diff options
| -rw-r--r-- | tensorflow/python/BUILD | 1 | ||||
| -rw-r--r-- | tensorflow/python/util/nest.py | 11 | ||||
| -rw-r--r-- | tensorflow/python/util/nest_test.py | 68 | ||||
| -rw-r--r-- | tensorflow/python/util/util.cc | 299 | ||||
| -rw-r--r-- | tensorflow/python/util/util.h | 4 | ||||
| -rw-r--r-- | tensorflow/python/util/util.i | 3 |
6 files changed, 284 insertions, 102 deletions
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index b5a0051c28..a7c60f5450 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3408,6 +3408,7 @@ py_test( ":math_ops", ":util", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py index d63f59a8c8..5aac559b9b 100644 --- a/tensorflow/python/util/nest.py +++ b/tensorflow/python/util/nest.py @@ -73,7 +73,7 @@ def _sequence_like(instance, args): Returns: `args` with the type of `instance`. """ - if isinstance(instance, dict): + if isinstance(instance, (dict, _collections.Mapping)): # Pack dictionaries in a deterministic order by sorting the keys. # Notice this means that we ignore the original order of `OrderedDict` # instances. This is intentional, to avoid potential bugs caused by mixing @@ -89,7 +89,7 @@ def _sequence_like(instance, args): def _yield_value(iterable): - if isinstance(iterable, dict): + if isinstance(iterable, (dict, _collections.Mapping)): # Iterate through dictionaries in a deterministic order by sorting the # keys. Notice this means that we ignore the original order of `OrderedDict` # instances. This is intentional, to avoid potential bugs caused by mixing @@ -215,7 +215,7 @@ def flatten_dict_items(dictionary): ValueError: If any key and value have not the same structure, or if keys are not unique. """ - if not isinstance(dictionary, dict): + if not isinstance(dictionary, (dict, _collections.Mapping)): raise TypeError("input must be a dictionary") flat_dictionary = {} for i, v in _six.iteritems(dictionary): @@ -455,7 +455,7 @@ def assert_shallow_structure(shallow_tree, input_tree, check_types=True): "structure has length %s, while shallow structure has length %s." % (len(input_tree), len(shallow_tree))) - if check_types and isinstance(shallow_tree, dict): + if check_types and isinstance(shallow_tree, (dict, _collections.Mapping)): if set(input_tree) != set(shallow_tree): raise ValueError( "The two structures don't have the same keys. Input " @@ -716,7 +716,7 @@ def yield_flat_paths(nest): # The _maybe_add_final_path_element function is used below in order to avoid # adding trailing slashes when the sub-element recursed into is a leaf. - if isinstance(nest, dict): + if isinstance(nest, (dict, _collections.Mapping)): for key in _sorted(nest): value = nest[key] for sub_path in yield_flat_paths(value): @@ -760,3 +760,4 @@ def flatten_with_joined_string_paths(structure, separator="/"): _pywrap_tensorflow.RegisterSequenceClass(_collections.Sequence) +_pywrap_tensorflow.RegisterMappingClass(_collections.Mapping) diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py index 2f12b25354..26c6ea4b01 100644 --- a/tensorflow/python/util/nest_test.py +++ b/tensorflow/python/util/nest_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import collections import time +from absl.testing import parameterized import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin @@ -33,7 +34,22 @@ from tensorflow.python.platform import test from tensorflow.python.util import nest -class NestTest(test.TestCase): +class _CustomMapping(collections.Mapping): + + def __init__(self, *args, **kwargs): + self._wrapped = dict(*args, **kwargs) + + def __getitem__(self, key): + return self._wrapped[key] + + def __iter__(self): + return iter(self._wrapped) + + def __len__(self): + return len(self._wrapped) + + +class NestTest(parameterized.TestCase, test.TestCase): PointXY = collections.namedtuple("Point", ["x", "y"]) # pylint: disable=invalid-name @@ -72,26 +88,32 @@ class NestTest(test.TestCase): with self.assertRaises(ValueError): nest.pack_sequence_as([5, 6, [7, 8]], ["a", "b", "c"]) + @parameterized.parameters({"mapping_type": collections.OrderedDict}, + {"mapping_type": _CustomMapping}) @test_util.assert_no_new_pyobjects_executing_eagerly - def testFlattenDictOrder(self): + def testFlattenDictOrder(self, mapping_type): """`flatten` orders dicts by key, including OrderedDicts.""" - ordered = collections.OrderedDict([("d", 3), ("b", 1), ("a", 0), ("c", 2)]) + ordered = mapping_type([("d", 3), ("b", 1), ("a", 0), ("c", 2)]) plain = {"d": 3, "b": 1, "a": 0, "c": 2} ordered_flat = nest.flatten(ordered) plain_flat = nest.flatten(plain) self.assertEqual([0, 1, 2, 3], ordered_flat) self.assertEqual([0, 1, 2, 3], plain_flat) - def testPackDictOrder(self): + @parameterized.parameters({"mapping_type": collections.OrderedDict}, + {"mapping_type": _CustomMapping}) + def testPackDictOrder(self, mapping_type): """Packing orders dicts by key, including OrderedDicts.""" - ordered = collections.OrderedDict([("d", 0), ("b", 0), ("a", 0), ("c", 0)]) + custom = mapping_type([("d", 0), ("b", 0), ("a", 0), ("c", 0)]) plain = {"d": 0, "b": 0, "a": 0, "c": 0} seq = [0, 1, 2, 3] - ordered_reconstruction = nest.pack_sequence_as(ordered, seq) + custom_reconstruction = nest.pack_sequence_as(custom, seq) plain_reconstruction = nest.pack_sequence_as(plain, seq) + self.assertIsInstance(custom_reconstruction, mapping_type) + self.assertIsInstance(plain_reconstruction, dict) self.assertEqual( - collections.OrderedDict([("d", 3), ("b", 1), ("a", 0), ("c", 2)]), - ordered_reconstruction) + mapping_type([("d", 3), ("b", 1), ("a", 0), ("c", 2)]), + custom_reconstruction) self.assertEqual({"d": 3, "b": 1, "a": 0, "c": 2}, plain_reconstruction) Abc = collections.namedtuple("A", ("b", "c")) # pylint: disable=invalid-name @@ -101,8 +123,10 @@ class NestTest(test.TestCase): # A nice messy mix of tuples, lists, dicts, and `OrderedDict`s. mess = [ "z", - NestTest.Abc(3, 4), - { + NestTest.Abc(3, 4), { + "d": _CustomMapping({ + 41: 4 + }), "c": [ 1, collections.OrderedDict([ @@ -111,17 +135,19 @@ class NestTest(test.TestCase): ]), ], "b": 5 - }, - 17 + }, 17 ] flattened = nest.flatten(mess) - self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 17]) + self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 4, 17]) structure_of_mess = [ 14, NestTest.Abc("a", True), { + "d": _CustomMapping({ + 41: 42 + }), "c": [ 0, collections.OrderedDict([ @@ -142,6 +168,10 @@ class NestTest(test.TestCase): self.assertIsInstance(unflattened_ordered_dict, collections.OrderedDict) self.assertEqual(list(unflattened_ordered_dict.keys()), ["b", "a"]) + unflattened_custom_mapping = unflattened[2]["d"] + self.assertIsInstance(unflattened_custom_mapping, _CustomMapping) + self.assertEqual(list(unflattened_custom_mapping.keys()), [41]) + def testFlatten_numpyIsNotFlattened(self): structure = np.array([1, 2, 3]) flattened = nest.flatten(structure) @@ -179,19 +209,23 @@ class NestTest(test.TestCase): self.assertFalse(nest.is_sequence(math_ops.tanh(ones))) self.assertFalse(nest.is_sequence(np.ones((4, 5)))) - def testFlattenDictItems(self): - dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", "d"))} + @parameterized.parameters({"mapping_type": _CustomMapping}, + {"mapping_type": dict}) + def testFlattenDictItems(self, mapping_type): + dictionary = mapping_type({(4, 5, (6, 8)): ("a", "b", ("c", "d"))}) flat = {4: "a", 5: "b", 6: "c", 8: "d"} self.assertEqual(nest.flatten_dict_items(dictionary), flat) with self.assertRaises(TypeError): nest.flatten_dict_items(4) - bad_dictionary = {(4, 5, (4, 8)): ("a", "b", ("c", "d"))} + bad_dictionary = mapping_type({(4, 5, (4, 8)): ("a", "b", ("c", "d"))}) with self.assertRaisesRegexp(ValueError, "not unique"): nest.flatten_dict_items(bad_dictionary) - another_bad_dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", ("d", "e")))} + another_bad_dictionary = mapping_type({ + (4, 5, (6, 8)): ("a", "b", ("c", ("d", "e"))) + }) with self.assertRaisesRegexp( ValueError, "Key had [0-9]* elements, but value had [0-9]* elements"): nest.flatten_dict_items(another_bad_dictionary) diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc index 366f8a0deb..f9e0b7e4d2 100644 --- a/tensorflow/python/util/util.cc +++ b/tensorflow/python/util/util.cc @@ -31,6 +31,8 @@ namespace { // Type object for collections.Sequence. This is set by RegisterSequenceClass. PyObject* CollectionsSequenceType = nullptr; +// Type object for collections.Mapping, set by RegisterMappingClass. +PyObject* CollectionsMappingType = nullptr; PyTypeObject* SparseTensorValueType = nullptr; const int kMaxItemsInCache = 1024; @@ -45,6 +47,23 @@ bool IsString(PyObject* o) { PyUnicode_Check(o); } +// Work around a writable-strings warning with Python 2's PyMapping_Keys macro, +// and while we're at it give them consistent behavior by making sure the +// returned value is a list. +// +// As with PyMapping_Keys, returns a new reference. +PyObject* MappingKeys(PyObject* o) { +#if PY_MAJOR_VERSION >= 3 + return PyMapping_Keys(o); +#else + static char key_method_name[] = "keys"; + Safe_PyObjectPtr raw_result(PyObject_CallMethod(o, key_method_name, nullptr)); + return PySequence_Fast( + raw_result.get(), + "The '.keys()' method of a custom mapping returned a non-sequence."); +#endif +} + // Equivalent to Python's 'o.__class__.__name__' // Note that '__class__' attribute is set only in new-style classes. // A lot of tensorflow code uses __class__ without checks, so it seems like @@ -85,6 +104,119 @@ string PyObjectToString(PyObject* o) { } } +class CachedTypeCheck { + public: + explicit CachedTypeCheck(std::function<int(PyObject*)> ternary_predicate) + : ternary_predicate_(std::move(ternary_predicate)) {} + + ~CachedTypeCheck() { + mutex_lock l(type_to_sequence_map_mu_); + for (const auto& pair : type_to_sequence_map_) { + Py_DECREF(pair.first); + } + } + + // Caches successful executions of the one-argument (PyObject*) callable + // "ternary_predicate" based on the type of "o". -1 from the callable + // indicates an unsuccessful check (not cached), 0 indicates that "o"'s type + // does not match the predicate, and 1 indicates that it does. Used to avoid + // calling back into Python for expensive isinstance checks. + int CachedLookup(PyObject* o) { + // Try not to return to Python - see if the type has already been seen + // before. + + auto* type = Py_TYPE(o); + + { + mutex_lock l(type_to_sequence_map_mu_); + auto it = type_to_sequence_map_.find(type); + if (it != type_to_sequence_map_.end()) { + return it->second; + } + } + + int check_result = ternary_predicate_(o); + + if (check_result == -1) { + return -1; // Type check error, not cached. + } + + // NOTE: This is never decref'd as long as the object lives, which is likely + // forever, but we don't want the type to get deleted as long as it is in + // the map. This should not be too much of a leak, as there should only be a + // relatively small number of types in the map, and an even smaller number + // that are eligible for decref. As a precaution, we limit the size of the + // map to 1024. + { + mutex_lock l(type_to_sequence_map_mu_); + if (type_to_sequence_map_.size() < kMaxItemsInCache) { + Py_INCREF(type); + type_to_sequence_map_.insert({type, check_result}); + } + } + + return check_result; + } + + private: + std::function<int(PyObject*)> ternary_predicate_; + mutex type_to_sequence_map_mu_; + std::unordered_map<PyTypeObject*, bool> type_to_sequence_map_ + GUARDED_BY(type_to_sequence_map_mu_); +}; + +// Returns 1 if `o` is considered a mapping for the purposes of Flatten(). +// Returns 0 otherwise. +// Returns -1 if an error occurred. +int IsMappingHelper(PyObject* o) { + static auto* const check_cache = new CachedTypeCheck([](PyObject* to_check) { + return PyObject_IsInstance(to_check, CollectionsMappingType); + }); + if (PyDict_Check(o)) return true; + if (TF_PREDICT_FALSE(CollectionsMappingType == nullptr)) { + PyErr_SetString( + PyExc_RuntimeError, + tensorflow::strings::StrCat( + "collections.Mapping type has not been set. " + "Please call RegisterMappingClass before using this module") + .c_str()); + return -1; + } + return check_cache->CachedLookup(o); +} + +// Returns 1 if `o` is considered a sequence for the purposes of Flatten(). +// Returns 0 otherwise. +// Returns -1 if an error occurred. +int IsSequenceHelper(PyObject* o) { + static auto* const check_cache = new CachedTypeCheck([](PyObject* to_check) { + int is_instance = PyObject_IsInstance(to_check, CollectionsSequenceType); + + // Don't cache a failed is_instance check. + if (is_instance == -1) return -1; + + return static_cast<int>(is_instance != 0 && !IsString(to_check)); + }); + // We treat dicts and other mappings as special cases of sequences. + if (IsMappingHelper(o)) return true; + if (PySet_Check(o) && !WarnedThatSetIsNotSequence) { + LOG(WARNING) << "Sets are not currently considered sequences, " + "but this may change in the future, " + "so consider avoiding using them."; + WarnedThatSetIsNotSequence = true; + } + if (TF_PREDICT_FALSE(CollectionsSequenceType == nullptr)) { + PyErr_SetString( + PyExc_RuntimeError, + tensorflow::strings::StrCat( + "collections.Sequence type has not been set. " + "Please call RegisterSequenceClass before using this module") + .c_str()); + return -1; + } + return check_cache->CachedLookup(o); +} + // Implements the same idea as tensorflow.util.nest._yield_value // During construction we check if the iterable is a dictionary. // If so, we construct a sequence from its sorted keys that will be used @@ -96,7 +228,12 @@ string PyObjectToString(PyObject* o) { // 'iterable' must not be modified while ValIterator is used. class ValIterator { public: - explicit ValIterator(PyObject* iterable) : dict_(nullptr), index_(0) { + explicit ValIterator(PyObject* iterable) + : dict_(nullptr), + mapping_(nullptr), + last_mapping_element_(nullptr), + seq_(nullptr), + index_(0) { if (PyDict_Check(iterable)) { dict_ = iterable; // PyDict_Keys returns a list, which can be used with @@ -108,6 +245,10 @@ class ValIterator { // bugs caused by mixing ordered and plain dicts (e.g., flattening // a dict but using a corresponding `OrderedDict` to pack it back). PyList_Sort(seq_); + } else if (IsMappingHelper(iterable)) { + mapping_ = iterable; + seq_ = MappingKeys(iterable); + PyList_Sort(seq_); } else { seq_ = PySequence_Fast(iterable, ""); } @@ -122,7 +263,9 @@ class ValIterator { PyObject* element = nullptr; if (index_ < size_) { // Both PySequence_Fast_GET_ITEM and PyDict_GetItem return borrowed - // references. + // references. For general mappings, ValIterator keeps a reference to the + // last retrieved element (and decrefs it before producing the next + // element) to abstract away the borrowed/new difference. element = PySequence_Fast_GET_ITEM(seq_, index_); ++index_; if (dict_ != nullptr) { @@ -132,85 +275,32 @@ class ValIterator { "Dictionary was modified during iteration over it"); return nullptr; } + } else if (mapping_ != nullptr) { + element = PyObject_GetItem(mapping_, element); + if (element == nullptr) { + PyErr_SetString(PyExc_RuntimeError, + "Mapping was modified during iteration over it"); + return nullptr; + } + last_mapping_element_.reset(element); } } return element; } private: - PyObject* seq_; + // Special casing for things that pass PyDict_Check (faster, no Python calls) PyObject* dict_; + + // General mappings which have custom Python logic + PyObject* mapping_; + Safe_PyObjectPtr last_mapping_element_; + + PyObject* seq_; Py_ssize_t size_; Py_ssize_t index_; }; -mutex g_type_to_sequence_map(LINKER_INITIALIZED); -std::unordered_map<PyTypeObject*, bool>* IsTypeSequenceMap() { - static auto* const m = new std::unordered_map<PyTypeObject*, bool>; - return m; -} - -// Returns 1 if `o` is considered a sequence for the purposes of Flatten(). -// Returns 0 otherwise. -// Returns -1 if an error occurred. -int IsSequenceHelper(PyObject* o) { - if (PyDict_Check(o)) return true; - if (PySet_Check(o) && !WarnedThatSetIsNotSequence) { - LOG(WARNING) << "Sets are not currently considered sequences, " - "but this may change in the future, " - "so consider avoiding using them."; - WarnedThatSetIsNotSequence = true; - } - if (TF_PREDICT_FALSE(CollectionsSequenceType == nullptr)) { - PyErr_SetString( - PyExc_RuntimeError, - tensorflow::strings::StrCat( - "collections.Sequence type has not been set. " - "Please call RegisterSequenceClass before using this module") - .c_str()); - return -1; - } - - // Try not to return to Python - see if the type has already been seen - // before. - - auto* type_to_sequence_map = IsTypeSequenceMap(); - auto* type = Py_TYPE(o); - - { - mutex_lock l(g_type_to_sequence_map); - auto it = type_to_sequence_map->find(type); - if (it != type_to_sequence_map->end()) { - return it->second; - } - } - - // NOTE: We explicitly release the g_type_to_sequence_map mutex, - // because PyObject_IsInstance() may release the GIL, allowing another thread - // concurrent entry to this function. - int is_instance = PyObject_IsInstance(o, CollectionsSequenceType); - - // Don't cache a failed is_instance check. - if (is_instance == -1) return -1; - - bool is_sequence = static_cast<int>(is_instance != 0 && !IsString(o)); - - // NOTE: This is never decref'd, but we don't want the type to get deleted - // as long as it is in the map. This should not be too much of a - // leak, as there should only be a relatively small number of types in the - // map, and an even smaller number that are eligible for decref. As a - // precaution, we limit the size of the map to 1024. - { - mutex_lock l(g_type_to_sequence_map); - if (type_to_sequence_map->size() < kMaxItemsInCache) { - Py_INCREF(type); - type_to_sequence_map->insert({type, is_sequence}); - } - } - - return is_sequence; -} - bool IsSparseTensorValueType(PyObject* o) { if (TF_PREDICT_FALSE(SparseTensorValueType == nullptr)) { return false; @@ -226,21 +316,35 @@ int IsSequenceForDataHelper(PyObject* o) { bool GetNextValuesForDict(PyObject* nested, std::vector<Safe_PyObjectPtr>* next_values) { - std::vector<PyObject*> result; - - PyObject* keys = PyDict_Keys(nested); - if (PyList_Sort(keys) == -1) return false; - Py_ssize_t size = PyList_Size(keys); + Safe_PyObjectPtr keys(PyDict_Keys(nested)); + if (PyList_Sort(keys.get()) == -1) return false; + Py_ssize_t size = PyList_Size(keys.get()); for (Py_ssize_t i = 0; i < size; ++i) { // We know that key and item will not be deleted because nested owns // a reference to them and callers of flatten must not modify nested // while the method is running. - PyObject* key = PyList_GET_ITEM(keys, i); + PyObject* key = PyList_GET_ITEM(keys.get(), i); PyObject* item = PyDict_GetItem(nested, key); Py_INCREF(item); next_values->emplace_back(item); } - Py_DECREF(keys); + return true; +} + +bool GetNextValuesForMapping(PyObject* nested, + std::vector<Safe_PyObjectPtr>* next_values) { + Safe_PyObjectPtr keys(MappingKeys(nested)); + if (keys.get() == nullptr) { + return false; + } + if (PyList_Sort(keys.get()) == -1) return false; + Py_ssize_t size = PyList_Size(keys.get()); + for (Py_ssize_t i = 0; i < size; ++i) { + PyObject* key = PyList_GET_ITEM(keys.get(), i); + // Unlike PyDict_GetItem, PyObject_GetItem returns a new reference. + PyObject* item = PyObject_GetItem(nested, key); + next_values->emplace_back(item); + } return true; } @@ -265,6 +369,9 @@ bool GetNextValues(PyObject* nested, if (PyDict_Check(nested)) { // if nested is dictionary, sort it by key and recurse on each