chore: import upstream snapshot with attribution

plugin/generateDetectionPlugin/CMakeLists.txt

@@ -0,0 +1,22 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2025 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.
+#
+
+add_plugin_source(
+    generateDetectionPlugin.cpp
+    generateDetectionPlugin.h
+)
+

plugin/generateDetectionPlugin/GenerateDetection_PluginConfig.yaml

@@ -0,0 +1,61 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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.
+#
+---
+name: GenerateDetection_TRT
+interface: "IPluginV2Ext"
+versions:
+  "1":
+    attributes:
+      - num_classes
+      - keep_topk
+      - score_threshold
+      - iou_threshold
+      - image_size
+    attribute_types:
+      num_classes: int32
+      keep_topk: int32
+      score_threshold: float32
+      iou_threshold: float32
+      image_size: int32
+    attribute_length:
+      num_classes: 1
+      keep_topk: 1
+      score_threshold: 1
+      iou_threshold: 1
+      image_size: 3
+    attribute_options:
+      num_classes:
+        min: "0"
+        max: "=pinf"
+      keep_topk:
+        min: "0"
+        max: "=pinf"
+      score_threshold:
+        min: "=0"
+        max: "=pinf"
+      iou_threshold:
+        min: "0"
+        max: "=pinf"
+      image_size:
+        min: "0, 0, 0"
+        max: "=pinf, =pinf, =pinf"
+    attributes_required:
+      - num_classes
+      - keep_topk
+      - score_threshold
+      - iou_threshold
+...

plugin/generateDetectionPlugin/README.md

@@ -0,0 +1,76 @@
+# generateDetection Plugin [DEPRECATED]
+
+**This plugin is deprecated since TensorRT 10.12 and will be removed in a future release. No alternatives are planned to be provided.**
+
+**Table Of Contents**
+- [Description](#description)
+    * [Structure](#structure)
+- [Parameters](#parameters)
+- [Additional resources](#additional-resources)
+- [License](#license)
+- [Changelog](#changelog)
+- [Known issues](#known-issues)
+
+## Description
+
+The `generateDetection` plugin performs bounding boxe refinement of MaskRCNN's detection head and generates the final detection output of MaskRCNN.   
+
+
+### Structure
+
+This plugin supports the NCHW format. It takes three input tensors: `delta_bbox`, `score` and `roi`
+
+`delta_bbox` is the refinement information of roi boxes generated from the `MultilevelProposeROI` plugin. `delta_bbox` tensor's shape is `[N, rois, num_classes*4, 1, 1]` where `N` is batch size,
+`rois` is the total number of ROI boxes candidates per image, and `num_classes*4` means 4 refinement elements (`[dy, dx, dh, dw]`) for each roi box as different classes.
+
+`score` is the predicted class scores of ROI boxes generated from MaskRCNN detection head of shape `[N, rois, num_classes, 1, 1]`. There is an `argmax` operation in `generateDetection` to determine the final class of detection
+candidates.   
+
+`roi` is the coordinates of ROI boxes candidates from the `MultilevelProposeROI` plugin of shape `[N, rois, 4]`. 
+
+This plugin generates output of shape `[N, keep_topk, 6]` where `keep_topk` is the maximum number of detections left after NMS and '6' means 6 elements of an detection `[y1, x1, y2, x2,
+class_label, score]`
+
+## Parameters
+
+This plugin has the plugin creator class `generateDetectionPluginCreator` and the plugin class `generateDetection`.
+  
+The following parameters were used to create `generateDetection` instance:
+
+| Type               | Parameter                          | Description
+|--------------------|------------------------------------|--------------------------------------------------------
+|`int`               |`num_classes`                       |Number of detection classes(including `background`). `num_classes=91` for COCO dataset
+|`int`               |`keep_topk`                         |Number of detections will be kept after NMS.  
+|`float`             |`score_threshold`                   |Confidence threshold value. This plugin will drop a detection if its class confidence(score) is under "score_threshold". 
+|`float`             |`iou_threshold`                     |IOU threshold value used in NMS.
+|`int[3]`            |`image_size`                        |Input image size in CHW. Defaults to [3,832,1344]
+
+## Limitations
+
+The number of anchors is capped at 2048 to support embedded devices with smaller shared memory capacity.
+
+To enable support for a device with higher memory, calls to `sortPerClass` and `KeepTopKGather` can be modified in `DetectionPostProcess` ([maskRCNNKernels.cu](https://github.com/NVIDIA/TensorRT/blob/main/plugin/common/kernels/maskRCNNKernels.cu)).
+
+## Additional resources
+
+
+
+## License
+
+For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) 
+documentation.
+
+
+## Changelog
+
+May 2025
+Add deprecation note.
+
+January 2022: The [Limitations](#limitations) section was added to this `README.md` file to document limitations of the plugin related to the maximum number of anchors it can support. 
+
+June 2020: First release of this `README.md` file.
+
+
+## Known issues
+
+There are no known issues in this plugin.

