509 lines
19 KiB
Python
509 lines
19 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Progress Bars
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=============
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Use or override one of the progress bar callbacks.
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"""
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import importlib
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import io
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import os
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import sys
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# check if ipywidgets is installed before importing tqdm.auto
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# to ensure it won't fail and a progress bar is displayed
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from typing import Optional, Union
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if importlib.util.find_spec('ipywidgets') is not None:
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from tqdm.auto import tqdm as _tqdm
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else:
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from tqdm import tqdm as _tqdm
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from pytorch_lightning.callbacks import Callback
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_PAD_SIZE = 5
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class tqdm(_tqdm):
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"""
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Custom tqdm progressbar where we append 0 to floating points/strings to prevent the progress bar from flickering
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"""
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@staticmethod
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def format_num(n) -> str:
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""" Add additional padding to the formatted numbers """
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should_be_padded = isinstance(n, (float, str))
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if not isinstance(n, str):
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n = _tqdm.format_num(n)
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if should_be_padded and 'e' not in n:
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if '.' not in n and len(n) < _PAD_SIZE:
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try:
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_ = float(n)
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except ValueError:
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return n
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n += '.'
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n += "0" * (_PAD_SIZE - len(n))
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return n
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class ProgressBarBase(Callback):
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r"""
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The base class for progress bars in Lightning. It is a :class:`~pytorch_lightning.callbacks.Callback`
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that keeps track of the batch progress in the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
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You should implement your highly custom progress bars with this as the base class.
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Example::
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class LitProgressBar(ProgressBarBase):
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def __init__(self):
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super().__init__() # don't forget this :)
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self.enable = True
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def disable(self):
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self.enable = False
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def on_train_batch_end(self, trainer, pl_module, outputs):
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super().on_train_batch_end(trainer, pl_module, outputs) # don't forget this :)
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percent = (self.train_batch_idx / self.total_train_batches) * 100
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sys.stdout.flush()
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sys.stdout.write(f'{percent:.01f} percent complete \r')
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bar = LitProgressBar()
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trainer = Trainer(callbacks=[bar])
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"""
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def __init__(self):
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self._trainer = None
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self._train_batch_idx = 0
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self._val_batch_idx = 0
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self._test_batch_idx = 0
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self._predict_batch_idx = 0
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@property
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def trainer(self):
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return self._trainer
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@property
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def train_batch_idx(self) -> int:
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"""
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The current batch index being processed during training.
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Use this to update your progress bar.
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"""
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return self._train_batch_idx
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@property
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def val_batch_idx(self) -> int:
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"""
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The current batch index being processed during validation.
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Use this to update your progress bar.
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"""
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return self._val_batch_idx
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@property
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def test_batch_idx(self) -> int:
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"""
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The current batch index being processed during testing.
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Use this to update your progress bar.
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"""
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return self._test_batch_idx
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@property
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def predict_batch_idx(self) -> int:
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"""
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The current batch index being processed during predicting.
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Use this to update your progress bar.
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"""
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return self._predict_batch_idx
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@property
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def total_train_batches(self) -> int:
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"""
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The total number of training batches during training, which may change from epoch to epoch.
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Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
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training dataloader is of infinite size.
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"""
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return self.trainer.num_training_batches
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@property
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def total_val_batches(self) -> int:
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"""
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The total number of validation batches during validation, which may change from epoch to epoch.
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Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
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validation dataloader is of infinite size.
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"""
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total_val_batches = 0
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if self.trainer.enable_validation:
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is_val_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
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total_val_batches = sum(self.trainer.num_val_batches) if is_val_epoch else 0
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return total_val_batches
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@property
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def total_test_batches(self) -> int:
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"""
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The total number of testing batches during testing, which may change from epoch to epoch.
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Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
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test dataloader is of infinite size.
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"""
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return sum(self.trainer.num_test_batches)
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@property
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def total_predict_batches(self) -> int:
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"""
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The total number of predicting batches during testing, which may change from epoch to epoch.
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Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
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predict dataloader is of infinite size.
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"""
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return sum(self.trainer.num_predict_batches)
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def disable(self):
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"""
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You should provide a way to disable the progress bar.
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The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the
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output on processes that have a rank different from 0, e.g., in multi-node training.
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"""
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raise NotImplementedError
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def enable(self):
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"""
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You should provide a way to enable the progress bar.
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The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training
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routines like the :ref:`learning rate finder <advanced/lr_finder:Learning Rate Finder>`
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to temporarily enable and disable the main progress bar.
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"""
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raise NotImplementedError
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def print(self, *args, **kwargs):
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"""
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You should provide a way to print without breaking the progress bar.
