// // ShapeConvolution3D.cpp // MNN // // Created by MNN on 2019/01/10. // Copyright © 2018, Alibaba Group Holding Limited // #include #include "shape/SizeComputer.hpp" #include "core/Macro.h" #include "core/TensorUtils.hpp" namespace MNN { class Convolution3DSizeComputer : public SizeComputer { public: virtual bool onComputeSize(const MNN::Op* op, const std::vector& inputs, const std::vector& outputs) const override { MNN_ASSERT(1 == inputs.size()); MNN_ASSERT(1 == outputs.size()); auto layer = op->main_as_Convolution3D()->common(); auto input = inputs[0]; if (input->buffer().dimensions != 5) { return false; } auto& outputBuffer = outputs[0]->buffer(); outputBuffer.dimensions = input->buffer().dimensions; outputBuffer.dim[0].extent = input->buffer().dim[0].extent; outputBuffer.dim[1].extent = layer->outputCount(); for (int i = 0; i < 3; ++i) { const int inputLength = input->length(i + 2), stride = (*layer->strides())[i]; if (inputLength <= 0) { return false; } int outputLength; if (layer->padMode() == PadMode_SAME) { outputLength = UP_DIV(inputLength, stride); } else { const int padl = layer->pads()->data()[i], kernel = layer->kernels()->data()[i], dialate = layer->dilates()->data()[i]; int padr = padl; if (layer->pads()->size() == 6) { padr = layer->pads()->data()[i+3]; } const int dialatedKernel = (kernel - 1) * dialate + 1; outputLength = (inputLength + padl + padr - dialatedKernel) / stride + 1; } outputBuffer.dim[i + 2].extent = outputLength; } outputBuffer.type = input->getType(); TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat; return true; } }; REGISTER_SHAPE(Convolution3DSizeComputer, OpType_Convolution3D); } // namespace MNN