chore: import upstream snapshot with attribution

plugin/proposalLayerPlugin/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(
+    proposalLayerPlugin.cpp
+    proposalLayerPlugin.h
+)
+

plugin/proposalLayerPlugin/ProposalLayer_PluginConfig.yaml

@@ -0,0 +1,54 @@
+#
+# 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: ProposalLayer_TRT
+interface: "IPluginV2Ext"
+versions:
+  "1":
+    attributes:
+      - prenms_topk
+      - keep_topk
+      - iou_threshold
+      - image_size
+    attribute_types:
+      prenms_topk: int32
+      keep_topk: int32
+      iou_threshold: float32
+      image_size: int32
+    attribute_length:
+      prenms_topk: 1
+      keep_topk: 1
+      iou_threshold: 1
+      image_size: 3
+    attribute_options:
+      prenms_topk:
+        min: "0"
+        max: "=1024"
+      keep_topk:
+        min: "0"
+        max: "=pinf"
+      iou_threshold:
+        min: "0"
+        max: "=pinf"
+      image_size:
+        min: "=3, 0, 0"
+        max: "=3, =1024, =1024" # dims 2 & 3 are capped to avoid timeout
+    attributes_required:
+      - prenms_topk
+      - keep_topk
+      - iou_threshold
+...

plugin/proposalLayerPlugin/README.md

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+# ProposalLayer 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 `ProposalLayer` plugin generates the first-stage detection (ROI candidates) out of the scores, refinement info from RPN (Region Proposal Network) and pre-defined anchors. It is used in `sampleUffMaskRCNN`.
+
+### Structure
+
+This plugin supports the NCHW format. It takes two input tenosrs: `object_score` and `object_delta` 
+
+`object_score` is the objectness score from RPN. `object_score`'s shape is `[N, anchors, 2, 1]` where `N` is the batch_size, `anchors` is the total number of anchors and `2` means 2 classes of objectness - foreground and background.
+
+`object_delta` is the refinement info from RPN of shape `[N, anchors, 4, 1]`. `4` refers to the 4 elements of refinement information - `[dy, dx, dh, dw]`
+
+This plugin generates one output tensor of shape `[N, keep_topk, 4]` where `keep_topk` is the maximum number of detections left after NMS and `4` refers to coordinates of ROI
+candidates `[y1, x1, y2, x2]`
+
+The default anchors are generated in this plugin during `initialization` method.
+For ResNet101 with 1024*1024 input image, the number of anchors can be computed as:
+```
+Anchors in feature map P2: 256*256*3 
+Anchors in feature map P3: 128*128*3
+Anchors in feature map P4: 64*64*3
+Anchors in feature map P5: 32*32*3
+Anchors in feature map P6(maxpooling): 16*16*3  
+
+total number of anchors: 87296*3 = 261888
+```
+
+## Parameters
+
+This plugin has the plugin creator class `ProposalLayerPluginCreator` and the plugin class `ProposalLayer`.
+  
+The following parameters were used to create `ProposalLayer` instance:
+
+| Type              | Parameter                        | Description
+|-------------------|----------------------------------|--------------------------------------------------------
+|`int`              |`prenms_topk`                     |The number of ROIs which will be kept before NMS. 
+|`int`              |`keep_topk`                       |Number of detections will be kept after NMS.
+|`float`            |`iou_threshold`                   |IOU threshold value used in NMS.
+|`int[3]`           |`image_size`                      |Input image size in CHW. Only supports C=3 and defaults to [3,1024,1024].
+
+## Limitations
+
+The number of anchors is capped at 1024 to support embedded devices with smaller shared memory capacity.
+
+To enable support for a device with higher memory, calls to `sortPerClass`, `PerClassNMS` and `KeepTopKGather` can be modified in `proposalRefineBatchClassNMS` ([maskRCNNKernels.cu](https://github.com/NVIDIA/TensorRT/blob/main/plugin/common/kernels/maskRCNNKernels.cu)).
+
+
+## Limitations
+
+The attribute `prenms_topk` is capped at 1024 to support embedded devices with smaller shared memory capacity.
+
+To enable support for a device with higher memory, calls to `sortPerClass`, `PerClassNMS` and `KeepTopKGather` can be modified in `proposalRefineBatchClassNMS` ([maskRCNNKernels.cu](https://github.com/NVIDIA/TensorRT/blob/main/plugin/common/kernels/maskRCNNKernels.cu)).
+
+
+## Additional resources
+
+The following resources provide a deeper understanding of the `ProposalLayer` plugin:
+
+- [MaskRCNN](https://github.com/matterport/Mask_RCNN)
+
+
+## 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. 
+
+July 2020: Add (optional) plugin parameter for specifying image size.
+
+June 2019: First release of proposeLayerPlugin.
+
+
+## Known issues
+
+There are no known issues in this plugin.

