wehub-resource-sync
515 lines
18 KiB
C++
515 lines
18 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "multilevelProposeROIPlugin.h"
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#include "common/plugin.h"
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#include "multilevelProposeROI/tlt_mrcnn_config.h"
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#include <algorithm>
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#include <cuda_runtime_api.h>
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#include <iostream>
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#include <math.h>
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#include <string_view>
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#include <fstream>
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using namespace nvinfer1;
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using namespace plugin;
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using nvinfer1::plugin::MultilevelProposeROI;
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using nvinfer1::plugin::MultilevelProposeROIPluginCreator;
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namespace
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{
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char const* const kMULTILEVELPROPOSEROI_PLUGIN_VERSION{"1"};
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char const* const kMULTILEVELPROPOSEROI_PLUGIN_NAME{"MultilevelProposeROI_TRT"};
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} // namespace
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MultilevelProposeROIPluginCreator::MultilevelProposeROIPluginCreator() noexcept
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{
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mPluginAttributes.clear();
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mPluginAttributes.emplace_back(PluginField("prenms_topk", nullptr, PluginFieldType::kINT32, 1));
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mPluginAttributes.emplace_back(PluginField("keep_topk", nullptr, PluginFieldType::kINT32, 1));
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mPluginAttributes.emplace_back(PluginField("fg_threshold", nullptr, PluginFieldType::kFLOAT32, 1));
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mPluginAttributes.emplace_back(PluginField("iou_threshold", nullptr, PluginFieldType::kFLOAT32, 1));
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mPluginAttributes.emplace_back(PluginField("image_size", nullptr, PluginFieldType::kINT32, 3));
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mFC.nbFields = mPluginAttributes.size();
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mFC.fields = mPluginAttributes.data();
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}
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char const* MultilevelProposeROIPluginCreator::getPluginName() const noexcept
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{
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return kMULTILEVELPROPOSEROI_PLUGIN_NAME;
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}
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char const* MultilevelProposeROIPluginCreator::getPluginVersion() const noexcept
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{
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return kMULTILEVELPROPOSEROI_PLUGIN_VERSION;
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}
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PluginFieldCollection const* MultilevelProposeROIPluginCreator::getFieldNames() noexcept
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{
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return &mFC;
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}
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IPluginV2Ext* MultilevelProposeROIPluginCreator::createPlugin(
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char const* name, PluginFieldCollection const* fc) noexcept
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{
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try
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{
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using namespace std::string_view_literals;
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plugin::validateRequiredAttributesExist({"prenms_topk", "keep_topk", "fg_threshold", "iou_threshold"}, fc);
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auto imageSize = TLTMaskRCNNConfig::IMAGE_SHAPE;
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PluginField const* fields = fc->fields;
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for (int32_t i = 0; i < fc->nbFields; ++i)
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{
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std::string_view const attrName = fields[i].name;
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if (attrName == "prenms_topk"sv)
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{
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PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
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mPreNMSTopK = *(static_cast<int32_t const*>(fields[i].data));
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}
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if (attrName == "keep_topk"sv)
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{
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PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
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mKeepTopK = *(static_cast<int32_t const*>(fields[i].data));
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}
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if (attrName == "fg_threshold"sv)
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{
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PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
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mFGThreshold = *(static_cast<float const*>(fields[i].data));
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}
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if (attrName == "iou_threshold"sv)
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{
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PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
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mIOUThreshold = *(static_cast<float const*>(fields[i].data));
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}
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if (attrName == "image_size"sv)
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{
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PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
