o vi&@sndZddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl m Z m Z m Z ddlZddlZddlZddlmmZddlmZddlmZddlmZddlmZddlmZm Z m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'dd l(m)Z)m*Z*dd l+m,Z,e-dZ.e-dZ/ej01re2dne2dZ3dZ4dZ5iZ6da7da8ej9dddidddZ:de;dedd#e;d e;dBde;fd$d%Z?dd&e;d e;dBde;fd'd(Z@d)e;deAfd*d+ZBd,d-ZCdd/e;fd0d1ZDdd2e;d3eEdeAfd4d5ZFd6e;dejGfd7d8ZHd9d:ZId;ejJddfdd?ZLdd;e ejJd@e:s r) formattercCsN|jj}|jj}|dkr|dkrdS|dkr|dkrdStd|d|d ) a\ Determine latent sequence indices based on model configuration. The latent sequence structure depends on the model's state representation: - state_t: Total number of latent frames in the sequence - min_num_conditional_frames: Number of frames used as conditional input Args: model: Cosmos model with config attributes Returns: tuple: (curr_state_start, curr_state_end, future_state_start, future_state_end) Examples: LIBERO (state_t=9, min_conditional_frames=4): Sequence: [blank, curr_proprio, curr_wrist_img, curr_third_person_img, action, future_proprio, future_wrist, future_third_person_img, value] Returns: (1, 3, 5, 7) RoboCasa (state_t=11, min_conditional_frames=5): Sequence: [blank, curr_proprio, curr_wrist_img, curr_third_person_img_left, curr_third_person_img_right, action, future_proprio, future_wrist_img, future_third_person_img_left, future_third_person_img_right, value] Returns: (1, 4, 6, 9) ALOHA (state_t=11, min_conditional_frames=5): Sequence: [blank, curr_proprio, curr_wrist_img1, curr_wrist_img2, curr_third_person_img, action, future_proprio, future_wrist1, future_wrist2, future_third_person_img, value] Returns: (1, 4, 6, 9) r) r)rrrzUnknown model config! state_t=z, min_num_conditional_frames=.)configstate_tmin_num_conditional_frames ValueError)modelr%Zmin_conditional_framesrrr$get_latent_indices_from_model_config=sr)checkpoint_pathreturncCs`|dus|dkr dSd|vr.|ds.|ds.|d}t|dkr.tdd|Ds.d SdS) z Check if a checkpoint path is a HuggingFace repository ID. Args: checkpoint_path (str): Path to checkpoint Returns: bool: True if it's a HF repo ID, False otherwise NF/./css|]}|dVqdS)r#N) startswith).0partrrr {sz(is_hf_checkpoint_path..T)r0splitlenany)r*partsrrris_hf_checkpoint_pathjs  r8repo_id cache_dircCstd||durtjdd}t||dd}td|tj|d}tj|r/|Sdd t|D}|rDtj||d S|S) an Download a Cosmos Policy checkpoint from HuggingFace and return the local path. Args: repo_id (str): HuggingFace repository ID (e.g., "nvidia/Cosmos-Policy-LIBERO-Predict2-2B") cache_dir (str, optional): Local cache directory. If None, uses HF default cache. Returns: str: Local path to the downloaded checkpoint directory z)Downloading checkpoint from HuggingFace: NHF_HOMET)r9r:Zresume_downloadz'Checkpoint downloaded successfully to: r(cSsg|] }|dr|qS)z.pt)endswith)r1frrr z*download_hf_checkpoint..r) printosenvirongetrpathjoinexistslistdir)r9r:Z local_dir model_dirpt_filesrrrdownload_hf_checkpoints   rJhf_pathcCsddlm}|dd}t|dkrtd|d|dd|d}|d}td ||d ur;tjd d }||||d }td ||S)a Download a single file from a HuggingFace repository and return the local path. Args: hf_path (str): HuggingFace file path in format "repo_id/filename" (e.g., "nvidia/Cosmos-Policy-LIBERO-Predict2-2B/libero_dataset_statistics.json") cache_dir (str, optional): Local cache directory. If None, uses HF default cache. Returns: str: Local path to the downloaded file r)hf_hub_downloadr-r/rzInvalid HF file path: z&. Expected format: 'org/repo/filename'rz#Downloading file from HuggingFace: Nr;)r9filenamer:z!File downloaded successfully to: ) huggingface_hubrLr4r5r'r@rArBrC)rKr:rLr7r9rMZ local_pathrrrdownload_hf_files  rOrDcCsV|dus|dkr |Sd|vr)|ds)|ds)|d}t|dkr)t||dS|S)a? Resolve a path - download from HuggingFace if it's an HF path, otherwise return as-is. Args: path (str): Path to resolve (can be local path or HF path in format "org/repo/filename") cache_dir (str, optional): Cache directory for HF downloads Returns: str: Resolved local path