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