refactor

models/spectral_normalization.py

@@ -1,9 +1,6 @@
 import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-from torch.autograd import Variable
+from torch import nn
 from torch.nn import Parameter
-from torch.optim.optimizer import Optimizer, required
 
 
 def l2normalize(v, eps=1e-12):
@@ -29,7 +26,6 @@ class SpectralNorm(nn.Module):
             v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data), u.data))
             u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
 
-        # sigma = torch.dot(u.data, torch.mv(w.view(height,-1).data, v.data))
         sigma = u.dot(w.view(height, -1).mv(v))
         setattr(self.module, self.name, w / sigma.expand_as(w))