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sparseConv3dlayer.cu
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#include "sparseConv3dlayer.h"
#include <fstream>
#include <iostream>
#include <iomanip> //设置输出格式
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <dirent.h>
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#include "params.h"
#include <time.h>
#include <chrono>
#include <cmath>
#include <string>
#include <string.h>
#include <malloc.h>
using namespace std::chrono;
using std::string;
using namespace nvinfer1;
using nvinfer1::SparseConv3dLayerPlugin;
using nvinfer1::SparseConv3dLayerPluginCreator;
using namespace std;
#define checkCudaErrors(status) \
{ \
if (status != 0) \
{ \
std::cout << "Cuda failure: " << cudaGetErrorString(status) \
<< " at line " << __LINE__ \
<< " in file " << __FILE__ \
<< " error status: " << status \
<< std::endl; \
abort(); \
} \
}
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
#define CUDA_MEM_ALIGN 256
static const char* PLUGIN_VERSION{"1"};
static const char* PLUGIN_NAME{"SparseConv3dLayerPlugin"};
// Static class fields initialization
PluginFieldCollection SparseConv3dLayerPluginCreator::mFC{};
std::vector<PluginField> SparseConv3dLayerPluginCreator::mPluginAttributes;
// Helper function for serializing plugin
template <typename T>
void writeToBuffer(char*& buffer, const T& val)
{
*reinterpret_cast<T*>(buffer) = val;
buffer += sizeof(T);
}
// Helper function for deserializing plugin
template <typename T>
T readFromBuffer(const char*& buffer)
{
T val = *reinterpret_cast<const T*>(buffer);
buffer += sizeof(T);
return val;
}
// Mimic np.round as in voxel generator in spconv implementation
int np_round(float x) {
// half way round to nearest-even
int x2 = int(x * 2.0f);
if(x != int(x) && x2 == x * 2.0f) {
return int(x / 2.0f + 0.5f) * 2;
}
return int(x + 0.5f);
}
// ALIGNPTR
int8_t* alignPtr(int8_t* ptr, uintptr_t to)
{
uintptr_t addr = (uintptr_t) ptr;
if (addr % to)
{
addr += to - addr % to;
}
return (int8_t*) addr;
}
// NEXTWORKSPACEPTR
int8_t* nextWorkspacePtr(int8_t* ptr, uintptr_t previousWorkspaceSize)
{
uintptr_t addr = (uintptr_t) ptr;
addr += previousWorkspaceSize;
return alignPtr((int8_t*) addr, CUDA_MEM_ALIGN);
}
// CALCULATE TOTAL WORKSPACE SIZE
size_t calculateTotalWorkspaceSize(size_t* workspaces, int count)
{
size_t total = 0;
for (int i = 0; i < count; i++)
{
total += workspaces[i];
if (workspaces[i] % CUDA_MEM_ALIGN)
{
total += CUDA_MEM_ALIGN - (workspaces[i] % CUDA_MEM_ALIGN);
}
}
return total;
}
__device__ void cuda_sleep(int64_t num_cycles)
{
int64_t cycles = 0;
int64_t start = clock64();
while(cycles < num_cycles)
{
cycles = clock64() - start;
}
}
SparseConv3dLayerPlugin::SparseConv3dLayerPlugin(
int in_channel, int out_channel,int max_voxels, int feature_num, int out_shape_x,int out_shape_y,int out_shape_z,
int spatial_shape_x, int spatial_shape_y, int spatial_shape_z,int ksize0,int ksize1, int ksize2,int stride0,int stride1,
int stride2, int padding0,int padding1,int padding2, int dilation, int out_padding, int weights_size, nvinfer1::Weights const& weights
) : in_channel_(in_channel), out_channel_(out_channel),max_voxels_(max_voxels), feature_num_(feature_num),
out_shape_x_(out_shape_x),out_shape_y_(out_shape_y),out_shape_z_(out_shape_z),
spatial_shape_x_(spatial_shape_x), spatial_shape_y_(spatial_shape_y), spatial_shape_z_(spatial_shape_z),ksize0_(ksize0),ksize1_(ksize1),ksize2_(ksize2),
stride0_(stride0),stride1_(stride1),stride2_(stride2), padding0_(padding0),padding1_(padding1),padding2_(padding2), dilation_(dilation), out_padding_(out_padding),weights_size_(weights_size)
{
weights_data_ = (float*)malloc(weights_size_*sizeof(float));
const float* temp_values = (const float*)weights.values;
for(int i = 0;i < weights_size_;i++)
{
weights_data_[i] = temp_values[i];
}
weights_.count = weights.count;
weights_.values = weights_data_;
// cudamalloc for conv
checkCudaErrors(cudaMalloc(&weights_dev_,sizeof(float)*ksize0_*ksize1_*ksize2_*in_channel_*out_channel_));
// copy to gpu
checkCudaErrors(cudaMemcpy(weights_dev_,weights_data_,sizeof(float)*ksize0_*ksize1_*ksize2_*in_channel_*out_channel_,cudaMemcpyHostToDevice));
}
SparseConv3dLayerPlugin::SparseConv3dLayerPlugin(const void* data, size_t length)
