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cc3b74b5e8
* [Fix] Fix lint * [Fix] Fix lint * Update mmengine/dist/utils.py Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> --------- Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
544 lines
17 KiB
Python
544 lines
17 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import collections.abc
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import functools
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import itertools
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import logging
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import re
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import subprocess
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import textwrap
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import warnings
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from collections import abc
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from importlib import import_module
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from inspect import getfullargspec, ismodule
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from itertools import repeat
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from typing import Any, Callable, Optional, Type, Union
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# From PyTorch internals
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def _ntuple(n):
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def parse(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return tuple(repeat(x, n))
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return parse
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to_1tuple = _ntuple(1)
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to_2tuple = _ntuple(2)
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to_3tuple = _ntuple(3)
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to_4tuple = _ntuple(4)
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to_ntuple = _ntuple
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def is_str(x):
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"""Whether the input is an string instance.
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Note: This method is deprecated since python 2 is no longer supported.
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"""
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return isinstance(x, str)
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def import_modules_from_strings(imports, allow_failed_imports=False):
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"""Import modules from the given list of strings.
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Args:
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imports (list | str | None): The given module names to be imported.
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allow_failed_imports (bool): If True, the failed imports will return
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None. Otherwise, an ImportError is raise. Defaults to False.
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Returns:
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list[module] | module | None: The imported modules.
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Examples:
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>>> osp, sys = import_modules_from_strings(
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... ['os.path', 'sys'])
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>>> import os.path as osp_
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>>> import sys as sys_
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>>> assert osp == osp_
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>>> assert sys == sys_
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"""
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if not imports:
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return
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single_import = False
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if isinstance(imports, str):
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single_import = True
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imports = [imports]
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if not isinstance(imports, list):
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raise TypeError(
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f'custom_imports must be a list but got type {type(imports)}')
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imported = []
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for imp in imports:
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if not isinstance(imp, str):
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raise TypeError(
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f'{imp} is of type {type(imp)} and cannot be imported.')
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try:
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imported_tmp = import_module(imp)
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except ImportError:
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if allow_failed_imports:
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warnings.warn(f'{imp} failed to import and is ignored.',
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UserWarning)
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imported_tmp = None
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else:
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raise ImportError(f'Failed to import {imp}')
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imported.append(imported_tmp)
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if single_import:
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imported = imported[0]
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return imported
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def iter_cast(inputs, dst_type, return_type=None):
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"""Cast elements of an iterable object into some type.
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Args:
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inputs (Iterable): The input object.
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dst_type (type): Destination type.
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return_type (type, optional): If specified, the output object will be
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converted to this type, otherwise an iterator.
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Returns:
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iterator or specified type: The converted object.
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"""
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if not isinstance(inputs, abc.Iterable):
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raise TypeError('inputs must be an iterable object')
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if not isinstance(dst_type, type):
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raise TypeError('"dst_type" must be a valid type')
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out_iterable = map(dst_type, inputs)
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if return_type is None:
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return out_iterable
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else:
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return return_type(out_iterable)
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def list_cast(inputs, dst_type):
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"""Cast elements of an iterable object into a list of some type.
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A partial method of :func:`iter_cast`.
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"""
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return iter_cast(inputs, dst_type, return_type=list)
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def tuple_cast(inputs, dst_type):
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"""Cast elements of an iterable object into a tuple of some type.
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A partial method of :func:`iter_cast`.
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"""
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return iter_cast(inputs, dst_type, return_type=tuple)
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def is_seq_of(seq: Any,
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expected_type: Union[Type, tuple],
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seq_type: Optional[Type] = None) -> bool:
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"""Check whether it is a sequence of some type.
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Args:
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seq (Sequence): The sequence to be checked.
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expected_type (type or tuple): Expected type of sequence items.
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seq_type (type, optional): Expected sequence type. Defaults to None.
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Returns:
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bool: Return True if ``seq`` is valid else False.
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Examples:
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>>> from mmengine.utils import is_seq_of
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>>> seq = ['a', 'b', 'c']
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>>> is_seq_of(seq, str)
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True
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>>> is_seq_of(seq, int)
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False
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"""
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if seq_type is None:
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exp_seq_type = abc.Sequence
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else:
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assert isinstance(seq_type, type)
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exp_seq_type = seq_type
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if not isinstance(seq, exp_seq_type):
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return False
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for item in seq:
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if not isinstance(item, expected_type):
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return False
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return True
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def is_list_of(seq, expected_type):
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"""Check whether it is a list of some type.
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A partial method of :func:`is_seq_of`.
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"""
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return is_seq_of(seq, expected_type, seq_type=list)
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def is_tuple_of(seq, expected_type):
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"""Check whether it is a tuple of some type.
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A partial method of :func:`is_seq_of`.
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"""
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return is_seq_of(seq, expected_type, seq_type=tuple)
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def slice_list(in_list, lens):
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"""Slice a list into several sub lists by a list of given length.
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Args:
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in_list (list): The list to be sliced.
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lens(int or list): The expected length of each out list.
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Returns:
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list: A list of sliced list.
