CHANGELOG

Add a github action workflow to run a trained model on the lidar-prod thresholds optimisation dataset (in order to automate thresholds optimization)

3.8.4

fix: move IoU appropriately to fix wrong device error created by a breaking change in torch when using DDP.

3.8.3

fix: prepare_data_per_node is a flag and was incorrectly used as a replacement for prepare_data.

3.8.2

fix: points not dropped case in subsampling when the subtile contains only one point
fix: type error in edge case when dropping points in DropPointsByClass (when there is only one remaining point)

3.8.1

fix: propagate input las format to output las (in particular epsg which comes either from input or config)

3.8.0

dev: log confusion matrices to Comet after each epoch.
fix: do not mix the two way to log IoUs to avoid known lightning Common Pitfalls.

3.7.1

fix: edge case when saving predictions under Classification channel, without saving entropy.

3.7.0

Update all versions of Pytorch, Pytorch Lightning, and Pytorch Geometric. Changes are retrocompatible for models trained with older versions (with adjustment to the configuration file).
Refactor logging of single-class IoUs to go from num_classes+1 torchmetrics instances to only 1.

3.6.1

Set urllib3<2 for comet logging to function and add back seaborn for plotting optimal LR graph.

3.6.0

Remove the "EPSG:2154" by default and use the metadata of the lidar file, unless a parameter is given.

3.5.2

Track ./tests/data/ dir including single-point-cloud.laz.

3.5.1

Run CICD operations for all branches prefixed with "staging-".

3.5.0

Abandon of option to get circular patches since it was never used.

3.4.12

Remove COPC datasets and dataloaders since they were abandonned and never used.

3.4.11

Unification of max length of lines (99) by applying black everywhere.

3.4.10

Migrate from setup.cfg to pyproject.toml and .flake8.

3.4.9

Support edge-case where source LAZ has no valid subtile (i.e. pre_filter=False for all candidate subtiles) during hdf5 creation

3.4.8

Raise an informative error in case of unexpected task_name

3.4.7

Remove tqdm when splitting a lidar tile to avoid cluttered logs during data preparation

3.4.6

Document the possible use of ign-pdal-tools for colorization

3.4.5

Set a default task_name (fit) to avoid common error at lauch time

3.4.4

Remove duplicated experiment configuration

3.4.3

Remove outdated and incorrect hydra parameter in config.yaml

3.4.2

Reconstruct absolute path of input LAS files explicitely, removing a costly glob operation

3.4.1

Fix dataset description for pacasam: there was an unwanted int-to-int mapping in classification_dict

3.4.0

Allow inference for the smallest possible patches (num_nodes=1) to have consistent inference behavior