chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
@@ -0,0 +1,68 @@
+//
+//  TRTBinary.cpp
+//  MNN
+//
+//  Created by MNN on 2019/09/11.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "TRTDetectionPostProcess.hpp"
+#include <core/TensorUtils.hpp>
+#include "schema/current/MNNPlugin_generated.h"
+using namespace std;
+namespace MNN {
+
+TRTDetectionPostProcess::TRTDetectionPostProcess(Backend *b, const Op *op, const std::vector<Tensor *> &inputs,
+                     const std::vector<Tensor *> &outputs)
+    : MNN::TRTCommonExecution(b, op) {
+    auto param = op->main_as_DetectionPostProcessParam();
+    param->UnPackTo(&mParam);
+    if (mParam.useRegularNMS) {
+        MNN_ERROR("TODO, use regular NMS to process decoded boxes!");
+        return;
+    }
+}
+
+std::vector<ITensor *> TRTDetectionPostProcess::onEncode(const std::vector<ITensor *> &xOp) {
+    auto plu = createPluginWithOutput(mOutputs);
+    plu->main.type  = MNNTRTPlugin::Parameter_DetectionPostProcessInfo;
+    plu->main.value = new MNNTRTPlugin::DetectionPostProcessInfoT;
+    auto detection     = plu->main.AsDetectionPostProcessInfo();
+
+    auto boxesEncoding = mInputs[0];
+    auto classPredictions = mInputs[1];
+    auto anchors = mInputs[2];
+
+    detection->numAnchors0 = mInputs[0]->length(1);
+    detection->scaleValues.push_back(mParam.centerSizeEncoding[0]);
+    detection->scaleValues.push_back(mParam.centerSizeEncoding[1]);
+    detection->scaleValues.push_back(mParam.centerSizeEncoding[2]);
+    detection->scaleValues.push_back(mParam.centerSizeEncoding[3]);
+
+    detection->numBoxes = boxesEncoding->length(1);
+    detection->boxCoordNum = boxesEncoding->length(2);
+    detection->numAnchors1 = anchors->length(0);
+    detection->anchorsCoordNum = anchors->length(1);
+
+    detection->numClassWithBackground = classPredictions->length(2);
+
+    detection->numClasses = mParam.numClasses;
+    detection->maxClassesPerAnchor = mParam.maxClassesPerDetection;
+
+    detection->maxDetections = mParam.maxDetections;
+    detection->iouThreshold = mParam.iouThreshold;
+    detection->nmsScoreThreshold = mParam.nmsScoreThreshold;
+
+    auto detectionPlugin = (nvinfer1::IPluginExt *)MNNTRTCreatePlugion(mOp, plu.get());
+    nvinfer1::IPluginLayer *plugin = mTrtBackend->getNetwork()->addPluginExt(&xOp[0], 3, *((nvinfer1::IPluginExt *)detectionPlugin));
+    if (plugin == nullptr) {
+        printf("Interp plugin == nullptr !!!\n");
+    }
+    mTrtBackend->pushReleaseLayer(detectionPlugin);
+    return {plugin->getOutput(0), plugin->getOutput(1), plugin->getOutput(2), plugin->getOutput(3)};
+}
+
+TRTCreatorRegister<TypedCreator<TRTDetectionPostProcess>> __detection_post_process_op(OpType_DetectionPostProcess);
+
+
+} // namespace MNN