mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			38 lines
		
	
	
		
			1.3 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			38 lines
		
	
	
		
			1.3 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
 | |
| //  ShapeTranspose.cpp
 | |
| //  MNN
 | |
| //
 | |
| //  Created by MNN on 2019/01/10.
 | |
| //  Copyright © 2018, Alibaba Group Holding Limited
 | |
| //
 | |
| 
 | |
| #include "shape/SizeComputer.hpp"
 | |
| #include "core/Macro.h"
 | |
| #include "core/TensorUtils.hpp"
 | |
| namespace MNN {
 | |
| 
 | |
| class TransposeComputer : public SizeComputer {
 | |
|     virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
 | |
|                                const std::vector<Tensor*>& outputs) const override {
 | |
|         const Tensor* input = inputs[0];
 | |
|         Tensor* perm        = inputs[1];
 | |
|         const int dims = input->buffer().dimensions;
 | |
|         if (perm->getType().code != halide_type_int || 32 != perm->getType().bits || dims != perm->buffer().dim[0].extent) {
 | |
|             return false;
 | |
|         }
 | |
|         auto permutation = perm->host<int32_t>();
 | |
|         outputs[0]->buffer().dimensions = dims;
 | |
|         outputs[0]->buffer().type = input->getType();
 | |
|         for (int i = 0; i < dims; ++i) {
 | |
|             const int32_t d                    = permutation[i];
 | |
|             outputs[0]->buffer().dim[i].extent = input->buffer().dim[d].extent;
 | |
|         }
 | |
|         TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
 | |
| 
 | |
|         return true;
 | |
|     }
 | |
| };
 | |
| 
 | |
| REGISTER_SHAPE_INPUTS(TransposeComputer, OpType_Transpose, {1});
 | |
| } // namespace MNN
 |