Nr,r-r.r)r:)r0r4r5rO)rDr:r7rrr resolve_paths    rPdataset_stats_pathcCs|dksJdt|}tj|sJd|t|d }t|}Wdn1s.wY|D] \}}t |||<q7|S)a Load dataset statistics from a JSON file. This function loads normalization statistics needed for action un-normalization and proprio rescaling. It handles both local paths and HuggingFace paths. Args: dataset_stats_path (str): Path to dataset statistics JSON file. Can be a local path or HF path (e.g., "nvidia/Cosmos-Policy-LIBERO-Predict2-2B/libero_dataset_statistics.json") Returns: dict: Dataset statistics with numpy arrays for keys like "actions_min", "actions_max", "proprio_min", "proprio_max" Raises: AssertionError: If dataset_stats_path is empty or file doesn't exist r,z>Must provide `dataset_stats_path` for action un-normalization!z$Dataset stats do not exist at path: rN) rPrArDrFopenjsonloaditemsnparray)rQr= dataset_statsZ stat_nameZ stat_valuerrrload_dataset_statss  rZcCs^td|j}t|rtd|t|}t|j||jdd\}}||t }||fS)ab Load and initialize the Cosmos model and configuration from checkpoint. Supports loading from: - HuggingFace repositories (e.g., "nvidia/Cosmos-Policy-LIBERO-Predict2-2B") - Local filesystem paths Args: cfg: Evaluation configuration object Returns: Tuple[torch.nn.Module, Config]: Tuple of (model, config) z(Instantiating pretrained Cosmos model...z!Detected HuggingFace repository: FZexperiment_nameZs3_checkpoint_dir config_fileZload_ema_to_reg) r@ ckpt_pathr8rJr r$r\evaltoDEVICE)cfgr*r(r$rrr get_models  rbcudadevicecCsV|j}t|rtd|t|}t|j||jdd\}}|||}||fS)a Initialize the "planning model" used for world model and value function predictions. The planning model is typically a model fine-tuned from the base Cosmos Policy checkpoint. Its world modeling and value function prediction capabilities are refined via training on policy rollouts. Supports loading from: - HuggingFace repositories (e.g., "nvidia/Cosmos-Policy-ALOHA-Planning-Model-Predict2-2B") - Local filesystem paths Args: cfg (Config): Configuration object with planning_model_config_name and planning_model_ckpt_path device (str): Device to load model on Returns: Tuple[torch.nn.Module, Config]: Tuple of (model, config) z4Detected HuggingFace repository for planning model: Fr[) planning_model_ckpt_pathr8r@rJr planning_model_config_namer\r^r_)rardr*planning_modelr$rrrget_planning_model*s  rht5_text_embeddings_path worker_idc Csl|durt|}|durtj|rtikr|d}t|dd}zg|Lt|d7}t|}t t j r;d|nd}| D]\}}t|t jrS||||<qBt|Wdn1scwYWdn1srwYtd|d ||ad aWdStytd YdSty} ztd | d WYd} ~ dSd} ~ wwdSdSdS)a% Initialize T5 text embeddings cache (for language instructions). Cache is a dict; key: language instruction (str), val: t5 embedding (torch.Tensor, shape (1, 512, 1024)). Args: t5_text_embeddings_path (str): Path to precomputed T5 text embeddings dictionary. Can be a local path or HF path (e.g., "nvidia/Cosmos-Policy-LIBERO-Predict2-2B/libero_t5_embeddings.pkl") worker_id (int): Worker ID (if using parallel inference) Returns: dict: T5 text embeddings cache N.locktimeoutrbcuda:rzLoaded T5 text embeddings from z onto device FzWarning: Could not acquire lock for T5 embeddings file after 30 seconds. Another process may be writing to it. Skipping cache load - embeddings will be computed on-demand.z,Warning: Error loading T5 embeddings cache: z(. Embeddings will be computed on-demand.)rPrArDrFt5_text_embeddings_cacherrSpicklerUtorchrdrc is_availablerV isinstanceTensorr_updater@t5_text_embeddings_path_global!t5_text_embeddings_newly_computedFileLockTimeout Exception) rirj lock_pathlockfiledatardkeyvalueerrrinit_t5_text_embeddings_cacheNsD          r task_labelcCsR|tvr t|}|Std|dt|}|t|<datd|dt|S)a  Get T5 embedding of language instruction from cache. If the embedding is not in cache, computes it and saves the updated cache to disk. Args: task_label (str): Task description string Returns: torch.Tensor: T5 text embedding z-Computing T5 embedding for new instruction: 