How to compute element-wise remainder of given input tensor in PyTorch?

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In this article, we are going to see how to compute the element-wise remainder in PyTorch. we have two methods to compute element-wise reminders one is torch.remainder() and the other one is torch.fmod() let’s go discuss both of them one by one.

torch.remainder() method

The PyTorch remainder() method computes the element-wise remainder of the division operation (dividend is divided by divisor). The dividend is a tensor whereas the divisor may be a scalar quantity or tensor. The values must be an integer and float only. before moving further let’s see the syntax of the given method.

Syntax: torch.remainder(input, other, out=None)

Parameters:

input (Tensor or Scalar) : the dividend element.

other (Tensor or Scalar) : the divisor element.

Return: This method returns a new tensor with remainder values.

Example 1:

The following program is to compute the element-wise remainder of two single-dimension tensors.

Python3

# importing torch 
import torch 
  
# define the dividend 
tens_1 = torch.tensor([5., -12., 25., -10., 30]) 
print("Dividend: ", tens_1) 
  
# define the divisor 
tens_2 = torch.tensor([5., -5., -6., 5., 8.]) 
print("Divisor: ", tens_2) 
  
# compute the remainder 
remainder = torch.remainder(tens_1, tens_2) 
  
# display result 
print("Remainder: ", remainder) 

Output:

Dividend: tensor([ 5., -12., 25., -10., 30.])

Divisor: tensor([ 5., -5., -6., 5., 8.])

Remainder: tensor([ 0., -2., -5., -0., 6.])

Example 2:

The following program is to compute the element-wise remainder of two 2D tensors.

Python3

# importing torch 
import torch 
  
# define the dividend 
tens_1 = torch.tensor([[5., -12.], 
                       [-10., 30.], ]) 
print("\n Dividend: \n", tens_1) 
  
# define the divisor 
tens_2 = torch.tensor([[5., -5.], 
                       [5., 8.], ]) 
  
print("\n Divisor: \n", tens_2) 
  
# compute the remainder 
remainder = torch.remainder(tens_1, tens_2) 
  
# display result 
print("\n Remainder: \n", remainder) 

Output:

 Dividend: 
 tensor([[  5., -12.],
        [-10.,  30.]])

 Divisor: 
 tensor([[ 5., -5.],
        [ 5.,  8.]])

 Remainder: 
 tensor([[ 0., -2.],
        [-0.,  6.]])

torch.fmod() method

This method gives also helps us to compute the element-wise remainder of division by the divisor. The divisor may be a number or a Tensor. When the divisor is zero it will return NaN. before moving further let’s see the syntax of the given method.

Syntax: torch.fmod(input, other)

Parameters:

input (Tensor) : the dividend.

other (Tensor or Scalar) : the divisor.

Return: This method returns a new tensor with remainder values.

Example 1:

The following program is to compute the element-wise remainder of two single-dimension tensors.

Python3

# importing torch 
import torch 
  
# define the dividend 
tens_1 = torch.tensor([5., -10., -17., 19., 20.]) 
print("\n\n Dividend: ", tens_1) 
  
# define the divisor 
tens_2 = torch.tensor([2., 5., 17., 7., 10.]) 
  
print("\n Divisor: ", tens_2) 
  
# compute the remainder using fmod() 
remainder = torch.fmod(tens_1, tens_2) 
  
# display result 
print("\n Remainder: ", remainder) 

Output:

 Dividend:  tensor([  5., -10., -17.,  19.,  20.])

 Divisor:  tensor([ 2.,  5., 17.,  7., 10.])

 Remainder:  tensor([1., -0., -0., 5., 0.])

Example 2:

The following program is to compute the element-wise remainder of two 2D tensors.

Python3

# importing torch 
import torch 
  
# define the dividend 
tens_1 = torch.tensor([[16., -12.], 
                       [-10., 30.], ]) 
print("\n\n Dividend: \n", tens_1) 
  
# define the divisor 
tens_2 = torch.tensor([[5., -6.], 
                       [5., 8.], ]) 
  
print("\n Divisor: \n", tens_2) 
  
# compute the remainder using fmod() 
remainder = torch.fmod(tens_1, tens_2) 
  
# display result 
print("\n Remainder:\n", remainder) 

Output:

 Dividend: 
 tensor([[ 16., -12.],
        [-10.,  30.]])

 Divisor: 
 tensor([[ 5., -6.],
        [ 5.,  8.]])

 Remainder:
 tensor([[1., -0.],
        [-0., 6.]])