutput(0);
nvinfer1::IElementWiseLayer* x1y1
= network->addElementWise(*anchorPointsTensor, *lt, nvinfer1::ElementWiseOperation::kSUB);
assert(x1y1 != nullptr);
std::string x1y1LayerName = "x1y1_" + std::to_string(layerIdx);
x1y1->setName(x1y1LayerName.c_str());
nvinfer1::ITensor* x1y1Tensor = x1y1->getOutput(0);
nvinfer1::IElementWiseLayer* x1y1 = network->addElementWise(*anchorPointsTensor, *lt,
nvinfer1::ElementWiseOperation::kSUB);
assert(x1y1 != nullptr);
std::string x1y1LayerName = "x1y1_" + std::to_string(layerIdx);
x1y1->setName(x1y1LayerName.c_str());
nvinfer1::ITensor* x1y1Tensor = x1y1->getOutput(0);
nvinfer1::IElementWiseLayer* x2y2
= network->addElementWise(*rb, *anchorPointsTensor, nvinfer1::ElementWiseOperation::kSUM);
assert(x2y2 != nullptr);
std::string x2y2LayerName = "x2y2_" + std::to_string(layerIdx);
x2y2->setName(x2y2LayerName.c_str());
nvinfer1::ITensor* x2y2Tensor = x2y2->getOutput(0);
nvinfer1::IElementWiseLayer* x2y2 = network->addElementWise(*rb, *anchorPointsTensor,
nvinfer1::ElementWiseOperation::kSUM);
assert(x2y2 != nullptr);
std::string x2y2LayerName = "x2y2_" + std::to_string(layerIdx);
x2y2->setName(x2y2LayerName.c_str());
nvinfer1::ITensor* x2y2Tensor = x2y2->getOutput(0);
std::vector<nvinfer1::ITensor*> concatInputs;
concatInputs.push_back(x1y1Tensor);
concatInputs.push_back(x2y2Tensor);
std::vector<nvinfer1::ITensor*> concatInputs;
concatInputs.push_back(x1y1Tensor);
concatInputs.push_back(x2y2Tensor);
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "concat_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(1);
output = concat->getOutput(0);
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "concat_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(1);
output = concat->getOutput(0);
channels = shuffleDims.d[0];
nvinfer1::Weights stridePointsWt{nvinfer1::DataType::kFLOAT, nullptr, channels};
val = new float[channels];
for (int i = 0; i < channels; ++i)
{
val[i] = weights[weightPtr];
weightPtr++;
}
stridePointsWt.values = val;
trtWeights.push_back(stridePointsWt);
channels = shuffleDims.d[0];
nvinfer1::Weights stridePointsWt {nvinfer1::DataType::kFLOAT, nullptr, channels};
val = new float[channels];
for (int i = 0; i < channels; ++i) {
val[i] = weights[weightPtr];
++weightPtr;
}
stridePointsWt.values = val;
trtWeights.push_back(stridePointsWt);
nvinfer1::IConstantLayer* stridePoints = network->addConstant(nvinfer1::Dims{2, {shuffleDims.d[0], 1}}, stridePointsWt);
assert(stridePoints != nullptr);
std::string stridePointsLayerName = "stride_points_" + std::to_string(layerIdx);
stridePoints->setName(stridePointsLayerName.c_str());
nvinfer1::ITensor* stridePointsTensor = stridePoints->getOutput(0);
nvinfer1::IConstantLayer* stridePoints = network->addConstant(nvinfer1::Dims{2, {shuffleDims.d[0], 1}}, stridePointsWt);
assert(stridePoints != nullptr);
std::string stridePointsLayerName = "stride_points_" + std::to_string(layerIdx);
stridePoints->setName(stridePointsLayerName.c_str());
nvinfer1::ITensor* stridePointsTensor = stridePoints->getOutput(0);
nvinfer1::IElementWiseLayer* pred
= network->addElementWise(*output, *stridePointsTensor, nvinfer1::ElementWiseOperation::kPROD);
assert(pred != nullptr);
std::string predLayerName = "pred_" + std::to_string(layerIdx);
pred->setName(predLayerName.c_str());
output = pred->getOutput(0);
nvinfer1::IElementWiseLayer* pred = network->addElementWise(*output, *stridePointsTensor,
nvinfer1::ElementWiseOperation::kPROD);
assert(pred != nullptr);
std::string predLayerName = "pred_" + std::to_string(layerIdx);
pred->setName(predLayerName.c_str());
output = pred->getOutput(0);
return output;
return output;
}

