Source code for pytorch_lightning.utilities.parsing

# Copyright The PyTorch Lightning team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Utilities used for parameter parsing."""

import copy
import inspect
import pickle
import types
from argparse import Namespace
from dataclasses import fields, is_dataclass
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union

from torch import nn
from typing_extensions import Literal

import pytorch_lightning as pl
from pytorch_lightning.utilities.rank_zero import rank_zero_warn

[docs]def str_to_bool_or_str(val: str) -> Union[str, bool]: """Possibly convert a string representation of truth to bool. Returns the input otherwise. Based on the python implementation distutils.utils.strtobool. True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values are 'n', 'no', 'f', 'false', 'off', and '0'. """ lower = val.lower() if lower in ("y", "yes", "t", "true", "on", "1"): return True if lower in ("n", "no", "f", "false", "off", "0"): return False return val
[docs]def str_to_bool(val: str) -> bool: """Convert a string representation of truth to bool. True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values are 'n', 'no', 'f', 'false', 'off', and '0'. Raises: ValueError: If ``val`` isn't in one of the aforementioned true or false values. >>> str_to_bool('YES') True >>> str_to_bool('FALSE') False """ val_converted = str_to_bool_or_str(val) if isinstance(val_converted, bool): return val_converted raise ValueError(f"invalid truth value {val_converted}")
[docs]def str_to_bool_or_int(val: str) -> Union[bool, int, str]: """Convert a string representation to truth of bool if possible, or otherwise try to convert it to an int. >>> str_to_bool_or_int("FALSE") False >>> str_to_bool_or_int("1") True >>> str_to_bool_or_int("2") 2 >>> str_to_bool_or_int("abc") 'abc' """ val_converted = str_to_bool_or_str(val) if isinstance(val_converted, bool): return val_converted try: return int(val_converted) except ValueError: return val_converted
[docs]def is_picklable(obj: object) -> bool: """Tests if an object can be pickled.""" try: pickle.dumps(obj) return True except (pickle.PickleError, AttributeError, RuntimeError): return False
[docs]def clean_namespace(hparams: Union[Dict[str, Any], Namespace]) -> None: """Removes all unpicklable entries from hparams.""" hparams_dict = hparams if isinstance(hparams, Namespace): hparams_dict = hparams.__dict__ del_attrs = [k for k, v in hparams_dict.items() if not is_picklable(v)] for k in del_attrs: rank_zero_warn(f"attribute '{k}' removed from hparams because it cannot be pickled") del hparams_dict[k]
[docs]def parse_class_init_keys(cls: Type["pl.LightningModule"]) -> Tuple[str, Optional[str], Optional[str]]: """Parse key words for standard ``self``, ``*args`` and ``**kwargs``. Examples: >>> class Model(): ... def __init__(self, hparams, *my_args, anykw=42, **my_kwargs): ... pass >>> parse_class_init_keys(Model) ('self', 'my_args', 'my_kwargs') """ init_parameters = inspect.signature(cls.__init__).parameters # docs claims the params are always ordered # init_params = list(init_parameters.values()) # self is always first n_self = init_params[0].name def _get_first_if_any( params: List[inspect.Parameter], param_type: Literal[inspect._ParameterKind.VAR_POSITIONAL, inspect._ParameterKind.VAR_KEYWORD], ) -> Optional[str]: for p in params: if p.kind == param_type: return return None n_args = _get_first_if_any(init_params, inspect.Parameter.VAR_POSITIONAL) n_kwargs = _get_first_if_any(init_params, inspect.Parameter.VAR_KEYWORD) return n_self, n_args, n_kwargs