'z'...Tz'... Finished!)rqr@r rysave_t5_text_embeddings_cache)rtext_embeddingrrrget_t5_embedding_from_caches  rc Cstdur tddStsdStd}t|dd}zz|mtjtr3td}td|tt|tdti}t D]\}}t |t j rQ|||<q@|||<q@ttd }t||Wdn1slwYtd t|d td aWdWdS1swYWdStytd d aYdSty}ztd|d atdWYd}~dSd}~ww)aC Save the T5 text embeddings cache to disk with automatic backup. Creates a backup of the original file before overwriting if new embeddings were computed. Uses file locking to prevent race conditions when multiple processes try to save simultaneously. If the lock cannot be acquired within 30 seconds, gracefully skips saving without crashing. This function is useful because it can prevent needing to recompute T5 embeddings that get added to the initial set of embeddings during the evaluations. Args: None Returns: None NzNWarning: No T5 embeddings path set. Cannot save new embeddings. Skipping save.rk rmz.backupz$Creating backup of T5 embeddings at z&Saving updated T5 embeddings cache to wbzSaved z T5 embeddings to FzWarning: Could not acquire lock for saving T5 embeddings after 30 seconds. Another process may be writing to it. Skipping save - embeddings will be recomputed if needed.z"Error saving T5 embeddings cache: z8Skipping save - embeddings will be recomputed if needed.)rxr@ryrrArDrFshutilcopy2rqrVrursrvrrSrrdumpr5rzr{)r|r}Z backup_pathZ save_datarrr=rrrrrsH      &  rimagescCsLt|tj}t|jdko|jddk}|jtjk}|r |r |s$JddS)z Validate input images format. Args: images (np.ndarray): Images to check Returns: None Raises: AssertionError: If image format is invalid rrzIncorrect images format detected! Make sure that the input images are a numpy array with shape (T, H, W, 3) and dtype np.uint8!N)rurWndarrayr5shapedtypeuint8)rZis_numpy_arrayZhas_correct_shapeZhas_correct_dtyperrrcheck_images_formats  rc Cs|j\}}}}||ksJd|d||dks Jd|t|dddd}g}|D]}t|d}t||} tj| ||gd d } || q/t |} | dddd t j} | S) a Apply test-time image transformations to match the image augmentations used at train time. At train time, we use random resized crops (90% area) and color jitter. At test time, we only need to do a 90% area center crop. Args: images (np.ndarray): Images of shape (T, H, W, C) and dtype np.uint8 Returns: np.ndarray: Images with test-time transformations applied z/Image height and width must be equal! Got H == z and W == rz)Number of channels should be 3! Got C == rrr/gml[?T) antialias)rrs from_numpypermuteintFZ center_cropresizeappendstackrnumpyastyperWr) r_HWCZ images_tensorresultsimgZ crop_sizeZimg_cropZ img_resizedZtransformed_imagesrrrapply_image_transformss     r flip_imagescCsRtj|dd}t||rt|}|jrt|dd}t|t}|jr't |}|S)ak Prepare images for Cosmos model input by resizing and cropping as needed. Args: images (List[np.ndarray]): List of input images as numpy arrays cfg: Configuration object with parameters flip_images (bool): Whether to flip images vertically across x-axis Returns: np.ndarray: Processed images ready for the model raxis_)quality) rWrrflipuduse_jpeg_compressionrrCOSMOS_IMAGE_SIZEtrained_with_image_augr)rrarZprocessed_imagesrrrprepare_images_for_models    r output_latent action_shapeaction_indicescCstj|jd|jd}||dd|ddddf}|j\}}}}||d} | jd} t|dks6Jd|d|d} | | ksPJd|d | d | d | | } | ddd| | f|| | } | || |d|d} tj| dd }|S) a Extract the predicted action chunk from the generated latent sequence. Args: output_latent (torch.Tensor): The full latent sequence generated by the model, with shape (B, C', T', H', W') action_shape (tuple): Target shape of the action chunk: (chunk_size, action_dim) action_indices (torch.Tensor): Batch indices specifying which index of the latent sequence contains the action Returns: torch.Tensor: Batch of extracted action chunks, with shape (B, chunk_size, action_dim) rrdNrrr/z>action_shape should have 2 elements: (chunk_size, action_dim)!z Action shape z requires z# elements, but the latent only has z elements!dim)rsarangerrdreshaper5mean)rrr