mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			45 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			45 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  ShapeTile.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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| #include "core/TensorUtils.hpp"
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| 
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| namespace MNN {
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| 
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| class TileComputer : 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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|         auto& input    = inputs[0]->buffer();
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|         auto multiples = inputs[1];
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|         MNN_ASSERT(multiples->getType().code == halide_type_int);
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|         auto& output = outputs[0]->buffer();
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|         // Expected multiples argument to be a 1-D vector of length input.dimensions
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|         MNN_ASSERT(1 == multiples->buffer().dimensions)
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|         MNN_ASSERT(input.dimensions == multiples->buffer().dim[0].extent);
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|         const int inputDims = input.dimensions;
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|         ::memcpy(output.dim, input.dim, input.dimensions * sizeof(halide_dimension_t));
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|         output.dimensions = inputDims;
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|         output.type       = input.type;
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|         
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|         for (int i = 0; i < inputDims; ++i) {
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|             output.dim[i].extent = input.dim[i].extent * multiples->host<int32_t>()[i];
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|         }
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|         TensorUtils::getDescribe(outputs[0])->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_INPUTS(TileComputer, OpType_Tile, {1});
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| 
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| } // namespace MNN
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