{
const char* d = reinterpret_cast<const char*>(data);
in_channel_ = readFromBuffer<int>(d);
out_channel_ = readFromBuffer<int>(d);
max_voxels_ = readFromBuffer<int>(d);
feature_num_ = readFromBuffer<int>(d);
out_shape_x_ = readFromBuffer<int>(d);
out_shape_y_ = readFromBuffer<int>(d);
out_shape_z_ = readFromBuffer<int>(d);
spatial_shape_x_ = readFromBuffer<int>(d);
spatial_shape_y_ = readFromBuffer<int>(d);
spatial_shape_z_ = readFromBuffer<int>(d);
ksize0_ = readFromBuffer<int>(d);
ksize1_ = readFromBuffer<int>(d);
ksize2_ = readFromBuffer<int>(d);
stride0_ = readFromBuffer<int>(d);
stride1_ = readFromBuffer<int>(d);
stride2_ = readFromBuffer<int>(d);
padding0_ = readFromBuffer<int>(d);
padding1_ = readFromBuffer<int>(d);
padding2_ = readFromBuffer<int>(d);
dilation_ = readFromBuffer<int>(d);
out_padding_ = readFromBuffer<int>(d);
weights_size_ = readFromBuffer<int>(d);
weights_.count = weights_size_;
weights_data_ = (float *)malloc(weights_size_*sizeof(float));
for(int i=0;i < weights_size_; i++)
{
weights_data_[i] = readFromBuffer<float>(d);
}
weights_.values = weights_data_;
// std::cout << "构造函数2 end" << std::endl;
// std::cout << in_channel_ << "," << out_channel_ << "," << max_voxels_ << "," << feature_num_ << "," << out_shape_x_ << "," <<
// out_shape_y_ << "," << out_shape_z_ << "," << spatial_shape_x_ << "," << spatial_shape_y_ << "," << spatial_shape_z_ <<
// "," << ksize0_ << "," << stride0_ << "," << padding0_ << "," << dilation_ << "," << out_padding_ << "," << weights_size_ << std::endl;
}
IPluginV2DynamicExt* SparseConv3dLayerPlugin::clone() const noexcept
{
auto* plugin = new SparseConv3dLayerPlugin(in_channel_, out_channel_,max_voxels_, feature_num_, out_shape_x_,out_shape_y_,out_shape_z_,
spatial_shape_x_, spatial_shape_y_, spatial_shape_z_,ksize0_,ksize1_,ksize2_,stride0_,stride1_,stride2_, padding0_,padding1_,padding2_, dilation_, out_padding_, weights_size_, weights_);
plugin->setPluginNamespace(mNamespace.c_str());
return plugin;
}
nvinfer1::DimsExprs SparseConv3dLayerPlugin::getOutputDimensions(
int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) noexcept
{
// assert(outputIndex >= 0 && outputIndex < this->getNbOutputs());
auto batch_size = inputs[0].d[0];
// int input_max_voxel_= inputs[0].d[1]->getConstantValue()*2;
// std::cout << input_max_voxel_ << "<<<<<<<<<<<<" << endl;
// auto batch_size = 1;
// std::cout << inputs[0].nbDims << " " << inputs[0].d[0]->getConstantValue() << " " << inputs[0].d[1]->getConstantValue() << " " << inputs[0].d[2]->getConstantValue() << std::endl;
// std::cout << inputs[1].nbDims << " " << inputs[1].d[0]->getConstantValue() << std::endl;
if (outputIndex == 0)
{
nvinfer1::DimsExprs dim0{};
dim0.nbDims = 3;
dim0.d[0] = batch_size;
dim0.d[1] = exprBuilder.constant(max_voxels_);
dim0.d[2] = exprBuilder.constant(out_channel_);
return dim0; // voxels 1 20000 4
}
if(outputIndex == 1){
nvinfer1::DimsExprs dim1{};
dim1.nbDims = 3;
dim1.d[0] = batch_size;
dim1.d[1] = exprBuilder.constant(max_voxels_);
dim1.d[2] = exprBuilder.constant(4);
return dim1; // coors 1 20000 4
}
nvinfer1::DimsExprs dim2{};
dim2.nbDims = 1;
dim2.d[0] = batch_size;
return dim2;
}
bool SparseConv3dLayerPlugin::supportsFormatCombination(
int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) noexcept
{
// PLUGIN_ASSERT(nbInputs == 2);
// PLUGIN_ASSERT(nbOutputs == 2);
const PluginTensorDesc& in = inOut[pos];
if (pos == 0) // voxelfeatures --- x, y, z, i dim: 1 20000 4
{
return (in.type == nvinfer1::DataType::kFLOAT) && (in.format == TensorFormat::kLINEAR);
}
if (pos == 1) // coors 1 20000 4
{
return (in.type == nvinfer1::DataType::kINT32) && (in.format == TensorFormat::kLINEAR);
}
if (pos == 2) // voxel_num
{
return (in.type == nvinfer1::DataType::kINT32) && (in.format == TensorFormat::kLINEAR);
}
if (pos == 3) // out_voxel_feature, dim: 1 x 20000 x out_channel
{
return (in.type == nvinfer1::DataType::kFLOAT) && (in.format == TensorFormat::kLINEAR);
}
if (pos == 4) // out_coors dim: 1x20000x4
{
return (in.type == nvinfer1::DataType::kINT32) && (in.format == TensorFormat::kLINEAR);
}
if (pos == 5) // out_voxel_num
{
return (in.type == nvinfer1::DataType::kINT32) && (in.format == TensorFormat::kLINEAR);
}
return false;
}
