1 files changed, 60 insertions, 0 deletions
diff --git a/src/text_recognizer/networks/transducer/test.py b/src/text_recognizer/networks/transducer/test.py
new file mode 100644
index 0000000..cadcecc
--- /dev/null
+++ b/src/text_recognizer/networks/transducer/test.py
@@ -0,0 +1,60 @@
+import torch
+from torch import nn
+
+from text_recognizer.networks.transducer import load_transducer_loss, Transducer
+import unittest
+
+
+class TestTransducer(unittest.TestCase):
+    def test_viterbi(self):
+        T = 5
+        N = 4
+        B = 2
+
+        # fmt: off
+        emissions1 = torch.tensor((
+            0, 4, 0, 1,
+            0, 2, 1, 1,
+            0, 0, 0, 2,
+            0, 0, 0, 2,
+            8, 0, 0, 2,
+            ),
+            dtype=torch.float,
+        ).view(T, N)
+        emissions2 = torch.tensor((
+            0, 2, 1, 7,
+            0, 2, 9, 1,
+            0, 0, 0, 2,
+            0, 0, 5, 2,
+            1, 0, 0, 2,
+            ),
+            dtype=torch.float,
+        ).view(T, N)
+        # fmt: on
+
+        # Test without blank:
+        labels = [[1, 3, 0], [3, 2, 3, 2, 3]]
+        transducer = Transducer(
+            tokens=["a", "b", "c", "d"],
+            graphemes_to_idx={"a": 0, "b": 1, "c": 2, "d": 3},
+            blank="none",
+        )
+        emissions = torch.stack([emissions1, emissions2], dim=0)
+        predictions = transducer.viterbi(emissions)
+        self.assertEqual([p.tolist() for p in predictions], labels)
+
+        # Test with blank without repeats:
+        labels = [[1, 0], [2, 2]]
+        transducer = Transducer(
+            tokens=["a", "b", "c"],
+            graphemes_to_idx={"a": 0, "b": 1, "c": 2},
+            blank="optional",
+            allow_repeats=False,
+        )
+        emissions = torch.stack([emissions1, emissions2], dim=0)
+        predictions = transducer.viterbi(emissions)
+        self.assertEqual([p.tolist() for p in predictions], labels)
+
+
+if __name__ == "__main__":
+    unittest.main()