!1216 add graph attention networks model and test file

Merge pull request !1216 from zhangdengcheng/master

tests/st/gnn/aggregator.py

@@ -322,7 +322,8 @@ class AttentionHead(nn.Cell):
             else:
                 ret = ret + input_feature
         # activation
-        ret = self.activation(ret)
+        if self.activation is not None:
+            ret = self.activation(ret)
         return ret
 
 
@@ -339,6 +340,8 @@ class AttentionAggregator(nn.Cell):
         coef_drop_ratio (float): Coefficient dropout ratio, default 0.0.
         activation (Cell): The output activation function, default nn.ELU().
         residual (bool): Whether to use residual connection, default False.
+        output_transform (str['concat', 'sum']): output transform for a layer,
+            default 'concat'
 
     Inputs:
         - **input_feature** (Tensor) - Tensor of shape : (batch_size, num_nodes, feature_dim).
@@ -360,7 +363,8 @@ class AttentionAggregator(nn.Cell):
                  in_drop=0.0,
                  coef_drop=0.0,
                  activation=nn.ELU(),
-                 residual=False):
+                 residual=False,
+                 output_transform='concat'):
         super(AttentionAggregator, self).__init__()
         self.num_heads = num_heads
         self.attns = []
@@ -372,9 +376,15 @@ class AttentionAggregator(nn.Cell):
                                             activation=activation,
                                             residual=residual))
         self.attns = nn.layer.CellList(self.attns)
+        if output_transform == 'concat':
+            self.out_trans = P.Concat(-1)
+        elif output_transform == 'sum':
+            self.out_trans = P.AddN()
+        else:
+            raise ValueError
 
     def construct(self, input_data, bias_mat):
         res = ()
         for i in range(self.num_heads):
             res += (self.attns[i](input_data, bias_mat),)
-        return P.Concat(-1)(res)
+        return self.out_trans(res)

tests/st/gnn/gat.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Graph Attention Networks."""
+import mindspore.nn as nn
+from mindspore._checkparam import check_bool, check_int_positive
+
+from aggregator import AttentionAggregator
+
+
+class GAT(nn.Cell):
+    """
+    Graph Attention Network
+
+    Args:
+        ftr_dims (int): Initial feature dimensions.
+        num_class (int): Num of class to identify.
+        num_nodes (int): Num of nodes in this graph.
+        hidden_units (list[int]): Num of hidden units at each layer.
+        num_heads (list[int]): Num of heads at each layer.
+        attn_drop (float): Drop out ratio of attention coefficient,
+            default 0.0.
+        ftr_drop (float): Drop out ratio of feature, default 0.0.
+        activation (Cell): Activation Function for output layer, default
+            nn.Elu().
+        residual (bool): Whether to use residual connection between
+            intermediate layers, default False.
+
+    Examples:
+        >>> ft_sizes = 1433
+        >>> num_class = 7
+        >>> num_nodes = 2708
+        >>> hid_units = [8]
+        >>> n_heads = [8, 1]
+        >>> activation = nn.ELU()
+        >>> residual = False
+        >>> input_data = Tensor(
+                np.array(np.random.rand(1, 2708, 1433), dtype=np.float32))
+        >>> biases = Tensor(np.array(np.random.rand(1, 2708, 2708), dtype=np.float32))
+        >>> net = GAT(ft_sizes,
+                      num_class,
+                      num_nodes,
+                      hidden_units=hid_units,
+                      num_heads=n_heads,
+                      attn_drop=0.6,
+                      ftr_drop=0.6,
+                      activation=activation,
+                      residual=residual)
+        >>> output = net(input_data, biases)
+    """
+
+    def __init__(self,
+                 ftr_dims,
+                 num_class,
+                 num_nodes,
+                 hidden_units,
+                 num_heads,
+                 attn_drop=0.0,
+                 ftr_drop=0.0,
+                 activation=nn.ELU(),
+                 residual=False):
+        super(GAT, self).__init__()
+        self.ftr_dims = check_int_positive(ftr_dims)
+        self.num_class = check_int_positive(num_class)
+        self.num_nodes = check_int_positive(num_nodes)
+        self.hidden_units = hidden_units
+        self.num_heads = num_heads
+        self.attn_drop = attn_drop
+        self.ftr_drop = ftr_drop
+        self.activation = activation
+        self.residual = check_bool(residual)
+        self.layers = []
+        # first layer
+        self.layers.append(AttentionAggregator(
+            self.ftr_dims,
+            self.hidden_units[0],
+            self.num_heads[0],
+            self.ftr_drop,
+            self.attn_drop,
+            self.activation,
+            residual=False))
+        # intermediate layer
+        for i in range(1, len(self.hidden_units)):
+            self.layers.append(AttentionAggregator(
+                self.hidden_units[i-1]*self.num_heads[i-1],
+                self.hidden_units[i],
+                self.num_heads[i],
+                self.ftr_drop,
+                self.attn_drop,
+                self.activation,
+                residual=self.residual))
+        # output layer
+        self.layers.append(AttentionAggregator(
+            self.hidden_units[-1]*self.num_heads[-2],
+            self.num_class,
+            self.num_heads[-1],
+            self.ftr_drop,
+            self.attn_drop,
+            activation=None,
+            residual=False,
+            output_transform='sum'))
+        self.layers = nn.layer.CellList(self.layers)
+
+    def construct(self, input_data, bias_mat):
+        for cell in self.layers:
+            input_data = cell(input_data, bias_mat)
+        return input_data/self.num_heads[-1]

tests/st/gnn/test_gat_model.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""test gat model."""
+import numpy as np
+
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.common.api import _executor
+from gat import GAT
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+def test_GAT():
+    ft_sizes = 1433
+    num_class = 7
+    num_nodes = 2708
+    hid_units = [8]
+    n_heads = [8, 1]
+    activation = nn.ELU()
+    residual = False
+    input_data = Tensor(
+        np.array(np.random.rand(1, 2708, 1433), dtype=np.float32))
+    biases = Tensor(np.array(np.random.rand(1, 2708, 2708), dtype=np.float32))
+    net = GAT(ft_sizes,
+              num_class,
+              num_nodes,
+              hidden_units=hid_units,
+              num_heads=n_heads,
+              attn_drop=0.6,
+              ftr_drop=0.6,
+              activation=activation,
+              residual=residual)
+    _executor.compile(net, input_data, biases)