o xiK@sddlmZddlZddlmZddlmZmZmZddl Z er&ddl m Z ej j dko1ej jdkZGdd d Zd0ddZd1ddZd2ddZ  d3d4ddZ  !d5d6d)d*Z  +d7d8d.d/ZdS)9) annotationsN)Sequence) TYPE_CHECKINGAnyCallable) _TableIndexc@seZdZUdZded<ded<ded<ded <d ed <d ed <          d2d3d"d#Zd4d&d'Z ( ) * d5d6d0d1ZdS)7ValidationDataLoggera0Logs validation data as a wandb.Table. ValidationDataLogger is intended to be used inside of library integrations in order to facilitate the process of optionally building a validation dataset and logging periodic predictions against such validation data using WandB best practices. Sequence | dict[str, Sequence]validation_inputs%Sequence | dict[str, Sequence] | Nonevalidation_targetszlist[_TableIndex]validation_indexesCallable | Noneprediction_row_processorwandb.Table | Noneclass_labels_tableboolinfer_missing_processorsNinputtargetwb_validation_datavalidation_datasetTinputstargetsindexeslist[_TableIndex] | Nonevalidation_row_processorinput_col_namestrtarget_col_name table_name artifact_type class_labelslist[str] | NonereturnNonec Csvt| trt| dkrtjdgdd| Dd} nd} |dur|dus%Jtjggd} t|tr?|D] }| |||q3n| ||t|trX|D] }| |||qLn| |||dur|| r|t|}t|}|dur||dur|t||| ||}|dur| |t || }| | dtj rtj || }nd}| |_||_||_||_||_| |_||_||_dS)a Initialize a new ValidationDataLogger. Args: inputs: A list of input vectors or dictionary of lists of input vectors (used if the model has multiple named inputs) targets: A list of target vectors or dictionary of lists of target vectors (used if the model has multiple named targets/putputs). Defaults to `None`. `targets` and `indexes` cannot both be `None`. indexes: An ordered list of `wandb.data_types._TableIndex` mapping the input items to their source table. This is most commonly retrieved by using `indexes = my_data_table.get_index()`. Defaults to `None`. `targets` and `indexes` cannot both be `None`. validation_row_processor: A function to apply to the validation data, commonly used to visualize the data. The function will receive an `ndx` (`int`) and a `row` (`dict`). If `inputs` is a list, then `row["input"]` will be the input data for the row. Else, it will be keyed based on the name of the input slot (corresponding to `inputs`). If `targets` is a list, then `row["target"]` will be the target data for the row. Else, it will be keyed based on `targets`. For example, if your input data is a single ndarray, but you wish to visualize the data as an image, then you can provide `lambda ndx, row: {"img": wandb.Image(row["input"])}` as the processor. If `None`, we will try to guess the appropriate processor. Ignored if `log_evaluation` is `False` or `val_keys` are present. Defaults to `None`. prediction_row_processor: Same as validation_row_processor, but applied to the model's output. `row["output"]` will contain the results of the model output. Defaults to `None`. input_col_name: The name to use for the input column. Defaults to `"input"`. target_col_name: The name to use for the target column. Defaults to `"target"`. table_name: The name to use for the validation table. Defaults to `"wb_validation_data"`. artifact_type: The artifact type to use for the validation data. Defaults to `"validation_dataset"`. class_labels: Optional list of labels to use in the inferred processors. If the model's `target` or `output` is inferred to be a class, we will attempt to map the class to these labels. Defaults to `None`. infer_missing_processors: Determines if processors are inferred if they are missing. Defaults to True. rlabelcSsg|]}|gqSr)).0r(r)r)\/home/ubuntu/.local/lib/python3.10/site-packages/wandb/sdk/integration_utils/data_logging.py \sz1ValidationDataLogger.__init__..columnsdataNvalidation_data) isinstancelistlenwandbTabledict add_column _make_example_infer_validation_row_processoradd_computed_columnsArtifactaddrun use_artifact get_indexrr rrrrlocal_validation_artifactr)selfrrrrrrr!r"r#r$rrlocal_validation_tablecol_name example_inputexample_targetr@r)r)r+__init__#sZ7            zValidationDataLogger.