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) / 6 class 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 CoordAtt(nn.Module): def __init__(self, inp, oup, reduction=32): super(CoordAtt, self).__init__() # height方向上的均值池化 self.pool_h = nn.AdaptiveAvgPool2d((None, 1)) # width方向上的均值池化 self.pool_w = nn.AdaptiveAvgPool2d((1, None)) 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 = x n, c, h, w = x.size() x_h = self.pool_h(x) x_w = self.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_h return out 嵌入CA注意力机制后出现这个问题怎么解决TypeError: init() takes from 3 to 4 positional arguments but 5 were given

根据您提供的代码，我看到在CoordAtt类的初始化函数中，没有发现任何与错误消息匹配的问题。根据错误提示，init()函数接受3到4个位置参数，但是在您的代码中没有传递额外的参数。

因此，可能的原因是您在其他地方调用了CoordAtt类的初始化函数，并传递了额外的参数。请检查一下您的代码，确保在初始化CoordAtt类时没有传递多余的参数。

另外，如果您在其他地方使用了CoordAtt类的实例，并且传递了额外的参数，那么可能需要修改相应的代码，以确保与CoordAtt类的初始化函数匹配。

如果问题仍然存在，请提供更多的代码和上下文信息，这样我可以更好地帮助您解决问题。

相关问题

class SelfAttention(nn.Module): def __init__(self, in_channels, reduction=4): super(SelfAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool1d(1) # print("in_channels", in_channels) self.fc1 = nn.Conv1d(in_channels, in_channels // reduction, 1, bias=False) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv1d(in_channels // reduction, in_channels, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, n = x.size() y = self.avg_pool(x) print("y.shape=", y.shape) # print("in_channels", in_channels) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x)我想在这个类中实现将传入的参数进行打印，你可以给我详细的代码吗？

你可以在 `__init__` 方法中添加以下代码：

print("in_channels:", in_channels)
print("reduction:", reduction)

在 `forward` 方法中添加以下代码：

print("x.shape:", x.shape)

这样就可以在打印的信息中看到传入的参数和输入的张量形状了。完整的代码如下：

class SelfAttention(nn.Module):
    def __init__(self, in_channels, reduction=4):
        super(SelfAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool1d(1) #
        print("in_channels:", in_channels)
        print("reduction:", reduction)
        self.fc1 = nn.Conv1d(in_channels, in_channels // reduction, 1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv1d(in_channels // reduction, in_channels, 1, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        print("x.shape:", x.shape)
        b, c, n = x.size()
        y = self.avg_pool(x)
        print("y.shape=", y.shape) #
        y = self.fc1(y)
        y = self.relu(y)
        y = self.fc2(y)
        y = self.sigmoid(y)
        return x * y.expand_as(x)

class MyNet(nn.Module): def __init__(self): super(MyNet, self).__init__() self.vgg16 = vgg16(pretrained=True) self.resnet18 = resnet18(pretrained=True) self.vgg16.classifier = nn.Identity() self.resnet18.fc = nn.Identity() self.fc = nn.Linear(25600, 2) def forward(self, x): x1 = self.vgg16(x) x2 = self.resnet18(x) x1 = x1.view(x1.size(0), -1) x2 = x2.view(x2.size(0), -1) x = torch.cat((x1, x2), dim=1) x = self.fc(x) return x 将以上代码加入DANet注意力机制

可以将 DAnet 注意力机制加入到 MyNet 的 forward 函数中，如下所示：

import torch
import torch.nn as nn

class DAnet(nn.Module):
    def __init__(self, in_channels, out_channels, reduction=16):
        super(DAnet, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, kernel_size=1, stride=1, padding=0)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(in_channels // reduction, out_channels, kernel_size=1, stride=1, padding=0)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x)
        y = self.conv1(y)
        y = self.relu(y)
        y = self.conv2(y)
        y = self.sigmoid(y)
        return x * y.expand_as(x)

class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.vgg16 = vgg16(pretrained=True)
        self.resnet18 = resnet18(pretrained=True)
        self.vgg16.classifier = nn.Identity()
        self.resnet18.fc = nn.Identity()
        self.danet1 = DAnet(512, 512)
        self.danet2 = DAnet(512, 512)
        self.fc = nn.Linear(25600, 2)

    def forward(self, x):
        x1 = self.vgg16.features(x)
        x1 = self.danet1(x1)
        x1 = self.vgg16.avgpool(x1)
        x1 = torch.flatten(x1, 1)
        
        x2 = self.resnet18.conv1(x)
        x2 = self.resnet18.bn1(x2)
        x2 = self.resnet18.relu(x2)
        x2 = self.resnet18.maxpool(x2)
        x2 = self.resnet18.layer1(x2)
        x2 = self.resnet18.layer2(x2)
        x2 = self.resnet18.layer3(x2)
        x2 = self.resnet18.layer4(x2)
        x2 = self.danet2(x2)
        x2 = self.resnet18.avgpool(x2)
        x2 = torch.flatten(x2, 1)

        x = torch.cat((x1, x2), dim=1)
        x = self.fc(x)
        return x

在 forward 函数中，我们首先对 VGG16 和 ResNet18 的特征提取部分进行计算，然后将 VGG16 的特征图 x1 和 ResNet18 的特征图 x2 分别输入到两个 DAnet 注意力机制中进行加权，最后将加权后的两个特征图拼接在一起，并通过全连接层进行分类。