mirror of https://github.com/alibaba/MNN.git
73 lines
3.7 KiB
C++
73 lines
3.7 KiB
C++
//
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// CoreMLInterp.cpp
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// MNN
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//
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// Created by MNN on 2021/05/27.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "CoreMLInterp.hpp"
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namespace MNN {
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CoreMLInterp::CoreMLInterp(MNN::Backend *b, const MNN::Op *op, const std::vector<Tensor *> &inputs, const std::vector<MNN::Tensor *> &outputs) : CoreMLCommonExecution(b, op) {
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initLayer();
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}
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ErrorCode CoreMLInterp::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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MNN_ASSERT(inputs.size() == 1 && outputs.size() == 1);
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auto interpParam = mOp->main_as_Interp();
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// ResizeBilinear: NPU; UpsampleLayer: GPU ?
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if (interpParam->resizeType() == 2) {
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mLayer_->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_RESIZE_BILINEAR;
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mLayer_->resizebilinear = mCoreMLBackend->create<CoreML__Specification__ResizeBilinearLayerParams>();
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core_ml__specification__resize_bilinear_layer_params__init(mLayer_->resizebilinear);
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mLayer_->resizebilinear->n_targetsize = 2;
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mLayer_->resizebilinear->targetsize = mCoreMLBackend->create<uint64_t>(mLayer_->resizebilinear->n_targetsize);
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mLayer_->resizebilinear->targetsize[0] = outputs[0]->height();
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mLayer_->resizebilinear->targetsize[1] = outputs[0]->width();
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mLayer_->resizebilinear->mode = mCoreMLBackend->create<CoreML__Specification__SamplingMode>();
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core_ml__specification__sampling_mode__init(mLayer_->resizebilinear->mode);
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mLayer_->resizebilinear->mode->samplingmethod = CORE_ML__SPECIFICATION__SAMPLING_MODE__METHOD__UPSAMPLE_MODE;
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if (interpParam->ctm() == CoordinateTransformationMode_AlignCorners) {
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mLayer_->resizebilinear->mode->samplingmethod = CORE_ML__SPECIFICATION__SAMPLING_MODE__METHOD__ALIGN_ENDPOINTS_MODE;
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}
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} else {
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mLayer_->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_UPSAMPLE;
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mLayer_->upsample = mCoreMLBackend->create<CoreML__Specification__UpsampleLayerParams>();
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core_ml__specification__upsample_layer_params__init(mLayer_->upsample);
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float heightScale = 1.0 / interpParam->heightScale();
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float widthScale = 1.0 / interpParam->widthScale();
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uint64_t heightScaleI = static_cast<uint64_t>(heightScale);
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uint64_t widthScaleI = static_cast<uint64_t>(widthScale);
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if (heightScale - heightScaleI == 0 && widthScale - widthScaleI == 0) {
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mLayer_->upsample->n_scalingfactor = 2;
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mLayer_->upsample->scalingfactor =
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mCoreMLBackend->create<uint64_t>(mLayer_->upsample->n_scalingfactor);
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mLayer_->upsample->scalingfactor[0] = heightScaleI;
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mLayer_->upsample->scalingfactor[1] = widthScaleI;
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} else {
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// scale
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mLayer_->upsample->n_fractionalscalingfactor = 2;
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mLayer_->upsample->fractionalscalingfactor =
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mCoreMLBackend->create<float>(mLayer_->upsample->n_fractionalscalingfactor);
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mLayer_->upsample->fractionalscalingfactor[0] = heightScale;
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mLayer_->upsample->fractionalscalingfactor[1] = widthScale;
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}
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// mode
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if (interpParam->resizeType() == 1 && mLayer_->upsample->n_fractionalscalingfactor != 2) {
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mLayer_->upsample->mode = CORE_ML__SPECIFICATION__UPSAMPLE_LAYER_PARAMS__INTERPOLATION_MODE__NN;
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} else {
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MNN_ERROR("[CoreML] Interp Don't support [Cubic, NearestneighborRound] mode.");
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return NOT_SUPPORT;
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}
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}
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setLayerInputsAndOutputs(mLayer_, {mCoreMLBackend->getTensorName(inputs[0])},
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{mCoreMLBackend->getTensorName(outputs[0])});
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mCoreMLBackend->addLayer(mLayer_);
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return NO_ERROR;
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}
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REGISTER_COREML_OP_CREATOR(CoreMLInterp, OpType_Interp)
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} // namespace MNN
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