srch

torch.index_select¶ torch. index_select (input, dim, index, *, out = None) → Tensor ¶ Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.. The returned tensor has the same number of dimensions as the original tensor (input).The dim th dimension has the same size as the length of index; other dimensions have …

11.03.2021 · Hi, I usually index tensors with lists of indices, like x = torch.as_tensor([[1,2,3,4,5], [6,7,8,9,0]]) index = [[0, 1, 1], [1, 1, 2]] # tensor([2, 7, 8]) x[index] Now I need index to be a tensor object, but doing this, I get an error: x = torch.as_tensor([[1,2,3,4,5], [6,7,8,9,0]]) index = torch.as_tensor( [[0, 1, 1], [1, 1, 2]]) # IndexError: index 2 is out of bounds for dimension 0 with ...

So, what is the purpose of gather function? Docs says: torch.gather(input, dim, index, out=None, sparse_grad=False) → TensorGathers values ...

In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays. Torch's indexing semantics ...

12.05.2018 · The only supported types are integers, slices, numpy scalars, or if indexing with a torch.LongTensor or torch.ByteTensor only a single Tensor may be passed. 2 Likes miguelvr (Miguel Varela Ramos) May 12, 2018, 9:14am

Indexing a tensor in the PyTorch C++ API works very similar to the Python API. All index types such as None / ... / integer / boolean / slice / tensor are ...

You can specify the corresponding row index as: import torch x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) y = torch.tensor([0, 2, ...

The torch_index package is designed for performing advanced indexing on PyTorch tensors. Beyond the support of basic indexing methods (vectorized indexing, ...

05.08.2018 · How about. In [1]: torch.nonzero ( (t == q).sum (dim=1) == t.size (1)) Out [1]: tensor ( [ [ 0], [ 3]]) Comparing t == q performs element-wise comparison between t and q, since you are looking for entire row match, you need to .sum (dim=1) along the rows and see what row is a perfect match == t.size (1).

dim: dimension along which index to add. '0' stands for column and '1' stands for row. · index: indices of the tensor to select from. It can be ...

torch::Tensor::index . torch::Tensor::index_put_ It’s also important to note that index types such as None / Ellipsis / Slice live in the torch::indexing namespace, and it’s recommended to put using namespace torch::indexing before any indexing code for convenient use of those index types.