optimised

architectures/WFNet.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.data import DataLoader
+import numpy as np
+from sklearn.metrics import classification_report
+from architectures.Model import Model
+
+'''class WF_Module(nn.Module):
+    """
+    Pure PyTorch Neural Network module graph.
+    Keeps parameter registration and autograd tracking separate from 
+    the framework's high-level Model abstractions to prevent recursion collisions.
+    """
+    def __init__(self, original_model: nn.Module, num_classes: int):
+        super().__init__()
+    
+        self.original_model = original_model
+        
+        # Target layer for weight filtering (layer4 block 1 conv2 or conv3 depending on arch)
+        last_layer = original_model.layer4[1]
+
+        # Some versions are limited to 2 convolutional layers
+        if hasattr(last_layer, "conv3"):
+            self.target_conv = last_layer.conv3
+        else:
+            self.target_conv = last_layer.conv2
+
+        # Completely freeze the original ResNet parameters
+        for param in self.parameters():
+            param.requires_grad = False
+            
+        # Initialize the alpha parameter matrix (Rows = Classes, Cols = Channels)
+        out_channels = self.target_conv.weight.shape[0]
+        self.alpha = nn.Parameter(torch.full((num_classes, out_channels), 3.0))'''
+
+'''
+    Poppi et_al's Single-shot multiclass unlearning.
+    This calculation happens only once to generate the mask. once the mask is generated,
+    Unlearning and remembering becomes a matter of switching gates on and off.'''
+'''
+    def forward(self, x: torch.Tensor, target_class_indices: torch.Tensor) -> torch.Tensor:
+        # we linearly loop through layers 1 to 4[block 1] (for ResNet) 
+        # for i in M_{|L|} do  l <- l[i]
+        x = self.original_model.maxpool(self.original_model.relu(self.original_model.bn1(self.original_model.conv1(x))))
+        x = self.original_model.layer1(x)
+        x = self.original_model.layer2(x)
+        x = self.original_model.layer3(x)
+        x = self.original_model.layer4[0](x)
+        
+        # The second block execute its internal transformations natively
+        # This handles conv1->conv2 (ResNet18) or conv1->conv2->conv3 (ResNet50) automatically!
+        # Xi+1 <- l(Xi, ˆwl)
+        x = self.original_model.layer4[1](x)
+        
+        # Apply mask dynamically to the completed block feature map
+        # wl <- αl[Yunl] ⊙ ˆwl
+        batch_alpha = self.alpha[target_class_indices]
+        mask = torch.sigmoid(batch_alpha).view(x.size(0), -1, 1, 1)
+        x = x * mask
+        
+        # Remaining standard head steps
+        x = self.original_model.avgpool(x)
+        x = torch.flatten(x, 1)
+        # so here we are returning the output logits
+        # the result of classification is then 
+        # argmax(x)
+        return self.original_model.fc(x)
+'''
+
+class WF_Module(nn.Module):
+    def __init__(self, original_model: nn.Module, num_classes: int, arch_enum):
+        super().__init__()
+        # If your model classes contain the raw inner torch model under an attribute, 
+        # extract it. Otherwise, use it directly.
+        self.original_model = getattr(original_model, "model", original_model)
+        
+        # Freeze the original model parameters completely
+        for param in self.original_model.parameters():
+            param.requires_grad = False
+            
+        # Target layer discovery using your clean Enum contract
+        self.target_layer = self._deduce_target_layer(self.original_model, arch_enum)
+        
+        # Derive channel dimensions dynamically from the deduced layer
+        out_channels = self._extract_channels(self.target_layer, self.original_model)
+        
+        # Initialize alpha parameter matrix (Rows = Classes, Cols = Channels)
+        self.alpha = nn.Parameter(torch.full((num_classes, out_channels), 3.0))
+        self._current_target_indices = None
+
+    def _deduce_target_layer(self, model: nn.Module, arch_enum) -> nn.Module:
+        """
+        Scans the architecture topology to target the final deep feature extraction block
+        right before global pooling/classification using strict Enum configurations.