batch_indicesZaction_latent_frame batch_sizeZlatent_channelsZlatent_hZlatent_wZflat_action_latentZnum_latent_elementsZnum_action_elementsZnum_action_chunksZall_action_chunksZfinal_action_chunkrrr)extract_action_chunk_from_latent_sequence-s"   r value_indicescCsXtj|jd|jd}||dd|ddddf}||jdd}tj|dd}|S)a Extract the predicted value from the generated latent sequence. Args: output_latent (torch.Tensor): The full latent sequence generated by the model, with shape (B, C', T', H', W') value_indices (torch.Tensor): Batch indices specifying which index of the latent sequence contains the value Returns: torch.Tensor: Batch of extracted values, with shape (B, value_dim) rrNrrr)rsrrrdrr)rrrZvalue_latent_frameZflat_value_latentZ final_valuerrr"extract_value_from_latent_sequenceZs  r?actionsrYscale_multipliercCsV|d}|d}|j}|d|jd}||}d|d|||}||}|S)a Unnormalize actions to the original dataset scale. This undoes the normalization used at train time. Args: actions (np.ndarray): Actions to be unnormalized dataset_stats (dict): Dataset statistics needed for the rescaling formula scale_multiplier (float): Multiplier to adjust scale from [-scale_multiplier,+scale_multiplier] back to [-1,+1] Returns: np.ndarray: Unnormalized actions actions_min actions_maxrr?r)rr)rrYrrroriginal_shaperrrunnormalize_actionsos rcCsR|}|d}|d}|sd||||d}n||||}||}|}|S)a Rescale (normalize) proprio to the range [-1,+1] or [0,+1], with optional scaling by scale_multiplier. Args: proprio (np.ndarray): Proprio to be rescaled dataset_stats (dict): Dataset statistics needed for rescaling formula non_negative_only (bool): Whether to use [0,+1] range (True) or [-1,+1] range (False) scale_multiplier (float): Multiplier to adjust final scale Returns: np.ndarray: Rescaled proprio Z proprio_minZ proprio_maxr/rr)propriorYnon_negative_onlyrarrZcurr_minZcurr_maxZ rescaled_arrrrrrescale_proprios rsampleorig_clean_latent_framesINDICES_TO_REPLACEc CsN|D]"}|dddd|ddddf|dddd|ddddf<q|S)aL Undo the latent injections so that VAE decoding will reconstruct images without visual artifacts. This is done by replacing the latent frames with the original (pre-injection) latent frames. The reason why this is needed is because the latent injection happens after the VAE encodes the raw image sequence. If the latent frames are decoded without removing the injections, the reoncstructed images have heavy visual distortions. To fix this, we replace the injected latent frames with the original (pre-injection) latent frames. These original frames really just correspond to blank/black (all zero) images that served as placeholders for the latent injections. Args: sample (torch.Tensor): Generated samples with latent injections, shape (B, C', T', H', W') orig_clean_latent_frames (torch.Tensor): Original clean (unnoised) latent frames in the condition object INDICES_TO_REPLACE (List[int]): Indices to replace with the original (pre-injection) latent frames Returns: torch.Tensor: Samples with latent injections undone Nr)rrrindexrrrundo_latent_injectionsBrr(future_wrist_image_latent_idxfuture_wrist_image2_latent_idxfuture_image_latent_idxfuture_image2_latent_idxtemporal_compression_factorc Cs |dur t|||}||dddd} | ddddd tj t j} |d | d } |d | d } |d | d } |d | d }i}|j rn| dd| f}||d <|j dkrn| dd| f}||d <|j r| dd| f}||d <|jdkr| dd|f}||d <|S)a Get predicted future images from generated samples. Args: model (torch.nn.Module): The model sample (torch.Tensor): Generated sample cfg (Config): Configuration object orig_clean_latent_frames (torch.Tensor): Original clean (unnoised) latent frames in the condition object INDICES_TO_REPLACE (List[int]): Indices to replace with original (pre-injection) latent frames future_wrist_image_latent_idx (int): Index of future wrist image in video future_wrist_image2_latent_idx (int): Index of future secondary wrist image in video future_image_latent_idx (int): Index of future primary image in