mirror of https://github.com/alibaba/MNN.git
61 lines
1.8 KiB
C++
61 lines
1.8 KiB
C++
//
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// CPUWhere.cpp
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// MNN
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//
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// Created by MNN on 2018/08/31.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUWhere.hpp"
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#include "backend/cpu/CPUBackend.hpp"
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namespace MNN {
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template <typename T>
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std::vector<int32_t> _collect(Tensor* t) {
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const T* ptr = t->host<T>();
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std::vector<int32_t> collect;
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for (int i = 0; i < t->elementSize(); i++) {
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if (ptr[i] > 0) {
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collect.push_back(i);
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}
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}
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return collect;
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}
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ErrorCode CPUWhere::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 outputData = outputs[0]->host<int32_t>();
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std::vector<int32_t> collect;
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if (ib.type == halide_type_of<float>()) {
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collect = _collect<float>(inputs[0]);
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} else if (ib.type == halide_type_of<int32_t>()) {
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collect = _collect<int32_t>(inputs[0]);
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} else if (ib.type == halide_type_of<uint8_t>()) {
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collect = _collect<uint8_t>(inputs[0]);
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}
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//MNN_ASSERT(outputs[0]->batch() == trueVec.size());
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for (int i = 0; i < collect.size(); i++) {
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int index = collect[i];
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for (int j = 0; j < ib.dimensions; j++) {
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int result = ib.dim[j].stride == 0 ? index : index / ib.dim[j].stride;
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index = index - result * ib.dim[j].stride;
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outputData[i * ib.dimensions + j] = result;
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}
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}
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return NO_ERROR;
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}
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class CPUWhereCreator : 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 override {
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return new CPUWhere(backend);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUWhereCreator, OpType_Where);
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} // namespace MNN
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