torch_geometric.transforms

Transforms are a general way to modify and customize Data or HeteroData objects, either by implicitly passing them as an argument to a Dataset, or by applying them explicitly to individual Data or HeteroData objects:

import torch_geometric.transforms as T
from torch_geometric.datasets import TUDataset

transform = T.Compose([T.ToUndirected(), T.AddSelfLoops()])

dataset = TUDataset(path, name='MUTAG', transform=transform)
data = dataset[0]  # Implicitly transform data on every access.

data = TUDataset(path, name='MUTAG')[0]
data = transform(data)  # Explicitly transform data.

General Transforms

BaseTransform	An abstract base class for writing transforms.
Compose	Composes several transforms together.
ComposeFilters	Composes several filters together.
ToDevice	Performs tensor device conversion, either for all attributes of the Data object or only the ones given by attrs (functional name: to_device).
ToSparseTensor	Converts the edge_index attributes of a homogeneous or heterogeneous data object into a transposed torch_sparse.SparseTensor or PyTorch torch.sparse.Tensor object with key adj_t (functional name: to_sparse_tensor).
Constant	Appends a constant value to each node feature x (functional name: constant).
NormalizeFeatures	Row-normalizes the attributes given in attrs to sum-up to one (functional name: normalize_features).
SVDFeatureReduction	Dimensionality reduction of node features via Singular Value Decomposition (SVD) (functional name: svd_feature_reduction).
RemoveTrainingClasses	Removes classes from the node-level training set as given by data.train_mask, e.g., in order to get a zero-shot label scenario (functional name: remove_training_classes).
RandomNodeSplit	Performs a node-level random split by adding train_mask, val_mask and test_mask attributes to the Data or HeteroData object (functional name: random_node_split).
RandomLinkSplit	Performs an edge-level random split into training, validation and test sets of a Data or a HeteroData object (functional name: random_link_split).
NodePropertySplit	Creates a node-level split with distributional shift based on a given node property, as proposed in the "Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts" paper (functional name: node_property_split).
IndexToMask	Converts indices to a mask representation (functional name: index_to_mask).
MaskToIndex	Converts a mask to an index representation (functional name: mask_to_index).
Pad	Applies padding to enforce consistent tensor shapes (functional name: pad).

Graph Transforms

ToUndirected	Converts a homogeneous or heterogeneous graph to an undirected graph such that \((j,i) \in \mathcal{E}\) for every edge \((i,j) \in \mathcal{E}\) (functional name: to_undirected).
OneHotDegree	Adds the node degree as one hot encodings to the node features (functional name: one_hot_degree).
TargetIndegree	Saves the globally normalized degree of target nodes (functional name: target_indegree).
LocalDegreeProfile	Appends the Local Degree Profile (LDP) from the "A Simple yet Effective Baseline for Non-attribute Graph Classification" paper (functional name: local_degree_profile).
AddSelfLoops	Adds self-loops to the given homogeneous or heterogeneous graph (functional name: add_self_loops).
AddRemainingSelfLoops	Adds remaining self-loops to the given homogeneous or heterogeneous graph (functional name: add_remaining_self_loops).
RemoveSelfLoops	Removes all self-loops in the given homogeneous or heterogeneous graph (functional name: remove_self_loops).
RemoveIsolatedNodes	Removes isolated nodes from the graph (functional name: remove_isolated_nodes).
RemoveDuplicatedEdges	Removes duplicated edges from a given homogeneous or heterogeneous graph.
KNNGraph	Creates a k-NN graph based on node positions data.pos (functional name: knn_graph).
RadiusGraph	Creates edges based on node positions data.pos to all points within a given distance (functional name: radius_graph).
ToDense	Converts a sparse adjacency matrix to a dense adjacency matrix with shape [num_nodes, num_nodes, *] (functional name: to_dense).
TwoHop	Adds the two hop edges to the edge indices (functional name: two_hop).
LineGraph	Converts a graph to its corresponding line-graph (functional name: line_graph).
LaplacianLambdaMax	Computes the highest eigenvalue of the graph Laplacian given by torch_geometric.utils.get_laplacian() (functional name: laplacian_lambda_max).
GDC	Processes the graph via Graph Diffusion Convolution (GDC) from the "Diffusion Improves Graph Learning" paper (functional name: gdc).
SIGN	The Scalable Inception Graph Neural Network module (SIGN) from the "SIGN: Scalable Inception Graph Neural Networks" paper (functional name: sign), which precomputes the fixed representations.
GCNNorm	Applies the GCN normalization from the "Semi-supervised Classification with Graph Convolutional Networks" paper (functional name: gcn_norm).
AddMetaPaths	Adds additional edge types to a HeteroData object between the source node type and the destination node type of a given metapath, as described in the "Heterogenous Graph Attention Networks" paper (functional name: add_metapaths).
AddRandomMetaPaths	Adds additional edge types similar to AddMetaPaths.
RootedEgoNets	Collects rooted \(k\)-hop EgoNets for each node in the graph, as described in the "From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness" paper.
RootedRWSubgraph	Collects rooted random-walk based subgraphs for each node in the graph, as described in the "From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness" paper.
LargestConnectedComponents	Selects the subgraph that corresponds to the largest connected components in the graph (functional name: largest_connected_components).
VirtualNode	Appends a virtual node to the given homogeneous graph that is connected to all other nodes, as described in the "Neural Message Passing for Quantum Chemistry" paper (functional name: virtual_node).
AddLaplacianEigenvectorPE	Adds the Laplacian eigenvector positional encoding from the "Benchmarking Graph Neural Networks" paper to the given graph (functional name: add_laplacian_eigenvector_pe).
AddRandomWalkPE	Adds the random walk positional encoding from the "Graph Neural Networks with Learnable Structural and Positional Representations" paper to the given graph (functional name: add_random_walk_pe).
FeaturePropagation	The feature propagation operator from the "On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features" paper (functional name: feature_propagation).
HalfHop	The graph upsampling augmentation from the "Half-Hop: A Graph Upsampling Approach for Slowing Down Message Passing" paper.

