import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear, ReLU, Sequential, LeakyReLU, Tanh, ELU
from dgl.nn.pytorch.conv import GINConv
from dgl.nn.pytorch.glob import SumPooling, AvgPooling, MaxPooling
from torch.nn import BatchNorm1d
from . import register_model
from .base import BaseAutoModel, activate_func
from copy import deepcopy
from ....utils import get_logger
LOGGER = get_logger("GINModel")
def set_default(args, d):
for k, v in d.items():
if k not in args:
args[k] = v
return args
class ApplyNodeFunc(nn.Module):
"""Update the node feature hv with MLP, BN and ReLU."""
def __init__(self, mlp):
super(ApplyNodeFunc, self).__init__()
self.mlp = mlp
self.bn = nn.BatchNorm1d(self.mlp.output_dim)
def forward(self, h):
h = self.mlp(h)
h = self.bn(h)
h = F.relu(h)
return h
class MLP(nn.Module):
"""MLP with linear output"""
def __init__(self, num_layers, input_dim, hidden_dim, output_dim):
"""MLP layers construction
Paramters
---------
num_layers: int
The number of linear layers
input_dim: int
The dimensionality of input features
hidden_dim: int
The dimensionality of hidden units at ALL layers
output_dim: int
The number of classes for prediction
"""
super(MLP, self).__init__()
self.linear_or_not = True # default is linear model
self.num_layers = num_layers
self.output_dim = output_dim
if num_layers < 1:
raise ValueError("number of layers should be positive!")
elif num_layers == 1:
# Linear model
self.linear = nn.Linear(input_dim, output_dim)
else:
# Multi-layer model
self.linear_or_not = False
self.linears = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
self.linears.append(nn.Linear(input_dim, hidden_dim))
for layer in range(num_layers - 2):
self.linears.append(nn.Linear(hidden_dim, hidden_dim))
self.linears.append(nn.Linear(hidden_dim, output_dim))
for layer in range(num_layers - 1):
self.batch_norms.append(nn.BatchNorm1d((hidden_dim)))
def forward(self, x):
if self.linear_or_not:
# If linear model
return self.linear(x)
else:
# If MLP
h = x
for i in range(self.num_layers - 1):
h = F.relu(self.batch_norms[i](self.linears[i](h)))
return self.linears[-1](h)
class GIN(torch.nn.Module):
"""GIN model"""
def __init__(self, args):
"""model parameters setting
Paramters
---------
num_layers: int
The number of linear layers in the neural network
num_mlp_layers: int
The number of linear layers in mlps
input_dim: int
The dimensionality of input features
hidden_dim: int
The dimensionality of hidden units at ALL layers
output_dim: int
The number of classes for prediction
final_dropout: float
dropout ratio on the final linear layer
eps: boolean
If True, learn epsilon to distinguish center nodes from neighbors
If False, aggregate neighbors and center nodes altogether.
neighbor_pooling_type: str
how to aggregate neighbors (sum, mean, or max)
graph_pooling_type: str
how to aggregate entire nodes in a graph (sum, mean or max)
"""
super(GIN, self).__init__()
self.args = args
missing_keys = list(
set(
[
"features_num",
"num_class",
"num_graph_features",
"num_layers",
"hidden",
"dropout",
"act",
"mlp_layers",
"eps",
]
)
- set(self.args.keys())
)
if len(missing_keys) > 0:
raise Exception("Missing keys: %s." % ",".join(missing_keys))
self.num_graph_features = self.args["num_graph_features"]
self.num_layers = self.args["num_layers"]
assert self.num_layers > 2, "Number of layers in GIN should not less than 3"
if not self.num_layers == len(self.args["hidden"]) + 1:
LOGGER.warn("Warning: layer size does not match the length of hidden units")
self.eps = True if self.args["eps"]=="True" else False
self.num_mlp_layers = self.args["mlp_layers"]
input_dim = self.args["features_num"]
hidden = self.args["hidden"]
neighbor_pooling_type = self.args["neighbor_pooling_type"]
graph_pooling_type = self.args["graph_pooling_type"]
if self.args["act"] == "leaky_relu":
act = LeakyReLU()
elif self.args["act"] == "relu":
act = ReLU()
elif self.args["act"] == "elu":
act = ELU()
elif self.args["act"] == "tanh":
act = Tanh()
else:
act = ReLU()
final_dropout = self.args["dropout"]
output_dim = self.args["num_class"]
# List of MLPs
self.ginlayers = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
for layer in range(self.num_layers - 1):
