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
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+//
+//  TRTDepthwiseDeconvolution.cpp
+//  MNN
+//
+//  Created by MNN on 2019/09/11.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "TRTDepthwiseDeconvolution.hpp"
+#include <core/TensorUtils.hpp>
+#include "core/ConvolutionCommon.hpp"
+#include "plugin/PreluPlugin.hpp"
+
+using namespace std;
+
+namespace MNN {
+
+TRTDepthwiseDeconvolution::TRTDepthwiseDeconvolution(Backend *b, const Op *op, const std::vector<Tensor *> &inputs,
+                               const std::vector<Tensor *> &outputs)
+    : MNN::TRTCommonExecution(b, op) {
+}
+
+std::vector<ITensor *> TRTDepthwiseDeconvolution::onEncode(const std::vector<ITensor *> &xOp) {
+#ifdef TRT_LOG
+    printf("TRTDepthwiseDeconvolution in\n");
+#endif
+    auto opName       = mOp->name()->str();
+    auto conv2D       = mOp->main_as_Convolution2D();
+    auto conv2DCommon = conv2D->common();
+
+    auto kernelX        = conv2DCommon->kernelX();
+    auto kernelY        = conv2DCommon->kernelY();
+    auto outputCount    = conv2DCommon->outputCount();
+    const float *source = nullptr;
+    int weightSize      = 0;
+
+    std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon;
+    ConvolutionCommon::getConvParameters(&quanCommon, backend(), mOp, &source, &weightSize);
+
+    nvinfer1::DimsHW NVKSize(kernelY, kernelX);
+    nvinfer1::DimsHW NVKSSize(conv2DCommon->strideY(), conv2DCommon->strideX());
+
+    TRTWeight weight{nvinfer1::DataType::kFLOAT, static_cast<void *>(const_cast<float *>(source)),
+                     static_cast<size_t>(weightSize)};
+
+    TRTWeight bias{nvinfer1::DataType::kFLOAT, static_cast<void *>(const_cast<float *>(conv2D->bias()->data())),
+                   static_cast<size_t>(conv2D->bias()->size())};
+    auto conv_layer =
+        mTrtBackend->getNetwork()->addDeconvolution(*xOp[0], outputCount, NVKSize, weight.get(), bias.get());
+
+    MNN_ASSERT(conv_layer != nullptr);
+    conv_layer->setStride(NVKSSize);
+    conv_layer->setNbGroups(outputCount);
+    auto pads = ConvolutionCommon::convolutionPad(mInputs[0], mOutputs[0], conv2DCommon);
+    conv_layer->setPadding(nvinfer1::DimsHW{pads.second, pads.first});
+
+    if (conv2DCommon->padMode() == PadMode_SAME) {
+        conv_layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
+    }
+    conv_layer->setName(mOp->name()->str().c_str());
+    auto relu  = conv2DCommon->relu();
+    auto relu6 = conv2DCommon->relu6();
+
+    if (relu) {
+        mActivationLayer = mTrtBackend->getNetwork()->addActivation(*conv_layer->getOutput(0), ActivationType::kRELU);
+    }
+
+    if (relu6) {
+        mActivationLayer = mTrtBackend->getNetwork()->addActivation(*conv_layer->getOutput(0), ActivationType::kCLIP);
+        mActivationLayer->setAlpha(0.);
+        mActivationLayer->setBeta(6.);
+    }
+
+    if (relu || relu6) {
+        return {mActivationLayer->getOutput(0)};
+    }
+    return {conv_layer->getOutput(0)};
+}
+
+TRTCreatorRegister<TypedCreator<TRTDepthwiseDeconvolution>> __depthwise_deconv_op(OpType_DeconvolutionDepthwise);
+
+} // namespace MNN