深度学习打卡第J1周：ResNet-50算法实战与解析

진영_2025-12-06 15:12

一、前期工作

复制代码 
import torch
import torch.nn as nn
from torchvision import transforms, datasets
import os,PIL,pathlib,warnings
warnings.filterwarnings("ignore")    #忽略警告信息

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

import matplotlib.pyplot as plt
from PIL import Image
import os

image_folder = './data/data/2Mild/'  # 指定图像文件夹路径

# 获取文件夹中的所有图像文件
image_files = [f for f in os.listdir(image_folder) if f.endswith((".jpg", ".png", ".jpeg"))]

fig, axes = plt.subplots(3, 8, figsize=(16, 6))  # 创建Matplotlib图像

# 使用列表推导式加载和显示图像
for ax, img_file in zip(axes.flat, image_files):
    img_path = os.path.join(image_folder, img_file)
    img = Image.open(img_path)
    ax.imshow(img)
    ax.axis('off')

# 显示图像
plt.tight_layout()
plt.show()

data_dir = './data/data/'

# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
    transforms.Normalize(           # 标准化处理->转换为标准正太分布（高斯分布），使模型更容易收敛
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])  # 其中 mean与std是从数据集中随机抽样计算得到的
])

total_data = datasets.ImageFolder(data_dir, transform=train_transforms)
total_data

total_data.class_to_idx

# 按8:2比例划分训练集和测试集
train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size

# 随机拆分数据集
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])

batch_size = 4

# 创建训练集数据加载器（打乱数据）
train_dl = torch.utils.data.DataLoader(
    train_dataset,
    batch_size=batch_size,
    shuffle=True
)

# 创建测试集数据加载器（不打乱数据）
test_dl = torch.utils.data.DataLoader(
    test_dataset,
    batch_size=batch_size
)

# 查看数据加载器的输出格式
for X, y in test_dl:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break

二、搭建网络模型

复制代码 
import torch
import torch.nn as nn

# Same Padding：自动计算卷积的填充大小
def autopad(k, p=None):  # kernel, padding
    # pad to 'same'
    if p is None:
        if isinstance(k, int):
            p = k // 2  # 如果k是整数，填充为k的一半
        else:
            p = [x // 2 for x in k]  # 如果k是列表，每个元素取一半
    return p


# Identity Block（残差块，输入输出维度一致）
class IdentityBlock(nn.Module):
    def __init__(self, in_channel, kernel_size, filters):
        super(IdentityBlock, self).__init__()
        filters1, filters2, filters3 = filters
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channel, filters1, 1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(filters1),
            nn.ReLU(True)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(filters1, filters2, kernel_size, stride=1, padding=autopad(kernel_size), bias=False),
            nn.BatchNorm2d(filters2),
            nn.ReLU(True)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(filters2, filters3, 1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(filters3)
        )
        self.relu = nn.ReLU(True)

    def forward(self, x):
        x1 = self.conv1(x)
        x1 = self.conv2(x1)
        x1 = self.conv3(x1)
        x = x1 + x  # 残差连接
        x = self.relu(x)
        return x


# Conv Block（残差块，输入输出维度不一致，需卷积调整维度）
class ConvBlock(nn.Module):
    def __init__(self, in_channel, kernel_size, filters, stride=2):
        super(ConvBlock, self).__init__()
        filters1, filters2, filters3 = filters
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channel, filters1, 1, stride=stride, padding=0, bias=False),
            nn.BatchNorm2d(filters1),
            nn.ReLU(True)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(filters1, filters2, kernel_size, stride=1, padding=autopad(kernel_size), bias=False),
            nn.BatchNorm2d(filters2),
            nn.ReLU(True)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(filters2, filters3, 1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(filters3)
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(in_channel, filters3, 1, stride=stride, padding=0, bias=False),
            nn.BatchNorm2d(filters3)
        )
        self.relu = nn.ReLU(True)

    def forward(self, x):
        x1 = self.conv1(x)
        x1 = self.conv2(x1)
        x1 = self.conv3(x1)
        x2 = self.conv4(x)  # 调整输入维度以匹配输出
        x = x1 + x2  # 残差连接
        x = self.relu(x)
        return x


# 构建ResNet-50网络
class ResNet50(nn.Module):
    def __init__(self, classes=1000):
        super(ResNet50, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, 7, stride=2, padding=3, bias=False, padding_mode='zeros'),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        self.conv2 = nn.Sequential(
            ConvBlock(64, 3, [64, 64, 256], stride=1),
            IdentityBlock(256, 3, [64, 64, 256]),
            IdentityBlock(256, 3, [64, 64, 256])
        )
        self.conv3 = nn.Sequential(
            ConvBlock(256, 3, [128, 128, 512]),
            IdentityBlock(512, 3, [128, 128, 512]),
            IdentityBlock(512, 3, [128, 128, 512]),
            IdentityBlock(512, 3, [128, 128, 512])
        )
        self.conv4 = nn.Sequential(
            ConvBlock(512, 3, [256, 256, 1024]),
            IdentityBlock(1024, 3, [256, 256, 1024]),
            IdentityBlock(1024, 3, [256, 256, 1024]),
            IdentityBlock(1024, 3, [256, 256, 1024]),
            IdentityBlock(1024, 3, [256, 256, 1024]),
            IdentityBlock(1024, 3, [256, 256, 1024])
        )
        self.conv5 = nn.Sequential(
            ConvBlock(1024, 3, [512, 512, 2048]),
            IdentityBlock(2048, 3, [512, 512, 2048]),
            IdentityBlock(2048, 3, [512, 512, 2048])
        )
        self.pool = nn.AvgPool2d(kernel_size=7, stride=7, padding=0)
        self.fc = nn.Linear(2048, classes)  # 这里classes设为3，对应分类任务

