Comments (8)
Can you give me the visualize result of the sampled points(maybe after farthest point sampling in my inference pipeline code)?
from toothgroupnetwork.
Thank you for your response,The first image is vertices and the second image is sampled_feats.
from toothgroupnetwork.
I changed 'new_fps' to 'fps' in 'resample_pcd' method,this is sampled_feats、first_results、result_ins_labels、result_sem_labels.
from toothgroupnetwork.
can you tell me what your graphic cards and Cuda version are?
This issue may be related to C++ (cuda kernel) function. So maybe you can see that the farthest point sampling or grouping algorithm in point transformer modules dose not work properly.
from toothgroupnetwork.
The graphics card is RTX 3090
from toothgroupnetwork.
from toothgroupnetwork.
Thank you very much for your help. I will continue to try different methods to solve this problem.
from toothgroupnetwork.
If you download the original pointops library, you might solve your problem. plz refer to #7
from toothgroupnetwork.
Related Issues (20)
- installing pointops HOT 7
- How to align axis before inference? HOT 1
- start_inference.py error : ZeroDivisionError: division by zero HOT 1
- how long should I wait for the first train step (the Farthest Point Sampling model) to be done ? HOT 7
- when I was training tsegnet (run_tooth_segmentation_module to True & pretrained_centroid_model_path) I got the following error HOT 1
- Training on custom data HOT 3
- bad result when inputting mirrored data HOT 5
- Training on other data HOT 1
- Train tgnet_fps and find the error HOT 4
- The A10 graphics card exhibits abnormal partitioning HOT 1
- Visualization code HOT 2
- Sweep : Create an understanding and flow diagram for this codebsae
- How to rotate data into the shape shown in the figure
- Sweep: Tell me abou the summary of the codebase
- The memory of the tgnet model
- data axes
- How to convert model weights(.h5 file) to onnx?
- ValueError: Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required by DBSCAN HOT 3
- error to run y to run inference_mid.py
- about inference result HOT 1
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