如何加载经过训练的模型来推断预测数据

Question

我训练并保存了 1000 个 epoch 的 CNN 模型，现在想检索验证数据（预测图像）。在下面的代码中，test_pred 和 test_real 输出验证集中的预测图像和真实图像。我是否应该加载并运行保存的模型再过 1 个时期以检索预测图像（由于数据量巨大，这将导致 CUDA 内存不足）？还是有其他方法？您可以在下面看到我的部分代码：

for epoch in range(epochs):
    mse_train_losses= []
    mae_train_losses = []
    N_train = []
    mse_val_losses = []
    mae_val_losses = []
    N_test = []
    
    if save_model:
        if epoch % 50 ==0:
            checkpoint = {'state_dict' : model.state_dict(),'optimizer' : optimizer.state_dict()}
            save_checkpoint(checkpoint)
   
    model.train()
    for data in train_loader:

        x_train_batch, y_train_batch = data[0].to(device, 
            dtype=torch.float), data[1].to(device, dtype=torch.float)  
        y_train_pred = model(x_train_batch)            # 1) Forward pass
        mse_train_loss = criterion(y_train_batch, y_train_pred, x_train_batch, mse) 
        mae_train_loss = criterion(y_train_batch, y_train_pred, x_train_batch, l1loss)  
        
        optimizer.zero_grad()                   
        mse_train_loss.backward()                        
        optimizer.step()                        
        
        mse_train_losses.append(mse_train_loss.item())
        mae_train_losses.append(mae_train_loss.item())
        N_train.append(len(x_train_batch))
        
                       
    test_pred=[] 
    test_real=[]
    model.eval()
    with torch.no_grad():
        for data in test_loader:
            x_test_batch, y_test_batch = data[0].to(device, 
                dtype=torch.float), data[1].to(device, dtype=torch.float)

            y_test_pred = model(x_test_batch)
            mse_val_loss = criterion(y_test_batch, y_test_pred, x_test_batch, mse)
            mae_val_loss = criterion(y_test_batch, y_test_pred, x_test_batch, l1loss)
            
            mse_val_losses.append(mse_val_loss.item())
            mae_val_losses.append(mae_val_loss.item())
            N_test.append(len(x_test_batch))
            
            test_pred.append(y_test_pred)                        
            test_real.append(y_test_batch)

Answer 1

当您将其附加到列表时，尝试在末尾使用 .cpu() ，如下所示：

test_pred.append(t_test_pred.cpu())

如何加载经过训练的模型来推断预测数据

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