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,