Organize test folders (#856)

Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>

tests/utils/test_io_utils.py

@@ -0,0 +1,87 @@
+# Copyright 2024 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 json
+from pathlib import Path
+from typing import Any
+
+import pytest
+
+from lerobot.common.utils.io_utils import deserialize_json_into_object
+
+
+@pytest.fixture
+def tmp_json_file(tmp_path: Path):
+    """Writes `data` to a temporary JSON file and returns the file's path."""
+
+    def _write(data: Any) -> Path:
+        file_path = tmp_path / "data.json"
+        with file_path.open("w", encoding="utf-8") as f:
+            json.dump(data, f)
+        return file_path
+
+    return _write
+
+
+def test_simple_dict(tmp_json_file):
+    data = {"name": "Alice", "age": 30}
+    json_path = tmp_json_file(data)
+    obj = {"name": "", "age": 0}
+    assert deserialize_json_into_object(json_path, obj) == data
+
+
+def test_nested_structure(tmp_json_file):
+    data = {"items": [1, 2, 3], "info": {"active": True}}
+    json_path = tmp_json_file(data)
+    obj = {"items": [0, 0, 0], "info": {"active": False}}
+    assert deserialize_json_into_object(json_path, obj) == data
+
+
+def test_tuple_conversion(tmp_json_file):
+    data = {"coords": [10.5, 20.5]}
+    json_path = tmp_json_file(data)
+    obj = {"coords": (0.0, 0.0)}
+    result = deserialize_json_into_object(json_path, obj)
+    assert result["coords"] == (10.5, 20.5)
+
+
+def test_type_mismatch_raises(tmp_json_file):
+    data = {"numbers": {"bad": "structure"}}
+    json_path = tmp_json_file(data)
+    obj = {"numbers": [0, 0]}
+    with pytest.raises(TypeError):
+        deserialize_json_into_object(json_path, obj)
+
+
+def test_missing_key_raises(tmp_json_file):
+    data = {"one": 1}
+    json_path = tmp_json_file(data)
+    obj = {"one": 0, "two": 0}
+    with pytest.raises(ValueError):
+        deserialize_json_into_object(json_path, obj)
+
+
+def test_extra_key_raises(tmp_json_file):
+    data = {"one": 1, "two": 2}
+    json_path = tmp_json_file(data)
+    obj = {"one": 0}
+    with pytest.raises(ValueError):
+        deserialize_json_into_object(json_path, obj)
+
+
+def test_list_length_mismatch_raises(tmp_json_file):
+    data = {"nums": [1, 2, 3]}
+    json_path = tmp_json_file(data)
+    obj = {"nums": [0, 0]}
+    with pytest.raises(ValueError):
+        deserialize_json_into_object(json_path, obj)