plugin/generateDetectionPlugin/generateDetectionPlugin.cpp

@@ -0,0 +1,350 @@
+/*
+ * 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 "generateDetectionPlugin.h"
+#include "common/plugin.h"
+#include <algorithm>
+#include <cuda_runtime_api.h>
+#include <string_view>
+
+using namespace nvinfer1;
+using namespace plugin;
+using nvinfer1::plugin::GenerateDetection;
+using nvinfer1::plugin::GenerateDetectionPluginCreator;
+
+#include <fstream>
+
+namespace
+{
+char const* const kGENERATEDETECTION_PLUGIN_VERSION{"1"};
+char const* const kGENERATEDETECTION_PLUGIN_NAME{"GenerateDetection_TRT"};
+} // namespace
+
+GenerateDetectionPluginCreator::GenerateDetectionPluginCreator() noexcept
+{
+    mPluginAttributes.clear();
+    mPluginAttributes.emplace_back(PluginField("num_classes", nullptr, PluginFieldType::kINT32, 1));
+    mPluginAttributes.emplace_back(PluginField("keep_topk", nullptr, PluginFieldType::kINT32, 1));
+    mPluginAttributes.emplace_back(PluginField("score_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* GenerateDetectionPluginCreator::getPluginName() const noexcept
+{
+    return kGENERATEDETECTION_PLUGIN_NAME;
+}
+
+char const* GenerateDetectionPluginCreator::getPluginVersion() const noexcept
+{
+    return kGENERATEDETECTION_PLUGIN_VERSION;
+}
+
+PluginFieldCollection const* GenerateDetectionPluginCreator::getFieldNames() noexcept
+{
+    return &mFC;
+}
+
+IPluginV2Ext* GenerateDetectionPluginCreator::createPlugin(char const* name, PluginFieldCollection const* fc) noexcept
+{
+    try
+    {
+        using namespace std::string_view_literals;
+        auto image_size = TLTMaskRCNNConfig::IMAGE_SHAPE;
+        PluginField const* fields = fc->fields;
+        plugin::validateRequiredAttributesExist({"num_classes", "keep_topk", "score_threshold", "iou_threshold"}, fc);
+
+        for (int32_t i = 0; i < fc->nbFields; ++i)
+        {
+            std::string_view const attrName = fields[i].name;
+            if (attrName == "num_classes"sv)
+            {
+                PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
+                mNbClasses = *(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 == "score_threshold"sv)
+            {
+                PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
+                mScoreThreshold = *(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, image_size.d);
+            }
+        }
+        return new GenerateDetection(mNbClasses, mKeepTopK, mScoreThreshold, mIOUThreshold, image_size);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+IPluginV2Ext* GenerateDetectionPluginCreator::deserializePlugin(
+    char const* name, void const* data, size_t length) noexcept
+{
+    try
+    {
+        return new GenerateDetection(data, length);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+GenerateDetection::GenerateDetection(int32_t num_classes, int32_t keep_topk, float score_threshold, float iou_threshold,
+    nvinfer1::Dims const& image_size)
+    : mNbClasses(num_classes)
+    , mKeepTopK(keep_topk)
+    , mScoreThreshold(score_threshold)
+    , mIOUThreshold(iou_threshold)
+    , mImageSize(image_size)
+{
+    mBackgroundLabel = 0;
+    PLUGIN_VALIDATE(mNbClasses > 0);
+    PLUGIN_VALIDATE(mKeepTopK > 0);
