mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			56 lines
		
	
	
		
			2.0 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			56 lines
		
	
	
		
			2.0 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  ShapeSliceTf.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 "core/Macro.h"
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| #include "core/SizeComputer.hpp"
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| 
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| namespace MNN {
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| 
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| class SliceTfComputer : public SizeComputer {
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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(inputs.size() == 3);
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|         MNN_ASSERT(outputs.size() == 1);
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| 
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|         auto input = inputs[0];
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|         // these two inputs should be const
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|         auto begin_tensor = inputs[1];
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|         auto size_tensor  = inputs[2];
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| 
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|         MNN_ASSERT(begin_tensor->buffer().dimensions == 1);
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|         MNN_ASSERT(size_tensor->buffer().dimensions == 1);
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|         MNN_ASSERT(input->buffer().dimensions >= 1);
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|         MNN_ASSERT(input->buffer().dimensions == begin_tensor->buffer().dim[0].extent);
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|         MNN_ASSERT(input->buffer().dimensions == size_tensor->buffer().dim[0].extent);
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| 
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|         auto output                 = outputs[0];
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|         output->buffer().dimensions = input->buffer().dimensions;
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|         output->buffer().type       = input->buffer().type;
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|         int dim                     = 0;
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|         for (int i = 0; i < input->buffer().dimensions; i++) {
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|             dim = size_tensor->host<int32_t>()[i];
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|             if (dim == -1 ) {
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|                 dim = input->buffer().dim[i].extent - begin_tensor->host<int32_t>()[i];
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|             }
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|             // size <= 0, this ouput is not useful, set the dimendsions 0
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|             if (dim <= 0) {
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|                 output->buffer().dimensions = 0;
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|                 break;
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|             }
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|             output->buffer().dim[i].extent = dim;
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|         }
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|         for (int i=0; i<outputs.size(); ++i) {
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|             TensorUtils::getDescribe(outputs[i])->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(SliceTfComputer, OpType_SliceTf, (std::vector<int>{1, 2}));
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| } // namespace MNN
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