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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.processor import TransitionKey
from lerobot.processor.converters import (
batch_to_transition,
create_transition,
to_tensor,
transition_to_batch,
)
from lerobot.utils.constants import ACTION, DONE, OBS_STATE, OBS_STR, REWARD
# Tests for the unified to_tensor function
def test_to_tensor_numpy_arrays():
"""Test to_tensor with various numpy arrays."""
# Regular numpy array
arr = np.array([1.0, 2.0, 3.0])
result = to_tensor(arr)
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0]))
# Different numpy dtypes should convert to float32 by default
int_arr = np.array([1, 2, 3], dtype=np.int64)
result = to_tensor(int_arr)
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0]))
# uint8 arrays (previously "preserved") should now convert
uint8_arr = np.array([100, 150, 200], dtype=np.uint8)
result = to_tensor(uint8_arr)
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert torch.allclose(result, torch.tensor([100.0, 150.0, 200.0]))
def test_to_tensor_numpy_scalars():
"""Test to_tensor with numpy scalars (0-dimensional arrays)."""
# numpy float32 scalar
scalar = np.float32(3.14)
result = to_tensor(scalar)
assert isinstance(result, torch.Tensor)
assert result.ndim == 0 # Should be 0-dimensional tensor
assert result.dtype == torch.float32
assert result.item() == pytest.approx(3.14)
# numpy int32 scalar
int_scalar = np.int32(42)
result = to_tensor(int_scalar)
assert isinstance(result, torch.Tensor)
assert result.ndim == 0
assert result.dtype == torch.float32
assert result.item() == pytest.approx(42.0)
def test_to_tensor_python_scalars():
"""Test to_tensor with Python scalars."""
# Python int
result = to_tensor(42)
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert result.item() == pytest.approx(42.0)
# Python float
result = to_tensor(3.14)
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert result.item() == pytest.approx(3.14)
def test_to_tensor_sequences():
"""Test to_tensor with lists and tuples."""
# List
result = to_tensor([1, 2, 3])
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0]))
# Tuple
result = to_tensor((4.5, 5.5, 6.5))
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert torch.allclose(result, torch.tensor([4.5, 5.5, 6.5]))
def test_to_tensor_existing_tensors():
"""Test to_tensor with existing PyTorch tensors."""
# Tensor with same dtype should pass through with potential device change
tensor = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)
result = to_tensor(tensor)
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert torch.allclose(result, tensor)
# Tensor with different dtype should convert
int_tensor = torch.tensor([1, 2, 3], dtype=torch.int64)
result = to_tensor(int_tensor)
assert isinstance(result, torch.Tensor)
assert result.dtype == torch.float32
assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0]))
def test_to_tensor_dictionaries():
"""Test to_tensor with nested dictionaries."""
# Simple dictionary
data = {"mean": [0.1, 0.2], "std": np.array([1.0, 2.0]), "count": 42}
result = to_tensor(data)
assert isinstance(result, dict)
assert isinstance(result["mean"], torch.Tensor)
assert isinstance(result["std"], torch.Tensor)
assert isinstance(result["count"], torch.Tensor)
assert torch.allclose(result["mean"], torch.tensor([0.1, 0.2]))
assert torch.allclose(result["std"], torch.tensor([1.0, 2.0]))
assert result["count"].item() == pytest.approx(42.0)
# Nested dictionary
nested = {
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[OBS_STR], dict)
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[OBS_STR]["mean"], torch.tensor([0.5, 0.6]))
def test_to_tensor_none_filtering():
"""Test that None values are filtered out from dictionaries."""
data = {"valid": [1, 2, 3], "none_value": None, "nested": {"valid": [4, 5], "also_none": None}}
result = to_tensor(data)
assert "none_value" not in result
assert "also_none" not in result["nested"]
assert "valid" in result
assert "valid" in result["nested"]
assert torch.allclose(result["valid"], torch.tensor([1.0, 2.0, 3.0]))
def test_to_tensor_dtype_parameter():
"""Test to_tensor with different dtype parameters."""
arr = np.array([1, 2, 3])
# Default dtype (float32)
result = to_tensor(arr)
assert result.dtype == torch.float32
# Explicit float32
result = to_tensor(arr, dtype=torch.float32)
assert result.dtype == torch.float32
# Float64
result = to_tensor(arr, dtype=torch.float64)
assert result.dtype == torch.float64
# Preserve original dtype
float64_arr = np.array([1.0, 2.0, 3.0], dtype=np.float64)
result = to_tensor(float64_arr, dtype=None)
assert result.dtype == torch.float64
def test_to_tensor_device_parameter():
"""Test to_tensor with device parameter."""
arr = np.array([1.0, 2.0, 3.0])
# CPU device (default)
result = to_tensor(arr, device="cpu")
assert result.device.type == "cpu"
# CUDA device (if available)
if torch.cuda.is_available():
result = to_tensor(arr, device="cuda")
assert result.device.type == "cuda"
def test_to_tensor_empty_dict():
"""Test to_tensor with empty dictionary."""
result = to_tensor({})
assert isinstance(result, dict)
assert len(result) == 0
def test_to_tensor_unsupported_type():
"""Test to_tensor with unsupported types raises TypeError."""
with pytest.raises(TypeError, match="Unsupported type for tensor conversion"):
to_tensor("unsupported_string")
with pytest.raises(TypeError, match="Unsupported type for tensor conversion"):
to_tensor(object())
def test_batch_to_transition_with_index_fields():
"""Test that batch_to_transition handles index and task_index fields correctly."""
# Create batch with index and task_index fields
batch = {
OBS_STATE: torch.randn(1, 7),
ACTION: torch.randn(1, 4),
REWARD: 1.5,
DONE: False,
"task": ["pick_cube"],
"index": torch.tensor([42], dtype=torch.int64),
"task_index": torch.tensor([3], dtype=torch.int64),
}
transition = batch_to_transition(batch)
# Check basic transition structure
assert TransitionKey.OBSERVATION in transition
assert TransitionKey.ACTION in transition
assert TransitionKey.COMPLEMENTARY_DATA in transition
# Check that index and task_index are in complementary_data
comp_data = transition[TransitionKey.COMPLEMENTARY_DATA]
assert "index" in comp_data
assert "task_index" in comp_data
assert "task" in comp_data
# Verify values
assert torch.equal(comp_data["index"], batch["index"])
assert torch.equal(comp_data["task_index"], batch["task_index"])
assert comp_data["task"] == batch["task"]
def testtransition_to_batch_with_index_fields():
"""Test that transition_to_batch handles index and task_index fields correctly."""
# Create transition with index and task_index in complementary_data
transition = create_transition(
observation={OBS_STATE: torch.randn(1, 7)},
action=torch.randn(1, 4),
reward=1.5,
done=False,
complementary_data={
"task": ["navigate"],
"index": torch.tensor([100], dtype=torch.int64),
"task_index": torch.tensor([5], dtype=torch.int64),
},
)
batch = transition_to_batch(transition)
# Check that index and task_index are in the batch
assert "index" in batch
assert "task_index" in batch
assert "task" in batch
# Verify values
assert torch.equal(batch["index"], transition[TransitionKey.COMPLEMENTARY_DATA]["index"])
assert torch.equal(batch["task_index"], transition[TransitionKey.COMPLEMENTARY_DATA]["task_index"])
assert batch["task"] == transition[TransitionKey.COMPLEMENTARY_DATA]["task"]
def test_batch_to_transition_without_index_fields():
"""Test that conversion works without index and task_index fields."""
# Batch without index/task_index
batch = {
OBS_STATE: torch.randn(1, 7),
ACTION: torch.randn(1, 4),
"task": ["pick_cube"],
}
transition = batch_to_transition(batch)
comp_data = transition[TransitionKey.COMPLEMENTARY_DATA]
# Should have task but not index/task_index
assert "task" in comp_data
assert "index" not in comp_data
assert "task_index" not in comp_data
def test_transition_to_batch_without_index_fields():
"""Test that conversion works without index and task_index fields."""
# Transition without index/task_index
transition = create_transition(
observation={OBS_STATE: torch.randn(1, 7)},
action=torch.randn(1, 4),
complementary_data={"task": ["navigate"]},
)
batch = transition_to_batch(transition)
# Should have task but not index/task_index
assert "task" in batch
assert "index" not in batch
assert "task_index" not in batch
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