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# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Contain small python utility functions
"""
import importlib.util
import re
from functools import lru_cache
from typing import Any, Dict, List, Union
import torch
import numpy as np
import yaml
from yaml import Dumper
def is_sci_notation(number: float) -> bool:
pattern = re.compile(r"^[+-]?\d+(\.\d*)?[eE][+-]?\d+$")
return bool(pattern.match(str(number)))
def float_representer(dumper: Dumper, number: Union[float, np.float32, np.float64]):
if is_sci_notation(number):
value = str(number)
if "." not in value and "e" in value:
value = value.replace("e", ".0e", 1)
else:
value = str(round(number, 3))
return dumper.represent_scalar("tag:yaml.org,2002:float", value)
yaml.add_representer(float, float_representer)
yaml.add_representer(np.float32, float_representer)
yaml.add_representer(np.float64, float_representer)
@lru_cache
def is_package_available(name: str) -> bool:
return importlib.util.find_spec(name) is not None
def union_two_dict(dict1: Dict[str, Any], dict2: Dict[str, Any]) -> Dict[str, Any]:
"""Union two dict. Will throw an error if there is an item not the same object with the same key."""
for key in dict2.keys():
if key in dict1:
assert dict1[key] == dict2[key], f"{key} in dict1 and dict2 are not the same object"
dict1[key] = dict2[key]
return dict1
def append_to_dict(data: Dict[str, List[Any]], new_data: Dict[str, Any]) -> None:
"""Append dict to a dict of list."""
for key, val in new_data.items():
if key not in data:
data[key] = []
data[key].append(val)
def unflatten_dict(data: Dict[str, Any], sep: str = "/") -> Dict[str, Any]:
unflattened = {}
for key, value in data.items():
pieces = key.split(sep)
pointer = unflattened
for piece in pieces[:-1]:
if piece not in pointer:
pointer[piece] = {}
pointer = pointer[piece]
pointer[pieces[-1]] = value
return unflattened
# origin
# def flatten_dict(data: Dict[str, Any], parent_key: str = "", sep: str = "/") -> Dict[str, Any]:
# flattened = {}
# for key, value in data.items():
# new_key = parent_key + sep + key if parent_key else key
# if isinstance(value, dict):
# flattened.update(flatten_dict(value, new_key, sep=sep))
# else:
# flattened[new_key] = value
# return flattened
# modified by wujunfei
def flatten_dict(d: Dict[str, Any], parent_key: str = '', sep: str = '/') -> Dict[str, Any]:
"""将嵌套字典展平，并确保值类型符合TensorBoard要求"""
items = []
for k, v in d.items():
new_key = f"{parent_key}{sep}{k}" if parent_key else k
if isinstance(v, dict):
items.extend(flatten_dict(v, new_key, sep=sep).items())
else:
# 转换值为支持的类型
if isinstance(v, (int, float, str, bool, torch.Tensor)):
items.append((new_key, v))
else:
# 将不支持的类型转换为字符串
items.append((new_key, str(v)))
return dict(items)
def convert_dict_to_str(data: Dict[str, Any]) -> str:
return yaml.dump(data, indent=2)
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