gnn.py

import torch
import torch.nn as nn
import numpy as np
from scipy.linalg import block_diag

import dgl.function as fn

from .aggregator import MaxPoolAggregator, MeanAggregator, LSTMAggregator
from .bundler import Bundler
from ..model_utils import masked_softmax
from model.loss import EntropyLoss


class GraphSageLayer(nn.Module):
    """
    GraphSage layer in Inductive learning paper by hamilton
    Here, graphsage layer is a reduced function in DGL framework
    """

    def __init__(self, in_feats, out_feats, activation, dropout,
                 aggregator_type, bn=False, bias=True):
        super(GraphSageLayer, self).__init__()
        self.use_bn = bn
        self.bundler = Bundler(in_feats, out_feats, activation, dropout,
                               bias=bias)
        self.dropout = nn.Dropout(p=dropout)

        if aggregator_type == "maxpool":
            self.aggregator = MaxPoolAggregator(in_feats, in_feats,
                                                activation, bias)
        elif aggregator_type == "lstm":
            self.aggregator = LSTMAggregator(in_feats, in_feats)
        else:
            self.aggregator = MeanAggregator()

    def forward(self, g, h):
        h = self.dropout(h)
        g.ndata['h'] = h
        if self.use_bn and not hasattr(self, 'bn'):
            device = h.device
            self.bn = nn.BatchNorm1d(h.size()[1]).to(device)
        g.update_all(fn.copy_src(src='h', out='m'), self.aggregator,
                     self.bundler)
        if self.use_bn:
            h = self.bn(h)
        h = g.ndata.pop('h')
        return h


class GraphSage(nn.Module):
    """
    Grahpsage network that concatenate several graphsage layer
    """

    def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation,
                 dropout, aggregator_type):
        super(GraphSage, self).__init__()
        self.layers = nn.ModuleList()

        # input layer
        self.layers.append(GraphSageLayer(in_feats, n_hidden, activation, dropout,
                                          aggregator_type))
        # hidden layers
        for _ in range(n_layers - 1):
            self.layers.append(GraphSageLayer(n_hidden, n_hidden, activation,
                                              dropout, aggregator_type))
        # output layer
        self.layers.append(GraphSageLayer(n_hidden, n_classes, None,
                                          dropout, aggregator_type))

    def forward(self, g, features):
        h = features
        for layer in self.layers:
            h = layer(g, h)
        return h


class DiffPoolBatchedGraphLayer(nn.Module):

    def __init__(self, input_dim, assign_dim, output_feat_dim,
                 activation, dropout, aggregator_type, link_pred):
        super(DiffPoolBatchedGraphLayer, self).__init__()
        self.embedding_dim = input_dim
        self.assign_dim = assign_dim
        self.hidden_dim = output_feat_dim
        self.link_pred = link_pred
        self.feat_gc = GraphSageLayer(
            input_dim,
            output_feat_dim,
            activation,
            dropout,
            aggregator_type)
        self.pool_gc = GraphSageLayer(
            input_dim,
            assign_dim,
            activation,
            dropout,
            aggregator_type)
        self.reg_loss = nn.ModuleList([])
        self.loss_log = {}
        self.reg_loss.append(EntropyLoss())

    def forward(self, g, h):
        feat = self.feat_gc(g, h)
        assign_tensor = self.pool_gc(g, h)
        device = feat.device
        assign_tensor_masks = []
        batch_size = len(g.batch_num_nodes())
        for g_n_nodes in g.batch_num_nodes():
            mask = torch.ones((g_n_nodes,
                               int(assign_tensor.size()[1] / batch_size)))
            assign_tensor_masks.append(mask)
        """
        The first pooling layer is computed on batched graph.
        We first take the adjacency matrix of the batched graph, which is block-wise diagonal.
        We then compute the assignment matrix for the whole batch graph, which will also be block diagonal
        """
        mask = torch.FloatTensor(
            block_diag(
                *
                assign_tensor_masks)).to(
            device=device)
        assign_tensor = masked_softmax(assign_tensor, mask,
                                       memory_efficient=False)
        h = torch.matmul(torch.t(assign_tensor), feat)
        adj = g.adjacency_matrix(ctx=device)
        adj_new = torch.sparse.mm(adj, assign_tensor)
        adj_new = torch.mm(torch.t(assign_tensor), adj_new)

        if self.link_pred:
            current_lp_loss = torch.norm(adj.to_dense() -
                                         torch.mm(assign_tensor, torch.t(assign_tensor))) / np.power(g.number_of_nodes(), 2)
            self.loss_log['LinkPredLoss'] = current_lp_loss

        for loss_layer in self.reg_loss:
            loss_name = str(type(loss_layer).__name__)
            self.loss_log[loss_name] = loss_layer(adj, adj_new, assign_tensor)

        return adj_new, h