__init__ predict_fnrcCs ||jS)aaProduce predictions by passing `validation_inputs` to `predict_fn`. 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Defaults to "val_row". table_name (str, optional): name of the prediction table. Defaults to "validation_predictions". commit (bool, optional): determines if commit should be called on the logged data. Defaults to False. r-N)rO)r4r5r1r6r7rrrr8r _infer_prediction_row_processorrrr:log) rArLrMrNr"rO pred_tablerCexample_predictionrDr)r)r+log_predictionss,     z$ValidationDataLogger.log_predictions) NNNNrrrrNT)rr rr rrrrrrrr r!r r"r r#r r$r%rrr&r')rGrr&r )rIrJrKT) rLr rMr rNr r"r rOrr&rP)__name__ __module__ __qualname____doc____annotations__rFrHrUr)r)r)r+r s2  nr r/rr&dict | Sequence | Any | NonecCsJt|tri}|D] }||d||<q |St|dr!|d}|Sd}|S)z1Used to make an example input, target, or output.r__len__N)r1r6hasattr)r/examplekeyr)r)r+r8s  r8r^Sequence | AnycCsBg}t|tst|drt|}|g}|dkr|t|d7}|S)z)Get the shape of an object if applicable.r\r)r1r r]r3_get_example_shape)r^shapelengthr)r)r+rasra lambda_fnrclosure_kwargsc sdfdd }|S) zVCreate a closure around a lambda function by binding `closure_kwargs` to the function.argsrkwargsr&cs&i}||||i|SN)update)rfrg_krerdr)r+closures  z_bind..closureN)rfrrgrr&rr))rdrerlr)rkr+_bindsrmrrpossible_base_exampleSequence | Any | Nonedict[str, Callable]cs*t|i}durOtdkrOdtjkrOtjjddddfdd |d <|}t|d koAt|ddhk}|sMfd d |d <|Stdkrddkrt |dt spt |drt | dt rdur~fdd |d<|Sdd |d<|Stdkrtjjdddddkrfdd |d<fdd |d<|}t|d kot|ddhk}|sLJfdd |d<|Std krt rdur|durt|krfdd |d<|Sdd |d<|Stdkrt rdd |d<|Stdkrt rd d |d!<|S)"zInfers a processor from a single example. Infers a processor from a single example with optional class_labels_table and base_example. Base example is useful for cases such as segmentation masks Nrnumpyz"Inferring processors require numpy)requiredr(cs|Srh) index_refargmaxndp)rnpr)r+sz7_infer_single_example_keyed_processor.. max_classcsfddtdDS)Ncsi|] }||qSr)r)r*i) class_namesrxr)r+ szK_infer_single_example_keyed_processor....rrangerv)rrbrxr+r{s scoretolistcs(|dtjkr|dS|dSNr)r3r/rtrvrr)r+r{'sclasscSs|dSrr)rvr)r)r+r{,sval csfddtdDS)Ncs.g|]}t|dr|n|qS)r)r]rr~rr)r+r,6s zK_infer_single_example_keyed_processor....rrrv)rbrr+r{6s nodec |Srh)rurvrzr)r+r{; rucrrh)argminrvrr)r+r{@rrcstj|d|ddidS)Nmasksr() mask_datar$)r)r4Image get_columnrvrr)r+r{HsimagecS t|Srhr4rrvr)r)r+r{SrrcSrrhrrvr)r)r+r{VrcSrrh)r4Videorvr)r)r+r{Yrvideo)rar3r/r4util get_moduleruniquesetr1intr]rCAN_INFER_IMAGE_AND_VIDEO)r^rrn processorsvalues is_one_hotr))rrrzrbr+%_infer_single_example_keyed_processorsr    B   5 /       rrrrDdict | SequencerEdict | Sequence | Anyrr r!c sBittr*D]}t|}|D]}tdd|||d|d|<qq n}t}|D]}tdd|||d|d|<q2t|trp|D] }t|||}|D]}tdd|||d|d|<qYqNn)|}t||tts|nd}|D]}tfdd|||d|d|<qfd d }|S) z7Infers the composite processor for the validation data.cS||||dSrhr)ndxrow key_processorr_r)r)r+r{l z1_infer_validation_row_processor..rr_:cSrrhr)rr)r)r+r{yrcSrrhr)rr)r)r+r{rNcs$||||tts|SdSrh)r1r6rrDrr)r+r{scfddDS)Nci|] }||qSr)r)r*r_rrsingle_processorsr)r+rzF_infer_validation_row_processor..processor..r)rrrrr+ processorz2_infer_validation_row_processor..processorr1r6rrm) rDrErrr!r_key_processorsp_keyrr)rDrrr+r9^s^      r9rIrToutput_col_namec sit|tr+|D] }t|||}|D]}tdd|||d|d|<qq n)|}t||tts7nd}|D]}tfdd|||d|d|<q<fdd}|S) z>Infers the composite processor for the prediction output data.cSrrhr)rr)r)r+r{rz1_infer_prediction_row_processor..rrNcs4||||tts|dSdS)NrJ)r1r6get_rowgetrrr)r+r{scr)Ncrr)r)rrr)r+rrzF_infer_prediction_row_processor..processor..r)rrrr+rrz2_infer_prediction_row_processor..processorr) rTrDrrrr_rrrr)rr+rQs8   rQ)r/rr&r[)r^r`)rdrrerr&r)NN)r^r`rrrnror&rp)Nrr) rDrrErrrrr r!r r&r)NrrI) rTrrDrrrrr rr r&r) __future__rsyscollections.abcrtypingrrrr4wandb.data_typesr version_infomajorminorrr r8rarmrr9rQr)r)r)r+s0    =   k P