:param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs.

这是一个技术问题，我可以回答。这段代码是一个 PyTorch 中的函数，它接受一个形状为 [N x C x ...] 的特征张量和一个形状为 [N x emb_channels] 的时间步嵌入张量，然后返回一个形状相同的输出张量。具体实现细节需要查看函数的源代码。

相关问题

class PointnetFPModule(nn.Module): r"""Propigates the features of one set to another""" def __init__(self, *, mlp: List[int], bn: bool = True): """ :param mlp: list of int :param bn: whether to use batchnorm """ super().__init__() self.mlp = pt_utils.SharedMLP(mlp, bn=bn) def forward( self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor ) -> torch.Tensor: """ :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features :param known: (B, m, 3) tensor of the xyz positions of the known features :param unknow_feats: (B, C1, n) tensor of the features to be propigated to :param known_feats: (B, C2, m) tensor of features to be propigated :return: new_features: (B, mlp[-1], n) tensor of the features of the unknown features """ if known is not None: dist, idx = pointnet2_utils.three_nn(unknown, known) dist_recip = 1.0 / (dist + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight) else: interpolated_feats = known_feats.expand(*known_feats.size()[0:2], unknown.size(1)) if unknow_feats is not None: new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) # (B, C2 + C1, n) else: new_features = interpolated_feats new_features = new_features.unsqueeze(-1) new_features = self.mlp(new_features) return new_features.squeeze(-1)运行时报错： File "/root/autodl-tmp/project/tools/../pointnet2_lib/pointnet2/pointnet2_modules.py", line 165, in forward new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) # (B, C2 + C1, n) RuntimeError: Sizes of tensors must match except in dimension 2. Got 64 and 256 (The offending index is 0)

这个错误是因为在执行 torch.cat 操作时，第二个张量 unknow_feats 的维度与 interpolated_feats 的维度不匹配。具体来说，unknow_feats 的第二个维度是 256，而 interpolated_feats 的第二个维度是 64。

你需要确保这两个张量的维度在进行 torch.cat 操作时是匹配的。如果你需要将它们拼接在一起，可以尝试调整 unknow_feats 的维度或者使用一个新的张量来存储它们的拼接结果。如果你不需要 unknow_feats，也可以直接将其设置为 None。

class GatherOperation(Function): @staticmethod def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: """ :param ctx: :param features: (B, C, N) :param idx: (B, npoint) index tensor of the features to gather :return: output: (B, C, npoint) """ assert features.is_contiguous() assert idx.is_contiguous() B, npoint = idx.size() _, C, N = features.size() output = torch.cuda.FloatTensor(B, C, npoint) pointnet2.gather_points_wrapper(B, C, N, npoint, features, idx, output) ctx.for_backwards = (idx, C, N) return output @staticmethod def backward(ctx, grad_out): idx, C, N = ctx.for_backwards B, npoint = idx.size() grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_()) grad_out_data = grad_out.data.contiguous() pointnet2.gather_points_grad_wrapper(B, C, N, npoint, grad_out_data, idx, grad_features.data) return grad_features, None gather_operation = GatherOperation.apply

这是一个 PyTorch 自定义函数 GatherOperation，用于实现从给定张量中根据索引提取特定元素的功能。具体地，输入 features 是形状为 (B, C, N) 的张量，表示 B 个样本中每个样本的 N 个特征向量，每个向量有 C 个元素。输入 idx 是形状为 (B, npoint) 的整数张量，表示要从 features 中提取的元素的索引。输出是形状为 (B, C, npoint) 的张量，表示提取的结果。

在 forward 函数中，首先对输入的 features 和 idx 进行了连续性检查，然后调用了一个 C++ 实现的 gather_points_wrapper 函数，将提取结果存储在 output 变量中，并将 idx、C、N 存储在 ctx 中以备反向传播使用。在 backward 函数中，首先从 ctx 中恢复 idx、C、N，然后创建一个和 features 相同形状的 grad_features 变量，调用 gather_points_grad_wrapper 函数计算 grad_features，并返回。

这个 GatherOperation 函数在 PointNet++ 中被广泛使用，用于实现点云分类和语义分割等任务中的采样和汇聚操作。