a d@sdZddlZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z ddlZddlmZddlmZddlmZdd lmZGd d d eeZGd d d eZdejfe e e e e e egefe dddZdS)z.Abstract base class used to build new loggers.N)ABC) defaultdict)AnyCallableDictMappingOptionalSequence)Logger)_DummyExperiment)rank_zero_experiment)ModelCheckpointc@s6eZdZdZeddddZeeedddZ dS) r z"Base class for experiment loggers.N)checkpoint_callbackreturncCsdS)zCalled after model checkpoint callback saves a new checkpoint. Args: checkpoint_callback: the model checkpoint callback instance N)selfrrri/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/pytorch_lightning/loggers/logger.pyafter_save_checkpoint"szLogger.after_save_checkpointrcCsdS)zvReturn the root directory where experiment logs get saved, or `None` if the logger does not save data locally.Nrrrrrsave_dir*szLogger.save_dir) __name__ __module__ __qualname____doc__r rpropertyrstrrrrrrr sr cseZdZdZddfdd ZeedddZeeddd d Z eeddd d Z ee dd dZ ee dddZ eddddZe edddZZS) 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 Nrcstt|_dSN)super__init__DummyExperiment _experimentr __class__rrr 7s zDummyLogger.__init__cCs|jS)z9Return the experiment object associated with this logger.)r"rrrr experiment;szDummyLogger.experimentargskwargsrcOsdSrrrr'r(rrr log_metrics@szDummyLogger.log_metricscOsdSrrr)rrrlog_hyperparamsCszDummyLogger.log_hyperparamscCsdS)zReturn the experiment name.rrrrrnameFszDummyLogger.namecCsdS)zReturn the experiment version.r,rrrrrversionKszDummyLogger.version)idxrcCs|Srr)rr/rrr __getitem__PszDummyLogger.__getitem__)r-rcCsttdddd}|S)zUAllows the DummyLogger to be called with arbitrary methods, to avoid AttributeErrors.Nr&c_sdSrr)r'r(rrrmethodWsz'DummyLogger.__getattr__..method)r)rr-r1rrr __getattr__TszDummyLogger.__getattr__)rrrrr rr!r%rr*r+rr-r.intr0rr2 __classcell__rrr#rr1sr)dicts agg_key_funcs default_funcrcs|pi}tttjdd|D}tt}|D]Z|}ddfdd|DD}t|dtrzt ||||<q0|p|||<q0t|S)aMerge 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': np.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|qSrr)r:vrrrr<r=csg|]}|qSr)get)r:Zd_inkrrr<r=r) list functoolsreduceoperatoror_rdictr? isinstance merge_dicts)r5r6r7r9Zd_outfnZ values_to_aggrr@rrI^s& rI)rrCrEabcr collectionsrtypingrrrrrr numpynpZlightning_fabric.loggersr Z FabricLoggerZlightning_fabric.loggers.loggerr r!r Z,pytorch_lightning.callbacks.model_checkpointr rmeanfloatrIrrrrs(       /