dataset fixing

datasets/shapenet.py

@@ -56,14 +56,18 @@ class CustomShapeNet(InMemoryDataset):
     def processed_file_names(self):
         return [f'{self.mode}.pt']
 
-    def __download(self):
+    def check_and_resolve_cloud_count(self):
-        dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
+        if self.raw_dir.exists():
+            dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
 
-        if dir_count:
+            if dir_count:
-            print(f'{dir_count} folders have been found....')
+                print(f'{dir_count} folders have been found....')
-            return dir_count
+                return dir_count
-        warn(ResourceWarning("No raw pointclouds have been found. Was this intentional?"))
+            else:
-        return dir_count
+                warn(ResourceWarning("No raw pointclouds have been found. Was this intentional?"))
+                return dir_count
+        warn(ResourceWarning("The raw data folder does not exist. Was this intentional?"))
+        return -1
 
     @property
     def num_classes(self):
@@ -87,6 +91,10 @@ class CustomShapeNet(InMemoryDataset):
                 print('Dataset Loaded')
                 break
             except FileNotFoundError:
+                status = self.check_and_resolve_cloud_count()
+                if status in [0, -1]:
+                    print(f'No dataset was loaded, status: {status}')
+                    break
                 self.process()
                 continue
         return data, slices

main_pipeline.py

@@ -42,9 +42,8 @@ def restore_logger_and_model(log_dir):
 
 def predict_prim_type(input_pc, model):
 
-    input_data = dict(norm=torch.tensor(np.array([input_pc[:, 3:6]], np.float)),
+    input_data = dict(norm=torch.tensor(np.array([input_pc[:, 3:6]], np.float)).unsqueeze(0),
-                      pos=torch.tensor(input_pc[:, 0:3]),
+                      pos=torch.tensor(input_pc[:, 0:3]).unsqueeze(0),
-                      y=np.zeros(input_pc.shape[0])
                       )
 
     batch_to_data = BatchToData()
@@ -71,7 +70,7 @@ if __name__ == '__main__':
 
     input_pc = normalize_pointcloud(input_pc)
 
-    grid_clusters = cluster_cubes(input_pc, [1,1,1], 2048)
+    grid_clusters = cluster_cubes(input_pc, [1,1,1], 1024)
 
     ps.init()
 

utils/pointcloud.py

@@ -1,6 +1,8 @@
 import numpy as np
 from sklearn.cluster import DBSCAN
 
+import open3d as o3d
+
 from pyod.models.knn import KNN
 from pyod.models.sod import SOD
 from pyod.models.abod import ABOD