dmlc--dgl
Quan (Andy) Gan
acd21a6d60
* csr and csc creation * fix * fix * fixes to adj transpose * fine * raise error if indptr did not match number of nodes * fix * huh? * oh Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
127 行
4.5 KiB
Python
可执行文件
127 行
4.5 KiB
Python
可执行文件
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from scipy.linalg import block_diag
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import dgl.function as fn
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from .aggregator import MaxPoolAggregator, MeanAggregator, LSTMAggregator
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from .bundler import Bundler
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from ..model_utils import masked_softmax
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from model.loss import EntropyLoss
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class GraphSageLayer(nn.Module):
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"""
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GraphSage layer in Inductive learning paper by hamilton
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Here, graphsage layer is a reduced function in DGL framework
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"""
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def __init__(self, in_feats, out_feats, activation, dropout,
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aggregator_type, bn=False, bias=True):
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super(GraphSageLayer, self).__init__()
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self.use_bn = bn
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self.bundler = Bundler(in_feats, out_feats, activation, dropout,
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bias=bias)
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self.dropout = nn.Dropout(p=dropout)
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if aggregator_type == "maxpool":
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self.aggregator = MaxPoolAggregator(in_feats, in_feats,
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activation, bias)
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elif aggregator_type == "lstm":
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self.aggregator = LSTMAggregator(in_feats, in_feats)
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else:
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self.aggregator = MeanAggregator()
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def forward(self, g, h):
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h = self.dropout(h)
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g.ndata['h'] = h
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if self.use_bn and not hasattr(self, 'bn'):
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device = h.device
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self.bn = nn.BatchNorm1d(h.size()[1]).to(device)
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g.update_all(fn.copy_src(src='h', out='m'), self.aggregator,
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self.bundler)
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if self.use_bn:
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h = self.bn(h)
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h = g.ndata.pop('h')
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return h
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class GraphSage(nn.Module):
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"""
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Grahpsage network that concatenate several graphsage layer
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"""
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def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation,
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dropout, aggregator_type):
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super(GraphSage, self).__init__()
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self.layers = nn.ModuleList()
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# input layer
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self.layers.append(GraphSageLayer(in_feats, n_hidden, activation, dropout,
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aggregator_type))
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# hidden layers
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for _ in range(n_layers - 1):
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self.layers.append(GraphSageLayer(n_hidden, n_hidden, activation,
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dropout, aggregator_type))
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# output layer
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self.layers.append(GraphSageLayer(n_hidden, n_classes, None,
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dropout, aggregator_type))
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def forward(self, g, features):
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h = features
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for layer in self.layers:
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h = layer(g, h)
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return h
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class DiffPoolBatchedGraphLayer(nn.Module):
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def __init__(self, input_dim, assign_dim, output_feat_dim,
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activation, dropout, aggregator_type, link_pred):
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super(DiffPoolBatchedGraphLayer, self).__init__()
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self.embedding_dim = input_dim
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self.assign_dim = assign_dim
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self.hidden_dim = output_feat_dim
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self.link_pred = link_pred
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self.feat_gc = GraphSageLayer(
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input_dim,
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output_feat_dim,
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activation,
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dropout,
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aggregator_type)
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self.pool_gc = GraphSageLayer(
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input_dim,
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assign_dim,
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activation,
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dropout,
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aggregator_type)
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self.reg_loss = nn.ModuleList([])
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self.loss_log = {}
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self.reg_loss.append(EntropyLoss())
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def forward(self, g, h):
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feat = self.feat_gc(g, h) # size = (sum_N, F_out), sum_N is num of nodes in this batch
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device = feat.device
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assign_tensor = self.pool_gc(g, h) # size = (sum_N, N_a), N_a is num of nodes in pooled graph.
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assign_tensor = F.softmax(assign_tensor, dim=1)
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assign_tensor = torch.split(assign_tensor, g.batch_num_nodes().tolist())
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assign_tensor = torch.block_diag(*assign_tensor) # size = (sum_N, batch_size * N_a)
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h = torch.matmul(torch.t(assign_tensor), feat)
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adj = g.adjacency_matrix(transpose=True, ctx=device)
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adj_new = torch.sparse.mm(adj, assign_tensor)
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adj_new = torch.mm(torch.t(assign_tensor), adj_new)
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if self.link_pred:
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current_lp_loss = torch.norm(adj.to_dense() -
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torch.mm(assign_tensor, torch.t(assign_tensor))) / np.power(g.number_of_nodes(), 2)
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self.loss_log['LinkPredLoss'] = current_lp_loss
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for loss_layer in self.reg_loss:
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loss_name = str(type(loss_layer).__name__)
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self.loss_log[loss_name] = loss_layer(adj, adj_new, assign_tensor)
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return adj_new, h
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