Spaces:
Sleeping
Sleeping
File size: 1,885 Bytes
148cade | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import torch.nn as nn
import torch.nn.functional as F
import torch
class EEGNet(nn.Module):
def __init__(self, n_channels=21, n_samples=1250, num_classes=2, dropout_rate=0.5):
super(EEGNet, self).__init__()
# Temporal convolution: learn temporal filters across time dimension
self.firstconv = nn.Sequential(
nn.Conv2d(1, 8, kernel_size=(1, 64), padding=(0, 32), bias=False), # shape: (B, 8, C, T)
nn.BatchNorm2d(8)
)
# Depthwise spatial convolution: one spatial filter per temporal filter
self.depthwiseConv = nn.Sequential(
nn.Conv2d(8, 16, kernel_size=(n_channels, 1), groups=8, bias=False), # shape: (B, 16, 1, T)
nn.BatchNorm2d(16),
nn.ELU(),
nn.AvgPool2d(kernel_size=(1, 4)),
nn.Dropout(dropout_rate)
)
# Separable convolution: combines temporal filters again
self.separableConv = nn.Sequential(
nn.Conv2d(16, 16, kernel_size=(1, 16), padding=(0, 8), bias=False),
nn.BatchNorm2d(16),
nn.ELU(),
nn.AvgPool2d(kernel_size=(1, 8)),
nn.Dropout(dropout_rate)
)
# Dynamically compute the flattened feature size after conv layers
dummy_input = torch.zeros(1, 1, n_channels, n_samples)
with torch.no_grad():
x = self.firstconv(dummy_input)
x = self.depthwiseConv(x)
x = self.separableConv(x)
flattened_size = x.reshape(1, -1).shape[1] # dynamically computed
# Final classification layer
self.classifier = nn.Linear(flattened_size, num_classes)
def forward(self, x):
x = self.firstconv(x)
x = self.depthwiseConv(x)
x = self.separableConv(x)
x = x.reshape(x.size(0), -1) # flatten
x = self.classifier(x)
return x |