D:\anaconda\envs\mamba\python.exe E:\ultralytics-v8.3.63\ultralytics\models\yolo\pwcmamba\ceshi.py
使用设备: cuda
解析第0层,from=-1,当前ch列表长度=1
解析第1层,from=-1,当前ch列表长度=1
解析第2层,from=-1,当前ch列表长度=2
解析第3层,from=-1,当前ch列表长度=3
解析第4层,from=-1,当前ch列表长度=4
解析第5层,from=-1,当前ch列表长度=5
解析第6层,from=-1,当前ch列表长度=6
解析第7层,from=-1,当前ch列表长度=7
解析第8层,from=-1,当前ch列表长度=8
解析第9层,from=-1,当前ch列表长度=9
解析第10层,from=-1,当前ch列表长度=10
解析第11层,from=-1,当前ch列表长度=11
解析第12层,from=[-1, -6],当前ch列表长度=12
解析第13层,from=-1,当前ch列表长度=13
解析第14层,from=-1,当前ch列表长度=14
解析第15层,from=-1,当前ch列表长度=15
解析第16层,from=[-1, -12],当前ch列表长度=16
解析第17层,from=-1,当前ch列表长度=17
解析第18层,from=-1,当前ch列表长度=18
解析第19层,from=[-1, -6],当前ch列表长度=19
解析第20层,from=-1,当前ch列表长度=20
解析第21层,from=-1,当前ch列表长度=21
解析第22层,from=[-1, -13],当前ch列表长度=22
解析第23层,from=-1,当前ch列表长度=23
解析第24层,from=[17, 20, 23],当前ch列表长度=24
Traceback (most recent call last):
File "E:\ultralytics-v8.3.63\ultralytics\models\yolo\pwcmamba\ceshi.py", line 15, in <module>
model = YOLO(cfg_path)
File "E:\ultralytics-v8.3.63\ultralytics\models\yolo\model.py", line 23, in __init__
super().__init__(model=model, task=task, verbose=verbose)
File "E:\ultralytics-v8.3.63\ultralytics\engine\model.py", line 144, in __init__
self._new(model, task=task, verbose=verbose)
File "E:\ultralytics-v8.3.63\ultralytics\engine\model.py", line 255, in _new
self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model
File "E:\ultralytics-v8.3.63\ultralytics\nn\tasks.py", line 334, in __init__
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
File "E:\ultralytics-v8.3.63\ultralytics\nn\tasks.py", line 332, in _forward
return self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)
File "E:\ultralytics-v8.3.63\ultralytics\nn\tasks.py", line 110, in forward
return self.predict(x, *args, **kwargs)
File "E:\ultralytics-v8.3.63\ultralytics\nn\tasks.py", line 128, in predict
return self._predict_once(x, profile, visualize, embed)
File "E:\ultralytics-v8.3.63\ultralytics\nn\tasks.py", line 149, in _predict_once
x = m(x) # run
File "D:\anaconda\envs\mamba\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\anaconda\envs\mamba\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "E:\ultralytics-v8.3.63\ultralytics\nn\modules\block.py", line 1293, in forward
x = self.ea_vss(x)
File "D:\anaconda\envs\mamba\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\anaconda\envs\mamba\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "E:\ultralytics-v8.3.63\ultralytics\nn\modules\block.py", line 1265, in forward
mamba_out.append(self.mamba(head_data))
File "D:\anaconda\envs\mamba\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\anaconda\envs\mamba\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "D:\anaconda\envs\mamba\lib\site-packages\mamba_ssm\modules\mamba_simple.py", line 146, in forward
out = mamba_inner_fn(
File "D:\anaconda\envs\mamba\lib\site-packages\mamba_ssm\ops\selective_scan_interface.py", line 306, in mamba_inner_fn
return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
File "D:\anaconda\envs\mamba\lib\site-packages\torch\autograd\function.py", line 539, in apply
return super().apply(*args, **kwargs) # type: ignore[misc]
File "D:\anaconda\envs\mamba\lib\site-packages\torch\cuda\amp\autocast_mode.py", line 113, in decorate_fwd
return fwd(*args, **kwargs)
File "D:\anaconda\envs\mamba\lib\site-packages\mamba_ssm\ops\selective_scan_interface.py", line 181, in forward
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, None, True)
RuntimeError: Expected x.is_cuda() to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)
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