better config handling

data/every_dream_validation.py

@@ -40,15 +40,26 @@ class EveryDreamValidator:
 
         self.log_writer = log_writer
 
-        self.config = {}
+        self.config = {
+            'batch_size': default_batch_size,
+            'every_n_epochs': 1,
+            'seed': 555
+        }
         if val_config_path is not None:
             with open(val_config_path, 'rt') as f:
-                self.config = json.load(f)
+                self.config.update(json.load(f))
 
-        self.batch_size = self.config.get('batch_size', default_batch_size)
-        self.every_n_epochs = self.config.get('every_n_epochs', 1)
-        self.seed = self.config.get('seed', 555)
-        self.val_data_root = self.config.get('val_data_root', None)
+    @property
+    def batch_size(self):
+        return self.config['batch_size']
+
+    @property
+    def every_n_epochs(self):
+        return self.config['every_n_epochs']
+
+    @property
+    def seed(self):
+        return self.config['seed']
 
     def prepare_validation_splits(self, train_items: list[ImageTrainItem], tokenizer: Any) -> list[ImageTrainItem]:
         """
@@ -115,7 +126,8 @@ class EveryDreamValidator:
                 flip_p=0.0,
                 seed=self.seed,
             )
-            val_items = resolver.resolve_root(self.val_data_root, args)
+            val_data_root = self.config['val_data_root']
+            val_items = resolver.resolve_root(val_data_root, args)
         else:
             raise ValueError(f"Unrecognized validation split mode '{val_split_mode}'")
         val_ed_batch = self._build_ed_batch(val_items, batch_size=self.batch_size, tokenizer=tokenizer, name='val')