Add test_dropout.

src/triacontagon/dropout.py

@@ -26,7 +26,7 @@ def dropout_sparse(x, keep_prob):
 def dropout_dense(x, keep_prob):
     # print('dropout_dense()')
     x = x.clone()
-    i = torch.nonzero(x)
+    i = torch.nonzero(x, as_tuple=False)
 
     n = keep_prob + torch.rand(len(i))
     n = (1. - torch.floor(n)).to(torch.bool)

tests/triacontagon/test_dropout.py

@@ -0,0 +1,26 @@
+from triacontagon.dropout import dropout_sparse, \
+    dropout_dense
+import torch
+import numpy as np
+
+
+def test_dropout_01():
+    for i in range(11):
+        torch.random.manual_seed(i)
+        a = torch.rand((5, 10))
+        a[a < .5] = 0
+
+        keep_prob=i/10. + np.finfo(np.float32).eps
+
+        torch.random.manual_seed(i)
+        b = dropout_dense(a, keep_prob=keep_prob)
+
+        torch.random.manual_seed(i)
+        c = dropout_sparse(a.to_sparse(), keep_prob=keep_prob)
+
+        print('keep_prob:', keep_prob)
+        print('a:', a.detach().cpu().numpy())
+        print('b:', b.detach().cpu().numpy())
+        print('c:', c, c.to_dense().detach().cpu().numpy())
+
+        assert torch.all(b == c.to_dense())