plugin/proposalLayerPlugin/proposalLayerPlugin.cpp

@@ -0,0 +1,384 @@
+/*
+ * 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 "proposalLayerPlugin.h"
+#include "common/mrcnn_config.h"
+#include "common/plugin.h"
+#include <algorithm>
+#include <cuda_runtime_api.h>
+#include <iostream>
+#include <math.h>
+#include <memory>
+#include <string_view>
+
+using namespace nvinfer1;
+using namespace plugin;
+using nvinfer1::plugin::ProposalLayer;
+using nvinfer1::plugin::ProposalLayerPluginCreator;
+
+namespace
+{
+char const* const kPROPOSALLAYER_PLUGIN_VERSION{"1"};
+char const* const kPROPOSALLAYER_PLUGIN_NAME{"ProposalLayer_TRT"};
+} // namespace
+
+ProposalLayerPluginCreator::ProposalLayerPluginCreator()
+{
+    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("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* ProposalLayerPluginCreator::getPluginName() const noexcept
+{
+    return kPROPOSALLAYER_PLUGIN_NAME;
+}
+
+char const* ProposalLayerPluginCreator::getPluginVersion() const noexcept
+{
+    return kPROPOSALLAYER_PLUGIN_VERSION;
+}
+
+PluginFieldCollection const* ProposalLayerPluginCreator::getFieldNames() noexcept
+{
+    return &mFC;
+}
+
+IPluginV2Ext* ProposalLayerPluginCreator::createPlugin(char const* name, PluginFieldCollection const* fc) noexcept
+{
+    try
+    {
+        auto imageSize = MaskRCNNConfig::IMAGE_SHAPE;
+        PluginField const* fields = fc->fields;
+        using namespace std::string_view_literals;
+        plugin::validateRequiredAttributesExist({"prenms_topk", "keep_topk", "iou_threshold"}, fc);
+        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 == "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* const dims = static_cast<int32_t const*>(fields[i].data);
+                std::copy_n(dims, 3, imageSize.d);
+            }
+        }
+        return new ProposalLayer(mPreNMSTopK, mKeepTopK, mIOUThreshold, imageSize);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+IPluginV2Ext* ProposalLayerPluginCreator::deserializePlugin(char const* name, void const* data, size_t length) noexcept
+{
+    try
+    {
+        return new ProposalLayer(data, length);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+ProposalLayer::ProposalLayer(
+    int32_t prenms_topk, int32_t keep_topk, float iou_threshold, nvinfer1::Dims const& imageSize)
+    : mPreNMSTopK(prenms_topk)
+    , mKeepTopK(keep_topk)
+    , mIOUThreshold(iou_threshold)
+    , mImageSize(imageSize)
+{
+    mBackgroundLabel = -1;
+    PLUGIN_VALIDATE(mPreNMSTopK > 0);
+    PLUGIN_VALIDATE(mPreNMSTopK <= 1024);
+    PLUGIN_VALIDATE(mKeepTopK > 0);
+    PLUGIN_VALIDATE(iou_threshold > 0.0F);
+    PLUGIN_VALIDATE(mImageSize.nbDims == 3);
+    PLUGIN_VALIDATE(mImageSize.d[0] == 3 && mImageSize.d[1] > 0 && mImageSize.d[2] > 0);
+
+    mParam.backgroundLabelId = -1;
+    mParam.numClasses = 1;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = 0.0;
+    mParam.iouThreshold = mIOUThreshold;
+
+    mType = DataType::kFLOAT;
+
+    generate_pyramid_anchors(imageSize);
+}
+
+int32_t ProposalLayer::getNbOutputs() const noexcept
+{
+    return 1;
+}
+
+int32_t ProposalLayer::initialize() noexcept
+{
+    // 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:
+    mAnchorBoxesDevice = std::make_shared<CudaBind<float>>(mAnchorsCnt * 4 * mMaxBatchSize);
+    int32_t batch_offset = sizeof(float) * mAnchorsCnt * 4;
+    uint8_t* device_ptr = static_cast<uint8_t*>(mAnchorBoxesDevice->mPtr);
+    for (int32_t i = 0; i < mMaxBatchSize; i++)
+    {
+        PLUGIN_CUASSERT(cudaMemcpy(static_cast<void*>(device_ptr + i * batch_offset),
+            static_cast<void*>(mAnchorBoxesHost.data()), batch_offset, cudaMemcpyHostToDevice));
+    }
+
+    return 0;
+}
+
+void ProposalLayer::terminate() noexcept {}
+
+void ProposalLayer::destroy() noexcept
+{
+    delete this;
+}
+
+bool ProposalLayer::supportsFormat(DataType type, PluginFormat format) const noexcept
+{
+    return (type == DataType::kFLOAT && format == PluginFormat::kLINEAR);
+}
+
+char const* ProposalLayer::getPluginType() const noexcept
+{
+    return kPROPOSALLAYER_PLUGIN_NAME;
+}
+
+char const* ProposalLayer::getPluginVersion() const noexcept
+{
+    return kPROPOSALLAYER_PLUGIN_VERSION;
+}
+
+IPluginV2Ext* ProposalLayer::clone() const noexcept
+{
+    try
+    {
+        auto plugin = std::make_unique<ProposalLayer>(*this);
+        plugin->setPluginNamespace(mNameSpace.c_str());
+        return plugin.release();
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+void ProposalLayer::setPluginNamespace(char const* libNamespace) noexcept
+{
+    mNameSpace = libNamespace;
+}
+
+char const* ProposalLayer::getPluginNamespace() const noexcept
+{
+    return mNameSpace.c_str();