10
nvdsinfer_custom_impl_Yolo/layers/reg_layer.h
View File

@@ -8,17 +8,11 @@
#include <map>
#include <vector>
#include <cassert>
#include "NvInfer.h"
nvinfer1::ITensor* regLayer(
int layerIdx,
std::map<std::string, std::string>& block,
std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights,
int& weightPtr,
nvinfer1::ITensor* input,
nvinfer1::ITensor* regLayer(int layerIdx, std::map<std::string, std::string>& block, std::vector<float>& weights,
std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

83
nvdsinfer_custom_impl_Yolo/layers/reorg_layer.cpp
View File

@@ -5,58 +5,55 @@
#include "reorg_layer.h"
nvinfer1::ITensor* reorgLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
#include <vector>
#include <cassert>
nvinfer1::ITensor*
reorgLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "reorg");
assert(block.at("type") == "reorg");
nvinfer1::Dims inputDims = input->getDimensions();
nvinfer1::Dims inputDims = input->getDimensions();
nvinfer1::ISliceLayer *slice1 = network->addSlice(
*input, nvinfer1::Dims{3, {0, 0, 0}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
nvinfer1::Dims{3, {1, 2, 2}});
assert(slice1 != nullptr);
std::string slice1LayerName = "slice1_" + std::to_string(layerIdx);
slice1->setName(slice1LayerName.c_str());
nvinfer1::ISliceLayer *slice1 = network->addSlice(*input, nvinfer1::Dims{3, {0, 0, 0}},
nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
assert(slice1 != nullptr);
std::string slice1LayerName = "slice1_" + std::to_string(layerIdx);
slice1->setName(slice1LayerName.c_str());
nvinfer1::ISliceLayer *slice2 = network->addSlice(
*input, nvinfer1::Dims{3, {0, 1, 0}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
nvinfer1::Dims{3, {1, 2, 2}});
assert(slice2 != nullptr);
std::string slice2LayerName = "slice2_" + std::to_string(layerIdx);
slice2->setName(slice2LayerName.c_str());
nvinfer1::ISliceLayer *slice2 = network->addSlice(*input, nvinfer1::Dims{3, {0, 1, 0}},
nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
assert(slice2 != nullptr);
std::string slice2LayerName = "slice2_" + std::to_string(layerIdx);
slice2->setName(slice2LayerName.c_str());
nvinfer1::ISliceLayer *slice3 = network->addSlice(
*input, nvinfer1::Dims{3, {0, 0, 1}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
nvinfer1::Dims{3, {1, 2, 2}});
assert(slice3 != nullptr);
std::string slice3LayerName = "slice3_" + std::to_string(layerIdx);
slice3->setName(slice3LayerName.c_str());
nvinfer1::ISliceLayer *slice3 = network->addSlice(*input, nvinfer1::Dims{3, {0, 0, 1}},
nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
assert(slice3 != nullptr);
std::string slice3LayerName = "slice3_" + std::to_string(layerIdx);
slice3->setName(slice3LayerName.c_str());
nvinfer1::ISliceLayer *slice4 = network->addSlice(
*input, nvinfer1::Dims{3, {0, 1, 1}}, nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}},
nvinfer1::Dims{3, {1, 2, 2}});
assert(slice4 != nullptr);
std::string slice4LayerName = "slice4_" + std::to_string(layerIdx);
slice4->setName(slice4LayerName.c_str());
nvinfer1::ISliceLayer *slice4 = network->addSlice(*input, nvinfer1::Dims{3, {0, 1, 1}},
nvinfer1::Dims{3, {inputDims.d[0], inputDims.d[1] / 2, inputDims.d[2] / 2}}, nvinfer1::Dims{3, {1, 2, 2}});
assert(slice4 != nullptr);
std::string slice4LayerName = "slice4_" + std::to_string(layerIdx);
slice4->setName(slice4LayerName.c_str());
std::vector<nvinfer1::ITensor*> concatInputs;
concatInputs.push_back(slice1->getOutput(0));
concatInputs.push_back(slice2->getOutput(0));
concatInputs.push_back(slice3->getOutput(0));
concatInputs.push_back(slice4->getOutput(0));
std::vector<nvinfer1::ITensor*> concatInputs;
concatInputs.push_back(slice1->getOutput(0));
concatInputs.push_back(slice2->getOutput(0));
concatInputs.push_back(slice3->getOutput(0));
concatInputs.push_back(slice4->getOutput(0));
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "concat_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(0);
output = concat->getOutput(0);
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "concat_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(0);
output = concat->getOutput(0);
return output;
return output;
}

