efe5916109e220a429a2cff110edb952d747466f,tests/keras/layers/test_convolutional.py,,test_upsampling_2d,#,351

Before Change

                    size=(length_row, length_col),
                    input_shape=input.shape[1:],
                    dim_ordering=dim_ordering)
                layer.input = K.variable(input)
                for train in [True, False]:
                    out = K.eval(layer.get_output(train))
                    if dim_ordering == "th":
                        assert out.shape[2] == length_row * input_nb_row
                        assert out.shape[3] == length_col * input_nb_col
                    else:  // tf
                        assert out.shape[1] == length_row * input_nb_row
                        assert out.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(out, expected_out)

                layer.get_config()


@pytest.mark.skipif(K._BACKEND != "theano", reason="Requires Theano backend")

After Change

                layer = convolutional.UpSampling2D(
                    size=(length_row, length_col),
                    dim_ordering=dim_ordering)
                layer.set_input(K.variable(input), shape=input.shape)

                out = K.eval(layer.output)
                if dim_ordering == "th":
                    assert out.shape[2] == length_row * input_nb_row
                    assert out.shape[3] == length_col * input_nb_col

Instances