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

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

lerobot/common/optim/schedulers.py

@@ -1,11 +1,31 @@
+#!/usr/bin/env python
+
+# 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 abc
 import math
 from dataclasses import asdict, dataclass
+from pathlib import Path
 
 import draccus
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LambdaLR, LRScheduler
 
+from lerobot.common.constants import SCHEDULER_STATE
+from lerobot.common.datasets.utils import write_json
+from lerobot.common.utils.io_utils import deserialize_json_into_object
+
 
 @dataclass
 class LRSchedulerConfig(draccus.ChoiceRegistry, abc.ABC):
@@ -89,3 +109,14 @@ class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig):
             return cosine_decay_schedule(current_step)
 
         return LambdaLR(optimizer, lr_lambda, -1)
+
+
+def save_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> None:
+    state_dict = scheduler.state_dict()
+    write_json(state_dict, save_dir / SCHEDULER_STATE)
+
+
+def load_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> LRScheduler:
+    state_dict = deserialize_json_into_object(save_dir / SCHEDULER_STATE, scheduler.state_dict())
+    scheduler.load_state_dict(state_dict)
+    return scheduler