+    PLUGIN_VALIDATE(score_threshold >= 0.0F);
+    PLUGIN_VALIDATE(iou_threshold > 0.0F);
+    PLUGIN_VALIDATE(mImageSize.nbDims == 3);
+    PLUGIN_VALIDATE(mImageSize.d[0] > 0 && mImageSize.d[1] > 0 && mImageSize.d[2] > 0);
+
+    mParam.backgroundLabelId = 0;
+    mParam.numClasses = mNbClasses;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = mScoreThreshold;
+    mParam.iouThreshold = mIOUThreshold;
+
+    mType = DataType::kFLOAT;
+}
+
+int32_t GenerateDetection::getNbOutputs() const noexcept
+{
+    return 1;
+}
+
+int32_t GenerateDetection::initialize() noexcept
+{
+    // Init the regWeight [10, 10, 5, 5]
+    mRegWeightDevice = std::make_shared<CudaBind<float>>(4);
+    PLUGIN_CUASSERT(cudaMemcpy(static_cast<void*>(mRegWeightDevice->mPtr),
+        static_cast<void const*>(TLTMaskRCNNConfig::DETECTION_REG_WEIGHTS), sizeof(float) * 4, cudaMemcpyHostToDevice));
+
+    //@Init the mValidCnt and mDecodedBboxes for max batch size
+    std::vector<int32_t> tempValidCnt(mMaxBatchSize, mAnchorsCnt);
+
+    mValidCnt = std::make_shared<CudaBind<int32_t>>(mMaxBatchSize);
+
+    PLUGIN_CUASSERT(cudaMemcpy(mValidCnt->mPtr, static_cast<void*>(tempValidCnt.data()),
+        sizeof(int32_t) * mMaxBatchSize, cudaMemcpyHostToDevice));
+
+    return 0;
+}
+
+void GenerateDetection::terminate() noexcept {}
+
+void GenerateDetection::destroy() noexcept
+{
+    delete this;
+}
+
+bool GenerateDetection::supportsFormat(DataType type, PluginFormat format) const noexcept
+{
+    return (type == DataType::kFLOAT && format == PluginFormat::kLINEAR);
+}
+
+char const* GenerateDetection::getPluginType() const noexcept
+{
+    return "GenerateDetection_TRT";
+}
+
+char const* GenerateDetection::getPluginVersion() const noexcept
+{
+    return "1";
+}
+
+IPluginV2Ext* GenerateDetection::clone() const noexcept
+{
+    try
+    {
+        return new GenerateDetection(*this);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+void GenerateDetection::setPluginNamespace(char const* libNamespace) noexcept
+{
+    mNameSpace = libNamespace;
+}
+
+char const* GenerateDetection::getPluginNamespace() const noexcept
+{
+    return mNameSpace.c_str();
+}
+
+size_t GenerateDetection::getSerializationSize() const noexcept
+{
+    return sizeof(int32_t) * 2 + sizeof(float) * 2 + sizeof(int32_t) * 2 + sizeof(nvinfer1::Dims);
+}
+
+void GenerateDetection::serialize(void* buffer) const noexcept
+{
+    char *d = reinterpret_cast<char*>(buffer), *a = d;
+    write(d, mNbClasses);
+    write(d, mKeepTopK);
+    write(d, mScoreThreshold);
+    write(d, mIOUThreshold);
+    write(d, mMaxBatchSize);
+    write(d, mAnchorsCnt);
+    write(d, mImageSize);
+    PLUGIN_ASSERT(d == a + getSerializationSize());
+}
+
+GenerateDetection::GenerateDetection(void const* data, size_t length)
+{
+    deserialize(static_cast<int8_t const*>(data), length);
+}
+
+void GenerateDetection::deserialize(int8_t const* data, size_t length)
+{
+    auto const* d{data};
+    int32_t num_classes = read<int32_t>(d);
+    int32_t keep_topk = read<int32_t>(d);
+    float score_threshold = read<float>(d);
+    float iou_threshold = read<float>(d);
+    mMaxBatchSize = read<int32_t>(d);
+    mAnchorsCnt = read<int32_t>(d);
+    mImageSize = read<nvinfer1::Dims3>(d);
