forked from mindspore-Ecosystem/mindspore
119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Graph Attention Networks."""
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import mindspore.nn as nn
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from mindspore._checkparam import Validator
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from aggregator import AttentionAggregator
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class GAT(nn.Cell):
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"""
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Graph Attention Network
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Args:
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ftr_dims (int): Initial feature dimensions.
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num_class (int): Num of class to identify.
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num_nodes (int): Num of nodes in this graph.
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hidden_units (list[int]): Num of hidden units at each layer.
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num_heads (list[int]): Num of heads at each layer.
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attn_drop (float): Drop out ratio of attention coefficient,
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default 0.0.
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ftr_drop (float): Drop out ratio of feature, default 0.0.
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activation (Cell): Activation Function for output layer, default
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nn.Elu().
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residual (bool): Whether to use residual connection between
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intermediate layers, default False.
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Examples:
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>>> ft_sizes = 1433
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>>> num_class = 7
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>>> num_nodes = 2708
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>>> hid_units = [8]
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>>> n_heads = [8, 1]
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>>> activation = nn.ELU()
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>>> residual = False
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>>> input_data = Tensor(
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np.array(np.random.rand(1, 2708, 1433), dtype=np.float32))
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>>> biases = Tensor(np.array(np.random.rand(1, 2708, 2708), dtype=np.float32))
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>>> net = GAT(ft_sizes,
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num_class,
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num_nodes,
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hidden_units=hid_units,
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num_heads=n_heads,
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attn_drop=0.6,
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ftr_drop=0.6,
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activation=activation,
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residual=residual)
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>>> output = net(input_data, biases)
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"""
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def __init__(self,
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ftr_dims,
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num_class,
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num_nodes,
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hidden_units,
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num_heads,
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attn_drop=0.0,
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ftr_drop=0.0,
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activation=nn.ELU(),
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residual=False):
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super(GAT, self).__init__()
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self.ftr_dims = Validator.check_positive_int(ftr_dims)
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self.num_class = Validator.check_positive_int(num_class)
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self.num_nodes = Validator.check_positive_int(num_nodes)
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self.hidden_units = hidden_units
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self.num_heads = num_heads
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self.attn_drop = attn_drop
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self.ftr_drop = ftr_drop
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self.activation = activation
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self.residual = Validator.check_bool(residual)
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self.layers = []
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# first layer
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self.layers.append(AttentionAggregator(
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self.ftr_dims,
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self.hidden_units[0],
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self.num_heads[0],
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self.ftr_drop,
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self.attn_drop,
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self.activation,
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residual=False))
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# intermediate layer
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for i in range(1, len(self.hidden_units)):
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self.layers.append(AttentionAggregator(
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self.hidden_units[i-1]*self.num_heads[i-1],
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self.hidden_units[i],
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self.num_heads[i],
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self.ftr_drop,
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self.attn_drop,
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self.activation,
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residual=self.residual))
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# output layer
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self.layers.append(AttentionAggregator(
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self.hidden_units[-1]*self.num_heads[-2],
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self.num_class,
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self.num_heads[-1],
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self.ftr_drop,
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self.attn_drop,
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activation=None,
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residual=False,
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output_transform='sum'))
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self.layers = nn.layer.CellList(self.layers)
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def construct(self, input_data, bias_mat):
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for cell in self.layers:
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input_data = cell(input_data, bias_mat)
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return input_data/self.num_heads[-1]
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