Fix linter issue

lerobot/scripts/train.py

@@ -53,84 +53,6 @@ from lerobot.configs.train import TrainPipelineConfig
 from lerobot.scripts.eval import eval_policy
 
 
-def make_optimizer_and_scheduler(cfg, policy):
-    if cfg.policy.name == "act":
-        optimizer_params_dicts = [
-            {
-                "params": [
-                    p
-                    for n, p in policy.named_parameters()
-                    if not n.startswith("model.backbone") and p.requires_grad
-                ]
-            },
-            {
-                "params": [
-                    p
-                    for n, p in policy.named_parameters()
-                    if n.startswith("model.backbone") and p.requires_grad
-                ],
-                "lr": cfg.training.lr_backbone,
-            },
-        ]
-        optimizer = torch.optim.AdamW(
-            optimizer_params_dicts,
-            lr=cfg.training.lr,
-            weight_decay=cfg.training.weight_decay,
-        )
-        lr_scheduler = None
-    elif cfg.policy.name == "diffusion":
-        optimizer = torch.optim.Adam(
-            policy.diffusion.parameters(),
-            cfg.training.lr,
-            cfg.training.adam_betas,
-            cfg.training.adam_eps,
-            cfg.training.adam_weight_decay,
-        )
-        from diffusers.optimization import get_scheduler
-
-        lr_scheduler = get_scheduler(
-            cfg.training.lr_scheduler,
-            optimizer=optimizer,
-            num_warmup_steps=cfg.training.lr_warmup_steps,
-            num_training_steps=cfg.training.offline_steps,
-        )
-    elif policy.name == "tdmpc":
-        optimizer = torch.optim.Adam(policy.parameters(), cfg.training.lr)
-        lr_scheduler = None
-
-    elif policy.name == "sac":
-        optimizer = torch.optim.Adam(
-            [
-                {"params": policy.actor.parameters(), "lr": policy.config.actor_lr},
-                {
-                    "params": policy.critic_ensemble.parameters(),
-                    "lr": policy.config.critic_lr,
-                },
-                {
-                    "params": policy.temperature.parameters(),
-                    "lr": policy.config.temperature_lr,
-                },
-            ]
-        )
-        lr_scheduler = None
-
-    elif cfg.policy.name == "vqbet":
-        from lerobot.common.policies.vqbet.modeling_vqbet import (
-            VQBeTOptimizer,
-            VQBeTScheduler,
-        )
-
-        optimizer = VQBeTOptimizer(policy, cfg)
-        lr_scheduler = VQBeTScheduler(optimizer, cfg)
-    elif cfg.policy.name == "hilserl_classifier":
-        optimizer = torch.optim.AdamW(policy.parameters(), cfg.policy.learning_rate)
-        lr_scheduler = None
-    else:
-        raise NotImplementedError()
-
-    return optimizer, lr_scheduler
-
-
 def update_policy(
     train_metrics: MetricsTracker,
     policy: PreTrainedPolicy,