tests/utils/test_logging_utils.py

@@ -0,0 +1,120 @@
+# Copyright 2024 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 pytest
+
+from lerobot.common.utils.logging_utils import AverageMeter, MetricsTracker
+
+
+@pytest.fixture
+def mock_metrics():
+    return {"loss": AverageMeter("loss", ":.3f"), "accuracy": AverageMeter("accuracy", ":.2f")}
+
+
+def test_average_meter_initialization():
+    meter = AverageMeter("loss", ":.2f")
+    assert meter.name == "loss"
+    assert meter.fmt == ":.2f"
+    assert meter.val == 0.0
+    assert meter.avg == 0.0
+    assert meter.sum == 0.0
+    assert meter.count == 0.0
+
+
+def test_average_meter_update():
+    meter = AverageMeter("accuracy")
+    meter.update(5, n=2)
+    assert meter.val == 5
+    assert meter.sum == 10
+    assert meter.count == 2
+    assert meter.avg == 5
+
+
+def test_average_meter_reset():
+    meter = AverageMeter("loss")
+    meter.update(3, 4)
+    meter.reset()
+    assert meter.val == 0.0
+    assert meter.avg == 0.0
+    assert meter.sum == 0.0
+    assert meter.count == 0.0
+
+
+def test_average_meter_str():
+    meter = AverageMeter("metric", ":.1f")
+    meter.update(4.567, 3)
+    assert str(meter) == "metric:4.6"
+
+
+def test_metrics_tracker_initialization(mock_metrics):
+    tracker = MetricsTracker(
+        batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=10
+    )
+    assert tracker.steps == 10
+    assert tracker.samples == 10 * 32
+    assert tracker.episodes == tracker.samples / (1000 / 50)
+    assert tracker.epochs == tracker.samples / 1000
+    assert "loss" in tracker.metrics
+    assert "accuracy" in tracker.metrics
+
+
+def test_metrics_tracker_step(mock_metrics):
+    tracker = MetricsTracker(
+        batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=5
+    )
+    tracker.step()
+    assert tracker.steps == 6
+    assert tracker.samples == 6 * 32
+    assert tracker.episodes == tracker.samples / (1000 / 50)
+    assert tracker.epochs == tracker.samples / 1000
+
+
+def test_metrics_tracker_getattr(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    assert tracker.loss == mock_metrics["loss"]
+    assert tracker.accuracy == mock_metrics["accuracy"]
+    with pytest.raises(AttributeError):
+        _ = tracker.non_existent_metric
+
+
+def test_metrics_tracker_setattr(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss = 2.0
+    assert tracker.loss.val == 2.0
+
+
+def test_metrics_tracker_str(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss.update(3.456, 1)
+    tracker.accuracy.update(0.876, 1)
+    output = str(tracker)
+    assert "loss:3.456" in output
+    assert "accuracy:0.88" in output
+
+
+def test_metrics_tracker_to_dict(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss.update(5, 2)
+    metrics_dict = tracker.to_dict()
+    assert isinstance(metrics_dict, dict)
+    assert metrics_dict["loss"] == 5  # average value
+    assert metrics_dict["steps"] == tracker.steps
+
+
+def test_metrics_tracker_reset_averages(mock_metrics):
+    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
+    tracker.loss.update(10, 3)
+    tracker.accuracy.update(0.95, 5)
+    tracker.reset_averages()
+    assert tracker.loss.avg == 0.0
+    assert tracker.accuracy.avg == 0.0

tests/utils/test_random_utils.py

@@ -0,0 +1,122 @@
+# Copyright 2024 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 random
+
+import numpy as np
+import pytest
+import torch
+
+from lerobot.common.utils.random_utils import (
+    deserialize_numpy_rng_state,
+    deserialize_python_rng_state,
+    deserialize_rng_state,
+    deserialize_torch_rng_state,
+    get_rng_state,
+    seeded_context,
+    serialize_numpy_rng_state,
+    serialize_python_rng_state,
+    serialize_rng_state,
+    serialize_torch_rng_state,
+    set_rng_state,
+    set_seed,
+)
+
+
+@pytest.fixture
+def fixed_seed():
+    """Fixture to set a consistent initial seed for each test."""
+    set_seed(12345)
+    yield
+
+
+def test_serialize_deserialize_python_rng(fixed_seed):
+    # Save state after generating val1
+    _ = random.random()
+    st = serialize_python_rng_state()
+    # Next random is val2
+    val2 = random.random()
+    # Restore the state, so the next random should match val2
+    deserialize_python_rng_state(st)
+    val3 = random.random()
+    assert val2 == val3
+
+
+def test_serialize_deserialize_numpy_rng(fixed_seed):
+    _ = np.random.rand()
+    st = serialize_numpy_rng_state()
+    val2 = np.random.rand()
+    deserialize_numpy_rng_state(st)
+    val3 = np.random.rand()
+    assert val2 == val3
+
+
+def test_serialize_deserialize_torch_rng(fixed_seed):
+    _ = torch.rand(1).item()
+    st = serialize_torch_rng_state()
+    val2 = torch.rand(1).item()
+    deserialize_torch_rng_state(st)
+    val3 = torch.rand(1).item()
+    assert val2 == val3
+
+
+def test_serialize_deserialize_rng(fixed_seed):
+    # Generate one from each library
+    _ = random.random()
+    _ = np.random.rand()
+    _ = torch.rand(1).item()
+    # Serialize
+    st = serialize_rng_state()
+    # Generate second set
+    val_py2 = random.random()
+    val_np2 = np.random.rand()
+    val_th2 = torch.rand(1).item()
+    # Restore, so the next draws should match val_py2, val_np2, val_th2
+    deserialize_rng_state(st)
+    assert random.random() == val_py2
+    assert np.random.rand() == val_np2
+    assert torch.rand(1).item() == val_th2
+
+
+def test_get_set_rng_state(fixed_seed):
+    st = get_rng_state()
+    val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    # Change states
+    random.random()
+    np.random.rand()
+    torch.rand(1)
+    # Restore
+    set_rng_state(st)
+    val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+    assert val1 == val2
+
+
+def test_set_seed():
+    set_seed(1337)
+    val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    set_seed(1337)
+    val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+    assert val1 == val2
+
+
+def test_seeded_context(fixed_seed):
+    val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    with seeded_context(1337):
+        seeded_val1 = (random.random(), np.random.rand(), torch.rand(1).item())
+    val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+    with seeded_context(1337):
+        seeded_val2 = (random.random(), np.random.rand(), torch.rand(1).item())
+
+    assert seeded_val1 == seeded_val2
+    assert all(a != b for a, b in zip(val1, seeded_val1, strict=True))  # changed inside the context
+    assert all(a != b for a, b in zip(val2, seeded_val2, strict=True))  # changed again after exiting