+    PLUGIN_VALIDATE(d == data + length);
+
+    mNbClasses = num_classes;
+    mKeepTopK = keep_topk;
+    mScoreThreshold = score_threshold;
+    mIOUThreshold = iou_threshold;
+
+    mParam.backgroundLabelId = 0;
+    mParam.numClasses = mNbClasses;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = mScoreThreshold;
+    mParam.iouThreshold = mIOUThreshold;
+
+    mType = DataType::kFLOAT;
+}
+
+void GenerateDetection::check_valid_inputs(nvinfer1::Dims const* inputs, int32_t nbInputDims) noexcept
+{
+    // classifier_delta_bbox[N, anchors, num_classes*4, 1, 1]
+    // classifier_class[N, anchors, num_classes, 1, 1]
+    // rpn_rois[N, anchors, 4]
+    PLUGIN_ASSERT(nbInputDims == 3);
+
+    // score
+    PLUGIN_ASSERT(inputs[1].nbDims == 4 && inputs[1].d[1] == mNbClasses);
+    // delta_bbox
+    PLUGIN_ASSERT(inputs[0].nbDims == 4 && inputs[0].d[1] == mNbClasses * 4);
+    // roi
+    PLUGIN_ASSERT(inputs[2].nbDims == 2 && inputs[2].d[1] == 4);
+}
+
+size_t GenerateDetection::getWorkspaceSize(int32_t batch_size) const noexcept
+{
+    RefineDetectionWorkSpace refine(batch_size, mAnchorsCnt, mParam, mType);
+    return refine.totalSize;
+}
+
+Dims GenerateDetection::getOutputDimensions(int32_t index, Dims const* inputs, int32_t nbInputDims) noexcept
+{
+
+    check_valid_inputs(inputs, nbInputDims);
+    PLUGIN_ASSERT(index == 0);
+
+    return {2, {mKeepTopK, 6}};
+}
+
+int32_t GenerateDetection::enqueue(
+    int32_t batch_size, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept
+{
+
+    void* detections = outputs[0];
+
+    // refine detection
+    RefineDetectionWorkSpace refDetcWorkspace(batch_size, mAnchorsCnt, mParam, mType);
+    cudaError_t status
+        = DetectionPostProcess(stream, batch_size, mAnchorsCnt, static_cast<float*>(mRegWeightDevice->mPtr),
+            static_cast<float>(mImageSize.d[1]), // Image Height
+            static_cast<float>(mImageSize.d[2]), // Image Width
+            DataType::kFLOAT,                    // mType,
+            mParam, refDetcWorkspace, workspace,
+            inputs[1],       // inputs[InScore]
+            inputs[0],       // inputs[InDelta],
+            mValidCnt->mPtr, // inputs[InCountValid],
+            inputs[2],       // inputs[ROI]
+            detections);
+
+    PLUGIN_ASSERT(status == cudaSuccess);
+    return status;
+}
+
+DataType GenerateDetection::getOutputDataType(
+    int32_t index, nvinfer1::DataType const* inputTypes, int32_t nbInputs) const noexcept
+{
+    // Only DataType::kFLOAT is acceptable by the plugin layer
+    return DataType::kFLOAT;
+}
+
+// Configure the layer with input and output data types.
+void GenerateDetection::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);
+    PLUGIN_ASSERT(inputDims[0].d[0] == inputDims[1].d[0] && inputDims[1].d[0] == inputDims[2].d[0]);
+
+    mAnchorsCnt = inputDims[2].d[0];
+    mType = inputTypes[0];
+    mMaxBatchSize = maxBatchSize;
+}
+
+// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
+void GenerateDetection::attachToContext(
+    cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) noexcept
+{
+}
+
+// Detach the plugin object from its execution context.
+void GenerateDetection::detachFromContext() noexcept {}

