Source code for cogdl.models.nn.mlp

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

from .. import BaseModel, register_model
from cogdl.utils import get_activation
from import Graph

[docs]@register_model("mlp") class MLP(BaseModel): r"""Multilayer perception with normalization .. math:: x^{(i+1)} = \sigma(W^{i}x^{(i)}) Parameters ---------- in_feats : int Size of each input sample. out_feats : int Size of each output sample. hidden_dim : int Size of hidden layer dimension. use_bn : bool, optional Apply batch normalization if True, default: `True). """
[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=16) parser.add_argument("--num-layers", type=int, default=2) parser.add_argument("--dropout", type=float, default=0.5)
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls( args.num_features, args.num_classes, args.hidden_size, args.num_layers, args.dropout, )
def __init__(self, in_feats, out_feats, hidden_size, num_layers, dropout=0.0, activation="relu", norm=None): super(MLP, self).__init__() self.norm = norm self.activation = get_activation(activation) self.dropout = dropout shapes = [in_feats] + [hidden_size] * (num_layers - 1) + [out_feats] self.mlp = nn.ModuleList([nn.Linear(shapes[layer], shapes[layer + 1]) for layer in range(num_layers)]) if norm is not None and num_layers > 1: if norm == "layernorm": self.norm_list = nn.ModuleList(nn.LayerNorm(x) for x in shapes[1:-1]) elif norm == "batchnorm": self.norm_list = nn.ModuleList(nn.BatchNorm1d(x) for x in shapes[1:-1]) else: raise NotImplementedError(f"{norm} is not implemented in CogDL.")
[docs] def forward(self, x, *args, **kwargs): if isinstance(x, Graph): x = x.x for i, fc in enumerate(self.mlp[:-1]): x = fc(x) if self.norm: x = self.norm_list[i](x) x = self.activation(x) x = F.dropout(x, p=self.dropout, x = self.mlp[-1](x) return x
[docs] def predict(self, data): return self.forward(data.x)