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

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

tests/test_schedulers.py

@@ -0,0 +1,81 @@
+from torch.optim.lr_scheduler import LambdaLR
+
+from lerobot.common.constants import SCHEDULER_STATE
+from lerobot.common.optim.schedulers import (
+    CosineDecayWithWarmupSchedulerConfig,
+    DiffuserSchedulerConfig,
+    VQBeTSchedulerConfig,
+    load_scheduler_state,
+    save_scheduler_state,
+)
+
+
+def test_diffuser_scheduler(optimizer):
+    config = DiffuserSchedulerConfig(name="cosine", num_warmup_steps=5)
+    scheduler = config.build(optimizer, num_training_steps=100)
+    assert isinstance(scheduler, LambdaLR)
+
+    optimizer.step()  # so that we don't get torch warning
+    scheduler.step()
+    expected_state_dict = {
+        "_get_lr_called_within_step": False,
+        "_last_lr": [0.0002],
+        "_step_count": 2,
+        "base_lrs": [0.001],
+        "last_epoch": 1,
+        "lr_lambdas": [None],
+        "verbose": False,
+    }
+    assert scheduler.state_dict() == expected_state_dict
+
+
+def test_vqbet_scheduler(optimizer):
+    config = VQBeTSchedulerConfig(num_warmup_steps=10, num_vqvae_training_steps=20, num_cycles=0.5)
+    scheduler = config.build(optimizer, num_training_steps=100)
+    assert isinstance(scheduler, LambdaLR)
+
+    optimizer.step()
+    scheduler.step()
+    expected_state_dict = {
+        "_get_lr_called_within_step": False,
+        "_last_lr": [0.001],
+        "_step_count": 2,
+        "base_lrs": [0.001],
+        "last_epoch": 1,
+        "lr_lambdas": [None],
+        "verbose": False,
+    }
+    assert scheduler.state_dict() == expected_state_dict
+
+
+def test_cosine_decay_with_warmup_scheduler(optimizer):
+    config = CosineDecayWithWarmupSchedulerConfig(
+        num_warmup_steps=10, num_decay_steps=90, peak_lr=0.01, decay_lr=0.001
+    )
+    scheduler = config.build(optimizer, num_training_steps=100)
+    assert isinstance(scheduler, LambdaLR)
+
+    optimizer.step()
+    scheduler.step()
+    expected_state_dict = {
+        "_get_lr_called_within_step": False,
+        "_last_lr": [0.0001818181818181819],
+        "_step_count": 2,
+        "base_lrs": [0.001],
+        "last_epoch": 1,
+        "lr_lambdas": [None],
+        "verbose": False,
+    }
+    assert scheduler.state_dict() == expected_state_dict
+
+
+def test_save_scheduler_state(scheduler, tmp_path):
+    save_scheduler_state(scheduler, tmp_path)
+    assert (tmp_path / SCHEDULER_STATE).is_file()
+
+
+def test_save_load_scheduler_state(scheduler, tmp_path):
+    save_scheduler_state(scheduler, tmp_path)
+    loaded_scheduler = load_scheduler_state(scheduler, tmp_path)
+
+    assert scheduler.state_dict() == loaded_scheduler.state_dict()