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										 |  |  | //
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							|  |  |  | //  ShapeDepthToSpace.cpp
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							|  |  |  | //  MNN
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							|  |  |  | //
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							|  |  |  | //  Created by MNN on 2019/07/16.
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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 DepthToSpaceSizeComputer : 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() == 1); | 
					
						
							|  |  |  |         MNN_ASSERT(outputs.size() == 1); | 
					
						
							|  |  |  |         MNN_ASSERT(inputs[0]->buffer().dimensions == 4); | 
					
						
							|  |  |  |          | 
					
						
							|  |  |  |         // here only implement NHWC
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							|  |  |  |         // TODO: implement NC4HW4
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							|  |  |  |         const int blockSize = op->main_as_DepthSpaceParam()->blockSize(); | 
					
						
							|  |  |  |         MNN_ASSERT(blockSize > 1); | 
					
						
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										 |  |  |         auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat; | 
					
						
							|  |  |  |         MNN_ASSERT(inputs[0]->channel() % (blockSize * blockSize) == 0); | 
					
						
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							|  |  |  |         auto& ib = inputs[0]->buffer(); | 
					
						
							|  |  |  |         auto& ob = outputs[0]->buffer(); | 
					
						
							|  |  |  |         ob.dimensions = ib.dimensions; | 
					
						
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										 |  |  |         ob.type = ib.type; | 
					
						
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										 |  |  |         if (format == MNN_DATA_FORMAT_NHWC) { | 
					
						
							|  |  |  |             ob.dim[0].extent = ib.dim[0].extent; | 
					
						
							|  |  |  |             ob.dim[1].extent = ib.dim[1].extent * blockSize; | 
					
						
							|  |  |  |             ob.dim[2].extent = ib.dim[2].extent * blockSize; | 
					
						
							|  |  |  |             ob.dim[3].extent = ib.dim[3].extent / (blockSize * blockSize); | 
					
						
							|  |  |  |         } else { | 
					
						
							|  |  |  |             // NCHW / NC4HW4
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							|  |  |  |             ob.dim[0].extent = ib.dim[0].extent; | 
					
						
							|  |  |  |             ob.dim[3].extent = ib.dim[3].extent * blockSize; | 
					
						
							|  |  |  |             ob.dim[2].extent = ib.dim[2].extent * blockSize; | 
					
						
							|  |  |  |             ob.dim[1].extent = ib.dim[1].extent / (blockSize * blockSize); | 
					
						
							|  |  |  |         } | 
					
						
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										 |  |  |         TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat; | 
					
						
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							|  |  |  |         return true; | 
					
						
							|  |  |  |     } | 
					
						
							|  |  |  | }; | 
					
						
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							|  |  |  | REGISTER_SHAPE(DepthToSpaceSizeComputer, OpType_DepthToSpace); | 
					
						
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							|  |  |  | } // namespace MNN
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