swin transformer流程和代码

### Swin Transformer工作原理 Swin Transformer是一种基于Transformer架构设计的新型卷积神经网络替代方案，特别适用于计算机视觉任务。该模型引入了分层结构和移位窗口机制来处理图像数据[^3]。 #### 分层结构 Swin Transformer采用层次化的设计理念，能够逐步提取不同尺度下的特征表示。每一层由多个阶段组成，每个阶段内部又包含了若干个基本模块——即Swin Transformer Block。这种多级抽象使得模型可以捕捉到更加丰富的空间关系信息。 #### 移位窗口机制 为了有效降低自注意力计算复杂度并保持局部感受野特性，Swin Transformer提出了独特的移位窗口策略。具体来说，在奇数层中使用固定位置划分不重叠的小方块作为窗口；而在偶数层则将这些窗口沿水平方向或垂直方向偏移半个宽度/高度形成交错排列的新布局形式。如此一来既保留了一定程度上的全局依赖建模能力，同时也大幅减少了内存消耗与运算量开销[^1]。 ### Python代码实现 以下是关于如何构建以及训练一个简单的Swin Transformer实例： ```python import torch from torchvision import models, transforms, datasets from timm.models.swin_transformer import swin_tiny_patch4_window7_224 as create_model def load_data(data_path): data_transform = { "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])]) } train_dataset = datasets.ImageFolder(root=f"{data_path}/train", transform=data_transform["train"]) val_dataset = datasets.ImageFolder(root=f"{data_path}/val", transform=data_transform["val"]) batch_size = 16 nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers print('Using {} dataloader workers every process'.format(nw)) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=nw) validate_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=nw) return train_loader, validate_loader if __name__ == '__main__': device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = create_model(num_classes=10).to(device) # 创建swin-tiny模型，指定分类数目为10类 loss_function = torch.nn.CrossEntropyLoss() optimizer = torch.optim.AdamW(model.parameters(), lr=0.0001) epochs = 10 best_acc = 0.0 save_path = './weights/swin.pth' train_steps = len(train_loader) for epoch in range(epochs): # Train phase model.train() running_loss = 0.0 train_bar = tqdm(train_loader, file=sys.stdout) for step, data in enumerate(train_bar): images, labels = data optimizer.zero_grad() logits = model(images.to(device)) loss = loss_function(logits, labels.to(device)) loss.backward() optimizer.step() # Print statistics running_loss += loss.item() # Validate after each epoch model.eval() acc = 0.0 with torch.no_grad(): val_bar = tqdm(validate_loader, file=sys.stdout) for val_data in val_bar: val_images, val_labels = val_data outputs = model(val_images.to(device)) predict_y = torch.max(outputs, dim=1)[1] acc += (predict_y == val_labels.to(device)).sum().item() val_accurate = acc / len(validate_loader.dataset) print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' % (epoch + 1, running_loss / train_steps, val_accurate)) if val_accurate > best_acc: best_acc = val_accurate torch.save(model.state_dict(), save_path) ``` 此段代码展示了完整的从加载数据集、定义损失函数优化器到最后保存最佳权重的过程。需要注意的是这里仅提供了一个基础版本用于演示目的，在实际项目开发过程中还需要根据需求调整超参数设置以及其他细节部分[^4]。