plugin/generateDetectionPlugin/generateDetectionPlugin.h

@@ -0,0 +1,138 @@
+/*
+ * 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.
+ */
+
+#ifndef TRT_GENERATE_DETECTION_PLUGIN_H
+#define TRT_GENERATE_DETECTION_PLUGIN_H
+#include <cuda_runtime_api.h>
+#include <memory>
+#include <string.h>
+#include <string>
+#include <vector>
+
+#include "NvInfer.h"
+#include "NvInferPlugin.h"
+#include "common/kernels/maskRCNNKernels.h"
+#include "multilevelProposeROI/tlt_mrcnn_config.h"
+
+namespace nvinfer1
+{
+namespace plugin
+{
+
+class GenerateDetection : public IPluginV2Ext
+{
+public:
+    GenerateDetection(int32_t num_classes, int32_t keep_topk, float score_threshold, float iou_threshold,
+        nvinfer1::Dims const& image_size);
+
+    GenerateDetection(void const* data, size_t length);
+
+    ~GenerateDetection() noexcept override = default;
+
+    int32_t getNbOutputs() const noexcept override;
+
+    Dims getOutputDimensions(int32_t index, Dims const* inputs, int32_t nbInputDims) noexcept override;
+
+    int32_t initialize() noexcept override;
+
+    void terminate() noexcept override;
+
+    void destroy() noexcept override;
+
+    size_t getWorkspaceSize(int32_t maxBatchSize) const noexcept override;
+
+    int32_t enqueue(int32_t batch_size, void const* const* inputs, void* const* outputs, void* workspace,
+        cudaStream_t stream) noexcept override;
+
+    size_t getSerializationSize() const noexcept override;
+
+    void serialize(void* buffer) const noexcept override;
+
+    bool supportsFormat(DataType type, PluginFormat format) const noexcept override;
+
+    char const* getPluginType() const noexcept override;
+
+    char const* getPluginVersion() const noexcept override;
+
+    IPluginV2Ext* clone() const noexcept override;
+
+    void setPluginNamespace(char const* libNamespace) noexcept override;
+
+    char const* getPluginNamespace() const noexcept override;
+
+    DataType getOutputDataType(
+        int32_t index, nvinfer1::DataType const* inputTypes, int32_t nbInputs) const noexcept override;
+
+    void attachToContext(
+        cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) noexcept override;
+
+    void 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 override;
+
+    void detachFromContext() noexcept override;
+
+private:
+    void deserialize(int8_t const* data, size_t length);
+    void check_valid_inputs(nvinfer1::Dims const* inputs, int32_t nbInputDims) noexcept;
+
+    int32_t mBackgroundLabel{};
+    int32_t mNbClasses{};
+    int32_t mKeepTopK{};
+    float mScoreThreshold{};
+    float mIOUThreshold{};
+
+    int32_t mMaxBatchSize{};
+    int32_t mAnchorsCnt{};
+    std::shared_ptr<CudaBind<int32_t>> mValidCnt; // valid cnt = number of input rois for every image.
+    nvinfer1::DataType mType{};
+    RefineNMSParameters mParam{};
+    std::shared_ptr<CudaBind<float>> mRegWeightDevice;
+
+    nvinfer1::Dims mImageSize{};
+
+    std::string mNameSpace;
+};
+
+class GenerateDetectionPluginCreator : public nvinfer1::pluginInternal::BaseCreator
+{
+public:
+    GenerateDetectionPluginCreator() noexcept;
+
+    ~GenerateDetectionPluginCreator() noexcept override {}
+
+    char const* getPluginName() const noexcept override;
+
+    char const* getPluginVersion() const noexcept override;
+
+    PluginFieldCollection const* getFieldNames() noexcept override;
+
+    IPluginV2Ext* createPlugin(char const* name, PluginFieldCollection const* fc) noexcept override;
+
+    IPluginV2Ext* deserializePlugin(char const* name, void const* data, size_t length) noexcept override;
+
+private:
+    PluginFieldCollection mFC;
+    int32_t mNbClasses{};
+    int32_t mKeepTopK{};
+    float mScoreThreshold{};
+    float mIOUThreshold{};
+    std::vector<PluginField> mPluginAttributes;
+};
+} // namespace plugin
+} // namespace nvinfer1
+#endif // TRT_GENERATE_DETECTION_PLUGIN_H