mirror of https://github.com/alibaba/MNN.git
114 lines
4.2 KiB
C++
114 lines
4.2 KiB
C++
//
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// ShapeSqueeze.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 "shape/SizeComputer.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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class UnSqueezeSizeComputer : 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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MNN_ASSERT(1 == outputs.size());
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const int* squeezeDim = nullptr;
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int squeezeDimSize = 0;
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if (nullptr != op->main_as_SqueezeParam()->squeezeDims()) {
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squeezeDim = op->main_as_SqueezeParam()->squeezeDims()->data();
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squeezeDimSize = op->main_as_SqueezeParam()->squeezeDims()->size();
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} else if (inputs.size() > 1) {
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squeezeDim = inputs[1]->host<int>();
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squeezeDimSize = inputs[1]->elementSize();
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}
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auto& ob = outputs[0]->buffer();
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auto& ib = inputs[0]->buffer();
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ob.dimensions = ib.dimensions + squeezeDimSize;
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uint32_t mask[MNN_MAX_TENSOR_DIM];
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::memset(mask, 0, sizeof(mask));
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for (int i = 0; i < squeezeDimSize; i++) {
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int axis = squeezeDim[i];
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if (axis < 0) {
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axis += ob.dimensions;
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}
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mask[axis] = 1;
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}
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int oDim = 0;
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for (int i = 0; i < ob.dimensions; i++) {
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ob.dim[i].extent = 1;
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if (mask[i] == 0) {
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ob.dim[i].extent = ib.dim[oDim].extent;
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oDim++;
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}
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}
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ob.type = inputs[0]->buffer().type;
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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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};
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class SqueezeSizeComputer : 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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MNN_ASSERT(1 == outputs.size());
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const int* squeezeDim = nullptr;
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int squeezeDimSize = 0;
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if (nullptr != op->main_as_SqueezeParam()->squeezeDims()) {
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squeezeDim = op->main_as_SqueezeParam()->squeezeDims()->data();
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squeezeDimSize = op->main_as_SqueezeParam()->squeezeDims()->size();
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} else if (inputs.size() > 1) {
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squeezeDim = inputs[1]->host<int>();
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squeezeDimSize = inputs[1]->elementSize();
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}
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uint32_t mask[MNN_MAX_TENSOR_DIM];
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::memset(mask, 0, sizeof(mask));
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auto& ob = outputs[0]->buffer();
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auto& ib = inputs[0]->buffer();
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for (int i = 0; i < squeezeDimSize; i++) {
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int axis = squeezeDim[i];
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if (axis < 0) {
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axis += ib.dimensions;
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}
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if (1 != ib.dim[axis].extent) {
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MNN_ERROR("Cannot Squeeze dim[%d], 1 is expected, %d is got. input shape:", axis, ib.dim[axis].extent);
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inputs[0]->printShape();
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return false;
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}
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mask[axis] = 1;
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}
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if (squeezeDimSize == 0) {
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for (int i = 0; i < ib.dimensions; ++i) {
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if (ib.dim[i].extent == 1) {
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mask[i] = 1;
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++squeezeDimSize;
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}
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}
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}
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// in = Tensor(shape=())
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// out = Squeeze(in) should also returns a tensor with shape=(), but
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// the `squeezeDimSize` and `ib.dimensions` are all 0.
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MNN_ASSERT(squeezeDimSize <= ib.dimensions);
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ob.dimensions = ib.dimensions - squeezeDimSize;
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int oDim = 0;
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for (int i = 0; i < ib.dimensions; i++) {
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if (mask[i] == 0) {
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ob.dim[oDim].extent = ib.dim[i].extent;
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oDim++;
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}
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}
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ob.type = inputs[0]->buffer().type;
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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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};
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REGISTER_SHAPE(SqueezeSizeComputer, OpType_Squeeze);
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REGISTER_SHAPE(UnSqueezeSizeComputer, OpType_Unsqueeze);
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} // namespace MNN
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