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import torch
from torch_geometric.data import Data
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
@functional_transform('svd_feature_reduction')
class SVDFeatureReduction(BaseTransform):
r"""Dimensionality reduction of node features via Singular Value
Decomposition (SVD) (functional name: :obj:`svd_feature_reduction`).
Args:
out_channels (int): The dimensionality of node features after
reduction.
"""
def __init__(self, out_channels: int):
self.out_channels = out_channels
def forward(self, data: Data) -> Data:
assert data.x is not None
if data.x.size(-1) > self.out_channels:
U, S, _ = torch.linalg.svd(data.x)
data.x = torch.mm(U[:, :self.out_channels],
torch.diag(S[:self.out_channels]))
return data
def __repr__(self) -> str:
return f'{self.__class__.__name__}({self.out_channels})'
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