+}
+
+size_t ProposalLayer::getSerializationSize() const noexcept
+{
+    return sizeof(int32_t) * 2 + sizeof(float) + sizeof(int32_t) * 2 + sizeof(nvinfer1::Dims);
+}
+
+void ProposalLayer::serialize(void* buffer) const noexcept
+{
+    char *d = reinterpret_cast<char*>(buffer), *a = d;
+    write(d, mPreNMSTopK);
+    write(d, mKeepTopK);
+    write(d, mIOUThreshold);
+    write(d, mMaxBatchSize);
+    write(d, mAnchorsCnt);
+    write(d, mImageSize);
+    PLUGIN_ASSERT(d == a + getSerializationSize());
+}
+
+ProposalLayer::ProposalLayer(void const* data, size_t length)
+{
+    deserialize(static_cast<int8_t const*>(data), length);
+}
+
+void ProposalLayer::deserialize(int8_t const* data, size_t length)
+{
+    auto const* d{data};
+    int32_t prenms_topk = read<int32_t>(d);
+    int32_t keep_topk = read<int32_t>(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);
+
+    mBackgroundLabel = -1;
+    mPreNMSTopK = prenms_topk;
+    mKeepTopK = keep_topk;
+    mIOUThreshold = iou_threshold;
+
+    mParam.backgroundLabelId = -1;
+    mParam.numClasses = 1;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = 0.0;
+    mParam.iouThreshold = mIOUThreshold;
+
+    mType = DataType::kFLOAT;
+
+    generate_pyramid_anchors(mImageSize);
+}
+
+void ProposalLayer::check_valid_inputs(nvinfer1::Dims const* inputs, int32_t nbInputDims)
+{
+    // object_score[N, anchors, 2, 1],
+    // foreground_delta[N, anchors, 4, 1],
+    // anchors should be generated inside
+    PLUGIN_ASSERT(nbInputDims == 2);
+    // foreground_score
+    PLUGIN_ASSERT(inputs[0].nbDims == 3 && inputs[0].d[1] == 2);
+    // foreground_delta
+    PLUGIN_ASSERT(inputs[1].nbDims == 3 && inputs[1].d[1] == 4);
+}
+
+size_t ProposalLayer::getWorkspaceSize(int32_t batch_size) const noexcept
+{
+
+    ProposalWorkSpace proposal(batch_size, mAnchorsCnt, mPreNMSTopK, mParam, mType);
+    return proposal.totalSize;
+}
+
+Dims ProposalLayer::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 ProposalLayer::generate_pyramid_anchors(nvinfer1::Dims const& imageDims)
+{
+    PLUGIN_VALIDATE(imageDims.nbDims == 3 && imageDims.d[0] == 3);
+
+    auto const& scales = MaskRCNNConfig::RPN_ANCHOR_SCALES;
+    auto const& ratios = MaskRCNNConfig::RPN_ANCHOR_RATIOS;
+    auto const& strides = MaskRCNNConfig::BACKBONE_STRIDES;
+    auto anchor_stride = MaskRCNNConfig::RPN_ANCHOR_STRIDE;
+
+    float const cy = imageDims.d[1] - 1;
+    float const cx = imageDims.d[2] - 1;
+
+    auto& anchors = mAnchorBoxesHost;
+    PLUGIN_VALIDATE(anchors.empty());
+
+    PLUGIN_VALIDATE(scales.size() == strides.size());
+    for (size_t s = 0; s < scales.size(); ++s)
+    {
+        float scale = scales[s];
+        int32_t stride = strides[s];
+
+        for (int32_t y = 0; y < imageDims.d[1]; y += anchor_stride * stride)
+            for (int32_t x = 0; x < imageDims.d[2]; x += anchor_stride * stride)
+                for (float r : ratios)
+                {
+                    float sqrt_r = sqrt(r);
+                    float h = scale / sqrt_r;
+                    float w = scale * sqrt_r;
+
+                    anchors.insert(anchors.end(),
+                        {(y - h / 2) / cy, (x - w / 2) / cx, (y + h / 2 - 1) / cy, (x + w / 2 - 1) / cx});
+                }
+    }
+
+    PLUGIN_VALIDATE(anchors.size() % 4 == 0);
+}
+
+int32_t ProposalLayer::enqueue(
+    int32_t batch_size, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept
+{
+
+    void* proposals = outputs[0];
+
+    // proposal
+    ProposalWorkSpace proposalWorkspace(batch_size, mAnchorsCnt, mPreNMSTopK, mParam, mType);
+    cudaError_t status = proposalRefineBatchClassNMS(stream, batch_size, mAnchorsCnt, mPreNMSTopK,
+        DataType::kFLOAT, // mType,
+        mParam, proposalWorkspace, workspace,
+        inputs[0], // inputs[object_score]
+        inputs[1], // inputs[bbox_delta],
+        mValidCnt->mPtr,
+        mAnchorBoxesDevice->mPtr, // inputs[anchors]
+        proposals);
+
+    PLUGIN_ASSERT(status == cudaSuccess);
+    return status;
+}
+
+// Return the DataType of the plugin output at the requested index
+DataType ProposalLayer::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 ProposalLayer::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]);
+
+    mAnchorsCnt = inputDims[0].d[0];
+    PLUGIN_ASSERT(mAnchorsCnt == (int32_t) (mAnchorBoxesHost.size() / 4));
+    mMaxBatchSize = maxBatchSize;
+}
+
+// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
+void ProposalLayer::attachToContext(
+    cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) noexcept
+{
+}
+
+// Detach the plugin object from its execution context.
+void ProposalLayer::detachFromContext() noexcept {}

