util_test.py 文件源码

def test_weighted_sum_handles_3d_attention_with_3d_matrix(self):
        batch_size = 1
        length_1 = 5
        length_2 = 2
        embedding_dim = 4
        sentence_array = numpy.random.rand(batch_size, length_2, embedding_dim)
        attention_array = numpy.random.rand(batch_size, length_1, length_2)
        sentence_tensor = Variable(torch.from_numpy(sentence_array).float())
        attention_tensor = Variable(torch.from_numpy(attention_array).float())
        aggregated_array = util.weighted_sum(sentence_tensor, attention_tensor).data.numpy()
        assert aggregated_array.shape == (batch_size, length_1, embedding_dim)
        for i in range(length_1):
            expected_array = (attention_array[0, i, 0] * sentence_array[0, 0] +
                              attention_array[0, i, 1] * sentence_array[0, 1])
            numpy.testing.assert_almost_equal(aggregated_array[0, i], expected_array,
                                              decimal=5)