Re-structure torch_geometric.nn.pool documentation (#6453)

CHANGELOG.md

@@ -59,7 +59,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
-- Re-structured the documentation ([#6420](https://github.com/pyg-team/pytorch_geometric/pull/6420), [#6423](https://github.com/pyg-team/pytorch_geometric/pull/6423), [#6429](https://github.com/pyg-team/pytorch_geometric/pull/6429), [#6440](https://github.com/pyg-team/pytorch_geometric/pull/6440), [#6443](https://github.com/pyg-team/pytorch_geometric/pull/6443), [#6445](https://github.com/pyg-team/pytorch_geometric/pull/6445), [#6452](https://github.com/pyg-team/pytorch_geometric/pull/6452))
+- Re-structured the documentation ([#6420](https://github.com/pyg-team/pytorch_geometric/pull/6420), [#6423](https://github.com/pyg-team/pytorch_geometric/pull/6423), [#6429](https://github.com/pyg-team/pytorch_geometric/pull/6429), [#6440](https://github.com/pyg-team/pytorch_geometric/pull/6440), [#6443](https://github.com/pyg-team/pytorch_geometric/pull/6443), [#6445](https://github.com/pyg-team/pytorch_geometric/pull/6445), [#6452](https://github.com/pyg-team/pytorch_geometric/pull/6452), [#6453](https://github.com/pyg-team/pytorch_geometric/pull/6453))
 - Fix the default arguments of `DataParallel` class ([#6376](https://github.com/pyg-team/pytorch_geometric/pull/6376))
 - Fix `ImbalancedSampler` on sliced `InMemoryDataset` ([#6374](https://github.com/pyg-team/pytorch_geometric/pull/6374))
 - Breaking Change: Changed the interface and implementation of `GraphMultisetTransformer` ([#6343](https://github.com/pyg-team/pytorch_geometric/pull/6343))

README.md

@@ -225,7 +225,7 @@ These GNN layers can be stacked together to create Graph Neural Network models.
 * **[SignedConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.SignedConv)** from Derr *et al.*: [Signed Graph Convolutional Network](https://arxiv.org/abs/1808.06354) (ICDM 2018) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/signed_gcn.py)]
 * **[DNAConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.DNAConv)** from Fey: [Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks](https://arxiv.org/abs/1904.04849) (ICLR-W 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/dna.py)]
 * **[PANConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.PANConv)** from Ma *et al.*: [Path Integral Based Convolution and Pooling for Graph Neural Networks](https://arxiv.org/abs/2006.16811) (NeurIPS 2020)
-* **[PointConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.PointConv)** (including **[Iterative Farthest Point Sampling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.fps)**, dynamic graph generation based on **[nearest neighbor](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.knn_graph)** or **[maximum distance](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.radius_graph)**, and **[k-NN interpolation](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.unpool.knn_interpolate)** for upsampling) from Qi *et al.*: [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/abs/1612.00593) (CVPR 2017) and [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413) (NIPS 2017) [[**Example1**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/pointnet2_classification.py), [**Example2**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/pointnet2_segmentation.py)]
+* **[PointConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.PointConv)** (including **[Iterative Farthest Point Sampling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.fps.html)**, dynamic graph generation based on **[nearest neighbor](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.knn_graph.html)** or **[maximum distance](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.radius_graph.html)**, and **[k-NN interpolation](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.unpool.knn_interpolate)** for upsampling) from Qi *et al.*: [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/abs/1612.00593) (CVPR 2017) and [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413) (NIPS 2017) [[**Example1**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/pointnet2_classification.py), [**Example2**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/pointnet2_segmentation.py)]
 * **[EdgeConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.EdgeConv)** from Wang *et al.*: [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/abs/1801.07829) (CoRR, 2018) [[**Example1**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/dgcnn_classification.py), [**Example2**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/dgcnn_segmentation.py)]
 * **[XConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.XConv)** from Li *et al.*: [PointCNN: Convolution On X-Transformed Points](https://arxiv.org/abs/1801.07791) (NeurIPS 2018) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/points/point_cnn.py)]
 * **[PPFConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.PPFConv)** from Deng *et al.*: [PPFNet: Global Context Aware Local Features for Robust 3D Point Matching](https://arxiv.org/abs/1802.02669) (CVPR 2018)
@@ -247,7 +247,7 @@ These GNN layers can be stacked together to create Graph Neural Network models.
 Graph pooling layers combine the vectorial representations of a set of nodes in a graph (or a subgraph) into a single vector representation that summarizes its properties of nodes.
 It is commonly applied to graph-level tasks, which require combining node features into a single graph representation.
 