tests/utils/test_train_utils.py

@@ -0,0 +1,97 @@
+# Copyright 2024 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.
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+from lerobot.common.constants import (
+    CHECKPOINTS_DIR,
+    LAST_CHECKPOINT_LINK,
+    OPTIMIZER_PARAM_GROUPS,
+    OPTIMIZER_STATE,
+    RNG_STATE,
+    SCHEDULER_STATE,
+    TRAINING_STATE_DIR,
+    TRAINING_STEP,
+)
+from lerobot.common.utils.train_utils import (
+    get_step_checkpoint_dir,
+    get_step_identifier,
+    load_training_state,
+    load_training_step,
+    save_checkpoint,
+    save_training_state,
+    save_training_step,
+    update_last_checkpoint,
+)
+
+
+def test_get_step_identifier():
+    assert get_step_identifier(5, 1000) == "000005"
+    assert get_step_identifier(123, 100_000) == "000123"
+    assert get_step_identifier(456789, 1_000_000) == "0456789"
+
+
+def test_get_step_checkpoint_dir():
+    output_dir = Path("/checkpoints")
+    step_dir = get_step_checkpoint_dir(output_dir, 1000, 5)
+    assert step_dir == output_dir / CHECKPOINTS_DIR / "000005"
+
+
+def test_save_load_training_step(tmp_path):
+    save_training_step(5000, tmp_path)
+    assert (tmp_path / TRAINING_STEP).is_file()
+
+
+def test_load_training_step(tmp_path):
+    step = 5000
+    save_training_step(step, tmp_path)
+    loaded_step = load_training_step(tmp_path)
+    assert loaded_step == step
+
+
+def test_update_last_checkpoint(tmp_path):
+    checkpoint = tmp_path / "0005"
+    checkpoint.mkdir()
+    update_last_checkpoint(checkpoint)
+    last_checkpoint = tmp_path / LAST_CHECKPOINT_LINK
+    assert last_checkpoint.is_symlink()
+    assert last_checkpoint.resolve() == checkpoint
+
+
+@patch("lerobot.common.utils.train_utils.save_training_state")
+def test_save_checkpoint(mock_save_training_state, tmp_path, optimizer):
+    policy = Mock()
+    cfg = Mock()
+    save_checkpoint(tmp_path, 10, cfg, policy, optimizer)
+    policy.save_pretrained.assert_called_once()
+    cfg.save_pretrained.assert_called_once()
+    mock_save_training_state.assert_called_once()
+
+
+def test_save_training_state(tmp_path, optimizer, scheduler):
+    save_training_state(tmp_path, 10, optimizer, scheduler)
+    assert (tmp_path / TRAINING_STATE_DIR).is_dir()
+    assert (tmp_path / TRAINING_STATE_DIR / TRAINING_STEP).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / RNG_STATE).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_STATE).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_PARAM_GROUPS).is_file()
+    assert (tmp_path / TRAINING_STATE_DIR / SCHEDULER_STATE).is_file()
+
+
+def test_save_load_training_state(tmp_path, optimizer, scheduler):
+    save_training_state(tmp_path, 10, optimizer, scheduler)
+    loaded_step, loaded_optimizer, loaded_scheduler = load_training_state(tmp_path, optimizer, scheduler)
+    assert loaded_step == 10
+    assert loaded_optimizer is optimizer
+    assert loaded_scheduler is scheduler