Comments (1)
The configs system is built so that hydra dynamically parses the configs and adapts the model input size according to the chosen features. Besides, it is also possible to pick from a list of supported features to be used for superpoint partition or as input pointwise features at training time.
In invite you to have a look at the configs for the different provided datasets, some have RGB, some have lidar intensity. Besides, you want to have a close look at how the partition_hf
and point_hf
are used in the configs and in the project, to understand the data pipeline.
If you want to create new point-wise attributes beyond the ones we support with configs/datamodule/semantic/_features.yaml
, you can. To this end, you will specify the name and dimensions of these new point attributes in this file. And if they need som careful preprocessing, you may need to build dedicated Transform
operations. You will need to dive deeper into the transforms of the existing datasets to see if what you need is not already here. But everything is thoroughly commented across the project, so you should be able to read though the code.
from superpoint_transformer.
Related Issues (20)
- mutable default <class 'hydra.conf.JobConf.JobConfig.OverrideDirname'> for field override_dirname is not allowed HOT 2
- Where are the results of semantic segmentation stored? HOT 11
- Issue in running demo HOT 5
- FRNN - RuntimeError: Unknown layout HOT 7
- Example training crashes with seeming integer overflow HOT 4
- Install script makes seemingly incorrect assumptions about PGEOF's environment HOT 17
- xy_tiling setting when process own data HOT 1
- ModuleNotFoundError: No module named 'src.dependencies.FRNN' HOT 1
- Transformer_blocks architecture in SPT HOT 1
- Training custom dataset with all points HOT 3
- Sinusoidal Learning rate when increasing both max_epochs and batch size HOT 10
- Norm_index HOT 1
- Is there any method to save the Partition L2 superpoint? HOT 9
- Can the model run on smaller GPU (6GB) ? HOT 1
- Error when installing pgeof. HOT 7
- Is my format of the Scannetv2 wrong? HOT 6
- Large variance in performance of re-trained models. HOT 3
- generating a 'processed' folder for a personalized dataset that has a structure similar to the S3DIS dataset HOT 3
- Empty list on Scatter_nearest_neighbor HOT 6
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from superpoint_transformer.