数字货币量化合约机器人/合约量化机器人系统开发详细及方案，数字货币合约量化机器人/量化合约机器人开发源码及案例

Dapp: Represents decentralized applications. They are applications that run backend code (mainly smart contracts written in Solidity) on decentralized networks or blockchains. Dapp can be built using front-end frameworks such as React, Vue, or Angular.

Smart contracts: They are computer programs stored on the blockchain that run when predetermined conditions are met. Smart contracts are written in Solidity language

int QuantizeConvPerChannel(const float weight, const int size, const float bias, int8_t quantizedWeight,
int32_t quantizedBias, float scale, const std::vector& inputScale,
const std::vector& outputScale, std::string method, bool mergeChannel) {
const int inputChannels = inputScale.size();
const int outputChannels = outputScale.size();
const int icXoc = inputChannels outputChannels;
DCHECK(size % icXoc == 0) << "Input Data Size Error!";

std::vector<float> quantizedWeightScale(outputChannels);

float inputScalexWeight = 1.0f;
if (mergeChannel) {
    if (method == "MAX_ABS"){
        SymmetricQuantizeWeight(weight, size, quantizedWeight, quantizedWeightScale.data(), outputChannels);
    }
    else if (method == "ADMM") {
        QuantizeWeightADMM(weight, size, quantizedWeight, quantizedWeightScale.data(), outputChannels);
    }
    inputScalexWeight = inputScale[0];
} else {
    const int kernelSize = size / icXoc;
    const int ocStride   = size / outputChannels;

    std::vector<float> weightMultiByInputScale(size);
    for (int oc = 0; oc < outputChannels; ++oc) {
        for (int ic = 0; ic < inputChannels; ++ic) {
            for (int i = 0; i < kernelSize; ++i) {
                const int index                = oc * ocStride + ic * kernelSize + i;
                weightMultiByInputScale[index] = inputScale[ic] * weight[index];
            }
        }
    }
    if (method == "MAX_ABS"){
        SymmetricQuantizeWeight(weightMultiByInputScale.data(), size, quantizedWeight, quantizedWeightScale.data(), outputChannels);
    }
    else if (method == "ADMM") {
        QuantizeWeightADMM(weightMultiByInputScale.data(), size, quantizedWeight, quantizedWeightScale.data(), outputChannels);
    }
}

for (int i = 0; i < outputChannels; ++i) {
    if (outputScale[i] == 0) {
        scale[i] = 0.0f;
    } else {
        scale[i] = inputScalexWeight * quantizedWeightScale[i] / outputScale[0];
    }
}

if (bias) {
    for (int i = 0; i < outputChannels; ++i) {
        if (inputScalexWeight == 0 || quantizedWeightScale[i] == 0) {
            quantizedBias[i] = 0;
        } else {
            quantizedBias[i] = static_cast<int32_t>(bias[i] / (inputScalexWeight * quantizedWeightScale[i]));
        }
    }
}

return 0;

}