Source code for cogdl.models.nn.gin

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

from .. import BaseModel
from cogdl.layers import MLP
from cogdl.layers import GINLayer
from cogdl.utils import split_dataset_general

[docs]class GIN(BaseModel): r"""Graph Isomorphism Network from paper `"How Powerful are Graph Neural Networks?" <>`__. Args: num_layers : int Number of GIN layers in_feats : int Size of each input sample out_feats : int Size of each output sample hidden_dim : int Size of each hidden layer dimension num_mlp_layers : int Number of MLP layers eps : float32, optional Initial `\epsilon` value, default: ``0`` pooling : str, optional Aggregator type to use, default: ``sum`` train_eps : bool, optional If True, `\epsilon` will be a learnable parameter, default: ``True`` """
[docs] @staticmethod def add_args(parser): parser.add_argument("--epsilon", type=float, default=0.0) parser.add_argument("--hidden-size", type=int, default=32) parser.add_argument("--num-layers", type=int, default=3) parser.add_argument("--num-mlp-layers", type=int, default=2) parser.add_argument("--dropout", type=float, default=0.5) parser.add_argument("--train-epsilon", dest="train_epsilon", action="store_false") parser.add_argument("--pooling", type=str, default="sum")
[docs] @classmethod def build_model_from_args(cls, args): return cls( args.num_layers, args.num_features, args.num_classes, args.hidden_size, args.num_mlp_layers, args.epsilon, args.pooling, args.train_epsilon, args.dropout, )
[docs] @classmethod def split_dataset(cls, dataset, args): return split_dataset_general(dataset, args)
def __init__( self, num_layers, in_feats, out_feats, hidden_dim, num_mlp_layers, eps=0, pooling="sum", train_eps=False, dropout=0.5, ): super(GIN, self).__init__() self.gin_layers = nn.ModuleList() self.batch_norm = nn.ModuleList() self.num_layers = num_layers for i in range(num_layers - 1): if i == 0: mlp = MLP(in_feats, hidden_dim, hidden_dim, num_mlp_layers, norm="batchnorm") else: mlp = MLP(hidden_dim, hidden_dim, hidden_dim, num_mlp_layers, norm="batchnorm") self.gin_layers.append(GINLayer(mlp, eps, train_eps)) self.batch_norm.append(nn.BatchNorm1d(hidden_dim)) self.linear_prediction = nn.ModuleList() for i in range(self.num_layers): if i == 0: self.linear_prediction.append(nn.Linear(in_feats, out_feats)) else: self.linear_prediction.append(nn.Linear(hidden_dim, out_feats)) self.dropout = nn.Dropout(dropout) self.criterion = torch.nn.CrossEntropyLoss()
[docs] def forward(self, batch): h = batch.x device = h.device batchsize = int(torch.max(batch.batch)) + 1 layer_rep = [h] for i in range(self.num_layers - 1): h = self.gin_layers[i](batch, h) h = self.batch_norm[i](h) h = F.relu(h) layer_rep.append(h) final_score = 0 for i in range(self.num_layers): hsize = layer_rep[i].shape[1] output = torch.zeros(batchsize, layer_rep[i].shape[1]).to(device) pooled = output.scatter_add_(dim=0, index=batch.batch.view(-1, 1).repeat(1, hsize), src=layer_rep[i]) final_score += self.dropout(self.linear_prediction[i](pooled)) return final_score