[Transform] [Doc] Rename transform to transforms and update doc (#3765)

* Update

* Update

* Update

* Fix

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* Update

* Update

* Fix

python/dgl/_dataloading/cluster_gcn.py

@@ -3,7 +3,7 @@ import os
 import pickle
 import numpy as np
 
-from ..transform import metis_partition_assignment
+from ..transforms import metis_partition_assignment
 from .. import backend as F
 from .dataloader import SubgraphIterator
 

python/dgl/_dataloading/dataloader.py

@@ -4,7 +4,7 @@ from collections.abc import Mapping, Sequence
 from abc import ABC, abstractproperty, abstractmethod
 import re
 import numpy as np
-from .. import transform
+from .. import transforms
 from ..base import NID, EID
 from .. import backend as F
 from .. import utils
@@ -83,7 +83,7 @@ class _EidExcluder():
             # to the mapping from the new graph to the old frontier.
             # So we need to test if located_eids is empty, and do the remapping ourselves.
             if len(located_eids) > 0:
-                frontier = transform.remove_edges(
+                frontier = transforms.remove_edges(
                     frontier, located_eids, store_ids=True)
                 frontier.edata[EID] = F.gather_row(parent_eids, frontier.edata[EID])
         else:
@@ -92,7 +92,7 @@ class _EidExcluder():
             new_eids = parent_eids.copy()
             for k, v in located_eids.items():
                 if len(v) > 0:
-                    frontier = transform.remove_edges(
+                    frontier = transforms.remove_edges(
                         frontier, v, etype=k, store_ids=True)
                     new_eids[k] = F.gather_row(parent_eids[k], frontier.edges[k].data[EID])
             frontier.edata[EID] = new_eids
@@ -455,7 +455,7 @@ class BlockSampler(Sampler):
             if not self.exclude_edges_in_frontier(g):
                 frontier = exclude_edges(frontier, exclude_eids, self.output_device)
 
-            block = transform.to_block(frontier, seed_nodes_out)
+            block = transforms.to_block(frontier, seed_nodes_out)
             if self.return_eids:
                 self.assign_block_eids(block, frontier)
 
@@ -838,7 +838,7 @@ class EdgeCollator(Collator):
         neg_pair_graph = heterograph(
             neg_edges, {ntype: self.g.number_of_nodes(ntype) for ntype in self.g.ntypes})
 
-        pair_graph, neg_pair_graph = transform.compact_graphs([pair_graph, neg_pair_graph])
+        pair_graph, neg_pair_graph = transforms.compact_graphs([pair_graph, neg_pair_graph])
         pair_graph.edata[EID] = induced_edges
 
         seed_nodes = pair_graph.ndata[NID]

python/dgl/_dataloading/shadow.py

 """ShaDow-GNN subgraph samplers."""
 from ..utils import prepare_tensor_or_dict
 from ..base import NID
-from .. import transform
+from .. import transforms
 from ..sampling import sample_neighbors
 from .neighbor import NeighborSamplingMixin
 from .dataloader import exclude_edges, Sampler
@@ -89,7 +89,7 @@ class ShaDowKHopSampler(NeighborSamplingMixin, Sampler):
             fanout = self.fanouts[i]
             frontier = sample_neighbors(
                 g, seed_nodes, fanout, replace=self.replace, prob=self.prob_arrays)
-            block = transform.to_block(frontier, seed_nodes)
+            block = transforms.to_block(frontier, seed_nodes)
             seed_nodes = block.srcdata[NID]
 
         subg = g.subgraph(seed_nodes, relabel_nodes=True)

python/dgl/_deprecate/graph.py

@@ -3692,14 +3692,14 @@ class DGLGraph(DGLBaseGraph):
     def line_graph(self, backtracking=True, shared=False):
         """Return the line graph of this graph.
 
-        See :func:`~dgl.transform.line_graph`.
+        See :func:`~dgl.transforms.line_graph`.
         """
         return dgl.line_graph(self, backtracking, shared)
 
     def reverse(self, share_ndata=False, share_edata=False):
         """Return the reverse of this graph.
 
-        See :func:`~dgl.transform.reverse`.
+        See :func:`~dgl.transforms.reverse`.
         """
         return dgl.reverse(self, share_ndata, share_edata)
 

python/dgl/contrib/graph_store.py

@@ -502,14 +502,14 @@ class BaseGraphStore(DGLGraph):
     def line_graph(self, backtracking=True, shared=False):
         """Return the line graph of this graph.
 
-        See :func:`~dgl.transform.line_graph`.
+        See :func:`~dgl.transforms.line_graph`.
         """
         raise Exception("Graph store doesn't support creating an line matrix.")
 
     def reverse(self, share_ndata=False, share_edata=False):
         """Return the reverse of this graph.
 
