def compute_weight(self):
return self.weight_v * (self.weight_g / norm(self.weight_v, 0))
def forward(self, input):
self.weight_v.data.mul_(self.mask)
_norm = norm(self.weight_v, 0) + 1e-8
weight = self.weight_v * (self.weight_g.exp() / _norm)
return F.conv2d(input, weight, self.bias, self.stride, self.padding,
self.dilation, self.groups)
def reset_parameters(self):
nn.init.normal_(self.weight_v, mean=0.0, std=0.05)
self.weight_g.data.copy_(norm(self.weight_v, 0))
if self.bias is not None:
nn.init.constant_(self.bias, 0)
def reset_parameters(self):
nn.init.normal_(self.weight_v, mean=0.0, std=0.05)
self.weight_v.data.mul_(self.mask)
_norm = norm(self.weight_v, 0).data + 1e-8
self.weight_g.data.copy_(_norm.log())
nn.init.constant_(self.bias, 0)