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import unittest |
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import torch |
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from fairseq.data import Dictionary |
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from fairseq.modules import CharacterTokenEmbedder |
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class TestCharacterTokenEmbedder(unittest.TestCase): |
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def test_character_token_embedder(self): |
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vocab = Dictionary() |
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vocab.add_symbol("hello") |
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vocab.add_symbol("there") |
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embedder = CharacterTokenEmbedder( |
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vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2 |
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) |
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test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]] |
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max_len = max(len(s) for s in test_sents) |
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input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad()) |
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for i in range(len(test_sents)): |
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input[i][0] = vocab.eos() |
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for j in range(len(test_sents[i])): |
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input[i][j + 1] = vocab.index(test_sents[i][j]) |
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input[i][j + 2] = vocab.eos() |
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embs = embedder(input) |
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assert embs.size() == (len(test_sents), max_len + 2, 5) |
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self.assertAlmostEqual(embs[0][0], embs[1][0]) |
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self.assertAlmostEqual(embs[0][0], embs[0][-1]) |
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self.assertAlmostEqual(embs[0][1], embs[2][1]) |
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self.assertAlmostEqual(embs[0][3], embs[1][1]) |
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embs.sum().backward() |
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assert embedder.char_embeddings.weight.grad is not None |
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def assertAlmostEqual(self, t1, t2): |
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self.assertEqual(t1.size(), t2.size(), "size mismatch") |
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self.assertLess((t1 - t2).abs().max(), 1e-6) |
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if __name__ == "__main__": |
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unittest.main() |
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