Method test_contour

tests/transforms/test_label_to_contour.py:145–175

(self)

class TestContour(unittest.TestCase):
    def test_contour(self):
        input_param = {"kernel_type": "Laplace"}

        for p in TEST_NDARRAYS:
            # check 5-dim input data
            test_cube, expected_output = gen_fixed_cube(p)
            for cube in test_cube:
                test_result_cube = LabelToContour(**input_param)(cube)
                self.assertEqual(test_result_cube.shape, cube.shape)

                channels = cube.shape[0]
                for channel in range(channels):
                    assert_allclose(test_result_cube[channel, ...], expected_output, type_test="tensor")

            # check 4-dim input data
            test_img, expected_output = gen_fixed_img(p)
            for img in test_img:
                channels = img.shape[0]
                test_result_img = LabelToContour(**input_param)(img)
                self.assertEqual(test_result_img.shape, img.shape)

                for channel in range(channels):
                    assert_allclose(test_result_img[channel, ...], expected_output, type_test="tensor")

        # check invalid input data
        error_input = torch.rand(1, 2)
        self.assertRaises(ValueError, LabelToContour(**input_param), error_input)
        error_input = torch.rand(1, 2, 3, 4, 5)
        self.assertRaises(ValueError, LabelToContour(**input_param), error_input)
        error_input = np.random.rand(1, 2, 3, 4, 5)
        self.assertRaises(ValueError, LabelToContour(**input_param), error_input)


if __name__ == "__main__":