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fix device propogation for noise and add noise tests
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-3
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2 files changed

+177
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‎source/extensions/omni.isaac.lab/omni/isaac/lab/utils/noise/noise_model.py

Lines changed: 53 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,21 @@
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def constant_noise(data: torch.Tensor, cfg: noise_cfg.ConstantNoiseCfg) -> torch.Tensor:
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"""Constant noise."""
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"""Applies a constant noise bias to a given data set.
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Args:
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data: The unmodified data set to apply noise to.
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cfg: The configuration parameters for constant noise.
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Returns:
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The data modified by the noise parameters provided.
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"""
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# fix tensor device for bias on first call and update config parameters
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if isinstance(cfg.bias,torch.Tensor):
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if cfg.bias.device is not data.device:
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cfg.bias = cfg.bias.to(data.device,)
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if cfg.operation == "add":
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return data + cfg.bias
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elif cfg.operation == "scale":
@@ -30,7 +44,25 @@ def constant_noise(data: torch.Tensor, cfg: noise_cfg.ConstantNoiseCfg) -> torch
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def uniform_noise(data: torch.Tensor, cfg: noise_cfg.UniformNoiseCfg) -> torch.Tensor:
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"""Uniform noise."""
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"""Applies a uniform noise to a given data set.
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Args:
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data: The unmodified data set to apply noise to.
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cfg: The configuration parameters for uniform noise.
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Returns:
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The data modified by the noise parameters provided.
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"""
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# fix tensor device for n_max on first call and update config parameters
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if isinstance(cfg.n_max,torch.Tensor):
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if cfg.n_max.device is not data.device:
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cfg.n_max = cfg.n_max.to(data.device)
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# fix tensor device for n_min on first call and update config parameters
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if isinstance(cfg.n_min,torch.Tensor):
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if cfg.n_min.device is not data.device:
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cfg.n_min = cfg.n_min.to(data.device)
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if cfg.operation == "add":
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return data + torch.rand_like(data) * (cfg.n_max - cfg.n_min) + cfg.n_min
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elif cfg.operation == "scale":
@@ -42,7 +74,25 @@ def uniform_noise(data: torch.Tensor, cfg: noise_cfg.UniformNoiseCfg) -> torch.T
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def gaussian_noise(data: torch.Tensor, cfg: noise_cfg.GaussianNoiseCfg) -> torch.Tensor:
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"""Gaussian noise."""
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"""Applies a gaussian noise to a given data set.
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Args:
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data: The unmodified data set to apply noise to.
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cfg: The configuration parameters for gaussian noise.
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Returns:
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The data modified by the noise parameters provided.
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"""
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# fix tensor device for mean on first call and update config parameters
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if isinstance(cfg.mean,torch.Tensor):
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if cfg.mean.device is not data.device:
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cfg.mean = cfg.mean.to(data.device)
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# fix tensor device for std on first call and update config parameters
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if isinstance(cfg.std,torch.Tensor):
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if cfg.std.device is not data.device:
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cfg.std = cfg.std.to(data.device)
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4696
if cfg.operation == "add":
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return data + cfg.mean + cfg.std * torch.randn_like(data)
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elif cfg.operation == "scale":

