mirror of https://github.com/alibaba/MNN.git
57 lines
2.0 KiB
C++
57 lines
2.0 KiB
C++
//
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// ShapeSliceTf.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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namespace MNN {
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class SliceTfComputer : 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(inputs.size() == 3);
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MNN_ASSERT(outputs.size() == 1);
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auto input = inputs[0];
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// these two inputs should be const
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auto begin_tensor = inputs[1];
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auto size_tensor = inputs[2];
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MNN_ASSERT(begin_tensor->buffer().dimensions == 1);
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MNN_ASSERT(size_tensor->buffer().dimensions == 1);
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MNN_ASSERT(input->buffer().dimensions >= 1);
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MNN_ASSERT(input->buffer().dimensions == begin_tensor->buffer().dim[0].extent);
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MNN_ASSERT(input->buffer().dimensions == size_tensor->buffer().dim[0].extent);
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auto output = outputs[0];
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output->buffer().dimensions = input->buffer().dimensions;
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output->buffer().type = input->buffer().type;
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int dim = 0;
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auto sizePtr = size_tensor->host<int32_t>();
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for (int i = 0; i < input->buffer().dimensions; i++) {
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dim = sizePtr[i];
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if (dim == -1 ) {
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auto begin = begin_tensor->host<int32_t>()[i];
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if (begin < 0) {
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begin += input->length(i);
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}
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dim = input->buffer().dim[i].extent - begin;
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}
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MNN_ASSERT(dim <= input->length(i));
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output->buffer().dim[i].extent = dim;
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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_INPUTS(SliceTfComputer, OpType_SliceTf, (std::vector<int>{1, 2}));
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} // namespace MNN
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