Source code for torcheeg.models.gnn.dgcnn
import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
def __init__(self, in_channels: int, out_channels: int, bias: bool=False):
super(GraphConvolution, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.weight = nn.Parameter(torch.FloatTensor(in_channels, out_channels))
nn.init.xavier_normal_(self.weight)
self.bias = None
if bias:
self.bias = nn.Parameter(torch.FloatTensor(out_channels))
nn.init.zeros_(self.bias)
def forward(self, x: torch.Tensor, adj: torch.Tensor) -> torch.Tensor:
out = torch.matmul(adj, x)
out = torch.matmul(out, self.weight)
if self.bias is not None:
return out + self.bias
else:
return out
class Linear(nn.Module):
def __init__(self, in_channels: int, out_channels: int, bias: bool=True):
super(Linear, self).__init__()
self.linear = nn.Linear(in_channels, out_channels, bias=bias)
nn.init.xavier_normal_(self.linear.weight)
if bias:
nn.init.zeros_(self.linear.bias)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
return self.linear(inputs)
def normalize_A(A: torch.Tensor, symmetry: bool=False) -> torch.Tensor:
A = F.relu(A)
if symmetry:
A = A + torch.transpose(A, 0, 1)
d = torch.sum(A, 1)
d = 1 / torch.sqrt(d + 1e-10)
D = torch.diag_embed(d)
L = torch.matmul(torch.matmul(D, A), D)
else:
d = torch.sum(A, 1)
d = 1 / torch.sqrt(d + 1e-10)
D = torch.diag_embed(d)
L = torch.matmul(torch.matmul(D, A), D)
return L
def generate_cheby_adj(A: torch.Tensor, num_layers: int) -> torch.Tensor:
support = []
for i in range(num_layers):
if i == 0:
support.append(torch.eye(A.shape[1]).to(A.device))
elif i == 1:
support.append(A)
else:
temp = torch.matmul(support[-1], A)
support.append(temp)
return support
class Chebynet(nn.Module):
def __init__(self, in_channels: int, num_layers: int, out_channels: int):
super(Chebynet, self).__init__()
self.num_layers = num_layers
self.gc1 = nn.ModuleList()
for i in range(num_layers):
self.gc1.append(GraphConvolution(in_channels, out_channels))
def forward(self, x: torch.Tensor, L: torch.Tensor) -> torch.Tensor:
adj = generate_cheby_adj(L, self.num_layers)
for i in range(len(self.gc1)):
if i == 0:
result = self.gc1[i](x, adj[i])
else:
result = result+ self.gc1[i](x, adj[i])
result = F.relu(result)
return result
[docs]class DGCNN(nn.Module):
r'''
Dynamical Graph Convolutional Neural Networks (DGCNN). For more details, please refer to the following information.
- Paper: Song T, Zheng W, Song P, et al. EEG emotion recognition using dynamical graph convolutional neural networks[J]. IEEE Transactions on Affective Computing, 2018, 11(3): 532-541.
- URL: https://ieeexplore.ieee.org/abstract/document/8320798
- Related Project: https://github.com/xueyunlong12589/DGCNN
Below is a recommended suite for use in emotion recognition tasks:
.. code-block:: python
from torcheeg.models import DGCNN
from torcheeg.datasets import SEEDDataset
from torcheeg import transforms
dataset = SEEDDataset(root_path='./Preprocessed_EEG',
offline_transform=transforms.BandDifferentialEntropy(band_dict={
"delta": [1, 4],
"theta": [4, 8],
"alpha": [8, 14],
"beta": [14, 31],
"gamma": [31, 49]
}),
online_transform=transforms.Compose([
transforms.ToTensor()
]),
label_transform=transforms.Compose([
transforms.Select('emotion'),
transforms.Lambda(lambda x: x + 1)
]))
model = DGCNN(in_channels=5, num_electrodes=62, hid_channels=32, num_layers=2, 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:`5`)
num_electrodes (int): The number of electrodes. (default: :obj:`62`)
num_layers (int): The number of graph convolutional layers. (default: :obj:`2`)
hid_channels (int): The number of hidden nodes in the first fully connected layer. (default: :obj:`32`)
num_classes (int): The number of classes to predict. (default: :obj:`2`)
'''
def __init__(self,
in_channels: int = 5,
num_electrodes: int = 62,
num_layers: int = 2,
hid_channels: int = 32,
num_classes: int = 2):
super(DGCNN, self).__init__()
self.in_channels = in_channels
self.num_electrodes = num_electrodes
self.hid_channels = hid_channels
self.num_layers = num_layers
self.num_classes = num_classes
self.layer1 = Chebynet(in_channels, num_layers, hid_channels)
self.BN1 = nn.BatchNorm1d(in_channels)
self.fc1 = Linear(num_electrodes * hid_channels, 64)
self.fc2 = Linear(64, num_classes)
self.A = nn.Parameter(torch.FloatTensor(num_electrodes, num_electrodes))
nn.init.xavier_normal_(self.A)
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
r'''
Args:
x (torch.Tensor): EEG signal representation, the ideal input shape is :obj:`[n, 62, 5]`. Here, :obj:`n` corresponds to the batch size, :obj:`62` corresponds to :obj:`num_electrodes`, and :obj:`5` 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 = self.BN1(x.transpose(1, 2)).transpose(1, 2)
L = normalize_A(self.A)
result = self.layer1(x, L)
result = result.reshape(x.shape[0], -1)
result = F.relu(self.fc1(result))
result = self.fc2(result)
return result