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auto const dims = static_cast<int32_t const*>(fields[i].data);
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std::copy_n(dims, 3, imageSize.d);
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}
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}
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return new MultilevelProposeROI(mPreNMSTopK, mKeepTopK, mFGThreshold, mIOUThreshold, imageSize);
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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IPluginV2Ext* MultilevelProposeROIPluginCreator::deserializePlugin(
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char const* name, void const* data, size_t length) noexcept
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{
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try
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{
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return new MultilevelProposeROI(data, length);
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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MultilevelProposeROI::MultilevelProposeROI(
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int32_t prenms_topk, int32_t keep_topk, float fg_threshold, float iou_threshold, const nvinfer1::Dims imageSize)
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: mPreNMSTopK(prenms_topk)
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, mKeepTopK(keep_topk)
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, mFGThreshold(fg_threshold)
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, mIOUThreshold(iou_threshold)
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, mImageSize(imageSize)
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{
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mBackgroundLabel = -1;
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PLUGIN_VALIDATE(mPreNMSTopK > 0);
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PLUGIN_VALIDATE(mPreNMSTopK <= 4096);
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PLUGIN_VALIDATE(mKeepTopK > 0);
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PLUGIN_VALIDATE(mIOUThreshold >= 0.0F);
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PLUGIN_VALIDATE(mFGThreshold >= 0.0F);
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PLUGIN_VALIDATE(mImageSize.nbDims == 3);
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PLUGIN_VALIDATE(mImageSize.d[0] > 0 && mImageSize.d[1] > 0 && mImageSize.d[2] > 0);
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mParam.backgroundLabelId = -1;
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mParam.numClasses = 1;
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mParam.keepTopK = mKeepTopK;
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mParam.scoreThreshold = mFGThreshold;
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mParam.iouThreshold = mIOUThreshold;
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mType = DataType::kFLOAT;
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mFeatureCnt = TLTMaskRCNNConfig::MAX_LEVEL - TLTMaskRCNNConfig::MIN_LEVEL + 1;
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generate_pyramid_anchors(mImageSize);
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}
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int32_t MultilevelProposeROI::getNbOutputs() const noexcept
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{
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return 1;
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}
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int32_t MultilevelProposeROI::initialize() noexcept
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{
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// Init the regWeight [1, 1, 1, 1]
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mRegWeightDevice = std::make_shared<CudaBind<float>>(4);
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std::vector<float> reg_weight(4, 1);
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PLUGIN_CUASSERT(cudaMemcpy(static_cast<void*>(mRegWeightDevice->mPtr), static_cast<void*>(reg_weight.data()),
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sizeof(float) * 4, cudaMemcpyHostToDevice));
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// Init the mValidCnt of max batch size
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std::vector<int32_t> tempValidCnt(mMaxBatchSize, mPreNMSTopK);
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mValidCnt = std::make_shared<CudaBind<int32_t>>(mMaxBatchSize);
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PLUGIN_CUASSERT(cudaMemcpy(mValidCnt->mPtr, static_cast<void*>(tempValidCnt.data()),
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sizeof(int32_t) * mMaxBatchSize, cudaMemcpyHostToDevice));
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// Init the anchors for batch size:
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for (int32_t i = 0; i < mFeatureCnt; i++)
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{
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int32_t i_anchors_cnt = mAnchorsCnt[i];
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auto i_anchors_host = mAnchorBoxesHost[i].data();
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auto i_anchors_device = std::make_shared<CudaBind<float>>(i_anchors_cnt * 4 * mMaxBatchSize);
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int32_t batch_offset = sizeof(float) * i_anchors_cnt * 4;
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uint8_t* device_ptr = static_cast<uint8_t*>(i_anchors_device->mPtr);
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for (int32_t i = 0; i < mMaxBatchSize; i++)
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{
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PLUGIN_CUASSERT(cudaMemcpy(static_cast<void*>(device_ptr + i * batch_offset),
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static_cast<void*>(i_anchors_host), batch_offset, cudaMemcpyHostToDevice));
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}
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mAnchorBoxesDevice.push_back(i_anchors_device);
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}
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// Init the temp storage for proposals from feature maps before concat
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std::vector<void*> score_tp;
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std::vector<void*> box_tp;
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for (int32_t i = 0; i < mFeatureCnt; i++)
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{
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if (mType == DataType::kFLOAT)
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{
