282 lines
11 KiB
Python
282 lines
11 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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Weights and Biases Logger
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-------------------------
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"""
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import operator
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import os
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from argparse import Namespace
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from pathlib import Path
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from typing import Any, Dict, Optional, Union
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from weakref import ReferenceType
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import torch.nn as nn
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from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
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from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
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from pytorch_lightning.utilities import _module_available, rank_zero_only
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.imports import _compare_version
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from pytorch_lightning.utilities.warnings import WarningCache
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warning_cache = WarningCache()
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_WANDB_AVAILABLE = _module_available("wandb")
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_WANDB_GREATER_EQUAL_0_10_22 = _compare_version("wandb", operator.ge, "0.10.22")
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try:
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import wandb
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from wandb.wandb_run import Run
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except ImportError:
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# needed for test mocks, these tests shall be updated
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wandb, Run = None, None
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class WandbLogger(LightningLoggerBase):
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r"""
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Log using `Weights and Biases <https://docs.wandb.ai/integrations/lightning>`_.
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Install it with pip:
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.. code-block:: bash
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pip install wandb
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Args:
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name: Display name for the run.
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save_dir: Path where data is saved (wandb dir by default).
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offline: Run offline (data can be streamed later to wandb servers).
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id: Sets the version, mainly used to resume a previous run.
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version: Same as id.
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anonymous: Enables or explicitly disables anonymous logging.
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project: The name of the project to which this run will belong.
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log_model: Log checkpoints created by :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint`
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as W&B artifacts.
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* if ``log_model == 'all'``, checkpoints are logged during training.
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* if ``log_model == True``, checkpoints are logged at the end of training, except when
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:paramref:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint.save_top_k` ``== -1``
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which also logs every checkpoint during training.
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* if ``log_model == False`` (default), no checkpoint is logged.
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prefix: A string to put at the beginning of metric keys.
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experiment: WandB experiment object. Automatically set when creating a run.
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\**kwargs: Arguments passed to :func:`wandb.init` like `entity`, `group`, `tags`, etc.
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Raises:
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ImportError:
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If required WandB package is not installed on the device.
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MisconfigurationException:
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If both ``log_model`` and ``offline``is set to ``True``.
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Example::
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from pytorch_lightning.loggers import WandbLogger
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from pytorch_lightning import Trainer
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# instrument experiment with W&B
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wandb_logger = WandbLogger(project='MNIST', log_model='all')
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trainer = Trainer(logger=wandb_logger)
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# log gradients and model topology
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wandb_logger.watch(model)
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See Also:
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- `Demo in Google Colab <http://wandb.me/lightning>`__ with model logging
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- `W&B Documentation <https://docs.wandb.ai/integrations/lightning>`__
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"""
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LOGGER_JOIN_CHAR = '-'
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def __init__(
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self,
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name: Optional[str] = None,
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save_dir: Optional[str] = None,
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offline: Optional[bool] = False,
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id: Optional[str] = None,
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anonymous: Optional[bool] = None,
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version: Optional[str] = None,
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project: Optional[str] = None,
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log_model: Optional[bool] = False,
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experiment=None,
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prefix: Optional[str] = '',
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sync_step: Optional[bool] = None,
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**kwargs
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):
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if wandb is None:
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raise ImportError(
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'You want to use `wandb` logger which is not installed yet,' # pragma: no-cover
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' install it with `pip install wandb`.'
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)
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if offline and log_model:
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raise MisconfigurationException(
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f'Providing log_model={log_model} and offline={offline} is an invalid configuration'
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' since model checkpoints cannot be uploaded in offline mode.\n'
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'Hint: Set `offline=False` to log your model.'
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)
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if log_model and not _WANDB_GREATER_EQUAL_0_10_22:
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warning_cache.warn(
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f'Providing log_model={log_model} requires wandb version >= 0.10.22'
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' for logging associated model metadata.\n'
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'Hint: Upgrade with `pip install --ugrade wandb`.'
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)
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if sync_step is not None:
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warning_cache.warn(
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"`WandbLogger(sync_step=(True|False))` is deprecated in v1.2.1 and will be removed in v1.5."
