1VhNHdZddlZddlZddlZddlZddlZddlZddlm Z ddl m Z ddl mZddl mZddl mZddZdd Zdd Zdd Zd Zd ZdZdZdZGddZGddej6ZGddeZGddeZGddeZGddZ y) zzExperimental public APIs for the HParams plugin. These are porcelain on top of `api_pb2` (`api.proto`) and `summary.py`. N)tf2) summary_pb2)api_pb2)metadata)plugin_data_pb2c6t|||}td|S)aWrite hyperparameter values for a single trial. Args: hparams: A `dict` mapping hyperparameters to the values used in this trial. Keys should be the names of `HParam` objects used in an experiment, or the `HParam` objects themselves. Values should be Python `bool`, `int`, `float`, or `string` values, depending on the type of the hyperparameter. The corresponding numpy types, like `np.float32`, are also permitted. trial_id: An optional `str` ID for the set of hyperparameter values used in this trial. Defaults to a hash of the hyperparameters. start_time_secs: The time that this trial started training, as seconds since epoch. Defaults to the current time. Returns: A tensor whose value is `True` on success, or `False` if no summary was written because no default summary writer was available. )hparamstrial_idstart_time_secsr ) hparams_pb_write_summary)r r r pbs V/home/dcms/DCMS/lib/python3.12/site-packages/tensorboard/plugins/hparams/summary_v2.pyr r $s&( ' B )R ((c 2|tj}t|}t||}tj||}t |D]}||}t |tr||j|_ -t |ttfr||j|_ Xt |tr||j|_}td|d|dt!|t#t$j&tj(|S)aCreate a summary encoding hyperparameter values for a single trial. Args: hparams: A `dict` mapping hyperparameters to the values used in this trial. Keys should be the names of `HParam` objects used in an experiment, or the `HParam` objects themselves. Values should be Python `bool`, `int`, `float`, or `string` values, depending on the type of the hyperparameter. trial_id: An optional `str` ID for the set of hyperparameter values used in this trial. Defaults to a hash of the hyperparameters. start_time_secs: The time that this trial started training, as seconds since epoch. Defaults to the current time. Returns: A TensorBoard `summary_pb2.Summary` message. ) group_namer zhparams[z] = z, of unsupported type )session_start_info)time_normalize_hparams_derive_session_group_namerSessionStartInfosorted isinstanceboolr bool_valuefloatint number_valuestr string_value TypeErrortype _summary_pbrSESSION_START_INFO_TAGHParamsPluginData)r r r rrhp_namehp_values rr r @s$))+ )G+Hg>J(99''? 7# h %=E  & &w / : 5#, /?G  & &w / < # &?G  & &w / <Hd8n6   ''))1  rc6t|||}td|S)aWrite a top-level experiment configuration. This configuration describes the hyperparameters and metrics that will be tracked in the experiment, but does not record any actual values of those hyperparameters and metrics. It can be created before any models are actually trained. Args: hparams: A list of `HParam` values. metrics: A list of `Metric` values. time_created_secs: The time that this experiment was created, as seconds since epoch. Defaults to the current time. Returns: A tensor whose value is `True` on success, or `False` if no summary was written because no default summary writer was available. )r metricstime_created_secshparams_config)hparams_config_pbr )r r)r*rs rr+r+qs'& + B *B //rcg}|D]h}tj|j|j|j}|j }||j ||j|j|Dcgc]}|j}}tj|||} ttjtj| Scc}w)aOCreate a top-level experiment configuration. This configuration describes the hyperparameters and metrics that will be tracked in the experiment, but does not record any actual values of those hyperparameters and metrics. It can be created before any models are actually trained. Args: hparams: A list of `HParam` values. metrics: A list of `Metric` values. time_created_secs: The time that this experiment was created, as seconds since epoch. Defaults to the current time. Returns: A TensorBoard `summary_pb2.Summary` message. )name description display_name) hparam_infos metric_infosr*) experiment)r HParamInfor.r/r0domainupdate_hparam_infoappendas_proto Experimentr#rEXPERIMENT_TAGrr%) r r)r*r1hparaminfor5metricr2r3s rr,r,s$L "!!