yolov5、YOLOv7、YOLOv8改進：注意力機制CA

論文題目：《Coordinate Attention for Efficient Mobile NetWork Design》
論文地址：??https://arxiv.org/pdf/2103.02907.pdf

請添加圖片描述

本文中，作者通過將位置信息嵌入到通道注意力中提出了一種新穎的移動網絡注意力機制，將其稱為“Coordinate Attention”。與通過2維全局池化將特征張量轉換為單個特征向量的通道注意力不同，Coordinate注意力將通道注意力分解為兩個1維特征編碼過程，分別沿2個空間方向聚合特征。這樣，可以沿一個空間方向捕獲遠程依賴關系，同時可以沿另一空間方向保留精確的位置信息。然后將生成的特征圖分別編碼為一對方向感知和位置敏感的attention map，可以將其互補地應用于輸入特征圖，以增強關注對象的表示。

不同于通道注意力將輸入通過2D全局池化轉化為單個特征向量，CoordAttention將通道注意力分解為兩個沿著不同方向聚合特征的1D特征編碼過程。這樣的好處是可以沿著一個空間方向捕獲長程依賴，沿著另一個空間方向保留精確的位置信息。然后，將生成的特征圖分別編碼，形成一對方向感知和位置敏感的特征圖，它們可以互補地應用到輸入特征圖來增強感興趣的目標的表示
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下面附上改進代碼

YOLOv5改進：

common中加入

class h_sigmoid(nn.Module):def __init__(self, inplace=True):super(h_sigmoid, self).__init__()self.relu = nn.ReLU6(inplace=inplace)def forward(self, x):return self.relu(x + 3) / 6class h_swish(nn.Module):def __init__(self, inplace=True):super(h_swish, self).__init__()self.sigmoid = h_sigmoid(inplace=inplace)def forward(self, x):return x * self.sigmoid(x)
class CA(nn.Module):# Coordinate Attention for Efficient Mobile Network Design'''Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for liftingmodel performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose anovel attention mechanism for mobile iscyy networks by embedding positional information into channel attention, whichwe call “coordinate attention”. Unlike channel attentionthat transforms a feature tensor to a single feature vector iscyy via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions, respectively'''def __init__(self, inp, oup, reduction=32):super(CA, self).__init__()mip = max(8, inp // reduction)self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0)self.bn1 = nn.BatchNorm2d(mip)self.act = h_swish()self.conv_h = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)self.conv_w = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)def forward(self, x):identity = xn,c,h,w = x.size()pool_h = nn.AdaptiveAvgPool2d((h, 1))pool_w = nn.AdaptiveAvgPool2d((1, w))x_h = pool_h(x)x_w = pool_w(x).permute(0, 1, 3, 2)y = torch.cat([x_h, x_w], dim=2)y = self.conv1(y)y = self.bn1(y)y = self.act(y) x_h, x_w = torch.split(y, [h, w], dim=2)x_w = x_w.permute(0, 1, 3, 2)a_h = self.conv_h(x_h).sigmoid()a_w = self.conv_w(x_w).sigmoid()out = identity * a_w * a_hreturn out

在yolo.py中注冊

找到parse.model模塊? ?加入下列代碼

        elif m in [CA]:c1, c2 = ch[f], args[0]if c2 != no:  # if not outputssc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]

添加配置文件

# YOLOv5 🚀, GPL-3.0 license# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth iscyy multiple
width_multiple: 0.50  # layer channel iscyy multiple
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2[-1, 1, Conv, [128, 3, 2]],  # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]],  # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]],  # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[-1, 1, CA, [1024]],[[17, 20, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

