Traceback (most recent call last): File "E:\yolov5-master\train.py", line 642, in <module> main(opt) File "E:\yolov5-master\train.py", line 531, in main train(opt.hyp, opt, device, callbacks) File "E:\yolov5-master\train.py", line 312, in train pred = model(imgs) # forward File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\yolo.py", line 209, in forward return self._forward_once(x, profile, visualize) # single-scale inference, train File "E:\yolov5-master\models\yolo.py", line 121, in _forward_once x = m(x) # run File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\common.py", line 167, in forward return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\container.py", line 217, in forward input = module(input) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\common.py", line 120, in forward return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "E:\yolov5-master\models\common.py", line 56, in forward return self.act(self.bn(self.conv(x))) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\modules\activation.py", line 396, in forward return F.silu(input, inplace=self.inplace) File "F:\conda\envs\yolov5\lib\site-packages\torch\nn\functional.py", line 2058, in silu return torch._C._nn.silu_(input) torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 6.00 GiB total capacity; 2.92 GiB already allocated; 951.00 MiB free; 3.01 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
时间: 2023-07-21 20:50:48 浏览: 148
这个错误提示是CUDA显存不足导致的。可能是模型过于复杂,超出了显存的限制。解决这个问题的方法是优化模型,减少显存使用,或者增加显存资源。你可以尝试以下解决方案:
1. 减小batch size,可以减少显存占用。
2. 将模型转移到CPU上运行。
3. 减小模型尺寸,可以减少显存占用。
4. 增加显存,可以更换显卡或增加显存条。
5. 优化代码,减少显存占用,例如使用inplace操作或减小tensor的维度。
相关问题
Traceback (most recent call last): File "E:\yolov5-master\train.py", line 642, in <module> main(opt) File "E:\yolov5-master\train.py", line 517, in main device = select_device(opt.device, batch_size=opt.batch_size) File "E:\yolov5-master\utils\torch_utils.py", line 118, in select_device assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ AssertionError: Invalid CUDA '--device 3' requested, use '--device cpu' or pass valid CUDA device(s)
根据你提供的错误信息,这是在运行YOLOv5的训练脚本时出现的错误。错误信息显示,你使用了无效的CUDA设备选项"--device 3",建议你检查一下你的CUDA设备是否正确安装并配置好了。你可以尝试使用有效的CUDA设备选项,例如"--device 0"或"--device 1"。如果你只想使用CPU进行训练,则可以使用"--device cpu"选项。
Traceback (most recent call last): File "D:\PyCharm\pytorch_sac-master\train.py", line 152, in <module> @hydra.main(config_path="config/train.yaml", strict=True) TypeError: main() got an unexpected keyword argument 'strict'
这个错误可能是因为你的 hydra 版本过低导致的,建议你升级 hydra 的版本。
你可以使用以下命令来升级 hydra:
```
pip install --upgrade hydra-core
```
如果你使用的是 conda 环境,则可以使用以下命令:
```
conda update hydra-core
```
升级后再次运行程序,看是否能够解决问题。