void SparseConv3dLayerPlugin::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) noexcept
{
}
size_t SparseConv3dLayerPlugin::getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const noexcept
{
int batchSize = inputs[0].dims.d[0];
int dense_voxel_num = out_shape_x_ * out_shape_y_ * out_shape_z_;
size_t grids_out_size = batchSize * dense_voxel_num * sizeof(int); // 41*1600*1408 -1
size_t indice_pairs_size = batchSize * ksize0_ * ksize1_ * ksize2_ * MAX_VOXELS * 2 * sizeof(int); // 27,2,16825 -1
size_t indice_num_size = batchSize * ksize0_ * ksize1_ * ksize2_ * sizeof(unsigned int); // 27 ,0
size_t valid_points_size = batchSize * MAX_VOXELS *27*4 * sizeof(unsigned int); // 20000*27*4
size_t valid_points_num_size = batchSize * MAX_VOXELS * sizeof(unsigned int);// 20000*1;
size_t workspaces[5];
workspaces[0] = grids_out_size;
workspaces[1] = indice_pairs_size;
workspaces[2] = indice_num_size;
workspaces[3] = valid_points_size;
workspaces[4] = valid_points_num_size;
return calculateTotalWorkspaceSize(workspaces, 5);
}
__global__ void getValidOutPosKernel(unsigned int *input_coords, unsigned int * valid_points,unsigned int* valid_points_num,
unsigned int *voxel_num,int ksize0,int ksize1,int ksize2,int stride0,int stride1,
int stride2, int padding0,int padding1,int padding2,
int dilation, int out_shape_x, int out_shape_y, int out_shape_z)
{
int voxelidx = blockIdx.x * blockDim.x + threadIdx.x;
if(voxelidx >= *voxel_num) return;
uint4 coords = ((uint4*)input_coords)[voxelidx];
// getValidOutPos
int NDim = 3;
int coords_[3] = {coords.y,coords.z,coords.w};
int ksize_[3] = {ksize0,ksize1,ksize2};
int stride_[3] = {stride0,stride1,stride2};
int padding_[3] = {padding0,padding1,padding2};
int dilation_[3] = {dilation,dilation,dilation};
int out_shape[3] = {out_shape_z,out_shape_y,out_shape_x};
int lowers[3] = {0,0,0};
int uppers[3] = {0,0,0};
int counter[3] = {0,0,0};
int counter_size[3] = {0,0,0};
int point_counter = 0;
int val = 0;
int num_points = 1;
int m = 1;
int offset = 0;
int valid = 0;
for (int i = 0; i < NDim; i++) {
lowers[i] = (coords_[i] - (ksize_[i] - 1) * dilation_[i] - 1 +
stride_[i] + padding_[i]) / stride_[i];
uppers[i] = (coords_[i] + padding_[i]) / stride_[i];
}
for (int i = 0; i < NDim; i++) {
counter_size[i] = ((uppers[i] - lowers[i]) / dilation_[i] + 1);
num_points *= counter_size[i];
}
for (int i = 0; i < num_points; i++) {
valid = 1;
m = 1;
offset = 0;
for (int j = NDim - 1; j >= 0; j--)
{ // 2,1,0
val = uppers[j] - counter[j] * dilation_[j];
atomicExch(valid_points+voxelidx*(27*4) + point_counter * 4 + j,val);
if (val < 0 || (val > out_shape[j] - 1)) {
valid = 0;
}
offset += m * (coords_[j] - val * stride_[j] + padding_[j]) / dilation_[j];
m *= ksize_[j];
}
atomicExch(valid_points + voxelidx*(27*4) +point_counter * 4 + NDim,offset);
if (valid) point_counter += 1;
counter[NDim - 1] += 1;
for (int c = NDim - 1; c >= 0; c--)
{
if (counter[c] == counter_size[c] && c > 0)
{
counter[c - 1] += 1;
counter[c] = 0;
}
}
}
atomicExch(valid_points_num+voxelidx,point_counter);
}
cudaError_t getValidOutPosKernel_launch(unsigned int *input_coords, unsigned int * valid_points,unsigned int* valid_points_num,
unsigned int *voxel_num,int ksize0,int ksize1,int ksize2,int stride0,
int stride1,int stride2, int padding0, int padding1,int padding2,
int dilation, int out_shape_x, int out_shape_y, int out_shape_z,cudaStream_t stream)
{
int threadNum = THREADS_FOR_VOXEL;
dim3 blocks((MAX_VOXELS+threadNum-1)/threadNum);
dim3 threads(threadNum);
getValidOutPosKernel<<<blocks, threads, 0, stream>>>
(input_coords, valid_points,valid_points_num,
voxel_num,ksize0,ksize1,ksize2,stride0,stride1,stride2, padding0,padding1,padding2,dilation,out_shape_x, out_shape_y, out_shape_z);
cudaError_t err = cudaGetLastError();
return err;
}
__global__ void getSparseIndicePairsKernel(unsigned int *input_coords, int * grids_out, int *indice_pairs, unsigned int *indice_num,
unsigned int * valid_points, unsigned int * valid_points_num, unsigned int *voxel_num,int out_shape_x,
int out_shape_y, int out_shape_z,unsigned int *output_coords, unsigned int *output_voxel_num)
{
int voxelidx = blockIdx.x * blockDim.x + threadIdx.x;
if(voxelidx >= *voxel_num) return;
int index = 0;
uint4 coords = ((uint4*)input_coords)[voxelidx];