11
nvdsinfer_custom_impl_Yolo/layers/reorg_layer.h
View File

@@ -3,19 +3,14 @@
* https://www.github.com/marcoslucianops
*/
#ifndef __REORGV5_LAYER_H__
#define __REORGV5_LAYER_H__
#ifndef __REORG_LAYER_H__
#define __REORG_LAYER_H__
#include <map>
#include <vector>
#include <cassert>
#include "NvInfer.h"
nvinfer1::ITensor* reorgLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* reorgLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

126
nvdsinfer_custom_impl_Yolo/layers/route_layer.cpp
View File

@@ -5,78 +5,70 @@
#include "route_layer.h"
nvinfer1::ITensor* routeLayer(
int layerIdx,
std::string& layers,
std::map<std::string, std::string>& block,
std::vector<nvinfer1::ITensor*> tensorOutputs,
nvinfer1::INetworkDefinition* network)
nvinfer1::ITensor*
routeLayer(int layerIdx, std::string& layers, std::map<std::string, std::string>& block,
std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "route");
assert(block.find("layers") != block.end());
assert(block.at("type") == "route");
assert(block.find("layers") != block.end());
std::string strLayers = block.at("layers");
std::vector<int> idxLayers;
size_t lastPos = 0, pos = 0;
while ((pos = strLayers.find(',', lastPos)) != std::string::npos)
{
int vL = std::stoi(trim(strLayers.substr(lastPos, pos - lastPos)));
idxLayers.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strLayers.length())
{
std::string lastV = trim(strLayers.substr(lastPos));
if (!lastV.empty())
idxLayers.push_back(std::stoi(lastV));
}
assert (!idxLayers.empty());
std::vector<nvinfer1::ITensor*> concatInputs;
for (uint i = 0; i < idxLayers.size(); ++i)
{
if (idxLayers[i] < 0)
idxLayers[i] = tensorOutputs.size() + idxLayers[i];
assert (idxLayers[i] >= 0 && idxLayers[i] < (int)tensorOutputs.size());
concatInputs.push_back(tensorOutputs[idxLayers[i]]);
if (i < idxLayers.size() - 1)
layers += std::to_string(idxLayers[i]) + ", ";
}
layers += std::to_string(idxLayers[idxLayers.size() - 1]);
std::string strLayers = block.at("layers");
std::vector<int> idxLayers;
size_t lastPos = 0, pos = 0;
while ((pos = strLayers.find(',', lastPos)) != std::string::npos) {
int vL = std::stoi(trim(strLayers.substr(lastPos, pos - lastPos)));
idxLayers.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strLayers.length()) {
std::string lastV = trim(strLayers.substr(lastPos));
if (!lastV.empty())
idxLayers.push_back(std::stoi(lastV));
}
assert (!idxLayers.empty());
std::vector<nvinfer1::ITensor*> concatInputs;
for (uint i = 0; i < idxLayers.size(); ++i) {
if (idxLayers[i] < 0)
idxLayers[i] = tensorOutputs.size() + idxLayers[i];
assert (idxLayers[i] >= 0 && idxLayers[i] < (int)tensorOutputs.size());
concatInputs.push_back(tensorOutputs[idxLayers[i]]);
if (i < idxLayers.size() - 1)
layers += std::to_string(idxLayers[i]) + ", ";
}
layers += std::to_string(idxLayers[idxLayers.size() - 1]);
