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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 | # 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 in headless mode
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows from here."""
from collections.abc import Generator
import pytest
import torch
from isaaclab.utils import DelayBuffer
@pytest.fixture
def delay_buffer():
"""Create a delay buffer for testing."""
device: str = "cpu"
batch_size: int = 10
history_length: int = 4
return DelayBuffer(history_length, batch_size=batch_size, device=device)
def _generate_data(batch_size: int, length: int, device: str) -> Generator[torch.Tensor]:
"""Data generator for testing the buffer."""
for step in range(length):
yield torch.full((batch_size, 1), step, dtype=torch.int, device=device)
def test_constant_time_lags(delay_buffer):
"""Test constant delay."""
const_lag: int = 3
batch_size: int = 10
delay_buffer.set_time_lag(const_lag)
all_data = []
for i, data in enumerate(_generate_data(batch_size, 20, delay_buffer.device)):
all_data.append(data)
# apply delay
delayed_data = delay_buffer.compute(data)
error = delayed_data - all_data[max(0, i - const_lag)]
assert torch.all(error == 0)
def test_reset(delay_buffer):
"""Test resetting the last two batch indices after iteration `reset_itr`."""
const_lag: int = 2
reset_itr = 10
batch_size: int = 10
delay_buffer.set_time_lag(const_lag)
all_data = []
for i, data in enumerate(_generate_data(batch_size, 20, delay_buffer.device)):
all_data.append(data)
# from 'reset_itr' iteration reset the last and second-to-last environments
if i == reset_itr:
delay_buffer.reset([-2, -1])
# apply delay
delayed_data = delay_buffer.compute(data)
# before 'reset_itr' is is similar to test_constant_time_lags
# after that indices [-2, -1] should be treated separately
if i < reset_itr:
error = delayed_data - all_data[max(0, i - const_lag)]
assert torch.all(error == 0)
else:
# error_regular = delayed_data[:-2] - all_data[max(0, i - const_lag)][:-2]
error2_reset = delayed_data[-2, -1] - all_data[max(reset_itr, i - const_lag)][-2, -1]
# assert torch.all(error_regular == 0)
assert torch.all(error2_reset == 0)
def test_random_time_lags(delay_buffer):
"""Test random delays."""
max_lag: int = 3
time_lags = torch.randint(
low=0, high=max_lag + 1, size=(delay_buffer.batch_size,), dtype=torch.int, device=delay_buffer.device
)
delay_buffer.set_time_lag(time_lags)
all_data = []
for i, data in enumerate(_generate_data(delay_buffer.batch_size, 20, delay_buffer.device)):
all_data.append(data)
# apply delay
delayed_data = delay_buffer.compute(data)
true_delayed_index = torch.maximum(i - delay_buffer.time_lags, torch.zeros_like(delay_buffer.time_lags))
true_delayed_index = true_delayed_index.tolist()
for i in range(delay_buffer.batch_size):
error = delayed_data[i] - all_data[true_delayed_index[i]][i]
assert torch.all(error == 0)
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