357 lines
13 KiB
C++
357 lines
13 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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*
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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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uint kNUM_CLASSES;
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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_r(
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const void* input, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
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const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
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const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
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const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
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cudaError_t cudaYoloLayer_nc(
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const void* input, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
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const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
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const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
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const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
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cudaError_t cudaYoloLayer(
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const void* input, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
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const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
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const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
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const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
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cudaError_t cudaRegionLayer(
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const void* input, void* softmax, void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* countData,
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const uint& batchSize, uint64_t& inputSize, uint64_t& outputSize, const float& scoreThreshold, const uint& netWidth,
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const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
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const void* anchors, cudaStream_t stream);
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cudaError_t sortDetections(
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void* d_indexes, void* d_scores, void* d_boxes, void* d_classes, void* bboxData, void* scoreData, void* countData,
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const uint& batchSize, uint64_t& outputSize, uint& topK, const uint& numOutputClasses, cudaStream_t stream);
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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_NetWidth);
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read(d, m_NetHeight);
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read(d, m_NumClasses);
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read(d, m_NewCoords);
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read(d, m_OutputSize);
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read(d, m_Type);
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read(d, m_TopK);
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read(d, m_ScoreThreshold);
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uint yoloTensorsSize;
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read(d, yoloTensorsSize);
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for (uint i = 0; i < yoloTensorsSize; ++i)
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{
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TensorInfo curYoloTensor;
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read(d, curYoloTensor.gridSizeX);
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read(d, curYoloTensor.gridSizeY);
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read(d, curYoloTensor.numBBoxes);
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read(d, curYoloTensor.scaleXY);
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uint anchorsSize;
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read(d, anchorsSize);
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for (uint j = 0; j < anchorsSize; j++)
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{
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float result;
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read(d, result);
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curYoloTensor.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 j = 0; j < maskSize; j++)
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{
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int result;
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read(d, result);
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curYoloTensor.mask.push_back(result);
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}
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m_YoloTensors.push_back(curYoloTensor);
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}
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kNUM_CLASSES = m_NumClasses;
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};
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YoloLayer::YoloLayer(
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const uint& netWidth, const uint& netHeight, const uint& numClasses, const uint& newCoords,
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const std::vector<TensorInfo>& yoloTensors, const uint64_t& outputSize, const uint& modelType, const uint& topK,
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const float& scoreThreshold) :
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m_NetWidth(netWidth),
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m_NetHeight(netHeight),
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m_NumClasses(numClasses),
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m_NewCoords(newCoords),
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m_YoloTensors(yoloTensors),
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m_OutputSize(outputSize),
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m_Type(modelType),
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m_TopK(topK),
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m_ScoreThreshold(scoreThreshold)
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{
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assert(m_NetWidth > 0);
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assert(m_NetHeight > 0);
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kNUM_CLASSES = 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 < 3);
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if (index == 0) {
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return nvinfer1::Dims3(m_TopK, 1, 4);
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}
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return nvinfer1::DimsHW(m_TopK, m_NumClasses);
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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 > 0);
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assert(format == nvinfer1::PluginFormat::kLINEAR);
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assert(inputDims != nullptr);
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}
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int32_t 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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void* countData = workspace;
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void* bboxData = outputs[0];
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void* scoreData = outputs[1];
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CUDA_CHECK(cudaMemsetAsync((int*)countData, 0, sizeof(int) * batchSize, stream));
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CUDA_CHECK(cudaMemsetAsync((float*)bboxData, 0, sizeof(float) * m_TopK * 4 * batchSize, stream));
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CUDA_CHECK(cudaMemsetAsync((float*)scoreData, 0, sizeof(float) * m_TopK * m_NumClasses * batchSize, stream));
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void* d_indexes;
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CUDA_CHECK(cudaMallocHost(&d_indexes, sizeof(int) * m_OutputSize * batchSize));
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CUDA_CHECK(cudaMemsetAsync((float*)d_indexes, 0, sizeof(int) * m_OutputSize * batchSize, stream));
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void* d_scores;
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CUDA_CHECK(cudaMallocHost(&d_scores, sizeof(float) * m_OutputSize * batchSize));
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CUDA_CHECK(cudaMemsetAsync((float*)d_scores, 0, sizeof(float) * m_OutputSize * batchSize, stream));
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void* d_boxes;
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CUDA_CHECK(cudaMallocHost(&d_boxes, sizeof(float) * m_OutputSize * 4 * batchSize));
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CUDA_CHECK(cudaMemsetAsync((float*)d_boxes, 0, sizeof(float) * m_OutputSize * 4 * batchSize, stream));
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void* d_classes;
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CUDA_CHECK(cudaMallocHost(&d_classes, sizeof(int) * m_OutputSize * batchSize));
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CUDA_CHECK(cudaMemsetAsync((float*)d_classes, 0, sizeof(int) * m_OutputSize * batchSize, stream));
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uint yoloTensorsSize = m_YoloTensors.size();
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for (uint i = 0; i < yoloTensorsSize; ++i)
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{
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TensorInfo& curYoloTensor = m_YoloTensors.at(i);
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uint numBBoxes = curYoloTensor.numBBoxes;
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float scaleXY = curYoloTensor.scaleXY;
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uint gridSizeX = curYoloTensor.gridSizeX;