-        See :func:`~dgl.transform.reverse`.
+        See :func:`~dgl.transforms.reverse`.
         """
         raise Exception("Graph store doesn't support reversing a matrix.")
 

python/dgl/data/citation_graph.py

@@ -20,7 +20,7 @@ from .. import batch
 from .. import backend as F
 from ..convert import graph as dgl_graph
 from ..convert import from_networkx, to_networkx
-from ..transform import reorder_graph
+from ..transforms import reorder_graph
 
 backend = os.environ.get('DGLBACKEND', 'pytorch')
 

python/dgl/data/gnn_benchmark.py

@@ -7,7 +7,7 @@ from .dgl_dataset import DGLBuiltinDataset
 from .utils import save_graphs, load_graphs, _get_dgl_url, deprecate_property, deprecate_class
 from ..convert import graph as dgl_graph
 from .. import backend as F
-from .. import transform
+from .. import transforms
 
 __all__ = ["AmazonCoBuyComputerDataset", "AmazonCoBuyPhotoDataset", "CoauthorPhysicsDataset", "CoauthorCSDataset",
            "CoraFullDataset", "AmazonCoBuy", "Coauthor", "CoraFull"]
@@ -39,7 +39,7 @@ class GNNBenchmarkDataset(DGLBuiltinDataset):
     def process(self):
         npz_path = os.path.join(self.raw_path, self.name + '.npz')
         g = self._load_npz(npz_path)
-        g = transform.reorder_graph(
+        g = transforms.reorder_graph(
             g, node_permute_algo='rcmk', edge_permute_algo='dst', store_ids=False)
         self._graph = g
         self._data = [g]
@@ -96,7 +96,7 @@ class GNNBenchmarkDataset(DGLBuiltinDataset):
             else:
                 labels = None
         g = dgl_graph((adj_matrix.row, adj_matrix.col))
-        g = transform.to_bidirected(g)
+        g = transforms.to_bidirected(g)
         g.ndata['feat'] = F.tensor(attr_matrix, F.data_type_dict['float32'])
         g.ndata['label'] = F.tensor(labels, F.data_type_dict['int64'])
         return g

python/dgl/data/minigc.py

@@ -7,7 +7,7 @@ from .dgl_dataset import DGLDataset
 from .utils import save_graphs, load_graphs, makedirs
 from .. import backend as F
 from ..convert import from_networkx
-from ..transform import add_self_loop
+from ..transforms import add_self_loop
 
 __all__ = ['MiniGCDataset']
 

python/dgl/data/qm9.py

@@ -6,7 +6,7 @@ import scipy.sparse as sp
 from .dgl_dataset import DGLDataset
 from .utils import download, _get_dgl_url
 from ..convert import graph as dgl_graph
-from ..transform import to_bidirected
+from ..transforms import to_bidirected
 from .. import backend as F
 
 class QM9Dataset(DGLDataset):

python/dgl/data/reddit.py

@@ -9,7 +9,7 @@ from .dgl_dataset import DGLBuiltinDataset
 from .utils import _get_dgl_url, generate_mask_tensor, load_graphs, save_graphs, deprecate_property
 from .. import backend as F
 from ..convert import from_scipy
-from ..transform import reorder_graph
+from ..transforms import reorder_graph
 
 
 class RedditDataset(DGLBuiltinDataset):

python/dgl/dataloading/base.py

@@ -3,7 +3,7 @@ from collections import Mapping
 from ..base import NID, EID
 from ..convert import heterograph
 from .. import backend as F
-from ..transform import compact_graphs
+from ..transforms import compact_graphs
 from ..frame import LazyFeature
 from ..utils import recursive_apply
 

python/dgl/dataloading/neighbor_sampler.py

 """Data loading components for neighbor sampling"""
 from ..base import NID, EID
-from ..transform import to_block
+from ..transforms import to_block
 from .base import BlockSampler
 
 class NeighborSampler(BlockSampler):

python/dgl/dataloading/shadow.py

 """ShaDow-GNN subgraph samplers."""
 from ..sampling.utils import EidExcluder
-from .. import transform
+from .. import transforms
 from ..base import NID
 from .base import set_node_lazy_features, set_edge_lazy_features
 
