yolov7添加gam模块

时间: 2023-11-26 19:44:33 浏览: 131

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yolov8添加注意力机制-学习记录

Yolov8添加注意力机制学习记录本文档记录了Yolov8添加注意力机制的学习记录，包括添加无参数注意力机制SimAM、添加一个通道数注意力机制EMA和添加二个通道数注意力机制GAM。一、Yolov8添加无参数注意力机制SimAM Yolov8添加无参数注意力机制SimAM可以提高目标检测模型的性能。SimAM注意力机制可以自动学习到不同通道的权重，提高模型的表达能力。添加SimAM注意力机制的步骤如下： 1. 新建simam_module.py文件，保存SimAM注意力机制的源码。 2. 在tasks.py文件中导入SimAM模块。 3. 修改yaml文件，加入SimAM模块，后续层数加1。 4. 测试yaml文件是否可行。添加SimAM注意力机制可以提高Yolov8模型的性能，对目标检测任务有很大的帮助。二、Yolov8添加一个通道数注意力机制EMA Yolov8添加一个通道数注意力机制EMA可以提高模型的表达能力。EMA注意力机制可以学习到不同通道的权重，提高模型的性能。添加EMA注意力机制的步骤如下： 1. 新建EMA_attention_module.py文件，保存EMA注意力机制的源码。 2. 在tasks.py文件中导入EMA模块，并添加两行代码。 3. 修改yaml文件，加入EMA模块，后续层数加1。 4. 测试yaml文件是否可行。添加EMA注意力机制可以提高Yolov8模型的性能，对目标检测任务有很大的帮助。三、Yolov8添加二个通道数注意力机制GAM Yolov8添加二个通道数注意力机制GAM可以提高模型的表达能力。GAM注意力机制可以学习到不同通道的权重，提高模型的性能。添加GAM注意力机制的步骤如下： 1. 新建GAM_Attention_module.py文件，保存GAM注意力机制的源码。 2. 在tasks.py文件中导入GAM模块，并对添加部分代码。 3. 修改yaml文件，加入GAM_Attention模块，后续层数相应修改。 4. 测试yaml文件是否可行。添加GAM注意力机制可以提高Yolov8模型的性能，对目标检测任务有很大的帮助。 Yolov8添加注意力机制可以提高模型的性能，对目标检测任务有很大的帮助。通过添加SimAM、EMA和GAM注意力机制，可以提高Yolov8模型的表达能力，提高目标检测的准确性。

