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