Source code for cogdl.models.nn.han

import torch.nn as nn

from cogdl.layers import HANLayer

from .. import BaseModel

[docs]class HAN(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("--num-nodes", type=int) parser.add_argument("--hidden-size", type=int, default=64) parser.add_argument("--num-layers", type=int, default=2) parser.add_argument("--num-edge", type=int, default=2)
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls( args.num_edge, args.num_features, args.hidden_size, args.num_classes, args.num_nodes, args.num_layers, )
def __init__(self, num_edge, w_in, w_out, num_class, num_nodes, num_layers): super(HAN, self).__init__() self.num_edge = num_edge self.num_nodes = num_nodes self.w_in = w_in self.w_out = w_out self.num_class = num_class self.num_layers = num_layers layers = [] for i in range(num_layers): if i == 0: layers.append(HANLayer(num_edge, w_in, w_out)) else: layers.append(HANLayer(num_edge, w_out, w_out)) self.layers = nn.ModuleList(layers) self.cross_entropy_loss = nn.CrossEntropyLoss() self.linear = nn.Linear(self.w_out, self.num_class)
[docs] def forward(self, graph): X = graph.x for i in range(self.num_layers): X = self.layers[i](graph, X) out = self.linear(X) return out