| 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) |