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0d80452 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | #!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
pytest.importorskip("transformers")
from lerobot.data_processing.sarm_annotations.subtask_annotation import (
Subtask,
SubtaskAnnotation,
Timestamp,
compute_temporal_proportions,
)
def make_annotation(subtasks: list[tuple[str, int, int]]) -> SubtaskAnnotation:
"""Helper to create SubtaskAnnotation from list of (name, start_sec, end_sec)."""
return SubtaskAnnotation(
subtasks=[
Subtask(
name=name,
timestamps=Timestamp(
start=f"{start // 60:02d}:{start % 60:02d}", end=f"{end // 60:02d}:{end % 60:02d}"
),
)
for name, start, end in subtasks
]
)
class TestComputeTemporalProportions:
"""Tests for compute_temporal_proportions (SARM Paper Formula 1).
Formula: ᾱ_k = (1/M) × Σ_i (L_{i,k} / T_i)
Key insight: This averages the PROPORTION of each subtask within each trajectory,
giving equal weight to all trajectories regardless of absolute length.
"""
def test_basic_two_trajectories_equal_proportions(self):
"""Test with two trajectories that have equal proportions."""
# Both trajectories: subtask1 = 50%, subtask2 = 50%
# Traj 1: T=100s, subtask1=50s, subtask2=50s
# Traj 2: T=200s, subtask1=100s, subtask2=100s
annotations = {
0: make_annotation([("subtask1", 0, 50), ("subtask2", 50, 100)]),
1: make_annotation([("subtask1", 0, 100), ("subtask2", 100, 200)]),
}
result = compute_temporal_proportions(annotations)
# Both should be 0.5
assert abs(result["subtask1"] - 0.5) < 1e-6
assert abs(result["subtask2"] - 0.5) < 1e-6
def test_paper_example_different_from_avg_durations(self):
"""Test that compute_temporal_proportions differs from naive average duration approach.
This is the key test showing the difference between:
- Paper formula: average of (L_i,k / T_i)
- Naive approach: mean(L_i,k) / sum(mean(L_i,j))
"""
# Episode 1: T=100s, subtask1=80s, subtask2=20s (proportions: 0.8, 0.2)
# Episode 2: T=200s, subtask1=40s, subtask2=160s (proportions: 0.2, 0.8)
annotations = {
0: make_annotation([("subtask1", 0, 80), ("subtask2", 80, 100)]),
1: make_annotation([("subtask1", 0, 40), ("subtask2", 40, 200)]),
}
result = compute_temporal_proportions(annotations)
# Paper formula:
# ᾱ_1 = (1/2) × (80/100 + 40/200) = (1/2) × (0.8 + 0.2) = 0.5
# ᾱ_2 = (1/2) × (20/100 + 160/200) = (1/2) × (0.2 + 0.8) = 0.5
assert abs(result["subtask1"] - 0.5) < 1e-6
assert abs(result["subtask2"] - 0.5) < 1e-6
def test_single_trajectory(self):
"""Test with a single trajectory."""
# T=100s, reach=30s, grasp=20s, lift=50s
annotations = {
0: make_annotation([("reach", 0, 30), ("grasp", 30, 50), ("lift", 50, 100)]),
}
result = compute_temporal_proportions(annotations)
assert abs(result["reach"] - 0.3) < 1e-6
assert abs(result["grasp"] - 0.2) < 1e-6
assert abs(result["lift"] - 0.5) < 1e-6
def test_sum_to_one(self):
"""Test that proportions always sum to 1."""
# Three episodes with varying proportions
annotations = {
0: make_annotation([("a", 0, 10), ("b", 10, 50), ("c", 50, 100)]), # 0.1, 0.4, 0.5
1: make_annotation([("a", 0, 20), ("b", 20, 70), ("c", 70, 100)]), # 0.2, 0.5, 0.3
2: make_annotation([("a", 0, 30), ("b", 30, 90), ("c", 90, 100)]), # 0.3, 0.6, 0.1
}
result = compute_temporal_proportions(annotations)
total = sum(result.values())
assert abs(total - 1.0) < 1e-6
def test_empty_annotations_returns_empty(self):
"""Test that empty annotations returns empty dict."""
result = compute_temporal_proportions({})
assert result == {}
def test_uniform_proportions(self):
"""Test with uniform proportions across subtasks."""
# Each subtask takes 25% of each episode
annotations = {
0: make_annotation([("a", 0, 25), ("b", 25, 50), ("c", 50, 75), ("d", 75, 100)]),
1: make_annotation([("a", 0, 50), ("b", 50, 100), ("c", 100, 150), ("d", 150, 200)]),
}
result = compute_temporal_proportions(annotations)
for name in ["a", "b", "c", "d"]:
assert abs(result[name] - 0.25) < 1e-6
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