mirror of https://github.com/alibaba/MNN.git
98 lines
3.3 KiB
C++
98 lines
3.3 KiB
C++
//
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// CPUSelect.cpp
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// MNN
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//
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// Created by MNN on 2019/5/22.
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// Copyright © 2018 Alibaba. All rights reserved.
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//
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#include "backend/cpu/CPUSelect.hpp"
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namespace MNN {
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static void ApplySelectAtAxis(int axis, int dimensions, const int* select,
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const float* input0, const float* input1, float* output,
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const std::vector<int>& select_shape,
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const std::vector<int>& input_shape,
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const std::vector<int>& select_stride,
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const std::vector<int>& input_stride) {
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if (axis >= dimensions) {
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if (*select) {
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*output = *input0;
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} else {
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*output = *input1;
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}
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return;
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}
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for (int i = 0; i < input_shape[axis]; ++i) {
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ApplySelectAtAxis(axis + 1, dimensions, select, input0, input1,
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output, select_shape, input_shape, select_stride, input_stride);
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input0 += input_stride[axis];
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input1 += input_stride[axis];
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output += input_stride[axis];
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if (select_shape[axis] == input_shape[axis]) {
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select += select_stride[axis];
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}
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}
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}
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ErrorCode CPUSelect::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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int dimensions = inputs[1]->dimensions();
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auto select = inputs[0];
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MNN_ASSERT(inputs.size() == 3);
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MNN_ASSERT(dimensions == inputs[2]->dimensions());
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for (int i = 0; i < dimensions; ++i) {
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MNN_ASSERT(inputs[1]->length(i) == inputs[2]->length(i));
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}
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MNN_ASSERT(select->dimensions() <= dimensions);
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for (int i = 0; i < select->dimensions(); ++i) {
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MNN_ASSERT(select->length(i) == 1 ||
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select->length(i) == inputs[1]->length(i));
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}
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auto outputPtr = outputs[0]->host<float>();
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auto input0Ptr = inputs[1]->host<float>();
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auto input1Ptr = inputs[2]->host<float>();
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auto selectPtr = select->host<int32_t>();
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std::vector<int> select_shape(dimensions);
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std::vector<int> input_shape(dimensions);
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std::vector<int> select_stride(dimensions);
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std::vector<int> input_stride(dimensions);
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for (int i = 0; i < dimensions; ++i) {
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if (i < select->dimensions()) {
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select_shape[i] = select->length(i);
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} else {
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select_shape[i] = 1;
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}
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input_shape[i] = inputs[1]->length(i);
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}
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for (int i = dimensions - 1; i >= 0; --i) {
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if (i == dimensions - 1) {
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select_stride[i] = 1;
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input_stride[i] = 1;
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} else {
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select_stride[i] = select_shape[i + 1] * select_stride[i + 1];
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input_stride[i] = input_shape[i + 1] * input_stride[i + 1];
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}
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}
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ApplySelectAtAxis(0, dimensions, selectPtr, input0Ptr, input1Ptr, outputPtr,
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select_shape, input_shape, select_stride, input_stride);
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return NO_ERROR;
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}
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class CPUSelectCreator : 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 CPUSelect(backend);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUSelectCreator, OpType_Select);
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} // namespace MNN
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