Python convBN Examples

Example #1

def resBlock(preAct, resParams, train, bn_updates, BA, GAselect):
    snrg = RandomStreams(12345)
    current_params = resParams[0]
    inAct, newRM, newRV = layers.convBNAct(preAct, current_params[:6], train)
    bn_updates.append((current_params[4], newRM))
    bn_updates.append((current_params[5], newRV))
    inAct = STEquant(inAct, BA)
    if GAselect == 2:
        inAct = quantizeGrad.quantizeGradL2(inAct)
    elif GAselect == 4:
        inAct = quantizeGrad.quantizeGradL4(inAct)
    elif GAselect == 6:
        inAct = quantizeGrad.quantizeGradL6(inAct)
    elif GAselect == 10:
        inAct = quantizeGrad.quantizeGradL10(inAct)
    elif GAselect == 12:
        inAct = quantizeGrad.quantizeGradL12(inAct)
    elif GAselect == 16:
        inAct = quantizeGrad.quantizeGradL16(inAct)
    elif GAselect == 18:
        inAct = quantizeGrad.quantizeGradL18(inAct)

    inAct = layers.dropout(inAct, train, 0.8, snrg)
    current_params = resParams[1]
    outAct, newRM, newRV = layers.convBN(inAct, current_params[:6], train)
    bn_updates.append((current_params[4], newRM))
    bn_updates.append((current_params[5], newRV))

    outAct = layers.slopedClipping(outAct + preAct)
    return outAct, bn_updates

Example #2

def resBlockStride(preAct, resParams, train, bn_updates, BA, GAselect):
    snrg = RandomStreams(12345)
    current_params = resParams[0]
    inAct, newRM, newRV = layers.convStrideBNAct(preAct, current_params[:6],
                                                 train)
    bn_updates.append((current_params[4], newRM))
    bn_updates.append((current_params[5], newRV))
    inAct = STEquant(inAct, BA)
    if GAselect == 8:
        inAct = quantizeGrad.quantizeGradL8(inAct)
    elif GAselect == 14:
        inAct = quantizeGrad.quantizeGradL14(inAct)

    inAct = layers.dropout(inAct, train, 0.8, snrg)
    current_params = resParams[1]
    outAct, newRM, newRV = layers.convBN(inAct, current_params[:6], train)
    bn_updates.append((current_params[4], newRM))
    bn_updates.append((current_params[5], newRV))

    current_params = resParams[2]
    shortCut, newRM, newRV = layers.convStrideBN(preAct, current_params[:6],
                                                 train)
    bn_updates.append((current_params[4], newRM))
    bn_updates.append((current_params[5], newRV))

    outAct = layers.slopedClipping(outAct + shortCut)
    return outAct, bn_updates

Example #3

File: FFAnalysis.py Project: charbel-sakr/Fixed-Point-Training

def resBlock(preAct, resParams, train):
    resActivations = []
    current_params = resParams[0]
    inAct, _, _ = layers.convBNAct(preAct, current_params, train)
    resActivations.append(inAct)

    current_params = resParams[1]
    outAct, _, _ = layers.convBN(inAct, current_params, train)

    outAct = layers.slopedClipping(outAct + preAct)
    resActivations.append(outAct)
    return outAct, resActivations

Example #4

def resBlock(preAct, resParams, train, BA1, BA2, BW1, BW2):
    current_params = resParams[0]
    current_params[0] = layers.quantizeWeight(current_params[0], BW1)
    inAct, _, _ = layers.convBNAct(preAct, current_params, train)
    inAct = layers.quantizeAct(inAct, BA1)
    inAct = 0.8 * inAct

    current_params = resParams[1]
    current_params[0] = layers.quantizeWeight(current_params[0], BW2)
    outAct, _, _ = layers.convBN(inAct, current_params, train)

    outAct = layers.slopedClipping(outAct + preAct)
    outAct = layers.quantizeAct(outAct, BA2)
    return outAct

Example #5

def resBlockStride(preAct, resParams, train, BA1, BA2, BW1, BW2, BW3):
    current_params = resParams[0]
    current_params[0] = layers.quantizeWeight(current_params[0], BW1)
    inAct, _, _ = layers.convStrideBNAct(preAct, current_params, train)
    inAct = 0.8 * inAct

    current_params = resParams[1]
    current_params[0] = layers.quantizeWeight(current_params[0], BW2)
    outAct, _, _ = layers.convBN(inAct, current_params, train)

    current_params = resParams[2]
    current_params[0] = layers.quantizeWeight(current_params[0], BW3)
    shortCut, _, _ = layers.convStrideBN(preAct, current_params, train)

    outAct = layers.slopedClipping(outAct + shortCut)
    return outAct

Example #6

def resBlockStride(preAct, resParams, train):
    resActivations = []
    current_params = resParams[0]
    inAct, _, _ = layers.convStrideBNAct(preAct, current_params, train)

    resActivations.append(inAct)
    inAct = 0.8 * inAct

    current_params = resParams[1]
    outAct, _, _ = layers.convBN(inAct, current_params, train)

    current_params = resParams[2]
    shortCut, _, _ = layers.convStrideBN(preAct, current_params, train)

    outAct = layers.slopedClipping(outAct + shortCut)
    resActivations.append(outAct)
    return outAct, resActivations

Example #7

def resBlock(preAct, resParams, train, bn_updates):
    snrg = RandomStreams(12345)
    resActivations = []
    current_params = resParams[0]
    inAct, newRM, newRV = layers.convBNAct(preAct, current_params, train)
    resActivations.append(inAct)
    bn_updates.append((current_params[4], newRM))
    bn_updates.append((current_params[5], newRV))

    inAct = layers.dropout(inAct, train, 0.8, snrg)

    current_params = resParams[1]
    outAct, newRM, newRV = layers.convBN(inAct, current_params, train)
    bn_updates.append((current_params[4], newRM))
    bn_updates.append((current_params[5], newRV))

    outAct = layers.slopedClipping(outAct + preAct)
    resActivations.append(outAct)
    return outAct, resActivations, bn_updates