mirror of
https://github.com/facebookresearch/fairseq.git
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71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
# Copyright (c) 2017-present, Facebook, Inc.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the LICENSE file in
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# the root directory of this source tree. An additional grant of patent rights
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# can be found in the PATENTS file in the same directory.
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import argparse
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import unittest
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import tests.utils as test_utils
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import torch
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from fairseq.data.backtranslation_dataset import BacktranslationDataset
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class TestBacktranslationDataset(unittest.TestCase):
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def setUp(self):
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self.tgt_dict, self.w1, self.w2, self.src_tokens, self.src_lengths, self.model = (
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test_utils.sequence_generator_setup()
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)
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backtranslation_args = argparse.Namespace()
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"""
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Same as defaults from fairseq/options.py
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"""
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backtranslation_args.backtranslation_unkpen = 0
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backtranslation_args.backtranslation_sampling = False
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backtranslation_args.backtranslation_max_len_a = 0
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backtranslation_args.backtranslation_max_len_b = 200
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backtranslation_args.backtranslation_beam = 2
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self.backtranslation_args = backtranslation_args
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dummy_src_samples = self.src_tokens
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self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
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def test_backtranslation_dataset(self):
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backtranslation_dataset = BacktranslationDataset(
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args=self.backtranslation_args,
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tgt_dataset=self.tgt_dataset,
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tgt_dict=self.tgt_dict,
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backtranslation_model=self.model,
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)
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dataloader = torch.utils.data.DataLoader(
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backtranslation_dataset,
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batch_size=2,
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collate_fn=backtranslation_dataset.collater,
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)
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backtranslation_batch_result = next(iter(dataloader))
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eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
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# Note that we sort by src_lengths and add left padding, so actually
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# ids will look like: [1, 0]
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expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
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expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
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generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
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tgt_tokens = backtranslation_batch_result["target"]
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self.assertTensorEqual(expected_src, generated_src)
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self.assertTensorEqual(expected_tgt, tgt_tokens)
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def assertTensorEqual(self, t1, t2):
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self.assertEqual(t1.size(), t2.size(), "size mismatch")
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self.assertEqual(t1.ne(t2).long().sum(), 0)
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if __name__ == "__main__":
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unittest.main()
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