if (concatInputs.size() == 1)
output = concatInputs[0];
else {
int axis = 0;
if (block.find("axis") != block.end())
axis = std::stoi(block.at("axis"));
if (axis < 0)
axis = concatInputs[0]->getDimensions().nbDims + axis;
if (concatInputs.size() == 1)
output = concatInputs[0];
else {
int axis = 0;
if (block.find("axis") != block.end())
axis = std::stoi(block.at("axis"));
if (axis < 0)
axis = concatInputs[0]->getDimensions().nbDims + axis;
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "route_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(axis);
output = concat->getOutput(0);
}
nvinfer1::IConcatenationLayer* concat = network->addConcatenation(concatInputs.data(), concatInputs.size());
assert(concat != nullptr);
std::string concatLayerName = "route_" + std::to_string(layerIdx);
concat->setName(concatLayerName.c_str());
concat->setAxis(axis);
output = concat->getOutput(0);
}
if (block.find("groups") != block.end())
{
nvinfer1::Dims prevTensorDims = output->getDimensions();
int groups = stoi(block.at("groups"));
int group_id = stoi(block.at("group_id"));
int startSlice = (prevTensorDims.d[0] / groups) * group_id;
int channelSlice = (prevTensorDims.d[0] / groups);
nvinfer1::ISliceLayer* slice = network->addSlice(
*output, nvinfer1::Dims{3, {startSlice, 0, 0}},
nvinfer1::Dims{3, {channelSlice, prevTensorDims.d[1], prevTensorDims.d[2]}}, nvinfer1::Dims{3, {1, 1, 1}});
assert(slice != nullptr);
std::string sliceLayerName = "slice_" + std::to_string(layerIdx);
slice->setName(sliceLayerName.c_str());
output = slice->getOutput(0);
}
if (block.find("groups") != block.end()) {
nvinfer1::Dims prevTensorDims = output->getDimensions();
int groups = stoi(block.at("groups"));
int group_id = stoi(block.at("group_id"));
int startSlice = (prevTensorDims.d[0] / groups) * group_id;
int channelSlice = (prevTensorDims.d[0] / groups);
nvinfer1::ISliceLayer* slice = network->addSlice(*output, nvinfer1::Dims{3, {startSlice, 0, 0}},
nvinfer1::Dims{3, {channelSlice, prevTensorDims.d[1], prevTensorDims.d[2]}}, nvinfer1::Dims{3, {1, 1, 1}});
assert(slice != nullptr);
std::string sliceLayerName = "slice_" + std::to_string(layerIdx);
slice->setName(sliceLayerName.c_str());
output = slice->getOutput(0);
}
return output;
return output;
}

9
nvdsinfer_custom_impl_Yolo/layers/route_layer.h
View File

@@ -6,14 +6,9 @@
#ifndef __ROUTE_LAYER_H__
#define __ROUTE_LAYER_H__
#include "NvInfer.h"
#include "../utils.h"
nvinfer1::ITensor* routeLayer(
int layerIdx,
std::string& layers,
std::map<std::string, std::string>& block,
std::vector<nvinfer1::ITensor*> tensorOutputs,
nvinfer1::INetworkDefinition* network);
nvinfer1::ITensor* routeLayer(int layerIdx, std::string& layers, std::map<std::string, std::string>& block,
std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network);
#endif

