dmlc--dgl
张天启
72ef642f29
* add sagpool example for pytorch backend * polish sagpool example for pytorch backend * [Example] SAGPool: use std variance * [Example] SAGPool: change to std * add sagpool example to index page * add graph property prediction tag to sagpool Co-authored-by: zhangtianqi <tianqizh@amazon.com>
124 行
4.6 KiB
Python
124 行
4.6 KiB
Python
import torch
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import torch.nn
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import torch.nn.functional as F
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import dgl
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from dgl.nn import GraphConv, AvgPooling, MaxPooling
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from layer import ConvPoolBlock, SAGPool
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class SAGNetworkHierarchical(torch.nn.Module):
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"""The Self-Attention Graph Pooling Network with hierarchical readout in paper
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`Self Attention Graph Pooling <https://arxiv.org/pdf/1904.08082.pdf>`
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Args:
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in_dim (int): The input node feature dimension.
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hid_dim (int): The hidden dimension for node feature.
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out_dim (int): The output dimension.
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num_convs (int, optional): The number of graph convolution layers.
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(default: 3)
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pool_ratio (float, optional): The pool ratio which determines the amount of nodes
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remain after pooling. (default: :obj:`0.5`)
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dropout (float, optional): The dropout ratio for each layer. (default: 0)
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"""
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def __init__(self, in_dim:int, hid_dim:int, out_dim:int, num_convs=3,
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pool_ratio:float=0.5, dropout:float=0.0):
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super(SAGNetworkHierarchical, self).__init__()
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self.dropout = dropout
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self.num_convpools = num_convs
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convpools = []
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for i in range(num_convs):
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_i_dim = in_dim if i == 0 else hid_dim
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_o_dim = hid_dim
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convpools.append(ConvPoolBlock(_i_dim, _o_dim, pool_ratio=pool_ratio))
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self.convpools = torch.nn.ModuleList(convpools)
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self.lin1 = torch.nn.Linear(hid_dim * 2, hid_dim)
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self.lin2 = torch.nn.Linear(hid_dim, hid_dim // 2)
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self.lin3 = torch.nn.Linear(hid_dim // 2, out_dim)
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def forward(self, graph:dgl.DGLGraph):
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feat = graph.ndata["feat"]
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final_readout = None
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for i in range(self.num_convpools):
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graph, feat, readout = self.convpools[i](graph, feat)
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if final_readout is None:
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final_readout = readout
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else:
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final_readout = final_readout + readout
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feat = F.relu(self.lin1(final_readout))
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feat = F.dropout(feat, p=self.dropout, training=self.training)
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feat = F.relu(self.lin2(feat))
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feat = F.log_softmax(self.lin3(feat), dim=-1)
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return feat
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class SAGNetworkGlobal(torch.nn.Module):
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"""The Self-Attention Graph Pooling Network with global readout in paper
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`Self Attention Graph Pooling <https://arxiv.org/pdf/1904.08082.pdf>`
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Args:
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in_dim (int): The input node feature dimension.
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hid_dim (int): The hidden dimension for node feature.
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out_dim (int): The output dimension.
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num_convs (int, optional): The number of graph convolution layers.
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(default: 3)
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pool_ratio (float, optional): The pool ratio which determines the amount of nodes
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remain after pooling. (default: :obj:`0.5`)
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dropout (float, optional): The dropout ratio for each layer. (default: 0)
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"""
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def __init__(self, in_dim:int, hid_dim:int, out_dim:int, num_convs=3,
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pool_ratio:float=0.5, dropout:float=0.0):
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super(SAGNetworkGlobal, self).__init__()
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self.dropout = dropout
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self.num_convs = num_convs
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convs = []
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for i in range(num_convs):
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_i_dim = in_dim if i == 0 else hid_dim
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_o_dim = hid_dim
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convs.append(GraphConv(_i_dim, _o_dim))
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self.convs = torch.nn.ModuleList(convs)
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concat_dim = num_convs * hid_dim
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self.pool = SAGPool(concat_dim, ratio=pool_ratio)
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self.avg_readout = AvgPooling()
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self.max_readout = MaxPooling()
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self.lin1 = torch.nn.Linear(concat_dim * 2, hid_dim)
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self.lin2 = torch.nn.Linear(hid_dim, hid_dim // 2)
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self.lin3 = torch.nn.Linear(hid_dim // 2, out_dim)
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def forward(self, graph:dgl.DGLGraph):
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feat = graph.ndata["feat"]
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conv_res = []
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for i in range(self.num_convs):
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feat = self.convs[i](graph, feat)
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conv_res.append(feat)
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conv_res = torch.cat(conv_res, dim=-1)
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graph, feat, _ = self.pool(graph, conv_res)
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feat = torch.cat([self.avg_readout(graph, feat), self.max_readout(graph, feat)], dim=-1)
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feat = F.relu(self.lin1(feat))
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feat = F.dropout(feat, p=self.dropout, training=self.training)
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feat = F.relu(self.lin2(feat))
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feat = F.log_softmax(self.lin3(feat), dim=-1)
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return feat
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def get_sag_network(net_type:str="hierarchical"):
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if net_type == "hierarchical":
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return SAGNetworkHierarchical
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elif net_type == "global":
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return SAGNetworkGlobal
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else:
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raise ValueError("SAGNetwork type {} is not supported.".format(net_type))
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