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CHANGELOG.md

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Changelog

All notable changes to this project will be documented in this file. The format is based on Keep a Changelog.

[2.0.5] - 2022-MM-DD

Added

  • Added a dynamically sized batch sampler for filling a mini-batch with a variable number of samples up to a maximum size (#4972)
  • Added fine grained options for setting bias and dropout per layer in the MLP model (#4981
  • Added EdgeCNN model (#4991)
  • Added scalable inference mode in BasicGNN with layer-wise neighbor loading (#4977)
  • Added inference benchmarks (#4892)
  • Added PyTorch 1.12 support (#4975)
  • Added unbatch_edge_index functionality for splitting an edge_index tensor according to a batch vector (#4903)
  • Added node-wise normalization mode in LayerNorm (#4944)
  • Added support for normalization_resolver (#4926, #4951, #4958, #4959)
  • Added notebook tutorial for torch_geometric.nn.aggr package to documentation (#4927)
  • Added support for follow_batch for lists or dictionaries of tensors (#4837)
  • Added Data.validate() and HeteroData.validate() functionality (#4885)
  • Added LinkNeighborLoader support to LightningDataModule (#4868)
  • Added predict() support to the LightningNodeData module (#4884)
  • Added time_attr argument to LinkNeighborLoader (#4877, #4908)
  • Added a filter_per_worker argument to data loaders to allow filtering of data within sub-processes (#4873)
  • Added a NeighborLoader benchmark script (#4815, #4862)
  • Added support for FeatureStore and GraphStore in NeighborLoader (#4817, #4851, #4854, #4856, #4857, #4882, #4883, #4929, #4992, #4962, #4968)
  • Added a normalize parameter to dense_diff_pool (#4847)
  • Added size=None explanation to jittable MessagePassing modules in the documentation (#4850)
  • Added documentation to the DataLoaderIterator class (#4838)
  • Added GraphStore support to Data and HeteroData (#4816)
  • Added FeatureStore support to Data and HeteroData (#4807, #4853)
  • Added support for dense aggregations in global_*_pool (#4827)
  • Added Python version requirement (#4825)
  • Added TorchScript support to JumpingKnowledge module (#4805)
  • Added a max_sample argument to AddMetaPaths in order to tackle very dense metapath edges (#4750)
  • Test HANConv with empty tensors (#4756, #4841)
  • Added the bias vector to the GCN model definition in the "Create Message Passing Networks" tutorial (#4755)
  • Added transforms.RootedSubgraph interface with two implementations: RootedEgoNets and RootedRWSubgraph (#3926)
  • Added ptr vectors for follow_batch attributes within Batch.from_data_list (#4723)
  • Added torch_geometric.nn.aggr package (#4687, #4721, #4731, #4762, #4749, #4779, #4863, #4864, #4865, #4866, #4872, #4934, #4935, #4957, #4973,#4973, 4986, #4995)
  • Added the DimeNet++ model (#4432, #4699, #4700, #4800)
  • Added an example of using PyG with PyTorch Ignite (#4487)
  • Added GroupAddRev module with support for reducing training GPU memory (#4671, #4701, #4715, #4730)
  • Added benchmarks via wandb (#4656, #4672, #4676)
  • Added unbatch functionality (#4628)
  • Confirm that to_hetero() works with custom functions, e.g., dropout_adj (4653)
  • Added the MLP.plain_last=False option (4652)
  • Added a check in HeteroConv and to_hetero() to ensure that MessagePassing.add_self_loops is disabled (4647)
  • Added HeteroData.subgraph() support (#4635)
  • Added the AQSOL dataset (#4626)
  • Added HeteroData.node_items() and HeteroData.edge_items() functionality (#4644)
  • Added PyTorch Lightning support in GraphGym (#4531, #4689, #4843)
  • Added support for returning embeddings in MLP models (#4625)
  • Added faster initialization of NeighborLoader in case edge indices are already sorted (via is_sorted=True) (#4620, #4702)
  • Added AddPositionalEncoding transform (#4521)
  • Added HeteroData.is_undirected() support (#4604)
  • Added the Genius and Wiki datasets to nn.datasets.LINKXDataset (#4570, #4600)
  • Added nn.glob.GlobalPooling module with support for multiple aggregations (#4582)
  • Added support for graph-level outputs in to_hetero (#4582)
  • Added CHANGELOG.md (#4581)

Changed

  • Fixed GenConv test (4993)
  • Fixed packaging tests for Python 3.10 (4982)
  • Changed act_dict (part of graphgym) to create individual instances instead of reusing the same ones everywhere (4978)
  • Fixed issue where one-hot tensors were passed to F.one_hot (4970)
  • Fixed bool arugments in argparse in benchmark/ (#4967)
  • Fixed BasicGNN for num_layers=1, which now respects a desired number of out_channels (#4943)
  • len(batch) will now return the number of graphs inside the batch, not the number of attributes (#4931)
  • Fixed data.subgraph generation for 0-dim tensors (#4932)
  • Removed unnecssary inclusion of self-loops when sampling negative edges (#4880)
  • Fixed InMemoryDataset inferring wrong len for lists of tensors (#4837)
  • Fixed Batch.separate when using it for lists of tensors (#4837)
  • Correct docstring for SAGEConv (#4852)
  • Fixed a bug in TUDataset where pre_filter was not applied whenever pre_transform was present
  • Renamed RandomTranslate to RandomJitter - the usage of RandomTranslate is now deprecated (#4828)
  • Do not allow accessing edge types in HeteroData with two node types when there exists multiple relations between these types (#4782)
  • Allow edge_type == rev_edge_type argument in RandomLinkSplit (#4757)
  • Fixed a numerical instability in the GeneralConv and neighbor_sample tests (#4754)
  • Fixed a bug in HANConv in which destination node features rather than source node features were propagated (#4753)
  • Fixed versions of checkout and setup-python in CI (#4751)
  • Fixed protobuf version (#4719)
  • Fixed the ranking protocol bug in the RGCN link prediction example (#4688)
  • Math support in Markdown (#4683)
  • Allow for setter properties in Data (#4682, #4686)
  • Allow for optional edge_weight in GCN2Conv (#4670)
  • Fixed the interplay between TUDataset and pre_transform that modify node features (#4669)
  • Make use of the pyg_sphinx_theme documentation template (#4664, #4667)
  • Refactored reading molecular positions from sdf file for qm9 datasets (4654)
  • Fixed MLP.jittable() bug in case return_emb=True (#4645, #4648)
  • The generated node features of StochasticBlockModelDataset are now ordered with respect to their labels (#4617)
  • Fixed typos in the documentation (#4616, #4824, #4895)
  • The bias argument in TAGConv is now actually applied (#4597)
  • Fixed subclass behaviour of process and download in Datsaet (#4586)
  • Fixed filtering of attributes for loaders in case __cat_dim__ != 0 (#4629)

Removed