mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			46 lines
		
	
	
		
			1.4 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			46 lines
		
	
	
		
			1.4 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  ShapeSvd.cpp
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| //  MNN
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| //
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| //  Created by MNN on 2022/07/14.
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| //  Copyright © 2018, Alibaba Group Holding Limited
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| //
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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 "math.h"
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| 
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| namespace MNN {
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| 
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| class SvdComputer : 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(inputs.size() == 1 && outputs.size() == 3);
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|         auto shape = inputs[0]->shape();
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|         MNN_ASSERT(shape.size() == 2);
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|         int row = shape[0];
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|         int col  = shape[1];
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|         // int single_num = std::min(row, col);
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|         int single_num = col;
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|         // w is [ single_num ]
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|         outputs[0]->buffer().dimensions = 1;
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|         outputs[0]->setLength(0, single_num);
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|         // u is [row, single_num ]
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|         outputs[1]->buffer().dimensions = 2;
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|         outputs[1]->setLength(0, row);
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|         outputs[1]->setLength(1, single_num);
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|         // vt is [single_num, col ]
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|         outputs[2]->buffer().dimensions = 2;
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|         outputs[2]->setLength(0, single_num);
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|         outputs[2]->setLength(1, col);
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|         for (int i = 0; i < 3; i++) {
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|             outputs[i]->buffer().type = inputs[0]->getType();
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|             TensorUtils::getDescribe(outputs[i])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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|         }
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|         return true;
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|     }
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| };
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| 
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| REGISTER_SHAPE(SvdComputer, OpType_Svd);
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| } // namespace MNN
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