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6b92ff7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import numpy as np
PRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53]
def radical_inverse(base, n):
val = 0
inv_base = 1.0 / base
inv_base_n = inv_base
while n > 0:
digit = n % base
val += digit * inv_base_n
n //= base
inv_base_n *= inv_base
return val
def halton_sequence(dim, n):
return [radical_inverse(PRIMES[dim], n) for dim in range(dim)]
def hammersley_sequence(dim, n, num_samples):
return [n / num_samples] + halton_sequence(dim - 1, n)
def sphere_hammersley_sequence(n, num_samples, offset=(0, 0), remap=False):
u, v = hammersley_sequence(2, n, num_samples)
u += offset[0] / num_samples
v += offset[1]
if remap:
u = 2 * u if u < 0.25 else 2 / 3 * u + 1 / 3
theta = np.arccos(1 - 2 * u) - np.pi / 2
phi = v * 2 * np.pi
return [phi, theta]
def set_random_seed(seed: int, deterministic: bool = False) -> None:
"""Best-effort seeding for reproducibility across common RNGs."""
import os
import random
import torch
if seed is None:
return
seed = int(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
if deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
try:
# warn_only keeps training/inference running if an op has no deterministic kernel.
torch.use_deterministic_algorithms(True, warn_only=True)
except TypeError:
torch.use_deterministic_algorithms(True)
except Exception:
pass |