video future_image2_latent_idx (int): Index of future secondary image in video temporal_compression_factor (int): Temporal compression factor for VAE Returns: Dict[str, Any]: Dictionary containing future image predictions Nrg_@rr/rrrfuture_wrist_imagefuture_wrist_image2 future_image future_image2)rdecodeclamprr_rsrrrrrWuse_wrist_imagenum_wrist_imagesuse_third_person_imagenum_third_person_images)r(rrarrrrrrrZgenerated_imagesZfuture_wrist_image_raw_idxZfuture_wrist_image2_raw_idxZfuture_image_raw_idxZfuture_image2_raw_idxfuture_image_predictionsrrrrrrr(get_future_images_from_generated_sampless. (  rall_value_predictions!value_ensemble_aggregation_schemecCs"|dkr t|d}nz|dkr*t|d}t|d}d}|||}n]|dkr=d}t||kd}nJ|dkrd}t|}||k}|d} |jd} | | } t||t|} | d} | | j dd }t| d k|t|}nt d |tj |ddd }|S) aG Aggregate value predictions from a list of value predictions. Args: value_ensemble_aggregation_scheme (str): Aggregation scheme to use for ensemble value predictions all_value_predictions (List[np.ndarray]): List of value predictions Returns: np.ndarray: Aggregated value predictions averagerZlcbrZ success_voteg?Z majority_meanr)minrzInvalid search value scheme: rmax) rsrrstdrsumrwhere zeros_likerr')rrvalue_predictionZ mean_valueZ std_valuebetaZsuccess_thresholdZstacked_valuesZ success_maskZ num_successZ num_totalZ success_ratioZ masked_valuesZ sum_successZ mean_successrrraggregate_value_predictionss0   r pred_dictscst|dkriSi}|dD]8fdd|D}t|dkr&d|<qtjdd|Ddd}tt|jddddtj}||<q|S)a Average a list of future image prediction dictionaries. This function is used when we have a future image prediction ensemble and we want to aggregate the predictions for visualization purposes. Args: pred_dicts (List[Dict[str, Any]]): List of future image prediction dictionaries Returns: Dict[str, Any]: Averaged future image predictions rcs$g|]}|ddur|qSN)rC)r1drrrr>Is$z4average_future_image_predictions..NcSsg|]}|tjqSr)rrWfloat32)r1vrrrr>Msrr) r5keysrWrcliproundrrr)rresultvalsstackedavgrrr average_future_image_predictions8s  $ rrrTobstask_label_or_embeddingseedrandomize_seednum_denoising_steps_action+generate_future_state_and_value_in_parallelrc <s |r tdd}tt|trt|} nt|tjr)tj |tj d } d\} } }}|j dkrA|d|dg}d}d } n6|j d krX|d|d|d g}d}d } d } n|j d kro|d|d|dg}d}d }d } nt d|j t||}d}|jr|d}|jrt||ddd}g}d}|| }t|}|tjt|dd|d 7}|jrt|td}|||}|d 7}d\}}|jr||}t|td}|||}|d 7}|jd kr||}t|td}|||}|d 7}nd}|jr-t|td}|||}|d 7}|jd kr-|| } t| td}!||!|}"|d 7}|||}#|d 7}|jrK|||}$|d 7}|jrr|||}%|d 7}|jd krp|||}&|d 7}nd}&|jr|||}'|d 7}|jd kr||!|}(|d 7}nd}(|||})|d 7}tj|dd}*tj|*dd}*t|*| d d d d f}*t|*d}*t|*j tj!d }*|jrt|"| dj tj d }+iddd|*d| #| d d j tj d dtj d g| tj d d!tj$| d t%t%ftj d d"|j&j'd|jr$|+ndd#|jr8tj |g| tj(d n tj dg| tj(d d$|jrWtj |g| tj(d n tj dg| tj(d d%|jrvtj |g| tj(d n tj dg| tj(d d&|jrtj |g| tj(d n tj dg| tj(d d'|jr|jd krtj |"g| tj(d n tj dg| tj(d d(tj |#g| tj(d d)|jrtj |$g| tj(d n tj dg| tj(d d*|jrtj |%g| tj(d n tj dg| tj(d d+|jr+|jd kr+tj |&g| tj(d n tj dg| tj(d d,|jrJtj |'g| tj(d n tj dg| tj(d |jrn|jd krntj |(g| tj(d n tj dg| tj(d tj |)g| tj(d d-},|j)|,| ||d|j*d.d/\}-}.tj+| f|#tj(|-j,d0}/t-|-|j.t/f|/d1 tj012|j3rt3||r?|j dkrgd2}0n|j d krgd3}0n|j d krgd3}0nt d|j t4||-5||.|0|jr|%nd|jr|jd kr|&nd|jr|'nd|jr|jd kr|(ndtd4 }1tj+| fdtj(|-j,d0}2t6|-|2}3|3d d }3tj7|3dd d5}3| d krg}4D]|4fd6d7t8t9DqH|4|rg}5t8| D]}6i}7|1:D] \}8}9|9|6|7|8<qo|5|7qg|5}1g}:t8| D] }6|:|3|6;q|:}3n#dfd8d7t8t9D|rd9d:|1:D}1|3d;}3t<|-|.