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auto i_scores_device = std::make_shared<CudaBind<float>>(mKeepTopK * mMaxBatchSize);
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auto i_bboxes_device = std::make_shared<CudaBind<float>>(mKeepTopK * 4 * mMaxBatchSize);
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mTempScores_float.push_back(i_scores_device);
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score_tp.push_back(static_cast<void*>(i_scores_device->mPtr));
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mTempBboxes_float.push_back(i_bboxes_device);
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box_tp.push_back(static_cast<void*>(i_bboxes_device->mPtr));
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}
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else if (mType == DataType::kHALF)
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{
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auto i_scores_device = std::make_shared<CudaBind<uint16_t>>(mKeepTopK * mMaxBatchSize);
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auto i_bboxes_device = std::make_shared<CudaBind<uint16_t>>(mKeepTopK * 4 * mMaxBatchSize);
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mTempScores_half.push_back(i_scores_device);
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score_tp.push_back(static_cast<void*>(i_scores_device->mPtr));
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mTempBboxes_half.push_back(i_bboxes_device);
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box_tp.push_back(static_cast<void*>(i_bboxes_device->mPtr));
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}
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}
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// Init the temp storage for pointer arrays of score and box:
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PLUGIN_CUASSERT(cudaMalloc(&mDeviceScores, sizeof(void*) * mFeatureCnt));
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PLUGIN_CUASSERT(cudaMalloc(&mDeviceBboxes, sizeof(void*) * mFeatureCnt));
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PLUGIN_CUASSERT(cudaMemcpy(mDeviceScores, score_tp.data(), sizeof(void*) * mFeatureCnt, cudaMemcpyHostToDevice));
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PLUGIN_CUASSERT(cudaMemcpy(mDeviceBboxes, box_tp.data(), sizeof(void*) * mFeatureCnt, cudaMemcpyHostToDevice));
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return 0;
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}
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void MultilevelProposeROI::terminate() noexcept {}
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void MultilevelProposeROI::destroy() noexcept
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{
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delete this;
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}
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bool MultilevelProposeROI::supportsFormat(DataType type, PluginFormat format) const noexcept
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{
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return ((type == DataType::kFLOAT || type == DataType::kHALF) && format == PluginFormat::kLINEAR);
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}
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char const* MultilevelProposeROI::getPluginType() const noexcept
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{
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return "MultilevelProposeROI_TRT";
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}
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char const* MultilevelProposeROI::getPluginVersion() const noexcept
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{
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return "1";
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}
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IPluginV2Ext* MultilevelProposeROI::clone() const noexcept
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{
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try
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{
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return new MultilevelProposeROI(*this);
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}
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catch (std::exception const& e)
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{
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caughtError(e);
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}
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return nullptr;
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}
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void MultilevelProposeROI::setPluginNamespace(char const* libNamespace) noexcept
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{
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mNameSpace = libNamespace;
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}
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char const* MultilevelProposeROI::getPluginNamespace() const noexcept
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{
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return mNameSpace.c_str();
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}
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size_t MultilevelProposeROI::getSerializationSize() const noexcept
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{
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return sizeof(int32_t) * 2 + sizeof(float) * 2 + sizeof(int32_t) * (mFeatureCnt + 1) + sizeof(nvinfer1::Dims)
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+ sizeof(DataType);
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}
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void MultilevelProposeROI::serialize(void* buffer) const noexcept
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{
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char *d = reinterpret_cast<char*>(buffer), *a = d;
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write(d, mPreNMSTopK);
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write(d, mKeepTopK);
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write(d, mFGThreshold);
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write(d, mIOUThreshold);
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write(d, mMaxBatchSize);
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for (int32_t i = 0; i < mFeatureCnt; i++)
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{
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write(d, mAnchorsCnt[i]);
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}
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write(d, mImageSize);