unsigned int index_z = 0;
unsigned int index_y = 0;
unsigned int index_x = 0;
unsigned int offset = 0;
int point_counter = valid_points_num[voxelidx];
for (int i = 0; i < point_counter; ++i)
{
index_z = *(valid_points + voxelidx*(27*4)+i*4+0);
index_y = *(valid_points + voxelidx*(27*4)+i*4+1);
index_x = *(valid_points + voxelidx*(27*4)+i*4+2);
offset = *(valid_points + voxelidx*(27*4)+i*4+3);
index = coords.x * out_shape_x * out_shape_y * out_shape_z +
index_z * out_shape_y * out_shape_x +
index_y * out_shape_x +
index_x;
if(grids_out[index] != -1)
{
atomicExch(output_coords + grids_out[index] * 4 + 1,index_z);
atomicExch(output_coords + grids_out[index] * 4 + 2,index_y);
atomicExch(output_coords + grids_out[index] * 4 + 3,index_x);
atomicExch(output_coords + grids_out[index] * 4 + 0,0); // batch_size == 1;
}
unsigned int old_num = atomicAdd(indice_num + offset, 1);
int * indice_pairs_address_1 = indice_pairs + offset*(2*MAX_VOXELS) + 1 * MAX_VOXELS + old_num;
int old_1 = atomicExch(indice_pairs_address_1,grids_out[index]); // output index
int * indice_pairs_address_0 = indice_pairs + offset*(2*MAX_VOXELS) + 0 * MAX_VOXELS + old_num;
int old_0 = atomicExch(indice_pairs_address_0,voxelidx); // input index
}
}
cudaError_t getSparseIndicePairsKernel_launch(unsigned int *input_coords, int * grids_out, int *indice_pairs,
unsigned int *indice_num,unsigned int * valid_points, unsigned int *valid_points_num,
unsigned int *voxel_num,int out_shape_x,
int out_shape_y, int out_shape_z,
unsigned int *output_coords, unsigned int *output_voxel_num,
cudaStream_t stream)
{
int threadNum = THREADS_FOR_VOXEL;
dim3 blocks((MAX_VOXELS+threadNum-1)/threadNum);
dim3 threads(threadNum);
getSparseIndicePairsKernel<<<blocks, threads, 0, stream>>>
(input_coords, grids_out, indice_pairs, indice_num,valid_points,valid_points_num, voxel_num,out_shape_x,out_shape_y,out_shape_z,
output_coords, output_voxel_num);
cudaError_t err = cudaGetLastError();
return err;
}
__global__ void fillGridKernel1(unsigned int *input_coords, int * grids_out,
unsigned int * valid_points, unsigned int * valid_points_num, unsigned int *voxel_num,int out_shape_x,
int out_shape_y, int out_shape_z)
{
int voxelidx = blockIdx.x * blockDim.x + threadIdx.x;
if(voxelidx >= *voxel_num) return;
int index = 0;
uint4 coords = ((uint4*)input_coords)[voxelidx];
unsigned int index_z = 0;
unsigned int index_y = 0;
unsigned int index_x = 0;
unsigned int offset = 0;
int point_counter = valid_points_num[voxelidx];
for (int i = 0; i < point_counter; ++i)
{
index_z = *(valid_points + voxelidx*(27*4)+i*4+0);
index_y = *(valid_points + voxelidx*(27*4)+i*4+1);
index_x = *(valid_points + voxelidx*(27*4)+i*4+2);
offset = *(valid_points + voxelidx*(27*4)+i*4+3);
index = coords.x * out_shape_x * out_shape_y * out_shape_z +
index_z * out_shape_y * out_shape_x +
index_y * out_shape_x +
index_x;
if(grids_out[index] == -1)
{
atomicExch(grids_out+index,1);
}
}
}
cudaError_t fillGridKernel_launch1(unsigned int *input_coords, int * grids_out,unsigned int * valid_points, unsigned int *valid_points_num,
unsigned int *voxel_num,int out_shape_x,
int out_shape_y, int out_shape_z,
cudaStream_t stream)
{
int threadNum = THREADS_FOR_VOXEL;
dim3 blocks((MAX_VOXELS+threadNum-1)/threadNum);
dim3 threads(threadNum);
fillGridKernel1<<<blocks, threads, 0, stream>>>
(input_coords, grids_out,valid_points,valid_points_num, voxel_num,out_shape_x,out_shape_y,out_shape_z);
cudaError_t err = cudaGetLastError();
return err;
}
__global__ void fillGridKernel2(int * grids_out,
unsigned int * output_voxel_num,int out_shape_x,
int out_shape_y, int out_shape_z)
{
int voxelidx = blockIdx.x * blockDim.x + threadIdx.x;
if (grids_out[voxelidx] == 1)
{
int old_value = atomicAdd(output_voxel_num,1);
grids_out[voxelidx] = old_value;
}
}
cudaError_t fillGridKernel_launch2(int * grids_out, unsigned int *output_voxel_num,
int out_shape_x,int out_shape_y, int out_shape_z,
cudaStream_t stream)
{
int threadNum = THREADS_FOR_VOXEL;
dim3 blocks((out_shape_x * out_shape_y*out_shape_z+threadNum-1)/threadNum);
dim3 threads(threadNum);
fillGridKernel2<<<blocks, threads, 0, stream>>>
(grids_out,output_voxel_num,out_shape_x,out_shape_y,out_shape_z);
cudaError_t err = cudaGetLastError();
