251 lines
9.2 KiB
Python
251 lines
9.2 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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from typing import Dict, Union
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.utilities import rank_zero_warn
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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, batch_idx):
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super().on_train_batch_end(trainer, pl_module, outputs, batch_idx) # 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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"""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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"""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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"""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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"""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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"""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 training
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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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"""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 validation
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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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"""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 test dataloader is
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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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"""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 predict dataloader
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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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"""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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"""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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"""You should provide a way to print without breaking the progress bar."""
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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 = 0
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def on_train_epoch_start(self, trainer, pl_module):
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self._train_batch_idx = trainer.fit_loop.epoch_loop.batch_progress.current.completed
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_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_epoch_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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def get_metrics(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Dict[str, Union[int, str]]:
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r"""
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Combines progress bar metrics collected from the trainer with standard metrics from get_standard_metrics.
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Implement this to override the items displayed in the progress bar.
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Here is an example of how to override the defaults:
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.. code-block:: python
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def get_metrics(self, trainer, model):
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# don't show the version number
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items = super().get_metrics(trainer, model)
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items.pop("v_num", None)
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return items
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Return:
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Dictionary with the items to be displayed in the progress bar.
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"""
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standard_metrics = pl_module.get_progress_bar_dict()
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pbar_metrics = trainer.progress_bar_metrics
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duplicates = list(standard_metrics.keys() & pbar_metrics.keys())
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if duplicates:
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rank_zero_warn(
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f"The progress bar already tracks a metric with the name(s) '{', '.join(duplicates)}' and"
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f" `self.log('{duplicates[0]}', ..., prog_bar=True)` will overwrite this value. "
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" If this is undesired, change the name or override `get_metrics()` in the progress bar callback.",
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UserWarning,
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)
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return {**standard_metrics, **pbar_metrics}
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def get_standard_metrics(trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Dict[str, Union[int, str]]:
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r"""
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Returns several standard metrics displayed in the progress bar, including the average loss value,
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split index of BPTT (if used) and the version of the experiment when using a logger.
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.. code-block::
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Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]
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Return:
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Dictionary with the standard metrics to be displayed in the progress bar.
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"""
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# call .item() only once but store elements without graphs
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running_train_loss = trainer.fit_loop.running_loss.mean()
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avg_training_loss = None
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if running_train_loss is not None:
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avg_training_loss = running_train_loss.cpu().item()
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elif pl_module.automatic_optimization:
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avg_training_loss = float("NaN")
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items_dict = {}
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if avg_training_loss is not None:
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items_dict["loss"] = f"{avg_training_loss:.3g}"
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if pl_module.truncated_bptt_steps > 0:
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items_dict["split_idx"] = trainer.fit_loop.split_idx
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if trainer.logger is not None and trainer.logger.version is not None:
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version = trainer.logger.version
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# show last 4 places of long version strings
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version = version[-4:] if isinstance(version, str) else version
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items_dict["v_num"] = version
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return items_dict
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