209 lines
6.5 KiB
C++
209 lines
6.5 KiB
C++
/*
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* Copyright (c) 2019-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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* Edited by Marcos Luciano
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* https://www.github.com/marcoslucianops
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*/
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#include "yoloPlugins.h"
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#include "NvInferPlugin.h"
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#include <cassert>
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#include <iostream>
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#include <memory>
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int kNUM_CLASSES;
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float kBETA_NMS;
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std::vector<float> kANCHORS;
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std::vector<std::vector<int>> kMASK;
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namespace {
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template <typename T>
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void write(char*& buffer, const T& val)
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{
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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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{
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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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cudaError_t cudaYoloLayer (
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const void* input, void* output, const uint& batchSize,
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const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
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const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const uint modelCoords, const float modelScale, const uint modelType);
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YoloLayer::YoloLayer (const void* data, size_t length)
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{
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const char *d = static_cast<const char*>(data);
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read(d, m_NumBoxes);
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read(d, m_NumClasses);
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read(d, m_GridSizeX);
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read(d, m_GridSizeY);
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read(d, m_OutputSize);
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read(d, m_type);
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read(d, m_new_coords);
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read(d, m_scale_x_y);
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read(d, m_beta_nms);
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uint anchorsSize;
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read(d, anchorsSize);
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for (uint i = 0; i < anchorsSize; i++) {
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float result;
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read(d, result);
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m_Anchors.push_back(result);
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}
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uint maskSize;
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read(d, maskSize);
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for (uint i = 0; i < maskSize; i++) {
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uint nMask;
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read(d, nMask);
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std::vector<int> pMask;
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for (uint f = 0; f < nMask; f++) {
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int result;
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read(d, result);
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pMask.push_back(result);
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}
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m_Mask.push_back(pMask);
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}
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kNUM_CLASSES = m_NumClasses;
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kBETA_NMS = m_beta_nms;
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kANCHORS = m_Anchors;
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kMASK = m_Mask;
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};
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YoloLayer::YoloLayer (
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const uint& numBoxes, const uint& numClasses, const uint& gridSizeX, const uint& gridSizeY, const uint model_type, const uint new_coords, const float scale_x_y, const float beta_nms, const std::vector<float> anchors, std::vector<std::vector<int>> mask) :
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m_NumBoxes(numBoxes),
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m_NumClasses(numClasses),
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m_GridSizeX(gridSizeX),
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m_GridSizeY(gridSizeY),
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m_type(model_type),
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m_new_coords(new_coords),
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m_scale_x_y(scale_x_y),
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m_beta_nms(beta_nms),
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m_Anchors(anchors),
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m_Mask(mask)
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{
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assert(m_NumBoxes > 0);
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assert(m_NumClasses > 0);
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assert(m_GridSizeX > 0);
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assert(m_GridSizeY > 0);
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m_OutputSize = m_GridSizeX * m_GridSizeY * (m_NumBoxes * (4 + 1 + m_NumClasses));
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};
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nvinfer1::Dims
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YoloLayer::getOutputDimensions(
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int index, const nvinfer1::Dims* inputs, int nbInputDims) noexcept
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{
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assert(index == 0);
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assert(nbInputDims == 1);
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return inputs[0];
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}
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bool YoloLayer::supportsFormat (
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nvinfer1::DataType type, nvinfer1::PluginFormat format) const noexcept {
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return (type == nvinfer1::DataType::kFLOAT &&
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format == nvinfer1::PluginFormat::kLINEAR);
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}
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void
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YoloLayer::configureWithFormat (
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const nvinfer1::Dims* inputDims, int nbInputs,
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const nvinfer1::Dims* outputDims, int nbOutputs,
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nvinfer1::DataType type, nvinfer1::PluginFormat format, int maxBatchSize) noexcept
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{
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assert(nbInputs == 1);
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assert (format == nvinfer1::PluginFormat::kLINEAR);
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assert(inputDims != nullptr);
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}
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int YoloLayer::enqueue(
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int batchSize, void const* const* inputs, void* const* outputs, void* workspace,
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cudaStream_t stream) noexcept
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{
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CHECK(cudaYoloLayer(
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inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
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m_OutputSize, stream, m_new_coords, m_scale_x_y, m_type));
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return 0;
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}
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size_t YoloLayer::getSerializationSize() const noexcept
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{
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int anchorsSum = 1;
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for (uint i = 0; i < m_Anchors.size(); i++) {
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anchorsSum += 1;
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}
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int maskSum = 1;
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for (uint i = 0; i < m_Mask.size(); i++) {
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maskSum += 1;
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for (uint f = 0; f < m_Mask[i].size(); f++) {
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maskSum += 1;
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}
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}
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return sizeof(m_NumBoxes) + sizeof(m_NumClasses) + sizeof(m_GridSizeX) + sizeof(m_GridSizeY) + sizeof(m_OutputSize) + sizeof(m_type)
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+ sizeof(m_new_coords) + sizeof(m_scale_x_y) + sizeof(m_beta_nms) + anchorsSum * sizeof(float) + maskSum * sizeof(int);
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}
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void YoloLayer::serialize(void* buffer) const noexcept
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{
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char *d = static_cast<char*>(buffer);
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write(d, m_NumBoxes);
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write(d, m_NumClasses);
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write(d, m_GridSizeX);
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write(d, m_GridSizeY);
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write(d, m_OutputSize);
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write(d, m_type);
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write(d, m_new_coords);
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write(d, m_scale_x_y);
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write(d, m_beta_nms);
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uint anchorsSize = m_Anchors.size();
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write(d, anchorsSize);
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for (uint i = 0; i < anchorsSize; i++) {
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write(d, m_Anchors[i]);
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}
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uint maskSize = m_Mask.size();
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write(d, maskSize);
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for (uint i = 0; i < maskSize; i++) {
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uint pMaskSize = m_Mask[i].size();
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write(d, pMaskSize);
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for (uint f = 0; f < pMaskSize; f++) {
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write(d, m_Mask[i][f]);
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}
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}
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kNUM_CLASSES = m_NumClasses;
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kBETA_NMS = m_beta_nms;
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kANCHORS = m_Anchors;
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kMASK = m_Mask;
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}
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nvinfer1::IPluginV2* YoloLayer::clone() const noexcept
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{
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return new YoloLayer (m_NumBoxes, m_NumClasses, m_GridSizeX, m_GridSizeY, m_type, m_new_coords, m_scale_x_y, m_beta_nms, m_Anchors, m_Mask);
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}
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REGISTER_TENSORRT_PLUGIN(YoloLayerPluginCreator); |