Merge branch 'master' of https://gitlab.lrz.de/mobile-ifi/point_to_primitive

# Conflicts:
#	main_pipeline.py

_parameters.py

@@ -25,9 +25,9 @@ main_arg_parser.add_argument("--data_npoints", type=int, default=1024, help="")
 main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
 main_arg_parser.add_argument("--data_refresh", type=strtobool, default=False, help="")
 main_arg_parser.add_argument("--data_dataset_type", type=str, default='ShapeNetPartSegDataset', help="")
-main_arg_parser.add_argument("--data_cluster_type", type=str, default='grid', help="")
-main_arg_parser.add_argument("--data_normals_as_cords", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--data_poly_as_plane", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--data_cluster_type", type=str, default='prim', help="")
+main_arg_parser.add_argument("--data_normals_as_cords", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--data_poly_as_plane", type=strtobool, default=False, help="")
 
 # Transformations
 # main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")

main.py

@@ -27,7 +27,7 @@ def run_lightning_loop(config_obj):
         checkpoint_callback = ModelCheckpoint(
             monitor='mean_loss',
             filepath=str(logger.log_dir / 'ckpt_weights'),
-            verbose=True, save_top_k=10,
+            verbose=True, save_top_k=3,
         )
 
         # =============================================================================

main_pipeline.py

@@ -67,15 +67,17 @@ if __name__ == '__main__':
     type_cluster_min_pts = 50
 
     model_path = Path('output') / 'PN2' / 'PN_9843bf499399786cfd58fe79fa1b3db8' / 'version_0'
+
     loaded_model = restore_logger_and_model(model_path)
     loaded_model.eval()
 
     transforms = Compose([NormalizeScale(), ])
     test_dataset = ShapeNetPartSegDataset('data', mode=GlobalVar.data_split.predict, collate_per_segment=False,
-                                          refresh=True, transform=transforms)
+                                          refresh=True, transform=transforms)  # , cluster_type='pc')
 
     grid_clusters = cluster_cubes(test_dataset[0], grid_clusters, max_points_per_cluster=grid_cluster_max_pts)
 
+
     ps.init()
 
     # ========================== Grid Clustering ==========================