mirror of https://github.com/alibaba/MNN.git
105 lines
3.5 KiB
C++
105 lines
3.5 KiB
C++
//
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// CPURelu.cpp
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// MNN
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//
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// Created by MNN on 2018/07/15.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPURelu.hpp"
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#include "backend/cpu/CPUBackend.hpp"
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#include "backend/cpu/compute/CommonOptFunction.h"
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#include "core/Macro.h"
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namespace MNN {
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ErrorCode CPURelu::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto& ib = inputs[0]->buffer();
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auto& ob = outputs[0]->buffer();
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const float* srcO = (const float*)ib.host;
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float* dstO = (float*)ob.host;
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auto size = inputs[0]->size() / sizeof(float);
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auto sizeQuad = size / 4;
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auto remain = size - sizeQuad * 4;
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MNNReluWithSlope(dstO, srcO, sizeQuad, mSlope);
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if (remain > 0) {
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MNNReluWithSlope(dstO + size - 4, srcO + size - 4, 1, mSlope);
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}
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return NO_ERROR;
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}
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ErrorCode CPURelu6::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto& ib = inputs[0]->buffer();
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auto& ob = outputs[0]->buffer();
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const float* srcO = (const float*)ib.host;
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float* dstO = (float*)ob.host;
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auto size = inputs[0]->size() / sizeof(float);
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MNNRelu6(dstO, srcO, size);
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return NO_ERROR;
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}
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CPUPRelu::CPUPRelu(Backend* b, const Op* op) : MNN::Execution(b) {
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auto c = op->main_as_PRelu();
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mSlope.reset(ALIGN_UP4(c->slopeCount()));
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mSlope.clear();
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::memcpy(mSlope.get(), c->slope()->data(), c->slopeCount() * sizeof(float));
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}
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ErrorCode CPUPRelu::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto& ib = inputs[0]->buffer();
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auto& ob = outputs[0]->buffer();
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const int width = ib.dim[3].extent;
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const int height = ib.dim[2].extent;
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const int channel = ib.dim[1].extent;
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const int batch = ib.dim[0].extent;
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const int depthQuad = UP_DIV(channel, 4);
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const int batchSize = depthQuad * 4 * width * height;
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const float* srcO = (const float*)ib.host;
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float* dstO = (float*)ob.host;
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int sizeQuad = width * height;
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for (int b = 0; b < batch; ++b) {
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auto src = srcO + b * batchSize;
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auto dst = dstO + b * batchSize;
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MNNReluWithSlopeChannel(dst, src, mSlope.get(), sizeQuad, depthQuad);
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}
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return NO_ERROR;
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}
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class CPUReluCreator : 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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if (op->type() == OpType_ReLU) {
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auto slope = 0.0f;
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if (nullptr != op->main() && OpParameter_Relu == op->main_type()) {
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slope = op->main_as_Relu()->slope();
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}
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return new CPURelu(backend, slope);
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}
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MNN_ASSERT(op->type() == OpType_PReLU);
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if (op->main_as_PRelu()->slopeCount() == 1) {
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return new CPURelu(backend, op->main_as_PRelu()->slope()->data()[0]);
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}
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return new CPUPRelu(backend, op);
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}
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};
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class CPURelu6Creator : 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 CPURelu6(backend);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUReluCreator, OpType_ReLU);
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REGISTER_CPU_OP_CREATOR(CPUReluCreator, OpType_PReLU);
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REGISTER_CPU_OP_CREATOR(CPURelu6Creator, OpType_ReLU6);
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} // namespace MNN
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