‎source/extensions/omni.isaac.lab/test/utils/test_noise.py

Lines changed: 124 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,124 @@
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# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
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# All rights reserved.
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#
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# SPDX-License-Identifier: BSD-3-Clause
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"""Launch Isaac Sim Simulator first."""
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from omni.isaac.lab.app import AppLauncher, run_tests
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# launch omniverse app
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app_launcher = AppLauncher(headless=True)
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simulation_app = app_launcher.app
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"""Rest everything follows."""
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import time
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import torch
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import unittest
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from dataclasses import MISSING
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import omni.isaac.lab.utils.noise as noise
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from omni.isaac.lab.utils import configclass
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class TestNoise(unittest.TestCase):
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"""Test different noise implementations."""
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def test_gaussian_noise(self):
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"""Test guassian_noise function."""
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for device in ["cpu","cuda"]:
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for noise_device in ["cpu","cuda"]:
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for op in ["add","scale","abs"]:
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with self.subTest(device=device, noise_device=noise_device, operation=op):
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# create random data set
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data = torch.rand(10000, 3, device=device)
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# define standard deviation and mean
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std = torch.tensor([0.1,0.2,0.3],device=noise_device)
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mean = torch.tensor([0.4,0.5,0.6],device=noise_device)
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# create noise config
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noise_cfg = noise.GaussianNoiseCfg(std=std,
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mean=mean,
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operation=op)
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for i in range(10):
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# apply noise
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noisy_data = noise_cfg.func(data,cfg=noise_cfg)
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# calculate resulting noise compared to original data set
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if op=="add":
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std_result, mean_result = torch.std_mean(noisy_data-data,dim=0)
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elif op=="scale":
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std_result, mean_result = torch.std_mean(noisy_data/data,dim=0)
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elif op=="abs":
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std_result, mean_result = torch.std_mean(noisy_data,dim=0)
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self.assertTrue(noise_cfg.mean.device,device)
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self.assertTrue(noise_cfg.std.device,device)
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torch.testing.assert_close(noise_cfg.std,std_result,atol=1e-2,rtol=1e-2)
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torch.testing.assert_close(noise_cfg.mean,mean_result,atol=1e-2,rtol=1e-2)
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def test_uniform_noise(self):
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"""Test uniform_noise function."""
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for device in ["cpu","cuda"]:
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for noise_device in ["cpu","cuda"]:
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for op in ["add","scale","abs"]:
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with self.subTest(device=device, noise_device=noise_device,operation=op):
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# create random data set
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data = torch.rand(10000, 3, device=device)
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# define uniform minimum and maximum
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n_min = torch.tensor([0.1,0.2,0.3],device=noise_device)
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n_max = torch.tensor([0.4,0.5,0.6],device=noise_device)
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# create noise config
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noise_cfg = noise.UniformNoiseCfg(n_max=n_max, n_min=n_min,operation=op)
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for i in range(10):
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# apply noise
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noisy_data = noise_cfg.func(data,cfg=noise_cfg)
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# calculate resulting noise compared to original data set
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if op=="add":
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min_result, _ = torch.min(noisy_data-data,dim=0)
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max_result, _ = torch.max(noisy_data-data,dim=0)
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elif op=="scale":
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min_result, _ = torch.min(torch.div(noisy_data,data),dim=0)
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max_result, _ = torch.max(torch.div(noisy_data,data),dim=0)
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elif op=="abs":
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min_result, _ = torch.min(noisy_data,dim=0)
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max_result, _ = torch.max(noisy_data,dim=0)
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self.assertTrue(noise_cfg.n_min.device,device)
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self.assertTrue(noise_cfg.n_max.device,device)
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self.assertTrue(all(torch.le(noise_cfg.n_min, min_result).tolist()))
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self.assertTrue(all(torch.ge(noise_cfg.n_max, max_result).tolist()))
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def test_constant_noise(self):
97+
"""Test constant_noise"""
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for device in ["cpu","cuda"]:
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for noise_device in ["cpu","cuda"]:
100+
for op in ["add","scale","abs"]:
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with self.subTest(device=device, noise_device=noise_device,operation=op):
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# create random data set
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data = torch.rand(10000, 3, device=device)
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# define a bias
105+
bias = torch.tensor([0.1,0.2,0.3],device=noise_device)
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# create noise config
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noise_cfg = noise.ConstantNoiseCfg(bias=bias, operation=op)
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for i in range(10):
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# apply noise
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noisy_data = noise_cfg.func(data,cfg=noise_cfg)
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# calculate resulting noise compared to original data set
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if op=="add":
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bias_result = noisy_data-data
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elif op=="scale":
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bias_result = noisy_data/data
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elif op=="abs":
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bias_result = noisy_data
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self.assertTrue(noise_cfg.bias.device,device)
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torch.testing.assert_close(noise_cfg.bias.repeat(data.shape[0],1),bias_result)
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if __name__ == "__main__":
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run_tests()

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