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"""
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if isinstance(lens, int):
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assert len(in_list) % lens == 0
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lens = [lens] * int(len(in_list) / lens)
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if not isinstance(lens, list):
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raise TypeError('"indices" must be an integer or a list of integers')
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elif sum(lens) != len(in_list):
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raise ValueError('sum of lens and list length does not '
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f'match: {sum(lens)} != {len(in_list)}')
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out_list = []
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idx = 0
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for i in range(len(lens)):
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out_list.append(in_list[idx:idx + lens[i]])
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idx += lens[i]
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return out_list
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def concat_list(in_list):
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"""Concatenate a list of list into a single list.
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Args:
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in_list (list): The list of list to be merged.
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Returns:
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list: The concatenated flat list.
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"""
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return list(itertools.chain(*in_list))
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def apply_to(data: Any, expr: Callable, apply_func: Callable):
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"""Apply function to each element in dict, list or tuple that matches with
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the expression.
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For examples, if you want to convert each element in a list of dict from
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`np.ndarray` to `Tensor`. You can use the following code:
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Examples:
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>>> from mmengine.utils import apply_to
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>>> import numpy as np
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>>> import torch
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>>> data = dict(array=[np.array(1)]) # {'array': [array(1)]}
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>>> result = apply_to(data, lambda x: isinstance(x, np.ndarray), lambda x: torch.from_numpy(x))
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>>> print(result) # {'array': [tensor(1)]}
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Args:
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data (Any): Data to be applied.
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expr (Callable): Expression to tell which data should be applied with
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the function. It should return a boolean.
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apply_func (Callable): Function applied to data.
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Returns:
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Any: The data after applying.
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""" # noqa: E501
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if isinstance(data, dict):
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# Keep the original dict type
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res = type(data)()
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for key, value in data.items():
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res[key] = apply_to(value, expr, apply_func)
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return res
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elif isinstance(data, tuple) and hasattr(data, '_fields'):
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# namedtuple
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return type(data)(*(apply_to(sample, expr, apply_func) for sample in data)) # type: ignore # noqa: E501 # yapf:disable
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elif isinstance(data, (tuple, list)):
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return type(data)(apply_to(sample, expr, apply_func) for sample in data) # type: ignore # noqa: E501 # yapf:disable
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elif expr(data):
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return apply_func(data)
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else:
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return data
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def check_prerequisites(
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prerequisites,
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checker,
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msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
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'found, please install them first.'): # yapf: disable
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"""A decorator factory to check if prerequisites are satisfied.
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Args:
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prerequisites (str of list[str]): Prerequisites to be checked.
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checker (callable): The checker method that returns True if a
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prerequisite is meet, False otherwise.
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msg_tmpl (str): The message template with two variables.
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Returns:
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decorator: A specific decorator.
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"""
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def wrap(func):
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@functools.wraps(func)
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def wrapped_func(*args, **kwargs):
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requirements = [prerequisites] if isinstance(
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prerequisites, str) else prerequisites
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missing = []
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for item in requirements:
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if not checker(item):
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missing.append(item)
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if missing:
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print(msg_tmpl.format(', '.join(missing), func.__name__))
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raise RuntimeError('Prerequisites not meet.')
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else:
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return func(*args, **kwargs)
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return wrapped_func
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return wrap
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def _check_py_package(package):
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try:
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import_module(package)
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except ImportError:
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return False
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else:
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return True
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def _check_executable(cmd):
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if subprocess.call(f'which {cmd}', shell=True) != 0:
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return False
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else:
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return True
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def requires_package(prerequisites):
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"""A decorator to check if some python packages are installed.
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Example:
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>>> @requires_package('numpy')
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>>> func(arg1, args):
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>>> return numpy.zeros(1)
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array([0.])
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>>> @requires_package(['numpy', 'non_package'])
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>>> func(arg1, args):
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>>> return numpy.zeros(1)
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ImportError
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"""
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return check_prerequisites(prerequisites, checker=_check_py_package)
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def requires_executable(prerequisites):
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"""A decorator to check if some executable files are installed.
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Example:
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>>> @requires_executable('ffmpeg')
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>>> func(arg1, args):
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>>> print(1)
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1
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"""
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return check_prerequisites(prerequisites, checker=_check_executable)
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def deprecated_api_warning(name_dict: dict,
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cls_name: Optional[str] = None) -> Callable:
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"""A decorator to check if some arguments are deprecate and try to replace
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deprecate src_arg_name to dst_arg_name.
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Args:
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name_dict(dict):
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key (str): Deprecate argument names.
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val (str): Expected argument names.
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Returns:
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func: New function.
