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#!/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 torch
from lerobot.processor import DataProcessorPipeline, TransitionKey
from lerobot.processor.converters import batch_to_transition, transition_to_batch
from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_PREFIX, OBS_STATE, REWARD, TRUNCATED
def _dummy_batch():
"""Create a dummy batch using the new format with observation.* and next.* keys."""
return {
f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128),
f"{OBS_IMAGE}.right": torch.randn(1, 3, 128, 128),
OBS_STATE: torch.tensor([[0.1, 0.2, 0.3, 0.4]]),
ACTION: torch.tensor([[0.5]]),
REWARD: 1.0,
DONE: False,
TRUNCATED: False,
"info": {"key": "value"},
}
def test_observation_grouping_roundtrip():
"""Test that observation.* keys are properly grouped and ungrouped."""
proc = DataProcessorPipeline([])
batch_in = _dummy_batch()
batch_out = proc(batch_in)
# Check that all observation.* keys are preserved
original_obs_keys = {k: v for k, v in batch_in.items() if k.startswith(OBS_PREFIX)}
reconstructed_obs_keys = {k: v for k, v in batch_out.items() if k.startswith(OBS_PREFIX)}
assert set(original_obs_keys.keys()) == set(reconstructed_obs_keys.keys())
# Check tensor values
assert torch.allclose(batch_out[f"{OBS_IMAGE}.left"], batch_in[f"{OBS_IMAGE}.left"])
assert torch.allclose(batch_out[f"{OBS_IMAGE}.right"], batch_in[f"{OBS_IMAGE}.right"])
assert torch.allclose(batch_out[OBS_STATE], batch_in[OBS_STATE])
# Check other fields
assert torch.allclose(batch_out[ACTION], batch_in[ACTION])
assert batch_out[REWARD] == batch_in[REWARD]
assert batch_out[DONE] == batch_in[DONE]
assert batch_out[TRUNCATED] == batch_in[TRUNCATED]
assert batch_out["info"] == batch_in["info"]
def test_batch_to_transition_observation_grouping():
"""Test that batch_to_transition correctly groups observation.* keys."""
batch = {
f"{OBS_IMAGE}.top": torch.randn(1, 3, 128, 128),
f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128),
OBS_STATE: [1, 2, 3, 4],
ACTION: torch.tensor([0.1, 0.2, 0.3, 0.4]),
REWARD: 1.5,
DONE: True,
TRUNCATED: False,
"info": {"episode": 42},
}
transition = batch_to_transition(batch)
# Check observation is a dict with all observation.* keys
assert isinstance(transition[TransitionKey.OBSERVATION], dict)
assert f"{OBS_IMAGE}.top" in transition[TransitionKey.OBSERVATION]
assert f"{OBS_IMAGE}.left" in transition[TransitionKey.OBSERVATION]
assert OBS_STATE in transition[TransitionKey.OBSERVATION]
# Check values are preserved
assert torch.allclose(
transition[TransitionKey.OBSERVATION][f"{OBS_IMAGE}.top"], batch[f"{OBS_IMAGE}.top"]
)
assert torch.allclose(
transition[TransitionKey.OBSERVATION][f"{OBS_IMAGE}.left"], batch[f"{OBS_IMAGE}.left"]
)
assert transition[TransitionKey.OBSERVATION][OBS_STATE] == [1, 2, 3, 4]
# Check other fields
assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([0.1, 0.2, 0.3, 0.4]))
assert transition[TransitionKey.REWARD] == 1.5
assert transition[TransitionKey.DONE]
assert not transition[TransitionKey.TRUNCATED]
assert transition[TransitionKey.INFO] == {"episode": 42}
assert transition[TransitionKey.COMPLEMENTARY_DATA] == {}
def test_transition_to_batch_observation_flattening():
"""Test that transition_to_batch correctly flattens observation dict."""
observation_dict = {
f"{OBS_IMAGE}.top": torch.randn(1, 3, 128, 128),
f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128),
OBS_STATE: [1, 2, 3, 4],
}
transition = {
TransitionKey.OBSERVATION: observation_dict,
TransitionKey.ACTION: "action_data",
TransitionKey.REWARD: 1.5,
TransitionKey.DONE: True,
TransitionKey.TRUNCATED: False,
TransitionKey.INFO: {"episode": 42},
TransitionKey.COMPLEMENTARY_DATA: {},
}
batch = transition_to_batch(transition)
# Check that observation.* keys are flattened back to batch
assert f"{OBS_IMAGE}.top" in batch
assert f"{OBS_IMAGE}.left" in batch
assert OBS_STATE in batch
# Check values are preserved
assert torch.allclose(batch[f"{OBS_IMAGE}.top"], observation_dict[f"{OBS_IMAGE}.top"])
assert torch.allclose(batch[f"{OBS_IMAGE}.left"], observation_dict[f"{OBS_IMAGE}.left"])
assert batch[OBS_STATE] == [1, 2, 3, 4]
# Check other fields are mapped to next.* format
assert batch[ACTION] == "action_data"
assert batch[REWARD] == 1.5
assert batch[DONE]
assert not batch[TRUNCATED]
assert batch["info"] == {"episode": 42}
def test_no_observation_keys():
"""Test behavior when there are no observation.* keys."""
