PyTorch Geometric Transforms Reference

PyTorch Geometric Transforms Reference

This document provides a comprehensive reference of all transforms available in torch_geometric.transforms.

Overview

Transforms modify Data or HeteroData objects before or during training. Apply them via:

# During dataset loading
dataset = MyDataset(root='/tmp', transform=MyTransform())

# Apply to individual data
transform = MyTransform()
data = transform(data)

# Compose multiple transforms
from torch_geometric.transforms import Compose
transform = Compose([Transform1(), Transform2(), Transform3()])

General Transforms

NormalizeFeatures

Purpose: Row-normalizes node features to sum to 1 Use case: Feature scaling, probability-like features

from torch_geometric.transforms import NormalizeFeatures
transform = NormalizeFeatures()

ToDevice

Purpose: Transfers data to specified device (CPU/GPU) Use case: GPU training, device management

from torch_geometric.transforms import ToDevice
transform = ToDevice('cuda')

RandomNodeSplit

Purpose: Creates train/val/test node masks Use case: Node classification splits Parameters: split='train_rest', num_splits, num_val, num_test

from torch_geometric.transforms import RandomNodeSplit
transform = RandomNodeSplit(num_val=0.1, num_test=0.2)

RandomLinkSplit

Purpose: Creates train/val/test edge splits Use case: Link prediction Parameters: num_val, num_test, is_undirected, split_labels

from torch_geometric.transforms import RandomLinkSplit
transform = RandomLinkSplit(num_val=0.1, num_test=0.2)

IndexToMask

Purpose: Converts indices to boolean masks Use case: Data preprocessing

from torch_geometric.transforms import IndexToMask
transform = IndexToMask()

MaskToIndex

Purpose: Converts boolean masks to indices Use case: Data preprocessing

from torch_geometric.transforms import MaskToIndex
transform = MaskToIndex()

FixedPoints

Purpose: Samples a fixed number of points Use case: Point cloud subsampling Parameters: num, replace, allow_duplicates

from torch_geometric.transforms import FixedPoints
transform = FixedPoints(1024)

ToDense

Purpose: Converts to dense adjacency matrices Use case: Small graphs, dense operations

from torch_geometric.transforms import ToDense
transform = ToDense(num_nodes=100)

ToSparseTensor

Purpose: Converts edge_index to SparseTensor Use case: Efficient sparse operations Parameters: remove_edge_index, fill_cache

from torch_geometric.transforms import ToSparseTensor
transform = ToSparseTensor()

Graph Structure Transforms

ToUndirected

Purpose: Converts directed graph to undirected Use case: Undirected graph algorithms Parameters: reduce='add' (how to handle duplicate edges)

from torch_geometric.transforms import ToUndirected
transform = ToUndirected()

AddSelfLoops

Purpose: Adds self-loops to all nodes Use case: GCN-style convolutions Parameters: fill_value (edge attribute for self-loops)

from torch_geometric.transforms import AddSelfLoops
transform = AddSelfLoops()

RemoveSelfLoops

Purpose: Removes all self-loops Use case: Cleaning graph structure

from torch_geometric.transforms import RemoveSelfLoops
transform = RemoveSelfLoops()

RemoveIsolatedNodes

Purpose: Removes nodes without edges Use case: Graph cleaning

from torch_geometric.transforms import RemoveIsolatedNodes
transform = RemoveIsolatedNodes()

RemoveDuplicatedEdges

Purpose: Removes duplicate edges Use case: Graph cleaning

from torch_geometric.transforms import RemoveDuplicatedEdges
transform = RemoveDuplicatedEdges()

LargestConnectedComponents

Purpose: Keeps only the largest connected component Use case: Focus on main graph structure Parameters: num_components (how many components to keep)

from torch_geometric.transforms import LargestConnectedComponents
transform = LargestConnectedComponents(num_components=1)

KNNGraph

Purpose: Creates edges based on k-nearest neighbors Use case: Point clouds, spatial data Parameters: k, loop, force_undirected, flow

from torch_geometric.transforms import KNNGraph
transform = KNNGraph(k=6)

