minor QoL improvements in offline pipeline

datasets/download_mt30.sh

@@ -0,0 +1 @@
+for i in {0..3}; do wget https://huggingface.co/datasets/nicklashansen/tdmpc2/resolve/main/mt30/chunk_${i}.pt?download=true && mv chunk_${i}.pt'?download=true' chunk_${i}.pt; done

datasets/download_mt80.sh

@@ -0,0 +1 @@
+for i in {0..19}; do wget https://huggingface.co/datasets/nicklashansen/tdmpc2/resolve/main/mt80/chunk_${i}.pt?download=true && mv chunk_${i}.pt'?download=true' chunk_${i}.pt; done

docker/environment.yaml

@@ -1,4 +1,4 @@
-name: graph
+name: tdmpc2
 channels:
   - pytorch-nightly
   - nvidia

tdmpc2/common/parser.py

@@ -53,7 +53,7 @@ def parse_cfg(cfg: OmegaConf) -> OmegaConf:
 	if cfg.multitask:
 		cfg.task_title = cfg.task.upper()
 		# Account for slight inconsistency in task_dim for the mt30 experiments
-		cfg.task_dim = 96 if cfg.task == 'mt80' or cfg.model_size in {1, 317} else 64
+		cfg.task_dim = 96 if cfg.task == 'mt80' or cfg.get('model_size', 5) in {1, 317} else 64
 	else:
 		cfg.task_dim = 0
 	cfg.tasks = TASK_SET.get(cfg.task, [cfg.task])

tdmpc2/trainer/offline_trainer.py

@@ -44,13 +44,12 @@ class OfflineTrainer(Trainer):
 			'Offline training only supports multitask training with mt30 or mt80 task sets.'
 
 		# Load data
-		assert self.cfg.task in self.cfg.data_dir, \
-			f'Expected data directory {self.cfg.data_dir} to contain {self.cfg.task}, ' \
-			f'please double-check your config.'
 		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}'
 		print(f'Found {len(fps)} files in {fp}')
+		assert len(fps) == (20 if self.cfg.task == 'mt80' else 4), \
+			f'Expected 20 files for mt80 task set, 4 files for mt30 task set, found {len(fps)} files.'
 	
 		# Create buffer for sampling
 		_cfg = deepcopy(self.cfg)
@@ -65,8 +64,9 @@ class OfflineTrainer(Trainer):
 				f'please double-check your config.'
 			for i in range(len(td)):
 				self.buffer.add(td[i])
-		assert self.buffer.num_eps == self.buffer.capacity, \
-			f'Buffer has {self.buffer.num_eps} episodes, expected {self.buffer.capacity} episodes.'
+		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.'
 		
 		print(f'Training agent for {self.cfg.steps} iterations...')
 		metrics = {}