Merge pull request 'feat/smoe_support' (#6) from feat/smoe_support into develop

Reviewed-on: #6

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "aiunn"
-version = "0.1.1"
+version = "0.1.2"
 description = "Finetuner for image upscaling using AIIA"
 readme = "README.md"
 requires-python = ">=3.10"

requirements.txt

@@ -2,4 +2,5 @@ torch
 aiia
 pillow
 torchvision
-sklearn
+sklearn
+https://gitea.fabelous.app/Machine-Learning/AIIA.git

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name="aiunn",
-    version="0.1.1",
+    version="0.1.2",
     packages=find_packages(where="src"),
     package_dir={"": "src"},
     install_requires=[

src/aiunn/__init__.py

@@ -3,4 +3,4 @@ from .upsampler.aiunn import aiuNN
 from .upsampler.config import aiuNNConfig
 from .inference.inference import aiuNNInference
 
-__version__ = "0.1.1"
+__version__ = "0.1.2"

src/aiunn/upsampler/aiunn.py

@@ -2,18 +2,18 @@ import os
 import torch
 import torch.nn as nn
 import warnings
-from aiia import AIIA, AIIAConfig, AIIABase
+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
@@ -28,11 +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):
         """
@@ -56,7 +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):
@@ -111,7 +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
@@ -142,9 +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")