[Bug] Yoloxpose's onnx model reasoning effect is poor, and multiple bounding boxes appear in single-player scenes #2384
Comments
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hi, the visualization results are aligned between pytorch model and onnxruntime, which means the conversion is good. You can also compare the evaluation results if possible. You can set a threshold and filter out boxes and keypoints. |
In fact, I simply modified the code about configuration file data preprocessing in change into In this way, yoloxpose can be deployed normally. If the code here is not modified by default, yoloxpose will report an error when converting the onnx format This shows that the data_preprocessor in the yolox configuration file does not seem to be supported by the deployment of mmpose. I am thinking whether the lack of this preprocessing leads to the offset of the onnx inference results. |
Thank you for your reply. When I remove the command --dump-info, the visual inference results have not changed, and multiple bounding boxes still appear at the same time. |
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hi, you need to set threshold as mentioned before.
This is onnxruntime result on my machine with same command for small model and the evaluation results are aligned with pytorch and onnxruntime. |
Thanks for your reply, I tried modifying the threshold to be close to the training configuration This still didn't change, when I tried |
When I tried testing using a multiplayer scenario, yolopose-s and yolopose-t showed similar results. It seems codebase_config = The values of yolopose-t yolopose-s |
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hi, I mean threshhold for bboxes or keypoints, jut like the following codes( could change to
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Thanks for your help, this is useful |
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This issue is marked as stale because it has been marked as invalid or awaiting response for 7 days without any further response. It will be closed in 5 days if the stale label is not removed or if there is no further response. |
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This issue is closed because it has been stale for 5 days. Please open a new issue if you have similar issues or you have any new updates now. |
Checklist
Describe the bug
I try to deploy yoloposex using mmdeploy. When I convert yoloxpose-tiny and yoloxpose-small to the onnx model, the visualization results of the onnx model and pytorch are poor, and multiple bounding boxes appear in the single-person scene, while the visualization results of pytorch inference directly in mmpose are normal.This does not seem to be a problem with onnx itself, I hope to get your help.
During the conversion process, there are many prompts like this one:
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 btreated as a constant in the future. This means that the trace might not generalize to other inputs!This is the visualization result of yoloposex-tiny in mmpose
This is the visualization result of yoloxpose-tiny after mmdeploy uses onnx export, including onnx visualization and pytorch visualization
（Visualization of onnx）
（Visualization of pytorch）
After using the onnx model of
yoloxpose-small, the visualization result is slightly betterReproduction
python tools/deploy.py /home/keli/download/mmdeploy/configs/mmpose/pose-detection_yolox-pose_onnxruntime_dynamic.py /home/keli/download/pose/projects/yolox-pose/configs/yolox-pose_s_8xb32-300e_coco.py /home/keli/download/pose/pthfile/yoloxpose/yolox-pose_s_8xb32-300e_coco-9f5e3924_20230321.pth demo/resources/human-pose.jpg --work-dir yoloxpose_onnx/yoloxpose_small --device cpu --show --dump-info
#pose-detection_yolox-pose_onnxruntime_dynamic.py
base = ['./pose-detection_static.py', '../base/backends/onnxruntime.py']
onnx_config = dict(
output_names=['dets', 'keypoints'],
dynamic_axes={
'input': {
0: 'batch',
},
'dets': {
0: 'batch',
},
'keypoints': {
0: 'batch'
}
})
codebase_config = dict(
post_processing=dict(
score_threshold=0.01,
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=5000,
keep_top_k=100,
background_label_id=-1,
))
Environment
Error traceback
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