63
nvdsinfer_custom_impl_Yolo/layers/shortcut_layer.cpp
View File

@@ -5,48 +5,41 @@
#include "shortcut_layer.h"
nvinfer1::ITensor* shortcutLayer(
int layerIdx,
std::string mode,
std::string activation,
std::string inputVol,
std::string shortcutVol,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* shortcutInput,
#include <cassert>
nvinfer1::ITensor*
shortcutLayer(int layerIdx, std::string mode, std::string activation, std::string inputVol, std::string shortcutVol,
std::map<std::string, std::string>& block, nvinfer1::ITensor* input, nvinfer1::ITensor* shortcutInput,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "shortcut");
assert(block.at("type") == "shortcut");
nvinfer1::ElementWiseOperation operation = nvinfer1::ElementWiseOperation::kSUM;
nvinfer1::ElementWiseOperation operation = nvinfer1::ElementWiseOperation::kSUM;
if (mode == "mul")
operation = nvinfer1::ElementWiseOperation::kPROD;
if (mode == "mul")
operation = nvinfer1::ElementWiseOperation::kPROD;
if (mode == "add" && inputVol != shortcutVol)
{
nvinfer1::ISliceLayer* slice = network->addSlice(
*shortcutInput, nvinfer1::Dims{3, {0, 0, 0}}, input->getDimensions(), nvinfer1::Dims{3, {1, 1, 1}});
assert(slice != nullptr);
std::string sliceLayerName = "slice_" + std::to_string(layerIdx);
slice->setName(sliceLayerName.c_str());
output = slice->getOutput(0);
}
else
{
output = shortcutInput;
}
if (mode == "add" && inputVol != shortcutVol) {
nvinfer1::ISliceLayer* slice = network->addSlice(*shortcutInput, nvinfer1::Dims{3, {0, 0, 0}}, input->getDimensions(),
nvinfer1::Dims{3, {1, 1, 1}});
assert(slice != nullptr);
std::string sliceLayerName = "slice_" + std::to_string(layerIdx);
slice->setName(sliceLayerName.c_str());
output = slice->getOutput(0);
}
else
output = shortcutInput;
nvinfer1::IElementWiseLayer* shortcut = network->addElementWise(*input, *output, operation);
assert(shortcut != nullptr);
std::string shortcutLayerName = "shortcut_" + std::to_string(layerIdx);
shortcut->setName(shortcutLayerName.c_str());
output = shortcut->getOutput(0);
nvinfer1::IElementWiseLayer* shortcut = network->addElementWise(*input, *output, operation);
assert(shortcut != nullptr);
std::string shortcutLayerName = "shortcut_" + std::to_string(layerIdx);
shortcut->setName(shortcutLayerName.c_str());
output = shortcut->getOutput(0);
output = activationLayer(layerIdx, activation, output, network);
assert(output != nullptr);
output = activationLayer(layerIdx, activation, output, network);
assert(output != nullptr);
return output;
return output;
}

13
nvdsinfer_custom_impl_Yolo/layers/shortcut_layer.h
View File

@@ -12,15 +12,8 @@
#include "activation_layer.h"
nvinfer1::ITensor* shortcutLayer(
int layerIdx,
std::string mode,
std::string activation,
std::string inputVol,
std::string shortcutVol,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* shortcut,
nvinfer1::INetworkDefinition* network);
nvinfer1::ITensor* shortcutLayer(int layerIdx, std::string mode, std::string activation, std::string inputVol,
std::string shortcutVol, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::ITensor* shortcut, nvinfer1::INetworkDefinition* network);
#endif

