| 86 | |
| 87 | |
| 88 | class Mlp(nn.Module): |
| 89 | def __init__(self, |
| 90 | in_features, |
| 91 | hidden_features=None, |
| 92 | out_features=None, |
| 93 | act_layer=nn.GELU, |
| 94 | drop=0.): |
| 95 | super().__init__() |
| 96 | out_features = out_features or in_features |
| 97 | hidden_features = hidden_features or in_features |
| 98 | self.fc1 = nn.Linear(in_features, hidden_features) |
| 99 | self.act = act_layer() |
| 100 | self.fc2 = nn.Linear(hidden_features, out_features) |
| 101 | self.drop = nn.Dropout(drop) |
| 102 | |
| 103 | def forward(self, x): |
| 104 | x = self.fc1(x) |
| 105 | x = self.act(x) |
| 106 | x = self.drop(x) |
| 107 | x = self.fc2(x) |
| 108 | x = self.drop(x) |
| 109 | return x |
| 110 | |
| 111 | |
| 112 | class ConvMixer(nn.Module): |