+        """
+        match arch_enum:
+            # --- RESNET FAMILY ---
+            case arch_enum.RESNET18 | arch_enum.RESNET34 | arch_enum.RESNET50 | arch_enum.WIDE_RESNET:
+                return model.layer4[-1]
+                
+            # --- GOOGLENET ---
+            case arch_enum.GOOGLENET:
+                return model.inception5b
+                
+            # --- INCEPTION V3 ---
+            case arch_enum.INCEPTION:
+                return model.Mixed_7c
+                
+            # --- DENSENET 121 ---
+            case arch_enum.DENSENET121:
+                return model.features.norm5
+                
+            # --- EFFICIENTNET ---
+            case arch_enum.EFFICIENTNET:
+                return model.features[-1]
+                
+            # --- SHUFFLENET ---
+            case arch_enum.SHUFFLENET:
+                return model.conv5
+                
+            case _:
+                # Robust Fallback Strategy
+                target = None
+                for module in model.modules():
+                    if isinstance(module, nn.Conv2d):
+                        target = module
+                if target is not None:
+                    return target
+                raise RuntimeError(f"Could not locate filtration anchor for Enum target: {arch_enum}")
+
+    def _extract_channels(self, target_layer: nn.Module, model: nn.Module) -> int:
+        """Helper to determine channel depth across varied layers types."""
+        if hasattr(target_layer, "out_channels"):
+            return target_layer.out_channels
+        if hasattr(target_layer, "num_features"): 
+            return target_layer.num_features
+        if hasattr(target_layer, "weight"):
+            return target_layer.weight.shape[0]
+            
+        # Classifier fallback mapping
+        if hasattr(model, "fc"):
+            return model.fc.in_features
+        if hasattr(model, "classifier"):
+            if isinstance(model.classifier, nn.Linear):
+                return model.classifier.in_features
+            if isinstance(model.classifier, nn.Sequential):
+                return model.classifier[0].in_features
+        return 512
+
+    def _filtration_hook(self, module: nn.Module, hook_input: tuple, hook_output: torch.Tensor) -> torch.Tensor:
+        if self._current_target_indices is None:
+            return hook_output
+            
+        batch_alpha = self.alpha[self._current_target_indices]
+        
+        if len(hook_output.shape) == 4:
+            mask = torch.sigmoid(batch_alpha).view(hook_output.size(0), -1, 1, 1)
+        else:
+            mask = torch.sigmoid(batch_alpha).view(hook_output.size(0), -1)
+            
+        return hook_output * mask
+
+    def forward(self, x: torch.Tensor, target_class_indices: torch.Tensor) -> torch.Tensor:
+        self._current_target_indices = target_class_indices
+        hook_handle = self.target_layer.register_forward_hook(self._filtration_hook)
+        try:
+            logits = self.original_model(x)
+        finally:
+            hook_handle.remove()
+            self._current_target_indices = None
+        return logits
+
+
+class WF_Net_Model(Model):
+    def __init__(self, device, size, original_model: nn.Module, target_class_index: int, arch):
+        self.device = device
+        self.size = size
+        self.wf_module = WF_Module(
+            arch_enum=arch,
+            original_model = original_model, 
+            num_classes = size
+        ).to(self.device)
+        
+        # this index indicates which row of the mask should be active (gate closed).
+        self.target_class_index = target_class_index
+        self.model = self.wf_module
+
+    def get(self):
+        return self.wf_module
+    
+    '''
+    We override the evaluate method from the base class, 
+    because how we evaluate is different here from that of a normal torch nn.Module object
+
+    '''
+    def evaluate(self, loader, mode="eval"):
+
+        self.wf_module.eval()
+        all_preds, all_labels = [], []
+        print(f"\nEvaluating Domain: [{mode}]...")
+        
+        with torch.no_grad():
+            for inputs, labels in loader:
+                inputs, labels = inputs.to(self.device), labels.to(self.device)
+                
+                # we apply the filter
+                gate_signals = torch.full((inputs.size(0),), self.target_class_index, dtype=torch.long, device=self.device)
+                
+                # pass prediction through the filter
+                outputs = self.wf_module(inputs, target_class_indices=gate_signals)
+
+                # return argmax(x)    
+                _, predicted = torch.max(outputs, 1)
+                all_preds.extend(predicted.cpu().numpy())
+                all_labels.extend(labels.cpu().numpy())
+
+        classes = sorted(list(set(all_labels)))
+        accuracy = 100 * (np.array(all_preds) == np.array(all_labels)).sum() / len(all_labels)
+        
+        print(f"Test Accuracy: {accuracy:.2f}%")
+        print(classification_report(all_labels, all_preds, labels=classes, zero_division=0))
+        report = classification_report(all_labels, all_preds, labels=classes, output_dict=True, zero_division=0)
+        
+        return accuracy, report
+    
+    def eval(self):
+        """Safely intercept any fallback base class calls targeting .eval()"""
+        self.wf_module.eval()