128 lines
3.9 KiB
C++
128 lines
3.9 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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#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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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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} //namespace
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// Forward declaration of cuda kernels
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cudaError_t cudaYoloLayerV3 (
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const void* input, void* output, const uint& batchSize,
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const uint& gridSize, const uint& numOutputClasses,
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const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream);
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YoloLayerV3::YoloLayerV3 (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_GridSize);
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read(d, m_OutputSize);
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};
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YoloLayerV3::YoloLayerV3 (
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const uint& numBoxes, const uint& numClasses, const uint& gridSize) :
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m_NumBoxes(numBoxes),
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m_NumClasses(numClasses),
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m_GridSize(gridSize)
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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_GridSize > 0);
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m_OutputSize = m_GridSize * m_GridSize * (m_NumBoxes * (4 + 1 + m_NumClasses));
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};
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nvinfer1::Dims
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YoloLayerV3::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 YoloLayerV3::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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YoloLayerV3::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 YoloLayerV3::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(cudaYoloLayerV3(
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inputs[0], outputs[0], batchSize, m_GridSize, m_NumClasses, m_NumBoxes,
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m_OutputSize, stream));
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return 0;
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}
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size_t YoloLayerV3::getSerializationSize() const noexcept
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{
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return sizeof(m_NumBoxes) + sizeof(m_NumClasses) + sizeof(m_GridSize) + sizeof(m_OutputSize);
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}
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void YoloLayerV3::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_GridSize);
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write(d, m_OutputSize);
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}
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nvinfer1::IPluginV2* YoloLayerV3::clone() const noexcept
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{
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return new YoloLayerV3 (m_NumBoxes, m_NumClasses, m_GridSize);
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}
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REGISTER_TENSORRT_PLUGIN(YoloLayerV3PluginCreator);
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