From deffbaba7ffb16ff57b56fe65f62df761f25fbd6 Mon Sep 17 00:00:00 2001 From: Mike Clark Date: Sun, 26 Jan 2020 15:19:09 +0000 Subject: [PATCH] for #330, use tqdm.auto in trainer (#752) * use tqdm.auto in trainer This will import the ipywidgets version of tqdm if available. This works nicely in notebooks by not filling up the log. In the terminal it will use the same old tqdm. We might also want to consider passing in the tqdm we want as an argument since there may be some edge cases where ipywidgets is available but the interface doesn't support it (e.g. vscode?) or isn't working. In which case people will get a warning message, but may want to configure it themselves. * use `from tqdm.auto` in eval loop * indents --- pytorch_lightning/trainer/evaluation_loop.py | 8 ++++---- pytorch_lightning/trainer/trainer.py | 12 ++++++------ 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/pytorch_lightning/trainer/evaluation_loop.py b/pytorch_lightning/trainer/evaluation_loop.py index e0148d60..b5e2fe95 100644 --- a/pytorch_lightning/trainer/evaluation_loop.py +++ b/pytorch_lightning/trainer/evaluation_loop.py @@ -127,7 +127,7 @@ import sys from abc import ABC, abstractmethod import torch -import tqdm +from tqdm.auto import tqdm from pytorch_lightning.utilities.debugging import MisconfigurationException @@ -293,9 +293,9 @@ class TrainerEvaluationLoopMixin(ABC): # main progress bar will already be closed when testing so initial position is free position = 2 * self.process_position + (not test) desc = 'Testing' if test else 'Validating' - pbar = tqdm.tqdm(desc=desc, total=max_batches, leave=test, position=position, - disable=not self.show_progress_bar, dynamic_ncols=True, - unit='batch', file=sys.stdout) + pbar = tqdm(desc=desc, total=max_batches, leave=test, position=position, + disable=not self.show_progress_bar, dynamic_ncols=True, + unit='batch', file=sys.stdout) setattr(self, f'{"test" if test else "val"}_progress_bar', pbar) # run evaluation diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py index 9cc9b559..e1c1464c 100644 --- a/pytorch_lightning/trainer/trainer.py +++ b/pytorch_lightning/trainer/trainer.py @@ -7,7 +7,7 @@ import logging import torch import torch.distributed as dist import torch.multiprocessing as mp -import tqdm +from tqdm.auto import tqdm from torch.optim.optimizer import Optimizer from pytorch_lightning.trainer.auto_mix_precision import TrainerAMPMixin @@ -850,13 +850,13 @@ class Trainer(TrainerIOMixin, ref_model.on_train_start() if not self.disable_validation and self.num_sanity_val_steps > 0: # init progress bars for validation sanity check - pbar = tqdm.tqdm(desc='Validation sanity check', + pbar = tqdm(desc='Validation sanity check', total=self.num_sanity_val_steps * len(self.get_val_dataloaders()), leave=False, position=2 * self.process_position, disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch') self.main_progress_bar = pbar # dummy validation progress bar - self.val_progress_bar = tqdm.tqdm(disable=True) + self.val_progress_bar = tqdm(disable=True) eval_results = self.evaluate(model, self.get_val_dataloaders(), self.num_sanity_val_steps, False) @@ -870,9 +870,9 @@ class Trainer(TrainerIOMixin, self.early_stop_callback.check_metrics(callback_metrics) # init progress bar - pbar = tqdm.tqdm(leave=True, position=2 * self.process_position, - disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch', - file=sys.stdout) + pbar = tqdm(leave=True, position=2 * self.process_position, + disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch', + file=sys.stdout) self.main_progress_bar = pbar # clear cache before training