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