a d-@szddlmZmZmZmZddlZddlmZddl m Z ddl m Z GdddeZ dd eeeeeffd d d ZdS) )AnyDictOptionalUnionN)Callback)_version)rank_zero_warnc @seZdZdZddddZeddddZeedd d Zeedd d Z eedd dZ eedddZ eedddZ ee dddZee dddZee dddZee dddZee dddZeee efdddZeee efddd Zeee efdd!d"Zeee efdd#d$Zeee efdd%d&Zeee efdd'd(Ze ed)d*d+Zddd,d-Zddd.d/Zddd0d1Ze e dd2d3d4Z!dd5edd6d7d8Z"dd5e#eee eee#eefffd9d:d;Z$dS)<ProgressBarBasea The base class for progress bars in Lightning. It is a :class:`~pytorch_lightning.callbacks.Callback` that keeps track of the batch progress in the :class:`~pytorch_lightning.trainer.trainer.Trainer`. You should implement your highly custom progress bars with this as the base class. Example:: class LitProgressBar(ProgressBarBase): def __init__(self): super().__init__() # don't forget this :) self.enable = True def disable(self): self.enable = False def on_train_batch_end(self, trainer, pl_module, outputs, batch_idx): super().on_train_batch_end(trainer, pl_module, outputs, batch_idx) # don't forget this :) percent = (self.train_batch_idx / self.total_train_batches) * 100 sys.stdout.flush() sys.stdout.write(f'{percent:.01f} percent complete \r') bar = LitProgressBar() trainer = Trainer(callbacks=[bar]) N)returncCsd|_d|_dSN)_trainer_current_eval_dataloader_idxselfrr/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/pytorch_lightning/callbacks/progress/base.py__init__2szProgressBarBase.__init__ pl.TrainercCs$|jdurtd|jjd|jS)NzThe `z*._trainer` reference has not been set yet.)r TypeError __class____name__rrrrtrainer6s zProgressBarBase.trainercCsdS)NzSanity Checkingrrrrrsanity_check_description<sz(ProgressBarBase.sanity_check_descriptioncCsdS)NZTrainingrrrrrtrain_description@sz!ProgressBarBase.train_descriptioncCsdS)NZ Validationrrrrrvalidation_descriptionDsz&ProgressBarBase.validation_descriptioncCsdS)NZTestingrrrrrtest_descriptionHsz ProgressBarBase.test_descriptioncCsdS)NZ Predictingrrrrrpredict_descriptionLsz#ProgressBarBase.predict_descriptioncCs|jjjjjjjjSr )rfit_loop epoch_loopval_loopbatch_progresstotal processedrrrr_val_processedPszProgressBarBase._val_processedcCs|jjjjjjS)zhThe number of batches processed during training. 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N)r rZnum_test_batchesrrrr%total_test_batches_current_dataloadersz5ProgressBarBase.total_test_batches_current_dataloadercCs|jdusJ|jj|jS)aThe total number of prediction batches, which may change from epoch to epoch for current dataloader. Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the predict dataloader is of infinite size. N)r rZnum_predict_batchesrrrr(total_predict_batches_current_dataloadersz8ProgressBarBase.total_predict_batches_current_dataloadercCs|jjjrt|jjSdS)aThe total number of validation batches, which may change from epoch to epoch for all val dataloaders. Use this to set the total number of iterations in the progress bar. 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The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training routines like the :ref:`learning rate finder `. to temporarily enable and disable the main progress bar. Nr;rrrrenableszProgressBarBase.enable)argskwargsr cOst|i|dS)zDYou should provide a way to print without breaking the progress bar.N)print)rr?r@rrrrAszProgressBarBase.printpl.LightningModule)r pl_modulestager cCs||_|js|dSr )r Zis_global_zeror=)rrrCrDrrrsetupszProgressBarBase.setuprrCr cCsTt||}|j}t||@}|rHtdd|d|ddi||S)af Combines progress bar metrics collected from the trainer with standard metrics from get_standard_metrics. Implement this to override the items displayed in the progress bar. Here is an example of how to override the defaults: .. code-block:: python def get_metrics(self, trainer, model): # don't show the version number items = super().get_metrics(trainer, model) items.pop("v_num", None) return items Return: Dictionary with the items to be displayed in the progress bar. z;The progress bar already tracks a metric with the name(s) 'z, z' and `self.log('rz', ..., prog_bar=True)` will overwrite this value. If this is undesired, change the name or override `get_metrics()` in the progress bar callback.)get_standard_metricsZprogress_bar_metricslistkeysrjoin)rrrCZstandard_metricsZ pbar_metrics duplicatesrrr get_metricss zProgressBarBase.get_metrics)%r __module__ __qualname____doc__rpropertyrstrrrrrrintr#r%r(r)r*rr4r+r-r.r/r1r6boolr9r:r=r>rrArErrLrrrrr sZ     r rrBrFcCs|jj}d}|dur&|}n|jr4td}i}|durL|d|d<|jdkrb|jj|d<|j rt |j }|durt |t r|dd}||d<|S) u Returns several standard metrics displayed in the progress bar, including the average loss value, split index of BPTT (if used) and the version of the experiment when using a logger. .. code-block:: Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10] Return: Dictionary with the standard metrics to be displayed in the progress bar. NNaNz.3gZlossr split_idxZv_num) rZ running_lossmeancpuitemZautomatic_optimizationr4Ztruncated_bptt_stepsrUloggersr isinstancerQ)rrCZrunning_train_lossZavg_training_lossZ items_dictversionrrrrGs$       rG)typingrrrrZpytorch_lightningplZpytorch_lightning.callbacksrZ"pytorch_lightning.utilities.loggerrZ%pytorch_lightning.utilities.rank_zerorr rQrRrGrrrrs   m