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = self.pool(x)
        x = torch.flatten(x, start_dim=1)
        x = self.fc(x)
        return x


# 实例化模型并移动到设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet50(classes=3).to(device)

# 统计模型参数数量以及其他指标
import torchsummary as summary
summary.summary(model, (3, 224, 224))

三、训练模型

复制代码 
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)  # 训练集的大小
    num_batches = len(dataloader)   # 批次数目（size/batch_size，向上取整）
    
    train_loss, train_acc = 0, 0  # 初始化训练损失和正确率
    
    for X, y in dataloader:  # 获取图片及其标签
        X, y = X.to(device), y.to(device)
        
        # 计算预测误差
        pred = model(X)             # 网络输出
        loss = loss_fn(pred, y)     # 计算网络输出和真实值之间的差距
        
        # 反向传播
        optimizer.zero_grad()  # grad属性归零
        loss.backward()        # 反向传播
        optimizer.step()       # 每一步自动更新
        
        # 记录acc和loss
        train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
        train_loss += loss.item()
    
    train_acc /= size       # 计算训练集整体正确率
    train_loss /= num_batches  # 计算训练集平均损失
    
    return train_acc, train_loss

def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)  # 测试集的大小
    num_batches = len(dataloader)   # 批次数目（size/batch_size，向上取整）
    test_loss, test_acc = 0, 0

    # 当不进行训练时，停止梯度更新，节省计算内存消耗
    with torch.no_grad():
        for imgs, target in dataloader:
            imgs, target = imgs.to(device), target.to(device)
            
            # 计算loss
            target_pred = model(imgs)
            loss = loss_fn(target_pred, target)

            test_loss += loss.item()
            test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()

    test_acc /= size
    test_loss /= num_batches

    return test_acc, test_loss

import copy
import torch
import torch.nn as nn
import torch.optim as optim

# 初始化优化器与损失函数
optimizer = optim.AdamW(model.parameters(), lr=1e-4)
loss_fn = nn.CrossEntropyLoss()  # 创建损失函数

epochs = 10  # 训练轮数

# 初始化指标记录列表
train_loss = []
train_acc = []
test_loss = []
test_acc = []

best_acc = 0  # 设置最佳准确率，作为保存最佳模型的指标

for epoch in range(epochs):
    # 训练阶段
    model.train()  # 开启训练模式（启用Dropout、BatchNorm等层的训练行为）
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
    
    # 测试阶段
    model.eval()  # 开启评估模式（禁用Dropout、固定BatchNorm等层的参数）
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
    
    # 保存最佳模型
    if epoch_test_acc > best_acc:
        best_acc = epoch_test_acc
        best_model = copy.deepcopy(model)  # 深拷贝当前最佳模型
    
    # 记录训练/测试指标
    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)
    
    # 获取当前学习率
    lr = optimizer.state_dict()['param_groups'][0]['lr']
    
    # 打印当前轮次的指标
    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
    print(template.format(epoch+1, 
                          epoch_train_acc*100, 
                          epoch_train_loss, 
                          epoch_test_acc*100, 
                          epoch_test_loss, 
                          lr))

# 保存最佳模型到文件
PATH = './best_model.pth'  # 保存的参数文件名
torch.save(best_model.state_dict(), PATH)  # 保存模型的参数状态字典

print('Done')

import matplotlib.pyplot as plt
# 隐藏警告
import warnings
warnings.filterwarnings("ignore")  # 忽略警告信息

# 配置Matplotlib显示（解决中文/负号显示问题）
plt.rcParams['font.sans-serif'] = ['SimHei']    # 正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False     # 正常显示负号
plt.rcParams['figure.dpi'] = 100               # 设置图像分辨率

from datetime import datetime
current_time = datetime.now()  # 获取当前时间

epochs_range = range(epochs)

# 创建画布并绘制子图
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
# 绘制训练/测试准确率曲线
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.xlabel(current_time)  # 横轴标注当前时间（打卡用）

plt.subplot(1, 2, 2)
# 绘制训练/测试损失曲线
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')

plt.show()
复制代码 
# 将参数加载到model当中
best_model.load_state_dict(torch.load(PATH, map_location=device))
# 用最佳模型评估测试集
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
# 输出最佳模型的测试指标
epoch_test_acc, epoch_test_loss
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