mirror of https://github.com/alibaba/MNN.git
38 lines
1.5 KiB
C++
38 lines
1.5 KiB
C++
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//
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// NNAPIScale.cpp
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// MNN
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//
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// Created by MNN on 2022/10/25.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "NNAPIScale.hpp"
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namespace MNN {
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NNAPIScale::NNAPIScale(MNN::Backend *b, const MNN::Op *op, const std::vector<Tensor *> &inputs, const std::vector<MNN::Tensor *> &outputs) : NNAPICommonExecution(b, op) {
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}
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ErrorCode NNAPIScale::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto scaleParam = mOp->main_as_Scale();
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auto weight = scaleParam->scaleData();
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auto bias = scaleParam->biasData();
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uint32_t channel = scaleParam->channels();
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auto inputShape = inputs[0]->shape(); // NCHW
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int inputSize = inputs[0]->elementSize();
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std::vector<uint32_t> dims {1, 1, 1, channel};
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// middle = input * weight
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auto middleIdx = buildTensor(ANEURALNETWORKS_TENSOR_FLOAT32, inputShape);
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auto inputIdxs = getTensorIdxs(inputs);
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inputIdxs.push_back(buildConstant(weight->data(), channel * sizeof(float), ANEURALNETWORKS_TENSOR_FLOAT32, dims));
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inputIdxs.push_back(buildScalar(ANEURALNETWORKS_FUSED_NONE));
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buildOperation(ANEURALNETWORKS_MUL, inputIdxs, {middleIdx});
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// output = middle + bias
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auto biasIdx = buildConstant(bias->data(), channel * sizeof(float), ANEURALNETWORKS_TENSOR_FLOAT32, dims);
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return buildOperation(ANEURALNETWORKS_ADD, {middleIdx, biasIdx, buildScalar(ANEURALNETWORKS_FUSED_NONE)}, getTensorIdxs(outputs));
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}
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REGISTER_NNAPI_OP_CREATOR(NNAPIScale, OpType_Scale)
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} // namespace MNN
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