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write(d, mType);
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PLUGIN_ASSERT(d == a + getSerializationSize());
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}
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MultilevelProposeROI::MultilevelProposeROI(void const* data, size_t length)
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{
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mFeatureCnt = TLTMaskRCNNConfig::MAX_LEVEL - TLTMaskRCNNConfig::MIN_LEVEL + 1;
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char const *d = reinterpret_cast<char const*>(data), *a = d;
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int32_t prenms_topk = read<int32_t>(d);
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int32_t keep_topk = read<int32_t>(d);
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float fg_threshold = read<float>(d);
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float iou_threshold = read<float>(d);
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mMaxBatchSize = read<int32_t>(d);
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PLUGIN_VALIDATE(mAnchorsCnt.size() == 0);
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for (int32_t i = 0; i < mFeatureCnt; i++)
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{
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mAnchorsCnt.push_back(read<int32_t>(d));
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}
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mImageSize = read<nvinfer1::Dims3>(d);
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mType = read<DataType>(d);
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PLUGIN_VALIDATE(d == a + length);
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mBackgroundLabel = -1;
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mPreNMSTopK = prenms_topk;
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mKeepTopK = keep_topk;
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mFGThreshold = fg_threshold;
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mIOUThreshold = iou_threshold;
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mParam.backgroundLabelId = -1;
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mParam.numClasses = 1;
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mParam.keepTopK = mKeepTopK;
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mParam.scoreThreshold = mFGThreshold;
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mParam.iouThreshold = mIOUThreshold;
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generate_pyramid_anchors(mImageSize);
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}
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void MultilevelProposeROI::check_valid_inputs(nvinfer1::Dims const* inputs, int32_t nbInputDims) noexcept
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{
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// x=2,3,4,5,6
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// foreground_delta_px [N, h_x * w_x * anchors_per_location, 4, 1],
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// foreground_score_px [N, h_x * w_x * anchors_per_location, 1, 1],
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// anchors should be generated inside
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PLUGIN_ASSERT(nbInputDims == 2 * mFeatureCnt);
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for (int32_t i = 0; i < 2 * mFeatureCnt; i += 2)
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{
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// foreground_delta
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PLUGIN_ASSERT(inputs[i].nbDims == 3 && inputs[i].d[1] == 4);
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// foreground_score
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PLUGIN_ASSERT(inputs[i + 1].nbDims == 3 && inputs[i + 1].d[1] == 1);
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}
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}
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size_t MultilevelProposeROI::getWorkspaceSize(int32_t batch_size) const noexcept
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{
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size_t total_size = 0;
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PLUGIN_ASSERT(mAnchorsCnt.size() == static_cast<size_t>(mFeatureCnt));
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// workspace for propose on each feature map
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for (int32_t i = 0; i < mFeatureCnt; i++)
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{
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MultilevelProposeROIWorkSpace proposal(batch_size, mAnchorsCnt[i], mPreNMSTopK, mParam, mType);
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total_size += proposal.totalSize;
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}
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// workspace for Concat and TopK
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ConcatTopKWorkSpace ct(batch_size, mFeatureCnt, mKeepTopK, mType);
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total_size += ct.totalSize;
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return total_size;
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}
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Dims MultilevelProposeROI::getOutputDimensions(int32_t index, Dims const* inputs, int32_t nbInputDims) noexcept
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{
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check_valid_inputs(inputs, nbInputDims);
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PLUGIN_ASSERT(index == 0);
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return {2, {mKeepTopK, 4}};
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}
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void MultilevelProposeROI::generate_pyramid_anchors(nvinfer1::Dims const& imageSize)
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{
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auto const image_dims = imageSize;
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auto const& anchor_scale = TLTMaskRCNNConfig::RPN_ANCHOR_SCALE;
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auto const& min_level = TLTMaskRCNNConfig::MIN_LEVEL;
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auto const& max_level = TLTMaskRCNNConfig::MAX_LEVEL;
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auto const& aspect_ratios = TLTMaskRCNNConfig::ANCHOR_RATIOS;
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// Generate anchors strides and scales
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std::vector<float> anchor_scales;
