433 lines
15 KiB
Python
433 lines
15 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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"""Abstract base class used to build new loggers."""
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import argparse
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import functools
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import operator
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from abc import ABC, abstractmethod
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from argparse import Namespace
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from functools import wraps
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from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Sequence, Union
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from weakref import ReferenceType
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import numpy as np
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
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from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_only
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def rank_zero_experiment(fn: Callable) -> Callable:
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"""Returns the real experiment on rank 0 and otherwise the DummyExperiment."""
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@wraps(fn)
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def experiment(self):
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@rank_zero_only
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def get_experiment():
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return fn(self)
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return get_experiment() or DummyExperiment()
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return experiment
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class LightningLoggerBase(ABC):
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"""Base class for experiment loggers.
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Args:
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agg_key_funcs:
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Dictionary which maps a metric name to a function, which will
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aggregate the metric values for the same steps.
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agg_default_func:
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Default function to aggregate metric values. If some metric name
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is not presented in the `agg_key_funcs` dictionary, then the
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`agg_default_func` will be used for aggregation.
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.. deprecated:: v1.6
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The parameters `agg_key_funcs` and `agg_default_func` are deprecated
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in v1.6 and will be removed in v1.8.
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Note:
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The `agg_key_funcs` and `agg_default_func` arguments are used only when
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one logs metrics with the :meth:`~LightningLoggerBase.agg_and_log_metrics` method.
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"""
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def __init__(
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self,
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agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
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agg_default_func: Optional[Callable[[Sequence[float]], float]] = None,
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):
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self._prev_step: int = -1
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self._metrics_to_agg: List[Dict[str, float]] = []
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if agg_key_funcs:
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self._agg_key_funcs = agg_key_funcs
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rank_zero_deprecation(
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"The `agg_key_funcs` parameter for `LightningLoggerBase` was deprecated in v1.6"
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" and will be removed in v1.8."
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)
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else:
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self._agg_key_funcs = {}
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if agg_default_func:
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self._agg_default_func = agg_default_func
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rank_zero_deprecation(
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"The `agg_default_func` parameter for `LightningLoggerBase` was deprecated in v1.6"
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" and will be removed in v1.8."
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)
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else:
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self._agg_default_func = np.mean
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def after_save_checkpoint(self, checkpoint_callback: "ReferenceType[ModelCheckpoint]") -> None:
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"""Called after model checkpoint callback saves a new checkpoint.
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Args:
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checkpoint_callback: the model checkpoint callback instance
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"""
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pass
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def update_agg_funcs(
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self,
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agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
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agg_default_func: Callable[[Sequence[float]], float] = np.mean,
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):
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"""Update aggregation methods.
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.. deprecated:: v1.6
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`update_agg_funcs` is deprecated in v1.6 and will be removed in v1.8.
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Args:
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agg_key_funcs:
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Dictionary which maps a metric name to a function, which will
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aggregate the metric values for the same steps.
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agg_default_func:
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Default function to aggregate metric values. If some metric name
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is not presented in the `agg_key_funcs` dictionary, then the
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`agg_default_func` will be used for aggregation.
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"""
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if agg_key_funcs:
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self._agg_key_funcs.update(agg_key_funcs)
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if agg_default_func:
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self._agg_default_func = agg_default_func
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rank_zero_deprecation(
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"`LightningLoggerBase.update_agg_funcs` was deprecated in v1.6 and will be removed in v1.8."
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)
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def agg_and_log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
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"""Aggregates and records metrics. This method doesn't log the passed metrics instantaneously, but instead
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it aggregates them and logs only if metrics are ready to be logged.
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.. deprecated:: v1.6
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This method is deprecated in v1.6 and will be removed in v1.8.
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Please use `LightningLoggerBase.log_metrics` instead.
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Args:
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metrics: Dictionary with metric names as keys and measured quantities as values
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step: Step number at which the metrics should be recorded
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"""
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self.log_metrics(metrics=metrics, step=step)
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@abstractmethod
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def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
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"""
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Records metrics.
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This method logs metrics as as soon as it received them. If you want to aggregate
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metrics for one specific `step`, use the
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:meth:`~pytorch_lightning.loggers.base.LightningLoggerBase.agg_and_log_metrics` method.
