mirror of https://github.com/alibaba/MNN.git
103 lines
4.3 KiB
C++
103 lines
4.3 KiB
C++
//
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// CPUGridSample.cpp
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// MNN
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//
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// Created by MNN on 2021/03/24.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUGridSample.hpp"
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#include <math.h>
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#include <string.h>
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#include "core/Concurrency.h"
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#include <algorithm>
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#include "backend/cpu/CPUBackend.hpp"
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#include "backend/cpu/compute/CommonOptFunction.h"
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#include "backend/cpu/compute/ConvOpt.h"
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#include "core/Macro.h"
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#include <math/Vec.hpp>
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using Vec4 = MNN::Math::Vec<float, 4>;
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namespace MNN {
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CPUGridSample::CPUGridSample(Backend *b, SampleMode mode, BorderMode paddingMode, bool alignCorners)
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: Execution(b) {
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mMode = mode;
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mPaddingMode = paddingMode;
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mAlignCorners = alignCorners;
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}
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ErrorCode CPUGridSample::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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int numberThread = static_cast<CPUBackend*>(backend())->threadNumber();
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auto core = static_cast<CPUBackend*>(backend())->functions();
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auto outputTensor = outputs[0];
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auto outH = outputTensor->buffer().dim[2].extent;
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auto outW = outputTensor->buffer().dim[3].extent;
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mTempCordBuffer.reset(Tensor::createDevice<uint8_t>({1, outH * outW * 2 * core->bytes}));
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auto res = backend()->onAcquireBuffer(mTempCordBuffer.get(), Backend::DYNAMIC);
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if (!res) {
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return OUT_OF_MEMORY;
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}
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backend()->onReleaseBuffer(mTempCordBuffer.get(), Backend::DYNAMIC);
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return NO_ERROR;
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}
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ErrorCode CPUGridSample::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto inputTensor = inputs[0];
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auto gridTensor = inputs[1];
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auto outputTensor = outputs[0];
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auto core = static_cast<CPUBackend*>(backend())->functions();
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auto inputPtr = inputTensor->host<uint8_t>();
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auto gridPtr = gridTensor->host<uint8_t>();
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auto outputPtr = outputTensor->host<uint8_t>();
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auto batches = inputTensor->buffer().dim[0].extent;
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auto channels = inputTensor->buffer().dim[1].extent;
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auto channelCUnit = UP_DIV(channels, core->pack);
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auto inH = inputTensor->buffer().dim[2].extent;
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auto inW = inputTensor->buffer().dim[3].extent;
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auto outH = outputTensor->buffer().dim[2].extent;
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auto outW = outputTensor->buffer().dim[3].extent;
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auto cordPtr = mTempCordBuffer->host<uint8_t>();
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auto threadCount = static_cast<CPUBackend*>(backend())->threadNumber();
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auto tileCount = channelCUnit * outH;
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for (auto b = 0; b < batches; ++b) {
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auto _inputPtr = inputPtr + b * inputTensor->buffer().dim[0].stride * core->bytes;
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auto _gridPtr = gridPtr + b * gridTensor->buffer().dim[0].stride * core->bytes;
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auto _outputPtr = outputPtr + b * outputTensor->buffer().dim[0].stride * core->bytes;
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// Compute cord
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core->MNNGridSampleComputeCord((float *)cordPtr, (const float *)_gridPtr, inH, inW, outH, outW, gridTensor->buffer().dim[1].stride, mAlignCorners);
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MNN_CONCURRENCY_BEGIN(tId, threadCount) {
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for (int index=tId; index < tileCount; index += threadCount) {
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auto c = index / outH;
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auto h = index % outH;
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auto inputC = _inputPtr + c * inW * inH * core->pack * core->bytes;
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auto outputC = _outputPtr + c * outW * outH * core->pack * core->bytes;
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auto cordH = cordPtr + h * outW * 2 * core->bytes;
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auto outputH = outputC + h * outW * core->pack * core->bytes;
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core->MNNGridSampleInterp((float *)outputH, (const float *)inputC, (const float *)cordH, inH, inW, outW, (mMode == SampleMode_NEAREST), (mPaddingMode == BorderMode_ZEROS));
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}
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}
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MNN_CONCURRENCY_END();
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}
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return NO_ERROR;
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}
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class CPUGridSampleCreator : 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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auto gridSampleParam = op->main_as_GridSample();
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auto mode = gridSampleParam->mode();
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auto paddingMode = gridSampleParam->paddingMode();
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auto alignCorners = gridSampleParam->alignCorners();
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return new CPUGridSample(backend, mode, paddingMode, alignCorners);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUGridSampleCreator, OpType_GridSample);
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} // namespace MNN
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