**,,      % %d +D! "5<<&FOO%>'..1 2 < < >>rc6|j}ttjjddxstjj }||5tjjj |dcdddS#1swYyxYw)a*Write a summary, returning the writing op. Args: name: As passed to `summary_scope`. pb: A `summary_pb2.Summary` message. Returns: A tensor whose value is `True` on success, or `False` if no summary was written because no default summary writer was available. summary_scopeNr)step)SerializeToStringgetattrtfsummary experimentalrX write_raw_pb)r.rraw_pbrXs rr r s ! ! #F ''$? $ :: # # t Dzz&&33F3CDDDs +BBctj}tj|}|jj ||tj }|S)zCreate a summary holding the given `HParamsPluginData` message. Args: tag: The `str` tag to use. hparams_plugin_data: The `HParamsPluginData` message to use. Returns: A TensorBoard `summary_pb2.Summary` message. )tagrtensor)rSummaryrcreate_summary_metadatarJadd NULL_TENSOR)rbhparams_plugin_datar]summary_metadatarJs rr#r#sR!!#G778KL MM   *83G3G  E NrcdeZdZdZd dZdZdZedZedZ edZ ed Z y) r@zA hyperparameter in an experiment. This class describes a hyperparameter in the abstract. It ranges over a domain of values, but is not bound to any particular value. Nc||_||_||_||_t |jt t dfstd|jy)a Create a hyperparameter object. Args: name: A string ID for this hyperparameter, which should be unique within an experiment. domain: An optional `Domain` object describing the values that this hyperparameter can take on. display_name: An optional human-readable display name (`str`). description: An optional Markdown string describing this hyperparameter. Raises: ValueError: If `domain` is not a `Domain`. Nznot a domain: )_name_domain _display_name _descriptionrDomainr"rA)selfr.r5r0r/s r__init__zHParam.__init__sP  )'$,,d(<=4<<AB B>rc<d|jd|jdS)Nz)rlrmrqs r__str__zHParam.__str__3s$(JJ ==rcd|jfd|jfd|jfd|jfg}dj d|D}d|zS)Nr.r5r0r/, c30K|]\}}|d|yw)=N).0rDrEs r z"HParam.__repr__..=s!Hv1Q"2!Hsz HParam(%s))rlrmrnrojoin)rqfields fields_strings r__repr__zHParam.__repr__6s^ TZZ t|| $ T// 0 D-- .    !H!HH m++rc|jSN)rlrus rr.z HParam.name@s zzrc|jSr)rmrus rr5z HParam.domainDs ||rc|jSr)rnrus rr0zHParam.display_nameHs!!!rc|jSr)rorus rr/zHParam.descriptionLs   r)NNN) __name__ __module__ __qualname____doc__rrrvrpropertyr.r5r0r/r{rrr@r@sg C,>,""!!rr@ceZdZdZej dZejefdZ ejdZ y)rpzThe domain of a hyperparameter. Domains are restricted to values of the simple types `float`, `int`, `str`, and `bool`. cy)z;Data type of this domain: `float`, `int`, `str`, or `bool`.Nr{rus rdtypez Domain.dtypeXs rcy)zSample a value from this domain uniformly at random. Args: rng: A `random.Random` interface; defaults to the `random` module itself. Raises: IndexError: If the domain is empty. Nr{rqrngs rsample_uniformzDomain.sample_uniform]s rcy)aUpdate an `HParamInfo` proto to include this domain. This should update the `type` field on the proto and exactly one of the `domain` variants on the proto. Args: hparam_info: An `api_pb2.HParamInfo` proto to modify. Nr{rq hparam_infos rr6zDomain.update_hparam_infojs rN) rrrrabcabstractpropertyrabstractmethodrandomrr6r{rrrprpQsX     !'         rrp) metaclasscdeZdZdZd dZdZdZedZedZ edZ e fd Z d Z y) IntIntervalz?A domain that takes on all integer values in a closed interval.Nct|tstd|t|tstd|||kDrt|d|||_||_y)a;Create an `IntInterval`. Args: min_value: The lower bound (inclusive) of the interval. max_value: The upper bound (inclusive) of the interval. Raises: TypeError: If `min_value` or `max_value` is not an `int`. ValueError: If `min_value > max_value`. zmin_value must be an int: zmax_value must be an int:  > N)rrr!rA _min_value _max_valuerq min_value max_values rrrzIntInterval.__init__zsZ)S)iIJ J)S)iIJ J y )Y?