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PluginFactory.h
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#ifndef __PLUGIN_FACTORY_H__
#define __PLUGIN_FACTORY_H__
#include <algorithm>
#include <cassert>
#include <iostream>
#include <cstring>
#include <sys/stat.h>
#include <map>
#include "NvCaffeParser.h"
#include "NvInferPlugin.h"
#include "upsample_layer.h"
using namespace nvinfer1;
using namespace nvcaffeparser1;
using namespace plugin;
#if 0
//Concat layer . TensorRT Concat only support cross channel
class ConcatPlugin : public IPlugin
{
public:
ConcatPlugin(int axis){ _axis = axis; };
// ConcatPlugin(int axis, const void* buffer, size_t size);
ConcatPlugin::ConcatPlugin(int axis, const void* buffer, size_t size)
{
assert(size == (18*sizeof(int)));
const int* d = reinterpret_cast<const int*>(buffer);
dimsConv4_3 = DimsCHW{d[0], d[1], d[2]};
dimsFc7 = DimsCHW{d[3], d[4], d[5]};
dimsConv6 = DimsCHW{d[6], d[7], d[8]};
dimsConv7 = DimsCHW{d[9], d[10], d[11]};
dimsConv8 = DimsCHW{d[12], d[13], d[14]};
dimsConv9 = DimsCHW{d[15], d[16], d[17]};
_axis = axis;
}
inline int getNbOutputs() const override {return 1;};
// Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override ;
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
{
assert(nbInputDims == 6);
if(_axis == 1)
{
top_concat_axis = inputs[0].d[0] + inputs[1].d[0] + inputs[2].d[0] + inputs[3].d[0] + inputs[4].d[0] + inputs[5].d[0];
return DimsCHW(top_concat_axis, 1, 1);
}else if(_axis == 2){
top_concat_axis = inputs[0].d[1] + inputs[1].d[1] + inputs[2].d[1] + inputs[3].d[1] + inputs[4].d[1] + inputs[5].d[1];
return DimsCHW(2, top_concat_axis, 1);
}else{//_param.concat_axis == 3
return DimsCHW(0, 0, 0);
}
}
// int initialize()
int initialize()
{
inputs_size = 6;//6个bottom层
if(_axis == 1)//c
{
top_concat_axis = dimsConv4_3.c() + dimsFc7.c() + dimsConv6.c() + dimsConv7.c() + dimsConv8.c() + dimsConv9.c();
bottom_concat_axis[0] = dimsConv4_3.c(); bottom_concat_axis[1] = dimsFc7.c(); bottom_concat_axis[2] = dimsConv6.c();
bottom_concat_axis[3] = dimsConv7.c(); bottom_concat_axis[4] = dimsConv8.c(); bottom_concat_axis[5] = dimsConv9.c();
concat_input_size_[0] = dimsConv4_3.h() * dimsConv4_3.w(); concat_input_size_[1] = dimsFc7.h() * dimsFc7.w();
concat_input_size_[2] = dimsConv6.h() * dimsConv6.w(); concat_input_size_[3] = dimsConv7.h() * dimsConv7.w();
concat_input_size_[4] = dimsConv8.h() * dimsConv8.w(); concat_input_size_[5] = dimsConv9.h() * dimsConv9.w();
num_concats_[0] = dimsConv4_3.c(); num_concats_[1] = dimsFc7.c(); num_concats_[2] = dimsConv6.c();
num_concats_[3] = dimsConv7.c(); num_concats_[4] = dimsConv8.c(); num_concats_[5] = dimsConv9.c();
}else if(_axis == 2){//h
top_concat_axis = dimsConv4_3.h() + dimsFc7.h() + dimsConv6.h() + dimsConv7.h() + dimsConv8.h() + dimsConv9.h();
bottom_concat_axis[0] = dimsConv4_3.h(); bottom_concat_axis[1] = dimsFc7.h(); bottom_concat_axis[2] = dimsConv6.h();
bottom_concat_axis[3] = dimsConv7.h(); bottom_concat_axis[4] = dimsConv8.h(); bottom_concat_axis[5] = dimsConv9.h();
concat_input_size_[0] = dimsConv4_3.w(); concat_input_size_[1] = dimsFc7.w(); concat_input_size_[2] = dimsConv6.w();
concat_input_size_[3] = dimsConv7.w(); concat_input_size_[4] = dimsConv8.w(); concat_input_size_[5] = dimsConv9.w();
num_concats_[0] = dimsConv4_3.c() * dimsConv4_3.h(); num_concats_[1] = dimsFc7.c() * dimsFc7.h();
