64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
import torch.nn as nn
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from torch.nn.parameter import Parameter
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from typing import Optional
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from layers.super_module import SuperModule
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from layers.super_module import SuperModule
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class SuperLinear(SuperModule):
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"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`"""
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def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
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super(SuperLinear, self).__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.weight = Parameter(torch.Tensor(out_features, in_features))
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if bias:
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self.bias = Parameter(torch.Tensor(out_features))
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else:
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self.register_parameter("bias", None)
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self.reset_parameters()
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def reset_parameters(self) -> None:
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init.kaiming_uniform_(self.weight, a=math.sqrt(5))
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if self.bias is not None:
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fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
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bound = 1 / math.sqrt(fan_in)
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init.uniform_(self.bias, -bound, bound)
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def forward(self, input: Tensor) -> Tensor:
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return F.linear(input, self.weight, self.bias)
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def extra_repr(self) -> str:
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return "in_features={:}, out_features={:}, bias={:}".format(
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self.in_features, self.out_features, self.bias is not None
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)
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class SuperMLP(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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