def get_init_args(frame: types.FrameType) -> Dict[str, Any]: _, _, _, local_vars = inspect.getargvalues(frame) if "__class__" not in local_vars: return {} cls = local_vars["__class__"] init_parameters = inspect.signature(cls.__init__).parameters self_var, args_var, kwargs_var = parse_class_init_keys(cls) filtered_vars = [n for n in (self_var, args_var, kwargs_var) if n] exclude_argnames = (*filtered_vars, "__class__", "frame", "frame_args") # only collect variables that appear in the signature local_args = {k: local_vars[k] for k in init_parameters.keys()} # kwargs_var might be None => raised an error by mypy if kwargs_var: local_args.update(local_args.get(kwargs_var, {})) local_args = {k: v for k, v in local_args.items() if k not in exclude_argnames} return local_args
[docs]def collect_init_args( frame: types.FrameType, path_args: List[Dict[str, Any]], inside: bool = False, classes: Tuple[Type, ...] = (), ) -> List[Dict[str, Any]]: """Recursively collects the arguments passed to the child constructors in the inheritance tree. Args: frame: the current stack frame path_args: a list of dictionaries containing the constructor args in all parent classes inside: track if we are inside inheritance path, avoid terminating too soon classes: the classes in which to inspect the frames Return: A list of dictionaries where each dictionary contains the arguments passed to the constructor at that level. The last entry corresponds to the constructor call of the most specific class in the hierarchy. """ _, _, _, local_vars = inspect.getargvalues(frame) # frame.f_back must be of a type types.FrameType for get_init_args/collect_init_args due to mypy if not isinstance(frame.f_back, types.FrameType): return path_args if "__class__" in local_vars and (not classes or issubclass(local_vars["__class__"], classes)): local_args = get_init_args(frame) # recursive update path_args.append(local_args) return collect_init_args(frame.f_back, path_args, inside=True, classes=classes) if not inside: return collect_init_args(frame.f_back, path_args, inside, classes=classes) return path_args
def flatten_dict(source: Dict[str, Any], result: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: if result is None: result = {} for k, v in source.items(): if isinstance(v, dict): _ = flatten_dict(v, result) else: result[k] = v return result
[docs]def save_hyperparameters( obj: Any, *args: Any, ignore: Optional[Union[Sequence[str], str]] = None, frame: Optional[types.FrameType] = None ) -> None: """See :meth:`~pytorch_lightning.LightningModule.save_hyperparameters`""" if len(args) == 1 and not isinstance(args, str) and not args[0]: # args[0] is an empty container return if not frame: current_frame = inspect.currentframe() # inspect.currentframe() return type is Optional[types.FrameType]: current_frame.f_back called only if available if current_frame: frame = current_frame.f_back if not isinstance(frame, types.FrameType): raise AttributeError("There is no `frame` available while being required.") if is_dataclass(obj): init_args = { getattr(obj, for f in fields(obj)} else: init_args = {} from pytorch_lightning.core.mixins import HyperparametersMixin for local_args in collect_init_args(frame, [], classes=(HyperparametersMixin,)): init_args.update(local_args) if ignore is None: ignore = [] elif isinstance(ignore, str): ignore = [ignore] elif isinstance(ignore, (list, tuple)): ignore = [arg for arg in ignore if isinstance(arg, str)] ignore = list(set(ignore)) init_args = {k: v for k, v in init_args.items() if k not in ignore} if not args: # take all arguments hp = init_args obj._hparams_name = "kwargs" if hp else None else: # take only listed arguments in `save_hparams` isx_non_str = [i for i, arg in enumerate(args) if not isinstance(arg, str)] if len(isx_non_str) == 1: hp = args[isx_non_str[0]] cand_names = [k for k, v in init_args.items() if v == hp] obj._hparams_name = cand_names[0] if cand_names else None else: hp = {arg: init_args[arg] for arg in args if isinstance(arg, str)} obj._hparams_name = "kwargs" # `hparams` are expected here obj._set_hparams(hp) # make deep copy so there is not other runtime changes reflected obj._hparams_initial = copy.deepcopy(obj._hparams) for k, v in obj._hparams.items(): if isinstance(v, nn.Module): rank_zero_warn( f"Attribute {k!r} is an instance of `nn.Module` and is already saved during checkpointing." f" It is recommended to ignore them using `self.save_hyperparameters(ignore=[{k!r}])`." )
