YOLOv7添加注意力机制和各种改进模块代码免费下载：完整代码
添加的部分模块代码：
########CBAM
class ChannelAttentionModule(nn.Module):
def __init__(self, c1, reduction=16):
super(ChannelAttentionModule, self).__init__()
mid_channel = c1 // reduction
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.shared_MLP = nn.Sequential(
nn.Linear(in_features=c1, out_features=mid_channel),
nn.LeakyReLU(0.1, inplace=True),
nn.Linear(in_features=mid_channel, out_features=c1)
)
self.act = nn.Sigmoid()
# self.act=nn.SiLU()
def forward(self, x):
avgout = self.shared_MLP(self.avg_pool(x).view(x.size(0), -1)).unsqueeze(2).unsqueeze(3)
maxout = self.shared_MLP(self.max_pool(x).view(x.size(0), -1)).unsqueeze(2).unsqueeze(3)
return self.act(avgout + maxout)
class SpatialAttentionModule(nn.Module):
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.act = 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.act(self.conv2d(out))
return out
class CBAM(nn.Module):
def __init__(self, c1, c2):
super(CBAM, self).__init__()
self.channel_attention = ChannelAttentionModule(c1)
self.spatial_attention = SpatialAttentionModule()
def forward(self, x):
out = self.channel_attention(x) * x
out = self.spatial_attention(out) * out
return out
##############CBAM
########SE
class SEAttention(nn.Module):
def __init__(self, channel=512,reduction=16):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
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)
########SE
#######GAM
class GAMAttention(nn.Module):
# https://paperswithcode.com/paper/global-attention-mechanism-retain-information
def __init__(self, c1, c2, group=True, rate=4):
super(GAMAttention, self).__init__()
self.channel_attention = nn.Sequential(
nn.Linear(c1, int(c1 / rate)),
nn.ReLU(inplace=True),
nn.Linear(int(c1 / rate), c1)
)
self.spatial_attention = nn.Sequential(
nn.Conv2d(c1, c1 // rate, kernel_size=7, padding=3, groups=rate) if group else nn.Conv2d(c1, int(c1 / rate),
kernel_size=7,
padding=3),
nn.BatchNorm2d(int(c1 / rate)),
nn.ReLU(inplace=True),
nn.Conv2d(c1 // rate, c2, kernel_size=7, padding=3, groups=rate) if group else nn.Conv2d(int(c1 / rate), c2,
kernel_size=7,
padding=3),
nn.BatchNorm2d(c2)
)
def forward(self, x):
b, c, h, w = x.shape
x_permute = x.permute(0, 2, 3, 1).view(b, -1, c)
x_att_permute = self.channel_attention(x_permute).view(b, h, w, c)
x_channel_att = x_att_permute.permute(0, 3, 1, 2)
x = x * x_channel_att
x_spatial_att = self.spatial_attention(x).sigmoid()
x_spatial_att = channel_shuffle(x_spatial_att, 4) # last shuffle
out = x * x_spatial_att
return out
def channel_shuffle(x, groups=2): ##shuffle channel
# RESHAPE----->transpose------->Flatten
B, C, H, W = x.size()
out = x.view(B, groups, C // groups, H, W).permute(0, 2, 1, 3, 4).contiguous()
out = out.view(B, C, H, W)
return out
#######GAM
#####NAMAttention 该注意力机制只有通道注意力机制的代码，空间的没有
import torch.nn as nn
import torch
from torch.nn import functional as F
class Channel_Att(nn.Module):
def __init__(self, channels, t=16):
super(Channel_Att, self).__init__()
self.channels = channels
self.bn2 = nn.BatchNorm2d(self.channels, affine=True)
def forward(self, x):
residual = x
x = self.bn2(x)
weight_bn = self.bn2.weight.data.abs() / torch.sum(self.bn2.weight.data.abs())
x = x.permute(0, 2, 3, 1).contiguous()
x = torch.mul(weight_bn, x)
x = x.permute(0, 3, 1, 2).contiguous()
x = torch.sigmoid(x) * residual #
return x
class NAMAttention(nn.Module):
def __init__(self, channels, out_channels=None, no_spatial=True):
super(NAMAttention, self).__init__()
self.Channel_Att = nn.Sequential(*(Channel_Att(channels)for _ in range(1)))
def forward(self, x):
# print(x.device)
#
# device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
x_out1 = self.Channel_Att(x)
return x_out1
#####NAMAttention
class RepGhostBottleneck1(nn.Module):
# RepGhostNeXt Bottleneck
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out
super().__init__()
self.c_ = int(c2 * e) # hidden channels
# attention mechanism can be used
self.m = nn.Sequential(*(RepGhostBottleneck(c1, c2, 2*self.c_) for _ in range(n)))
def forward(self, x):
return self.m(x)