|,t<|#|$|%|&|'|(|)d;||| d<};|r|1|;d=<|3|;d><Wd|;SWd|;S1swY|;S)?a Generate action predictions with the policy. Args: cfg (Config): Configuration object with parameters model (torch.nn.Module): The policy model dataset_stats (dict): Dataset statistics needed for rescaling formula obs (Dict[str, Any]): Observation dictionary task_label_or_embedding (Any): Text description of the task or T5 embedding of the task seed (int): Seed for sampling from the model randomize_seed (bool): Whether to randomize the seed for sampling actions in each query (still depends on base seed) num_denoising_steps_action (int): Number of denoising steps to use for action prediction generate_future_state_and_value_in_parallel (bool): Whether to generate future state and value in parallel with the actions worker_id (int): Worker ID (if using parallel inference) batch_size (int): Batch size for inference Returns: Dict[str, Any]: Dictionary containing actions and related predictions )r)rrrrlibero wrist_image primary_imagerrrobocasasecondary_imager/alohaleft_wrist_imageright_wrist_image Eval suite not implemented yet: NrFr)rrr)Ztotal_num_copiesNNr)rrrr/rZ dataset_nameZ video_dataZvideoZt5_text_embeddingsZfpsZ padding_masknum_conditional_framescurrent_proprio_latent_idxcurrent_wrist_image_latent_idxcurrent_wrist_image2_latent_idxcurrent_image_latent_idxcurrent_image2_latent_idxaction_latent_idxfuture_proprio_latent_idxrrr)rvalue_latent_idxT)n_sample num_stepsris_negative_promptuse_variance_scalereturn_orig_clean_latent_framesrrd)rrrrrrrrrr"rrcg|]}|qSrrr1i)actrrr>zget_action..cr rrr!)rrrr>r$cS"i|] \}}|dur||dqSNrrr1krrrr "zget_action..)rrrrrrr)rgenerated_latentr data_batchlatent_indicesall_camera_imagesrrrr)=secretsrandbitsrsinference_moderustrrrWrtensorbfloat16rcsuiter'r use_proprionormalize_propriorrr expand_dimsrcopy"COSMOS_TEMPORAL_COMPRESSION_FACTORrrrr concatenatetile transposerr_rrrepeatzerosrr$r&int64generate_samples_from_batchrfullrdr chunk_sizerrrrrrclonerrranger5rVitemdict),sz/get_future_state_prediction..cg|]}|qSrrr!rrrr>0r$FTrrrrrskip_vae_encodingprevious_generated_latentrr/rcSs(i|]\}}|t|dr|n|qS)rD)hasattrrDr'rrrr)Ts(z/get_future_state_prediction..rcSr%r&rr'rrrr)r*)rfuture_state_samples_listN)r/r0rsr1r5r'r$r&rErWlinspacerrtolistrArrrrDrrrrr:rVlowerrrG) rar(r,rHrrrrrrrrrOrjrrPrQrRrr]Zfuture_state_seedsZ!future_state_denoising_steps_listrIr"Zsample_future_state_iZpred_irZ"generated_latent_with_future_staterJr(rrLrrXrget_future_state_predictions/                        ranum_denoising_steps_valueuse_ensemble_value_predictions!num_value_predictions_in_ensemblec  s8|r tddt|jjd|d<|j|d<g} |D]} | r|dks,Jd|r8ddt| D} n fd dt| D} t d||  t  }t| D]>}|||| |d |j d | d }|j|fi|}tj|fd tj|jd}t||}|dd}tj|ddd}| |qSq |rtdd}n}|||d |j d | d }|j|fi|}tj|fd tj|jd}t||}|dd}tj|ddd}| |q | rt| |j}n| d}|dkrg}t|D] }|||q|}n|d}t|d}Wd|S1swY|S)a Generate value predictions with the value function model. Args: cfg (Config): Configuration object with parameters model (torch.nn.Module): The value function model data_batch (Dict[str, Any]): Data batch for video model generation future_state_samples_list (List[torch.Tensor]): List of future state samples seed (int): Seed for sampling from the model randomize_seed (bool): Whether to randomize the seed for sampling actions in each query (still depends on base seed) num_denoising_steps_value (int): Number of denoising steps to use for value prediction worker_id (int): Worker ID (if using parallel inference) batch_size (int): Batch size for inference use_ensemble_value_predictions (bool): Whether to use ensemble value predictions num_value_predictions_in_ensemble (int): Number of value predictions in ensemble Returns: Dict[str, Any]: Dictionary containing value predictions rrrr.mask_current_state_action_for_value_prediction;Ensemble value predictions only supported for batch size 1!cSrSrTrUrVrrrr>r?z(get_value_prediction..crWrrr!rXrrr>r$FTrYrrr/rr)rN)r/r0rsr1r$r%rerErWr^rrr_rrArBr@rdrrrrrrFrG)rar(r,r]rrrbrjrrcrdrZ fs_sample value_seedsvalue_denoising_steps_listr"Zgenerate_value_sample_kwargsgenerated_latent_with_valuerr value_seedrKrLrrXrget_value_predictions!             ^^rk action_samplec  s|r tddt|jjd|d<|j|d<g} | r|dks'Jd|r3ddt| D} n fd dt| D} t d||  t  } t| D]8}|j ||| || |d |jd |d }tj|fd tj|jd}t||}|dd}tj|ddd}| |qNn>|rtdd}n}|j ||||d |jd |d }tj|fd tj|jd}t||}|dd}tj|ddd}| || rt| |j}n| d}|dkrg}t|D] }|||q|}n|d}td|d}Wd|S1swY|S)a Generate Q-value predictions with the value function model. This is a variant that takes the current state and action as conditioning and generates Q(s, a). Note that this requires a separate checkpoint that is trained to predict values conditioned on the current state and action. Args: cfg (Config): Configuration object with parameters model (torch.nn.Module): The value function model data_batch (Dict[str, Any]): Data batch for video model generation action_sample (torch.Tensor): Generated action sample seed (int): Seed for sampling from the model randomize_seed (bool): Whether to randomize the seed for sampling actions in each query (still depends on base seed) num_denoising_steps_value (int): Number of denoising steps to use for value prediction worker_id (int): Worker ID (if using parallel inference) batch_size (int): Batch size for inference use_ensemble_value_predictions (bool): Whether to use ensemble value predictions num_value_predictions_in_ensemble (int): Number of value predictions in ensemble Returns: Dict[str, Any]: Dictionary containing Q-value predictions rrrr'mask_future_state_for_qvalue_predictionrfcSrSrTrUrVrrrr>Mr?z)get_qvalue_prediction..crWrrr!rXrrr>Or$FTrYrrr/rrN)rr)r/r0rsr1r$r%rmrErWr^rrr_rArrBr@rdrrrrrrFrG)rar(r,rlrrrbrjrrcrdrrgrhr"rirrrjrKrLrrXrget_qvalue_predictions$            XXrnc@s0eZdZdZddZddZddZdd Zd S) WorkerPoolManagerzd Manages a pool of persistent parallel workers across multiple GPUs (for best-of-N search). cCs>||_||_||_i|_i|_t|_t|_i|_d|_ dS)NF) rarYavailable_gpusworkers task_queuesr result_queue error_queueshutdown_events initialized)selfrarYrprrr__init__s zWorkerPoolManager.__init__c Cs|jrdStd|j|jD],}t}t}tt||j|j||j |j |fd}| ||j |<||j |<||j|<qt}t}d}t|t|jkrt||kr[tdz)|j s|j }|ddkr~||dtd |dd |j raWn tyYnwzA|j s|j } | dd krtd | dd | dd| vrtd| dd| dtd | d|j rWntytyYnwtdt|t|jksOd|_tdt|jddS)z/Start persistent workers on all available GPUs.Nz%Starting persistent workers on GPUs: )targetargsiXzWorker initialization timeouttype init_completegpu_idzWorker on GPU z initialized successfully init_errorz$Worker initialization failed on GPU : error tracebackzGPU z traceback: g?TzAll z! workers initialized successfully)rvr@rpr r r persistent_parallel_workerrarYrsrtstartrqrrrusettimer5 RuntimeErrorempty get_nowaitrCaddr{sleep) rwr} task_queueshutdown_eventworkerZinitialized_workers start_timernrrrrr start_workerssr              zWorkerPoolManager.start_workersc Cs|jstdg}t|jjD]%}tt}|j|t |j}|||d}|j | || |qi}g} t } |jj} t ||jjkrt | | krz%|jsu|j} | ddkrp| d|vrp| || d<|jrWWn tyYnwz$|js|j} | ddkr| d|vr| | |jrWn tyYnwt dt ||jjkrt | | ksQg}|D]}||vr||} | | dq| rtd t | d | D]} td | dd | d d| dq|S)z+Submit tasks to workers and return results.z4Workers not initialized. Call start_workers() first.)task_idobservation_dicttask_descriptionr{rr task_error{Gz?rLzParallel inference errors: z tasks failedzTask z on GPU r}rr)rvrrEranum_queries_best_of_nr2uuiduuid4rpr5rrputrrparallel_timeoutrsrrrCr{rtrr@)rwrrZtask_idsZ query_idxrr}taskrerrorsrrnrr query_resultsrrr submit_taskssd               (zWorkerPoolManager.submit_tasksc Cs|jsdStd|jD]}|q|jD]}z|dWqty,Yqw|j D]E\}}z%|j dd| rZtd|| |j dd| rZ| Wq2tyw}ztd|d|WYd}~q2d}~wwd |_td dS) zShutdown all workers.NzShutting down worker pool...rrmz Force terminating worker on GPU rz"Error shutting down worker on GPU rFzWorker pool shutdown complete)rvr@ruvaluesrrrrr{rqrVrEis_alive terminatekill)rwrrr}rrrrrshutdowns6      zWorkerPoolManager.shutdownN)__name__ __module__ __qualname____doc__rxrrrrrrrros  : 8roc+sd}d}ztjtj|td|} tjs%td|td|dt|\}} | | }td||j dkr_td|dt |\}} | | }td |nd \}} t |j |d ||d d |sҐz|jdd} | durWWdS| d} | d}| d}g}g}g}i}t}t||||||j||j|j|jp|jp|j |d }|dkrt|}td|d|dd|d|d<||d|jrHt}t|f|dur|n||d|d|d|dd|dd|dd |dd!|dd"|j||j|j|j|j|jd#}|dkr:t|}td|d$|dd|d%|d%<||d%n |jsR|d%|d%<|jrt}t||durc|n||d|d&|j||j|j |j!|j"d' }|dkrt|}td|d(|dd|d)|d)<||d)td*|d+|d)d,n_|jrt}t#||dur|n||d|d|j||j|j |j!