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Args:
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metrics: Dictionary with metric names as keys and measured quantities as values
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step: Step number at which the metrics should be recorded
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"""
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pass
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@abstractmethod
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def log_hyperparams(self, params: argparse.Namespace, *args, **kwargs):
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"""Record hyperparameters.
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Args:
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params: :class:`~argparse.Namespace` containing the hyperparameters
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args: Optional positional arguments, depends on the specific logger being used
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kwargs: Optional keyword arguments, depends on the specific logger being used
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"""
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def log_graph(self, model: "pl.LightningModule", input_array=None) -> None:
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"""Record model graph.
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Args:
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model: lightning model
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input_array: input passes to `model.forward`
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"""
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pass
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def save(self) -> None:
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"""Save log data."""
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def finalize(self, status: str) -> None:
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"""Do any processing that is necessary to finalize an experiment.
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Args:
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status: Status that the experiment finished with (e.g. success, failed, aborted)
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"""
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self.save()
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def close(self) -> None:
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"""Do any cleanup that is necessary to close an experiment.
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See deprecation warning below.
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.. deprecated:: v1.5
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This method is deprecated in v1.5 and will be removed in v1.7.
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Please use `LightningLoggerBase.finalize` instead.
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"""
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rank_zero_deprecation(
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"`LightningLoggerBase.close` method is deprecated in v1.5 and will be removed in v1.7."
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" Please use `LightningLoggerBase.finalize` instead."
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)
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self.save()
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@property
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def save_dir(self) -> Optional[str]:
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"""Return the root directory where experiment logs get saved, or `None` if the logger does not save data
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locally."""
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return None
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@property
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def group_separator(self):
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"""Return the default separator used by the logger to group the data into subfolders."""
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return "/"
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@property
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@abstractmethod
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def name(self) -> str:
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"""Return the experiment name."""
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@property
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@abstractmethod
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def version(self) -> Union[int, str]:
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"""Return the experiment version."""
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class LoggerCollection(LightningLoggerBase):
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"""The :class:`LoggerCollection` class is used to iterate all logging actions over the given `logger_iterable`.
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.. deprecated:: v1.6
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`LoggerCollection` is deprecated in v1.6 and will be removed in v1.8.
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Directly pass a list of loggers to the Trainer and access the list via the `trainer.loggers` attribute.
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Args:
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logger_iterable: An iterable collection of loggers
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"""
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def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
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super().__init__()
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self._logger_iterable = logger_iterable
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rank_zero_deprecation(
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"`LoggerCollection` is deprecated in v1.6 and will be removed in v1.8. Directly pass a list of loggers"
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" to the Trainer and access the list via the `trainer.loggers` attribute."
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)
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def __getitem__(self, index: int) -> LightningLoggerBase:
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return list(self._logger_iterable)[index]
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def after_save_checkpoint(self, checkpoint_callback: "ReferenceType[ModelCheckpoint]") -> None:
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for logger in self._logger_iterable:
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logger.after_save_checkpoint(checkpoint_callback)
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def update_agg_funcs(
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self,
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agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
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agg_default_func: Callable[[Sequence[float]], float] = np.mean,
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):
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for logger in self._logger_iterable:
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logger.update_agg_funcs(agg_key_funcs, agg_default_func)
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@property
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def experiment(self) -> List[Any]:
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"""Returns a list of experiment objects for all the loggers in the logger collection."""
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return [logger.experiment for logger in self._logger_iterable]
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def agg_and_log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
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for logger in self._logger_iterable:
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logger.agg_and_log_metrics(metrics=metrics, step=step)
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def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
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for logger in self._logger_iterable:
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logger.log_metrics(metrics=metrics, step=step)
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def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
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for logger in self._logger_iterable:
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logger.log_hyperparams(params)
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def log_graph(self, model: "pl.LightningModule", input_array=None) -> None:
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for logger in self._logger_iterable:
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logger.log_graph(model, input_array)
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def log_text(self, *args, **kwargs) -> None:
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for logger in self._logger_iterable:
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logger.log_text(*args, **kwargs)
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def log_image(self, *args, **kwargs) -> None:
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for logger in self._logger_iterable:
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logger.log_image(*args, **kwargs)
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def save(self) -> None:
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for logger in self._logger_iterable:
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logger.save()
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def finalize(self, status: str) -> None:
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for logger in self._logger_iterable:
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logger.finalize(status)
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def close(self) -> None:
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"""
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.. deprecated:: v1.5
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This method is deprecated in v1.5 and will be removed in v1.7.
