281 lines
10 KiB
Plaintext
281 lines
10 KiB
Plaintext
#include "yololayer.h"
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#include "cuda_utils.h"
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#include <cassert>
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#include <vector>
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#include <iostream>
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namespace Tn {
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template<typename T>
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void write(char*& buffer, const T& val) {
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*reinterpret_cast<T*>(buffer) = val;
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buffer += sizeof(T);
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}
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template<typename T>
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void read(const char*& buffer, T& val) {
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val = *reinterpret_cast<const T*>(buffer);
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buffer += sizeof(T);
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}
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}
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namespace nvinfer1 {
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YoloLayerPlugin::YoloLayerPlugin(int classCount, int netWidth, int netHeight, int maxOut, bool is_segmentation, const std::vector<YoloKernel>& vYoloKernel) {
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mClassCount = classCount;
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mYoloV5NetWidth = netWidth;
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mYoloV5NetHeight = netHeight;
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mMaxOutObject = maxOut;
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is_segmentation_ = is_segmentation;
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mYoloKernel = vYoloKernel;
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mKernelCount = vYoloKernel.size();
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CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
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size_t AnchorLen = sizeof(float)* kNumAnchor * 2;
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for (int ii = 0; ii < mKernelCount; ii++) {
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CUDA_CHECK(cudaMalloc(&mAnchor[ii], AnchorLen));
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const auto& yolo = mYoloKernel[ii];
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CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
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}
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}
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YoloLayerPlugin::~YoloLayerPlugin() {
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for (int ii = 0; ii < mKernelCount; ii++) {
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CUDA_CHECK(cudaFree(mAnchor[ii]));
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}
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CUDA_CHECK(cudaFreeHost(mAnchor));
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}
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// create the plugin at runtime from a byte stream
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YoloLayerPlugin::YoloLayerPlugin(const void* data, size_t length) {
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using namespace Tn;
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const char *d = reinterpret_cast<const char *>(data), *a = d;
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read(d, mClassCount);
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read(d, mThreadCount);
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read(d, mKernelCount);
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read(d, mYoloV5NetWidth);
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read(d, mYoloV5NetHeight);
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read(d, mMaxOutObject);
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read(d, is_segmentation_);
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mYoloKernel.resize(mKernelCount);
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auto kernelSize = mKernelCount * sizeof(YoloKernel);
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memcpy(mYoloKernel.data(), d, kernelSize);
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d += kernelSize;
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CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
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size_t AnchorLen = sizeof(float)* kNumAnchor * 2;
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for (int ii = 0; ii < mKernelCount; ii++) {
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CUDA_CHECK(cudaMalloc(&mAnchor[ii], AnchorLen));
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const auto& yolo = mYoloKernel[ii];
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CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
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}
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assert(d == a + length);
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}
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void YoloLayerPlugin::serialize(void* buffer) const TRT_NOEXCEPT {
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using namespace Tn;
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char* d = static_cast<char*>(buffer), *a = d;
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write(d, mClassCount);
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write(d, mThreadCount);
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write(d, mKernelCount);
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write(d, mYoloV5NetWidth);
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write(d, mYoloV5NetHeight);
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write(d, mMaxOutObject);
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write(d, is_segmentation_);
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auto kernelSize = mKernelCount * sizeof(YoloKernel);
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memcpy(d, mYoloKernel.data(), kernelSize);
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d += kernelSize;
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assert(d == a + getSerializationSize());
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}
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size_t YoloLayerPlugin::getSerializationSize() const TRT_NOEXCEPT {
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size_t s = sizeof(mClassCount) + sizeof(mThreadCount) + sizeof(mKernelCount);
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s += sizeof(YoloKernel) * mYoloKernel.size();
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s += sizeof(mYoloV5NetWidth) + sizeof(mYoloV5NetHeight);
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s += sizeof(mMaxOutObject) + sizeof(is_segmentation_);
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return s;
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}
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int YoloLayerPlugin::initialize() TRT_NOEXCEPT {
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return 0;
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}
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Dims YoloLayerPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims) TRT_NOEXCEPT {
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//output the result to channel
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int totalsize = mMaxOutObject * sizeof(Detection) / sizeof(float);
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return Dims3(totalsize + 1, 1, 1);
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}
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// Set plugin namespace
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void YoloLayerPlugin::setPluginNamespace(const char* pluginNamespace) TRT_NOEXCEPT {
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mPluginNamespace = pluginNamespace;
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}
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const char* YoloLayerPlugin::getPluginNamespace() const TRT_NOEXCEPT {
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return mPluginNamespace;
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}
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// Return the DataType of the plugin output at the requested index
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DataType YoloLayerPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const TRT_NOEXCEPT {
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return DataType::kFLOAT;
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}
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// Return true if output tensor is broadcast across a batch.
