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 +1,356 @@
+/*
+ * 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 "detectionLayerPlugin.h"
+#include "common/plugin.h"
+
+#include <memory>
+#include <string_view>
+
+using namespace nvinfer1;
+using namespace plugin;
+using nvinfer1::plugin::DetectionLayer;
+using nvinfer1::plugin::DetectionLayerPluginCreator;
+
+namespace
+{
+char const* const kDETECTIONLAYER_PLUGIN_VERSION{"1"};
+char const* const kDETECTIONLAYER_PLUGIN_NAME{"DetectionLayer_TRT"};
+} // namespace
+
+DetectionLayerPluginCreator::DetectionLayerPluginCreator()
+{
+    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));
+
+    mFC.nbFields = mPluginAttributes.size();
+    mFC.fields = mPluginAttributes.data();
+}
+
+char const* DetectionLayerPluginCreator::getPluginName() const noexcept
+{
+    return kDETECTIONLAYER_PLUGIN_NAME;
+}
+
+char const* DetectionLayerPluginCreator::getPluginVersion() const noexcept
+{
+    return kDETECTIONLAYER_PLUGIN_VERSION;
+}
+
+PluginFieldCollection const* DetectionLayerPluginCreator::getFieldNames() noexcept
+{
+    return &mFC;
+}
+
+IPluginV2Ext* DetectionLayerPluginCreator::createPlugin(char const* /*name*/, PluginFieldCollection const* fc) noexcept
+{
+    using namespace std::string_view_literals;
+    try
+    {
+        PLUGIN_VALIDATE(fc != nullptr);
+        plugin::validateRequiredAttributesExist({"num_classes", "keep_topk", "score_threshold", "iou_threshold"}, fc);
+        PluginField const* fields = fc->fields;
+        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));
+            }
+        }
+        return new DetectionLayer(mNbClasses, mKeepTopK, mScoreThreshold, mIOUThreshold);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+IPluginV2Ext* DetectionLayerPluginCreator::deserializePlugin(
+    char const* /*name*/, void const* data, size_t length) noexcept
+{
+    try
+    {
+        return new DetectionLayer(data, length);
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+DetectionLayer::DetectionLayer(int32_t numClasses, int32_t keepTopk, float scoreThreshold, float iouThreshold)
+    : mNbClasses(numClasses)
+    , mKeepTopK(keepTopk)
+    , mScoreThreshold(scoreThreshold)
+    , mIOUThreshold(iouThreshold)
+{
+    mBackgroundLabel = 0;
+    PLUGIN_VALIDATE(mNbClasses > 0);
+    PLUGIN_VALIDATE(mKeepTopK > 0);
+    PLUGIN_VALIDATE(mScoreThreshold >= 0.F);
+    PLUGIN_VALIDATE(mIOUThreshold > 0.F);
+
+    mParam.backgroundLabelId = 0;
+    mParam.numClasses = mNbClasses;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = mScoreThreshold;
+    mParam.iouThreshold = mIOUThreshold;
+
+    mType = DataType::kFLOAT;
+}
+
+int32_t DetectionLayer::getNbOutputs() const noexcept
+{
+    return 1;
+}
+
+int32_t DetectionLayer::initialize() noexcept
+{
+    try
+    {
+        // 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 STATUS_SUCCESS;
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return STATUS_FAILURE;
+}
+
+void DetectionLayer::terminate() noexcept {}
+
+void DetectionLayer::destroy() noexcept
+{
+    delete this;
+}
+
+bool DetectionLayer::supportsFormat(DataType type, PluginFormat format) const noexcept
+{
+    return (type == DataType::kFLOAT && format == PluginFormat::kLINEAR);
+}
+
+char const* DetectionLayer::getPluginType() const noexcept
+{
+    return kDETECTIONLAYER_PLUGIN_NAME;
+}
+
+char const* DetectionLayer::getPluginVersion() const noexcept
+{
+    return kDETECTIONLAYER_PLUGIN_VERSION;
+}
+
+IPluginV2Ext* DetectionLayer::clone() const noexcept
+{
+    try
+    {
+        auto plugin = std::make_unique<DetectionLayer>(*this);
+        plugin->setPluginNamespace(mNameSpace.c_str());
+        return plugin.release();
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return nullptr;
+}
+
+void DetectionLayer::setPluginNamespace(char const* libNamespace) noexcept
+{
+    try
+    {
+        PLUGIN_VALIDATE(libNamespace != nullptr);
+        mNameSpace = libNamespace;
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+}