-* **[Top-K Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.TopKPooling)** from Gao and Ji: [Graph U-Nets](https://arxiv.org/abs/1905.05178) (ICML 2019), Cangea *et al.*: [Towards Sparse Hierarchical Graph Classifiers](https://arxiv.org/abs/1811.01287) (NeurIPS-W 2018) and Knyazev *et al.*: [Understanding Attention and Generalization in Graph Neural Networks](https://arxiv.org/abs/1905.02850) (ICLR-W 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_topk_pool.py)]
+* **[Top-K Pooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.TopKPooling.html)** from Gao and Ji: [Graph U-Nets](https://arxiv.org/abs/1905.05178) (ICML 2019), Cangea *et al.*: [Towards Sparse Hierarchical Graph Classifiers](https://arxiv.org/abs/1811.01287) (NeurIPS-W 2018) and Knyazev *et al.*: [Understanding Attention and Generalization in Graph Neural Networks](https://arxiv.org/abs/1905.02850) (ICLR-W 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_topk_pool.py)]
 * **[DiffPool](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.dense.diff_pool.dense_diff_pool)** from Ying *et al.*: [Hierarchical Graph Representation Learning with Differentiable Pooling](https://arxiv.org/abs/1806.08804) (NeurIPS 2018) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_diff_pool.py)]
 
