def instanceNorm2dBackward(grad,
data,
extscale,
savemean,
saveinvvar,
epsilon,
affine=True):
batchsize, maps = grad.shape[:2]
outgrad = grad.reshape(1, batchsize * grad.shape[1], grad.shape[2],
grad.shape[3])
indata = data.reshape(1, batchsize * data.shape[1], data.shape[2],
data.shape[3])
ingrad, scalegrad, bgrad = MIOpen.batchNorm2dBackward(
indata, outgrad, extscale, savemean, saveinvvar, epsilon)
if affine and batchsize > 1:
scalegrad = CLBlas.sumOnMatrix(scalegrad.reshape(batchsize,
-1)).reshape(
1, maps, 1, 1)
bgrad = CLBlas.sumOnMatrix(bgrad.reshape(batchsize,
-1)).reshape(1, maps, 1, 1)
if affine:
return ingrad.reshape(grad.shape), scalegrad, bgrad
else:
return ingrad.reshape(grad.shape)
def wrapBatchNormNdBackward(data,
grad,
scale,
savemean,
saveinvvar,
epsilon,
mode=None):
return MIOpen.batchNorm2dBackward(data, grad, scale, savemean,
saveinvvar, epsilon)