Source code for cogdl.models.nn.dgi

import numpy as np
import torch
import torch.nn as nn

from .. import BaseModel, register_model
from cogdl.utils import get_activation, spmm
from cogdl.trainers.self_supervised_trainer import SelfSupervisedTrainer
from cogdl.data.sampler import NeighborSampler
from cogdl.models.nn.graphsage import Graphsage
from cogdl.utils.evaluator import cross_entropy_loss


# Borrowed from https://github.com/PetarV-/DGI
class GCN(nn.Module):
    def __init__(self, in_ft, out_ft, act, bias=True):
        super(GCN, self).__init__()
        self.fc = nn.Linear(in_ft, out_ft, bias=False)
        self.act = nn.PReLU() if act == "prelu" else get_activation(act)

        if bias:
            self.bias = nn.Parameter(torch.FloatTensor(out_ft))
            self.bias.data.fill_(0.0)
        else:
            self.register_parameter("bias", None)

        for m in self.modules():
            self.weights_init(m)

    def weights_init(self, m):
        if isinstance(m, nn.Linear):
            torch.nn.init.xavier_uniform_(m.weight.data)
            if m.bias is not None:
                m.bias.data.fill_(0.0)

    # Shape of seq: (batch, nodes, features)
    def forward(self, graph, seq, sparse=False):
        seq_fts = self.fc(seq)
        if len(seq_fts.shape) > 2:
            if sparse:
                out = torch.unsqueeze(spmm(graph, torch.squeeze(seq_fts, 0)), 0)
            else:
                out = torch.bmm(graph, seq_fts)
        else:
            if sparse:
                out = spmm(graph, torch.squeeze(seq_fts, 0))
            else:
                out = torch.mm(graph, seq_fts)
        if self.bias is not None:
            out += self.bias

        return self.act(out)


# Borrowed from https://github.com/PetarV-/DGI
class AvgReadout(nn.Module):
    def __init__(self):
        super(AvgReadout, self).__init__()

    def forward(self, seq, msk):
        dim = len(seq.shape) - 2
        if msk is None:
            return torch.mean(seq, dim)
        else:
            return torch.sum(seq * msk, dim) / torch.sum(msk)


# Borrowed from https://github.com/PetarV-/DGI
class Discriminator(nn.Module):
    def __init__(self, n_h):
        super(Discriminator, self).__init__()
        self.f_k = nn.Bilinear(n_h, n_h, 1)

        for m in self.modules():
            self.weights_init(m)

    def weights_init(self, m):
        if isinstance(m, nn.Bilinear):
            torch.nn.init.xavier_uniform_(m.weight.data)
            if m.bias is not None:
                m.bias.data.fill_(0.0)

    def forward(self, c, h_pl, h_mi, s_bias1=None, s_bias2=None):
        c_x = torch.unsqueeze(c, 0)
        c_x = c_x.expand_as(h_pl)

        sc_1 = torch.squeeze(self.f_k(h_pl, c_x), 1)
        sc_2 = torch.squeeze(self.f_k(h_mi, c_x), 1)

        if s_bias1 is not None:
            sc_1 += s_bias1
        if s_bias2 is not None:
            sc_2 += s_bias2

        logits = torch.cat((sc_1, sc_2))

