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Running
on
Zero
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from torch import nn
import torch
## Referencing the paper: https://arxiv.org/pdf/1709.01507
class SELayer(nn.Module):
"""
Squeeze-and-Excitation Layer (SELayer) for enhancing channel-wise feature responses.
The SELayer implements the Squeeze-and-Excitation block as proposed in the paper,
which adaptively recalibrates channel-wise feature responses by modeling the interdependencies
between channels.
Args:
channel (int): The number of input channels.
reduction (int): The reduction ratio for the number of channels in the bottleneck.
Default is 16.
"""
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
# Adaptive average pooling to generate a global descriptor
self.avg_pool = nn.AdaptiveAvgPool2d(1)
# Fully connected layers for the real part
self.fc_r = nn.Sequential(
nn.Linear(channel, channel // reduction), # Reduce channels
nn.ReLU(inplace=True), # Activation function
nn.Linear(channel // reduction, channel), # Restore channels
nn.Sigmoid() # Sigmoid activation to scale outputs
)
# Fully connected layers for the imaginary part
self.fc_i = nn.Sequential(
nn.Linear(channel, channel // reduction), # Reduce channels
nn.ReLU(inplace=True), # Activation function
nn.Linear(channel // reduction, channel), # Restore channels
nn.Sigmoid() # Sigmoid activation to scale outputs
)
def forward(self, x):
"""
Forward pass for the SELayer.
The forward method applies the squeeze-and-excitation operation on the input tensor `x`.
It computes the channel-wise attention weights for both the real and imaginary parts
of the input.
Args:
x (torch.Tensor): Input tensor of shape (B, C, D, H, W), where:
B - batch size,
C - number of channels,
D - depth,
H - height,
W - width.
Returns:
torch.Tensor: Output tensor after applying channel-wise attention,
same shape as input `x`.
"""
# Extract the batch size and number of channels
b, c, _, _, _ = x.size()
# Compute the squeeze operation for the real part
x_r = self.avg_pool(x[:, :, :, :, 0]).view(b, c) # Global average pooling
# Compute the squeeze operation for the imaginary part
x_i = self.avg_pool(x[:, :, :, :, 1]).view(b, c) # Global average pooling
# Calculate channel-wise attention for the real part
y_r = self.fc_r(x_r).view(b, c, 1, 1, 1) - self.fc_i(x_i).view(b, c, 1, 1, 1)
# Calculate channel-wise attention for the imaginary part
y_i = self.fc_r(x_i).view(b, c, 1, 1, 1) + self.fc_i(x_r).view(b, c, 1, 1, 1)
# Concatenate real and imaginary attention weights along the channel dimension
y = torch.cat([y_r, y_i], 4)
# Scale the input features by the attention weights
return x * y
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