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bool YoloLayerPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const TRT_NOEXCEPT {
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return false;
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}
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// Return true if plugin can use input that is broadcast across batch without replication.
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bool YoloLayerPlugin::canBroadcastInputAcrossBatch(int inputIndex) const TRT_NOEXCEPT {
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return false;
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}
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void YoloLayerPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) TRT_NOEXCEPT {}
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// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
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void YoloLayerPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) TRT_NOEXCEPT {}
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// Detach the plugin object from its execution context.
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void YoloLayerPlugin::detachFromContext() TRT_NOEXCEPT {}
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const char* YoloLayerPlugin::getPluginType() const TRT_NOEXCEPT {
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return "YoloLayer_TRT";
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}
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const char* YoloLayerPlugin::getPluginVersion() const TRT_NOEXCEPT {
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return "1";
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}
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void YoloLayerPlugin::destroy() TRT_NOEXCEPT {
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delete this;
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}
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// Clone the plugin
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IPluginV2IOExt* YoloLayerPlugin::clone() const TRT_NOEXCEPT {
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YoloLayerPlugin* p = new YoloLayerPlugin(mClassCount, mYoloV5NetWidth, mYoloV5NetHeight, mMaxOutObject, is_segmentation_, mYoloKernel);
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p->setPluginNamespace(mPluginNamespace);
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return p;
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}
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__device__ float Logist(float data) { return 1.0f / (1.0f + expf(-data)); };
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__global__ void CalDetection(const float *input, float *output, int noElements,
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const int netwidth, const int netheight, int maxoutobject, int yoloWidth,
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int yoloHeight, const float anchors[kNumAnchor * 2], int classes, int outputElem, bool is_segmentation) {
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int idx = threadIdx.x + blockDim.x * blockIdx.x;
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if (idx >= noElements) return;
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int total_grid = yoloWidth * yoloHeight;
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int bnIdx = idx / total_grid;
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idx = idx - total_grid * bnIdx;
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int info_len_i = 5 + classes;
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if (is_segmentation) info_len_i += 32;
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const float* curInput = input + bnIdx * (info_len_i * total_grid * kNumAnchor);
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for (int k = 0; k < kNumAnchor; ++k) {
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float box_prob = Logist(curInput[idx + k * info_len_i * total_grid + 4 * total_grid]);
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if (box_prob < kIgnoreThresh) continue;
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int class_id = 0;
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float max_cls_prob = 0.0;
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for (int i = 5; i < 5 + classes; ++i) {
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float p = Logist(curInput[idx + k * info_len_i * total_grid + i * total_grid]);
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if (p > max_cls_prob) {
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max_cls_prob = p;
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class_id = i - 5;
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}
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}
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float *res_count = output + bnIdx * outputElem;
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int count = (int)atomicAdd(res_count, 1);
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if (count >= maxoutobject) return;
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char *data = (char*)res_count + sizeof(float) + count * sizeof(Detection);
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Detection *det = (Detection*)(data);
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int row = idx / yoloWidth;
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int col = idx % yoloWidth;
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det->bbox[0] = (col - 0.5f + 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 0 * total_grid])) * netwidth / yoloWidth;
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det->bbox[1] = (row - 0.5f + 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 1 * total_grid])) * netheight / yoloHeight;
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det->bbox[2] = 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 2 * total_grid]);
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det->bbox[2] = det->bbox[2] * det->bbox[2] * anchors[2 * k];
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det->bbox[3] = 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 3 * total_grid]);
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det->bbox[3] = det->bbox[3] * det->bbox[3] * anchors[2 * k + 1];
