dmlc--dgl
hbsun2113
8c750170e8
* simple implemention of GraphSAGE * update the abstract method and `torch.nn` modules are more utilized
256 行
8.5 KiB
Python
256 行
8.5 KiB
Python
"""
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Inductive Representation Learning on Large Graphs
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Paper: http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf
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Code: https://github.com/williamleif/graphsage-simple
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Simple reference implementation of GraphSAGE.
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"""
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import argparse
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import time
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import abc
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import numpy as np
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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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from dgl import DGLGraph
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from dgl.data import register_data_args, load_data
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import dgl.function as fn
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class Aggregator(nn.Module):
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def __init__(self, g, in_feats, out_feats, activation=None, bias=True):
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super(Aggregator, self).__init__()
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self.g = g
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self.linear = nn.Linear(in_feats, out_feats, bias=bias) # (F, EF) or (2F, EF)
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self.activation = activation
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nn.init.xavier_uniform_(self.linear.weight, gain=nn.init.calculate_gain('relu'))
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def forward(self, node):
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nei = node.mailbox['m'] # (B, N, F)
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h = node.data['h'] # (B, F)
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h = self.concat(h, nei, node) # (B, F) or (B, 2F)
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h = self.linear(h) # (B, EF)
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if self.activation:
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h = self.activation(h)
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norm = torch.pow(h, 2)
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norm = torch.sum(norm, 1, keepdim=True)
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norm = torch.pow(norm, -0.5)
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norm[torch.isinf(norm)] = 0
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# h = h * norm
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return {'h': h}
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@abc.abstractmethod
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def concat(self, h, nei, nodes):
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raise NotImplementedError
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class MeanAggregator(Aggregator):
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def __init__(self, g, in_feats, out_feats, activation, bias):
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super(MeanAggregator, self).__init__(g, in_feats, out_feats, activation, bias)
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def concat(self, h, nei, nodes):
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degs = self.g.in_degrees(nodes.nodes()).float()
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if h.is_cuda:
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degs = degs.cuda(h.device)
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concatenate = torch.cat((nei, h.unsqueeze(1)), 1)
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concatenate = torch.sum(concatenate, 1) / degs.unsqueeze(1)
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return concatenate # (B, F)
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class PoolingAggregator(Aggregator):
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def __init__(self, g, in_feats, out_feats, activation, bias): # (2F, F)
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super(PoolingAggregator, self).__init__(g, in_feats*2, out_feats, activation, bias)
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self.mlp = PoolingAggregator.MLP(in_feats, in_feats, F.relu, False, True)
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def concat(self, h, nei, nodes):
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nei = self.mlp(nei) # (B, F)
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concatenate = torch.cat((nei, h), 1) # (B, 2F)
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return concatenate
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class MLP(nn.Module):
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def __init__(self, in_feats, out_feats, activation, dropout, bias): # (F, F)
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super(PoolingAggregator.MLP, self).__init__()
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self.linear = nn.Linear(in_feats, out_feats, bias=bias) # (F, F)
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self.dropout = nn.Dropout(p=dropout)
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self.activation = activation
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nn.init.xavier_uniform_(self.linear.weight, gain=nn.init.calculate_gain('relu'))
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def forward(self, nei):
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nei = self.dropout(nei) # (B, N, F)
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nei = self.linear(nei)
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if self.activation:
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nei = self.activation(nei)
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max_value = torch.max(nei, dim=1)[0] # (B, F)
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return max_value
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class GraphSAGELayer(nn.Module):
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def __init__(self,
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g,
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in_feats,
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out_feats,
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activation,
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dropout,
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aggregator_type,
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bias=True,
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):
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super(GraphSAGELayer, self).__init__()
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self.g = g
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self.dropout = nn.Dropout(p=dropout)
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if aggregator_type == "pooling":
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self.aggregator = PoolingAggregator(g, in_feats, out_feats, activation, bias)
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else:
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self.aggregator = MeanAggregator(g, in_feats, out_feats, activation, bias)
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def forward(self, h):
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h = self.dropout(h)
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self.g.ndata['h'] = h
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self.g.update_all(fn.copy_src(src='h', out='m'), self.aggregator)
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h = self.g.ndata.pop('h')
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return h
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class GraphSAGE(nn.Module):
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def __init__(self,
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g,
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in_feats,
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n_hidden,
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n_classes,
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n_layers,
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activation,
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dropout,
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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(g, in_feats, n_hidden, activation, dropout, aggregator_type))
