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										 |  |  | //
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							|  |  |  | //  ShapeBroadcastTo.cpp
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							|  |  |  | //  MNN
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							|  |  |  | //
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							|  |  |  | //  Created by MNN on 2019/12/2.
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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 ShapeBroadcastTo : public SizeComputer { | 
					
						
							|  |  |  |     virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs, | 
					
						
							|  |  |  |                                const std::vector<Tensor*>& outputs) const override { | 
					
						
							|  |  |  |         MNN_ASSERT(inputs.size() == 2); | 
					
						
							|  |  |  |         MNN_ASSERT(outputs.size() == 1); | 
					
						
							|  |  |  |         auto input  = inputs[0]; | 
					
						
							|  |  |  |         auto shape  = inputs[1]; | 
					
						
							|  |  |  |         auto output = outputs[0]; | 
					
						
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										 |  |  |         int inputDims = input->dimensions(); | 
					
						
							|  |  |  |         int shapeDims = shape->elementSize(); | 
					
						
							|  |  |  |         output->buffer().dimensions = inputDims > shapeDims ? inputDims : shapeDims; | 
					
						
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										 |  |  |         const int dimension = output->dimensions(); | 
					
						
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										 |  |  |         const int* shapeData        = shape->host<int>(); | 
					
						
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										 |  |  |         for (int i = 1; i <= dimension; ++i) { | 
					
						
							|  |  |  |             int inputDim = 1, shapeDim = 1; | 
					
						
							|  |  |  |             if (i <= inputDims) { | 
					
						
							|  |  |  |                 inputDim = input->length(inputDims - i); | 
					
						
							|  |  |  |             } | 
					
						
							|  |  |  |             if (i <= shapeDims) { | 
					
						
							|  |  |  |                 shapeDim = shapeData[shapeDims - i]; | 
					
						
							|  |  |  |             } | 
					
						
							|  |  |  |             if (shapeDim <= 1) { | 
					
						
							|  |  |  |                 // shapeDim is {-1,0,1}, keep inputDim
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							|  |  |  |                 output->setLength(dimension - i, inputDim); | 
					
						
							|  |  |  |             } else { | 
					
						
							|  |  |  |                 // broadcast inputDim to shapeDim, need shapDim % inputDim == 0
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							|  |  |  |                 // inputDim == 0, need shapeDim <= 0 keep dim
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							|  |  |  |                 MNN_ASSERT(inputDim != 0); | 
					
						
							|  |  |  |                 MNN_ASSERT(shapeDim % inputDim == 0); | 
					
						
							|  |  |  |                 output->setLength(dimension - i, shapeDim); | 
					
						
							|  |  |  |             } | 
					
						
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										 |  |  |         } | 
					
						
							|  |  |  |         output->buffer().type                             = input->buffer().type; | 
					
						
							|  |  |  |         TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(input)->dimensionFormat; | 
					
						
							|  |  |  |         return true; | 
					
						
							|  |  |  |     } | 
					
						
							|  |  |  | }; | 
					
						
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							|  |  |  | REGISTER_SHAPE_INPUTS(ShapeBroadcastTo, OpType_BroadcastTo, {1}); | 
					
						
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							|  |  |  | } // namespace MNN
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