chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""Tensorflow modules for graph global pooling."""
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# pylint: disable= no-member, arguments-differ, invalid-name, W0235
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import tensorflow as tf
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from tensorflow.keras import layers
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from ...readout import (
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max_nodes,
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mean_nodes,
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softmax_nodes,
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sum_nodes,
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topk_nodes,
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)
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__all__ = [
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"SumPooling",
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"AvgPooling",
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"MaxPooling",
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"SortPooling",
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"WeightAndSum",
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"GlobalAttentionPooling",
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]
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class SumPooling(layers.Layer):
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r"""Apply sum pooling over the nodes in the graph.
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.. math::
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r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k
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"""
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def __init__(self):
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super(SumPooling, self).__init__()
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def call(self, graph, feat):
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r"""Compute sum pooling.
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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
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The input feature with shape :math:`(N, *)` where
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:math:`N` is the number of nodes in the graph.
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Returns
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-------
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tf.Tensor
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The output feature with shape :math:`(B, *)`, where
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:math:`B` refers to the batch size.
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"""
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with graph.local_scope():
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graph.ndata["h"] = feat
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readout = sum_nodes(graph, "h")
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return readout
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class AvgPooling(layers.Layer):
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r"""Apply average pooling over the nodes in the graph.
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.. math::
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r^{(i)} = \frac{1}{N_i}\sum_{k=1}^{N_i} x^{(i)}_k
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"""
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def __init__(self):
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super(AvgPooling, self).__init__()
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def call(self, graph, feat):
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r"""Compute average pooling.
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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
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The input feature with shape :math:`(N, *)` where
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:math:`N` is the number of nodes in the graph.
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Returns
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-------
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tf.Tensor
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The output feature with shape :math:`(B, *)`, where
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:math:`B` refers to the batch size.
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"""
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with graph.local_scope():
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graph.ndata["h"] = feat
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readout = mean_nodes(graph, "h")
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return readout
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class MaxPooling(layers.Layer):
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r"""Apply max pooling over the nodes in the graph.
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.. math::
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r^{(i)} = \max_{k=1}^{N_i}\left( x^{(i)}_k \right)
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"""
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def __init__(self):
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super(MaxPooling, self).__init__()
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def call(self, graph, feat):
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r"""Compute max pooling.
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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
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The input feature with shape :math:`(N, *)` where
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:math:`N` is the number of nodes in the graph.
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Returns
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-------
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tf.Tensor
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The output feature with shape :math:`(B, *)`, where
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:math:`B` refers to the batch size.
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"""
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with graph.local_scope():
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graph.ndata["h"] = feat
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readout = max_nodes(graph, "h")
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return readout
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class SortPooling(layers.Layer):
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r"""Sort Pooling from `An End-to-End Deep Learning Architecture for Graph Classification
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<https://www.cse.wustl.edu/~ychen/public/DGCNN.pdf>`__
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Parameters
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----------
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k : int
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The number of nodes to hold for each graph.
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"""
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def __init__(self, k):
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super(SortPooling, self).__init__()
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self.k = k
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def call(self, graph, feat):
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r"""Compute sort pooling.
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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
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The input node feature with shape :math:`(N, D)` where
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:math:`N` is the number of nodes in the graph.
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Returns
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-------
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tf.Tensor
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The output feature with shape :math:`(B, k * D)`, where
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:math:`B` refers to the batch size.
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"""
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with graph.local_scope():
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# Sort the feature of each node in ascending order.
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feat = tf.sort(feat, -1)
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graph.ndata["h"] = feat
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# Sort nodes according to their last features.
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ret = tf.reshape(
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topk_nodes(graph, "h", self.k, sortby=-1)[0],
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(-1, self.k * feat.shape[-1]),
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)
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return ret
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class GlobalAttentionPooling(layers.Layer):
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r"""Global Attention Pooling from `Gated Graph Sequence Neural Networks
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<https://arxiv.org/abs/1511.05493.pdf>`__
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.. math::
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r^{(i)} = \sum_{k=1}^{N_i}\mathrm{softmax}\left(f_{gate}
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\left(x^{(i)}_k\right)\right) f_{feat}\left(x^{(i)}_k\right)
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Parameters
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----------
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gate_nn : tf.layers.Layer
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A neural network that computes attention scores for each feature.
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feat_nn : tf.layers.Layer, optional
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A neural network applied to each feature before combining them
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with attention scores.
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"""
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def __init__(self, gate_nn, feat_nn=None):
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super(GlobalAttentionPooling, self).__init__()
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self.gate_nn = gate_nn
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self.feat_nn = feat_nn
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def call(self, graph, feat):
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r"""Compute global attention pooling.
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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
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The input node feature with shape :math:`(N, D)` where
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:math:`N` is the number of nodes in the graph.
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Returns
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-------
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tf.Tensor
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The output feature with shape :math:`(B, *)`, where
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:math:`B` refers to the batch size.
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"""
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with graph.local_scope():
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gate = self.gate_nn(feat)
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assert (
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gate.shape[-1] == 1
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), "The output of gate_nn should have size 1 at the last axis."
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feat = self.feat_nn(feat) if self.feat_nn else feat
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graph.ndata["gate"] = gate
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gate = softmax_nodes(graph, "gate")
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graph.ndata.pop("gate")
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graph.ndata["r"] = feat * gate
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readout = sum_nodes(graph, "r")
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graph.ndata.pop("r")
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return readout
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class WeightAndSum(layers.Layer):
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"""Compute importance weights for atoms and perform a weighted sum.
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Parameters
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----------
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in_feats : int
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Input atom feature size
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"""
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def __init__(self, in_feats):
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super(WeightAndSum, self).__init__()
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self.in_feats = in_feats
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self.atom_weighting = tf.keras.Sequential(
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layers.Dense(1), layers.Activation(tf.nn.sigmoid)
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)
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def call(self, g, feats):
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"""Compute molecule representations out of atom representations
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Parameters
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----------
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g : DGLGraph
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DGLGraph with batch size B for processing multiple molecules in parallel
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feats : FloatTensor of shape (N, self.in_feats)
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Representations for all atoms in the molecules
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* N is the total number of atoms in all molecules
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Returns
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-------
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FloatTensor of shape (B, self.in_feats)
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Representations for B molecules
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"""
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with g.local_scope():
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g.ndata["h"] = feats
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g.ndata["w"] = self.atom_weighting(g.ndata["h"])
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h_g_sum = sum_nodes(g, "h", "w")
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return h_g_sum
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