Source code for cogdl.models.nn.graphsage

from typing import Any
import random

import torch
import torch.nn as nn
import torch.nn.functional as F

from cogdl.layers import MeanAggregator, SumAggregator
from cogdl.trainers.sampled_trainer import NeighborSamplingTrainer

from .. import BaseModel, register_model
from import Data

def sage_sampler(adjlist, edge_index, num_sample):
    if adjlist == {}:
        edge_index = edge_index.t().cpu().tolist()
        for i in edge_index:
            if not (i[0] in adjlist):
                adjlist[i[0]] = [i[1]]

    sample_list = []
    for i in adjlist:
        list = [[i, j] for j in adjlist[i]]
        if len(list) > num_sample:
            list = random.sample(list, num_sample)

    edge_idx = torch.as_tensor(sample_list, dtype=torch.long).t()
    return edge_idx

class GraphSAGELayer(nn.Module):
    def __init__(self, in_feats, out_feats, normalize=False, aggr="mean"):
        super(GraphSAGELayer, self).__init__()
        self.in_feats = in_feats
        self.out_feats = out_feats
        self.normalize = normalize
        if aggr == "mean":
            self.aggr = MeanAggregator(in_feats, out_feats)
        elif aggr == "sum":
            self.aggr = SumAggregator(in_feats, out_feats)
            raise NotImplementedError

    def forward(self, x, edge_index, edge_weight=None):
        if edge_weight is None:
            edge_weight = torch.ones(edge_index.shape[1]).float().to(x.device)
        adj_sp = torch.sparse_coo_tensor(
            size=(x.shape[0], x.shape[0]),
        out = self.aggr(x, adj_sp)
        if self.normalize:
            out = F.normalize(out, p=2.0, dim=-1)
        return out

[docs]@register_model("graphsage") class Graphsage(BaseModel):
[docs] @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # fmt: off parser.add_argument("--hidden-size", type=int, nargs='+', default=[128]) parser.add_argument("--num-layers", type=int, default=2) parser.add_argument("--sample-size", type=int, nargs='+', default=[10, 10]) parser.add_argument("--dropout", type=float, default=0.5) parser.add_argument("--batch-size", type=int, default=128)
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls( args.num_features, args.num_classes, args.hidden_size, args.num_layers, args.sample_size, args.dropout, )
[docs] def sampling(self, edge_index, num_sample): return sage_sampler(self.adjlist, edge_index, num_sample)
def __init__(self, num_features, num_classes, hidden_size, num_layers, sample_size, dropout): super(Graphsage, self).__init__() assert num_layers == len(sample_size) self.adjlist = {} self.num_features = num_features self.num_classes = num_classes self.hidden_size = hidden_size self.num_layers = num_layers self.sample_size = sample_size self.dropout = dropout shapes = [num_features] + hidden_size + [num_classes] self.convs = nn.ModuleList([GraphSAGELayer(shapes[layer], shapes[layer + 1]) for layer in range(num_layers)])
[docs] def mini_forward(self, x, edge_index): for i in range(self.num_layers): edge_index_sp = self.sampling(edge_index, self.sample_size[i]).to(x.device) x = self.convs[i](x, edge_index_sp) if i != self.num_layers - 1: x = F.relu(x) x = F.dropout(x, p=self.dropout, return x
[docs] def mini_loss(self, data): return self.loss_fn( self.mini_forward(data.x, data.edge_index)[data.train_mask], data.y[data.train_mask], )
[docs] def predict(self, data): return self.forward(data.x, data.edge_index)
[docs] def forward(self, *args): assert len(args) == 2 if isinstance(args[1], torch.Tensor): return self.mini_forward(*args) else: x, adjs = args for i, (src_id, edge_index, size) in enumerate(adjs): edge_index = output = self.convs[i](x, edge_index) x = output[0 : size[1]] if i != self.num_layers - 1: x = F.relu(x) x = F.dropout(x, p=self.dropout, return x
[docs] def node_classification_loss(self, *args): if isinstance(args[0], Data): return self.mini_loss(*args) else: x, adjs, y = args pred = self.forward(x, adjs) return self.loss_fn(pred, y)
[docs] def inference(self, x_all, data_loader): for i in range(len(self.convs)): output = [] for src_id, edge_index, size in data_loader: x = x_all[src_id].to(self.device) edge_index = x = self.convs[i](x, edge_index) x = x[: size[1]] if i != self.num_layers - 1: x = F.relu(x) output.append(x.cpu()) x_all =, dim=0) return x_all
[docs] @staticmethod def get_trainer(task: Any, args: Any): if args.dataset not in ["cora", "citeseer"]: return NeighborSamplingTrainer if hasattr(args, "use_trainer"): return NeighborSamplingTrainer
[docs] def set_data_device(self, device): self.device = device