wehub-resource-sync
338 lines
9.3 KiB
Python
338 lines
9.3 KiB
Python
from typing import Optional
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import dgl
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import torch
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import torch.nn
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from dgl import DGLGraph
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from dgl.nn import GraphConv
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from torch import Tensor
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class GraphConvWithDropout(GraphConv):
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"""
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A GraphConv followed by a Dropout.
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"""
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def __init__(
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self,
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in_feats,
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out_feats,
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dropout=0.3,
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norm="both",
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weight=True,
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bias=True,
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activation=None,
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allow_zero_in_degree=False,
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):
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super(GraphConvWithDropout, self).__init__(
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in_feats,
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out_feats,
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norm,
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weight,
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bias,
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activation,
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allow_zero_in_degree,
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)
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self.dropout = torch.nn.Dropout(p=dropout)
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def call(self, graph, feat, weight=None):
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feat = self.dropout(feat)
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return super(GraphConvWithDropout, self).call(graph, feat, weight)
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class Discriminator(torch.nn.Module):
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"""
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Description
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-----------
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A discriminator used to let the network to discrimate
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between positive (neighborhood of center node) and
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negative (any neighborhood in graph) samplings.
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Parameters
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----------
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feat_dim : int
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The number of channels of node features.
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"""
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def __init__(self, feat_dim: int):
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super(Discriminator, self).__init__()
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self.affine = torch.nn.Bilinear(feat_dim, feat_dim, 1)
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self.reset_parameters()
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def reset_parameters(self):
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torch.nn.init.xavier_uniform_(self.affine.weight)
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torch.nn.init.zeros_(self.affine.bias)
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def forward(
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self,
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h_x: Tensor,
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h_pos: Tensor,
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h_neg: Tensor,
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bias_pos: Optional[Tensor] = None,
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bias_neg: Optional[Tensor] = None,
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):
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"""
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Parameters
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----------
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h_x : torch.Tensor
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Node features, shape: :obj:`(num_nodes, feat_dim)`
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h_pos : torch.Tensor
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The node features of positive samples
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It has the same shape as :obj:`h_x`
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h_neg : torch.Tensor
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The node features of negative samples
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It has the same shape as :obj:`h_x`
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bias_pos : torch.Tensor
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Bias parameter vector for positive scores
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shape: :obj:`(num_nodes)`
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bias_neg : torch.Tensor
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Bias parameter vector for negative scores
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shape: :obj:`(num_nodes)`
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Returns
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-------
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(torch.Tensor, torch.Tensor)
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The output scores with shape (2 * num_nodes,), (num_nodes,)
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"""
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score_pos = self.affine(h_pos, h_x).squeeze()
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score_neg = self.affine(h_neg, h_x).squeeze()
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if bias_pos is not None:
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score_pos = score_pos + bias_pos
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if bias_neg is not None:
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score_neg = score_neg + bias_neg
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logits = torch.cat((score_pos, score_neg), 0)
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return logits, score_pos
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class DenseLayer(torch.nn.Module):
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"""
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Description
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-----------
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Dense layer with a linear layer and an activation function
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"""
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def __init__(
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self, in_dim: int, out_dim: int, act: str = "prelu", bias=True
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):
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super(DenseLayer, self).__init__()
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self.lin = torch.nn.Linear(in_dim, out_dim, bias=bias)
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self.act_type = act.lower()
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self.reset_parameters()
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def reset_parameters(self):
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torch.nn.init.xavier_uniform_(self.lin.weight)
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if self.lin.bias is not None:
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torch.nn.init.zeros_(self.lin.bias)
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if self.act_type == "prelu":
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self.act = torch.nn.PReLU()
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else:
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self.act = torch.relu
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def forward(self, x):
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x = self.lin(x)
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return self.act(x)
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class IndexSelect(torch.nn.Module):
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"""
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Description
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-----------
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The index selection layer used by VIPool
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Parameters
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----------
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pool_ratio : float
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The pooling ratio (for keeping nodes). For example,
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if `pool_ratio=0.8`, 80\% nodes will be preserved.
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hidden_dim : int
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The number of channels in node features.
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act : str, optional
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The activation function type.
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Default: :obj:`'prelu'`
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dist : int, optional
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DO NOT USE THIS PARAMETER
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"""
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def __init__(
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self,
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pool_ratio: float,
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hidden_dim: int,
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act: str = "prelu",
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dist: int = 1,
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):
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super(IndexSelect, self).__init__()
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self.pool_ratio = pool_ratio
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self.dist = dist
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self.dense = DenseLayer(hidden_dim, hidden_dim, act)
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self.discriminator = Discriminator(hidden_dim)
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self.gcn = GraphConvWithDropout(hidden_dim, hidden_dim)
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def forward(
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self,
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graph: DGLGraph,
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h_pos: Tensor,
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h_neg: Tensor,
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bias_pos: Optional[Tensor] = None,
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bias_neg: Optional[Tensor] = None,
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):
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"""
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Description
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-----------
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Perform index selection
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Parameters
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----------
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graph : dgl.DGLGraph
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Input graph.
