获取相应的索引。使用示例：```import torch 创建一个张量 x = torch.randn(3, 4)在第1维上按升序排序 sorted_x, indices = torch.sort(x, dim=1)print(sorted_x)print(indices)在第0维上按降序排序 sorted_x, indices = torch.sort(x, dim=0, descending=True)print(sorted_x)print(indices)```
descending:排序方式(从小到大和从大到小),默认为从小到大排序(即descending=False) 示例 torch.sort() import torch a = torch.tensor([[2,3,1],[0,5,6]]) print(torch.sort(a)) print(torch.sort(a,dim=0)) print(torch.sort(a,dim=0,descending=True)) >>>torch.return_types.sort( values=...
softmax(CoAtt, dim=-1) new_x = torch.cat([torch.bmm(CoAtt, x), x], -1) sorted_x_len, indx = torch.sort(x_len, 0, descending=True) new_x = pack_padded_sequence(new_x[indx], sorted_x_len.data.tolist(), batch_first=True) h0 = to_cuda(torch.zeros(2, x_len.size(0...
3.3 dim = 0 表示对每列中的元素进行降序排序,descending=True表示降序排序 x=torch.randn(3,4)sorted,indices=torch.sort(x,dim=0,descending=True)x,sorted,indices 输出结果如下:(tensor([[ 0.9142,-0.2178,0.5602,2.3951],[-0.6977,0.4915,0.3988,0.6406],[ 0.4880,1.1646,-0.3466,0.5801]]),tensor([[ ...
_, idx_sort = torch.sort(x_lengths, dim=0, descending=True)#_, idx_unsort = torch.sort(idx_sort, dim=0) x = x.index_select(0, idx_sort) x_lengths = x_lengths[idx_sort] x_packed = nn.utils.rnn.pack_padded_sequence(x, x_lengths, batch_first=True) ...
i, idx= a.sort(dim=1, descending=True) print(i) print(idx) print() j, rank= idx.sort(dim=1) print(rank) 返回: tensor([[2.3326,0.0275, -0.0799,0.4156], [-2.2066,1.7997, -2.2767,0.4704], [-0.6980,0.2285,1.0018, -0.8874]]) ...
dim = -1,按照行排序,dim= 1按照列排序,descending=True,则递减排序,否则递增 3.例子 按照行排序 logits = torch.tensor([[[-0.5816, -0.3873, -1.0215, -1.0145, 0.4053], [ 0.7265, 1.4164, 1.3443, 1.2035, 1.8823], [-0.4451, 0.1673, 1.2590, -2.0757, 1.7255], ...
torch.sort(input, dim=- 1, descending=False, stable=False, *, out=None) 参数: input(Tensor) -输入张量。 dim(int,可选的) -要排序的维度 descending(bool,可选的) -控制排序顺序(升序或降序) stable(bool,可选的) -使排序例程稳定,从而保证保留等效元素的顺序。 关键字参数: out(tuple,可选的)...
Out[174]:tensor([[4,9,7,8,5],[3,5,9,1,4]])# 现在进行第一次的sort,返回的是元素降序的对应索引_,loss_idx=b.sort(dim=1,descending=True)loss_idx Out[176]:tensor([ [1,3,2,4,0], [2,1,4,0,3]])# 进行第二次的sort,得到原Tensor的元素按dim指定维度,排第几,索引变成了排名_,...
Sort(0, False) 'sort descending End Function Public Function SortHoleChartTable() As Boolean Debug.Print ("Table type selected: swTableAnnotation_HoleChart") Dim swSpecTable As IHoleTableAnnotation Set swSpecTable = swTable Dim status As Boolean status = swSpecTable.Sort(0, True) 'sort ...