num_concats_[2] = dimsConv6.c() * dimsConv6.h(); num_concats_[3] = dimsConv7.c() * dimsConv7.h();
num_concats_[4] = dimsConv8.c() * dimsConv8.h(); num_concats_[5] = dimsConv9.c() * dimsConv9.h();
}else{//_param.concat_axis == 3 , w
top_concat_axis = dimsConv4_3.w() + dimsFc7.w() + dimsConv6.w() + dimsConv7.w() + dimsConv8.w() + dimsConv9.w();
bottom_concat_axis[0] = dimsConv4_3.w(); bottom_concat_axis[1] = dimsFc7.w(); bottom_concat_axis[2] = dimsConv6.w();
bottom_concat_axis[3] = dimsConv7.w(); bottom_concat_axis[4] = dimsConv8.w(); bottom_concat_axis[5] = dimsConv9.w();
concat_input_size_[0] = 1; concat_input_size_[1] = 1; concat_input_size_[2] = 1;
concat_input_size_[3] = 1; concat_input_size_[4] = 1; concat_input_size_[5] = 1;
return 0;
}
return 0;
}
// inline void terminate() override;
void terminate()
{
//CUDA_CHECK(cudaFree(scale_data));
delete[] bottom_concat_axis;
delete[] concat_input_size_;
delete[] num_concats_;
}
inline size_t getWorkspaceSize(int) const override { return 0; };
//int enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) override;
int enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream)
{
float *top_data = reinterpret_cast<float*>(outputs[0]);
int offset_concat_axis = 0;
const bool kForward = true;
for (int i = 0; i < inputs_size; ++i) {
const float *bottom_data = reinterpret_cast<const float*>(inputs[i]);
const int nthreads = num_concats_[i] * concat_input_size_[i];
//const int nthreads = bottom_concat_size * num_concats_[i];
ConcatLayer(nthreads, bottom_data, kForward, num_concats_[i], concat_input_size_[i], top_concat_axis, bottom_concat_axis[i], offset_concat_axis, top_data, stream);
offset_concat_axis += bottom_concat_axis[i];
}
return 0;
}
// size_t getSerializationSize() override;
size_t getSerializationSize()
{
return 18*sizeof(int);
}
// void serialize(void* buffer) override;
void serialize(void* buffer)
{
int* d = reinterpret_cast<int*>(buffer);
d[0] = dimsConv4_3.c(); d[1] = dimsConv4_3.h(); d[2] = dimsConv4_3.w();
d[3] = dimsFc7.c(); d[4] = dimsFc7.h(); d[5] = dimsFc7.w();
d[6] = dimsConv6.c(); d[7] = dimsConv6.h(); d[8] = dimsConv6.w();
d[9] = dimsConv7.c(); d[10] = dimsConv7.h(); d[11] = dimsConv7.w();
d[12] = dimsConv8.c(); d[13] = dimsConv8.h(); d[14] = dimsConv8.w();
d[15] = dimsConv9.c(); d[16] = dimsConv9.h(); d[17] = dimsConv9.w();
}
// void configure(const Dims*inputs, int nbInputs, const Dims* outputs, int nbOutputs, int) override;
void configure(const Dims*inputs, int nbInputs, const Dims* outputs, int nbOutputs, int)
{
dimsConv4_3 = DimsCHW{inputs[0].d[0], inputs[0].d[1], inputs[0].d[2]};
dimsFc7 = DimsCHW{inputs[1].d[0], inputs[1].d[1], inputs[1].d[2]};
dimsConv6 = DimsCHW{inputs[2].d[0], inputs[2].d[1], inputs[2].d[2]};
dimsConv7 = DimsCHW{inputs[3].d[0], inputs[3].d[1], inputs[3].d[2]};
dimsConv8 = DimsCHW{inputs[4].d[0], inputs[4].d[1], inputs[4].d[2]};
dimsConv9 = DimsCHW{inputs[5].d[0], inputs[5].d[1], inputs[5].d[2]};
}
protected:
DimsCHW dimsConv4_3, dimsFc7, dimsConv6, dimsConv7, dimsConv8, dimsConv9;
int inputs_size;
int top_concat_axis;//top 层 concat后的维度
int* bottom_concat_axis = new int[9];//记录每个bottom层concat维度的shape
int* concat_input_size_ = new int[9];
int* num_concats_ = new int[9];
int _axis;
};
#endif
//SSD Reshape layer : shape{0,-1,21}
template<int OutC>
class Reshape : public IPlugin
{
public:
Reshape() {}
Reshape(const void* buffer, size_t size)
{
assert(size == sizeof(mCopySize));
mCopySize = *reinterpret_cast<const size_t*>(buffer);
}
int getNbOutputs() const override
{
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override
{
assert(nbInputDims == 1);
assert(index == 0);
assert(inputs[index].nbDims == 3);
assert((inputs[0].d[0])*(inputs[0].d[1]) % OutC == 0);
return DimsCHW(OutC, inputs[0].d[0] * inputs[0].d[1] / OutC, inputs[0].d[2]);
}
int initialize() override
{
return 0;
}
void terminate() override
{
}
size_t getWorkspaceSize(int) const override
{
return 0;
}
// currently it is not possible for a plugin to execute "in place". Therefore we memcpy the data from the input to the output buffer
int enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) override
{
CHECK(cudaMemcpyAsync(outputs[0], inputs[0], mCopySize * batchSize, cudaMemcpyDeviceToDevice, stream));
return 0;
}
size_t getSerializationSize() override
{
return sizeof(mCopySize);
}
void serialize(void* buffer) override
{
*reinterpret_cast<size_t*>(buffer) = mCopySize;
}
void configure(const Dims*inputs, int nbInputs, const Dims* outputs, int nbOutputs, int) override
{
mCopySize = inputs[0].d[0] * inputs[0].d[1] * inputs[0].d[2] * sizeof(float);
}
protected:
size_t mCopySize;
};
class SofaMaxChannelLayer: public IPlugin
{
public:
SofaMaxChannelLayer(int axis): _axis(axis),inner_num_(1),outer_num_(3462){}
SofaMaxChannelLayer(int axis,const void* buffer,size_t size)
{
_axis = axis;
}
inline int getNbOutputs() const override { return 1; };
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override
{
return DimsCHW(1,3462, 2);
}
int initialize() override
{
return 0;
}
inline void terminate() override
{
}
inline size_t getWorkspaceSize(int) const override { return 0; }
int enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) override
{
return 0;
}
size_t getSerializationSize() override
{
return 0;
}
void serialize(void* buffer) override
{
}
void configure(const Dims* inputs, int nbInputs, const Dims* outputs, int nbOutputs, int) override
{
#if 0
int dim = bottom[0]->count() / outer_num_;
caffe_copy(bottom[0]->count(), bottom_data, top_data);
// We need to subtract the max to avoid numerical issues, compute the exp,
// and then normalize.
for (int i = 0; i < outer_num_; ++i) {
// initialize scale_data to the first plane
caffe_copy(inner_num_, bottom_data + i * dim, scale_data);
for (int j = 0; j < ; j++) {
for (int k = 0; k < inner_num_; k++) {
scale_data[k] = std::max(scale_data[k],
bottom_data[i * dim + j * inner_num_ + k]);
}
}
// subtraction
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_,
1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data);
// exponentiation
caffe_exp<Dtype>(dim, top_data, top_data);
// sum after exp
caffe_cpu_gemv<Dtype>(CblasTrans, channels, inner_num_, 1.,
top_data, sum_multiplier_.cpu_data(), 0., scale_data);
// division
for (int j = 0; j < channels; j++) {
caffe_div(inner_num_, top_data, scale_data, top_data);
top_data += inner_num_;
}
}
#endif
}
protected:
int _axis;
int _size;
float* scale = new float [3462]; //scale
int inner_num_;
int outer_num_;
};
# if 0
__global__ void ReLUForward1(const int n, const float* in, float* out,
float negative_slope) {
CUDA_KERNEL_LOOP(index, n) {
printf("ReLUForwardsss 01 enqueue line=%d",__LINE__);
