前言：人工智能是一门研究如何使机器能够智能地行动的学科，也是现代人工智能技术制造最核心的技术之一。
人工智能是一门研究如何使机器能够智能地行动的学科，也是现代人工智能技术制造*核心的技术之一。随着人工智能的发展，利用大数据和机器学习等技术，使制造过程更加智能化和自动化。例如，【系统176搭建·0206-可电可微5616-】生产计划和调度等工作可以由机器自动完成，同时通过机器学习技术，制造过程中的一些异常情况也可以被及时识别和处理。void Calibration::_updateScale() for(const auto&op:_originaleModel->oplists){ std::vector<std::string>::iterator iter=std::find(_skip_quant_ops.begin(),_skip_quant_ops.end(),op->name); if(iter!=_skip_quant_ops.end()){ continue; } const auto opType=op->type; if(opType!=MNN::OpType_Convolution&&opType!=MNN::OpType_ConvolutionDepthwise&& opType!=MNN::OpType_Eltwise){ continue; } auto tensorsPair=_opInfo.find(op->name); if(tensorsPair==_opInfo.end()){ MNN_ERROR("Can't find tensors for%sn",op->name.c_str()); if(opType==MNN::OpType_Eltwise){ auto param=op->main.AsEltwise(); //Now only support AddInt8 if(param->type!=MNN::EltwiseType_SUM){ continue; } const auto&inputScale0=_scales[tensorsPair->second.first[0]]; const auto&inputScale1=_scales[tensorsPair->second.first[1]]; const auto&outputScale=_scales[tensorsPair->second.second[0]]; const int outputScaleSize=outputScale.size(); std::vector<float>outputInvertScale(outputScaleSize); Helper::invertData(outputInvertScale.data(),outputScale.data(),outputScaleSize); op->type=MNN::OpType_EltwiseInt8; op->main.Reset(); op->main.type=MNN::OpParameter_EltwiseInt8; auto eltwiseInt8Param=new MNN::EltwiseInt8T; auto input0ScaleParam=new MNN::QuantizedFloatParamT; auto input1ScaleParam=new MNN::QuantizedFloatParamT; auto outputScaleParam=new MNN::QuantizedFloatParamT; input0ScaleParam->tensorScale=inputScale0; input1ScaleParam->tensorScale=inputScale1; outputScaleParam->tensorScale=outputInvertScale; eltwiseInt8Param->inputQuan0=std::unique_ptr<MNN::QuantizedFloatParamT>(input0ScaleParam); eltwiseInt8Param->inputQuan1=std::unique_ptr<MNN::QuantizedFloatParamT>(input1ScaleParam); eltwiseInt8Param->outputQuan=std::unique_ptr<MNN::QuantizedFloatParamT>(outputScaleParam); op->main.value=eltwiseInt8Param; continue; //below is Conv/DepthwiseConv const auto&inputScale=_scales[tensorsPair->second.first[0]]; auto param=op->main.AsConvolution2D(); const int channles=param->common->outputCount; const int weightSize=param->weight.size(); param->symmetricQuan.reset(new MNN::QuantizedFloatParamT); //quantizedParam是param->symmetricQuan的引用 auto&quantizedParam=param->symmetricQuan; quantizedParam->scale.resize(channles); quantizedParam->weight.resize(weightSize); quantizedParam->bias.resize(channles); if(opType==MNN::OpType_Convolution){ QuantizeConvPerChannel(param->weight.data(),param->weight.size(),param->bias.data(), quantizedParam->weight.data(),quantizedParam->bias.data(), quantizedParam->scale.data(),inputScale,outputScale,_weightQuantizeMethod,_weightClampValue); op->type=MNN::OpType_ConvInt8; }else if(opType==MNN::OpType_ConvolutionDepthwise){ QuantizeDepthwiseConv(param->weight.data(),param->weight.size(),param->bias.data(),
