Traceback (most recent call last): File "train.py", line 354, in <module> fit_one_epoch(model_train, model, yolo_loss, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, save_period, save_dir) File "/hy-tmp/yolov5-pytorch-bilibili/yolov5-pytorch-bilibili/utils/utils_fit.py", line 34, in fit_one_epoch outputs = model_train(images) File "/usr/local/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/miniconda3/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 169, in forward return self.module(*inputs[0], **kwargs[0]) File "/usr/local/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/hy-tmp/yolov5-pytorch-bilibili/yolov5-pytorch-bilibili/nets/yolo.py", line 102, in forward self.h3 = self.bottlenecklstm3(P3, self.h3, self.c3) # lstm File "/usr/local/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/hy-tmp/yolov5-pytorch-bilibili/yolov5-pytorch-bilibili/nets/bottleneck_lstm.py", line 141, in forward new_h, new_c = self.cell(inputs, h, c) File "/usr/local/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/hy-tmp/yolov5-pytorch-bilibili/yolov5-pytorch-bilibili/nets/bottleneck_lstm.py", line 68, in forward y = torch.cat((x, h),1) RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument tensors in method wrapper_cat)

这个错误通常是由于将位于不同设备（如GPU和CPU）上的张量传递给需要所有张量都在同一设备上的函数或方法，例如`torch.cat()`方法。要解决这个问题，需要将所有张量都移动到同一设备上，或者使用支持跨设备的函数（如`torch.nn.DataParallel()`）来处理它们。可以使用`tensor.to(device)`方法将张量移动到指定设备上，其中`device`可以是字符串（如`"cuda:0"`）或`torch.device()`对象。在这个特定的错误中，很可能是将GPU上的张量与CPU上的张量拼接在一起，因此需要将所有张量移动到同一设备上。可以使用`tensor.to(device)`方法将张量移动到指定设备上，其中`device`可以是字符串（如`"cuda:0"`）或`torch.device()`对象。

相关问题

Epoch 1/200: 0%| | 0/11 [00:00<?, ?it/s<class 'dict'>]Traceback (most recent call last): File "C:/Users/vipuser/Desktop/yolov4-pytorch-master/train.py", line 550, in <module> fit_one_epoch(model_train, model, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank) File "C:\Users\vipuser\Desktop\yolov4-pytorch-master\utils\utils_fit.py", line 42, in fit_one_epoch loss_value_all += loss_item TypeError: unsupported operand type(s) for +=: 'int' and 'tuple' Epoch 1/200: 0%| | 0/11 [00:26<?, ?it/s<class 'dict'>]

这个错误是由于你的代码中的`loss_value_all`变量被错误地设置为元组类型，而不是整数类型。你需要检查你的代码，找到这个变量的定义，并将其更改为整数类型。

一种可能的解决方法是在定义`loss_value_all`时，将其初始化为0，如下所示：

loss_value_all = 0

然后，在每个批次训练时，你需要将批次损失值转换为一个标量，例如使用`.item()`方法，然后将其添加到`loss_value_all`中，如下所示：

loss_value_all += loss_item.item()

这样就能够将批次损失值添加到`loss_value_all`变量中，而不会出现类型错误。

Traceback (most recent call last): File "/content/faster-rcnn-pytorch/train.py", line 439, in <module> fit_one_epoch(model, train_util, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir) File "/content/faster-rcnn-pytorch/utils/utils_fit.py", line 27, in fit_one_epoch rpn_loc, rpn_cls, roi_loc, roi_cls, total = train_util.train_step(images, boxes, labels, 1, fp16, scaler) File "/content/faster-rcnn-pytorch/nets/frcnn_training.py", line 327, in train_step losses = self.forward(imgs, bboxes, labels, scale) File "/content/faster-rcnn-pytorch/nets/frcnn_training.py", line 266, in forward gt_rpn_label = torch.Tensor(gt_rpn_label).type_as(rpn_locs).long() File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler _error_if_any_worker_fails() RuntimeError: DataLoader worker (pid 9618) is killed by signal: Killed.

这是一个 RuntimeError 错误，错误信息显示在使用 DataLoader 过程中，其中的一个 worker 被杀死了。可能是由于内存不足导致的，或者是系统资源不足导致的。你可以尝试减小 batch_size 或者调整其他超参数，以减少内存占用。另外，也可以尝试在训练代码中加入内存监控和释放代码，及时释放不必要的内存，以避免内存不足的问题。