chore: import upstream snapshot with attribution

python/dgl/nn/tensorflow/conv/ginconv.py

@@ -0,0 +1,111 @@
+"""Tensorflow Module for Graph Isomorphism Network layer"""
+# pylint: disable= no-member, arguments-differ, invalid-name
+import tensorflow as tf
+from tensorflow.keras import layers
+
+from .... import function as fn
+from ....utils import expand_as_pair
+
+
+class GINConv(layers.Layer):
+    r"""Graph Isomorphism Network layer from `How Powerful are Graph
+    Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__
+
+    .. math::
+        h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} +
+        \mathrm{aggregate}\left(\left\{h_j^{l}, j\in\mathcal{N}(i)
+        \right\}\right)\right)
+
+    Parameters
+    ----------
+    apply_func : callable activation function/layer or None
+        If not None, apply this function to the updated node feature,
+        the :math:`f_\Theta` in the formula.
+    aggregator_type : str
+        Aggregator type to use (``sum``, ``max`` or ``mean``).
+    init_eps : float, optional
+        Initial :math:`\epsilon` value, default: ``0``.
+    learn_eps : bool, optional
+        If True, :math:`\epsilon` will be a learnable parameter. Default: ``False``.
+
+    Example
+    -------
+    >>> import dgl
+    >>> import numpy as np
+    >>> import tensorflow as tf
+    >>> from dgl.nn import GINConv
+    >>>
+    >>> with tf.device("CPU:0"):
+    >>>     g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
+    >>>     feat = tf.ones((6, 10))
+    >>>     lin = tf.keras.layers.Dense(10)
+    >>>     conv = GINConv(lin, 'max')
+    >>>     res = conv(g, feat)
+    >>>     res
+    <tf.Tensor: shape=(6, 10), dtype=float32, numpy=
+    array([[-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
+            1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
+        [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
+            1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
+        [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
+            1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
+        [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
+            1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
+        [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
+            1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
+        [-0.0545128 ,  0.9525287 , -0.15352362, -0.9979155 , -0.18199593,
+            0.55207   ,  1.2442873 , -0.17693758,  0.67841303,  0.8633929 ]],
+        dtype=float32)>
+    """
+
+    def __init__(
+        self, apply_func, aggregator_type, init_eps=0, learn_eps=False
+    ):
+        super(GINConv, self).__init__()
+        self.apply_func = apply_func
+        if aggregator_type == "sum":
+            self._reducer = fn.sum
+        elif aggregator_type == "max":
+            self._reducer = fn.max
+        elif aggregator_type == "mean":
+            self._reducer = fn.mean
+        else:
+            raise KeyError(
+                "Aggregator type {} not recognized.".format(aggregator_type)
+            )
+        # to specify whether eps is trainable or not.
+        self.eps = tf.Variable(
+            initial_value=[init_eps], dtype=tf.float32, trainable=learn_eps
+        )
+
+    def call(self, graph, feat):
+        r"""Compute Graph Isomorphism Network layer.
+
+        Parameters
+        ----------
+        graph : DGLGraph
+            The graph.
+        feat : tf.Tensor or pair of tf.Tensor
+            If a tf.Tensor is given, the input feature of shape :math:`(N, D_{in})` where
+            :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes.
+            If a pair of tf.Tensor is given, the pair must contain two tensors of shape
+            :math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`.
+            If ``apply_func`` is not None, :math:`D_{in}` should
+            fit the input dimensionality requirement of ``apply_func``.
+
+        Returns
+        -------
+        tf.Tensor
+            The output feature of shape :math:`(N, D_{out})` where
+            :math:`D_{out}` is the output dimensionality of ``apply_func``.
+            If ``apply_func`` is None, :math:`D_{out}` should be the same
+            as input dimensionality.
+        """
+        with graph.local_scope():
+            feat_src, feat_dst = expand_as_pair(feat, graph)
+            graph.srcdata["h"] = feat_src
+            graph.update_all(fn.copy_u("h", "m"), self._reducer("m", "neigh"))
+            rst = (1 + self.eps) * feat_dst + graph.dstdata["neigh"]
+            if self.apply_func is not None:
+                rst = self.apply_func(rst)
+            return rst