Python tensorflow.python.ops.variables._all_saveable_objects() Examples
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Example #1
Source File: model.py From ebonite with Apache License 2.0 | 5 votes |
def _is_graph_frozen() -> bool: """ Checks if graph in current graph is frozen :return: `True` or `False` """ from tensorflow.python.ops import variables return not bool(variables._all_saveable_objects())
Example #2
Source File: tf_model.py From DeepPavlov with Apache License 2.0 | 5 votes |
def _get_saveable_variables(exclude_scopes=tuple()): # noinspection PyProtectedMember all_vars = variables._all_saveable_objects() vars_to_train = [var for var in all_vars if all(sc not in var.name for sc in exclude_scopes)] return vars_to_train
Example #3
Source File: builder_impl.py From lambda-packs with MIT License | 4 votes |
def add_meta_graph(self, tags, signature_def_map=None, assets_collection=None, legacy_init_op=None, clear_devices=False, main_op=None): """Adds the current meta graph to the SavedModel. Creates a Saver in the current scope and uses the Saver to export the meta graph def. Invoking this API requires the `add_meta_graph_and_variables()` API to have been invoked before. Args: tags: The set of tags to annotate the meta graph def with. signature_def_map: The map of signature defs to be added to the meta graph def. assets_collection: Assets collection to be saved with SavedModel. Note that this collection should be a subset of the assets saved as part of the first meta graph in the SavedModel. legacy_init_op: Legacy support for op or group of ops to execute after the restore op upon a load. clear_devices: Set to true if the device info on the default graph should be cleared. main_op: Op or group of ops to execute when the graph is loaded. Raises: AssertionError: If the variables for the SavedModel have not been saved yet. """ if not self._has_saved_variables: raise AssertionError( "Graph state including variables and assets has not been saved yet. " "Please invoke `add_meta_graph_and_variables()` first.") # Validate the signature def map to ensure all included TensorInfos are # properly populated. self._validate_signature_def_map(signature_def_map) # Save asset files and write them to disk, if any. self._save_and_write_assets(assets_collection) if main_op is None: # Add legacy init op to the SavedModel. self._maybe_add_legacy_init_op(legacy_init_op) else: self._add_main_op(main_op) # Initialize a saver to generate a sharded output for all saveables in the # current scope. saver = tf_saver.Saver( variables._all_saveable_objects(), # pylint: disable=protected-access sharded=True, write_version=saver_pb2.SaverDef.V2, allow_empty=True) meta_graph_def = saver.export_meta_graph(clear_devices=clear_devices) # Tag the meta graph def and add it to the SavedModel. self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map)
Example #4
Source File: saver.py From lambda-packs with MIT License | 4 votes |
def build(self): """Builds saver_def.""" if self._is_built: return self._is_built = True if not self.saver_def: if self._builder is None: self._builder = BaseSaverBuilder(self._write_version) if self._var_list is None: # pylint: disable=protected-access self._var_list = variables._all_saveable_objects() if not self._var_list: if self._allow_empty: self._is_empty = True return else: raise ValueError("No variables to save") self._is_empty = False self.saver_def = self._builder.build( self._var_list, reshape=self._reshape, sharded=self._sharded, max_to_keep=self._max_to_keep, keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours, name=self._name, restore_sequentially=self._restore_sequentially) elif self.saver_def and self._name: # Since self._name is used as a name_scope by builder(), we are # overloading the use of this field to represent the "import_scope" as # well. self.saver_def.filename_tensor_name = ops.prepend_name_scope( self.saver_def.filename_tensor_name, self._name) self.saver_def.save_tensor_name = ops.prepend_name_scope( self.saver_def.save_tensor_name, self._name) self.saver_def.restore_op_name = ops.prepend_name_scope( self.saver_def.restore_op_name, self._name) self._check_saver_def() # Updates next checkpoint time. self._next_checkpoint_time = ( time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) self._last_checkpoints = []
Example #5
Source File: saver.py From auto-alt-text-lambda-api with MIT License | 4 votes |
def build(self): """Builds saver_def.""" if self._is_built: return self._is_built = True if not self.saver_def: if self._builder is None: self._builder = BaseSaverBuilder(self._write_version) if self._var_list is None: # pylint: disable=protected-access self._var_list = variables._all_saveable_objects() if not self._var_list: if self._allow_empty: self._is_empty = True return else: raise ValueError("No variables to save") self._is_empty = False self.saver_def = self._builder.build( self._var_list, reshape=self._reshape, sharded=self._sharded, max_to_keep=self._max_to_keep, keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours, name=self._name, restore_sequentially=self._restore_sequentially) elif self.saver_def and self._name: # Since self._name is used as a name_scope by builder(), we are # overloading the use of this field to represent the "import_scope" as # well. self.saver_def.filename_tensor_name = ops.prepend_name_scope( self.saver_def.filename_tensor_name, self._name) self.saver_def.save_tensor_name = ops.prepend_name_scope( self.saver_def.save_tensor_name, self._name) self.saver_def.restore_op_name = ops.prepend_name_scope( self.saver_def.restore_op_name, self._name) self._check_saver_def() # Updates next checkpoint time. self._next_checkpoint_time = ( time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) self._last_checkpoints = []
