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406662d | 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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from isaaclab.app import AppLauncher
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import pytest
import torch
import isaaclab.utils.noise as noise
@pytest.mark.parametrize("device", ["cpu", "cuda:0"])
@pytest.mark.parametrize("noise_device", ["cpu", "cuda:0"])
@pytest.mark.parametrize("op", ["add", "scale", "abs"])
def test_gaussian_noise(device, noise_device, op):
"""Test guassian_noise function."""
# create random data set
data = torch.rand(10000, 3, device=device)
# define standard deviation and mean
std = torch.tensor([0.1, 0.2, 0.3], device=noise_device)
mean = torch.tensor([0.4, 0.5, 0.6], device=noise_device)
# create noise config
noise_cfg = noise.GaussianNoiseCfg(std=std, mean=mean, operation=op)
for i in range(10):
# apply noise
noisy_data = noise_cfg.func(data, cfg=noise_cfg)
# calculate resulting noise compared to original data set
if op == "add":
std_result, mean_result = torch.std_mean(noisy_data - data, dim=0)
elif op == "scale":
std_result, mean_result = torch.std_mean(noisy_data / data, dim=0)
elif op == "abs":
std_result, mean_result = torch.std_mean(noisy_data, dim=0)
assert str(noise_cfg.mean.device) == device
assert str(noise_cfg.std.device) == device
torch.testing.assert_close(noise_cfg.std, std_result, atol=1e-2, rtol=1e-2)
torch.testing.assert_close(noise_cfg.mean, mean_result, atol=1e-2, rtol=1e-2)
@pytest.mark.parametrize("device", ["cpu", "cuda:0"])
@pytest.mark.parametrize("noise_device", ["cpu", "cuda:0"])
@pytest.mark.parametrize("op", ["add", "scale", "abs"])
def test_uniform_noise(device, noise_device, op):
"""Test uniform_noise function."""
# create random data set
data = torch.rand(10000, 3, device=device)
# define uniform minimum and maximum
n_min = torch.tensor([0.1, 0.2, 0.3], device=noise_device)
n_max = torch.tensor([0.4, 0.5, 0.6], device=noise_device)
# create noise config
noise_cfg = noise.UniformNoiseCfg(n_max=n_max, n_min=n_min, operation=op)
for i in range(10):
# apply noise
noisy_data = noise_cfg.func(data, cfg=noise_cfg)
# calculate resulting noise compared to original data set
if op == "add":
min_result, _ = torch.min(noisy_data - data, dim=0)
max_result, _ = torch.max(noisy_data - data, dim=0)
elif op == "scale":
min_result, _ = torch.min(torch.div(noisy_data, data), dim=0)
max_result, _ = torch.max(torch.div(noisy_data, data), dim=0)
elif op == "abs":
min_result, _ = torch.min(noisy_data, dim=0)
max_result, _ = torch.max(noisy_data, dim=0)
assert str(noise_cfg.n_min.device) == device
assert str(noise_cfg.n_max.device) == device
# add a small epsilon to accommodate for floating point error
assert all(torch.le(noise_cfg.n_min - 1e-5, min_result).tolist())
assert all(torch.ge(noise_cfg.n_max + 1e-5, max_result).tolist())
@pytest.mark.parametrize("device", ["cpu", "cuda:0"])
@pytest.mark.parametrize("noise_device", ["cpu", "cuda:0"])
@pytest.mark.parametrize("op", ["add", "scale", "abs"])
def test_constant_noise(device, noise_device, op):
"""Test constant_noise"""
# create random data set
data = torch.rand(10000, 3, device=device)
# define a bias
bias = torch.tensor([0.1, 0.2, 0.3], device=noise_device)
# create noise config
noise_cfg = noise.ConstantNoiseCfg(bias=bias, operation=op)
for i in range(10):
# apply noise
noisy_data = noise_cfg.func(data, cfg=noise_cfg)
# calculate resulting noise compared to original data set
if op == "add":
bias_result = noisy_data - data
elif op == "scale":
bias_result = noisy_data / data
elif op == "abs":
bias_result = noisy_data
assert str(noise_cfg.bias.device) == device
torch.testing.assert_close(noise_cfg.bias.repeat(data.shape[0], 1), bias_result)
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