mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			65 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			65 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  CPUQuantizedLogistic.cpp
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| //  MNN
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| //
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| //  Created by MNN on 2018/12/12.
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| //  Copyright © 2018, Alibaba Group Holding Limited
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| //
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| #include "backend/cpu/CPUBackend.hpp"
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| #ifdef MNN_SUPPORT_DEPRECATED_OP
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| #include "backend/cpu/CPUQuantizedLogistic.hpp"
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| #include "backend/cpu/CPUFixedPoint.hpp"
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| #include "backend/cpu/CPUQuantizationUtils.hpp"
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| #include "core/Macro.h"
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| #include "backend/cpu/compute/OptimizedComputer.hpp"
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| 
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| namespace MNN {
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| 
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| CPUQuantizedLogistic::CPUQuantizedLogistic(Backend *backend, const Op *op) : Execution(backend) {
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|     mLogisticParam = op->main_as_QuantizedLogistic();
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| }
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| 
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| ErrorCode CPUQuantizedLogistic::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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|     MNN_ASSERT(1 == inputs.size() && 1 == outputs.size());
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|     MNN_ASSERT(0 == mLogisticParam->outputQuantizedParam()->zeroPoint() &&
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|                1. / 256 == mLogisticParam->outputQuantizedParam()->scale());
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| 
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|     static constexpr int kInputIntegerBits = 4;
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|     const double inputRealMultiplier =
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|         mLogisticParam->inputQuantizedParam()->scale() * static_cast<double>(1 << (31 - kInputIntegerBits));
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|     QuantizeMultiplierGreaterThanOne(inputRealMultiplier, &mInputMultiplier, &mInputLeftShift);
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|     mInputZeroPoint = mLogisticParam->inputQuantizedParam()->zeroPoint();
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|     mInputRangeRadius = CalculateInputRadius(kInputIntegerBits, mInputLeftShift);
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|     return NO_ERROR;
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| }
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| 
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| ErrorCode CPUQuantizedLogistic::onExecute(const std::vector<MNN::Tensor *> &inputs,
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|                                           const std::vector<MNN::Tensor *> &outputs) {
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|     auto input = inputs[0], output = outputs[0];
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|     std::vector<int> inputDims, outputDims;
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|     for (int i = 0; i < input->buffer().dimensions; i++) {
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|         inputDims.push_back(input->buffer().dim[i].extent);
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|     }
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|     for (int i = 0; i < output->buffer().dimensions; i++) {
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|         outputDims.push_back(output->buffer().dim[i].extent);
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|     }
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| 
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|     Optimized::Logistic(input->host<uint8_t>(), inputDims, mInputZeroPoint,
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|                         mInputRangeRadius, mInputMultiplier, mInputLeftShift, output->host<uint8_t>(), outputDims);
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| 
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|     return NO_ERROR;
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| }
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| 
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| class CPUQuantizedLogisticCreator : public CPUBackend::Creator {
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| public:
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|     virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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|                                 const MNN::Op *op, Backend *backend) const {
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|         return new CPUQuantizedLogistic(backend, op);
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|     }
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| };
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| } // namespace MNN
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| #endif
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| namespace MNN {
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| REGISTER_CPU_OP_CREATOR_OLD(CPUQuantizedLogisticCreator, OpType_QuantizedLogistic);
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| };
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