Eval Error

[mahao18cm]

System Info

Name: lerobot
Version: 0.1.0
torch 2.7.1
Python 3.10.18
numpy  2.2.6

Information

• One of the scripts in the examples/ folder of LeRobot
• My own task or dataset (give details below)

Reproduction

python lerobot/scripts/eval.py
--policy.path=outputs/train/diffusion_pusht/checkpoints/005000/pretrained_model
--env.type=pusht
--eval.batch_size=10
--eval.n_episodes=10
--policy.use_amp=false
--policy.device=cuda

Error:
Traceback (most recent call last):
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/parsers/decoding.py", line 159, in decode_dataclass
instance = origin(**init_args)
File "", line 9, in init
File "/home/root/work/Users/XXXX/lerobot/lerobot/configs/eval.py", line 43, in post_init
self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
File "/home/root/work/Users/XXXXX/lerobot/lerobot/configs/policies.py", line 160, in from_pretrained
config_file = hf_hub_download(
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
validate_repo_id(arg_value)
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
raise HFValidationError(
huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/efsbj4/Users/XXXXX/lerobot/outputs/train/checkpoints/020000/pretrained_model'. Use repo_type argument if needed.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/lerobot/lerobot/scripts/eval.py", line 508, in
eval_main()
File "lerobot/lerobot/configs/parser.py", line 225, in wrapper_inner
cfg = draccus.parse(config_class=argtype, config_path=config_path, args=cli_args)
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/argparsing.py", line 211, in parse
return parser.parse_args(args)
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/argparsing.py", line 102, in parse_args
args, _ = self.parse_known_args(args, namespace, is_parse_args=True)
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/argparsing.py", line 136, in parse_known_args
parsed_t = self._postprocessing(parsed_args)
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/argparsing.py", line 180, in _postprocessing
cfg = decoding.decode(self.config_class, deflat_d)
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/parsers/registry_utils.py", line 78, in wrapper
return base_func(*args, **kw)
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/parsers/decoding.py", line 48, in decode
return get_decoding_fn(cls)(raw_value, ()) # type: ignore
File "/home/root/anaconda3/envs/PyTorch-2.1.0/lib/python3.10/site-packages/draccus/parsers/decoding.py", line 161, in decode_dataclass
raise ParsingError(f"Couldn't instantiate class {stringify_type(cls)} using the given arguments.") from e
draccus.utils.ParsingError: Couldn't instantiate class EvalPipelineConfig using the given arguments.

Expected behavior

how to evaluate the model trained on our dataset?