scene_name.txt_sem_label.npy

[Changyangli]

Hi,

Thanks again for releasing the source code. I have one more question regarding the gorilla-3d evaluator: For line 212 in "test.py" that evaluator.process(inputs, outputs), the gorilla-3d evaluator tries to read gt labels expected to be saved in "scene_name.txt_sem_label.npy" files. Where actually can I get those files? Seems that the "prepare_data.sh" does not generate such gt files as well. Thank!

[lzhnb]

Oh, thanks for your question.
I may miss the generation of semantic label, my label files have been generated long ago, so I do not meet this problem and I forget this preprocessing.
I will add the generation as soon as possible.

Thanks!!!

[lzhnb]

If you want to run the inference, you can ignore the evaluator and run the inst_evaluator only, which can process the instance segmentation evaluation individually.

[lzhnb]

I've fixed the semantic evaluation.

Please update the gorilla3d (git pull origin dev) and run the following script in the dataset root:(which is the last command in prepare_data.sh)

# prepare validation dataset gt
python -m gorilla3d.preprocessing.scannetv2.segmentation.prepare_data_inst_gttxt --data-split val

it will generate the semantic segmentation label.

After that, you should evaluation the semantic segmentation successfully. The inference script:

CUDA_VISIBLE_DEVICES=0 python test.py --config config/default.yaml --pretrain pretrain.pth --eval --semantic

[Changyangli]

I've fixed the semantic evaluation.

Please update the gorilla3d (git pull origin dev) and run the following script in the dataset root:(which is the last command in prepare_data.sh)

# prepare validation dataset gt
python -m gorilla3d.preprocessing.scannetv2.segmentation.prepare_data_inst_gttxt --data-split val

it will generate the semantic segmentation label.

After that, you should evaluation the semantic segmentation successfully. The inference script:

CUDA_VISIBLE_DEVICES=0 python test.py --config config/default.yaml --pretrain pretrain.pth --eval --semantic

Cool, thanks for fixing the issue and replying so quickly! And again, thanks for sharing this amazing repo!