feat: 实现 NeRF 和 CNN 两种算法

- 新增 NeRF 和 CNN 的代码实现
- 添加实验报告和使用说明文档- 创建数据集说明文件
- 生成测试结果和可视化输出

README.md

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+# NeRF和CNN实验项目
+
+## 项目结构
+- `nerf/`：NeRF算法实现代码
+- `cnn/`：CNN图像识别算法代码
+- `data/`：数据集存储目录
+- `results/`：训练结果保存目录
+- `docs/`：实验报告文档
+
+## 环境依赖
+```bash
+# 安装PyTorch（根据CUDA版本选择对应命令）
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+
+# 安装其他依赖
+pip install numpy opencv-python matplotlib argparse
+```
+
+## 使用说明
+### 运行NeRF实验
+```bash
+cd nerf
+python nerf/train_nerf.py
+python nerf/test_nerf.py
+```
+
+### 运行CNN实验
+```bash
+cd cnn
+python cnn.py --data_path ../data
+```
+
+## 实验报告
+详见docs/experiment_report.md文件

cnn/cnn.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+import torchvision
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+import os
+
+# 设置中文字体
+plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体显示中文
+plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号
+
+# CNN图像识别模型定义
+class SimpleCNN(nn.Module):
+    def __init__(self, num_classes=10):
+        super(SimpleCNN, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(128 * 8 * 8, 512),
+            nn.ReLU(),
+            nn.Linear(512, num_classes)
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+# 数据预处理
+def get_dataloaders(data_path, batch_size=64):
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+    ])
+
+    trainset = torchvision.datasets.CIFAR10(root=data_path, train=True,
+                                            download=True, transform=transform)
+    trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
+
+    testset = torchvision.datasets.CIFAR10(root=data_path, train=False,
+                                           download=True, transform=transform)
+    testloader = DataLoader(testset, batch_size=batch_size, shuffle=False)
+
+    return trainloader, testloader
+
+# 测试函数
+def test_cnn(model, testloader, device):
+    model.eval()  # 设置为评估模式
+    correct = 0
+    total = 0
+    with torch.no_grad():  # 不需要计算梯度
+        for data in testloader:
+            images, labels = data
+            images, labels = images.to(device), labels.to(device)
+            outputs = model(images)
+            _, predicted = torch.max(outputs.data, 1)  # 获取预测结果
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()  # 计算正确预测数
+
+    accuracy = 100 * correct / total  # 计算准确率
+    print(f'测试准确率: {accuracy:.2f}%')  # 打印准确率
+    return accuracy
+
+# 展示预测结果函数
+def show_sample_predictions(model, testloader, device, num_samples=6):
+    model.eval()
+    data_iter = iter(testloader)
+    images, labels = next(data_iter)
+    images, labels = images.to(device), labels.to(device)
+    
+    outputs = model(images)
+    _, predicted = torch.max(outputs, 1)
+    
+    # 转换回CPU并显示图像
+    images = images.cpu()
+    labels = labels.cpu()
+    predicted = predicted.cpu()
+    
+    # 创建图像展示
+    fig, axes = plt.subplots(1, num_samples, figsize=(15, 3))
+    for i in range(num_samples):
+        img = transforms.ToPILImage()(images[i])
+        axes[i].imshow(img)
+        axes[i].set_title('实际: %d\n预测: %d' % (labels[i].item(), predicted[i].item()))
+        axes[i].axis('off')
+    
+    plt.tight_layout()
+    # 确保结果目录存在
+    os.makedirs('results/cnn', exist_ok=True)
+    plt.savefig('results/cnn/sample_predictions.png')
+    plt.close()
+
+# 训练函数
+def train_cnn():
+    # 参数设置
+    parser = argparse.ArgumentParser(description='Train CNN')
+    parser.add_argument('--data_path', type=str, default='./data', help='Dataset path')
+    args = parser.parse_args()
+
+    # 设备配置
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # 数据加载
+    trainloader, testloader = get_dataloaders(args.data_path)
+
+    # 模型初始化
+    model = SimpleCNN().to(device)
+
+    # 损失函数和优化器
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+    # 训练循环
+    for epoch in range(10):  # loop over the dataset multiple times
+        running_loss = 0.0
+        for i, data in enumerate(trainloader, 0):
+            inputs, labels = data
+            inputs, labels = inputs.to(device), labels.to(device)
+
+            # 零梯度
+            optimizer.zero_grad()
+
+            # 前向传播 + 反向传播 + 优化
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            # 打印统计信息
+            running_loss += loss.item()
+            if i % 100 == 99:  # 每100个小批量打印一次
+                print(f'[Epoch {epoch + 1}, Batch {i + 1}] loss: {running_loss / 100:.3f}')
+                running_loss = 0.0
+
+        # 保存模型
+        torch.save(model.state_dict(), f'cnn_model_epoch{epoch}.pth')
+    
+    # 测试模型
+    print('开始测试模型...')
+    accuracy = test_cnn(model, testloader, device)
+    
+    # 展示样本预测结果
+    show_sample_predictions(model, testloader, device)
+
+    # 保存测试结果
+    with open('results/cnn_accuracy.txt', 'w') as f:
+        f.write(f'Test Accuracy: {accuracy:.2f}%')
+
+    print('Finished Training')
+
+if __name__ == '__main__':
+    train_cnn()

cnn/results/cnn_accuracy.txt

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+Test Accuracy: 72.94%

data/nerf/lego/predictions/pred_0.json

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data/nerf/lego/predictions/pred_1.json

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data/nerf/lego/predictions/pred_10.json

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data/nerf/lego/predictions/pred_100.json

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data/nerf/lego/predictions/pred_101.json

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data/nerf/lego/predictions/pred_102.json

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data/nerf/lego/predictions/pred_103.json

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data/nerf/lego/predictions/pred_104.json

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data/nerf/lego/predictions/pred_106.json

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data/nerf/lego/predictions/pred_107.json

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data/nerf/lego/predictions/pred_108.json

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data/nerf/lego/predictions/pred_11.json

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