249 lines
8.3 KiB
Python
249 lines
8.3 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 inspect
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import pickle
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from argparse import Namespace
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from typing import Dict, Union
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from pytorch_lightning.utilities import rank_zero_warn
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def str_to_bool_or_str(val: str) -> Union[str, bool]:
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"""Possibly convert a string representation of truth to bool.
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Returns the input otherwise.
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Based on the python implementation distutils.utils.strtobool
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True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
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are 'n', 'no', 'f', 'false', 'off', and '0'.
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"""
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lower = val.lower()
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if lower in ('y', 'yes', 't', 'true', 'on', '1'):
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return True
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elif lower in ('n', 'no', 'f', 'false', 'off', '0'):
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return False
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else:
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return val
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def str_to_bool(val: str) -> bool:
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"""Convert a string representation of truth to bool.
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True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
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are 'n', 'no', 'f', 'false', 'off', and '0'. Raises ValueError if
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'val' is anything else.
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>>> str_to_bool('YES')
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True
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>>> str_to_bool('FALSE')
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False
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"""
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val = str_to_bool_or_str(val)
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if isinstance(val, bool):
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return val
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raise ValueError(f'invalid truth value {val}')
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def is_picklable(obj: object) -> bool:
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"""Tests if an object can be pickled"""
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try:
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pickle.dumps(obj)
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return True
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except pickle.PicklingError:
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return False
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def clean_namespace(hparams):
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"""Removes all unpicklable entries from hparams"""
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hparams_dict = hparams
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if isinstance(hparams, Namespace):
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hparams_dict = hparams.__dict__
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del_attrs = [k for k, v in hparams_dict.items() if not is_picklable(v)]
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for k in del_attrs:
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rank_zero_warn(f"attribute '{k}' removed from hparams because it cannot be pickled", UserWarning)
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del hparams_dict[k]
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def get_init_args(frame) -> dict:
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_, _, _, local_vars = inspect.getargvalues(frame)
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if '__class__' not in local_vars:
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return
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cls = local_vars['__class__']
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spec = inspect.getfullargspec(cls.__init__)
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init_parameters = inspect.signature(cls.__init__).parameters
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self_identifier = spec.args[0] # "self" unless user renames it (always first arg)
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varargs_identifier = spec.varargs # by convention this is named "*args"
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kwargs_identifier = spec.varkw # by convention this is named "**kwargs"
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exclude_argnames = (
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varargs_identifier, kwargs_identifier, self_identifier, '__class__', 'frame', 'frame_args'
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)
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# only collect variables that appear in the signature
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local_args = {k: local_vars[k] for k in init_parameters.keys()}
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local_args.update(local_args.get(kwargs_identifier, {}))
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local_args = {k: v for k, v in local_args.items() if k not in exclude_argnames}
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return local_args
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def collect_init_args(frame, path_args: list, inside: bool = False) -> list:
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"""
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Recursively collects the arguments passed to the child constructors in the inheritance tree.
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Args:
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frame: the current stack frame
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path_args: a list of dictionaries containing the constructor args in all parent classes
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inside: track if we are inside inheritance path, avoid terminating too soon
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Return:
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A list of dictionaries where each dictionary contains the arguments passed to the
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constructor at that level. The last entry corresponds to the constructor call of the
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most specific class in the hierarchy.
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"""
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_, _, _, local_vars = inspect.getargvalues(frame)
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if '__class__' in local_vars:
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local_args = get_init_args(frame)
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# recursive update
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path_args.append(local_args)
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return collect_init_args(frame.f_back, path_args, inside=True)
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elif not inside:
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return collect_init_args(frame.f_back, path_args, inside)
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else:
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return path_args
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def flatten_dict(source, result=None):
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if result is None:
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result = {}
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for k, v in source.items():
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if isinstance(v, dict):
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_ = flatten_dict(v, result)
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else:
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result[k] = v
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return result
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class AttributeDict(Dict):
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"""Extended dictionary accesisable with dot notation.
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>>> ad = AttributeDict({'key1': 1, 'key2': 'abc'})
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>>> ad.key1
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1
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>>> ad.update({'my-key': 3.14})
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>>> ad.update(mew_key=42)
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>>> ad.key1 = 2
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>>> ad
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"key1": 2
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"key2": abc
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"mew_key": 42
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"my-key": 3.14
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"""
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def __getattr__(self, key):
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try:
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return self[key]
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except KeyError:
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raise AttributeError(f'Missing attribute "{key}"')
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def __setattr__(self, key, val):
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self[key] = val
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def __repr__(self):
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if not len(self):
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return ""
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max_key_length = max([len(str(k)) for k in self])
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tmp_name = '{:' + str(max_key_length + 3) + 's} {}'
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rows = [tmp_name.format(f'"{n}":', self[n]) for n in sorted(self.keys())]
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out = '\n'.join(rows)
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return out
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def lightning_hasattr(model, attribute):
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""" Special hasattr for lightning. Checks for attribute in model namespace,
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the old hparams namespace/dict, and the datamodule. """
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trainer = model.trainer
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# Check if attribute in model
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if hasattr(model, attribute):
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attr = True
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# Check if attribute in model.hparams, either namespace or dict
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elif hasattr(model, 'hparams'):
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if isinstance(model.hparams, dict):
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attr = attribute in model.hparams
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else:
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attr = hasattr(model.hparams, attribute)
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# Check if the attribute in datamodule (datamodule gets registered in Trainer)
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elif trainer is not None and trainer.datamodule is not None and hasattr(trainer.datamodule, attribute):
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attr = getattr(trainer.datamodule, attribute)
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else:
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attr = False
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return attr
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def lightning_getattr(model, attribute):
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""" Special getattr for lightning. Checks for attribute in model namespace,
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the old hparams namespace/dict, and the datamodule. """
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trainer = model.trainer
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# Check if attribute in model
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if hasattr(model, attribute):
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attr = getattr(model, attribute)
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# Check if attribute in model.hparams, either namespace or dict
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elif hasattr(model, 'hparams'):
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if isinstance(model.hparams, dict):
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attr = model.hparams[attribute]
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else:
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attr = getattr(model.hparams, attribute)
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# Check if the attribute in datamodule (datamodule gets registered in Trainer)
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elif trainer is not None and trainer.datamodule is not None and hasattr(trainer.datamodule, attribute):
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attr = getattr(trainer.datamodule, attribute)
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else:
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raise ValueError(f'{attribute} is neither stored in the model namespace'
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' nor the `hparams` namespace/dict, nor the datamodule.')
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return attr
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def lightning_setattr(model, attribute, value):
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""" Special setattr for lightning. Checks for attribute in model namespace
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and the old hparams namespace/dict.
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Will also set the attribute on datamodule, if it exists.
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"""
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if not lightning_hasattr(model, attribute):
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raise ValueError(f'{attribute} is neither stored in the model namespace'
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' nor the `hparams` namespace/dict, nor the datamodule.')
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trainer = model.trainer
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# Check if attribute in model
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if hasattr(model, attribute):
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setattr(model, attribute, value)
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# Check if attribute in model.hparams, either namespace or dict
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elif hasattr(model, 'hparams'):
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if isinstance(model.hparams, dict):
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model.hparams[attribute] = value
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else:
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setattr(model.hparams, attribute, value)
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# Check if the attribute in datamodule (datamodule gets registered in Trainer)
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if trainer is not None and trainer.datamodule is not None and hasattr(trainer.datamodule, attribute):
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setattr(trainer.datamodule, attribute, value)
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