return err;
}
__global__ void sparseGatherConvScatter(float* input_voxel_features,int *indice_pairs,unsigned int *indice_num,
float *output_voxel_features,int kernel_volume_index,float* weights_dev_,int in_channel, int out_channel)
{
int act_idx = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int num_act = indice_num[kernel_volume_index];
if(act_idx >= num_act) return;
int input_index = *(indice_pairs + kernel_volume_index * 2*MAX_VOXELS + 0*MAX_VOXELS + act_idx);
// // get kernel in act_idx positon
int kernel_num = in_channel * out_channel;
// get position for output
int output_index = *(indice_pairs + kernel_volume_index * 2 * MAX_VOXELS + 1*MAX_VOXELS + act_idx);
for(int i=0; i < out_channel; i++)
{
float current_sum = 0.0;
for(int j=0;j<in_channel;j++)
{
current_sum += input_voxel_features[input_index * in_channel + j] * weights_dev_[kernel_volume_index * kernel_num+i+j*out_channel];
}
float old_value = atomicAdd(output_voxel_features + output_index * out_channel + i,current_sum);
}
}
cudaError_t sparseGatherConvScatter_launch(float* input_voxel_features,int *indice_pairs,unsigned int *indice_num,
float *output_voxel_features,int kernel_volume_index,float* weights_dev_,
int in_channel, int out_channel, cudaStream_t stream)
{
int threadNum = THREADS_FOR_VOXEL;
dim3 blocks((MAX_VOXELS+threadNum-1)/threadNum);
dim3 threads(threadNum);
sparseGatherConvScatter<<<blocks, threads, 0, stream>>>
(input_voxel_features,indice_pairs,indice_num,output_voxel_features,kernel_volume_index,weights_dev_,in_channel,out_channel);
cudaError_t err = cudaGetLastError();
return err;
}
int SparseConv3dLayerPlugin::enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) noexcept
{
// std::cout << "enqueue start" << std::endl;
int batchSize = inputDesc[0].dims.d[0];
//TRT-input
// std::cout << "batch_size: " << batchSize << std::endl;
float *input_voxel_features = const_cast<float *>((const float*)inputs[0]);
unsigned int *input_coords = const_cast<unsigned int *>((const unsigned int*)inputs[1]);
unsigned int * voxel_num = const_cast<unsigned int *>((const unsigned int *)inputs[2]);
//TRT-output
float *output_voxel_features = (float *)(outputs[0]); // 1 * 20000 * 27 * 32
unsigned int *output_coords = (unsigned int *)(outputs[1]); // 1* 20000*27*4
unsigned int *output_voxel_num = (unsigned int *)(outputs[2]); // 1
int dense_voxel_num = out_shape_x_ * out_shape_y_ * out_shape_z_;
size_t grids_out_size = batchSize * dense_voxel_num * sizeof(int); // 41*1600*1408 -1
size_t indice_pairs_size = batchSize * ksize0_ * ksize1_ * ksize2_ * MAX_VOXELS * 2 * sizeof(int); // 27,2,16825 -1
size_t indice_num_size = batchSize * ksize0_ * ksize1_ * ksize2_ * sizeof(unsigned int); // 27 ,0
size_t valid_points_size = batchSize * MAX_VOXELS * 27*4 * sizeof(unsigned int); // 20000*256*4
size_t valid_points_num_size = batchSize * MAX_VOXELS * sizeof(unsigned int);// 20000*1;
// std::cout << out_shape_x_ << "," << out_shape_y_ << "," << out_shape_z_ << "," << ksize0_ << "," << max_voxels_ << "," << out_channel_ << std::endl;
size_t workspaces[5];
workspaces[0] = grids_out_size;
workspaces[1] = indice_pairs_size;
workspaces[2] = indice_num_size;
workspaces[3] = valid_points_size;
workspaces[4] = valid_points_num_size;
size_t total_workspace = calculateTotalWorkspaceSize(workspaces, 5);
int* grids_out = static_cast<int*>(workspace);
int* indice_pairs = reinterpret_cast<int*>(
nextWorkspacePtr(reinterpret_cast<int8_t*>(grids_out), grids_out_size));
unsigned int* indice_num = reinterpret_cast<unsigned int*>(
nextWorkspacePtr(reinterpret_cast<int8_t*>(indice_pairs), indice_pairs_size));
unsigned int* valid_points = reinterpret_cast<unsigned int*>(
nextWorkspacePtr(reinterpret_cast<int8_t*>(indice_num), indice_num_size));
unsigned int* valid_points_num = reinterpret_cast<unsigned int*>(
nextWorkspacePtr(reinterpret_cast<int8_t*>(valid_points), valid_points_size));
// Initialize workspace memory
checkCudaErrors(cudaMemsetAsync(grids_out, -1, grids_out_size, stream)); // total_workspace
checkCudaErrors(cudaMemsetAsync(indice_pairs, -1, indice_pairs_size, stream)); // total_workspace
checkCudaErrors(cudaMemsetAsync(indice_num, 0, indice_num_size, stream)); // total_workspace