?到此YOLOv5就改好了

下面介紹yolov7

同樣在common中加入以下代碼

class h_sigmoid(nn.Module):def __init__(self, inplace=True):super(h_sigmoid, self).__init__()self.relu = nn.ReLU6(inplace=inplace)def forward(self, x):return self.relu(x + 3) / 6class h_swish(nn.Module):def __init__(self, inplace=True):super(h_swish, self).__init__()self.sigmoid = h_sigmoid(inplace=inplace)def forward(self, x):return x * self.sigmoid(x)
class CA(nn.Module):# Coordinate Attention for Efficient Mobile Network Design'''Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for liftingmodel performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose anovel attention mechanism for mobile iscyy networks by embedding positional information into channel attention, whichwe call “coordinate attention”. Unlike channel attentionthat transforms a feature tensor to a single feature vector iscyy via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions, respectively'''def __init__(self, inp, oup, reduction=32):super(CA, self).__init__()mip = max(8, inp // reduction)self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0)self.bn1 = nn.BatchNorm2d(mip)self.act = h_swish()self.conv_h = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)self.conv_w = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)def forward(self, x):identity = xn,c,h,w = x.size()pool_h = nn.AdaptiveAvgPool2d((h, 1))pool_w = nn.AdaptiveAvgPool2d((1, w))x_h = pool_h(x)x_w = pool_w(x).permute(0, 1, 3, 2)y = torch.cat([x_h, x_w], dim=2)y = self.conv1(y)y = self.bn1(y)y = self.act(y) x_h, x_w = torch.split(y, [h, w], dim=2)x_w = x_w.permute(0, 1, 3, 2)a_h = self.conv_h(x_h).sigmoid()a_w = self.conv_w(x_w).sigmoid()out = identity * a_w * a_hreturn out

在yolo.py中注冊

找到parse.model模塊? ?加入下列代碼

        elif m in [CA]:c1, c2 = ch[f], args[0]if c2 != no:  # if not outputssc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]

添加配置文件

# YOLOv7 🚀, GPL-3.0 license
# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel iscyy multiple# anchors
anchors:- [12,16, 19,36, 40,28]  # P3/8- [36,75, 76,55, 72,146]  # P4/16- [142,110, 192,243, 459,401]  # P5/32# yolov7 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [32, 3, 1]],  # 0[-1, 1, Conv, [64, 3, 2]],  # 1-P1/2[-1, 1, Conv, [64, 3, 1]],[-1, 1, Conv, [128, 3, 2]],  # 3-P2/4 [-1, 1, C3, [128]], [-1, 1, Conv, [256, 3, 2]], [-1, 1, MP, []],[-1, 1, Conv, [128, 1, 1]],[-3, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [128, 3, 2]],[[-1, -3], 1, Concat, [1]],  # 16-P3/8[-1, 1, Conv, [128, 1, 1]],[-2, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[[-1, -3, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [512, 1, 1]],[-1, 1, MP, []],[-1, 1, Conv, [256, 1, 1]],[-3, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 2]],[[-1, -3], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1]],[-2, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[[-1, -3, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [1024, 1, 1]],          [-1, 1, MP, []],[-1, 1, Conv, [512, 1, 1]],[-3, 1, Conv, [512, 1, 1]],[-1, 1, Conv, [512, 3, 2]],[[-1, -3], 1, Concat, [1]],[-1, 1, C3, [1024]],[-1, 1, Conv, [256, 3, 1]],]# yolov7 head by iscyy
head:[[-1, 1, SPPCSPC, [512]],[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[31, 1, Conv, [256, 1, 1]],[[-1, -2], 1, Concat, [1]],[-1, 1, C3, [128]],[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[18, 1, Conv, [128, 1, 1]],[[-1, -2], 1, Concat, [1]],[-1, 1, C3, [128]],[-1, 1, MP, []],[-1, 1, Conv, [128, 1, 1]],[-3, 1, CA, [128]],[-1, 1, Conv, [128, 3, 2]],[[-1, -3, 44], 1, Concat, [1]],[-1, 1, C3, [256]], [-1, 1, MP, []],[-1, 1, Conv, [256, 1, 1]],[-3, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 2]], [[-1, -3, 39], 1, Concat, [1]],[-1, 3, C3, [512]],# 檢測頭 -----------------------------[49, 1, RepConv, [256, 3, 1]],[55, 1, RepConv, [512, 3, 1]],[61, 1, RepConv, [1024, 3, 1]],[[62,63,64], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)]

至此v7就配置完成了

v8的配置同v5是一樣的。