error to run y to run inference_mid.py

[liiad]

try to run inference_mid.py --input_path D:\Tooth\3D_scans_per_patient_obj_files_b2 --save_path D:\Tooth\S ,errors,How to Sovle?
"C:\Program Files\Python39\python.exe" D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\inference_mid.py --input_path D:\Tooth\3D_scans_per_patient_obj_files_b2 --save_path D:\Tooth\S
The Zen of Python, by Tim Peters

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Simple is better than complex.
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Flat is better than nested.
Sparse is better than dense.
Readability counts.
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Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
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If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
Processing: 0 : D:\Tooth\3D_scans_per_patient_obj_files_b2\00OMSZGW\00OMSZGW_lower.obj
Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required by DBSCAN.
Traceback (most recent call last):
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\predict_utils.py", line 101, in predict
pred_result = self.chl_pipeline(scan_path)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\inference_pipeline_mid.py", line 52, in call
first_results = self.get_first_module_results(input_cuda_feats, self.first_module)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\inference_pipeline_mid.py", line 176, in get_first_module_results
output = base_model([points])
File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\models\modules\grouping_network_module.py", line 65, in forward
fg_points_labels_ls = tu.get_clustering_labels(b_moved_points, whole_cls_1)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\tsg_utils.py", line 88, in get_clustering_labels
clustering = DBSCAN(eps=0.03, min_samples=60).fit(moved_points[super_point_cond, :], 3)
File "C:\Program Files\Python39\lib\site-packages\sklearn\base.py", line 1474, in wrapper
return fit_method(estimator, *args, **kwargs)
File "C:\Program Files\Python39\lib\site-packages\sklearn\cluster_dbscan.py", line 393, in fit
X = self._validate_data(X, accept_sparse="csr")
File "C:\Program Files\Python39\lib\site-packages\sklearn\base.py", line 633, in _validate_data
out = check_array(X, input_name="X", **check_params)
File "C:\Program Files\Python39\lib\site-packages\sklearn\utils\validation.py", line 1072, in check_array
raise ValueError(
ValueError: Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required by DBSCAN.

Traceback (most recent call last):
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\inference_mid.py", line 65, in
pred_obj.process(stl_path_ls[i], os.path.join(args.save_path, os.path.basename(stl_path_ls[i]).replace(".obj", ".json")))
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\predict_utils.py", line 140, in process
labels, instances, jaw = self.predict([input_path])
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\predict_utils.py", line 101, in predict
pred_result = self.chl_pipeline(scan_path)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\inference_pipeline_mid.py", line 52, in call
first_results = self.get_first_module_results(input_cuda_feats, self.first_module)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\inference_pipeline_mid.py", line 176, in get_first_module_results
output = base_model([points])
File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\models\modules\grouping_network_module.py", line 65, in forward
fg_points_labels_ls = tu.get_clustering_labels(b_moved_points, whole_cls_1)
File "D:\Tooth\3DTeethSeg_MICCAI\ToothGroupNetwork-challenge_branch\tsg_utils.py", line 88, in get_clustering_labels
clustering = DBSCAN(eps=0.03, min_samples=60).fit(moved_points[super_point_cond, :], 3)
File "C:\Program Files\Python39\lib\site-packages\sklearn\base.py", line 1474, in wrapper
return fit_method(estimator, *args, **kwargs)
File "C:\Program Files\Python39\lib\site-packages\sklearn\cluster_dbscan.py", line 393, in fit
X = self._validate_data(X, accept_sparse="csr")
File "C:\Program Files\Python39\lib\site-packages\sklearn\base.py", line 633, in _validate_data
out = check_array(X, input_name="X", **check_params)
File "C:\Program Files\Python39\lib\site-packages\sklearn\utils\validation.py", line 1072, in check_array
raise ValueError(
ValueError: Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required by DBSCAN.

[limhoyeon]

It could be the issue with point ops library in my repository.
Could you replace the point ops library with the one described in the bottom section of README?

[liiad]

I had replaced ops bottom，but it doesn't work properly on the rtx4090, do you have any other advise

…

---- Replied Message ---- | From | ***@***.***> | | Date | 07/31/2024 13:41 | | To | limhoyeon/ToothGroupNetwork ***@***.***> | | Cc | liiad ***@***.***>, Author ***@***.***> | | Subject | Re: [limhoyeon/ToothGroupNetwork] error to run y to run inference_mid.py (Issue #42) | It could be the issue with point ops library in my repository. Could you replace the point ops library with the one described in the bottom section of README? — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: ***@***.***>

[limhoyeon]

Did you check whether the input point cloud is correctly loaded or not?