How to perform element-wise division on tensors in PyTorch?
Last Updated :
02 Mar, 2022
In this article, we will understand how to perform element-wise division of two tensors in PyTorch. To perform the element-wise division of tensors, we can apply the torch.div() method. It takes two tensors (dividend and divisor) as the inputs and returns a new tensor with the element-wise division result. We can use the below syntax to compute the element-wise division-
Syntax: torch.div(input, other, rounding_mode=None)
Parameters:
- input: the first input tensor (dividend).
- other: the second input tensor (divisor).
- rounding_mode: The type of rounding applied to the result.
Return: it returns a new tensor with element-wise division of the tensor input by the tensor other.
Example:
Inputs:
tensor([10., 25., 30.])
tensor([2., -5., 10.])
Output:
tensor([5., -5., 3.])
Inputs:
tensor([3., 0., 23.])
tensor([2., -5., 0.])
Output:
tensor([1.5, -0., inf])
Let’s understand how the element-wise division of tensors works with the help of some Python examples.
Example 1:
In the example below, we perform the element-wise division of two 1-D tensors using the PyTorch method torch.div().
Python3
import torch
A = torch.tensor([0.0312, 0.3401, 0.1645, -1.0781])
print("Tensor A:\n", A)
B = torch.tensor([-1.8584, 0.5706, -0.8994, 2.2492])
print("\nTensor B:\n", B)
result = torch.div(A, B)
print("\nElement-wise Division Output:\n", result)
|
Output:
Tensor A:
tensor([ 0.0312, 0.3401, 0.1645, -1.0781])
Tensor B:
tensor([-1.8584, 0.5706, -0.8994, 2.2492])
Element-wise Division Output:
tensor([-0.0168, 0.5960, -0.1829, -0.4793])
Example 2:
In the example below, we perform the element-wise division of a 2D tensor by a 2D tensor using the PyTorch method torch.div(). We also apply different rounding modes.
Python3
import torch
a = torch. tensor([[-1.8665, 0.6341, 0.8920],
[-0.1712, 0.3949, 1.9414],
[-1.2088, -1.0375, -1.3087],
[0.9161, -0.2972, 1.5289]])
print("Tensor a:\n", a)
b = torch. tensor([[-0.2187, 0.5252, -0.5840],
[1.5293, -0.4514, 1.8490],
[-0.7269, -0.1561, -0.0629],
[-0.5379, -0.9751, 0.6541]])
print("\nTensor b:\n", b)
print("\nElement-wise Division:")
result1 = torch.div(a, b)
print("\nResult:\n", result1)
result2 = torch.div(a, b, rounding_mode='trunc')
print("\nResult with rounding_mode='trunc':\n", result2)
result3 = torch.div(a, b, rounding_mode='floor')
print("\nResult with rounding_mode='floor':\n", result3)
|
Output:
Tensor a:
tensor([[-1.8665, 0.6341, 0.8920],
[-0.1712, 0.3949, 1.9414],
[-1.2088, -1.0375, -1.3087],
[ 0.9161, -0.2972, 1.5289]])
Tensor b:
tensor([[-0.2187, 0.5252, -0.5840],
[ 1.5293, -0.4514, 1.8490],
[-0.7269, -0.1561, -0.0629],
[-0.5379, -0.9751, 0.6541]])
Element-wise Division:
Result:
tensor([[ 8.5345, 1.2073, -1.5274],
[-0.1119, -0.8748, 1.0500],
[ 1.6630, 6.6464, 20.8060],
[-1.7031, 0.3048, 2.3374]])
Result with rounding_mode='trunc':
tensor([[ 8., 1., -1.],
[-0., -0., 1.],
[ 1., 6., 20.],
[-1., 0., 2.]])
Result with rounding_mode='floor':
tensor([[ 8., 1., -2.],
[-1., -1., 1.],
[ 1., 6., 20.],
[-2., 0., 2.]])
Example 3:
In the below example, we perform the element-wise division of a 3-D tensor by a 1-D tensor using the PyTorch method torch.div().
Python3
import torch
a = torch.randn(3, 2, 2)
b = torch.randn(2)
print("Tensor a :\n", a)
print("\nTensor b :\n", b)
result = torch.div(a, b)
print("\nElementwise Division Output :\n", result)
|
Output:
Tensor a :
tensor([[[-0.7549, 1.8301],
[-0.5545, 1.3180]],
[[ 0.1159, 0.8394],
[ 0.0452, -1.2860]],
[[ 0.3850, -0.9654],
[-1.5530, -0.8627]]])
Tensor b :
tensor([-1.2378, 0.2153])
Elementwise Division Output :
tensor([[[ 0.6098, 8.5011],
[ 0.4480, 6.1226]],
[[-0.0937, 3.8991],
[-0.0365, -5.9739]],
[[-0.3110, -4.4844],
[ 1.2546, -4.0074]]])
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