a d'@s^dZddlmZmZmZmZmZddlmZddl m Z ddl Z ddl mZGdddZdS) z* Base class used to build new callbacks. )AnyDictListOptionalType)Tensor) OptimizerN) STEP_OUTPUTc@steZdZdZeedddZeeddddZe eddd Z d d ed d ddZ d d ed d ddZ d d d dddZ d d d dddZd d d dddZd d d dddZd d e ed dddZd d ee ed ddd Zd d d dd!d"Zd d d dd#d$Zd d d dd%d&Zd d d dd'd(Zd d d dd)d*Zd d d dd+d,Zd d d dd-d.Zd d ee d d/d0d1Zd d e eed d2d3d4Zd d eee eed d5d6d7Z d d e eed d2d8d9Z!d d eee eed d5d:d;Z"d d e eed d2dd?Z$d d d dd@dAZ%d d d ddBdCZ&d d d ddDdEZ'd d d ddFdGZ(d d d ddHdIZ)d d d ddJdKZ*d d d ddLdMZ+d d d ddNdOZ,d d e-d dPdQdRZ.e/ee fddSdTZ0e/ee fd dUdVdWZ1d d e/ee fd dXdYdZZ2d d e/ee fd dXd[d\Z3d d e4d d]d^d_Z5d d d dd`daZ6d d e7ed dbdcddZ8d d e7d dedfdgZ9d S)hCallbackzz Abstract base class used to build new callbacks. Subclass this class and override any of the relevant hooks )returncCs|jjS)atIdentifier for the state of the callback. Used to store and retrieve a callback's state from the checkpoint dictionary by ``checkpoint["callbacks"][state_key]``. Implementations of a callback need to provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of multiple instances of that callback. ) __class__ __qualname__selfrm/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/pytorch_lightning/callbacks/callback.py state_key#szCallback.state_keycCst|S)z7State key for checkpoints saved prior to version 1.5.0.)typerrrr_legacy_state_key-szCallback._legacy_state_key)kwargsr cKs|jjt|S)zFormats a set of key-value pairs into a state key string with the callback class name prefixed. Useful for defining a :attr:`state_key`. Args: **kwargs: A set of key-value pairs. Must be serializable to :class:`str`. )r r repr)rrrrr_generate_state_key2szCallback._generate_state_keyz pl.Trainerzpl.LightningModuleN)trainer pl_modulestager cCsdS)z9Called when fit, validate, test, predict, or tune begins.Nrrrrrrrrsetup;szCallback.setupcCsdS)z7Called when fit, validate, test, predict, or tune ends.Nrrrrrteardown>szCallback.teardown)rrr cCsdS)zCalled when fit begins.Nrrrrrrr on_fit_startAszCallback.on_fit_startcCsdS)zCalled when fit ends.Nrrrrr on_fit_endDszCallback.on_fit_endcCsdS)z/Called when the validation sanity check starts.Nrrrrron_sanity_check_startGszCallback.on_sanity_check_startcCsdS)z-Called when the validation sanity check ends.Nrrrrron_sanity_check_endJszCallback.on_sanity_check_end)rrbatch batch_idxr cCsdS)z#Called when the train batch begins.Nr)rrrr#r$rrron_train_batch_startMszCallback.on_train_batch_start)rroutputsr#r$r cCsdS)zCalled when the train batch ends. Note: The value ``outputs["loss"]`` here will be the normalized value w.r.t ``accumulate_grad_batches`` of the loss returned from ``training_step``. Nr)rrrr&r#r$rrron_train_batch_endRszCallback.on_train_batch_endcCsdS)z#Called when the train epoch begins.Nrrrrron_train_epoch_start\szCallback.on_train_epoch_startcCsdS)aICalled when the train epoch ends. To access all batch outputs at the end of the epoch, either: 1. Implement `training_epoch_end` in the `LightningModule` and access outputs via the module OR 2. Cache data across train batch hooks inside the callback implementation to post-process in this hook. Nrrrrron_train_epoch_end_szCallback.on_train_epoch_endcCsdS)z!Called when the val epoch begins.Nrrrrron_validation_epoch_starthsz"Callback.on_validation_epoch_startcCsdS)zCalled when the val epoch ends.Nrrrrron_validation_epoch_endksz Callback.on_validation_epoch_endcCsdS)z"Called when the test epoch begins.Nrrrrron_test_epoch_startnszCallback.on_test_epoch_startcCsdS)z Called when the test epoch ends.Nrrrrron_test_epoch_endqszCallback.on_test_epoch_endcCsdS)z%Called