mirror of https://github.com/alibaba/MNN.git
104 lines
4.3 KiB
C++
104 lines
4.3 KiB
C++
//
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// ShapeSlice.cpp
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// MNN
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//
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// Created by MNN on 2019/01/10.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "shape/SizeComputer.hpp"
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#include "core/Macro.h"
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#include <algorithm>
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namespace MNN {
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class SliceComputer : public SizeComputer {
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virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
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const std::vector<Tensor*>& outputs) const override {
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MNN_ASSERT(1 == inputs.size());
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auto outputSize = (int)outputs.size();
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auto slice = op->main_as_Slice();
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auto& input = inputs[0]->buffer();
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int axis = slice->axis();
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if (axis < 0) {
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axis += input.dimensions;
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}
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if (MNN::NetSource_CAFFE == slice->sourceType()) {
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// caffe Slice
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int previous = 0;
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for (int i = 0; i < slice->slicePoints()->size(); ++i) {
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int sliceIndex = slice->slicePoints()->data()[i];
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auto& output = outputs[i]->buffer();
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output.dimensions = input.dimensions;
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::memcpy(output.dim, input.dim, input.dimensions * sizeof(halide_dimension_t));
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output.type = input.type;
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output.dim[axis].extent = sliceIndex - previous;
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previous = sliceIndex;
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}
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// Compute Last
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auto& output = outputs[outputs.size() - 1]->buffer();
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output.dimensions = input.dimensions;
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output.type = input.type;
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::memcpy(output.dim, input.dim, input.dimensions * sizeof(halide_dimension_t));
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output.dim[axis].extent = input.dim[axis].extent - previous;
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} else {
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// tensorflow Split
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if (nullptr == slice->slicePoints() || 1 == slice->slicePoints()->size()) {
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// scalar
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int numSplits = outputSize;
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if (nullptr != slice->slicePoints() && slice->slicePoints()->data()[0] < outputSize) {
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numSplits = slice->slicePoints()->data()[0];
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}
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MNN_ASSERT(0 == input.dim[axis].extent % numSplits);
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const int splitDim = input.dim[axis].extent / numSplits;
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for (int i = 0; i < numSplits; i++) {
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auto& output = outputs[i]->buffer();
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output.dimensions = input.dimensions;
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output.type = input.type;
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::memcpy(output.dim, input.dim, input.dimensions * sizeof(halide_dimension_t));
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output.dim[axis].extent = splitDim;
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}
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} else {
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// one dimension tensor, ex: [5,30]=>[5,4]+[5,15]+[5,11], slicePoints is [4, 15, 11]
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int numberSplits = slice->slicePoints()->size();
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MNN_ASSERT(0 < numberSplits);
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numberSplits = std::min(numberSplits, outputSize);
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int determineTensorIndex = -1;
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int maxSize = 0;
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for (int i = 0; i < numberSplits; i++) {
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auto& output = outputs[i]->buffer();
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output.type = input.type;
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output.dimensions = input.dimensions;
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::memcpy(output.dim, input.dim, input.dimensions * sizeof(halide_dimension_t));
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auto length = slice->slicePoints()->data()[i];
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if (-1 != length) {
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output.dim[axis].extent = length;
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maxSize += length;
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} else {
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if (determineTensorIndex >= 0) {
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// Don't support two -1 points
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return false;
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}
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determineTensorIndex = i;
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}
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}
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if (determineTensorIndex >= 0) {
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auto& output = outputs[determineTensorIndex]->buffer();
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output.dim[axis].extent = input.dim[axis].extent - maxSize;
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}
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}
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}
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for (int i=0; i<outputs.size(); ++i) {
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TensorUtils::getDescribe(outputs[i])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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}
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return true;
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}
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};
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REGISTER_SHAPE(SliceComputer, OpType_Slice);
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} // namespace MNN
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