这就是我用的

args = {
        'batch_size': 32,
        'workers': 4,
        'train_dir': 'train',
        'val_dir': 'val',
        'lr': 1e-3,
        'momentum': 0.9,
        'weight_decay': 1e-4
    }
args = namedtuple('Args', ' '.join(list(args.keys())))(**args)

print (args.lr)

我发现自己想知道python生态系统中“字典键作为attr”的当前状态。正如一些评论者所指出的，这可能不是你想要从头开始的东西，因为有几个陷阱和脚枪，其中一些非常微妙。此外，我不建议使用Namespace作为基类，我已经走上了那条路，它并不漂亮。

幸运的是，有几个开源包提供了这个功能，可以安装了!不幸的是，有几个包。以下是截至2019年12月的概要。

竞争者(最近提交到|#提交|#投稿|覆盖率%):

上瘾者(2021-01-05 | 229 | | 100%)22 蒙克(2021-01-22 | 166 | 17 | ?) easydict (2021-02-28 | 54 | 7% | ?) attrdict(| 108 | 5 |地址:100%) prodict (2021-03-06 | 100 | 2 | ?)

不再保养或保养不足:

treedict (2014-03-28 | 95 | 2 | ?) bunch (2012-03-12 | 20% | 2 | ?) NeoBunch

目前我推荐咀嚼或上瘾。它们拥有最多的提交、贡献者和发布，这意味着它们都有一个健康的开源代码库。他们有最干净的自述。Md, 100%的覆盖率，以及一组好看的测试。

我在这场比赛中没有一只狗(现在!)，除了滚动我自己的dict/attr代码，浪费了大量的时间，因为我不知道所有这些选项:)。我可能会在未来贡献给addict/munch，因为我宁愿看到一个完整的包，而不是一堆碎片化的包。如果你喜欢它们，就投稿吧!特别是，看起来munch可以使用codecov徽章，addict可以使用python版本徽章。

瘾君子优点:

递归初始化(foo.a.b.c = 'bar')，类字典参数成为成瘾。Dict

成瘾的缺点:

阴影打字。词典，如果你从成瘾进口词典 不检查密钥。由于允许递归init，如果你拼错了一个键，你只是创建一个新属性，而不是KeyError(感谢AljoSt)

蒙克优点:

独特的命名 内置的JSON和YAML的ser/de函数

蒙克缺点:

没有递归初始化(你不能构造foo.a.b.c = 'bar'，你必须设置foo.a.b.c = 'bar')。A，然后foo, A。b等。

其中我发表评论

Many moons ago, when I used text editors to write python, on projects with only myself or one other dev, I liked the style of dict-attrs, the ability to insert keys by just declaring foo.bar.spam = eggs. Now I work on teams, and use an IDE for everything, and I have drifted away from these sorts of data structures and dynamic typing in general, in favor of static analysis, functional techniques and type hints. I've started experimenting with this technique, subclassing Pstruct with objects of my own design:

class  BasePstruct(dict):
    def __getattr__(self, name):
        if name in self.__slots__:
            return self[name]
        return self.__getattribute__(name)

    def __setattr__(self, key, value):
        if key in self.__slots__:
            self[key] = value
            return
        if key in type(self).__dict__:
            self[key] = value
            return
        raise AttributeError(
            "type object '{}' has no attribute '{}'".format(type(self).__name__, key))


class FooPstruct(BasePstruct):
    __slots__ = ['foo', 'bar']

This gives you an object which still behaves like a dict, but also lets you access keys like attributes, in a much more rigid fashion. The advantage here is I (or the hapless consumers of your code) know exactly what fields can and can't exist, and the IDE can autocomplete fields. Also subclassing vanilla dict means json serialization is easy. I think the next evolution in this idea would be a custom protobuf generator which emits these interfaces, and a nice knock-on is you get cross-language data structures and IPC via gRPC for nearly free.

如果您决定使用attrt -dicts，那么为了您自己(和您的队友)的理智，有必要记录期望哪些字段。

请随意编辑/更新这篇文章，以保持它的最新!

我根据这个线程的输入创建了这个。我需要使用odect，所以我必须覆盖get和设置attr。我认为这应该适用于大多数特殊用途。

用法如下:

# Create an ordered dict normally...
>>> od = OrderedAttrDict()
>>> od["a"] = 1
>>> od["b"] = 2
>>> od
OrderedAttrDict([('a', 1), ('b', 2)])

# Get and set data using attribute access...
>>> od.a
1
>>> od.b = 20
>>> od
OrderedAttrDict([('a', 1), ('b', 20)])

# Setting a NEW attribute only creates it on the instance, not the dict...
>>> od.c = 8
>>> od
OrderedAttrDict([('a', 1), ('b', 20)])
>>> od.c
8

类:

class OrderedAttrDict(odict.OrderedDict):
    """
    Constructs an odict.OrderedDict with attribute access to data.

