o zi @sdZddlZddlZddlZddlmZddlmZddlm Z m Z m Z m Z m Z mZddlmZddlmZddlmZdd lmZdd lmZGd d d eeZGd ddeZdejfdee de e de eegefde fddZdS)z.Abstract base class used to build new loggers.N)ABC) defaultdict)AnyCallableDictMappingOptionalSequence)override)Logger)_DummyExperiment)rank_zero_experiment)ModelCheckpointc@s8eZdZdZdeddfddZedeefddZ dS) r z"Base class for experiment loggers.checkpoint_callbackreturnNcCdS)zCalled after model checkpoint callback saves a new checkpoint. Args: checkpoint_callback: the model checkpoint callback instance N)selfrrrT/home/ubuntu/.local/lib/python3.10/site-packages/pytorch_lightning/loggers/logger.pyafter_save_checkpoint"szLogger.after_save_checkpointcCr)zvReturn the root directory where experiment logs get saved, or `None` if the logger does not save data locally.Nrrrrrsave_dir+zLogger.save_dir) __name__ __module__ __qualname____doc__rrpropertyrstrrrrrrr s  r cseZdZdZdfdd ZedefddZede d e ddfd d Z ede d e ddfd d Z eede fddZ eede fddZdeddfddZde defddZZS) DummyLoggerzDummy logger for internal use. It is useful if we want to disable user's logger for a feature, but still ensure that user code can run rNcstt|_dSN)super__init__DummyExperiment _experimentr __class__rrr"9s  zDummyLogger.__init__cCs|jS)z9Return the experiment object associated with this logger.)r$rrrr experiment=szDummyLogger.experimentargskwargscOdSr rrr(r)rrr log_metricsBzDummyLogger.log_metricscOr*r rr+rrrlog_hyperparamsFr-zDummyLogger.log_hyperparamscCr)zReturn the experiment name.rrrrrnameJrzDummyLogger.namecCr)zReturn the experiment version.r/rrrrrversionPrzDummyLogger.versionidxcCs|Sr r)rr2rrr __getitem__Vr-zDummyLogger.__getitem__r0cCsdtdtddfdd}|S)zUAllows the DummyLogger to be called with arbitrary methods, to avoid AttributeErrors.r(r)rNc_r*r r)r(r)rrrmethod]sz'DummyLogger.__getattr__..method)r)rr0r4rrr __getattr__ZszDummyLogger.__getattr__)rN)rrrrr"rr#r'r rr,r.rr0r1intr3rr5 __classcell__rrr%rr2s"rdicts agg_key_funcs default_funcrcs|pi}tttjdd|D}tt}|D]-|}ddfdd|DD}t|dtr=t ||||<q|p@|||<qt|S)a#Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function. Args: dicts: Sequence of dictionaries to be merged. agg_key_funcs: Mapping from key name to function. This function will aggregate a list of values, obtained from the same key of all dictionaries. If some key has no specified aggregation function, the default one will be used. Default is: ``None`` (all keys will be aggregated by the default function). default_func: Default function to aggregate keys, which are not presented in the `agg_key_funcs` map. Returns: Dictionary with merged values. Examples: >>> import pprint >>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1, 'd': {'d1': 1, 'd3': 3}} >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1, 'd': {'d1': 2, 'd2': 3}} >>> d3 = {'a': 1.1, 'v': 2.3, 'd': {'d3': 3, 'd4': {'d5': 1}}} >>> dflt_func = min >>> agg_funcs = {'a': statistics.mean, 'v': max, 'd': {'d1': sum}} >>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func)) {'a': 1.3, 'b': 2.0, 'c': 1, 'd': {'d1': 3, 'd2': 3, 'd3': 3, 'd4': {'d5': 1}}, 'v': 2.3} cSsg|]}t|qSr)setkeys).0drrr zmerge_dicts..cSsg|]}|dur|qSr r)r=vrrrr?r@csg|]}|qSr)get)r=d_inkrrr?sr) list functoolsreduceoperatoror_rdictrB isinstance merge_dicts)r8r9r:r<d_outfn values_to_aggrrDrrMds& rM)rrGrI statisticsabcr collectionsrtypingrrrrrr typing_extensionsr lightning_fabric.loggersr FabricLoggerlightning_fabric.loggers.loggerr r#r ,pytorch_lightning.callbacks.model_checkpointrrmeanfloatrMrrrrs2         4