### 基于 PyTorch 的花卉识别代码 以下是基于 PyTorch 实现的一个简单花卉分类模型的示例代码。该代码涵盖了数据加载、模型定义、训练过程以及测试部分。 #### 数据预处理 为了适应不同尺寸的图像，可以使用 `torchvision.transforms` 对输入图片进行统一大小调整并转换为张量形式： ```python import torch from torchvision import datasets, transforms, models from torch.utils.data import DataLoader # 定义数据变换操作 data_transforms = { 'train': transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) } # 加载数据集 data_dir = './flower_data' # 替换为实际的数据路径 image_datasets = {x: datasets.ImageFolder(f'{data_dir}/{x}', data_transforms[x]) for x in ['train', 'val']} dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['train'].classes ``` #### 模型定义 这里采用迁移学习的方式，利用预训练的 ResNet-18 模型作为基础网络，并替换其最后的全连接层以适配当前任务中的类别数（即 5 类）。 ```python device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_ft = models.resnet18(pretrained=True) for param in model_ft.parameters(): param.requires_grad = False num_ftrs = model_ft.fc.in_features model_ft.fc = torch.nn.Linear(num_ftrs, len(class_names)) model_ft = model_ft.to(device) criterion = torch.nn.CrossEntropyLoss() optimizer_ft = torch.optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9) ``` #### 训练函数 编写一个通用的训练循环来优化模型参数。 ```python def train_model(model, criterion, optimizer, num_epochs=25): best_acc = 0.0 for epoch in range(num_epochs): print(f'Epoch {epoch}/{num_epochs - 1}') for phase in ['train', 'val']: if phase == 'train': model.train() else: model.eval() running_loss = 0.0 running_corrects = 0 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) optimizer.zero_grad() with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) if phase == 'train': loss.backward() optimizer.step() running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}') if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc print('Best val Acc: {:4f}'.format(best_acc)) return model model_trained = train_model(model_ft, criterion, optimizer_ft, num_epochs=10) ``` #### 测试模型 完成训练后，可以通过以下方式评估模型性能或预测新样本标签。 ```python def visualize_model(model, num_images=6): was_training = model.training model.eval() images_so_far = 0 fig = plt.figure() with torch.no_grad(): for i, (inputs, labels) in enumerate(dataloaders['val']): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) _, preds = torch.max(outputs, 1) for j in range(inputs.size()[0]): images_so_far += 1 ax = plt.subplot(num_images//2, 2, images_so_far) ax.axis('off') ax.set_title(f'predicted: {class_names[preds[j]]}') imshow(inputs.cpu().data[j]) if images_so_far == num_images: model.train(mode=was_training) return model.train(mode=was_training) visualize_model(model_trained) plt.show() ``` --- ### 注意事项 上述代码假设已经下载好花卉数据集并按照标准结构存储在本地文件夹中[^1]。如果需要调用远程 API 获取数据，则可以根据具体接口文档修改数据获取逻辑[^2]。