out[index] = in[index] > 0 ? in[index] : in[index] * negative_slope;
}
}
#endif
//SSD Flatten layer
class LReluLayer : public IPlugin
{
public:
LReluLayer(){}
LReluLayer(float para):para_(para)
{
std::cout<<"LReluLayer0"<<std::endl;
}
LReluLayer(const void* buffer,size_t sz, float para):para_(para)
{
assert(sz == 4 * sizeof(int));
const int* d = reinterpret_cast<const int*>(buffer);
float* p=(float*)(d+3);
para_=p[0];
channel_=d[0];
w_=d[1];
h_=d[2];
//std::cout<<"LReluLayer1"<<para_ <<" " <<channel_<<" "<<w_ <<" "<<h_ <<std::endl;
para_=0.1;
}
inline int getNbOutputs() const override { return 1; };
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override
{
std::cout<<"getOutputDimensions channel"<<inputs[0].d[0]<<"h:"<<inputs[0].d[1]<<"w:"<<inputs[0].d[2]<<std::endl;
channel_=inputs[0].d[0];
w_=inputs[0].d[1];
h_=inputs[0].d[2];
return DimsCHW(inputs[0].d[0], inputs[0].d[1] , inputs[0].d[2] );
}
int initialize() override
{
return 0;
}
inline void terminate() override
{
}
inline size_t getWorkspaceSize(int) const override { return 0; }
int enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) override
{
// std::cout<<"LReluLayer1 enqueue : "<<batchSize<<"c:"<<channel_<<"w:"<<w_<<"h:"<<h_<<"para_"<<para_<<std::endl;
ReluForward_gpu((const float*)inputs[0],(float*)outputs[0],batchSize,channel_,w_,h_,para_);
//ReLUForward1<<<CAFFE_GET_BLOCKS(batchSize*channel_*w_*h_), CAFFE_CUDA_NUM_THREADS>>>(batchSize*channel_*w_*h_,inputs[0],outputs[0],para_);
//std::cout<<"flatten enqueue:"<<batchSize<<";"<<_size<<std::endl;
//CHECK(cudaMemcpyAsync(outputs[0],inputs[0],batchSize*_size*sizeof(float),cudaMemcpyDeviceToDevice,stream));
//Forward_gpu (
// (float*)inputs[0],1,channel_, w_, h_, stride_, (float*)outputs[0] );
//CHECK(cudaMemcpyAsync(outputs[0], inputs[0], mCopySize * batchSize, cudaMemcpyDeviceToDevice, stream));
return 0;
}
size_t getSerializationSize() override
{
return sizeof(int)*3+sizeof(float);
}
void serialize(void* buffer) override
{
//
//write(q+3, (float)para_);
float* q = reinterpret_cast<float*>(buffer);
int* d = reinterpret_cast<int*>(buffer);
d[0] = channel_; d[1] = w_; d[2] = h_;
q[4]=para_;
//serializeFromDevice(d, mKernelWeights);
//serializeFromDevice(d, mBiasWeights);
}
void configure(const Dims*inputs, int nbInputs, const Dims* outputs, int nbOutputs, int) override
{
// dimBottom = DimsCHW(inputs[0].d[0], inputs[0].d[1], inputs[0].d[2]);
channel_=inputs[0].d[0];
w_=inputs[0].d[1];
h_=inputs[0].d[2];
}
protected:
float para_;
int channel_;
int w_;
int h_;
};
//SSD Flatten layer
class UpsampleLayer : public IPlugin
{
public:
UpsampleLayer(){}
UpsampleLayer(size_t stride):stride_(stride)
{
std::cout<<"UpsampleLayer0"<<std::endl;
}
UpsampleLayer(const void* buffer,size_t sz, size_t stride):stride_(stride)
{
const int* d = reinterpret_cast<const int*>(buffer);
channel_=d[0];
w_=d[1];
h_=d[2];
std::cout<<"UpsampleLayer1"<<std::endl;
}
inline int getNbOutputs() const override { return 1; };
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override
{
std::cout<<"channel"<<inputs[0].d[0]<<"h:"<<inputs[0].d[1]<<"w:"<<inputs[0].d[2]<<std::endl;
channel_=inputs[0].d[0];
w_=inputs[0].d[1];
h_=inputs[0].d[2];
return DimsCHW(inputs[0].d[0], inputs[0].d[1]*stride_, inputs[0].d[2]*stride_);
}
int initialize() override
{
return 0;
}
inline void terminate() override
