When running this code snippet. I get the Type Error below. Any guidance would be appreciated
_!python clip_objecttracker.py --source ./data/video/cars.mp4 --detection-engine yolov5
/usr/local/lib/python3.7/dist-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
video 1/1 (1/266) /content/zero-shot-object-tracking/zero-shot-object-tracking/data/video/cars.mp4: yolov5 inference
[Detections]
1 persons, 8 cars, 2 trucks,
Traceback (most recent call last):
File "clip_object_tracker.py", line 361, in
detect()
File "clip_object_tracker.py", line 249, in detect
class_nums = np.array([d.class_num for d in detections])
File "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py", line 678, in array
return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
When running this code snippet. I get the Type Error below. Any guidance would be appreciated _!python clip_objecttracker.py --source ./data/video/cars.mp4 --detection-engine yolov5
/usr/local/lib/python3.7/dist-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] video 1/1 (1/266) /content/zero-shot-object-tracking/zero-shot-object-tracking/data/video/cars.mp4: yolov5 inference
[Detections] 1 persons, 8 cars, 2 trucks, Traceback (most recent call last): File "clip_object_tracker.py", line 361, in
detect()
File "clip_object_tracker.py", line 249, in detect
class_nums = np.array([d.class_num for d in detections])
File "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py", line 678, in array
return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.