在pytorch中将索引选定张量添加到另一个具有重叠-Python问题

Add a index selected tensor to another tensor with overlapping indices in pytorch(在pytorch中将索引选定张量添加到另一个具有重叠索引的张量)

本文介绍了在pytorch中将索引选定张量添加到另一个具有重叠索引的张量的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

这是这个问题的后续问题.我想在 pytorch 中做同样的事情.是否有可能做到这一点?如果是，如何?

导入火炬image = torch.tensor([[246, 50, 101], [116, 1, 113], [187, 110, 64]])iy = torch.tensor([[1, 0, 2], [1, 0, 2], [2, 2, 2]])ix = torch.tensor([[0, 2, 1], [1, 2, 0], [0, 1, 2]])warped_image = torch.zeros(size=image.shape)我需要像 torch.add.at(warped_image, (iy, ix), image) 之类的东西，它给出的输出为
[[ 0. 0. 51.][246.116. 0.][300.211. 64.]]注意 (0,1) 和 (1,1) 处的索引指向相同的位置 (0,2).所以，我想要 warped_image[0,2] = image[0,1] + image[1,1] = 51.
 解决方案 
您正在寻找的是 torch.Tensor.index_put_ 将 accumulate 参数设置为 True:
<预><代码>>>>warped_image = torch.zeros_like(图像)>>>warped_image.index_put_((iy, ix), image,accumulate=True)张量([[ 0, 0, 51],[246, 116, 0],[300, 211, 64]])
或者，使用外置版本torch.index_put:
<预><代码>>>>torch.index_put(torch.zeros_like(图像)，(iy，ix)，图像，累积=真)张量([[ 0, 0, 51],[246, 116, 0],[300, 211, 64]])

This is a follow up question to this question. I want to do the exactly same thing in pytorch. Is it possible to do this? If yes, how?

import torch
image = torch.tensor([[246,  50, 101], [116,   1, 113], [187, 110,  64]])
iy = torch.tensor([[1, 0, 2], [1, 0, 2], [2, 2, 2]])
ix = torch.tensor([[0, 2, 1], [1, 2, 0], [0, 1, 2]])
warped_image = torch.zeros(size=image.shape)

I need something like torch.add.at(warped_image, (iy, ix), image) that gives the output as

[[  0.   0.  51.]
 [246. 116.   0.]
 [300. 211.  64.]]

Note that the indices at (0,1) and (1,1) point to the same location (0,2). So, I want warped_image[0,2] = image[0,1] + image[1,1] = 51.

解决方案

What you are looking for is torch.Tensor.index_put_ with the accumulate argument set to True:

>>> warped_image = torch.zeros_like(image)

>>> warped_image.index_put_((iy, ix), image, accumulate=True)
tensor([[  0,   0,  51],
        [246, 116,   0],
        [300, 211,  64]])

Or, using the out-place version torch.index_put:

>>> torch.index_put(torch.zeros_like(image), (iy, ix), image, accumulate=True)
tensor([[  0,   0,  51],
        [246, 116,   0],
        [300, 211,  64]])

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