{
}
inline size_t getWorkspaceSize(int) const override { return 0; }
int enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) override
{
// std::cout<<"UpsampleLayer1 enqueue"<<std::endl;
//std::cout<<"flatten enqueue:"<<batchSize<<";"<<_size<<std::endl;
//CHECK(cudaMemcpyAsync(outputs[0],inputs[0],batchSize*_size*sizeof(float),cudaMemcpyDeviceToDevice,stream));
Forward_gpu((float*)inputs[0],1,channel_, w_, h_, stride_, (float*)outputs[0] );
return 0;
}
size_t getSerializationSize() override
{
return 4*sizeof(int);
}
void serialize(void* buffer) override
{
int* d = reinterpret_cast<int*>(buffer);
d[0] = channel_; d[1] = w_; d[2] = h_;
d[3]=stride_;
}
void configure(const Dims*inputs, int nbInputs, const Dims* outputs, int nbOutputs, int) override
{
// dimBottom = DimsCHW(inputs[0].d[0], inputs[0].d[1], inputs[0].d[2]);
channel_=inputs[0].d[0];
w_=inputs[0].d[1];
h_=inputs[0].d[2];
}
protected:
int stride_;
int channel_;
int w_;
int h_;
};
//SSD Flatten layer
class FlattenLayer : public IPlugin
{
public:
FlattenLayer(){}
FlattenLayer(const void* buffer,size_t size)
{
assert(size == 3 * sizeof(int));
const int* d = reinterpret_cast<const int*>(buffer);
_size = d[0] * d[1] * d[2];
dimBottom = DimsCHW{d[0], d[1], d[2]};
}
inline int getNbOutputs() const override { return 1; };
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override
{
assert(1 == nbInputDims);
assert(0 == index);
assert(3 == inputs[index].nbDims);
_size = inputs[0].d[0] * inputs[0].d[1] * inputs[0].d[2];
return DimsCHW(_size, 1, 1);
}
int initialize() override
{
return 0;
}
inline void terminate() override
{
}
inline size_t getWorkspaceSize(int) const override { return 0; }
int enqueue(int batchSize, const void*const *inputs, void** outputs, void*, cudaStream_t stream) override
{
std::cout<<"flatten enqueue:"<<batchSize<<";"<<_size<<std::endl;
CHECK(cudaMemcpyAsync(outputs[0],inputs[0],batchSize*_size*sizeof(float),cudaMemcpyDeviceToDevice,stream));
return 0;
}
size_t getSerializationSize() override
{
return 3 * sizeof(int);
}
void serialize(void* buffer) override
{
int* d = reinterpret_cast<int*>(buffer);
d[0] = dimBottom.c(); d[1] = dimBottom.h(); d[2] = dimBottom.w();
}
void configure(const Dims*inputs, int nbInputs, const Dims* outputs, int nbOutputs, int) override
{
dimBottom = DimsCHW(inputs[0].d[0], inputs[0].d[1], inputs[0].d[2]);
}
protected:
DimsCHW dimBottom;
int _size;
};
class PluginFactory : public nvinfer1::IPluginFactory, public nvcaffeparser1::IPluginFactory {
public:
// caffe parser plugin implementation
bool isPlugin(const char* name) override
{
printf("isPlugin %s\n",name);
return (!strcmp(name, "conv11_mbox_loc_perm")
|| !strcmp(name, "conv11_mbox_conf_flat")
|| !strcmp(name, "conv11_mbox_conf_perm")
|| !strcmp(name, "conv11_mbox_loc_flat")
|| !strcmp(name, "conv11_mbox_priorbox")
|| !strcmp(name, "conv13_mbox_loc_perm")
|| !strcmp(name, "conv13_mbox_loc_flat")
|| !strcmp(name, "conv13_mbox_conf_perm")
|| !strcmp(name, "conv13_mbox_conf_flat")
|| !strcmp(name, "conv13_mbox_priorbox")
|| !strcmp(name, "conv14_2_mbox_loc_perm")
|| !strcmp(name, "conv14_2_mbox_loc_flat")
|| !strcmp(name, "conv14_2_mbox_conf_perm")
|| !strcmp(name, "conv14_2_mbox_conf_flat")
|| !strcmp(name, "conv14_2_mbox_priorbox")
|| !strcmp(name, "conv15_2_mbox_loc_perm")
|| !strcmp(name, "conv15_2_mbox_loc_flat")