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" Metrics are now logged separately and automatically synchronized.", DeprecationWarning
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)
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super().__init__()
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self._offline = offline
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self._log_model = log_model
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self._prefix = prefix
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self._experiment = experiment
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self._logged_model_time = {}
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self._checkpoint_callback = None
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# set wandb init arguments
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anonymous_lut = {True: 'allow', False: None}
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self._wandb_init = dict(
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name=name,
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project=project,
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id=version or id,
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dir=save_dir,
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resume='allow',
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anonymous=anonymous_lut.get(anonymous, anonymous)
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)
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self._wandb_init.update(**kwargs)
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# extract parameters
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self._save_dir = self._wandb_init.get('dir')
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self._name = self._wandb_init.get('name')
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self._id = self._wandb_init.get('id')
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def __getstate__(self):
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state = self.__dict__.copy()
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# args needed to reload correct experiment
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state['_id'] = self._experiment.id if self._experiment is not None else None
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# cannot be pickled
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state['_experiment'] = None
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return state
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@property
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@rank_zero_experiment
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def experiment(self) -> Run:
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r"""
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Actual wandb object. To use wandb features in your
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:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
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Example::
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self.logger.experiment.some_wandb_function()
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"""
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if self._experiment is None:
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if self._offline:
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os.environ['WANDB_MODE'] = 'dryrun'
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self._experiment = wandb.init(**self._wandb_init) if wandb.run is None else wandb.run
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# define default x-axis (for latest wandb versions)
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if getattr(self._experiment, "define_metric", None):
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self._experiment.define_metric("trainer/global_step")
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self._experiment.define_metric("*", step_metric='trainer/global_step', step_sync=True)
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return self._experiment
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def watch(self, model: nn.Module, log: str = 'gradients', log_freq: int = 100):
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self.experiment.watch(model, log=log, log_freq=log_freq)
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@rank_zero_only
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def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
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params = self._convert_params(params)
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params = self._flatten_dict(params)
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params = self._sanitize_callable_params(params)
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self.experiment.config.update(params, allow_val_change=True)
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@rank_zero_only
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def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
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assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'
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metrics = self._add_prefix(metrics)
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if step is not None:
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self.experiment.log({**metrics, 'trainer/global_step': step})
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else:
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self.experiment.log(metrics)
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@property
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def save_dir(self) -> Optional[str]:
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return self._save_dir
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@property
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def name(self) -> Optional[str]:
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# don't create an experiment if we don't have one
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return self._experiment.project_name() if self._experiment else self._name
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@property
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def version(self) -> Optional[str]:
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# don't create an experiment if we don't have one
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return self._experiment.id if self._experiment else self._id
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def after_save_checkpoint(self, checkpoint_callback: 'ReferenceType[ModelCheckpoint]') -> None:
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# log checkpoints as artifacts
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if self._log_model == 'all' or self._log_model is True and checkpoint_callback.save_top_k == -1:
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self._scan_and_log_checkpoints(checkpoint_callback)
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elif self._log_model is True:
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self._checkpoint_callback = checkpoint_callback
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@rank_zero_only
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def finalize(self, status: str) -> None:
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# log checkpoints as artifacts
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if self._checkpoint_callback:
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self._scan_and_log_checkpoints(self._checkpoint_callback)
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def _scan_and_log_checkpoints(self, checkpoint_callback: 'ReferenceType[ModelCheckpoint]') -> None:
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# get checkpoints to be saved with associated score
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checkpoints = {
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checkpoint_callback.last_model_path: checkpoint_callback.current_score,
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checkpoint_callback.best_model_path: checkpoint_callback.best_model_score,
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**checkpoint_callback.best_k_models
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}
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checkpoints = sorted([(Path(p).stat().st_mtime, p, s) for p, s in checkpoints.items() if Path(p).is_file()])
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checkpoints = [
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c for c in checkpoints if c[1] not in self._logged_model_time.keys() or self._logged_model_time[c[1]] < c[0]
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]
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# log iteratively all new checkpoints
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for t, p, s in checkpoints:
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metadata = {
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'score': s,
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'original_filename': Path(p).name,
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'ModelCheckpoint': {
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k: getattr(checkpoint_callback, k)
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for k in [
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'monitor', 'mode', 'save_last', 'save_top_k', 'save_weights_only', '_every_n_train_steps',
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'_every_n_val_epochs'
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]
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# ensure it does not break if `ModelCheckpoint` args change
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if hasattr(checkpoint_callback, k)
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}
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} if _WANDB_GREATER_EQUAL_0_10_22 else None
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artifact = wandb.Artifact(name=f"model-{self.experiment.id}", type="model", metadata=metadata)
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artifact.add_file(p, name='model.ckpt')
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aliases = ["latest", "best"] if p == checkpoint_callback.best_model_path else ["latest"]
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self.experiment.log_artifact(artifact, aliases=aliases)
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# remember logged models - timestamp needed in case filename didn't change (lastkckpt or custom name)
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self._logged_model_time[p] = t
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