chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""Tensorflow Module for GraphSAGE layer"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import tensorflow as tf
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from tensorflow.keras import layers
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from .... import function as fn
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from ....base import DGLError
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from ....utils import check_eq_shape, expand_as_pair
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class SAGEConv(layers.Layer):
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r"""GraphSAGE layer from `Inductive Representation Learning on
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Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__
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.. math::
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h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate}
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\left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)
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h_{i}^{(l+1)} &= \sigma \left(W \cdot \mathrm{concat}
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(h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1}) \right)
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h_{i}^{(l+1)} &= \mathrm{norm}(h_{i}^{(l+1)})
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Parameters
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----------
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in_feats : int, or pair of ints
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Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`.
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GATConv can be applied on homogeneous graph and unidirectional
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`bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
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If the layer applies on a unidirectional bipartite graph, ``in_feats``
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specifies the input feature size on both the source and destination nodes. If
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a scalar is given, the source and destination node feature size would take the
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same value.
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If aggregator type is ``gcn``, the feature size of source and destination nodes
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are required to be the same.
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out_feats : int
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Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`.
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aggregator_type : str
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Aggregator type to use (``mean``, ``gcn``, ``pool``, ``lstm``).
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feat_drop : float
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Dropout rate on features, default: ``0``.
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bias : bool
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If True, adds a learnable bias to the output. Default: ``True``.
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norm : callable activation function/layer or None, optional
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If not None, applies normalization to the updated node features.
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activation : callable activation function/layer or None, optional
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If not None, applies an activation function to the updated node features.
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Default: ``None``.
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Examples
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--------
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>>> import dgl
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>>> import numpy as np
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>>> import tensorflow as tf
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>>> from dgl.nn import SAGEConv
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>>>
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>>> # Case 1: Homogeneous graph
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>>> with tf.device("CPU:0"):
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
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>>> g = dgl.add_self_loop(g)
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>>> feat = tf.ones((6, 10))
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>>> conv = SAGEConv(10, 2, 'pool')
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>>> res = conv(g, feat)
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>>> res
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<tf.Tensor: shape=(6, 2), dtype=float32, numpy=
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array([[-3.6633523 , -0.90711546],
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[-3.6633523 , -0.90711546],
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[-3.6633523 , -0.90711546],
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[-3.6633523 , -0.90711546],
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[-3.6633523 , -0.90711546],
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[-3.6633523 , -0.90711546]], dtype=float32)>
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>>> # Case 2: Unidirectional bipartite graph
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>>> with tf.device("CPU:0"):
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>>> u = [0, 1, 0, 0, 1]
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>>> v = [0, 1, 2, 3, 2]
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>>> g = dgl.heterograph({('_N', '_E', '_N'):(u, v)})
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>>> u_fea = tf.convert_to_tensor(np.random.rand(2, 5))
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>>> v_fea = tf.convert_to_tensor(np.random.rand(4, 5))
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>>> conv = SAGEConv((5, 10), 2, 'mean')
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>>> res = conv(g, (u_fea, v_fea))
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>>> res
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<tf.Tensor: shape=(4, 2), dtype=float32, numpy=
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array([[-0.59453356, -0.4055441 ],
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[-0.47459763, -0.717764 ],
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[ 0.3221837 , -0.29876417],
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[-0.63356155, 0.09390211]], dtype=float32)>
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"""
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def __init__(
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self,
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in_feats,
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out_feats,
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aggregator_type,
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feat_drop=0.0,
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bias=True,
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norm=None,
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activation=None,
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):
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super(SAGEConv, self).__init__()
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valid_aggre_types = {"mean", "gcn", "pool", "lstm"}
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if aggregator_type not in valid_aggre_types:
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raise DGLError(
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"Invalid aggregator_type. Must be one of {}. "
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"But got {!r} instead.".format(
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valid_aggre_types, aggregator_type
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)
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)
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self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
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self._out_feats = out_feats
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self._aggre_type = aggregator_type
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self.norm = norm
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self.feat_drop = layers.Dropout(feat_drop)
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self.activation = activation
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# aggregator type: mean/pool/lstm/gcn
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if aggregator_type == "pool":
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self.fc_pool = layers.Dense(self._in_src_feats)
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if aggregator_type == "lstm":
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self.lstm = layers.LSTM(units=self._in_src_feats)
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if aggregator_type != "gcn":
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self.fc_self = layers.Dense(out_feats, use_bias=bias)
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self.fc_neigh = layers.Dense(out_feats, use_bias=bias)
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def _lstm_reducer(self, nodes):
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"""LSTM reducer
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NOTE(zihao): lstm reducer with default schedule (degree bucketing)
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is slow, we could accelerate this with degree padding in the future.
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"""
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m = nodes.mailbox["m"] # (B, L, D)
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rst = self.lstm(m)
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return {"neigh": rst}
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def call(self, graph, feat):
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r"""Compute GraphSAGE layer.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : tf.Tensor or pair of tf.Tensor
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If a tf.Tensor is given, it represents the input feature of shape
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:math:`(N, D_{in})`
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where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes.
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If a pair of tf.Tensor is given, the pair must contain two tensors of shape
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:math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`.
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Returns
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-------
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tf.Tensor
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The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
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is size of output feature.
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"""
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with graph.local_scope():
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if isinstance(feat, tuple):
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feat_src = self.feat_drop(feat[0])
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feat_dst = self.feat_drop(feat[1])
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else:
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feat_src = feat_dst = self.feat_drop(feat)
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if graph.is_block:
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feat_dst = feat_src[: graph.number_of_dst_nodes()]
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h_self = feat_dst
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# Handle the case of graphs without edges
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if graph.num_edges() == 0:
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graph.dstdata["neigh"] = tf.cast(
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tf.zeros((graph.number_of_dst_nodes(), self._in_src_feats)),
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tf.float32,
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)
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if self._aggre_type == "mean":
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graph.srcdata["h"] = feat_src
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graph.update_all(fn.copy_u("h", "m"), fn.mean("m", "neigh"))
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h_neigh = graph.dstdata["neigh"]
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elif self._aggre_type == "gcn":
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check_eq_shape(feat)
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graph.srcdata["h"] = feat_src
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graph.dstdata["h"] = feat_dst # same as above if homogeneous
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graph.update_all(fn.copy_u("h", "m"), fn.sum("m", "neigh"))
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# divide in_degrees
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degs = tf.cast(graph.in_degrees(), tf.float32)
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h_neigh = (graph.dstdata["neigh"] + graph.dstdata["h"]) / (
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tf.expand_dims(degs, -1) + 1
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)
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elif self._aggre_type == "pool":
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graph.srcdata["h"] = tf.nn.relu(self.fc_pool(feat_src))
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graph.update_all(fn.copy_u("h", "m"), fn.max("m", "neigh"))
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h_neigh = graph.dstdata["neigh"]
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elif self._aggre_type == "lstm":
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graph.srcdata["h"] = feat_src
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graph.update_all(fn.copy_u("h", "m"), self._lstm_reducer)
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h_neigh = graph.dstdata["neigh"]
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else:
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raise KeyError(
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"Aggregator type {} not recognized.".format(
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self._aggre_type
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)
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)
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# GraphSAGE GCN does not require fc_self.
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if self._aggre_type == "gcn":
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rst = self.fc_neigh(h_neigh)
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else:
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rst = self.fc_self(h_self) + self.fc_neigh(h_neigh)
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# activation
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if self.activation is not None:
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rst = self.activation(rst)
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# normalization
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if self.norm is not None:
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rst = self.norm(rst)
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return rst
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