resnet gpu源码

### ResNet在医学图像分类中的代码实现 ResNet是一种深度卷积神经网络，其核心思想是通过残差块引入恒等映射，从而缓解梯度消失问题并允许训练更深的网络[^3]。以下是一个基于ResNet-50架构的医学图像分类任务的代码实现示例。该代码使用了PyTorch框架，并以多类别分类任务为目标。 #### 1. 环境准备 首先需要安装必要的库： ```bash pip install torch torchvision matplotlib numpy ``` #### 2. 数据集加载与预处理 医学图像分类通常需要对图像进行标准化和增强处理。以下代码展示了如何加载和预处理数据集： ```python import torch from torchvision import datasets, transforms from torch.utils.data import DataLoader # 定义数据预处理步骤 transform = transforms.Compose([ transforms.Resize((224, 224)), # 调整图像大小 transforms.ToTensor(), # 转换为张量 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 标准化 ]) # 加载数据集（假设数据集路径为"data"） train_dataset = datasets.ImageFolder(root='data/train', transform=transform) val_dataset = datasets.ImageFolder(root='data/val', transform=transform) # 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) ``` #### 3. 模型定义 ResNet-50模型可以通过`torchvision.models`直接加载，并根据任务需求调整最后一层全连接层的输出维度： ```python import torch.nn as nn import torchvision.models as models # 加载预训练的ResNet-50模型 model = models.resnet50(pretrained=True) # 替换最后一层全连接层以适应医学图像分类任务 num_classes = 9 # 假设有9个类别 model.fc = nn.Linear(model.fc.in_features, num_classes) # 将模型移动到GPU（如果可用） device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) ``` #### 4. 损失函数与优化器 选择交叉熵损失函数和Adam优化器来训练模型： ```python import torch.optim as optim # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) ``` #### 5. 训练过程 以下是模型训练的主要部分： ```python def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=10): for epoch in range(num_epochs): model.train() running_loss = 0.0 correct = 0 total = 0 # 训练阶段 for images, labels in train_loader: images, labels = images.to(device), labels.to(device) # 前向传播 outputs = model(images) loss = criterion(outputs, labels) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() # 统计损失和准确率 running_loss += loss.item() _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() train_loss = running_loss / len(train_loader) train_acc = 100 * correct / total # 验证阶段 model.eval() val_correct = 0 val_total = 0 with torch.no_grad(): for images, labels in val_loader: images, labels = images.to(device), labels.to(device) outputs = model(images) _, predicted = torch.max(outputs.data, 1) val_total += labels.size(0) val_correct += (predicted == labels).sum().item() val_acc = 100 * val_correct / val_total print(f'Epoch [{epoch+1}/{num_epochs}], ' f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}%, ' f'Val Acc: {val_acc:.2f}%') # 开始训练 train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=10) ``` #### 6. 测试与推理 完成训练后，可以使用测试集评估模型性能或进行单张图像的推理： ```python def predict_image(image_path, model, transform): from PIL import Image image = Image.open(image_path).convert('RGB') image = transform(image).unsqueeze(0).to(device) output = model(image) _, predicted = torch.max(output.data, 1) return predicted.item() # 示例预测 image_path = 'data/test/image.png' prediction = predict_image(image_path, model, transform) print(f'Predicted Class: {prediction}') ``` ### 总结 上述代码展示了一个完整的基于ResNet-50的医学图像分类流程，包括数据预处理、模型定义、训练过程以及推理部分。此代码适用于多类别分类任务，并且可以扩展到其他医学图像数据集[^4]。

下面是使用PyTorch的ResNet-18模型训练自己的数据集的源代码示例： ```python import torch import torchvision import torchvision.transforms as transforms import torchvision.models as models import torch.optim as optim import torch.nn as nn # 定义数据预处理 transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # 加载自定义数据集 train_dataset = torchvision.datasets.ImageFolder(root='path_to_train_data', transform=transform) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=2) # 加载预训练的ResNet-18模型 model = models.resnet18(pretrained=True) # 冻结模型的参数 for param in model.parameters(): param.requires_grad = False # 替换最后一层全连接层 num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, num_classes) # 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9) # 将模型移动到GPU device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = model.to(device) # 训练模型 num_epochs = 10 for epoch in range(num_epochs): running_loss = 0.0 for i, data in enumerate(train_loader, 0): inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if i % 200 == 199: print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 200)) running_loss = 0.0 print('训练完成！') ``` 注意替换代码中的`path_to_train_data`为你自己的训练数据集的路径。此外，还可以调整超参数并将模型训练在GPU上，以加快训练速度。