| #!/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. | |
| from torch.optim import Optimizer | |
| from torch.optim.lr_scheduler import LRScheduler | |
| from lerobot.configs.train import TrainPipelineConfig | |
| from lerobot.policies.pretrained import PreTrainedPolicy | |
| def make_optimizer_and_scheduler( | |
| cfg: TrainPipelineConfig, policy: PreTrainedPolicy | |
| ) -> tuple[Optimizer, LRScheduler | None]: | |
| """Generates the optimizer and scheduler based on configs. | |
| Args: | |
| cfg (TrainPipelineConfig): The training config that contains optimizer and scheduler configs | |
| policy (PreTrainedPolicy): The policy config from which parameters and presets must be taken from. | |
| Returns: | |
| tuple[Optimizer, LRScheduler | None]: The couple (Optimizer, Scheduler). Scheduler can be `None`. | |
| """ | |
| params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters() | |
| optimizer = cfg.optimizer.build(params) | |
| lr_scheduler = cfg.scheduler.build(optimizer, cfg.steps) if cfg.scheduler is not None else None | |
| return optimizer, lr_scheduler | |