import torch.nn as nn
import torch.nn.functional as F
from .. import BaseModel, register_model
from cogdl.utils import get_activation
from cogdl.data 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, training=self.training)
x = self.mlp[-1](x)
return x
[docs] def predict(self, data):
return self.forward(data.x)