|| !strcmp(name, "conv15_2_mbox_conf_perm")
|| !strcmp(name, "conv15_2_mbox_conf_flat")
|| !strcmp(name, "conv15_2_mbox_priorbox")
|| !strcmp(name, "conv16_2_mbox_loc_perm")
|| !strcmp(name, "conv16_2_mbox_loc_flat")
|| !strcmp(name, "conv16_2_mbox_conf_perm")
|| !strcmp(name, "conv16_2_mbox_conf_flat")
|| !strcmp(name, "conv16_2_mbox_priorbox")
|| !strcmp(name, "conv17_2_mbox_loc_perm")
|| !strcmp(name, "conv17_2_mbox_loc_flat")
|| !strcmp(name, "conv17_2_mbox_conf_perm")
|| !strcmp(name, "conv17_2_mbox_conf_flat")
|| !strcmp(name, "conv17_2_mbox_priorbox")
|| !strcmp(name, "mbox_conf_reshape")
|| !strcmp(name, "mbox_conf_flatten")
|| !strcmp(name, "detection_out")
|| !strcmp(name, "mbox_loc")
|| !strcmp(name, "mbox_conf")
|| !strcmp(name, "mbox_priorbox")
|| !strcmp(name, "mbox_conf_softmax")
|| !strcmp(name, "layer85-upsample")
|| !strcmp(name, "layer97-upsample")
|| !strcmp(name, "layer104-act")
|| !strcmp(name, "layer28-act")
|| !strcmp(name, "layer0-act")
|| !strcmp(name, "layer2-act")
|| !strcmp(name, "layer1-act")
|| !strcmp(name, "layer103-act")
|| !strcmp(name, "layer102-act")
|| !strcmp(name, "layer101-act")
|| !strcmp(name, "layer100-act")
|| !strcmp(name, "layer99-act")
|| !strcmp(name, "layer96-act")
|| !strcmp(name, "layer92-act")
|| !strcmp(name, "layer91-act")
|| !strcmp(name, "layer90-act")
|| !strcmp(name, "layer89-act")
|| !strcmp(name, "layer88-act")
|| !strcmp(name, "layer87-act")
|| !strcmp(name, "layer84-act")
|| !strcmp(name, "layer80-act")
|| !strcmp(name, "layer79-act")
|| !strcmp(name, "layer78-act")
|| !strcmp(name, "layer77-act")
|| !strcmp(name, "layer76-act")
|| !strcmp(name, "layer75-act")
|| !strcmp(name, "layer73-act")
|| !strcmp(name, "layer72-act")
|| !strcmp(name, "layer70-act")
|| !strcmp(name, "layer69-act")
|| !strcmp(name, "layer67-act")
|| !strcmp(name, "layer66-act")
|| !strcmp(name, "layer64-act")
|| !strcmp(name, "layer63-act")
|| !strcmp(name, "layer62-act")
|| !strcmp(name, "layer60-act")
|| !strcmp(name, "layer59-act")
|| !strcmp(name, "layer57-act")
|| !strcmp(name, "layer56-act")
|| !strcmp(name, "layer54-act")
|| !strcmp(name, "layer53-act")
//
|| !strcmp(name, "layer51-act")
|| !strcmp(name, "layer50-act")
|| !strcmp(name, "layer48-act")
|| !strcmp(name, "layer47-act")
|| !strcmp(name, "layer45-act")
|| !strcmp(name, "layer44-act")
|| !strcmp(name, "layer42-act")
|| !strcmp(name, "layer41-act")
|| !strcmp(name, "layer39-act")
|| !strcmp(name, "layer38-act")
|| !strcmp(name, "layer37-act")
|| !strcmp(name, "layer35-act")
|| !strcmp(name, "layer34-act")
|| !strcmp(name, "layer32-act")
|| !strcmp(name, "layer31-act")
|| !strcmp(name, "layer29-act")
|| !strcmp(name, "layer26-act")
|| !strcmp(name, "layer25-act")
|| !strcmp(name, "layer23-act")
|| !strcmp(name, "layer22-act")
|| !strcmp(name, "layer20-act")
|| !strcmp(name, "layer19-act")
|| !strcmp(name, "layer17-act")
|| !strcmp(name, "layer16-act")
|| !strcmp(name, "layer14-act")
|| !strcmp(name, "layer13-act")
|| !strcmp(name, "layer12-act")
|| !strcmp(name, "layer10-act")
|| !strcmp(name, "layer9-act")
|| !strcmp(name, "layer7-act")
|| !strcmp(name, "layer6-act")
|| !strcmp(name, "layer5-act")
|| !strcmp(name, "layer3-act")
);
}
virtual IPlugin* createPlugin(const char* layerName, const Weights* weights, int nbWeights) override
{