Example #6
Source File: saver.py From deep_image_model with Apache License 2.0 | 4 votes |
def build(self): """Builds saver_def.""" if self._is_built: return self._is_built = True if not self.saver_def: if self._builder is None: self._builder = BaseSaverBuilder(self._write_version) if self._var_list is None: # pylint: disable=protected-access self._var_list = variables._all_saveable_objects() if not self._var_list: if self._allow_empty: self._is_empty = True return else: raise ValueError("No variables to save") self._is_empty = False self.saver_def = self._builder.build( self._var_list, reshape=self._reshape, sharded=self._sharded, max_to_keep=self._max_to_keep, keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours, name=self._name, restore_sequentially=self._restore_sequentially) elif self.saver_def and self._name: # Since self._name is used as a name_scope by builder(), we are # overloading the use of this field to represent the "import_scope" as # well. self.saver_def.filename_tensor_name = ops.prepend_name_scope( self.saver_def.filename_tensor_name, self._name) self.saver_def.save_tensor_name = ops.prepend_name_scope( self.saver_def.save_tensor_name, self._name) self.saver_def.restore_op_name = ops.prepend_name_scope( self.saver_def.restore_op_name, self._name) self._check_saver_def() # Updates next checkpoint time. self._next_checkpoint_time = ( time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) self._last_checkpoints = []
Example #7
Source File: saver.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
def _build(self, checkpoint_path, build_save, build_restore): """Builds saver_def.""" if context.in_graph_mode(): if self._is_built: return self._is_built = True if not self.saver_def or context.in_eager_mode(): if self._builder is None: self._builder = BaseSaverBuilder(self._write_version) if self._var_list is None: # pylint: disable=protected-access self._var_list = variables._all_saveable_objects() if not self._var_list: if self._allow_empty: self._is_empty = True return else: raise ValueError("No variables to save") self._is_empty = False self.saver_def = self._builder._build_internal( # pylint: disable=protected-access self._var_list, reshape=self._reshape, sharded=self._sharded, max_to_keep=self._max_to_keep, keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours, name=self._name, restore_sequentially=self._restore_sequentially, filename=checkpoint_path, build_save=build_save, build_restore=build_restore) elif self.saver_def and self._name: # Since self._name is used as a name_scope by builder(), we are # overloading the use of this field to represent the "import_scope" as # well. self.saver_def.filename_tensor_name = ops.prepend_name_scope( self.saver_def.filename_tensor_name, self._name) self.saver_def.save_tensor_name = ops.prepend_name_scope( self.saver_def.save_tensor_name, self._name) self.saver_def.restore_op_name = ops.prepend_name_scope( self.saver_def.restore_op_name, self._name) self._check_saver_def() # Updates next checkpoint time. self._next_checkpoint_time = ( time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) self._last_checkpoints = [] self._checkpoints_to_be_deleted = []
Example #8
Source File: saver.py From keras-lambda with MIT License | 4 votes |
def build(self): """Builds saver_def.""" if self._is_built: return self._is_built = True if not self.saver_def: if self._builder is None: self._builder = BaseSaverBuilder(self._write_version) if self._var_list is None: # pylint: disable=protected-access self._var_list = variables._all_saveable_objects() if not self._var_list: if self._allow_empty: self._is_empty = True return else: raise ValueError("No variables to save") self._is_empty = False self.saver_def = self._builder.build( self._var_list, reshape=self._reshape, sharded=self._sharded, max_to_keep=self._max_to_keep, keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours, name=self._name, restore_sequentially=self._restore_sequentially) elif self.saver_def and self._name: # Since self._name is used as a name_scope by builder(), we are # overloading the use of this field to represent the "import_scope" as # well. self.saver_def.filename_tensor_name = ops.prepend_name_scope( self.saver_def.filename_tensor_name, self._name) self.saver_def.save_tensor_name = ops.prepend_name_scope( self.saver_def.save_tensor_name, self._name) self.saver_def.restore_op_name = ops.prepend_name_scope( self.saver_def.restore_op_name, self._name) self._check_saver_def() # Updates next checkpoint time. self._next_checkpoint_time = ( time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) self._last_checkpoints = []