101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# 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.
|
||
|
|
# ==============================================================================
|
||
|
|
"""TensorBoard is a webapp for understanding TensorFlow runs and graphs."""
|
||
|
|
|
||
|
|
|
||
|
|
import functools
|
||
|
|
import threading
|
||
|
|
import types
|
||
|
|
|
||
|
|
|
||
|
|
def lazy_load(name):
|
||
|
|
"""Decorator to define a function that lazily loads the module 'name'.
|
||
|
|
|
||
|
|
This can be used to defer importing troublesome dependencies - e.g. ones that
|
||
|
|
are large and infrequently used, or that cause a dependency cycle -
|
||
|
|
until they are actually used.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
name: the fully-qualified name of the module; typically the last segment
|
||
|
|
of 'name' matches the name of the decorated function
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Decorator function that produces a lazy-loading module 'name' backed by the
|
||
|
|
underlying decorated function.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def wrapper(load_fn):
|
||
|
|
# Wrap load_fn to call it exactly once and update __dict__ afterwards to
|
||
|
|
# make future lookups efficient (only failed lookups call __getattr__).
|
||
|
|
@_memoize
|
||
|
|
def load_once(self):
|
||
|
|
if load_once.loading:
|
||
|
|
raise ImportError(
|
||
|
|
"Circular import when resolving LazyModule %r" % name
|
||
|
|
)
|
||
|
|
load_once.loading = True
|
||
|
|
try:
|
||
|
|
module = load_fn()
|
||
|
|
finally:
|
||
|
|
load_once.loading = False
|
||
|
|
self.__dict__.update(module.__dict__)
|
||
|
|
load_once.loaded = True
|
||
|
|
return module
|
||
|
|
|
||
|
|
load_once.loading = False
|
||
|
|
load_once.loaded = False
|
||
|
|
|
||
|
|
# Define a module that proxies getattr() and dir() to the result of calling
|
||
|
|
# load_once() the first time it's needed. The class is nested so we can close
|
||
|
|
# over load_once() and avoid polluting the module's attrs with our own state.
|
||
|
|
class LazyModule(types.ModuleType):
|
||
|
|
def __getattr__(self, attr_name):
|
||
|
|
return getattr(load_once(self), attr_name)
|
||
|
|
|
||
|
|
def __dir__(self):
|
||
|
|
return dir(load_once(self))
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
if load_once.loaded:
|
||
|
|
return "<%r via LazyModule (loaded)>" % load_once(self)
|
||
|
|
return (
|
||
|
|
"<module %r via LazyModule (not yet loaded)>"
|
||
|
|
% self.__name__
|
||
|
|
)
|
||
|
|
|
||
|
|
return LazyModule(name)
|
||
|
|
|
||
|
|
return wrapper
|
||
|
|
|
||
|
|
|
||
|
|
def _memoize(f):
|
||
|
|
"""Memoizing decorator for f, which must have exactly 1 hashable
|
||
|
|
argument."""
|
||
|
|
nothing = object() # Unique "no value" sentinel object.
|
||
|
|
cache = {}
|
||
|
|
# Use a reentrant lock so that if f references the resulting wrapper we die
|
||
|
|
# with recursion depth exceeded instead of deadlocking.
|
||
|
|
lock = threading.RLock()
|
||
|
|
|
||
|
|
@functools.wraps(f)
|
||
|
|
def wrapper(arg):
|
||
|
|
if cache.get(arg, nothing) is nothing:
|
||
|
|
with lock:
|
||
|
|
if cache.get(arg, nothing) is nothing:
|
||
|
|
cache[arg] = f(arg)
|
||
|
|
return cache[arg]
|
||
|
|
|
||
|
|
return wrapper
|