update offline trainer to use new torch.load api

tdmpc2/trainer/offline_trainer.py

@@ -38,13 +38,9 @@ class OfflineTrainer(Trainer):
 				f'episode_reward+{self.cfg.tasks[task_idx]}': np.nanmean(ep_rewards),
 				f'episode_success+{self.cfg.tasks[task_idx]}': np.nanmean(ep_successes),})
 		return results
-				
-	def train(self):
-		"""Train a TD-MPC2 agent."""
-		assert self.cfg.multitask and self.cfg.task in {'mt30', 'mt80'}, \
-			'Offline training only supports multitask training with mt30 or mt80 task sets.'
-
-		# Load data
+	
+	def _load_dataset(self):
+		"""Load dataset for offline training."""
 		fp = Path(os.path.join(self.cfg.data_dir, '*.pt'))
 		fps = sorted(glob(str(fp)))
 		assert len(fps) > 0, f'No data found at {fp}'
@@ -59,7 +55,7 @@ class OfflineTrainer(Trainer):
 		_cfg.steps = _cfg.buffer_size
 		self.buffer = Buffer(_cfg)
 		for fp in tqdm(fps, desc='Loading data'):
-			td = torch.load(fp)
+			td = torch.load(fp, weights_only=False)
 			assert td.shape[1] == _cfg.episode_length, \
 				f'Expected episode length {td.shape[1]} to match config episode length {_cfg.episode_length}, ' \
 				f'please double-check your config.'
@@ -68,6 +64,12 @@ class OfflineTrainer(Trainer):
 		expected_episodes = _cfg.buffer_size // _cfg.episode_length
 		assert self.buffer.num_eps == expected_episodes, \
 			f'Buffer has {self.buffer.num_eps} episodes, expected {expected_episodes} episodes.'
+
+	def train(self):
+		"""Train a TD-MPC2 agent."""
+		assert self.cfg.multitask and self.cfg.task in {'mt30', 'mt80'}, \
+			'Offline training only supports multitask training with mt30 or mt80 task sets.'
+		self._load_dataset()
 		
 		print(f'Training agent for {self.cfg.steps} iterations...')
 		metrics = {}