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