Classes Fixed abnd debugging

utils/module_mixins.py

@@ -15,14 +15,14 @@ import matplotlib.pyplot as plt
 from torch import nn
 from torch.optim import Adam
 from torch_geometric.data import Data, DataLoader
+from torch_geometric.transforms import Compose, FixedPoints, NormalizeScale
 
 from torchcontrib.optim import SWA
 
 
 from ml_lib.modules.util import LightningBaseModule
 from ml_lib.utils.tools import to_one_hot
-
-from .project_settings import GlobalVar
+from utils.project_settings import dataSplit
 
 
 class BaseOptimizerMixin:
@@ -116,7 +116,7 @@ class BaseValMixin:
         y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().float().numpy()
         y_pred_max = np.argmax(y_pred, axis=1)
 
-        class_names = {val: key for key, val in GlobalVar.classes.items()}
+        class_names = {val: key for key, val in self.dataset.test_dataset.classes.items()}
         ######################################################################################
         #
         # F1 SCORE
@@ -223,21 +223,30 @@ class DatasetMixin:
         # Dataset
         # =============================================================================
         # Data Augmentations or Utility Transformations
+        transforms = Compose(
+            [
+                FixedPoints(8096),
 
+                NormalizeScale()
+            ]
+        )
+        test_kwargs = kwargs.copy()
+        test_kwargs.update(transform=transforms)
         # Dataset
         dataset = Namespace(
             **dict(
                 # TRAIN DATASET
-                train_dataset=dataset_class(self.params.root, mode=GlobalVar.data_split.train,
+                train_dataset=dataset_class(self.params.root, mode=dataSplit.train, collate_per_segment=True,
                                             **kwargs),
 
+
                 # VALIDATION DATASET
-                val_dataset=dataset_class(self.params.root, mode=GlobalVar.data_split.devel,
-                                          **kwargs),
+                val_dataset=dataset_class(self.params.root, mode=dataSplit.devel, collate_per_segment=False,
+                                          **test_kwargs),
 
                 # TEST DATASET
-                test_dataset=dataset_class(self.params.root, mode=GlobalVar.data_split.predict,
-                                           **kwargs),
+                test_dataset=dataset_class(self.params.root, mode=dataSplit.predict, collate_per_segment=False,
+                                           **test_kwargs),
             )
         )
         return dataset

utils/pointcloud.py

@@ -1,17 +1,17 @@
 import numpy as np
 from sklearn.cluster import DBSCAN
 
-#import open3d as o3d
+# import open3d as o3d
 
 from pyod.models.lof import LOF
 
 from torch_geometric.data import Data
 
-from utils.project_settings import Classes
+from utils.project_settings import classesAll
 
 
 def polytopes_to_planes(pc):
-    pc[(pc[:, 6] == float(Classes.Box)) or (pc[:, 6] == float(Classes.Polytope)), 6] = float(Classes.Plane)
+    pc[(pc[:, 6] == float(classesAll.Box)) or (pc[:, 6] == float(classesAll.Polytope)), 6] = float(classesAll.Plane)
     return pc
 
 

utils/project_settings.py

@@ -19,8 +19,7 @@ class DataClass(Namespace):
         return self.__dict__()[item]
 
 
-class Classes(DataClass):
-
+class ClassesALL(DataClass):
     # Object Classes for Point Segmentation
     Sphere = 0
     Cylinder = 1
@@ -29,10 +28,11 @@ class Classes(DataClass):
     Plane = 4       #
 
 
-class Settings(DataClass):
-    P2G = 'grid'
-    P2P = 'prim'
-    PN2 = 'pc'
+class ClassesPolyAsPlane(DataClass):
+    # Object Classes for Point Segmentation
+    Sphere = 0
+    Cylinder = 1
+    Plane = 2         # All SubTypes of Planes
 
 
 class ClusterTypes(DataClass):
@@ -40,6 +40,7 @@ class ClusterTypes(DataClass):
     grid = 'grid'
     none = ''
 
+
 class DataSplit(DataClass):
     # DATA SPLIT OPTIONS
     train = 'train'
@@ -47,18 +48,7 @@ class DataSplit(DataClass):
     test = 'test'
     predict = 'predict'
 
-
-class GlobalVar(DataClass):
-    # Variables for plotting
-    PADDING = 0.25
-    DPI = 50
-
-    data_split = DataSplit()
-
-    classes = Classes()
-
-    grid_count = 12
-
-    prim_count = -1
-
-    settings = Settings()
+classesAll = ClassesALL()
+classesPolyAsPlane= ClassesPolyAsPlane()
+clusterTypes = ClusterTypes()
+dataSplit=DataSplit()