Example #1
0
    def test_beam_search(self):
        vocab = text_utils.VocabFromText(self.VOCAB_EXAMPLE_SENTENCES)
        model_config = self.config.model_config.butd
        model = TestDecoderModel(model_config, vocab)
        model.build()
        model.eval()

        expected_tokens = {
            1: [1.0, 23.0, 1.0, 24.0, 29.0, 37.0, 40.0, 17.0, 29.0, 2.0],
            2: [1.0, 0.0, 8.0, 1.0, 28.0, 25.0, 2.0],
            8: [1.0, 34.0, 1.0, 13.0, 1.0, 2.0],
            16: [1.0, 25.0, 18.0, 2.0],
        }

        for batch_size in [1, 2, 8, 16]:
            samples = []
            for _ in range(batch_size):
                sample = Sample()
                sample.dataset_name = "coco"
                sample.dataset_type = "test"
                sample.image_feature_0 = torch.randn(100, 2048)
                sample.answers = torch.zeros((5, 10), dtype=torch.long)
                samples.append(sample)

            sample_list = SampleList(samples)
            tokens = model(sample_list)["captions"]
            self.assertEqual(np.trim_zeros(tokens[0].tolist()),
                             expected_tokens[batch_size])
Example #2
0
    def test_nucleus_sampling(self):
        vocab = text_utils.VocabFromText(self.VOCAB_EXAMPLE_SENTENCES)

        model_config = self.config.model_config.butd
        model = TestDecoderModel(model_config, vocab)
        model.build()
        model.eval()

        sample = Sample()
        sample.dataset_name = "coco"
        sample.dataset_type = "test"
        sample.image_feature_0 = torch.randn(100, 2048)
        sample.answers = torch.zeros((5, 10), dtype=torch.long)
        sample_list = SampleList([sample])

        tokens = model(sample_list)["captions"]

        # these are expected tokens for sum_threshold = 0.5

        # Because of a bug fix in https://github.com/pytorch/pytorch/pull/47386
        # the torch.Tensor.multinomail will generate different random sequence.
        # TODO: Remove this hack after OSS uses later version of PyTorch.
        if LegacyVersion(torch.__version__) > LegacyVersion("1.7.1"):
            expected_tokens = [1.0, 23.0, 38.0, 30.0, 5.0, 11.0, 2.0]
        else:
            expected_tokens = [
                1.0,
                29.0,
                11.0,
                11.0,
                39.0,
                10.0,
                31.0,
                4.0,
                19.0,
                39.0,
                2.0,
            ]

        self.assertEqual(tokens[0].tolist(), expected_tokens)