212
nvdsinfer_custom_impl_Yolo/layers/shuffle_layer.cpp
View File

@@ -5,119 +5,133 @@
#include "shuffle_layer.h"
nvinfer1::ITensor* shuffleLayer(
int layerIdx,
std::string& layer,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
std::vector<nvinfer1::ITensor*> tensorOutputs,
nvinfer1::INetworkDefinition* network)
nvinfer1::ITensor*
shuffleLayer(int layerIdx, std::string& layer, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "shuffle");
assert(block.at("type") == "shuffle");
nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*input);
assert(shuffle != nullptr);
std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
shuffle->setName(shuffleLayerName.c_str());
nvinfer1::IShuffleLayer* shuffle = network->addShuffle(*input);
assert(shuffle != nullptr);
std::string shuffleLayerName = "shuffle_" + std::to_string(layerIdx);
shuffle->setName(shuffleLayerName.c_str());
if (block.find("reshape") != block.end())
{
std::string strReshape = block.at("reshape");
std::vector<int32_t> reshape;
size_t lastPos = 0, pos = 0;
while ((pos = strReshape.find(',', lastPos)) != std::string::npos)
{
int vL = std::stoi(trim(strReshape.substr(lastPos, pos - lastPos)));
reshape.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strReshape.length())
{
std::string lastV = trim(strReshape.substr(lastPos));
if (!lastV.empty())
reshape.push_back(std::stoi(lastV));
}
assert(!reshape.empty());
if (block.find("reshape") != block.end()) {
int from = -1;
if (block.find("from") != block.end())
from = std::stoi(block.at("from"));
int from = -1;
if (block.find("from") != block.end())
from = std::stoi(block.at("from"));
if (from < 0)
from = tensorOutputs.size() + from;
if (from < 0)
from = tensorOutputs.size() + from;
layer = std::to_string(from);
layer = std::to_string(from);
nvinfer1::Dims inputTensorDims = tensorOutputs[from]->getDimensions();
nvinfer1::Dims inputTensorDims = tensorOutputs[from]->getDimensions();
int32_t l = inputTensorDims.d[1] * inputTensorDims.d[2];
nvinfer1::Dims reshapeDims;
reshapeDims.nbDims = reshape.size();
for (uint i = 0; i < reshape.size(); ++i)
if (reshape[i] == 0)
reshapeDims.d[i] = l;
else
reshapeDims.d[i] = reshape[i];
shuffle->setReshapeDimensions(reshapeDims);
std::string strReshape = block.at("reshape");
std::vector<int32_t> reshape;
size_t lastPos = 0, pos = 0;
while ((pos = strReshape.find(',', lastPos)) != std::string::npos) {
std::string V = trim(strReshape.substr(lastPos, pos - lastPos));
if (V == "c")
reshape.push_back(inputTensorDims.d[0]);
else if (V == "ch")
reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1]);
else if (V == "cw")
reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[2]);
else if (V == "h")
reshape.push_back(inputTensorDims.d[1]);
else if (V == "hw")
reshape.push_back(inputTensorDims.d[1] * inputTensorDims.d[2]);
else if (V == "w")
reshape.push_back(inputTensorDims.d[2]);
else if (V == "chw")
reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1] * inputTensorDims.d[2]);
else
reshape.push_back(std::stoi(V));
lastPos = pos + 1;
}
if (block.find("transpose1") != block.end())
{
std::string strTranspose1 = block.at("transpose1");
std::vector<int32_t> transpose1;
size_t lastPos = 0, pos = 0;
while ((pos = strTranspose1.find(',', lastPos)) != std::string::npos)
{
int vL = std::stoi(trim(strTranspose1.substr(lastPos, pos - lastPos)));
transpose1.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strTranspose1.length())
{