 <details>
@@ -258,13 +258,13 @@ It is commonly applied to graph-level tasks, which require combining node featur
 * **[Sort Aggregation](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.aggr.SortAggregation.html)** from Zhang *et al.*: [An End-to-End Deep Learning Architecture for Graph Classification](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf) (AAAI 2018) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/sort_pool.py)]
 * **[MinCut Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.dense.mincut_pool.dense_mincut_pool)** from Bianchi *et al.*: [Spectral Clustering with Graph Neural Networks for Graph Pooling](https://arxiv.org/abs/1907.00481) (ICML 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_mincut_pool.py)]
 * **[DMoN Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.dense.dmon_pool.DMoNPooling)** from Tsitsulin *et al.*: [Graph Clustering with Graph Neural Networks](https://arxiv.org/abs/2006.16904) (CoRR 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_dmon_pool.py)]
-* **[Graclus Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.graclus)** from Dhillon *et al.*: [Weighted Graph Cuts without Eigenvectors: A Multilevel Approach](http://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf) (PAMI 2007) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mnist_graclus.py)]
-* **[Voxel Grid Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.voxel_grid)** from, *e.g.*, Simonovsky and Komodakis: [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://arxiv.org/abs/1704.02901) (CVPR 2017) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mnist_voxel_grid.py)]
-* **[SAG Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.SAGPooling)** from Lee *et al.*: [Self-Attention Graph Pooling](https://arxiv.org/abs/1904.08082) (ICML 2019) and Knyazev *et al.*: [Understanding Attention and Generalization in Graph Neural Networks](https://arxiv.org/abs/1905.02850) (ICLR-W 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/sag_pool.py)]
-* **[Edge Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.EdgePooling)** from Diehl *et al.*: [Towards Graph Pooling by Edge Contraction](https://graphreason.github.io/papers/17.pdf) (ICML-W 2019) and Diehl: [Edge Contraction Pooling for Graph Neural Networks](https://arxiv.org/abs/1905.10990) (CoRR 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/edge_pool.py)]
-* **[ASAPooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.ASAPooling)** from Ranjan *et al.*: [ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations](https://arxiv.org/abs/1911.07979) (AAAI 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/asap.py)]
-* **[PANPooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.PANPooling)** from Ma *et al.*: [Path Integral Based Convolution and Pooling for Graph Neural Networks](https://arxiv.org/abs/2006.16811) (NeurIPS 2020)
-* **[MemPooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.MemPooling)** from Khasahmadi *et al.*: [Memory-Based Graph Networks](https://arxiv.org/abs/2002.09518) (ICLR 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mem_pool.py)]
+* **[Graclus Pooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.graclus.html)** from Dhillon *et al.*: [Weighted Graph Cuts without Eigenvectors: A Multilevel Approach](http://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf) (PAMI 2007) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mnist_graclus.py)]
+* **[Voxel Grid Pooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.voxel_grid.html)** from, *e.g.*, Simonovsky and Komodakis: [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://arxiv.org/abs/1704.02901) (CVPR 2017) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mnist_voxel_grid.py)]
+* **[SAG Pooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.SAGPooling.html)** from Lee *et al.*: [Self-Attention Graph Pooling](https://arxiv.org/abs/1904.08082) (ICML 2019) and Knyazev *et al.*: [Understanding Attention and Generalization in Graph Neural Networks](https://arxiv.org/abs/1905.02850) (ICLR-W 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/sag_pool.py)]
+* **[Edge Pooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.EdgePooling.html)** from Diehl *et al.*: [Towards Graph Pooling by Edge Contraction](https://graphreason.github.io/papers/17.pdf) (ICML-W 2019) and Diehl: [Edge Contraction Pooling for Graph Neural Networks](https://arxiv.org/abs/1905.10990) (CoRR 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/edge_pool.py)]
+* **[ASAPooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.ASAPooling.html)** from Ranjan *et al.*: [ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations](https://arxiv.org/abs/1911.07979) (AAAI 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/asap.py)]
+* **[PANPooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.PANPooling.html)** from Ma *et al.*: [Path Integral Based Convolution and Pooling for Graph Neural Networks](https://arxiv.org/abs/2006.16811) (NeurIPS 2020)
+* **[MemPooling](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.pool.MemPooling.html)** from Khasahmadi *et al.*: [Memory-Based Graph Networks](https://arxiv.org/abs/2002.09518) (ICLR 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mem_pool.py)]
 * **[Graph Multiset Transformer](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.aggr.GraphMultisetTransformer.html)** from Baek *et al.*: [Accurate Learning of Graph Representations with Graph Multiset Pooling](https://arxiv.org/abs/2102.11533) (ICLR 2021) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_gmt.py)]
 * **[Equilibrium Aggregation](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.aggr.EquilibriumAggregation.html)** from Bartunov *et al.*: [](https://arxiv.org/abs/2202.12795) (UAI 2022) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/equilibrium_median.py)]
 </details>

docs/source/modules/nn.rst

@@ -178,16 +178,14 @@ Pooling Layers
 --------------
 
 .. currentmodule:: torch_geometric.nn.pool
+
 .. autosummary::
    :nosignatures:
-   {% for cls in torch_geometric.nn.pool.classes %}
-     {{ cls }}
-   {% endfor %}
+   :toctree: ../generated
 
-.. automodule:: torch_geometric.nn.pool
-   :members:
-   :undoc-members:
-   :exclude-members: training
+   {% for name in torch_geometric.nn.pool.classes %}
+     {{ name }}
+   {% endfor %}
 
 Unpooling Layers
 ----------------

torch_geometric/datasets/zinc.py

@@ -35,7 +35,7 @@ class ZINC(InMemoryDataset):
 
     Args:
         root (str): Root directory where the dataset should be saved.
-        subset (boolean, optional): If set to :obj:`True`, will only load a
+        subset (bool, optional): If set to :obj:`True`, will only load a
             subset of the dataset (12,000 molecular graphs), following the
             `"Benchmarking Graph Neural Networks"
             <https://arxiv.org/abs/2003.00982>`_ paper. (default: :obj:`False`)