コード例 #1
0
class CacheManager(object):
    """The librosa cache manager class wraps joblib.Memory
    with a __call__ attribute, so that it may act as a function.

    Additionally, it provides a caching level filter, so that
    different functions can be cached or not depending on the user's
    preference for speed vs. storage usage.
    """
    def __init__(self, *args, **kwargs):

        level = kwargs.pop("level", 10)

        # Initialize the memory object
        self.memory = Memory(*args, **kwargs)
        # The level parameter controls which data we cache
        # smaller numbers mean less caching
        self.level = level

    def __call__(self, level):
        """Example usage:

        @cache(level=2)
        def semi_important_function(some_arguments):
            ...
        """
        def wrapper(function):
            """Decorator function.  Adds an input/output cache to
            the specified function."""
            if self.memory.location is not None and self.level >= level:
                return _decorator_apply(self.memory.cache, function)

            else:
                return function

        return wrapper

    def clear(self, *args, **kwargs):
        return self.memory.clear(*args, **kwargs)

    def eval(self, *args, **kwargs):
        return self.memory.eval(*args, **kwargs)

    def format(self, *args, **kwargs):
        return self.memory.format(*args, **kwargs)

    def reduce_size(self, *args, **kwargs):
        return self.memory.reduce_size(*args, **kwargs)

    def warn(self, *args, **kwargs):
        return self.memory.warn(*args, **kwargs)
コード例 #2
0
class CacheManager(object):
    '''The librosa cache manager class wraps joblib.Memory
    with a __call__ attribute, so that it may act as a function.

    Additionally, it provides a caching level filter, so that
    different functions can be cached or not depending on the user's
    preference for speed vs. storage usage.
    '''
    def __init__(self, *args, **kwargs):

        level = kwargs.pop('level', 10)

        # Initialize the memory object
        self.memory = Memory(*args, **kwargs)
        # The level parameter controls which data we cache
        # smaller numbers mean less caching
        self.level = level

    def __call__(self, level):
        '''Example usage:

        @cache(level=2)
        def semi_important_function(some_arguments):
            ...
        '''
        def wrapper(function):
            '''Decorator function.  Adds an input/output cache to
            the specified function.'''

            from decorator import FunctionMaker

            def decorator_apply(dec, func):
                """Decorate a function by preserving the signature even if dec
                is not a signature-preserving decorator.

                This recipe is derived from
                http://micheles.googlecode.com/hg/decorator/documentation.html#id14
                """

                return FunctionMaker.create(func,
                                            'return decorated(%(signature)s)',
                                            dict(decorated=dec(func)),
                                            __wrapped__=func)

            if self.memory.location is not None and self.level >= level:
                return decorator_apply(self.memory.cache, function)

            else:
                return function

        return wrapper

    def clear(self, *args, **kwargs):
        return self.memory.clear(*args, **kwargs)

    def eval(self, *args, **kwargs):
        return self.memory.eval(*args, **kwargs)

    def format(self, *args, **kwargs):
        return self.memory.format(*args, **kwargs)

    def reduce_size(self, *args, **kwargs):
        return self.memory.reduce_size(*args, **kwargs)

    def warn(self, *args, **kwargs):
        return self.memory.warn(*args, **kwargs)
コード例 #3
0
ファイル: _cache.py プロジェクト: ai-learn-use/librosa
class CacheManager(object):
    '''The librosa cache manager class wraps joblib.Memory
    with a __call__ attribute, so that it may act as a function.

    Additionally, it provides a caching level filter, so that
    different functions can be cached or not depending on the user's
    preference for speed vs. storage usage.
    '''

    def __init__(self, *args, **kwargs):

        level = kwargs.pop('level', 10)

        # Initialize the memory object
        self.memory = Memory(*args, **kwargs)
        # The level parameter controls which data we cache
        # smaller numbers mean less caching
        self.level = level

    def __call__(self, level):
        '''Example usage:

        @cache(level=2)
        def semi_important_function(some_arguments):
            ...
        '''
        def wrapper(function):
            '''Decorator function.  Adds an input/output cache to
            the specified function.'''

            from decorator import FunctionMaker

            def decorator_apply(dec, func):
                """Decorate a function by preserving the signature even if dec
                is not a signature-preserving decorator.

                This recipe is derived from
                http://micheles.googlecode.com/hg/decorator/documentation.html#id14
                """

                return FunctionMaker.create(
                    func, 'return decorated(%(signature)s)',
                    dict(decorated=dec(func)), __wrapped__=func)

            if self.memory.location is not None and self.level >= level:
                return decorator_apply(self.memory.cache, function)

            else:
                return function
        return wrapper

    def clear(self, *args, **kwargs):
        return self.memory.clear(*args, **kwargs)

    def eval(self, *args, **kwargs):
        return self.memory.eval(*args, **kwargs)

    def format(self, *args, **kwargs):
        return self.memory.format(*args, **kwargs)

    def reduce_size(self, *args, **kwargs):
        return self.memory.reduce_size(*args, **kwargs)

    def warn(self, *args, **kwargs):
        return self.memory.warn(*args, **kwargs)