DataSet Modifications

_parameters.py

@@ -36,7 +36,7 @@ main_arg_parser.add_argument("--data_poly_as_plane", type=strtobool, default=Fal
 # Training
 main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
 main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
-main_arg_parser.add_argument("--train_epochs", type=int, default=200, help="")
+main_arg_parser.add_argument("--train_epochs", type=int, default=25, help="")
 main_arg_parser.add_argument("--train_batch_size", type=int, default=10, help="")
 main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
 main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-8, help="")

datasets/shapenet.py

@@ -111,7 +111,7 @@ class CustomShapeNet(InMemoryDataset):
                 os.remove(filepath)
                 try:
                     config_path.unlink()
-                except:
+                except FileNotFoundError:
                     pass
                 print('Processed Location "Refreshed" (We deleted the Files)')
             except FileNotFoundError:
@@ -138,7 +138,7 @@ class CustomShapeNet(InMemoryDataset):
         return data, slices
 
     def _build_config(self):
-        conf_dict = {key:str(val) for key, val in self.__dict__.items() if '__' not in key and key not in [
+        conf_dict = {key: str(val) for key, val in self.__dict__.items() if '__' not in key and key not in [
             'classes', 'refresh', 'transform', 'data', 'slices'
         ]}
         return conf_dict
@@ -194,10 +194,9 @@ class CustomShapeNet(InMemoryDataset):
                             continue
                         tensor = tensor.unsqueeze(0)
                     if self.poly_as_plane:
-                        tensor[:, -2][tensor[:, -2] == float(self.classes.Plane)] = 4.0
+                        tensor[:, -2][tensor[:, -2] == float(self.classes.Plane)] = 2.0
-                        tensor[:, -2][tensor[:, -2] == float(self.classes.Box)] = 4.0
+                        tensor[:, -2][tensor[:, -2] == float(self.classes.Box)] = 2.0
-                        tensor[:, -2][tensor[:, -2] == float(self.classes.Polytope)] = 4.0
+                        tensor[:, -2][tensor[:, -2] == float(self.classes.Polytope)] = 2.0
-                        tensor[:, -2][tensor[:, -2] == self.classes.Torus] = 3.0
                     src[key] = tensor
 
                 for key, values in src.items():

main_pipeline.py

@@ -5,22 +5,19 @@ import torch
 import polyscope as ps
 import numpy as np
 
-from torch.utils.data import DataLoader
-
 # Dataset and Dataloaders
 # =============================================================================
 
 # Transforms
-from torch_geometric.transforms import Compose, NormalizeScale, RandomFlip
+from torch_geometric.transforms import Compose, NormalizeScale
 
 from ml_lib.point_toolset.point_io import BatchToData
 from ml_lib.utils.model_io import SavedLightningModels
 
-
 # Datasets
 from datasets.shapenet import ShapeNetPartSegDataset
 from models import PointNet2
-from utils.pointcloud import cluster_cubes, append_onehotencoded_type, label2color, hierarchical_clustering, \
+from utils.pointcloud import cluster_cubes, append_onehotencoded_type, label2color, \
     write_clusters, cluster2Color, cluster_dbscan
 from utils.project_settings import GlobalVar, DataClass
 
@@ -66,7 +63,7 @@ if __name__ == '__main__':
     type_cluster_eps = 0.1
     type_cluster_min_pts = 100
 
-    model_path = Path('output') / 'PN2' / 'PN_9843bf499399786cfd58fe79fa1b3db8' / 'version_0'
+    model_path = Path('output') / 'PN2' / 'PN_f0d6bc0b9bf95a7e64f31a7df3c820d0' / 'version_0'
 
     loaded_model = restore_logger_and_model(model_path)
     loaded_model.eval()
@@ -75,7 +72,7 @@ if __name__ == '__main__':
     test_dataset = ShapeNetPartSegDataset('data', mode=GlobalVar.data_split.predict, collate_per_segment=False,
                                           refresh=True, transform=transforms)  # , cluster_type='pc')
 
-    grid_clusters = cluster_cubes(test_dataset[0], grid_clusters, max_points_per_cluster=grid_cluster_max_pts)
+    grid_clusters = cluster_cubes(test_dataset[1], grid_clusters, max_points_per_cluster=grid_cluster_max_pts)
 
     ps.init()
 
@@ -90,8 +87,8 @@ if __name__ == '__main__':
         pc_with_prim_type = predict_prim_type(grid_cluster_pc, loaded_model)
 
         # Re-Map Primitive type for 1-hot-encoding.
-        pc_with_prim_type[:, 6][pc_with_prim_type[:, 6] == 0.0] = 0.0 # Sphere
+        pc_with_prim_type[:, 6][pc_with_prim_type[:, 6] == 0.0] = 0.0  # Sphere
-        pc_with_prim_type[:, 6][pc_with_prim_type[:, 6] == 1.0] = 1.0 # Cylinder
+        pc_with_prim_type[:, 6][pc_with_prim_type[:, 6] == 1.0] = 1.0  # Cylinder
         pc_with_prim_type[:, 6][pc_with_prim_type[:, 6] == 3.0] = 2.0  # Box
         pc_with_prim_type[:, 6][pc_with_prim_type[:, 6] == 4.0] = 2.0  # Polytope
         pc_with_prim_type[:, 6][pc_with_prim_type[:, 6] == 6.0] = 2.0  # Plane

utils/project_settings.py

@@ -24,11 +24,9 @@ class Classes(DataClass):
     # Object Classes for Point Segmentation
     Sphere = 0
     Cylinder = 1
-    Cone = 2
+    Box = 2         # All SubTypes of Planes
-    Box = 3         # All SubTypes of Planes
+    Polytope = 3    #
-    Polytope = 4    #
+    Plane = 4       #
-    Torus = 5
-    Plane = 6       #
 
 
 class Settings(DataClass):