a Sicz@sdZddlZddlmmZddlmZddlm Z ddl m Z ddl mZddl mZddl mZGd d d ZGd d d eZGd ddeZddZddZddZddZddZddZddZdS)zUtilities for `Model.compile`.N)losses)metrics) saving_lib) generic_utils) losses_utils)tf_utilsc@sBeZdZdZdddZddZddZd d Zd d Zd dZ dS) ContainerzBase Container class.NcCs ||_dSN) _output_names)self output_namesr V/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/engine/compile_utils.py__init__"szContainer.__init__cCs|jdurt||_dSr )r create_pseudo_output_namesr y_predr r rbuild%s zContainer.buildcsJt||jt|tjsFtj|rFtjfdd|S)aConvenience method to conform `struct` to `outputs` structure. Mappings performed: (1) Map a dict to a list of outputs, using the output names. (2) Fill missing keys in a dict w/ `None`s. (3) Map a single item to all outputs. Args: outputs: Model predictions. struct: Arbitrary nested structure (e.g. of labels, sample_weights, losses, or metrics). Returns: Mapping of `struct` to `outputs` structure. csSr r _structr r@z/Container._conform_to_outputs..)map_to_output_namesr map_missing_dict_keystfnest is_nested map_structure)r outputsrr rr_conform_to_outputs+s  zContainer._conform_to_outputscsHsSttj|dkfddtjfdd|S)aDetermines if losses / metrics should be applied to all outputs. NOTE: This method should only be called for Metrics / Losses, not for y_true / sample_weight. Args: outputs: Model predictions. objects: Arbitrary nested structure (e.g. of losses or metrics) Returns: Arbitrary nested structure of objects, maybe copied to each output. Applies a Loss / Metric to all outputs. csrtjjSSr )rrr _copy_objectr )objectsr should_copy_objectsr r _broadcast_fnZsz._broadcast_fncsSr r r)r&r rr_rz7Container._maybe_broadcast_to_outputs..)_should_broadcastlenrrflattenr)r r r$r )r&r$r r%r_maybe_broadcast_to_outputsCs  z%Container._maybe_broadcast_to_outputscCstdSr NotImplementedError)r r$r r rr'aszContainer._should_broadcastcCstdSr r+r objr r rr#dszContainer._copy_object)N) __name__ __module__ __qualname____doc__rrr!r*r'r#r r r rrs rcseZdZdZdfdd ZddZeddZed d Z fd d Z ed dZ ddZ dddZ ddZddZddZddZZS)LossesContaineraA container class for losses passed to `Model.compile()`. Args: losses: Struct of loss function(s). See `Model.compile()` doc for more information. loss_weights: Weights of the losses contributions of different model outputs. See `Model.compile()` doc for more information. output_names: List of string. Per-output metric names. total_loss_mean: A `keras.metrics.Mean` instance that is used to track the mean of all losses (including compiled and regularization losses). NcsLtt|j|d||_||_||_||_d|_|p>tj dd|_ d|_ dS)Nr lossnameF) superr3r _user_losses_user_loss_weights_losses _loss_weights_per_output_metrics metrics_modMean_total_loss_mean_built)r r loss_weightsr total_loss_mean __class__r rruszLossesContainer.__init__cCs.tj|j|_dd|jDt|jdS)NcSsg|]}|durt|qSr )rserialize_keras_object).0r.r r r sz.LossesContainer.get_config..)rrC)rrr)r;rrFr@r r r r get_configszLossesContainer.get_configcCsPi}|D]4\}}t|tr2dd|D||<q t|||<q |fi|S)z:Returns the `LossesContainer` instance given the `config`.cSsg|]}t|qSr )rdeserialize_keras_object)rGitemr r rrHsz/LossesContainer.from_config..)items isinstancelistrrK)clsconfigZdeserialized_configkeyvaluer r r from_configs   zLossesContainer.from_configcCs.