mirror of https://github.com/alibaba/MNN.git
94 lines
3.6 KiB
C++
94 lines
3.6 KiB
C++
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//
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// CPUPadding.cpp
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// MNN
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//
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// Created by MNN on 2019/6/24.
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// Copyright © 2018 Alibaba. All rights reserved.
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//
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#include "CPUPadding.hpp"
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#include "Macro.h"
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#include "TensorUtils.hpp"
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namespace MNN {
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ErrorCode CPUPadding::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto input = inputs[0];
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auto output = outputs[0];
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auto padding = inputs[1]->host<int32_t>();
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::memset(output->host<char>(), 0, output->size());
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auto bytes = input->getType().bytes();
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auto unit = input->length(3) * bytes;
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for (int b = 0; b < input->length(0); ++b) {
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auto outputB = output->host<char>() + output->stride(0) * (b + padding[2 * 0]) * bytes;
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auto inputB = input->host<char>() + input->stride(0) * b * bytes;
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for (int h = 0; h < input->length(1); ++h) {
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auto outputH = outputB + output->stride(1) * (h + padding[2 * 1]) * bytes;
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auto inputH = inputB + input->stride(1) * h * bytes;
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for (int w = 0; w < input->length(2); ++w) {
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auto outputW = outputH + output->stride(2) * (w + padding[2 * 2]) * bytes;
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auto inputW = inputH + input->stride(2) * w * bytes;
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::memcpy(outputW + padding[3 * 2] * bytes, inputW, unit);
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}
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}
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}
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return NO_ERROR;
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}
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ErrorCode CPUPaddingPacked::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto input = inputs[0];
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auto output = outputs[0];
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auto iw = input->width();
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auto ih = input->height();
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auto ic = input->channel();
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auto ib = input->batch();
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auto ow = output->width();
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auto oh = output->height();
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auto icC4 = UP_DIV(ic, 4);
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auto padding = inputs[1]->host<int32_t>();
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::memset(output->host<float>(), 0, output->size());
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for (int n = 0; n < ib; ++n) {
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auto inputN = input->host<float>() + input->stride(0) * n;
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auto outputN = output->host<float>() + output->stride(0) * (padding[2 * 0] + n);
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for (int c = 0; c < icC4; ++c) {
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auto inputC = inputN + c * iw * ih * 4;
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auto outputC = outputN + c * ow * oh * 4;
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for (int h = 0; h < ih; ++h) {
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auto inputH = inputC + h * iw * 4;
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auto outputH = outputC + (h + padding[2 * 2]) * ow * 4;
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::memcpy(outputH + padding[2 * 3] * 4, inputH, iw * 4 * sizeof(float));
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}
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}
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}
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return NO_ERROR;
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}
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class CPUPaddingCreator : 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 (inputs[0]->dimensions() != 4) {
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MNN_ERROR("Currently padding only support NHWC or NC4HW4\n");
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return nullptr;
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}
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auto padding = inputs[1];
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auto paddingPtr = padding->host<int32_t>();
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if (TensorUtils::getDescribe(inputs[0])->dimensionFormat == MNN_DATA_FORMAT_NHWC) {
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return new CPUPadding(backend);
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}
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if (paddingPtr[2] != 0 || paddingPtr[3] != 0) {
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MNN_ERROR("Currently padding NC4HW4 don't support channel padding\n");
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return nullptr;
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}
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if (inputs[0]->buffer().type.code != halide_type_float) {
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MNN_ERROR("Currently padding NC4HW4 only support float padding\n");
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return nullptr;
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}
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return new CPUPaddingPacked(backend);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUPaddingCreator, OpType_Padding);
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}; // namespace MNN
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