if layer == 0:
mlp = MLP(self.num_mlp_layers, input_dim, hidden[layer], hidden[layer])
else:
mlp = MLP(self.num_mlp_layers, hidden[layer-1], hidden[layer], hidden[layer])
self.ginlayers.append(
GINConv(ApplyNodeFunc(mlp), neighbor_pooling_type, 0, self.eps))
self.batch_norms.append(nn.BatchNorm1d(hidden[layer]))
# Linear function for graph poolings of output of each layer
# which maps the output of different layers into a prediction score
self.linears_prediction = torch.nn.ModuleList()
for layer in range(self.num_layers):
if layer == 0:
self.linears_prediction.append(
nn.Linear(input_dim, output_dim))
else:
self.linears_prediction.append(
nn.Linear(hidden[layer-1], output_dim))
self.drop = nn.Dropout(final_dropout)
if graph_pooling_type == 'sum':
self.pool = SumPooling()
elif graph_pooling_type == 'mean':
self.pool = AvgPooling()
elif graph_pooling_type == 'max':
self.pool = MaxPooling()
else:
raise NotImplementedError
def forward(self, data):
x = data.ndata.pop('feat')
if self.num_graph_features > 0:
graph_feature = data.gf
# list of hidden representation at each layer (including input)
hidden_rep = [x]
for i in range(self.num_layers - 1):
x = self.ginlayers[i](data, x)
x = self.batch_norms[i](x)
x = activate_func(x, self.args["act"])
hidden_rep.append(x)
score_over_layer = 0
# perform pooling over all nodes in each graph in every layer
for i, h in enumerate(hidden_rep):
pooled_h = self.pool(data, h)
score_over_layer += self.drop(self.linears_prediction[i](pooled_h))
return score_over_layer
[docs]@register_model("gin-model")
class AutoGIN(BaseAutoModel):
r"""
AutoGIN. The model used in this automodel is GIN, i.e., the graph isomorphism network from the `"How Powerful are
Graph Neural Networks?" <https://arxiv.org/abs/1810.00826>`_ paper. The layer is
.. math::
\mathbf{x}^{\prime}_i = h_{\mathbf{\Theta}} \left( (1 + \epsilon) \cdot
\mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j \right)
or
.. math::
\mathbf{X}^{\prime} = h_{\mathbf{\Theta}} \left( \left( \mathbf{A} +
(1 + \epsilon) \cdot \mathbf{I} \right) \cdot \mathbf{X} \right),
here :math:`h_{\mathbf{\Theta}}` denotes a neural network, *.i.e.* an MLP.
Parameters
----------
num_features: `int`.
The dimension of features.
num_classes: `int`.
The number of classes.
device: `torch.device` or `str`
The device where model will be running on.
init: `bool`.
If True(False), the model will (not) be initialized.
"""
def __init__(
self,
num_features=None,
num_classes=None,
device=None,
num_graph_features=0,
**args
):
super().__init__(num_features, num_classes, device, num_graph_features=num_graph_features, **args)
self.num_graph_features = num_graph_features
self.hyper_parameter_space = [
{
"parameterName": "num_layers",
"type": "DISCRETE",
"feasiblePoints": "4,5,6",
},
{
"parameterName": "hidden",
"type": "NUMERICAL_LIST",
"numericalType": "INTEGER",
"length": 5,
"minValue": [8, 8, 8, 8, 8],
"maxValue": [64, 64, 64, 64, 64],
"scalingType": "LOG",
"cutPara": ("num_layers",),
"cutFunc": lambda x: x[0] - 1,
},
{
"parameterName": "dropout",
"type": "DOUBLE",
"maxValue": 0.9,
"minValue": 0.1,
"scalingType": "LINEAR",
},
{
"parameterName": "act",
"type": "CATEGORICAL",
"feasiblePoints": ["leaky_relu", "relu", "elu", "tanh"],
},
{
"parameterName": "eps",
"type": "CATEGORICAL",
"feasiblePoints": [True, False],
},
{
"parameterName": "mlp_layers",
"type": "DISCRETE",
"feasiblePoints": "2,3,4",
},
{
"parameterName": "neighbor_pooling_type",
"type": "CATEGORICAL",
"feasiblePoints": ["sum", "mean", "max"],
},
{
"parameterName": "graph_pooling_type",
"type": "CATEGORICAL",
"feasiblePoints": ["sum", "mean", "max"],
},
]
self.hyper_parameters = {
"num_layers": 5,
"hidden": [64,64,64,64],
"dropout": 0.5,
"act": "relu",
"eps": "False",
"mlp_layers": 2,
"neighbor_pooling_type": "sum",
"graph_pooling_type": "sum"
}
def from_hyper_parameter(self, hp, **kwargs):
return super().from_hyper_parameter(hp, num_graph_features=self.num_graph_features, **kwargs)
def _initialize(self):
# """Initialize model."""
self._model = GIN({
"features_num": self.input_dimension,
"num_class": self.output_dimension,
"num_graph_features": self.num_graph_features,
**self.hyper_parameters
}).to(self.device)