diff --git a/python/torch_bindings.py b/python/torch_bindings.py
index d6be6d7..ead81f1 100644
--- a/python/torch_bindings.py
+++ b/python/torch_bindings.py
@@ -75,6 +75,9 @@ def forward(ctx, gmaker, center, coords, types, radii):
         ctx.save_for_backward(coords, types, radii)
         ctx.gmaker = gmaker
         ctx.center = center
+        if len(types.shape) != 2:
+            raise ValueError('Vector types required in Coords2Grid.')
+
         shape = gmaker.grid_dimensions(types.shape[1]) #ntypes == nchannels
         output = torch.empty(*shape,dtype=coords.dtype,device=coords.device)
         gmaker.forward(center, coords, types, radii, output)
@@ -97,13 +100,16 @@ class BatchedCoords2GridFunction(torch.autograd.Function):
     
     @staticmethod
     def forward(ctx, gmaker, center, coords, types, radii):
-        '''coords are Nx3, types are NxT, radii are N'''
+        '''coords are BxNx3, types are BxNxT, radii are BxN'''
         ctx.save_for_backward(coords, types, radii)
         ctx.gmaker = gmaker
         ctx.center = center
         batch_size = coords.shape[0]
         if batch_size != types.shape[0] or batch_size != radii.shape[0]:
             raise RuntimeError("Inconsistent batch sizes in Coords2Grid inputs")
+            
+        if len(types.shape) != 3:
+            raise ValueError('Vector types required in BatchedCoords2Grid.')            
         shape = gmaker.grid_dimensions(types.shape[2]) #ntypes == nchannels
         output = torch.empty(batch_size,*shape,dtype=coords.dtype,device=coords.device)
         for i in range(batch_size):