要在YOLOv7中添加GAM模块，需要进行以下步骤： 1. 在YOLOv7的代码中添加GAM模块的定义，可以参考其他模块的定义方式进行添加。 2. 在YOLOv7的网络结构中添加GAM模块，可以参考其他模块的添加方式进行添加。 3. 在YOLOv7的训练过程中，将GAM模块加入到loss计算中，以便进行反向传播。具体实现方式可以参考以下代码： ```python # 定义GAM模块 class GAM(nn.Module): def __init__(self, in_channels, reduction=16): super(GAM, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(in_channels, in_channels // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(in_channels // reduction, in_channels, 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 # 在YOLOv7的网络结构中添加GAM模块 class YOLOv7(nn.Module): def __init__(self): super(YOLOv7, self).__init__() self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1) self.bn1 = nn.BatchNorm2d(32) self.relu1 = nn.LeakyReLU(0.1) self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(64) self.relu2 = nn.LeakyReLU(0.1) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) self.bn3 = nn.BatchNorm2d(128) self.relu3 = nn.LeakyReLU(0.1) self.conv4 = nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0) self.bn4 = nn.BatchNorm2d(64) self.relu4 = nn.LeakyReLU(0.1) self.conv5 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) self.bn5 = nn.BatchNorm2d(128) self.relu5 = nn.LeakyReLU(0.1) self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv6 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) self.bn6 = nn.BatchNorm2d(256) self.relu6 = nn.LeakyReLU(0.1) self.conv7 = nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0) self.bn7 = nn.BatchNorm2d(128) self.relu7 = nn.LeakyReLU(0.1) self.conv8 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) self.bn8 = nn.BatchNorm2d(256) self.relu8 = nn.LeakyReLU(0.1) self.pool8 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv9 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) self.bn9 = nn.BatchNorm2d(512) self.relu9 = nn.LeakyReLU(0.1) self.conv10 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0) self.bn10 = nn.BatchNorm2d(256) self.relu10 = nn.LeakyReLU(0.1) self.conv11 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) self.bn11 = nn.BatchNorm2d(512) self.relu11 = nn.LeakyReLU(0.1) self.conv12 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0) self.bn12 = nn.BatchNorm2d(256) self.relu12 = nn.LeakyReLU(0.1) self.conv13 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) self.bn13 = nn.BatchNorm2d(512) self.relu13 = nn.LeakyReLU(0.1) self.gam13 = GAM(512) self.conv14 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1) self.bn14 = nn.BatchNorm2d(1024) self.relu14 = nn.LeakyReLU(0.1) self.conv15 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0) self.bn15 = nn.BatchNorm2d(512) self.relu15 = nn.LeakyReLU(0.1) self.conv16 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1) self.bn16 = nn.BatchNorm2d(1024) self.relu16 = nn.LeakyReLU(0.1) self.conv17 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0) self.bn17 = nn.BatchNorm2d(512) self.relu17 = nn.LeakyReLU(0.1) self.conv18 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1) self.bn18 = nn.BatchNorm2d(1024) self.relu18 = nn.LeakyReLU(0.1) self.gam18 = GAM(1024) self.conv19 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1) self.bn19 = nn.BatchNorm2d(1024) self.relu19 = nn.LeakyReLU(0.1) self.conv20 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1) self.bn20 = nn.BatchNorm2d(1024) self.relu20 = nn.LeakyReLU(0.1) self.conv21 = nn.Conv2d(3072, 1024, kernel_size=3, stride=1, padding=1) self.bn21 = nn.BatchNorm2d(1024) self.relu21 = nn.LeakyReLU(0.1) self.conv22 = nn.Conv2d(1024, 75, kernel_size=1, stride=1, padding=0) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu1(x) x = self.pool1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu2(x) x = self.pool2(x) x = self.conv3(x) x = self.bn3(x) x = self.relu3(x) x = self.conv4(x) x = self.bn4(x) x = self.relu4(x) x = self.conv5(x) x = self.bn5(x) x = self.relu5(x) x = self.pool5(x) x = self.conv6(x) x = self.bn6(x) x = self.relu6(x) x = self.conv7(x) x = self.bn7(x) x = self.relu7(x) x = self.conv8(x) x = self.bn8(x) x = self.relu8(x) x = self.pool8(x) x = self.conv9(x) x = self.bn9(x) x = self.relu9(x) x = self.conv10(x) x = self.bn10(x) x = self.relu10(x) x = self.conv11(x) x = self.bn11(x) x = self.relu11(x) x = self.conv12(x) x = self.bn12(x) x = self.relu12(x) x = self.conv13(x) x = self.bn13(x) x = self.relu13(x) x = self.gam13(x) x = self.conv14(x) x = self.bn14(x) x = self.relu14(x) x = self.conv15(x) x = self.bn15(x) x = self.relu15(x) x = self.conv16(x) x = self.bn16(x) x = self.relu16(x) x = self.conv17(x) x = self.bn17(x) x = self.relu17(x) x = self.conv18(x) x = self.bn18(x) x = self.relu18(x) x = self.gam18(x) x = self.conv19(x) x = self.bn19(x) x = self.relu19(x) x = self.conv20(x) x = self.bn20(x) x = self.relu20(x) # 将三个不同尺度的特征图进行拼接 y = F.interpolate(x, scale_factor=2, mode='nearest') y = torch.cat([y, self.bn17(self.conv17(self.bn16(self.conv16(self.bn15(self.conv15(y))))))], 1) y = F.interpolate(y, scale_factor=2, mode='nearest') y = torch.cat([y, self.bn11(self.conv11(self.bn10(self.conv10(self.bn9(self.conv9(y))))))], 1) y = F.interpolate(y, scale_factor=2, mode='nearest') y = torch.cat([y, self.bn4(self.conv4(self.bn3(self.conv3(y))))], 1) y = self.conv21(y) y = self.bn21(y) y = self.relu21(y) y = self.conv22(y) return y # 在YOLOv7的训练过程中，将GAM模块加入到loss计算中 class YOLOv7Loss(nn.Module): def __init__(self): super(YOLOv7Loss, self).__init__() def forward(self, output, target): # 计算loss return loss ```

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