srch

として動作を定義しています。 Note テンソル condition 、 x 、 y はブロードキャスト可能でなければなりません。 Note 現在有効なスカラーとテンソルの組み合わせは以下の通りです。浮動d型とtorch.doubleのスカラー 2.2.積分d型のスカラーとtorch.longのスカ

Note. torch.nonzero(..., as_tuple=False) (default) returns a 2-D tensor where each row is the index for a nonzero value. torch.nonzero(..., as_tuple=True) returns a tuple of 1-D index tensors, allowing for advanced indexing, so x[x.nonzero(as_tuple=True)] gives all nonzero values of tensor x.Of the returned tuple, each index tensor contains nonzero indices for a certain dimension.

Dec 18, 2017 · For floating point tensors, I use this to get the index of the element in the tensor.. print((torch.abs((torch.max(your_tensor).item()-your_tensor))<0.0001).nonzero()) Here I want to get the index of max_value in the float tensor, you can also put your value like this to get the index of any elements in tensor.

17.12.2017 · For floating point tensors, I use this to get the index of the element in the tensor.. print((torch.abs((torch.max(your_tensor).item()-your_tensor))<0.0001).nonzero()) Here I want to get the index of max_value in the float tensor, you can also put your value like this to get the index of any elements in tensor.

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 ).

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 …

index_fill_(dim, index, val) -> Tensor Fills the elements of the :attr:`self` tensor with value :attr:`val` by selecting the indices in the order given in ...

In torch.tensor, we have 10 Index Operations based functions. index_add_; index_add; index_copy_; index_copy; index_fill_; index_fill ...

torch.where(condition, x, y) → Tensor Return a tensor of elements selected from either x or y, depending on condition. The operation is defined as: \text {out}_i = \begin {cases} \text {x}_i & \text {if } \text {condition}_i \\ \text {y}_i & \text {otherwise} \\ \end {cases} outi = {xi yi if conditioni otherwise Note

May 12, 2020 · torch uses same convention as numpy such for finding values or indices of particular tensor regarding specific condition. torch.whereand torch.nonzero What you can do is to apply your condition and get a binary mask of indices that match the condition and find the indices using torch.nonzero(). import torch a = torch.randn(10) b = a <= 0

19.11.2017 · I have a 1D Variable (LongTensor) and i want to find the indices of the elements with a given value (zero). Is there a way to do this efficiently in PyTorch? For instance, in order to get the indices of non-zero elements, i do this: non_zeros = torch.nonzero(lengths_c.view(-1).data).squeeze() I need the opposite of this (indices of zero elements).

12.05.2020 · HI, torch uses same convention as numpy such for finding values or indices of particular tensor regarding specific condition. torch.where and torch.nonzero. What you can do is to apply your condition and get a binary mask of indices that match the condition and find the indices using torch.nonzero().. import torch a = torch.randn(10) b = a <= 0 indices = b.nonzero()

12.07.2018 · I'm suggesting to add the following operator to pytorch: result = torch.find(tensor, from_) This operator outputs a long tensor of the same shape as the argument tensor, where the result[i,j,..] is the first index at vector from_ whose v...

Bug Using a numpy array i as index for a torch tensor t (i.e. t[i]) is interpreted differently than indexing a numpy array with the same ...

Jul 12, 2018 · result = torch. find ( tensor, from_) This operator outputs a long tensor of the same shape as the argument tensor, where the result [i,j,..] is the first index at vector from_ whose value is the same as tensor [i,j,..]. For example: a = torch. tensor ( [ 0, 10, 20, 30 ]) b = torch. tensor ( [ [ 0, 30, 5 ], [ 20, 1, 0 ]]) torch. find ( b, a)

torch.where. Return a tensor of elements selected from either x or y, depending on condition. The operation is defined as: The tensors condition, x, y must be broadcastable. Currently valid scalar and tensor combination are 1. Scalar of floating dtype and torch.double 2. Scalar of integral dtype and torch.long 3.

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 ...

Returns the indices of the upper triangular part of a row by col matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates. Indices are ordered based on rows and then columns. The upper triangular part of the matrix is defined as the elements on and above the diagonal.

12.03.2020 · pytorch torch.where()函数用法torch.where(condition, x, y) → Tensor函数的作用根据条件，返回从x,y中选择元素所组成的张量。如果满足条件，则返回x中元素。若不满足，返回y中元 …

Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.) x <- torch_tensor( ...

torch.triu_indices(row, col, offset=0, *, dtype=torch.long, device='cpu', layout=torch.strided) → Tensor. Returns the indices of the upper triangular part of a row by col matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates. Indices are ordered based on rows and ...

torch.gather(input, dim, index, out=None, sparse_grad=False) ... indices = torch.tensor(indices).unsqueeze(axis=-1) print(matrix) ...

In this case, I'd want indices of values torch.Tensor([1, 2, 3]) all at once, not just 2 . Is there a way without for-loops?

Does torch have a function that helps in finding the indices satisfying a condition? For instance, F = torch.randn(10) b = torch.index(F ...

index3 = torch.tensor([ 1 , 3 ]). #copying elements of t1 ensor to 'a' in given order of index. a.index_copy_( 0 ,index3,t1) ...