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"""
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def api_warning_wrapper(old_func):
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@functools.wraps(old_func)
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def new_func(*args, **kwargs):
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# get the arg spec of the decorated method
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args_info = getfullargspec(old_func)
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# get name of the function
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func_name = old_func.__name__
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if cls_name is not None:
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func_name = f'{cls_name}.{func_name}'
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if args:
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arg_names = args_info.args[:len(args)]
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for src_arg_name, dst_arg_name in name_dict.items():
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if src_arg_name in arg_names:
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warnings.warn(
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f'"{src_arg_name}" is deprecated in '
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f'`{func_name}`, please use "{dst_arg_name}" '
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'instead', DeprecationWarning)
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arg_names[arg_names.index(src_arg_name)] = dst_arg_name
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if kwargs:
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for src_arg_name, dst_arg_name in name_dict.items():
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if src_arg_name in kwargs:
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assert dst_arg_name not in kwargs, (
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f'The expected behavior is to replace '
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f'the deprecated key `{src_arg_name}` to '
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f'new key `{dst_arg_name}`, but got them '
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f'in the arguments at the same time, which '
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f'is confusing. `{src_arg_name} will be '
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f'deprecated in the future, please '
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f'use `{dst_arg_name}` instead.')
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warnings.warn(
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f'"{src_arg_name}" is deprecated in '
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f'`{func_name}`, please use "{dst_arg_name}" '
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'instead', DeprecationWarning)
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kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
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# apply converted arguments to the decorated method
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output = old_func(*args, **kwargs)
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return output
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return new_func
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return api_warning_wrapper
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def is_method_overridden(method: str, base_class: type,
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derived_class: Union[type, Any]) -> bool:
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"""Check if a method of base class is overridden in derived class.
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Args:
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method (str): the method name to check.
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base_class (type): the class of the base class.
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derived_class (type | Any): the class or instance of the derived class.
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"""
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assert isinstance(base_class, type), \
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"base_class doesn't accept instance, Please pass class instead."
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if not isinstance(derived_class, type):
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derived_class = derived_class.__class__
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base_method = getattr(base_class, method)
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derived_method = getattr(derived_class, method)
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return derived_method != base_method
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def has_method(obj: object, method: str) -> bool:
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"""Check whether the object has a method.
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Args:
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method (str): The method name to check.
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obj (object): The object to check.
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Returns:
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bool: True if the object has the method else False.
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"""
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return hasattr(obj, method) and callable(getattr(obj, method))
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def deprecated_function(since: str, removed_in: str,
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instructions: str) -> Callable:
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"""Marks functions as deprecated.
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Throw a warning when a deprecated function is called, and add a note in the
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docstring. Modified from https://github.com/pytorch/pytorch/blob/master/torch/onnx/_deprecation.py
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Args:
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since (str): The version when the function was first deprecated.
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removed_in (str): The version when the function will be removed.
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instructions (str): The action users should take.
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Returns:
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Callable: A new function, which will be deprecated soon.
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""" # noqa: E501
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from mmengine import print_log
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def decorator(function):
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@functools.wraps(function)
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def wrapper(*args, **kwargs):
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print_log(
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f"'{function.__module__}.{function.__name__}' "
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f'is deprecated in version {since} and will be '
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f'removed in version {removed_in}. Please {instructions}.',
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logger='current',
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level=logging.WARNING,
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)
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return function(*args, **kwargs)
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indent = ' '
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# Add a deprecation note to the docstring.
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docstring = function.__doc__ or ''
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# Add a note to the docstring.
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deprecation_note = textwrap.dedent(f"""\
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.. deprecated:: {since}
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Deprecated and will be removed in version {removed_in}.
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Please {instructions}.
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""")
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# Split docstring at first occurrence of newline
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pattern = '\n\n'
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summary_and_body = re.split(pattern, docstring, 1)
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if len(summary_and_body) > 1:
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summary, body = summary_and_body
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body = textwrap.indent(textwrap.dedent(body), indent)
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summary = '\n'.join(
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[textwrap.dedent(string) for string in summary.split('\n')])
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summary = textwrap.indent(summary, prefix=indent)
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# Dedent the body. We cannot do this with the presence of the
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# summary because the body contains leading whitespaces when the
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# summary does not.
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new_docstring_parts = [
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deprecation_note, '\n\n', summary, '\n\n', body
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]
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else:
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summary = summary_and_body[0]
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summary = '\n'.join(
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[textwrap.dedent(string) for string in summary.split('\n')])
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summary = textwrap.indent(summary, prefix=indent)
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new_docstring_parts = [deprecation_note, '\n\n', summary]
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wrapper.__doc__ = ''.join(new_docstring_parts)
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return wrapper
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return decorator
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def get_object_from_string(obj_name: str):
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"""Get object from name.
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Args:
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obj_name (str): The name of the object.
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Examples:
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>>> get_object_from_string('torch.optim.sgd.SGD')
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>>> torch.optim.sgd.SGD
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"""
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parts = iter(obj_name.split('.'))
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module_name = next(parts)
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# import module
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while True:
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try:
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module = import_module(module_name)
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part = next(parts)
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# mmcv.ops has nms.py and nms function at the same time. So the
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# function will have a higher priority
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obj = getattr(module, part, None)
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if obj is not None and not ismodule(obj):
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break
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module_name = f'{module_name}.{part}'
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except StopIteration:
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# if obj is a module
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return module
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except ImportError:
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return None
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# get class or attribute from module
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obj = module
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while True:
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try:
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|
obj = getattr(obj, part)
|
|
part = next(parts)
|
|
except StopIteration:
|
|
return obj
|
|
except AttributeError:
|
|
return None
|