Implement XConv operator. Begin implementing PointCNN.

tests/test_basic.py

@@ -1,12 +1,30 @@
 import unittest
 
-from context import pointcnn
+import torch
+from torch.autograd import Variable
+import numpy as np
+
+from pointcnn.core import XConv
 
 class BasicTests(unittest.TestCase):
     """ Basic test cases """
 
-    def test_example(self):
+    def test_xconv_shape(self):
         self.assertTrue(True)
+        np.random.seed(0)
+
+        N = 4
+        D = 3
+        C_in = 8
+        C_out = 32
+        N_neighbors = 100
+
+        model = XConv(C_in, C_out, D, N_neighbors)
+        test_p = Variable(torch.from_numpy(np.random.rand(N,D).astype(np.float32)))
+        test_P = Variable(torch.from_numpy(np.random.rand(N,N_neighbors,D).astype(np.float32)))
+        test_F = Variable(torch.from_numpy(np.random.rand(N,N_neighbors,C_in).astype(np.float32)))
+        test_out = model(test_p, test_P, test_F)
+        self.assertEqual(test_out.size(), (N, C_out))
 
 if __name__ == "__main__":
     unittest.main()