| 9 | |
| 10 | |
| 11 | class LinearNorm(nn.Module): |
| 12 | def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): |
| 13 | super(LinearNorm, self).__init__() |
| 14 | self.linear_layer = nn.Linear(in_dim, out_dim, bias=bias) |
| 15 | nn.init.xavier_uniform_(self.linear_layer.weight, gain=nn.init.calculate_gain(w_init_gain)) |
| 16 | |
| 17 | def forward(self, x): |
| 18 | return self.linear_layer(x) |
| 19 | |
| 20 | |
| 21 | class LayerNorm(nn.Module): |