chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
@@ -0,0 +1,514 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "multilevelProposeROIPlugin.h"
+#include "common/plugin.h"
+#include "multilevelProposeROI/tlt_mrcnn_config.h"
+#include <algorithm>
+#include <cuda_runtime_api.h>
+#include <iostream>
+#include <math.h>
+#include <string_view>
+
+#include <fstream>
+
+using namespace nvinfer1;
+using namespace plugin;
+using nvinfer1::plugin::MultilevelProposeROI;
+using nvinfer1::plugin::MultilevelProposeROIPluginCreator;
+
+namespace
+{
+char const* const kMULTILEVELPROPOSEROI_PLUGIN_VERSION{"1"};
+char const* const kMULTILEVELPROPOSEROI_PLUGIN_NAME{"MultilevelProposeROI_TRT"};
+} // namespace
+
+MultilevelProposeROIPluginCreator::MultilevelProposeROIPluginCreator() noexcept
+{
+    mPluginAttributes.clear();
+    mPluginAttributes.emplace_back(PluginField("prenms_topk", nullptr, PluginFieldType::kINT32, 1));
+    mPluginAttributes.emplace_back(PluginField("keep_topk", nullptr, PluginFieldType::kINT32, 1));
+    mPluginAttributes.emplace_back(PluginField("fg_threshold", nullptr, PluginFieldType::kFLOAT32, 1));
+    mPluginAttributes.emplace_back(PluginField("iou_threshold", nullptr, PluginFieldType::kFLOAT32, 1));
+    mPluginAttributes.emplace_back(PluginField("image_size", nullptr, PluginFieldType::kINT32, 3));
+
+    mFC.nbFields = mPluginAttributes.size();
+    mFC.fields = mPluginAttributes.data();
+}
+
+char const* MultilevelProposeROIPluginCreator::getPluginName() const noexcept
+{
+    return kMULTILEVELPROPOSEROI_PLUGIN_NAME;
+}
+
+char const* MultilevelProposeROIPluginCreator::getPluginVersion() const noexcept
+{
+    return kMULTILEVELPROPOSEROI_PLUGIN_VERSION;
+}
+
+PluginFieldCollection const* MultilevelProposeROIPluginCreator::getFieldNames() noexcept
+{
+    return &mFC;
+}
+
+IPluginV2Ext* MultilevelProposeROIPluginCreator::createPlugin(
+    char const* name, PluginFieldCollection const* fc) noexcept
+{
+    try
+    {
+        using namespace std::string_view_literals;
+        plugin::validateRequiredAttributesExist({"prenms_topk", "keep_topk", "fg_threshold", "iou_threshold"}, fc);
+        auto imageSize = TLTMaskRCNNConfig::IMAGE_SHAPE;
+        PluginField const* fields = fc->fields;
+        for (int32_t i = 0; i < fc->nbFields; ++i)
+        {
+            std::string_view const attrName = fields[i].name;
+            if (attrName == "prenms_topk"sv)
+            {
+                PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
+                mPreNMSTopK = *(static_cast<int32_t const*>(fields[i].data));
+            }
+            if (attrName == "keep_topk"sv)
+            {
+                PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
+                mKeepTopK = *(static_cast<int32_t const*>(fields[i].data));
+            }
+            if (attrName == "fg_threshold"sv)
+            {
+                PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
+                mFGThreshold = *(static_cast<float const*>(fields[i].data));
+            }
+            if (attrName == "iou_threshold"sv)
+            {
+                PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
+                mIOUThreshold = *(static_cast<float const*>(fields[i].data));
+            }
+            if (attrName == "image_size"sv)
+            {
+                PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
+                auto const dims = static_cast<int32_t const*>(fields[i].data);
+                std::copy_n(dims, 3, imageSize.d);
+            }
+        }
+        return new MultilevelProposeROI(mPreNMSTopK, mKeepTopK, mFGThreshold, mIOUThreshold, imageSize);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+IPluginV2Ext* MultilevelProposeROIPluginCreator::deserializePlugin(