checkCudaErrors(cudaMemsetAsync(valid_points, 0, valid_points_size, stream)); // total_workspace
checkCudaErrors(cudaMemsetAsync(valid_points_num, 0, valid_points_num_size, stream)); // total_workspace
// FOR tensorrt output
unsigned int output_voxel_features_size = batchSize * max_voxels_ * out_channel_ * sizeof(float);
unsigned int output_coords_size = batchSize * max_voxels_ * 4 * sizeof(unsigned int);
unsigned int output_voxel_num_size = batchSize * sizeof(unsigned int);
// unsigned int params_data_size = batchSize * sizeof(unsigned int);
checkCudaErrors(cudaMemsetAsync(output_voxel_features, 0, output_voxel_features_size, stream));
checkCudaErrors(cudaMemsetAsync(output_coords, 0, output_coords_size, stream));
checkCudaErrors(cudaMemsetAsync(output_voxel_num, 0, output_voxel_num_size, stream));
// unsigned int voxel_num_cpu = 0;
// float * voxel_features_cpu = (float*)malloc(sizeof(float)*20000*4);
// unsigned int * coors_cpu = (unsigned int*)malloc(sizeof(unsigned int)*20000*4);
// CHECK(cudaMemcpyAsync(&voxel_num_cpu, voxel_num, 1 * sizeof(unsigned int), cudaMemcpyDeviceToHost, stream));
// CHECK(cudaMemcpyAsync(voxel_features_cpu, input_voxel_features, 1 * 20000 * 4* sizeof(float), cudaMemcpyDeviceToHost, stream));
// CHECK(cudaMemcpyAsync(coors_cpu, input_coords, 1 * 20000 * 4* sizeof(unsigned int), cudaMemcpyDeviceToHost, stream));
// string save_path = "./cuda_000000.txt";
// std::ofstream out_txt_file;
// out_txt_file.open(save_path.c_str(),ios::out | ios::trunc);
// // out_txt_file << fixed;
// out_txt_file << setiosflags(ios::fixed) << setprecision(6);
// int seconds = 1;
// out_txt_file << seconds << std::endl;
// for(int i=0;i < voxel_num_cpu; i++)
// {
// for(int j=0;j<4;j++)
// {
// out_txt_file << *(voxel_features_cpu + i * 4+j) << ",";
// }
// out_txt_file << *(coors_cpu + i * 4) << "," << *(coors_cpu + i * 4+1) << ","
// << *(coors_cpu + i * 4 + 2) << "," << *(coors_cpu+i*4+3) << std::endl;;
// // std::cout << "index : " << i << " " << "voxel: " << *(voxel_feature + i * 4) << "," << *(voxel_feature + i * 4+1) << ","
// // << *(voxel_feature + i * 4+2) << "," << *(voxel_feature + i * 4+3) << ",||||"<< *(coors + i * 4) << "," << *(coors + i * 4+1) << ","
// // << *(coors + i * 4 + 2) << "," << *(coors+i*4+3)
// // << std::endl;
// }
// out_txt_file.close();
// // return 0;
// free(voxel_features_cpu);
// free(coors_cpu);
checkCudaErrors(getValidOutPosKernel_launch(input_coords, valid_points,valid_points_num,
voxel_num,ksize0_,ksize1_,ksize2_,stride0_,stride1_,stride2_, padding0_,padding1_,padding2_, dilation_, out_shape_x_,out_shape_y_, out_shape_z_,stream));
// // c++ fill grid
// // create memory
// // int * grids_out,unsigned int * valid_points, unsigned int * valid_points_num, unsigned int *voxel_num,int out_shape_x,
// // int out_shape_y, int out_shape_z, unsigned int *output_voxel_num
// // cout << "voxel_num" << *(voxel_num) << std::endl;
// unsigned int voxel_num_cpu_ = 0;
// unsigned int cpu_out_voxel_num_ = 0;
// // size_t grids_out_size = batchSize * dense_voxel_num * sizeof(int); // 41*1600*1408 -1
// // size_t valid_points_size = batchSize * MAX_VOXELS * 256*4 * sizeof(unsigned int); // 20000*256*4
// // size_t valid_points_num_size = batchSize * MAX_VOXELS * sizeof(unsigned int);// 20000*1;
// int *grids_out_cpu = (int*)malloc(grids_out_size);
// unsigned int * valid_points_cpu = (unsigned int*)malloc(valid_points_size);
// unsigned int * valid_points_num_cpu = (unsigned int*)malloc(valid_points_num_size);
// // cout << "out_shape_x" << out_shape_x_ << std::endl;
// // copy to cpu
// CHECK(cudaMemcpyAsync(&voxel_num_cpu_, voxel_num, 1 * sizeof(unsigned int), cudaMemcpyDeviceToHost, stream));
// // CHECK(cudaMemcpyAsync(&cpu_out_voxel_num_, output_voxel_num, 1 * sizeof(unsigned int), cudaMemcpyDeviceToHost, stream));
// CHECK(cudaMemcpyAsync(grids_out_cpu, grids_out, grids_out_size, cudaMemcpyDeviceToHost, stream));
// CHECK(cudaMemcpyAsync(valid_points_cpu, valid_points, valid_points_size, cudaMemcpyDeviceToHost, stream));
// CHECK(cudaMemcpyAsync(valid_points_num_cpu, valid_points_num, valid_points_num_size, cudaMemcpyDeviceToHost, stream));
// int point_counter = 0;
// int index = 0;
// unsigned int index_z = 0;
// unsigned int index_y = 0;
// unsigned int index_x = 0;