@ @##rc<d|jd|jdSN[rx]rrrus rrvzIntInterval.__str__!__doo>>rc<d|jd|jdS)Nz IntInterval(rx)rrus rrzIntInterval.__repr__s(,IIrctSr)rrus rrzIntInterval.dtypes rc|jSrrrus rrzIntInterval.min_value rc|jSrrrus rrzIntInterval.max_valuerrcN|j|j|jSr)randintrrrs rrzIntInterval.sample_uniform{{4??DOO< max_value`. zmin_value must be a float: zmax_value must be a float: rN)rrr!rArrrs rrrzRealInterval.__init__sZ)U+yJK K)U+yJK K y )Y?@ @##rc<d|jd|jdSrrrus rrvzRealInterval.__str__rrc<d|jd|jdS)Nz RealInterval(rxrrrus rrzRealInterval.__repr__s)-$//JJrctSr)rrus rrzRealInterval.dtypes rc|jSrrrus rrzRealInterval.min_valuerrc|jSrrrus rrzRealInterval.max_valuerrcN|j|j|jSr)uniformrrrs rrzRealInterval.sample_uniformrrctj|_|j|j_|j |j_yrrrs rr6zRealInterval.update_hparam_infos4"44 04 ##-04 ##-rrrr{rrrrsaF$(?K"(=@rrcTeZdZdZd dZdZdZedZedZ e fdZ d Z y) DiscretezhA domain that takes on a fixed set of values. These values may be of any (single) domain type. Nct||_|0|jrt|jd}n td|tt t tfvrtd|||_|jD]>}t||jrtd|d|jjd|jjy)aConstruct a discrete domain. Args: values: A iterable of the values in this domain. dtype: The Python data type of values in this domain: one of `int`, `float`, `bool`, or `str`. If `values` is non-empty, `dtype` may be `None`, in which case it will be inferred as the type of the first element of `values`. Raises: ValueError: If `values` is empty but no `dtype` is specified. ValueError: If `dtype` or its inferred value is not `int`, `float`, `bool`, or `str`. TypeError: If an element of `values` is not an instance of `dtype`. Nrz$Empty domain with no dtype specifiedzUnknown dtype: zdtype mismatch: not isinstance(rxr) list_valuesr"rArrrr_dtyperr!rsort)rqvaluesrrJs rrrzDiscrete.__init__s"F| =||T\\!_- !GHH eT3/ /E;< < \\ EeT[[1dkk224  rcLddjd|jDzS)Nz{%s}rxc32K|]}t|ywr)repr)r|xs rr}z#Discrete.__str__..s"Aq47"As)r~rrus rrvzDiscrete.__str__s "ADLL"AABBrc"d|jdS)Nz Discrete(r)rrus rrzDiscrete.__repr__s!%//rc|jSr)rrus rrzDiscrete.dtype s {{rc,t|jSr)rrrus rrzDiscrete.valuessDLL!!rc8|j|jSr)choicerrs rrzDiscrete.sample_uniformszz$,,''rc8ttjttjttj t tji|j|_ |jd|jj|jy)Ndomain_discrete)rrrrrDATA_TYPE_BOOLrDATA_TYPE_STRINGrr" ClearFieldrextendrrs rr6zDiscrete.update_hparam_infoso ** 7,, '(( ))  ++   01##**4<<8rr) rrrrrrrvrrrrrrr6r{rrrrsO DC0"""((9rrcVeZdZdZej ZejZ ddZ dZ y)MetriczA metric in an experiment. A metric is a real-valued function of a model. Each metric is associated with a TensorBoard scalar summary, which logs the metric's value as the model trains. Nc||_||_||_||_||_|jdt j t jfvrtd|jy)a_ Args: tag: The tag name of the scalar summary that corresponds to this metric (as a `str`). group: An optional string listing the subdirectory under the session's log directory containing summaries for this metric. For instance, if summaries for training runs are written to events files in `ROOT_LOGDIR/SESSION_ID/train`, then `group` should be `"train"`. Defaults to the empty string: i.e., summaries are expected to be written to the session logdir. display_name: An optional human-readable display name. description: An optional Markdown string with a human-readable description of this metric, to appear in TensorBoard. dataset_type: Either `Metric.TRAINING` or `Metric.VALIDATION`, or `None`. Nzinvalid dataset type: ) _tag_grouprnro _dataset_typerTRAINING VALIDATIONrA)rqrbgroupr0r/ dataset_types rrrzMetric.__init__,sc2  )')   dFOOV=N=N%O O4;M;MOP P Prctjtj|j|j|j |j |jS)N)rrb)r.r0r/r)r MetricInfo MetricNamerrrnrorrus rr8zMetric.as_protoMsN!!##kkII++))++  r)NNNN) rrrrrDATASET_TRAININGrDATASET_VALIDATIONrrrr8r{rrrr!s9''H++J  QB  rrrr)!rrrRrPrrnumpyrGtensorboard.compatrr\tensorboard.compat.protortensorboard.plugins.hparamsrrrr r r+r,rrBrr r#r@ABCMetarprrrrr{rrrs   (0/07)8.b06&R6& ?D($8!8!v# s{{# L1@&1@h/@6/@dA9vA9H5 5 r