+    char const* name, void const* data, size_t length) noexcept
+{
+    try
+    {
+        return new MultilevelProposeROI(data, length);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+MultilevelProposeROI::MultilevelProposeROI(
+    int32_t prenms_topk, int32_t keep_topk, float fg_threshold, float iou_threshold, const nvinfer1::Dims imageSize)
+    : mPreNMSTopK(prenms_topk)
+    , mKeepTopK(keep_topk)
+    , mFGThreshold(fg_threshold)
+    , mIOUThreshold(iou_threshold)
+    , mImageSize(imageSize)
+{
+    mBackgroundLabel = -1;
+    PLUGIN_VALIDATE(mPreNMSTopK > 0);
+    PLUGIN_VALIDATE(mPreNMSTopK <= 4096);
+    PLUGIN_VALIDATE(mKeepTopK > 0);
+    PLUGIN_VALIDATE(mIOUThreshold >= 0.0F);
+    PLUGIN_VALIDATE(mFGThreshold >= 0.0F);
+    PLUGIN_VALIDATE(mImageSize.nbDims == 3);
+    PLUGIN_VALIDATE(mImageSize.d[0] > 0 && mImageSize.d[1] > 0 && mImageSize.d[2] > 0);
+
+    mParam.backgroundLabelId = -1;
+    mParam.numClasses = 1;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = mFGThreshold;
+    mParam.iouThreshold = mIOUThreshold;
+
+    mType = DataType::kFLOAT;
+
+    mFeatureCnt = TLTMaskRCNNConfig::MAX_LEVEL - TLTMaskRCNNConfig::MIN_LEVEL + 1;
+
+    generate_pyramid_anchors(mImageSize);
+}
+
+int32_t MultilevelProposeROI::getNbOutputs() const noexcept
+{
+    return 1;
+}
+
+int32_t MultilevelProposeROI::initialize() noexcept
+{
+    // Init the regWeight [1, 1, 1, 1]
+    mRegWeightDevice = std::make_shared<CudaBind<float>>(4);
+    std::vector<float> reg_weight(4, 1);
+    PLUGIN_CUASSERT(cudaMemcpy(static_cast<void*>(mRegWeightDevice->mPtr), static_cast<void*>(reg_weight.data()),
+        sizeof(float) * 4, cudaMemcpyHostToDevice));
+
+    // Init the mValidCnt of max batch size
+    std::vector<int32_t> tempValidCnt(mMaxBatchSize, mPreNMSTopK);
+
+    mValidCnt = std::make_shared<CudaBind<int32_t>>(mMaxBatchSize);
+
+    PLUGIN_CUASSERT(cudaMemcpy(mValidCnt->mPtr, static_cast<void*>(tempValidCnt.data()),
+        sizeof(int32_t) * mMaxBatchSize, cudaMemcpyHostToDevice));
+
+    // Init the anchors for batch size:
+    for (int32_t i = 0; i < mFeatureCnt; i++)
+    {
+        int32_t i_anchors_cnt = mAnchorsCnt[i];
+        auto i_anchors_host = mAnchorBoxesHost[i].data();
+        auto i_anchors_device = std::make_shared<CudaBind<float>>(i_anchors_cnt * 4 * mMaxBatchSize);
+        int32_t batch_offset = sizeof(float) * i_anchors_cnt * 4;
+        uint8_t* device_ptr = static_cast<uint8_t*>(i_anchors_device->mPtr);
+        for (int32_t i = 0; i < mMaxBatchSize; i++)
+        {
+            PLUGIN_CUASSERT(cudaMemcpy(static_cast<void*>(device_ptr + i * batch_offset),
+                static_cast<void*>(i_anchors_host), batch_offset, cudaMemcpyHostToDevice));
+        }
+        mAnchorBoxesDevice.push_back(i_anchors_device);
+    }
+
+    // Init the temp storage for proposals from feature maps before concat
+    std::vector<void*> score_tp;
+    std::vector<void*> box_tp;
+    for (int32_t i = 0; i < mFeatureCnt; i++)
+    {
+        if (mType == DataType::kFLOAT)
+        {
+            auto i_scores_device = std::make_shared<CudaBind<float>>(mKeepTopK * mMaxBatchSize);
+            auto i_bboxes_device = std::make_shared<CudaBind<float>>(mKeepTopK * 4 * mMaxBatchSize);
+            mTempScores_float.push_back(i_scores_device);
+            score_tp.push_back(static_cast<void*>(i_scores_device->mPtr));
+            mTempBboxes_float.push_back(i_bboxes_device);
+            box_tp.push_back(static_cast<void*>(i_bboxes_device->mPtr));
+        }
+        else if (mType == DataType::kHALF)
+        {
+            auto i_scores_device = std::make_shared<CudaBind<uint16_t>>(mKeepTopK * mMaxBatchSize);
+            auto i_bboxes_device = std::make_shared<CudaBind<uint16_t>>(mKeepTopK * 4 * mMaxBatchSize);
