#include "decode.h" #include "stdio.h" #include "device_launch_parameters.h" namespace nvinfer1 { DecodePlugin::DecodePlugin() { } DecodePlugin::~DecodePlugin() { } // create the plugin at runtime from a byte stream DecodePlugin::DecodePlugin(const void* data, size_t length) { } void DecodePlugin::serialize(void* buffer) const TRT_NOEXCEPT { } size_t DecodePlugin::getSerializationSize() const TRT_NOEXCEPT { return 0; } int DecodePlugin::initialize() TRT_NOEXCEPT { return 0; } Dims DecodePlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims) TRT_NOEXCEPT { //output the result to channel int totalCount = 0; totalCount += decodeplugin::INPUT_H / 8 * decodeplugin::INPUT_W / 8 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); totalCount += decodeplugin::INPUT_H / 16 * decodeplugin::INPUT_W / 16 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); totalCount += decodeplugin::INPUT_H / 32 * decodeplugin::INPUT_W / 32 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); return Dims3(totalCount + 1, 1, 1); } // Set plugin namespace void DecodePlugin::setPluginNamespace(const char* pluginNamespace) TRT_NOEXCEPT { mPluginNamespace = pluginNamespace; } const char* DecodePlugin::getPluginNamespace() const TRT_NOEXCEPT { return mPluginNamespace; } // Return the DataType of the plugin output at the requested index DataType DecodePlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const TRT_NOEXCEPT { return DataType::kFLOAT; } // Return true if output tensor is broadcast across a batch. bool DecodePlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const TRT_NOEXCEPT { return false; } // Return true if plugin can use input that is broadcast across batch without replication. bool DecodePlugin::canBroadcastInputAcrossBatch(int inputIndex) const TRT_NOEXCEPT { return false; } void DecodePlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) TRT_NOEXCEPT { } // Attach the plugin object to an execution context and grant the plugin the access to some context resource. void DecodePlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) TRT_NOEXCEPT { } // Detach the plugin object from its execution context. void DecodePlugin::detachFromContext() TRT_NOEXCEPT {} const char* DecodePlugin::getPluginType() const TRT_NOEXCEPT { return "Decode_TRT"; } const char* DecodePlugin::getPluginVersion() const TRT_NOEXCEPT { return "1"; } void DecodePlugin::destroy() TRT_NOEXCEPT { delete this; } // Clone the plugin IPluginV2IOExt* DecodePlugin::clone() const TRT_NOEXCEPT { DecodePlugin* p = new DecodePlugin(); p->setPluginNamespace(mPluginNamespace); return p; } __device__ float Logist(float data) { return 1. / (1. + expf(-data)); }; __global__ void CalDetection(const float* input, float* output, int num_elem, int step, int anchor, int output_elem) { int idx = threadIdx.x + blockDim.x * blockIdx.x; if (idx >= num_elem) return; int h = decodeplugin::INPUT_H / step; int w = decodeplugin::INPUT_W / step; int total_grid = h * w; int bn_idx = idx / total_grid; idx = idx - bn_idx * total_grid; int y = idx / w; int x = idx % w; const float* cur_input = input + bn_idx * (4 + 2 + 10) * 2 * total_grid; const float* bbox_reg = &cur_input[0]; const float* cls_reg = &cur_input[2 * 4 * total_grid]; const float* lmk_reg = &cur_input[2 * 4 * total_grid + 2 * 2 * total_grid]; for (int k = 0; k < 2; ++k) { float conf1 = cls_reg[idx + k * total_grid * 2]; float conf2 = cls_reg[idx + k * total_grid * 2 + total_grid]; conf2 = expf(conf2) / (expf(conf1) + expf(conf2)); if (conf2 <= 0.02) continue; float* res_count = output + bn_idx * output_elem; int count = (int)atomicAdd(res_count, 1); char* data = (char*)res_count + sizeof(float) + count * sizeof(decodeplugin::Detection); decodeplugin::Detection* det = (decodeplugin::Detection*)(data); float prior[4]; prior[0] = ((float)x + 0.5) / w; prior[1] = ((float)y + 0.5) / h; prior[2] = (float)anchor * (k + 1) / decodeplugin::INPUT_W; prior[3] = (float)anchor * (k + 1) / decodeplugin::INPUT_H; //Location det->bbox[0] = prior[0] + bbox_reg[idx + k * total_grid * 4] * 0.1 * prior[2]; det->bbox[1] = prior[1] + bbox_reg[idx + k * total_grid * 4 + total_grid] * 0.1 * prior[3]; det->bbox[2] = prior[2] * expf(bbox_reg[idx + k * total_grid * 4 + total_grid * 2] * 0.2); det->bbox[3] = prior[3] * expf(bbox_reg[idx + k * total_grid * 4 + total_grid * 3] * 0.2); det->bbox[0] -= det->bbox[2] / 2; det->bbox[1] -= det->bbox[3] / 2; det->bbox[2] += det->bbox[0]; det->bbox[3] += det->bbox[1]; det->bbox[0] *= decodeplugin::INPUT_W; det->bbox[1] *= decodeplugin::INPUT_H; det->bbox[2] *= decodeplugin::INPUT_W; det->bbox[3] *= decodeplugin::INPUT_H; det->class_confidence = conf2; for (int i = 0; i < 10; i += 2) { det->landmark[i] = prior[0] + lmk_reg[idx + k * total_grid * 10 + total_grid * i] * 0.1 * prior[2]; det->landmark[i + 1] = prior[1] + lmk_reg[idx + k * total_grid * 10 + total_grid * (i + 1)] * 0.1 * prior[3]; det->landmark[i] *= decodeplugin::INPUT_W; det->landmark[i + 1] *= decodeplugin::INPUT_H; } } } void DecodePlugin::forwardGpu(const float* const* inputs, float* output, cudaStream_t stream, int batchSize) { int num_elem = 0; int base_step = 8; int base_anchor = 16; int thread_count; int totalCount = 1; totalCount += decodeplugin::INPUT_H / 8 * decodeplugin::INPUT_W / 8 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); totalCount += decodeplugin::INPUT_H / 16 * decodeplugin::INPUT_W / 16 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); totalCount += decodeplugin::INPUT_H / 32 * decodeplugin::INPUT_W / 32 * 2 * sizeof(decodeplugin::Detection) / sizeof(float); for (int idx = 0; idx < batchSize; ++idx) { cudaMemsetAsync(output + idx * totalCount, 0, sizeof(float), stream); } for (unsigned int i = 0; i < 3; ++i) { num_elem = batchSize * decodeplugin::INPUT_H / base_step * decodeplugin::INPUT_W / base_step; thread_count = (num_elem < thread_count_) ? num_elem : thread_count_; CalDetection << < (num_elem + thread_count - 1) / thread_count, thread_count, 0, stream >> > (inputs[i], output, num_elem, base_step, base_anchor, totalCount); base_step *= 2; base_anchor *= 4; } } int DecodePlugin::enqueue(int batchSize, const void* const* inputs, void* TRT_CONST_ENQUEUE* outputs, void* workspace, cudaStream_t stream) TRT_NOEXCEPT { //GPU //CUDA_CHECK(cudaStreamSynchronize(stream)); forwardGpu((const float* const*)inputs, (float*)outputs[0], stream, batchSize); return 0; }; PluginFieldCollection DecodePluginCreator::mFC{}; std::vector DecodePluginCreator::mPluginAttributes; DecodePluginCreator::DecodePluginCreator() { mPluginAttributes.clear(); mFC.nbFields = mPluginAttributes.size(); mFC.fields = mPluginAttributes.data(); } const char* DecodePluginCreator::getPluginName() const TRT_NOEXCEPT { return "Decode_TRT"; } const char* DecodePluginCreator::getPluginVersion() const TRT_NOEXCEPT { return "1"; } const PluginFieldCollection* DecodePluginCreator::getFieldNames() TRT_NOEXCEPT { return &mFC; } IPluginV2IOExt* DecodePluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) TRT_NOEXCEPT { DecodePlugin* obj = new DecodePlugin(); obj->setPluginNamespace(mNamespace.c_str()); return obj; } IPluginV2IOExt* DecodePluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength) TRT_NOEXCEPT { // This object will be deleted when the network is destroyed, which will // call PReluPlugin::destroy() DecodePlugin* obj = new DecodePlugin(serialData, serialLength); obj->setPluginNamespace(mNamespace.c_str()); return obj; } }