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"""
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print(*args, **kwargs)
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def on_init_end(self, trainer):
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self._trainer = trainer
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def on_train_start(self, trainer, pl_module):
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self._train_batch_idx = trainer.batch_idx
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def on_train_epoch_start(self, trainer, pl_module):
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self._train_batch_idx = 0
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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self._train_batch_idx += 1
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def on_validation_start(self, trainer, pl_module):
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self._val_batch_idx = 0
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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self._val_batch_idx += 1
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def on_test_start(self, trainer, pl_module):
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self._test_batch_idx = 0
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def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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self._test_batch_idx += 1
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def on_predict_start(self, trainer, pl_module):
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self._predict_batch_idx = 0
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def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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self._predict_batch_idx += 1
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class ProgressBar(ProgressBarBase):
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r"""
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This is the default progress bar used by Lightning. It prints to `stdout` using the
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:mod:`tqdm` package and shows up to four different bars:
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- **sanity check progress:** the progress during the sanity check run
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- **main progress:** shows training + validation progress combined. It also accounts for
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multiple validation runs during training when
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:paramref:`~pytorch_lightning.trainer.trainer.Trainer.val_check_interval` is used.
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- **validation progress:** only visible during validation;
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shows total progress over all validation datasets.
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- **test progress:** only active when testing; shows total progress over all test datasets.
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For infinite datasets, the progress bar never ends.
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If you want to customize the default ``tqdm`` progress bars used by Lightning, you can override
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specific methods of the callback class and pass your custom implementation to the
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:class:`~pytorch_lightning.trainer.trainer.Trainer`:
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Example::
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class LitProgressBar(ProgressBar):
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def init_validation_tqdm(self):
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bar = super().init_validation_tqdm()
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bar.set_description('running validation ...')
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return bar
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bar = LitProgressBar()
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trainer = Trainer(callbacks=[bar])
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Args:
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refresh_rate:
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Determines at which rate (in number of batches) the progress bars get updated.
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Set it to ``0`` to disable the display. By default, the
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:class:`~pytorch_lightning.trainer.trainer.Trainer` uses this implementation of the progress
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bar and sets the refresh rate to the value provided to the
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:paramref:`~pytorch_lightning.trainer.trainer.Trainer.progress_bar_refresh_rate` argument in the
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:class:`~pytorch_lightning.trainer.trainer.Trainer`.
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process_position:
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Set this to a value greater than ``0`` to offset the progress bars by this many lines.
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This is useful when you have progress bars defined elsewhere and want to show all of them
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together. This corresponds to
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:paramref:`~pytorch_lightning.trainer.trainer.Trainer.process_position` in the
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:class:`~pytorch_lightning.trainer.trainer.Trainer`.
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"""
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def __init__(self, refresh_rate: int = 1, process_position: int = 0):
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super().__init__()
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self._refresh_rate = refresh_rate
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self._process_position = process_position
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self._enabled = True
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self.main_progress_bar = None
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self.val_progress_bar = None
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self.test_progress_bar = None
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def __getstate__(self):
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# can't pickle the tqdm objects
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state = self.__dict__.copy()
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state['main_progress_bar'] = None
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state['val_progress_bar'] = None
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state['test_progress_bar'] = None
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return state
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@property
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def refresh_rate(self) -> int:
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return self._refresh_rate
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@property
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def process_position(self) -> int:
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return self._process_position
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@property
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def is_enabled(self) -> bool:
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return self._enabled and self.refresh_rate > 0
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@property
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def is_disabled(self) -> bool:
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return not self.is_enabled
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def disable(self) -> None:
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self._enabled = False
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def enable(self) -> None:
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self._enabled = True
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def init_sanity_tqdm(self) -> tqdm:
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""" Override this to customize the tqdm bar for the validation sanity run. """
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bar = tqdm(
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desc='Validation sanity check',
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position=(2 * self.process_position),
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disable=self.is_disabled,
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leave=False,
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dynamic_ncols=True,
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file=sys.stdout,
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)
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return bar
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def init_train_tqdm(self) -> tqdm:
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""" Override this to customize the tqdm bar for training. """
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bar = tqdm(
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desc='Training',
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initial=self.train_batch_idx,
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position=(2 * self.process_position),
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disable=self.is_disabled,
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leave=True,
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dynamic_ncols=True,
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file=sys.stdout,
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smoothing=0,
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)
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return bar
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def init_predict_tqdm(self) -> tqdm:
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""" Override this to customize the tqdm bar for predicting. """
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bar = tqdm(
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desc='Predicting',
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initial=self.train_batch_idx,
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position=(2 * self.process_position),
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disable=self.is_disabled,
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leave=True,
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dynamic_ncols=True,
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file=sys.stdout,
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smoothing=0,
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)
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return bar
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def init_validation_tqdm(self) -> tqdm:
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""" Override this to customize the tqdm bar for validation. """
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# The main progress bar doesn't exist in `trainer.validate()`
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has_main_bar = self.main_progress_bar is not None