+            mTempScores_half.push_back(i_scores_device);
+            score_tp.push_back(static_cast<void*>(i_scores_device->mPtr));
+            mTempBboxes_half.push_back(i_bboxes_device);
+            box_tp.push_back(static_cast<void*>(i_bboxes_device->mPtr));
+        }
+    }
+
+    // Init the temp storage for pointer arrays of score and box:
+    PLUGIN_CUASSERT(cudaMalloc(&mDeviceScores, sizeof(void*) * mFeatureCnt));
+    PLUGIN_CUASSERT(cudaMalloc(&mDeviceBboxes, sizeof(void*) * mFeatureCnt));
+
+    PLUGIN_CUASSERT(cudaMemcpy(mDeviceScores, score_tp.data(), sizeof(void*) * mFeatureCnt, cudaMemcpyHostToDevice));
+    PLUGIN_CUASSERT(cudaMemcpy(mDeviceBboxes, box_tp.data(), sizeof(void*) * mFeatureCnt, cudaMemcpyHostToDevice));
+
+    return 0;
+}
+
+void MultilevelProposeROI::terminate() noexcept {}
+
+void MultilevelProposeROI::destroy() noexcept
+{
+    delete this;
+}
+
+bool MultilevelProposeROI::supportsFormat(DataType type, PluginFormat format) const noexcept
+{
+    return ((type == DataType::kFLOAT || type == DataType::kHALF) && format == PluginFormat::kLINEAR);
+}
+
+char const* MultilevelProposeROI::getPluginType() const noexcept
+{
+    return "MultilevelProposeROI_TRT";
+}
+
+char const* MultilevelProposeROI::getPluginVersion() const noexcept
+{
+    return "1";
+}
+
+IPluginV2Ext* MultilevelProposeROI::clone() const noexcept
+{
+    try
+    {
+        return new MultilevelProposeROI(*this);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+void MultilevelProposeROI::setPluginNamespace(char const* libNamespace) noexcept
+{
+    mNameSpace = libNamespace;
+}
+
+char const* MultilevelProposeROI::getPluginNamespace() const noexcept
+{
+    return mNameSpace.c_str();
+}
+
+size_t MultilevelProposeROI::getSerializationSize() const noexcept
+{
+    return sizeof(int32_t) * 2 + sizeof(float) * 2 + sizeof(int32_t) * (mFeatureCnt + 1) + sizeof(nvinfer1::Dims)
+        + sizeof(DataType);
+}
+
+void MultilevelProposeROI::serialize(void* buffer) const noexcept
+{
+    char *d = reinterpret_cast<char*>(buffer), *a = d;
+    write(d, mPreNMSTopK);
+    write(d, mKeepTopK);
+    write(d, mFGThreshold);
+    write(d, mIOUThreshold);
+    write(d, mMaxBatchSize);
+    for (int32_t i = 0; i < mFeatureCnt; i++)
+    {
+        write(d, mAnchorsCnt[i]);
+    }
+    write(d, mImageSize);
+    write(d, mType);
+    PLUGIN_ASSERT(d == a + getSerializationSize());
+}
+
+MultilevelProposeROI::MultilevelProposeROI(void const* data, size_t length)
+{
+    mFeatureCnt = TLTMaskRCNNConfig::MAX_LEVEL - TLTMaskRCNNConfig::MIN_LEVEL + 1;
+
+    char const *d = reinterpret_cast<char const*>(data), *a = d;
+    int32_t prenms_topk = read<int32_t>(d);
+    int32_t keep_topk = read<int32_t>(d);
+    float fg_threshold = read<float>(d);
+    float iou_threshold = read<float>(d);
+    mMaxBatchSize = read<int32_t>(d);
+    PLUGIN_VALIDATE(mAnchorsCnt.size() == 0);
+    for (int32_t i = 0; i < mFeatureCnt; i++)
+    {
+        mAnchorsCnt.push_back(read<int32_t>(d));
+    }
+    mImageSize = read<nvinfer1::Dims3>(d);
+    mType = read<DataType>(d);
+    PLUGIN_VALIDATE(d == a + length);
+
+    mBackgroundLabel = -1;
+    mPreNMSTopK = prenms_topk;
+    mKeepTopK = keep_topk;
+    mFGThreshold = fg_threshold;
+    mIOUThreshold = iou_threshold;
+
+    mParam.backgroundLabelId = -1;
+    mParam.numClasses = 1;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = mFGThreshold;
+    mParam.iouThreshold = mIOUThreshold;
+
+    generate_pyramid_anchors(mImageSize);
+}
+
+void MultilevelProposeROI::check_valid_inputs(nvinfer1::Dims const* inputs, int32_t nbInputDims) noexcept
+{
+    // x=2,3,4,5,6
+    // foreground_delta_px [N, h_x * w_x * anchors_per_location, 4, 1],
+    // foreground_score_px [N, h_x * w_x * anchors_per_location, 1, 1],
