util_test.py 文件源码

def test_add_positional_features(self):
        # This is hard to test, so we check that we get the same result as the
        # original tensorflow implementation:
        # https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/layers/common_attention.py#L270
        tensor2tensor_result = numpy.asarray([[0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 1.00000000e+00],
                                              [8.41470957e-01, 9.99999902e-05, 5.40302277e-01, 1.00000000e+00],
                                              [9.09297407e-01, 1.99999980e-04, -4.16146845e-01, 1.00000000e+00]])

        tensor = Variable(torch.zeros([2, 3, 4]))
        result = util.add_positional_features(tensor, min_timescale=1.0, max_timescale=1.0e4)
        numpy.testing.assert_almost_equal(result[0].data.cpu().numpy(), tensor2tensor_result)
        numpy.testing.assert_almost_equal(result[1].data.cpu().numpy(), tensor2tensor_result)

        # Check case with odd number of dimensions.
        tensor2tensor_result = numpy.asarray([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,
                                               1.00000000e+00, 1.00000000e+00, 0.00000000e+00],
                                              [8.41470957e-01, 9.99983307e-03, 9.99999902e-05, 5.40302277e-01,
                                               9.99949992e-01, 1.00000000e+00, 0.00000000e+00],
                                              [9.09297407e-01, 1.99986659e-02, 1.99999980e-04, -4.16146815e-01,
                                               9.99800026e-01, 1.00000000e+00, 0.00000000e+00]])

        tensor = Variable(torch.zeros([2, 3, 7]))
        result = util.add_positional_features(tensor, min_timescale=1.0, max_timescale=1.0e4)
        numpy.testing.assert_almost_equal(result[0].data.cpu().numpy(), tensor2tensor_result)
        numpy.testing.assert_almost_equal(result[1].data.cpu().numpy(), tensor2tensor_result)