def instanceNorm2d(data, scale, bias, epsilon=1e-5):
batchsize = data.shape[0]
if batchsize > 1:
extscale = Utils.tile(scale, batchsize, axis=1)
extbias = Utils.tile(bias, batchsize, axis=1)
else:
extscale = scale
extbias = bias
indata = data.reshape(1, batchsize * data.shape[1], data.shape[2],
data.shape[3])
mean = Driver.empty(queue, (1, indata.shape[1], 1, 1),
dtype=np.float32,
allocator=memPool)
var = Driver.empty(queue, (1, indata.shape[1], 1, 1),
dtype=np.float32,
allocator=memPool)
outdata, savemean, saveinvvar = MIOpen.batchNorm2d(indata,
extscale,
extbias,
mean,
var,
epsilon,
test=False)
return outdata.reshape(data.shape), savemean, saveinvvar, extscale
def wrapBatchNormNd(data,
scale,
bias,
mean,
var,
epsilon,
factor,
test,
mode=None,
out=None):
return MIOpen.batchNorm2d(data,
scale,
bias,
mean,
var,
epsilon,
factor,
test,
out=out)