Source code for cogdl.models.nn.pyg_cheb

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn.conv import ChebConv

from .. import BaseModel, register_model

[docs]@register_model("chebyshev") class Chebyshev(BaseModel):
[docs] @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # fmt: off parser.add_argument("--num-features", type=int) parser.add_argument("--num-classes", type=int) parser.add_argument("--hidden-size", type=int, default=64) parser.add_argument("--num-layers", type=int, default=2) parser.add_argument("--dropout", type=float, default=0.5) parser.add_argument("--filter-size", type=int, default=5)
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls( args.num_features, args.hidden_size, args.num_classes, args.num_layers, args.dropout, args.filter_size, )
def __init__(self, in_feats, hidden_size, out_feats, num_layers, dropout, filter_size): super(Chebyshev, self).__init__() self.num_features = in_feats self.num_classes = out_feats self.hidden_size = hidden_size self.num_layers = num_layers self.dropout = dropout self.filter_size = filter_size shapes = [in_feats] + [hidden_size] * (num_layers - 1) + [out_feats] self.convs = nn.ModuleList( [ChebConv(shapes[layer], shapes[layer + 1], filter_size) for layer in range(num_layers)] )
[docs] def forward(self, x, edge_index): for conv in self.convs[:-1]: x = F.relu(conv(x, edge_index)) x = F.dropout(x, p=self.dropout, x = self.convs[-1](x, edge_index) return x
[docs] def predict(self, data): return self.forward(data.x, data.edge_index)