+    // anchors should be generated inside
+    PLUGIN_ASSERT(nbInputDims == 2 * mFeatureCnt);
+    for (int32_t i = 0; i < 2 * mFeatureCnt; i += 2)
+    {
+        // foreground_delta
+        PLUGIN_ASSERT(inputs[i].nbDims == 3 && inputs[i].d[1] == 4);
+        // foreground_score
+        PLUGIN_ASSERT(inputs[i + 1].nbDims == 3 && inputs[i + 1].d[1] == 1);
+    }
+}
+
+size_t MultilevelProposeROI::getWorkspaceSize(int32_t batch_size) const noexcept
+{
+    size_t total_size = 0;
+    PLUGIN_ASSERT(mAnchorsCnt.size() == static_cast<size_t>(mFeatureCnt));
+
+    // workspace for propose on each feature map
+    for (int32_t i = 0; i < mFeatureCnt; i++)
+    {
+
+        MultilevelProposeROIWorkSpace proposal(batch_size, mAnchorsCnt[i], mPreNMSTopK, mParam, mType);
+        total_size += proposal.totalSize;
+    }
+
+    // workspace for Concat and TopK
+    ConcatTopKWorkSpace ct(batch_size, mFeatureCnt, mKeepTopK, mType);
+    total_size += ct.totalSize;
+
+    return total_size;
+}
+
+Dims MultilevelProposeROI::getOutputDimensions(int32_t index, Dims const* inputs, int32_t nbInputDims) noexcept
+{
+
+    check_valid_inputs(inputs, nbInputDims);
+    PLUGIN_ASSERT(index == 0);
+
+    return {2, {mKeepTopK, 4}};
+}
+
+void MultilevelProposeROI::generate_pyramid_anchors(nvinfer1::Dims const& imageSize)
+{
+    auto const image_dims = imageSize;
+
+    auto const& anchor_scale = TLTMaskRCNNConfig::RPN_ANCHOR_SCALE;
+    auto const& min_level = TLTMaskRCNNConfig::MIN_LEVEL;
+    auto const& max_level = TLTMaskRCNNConfig::MAX_LEVEL;
+    auto const& aspect_ratios = TLTMaskRCNNConfig::ANCHOR_RATIOS;
+
+    // Generate anchors strides and scales
+    std::vector<float> anchor_scales;
+    std::vector<int32_t> anchor_strides;
+    for (int32_t i = min_level; i < max_level + 1; i++)
+    {
+        int32_t stride = static_cast<int32_t>(pow(2.0, i));
+        anchor_strides.push_back(stride);
+        anchor_scales.push_back(stride * anchor_scale);
+    }
+
+    auto& anchors = mAnchorBoxesHost;
+    PLUGIN_VALIDATE(anchors.size() == 0);
+
+    PLUGIN_VALIDATE(anchor_scales.size() == anchor_strides.size());
+    for (size_t s = 0; s < anchor_scales.size(); ++s)
+    {
+        float scale = anchor_scales[s];
+        int32_t stride = anchor_strides[s];
+
+        std::vector<float> s_anchors;
+        for (int32_t y = stride / 2; y < image_dims.d[1]; y += stride)
+            for (int32_t x = stride / 2; x < image_dims.d[2]; x += stride)
+                for (auto r : aspect_ratios)
+                {
+                    float h = scale * r.second;
+                    float w = scale * r.first;
+
+                    // Using y+h/2 instead of y+h/2-1 for alignment with TLT implementation
+                    s_anchors.insert(s_anchors.end(), {(y - h / 2), (x - w / 2), (y + h / 2), (x + w / 2)});
+                }
+
+        anchors.push_back(s_anchors);
+    }
+
+    PLUGIN_VALIDATE(anchors.size() == static_cast<size_t>(max_level - min_level + 1));
+}
+
+int32_t MultilevelProposeROI::enqueue(
+    int32_t batch_size, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept
+{
+
+    void* final_proposals = outputs[0];
+    size_t kernel_workspace_offset = 0;
+    cudaError_t status;
+
+    std::vector<void*> mTempScores;
+    std::vector<void*> mTempBboxes;
+
+    for (int32_t i = 0; i < mFeatureCnt; i++)
+    {
+        if (mType == DataType::kFLOAT)
+        {
+            mTempScores.push_back(mTempScores_float[i]->mPtr);
+            mTempBboxes.push_back(mTempBboxes_float[i]->mPtr);
+        }
+        else if (mType == DataType::kHALF)
+        {
+            mTempScores.push_back(mTempScores_half[i]->mPtr);
+            mTempBboxes.push_back(mTempBboxes_half[i]->mPtr);
+        }
+    }
+
+    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 {}