mirror of https://github.com/alibaba/MNN.git
77 lines
3.0 KiB
C++
77 lines
3.0 KiB
C++
//
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// ShapeDeconvolution.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/SizeComputer.hpp"
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namespace MNN {
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class DeconvolutionSizeComputer : 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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auto layer = op->main_as_Convolution2D()->common();
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auto inputTensor = inputs[0];
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int input_width = inputTensor->width();
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int input_height = inputTensor->height();
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int sH = layer->strideY();
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int sW = layer->strideX();
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int kH = layer->kernelY();
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int kW = layer->kernelX();
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int pH = layer->padY();
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int pW = layer->padX();
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int dH = layer->dilateY();
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int dW = layer->dilateX();
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int output_width;
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int output_height;
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if (layer->padMode() == PadMode_SAME) { // Tensorflow support
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output_width = input_width * sW;
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output_height = input_height * sH;
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} else {
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if (nullptr != layer->pads()) {
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MNN_ASSERT(layer->pads()->size() >= 4);
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output_width = (input_width - 1) * sW + dW * (kW - 1) + 1 - layer->pads()->data()[1] - layer->pads()->data()[3];
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output_height = (input_height - 1) * sH + dH * (kH - 1) + 1 - layer->pads()->data()[0] - layer->pads()->data()[2];
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} else {
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output_width = (input_width - 1) * sW + dW * (kW - 1) + 1 - pW * 2;
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output_height = (input_height - 1) * sH + dH * (kH - 1) + 1 - pH * 2;
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}
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}
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auto& outputBuffer = outputs[0]->buffer();
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outputBuffer.type = inputs[0]->getType();
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outputBuffer.dimensions = inputTensor->buffer().dimensions;
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outputBuffer.dim[0].extent = inputTensor->buffer().dim[0].extent;
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outputBuffer.dim[1].extent = op->main_as_Convolution2D()->common()->outputCount();
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outputBuffer.dim[2].extent = output_height;
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outputBuffer.dim[3].extent = output_width;
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
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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_Convolution2D()->common();
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auto kw = layer->kernelX();
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auto kh = layer->kernelY();
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auto group = layer->group();
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auto ic = inputs[0]->channel();
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auto oc = outputs[0]->channel();
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auto oSize = inputs[0]->width() * inputs[0]->height() * inputs[0]->batch();
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return (float)oSize * kw * kh * (ic * oc / group) / FLOPS_M;
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}
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};
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REGISTER_SHAPE(DeconvolutionSizeComputer, OpType_Deconvolution);
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REGISTER_SHAPE(DeconvolutionSizeComputer, OpType_DeconvolutionDepthwise);
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} // namespace MNN
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