+
+char const* DetectionLayer::getPluginNamespace() const noexcept
+{
+    return mNameSpace.c_str();
+}
+
+size_t DetectionLayer::getSerializationSize() const noexcept
+{
+    return sizeof(int32_t) * 2 + sizeof(float) * 2 + sizeof(int32_t) * 2;
+}
+
+void DetectionLayer::serialize(void* buffer) const noexcept
+{
+    auto* d = reinterpret_cast<uint8_t*>(buffer);
+    auto* const a = d;
+    write(d, mNbClasses);
+    write(d, mKeepTopK);
+    write(d, mScoreThreshold);
+    write(d, mIOUThreshold);
+    write(d, mMaxBatchSize);
+    write(d, mAnchorsCnt);
+    PLUGIN_ASSERT(d == a + getSerializationSize());
+}
+
+DetectionLayer::DetectionLayer(void const* data, size_t length)
+{
+    auto const* d = reinterpret_cast<uint8_t const*>(data);
+    auto const* const a = d;
+    mNbClasses = read<int32_t>(d);
+    mKeepTopK = read<int32_t>(d);
+    mScoreThreshold = read<float>(d);
+    mIOUThreshold = read<float>(d);
+    mMaxBatchSize = read<int32_t>(d);
+    mAnchorsCnt = read<int32_t>(d);
+    PLUGIN_VALIDATE(d == a + length);
+
+    mParam.backgroundLabelId = 0;
+    mParam.numClasses = mNbClasses;
+    mParam.keepTopK = mKeepTopK;
+    mParam.scoreThreshold = mScoreThreshold;
+    mParam.iouThreshold = mIOUThreshold;
+
+    mType = DataType::kFLOAT;
+}
+
+void DetectionLayer::checkValidInputs(nvinfer1::Dims const* inputs, int32_t nbInputDims)
+{
+    // classifier_delta_bbox[N, anchors, num_classes*4, 1, 1]
+    // classifier_class[N, anchors, num_classes, 1, 1]
+    // rpn_rois[N, anchors, 4]
+    PLUGIN_VALIDATE(nbInputDims == 3);
+    // delta_bbox
+    PLUGIN_VALIDATE(inputs[0].nbDims == 4 && inputs[0].d[1] == mNbClasses * 4);
+    // score
+    PLUGIN_VALIDATE(inputs[1].nbDims == 4 && inputs[1].d[1] == mNbClasses);
+    // roi
+    PLUGIN_VALIDATE(inputs[2].nbDims == 2 && inputs[2].d[1] == 4);
+}
+
+size_t DetectionLayer::getWorkspaceSize(int32_t batchSize) const noexcept
+{
+    RefineDetectionWorkSpace refine(batchSize, mAnchorsCnt, mParam, mType);
+    return refine.totalSize;
+}
+
+Dims DetectionLayer::getOutputDimensions(int32_t index, Dims const* inputs, int32_t nbInputDims) noexcept
+{
+    try
+    {
+        checkValidInputs(inputs, nbInputDims);
+        PLUGIN_VALIDATE(index == 0);
+        // [N, anchors, (y1, x1, y2, x2, class_id, score)]
+        return {2, {mKeepTopK, 6}};
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return Dims{};
+}
+
+int32_t DetectionLayer::enqueue(
+    int32_t batchSize, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept
+{
+    try
+    {
+        PLUGIN_VALIDATE(inputs != nullptr);
+        PLUGIN_VALIDATE(outputs != nullptr);
+        void* detections = outputs[0];
+
+        // refine detection
+        RefineDetectionWorkSpace refDetcWorkspace(batchSize, mAnchorsCnt, mParam, mType);
+        cudaError_t status = RefineBatchClassNMS(stream, batchSize, mAnchorsCnt,
+            DataType::kFLOAT, // mType,
+            mParam, refDetcWorkspace, workspace,
+            inputs[1],       // inputs[InScore]
+            inputs[0],       // inputs[InDelta],
+            mValidCnt->mPtr, // inputs[InCountValid],
+            inputs[2],       // inputs[ROI]
+            detections);
+
+        return status;
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+    return STATUS_FAILURE;
+}
+
+DataType DetectionLayer::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 DetectionLayer::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
+{
+    try
+    {
+        checkValidInputs(inputDims, nbInputs);
+        PLUGIN_VALIDATE(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;
+    }
+    catch (std::exception const& e)
+    {
+        caughtError(e);
+    }
+}
+
+// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
+void DetectionLayer::attachToContext(
+    cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) noexcept
+{
+}
+
+// Detach the plugin object from its execution context.
+void DetectionLayer::detachFromContext() noexcept {}