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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 numpy as np
import pytest
import torch
from lerobot.configs.types import FeatureType, PipelineFeatureType
from lerobot.processor import TransitionKey, VanillaObservationProcessorStep
from lerobot.processor.converters import create_transition
from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
from tests.conftest import assert_contract_is_typed
def test_process_single_image():
"""Test processing a single image."""
processor = VanillaObservationProcessorStep()
# Create a mock image (H, W, C) format, uint8
image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
observation = {"pixels": image}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that the image was processed correctly
assert OBS_IMAGE in processed_obs
processed_img = processed_obs[OBS_IMAGE]
# Check shape: should be (1, 3, 64, 64) - batch, channels, height, width
assert processed_img.shape == (1, 3, 64, 64)
# Check dtype and range
assert processed_img.dtype == torch.float32
assert processed_img.min() >= 0.0
assert processed_img.max() <= 1.0
def test_process_image_dict():
"""Test processing multiple images in a dictionary."""
processor = VanillaObservationProcessorStep()
# Create mock images
image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
image2 = np.random.randint(0, 256, size=(48, 48, 3), dtype=np.uint8)
observation = {"pixels": {"camera1": image1, "camera2": image2}}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that both images were processed
assert f"{OBS_IMAGES}.camera1" in processed_obs
assert f"{OBS_IMAGES}.camera2" in processed_obs
# Check shapes
assert processed_obs[f"{OBS_IMAGES}.camera1"].shape == (1, 3, 32, 32)
assert processed_obs[f"{OBS_IMAGES}.camera2"].shape == (1, 3, 48, 48)
def test_process_batched_image():
"""Test processing already batched images."""
processor = VanillaObservationProcessorStep()
# Create a batched image (B, H, W, C)
image = np.random.randint(0, 256, size=(2, 64, 64, 3), dtype=np.uint8)
observation = {"pixels": image}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that batch dimension is preserved
assert processed_obs[OBS_IMAGE].shape == (2, 3, 64, 64)
def test_invalid_image_format():
"""Test error handling for invalid image formats."""
processor = VanillaObservationProcessorStep()
# Test wrong channel order (channels first)
image = np.random.randint(0, 256, size=(3, 64, 64), dtype=np.uint8)
observation = {"pixels": image}
transition = create_transition(observation=observation)
with pytest.raises(ValueError, match="Expected channel-last images"):
processor(transition)
def test_invalid_image_dtype():
"""Test error handling for invalid image dtype."""
processor = VanillaObservationProcessorStep()
# Test wrong dtype
image = np.random.rand(64, 64, 3).astype(np.float32)
observation = {"pixels": image}
transition = create_transition(observation=observation)
with pytest.raises(ValueError, match="Expected torch.uint8 images"):
processor(transition)
def test_no_pixels_in_observation():
"""Test processor when no pixels are in observation."""
processor = VanillaObservationProcessorStep()
observation = {"other_data": np.array([1, 2, 3])}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Should preserve other data unchanged
assert "other_data" in processed_obs
np.testing.assert_array_equal(processed_obs["other_data"], np.array([1, 2, 3]))
def test_none_observation():
"""Test processor with None observation."""
processor = VanillaObservationProcessorStep()
transition = create_transition(observation={})
result = processor(transition)
assert result == transition
def test_serialization_methods():
"""Test serialization methods."""
processor = VanillaObservationProcessorStep()
# Test get_config
config = processor.get_config()
assert isinstance(config, dict)
# Test state_dict
state = processor.state_dict()
assert isinstance(state, dict)
# Test load_state_dict (should not raise)
processor.load_state_dict(state)
# Test reset (should not raise)
processor.reset()
def test_process_environment_state():
"""Test processing environment_state."""
processor = VanillaObservationProcessorStep()
env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
observation = {"environment_state": env_state}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that environment_state was renamed and processed
assert OBS_ENV_STATE in processed_obs
assert "environment_state" not in processed_obs
processed_state = processed_obs[OBS_ENV_STATE]
assert processed_state.shape == (1, 3) # Batch dimension added
assert processed_state.dtype == torch.float32
torch.testing.assert_close(processed_state, torch.tensor([[1.0, 2.0, 3.0]]))
def test_process_agent_pos():
"""Test processing agent_pos."""
processor = VanillaObservationProcessorStep()
agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
observation = {"agent_pos": agent_pos}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that agent_pos was renamed and processed
assert OBS_STATE in processed_obs
assert "agent_pos" not in processed_obs
processed_state = processed_obs[OBS_STATE]
assert processed_state.shape == (1, 3) # Batch dimension added
assert processed_state.dtype == torch.float32
torch.testing.assert_close(processed_state, torch.tensor([[0.5, -0.5, 1.0]]))
def test_process_batched_states():
"""Test processing already batched states."""
processor = VanillaObservationProcessorStep()
env_state = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
agent_pos = np.array([[0.5, -0.5], [1.0, -1.0]], dtype=np.float32)
observation = {"environment_state": env_state, "agent_pos": agent_pos}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that batch dimensions are preserved
assert processed_obs[OBS_ENV_STATE].shape == (2, 2)
assert processed_obs[OBS_STATE].shape == (2, 2)
def test_process_both_states():
"""Test processing both environment_state and agent_pos."""
processor = VanillaObservationProcessorStep()
env_state = np.array([1.0, 2.0], dtype=np.float32)
agent_pos = np.array([0.5, -0.5], dtype=np.float32)
observation = {"environment_state": env_state, "agent_pos": agent_pos, "other_data": "keep_me"}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that both states were processed
assert OBS_ENV_STATE in processed_obs
assert OBS_STATE in processed_obs
# Check that original keys were removed
assert "environment_state" not in processed_obs
assert "agent_pos" not in processed_obs
# Check that other data was preserved
assert processed_obs["other_data"] == "keep_me"
def test_no_states_in_observation():
"""Test processor when no states are in observation."""
processor = VanillaObservationProcessorStep()
observation = {"other_data": np.array([1, 2, 3])}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Should preserve data unchanged
np.testing.assert_array_equal(processed_obs, observation)
def test_complete_observation_processing():
"""Test processing a complete observation with both images and states."""
processor = VanillaObservationProcessorStep()
# Create mock data
image = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
observation = {
"pixels": image,
"environment_state": env_state,
"agent_pos": agent_pos,
"other_data": "preserve_me",
}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
# Check that image was processed
assert OBS_IMAGE in processed_obs
assert processed_obs[OBS_IMAGE].shape == (1, 3, 32, 32)
# Check that states were processed
assert OBS_ENV_STATE in processed_obs
assert OBS_STATE in processed_obs
# Check that original keys were removed
assert "pixels" not in processed_obs
assert "environment_state" not in processed_obs
assert "agent_pos" not in processed_obs
# Check that other data was preserved
assert processed_obs["other_data"] == "preserve_me"
def test_image_only_processing():
"""Test processing observation with only images."""
processor = VanillaObservationProcessorStep()
image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
observation = {"pixels": image}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
assert OBS_IMAGE in processed_obs
assert len(processed_obs) == 1
def test_state_only_processing():
"""Test processing observation with only states."""
processor = VanillaObservationProcessorStep()
agent_pos = np.array([1.0, 2.0], dtype=np.float32)
observation = {"agent_pos": agent_pos}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
assert OBS_STATE in processed_obs
assert "agent_pos" not in processed_obs
def test_empty_observation():
"""Test processing empty observation."""
processor = VanillaObservationProcessorStep()
observation = {}
transition = create_transition(observation=observation)
result = processor(transition)
processed_obs = result[TransitionKey.OBSERVATION]
assert processed_obs == {}
def test_equivalent_to_original_function():
"""Test that ObservationProcessor produces equivalent results to preprocess_observation."""
# Import the original function for comparison
from lerobot.envs.utils import preprocess_observation
processor = VanillaObservationProcessorStep()
# Create test data similar to what the original function expects
image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
observation = {"pixels": image, "environment_state": env_state, "agent_pos": agent_pos}
# Process with original function
original_result = preprocess_observation(observation)
# Process with new processor
transition = create_transition(observation=observation)
processor_result = processor(transition)[TransitionKey.OBSERVATION]
# Compare results
assert set(original_result.keys()) == set(processor_result.keys())
for key in original_result:
torch.testing.assert_close(original_result[key], processor_result[key])
def test_equivalent_with_image_dict():
"""Test equivalence with dictionary of images."""
from lerobot.envs.utils import preprocess_observation
processor = VanillaObservationProcessorStep()
# Create test data with multiple cameras
image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
image2 = np.random.randint(0, 256, size=(48, 48, 3), dtype=np.uint8)
agent_pos = np.array([1.0, 2.0], dtype=np.float32)
observation = {"pixels": {"cam1": image1, "cam2": image2}, "agent_pos": agent_pos}
# Process with original function
original_result = preprocess_observation(observation)
# Process with new processor
transition = create_transition(observation=observation)
processor_result = processor(transition)[TransitionKey.OBSERVATION]
# Compare results
assert set(original_result.keys()) == set(processor_result.keys())
for key in original_result:
torch.testing.assert_close(original_result[key], processor_result[key])
def test_image_processor_features_pixels_to_image(policy_feature_factory):
processor = VanillaObservationProcessorStep()
features = {
PipelineFeatureType.OBSERVATION: {
"pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
"keep": policy_feature_factory(FeatureType.ENV, (1,)),
},
}
out = processor.transform_features(features.copy())
assert (
OBS_IMAGE in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][OBS_IMAGE]
== features[PipelineFeatureType.OBSERVATION]["pixels"]
)
assert "pixels" not in out[PipelineFeatureType.OBSERVATION]
assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"]
assert_contract_is_typed(out)
def test_image_processor_features_observation_pixels_to_image(policy_feature_factory):
processor = VanillaObservationProcessorStep()
features = {
PipelineFeatureType.OBSERVATION: {
"observation.pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
"keep": policy_feature_factory(FeatureType.ENV, (1,)),
},
}
out = processor.transform_features(features.copy())
assert (
OBS_IMAGE in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][OBS_IMAGE]
== features[PipelineFeatureType.OBSERVATION]["observation.pixels"]
)
assert "observation.pixels" not in out[PipelineFeatureType.OBSERVATION]
assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"]
assert_contract_is_typed(out)
def test_image_processor_features_multi_camera_and_prefixed(policy_feature_factory):
processor = VanillaObservationProcessorStep()
features = {
PipelineFeatureType.OBSERVATION: {
"pixels.front": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
"pixels.wrist": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
"observation.pixels.rear": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
"keep": policy_feature_factory(FeatureType.ENV, (7,)),
},
}
out = processor.transform_features(features.copy())
assert (
f"{OBS_IMAGES}.front" in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.front"]
== features[PipelineFeatureType.OBSERVATION]["pixels.front"]
)
assert (
f"{OBS_IMAGES}.wrist" in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.wrist"]
== features[PipelineFeatureType.OBSERVATION]["pixels.wrist"]
)
assert (
f"{OBS_IMAGES}.rear" in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.rear"]
== features[PipelineFeatureType.OBSERVATION]["observation.pixels.rear"]
)
assert (
"pixels.front" not in out[PipelineFeatureType.OBSERVATION]
and "pixels.wrist" not in out[PipelineFeatureType.OBSERVATION]
and "observation.pixels.rear" not in out[PipelineFeatureType.OBSERVATION]
)
assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"]
assert_contract_is_typed(out)
def test_state_processor_features_environment_and_agent_pos(policy_feature_factory):
processor = VanillaObservationProcessorStep()
features = {
PipelineFeatureType.OBSERVATION: {
"environment_state": policy_feature_factory(FeatureType.STATE, (3,)),
"agent_pos": policy_feature_factory(FeatureType.STATE, (7,)),
"keep": policy_feature_factory(FeatureType.ENV, (1,)),
},
}
out = processor.transform_features(features.copy())
assert (
OBS_ENV_STATE in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][OBS_ENV_STATE]
== features[PipelineFeatureType.OBSERVATION]["environment_state"]
)
assert (
OBS_STATE in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][OBS_STATE]
== features[PipelineFeatureType.OBSERVATION]["agent_pos"]
)
assert (
"environment_state" not in out[PipelineFeatureType.OBSERVATION]
and "agent_pos" not in out[PipelineFeatureType.OBSERVATION]
)
assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"]
assert_contract_is_typed(out)
def test_state_processor_features_prefixed_inputs(policy_feature_factory):
proc = VanillaObservationProcessorStep()
features = {
PipelineFeatureType.OBSERVATION: {
OBS_ENV_STATE: policy_feature_factory(FeatureType.STATE, (2,)),
"observation.agent_pos": policy_feature_factory(FeatureType.STATE, (4,)),
},
}
out = proc.transform_features(features.copy())
assert (
OBS_ENV_STATE in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][OBS_ENV_STATE]
== features[PipelineFeatureType.OBSERVATION][OBS_ENV_STATE]
)
assert (
OBS_STATE in out[PipelineFeatureType.OBSERVATION]
and out[PipelineFeatureType.OBSERVATION][OBS_STATE]
== features[PipelineFeatureType.OBSERVATION]["observation.agent_pos"]
)
assert (
"environment_state" not in out[PipelineFeatureType.OBSERVATION]
and "agent_pos" not in out[PipelineFeatureType.OBSERVATION]
)
assert_contract_is_typed(out)
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