test_convolutional.py 文件源码

def test_upsampling_2d():
    nb_samples = 2
    stack_size = 2
    input_nb_row = 11
    input_nb_col = 12

    for dim_ordering in ['th', 'tf']:
        if dim_ordering == 'th':
            input = np.random.rand(nb_samples, stack_size, input_nb_row,
                                   input_nb_col)
        else:  # tf
            input = np.random.rand(nb_samples, input_nb_row, input_nb_col,
                                   stack_size)

        for length_row in [2, 3, 9]:
            for length_col in [2, 3, 9]:
                layer = convolutional.UpSampling2D(
                    size=(length_row, length_col),
                    dim_ordering=dim_ordering)
                layer.build(input.shape)
                output = layer(K.variable(input))
                np_output = K.eval(output)
                if dim_ordering == 'th':
                    assert np_output.shape[2] == length_row * input_nb_row
                    assert np_output.shape[3] == length_col * input_nb_col
                else:  # tf
                    assert np_output.shape[1] == length_row * input_nb_row
                    assert np_output.shape[2] == length_col * input_nb_col

                # compare with numpy
                if dim_ordering == 'th':
                    expected_out = np.repeat(input, length_row, axis=2)
                    expected_out = np.repeat(expected_out, length_col, axis=3)
                else:  # tf
                    expected_out = np.repeat(input, length_row, axis=1)
                    expected_out = np.repeat(expected_out, length_col, axis=2)

                assert_allclose(np_output, expected_out)