improved and simplified config

src/aiunn/upsampler/aiunn.py

@@ -2,19 +2,18 @@ import os
 import torch
 import torch.nn as nn
 import warnings
-from aiia import AIIAConfig, AIIABase
-from aiia.model.Model import AIIA
+from aiia.model.Model import AIIA, AIIAConfig, AIIABase
 from .config import aiuNNConfig
 import warnings
 
 
 class aiuNN(AIIA):
-    def __init__(self, base_model: AIIABase):
+    def __init__(self, base_model: AIIA, config:aiuNNConfig):
         super().__init__(base_model.config)
         self.base_model = base_model
         
         # Pass the unified base configuration using the new parameter.
-        self.config = aiuNNConfig(base_config=base_model.config)
+        self.config = config
         
         # Enhanced approach
         scale_factor = self.config.upsample_scale
@@ -29,11 +28,12 @@ class aiuNN(AIIA):
 
 
     def forward(self, x):
-        x = self.base_model(x)
-        x = self.upsample(x)
-        x = self.to_rgb(x)  # Ensures output has 3 channels.
+        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
 
+
     @classmethod
     def load(cls, path, precision: str = None, **kwargs):
         """
@@ -57,7 +57,7 @@ class aiuNN(AIIA):
         state_dict = torch.load(os.path.join(path, "model.pth"), map_location=device)
         
         # Import all model types
-        from aiia import AIIABase, AIIABaseShared, AIIAExpert, AIIAmoe, AIIAchunked, AIIArecursive
+        from aiia.model.Model import AIIABase, AIIABaseShared, AIIAExpert, AIIAmoe, AIIAchunked, AIIArecursive
         
         # Helper function to detect base class type from key patterns
         def detect_base_class_type(keys_prefix):
@@ -112,7 +112,7 @@ class aiuNN(AIIA):
             base_model = base_class(config, **kwargs)
         
         # Create the aiuNN model with the detected base model
-        model = cls(base_model)
+        model = cls(base_model, config=base_model.config)
         
         # Handle precision conversion
         dtype = None
@@ -143,9 +143,10 @@ if __name__ == "__main__":
     from aiia import AIIABase, AIIAConfig
     # Create a configuration and build a base model.
     config = AIIAConfig()
+    ai_config = aiuNNConfig()
     base_model = AIIABase(config)
     # Instantiate Upsampler from the base model (works correctly).
-    upsampler = aiuNN(base_model)
+    upsampler = aiuNN(base_model, config=ai_config)
 
     # Save the model (both configuration and weights).
     upsampler.save("hehe")