Normalization and transforms for batch_to_data class

point_toolset/point_io.py

@@ -5,16 +5,17 @@ from torch_geometric.data import Data
 
 
 class BatchToData(object):
-    def __init__(self):
+    def __init__(self, transforms=None):
         super(BatchToData, self).__init__()
+        self.transforms = transforms if transforms else lambda x: x
 
-    def __call__(self, batch_x: torch.Tensor, batch_pos: torch.Tensor,
+    def __call__(self, batch_norm: torch.Tensor, batch_pos: 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)
+        batch_size, num_points, _ = batch_norm.shape  # (batch_size, num_points, 3)
 
-        x = batch_x.reshape(batch_size * num_points, -1)
+        norm = batch_norm.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) 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
@@ -24,5 +25,8 @@ class BatchToData(object):
         batch = batch.view(-1)
 
         data = Data()
-        data.x, data.pos, data.batch, data.yl, data.yc = x, pos, batch, batch_y_l, batch_y_c
+        data.norm, data.pos, data.batch, data.yl, data.yc = norm, pos, batch, batch_y_l, batch_y_c
+
+        data = self.transforms(data)
+
         return data