RadiusGraph

Purpose: Creates edges within a radius Use case: Point clouds, spatial data Parameters: r, loop, max_num_neighbors, flow

from torch_geometric.transforms import RadiusGraph
transform = RadiusGraph(r=0.1)

Delaunay

Purpose: Computes Delaunay triangulation Use case: 2D/3D spatial graphs

from torch_geometric.transforms import Delaunay
transform = Delaunay()

FaceToEdge

Purpose: Converts mesh faces to edges Use case: Mesh processing

from torch_geometric.transforms import FaceToEdge
transform = FaceToEdge()

LineGraph

Purpose: Converts graph to its line graph Use case: Edge-centric analysis Parameters: force_directed

from torch_geometric.transforms import LineGraph
transform = LineGraph()

GDC

Purpose: Graph Diffusion Convolution preprocessing Use case: Improved message passing Parameters: self_loop_weight, normalization_in, normalization_out, diffusion_kwargs

from torch_geometric.transforms import GDC
transform = GDC(self_loop_weight=1, normalization_in='sym',
                diffusion_kwargs=dict(method='ppr', alpha=0.15))

SIGN

Purpose: Scalable Inception Graph Neural Networks preprocessing Use case: Efficient multi-scale features Parameters: K (number of hops)

from torch_geometric.transforms import SIGN
transform = SIGN(K=3)

Feature Transforms

OneHotDegree

Purpose: One-hot encodes node degree Use case: Degree as feature Parameters: max_degree, cat (concatenate with existing features)

from torch_geometric.transforms import OneHotDegree
transform = OneHotDegree(max_degree=100)

LocalDegreeProfile

Purpose: Appends local degree profile Use case: Structural node features

from torch_geometric.transforms import LocalDegreeProfile
transform = LocalDegreeProfile()

Constant

Purpose: Adds constant features to nodes Use case: Featureless graphs Parameters: value, cat

from torch_geometric.transforms import Constant
transform = Constant(value=1.0)

TargetIndegree

Purpose: Saves in-degree as target Use case: Degree prediction Parameters: norm, max_value

from torch_geometric.transforms import TargetIndegree
transform = TargetIndegree(norm=False)

AddRandomWalkPE

Purpose: Adds random walk positional encoding Use case: Positional information Parameters: walk_length, attr_name

from torch_geometric.transforms import AddRandomWalkPE
transform = AddRandomWalkPE(walk_length=20)

AddLaplacianEigenvectorPE

Purpose: Adds Laplacian eigenvector positional encoding Use case: Spectral positional information Parameters: k (number of eigenvectors), attr_name

from torch_geometric.transforms import AddLaplacianEigenvectorPE
transform = AddLaplacianEigenvectorPE(k=10)

AddMetaPaths

Purpose: Adds meta-path induced edges Use case: Heterogeneous graphs Parameters: metapaths, drop_orig_edges, drop_unconnected_nodes

from torch_geometric.transforms import AddMetaPaths
metapaths = [[('author', 'paper'), ('paper', 'author')]]  # Co-authorship
transform = AddMetaPaths(metapaths)

SVDFeatureReduction

Purpose: Reduces feature dimensionality via SVD Use case: Dimensionality reduction Parameters: out_channels

from torch_geometric.transforms import SVDFeatureReduction
transform = SVDFeatureReduction(out_channels=64)

Vision/Spatial Transforms

Center

Purpose: Centers node positions Use case: Point cloud preprocessing

from torch_geometric.transforms import Center
transform = Center()

NormalizeScale

Purpose: Normalizes positions to unit sphere Use case: Point cloud normalization

from torch_geometric.transforms import NormalizeScale
transform = NormalizeScale()

NormalizeRotation

Purpose: Rotates to principal components Use case: Rotation-invariant learning Parameters: max_points

from torch_geometric.transforms import NormalizeRotation
transform = NormalizeRotation()

Distance

Purpose: Saves Euclidean distance as edge attribute Use case: Spatial graphs Parameters: norm, max_value, cat

from torch_geometric.transforms import Distance
transform = Distance(norm=False, cat=False)