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# hidden layers
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for i in range(n_layers - 1):
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self.layers.append(GraphSAGELayer(g, n_hidden, n_hidden, activation, dropout, aggregator_type))
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# output layer
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self.layers.append(GraphSAGELayer(g, n_hidden, n_classes, None, dropout, aggregator_type))
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def forward(self, features):
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h = features
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for layer in self.layers:
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h = layer(h)
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return h
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def evaluate(model, features, labels, mask):
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model.eval()
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with torch.no_grad():
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logits = model(features)
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logits = logits[mask]
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labels = labels[mask]
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_, indices = torch.max(logits, dim=1)
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correct = torch.sum(indices == labels)
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return correct.item() * 1.0 / len(labels)
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def main(args):
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# load and preprocess dataset
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data = load_data(args)
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features = torch.FloatTensor(data.features)
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labels = torch.LongTensor(data.labels)
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train_mask = torch.ByteTensor(data.train_mask)
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val_mask = torch.ByteTensor(data.val_mask)
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test_mask = torch.ByteTensor(data.test_mask)
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in_feats = features.shape[1]
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n_classes = data.num_labels
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n_edges = data.graph.number_of_edges()
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print("""----Data statistics------'
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#Edges %d
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#Classes %d
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#Train samples %d
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#Val samples %d
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#Test samples %d""" %
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(n_edges, n_classes,
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train_mask.sum().item(),
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val_mask.sum().item(),
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test_mask.sum().item()))
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if args.gpu < 0:
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cuda = False
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else:
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cuda = True
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torch.cuda.set_device(args.gpu)
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features = features.cuda()
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labels = labels.cuda()
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train_mask = train_mask.cuda()
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val_mask = val_mask.cuda()
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test_mask = test_mask.cuda()
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print("use cuda:", args.gpu)
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# graph preprocess and calculate normalization factor
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g = DGLGraph(data.graph)
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n_edges = g.number_of_edges()
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# create GraphSAGE model
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model = GraphSAGE(g,
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in_feats,
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args.n_hidden,
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n_classes,
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args.n_layers,
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F.relu,
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args.dropout,
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args.aggregator_type
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)
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if cuda:
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model.cuda()
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loss_fcn = torch.nn.CrossEntropyLoss()
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# use optimizer
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optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
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# initialize graph
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dur = []
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for epoch in range(args.n_epochs):
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model.train()
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if epoch >= 3:
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t0 = time.time()
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# forward
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logits = model(features)
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loss = loss_fcn(logits[train_mask], labels[train_mask])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if epoch >= 3:
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dur.append(time.time() - t0)
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acc = evaluate(model, features, labels, val_mask)
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print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
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"ETputs(KTEPS) {:.2f}".format(epoch, np.mean(dur), loss.item(),
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acc, n_edges / np.mean(dur) / 1000))
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print()
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acc = evaluate(model, features, labels, test_mask)
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print("Test Accuracy {:.4f}".format(acc))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='GCN')
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register_data_args(parser)
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parser.add_argument("--dropout", type=float, default=0.5,
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help="dropout probability")
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parser.add_argument("--gpu", type=int, default=-1,
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help="gpu")
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parser.add_argument("--lr", type=float, default=1e-2,
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help="learning rate")
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parser.add_argument("--n-epochs", type=int, default=200,
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help="number of training epochs")
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parser.add_argument("--n-hidden", type=int, default=16,
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help="number of hidden gcn units")
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parser.add_argument("--n-layers", type=int, default=1,
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help="number of hidden gcn layers")
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parser.add_argument("--weight-decay", type=float, default=5e-4,
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help="Weight for L2 loss")
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parser.add_argument("--aggregator-type", type=str, default="mean",
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help="Weight for L2 loss")
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args = parser.parse_args()
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print(args)
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main(args)
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