477 lines
16 KiB
Python
477 lines
16 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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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, MutableMapping, Optional, Sequence, Tuple, Union
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import numpy as np
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import torch
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.utilities import rank_zero_only
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class LightningLoggerBase(ABC):
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"""
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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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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: Callable[[Sequence[float]], float] = np.mean
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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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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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"""
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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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"""
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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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Step 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._reduce_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 _reduce_agg_metrics(self):
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"""Aggregate accumulated metrics."""
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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 _finalize_agg_metrics(self):
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"""This shall be called before save/close."""
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agg_step, metrics_to_log = self._reduce_agg_metrics()
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self._metrics_to_agg = []
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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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def agg_and_log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
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"""
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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:
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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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"""
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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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@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 _sanitize_callable_params(params: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Sanitize callable params dict, e.g. ``{'a': <function_**** at 0x****>} -> {'a': 'function_****'}``.
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Args:
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params: Dictionary containing the hyperparameters
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Returns:
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dictionary with all callables sanitized
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"""
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def _sanitize_callable(val):
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# Give them one chance to return a value. Don't go rabbit hole of recursive call
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if isinstance(val, Callable):
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try:
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_val = val()
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if isinstance(_val, Callable):
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return val.__name__
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return _val
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except Exception:
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return getattr(val, "__name__", None)
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return val
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return {key: _sanitize_callable(val) for key, val in params.items()}
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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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"""
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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 containing the hyperparameters
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delimiter: Delimiter to express the hierarchy. Defaults to ``'/'``.
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Returns:
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Flattened 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, MutableMapping):
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for key, value in input_dict.items():
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if isinstance(value, (MutableMapping, 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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"""
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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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"""
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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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"""
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def log_graph(self, model: LightningModule, input_array=None) -> None:
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"""
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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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self._finalize_agg_metrics()
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def finalize(self, status: str) -> None:
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"""
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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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self.save()
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@property
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def save_dir(self) -> Optional[str]:
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"""
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Return the root directory where experiment logs get saved, or `None` if the logger does not
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save data locally.
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"""
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return None
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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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"""
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The :class:`LoggerCollection` class is used to iterate all logging actions over
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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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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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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, 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, 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: 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 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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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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# 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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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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class DummyExperiment(object):
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""" Dummy experiment """
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def nop(*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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class DummyLogger(LightningLoggerBase):
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""" Dummy logger for internal use. Is usefull if we want to disable users
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logger for a feature, but still secure that users code can run """
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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):
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return self._experiment
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def log_metrics(self, metrics, step):
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pass
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def log_hyperparams(self, params):
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pass
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@property
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def name(self):
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pass
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@property
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def version(self):
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pass
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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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"""
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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, '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 dict()
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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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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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