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HuguesTHOMAS 2020-04-27 19:06:34 -04:00
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doc/object_segmentation_guide.md
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## Object Part Segmentation on ShapeNetPart
### Data
ShapeNetPart dataset can be downloaded <a href="https://shapenet.cs.stanford.edu/ericyi/shapenetcore_partanno_segmentation_benchmark_v0.zip">here (635 MB)</a>. Uncompress the folder and move it to `Data/ShapeNetPart/shapenetcore_partanno_segmentation_benchmark_v0`.
### Training
Simply run the following script to start the training:
python3 training_ShapeNetPart.py
Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called `ShapeNetPartConfig`, and the first run of this script might take some time to precompute dataset structures.
### Plot a logged training
When you start a new training, it is saved in a `results` folder. A dated log folder will be created, containing many information including loss values, validation metrics, model checkpoints, etc.
In `plot_convergence.py`, you will find detailed comments explaining how to choose which training log you want to plot. Follow them and then run the script :
python3 plot_convergence.py
### Test the trained model
The test script is the same for all models (segmentation or classification). In `test_any_model.py`, you will find detailed comments explaining how to choose which logged trained model you want to test. Follow them and then run the script :
python3 test_any_model.py

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## Scene Segmentation on S3DIS
### Data
We consider our experiment folder is located at `XXXX/Experiments/KPConv-PyTorch`. And we use a common Data folder
loacated at `XXXX/Data`. Therefore the relative path to the Data folder is `../../Data`.
SemanticKitti dataset can be downloaded <a href="http://semantic-kitti.org/dataset.html#download">here (80 GB)</a>.
Download the three file named:
* `data_odometry_velodyne.zip`, [`data_odometry_velodyne.zip`](http://www.cvlibs.net/download.php?file=data_odometry_velodyne.zip)
uncompress the data and move it to `../../Data/S3DIS`.
http://www.cvlibs.net/download.php?file=data_odometry_velodyne.zip
N.B. If you want to place your data anywhere else, you just have to change the variable
`self.path` of `S3DISDataset` class ([here](https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/afa18c92f00c6ed771b61cb08b285d2f93446ea4/datasets/S3DIS.py#L88)).
### Training
Simply run the following script to start the training:
python3 training_S3DIS.py
Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called `S3DISConfig`, and the first run of this script might take some time to precompute dataset structures.
### Plot a logged training
When you start a new training, it is saved in a `results` folder. A dated log folder will be created, containing many information including loss values, validation metrics, model checkpoints, etc.
In `plot_convergence.py`, you will find detailed comments explaining how to choose which training log you want to plot. Follow them and then run the script :
python3 plot_convergence.py
### Test the trained model
The test script is the same for all models (segmentation or classification). In `test_any_model.py`, you will find detailed comments explaining how to choose which logged trained model you want to test. Follow them and then run the script :
python3 test_any_model.py