From 053a25432740ac66281a51c7a19d675064204fe5 Mon Sep 17 00:00:00 2001 From: HuguesTHOMAS Date: Mon, 27 Apr 2020 19:06:34 -0400 Subject: [PATCH] Corrections --- doc/object_segmentation_guide.md | 29 --------------------- doc/slam_segmentation_guide.md | 43 ++++++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+), 29 deletions(-) delete mode 100644 doc/object_segmentation_guide.md create mode 100644 doc/slam_segmentation_guide.md diff --git a/doc/object_segmentation_guide.md b/doc/object_segmentation_guide.md deleted file mode 100644 index 60d9dbd..0000000 --- a/doc/object_segmentation_guide.md +++ /dev/null @@ -1,29 +0,0 @@ - -## Object Part Segmentation on ShapeNetPart - -### Data - -ShapeNetPart dataset can be downloaded here (635 MB). 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 diff --git a/doc/slam_segmentation_guide.md b/doc/slam_segmentation_guide.md new file mode 100644 index 0000000..5f8cf5b --- /dev/null +++ b/doc/slam_segmentation_guide.md @@ -0,0 +1,43 @@ + +## 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 here (80 GB). +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