Deserialize JSON and create class objects when loading

generic_trainer/configs.py

@@ -3,6 +3,8 @@
 from typing import Any, Callable, Optional, Union
 import json
 import os
+import re
+import importlib
 
 import torch
 from torch.utils.data import Dataset
@@ -15,6 +17,10 @@
 
 @dataclasses.dataclass
 class OptionContainer:
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.globals = {}
+
     def __str__(self):
         s = ''
         for key in self.__dict__.keys():
@@ -40,6 +46,10 @@ def get_serializable_dict(self):
             d[key] = v
         return d
 
+    def deserizalize_dict(self, d):
+        for key in d.keys():
+            self.__dict__[key] = self.string_to_object(key, d[key])
+
     def dump_to_json(self, filename):
         try:
             f = open(filename, 'w')
@@ -49,14 +59,16 @@ def dump_to_json(self, filename):
         except:
             print('Failed to dump json.')
 
-    def load_from_json(self, filename):
+    def load_from_json(self, filename, namespace=None):
         """
         This function only overwrites entries contained in the JSON file. Unspecified entries are unaffected.
         """
+        if namespace is not None:
+            for key in namespace.keys():
+                globals()[key] = namespace[key]
         f = open(filename, 'r')
         d = json.load(f)
-        for key in d.keys():
-            self.__dict__[key] = self.string_to_object(key, d[key])
+        self.deserizalize_dict(d)
         f.close()
 
     def string_to_object(self, key, value):
@@ -66,6 +78,21 @@ def string_to_object(self, key, value):
         :param value: str.
         :return: object.
         """
+        # Value is a class handle
+        if isinstance(value, (list, tuple)):
+            value = [self.string_to_object(key, v) for v in value]
+        elif isinstance(value, str) and (res := re.match(r"<class '(.+)'>", value)):
+            class_import_path = res.groups()[0].split('.')
+            value = getattr(importlib.import_module('.'.join(class_import_path[:-1])), class_import_path[-1])
+        elif value in ['True', 'False']:
+            value = True if value == 'True' else False
+        else:
+            for caster in (int, float):
+                try:
+                    value = caster(value)
+                    break
+                except (ValueError, TypeError):
+                    pass
         return value
 
     def object_to_string(self, key, value):
@@ -77,8 +104,12 @@ def object_to_string(self, key, value):
         """
         if isinstance(value, OptionContainer):
             value = value.get_serializable_dict()
+        elif isinstance(value, dict):
+            value = value
         elif isinstance(value, (tuple, list)):
             value = [self.object_to_string(key, x) for x in value]
+        elif value is None:
+            value = None
         else:
             value = str(value)
         return value
@@ -90,7 +121,14 @@ def object_to_string(self, key, value):
 
 @dataclasses.dataclass
 class ModelParameters(OptionContainer):
-    pass
+
+    def string_to_object(self, key, value):
+        value = super().string_to_object(key, value)
+        if 'model_params' in key and isinstance(value, dict):
+            config = ModelParameters()
+            config.deserizalize_dict(value)
+            value = config
+        return value
 
 
 # =============================
@@ -161,6 +199,18 @@ class Config(OptionContainer):
     Task type. Can be 'classification', 'regression'. Currently this only affects the logging of the loss tracker.
     """
 
+    def string_to_object(self, key, value):
+        value = super().string_to_object(key, value)
+        if 'model_params' in key and isinstance(value, dict):
+            config = ModelParameters()
+            config.deserizalize_dict(value)
+            value = config
+        if key == 'parallelization_params' and isinstance(value, dict):
+            config = ParallelizationConfig()
+            config.deserizalize_dict(value)
+            value = config
+        return value
+
 
 @dataclasses.dataclass
 class InferenceConfig(Config):
@@ -185,6 +235,10 @@ class InferenceConfig(Config):
     processed variables.
     """
 
+    def string_to_object(self, key, value):
+        if key == 'model_save_dir':
+            self.pretrained_model_path = os.path.join(value, 'best_model.pth')
+
 
 @dataclasses.dataclass
 class TrainingConfig(Config):
@@ -289,6 +343,24 @@ class TrainingConfig(Config):
     save_onnx: bool = False
     """If True, ONNX models are saved along with state dicts."""
 
+    def string_to_object(self, key, value):
+        value = super().string_to_object(key, value)
+        if key == 'loss_function' and not isinstance(value, (list, tuple)):
+            try:
+                value = eval(value)
+            except Exception as e:
+                print(
+                    "When loading loss_function from JSON, the following error occurred:'\n{}\n"
+                    "To create a loss function object, its class name must be in the global namespace. You can "
+                    "import the proper classes in your driver script using from ... import ..., and pass "
+                    "globals() to load_from_json:\n"
+                    "    configs.load_from_json(filename, namespace=globals())\n".format(e)
+                )
+        elif key == 'loss_tracker_params':
+            configs = LossTrackerParameters()
+            configs.deserizalize_dict(value)
+            value = configs
+        return value
 
 @dataclasses.dataclass
 class PretrainingConfig(TrainingConfig):