std::string lastV = trim(strTranspose1.substr(lastPos));
if (!lastV.empty())
transpose1.push_back(std::stoi(lastV));
}
assert(!transpose1.empty());
nvinfer1::Permutation permutation1;
for (uint i = 0; i < transpose1.size(); ++i)
permutation1.order[i] = transpose1[i];
shuffle->setFirstTranspose(permutation1);
if (lastPos < strReshape.length()) {
std::string lastV = trim(strReshape.substr(lastPos));
if (!lastV.empty()) {
if (lastV == "c")
reshape.push_back(inputTensorDims.d[0]);
else if (lastV == "ch")
reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1]);
else if (lastV == "cw")
reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[2]);
else if (lastV == "h")
reshape.push_back(inputTensorDims.d[1]);
else if (lastV == "hw")
reshape.push_back(inputTensorDims.d[1] * inputTensorDims.d[2]);
else if (lastV == "w")
reshape.push_back(inputTensorDims.d[2]);
else if (lastV == "chw")
reshape.push_back(inputTensorDims.d[0] * inputTensorDims.d[1] * inputTensorDims.d[2]);
else
reshape.push_back(std::stoi(lastV));
}
}
assert(!reshape.empty());
if (block.find("transpose2") != block.end())
{
std::string strTranspose2 = block.at("transpose2");
std::vector<int32_t> transpose2;
size_t lastPos = 0, pos = 0;
while ((pos = strTranspose2.find(',', lastPos)) != std::string::npos)
{
int vL = std::stoi(trim(strTranspose2.substr(lastPos, pos - lastPos)));
transpose2.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strTranspose2.length())
{
std::string lastV = trim(strTranspose2.substr(lastPos));
if (!lastV.empty())
transpose2.push_back(std::stoi(lastV));
}
assert(!transpose2.empty());
nvinfer1::Dims reshapeDims;
reshapeDims.nbDims = reshape.size();
nvinfer1::Permutation permutation2;
for (uint i = 0; i < transpose2.size(); ++i)
permutation2.order[i] = transpose2[i];
for (uint i = 0; i < reshape.size(); ++i)
reshapeDims.d[i] = reshape[i];
shuffle->setSecondTranspose(permutation2);
shuffle->setReshapeDimensions(reshapeDims);
}
if (block.find("transpose1") != block.end()) {
std::string strTranspose1 = block.at("transpose1");
std::vector<int32_t> transpose1;
size_t lastPos = 0, pos = 0;
while ((pos = strTranspose1.find(',', lastPos)) != std::string::npos) {
int vL = std::stoi(trim(strTranspose1.substr(lastPos, pos - lastPos)));
transpose1.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strTranspose1.length()) {
std::string lastV = trim(strTranspose1.substr(lastPos));
if (!lastV.empty())
transpose1.push_back(std::stoi(lastV));
}
assert(!transpose1.empty());
output = shuffle->getOutput(0);
nvinfer1::Permutation permutation1;
for (uint i = 0; i < transpose1.size(); ++i)
permutation1.order[i] = transpose1[i];
return output;
shuffle->setFirstTranspose(permutation1);
}
if (block.find("transpose2") != block.end()) {
std::string strTranspose2 = block.at("transpose2");
std::vector<int32_t> transpose2;
size_t lastPos = 0, pos = 0;
while ((pos = strTranspose2.find(',', lastPos)) != std::string::npos) {
int vL = std::stoi(trim(strTranspose2.substr(lastPos, pos - lastPos)));
transpose2.push_back(vL);
lastPos = pos + 1;
}
if (lastPos < strTranspose2.length()) {
std::string lastV = trim(strTranspose2.substr(lastPos));
if (!lastV.empty())
transpose2.push_back(std::stoi(lastV));
}
assert(!transpose2.empty());
nvinfer1::Permutation permutation2;
for (uint i = 0; i < transpose2.size(); ++i)
permutation2.order[i] = transpose2[i];
shuffle->setSecondTranspose(permutation2);
}
output = shuffle->getOutput(0);
return output;
}