|js gSddtj|jD}|jg|S)zPer-output loss metrics.cSsg|]}|dur|qSr r )rG metric_objr r rrHsz+LossesContainer.metrics..)rArrr)r=r@)r per_output_metricsr r rrs  zLossesContainer.metricscstt|||||j|_|||j|_tj|j |j|_tj |j|_|||j |_ |||j |_ tj |j |_ | d|_ dS)zOne-time setup of loss objects.TN)r8r3rr*r;r!rrr_get_loss_objectr)r<_create_metricsrArrDr rrs zLossesContainer.buildcCs|jSr rArIr r rbuiltszLossesContainer.builtcCsft|jdkrdg|_nJg|_t|j|jD]4\}}|durJ|jdq,|jt|dq,dS)zBCreates per-output loss metrics, but only for multi-output Models.r"N_loss)r(r r=zipr;appendr>r?)r loss_obj output_namer r rrXs  zLossesContainer._create_metricscCs|||}|||}|js(||tj|}tj|}tj|}g}g}d}||||j|j|jf}t |D]\} } } } } }| dusv| durqvt | | | \} } } t | | t | } | | | | d}|}| jt jjkr|tjj9}|durt| r| }nt| d}|dur6|j||d| durP|| 9}|| 9}| jt jjksp| jt jjkrzt |}||||qv|rt |}t|}|||t ||rt |}t|}|jj||dt |}t|}|SdSdS)aComputes the overall loss. Args: y_true: An arbitrary structure of Tensors representing the ground truth. y_pred: An arbitrary structure of Tensors representing a Model's outputs. sample_weight: An arbitrary structure of Tensors representing the per-sample loss weights. If one Tensor is passed, it is used for all losses. If multiple Tensors are passed, the structure should match `y_pred`. regularization_losses: Additional losses to be added to the total loss. Returns: The total loss as a `tf.Tensor`, or `None` if no loss results. N sample_weightr) r!rArrrr)r;r<r=r\match_dtype_and_rankr apply_maskget_mask reduction ReductionV2SUM distribute get_strategynum_replicas_in_syncr is_raggednrowsshape update_stateSUM_OVER_BATCH_SIZEAUTOscale_loss_for_distributionr]cast_losses_to_common_dtypeadd_nr@)r y_truerraregularization_losses loss_valuesZtotal_loss_mean_values batch_dimzip_argsy_ty_pswr^ loss_weightrU loss_valueZtotal_loss_mean_valuereg_lossZtotal_total_loss_mean_value total_lossr r r__call__s                    zLossesContainer.__call__cCs>|js dS|jgtj|j}|D]}|dur$|q$dS)z!Resets the state of loss metrics.N)rAr@rrr)r= reset_stater rrUr r rrGs zLossesContainer.reset_statecCsX|dur dSt|}t|tjsNt|}|dur@td|tj||d}d|_|S)aReturns a `Loss` object. Converts the user-supplied loss to a `Loss` object. Also allows `SUM_OVER_BATCH_SIZE` reduction to be used for this loss. Args: loss: A string, function, or `Loss` object. Returns: A `Loss` object. Nz%Loss should be a callable, received: r6T) losses_modgetrNLossget_custom_object_name ValueErrorLossFunctionWrapper_allow_sum_over_batch_size)r r5 loss_namer r rrWRs   z LossesContainer._get_loss_objectcCstj| Sr rrrr-r r rr'jsz!LossesContainer._should_broadcastcCs|Sr r r-r r rr#mszLossesContainer._copy_object)NNN)NN)r/r0r1r2rrJ classmethodrTpropertyrrrZrXrrrWr'r# __classcell__r r rDrr3hs$     o r3cseZdZdZd"fdd ZddZedd Zed d Zed d Z fddZ eddZ ddZ ddZ d#ddZddZddZddZddZd d!ZZS)$MetricsContainerz8A container class for metrics passed to `Model.compile`.NFcsFtt|j|d|||||_||_||_||_d|_||_ dS)aInitializes a container for metrics. Arguments: metrics: see the `metrics` argument from `tf.keras.Model.compile`. weighted_metrics: see the `weighted_metrics` argument from `tf.keras.Model.compile`. output_names: A list of strings of names of outputs for the model. from_serialized: Whether the model being compiled is from a serialized model. Used to avoid redundantly applying pre-processing renaming steps. r4FN) r8rr_check_duplicated_metrics _user_metrics_user_weighted_metrics_metrics_weighted_metricsrA_from_serialized)r rweighted_metricsr from_serializedrDr rrts zMetricsContainer.__init__cCsht}g}tj|tj|D].