我已经将模型和数据设置到相同的设备上，但总是会引发这样的错误：

RuntimeError: 输入类型（torch.cuda.FloatTensor）和权重类型（torch.FloatTensor）应相同

以下是我的训练代码，希望你能解答这个问题。谢谢！

def train(train_img_path, train_label_path, pths_path, interval, log_file):    file_num = len(os.listdir(train_img_path))    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")    net = EAST(extractor=extractor, geometry_mode=geometry_mode, pretrained=True)    net = net.to(device)    trainset = custom_dataset(train_img_path, train_label_path)    train_loader = data.DataLoader(trainset, batch_size=batch_size,                                   shuffle=True, num_workers=num_workers, drop_last=True)    optimizer = optim.SGD(net.parameters(), lr=initial_lr, momentum=momentum, weight_decay=weight_decay_sgd)    criterion = Loss(weight_geo, weight_angle, geometry_mode="RBOX")    net.train()    epoch_loss = 0.    for epoch in range(max_epoch):        epoch_time = time.time()        for i, (img, score_gt, geo_gt, ignored_map) in enumerate(train_loader):            start_time = time.time()            img, score_gt, geo_gt, ignored_map = img.to(device), score_gt.to(device),\                                                 geo_gt.to(device), ignored_map.to(device)            score_pred, geo_pred = net(img)            total_loss, score_loss, loss_AABB, loss_angle = criterion(score_pred, geo_pred, score_gt, geo_gt, ignored_map)            epoch_loss += total_loss.item()            optimizer.zero_grad()            total_loss.backward()            optimizer.step()

回答：

我怀疑你的损失函数内部有自己的参数，因此你也应该

criterion = Loss(weight_geo, weight_angle, geometry_mode="RBOX").to(device)

如果你能提供完整的错误追踪，指出是哪一行确切地引发了错误，就更容易发现问题所在了。