def _export_mode(mode, has_saved_vars, builder, model, custom_objects, checkpoint_path, input_signature): """Exports a model, and optionally saves new vars from the clone model. Args: mode: A `tf.estimator.ModeKeys` string. has_saved_vars: A `boolean` indicating whether the SavedModel has already exported variables. builder: A `SavedModelBuilder` object. model: A `tf.keras.Model` object. custom_objects: A dictionary mapping string names to custom classes or functions. checkpoint_path: String path to checkpoint. input_signature: Nested TensorSpec containing the expected inputs. Can be `None`, in which case the signature will be inferred from the model. Raises: ValueError: If the train/eval mode is being exported, but the model does not have an optimizer. """ from tensorflow.python.keras import models as models_lib # pylint: disable=g-import-not-at-top compile_clone = (mode != mode_keys.ModeKeys.PREDICT) if compile_clone and not model.optimizer: raise ValueError( 'Model does not have an optimizer. Cannot export mode %s' % mode) model_graph = ops.get_default_graph() with ops.Graph().as_default() as g, K.learning_phase_scope( mode == mode_keys.ModeKeys.TRAIN): if input_signature is None: input_tensors = None else: input_tensors = nest.map_structure(create_placeholder, input_signature) # Clone the model into blank graph. This will create placeholders for inputs # and targets. clone = models_lib.clone_and_build_model(model, input_tensors=input_tensors, custom_objects=custom_objects, compile_clone=compile_clone) # Make sure that iterations variable is added to the global step collection, # to ensure that, when the SavedModel graph is loaded, the iterations # variable is returned by `tf.train.get_global_step()`. This is required for # compatibility with the SavedModelEstimator. if compile_clone: g.add_to_collection(ops.GraphKeys.GLOBAL_STEP, clone.optimizer.iterations) # Extract update and train ops from train/test/predict functions. train_op = None if mode == mode_keys.ModeKeys.TRAIN: clone._make_train_function() train_op = clone.train_function.updates_op elif mode == mode_keys.ModeKeys.TEST: clone._make_test_function() else: clone._make_predict_function() g.get_collection_ref(ops.GraphKeys.UPDATE_OPS).extend( clone.state_updates) clone_var_list = checkpointable_utils.named_saveables(clone) with session.Session().as_default(): if has_saved_vars: # Confirm all variables in the clone have an entry in the checkpoint. status = clone.load_weights(checkpoint_path) status.assert_existing_objects_matched() else: # Confirm that variables between the clone and model match up exactly, # not counting optimizer objects. Optimizer objects are ignored because # if the model has not trained, the slot variables will not have been # created yet. # TODO(b/113179535): Replace with checkpointable equivalence. _assert_same_non_optimizer_objects(model, model_graph, clone, g) # TODO(b/113178242): Use value transfer for checkpointable objects. clone.load_weights(checkpoint_path) # Add graph and variables to SavedModel. # TODO(b/113134168): Switch to add_meta_graph_and_variables. clone.save_weights(checkpoint_path, save_format='tf', overwrite=True) builder._has_saved_variables = True # Add graph to the SavedModel builder. builder.add_meta_graph(model_utils.EXPORT_TAG_MAP[mode], signature_def_map=_create_signature_def_map( clone, mode), saver=saver_lib.Saver(clone_var_list), init_op=variables.local_variables_initializer(), train_op=train_op) return None
def _export_mode( mode, has_saved_vars, builder, model, custom_objects, checkpoint_path): """Export a model, and optionally save new vars from the clone model. Args: mode: A `tf.estimator.ModeKeys` string. has_saved_vars: A `boolean` indicating whether the SavedModel has already exported variables. builder: A `SavedModelBuilder` object. model: A `tf.keras.Model` object. custom_objects: A dictionary mapping string names to custom classes or functions. checkpoint_path: String path to checkpoint. Raises: ValueError: If the train/eval mode is being exported, but the model does not have an optimizer. """ compile_clone = (mode != model_fn_lib.ModeKeys.PREDICT) if compile_clone and not model.optimizer: raise ValueError( 'Model does not have an optimizer. Cannot export mode %s' % mode) model_graph = ops.get_default_graph() with ops.Graph().as_default() as g: K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN) # Clone the model into blank graph. This will create placeholders for inputs # and targets. clone = models_lib.clone_and_build_model( model, custom_objects=custom_objects, compile_clone=compile_clone) # Make sure that iterations variable is added to the global step collection, # to ensure that, when the SavedModel graph is loaded, the iterations # variable is returned by `tf.train.get_global_step()`. This is required for # compatibility with the SavedModelEstimator. if compile_clone: g.add_to_collection(ops.GraphKeys.GLOBAL_STEP, clone.optimizer.iterations) # Extract update and train ops from train/test/predict functions. train_op = None if mode == model_fn_lib.ModeKeys.TRAIN: clone._make_train_function() train_op = clone.train_function.updates_op elif mode == model_fn_lib.ModeKeys.EVAL: clone._make_test_function() else: clone._make_predict_function() g.get_collection_ref(ops.GraphKeys.UPDATE_OPS).extend(clone.state_updates) clone_var_list = checkpointable_utils.named_saveables(clone) with session.Session().as_default(): if has_saved_vars: # Confirm all variables in the clone have an entry in the checkpoint. status = clone.load_weights(checkpoint_path) status.assert_existing_objects_matched() else: # Confirm that variables between the clone and model match up exactly, # not counting optimizer objects. Optimizer objects are ignored because # if the model has not trained, the slot variables will not have been # created yet. # TODO(b/113179535): Replace with checkpointable equivalence. _assert_same_non_optimizer_objects(model, model_graph, clone, g) # TODO(b/113178242): Use value transfer for checkpointable objects. clone.load_weights(checkpoint_path) # Add graph and variables to SavedModel. # TODO(b/113134168): Switch to add_meta_graph_and_variables. clone.save_weights(checkpoint_path, save_format='tf', overwrite=True) builder._has_saved_variables = True # Add graph to the SavedModel builder. builder.add_meta_graph( model_fn_lib.EXPORT_TAG_MAP[mode], signature_def_map=_create_signature_def_map(clone, mode), saver=saver_lib.Saver(clone_var_list), init_op=variables.local_variables_initializer(), train_op=train_op) return None
def _get_var_list(model): """Returns list of all checkpointed saveable objects in the model.""" return checkpointable_utils.named_saveables(model)
def _get_var_list(model): """Return list of all checkpointed saveable objects in the model.""" return checkpointable_utils.named_saveables(model)