// there's no way to pass parameters through from the model definition, so we have to define it here explicitly
if(!strcmp(layerName, "conv4_3_norm")){
//INvPlugin * plugin::createSSDNormalizePlugin (const Weights *scales, bool acrossSpatial, bool channelShared, float eps)
_nvPlugins[layerName] = plugin::createSSDNormalizePlugin(weights,false,false,1e-10);
return _nvPlugins.at(layerName);
}else if(!strcmp(layerName, "conv11_mbox_loc_perm")
|| !strcmp(layerName, "conv11_mbox_conf_perm")
|| !strcmp(layerName, "conv13_mbox_loc_perm")
|| !strcmp(layerName,"conv13_mbox_conf_perm")
|| !strcmp(layerName,"conv14_2_mbox_loc_perm")
|| !strcmp(layerName,"conv14_2_mbox_conf_perm")
|| !strcmp(layerName,"conv15_2_mbox_loc_perm")
|| !strcmp(layerName,"conv15_2_mbox_conf_perm")
|| !strcmp(layerName,"conv16_2_mbox_loc_perm")
|| !strcmp(layerName,"conv16_2_mbox_conf_perm")
|| !strcmp(layerName,"conv17_2_mbox_loc_perm")
|| !strcmp(layerName,"conv17_2_mbox_conf_perm")
){
_nvPlugins[layerName] = plugin::createSSDPermutePlugin(Quadruple({0,2,3,1}));
return _nvPlugins.at(layerName);
} else if(!strcmp(layerName,"conv11_mbox_priorbox")){
plugin::PriorBoxParameters params = {0};
float minSize[1] = {60.0f};
//float maxSize[1] = {60.0f};
float aspectRatios[1] = {2.0f};
params.minSize = (float*)minSize;
//params.maxSize = (float*)maxSize;
params.aspectRatios = (float*)aspectRatios;
params.numMinSize = 1;
params.numMaxSize = 0;
params.numAspectRatios = 1;
params.flip = true;
params.clip = false;
params.variance[0] = 0.10000000149;
params.variance[1] = 0.10000000149;
params.variance[2] = 0.20000000298;
params.variance[3] = 0.20000000298;
params.imgH = 0;
params.imgW = 0;
params.stepH = 0.0f;
params.stepW = 0.0f;
params.offset = 0.5f;
_nvPlugins[layerName] = plugin::createSSDPriorBoxPlugin(params);
return _nvPlugins.at(layerName);
}else if(!strcmp(layerName,"conv13_mbox_priorbox")){
plugin::PriorBoxParameters params = {0};
float minSize[1] = {105.0f};
float maxSize[1] = {150.0f};
float aspectRatios[2] = {2.0f, 3.0f};
params.minSize = (float*)minSize;
params.maxSize = (float*)maxSize;
params.aspectRatios = (float*)aspectRatios;
params.numMinSize = 1;
params.numMaxSize = 1;
params.numAspectRatios = 2;
params.flip = true;
params.clip = false;
params.variance[0] = 0.10000000149;
params.variance[1] = 0.10000000149;
params.variance[2] = 0.20000000298;
params.variance[3] = 0.20000000298;
params.imgH = 0;
params.imgW = 0;
params.stepH = 0.0f;
params.stepW = 0.0f;
params.offset = 0.5f;
_nvPlugins[layerName] = plugin::createSSDPriorBoxPlugin(params);
return _nvPlugins.at(layerName);
}else if(!strcmp(layerName,"conv14_2_mbox_priorbox")){
plugin::PriorBoxParameters params = {0};
float minSize[1] = {150.0f};
float maxSize[1] = {195.0f};
float aspectRatios[2] = {2.0f, 3.0f};
params.minSize = (float*)minSize;
params.maxSize = (float*)maxSize;
params.aspectRatios = (float*)aspectRatios;
params.numMinSize = 1;
params.numMaxSize = 1;
params.numAspectRatios = 2;
params.flip = true;
params.clip = false;
params.variance[0] = 0.10000000149;
params.variance[1] = 0.10000000149;
params.variance[2] = 0.20000000298;
params.variance[3] = 0.20000000298;
params.imgH = 0;
params.imgW = 0;
params.stepH = 0.0f;
params.stepW = 0.0f;
params.offset = 5.0f;
_nvPlugins[layerName] = plugin::createSSDPriorBoxPlugin(params);
return _nvPlugins.at(layerName);
}else if(!strcmp(layerName,"conv15_2_mbox_priorbox")){
