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- # Copyright (c) Facebook, Inc. and its affiliates.
- #
- # This source code is licensed under the MIT license found in the
- # LICENSE file in the root directory of this source tree.
- import unittest
- import torch
- from fairseq.data import Dictionary
- from fairseq.modules import CharacterTokenEmbedder
- class TestCharacterTokenEmbedder(unittest.TestCase):
- def test_character_token_embedder(self):
- vocab = Dictionary()
- vocab.add_symbol("hello")
- vocab.add_symbol("there")
- embedder = CharacterTokenEmbedder(
- vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2
- )
- test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]]
- max_len = max(len(s) for s in test_sents)
- input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad())
- for i in range(len(test_sents)):
- input[i][0] = vocab.eos()
- for j in range(len(test_sents[i])):
- input[i][j + 1] = vocab.index(test_sents[i][j])
- input[i][j + 2] = vocab.eos()
- embs = embedder(input)
- assert embs.size() == (len(test_sents), max_len + 2, 5)
- self.assertAlmostEqual(embs[0][0], embs[1][0])
- self.assertAlmostEqual(embs[0][0], embs[0][-1])
- self.assertAlmostEqual(embs[0][1], embs[2][1])
- self.assertAlmostEqual(embs[0][3], embs[1][1])
- embs.sum().backward()
- assert embedder.char_embeddings.weight.grad is not None
- def assertAlmostEqual(self, t1, t2):
- self.assertEqual(t1.size(), t2.size(), "size mismatch")
- self.assertLess((t1 - t2).abs().max(), 1e-6)
- if __name__ == "__main__":
- unittest.main()
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