html5+css3网站模板网站编辑seo

DAY 43 复习日

作业：
kaggle找到一个图像数据集，用cnn网络进行训练并且用grad-cam做可视化

@浙大疏锦行

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选择 Dogs vs Cats 数据集（Kaggle经典二分类问题）

完整代码实现

1. 设置环境并加载数据

import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets, models
from torch.utils.data import DataLoader, random_split
import matplotlib.pyplot as plt
import cv2
from PIL import Image# 检查GPU可用性
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")# 数据预处理
transform = transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])# 加载数据集
dataset = datasets.ImageFolder('./dogs-vs-cats/train', transform=transform)# 划分训练集和验证集（80%训练，20%验证）
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)# 查看类别
print(f"Classes: {dataset.classes}")

2. 定义CNN模型（使用预训练的ResNet18）

class DogCatCNN(nn.Module):def __init__(self):super(DogCatCNN, self).__init__()# 使用预训练的ResNet18self.resnet = models.resnet18(pretrained=True)# 冻结所有卷积层参数（可选）for param in self.resnet.parameters():param.requires_grad = False# 替换最后的全连接层（适应我们的二分类问题）num_features = self.resnet.fc.in_featuresself.resnet.fc = nn.Sequential(nn.Linear(num_features, 256),nn.ReLU(),nn.Dropout(0.5),nn.Linear(256, 2)  # 2 classes: dog, cat)def forward(self, x):return self.resnet(x)model = DogCatCNN().to(device)

3. 训练函数

def train_model(model, criterion, optimizer, num_epochs=10):best_acc = 0.0for epoch in range(num_epochs):print(f'Epoch {epoch+1}/{num_epochs}')print('-' * 10)# 训练阶段model.train()running_loss = 0.0running_corrects = 0for inputs, labels in train_loader:inputs = inputs.to(device)labels = labels.to(device)optimizer.zero_grad()outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)epoch_loss = running_loss / len(train_loader.dataset)epoch_acc = running_corrects.double() / len(train_loader.dataset)print(f'Train Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')# 验证阶段model.eval()val_loss = 0.0val_corrects = 0with torch.no_grad():for inputs, labels in val_loader:inputs = inputs.to(device)labels = labels.to(device)outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)val_loss += loss.item() * inputs.size(0)val_corrects += torch.sum(preds == labels.data)val_loss = val_loss / len(val_loader.dataset)val_acc = val_corrects.double() / len(val_loader.dataset)print(f'Val Loss: {val_loss:.4f} Acc: {val_acc:.4f}\n')# 保存最佳模型if val_acc > best_acc:best_acc = val_acctorch.save(model.state_dict(), 'best_model.pth')print(f'Best val Acc: {best_acc:.4f}')return model# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)# 训练模型
model = train_model(model, criterion, optimizer, num_epochs=10)

4. Grad-CAM可视化实现

class GradCAM:def __init__(self, model, target_layer):self.model = modelself.target_layer = target_layerself.gradients = Noneself.activations = None# 注册钩子target_layer.register_forward_hook(self.save_activations)target_layer.register_backward_hook(self.save_gradients)def save_activations(self, module, input, output):self.activations = outputdef save_gradients(self, module, grad_input, grad_output):self.gradients = grad_output[0]def forward(self, x):return self.model(x)def __call__(self, x, class_idx=None):# 前向传播output = self.forward(x)if class_idx is None:class_idx = torch.argmax(output, dim=1).item()# 反向传播self.model.zero_grad()one_hot = torch.zeros_like(output)one_hot[0][class_idx] = 1output.backward(gradient=one_hot, retain_graph=True)# 计算权重pooled_gradients = torch.mean(self.gradients, dim=[0, 2, 3])# 加权特征图activations = self.activations[0]for i in range(activations.size(0)):activations[i, :, :] *= pooled_gradients[i]# 生成热图heatmap = torch.mean(activations, dim=0).detach().cpu().numpy()heatmap = np.maximum(heatmap, 0)heatmap /= np.max(heatmap)return heatmapdef visualize_gradcam(model, image_path, target_layer):# 加载并预处理图像img = Image.open(image_path).convert('RGB')img_tensor = transform(img).unsqueeze(0).to(device)# 获取预测类别model.eval()with torch.no_grad():output = model(img_tensor)pred_class = torch.argmax(output, dim=1).item()# 创建Grad-CAMgrad_cam = GradCAM(model, target_layer)heatmap = grad_cam(img_tensor, pred_class)# 处理原始图像img_np = np.array(img.resize((224, 224)))img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)# 生成热图叠加heatmap = cv2.resize(heatmap, (img_np.shape[1], img_np.shape[0]))heatmap = np.uint8(255 * heatmap)heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)superimposed_img = heatmap * 0.4 + img_np * 0.6superimposed_img = np.clip(superimposed_img, 0, 255).astype(np.uint8)# 显示结果plt.figure(figsize=(10, 5))plt.subplot(1, 2, 1)plt.imshow(cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB))plt.title(f'Original (Pred: {dataset.classes[pred_class]})')plt.axis('off')plt.subplot(1, 2, 2)plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))plt.title('Grad-CAM')plt.axis('off')plt.show()# 选择目标层（ResNet的最后一个卷积层）
target_layer = model.resnet.layer4[-1].conv2# 可视化示例
test_image_path = './dogs-vs-cats/train/dog.100.jpg' 
visualize_gradcam(model, test_image_path, target_layer)