fixed model loading due to a bug

pyproject.toml

@@ -8,9 +8,23 @@ target-version = ['py37']
 include = '\.pyi?$'
 
 [project]
-name = "AIIA"
-version = "0.1.1"  # Replace with your desired version number
-description = "AIIA Deep Learning Model"
+name = "aiia"
+version = "0.1.1"
+description = "AIIA Deep Learning Model Implementation"
+readme = "README.md"
 authors = [
     { name="Falko Habel", email="falko.habel@gmx.de" }
+]
+dependencies = [
+    "torch>=2.5.0",
+    "numpy",
+    "tqdm",
+    "pytest",
+    "pillow"
+]
+requires-python = ">=3.7"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent"
 ]

run.py

@@ -1,27 +1,6 @@
-data_path1 = "/root/training_data/vision-dataset/images_pretrain.parquet"
-data_path2 = "/root/training_data/vision-dataset/vector_img_pretrain.parquet"
-    
-from aiia.model import AIIABase
-from aiia.model.config import AIIAConfig
-from aiia.pretrain import Pretrainer
 
-# Create your model
-config = AIIAConfig(model_name="AIIA-Base-512x20k")
-model = AIIABase(config)
 
-# Initialize pretrainer with the model
-pretrainer = Pretrainer(model, learning_rate=config.learning_rate, config=config)
 
-# List of dataset paths
-dataset_paths = [
-    data_path1,
-    data_path2
-]
+from aiia import AIIABase
 
-# Start training with multiple datasets
-pretrainer.train(
-    dataset_paths=dataset_paths,
-    num_epochs=10,
-    batch_size=2,
-    sample_size=10000
-)
+model = AIIABase.load(path="AIIA-base-512", precision="bf16")

setup.py

@@ -1,25 +1,6 @@
 from setuptools import setup, find_packages
 
 setup(
-    name="aiia",
-    version="0.1.1",
     packages=find_packages(where="src"),
     package_dir={"": "src"},
-    install_requires=[
-        "torch>=1.8.0",
-        "numpy>=1.19.0",
-        "tqdm>=4.62.0",
-    ],
-    author="Falko Habel",
-    author_email="falko.habel@gmx.de",
-    description="AIIA deep learning model implementation",
-    long_description=open("README.md").read(),
-    long_description_content_type="text/markdown",
-    url="https://gitea.fabelous.app/Maschine-Learning/AIIA.git",
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: Creative Commons Attribution-NonCommercial 4.0 International",
-        "Operating System :: OS Independent",
-    ],
-    python_requires=">=3.10",
-)
+)

src/aiia/model/Model.py

@@ -24,17 +24,6 @@ class AIIA(nn.Module):
 
     @classmethod
     def load(cls, path, precision: str = None):
-        """
-        Load the model from the given path.
-        
-        Parameters:
-        - path (str): The directory containing the saved model.
-        - precision (str, optional): The desired precision for model weights.
-          Options are:
-              'fp16'   -> load weights with torch.float16,
-              'bf16'   -> load weights with torch.bfloat16.
-          If precision is None, default torch.float32 is used.
-        """
         config = AIIAConfig.load(path)
         model = cls(config)
         device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -44,7 +33,6 @@ class AIIA(nn.Module):
             if precision.lower() == 'fp16':
                 dtype = torch.float16
             elif precision.lower() == 'bf16':
-                # For CUDA devices, check whether BF16 is supported. If not, fallback to FP16.
                 if device == 'cuda' and not torch.cuda.is_bf16_supported():
                     warnings.warn("BF16 is not supported on this GPU. Falling back to FP16.")
                     dtype = torch.float16
@@ -53,14 +41,19 @@ class AIIA(nn.Module):
             else:
                 raise ValueError("Unsupported precision. Use 'fp16', 'bf16', or leave as None.")
 
+        # Load the state dictionary normally (without dtype argument)
+        model_dict = torch.load(f"{path}/model.pth", map_location=device)
+
+        # If a precision conversion is requested, cast each tensor in the state dict to the target dtype.
         if dtype is not None:
-            model_dict = torch.load(f"{path}/model.pth", map_location=device, dtype=dtype)
-        else:
-            model_dict = torch.load(f"{path}/model.pth", map_location=device)
-        
+            for key, param in model_dict.items():
+                if torch.is_tensor(param):
+                    model_dict[key] = param.to(dtype)
+
         model.load_state_dict(model_dict)
         return model
 
+
 class AIIABase(AIIA):
     def __init__(self, config: AIIAConfig, **kwargs):
         super().__init__(config=config, **kwargs)