batch = {
ACTION: torch.tensor([1.0, 2.0]),
REWARD: 2.0,
DONE: False,
TRUNCATED: True,
"info": {"test": "no_obs"},
}
transition = batch_to_transition(batch)
# Observation should be None when no observation.* keys
assert transition[TransitionKey.OBSERVATION] is None
# Check other fields
assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([1.0, 2.0]))
assert transition[TransitionKey.REWARD] == 2.0
assert not transition[TransitionKey.DONE]
assert transition[TransitionKey.TRUNCATED]
assert transition[TransitionKey.INFO] == {"test": "no_obs"}
# Round trip should work
reconstructed_batch = transition_to_batch(transition)
assert torch.allclose(reconstructed_batch[ACTION], torch.tensor([1.0, 2.0]))
assert reconstructed_batch[REWARD] == 2.0
assert not reconstructed_batch[DONE]
assert reconstructed_batch[TRUNCATED]
assert reconstructed_batch["info"] == {"test": "no_obs"}
def test_minimal_batch():
"""Test with minimal batch containing only observation.* and action."""
batch = {OBS_STATE: "minimal_state", ACTION: torch.tensor([0.5])}
transition = batch_to_transition(batch)
# Check observation
assert transition[TransitionKey.OBSERVATION] == {OBS_STATE: "minimal_state"}
assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([0.5]))
# Check defaults
assert transition[TransitionKey.REWARD] == 0.0
assert not transition[TransitionKey.DONE]
assert not transition[TransitionKey.TRUNCATED]
assert transition[TransitionKey.INFO] == {}
assert transition[TransitionKey.COMPLEMENTARY_DATA] == {}
# Round trip
reconstructed_batch = transition_to_batch(transition)
assert reconstructed_batch[OBS_STATE] == "minimal_state"
assert torch.allclose(reconstructed_batch[ACTION], torch.tensor([0.5]))
assert reconstructed_batch[REWARD] == 0.0
assert not reconstructed_batch[DONE]
assert not reconstructed_batch[TRUNCATED]
assert reconstructed_batch["info"] == {}
def test_empty_batch():
"""Test behavior with empty batch."""
batch = {}
transition = batch_to_transition(batch)
# All fields should have defaults
assert transition[TransitionKey.OBSERVATION] is None
assert transition[TransitionKey.ACTION] is None
assert transition[TransitionKey.REWARD] == 0.0
assert not transition[TransitionKey.DONE]
assert not transition[TransitionKey.TRUNCATED]
assert transition[TransitionKey.INFO] == {}
assert transition[TransitionKey.COMPLEMENTARY_DATA] == {}
# Round trip
reconstructed_batch = transition_to_batch(transition)
assert reconstructed_batch[ACTION] is None
assert reconstructed_batch[REWARD] == 0.0
assert not reconstructed_batch[DONE]
assert not reconstructed_batch[TRUNCATED]
assert reconstructed_batch["info"] == {}
def test_complex_nested_observation():
"""Test with complex nested observation data."""
batch = {
f"{OBS_IMAGE}.top": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567890},
f"{OBS_IMAGE}.left": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567891},
OBS_STATE: torch.randn(7),
ACTION: torch.randn(8),
REWARD: 3.14,
DONE: False,
TRUNCATED: True,
"info": {"episode_length": 200, "success": True},
}
transition = batch_to_transition(batch)
reconstructed_batch = transition_to_batch(transition)
# Check that all observation keys are preserved
original_obs_keys = {k for k in batch if k.startswith(OBS_PREFIX)}
reconstructed_obs_keys = {k for k in reconstructed_batch if k.startswith(OBS_PREFIX)}
assert original_obs_keys == reconstructed_obs_keys
# Check tensor values
assert torch.allclose(batch[OBS_STATE], reconstructed_batch[OBS_STATE])
# Check nested dict with tensors
assert torch.allclose(
batch[f"{OBS_IMAGE}.top"]["image"], reconstructed_batch[f"{OBS_IMAGE}.top"]["image"]
)
assert torch.allclose(
batch[f"{OBS_IMAGE}.left"]["image"], reconstructed_batch[f"{OBS_IMAGE}.left"]["image"]
)
# Check action tensor
assert torch.allclose(batch[ACTION], reconstructed_batch[ACTION])
# Check other fields
assert batch[REWARD] == reconstructed_batch[REWARD]
assert batch[DONE] == reconstructed_batch[DONE]
assert batch[TRUNCATED] == reconstructed_batch[TRUNCATED]
assert batch["info"] == reconstructed_batch["info"]
def test_custom_converter():
"""Test that custom converters can still be used."""
def to_tr(batch):
# Custom converter that modifies the reward
tr = batch_to_transition(batch)
# Double the reward
reward = tr.get(TransitionKey.REWARD, 0.0)
new_tr = tr.copy()
new_tr[TransitionKey.REWARD] = reward * 2 if reward is not None else 0.0
return new_tr
def to_batch(tr):
batch = transition_to_batch(tr)
return batch
processor = DataProcessorPipeline(steps=[], to_transition=to_tr, to_output=to_batch)
batch = {
OBS_STATE: torch.randn(1, 4),
ACTION: torch.randn(1, 2),
REWARD: 1.0,
DONE: False,
}
result = processor(batch)
# Check the reward was doubled by our custom converter
assert result[REWARD] == 2.0
assert torch.allclose(result[OBS_STATE], batch[OBS_STATE])
assert torch.allclose(result[ACTION], batch[ACTION])
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