torch.index_select

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于 2022-08-19 17:47:32 首次发布

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torch.index_select函数用于根据指定维度上的索引返回一个新的张量，该张量与原始张量具有相同数量的维度，指定维度的大小与索引张量的长度相同，其他维度的大小保持不变。注意返回的张量不共享原始张量的存储。如果索引张量形状不正确，会自动调整并可能重新分配存储。

PyTorch 2.5

PyTorch

Cuda

PyTorch 是一个开源的 Python 机器学习库，基于 Torch 库，底层由 C++ 实现，应用于人工智能领域，如计算机视觉和自然语言处理

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 dimth dimension has the same size as the length of index; other dimensions have the same size as in the original tensor.

Note

The returned tensor does not use the same storage as the original tensor. If out has a different shape than expected, we silently change it to the correct shape, reallocating the underlying storage if necessary.

Parameters

input (Tensor) – the input tensor.
dim (int) – the dimension in which we index
index (LongTensor) – the 1-D tensor containing the indices to index
out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-0.4664,  0.2647, -0.1228, -1.1068],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> indices = torch.tensor([0, 2])
>>> torch.index_select(x, 0, indices)
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> torch.index_select(x, 1, indices)
tensor([[ 0.1427, -0.5414],
        [-0.4664, -0.1228],
        [-1.1734,  0.7230]])

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