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modules.py
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modules.py
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import torch
import torch.nn as nn
from torch.nn import functional as F
# ==================================================================================#
# 以下为构建网络的基本卷积模块，上采样模块以及下采样模块
# ==================================================================================#
class Conv(nn.Module):
def __init__(self, C_in, C_out):
super(Conv, self).__init__()
self.layer = nn.Sequential(
nn.Conv2d(C_in, C_out, 3, 1, 1),
nn.BatchNorm2d(C_out),
# 防止过拟合
# nn.Dropout(0.3),
nn.LeakyReLU(),
nn.Conv2d(C_out, C_out, 3, 1, 1),
nn.BatchNorm2d(C_out),
# 防止过拟合
# nn.Dropout(0.4),
nn.LeakyReLU(),
)
def forward(self, x):
return self.layer(x)
# 下采样模块
class DownSampling(nn.Module):
def __init__(self, C):
super(DownSampling, self).__init__()
self.Down = nn.Sequential(
# 使用卷积进行2倍的下采样，通道数不变
nn.Conv2d(C, C, 3, 2, 1),
nn.BatchNorm2d(C),
nn.LeakyReLU()
)
def forward(self, x):
return self.Down(x)
# 上采样模块
class UpSampling(nn.Module):
def __init__(self, C):
super(UpSampling, self).__init__()
# 特征图大小扩大2倍，通道数减半
self.Up = nn.Conv2d(C, C // 2, 1, 1)
def forward(self, x, r):
# 使用邻近插值进行下采样
up = F.interpolate(x, scale_factor=2, mode="nearest")
x = self.Up(up)
# 拼接，当前上采样的，和之前下采样过程中的
return torch.cat((x, r), 1)
# ==================================================================================#
# 以下为一些注意力模块以及特征融合模块，例如SE模块，CBAM模块，ASPP模块
# ==================================================================================#
class ChannelAttentionModule(nn.Module):
"""
通道注意力机制
"""
def __init__(self, channel, ratio=16):
super(ChannelAttentionModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.shared_MLP = nn.Sequential(
nn.Conv2d(channel, channel // ratio, 1, bias=False),
nn.ReLU(),
nn.Conv2d(channel // ratio, channel, 1, bias=False)
)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avgout = self.shared_MLP(self.avg_pool(x))
maxout = self.shared_MLP(self.max_pool(x))
return self.sigmoid(avgout + maxout)
class SpatialAttentionModule(nn.Module):
"""
空间注意力机制
"""
def __init__(self):
super(SpatialAttentionModule, self).__init__()
self.conv2d = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=7, stride=1, padding=3)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avgout = torch.mean(x, dim=1, keepdim=True)
maxout, _ = torch.max(x, dim=1, keepdim=True)
out = torch.cat([avgout, maxout], dim=1)
out = self.sigmoid(self.conv2d(out))
return out
class CBAM(nn.Module):
"""
空间注意力机制与通道注意力机制结合
"""
def __init__(self, channel):
super(CBAM, self).__init__()
self.channel_attention = ChannelAttentionModule(channel)
self.spatial_attention = SpatialAttentionModule()
def forward(self, x):
out = self.channel_attention(x) * x
out = self.spatial_attention(out) * out
return out
class Squeeze_Excite_Block(nn.Module):
"""
SE模块通道注意力机制
"""
def __init__(self, channel, reduction=16):
super(Squeeze_Excite_Block, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.LeakyReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid(),
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
class ASPP(nn.Module):
"""
Atrous Spatial Pyramid Pooling
受到SPP的启发，语义分割模型DeepLabv2中提出了ASPP模块，
该模块使用具有不同采样率的多个并行空洞卷积层。为每个采样
率提取的特征在单独的分支中进一步处理，并融合以生成最终结
果。该模块通过不同的空洞rate构建不同感受野的卷积核，用
来获取多尺度物体信息.
"""
def __init__(self, in_dims, out_dims, rate=[6, 12, 18]):
super(ASPP, self).__init__()
self.aspp_block1 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[0], dilation=rate[0]
),
nn.LeakyReLU(inplace=True),
nn.BatchNorm2d(out_dims),
)
self.aspp_block2 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[1], dilation=rate[1]
),
nn.LeakyReLU(inplace=True),
nn.BatchNorm2d(out_dims),
)
self.aspp_block3 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[2], dilation=rate[2]
),
nn.LeakyReLU(inplace=True),
nn.BatchNorm2d(out_dims),
)
self.output = nn.Conv2d(len(rate) * out_dims, out_dims, 1)
self._init_weights()
def forward(self, x):
x1 = self.aspp_block1(x)
x2 = self.aspp_block2(x)
x3 = self.aspp_block3(x)
out = torch.cat([x1, x2, x3], dim=1)
return self.output(out)
def _init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()