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chore: replace hard-coded obs values with constants throughout all the source code (#2037)

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* chore: replace hard-coded OBS values with constants throughout all the source code

* chore(tests): replace hard-coded OBS values with constants throughout all the test code
This commit is contained in:
Steven Palma
2025-09-25 15:36:47 +02:00
committed by GitHub
parent ddba994d73
commit 43d878a102
52 changed files with 659 additions and 649 deletions
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33
tests/datasets/test_compute_stats.py
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@@ -28,6 +28,7 @@ from lerobot.datasets.compute_stats import (
sample_images,
sample_indices,
)
from lerobot.utils.constants import OBS_IMAGE, OBS_STATE
def mock_load_image_as_numpy(path, dtype, channel_first):
@@ -136,21 +137,21 @@ def test_get_feature_stats_single_value():
def test_compute_episode_stats():
episode_data = {
"observation.image": [f"image_{i}.jpg" for i in range(100)],
"observation.state": np.random.rand(100, 10),
OBS_IMAGE: [f"image_{i}.jpg" for i in range(100)],
OBS_STATE: np.random.rand(100, 10),
}
features = {
"observation.image": {"dtype": "image"},
"observation.state": {"dtype": "numeric"},
OBS_IMAGE: {"dtype": "image"},
OBS_STATE: {"dtype": "numeric"},
}
with patch("lerobot.datasets.compute_stats.load_image_as_numpy", side_effect=mock_load_image_as_numpy):
stats = compute_episode_stats(episode_data, features)
assert "observation.image" in stats and "observation.state" in stats
assert stats["observation.image"]["count"].item() == 100
assert stats["observation.state"]["count"].item() == 100
assert stats["observation.image"]["mean"].shape == (3, 1, 1)
assert OBS_IMAGE in stats and OBS_STATE in stats
assert stats[OBS_IMAGE]["count"].item() == 100
assert stats[OBS_STATE]["count"].item() == 100
assert stats[OBS_IMAGE]["mean"].shape == (3, 1, 1)
def test_assert_type_and_shape_valid():
@@ -224,38 +225,38 @@ def test_aggregate_feature_stats():
def test_aggregate_stats():
all_stats = [
{
"observation.image": {
OBS_IMAGE: {
"min": [1, 2, 3],
"max": [10, 20, 30],
"mean": [5.5, 10.5, 15.5],
"std": [2.87, 5.87, 8.87],
"count": 10,
},
"observation.state": {"min": 1, "max": 10, "mean": 5.5, "std": 2.87, "count": 10},
OBS_STATE: {"min": 1, "max": 10, "mean": 5.5, "std": 2.87, "count": 10},
"extra_key_0": {"min": 5, "max": 25, "mean": 15, "std": 6, "count": 6},
},
{
"observation.image": {
OBS_IMAGE: {
"min": [2, 1, 0],
"max": [15, 10, 5],
"mean": [8.5, 5.5, 2.5],
"std": [3.42, 2.42, 1.42],
"count": 15,
},
"observation.state": {"min": 2, "max": 15, "mean": 8.5, "std": 3.42, "count": 15},
OBS_STATE: {"min": 2, "max": 15, "mean": 8.5, "std": 3.42, "count": 15},
"extra_key_1": {"min": 0, "max": 20, "mean": 10, "std": 5, "count": 5},
},
]
expected_agg_stats = {
"observation.image": {
OBS_IMAGE: {
"min": [1, 1, 0],
"max": [15, 20, 30],
"mean": [7.3, 7.5, 7.7],
"std": [3.5317, 4.8267, 8.5581],
"count": 25,
},
"observation.state": {
OBS_STATE: {
"min": 1,
"max": 15,
"mean": 7.3,
@@ -283,7 +284,7 @@ def test_aggregate_stats():
for fkey, stats in ep_stats.items():
for k in stats:
stats[k] = np.array(stats[k], dtype=np.int64 if k == "count" else np.float32)
if fkey == "observation.image" and k != "count":
if fkey == OBS_IMAGE and k != "count":
stats[k] = stats[k].reshape(3, 1, 1) # for normalization on image channels
else:
stats[k] = stats[k].reshape(1)
@@ -292,7 +293,7 @@ def test_aggregate_stats():
for fkey, stats in expected_agg_stats.items():
for k in stats:
stats[k] = np.array(stats[k], dtype=np.int64 if k == "count" else np.float32)
if fkey == "observation.image" and k != "count":
if fkey == OBS_IMAGE and k != "count":
stats[k] = stats[k].reshape(3, 1, 1) # for normalization on image channels
else:
stats[k] = stats[k].reshape(1)