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Please use `LoggerCollection.finalize` instead.
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"""
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rank_zero_deprecation(
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"`LoggerCollection.close` method is deprecated in v1.5 and will be removed in v1.7."
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" Please use `LoggerCollection.finalize` instead."
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)
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for logger in self._logger_iterable:
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logger.close()
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@property
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def save_dir(self) -> Optional[str]:
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"""Returns ``None`` as checkpoints should be saved to default / chosen location when using multiple
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loggers."""
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# Checkpoints should be saved to default / chosen location when using multiple loggers
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return None
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@property
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def name(self) -> str:
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"""Returns the unique experiment names for all the loggers in the logger collection joined by an
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underscore."""
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return "_".join(dict.fromkeys(str(logger.name) for logger in self._logger_iterable))
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@property
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def version(self) -> str:
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"""Returns the unique experiment versions for all the loggers in the logger collection joined by an
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underscore."""
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return "_".join(dict.fromkeys(str(logger.version) for logger in self._logger_iterable))
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class DummyExperiment:
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"""Dummy experiment."""
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def nop(self, *args, **kw):
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pass
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def __getattr__(self, _):
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return self.nop
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def __getitem__(self, idx) -> "DummyExperiment":
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# enables self.logger.experiment[0].add_image(...)
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return self
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def __setitem__(self, *args, **kwargs) -> None:
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pass
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class DummyLogger(LightningLoggerBase):
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"""Dummy logger for internal use.
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It is useful if we want to disable user's logger for a feature, but still ensure that user code can run
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"""
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def __init__(self):
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super().__init__()
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self._experiment = DummyExperiment()
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@property
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def experiment(self) -> DummyExperiment:
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"""Return the experiment object associated with this logger."""
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return self._experiment
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def log_metrics(self, *args, **kwargs) -> None:
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pass
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def log_hyperparams(self, *args, **kwargs) -> None:
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pass
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@property
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def name(self) -> str:
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"""Return the experiment name."""
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return ""
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@property
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def version(self) -> str:
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"""Return the experiment version."""
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return ""
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def __getitem__(self, idx) -> "DummyLogger":
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# enables self.logger[0].experiment.add_image(...)
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return self
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def __iter__(self):
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# if DummyLogger is substituting a logger collection, pretend it is empty
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yield from ()
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def merge_dicts(
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dicts: Sequence[Mapping],
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agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
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default_func: Callable[[Sequence[float]], float] = np.mean,
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) -> Dict:
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"""Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given
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function.
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Args:
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dicts:
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Sequence of dictionaries to be merged.
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agg_key_funcs:
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Mapping from key name to function. This function will aggregate a
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list of values, obtained from the same key of all dictionaries.
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If some key has no specified aggregation function, the default one
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will be used. Default is: ``None`` (all keys will be aggregated by the
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default function).
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default_func:
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Default function to aggregate keys, which are not presented in the
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`agg_key_funcs` map.
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Returns:
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Dictionary with merged values.
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Examples:
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>>> import pprint
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>>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1, 'd': {'d1': 1, 'd3': 3}}
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>>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1, 'd': {'d1': 2, 'd2': 3}}
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>>> d3 = {'a': 1.1, 'v': 2.3, 'd': {'d3': 3, 'd4': {'d5': 1}}}
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>>> dflt_func = min
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>>> agg_funcs = {'a': np.mean, 'v': max, 'd': {'d1': sum}}
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>>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func))
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{'a': 1.3,
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'b': 2.0,
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'c': 1,
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'd': {'d1': 3, 'd2': 3, 'd3': 3, 'd4': {'d5': 1}},
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'v': 2.3}
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"""
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agg_key_funcs = agg_key_funcs or {}
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keys = list(functools.reduce(operator.or_, [set(d.keys()) for d in dicts]))
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d_out = {}
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for k in keys:
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fn = agg_key_funcs.get(k)
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values_to_agg = [v for v in [d_in.get(k) for d_in in dicts] if v is not None]
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if isinstance(values_to_agg[0], dict):
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d_out[k] = merge_dicts(values_to_agg, fn, default_func)
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else:
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d_out[k] = (fn or default_func)(values_to_agg)
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return d_out
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