mirror of https://github.com/alibaba/MNN.git
64 lines
1.9 KiB
C++
64 lines
1.9 KiB
C++
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// CPUSpatialProduct.cpp
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// MNN
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//
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// Created by MNN on 2018/07/19.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUSpatialProduct.hpp"
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#include "backend/cpu/CPUBackend.hpp"
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#include "core/Macro.h"
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#ifdef MNN_USE_NEON
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#include <arm_neon.h>
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#endif
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namespace MNN {
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CPUSpatialProduct::CPUSpatialProduct(Backend *b) : MNN::Execution(b) {
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// nothing to do
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}
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ErrorCode CPUSpatialProduct::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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// Assume
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// bottom[0] dim CxHxW
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// bottom[1] dim 1xHxW
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// top[0] dim CxHxW
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auto inputTensor = inputs[0];
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auto outputTensor = outputs[0];
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int w = inputTensor->width();
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int h = inputTensor->height();
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int channels = UP_DIV(inputTensor->channel(), 4);
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int size = w * h;
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auto inputT1 = inputs[1];
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// second, top[0](CxHxW) = bottom[0](CxHxW) * bottom[1](CxHxW)
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for (int q = 0; q < channels; q++) {
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const float *ptr = inputTensor->host<float>() + q * size * 4;
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const float *ptr1 = inputT1->host<float>();
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float *outptr = outputTensor->host<float>() + q * size * 4;
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for (int v = 0; v < size; ++v) {
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#ifdef MNN_USE_NEON
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vst1q_f32(outptr + 4 * v, vld1q_f32(ptr + 4 * v) * vdupq_n_f32(ptr1[4 * v]));
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#else
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for (int j = 0; j < 4; ++j) {
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outptr[4 * v + j] = ptr[4 * v + j] * ptr1[4 * v + 0];
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}
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#endif
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}
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}
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return NO_ERROR;
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}
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class CPUSpatialProductCreator : public CPUBackend::Creator {
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public:
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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const MNN::Op *op, Backend *backend) const {
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return new CPUSpatialProduct(backend);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUSpatialProductCreator, OpType_SpatialProduct);
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} // namespace MNN
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