J5打卡——pytorch实现DenseNet+SE-Net猴痘分类

  🍨 本文为🔗365天深度学习训练营中的学习记录博客🍖 原作者：K同学啊

1.检查GPU import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision from torchvision import transforms, datasets import os,PIL,pathlib device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device

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2.查看数据 import os,PIL,random,pathlib data_dir = 'data/45-data/' data_dir = pathlib.Path(data_dir) data_paths = list(data_dir.glob('*')) classeNames = [str(path).split("\\")[2] for path in data_paths] classeNames ​​ 3.划分数据集 total_datadir = 'data/45-data' 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=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。 ]) total_data = datasets.ImageFolder(total_datadir,transform=train_transforms) total_data train_size = int(0.8 * len(total_data)) test_size = len(total_data) - train_size train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size]) train_dataset, test_dataset train_size,test_size batch_size = 32 train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1) test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=1) 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 ​​​​​​​​

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4.创建模型 import torch import torch.nn as nn import torch.nn.functional as F # SE注意力机制 class SqueezeExcitation(nn.Module): def __init__(self, in_channels, reduction=16): super(SqueezeExcitation, self).__init__() self.global_avg_pool = nn.AdaptiveAvgPool2d(1) self.fc1 = nn.Linear(in_channels, in_channels // reduction, bias=False) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Linear(in_channels // reduction, in_channels, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _, _ = x.size() y = self.global_avg_pool(x).view(b, c) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y).view(b, c, 1, 1) return x * y # DenseNet的Conv Block class ConvBlock(nn.Module): def __init__(self, in_channels, growth_rate): super(ConvBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_channels) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_channels, 4 * growth_rate, kernel_size=1, bias=False) self.bn2 = nn.BatchNorm2d(4 * growth_rate) self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False) def forward(self, x): out = self.conv1(self.relu(self.bn1(x))) out = self.conv2(self.relu(self.bn2(out))) return torch.cat([x, out], 1) # Dense Block class DenseBlock(nn.Module): def __init__(self, num_layers, in_channels, growth_rate): super(DenseBlock, self).__init__() layers = [] for i in range(num_layers): layers.append(ConvBlock(in_channels + i * growth_rate, growth_rate)) self.block = nn.Sequential(*layers) def forward(self, x): return self.block(x) # Transition Block class TransitionBlock(nn.Module): def __init__(self, in_channels, out_channels): super(TransitionBlock, self).__init__() self.bn = nn.BatchNorm2d(in_channels) self.relu = nn.ReLU(inplace=True) self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) self.pool = nn.AvgPool2d(kernel_size=2, stride=2) def forward(self, x): x = self.conv(self.relu(self.bn(x))) x = self.pool(x) return x # DenseNet class DenseNet(nn.Module): def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, num_classes=3): super(DenseNet, self).__init__() # 初始卷积层 self.features = nn.Sequential( nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False), nn.BatchNorm2d(num_init_features), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1) ) num_features = num_init_features layers = [] for i, num_layers in enumerate(block_config): layers.append(DenseBlock(num_layers, num_features, growth_rate)) num_features += num_layers * growth_rate if i != len(block_config) - 1: layers.append(TransitionBlock(num_features, num_features // 2)) num_features = num_features // 2 self.dense_blocks = nn.Sequential(*layers) self.se = SqueezeExcitation(num_features) self.final_bn = nn.BatchNorm2d(num_features) self.final_relu = nn.ReLU(inplace=True) self.global_avg_pool = nn.AdaptiveAvgPool2d(1) self.classifier = nn.Linear(num_features, num_classes) def forward(self, x): x = self.features(x) x = self.dense_blocks(x) x = self.se(x) x = self.final_relu(self.final_bn(x)) x = self.global_avg_pool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x # 定义不同版本的 DenseNet def densenet121(num_classes=3): return DenseNet(growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, num_classes=num_classes) def densenet169(num_classes=3): return DenseNet(growth_rate=32, block_config=(6, 12, 32, 32), num_init_features=64, num_classes=num_classes) def densenet201(num_classes=3): return DenseNet(growth_rate=32, block_config=(6, 12, 48, 32), num_init_features=64, num_classes=num_classes) model=densenet121().to(device) model ​ 5.编译及训练模型   loss_fn = nn.CrossEntropyLoss() # 创建损失函数 learn_rate = 1e-4 # 学习率 opt = torch.optim.SGD(model.parameters(),lr=learn_rate) # 训练循环 def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) # 训练集的大小，一共60000张图片 num_batches = len(dataloader) # 批次数目，1875（60000/32） 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，targets为真实值，计算二者差值即为损失 # 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与loss train_acc += (pred.argmax(1) == y).type(torch.float).sum().item() train_loss += loss.item() train_acc /= size train_loss /= num_batches return train_acc, train_loss def test (dataloader, model, loss_fn): size = len(dataloader.dataset) # 测试集的大小，一共10000张图片 num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，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 epochs = 20 train_loss = [] train_acc = [] test_loss = [] test_acc = [] for epoch in range(epochs): model.train() epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt) model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn) train_acc.append(epoch_train_acc) train_loss.append(epoch_train_loss) test_acc.append(epoch_test_acc) test_loss.append(epoch_test_loss) template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}') print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss)) print('Done') ​​​​​​​​​ 6.结果可视化 import matplotlib.pyplot as plt #隐藏警告 import warnings warnings.filterwarnings("ignore") #忽略警告信息 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.rcParams['figure.dpi'] = 100 #分辨率 from datetime import datetime current_time = datetime.now() # 获取当前时间 epochs_range = range(epochs) plt.figure(figsize=(12, 3)) plt.subplot(1, 2, 1) 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) # 打卡请带上时间戳，否则代码截图无效 plt.subplot(1, 2, 2) 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() ​​ ​​​​​7.预测图片 from PIL import Image classes = list(total_data.class_to_idx) def predict_one_image(image_path, model, transform, classes): test_img = Image.open(image_path).convert('RGB') # plt.imshow(test_img) # 展示预测的图片 test_img = transform(test_img) img = test_img.to(device).unsqueeze(0) model.eval() output = model(img) _,pred = torch.max(output,1) pred_class = classes[pred] print(f'预测结果是：{pred_class}') # 预测训练集中的某张照片 predict_one_image(image_path='data/45-data/Others/NM01_01_05.jpg', model=model, transform=train_transforms, classes=classes) ​​​ 总结：