}t|tjs4q"||vrF||||q"|rdt d|dS)a/Check and raise error when user provided metrics has any duplications. Note that metrics are stateful container, a shared metric instance between model.metric and model.weighted_metric will make the same intance to be udpated twice, and report wrong value. Args: metrics: User provided metrics list. weighted_metrics: User provided weighted metrics list. Raises: ValueError, when duplicated metrics instance discovered in user provided metrics and weighted metrics. zFound duplicated metrics object in the user provided metrics and weighted metrics. This will cause the same metric object to be updated multiple times, and report wrong results. Duplicated items: N) setrrr)rNr>Metricr]addr)r rrseenZ duplicatedxr r rrs   z*MetricsContainer._check_duplicated_metricscCs|js gS|jS)zAll metrics in this container.)rA_metrics_in_orderrIr r rrszMetricsContainer.metricscCs|js dStj|jS)zAMetrics in the container that should not be passed sample_weight.N)rArrr)rrIr r runweighted_metricssz#MetricsContainer.unweighted_metricscCs|js dStj|jS)z@Metrics in this container that should be passed `sample_weight`.N)rArrr)rrIr r rrsz!MetricsContainer.weighted_metricscstt|||||j|_|||j|_|||j|_|||j|_tjj |}tjj |}tjj |j|_tjj |j|_tjj ||j |j|||_tjj ||j |j|||_tjj j ||jdd|_tjj j ||jdd|_|js||d|_dS)z!One-time setup of metric objects.F) check_typesTN)r8rrr*rr!rr __internal__r list_to_tuplemap_structure_up_to_get_metric_objects flatten_up_tor_set_metric_names_create_ordered_metricsrA)r rrtrDr rrsFzMetricsContainer.buildcCs|jSr rYrIr r rrZszMetricsContainer.builtc Cst}t|jdk}|j|j|jf}t|D]\}}}|D]J}|durHq:|r\|d|j|_|j|vrxtd|jd||jq:|D]}|durq|r|d|j|vr|d|j|_q|d|j|_n|j|vrd|j|_|j|vrtd|jd ||jqq,dS) zSets unique metric names.r"Nrz&Found two metrics with the same name: z?. All the metrics added to the model need to have unique names. _weighted_ weighted_z/Found two weighted metrics with the same name: z>.All the metrics added to the model need to have unique names.) rr(r rrr\_namerr) r metric_namesis_multi_outputrxr_output_metricsweighted_output_metricsmwmr r rrs@     z"MetricsContainer._set_metric_namescCsng|_t|j|jD]T\}}tj|D]}|dur(|j|q(tj|D]}|durN|j|qNqdS)z@Cache the flat order needed when return metrics, for backcompat.N)rr\rrrrr)r])r routput_weighted_metricsrrr r rr.s z(MetricsContainer._create_ordered_metricsc Cs.|||}|||}|js*|||tj|}|durJtj|ng}tj|}||||j|jf}t|D]\}}}}} |dustt dd|Drt dd| Drqtt |||\}}}t |} t ||| }|D]} | durq| j||| dq| D]$} | durq| j|||dqqtdS)z(Updates the state of per-output metrics.Ncss|]}|duVqdSr r rGrr r r Qrz0MetricsContainer.update_state..css|]}|duVqdSr r )rGrr r rrRrr`)r!rArrrr)rrr\allrbrrdrcrn) r rtrrarxryrzr{ metric_objsweighted_metric_objsmaskrUweighted_metric_objr r rrn;s>       zMetricsContainer.update_statecCsL|jr|j}ntj|jtj|j}|D]}t|tj r.| q.dS)z4Resets the state of all `Metric`s in this container.N) rArrrr)rrrNr>rrrr r rrds zMetricsContainer.reset_statecs"tj|}fdd|DS)z2Convert user-supplied metrics to `Metric` objects.csg|]}|qSr )_get_metric_objectrr rzryr rrHwrz8MetricsContainer._get_metric_objects..)rrr))r rryrzr rrrts z$MetricsContainer._get_metric_objectsc Cs0|dur dSt|dvr(t|}nt|j}t|j}|jd}|jd}|dk} ||kp~|dko~|dk} t|dvr| rtj}q| rtj}qtj }n| rtj }n| rtj }ntj }t |tjrd|_t |tjs,t |tr|} n t|} | durtd|tj|| d}|S) zConverts user-supplied metric to a `Metric` object. Args: metric: A string, function, or `Metric` object. y_t: Sample of label. y_p: Sample of output. Returns: A `Metric` object. N)accuracyacc crossentropycer")rrTz'Metric should be a callable, received: r6)strlowerr>rr(rmas_listbinary_accuracysparse_categorical_accuracycategorical_accuracybinary_crossentropysparse_categorical_crossentropycategorical_crossentropyrNrrrrrrMeanMetricWrapper) r metricryrzrUy_t_ranky_p_rank