10
nvdsinfer_custom_impl_Yolo/layers/shuffle_layer.h
View File

@@ -6,15 +6,9 @@
#ifndef __SHUFFLE_LAYER_H__
#define __SHUFFLE_LAYER_H__
#include "NvInfer.h"
#include "../utils.h"
nvinfer1::ITensor* shuffleLayer(
int layerIdx,
std::string& layer,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
std::vector<nvinfer1::ITensor*> tensorOutputs,
nvinfer1::INetworkDefinition* network);
nvinfer1::ITensor* shuffleLayer(int layerIdx, std::string& layer, std::map<std::string, std::string>& block,
nvinfer1::ITensor* input, std::vector<nvinfer1::ITensor*> tensorOutputs, nvinfer1::INetworkDefinition* network);
#endif

30
nvdsinfer_custom_impl_Yolo/layers/softmax_layer.cpp
View File

@@ -5,25 +5,25 @@
#include "softmax_layer.h"
nvinfer1::ITensor* softmaxLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
#include <cassert>
nvinfer1::ITensor*
softmaxLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "softmax");
assert(block.find("axes") != block.end());
assert(block.at("type") == "softmax");
assert(block.find("axes") != block.end());
int axes = std::stoi(block.at("axes"));
int axes = std::stoi(block.at("axes"));
nvinfer1::ISoftMaxLayer* softmax = network->addSoftMax(*input);
assert(softmax != nullptr);
std::string softmaxLayerName = "softmax_" + std::to_string(layerIdx);
softmax->setName(softmaxLayerName.c_str());
softmax->setAxes(1 << axes);
output = softmax->getOutput(0);
nvinfer1::ISoftMaxLayer* softmax = network->addSoftMax(*input);
assert(softmax != nullptr);
std::string softmaxLayerName = "softmax_" + std::to_string(layerIdx);
softmax->setName(softmaxLayerName.c_str());
softmax->setAxes(1 << axes);
output = softmax->getOutput(0);
return output;
return output;
}

6
nvdsinfer_custom_impl_Yolo/layers/softmax_layer.h
View File

@@ -7,14 +7,10 @@
#define __SOFTMAX_LAYER_H__
#include <map>
#include <cassert>
#include "NvInfer.h"
nvinfer1::ITensor* softmaxLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* softmaxLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif

34
nvdsinfer_custom_impl_Yolo/layers/upsample_layer.cpp
View File

@@ -5,28 +5,28 @@
#include "upsample_layer.h"
nvinfer1::ITensor* upsampleLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
#include <cassert>
nvinfer1::ITensor*
upsampleLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network)
{
nvinfer1::ITensor* output;
nvinfer1::ITensor* output;
assert(block.at("type") == "upsample");
assert(block.find("stride") != block.end());
assert(block.at("type") == "upsample");
assert(block.find("stride") != block.end());
int stride = std::stoi(block.at("stride"));
int stride = std::stoi(block.at("stride"));
float scale[3] = {1, static_cast<float>(stride), static_cast<float>(stride)};
float scale[3] = {1, static_cast<float>(stride), static_cast<float>(stride)};
nvinfer1::IResizeLayer* resize = network->addResize(*input);
assert(resize != nullptr);
std::string resizeLayerName = "upsample_" + std::to_string(layerIdx);
resize->setName(resizeLayerName.c_str());
resize->setResizeMode(nvinfer1::ResizeMode::kNEAREST);
resize->setScales(scale, 3);
output = resize->getOutput(0);
nvinfer1::IResizeLayer* resize = network->addResize(*input);
assert(resize != nullptr);
std::string resizeLayerName = "upsample_" + std::to_string(layerIdx);
resize->setName(resizeLayerName.c_str());
resize->setResizeMode(nvinfer1::ResizeMode::kNEAREST);
resize->setScales(scale, 3);
output = resize->getOutput(0);
return output;
return output;
}

6
nvdsinfer_custom_impl_Yolo/layers/upsample_layer.h
View File

@@ -7,14 +7,10 @@
#define __UPSAMPLE_LAYER_H__
#include <map>
#include <cassert>
#include "NvInfer.h"
nvinfer1::ITensor* upsampleLayer(
int layerIdx,
std::map<std::string, std::string>& block,
nvinfer1::ITensor* input,
nvinfer1::ITensor* upsampleLayer(int layerIdx, std::map<std::string, std::string>& block, nvinfer1::ITensor* input,
nvinfer1::INetworkDefinition* network);
#endif