Create Custom ResNet SHAP

Custom ResNet SHAP

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+import shap
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import models
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Custom BasicBlock to avoid in-place operations
+class CustomBasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None):
+        super(CustomBasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = norm_layer(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x.clone()
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = F.relu(out.clone(), inplace=False)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x.clone())
+
+        out = out.clone() + identity  # Clone before addition to avoid in-place modification
+        out = F.relu(out.clone(), inplace=False)
+
+        return out
+
+# Custom ResNet using CustomBasicBlock
+class CustomResNet(nn.Module):
+    def __init__(self, block, layers, num_classes=1000):
+        super(CustomResNet, self).__init__()
+        self.inplanes = 64
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=False)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        norm_layer = nn.BatchNorm2d
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, groups=1, base_width=64, dilation=1, norm_layer=norm_layer))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups=1, base_width=64, dilation=1, norm_layer=norm_layer))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x.clone())  # Clone to avoid in-place operation
+        x = self.maxpool(x)
+
+        x = self.layer1(x.clone())  # Clone to avoid in-place operation
+        x = self.layer2(x.clone())  # Clone to avoid in-place operation
+        x = self.layer3(x.clone())  # Clone to avoid in-place operation
+        x = self.layer4(x.clone())  # Clone to avoid in-place operation
+
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.fc(x.clone())  # Clone to avoid in-place operation
+
+        return x
+
+# Initialize the custom model with pre-trained weights
+model = CustomResNet(CustomBasicBlock, [2, 2, 2, 2])
+state_dict = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1).state_dict()
+model.load_state_dict(state_dict)
+model.eval()
+
+# Initialize SHAP explainer with the custom model
+explainer = shap.DeepExplainer(model, torch.randn(1, 3, 224, 224))
+
+# Generate SHAP values for an input image
+sample_image = torch.randn(1, 3, 224, 224)
+shap_values = explainer.shap_values(sample_image, check_additivity=False)
+
+# Convert SHAP values and sample image to numpy for SHAP visualization
+shap_values_class_0 = shap_values[0][0]  # Extract SHAP values for the first class
+sample_image_np = sample_image.squeeze().permute(1, 2, 0).detach().numpy()
+
+# Normalize sample image and SHAP values to range [0, 1] for visualization
+sample_image_np = np.clip(sample_image_np, 0, 1)
+shap_min, shap_max = shap_values_class_0.min(), shap_values_class_0.max()
+shap_values_class_0 = (shap_values_class_0 - shap_min) / (shap_max - shap_min)
+
+# Ensure both `sample_image_np` and `shap_values_class_0` are NumPy arrays with correct shapes for image_plot
+sample_image_np = np.array([sample_image_np])  # Add batch dimension for SHAP
+shap_values_class_0 = np.array([shap_values_class_0])  # Add batch dimension for SHAP
+
+# Visualize SHAP values for the first class
+shap.image_plot(shap_values_class_0, sample_image_np)