mirror of https://github.com/alibaba/MNN.git
78 lines
2.8 KiB
C++
78 lines
2.8 KiB
C++
//
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// ShapeConvolution3D.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 <math.h>
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#include "core/Macro.h"
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#include "core/SizeComputer.hpp"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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class Convolution3DSizeComputer : public SizeComputer {
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public:
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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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MNN_ASSERT(1 == outputs.size());
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auto layer = op->main_as_Convolution3D()->common();
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for (auto stride: *layer->strides()) {
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MNN_ASSERT(stride == 1);
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}
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for (auto dilate: *layer->dilates()) {
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MNN_ASSERT(dilate == 1);
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}
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auto input = inputs[0];
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if (input->buffer().dimensions != 5) {
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return false;
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}
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auto& outputBuffer = outputs[0]->buffer();
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outputBuffer.dimensions = input->buffer().dimensions;
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outputBuffer.dim[0].extent = input->buffer().dim[0].extent;
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outputBuffer.dim[1].extent = layer->outputCount();
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for (int i = 0; i < 3; ++i) {
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const int inputLength = input->length(i + 2), stride = (*layer->strides())[i];
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if (inputLength <= 0) {
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return false;
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}
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int outputLength;
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if (layer->padMode() == PadMode_SAME) {
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outputLength = UP_DIV(inputLength, stride);
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} else {
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const int pad = (*layer->pads())[i], kernel = (*layer->kernels())[i], dialate = (*layer->dilates())[i];
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const int dialatedKernel = (kernel - 1) * dialate + 1;
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outputLength = (inputLength + 2 * pad - dialatedKernel) / stride + 1;
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}
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outputBuffer.dim[i + 2].extent = outputLength;
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}
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outputBuffer.type = input->getType();
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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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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 layer = op->main_as_Convolution3D()->common();
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int oSize = outputs[0]->length(1);
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float flopsPerElement = inputs[0]->length(1);
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for (int i = 0; i < 3; ++i) {
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flopsPerElement *= (*layer->kernels())[i];
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oSize *= outputs[0]->length(i + 2);
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}
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float flops = oSize * flopsPerElement / FLOPS_M;
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return flops;
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}
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};
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REGISTER_SHAPE(Convolution3DSizeComputer, OpType_Convolution3D);
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} // namespace MNN
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