qi <ddlZddlZddlZddlmZddlmZddlm cm Z ddl m cmZddlmcmZddlmZddlmZddlmZdddddddejdejezdejdzd eeefdzd edzd eeejfdzd edzd ej fdZ!dS)N)Any) checkpoints)config)repack_transforms sample_kwargsdefault_prompt norm_statspytorch_device train_configcheckpoint_dirrrrr r returnc x|ptj}tjt |}t j|d}t j|}tj d|r6|j ||} | j dn;|j t!j|dz t$j} |j|j|j } |3| jt1dt3j|dz | j}|r7|5 d dl} | jrd nd }n#t<$rd }YnwxYwt?j | g|j!tj"|| j#j!tj$|| j% | j&j!g| j&j'tj(|| j% | j#j'|j'||j)||r|nd S)a=Create a policy from a trained checkpoint. Args: train_config: The training config to use to create the model. checkpoint_dir: The directory to load the model from. repack_transforms: Optional transforms that will be applied before any other transforms. sample_kwargs: The kwargs to pass to the `sample_actions` method. If not provided, the default kwargs will be used. default_prompt: The default prompt to use for the policy. Will inject the prompt into the input data if it doesn't already exist. norm_stats: The norm stats to use for the policy. If not provided, the norm stats will be loaded from the checkpoint directory. pytorch_device: Device to use for PyTorch models (e.g., "cpu", "cuda", "cuda:0"). If None and is_pytorch=True, will use "cuda" if available, otherwise "cpu". Note: The function automatically detects whether the model is PyTorch-based by checking for the presence of "model.safensors" in the checkpoint directory. zmodel.safetensorszLoading model...bfloat16params)dtypeNz(Asset id is required to load norm stats.assetsrcudacpu) use_quantiles) transformsoutput_transformsrmetadata is_pytorchr )*rGroupdownloadmaybe_downloadstrospathjoinexistslogginginfomodel load_pytorchpaligemma_with_expertto_bfloat16_for_selected_paramsload_modelrestore_paramsjnprdatacreate assets_dirsasset_id ValueError _checkpointsload_norm_statstorchr is_available ImportError_policyPolicyinputsInjectDefaultPromptdata_transforms Normalizeuse_quantile_normmodel_transformsoutputs Unnormalizepolicy_metadata) r r rrrr r weight_pathrr$ data_configr3s >/home/robot-lab/Pi0.5_yam/src/openpi/policies/policy_config.pycreate_trained_policyrDsp:*?Z-=-?-?,S-@-@AAN',,~/BCCK ,,J L#$$$n"// kJJ #CCJOOOO"''(=nx>W_b_k(l(l(lmm#**<+C\EWXXK   'GHH H!1.82K[Mabb #n, # LLL',z'>'>'@'@KVVeNN # # #"NNN # >   %  *> : :  ( /   ;;X Y Y Y   ) 0    ) 1  ":[=Z [ [ [  ( 0  &  $-)3=~~%   s!E>> F  F )"r"rpathlibtypingr jax.numpynumpyr+openpi.models.modelmodelsr$r)openpi.policies.policypoliciespolicyr6openpi.shared.downloadsharedropenpi.trainingrr1r_configopenpi.transformsr TrainConfigPathrrdict NormStatsr7rDrCrYs $$$$$$$$$((((((((()))))))))777777------&&&&&&26+/!%9=!%NNN%NL3&N"'$. N S>D( N $J NS*../$6N$JN ^NNNNNNrX