o niML @sddlZddlmZddlmZmZmZmZmZm Z m Z ddl Z er(ddl m Z ejjdko3ejjdkZGdddZd ed e e eeeffd d Zd e eeffddZdeded efddZ  d&d e eefde dde e eefd eeeffddZ   d'de eefde eeefde ddeded ef dd Z   !d(d"e eefde eefde dded#ed ef d$d%ZdS))N)Sequence) TYPE_CHECKINGAnyCallableDictListOptionalUnion) _TableIndexc@s`eZdZUdZeeeeeffed<e eeeeeffed<e ded<e e ed<e ded<e ed <  d(deeeeeffde eeeeeffde e dde e de e dedededede e ed e dd fddZ de deeeeefffddZ  ! d)d"eeeeeffd#ed$eded%e dejjf d&d'Zd S)*ValidationDataLoggera0Logs 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. validation_inputsvalidation_targetsr validation_indexesprediction_row_processor wandb.Tableclass_labels_tableinfer_missing_processorsNinputtargetwb_validation_datavalidation_datasetTinputstargetsindexesvalidation_row_processorinput_col_nametarget_col_name table_name artifact_type class_labelsreturnc 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$f/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/wandb/sdk/integration_utils/data_logging.py Zsz1ValidationDataLogger.__init__..columnsdataNvalidation_data) isinstancelistlenwandbTabledict add_column _make_example_infer_validation_row_processoradd_computed_columnsArtifactaddrun use_artifact get_indexrrrrrrlocal_validation_artifactr)selfrrrrrrrrr r!rrlocal_validation_tablecol_name example_inputexample_targetr;r$r$r&__init__!sZ7            zValidationDataLogger.__init__ predict_fncCs ||jS)aaProduce predictions by passing `validation_inputs` to `predict_fn`. Args: predict_fn (Callable): Any function which can accept `validation_inputs` and produce a list of vectors or dictionary of lists of vectors Returns: (Sequence | Dict[str, Sequence]): The returned value of predict_fn )r)r<rBr$r$r&make_predictionss z%ValidationDataLogger.make_predictionsoutputval_rowvalidation_predictions predictionsprediction_col_nameval_ndx_col_namecommitc Cstjggd}t|tr|D] }||||qn||||||j|jdurK|jrKt|}t|j } |durK| durKt || |j |j ||_|jdurV| |jtj||i|d|S)a Log a set of predictions. Intended usage: vl.log_predictions(vl.make_predictions(self.model.predict)) Args: predictions (Sequence | Dict[str, Sequence]): A list of prediction vectors or dictionary of lists of prediction vectors prediction_col_name (str, optional): the name of the prediction column. Defaults to "output". val_ndx_col_name (str, optional): The name of the column linking prediction table to the validation ata table. Defaults to "val_row". table_name (str, optional): name of the prediction table. Defaults to "validation_predictions". commit (bool, optional): determines if commit should be called on the logged data. Defaults to False. r(N)rJ)r/r0r,r1r2rrrr3r_infer_prediction_row_processorrrr5log) r<rGrHrIrrJ pred_tabler>example_predictionr?r$r$r&log_predictionss,     z$ValidationDataLogger.log_predictions) NNNNrrrrNT)rDrErFT)__name__ __module__ __qualname____doc__r rrstr__annotations__rrrboolrArCr/ data_typesr0rOr$r$r$r&r s          n r r*r"cCsJt|tri}|D] }||d||<q |St|dr!|d}|Sd}|S)z1Used to make an example input, target, or output.r__len__N)r,r1hasattr)r*examplekeyr$r$r&r3s  r3rZcCsBg}t|tst|drt|}|g}|dkr|t|d7}|S)z)Get the shape of an object if applicable.rXr)r,rTrYr._get_example_shape)rZshapelengthr$r$r&r\sr\ lambda_fnclosure_kwargsc s dtdtdtffdd }|S)zVCreate a closure around a lambda function by binding `closure_kwargs` to the function.argskwargsr"cs&i}||||i|SN)update)rarb_kr`r_r$r&closures  z_bind..closure)r)r_r`rgr$rfr&_bindsrhrrpossible_base_examplecs*t|i}durOtdkrOdtjkrOtjjddddfdd |d <|}t|d koAt|ddhk}|sMfd d |d <|Stdkrddkrt |dt spt |drt | dt rdur~fdd |d<|Sdd |d<|Stdkrtjjdddddkrfdd |d<fdd |d<|}t|d kot|ddhk}|sLJfdd |d<|Std krt rdur|durt|krfdd |d<|Sdd |d<|Stdkrt rdd |d<|Stdkrt rd d |d!<|S)"zInfers a processor from a single example. Infers a processor from a single example with optional class_labels_table and base_example. Base example is useful for cases such as segmentation masks Nrnumpyz"Inferring processors require numpy)requiredr#cs|Src) index_refargmaxndp)rnpr$r&sz7_infer_single_example_keyed_processor.. max_classcsfddtdDS)Ncsi|] }||qSr$r$r%i) class_namesrqr$r& szK_infer_single_example_keyed_processor....rrangero)ryr]rqr&rts scoretolistcs(|dtjkr|dS|dSNr)r.r*rmrorr$r&rt'sclasscSs|dSrr$ror$r$r&rt,sval csfddtdDS)Ncs.g|]}t|dr|n|qS)r)rYrrwr}r$r&r'6s zK_infer_single_example_keyed_processor....rr{ro)r]r}r&rt6s nodec |Src)rnrorsr$r&rt; rncrrc)argminrorr$r&rt@rrcstj|d|ddidS)Nmasksr#) mask_datar!)r)r/Image get_columnrorr$r&rtHsimagecS t|Srcr/rror$r$r&rtSrr cSrrcrror$r$r&rtVrcSrrc)r/Videoror$r$r&rtYrvideo)r\r.r*r/util get_moduleruniquesetr,intrYrCAN_INFER_IMAGE_AND_VIDEO)rZrri processorsvalues is_one_hotr$)rryrsr]r&%_infer_single_example_keyed_processorsr    B   5 /       rrrr?r@rrc 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||||dSrcr$ndxrow key_processorr[r$r$r&rtl z1_infer_validation_row_processor..rr[:cSrrcr$rr$r$r&rtyrcSrrcr$rr$r$r&rtrNcs$||||tts|SdSrc)r,r1rr?rr$r&rtscfddDS)Nci|] }||qSr$r$r%r[rrsingle_processorsr$r&rzzF_infer_validation_row_processor..processor..r$rrrrr& processorz2_infer_validation_row_processor..processorr,r1rrh) r?r@rrrr[key_processorsp_keyrr$r?rrr&r4^s^      r4rDrNoutput_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.cSrrcr$rr$r$r&rtrz1_infer_prediction_row_processor..rrNcs4||||tts|dSdS)NrE)r,r1get_rowgetrrr$r&rtscr)Ncrr$r$rrr$r&rzrzF_infer_prediction_row_processor..processor..r$rrrr&rrz2_infer_prediction_row_processor..processorr) rNr?rrrr[rrrr$rr&rKs8   rK)NN)Nrr)NrrD)syscollections.abcrtypingrrrrrrr r/wandb.data_typesr version_infomajorminorrr r3r\rhrTrr4rKr$r$r$r&sn $  ?    k   P