y_t_last_dim y_p_last_dim is_binaryis_sparse_categorical metric_namer r rrysH     z#MetricsContainer._get_metric_objectcCs2tj|sdSt|ttfo0tdd|D S)NTcss|]}tj|VqdSr r)rGor r rrsz5MetricsContainer._should_broadcast..)rrrrNrOtupleanyr-r r rr's  z"MetricsContainer._should_broadcastcCs t|tjr|j|S|Sr )rNr>rrErTrJr-r r rr#s zMetricsContainer._copy_object)NNNF)N)r/r0r1r2rrrrrrrrZrrrnrrrr'r#rr r rDrrqs0#    3 , )? rcCs t|ddS)z2Create pseudo output names for a subclassed Model.output_prefix_create_pseudo_names)r r r rrsrcCs t|ddS)z1Create pseudo input names for a subclassed Model.input_rr)inputsr r rcreate_pseudo_input_namessrcCs|dd}ttjj|}tj||}g}|D]F}|sB|d}n*ddd|D}t|dtrl||}| |q0|S)aCCreates pseudo {input | output} names for subclassed Models. Warning: this function should only be used to define default names for `Metics` and `SavedModel`. No other use cases should rely on a `Model`'s input or output names. Example with dict: `{'a': [x1, x2], 'b': x3}` becomes: `['a_1', 'a_2', 'b']` Example with list: `[x, y]` becomes: `['output_1', 'output_2']` Args: tensors: `Model`'s outputs or inputs. prefix: 'output_' for outputs, 'input_' for inputs. Returns: Flattened list of pseudo names. cSst|tr|dS|S)Nr")rNint)eler r r one_indexs z'_create_pseudo_names..one_index1rcss|]}t|VqdSr )r)rGpr r rrrz'_create_pseudo_names..r) rOrrryield_flat_pathsrjoinrNrr])tensorsrr flat_pathsnamespathr7r r rrs  rcstj| }| o4t|ttfo4tdd|D }|s>|rttr|pRt|}t fdd|D}rt d d|ddt |d kr|d S|SSd S) aAMaps a dict to a list using `output_names` as keys. This is a convenience feature only. When a `Model`'s outputs are a list, you can specify per-output losses and metrics as a dict, where the keys are the output names. If you specify per-output losses and metrics via the same structure as the `Model`'s outputs (recommended), no mapping is performed. For the Functional API, the output names are the names of the last layer of each output. For the Subclass API, the output names are determined by `create_pseudo_output_names` (For example: `['output_1', 'output_2']` for a list of outputs). This mapping preserves backwards compatibility for `compile` and `fit`. Args: y_pred: Sample outputs of the Model, to determine if this convenience feature should be applied (`struct` is returned unmodified if `y_pred` isn't a flat list). output_names: List. The names of the outputs of the Model. struct: The structure to map. Returns: `struct` mapped to a list in same order as `output_names`. css|]}tj|VqdSr r)rGrzr r rrrz&map_to_output_names..csg|]}|dqSr )pop)rGr7rr rrH#rz'map_to_output_names..zOFound unexpected losses or metrics that do not correspond to any Model output: z. Valid mode output names: z. Received struct is: .r"rN) rrrrNrOrrdictrcopyrkeysr()rr r single_outputoutputs_are_flat_list new_structr rrrs0    rcCsDt|trt|ts|St|}|D]}||vr*d||<q*|S)z@Replaces missing dict keys in `struct` with `None` placeholders.N)rNrrr)rrkr r rr2s   rcCs|jjdkr&|jjdkr&tj|dd}|durT|jjdkrT|jjdkrTtj|dd}|jjrd|jjst|jjr|jjrt||j}|durt||j}|||fS)z$Match dtype and rank of predictions.r"r)axisN)rmrankr expand_dimsdtype is_floating is_integercast)ryrzr{r r rrb=srbcCs@t|dr|jSt|dr |jSt|dr8t|jjSdSdS)zReturns the name to use for a custom loss or metric callable. Args: obj: Custom loss of metric callable Returns: Name to use, or `None` if the object was not recognized. r7r/rEN)hasattrr7r/r to_snake_caserE)r.r r rrRs   r)r2rtensorflow.compat.v2compatv2rkerasrrrr>keras.saving.experimentalr keras.utilsrrrrr3rrrrrrrbrr r r rs,      I X-4