mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			50 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			50 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  ShapeReduceJoin.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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| 
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| #include "shape/SizeComputer.hpp"
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| #include "core/Macro.h"
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| 
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| namespace MNN {
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| class ReduceJoinComputer : 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(2 == inputs.size());
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|         MNN_ASSERT(1 == outputs.size());
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| 
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|         auto output = outputs[0];
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|         auto input  = inputs[0];
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|         auto axis   = inputs[1];
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| 
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|         // support reduce 1 dimension, only
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|         MNN_ASSERT(axis->size() == axis->buffer().type.bytes());
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| 
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|         MNN_ASSERT(axis->host<int32_t>()[0] >= 0);
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|         std::vector<int> shape;
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|         for (int i = 0; i < input->buffer().dimensions; i++) {
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|             if (i != axis->host<int32_t>()[0]) {
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|                 shape.push_back(input->buffer().dim[i].extent);
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|             } else {
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|                 if (op->main_as_ReduceJoin()->keepDims()) {
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|                     shape.push_back(1);
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|                 }
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|             }
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|         }
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|         output->buffer().dimensions = (int)shape.size();
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|         for (int i = 0; i < shape.size(); i++) {
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|             output->buffer().dim[i].extent = shape[i];
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|         }
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|         output->setType(DataType_DT_STRING);
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|         TensorUtils::getDescribe(outputs[0])->dimensionFormat = MNN_DATA_FORMAT_NHWC;
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|         return true;
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|     }
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| };
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| 
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| REGISTER_SHAPE_INPUTS(ReduceJoinComputer, OpType_ReduceJoin, {1});
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| } // namespace MNN
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