class PointnetSAModuleMSG(_PointnetSAModuleBase): """ Pointnet set abstraction layer with multiscale grouping and attention mechanism """ 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() # Add attention module self.attentions = 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 # Add attention module for each scale self.attentions.append(Attention(mlp_spec[-1])) 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) xyz coordinates of the points :param features: (B, N, C) input features :return: (B, npoint, mlp[-1]) tensor """ new_features_list = [] for i in range(len(self.groupers)): grouper = self.groupers[i] mlp = self.mlps[i] attention = self.attentions[i] # Group points and features grouped_xyz, grouped_features = grouper(xyz, features) # Apply MLP to each group grouped_features = mlp(grouped_features) # Apply attention mechanism to the features of each group grouped_features = attention(grouped_features) # Perform pooling over each group if self.pool_method == 'max_pool': pooled_features = torch.max(grouped_features, dim=2)[0] else: pooled_features = torch.mean(grouped_features, dim=2) new_features_list.append(pooled_features) # Concatenate features from different scales new_features = torch.cat(new_features_list, dim=1) return new_features在该类中使用的QueryAndGroup类会主动将该类所继承的父类的返回值传入QueryAndGroup类中的forward函数吗

是的，该类中使用的QueryAndGroup类会调用其继承的父类_PointnetSAModuleBase的forward函数，并将其返回值传入QueryAndGroup类中的forward函数中。具体来说，QueryAndGroup类的forward函数接收点云数据和特征数据作为输入，然后调用父类_PointnetSAModuleBase的forward函数对点云数据进行处理，得到每个点所属的局部区域以及该区域内的点的特征，最后将点的坐标和特征数据传入MLP进行处理，得到该局部区域的特征表示。这些处理结果会被QueryAndGroup类返回供_PointnetSAModuleMSG类使用。