Understand tensor.contiguous() with Examples: How to Use?

In this tutorial, we will use some examples to show you how to understnd and use tensor.contiguous() in PyTorch.

tensor.contiguous()

It is defined as:

Tensor.contiguous(memory_format=torch.contiguous_format)

It will return a contiguous in memory tensor containing the same data as self tensor.

As to tensor.view() function, it should be implemented on a contiguous tensor.

For example:

import torch
x = torch.tensor([[1, 2, 2],[2, 1, 3]])

x = x.transpose(0, 1)
print(x)
y = x.view(-1)
print(y)

In this code, we transpose tensor x, then change its shape with tensor.view() function.

Run this code, we will see this error.

y = x.view(-1)
RuntimeError: view size is not compatible with input tensor’s size and stride

In order make tensor.view() work, we can get a contiguous tensor.

For example:

import torch
x = torch.tensor([[1, 2, 2],[2, 1, 3]])

x = x.transpose(0, 1)
print(x)
x = x.contiguous()
y = x.view(-1)
print(y)

Run this code, we will see:

tensor([[1, 2],
        [2, 1],
        [2, 3]])
tensor([1, 2, 2, 1, 2, 3])

In this example, we use x.contiguous() to get a contiguous tensor before using x.view(), then x.view() can work well.

From above, we can know how to use tensor.contiguous() correctly.