Dataset for whole pointclouds with farthest point sampling _incomplete_

datasets/_point_dataset.py

@@ -0,0 +1,36 @@
+from abc import ABC
+from pathlib import Path
+
+from torch.utils.data import Dataset
+from ml_lib.point_toolset.sampling import FarthestpointSampling
+
+
+class _Point_Dataset(ABC, Dataset):
+
+    @property
+    def setting(self) -> str:
+        raise NotImplementedError
+
+    headers = ['x', 'y', 'z', 'nx', 'ny', 'nz', 'label', 'cl_idx']
+
+    def __init__(self, root=Path('data'), sampling_k=2048, transforms=None, load_preprocessed=True, *args, **kwargs):
+        super(_Point_Dataset, self).__init__()
+
+        self.load_preprocessed = load_preprocessed
+        self.transforms = transforms if transforms else lambda x: x
+        self.sampling_k = sampling_k
+        self.sampling = FarthestpointSampling(K=self.sampling_k)
+        self.root = Path(root)
+        self.raw = root / 'raw'
+        self.processed_ext = '.pik'
+        self.raw_ext = '.xyz'
+        self.processed = root / self.setting
+
+        self._files = list(self.raw.glob(f'*{self.setting}*'))
+
+
+    def __len__(self):
+        raise NotImplementedError
+
+    def __getitem__(self, item):
+        raise NotImplementedError