    Setting a NEW attribute only creates it on the instance, not the dict.
    Setting an attribute that is a key in the data will set the dict data but 
    will not create a new instance attribute
    """
    def __getattr__(self, attr):
        """
        Try to get the data. If attr is not a key, fall-back and get the attr
        """
        if self.has_key(attr):
            return super(OrderedAttrDict, self).__getitem__(attr)
        else:
            return super(OrderedAttrDict, self).__getattr__(attr)


    def __setattr__(self, attr, value):
        """
        Try to set the data. If attr is not a key, fall-back and set the attr
        """
        if self.has_key(attr):
            super(OrderedAttrDict, self).__setitem__(attr, value)
        else:
            super(OrderedAttrDict, self).__setattr__(attr, value)

这是一个非常酷的模式，已经在线程中提到了，但如果你只是想把字典转换成一个在IDE中使用自动完成的对象，等等:

class ObjectFromDict(object):
    def __init__(self, d):
        self.__dict__ = d

解决方案是:

DICT_RESERVED_KEYS = vars(dict).keys()


class SmartDict(dict):
    """
    A Dict which is accessible via attribute dot notation
    """
    def __init__(self, *args, **kwargs):
        """
        :param args: multiple dicts ({}, {}, ..)
        :param kwargs: arbitrary keys='value'

        If ``keyerror=False`` is passed then not found attributes will
        always return None.
        """
        super(SmartDict, self).__init__()
        self['__keyerror'] = kwargs.pop('keyerror', True)
        [self.update(arg) for arg in args if isinstance(arg, dict)]
        self.update(kwargs)

    def __getattr__(self, attr):
        if attr not in DICT_RESERVED_KEYS:
            if self['__keyerror']:
                return self[attr]
            else:
                return self.get(attr)
        return getattr(self, attr)

    def __setattr__(self, key, value):
        if key in DICT_RESERVED_KEYS:
            raise AttributeError("You cannot set a reserved name as attribute")
        self.__setitem__(key, value)

    def __copy__(self):
        return self.__class__(self)

    def copy(self):
        return self.__copy__()

由于以下原因，我对现有的选项不满意，于是我开发了MetaDict。它的行为完全类似于dict，但支持点表示法和IDE自动补全，而没有其他解决方案的缺点和潜在的名称空间冲突。所有功能和使用示例都可以在GitHub上找到(见上面的链接)。

完全披露:我是MetaDict的作者。

我在尝试其他解决方案时遇到的缺点/限制:

Addict No key autocompletion in IDE Nested key assignment cannot be turned off Newly assigned dict objects are not converted to support attribute-style key access Shadows inbuilt type Dict Prodict No key autocompletion in IDE without defining a static schema (similar to dataclass) No recursive conversion of dict objects when embedded in list or other inbuilt iterables AttrDict No key autocompletion in IDE Converts list objects to tuple behind the scenes Munch Inbuilt methods like items(), update(), etc. can be overwritten with obj.items = [1, 2, 3] No recursive conversion of dict objects when embedded in list or other inbuilt iterables EasyDict Only strings are valid keys, but dict accepts all hashable objects as keys Inbuilt methods like items(), update(), etc. can be overwritten with obj.items = [1, 2, 3] Inbuilt methods don't behave as expected: obj.pop('unknown_key', None) raises an AttributeError

你可以用我刚做的这个类来做。对于这个类，您可以像使用另一个字典(包括json序列化)一样使用Map对象，或者使用点表示法。希望对你有所帮助:

class Map(dict):
    """
    Example:
    m = Map({'first_name': 'Eduardo'}, last_name='Pool', age=24, sports=['Soccer'])
    """
    def __init__(self, *args, **kwargs):
        super(Map, self).__init__(*args, **kwargs)
        for arg in args:
            if isinstance(arg, dict):
                for k, v in arg.iteritems():
                    self[k] = v

        if kwargs:
            for k, v in kwargs.iteritems():
                self[k] = v

    def __getattr__(self, attr):
        return self.get(attr)

    def __setattr__(self, key, value):
        self.__setitem__(key, value)

    def __setitem__(self, key, value):
        super(Map, self).__setitem__(key, value)
        self.__dict__.update({key: value})

    def __delattr__(self, item):
        self.__delitem__(item)

    def __delitem__(self, key):
        super(Map, self).__delitem__(key)
        del self.__dict__[key]

使用例子:

m = Map({'first_name': 'Eduardo'}, last_name='Pool', age=24, sports=['Soccer'])
# Add new key
m.new_key = 'Hello world!'
print m.new_key
print m['new_key']
# Update values
m.new_key = 'Yay!'
# Or
m['new_key'] = 'Yay!'
# Delete key
del m.new_key
# Or
del m['new_key']