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uint gridSizeY = curYoloTensor.gridSizeY;
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std::vector<float> anchors = curYoloTensor.anchors;
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std::vector<int> mask = curYoloTensor.mask;
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void* v_anchors;
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void* v_mask;
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if (anchors.size() > 0) {
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float* f_anchors = anchors.data();
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CUDA_CHECK(cudaMallocHost(&v_anchors, sizeof(float) * anchors.size()));
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CUDA_CHECK(cudaMemcpy(v_anchors, f_anchors, sizeof(float) * anchors.size(), cudaMemcpyHostToDevice));
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}
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if (mask.size() > 0) {
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int* f_mask = mask.data();
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CUDA_CHECK(cudaMallocHost(&v_mask, sizeof(int) * mask.size()));
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CUDA_CHECK(cudaMemcpy(v_mask, f_mask, sizeof(int) * mask.size(), cudaMemcpyHostToDevice));
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}
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uint64_t inputSize = gridSizeX * gridSizeY * (numBBoxes * (4 + 1 + m_NumClasses));
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if (m_Type == 2) { // YOLOR incorrect param: scale_x_y = 2.0
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CUDA_CHECK(cudaYoloLayer_r(
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inputs[i], d_indexes, d_scores, d_boxes, d_classes, countData, batchSize, inputSize, m_OutputSize,
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m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, 2.0, v_anchors,
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v_mask, stream));
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}
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else if (m_Type == 1) {
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if (m_NewCoords) {
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CUDA_CHECK(cudaYoloLayer_nc(
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inputs[i], d_indexes, d_scores, d_boxes, d_classes, countData, batchSize, inputSize, m_OutputSize,
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m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, scaleXY,
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v_anchors, v_mask, stream));
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}
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else {
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CUDA_CHECK(cudaYoloLayer(
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inputs[i], d_indexes, d_scores, d_boxes, d_classes, countData, batchSize, inputSize, m_OutputSize,
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m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, scaleXY,
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v_anchors, v_mask, stream));
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}
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}
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else {
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void* softmax;
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CUDA_CHECK(cudaMallocHost(&softmax, sizeof(float) * inputSize * batchSize));
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CUDA_CHECK(cudaMemsetAsync((float*)softmax, 0, sizeof(float) * inputSize * batchSize));
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CUDA_CHECK(cudaRegionLayer(
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inputs[i], softmax, d_indexes, d_scores, d_boxes, d_classes, countData, batchSize, inputSize, m_OutputSize,
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m_ScoreThreshold, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, v_anchors,
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stream));
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CUDA_CHECK(cudaFreeHost(softmax));
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}
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if (anchors.size() > 0) {
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CUDA_CHECK(cudaFreeHost(v_anchors));
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}
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if (mask.size() > 0) {
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CUDA_CHECK(cudaFreeHost(v_mask));
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}
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}
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CUDA_CHECK(sortDetections(
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d_indexes, d_scores, d_boxes, d_classes, bboxData, scoreData, countData, batchSize, m_OutputSize, m_TopK,
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m_NumClasses, stream));
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CUDA_CHECK(cudaFreeHost(d_indexes));
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CUDA_CHECK(cudaFreeHost(d_scores));
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CUDA_CHECK(cudaFreeHost(d_boxes));
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CUDA_CHECK(cudaFreeHost(d_classes));
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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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size_t totalSize = 0;
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totalSize += sizeof(m_NetWidth);
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totalSize += sizeof(m_NetHeight);
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totalSize += sizeof(m_NumClasses);
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totalSize += sizeof(m_NewCoords);
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totalSize += sizeof(m_OutputSize);
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totalSize += sizeof(m_Type);
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totalSize += sizeof(m_TopK);
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totalSize += sizeof(m_ScoreThreshold);
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uint yoloTensorsSize = m_YoloTensors.size();
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totalSize += sizeof(yoloTensorsSize);
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for (uint i = 0; i < yoloTensorsSize; ++i)
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{
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const TensorInfo& curYoloTensor = m_YoloTensors.at(i);
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totalSize += sizeof(curYoloTensor.gridSizeX);
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totalSize += sizeof(curYoloTensor.gridSizeY);
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totalSize += sizeof(curYoloTensor.numBBoxes);
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totalSize += sizeof(curYoloTensor.scaleXY);
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totalSize += sizeof(uint) + sizeof(curYoloTensor.anchors[0]) * curYoloTensor.anchors.size();
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totalSize += sizeof(uint) + sizeof(curYoloTensor.mask[0]) * curYoloTensor.mask.size();
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}
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return totalSize;
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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_NetWidth);
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write(d, m_NetHeight);
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write(d, m_NumClasses);
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write(d, m_NewCoords);
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write(d, m_OutputSize);
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write(d, m_Type);
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write(d, m_TopK);
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write(d, m_ScoreThreshold);
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uint yoloTensorsSize = m_YoloTensors.size();
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write(d, yoloTensorsSize);
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for (uint i = 0; i < yoloTensorsSize; ++i)
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{
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const TensorInfo& curYoloTensor = m_YoloTensors.at(i);
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write(d, curYoloTensor.gridSizeX);
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write(d, curYoloTensor.gridSizeY);
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write(d, curYoloTensor.numBBoxes);
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write(d, curYoloTensor.scaleXY);
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uint anchorsSize = curYoloTensor.anchors.size();
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write(d, anchorsSize);
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for (uint j = 0; j < anchorsSize; ++j)
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{
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write(d, curYoloTensor.anchors[j]);
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}
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uint maskSize = curYoloTensor.mask.size();
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write(d, maskSize);
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for (uint j = 0; j < maskSize; ++j)
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{
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write(d, curYoloTensor.mask[j]);
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}
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}
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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 (
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m_NetWidth, m_NetHeight, m_NumClasses, m_NewCoords, m_YoloTensors, m_OutputSize, m_Type, m_TopK,
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m_ScoreThreshold);
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}
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REGISTER_TENSORRT_PLUGIN(YoloLayerPluginCreator);
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