351 lines
12 KiB
Python
351 lines
12 KiB
Python
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 Union, Optional, Dict, Iterable, Any, Callable, List, Sequence, Mapping, Tuple
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import numpy as np
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import torch
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def rank_zero_only(fn: Callable):
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"""Decorate a logger method to run it only on the process with rank 0.
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Args:
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fn: Function to decorate
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"""
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@wraps(fn)
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def wrapped_fn(self, *args, **kwargs):
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if self.rank == 0:
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fn(self, *args, **kwargs)
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return wrapped_fn
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class LightningLoggerBase(ABC):
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"""Base class for experiment loggers."""
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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: Callable[[Sequence[float]], float] = np.mean
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):
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"""
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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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Notes:
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`agg_key_funcs` and `agg_default_func` are used only when one logs metrics with
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`LightningLoggerBase.agg_and_log_metrics` method.
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"""
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self._rank = 0
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self._prev_step = -1
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self._metrics_to_agg: List[Dict[str, float]] = []
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self._agg_key_funcs = agg_key_funcs if agg_key_funcs else {}
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self._agg_default_func = agg_default_func
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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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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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@property
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@abstractmethod
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def experiment(self) -> Any:
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"""Return the experiment object associated with this logger"""
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def _aggregate_metrics(
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self, metrics: Dict[str, float], step: Optional[int] = None
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) -> Tuple[int, Optional[Dict[str, float]]]:
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"""Aggregates metrics.
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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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Returns:
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sStep and aggregated metrics. The return value could be None. In such case, metrics
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are added to the aggregation list, but not aggregated yet.
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"""
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# if you still receiving metric from the same step, just accumulate it
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if step == self._prev_step:
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self._metrics_to_agg.append(metrics)
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return step, None
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# compute the metrics
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agg_step, agg_mets = self._finalize_agg_metrics()
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# as new step received reset accumulator
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self._metrics_to_agg = [metrics]
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self._prev_step = step
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return agg_step, agg_mets
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def _finalize_agg_metrics(self):
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"""Aggregate accumulated metrics. This shall be called in close."""
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# compute the metrics
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if not self._metrics_to_agg:
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agg_mets = None
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elif len(self._metrics_to_agg) == 1:
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agg_mets = self._metrics_to_agg[0]
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else:
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agg_mets = merge_dicts(self._metrics_to_agg, self._agg_key_funcs, self._agg_default_func)
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return self._prev_step, agg_mets
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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.
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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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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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agg_step, metrics_to_log = self._aggregate_metrics(metrics=metrics, step=step)
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if metrics_to_log is not None:
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self.log_metrics(metrics=metrics_to_log, step=agg_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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"""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 `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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@staticmethod
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def _convert_params(params: Union[Dict[str, Any], Namespace]) -> Dict[str, Any]:
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# in case converting from namespace
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if isinstance(params, Namespace):
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params = vars(params)
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if params is None:
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params = {}
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return params
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@staticmethod
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def _flatten_dict(params: Dict[str, Any], delimiter: str = '/') -> Dict[str, Any]:
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"""Flatten hierarchical dict e.g. {'a': {'b': 'c'}} -> {'a/b': 'c'}.
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Args:
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params: Dictionary contains hparams
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delimiter: Delimiter to express the hierarchy. Defaults to '/'.
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Returns:
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Flatten dict.
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Examples:
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>>> LightningLoggerBase._flatten_dict({'a': {'b': 'c'}})
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{'a/b': 'c'}
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>>> LightningLoggerBase._flatten_dict({'a': {'b': 123}})
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{'a/b': 123}
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"""
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def _dict_generator(input_dict, prefixes=None):
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prefixes = prefixes[:] if prefixes else []
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if isinstance(input_dict, dict):
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for key, value in input_dict.items():
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if isinstance(value, (dict, Namespace)):
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value = vars(value) if isinstance(value, Namespace) else value
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for d in _dict_generator(value, prefixes + [key]):
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yield d
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else:
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yield prefixes + [key, value if value is not None else str(None)]
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else:
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yield prefixes + [input_dict if input_dict is None else str(input_dict)]
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return {delimiter.join(keys): val for *keys, val in _dict_generator(params)}
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@staticmethod
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def _sanitize_params(params: Dict[str, Any]) -> Dict[str, Any]:
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"""Returns params with non-primitvies converted to strings for logging
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>>> params = {"float": 0.3,
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... "int": 1,
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... "string": "abc",
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... "bool": True,
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... "list": [1, 2, 3],
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... "namespace": Namespace(foo=3),
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... "layer": torch.nn.BatchNorm1d}
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>>> import pprint
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>>> pprint.pprint(LightningLoggerBase._sanitize_params(params)) # doctest: +NORMALIZE_WHITESPACE
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{'bool': True,
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'float': 0.3,
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'int': 1,
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'layer': "<class 'torch.nn.modules.batchnorm.BatchNorm1d'>",
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'list': '[1, 2, 3]',
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'namespace': 'Namespace(foo=3)',
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'string': 'abc'}
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"""
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return {k: v if type(v) in [bool, int, float, str, torch.Tensor] else str(v) for k, v in params.items()}
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@abstractmethod
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def log_hyperparams(self, params: argparse.Namespace):
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"""Record hyperparameters.
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Args:
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params: argparse.Namespace containing the hyperparameters
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"""
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def save(self) -> None:
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"""Save log data."""
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pass
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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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pass
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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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agg_step, metrics_to_log = self._finalize_agg_metrics()
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if metrics_to_log is not None:
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self.log_metrics(metrics=metrics_to_log, step=agg_step)
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@property
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def rank(self) -> int:
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"""Process rank. In general, metrics should only be logged by the process with rank 0."""
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return self._rank
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@rank.setter
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def rank(self, value: int) -> None:
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"""Set the process rank."""
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self._rank = value
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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 `LoggerCollection` class is used to iterate all logging actions over the given `logger_iterable`.
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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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def __getitem__(self, index: int) -> LightningLoggerBase:
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return [logger for logger in self._logger_iterable][index]
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@property
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def experiment(self) -> List[Any]:
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return [logger.experiment for logger in self._logger_iterable]
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def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
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[logger.log_metrics(metrics, step) for logger in self._logger_iterable]
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def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
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[logger.log_hyperparams(params) for logger in self._logger_iterable]
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def save(self) -> None:
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[logger.save() for logger in self._logger_iterable]
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def finalize(self, status: str) -> None:
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[logger.finalize(status) for logger in self._logger_iterable]
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def close(self) -> None:
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[logger.close() for logger in self._logger_iterable]
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@LightningLoggerBase.rank.setter
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def rank(self, value: int) -> None:
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self._rank = value
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for logger in self._logger_iterable:
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logger.rank = value
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@property
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def name(self) -> str:
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return '_'.join([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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return '_'.join([str(logger.version) for logger in self._logger_iterable])
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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
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same keys with some given 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}
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>>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1}
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>>> d3 = {'a': 1.1, 'v': 2.3}
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>>> dflt_func = min
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>>> agg_funcs = {'a': np.mean, 'v': max}
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>>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func))
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{'a': 1.3, 'b': 2.0, 'c': 1, 'v': 2.3}
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"""
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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, default_func) if agg_key_funcs else default_func
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agg_val = fn([v for v in [d_in.get(k) for d_in in dicts] if v is not None])
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d_out[k] = agg_val
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return d_out
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