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Indexing and slicing a tensor in PyTorch

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by kanika dot icon Updated: Apr 6, 2023

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Indexing and slicing a tensor in PyTorch refers to selecting a specific part of a tensor, which can be done using a combination of indices and slices. This is useful for selecting tensor parts, such as a subset of rows or columns or a certain number of elements along a certain dimension. Indexing and slicing can be used to select and manipulate tensor parts, which can be used for various operations, such as creating sub-tensors from a larger tensor or applying certain operations to only a subset of elements in a tensor. 


A tensor in Python is a multi-dimensional array used to store numerical data. It is a fundamental data structure in deep learning models like convolutional neural networks (CNNs). Tensors are usually represented as a matrix of numbers and can be manipulated using various operations such as addition, multiplication, and division. 


Indexing and slicing of tensors in PyTorch are the same as indexing and slicing lists in Python. 

  • To retrieve a single tensor element, use the indexing operator [] with the corresponding indices. 
  • To slice a tensor, use the slicing operator: with the corresponding indices. 


Here is an example of indexing and slicing a tensor in PyTorch. 



Fig 1: Preview of the output that you will get on indexing a tensor in PyTorch.



Fig 2: Preview of the output that you will get on slicing a tensor in PyTorch.

Codes


In this solution, we use the torch.tensor Function of the PyTorch library

How Pytorch Tensor get the index of specific value

PythondotLines of Code : 3dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
t = torch.Tensor([1, 2, 3])
print ((t == 2).nonzero())
Pytorch select tensor

Lines of Code : 9dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import torch
A = torch.rand((3,5,500))
first_three_rows = A[:, :3, :]

import torch
A = torch.rand((3,5,500))
indices = [2,4,5]
result = torch.cat([A[idx, :index, :] for idx, index in enumerate(indices)] , dim=0)

Instructions

Follow the steps carefully to get the output easily.

  1. Install Jupyter Notebook on your computer.
  2. Open terminal and install the required libraries with following commands.
  3. Install pytorch - pip install torch.
  4. Copy the codes using the "Copy" button above, and paste it into your IDE's Python file.
  5. Print Result in slicing.
  6. Run the file to perform Indexing and slicing a tensor in PyTorch.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "Indexing and slicing a tensor in PyTorch" in kandi. You can try any such use case!

Dependent Libraries


pytorchby pytorch

Python doticonstar image 67874 doticonVersion:v2.0.1doticon
License: Others (Non-SPDX)

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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            pytorchby pytorch

            Python doticon star image 67874 doticonVersion:v2.0.1doticon License: Others (Non-SPDX)

            Tensors and Dynamic neural networks in Python with strong GPU acceleration
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have PyTorch that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the PyTorch page in kandi.


                      You can search for any dependent library on kandi like PyTorch

                      Environment Tested


                      I tested this solution in the following versions. Be mindful of changes when working with other versions.

                      1. The solution is created in Python 3.9.6
                      2. The solution is tested on PyTorch 2.0.0+cpu version.


                      Using this solution, we are able to perform indexing and slicing of tensor in PyTorch in Python with simple steps. PyTorch is also used in Computer Vision and Generative Adversarial Networks.

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