|j"d- }|dkrt|}td|d(|dd|d)|d)<||d)td*|d+|d)d,n|d)|d)<|j$d.krM|jrJd/t%|\}}}}t&d0|j$d.D]#}|d&D]}|'}|'} |dddd||d.f| dddd||d.f<|d}!|j(j)|!d1<d2|!d3<|jrpt*+d4d5}"n|j|}"d.}#|j,|!|#|j|"d2|j-d6| d6d7 \}$}%|dd8}&tj.|#f|&tj/|$jd9}'t0|$|j1t2f|'d: tj345|j6rt6|dfd;d<t&t7D|t|f|dur|n||d|$|%|dd|dd|dd |dd!|dd"|j||j|j|j|j|jd#}||d%t||dur|n||d|d&|j||j|j |j!|j"d' }|d)|d)<||d)td*|d.d+|d)d,q/q(||d=<||d><||d?<t8|dtj9rj|d@4|d@<d%|vr|d%:D]\}(})t8|)tj9r|)4|d%|(<qu||| dA|dBWn5t;y}*z(t8|*tvr| ddDndDdEt?|*t@AdFWYd}*~*nd}*~*ww|rwWdSWdSt;y}*z||dGt?|*t@AdHWYd}*~*dSd}*~*ww)Iz Persistent parallel worker function that loads the model once and handles queries in a while loop. Used in best-of-N planning with N GPUs working in parallel. Runs on one specific GPU and processes tasks from a task queue. NrpzCUDA not available on GPU zLoading model on GPU z...z!Model loaded successfully on GPU r,zLoading planning model on GPU z*Planning model loaded successfully on GPU r )rjr|)r}r{rrmrrr)rrrrrjrz -- Query z: Action query time = z.3fz secrr,r+rr-rrrrr)r(r,rHrrrrrrrrrOrPrQrRz': Future state prediction query time = rr])r(r,r]rrrbrcrdz : Value prediction query time = rzQuery z: Value prediction: z.4f)r(r,rlrrrbrcrdrz 1 not supported for Q(s, a) value prediction!r/rFrerrT)rrrrrrZr[rrr)rcr rrr!Z next_actionsrrr>!r$z.persistent_parallel_worker..!future_image_predictions_by_depthvalue_predictions_by_depthactions_by_depthactionr)r}rr{rLrunknownr)r}rr{rrr~)r}r{rr)Brsrcinit set_devicerdrtrr@rbr_rerhrriris_setrCrrMrrrar_future_predictionar_value_predictionar_qvalue_predictionrrarOrPrQrRrkrbrcrdrn search_depthr)rErDr$r&r/r0rArrBr@rrCrrrrrr5rurvrVr{queueEmptylocalsr2r format_exc)+r}rarYrrsrtrr(rgrdr$Zplanning_model_configrr observationrrrrrLraction_return_dictZ query_timeZfuture_state_return_dictZvalue_return_dictZcurr_state_startZcurr_state_endZfuture_state_startZfuture_state_enddepthZfuture_state_latentZ'next_generated_latent_with_future_stateZ(rearranged_next_latent_with_future_stater,rrZ!next_generated_latent_with_actionZnext_orig_clean_latent_framesrrrrrrrrr;s                                "p  *r<c Cs|js td|j|d|dd}d|vr|d|d<d|vr(|d|d<d|vr2|d|d<d|vr<|d|d<z|||}|rH|WSWd Sty_}z td ||d }~ww) a Query the model in parallel across multiple GPUs using a persistent worker pool. This function is useful when doing best-of-N search with N GPUs working in parallel. Args: cfg (Config): Configuration object observation (Dict[str, Any]): Input observation dict with keys like "primary_image", "wrist_image", "proprio", etc. task_description (str): Task description string worker_pool (WorkerPoolManager): WorkerPoolManager instance timeout (int): Timeout for the operation (uses cfg.parallel_timeout from worker_pool) Returns: List[Dict[str, Any]]: List of return_dict dictionaries zUParallel inference requires initialized worker pool, but got worker_pool.initialized=rr)rrrr r rzParallel inference failed: N)rvrrr{r@)rarrZ worker_poolrnrrrrrrquery_model_parallels8     rhttp://0.0.0.0:8777/actrserver_endpointcCstj||d}|S)a  Get action from remote policy inference server. Args: observation (Dict[str, Any]): Observation data to send to server server_endpoint (str): URL of the inference server Returns: Dict[str, Any]: Action response from server )rT)requestspostrT)rrresponserrrget_action_from_servers  rr)rcr&)F)r)Fr)NNNNNNr)rFrTrr)rFrrrFrrN)rFrrrFr)r)r)brrTrArrrr/rrrrtypingrrrrrWrrsZ!torchvision.transforms.functional transforms functionalrZfilelockrrrzrNrPILrtorch.multiprocessingr r r Z0cosmos_policy._src.predict2.inference.get_t5_embr Z.cosmos_policy._src.predict2.utils.model_loaderr Zcosmos_policy.constantsrZ$cosmos_policy.datasets.dataset_utilsrrZcosmos_policy.utils.utilsrstrftimeZDATEZ DATE_TIMErcrtrdr`rr:rqrxryset_printoptionsr)r2boolr8rJrOrPrGrZrbrhrrrvrrrrrrtuplerrrrrrnnModulerrrrMrarkrnrorrrrrrrs          -'("$5:$# -     < &:!            C           !L :