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h_pos : torch.Tensor
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The node features of positive samples
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It has the same shape as :obj:`h_x`
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h_neg : torch.Tensor
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The node features of negative samples
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It has the same shape as :obj:`h_x`
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bias_pos : torch.Tensor
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Bias parameter vector for positive scores
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shape: :obj:`(num_nodes)`
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bias_neg : torch.Tensor
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Bias parameter vector for negative scores
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shape: :obj:`(num_nodes)`
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"""
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# compute scores
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h_pos = self.dense(h_pos)
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h_neg = self.dense(h_neg)
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embed = self.gcn(graph, h_pos)
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h_center = torch.sigmoid(embed)
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logit, logit_pos = self.discriminator(
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h_center, h_pos, h_neg, bias_pos, bias_neg
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)
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scores = torch.sigmoid(logit_pos)
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# sort scores
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scores, idx = torch.sort(scores, descending=True)
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# select top-k
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num_nodes = graph.num_nodes()
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num_select_nodes = int(self.pool_ratio * num_nodes)
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size_list = [num_select_nodes, num_nodes - num_select_nodes]
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select_scores, _ = torch.split(scores, size_list, dim=0)
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select_idx, non_select_idx = torch.split(idx, size_list, dim=0)
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return logit, select_scores, select_idx, non_select_idx, embed
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class GraphPool(torch.nn.Module):
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"""
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Description
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-----------
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The pooling module for graph
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Parameters
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----------
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hidden_dim : int
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The number of channels of node features.
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use_gcn : bool, optional
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Whether use gcn in down sampling process.
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default: :obj:`False`
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"""
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def __init__(self, hidden_dim: int, use_gcn=False):
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super(GraphPool, self).__init__()
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self.use_gcn = use_gcn
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self.down_sample_gcn = (
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GraphConvWithDropout(hidden_dim, hidden_dim) if use_gcn else None
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)
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def forward(
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self,
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graph: DGLGraph,
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feat: Tensor,
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select_idx: Tensor,
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non_select_idx: Optional[Tensor] = None,
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scores: Optional[Tensor] = None,
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pool_graph=False,
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):
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"""
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Description
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-----------
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Perform graph pooling.
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Parameters
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----------
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graph : dgl.DGLGraph
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The input graph
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feat : torch.Tensor
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The input node feature
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select_idx : torch.Tensor
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The index in fine graph of node from
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coarse graph, this is obtained from
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previous graph pooling layers.
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non_select_idx : torch.Tensor, optional
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The index that not included in output graph.
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default: :obj:`None`
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scores : torch.Tensor, optional
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Scores for nodes used for pooling and scaling.
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default: :obj:`None`
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pool_graph : bool, optional
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Whether perform graph pooling on graph topology.
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default: :obj:`False`
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"""
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if self.use_gcn:
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feat = self.down_sample_gcn(graph, feat)
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feat = feat[select_idx]
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if scores is not None:
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feat = feat * scores.unsqueeze(-1)
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if pool_graph:
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num_node_batch = graph.batch_num_nodes()
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graph = dgl.node_subgraph(graph, select_idx)
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graph.set_batch_num_nodes(num_node_batch)
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return feat, graph
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else:
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return feat
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class GraphUnpool(torch.nn.Module):
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"""
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Description
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-----------
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The unpooling module for graph
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Parameters
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----------
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hidden_dim : int
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The number of channels of node features.
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"""
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def __init__(self, hidden_dim: int):
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super(GraphUnpool, self).__init__()
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self.up_sample_gcn = GraphConvWithDropout(hidden_dim, hidden_dim)
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def forward(self, graph: DGLGraph, feat: Tensor, select_idx: Tensor):
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"""
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Description
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-----------
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Perform graph unpooling
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Parameters
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----------
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graph : dgl.DGLGraph
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The input graph
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feat : torch.Tensor
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The input node feature
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select_idx : torch.Tensor
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The index in fine graph of node from
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coarse graph, this is obtained from
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previous graph pooling layers.
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"""
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fine_feat = torch.zeros(
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(graph.num_nodes(), feat.size(-1)), device=feat.device
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)
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fine_feat[select_idx] = feat
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fine_feat = self.up_sample_gcn(graph, fine_feat)
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return fine_feat
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