这段代码实现了一个基于DenseNet的图像分类任务，包含了数据加载、模型定义、训练、测试、结果可视化以及单张图片预测的全流程。以下是代码的主要步骤和功能总结：

1. 检查GPU    - 使用`torch.cuda.is_available()`检查是否有可用的GPU，并将模型和数据加载到GPU上。

2. 查看数据    - 定义了数据集的路径，并使用`pathlib.Path`来管理路径。    - 通过`glob`方法获取数据集中的所有类别，并生成类别名称列表。

3. 划分数据集    - 使用`transforms.Compose`定义数据预处理操作，包括调整大小、转换为Tensor、标准化等。    - 使用`datasets.ImageFolder`加载数据集，并将其划分为训练集和测试集（80%训练，20%测试）。    - 使用`DataLoader`将数据集加载为批量数据，方便后续训练和测试。

4. 创建模型    - 定义了`SqueezeExcitation`（SE注意力机制）模块，用于增强模型的表达能力。    - 定义了`ConvBlock`（卷积块）和`DenseBlock`（密集块），这些是DenseNet的核心组件。    - 定义了`TransitionBlock`（过渡块），用于减少特征图的尺寸。    - 定义了`DenseNet`模型，包含了初始卷积层、多个密集块、过渡块、SE注意力机制以及最后的分类层。    - 提供了`densenet121`、`densenet169`、`densenet201`等不同版本的DenseNet模型。

 5. 编译及训练模型    - 使用`CrossEntropyLoss`作为损失函数，`SGD`作为优化器。    - 定义了`train`和`test`函数，分别用于训练和测试模型。    - 进行了20个epoch的训练，并在每个epoch结束后记录训练和测试的准确率及损失。

6. 结果可视化    - 使用`matplotlib`绘制了训练和测试的准确率曲线以及损失曲线。    - 在图中添加了时间戳，确保代码的可追溯性。

 7. 预测图片    - 定义了一个函数`predict_one_image`，用于对单张图片进行预测。    - 通过加载图片、预处理、模型推理等步骤，输出图片的预测类别。

创新：

创新点：在DenseNet的基础上，引入了Squeeze-and-Excitation (SE) 注意力机制。

作用：SE模块通过学习通道之间的依赖关系，动态调整每个通道的特征权重，从而增强模型对重要特征的关注能力。

优势：这种机制可以显著提升模型的表达能力，尤其是在复杂的图像分类任务中，能够更好地捕捉到关键特征。