File "main.py", line 47, in <module> exp.train(args) File "/root/autodl-tmp/SimVP-Simpler-yet-Better-Video-Prediction-master-mnist/SimVP-Simpler-yet-Better-Video-Prediction-master/exp.py", line 186, in train loss.backward() File "/root/miniconda3/lib/python3.8/site-packages/torch/_tensor.py", line 363, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/root/miniconda3/lib/python3.8/site-packages/torch/autograd/__init__.py", line 173, in backward Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.

这个错误通常表示在计算图中进行了多反向传播，或者在已经释放保存的中间张量之后直接访问了它们。这可能是由于在调用 `.backward()` 或 `autograd.grad()` 之后尝试进行第二次反向传播，或者在释放保存的张量后仍然访问它们。

要解决这个问题，你可以尝试以下几个步骤：

1. 确保你在每次迭代中只调用一次 `.backward()`。如果你需要多次反向传播，请确保在每次调用之前使用 `torch.autograd.grad()` 清除梯度。

2. 如果你需要在调用 `.backward()` 后访问保存的张量，请确保指定 `retain_graph=True` 参数。这将保留计算图，并允许你在反向传播之后访问保存的张量。

3. 检查你的代码，确保没有直接访问已经被释放的中间张量。如果你需要在反向传播之后访问这些张量，你可以使用 `torch.autograd.grad()` 来获取它们。

如果你仍然遇到困难，请提供更多的代码上下文，以便我更好地帮助你解决问题。

相关问题

File "main.py", line 49, in <module> mse = exp.test(args) File "/root/autodl-tmp/SimVP-Simpler-yet-Better-Video-Prediction-master-mnist/SimVP-Simpler-yet-Better-Video-Prediction-master/exp.py", line 255, in test plt.imshow(inputs[i, j].transpose(1,2,0)/255.0) File "/root/miniconda3/lib/python3.8/site-packages/matplotlib/pyplot.py", line 2695, in imshow __ret = gca().imshow( File "/root/miniconda3/lib/python3.8/site-packages/matplotlib/__init__.py", line 1442, in inner return func(ax, *map(sanitize_sequence, args), **kwargs) File "/root/miniconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py", line 5665, in imshow im.set_data(X) File "/root/miniconda3/lib/python3.8/site-packages/matplotlib/image.py", line 710, in set_data raise TypeError("Invalid shape {} for image data" TypeError: Invalid shape (32, 32, 2) for image data

根据报错信息，出现了一个类型错误。错误的原因是在plt.imshow函数中，尝试将形状为(32, 32, 2)的数据作为图像数据进行显示，但是该形状的数据不符合图像数据的要求。

要解决这个错误，你可以检查输入的数据和其形状是否正确。可能的原因是输入的数据维度或通道数不正确，应该调整数据的形状或维度使其符合图像数据的要求。

另外，你可以查看输入数据的具体内容，确认其是否与预期相符。可以使用print语句输出输入数据的形状和相关信息，以便进行进一步的排查和调试。

Traceback (most recent call last): File "/root/autodl-tmp/ultralytics-main/run.py", line 7, in <module> model.train(data='/root/autodl-tmp/ultralytics-main/traindata3/data.yaml') File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/model.py", line 371, in train self.trainer.train() File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/trainer.py", line 192, in train self._do_train(world_size) File "/root/autodl-tmp/ultralytics-main/ultralytics/yolo/engine/trainer.py", line 328, in _do_train preds = self.model(batch['img']) File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/tasks.py", line 219, in forward return self._forward_once(x, profile, visualize) # single-scale inference, train File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/tasks.py", line 70, in _forward_once x = m(x) # run File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/root/autodl-tmp/ultralytics-main/ultralytics/nn/modules/block.py", line 183, in forward return self.cv2(torch.cat(y, 1)) torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 100.00 MiB (GPU 0; 23.65 GiB total capacity; 6.18 GiB already allocated; 98.56 MiB free; 6.21 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF terminate called without an active exception Aborted (core dumped)

这是一个CUDA out of memory错误，说明你的GPU显存已经不足以支持你的模型在训练时的计算了。你可以尝试减小batch size或者使用更高显存的GPU来解决这个问题。另外，你也可以尝试使用PyTorch提供的一些内存管理的工具，比如设置max_split_size_mb参数来避免内存碎片化。