plugin/proposalLayerPlugin/proposalLayerPlugin.h

@@ -0,0 +1,139 @@
+/*
+ * 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_PROPOSAL_LAYER_PLUGIN_H
+#define TRT_PROPOSAL_LAYER_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"
+
+namespace nvinfer1
+{
+namespace plugin
+{
+
+class ProposalLayer : public IPluginV2Ext
+{
+public:
+    ProposalLayer(int32_t prenms_topk, int32_t keep_topk, float iou_threshold, nvinfer1::Dims const& image_size);
+
+    ProposalLayer(void const* data, size_t length);
+
+    ~ProposalLayer() 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;
+
+    // void configureWithFormat(const Dims* inputs, int32_t nbInputs, const Dims* outputDims, int32_t nbOutputs,
+    // nvinfer1::DataType type, nvinfer1::PluginFormat format, int32_t maxBatchSize) 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);
+    void generate_pyramid_anchors(nvinfer1::Dims const& imageDims);
+
+    int32_t mBackgroundLabel{};
+    int32_t mPreNMSTopK{};
+    int32_t mKeepTopK{};
+    float mIOUThreshold{};
+
+    int32_t mMaxBatchSize{};
+    int32_t mAnchorsCnt{};
+    std::shared_ptr<CudaBind<int32_t>> mValidCnt; // valid cnt = number of input roi for every image.
+    std::shared_ptr<CudaBind<float>>
+        mAnchorBoxesDevice; // [N, anchors(261888 for resnet101 + 1024*1024), (y1, x1, y2, x2)]
+    std::vector<float> mAnchorBoxesHost;
+
+    nvinfer1::DataType mType{};
+    nvinfer1::Dims mImageSize{};
+    RefineNMSParameters mParam{};
+
+    std::string mNameSpace;
+};
+
+class ProposalLayerPluginCreator : public nvinfer1::pluginInternal::BaseCreator
+{
+public:
+    ProposalLayerPluginCreator();
+
+    ~ProposalLayerPluginCreator() 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 mPreNMSTopK;
+    int32_t mKeepTopK;
+    float mIOUThreshold;
+    std::vector<PluginField> mPluginAttributes;
+};
+} // namespace plugin
+} // namespace nvinfer1
+#endif // TRT_PROPOSAL_LAYER_PLUGIN_H