// unsigned int offset = 0;
// for(int i=0;i<voxel_num_cpu_;i++)
// {
// int point_counter = valid_points_num_cpu[i];
// for(int j=0;j<point_counter;j++)
// {
// index_z = *(valid_points_cpu + i*(256*4)+j*4+0);
// index_y = *(valid_points_cpu + i*(256*4)+j*4+1);
// index_x = *(valid_points_cpu + i*(256*4)+j*4+2);
// offset = *(valid_points_cpu + i*(256*4)+j*4+3);
// index = 0 * out_shape_x_ * out_shape_y_ * out_shape_z_ + index_z * out_shape_y_ * out_shape_x_ + index_y * out_shape_x_ + index_x;
// if (grids_out_cpu[index] == -1)
// {
// grids_out_cpu[index] = cpu_out_voxel_num_;
// cpu_out_voxel_num_++;
// }
// }
// }
// std::cout << "cpu_out_voxel_num_" << cpu_out_voxel_num_ << std::endl;
// // copy to gpu
// // CHECK(cudaMemcpyAsync(&voxel_num_cpu_, voxel_num, 1 * sizeof(unsigned int), cudaMemcpyDeviceToHost, stream));
// CHECK(cudaMemcpyAsync(output_voxel_num, &cpu_out_voxel_num_, 1 * sizeof(unsigned int), cudaMemcpyHostToDevice, stream));
// CHECK(cudaMemcpyAsync(grids_out, grids_out_cpu, grids_out_size, cudaMemcpyHostToDevice, stream));
// free(grids_out_cpu);
// free(valid_points_cpu);
// free(valid_points_num_cpu);
checkCudaErrors(fillGridKernel_launch1(input_coords,grids_out,valid_points,valid_points_num, voxel_num,
out_shape_x_,out_shape_y_,out_shape_z_,stream));
checkCudaErrors(fillGridKernel_launch2(grids_out,output_voxel_num,out_shape_x_,out_shape_y_,out_shape_z_,stream));
checkCudaErrors(getSparseIndicePairsKernel_launch(input_coords,grids_out,indice_pairs,indice_num,valid_points,valid_points_num, voxel_num,
out_shape_x_,out_shape_y_,out_shape_z_,output_coords, output_voxel_num,stream));
// //save input features
// float* voxel_features_cpu = (float*)malloc(sizeof(float)*40000*16);
// CHECK(cudaMemcpyAsync(voxel_features_cpu, input_voxel_features, 40000*16 * sizeof(float), cudaMemcpyDeviceToHost, stream));
// std::ofstream out_txt_file;
// std::string save_path = "./000000_trt_input_feature.txt";
// out_txt_file.open(save_path.c_str(),ios::out | ios::trunc);
// // out_txt_file << fixed;
// out_txt_file << setiosflags(ios::fixed) << setprecision(6);
// // int voxel_num_cpu = 16825;
// int output_channel = 16;
// for(int i=0; i < voxel_num_cpu; i++)
// {
// for(int j=0;j<output_channel;j++)
// {
// out_txt_file << *(voxel_features_cpu + i * output_channel+j) << ",";
// }
// out_txt_file << std::endl;
// // std::cout << "index : " << i << " " << "voxel: " << *(voxel_feature + i * 4) << "," << *(voxel_feature + i * 4+1) << ","
// // << *(voxel_feature + i * 4+2) << "," << *(voxel_feature + i * 4+3) << ",||||"<< *(coors + i * 4) << "," << *(coors + i * 4+1) << ","
// // << *(coors + i * 4 + 2) << "," << *(coors+i*4+3)
// // << std::endl;
// }
// out_txt_file.close();
int kernel_volume = ksize0_ * ksize1_ * ksize2_;
for(int i = 0; i < kernel_volume; i++)
{
checkCudaErrors(sparseGatherConvScatter_launch(input_voxel_features,indice_pairs,indice_num,output_voxel_features,i,weights_dev_,
in_channel_,out_channel_,stream));
}
// std::cout << "enqueue finished >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>" << std::endl;
return 0;
}
nvinfer1::DataType SparseConv3dLayerPlugin::getOutputDataType(
int index, const nvinfer1::DataType* inputTypes, int nbInputs) const noexcept
{
if(index == 0)
{
// cout << "inputTypes[0]: " << inputTypes[0] << std::endl;
return inputTypes[0];
// return nvinfer1::DataType::kFLOAT;
}
return inputTypes[1];
// return nvinfer1::DataType::kINT32;
}
const char* SparseConv3dLayerPlugin::getPluginType() const noexcept
{
return PLUGIN_NAME;
}
const char* SparseConv3dLayerPlugin::getPluginVersion() const noexcept
{
return PLUGIN_VERSION;
}
int SparseConv3dLayerPlugin::getNbOutputs() const noexcept
{
return 3;
}
int SparseConv3dLayerPlugin::initialize() noexcept
{
return 0;
}
void SparseConv3dLayerPlugin::terminate() noexcept
{
// // cudafree
cudaFree(weights_dev_);
// //c free
// free(weights_data_);
}
SparseConv3dLayerPlugin::~SparseConv3dLayerPlugin()
{
terminate();
}
size_t SparseConv3dLayerPlugin::getSerializationSize() const noexcept
{
return ksize0_*ksize1_*ksize2_*in_channel_*out_channel_ * sizeof(float) + 22 * sizeof(int);
}
void SparseConv3dLayerPlugin::serialize(void* buffer) const noexcept
{
char* d = reinterpret_cast<char*>(buffer);
