util_test.py 文件源码

def test_last_dim_softmax_handles_mask_correctly(self):
        batch_size = 1
        length_1 = 4
        length_2 = 3
        num_options = 5
        options_array = numpy.zeros((batch_size, length_1, length_2, num_options))
        for i in range(length_1):
            for j in range(length_2):
                options_array[0, i, j] = [2, 4, 0, 1, 6]
        mask = Variable(torch.IntTensor([[1, 1, 1, 1, 0]]))
        options_tensor = Variable(torch.from_numpy(options_array).float())
        softmax_tensor = util.last_dim_softmax(options_tensor, mask).data.numpy()
        assert softmax_tensor.shape == (batch_size, length_1, length_2, num_options)
        for i in range(length_1):
            for j in range(length_2):
                assert_almost_equal(softmax_tensor[0, i, j],
                                    [0.112457, 0.830953, 0.015219, 0.041371, 0.0],
                                    decimal=5)