MNN/source/shape/ShapeBroadcastTo.cpp

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//
// ShapeBroadcastTo.cpp
// MNN
//
// Created by MNN on 2019/12/2.
// Copyright © 2018, Alibaba Group Holding Limited
//
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#include "shape/SizeComputer.hpp"
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#include "core/Macro.h"
#include "core/TensorUtils.hpp"
namespace MNN {
class ShapeBroadcastTo : public SizeComputer {
virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {
MNN_ASSERT(inputs.size() == 2);
MNN_ASSERT(outputs.size() == 1);
auto input = inputs[0];
auto shape = inputs[1];
auto output = outputs[0];
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int inputDims = input->dimensions();
int shapeDims = shape->elementSize();
output->buffer().dimensions = inputDims > shapeDims ? inputDims : shapeDims;
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const int dimension = output->dimensions();
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const int* shapeData = shape->host<int>();
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for (int i = 1; i <= dimension; ++i) {
int inputDim = 1, shapeDim = 1;
if (i <= inputDims) {
inputDim = input->length(inputDims - i);
}
if (i <= shapeDims) {
shapeDim = shapeData[shapeDims - i];
}
if (shapeDim <= 1) {
// shapeDim is {-1,0,1}, keep inputDim
output->setLength(dimension - i, inputDim);
} else {
// broadcast inputDim to shapeDim, need shapDim % inputDim == 0
// inputDim == 0, need shapeDim <= 0 keep dim
MNN_ASSERT(inputDim != 0);
MNN_ASSERT(shapeDim % inputDim == 0);
output->setLength(dimension - i, shapeDim);
}
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}
output->buffer().type = input->buffer().type;
TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(input)->dimensionFormat;
return true;
}
};
REGISTER_SHAPE_INPUTS(ShapeBroadcastTo, OpType_BroadcastTo, {1});
} // namespace MNN