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bar = tqdm(
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desc='Validating',
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position=(2 * self.process_position + has_main_bar),
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disable=self.is_disabled,
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leave=False,
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dynamic_ncols=True,
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file=sys.stdout
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)
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return bar
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def init_test_tqdm(self) -> tqdm:
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""" Override this to customize the tqdm bar for testing. """
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bar = tqdm(
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desc="Testing",
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position=(2 * self.process_position),
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disable=self.is_disabled,
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leave=True,
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dynamic_ncols=True,
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file=sys.stdout
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)
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return bar
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def on_sanity_check_start(self, trainer, pl_module):
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super().on_sanity_check_start(trainer, pl_module)
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self.val_progress_bar = self.init_sanity_tqdm()
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self.main_progress_bar = tqdm(disable=True) # dummy progress bar
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def on_sanity_check_end(self, trainer, pl_module):
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super().on_sanity_check_end(trainer, pl_module)
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self.main_progress_bar.close()
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self.val_progress_bar.close()
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def on_train_start(self, trainer, pl_module):
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super().on_train_start(trainer, pl_module)
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self.main_progress_bar = self.init_train_tqdm()
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def on_train_epoch_start(self, trainer, pl_module):
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super().on_train_epoch_start(trainer, pl_module)
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total_train_batches = self.total_train_batches
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total_val_batches = self.total_val_batches
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if total_train_batches != float('inf'):
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# val can be checked multiple times per epoch
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val_checks_per_epoch = total_train_batches // trainer.val_check_batch
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total_val_batches = total_val_batches * val_checks_per_epoch
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total_batches = total_train_batches + total_val_batches
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reset(self.main_progress_bar, total_batches)
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self.main_progress_bar.set_description(f'Epoch {trainer.current_epoch}')
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
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if self._should_update(self.train_batch_idx, self.total_train_batches + self.total_val_batches):
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self._update_bar(self.main_progress_bar)
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self.main_progress_bar.set_postfix(trainer.progress_bar_dict)
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def on_validation_start(self, trainer, pl_module):
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super().on_validation_start(trainer, pl_module)
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if trainer.sanity_checking:
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reset(self.val_progress_bar, sum(trainer.num_sanity_val_batches))
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else:
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self._update_bar(self.main_progress_bar) # fill up remaining
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self.val_progress_bar = self.init_validation_tqdm()
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reset(self.val_progress_bar, self.total_val_batches)
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
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if self._should_update(self.val_batch_idx, self.total_val_batches):
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self._update_bar(self.val_progress_bar)
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self._update_bar(self.main_progress_bar)
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def on_validation_end(self, trainer, pl_module):
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super().on_validation_end(trainer, pl_module)
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if self.main_progress_bar is not None:
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self.main_progress_bar.set_postfix(trainer.progress_bar_dict)
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self.val_progress_bar.close()
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def on_train_end(self, trainer, pl_module):
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super().on_train_end(trainer, pl_module)
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self.main_progress_bar.close()
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def on_test_start(self, trainer, pl_module):
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super().on_test_start(trainer, pl_module)
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self.test_progress_bar = self.init_test_tqdm()
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self.test_progress_bar.total = convert_inf(self.total_test_batches)
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def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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super().on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
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if self._should_update(self.test_batch_idx, self.total_test_batches):
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self._update_bar(self.test_progress_bar)
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def on_test_end(self, trainer, pl_module):
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super().on_test_end(trainer, pl_module)
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self.test_progress_bar.close()
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def on_predict_start(self, trainer, pl_module):
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super().on_predict_start(trainer, pl_module)
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self.predict_progress_bar = self.init_predict_tqdm()
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self.predict_progress_bar.total = convert_inf(self.total_predict_batches)
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def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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super().on_predict_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
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if self._should_update(self.predict_batch_idx, self.total_predict_batches):
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self._update_bar(self.predict_progress_bar)
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def on_predict_end(self, trainer, pl_module):
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self.predict_progress_bar.close()
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def print(
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self, *args, sep: str = ' ', end: str = os.linesep, file: Optional[io.TextIOBase] = None, nolock: bool = False
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):
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active_progress_bar = None
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if not self.main_progress_bar.disable:
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active_progress_bar = self.main_progress_bar
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elif not self.val_progress_bar.disable:
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active_progress_bar = self.val_progress_bar
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elif not self.test_progress_bar.disable:
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active_progress_bar = self.test_progress_bar
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if active_progress_bar is not None:
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s = sep.join(map(str, args))
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active_progress_bar.write(s, end=end, file=file, nolock=nolock)
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def _should_update(self, current, total):
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return self.is_enabled and (current % self.refresh_rate == 0 or current == total)
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def _update_bar(self, bar: Optional[tqdm]) -> None:
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""" Updates the bar by the refresh rate without overshooting. """
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if bar is None:
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return
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if bar.total is not None:
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delta = min(self.refresh_rate, bar.total - bar.n)
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else:
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# infinite / unknown size
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delta = self.refresh_rate
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if delta > 0:
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bar.update(delta)
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|
|
|
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def convert_inf(x: Optional[Union[int, float]]) -> Optional[Union[int, float]]:
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|
""" The tqdm doesn't support inf values. We have to convert it to None. """
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|
if x == float('inf'):
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|
return None
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|
return x
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|
|
|
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def reset(bar: tqdm, total: Optional[int] = None) -> None:
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|
""" Resets the tqdm bar to 0 progress with a new total, unless it is disabled. """
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|
if not bar.disable:
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|
bar.reset(total=convert_inf(total))
|