def test_fail(self):
t1 = SpatialPadd("image", [10, 5])
data = t1(self.all_data["2D"])
# Check that error is thrown when inverse are used out of order.
t2 = ResizeWithPadOrCropd("image", [10, 5])
with self.assertRaises(RuntimeError):
t2.inverse(data)
def test_pad_shape(self, input_param, input_data, expected_val):
for p in TEST_NDARRAYS_ALL:
if isinstance(
p(0),
torch.Tensor) and ("constant_values" in input_param
or input_param["mode"] == "reflect"):
continue
padcropper = ResizeWithPadOrCropd(**input_param)
input_data["img"] = p(input_data["img"])
result = padcropper(input_data)
np.testing.assert_allclose(result["img"].shape, expected_val)
inv = padcropper.inverse(result)
for k in input_data:
self.assertTupleEqual(inv[k].shape, input_data[k].shape)