New Dataset for per spatial cluster training

point_toolset/point_io.py

@@ -1,3 +1,5 @@
+from typing import Union
+
 import torch
 from torch_geometric.data import Data
 
@@ -7,15 +9,15 @@ class BatchToData(object):
         super(BatchToData, self).__init__()
 
     def __call__(self, batch_x: torch.Tensor, batch_pos: torch.Tensor,
-                 batch_y_l: torch.Tensor, batch_y_c: torch.Tensor):
+                 batch_y_l: Union[torch.Tensor, None] = None, batch_y_c: Union[torch.Tensor, None] = None):
         # Convert to torch_geometric.data.Data type
         # data = data.transpose(1, 2).contiguous()
         batch_size, num_points, _ = batch_x.shape  # (batch_size, num_points, 3)
 
         x = batch_x.reshape(batch_size * num_points, -1)
         pos = batch_pos.reshape(batch_size * num_points, -1)
-        batch_y_l = batch_y_l.reshape(batch_size * num_points)
-        batch_y_c = batch_y_c.reshape(batch_size * num_points)
+        batch_y_l = batch_y_l.reshape(batch_size * num_points) if batch_y_l is not None else batch_y_l
+        batch_y_c = batch_y_c.reshape(batch_size * num_points) if batch_y_c is not None else batch_y_c
         batch = torch.zeros((batch_size, num_points), device=pos.device, dtype=torch.long)
         for i in range(batch_size):
             batch[i] = i

point_toolset/sampling.py

@@ -19,11 +19,7 @@ class RandomSampling(_Sampler):
         super(RandomSampling, self).__init__(*args, **kwargs)
 
     def __call__(self, pts, *args, **kwargs):
-        if pts.shape[0] < self.k:
-            return pts
-
-        else:
-            rnd_indexs = np.random.choice(np.arange(pts.shape[0]), self.k, replace=False)
+        rnd_indexs = np.random.choice(np.arange(pts.shape[0]), min(self.k, pts.shape[0]), replace=False)
         return rnd_indexs