File size: 3,310 Bytes
d6b3397 | 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 | # 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
"""Unit tests for benchmark training-metric logging helpers."""
from __future__ import annotations
import pytest
from scripts.benchmarks.utils import SUCCESS_RATE_LOG_TAGS, log_rl_training_metrics
class _FakeBenchmark:
"""Collect benchmark measurements without initializing benchmark backends."""
def __init__(self):
self.measurements: list[tuple[str, str, object, str]] = []
def add_measurement(self, phase, measurement):
self.measurements.append((phase, measurement.name, measurement.value, getattr(measurement, "unit", "")))
def measurement_by_name(self, name: str):
return next(m for m in self.measurements if m[1] == name)
@pytest.mark.parametrize(
"workflow,reward_tag,episode_length_tag",
[
("rl_games", "rewards/iter", "episode_lengths/iter"),
("rsl_rl", "Train/mean_reward", "Train/mean_episode_length"),
],
)
def test_log_rl_training_metrics_skips_missing_short_run_scalars(
workflow: str, reward_tag: str, episode_length_tag: str, capsys: pytest.CaptureFixture[str]
):
"""Short benchmark runs may finish before reward and episode-length scalars are emitted."""
benchmark = _FakeBenchmark()
log_rl_training_metrics(
benchmark,
log_data={},
reward_tag=reward_tag,
episode_length_tag=episode_length_tag,
task="Isaac-Ant-v0",
workflow=workflow,
should_check_convergence=True,
)
assert benchmark.measurements == []
output = capsys.readouterr().out
assert f"TensorBoard log is missing '{reward_tag}'" in output
assert f"TensorBoard log is missing '{episode_length_tag}'" in output
assert f"Cannot check convergence because '{reward_tag}' was not logged" in output
@pytest.mark.parametrize(
"workflow,reward_tag,episode_length_tag",
[
("rl_games", "rewards/iter", "episode_lengths/iter"),
("rsl_rl", "Train/mean_reward", "Train/mean_episode_length"),
],
)
def test_log_rl_training_metrics_logs_present_normal_run_scalars(
workflow: str, reward_tag: str, episode_length_tag: str, capsys: pytest.CaptureFixture[str]
):
"""Normal runs with reward and episode-length scalars should log train metrics."""
benchmark = _FakeBenchmark()
log_rl_training_metrics(
benchmark,
log_data={
reward_tag: [1.0, 2.0, 3.0],
episode_length_tag: [10.0, 11.0],
SUCCESS_RATE_LOG_TAGS[0]: [0.25, 0.5],
},
reward_tag=reward_tag,
episode_length_tag=episode_length_tag,
task="Isaac-Ant-v0",
workflow=workflow,
)
assert benchmark.measurement_by_name("Rewards")[2] == [1.0, 2.0, 3.0]
assert benchmark.measurement_by_name("Max Rewards")[2] == 3.0
assert benchmark.measurement_by_name("Episode Lengths")[2] == [10.0, 11.0]
assert benchmark.measurement_by_name("Max Episode Lengths")[2] == 11.0
assert benchmark.measurement_by_name("Success Rates")[2] == [0.25, 0.5]
assert benchmark.measurement_by_name("success_rate")[2] == 0.5
assert "TensorBoard log is missing" not in capsys.readouterr().out
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