mirror of https://github.com/alibaba/MNN.git
62 lines
2.1 KiB
C++
62 lines
2.1 KiB
C++
//
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// ShapeGatherV2.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 "core/Macro.h"
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#include "core/SizeComputer.hpp"
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namespace MNN {
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class GatherV2Computer : 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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auto params = inputs[0];
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auto indices = inputs[1];
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MNN_ASSERT(indices->getType().code == halide_type_int);
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int axis = 0;
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if (inputs.size() == 3) {
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auto axis_tensor = inputs[2];
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axis = axis_tensor->host<int32_t>()[0];
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}
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MNN_ASSERT(axis > -params->buffer().dimensions && axis < params->buffer().dimensions);
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if (axis < 0) {
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axis = params->buffer().dimensions + axis;
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}
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const int gather_dim_size = params->buffer().dim[axis].extent;
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MNN_ASSERT(gather_dim_size <= std::numeric_limits<int32_t>::max());
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const int numDimensions = params->buffer().dimensions + indices->buffer().dimensions - 1;
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MNN_ASSERT(axis <= numDimensions);
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std::vector<int> result_shape;
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for (int i = 0; i < axis; i++) {
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result_shape.push_back(params->buffer().dim[i].extent);
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}
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for (int i = 0; i < indices->buffer().dimensions; i++) {
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result_shape.push_back(indices->buffer().dim[i].extent);
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}
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for (int i = axis + 1; i < params->buffer().dimensions; i++) {
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result_shape.push_back(params->buffer().dim[i].extent);
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}
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outputs[0]->buffer().dimensions = (int)result_shape.size();
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outputs[0]->buffer().type = params->buffer().type;
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for (int i = 0; i < result_shape.size(); i++) {
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outputs[0]->buffer().dim[i].extent = result_shape.at(i);
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}
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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return true;
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}
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};
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REGISTER_SHAPE_INPUTS(GatherV2Computer, OpType_GatherV2, (std::vector<int>{2}));
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} // namespace MNN
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