Working on fixing decoder transformer

1 files changed, 0 insertions, 60 deletions

diff --git a/text_recognizer/networks/transducer/test.py b/text_recognizer/networks/transducer/test.py
deleted file mode 100644
index cadcecc..0000000
--- a/text_recognizer/networks/transducer/test.py
+++ /dev/null
@@ -1,60 +0,0 @@
-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()