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std::vector<int32_t> anchor_strides;
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for (int32_t i = min_level; i < max_level + 1; i++)
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{
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int32_t stride = static_cast<int32_t>(pow(2.0, i));
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anchor_strides.push_back(stride);
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anchor_scales.push_back(stride * anchor_scale);
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}
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auto& anchors = mAnchorBoxesHost;
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PLUGIN_VALIDATE(anchors.size() == 0);
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PLUGIN_VALIDATE(anchor_scales.size() == anchor_strides.size());
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for (size_t s = 0; s < anchor_scales.size(); ++s)
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{
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float scale = anchor_scales[s];
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int32_t stride = anchor_strides[s];
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std::vector<float> s_anchors;
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for (int32_t y = stride / 2; y < image_dims.d[1]; y += stride)
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for (int32_t x = stride / 2; x < image_dims.d[2]; x += stride)
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for (auto r : aspect_ratios)
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{
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float h = scale * r.second;
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float w = scale * r.first;
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// Using y+h/2 instead of y+h/2-1 for alignment with TLT implementation
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s_anchors.insert(s_anchors.end(), {(y - h / 2), (x - w / 2), (y + h / 2), (x + w / 2)});
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}
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anchors.push_back(s_anchors);
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}
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PLUGIN_VALIDATE(anchors.size() == static_cast<size_t>(max_level - min_level + 1));
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}
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int32_t MultilevelProposeROI::enqueue(
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int32_t batch_size, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept
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{
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void* final_proposals = outputs[0];
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size_t kernel_workspace_offset = 0;
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cudaError_t status;
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std::vector<void*> mTempScores;
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std::vector<void*> mTempBboxes;
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for (int32_t i = 0; i < mFeatureCnt; i++)
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{
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if (mType == DataType::kFLOAT)
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{
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mTempScores.push_back(mTempScores_float[i]->mPtr);
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mTempBboxes.push_back(mTempBboxes_float[i]->mPtr);
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}
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else if (mType == DataType::kHALF)
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{
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mTempScores.push_back(mTempScores_half[i]->mPtr);
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mTempBboxes.push_back(mTempBboxes_half[i]->mPtr);
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}
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}
|
|
|
|
for (int32_t i = 0; i < mFeatureCnt; i++)
|
|
{
|
|
MultilevelProposeROIWorkSpace proposal_ws(batch_size, mAnchorsCnt[i], mPreNMSTopK, mParam, mType);
|
|
status = MultilevelPropose(stream, batch_size, mAnchorsCnt[i], mPreNMSTopK,
|
|
static_cast<float*>(mRegWeightDevice->mPtr),
|
|
static_cast<float>(mImageSize.d[1]), // Input Height
|
|
static_cast<float>(mImageSize.d[2]),
|
|
mType, // mType,
|
|
mParam, proposal_ws, static_cast<uint8_t*>(workspace) + kernel_workspace_offset,
|
|
inputs[2 * i + 1], // inputs[object_score],
|
|
inputs[2 * i], // inputs[bbox_delta]
|
|
mValidCnt->mPtr,
|
|
mAnchorBoxesDevice[i]->mPtr, // inputs[anchors]
|
|
mTempScores[i], // temp scores [batch_size, topk, 1]
|
|
mTempBboxes[i]); // temp
|
|
PLUGIN_ASSERT(status == cudaSuccess);
|
|
kernel_workspace_offset += proposal_ws.totalSize;
|
|
}
|
|
|
|
ConcatTopKWorkSpace ctopk_ws(batch_size, mFeatureCnt, mKeepTopK, mType);
|
|
status = ConcatTopK(stream, batch_size, mFeatureCnt, mKeepTopK, mType,
|
|
static_cast<uint8_t*>(workspace) + kernel_workspace_offset, ctopk_ws, reinterpret_cast<void**>(mDeviceScores),
|
|
reinterpret_cast<void**>(mDeviceBboxes), final_proposals);
|
|
|
|
PLUGIN_ASSERT(status == cudaSuccess);
|
|
return status;
|
|
}
|
|
|
|
// Return the DataType of the plugin output at the requested index
|
|
DataType MultilevelProposeROI::getOutputDataType(
|
|
int32_t index, nvinfer1::DataType const* inputTypes, int32_t nbInputs) const noexcept
|
|
{
|
|
// Only DataType::kFLOAT is acceptable by the plugin layer
|
|
if ((inputTypes[0] == DataType::kFLOAT) || (inputTypes[0] == DataType::kHALF))
|
|
return inputTypes[0];
|
|
return DataType::kFLOAT;
|
|
}
|
|
|
|
// Configure the layer with input and output data types.
|
|
void MultilevelProposeROI::configurePlugin(Dims const* inputDims, int32_t nbInputs, Dims const* outputDims,
|
|
int32_t nbOutputs, DataType const* inputTypes, DataType const* outputTypes, bool const* inputIsBroadcast,
|
|
bool const* outputIsBroadcast, PluginFormat floatFormat, int32_t maxBatchSize) noexcept
|
|
{
|
|
check_valid_inputs(inputDims, nbInputs);
|
|
|
|
mAnchorsCnt.clear();
|
|
for (int32_t i = 0; i < mFeatureCnt; i++)
|
|
{
|
|
mAnchorsCnt.push_back(inputDims[2 * i].d[0]);
|
|
PLUGIN_ASSERT(mAnchorsCnt[i] == (int32_t) (mAnchorBoxesHost[i].size() / 4));
|
|
}
|
|
|
|
mMaxBatchSize = maxBatchSize;
|
|
|
|
mType = inputTypes[0];
|
|
}
|
|
|
|
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
|
|
void MultilevelProposeROI::attachToContext(
|
|
cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) noexcept
|
|
{
|
|
}
|
|
|
|
// Detach the plugin object from its execution context.
|
|
void MultilevelProposeROI::detachFromContext() noexcept {}
|