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("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): b, c, n = x.size() print("x.shape=", x.shape) y = self.avg_pool(x) print("y.shape=", y.shape) # print("channel_out", channel_out) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x)我如果已将以上的SelfAttention模块加入到了PointRCNN中，及构建self.SA_modules.append( nn.Sequential( PointnetSAModuleMSG( npoint=cfg.RPN.SA_CONFIG.NPOINTS[k], radii=cfg.RPN.SA_CONFIG.RADIUS[k], nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k], mlps=mlps[:2], use_xyz=use_xyz, bn=cfg.RPN.USE_BN, ), SelfAttention(channel_out) ) )，在每一个SA模块后增加一层注意力机制模块，我想问的是SelfAttention中的forward函数什么时候被执行，是不是只有要最后输出全局特征时才执行forward函数，在构建网络时自动执行init函数

是的，SelfAttention类中的forward函数只有在网络执行前向传播时才会被调用。在你的代码中，当你构建网络时，会自动执行每个模块的init函数来初始化模块的参数。但是只有在执行网络的前向传播时，模块的forward函数才会被调用，因为在前向传播时需要计算模块的输出。所以只有当你调用模型的forward函数时，模块的forward函数才会被执行。在你的模型中，当你调用self.SA_modules的forward函数时，SelfAttention模块的forward函数才会被执行。

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)

这是类的定义，class SelfAttention(nn.Module): def __init__(self, in_channels, reduction=4): super(SelfAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool1d(1) 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) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x)，而这是使用该类的位置：self.SA_modules.append( nn.Sequential( PointnetSAModuleMSG( npoint=cfg.RPN.SA_CONFIG.NPOINTS[k], radii=cfg.RPN.SA_CONFIG.RADIUS[k], nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k], mlps=mlps, use_xyz=use_xyz, bn=cfg.RPN.USE_BN, channel_out=channel_out ), SelfAttention(channel_out=channel_out) ) )，但是运行时报错：File "/root/autodl-tmp/project/tools/../lib/net/pointnet2_msg.py", line 125, in __init__ SelfAttention(channel_out=channel_out) TypeError: __init__() got an unexpected keyword argument 'channel_out'，为什么呢？如何解决

在你的代码中，SelfAttention类的初始化方法中只接收两个参数in_channels和reduction，而你在使用该类时传递了一个名为channel_out的参数，导致了TypeError。

解决方法是修改SelfAttention类的定义，为其增加一个名为channel_out的参数，并在初始化方法中接收和使用该参数。修改后的类定义如下：

class SelfAttention(nn.Module):
    def __init__(self, in_channels, channel_out, reduction=4):
        super(SelfAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool1d(1)
        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, channel_out, 1, bias=False)
        self.sigmoid = nn.Sigmoid()
    
    def forward(self, x):
        b, c, n = x.size()
        y = self.avg_pool(x)
        y = self.fc1(y)
        y = self.relu(y)
        y = self.fc2(y)
        y = self.sigmoid(y)
        return x * y.expand_as(x)

然后在使用SelfAttention类时，需要传递in_channels和channel_out参数，例如：

self.SA_modules.append(
    nn.Sequential(
        PointnetSAModuleMSG(
            npoint=cfg.RPN.SA_CONFIG.NPOINTS[k],
            radii=cfg.RPN.SA_CONFIG.RADIUS[k],
            nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k],
            mlps=mlps,
            use_xyz=use_xyz,
            bn=cfg.RPN.USE_BN,
            channel_out=channel_out
        ),
        SelfAttention(in_channels=channel_out, channel_out=channel_out)
    )
)