updated model for moe

pyproject.toml

@@ -10,7 +10,7 @@ include = '\.pyi?$'
 
 [project]
 name = "aiia"
-version = "0.1.3"
+version = "0.1.4"
 description = "AIIA Deep Learning Model Implementation"
 readme = "README.md"
 authors = [

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = aiia
-version = 0.1.3
+version = 0.1.4
 author = Falko Habel
 author_email = falko.habel@gmx.de
 description = AIIA deep learning model implementation

src/aiia/__init__.py

@@ -4,4 +4,4 @@ from .data.DataLoader import DataLoader
 from .pretrain.pretrainer import Pretrainer, ProjectionHead
 
 
-__version__ = "0.1.3"
+__version__ = "0.1.4"

src/aiia/data/DataLoader.py

@@ -177,7 +177,7 @@ class AIIADataset(torch.utils.data.Dataset):
         self.items = items
         self.pretraining = pretraining
         self.transform = transforms.Compose([
-            transforms.Resize((224, 224)),
+            transforms.Resize((410, 410)),
             transforms.ToTensor()
         ])
         
@@ -193,7 +193,7 @@ class AIIADataset(torch.utils.data.Dataset):
                 raise ValueError(f"Invalid image at index {idx}")
                 
             image = self.transform(image)
-            if image.shape != (3, 224, 224):
+            if image.shape != (3, 410, 410):
                 raise ValueError(f"Invalid image shape at index {idx}: {image.shape}")
             
             if task == 'denoise':
@@ -215,7 +215,7 @@ class AIIADataset(torch.utils.data.Dataset):
                 if not isinstance(image, Image.Image):
                     raise ValueError(f"Invalid image at index {idx}")
                 image = self.transform(image)
-                if image.shape != (3, 224, 224):
+                if image.shape != (3, 410, 410):
                     raise ValueError(f"Invalid image shape at index {idx}: {image.shape}")
                 return image, label
             else:
@@ -223,6 +223,6 @@ class AIIADataset(torch.utils.data.Dataset):
                     image = self.transform(item)
                 else:
                     image = self.transform(item[0])
-                if image.shape != (3, 224, 224):
+                if image.shape != (3, 410, 410):
                     raise ValueError(f"Invalid image shape at index {idx}: {image.shape}")
                 return image

src/aiia/model/Model.py

@@ -177,35 +177,68 @@ class AIIAExpert(AIIA):
         # Process input through the base CNN
         return self.base_cnn(x)
 
-
 class AIIAmoe(AIIA):
     def __init__(self, config: AIIAConfig, num_experts: int = 3, base_class=AIIABase, **kwargs):
-        super().__init__(config=config, **kwargs)
-        self.config = self.config
-        
-        # Update config with new parameters if provided
+        super().__init__()
+        self.config = config
+
+        # Update the config to include the number of experts.
         self.config.num_experts = num_experts
-        
-        # Initialize multiple experts using chosen base class
+
+        # Initialize multiple experts from the chosen base class.
         self.experts = nn.ModuleList([
-            AIIAExpert(self.config, base_class=base_class, **kwargs) 
-            for _ in range(self.config.num_experts)
+            AIIAExpert(self.config, base_class=base_class, **kwargs)
+            for _ in range(num_experts)
         ])
-        
-        # Create gating network
+
+        # To generate gating weights, we first need to determine the feature dimension.
+        # Each expert is assumed to return an output of shape (B, C, H, W); after averaging over H and W, 
+        # we obtain a tensor of shape (B, C) where C is the number of channels (here assumed to be 410).
+        gate_in_features = 410  # Adjust this if your expert output changes.
+
+        # Create a gating network that maps the aggregated features to num_experts weights.
         self.gate = nn.Sequential(
-            nn.Linear(self.config.hidden_size, self.config.num_experts),
+            nn.Linear(gate_in_features, num_experts),
             nn.Softmax(dim=1)
         )
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the Mixture-of-Experts model.
+
+        Args:
+            x (torch.Tensor): Input tensor
+
+        Returns:
+            torch.Tensor: Merged output tensor from all experts
+        """
+        # Stack the outputs from each expert.
+        # Each expert's output should have shape (B, C, H, W). After stacking, expert_outputs has shape:
+        # (B, num_experts, C, H, W)
         expert_outputs = torch.stack([expert(x) for expert in self.experts], dim=1)
-        gate_weights = self.gate(torch.mean(expert_outputs, (2, 3)))
-        merged_output = torch.sum(
-            expert_outputs * gate_weights.unsqueeze(2).unsqueeze(3), dim=1
-        )
+
+        # Aggregate spatial features: average across the spatial dimensions (H, W).
+        # This results in a tensor with shape (B, num_experts, C)
+        spatial_avg = torch.mean(expert_outputs, dim=(3, 4))
+
+        # To feed the gating network, further average across the expert dimension,
+        # obtaining a tensor of shape (B, C) that represents the global feature summary.
+        gate_input = torch.mean(spatial_avg, dim=1)
+
+        # Compute gating weights using the gating network.
+        # The output gate_weights has shape (B, num_experts)
+        gate_weights = self.gate(gate_input)
+
+        # Expand the gate weights to match the expert outputs shape so they can be combined.
+        # After unsqueezing, gate_weights has shape (B, num_experts, 1, 1, 1)
+        gate_weights_expanded = gate_weights.unsqueeze(2).unsqueeze(3).unsqueeze(4)
+
+        # Multiply each expert's output by its corresponding gating weight and sum over experts.
+        # The merged_output retains the shape (B, C, H, W)
+        merged_output = torch.sum(expert_outputs * gate_weights_expanded, dim=1)
         return merged_output
 
+
 class AIIAchunked(AIIA):
     def __init__(self, config: AIIAConfig, patch_size: int = 16, base_class=AIIABase, **kwargs):
         super().__init__(config=config, **kwargs)