class PointnetSAModuleMSG(_PointnetSAModuleBase): """Pointnet set abstraction layer with multiscale grouping""" def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): """ :param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples in each ball query :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale :param bn: whether to use batchnorm :param use_xyz: :param pool_method: max_pool / avg_pool :param instance_norm: whether to use instance_norm """ super().__init__() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) self.pool_method = pool_method这是PointnetSAModuleMSG的代码，而这是selfattention的代码：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)；我想将SelfAttention作为PointnetSAModuleMSG的子模块，我是为了加入SA注意力机制，所以需要对PointnetSAModuleMSG进行修改。我想在每个SA模块中添加一个注意力机制，以使得网络可以更好地聚焦于重要的点。具体实现方式是在每个SA模块的最后一层MLP后加入一个Self-Attention层，（如SelfAttention类所示）用于计算每个点的注意力分数。你可以给我写出详细的修改代码吗？

好的，以下是修改后的代码：

class PointnetSAModuleMSG(SelfAttention, _PointnetSAModuleBase):
    """Pointnet set abstraction layer with multiscale grouping and self-attention"""

    def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True,
                 use_xyz: bool = True, pool_method='max_pool', instance_norm=False, reduction=4):
        """
        :param npoint: int
        :param radii: list of float, list of radii to group with
        :param nsamples: list of int, number of samples in each ball query
        :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
        :param bn: whether to use batchnorm
        :param use_xyz:
        :param pool_method: max_pool / avg_pool
        :param instance_norm: whether to use instance_norm
        :param reduction: reduction factor for self-attention
        """
        super().__init__(in_channels=mlps[-1][-1], reduction=reduction)
        assert len(radii) == len(nsamples) == len(mlps)

        self.npoint = npoint
        self.groupers = nn.ModuleList()
        self.mlps = nn.ModuleList()
        for i in range(len(radii)):
            radius = radii[i]
            nsample = nsamples[i]
            self.groupers.append(
                pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
                if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
            )
            mlp_spec = mlps[i]
            if use_xyz:
                mlp_spec[0] += 3
            mlp_spec.append(mlp_spec[-1]) # Add output channel for self-attention
            self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm))

        self.pool_method = pool_method

    def forward(self, xyz, features):
        """
        :param xyz: (B, N, 3) tensor of the xyz coordinates of the features
        :param features: (B, C, N) tensor of the descriptors of the features
        :return: (B, sum(mlps[-1]), npoint) tensor of the global features of each subsampled ball
        """
        new_features_list = []
        for i in range(len(self.groupers)):
            new_features = self.groupers[i](xyz, features)
            new_features = self.mlps[i](new_features)
            new_features = self.forward_self_attention(new_features) # Apply self-attention
            new_features = getattr(torch, self.pool_method)(new_features, 2)  # (B, mlp[-1], npoint)
            new_features_list.append(new_features)
        return torch.cat(new_features_list, dim=1)  # (B, sum(mlps[-1]), npoint)

在这个新的 `PointnetSAModuleMSG` 类中，我们继承了 `SelfAttention` 和 `_PointnetSAModuleBase`，并在构造函数中增加了一个新的参数 `reduction`，它用于控制 self-attention 的降维比例。

在 `__init__` 函数中，我们首先调用 `super()` 来初始化 `SelfAttention` 类，然后按照原来的方式初始化 `PointnetSAModuleMSG` 类。不同的是，在最后一层 MLP 之后，我们加入了一个新的输出通道，用于 self-attention 的计算。

在 `forward` 函数中，我们首先调用原来的方法获取每个球的子采样点集，然后将这些点集输入到新的 MLP 中。在这个 MLP 的最后一层之后，我们使用 `forward_self_attention` 函数对特征进行自注意力计算。最后，我们使用池化函数对每个子采样球的特征进行池化，并将它们拼接在一起，形成一个全局特征的张量。