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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 argparse | |
| import unittest | |
| import tests.utils as test_utils | |
| import torch | |
| from fairseq.sequence_scorer import SequenceScorer | |
| class TestSequenceScorer(unittest.TestCase): | |
| def test_sequence_scorer(self): | |
| # construct dummy dictionary | |
| d = test_utils.dummy_dictionary(vocab_size=2) | |
| self.assertEqual(d.pad(), 1) | |
| self.assertEqual(d.eos(), 2) | |
| self.assertEqual(d.unk(), 3) | |
| eos = d.eos() | |
| w1 = 4 | |
| w2 = 5 | |
| # construct dataloader | |
| data = [ | |
| { | |
| "source": torch.LongTensor([w1, w2, eos]), | |
| "target": torch.LongTensor([w1, w2, w1, eos]), | |
| }, | |
| { | |
| "source": torch.LongTensor([w2, eos]), | |
| "target": torch.LongTensor([w2, w1, eos]), | |
| }, | |
| { | |
| "source": torch.LongTensor([w2, eos]), | |
| "target": torch.LongTensor([w2, eos]), | |
| }, | |
| ] | |
| data_itr = test_utils.dummy_dataloader(data) | |
| # specify expected output probabilities | |
| args = argparse.Namespace() | |
| unk = 0.0 | |
| args.beam_probs = [ | |
| # step 0: | |
| torch.FloatTensor( | |
| [ | |
| # eos w1 w2 | |
| [0.0, unk, 0.6, 0.4], # sentence 1 | |
| [0.0, unk, 0.4, 0.6], # sentence 2 | |
| [0.0, unk, 0.7, 0.3], # sentence 3 | |
| ] | |
| ), | |
| # step 1: | |
| torch.FloatTensor( | |
| [ | |
| # eos w1 w2 | |
| [0.0, unk, 0.2, 0.7], # sentence 1 | |
| [0.0, unk, 0.8, 0.2], # sentence 2 | |
| [0.7, unk, 0.1, 0.2], # sentence 3 | |
| ] | |
| ), | |
| # step 2: | |
| torch.FloatTensor( | |
| [ | |
| # eos w1 w2 | |
| [0.10, unk, 0.50, 0.4], # sentence 1 | |
| [0.15, unk, 0.15, 0.7], # sentence 2 | |
| [0.00, unk, 0.00, 0.0], # sentence 3 | |
| ] | |
| ), | |
| # step 3: | |
| torch.FloatTensor( | |
| [ | |
| # eos w1 w2 | |
| [0.9, unk, 0.05, 0.05], # sentence 1 | |
| [0.0, unk, 0.00, 0.0], # sentence 2 | |
| [0.0, unk, 0.00, 0.0], # sentence 3 | |
| ] | |
| ), | |
| ] | |
| expected_scores = [ | |
| [0.6, 0.7, 0.5, 0.9], # sentence 1 | |
| [0.6, 0.8, 0.15], # sentence 2 | |
| [0.3, 0.7], # sentence 3 | |
| ] | |
| task = test_utils.TestTranslationTask.setup_task(args, d, d) | |
| model = task.build_model(args) | |
| scorer = SequenceScorer(task.target_dictionary) | |
| for sample in data_itr: | |
| hypos = task.inference_step(scorer, [model], sample) | |
| for id, hypos_id in zip(sample["id"].tolist(), hypos): | |
| self.assertHypoTokens(hypos_id[0], data[id]["target"]) | |
| self.assertHypoScore(hypos_id[0], expected_scores[id]) | |
| def assertHypoTokens(self, hypo, tokens): | |
| self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens)) | |
| def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): | |
| pos_scores = torch.FloatTensor(pos_probs).log() | |
| self.assertAlmostEqual(hypo["positional_scores"], pos_scores) | |
| self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) | |
| score = pos_scores.sum() | |
| if normalized: | |
| score /= pos_scores.numel() ** lenpen | |
| self.assertLess(abs(score - hypo["score"]), 1e-6) | |
| def assertAlmostEqual(self, t1, t2): | |
| self.assertEqual(t1.size(), t2.size(), "size mismatch") | |
| self.assertLess((t1 - t2).abs().max(), 1e-4) | |
| def assertTensorEqual(self, t1, t2): | |
| self.assertEqual(t1.size(), t2.size(), "size mismatch") | |
| self.assertEqual(t1.ne(t2).long().sum(), 0) | |
| if __name__ == "__main__": | |
| unittest.main() | |