Vision Transforms

Distance	Saves the Euclidean distance of linked nodes in its edge attributes (functional name: distance).
Cartesian	Saves the relative Cartesian coordinates of linked nodes in its edge attributes (functional name: cartesian).
LocalCartesian	Saves the relative Cartesian coordinates of linked nodes in its edge attributes (functional name: local_cartesian).
Polar	Saves the polar coordinates of linked nodes in its edge attributes (functional name: polar).
Spherical	Saves the spherical coordinates of linked nodes in its edge attributes (functional name: spherical).
PointPairFeatures	Computes the rotation-invariant Point Pair Features (functional name: point_pair_features).
Center	Centers node positions data.pos around the origin (functional name: center).
NormalizeRotation	Rotates all points according to the eigenvectors of the point cloud (functional name: normalize_rotation).
NormalizeScale	Centers and normalizes node positions to the interval \((-1, 1)\) (functional name: normalize_scale).
RandomJitter	Translates node positions by randomly sampled translation values within a given interval (functional name: random_jitter).
RandomFlip	Flips node positions along a given axis randomly with a given probability (functional name: random_flip).
LinearTransformation	Transforms node positions data.pos with a square transformation matrix computed offline (functional name: linear_transformation).
RandomScale	Scales node positions by a randomly sampled factor \(s\) within a given interval, e.g., resulting in the transformation matrix (functional name: random_scale).
RandomRotate	Rotates node positions around a specific axis by a randomly sampled factor within a given interval (functional name: random_rotate).
RandomShear	Shears node positions by randomly sampled factors \(s\) within a given interval, e.g., resulting in the transformation matrix (functional name: random_shear).
FaceToEdge	Converts mesh faces [3, num_faces] to edge indices [2, num_edges] (functional name: face_to_edge).
SamplePoints	Uniformly samples a fixed number of points on the mesh faces according to their face area (functional name: sample_points).
FixedPoints	Samples a fixed number of points and features from a point cloud (functional name: fixed_points).
GenerateMeshNormals	Generate normal vectors for each mesh node based on neighboring faces (functional name: generate_mesh_normals).
Delaunay	Computes the delaunay triangulation of a set of points (functional name: delaunay).
ToSLIC	Converts an image to a superpixel representation using the skimage.segmentation.slic() algorithm, resulting in a torch_geometric.data.Data object holding the centroids of superpixels in data.pos and their mean color in data.x (functional name: to_slic).
GridSampling	Clusters points into fixed-sized voxels (functional name: grid_sampling).