Remove offline training, refactor train.py and logging/checkpointing (#670)

Co-authored-by: Remi <remi.cadene@huggingface.co>

tests/test_optimizers.py

@@ -0,0 +1,43 @@
+import pytest
+import torch
+
+from lerobot.common.constants import (
+    OPTIMIZER_PARAM_GROUPS,
+    OPTIMIZER_STATE,
+)
+from lerobot.common.optim.optimizers import (
+    AdamConfig,
+    AdamWConfig,
+    SGDConfig,
+    load_optimizer_state,
+    save_optimizer_state,
+)
+
+
+@pytest.mark.parametrize(
+    "config_cls, expected_class",
+    [
+        (AdamConfig, torch.optim.Adam),
+        (AdamWConfig, torch.optim.AdamW),
+        (SGDConfig, torch.optim.SGD),
+    ],
+)
+def test_optimizer_build(config_cls, expected_class, model_params):
+    config = config_cls()
+    optimizer = config.build(model_params)
+    assert isinstance(optimizer, expected_class)
+    assert optimizer.defaults["lr"] == config.lr
+
+
+def test_save_optimizer_state(optimizer, tmp_path):
+    save_optimizer_state(optimizer, tmp_path)
+    assert (tmp_path / OPTIMIZER_STATE).is_file()
+    assert (tmp_path / OPTIMIZER_PARAM_GROUPS).is_file()
+
+
+def test_save_and_load_optimizer_state(model_params, optimizer, tmp_path):
+    save_optimizer_state(optimizer, tmp_path)
+    loaded_optimizer = AdamConfig().build(model_params)
+    loaded_optimizer = load_optimizer_state(loaded_optimizer, tmp_path)
+
+    torch.testing.assert_close(optimizer.state_dict(), loaded_optimizer.state_dict())