add gin test

pgl/layers/conv.py

@@ -180,7 +180,13 @@ def gat(gw,
     return output
 
 
-def gin(gw, feature, name, init_eps=0.0, train_eps=False, apply_func=None):
+def gin(gw,
+        feature,
+        hidden_size,
+        activation,
+        name,
+        init_eps=0.0,
+        train_eps=False):
     """Implementation of Graph Isomorphism Network (GIN) layer.
 
     This is an implementation of the paper How Powerful are Graph Neural Networks?
@@ -193,19 +199,18 @@ def gin(gw, feature, name, init_eps=0.0, train_eps=False, apply_func=None):
 
         name: GIN layer names.
 
+        hidden_size: The hidden size for gin.
+
+        activation: The activation for the output.
+
         init_eps: float, optional
             Initial :math:`\epsilon` value, default is 0.
 
         train_eps: bool, optional
             if True, :math:`\epsilon` will be a learnable parameter.
 
-        apply_func: Callable activation function or None. 
-            Default is None. If not None, apply this function to the updated feature.
-
     Return:
-        A tensor with shape (num_nodes, output_size) where ``output_size`` is the 
-        output dimensionality of ``apply_func``. If ``apply_func`` is None, ``output_size``
-        should be the same as ``feature_size``.
+        A tensor with shape (num_nodes, hidden_size).
     """
 
     def send_src_copy(src_feat, dst_feat, edge_feat):
@@ -214,8 +219,9 @@ def gin(gw, feature, name, init_eps=0.0, train_eps=False, apply_func=None):
     epsilon = fluid.layers.create_parameter(
         shape=[1, 1],
         dtype="float32",
-        attr=F.ParamAttr(name="%s_eps" % name),
-        default_initializer=F.initializer.ConstantInitializer(value=init_eps))
+        attr=fluid.ParamAttr(name="%s_eps" % name),
+        default_initializer=fluid.initializer.ConstantInitializer(
+            value=init_eps))
 
     if not train_eps:
         epsilon.stop_gradient = True
@@ -223,7 +229,17 @@ def gin(gw, feature, name, init_eps=0.0, train_eps=False, apply_func=None):
     msg = gw.send(send_src_copy, nfeat_list=[("h", feature)])
     output = gw.recv(msg, "sum") + (1.0 + epsilon) * feature
 
-    if apply_func is not None:
-        output = apply_func(output, name)
+    output = fluid.layers.fc(output,
+                             size=hidden_size,
+                             bias_attr=False,
+                             param_attr=fluid.ParamAttr(name="%s_w" % name))
+
+    bias = fluid.layers.create_parameter(
+        shape=[hidden_size],
+        dtype='float32',
+        is_bias=True,
+        attr=fluid.ParamAttr(name="%s_b" % name))
+
+    output = fluid.layers.elementwise_add(output, bias, act=activation)
 
     return output

pgl/tests/test_gin.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+    This file is for testing gin layer.
+"""
+from __future__ import division
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import unicode_literals
+import unittest
+import numpy as np
+
+import paddle.fluid as F
+import paddle.fluid.layers as L
+
+from pgl.layers.conv import gin
+from pgl import graph
+from pgl import graph_wrapper
+
+
+class GinTest(unittest.TestCase):
+    """GinTest
+    """
+
+    def test_gin(self):
+        """test_gin
+        """
+        np.random.seed(1)
+        hidden_size = 8
+
+        num_nodes = 10
+
+        edges = [(1, 4), (0, 5), (1, 9), (1, 8), (2, 8), (2, 5), (3, 6),
+                 (3, 7), (3, 4), (3, 8)]
+        inver_edges = [(v, u) for u, v in edges]
+        edges.extend(inver_edges)
+
+        node_feat = {"feature": np.random.rand(10, 4).astype("float32")}
+
+        g = graph.Graph(num_nodes=num_nodes, edges=edges, node_feat=node_feat)
+
+        use_cuda = False
+        place = F.GPUPlace(0) if use_cuda else F.CPUPlace()
+
+        prog = F.Program()
+        startup_prog = F.Program()
+        with F.program_guard(prog, startup_prog):
+            gw = graph_wrapper.GraphWrapper(
+                name='graph',
+                place=place,
+                node_feat=g.node_feat_info(),
+                edge_feat=g.edge_feat_info())
+
+            output = gin(gw,
+                         gw.node_feat['feature'],
+                         hidden_size=hidden_size,
+                         activation='relu',
+                         name='gin',
+                         init_eps=1,
+                         train_eps=True)
+
+        exe = F.Executor(place)
+        exe.run(startup_prog)
+        ret = exe.run(prog, feed=gw.to_feed(g), fetch_list=[output])
+
+        self.assertEqual(ret[0].shape[0], num_nodes)
+        self.assertEqual(ret[0].shape[1], hidden_size)
+
+
+if __name__ == "__main__":
+    unittest.main()