layers -> xlayers

lib/xlayers/mlp.py

@@ -0,0 +1,29 @@
+import torch.nn as nn
+from typing import Optional
+
+
+class MLP(nn.Module):
+    # MLP: FC -> Activation -> Drop -> FC -> Drop
+    def __init__(
+        self,
+        in_features,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer=nn.GELU,
+        drop: Optional[float] = None,
+    ):
+        super(MLP, self).__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop or 0)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x