        return logits


[docs]@register_model("dgi") class DGIModel(BaseModel):
[docs] @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # fmt: off parser.add_argument("--hidden-size", type=int, default=512) parser.add_argument("--max-epoch", type=int, default=1000) parser.add_argument("--activation", type=str, default="prelu") parser.add_argument("--patience", type=int, default=20)
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls(args.num_features, args.hidden_size, args.activation)
def __init__(self, in_feats, hidden_size, activation): super(DGIModel, self).__init__() self.gcn = GCN(in_feats, hidden_size, activation) self.read = AvgReadout() self.sigm = nn.Sigmoid() self.disc = Discriminator(hidden_size) self.loss_f = nn.BCEWithLogitsLoss() self.cache = None self.sparse = True def _forward(self, graph, seq1, seq2, sparse, msk): h_1 = self.gcn(graph, seq1, sparse) c = self.read(h_1, msk) c = self.sigm(c) h_2 = self.gcn(graph, seq2, sparse) ret = self.disc(c, h_1, h_2) return ret
[docs] def forward(self, graph): graph.sym_norm() x = graph.x idx = np.random.permutation(graph.num_nodes) shuf_fts = x[idx, :] logits = self._forward(graph, x, shuf_fts, True, None) return logits
[docs] def loss(self, data): if self.cache is None: num_nodes = data.num_nodes lbl_1 = torch.ones(1, num_nodes) lbl_2 = torch.zeros(1, num_nodes) self.cache = {"labels": torch.cat((lbl_1, lbl_2), 1).to(data.x.device)} labels = self.cache["labels"].to(data.x.device) logits = self.forward(data) logits = logits.unsqueeze(0) loss = self.loss_f(logits, labels) return loss
[docs] def node_classification_loss(self, data): return self.loss(data)
# Detach the return variables
[docs] def embed(self, data, msk=None): h_1 = self.gcn(data, data.x, self.sparse) # c = self.read(h_1, msk) return h_1.detach() # , c.detach()
[docs] @staticmethod def get_trainer(task, args): return SelfSupervisedTrainer
""" @register_model("dgi_sampling") class DGISamplingModel(BaseModel): @staticmethod def add_args(parser): # fmt: off parser.add_argument("--hidden-size", type=int, default=512) parser.add_argument("--max-epoch", type=int, default=1000) parser.add_argument("--num-layers", type=int, default=3) parser.add_argument("--patience", type=int, default=20) parser.add_argument('--sample-size', type=int, nargs='+', default=[10, 10, 25]) parser.add_argument('--batch-size', type=int, default=256) # fmt: on @classmethod def build_model_from_args(cls, args): return cls(args.num_features, args.hidden_size, args.num_layers, args.sample_size, args.batch_size, args.device_id) def __init__(self, in_feats, hidden_size, num_layers, sample_size, batch_size, device): super(DGISamplingModel, self).__init__() self.gcn = Graphsage(in_feats, 7, [hidden_size] * (num_layers - 1), num_layers, sample_size, 0.3) self.device = "cpu" if device is None else device[0] self.gcn.set_data_device(self.device) self.read = AvgReadout() self.sigm = nn.Sigmoid() self.disc = Discriminator(hidden_size) self.loss_f = nn.BCEWithLogitsLoss() self.cache = None self.sparse = True self.sample_size = sample_size self.batch_size = batch_size self.train_loader = None self.test_loader = None def forward(self, data): data = data.apply(lambda x: x.cpu()) if self.train_loader is None: self.train_loader = NeighborSampler( data=data, mask=None, sizes=self.sample_size, batch_size=self.batch_size, num_workers=4, shuffle=True, ) self.test_loader = NeighborSampler( data=data, mask=None, sizes=[-1], batch_size=self.batch_size, shuffle=False, ) for target_id, n_id, adjs in self.train_loader: x = data.x[n_id].to(self.device) return target_id, self.gcn(x, adjs) idx = np.random.permutation(x.shape[0]) shuf_fts = x[idx, :] h_1 = self.gcn(x, adjs) c = self.read(h_1, None) c = self.sigm(c) h_2 = self.gcn(shuf_fts, adjs) ret = self.disc(c, h_1, h_2) print(ret) return ret def loss(self, data): if self.cache is None: lbl_1 = torch.ones(1, self.batch_size) lbl_2 = torch.zeros(1, self.batch_size) self.cache = {"labels": torch.cat((lbl_1, lbl_2), 1).to(self.device)} labels = self.cache["labels"].to(self.device) target_id, logits = self.forward(data) return cross_entropy_loss(logits, data.y[target_id].to(self.device)) logits = self.forward(data) logits = logits.unsqueeze(0) loss = self.loss_f(logits, labels) return loss def node_classification_loss(self, data): return self.loss(data) # Detach the return variables def embed(self, data, msk=None): logits = self.gcn.inference(data.x, self.test_loader) return logits.detach() @staticmethod def get_trainer(task, args): return SelfSupervisedTrainer """