Cartesian

Purpose: Saves relative Cartesian coordinates as edge attributes Use case: Spatial relationships Parameters: norm, max_value, cat

from torch_geometric.transforms import Cartesian
transform = Cartesian(norm=False)

Polar

Purpose: Saves polar coordinates as edge attributes Use case: 2D spatial graphs Parameters: norm, max_value, cat

from torch_geometric.transforms import Polar
transform = Polar(norm=False)

Spherical

Purpose: Saves spherical coordinates as edge attributes Use case: 3D spatial graphs Parameters: norm, max_value, cat

from torch_geometric.transforms import Spherical
transform = Spherical(norm=False)

LocalCartesian

Purpose: Saves coordinates in local coordinate system Use case: Local spatial features Parameters: norm, cat

from torch_geometric.transforms import LocalCartesian
transform = LocalCartesian()

PointPairFeatures

Purpose: Computes point pair features Use case: 3D registration, correspondence Parameters: cat

from torch_geometric.transforms import PointPairFeatures
transform = PointPairFeatures()

Data Augmentation

RandomJitter

Purpose: Randomly jitters node positions Use case: Point cloud augmentation Parameters: translate, scale

from torch_geometric.transforms import RandomJitter
transform = RandomJitter(0.01)

RandomFlip

Purpose: Randomly flips positions along axis Use case: Geometric augmentation Parameters: axis, p (probability)

from torch_geometric.transforms import RandomFlip
transform = RandomFlip(axis=0, p=0.5)

RandomScale

Purpose: Randomly scales positions Use case: Scale augmentation Parameters: scales (min, max)

from torch_geometric.transforms import RandomScale
transform = RandomScale((0.9, 1.1))

RandomRotate

Purpose: Randomly rotates positions Use case: Rotation augmentation Parameters: degrees (range), axis (rotation axis)

from torch_geometric.transforms import RandomRotate
transform = RandomRotate(degrees=15, axis=2)

RandomShear

Purpose: Randomly shears positions Use case: Geometric augmentation Parameters: shear (range)

from torch_geometric.transforms import RandomShear
transform = RandomShear(0.1)

RandomTranslate

Purpose: Randomly translates positions Use case: Translation augmentation Parameters: translate (range)

from torch_geometric.transforms import RandomTranslate
transform = RandomTranslate(0.1)

LinearTransformation

Purpose: Applies linear transformation matrix Use case: Custom geometric transforms Parameters: matrix

from torch_geometric.transforms import LinearTransformation
import torch
matrix = torch.eye(3)
transform = LinearTransformation(matrix)

Mesh Processing

SamplePoints

Purpose: Samples points uniformly from mesh Use case: Mesh to point cloud conversion Parameters: num, remove_faces, include_normals

from torch_geometric.transforms import SamplePoints
transform = SamplePoints(num=1024)

GenerateMeshNormals

Purpose: Generates face/vertex normals Use case: Mesh processing

from torch_geometric.transforms import GenerateMeshNormals
transform = GenerateMeshNormals()

FaceToEdge

Purpose: Converts mesh faces to edges Use case: Mesh to graph conversion Parameters: remove_faces

from torch_geometric.transforms import FaceToEdge
transform = FaceToEdge()

Sampling and Splitting

GridSampling

Purpose: Clusters points in voxel grid Use case: Point cloud downsampling Parameters: size (voxel size), start, end

from torch_geometric.transforms import GridSampling
transform = GridSampling(size=0.1)

FixedPoints

Purpose: Samples fixed number of points Use case: Uniform point cloud size Parameters: num, replace, allow_duplicates

from torch_geometric.transforms import FixedPoints
transform = FixedPoints(num=2048, replace=False)

RandomScale

Purpose: Randomly scales by sampling from range Use case: Scale augmentation (already listed above)

VirtualNode

Purpose: Adds a virtual node connected to all nodes Use case: Global information propagation

from torch_geometric.transforms import VirtualNode
transform = VirtualNode()

Specialized Transforms

ToSLIC

Purpose: Converts images to superpixel graphs (SLIC algorithm) Use case: Image as graph Parameters: num_segments, compactness, add_seg, add_img

from torch_geometric.transforms import ToSLIC
transform = ToSLIC(num_segments=75)

