def testScaleAndTranslateDown(self, dtype, method, antialias):
image_shape = [1, 6, 7, 1]
target_shape = [1, 3, 3, 1]
data = [
51, 38, 32, 89, 41, 21, 97, 51, 33, 87, 89, 34, 21, 97, 43, 25, 25, 92,
41, 11, 84, 11, 55, 111, 23, 99, 50, 83, 13, 92, 52, 43, 90, 43, 14, 89,
71, 32, 23, 23, 35, 93
]
if antialias:
expected_data = {}
expected_data["linear"] = [
43.5372, 59.3694, 53.6907, 49.3221, 56.8168, 55.4849, 0, 0, 0
]
expected_data["lanczos3"] = [
43.2884, 57.9091, 54.6439, 48.5856, 58.2427, 53.7551, 0, 0, 0
]
expected_data["lanczos5"] = [
43.9209, 57.6360, 54.9575, 48.9272, 58.1865, 53.1948, 0, 0, 0
]
expected_data["cubic"] = [
42.9935, 59.1687, 54.2138, 48.2640, 58.2678, 54.4088, 0, 0, 0
]
else:
expected_data = {}
expected_data["linear"] = [
43.6071, 89, 59, 37.1785, 27.2857, 58.3571, 0, 0, 0
]
expected_data["lanczos3"] = [
44.1390, 87.8786, 63.3111, 25.1161, 20.8795, 53.6165, 0, 0, 0
]
expected_data["lanczos5"] = [
44.8835, 85.5896, 66.7231, 16.9983, 19.8891, 47.1446, 0, 0, 0
]
expected_data["cubic"] = [
43.6426, 88.8854, 60.6638, 31.4685, 22.1204, 58.3457, 0, 0, 0
]
x = np.array(data, dtype=dtype).reshape(image_shape)
expected = np.array(
expected_data[method], dtype=dtype).reshape(target_shape)
scale_a = jnp.array([1.0, 0.35, 0.4, 1.0], dtype=jnp.float32)
translation_a = jnp.array([0.0, 0.2, 0.1, 0.0], dtype=jnp.float32)
output = image.scale_and_translate(
x, target_shape, (0,1,2,3),
scale_a, translation_a, method, antialias=antialias)
self.assertAllClose(output, expected, atol=2e-03)
# Tests that running with just a subset of dimensions that have non-trivial
# scale and translation.
output = image.scale_and_translate(
x, target_shape, (1,2),
scale_a[1:3], translation_a[1:3], method, antialias=antialias)
self.assertAllClose(output, expected, atol=2e-03)
def testScaleAndTranslateUp(self, dtype, image_shape, target_shape, scale,
translation, method):
data = [64, 32, 32, 64, 50, 100]
# Note zeros occur in the output because the sampling location is outside
# the boundaries of the input image.
expected_data = {}
expected_data["linear"] = [
0.0, 0.0, 0.0, 0.0, 56.0, 40.0, 32.0, 0.0, 52.0, 44.0, 40.0, 0.0,
44.0, 52.0, 56.0, 0.0, 45.625, 63.875, 73.0, 0.0, 56.875, 79.625,
91.0, 0.0
]
expected_data["lanczos3"] = [
0.0, 0.0, 0.0, 0.0, 59.6281, 38.4313, 22.23, 0.0, 52.0037, 40.6454,
31.964, 0.0, 41.0779, 47.9383, 53.1818, 0.0, 43.0769, 67.1244,
85.5045, 0.0, 56.4713, 83.5243, 104.2017, 0.0
]
expected_data["lanczos5"] = [
0.0, 0.0, 0.0, 0.0, 60.0223, 40.6694, 23.1219, 0.0, 51.2369,
39.5593, 28.9709, 0.0, 40.8875, 46.5604, 51.7041, 0.0, 43.5299,
67.7223, 89.658, 0.0, 56.784, 83.984, 108.6467, 0.0
]
expected_data["cubic"] = [
0.0, 0.0, 0.0, 0.0, 59.0252, 36.9748, 25.8547, 0.0, 53.3386,
41.4789, 35.4981, 0.0, 41.285, 51.0051, 55.9071, 0.0, 42.151,
65.8032, 77.731, 0.0, 55.823, 83.9288, 98.1026, 0.0
]
x = np.array(data, dtype=dtype).reshape(image_shape)
# Should we test different float types here?
scale_a = jnp.array(scale, dtype=jnp.float32)
translation_a = jnp.array(translation, dtype=jnp.float32)
output = image.scale_and_translate(x, target_shape,
range(len(image_shape)), scale_a,
translation_a, method)
expected = np.array(expected_data[method],
dtype=dtype).reshape(target_shape)
self.assertAllClose(output, expected, atol=2e-03)
