def hybrid_forward(self, F, x, gamma, beta):
if self._axis == 1:
return F.InstanceNorm(x,
gamma,
beta,
name='fwd',
eps=self._epsilon)
x = x.swapaxes(1, self._axis)
return F.InstanceNorm(x, gamma, beta, name='fwd',
eps=self._epsilon).swapaxes(1, self._axis)
def verify_instance_norm_rewrite(shp, eps):
# assert len(shp) == 4 # NCHW
assert len(shp) >= 3
vshp = (shp[1], )
data_np = np.random.uniform(size=shp)
gamma_np = np.random.uniform(size=vshp)
beta_np = np.random.uniform(size=vshp)
x = nd.array(data_np)
gamma = nd.array(gamma_np)
beta = nd.array(beta_np)
# org op
y = nd.InstanceNorm(x, gamma=gamma, beta=beta, eps=eps)
# rewrite op
axis = [i for i in range(len(shp)) if i != 1]
for i in axis:
gamma = nd.expand_dims(gamma, axis=i)
beta = nd.expand_dims(beta, axis=i)
n = np.product(shp[2:])
mean = nd.sum(x, axis=axis, keepdims=True) / n
dev = x - mean
var = nd.sum(dev * dev, axis=axis, keepdims=True) / n
std = nd.sqrt(var) + eps
frac = dev / std
z = frac * gamma + beta
# compare
assert z.shape == y.shape
zn, zp = get_norm(z)
yn, yp = get_norm(y)
rn = np.linalg.norm(zp - yp)
print(zn, yn, rn)
def hybrid_forward(self, F, x, gamma, beta):
return F.InstanceNorm(x, gamma, beta, name='fwd', **self._kwargs)