GCNNorm

Purpose: Applies GCN-style normalization to edges Use case: Preprocessing for GCN Parameters: add_self_loops

from torch_geometric.transforms import GCNNorm
transform = GCNNorm(add_self_loops=True)

LaplacianLambdaMax

Purpose: Computes largest Laplacian eigenvalue Use case: ChebConv preprocessing Parameters: normalization, is_undirected

from torch_geometric.transforms import LaplacianLambdaMax
transform = LaplacianLambdaMax(normalization='sym')

NormalizeRotation

Purpose: Rotates mesh/point cloud to align with principal axes Use case: Canonical orientation Parameters: max_points

from torch_geometric.transforms import NormalizeRotation
transform = NormalizeRotation()

Compose and Apply

Compose

Purpose: Chains multiple transforms Use case: Complex preprocessing pipelines

from torch_geometric.transforms import Compose
transform = Compose([
    Center(),
    NormalizeScale(),
    KNNGraph(k=6),
    Distance(norm=False),
])

BaseTransform

Purpose: Base class for custom transforms Use case: Implementing custom transforms

from torch_geometric.transforms import BaseTransform

class MyTransform(BaseTransform):
    def __init__(self, param):
        self.param = param

    def __call__(self, data):
        # Modify data
        data.x = data.x * self.param
        return data

Common Transform Combinations

Node Classification Preprocessing

transform = Compose([
    NormalizeFeatures(),
    RandomNodeSplit(num_val=0.1, num_test=0.2),
])

Point Cloud Processing

transform = Compose([
    Center(),
    NormalizeScale(),
    RandomRotate(degrees=15, axis=2),
    RandomJitter(0.01),
    KNNGraph(k=6),
    Distance(norm=False),
])

Mesh to Graph

transform = Compose([
    FaceToEdge(remove_faces=True),
    GenerateMeshNormals(),
    Distance(norm=True),
])

Graph Structure Enhancement

transform = Compose([
    ToUndirected(),
    AddSelfLoops(),
    RemoveIsolatedNodes(),
    GCNNorm(),
])

Heterogeneous Graph Preprocessing

transform = Compose([
    AddMetaPaths(metapaths=[
        [('author', 'paper'), ('paper', 'author')],
        [('author', 'paper'), ('paper', 'conference'), ('conference', 'paper'), ('paper', 'author')]
    ]),
    RandomNodeSplit(split='train_rest', num_val=0.1, num_test=0.2),
])

Link Prediction

transform = Compose([
    NormalizeFeatures(),
    RandomLinkSplit(num_val=0.1, num_test=0.2, is_undirected=True),
])

Usage Tips

1. Order matters: Apply structural transforms before feature transforms
2. Caching: Some transforms (like GDC) are expensive—apply once
3. Augmentation: Use Random* transforms during training only
4. Compose sparingly: Too many transforms slow down data loading
5. Custom transforms: Inherit from BaseTransform for custom logic
6. Pre-transforms: Apply expensive transforms once during dataset processing:

   dataset = MyDataset(root='/tmp', pre_transform=ExpensiveTransform())
   

7. Dynamic transforms: Apply cheap transforms during training:

   dataset = MyDataset(root='/tmp', transform=CheapTransform())
   

Performance Considerations

Expensive transforms (apply as pre_transform):

• GDC
• SIGN
• KNNGraph (for large point clouds)
• AddLaplacianEigenvectorPE
• SVDFeatureReduction

Cheap transforms (apply as transform):

• NormalizeFeatures
• ToUndirected
• AddSelfLoops
• Random* augmentations
• ToDevice

Example:

from torch_geometric.datasets import Planetoid
from torch_geometric.transforms import Compose, GDC, NormalizeFeatures

# Expensive preprocessing done once
pre_transform = GDC(
    self_loop_weight=1,
    normalization_in='sym',
    diffusion_kwargs=dict(method='ppr', alpha=0.15)
)

# Cheap transform applied each time
transform = NormalizeFeatures()

dataset = Planetoid(
    root='/tmp/Cora',
    name='Cora',
    pre_transform=pre_transform,
    transform=transform
)