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import math
import torch.nn as nn
class CA_layer(nn.Module):
def __init__(self, channel, reduction=16):
super(CA_layer, self).__init__()
# global average pooling
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False),
nn.GELU(),
nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False),
# nn.Sigmoid()
)
def forward(self, x):
y = self.fc(self.gap(x))
return x * y.expand_as(x)
class Simple_CA_layer(nn.Module):
def __init__(self, channel):
super(Simple_CA_layer, self).__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Conv2d(
in_channels=channel,
out_channels=channel,
kernel_size=1,
padding=0,
stride=1,
groups=1,
bias=True,
)
def forward(self, x):
return x * self.fc(self.gap(x))
class ECA_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel):
super(ECA_layer, self).__init__()
b = 1
gamma = 2
k_size = int(abs(math.log(channel, 2) + b) / gamma)
k_size = k_size if k_size % 2 else k_size + 1
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(
1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
)
# self.sigmoid = nn.Sigmoid()
def forward(self, x):
# x: input features with shape [b, c, h, w]
# b, c, h, w = x.size()
# feature descriptor on the global spatial information
y = self.avg_pool(x)
# Two different branches of ECA module
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
# Multi-scale information fusion
# y = self.sigmoid(y)
return x * y.expand_as(x)
class ECA_MaxPool_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel):
super(ECA_MaxPool_layer, self).__init__()
b = 1
gamma = 2
k_size = int(abs(math.log(channel, 2) + b) / gamma)
k_size = k_size if k_size % 2 else k_size + 1
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.conv = nn.Conv1d(
1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
)
# self.sigmoid = nn.Sigmoid()
def forward(self, x):
# x: input features with shape [b, c, h, w]
# b, c, h, w = x.size()
# feature descriptor on the global spatial information
y = self.max_pool(x)
# Two different branches of ECA module
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
# Multi-scale information fusion
# y = self.sigmoid(y)
return x * y.expand_as(x)
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