deprecate (de)serialize_value_or_type

- add deprecation warning, remove functionality
- replace all occurences with the corresponding new functions

Author: vpratz

bayesflow/networks/point_inference_network.py

@@ -1,11 +1,7 @@
 import keras
-from keras.saving import (
-    deserialize_keras_object as deserialize,
-    serialize_keras_object as serialize,
-    register_keras_serializable as serializable,
-)
 
-from bayesflow.utils import model_kwargs, find_network, serialize_value_or_type, deserialize_value_or_type
+from bayesflow.utils import model_kwargs, find_network
+from bayesflow.utils.serialization import deserialize, serializable, serialize
 from bayesflow.types import Shape, Tensor
 from bayesflow.scores import ScoringRule, ParametricDistributionScore
 from bayesflow.utils.decorators import allow_batch_size
@@ -30,10 +26,10 @@ def __init__(
         self.subnet = find_network(subnet, **kwargs.get("subnet_kwargs", {}))
 
         self.config = {
+            "subnet": serialize(subnet),
+            "scores": serialize(scores),
             **kwargs,
         }
-        self.config = serialize_value_or_type(self.config, "subnet", subnet)
-        self.config["scores"] = serialize(self.scores)
 
     def build(self, xz_shape: Shape, conditions_shape: Shape = None) -> None:
         """Builds all network components based on shapes of conditions and targets.
@@ -119,7 +115,7 @@ def get_config(self):
     def from_config(cls, config):
         config = config.copy()
         config["scores"] = deserialize(config["scores"])
-        config = deserialize_value_or_type(config, "subnet")
+        config["subnet"] = deserialize(config["subnet"])
         return cls(**config)
 
     def call(

bayesflow/scores/scoring_rule.py

@@ -1,10 +1,10 @@
 import math
 
 import keras
-from keras.saving import register_keras_serializable as serializable
 
 from bayesflow.types import Shape, Tensor
-from bayesflow.utils import find_network, serialize_value_or_type, deserialize_value_or_type
+from bayesflow.utils import find_network
+from bayesflow.utils.serialization import deserialize, serializable, serialize
 
 
 @serializable(package="bayesflow.scores")
@@ -51,23 +51,16 @@ def __init__(
         self.config = {"subnets_kwargs": self.subnets_kwargs}
 
     def get_config(self):
-        self.config["subnets"] = {
-            key: serialize_value_or_type({}, "subnet", subnet) for key, subnet in self.subnets.items()
-        }
-        self.config["links"] = {key: serialize_value_or_type({}, "link", link) for key, link in self.links.items()}
+        self.config["subnets"] = {key: serialize(subnet) for key, subnet in self.subnets.items()}
+        self.config["links"] = {key: serialize(link) for key, link in self.links.items()}
 
         return self.config
 
     @classmethod
     def from_config(cls, config):
         config = config.copy()
-        config["subnets"] = {
-            key: deserialize_value_or_type(subnet_dict, "subnet")["subnet"]
-            for key, subnet_dict in config["subnets"].items()
-        }
-        config["links"] = {
-            key: deserialize_value_or_type(link_dict, "link")["link"] for key, link_dict in config["links"].items()
-        }
+        config["subnets"] = {key: deserialize(subnet_dict) for key, subnet_dict in config["subnets"].items()}
+        config["links"] = {key: deserialize(link_dict) for key, link_dict in config["links"].items()}
 
         return cls(**config)
 

bayesflow/utils/serialization.py

@@ -5,6 +5,7 @@
 import keras
 import numpy as np
 import sys
+from warnings import warn
 
 # this import needs to be exactly like this to work with monkey patching
 from keras.saving import deserialize_keras_object
@@ -19,77 +20,21 @@
 
 
 def serialize_value_or_type(config, name, obj):
-    """Serialize an object that can be either a value or a type
-    and add it to a copy of the supplied dictionary.
-
-    Parameters
-    ----------
-    config  : dict
-        Dictionary to add the serialized object to. This function does not
-        modify the dictionary in place, but returns a modified copy.
-    name    : str
-        Name of the obj that should be stored. Required for later deserialization.
-    obj     : object or type
-        The object to serialize. If `obj` is of type `type`, we use
-        `keras.saving.get_registered_name` to obtain the registered type name.
-        If it is not a type, we try to serialize it as a Keras object.
-
-    Returns
-    -------
-    updated_config  : dict
-        Updated dictionary with a new key `"_bayesflow_<name>_type"` or
-        `"_bayesflow_<name>_val"`. The prefix is used to avoid name collisions,
-        the suffix indicates how the stored value has to be deserialized.
-
-    Notes
-    -----
-    We allow strings or `type` parameters at several places to instantiate objects
-    of a given type (e.g., `subnet` in `CouplingFlow`). As `type` objects cannot
-    be serialized, we have to distinguish the two cases for serialization and
-    deserialization. This function is a helper function to standardize and
-    simplify this.
-    """
-    updated_config = config.copy()
-    if isinstance(obj, type):
-        updated_config[f"{PREFIX}{name}_type"] = keras.saving.get_registered_name(obj)
-    else:
-        updated_config[f"{PREFIX}{name}_val"] = keras.saving.serialize_keras_object(obj)
-    return updated_config
+    """This function is deprecated."""
+    warn(
+        "This method is deprecated. It was replaced by bayesflow.utils.serialization.serialize.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
 
 
 def deserialize_value_or_type(config, name):
-    """Deserialize an object that can be either a value or a type and add
-    it to the supplied dictionary.
-
-    Parameters
-    ----------
-    config  : dict
-        Dictionary containing the object to deserialize. If a type was
-        serialized, it should contain the key `"_bayesflow_<name>_type"`.
-        If an object was serialized, it should contain the key
-        `"_bayesflow_<name>_val"`. In a copy of this dictionary,
-        the item will be replaced with the key `name`.
-    name    : str
-        Name of the object to deserialize.
-
-    Returns
-    -------
-    updated_config  : dict
-        Updated dictionary with a new key `name`, with a value that is either
-        a type or an object.
-
-    See Also
-    --------
-    serialize_value_or_type
-    """
-    updated_config = config.copy()
-    if f"{PREFIX}{name}_type" in config:
-        updated_config[name] = keras.saving.get_registered_object(config[f"{PREFIX}{name}_type"])
-        del updated_config[f"{PREFIX}{name}_type"]
-    elif f"{PREFIX}{name}_val" in config:
-        updated_config[name] = keras.saving.deserialize_keras_object(config[f"{PREFIX}{name}_val"])
-        del updated_config[f"{PREFIX}{name}_val"]
-    return updated_config
+    """This function is deprecated."""
+    warn(
+        "This method is deprecated. It was replaced by bayesflow.utils.serialization.deserialize.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
 
 
 def deserialize(config: dict, custom_objects=None, safe_mode=True, **kwargs):

docsrc/source/development/serialization.md

@@ -21,8 +21,6 @@ We mainly provide three convenience functions:
 - The {py:func}`~bayesflow.utils.serialization.serialize` function, which adds support for serializing classes.
 - Its counterpart {py:func}`~bayesflow.utils.serialization.deserialize`, adds support to deserialize classes.
 
-_Note: The `(de)serialize_value_or_type` functions are made obsolete by the functions given above and will probably be deprecated soon._
-
 ## Usage
 
 To use the adapted serialization functions, you have to use them in the `get_config` and `from_config` method. Please refer to existing classes in the library for usage examples.