more loss tests

layers/losses.py

@@ -18,7 +18,7 @@ class L1LossMasked(nn.Module):
             length: A Variable containing a LongTensor of size (batch,)
                 which contains the length of each data in a batch.
         Returns:
-            loss: An average loss value masked by the length.
+            loss: An average loss value in range [0, 1] masked by the length.
         """
         # mask: (batch, max_len, 1)
         target.requires_grad = False
@@ -44,7 +44,7 @@ class MSELossMasked(nn.Module):
             length: A Variable containing a LongTensor of size (batch,)
                 which contains the length of each data in a batch.
         Returns:
-            loss: An average loss value masked by the length.
+            loss: An average loss value in range [0, 1] masked by the length.
         """
         # mask: (batch, max_len, 1)
         target.requires_grad = False

tests/test_layers.py

@@ -118,6 +118,7 @@ class EncoderTests(unittest.TestCase):
 
 class L1LossMaskedTests(unittest.TestCase):
     def test_in_out(self):
+        # test input == target
         layer = L1LossMasked()
         dummy_input = T.ones(4, 8, 128).float()
         dummy_target = T.ones(4, 8, 128).float()
@@ -125,11 +126,14 @@ class L1LossMaskedTests(unittest.TestCase):
         output = layer(dummy_input, dummy_target, dummy_length)
         assert output.item() == 0.0
 
+        # test input != target
         dummy_input = T.ones(4, 8, 128).float()
         dummy_target = T.zeros(4, 8, 128).float()
         dummy_length = (T.ones(4) * 8).long()
         output = layer(dummy_input, dummy_target, dummy_length)
         assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
+
+        # test if padded values of input makes any difference
         dummy_input = T.ones(4, 8, 128).float()
         dummy_target = T.zeros(4, 8, 128).float()
         dummy_length = (T.arange(5, 9)).long()
@@ -137,3 +141,11 @@ class L1LossMaskedTests(unittest.TestCase):
             (sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
         output = layer(dummy_input + mask, dummy_target, dummy_length)
         assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
+
+        dummy_input = T.rand(4, 8, 128).float()
+        dummy_target = dummy_input.detach()
+        dummy_length = (T.arange(5, 9)).long()
+        mask = (
+            (sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
+        output = layer(dummy_input + mask, dummy_target, dummy_length)
+        assert output.item() == 0, "0 vs {}".format(output.data[0])