dataset modification

datasets/shapenet.py

@@ -8,7 +8,6 @@ from collections import defaultdict
 import os
 from torch.utils.data import Dataset
 from tqdm import tqdm
-import glob
 
 import torch
 from torch_geometric.data import InMemoryDataset
@@ -45,7 +44,9 @@ class CustomShapeNet(InMemoryDataset):
         assert mode in self.modes.keys(), f'"mode" must be one of {self.modes.keys()}'
 
         # Set the Dataset Parameters
-        self.collate_per_segment, self.mode, self.refresh = collate_per_segment, mode, refresh
+        self.collate_per_segment = collate_per_segment
+        self.mode = mode
+        self.refresh = refresh
         self.with_normals = with_normals
         root_dir = Path(root_dir)
         super(CustomShapeNet, self).__init__(root_dir, transform, pre_transform, pre_filter)
@@ -57,15 +58,15 @@ class CustomShapeNet(InMemoryDataset):
         return [f'{self.mode}.pt']
 
     def check_and_resolve_cloud_count(self):
-        if self.raw_dir.exists():
+        if (self.raw_dir / self.mode).exists():
-            dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
+            file_count = len([cloud for cloud in (self.raw_dir / self.mode).iterdir() if cloud.is_file()])
 
-            if dir_count:
+            if file_count:
-                print(f'{dir_count} folders have been found....')
+                print(f'{file_count} files have been found....')
-                return dir_count
+                return file_count
             else:
                 warn(ResourceWarning("No raw pointclouds have been found. Was this intentional?"))
-                return dir_count
+                return file_count
         warn(ResourceWarning("The raw data folder does not exist. Was this intentional?"))
         return -1
 
@@ -99,7 +100,7 @@ class CustomShapeNet(InMemoryDataset):
                 continue
         return data, slices
 
-    def _transform_and_filter(self, data):
+    def _pre_transform_and_filter(self, data):
         # ToDo: ANy filter to apply? Then do it here.
         if self.pre_filter is not None and not self.pre_filter(data):
             data = self.pre_filter(data)
@@ -133,7 +134,9 @@ class CustomShapeNet(InMemoryDataset):
                 src[key] = torch.tensor(values, dtype=torch.double).squeeze()
 
             if not self.collate_per_segment:
-                src = dict(all=torch.stack([x for x in src.values()]))
+                src = dict(
+                    all=torch.cat(tuple(src.values()))
+                )
 
             for key, values in src.items():
                 try:
@@ -157,17 +160,18 @@ class CustomShapeNet(InMemoryDataset):
                 if self.collate_per_segment:
                     data = Data(**attr_dict)
                 else:
-                    if not data:
+                    if data is None:
                         data = defaultdict(list)
                     # points=points, norm=points[:, 3:]
                     for key, val in attr_dict.items():
                         data[key].append(val)
+                    # data = Data(**data)
 
-                data = self._transform_and_filter(data)
+                # data = self._pre_transform_and_filter(data)
                 if self.collate_per_segment:
                     datasets[self.mode].append(data)
             if not self.collate_per_segment:
-                # Todo: What is this?
+                # This is just to be sure, but should not be needed, since src[all] == all there is in this cloud
                 datasets[self.mode].append(Data(**{key: torch.cat(data[key]) for key in data.keys()}))
 
         if datasets[self.mode]:
@@ -198,8 +202,10 @@ class ShapeNetPartSegDataset(Dataset):
 
         # Resample to fixed number of points
         try:
-            npoints = self.npoints if self.mode != 'predict' else data.pos.shape[0]
+            npoints = self.npoints if self.mode != DataSplit.predict else data.pos.shape[0]
-            choice = np.random.choice(data.pos.shape[0], npoints, replace=False if self.mode == 'predict' else True)
+            choice = np.random.choice(data.pos.shape[0], npoints,
+                                      replace=False if self.mode == DataSplit.predict else True
+                                      )
         except ValueError:
             choice = []
 

main_pipeline.py

@@ -57,7 +57,7 @@ def predict_prim_type(input_pc, model):
 
 if __name__ == '__main__':
 
-    input_pc_path = Path('data') / 'pc' / 'pc.txt'
+    input_pc_path = Path('data') / 'pc' / 'test.xyz'
 
     model_path = Path('output') / 'PN2' / 'PN_26512907a2de0664bfad2349a6bffee3' / 'version_0'
     # config_filename = 'config.ini'
@@ -66,15 +66,19 @@ if __name__ == '__main__':
     loaded_model = restore_logger_and_model(model_path)
     loaded_model.eval()
 
-    input_pc = read_pointcloud(input_pc_path, ' ', False)
+     #input_pc = read_pointcloud(input_pc_path, ' ', False)
 
-    input_pc = normalize_pointcloud(input_pc)
+    # input_pc = normalize_pointcloud(input_pc)
 
-    grid_clusters = cluster_cubes(input_pc, [1,1,1], 1024)
+    # TEST DATASET
+    test_dataset = ShapeNetPartSegDataset('data', mode=GlobalVar.data_split.predict, collate_per_segment=False,
+                                          npoints=1024, refresh=True)
+
+    grid_clusters = cluster_cubes(test_dataset[0], [3, 3, 3], max_points_per_cluster=1024)
 
     ps.init()
 
-    for i,grid_cluster_pc in enumerate(grid_clusters):
+    for i, grid_cluster_pc in enumerate(grid_clusters):
 
         print("Cluster pointcloud size: {}".format(grid_cluster_pc.shape[0]))
 

utils/pointcloud.py

@@ -17,6 +17,7 @@ from pyod.models.loci import LOCI
 from pyod.models.hbos import HBOS
 from pyod.models.lscp import LSCP
 from pyod.models.feature_bagging import FeatureBagging
+from torch_geometric.data import Data
 
 from utils.project_settings import Classes
 
@@ -116,6 +117,10 @@ def cluster_cubes(data, cluster_dims, max_points_per_cluster=-1, min_points_per_
         print("no need to cluster.")
         return [farthest_point_sampling(data, max_points_per_cluster)]
 
+    if isinstance(data, Data):
+        import torch
+        data = torch.cat((data.pos, data.norm, data.y.double().unsqueeze(-1)), dim=-1).numpy()
+
     max = data[:, :3].max(axis=0)
     max += max * 0.01
 

utils/project_settings.py

@@ -43,6 +43,7 @@ class DataSplit(DataClass):
     train = 'train'
     devel = 'devel'
     test = 'test'
+    predict = 'predict'
 
 
 class GlobalVar(DataClass):