There is no pi (π) function in PyTorch with some version, so some people suggested to use math.pi or np.pi for conversion into torch tensor. For me, torch.pi works with a notebook without the GPU, but not with a computer with the GPU.
Here is the code to compare different implementations of the Pi function.
import torch
import numpy as np
import time
import math
#test pi
time_start = time.perf_counter()
pi_math = torch.tensor(math.pi)
time_elapsed_test = (time.perf_counter() - time_start)
print('math pi time =',time_elapsed_test)
time_start = time.perf_counter()
pi_np = torch.tensor(np.pi)
time_elapsed_test = (time.perf_counter() - time_start)
print('np pi time =',time_elapsed_test)
time_start = time.perf_counter()
pi_torch = torch.pi
time_elapsed_test = (time.perf_counter() - time_start)
print('torch pi time =',time_elapsed_test)
time_start = time.perf_counter()
PI = torch.acos(torch.Tensor([-1]))
time_elapsed_test = (time.perf_counter() - time_start)
print('PI time =',time_elapsed_test)
Results for computation time:
math pi time = 3.6502000057225814e-05
np pi time = 1.9006000002264045e-05
torch pi time = 8.009999419300584e-07
PI time = 0.00035780000007434865
Hence torch.pi is the fastest.
Without torch.pi, you may use torch.tensor(np.pi)
For an accurate pi, cast the type to float64:
torch.tensor(np.pi, dtype=torch.float64)
Example:
(Pdb) torch.tensor(np.pi)*100000
tensor(314159.2812)
(Pdb) torch.tensor(np.pi, dtype=torch.float64)*100000
tensor(314159.2654, dtype=torch.float64)
MATLAB:
pi = 3.141592653589793
Reference:
