def testFromLibraryCyclicGradFuncs(self): @function.Defun(dtypes.float32) def F1(x): return math_ops.exp(x) - math_ops.exp(-x) @function.Defun(dtypes.float32) def F2(x): return math_ops.exp(x) - math_ops.exp(-x) # Create invalid function def library where F1 has gradient function F2 and # F2 has gradient function F1 library = function_pb2.FunctionDefLibrary() library.function.extend([F1.definition, F2.definition]) gradient1 = function_pb2.GradientDef() gradient1.function_name = F1.name gradient1.gradient_func = F2.name gradient2 = function_pb2.GradientDef() gradient2.function_name = F2.name gradient2.gradient_func = F1.name library.gradient.extend([gradient1, gradient2]) with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary contains cyclic gradient functions!"): function._from_library(library)
def testFromLibraryCyclicGradFuncs(self): @function.Defun(dtypes.float32) def F1(x): return math_ops.exp(x) - math_ops.exp(-x) @function.Defun(dtypes.float32) def F2(x): return math_ops.exp(x) - math_ops.exp(-x) # Create invalid function def library where F1 has gradient function F2 and # F2 has gradient function F1 library = function_pb2.FunctionDefLibrary() library.function.extend([F1.definition, F2.definition]) gradient1 = function_pb2.GradientDef() gradient1.function_name = F1.name gradient1.gradient_func = F2.name gradient2 = function_pb2.GradientDef() gradient2.function_name = F2.name gradient2.gradient_func = F1.name library.gradient.extend([gradient1, gradient2]) with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary contains cyclic gradient functions!"): function._from_library(library)
def testFromLibraryMissingFuncDef(self): @function.Defun(dtypes.float32, dtypes.float32) def G1(x, dy): return x * dy @function.Defun(dtypes.float32) def F1(x): return math_ops.exp(x) - math_ops.exp(-x) gradient = function_pb2.GradientDef() gradient.function_name = F1.name gradient.gradient_func = G1.name # Create invalid function def that is missing G1 function def library = function_pb2.FunctionDefLibrary() library.gradient.extend([gradient]) library.function.extend([F1.definition]) with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary missing 'G1_[0-9a-zA-Z]{8,11}' FunctionDef"): function._from_library(library) # Create invalid function def that is missing F1 function def library = function_pb2.FunctionDefLibrary() library.gradient.extend([gradient]) library.function.extend([G1.definition]) with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary missing 'F1_[0-9a-zA-Z]{8,11}' FunctionDef"): function._from_library(library)
def testFromLibraryMissingFuncDef(self): @function.Defun(dtypes.float32, dtypes.float32) def G1(x, dy): return x * dy @function.Defun(dtypes.float32) def F1(x): return math_ops.exp(x) - math_ops.exp(-x) gradient = function_pb2.GradientDef() gradient.function_name = F1.name gradient.gradient_func = G1.name # Create invalid function def that is missing G1 function def library = function_pb2.FunctionDefLibrary() library.gradient.extend([gradient]) library.function.extend([F1.definition]) with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary missing 'G1_........' FunctionDef"): function._from_library(library) # Create invalid function def that is missing F1 function def library = function_pb2.FunctionDefLibrary() library.gradient.extend([gradient]) library.function.extend([G1.definition]) with self.assertRaisesRegexp( ValueError, "FunctionDefLibrary missing 'F1_........' FunctionDef"): function._from_library(library)
def testFromLibrary(self): # Define some functions with different gradient functions. Note that many of # the below functions are identical since function bodies don't matter for # this test. @function.Defun(dtypes.float32, dtypes.float32) def G1(x, dy): return x * dy @function.Defun(dtypes.float32, dtypes.float32) def G2(x, dy): return x * dy # F1 and F2 have the same gradient function @function.Defun(dtypes.float32, grad_func=G1) def F1(x): return math_ops.exp(x) - math_ops.exp(-x) @function.Defun(dtypes.float32, grad_func=G1) def F2(x): return math_ops.exp(x) - math_ops.exp(-x) # F3 has a different gradient function @function.Defun(dtypes.float32, grad_func=G2) def F3(x): return math_ops.exp(x) - math_ops.exp(-x) # F4 has no gradient function @function.Defun(dtypes.float32) def F4(x): return math_ops.exp(x) - math_ops.exp(-x) # Instantiate all functions g = ops.Graph() with g.as_default(): c = constant_op.constant(1.0, dtypes.float32) f1 = F1(c) f2 = F2(c) f3 = F3(c) f4 = F4(c) gradients_impl.gradients([f1, f2, f3, f4], c) library = g.as_graph_def().library new_funcs = function._from_library(library) def CheckNewFunc(func): new_func = [f for f in new_funcs if f.name == func.name] self.assertEqual(len(new_func), 1) self.expectFunctionsEqual(func, new_func=new_func[0]) CheckNewFunc(G1) CheckNewFunc(G2) CheckNewFunc(F1) CheckNewFunc(F2) CheckNewFunc(F3) CheckNewFunc(F4)
def testFromLibrary(self): # Define some functions with different gradient functions. Note that many of # the below functions are identical since function bodies don't matter for # this test. @function.Defun(dtypes.float32, dtypes.float32) def G1(x, dy): return x * dy @function.Defun(dtypes.float32, dtypes.float32) def G2(x, dy): return x * dy # F1 and F2 have the same gradient function @function.Defun(dtypes.float32, grad_func=G1) def F1(x): return math_ops.exp(x) - math_ops.exp(-x) @function.Defun(dtypes.float32, grad_func=G1) def F2(x): return math_ops.exp(x) - math_ops.exp(-x) # F3 has a different gradient function @function.Defun(dtypes.float32, grad_func=G2) def F3(x): return math_ops.exp(x) - math_ops.exp(-x) # F4 has no gradient function @function.Defun(dtypes.float32) def F4(x): return math_ops.exp(x) - math_ops.exp(-x) # Instantiate all functions g = ops.Graph() with g.as_default(): c = constant_op.constant(1.0, dtypes.float32) f1 = F1(c) f2 = F2(c) f3 = F3(c) f4 = F4(c) gradients_impl.gradients([f1, f2, f3, f4], c) library = g.as_graph_def().library new_funcs = function._from_library(library) def CheckNewFunc(func): new_func = [f for f in new_funcs if f.name == func.name] self.assertEqual(len(new_func), 1) self.expectFunctionsEqual(func, new_func=new_func[0]) CheckNewFunc(G1) CheckNewFunc(G2) CheckNewFunc(F1) CheckNewFunc(F2) CheckNewFunc(F3) CheckNewFunc(F4)
def testFromLibraryEmptyLib(self): library = function_pb2.FunctionDefLibrary() self.assertEqual(len(function._from_library(library)), 0)
def import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None): """Imports the graph from `graph_def` into the current default `Graph`. This function provides a way to import a serialized TensorFlow [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and extract individual objects in the `GraphDef` as @{tf.Tensor} and @{tf.Operation} objects. Once extracted, these objects are placed into the current default `Graph`. See @{tf.Graph.as_graph_def} for a way to create a `GraphDef` proto. Args: graph_def: A `GraphDef` proto containing operations to be imported into the default graph. input_map: A dictionary mapping input names (as strings) in `graph_def` to `Tensor` objects. The values of the named input tensors in the imported graph will be re-mapped to the respective `Tensor` values. return_elements: A list of strings containing operation names in `graph_def` that will be returned as `Operation` objects; and/or tensor names in `graph_def` that will be returned as `Tensor` objects. name: (Optional.) A prefix that will be prepended to the names in `graph_def`. Note that this does not apply to imported function names. Defaults to `"import"`. op_dict: (Optional.) A dictionary mapping op type names to `OpDef` protos. Must contain an `OpDef` proto for each op type named in `graph_def`. If omitted, uses the `OpDef` protos registered in the global registry. producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped) list of `OpDef`s used by the producer of the graph. If provided, attrs for ops in `graph_def` that are not in `op_dict` that have their default value according to `producer_op_list` will be removed. This will allow some more `GraphDef`s produced by later binaries to be accepted by earlier binaries. Returns: A list of `Operation` and/or `Tensor` objects from the imported graph, corresponding to the names in `return_elements`. Raises: TypeError: If `graph_def` is not a `GraphDef` proto, `input_map` is not a dictionary mapping strings to `Tensor` objects, or `return_elements` is not a list of strings. ValueError: If `input_map`, or `return_elements` contains names that do not appear in `graph_def`, or `graph_def` is not well-formed (e.g. it refers to an unknown tensor). """ # Type checks for inputs. if not isinstance(graph_def, graph_pb2.GraphDef): # `graph_def` could be a dynamically-created message, so try a duck-typed # approach try: old_graph_def = graph_def graph_def = graph_pb2.GraphDef() graph_def.MergeFrom(old_graph_def) except TypeError: raise TypeError('graph_def must be a GraphDef proto.') if input_map is None: input_map = {} else: if not (isinstance(input_map, dict) and all(isinstance(k, compat.bytes_or_text_types) for k in input_map.keys())): raise TypeError('input_map must be a dictionary mapping strings to ' 'Tensor objects.') if return_elements is not None: return_elements = tuple(return_elements) if not all(isinstance(x, compat.bytes_or_text_types) for x in return_elements): raise TypeError('return_elements must be a list of strings.') # Use a canonical representation for all tensor names. input_map = {_CanonicalInputName(k): v for k, v in input_map.items()} used_input_keys = set() name_to_op = {} if op_dict is None: op_dict = op_def_registry.get_registered_ops() if producer_op_list is None: producer_op_dict = None else: producer_op_dict = {op.name: op for op in producer_op_list.op} g = ops.get_default_graph() # Add any functions defined in `graph_def` to `g` if graph_def.library and graph_def.library.function: # Copy op_dict so we don't clobber the original op_dict = copy.copy(op_dict) # pylint: disable=protected-access # Note that we do not prepend `name` to the function name. The reasoning is # that function names are similar to op definition names, which currently do # not have a scoped name or namespace scheme. functions = function._from_library(graph_def.library) for f in functions: g._add_function(f) op_dict[f.name] = f.definition.signature # pylint: enable=protected-access # LINT.IfChange with ops.name_scope(name, 'import', input_map.values()) as scope: # TODO(ashankar): Should this just copy over or should it do some # more nuanced merging? For example, the graph may already have some # marked "bad versions" and we don't want to lose those because of # what's in graph_def.versions? The C++ ImporGraphDef does something # more nuanced. g.graph_def_versions.CopyFrom(graph_def.versions) if not all(isinstance(v, ops.Tensor) for v in input_map.values()): if not scope: # The caller must have passed `name=''`. raise ValueError( 'tf.import_graph_def() requires a non-empty `name` if `input_map` ' 'contains non-Tensor values. Try calling tf.convert_to_tensor() on ' '`input_map` values before calling tf.import_graph_def().') with ops.name_scope('_inputs'): input_map = {k: ops.convert_to_tensor(v) for k, v in input_map.items()} # NOTE(mrry): We do this in two passes, because there may be a cycle in # `graph_def`. # 1. Add operations without their inputs. for node in graph_def.node: # Set any default attr values that aren't present. if node.op not in op_dict: raise ValueError('No op named %s in defined operations.' % node.op) op_def = op_dict[node.op] for attr_def in op_def.attr: key = attr_def.name if attr_def.HasField('default_value'): value = node.attr[key] if value is None or value.WhichOneof('value') is None: node.attr[key].CopyFrom(attr_def.default_value) if producer_op_dict: # Remove any default attr values that aren't in op_def. if node.op in producer_op_dict: producer_op_def = producer_op_dict[node.op] # We make a copy of node.attr to iterate through since we # may modify node.attr inside the loop. for key in list(node.attr): if _FindAttrInOpDef(key, op_def) is None: # No attr_def in consumer, look in producer. attr_def = _FindAttrInOpDef(key, producer_op_def) if (attr_def and attr_def.HasField('default_value') and node.attr[key] == attr_def.default_value): # Unknown attr had default value in producer, delete it # so it can be understood by consumer. del node.attr[key] output_types = _OutputTypes(node, op_dict) name_to_op[node.name] = g.create_op( node.op, [], output_types, name=node.name, attrs=node.attr, compute_shapes=False, compute_device=False, op_def=op_def) # 2. Add inputs to the operations. for node in graph_def.node: op = name_to_op[node.name] input_types = _InputTypes(node, op_dict) # Rewrite the colocation attributes in the graph, since the # names of new ops may have changed. for key, value in op.node_def.attr.items(): if key == '_class': class_values = value.list new_class_values = [] for class_value in class_values.s: if class_value.startswith(b'loc:@'): op_to_bind_to = class_value[5:].decode() # Find the op by its original name. if op_to_bind_to not in name_to_op: raise ValueError('Specified colocation to an op that ' 'does not exist during import: %s in %s' % ( op_to_bind_to, node.name)) original_op = name_to_op[op_to_bind_to] new_class_values.append(compat.as_bytes( 'loc:@' + original_op.name)) else: new_class_values.append(class_value) value.list.CopyFrom(attr_value_pb2.AttrValue.ListValue( s=new_class_values)) # NOTE(mrry): We cannot use zip here because control inputs do not appear # in the list of input_types. for i, input_name in enumerate( [_CanonicalInputName(x) for x in node.input]): if _IsControlInput(input_name): # (a) Input is a control input that should be taken from an op # in "graph_def". try: source_op = name_to_op[input_name[1:]] except KeyError: raise ValueError( _InvalidNodeMessage( node, 'Control input %r not found in graph_def.' % (input_name,))) # pylint: disable=protected-access op._add_control_input(source_op) # pylint: enable=protected-access else: try: input_type = input_types[i] except IndexError: raise ValueError(_InvalidNodeMessage( node, 'More inputs specified (%r) than the op expects.' % (input_name,))) if input_name in input_map: # (b) Input should be replaced by a tensor from the caller. source_tensor = input_map[input_name] used_input_keys.add(input_name) else: # (c) Input should be taken from an op in `graph_def`. operation_name, output_index = _ParseTensorName(input_name) try: source_op = name_to_op[operation_name] source_tensor = list(source_op.values())[output_index] except (KeyError, IndexError): raise ValueError( _InvalidNodeMessage( node, 'Input tensor %r not found in graph_def.' % (input_name,))) try: # pylint: disable=protected-access op._add_input(source_tensor, dtype=input_type) # pylint: enable=protected-access except TypeError as te: raise ValueError(_InvalidNodeMessage( node, 'Input tensor %r %s' % (input_name, te))) # pylint: disable=protected-access if op._input_dtypes != input_types: raise ValueError( _InvalidNodeMessage( node, 'Input types mismatch (expected %r but got %r)' % (', '.join(dtypes.as_dtype(x).name for x in input_types), ', '.join(x.name for x in op._input_dtypes)))) # pylint: enable=protected-access if not g._is_function(op.type): # pylint: disable=protected-access # Execute shape inference for this op. # NOTE(mrry): If the graph contains a cycle, the full shape information # may not be available for this op's inputs. ops.set_shapes_for_outputs(op) # For nodes with _output_shapes set, set the output shapes. if '_output_shapes' in op.node_def.attr: for i, output in enumerate(op.outputs): dims = op.node_def.attr['_output_shapes'].list.shape[i] output_shape = tensor_shape.TensorShape( None if dims.unknown_rank else [dim.size if dim.size >= 0 else None for dim in dims.dim]) try: output.set_shape(output_shape) except ValueError as e: # If the output shape is incompatible with what is inferred # by the graph for a very specific whitelist of ops, then we # ignore this output shape. This can happen if there is a # bug in the shape function for some operation, and the # serialized graph def has the incorrect shape set when # running on a newer binary with the fixed shape function. # This is an escape hatch that allows us to correct shape # functions that are not critical to correct execution but # would cause graphs to fail if imported after correcting. # # This can be removed after 2017/03/08. if op.type in ['RandomShuffleQueue', 'PaddingFIFOQueue', 'FIFOQueue', 'PriorityQueue', 'QueueSize', 'Stack', 'Barrier', 'BarrierReadySize', 'BarrierIncompleteSize', 'HashTable', 'MutableHashTable', 'MutableHashTableOfTensors', 'Mutex', 'CuckooTable', 'IndexTable', 'WholeFileReader', 'TextLineReader', 'FixedLengthRecordReader', 'TFRecordReader', 'IdentityReader', 'RefSwitch', 'RefEnter', 'RefNextIteration', 'RefMerge', 'RefIdentity']: pass elif op.type in [ 'ConditionalAccumulator', 'SparseConditionalAccumulator', 'Table' ]: # This can be removed after 2017/04/24. pass else: raise e del op.node_def.attr['_output_shapes'] # Apply device functions for this op. # NOTE(mrry): We do this after configuring the inputs, because # the result of the device functions may depend on the inputs. with _MaybeDevice(node.device): g._apply_device_functions(op) # pylint: disable=protected-access # Treat unused input mappings as an error, because they are likely to be # due to a typo. unused_input_keys = frozenset(input_map.keys()).difference(used_input_keys) if unused_input_keys: raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(unused_input_keys)) if return_elements is None: return None else: ret = [] for name in return_elements: name = compat.as_str(name) if ':' in name: try: operation_name, output_index = _ParseTensorName(name) ret.append(name_to_op[operation_name].outputs[output_index]) except (ValueError, KeyError, IndexError): raise ValueError( 'Requested return_element %r not found in graph_def.' % name) else: try: ret.append(name_to_op[name]) except KeyError: raise ValueError( 'Requested return_element %r not found in graph_def.' % name) return ret
def import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None): """Imports the graph from `graph_def` into the current default `Graph`. This function provides a way to import a serialized TensorFlow [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and extract individual objects in the `GraphDef` as @{tf.Tensor} and @{tf.Operation} objects. Once extracted, these objects are placed into the current default `Graph`. See @{tf.Graph.as_graph_def} for a way to create a `GraphDef` proto. Args: graph_def: A `GraphDef` proto containing operations to be imported into the default graph. input_map: A dictionary mapping input names (as strings) in `graph_def` to `Tensor` objects. The values of the named input tensors in the imported graph will be re-mapped to the respective `Tensor` values. return_elements: A list of strings containing operation names in `graph_def` that will be returned as `Operation` objects; and/or tensor names in `graph_def` that will be returned as `Tensor` objects. name: (Optional.) A prefix that will be prepended to the names in `graph_def`. Note that this does not apply to imported function names. Defaults to `"import"`. op_dict: (Optional.) A dictionary mapping op type names to `OpDef` protos. Must contain an `OpDef` proto for each op type named in `graph_def`. If omitted, uses the `OpDef` protos registered in the global registry. producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped) list of `OpDef`s used by the producer of the graph. If provided, attrs for ops in `graph_def` that are not in `op_dict` that have their default value according to `producer_op_list` will be removed. This will allow some more `GraphDef`s produced by later binaries to be accepted by earlier binaries. Returns: A list of `Operation` and/or `Tensor` objects from the imported graph, corresponding to the names in `return_elements`. Raises: TypeError: If `graph_def` is not a `GraphDef` proto, `input_map` is not a dictionary mapping strings to `Tensor` objects, or `return_elements` is not a list of strings. ValueError: If `input_map`, or `return_elements` contains names that do not appear in `graph_def`, or `graph_def` is not well-formed (e.g. it refers to an unknown tensor). """ # Type checks for inputs. if not isinstance(graph_def, graph_pb2.GraphDef): # `graph_def` could be a dynamically-created message, so try a duck-typed # approach try: old_graph_def = graph_def graph_def = graph_pb2.GraphDef() graph_def.MergeFrom(old_graph_def) except TypeError: raise TypeError('graph_def must be a GraphDef proto.') if input_map is None: input_map = {} else: if not (isinstance(input_map, dict) and all(isinstance(k, compat.bytes_or_text_types) for k in input_map.keys())): raise TypeError('input_map must be a dictionary mapping strings to ' 'Tensor objects.') if return_elements is not None: return_elements = tuple(return_elements) if not all(isinstance(x, compat.bytes_or_text_types) for x in return_elements): raise TypeError('return_elements must be a list of strings.') # Use a canonical representation for all tensor names. input_map = {_CanonicalInputName(k): v for k, v in input_map.items()} used_input_keys = set() name_to_op = {} if op_dict is None: op_dict = op_def_registry.get_registered_ops() if producer_op_list is None: producer_op_dict = None else: producer_op_dict = {op.name: op for op in producer_op_list.op} g = ops.get_default_graph() # Add any functions defined in `graph_def` to `g` if graph_def.library and graph_def.library.function: # Copy op_dict so we don't clobber the original op_dict = copy.copy(op_dict) # pylint: disable=protected-access # Note that we do not prepend `name` to the function name. The reasoning is # that function names are similar to op definition names, which currently do # not have a scoped name or namespace scheme. functions = function._from_library(graph_def.library) for f in functions: f.add_to_graph(g) op_dict[f.name] = f.definition.signature # pylint: enable=protected-access # LINT.IfChange with ops.name_scope(name, 'import', input_map.values()) as scope: # TODO(ashankar): Should this just copy over or should it do some # more nuanced merging? For example, the graph may already have some # marked "bad versions" and we don't want to lose those because of # what's in graph_def.versions? The C++ ImporGraphDef does something # more nuanced. g.graph_def_versions.CopyFrom(graph_def.versions) if not all(isinstance(v, ops.Tensor) for v in input_map.values()): if not scope: # The caller must have passed `name=''`. raise ValueError( 'tf.import_graph_def() requires a non-empty `name` if `input_map` ' 'contains non-Tensor values. Try calling tf.convert_to_tensor() on ' '`input_map` values before calling tf.import_graph_def().') with ops.name_scope('_inputs'): input_map = {k: ops.convert_to_tensor(v) for k, v in input_map.items()} # NOTE(mrry): We do this in two passes, because there may be a cycle in # `graph_def`. # 1. Add operations without their inputs. for node in graph_def.node: # Check to see if this op's name matches a previously seen op if node.name in name_to_op: raise ValueError('Duplicate name \'%s\' in GraphDef.' % node.name) # Set any default attr values that aren't present. if node.op not in op_dict: raise ValueError('No op named %s in defined operations.' % node.op) op_def = op_dict[node.op] for attr_def in op_def.attr: key = attr_def.name if attr_def.HasField('default_value'): value = node.attr[key] if value is None or value.WhichOneof('value') is None: node.attr[key].CopyFrom(attr_def.default_value) if producer_op_dict: # Remove any default attr values that aren't in op_def. if node.op in producer_op_dict: producer_op_def = producer_op_dict[node.op] # We make a copy of node.attr to iterate through since we # may modify node.attr inside the loop. for key in list(node.attr): if _FindAttrInOpDef(key, op_def) is None: # No attr_def in consumer, look in producer. attr_def = _FindAttrInOpDef(key, producer_op_def) if (attr_def and attr_def.HasField('default_value') and node.attr[key] == attr_def.default_value): # Unknown attr had default value in producer, delete it # so it can be understood by consumer. del node.attr[key] output_types = _OutputTypes(node, op_dict) name_to_op[node.name] = g.create_op( node.op, [], output_types, name=node.name, attrs=node.attr, compute_shapes=False, compute_device=False, op_def=op_def) # Maps from a node to the ops it is colocated with, if colocation # is specified in the attributes. colocation_pairs = collections.defaultdict(list) # 2. Add inputs to the operations. for node in graph_def.node: op = name_to_op[node.name] input_types = _InputTypes(node, op_dict) apply_device_function = True # Rewrite the colocation attributes in the graph, since the # names of new ops may have changed. for key, value in op.node_def.attr.items(): if key == '_class': class_values = value.list new_class_values = [] for class_value in class_values.s: if class_value.startswith(b'loc:@'): op_to_bind_to = class_value[5:].decode() # Find the op by its original name. if op_to_bind_to not in name_to_op: raise ValueError('Specified colocation to an op that ' 'does not exist during import: %s in %s' % ( op_to_bind_to, node.name)) original_op = name_to_op[op_to_bind_to] new_class_values.append(compat.as_bytes( 'loc:@' + original_op.name)) if op_to_bind_to != node.name: # Keep track of this mapping for a later phase. colocation_pairs[op].append(original_op) # Don't apply this op's device function, # the colocation constraint will ensure # the proper device gets assigned at runtime. apply_device_function = False else: new_class_values.append(class_value) value.list.CopyFrom(attr_value_pb2.AttrValue.ListValue( s=new_class_values)) # NOTE(mrry): We cannot use zip here because control inputs do not appear # in the list of input_types. for i, input_name in enumerate( [_CanonicalInputName(x) for x in node.input]): if _IsControlInput(input_name): # (a) Input is a control input that should be taken from an op # in "graph_def". try: source_op = name_to_op[input_name[1:]] except KeyError: raise ValueError( _InvalidNodeMessage( node, 'Control input %r not found in graph_def.' % (input_name,))) # pylint: disable=protected-access op._add_control_input(source_op) # pylint: enable=protected-access else: try: input_type = input_types[i] except IndexError: raise ValueError(_InvalidNodeMessage( node, 'More inputs specified (%r) than the op expects.' % (input_name,))) if input_name in input_map: # (b) Input should be replaced by a tensor from the caller. source_tensor = input_map[input_name] used_input_keys.add(input_name) else: # (c) Input should be taken from an op in `graph_def`. operation_name, output_index = _ParseTensorName(input_name) try: source_op = name_to_op[operation_name] source_tensor = list(source_op.values())[output_index] except (KeyError, IndexError): raise ValueError( _InvalidNodeMessage( node, 'Input tensor %r not found in graph_def.' % (input_name,))) try: # pylint: disable=protected-access op._add_input(source_tensor, dtype=input_type) # pylint: enable=protected-access except TypeError as te: raise ValueError(_InvalidNodeMessage( node, 'Input tensor %r %s' % (input_name, te))) # pylint: disable=protected-access if op._input_dtypes != input_types: raise ValueError( _InvalidNodeMessage( node, 'Input types mismatch (expected %r but got %r)' % (', '.join(dtypes.as_dtype(x).name for x in input_types), ', '.join(x.name for x in op._input_dtypes)))) # pylint: enable=protected-access if not g._is_function(op.type): # pylint: disable=protected-access # Execute shape inference for this op. # NOTE(mrry): If the graph contains a cycle, the full shape information # may not be available for this op's inputs. ops.set_shapes_for_outputs(op) # For nodes with _output_shapes set, set the output shapes. if '_output_shapes' in op.node_def.attr: for i, output in enumerate(op.outputs): dims = op.node_def.attr['_output_shapes'].list.shape[i] output_shape = tensor_shape.TensorShape( None if dims.unknown_rank else [dim.size if dim.size >= 0 else None for dim in dims.dim]) try: output.set_shape(output_shape) except ValueError as e: # If the output shape is incompatible with what is inferred # by the graph for a very specific whitelist of ops, then we # ignore this output shape. This can happen if there is a # bug in the shape function for some operation, and the # serialized graph def has the incorrect shape set when # running on a newer binary with the fixed shape function. # This is an escape hatch that allows us to correct shape # functions that are not critical to correct execution but # would cause graphs to fail if imported after correcting. # # This can be removed after 2017/03/08. if op.type in ['RandomShuffleQueue', 'PaddingFIFOQueue', 'FIFOQueue', 'PriorityQueue', 'QueueSize', 'Stack', 'Barrier', 'BarrierReadySize', 'BarrierIncompleteSize', 'HashTable', 'MutableHashTable', 'MutableHashTableOfTensors', 'Mutex', 'CuckooTable', 'IndexTable', 'WholeFileReader', 'TextLineReader', 'FixedLengthRecordReader', 'TFRecordReader', 'IdentityReader', 'LMDBReader', 'RefSwitch', 'RefEnter', 'RefNextIteration', 'RefMerge', 'RefIdentity']: pass elif op.type in [ 'ConditionalAccumulator', 'SparseConditionalAccumulator', 'Table' ]: # This can be removed after 2017/04/24. pass else: raise e del op.node_def.attr['_output_shapes'] # NOTE(mrry): We do this after configuring the inputs, because # the result of the device functions may depend on the inputs. if apply_device_function: with _MaybeDevice(node.device): g._apply_device_functions(op) # pylint: disable=protected-access # The following loop populates the device field of ops that are # colocated with another op. This is implied by the colocation # attribute, but we propagate the device field for completeness. for op, coloc_op_list in colocation_pairs.items(): coloc_device = None # Find any device in the list of colocated ops that have a # device, if it exists. We assume that if multiple ops # have devices, they refer to the same device. Otherwise, a # runtime error will occur since the colocation property # cannot be guaranteed. # # One possible improvement is to try to check for compatibility # of all devices in this list at import time here, which would # require implementing a compatibility function for device specs # in python. for coloc_op in coloc_op_list: if coloc_op.device: coloc_device = pydev.DeviceSpec.from_string(coloc_op.device) break if coloc_device: op._set_device(coloc_device) # pylint: disable=protected-access # Treat unused input mappings as an error, because they are likely to be # due to a typo. unused_input_keys = frozenset(input_map.keys()).difference(used_input_keys) if unused_input_keys: raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(unused_input_keys)) if return_elements is None: return None else: ret = [] for name in return_elements: name = compat.as_str(name) if ':' in name: try: operation_name, output_index = _ParseTensorName(name) ret.append(name_to_op[operation_name].outputs[output_index]) except (ValueError, KeyError, IndexError): raise ValueError( 'Requested return_element %r not found in graph_def.' % name) else: try: ret.append(name_to_op[name]) except KeyError: raise ValueError( 'Requested return_element %r not found in graph_def.' % name) return ret
def testFromLibraryEmptyLib(self): library = function_pb2.FunctionDefLibrary() self.assertEqual(len(function._from_library(library)), 0)
def import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None): """Imports the graph from `graph_def` into the current default `Graph`. This function provides a way to import a serialized TensorFlow [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and extract individual objects in the `GraphDef` as @{tf.Tensor} and @{tf.Operation} objects. Once extracted, these objects are placed into the current default `Graph`. See @{tf.Graph.as_graph_def} for a way to create a `GraphDef` proto. Args: graph_def: A `GraphDef` proto containing operations to be imported into the default graph. input_map: A dictionary mapping input names (as strings) in `graph_def` to `Tensor` objects. The values of the named input tensors in the imported graph will be re-mapped to the respective `Tensor` values. return_elements: A list of strings containing operation names in `graph_def` that will be returned as `Operation` objects; and/or tensor names in `graph_def` that will be returned as `Tensor` objects. name: (Optional.) A prefix that will be prepended to the names in `graph_def`. Note that this does not apply to imported function names. Defaults to `"import"`. op_dict: (Optional.) Deprecated, do not use. producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped) list of `OpDef`s used by the producer of the graph. If provided, unrecognized attrs for ops in `graph_def` that have their default value according to `producer_op_list` will be removed. This will allow some more `GraphDef`s produced by later binaries to be accepted by earlier binaries. Returns: A list of `Operation` and/or `Tensor` objects from the imported graph, corresponding to the names in `return_elements`. Raises: TypeError: If `graph_def` is not a `GraphDef` proto, `input_map` is not a dictionary mapping strings to `Tensor` objects, or `return_elements` is not a list of strings. ValueError: If `input_map`, or `return_elements` contains names that do not appear in `graph_def`, or `graph_def` is not well-formed (e.g. it refers to an unknown tensor). """ graph_def = _ProcessGraphDefParam(graph_def) input_map = _ProcessInputMapParam(input_map) return_elements = _ProcessReturnElementsParam(return_elements) op_dict = op_def_registry.get_registered_ops() if producer_op_list is not None: # TODO(skyewm): make a copy of graph_def so we're not mutating the argument? _RemoveDefaultAttrs(op_dict, producer_op_list, graph_def) graph = ops.get_default_graph() if graph._c_graph: # pylint: disable=protected-access with ops.name_scope(name, 'import', input_map.values()) as scope: # Save unique prefix generated by name_scope if scope: assert scope.endswith('/') prefix = scope[:-1] else: prefix = '' # Generate any input map tensors inside name scope input_map = _ConvertInputMapValues(name, input_map) scoped_options = c_api_util.ScopedTFImportGraphDefOptions() options = scoped_options.options _PopulateTFImportGraphDefOptions(options, prefix, input_map, return_elements) with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: try: with errors.raise_exception_on_not_ok_status() as status: results = c_api.TF_GraphImportGraphDefWithResults( graph._c_graph, serialized, options, status) # pylint: disable=protected-access except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) _ProcessNewOps(graph) # Create _DefinedFunctions for any imported functions. # # We do this by creating _DefinedFunctions directly from `graph_def`, and # adding them to `graph`. Adding an existing function to a TF_Graph is a # no-op, so this only has the effect of updating the Python state (usually # _DefinedFunction.add_to_graph also adds the function to the TF_Graph). # # TODO(skyewm): fetch the TF_Functions directly from the TF_Graph # TODO(skyewm): avoid sending serialized FunctionDefs back to the TF_Graph if graph_def.library and graph_def.library.function: # pylint: disable=protected-access functions = function._from_library(graph_def.library) for f in functions: f.add_to_graph(graph) # pylint: enable=protected-access # Treat input mappings that don't appear in the graph as an error, because # they are likely to be due to a typo. missing_unused_input_keys = ( c_api.TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( results)) if missing_unused_input_keys: missing_unused_input_keys = [compat.as_str(s) for s in missing_unused_input_keys] raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(missing_unused_input_keys)) if return_elements is None: return None else: return _GatherReturnElements(return_elements, graph, results) else: g = graph # Use a canonical representation for all tensor names. input_map = {_CanonicalInputName(k): v for k, v in input_map.items()} used_input_keys = set() name_to_op = {} # Add any functions defined in `graph_def` to `g` if graph_def.library and graph_def.library.function: # Copy op_dict so we don't clobber the original op_dict = copy.copy(op_dict) # pylint: disable=protected-access # Note that we do not prepend `name` to the function name. The reasoning # is that function names are similar to op definition names, which # currently do not have a scoped name or namespace scheme. functions = function._from_library(graph_def.library) for f in functions: f.add_to_graph(g) op_dict[f.name] = f.definition.signature # pylint: enable=protected-access # LINT.IfChange with ops.name_scope(name, 'import', input_map.values()) as scope: # TODO(ashankar): Should this just copy over or should it do some # more nuanced merging? For example, the graph may already have some # marked "bad versions" and we don't want to lose those because of # what's in graph_def.versions? The C++ ImporGraphDef does something # more nuanced. g.graph_def_versions.CopyFrom(graph_def.versions) input_map = _ConvertInputMapValues(name, input_map) # NOTE(mrry): We do this in two passes, because there may be a cycle in # `graph_def`. # 1. Add operations without their inputs. for node in graph_def.node: # Check to see if this op's name matches a previously seen op if node.name in name_to_op: raise ValueError('Duplicate name \'%s\' in GraphDef.' % node.name) # Set any default attr values that aren't present. if node.op not in op_dict: raise ValueError('No op named %s in defined operations.' % node.op) op_def = op_dict[node.op] for attr_def in op_def.attr: key = attr_def.name if attr_def.HasField('default_value'): value = node.attr[key] if value is None or value.WhichOneof('value') is None: node.attr[key].CopyFrom(attr_def.default_value) output_types = _OutputTypes(node, op_dict) name_to_op[node.name] = g.create_op( node.op, [], output_types, name=node.name, attrs=node.attr, compute_shapes=False, compute_device=False, op_def=op_def) # Maps from a node to the ops it is colocated with, if colocation # is specified in the attributes. colocation_pairs = collections.defaultdict(list) # 2. Add inputs to the operations. for node in graph_def.node: op = name_to_op[node.name] input_types = _InputTypes(node, op_dict) apply_device_function = True # Rewrite the colocation attributes in the graph, since the # names of new ops may have changed. for key, value in op.node_def.attr.items(): if key == '_class': class_values = value.list new_class_values = [] for class_value in class_values.s: if class_value.startswith(b'loc:@'): op_to_bind_to = class_value[5:].decode() # Find the op by its original name. if op_to_bind_to not in name_to_op: raise ValueError('Specified colocation to an op that ' 'does not exist during import: %s in %s' % ( op_to_bind_to, node.name)) original_op = name_to_op[op_to_bind_to] new_class_values.append(compat.as_bytes( 'loc:@' + original_op.name)) if op_to_bind_to != node.name: # Keep track of this mapping for a later phase. colocation_pairs[op].append(original_op) # Don't apply this op's device function, # the colocation constraint will ensure # the proper device gets assigned at runtime. apply_device_function = False else: new_class_values.append(class_value) value.list.CopyFrom(attr_value_pb2.AttrValue.ListValue( s=new_class_values)) # NOTE(mrry): We cannot use zip here because control inputs do not # appear in the list of input_types. for i, input_name in enumerate( [_CanonicalInputName(x) for x in node.input]): if _IsControlInput(input_name): # (a) Input is a control input that should be taken from an op # in "graph_def". try: source_op = name_to_op[input_name[1:]] except KeyError: raise ValueError( _InvalidNodeMessage( node, 'Control input %r not found in graph_def.' % (input_name,))) # pylint: disable=protected-access op._add_control_input(source_op) # pylint: enable=protected-access else: try: input_type = input_types[i] except IndexError: raise ValueError(_InvalidNodeMessage( node, 'More inputs specified (%r) than the op expects.' % (input_name,))) if input_name in input_map: # (b) Input should be replaced by a tensor from the caller. source_tensor = input_map[input_name] used_input_keys.add(input_name) else: # (c) Input should be taken from an op in `graph_def`. operation_name, output_index = _ParseTensorName(input_name) try: source_op = name_to_op[operation_name] source_tensor = list(source_op.values())[output_index] except (KeyError, IndexError): raise ValueError( _InvalidNodeMessage( node, 'Input tensor %r not found in graph_def.' % (input_name,))) try: # pylint: disable=protected-access op._add_input(source_tensor, dtype=input_type) # pylint: enable=protected-access except TypeError as te: raise ValueError(_InvalidNodeMessage( node, 'Input tensor %r %s' % (input_name, te))) # pylint: disable=protected-access if op._input_types != input_types: raise ValueError( _InvalidNodeMessage( node, 'Input types mismatch (expected %r but got %r)' % (', '.join(dtypes.as_dtype(x).name for x in input_types), ', '.join(x.name for x in op._input_types)))) # pylint: enable=protected-access if not g._is_function(op.type): # pylint: disable=protected-access # Execute shape inference for this op. # NOTE(mrry): If the graph contains a cycle, the full shape # information may not be available for this op's inputs. ops.set_shapes_for_outputs(op) # For nodes with _output_shapes set, set the output shapes. if '_output_shapes' in op.node_def.attr: for i, output in enumerate(op.outputs): dims = op.node_def.attr['_output_shapes'].list.shape[i] output_shape = tensor_shape.TensorShape( None if dims.unknown_rank else [dim.size if dim.size >= 0 else None for dim in dims.dim]) try: output.set_shape(output_shape) except ValueError as e: # If the output shape is incompatible with what is inferred # by the graph for a very specific whitelist of ops, then we # ignore this output shape. This can happen if there is a # bug in the shape function for some operation, and the # serialized graph def has the incorrect shape set when # running on a newer binary with the fixed shape function. # This is an escape hatch that allows us to correct shape # functions that are not critical to correct execution but # would cause graphs to fail if imported after correcting. # # This can be removed after 2017/03/08. if op.type in ['RandomShuffleQueue', 'PaddingFIFOQueue', 'FIFOQueue', 'PriorityQueue', 'QueueSize', 'Stack', 'Barrier', 'BarrierReadySize', 'BarrierIncompleteSize', 'HashTable', 'MutableHashTable', 'MutableHashTableOfTensors', 'Mutex', 'CuckooTable', 'IndexTable', 'WholeFileReader', 'TextLineReader', 'FixedLengthRecordReader', 'TFRecordReader', 'IdentityReader', 'LMDBReader', 'RefSwitch', 'RefEnter', 'RefNextIteration', 'RefMerge', 'RefIdentity']: pass elif op.type in [ 'ConditionalAccumulator', 'SparseConditionalAccumulator', 'Table' ]: # This can be removed after 2017/04/24. pass else: raise e del op.node_def.attr['_output_shapes'] # NOTE(mrry): We do this after configuring the inputs, because # the result of the device functions may depend on the inputs. if apply_device_function: with _MaybeDevice(node.device): g._apply_device_functions(op) # pylint: disable=protected-access # The following loop populates the device field of ops that are # colocated with another op. This is implied by the colocation # attribute, but we propagate the device field for completeness. for op, coloc_op_list in colocation_pairs.items(): coloc_device = None # Find any device in the list of colocated ops that have a # device, if it exists. We assume that if multiple ops # have devices, they refer to the same device. Otherwise, a # runtime error will occur since the colocation property # cannot be guaranteed. # # One possible improvement is to try to check for compatibility # of all devices in this list at import time here, which would # require implementing a compatibility function for device specs # in python. for coloc_op in coloc_op_list: if coloc_op.device: coloc_device = pydev.DeviceSpec.from_string(coloc_op.device) break if coloc_device: op._set_device(coloc_device) # pylint: disable=protected-access # Treat input mappings that don't appear in the graph as an error, # because they are likely to be due to a typo. def _IsImportedNodeOutput(tensor_name): operation_name, output_index = _ParseTensorName(tensor_name) try: return output_index < len(name_to_op[operation_name].outputs) except KeyError: return False absent_input_keys = [ k for k in frozenset(input_map.keys()).difference(used_input_keys) if not _IsImportedNodeOutput(k)] if absent_input_keys: raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(absent_input_keys)) if return_elements is None: return None else: ret = [] for name in return_elements: name = compat.as_str(name) if ':' in name: try: operation_name, output_index = _ParseTensorName(name) ret.append(name_to_op[operation_name].outputs[output_index]) except (ValueError, KeyError, IndexError): raise ValueError( 'Requested return_element %r not found in graph_def.' % name) else: try: ret.append(name_to_op[name]) except KeyError: raise ValueError( 'Requested return_element %r not found in graph_def.' % name) return ret
def import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None): """Imports the graph from `graph_def` into the current default `Graph`. This function provides a way to import a serialized TensorFlow [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and extract individual objects in the `GraphDef` as @{tf.Tensor} and @{tf.Operation} objects. Once extracted, these objects are placed into the current default `Graph`. See @{tf.Graph.as_graph_def} for a way to create a `GraphDef` proto. Args: graph_def: A `GraphDef` proto containing operations to be imported into the default graph. input_map: A dictionary mapping input names (as strings) in `graph_def` to `Tensor` objects. The values of the named input tensors in the imported graph will be re-mapped to the respective `Tensor` values. return_elements: A list of strings containing operation names in `graph_def` that will be returned as `Operation` objects; and/or tensor names in `graph_def` that will be returned as `Tensor` objects. name: (Optional.) A prefix that will be prepended to the names in `graph_def`. Note that this does not apply to imported function names. Defaults to `"import"`. op_dict: (Optional.) Deprecated, do not use. producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped) list of `OpDef`s used by the producer of the graph. If provided, unrecognized attrs for ops in `graph_def` that have their default value according to `producer_op_list` will be removed. This will allow some more `GraphDef`s produced by later binaries to be accepted by earlier binaries. Returns: A list of `Operation` and/or `Tensor` objects from the imported graph, corresponding to the names in `return_elements`. Raises: TypeError: If `graph_def` is not a `GraphDef` proto, `input_map` is not a dictionary mapping strings to `Tensor` objects, or `return_elements` is not a list of strings. ValueError: If `input_map`, or `return_elements` contains names that do not appear in `graph_def`, or `graph_def` is not well-formed (e.g. it refers to an unknown tensor). """ op_dict = op_def_registry.get_registered_ops() graph_def = _ProcessGraphDefParam(graph_def, op_dict) input_map = _ProcessInputMapParam(input_map) return_elements = _ProcessReturnElementsParam(return_elements) if producer_op_list is not None: # TODO(skyewm): make a copy of graph_def so we're not mutating the argument? _RemoveDefaultAttrs(op_dict, producer_op_list, graph_def) graph = ops.get_default_graph() if graph._c_graph: # pylint: disable=protected-access with ops.name_scope(name, 'import', input_map.values()) as scope: # Save unique prefix generated by name_scope if scope: assert scope.endswith('/') prefix = scope[:-1] else: prefix = '' # Generate any input map tensors inside name scope input_map = _ConvertInputMapValues(name, input_map) scoped_options = c_api_util.ScopedTFImportGraphDefOptions() options = scoped_options.options _PopulateTFImportGraphDefOptions(options, prefix, input_map, return_elements) # _ProcessNewOps mutates the new operations. _lock ensures a Session.run # call cannot occur between creating the TF_Operations in the # TF_GraphImportGraphDefWithResults call and mutating the them in # _ProcessNewOps. with graph._lock: # pylint: disable=protected-access with c_api_util.tf_buffer( graph_def.SerializeToString()) as serialized: try: with errors.raise_exception_on_not_ok_status() as status: results = c_api.TF_GraphImportGraphDefWithResults( graph._c_graph, serialized, options, status) # pylint: disable=protected-access except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) _ProcessNewOps(graph) # Create _DefinedFunctions for any imported functions. # # We do this by creating _DefinedFunctions directly from `graph_def`, and # adding them to `graph`. Adding an existing function to a TF_Graph is a # no-op, so this only has the effect of updating the Python state (usually # _DefinedFunction.add_to_graph also adds the function to the TF_Graph). # # TODO(skyewm): fetch the TF_Functions directly from the TF_Graph # TODO(skyewm): avoid sending serialized FunctionDefs back to the TF_Graph if graph_def.library and graph_def.library.function: # pylint: disable=protected-access functions = function._from_library(graph_def.library) for f in functions: f.add_to_graph(graph) # pylint: enable=protected-access # Treat input mappings that don't appear in the graph as an error, because # they are likely to be due to a typo. missing_unused_input_keys = ( c_api.TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( results)) if missing_unused_input_keys: missing_unused_input_keys = [ compat.as_str(s) for s in missing_unused_input_keys ] raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(missing_unused_input_keys)) if return_elements is None: return None else: return _GatherReturnElements(return_elements, graph, results) else: g = graph # Use a canonical representation for all tensor names. input_map = {_CanonicalInputName(k): v for k, v in input_map.items()} used_input_keys = set() name_to_op = {} # Add any functions defined in `graph_def` to `g` if graph_def.library and graph_def.library.function: # Copy op_dict so we don't clobber the original op_dict = copy.copy(op_dict) # pylint: disable=protected-access # Note that we do not prepend `name` to the function name. The reasoning # is that function names are similar to op definition names, which # currently do not have a scoped name or namespace scheme. functions = function._from_library(graph_def.library) for f in functions: f.add_to_graph(g) op_dict[f.name] = f.definition.signature # pylint: enable=protected-access # LINT.IfChange with ops.name_scope(name, 'import', input_map.values()) as scope: # TODO(ashankar): Should this just copy over or should it do some # more nuanced merging? For example, the graph may already have some # marked "bad versions" and we don't want to lose those because of # what's in graph_def.versions? The C++ ImporGraphDef does something # more nuanced. g.graph_def_versions.CopyFrom(graph_def.versions) input_map = _ConvertInputMapValues(name, input_map) # NOTE(mrry): We do this in two passes, because there may be a cycle in # `graph_def`. # 1. Add operations without their inputs. for node in graph_def.node: # Check to see if this op's name matches a previously seen op if node.name in name_to_op: raise ValueError('Duplicate name \'%s\' in GraphDef.' % node.name) if node.op not in op_dict: raise ValueError('No op named %s in defined operations.' % node.op) op_def = op_dict[node.op] output_types = _OutputTypes(node, op_dict) name_to_op[node.name] = g.create_op(node.op, [], output_types, name=node.name, attrs=node.attr, compute_shapes=False, compute_device=False, op_def=op_def) # Maps from a node to the ops it is colocated with, if colocation # is specified in the attributes. colocation_pairs = collections.defaultdict(list) # 2. Add inputs to the operations. for node in graph_def.node: op = name_to_op[node.name] input_types = _InputTypes(node, op_dict) apply_device_function = True # Rewrite the colocation attributes in the graph, since the # names of new ops may have changed. for key, value in op.node_def.attr.items(): if key == '_class': class_values = value.list new_class_values = [] for class_value in class_values.s: if class_value.startswith(b'loc:@'): op_to_bind_to = class_value[5:].decode() # Find the op by its original name. if op_to_bind_to not in name_to_op: raise ValueError( 'Specified colocation to an op that ' 'does not exist during import: %s in %s' % (op_to_bind_to, node.name)) original_op = name_to_op[op_to_bind_to] new_class_values.append( compat.as_bytes('loc:@' + original_op.name)) if op_to_bind_to != node.name: # Keep track of this mapping for a later phase. colocation_pairs[op].append(original_op) # Don't apply this op's device function, # the colocation constraint will ensure # the proper device gets assigned at runtime. apply_device_function = False else: new_class_values.append(class_value) value.list.CopyFrom( attr_value_pb2.AttrValue.ListValue( s=new_class_values)) # NOTE(mrry): We cannot use zip here because control inputs do not # appear in the list of input_types. for i, input_name in enumerate( [_CanonicalInputName(x) for x in node.input]): if _IsControlInput(input_name): # (a) Input is a control input that should be taken from an op # in "graph_def". try: source_op = name_to_op[input_name[1:]] except KeyError: raise ValueError( _InvalidNodeMessage( node, 'Control input %r not found in graph_def.' % (input_name, ))) # pylint: disable=protected-access op._add_control_input(source_op) # pylint: enable=protected-access else: try: input_type = input_types[i] except IndexError: raise ValueError( _InvalidNodeMessage( node, 'More inputs specified (%r) than the op expects.' % (input_name, ))) if input_name in input_map: # (b) Input should be replaced by a tensor from the caller. source_tensor = input_map[input_name] used_input_keys.add(input_name) else: # (c) Input should be taken from an op in `graph_def`. operation_name, output_index = _ParseTensorName( input_name) try: source_op = name_to_op[operation_name] source_tensor = list( source_op.values())[output_index] except (KeyError, IndexError): raise ValueError( _InvalidNodeMessage( node, 'Input tensor %r not found in graph_def.' % (input_name, ))) try: # pylint: disable=protected-access op._add_input(source_tensor, dtype=input_type) # pylint: enable=protected-access except TypeError as te: raise ValueError( _InvalidNodeMessage( node, 'Input tensor %r %s' % (input_name, te))) # pylint: disable=protected-access if op._input_types != input_types: raise ValueError( _InvalidNodeMessage( node, 'Input types mismatch (expected %r but got %r)' % (', '.join( dtypes.as_dtype(x).name for x in input_types), ', '.join( x.name for x in op._input_types)))) # pylint: enable=protected-access if not g._is_function(op.type): # pylint: disable=protected-access # Execute shape inference for this op. # NOTE(mrry): If the graph contains a cycle, the full shape # information may not be available for this op's inputs. ops.set_shapes_for_outputs(op) # For nodes with _output_shapes set, set the output shapes. if '_output_shapes' in op.node_def.attr: for i, output in enumerate(op.outputs): dims = op.node_def.attr['_output_shapes'].list.shape[i] output_shape = tensor_shape.TensorShape( None if dims.unknown_rank else [ dim.size if dim.size >= 0 else None for dim in dims.dim ]) try: output.set_shape(output_shape) except ValueError as e: # If the output shape is incompatible with what is inferred # by the graph for a very specific whitelist of ops, then we # ignore this output shape. This can happen if there is a # bug in the shape function for some operation, and the # serialized graph def has the incorrect shape set when # running on a newer binary with the fixed shape function. # This is an escape hatch that allows us to correct shape # functions that are not critical to correct execution but # would cause graphs to fail if imported after correcting. # # This can be removed after 2017/03/08. if op.type in [ 'RandomShuffleQueue', 'PaddingFIFOQueue', 'FIFOQueue', 'PriorityQueue', 'QueueSize', 'Stack', 'Barrier', 'BarrierReadySize', 'BarrierIncompleteSize', 'HashTable', 'MutableHashTable', 'MutableHashTableOfTensors', 'Mutex', 'CuckooTable', 'IndexTable', 'WholeFileReader', 'TextLineReader', 'FixedLengthRecordReader', 'TFRecordReader', 'IdentityReader', 'LMDBReader', 'RefSwitch', 'RefEnter', 'RefNextIteration', 'RefMerge', 'RefIdentity' ]: pass elif op.type in [ 'ConditionalAccumulator', 'SparseConditionalAccumulator', 'Table' ]: # This can be removed after 2017/04/24. pass else: raise e del op.node_def.attr['_output_shapes'] # NOTE(mrry): We do this after configuring the inputs, because # the result of the device functions may depend on the inputs. if apply_device_function: with _MaybeDevice(node.device): g._apply_device_functions(op) # pylint: disable=protected-access # The following loop populates the device field of ops that are # colocated with another op. This is implied by the colocation # attribute, but we propagate the device field for completeness. for op, coloc_op_list in colocation_pairs.items(): coloc_device = None # Find any device in the list of colocated ops that have a # device, if it exists. We assume that if multiple ops # have devices, they refer to the same device. Otherwise, a # runtime error will occur since the colocation property # cannot be guaranteed. # # One possible improvement is to try to check for compatibility # of all devices in this list at import time here, which would # require implementing a compatibility function for device specs # in python. for coloc_op in coloc_op_list: if coloc_op.device: coloc_device = pydev.DeviceSpec.from_string( coloc_op.device) break if coloc_device: op._set_device(coloc_device) # pylint: disable=protected-access # Treat input mappings that don't appear in the graph as an error, # because they are likely to be due to a typo. def _IsImportedNodeOutput(tensor_name): operation_name, output_index = _ParseTensorName(tensor_name) try: return output_index < len( name_to_op[operation_name].outputs) except KeyError: return False absent_input_keys = [ k for k in frozenset(input_map.keys()).difference( used_input_keys) if not _IsImportedNodeOutput(k) ] if absent_input_keys: raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(absent_input_keys)) if return_elements is None: return None else: ret = [] for name in return_elements: name = compat.as_str(name) if ':' in name: try: operation_name, output_index = _ParseTensorName( name) ret.append(name_to_op[operation_name]. outputs[output_index]) except (ValueError, KeyError, IndexError): raise ValueError( 'Requested return_element %r not found in graph_def.' % name) else: try: ret.append(name_to_op[name]) except KeyError: raise ValueError( 'Requested return_element %r not found in graph_def.' % name) return ret
def import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None): """Imports the graph from `graph_def` into the current default `Graph`. This function provides a way to import a serialized TensorFlow [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and extract individual objects in the `GraphDef` as @{tf.Tensor} and @{tf.Operation} objects. Once extracted, these objects are placed into the current default `Graph`. See @{tf.Graph.as_graph_def} for a way to create a `GraphDef` proto. Args: graph_def: A `GraphDef` proto containing operations to be imported into the default graph. input_map: A dictionary mapping input names (as strings) in `graph_def` to `Tensor` objects. The values of the named input tensors in the imported graph will be re-mapped to the respective `Tensor` values. return_elements: A list of strings containing operation names in `graph_def` that will be returned as `Operation` objects; and/or tensor names in `graph_def` that will be returned as `Tensor` objects. name: (Optional.) A prefix that will be prepended to the names in `graph_def`. Note that this does not apply to imported function names. Defaults to `"import"`. op_dict: (Optional.) Deprecated, do not use. producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped) list of `OpDef`s used by the producer of the graph. If provided, unrecognized attrs for ops in `graph_def` that have their default value according to `producer_op_list` will be removed. This will allow some more `GraphDef`s produced by later binaries to be accepted by earlier binaries. Returns: A list of `Operation` and/or `Tensor` objects from the imported graph, corresponding to the names in `return_elements`. Raises: TypeError: If `graph_def` is not a `GraphDef` proto, `input_map` is not a dictionary mapping strings to `Tensor` objects, or `return_elements` is not a list of strings. ValueError: If `input_map`, or `return_elements` contains names that do not appear in `graph_def`, or `graph_def` is not well-formed (e.g. it refers to an unknown tensor). """ op_dict = op_def_registry.get_registered_ops() graph_def = _ProcessGraphDefParam(graph_def, op_dict) input_map = _ProcessInputMapParam(input_map) return_elements = _ProcessReturnElementsParam(return_elements) if producer_op_list is not None: # TODO(skyewm): make a copy of graph_def so we're not mutating the argument? _RemoveDefaultAttrs(op_dict, producer_op_list, graph_def) graph = ops.get_default_graph() with ops.name_scope(name, 'import', input_map.values()) as scope: # Save unique prefix generated by name_scope if scope: assert scope.endswith('/') prefix = scope[:-1] else: prefix = '' # Generate any input map tensors inside name scope input_map = _ConvertInputMapValues(name, input_map) scoped_options = c_api_util.ScopedTFImportGraphDefOptions() options = scoped_options.options _PopulateTFImportGraphDefOptions(options, prefix, input_map, return_elements) # _ProcessNewOps mutates the new operations. _mutation_lock ensures a # Session.run call cannot occur between creating the TF_Operations in the # TF_GraphImportGraphDefWithResults call and mutating the them in # _ProcessNewOps. with graph._mutation_lock(): # pylint: disable=protected-access with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: try: results = c_api.TF_GraphImportGraphDefWithResults( graph._c_graph, serialized, options) # pylint: disable=protected-access results = c_api_util.ScopedTFImportGraphDefResults(results) except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) # Create _DefinedFunctions for any imported functions. # # We do this by creating _DefinedFunctions directly from `graph_def`, and # adding them to `graph`. Adding an existing function to a TF_Graph is a # no-op, so this only has the effect of updating the Python state (usually # _DefinedFunction.add_to_graph also adds the function to the TF_Graph). # # TODO(skyewm): fetch the TF_Functions directly from the TF_Graph # TODO(skyewm): avoid sending serialized FunctionDefs back to the TF_Graph # TODO(b/74620627): move this after _ProcessNewOps outside the lock once # _USE_C_SHAPES is removed. if graph_def.library and graph_def.library.function: # pylint: disable=protected-access functions = function._from_library(graph_def.library) for f in functions: f.add_to_graph(graph) # pylint: enable=protected-access _ProcessNewOps(graph) # Treat input mappings that don't appear in the graph as an error, because # they are likely to be due to a typo. missing_unused_input_keys = ( c_api.TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( results.results)) if missing_unused_input_keys: missing_unused_input_keys = [ compat.as_str(s) for s in missing_unused_input_keys ] raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(missing_unused_input_keys)) if return_elements is None: return None else: return _GatherReturnElements(return_elements, graph, results.results)
def import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None): """Imports the graph from `graph_def` into the current default `Graph`. This function provides a way to import a serialized TensorFlow [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and extract individual objects in the `GraphDef` as `tf.Tensor` and `tf.Operation` objects. Once extracted, these objects are placed into the current default `Graph`. See `tf.Graph.as_graph_def` for a way to create a `GraphDef` proto. Args: graph_def: A `GraphDef` proto containing operations to be imported into the default graph. input_map: A dictionary mapping input names (as strings) in `graph_def` to `Tensor` objects. The values of the named input tensors in the imported graph will be re-mapped to the respective `Tensor` values. return_elements: A list of strings containing operation names in `graph_def` that will be returned as `Operation` objects; and/or tensor names in `graph_def` that will be returned as `Tensor` objects. name: (Optional.) A prefix that will be prepended to the names in `graph_def`. Note that this does not apply to imported function names. Defaults to `"import"`. op_dict: (Optional.) Deprecated, do not use. producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped) list of `OpDef`s used by the producer of the graph. If provided, unrecognized attrs for ops in `graph_def` that have their default value according to `producer_op_list` will be removed. This will allow some more `GraphDef`s produced by later binaries to be accepted by earlier binaries. Returns: A list of `Operation` and/or `Tensor` objects from the imported graph, corresponding to the names in `return_elements`. Raises: TypeError: If `graph_def` is not a `GraphDef` proto, `input_map` is not a dictionary mapping strings to `Tensor` objects, or `return_elements` is not a list of strings. ValueError: If `input_map`, or `return_elements` contains names that do not appear in `graph_def`, or `graph_def` is not well-formed (e.g. it refers to an unknown tensor). """ op_dict = op_def_registry.get_registered_ops() graph_def = _ProcessGraphDefParam(graph_def, op_dict) input_map = _ProcessInputMapParam(input_map) return_elements = _ProcessReturnElementsParam(return_elements) if producer_op_list is not None: # TODO(skyewm): make a copy of graph_def so we're not mutating the argument? _RemoveDefaultAttrs(op_dict, producer_op_list, graph_def) graph = ops.get_default_graph() with ops.name_scope(name, 'import', input_map.values()) as scope: # Save unique prefix generated by name_scope if scope: assert scope.endswith('/') prefix = scope[:-1] else: prefix = '' # Generate any input map tensors inside name scope input_map = _ConvertInputMapValues(name, input_map) scoped_options = c_api_util.ScopedTFImportGraphDefOptions() options = scoped_options.options _PopulateTFImportGraphDefOptions(options, prefix, input_map, return_elements) # _ProcessNewOps mutates the new operations. _mutation_lock ensures a # Session.run call cannot occur between creating the TF_Operations in the # TF_GraphImportGraphDefWithResults call and mutating the them in # _ProcessNewOps. with graph._mutation_lock(): # pylint: disable=protected-access with c_api_util.tf_buffer(graph_def.SerializeToString()) as serialized: try: results = c_api.TF_GraphImportGraphDefWithResults( graph._c_graph, serialized, options) # pylint: disable=protected-access results = c_api_util.ScopedTFImportGraphDefResults(results) except errors.InvalidArgumentError as e: # Convert to ValueError for backwards compatibility. raise ValueError(str(e)) # Create _DefinedFunctions for any imported functions. # # We do this by creating _DefinedFunctions directly from `graph_def`, and # adding them to `graph`. Adding an existing function to a TF_Graph is a # no-op, so this only has the effect of updating the Python state (usually # _DefinedFunction.add_to_graph also adds the function to the TF_Graph). # # TODO(skyewm): fetch the TF_Functions directly from the TF_Graph # TODO(skyewm): avoid sending serialized FunctionDefs back to the TF_Graph # TODO(b/74620627): move this after _ProcessNewOps outside the lock once # _USE_C_SHAPES is removed. if graph_def.library and graph_def.library.function: # pylint: disable=protected-access functions = function._from_library(graph_def.library) for f in functions: f.add_to_graph(graph) # pylint: enable=protected-access _ProcessNewOps(graph) # Treat input mappings that don't appear in the graph as an error, because # they are likely to be due to a typo. missing_unused_input_keys = ( c_api.TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( results.results)) if missing_unused_input_keys: missing_unused_input_keys = [ compat.as_str(s) for s in missing_unused_input_keys ] raise ValueError( 'Attempted to map inputs that were not found in graph_def: [%s]' % ', '.join(missing_unused_input_keys)) if return_elements is None: return None else: return _GatherReturnElements(return_elements, graph, results.results)