Source code for cogdl.models.nn.gcnii

import math

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

from cogdl.layers import GCNIILayer
from .. import BaseModel

[docs]class GCNII(BaseModel): """ Implementation of GCNII in paper `"Simple and Deep Graph Convolutional Networks" <>`_. Parameters ----------- in_feats : int Size of each input sample hidden_size : int Size of each hidden unit out_feats : int Size of each out sample num_layers : int dropout : float alpha : float Parameter of initial residual connection lmbda : float Parameter of identity mapping wd1 : float Weight-decay for Fully-connected layers wd2 : float Weight-decay for convolutional layers """
[docs] @staticmethod def add_args(parser): parser.add_argument("--hidden-size", type=int, default=64) parser.add_argument("--num-layers", type=int, default=64) parser.add_argument("--lambda", dest="lmbda", type=float, default=0.5) parser.add_argument("--alpha", type=float, default=0.1) parser.add_argument("--dropout", type=float, default=0.6) parser.add_argument("--wd1", type=float, default=5e-4) parser.add_argument("--wd2", type=float, default=5e-4) parser.add_argument("--residual", action="store_true")
[docs] @classmethod def build_model_from_args(cls, args): return cls( in_feats=args.num_features, hidden_size=args.hidden_size, out_feats=args.num_classes, num_layers=args.num_layers, dropout=args.dropout, alpha=args.alpha, lmbda=args.lmbda, wd1=args.wd1, wd2=args.wd2, residual=args.residual, actnn=args.actnn, )
def __init__( self, in_feats, hidden_size, out_feats, num_layers, dropout=0.5, alpha=0.1, lmbda=1, wd1=0.0, wd2=0.0, residual=False, actnn=False, ): super(GCNII, self).__init__() Layer = GCNIILayer Linear = nn.Linear Dropout = nn.Dropout ReLU = nn.ReLU(inplace=True) if actnn: try: from cogdl.layers.actgcnii_layer import ActGCNIILayer from actnn.layers import QLinear, QReLU, QDropout except Exception: print("Please install the actnn library first.") exit(1) Layer = ActGCNIILayer Linear = QLinear Dropout = QDropout ReLU = QReLU() self.fc_layers = nn.ModuleList() self.fc_layers.append(Linear(in_feats, hidden_size)) self.fc_layers.append(Linear(hidden_size, out_feats)) self.dropout = Dropout(dropout) self.activation = ReLU self.alpha = alpha self.lmbda = lmbda self.wd1 = wd1 self.wd2 = wd2 self.layers = nn.ModuleList( Layer(hidden_size, self.alpha, math.log(self.lmbda / (i + 1) + 1), residual) for i in range(num_layers) ) self.fc_parameters = list(self.fc_layers.parameters()) self.conv_parameters = list(self.layers.parameters())
[docs] def forward(self, graph): graph.sym_norm() x = graph.x init_h = self.dropout(x) init_h = self.activation(self.fc_layers[0](init_h)) h = init_h for layer in self.layers: h = self.dropout(h) h = layer(graph, h, init_h) h = self.activation(h) h = self.dropout(h) out = self.fc_layers[1](h) return out
[docs] def predict(self, graph): return self.forward(graph)
[docs] def get_optimizer(self, args): return torch.optim.Adam( [ {"params": self.fc_parameters, "weight_decay": self.wd1}, {"params": self.conv_parameters, "weight_decay": self.wd2}, ],, )