value return GetNextValuesForDict(nested, next_values); + } else if (IsMappingHelper(nested)) { + // same treatment as dictionaries, but for custom mapping types + return GetNextValuesForMapping(nested, next_values); } // iterate and recurse return GetNextValuesForIterable(nested, next_values); @@ -276,6 +383,9 @@ bool GetNextValuesForData(PyObject* nested, if (PyDict_Check(nested)) { // if nested is dictionary, sort it by key and recurse on each value return GetNextValuesForDict(nested, next_values); + } else if (IsMappingHelper(nested)) { + // same treatment as dictionaries, but for custom mapping types + return GetNextValuesForMapping(nested, next_values); } else if (IsSparseTensorValueType(nested)) { // if nested is a SparseTensorValue, just return itself as a single item Py_INCREF(nested); @@ -320,8 +430,8 @@ bool FlattenHelper( // 'dict1' and 'dict2' are assumed to be Python dictionaries. void SetDifferentKeysError(PyObject* dict1, PyObject* dict2, string* error_msg, bool* is_type_error) { - PyObject* k1 = PyDict_Keys(dict1); - PyObject* k2 = PyDict_Keys(dict2); + PyObject* k1 = MappingKeys(dict1); + PyObject* k2 = MappingKeys(dict2); *is_type_error = false; *error_msg = tensorflow::strings::StrCat( "The two dictionaries don't have the same set of keys. " @@ -423,6 +533,24 @@ bool AssertSameStructureHelper(PyObject* o1, PyObject* o2, bool check_types, return true; } } + } else if (IsMappingHelper(o1)) { + // Fallback for custom mapping types. Instead of using PyDict methods + // which stay in C, we call iter(o1). + if (PyMapping_Size(o1) != PyMapping_Size(o2)) { + SetDifferentKeysError(o1, o2, error_msg, is_type_error); + return true; + } + + Safe_PyObjectPtr iter(PyObject_GetIter(o1)); + PyObject* key; + while ((key = PyIter_Next(iter.get())) != nullptr) { + if (!PyMapping_HasKey(o2, key)) { + SetDifferentKeysError(o1, o2, error_msg, is_type_error); + Py_DECREF(key); + return true; + } + Py_DECREF(key); + } } } @@ -470,6 +598,19 @@ void RegisterSequenceClass(PyObject* sequence_class) { CollectionsSequenceType = sequence_class; } +void RegisterMappingClass(PyObject* mapping_class) { + if (!PyType_Check(mapping_class)) { + PyErr_SetString( + PyExc_TypeError, + tensorflow::strings::StrCat( + "Expecting a class definition for `collections.Mapping`. Got ", + Py_TYPE(mapping_class)->tp_name) + .c_str()); + return; + } + CollectionsMappingType = mapping_class; +} + void RegisterSparseTensorValueClass(PyObject* sparse_tensor_value_class) { if (!PyType_Check(sparse_tensor_value_class)) { PyErr_SetString( diff --git a/tensorflow/python/util/util.h b/tensorflow/python/util/util.h index 70efc10c9a..41dcc969f8 100644 --- a/tensorflow/python/util/util.h +++ b/tensorflow/python/util/util.h @@ -118,7 +118,9 @@ PyObject* Flatten(PyObject* nested); // the type from the module. This approach also requires some trigger from // Python so that we know that Python interpreter had been initialzied. void RegisterSequenceClass(PyObject* sequence_class); -// Similar to the above function, except for the +// Like RegisterSequenceClass, but for collections.Mapping. +void RegisterMappingClass(PyObject* mapping_class); +// Similar to the above functions, except for the // sparse_tensor.SparseTensorValue class. void RegisterSparseTensorValueClass(PyObject* sparse_tensor_value_class); diff --git a/tensorflow/python/util/util.i b/tensorflow/python/util/util.i index 9f3b11b982..6ad1484295 100644 --- a/tensorflow/python/util/util.i +++ b/tensorflow/python/util/util.i @@ -31,6 +31,9 @@ limitations under the License. %unignore tensorflow::swig::RegisterSequenceClass; %noexception tensorflow::swig::RegisterSequenceClass; +%unignore tensorflow::swig::RegisterMappingClass; +%noexception tensorflow::swig::RegisterMappingClass; + %unignore tensorflow::swig::RegisterSparseTensorValueClass; %noexception tensorflow::swig::RegisterSparseTensorValueClass; |