Source code for torcheeg.models.pyg.gin
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GINConv
from torch_geometric.data import Batch
[docs]class GIN(nn.Module):
r'''
A simple but effective graph isomorphism network (GIN) structure from the book of Zhang et al. For more details, please refer to the following information.
- Book: Zhang X, Yao L. Deep Learning for EEG-Based Brain-Computer Interfaces: Representations, Algorithms and Applications[M]. 2021.
- URL: https://www.worldscientific.com/worldscibooks/10.1142/q0282#t=aboutBook
- Related Project: https://github.com/xiangzhang1015/Deep-Learning-for-BCI/blob/master/pythonscripts/4-3_GIN.py
Below is a recommended suite for use in emotion recognition tasks:
.. code-block:: python
from torcheeg.datasets import DEAPDataset
from torcheeg import transforms
from torcheeg.transforms.pyg import ToG
from torcheeg.datasets.constants import SEED_STANDARD_ADJACENCY_MATRIX
from torcheeg.models import GIN
from torch_geometric.data import DataLoader
dataset = DEAPDataset(root_path='./data_preprocessed_python',
offline_transform=transforms.BandDifferentialEntropy(),
online_transform=ToG(SEED_STANDARD_ADJACENCY_MATRIX),
label_transform=transforms.Compose([
transforms.Select('valence'),
transforms.Binary(5.0),
]))
model = GIN(in_channels=4, hid_channels=64, num_classes=2)
x, y = next(iter(DataLoader(dataset, batch_size=64)))
model(x)
Args:
in_channels (int): The feature dimension of each electrode. (default: :obj:`4`)
hid_channels (int): The number of hidden nodes in the GRU layers and the fully connected layer. (default: :obj:`64`)
num_classes (int): The number of classes to predict. (default: :obj:`2`)
'''
def __init__(self,
in_channels: int = 4,
hid_channels: int = 64,
num_classes: int = 3):
super(GIN, self).__init__()
self.in_channels = in_channels
self.hid_channels = hid_channels
self.num_classes = num_classes
nn1 = nn.Sequential(nn.Linear(in_channels, hid_channels), nn.ReLU(),
nn.Linear(hid_channels, hid_channels))
self.conv1 = GINConv(nn1)
self.bn1 = nn.BatchNorm1d(hid_channels)
nn2 = nn.Sequential(nn.Linear(hid_channels, hid_channels), nn.ReLU(),
nn.Linear(hid_channels, hid_channels))
self.conv2 = GINConv(nn2)
self.bn2 = nn.BatchNorm1d(hid_channels)
nn3 = nn.Sequential(nn.Linear(hid_channels, hid_channels), nn.ReLU(),
nn.Linear(hid_channels, hid_channels))
self.conv3 = GINConv(nn3)
self.bn3 = nn.BatchNorm1d(hid_channels)
self.fc1 = nn.Linear(hid_channels, hid_channels)
self.fc2 = nn.Linear(hid_channels, num_classes)
[docs] def forward(self, data: Batch) -> torch.Tensor:
r'''
Args:
data (torch_geometric.data.Batch): EEG signal representation, the ideal input shape of data.x is :obj:`[n, 62, 4]`. Here, :obj:`n` corresponds to the batch size, :obj:`62` corresponds to the number of electrodes, and :obj:`4` corresponds to :obj:`in_channels`.
Returns:
torch.Tensor[number of sample, number of classes]: the predicted probability that the samples belong to the classes.
'''
x, edge_index, batch = data.x, data.edge_index, data.num_graphs
x = x.reshape([-1, self.in_channels])
x = F.relu(self.conv1(x, edge_index))
x = self.bn1(x)
x = F.relu(self.conv2(x, edge_index))
x = self.bn2(x)
x = F.relu(self.conv3(x, edge_index))
x = self.bn3(x)
x = x.view(batch, -1, self.hid_channels)
x = x.sum(dim=1)
x = F.dropout(x, p=0.3, training=self.training)
x = self.fc2(x)
return x