The following are 30 code examples of torch.index_select (). Select (input, dim, index) → tensor ¶ slices the input tensor along the selected dimension at the given index. Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is.
`torch.index_select` will return a random value if input tensor is
See the parameters, return value and an example of the function.
W[ index, :] second way:
You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the. In pytorch, the.index_select() function extracts specific elements from a tensor along a specified dimension based on indices and returns a new tensor. Indices = torch.tensor([0, 2]) selected_rows = torch.index_select(tensor, 0, indices) print(selected_rows) This function returns a view of the original tensor with the given dimension.
I could achieve a batch index_select by following two ways: Use this to pick entire rows or columns. You can use the torch.index_select() function (or the tensor.index_select() method) to select multiple dimensions from a tensor and return a new tensor with the same number of. How these work and can be used to embed words with word vectors in a.
Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is.
We call torch.index_select with x as the input tensor, dim=0 to select rows, and indices to specify which rows to select. We define indices as a 1d tensor containing 0 and 2, indicating we want. Suppose we have access to index(t, indices) where passing a list of raw. Torch.index_select torch.index_select(input, dim, index, *, out=none) → tensor.
256 is the batch size. Index_select (dim, index) → tensor ¶ see torch.index_select() I’m trying to write my own index_select to understand how this is done under the hood efficiently.