plugin::PriorBoxParameters params = {0};
float minSize[1] = {195.0f};
float maxSize[1] = {240.0f};
float aspectRatios[2] = {2.0f, 3.0f};
params.minSize = (float*)minSize;
params.maxSize = (float*)maxSize;
params.aspectRatios = (float*)aspectRatios;
params.numMinSize = 1;
params.numMaxSize = 1;
params.numAspectRatios = 2;
params.flip = true;
params.clip = false;
params.variance[0] = 0.10000000149;
params.variance[1] = 0.10000000149;
params.variance[2] = 0.20000000298;
params.variance[3] = 0.20000000298;
params.imgH = 0;
params.imgW = 0;
params.stepH = 0.0f;
params.stepW = 0.0f;
params.offset = 0.5f;
_nvPlugins[layerName] = plugin::createSSDPriorBoxPlugin(params);
return _nvPlugins.at(layerName);
}else if(!strcmp(layerName,"conv16_2_mbox_priorbox")){
plugin::PriorBoxParameters params = {0};
float minSize[1] = {240.0f};
float maxSize[1] = {285.0f};
float aspectRatios[2] = {2.0f,3.0f};
params.minSize = (float*)minSize;
params.maxSize = (float*)maxSize;
params.aspectRatios = (float*)aspectRatios;
params.numMinSize = 1;
params.numMaxSize = 1;
params.numAspectRatios = 2;
params.flip = true;
params.clip = false;
params.variance[0] = 0.10000000149;
params.variance[1] = 0.10000000149;
params.variance[2] = 0.20000000298;
params.variance[3] = 0.20000000298;
params.imgH = 0;
params.imgW = 0;
params.stepH = 0.0f;
params.stepW = 0.0f;
params.offset = 0.5f;
_nvPlugins[layerName] = plugin::createSSDPriorBoxPlugin(params);
return _nvPlugins.at(layerName);
}else if(!strcmp(layerName,"conv17_2_mbox_priorbox")){
plugin::PriorBoxParameters params = {0};
float minSize[1] = {285.0f};
float maxSize[1] = {300.0f};
float aspectRatios[2] = {2.0f,3.0f};
params.minSize = (float*)minSize;
params.maxSize = (float*)maxSize;
params.aspectRatios = (float*)aspectRatios;
params.numMinSize = 1;
params.numMaxSize = 1;
params.numAspectRatios = 2;
params.flip = true;
params.clip = false;
params.variance[0] = 0.10000000149;
params.variance[1] = 0.10000000149;
params.variance[2] = 0.20000000298;
params.variance[3] = 0.20000000298;
params.imgH = 0;
params.imgW = 0;
params.stepH = 0.0f;
params.stepW = 0.0f;
params.offset = 0.5f;
_nvPlugins[layerName] = plugin::createSSDPriorBoxPlugin(params);
return _nvPlugins.at(layerName);
}else if(!strcmp(layerName,"detection_out")){
/*
bool shareLocation
bool varianceEncodedInTarget
int backgroundLabelId
int numClasses
int topK
int keepTopK
float confidenceThreshold
float nmsThreshold
CodeType_t codeType
*/
plugin::DetectionOutputParameters params = {0};
params.numClasses = 2;
params.shareLocation = true;
params.varianceEncodedInTarget = false;
params.backgroundLabelId = 0;
params.keepTopK = 100;
params.codeType = CENTER_SIZE;
params.nmsThreshold = 0.45;
params.topK = 100;
params.confidenceThreshold = 0.25;
std::cout << "detection_out..." << std::endl;
_nvPlugins[layerName] = plugin::createSSDDetectionOutputPlugin(params);
return _nvPlugins.at(layerName);
}else if (
!strcmp(layerName, "conv11_mbox_conf_flat")
||!strcmp(layerName,"conv11_mbox_loc_flat")
||!strcmp(layerName,"conv13_mbox_loc_flat")
||!strcmp(layerName,"conv13_mbox_conf_flat")
||!strcmp(layerName,"conv14_2_mbox_loc_flat")
||!strcmp(layerName,"conv14_2_mbox_conf_flat")
||!strcmp(layerName,"conv15_2_mbox_loc_flat")
||!strcmp(layerName,"conv15_2_mbox_conf_flat")
||!strcmp(layerName,"conv16_2_mbox_loc_flat")