moved to transformer support, currently dropped fp16 load support

src/aiunn/upsampler/aiunn.py

@@ -9,13 +9,12 @@ import warnings
 
 
 class aiuNN(PreTrainedModel):
-    def __init__(self, base_model: PreTrainedModel, config:aiuNNConfig):
-        super().__init__(base_model.config)
-        self.base_model = base_model
-        
+    config_class = aiuNNConfig
+    def __init__(self, config: aiuNNConfig):
+        super().__init__(config)
         # Pass the unified base configuration using the new parameter.
         self.config = config
-        
+
         # Enhanced approach
         scale_factor = self.config.upsample_scale
         out_channels = self.base_model.config.num_channels * (scale_factor ** 2)
@@ -27,13 +26,19 @@ class aiuNN(PreTrainedModel):
         )
         self.pixel_shuffle = nn.PixelShuffle(scale_factor)
 
-
+    def load_base_model(self, base_model: PreTrainedModel):
+        self.base_model = base_model
+        
     def forward(self, x):
+        if self.base_model is None:
+            raise ValueError("Base model is not loaded. Call 'load_base_model' before forwarding.")
         x = self.base_model(x)  # Get base features
         x = self.pixel_shuffle_conv(x)  # Expand channels for shuffling
         x = self.pixel_shuffle(x)  # Rearrange channels into spatial dimensions
         return x
 
+
+
 if __name__ == "__main__":
     from aiia import AIIABase, AIIAConfig
     # Create a configuration and build a base model.
@@ -41,11 +46,11 @@ if __name__ == "__main__":
     ai_config = aiuNNConfig()
     base_model = AIIABase(config)
     # Instantiate Upsampler from the base model (works correctly).
-    upsampler = aiuNN(base_model, config=ai_config)
-
+    upsampler = aiuNN(config=ai_config)
+    upsampler.load_base_model(base_model)
     # Save the model (both configuration and weights).
     upsampler.save_pretrained("aiunn")
 
     # Now load using the overridden load method; this will load the complete model.
-    upsampler_loaded = aiuNN.load("aiunn", precision="bf16")
+    upsampler_loaded = aiuNN.from_pretrained("aiunn")
     print("Updated configuration:", upsampler_loaded.config.__dict__)