def test_unbound_instance_cache(self):
class TestClass(object):
def method(self):
pass
c = cache.UnboundInstanceCache()
o1 = TestClass()
dummy = object()
c[o1.method][1] = dummy
self.assertTrue(c.has(o1.method, 1))
self.assertFalse(c.has(o1.method, 2))
self.assertIs(c[o1.method][1], dummy)
self.assertEqual(len(c), 1)
o2 = TestClass()
self.assertTrue(c.has(o2.method, 1))
self.assertIs(c[o2.method][1], dummy)
self.assertEqual(len(c), 1)
from __future__ import division
from __future__ import print_function
import functools
import inspect
import sys
import unittest
from tensorflow.python.autograph.core import config
from tensorflow.python.autograph.pyct import cache
from tensorflow.python.autograph.pyct import inspect_utils
from tensorflow.python.autograph.utils import ag_logging as logging
from tensorflow.python.eager import function
from tensorflow.python.util import tf_inspect
_ALLOWLIST_CACHE = cache.UnboundInstanceCache()
def _is_of_known_loaded_module(f, module_name):
mod = sys.modules.get(module_name, None)
if mod is None:
return False
if any(v is not None for v in mod.__dict__.values() if f is v):
return True
return False
def _is_known_loaded_type(f, module_name, entity_name):
"""Tests whether the function or method is an instance of a known type."""
if (module_name not in sys.modules
or not hasattr(sys.modules[module_name], entity_name)):
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = continue_statements.transform(node, ctx)
node = return_statements.transform(node, ctx)
if ctx.user.options.uses(converter.Feature.LISTS):
node = lists.transform(node, ctx)
node = slices.transform(node, ctx)
node = call_trees.transform(node, ctx)
node = control_flow.transform(node, ctx)
node = conditional_expressions.transform(node, ctx)
node = logical_expressions.transform(node, ctx)
return node
_TRANSPILER = AutoGraphTranspiler()
_WHITELIST_CACHE = cache.UnboundInstanceCache()
custom_vars = None
# TODO(mdan): Superfluous function, remove.
# TODO(mdan): Put these extra fields inside __autograph_info__.
def convert(entity, program_ctx):
"""Applies AutoGraph to entity."""
if not hasattr(entity, '__code__'):
raise ValueError('Cannot apply autograph to a function that doesn\'t '
'expose a __code__ object. If this is a @tf.function,'
' try passing f.python_function instead.')
_create_custom_vars(program_ctx)
