format

lerobot/scripts/train.py

@@ -16,10 +16,10 @@ from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import Logger, log_output_dir
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.utils.utils import (
-        format_big_number,
-        get_safe_torch_device,
-        init_logging,
-        set_global_seed,
+    format_big_number,
+    get_safe_torch_device,
+    init_logging,
+    set_global_seed,
 )
 from lerobot.scripts.eval import eval_policy
 
@@ -40,7 +40,7 @@ def update_policy(policy, batch, optimizer, lr_scheduler=None):
     loss.backward()
 
     # Diffusion
-    model = policy.diffusion if hasattr(policy, "diffusion") else policy # TODO: hacky, remove this line
+    model = policy.diffusion if hasattr(policy, "diffusion") else policy  # TODO: hacky, remove this line
     grad_norm = torch.nn.utils.clip_grad_norm_(
         model.parameters(),
         policy.cfg.grad_clip_norm,
@@ -54,7 +54,7 @@ def update_policy(policy, batch, optimizer, lr_scheduler=None):
 
     if hasattr(policy, "ema") and policy.ema is not None:
         policy.ema.step(model)
-    
+
     info = {
         "loss": loss.item(),
         "grad_norm": float(grad_norm),
@@ -280,21 +280,13 @@ def train(cfg: dict, out_dir=None, job_name=None):
     # Temporary hack to move optimizer out of policy
     if isinstance(policy, ActPolicy):
         optimizer_params_dicts = [
+            {"params": [p for n, p in policy.named_parameters() if not n.startswith("backbone") and p.requires_grad]},
             {
-                "params": [
-                    p for n, p in policy.named_parameters() if not n.startswith("backbone") and p.requires_grad
-                ]
-            },
-            {
-                "params": [
-                    p for n, p in policy.named_parameters() if n.startswith("backbone") and p.requires_grad
-                ],
+                "params": [p for n, p in policy.named_parameters() if n.startswith("backbone") and p.requires_grad],
                 "lr": policy.cfg.lr_backbone,
             },
         ]
-        optimizer = torch.optim.AdamW(
-            optimizer_params_dicts, lr=policy.cfg.lr, weight_decay=policy.cfg.weight_decay
-        )
+        optimizer = torch.optim.AdamW(optimizer_params_dicts, lr=policy.cfg.lr, weight_decay=policy.cfg.weight_decay)
         lr_scheduler = None
     elif isinstance(policy, DiffusionPolicy):
         optimizer = torch.optim.Adam(
@@ -313,8 +305,6 @@ def train(cfg: dict, out_dir=None, job_name=None):
             last_epoch=global_step - 1,
         )
 
-
-
     num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
     num_total_params = sum(p.numel() for p in policy.parameters())
 
@@ -397,9 +387,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
     # create dataloader for online training
     concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset])
     weights = [1.0] * len(concat_dataset)
-    sampler = torch.utils.data.WeightedRandomSampler(
-        weights, num_samples=len(concat_dataset), replacement=True
-    )
+    sampler = torch.utils.data.WeightedRandomSampler(weights, num_samples=len(concat_dataset), replacement=True)
     dataloader = torch.utils.data.DataLoader(
         concat_dataset,
         num_workers=4,