writeToBuffer<int>(d, in_channel_);
writeToBuffer<int>(d, out_channel_);
writeToBuffer<int>(d, max_voxels_);
writeToBuffer<int>(d, feature_num_);
writeToBuffer<int>(d, out_shape_x_);
writeToBuffer<int>(d, out_shape_y_);
writeToBuffer<int>(d, out_shape_z_);
writeToBuffer<int>(d, spatial_shape_x_);
writeToBuffer<int>(d, spatial_shape_y_);
writeToBuffer<int>(d, spatial_shape_z_);
writeToBuffer<int>(d, ksize0_);
writeToBuffer<int>(d, ksize1_);
writeToBuffer<int>(d, ksize2_);
writeToBuffer<int>(d, stride0_);
writeToBuffer<int>(d, stride1_);
writeToBuffer<int>(d, stride2_);
writeToBuffer<int>(d, padding0_);
writeToBuffer<int>(d, padding1_);
writeToBuffer<int>(d, padding2_);
writeToBuffer<int>(d, dilation_);
writeToBuffer<int>(d, out_padding_);
writeToBuffer<int>(d, weights_size_);
const float * data = (const float*)weights_.values;
for(int i=0; i < weights_size_; i++)
{
writeToBuffer<float>(d,data[i]);
}
}
void SparseConv3dLayerPlugin::destroy() noexcept
{
delete this;
}
void SparseConv3dLayerPlugin::setPluginNamespace(const char* libNamespace) noexcept
{
mNamespace = libNamespace;
}
const char* SparseConv3dLayerPlugin::getPluginNamespace() const noexcept
{
return mNamespace.c_str();
}
// __device__ float Logist(float data){ return 1.0f / (1.0f + expf(-data)); };
SparseConv3dLayerPluginCreator::SparseConv3dLayerPluginCreator()
{
mPluginAttributes.clear();
mPluginAttributes.emplace_back(PluginField("in_channel", nullptr, PluginFieldType::kINT32, 1)); // 4
mPluginAttributes.emplace_back(PluginField("out_channel", nullptr, PluginFieldType::kINT32, 1)); //16
mPluginAttributes.emplace_back(PluginField("max_voxels", nullptr, PluginFieldType::kINT32, 1)); //16825
mPluginAttributes.emplace_back(PluginField("feature_num", nullptr, PluginFieldType::kINT32, 1)); //4
mPluginAttributes.emplace_back(PluginField("out_shape", nullptr, PluginFieldType::kINT32, 1)); // [41,1600,1408]
mPluginAttributes.emplace_back(PluginField("spatial_shape", nullptr, PluginFieldType::kINT32, 1)); // [41,1600,1408]
mPluginAttributes.emplace_back(PluginField("ksize", nullptr, PluginFieldType::kINT32, 1)); // [3,3,3]
mPluginAttributes.emplace_back(PluginField("stride", nullptr, PluginFieldType::kINT32, 1)); // [1,1,1]
mPluginAttributes.emplace_back(PluginField("padding", nullptr, PluginFieldType::kINT32, 1)); // [0,0,0]
mPluginAttributes.emplace_back(PluginField("dilation", nullptr, PluginFieldType::kINT32, 1)); // [1,1,1]
mPluginAttributes.emplace_back(PluginField("out_padding", nullptr, PluginFieldType::kINT32, 1)); //[0,0,0]
mPluginAttributes.emplace_back(PluginField("weights", nullptr, PluginFieldType::kFLOAT32, 1)); // 3x3x4x16
mFC.nbFields = mPluginAttributes.size();
mFC.fields = mPluginAttributes.data();
}
const char* SparseConv3dLayerPluginCreator::getPluginName() const noexcept
{
return PLUGIN_NAME;
}
const char* SparseConv3dLayerPluginCreator::getPluginVersion() const noexcept
{
return PLUGIN_VERSION;
}
const PluginFieldCollection* SparseConv3dLayerPluginCreator::getFieldNames() noexcept
{
return &mFC;
}
IPluginV2* SparseConv3dLayerPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) noexcept
{
// std::cout << "createplugin start <<<<<<<<<<<<<<<<<<<<<<" << std::endl;
const PluginField* fields = fc->fields;
int nbFields = fc->nbFields;
// std::cout << nbFields << std::endl;
int in_channel = 0;
int out_channel = 0;
int max_voxels = 0;
int feature_num = 0;
int out_shape_x = 0;
int out_shape_y = 0;
int out_shape_z = 0;
int spatial_shape_x = 0;
int spatial_shape_y = 0;
int spatial_shape_z = 0;
int ksize0 = 0;
int ksize1 = 0;
int ksize2 = 0;
int stride0 = 0;
int stride1 = 0;
int stride2 = 0;
int padding0 = 0;
int padding1 = 0;
int padding2 = 0;
int dilation = 0;
int out_padding = 0;
int weights_size = 0;
const float *weight;
// std::cout << fields[0].name << std::endl;
for (int i = 0; i < nbFields; ++i)
{
const char* attr_name = fields[i].name;
// std::cout << " createplugn <<" << attr_name << std::endl;
if (!strcmp(attr_name, "in_channel"))
{
const int* d = static_cast<const int*>(fields[i].data);
in_channel = d[0];
// std::cout << "in_channel <<<<" << in_channel << std::endl;
}
else if (!strcmp(attr_name, "out_channel"))
{
const int* d = static_cast<const int*>(fields[i].data);
out_channel = d[0];