[docs]class AttributeDict(Dict): """Extended dictionary accessible with dot notation. >>> ad = AttributeDict({'key1': 1, 'key2': 'abc'}) >>> ad.key1 1 >>> ad.update({'my-key': 3.14}) >>> ad.update(new_key=42) >>> ad.key1 = 2 >>> ad "key1": 2 "key2": abc "my-key": 3.14 "new_key": 42 """ def __getattr__(self, key: str) -> Optional[Any]: try: return self[key] except KeyError as exp: raise AttributeError(f'Missing attribute "{key}"') from exp def __setattr__(self, key: str, val: Any) -> None: self[key] = val def __repr__(self) -> str: if not len(self): return "" max_key_length = max(len(str(k)) for k in self) tmp_name = "{:" + str(max_key_length + 3) + "s} {}" rows = [tmp_name.format(f'"{n}":', self[n]) for n in sorted(self.keys())] out = "\n".join(rows) return out
def _lightning_get_all_attr_holders(model: "pl.LightningModule", attribute: str) -> List[Any]: """Special attribute finding for Lightning. Gets all of the objects or dicts that holds attribute. Checks for attribute in model namespace, the old hparams namespace/dict, and the datamodule. """ holders: List[Any] = [] # Check if attribute in model if hasattr(model, attribute): holders.append(model) # Check if attribute in model.hparams, either namespace or dict if hasattr(model, "hparams"): if attribute in model.hparams: holders.append(model.hparams) trainer = model._trainer # Check if the attribute in datamodule (datamodule gets registered in Trainer) if trainer is not None and trainer.datamodule is not None and hasattr(trainer.datamodule, attribute): holders.append(trainer.datamodule) return holders def _lightning_get_first_attr_holder(model: "pl.LightningModule", attribute: str) -> Optional[Any]: """Special attribute finding for Lightning. Gets the object or dict that holds attribute, or None. Checks for attribute in model namespace, the old hparams namespace/dict, and the datamodule, returns the last one that has it. """ holders = _lightning_get_all_attr_holders(model, attribute) if len(holders) == 0: return None # using the last holder to preserve backwards compatibility return holders[-1]
[docs]def lightning_hasattr(model: "pl.LightningModule", attribute: str) -> bool: """Special hasattr for Lightning. Checks for attribute in model namespace, the old hparams namespace/dict, and the datamodule. """ return _lightning_get_first_attr_holder(model, attribute) is not None
[docs]def lightning_getattr(model: "pl.LightningModule", attribute: str) -> Optional[Any]: """Special getattr for Lightning. Checks for attribute in model namespace, the old hparams namespace/dict, and the datamodule. Raises: AttributeError: If ``model`` doesn't have ``attribute`` in any of model namespace, the hparams namespace/dict, and the datamodule. """ holder = _lightning_get_first_attr_holder(model, attribute) if holder is None: raise AttributeError( f"{attribute} is neither stored in the model namespace" " nor the `hparams` namespace/dict, nor the datamodule." ) if isinstance(holder, dict): return holder[attribute] return getattr(holder, attribute)
[docs]def lightning_setattr(model: "pl.LightningModule", attribute: str, value: Any) -> None: """Special setattr for Lightning. Checks for attribute in model namespace and the old hparams namespace/dict. Will also set the attribute on datamodule, if it exists. Raises: AttributeError: If ``model`` doesn't have ``attribute`` in any of model namespace, the hparams namespace/dict, and the datamodule. """ holders = _lightning_get_all_attr_holders(model, attribute) if len(holders) == 0: raise AttributeError( f"{attribute} is neither stored in the model namespace" " nor the `hparams` namespace/dict, nor the datamodule." ) for holder in holders: if isinstance(holder, dict): holder[attribute] = value else: setattr(holder, attribute, value)

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