vitae高光谱图像分类

Vitae Transformer是一种新型的Transformer模型，它在高光谱图像分类任务中表现出色。下面是使用Vitae Transformer进行高光谱图像分类的步骤：

1. 准备数据集：将高光谱图像数据集划分为训练集和测试集，并将其转换为模型可接受的格式。

2. 定义模型：使用PyTorch实现Vitae Transformer模型，并根据数据集的特点进行相应的调整。

   import torch
   import torch.nn as nn
   from transformers import ViTaeTransformer
   
   class ViTaeClassifier(nn.Module):
       def __init__(self, num_classes):
           super(ViTaeClassifier, self).__init__()
           self.transformer = ViTaeTransformer()
           self.fc = nn.Linear(self.transformer.config.hidden_size, num_classes)
           
       def forward(self, x):
           x = self.transformer(x)
           x = self.fc(x.pooler_output)
           return x

3. 训练模型：使用训练集对模型进行训练，并在验证集上进行验证，以确定最佳的超参数和模型结构。

   import torch.optim as optim
   from torch.utils.data import DataLoader
   from sklearn.metrics import accuracy_score
   
   # 定义超参数
   lr = 1e-4
   batch_size = 32
   num_epochs = 10
   
   # 准备数据集
   train_dataset = ...
   test_dataset = ...
   train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
   test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
   
   # 定义模型和优化器
   model = ViTaeClassifier(num_classes=10)
   optimizer = optim.Adam(model.parameters(), lr=lr)
   
   # 训练模型
   for epoch in range(num_epochs):
       model.train()
       for batch_idx, (data, target) in enumerate(train_loader):
           optimizer.zero_grad()
           output = model(data)
           loss = nn.CrossEntropyLoss()(output, target)
           loss.backward()
           optimizer.step()
           
       model.eval()
       with torch.no_grad():
           test_acc = []
           for data, target in test_loader:
               output = model(data)
               pred = output.argmax(dim=1)
               acc = accuracy_score(pred.cpu().numpy(), target.cpu().numpy())
               test_acc.append(acc)
           print('Epoch: {}, Test Accuracy: {:.2f}%'.format(epoch, 100 * sum(test_acc) / len(test_acc)))

4. 测试模型：使用测试集对模型进行测试，并计算模型的准确率。

   model.eval()
   with torch.no_grad():
       test_acc = []
       for data, target in test_loader:
           output = model(data)
           pred = output.argmax(dim=1)
           acc = accuracy_score(pred.cpu().numpy(), target.cpu().numpy())
           test_acc.append(acc)
       print('Test Accuracy: {:.2f}%'.format(100 * sum(test_acc) / len(test_acc)))