这是我尝试运行的代码，但出现错误。
from ultralytics import YOLO
model = YOLO(r"C:\Users\skahi\Downloads\runs-20240406T064851Z-001\runs\detect\train\weights\best.pt")
results = model(r"D:\datasets\A_Z Handwritten Data\testimg1.png", save=True)
输出应该是给定图像的检测次数。
但我收到此错误：
NotImplementedError Traceback (most recent call last)
Cell In[10], line 1
----> 1 model(r"D:\datasets\A_Z Handwritten Data\testimg1.png", save=True)
File ~\anaconda3\Lib\site-packages\ultralytics\engine\model.py:169, in Model.__call__(self, source, stream, **kwargs)
146 def __call__(
147 self,
148 source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
149 stream: bool = False,
150 **kwargs,
151 ) -> list:
152 """
153 An alias for the predict method, enabling the model instance to be callable.
154
(...)
167 (List[ultralytics.engine.results.Results]): A list of prediction results, encapsulated in the Results class.
168 """
--> 169 return self.predict(source, stream, **kwargs)
File ~\anaconda3\Lib\site-packages\ultralytics\engine\model.py:439, in Model.predict(self, source, stream, predictor, **kwargs)
437 if prompts and hasattr(self.predictor, "set_prompts"): # for SAM-type models
438 self.predictor.set_prompts(prompts)
--> 439 return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
File ~\anaconda3\Lib\site-packages\ultralytics\engine\predictor.py:168, in BasePredictor.__call__(self, source, model, stream, *args, **kwargs)
166 return self.stream_inference(source, model, *args, **kwargs)
167 else:
--> 168 return list(self.stream_inference(source, model, *args, **kwargs))
File ~\anaconda3\Lib\site-packages\torch\utils\_contextlib.py:35, in _wrap_generator.<locals>.generator_context(*args, **kwargs)
32 try:
33 # Issuing `None` to a generator fires it up
34 with ctx_factory():
---> 35 response = gen.send(None)
37 while True:
38 try:
39 # Forward the response to our caller and get its next request
File ~\anaconda3\Lib\site-packages\ultralytics\engine\predictor.py:255, in BasePredictor.stream_inference(self, source, model, *args, **kwargs)
253 # Postprocess
254 with profilers[2]:
--> 255 self.results = self.postprocess(preds, im, im0s)
256 self.run_callbacks("on_predict_postprocess_end")
258 # Visualize, save, write results
File ~\anaconda3\Lib\site-packages\ultralytics\models\yolo\detect\predict.py:25, in DetectionPredictor.postprocess(self, preds, img, orig_imgs)
23 def postprocess(self, preds, img, orig_imgs):
24 """Post-processes predictions and returns a list of Results objects."""
---> 25 preds = ops.non_max_suppression(
26 preds,
27 self.args.conf,
28 self.args.iou,
29 agnostic=self.args.agnostic_nms,
30 max_det=self.args.max_det,
31 classes=self.args.classes,
32 )
34 if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
35 orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
File ~\anaconda3\Lib\site-packages\ultralytics\utils\ops.py:282, in non_max_suppression(prediction, conf_thres, iou_thres, classes, agnostic, multi_label, labels, max_det, nc, max_time_img, max_nms, max_wh, in_place, rotated)
280 else:
281 boxes = x[:, :4] + c # boxes (offset by class)
--> 282 i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
283 i = i[:max_det] # limit detections
285 # # Experimental
286 # merge = False # use merge-NMS
287 # if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
(...)
294 # if redundant:
295 # i = i[iou.sum(1) > 1] # require redundancy
File ~\anaconda3\Lib\site-packages\torchvision\ops\boxes.py:41, in nms(boxes, scores, iou_threshold)
39 _log_api_usage_once(nms)
40 _assert_has_ops()
---> 41 return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
File ~\anaconda3\Lib\site-packages\torch\_ops.py:755, in OpOverloadPacket.__call__(self, *args, **kwargs)
750 def __call__(self, *args, **kwargs):
751 # overloading __call__ to ensure torch.ops.foo.bar()
752 # is still callable from JIT
753 # We save the function ptr as the `op` attribute on
754 # OpOverloadPacket to access it here.
--> 755 return self._op(*args, **(kwargs or {}))
NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, Meta, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
CPU: registered at C:\actions-runner\_work\vision\vision\pytorch\vision\torchvision\csrc\ops\cpu\nms_kernel.cpp:112 [kernel]
Meta: registered at /dev/null:440 [kernel]
QuantizedCPU: registered at C:\actions-runner\_work\vision\vision\pytorch\vision\torchvision\csrc\ops\quantized\cpu\qnms_kernel.cpp:124 [kernel]
BackendSelect: fallthrough registered at ..\aten\src\ATen\core\BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:154 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at ..\aten\src\ATen\functorch\DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at ..\aten\src\ATen\FunctionalizeFallbackKernel.cpp:324 [backend fallback]
Named: registered at ..\aten\src\ATen\core\NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at ..\aten\src\ATen\ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at ..\aten\src\ATen\native\NegateFallback.cpp:19 [backend fallback]
ZeroTensor: registered at ..\aten\src\ATen\ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:86 [backend fallback]
AutogradOther: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:53 [backend fallback]
AutogradCPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:57 [backend fallback]
AutogradCUDA: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:65 [backend fallback]
AutogradXLA: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:69 [backend fallback]
AutogradMPS: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:77 [backend fallback]
AutogradXPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:61 [backend fallback]
AutogradHPU: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:90 [backend fallback]
AutogradLazy: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:73 [backend fallback]
AutogradMeta: registered at ..\aten\src\ATen\core\VariableFallbackKernel.cpp:81 [backend fallback]
Tracer: registered at ..\torch\csrc\autograd\TraceTypeManual.cpp:297 [backend fallback]
AutocastCPU: registered at C:\actions-runner\_work\vision\vision\pytorch\vision\torchvision\csrc\ops\autocast\nms_kernel.cpp:34 [kernel]
AutocastCUDA: registered at C:\actions-runner\_work\vision\vision\pytorch\vision\torchvision\csrc\ops\autocast\nms_kernel.cpp:27 [kernel]
FuncTorchBatched: registered at ..\aten\src\ATen\functorch\LegacyBatchingRegistrations.cpp:720 [backend fallback]
BatchedNestedTensor: registered at ..\aten\src\ATen\functorch\LegacyBatchingRegistrations.cpp:746 [backend fallback]
FuncTorchVmapMode: fallthrough registered at ..\aten\src\ATen\functorch\VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at ..\aten\src\ATen\LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at ..\aten\src\ATen\VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at ..\aten\src\ATen\functorch\TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:162 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at ..\aten\src\ATen\functorch\DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:166 [backend fallback]
PythonDispatcher: registered at ..\aten\src\ATen\core\PythonFallbackKernel.cpp:158 [backend fallback]
正如@hanna_liavoshka 所提到的，
NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend
表示 torchvision 的 NMS 操作（非极大值抑制）操作未正确配置为使用 CUDA。确保您的环境中有支持 CUDA 的 torchvision。