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) def get_model(input_channels=6, use_xyz=True): return Pointnet2MSG(input_channels=input_channels, use_xyz=use_xyz) class Pointnet2MSG(nn.Module): def init(self, input_channels=6, use_xyz=True): super().init() self.SA_modules = nn.ModuleList() channel_in = input_channels skip_channel_list = [input_channels] for k in range(cfg.RPN.SA_CONFIG.NPOINTS.len()): mlps = cfg.RPN.SA_CONFIG.MLPS[k].copy() channel_out = 0 for idx in range(mlps.len()): mlps[idx] = [channel_in] + mlps[idx] channel_out += mlps[idx][-1] mlps.append(channel_out) 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 ), SelfAttention(channel_out) ) ) skip_channel_list.append(channel_out) channel_in = channel_out self.FP_modules = nn.ModuleList() for k in range(cfg.RPN.FP_MLPS.len()): pre_channel = cfg.RPN.FP_MLPS[k + 1][-1] if k + 1 < len(cfg.RPN.FP_MLPS) else channel_out self.FP_modules.append( PointnetFPModule( mlp=[pre_channel + skip_channel_list[k]] + cfg.RPN.FP_MLPS[k] ) )根据如上代码，如果要在Pointnet2MSG类中的forward函数调用SA_modules的话需要传入哪些参数，几个参数？

在Pointnet2MSG类中的forward函数调用SA_modules时，需要传入点云数据x以及额外的参数，具体如下：

def forward(self, x):
    l_xyz, l_features = [None] * self.num_layers, [None] * self.num_layers
    # SA_modules
    for i in range(self.num_layers):
        # SA_modules需要传入的参数包括：输入点云数据x，输入点云xyz坐标l_xyz[i-1]，输入点云特征向量l_features[i-1]
        xyz, features = self.SA_modules[i][0](x if i == 0 else l_xyz[i-1], l_features[i-1])
        l_xyz[i], l_features[i] = xyz, features
        # SelfAttention模块需要传入的参数：输入特征向量l_features[i]
        l_features[i] = self.SA_modules[i][1](l_features[i])
    # FP_modules
    for i in range(self.num_layers-1, -1, -1):
        # FP_modules需要传入的参数包括：上一层的输出xyz坐标l_xyz[i+1]，上一层的输出特征向量l_features[i+1]，当前层的输入xyz坐标l_xyz[i]，当前层的输入特征向量l_features[i]
        l_features[i] = self.FP_modules[i](l_xyz[i+1], l_xyz[i], l_features[i+1], l_features[i])
    return l_features[0]

其中，SA_modules需要传入的参数包括：输入点云数据x，输入点云xyz坐标l_xyz[i-1]，输入点云特征向量l_features[i-1]，SelfAttention模块需要传入的参数为输入特征向量l_features[i]。FP_modules需要传入的参数包括：上一层的输出xyz坐标l_xyz[i+1]，上一层的输出特征向量l_features[i+1]，当前层的输入xyz坐标l_xyz[i]，当前层的输入特征向量l_features[i]。