Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,738 Bytes
8e8cd3e |
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
from torch import nn
import torch
class SELayer(nn.Module):
"""
Squeeze-and-Excitation (SE) Layer.
This layer implements the Squeeze-and-Excitation mechanism, which adaptively
recalibrates channel-wise feature responses by explicitly modeling
interdependencies between channels. It enhances the representational power
of a neural network by emphasizing informative features while suppressing
less useful ones.
Args:
channel (int): The number of input channels.
reduction (int, optional): Reduction ratio for the dimensionality
of the intermediate representations. Default is 16.
"""
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
# Adaptive average pooling to reduce spatial dimensions to 1x1
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.avg_pool_layer = nn.Sequential(
nn.Linear(channel, channel // reduction), # First linear layer
nn.ReLU(inplace=True), # Activation layer
nn.Linear(channel // reduction, channel), # Second linear layer
nn.Sigmoid() # Sigmoid activation for scaling
)
# Adaptive max pooling to reduce spatial dimensions to 1x1
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.max_pool_layer = nn.Sequential(
nn.Linear(channel, channel // reduction), # First linear layer
nn.ReLU(inplace=True), # Activation layer
nn.Linear(channel // reduction, channel), # Second linear layer
nn.Sigmoid() # Sigmoid activation for scaling
)
def forward(self, x):
"""
Forward pass for the SE Layer.
Args:
x (Tensor): Input tensor of shape (B, C, H, W) where:
B = batch size,
C = number of channels,
H = height,
W = width.
Returns:
Tensor: Output tensor of the same shape as input `x` after
applying the squeeze-and-excitation mechanism.
"""
b, c, _, _ = x.size() # Unpack input dimensions
x_avg = self.avg_pool(x).view(b, c) # Squeeze: apply average pooling
x_avg = self.avg_pool_layer(x_avg).view(b, c, 1, 1) # Excitation: pass through layers
x_max = self.max_pool(x).view(b, c) # Squeeze: apply max pooling
x_max = self.max_pool_layer(x_max).view(b, c, 1, 1) # Excitation: pass through layers
# Scale the input features by the computed channel weights
y = (x_avg + x_max) * x
return y # Return the recalibrated output
|