def forward(self, inputs, device):
x, gamma, beta = inputs
y = functions.group_normalization(x,
self.groups,
gamma,
beta,
eps=self.eps)
return y,
def depthwise_normalization(x):
x = chainer.as_variable(x)
dim = x.shape[1]
xp = x.xp
dtype = x.dtype
gamma = xp.ones(dim, dtype=dtype)
beta = xp.zeros(dim, dtype=dtype)
return F.group_normalization(x, dim, gamma, beta)
def func(*args_):
return functions.group_normalization(
*[args_[0], self.groups, args_[1], args_[2]])
def func(*args_):
return functions.group_normalization(
*[args_[0], self.groups, args_[1], args_[2]])
def forward(self, inputs, device):
x, gamma, beta = inputs
y = functions.group_normalization(x, self.groups, gamma, beta,
eps=self.eps)
return y,