torch.index_select(input, dim, index) 函数返回的是沿着输入张量的指定维度的指定索引号进行索引的张量子集,函数参数有: input(Tensor) - 需要进行索引操作的输入张量; dim(int) - 需要对输入张量进行索引的维度; index(LongTensor) - 包含索引号的 1D 张量; 一维例子: 二维例子: 4. torch.nonzero()和torc...
x1,x2,x3 = torch.split(torch.index_select(verts,0, faces[:,0]) - torch.index_select(verts,0, faces[:,1]),1, dim =1) y1,y2,y3 = torch.split(torch.index_select(verts,0, faces[:,1]) - torch.index_select(verts,0, faces[:,2]),1, dim =1) a = (x2*y3 - x3*y2)**...
9。 index_select: 通过从目标张量中进行选择,可以返回一个带有上述索引的张量。语法:torch.index_select(input, dim, index, out=None) 参数:Input (Tensor): Tensor from which the index will be selected. Dimension (int): The dimension selected along it. Index: An index containing an index....
index_select(dim=0, index=mention_ids) # [lamda*word_num] mention_end_tensor = torch.from_numpy(mention_end).to(self.device).index_select(dim=0, index=mention_ids) # [lamda*word_num] mention_emb = candidate_mention_emb.index_select(index=mention_ids, dim=0) # [lamda*word_num,emb...
3.2,gather 和 torch.index_select 算子 gather算子的用法比较难以理解,在翻阅了官方文档和网上资料后,我有了一些自己的理解。 1,gather是不规则的切片提取算子(Gathers values along an axis specified by dim. 在指定维度上根据索引 index 来选取数据)。函数定义如下: ...
RuntimeError: index_add_cuda_ does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True)'... 解决方案: 自己定义一个确定性的实现,替换调用的接口。对于torch.index_select 这个接口,可以有如下的实现。 def...
torch.index_select(input,dim,index,out=None) → Tensor Returns a new tensor which indexes theinputtensor along dimensiondimusing the entries inindexwhich is a LongTensor. The returned tensor has the same number of dimensions as the original tensor (input). Thedimth dimension has the same size...
As the picture below shows, torch.index_select outperforms regular indexing when the tensor size is small, but is outperformed when the size is large. Is this expected behavior? If I have to do indexing many times, am I expected to choose the indexing method based on the size of the ...
scatter_(-1, index, 1.0) hardwts = one_h - probs.detach() + probs if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()): continue else: break feature = self.stem(inputs) for i, cell in enumerate(self.cells): if isinstance(cell, ...
E[t].index_put_(select, subE.transpose(0,1))returnC.transpose(1,2), E.transpose(1,2) 开发者ID:elbayadm,项目名称:attn2d,代码行数:22,代码来源:dynamic_controls.py 注:本文中的torch.empty_like方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的...