我的验证数据有150张图像，但在使用模型进行预测时，我的预测结果长度只有22，我不明白这是为什么？

total_v=0correct_v=0with torch.no_grad():    model.eval()    for data_v, target_v in (validloader):        if SK:            target_v = torch.tensor(np.where(target_v.numpy() == 2, 1, 0).astype(np.longlong))        else:            target_v = torch.tensor(np.where(target_v.numpy() == 0, 1, 0).astype(np.longlong))                     data_v, target_v = data_v.to(device), target_v.to(device)                 outputs_v = model(data_v)        loss_v = criterion(outputs_v, target_v)        batch_loss += loss_v.item()        _,pred_v = torch.max(outputs_v, dim=1)        correct_v += torch.sum(pred_v==target_v).item()        total_v += target_v.size(0)    val_acc.append(100 * correct_v/total_v)    val_loss.append(batch_loss/len(validloader))    network_learned = batch_loss < valid_loss_min    print(f'validation loss: {np.mean(val_loss):.4f}, validation acc: {(100 *           correct_v/total_v):.4f}\n')

这是我的模型

model = models.resnet50(pretrained = True)num_ftrs = model.fc.in_featuresmodel.fc = nn.Linear(num_ftrs, 2)model.to(device)criterion = nn.CrossEntropyLoss()optimizer = optim.Adagrad(model.parameters())

回答：

如果你想要得到所有的预测结果，你应该存储每个批次的预测结果，并在迭代结束时将它们连接起来

...all_preds = []for data_v, target_v in validloader:     ....     _,pred_v = torch.max(outputs_v, dim=1)    all_preds.append(pred_v)    ....all_preds = torch.cat(all_preds).cpu().numpy()print(len(all_preds))