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chore: replace hard-coded action values with constants throughout all the source code (#2055)

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

* chore(tests): replace hard-coded action values with constants throughout all the test code
This commit is contained in:
Steven Palma
2025-09-26 13:33:18 +02:00
committed by GitHub
parent 9627765ce2
commit d2782cf66b
47 changed files with 269 additions and 255 deletions
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14
tests/processor/test_converters.py
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@@ -9,7 +9,7 @@ from lerobot.processor.converters import (
to_tensor,
transition_to_batch,
)
from lerobot.utils.constants import OBS_STATE, OBS_STR
from lerobot.utils.constants import ACTION, OBS_STATE, OBS_STR
# Tests for the unified to_tensor function
@@ -118,16 +118,16 @@ def test_to_tensor_dictionaries():
# Nested dictionary
nested = {
"action": {"mean": [0.1, 0.2], "std": [1.0, 2.0]},
ACTION: {"mean": [0.1, 0.2], "std": [1.0, 2.0]},
OBS_STR: {"mean": np.array([0.5, 0.6]), "count": 10},
}
result = to_tensor(nested)
assert isinstance(result, dict)
assert isinstance(result["action"], dict)
assert isinstance(result[ACTION], dict)
assert isinstance(result[OBS_STR], dict)
assert isinstance(result["action"]["mean"], torch.Tensor)
assert isinstance(result[ACTION]["mean"], torch.Tensor)
assert isinstance(result[OBS_STR]["mean"], torch.Tensor)
assert torch.allclose(result["action"]["mean"], torch.tensor([0.1, 0.2]))
assert torch.allclose(result[ACTION]["mean"], torch.tensor([0.1, 0.2]))
assert torch.allclose(result[OBS_STR]["mean"], torch.tensor([0.5, 0.6]))
@@ -200,7 +200,7 @@ def test_batch_to_transition_with_index_fields():
# Create batch with index and task_index fields
batch = {
OBS_STATE: torch.randn(1, 7),
"action": torch.randn(1, 4),
ACTION: torch.randn(1, 4),
"next.reward": 1.5,
"next.done": False,
"task": ["pick_cube"],
@@ -262,7 +262,7 @@ def test_batch_to_transition_without_index_fields():
# Batch without index/task_index
batch = {
OBS_STATE: torch.randn(1, 7),
"action": torch.randn(1, 4),
ACTION: torch.randn(1, 4),
"task": ["pick_cube"],
}