Source code for torcheeg.models.gan.bgan
from typing import Tuple
import torch
import torch.nn as nn
[docs]class BGenerator(nn.Module):
r'''
TorchEEG provides an EEG feature generator based on CNN architecture and GAN for generating EEG grid representations of different frequency bands based on a given class label.
.. code-block:: python
import torch
from torcheeg.models.gan.bgan import BGenerator
g_model = BGenerator(in_channels=128)
z = torch.normal(mean=0, std=1, size=(1, 128))
fake_X = g_model(z)
Args:
in_channels (int): The input feature dimension (of noise vectors). (default: :obj:`128`)
out_channels (int): The generated feature dimension of each electrode. (default: :obj:`4`)
grid_size (tuple): Spatial dimensions of grid-like EEG representation. (default: :obj:`(9, 9)`)
'''
def __init__(self,
in_channels: int = 128,
out_channels: int = 4,
grid_size: Tuple[int, int] = (9, 9)):
super(BGenerator, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.grid_size = grid_size
self.deproj = nn.Sequential(
nn.Linear(in_channels, in_channels * 4 * 3 * 3), nn.LeakyReLU())
self.deconv1 = nn.Sequential(
nn.ConvTranspose2d(in_channels * 4,
in_channels * 2,
kernel_size=3,
stride=2,
padding=1,
bias=True), nn.BatchNorm2d(in_channels * 2),
nn.LeakyReLU())
self.deconv2 = nn.Sequential(
nn.ConvTranspose2d(in_channels * 2,
in_channels * 2,
kernel_size=3,
stride=1,
padding=1,
bias=True), nn.BatchNorm2d(in_channels * 2),
nn.LeakyReLU())
self.deconv3 = nn.Sequential(
nn.ConvTranspose2d(in_channels * 2,
in_channels,
kernel_size=3,
stride=2,
padding=1,
bias=True), nn.BatchNorm2d(in_channels),
nn.LeakyReLU())
self.deconv4 = nn.ConvTranspose2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True)
[docs] def forward(self, x: torch.Tensor):
r'''
Args:
x (torch.Tensor): a random vector, the ideal input shape is :obj:`[n, 128]`. Here, :obj:`n` corresponds to the batch size, and :obj:`128` corresponds to :obj:`in_channels`.
Returns:
torch.Tensor[n, 4, 9, 9]: the generated fake EEG signals. Here, :obj:`4` corresponds to the :obj:`out_channels`, and :obj:`(9, 9)` corresponds to the :obj:`grid_size`.
'''
x = self.deproj(x)
x = x.view(-1, self.in_channels * 4, 3, 3)
x = self.deconv1(x)
x = self.deconv2(x)
x = self.deconv3(x)
x = self.deconv4(x)
return x
[docs]class BDiscriminator(nn.Module):
r'''
TorchEEG provides an EEG feature generator based on CNN architecture and GAN for generating EEG grid representations of different frequency bands based on a given class label.
.. code-block:: python
g_model = BGenerator(in_channels=128)
d_model = BDiscriminator(in_channels=4)
z = torch.normal(mean=0, std=1, size=(1, 128))
fake_X = g_model(z)
disc_X = d_model(fake_X)
Args:
in_channels (int): The feature dimension of each electrode. (default: :obj:`4`)
grid_size (tuple): Spatial dimensions of grid-like EEG representation. (default: :obj:`(9, 9)`)
hid_channels (int): The number of hidden nodes in the first fully connected layer. (default: :obj:`32`)
'''
def __init__(self,
in_channels: int = 4,
grid_size: Tuple[int, int] = (9, 9),
hid_channels: int = 64):
super(BDiscriminator, self).__init__()
self.in_channels = in_channels
self.grid_size = grid_size
self.hid_channels = hid_channels
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels,
hid_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True), nn.BatchNorm2d(hid_channels), nn.LeakyReLU())
self.conv2 = nn.Sequential(
nn.Conv2d(hid_channels,
hid_channels * 2,
kernel_size=3,
stride=2,
padding=1,
bias=True), nn.BatchNorm2d(hid_channels * 2),
nn.LeakyReLU())
self.conv3 = nn.Sequential(
nn.Conv2d(hid_channels * 2,
hid_channels * 2,
kernel_size=3,
stride=1,
padding=1,
bias=True), nn.BatchNorm2d(hid_channels * 2),
nn.LeakyReLU())
self.conv4 = nn.Sequential(
nn.Conv2d(hid_channels * 2,
hid_channels * 4,
kernel_size=3,
stride=2,
padding=1,
bias=True), nn.BatchNorm2d(hid_channels * 4),
nn.LeakyReLU())
self.proj = nn.Linear(self.feature_dim, 1)
@property
def feature_dim(self):
with torch.no_grad():
mock_eeg = torch.zeros(1, self.in_channels, *self.grid_size)
mock_eeg = self.conv1(mock_eeg)
mock_eeg = self.conv2(mock_eeg)
mock_eeg = self.conv3(mock_eeg)
mock_eeg = self.conv4(mock_eeg)
return mock_eeg.flatten(start_dim=1).shape[-1]
[docs] def forward(self, x: torch.Tensor):
r'''
Args:
x (torch.Tensor): EEG signal representation, the ideal input shape is :obj:`[n, 4, 9, 9]`. Here, :obj:`n` corresponds to the batch size, :obj:`4` corresponds to the :obj:`in_channels`, and :obj:`(9, 9)` corresponds to the :obj:`grid_size`.
Returns:
torch.Tensor[n, 1]: Predicts the result of whether a given sample is a fake sample or not. Here, :obj:`n` corresponds to the batch size.
'''
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = x.flatten(start_dim=1)
x = self.proj(x)
return x