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det->conf = box_prob * max_cls_prob;
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det->class_id = class_id;
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for (int i = 0; is_segmentation && i < 32; i++) {
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det->mask[i] = curInput[idx + k * info_len_i * total_grid + (i + 5 + classes) * total_grid];
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}
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}
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}
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void YoloLayerPlugin::forwardGpu(const float* const* inputs, float *output, cudaStream_t stream, int batchSize) {
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int outputElem = 1 + mMaxOutObject * sizeof(Detection) / sizeof(float);
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for (int idx = 0; idx < batchSize; ++idx) {
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CUDA_CHECK(cudaMemsetAsync(output + idx * outputElem, 0, sizeof(float), stream));
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}
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int numElem = 0;
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for (unsigned int i = 0; i < mYoloKernel.size(); ++i) {
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const auto& yolo = mYoloKernel[i];
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numElem = yolo.width * yolo.height * batchSize;
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if (numElem < mThreadCount) mThreadCount = numElem;
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CalDetection << < (numElem + mThreadCount - 1) / mThreadCount, mThreadCount, 0, stream >> >
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(inputs[i], output, numElem, mYoloV5NetWidth, mYoloV5NetHeight, mMaxOutObject, yolo.width, yolo.height, (float*)mAnchor[i], mClassCount, outputElem, is_segmentation_);
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}
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}
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int YoloLayerPlugin::enqueue(int batchSize, const void* const* inputs, void* TRT_CONST_ENQUEUE* outputs, void* workspace, cudaStream_t stream) TRT_NOEXCEPT {
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forwardGpu((const float* const*)inputs, (float*)outputs[0], stream, batchSize);
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return 0;
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}
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PluginFieldCollection YoloPluginCreator::mFC{};
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std::vector<PluginField> YoloPluginCreator::mPluginAttributes;
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YoloPluginCreator::YoloPluginCreator() {
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mPluginAttributes.clear();
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mFC.nbFields = mPluginAttributes.size();
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mFC.fields = mPluginAttributes.data();
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}
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const char* YoloPluginCreator::getPluginName() const TRT_NOEXCEPT {
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return "YoloLayer_TRT";
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}
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const char* YoloPluginCreator::getPluginVersion() const TRT_NOEXCEPT {
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return "1";
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}
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const PluginFieldCollection* YoloPluginCreator::getFieldNames() TRT_NOEXCEPT {
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return &mFC;
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}
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IPluginV2IOExt* YoloPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) TRT_NOEXCEPT {
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assert(fc->nbFields == 2);
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assert(strcmp(fc->fields[0].name, "netinfo") == 0);
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assert(strcmp(fc->fields[1].name, "kernels") == 0);
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int *p_netinfo = (int*)(fc->fields[0].data);
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int class_count = p_netinfo[0];
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int input_w = p_netinfo[1];
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int input_h = p_netinfo[2];
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int max_output_object_count = p_netinfo[3];
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bool is_segmentation = (bool)p_netinfo[4];
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std::vector<YoloKernel> kernels(fc->fields[1].length);
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memcpy(&kernels[0], fc->fields[1].data, kernels.size() * sizeof(YoloKernel));
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YoloLayerPlugin* obj = new YoloLayerPlugin(class_count, input_w, input_h, max_output_object_count, is_segmentation, kernels);
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obj->setPluginNamespace(mNamespace.c_str());
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return obj;
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}
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IPluginV2IOExt* YoloPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength) TRT_NOEXCEPT {
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// This object will be deleted when the network is destroyed, which will
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// call YoloLayerPlugin::destroy()
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YoloLayerPlugin* obj = new YoloLayerPlugin(serialData, serialLength);
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obj->setPluginNamespace(mNamespace.c_str());
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return obj;
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}
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}
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