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# 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."""
from dataclasses import MISSING
import pytest
import torch
import isaaclab.utils.modifiers as modifiers
from isaaclab.utils import configclass
@configclass
class ModifierTestCfg:
"""Configuration for testing modifiers."""
cfg: modifiers.ModifierCfg = MISSING
init_data: torch.Tensor = MISSING
result: torch.Tensor = MISSING
num_iter: int = 10
def test_scale_modifier():
"""Test scale modifier."""
# create test data
init_data = torch.tensor([1.0, 2.0, 3.0])
scale = 2.0
result = torch.tensor([2.0, 4.0, 6.0])
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.ModifierCfg(func=modifiers.scale, params={"multiplier": scale}),
init_data=init_data,
result=result,
)
# test modifier
for _ in range(test_cfg.num_iter):
output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params)
assert torch.allclose(output, test_cfg.result)
def test_bias_modifier():
"""Test bias modifier."""
# create test data
init_data = torch.tensor([1.0, 2.0, 3.0])
bias = 1.0
result = torch.tensor([2.0, 3.0, 4.0])
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.ModifierCfg(func=modifiers.bias, params={"value": bias}),
init_data=init_data,
result=result,
)
# test modifier
for _ in range(test_cfg.num_iter):
output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params)
assert torch.allclose(output, test_cfg.result)
def test_clip_modifier():
"""Test clip modifier."""
# create test data
init_data = torch.tensor([1.0, 2.0, 3.0])
min_val = 1.5
max_val = 2.5
result = torch.tensor([1.5, 2.0, 2.5])
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (min_val, max_val)}),
init_data=init_data,
result=result,
)
# test modifier
for _ in range(test_cfg.num_iter):
output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params)
assert torch.allclose(output, test_cfg.result)
def test_clip_no_upper_bound_modifier():
"""Test clip modifier with no upper bound."""
# create test data
init_data = torch.tensor([1.0, 2.0, 3.0])
min_val = 1.5
result = torch.tensor([1.5, 2.0, 3.0])
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (min_val, None)}),
init_data=init_data,
result=result,
)
# test modifier
for _ in range(test_cfg.num_iter):
output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params)
assert torch.allclose(output, test_cfg.result)
def test_clip_no_lower_bound_modifier():
"""Test clip modifier with no lower bound."""
# create test data
init_data = torch.tensor([1.0, 2.0, 3.0])
max_val = 2.5
result = torch.tensor([1.0, 2.0, 2.5])
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.ModifierCfg(func=modifiers.clip, params={"bounds": (None, max_val)}),
init_data=init_data,
result=result,
)
# test modifier
for _ in range(test_cfg.num_iter):
output = test_cfg.cfg.func(test_cfg.init_data, **test_cfg.cfg.params)
assert torch.allclose(output, test_cfg.result)
def test_torch_relu_modifier():
"""Test torch relu modifier."""
# create test data
init_data = torch.tensor([-1.0, 0.0, 1.0])
result = torch.tensor([0.0, 0.0, 1.0])
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.ModifierCfg(func=torch.nn.functional.relu),
init_data=init_data,
result=result,
)
# test modifier
for _ in range(test_cfg.num_iter):
output = test_cfg.cfg.func(test_cfg.init_data)
assert torch.allclose(output, test_cfg.result)
@pytest.mark.parametrize("device", ["cpu", "cuda:0"])
def test_digital_filter(device):
"""Test digital filter modifier."""
# create test data
init_data = torch.tensor([0.0, 0.0, 0.0], device=device)
A = [0.0, 0.1]
B = [0.5, 0.5]
result = torch.tensor([-0.45661893, -0.45661893, -0.45661893], device=device)
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.DigitalFilterCfg(A=A, B=B), init_data=init_data, result=result, num_iter=16
)
# create a modifier instance
modifier_obj = test_cfg.cfg.func(test_cfg.cfg, test_cfg.init_data.shape, device=device)
# test the modifier
theta = torch.tensor([0.0], device=device)
delta = torch.pi / torch.tensor([8.0, 8.0, 8.0], device=device)
for _ in range(5):
# reset the modifier
modifier_obj.reset()
# apply the modifier multiple times
for i in range(test_cfg.num_iter):
data = torch.sin(theta + i * delta)
processed_data = modifier_obj(data)
assert data.shape == processed_data.shape, "Modified data shape does not equal original"
# check if the modified data is close to the expected result
torch.testing.assert_close(processed_data, test_cfg.result)
@pytest.mark.parametrize("device", ["cpu", "cuda:0"])
def test_integral(device):
"""Test integral modifier."""
# create test data
init_data = torch.tensor([0.0], device=device)
dt = 1.0
result = torch.tensor([12.5], device=device)
# create test config
test_cfg = ModifierTestCfg(
cfg=modifiers.IntegratorCfg(dt=dt),
init_data=init_data,
result=result,
num_iter=6,
)
# create a modifier instance
modifier_obj = test_cfg.cfg.func(test_cfg.cfg, test_cfg.init_data.shape, device=device)
# test the modifier
delta = torch.tensor(1.0, device=device)
for _ in range(5):
# reset the modifier
modifier_obj.reset()
# clone the data to avoid modifying the original
data = test_cfg.init_data.clone()
# apply the modifier multiple times
for _ in range(test_cfg.num_iter):
processed_data = modifier_obj(data)
data = data + delta
assert data.shape == processed_data.shape, "Modified data shape does not equal original"
# check if the modified data is close to the expected result
torch.testing.assert_close(processed_data, test_cfg.result)
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