Source code for physicsnemo.nn.module.weight_norm
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import torch
import torch.nn as nn
import torch.nn.functional as F
import physicsnemo # noqa: F401 for docs
Tensor = torch.Tensor
[docs]
class WeightNormLinear(nn.Module):
"""Weight Norm Layer for 1D Tensors
Parameters
----------
in_features : int
Size of the input features
out_features : int
Size of the output features
bias : bool, optional
Apply the bias to the output of linear layer, by default True
Example
-------
>>> wnorm = physicsnemo.nn.WeightNormLinear(2,4)
>>> input = torch.rand(2,2)
>>> output = wnorm(input)
>>> output.size()
torch.Size([2, 4])
"""
def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.empty((out_features, in_features)))
self.weight_g = nn.Parameter(torch.empty((out_features, 1)))
if bias:
self.bias = nn.Parameter(torch.empty(out_features))
else:
self.register_parameter("bias", None)
self.reset_parameters()
[docs]
def reset_parameters(self) -> None:
"""Reset normalization weights"""
nn.init.xavier_uniform_(self.weight)
nn.init.constant_(self.weight_g, 1.0)
if self.bias is not None:
nn.init.constant_(self.bias, 0.0)
[docs]
def forward(self, input: Tensor) -> Tensor:
norm = self.weight.norm(dim=1, p=2, keepdim=True)
weight = self.weight_g * self.weight / norm
return F.linear(input, weight, self.bias)
