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
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
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
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
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
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
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