Added normalization schemes and style checks

lerobot/common/logger.py

@@ -25,13 +25,13 @@ from glob import glob
 from pathlib import Path
 
 import torch
-import wandb
 from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from omegaconf import DictConfig, OmegaConf
 from termcolor import colored
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
 
+import wandb
 from lerobot.common.policies.policy_protocol import Policy
 from lerobot.common.utils.utils import get_global_random_state, set_global_random_state
 

lerobot/common/policies/hilserl/classifier/configuration_classifier.py

@@ -2,8 +2,6 @@ import json
 import os
 from dataclasses import asdict, dataclass
 
-import torch
-
 
 @dataclass
 class ClassifierConfig:

lerobot/common/policies/hilserl/classifier/modeling_classifier.py

@@ -23,9 +23,11 @@ class ClassifierOutput:
         self.hidden_states = hidden_states
 
     def __repr__(self):
-        return (f"ClassifierOutput(logits={self.logits}, "
-                f"probabilities={self.probabilities}, "
-                f"hidden_states={self.hidden_states})")
+        return (
+            f"ClassifierOutput(logits={self.logits}, "
+            f"probabilities={self.probabilities}, "
+            f"hidden_states={self.hidden_states})"
+        )
 
 
 class Classifier(

lerobot/scripts/train.py

@@ -95,11 +95,13 @@ def make_optimizer_and_scheduler(cfg, policy):
         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},
-            ])
+        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":

lerobot/scripts/train_hilserl_classifier.py

@@ -22,7 +22,6 @@ from pprint import pformat
 import hydra
 import torch
 import torch.nn as nn
-import wandb
 from deepdiff import DeepDiff
 from omegaconf import DictConfig, OmegaConf
 from termcolor import colored
@@ -31,6 +30,7 @@ from torch.cuda.amp import GradScaler
 from torch.utils.data import DataLoader, WeightedRandomSampler, random_split
 from tqdm import tqdm
 
+import wandb
 from lerobot.common.datasets.factory import resolve_delta_timestamps
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.logger import Logger