mirror of https://github.com/alibaba/MNN.git
92 lines
3.5 KiB
C++
92 lines
3.5 KiB
C++
//
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// ShapePool3D.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 "shape/SizeComputer.hpp"
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#include "core/Macro.h"
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namespace MNN {
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class Pool3DSizeComputer : 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 input = inputs[0];
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auto output = outputs[0];
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auto layer = op->main_as_Pool3D();
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auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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// only check channel dim when global pool
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int maxCheckDim = (layer->isGlobal() ? 1 :input->buffer().dimensions - 1);
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for (unsigned int i = 1; i <= maxCheckDim; ++i) {
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if (input->buffer().dim[i].extent <= 0) {
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return false;
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}
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}
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output->buffer().dimensions = input->buffer().dimensions;
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::memcpy(output->buffer().dim, input->buffer().dim, input->buffer().dimensions * sizeof(halide_dimension_t));
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if (layer->isGlobal()) {
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if (format == MNN_DATA_FORMAT_NHWC) {
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// N [1...] C
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for (int d = 1; d < output->dimensions() - 1; d++) {
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output->buffer().dim[d].extent = 1;
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}
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} else {
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// N C [1...]
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for (int d = 2; d < output->dimensions(); d++) {
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output->buffer().dim[d].extent = 1;
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}
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}
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} else {
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int offset = format == MNN_DATA_FORMAT_NHWC ? 1 : 2;
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for (unsigned int i = 0; i < input->dimensions() - 2; ++i) {
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int inputLength = input->buffer().dim[i + 2].extent, outputLength = 0;
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const int kernel = (*layer->kernels())[i], stride = (*layer->strides())[i];
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if (layer->padType() == PoolPadType_CAFFE) {
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int pad = (*layer->pads())[i];
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outputLength = (inputLength + 2 * pad - kernel) / stride + 1;
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} else if (layer->padType() == PoolPadType_SAME) {
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outputLength = UP_DIV(inputLength, stride);
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} else if (layer->padType() == PoolPadType_VALID) {
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outputLength = (inputLength - kernel) / stride + 1;
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} else {
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MNN_ERROR("PoolPadType %d not support\n", layer->padType());
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}
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if (outputLength <= 0) {
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return false;
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}
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output->buffer().dim[i + offset].extent = outputLength;
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}
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}
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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output->buffer().type = input->buffer().type;
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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 size = (float)outputs[0]->elementSize() / 1024.0f / 1024.0f;
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auto layer = op->main_as_Pool3D();
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float flopsPerElement = 1;
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if (layer->kernels() == nullptr) {
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return size * flopsPerElement;
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}
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for (auto kernel: *layer->kernels()) {
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flopsPerElement *= kernel;
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}
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return size * flopsPerElement;
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}
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};
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REGISTER_SHAPE(Pool3DSizeComputer, OpType_Pooling3D);
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} // namespace MNN
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