@@ -86,7 +86,7 @@ class ShaDowKHopSampler(object):
             frontier = g.sample_neighbors(
                 seed_nodes, fanout, output_device=self.output_device,
                 replace=self.replace, prob=self.prob, exclude_edges=exclude_edges)
-            block = transform.to_block(frontier, seed_nodes)
+            block = transforms.to_block(frontier, seed_nodes)
             seed_nodes = block.srcdata[NID]
 
         subg = g.subgraph(seed_nodes, relabel_nodes=True, output_device=self.output_device)

python/dgl/distributed/dist_graph.py

@@ -9,7 +9,7 @@ import numpy as np
 from ..heterograph import DGLHeteroGraph
 from ..convert import heterograph as dgl_heterograph
 from ..convert import graph as dgl_graph
-from ..transform import compact_graphs
+from ..transforms import compact_graphs
 from .. import heterograph_index
 from .. import backend as F
 from ..base import NID, EID, NTYPE, ETYPE, ALL, is_all

python/dgl/nn/mxnet/conv/agnnconv.py

@@ -11,13 +11,8 @@ from ....utils import expand_as_pair
 
 
 class AGNNConv(nn.Block):
-    r"""
-
-    Description
-    -----------
-    Attention-based Graph Neural Network layer from paper `Attention-based
-    Graph Neural Network for Semi-Supervised Learning
-    <https://arxiv.org/abs/1803.03735>`__.
+    r"""Attention-based Graph Neural Network layer from `Attention-based Graph Neural Network for
+    Semi-Supervised Learning <https://arxiv.org/abs/1803.03735>`__
 
     .. math::
         H^{l+1} = P H^{l}

python/dgl/nn/mxnet/conv/appnpconv.py

@@ -7,13 +7,9 @@ from mxnet.gluon import nn
 from .... import function as fn
 
 class APPNPConv(nn.Block):
-    r"""
-
-    Description
-    -----------
-    Approximate Personalized Propagation of Neural Predictions
-    layer from paper `Predict then Propagate: Graph Neural Networks
-    meet Personalized PageRank <https://arxiv.org/pdf/1810.05997.pdf>`__.
+    r"""Approximate Personalized Propagation of Neural Predictions layer from `Predict then
+    Propagate: Graph Neural Networks meet Personalized PageRank
+    <https://arxiv.org/pdf/1810.05997.pdf>`__
 
     .. math::
         H^{0} &= X

python/dgl/nn/mxnet/conv/chebconv.py

@@ -10,13 +10,8 @@ from .... import broadcast_nodes, function as fn
 
 
 class ChebConv(nn.Block):
-    r"""
-
-    Description
-    -----------
-    Chebyshev Spectral Graph Convolution layer from paper `Convolutional
-    Neural Networks on Graphs with Fast Localized Spectral Filtering
-    <https://arxiv.org/pdf/1606.09375.pdf>`__.
+    r"""Chebyshev Spectral Graph Convolution layer from `Convolutional Neural Networks on Graphs
+    with Fast Localized Spectral Filtering <https://arxiv.org/pdf/1606.09375.pdf>`__
 
     .. math::
         h_i^{l+1} &= \sum_{k=0}^{K-1} W^{k, l}z_i^{k, l}

python/dgl/nn/mxnet/conv/densechebconv.py

@@ -7,13 +7,8 @@ from mxnet.gluon import nn
 
 
 class DenseChebConv(nn.Block):
-    r"""
-
-    Description
-    -----------
-    Chebyshev Spectral Graph Convolution layer from paper `Convolutional
-    Neural Networks on Graphs with Fast Localized Spectral Filtering
-    <https://arxiv.org/pdf/1606.09375.pdf>`__.
+    r"""Chebyshev Spectral Graph Convolution layer from `Convolutional Neural Networks on Graphs
+    with Fast Localized Spectral Filtering <https://arxiv.org/pdf/1606.09375.pdf>`__
 
     We recommend to use this module when applying ChebConv on dense graphs.
 

python/dgl/nn/mxnet/conv/densegraphconv.py

@@ -7,12 +7,9 @@ from mxnet.gluon import nn
 
 
 class DenseGraphConv(nn.Block):
-    """
+    """Graph Convolutional layer from `Semi-Supervised Classification with Graph
+    Convolutional Networks <https://arxiv.org/abs/1609.02907>`__
 
-    Description
-    -----------
-    Graph Convolutional Network layer where the graph structure
-    is given by an adjacency matrix.
     We recommend user to use this module when applying graph convolution on
     dense graphs.
 

python/dgl/nn/mxnet/conv/densesageconv.py

@@ -8,12 +8,9 @@ from ....utils import check_eq_shape
 
 
 class DenseSAGEConv(nn.Block):
-    """
+    """GraphSAGE layer from `Inductive Representation Learning on Large Graphs
+    <https://arxiv.org/abs/1706.02216>`__
 
-    Description
-    -----------
-    GraphSAGE layer where the graph structure is given by an
-    adjacency matrix.
     We recommend to use this module when appying GraphSAGE on dense graphs.
 
     Note that we only support gcn aggregator in DenseSAGEConv.