[Bug] Tried to run the example in the mmdeploy tutorial but failed about mmdeploy

[Ochre-amber]

I also tried to run the example from the mmdetection tutorial, but the same run failed.
(https://mmdetection.readthedocs.io/zh-cn/v3.0.0/user_guides/deploy.html)

from mmdeploy.apis import torch2onnx
from mmdeploy.backend.sdk.export_info import export2SDK

img = 'demo/demo.jpg'
work_dir = 'mmdeploy_models/mmdet/onnx'
save_file = 'end2end.onnx'
deploy_cfg = '../mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
model_cfg = 'configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model_checkpoint = 'faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
device = 'cpu'

# 1. convert model to onnx
torch2onnx(img, work_dir, save_file, deploy_cfg, model_cfg,
           model_checkpoint, device)

# 2. extract pipeline info for inference by MMDeploy SDK
export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint,
           device=device)

D:\Coding\anaconda\envs\tensoryolo\python.exe D:\Programs\last\project\mmdetection\to_onnx.py 
05/05 11:13:23 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized.
05/05 11:13:23 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized.
Loads checkpoint by local backend from path: faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
05/05 11:13:23 - mmengine - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future. 
05/05 11:13:23 - mmengine - INFO - Export PyTorch model to ONNX: mmdeploy_models/mmdet/onnx\end2end.onnx.
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\core\optimizers\function_marker.py:160: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  ys_shape = tuple(int(s) for s in ys.shape)
D:\Programs\last\project\mmdetection\mmdet\models\dense_heads\anchor_head.py:115: UserWarning: DeprecationWarning: anchor_generator is deprecated, please use "prior_generator" instead
  warnings.warn('DeprecationWarning: anchor_generator is deprecated, '
D:\Programs\last\project\mmdetection\mmdet\models\task_modules\prior_generators\anchor_generator.py:356: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors`` 
  warnings.warn('``grid_anchors`` would be deprecated soon. '
D:\Programs\last\project\mmdetection\mmdet\models\task_modules\prior_generators\anchor_generator.py:392: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors`` 
  warnings.warn(
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\codebase\mmdet\models\dense_heads\rpn_head.py:89: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\pytorch\functions\topk.py:28: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  k = torch.tensor(k, device=input.device, dtype=torch.long)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\codebase\mmdet\models\task_modules\coders\delta_xywh_bbox_coder.py:38: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert pred_bboxes.size(0) == bboxes.size(0)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\codebase\mmdet\models\task_modules\coders\delta_xywh_bbox_coder.py:40: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert pred_bboxes.size(1) == bboxes.size(1)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\codebase\mmdet\deploy\utils.py:48: TracerWarning: Using len to get tensor shape might cause the trace to be incorrect. Recommended usage would be tensor.shape[0]. Passing a tensor of different shape might lead to errors or silently give incorrect results.
  assert len(max_shape) == 2, '`max_shape` should be [h, w]'
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\mmcv\ops\nms.py:270: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  iou_threshold = torch.tensor([iou_threshold], dtype=torch.float32)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\mmcv\ops\nms.py:271: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  score_threshold = torch.tensor([score_threshold], dtype=torch.float32)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\mmcv\ops\nms.py:44: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  score_threshold = float(score_threshold)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\mmcv\ops\nms.py:45: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  iou_threshold = float(iou_threshold)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmcv\ops\nms.py:123: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert boxes.size(1) == 4
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmcv\ops\nms.py:124: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert boxes.size(0) == scores.size(0)
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmdeploy\codebase\mmdet\models\roi_heads\standard_roi_head.py:41: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  rois_dims = int(rois.shape[-1])
D:\Coding\anaconda\envs\tensoryolo\lib\site-packages\mmcv\ops\roi_align.py:78: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!'

Process finished with exit code -1073741819 (0xC0000005)

[fat-921]

请问你解决了吗？我的环境跟你一样，也遇到了这个问题。。

[Ochre-amber]

@fat-921 我还没有解决，尝试修改环境配置，但还没有找到问题的原因。事实上，我当前版本中所有模型的转换都没有成功。