mirror of https://github.com/alibaba/MNN.git
54 lines
2.0 KiB
C++
54 lines
2.0 KiB
C++
//
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// ShapeGridSample.cpp
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// MNN
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//
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// Created by MNN on 2021/03/24.
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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 GridSampleSizeComputer : 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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// https://pytorch.org/docs/1.7.1/nn.functional.html?highlight=grid_sample#torch.nn.functional.grid_sample
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// inputs[0] is input, inputs[1] is grid
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MNN_ASSERT(2 == inputs.size());
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MNN_ASSERT(1 == outputs.size());
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MNN_ASSERT(4 == inputs[0]->buffer().dimensions && 4 == inputs[1]->buffer().dimensions);
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MNN_ASSERT(inputs[0]->buffer().dim[0].extent == inputs[1]->buffer().dim[0].extent);
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MNN_ASSERT(2 == inputs[1]->buffer().dim[3].extent);
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auto &ibInput0 = inputs[0]->buffer();
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auto &ibInput1 = inputs[1]->buffer();
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auto &ob = outputs[0]->buffer();
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ob.dimensions = ibInput1.dimensions;
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ob.dim[0].extent = ibInput0.dim[0].extent;
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ob.dim[1].extent = ibInput0.dim[1].extent;
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ob.dim[2].extent = ibInput1.dim[1].extent;
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ob.dim[3].extent = ibInput1.dim[2].extent;
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ob.type = ibInput0.type;
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(
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inputs[0])->dimensionFormat;
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return true;
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}
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virtual float onComputeFlops(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 gridSampleParam = op->main_as_GridSample();
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if (gridSampleParam->mode() == MNN::SampleMode_BILINEAR) {
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return 4 * SizeComputer::onComputeFlops(op, inputs, outputs);
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}
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return SizeComputer::onComputeFlops(op, inputs, outputs);
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}
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};
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REGISTER_SHAPE(GridSampleSizeComputer, OpType_GridSample);
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} // namespace MNN
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