when the predict epoch begins.Nrrrrron_predict_epoch_starttszCallback.on_predict_epoch_start)rrr&r cCsdS)z#Called when the predict epoch ends.Nr)rrrr&rrron_predict_epoch_endwszCallback.on_predict_epoch_end)rrr#r$dataloader_idxr cCsdS)z(Called when the validation batch begins.Nrrrrr#r$r0rrron_validation_batch_startzsz"Callback.on_validation_batch_start)rrr&r#r$r0r cCsdS)z&Called when the validation batch ends.Nrrrrr&r#r$r0rrron_validation_batch_ends z Callback.on_validation_batch_endcCsdS)z"Called when the test batch begins.Nrr1rrron_test_batch_startszCallback.on_test_batch_startcCsdS)z Called when the test batch ends.Nrr3rrron_test_batch_ends zCallback.on_test_batch_endcCsdS)z%Called when the predict batch begins.Nrr1rrron_predict_batch_startszCallback.on_predict_batch_startcCsdS)z#Called when the predict batch ends.Nrr3rrron_predict_batch_ends zCallback.on_predict_batch_endcCsdS)zCalled when the train begins.Nrrrrron_train_startszCallback.on_train_startcCsdS)zCalled when the train ends.Nrrrrr on_train_endszCallback.on_train_endcCsdS)z'Called when the validation loop begins.Nrrrrron_validation_startszCallback.on_validation_startcCsdS)z%Called when the validation loop ends.Nrrrrron_validation_endszCallback.on_validation_endcCsdS)zCalled when the test begins.Nrrrrr on_test_startszCallback.on_test_startcCsdS)zCalled when the test ends.Nrrrrr on_test_endszCallback.on_test_endcCsdS)zCalled when the predict begins.Nrrrrron_predict_startszCallback.on_predict_startcCsdS)zCalled when predict ends.Nrrrrron_predict_endszCallback.on_predict_end)rr exceptionr cCsdS)zACalled when any trainer execution is interrupted by an exception.Nr)rrrrArrr on_exceptionszCallback.on_exceptioncCsiS)zCalled when saving a checkpoint, implement to generate callback's ``state_dict``. Returns: A dictionary containing callback state. rrrrr state_dictszCallback.state_dict)rCr cCsdS)zCalled when loading a checkpoint, implement to reload callback state given callback's ``state_dict``. Args: state_dict: the callback state returned by ``state_dict``. Nr)rrCrrrload_state_dictszCallback.load_state_dict)rr checkpointr cCsdS)a Called when saving a checkpoint to give you a chance to store anything else you might want to save. Args: trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance. pl_module: the current :class:`~pytorch_lightning.core.module.LightningModule` instance. checkpoint: the checkpoint dictionary that will be saved. NrrrrrErrron_save_checkpointszCallback.on_save_checkpointcCsdS)ap Called when loading a model checkpoint, use to reload state. Args: trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance. pl_module: the current :class:`~pytorch_lightning.core.module.LightningModule` instance. checkpoint: the full checkpoint dictionary that got loaded by the Trainer. NrrFrrron_load_checkpointszCallback.on_load_checkpoint)rrlossr cCsdS)z"Called before ``loss.backward()``.Nr)rrrrIrrron_before_backwardszCallback.on_before_backwardcCsdS)zCCalled after ``loss.backward()`` and before optimizers are stepped.Nrrrrron_after_backwardszCallback.on_after_backward)rr optimizeropt_idxr cCsdS)z#Called before ``optimizer.step()``.Nr)rrrrLrMrrron_before_optimizer_stepsz!Callback.on_before_optimizer_step)rrrLr cCsdS)z(Called before ``optimizer.zero_grad()``.Nr)rrrrLrrron_before_zero_gradszCallback.on_before_zero_grad):__name__ __module__r __doc__propertystrrrrrrrrrr r!r"intr%r r'r(r)r*r+r,r-r.rr/r2rr4r5r6r7r8r9r:r;r<r=r>r?r@ BaseExceptionrBrrCrDrGrHrrJrKrrNrOrrrrr s                 r )rRtypingrrrrrZtorchrZ torch.optimrZpytorch_lightningplZ!pytorch_lightning.utilities.typesr r rrrrs